pax_global_header00006660000000000000000000000064142662537470014532gustar00rootroot0000000000000052 comment=64f617714333709782ef9ad404c58d3acef66f60 pybel-0.15.5/000077500000000000000000000000001426625374700127355ustar00rootroot00000000000000pybel-0.15.5/.bumpversion.cfg000066400000000000000000000015251426625374700160500ustar00rootroot00000000000000[bumpversion] current_version = 0.15.5 commit = True tag = False parse = (?P\d+)\.(?P\d+)\.(?P\d+)(?:-(?P[0-9A-Za-z-]+(?:\.[0-9A-Za-z-]+)*))?(?:\+(?P[0-9A-Za-z-]+(?:\.[0-9A-Za-z-]+)*))? serialize = {major}.{minor}.{patch}-{release}+{build} {major}.{minor}.{patch}+{build} {major}.{minor}.{patch}-{release} {major}.{minor}.{patch} [bumpversion:part:release] optional_value = production first_value = dev values = dev production [bumpverion:part:build] values = [0-9A-Za-z-]+ [bumpversion:file:setup.cfg] search = version = {current_version} replace = version = {new_version} [bumpversion:file:docs/source/conf.py] search = release = '{current_version}' replace = release = '{new_version}' [bumpversion:file:src/pybel/version.py] search = VERSION = "{current_version}" replace = VERSION = "{new_version}" pybel-0.15.5/.flake8000066400000000000000000000020401426625374700141040ustar00rootroot00000000000000######################### # Flake8 Configuration # # (.flake8) # # (formerly in tox.ini) # ######################### [flake8] ignore = # max line length E501 F401 # star imports F403 # docstring in magic method D105 # line break before binary operator W503 # Docstring in __init__ D107 # Complaining about assert statements S101 # Complains about random number generators S311 # Complains about pickles S301, S403 # Redefinition in CLI F811 # Error names N818 # pep8 is wrong E203 # FIXME later remove this D401 exclude = .tox, .git, __pycache__, docs/source/conf.py, build, dist, tests/fixtures/*, *.pyc, *.egg-info, .cache, .eggs max-complexity = 25 max-line-length = 120 import-order-style = pycharm application-import-names = pybel bel_resources tests format = ${cyan}%(path)s${reset}:${yellow_bold}%(row)d${reset}:${green_bold}%(col)d${reset}: ${red_bold}%(code)s${reset} %(text)s pybel-0.15.5/.github/000077500000000000000000000000001426625374700142755ustar00rootroot00000000000000pybel-0.15.5/.github/workflows/000077500000000000000000000000001426625374700163325ustar00rootroot00000000000000pybel-0.15.5/.github/workflows/tests.yml000066400000000000000000000042161426625374700202220ustar00rootroot00000000000000name: Tests on: [ push ] jobs: lint: name: Lint runs-on: ubuntu-latest strategy: matrix: python-version: [ "3.6", "3.10" ] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: pip install tox - name: Check manifest run: tox -e manifest - name: Check code quality with flake8 run: tox -e flake8 - name: Check package metadata with Pyroma run: tox -e pyroma # - name: Check static typing with MyPy # run: tox -e mypy # # Allow failure, see https://github.community/t/continue-on-error-allow-failure-ui-indication/16773 # if: succeeded() || failed() docs: name: Documentation runs-on: ubuntu-latest strategy: matrix: python-version: [ "3.6", "3.10" ] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: pip install tox - name: Check RST conformity with doc8 run: tox -e doc8 - name: Check README.rst run: tox -e doc8 - name: Check documentation build with Sphinx run: tox -e docs tests: name: Tests runs-on: ${{ matrix.os }} strategy: matrix: os: [ ubuntu-latest, windows-latest ] python-version: [ "3.6", "3.7", "3.8", "3.9", "3.10" ] exclude: - os: windows-latest python-version: 3.7 - os: windows-latest python-version: 3.8 - os: windows-latest python-version: 3.9 steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: pip install tox - name: Test with pytest run: tox -e py pybel-0.15.5/.gitignore000066400000000000000000000037531426625374700147350ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # IPython Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # dotenv .env # virtualenv venv/ ENV/ # Spyder project settings .spyderproject # Rope project settings .ropeproject # PyCharm project settings .idea/* *.pickle *.gpickle scratch scratch/* .pytest_cache ### OSX ### # General .DS_Store .AppleDouble .LSOverride # Icon must end with two \r Icon # Thumbnails ._* # Files that might appear in the root of a volume .DocumentRevisions-V100 .fseventsd .Spotlight-V100 .TemporaryItems .Trashes .VolumeIcon.icns .com.apple.timemachine.donotpresent # Directories potentially created on remote AFP share .AppleDB .AppleDesktop Network Trash Folder Temporary Items .apdisk ### Vim ### # Swap [._]*.s[a-v][a-z] [._]*.sw[a-p] [._]s[a-rt-v][a-z] [._]ss[a-gi-z] [._]sw[a-p] # Session Session.vim # Temporary .netrwhist *~ # Auto-generated tag files tags # Persistent undo [._]*.un~ .mypy_cache notebooks/hipathia_demo/covid19/covid19.bel.nodelink.json notebooks/hipathia_demo/hemekg/hemekg.bel.nodelink.json notebooks/hipathia_demo/cbn/source notebooks/hipathia_demo/cbn/Human-2.0.zip notebooks/hipathia_demo/covid19/covid19-grounded.bel.nodelink.json pybel-0.15.5/.readthedocs.yml000066400000000000000000000003711426625374700160240ustar00rootroot00000000000000# See: https://docs.readthedocs.io/en/latest/config-file/v2.html version: 2 build: image: latest python: version: "3.8" install: - method: pip path: . extra_requirements: - docs - jupyter - grounding pybel-0.15.5/AUTHORS.rst000066400000000000000000000012631426625374700146160ustar00rootroot00000000000000Authors ======= The following have contributed to the development, maintenance, and testing of PyBEL. Maintainer ---------- `Charles Tapley Hoyt `_ Contributors ------------ - `Andrej Konotopez `_ - `Christian Ebeling `_ - `Scott Colby `_ - `Daniel Domingo-Fernández `_ - `Ben Gyori `_ - `Nicola Soranzo `_ - `Steffen Möller `_ - `tehw0lf `_ - `Jeremy Zucker `_ - `Aman Choudhri `_ pybel-0.15.5/CHANGELOG.rst000066400000000000000000001433341426625374700147660ustar00rootroot00000000000000Change Log ========== All notable changes to this project will be documented in this file. The format is based on `Keep a Changelog `_ and this project adheres to `Semantic Versioning `_ `0.15.3 `_ - 2021-04-19 -------------------------------------------------------------------------------- Added ~~~~~ - Exposed transitivities parsed via nested statements through ``pybel.BELGraph.transitivities`` (https://github.com/pybel/pybel/issues/490). Fixed ~~~~~ - Some dependencies updated their interfaces (https://github.com/pybel/pybel/pull/491) `0.15.2 `_ - 2021-03-21 -------------------------------------------------------------------------------- Added ~~~~~ - Support for direct regulations via ``pybel.constants.DIRECTLY_REGUALTES`` and ``pybel.BELGraph.add_directly_regulates`` `0.15.1 `_ - 2021-02-07 -------------------------------------------------------------------------------- - Add support for named reactions (https://github.com/pybel/pybel/pull/485) - Switch to GitHub Actions - Use `PyStow `_ for file management - Use `Bioregistry `_ for prefix normalization `0.15.0 `_ - 2020-12-17 --------------------------------------------------------------------------------- Added ~~~~~ - Support for homomultimers in ML triples export - Graph dispatches allow quick access to PyBEL functionality (https://github.com/pybel/pybel/pull/449) - Add "Streamable" BEL I/O (https://github.com/pybel/pybel/pull/451) - ``pybel.to_triples`` allows direct export of ML-ready triples to a numpy ndarray. - ``pybel.parse`` function allows for quick parsing of BEL strings - Add JSON Schema for validating nodes and edges (https://github.com/pybel/pybel/pull/450) thanks @aman527 - Add BEL Repository functionality, previously located in https://github.com/pybel/bel-repository - Pickling can be done with gzip to greatly reduce size both on files (https://github.com/pybel/pybel/commit/d90578cb) and on bytes (https://github.com/pybel/pybel/commit/fcc99952) Changed ~~~~~~~ - PyBEL now supports Python 3.6+ only. - ``pybel.to_tsv`` renamed to ``pybel.to_triples_file`` - The citation entry in PyBEL JSON datastructure now uses ``namespace``/``identifier``/``name`` instead of ``db``/``db_id``. The corresponding constants ``pybel.constants.CITATION_DB``/``pybel.constants.CITATION_DB_ID``/ ``pybel.constants.CITATION_DB_NAME`` have been removed (https://github.com/pybel/pybel/pull/453). - The citation entry now relies on a subclass of ``pybel.language.Entity``, which means empty strings are no longer allowed. - The inconsistent usage of ``subject``/``source`` as well as ``object``/``target`` has been normalized everywhere. This means the constants ``pybel.constants.SUBJECT``/``pybel.constants.OBJECT`` were removed and new constants ``pybel.constants.SOURCE_MODIFIER``/``pybel.constants.TARGET_MODIFIER`` were added (https://github.com/pybel/pybel/pull/453). - Remove the BEL default namespace. All usages get normalized to controlled vocabularies (mostly GO) automatically (https://github.com/pybel/pybel/pull/455). - Improve namespace/annotation database insertion with Pandas (https://github.com/pybel/pybel/pull/454) - Pickling now uses protocol 5 by default assisted by the ``pickle5`` backport on Python 3.6 (https://github.com/pybel/pybel/commit/679dcab7) - Database now stores graphs in gzipped pickles (https://github.com/pybel/pybel/commit/9ee9bf21) - Unspecified molecular activities now use the top-level GO term (https://github.com/pybel/pybel/commit/d56993e6) - New annotation storage format using lists of annotations instead of dictionaries (https://github.com/pybel/pybel/pull/461) - Citations now wrapped in dedicated data structure (https://github.com/pybel/pybel/pull/468) Fixed ~~~~~ - Parser now correctly supports dashes and dots in namespaces as well as in names without quoting (https://github.com/pybel/pybel/pull/460) - Bug in creating list abundances with name and list definition (https://github.com/pybel/pybel/pull/465) `0.14.10 `_ - 2020-06-15 ----------------------------------------------------------------------------------- Added ~~~~~ - Importer from `EMMAA `_ (https://github.com/pybel/pybel/pull/432) - I/O for Amazon S3 (https://github.com/pybel/pybel/pull/431) - Improve TSV exporter (d7d12878, 74d51c1c, e1082523, 6ffc1df6) - Add identifier-based entity remapper (ba8aa933) - Add annotation grounding (https://github.com/pybel/pybel/pull/435, https://github.com/pybel/pybel/pull/443) - Add HiPathia export examples (https://github.com/pybel/pybel/pull/422) Changed ~~~~~~~ - Updated default BEL Commons instance from https://bel-commons.scai.fraunhofer.de to https://bel-commons-dev.scai.fraunhofer.de - Add more namespaces for JGIF parsing to support CBN and BioDati import (9f74122d and https://github.com/pybel/pybel/pull/435; thanks @djinnome) - Make Jupyter notebook export accessible from top level at ``pybel.to_jupyter`` (4d76faad) Fixed ~~~~~ - Fix bug in display of nice labels in Jupyter notebook (775bdc30) Removed ~~~~~~~ - Remove default service URL for BEL Commons exporter. The Fraunhofer instance was taken down (a9a540fb). `0.14.9 `_ - 2020-04-25 --------------------------------------------------------------------------------- Changed ~~~~~~~ - Updated GraphDati and BioDati exports (https://github.com/pybel/pybel/commit/c9f95344b72ff86239c8987d6b534000ba509a1f) - Refactoring of ``pybel.struct.filters`` and ``pybel.struct.mutation`` - some imports might have to be updated Added ~~~~~ - Importer from `Fraunhofer OrientDB `_ (https://github.com/pybel/pybel/pull/429) - Exporter for `SPIA `_ analytical tool (https://github.com/pybel/pybel/pull/430) `0.14.8 `_ - 2020-04-24 --------------------------------------------------------------------------------- Changed ~~~~~~~ - ``pybel.post_graphdati()`` has been renamed to ``pybel.to_biodati()`` - ``pybel.to_web()`` has been renamed to ``pybel.to_bel_commons()`` - ``pybel.from_web()`` has been renamed to ``pybel.from_bel_commons()`` Added ~~~~~ - Content importers from GraphDati format with ``pybel.from_graphdati()`` and ``pybel.from_graphdati()`` and enable usage of respective extensions ``*.bel.graphdati.json`` and ``*.bel.graphdati.json.gz`` with ``pybel.load()`` (https://github.com/pybel/pybel/pull/425) - Content importer from BioDati with ``pybel.from_biodati()`` (https://github.com/pybel/pybel/pull/425) - Add direct function for loading CBN files (so you don't have to load the JSON first) with ``pybel.from_cbn_jgif_file()`` - Added ``pybel.grounding.ground()`` function that uses the unlisted Python 3.7+ dependency, ``pyobo``` to ground/normalize entities in a given BEL graph. This also takes care of upgrading legacy namespace names and mapping SCOMP/SFAM via FamPlex. (https://github.com/pybel/pybel/pull/426) `0.14.7 `_ - 2020-04-16 --------------------------------------------------------------------------------- Added ~~~~~ - Support for transcription factor relationships in TSV (machine learning) output Fixed ~~~~~ - Fixed incorrect parsing of OBO-style identifiers (https://github.com/pybel/pybel/pull/421) - Make sure pop() works in translocations (https://github.com/pybel/pybel/pull/421) - None and non-empty string checks in DSL (3156d519) - Fixed Jupyter export (the interface changed in Jinja2; d6e7e619) `0.14.6 `_ - 2020-04-01 --------------------------------------------------------------------------------- Added ~~~~~ - Add PyKEEN import hooks, so you can automatically load up a BEL file for machine learning with `PyKEEN `_. - Update TSV exporter for better ML-ready triples for PyKEEN - Added INDRA I/O options and `pybel.from_bel_script_gz` option - Add HiPathia Exporter (https://github.com/pybel/pybel/pull/414) - Add PyNPA Exporter (https://github.com/pybel/pybel/pull/413) - Add universal I/O functions `pybel.load` and `pybel.dump` (https://github.com/pybel/pybel/pull/417) `0.14.5 `_ - 2020-02-26 --------------------------------------------------------------------------------- Added ~~~~~ - Gzip variant of umbrella nodelink exporter - More entry points for exporting Fixed ~~~~~ - OBO-style export was broken if name and identifier weren't available. This works with whatever is available now - If CURIE is requested and both identifier and name are available, defaults to identifier. `0.14.4 `_ - 2020-02-25 --------------------------------------------------------------------------------- Added ~~~~~ - Added support for BEP-0005 - Added BEP-0001 support (population abundance; https://github.com/pybel/pybel/issues/402) - Added BEP-0003 support (noCorrelation relation; https://github.com/pybel/pybel/issues/403) - Added BEP-0012 support (correlation relation; https://github.com/pybel/pybel/issues/403) - Added BEP-0011 support (binds relation; https://github.com/pybel/pybel/issues/403) - Add GraphDati exporter and BioDati uploader (https://github.com/pybel/pybel/issues/407) - Add Hetionet importer (https://github.com/pybel/pybel/issues/406) - Add several more I/O functions (``pybel.to_bel_script_gz``, etc.) Removed ~~~~~~~ - Removed support for ``label`` relation - Removed support for node attributes and description Updated ~~~~~~~ - Updated programmatic citation handling. Now a tuple of strings (database, identifier) can be passed anywhere a citation is needed. Fixed ~~~~~ - Fixed output of BEP-0008 (OBO-style identifiers) - Fixed convenience functions for ``BELGraph.add_inhibits`` and ``BELGraph.add_activates`` (there was a typo and it was adding the opposite relation in both) - Fixed that graph edge adder functions don't add two-way edges (https://github.com/pybel/pybel/issues/409) `0.14.3 `_ - 2020-01-08 --------------------------------------------------------------------------------- Added ~~~~~ - Umbrella node-link JSON exporter(https://github.com/pybel/pybel/pull/400) - GraphML exporter with umbrella nodes (https://github.com/pybel/pybel/pull/400) `0.14.2 `_ - 2019-11-26 --------------------------------------------------------------------------------- Added ~~~~~ - Added several IO functions and convenience functions (gzipped wrappers, etc.) `0.14.1 `_ - 2019-11-26 --------------------------------------------------------------------------------- Fixed ~~~~~ - Fixed autoflushing in manager `0.14.0 `_ - 2019-11-15 --------------------------------------------------------------------------------- Added ~~~~~ - Add metagraph for nested statements - Add xrefs to DSL model - Add OBO-style identifier parsing and export - Add TSV exporter that does reasoning over edges (originally from BioKEEN) Changed ~~~~~~~ - DSL format now stores all data in a 'concept' entry - Try looking up namespace to identifiers mapping by default - Changed name of pybel.to_bel_path to pybel.to_bel_script - Used magic to combine ``to_*_file`` and ``to_*_path`` functions `0.13.2 `_ - 2019-04-24 --------------------------------------------------------------------------------- Added ~~~~~ - BELGraph class now has built-in summaries for authors and citations - Added first Jupyter notebook into documentation. More to come! Changed ~~~~~~~ - Authors are always stored as lists inside edges - Nodes in node-link JSON always have the BEL string included - Updated documentation for data model and DSL - Enforce keyword argument usage in BELGraph.add_qualified_edge - Use iterator in pybel.union so graphs can be lazily loaded and combine Removed ~~~~~~~ - Remove remaining traces of namespace hierarchy table Fixed ~~~~~ - Union function also takes union of locally defined annotations now - Handling of special translocations (sec, surf; https://github.com/pybel/pybel/issues/377) - Fixed public header in pybel.to_web - Fixed public interface to pipeline and queries - Fixed parsing of gene methylations/modifications `0.13.1 `_ - 2019-01-14 --------------------------------------------------------------------------------- Fixed ~~~~~ - Fix handling of node JSON with identifier but not name (https://github.com/pybel/pybel/issues/375) - Fix handling of isolated nodes in `pybel.union` (https://github.com/pybel/pybel/issues/373) `0.13.0 `_ - 2019-01-07 --------------------------------------------------------------------------------- Added ~~~~~ - Add JSON to node and edge SQL models (https://github.com/pybel/pybel/pull/358) - Add more properties to the Fragment class - Node pruning command to CLI - Type hints (https://github.com/pybel/pybel/issues/369) Changed ~~~~~~~ - Use a declarative setup (https://github.com/pybel/pybel/issues/360) - Pass flake8 (https://github.com/pybel/pybel/issues/363) - Change handling of locally defined namespaces in parser - Excise `pybel.resources` module to new package [`bel_resources`](https://github.com/cthoyt/bel-resources) Fixed ~~~~~ - Add sha512 to JSON of edges' nodes when retrieving from the database - Add nested sha512 identifiers when outputting node-link (https://github.com/pybel/pybel/issues/370) - Fixed handling of invalid entities in the BEL parser (https://github.com/pybel/pybel/issues/368) - Fixed merging of locally defined annotations when using `pybel.union` (https://github.com/pybel/pybel/issues/372) Removed ~~~~~~~ - Dropped Python 2.7 support (https://github.com/pybel/pybel/issues/285) - Dropped Python 3.4 support (https://github.com/pybel/pybel/issues/286) `0.12.2 `_ - 2018-11-19 --------------------------------------------------------------------------------- Added ~~~~~ - Serialization functions can be accessed directly from the BELGraph class (https://github.com/pybel/pybel/pull/344) - Added several useful node filter functions (Thanks @ddomingof; https://github.com/pybel/pybel/pull/347) - Add a function for removing extraneous citation metadata Changed ~~~~~~~ - pybel.struct.graph.BELgraph.summarize() now prints the number of warnings, even if it is zero Fixed ~~~~~ - Platform specificity for requirements in setup.py (Thanks @scolby33; https://github.com/pybel/pybel/pull/346) - Print statement problem (Thanks @smoe; https://github.com/pybel/pybel/pull/351) - Import paths for INDRA (Thanks @bgyori; https://github.com/pybel/pybel/pull/339 - Improvements on flake8 status (Thanks @tehw0lf; https://github.com/pybel/pybel/pull/353) - Ensure complexes have at least one member (Thanks @10mubeen for pointing this out) - Make "Other" as the default namespace domain for generating BEL namespace files `0.12.1 `_ - 2018-09-13 --------------------------------------------------------------------------------- Fixed ~~~~~ - Wrong names in CLI - Add missing star import for pybel.dsl.ListAbundance Changed ~~~~~~~ - Update iteration over BEL files to read in one pass Added ~~~~~ - More summary functions in pybel.struct `0.12.0 `_ - 2018-09-06 ---------------------------------------------------------------------------------- Changed ~~~~~~~ - Update edge hashing algorithm (this invalidates old hashes) - Edge hashes are now used as keys instead of being put inside edge data dictionaries - Improved graph operations with new location of edge hashes - Update Node/Link JSON schema - Improve __contains__ and has_node functions to handle DSL objects - Require usage of DSL when creating BELGraph instances - Use DSL completely in ORM - Add SHA512 to authors to avoid issues with MySQL's collation Removed ~~~~~~~ - Remove ``pybel.tokens.node_to_tuple`` function and ``pybel.tokens.node_to_bel`` functions - All tuple-related functions in the DSL (AKA the tupleectomy) `0.11.11 `_ - 2018-07-31 ------------------------------------------------------------------------------------ Added ~~~~~ - Automatic generation of CLI documentation with ``sphinx-click`` - Several edge creation convenience functions to the ``BELGraph`` - Graph summary functions Changed ~~~~~~~ - Improve Drop networks (Thanks @scolby33) (https://github.com/pybel/pybel/pull/319) - Huge improvements to documentation and code style reccomended by flake8 Fixed ~~~~~ - Fixed handling of tuples (64d0685) Removed ~~~~~~~ - Remove function ``BELGraph.iter_data`` `0.11.10 `_ - 2018-07-23 ----------------------------------------------------------------------------------- Added ~~~~~ - Several subgraph functions (https://github.com/pybel/pybel/pull/315) Changed ~~~~~~~ - Better SQL implementation of get_recent_networks (https://github.com/pybel/pybel/pull/312) `0.11.9 `_ - 2018-07-?? --------------------------------------------------------------------------------- Removed ~~~~~~~ - Removed CX and NDEx IO in favor of https://github.com/pybel/pybel-cx Changed ~~~~~~~ - Better (less annoying) logging for deprecated transformations - Turn off SQL echoing by default - Update getting annotation entries - Better options for using TQDM while parsing Added ~~~~~ - Flag to INDRA machine to run locally - Add require annotations option to parser (https://github.com/pybel/pybel/issues/255) - Data missing key node predicate builder `0.11.8 `_ - 2018-06-27 --------------------------------------------------------------------------------- Added ~~~~~ - Deprecation system for pipeline functions (for when they're renamed) Changed ~~~~~~~ - Rely on edge predicates more heavily in selection/induction/expansion transformations - Rename several functions related to the "central dogma" for more clarity `0.11.7 `_ - 2018-06-26 --------------------------------------------------------------------------------- Fixed ~~~~~ - Bug where data did not get copied to sub-graphs on induction (https://github.com/pybel/pybel/issues/#307) `0.11.6 `_ - 2018-06-25 --------------------------------------------------------------------------------- Added ~~~~~ - Added get_annotation_values function to pybel.struct.summary Removed ~~~~~~~ - Removed Manager.ensure function Fixed ~~~~~ - Fixed a bug in Manager.from_connection (https://github.com/pybel/pybel/issues/#306) `0.11.5 `_ - 2018-06-22 --------------------------------------------------------------------------------- Changed ~~~~~~~ - Changed arguments in pybel.struct.mutations.get_subgraphs_by_annotation - Moved utility functions in pybel.struct.mutations `0.11.4 `_ - 2018-06-22 --------------------------------------------------------------------------------- Changed ~~~~~~~ - Use BELGraph.fresh_copy instead of importing the class in mutator functions Added ~~~~~ - Add pipeline (https://github.com/pybel/pybel/issues/301) - Testing of neighborhood functions - Added several transformation and grouping functions for BELGraph - INDRA Machine in CLI Fixed ~~~~~ - Add missing field from BaseAbundance (https://github.com/pybel/pybel/issues/302) `0.11.3 `_ - 2018-06-04 --------------------------------------------------------------------------------- Added ~~~~~ - Made testing code and date install as part of main package(https://github.com/pybel/pybel/pull/298) Removed ~~~~~~~ - Remove extension hook and extension loader (https://github.com/pybel/pybel/pull/300) `0.11.2 `_ - 2018-05-10 --------------------------------------------------------------------------------- Added ~~~~~ - Calculation of SHA512 hash to DSL abundances - Documented the deployment extra for setup.py - Added to and from JSON path IO functions - PMI Contact for CBN import and more default namespaces - Added common query builders to SQLAlchemy models Fixed ~~~~~ - Fixed name/version lookup in the database - Safer creation of directories (https://github.com/pybel/pybel/issues/#284) - Make export to GraphML more boring and permissive - Implement to_tuple for CentralDogma (https://github.com/pybel/pybel/issues/#281) - Unicode compatibility error. Thanks @bgyori! (https://github.com/pybel/pybel/pull/289) Changed ~~~~~~~ - Made parsing of fragments permissive to quoting (https://github.com/pybel/pybel/issues/#282) - Update citation handling - Update namespace methods in CLI - Added ``as_bel`` method to DSL - Update authentication with BEL Commons (https://github.com/pybel/pybel/commit/4f6b8b0ecab411e1d2b110e00c8bac77ace88308) - Unpin SQLAlchemy version. Most up-to-date should remain safe. Removed ~~~~~~~ - Removed static function ``pybel.BELGraph.hash_node`` since it just wraps ``pybel.utils.node_to_tuple`` - Removed unnecessary configuration editing from CLI - Removed OWL Parser (https://github.com/pybel/pybel/issues/290) - Removed support for BELEQ files (https://github.com/pybel/pybel/issues/294) - Remove artifactory code and migrated to https://github.com/pybel/pybel-artifactory. (https://github.com/pybel/pybel/issues/292) `0.11.1 `_ - 2018-02-19 --------------------------------------------------------------------------------- Added ~~~~~ - Added additional DSL shortcuts for building edges with the BELGraph - Added example graphs (statins, BRAF, orthology examples) - Added knowledge transfer function - Added progress bar for parser `0.11.0 `_ - 2018-02-07 --------------------------------------------------------------------------------- Changed ~~~~~~~ - Updated SQL schema and made new minimum unpickle version 0.11.0. - Parser now uses a compact representation of annotations instead of exploding to multiple edges (https://github.com/pybel/pybel/issues/261) - Update annotation filtering functions to reflect new data format (https://github.com/pybel/pybel/issues/262) - Update GraphML Output (https://github.com/pybel/pybel/issues/260) - Better error message when missing namespace resource (https://github.com/pybel/pybel/issues/265) Fixed ~~~~~ - Fixed more problems with edge store and testing (https://github.com/pybel/pybel/issues/225, https://github.com/pybel/pybel/issues/256, https://github.com/pybel/pybel/issues/257) - Fixed windows testing (https://github.com/pybel/pybel/issues/243) - Fixed broken network cascade, but is still slow (https://github.com/pybel/pybel/issues/256, https://github.com/pybel/pybel/issues/257, https://github.com/pybel/pybel/issues/259) - Fixed JGIF import (https://github.com/pybel/pybel/issues/266) and added scripts directory (3dc6b1f) - Fix extras in setup.py and requirements.txt Added ~~~~~ - Additional regex format for date parsing from PubMed (https://github.com/pybel/pybel/issues/259) - Add labels to nodes in GraphML output (https://github.com/pybel/pybel/issues/260) - Add edge predicate builders (https://github.com/pybel/pybel/issues/262) - Testing on multiple databases (SQLite, MySQL, PostgreSQL) (https://github.com/pybel/pybel/issues/238) - Added ``pybel.struct.mutations`` module - Added graph-based equivalency checking - Add more documentation to BELGraph (https://github.com/pybel/pybel/issues/271) `0.10.1 `_ - 2017-12-28 --------------------------------------------------------------------------------- Fixed ~~~~~ - Fixed truncation description parsing to handle double quotes Changed ~~~~~~~ - Made DSL functions into classes to allow inheritance and isinstance checking as well as preliminary to_tuple functionality Added ~~~~~ - Added more edge predicates (has_activity, has_degree, has_translocation, has_annotation) `0.10.0 `_ - 2017-12-22 -------------------------------------------------------------------------------- Changed ~~~~~~~ - Updated SQL schema and made new minimum unpickle version 0.10.0. - Moved `pybel.parser.language` to `pybel.language` - Moved `pybel.parser.canoncalize` to `pybel.tokens` - Overhaul of `pybel.struct.filters` - included many more functions, tests, and updated nomenclature - Update canoncalize functions to be generally reusable (take node data dictionaries) - Make NDEx2, Neo4j, OWL parsing, and INDRA setup.py install extras Fixed ~~~~~ - Names defined by regular expressions can now be included in the database cache (https://github.com/pybel/pybel/issues/250, https://github.com/pybel/pybel/issues/251) - Fixed ``Manager.has_name_version`` (https://github.com/pybel/pybel/issues/246) - Fixed CX output and upgraded to NDEx2 client - When joining graphs, keep their metadata (https://github.com/pybel/pybel/commit/affaecc73d2b4affa8aeecb3834ed7c6f5697cac) Added ~~~~~ - Included partOf relationship in BEL language (https://github.com/pybel/pybel/issues/244) - Added additional date formats to parse from PubMed (https://github.com/pybel/pybel/issues/239) - Filled out many more DSL functions and added testing - Added ability to set relationship parsing policy in BEL Parser (https://github.com/pybel/pybel/commit/09614465d80d2931e901fd54d067a5151e327283) - Implemented from PyBEL Web Function - Implemented to INDRA function `0.9.7 `_ - 2017-11-20 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Use ``HASH`` as dictionary key instead of ``ID`` - Allow DSL to create nodes without names but with identifiers - Rename instance variables in parsers for consistency - Greater usage of DSL in parser `0.9.6 `_ - 2017-11-12 ------------------------------------------------------------------------------ Added ~~~~~ - Additional keyword arguments for JSON output functions Changed ~~~~~~~ - Updated parser intermediate data structure. Should have no affect on end users. - Smarter serialization of PyBEL data dictionaries to BEL Fixed ~~~~~ - Better handling of citations that have authors pre-parsed into lists (https://github.com/pybel/pybel/issues/247) `0.9.5 `_ - 2017-11-07 ------------------------------------------------------------------------------ Added ~~~~~ - Updates to DSL - More node filters and predicates - Added "partOf" relationship (https://github.com/pybel/pybel/issues/244) - Added more regular expressions for date parsing (https://github.com/pybel/pybel/issues/239) Fixed ~~~~~ - Fixed incorrect checking of network storage (https://github.com/pybel/pybel/issues/246) Changed ~~~~~~~ - Reorganized resources module to reduce dependencies on PyBEL Tools, which has lots of other big requirements - Moved ``pybel.summary`` module to ``pybel.struct.summary`` `0.9.4 `_ - 2017-11-03 ------------------------------------------------------------------------------ Fixed ~~~~~ - Problem with uploading products, reactants, and members to NDEx (#230) - Checking for adding uncachable nodes when populating edge store Added ~~~~~ - Database seeding functions - Citation management - Added PubMed Central as type in citation Removed ~~~~~~~ - Don't keep blobs in node or edge cache anymore `0.9.3 `_ - 2017-10-19 ------------------------------------------------------------------------------ Added ~~~~~ - Convenience functions for adding qualified and unqualified edges to BELGraph class - Sialic Acid Example BEL Graph - EGF Example BEL Graph - Added PyBEL Web export and stub for import - BioPAX Import - Dedicated BEL Syntax error Changed ~~~~~~~ - Update the BEL Script canonicalization rules to group citations then evidences better - Removed requirement of annotation entry in edge data dictionaries - Confident enough to make using the edge store True by default Fixed ~~~~~ - Fixed unset list parsing so it doesn't need quotes (#234) Removed ~~~~~~~ - In-memory caching of authors `0.9.2 `_ - 2017-09-27 ------------------------------------------------------------------------------ Fixed ~~~~~ - JSON Serialization bug for authors in Citation Model `0.9.1 `_ - 2017-09-26 ------------------------------------------------------------------------------ Added ~~~~~ - INDRA Import - Usage of built-in operators on BEL Graphs Changed ~~~~~~~ - Update list recent networks function to work better with SQL 99 compliant (basically everything except the old version of MySQL and SQLite) RDBMS - Better tests for queries to edge store - Better testing when extensions not installed (c1ac850) - Update documentation to new OpenBEL website links Fixed ~~~~~ - Fix crash when uploading network to edge store that has annotation pattern definitions (still needs some work though) - Added foreign keys for first and last authors in Citation model (requires database rebuild) - Froze NetworkX version at 1.11 since 2.0 breaks everything Removed ~~~~~~~ - Don't cache SQLAlchemy models locally (3d7d238) `0.9.0 `_ - 2017-09-19 ------------------------------------------------------------------------------ Added ~~~~~ - Option for setting scopefunc in Manager - Include extra citation information on inserting graph to database that might have come from citation enrichment - Node model to tuple and json functions are now complete Changed ~~~~~~~ - Added members lists to the node data dictionaries for complex and composite nodes - Added reactants and products lists to the node data dictionaries for reaction nodes Fixed ~~~~~~~ - GOCC and other location caching problem - Node tuples for reactions are now using standard node tuples for reactants and products. This was a huge issue but it had never come up before. DANGER - this means all old code will still work, but any node-tuple reliant code will have unexpected results. This also means that the node hashes in the database for all reactions will now be outdated, so the minimum version is being bumped. `0.8.1 `_ - 2017-09-08 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Change CacheManager class name to Manager - Change references from build_manager to Manager.ensure - Automatically update default database to minimum import version - Constants for extra citation fields and update to_json for Citation model Fixed ~~~~~ - Bug in author insertion for non-unique authors `0.8.0 `_ - 2017-09-08 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Made new minimum unpickle version 0.8.0. From now on, all unpickle changes (before a 1.0.0 release) will be accompanied by a minor version bump. - Overall better handling of citation insertion - Updated data models. Added to Citation model and renamed namespaceEntry in Node model. - Better init function for BELGraph - Force name and version to not be null in the database - Update pickle references to use six module - Update base cache manager - better connection handling and more exposed arguments Added ~~~~~ - Get graph functions to cache manager - Added more useful functions to cache manager - Kwargs for setting name, version, and description in BELGraph init - Getters and setters for version and description in BELGraph - Node data to tuple functions (https://github.com/pybel/pybel/issues/145) `0.7.3 `_ - 2017-09-05 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Update logging for parsing of bad version strings - Change where kwargs go in parse_lines function - Make non-standard parsing modes part of kwargs Fixed ~~~~~ - On-purpose singletons now properly identified (https://github.com/pybel/pybel/issues/218) Added ~~~~~ - CLI command for set connection (https://github.com/pybel/pybel/issues/220) - GEF and GAP activities added for INDRA `0.7.2 `_ - 2017-08-10 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Externalized more parsing constants - Updated version management - Keep track of all singleton lines in parsing - Update CLI - Update JGIF export from CBN Fixed ~~~~~ - Change node hashing ot only use type and reference Added ~~~~~ - Node intersection merge - Get most recent network by name in manager `0.7.1 `_ - 2017-07-25 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Externalized some PyParsing elements Fixed ~~~~~ - Version string tokenization `0.7.0 `_ - 2017-07-21 ------------------------------------------------------------------------------ Added ~~~~~ - Added Project key to document metadata parser (https://github.com/pybel/pybel/issues/215) - Reusable protocols for hashing nodes and edges Fixed ~~~~~ - Edge store working (https://github.com/pybel/pybel/issues/212) Changed ~~~~~~~ - Update resource urls (https://github.com/pybel/pybel/issues/211) - General improvements to exception handling - Made new minimum unpickle version 0.7.0 `0.6.2 `_ - 2017-06-28 ------------------------------------------------------------------------------ Added ~~~~~ - Environment variable for data locations - Add get network by ids merger `0.6.1 `_ - 2017-06-25 ------------------------------------------------------------------------------ Added ~~~~~ - Node and edge filter framework (https://github.com/pybel/pybel/issues/206) - Network joining (https://github.com/pybel/pybel/issues/205 and https://github.com/pybel/pybel/issues/204) - More thorough tests of IO Fixed ~~~~~ - Bug when getting multiple networks by identifier (https://github.com/pybel/pybel/issues/208) - Arguments to exceptions mixed up Changed ~~~~~~~ - Use context in command line interface to streamline code - Remove old, unused code `0.6.0 `_ - 2017-06-11 ------------------------------------------------------------------------------- Changed ~~~~~~~ - Merge OWL and BEL namespaces (https://github.com/pybel/pybel/issues/118) - Remove lots of unused/redundant code - Lots of functions renamed and moved... Sorry people. Added ~~~~~ - Multiple options for graph joining - Filter functions (https://github.com/pybel/pybel/issues/206) `0.5.11 `_ - 2017-06-07 --------------------------------------------------------------------------------- Changed ~~~~~~~ - Added line numbers to parsing exceptions - Update minimum pickle parsing from 0.5.10 to 0.5.11 to reflect changes in parsing exceptions `0.5.10 `_ - 2017-06-06 -------------------------------------------------------------------------------- Added ~~~~~ - Network outer join (https://github.com/pybel/pybel/issues/205) - Network full join with hash (https://github.com/pybel/pybel/issues/204 and https://github.com/pybel/pybel/issues/204) - Option to suppress singleton warnings (https://github.com/pybel/pybel/issues/200) Changed ~~~~~~~ - Moved :mod:`pybel.graph` to :mod:`pybel.struct.graph` - Parse exceptions are renamed - Update minimum pickle parsing from 0.5.4 to 0.5.10 to reflect changes in parsing execeptions and project structure Fixed ~~~~~ - Rewrote the CSV Exporter (https://github.com/pybel/pybel/issues/201) `0.5.9 `_ - 2017-05-28 ------------------------------------------------------------------------------ Added ~~~~~ - JGIF interchange (https://github.com/pybel/pybel/issues/193) and (https://github.com/pybel/pybel/issues/194) - Configuration file parsing (https://github.com/pybel/pybel/issues/197) `0.5.8 `_ - 2017-05-25 ------------------------------------------------------------------------------ Changed ~~~~~~~ - CX is now unstreamified on load, making compatibility with other CX sources (like NDEx) possible - Testing now enables ``PYBEL_TEST_CONNECTION`` environment variable to set a persistient database - Testing data cut down to reduce memory consumption Added ~~~~~ - NDEx upload and download `0.5.7 `_ - 2017-05-20 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Public IO changed for to/from_json and to/from_cx (https://github.com/pybel/pybel/issues/192) - Better error output for metadata failure (https://github.com/pybel/pybel/issues/191) Added ~~~~~ - Add BEL script line to edges (https://github.com/pybel/pybel/issues/155) - Export to GSEA gene list (https://github.com/pybel/pybel/issues/189) - Non-caching of namespaces support (https://github.com/pybel/pybel/issues/190) Note: I made a mistake with the release on 0.5.6, so I just bumped the patch one more. `0.5.5 `_ - 2017-05-08 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Updated CX output to have full provenance and list definitions (https://github.com/pybel/pybel/issues/180) Added ~~~~~ - DOI and URL are now acceptable citation types (https://github.com/pybel/pybel/issues/188) - Citation can now be given as a double of type and reference (https://github.com/pybel/pybel/issues/187) `0.5.4 `_ - 2017-04-28 ------------------------------------------------------------------------------ Fixed ~~~~~ - MySQL truncations of large BLOBs - Session management problems Changed ~~~~~~~ - If a namespace/annotation was redefined, will now thrown an exception instead of just a logging a warning - Update minimum pickle parsing from 0.5.3 to 0.5.4 to reflect changes in parse exceptions Added ~~~~~ - Ability to drop graph that isn't in graph store from CLI `0.5.3 `_ - 2017-04-19 ------------------------------------------------------------------------------ Added ~~~~~ - Lenient parsing mode for unqualified translocations (https://github.com/pybel/pybel/issues/178) Changed ~~~~~~~ - Check for dead URLs at BEL framework (https://github.com/pybel/pybel/issues/177) - Don't throw warnings for versions that are in YYYYMMDD format (https://github.com/pybel/pybel/issues/175) - Include character positions in some exceptions (https://github.com/pybel/pybel/issues/176) - Update minimum pickle parsing from 0.4.2 to 0.5.3 to reflect the new parse exceptions's names and arguments `0.5.2 `_ - 2017-04-16 ------------------------------------------------------------------------------ Fixed ~~~~~ - Ensure existence of namespaces/annotations during graph upload (https://github.com/pybel/pybel/issues/165) `0.5.1 `_ - 2017-04-10 ------------------------------------------------------------------------------ Added ~~~~~ - Parsing of labels (https://github.com/pybel/pybel/issues/173) Fixed ~~~~~ - Parsing of hasComponents lists (https://github.com/pybel/pybel/issues/172) `0.5.0 `_ - 2017-04-07 ------------------------------------------------------------------------------ Added ~~~~~ - Debugging on lines starting with #: comments (https://github.com/pybel/pybel/issues/162) - Added missing relations in pybel constants (https://github.com/pybel/pybel/issues/161) Changed ~~~~~~~ - Merge definition and graph cache (https://github.com/pybel/pybel/issues/164) - Warn when not using semantic versioning (https://github.com/pybel/pybel/issues/160) `0.4.4 `_ - 2017-04-03 ------------------------------------------------------------------------------ Added ~~~~~ - File paths in definition parsing (https://github.com/pybel/pybel/issues/158) - Quotes around variant string (https://github.com/pybel/pybel/issues/156) Changed ~~~~~~~ - Reorganized package to split line parsing from core data structure (https://github.com/pybel/pybel/issues/154) `0.4.3 `_ - 2017-03-21 ------------------------------------------------------------------------------ Added ~~~~~ - Documentation for constants (https://github.com/pybel/pybel/issues/146) - Date validation on parse-time (https://github.com/pybel/pybel/issues/147) Changed ~~~~~~~ - Externalized strings from modifier parsers - Move ``pybel.cx.hash_tuple`` to ``pybel.utils.hash_tuple`` (https://github.com/pybel/pybel/issues/144) Fixed ~~~~~ - Output to CX on CLI crashing (https://github.com/pybel/pybel/issues/152) - Assignment of graph metadata on reload (https://github.com/pybel/pybel/issues/153) `0.4.2 `_ - 2017-03-16 ------------------------------------------------------------------------------ Added ~~~~~ - Node property data model and I/O - Edge property data model and I/O Changed ~~~~~~~ - Update version checking to be more lenient. v0.4.2 is now the minimum for reloading a graph Removed ~~~~~~~ - Origin completion option on BEL parsing. See `PyBEL Tools `_ `0.4.1 `_ - 2017-03-11 ------------------------------------------------------------------------------ Added ~~~~~ - More output options for BEL - Explicit parsing of hasVariant, hasReactant, and hasProduct Fixed ~~~~~ - Allow parsing of non-standard ordering of annotations - Superfluous output of single nodes when writing BEL scripts `0.4.0 `_ - 2017-03-07 ------------------------------------------------------------------------------- Added ~~~~~ - Stable CX import and export - Edge Store data models and loading - Alternative control parsing technique without citation clearing - Node name calculator `0.3.11 `_ - 2017-03-05 --------------------------------------------------------------------------------- Fixed ~~~~~ - Fixed has_members not adding annotations tag - Reliance on node identifiers in canonicalization of complexes and composites - Fixed graph iterator filter `0.3.10 `_ - 2017-03-01 -------------------------------------------------------------------------------- Added ~~~~~ - Shortcut for adding unqualified edges Fixed ~~~~~ - All edges have annotations dictionary now - JSON Export doesn't crash if there aren't list annotations - All exceptions have __str__ function for stringification by JSON export if desired `0.3.9 `_ - 2017-02-21 ------------------------------------------------------------------------------ Added ~~~~~ - Experimental CX export for use with NDEx Changed ~~~~~~~ - Better testing with thorough BEL Fixed ~~~~~ - ParseResult objects no longer propogate through graph - Fixed outputting to JSON Removed ~~~~~~~ - Support for importing GraphML is no longer continued because there's too much information loss `0.3.8 `_ - 2017-02-12 ------------------------------------------------------------------------------ Added ~~~~~ - Annotation pattern definitions - Alternative json output to in-memory dictionary Changed ~~~~~~~ - Removed url rewriting for OpenBEL Framework - Group all annotations in edge data (see Data Model in docs) `0.3.7 `_ - 2017-02-06 ------------------------------------------------------------------------------ Added ~~~~~ - Added equivalentTo relation - Added OWL annotation support - Version integrity checking - Dump cache functionality Changed ~~~~~~~ - Merged GENE, GENE_VARIANT, and GENE_FUSION `0.3.6 `_ - 2017-02-03 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Switch ontospy dependency to onto2nx for Windows support `0.3.5 `_ - 2017-01-30 ------------------------------------------------------------------------------ Added ~~~~~ - Add thorough testing of BEL document Changed ~~~~~~~ - Improved string externalization - Update to data model for fusions - Improved parser performance `0.3.4 `_ - 2017-01-22 ------------------------------------------------------------------------------ Added ~~~~~ - Codec support for opening files by path Changed ~~~~~~~ - Protein modifications, gene modifications, and variants are now stored as dictionaries in the latent data structure - Many constants have been externalized - BEL default names, like kinaseActivity are automatically assigned a sentinel value as a namespace `0.3.3 `_ - 2017-01-18 ------------------------------------------------------------------------------ Added ~~~~~ - Make HGVS parsing less complicated by storing as strings - add warning tracking `0.3.2 `_ - 2017-01-13 ------------------------------------------------------------------------------ Added ~~~~~ - Gene modification support - Namespace equivalence mapping data models and manager - Extension loading Changed ~~~~~~~ - Better testing (local files only with mocks) - Better names for exceptions and warnings `0.3.1 `_ - 2017-01-03 ------------------------------------------------------------------------------ Added ~~~~~ - Bytes IO of BEL Graphs - Graph caching and Graph Cache Manager Fixed ~~~~~ - Annotations weren't getting cached because *somebody* forgot to add the urls. Fixed. - Removed typos in default namespace list Changed ~~~~~~~ - More explicit tests and overall test case refactoring - Better handling of BEL script metadata `0.3.0 `_ - 2016-12-29 ------------------------------------------------------------------------------ Added ~~~~~ - OWL namespace support and caching - Full support for BEL canonicalization and output Fixed ~~~~~ - Rewrote namespace cache and SQLAlchemy models Removed ~~~~~~~ - Removed unnecessary pandas and matplotlib dependencies `0.2.6 `_ - 2016-11-19 ------------------------------------------------------------------------------ Added ~~~~~ - Canonical BEL terms added to nodes on parsing - Fragment parsing - Support for alternative names for evidence (SupportingText) - More explicit support of unqualified edges - Created top-level constants file Fixed ~~~~~ - Fix incorrect HGVS protein truncation parsing - Fix missing location option in abundance tag parsing - Fix json input/output Removed ~~~~~~~ - Deleted junk code from mapper and namespace cache manager `0.2.5 `_ - 2016-11-13 ------------------------------------------------------------------------------ Added ~~~~~ - Nested statement parsing support - Fusion parsing support Fixed ~~~~~ - Fixed graphml input/output - Changed encodings of python files to utf-8 - Fixed typos in language.py `0.2.4 `_ - 2016-11-13 ------------------------------------------------------------------------------ Added ~~~~~ - Neo4J CLI output - Edge and node filtering - Assertions of document metadata key - Added BEL 2.0 protein modification default mapping support Changed ~~~~~~~ - Rewrite HGVS parsing - Updated canonicalization Fixed ~~~~~ - Typo in amino acid dictionary - Assertion of citation `0.2.3 `_ - 2016-11-09 ------------------------------------------------------------------------------ Changed ~~~~~~~ - Made logging lazy and updated logging codes - Update rewriting of old statements - Explicitly streamlined MatchFirst statements; huge speed improvements `0.2.2 `_ - 2016-10-25 ------------------------------------------------------------------------------ Removed ~~~~~~~ - Documentation is no longer stored in version control - Fixed file type in CLI `0.2.1 `_ - 2016-10-25 [YANKED] --------------------------------------------------------------------------------------- Added ~~~~~ - Added CLI for data manager 0.2.0 - 2016-10-22 ------------------ Added ~~~~~ - Added definition cache manager pybel-0.15.5/CONTRIBUTING.md000066400000000000000000000114651426625374700151750ustar00rootroot00000000000000# Contributing Contributions to this repository are welcomed and encouraged. ## Code Contribution This project uses the [GitHub Flow](https://guides.github.com/introduction/flow) model for code contributions. Follow these steps: 1. [Create a fork](https://help.github.com/articles/fork-a-repo) of the upstream repository at [`pybel/pybel`](https://github.com/pybel/pybel) on your GitHub account (or in one of your organizations) 2. [Clone your fork](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository) with `git clone https://github.com//pybel.git` 3. Make and commit changes to your fork with `git commit` 4. Push changes to your fork with `git push` 5. Repeat steps 3 and 4 as needed 6. Submit a pull request back to the upstream repository ### Merge Model This repository uses [squash merges](https://docs.github.com/en/github/collaborating-with-pull-requests/incorporating-changes-from-a-pull-request/about-pull-request-merges#squash-and-merge-your-pull-request-commits) to group all related commits in a given pull request into a single commit upon acceptance and merge into the main branch. This has several benefits: 1. Keeps the commit history on the main branch focused on high-level narrative 2. Enables people to make lots of small commits without worrying about muddying up the commit history 3. Commits correspond 1-to-1 with pull requests ### Code Style This project encourages the use of optional static typing. It uses [`mypy`](http://mypy-lang.org/) as a type checker and [`sphinx_autodoc_typehints`](https://github.com/agronholm/sphinx-autodoc-typehints) to automatically generate documentation based on type hints. You can check if your code passes `mypy` with `tox -e mypy`. This project uses [`black`](https://github.com/psf/black) to automatically enforce a consistent code style. You can apply `black` and other pre-configured linters with `tox -e lint`. This project uses [`flake8`](https://flake8.pycqa.org) and several plugins for additional checks of documentation style, security issues, good variable nomenclature, and more ( see [`tox.ini`](tox.ini) for a list of flake8 plugins). You can check if your code passes `flake8` with `tox -e flake8`. Each of these checks are run on each commit using GitHub Actions as a continuous integration service. Passing all of them is required for accepting a contribution. If you're unsure how to address the feedback from one of these tools, please say so either in the description of your pull request or in a comment, and we will help you. ### Logging Python's builtin `print()` should not be used (except when writing to files), it's checked by the [`flake8-print`](https://github.com/jbkahn/flake8-print) plugin to `flake8`. If you're in a command line setting or `main()` function for a module, you can use `click.echo()`. Otherwise, you can use the builtin `logging` module by adding `logger = logging.getLogger(__name__)` below the imports at the top of your file. ### Documentation All public functions (i.e., not starting with an underscore `_`) must be documented using the [sphinx documentation format](https://sphinx-rtd-tutorial.readthedocs.io/en/latest/docstrings.html#the-sphinx-docstring-format). The [`darglint`](https://github.com/terrencepreilly/darglint) plugin to `flake8` reports on functions that are not fully documented. This project uses [`sphinx`](https://www.sphinx-doc.org) to automatically build documentation into a narrative structure. You can check that the documentation builds properly in an isolated environment with `tox -e docs-test` and actually build it locally with `tox -e docs`. ### Testing Functions in this repository should be unit tested. These can either be written using the `unittest` framework in the `tests/` directory or as embedded doctests. You can check that the unit tests pass with `tox -e py` and that the doctests pass with `tox -e doctests`. These tests are required to pass for accepting a contribution. ### Syncing your fork If other code is updated before your contribution gets merged, you might need to resolve conflicts against the main branch. After cloning, you should add the upstream repository with ```shell $ git remote add pybel https://github.com/pybel/pybel.git ``` Then, you can merge upstream code into your branch. You can also use the GitHub UI to do this by following [this tutorial](https://docs.github.com/en/github/collaborating-with-pull-requests/working-with-forks/syncing-a-fork). ### Python Version Compatibility This project aims to support all versions of Python that have not passed their end-of-life dates. After end-of-life, the version will be removed from the Trove qualifiers in the [`setup.cfg`](setup.cfg) and from the GitHub Actions testing configuration. See https://endoflife.date/python for a timeline of Python release and end-of-life dates. pybel-0.15.5/LICENSE000066400000000000000000000020711426625374700137420ustar00rootroot00000000000000MIT License Copyright (c) 2016-2022 Charles Tapley Hoyt Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. pybel-0.15.5/MANIFEST.in000066400000000000000000000010631426625374700144730ustar00rootroot00000000000000graft src graft tests prune scripts prune notebooks recursive-include docs/source *.py recursive-include docs/source *.rst recursive-include docs/source *.png recursive-include src/pybel/testing/bel *.bel recursive-include src/pybel/testing/belanno *.belanno recursive-include src/pybel/testing/belns *.belns include docs/Makefile global-exclude *.py[cod] __pycache__ *.so *.dylib .DS_Store *.gpickle exclude .bumpversion.cfg exclude .readthedocs.yml exclude .flake8 exclude tox.ini exclude CONTRIBUTING.md CHANGELOG.rst AUTHORS.rst include LICENSE README.rst pybel-0.15.5/README.rst000066400000000000000000000405351426625374700144330ustar00rootroot00000000000000PyBEL |zenodo| |build| |coverage| |documentation| |bioregistry| |black| ======================================================================= `PyBEL `_ is a pure Python package for parsing and handling biological networks encoded in the `Biological Expression Language `_ (BEL). It facilitates data interchange between data formats like `NetworkX `_, Node-Link JSON, `JGIF `_, CSV, SIF, `Cytoscape `_, `CX `_, `INDRA `_, and `GraphDati `_; database systems like SQL and `Neo4J `_; and web services like `NDEx `_, `BioDati Studio `_, and `BEL Commons `_. It also provides exports for analytical tools like `HiPathia `_, `Drug2ways `_ and `SPIA `_; machine learning tools like `PyKEEN `_ and `OpenBioLink `_; and others. Its companion package, `PyBEL Tools `_, contains a suite of functions and pipelines for analyzing the resulting biological networks. We realize that we have a name conflict with the python wrapper for the cheminformatics package, OpenBabel. If you're looking for their python wrapper, see `here `_. Citation -------- If you find PyBEL useful for your work, please consider citing: .. [1] Hoyt, C. T., *et al.* (2017). `PyBEL: a Computational Framework for Biological Expression Language `_. *Bioinformatics*, 34(December), 1–2. Installation |pypi_version| |python_versions| |pypi_license| ------------------------------------------------------------ PyBEL can be installed easily from `PyPI `_ with the following code in your favorite shell: .. code-block:: sh $ pip install pybel or from the latest code on `GitHub `_ with: .. code-block:: sh $ pip install git+https://github.com/pybel/pybel.git See the `installation documentation `_ for more advanced instructions. Also, check the change log at `CHANGELOG.rst `_. Getting Started --------------- More examples can be found in the `documentation `_ and in the `PyBEL Notebooks `_ repository. Compiling and Saving a BEL Graph ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This example illustrates how the a BEL document from the `Human Brain Pharmacome `_ project can be loaded and compiled directly from GitHub. .. code-block:: python >>> import pybel >>> url = 'https://raw.githubusercontent.com/pharmacome/conib/master/hbp_knowledge/proteostasis/kim2013.bel' >>> graph = pybel.from_bel_script_url(url) Other functions for loading BEL content from many formats can be found in the `I/O documentation `_. Note that PyBEL can handle `BEL 1.0 `_ and `BEL 2.0+ `_ simultaneously. After you have a BEL graph, there are numerous ways to save it. The ``pybel.dump`` function knows how to output it in many formats based on the file extension you give. For all of the possibilities, check the `I/O documentation `_. .. code-block:: python >>> import pybel >>> graph = ... >>> # write as BEL >>> pybel.dump(graph, 'my_graph.bel') >>> # write as Node-Link JSON for network viewers like D3 >>> pybel.dump(graph, 'my_graph.bel.nodelink.json') >>> # write as GraphDati JSON for BioDati >>> pybel.dump(graph, 'my_graph.bel.graphdati.json') >>> # write as CX JSON for NDEx >>> pybel.dump(graph, 'my_graph.bel.cx.json') >>> # write as INDRA JSON for INDRA >>> pybel.dump(graph, 'my_graph.indra.json') Summarizing the Contents of the Graph ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The ``BELGraph`` object has several "dispatches" which are properties that organize its various functionalities. One is the ``BELGraph.summarize`` dispatch, which allows for printing summaries to the console. These examples will use the `RAS Model `_ from EMMAA, so you'll have to be sure to ``pip install indra`` first. The graph can be acquired and summarized with ``BELGraph.summarize.statistics()`` as in: .. code-block:: python >>> import pybel >>> graph = pybel.from_emmaa('rasmodel', date='2020-05-29-17-31-58') # Needs >>> graph.summarize.statistics() --------------------- ------------------- Name rasmodel Version 2020-05-29-17-31-58 Number of Nodes 126 Number of Namespaces 5 Number of Edges 206 Number of Annotations 4 Number of Citations 1 Number of Authors 0 Network Density 1.31E-02 Number of Components 1 Number of Warnings 0 --------------------- ------------------- The number of nodes of each type can be summarized with ``BELGraph.summarize.nodes()`` as in: .. code-block:: python >>> graph.summarize.nodes(examples=False) Type (3) Count ------------ ------- Protein 97 Complex 27 Abundance 2 The number of nodes with each namespace can be summarized with ``BELGraph.summarize.namespaces()`` as in: .. code-block:: python >>> graph.summarize.namespaces(examples=False) Namespace (4) Count --------------- ------- HGNC 94 FPLX 3 CHEBI 1 TEXT 1 The edges can be summarized with ``BELGraph.summarize.edges()`` as in: .. code-block:: python >>> graph.summarize.edges(examples=False) Edge Type (12) Count --------------------------------- ------- Protein increases Protein 64 Protein hasVariant Protein 48 Protein partOf Complex 47 Complex increases Protein 20 Protein decreases Protein 9 Complex directlyIncreases Protein 8 Protein increases Complex 3 Abundance partOf Complex 3 Protein increases Abundance 1 Complex partOf Complex 1 Protein decreases Abundance 1 Abundance decreases Protein 1 Grounding the Graph ~~~~~~~~~~~~~~~~~~~ Not all BEL graphs contain both the name and identifier for each entity. Some even use non-standard prefixes (also called **namespaces** in BEL). Usually, BEL graphs are validated against controlled vocabularies, so the following demo shows how to add the corresponding identifiers to all nodes. .. code-block:: python from urllib.request import urlretrieve url = 'https://github.com/cthoyt/selventa-knowledge/blob/master/selventa_knowledge/large_corpus.bel.nodelink.json.gz' urlretrieve(url, 'large_corpus.bel.nodelink.json.gz') import pybel graph = pybel.load('large_corpus.bel.nodelink.json.gz') import pybel.grounding grounded_graph = pybel.grounding.ground(graph) Note: you have to install ``pyobo`` for this to work and be running Python 3.7+. Displaying a BEL Graph in Jupyter ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ After installing ``jinja2`` and ``ipython``, BEL graphs can be displayed in Jupyter notebooks. .. code-block:: python >>> from pybel.examples import sialic_acid_graph >>> from pybel.io.jupyter import to_jupyter >>> to_jupyter(sialic_acid_graph) Using the Parser ~~~~~~~~~~~~~~~~ If you don't want to use the ``pybel.BELGraph`` data structure and just want to turn BEL statements into JSON for your own purposes, you can directly use the ``pybel.parse()`` function. .. code-block:: python >>> import pybel >>> pybel.parse('p(hgnc:4617 ! GSK3B) regulates p(hgnc:6893 ! MAPT)') {'source': {'function': 'Protein', 'concept': {'namespace': 'hgnc', 'identifier': '4617', 'name': 'GSK3B'}}, 'relation': 'regulates', 'target': {'function': 'Protein', 'concept': {'namespace': 'hgnc', 'identifier': '6893', 'name': 'MAPT'}}} This functionality can also be exposed through a Flask-based web application with ``python -m pybel.apps.parser`` after installing ``flask`` with ``pip install flask``. Note that the first run requires about a ~2 second delay to generate the parser, after which each parse is very fast. Using the CLI ~~~~~~~~~~~~~ PyBEL also installs a command line interface with the command :code:`pybel` for simple utilities such as data conversion. In this example, a BEL document is compiled then exported to `GraphML `_ for viewing in Cytoscape. .. code-block:: sh $ pybel compile ~/Desktop/example.bel $ pybel serialize ~/Desktop/example.bel --graphml ~/Desktop/example.graphml In Cytoscape, open with :code:`Import > Network > From File`. Contributing ------------ Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See `CONTRIBUTING.rst `_ for more information on getting involved. Acknowledgements ---------------- Support ~~~~~~~ The development of PyBEL has been supported by several projects/organizations (in alphabetical order): - `The Cytoscape Consortium `_ - `Enveda Biosciences `_ - `Fraunhofer Center for Machine Learning `_ - `Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) `_ - `Harvard Program in Therapeutic Science - Laboratory of Systems Pharmacology `_ - `University of Bonn `_ Funding ~~~~~~~ - DARPA Young Faculty Award W911NF2010255 (PI: Benjamin M. Gyori). - The `European Union `_, `European Federation of Pharmaceutical Industries and Associations (EFPIA) `_, and `Innovative Medicines Initiative `_ Joint Undertaking under `AETIONOMY `_ [grant number 115568], resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. Logo ~~~~ The PyBEL `logo `_ was designed by `Scott Colby `_. .. |build| image:: https://github.com/pybel/pybel/workflows/Tests/badge.svg :target: https://github.com/pybel/pybel/actions :alt: Build Status .. |coverage| image:: https://codecov.io/gh/pybel/pybel/coverage.svg?branch=develop :target: https://codecov.io/gh/pybel/pybel/branch/develop :alt: Development Coverage Status .. |documentation| image:: https://readthedocs.org/projects/pybel/badge/?version=latest :target: http://pybel.readthedocs.io/en/latest/ :alt: Development Documentation Status .. |climate| image:: https://codeclimate.com/github/pybel/pybel/badges/gpa.svg :target: https://codeclimate.com/github/pybel/pybel :alt: Code Climate .. |python_versions| image:: https://img.shields.io/pypi/pyversions/PyBEL.svg :target: https://pypi.python.org/pypi/pybel :alt: Stable Supported Python Versions .. |pypi_version| image:: https://img.shields.io/pypi/v/PyBEL.svg :target: https://pypi.python.org/pypi/pybel :alt: Current version on PyPI .. |pypi_license| image:: https://img.shields.io/pypi/l/PyBEL.svg :target: https://github.com/pybel/pybel/blob/master/LICENSE :alt: MIT License .. |zenodo| image:: https://zenodo.org/badge/68376693.svg :target: https://zenodo.org/badge/latestdoi/68376693 .. |bioregistry| image:: 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Rcj̈́TR^8b"WvQ%CGVH(\4Nڿ砨Y*!YVy l*ԁJaITPR1I!L”~(BOa&$g@NS S)<݊Su*15x#o0R!kU }"7voP n0v%]륇4 &YCC@'Ӌ*fSB#$Pv1> R +(W«f1yi* 3{أe(֐\KK`|@GBB! #d~v\9Ŭ-'OT18)ԑIm|^bͥ8QCfut]xE[JSbQ%c0ft * D pI-Ʈ,i"\dL\H_KZԟz ۆ̰p{@$ H+ a2}FH6;( J2Bm&Kܢk4Ta?[^aG Էp{NHaZL+?:jWNCx8ї8 _͠Ճd$8JOL)(ܦ#MZ^O(0B)q@I7QD!N?3ix(4<#@PکB,JkdLw`e bhAʛi*|(4*v3U7hk|_Qf9Љ:¢JaQ~ݔSP%mpLUX0zB1+8d &tcf.FbU aX酪srOR s&5<f d|X1fSoCiw֍iBf?0M^?t`a/N"MHzHtktnVHTe)}9rhnH$haD~}-D2؏iG$68$3bTtIENDB`pybel-0.15.5/docs/source/conf.py000066400000000000000000000246301426625374700164710ustar00rootroot00000000000000# -*- coding: utf-8 -*- import os import re import sys from datetime import date # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('../../src')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.viewcode', 'sphinx_autodoc_typehints', 'sphinx_click.ext', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. # # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = 'PyBEL' copyright = f'2016-{date.today().year}, Charles Tapley Hoyt' author = 'Charles Tapley Hoyt' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The full version, including alpha/beta/rc tags. release = '0.15.5' # The short X.Y version. parsed_version = re.match( '(?P\d+)\.(?P\d+)\.(?P\d+)(?:-(?P[0-9A-Za-z-]+(?:\.[0-9A-Za-z-]+)*))?(?:\+(?P[0-9A-Za-z-]+(?:\.[0-9A-Za-z-]+)*))?', release ) version = parsed_version.expand('\g.\g.\g') if parsed_version.group('release'): tags.add('prerelease') # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # # today = '' # # Else, today_fmt is used as the format for a strftime call. # # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = [ '**/.ipynb_checkpoints', ] # The reST default role (used for this markup: `text`) to use for all # documents. # # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False todo_include_todos = True # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. # " v documentation" by default. # # html_title = u'PyBEL vX.Y.Z' # A shorter title for the navigation bar. Default is the same as html_title. # # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = 'PyBEL-square-100.png' # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". # html_static_path = ['_static'] html_static_path = [] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # # html_extra_path = [] # If not None, a 'Last updated on:' timestamp is inserted at every page # bottom, using the given strftime format. # The empty string is equivalent to '%b %d, %Y'. # # html_last_updated_fmt = None # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # # html_additional_pages = {} # If false, no module index is generated. # # html_domain_indices = True # If false, no index is generated. # # html_use_index = True # If true, the index is split into individual pages for each letter. # # html_split_index = False # If true, links to the reST sources are added to the pages. # # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh' # # html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # 'ja' uses this config value. # 'zh' user can custom change `jieba` dictionary path. # # html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. # # html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'PyBELdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'PyBEL.tex', u'PyBEL Documentation', u'Charles Tapley Hoyt', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # # latex_use_parts = False # If true, show page references after internal links. # # latex_show_pagerefs = False # If true, show URL addresses after external links. # # latex_show_urls = False # Documents to append as an appendix to all manuals. # # latex_appendices = [] # It false, will not define \strong, \code, itleref, \crossref ... but only # \sphinxstrong, ..., \sphinxtitleref, ... To help avoid clash with user added # packages. # # latex_keep_old_macro_names = True # If false, no module index is generated. # # latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'pybel', u'PyBEL Documentation', [author], 1) ] # If true, show URL addresses after external links. # # man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'PyBEL', u'PyBEL Documentation', author, 'PyBEL', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. # # texinfo_appendices = [] # If false, no module index is generated. # # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # # texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. # # texinfo_no_detailmenu = False # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { 'python': ('https://docs.python.org/3', None), 'networkx': ('https://networkx.github.io/documentation/latest/', None), 'py2neo': ('https://py2neo.org/2021.0/', None), 'sqlalchemy': ('https://docs.sqlalchemy.org/en/latest', None), 'indra': ('https://indra.readthedocs.io/en/latest/', None), 'bio2bel': ('https://bio2bel.readthedocs.io/en/latest/', None), 'requests': ('https://requests.kennethreitz.org/en/master/', None), 'setuptools': ('https://setuptools.readthedocs.io/en/latest/', None), } autodoc_member_order = 'bysource' autoclass_content = 'both' if os.environ.get('READTHEDOCS', None): tags.add('readthedocs') pybel-0.15.5/docs/source/index.rst000066400000000000000000000064551426625374700170400ustar00rootroot00000000000000PyBEL |release| Documentation ============================= Biological Expression Language (BEL) is a domain-specific language that enables the expression of complex molecular relationships and their context in a machine-readable form. Its simple grammar and expressive power have led to its successful use in the to describe complex disease networks with several thousands of relationships. PyBEL is a pure Python software package that parses BEL documents, validates their semantics, and facilitates data interchange between common formats and database systems like JSON, CSV, Excel, SQL, CX, and Neo4J. Its companion package, `PyBEL-Tools `_, contains a library of functions for analysis of biological networks. For result-oriented guides, see the `PyBEL-Notebooks `_ repository. Installation is as easy as getting the code from `PyPI `_ with :code:`python3 -m pip install pybel`. See the :doc:`installation ` documentation. For citation information, see the :doc:`references ` page. PyBEL is tested on Python 3.5+ on Mac OS and Linux using `Travis CI `_ as well as on Windows using `AppVeyor `_. .. seealso:: - Specified by `BEL 1.0 `_, `BEL 2.0 `_, and `BEL 2.0+ `_ - Documented on `Read the Docs `_ - Versioned on `GitHub `_ - Tested on `Travis CI `_ - Distributed by `PyPI `_ .. toctree:: :maxdepth: 2 :caption: Getting Started :name: start introduction/overview introduction/installation .. toctree:: :maxdepth: 2 :caption: Data Structure :name: data reference/struct/datamodel reference/struct/examples reference/struct/filters reference/struct/grouping reference/struct/operators reference/struct/pipeline reference/struct/query reference/struct/summary .. toctree:: :maxdepth: 2 :caption: Mutations :name: mutations reference/mutations/mutations reference/mutations/collapse reference/mutations/deletion reference/mutations/expansion reference/mutations/induction reference/mutations/induction_expansion reference/mutations/inference reference/mutations/metadata .. toctree:: :maxdepth: 2 :caption: Conversion :name: conversion reference/io .. toctree:: :caption: Database :name: database reference/database/manager reference/database/models .. toctree:: :maxdepth: 2 :caption: Topic Guide :name: topics topics/cookbook topics/cli .. toctree:: :caption: Reference :name: reference reference/constants reference/parser reference/dsl reference/logging .. toctree:: :caption: Project :name: project meta/references meta/postmortem meta/technology Indices and Tables ------------------ * :ref:`genindex` * :ref:`modindex` * :ref:`search` pybel-0.15.5/docs/source/introduction/000077500000000000000000000000001426625374700177065ustar00rootroot00000000000000pybel-0.15.5/docs/source/introduction/installation.rst000066400000000000000000000056151426625374700231500ustar00rootroot00000000000000Installation ============ The latest stable code can be installed from `PyPI `_ with: .. code-block:: sh $ python3 -m pip install pybel The most recent code can be installed from the source on `GitHub `_ with: .. code-block:: sh $ python3 -m pip install git+https://github.com/pybel/pybel.git For developers, the repository can be cloned from `GitHub `_ and installed in editable mode with: .. code-block:: sh $ git clone https://github.com/pybel/pybel.git $ cd pybel $ python3 -m pip install -e . Extras ------ The ``setup.py`` makes use of the ``extras_require`` argument of :func:`setuptools.setup` in order to make some heavy packages that support special features of PyBEL optional to install, in order to make the installation more lean by default. A single extra can be installed from PyPI like :code:`python3 -m pip install pybel[neo4j]` or multiple can be installed using a list like :code:`python3 -m pip install pybel[neo4j,indra]`. Likewise, for developer installation, extras can be installed in editable mode with :code:`python3 -m pip install -e .[neo4j]` or multiple can be installed using a list like :code:`python3 -m pip install -e .[neo4j,indra]`. The available extras are: neo4j ~~~~~ This extension installs the :mod:`py2neo` package to support upload and download to Neo4j databases. .. seealso:: - :func:`pybel.to_neo4j` indra ~~~~~ This extra installs support for :mod:`indra`, the integrated network dynamical reasoner and assembler. Because it also represents biology in BEL-like statements, many statements from PyBEL can be converted to INDRA, and visa-versa. This package also enables the import of BioPAX, SBML, and SBGN into BEL. .. seealso:: - :func:`pybel.from_biopax` - :func:`pybel.from_indra_statements` - :func:`pybel.from_indra_pickle` - :func:`pybel.to_indra` jupyter ~~~~~~~ This extra installs support for visualizing BEL graphs in Jupyter notebooks. .. seealso:: - :func:`pybel.io.jupyter.to_html` - :func:`pybel.io.jupyter.to_jupyter` Caveats ------- - PyBEL extends the :code:`networkx` for its core data structure. Many of the graphical aspects of :code:`networkx` depend on :code:`matplotlib`, which is an optional dependency. - If :code:`HTMLlib5` is installed, the test that's supposed to fail on a web page being missing actually tries to parse it as RDFa, and doesn't fail. Disregard this. Upgrading --------- During the current development cycle, programmatic access to the definition and graph caches might become unstable. If you have any problems working with the database, try removing it with one of the following commands: 1. Running :code:`pybel manage drop` (unix) 2. Running :code:`python3 -m pybel manage drop` (windows) 3. Removing the folder :code:`~/.pybel` PyBEL will build a new database and populate it on the next run. pybel-0.15.5/docs/source/introduction/overview.rst000066400000000000000000000332351426625374700223140ustar00rootroot00000000000000Overview ======== Background on Systems Biology Modeling -------------------------------------- Biological Expression Language (BEL) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Biological Expression Language (BEL) is a domain specific language that enables the expression of complex molecular relationships and their context in a machine-readable form. Its simple grammar and expressive power have led to its successful use to describe complex disease networks with several thousands of relationships. For a detailed explanation, see the BEL `1.0 `_ and `2.0 `_, and `2.0+ `_ specifications. BEL Community Links ~~~~~~~~~~~~~~~~~~~ - BEL `Community Portal `_ - BEL `Google Group `_ Design Considerations --------------------- Missing Namespaces and Improper Names ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The use of openly shared controlled vocabularies (namespaces) within BEL facilitates the exchange and consistency of information. Finding the correct :code:`namespace:name` pair is often a difficult part of the curation process. Outdated Namespaces ~~~~~~~~~~~~~~~~~~~ BEL provides a variety of `namespaces `_ covering each of the BEL function types. Selventa used to provide BEL namespace files generated by the deprecated project at ``https://github.com/OpenBEL/resource-generator`` and hosted at the abandoned website ``http://www.belframework.org/``. Newer versions of these namespaces can be found at https://github.com/pharmacome/conso/tree/master/external. Generating New Namespaces ~~~~~~~~~~~~~~~~~~~~~~~~~ In some cases, it is appropriate to design a new namespace, using the `custom namespace specification `_ provided by the OpenBEL Framework. Packages for generating namespace, annotation, and knowledge resources have been grouped in the `Bio2BEL `_ organization on GitHub. Synonym Issues ~~~~~~~~~~~~~~ Due to the huge number of terms across many namespaces, it's difficult for curators to know the domain-specific synonyms that obscure the controlled/preferred term. However, the issue of synonym resolution and semantic searching has already been generally solved by the use of ontologies. Besides just a controlled vocabulary, they also a hierarchical model of knowledge, synonyms with cross-references to databases and other ontologies, and other information semantic reasoning. Ontologies in the biomedical domain can be found at `OBO `_ and `EMBL-EBI OLS `_. Additionally, as a tool for curators, the EMBL Ontology Lookup Service (OLS) allows for semantic searching. Simple queries for the terms 'mitochondrial dysfunction' and 'amyloid beta-peptides' immediately returned results from relevant ontologies, and ended a long debate over how to represent these objects within BEL. EMBL-EBI also provides a programmatic API to the OLS service, for searching terms (http://www.ebi.ac.uk/ols/api/search?q=folic%20acid) and suggesting resolutions (http://www.ebi.ac.uk/ols/api/suggest?q=folic+acid) Implementation -------------- PyBEL is implemented using the PyParsing module. It provides flexibility and incredible speed in parsing compared to regular expression implementation. It also allows for the addition of parsing action hooks, which allow the graph to be checked semantically at compile-time. It uses SQLite to provide a consistent and lightweight caching system for external data, such as namespaces, annotations, ontologies, and SQLAlchemy to provide a cross-platform interface. The same data management system is used to store graphs for high-performance querying. Extensions to BEL ----------------- The PyBEL compiler is fully compliant with both BEL v1.0 and v2.0 and automatically upgrades legacy statements. Additionally, PyBEL includes several additions to the BEL specification to enable expression of important concepts in molecular biology that were previously missing and to facilitate integrating new data types. A short example is the inclusion of protein oxidation in the default BEL namespace for protein modifications. Other, more elaborate additions are outlined below. Syntax for Epigenetics ~~~~~~~~~~~~~~~~~~~~~~ PyBEL introduces the gene modification function, gmod(), as a syntax for encoding epigenetic modifications. Its usage mirrors the pmod() function for proteins and includes arguments for methylation. For example, the methylation of NDUFB6 was found to be negatively correlated with its expression in a study of insulin resistance and Type II diabetes. This can now be expressed in BEL such as in the following statement: ``g(HGNC:NDUFB6, gmod(Me)) negativeCorrelation r(HGNC:NDUFB6)`` References: - https://www.ncbi.nlm.nih.gov/pubmed/17948130 - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4655260/ .. note:: This syntax is currently under consideration as `BEP-0006 `_. Definition of Namespaces as Regular Expressions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BEL imposes the constraint that each identifier must be qualified with an enumerated namespace to enable semantic interoperability and data integration. However, enumerating a namespace with potentially billions of names, such as dbSNP, poses a computational issue. PyBEL introduces syntax for defining namespaces with a consistent pattern using a regular expression to overcome this issue. For these namespaces, semantic validation can be perform in post-processing against the underlying database. The dbSNP namespace can be defined with a syntax familiar to BEL annotation definitions with regular expressions as follows: ``DEFINE NAMESPACE dbSNP AS PATTERN "rs[0-9]+"`` .. note:: This syntax was proposed with `BEP-0005 `_ and has been officially accepted as part of the BEL 2.1 specification. Definition of Resources using OWL ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Previous versions of PyBEL until 0.11.2 had an alternative namespace definition. Now it is recommended to either generate namespace files with reproducible build scripts following the Bio2BEL framework, or to directly add them to the database with the Bio2BEL :class:`bio2bel.manager.namespace_manager.NamespaceManagerMixin` extension. Things to Consider ------------------ Do All Statements Need Supporting Text? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Yes! All statements must be minimally qualified with a citation and evidence (now called SupportingText in BEL 2.0) to maintain provenance. Statements without evidence can't be traced to their source or evaluated independently from the curator, so they are excluded. Multiple Annotations ~~~~~~~~~~~~~~~~~~~~ All single annotations are considered as single element sets. When multiple annotations are present, all are unioned and attached to a given edge. .. code:: SET Citation = {"PubMed","Example Article","12345"} SET ExampleAnnotation1 = {"Example Value 11", "Example Value 12"} SET ExampleAnnotation2 = {"Example Value 21", "Example Value 22"} p(HGNC:YFG1) -> p(HGNC:YFG2) Namespace and Annotation Name Choices ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :code:`*.belns` and :code:`*.belanno` configuration files include an entry called "Keyword" in their respective [Namespace] and [AnnotationDefinition] sections. To maintain understandability between BEL documents, PyBEL warns when the names given in :code:`*.bel` documents do not match their respective resources. For now, capitalization is not considered, but in the future, PyBEL will also warn when capitalization is not properly stylized, like forgetting the lowercase 'h' in "ChEMBL". Why Not Nested Statements? ~~~~~~~~~~~~~~~~~~~~~~~~~~ BEL has different relationships for modeling direct and indirect causal relations. Direct ****** - :code:`A => B` means that `A` directly increases `B` through a physical process. - :code:`A =| B` means that `A` directly decreases `B` through a physical process. Indirect ******** The relationship between two entities can be coded in BEL, even if the process is not well understood. - :code:`A -> B` means that `A` indirectly increases `B`. There are hidden elements in `X` that mediate this interaction through a pathway direct interactions :code:`A (=> or =|) X_1 (=> or =|) ... X_n (=> or =|) B`, or through a set of multiple pathways that constitute a network. - :code:`A -| B` means that `A` indirectly decreases `B`. Like for :code:`A -> B`, this process involves hidden components with varying activities. Increasing Nested Relationships ******************************* BEL also allows object of a relationship to be another statement. - :code:`A => (B => C)` means that `A` increases the process by which `B` increases `C`. The example in the BEL Spec :code:`p(HGNC:GATA1) => (act(p(HGNC:ZBTB16)) => r(HGNC:MPL))` represents GATA1 directly increasing the process by which ZBTB16 directly increases MPL. Before, directly increasing was used to specify physical contact, so it's reasonable to conclude that :code:`p(HGNC:GATA1) => act(p(HGNC:ZBTB16))`. The specification cites examples when `B` is an activity that only is affected in the context of `A` and `C`. This complicated enough that it is both impractical to standardize during curation, and impractical to represent in a network. - :code:`A -> (B => C)` can be interpreted by assuming that `A` indirectly increases `B`, and because of monotonicity, conclude that :code:`A -> C` as well. - :code:`A => (B -> C)` is more difficult to interpret, because it does not describe which part of process :code:`B -> C` is affected by `A` or how. Is it that :code:`A => B`, and :code:`B => C`, so we conclude :code:`A -> C`, or does it mean something else? Perhaps `A` impacts a different portion of the hidden process in :code:`B -> C`. These statements are ambiguous enough that they should be written as just :code:`A => B`, and :code:`B -> C`. If there is no literature evidence for the statement :code:`A -> C`, then it is not the job of the curator to make this inference. Identifying statements of this might be the goal of a bioinformatics analysis of the BEL network after compilation. - :code:`A -> (B -> C)` introduces even more ambiguity, and it should not be used. - :code:`A => (B =| C)` states `A` increases the process by which `B` decreases `C`. One interpretation of this statement might be that :code:`A => B` and :code:`B =| C`. An analysis could infer :code:`A -| C`. Statements in the form of :code:`A -> (B =| C)` can also be resolved this way, but with added ambiguity. Decreasing Nested Relationships ******************************* While we could agree on usage for the previous examples, the decrease of a nested statement introduces an unreasonable amount of ambiguity. - :code:`A =| (B => C)` could mean `A` decreases `B`, and `B` also increases `C`. Does this mean A decreases C, or does it mean that C is still increased, but just not as much? Which of these statements takes precedence? Or do their effects cancel? The same can be said about :code:`A -| (B => C)`, and with added ambiguity for indirect increases :code:`A -| (B -> C)` - :code:`A =| (B =| C)` could mean that `A` decreases `B` and `B` decreases `C`. We could conclude that `A` increases `C`, or could we again run into the problem of not knowing the precedence? The same is true for the indirect versions. Recommendations for Use in PyBEL ******************************** After considering the ambiguity of nested statements to be a great risk to clarity, and PyBEL disables the usage of nested statements by default. See the Input and Output section for different parser settings. At Fraunhofer SCAI, curators resolved these statements to single statements to improve the precision and readability of our BEL documents. While most statements in the form :code:`A rel1 (B rel2 C)` can be reasonably expanded to :code:`A rel1 B` and :code:`B rel2 C`, the few that cannot are the difficult-to-interpret cases that we need to be careful about in our curation and later analyses. Why Not RDF? ~~~~~~~~~~~~ Current bel2rdf serialization tools build URLs with the OpenBEL Framework domain as a namespace, rather than respect the original namespaces of original entities. This does not follow the best practices of the semantic web, where URL’s representing an object point to a real page with additional information. For example, UniProt does an exemplary job of this. Ultimately, using non-standard URLs makes harmonizing and data integration difficult. Additionally, the RDF format does not easily allow for the annotation of edges. A simple statement in BEL that one protein up-regulates another can be easily represented in a triple in RDF, but when the annotations and citation from the BEL document need to be included, this forces RDF serialization to use approaches like representing the statement itself as a node. RDF was not intended to represent this type of information, but more properly for locating resources (hence its name). Furthermore, many blank nodes are introduced throughout the process. This makes RDF incredibly difficult to understand or work with. Later, writing queries in SPARQL becomes very difficult because the data format is complicated and the language is limited. For example, it would be incredibly complicated to write a query in SPARQL to get the objects of statements from publications by a certain author. pybel-0.15.5/docs/source/meta/000077500000000000000000000000001426625374700161135ustar00rootroot00000000000000pybel-0.15.5/docs/source/meta/postmortem.rst000066400000000000000000000022271426625374700210610ustar00rootroot00000000000000Current Issues ============== Speed ----- Speed is still an issue, because documents above 100K lines still take a couple minutes to run. This issue is exacerbated by (optionally) logging output to the console, which can make it more than 3x or 4x as slow. Namespaces ---------- The default namespaces from OpenBEL do not follow a standard file format. They are similar to INI config files, but do not use consistent delimiters. Also, many of the namespaces don't respect that the delimiter should not be used in the namespace names. There are also lots of names with strange characters, which may have been caused by copying from a data source that had specfic escape characters without proper care. Testing ------- Testing was very difficult because the example documents on the OpenBEL website had many semantic errors, such as using names and annotation values that were not defined within their respective namespace and annotation definition files. They also contained syntax errors like naked names, which are not only syntatically incorrect, but lead to bad science; and improper usage of activities, like illegally nesting an activity within a composite statement. pybel-0.15.5/docs/source/meta/references.rst000066400000000000000000000037111426625374700207700ustar00rootroot00000000000000References ========== If you find PyBEL useful for your work, please consider citing [1]_: .. [1] Hoyt, C. T., *et al.* (2017). `PyBEL: a Computational Framework for Biological Expression Language `_. Bioinformatics, 34(December), 1–2. Related Publications -------------------- We have used PyBEL in several other projects and publications. Below is a sample: - Domingo-Fernández, D., Mubeen, S., Marin-Llao, J., Hoyt, C. T., & Hofmann-Apitius, M. (2018). PathMe: Merging and exploring mechanistic pathway knowledge. bioRxiv, 451625. - Domingo-Fernández, D., Hoyt, C. T., Alvarez, C. B., Marin-Llao, J., Hofmann-Apitius, M. (2018). ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases. Npj Systems Biology and Applications, 5(1), 3. https://doi.org/10.1038/s41540-018-0078-8 - Hoyt, C. T., et al. (2018). A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities. Database, 2018(1). https://doi.org/10.1093/database/bay050 - Hoyt, C. T., et al. (2019). Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language. BioRxiv, 536409. https://doi.org/10.1101/536409 - Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11. https://doi.org/10.1093/database/bay126 - Ali, M., et al. (2019). BioKEEN: A library for learning and evaluating biological knowledge graph embeddings. Bioinformatics, btz117. https://doi.org/10.1093/bioinformatics/btz117 Software using PyBEL -------------------- - https://github.com/cthoyt/bel-repository - https://github.com/bio2bel - https://github.com/sorgerlab/indra - https://github.com/smartDataAnalytics/biokeen BEL Content ----------- - https://github.com/neurommsig/neurommsig-knowledge - https://github.com/pharmacome/knowledge pybel-0.15.5/docs/source/meta/technology.rst000066400000000000000000000133241426625374700210230ustar00rootroot00000000000000Technology ========== This page is meant to describe the development stack for PyBEL, and should be a useful introduction for contributors. Versioning ---------- PyBEL is versioned on GitHub so changes in its code can be tracked over time and to make use of the variety of software development plugins. Code is produced following the `Git Flow `_ philosophy, which means that new features are coded in branches off of the development branch and merged after they are triaged. Finally, develop is merged into master for releases. If there are bugs in releases that need to be fixed quickly, "hot fix" branches from master can be made, then merged back to master and develop after fixing the problem. Testing in PyBEL ---------------- PyBEL is written with extensive unit testing and integration testing. Whenever possible, test- driven development is practiced. This means that new ideas for functions and features are encoded as blank classes/functions and directly writing tests for the desired output. After tests have been written that define how the code should work, the implementation can be written. Test-driven development requires us to think about design before making quick and dirty implementations. This results in better code. Additionally, thorough testing suites make it possible to catch when changes break existing functionality. Tests are written with the standard :mod:`unittest` library. Unit Testing ~~~~~~~~~~~~ Unit tests check that the functionality of the different parts of PyBEL work independently. An example unit test can be found in :code:`tests.test_parse_bel.TestAbundance.test_short_abundance`. It ensures that the parser is able to handle a given string describing the abundance of a chemical/other entity in BEL. It tests that the parser produces the correct output, that the BEL statement is converted to the correct internal representation. In this example, this is a tuple describing the abundance of oxygen atoms. Finally, it tests that this representation is added as a node in the underlying BEL graph with the appropriate attributes added. Integration Testing ~~~~~~~~~~~~~~~~~~~ Integration tests are more high level, and ensure that the software accomplishes more complicated goals by using many components. An example integration test is found in tests.test_import.TestImport.test_from_fileURL. This test ensures that a BEL script can be read and results in a NetworkX object that contains all of the information described in the script Tox ~~~ While IDEs like PyCharm provide excellent testing tools, they are not programmatic. `Tox `_ is python package that provides a CLI interface to run automated testing procedures (as well as other build functions, that aren't important to explain here). In PyBEL, it is used to run the unit tests in the :code:`tests` folder with the :mod:`pytest` harness. It also runs :code:`check-manifest`, builds the documentation with :mod:`sphinx`, and computes the code coverage of the tests. The entire procedure is defined in :code:`tox.ini`. Tox also allows test to be done on many different versions of Python. Continuous Integration ~~~~~~~~~~~~~~~~~~~~~~ Continuous integration is a philosophy of automatically testing code as it changes. PyBEL makes use of the Travis CI server to perform testing because of its tight integration with GitHub. Travis automatically installs git hooks inside GitHub so it knows when a new commit is made. Upon each commit, Travis downloads the newest commit from GitHub and runs the tests configured in the :code:`.travis.yml` file in the top level of the PyBEL repository. This file effectively instructs the Travis CI server to run Tox. It also allows for the modification of the environment variables. This is used in PyBEL to test many different versions of python. Code Coverage ~~~~~~~~~~~~~ After building, Travis sends code coverage results to `codecov.io `_. This site helps visualize untested code and track the improvement of testing coverage over time. It also integrates with GitHub to show which feature branches are inadequately tested. In development of PyBEL, inadequately tested code is not allowed to be merged into develop. Versioning ~~~~~~~~~~ PyBEL uses semantic versioning. In general, the project's version string will has a suffix :code:`-dev` like in :code:`0.3.4-dev` throughout the development cycle. After code is merged from feature branches to develop and it is time to deploy, this suffix is removed and develop branch is merged into master. The version string appears in multiple places throughout the project, so BumpVersion is used to automate the updating of these version strings. See .bumpversion.cfg for more information. Deployment ---------- PyBEL is also distributed through PyPI (pronounced Py-Pee-Eye). Travis CI has a wonderful integration with PyPI, so any time a tag is made on the master branch (and also assuming the tests pass), a new distribution is packed and sent to PyPI. Refer to the "deploy" section at the bottom of the :code:`.travis.yml` file for more information, or the Travis CI `PyPI deployment documentation `_. As a side note, Travis CI has an encryption tool so the password for the PyPI account can be displayed publicly on GitHub. Travis decrypts it before performing the upload to PyPI. Steps ~~~~~ 1. :code:`bumpversion release` on development branch 2. Push to git 3. After tests pass, merge develop in to master 4. After tests pass, create a tag on GitHub with the same name as the version number (on master) 5. Travis will automatically deploy to PyPI after tests pass. After checking deployment has been successful, switch to develop and :code:`bumpversion patch` pybel-0.15.5/docs/source/reference/000077500000000000000000000000001426625374700171235ustar00rootroot00000000000000pybel-0.15.5/docs/source/reference/constants.rst000066400000000000000000000001611426625374700216670ustar00rootroot00000000000000Constants ========= .. automodule:: pybel.constants :members: .. automodule:: pybel.language :members: pybel-0.15.5/docs/source/reference/database/000077500000000000000000000000001426625374700206675ustar00rootroot00000000000000pybel-0.15.5/docs/source/reference/database/manager.rst000066400000000000000000000011441426625374700230330ustar00rootroot00000000000000Manager ======= Manager API ----------- The BaseManager takes care of building and maintaining the connection to the database via SQLAlchemy. .. autoclass:: pybel.manager.BaseManager :members: The Manager collates multiple groups of functions for interacting with the database. For sake of code clarity, they are separated across multiple classes that are documented below. .. autoclass:: pybel.manager.Manager :members: :show-inheritance: Manager Components ------------------ .. autoclass:: pybel.manager.NetworkManager :members: .. autoclass:: pybel.manager.QueryManager :members: pybel-0.15.5/docs/source/reference/database/models.rst000066400000000000000000000001011426625374700226740ustar00rootroot00000000000000Models ====== .. automodule:: pybel.manager.models :members: pybel-0.15.5/docs/source/reference/dsl.rst000066400000000000000000000031341426625374700204400ustar00rootroot00000000000000Internal Domain Specific Language ================================= .. automodule:: pybel.dsl Primitives ---------- .. autoclass:: pybel.dsl.Entity .. autoclass:: pybel.dsl.BaseEntity .. autoclass:: pybel.dsl.BaseAbundance .. autoclass:: pybel.dsl.ListAbundance Named Entities -------------- .. autoclass:: pybel.dsl.Abundance .. autoclass:: pybel.dsl.BiologicalProcess .. autoclass:: pybel.dsl.Pathology .. autoclass:: pybel.dsl.Population Central Dogma ------------- .. autoclass:: pybel.dsl.CentralDogma .. autoclass:: pybel.dsl.Gene .. autoclass:: pybel.dsl.Transcribable .. autoclass:: pybel.dsl.Rna .. autoclass:: pybel.dsl.MicroRna .. autoclass:: pybel.dsl.Protein Variants ~~~~~~~~ .. autoclass:: pybel.dsl.Variant .. autoclass:: pybel.dsl.ProteinModification .. autoclass:: pybel.dsl.GeneModification .. autoclass:: pybel.dsl.Hgvs .. autoclass:: pybel.dsl.HgvsReference .. autoclass:: pybel.dsl.HgvsUnspecified .. autoclass:: pybel.dsl.ProteinSubstitution .. autoclass:: pybel.dsl.Fragment Fusions ------- .. autoclass:: pybel.dsl.FusionBase .. autoclass:: pybel.dsl.GeneFusion .. autoclass:: pybel.dsl.RnaFusion .. autoclass:: pybel.dsl.ProteinFusion Fusion Ranges ~~~~~~~~~~~~~ .. autoclass:: pybel.dsl.FusionRangeBase .. autoclass:: pybel.dsl.EnumeratedFusionRange .. autoclass:: pybel.dsl.MissingFusionRange List Abundances ~~~~~~~~~~~~~~~ .. autoclass:: pybel.dsl.ComplexAbundance .. autoclass:: pybel.dsl.CompositeAbundance .. autoclass:: pybel.dsl.Reaction Utilities --------- The following functions are useful to build DSL objects from dictionaries: .. autofunction:: pybel.tokens.parse_result_to_dsl pybel-0.15.5/docs/source/reference/io.rst000066400000000000000000000162111426625374700202650ustar00rootroot00000000000000Input and Output ================ .. automodule:: pybel.io .. autofunction:: pybel.load .. autofunction:: pybel.dump Import ------ Parsing Modes ~~~~~~~~~~~~~ The PyBEL parser has several modes that can be enabled and disabled. They are described below. Allow Naked Names ***************** By default, this is set to :code:`False`. The parser does not allow identifiers that are not qualified with namespaces (*naked names*), like in :code:`p(YFG)`. A proper namespace, like :code:`p(HGNC:YFG)` must be used. By setting this to :code:`True`, the parser becomes permissive to naked names. In general, this is bad practice and this feature will be removed in the future. Allow Nested ************ By default, this is set to :code:`False`. The parser does not allow nested statements is disabled. See `overview`. By setting this to :code:`True` the parser will accept nested statements one level deep. Citation Clearing ***************** By default, this is set to :code:`True`. While the BEL specification clearly states how the language should be used as a state machine, many BEL documents do not conform to the strict :code:`SET`/:code:`UNSET` rules. To guard against annotations accidentally carried from one set of statements to the next, the parser has two modes. By default, in citation clearing mode, when a :code:`SET CITATION` command is reached, it will clear all other annotations (except the :code:`STATEMENT_GROUP`, which has higher priority). This behavior can be disabled by setting this to :code:`False` to re-enable strict parsing. Reference ~~~~~~~~~ .. autofunction:: pybel.from_bel_script .. autofunction:: pybel.from_bel_script_url .. autofunction:: pybel.to_bel_script Hetionet ~~~~~~~~ .. automodule:: pybel.io.hetionet .. autofunction:: pybel.from_hetionet_json .. autofunction:: pybel.from_hetionet_file .. autofunction:: pybel.from_hetionet_gz .. autofunction:: pybel.get_hetionet Transport --------- All transport pairs are reflective and data-preserving. Bytes ~~~~~ .. automodule:: pybel.io.gpickle .. autofunction:: pybel.from_bytes .. autofunction:: pybel.to_bytes .. autofunction:: pybel.from_bytes_gz .. autofunction:: pybel.to_bytes_gz .. autofunction:: pybel.from_pickle .. autofunction:: pybel.to_pickle .. autofunction:: pybel.from_pickle_gz .. autofunction:: pybel.to_pickle_gz Node-Link JSON ~~~~~~~~~~~~~~ .. automodule:: pybel.io.nodelink .. autofunction:: pybel.from_nodelink .. autofunction:: pybel.to_nodelink .. autofunction:: pybel.from_nodelink_jsons .. autofunction:: pybel.to_nodelink_jsons .. autofunction:: pybel.from_nodelink_file .. autofunction:: pybel.to_nodelink_file .. autofunction:: pybel.from_nodelink_gz .. autofunction:: pybel.to_nodelink_gz Streamable BEL (JSONL) ~~~~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.io.sbel .. autofunction:: pybel.from_sbel .. autofunction:: pybel.to_sbel .. autofunction:: pybel.from_sbel_file .. autofunction:: pybel.to_sbel_file .. autofunction:: pybel.from_sbel_gz .. autofunction:: pybel.to_sbel_gz Cyberinfrastructure Exchange ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.io.cx .. autofunction:: pybel.from_cx .. autofunction:: pybel.to_cx .. autofunction:: pybel.from_cx_jsons .. autofunction:: pybel.to_cx_jsons .. autofunction:: pybel.from_cx_file .. autofunction:: pybel.to_cx_file .. autofunction:: pybel.from_cx_gz .. autofunction:: pybel.to_cx_gz JSON Graph Interchange Format ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.io.jgif .. autofunction:: pybel.from_jgif .. autofunction:: pybel.to_jgif .. autofunction:: pybel.from_jgif_jsons .. autofunction:: pybel.to_jgif_jsons .. autofunction:: pybel.from_jgif_file .. autofunction:: pybel.to_jgif_file .. autofunction:: pybel.from_jgif_gz .. autofunction:: pybel.to_jgif_gz .. autofunction:: pybel.post_jgif .. autofunction:: pybel.from_cbn_jgif .. autofunction:: pybel.from_cbn_jgif_file GraphDati ~~~~~~~~~ .. automodule:: pybel.io.graphdati .. autofunction:: pybel.to_graphdati .. autofunction:: pybel.from_graphdati .. autofunction:: pybel.to_graphdati_file .. autofunction:: pybel.from_graphdati_file .. autofunction:: pybel.to_graphdati_gz .. autofunction:: pybel.from_graphdati_gz .. autofunction:: pybel.to_graphdati_jsons .. autofunction:: pybel.from_graphdati_jsons .. autofunction:: pybel.to_graphdati_jsonl .. autofunction:: pybel.to_graphdati_jsonl_gz INDRA ~~~~~ .. automodule:: pybel.io.indra .. autofunction:: pybel.from_indra_statements .. autofunction:: pybel.from_indra_statements_json .. autofunction:: pybel.from_indra_statements_json_file .. autofunction:: pybel.to_indra_statements .. autofunction:: pybel.to_indra_statements_json .. autofunction:: pybel.to_indra_statements_json_file .. autofunction:: pybel.from_biopax Visualization ------------- Jupyter ~~~~~~~ .. automodule:: pybel.io.jupyter .. autofunction:: pybel.to_jupyter Analytical Services ------------------- PyNPA ~~~~~ .. automodule:: pybel.io.pynpa .. autofunction:: pybel.to_npa_directory .. autofunction:: pybel.to_npa_dfs HiPathia ~~~~~~~~ .. automodule:: pybel.io.hipathia .. autofunction:: pybel.to_hipathia .. autofunction:: pybel.to_hipathia_dfs .. autofunction:: pybel.from_hipathia_paths .. autofunction:: pybel.from_hipathia_dfs SPIA ~~~~ .. automodule:: pybel.io.spia .. autofunction:: pybel.to_spia_dfs .. autofunction:: pybel.to_spia_excel .. autofunction:: pybel.to_spia_tsvs PyKEEN ~~~~~~ .. automodule:: pybel.io.pykeen .. autofunction:: pybel.io.pykeen.get_triples_from_bel .. autofunction:: pybel.io.pykeen.get_triples_from_bel_nodelink .. autofunction:: pybel.io.pykeen.get_triples_from_bel_pickle .. autofunction:: pybel.io.pykeen.get_triples_from_bel_commons Machine Learning ~~~~~~~~~~~~~~~~ .. automodule:: pybel.io.triples .. autofunction:: pybel.to_triples .. autofunction:: pybel.to_triples_file .. autofunction:: pybel.to_edgelist Web Services ------------ BEL Commons ~~~~~~~~~~~ .. automodule:: pybel.io.bel_commons_client .. autofunction:: pybel.from_bel_commons .. autofunction:: pybel.to_bel_commons Amazon Simple Storage Service (S3) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.io.aws .. autofunction:: pybel.to_s3 .. autofunction:: pybel.from_s3 BioDati ~~~~~~~ .. automodule:: pybel.io.biodati_client .. autofunction:: pybel.to_biodati .. autofunction:: pybel.from_biodati Fraunhofer OrientDB ~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.io.fraunhofer_orientdb .. autofunction:: pybel.from_fraunhofer_orientdb EMMAA ~~~~~~ .. automodule:: pybel.io.emmaa .. autofunction:: pybel.from_emmaa Databases --------- SQL Databases ~~~~~~~~~~~~~ .. automodule:: pybel.manager.database_io .. autofunction:: pybel.from_database .. autofunction:: pybel.to_database Neo4j ~~~~~ .. automodule:: pybel.io.neo4j .. autofunction:: pybel.to_neo4j Lossy Export ------------ Umbrella Node-Link JSON ~~~~~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.io.umbrella_nodelink .. autofunction:: pybel.to_umbrella_nodelink .. autofunction:: pybel.to_umbrella_nodelink_file .. autofunction:: pybel.to_umbrella_nodelink_gz GraphML ~~~~~~~ .. automodule:: pybel.io.graphml .. autofunction:: pybel.to_graphml Miscellaneous ~~~~~~~~~~~~~ .. automodule:: pybel.io.extras .. autofunction:: pybel.to_csv .. autofunction:: pybel.to_sif .. autofunction:: pybel.to_gsea pybel-0.15.5/docs/source/reference/logging.rst000066400000000000000000000001211426625374700212750ustar00rootroot00000000000000Logging Messages ================ .. automodule:: pybel.exceptions :members: pybel-0.15.5/docs/source/reference/mutations/000077500000000000000000000000001426625374700211465ustar00rootroot00000000000000pybel-0.15.5/docs/source/reference/mutations/collapse.rst000066400000000000000000000001171426625374700235010ustar00rootroot00000000000000Collapse ======== .. automodule:: pybel.struct.mutation.collapse :members: pybel-0.15.5/docs/source/reference/mutations/deletion.rst000066400000000000000000000001171426625374700235020ustar00rootroot00000000000000Deletion ======== .. automodule:: pybel.struct.mutation.deletion :members: pybel-0.15.5/docs/source/reference/mutations/expansion.rst000066400000000000000000000001221426625374700236770ustar00rootroot00000000000000Expansion ========= .. automodule:: pybel.struct.mutation.expansion :members: pybel-0.15.5/docs/source/reference/mutations/induction.rst000066400000000000000000000001221426625374700236670ustar00rootroot00000000000000Induction ========= .. automodule:: pybel.struct.mutation.induction :members: pybel-0.15.5/docs/source/reference/mutations/induction_expansion.rst000066400000000000000000000001701426625374700257560ustar00rootroot00000000000000Induction and Expansion ======================= .. automodule:: pybel.struct.mutation.induction_expansion :members: pybel-0.15.5/docs/source/reference/mutations/inference.rst000066400000000000000000000001221426625374700236310ustar00rootroot00000000000000Inference ========= .. automodule:: pybel.struct.mutation.inference :members: pybel-0.15.5/docs/source/reference/mutations/metadata.rst000066400000000000000000000002751426625374700234640ustar00rootroot00000000000000Metadata ======== .. automodule:: pybel.struct.mutation.metadata :members: .. autofunction:: pybel.manager.enrich_pubmed_citations .. autofunction:: pybel.manager.enrich_pmc_citations pybel-0.15.5/docs/source/reference/mutations/mutations.rst000066400000000000000000000001161426625374700237210ustar00rootroot00000000000000Mutations ========= .. automodule:: pybel.struct.mutation.utils :members: pybel-0.15.5/docs/source/reference/parser.rst000066400000000000000000000023061426625374700211520ustar00rootroot00000000000000Parsers ======= This page is for users who want to squeeze the most bizarre possibilities out of PyBEL. Most users will not need this reference. PyBEL makes extensive use of the PyParsing module. The code is organized to different modules to reflect the different faces ot the BEL language. These parsers support BEL 2.0 and have some backwards compatibility for rewriting BEL v1.0 statements as BEL v2.0. The biologist and bioinformatician using this software will likely never need to read this page, but a developer seeking to extend the language will be interested to see the inner workings of these parsers. See: https://github.com/OpenBEL/language/blob/master/version_2.0/MIGRATE_BEL1_BEL2.md BEL Parser ---------- .. autoclass:: pybel.parser.parse_bel.BELParser :members: .. autofunction:: pybel.io.line_utils.parse_lines Metadata Parser --------------- .. autoclass:: pybel.parser.parse_metadata.MetadataParser :members: Control Parser -------------- .. autoclass:: pybel.parser.parse_control.ControlParser :members: Concept Parser -------------- .. autoclass:: pybel.parser.parse_concept.ConceptParser :members: Sub-Parsers ----------- .. automodule:: pybel.parser.modifiers :members: pybel-0.15.5/docs/source/reference/struct/000077500000000000000000000000001426625374700204475ustar00rootroot00000000000000pybel-0.15.5/docs/source/reference/struct/datamodel.rst000066400000000000000000000437601426625374700231450ustar00rootroot00000000000000Data Model ========== .. automodule:: pybel.struct Constants --------- These documents refer to many aspects of the data model using constants, which can be found in the top-level module :mod:`pybel.constants`. Terms describing abundances, annotations, and other internal data are designated in :mod:`pybel.constants` with full-caps, such as :data:`pybel.constants.FUNCTION` and :data:`pybel.constants.PROTEIN`. For normal usage, we suggest referring to values in dictionaries by these constants, in case the hard-coded strings behind these constants change. Function Nomenclature ~~~~~~~~~~~~~~~~~~~~~ The following table shows PyBEL's internal mapping from BEL functions to its own constants. This can be accessed programatically via :data:`pybel.parser.language.abundance_labels`. +-------------------------------------------+-----------------------------------+--------------------------------------+ | BEL Function | PyBEL Constant | PyBEL DSL | +===========================================+===================================+======================================+ | ``a()``, ``abundance()`` |:data:`pybel.constants.ABUNDANCE` |:class:`pybel.dsl.Abundance` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``g()``, ``geneAbundance()`` |:data:`pybel.constants.GENE` |:class:`pybel.dsl.Gene` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``r()``, ``rnaAbunance()`` |:data:`pybel.constants.RNA` |:class:`pybel.dsl.Rna` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``m()``, ``microRNAAbundance()`` |:data:`pybel.constants.MIRNA` |:class:`pybel.dsl.MicroRna` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``p()``, ``proteinAbundance()`` |:data:`pybel.constants.PROTEIN` |:class:`pybel.dsl.Protein` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``bp()``, ``biologicalProcess()`` |:data:`pybel.constants.BIOPROCESS` |:class:`pybel.dsl.BiologicalProcess` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``path()``, ``pathology()`` |:data:`pybel.constants.PATHOLOGY` |:class:`pybel.dsl.Pathology` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``complex()``, ``complexAbundance()`` |:data:`pybel.constants.COMPLEX` |:class:`pybel.dsl.ComplexAbundance` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``composite()``, ``compositeAbundance()`` |:data:`pybel.constants.COMPOSITE` |:class:`pybel.dsl.CompositeAbundance` | +-------------------------------------------+-----------------------------------+--------------------------------------+ | ``rxn()``, ``reaction()`` |:data:`pybel.constants.REACTION` |:class:`pybel.dsl.Reaction` | +-------------------------------------------+-----------------------------------+--------------------------------------+ Graph ----- .. autoclass:: pybel.BELGraph :exclude-members: nodes_iter, edges_iter, add_warning, fresh_copy, document :members: .. automethod:: __add__ .. automethod:: __iadd__ .. automethod:: __and__ .. automethod:: __iand__ Dispatches ~~~~~~~~~~ Dispatches are classes that enable easy access to summary, mutation, and other functions that consume graphs directly through the :class:`pybel.BELGraph` interface. .. autoclass:: pybel.struct.graph.CountDispatch :members: .. autoclass:: pybel.struct.graph.InduceDispatch :members: .. autoclass:: pybel.struct.graph.SummarizeDispatch :members: .. autoclass:: pybel.struct.graph.ExpandDispatch :members: .. autoclass:: pybel.struct.graph.PlotDispatch :members: Nodes ----- Nodes (or *entities*) in a :class:`pybel.BELGraph` represent physical entities' abundances. Most contain information about the identifier for the entity using a namespace/name pair. The PyBEL parser converts BEL terms to an internal representation using an internal domain specific language (DSL) that allows for writing BEL directly in Python. For example, after the BEL term :code:`p(hgnc:GSK3B)` is parsed, it is instantiated as a Python object using the DSL function corresponding to the ``p()`` function in BEL, :class:`pybel.dsl.Protein`, like: .. code:: python from pybel.dsl import Protein gsk3b_protein = Protein(namespace='hgnc', name='GSK3B') :class:`pybel.dsl.Protein`, like the others mentioned before, inherit from :class:`pybel.dsl.BaseEntity`, which itself inherits from :class:`dict`. Therefore, the resulting object can be used like a dict that looks like: .. code-block:: python from pybel.constants import * { FUNCTION: PROTEIN, NAMESPACE: 'hgnc', NAME: 'GSK3B', } Alternatively, it can be used in more exciting ways, outlined later in the documentation for :mod:`pybel.dsl`. Variants ~~~~~~~~ The addition of a variant tag results in an entry called 'variants' in the data dictionary associated with a given node. This entry is a list with dictionaries describing each of the variants. All variants have the entry 'kind' to identify whether it is a post-translational modification (PTM), gene modification, fragment, or HGVS variant. .. warning:: The canonical ordering for the elements of the ``VARIANTS`` list correspond to the sorted order of their corresponding node tuples using :func:`pybel.parser.canonicalize.sort_dict_list`. Rather than directly modifying the BELGraph's structure, use :meth:`pybel.BELGraph.add_node_from_data`, which takes care of automatically canonicalizing this dictionary. .. automodule:: pybel.parser.modifiers.variant Gene Substitutions ~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.parser.modifiers.gene_substitution Gene Modifications ~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.parser.modifiers.gene_modification Protein Substitutions ~~~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.parser.modifiers.protein_substitution Protein Modifications ~~~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.parser.modifiers.protein_modification Protein Truncations ~~~~~~~~~~~~~~~~~~~ .. automodule:: pybel.parser.modifiers.truncation Protein Fragments ~~~~~~~~~~~~~~~~~ .. automodule:: pybel.parser.modifiers.fragment Fusions ~~~~~~~ .. automodule:: pybel.parser.modifiers.fusion Unqualified Edges ----------------- Unqualified edges are automatically inferred by PyBEL and do not contain citations or supporting evidence. Variant and Modifications' Parent Relations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ All variants, modifications, fragments, and truncations are connected to their parent entity with an edge having the relationship :code:`hasParent`. For :code:`p(hgnc:GSK3B, var(p.Gly123Arg))`, the following edge is inferred: .. code:: p(hgnc:GSK3B, var(p.Gly123Arg)) hasParent p(hgnc:GSK3B) All variants have this relationship to their reference node. BEL does not specify relationships between variants, such as the case when a given phosphorylation is necessary to make another one. This knowledge could be encoded directly like BEL, since PyBEL does not restrict users from manually asserting unqualified edges. List Abundances ~~~~~~~~~~~~~~~ Complexes and composites that are defined by lists. As of version 0.9.0, they contain a list of the data dictionaries that describe their members. For example :code:`complex(p(hgnc:FOS), p(hgnc:JUN))` becomes: .. code-block:: python from pybel.constants import * { FUNCTION: COMPLEX, MEMBERS: [ { FUNCTION: PROTEIN, NAMESPACE: 'hgnc', NAME: 'FOS', }, { FUNCTION: PROTEIN, NAMESPACE: 'hgnc', NAME: 'JUN', } ] } The following edges are also inferred: .. code:: complex(p(hgnc:FOS), p(hgnc:JUN)) hasMember p(hgnc:FOS) complex(p(hgnc:FOS), p(hgnc:JUN)) hasMember p(hgnc:JUN) .. seealso:: BEL 2.0+ Tutorial on `complex abundances `_ Similarly, :code:`composite(a(CHEBI:malonate), p(hgnc:JUN))` becomes: .. code-block:: python from pybel.constants import * { FUNCTION: COMPOSITE, MEMBERS: [ { FUNCTION: ABUNDANCE, NAMESPACE: 'CHEBI', NAME: 'malonate', }, { FUNCTION: PROTEIN, NAMESPACE: 'hgnc', NAME: 'JUN', } ] } The following edges are inferred: .. code:: composite(a(CHEBI:malonate), p(hgnc:JUN)) hasComponent a(CHEBI:malonate) composite(a(CHEBI:malonate), p(hgnc:JUN)) hasComponent p(hgnc:JUN) .. warning:: The canonical ordering for the elements of the :data:`pybel.constantsMEMBERS` list correspond to the sorted order of their corresponding node tuples using :func:`pybel.parser.canonicalize.sort_dict_list`. Rather than directly modifying the BELGraph's structure, use :meth:`BELGraph.add_node_from_data`, which takes care of automatically canonicalizing this dictionary. .. seealso:: BEL 2.0+ Tutorial on `composite abundances `_ Reactions ~~~~~~~~~ The usage of a reaction causes many nodes and edges to be created. The following example will illustrate what is added to the network for .. code:: rxn(reactants(a(CHEBI:"(3S)-3-hydroxy-3-methylglutaryl-CoA"), a(CHEBI:"NADPH"), \ a(CHEBI:"hydron")), products(a(CHEBI:"mevalonate"), a(CHEBI:"NADP(+)"))) As of version 0.9.0, the reactants' and products' data dictionaries are included as sub-lists keyed ``REACTANTS`` and ``PRODUCTS``. It becomes: .. code-block:: python from pybel.constants import * { FUNCTION: REACTION REACTANTS: [ { FUNCTION: ABUNDANCE, NAMESPACE: 'CHEBI', NAME: '(3S)-3-hydroxy-3-methylglutaryl-CoA' }, { FUNCTION: ABUNDANCE, NAMESPACE: 'CHEBI', NAME: 'NADPH', }, { FUNCTION: ABUNDANCE, NAMESPACE: 'CHEBI', NAME: 'hydron', } ], PRODUCTS: [ { FUNCTION: ABUNDANCE, NAMESPACE: 'CHEBI', NAME: 'mevalonate', }, { FUNCTION: ABUNDANCE, NAMESPACE: 'CHEBI', NAME: 'NADP(+)', } ] } .. warning:: The canonical ordering for the elements of the ``REACTANTS`` and ``PRODUCTS`` lists correspond to the sorted order of their corresponding node tuples using :func:`pybel.parser.canonicalize.sort_dict_list`. Rather than directly modifying the BELGraph's structure, use :meth:`BELGraph.add_node_from_data`, which takes care of automatically canonicalizing this dictionary. The following edges are inferred, where :code:`X` represents the previous reaction, for brevity: .. code:: X hasReactant a(CHEBI:"(3S)-3-hydroxy-3-methylglutaryl-CoA") X hasReactant a(CHEBI:"NADPH") X hasReactant a(CHEBI:"hydron") X hasProduct a(CHEBI:"mevalonate") X hasProduct a(CHEBI:"NADP(+)")) .. seealso:: BEL 2.0+ tutorial on `reactions `_ Edges ----- Design Choices ~~~~~~~~~~~~~~ In the OpenBEL Framework, modifiers such as activities (kinaseActivity, etc.) and transformations (translocations, degradations, etc.) were represented as their own nodes. In PyBEL, these modifiers are represented as a property of the edge. In reality, an edge like :code:`sec(p(hgnc:A)) -> activity(p(hgnc:B), ma(kinaseActivity))` represents a connection between :code:`hgnc:A` and :code:`hgnc:B`. Each of these modifiers explains the context of the relationship between these physical entities. Further, querying a network where these modifiers are part of a relationship is much more straightforward. For example, finding all proteins that are upregulated by the kinase activity of another protein now can be directly queried by filtering all edges for those with a subject modifier whose modification is molecular activity, and whose effect is kinase activity. Having fewer nodes also allows for a much easier display and visual interpretation of a network. The information about the modifier on the subject and activity can be displayed as a color coded source and terminus of the connecting edge. The compiler in OpenBEL framework created nodes for molecular activities like :code:`kin(p(hgnc:YFG))` and induced an edge like :code:`p(hgnc:YFG) actsIn kin(p(hgnc:YFG))`. For transformations, a statement like :code:`tloc(p(hgnc:YFG), GO:intracellular, GO:"cell membrane")` also induced :code:`tloc(p(hgnc:YFG), GO:intracellular, GO:"cell membrane") translocates p(hgnc:YFG)`. In PyBEL, we recognize that these modifications are actually annotations to the type of relationship between the subject's entity and the object's entity. ``p(hgnc:ABC) -> tloc(p(hgnc:YFG), GO:intracellular, GO:"cell membrane")`` is about the relationship between :code:`p(hgnc:ABC)` and :code:`p(hgnc:YFG)`, while the information about the translocation qualifies that the object is undergoing an event, and not just the abundance. This is a confusion with the use of :code:`proteinAbundance` as a keyword, and perhaps is why many people prefer to use just the keyword :code:`p` Example Edge Data Structure ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Because this data is associated with an edge, the node data for the subject and object are not included explicitly. However, information about the activities, modifiers, and transformations on the subject and object are included. Below is the "skeleton" for the edge data model in PyBEL: .. code-block:: python from pybel.constants import * { SUBJECT: { # ... modifications to the subject node. Only present if non-empty. }, RELATION: POSITIVE_CORRELATION, OBJECT: { # ... modifications to the object node. Only present if non-empty. }, EVIDENCE: ..., CITATION : { CITATION_TYPE: CITATION_TYPE_PUBMED, CITATION_REFERENCE: ..., CITATION_DATE: 'YYYY-MM-DD', CITATION_AUTHORS: 'Jon Snow|John Doe', }, ANNOTATIONS: { 'Disease': { 'Colorectal Cancer': True, }, # ... additional annotations as tuple[str,dict[str,bool]] pairs }, } Each edge must contain the ``RELATION``, ``EVIDENCE``, and ``CITATION`` entries. The ``CITATION`` must minimally contain ``CITATION_TYPE`` and ``CITATION_REFERENCE`` since these can be used to look up additional metadata. .. note:: Since version 0.10.2, annotations now always appear as dictionaries, even if only one value is present. Activities ~~~~~~~~~~ Modifiers are added to this structure as well. Under this schema, :code:`p(hgnc:GSK3B, pmod(P, S, 9)) pos act(p(hgnc:GSK3B), ma(kin))` becomes: .. code-block:: python from pybel.constants import * { RELATION: POSITIVE_CORRELATION, OBJECT: { MODIFIER: ACTIVITY, EFFECT: { NAME: 'kin', NAMESPACE: BEL_DEFAULT_NAMESPACE, } }, CITATION: { ... }, EVIDENCE: ..., ANNOTATIONS: { ... }, } Activities without molecular activity annotations do not contain an :data:`pybel.constants.EFFECT` entry: Under this schema, :code:`p(hgnc:GSK3B, pmod(P, S, 9)) pos act(p(hgnc:GSK3B))` becomes: .. code-block:: python from pybel.constants import * { RELATION: POSITIVE_CORRELATION, OBJECT: { MODIFIER: ACTIVITY }, CITATION: { ... }, EVIDENCE: ..., ANNOTATIONS: { ... }, } Locations ~~~~~~~~~ .. automodule:: pybel.parser.modifiers.location Translocations ~~~~~~~~~~~~~~ Translocations have their own unique syntax. :code:`p(hgnc:YFG1) -> sec(p(hgnc:YFG2))` becomes: .. code-block:: python from pybel.constants import * { RELATION: INCREASES, OBJECT: { MODIFIER: TRANSLOCATION, EFFECT: { FROM_LOC: { NAMESPACE: 'GO', NAME: 'intracellular', }, TO_LOC: { NAMESPACE: 'GO', NAME: 'extracellular space', } } }, CITATION: { ... }, EVIDENCE: ..., ANNOTATIONS: { ... }, } .. seealso:: BEL 2.0+ tutorial on `translocations `_ Degradations ~~~~~~~~~~~~ Degradations are more simple, because there's no ::data:`pybel.constants.EFFECT` entry. :code:`p(hgnc:YFG1) -> deg(p(hgnc:YFG2))` becomes: .. code-block:: python from pybel.constants import * { RELATION: INCREASES, OBJECT: { MODIFIER: DEGRADATION, }, CITATION: { ... }, EVIDENCE: ..., ANNOTATIONS: { ... }, } .. warning:: Degradations only provide syntax sugar and will be automatically upgraded in a future version of PyBEL such that: - ``deg(X) -> Y`` is upgraded to ``X -| Y`` - ``deg(X) -| Y`` is upgraded to ``X -> Y`` - ``deg(X) => Y`` is upgraded to ``X =| Y`` - ``deg(X) cnc Y`` is upgraded to ``X cnc Y`` - ``X -> deg(Y)`` is upgraded to ``X -| Y`` - ``X => deg(Y)`` is upgraded to ``X =| Y`` - ``X cnc deg(Y)`` is upgraded to ``X cnc Y`` - ``X -| deg(Y)`` is undefined pybel-0.15.5/docs/source/reference/struct/examples.rst000066400000000000000000000010131426625374700230120ustar00rootroot00000000000000Example Networks ================ .. automodule:: pybel.examples .. automodule:: pybel.examples.egf_example .. py:data:: pybel.examples.egf_graph .. automodule:: pybel.examples.sialic_acid_example .. py:data:: pybel.examples.sialic_acid_graph .. automodule:: pybel.examples.braf_example .. py:data:: pybel.examples.braf_example_graph .. automodule:: pybel.examples.statin_example .. py:data:: pybel.examples.statin_graph .. automodule:: pybel.examples.tloc_example .. py:data:: pybel.examples.ras_tloc_graph pybel-0.15.5/docs/source/reference/struct/filters.rst000066400000000000000000000001031426625374700226430ustar00rootroot00000000000000Filters ======= .. automodule:: pybel.struct.filters :members: pybel-0.15.5/docs/source/reference/struct/grouping.rst000066400000000000000000000001061426625374700230300ustar00rootroot00000000000000Grouping ======== .. automodule:: pybel.struct.grouping :members: pybel-0.15.5/docs/source/reference/struct/operators.rst000066400000000000000000000003271426625374700232210ustar00rootroot00000000000000Operations ========== This page outlines operations that can be done to BEL graphs. .. autofunction:: pybel.struct.left_full_join .. autofunction:: pybel.struct.left_outer_join .. autofunction:: pybel.struct.union pybel-0.15.5/docs/source/reference/struct/pipeline.rst000066400000000000000000000004011426625374700230010ustar00rootroot00000000000000Pipeline ======== .. autoclass:: pybel.Pipeline :members: Transformation Decorators ------------------------- .. automodule:: pybel.struct.pipeline.decorators :members: Exceptions ---------- .. automodule:: pybel.struct.pipeline.exc :members: pybel-0.15.5/docs/source/reference/struct/query.rst000066400000000000000000000000751426625374700223500ustar00rootroot00000000000000Query ===== .. automodule:: pybel.struct.query :members: pybel-0.15.5/docs/source/reference/struct/summary.rst000066400000000000000000000001671426625374700227020ustar00rootroot00000000000000Summary ======= .. automodule:: pybel.struct.summary :members: .. automodule:: pybel.struct.getters :members: pybel-0.15.5/docs/source/topics/000077500000000000000000000000001426625374700164665ustar00rootroot00000000000000pybel-0.15.5/docs/source/topics/cli.rst000066400000000000000000000007571426625374700200000ustar00rootroot00000000000000Command Line Interface ====================== .. note:: The command line wrapper might not work on Windows. Use :code:`python3 -m pybel` if it has issues. PyBEL automatically installs the command :code:`pybel`. This command can be used to easily compile BEL documents and convert to other formats. See :code:`pybel --help` for usage details. This command makes logs of all conversions and warnings to the directory :code:`~/.pybel/`. .. click:: pybel.cli:main :prog: pybel :show-nested: pybel-0.15.5/docs/source/topics/cookbook.rst000066400000000000000000000015461426625374700210340ustar00rootroot00000000000000Cookbook ======== An extensive set of examples can be found on the `PyBEL Notebooks `_ repository on GitHub. These notebooks contain basic usage and also make numerous references to the analytical package `PyBEL Tools `_ Configuration ------------- The default connection string can be set as an environment variable in your ``~/.bashrc``. If you're using MySQL or MariaDB, it could look like this: .. code:: $ export PYBEL_CONNECTION="mysql+pymysql://user:password@server_name/database_name?charset=utf8" Prepare a Cytoscape Network ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Load, compile, and export to Cytoscape format: .. code-block:: sh $ pybel convert --path ~/Desktop/example.bel --graphml ~/Desktop/example.graphml In Cytoscape, open with :code:`Import > Network > From File`. pybel-0.15.5/notebooks/000077500000000000000000000000001426625374700147405ustar00rootroot00000000000000pybel-0.15.5/notebooks/Compiling a BEL Document.ipynb000066400000000000000000001343441426625374700222600ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading BEL Documents\n", "\n", "We'll always start by importing `pybel`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "from urllib.request import urlretrieve\n", "\n", "import pybel\n", "import logging\n", "\n", "\n", "logging.getLogger('pybel').setLevel(logging.DEBUG)\n", "logging.basicConfig(level=logging.DEBUG)\n", "logging.getLogger('urllib3').setLevel(logging.WARNING)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.15.0-dev\n" ] } ], "source": [ "print(pybel.get_version())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:pybel.manager.base_manager:auto flush: True, auto commit: False, expire on commmit: True\n" ] } ], "source": [ "DESKTOP_PATH = os.path.join(os.path.expanduser('~'), 'Desktop')\n", "manager = pybel.Manager(f'sqlite:///{DESKTOP_PATH}/pybel_example_database.db')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll download and parse a BEL document from the Human Brain Pharmacome project describing the 2018 paper from Boland *et al.*, \"Promoting the clearance of neurotoxic proteins in neurodegenerative disorders of ageing\"." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "url = 'https://raw.githubusercontent.com/pharmacome/conib/master/hbp_knowledge/tau/boland2018.bel'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A BEL document can be downloaded and parsed from a URL using `pybel.from_bel_script_url`. Keep in mind, the first time we load a given BEL document, various BEL resources that are referenced in the document must be cached. Be patient - this can take up to ten minutes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:pybel.io.lines:Loading from url: https://raw.githubusercontent.com/pharmacome/conib/master/hbp_knowledge/tau/boland2018.bel\n", "INFO:pybel.io.line_utils:Finished parsing document section in 0.00 seconds\n", "INFO:pybel.io.line_utils:Finished parsing definitions section in 0.00 seconds\n", "downloading namespaces: 0%| | 0/13 [00:003)-beta-D-galactosyl-(1->4)-beta-D-glucose"}, "id": "8bded7bc3765b30b86c69ab575d472e7", "bel": "a(CHEBI:\"N-acetyl-alpha-neuraminyl-(2->3)-beta-D-galactosyl-(1->4)-beta-D-glucose\")"}, {"function": "Abundance", "concept": {"namespace": "CHEBI", "name": "N-benzyloxycarbonyl-L-leucyl-L-leucyl-L-leucinal"}, "id": "453b667ef165c1c01b33e07ea2348f10", "bel": "a(CHEBI:\"N-benzyloxycarbonyl-L-leucyl-L-leucyl-L-leucinal\")"}, {"function": "Abundance", "concept": {"namespace": "CHEBI", "name": "N-methyl-D-aspartic acid"}, "id": "daff1265d96c4fda3d24736ec37b0bdb", "bel": "a(CHEBI:\"N-methyl-D-aspartic acid\")"}, {"function": "Abundance", "concept": {"namespace": "CHEBI", "name": "NF-kappaB inhibitor"}, "id": "48323061eff58e388d8815c27d17d1e8", "bel": "a(CHEBI:\"NF-kappaB inhibitor\")"}, {"function": "Abundance", "concept": {"namespace": "CHEBI", "name": "NMDA receptor antagonist"}, "id": "98259a7a4485d8deb63c87637c5bed63", "bel": "a(CHEBI:\"NMDA receptor antagonist\")"}, {"function": "Abundance", "concept": {"namespace": "CHEBI", "name": "O-acetyl-L-serine"}, "id": "327d407e9351673ab8d9547b197c19a8", "bel": "a(CHEBI:\"O-acetyl-L-serine\")"}, {"function": "Abundance", "concept": {"namespace": "CHEBI", "name": "PPARgamma agonist"}, "id": "3827c5b0aa5849e290f6170e295e971b", "bel": "a(CHEBI:\"PPARgamma agonist\")"}, {"function": "Abundance", "concept": {"namespace": "CHEBI", "name": "S-adenosyl-L-homocysteine"}, "id": "8ef83f6c1ce56a37b7eb08266586360f", "bel": 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"products": [{"function": "Abundance", "concept": {"namespace": "CHEBI", "name": "amyloid-beta"}, "id": "e9557ea6ec74113b73fe23f4bd76b524", "bel": "a(CHEBI:\"amyloid-beta\")"}], "id": "396d3e35bdf0ee5a25a72d7b7c55a94a", "bel": "rxn(reactants(p(HGNC:APP), p(HGNC:CTSD)), products(a(CHEBI:\"amyloid-beta\")))"}, {"function": "Reaction", "reactants": [{"function": "Protein", "concept": {"namespace": "HGNC", "name": "CDK5R1"}, "id": "386aa7422eaa152faa763b873a474731", "bel": "p(HGNC:CDK5R1)"}], "products": [{"function": "Protein", "concept": {"namespace": "CONSO", "name": "CDK5R1 p25"}, "id": "47a8a0f49c7c00238a4836e8c6bfba21", "bel": "p(CONSO:\"CDK5R1 p25\")"}], "id": "3d926b8102c0d85bcdaa6338a6474c1d", "bel": "rxn(reactants(p(HGNC:CDK5R1)), products(p(CONSO:\"CDK5R1 p25\")))"}, {"function": "Reaction", "reactants": [{"function": "Protein", "concept": {"namespace": "HGNC", "name": "NOTCH1"}, "id": "27f2f587c4ffaeb8ec45fb9b21c7b8c5", "bel": "p(HGNC:NOTCH1)"}], "products": [{"function": 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"citation": {"db": "Other", "db_id": "123"}, "source": 78, "target": 2327, "key": "32692dbab6be257093b8a62dd9f55bbe"}, {"line": 117, "relation": "isA", "evidence": "The statements inside this citation is included to connect two entity types or triples, which will exist as islands/subnetworks in the big model.", "citation": {"db": "Other", "db_id": "123"}, "source": 78, "target": 80, "key": "cf874189e8632b606c697ca3d477bef6"}, {"line": 116, "relation": "equivalentTo", "evidence": "The statements inside this citation is included to connect two entity types or triples, which will exist as islands/subnetworks in the big model.", "citation": {"db": "Other", "db_id": "123"}, "source": 2327, "target": 78, "key": "66878ffe84ffecdc641414c7de590dbb"}, {"line": 118, "relation": "isA", "evidence": "The statements inside this citation is included to connect two entity types or triples, which will exist as islands/subnetworks in the big model.", "citation": {"db": "Other", "db_id": "123"}, "source": 2327, "target": 80, "key": "123dcc07c2c2bc2cec6e6fe9c6448e87"}, {"line": 238, "relation": "positiveCorrelation", "evidence": "gamma-Secretase comprises a molecular complex of four integral membrane proteins - presenilin, nicastrin, APH-1 and PEN-2 - and its molecular mechanism remains under extensive scrutiny. The ratio of Abeta(42) over Abeta(40) is increased by familial Alzheimer's disease mutations occurring in the presenilin genes or in APP, near the gamma-secretase cleavage site.", "citation": {"db": "PubMed", "db_id": "16696577"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2327, "target": 3823, "key": "9b5e328f9ad32785e95de012c81ef42b"}, {"relation": "partOf", "source": 2327, "target": 952, "key": "77f612f3a613801a8a7294313f0e3c64"}, {"line": 3916, "relation": "directlyDecreases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Synapses": true}}, "source": 2327, "target": 492, "key": "cd904e77479946300cc9ad491efe31da"}, {"line": 3917, "relation": "directlyDecreases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Synapses": true}}, "source": 2327, "target": 491, "key": "cc4cdaf3c542b3ffa02570d5d2a7cf53"}, {"line": 16012, "relation": "increases", "evidence": "Exposure of neuronal cultures to subtoxic concentrations of beta-amyloid peptide 1-40 (1-10microM) or the fragment 25-35 up-regulated both bcl-xL mRNA and Bcl-xL protein levels, determined by reverse transcriptase-polymerase chain reaction and western blot analysis.", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "source": 2327, "target": 3945, "key": "211a2925687d11f3e6c470a7851457f5"}, {"line": 16013, "relation": "increases", "evidence": "Exposure of neuronal cultures to subtoxic concentrations of beta-amyloid peptide 1-40 (1-10microM) or the fragment 25-35 up-regulated both bcl-xL mRNA and Bcl-xL protein levels, determined by reverse transcriptase-polymerase chain reaction and western blot analysis.", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "source": 2327, "target": 2394, "key": "7bdd702fd20482485ad955b880770655"}, {"line": 20040, "relation": "decreases", "evidence": "Abeta may contribute to the reduction in CBF in AD, as both Abeta42 and Abeta42 induce cerebrovascular dysfunction.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2327, "target": 832, "key": "d0146b6985120fa14ae38036fa93834a"}, {"relation": "partOf", "source": 2327, "target": 1036, "key": "4f49b8affb004fc8b198dc13848e8de3"}, {"relation": "partOf", "source": 2327, "target": 1046, "key": "da208f404a46e6ccefa5082a92cc8d94"}, {"relation": "partOf", "source": 2327, "target": 1126, "key": "e137aed23bf5973d1d4a5200db9b9a6a"}, {"line": 28304, "relation": "negativeCorrelation", "evidence": "We show that knock-down of ATXN1 significantly increases the levels of both Abeta40 and Abeta42. This effect could be rescued with concurrent overexpression of ATXN1. Moreover, overexpression of ATXN1 decreased Abeta levels. ", "citation": {"db": "PubMed", "db_id": "20139999"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2327, "target": 2371, "key": "cca52cb2f41f901712d266fd53f45733"}, {"line": 31709, "relation": "association", "evidence": "XB51 protein is known to interact with the amino-terminal of the X11L protein and to be involved in Abeta40 generation, a hallmark of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19035353"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2327, "target": 3097, "key": "9caadef97806fd192d123377e84579ce"}, {"line": 33481, "relation": "increases", "evidence": "Likewise, Abeta(1-40) led to activation of both JNK (c-Jun-NH2-terminal kinase)/c-Jun and nuclear factor-kappaB, resulting in iNOS upregulation in both brain structures.", "citation": {"db": "PubMed", "db_id": "17507561"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2327, "target": 3112, "key": "9e6648c680675e2b88d91af66f76f55e"}, {"line": 33483, "relation": "increases", "evidence": "Likewise, Abeta(1-40) led to activation of both JNK (c-Jun-NH2-terminal kinase)/c-Jun and nuclear factor-kappaB, resulting in iNOS upregulation in both brain structures.", "citation": {"db": "PubMed", "db_id": "17507561"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2327, "target": 3002, "key": "2ad0cd351b1f8e764e1d953b1c94bdc7"}, {"line": 33485, "relation": "increases", "evidence": "Likewise, Abeta(1-40) led to activation of both JNK (c-Jun-NH2-terminal kinase)/c-Jun and nuclear factor-kappaB, resulting in iNOS upregulation in both brain structures.", "citation": {"db": "PubMed", "db_id": "17507561"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2327, "target": 3123, "key": "f81727f288a19a9927cb9926c4a4f737"}, {"line": 33491, "relation": "increases", "evidence": "Abeta(1-40) administration induced an increase in TNF-alpha expression and oxidative alterations in prefrontal cortex and hippocampus.", "citation": {"db": "PubMed", "db_id": "17507561"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 2327, "target": 3472, "key": "b58caf7a4365734314356db681c108e0"}, {"line": 33575, "relation": "increases", "evidence": "Soluble Abeta40, the major amyloid precursor protein cleavage product, by itself stimulates astrocytes to express NOS-2 and make NO, possibly by activating p75(NTR) receptors, which they share with neurons, and can considerably amplify NOS-2 expression by the pro-inflammatory cytokine trio. ", "citation": {"db": "PubMed", "db_id": "17385278"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2327, "target": 3117, "key": "4c128399778d9b5f56b897e7e900fc31"}, {"relation": "partOf", "source": 2327, "target": 1225, "key": "fa617fa1044ec1175ffecd5359f3b39f"}, {"relation": "partOf", "source": 2327, "target": 1224, "key": "34a207a9da4c83fbd56d0a74bdb1b162"}, {"line": 46763, "relation": "positiveCorrelation", "evidence": "Both neurogranin and YKL‐40 correlated with tau as well as with Abeta40 in all studied diagnostic groups", "citation": {"db": "PubMed", "db_id": "26783546"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2327, "target": 2509, "key": "f17ff94f64deac6ac884c5aa8bb8d443"}, {"line": 46764, "relation": "positiveCorrelation", "evidence": "Both neurogranin and YKL‐40 correlated with tau as well as with Abeta40 in all studied diagnostic groups", "citation": {"db": "PubMed", "db_id": "26783546"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2327, "target": 3141, "key": "1ab4c5c68038acd0f70c5083108ad226"}, {"relation": "hasVariant", "source": 2315, "target": 2327, "key": "c4cba6d4f86bb36fbbbbe41cae7364a5"}, {"line": 3744, "relation": "increases", "evidence": "An interruption of the bidirectional trafficking of APP between the TGN and endosomes, particularly retromer-mediated retrieval of APP from early endosomes to the TGN, resulted in the accumulation of endocytosed APP in early endosomes with reduced APP processing. These data suggest that Abeta(40) is generated predominantly in the TGN, relying on an endocytosed pool of APP recycled from early endosomes to the TGN.", "citation": {"db": "PubMed", "db_id": "22711829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}}}, "source": 2315, "target": 2327, "key": "de63ddee9dee400aa0de579cdcad0100"}, {"relation": "hasVariant", "source": 2315, "target": 2328, "key": "8accc2490bf4ffa926ce3da611f288fc"}, {"line": 2682, "relation": "decreases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER. This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation"}, "source": 2315, "target": 2328, "key": "f9bc83d9d7555cf65209332f3079a852"}, {"line": 3465, "relation": "increases", "evidence": "The low-density lipoprotein receptor (LDLR) has the highest affinity for apoE and plays an important role in brain cholesterol metabolism.These data suggest that increased APP expression and Abeta exposure alters microtubule function, leading to reduced transport of LDLR to the plasma membrane. Consequent deleterious effects on apoE uptake and function will have implications for AD pathogenesis and/or progression", "citation": {"db": "PubMed", "db_id": "20049331"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2328, "key": "b76c6d98c44ef47d2fff0eb20db143e5"}, {"line": 3735, "relation": "increases", "evidence": "Depletion of Hrs and Tsg101, acting early in the multivesicular body pathway, retained APP in early endosomes and reduced Abeta(40) production. Conversely, depletion of CHMP6 and VPS4, acting late in the pathway, rerouted endosomal APP to the TGN for enhanced APP processing. We found that VPS35 (retromer)-mediated APP recycling to the TGN was required for efficient Abeta(40) production.", "citation": {"db": "PubMed", "db_id": "22711829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}}}, "source": 2315, "target": 2328, "key": "3662606127b0a79b0812b53b6ac74181"}, {"line": 34361, "relation": "increases", "evidence": "The formation of beta A4 amyloid in the brains of individuals with Alzheimer's disease requires the proteolytic cleavage of amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "10605825"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 2328, "key": "ad5ca0e5cf710d8e369ac2bf4d32508a"}, {"line": 34368, "relation": "increases", "evidence": "Several lines of evidence suggest that cathepsin D, the major lysosomal/endosomal aspartic protease, may be involved in this process. ", "citation": {"db": "PubMed", "db_id": "10605825"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 2328, "key": "7df72c1d9be5b1e7c00a6eefd7a7ec40"}, {"line": 35729, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 2328, "key": "fccf2903be15c1f267114c6d3108888b"}, {"relation": "hasVariant", "source": 2315, "target": 2318, "key": "b3d629a6e8f6b32761171c652ad56b5c"}, {"relation": "hasVariant", "source": 2315, "target": 2319, "key": "43882da6b909ccaedea373a6340dcc3a"}, {"relation": "hasVariant", "source": 2315, "target": 2324, "key": "1850984ecd5244246e133e51ac663421"}, {"relation": "hasVariant", "source": 2315, "target": 2326, "key": "a5b0e930ad7e9f8d43734ff070ea83a0"}, {"line": 201, "relation": "association", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2315, "target": 2948, "key": "444c2baeb4b63c77e6932ac890527010"}, {"line": 48387, "relation": "association", "evidence": "The transport of amyloid precursor protein is mediated through its interaction with kinesin light-chain 1 (KNS2).", "citation": {"db": "PubMed", "db_id": "15364413"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Innate immune system subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2948, "key": "90f3e130e50ef0a6853c94bf0c381eb7"}, {"line": 48414, "relation": "association", "evidence": "Second, reduced transport of APP by KLC1vE triggers an ER stress response that activates the amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "25394182"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Innate immune system subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2948, "key": "a46af8cad568c68f9fe4da483314bbda"}, {"line": 202, "relation": "association", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2315, "target": 2532, "key": "141cb06413b10c6c24e490696730608b"}, {"line": 217, "relation": "association", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation"}, "source": 2315, "target": 484, "key": "0749576c76e4ad896b18d4546efce6fb"}, {"relation": "hasVariant", "source": 2315, "target": 2351, "key": "6073388d6923da31d02ff148c6494b21"}, {"line": 393, "relation": "decreases", "evidence": "Abeta can also cause mitochondrial oxidative stress and dysregulation of Ca2+ homeostasis resulting in impairment of the electron transport chain (ETC), increased production of superoxide anion radical. and decreased production of ATP.", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2315, "target": 190, "key": "f4d7a4ffd622868b88784bccbb23e5d4"}, {"line": 1595, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2315, "target": 190, "key": "3efbbe672bc822d94451f0dfe3d78c17"}, {"relation": "partOf", "source": 2315, "target": 1183, "key": "df52bc6706c9bbede906c7bce3c0b8b2"}, {"relation": "hasVariant", "source": 2315, "target": 2343, "key": "ce1ea026164fbeaa93b93d25c1dcd792"}, {"relation": "partOf", "source": 2315, "target": 979, "key": "0e0a47b72f86c7c689d868e3bf7bf156"}, {"line": 1235, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2885, "key": "50f37207fc8bde93547afbda579b86e2"}, {"relation": "partOf", "source": 2315, "target": 1219, "key": "fd2d47c53da7de630bfe0c4c8d37383f"}, {"line": 1581, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 696, "key": "cd9b3e515693abb0597e811cba9accb1"}, {"line": 1589, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Electron transport chain": true}, "Confidence": {"Very High": true}}, "source": 2315, "target": 546, "key": "b54404765cfa22b1973615d241875b32"}, {"line": 1593, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2315, "target": 615, "key": "22568fa2d399d653c7c57713abd83ddb"}, {"line": 1597, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2315, "target": 614, "key": "3395b4a71b39e341c1d800f8f9a0a3d8"}, {"line": 4458, "relation": "decreases", "evidence": "A large body of literature has suggested an important role for a number of factors including oxidative stress, mitochondrial DNA mutations, imbalance in calcium homeostasis and aging in the dysfunction of mitochondrial complexes (28-34, 40, 46, 47). In addition, recent studies have also implicated a role for targeting and accumulation of plasma membrane APP and cytosolic alpha synuclein to mitochondria in the pathogenesis of mitochondrial dysfunction in Alzheimer's and Parkinson’s diseases, respectively.", "citation": {"db": "PubMed", "db_id": "19619643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Mitochondria": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 614, "key": "0b2a6ffb8ca9312aa6b2665a80790025"}, {"line": 37927, "relation": "increases", "evidence": "However, normal physiological functions of endogenous APP are not thoroughly understood but are thought to be involved in the stabilizing contact points between synapses and maintaining mitochondrial functions", "citation": {"db": "PubMed", "db_id": "19619643"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 614, "key": "24aa459be9e055653ecb6cbb4d5ef2f1"}, {"line": 1603, "relation": "increases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 478, "key": "f4317dfa74f05e34fe3655a1b2566538"}, {"line": 26595, "relation": "increases", "evidence": "Expression of familial Alzheimer's disease (FAD) mutants of APP in primary neurons causes both intracellular accumulation of the C-terminal beta-secretase cleavage product of APP and increased secretion of Abeta, and eventually results in apoptotic death of the cells.", "citation": {"db": "PubMed", "db_id": "11744168"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 478, "key": "c4c088d7dde298d7193444cd570a29cf"}, {"relation": "hasVariant", "source": 2315, "target": 2323, "key": "f4dd0ff12733560621053a06b7f79fd1"}, {"relation": "hasVariant", "source": 2315, "target": 2325, "key": "f534e4f0d3a7164f604cffb8ebe61633"}, {"relation": "hasVariant", "source": 2315, "target": 2345, "key": "6f4400d1b59c02d3d2d94868b2d34889"}, {"relation": "hasVariant", "source": 2315, "target": 2348, "key": "aaa869529f655b994949e622834955af"}, {"relation": "partOf", "source": 2315, "target": 1686, "key": "89686e69653f64ec32efadb9eaf9145c"}, {"relation": "hasVariant", "source": 2315, "target": 2339, "key": "17b4690d025694885c6958f358491995"}, {"relation": "partOf", "source": 2315, "target": 1171, "key": "fa8f263a135dd0df791530c8c42dec5a"}, {"line": 1819, "relation": "increases", "evidence": "Recent data have demonstrated that AbetaPP may signal to the nucleus also using a Abeta-secretase-independent mechanism that involves membrane sequestration and phosphorylation of Tip60.More recently, Stante et al. have suggested that the presence of Fe65 into the nucleus may have a protective role, and that its translocation depends on AbetaPP. They propose that DNA repair defects could significantly contribute to the neurodysfunction and neurodegeneration observed in AD, and that an involvement of the Fe65-APP complex in the response of the cells to DNA damage and in the DNA repair machinery could represent a possible mechanism contributing to neuronal degeneration observed in AD pathology", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2943, "key": "d4e32888a2678e3195d7a2100072dc96"}, {"relation": "partOf", "source": 2315, "target": 1092, "key": "f684b3aa4397c31cb40e25b2dd43c706"}, {"relation": "partOf", "source": 2315, "target": 1208, "key": "2016e9565011a684534b134c86cd4751"}, {"relation": "partOf", "source": 2315, "target": 1170, "key": "a0ad283f4707d78c9474f647a905b696"}, {"relation": "partOf", "source": 2315, "target": 1678, "key": "2ad1e40ffbf753c28f06a1cc3d98655d"}, {"relation": "partOf", "source": 2315, "target": 1164, "key": "2e09e4b8c9eaa142530b6bef012989e2"}, {"relation": "hasVariant", "source": 2315, "target": 2340, "key": "f693584ca0de9e25a09076fd37636bc2"}, {"relation": "partOf", "source": 2315, "target": 1207, "key": "4af3d53d8b30a44146b65c546305d7b7"}, {"relation": "partOf", "source": 2315, "target": 1209, "key": "93ead301d13d2ba858bde207e688d960"}, {"relation": "partOf", "source": 2315, "target": 1173, "key": "f9594cf668b7921b7372ccecd0487bd2"}, {"line": 2161, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DNA synthesis": true, "Notch signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 531, "key": "46cf7dc8822f9deb132e0cccda7bf5ee"}, {"line": 2166, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3852, "key": "df32ab02776e49a58a77974eda840b01"}, {"line": 2215, "relation": "association", "evidence": "As previously discussed, AbetaPP regulates ERK1/2 levels, its phosphorylation/translocation to the centrosome, and cell proliferation rate.Additionally, in the same study, we showed that also PS1 interacts with Grb2 in the centrosomes and modulates ERK1/2 signaling.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2173, "key": "9abbd44be684a08a5754701f6963cb93"}, {"line": 35539, "relation": "association", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 2173, "key": "9a7eff9a8034b74b319154088cee37e2"}, {"line": 2216, "relation": "association", "evidence": "As previously discussed, AbetaPP regulates ERK1/2 levels, its phosphorylation/translocation to the centrosome, and cell proliferation rate.Additionally, in the same study, we showed that also PS1 interacts with Grb2 in the centrosomes and modulates ERK1/2 signaling.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2174, "key": "7d5215ce487edc8833d2befaeda0a85b"}, {"line": 2266, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 2981, "key": "ab3223d34c4791cf45cbf7608733e370"}, {"relation": "partOf", "source": 2315, "target": 1205, "key": "c2099efd77653531727207e6430d2730"}, {"line": 2280, "relation": "increases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2315, "target": 80, "key": "b1bb0b4939152d9bf8654cf12184881b"}, {"line": 2405, "relation": "increases", "evidence": "Alzheimer's disease (AD), one of the major causes of disability and mortality in Western societies, is a progressive age-related neurodegenerative disorder. Increasing evidence suggests that the etiology of AD may involve disruptions of zinc (Zn) homeostasis. This review discusses current evidence supporting a potential role of Zn and zinc transporters (ZnTs) in processing of the amyloid beta protein precursor (APP) and amyloid beta (Abeta) peptide generation and aggregation.", "citation": {"db": "PubMed", "db_id": "22447723"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 80, "key": "b25fc5be63baaf468382999a83a0ac9c"}, {"line": 4154, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2315, "target": 80, "key": "8701f6c130088ac54e255762f7f4de92"}, {"line": 5460, "relation": "increases", "evidence": "Cirrito et al. [26] also show that synaptic activity-induced increase in endocytosis drives more APP into the endocytic compartment, ultimately resulting in increased Abeta production and release. Abeta produced in the endocytic pathway is then brought to the cell surface where it is released into the extracellular fluid [70]. Inhibition of endocytosis reduces APP internalization and reduces Abeta production and release in cell lines", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2315, "target": 80, "key": "50c192f2784da9688f6361814863f59b"}, {"line": 26409, "relation": "increases", "evidence": "Abeta is derived from proteolytic processing of the amyloid precursor protein (APP), which interacts with several members of the LDLR family.", "citation": {"db": "PubMed", "db_id": "17185504"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 80, "key": "9133614b4a067bc8d1932c8b6a790ce1"}, {"line": 26590, "relation": "increases", "evidence": "Expression of familial Alzheimer's disease (FAD) mutants of APP in primary neurons causes both intracellular accumulation of the C-terminal beta-secretase cleavage product of APP and increased secretion of Abeta, and eventually results in apoptotic death of the cells.", "citation": {"db": "PubMed", "db_id": "11744168"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2315, "target": 80, "key": "024df50bc935fc0254d5b8565c6a8b7c"}, {"line": 36083, "relation": "decreases", "evidence": "Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase ABeta¸ production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of ABeta¸. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Axons": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Axons"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 2315, "target": 80, "key": "a4c6fc14eabf307a32965180b495d9bc"}, {"line": 37904, "relation": "increases", "evidence": "Iron was further demonstrated to modulate expression of the Alzheimer's amyloid precursor holo-protein (APP) by a mechanism similar to that of regulation of ferritin-L and -H mRNA translation through an iron-responsive element (IRE) in their 5' untranslated regions (UTRs). Here, we discuss two aspects of the link between iron and AD, in relation to the recently discovered IRE in the 5'UTR of APP mRNA. The first is the physiological aspect: a compensatory neuroprotective response of amyloid-ß protein (ABeta¸) in reducing iron-induced neurotoxicity. Thus, given that ABeta¸ possesses iron chelation sites, it is hypothesized that OS-induced intracellular iron may stimulate APP holo-protein translation (via the APP 5'UTR) and subsequently the generation of its cleavage product, ABeta¸, as a compensatory response that eventually reduces OS.", "citation": {"db": "PubMed", "db_id": "19090990"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 80, "key": "e8af47da265bd1849bfb3b92b5bc1986"}, {"line": 44606, "relation": "positiveCorrelation", "evidence": "hypomethylated APP, individuals, which in turn produces more APP, which is further cleaved to build up Abeta levels", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 80, "key": "f590a98f6531839f2a85ab10d766dba3"}, {"line": 2403, "relation": "association", "evidence": "Alzheimer's disease (AD), one of the major causes of disability and mortality in Western societies, is a progressive age-related neurodegenerative disorder. Increasing evidence suggests that the etiology of AD may involve disruptions of zinc (Zn) homeostasis. This review discusses current evidence supporting a potential role of Zn and zinc transporters (ZnTs) in processing of the amyloid beta protein precursor (APP) and amyloid beta (Abeta) peptide generation and aggregation.", "citation": {"db": "PubMed", "db_id": "22447723"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 3378, "key": "4f0902f1f7365f15e12be21cdc79c9dd"}, {"relation": "partOf", "source": 2315, "target": 1094, "key": "afe64049f9de5922ba0bb848faee88cc"}, {"line": 2416, "relation": "association", "evidence": "Several studies found that FE65, a cytoplasmic adaptor protein, interacts with APP and LRP1, altering the trafficking and processing of APP. We have previously shown that FE65 interacts with the ApoE receptor, ApoER2, altering its trafficking and processing. Interestingly, it has been shown that FE65 can act as a linker between APP and LRP1 or ApoER2.", "citation": {"db": "PubMed", "db_id": "22429478"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 1094, "key": "36f2d823fc4b1cc94eb61ffb0dafd772"}, {"relation": "hasVariant", "source": 2315, "target": 2341, "key": "a2e30f48067560209fae8769e1e73ac5"}, {"line": 2450, "relation": "positiveCorrelation", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 2341, "key": "7b49ed55075821509bf74b1cfab343d1"}, {"relation": "partOf", "source": 2315, "target": 1210, "key": "b00e7aa4f6cf582cf38df6c52bb2ef11"}, {"relation": "partOf", "source": 2315, "target": 1080, "key": "81c071ba93be2ebcb31935834118c6e6"}, {"relation": "hasVariant", "source": 2315, "target": 2338, "key": "fb94d8ac8f0121cbeb6a52d76739eeb1"}, {"line": 2712, "relation": "increases", "evidence": "In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. ", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 684, "key": "a57c311515c15bab125f1212dbf7a253"}, {"relation": "hasVariant", "source": 2315, "target": 2330, "key": "8444befc413f91c28d25f74a07e05a35"}, {"relation": "partOf", "source": 2315, "target": 1192, "key": "51013150b1a6ee59d41901c42024204e"}, {"relation": "hasVariant", "source": 2315, "target": 2332, "key": "1befe2cd6522380c68298c8d48ff61d0"}, {"relation": "partOf", "source": 2315, "target": 1676, "key": "903e250b97d267c439600467ceda2d12"}, {"relation": "hasVariant", "source": 2315, "target": 2333, "key": "07d9432631e2122497ab568738dbf05b"}, {"relation": "partOf", "source": 2315, "target": 1184, "key": "f2631b93f13a6416fbd9800296ad491e"}, {"relation": "partOf", "source": 2315, "target": 1003, "key": "ebfc95b8b1cc2da267ca1a3e5a30cb51"}, {"relation": "partOf", "source": 2315, "target": 1076, "key": "b25f95edb3c796ea2367be5e52ddba69"}, {"relation": "partOf", "source": 2315, "target": 1082, "key": "c18832fda7d7e1c4a220085de6581bbe"}, {"relation": "partOf", "source": 2315, "target": 1088, "key": "89cdb137b7cc422cc1e3fd3a5063a555"}, {"line": 5199, "relation": "increases", "evidence": "Higher APP expression and elevated Abeta levels cause greater than required Cu export, leading to increased Cu in cerebrospinal fluid (CSF) and serum, and an intracellular (IC) Cu deficiency in the brain. Cu-deficient superoxide dismutase (SOD1) contributes to the reduced antioxidant capacity of the brain, allowing further oxidative stress.", "citation": {"db": "PubMed", "db_id": "15910549"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Free radical formation subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 100, "key": "3f38f94f5d69658c5b4ea7dd3fbed839"}, {"relation": "partOf", "source": 2315, "target": 1675, "key": "c1e3b60c22b9d5ea55c51e3be6a74885"}, {"line": 5935, "relation": "association", "evidence": "These results demonstrate that Src-mediated phosphorylation of Mint2 regulates the APP endocytic sorting pathway, providing a mechanism for regulating Abeta secretion.", "citation": {"db": "PubMed", "db_id": "22787047"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2315, "target": 2297, "key": "521ef44801e49aefe52c817b33c42e1f"}, {"relation": "partOf", "source": 2315, "target": 1148, "key": "1474aeda4b66061cbeb7f187ea718652"}, {"relation": "partOf", "source": 2315, "target": 1154, "key": "a9983ec85f83227071bea4030950f046"}, {"relation": "partOf", "source": 2315, "target": 1155, "key": "4e3749d4dd6719c7a7edcbd1c4f7f9e8"}, {"relation": "partOf", "source": 2315, "target": 1156, "key": "482022f4170c2d7985aca688b053d882"}, {"line": 6378, "relation": "positiveCorrelation", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 842, "key": "12106edd8d16825df00e9e94400000ad"}, {"line": 8484, "relation": "association", "evidence": "We show that miRNAs belonging to the miR-20a family (that is, miR-20a, miR-17-5p and miR-106b) could regulate APP expression in vitro and at the endogenous level in neuronal cell lines. A tight correlation between these miRNAs and APP was found during brain development and in differentiating neurons. We thus identify miRNAs as novel endogenous regulators of APP expression, suggesting that variations in miRNA expression could contribute to changes in APP expression in the brain during development and disease. This possibility is further corroborated by the observation that a statistically significant decrease in miR-106b expression was found in sporadic AD patients.", "citation": {"db": "PubMed", "db_id": "19110058"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2315, "target": 2091, "key": "95191001504bf0dd8efe9f12227c931d"}, {"line": 45992, "relation": "association", "evidence": "The levels of DNA methylation in promoters of APP, BACE1, and PS1 genes are decreased after anisomycin treatment.", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2315, "target": 2091, "key": "7beea0c46cd26885a3fc4a624d2d0c23"}, {"line": 9061, "relation": "association", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 3000, "key": "554f6f63c6ad6e2bab2bea580d1a1eb3"}, {"line": 9062, "relation": "association", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 2990, "key": "8d0bd17e1173332221b4f7806957a4f4"}, {"line": 9068, "relation": "increases", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3823, "key": "cece888d7341e74eab6c59408fa5678a"}, {"line": 9080, "relation": "increases", "evidence": "APP (amyloid precursor protein) and LRP1 (low-density lipoprotein receptor-related protein 1) have been implicated in the pathogenesis of AD (Alzheimer's disease). They are functionally linked by Fe65, a PTB (phosphotyrosine-binding)-domain-containing adaptor protein that binds to intracellular NPxY-motifs of APP and LRP1, thereby influencing expression levels, cellular trafficking and processing. Additionally, Fe65 has been reported to mediate nuclear signalling in combination with intracellular domains of APP and LRP1. We have previously identified another adaptor protein, GULP1 (engulfment adaptor PTB-domain-containing 1). In the present study we characterize and compare nuclear trafficking and transactivation of GULP1 and Fe65 together with APP and LRP1 and report differential nuclear trafficking of adaptors when APP or LRP1 are co-expressed. The observed effects were additionally supported by a reporter-plasmid-based transactivation assay. The results from the present study indicate that Fe65 might have signalling properties together with APP and LRP1, whereas GULP1 only mediates LRP1 transactivation.", "citation": {"db": "PubMed", "db_id": "23167255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3823, "key": "71f5c0982af959fa44bc51870c36570b"}, {"line": 27153, "relation": "association", "evidence": "Expression levels of the amyloid precursor protein (APP) and beta-site amyloid (Abeta) cleaving enzyme 1 (BACE1) have been implicated in Alzheimer disease (AD) progression.", "citation": {"db": "PubMed", "db_id": "19462468"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 3823, "key": "458ad28ffafb0fac20a14b36cda53730"}, {"line": 31403, "relation": "association", "evidence": "Amyloid precursor protein (APP) is a widely expressed transmembrane protein of unknown function that is involved in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "16797788"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3823, "key": "18cfadf785574b21c616c494680ac893"}, {"line": 32417, "relation": "association", "evidence": "The amyloid precursor protein (APP) and the presenilins 1 and 2 are genetically linked to the development of familial Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3823, "key": "5eea56b2452aad3b6da47e1cd29eecdb"}, {"line": 33377, "relation": "association", "evidence": "The beta-amyloid precursor protein APP and the microtubule-associated protein Tau play a crucial role in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3823, "key": "f42ab0b1249f2a06dd6b17e217ed2e71"}, {"line": 9201, "relation": "association", "evidence": "It was found that miRNAs hsa-mir-106a and hsa-mir-520c could bind to their predicted target sequences in the APP 3′UTR and negatively regulate APP expression.94 Another recent study showed that miR-101 is a negative regulator of APP expression and could affect the accumulation of Abeta, suggesting a possible role for miR-101 in neuropathological conditions.", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2090, "key": "60fe9af05ff5ac7b584babcc81efc1c3"}, {"line": 45740, "relation": "association", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 2315, "target": 2090, "key": "ca304922b8337852f1b470e030e16843"}, {"line": 45969, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2315, "target": 2090, "key": "b68ccc713ef4247ab28083ca1226ad53"}, {"line": 9202, "relation": "association", "evidence": "It was found that miRNAs hsa-mir-106a and hsa-mir-520c could bind to their predicted target sequences in the APP 3′UTR and negatively regulate APP expression.94 Another recent study showed that miR-101 is a negative regulator of APP expression and could affect the accumulation of Abeta, suggesting a possible role for miR-101 in neuropathological conditions.", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2123, "key": "bd2709b2b2f23d06a9ba193993d77ca5"}, {"line": 45741, "relation": "association", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 2315, "target": 2123, "key": "3de204a56f162e06e0568c4b3cfa1a59"}, {"line": 45970, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2315, "target": 2123, "key": "9ae5933772ff55200f976269ff5eae27"}, {"relation": "partOf", "source": 2315, "target": 1147, "key": "3fba48ca9560c940d7fa69844cc3bd7f"}, {"line": 13407, "relation": "increases", "evidence": "In immunoblotting analysis, nefiracetam treatment increased the PKCalpha activity with a bell-shaped dose-response relationship peaking at 10 nM, thereby increasing phosphorylation of PKC substrate and NMDA receptor. Nefiracetam treatment did not affect the PKA activity.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"NMDA receptor": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2780, "key": "ad16e6b65e844377ecbdb0835658180d"}, {"line": 14486, "relation": "association", "evidence": "Interestingly, the analysis of c-Fos expression revealed that the APPswe mutation disrupted dentate gyrus and amygdala function, as well as altering the influence of these regions on the neural network dynamics activated during context memory retrieval.", "citation": {"db": "PubMed", "db_id": "24628842"}, "annotations": {"MeSHAnatomy": {"Dentate Gyrus": true, "Amygdala": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 2315, "target": 2699, "key": "6ab4619e904ef0b08e4984edfa7dbbf2"}, {"line": 45273, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2699, "key": "83db070ff70ef6da5ca4af60372896c8"}, {"line": 48983, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Whereas APP affected Egr1 promoter activity by reducing access of the CREB transcription factor.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2699, "key": "5e481312a2d903740e43fe604b707b71"}, {"line": 15760, "relation": "association", "evidence": "Furthermore, recent studies have demonstrated that age-related androgen depletion results in accumulation of beta-amyloid protein and thereby acts as a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2315, "target": 209, "key": "75cf341593e05841c98cfe98d93a4c4f"}, {"line": 19808, "relation": "association", "evidence": "Mifepristone alters amyloid precursor protein processing to preclude amyloid beta and also reduces tau pathology.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 304, "key": "860a8c2b361a6440c50ee330c75bb5ee"}, {"line": 23713, "relation": "association", "evidence": "Over the last several years a number of reports have emerged suggesting that at least some ChEI might take part in betaAPP metabolism, influencing its secretion and Abeta differential cleavage. Moreover, we (Sob6w and Kloszewska 2005) and others (Basun et al. 2002, Zimmermann et al. 2005) have shown that the treatment with ChEI might influence BAPP metabolism in AD patients as measured by changes in plasma (including platelet-derived) metabolites. In our previous pilot study we have demonstrated that short-term treatment with ChEI rivastigmine exhibits a significant effect on plasma concentrations of Abeta-42 (mean increase after treatment reached 7.8 ± 8.4 pg/ml) with a negative correlation to patients age, while no changes in Abeta-40 levels were detected.", "citation": {"db": "PubMed", "db_id": "17691220"}, "annotations": {"Species": {"10116": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 344, "key": "f4cb633e9be8c7dbf7fff6666bd5b793"}, {"relation": "partOf", "source": 2315, "target": 1026, "key": "11ec8c7b097432d97baa2348e2f90650"}, {"relation": "partOf", "source": 2315, "target": 1035, "key": "60ca34139e7d57d2590431ab99f597f4"}, {"relation": "partOf", "source": 2315, "target": 1038, "key": "9503479d90c0f66a468e5524eef97591"}, {"relation": "hasVariant", "source": 2315, "target": 2334, "key": "b3c3db7f014c57716dfa3832d3b1b82e"}, {"relation": "partOf", "source": 2315, "target": 1042, "key": "a282c878071f61562f706d98b21cd1bf"}, {"relation": "partOf", "source": 2315, "target": 1044, "key": "d201c37c11cf1386fa4672c5cd925503"}, {"relation": "partOf", "source": 2315, "target": 1153, "key": "fb53656cd6d453318b2953e45a59a628"}, {"relation": "partOf", "source": 2315, "target": 1152, "key": "8d181e76d7b29fb8a40a0c79af23de1f"}, {"relation": "partOf", "source": 2315, "target": 1151, "key": "0443cf135b1113ee582cbf97c0ac3992"}, {"line": 24972, "relation": "association", "evidence": "ADAM19 interacts with APP", "citation": {"db": "PubMed", "db_id": "17112471"}, "annotations": {"Confidence": {"High": true}}, "source": 2315, "target": 2251, "key": "d2537af10d6f27a5881ea2ae3524683c"}, {"relation": "partOf", "source": 2315, "target": 1125, "key": "9a19107e679623f00e628956602e8b15"}, {"line": 25866, "relation": "association", "evidence": "Co-expression and pulse-chase experiments showed that a functional apoE:APP interaction occurs intracellularly which directly affects maturation and subsequently the secretion kinetics of APP.", "citation": {"db": "PubMed", "db_id": "11523796"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2315, "target": 1125, "key": "3836e85cee9497608f346fdeee41974b"}, {"relation": "hasVariant", "source": 2315, "target": 2321, "key": "b1419e29744b5cf3489eb3fda6a469f9"}, {"relation": "partOf", "source": 2315, "target": 1045, "key": "380216d6e770a2d5ef687d01140febf0"}, {"line": 25355, "relation": "positiveCorrelation", "evidence": "Elevation of active ADAM10 correlates with increased alpha-CTF cleavage, and elevated sAPP-alpha.", "citation": {"db": "PubMed", "db_id": "16624814"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 2249, "key": "ea26e4d69ad4cd5d120129948b4694c2"}, {"relation": "partOf", "source": 2315, "target": 1052, "key": "12566606788f2dc3266df4ba3c51fe97"}, {"relation": "partOf", "source": 2315, "target": 1055, "key": "72f0949993bf97f9b2a6fe2682884ff4"}, {"relation": "partOf", "source": 2315, "target": 1066, "key": "59d6c8248253233319e27d4532b3ea87"}, {"line": 26179, "relation": "association", "evidence": "Moreover, our recent studies further demonstrated that (1) apoE mediates sulfatide depletion in amyloid-beta precursor protein transgenic mice; (2) sulfatides enhance amyloid beta (Abeta) peptides binding to apoE-associated particles; (3) Abeta42 content notably correlates with sulfatide content in CSF;(4) sulfatides markedly enhance the uptake of Abeta peptides; and (5) abnormal sulfatide-facilitated Abeta uptake results in the accumulation of Abeta in lysosomes.", "citation": {"db": "PubMed", "db_id": "20052565"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 116, "key": "9b3b84808b01144e350764360edcb5ea"}, {"line": 26345, "relation": "association", "evidence": "We conclude that astrocytes: (i) strongly regulate neuronal APP expression in primary neurons, and (ii) promote the amyloidogenic pathway in an apoE4-dependent manner. Thus, apoE and astrocytic factor(s) may pmodulate the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11553277"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 418, "key": "74a20727ba1dbf5a724a7c905fb091f9"}, {"relation": "partOf", "source": 2315, "target": 1142, "key": "b359376e34c7df9719d8a4eeb038886b"}, {"relation": "partOf", "source": 2315, "target": 1143, "key": "1ec457734dccb3138ca9d079784df7b6"}, {"relation": "partOf", "source": 2315, "target": 1144, "key": "e5d1c7e199da4edd1c91ac01913e79ab"}, {"line": 26589, "relation": "increases", "evidence": "Expression of familial Alzheimer's disease (FAD) mutants of APP in primary neurons causes both intracellular accumulation of the C-terminal beta-secretase cleavage product of APP and increased secretion of Abeta, and eventually results in apoptotic death of the cells.", "citation": {"db": "PubMed", "db_id": "11744168"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2375, "key": "5cfe27c04cdbfcc88001bb3f25aa04da"}, {"relation": "partOf", "source": 2315, "target": 1146, "key": "46b56a32b22cb026b4a5e5a34391c5cc"}, {"relation": "hasVariant", "source": 2315, "target": 2329, "key": "220fd599c9d5e26e0141760eb3dfa21f"}, {"line": 27016, "relation": "association", "evidence": "The findings link hypercholesterolemia with cognitive dysfunction potentially mediated by increased neuroinflammation and APP processing in a non-transgenic mouse pmodel.", "citation": {"db": "PubMed", "db_id": "18410513"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 3913, "key": "8e594e3bdcfb0fa0f8d89708ee342cfd"}, {"relation": "partOf", "source": 2315, "target": 1145, "key": "75f95d339d47b8b686b2c398dbafec65"}, {"relation": "hasVariant", "source": 2315, "target": 2346, "key": "16b62ef0827290da728d1d9cf656ae7f"}, {"relation": "hasVariant", "source": 2315, "target": 2349, "key": "e789e88370426529f313592242e19fc8"}, {"relation": "partOf", "source": 2315, "target": 1203, "key": "8ed7348f6787790ba467647e0d22dee1"}, {"relation": "partOf", "source": 2315, "target": 1204, "key": "4e9bbc35e727d59fa70b86b7e17ad577"}, {"relation": "hasVariant", "source": 2315, "target": 2347, "key": "b4d0162fc4e5a02cbf946a287f61c318"}, {"relation": "hasVariant", "source": 2315, "target": 2344, "key": "4a076c2443347ff712fbe18a39bd9c4d"}, {"relation": "partOf", "source": 2315, "target": 1206, "key": "c3f151abee5a0397f5d395589bf06e29"}, {"line": 28491, "relation": "increases", "evidence": "Alzheimer precursor protein interaction with the Nogo-66 receptor reduces amyloid-beta plaque deposition.", "citation": {"db": "PubMed", "db_id": "16452662"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 1206, "key": "37e90ceeaa519d261827dbcaff8ec1cd"}, {"line": 28493, "relation": "association", "evidence": "Alzheimer precursor protein interaction with the Nogo-66 receptor reduces amyloid-beta plaque deposition.", "citation": {"db": "PubMed", "db_id": "16452662"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 3332, "key": "22ad6582216c986ed3faa032fa28f142"}, {"relation": "partOf", "source": 2315, "target": 916, "key": "f057fc386a4be050a6dc6794a323ca27"}, {"relation": "partOf", "source": 2315, "target": 1075, "key": "f9d1fe614ab0ea0da9fb8912ac5109ad"}, {"relation": "partOf", "source": 2315, "target": 1187, "key": "a2488dcdb6e798bdaa6b7523c7c36f45"}, {"relation": "partOf", "source": 2315, "target": 1211, "key": "0a52c8605cdabec306e411211f5e3248"}, {"line": 28655, "relation": "increases", "evidence": "Loss of APP leads to aberrant localization of CHT at the neuromuscular synapses and reduced CHT activity at cholinergic projections.", "citation": {"db": "PubMed", "db_id": "17709753"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 3379, "key": "0e85eb1552ca8d8c75033c6638fc49b5"}, {"line": 28661, "relation": "regulates", "evidence": "At the cellular level, we show that APP and CHT can be found in Rab5-positive endosomal compartments and that APP affects CHT endocytosis.", "citation": {"db": "PubMed", "db_id": "17709753"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2315, "target": 3379, "key": "d1993c2c9113492c2807da0f272c05df"}, {"relation": "partOf", "source": 2315, "target": 1212, "key": "736b2de6ac30c6a2c6e182a02661ebd6"}, {"line": 28702, "relation": "association", "evidence": "Despite the wealth of in vivo and in vitro data that have accumulated regarding the connection of APP to kinesin transport, it is not yet clear if APP is coupled to its specific motor protein via an intracellular interaction partner, such as the c-Jun N-terminal kinase-interacting protein, or by yet another unknown molecular mechanism", "citation": {"db": "PubMed", "db_id": "17047360"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 3403, "key": "d0d31ae8fbad75d61c384858470f96a4"}, {"relation": "partOf", "source": 2315, "target": 1215, "key": "9e70f4c92274c8f4a2c7616b84362ff4"}, {"line": 28718, "relation": "association", "evidence": "The beta-amyloid precursor protein (APP) shares intracellular and extracellular-binding partners with the family of receptors for apolipoprotein E (apoE). ", "citation": {"db": "PubMed", "db_id": "18415033"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2312, "key": "01abd698a8f5bd3a18755749d33ddbb3"}, {"relation": "partOf", "source": 2315, "target": 1217, "key": "ff8cee30ce59b85445ba412df1bc7524"}, {"relation": "partOf", "source": 2315, "target": 1218, "key": "89a269ab0b116fa4b3c1c301f0d268c3"}, {"relation": "partOf", "source": 2315, "target": 1220, "key": "24a8d472128a197f7c8b7ce2770c541e"}, {"relation": "partOf", "source": 2315, "target": 1221, "key": "7468779ff8c1ed1b770151422784ea94"}, {"line": 30176, "relation": "increases", "evidence": "We have previously reported that overexpression of wild-type amyloid precursor protein (APP) in postmitotic neurons induces cleavage-dependent activation of caspase-3 both in vivo and in vitro.", "citation": {"db": "PubMed", "db_id": "12425945"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 2444, "key": "a63f1daaf5c6840067a6ace4afeb10e0"}, {"line": 30181, "relation": "increases", "evidence": "Overexpression of wild-type APP significantly increased intracellular (45)Ca(2+) content prior to the activation of caspase-3 in NT2-derived neurons.", "citation": {"db": "PubMed", "db_id": "12425945"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 2444, "key": "775ee31ca3d2ae5c5f0d441d1c5ef145"}, {"line": 30182, "relation": "increases", "evidence": "Overexpression of wild-type APP significantly increased intracellular (45)Ca(2+) content prior to the activation of caspase-3 in NT2-derived neurons.", "citation": {"db": "PubMed", "db_id": "12425945"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 94, "key": "ff5e97ba3e26231548f0ab083c826638"}, {"line": 30193, "relation": "increases", "evidence": "Furthermore, calpain, a Ca(2+)-dependent cysteine protease, was activated in neurons overexpressing APP as assessed by increased levels of calpain-cleaved alpha-fodrin and autolytic mu-calpain fragments. ", "citation": {"db": "PubMed", "db_id": "12425945"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Calpastatin-calpain subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 2435, "key": "fd8f7be9d4818cc331a726da33bdb7b1"}, {"relation": "partOf", "source": 2315, "target": 1161, "key": "87e99fc394cbc0feb2f17a969573eb86"}, {"relation": "partOf", "source": 2315, "target": 1167, "key": "472d194d9c8a98a63409f56b87bdc152"}, {"line": 31077, "relation": "association", "evidence": "Low density lipoprotein receptor-related protein (LRP) participates in the uptake and degradation of several ligands implicated in neuronal pathophysiology including apolipoprotein E (apoE), activated alpha(2) -macroglobulin (alpha(2)M*) and beta-amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "10797543"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2315, "target": 2970, "key": "62c19d6b2e4817902bf5dad54e239ea8"}, {"line": 31153, "relation": "association", "evidence": "Increasing evidence suggests that the low density lipoprotein receptor-related protein ( LRP ) affects the processing of amyloid precursor protein ( APP ) and amyloid beta ( Abeta ) protein production as well as mediates the clearance of Abeta from the brain.", "citation": {"db": "PubMed", "db_id": "15772078"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2970, "key": "24fe64340e0344bed4cd9e200179f74e"}, {"relation": "partOf", "source": 2315, "target": 1200, "key": "102d540751bbda7dccb10968e85333b8"}, {"relation": "partOf", "source": 2315, "target": 1175, "key": "2475ff6ee93ba2313a7a5aa322536ea3"}, {"relation": "partOf", "source": 2315, "target": 1185, "key": "39dae641111f2168e4ffa9113004e785"}, {"relation": "partOf", "source": 2315, "target": 1201, "key": "cd6156db4057a03aac85da7769a71fe8"}, {"relation": "partOf", "source": 2315, "target": 1189, "key": "b29a4b16e3ad0f2d2bcbbeb5ac4e8f77"}, {"relation": "partOf", "source": 2315, "target": 1190, "key": "e027adb9e19546a8e86a17dbe8eb1ecf"}, {"relation": "partOf", "source": 2315, "target": 1193, "key": "5e74512551d1b7e40cdd25238c1fff2d"}, {"relation": "partOf", "source": 2315, "target": 1081, "key": "21b0641a03ad4898687d4ef3229c43f5"}, {"relation": "partOf", "source": 2315, "target": 1202, "key": "6df1af933298a797e273c5a579991e0c"}, {"relation": "partOf", "source": 2315, "target": 1160, "key": "4d2540ce8758a766d04385ac94cad129"}, {"relation": "partOf", "source": 2315, "target": 1186, "key": "bfd361d7cb377ab61df76bd2cf6402b1"}, {"relation": "partOf", "source": 2315, "target": 1194, "key": "47b36da5263580ac46933b6a4c75f5c8"}, {"relation": "partOf", "source": 2315, "target": 1191, "key": "c84a13f951936525ac1414a2925cbb57"}, {"relation": "partOf", "source": 2315, "target": 1198, "key": "8ab0286cc5d7d564e916f9b1348a5b5a"}, {"line": 33156, "relation": "increases", "evidence": "Neurons overexpressing APP or APP(V642I) show increased APP-BP1 protein levels in lipid rafts. ", "citation": {"db": "PubMed", "db_id": "14557245"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3087, "key": "1c12ec273dad603a9761b0a0dcaebd3d"}, {"line": 38009, "relation": "association", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2315, "target": 3087, "key": "48254657fbeb5ce782cc7975a0dc4763"}, {"relation": "hasVariant", "source": 2315, "target": 2350, "key": "fb8f55709eea8fa7eebabbf33a37880a"}, {"relation": "partOf", "source": 2315, "target": 1168, "key": "17b0d535d9a98b0e015a43eed2888563"}, {"relation": "partOf", "source": 2315, "target": 1176, "key": "fcb59ddbe90b521a6e89a46ae380d8f7"}, {"relation": "partOf", "source": 2315, "target": 1073, "key": "0e4decc61aa93ebc1530f5ad7adfad77"}, {"relation": "partOf", "source": 2315, "target": 1222, "key": "da1d91c49a3cd1fc36acb3f10e0361f1"}, {"relation": "partOf", "source": 2315, "target": 1188, "key": "185f4453ad15b9e294f250909fc30a96"}, {"relation": "partOf", "source": 2315, "target": 1097, "key": "574babc965d4929a6716a6ece832f252"}, {"relation": "hasVariant", "source": 2315, "target": 2316, "key": "312bfa6fda1111d3c94ecc58e6047006"}, {"relation": "partOf", "source": 2315, "target": 1121, "key": "c7da5299c557fbff7667f86eaef479ab"}, {"relation": "partOf", "source": 2315, "target": 1123, "key": "2f1ef0fc5d4ec7bbacb51cc2f275de3c"}, {"relation": "partOf", "source": 2315, "target": 1157, "key": "31183f857068a40a25e53b283771172d"}, {"relation": "partOf", "source": 2315, "target": 1216, "key": "d044445d31a0b8aec170c16547864c46"}, {"relation": "partOf", "source": 2315, "target": 1181, "key": "bdbc762a7a9cd85596f953c9064f2baa"}, {"relation": "partOf", "source": 2315, "target": 1178, "key": "2b00be64f7089390c0f96cf220b72e9e"}, {"relation": "partOf", "source": 2315, "target": 1163, "key": "4e38a9d4f02bd96ac4150786271c5957"}, {"relation": "partOf", "source": 2315, "target": 1162, "key": "4d98846def594768d430a92b94757729"}, {"line": 34392, "relation": "association", "evidence": "A major pre-beta-amyloid protein695 (APP695) processing activity from Alzheimer's disease brain extracts was identified and found to be indistinguishable from the activity of cathepsin D", "citation": {"db": "PubMed", "db_id": "7523115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2315, "target": 2593, "key": "78e49a1a3bba2974f8ccda86a8f1ec9b"}, {"relation": "partOf", "source": 2315, "target": 1159, "key": "99d81261f56bcad0688f77204f54cb6b"}, {"relation": "partOf", "source": 2315, "target": 1110, "key": "5df86877e9cd71879679f3b883706fb6"}, {"relation": "partOf", "source": 2315, "target": 1107, "key": "750657334dd03cb42bebfa07fefe313a"}, {"relation": "partOf", "source": 2315, "target": 1150, "key": "d60b7af80bbb257a94db0eb412508989"}, {"relation": "partOf", "source": 2315, "target": 1195, "key": "cd2b9227828995dbfb6a7a6f0add2c57"}, {"relation": "partOf", "source": 2315, "target": 1199, "key": "3c036df4c6279f726ef68ad7fa85a317"}, {"relation": "partOf", "source": 2315, "target": 1687, "key": "2b36fe9726ef4cf764478d758bfba452"}, {"relation": "partOf", "source": 2315, "target": 1174, "key": "b9cdea2242d80e073dd370cf9a097cad"}, {"relation": "partOf", "source": 2315, "target": 1172, "key": "4c474ac99e09ddbd5aafe9842890fc58"}, {"relation": "partOf", "source": 2315, "target": 1093, "key": "6ca36e9cc0e79dc34bbccfbd2ff9d782"}, {"line": 35981, "relation": "decreases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER.This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}}, "subject": {"modifier": "Translocation"}, "source": 2315, "target": 1093, "key": "67eee0f185860aabf31888fa01ff3efb"}, {"line": 36019, "relation": "decreases", "evidence": "APP is synthesized in the endoplasmic reticulum (ER) and then transported through the Golgi apparatus to the trans-Golgi-network (TGN) where the highest concentration of APP is found in neurons at steady state. Abeta is generated in the ER and Golgi/TGN. From the TGN, APP can be transported in TGN-derived secretory vesicles to the cell surface where it is either cleaved by alpha-secretase to produce a soluble molecule, sAPPalpha [37], or re-internalized via an endosomal/lysosomal degradation pathway", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}, "toLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}}}, "source": 2315, "target": 4101, "key": "73e222289fd6e38fc5c94200fce0d5b3"}, {"line": 36020, "relation": "increases", "evidence": "APP is synthesized in the endoplasmic reticulum (ER) and then transported through the Golgi apparatus to the trans-Golgi-network (TGN) where the highest concentration of APP is found in neurons at steady state. Abeta is generated in the ER and Golgi/TGN. From the TGN, APP can be transported in TGN-derived secretory vesicles to the cell surface where it is either cleaved by alpha-secretase to produce a soluble molecule, sAPPalpha [37], or re-internalized via an endosomal/lysosomal degradation pathway", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}, "toLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}}}, "source": 2315, "target": 4099, "key": "92ba37a0f459deeb1b5afd882e3af5ae"}, {"line": 36526, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2315, "target": 4099, "key": "ee1ea06919ff0b13dfa039760a3e2646"}, {"relation": "partOf", "source": 2315, "target": 1196, "key": "5489231b99d918901ecc5cd6aafa022e"}, {"relation": "partOf", "source": 2315, "target": 1166, "key": "77e7f17b15d46dd468c1fa0622f95a92"}, {"relation": "partOf", "source": 2315, "target": 1165, "key": "bc7e891d2d43c987d2601dca40301776"}, {"line": 37261, "relation": "increases", "evidence": "Several lines of evidence have revealed that APP may be involved in cell adhesion, cell migration, and neurite outgrowth ", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 497, "key": "953bfe23b7ef125e9accc237bbcfb225"}, {"line": 37888, "relation": "increases", "evidence": "Cell Adhesion An RHDS motif near the extralumenal portion of APP or at the C terminus of APPs lying within the ABeta¸ region appears to promote cell adhesion. It is believed that this region acts in an integrin-like manner and can, accordingly, be blocked by RGDS peptide sequence derived from the fibronectin-binding domain. Similarly, APP colocalizes with integrins on the surface of axons and at sites of adhesion. Evidence of interaction with laminin and collagen provides further evidence of adhesion-promoting properties. Interestingly, because the RHDS sequence is contained within the N terminus of ABeta¸, similar cell adhesion-promoting properties have also been attributed to the ABeta¸ peptide itself. This latter property is, however, difficult to tease out in view of the cytotoxicity of ABeta¸ peptide when tested in a variety of cell systems in vitro. Furthermore, it is difficult to separate the cell adhesion-from the neurite outgrowth-promoting roles of APP. Clearly, these are probably somewhat inseparable, as neuronal migration, neurite outgrowth, and even synaptogenesis would involve substrate adhesion.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Inflammatory response subgraph": true, "Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 497, "key": "58875c04f32a77c9688f3e70cde29c3d"}, {"line": 37262, "relation": "increases", "evidence": "Several lines of evidence have revealed that APP may be involved in cell adhesion, cell migration, and neurite outgrowth ", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 509, "key": "9fa6a0402ec8b13c6f394a565eae13a2"}, {"line": 37263, "relation": "increases", "evidence": "Several lines of evidence have revealed that APP may be involved in cell adhesion, cell migration, and neurite outgrowth ", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 652, "key": "93297d1e7ef9bc5c7bc6e11b2f4a039a"}, {"line": 37285, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 652, "key": "a4e2f367fcd36d13e35882ba47fedbb0"}, {"line": 37536, "relation": "increases", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 652, "key": "18de4aebaeb74077c31317603619869e"}, {"line": 37554, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 652, "key": "3dbb40569ebc13cde3f27373a3d04305"}, {"line": 37272, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 3306, "key": "8e7c46d185057f2b85d4122c76f05419"}, {"line": 37327, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 3306, "key": "66fd1edb85237c21c8091113c7a28508"}, {"line": 37278, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2616, "key": "41b664200495ee0250b4020ae4454259"}, {"line": 37283, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2927, "key": "5b11ecf9abd2dc07c272f675edd7078f"}, {"line": 37328, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 2927, "key": "9547343d442ec03ef377c0a3696ed27c"}, {"line": 37387, "relation": "increases", "evidence": "The cleavage of APP by a-secretase results in the generation of APPs, which might have biological functions in growth regulation and neuroprotection, and, in the case of forms containing the Kunitz proteinase inhibitor domain, in blood coagulation. The C-terminal, 83- residue APP fragments (C83) remaining in the cell membrane have a relative long half-life and can be detected to different extents in metabolically labeled cells.", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 730, "key": "4b065d7c837b13afe5d1757bcea3c8cc"}, {"line": 37388, "relation": "increases", "evidence": "The cleavage of APP by a-secretase results in the generation of APPs, which might have biological functions in growth regulation and neuroprotection, and, in the case of forms containing the Kunitz proteinase inhibitor domain, in blood coagulation. The C-terminal, 83- residue APP fragments (C83) remaining in the cell membrane have a relative long half-life and can be detected to different extents in metabolically labeled cells.", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 431, "key": "056a119c71b324b8a65ab3820980ee12"}, {"relation": "partOf", "source": 2315, "target": 1213, "key": "af2afa7624cda2f3a827d08555d72d67"}, {"relation": "partOf", "source": 2315, "target": 1214, "key": "c812dac1e48c065138a5e73b69622a12"}, {"line": 37395, "relation": "increases", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 817, "key": "0f8ab8a41b3ca4f8e49006b3ac21dc92"}, {"relation": "partOf", "source": 2315, "target": 971, "key": "69bc8ce259579f671246ab940aeb4f59"}, {"line": 37397, "relation": "decreases", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2315, "target": 4089, "key": "2acd1dd6f4f7a6cbca89c384a3c6b55b"}, {"line": 37399, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 2760, "key": "2f1712fad4150ca4f75ee9db906a9160"}, {"line": 37400, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 2761, "key": "7c404c9f1232e859e7e025775bed3ac7"}, {"line": 37401, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 2762, "key": "93ca31e0108c8f81f8705422c5312bf4"}, {"line": 37402, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 2763, "key": "babb8076c5e60c7223d5d0f0bf8663f2"}, {"line": 37403, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 2764, "key": "36aff00d23ba543f44391835aa6429eb"}, {"line": 37404, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2315, "target": 2765, "key": "3de5110e0150dc93e5933f396fce436d"}, {"relation": "partOf", "source": 2315, "target": 1096, "key": "6c40a8c18eabb1af972377b356e7840a"}, {"relation": "partOf", "source": 2315, "target": 1223, "key": "3eb0dbab2e4070a8e1527d83445f2fdd"}, {"line": 37465, "relation": "association", "evidence": "APP/APLP expression is up-regulated during neuronal maturation and differentiation, undergoes rapid anterograde transport, and is targeted in vesicles distinct from synaptophysin transport vesicles to synaptic sites", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 649, "key": "d3465e2cabf0d86f4c67b91e6ce9f06a"}, {"line": 38008, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "source": 2315, "target": 649, "key": "dfcbaa4de2fb9a3c4caa77c7deb276c4"}, {"relation": "partOf", "source": 2315, "target": 1158, "key": "4b41f78da58d859e507d2c2f154d8f77"}, {"line": 37477, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 516, "key": "9d8d3879067a3dd05910bacc1472213a"}, {"line": 37479, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 514, "key": "11a434793009d569d2be7c8d5d614b30"}, {"relation": "partOf", "source": 2315, "target": 1177, "key": "f99aafe7f5aa9c1c57def28350ca9c59"}, {"relation": "partOf", "source": 2315, "target": 1182, "key": "9092c030d6c391fdab63e930f759f030"}, {"line": 37493, "relation": "increases", "evidence": "Like NX/NL and SynCAM-mediated synaptic adhesion in which extracellular sequences engage transsynaptic interactions and the intracellular domains recruit pre- or postsynaptic complexes (reviewed in Dalva et al. 2007), both the extracellular and intracellular domains of APP are required to mediate the synaptogenic activity. Consistent with Soba et al. (2005), the E1 domain plays a more active role in synaptic adhesion. Interestingly, the highly conserved GYENPTY sequence of the APP intracellular domain could form a tripartite complex with Munc 18 interacting protein (Mint/X11) and calcium/calmodulin-dependent serine protein kinase (CASK) similar to that of neurexin and SynCAM (Hata et al. 1996; Biederer and Südhof 2000; Biederer et al. 2002), and the SynCAM carboxy-terminal sequence could functionally replace the corresponding APP domain in the coculture assay (Wang et al. 2009), suggesting that the Mint/CASK complexes may be the common mediators for the different classes of synaptic adhesion proteins. Thus, the precise role of APP-mediated synaptic adhesion in central synapses, whether it involves interaction with other SAMs, and the relationship between APP-mediated synaptogenesis and synaptic dysfunction occurring in AD are interesting questions that warrant further investigation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 787, "key": "2c0aed676ad12a9f562734e459aeab45"}, {"line": 37564, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 787, "key": "7b210238cda261dbbfc7932eca5f684c"}, {"line": 37597, "relation": "increases", "evidence": "APP is expressed pre- and postsynaptically and promotes synapse formation via trans-synaptic interactions of its extracellular domains. Full-length APP also may promote dendritic spine formation as well as surface expression of GluA2-containing AMPA receptors and GluN2B-containing NMDA receptors. Enhanced synaptic activity drives APP processing via the amyloidogenic ß -secretase pathway, leading to subsequent spine loss and downregulation of glutamate receptors in a negative feedback loop.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 787, "key": "d04aa29407421941644dec35d0fce527"}, {"relation": "partOf", "source": 2315, "target": 1087, "key": "005aafbb0a4a8002f49379ba859194c0"}, {"relation": "partOf", "source": 2315, "target": 1149, "key": "aa20807af9a05866aee3cde033c0fd4b"}, {"relation": "partOf", "source": 2315, "target": 1180, "key": "a12b6fc9eff71e41e88ec34eac36545c"}, {"line": 37505, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3091, "key": "7d34c9d2ac817e0bd7832a16c9825e6e"}, {"line": 37506, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 512, "key": "5a317df0465257e0d8b5ac662332fabc"}, {"line": 37584, "relation": "increases", "evidence": "Interestingly, a recent study using heterologous coculture systems has demonstrated that the extracellular domain of APP is especially important for promoting synapse formation. These findings suggest that trans-synaptic interactions between pre- and postsynaptic APP contribute to the adhesion of synapses", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 512, "key": "28aa261c484fd24f1df3052aeb69cb2d"}, {"line": 37933, "relation": "increases", "evidence": "However, normal physiological functions of endogenous APP are not thoroughly understood but are thought to be involved in the stabilizing contact points between synapses and maintaining mitochondrial functions", "citation": {"db": "PubMed", "db_id": "19619643"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 512, "key": "d4ca03678b06009ba070c1cdd5b21f35"}, {"line": 37508, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3138, "key": "b71aadca59a0747a9c5c472f61d8a983"}, {"line": 37510, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2542, "key": "4d4b264cd96cbdc8d41b555c7d1367a4"}, {"line": 37518, "relation": "association", "evidence": "Moreover, gain- or loss-of-function studies with either intraventricular APPsa infusion, down-regulation by antibody infusion or pharmacological inhibition of a-secretase coherently showed a function for APPsa in spatial memory and for LTP", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 738, "key": "02b4f123acb04544a00bae0b4d81a227"}, {"line": 37528, "relation": "association", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 2656, "key": "651d765ead34f57b13d732f8a0d3f139"}, {"line": 37532, "relation": "increases", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2991, "key": "fb056c1a5abff1640be9895e875ae29d"}, {"line": 37533, "relation": "increases", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 822, "key": "ab9754340365ac7c22f8e0fa48b2cf88"}, {"line": 37548, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 822, "key": "2f64fb81ea98495f78b8a374d508a58a"}, {"line": 37535, "relation": "decreases", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2488, "key": "f12d08a79b7aa99ddda07584c702ce8b"}, {"line": 37551, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 743, "key": "e9053f8e9f94cdc1d14eafeb3f6d764a"}, {"line": 37557, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2315, "target": 646, "key": "06ac136184d26b6482381055251e0b2e"}, {"line": 37567, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 737, "key": "cda98ef02946e25f148756e9349313fb"}, {"line": 37570, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 758, "key": "cb19055d395963be390247d90c412490"}, {"line": 37593, "relation": "association", "evidence": "APP is expressed pre- and postsynaptically and promotes synapse formation via trans-synaptic interactions of its extracellular domains. Full-length APP also may promote dendritic spine formation as well as surface expression of GluA2-containing AMPA receptors and GluN2B-containing NMDA receptors. Enhanced synaptic activity drives APP processing via the amyloidogenic ß -secretase pathway, leading to subsequent spine loss and downregulation of glutamate receptors in a negative feedback loop.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2772, "key": "ff13eacff4ab377d99f3c9843ddf42e2"}, {"line": 37623, "relation": "increases", "evidence": "Interestingly, we found that APP also affects excitatory synaptic transmission by altering AMPA receptor (AMPAR) and NMDA receptor (NMDAR) trafficking. Recently, we demonstrated that APP increases cell surface levels of the GluA2 (or GluR2) subunit of AMPA receptors (or GluAs), but does not alter levels of GluA1 (or GluR1), suggesting that APP regulates certain AMPAR subunits, specifically GluA2. Considering that alterations in AMPAR subunit expression (particularly in the synaptic content of GluA2-containing AMPARs) can impact synaptic transmission and plasticity, these changes may also potentially alter the function of excitatory synapses. The increase in GluA2 levels is expected to enhance excitatory synaptic transmission, especially because it occurred in the absence of a decrease in GluA1, suggesting an overall increase in AMPAR number at synapses.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2772, "key": "b436f7fc5af6d857c697afe82f16585a"}, {"line": 37602, "relation": "increases", "evidence": "APP is expressed pre- and postsynaptically and promotes synapse formation via trans-synaptic interactions of its extracellular domains. Full-length APP also may promote dendritic spine formation as well as surface expression of GluA2-containing AMPA receptors and GluN2B-containing NMDA receptors. Enhanced synaptic activity drives APP processing via the amyloidogenic ß -secretase pathway, leading to subsequent spine loss and downregulation of glutamate receptors in a negative feedback loop.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2779, "key": "cf38842752847d30cdd56b43433db83b"}, {"relation": "partOf", "source": 2315, "target": 1684, "key": "01c2c325917bf4cde0f5e58ef4417b2f"}, {"relation": "partOf", "source": 2315, "target": 1685, "key": "333b6472606b8a403803cac9ee9d2bd5"}, {"line": 37624, "relation": "increases", "evidence": "Interestingly, we found that APP also affects excitatory synaptic transmission by altering AMPA receptor (AMPAR) and NMDA receptor (NMDAR) trafficking. Recently, we demonstrated that APP increases cell surface levels of the GluA2 (or GluR2) subunit of AMPA receptors (or GluAs), but does not alter levels of GluA1 (or GluR1), suggesting that APP regulates certain AMPAR subunits, specifically GluA2. Considering that alterations in AMPAR subunit expression (particularly in the synaptic content of GluA2-containing AMPARs) can impact synaptic transmission and plasticity, these changes may also potentially alter the function of excitatory synapses. The increase in GluA2 levels is expected to enhance excitatory synaptic transmission, especially because it occurred in the absence of a decrease in GluA1, suggesting an overall increase in AMPAR number at synapses.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 790, "key": "3b36c52b5996d5beb6ee397d54767b46"}, {"line": 37633, "relation": "increases", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "object": {"modifier": "Translocation"}, "source": 2315, "target": 2781, "key": "172aa1f1ccbe9a9f40aa48da3538c3f5"}, {"line": 37635, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "source": 2315, "target": 726, "key": "95a1a9a3ad3dcfa19072d882074591bb"}, {"line": 37637, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "source": 2315, "target": 761, "key": "5395ac482a1585137575d955859a4669"}, {"line": 37640, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "source": 2315, "target": 812, "key": "53457f70d03b3f927f9d734fcc6e37dd"}, {"relation": "partOf", "source": 2315, "target": 1672, "key": "7e3a61e85f2ab1e1efa5a017b3419817"}, {"relation": "partOf", "source": 2315, "target": 1688, "key": "355f7ed3fdd2df1bf5f8a248ec52e1b5"}, {"relation": "partOf", "source": 2315, "target": 1095, "key": "086e7758aa0d46afbf052086805af472"}, {"line": 37880, "relation": "association", "evidence": "APPs is constitutively released from cells following a-secretase cleavage, these findings indicated that APP has autocrine and paracrine functions in growth regulation.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cytokine signaling subgraph": true}}, "source": 2315, "target": 536, "key": "30611500c70a42cac2721a37cf368dcd"}, {"relation": "partOf", "source": 2315, "target": 1683, "key": "6ed1ff494964b0f87e65a1a3ddfc9449"}, {"relation": "partOf", "source": 2315, "target": 1681, "key": "98597d2728a37c300f3b3dfd8e1c1d9d"}, {"line": 37906, "relation": "decreases", "evidence": "Iron was further demonstrated to modulate expression of the Alzheimer's amyloid precursor holo-protein (APP) by a mechanism similar to that of regulation of ferritin-L and -H mRNA translation through an iron-responsive element (IRE) in their 5' untranslated regions (UTRs). Here, we discuss two aspects of the link between iron and AD, in relation to the recently discovered IRE in the 5'UTR of APP mRNA. The first is the physiological aspect: a compensatory neuroprotective response of amyloid-ß protein (ABeta¸) in reducing iron-induced neurotoxicity. Thus, given that ABeta¸ possesses iron chelation sites, it is hypothesized that OS-induced intracellular iron may stimulate APP holo-protein translation (via the APP 5'UTR) and subsequently the generation of its cleavage product, ABeta¸, as a compensatory response that eventually reduces OS.", "citation": {"db": "PubMed", "db_id": "19090990"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 2315, "target": 721, "key": "4dbc0de78aaa091ac2bff3d2f535a9ac"}, {"relation": "partOf", "source": 2315, "target": 1682, "key": "b649974483c701ce08064c8c5c72de9c"}, {"line": 37977, "relation": "association", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "MAPK-ERK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 3005, "key": "20a7da5853f187fa2181810313650642"}, {"line": 38005, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "source": 2315, "target": 2755, "key": "f2263610060f9cfb4f9c8c7e15a8cb83"}, {"line": 38012, "relation": "association", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2315, "target": 3162, "key": "ad75e4725a2fe1632adaaba111368dcf"}, {"line": 38014, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "source": 2315, "target": 445, "key": "1d8586e01137fa388fb5cd47479f6196"}, {"relation": "partOf", "source": 2315, "target": 1169, "key": "d255ab66f6c0a5695d99230da908fe41"}, {"line": 38025, "relation": "increases", "evidence": "Considering all these aspects, it is possible to hypothesize that posttranslational modifications of ABeta¸PP, or in its CTFs, such as a selective phosphorylation, might couple them, to different cellular pathways. These observation supports the hypothesis that ABeta¸PP may act as a receptor/transducer molecule in multiple cell-signaling events, the comprehension of which may have implications either for the normal biological function of ABeta¸PP, for its processing and for its pathological role in the genesis of AD", "citation": {"db": "PubMed", "db_id": "22496686 "}, "source": 2315, "target": 515, "key": "d92c79bba66e5aae11abedd71cce0930"}, {"line": 38033, "relation": "increases", "evidence": "These findings indicate that a fraction of APP, including its amino-terminal portion, may be localized in the nucleus as well as in the nucleolus, suggesting an important role of APP in RNA metabolism and other intra-nucleolus functions.", "citation": {"db": "PubMed", "db_id": "22659497"}, "source": 2315, "target": 454, "key": "eee88ab650eba0efabc7d4a65f90f2b1"}, {"relation": "partOf", "source": 2315, "target": 1197, "key": "fb7a7df2109d621791c9c596974ee722"}, {"relation": "hasVariant", "source": 2315, "target": 2336, "key": "72fe3d0c48e3140413a7785cbf133532"}, {"relation": "hasVariant", "source": 2315, "target": 2335, "key": "5711b9cc149c1b7ce854ab80b2d155bd"}, {"relation": "hasVariant", "source": 2315, "target": 2337, "key": "8968faf00fdf1f92e39f9fc34421d847"}, {"line": 38581, "relation": "association", "evidence": "As an adaptor protein involved in protein sorting and trafficking, X11 has been suggested as affecting APP trafficking/metabolism by interacting with AICD, leading to reduced Ab production. X11 has also been found to suppress the transactivation of AICD, possibly by competing with AICD for the recruitment of Fe65, as they share the same binding motif", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation"}, "source": 2315, "target": 1079, "key": "1b68d0c8fedeebd20c2621e2ba505c73"}, {"line": 40036, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 261, "key": "7d7edce1538c87c139c8e75813c3de7b"}, {"line": 40040, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 3468, "key": "7b37c3cee204e9f9bf9f0deb3445daea"}, {"line": 40041, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2315, "target": 3112, "key": "e05875bbe2e86f740ddc721ad351ab24"}, {"relation": "hasVariant", "source": 2315, "target": 2322, "key": "4e99f68b1f1805513b8f0de10af79c00"}, {"line": 43792, "relation": "increases", "evidence": "Although the role of AICD as a gene transcription inducer is still controversial, several targets of AICD- or APP-mediated transcriptional activation are reportedly regulated by APP nuclear signaling, including APP itself, BACE, Tip60, GSK-3ß, p53, Mn-SOD, KAI1, Neprilysin, transgelin, a2actin, S100a9, and other genes", "citation": {"db": "PubMed", "db_id": "21034527"}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 2315, "target": 3334, "key": "91d34ee9cc2ad4ca98f33294e00141c2"}, {"line": 44430, "relation": "orthologous", "evidence": "developmental exposure of rodents to the heavy metal lead (Pb) increases APP (amyloid precursor protein) and Abeta production later in the aging brain", "citation": {"db": "PubMed", "db_id": "18157652"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 2315, "target": 3584, "key": "ec8eb18a23b5ad6744669a5ef31c0a01"}, {"relation": "hasVariant", "source": 2315, "target": 2342, "key": "a21d80941b166cc8bf7daa6ad507095f"}, {"relation": "hasVariant", "source": 2315, "target": 2320, "key": "13337814e4b234e6f84587c64004c7ca"}, {"relation": "hasVariant", "source": 2315, "target": 2331, "key": "ec31192b09253e59157a7f650d3ba073"}, {"relation": "hasVariant", "source": 2315, "target": 2317, "key": "6eb22bce50d088d7cb8081a7192da0b6"}, {"line": 45272, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2658, "key": "3397372022ae0c16fce0ea122f5a2986"}, {"line": 48979, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Whereas APP affected Egr1 promoter activity by reducing access of the CREB transcription factor.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"Published": {"Epilepsy comorbidity paper": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2658, "key": "73830ae969b9007acdc54f6923acc028"}, {"line": 45274, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2397, "key": "e1642378d358952a42c26755ea7b637d"}, {"line": 48985, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Whereas APP affected Egr1 promoter activity by reducing access of the CREB transcription factor.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2397, "key": "36c47fb881f7a7e23482f37215cff3e9"}, {"line": 45275, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2355, "key": "d4701d6c6099456055b462b1d76e6f4c"}, {"line": 48987, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Whereas APP affected Egr1 promoter activity by reducing access of the CREB transcription factor.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2355, "key": "b00d2537f1ee6f00e7692943320ca221"}, {"line": 45293, "relation": "increases", "evidence": "APP Specifically Regulates Acetylation of Histone H4 at Lysines 5 and 12", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2834, "key": "e992cadb9f562f34e79bb356b8cd99e6"}, {"line": 45298, "relation": "increases", "evidence": "APP Specifically Regulates Acetylation of Histone H4 at Lysines 5 and 12", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2315, "target": 2833, "key": "0227c7222a9bc990889fd89c7820a360"}, {"line": 45411, "relation": "association", "evidence": "Networks analyses suggest that RPL13 interacts with PTK2B and APP", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 2315, "target": 3321, "key": "28f0d6abd4105c5432d85dafe4bf664f"}, {"line": 45453, "relation": "positiveCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 2315, "target": 1747, "key": "9b2644b3fd2266cb5f543807c98a281a"}, {"line": 46141, "relation": "negativeCorrelation", "evidence": "APP gene sequence data that suggests there are multiple potential sites for CpG methylation both within and around the APP gene, and that at least one of these sites is hypomethylated in brain tissue from an AD patient. That results in Increased levels of APP proteins and mRNA ", "citation": {"db": "PubMed", "db_id": "8746452"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2315, "target": 1747, "key": "fafc8afc13f732eec8f63a5dca2c73fa"}, {"line": 46290, "relation": "negativeCorrelation", "evidence": "The Pb exposure acted to inhibit DNA methylation patterns, thus setting the responsiveness of the APP promoter and the expression of the APP gene at a higher level. ", "citation": {"db": "PubMed", "db_id": "22764079"}, "source": 2315, "target": 1747, "key": "fc32a3d19fe2c43090b8a6708d153b70"}, {"relation": "partOf", "source": 2315, "target": 1179, "key": "791464228dee4c109464e233b750cec8"}, {"line": 47347, "relation": "isA", "evidence": "Binding to HSPGs requires a heparin/heparan sulfate-binding domain consisting of a stretch of positively charged lysines or arginines on the ligand. Prion protein, beta-amyloid, tau, and alpha-synuclein all have putative heparin-binding domains(25, 44–46).", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "source": 2315, "target": 49, "key": "b9f2209303cfed22c78ada194a81dc5a"}, {"line": 48989, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Whereas APP affected Egr1 promoter activity by reducing access of the CREB transcription factor.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2315, "target": 2162, "key": "80ff1e93a54a40e5d67624c6e36051c5"}, {"line": 191, "relation": "positiveCorrelation", "evidence": "Here, we show that siRNA-mediated loss of calsyntenin-1 in cultured neurons alters APP processing to increase production of Abeta. We also show that calsyntenin-1 is reduced in Alzheimer's disease brains and that the extent of this reduction correlates with increased Abeta levels.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Calsyntenin subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Degradation"}, "source": 80, "target": 2532, "key": "ee024f310b0168f9252a5867c50bcf30"}, {"line": 207, "relation": "association", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 80, "target": 484, "key": "bdb2824c944b85113c4d07cdc62d7e04"}, {"line": 2652, "relation": "association", "evidence": "Although there are numerous studies regarding Alzheimer's disease (AD), the cause and progression of AD are still not well understood. The researches in the past decade implicated amyloid-beta (Abeta) overproduction as a causative event in disease pathogenesis, but still failed to clarify the mechanism of pathology from Abeta production to central neural system defects in AD. The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.For this hypothesis, the factors related with the initiation of AD pathology are not only limited to the neurons per se but also expanded to the microenvironment around neurons, such as the secretion of BDNF from astrocytes. The modification of the origin in this pathway may contribute to slow down the disease progression of AD.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 484, "key": "95a815e6ea7effbaea30774baa0eed6d"}, {"line": 474, "relation": "increases", "evidence": "The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2445, "key": "5c465c47534cb15e98f43df752efbd37"}, {"line": 489, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 80, "target": 170, "key": "6e01d3dbaafe47c1e5e67cfe0b3ab3aa"}, {"line": 1546, "relation": "increases", "evidence": "Biochemical and morphological alterations of mitochondria may play an important role in the pathogenesis of Alzheimer's disease (AD). Particularly, mitochondrial dysfunction is a hallmark of amyloid-beta-induced neuronal toxicity in Alzheimer's disease. The recent emphasis on the intracellular biology of amyloid-beta and its precursor protein (APP) has led researchers to consider the possibility that mitochondria-associated and mitochondrial amyloid-beta may directly cause neurotoxicity. Both proteins are known to localize to mitochondrial membranes, block the transport of nuclear-encoded mitochondrial proteins to mitochondria, interact with mitochondrial proteins, disrupt the electron transport chain, increase reactive oxygen species production, cause mitochondrial damage, and prevent neurons from functioning normally. ", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 170, "key": "2db5e0db7cfd203e36033c72bf1f5704"}, {"line": 44503, "relation": "increases", "evidence": "Increased Abeta levels promoted the production of reactive oxygen species (ROS)", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 170, "key": "28fb23fb14c387597f3fb72ebf4dc631"}, {"line": 507, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true, "Nitric oxide subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 3875, "key": "5db55d540b766c6a3d6cab949a92348b"}, {"line": 515, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 156, "key": "5234e8a77699ac372ac601e10933adc7"}, {"line": 530, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 3472, "key": "33fc6ffbab7ef35d2fc11af2823ef237"}, {"line": 535, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 2885, "key": "58bf8823af5f71696a81e0f02978d8ea"}, {"line": 1374, "relation": "increases", "evidence": "A temporal sequence was observed whereby Abeta accumulation is followed by expression of IL-1Abeta and eventually, of CXCL1, in the hippocampus and olfactory bulb but not the cortex.", "citation": {"db": "PubMed", "db_id": "21295112"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2885, "key": "29f632517f6d7d5151e6340e1eeafaef"}, {"line": 24905, "relation": "positiveCorrelation", "evidence": "The proinflammatory cytokine interleukin (IL)-1beta is up-regulated in microglial cells surrounding amyloid plaques, leading to the hypothesis that IL-1beta is a risk factor for Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2885, "key": "f7202563a741d307f30dd06572377f10"}, {"line": 557, "relation": "increases", "evidence": "Our results show that oligomeric Abeta specifically induces CaN activity and promotes CaN-dependent CREB and Bcl-2 Asociated death Protein (BAD) dephosphorylation and cell death", "citation": {"db": "PubMed", "db_id": "18782350"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 872, "key": "75ea02e5f21fef1678b2516ddbefc3fc"}, {"line": 621, "relation": "increases", "evidence": "In Alzheimer disease, it has been proposed that the peptide beta amyloid promotes GSK3 activation, resulting in tau phosphorylation", "citation": {"db": "PubMed", "db_id": "19782073"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Tau protein subgraph": true, "Amyloidogenic subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2178, "key": "708bd6b2d551be7fea05ec6ffa749d15"}, {"line": 33499, "relation": "increases", "evidence": "Compared to vehicle, Abeta increased GSK3 activity, and was associated with elevations in levels of ptau, caspase-3, the tau kinase phospho-c-jun N-terminal kinase (pJNK), neuronal DNA fragmentation, and gliosis.", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2178, "key": "76609f8d1f3c787cbdb18cc3c9e7d461"}, {"relation": "partOf", "source": 80, "target": 1658, "key": "42bb821e65ff9215297f38e58ef65b6a"}, {"line": 707, "relation": "increases", "evidence": "Caspase-2 mediates neuronal cell death induced by beta-amyloid", "citation": {"db": "PubMed", "db_id": "10662829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 80, "target": 2443, "key": "cf06ad03ec347caf4b176c7f226636b0"}, {"line": 883, "relation": "association", "evidence": "beta APs enhanced both kainate and NMDA neurotoxicity, indicating that the effect was not specific for a particular subtype of glutamate receptor.", "citation": {"db": "PubMed", "db_id": "1346802"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 3548, "key": "f9d40383dcdb8fc7f0b1f9810e6c3e88"}, {"line": 47070, "relation": "association", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3548, "key": "10e198df85337641e47641b93ee65e7c"}, {"line": 884, "relation": "association", "evidence": "beta APs enhanced both kainate and NMDA neurotoxicity, indicating that the effect was not specific for a particular subtype of glutamate receptor.", "citation": {"db": "PubMed", "db_id": "1346802"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 736, "key": "6aaf410f124989b6723e3f930589397c"}, {"line": 890, "relation": "increases", "evidence": "beta amyloid peptides cause an elevation in intracellular calcium levels and enhance calcium responses to depolarization and calcium ion-ophore.", "citation": {"db": "PubMed", "db_id": "1346802"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular space"}, "toLoc": {"namespace": "GO", "name": "intracellular part"}}}, "source": 80, "target": 94, "key": "5d19caf9ea028459a84da480db7025cf"}, {"line": 3931, "relation": "directlyIncreases", "evidence": "Abeta forms oligomers which can insert into the plasma membrane and form pores through which Ca2+ passes into the cytoplasm", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Extracellular Space"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 80, "target": 94, "key": "6e688ff3090de0c97285405495f1ec38"}, {"line": 5540, "relation": "increases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 80, "target": 94, "key": "0e0d18f2f261a7f18b86cf9083b8efa1"}, {"line": 904, "relation": "decreases", "evidence": "Creatine kinase(CK) and beta-actin have increased carbonyl groups, an index of protein oxidation, and Glt-1, the principal glutamate transporter, has increased binding of the lipid peroxidation product, 4-hydroxy-2-nonenal (HNE). Abeta inhibits CK and causes lipid peroxidation, leading to HNE formation.", "citation": {"db": "PubMed", "db_id": "12607822"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 80, "target": 2530, "key": "9ec647d0fa8f94d38b69ed6b901908d2"}, {"line": 1292, "relation": "increases", "evidence": "Here we show that the buildup of Abeta increases the mammalian target of rapamycin (mTOR) signaling, whereas decreasing mTOR signaling reduces Abeta levels, thereby highlighting an interrelation between mTOR signaling and Abeta. The mTOR pathway plays a central role in controlling protein homeostasis and hence, neuronal functions; indeed mTOR signaling regulates different forms of learning and memory.", "citation": {"db": "PubMed", "db_id": "20178983"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3076, "key": "aa6fe7adaa22799eb5a6e8c179e61803"}, {"line": 1360, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 2606, "key": "977cba88ce8d6abd8087f2ea8deb68d5"}, {"line": 1361, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 2603, "key": "eeea0e6d35e8f8d14ba722e6d47337a1"}, {"line": 1541, "relation": "decreases", "evidence": "Biochemical and morphological alterations of mitochondria may play an important role in the pathogenesis of Alzheimer's disease (AD). Particularly, mitochondrial dysfunction is a hallmark of amyloid-beta-induced neuronal toxicity in Alzheimer's disease. The recent emphasis on the intracellular biology of amyloid-beta and its precursor protein (APP) has led researchers to consider the possibility that mitochondria-associated and mitochondrial amyloid-beta may directly cause neurotoxicity. Both proteins are known to localize to mitochondrial membranes, block the transport of nuclear-encoded mitochondrial proteins to mitochondria, interact with mitochondrial proteins, disrupt the electron transport chain, increase reactive oxygen species production, cause mitochondrial damage, and prevent neurons from functioning normally. ", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Electron transport chain": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 546, "key": "987e4e0a9c4245f2ec99cd5f92f2ec15"}, {"line": 1590, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Electron transport chain": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 546, "key": "a4ccdae94a47580fd1ffaa69586d6ab7"}, {"line": 1557, "relation": "decreases", "evidence": "Biochemical and morphological alterations of mitochondria may play an important role in the pathogenesis of Alzheimer's disease (AD). Particularly, mitochondrial dysfunction is a hallmark of amyloid-beta-induced neuronal toxicity in Alzheimer's disease. The recent emphasis on the intracellular biology of amyloid-beta and its precursor protein (APP) has led researchers to consider the possibility that mitochondria-associated and mitochondrial amyloid-beta may directly cause neurotoxicity. Both proteins are known to localize to mitochondrial membranes, block the transport of nuclear-encoded mitochondrial proteins to mitochondria, interact with mitochondrial proteins, disrupt the electron transport chain, increase reactive oxygen species production, cause mitochondrial damage, and prevent neurons from functioning normally. ", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 621, "key": "1260e42b61b3cac03e0a613079d9c8d5"}, {"line": 1558, "relation": "decreases", "evidence": "Biochemical and morphological alterations of mitochondria may play an important role in the pathogenesis of Alzheimer's disease (AD). Particularly, mitochondrial dysfunction is a hallmark of amyloid-beta-induced neuronal toxicity in Alzheimer's disease. The recent emphasis on the intracellular biology of amyloid-beta and its precursor protein (APP) has led researchers to consider the possibility that mitochondria-associated and mitochondrial amyloid-beta may directly cause neurotoxicity. Both proteins are known to localize to mitochondrial membranes, block the transport of nuclear-encoded mitochondrial proteins to mitochondria, interact with mitochondrial proteins, disrupt the electron transport chain, increase reactive oxygen species production, cause mitochondrial damage, and prevent neurons from functioning normally. ", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 619, "key": "954c35d7c774e6a590d889eab2d763e0"}, {"line": 1594, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 615, "key": "ad14dcc722ee7b3fd47752c33d15753f"}, {"line": 1596, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 190, "key": "1bb2955d83b57fe009c4655e212479e3"}, {"line": 1598, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 614, "key": "7cbff189264d1685c12d60d41bb4b00d"}, {"line": 1607, "relation": "increases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 478, "key": "3616ced01b27dac782c294118de348e5"}, {"line": 3260, "relation": "positiveCorrelation", "evidence": "The role of miR-124 on the expression of Abeta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the Abeta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}}, "source": 80, "target": 478, "key": "9b5ab5cb215b1bf261e3e8e513da733b"}, {"line": 8899, "relation": "increases", "evidence": "The role of miR-124 on the expression of beta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the beta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 478, "key": "0a2ce9d57b6938db3b0eeb8258c2217c"}, {"relation": "partOf", "source": 80, "target": 927, "key": "ca230ac8104fd1f421930fa5a6157ad6"}, {"line": 1740, "relation": "association", "evidence": "The K16N mutation is located exactly at the a-secretase cleavage site and influences both APP and Abeta. First, due to the K16N mutation APP secretion is affected and a higher amount of Abeta peptides is being produced.", "citation": {"db": "PubMed", "db_id": "22514144"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 80, "target": 2348, "key": "9c5363f29aff32faf009077f74682a8f"}, {"line": 2151, "relation": "regulates", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2173, "key": "b467613cd9c925f854060a7196aaeb2a"}, {"line": 7718, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2173, "key": "422dbb0a5c45a915a784d7368788733d"}, {"line": 2234, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 80, "target": 2312, "key": "9c09c7c824f2734b726f6fc2a5512a43"}, {"line": 26021, "relation": "association", "evidence": "Moreover, apoE-positive newly formed plaques were seen more frequently in APOE epsilon4/4 cases than in non-APOE epsilon4/4 individuals, thereby underlining the potentially crucial role of apoE for the development of Abeta deposits.", "citation": {"db": "PubMed", "db_id": "16195918"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 2312, "key": "ddd2e4840773826e167d0f433612bf17"}, {"line": 45146, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 80, "target": 2312, "key": "715a87f55e9004d3581fb703e624541e"}, {"line": 2281, "relation": "decreases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3306, "key": "8eb5e5be617a9177eb21e19cb319586d"}, {"line": 2285, "relation": "decreases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "NMDA receptor": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 702, "key": "dcb393da03325da1b2b011cc80d140b7"}, {"line": 2288, "relation": "decreases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "NMDA receptor": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2777, "key": "fbf86ac80bc57c11e11bfadf45184a4a"}, {"line": 2289, "relation": "decreases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "NMDA receptor": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2780, "key": "aa1d9a0a9a66dc5765cd25c6ecb7d3ff"}, {"line": 2370, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 491, "key": "12750be7605755fcead0a8bd65e823a1"}, {"line": 3945, "relation": "decreases", "evidence": "Abeta can also interact with Fe2+ and Cu+ to generate hydrogen peroxide and hydroxyl radical (OH.) resulting in membrane lipid peroxidation which generates toxic aldehydes that impair the function of membrane ion-motive ATPases (Na+ and Ca2+ pumps)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}, "CellStructure": {"Cell Membrane": true}}, "source": 80, "target": 491, "key": "b131be8a2bd020e9076f6f5e381613df"}, {"line": 6556, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 491, "key": "e06b9971423be6533712db399dd1a455"}, {"line": 2371, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 628, "key": "0e0f391509b15dce8ec93b5072070a98"}, {"line": 2520, "relation": "increases", "evidence": "Two possible models for involvement of HRD1 in the pathogenesis of AD. Model 1 (cause of AD): Unknown stress initiates insolubilization of HRD1 protein, resulting in a decrease in the functional HRD1 protein in the ER membrane. Subsequently, APP accumulates in the ER and is processed into Abeta that induces hyperphosphorylation of tau protein (ptau). Finally, accumulated Abeta and/or p-tau causes neurodegeneration leading to AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "source": 80, "target": 3015, "key": "3120095152a706d22138c607729834d6"}, {"line": 6549, "relation": "increases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3015, "key": "53d58623128a88b210000ad2c0bc39ba"}, {"line": 29252, "relation": "association", "evidence": "We have shown that interaction of CD40 with CD40L enables microglial activation in response to amyloid-beta peptide (Abeta), which is associated with Alzheimer's disease (AD)-like neuronal tau hyperphosphorylation in vivo.", "citation": {"db": "PubMed", "db_id": "12402041"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true, "Tau protein subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3015, "key": "b29795ad79d7a7092bbafc3b7a1a8954"}, {"line": 33502, "relation": "positiveCorrelation", "evidence": "Compared to vehicle, Abeta increased GSK3 activity, and was associated with elevations in levels of ptau, caspase-3, the tau kinase phospho-c-jun N-terminal kinase (pJNK), neuronal DNA fragmentation, and gliosis.", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 3015, "key": "0c242c282d0cf44d375c11f201538279"}, {"line": 2521, "relation": "positiveCorrelation", "evidence": "Two possible models for involvement of HRD1 in the pathogenesis of AD. Model 1 (cause of AD): Unknown stress initiates insolubilization of HRD1 protein, resulting in a decrease in the functional HRD1 protein in the ER membrane. Subsequently, APP accumulates in the ER and is processed into Abeta that induces hyperphosphorylation of tau protein (ptau). Finally, accumulated Abeta and/or p-tau causes neurodegeneration leading to AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "source": 80, "target": 3823, "key": "64a1d0bad6ecb2b9cd423b1170cbf821"}, {"line": 3526, "relation": "increases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "aae918ffb075f9afb6a18d9e838aeeaa"}, {"line": 3612, "relation": "increases", "evidence": "Recently, many amyloid PET-positive and cognitively normal subjects were found in PiB-PET studies. PiB-PET studies on healthy subjects have also shown that apolipoprotein (APO) E4 boosts the accumulation of amyloid-beta and may consequently accelerate the pathogenesis of AD", "citation": {"db": "PubMed", "db_id": "20675880"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "920e878c7d874fe229a0fc93ea902515"}, {"line": 5168, "relation": "increases", "evidence": "beta-Amyloid (Abeta) plays a central role in Alzheimer's disease (AD) pathogenesis. Neurons are major sources of Abeta in the brain. However, astrocytes outnumber neurons by at least five-fold. Thus, even a small level of astrocytic Abeta production could make a significant contribution to Abeta burden in AD. Moreover, activated astrocytes may increase Abeta generation. beta-Site APP cleaving enzyme 1 (BACE1) cleavage of amyloid precursor protein (APP) initiates Abeta production.", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}}, "source": 80, "target": 3823, "key": "f30987b716dbe71abc68f4e8004cd2c6"}, {"line": 5228, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 80, "target": 3823, "key": "3647e6628e51fb794b79da7a63bdc170"}, {"line": 6076, "relation": "increases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 3823, "key": "848e23bd0392035a486708b7ec8da503"}, {"line": 9187, "relation": "increases", "evidence": "The amyloid-beta (Abeta) peptide is the derivative of amyloid precursor protein (APP) generated through sequential proteolytic processing by beta- and gamma-secretases. Excessive accumulation of Abeta, the main constituent of amyloid plaques, has been implicated in the etiology of Alzheimer disease (AD). ", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "8de57c6bfc9e8b111522247794e926c9"}, {"line": 25987, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulation of beta-amyloid (Abeta).", "citation": {"db": "PubMed", "db_id": "15331417"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "59fa22a4d94b42444f57d4c07dd00d3c"}, {"line": 26083, "relation": "association", "evidence": "Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "117c9ffcfc8bdcdddb1ddd2245eeeced"}, {"line": 26403, "relation": "association", "evidence": "Amyloid-beta peptide (Abeta) production and accumulation in the brain is a central event in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "17185504"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "2c7d3a19bcdded739f008a49eb11bfa3"}, {"line": 26657, "relation": "increases", "evidence": "According to the amyloid hypothesis, accumulation of Abeta is the primary influence driving AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "15218540"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "2af5f066d0c610f7e149ba6c06f978f3"}, {"line": 26781, "relation": "association", "evidence": "Amyloid-beta peptides (Abeta) are widely presumed to play a causal role in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "16027115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "1d4c5b3e49c6194684ca5ab8925879ad"}, {"line": 26843, "relation": "association", "evidence": "Amyloid beta-peptide (Abeta), which is a product of the proteolytic effect of beta-secretase (BACE) on an amyloid precursor protein, is closely associated with Alzheimer's disease (AD) pathogenesis.", "citation": {"db": "PubMed", "db_id": "17205046"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "27175b3455fad0f194bc5d3444d39ef1"}, {"line": 26872, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulation of the neurotoxic peptide amyloid-beta (Abeta), which is produced by sequential cleavage of amyloid precursor protein (APP) by the aspartyl protease beta-secretase and the presenilin-dependent protease gamma-secretase.", "citation": {"db": "PubMed", "db_id": "17360493"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "6a95e70021b9f53dc5157ce52f837539"}, {"line": 27079, "relation": "association", "evidence": "Amyloid-beta (Abeta) is either directly involved in the pathogenesis of Alzheimer's disease (AD) or tightly correlated with other primary pathogenic factors.", "citation": {"db": "PubMed", "db_id": "19199126"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "5d42e9eb5315f1a7c1cf5f2df73ed4a2"}, {"line": 27125, "relation": "association", "evidence": "Accumulation of beta-amyloid peptide (Abeta) in the brain is a primary influence driving Alzheimer's disease (AD) pathogenesis.", "citation": {"db": "PubMed", "db_id": "19355846"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "b469c392f8f43fec90e9ffafcd1f98c2"}, {"line": 27152, "relation": "association", "evidence": "Expression levels of the amyloid precursor protein (APP) and beta-site amyloid (Abeta) cleaving enzyme 1 (BACE1) have been implicated in Alzheimer disease (AD) progression.", "citation": {"db": "PubMed", "db_id": "19462468"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "5e18fddcafede39573885db92aefcedc"}, {"line": 27185, "relation": "association", "evidence": "Clearly, AD is associated with accumulation of amyloid beta (Abeta) in the brain. ", "citation": {"db": "PubMed", "db_id": "19698775"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "b8604ac18c421b6ae2d430965de5b982"}, {"line": 27291, "relation": "association", "evidence": "It has been suggested that cholesterol may pmodulate amyloid-beta (Abeta) formation, a causative factor of Alzheimer's disease (AD), by regulating distribution of the three key proteins in the pathogenesis of AD (beta-amyloid precursor protein (APP), beta-secretase (BACE1) and/or presenilin 1 (PS1)) within lipid rafts.", "citation": {"db": "PubMed", "db_id": "20138836"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "a2b81fd6c830de98c10191d8e8b69e86"}, {"line": 27301, "relation": "association", "evidence": "Accumulation of amyloid-beta (Abeta) peptide and deposition of hyperphosphorylated tau protein are two major pathological hallmarks of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "a959226524f96234e8a4630e4675bb6f"}, {"line": 27687, "relation": "association", "evidence": "BACE1 is a membrane-bound aspartic protease that cleaves the amyloid precursor protein (APP) at the beta-secretase site, a critical step in the Alzheimer disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "12473667"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "994636e12ae8c892d9f9b55875cce74a"}, {"line": 27763, "relation": "association", "evidence": "The cerebral deposition of amyloid beta-peptide (Abeta) is a major factor in the etiology of Alzheimer's disease. beta-Secretase (BACE) initiates the generation of Abeta by cleaving the amyloid precursor protein at the beta-site and is therefore a prime target for therapeutic intervention.", "citation": {"db": "PubMed", "db_id": "14622952"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "f3ece8f23f96dd7c9745af37382a9dc3"}, {"line": 27936, "relation": "association", "evidence": "Beta-secretase [beta-site amyloid precursor protein-cleaving enzyme 1 (BACE1)] is the key rate-limiting enzyme for the production of the beta-amyloid (Abeta) peptide involved in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "16306400"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "a1fb2581c08a8cf4170642076733fcad"}, {"line": 28094, "relation": "association", "evidence": "The amyloid beta (Abeta) peptide is responsible for toxic amyloid plaque formation and is central to the aetiology of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "17541560"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "91647f6a2cc31b949993ed0d4e8074d4"}, {"line": 30948, "relation": "increases", "evidence": "Elevation of intracranial soluble amyloid-beta (Abeta) levels has been implicated in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "f6766cc24ea1075ffa3f9eae59aaeb82"}, {"line": 33125, "relation": "association", "evidence": "A beta is thought to play a role in the pathogenesis of Alzheimer's disease, and, hence, considerable effort has been invested in defining the means by which A beta is generated from the APPs.", "citation": {"db": "PubMed", "db_id": "8626687"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "7e6e67236538efa5047066b863fbab5e"}, {"line": 47058, "relation": "association", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "d0a7124431b97896a89f7fa48dc29cc3"}, {"line": 47700, "relation": "increases", "evidence": "Thus, behavioral stressors can rapidly increase ISF Abeta through neuronal activity in a CRF-dependent manner, and the results suggest a mechanism by which behavioral stress may affect Alzheimer's disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "17551018"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Anatomy": {"interstitial fluid": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3823, "key": "4266e82298b7f53f20462cb71a9a5dc2"}, {"relation": "partOf", "source": 80, "target": 1657, "key": "d256862c2ec0239a4cf018a9354c19c0"}, {"relation": "partOf", "source": 80, "target": 924, "key": "16fc0c97167953c48bfae758e26f7d1b"}, {"line": 2650, "relation": "decreases", "evidence": "Although there are numerous studies regarding Alzheimer's disease (AD), the cause and progression of AD are still not well understood. The researches in the past decade implicated amyloid-beta (Abeta) overproduction as a causative event in disease pathogenesis, but still failed to clarify the mechanism of pathology from Abeta production to central neural system defects in AD. The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.For this hypothesis, the factors related with the initiation of AD pathology are not only limited to the neurons per se but also expanded to the microenvironment around neurons, such as the secretion of BDNF from astrocytes. The modification of the origin in this pathway may contribute to slow down the disease progression of AD.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3147, "key": "7ad1484c1a5d7ae207c4f621de5e9353"}, {"line": 2651, "relation": "decreases", "evidence": "Although there are numerous studies regarding Alzheimer's disease (AD), the cause and progression of AD are still not well understood. The researches in the past decade implicated amyloid-beta (Abeta) overproduction as a causative event in disease pathogenesis, but still failed to clarify the mechanism of pathology from Abeta production to central neural system defects in AD. The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.For this hypothesis, the factors related with the initiation of AD pathology are not only limited to the neurons per se but also expanded to the microenvironment around neurons, such as the secretion of BDNF from astrocytes. The modification of the origin in this pathway may contribute to slow down the disease progression of AD.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2397, "key": "9d76ee02e825038c296dc4570b2a38a3"}, {"line": 2778, "relation": "increases", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2794, "key": "dd811b1e8d0865dc7af2089daf6fb56d"}, {"line": 33050, "relation": "increases", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2794, "key": "29888dacf033765f57b32058f616ad86"}, {"line": 2779, "relation": "increases", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 645, "key": "5783b285b65c8b0fa26c8c3a86a6011e"}, {"line": 3461, "relation": "association", "evidence": "The low-density lipoprotein receptor (LDLR) has the highest affinity for apoE and plays an important role in brain cholesterol metabolism.These data suggest that increased APP expression and Abeta exposure alters microtubule function, leading to reduced transport of LDLR to the plasma membrane. Consequent deleterious effects on apoE uptake and function will have implications for AD pathogenesis and/or progression", "citation": {"db": "PubMed", "db_id": "20049331"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 391, "key": "ba4b42b46341174147cb9c03f620ce5e"}, {"line": 3525, "relation": "decreases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 820, "key": "9f08de6a07b26de236279ca9c73305e9"}, {"line": 4481, "relation": "decreases", "evidence": "Soluble Abeta oligomers can rapidly disrupt synaptic memory mechanisms at extremely low concentrations via stress-activated kinases and oxidative/nitrosative stress mediators.", "citation": {"db": "PubMed", "db_id": "17956317"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 80, "target": 820, "key": "75d5edf3c256fcb2a26ab81c9962281d"}, {"line": 26372, "relation": "negativeCorrelation", "evidence": "Coupled with recent studies showing that synthetic and naturally occurring Abeta oligomers can inhibit hippocampal long-term potentiation, the in vivo age-dependent accumulation of SDS-soluble Abeta dimers in lipid rafts at the time when memory impairment begins in Tg2576 mice provides strong evidence linking Abeta oligomers to memory impairment.", "citation": {"db": "PubMed", "db_id": "15084661"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 820, "key": "479a9bb0eff8dba465f4bd6a39ed1a1b"}, {"line": 3575, "relation": "association", "evidence": "Yet several studies have demonstrated that oligomeric Abeta affects the cellular cholesterol level, which in turn has a variety of effects on AD related pathologies, including modulation of tau phosphorylation, synapse formation and maintenance of its function, and the neurodegenerative process.", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 231, "key": "35e836d6463662ae939d0ac90807a8f3"}, {"line": 4133, "relation": "association", "evidence": "Apolipoprotein E is the main lipid carrier in the brain and the best-established risk factor for late-onset Alzheimer's disease. Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. Finally, compounds that modulate cholesterol metabolism affect amyloid-beta generation.", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 231, "key": "d5ddb81df379bac5568a7b74d0bc46c6"}, {"line": 26082, "relation": "association", "evidence": "Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 231, "key": "b73380418df723facfb024e7ed95a628"}, {"line": 27002, "relation": "association", "evidence": "In addition, growing evidence suggests a role of cholesterol in Alzheimer disease pathology and Abeta generation. ", "citation": {"db": "PubMed", "db_id": "18308724"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 231, "key": "7e8b778d7272cfb3dd27e10b91f58d1c"}, {"line": 27292, "relation": "association", "evidence": "It has been suggested that cholesterol may pmodulate amyloid-beta (Abeta) formation, a causative factor of Alzheimer's disease (AD), by regulating distribution of the three key proteins in the pathogenesis of AD (beta-amyloid precursor protein (APP), beta-secretase (BACE1) and/or presenilin 1 (PS1)) within lipid rafts.", "citation": {"db": "PubMed", "db_id": "20138836"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 231, "key": "aaaae80e4e8976c2299d99d73e837fd0"}, {"line": 3944, "relation": "decreases", "evidence": "Abeta can also interact with Fe2+ and Cu+ to generate hydrogen peroxide and hydroxyl radical (OH.) resulting in membrane lipid peroxidation which generates toxic aldehydes that impair the function of membrane ion-motive ATPases (Na+ and Ca2+ pumps)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}, "CellStructure": {"Cell Membrane": true}}, "source": 80, "target": 781, "key": "da7bcfc067a7ec085570060bad01d426"}, {"line": 4479, "relation": "increases", "evidence": "Soluble Abeta oligomers can rapidly disrupt synaptic memory mechanisms at extremely low concentrations via stress-activated kinases and oxidative/nitrosative stress mediators.", "citation": {"db": "PubMed", "db_id": "17956317"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 80, "target": 2187, "key": "9f991d40512a0e9f75f68d79b9709de1"}, {"line": 4991, "relation": "negativeCorrelation", "evidence": "Interaction of CCL2 with its receptor CCR2 regulates mononuclear phagocyte accumulation. CCR2 deficiency leads to lower mononuclear phagocyte accumulation and is associated with higher brain Abeta levels, specifically around blood vessels, suggesting that monocytes accumulate at sites of Abeta deposition in an initial attempt to clear these deposits and stop or delay their neurotoxic effects.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Chemokine signaling subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 80, "target": 1318, "key": "550c1c41a09fea41f164c0c6548f137e"}, {"line": 5047, "relation": "increases", "evidence": "Neuritic plaques in the brain of Alzheimer's disease patients are characterized by beta-amyloid deposits associated with a glia-mediated inflammatory response.", "citation": {"db": "PubMed", "db_id": "15817521"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 532, "key": "8c72fd499c41f42d4d59f70a3061e014"}, {"line": 5200, "relation": "increases", "evidence": "Higher APP expression and elevated Abeta levels cause greater than required Cu export, leading to increased Cu in cerebrospinal fluid (CSF) and serum, and an intracellular (IC) Cu deficiency in the brain. Cu-deficient superoxide dismutase (SOD1) contributes to the reduced antioxidant capacity of the brain, allowing further oxidative stress.", "citation": {"db": "PubMed", "db_id": "15910549"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Free radical formation subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 100, "key": "3dffc4c81f55494dd9a83302e094ec2a"}, {"line": 5208, "relation": "increases", "evidence": "Higher APP expression and elevated Abeta levels cause greater than required Cu export, leading to increased Cu in cerebrospinal fluid (CSF) and serum, and an intracellular (IC) Cu deficiency in the brain. Cu-deficient superoxide dismutase (SOD1) contributes to the reduced antioxidant capacity of the brain, allowing further oxidative stress.", "citation": {"db": "PubMed", "db_id": "15910549"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 99, "key": "5c9708afdbe6b5cdc00750e86a35a3aa"}, {"line": 5209, "relation": "increases", "evidence": "Higher APP expression and elevated Abeta levels cause greater than required Cu export, leading to increased Cu in cerebrospinal fluid (CSF) and serum, and an intracellular (IC) Cu deficiency in the brain. Cu-deficient superoxide dismutase (SOD1) contributes to the reduced antioxidant capacity of the brain, allowing further oxidative stress.", "citation": {"db": "PubMed", "db_id": "15910549"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 535, "key": "fe93cd4f41e21cd3c7a3d89deb17a4e3"}, {"line": 5250, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3374, "key": "0ba2b49d2cac368aaf5799dc6d81ce0d"}, {"line": 5251, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3372, "key": "ca20f0b97572903fd4814e32b59ae45d"}, {"line": 5252, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3373, "key": "91ca1946ef903fa1426b0bd821163b5b"}, {"line": 5253, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3371, "key": "a09362b07a28d1e8f1b47687c2bcbd59"}, {"relation": "partOf", "source": 80, "target": 939, "key": "33bf4734591888fa017d2e5c6460c004"}, {"line": 5542, "relation": "decreases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 80, "target": 688, "key": "ea9d4f7e5bdb1900d29931498866b46f"}, {"line": 37050, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 688, "key": "e7d58352fe238f2f4c5c8f4ac549693d"}, {"line": 5946, "relation": "increases", "evidence": "The amyloid precursor protein (APP) is a large, ubiquitous integral membrane protein with a small amyloid-beta (Abeta) domain. In the human brain, endosomal processing of APP produces neurotoxic Abeta-peptides, which are involved in Alzheimer's disease. Here, we show that the Abeta sequence exerts a physiological function when still present in the unprocessed APP molecule. From the extracellular site, Abeta concentrates APP molecules into plasmalemmal membrane protein clusters. Moreover, Abeta stabilization of clusters is a prerequisite for their targeting to endocytic clathrin structures. Therefore, we conclude that the Abeta domain directly mediates a central step in APP trafficking, driving its own conversion into neurotoxic peptides.", "citation": {"db": "PubMed", "db_id": "22455924"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2315, "key": "4344ae94f3f6bee6dfbe7628f590e4bc"}, {"line": 44606, "relation": "positiveCorrelation", "evidence": "hypomethylated APP, individuals, which in turn produces more APP, which is further cleaved to build up Abeta levels", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2315, "key": "b1d76fcb9aa9ab753d4ed607881b87c2"}, {"line": 6075, "relation": "negativeCorrelation", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 523, "key": "62a2d5d3efd10c559057b3d34ebc09fe"}, {"line": 6077, "relation": "increases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 3866, "key": "8a29aed6f7538a53fa449e477cc67fe8"}, {"line": 6078, "relation": "decreases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 839, "key": "af20d382956d7fb1970d50fd4e696a21"}, {"line": 6585, "relation": "increases", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3889, "key": "5c2f39297fccf373369dbed02a090b73"}, {"line": 6592, "relation": "association", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Synuclein subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3384, "key": "ab93554612b85babdd5c0d0f5b51726c"}, {"line": 6868, "relation": "positiveCorrelation", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 80, "target": 573, "key": "42a3fc9b154a847e0eced1794404490b"}, {"line": 7368, "relation": "association", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Disease": {"type 2 diabetes mellitus": true}}, "source": 80, "target": 3915, "key": "1fc35b8f81912fab995a34a06706f80a"}, {"line": 7715, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 750, "key": "355b194ae2ae68bcb903c0e76ee6c380"}, {"relation": "partOf", "source": 80, "target": 934, "key": "deea9fcdfbfaeb6fbcb55e06567ce1fa"}, {"line": 8104, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 80, "target": 934, "key": "9f7200adc2abee271c39f6232004c7b5"}, {"line": 8105, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 80, "target": 601, "key": "a8b003ce7d068bfa4c718de0d61fad89"}, {"relation": "partOf", "source": 80, "target": 915, "key": "44c53d8b95b3632d3feb8e1e988b30e5"}, {"line": 8106, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 80, "target": 915, "key": "1a3d6fb6d9643185dc1cdaa2245922f2"}, {"relation": "partOf", "source": 80, "target": 909, "key": "8002e46f7aebc81bd783cdd91e05e691"}, {"line": 8107, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 80, "target": 909, "key": "f10182e06a37be73c164c2b9e40a7552"}, {"line": 8898, "relation": "association", "evidence": "The role of miR-124 on the expression of beta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the beta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2082, "key": "4b591f65708d9de6f3378158ac80f7a0"}, {"line": 9165, "relation": "decreases", "evidence": "Target gene repression mediated by miRNAs miR-181c and miR-9 both of which are down-regulated by amyloid-beta. MicroRNAs (miRNAs) are small non-coding RNA regulators of protein synthesis that are essential for normal brain development and function. Their profiles are significantly altered in neurodegenerative diseases such as Alzheimer's disease (AD) that is characterized by amyloid-beta (Abeta) and tau deposition in brain. How deregulated miRNAs contribute to AD is not understood, as their dysfunction could be both a cause and a consequence of disease. To address this question we had previously profiled miRNAs in models of AD. This identified miR-9 and -181c as being down-regulated by Abeta in hippocampal cultures. Interestingly, there was a remarkable overlap with those miRNAs that are deregulated in Abeta-depositing APP23 transgenic mice and in human AD tissue. While the Abeta precursor protein APP itself is a target of miRNA regulation, the challenge resides in identifying further targets. Here, we expand the repertoire of miRNA target genes by identifying the 3' untranslated regions (3' UTRs) of TGFBI, TRIM2, SIRT1 and BTBD3 as being repressed by miR-9 and -181c, either alone or in combination. Taken together, our study identifies putative target genes of miRNAs miR-9 and 181c, which may function in brain homeostasis and disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "21720722"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2107, "key": "c80a6742e951f0563e984404f38c410a"}, {"line": 9166, "relation": "decreases", "evidence": "Target gene repression mediated by miRNAs miR-181c and miR-9 both of which are down-regulated by amyloid-beta. MicroRNAs (miRNAs) are small non-coding RNA regulators of protein synthesis that are essential for normal brain development and function. Their profiles are significantly altered in neurodegenerative diseases such as Alzheimer's disease (AD) that is characterized by amyloid-beta (Abeta) and tau deposition in brain. How deregulated miRNAs contribute to AD is not understood, as their dysfunction could be both a cause and a consequence of disease. To address this question we had previously profiled miRNAs in models of AD. This identified miR-9 and -181c as being down-regulated by Abeta in hippocampal cultures. Interestingly, there was a remarkable overlap with those miRNAs that are deregulated in Abeta-depositing APP23 transgenic mice and in human AD tissue. While the Abeta precursor protein APP itself is a target of miRNA regulation, the challenge resides in identifying further targets. Here, we expand the repertoire of miRNA target genes by identifying the 3' untranslated regions (3' UTRs) of TGFBI, TRIM2, SIRT1 and BTBD3 as being repressed by miR-9 and -181c, either alone or in combination. Taken together, our study identifies putative target genes of miRNAs miR-9 and 181c, which may function in brain homeostasis and disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "21720722"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2086, "key": "5383d29686323f470741e008559a134a"}, {"line": 9794, "relation": "biomarkerFor", "evidence": "In conclusion, the level of Abeta autoantibody is dramatically elevated in patient serum of T2DM, and, as such, might be used as a possible biomarker for T2DM.", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 3850, "key": "e52002b8c755b98580f8f9d379b4eb9d"}, {"relation": "partOf", "source": 80, "target": 928, "key": "4e398f54fd1cc9818f5c3dc30bad2847"}, {"line": 10127, "relation": "association", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2899, "key": "cfbd82e5c5cf01c45abc3893d7863b6f"}, {"line": 10142, "relation": "negativeCorrelation", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2899, "key": "d48a65fa5acaa63b2df8451cee4be15c"}, {"relation": "partOf", "source": 80, "target": 931, "key": "e73218f8209ec359030bf7d75c6f66d4"}, {"line": 10349, "relation": "negativeCorrelation", "evidence": "As far as the metabolism of amyloid precursor protein (APP) in late-onset sporadic Alzheimer disease is concerned, neuronal insulin receptor dysfunction may result in the intracellular accumulation of Abeta and in subsequent cellular damage.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2900, "key": "5801b63409e7647a8c7e43e2dffa932e"}, {"relation": "partOf", "source": 80, "target": 941, "key": "42360506eacfe45473d97a765242dac5"}, {"relation": "partOf", "source": 80, "target": 942, "key": "bfe4560c07c367b330d914fcac42f203"}, {"relation": "partOf", "source": 80, "target": 943, "key": "a0486a12a6a59567e3595b537b8d7c62"}, {"relation": "partOf", "source": 80, "target": 917, "key": "20cc6043ae9d00195a2969f8a84aa9b2"}, {"line": 11627, "relation": "increases", "evidence": "Moreover, in examination of this pathway in another cell type pertinent to AD, we find that Abeta induces a proinflammatory response in microglia as evidenced by increased leukotriene B4 release. We show that both dipyridamole and compounds which increase cGMP levels prevent Abeta-induced microglial inflammation. Our results suggest that therapeutic intervention aimed at reduction of microglial-mediated inflammation via inhibition of cGMP-PDE or elevation of cGMP may be beneficial in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 80, "target": 591, "key": "5e82dcbebe599c3a820a1726021e4358"}, {"line": 11628, "relation": "increases", "evidence": "Moreover, in examination of this pathway in another cell type pertinent to AD, we find that Abeta induces a proinflammatory response in microglia as evidenced by increased leukotriene B4 release. We show that both dipyridamole and compounds which increase cGMP levels prevent Abeta-induced microglial inflammation. Our results suggest that therapeutic intervention aimed at reduction of microglial-mediated inflammation via inhibition of cGMP-PDE or elevation of cGMP may be beneficial in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 80, "target": 577, "key": "238d8fa88041d5587012f3e56cc357d9"}, {"line": 39013, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 577, "key": "765c7fb29dafc0318f57521e2e9047cf"}, {"line": 11629, "relation": "increases", "evidence": "Moreover, in examination of this pathway in another cell type pertinent to AD, we find that Abeta induces a proinflammatory response in microglia as evidenced by increased leukotriene B4 release. We show that both dipyridamole and compounds which increase cGMP levels prevent Abeta-induced microglial inflammation. Our results suggest that therapeutic intervention aimed at reduction of microglial-mediated inflammation via inhibition of cGMP-PDE or elevation of cGMP may be beneficial in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 80, "target": 3920, "key": "5dd33ffcb0a42503ef931eb613a2ba9a"}, {"line": 19553, "relation": "association", "evidence": "Cyclooxygenase-1 null mice show reduced neuroinflammation in response to beta-amyloid.", "citation": {"db": "PubMed", "db_id": "20157512"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3920, "key": "e56fb8406413fd5bc9042a55c93545f7"}, {"line": 14510, "relation": "positiveCorrelation", "evidence": "In temporal cortex, analysis revealed a significant correlation between MMP9 activity and amyloid-beta42. In accordance with our analysis in adult brains, MMP9 activation positively correlated with amyloid-beta42 levels", "citation": {"db": "PubMed", "db_id": "24519975"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3062, "key": "369b1c088dca3e80901aa62e23171107"}, {"line": 14763, "relation": "decreases", "evidence": "Isorhynchophylline (IRN), an alkaloid isolated from Uncaria rhynchophylla, has been reported to improve cognitive impairment induced by beta-amyloid in rats.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Confidence": {"Medium": true}, "Species": {"10116": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 80, "target": 812, "key": "a35245e94f3f6e05c34bafc2678be712"}, {"line": 16004, "relation": "increases", "evidence": "Up-regulation of Bcl-xL in response to subtoxic beta-amyloid: role in neuronal resistance against apoptotic and oxidative injury.", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}}, "source": 80, "target": 2394, "key": "dbbdf9d453aa97c22e91df21a94bf4c5"}, {"line": 16035, "relation": "increases", "evidence": "Cells overexpressing Bcl-xL were significantly protected from beta-amyloid neurotoxicity and staurosporine-induced apoptosis compared to vector-transfected controls.", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}}, "source": 80, "target": 3824, "key": "f312296ebda49ab24a3c9f209fd8f00c"}, {"relation": "partOf", "source": 80, "target": 1660, "key": "fe864c93714bb715b3d965e617d26137"}, {"line": 18314, "relation": "increases", "evidence": "Neurons in both AD brain and Abeta-treated cultures exhibited FasL upregulation and changes in immunoreactivity for Fas receptor.", "citation": {"db": "PubMed", "db_id": "12742739"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 80, "target": 2690, "key": "0dcf028bc16a3b27f8ad69b1f7a14eae"}, {"line": 18396, "relation": "increases", "evidence": "These findings raise the possibility that the JNK pathway may also contribute to Abeta-dependent death in AD patients.", "citation": {"db": "PubMed", "db_id": "11567045"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 505, "key": "fe81d5c37c110b1652f01798ef4d7869"}, {"line": 19209, "relation": "increases", "evidence": "E2F1 was distributed throughout the cytoplasm and neurites of PC12 cells in response to Abeta and in the cytoplasm of cells in AD brain.", "citation": {"db": "PubMed", "db_id": "11640947"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "Amyloidogenic subgraph": true}, "CellStructure": {"Neurites": true, "Cytoplasm": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}, "CellLine": {"PC-12 cell": true}}, "subject": {"location": {"namespace": "MESH", "name": "Cytoplasm"}}, "source": 80, "target": 2650, "key": "3bb407d960fa89f7985d21b52e9f1218"}, {"line": 19584, "relation": "positiveCorrelation", "evidence": "However, COX-1 immunopositive microglia were found in association with Abeta plaques, and the density of COX-1 immunopositive microglia in AD fusiform cortex was increased.", "citation": {"db": "PubMed", "db_id": "10560656"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3277, "key": "14ddf9f3c532258134f5e7456e464d43"}, {"line": 19614, "relation": "positiveCorrelation", "evidence": "In AD brains, COX-1-positive microglial cells were primarily associated with amyloid beta plaques, while the number of COX-2-positive neurons was increased compared to that in control brains.", "citation": {"db": "PubMed", "db_id": "11194936"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Neurons": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3277, "key": "9be1fbf9c5291945493922df8b1fc7e0"}, {"line": 20248, "relation": "increases", "evidence": "C/EBP homologous protein (CHOP), a pro-apoptotic ER stress protein, was expressed at high levels but glucose-regulated protein 78 (GRP78), an anti-apoptotic ER stress protein with chaperone activity, was only slightly affected by treatment with beta-amyloid.", "citation": {"db": "PubMed", "db_id": "23558999"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Response DNA damage": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2622, "key": "ea50436c5993e388c9224317a17e3767"}, {"line": 20249, "relation": "causesNoChange", "evidence": "C/EBP homologous protein (CHOP), a pro-apoptotic ER stress protein, was expressed at high levels but glucose-regulated protein 78 (GRP78), an anti-apoptotic ER stress protein with chaperone activity, was only slightly affected by treatment with beta-amyloid.", "citation": {"db": "PubMed", "db_id": "23558999"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Response DNA damage": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2849, "key": "560d5935f32bc5d0022eb6aa88a19ffa"}, {"line": 20657, "relation": "increases", "evidence": "We have shown previously that activation of PKR in Abeta-triggered apoptotic process.", "citation": {"db": "PubMed", "db_id": "16532272"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2662, "key": "48f075e21dbedd899304a349217d55f8"}, {"line": 20696, "relation": "negativeCorrelation", "evidence": "Deficiency of ABCC1 substantially increased cerebral Abeta levels without altering the expression of most enzymes that would favor the production of Abeta from the Abeta precursor protein.", "citation": {"db": "PubMed", "db_id": "21881209"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 3579, "key": "d6157d0e21a8208febeb8e45ae5f3d74"}, {"line": 20893, "relation": "increases", "evidence": "Amyloid beta-protein stimulates the expression of urokinase-type plasminogen activator (uPA) and its receptor (uPAR) in human cerebrovascular smooth muscle cells.", "citation": {"db": "PubMed", "db_id": "12754271"}, "annotations": {"Cell": {"regular cardiac myocyte": true}, "Species": {"9606": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3201, "key": "9e63ad032236fdc72e3def6f19afb60e"}, {"line": 20894, "relation": "increases", "evidence": "Amyloid beta-protein stimulates the expression of urokinase-type plasminogen activator (uPA) and its receptor (uPAR) in human cerebrovascular smooth muscle cells.", "citation": {"db": "PubMed", "db_id": "12754271"}, "annotations": {"Cell": {"regular cardiac myocyte": true}, "Species": {"9606": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3202, "key": "c5ad2d313c17cd10ee850dda7cbc72cf"}, {"relation": "partOf", "source": 80, "target": 937, "key": "71641bb8fdce4344a9c69221c4ba2933"}, {"line": 21752, "relation": "increases", "evidence": "The extracellular amyloid-beta deposition in AD brains could be a causative factor that activates p70S6K. We hypothesized that amyloid-beta deposition activates p70S6K whose anti-apoptotic property subsequently keeps neurons from entering into the apoptotic process.", "citation": {"db": "PubMed", "db_id": "18688088"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3327, "key": "ace142c4794dba342da97f29a72e3989"}, {"line": 21961, "relation": "association", "evidence": "Moreover, 5-LO targeted gene disruption or its in vivo selective pharmacological inhibition results in a significant reduction of Abeta, CREB and gamma-secretase levels. These data establish a novel functional role for 5-LO in regulating endogenous formation of Abeta levels in the central nervous system.", "citation": {"db": "PubMed", "db_id": "21280074"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Eicosanoids signaling subgraph": true}}, "source": 80, "target": 2288, "key": "b0940bf2ca7d04c3005f73d7967ade8b"}, {"line": 22692, "relation": "positiveCorrelation", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3123, "key": "32ceea9eb33b112d20e6421342bca9ac"}, {"line": 22700, "relation": "negativeCorrelation", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 80, "target": 3123, "key": "466d3a1bf6d350c4d8c7ea0e52f3a4b5"}, {"relation": "partOf", "source": 80, "target": 908, "key": "21c49009ab84b46e0d13731f88562f57"}, {"relation": "partOf", "source": 80, "target": 930, "key": "aa3aec7449ef237c8d5f678b4db2b9cd"}, {"relation": "partOf", "source": 80, "target": 910, "key": "935d5e8288276ce720f3ecbb4849e1c3"}, {"relation": "partOf", "source": 80, "target": 911, "key": "47842a322f589e93939c60b393fc5b57"}, {"relation": "partOf", "source": 80, "target": 912, "key": "68b52d4f4ed77955786f1d233b5a70c0"}, {"relation": "partOf", "source": 80, "target": 914, "key": "25a66921044b34f122ec9b97292750a1"}, {"line": 26294, "relation": "association", "evidence": "It appears that the efficiency of binding between each of three main apoE isoforms and Abeta correlates inversely with the risk of developing late-onset familial AD and may indicate possible involvement of apoE in the binding and clearance of Abeta in vivo.", "citation": {"db": "PubMed", "db_id": "9265639"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 914, "key": "7983b81ac4fd1e2ff0470a81f07a9699"}, {"line": 25643, "relation": "association", "evidence": "Abeta progressively accumulates in mitochondria and mediates mitochondrial toxicity", "citation": {"db": "PubMed", "db_id": "17424907"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 430, "key": "52e7fb47ab6f7a82061904e3bbda67f6"}, {"line": 25682, "relation": "increases", "evidence": "Direct addition of Ab(1– 42) to the microglia increased their expression of M-CSF. ", "citation": {"db": "PubMed", "db_id": "15882940"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2566, "key": "d27d1d25343c462b1bd3689d51b7ded0"}, {"line": 25690, "relation": "increases", "evidence": "In each case, the Abstimulation of M-CSF secretion was significantly blocked by treatment of cultures with anti-RAGE F(ab')2.", "citation": {"db": "PubMed", "db_id": "15882940"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2566, "key": "1f6bd16563b4b90c9566b11cd99e3d3d"}, {"relation": "partOf", "source": 80, "target": 1659, "key": "4205e77a75085de5ac12feec637015ec"}, {"relation": "partOf", "source": 80, "target": 913, "key": "27c5bd41ffd371a16d780cf1d5613a7a"}, {"line": 25876, "relation": "association", "evidence": "The findings suggest that in cells that express both apoE and APP, such as astrocytes and microglia, a functional apoE:APP interaction may occur which pmodulates APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "11523796"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 1125, "key": "7c36e8dd8c03b769fd68c0aaa6f92dc3"}, {"relation": "partOf", "source": 80, "target": 933, "key": "0a9bacda386c75d3cc71a5e8040ece77"}, {"line": 26858, "relation": "increases", "evidence": "Aggregated Abeta induced IFN-gamma production from co-culture of astrocytes and microglia, and IFN-gamma elicited tumor necrosis factor (TNF)-alpha secretion in wild type (WT) but not GRKO microglia co-cultured with astrocytes. ", "citation": {"db": "PubMed", "db_id": "17255335"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Interferon signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2870, "key": "a427c2b3958c1c7121df9705eb14f48a"}, {"relation": "partOf", "source": 80, "target": 940, "key": "7738bbd6e3c220c279f192f1e860ac18"}, {"line": 27316, "relation": "association", "evidence": "Leptin, an adipocytokine involved in cell survival and in learning, has been demonstrated to regulate Abeta production and tau hyperphosphorylation in transgenic mice for AD. ", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2961, "key": "6ed63fe8a20cf9917d91adb8d1ce2cf4"}, {"line": 27375, "relation": "increases", "evidence": "abaton et al. have presented a pmodel of both non-pathological and pathological Abeta activities and suggest potential therapeutic pathways based on their proposed framework of Abeta acting as the signal that induces a kinase cascade, ultimately stimulating transcription factors that upregulate genes such as BACE1.", "citation": {"db": "PubMed", "db_id": "20451519"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2375, "key": "ab9534a7fe335d37bd7ec63a932f6e92"}, {"line": 27389, "relation": "increases", "evidence": "Interestingly, treatment of cultured primary neurons with amyloid-beta (Abeta) peptides caused an increase in the level of beta-site APP-cleaving enzyme 1 (BACE1), the key enzyme responsible for APP processing and Abeta production.", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2375, "key": "508ab549ab7bb281ee38bd1471d8c3fb"}, {"line": 27447, "relation": "increases", "evidence": "Interestingly, treatment of cultured primary neurons with amyloid-beta (Abeta) peptides caused an increase in the level of beta-site APP-cleaving enzyme 1 (BACE1), the key enzyme responsible for APP processing and Abeta production. This effect was inhibited by CAST overexpression. ", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 2375, "key": "6768778802a4d716139dd310e506c012"}, {"line": 27729, "relation": "association", "evidence": "Here we report evidence that heparan sulfate (HS) interacts with beta-site APP-cleaving enzyme (BACE) 1 and regulates its cleavage of APP.", "citation": {"db": "PubMed", "db_id": "14530380"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 960, "key": "3642e88b9a90270707c0e322594dd783"}, {"relation": "partOf", "source": 80, "target": 916, "key": "d97bb5d518e7f4e0cb730e9d9782d392"}, {"relation": "partOf", "source": 80, "target": 926, "key": "33aeec55e9a5a56cd60cdffa891a0491"}, {"relation": "partOf", "source": 80, "target": 936, "key": "29297d011384e42d7831862ded67ace0"}, {"relation": "partOf", "source": 80, "target": 944, "key": "2bd730669565e375ef7bcc3459d56223"}, {"relation": "partOf", "source": 80, "target": 945, "key": "c0172756ad5efab2a9fbbfe5446bbdc0"}, {"relation": "partOf", "source": 80, "target": 919, "key": "5c39ae306560764b7a10284ee74525ae"}, {"relation": "partOf", "source": 80, "target": 920, "key": "e97ead1e05f4bbd5aa2172b35b175a9f"}, {"relation": "partOf", "source": 80, "target": 905, "key": "b5bc6a885f73d810dcd2337c9092d07c"}, {"relation": "partOf", "source": 80, "target": 906, "key": "53c04abcebc446f546b0e52adf434aa8"}, {"relation": "partOf", "source": 80, "target": 921, "key": "8d647595fca69afa0eb1b42dad84eef4"}, {"relation": "partOf", "source": 80, "target": 932, "key": "54b71f38a337c947c0f96f51d9471180"}, {"relation": "partOf", "source": 80, "target": 925, "key": "ec2597a27fe4ccb12b62da92b14380ff"}, {"relation": "partOf", "source": 80, "target": 947, "key": "610dcc8ae304c13e2336bcd6a33b8d15"}, {"relation": "partOf", "source": 80, "target": 948, "key": "a02fc34df5856b1241971a91810ea8ef"}, {"relation": "partOf", "source": 80, "target": 922, "key": "3d62f7e50b48b03d4ac38096aebda594"}, {"line": 29253, "relation": "increases", "evidence": "We have shown that interaction of CD40 with CD40L enables microglial activation in response to amyloid-beta peptide (Abeta), which is associated with Alzheimer's disease (AD)-like neuronal tau hyperphosphorylation in vivo.", "citation": {"db": "PubMed", "db_id": "12402041"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true, "Tau protein subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 1324, "key": "1ff8aeda98086a29e17e3b8aab292537"}, {"line": 29553, "relation": "association", "evidence": "Cyclin-dependent kinase 5 (Cdk5) activity is significantly increased in AD and contributes to all three hallmarks: neurotoxic amyloid-beta (Abeta), neurofibrillary tangles (NFT), and extensive cell death.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 80, "target": 2487, "key": "80c29abbf6e27837dddfe6540f6bb486"}, {"relation": "partOf", "source": 80, "target": 929, "key": "e59330c614ee69050687d7f14af15e86"}, {"line": 31154, "relation": "association", "evidence": "Increasing evidence suggests that the low density lipoprotein receptor-related protein ( LRP ) affects the processing of amyloid precursor protein ( APP ) and amyloid beta ( Abeta ) protein production as well as mediates the clearance of Abeta from the brain.", "citation": {"db": "PubMed", "db_id": "15772078"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2970, "key": "6024fbfbe86bbad84e45d60f0ef10130"}, {"line": 31589, "relation": "association", "evidence": "Nicastrin also binds carboxy-terminal derivatives of beta-amyloid precursor protein ( betaAPP ) , and pmodulates the production of the amyloid beta-peptide ( A beta ) from these derivatives.", "citation": {"db": "PubMed", "db_id": "10993067"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3094, "key": "914197409c6e4c8b33c71c493e9f77af"}, {"line": 31599, "relation": "association", "evidence": "Increasing evidences have shown that nicastrin (NCSTN) plays a crucial role in gamma-cleavage of the amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "16423463"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3094, "key": "f2403bb644669cc2d57271704b7d801b"}, {"line": 31610, "relation": "association", "evidence": "Increasing evidences have shown that nicastrin (NCSTN) plays a crucial role in gamma-cleavage of the amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "19394408"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3094, "key": "09e57663df032cd72668a44874edee4c"}, {"relation": "partOf", "source": 80, "target": 935, "key": "2799caadfb54c34de7f3ac39a9346179"}, {"line": 33505, "relation": "increases", "evidence": "Compared to vehicle, Abeta increased GSK3 activity, and was associated with elevations in levels of ptau, caspase-3, the tau kinase phospho-c-jun N-terminal kinase (pJNK), neuronal DNA fragmentation, and gliosis.", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 2444, "key": "cdffa057ce0a2a99fdb31228ef44b4db"}, {"line": 33507, "relation": "increases", "evidence": "Compared to vehicle, Abeta increased GSK3 activity, and was associated with elevations in levels of ptau, caspase-3, the tau kinase phospho-c-jun N-terminal kinase (pJNK), neuronal DNA fragmentation, and gliosis.", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 3004, "key": "73b3b7e26fb0eb0249d506399651fa63"}, {"line": 33528, "relation": "increases", "evidence": "We report that beta-amyloid (Abeta), a death-promoting peptide implicated in the pathophysiology of AD, induces the proapoptotic protein Bcl-2 interacting mediator of cell death (Bim) in cultured hippocampal and cortical neurons. ", "citation": {"db": "PubMed", "db_id": "17251431"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}}, "source": 80, "target": 2396, "key": "cbc38b9e72fbbc1ee408bd31d6e2cf92"}, {"line": 33548, "relation": "association", "evidence": "Our observations indicate that Bim is a proapoptotic effector of Abeta and of dysregulated cell cycle proteins in AD and identify both Bim and cell cycle elements as potential therapeutic targets.", "citation": {"db": "PubMed", "db_id": "17251431"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2396, "key": "9eac3f44f5199b61c4a1517ec9988f9a"}, {"line": 33561, "relation": "association", "evidence": "A possible cell surface target for Abetas is the p75 neurotrophin receptor (p75(NTR)).", "citation": {"db": "PubMed", "db_id": "17385278"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3117, "key": "b00e2a50ec3f0c61693f6280c1754be7"}, {"line": 33788, "relation": "association", "evidence": "SorLA has been shown to be down regulated in Alzheimer's disease brains, interact with ApoE, and pmodulate Abeta production", "citation": {"db": "PubMed", "db_id": "16930450"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3397, "key": "eaca14e88e5d7f5587c0c089882136ba"}, {"relation": "partOf", "source": 80, "target": 946, "key": "853a96ca9a1e8f9584476b2fe729b3e8"}, {"line": 33965, "relation": "increases", "evidence": "The receptor activation assays revealed that apoE as well as beta amyloid activated the CASR and that the level of activation appeared to be isoform dependent for apoE.", "citation": {"db": "PubMed", "db_id": "19035514"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2451, "key": "5f18032f677ec5df232c0ef95779a3bf"}, {"relation": "partOf", "source": 80, "target": 918, "key": "f7d85f7107bfe0126cdb16e3836b9e15"}, {"relation": "partOf", "source": 80, "target": 938, "key": "91497c957810ec81985a7e52171026dd"}, {"relation": "partOf", "source": 80, "target": 923, "key": "740f61e0e24531a1e6163dde4c29609c"}, {"line": 36377, "relation": "increases", "evidence": "A youthful role for ABeta¸ may enhance neuronal plasticity to help the remaining neural circuits compensate for lost or broken circuits and improve overall network performance and neurological function. Improving network activity may also help to prevent the inexorable loss of neuronal processes and cell bodies that occurs in AD and other neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 745, "key": "dc3e92aab6b5c70b813b1794b3c849e7"}, {"relation": "partOf", "source": 80, "target": 907, "key": "c50939bfbdec663f7f425f6851cd5a41"}, {"line": 36595, "relation": "decreases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 760, "key": "0590494c6af5c4be453737f2354a94f8"}, {"line": 36597, "relation": "decreases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 123, "key": "937d35eb4dcedce0ddacf09bd6ec4c10"}, {"line": 36598, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 822, "key": "d2fdb76b60e21b2fb08cb7a62a6c0ca1"}, {"line": 36609, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2770, "key": "573107ab388fa748bc9a2e8400665fa1"}, {"line": 36611, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2772, "key": "904c14bcd3a0ca26526746173d2a5b9a"}, {"line": 36613, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2773, "key": "7706383e3b41a1ee1d753ff84ce87141"}, {"line": 36615, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2774, "key": "b0adda082a1f4e20218301b8e1437808"}, {"line": 36618, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2776, "key": "ee3a2146b49af8fcc3c6f0287cdf0f97"}, {"line": 36620, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2779, "key": "32494f7bf31584c14f8015ac33c3edfd"}, {"line": 36622, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2781, "key": "ea7c05bfe4fb375777dbb03204c64a92"}, {"line": 36624, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2782, "key": "3d25c3d5cc18baf9f08d825ecd736342"}, {"line": 36626, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2783, "key": "f73126487ab34c261f9b1bd9cf47ad17"}, {"line": 36628, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2784, "key": "c9eee0a64e16b7b02b2f15707ad3c254"}, {"line": 36633, "relation": "association", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 647, "key": "8a8025c71bc497ee757aa2353eca1315"}, {"line": 36634, "relation": "increases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 488, "key": "5f1443b684d3368f0317b163ec9d1ccc"}, {"line": 37051, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 635, "key": "22609de1583180fc674cac4b21509d5e"}, {"line": 37055, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2413, "key": "3b6c9e5b2bca70196f28046b694969a0"}, {"line": 37057, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2414, "key": "2e597bdb3db641630a2ae19e8bf286d2"}, {"line": 37059, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2415, "key": "f97243ae45a0116eeb1186918e35967d"}, {"line": 37061, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2416, "key": "ed06b5c561e778e9e26ab98eb15b9652"}, {"line": 37063, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2417, "key": "2752763e0ae19cde1ccb26dd4efb84c1"}, {"line": 37065, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2418, "key": "efba67c2627fc82406eb7b326b2d317e"}, {"line": 37067, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2419, "key": "e058a1eb6dd70715cfe4b9446d1702ac"}, {"line": 37069, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 2420, "key": "eac8f168e45ed34e8470aa01880812cf"}, {"line": 37889, "relation": "increases", "evidence": "Cell Adhesion An RHDS motif near the extralumenal portion of APP or at the C terminus of APPs lying within the ABeta¸ region appears to promote cell adhesion. It is believed that this region acts in an integrin-like manner and can, accordingly, be blocked by RGDS peptide sequence derived from the fibronectin-binding domain. Similarly, APP colocalizes with integrins on the surface of axons and at sites of adhesion. Evidence of interaction with laminin and collagen provides further evidence of adhesion-promoting properties. Interestingly, because the RHDS sequence is contained within the N terminus of ABeta¸, similar cell adhesion-promoting properties have also been attributed to the ABeta¸ peptide itself. This latter property is, however, difficult to tease out in view of the cytotoxicity of ABeta¸ peptide when tested in a variety of cell systems in vitro. Furthermore, it is difficult to separate the cell adhesion-from the neurite outgrowth-promoting roles of APP. Clearly, these are probably somewhat inseparable, as neuronal migration, neurite outgrowth, and even synaptogenesis would involve substrate adhesion.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Inflammatory response subgraph": true, "Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 497, "key": "c509749a8393f0fcc2da0f47d969bcc6"}, {"line": 39105, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3467, "key": "c548494e4de8383ed833bde070c7a3a2"}, {"line": 39107, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3468, "key": "0fa9f1e754a0ed776f7d488e3f55962a"}, {"line": 39112, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3120, "key": "7f7785beefe521ed8d927a9638797b1b"}, {"line": 39114, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2707, "key": "605b627c148f02a0bf29517eddac4660"}, {"line": 39116, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2708, "key": "b76e8f54ecc526edcbd99558a5de4cb1"}, {"line": 39118, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 2271, "key": "65e69c6a0efb6af40c9b6c263badb642"}, {"line": 39123, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3069, "key": "ee0c55a2f99a699d15a390f89134751f"}, {"line": 39125, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3340, "key": "e2e60650b0e5cf8f57063383b21d8f49"}, {"line": 39127, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 80, "target": 3286, "key": "8187651c659928ca9330b966d3639694"}, {"line": 40188, "relation": "association", "evidence": "Activated microglia play a critical role in amyloid clearance, but chronic deregulation of CNS inflammatory pathways results in secretion of neurotoxic mediators that ultimately contribute to neurodegeneration in AD.", "citation": {"db": "PubMed", "db_id": "24369524"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Microglia": true, "Bodily Secretions": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 609, "key": "7199eebe5efd9e4d6525797b716c0f09"}, {"line": 41598, "relation": "increases", "evidence": "Notably, beta-amyloid (Abeta) deposition induces microglial activation and the subsequent production of proinflammatory neurotoxic factors.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 609, "key": "c972f97962b096bf666b2dfc56745d3c"}, {"relation": "partOf", "source": 80, "target": 949, "key": "a2f86ebf02d8c34720d471021b524a01"}, {"line": 43584, "relation": "increases", "evidence": "beta-Amyloid of Alzheimer's disease induces reactive gliosis that inhibits axonal outgrowth.", "citation": {"db": "PubMed", "db_id": "8287928"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Autophagy signaling subgraph": true, "Amyloidogenic subgraph": true}}, "source": 80, "target": 3912, "key": "9c41844cefb5a212e3715410a3906694"}, {"line": 44458, "relation": "positiveCorrelation", "evidence": "We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age.", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3584, "key": "26e652a9d31a4bb4ee5b3ccc56957c30"}, {"line": 44792, "relation": "positiveCorrelation", "evidence": "Levels of BACE1 protein, enzymatic activity and beta-CTF elevate with age in the cerebrum, suggesting a functional role of BACE1 in Abeta overproduction.", "citation": {"db": "PubMed", "db_id": "21725719"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3593, "key": "50a534ee2b28163487ce74c905f59807"}, {"line": 44806, "relation": "positiveCorrelation", "evidence": "beta-secretase-1 (BACE1) elevation relative to Abeta accumulation and synaptic/neuritic alterations in the forebrain, using transgenic mice harboring familial AD (FAD) mutations (5XFAD and 2XFAD) as models", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "DiseaseState": {"Familial Alzheimers Disease": true}, "KnockoutMice": {"App transgenic": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 3593, "key": "e92b47044346e53dc5a971131cf47bac"}, {"line": 45145, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 80, "target": 3258, "key": "a4a4e8301ce179b29392e0daf3d72529"}, {"line": 45155, "relation": "increases", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 80, "target": 3820, "key": "b5aa90f8e44c2821b074c34384417a5f"}, {"line": 46571, "relation": "increases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 80, "target": 648, "key": "cf912825747dbcb9263515364af7ab30"}, {"line": 47059, "relation": "association", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Confidence": {"Medium": true}}, "source": 80, "target": 568, "key": "e2846c940ee5679e6eda231f43e63609"}, {"line": 47068, "relation": "increases", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 569, "key": "aa7f1f838f2cdf81f0d1573d1062741a"}, {"line": 47069, "relation": "increases", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "source": 80, "target": 35, "key": "df0ce01e960ec03d986e31be606db7de"}, {"line": 48646, "relation": "increases", "evidence": "Although the mechanism of Abeta action in the pathogenesis of Alzheimer's disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1)", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 80, "target": 2629, "key": "09bc8e3a7b32f855246838fbb32543c5"}, {"line": 48659, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}}, "object": {"location": {"namespace": "GO", "name": "intracellular"}}, "source": 80, "target": 2538, "key": "bb77fbcbdb6eb32a275f37c7e7953d0a"}, {"line": 48660, "relation": "decreases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 80, "target": 2538, "key": "d19b6f113ced2be44c3842587f7b1501"}, {"line": 49134, "relation": "increases", "evidence": "However, MMPs can degrade both soluble and fibrillar forms of amyloid-beta (Abeta). It has also been shown that Abeta enhances the expression of MMPs in neuroglial cultures and induces the release of TIMP-1 by brain cell", "citation": {"db": "PubMed", "db_id": "23792694"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Microglia": true}, "Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "source": 80, "target": 2194, "key": "6f042196f9fbc65061af29f22fe94475"}, {"line": 49135, "relation": "increases", "evidence": "However, MMPs can degrade both soluble and fibrillar forms of amyloid-beta (Abeta). It has also been shown that Abeta enhances the expression of MMPs in neuroglial cultures and induces the release of TIMP-1 by brain cell", "citation": {"db": "PubMed", "db_id": "23792694"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Microglia": true}, "Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "source": 80, "target": 3463, "key": "fbaad5861be1098e525d64ee0f3a7eae"}, {"line": 49137, "relation": "increases", "evidence": "However, MMPs can degrade both soluble and fibrillar forms of amyloid-beta (Abeta). It has also been shown that Abeta enhances the expression of MMPs in neuroglial cultures and induces the release of TIMP-1 by brain cell", "citation": {"db": "PubMed", "db_id": "23792694"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Microglia": true}, "Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 80, "target": 3463, "key": "fb375b9125329546fde1b745de6f7216"}, {"line": 49455, "relation": "negativeCorrelation", "evidence": "There is intriguing evidence that homocysteine levels may be related to plasma levels of amyloid peptides in individuals with AD,12,13 and that reduction of homocysteine levels may lower amyloid levels.", "citation": {"db": "PubMed", "db_id": "18854539"}, "source": 80, "target": 275, "key": "85c4cfaaeb08e1f3fe6b1edb302ed9c7"}, {"line": 120, "relation": "equivalentTo", "evidence": "The statements inside this citation is included to connect two entity types or triples, which will exist as islands/subnetworks in the big model.", "citation": {"db": "Other", "db_id": "123"}, "source": 79, "target": 2328, "key": "a25474506d6128790d9ae75b78a67d36"}, {"line": 121, "relation": "isA", "evidence": "The statements inside this citation is included to connect two entity types or triples, which will exist as islands/subnetworks in the big model.", "citation": {"db": "Other", "db_id": "123"}, "source": 79, "target": 80, "key": "ba9a60cd3a2e0698d665dd4e97febbdb"}, {"line": 5359, "relation": "increases", "evidence": "Excessive ROS are locally generated in response to synaptic Abeta oligomer binding. This ROS formation can be totally blocked by the mitochondrial uncoupler, 2,4-dinitrophenol which suggests a central role of mitochondria in Abeta-induced oxidative stress.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 79, "target": 170, "key": "2a2408838b84640c3bc824b0f07c85cd"}, {"line": 48627, "relation": "increases", "evidence": "Specifically, conditional loss of beta1-integrin prevented Abeta42O-induced Cofilin activation, and allosteric modulation or activation of beta1-integrin significantly reduced Abeta42O binding to neurons while blocking Abeta42O-induced reactive oxygen species (ROS) production, mitochondrial dysfunction, depletion of F-actin/focal Vinculin, and apoptotic process.", "citation": {"db": "PubMed", "db_id": "25698445"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 79, "target": 170, "key": "a8177459401e4f79ba4f70c75fddb2e1"}, {"line": 5363, "relation": "increases", "evidence": "Excessive ROS are locally generated in response to synaptic Abeta oligomer binding. This ROS formation can be totally blocked by the mitochondrial uncoupler, 2,4-dinitrophenol which suggests a central role of mitochondria in Abeta-induced oxidative stress.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 79, "target": 842, "key": "9198aa3e066da5a8c33308630ca7dbb2"}, {"line": 5393, "relation": "association", "evidence": "Many studies suggest the possible involvement of oxidative stress and calcium dysfunction in Abeta toxicity.The question as to why brain synaptic ROS levels increase with age is uncertain, but may involve lack of use followed by acute overstimulation of excitatory NMDARs that leads to excessive ROS, related to excess Ca2+ entry into mitochondria. Dysregulation of NMDAR function induced by Abeta binding to neuronal synapses may lead to synaptic mitochondrial dysfunction and excessive ROS formation.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Calcium-dependent signal transduction": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 79, "target": 842, "key": "43ffc55a0b4e49bdd17a1f842b1c5e30"}, {"line": 5381, "relation": "association", "evidence": "Many studies suggest the possible involvement of oxidative stress and calcium dysfunction in Abeta toxicity.The question as to why brain synaptic ROS levels increase with age is uncertain, but may involve lack of use followed by acute overstimulation of excitatory NMDARs that leads to excessive ROS, related to excess Ca2+ entry into mitochondria. Dysregulation of NMDAR function induced by Abeta binding to neuronal synapses may lead to synaptic mitochondrial dysfunction and excessive ROS formation.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Calcium-dependent signal transduction": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 79, "target": 491, "key": "cd0903b413cd0ed7d7f8d7dddc87b909"}, {"line": 48599, "relation": "increases", "evidence": "Specifically, conditional loss of beta1-integrin prevented Abeta42O-induced Cofilin activation, and allosteric modulation or activation of beta1-integrin significantly reduced Abeta42O binding to neurons while blocking Abeta42O-induced reactive oxygen species (ROS) production, mitochondrial dysfunction, depletion of F-actin/focal Vinculin, and apoptotic process.", "citation": {"db": "PubMed", "db_id": "25698445"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 79, "target": 2507, "key": "f8a617dba63a599fb2e9df26d83e23e5"}, {"line": 48600, "relation": "decreases", "evidence": "Specifically, conditional loss of beta1-integrin prevented Abeta42O-induced Cofilin activation, and allosteric modulation or activation of beta1-integrin significantly reduced Abeta42O binding to neurons while blocking Abeta42O-induced reactive oxygen species (ROS) production, mitochondrial dysfunction, depletion of F-actin/focal Vinculin, and apoptotic process.", "citation": {"db": "PubMed", "db_id": "25698445"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 79, "target": 3517, "key": "4f52b1d52fda2c5270bf61ca8dc48d10"}, {"line": 48620, "relation": "increases", "evidence": "Specifically, conditional loss of beta1-integrin prevented Abeta42O-induced Cofilin activation, and allosteric modulation or activation of beta1-integrin significantly reduced Abeta42O binding to neurons while blocking Abeta42O-induced reactive oxygen species (ROS) production, mitochondrial dysfunction, depletion of F-actin/focal Vinculin, and apoptotic process.", "citation": {"db": "PubMed", "db_id": "25698445"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 79, "target": 478, "key": "135aa7295fbe567d96c2dbf7e7a577ef"}, {"line": 120, "relation": "equivalentTo", "evidence": "The statements inside this citation is included to connect two entity types or triples, which will exist as islands/subnetworks in the big model.", "citation": {"db": "Other", "db_id": "123"}, "source": 2328, "target": 79, "key": "622f64094d371670cfdf948fe595e092"}, {"line": 122, "relation": "isA", "evidence": "The statements inside this citation is included to connect two entity types or triples, which will exist as islands/subnetworks in the big model.", "citation": {"db": "Other", "db_id": "123"}, "source": 2328, "target": 80, "key": "168598b66591b423bcc26cf6f896d76b"}, {"line": 16893, "relation": "increases", "evidence": "First, Pin1 inhibits the production of Abeta, and enhances the activity of eNOS. Second, Abeta and eNOS form a mutual inhibition system. Third, the well-balanced feedback signaling loop avoids the development of AD, HTN, and CAA by inhibiting the frequent pathological characteristics of these diseases, including Abeta deposition in cerebral microvessels and cerebral microbleeds.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 80, "key": "97e1b282e73798ec33d10db8f718acff"}, {"line": 237, "relation": "positiveCorrelation", "evidence": "gamma-Secretase comprises a molecular complex of four integral membrane proteins - presenilin, nicastrin, APH-1 and PEN-2 - and its molecular mechanism remains under extensive scrutiny. The ratio of Abeta(42) over Abeta(40) is increased by familial Alzheimer's disease mutations occurring in the presenilin genes or in APP, near the gamma-secretase cleavage site.", "citation": {"db": "PubMed", "db_id": "16696577"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 3823, "key": "c45ddc86536b57ea7460dec095e85fb9"}, {"line": 330, "relation": "positiveCorrelation", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 3823, "key": "bd1fc347d9eeaf7af5a4f95e963f92b8"}, {"line": 3596, "relation": "biomarkerFor", "evidence": "Our findings support the notion that CSF tau and Abeta(1-42) may be useful biomarkers in the early identification of AD in MCI subjects.", "citation": {"db": "PubMed", "db_id": "14699432"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3823, "key": "243b7414f95c045311c6ad706d9008a8"}, {"line": 3625, "relation": "association", "evidence": "Amyloid beta-peptide 1-42 (Abeta(1-42)) and hyperphosphorylated tubulin associated unit (tau) isoforms appear to be the most sensitive and specific CSF biomarkers, the combination of these biomarkers depicting the best diagnosis value for AD.", "citation": {"db": "PubMed", "db_id": "18584921"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3823, "key": "5028cabadcb9e079b10d900ae3f1a388"}, {"line": 16889, "relation": "increases", "evidence": "First, Pin1 inhibits the production of Abeta, and enhances the activity of eNOS. Second, Abeta and eNOS form a mutual inhibition system. Third, the well-balanced feedback signaling loop avoids the development of AD, HTN, and CAA by inhibiting the frequent pathological characteristics of these diseases, including Abeta deposition in cerebral microvessels and cerebral microbleeds.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"Low": true}}, "source": 2328, "target": 3823, "key": "11833dbd79b390c0da344ffcca3611ad"}, {"line": 27925, "relation": "biomarkerFor", "evidence": "The aspartic protease beta-secretase (BACE) cleaves the amyloid precursor protein into a 42 residue beta-peptide, which is the principal biochemical marker of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16216580"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3823, "key": "8b2b444631023290d6bbd5ea890a6c14"}, {"line": 31989, "relation": "association", "evidence": "PrP(C) decreases amyloid-beta (Abeta) production, which is involved in AD pathogenesis, by inhibiting beta-secretase (BACE1) activity.", "citation": {"db": "PubMed", "db_id": "23577068"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3823, "key": "d2f0109e3fc038b926bc99b6f2ae1bf8"}, {"line": 35172, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 2328, "target": 3823, "key": "8e14d219c1f3f5e2b96cf29132c88dcc"}, {"line": 43458, "relation": "increases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 2328, "target": 3823, "key": "1f2928796f7e6508fa7639a2a5b02d63"}, {"line": 44432, "relation": "increases", "evidence": "developmental exposure of rodents to the heavy metal lead (Pb) increases APP (amyloid precursor protein) and Abeta production later in the aging brain", "citation": {"db": "PubMed", "db_id": "18157652"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 2328, "target": 3823, "key": "cae8deb9966e4fb8c18e793d699c8b51"}, {"line": 377, "relation": "increases", "evidence": "Abeta can also cause mitochondrial oxidative stress and dysregulation of Ca2+ homeostasis resulting in impairment of the electron transport chain (ETC), increased production of superoxide anion radical. and decreased production of ATP.", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Response to oxidative stress": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 775, "key": "ed6324f29c737a33deb7929dc028cdbf"}, {"line": 1697, "relation": "increases", "evidence": "Amyloid-beta accumulation in the synapses directly disturbs mitochondrial function, causing oxidative stress, decreased ATP, and increased Ca2+ influx. Furthermore, the interaction of mitochondrial amyloid-beta with its binding proteins, such as ABAD and CypD, exacerbates amyloid-beta-induced mitochondria and neuronal stress and malfunction.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "CellStructure": {"Synapses": true}, "Confidence": {"High": true}}, "source": 2328, "target": 775, "key": "30b88631a66f1b2b3beda53b856bc3b5"}, {"line": 389, "relation": "decreases", "evidence": "Abeta can also cause mitochondrial oxidative stress and dysregulation of Ca2+ homeostasis resulting in impairment of the electron transport chain (ETC), increased production of superoxide anion radical. and decreased production of ATP.", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 615, "key": "04f478f859d56784cb15949d202f7bbc"}, {"line": 390, "relation": "decreases", "evidence": "Abeta can also cause mitochondrial oxidative stress and dysregulation of Ca2+ homeostasis resulting in impairment of the electron transport chain (ETC), increased production of superoxide anion radical. and decreased production of ATP.", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 546, "key": "b9fd8f78ad88847a9c37368a8e884174"}, {"line": 391, "relation": "increases", "evidence": "Abeta can also cause mitochondrial oxidative stress and dysregulation of Ca2+ homeostasis resulting in impairment of the electron transport chain (ETC), increased production of superoxide anion radical. and decreased production of ATP.", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 359, "key": "e7fcbbe02c1b1fcb82e47a398e50104c"}, {"line": 392, "relation": "decreases", "evidence": "Abeta can also cause mitochondrial oxidative stress and dysregulation of Ca2+ homeostasis resulting in impairment of the electron transport chain (ETC), increased production of superoxide anion radical. and decreased production of ATP.", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 190, "key": "1d6ba5f6ba550854c3f651252d8ba184"}, {"line": 1701, "relation": "decreases", "evidence": "Amyloid-beta accumulation in the synapses directly disturbs mitochondrial function, causing oxidative stress, decreased ATP, and increased Ca2+ influx. Furthermore, the interaction of mitochondrial amyloid-beta with its binding proteins, such as ABAD and CypD, exacerbates amyloid-beta-induced mitochondria and neuronal stress and malfunction.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "CellStructure": {"Synapses": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 190, "key": "72acc04949af6877f9c3f1d61128cc94"}, {"line": 495, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true, "Nitric oxide subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 156, "key": "afef4fd228ce6953845bb953d15983eb"}, {"line": 15117, "relation": "increases", "evidence": "We have shown with cultured cerebral cortical normal (i.e., untransformed) adult human astrocytes (NAHAs) that exogenous amyloid-beta peptides (Abetas) stimulate the astrocytes to make and secrete large amounts of Abetas and nitric oxide by a mechanism mediated through the calcium-sensing receptor (CaSR).", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true, "Cerebral Cortex": true}, "Species": {"9606": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 156, "key": "f7ba336e48000fa95113837ffdd3d353"}, {"line": 588, "relation": "increases", "evidence": "Application of the amyloid beta-peptide A beta(1-42) induces the conversion of p35 to p25 in primary cortical neurons", "citation": {"db": "PubMed", "db_id": "10830966"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2328, "target": 4105, "key": "8fc6fbbbb0a1b5a636930b138e298e77"}, {"line": 747, "relation": "decreases", "evidence": "Abeta-dependent inactivation of the JAK2/STAT3 axis causes memory loss through cholinergic dysfunction.", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2328, "target": 2933, "key": "ff710326b9d0e3f821073ae6d5f72820"}, {"line": 819, "relation": "association", "evidence": "Alpha synuclein also contributes to the intracellular inclusions of multiple system atrophy, and a fragment has been found in senile plaques in Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "10491577"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synuclein subgraph": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 3384, "key": "1b9ea96bfa59905bdc9204a559465868"}, {"relation": "partOf", "source": 2328, "target": 1047, "key": "b881cced8be7d28034df8f1b791d657a"}, {"line": 1207, "relation": "increases", "evidence": "Our results suggest that SRs play a role on inflammatory activation, inducing production of NO and IL1Abeta, and show potentiation by Abeta. Potentiation of the inflammatory response of Abeta could be meaningful for the activation of glia observed in AD.We propose that scavenger receptors (SR) participate in the activation of glia by Abeta.", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3069, "key": "7cb9804ee913311db5eb90ed1c922901"}, {"line": 1268, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 2328, "target": 3341, "key": "8e977aa4c1f0929597bd27eea37e67cf"}, {"line": 1269, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 2328, "target": 3340, "key": "db5c177373d87aa74a4b957fc7039150"}, {"line": 1270, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 2328, "target": 3339, "key": "2e4a77c883aa1aeb062cf88deffbb112"}, {"line": 1351, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2328, "target": 2455, "key": "b5cb2bbda4bd74fe84ce1d2ed8720008"}, {"line": 1388, "relation": "positiveCorrelation", "evidence": "Cathepsin D, the most abundant lysosomal and endosomal aspartyl protease, shows beta and gamma secretase activity in vitro by cleaving the amyloid precursor protein (APP) into amyloid beta protein (Abeta). Polymorphism at position 224, C224T, on exon 2 of cathepsin D gene (CTSD) has been associated with an increased risk for Alzheimer's disease (AD) ", "citation": {"db": "PubMed", "db_id": "20597865"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 1792, "key": "e0a6c9c1abc875e7101169a42b5a0829"}, {"line": 1582, "relation": "decreases", "evidence": "APP and amyloid-beta may block mitochondrial translocation of nuclear-encoded proteins, such as components of the electron transport chain, impairing mitochondrial function. Intramitochondrial amyloid-beta is able to perturb mitochondrial function in several ways by directly influencing extracellular transport chain complex activities, impairing mitochondrial dynamics, or disturbing calcium storage, thus increasing apoptotic pathways. Moreover, amyloid-beta interacts with mitochondrial matrix components inducing an improper mitochondrial complex function leads to a decreased mitochondrial membrane potential of the organelle and impairing ATP formation.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 696, "key": "519a3bdbde115a42aaacb26f3954ac73"}, {"line": 2375, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 846, "key": "4c7ac5294711439018e56ac610ecc635"}, {"line": 2455, "relation": "negativeCorrelation", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 3439, "key": "e7d67b67b3cedf79b274d52b67545240"}, {"relation": "partOf", "source": 2328, "target": 1228, "key": "ff9311509cc0bae68f8156ae3e8c065a"}, {"relation": "partOf", "source": 2328, "target": 1245, "key": "84ec3e4f5965baa45b9d6ef14afe60df"}, {"line": 3420, "relation": "directlyDecreases", "evidence": "Molecular mechanisms of APP cleavage products in the regulation of GD3S enzyme activity. (A) In absence of Ab peptides a-series ganglioside GM3 binds to GD3S and is converted to the b-series ganglioside GD3. In presence of Ab, Ab binds ganglioside GM3, forming an Ab-GM3 complex. This complex still binds to GD3S, but cannot be converted to GD3. (B) Dual function of Ab and AICD in GD3S regulation. Ab reduces enzyme activity of GD3S by forming an Ab-GM3 complex, resulting in reduced turnover of GM3 to GD3. AICD binds the adaptor protein Fe65 and reduces GD3S gene transcription, which also results in reduced turnover of GM3 to GD3.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 3423, "key": "f84dc4341e521cf426ff6b9a70b58f37"}, {"relation": "partOf", "source": 2328, "target": 902, "key": "863e1c75f3053d2c9ccc66cfbcde6b78"}, {"line": 3421, "relation": "directlyIncreases", "evidence": "Molecular mechanisms of APP cleavage products in the regulation of GD3S enzyme activity. (A) In absence of Ab peptides a-series ganglioside GM3 binds to GD3S and is converted to the b-series ganglioside GD3. In presence of Ab, Ab binds ganglioside GM3, forming an Ab-GM3 complex. This complex still binds to GD3S, but cannot be converted to GD3. (B) Dual function of Ab and AICD in GD3S regulation. Ab reduces enzyme activity of GD3S by forming an Ab-GM3 complex, resulting in reduced turnover of GM3 to GD3. AICD binds the adaptor protein Fe65 and reduces GD3S gene transcription, which also results in reduced turnover of GM3 to GD3.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 902, "key": "279fd5f1099a5aa71735a33756c4b4c9"}, {"relation": "partOf", "source": 2328, "target": 1127, "key": "5a00eb2d277e0521fb827ca5abb8d289"}, {"line": 26136, "relation": "increases", "evidence": "Importantly, ApoE binds to Abeta and this, too, is influenced by its lipidation status", "citation": {"db": "PubMed", "db_id": "18549781"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"APOE subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2328, "target": 1127, "key": "cd3193a027e9fb79427c954ca5ca89d2"}, {"line": 3466, "relation": "directlyDecreases", "evidence": "The low-density lipoprotein receptor (LDLR) has the highest affinity for apoE and plays an important role in brain cholesterol metabolism.These data suggest that increased APP expression and Abeta exposure alters microtubule function, leading to reduced transport of LDLR to the plasma membrane. Consequent deleterious effects on apoE uptake and function will have implications for AD pathogenesis and/or progression", "citation": {"db": "PubMed", "db_id": "20049331"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "intracellular"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 2328, "target": 2960, "key": "ec763579c5e6e171e9364fa8c3db4d25"}, {"line": 3914, "relation": "directlyDecreases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Synapses": true}}, "source": 2328, "target": 492, "key": "47aaee5f40b448a175a709a3991cfc80"}, {"line": 37237, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2328, "target": 492, "key": "bb40bf8c6033934792002df9425361ce"}, {"line": 3915, "relation": "directlyDecreases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Synapses": true}}, "source": 2328, "target": 491, "key": "2d1a4e3979ef4fa758d6e4a6898fcc3c"}, {"line": 10504, "relation": "association", "evidence": "A growing number of reports suggest that elevated levels of extracellular Alzheimer's beta-amyloid protein alter the homeostasis of free [Ca(2+)](i) in different cell types of the mammalian brain.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}}, "source": 2328, "target": 491, "key": "327f20307b25a148d75b66dcb552f829"}, {"line": 3936, "relation": "increases", "evidence": "The interaction of Abeta with the plasma membrane may be facilitated by binding to phosphatidylserine (PtdS); age/AD-related mitochondrial impairment (ATP depletion) may trigger flipping of PtdS from the inner portion of the plasma membrane to the cell surface. The PtdS flipping may also result from Ca2+ influx or release from the endoplasmic reticulum (ER) or mitochondria which can activate a phospholipid scramblase (PLSCR1)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}, "CellStructure": {"Cell Membrane": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}}}, "source": 2328, "target": 160, "key": "35e9f9e59a83eb950dabc0bc10f21a6f"}, {"line": 4029, "relation": "directlyDecreases", "evidence": "Amyloidogenic APP processing may prevent a-secretase (a) cleavage of APP which would otherwise generate a secreted form of APP (sAPPa).", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2328, "target": 2249, "key": "20900d0c79522e1d5fc5f709ca5abbb4"}, {"line": 4030, "relation": "directlyDecreases", "evidence": "Amyloidogenic APP processing may prevent a-secretase (a) cleavage of APP which would otherwise generate a secreted form of APP (sAPPa).", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Low": true}}, "source": 2328, "target": 2137, "key": "6729d87e9b12071026d2f9be09b87fb8"}, {"relation": "partOf", "source": 2328, "target": 1689, "key": "04bb930bfc52e6be51caf3bdf3f558ae"}, {"relation": "partOf", "source": 2328, "target": 1691, "key": "60edfcd412b0d10b4a3832857ffc13d7"}, {"relation": "partOf", "source": 2328, "target": 1694, "key": "238902cc9cd6cd9ffb7381c4a8d1cd5e"}, {"line": 4847, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2328, "target": 3258, "key": "55bcafa4caf1918b7b68cb818b2dac98"}, {"line": 32486, "relation": "association", "evidence": "Presenilin 1 (PS1) plays a critical role in the gamma-secretase processing of the amyloid precursor protein to generate the beta-amyloid peptide, which accumulates in plaques in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "18299393"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3258, "key": "d47b3fd0615d620466cc0edf07fbd4af"}, {"line": 4850, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2328, "target": 3268, "key": "75a73daf47ce723645db8244f6be9482"}, {"line": 5214, "relation": "decreases", "evidence": "Higher APP expression and elevated Abeta levels cause greater than required Cu export, leading to increased Cu in cerebrospinal fluid (CSF) and serum, and an intracellular (IC) Cu deficiency in the brain. Cu-deficient superoxide dismutase (SOD1) contributes to the reduced antioxidant capacity of the brain, allowing further oxidative stress.", "citation": {"db": "PubMed", "db_id": "15910549"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}}, "source": 2328, "target": 99, "key": "c39f009a8ea033ee5363412029b5e811"}, {"line": 5257, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 2843, "key": "6c1664b4ef83eb22b67599ee8f47741c"}, {"line": 5278, "relation": "directlyDecreases", "evidence": "Mitochondrial Abeta-binding alcohol dehydrogenase (ABAD): ABAD is a member of the short chain dehydrogenase reductase family in mitochondria that binds Abeta. Binding of Abeta to ABAD distorts the enzymebetas structure, rendering it inactive. In neurons, ABAD is predominately localized to mitochondria. Upon binding ABAD, Abeta triggers events leading to neuronal apoptosis through a mitochondrial pathway.Interestingly, mitochondrial ABAD is upregulated in neurons from AD patients. The ABAD-Abeta complex has been hypothesized to induce oxidant stress and mitochondrial dysfunction. Increased expression of ABAD exacerbates Abeta-mediated mitochondrial and neuronal stress. Abeta binding to ABAD causes free radical production and neuronal apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true, "Free radical formation subgraph": true, "Metabolism of steroid hormones subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2328, "target": 2843, "key": "89f71e06293009eee880d160027b81ff"}, {"line": 37149, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2843, "key": "7dbfbd1bf4c57be1050b1ca2288d0c67"}, {"line": 5260, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3215, "key": "b48aebf32da5f0d013ed165bf2511389"}, {"relation": "partOf", "source": 2328, "target": 1243, "key": "d8b2dd154ed85f302cc9be334cab96dd"}, {"relation": "partOf", "source": 2328, "target": 1229, "key": "24d9a9d3935ce6936df0162e0612c4e7"}, {"line": 6541, "relation": "increases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 879, "key": "688daa049e97a9fba58e9cb50b4a3438"}, {"line": 6810, "relation": "negativeCorrelation", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "UserdefinedCellLine": {"App transgenic": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2328, "target": 345, "key": "150384e151909517f7da9a033ec808b7"}, {"line": 7353, "relation": "positiveCorrelation", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3857, "key": "bc5f45eefcc3a7730bf42e32bf1e43b2"}, {"line": 8052, "relation": "association", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2328, "target": 735, "key": "8719446a714e987e6b91c2c711ef9a55"}, {"line": 8155, "relation": "negativeCorrelation", "evidence": "Abeta degrading activity by IDE was shown to be lower in AD brains than in the controls [71]. Moreover, the amount of hippocampal IDE protein was also found to reduce in AD brains as compared to the controls [15]. When the IDE gene was deleted in mouse model, Abeta levels in the brain were elevated [27] and [58], suggesting IDE activity is critical in determining the amount of brain Abeta in vivo. More significantly, enhanced IDE activity in the IDE and APP double transgenic mice decreased their brain Abeta levels, and prevented the formation of AD pathology [52].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2867, "key": "030de7a353421a95716c228d4cda14f9"}, {"line": 10077, "relation": "association", "evidence": "Insulin-degrading enzyme (IDE) is central to the turnover of insulin and degrades amyloid beta (Abeta) in the mammalian brain.", "citation": {"db": "PubMed", "db_id": "18411275"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2328, "target": 2867, "key": "cf87829176e8d4a78d98377a272a8dfa"}, {"line": 8209, "relation": "decreases", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2328, "target": 1483, "key": "f4260f1c08e3c286af6a147a881fd8d5"}, {"line": 8876, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2098, "key": "e863a13cfb4ac5762fd1caeb320f0c56"}, {"line": 8879, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2107, "key": "07ab5d2ea2d4d335e337c081003e0533"}, {"line": 8882, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2086, "key": "2de7f0e04bd2dcb0bb41d0ac8b07c04e"}, {"line": 8885, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2115, "key": "556ef7ac1f974e74f4dbc442727c4cba"}, {"line": 9104, "relation": "association", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2312, "key": "2bd70223319d5974b23bb06ea0e75530"}, {"line": 26137, "relation": "association", "evidence": "Importantly, ApoE binds to Abeta and this, too, is influenced by its lipidation status", "citation": {"db": "PubMed", "db_id": "18549781"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"APOE subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2328, "target": 2312, "key": "89316f095073ebbfb31ab4a1c91c0ec8"}, {"line": 39597, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2328, "target": 2312, "key": "f720265f02d3c8490e9f79345c853a31"}, {"line": 43211, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 2312, "key": "f5d52c07cdabbe026fa6d495a7371d71"}, {"line": 9761, "relation": "association", "evidence": "Abeta autoantibody levels were increased in T2DM compared with age-matched controls by 45.4 +/- 8.1% (p< 0.001).", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3850, "key": "05c0fa3e8b1320979a2c1c6033b12ad1"}, {"line": 9771, "relation": "positiveCorrelation", "evidence": "Abeta autoantibody levels in the T2DM group were positively correlated with the levels of cholesterol (p=0.011),", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"High": true}}, "source": 2328, "target": 231, "key": "8adf6c2d2d20a24f85e6e7b39d58c642"}, {"line": 9782, "relation": "positiveCorrelation", "evidence": "low density lipoprotein cholesterol (p=0.020), and triglycerides (p=0.039).", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 405, "key": "fb905b494cf22b02d5bfa411e0534943"}, {"line": 9783, "relation": "positiveCorrelation", "evidence": "low density lipoprotein cholesterol (p=0.020), and triglycerides (p=0.039).", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 365, "key": "d54729e3cc54576f0fddbccdf7f689d8"}, {"relation": "partOf", "source": 2328, "target": 1230, "key": "c3f60425d9c28ed17d2e6cb5565f7a3c"}, {"line": 10492, "relation": "increases", "evidence": "Alzheimer's beta-amyloid, human islet amylin, and prion protein fragment evoke intracellular free calcium elevations by a common mechanism in a hypothalamic GnRH neuronal cell line.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"MeSHAnatomy": {"Hypothalamus": true}, "Subgraph": {"Calcium-dependent signal transduction": true}}, "source": 2328, "target": 94, "key": "9bfb0a260f939437e9e049f95a6c1225"}, {"line": 10547, "relation": "increases", "evidence": "Abeta1-42 and human amylin (hAmylin) increase cytosolic cAMP and Ca(2+), trigger multiple pathways involving the signal transduction mediators protein kinase A, MAPK, Akt, and cFos.", "citation": {"db": "PubMed", "db_id": "22500019"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2328, "target": 94, "key": "7c543dae7d79caa49bdbe645ca878037"}, {"line": 36942, "relation": "increases", "evidence": "Protein kinase C: PKC is part of a multigene family of serine-threonine kinases central to many signal transduction pathways [138] with a prominent role in memory [139]. It is likely that ABeta¸-induced increases in cytosolic Ca2+ signals are transmitted to PKC for PKC-mediated transcriptional activation. In addition, PKC activates ERK by interacting with Ras or Raf-1 [140] to initiate CREB phosphorylation. While PKC levels decline in AD [141], their activation restores K+ channel function in cells from AD patients [142]. In addition, activation of PKC directly or indirectly enhances the a-processing cleavage of APP [143].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 94, "key": "ee8e34b6524e07d170873f19860e4010"}, {"line": 37239, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2328, "target": 94, "key": "4568a1efefb1be4f64c0a5f641c6a558"}, {"line": 10548, "relation": "increases", "evidence": "Abeta1-42 and human amylin (hAmylin) increase cytosolic cAMP and Ca(2+), trigger multiple pathways involving the signal transduction mediators protein kinase A, MAPK, Akt, and cFos.", "citation": {"db": "PubMed", "db_id": "22500019"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2328, "target": 20, "key": "a36282d8c4c70d3eb721b8b76a7e2e8a"}, {"line": 10552, "relation": "increases", "evidence": "Abeta1-42 and human amylin (hAmylin) increase cytosolic cAMP and Ca(2+), trigger multiple pathways involving the signal transduction mediators protein kinase A, MAPK, Akt, and cFos.", "citation": {"db": "PubMed", "db_id": "22500019"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Akt subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2153, "key": "9c8beca401b81dbffaf379e3e6807a51"}, {"line": 10554, "relation": "increases", "evidence": "Abeta1-42 and human amylin (hAmylin) increase cytosolic cAMP and Ca(2+), trigger multiple pathways involving the signal transduction mediators protein kinase A, MAPK, Akt, and cFos.", "citation": {"db": "PubMed", "db_id": "22500019"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 706, "key": "2c40750e2b143fe1777a29810138bb88"}, {"line": 10555, "relation": "increases", "evidence": "Abeta1-42 and human amylin (hAmylin) increase cytosolic cAMP and Ca(2+), trigger multiple pathways involving the signal transduction mediators protein kinase A, MAPK, Akt, and cFos.", "citation": {"db": "PubMed", "db_id": "22500019"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2699, "key": "9a02693c1c7a98599db783213e321ca3"}, {"line": 11105, "relation": "positiveCorrelation", "evidence": "PEN-2 is an integral membrane protein that is a necessary component of the gamma-secretase complex, which is central in the pathogenesis of Alzheimer's disease and is also required for Notch signaling. In the absence of PEN-2, Notch signaling fails to guide normal development in Caenorhabditis elegans, and amyloid beta peptide is not generated from the amyloid precursor protein", "citation": {"db": "PubMed", "db_id": "12639958"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 2328, "target": 3272, "key": "35914677bea0ead6264af149a0f70b5f"}, {"line": 11181, "relation": "negativeCorrelation", "evidence": "CSF levels of total but not free IgG autoAbs against galanin were increased in AD, resulting in increased percentage of galanin autoAbs present as immune complexes. CSF levels of galanin total autoAbs and α-MSH free autoAbs correlated negatively with the severity of cognitive impairment as measured by MMSE. Both total and free autoAbs against galanin and α-MSH in CSF correlated negatively with age in AD patients but not in controls. CSF levels of galanin autoAbs and free α-MSH AutoAbs negatively correlated with CSF levels of t-Tau, p-Tau and ratios of t-Tau/Abeta42 or p-Tau/Abeta42 in AD patients but not in controls.", "citation": {"db": "PubMed", "db_id": "22078238"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}, "Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Tau protein subgraph": true}}, "source": 2328, "target": 2737, "key": "e6faf8746fe862df8884c87d847cb535"}, {"line": 11223, "relation": "association", "evidence": "Seladin-1 was considered a novel neuroprotective factor, because of its anti-apoptotic activity. Subsequently, it was demonstrated that seladin-1 has also enzymatic activity [3-beta-hydroxysterol delta-24-reductase, (DHCR24)], which catalyzes the synthesis of cholesterol from desmosterol. The amount of membrane cholesterol may play an important role both in protecting neuronal cells against toxic insults and in inhibiting the production of beta-amyloid. We demonstrated that seladin-1 overexpression increases the amount of membrane cholesterol and induces resistance against beta-amyloid aggregates in neuroblastoma cells, whereas a specific inhibitor of DHCR24 increased cell vulnerability. We also hypothesized that seladin-1 might be a mediator of the neuroprotective effects of estrogens. We first demonstrated that, in human fetal neuroepithelial cells (FNC), 17beta-estradiol, raloxifene, and tamoxifen exert protective effects against beta-amyloid toxicity and oxidative stress. In addition, these molecules significantly increased the expression of seladin-1 and the amount of cell cholesterol. Then, we showed that, upon seladin-1 silencing, the protective effects of estrogens were abolished, thus indicating this factor as a fundamental mediator of estrogen-mediated neuroprotection, at least in FNC cells. Furthermore, we detected the presence of functionally active half-palindromic estrogen responsive elements upstream the coding region of the seladin-1 gene. Overall, our results indicate that seladin-1 may be viewed as a multi-faceted protein, which conjugates both the neuroprotective properties of estrogens and the important functions of cholesterol in maintaining brain homeostasis", "citation": {"db": "PubMed", "db_id": "21396986"}, "annotations": {"Confidence": {"Medium": true}, "UserdefinedCellLine": {"Neuroblastoma cell": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}}, "subject": {"modifier": "Degradation"}, "source": 2328, "target": 2627, "key": "19cf2d2480469d1ec7b6a42dda7b8e24"}, {"relation": "partOf", "source": 2328, "target": 953, "key": "31bda3e26ba68cd39576072f5f650c32"}, {"line": 11382, "relation": "decreases", "evidence": "We tested the effects of native GIP and the agonist N-AcGIP on synaptic plasticity [long-term potentiation (LTP)] in the hippocampus [15 nmol, administered intracerebroventricularly (icv)] and report for the first time that both peptides have enhancing effects on LTP. In contrast, the antagonist of GIP, Pro(3)GIP (15 nmol icv), reduced LTP. Injection of beta-amyloid(25-35) (100 nmol), a peptide that aggregates in brains of AD patients, also impaired LTP. The injection of N-AcGIP (15 nmol icv) 30 min prior to injection of amyloid(25-35) (100 nmol icv) fully reversed the impairment of LTP induced by beta-amyloid. The results demonstrate for the first time that GIP (particularly enzyme-resistant forms) not only directly modulates neurotransmitter release and LTP formation, but also protects synapses from the detrimental effects of beta-amyloid fragments on LTP formation. ", "citation": {"db": "PubMed", "db_id": "18234983"}, "annotations": {"Species": {"10116": true}}, "subject": {"modifier": "Activity"}, "source": 2328, "target": 597, "key": "a7b96202939c8ca27e8d83405e540bbe"}, {"line": 36582, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 597, "key": "3437016b3ef1420c28f0094070d4703d"}, {"line": 38424, "relation": "increases", "evidence": "Moreover, two groups recently reported that low doses (picomolar) of Ab can positively modulate synaptic plasticity and memory by increasing long-term potentiation (Morley et al. 2008; Puzzo et al. 2008), revealing a novel physiological function of Ab under normal conditions.", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 2328, "target": 597, "key": "8e5d618563970d7de80ed832857ca1ee"}, {"line": 11599, "relation": "increases", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 824, "key": "393f0cb60b22dfa0a2b89a8b2e206303"}, {"line": 11604, "relation": "decreases", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 825, "key": "1a90cd0c5ddcb5bd363cc8c6629052e3"}, {"line": 11605, "relation": "decreases", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 660, "key": "a73593f982db6c3db9fae35bc602095b"}, {"line": 11609, "relation": "association", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 243, "key": "b9947db228c787767a323ffe858e2e6c"}, {"line": 11617, "relation": "decreases", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "source": 2328, "target": 21, "key": "4de0223514ad9bd6d8b53da34dfe337f"}, {"line": 11661, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 434, "key": "4dae55fd621a4097905e2c3901427ec8"}, {"line": 12227, "relation": "decreases", "evidence": "After the treatment, memantine-treated mice had restored cognition and significantly reduced the levels of insoluble amyloid-beta (Abeta), Abeta dodecamers (Abeta*56), prefibrillar soluble oligomers, and fibrillar oligomers. The effects on pathology were stronger in older, more impaired animals. Memantine treatment also was associated with a decline in the levels of total tau and hyperphosphorylated tau. Finally, memantine pre-incubation prevented Abeta-induced inhibition of long-term potentiation in hippocampal slices of cognitively normal mice.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"NMDA receptor": true, "Non-amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 839, "key": "adca619937896194f21bfa288a1cb1cd"}, {"line": 12399, "relation": "association", "evidence": "In this study, we show that at nanomolar-low micromolar concentrations, etazolate, a selective GABA(A) receptor modulator, stimulates sAPPalpha production in rat cortical neurons and in guinea pig brains. Etazolate (20 nM-2 microM) dose-dependently protected rat cortical neurons against Abeta-induced toxicity. The neuroprotective effects of etazolate were fully blocked by GABA(A) receptor antagonists indicating that this neuroprotection was due to GABA(A) receptor signalling. This indicating that etazolate exerts its neuroprotective effect via sAPPalpha induction.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 2328, "target": 648, "key": "a467353db3cd282ef37d4d2b20a05a4a"}, {"line": 38093, "relation": "increases", "evidence": "Humanin (HN) is a short neuroprotective peptide abolishing Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2328, "target": 648, "key": "a88e3a997a58f6e648b1233678103e60"}, {"line": 47035, "relation": "association", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 648, "key": "c8c819c6cf71e09a9ca534ca39741123"}, {"line": 47766, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 648, "key": "a2d2c3d3fee3a8078d9a69e6118a5b0d"}, {"line": 13118, "relation": "increases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Inflammatory response subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 645, "key": "49b4bf072e98159cff9c0094e8d71132"}, {"line": 35559, "relation": "increases", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 645, "key": "b460ca6bc38926548d3c4fb41e198d2d"}, {"line": 47724, "relation": "increases", "evidence": "Although the mechanism of Ab action in the pathogenesis of Alzheimer’s disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1), whereas the silencing of Dkk1 blocks Ab neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}}, "source": 2328, "target": 645, "key": "5ff9529be08f1ea9bece08011db84d43"}, {"line": 13782, "relation": "increases", "evidence": "Activation of nuclear factor-kappa B by beta-amyloid peptides and interferon-gamma in murine microglia.", "citation": {"db": "PubMed", "db_id": "9209268"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Interferon signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 871, "key": "e40819a3ed3e5868b610f65ebb5382f7"}, {"relation": "partOf", "source": 2328, "target": 1690, "key": "16afa8172da1fcfa325a6341e6510b0e"}, {"line": 14127, "relation": "increases", "evidence": "In vitro exposure of mouse primary neurons to Abeta1-42 caused a gradual increases in CysLT1R expression.", "citation": {"db": "PubMed", "db_id": "24269024"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "source": 2328, "target": 4044, "key": "1ce7c40f5aaf7f833fa38b8188ee903b"}, {"line": 41819, "relation": "increases", "evidence": "Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 4044, "key": "0c55dab1829b9b475ea9bbbd2b8aa311"}, {"line": 41838, "relation": "increases", "evidence": "Abeta1-42-mediated increase of CysLT1R expression was associated with Abeta1-42-induced cytotoxicity as measured by MTT reduction assay and lactate dehydrogenase (LDH) release assay.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2328, "target": 4044, "key": "e851a5a279b637bf7458fa5455f5daa1"}, {"line": 41845, "relation": "increases", "evidence": "Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2328, "target": 4044, "key": "56747864f5fda34a7063d950be190e97"}, {"line": 14139, "relation": "increases", "evidence": "In vivo bilateral intrahippocampal injection of Abeta1-42 also elicited time-dependent increases of CysLT1R expression in the hippocampus and cortex of mice.", "citation": {"db": "PubMed", "db_id": "24269024"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}}, "source": 2328, "target": 2615, "key": "4f9b0c95259ac681bca79bf70327b7f8"}, {"line": 14983, "relation": "positiveCorrelation", "evidence": "Conversely, oligomer-enriched preparations of Abeta1-42 increased soluble NG2 levels in the supernatants.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Confidence": {"High": true}}, "source": 2328, "target": 2577, "key": "a5dea130c59de77d8b9d6a568e0d5aaf"}, {"line": 14989, "relation": "positiveCorrelation", "evidence": "There was also a trend toward increased MMP-9 activity observed after oligomeric Abeta1-42 exposure.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Confidence": {"High": true}}, "source": 2328, "target": 3062, "key": "f5839dd85d70cf6b652bfeda7a4f380e"}, {"line": 14996, "relation": "negativeCorrelation", "evidence": "In agreement with the altered sNG2 levels, we found decreased MMP-9 activity after fibrillar Abeta_42 exposure and a trend toward increased MMP-9 activity after oligomeric Abeta_42 exposure.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3062, "key": "5e83bbde1444a80872f713ecb8c28633"}, {"line": 15084, "relation": "increases", "evidence": "The Abeta Peptides-Activated Calcium-Sensing Receptor Stimulates the Production and Secretion of Vascular Endothelial Growth Factor-A by Normoxic Adult Human Cortical Astrocytes.", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Bodily Secretions": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2451, "key": "1dc204b530a362f6c0be930f29a97180"}, {"line": 15113, "relation": "association", "evidence": "We have shown with cultured cerebral cortical normal (i.e., untransformed) adult human astrocytes (NAHAs) that exogenous amyloid-beta peptides (Abetas) stimulate the astrocytes to make and secrete large amounts of Abetas and nitric oxide by a mechanism mediated through the calcium-sensing receptor (CaSR).", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true, "Cerebral Cortex": true}, "Species": {"9606": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 2451, "key": "9d83858da587ed5df10b0d20a98aac28"}, {"line": 15134, "relation": "increases", "evidence": "Here, we report that exogenous Abetas stimulate the NAHAs to produce and secrete even VEGF-A through a CaSR-mediated mechanism.", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 3519, "key": "3a2e8ad4e27e0249d085a56db3ca7322"}, {"line": 15469, "relation": "increases", "evidence": "Pathogenic A beta induces the expression and activation of matrix metalloproteinase-2 in human cerebrovascular smooth muscle cells.", "citation": {"db": "PubMed", "db_id": "12753080"}, "annotations": {"Cell": {"regular cardiac myocyte": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3059, "key": "14e74820ac6baf63c8cdd2dec5ddc941"}, {"line": 15758, "relation": "negativeCorrelation", "evidence": "Furthermore, recent studies have demonstrated that age-related androgen depletion results in accumulation of beta-amyloid protein and thereby acts as a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 209, "key": "2db8d22a4f53ee94584151c0074d6448"}, {"line": 16631, "relation": "positiveCorrelation", "evidence": "Additionally, there was a significant positive correlation between OPN staining intensity and both amyloid-beta load (p(2) = 0.25; P < 0.05; n = 20) and aging (p(2) = 0.32; P < 0.01; n = 20) among all control and AD subjects.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3409, "key": "21282a419a3e5b8f092150ad38e19229"}, {"line": 16786, "relation": "decreases", "evidence": "beta-amyloid decreases detectable endothelial nitric oxide synthase in human erythrocytes: a role for membrane acetylcholinesterase.", "citation": {"db": "PubMed", "db_id": "22431227"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Endothelium": true}, "Cell": {"erythrocyte": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3124, "key": "2ef190fbc8e6b6551adce6cf36cfe4f0"}, {"line": 16855, "relation": "association", "evidence": "Herein, we hypothesize that a feedback signaling loop, consisted of Pin1, endothelial nitric oxide synthase (eNOS), and amyloid-beta (Abeta), may contribute to the interesting pathological phenomenon.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 2328, "target": 3124, "key": "a92bcedac421e17c7417a191346bea4f"}, {"line": 16881, "relation": "decreases", "evidence": "First, Pin1 inhibits the production of Abeta, and enhances the activity of eNOS. Second, Abeta and eNOS form a mutual inhibition system. Third, the well-balanced feedback signaling loop avoids the development of AD, HTN, and CAA by inhibiting the frequent pathological characteristics of these diseases, including Abeta deposition in cerebral microvessels and cerebral microbleeds.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3124, "key": "3272f7bd686a1c8c7d89c42d1dbe87d9"}, {"line": 16803, "relation": "decreases", "evidence": "Concurrently, Abeta alters erythrocyte cell morphology, decreases nitrites and nitrates levels, and affects membrane acetylcholinesterase activity.", "citation": {"db": "PubMed", "db_id": "22431227"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Cell": {"erythrocyte": true}, "Confidence": {"High": true}}, "source": 2328, "target": 315, "key": "6bcd2f50e6b707e6f542fd30f707e2c8"}, {"line": 16806, "relation": "decreases", "evidence": "Concurrently, Abeta alters erythrocyte cell morphology, decreases nitrites and nitrates levels, and affects membrane acetylcholinesterase activity.", "citation": {"db": "PubMed", "db_id": "22431227"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Cell": {"erythrocyte": true}, "Confidence": {"High": true}}, "source": 2328, "target": 314, "key": "dc0e2ea61a5f400ddcc1fc268aa29394"}, {"line": 16809, "relation": "association", "evidence": "Concurrently, Abeta alters erythrocyte cell morphology, decreases nitrites and nitrates levels, and affects membrane acetylcholinesterase activity.", "citation": {"db": "PubMed", "db_id": "22431227"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Cell": {"erythrocyte": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2244, "key": "9ce0abeeeabf246ddac094f01ff897c9"}, {"line": 24844, "relation": "association", "evidence": "At this point, inhibitors able to interact at the peripheral binding site are of particular relevance, as they might disrupt the interactions between the enzyme acetylcholinesterase and the beta-amyloid peptide. ", "citation": {"db": "PubMed", "db_id": "15544503"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2244, "key": "877e0c8eaca78a2d43dbd668cc77beb4"}, {"line": 16859, "relation": "association", "evidence": "Herein, we hypothesize that a feedback signaling loop, consisted of Pin1, endothelial nitric oxide synthase (eNOS), and amyloid-beta (Abeta), may contribute to the interesting pathological phenomenon.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 2328, "target": 3192, "key": "2bd3f64105f62d9054da69407931a93a"}, {"line": 17666, "relation": "negativeCorrelation", "evidence": "Up-regulation of P-glycoprotein reduces intracellular accumulation of beta amyloid: investigation of P-glycoprotein as a novel therapeutic target for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 2232, "key": "ed4f54044485b92119fe45c054419268"}, {"line": 17687, "relation": "negativeCorrelation", "evidence": "Also, fluorescent micrographs showed an inverse relationship between levels of P-gp expression and 5-carboxyfluorescein labelled Abeta (FAM-Abeta42) intracellular accumulation.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 2232, "key": "85aef1afd9936d3e6997eea09c827102"}, {"line": 17855, "relation": "positiveCorrelation", "evidence": "Here, we show that CCL4 mRNA and protein are overexpressed in the brains of APPswe/PS1ΔE9 (APP/PS1) double-transgenic mice, a model of cerebral amyloid deposition; expression was minimal in brains from nontransgenic littermates or single-mutant controls.", "citation": {"db": "PubMed", "db_id": "24607962"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Cerebrum": true}, "Species": {"10090": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2328, "target": 2458, "key": "467a638eb28f6b6a42e1273ac4080711"}, {"line": 18772, "relation": "association", "evidence": "The selective distribution of MMP-3 in the human brain suggests that MMP-3 might play an important role in the pathogenesis of AD, especially in the degradation of beta-amyloid protein.", "citation": {"db": "PubMed", "db_id": "10672313"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2328, "target": 3060, "key": "5227898c7db0092c2bb07cac94825804"}, {"line": 18888, "relation": "negativeCorrelation", "evidence": "Although the complete loss of tPA was developmentally fatal to Tg2576 mice, tPA-heterozygous Tg2576 mice expressed the more severe degenerative phenotypes than tPA wild-type Tg2576 mice, including abnormal and unhealthy growth, shorter life spans, significantly enhanced Abeta levels, and the deposition of more and larger amyloid plaques in the brain.", "citation": {"db": "PubMed", "db_id": "24126163"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3200, "key": "14260fc6f51b7ed5bb852c1fc31904e6"}, {"line": 18971, "relation": "increases", "evidence": "Assembled A beta is a potent stimulator of tissue-type plasminogen activator (tPA) in vitro.", "citation": {"db": "PubMed", "db_id": "10471309"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3200, "key": "39f91b90edc6a69986ec1ad5d9823ca7"}, {"line": 18990, "relation": "increases", "evidence": "Our results indicate that beta-sheet secondary structure of A beta, which can be promoted by plasmin cleavage, stimulates tPA activity.", "citation": {"db": "PubMed", "db_id": "10471309"}, "annotations": {"Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3200, "key": "e08c81547898ee56f77a3c7675bda151"}, {"relation": "partOf", "source": 2328, "target": 1241, "key": "028bfb22cdd19013854762746d14821a"}, {"line": 19191, "relation": "increases", "evidence": "We found that pRb exhibited increased levels of Ser795 phosphorylation in response to Abeta in the nucleus of PC12 cells and also in the nucleus of a subset of neurons during AD.", "citation": {"db": "PubMed", "db_id": "11640947"}, "annotations": {"CellLine": {"PC-12 cell": true}, "Disease": {"Alzheimer's disease": true}, "CellStructure": {"Cell Nucleus": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Subgraph": {"Retinoblastoma subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3301, "key": "e5efce27d5085a6d0af3ec4bc7181ebe"}, {"line": 19377, "relation": "decreases", "evidence": "Amyloid-beta inhibits thrombospondin 1 release from cultured astrocytes: effects on synaptic protein expression.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 3459, "key": "d472e8cd0bb8c05901b9a5f05453c984"}, {"line": 19401, "relation": "decreases", "evidence": "These findings suggest that Abeta-mediated reduction in astrocytic TSP-1 release, possibly related to oxidative stress, contributes to the loss of synaptophysin in neurons.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Neurons": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 3459, "key": "00dd69d39450fa71a68de4d4b0dcabf4"}, {"line": 19402, "relation": "decreases", "evidence": "These findings suggest that Abeta-mediated reduction in astrocytic TSP-1 release, possibly related to oxidative stress, contributes to the loss of synaptophysin in neurons.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2328, "target": 3438, "key": "ed382ada01141dfe5f97858e6758e701"}, {"line": 19941, "relation": "increases", "evidence": "We previously showed ECE-2 and ET-1 to be elevated in postmortem temporal cortex from AD patients, and ECE-2 expression and ET-1 release to be upregulated by Abeta42 in vitro.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Species": {"9606": true}}, "source": 2328, "target": 2651, "key": "001b20ab64c659d1447c0963e003f6c0"}, {"line": 19942, "relation": "increases", "evidence": "We previously showed ECE-2 and ET-1 to be elevated in postmortem temporal cortex from AD patients, and ECE-2 expression and ET-1 release to be upregulated by Abeta42 in vitro.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Species": {"9606": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 2653, "key": "594b85d5b91e7c732985de908812d5d7"}, {"line": 19959, "relation": "increases", "evidence": "In primary cultures of human brain endothelial cells, both Abeta40 and Abeta42 caused a significant increase in ET-1 release, the increase being particularly pronounced with Abeta40.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Cell": {"endothelial cell": true}, "Species": {"9606": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 2653, "key": "4c007915f645b865ffc9ae6fbb61ce71"}, {"line": 20048, "relation": "increases", "evidence": "Abeta42 causes increased neuronal production and release of endothelin-1 (ET-1), a potent vasoconstrictor, and upregulation of endothelin-converting enzyme-2 (ECE-2), the enzyme which cleaves ET-1 from its inactive precursor.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2653, "key": "efddcc133e92c239cfed92199470e0c2"}, {"relation": "partOf", "source": 2328, "target": 1693, "key": "6cfcd7234b62edfb039563a57e1fb4f4"}, {"line": 20041, "relation": "decreases", "evidence": "Abeta may contribute to the reduction in CBF in AD, as both Abeta42 and Abeta42 induce cerebrovascular dysfunction.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 832, "key": "9d25eca40da899a9e2070760e7d65e6a"}, {"line": 20049, "relation": "increases", "evidence": "Abeta42 causes increased neuronal production and release of endothelin-1 (ET-1), a potent vasoconstrictor, and upregulation of endothelin-converting enzyme-2 (ECE-2), the enzyme which cleaves ET-1 from its inactive precursor.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2652, "key": "446164436cc3429d04be803b2dc2a42a"}, {"relation": "partOf", "source": 2328, "target": 1231, "key": "210e98605d22de6a62bcfadac0b5da1b"}, {"line": 20630, "relation": "increases", "evidence": "We report here that Abeta activates the ER stress response factor X-box binding protein 1 (XBP1) in transgenic flies and in mammalian cultured neurons, yielding its active form, the transcription factor XBP1s. XBP1s shows neuroprotective activity in two different AD models, flies expressing Abeta and mammalian cultured neurons treated with Abeta oligomers.", "citation": {"db": "PubMed", "db_id": "21389082"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3537, "key": "0517fdf86129199c1311bebe34b0d844"}, {"line": 20741, "relation": "negativeCorrelation", "evidence": "In the brains of Abcg2 knockout mice, NF-kB activation as a result of Abcg2 deficiency increased Abeta deposition compared to controls. This result was further confirmed in vitro in N2a-695 cells where overexpression of ABCG2 significantly decreased the processing rate of APP and Abeta production as compared with controls.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2328, "target": 2239, "key": "f5abdc285f89363ed25e0310f21c8546"}, {"line": 20853, "relation": "increases", "evidence": "In this study we demonstrate that uPAR mRNA and protein expression is induced following incubation of human post-mortem brain-derived microglia with fibrillar amyloid beta (Abeta) peptide.", "citation": {"db": "PubMed", "db_id": "11814408"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Microglia": true}, "Species": {"9606": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 2328, "target": 3202, "key": "a149ede9dddb2bab67fb329b2e95b57b"}, {"line": 21118, "relation": "negativeCorrelation", "evidence": "Lower CSF ACE protein level, and to a lesser extent serum ACE protein level and CSF ACE activity, were associated with lower CSF Abeta, indicating more brain Abeta pathology; adjusted regression coefficients (B) (95% CI) per SD increase were 0.09 (0.04; 0.15), 0.06 (0.00; 0.12) and 0.05 (0.00; 0.11), respectively. Further, lower CSF ACE protein level was associated with lower CSF tau and ptau levels; adjusted B's (95% CI) per SD increase were 0.15 (0.06; 0.25) and 0.17 (0.10; 0.25), respectively.These results strengthen the hypothesis that ACE degrades Abeta.", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Anatomy": {"cerebrospinal fluid": true, "serum": true}, "Confidence": {"Medium": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2328, "target": 2243, "key": "9df6ddaae2b4d1af2a40b93e26c398b5"}, {"line": 21413, "relation": "increases", "evidence": "Amyloid beta protein activates PKC-delta and induces translocation of myristoylated alanine-rich C kinase substrate (MARCKS) in microglia.", "citation": {"db": "PubMed", "db_id": "11290384"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3238, "key": "62152c97599a3c776c0dcb6f6abaa5db"}, {"line": 21418, "relation": "increases", "evidence": "We found that the kinase activity of PKC-delta but not that of PKC-alpha or -epsilon is increased by stimulation of microglia with Abeta, with a striking tyrosine phosphorylation of PKC-delta.", "citation": {"db": "PubMed", "db_id": "11290384"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2328, "target": 3242, "key": "2a9fd3f664bd2d60b1f383bb5921e05e"}, {"line": 21431, "relation": "increases", "evidence": "Abeta induced translocation of MARCKS from the membrane fraction to the cytosolic fraction.", "citation": {"db": "PubMed", "db_id": "11290384"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "CellStructure": {"Cell Membrane": true, "Cytosol": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Cytosol"}}}, "source": 2328, "target": 3041, "key": "d58cdcce0bb7c857d853c3add701a504"}, {"line": 21438, "relation": "increases", "evidence": "Taken together with our previous observations that Abeta-induced phosphorylation of MARCKS and chemotaxis of microglia are inhibited by either tyrosine kinase or PKC inhibitors, our results provide evidence that Abeta induces phosphorylation and translocation of MARCKS through the tyrosine kinase-PKC-delta signaling pathway in microglia.", "citation": {"db": "PubMed", "db_id": "11290384"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2328, "target": 3042, "key": "285f7b93d8e9f9981d83d14b55348b24"}, {"line": 22013, "relation": "association", "evidence": "Several of the Abeta42/43 -increasing mutants severely impaired the cleavages of Notch1 and CD44 substrates, which were not affected by any other L383 mutation.", "citation": {"db": "PubMed", "db_id": "23237322"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2476, "key": "5a8af2a9f9e10750faaf33a83e07d8d2"}, {"line": 22021, "relation": "association", "evidence": "Several of the Abeta42/43 -increasing mutants severely impaired the cleavages of Notch1 and CD44 substrates, which were not affected by any other L383 mutation.", "citation": {"db": "PubMed", "db_id": "23237322"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3126, "key": "e1544089250edc383bd5f0dd0a9f42c1"}, {"line": 22151, "relation": "increases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Species": {"10090": true}}, "source": 2328, "target": 3623, "key": "4f93addc91b4c9630d01c45a8aadc5fc"}, {"line": 22220, "relation": "decreases", "evidence": "Montelukast also suppressed the Abeta_42-induced Bcl-2 decrease and Caspase-3 activation. Therefore, montelukast may exhibit a potent, anti-apoptotic effect, which contributes to the blockade of apoptotic responses induced by Abeta_42.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2328, "target": 3597, "key": "77b61c9a178ee1fda3130098bf20d5e9"}, {"line": 22221, "relation": "increases", "evidence": "Montelukast also suppressed the Abeta_42-induced Bcl-2 decrease and Caspase-3 activation. Therefore, montelukast may exhibit a potent, anti-apoptotic effect, which contributes to the blockade of apoptotic responses induced by Abeta_42.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3600, "key": "2e162b98706aff76ebb3592a54b424e5"}, {"line": 22664, "relation": "increases", "evidence": "In Alzheimer's disease (AD), affected neurons accumulate beta amyloid protein, components of which can induce mouse microglia to express the high-output isoform of nitric oxide synthase (NOS2) in vitro. Products of NOS2 can be neurotoxic. In mice, NOS2 is normally suppressed by transforming growth factor beta 1 (TGF-beta 1). Expression of TGF-beta 1 is decreased in brains from AD patients, a situation that might be permissive for accumulation of NOS2.", "citation": {"db": "PubMed", "db_id": "8879214"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3689, "key": "02e3764d3e123a714abbc000db384c6c"}, {"line": 24093, "relation": "negativeCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2871, "key": "f54d9572180751e7a206fb7cc0f0a2d7"}, {"line": 36826, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2871, "key": "a806ad6c4c9865709b20a14486e2afc3"}, {"line": 24096, "relation": "positiveCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3861, "key": "1e9ee60736856bb4ca8bb9cd94abb22e"}, {"relation": "partOf", "source": 2328, "target": 1027, "key": "c5087ddb37ebfd83ba75664add470f79"}, {"relation": "partOf", "source": 2328, "target": 1037, "key": "d6f1888226e66aa38b151e5fdcf4548b"}, {"relation": "partOf", "source": 2328, "target": 1039, "key": "2ffbd082633c319e38e86d6f8cd6a074"}, {"line": 25066, "relation": "increases", "evidence": "The receptor for advanced glycation end products (RAGE) is a cell-bound receptor of the immunoglobulin superfamily which may be activated by a variety of proinflammatory ligands including advanced glycoxidation end products, S100/calgranulins, high mobility group box 1, and amyloid beta-peptide.", "citation": {"db": "PubMed", "db_id": "16842191"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2271, "key": "907cef0d8eada2fb979952e96d3628d0"}, {"relation": "partOf", "source": 2328, "target": 1227, "key": "68a5fe7c34e7e53861fc83f845a987c0"}, {"relation": "partOf", "source": 2328, "target": 1067, "key": "913d64c1c3293c080a4f4bf014eb5a3e"}, {"line": 25777, "relation": "association", "evidence": "Particularly, it has been shown that agrin is associated with the pathological lesions of Alzheimer's disease (AD) and may contribute to the formation of beta-amyloid (Abeta) plaques in AD", "citation": {"db": "PubMed", "db_id": "16037493"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}}, "source": 2328, "target": 2273, "key": "12e6dae5d81ed5670787110e16a861e7"}, {"line": 26181, "relation": "association", "evidence": "Moreover, our recent studies further demonstrated that (1) apoE mediates sulfatide depletion in amyloid-beta precursor protein transgenic mice; (2) sulfatides enhance amyloid beta (Abeta) peptides binding to apoE-associated particles; (3) Abeta42 content notably correlates with sulfatide content in CSF;(4) sulfatides markedly enhance the uptake of Abeta peptides; and (5) abnormal sulfatide-facilitated Abeta uptake results in the accumulation of Abeta in lysosomes.", "citation": {"db": "PubMed", "db_id": "20052565"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 116, "key": "99d4e5c5773a825695181b8101f275a3"}, {"relation": "partOf", "source": 2328, "target": 900, "key": "7fb8770727fb3de0562a224db402e10e"}, {"relation": "partOf", "source": 2328, "target": 1246, "key": "23058a15a2973856863d25d6267370f2"}, {"line": 28733, "relation": "association", "evidence": "These results suggest that the breakdown of HRD1-mediated ERAD causes Abeta generation and ER stress, possibly linked to AD.", "citation": {"db": "PubMed", "db_id": "20237263"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 835, "key": "7cc940d7f5965faa8dbebb44c70afcd3"}, {"relation": "partOf", "source": 2328, "target": 1247, "key": "ed730a15afbeff95463a2088c5eebd91"}, {"line": 29273, "relation": "association", "evidence": "CD40 ligation in the presence of Abeta(1-42) leads to adaptive activation of microglia, as evidenced by increased co-localization of MHC class II with Abeta. ", "citation": {"db": "PubMed", "db_id": "15688347"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 2224, "key": "df16e9fca1adc182e53bd4ff181cbd73"}, {"line": 29388, "relation": "decreases", "evidence": "We further observed that treatment with Abeta(42) decreased cellular N-cadherin expression through NMDA receptors accompanied by increased phosphorylation of both p38 MAPK and Tau in murine primary neurons. ", "citation": {"db": "PubMed", "db_id": "21177868"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2483, "key": "3a17a5eb279bb62b510d7d3a427cb70b"}, {"line": 29389, "relation": "increases", "evidence": "We further observed that treatment with Abeta(42) decreased cellular N-cadherin expression through NMDA receptors accompanied by increased phosphorylation of both p38 MAPK and Tau in murine primary neurons. ", "citation": {"db": "PubMed", "db_id": "21177868"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2999, "key": "8b4d6775c983e752dc6578cd0a19ea4b"}, {"line": 29390, "relation": "increases", "evidence": "We further observed that treatment with Abeta(42) decreased cellular N-cadherin expression through NMDA receptors accompanied by increased phosphorylation of both p38 MAPK and Tau in murine primary neurons. ", "citation": {"db": "PubMed", "db_id": "21177868"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3015, "key": "94a0d85c397a2ebf7a14d31b4a995a5f"}, {"line": 30301, "relation": "association", "evidence": "A synthetic peptide, S3 peptide, which acts as a specific competitor for ADF/cofilin phosphorylation by LIMK1, inhibited fAbeta-induced ADF/cofilin phosphorylation, preventing actin filament repmodeling and neuronal degeneration, indicating the involvement of LIMK1 in Abeta-induced neuronal degeneration in vitro.", "citation": {"db": "PubMed", "db_id": "16775141"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2328, "target": 2963, "key": "37ffb83c15e359d35f79e65867ac9637"}, {"line": 30384, "relation": "increases", "evidence": "In SH-SY5Y cells, results showed that Abeta42 induced a large increase in phosphorylated PKR and FADD levels and a physical interaction between PKR and FADD in the nucleus, also observed in the cortex of APP(SL)PS1 KI mice.", "citation": {"db": "PubMed", "db_id": "19889624"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2663, "key": "66be8cd3d0a9425cb7537d7567bb3b05"}, {"line": 30385, "relation": "increases", "evidence": "In SH-SY5Y cells, results showed that Abeta42 induced a large increase in phosphorylated PKR and FADD levels and a physical interaction between PKR and FADD in the nucleus, also observed in the cortex of APP(SL)PS1 KI mice.", "citation": {"db": "PubMed", "db_id": "19889624"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2688, "key": "769f4267a19701b0c0fccdf99c8cb205"}, {"line": 30954, "relation": "decreases", "evidence": "Intracerebroventricular administration of Abeta1-42 downregulated p-STAT3 whereas passive immunization with anti-Abeta antibody conversely restored hippocampal p-STAT3 levels in Tg2576 mice, paralleling the decrease in the brain Abeta burden. Abeta1-42 consistently pmodulated p-STAT3 levels in primary neurons.", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Serotonergic subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3426, "key": "5a0437fbcc9a5263a0b7fe9b7ef1b8b7"}, {"relation": "partOf", "source": 2328, "target": 1234, "key": "12c56072479e13473254c02b317f3671"}, {"line": 31168, "relation": "association", "evidence": "Recombinant LRP cluster IV ( LRP-IV ) bound Abeta in plasma in mice and Alzheimer 's disease-affected humans with compromised sLRP-mediated AAbetabinding , and reduced Abeta-related pathology and dysfunction in a mouse pmodel of Alzheimer disease , suggesting that LRP-IV can effectively replace native sLRP and clear AAbeta", "citation": {"db": "PubMed", "db_id": "17694066"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2328, "target": 2970, "key": "7f0b09aa3c5de5e0f2023877aadb83e4"}, {"relation": "partOf", "source": 2328, "target": 1239, "key": "80b18d84f56a34e51d2c4f33ebd1e1c1"}, {"line": 31260, "relation": "association", "evidence": "Peptidylarginine deiminase (PAD II) is an enzyme that uses arginine as a substrate and we now show that PAD II not only binds with the peptides Abeta(1-40), Abeta(22-35), Abeta(17-28), Abeta(25-35) and Abeta(32-35) but assists in the proteolytic degradation of these peptides with the concomitant formation of insoluble fibrils.", "citation": {"db": "PubMed", "db_id": "20224908"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2328, "target": 3160, "key": "4aa13257bff29b4f307fa3fdf1751121"}, {"relation": "partOf", "source": 2328, "target": 1244, "key": "b97cd64cadb2d354e5ba9baf20db1db4"}, {"relation": "partOf", "source": 2328, "target": 1240, "key": "d920741479e3d9f3d2eca41e1670c726"}, {"relation": "partOf", "source": 2328, "target": 1236, "key": "ff21c2861f4cd7f2b02f3a052f0c41cb"}, {"relation": "partOf", "source": 2328, "target": 1237, "key": "346eee9f7f111576c71bd7abeadd882a"}, {"relation": "partOf", "source": 2328, "target": 1235, "key": "362957a2fd619fb35a0fc2bffdc06e17"}, {"relation": "partOf", "source": 2328, "target": 1242, "key": "a02779a302f8fdbc91be522fbb2db979"}, {"line": 33956, "relation": "association", "evidence": "Our recent studies demonstrated that alpha 1-antichymotrypsin (ACT), a serine protease inhibitor, was associated with the beta-protein in the brain amyloid deposits of Alzheimer's disease, aged human controls and aged monkeys, suggesting a role for the inhibitor in the amyloid deposition.", "citation": {"db": "PubMed", "db_id": "2190106"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3350, "key": "6afb4ede9fe60a61b49480aba031455e"}, {"line": 43219, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3350, "key": "ec2785ed2b566c6bb7f27e00b44d6cbe"}, {"line": 34843, "relation": "association", "evidence": "Abnormal neuritic sprouting is a prominent feature of Alzheimer's disease (AD), and the Thy-1 glycoprotein has a role in neurite growth in culture. We therefore investigated the distribution of Thy-1 immunoreactivity in the hippocampus of normal elderly patients and of AD patients. Some Thy-1-immunoreactive dystrophic neurites entered senile plaques. The data confirm that there is extensive growth of abnormal neurites in AD and suggest that Thy-1 is involved in this process.", "citation": {"db": "PubMed", "db_id": "1347079"}, "annotations": {"Subgraph": {"Cell-cell communication subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 2328, "target": 3461, "key": "1bed31bf08880a5b6624f0fcdc9c0ed7"}, {"line": 34998, "relation": "decreases", "evidence": "Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 2328, "target": 3513, "key": "72d52a83ef06af6eb212966614e98e20"}, {"line": 35037, "relation": "decreases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3147, "key": "509e74baaa58b6b18eee0f9e0e5cd081"}, {"line": 35038, "relation": "decreases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2397, "key": "e5a56143beb5b845fb8872359b9c311a"}, {"line": 35141, "relation": "decreases", "evidence": "Utilizing a novel microfluidic culture chamber, we demonstrate that Abeta oligomers compromise BDNF-mediated retrograde transport by impairing endosomal vesicle velocities, resulting in impaired downstream signaling driven by BDNF/TrkB, including ERK5 activation, and CREB-dependent gene regulation. Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2397, "key": "01e1584bd7e060ac865afafe404c7c3d"}, {"line": 39202, "relation": "decreases", "evidence": "We found that Abeta1-42 significantly decreases the expression of brain-derived neurotrophic factor (BDNF) in DCs derived from AD patients but not from control subjects. Thus, possibly due to their Abeta-induced reduction of neurotrophic support to neurons, DCs from AD patients might contribute to brain damage by playing a part in Abeta-dependent neuronal toxicity.", "citation": {"db": "PubMed", "db_id": "23578995"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true}}, "source": 2328, "target": 2397, "key": "422c81a1a9c686eb320b21175e8e3d6c"}, {"line": 39713, "relation": "decreases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chaperone subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 2397, "key": "c9c760803cabed148b92964dad819ddd"}, {"line": 43833, "relation": "increases", "evidence": "We found that frontal CC regions were preserved with respect to the posterior ones in aMCI; in these individuals significant correlations were seen between DTI-derived metrics in frontal-parietal CC areas and Abeta42-stimulated BDNF-producing CD4+ T lymphocytes and PDL-1-expressing CD14+ cells.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"MeSHAnatomy": {"T-Lymphocytes": true}, "Subgraph": {"T cells signaling": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2397, "key": "32cbba9c85e756cd2f4ff21b6fb0a4e4"}, {"line": 35039, "relation": "directlyDecreases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 1295, "key": "aa02016d53a79d7fdf610979b8cc15e4"}, {"line": 35043, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3812, "key": "22d22ad693b92faac3c7c79ab142f647"}, {"line": 35136, "relation": "decreases", "evidence": "Utilizing a novel microfluidic culture chamber, we demonstrate that Abeta oligomers compromise BDNF-mediated retrograde transport by impairing endosomal vesicle velocities, resulting in impaired downstream signaling driven by BDNF/TrkB, including ERK5 activation, and CREB-dependent gene regulation. Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 2328, "target": 4030, "key": "4eea72b78096a4cb7300b4797981bc24"}, {"relation": "partOf", "source": 2328, "target": 1238, "key": "70945bd2dc423968357ae670d014ca33"}, {"line": 35177, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 2328, "target": 3118, "key": "715cd49c883312bd39d4621725a6249b"}, {"line": 35183, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3112, "key": "85456db64c21108fa85ab1d214307002"}, {"line": 35184, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 2328, "target": 1473, "key": "1d77016b002a63fd59214f1a5e366c50"}, {"relation": "partOf", "source": 2328, "target": 1692, "key": "1e6aa74254af5c9581d616b4f05bb770"}, {"line": 35332, "relation": "regulates", "evidence": "Genetically, AD is linked to mutations in few proteins amyloid precursor protein (APP) and presenilin 1 and 2 (PS1 and PS2). The molecular mechanisms underlying neurodegeneration in AD as well as the physiological function of APP are not yet known. A recent theory has proposed that APP and PS1 modulate intracellular signals to induce cell-cycle abnormalities responsible for neuronal death and possibly amyloid deposition.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "source": 2328, "target": 503, "key": "b84fcea162ec852044a11a120970220b"}, {"line": 35553, "relation": "increases", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2794, "key": "b7dacd5142702e0d87137427445ccb41"}, {"line": 35656, "relation": "increases", "evidence": "Our results show that oligomeric Abeta specifically induces CaN activity and promotes CaN-dependent CREB and Bcl-2 Asociated death Protein (BAD) dephosphorylation and cell death", "citation": {"db": "PubMed", "db_id": "18782350"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 872, "key": "7fe8e97938ae7e18cb5e9f92e9c16feb"}, {"line": 35896, "relation": "decreases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 483, "key": "c6595a6fa957583430994867fc72fc3f"}, {"line": 36317, "relation": "increases", "evidence": "Cellular uptake and degradation by glial cells is one means by which ABeta¸ may be cleared from the brain. In the current study, we demonstrate that modulating levels of the low-density lipoprotein receptor (LDLR), a cell surface receptor that regulates the amount of apolipoprotein E (apoE) in the brain, altered both the uptake and degradation of ABeta¸ by astrocytes. Deletion of LDLR caused a decrease in ABeta¸ uptake, while increasing LDLR levels significantly enhanced both the uptake and clearance of ABeta¸. Increasing LDLR levels also enhanced the cellular degradation of ABeta¸ and facilitated the vesicular transport of ABeta¸ to lysosomes. Despite the fact that LDLR regulated the uptake of apoE by astrocytes, we found that the effect of LDLR on ABeta¸ uptake and clearance occurred in the absence of apoE. Finally, we provide evidence that ABeta¸ can directly bind to LDLR, suggesting an interaction between LDLR and ABeta¸ could be responsible for LDLR-mediated ABeta¸ uptake. Therefore, these results identify LDLR as a receptor that mediates ABeta¸ uptake and clearance by astrocytes, and provide evidence that increasing glial LDLR levels may promote ABeta¸ degradation within the brain", "citation": {"db": "PubMed", "db_id": "22383525"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "COP-Coated Vesicles"}, "toLoc": {"namespace": "MESH", "name": "Lysosomes"}}}, "object": {"modifier": "Degradation"}, "source": 2328, "target": 2328, "key": "22e08ee83f475c2a04f30f821c9b19d6"}, {"relation": "partOf", "source": 2328, "target": 1233, "key": "bb8fe52edb100c20d0a75865364704eb"}, {"line": 36337, "relation": "increases", "evidence": "The proposed amyloid-like conformation of peptide/protein hormones in secretory granules may explain the processes of granule formation including hormone selection, membrane surrounding as well as the inert hormone storage, and subsequently the release of hormones from the granules: It is proposed that in the Golgi, amyloid aggregation of the (pro)hormone is initiated spontaneously above a critical (pro)hormone concentration or/and in presence of helper molecules such as GAGs in parallel to a possible prohormone processing. Alternatively, since the prohormone may aggregate less into an amyloid entity than its hormone counterpart, the prohormone processing at critical hormone concentrations may initiate the aggregation. Since the formation of amyloid fibrils is amino acid sequence-specific, the initiated amyloid aggregation of the (pro)hormone is selective yielding granule cores composed of specific hormones only. Specific coaggregation of some hormones may be possible since some amyloid proteins are able to cross-seed. The amyloid aggregation sorts thereby the protein/peptide hormones into secretory granule cores, concentrates them to the highest density possible and excludes non-aggregation-prone constitutively secreted proteins. During the aggregation process the hormone amyloids get surrounded by membrane, separate from the golgi and form mature granules. The membrane attraction may be spontaneous since membrane binding seems to be an inherent property of amyloid aggregates. Alternatively, since the cross-ß sheet represents a single structural epitope it may serve as a possible recognition motive of an unknown granule-recruiting membrane protein. Once the secretory granules are formed they can be stored for long durations since the amyloid entity provides a very stable depot. Upon signaling, secretory granules are secreted and the cross-ß sheet structure of the amyloid enables a controlled release of monomeric, functional hormone, which might be supported by chaperones.", "citation": {"db": "PubMed", "db_id": "19541956"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 572, "key": "2b7fc827fa12695bd832ea04c0097ea1"}, {"line": 36370, "relation": "decreases", "evidence": "ABeta¸ was proposed as a regulator of ion channel function [27] and as essential for neuronal health . ABeta¸ is secreted from neurons in response to synaptic activity and that ABeta¸, in turn, down regulates synaptic transmission [29]. This negative feedback loop could operate as a physiological homeostatic mechanism to limit levels of neuronal activity", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 827, "key": "828a7ca5ba2c678159838ff1d8c3c5d7"}, {"line": 36581, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 820, "key": "a0a3854c3717366949dbb1de4da035b3"}, {"line": 37034, "relation": "increases", "evidence": "Dysregulation of intracellular calcium signaling has been implicated in the pathogenesis of Alzheimer’s disease [150]. ABeta¸ is known to act through multiple targets [151] including Ca2+ channels and various receptors in membranes. Synthetic ABeta¸ binds to the calcium permeable nAChRs with high affinity [152]. ABeta¸42 administered in the low picomolar range activates nAChRs at presynaptic nerve endings of synaptosomes [83, 153]. Under normal conditions, activation of nAChRs is necessary for the ABeta¸-induced increase in synaptic plasticity and memory [23]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the peptide with the nAChR. In addition, ABeta¸ enhances transmitter release by transient increase of glutamate release from the presynaptic terminal that results from brief periods of high frequency stimulation with Ca2+ buildup within the terminal that triggers mechanisms of short-term synaptic plasticity [154].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 820, "key": "356e37d37449cfb0d2a5760044ffa0e5"}, {"line": 36660, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2910, "key": "1648183d8525fb378fa245d3705a2573"}, {"line": 36661, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2915, "key": "b3dbf9f5a38895fd4a10046c484dae3a"}, {"line": 36662, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3355, "key": "f9224119717d485cdc4010ac0c1c80ab"}, {"line": 36663, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3357, "key": "28edf77e240c994d6347147956da0739"}, {"line": 36664, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3359, "key": "4b384ebd0a7d18adf9a2c0932175753a"}, {"line": 36665, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3361, "key": "0f1c7ff6edb386ed0585a6c268e1f7b6"}, {"line": 36878, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2796, "key": "fe8b0cde55d2720202cc9e7f5dc55ac2"}, {"line": 36904, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2163, "key": "5217b6c708ce72f9fe88191e6b2bdcef"}, {"line": 36926, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 448, "key": "2352449457c9196f5e0ad4aecb9afea4"}, {"line": 36938, "relation": "increases", "evidence": "Protein kinase C: PKC is part of a multigene family of serine-threonine kinases central to many signal transduction pathways [138] with a prominent role in memory [139]. It is likely that ABeta¸-induced increases in cytosolic Ca2+ signals are transmitted to PKC for PKC-mediated transcriptional activation. In addition, PKC activates ERK by interacting with Ras or Raf-1 [140] to initiate CREB phosphorylation. While PKC levels decline in AD [141], their activation restores K+ channel function in cells from AD patients [142]. In addition, activation of PKC directly or indirectly enhances the a-processing cleavage of APP [143].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 715, "key": "002dae0da4758219096e059df8fdf335"}, {"relation": "partOf", "source": 2328, "target": 1226, "key": "9fb5393185e7b806ef5025c51f2c38ad"}, {"line": 36994, "relation": "increases", "evidence": "Dysregulation of intracellular calcium signaling has been implicated in the pathogenesis of Alzheimer’s disease [150]. ABeta¸ is known to act through multiple targets [151] including Ca2+ channels and various receptors in membranes. Synthetic ABeta¸ binds to the calcium permeable nAChRs with high affinity [152]. ABeta¸42 administered in the low picomolar range activates nAChRs at presynaptic nerve endings of synaptosomes [83, 153]. Under normal conditions, activation of nAChRs is necessary for the ABeta¸-induced increase in synaptic plasticity and memory [23]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the peptide with the nAChR. In addition, ABeta¸ enhances transmitter release by transient increase of glutamate release from the presynaptic terminal that results from brief periods of high frequency stimulation with Ca2+ buildup within the terminal that triggers mechanisms of short-term synaptic plasticity [154].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2514, "key": "4751a1abf624ed052f341c0a49727f4b"}, {"line": 37030, "relation": "increases", "evidence": "Dysregulation of intracellular calcium signaling has been implicated in the pathogenesis of Alzheimer’s disease [150]. ABeta¸ is known to act through multiple targets [151] including Ca2+ channels and various receptors in membranes. Synthetic ABeta¸ binds to the calcium permeable nAChRs with high affinity [152]. ABeta¸42 administered in the low picomolar range activates nAChRs at presynaptic nerve endings of synaptosomes [83, 153]. Under normal conditions, activation of nAChRs is necessary for the ABeta¸-induced increase in synaptic plasticity and memory [23]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the peptide with the nAChR. In addition, ABeta¸ enhances transmitter release by transient increase of glutamate release from the presynaptic terminal that results from brief periods of high frequency stimulation with Ca2+ buildup within the terminal that triggers mechanisms of short-term synaptic plasticity [154].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 688, "key": "5a65ff3235121cf82db84540087230c3"}, {"line": 38423, "relation": "increases", "evidence": "Moreover, two groups recently reported that low doses (picomolar) of Ab can positively modulate synaptic plasticity and memory by increasing long-term potentiation (Morley et al. 2008; Puzzo et al. 2008), revealing a novel physiological function of Ab under normal conditions.", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 2328, "target": 688, "key": "18fb42b27aaafa7c8a81f9075e5d3f23"}, {"line": 37038, "relation": "increases", "evidence": "Dysregulation of intracellular calcium signaling has been implicated in the pathogenesis of Alzheimer’s disease [150]. ABeta¸ is known to act through multiple targets [151] including Ca2+ channels and various receptors in membranes. Synthetic ABeta¸ binds to the calcium permeable nAChRs with high affinity [152]. ABeta¸42 administered in the low picomolar range activates nAChRs at presynaptic nerve endings of synaptosomes [83, 153]. Under normal conditions, activation of nAChRs is necessary for the ABeta¸-induced increase in synaptic plasticity and memory [23]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the peptide with the nAChR. In addition, ABeta¸ enhances transmitter release by transient increase of glutamate release from the presynaptic terminal that results from brief periods of high frequency stimulation with Ca2+ buildup within the terminal that triggers mechanisms of short-term synaptic plasticity [154].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 123, "key": "155cfc8fa231c357af1f5568480b4e50"}, {"line": 37075, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 2328, "target": 3555, "key": "4d1151e65be2684cd6c5f7dfa2bd0c75"}, {"line": 37079, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2328, "target": 2413, "key": "5d5cab27092d1f8baec44d5ddca29a41"}, {"line": 37081, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2328, "target": 2414, "key": "9ed0ac4b48b13a040c509f14c412db33"}, {"line": 37083, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2328, "target": 2415, "key": "83069b2246fe93fca4bdda6afa5d4ba4"}, {"line": 37085, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2328, "target": 2416, "key": "cb156817293e4887f17ad956ec7c7764"}, {"line": 37087, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2328, "target": 2417, "key": "bce34262c177325c5332f45fa10d7058"}, {"line": 37089, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2328, "target": 2419, "key": "b1a0dd26a4b13461bc02ed82401ffb04"}, {"line": 37091, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2328, "target": 2420, "key": "01dd1bf8bc22b3f76442e05534e5b291"}, {"line": 37114, "relation": "association", "evidence": "Cholesterol transport: High cholesterol levels have been linked to overproduction of ABeta¸ and are a risk factor for AD. One of the physiological functions of ABeta¸ has been suggested to control cholesterol transport [167]. Prevalence of AD is reduced among people treated with inhibitors of cholesterol biosynthesis, statins [168, 169] and animal studies support these results [170]. In vitro and in vivo studies have shown that cholesterol modulates APP processing and affects APP mRNA expression [171]. Another mechanism is the increased binding of ABeta¸ to ApoE4 over non-E4 alleles. ApoE is a lipid and cholesterol transport protein responsible for the efflux of cholesterol from neurons to form stable complexes both in vitro and in vivo [172]. Allele ApoE4 is a major risk factor in AD [173]. This relationship might promote synaptogenesis, since in vitro studies have demonstrated that cholesterol released by astroglia increases synaptogenesis [174, 175] with resulting modulation of spike rates [176]. Together, this evidence indicates that one of the physiological functions of APP might be to control cholesterol movement across neuronal membranes [167].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 530, "key": "cc82af95e613ccdd3e528a98e09221f1"}, {"line": 37130, "relation": "decreases", "evidence": "Antioxidant: The three histidine residues in ABeta¸ control the redox activity of iron, indicating that ABeta¸ is likely to be an important antioxidant. ABeta¸40 at 5 µM was found to protect primary neuronal cultures from the neurotoxicity of iron [94]. Nakamura et al. [177] found that ABeta¸40 or ABeta¸42 inhibits Fe3+ or Cu+2-catalyzed ascorbate oxidation and hydroxyl radical generation. Nanomolar concentrations of ABeta¸ can block neuronal apoptosis following oxidative damage, which suggests that ABeta¸ has a protective role against oxidative stress [178] and is essential for neuronal survival [28, 94]. Monomeric ABeta¸40 was found to protect neurons cultured in a medium containing 1.5 µM Fe2+ without antioxidant molecules. However, the antioxidant protection of monomeric ABeta¸40 depends on the type of oxidant used. ABeta¸40 inhibits cultured neurondeath caused by Cu2+, Fe2+, and Fe3+ but does not protect neurons against H2O2-induced damage [94]. In cerebral cortical neuronal cultures, monomeric ABeta¸40 inhibits the reduction of Fe3+ induced by vitamin C and the generation of superoxides and prevents lipid peroxidation induced by Fe2+ [94]. Moreover, monomeric forms of ABeta¸42 also exhibited antioxidant and neuroprotective effects. However, oligomeric or aggregated ABeta¸40 and ABeta¸42 were devoid of such antioxidant activity and their neuroprotective activity was demolished. Thus, depriving neurons of the protective activity of ABeta¸42 monomers may also be an important factor in neurodegeneration [97]. These findings provide novel insights on a normal antioxidant role of ABeta¸ and indicate that monomeric ABeta¸ protects neurons by quenching metal-inducible oxygen radical generation and thereby inhibits neurotoxicity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true, "Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 138, "key": "31c76fed8e4d212b61c77856b4bec1bd"}, {"line": 37131, "relation": "decreases", "evidence": "Antioxidant: The three histidine residues in ABeta¸ control the redox activity of iron, indicating that ABeta¸ is likely to be an important antioxidant. ABeta¸40 at 5 µM was found to protect primary neuronal cultures from the neurotoxicity of iron [94]. Nakamura et al. [177] found that ABeta¸40 or ABeta¸42 inhibits Fe3+ or Cu+2-catalyzed ascorbate oxidation and hydroxyl radical generation. Nanomolar concentrations of ABeta¸ can block neuronal apoptosis following oxidative damage, which suggests that ABeta¸ has a protective role against oxidative stress [178] and is essential for neuronal survival [28, 94]. Monomeric ABeta¸40 was found to protect neurons cultured in a medium containing 1.5 µM Fe2+ without antioxidant molecules. However, the antioxidant protection of monomeric ABeta¸40 depends on the type of oxidant used. ABeta¸40 inhibits cultured neurondeath caused by Cu2+, Fe2+, and Fe3+ but does not protect neurons against H2O2-induced damage [94]. In cerebral cortical neuronal cultures, monomeric ABeta¸40 inhibits the reduction of Fe3+ induced by vitamin C and the generation of superoxides and prevents lipid peroxidation induced by Fe2+ [94]. Moreover, monomeric forms of ABeta¸42 also exhibited antioxidant and neuroprotective effects. However, oligomeric or aggregated ABeta¸40 and ABeta¸42 were devoid of such antioxidant activity and their neuroprotective activity was demolished. Thus, depriving neurons of the protective activity of ABeta¸42 monomers may also be an important factor in neurodegeneration [97]. These findings provide novel insights on a normal antioxidant role of ABeta¸ and indicate that monomeric ABeta¸ protects neurons by quenching metal-inducible oxygen radical generation and thereby inhibits neurotoxicity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true, "Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 101, "key": "206d1c8137a5e35b18ad4181395ab1f9"}, {"line": 37132, "relation": "decreases", "evidence": "Antioxidant: The three histidine residues in ABeta¸ control the redox activity of iron, indicating that ABeta¸ is likely to be an important antioxidant. ABeta¸40 at 5 µM was found to protect primary neuronal cultures from the neurotoxicity of iron [94]. Nakamura et al. [177] found that ABeta¸40 or ABeta¸42 inhibits Fe3+ or Cu+2-catalyzed ascorbate oxidation and hydroxyl radical generation. Nanomolar concentrations of ABeta¸ can block neuronal apoptosis following oxidative damage, which suggests that ABeta¸ has a protective role against oxidative stress [178] and is essential for neuronal survival [28, 94]. Monomeric ABeta¸40 was found to protect neurons cultured in a medium containing 1.5 µM Fe2+ without antioxidant molecules. However, the antioxidant protection of monomeric ABeta¸40 depends on the type of oxidant used. ABeta¸40 inhibits cultured neurondeath caused by Cu2+, Fe2+, and Fe3+ but does not protect neurons against H2O2-induced damage [94]. In cerebral cortical neuronal cultures, monomeric ABeta¸40 inhibits the reduction of Fe3+ induced by vitamin C and the generation of superoxides and prevents lipid peroxidation induced by Fe2+ [94]. Moreover, monomeric forms of ABeta¸42 also exhibited antioxidant and neuroprotective effects. However, oligomeric or aggregated ABeta¸40 and ABeta¸42 were devoid of such antioxidant activity and their neuroprotective activity was demolished. Thus, depriving neurons of the protective activity of ABeta¸42 monomers may also be an important factor in neurodegeneration [97]. These findings provide novel insights on a normal antioxidant role of ABeta¸ and indicate that monomeric ABeta¸ protects neurons by quenching metal-inducible oxygen radical generation and thereby inhibits neurotoxicity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true, "Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 729, "key": "84f0467648649cf6ef87eeb363da8688"}, {"line": 37142, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2776, "key": "b41168ee81ece9a7ecb9602f0de399a7"}, {"line": 37143, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2779, "key": "613c248aa6c60bdd65b11cf1da6d47b2"}, {"line": 37144, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2781, "key": "ff750e2f66d3f36a5a189a2a205bd5fa"}, {"line": 37145, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2782, "key": "04ba3e6af330a6e1612a6d437fd4ada5"}, {"line": 37146, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2783, "key": "8573f99212a4ad942366ead7c09057e6"}, {"line": 37147, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 2784, "key": "05d8a330761b8ecc3e87fd74e9f642ea"}, {"line": 37148, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3214, "key": "4e783a805f0cefff46d18334a48a7193"}, {"line": 37690, "relation": "increases", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2328, "target": 649, "key": "1fbab2106d372259104cab0e76bbc8ff"}, {"line": 38045, "relation": "association", "evidence": "Two recent studies (Lauren et al., 2009; Nikolaev et al., 2009) now connect the physiological and pathological functions of APP processing products. Lauren et al. show that ABeta¸42 binds to the cellular prion protein (PrP), which itself can cause neuropathology when misfolded. In a separate study, Nikolaev et al. report that the N-terminal fragment of APP (N-APP) interacts with death receptor 6 (DR6), resulting in pruning of axons and neurons during development of the central nervous system (CNS).These studies suggest that APP processing constitutes a complex signaling center that serves multiple physiological functions that could trigger pathological events when deregulated during disease.", "citation": {"db": "PubMed", "db_id": "19524503"}, "source": 2328, "target": 3254, "key": "70207816019311acd58de9958dd94a8d"}, {"line": 38106, "relation": "negativeCorrelation", "evidence": "we found that the Nogo-66 receptor (NgR) interacts physically with both Abeta and the amyloid precursor protein (APP). The inverse correlation of Abeta levels with NgR levels within the brain may reflect regulation of Abeta production and/or Abeta clearance.", "citation": {"db": "PubMed", "db_id": "17182778"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3331, "key": "9d564f07dd5bf0a622ed16ef4d8d0953"}, {"line": 38767, "relation": "negativeCorrelation", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2328, "target": 3123, "key": "7f8999e5eb9a768ad46c8a5714991d70"}, {"line": 38903, "relation": "increases", "evidence": "The heme oxygenases (HOs), responsible for the degradation of heme to biliverdin/bilirubin, free iron and CO,/ have been heavily implicated in mammalian CNS aging and disease. In Alzheimer disease and mild cognitive impairment, / immunoreactive HO-1 protein is over-expressed in neurons and astrocytes of the cerebral cortex and hippocampus relative to / age-matched, cognitively intact controls and co-localizes to senile plaques, neurofibrillary tangles, and corpora amylacea./ In 'stressed' astroglia, HO-1 hyperactivity promotes mitochondrial sequestration of non-transferrin iron and macroautophagy/ and may thereby contribute to the pathological iron deposition and bioenergetic failure amply documented in Alzheimer/ disease, Parkinson disease and other aging-related neurodegenerative disorders. Glial HO-1 expression may also impact cell/ survival and neuroplasticity in these conditions by modulating brain sterol metabolism and proteosomal degradation of/ neurotoxic protein aggregates.", "citation": {"db": "PubMed", "db_id": "19457088"}, "source": 2328, "target": 2839, "key": "b7068175f8956bdb1f332524d963f3d6"}, {"line": 38915, "relation": "increases", "evidence": "For example, when the brain is injured, microglia become activated by Abeta deposits and recruit astrocytes / by secreting acute-phase proteins such as complement factors and cytokines. Reactive microglia and astrocytes additionally / generate proinflammatory mediators, including cytokines, chemokines, prostaglandins, neurotoxic secretory products, / reactive oxygen species, and nitric oxide (Griffin et al., 1998; Tuppo and Arias, 2005). Cytokines and chemokines, / in turn, stimulate the synthesis of other enzymes, such as COXs and prostaglandin synthases. In AD, the expression / of COX-2, the inducible isoform, increases in response to inflammatory agents in neurons and glial cells (Pasinetti and / Aisen, 1998; Sairanen et al., 1998). Because COX is the rate-limiting enzyme in the production of prostaglandins (O'Banion,/ 1999; Smith et al., 1991), the increase in COX activity leads to an increase in prostaglandin production (Consilvio et al.,/ 2004). ", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 2328, "target": 3912, "key": "711eb21194411d26d349f43baef6f15c"}, {"line": 39024, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3912, "key": "ea76ea2d5218caa33bca9f9eb8d9d73e"}, {"line": 39280, "relation": "increases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 2879, "key": "672e30a77f3728b4aeb484fd355a2b18"}, {"line": 39282, "relation": "increases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 2889, "key": "129940420960475bec3945f45cdcb4b0"}, {"line": 39291, "relation": "increases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 2468, "key": "0cfd312b7a2c2a8c84ec0def0a909dd6"}, {"line": 39293, "relation": "increases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 3084, "key": "7699d9b1b96760bca473694250e9f4c1"}, {"line": 39295, "relation": "increases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 2328, "target": 3467, "key": "c470007523bb5678558ea64e93b031e0"}, {"line": 39568, "relation": "positiveCorrelation", "evidence": "Epidemiological studies, indicating that non-steroidal anti-inflammatory drugs (NSAIDs) decrease the risk of developing AD, have encouraged the study on the role of inflammation in AD. The best-characterized action of most NSAIDs is the inhibition of cyclooxygenase (COX). The expression of the constitutively expressed COX-1 and the inflammatory induced COX-2 has been intensively investigated in AD brain and different disease models for AD. Despite these studies, clinical trials with NSAIDs or selective COX-2 inhibitors showed little or no effect on clinical progression of AD. The expression levels of COX-1 and COX-2 change in the different stages of AD pathology. In an early stage, when low-fibrillar Abeta deposits are present and only very few neurofibrillary tangles are observed in the cortical areas, COX-2 is increased in neurons. The increased neuronal COX-2 expression parallels and colocalizes with the expression of cell cycle proteins. COX-1 is primarily expressed in microglia, which are associated with fibrillar Abeta deposits. This suggests that in AD brain COX-1 and COX-2 are involved in inflammatory and regenerating pathways respectively. In this review we will discuss the role of COX-1 and COX-2 in the different stages of AD pathology.", "citation": {"db": "PubMed", "db_id": "18537664"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 2328, "target": 3277, "key": "ba0172c169c1a210f6803c8da152dcd3"}, {"line": 39602, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2328, "target": 3556, "key": "70a8225ebe65ab64874d72d1ae4b6663"}, {"relation": "partOf", "source": 2328, "target": 1232, "key": "99c4520c75f391d3d4e420776b953334"}, {"line": 40501, "relation": "increases", "evidence": "Oligomeric amyloid beta induces IL-1beta processing via production of ROS: implication in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 2885, "key": "bec28288f1d1da8ee8846f886d6815a4"}, {"line": 40505, "relation": "increases", "evidence": "Oligomeric amyloid beta induces IL-1beta processing via production of ROS: implication in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 170, "key": "9ac0d262ec68f106da555490d11b8957"}, {"line": 44507, "relation": "increases", "evidence": "Increased Abeta levels promoted the production of reactive oxygen species (ROS)", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 170, "key": "52cbc16402252e7dbc277aae756549c3"}, {"line": 44562, "relation": "increases", "evidence": "hypomethylation of the APP promoter for example can increase the ceiling of expression of the APP gene in response to aging processes driving overproduction of APP and Abeta levels. The increased Abeta levels then facilitate ROS production with their pro-oxidant properties, damaging the DNA. ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2328, "target": 170, "key": "48a0f151f8d8c7b0e2d1b7ce7bf1ed10"}, {"line": 44637, "relation": "increases", "evidence": "Developmental exposure to lead (Pb) has been shown to elevate the Alzheimer's disease (AD) related beta-amyloid peptide (Abeta), which is known to generate reactive oxygen species in the aging brain. This study measures the lifetime cerebral 8-hydroxy-2'-deoxyguanosine (oxo8dG) levels and the activity of the DNA repair enzyme 8-oxoguanine DNA glycosylase (Ogg1) in rats developmentally exposed to Pb.", "citation": {"db": "PubMed", "db_id": "16484331"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Developmental_Phase__of_patient": {"Old": true}, "Confidence": {"High": true}}, "source": 2328, "target": 170, "key": "e11d00f83172111178f33ed66b88219f"}, {"relation": "partOf", "source": 2328, "target": 1248, "key": "31d5c469c4539b14c29815cd79805da3"}, {"line": 42883, "relation": "increases", "evidence": "Abeta aggregation is closely associated with neurotoxicity, oxidative stress, and neuronal inflammation.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "MeSHAnatomy": {"Brain": true}, "MeSHDisease": {"Inflammation": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3920, "key": "7970dc9441dd61fff30be108eb6862a9"}, {"line": 43327, "relation": "increases", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3706, "key": "32d50209071d30372cead7745f4bd56f"}, {"line": 43358, "relation": "increases", "evidence": "Here we report that the expression of COX-2 and glial fibrillary acidic protein were enhanced and that of/ peroxisome proliferator-activated receptor gamma (PPARgamma) was decreased in Abeta(25-35)-treated astrocytes. In line/ with these results, nuclear factor-kappaB translocation was increased in the presence of Abeta.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Prostaglandin subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 3706, "key": "ba18e0b5eb3ba33187251f404d8aaa18"}, {"line": 43333, "relation": "increases", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3660, "key": "d6a2d3ead22ac75921998f489a045822"}, {"line": 43334, "relation": "increases", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3662, "key": "48443b7926ed69f39ba75c6859b2a72d"}, {"line": 43354, "relation": "increases", "evidence": "Here we report that the expression of COX-2 and glial fibrillary acidic protein were enhanced and that of/ peroxisome proliferator-activated receptor gamma (PPARgamma) was decreased in Abeta(25-35)-treated astrocytes. In line/ with these results, nuclear factor-kappaB translocation was increased in the presence of Abeta.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Prostaglandin subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3636, "key": "2afa3b4e1e1cf18c45edf7711d6e6882"}, {"line": 43366, "relation": "decreases", "evidence": "Here we report that the expression of COX-2 and glial fibrillary acidic protein were enhanced and that of/ peroxisome proliferator-activated receptor gamma (PPARgamma) was decreased in Abeta(25-35)-treated astrocytes. In line/ with these results, nuclear factor-kappaB translocation was increased in the presence of Abeta.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Prostaglandin subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3699, "key": "64518675565cef37150cc6862412f4e9"}, {"line": 43377, "relation": "increases", "evidence": "Here we report that the expression of COX-2 and glial fibrillary acidic protein were enhanced and that of/ peroxisome proliferator-activated receptor gamma (PPARgamma) was decreased in Abeta(25-35)-treated astrocytes. In line/ with these results, nuclear factor-kappaB translocation was increased in the presence of Abeta.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 2328, "target": 3685, "key": "09face887a2dd5daf7bc29d5de199391"}, {"line": 43534, "relation": "increases", "evidence": "PKR inhibition prevented Abeta42-induced activation of IκB and NF-κB, strongly decreased production and release/ of tumor necrosis factor (TNFα) and interleukin (IL)-1beta, and limited apoptotic process.", "citation": {"db": "PubMed", "db_id": "21699726"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3685, "key": "0ff43ad4ca5d9b13cfac8e3afdf7588b"}, {"line": 43427, "relation": "increases", "evidence": "Chronic neuroinflammatory processes including glial activation may play a role in the pathogenesis of/ Alzheimer's disease (AD). The immune and inflammatory mediator CD40 ligand (CD40L) can augment the activation of/ cultured microglia by amyloid beta-protein (Abeta) and promote neuron death. We investigated whether CD40L is/ increased in AD and in animal models of AD and neuroinflammation. These findings indicate that astrocytes are / the predominant source of CD40L in brain, and are consistent with the proposed role of CD40L-mediated neurotoxic/ inflammation in AD.", "citation": {"db": "PubMed", "db_id": "11755016"}, "annotations": {"Cell": {"microglial cell": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2328, "target": 609, "key": "c351290e925ffdba4593c18c070a6dae"}, {"line": 43476, "relation": "decreases", "evidence": "Ultrastructural detection of choline acetyl transferase (ChAT)-immunostaining in cerebral cortical sections/ of transgenic mice clearly demonstrated degeneration of ChAT-immunoreactive fibres in the environment of beta-amyloid/ plaques and activated glial cells suggesting a role of beta-amyloid and/or inflammation in specific degeneration of / cholinergic synaptic structures.", "citation": {"db": "PubMed", "db_id": "12788508"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 3611, "key": "4e535893d5238c159ead884e8cb1e51f"}, {"line": 43485, "relation": "decreases", "evidence": "Ultrastructural detection of choline acetyl transferase (ChAT)-immunostaining in cerebral cortical sections/ of transgenic mice clearly demonstrated degeneration of ChAT-immunoreactive fibres in the environment of beta-amyloid/ plaques and activated glial cells suggesting a role of beta-amyloid and/or inflammation in specific degeneration of / cholinergic synaptic structures.", "citation": {"db": "PubMed", "db_id": "12788508"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 689, "key": "7a47eaea8bd11a69c8cc37e4cf257d90"}, {"line": 43503, "relation": "increases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "source": 2328, "target": 3649, "key": "b6ac6d2dbb5a553e4c12ddf1c19c296e"}, {"line": 43526, "relation": "increases", "evidence": "PKR inhibition prevented Abeta42-induced activation of IκB and NF-κB, strongly decreased production and release/ of tumor necrosis factor (TNFα) and interleukin (IL)-1beta, and limited apoptotic process.", "citation": {"db": "PubMed", "db_id": "21699726"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3687, "key": "122d8ee5302069d1bcb4920611e0f6ad"}, {"line": 44556, "relation": "negativeCorrelation", "evidence": "hypomethylation of the APP promoter for example can increase the ceiling of expression of the APP gene in response to aging processes driving overproduction of APP and Abeta levels. The increased Abeta levels then facilitate ROS production with their pro-oxidant properties, damaging the DNA. ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2328, "target": 1747, "key": "1a8017765d6e1600ba63c39c3c811969"}, {"line": 45967, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2328, "target": 1747, "key": "120f5817ee8b0314797e7e63b4480446"}, {"line": 45331, "relation": "increases", "evidence": "external application of beta-amyloid cerebral endothelial cell cultures results in extensive methylation at the neprilysin (NPE) gene promoter", "citation": {"db": "PubMed", "db_id": "21419233"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2328, "target": 1876, "key": "30192d7318963981b253786e0c8f9bb8"}, {"line": 45905, "relation": "increases", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2328, "target": 478, "key": "1b6e213318efc9177b8e042212202150"}, {"line": 45973, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2328, "target": 1756, "key": "f4088dedb38b838a929518e0333d3a69"}, {"line": 45976, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2328, "target": 1926, "key": "d1393f801a4d506cd60de76d6abf157e"}, {"line": 46318, "relation": "increases", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2328, "target": 2804, "key": "d0eb828d1d6feed050cb1a8cd18fff72"}, {"line": 46535, "relation": "decreases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Low": true}, "CellStructure": {"Mitochondria": true}, "Subgraph": {"Chaperone subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 894, "key": "b8326d6a3f94f2cd0929f63169f75a07"}, {"line": 46648, "relation": "increases", "evidence": "We found Abeta oligomer accumulation in the endoplasmic reticulum (ER), endosomes/lysosomes, and mitochondria in hippocampal neurons of 22-month-old mice. We also detected up-regulation of Grp78 and HRD1 (an E3 ubiquitin ligase), leakage of cathepsin D from endosomes/lysosomes into cytoplasm, cytochrome c release from mitochondria, and activation of caspase-3 in the hippocampi of 18-month-old mice.", "citation": {"db": "PubMed", "db_id": "21488093"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 2328, "target": 3650, "key": "92c369dafd6373042321901a1361fa87"}, {"line": 47031, "relation": "decreases", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 602, "key": "438d56e59645effc64ff02e4e6fae597"}, {"line": 47036, "relation": "association", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2328, "target": 3548, "key": "ffd14bfa2f22272cabc0d4be06da69d7"}, {"line": 47037, "relation": "association", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 527, "key": "9ee94ac67512681332af7f658b8dd9ab"}, {"line": 47479, "relation": "association", "evidence": "overexpression of CRF or exposure to chronic stress in rodents can induce phosphorylation and solubility changes in the microtubule-associated protein, tau, a process that is reliant on CRFR1. Furthermore, exposing rodents to chronic emotional stress results in increased phosphorylation and decreased solubility of the tau protein; changes that are also strictly dependent on CRFR1 signaling. In addition to work on tau, several reports demonstrate that CRF or stress exposure can impact Abeta production and accumulation in AD models and that stress-induced Abeta plaque formation in adult AD mice can be reduced by CRFR1 antagonism . In particular, our recently published work demonstrates that genetic ablation of CRFR1 greatly reduces the production of APP CTFs and accumulation of Abeta in the brains of AD mice. ", "citation": {"db": "PubMed", "db_id": "26555315"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 3773, "key": "6ed064201177dcc23be84999f0bbc438"}, {"line": 47508, "relation": "increases", "evidence": "In cells, CRF treatment increases Abeta production and triggers CRF receptor 1 (CRFR1) and gamma-secretase internalization. Co-immunoprecipitation studies establish that gamma-secretase associates with CRFR1; this is mediated by beta-arrestin binding motifs. ", "citation": {"db": "PubMed", "db_id": "25964433"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 3773, "key": "f700a7c19a8d4c680815720a4cf5147a"}, {"line": 47643, "relation": "increases", "evidence": "In cells, CRF treatment increases Abeta production and triggers CRF receptor 1 (CRFR1) and gamma-secretase internalization. Co-immunoprecipitation studies establish that gamma-secretase associates with CRFR1; this is mediated by beta-arrestin binding motifs. ", "citation": {"db": "PubMed", "db_id": "25964433"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2328, "target": 2562, "key": "31074b048bceb8dcc8cb1fe7858157e0"}, {"line": 47722, "relation": "increases", "evidence": "Although the mechanism of Ab action in the pathogenesis of Alzheimer’s disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1), whereas the silencing of Dkk1 blocks Ab neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}}, "source": 2328, "target": 2629, "key": "e6c29dff0a18e337b1a4ca7048f3959e"}, {"line": 48108, "relation": "increases", "evidence": "Although the mechanism of Abeta action in the pathogenesis of Alzheimer's disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1), whereas the silencing of Dkk1 blocks Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 2629, "key": "1a74acc040bd16bd2cbfafe958af1403"}, {"line": 48109, "relation": "negativeCorrelation", "evidence": "Although the mechanism of Abeta action in the pathogenesis of Alzheimer's disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1), whereas the silencing of Dkk1 blocks Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2328, "target": 2629, "key": "fd8bdcd7b6156e6ecedc66fce66f2235"}, {"line": 48124, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2629, "key": "3362c21e60cd26a1b297915abb9bf86c"}, {"line": 48155, "relation": "increases", "evidence": "Thus, we have identified a pathway whereby Abeta induces a clusterin/p53/Dkk1/wnt-PCP-JNK pathway, which drives the upregulation of several genes that mediate the development of AD-like neuropathologies, thereby providing new mechanistic insights into the action of Abeta in neurodegenerative diseases.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2629, "key": "9e9b8412347ad20c5aff7184b9ed6c0e"}, {"line": 47758, "relation": "positiveCorrelation", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2538, "key": "bbfea9dc045a88e10b3e7944d18cce7d"}, {"line": 47770, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "object": {"location": {"namespace": "MESH", "name": "Cytosol"}}, "source": 2328, "target": 2538, "key": "2693b5e78007542b26b319b1f83eb2ab"}, {"line": 47774, "relation": "decreases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 2538, "key": "ac098a0e6d3372877bb5abaad9acfe70"}, {"line": 48128, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"location": {"namespace": "MESH", "name": "Cytosol"}}, "source": 2328, "target": 2538, "key": "6db84a47455afd850a9ace575b4acda0"}, {"line": 48132, "relation": "decreases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2328, "target": 2538, "key": "0b4fc5dc34169d610b03b156b192d88e"}, {"line": 47806, "relation": "positiveCorrelation", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 2328, "target": 2658, "key": "dd7e7ce099e0da23d27b31ae8df0d72e"}, {"line": 47807, "relation": "positiveCorrelation", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 2328, "target": 3086, "key": "99af69284a1bdc67dd1cae20d51a58d2"}, {"line": 47808, "relation": "positiveCorrelation", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 2328, "target": 2952, "key": "cea421f2e6364648630ab2fc7f8e57ef"}, {"line": 48159, "relation": "increases", "evidence": "Thus, we have identified a pathway whereby Abeta induces a clusterin/p53/Dkk1/wnt-PCP-JNK pathway, which drives the upregulation of several genes that mediate the development of AD-like neuropathologies, thereby providing new mechanistic insights into the action of Abeta in neurodegenerative diseases.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2328, "target": 463, "key": "9fb434d896180b012bad212988369982"}, {"relation": "partOf", "source": 2328, "target": 1656, "key": "2874fb7f52f98035f60843dc75f8c6c8"}, {"line": 49102, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "source": 2328, "target": 3463, "key": "e18ee984ded31452d15a7ff12e2d5641"}, {"line": 49117, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 2194, "key": "2aa0a0bbf43e4aad332fe77ee762be6d"}, {"line": 49346, "relation": "association", "evidence": "On top of the observation that vitamin D supplementation leads to improved cognitive function, all the studies in an AD-like context have also shown that vitamin D treatment, regardless of the model tested, the dosage, the molecule chosen, and the time of treatment decreases the amyloid burden, suggesting a link between vitamin D function and amyloidogenesis.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 2328, "target": 187, "key": "c202631320cfaa34cecadb40bc73b18d"}, {"line": 126, "relation": "decreases", "evidence": "The statements inside this citation is included to connect two entity types or triples, which will exist as islands/subnetworks in the big model.", "citation": {"db": "Other", "db_id": "123"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 118, "target": 868, "key": "1ffe26828b6c61cc6abfa3dd19517450"}, {"line": 13079, "relation": "decreases", "evidence": "The outcomes of the clinical trials of the gamma-secretase inhibitor Semagacestat (LY-450139) and the gamma-secretase modulator (GSM) Tarenflurbil were disappointing, but may not represent the end of the gamma-secretase era. gamma-Secretase modulators, by definition, only block the gamma-secretase cleavage of amyloid-beta protein precursor (AbetaPP) to generate the longer, 42-residue amyloid-beta (Abeta42) without changing the production of total Abeta. The first generation GSMs were shown to block Abeta42 generation while increasing Abeta38. The non-steroidal anti-inflammatory drug, Tarenflurbil, binds to AbetaPP and shifts the cleavage site from Abeta42 to Abeta38", "citation": {"db": "PubMed", "db_id": "22710916"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 118, "target": 868, "key": "883f77404b1a0f94c0d732f77223eb89"}, {"line": 13186, "relation": "decreases", "evidence": "gamma-Secretase inhibitor believed to be associated with the inhibition of the cleavage of Notch, a transmembrane receptor involved in regulating cell-fate decisions.", "citation": {"db": "PubMed", "db_id": "22087836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 118, "target": 868, "key": "807223b886f3487728a8502b3304e0d4"}, {"line": 13081, "relation": "decreases", "evidence": "The outcomes of the clinical trials of the gamma-secretase inhibitor Semagacestat (LY-450139) and the gamma-secretase modulator (GSM) Tarenflurbil were disappointing, but may not represent the end of the gamma-secretase era. gamma-Secretase modulators, by definition, only block the gamma-secretase cleavage of amyloid-beta protein precursor (AbetaPP) to generate the longer, 42-residue amyloid-beta (Abeta42) without changing the production of total Abeta. The first generation GSMs were shown to block Abeta42 generation while increasing Abeta38. The non-steroidal anti-inflammatory drug, Tarenflurbil, binds to AbetaPP and shifts the cleavage site from Abeta42 to Abeta38", "citation": {"db": "PubMed", "db_id": "22710916"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 118, "target": 2328, "key": "bb6d228b06812543697330e957dd87e4"}, {"line": 13082, "relation": "increases", "evidence": "The outcomes of the clinical trials of the gamma-secretase inhibitor Semagacestat (LY-450139) and the gamma-secretase modulator (GSM) Tarenflurbil were disappointing, but may not represent the end of the gamma-secretase era. gamma-Secretase modulators, by definition, only block the gamma-secretase cleavage of amyloid-beta protein precursor (AbetaPP) to generate the longer, 42-residue amyloid-beta (Abeta42) without changing the production of total Abeta. The first generation GSMs were shown to block Abeta42 generation while increasing Abeta38. The non-steroidal anti-inflammatory drug, Tarenflurbil, binds to AbetaPP and shifts the cleavage site from Abeta42 to Abeta38", "citation": {"db": "PubMed", "db_id": "22710916"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 118, "target": 411, "key": "4556b35bebbd345dade2440408d8152f"}, {"line": 13187, "relation": "decreases", "evidence": "gamma-Secretase inhibitor believed to be associated with the inhibition of the cleavage of Notch, a transmembrane receptor involved in regulating cell-fate decisions.", "citation": {"db": "PubMed", "db_id": "22087836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 118, "target": 4106, "key": "a77f7c670e8ef8804d3e09bf1d8736d4"}, {"line": 181, "relation": "increases", "evidence": "Processing of APP to produce Ab involves cleavage by b-site APP cleaving enzyme-1 (BACE1) and g-secretase that process APP at the N- and C-termini, respectively, of the Ab sequence.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4096, "key": "6eb1c6733082a2a8a2a2ed1f96ab97c4"}, {"line": 303, "relation": "increases", "evidence": "Amyloid plaques consist primarily of amyloid beta protein (Abeta), which is produced when APP is cleaved by beta-secretase and then cleaved again by gamma-secretase as part of the amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "22702962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4096, "key": "66ba648fba87ba3efa066e9374dd6597"}, {"line": 466, "relation": "increases", "evidence": "Abeta is generated from APP by concerted proteolysis by Abeta-secretase, which generates carboxyl-terminal fragments (CTFs) of APP, and then by gamma-secretase.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4096, "key": "b8871467552d7aee19e11ad30e0e0bf9"}, {"line": 8414, "relation": "directlyIncreases", "evidence": "Processing of APP to produce Ab involves cleavage by b-site APP cleaving enzyme-1 (BACE1) and g-secretase that process APP at the N- and C-termini, respectively, of the Ab sequence.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4096, "key": "23d69ccf20023aa7b3ebcaf2204930bd"}, {"line": 9185, "relation": "directlyIncreases", "evidence": "The amyloid-beta (Abeta) peptide is the derivative of amyloid precursor protein (APP) generated through sequential proteolytic processing by beta- and gamma-secretases. Excessive accumulation of Abeta, the main constituent of amyloid plaques, has been implicated in the etiology of Alzheimer disease (AD). ", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4096, "key": "3ebc601f48cdd04481c8f37feba53d3b"}, {"line": 13080, "relation": "increases", "evidence": "The outcomes of the clinical trials of the gamma-secretase inhibitor Semagacestat (LY-450139) and the gamma-secretase modulator (GSM) Tarenflurbil were disappointing, but may not represent the end of the gamma-secretase era. gamma-Secretase modulators, by definition, only block the gamma-secretase cleavage of amyloid-beta protein precursor (AbetaPP) to generate the longer, 42-residue amyloid-beta (Abeta42) without changing the production of total Abeta. The first generation GSMs were shown to block Abeta42 generation while increasing Abeta38. The non-steroidal anti-inflammatory drug, Tarenflurbil, binds to AbetaPP and shifts the cleavage site from Abeta42 to Abeta38", "citation": {"db": "PubMed", "db_id": "22710916"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 868, "target": 4096, "key": "fdaee706741ea55113ce76c73f75c80c"}, {"line": 36062, "relation": "increases", "evidence": "In addition, APP can be cleaved by a- and g-secretases and this precludes Ab production since a-secretase cleaves APP within the Ab sequence", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4096, "key": "f5615004585fed9fdbfd4aecdbd81aec"}, {"line": 232, "relation": "increases", "evidence": "gamma-Secretase comprises a molecular complex of four integral membrane proteins - presenilin, nicastrin, APH-1 and PEN-2 - and its molecular mechanism remains under extensive scrutiny. The ratio of Abeta(42) over Abeta(40) is increased by familial Alzheimer's disease mutations occurring in the presenilin genes or in APP, near the gamma-secretase cleavage site.", "citation": {"db": "PubMed", "db_id": "16696577"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 2328, "key": "a273e12e8980fc175907239e80afa1da"}, {"line": 271, "relation": "association", "evidence": "In this issue of Nature Medicine, Thathiah et al.4 now provide provocative evidence that the adaptor protein beta mediates the Abeta-altering effects of these GPCRs by promoting Abeta generation. This newly uncovered function of beta-arrestin 2 suggests it could be targeted to decrease amyloid pathology in patients with Alzheimer's disease. Production of the amyloid-beta peptide in Alzheimer's disease by the gamma-secretase complex can be regulated by certain G protein coupled receptors. This regulation seems to be mediated by beta-arrestin-2, whose expression was found to be elevated in Alzheimer's disease brains.Recruitment of beta-arrestin 2 to a GPCR leads to interaction with the gamma-secretase complex via the Aph-1 subunit. Other members of the complex include presenilin-1 (PS-1), nicastrin (Nct) and Pen-2. The complex then moves laterally into lipid rafts, where gamma-secretase activation is enhanced. Internalization may also occur to localize gamma-secretase to late endosomes, where its activation is also increased. Cleavage of APP by beta-secretase (BACE1) to release soluble APP (sAPPb) followed by gamma-secretase produces Abeta and APP intracellular domain (AICD). Increased production and secretion of Abeta from cells can lead to extracellular Abeta aggregation in the form of plaques. Mutagenesis of GPR3 in regions of the protein that specifically interact with either G protein or beta-arrestin 2 further showed that beta-arrestin 2, not G protein, mediates the ability of GPR3 to increase Abeta levels.", "citation": {"db": "PubMed", "db_id": "23296004"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 1111, "key": "215bafee92fb8bb189a222147abd8fa3"}, {"line": 272, "relation": "increases", "evidence": "In this issue of Nature Medicine, Thathiah et al.4 now provide provocative evidence that the adaptor protein beta mediates the Abeta-altering effects of these GPCRs by promoting Abeta generation. This newly uncovered function of beta-arrestin 2 suggests it could be targeted to decrease amyloid pathology in patients with Alzheimer's disease. Production of the amyloid-beta peptide in Alzheimer's disease by the gamma-secretase complex can be regulated by certain G protein coupled receptors. This regulation seems to be mediated by beta-arrestin-2, whose expression was found to be elevated in Alzheimer's disease brains.Recruitment of beta-arrestin 2 to a GPCR leads to interaction with the gamma-secretase complex via the Aph-1 subunit. Other members of the complex include presenilin-1 (PS-1), nicastrin (Nct) and Pen-2. The complex then moves laterally into lipid rafts, where gamma-secretase activation is enhanced. Internalization may also occur to localize gamma-secretase to late endosomes, where its activation is also increased. Cleavage of APP by beta-secretase (BACE1) to release soluble APP (sAPPb) followed by gamma-secretase produces Abeta and APP intracellular domain (AICD). Increased production and secretion of Abeta from cells can lead to extracellular Abeta aggregation in the form of plaques. Mutagenesis of GPR3 in regions of the protein that specifically interact with either G protein or beta-arrestin 2 further showed that beta-arrestin 2, not G protein, mediates the ability of GPR3 to increase Abeta levels.", "citation": {"db": "PubMed", "db_id": "23296004"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 80, "key": "8a818a40d81f044ae791dd286d76c61f"}, {"line": 283, "relation": "increases", "evidence": "Moreover, we find that PLD1 also regulates PS1 trafficking and that PLD1 overexpression promotes cell surface accumulation of PS1 in an APP-independent manner. Our results clearly elucidate a physiological function of APP in regulating protein trafficking and suggest that intracellular trafficking of PS1/gamma-secretase is regulated by multiple factors, including APP and PLD1.", "citation": {"db": "PubMed", "db_id": "19276086"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 80, "key": "11bc1a6cb9c1c9f0d44f301e270f78ed"}, {"line": 10427, "relation": "increases", "evidence": "In this study, we demonstrate that ER stress induces presenilin-1 expression through activating transcription factor 4 (ATF4), resulting in increased amyloid-beta (Abeta) secretion by gamma-secretase activity, which is suppressed by quercetin by modifying UPR signaling.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "MeSHAnatomy": {"Bodily Secretions": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 80, "key": "196205478eb797e31ea31000c1f4eed1"}, {"line": 2045, "relation": "increases", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 868, "target": 2200, "key": "ed937fabf636761c912764d9974e5a8b"}, {"line": 2108, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2970, "key": "56b17c53a647b0a864e4a4d804bfb913"}, {"line": 2241, "relation": "increases", "evidence": "Beside its role as Abeta chaperone, ApoE might modulate specific internalization and signaling events via binding to its receptors. Some of them possess shared adaptors with AbetaPP; in particular Fe65 and JIP1 bind to LRP8, LRP1, and megalin. Indeed gamma-secretase cleavage regulates the intramembrane proteolysis of LRP8, LRP1, and of SOR-1/LRP11. It is tempting to speculate that LRPs could affect AbetaPP processing and signaling (and vice versa) through gamma-secretase and ApoE-mediated stimuli.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2970, "key": "294c9436f2c617f9513d8df4cc27c38f"}, {"line": 2109, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2974, "key": "830494d5f2df5066f206ef123eb5e0ab"}, {"line": 2110, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2976, "key": "d002109852f89cc48855d4eca33a3eb5"}, {"line": 2111, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2977, "key": "28ab18124499c08d89289f7a3c84bc04"}, {"line": 2112, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2979, "key": "ef82c897ff275626c3d47673fd869861"}, {"line": 2113, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2981, "key": "ebae7afd4c1b45093818ae40fce59b35"}, {"line": 2240, "relation": "increases", "evidence": "Beside its role as Abeta chaperone, ApoE might modulate specific internalization and signaling events via binding to its receptors. Some of them possess shared adaptors with AbetaPP; in particular Fe65 and JIP1 bind to LRP8, LRP1, and megalin. Indeed gamma-secretase cleavage regulates the intramembrane proteolysis of LRP8, LRP1, and of SOR-1/LRP11. It is tempting to speculate that LRPs could affect AbetaPP processing and signaling (and vice versa) through gamma-secretase and ApoE-mediated stimuli.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2981, "key": "1a3667c128cb00eb42471badf28ee7a5"}, {"line": 2114, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2971, "key": "c67f92fe92d78101456a06e25a71a66c"}, {"line": 2115, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2972, "key": "76070a58ad01c8f484a7fe062aca0d05"}, {"line": 2242, "relation": "increases", "evidence": "Beside its role as Abeta chaperone, ApoE might modulate specific internalization and signaling events via binding to its receptors. Some of them possess shared adaptors with AbetaPP; in particular Fe65 and JIP1 bind to LRP8, LRP1, and megalin. Indeed gamma-secretase cleavage regulates the intramembrane proteolysis of LRP8, LRP1, and of SOR-1/LRP11. It is tempting to speculate that LRPs could affect AbetaPP processing and signaling (and vice versa) through gamma-secretase and ApoE-mediated stimuli.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2972, "key": "d4ebf28224e3c80a094cb88a75224242"}, {"line": 2918, "relation": "association", "evidence": "Mutations in presenilin2 (PS2), a homolog of PS1, are also associated with FAD. While the precise mechanism on how these mutations cause AD is unknown, multiple theories have arisen to explain the role of PS1 and PS2 mutations on AD pathogenesis. These mutations lead to abnormal function of gamma- secretase, the beta-catenin pathway, calcium homoeostasis and the lysosomal/autophagy pathway as well as chaperones. Among these hypotheses, the effect of PS mutations on gamma-secretase has been extensively investigated. gamma-Secretase is composed of at least four subunits: PS, Nicastrin, Aph1 and Pen2; with a total of 19 putative transmembrane domains.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 868, "target": 1667, "key": "9b227b45d7d3f64d07a7e3eaa8abe8d7"}, {"line": 2930, "relation": "increases", "evidence": "gamma-Secretase cleaves multiple substrates including the amyloid precursor protein (APP), Notch, and other type I membrane proteins.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 868, "target": 2332, "key": "44c2fcb2f4581c98249f4a2d3bd58d96"}, {"line": 2931, "relation": "increases", "evidence": "gamma-Secretase cleaves multiple substrates including the amyloid precursor protein (APP), Notch, and other type I membrane proteins.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 868, "target": 2201, "key": "0bdfd31f1a14ef87971e6916b626cd64"}, {"line": 3296, "relation": "increases", "evidence": "Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and Abeta production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2315, "key": "2583ac5633dbbc296abe325204a53610"}, {"line": 35728, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 868, "target": 2315, "key": "cd29e2dc74ba8c18c17c9002d422c955"}, {"relation": "partOf", "source": 868, "target": 1003, "key": "874f570c442f0aa9cba319d28f0e6467"}, {"line": 4503, "relation": "increases", "evidence": "beta- and gamma-secretase cleave the amyloid precursor protein (APP) to release the amyloidogenic beta-amyloid peptides (Abeta) and the APP intracellular domain (AICD).", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4097, "key": "a865a6449f8c05d0f32c309760adebed"}, {"line": 6091, "relation": "increases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4101, "key": "1b73fe87d02506d9e6b4bce5fbc1173a"}, {"line": 6094, "relation": "increases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 717, "key": "60e0087ce7356ccbaa8ba045bf4a872f"}, {"line": 11095, "relation": "increases", "evidence": "PEN-2 is an integral membrane protein that is a necessary component of the gamma-secretase complex, which is central in the pathogenesis of Alzheimer's disease and is also required for Notch signaling. In the absence of PEN-2, Notch signaling fails to guide normal development in Caenorhabditis elegans, and amyloid beta peptide is not generated from the amyloid precursor protein", "citation": {"db": "PubMed", "db_id": "12639958"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 868, "target": 453, "key": "469bbfb25feaa22c2fa8c757f2949606"}, {"line": 13153, "relation": "increases", "evidence": "There are 4 classes of potentially disease-modifying treatments that have successfully advanced to later-stage clinical trials: (1) immunotherapies, (2) secretase inhibitors, (3) selective Abeta42-lowering agents (SALAs), and (4) anti-Abeta aggregation agents. Gamma-secretase has many biologically essential substrates. One physiologically important gamma-secretase substrate is the Notch signaling protein", "citation": {"db": "PubMed", "db_id": "17599166"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 453, "key": "a64320a650dcca8b1cc02cf321c5bf1d"}, {"line": 21452, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 868, "target": 3823, "key": "2aae002042932babbb3e64193db71324"}, {"line": 21455, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 868, "target": 3545, "key": "638f7c499a92b025d449018e4849fdba"}, {"line": 35735, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 868, "target": 684, "key": "fd5cde34ed2c726c7a1d5f94aadbcfcd"}, {"line": 43247, "relation": "directlyIncreases", "evidence": "Processing of APP to produce Ab involves cleavage by b-site APP cleaving enzyme-1 (BACE1) and g-secretase that process APP at the N- and C-termini, respectively, of the Ab sequence.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 868, "target": 4107, "key": "fda6ae9a21d28e1f38982fc84959b1b1"}, {"line": 47509, "relation": "association", "evidence": "In cells, CRF treatment increases Abeta production and triggers CRF receptor 1 (CRFR1) and gamma-secretase internalization. Co-immunoprecipitation studies establish that gamma-secretase associates with CRFR1; this is mediated by beta-arrestin binding motifs. ", "citation": {"db": "PubMed", "db_id": "25964433"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 868, "target": 3773, "key": "d0013fd276295f81a51203b1dfda9da5"}, {"line": 47644, "relation": "association", "evidence": "In cells, CRF treatment increases Abeta production and triggers CRF receptor 1 (CRFR1) and gamma-secretase internalization. Co-immunoprecipitation studies establish that gamma-secretase associates with CRFR1; this is mediated by beta-arrestin binding motifs. ", "citation": {"db": "PubMed", "db_id": "25964433"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 868, "target": 2562, "key": "402e8ab9941e1bf50a235522d157488b"}, {"line": 162, "relation": "isA", "evidence": "We previously showed that beta-amyloid precursor protein (APP) is cleaved not only in the middle of the membrane (gamma-cleavage) but also at novel cleavage sites close to the membrane/cytoplasmic boundary (ε-cleavage), releasing APP intracellular domains (AICDs) 49−99 and 50−99. To learn more about the relationship between gamma- and ε-cleavage, C-terminally truncated carboxyl-terminal fragments (CTFs) of APP, especially CTFs1−48 and 1−49 (the postulated products that are generated by ε-cleavage), were transiently expressed in CHO cells. Most importantly, the cells expressing CTF1−49 secreted predominantly amyloid beta-protein (Abeta) 40, while those expressing CTF1−48 secreted preferentially Abeta42. This supports our assumption that ε-cleavage precedes Αbeta production and that preceding ε-cleavage determines the preference for the final Abeta species. The gamma-secretase inhibitors, L-685,458 and DAPT, suppressed Abeta production from CTF1−49. Regarding Abeta production from CTF1−48, L-685,458 suppressed it, but DAPT failed to do so. A dominant negative mutant of presenilin 1 suppressed the production of Abeta40 and 42 from both CTFs1−48 and 1−49. These data should shed significant light into the mechanism of Abeta production.", "citation": {"db": "PubMed", "db_id": "15491160"}, "annotations": {"TextLocation": {"Abstract": true}}, "source": 2318, "target": 2132, "key": "a82a65b6a3a1589d299dda2f06b7b448"}, {"line": 163, "relation": "isA", "evidence": "We previously showed that beta-amyloid precursor protein (APP) is cleaved not only in the middle of the membrane (gamma-cleavage) but also at novel cleavage sites close to the membrane/cytoplasmic boundary (ε-cleavage), releasing APP intracellular domains (AICDs) 49−99 and 50−99. To learn more about the relationship between gamma- and ε-cleavage, C-terminally truncated carboxyl-terminal fragments (CTFs) of APP, especially CTFs1−48 and 1−49 (the postulated products that are generated by ε-cleavage), were transiently expressed in CHO cells. Most importantly, the cells expressing CTF1−49 secreted predominantly amyloid beta-protein (Abeta) 40, while those expressing CTF1−48 secreted preferentially Abeta42. This supports our assumption that ε-cleavage precedes Αbeta production and that preceding ε-cleavage determines the preference for the final Abeta species. The gamma-secretase inhibitors, L-685,458 and DAPT, suppressed Abeta production from CTF1−49. Regarding Abeta production from CTF1−48, L-685,458 suppressed it, but DAPT failed to do so. A dominant negative mutant of presenilin 1 suppressed the production of Abeta40 and 42 from both CTFs1−48 and 1−49. These data should shed significant light into the mechanism of Abeta production.", "citation": {"db": "PubMed", "db_id": "15491160"}, "annotations": {"TextLocation": {"Abstract": true}}, "source": 2319, "target": 2132, "key": "b47851c4a58fdec6b3c3213b725b3810"}, {"line": 165, "relation": "isA", "evidence": "We previously showed that beta-amyloid precursor protein (APP) is cleaved not only in the middle of the membrane (gamma-cleavage) but also at novel cleavage sites close to the membrane/cytoplasmic boundary (ε-cleavage), releasing APP intracellular domains (AICDs) 49−99 and 50−99. To learn more about the relationship between gamma- and ε-cleavage, C-terminally truncated carboxyl-terminal fragments (CTFs) of APP, especially CTFs1−48 and 1−49 (the postulated products that are generated by ε-cleavage), were transiently expressed in CHO cells. Most importantly, the cells expressing CTF1−49 secreted predominantly amyloid beta-protein (Abeta) 40, while those expressing CTF1−48 secreted preferentially Abeta42. This supports our assumption that ε-cleavage precedes Αbeta production and that preceding ε-cleavage determines the preference for the final Abeta species. The gamma-secretase inhibitors, L-685,458 and DAPT, suppressed Abeta production from CTF1−49. Regarding Abeta production from CTF1−48, L-685,458 suppressed it, but DAPT failed to do so. A dominant negative mutant of presenilin 1 suppressed the production of Abeta40 and 42 from both CTFs1−48 and 1−49. These data should shed significant light into the mechanism of Abeta production.", "citation": {"db": "PubMed", "db_id": "15491160"}, "annotations": {"TextLocation": {"Abstract": true}}, "source": 2324, "target": 3563, "key": "b626823e627ff5dffabad292de57e75a"}, {"line": 1506, "relation": "increases", "evidence": "AICD was shown to induce the expression of genes having functional roles in actin organization and dynamics, including transgelin (SM22) and alpha2-actin, resulting in a loss of organized filamentous actin structures within the cell. In fact, overexpression of transgelin, a proposed AICD target gene, causes destabilization of actin filaments, depolarization of mitochondrial membrane potential (DeltaPsim), and significant alteration of mitochondrial distribution and morphology in human SHEP neuroblastoma cells and primary neurons. These data demonstrate that induction of AICD/APP significantly alters cytoskeletal dynamics and mitochondrial function in neuronal cells by interacting with JIP1b or Fe65.", "citation": {"db": "PubMed", "db_id": "21034527"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 3441, "key": "f2eebe0f326e276f84b2d328bee0256a"}, {"line": 1509, "relation": "increases", "evidence": "AICD was shown to induce the expression of genes having functional roles in actin organization and dynamics, including transgelin (SM22) and alpha2-actin, resulting in a loss of organized filamentous actin structures within the cell. In fact, overexpression of transgelin, a proposed AICD target gene, causes destabilization of actin filaments, depolarization of mitochondrial membrane potential (DeltaPsim), and significant alteration of mitochondrial distribution and morphology in human SHEP neuroblastoma cells and primary neurons. These data demonstrate that induction of AICD/APP significantly alters cytoskeletal dynamics and mitochondrial function in neuronal cells by interacting with JIP1b or Fe65.", "citation": {"db": "PubMed", "db_id": "21034527"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 2246, "key": "58553436bb9d4bde5798963b1ec36331"}, {"relation": "partOf", "source": 3563, "target": 1677, "key": "be9e42798ef428b34f22a13ac5ed9915"}, {"line": 2798, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-b, may also contribute to AICD-related cytotoxicity by up-regulating tau hyperphosphorylation. GSK-3b activation and collapsin response mediator protein 2 (CRMP2) phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 3482, "key": "920a9a63bdb459dad82ca9404015f292"}, {"line": 35590, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3482, "key": "027c40cf034ebbc73ee99788dca7a3e7"}, {"line": 35591, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 3482, "key": "36abafcee26bb46e6addeb9425aa699f"}, {"line": 2810, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-b, may also contribute to AICD-related cytotoxicity by up-regulating tau hyperphosphorylation. GSK-3b activation and collapsin response mediator protein 2 (CRMP2) phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 2794, "key": "e3cd7767158a5d09b0ed94980685e441"}, {"line": 4515, "relation": "directlyIncreases", "evidence": "In this study, we found the AICD to strongly inhibit Wnt-induced transcriptional reporter activity, and to counteract Wnt-induced c-Myc expression. Loss of the AICD resulted in an increased responsiveness to Wnt/beta-catenin-mediated transcription. Mechanically, the AICD was found to interact with glycogen synthase kinase 3 beta (GSK3beta) and promote its kinase activity. The subsequent AICD-strengthened Axin-GSK3beta complex potentiates beta-catenin poly-ubiquitination.", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 2794, "key": "1931cc3ed18d0c83d6cc58afc340a271"}, {"line": 2813, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-b, may also contribute to AICD-related cytotoxicity by up-regulating tau hyperphosphorylation. GSK-3b activation and collapsin response mediator protein 2 (CRMP2) phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2642, "key": "651fba264924c1554da303c5feb3455c"}, {"line": 2826, "relation": "association", "evidence": "However, C31, a short form of AICD generated by caspase cleavage, has been reported to directly activate caspase 3 in the tumor cell death process.C31 also appears to induce a caspase-independent toxicity by selectively increasing Ab42", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Complement system subgraph": true}, "Confidence": {"Medium": true}}, "source": 3563, "target": 2330, "key": "1f439d2a2cd3adc2c21d6b70f7d75f6d"}, {"line": 2841, "relation": "association", "evidence": "APP-binding protein 1 reportedly interacts with AICD and activates the neddylation pathway, further down-regulating the level of b-catenin and potentially resulting in apoptotic process. In addition, cellular Ca2+ homeostasis appears to be modulated by AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2301, "key": "193ceea0a55dd8f918bb3d3ecb75d7dd"}, {"relation": "partOf", "source": 3563, "target": 1108, "key": "08b123420a366823386562e45e962b75"}, {"line": 2849, "relation": "association", "evidence": "APP-binding protein 1 reportedly interacts with AICD and activates the neddylation pathway, further down-regulating the level of b-catenin and potentially resulting in apoptotic process. In addition, cellular Ca2+ homeostasis appears to be modulated by AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 491, "key": "f937d4d118a82c7a6ac72a0b6b63c6eb"}, {"line": 3975, "relation": "decreases", "evidence": "Amyloidogenic processing also generates an intracellular APP domain (AICD) which can translocate to the nucleus and modify gene transcription in ways that perturb Ca2+ homeostasis", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 3563, "target": 491, "key": "948d2a06c4ee93c8af3ca14a58a0b3f9"}, {"relation": "partOf", "source": 3563, "target": 1105, "key": "855e043c27e31ddf65e33b5380692e5f"}, {"line": 3974, "relation": "association", "evidence": "Amyloidogenic processing also generates an intracellular APP domain (AICD) which can translocate to the nucleus and modify gene transcription in ways that perturb Ca2+ homeostasis", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 3563, "target": 766, "key": "cf6c4c1007d67d6980b4ef3b5799aebc"}, {"line": 37943, "relation": "increases", "evidence": "As far as AICD fragments are concern, it was reported that, after binding Fe65 (Figure 1), an adaptor protein mediating assembly of multimolecular complexes through a variety of protein-interaction domains, and the histone acetyltransferase Tip60, AICD translocate into the nucleus where it acts as gene transcription regulators", "citation": {"db": "PubMed", "db_id": "22496686 "}, "source": 3563, "target": 766, "key": "a2e1774688588e1093bc7f02399deb2a"}, {"line": 4509, "relation": "decreases", "evidence": "In this study, we found the AICD to strongly inhibit Wnt-induced transcriptional reporter activity, and to counteract Wnt-induced c-Myc expression. Loss of the AICD resulted in an increased responsiveness to Wnt/beta-catenin-mediated transcription. Mechanically, the AICD was found to interact with glycogen synthase kinase 3 beta (GSK3beta) and promote its kinase activity. The subsequent AICD-strengthened Axin-GSK3beta complex potentiates beta-catenin poly-ubiquitination.", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Wnt signaling subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 2221, "key": "87231aba2968132157a6577e5eb228bc"}, {"line": 4512, "relation": "decreases", "evidence": "In this study, we found the AICD to strongly inhibit Wnt-induced transcriptional reporter activity, and to counteract Wnt-induced c-Myc expression. Loss of the AICD resulted in an increased responsiveness to Wnt/beta-catenin-mediated transcription. Mechanically, the AICD was found to interact with glycogen synthase kinase 3 beta (GSK3beta) and promote its kinase activity. The subsequent AICD-strengthened Axin-GSK3beta complex potentiates beta-catenin poly-ubiquitination.", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Beta-Catenin subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 2586, "key": "2a982ff65f3156e3d28571ce4d0c7036"}, {"line": 4519, "relation": "increases", "evidence": "In this study, we found the AICD to strongly inhibit Wnt-induced transcriptional reporter activity, and to counteract Wnt-induced c-Myc expression. Loss of the AICD resulted in an increased responsiveness to Wnt/beta-catenin-mediated transcription. Mechanically, the AICD was found to interact with glycogen synthase kinase 3 beta (GSK3beta) and promote its kinase activity. The subsequent AICD-strengthened Axin-GSK3beta complex potentiates beta-catenin poly-ubiquitination.", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}}, "source": 3563, "target": 1016, "key": "1755f28dfd4de3ac0f768528d8424f84"}, {"line": 4525, "relation": "directlyDecreases", "evidence": "Taken together, our results identify the AICD as a novel inhibitory factor of the canonical Wnt signalling pathway and suggest its regulatory role in neuronal cell proliferation and differentiation.", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Wnt signaling subgraph": true}}, "source": 3563, "target": 462, "key": "39e6d36e98d77bd7d72ede984008f429"}, {"relation": "partOf", "source": 3563, "target": 1041, "key": "dfc3cb0bbddb4af16cf23a8fc98fdb7a"}, {"line": 24551, "relation": "association", "evidence": "c-Abl pmodulates AICD dependent cellular responses: transcriptional induction and apoptotic process.", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 3563, "target": 2240, "key": "389ad03195f949a4806910701a3bd0e1"}, {"line": 24578, "relation": "association", "evidence": "Our results show that c-Abl pmodulates AICD dependent cellular responses, transcriptional induction as well as the apoptotic response, which could participate in the onset and progression of the neurodegenerative pathology, observed in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 3563, "target": 2240, "key": "ff7ae8e7e7b92ecb08a87f0d157566b0"}, {"line": 24552, "relation": "increases", "evidence": "c-Abl pmodulates AICD dependent cellular responses: transcriptional induction and apoptotic process.", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 3563, "target": 794, "key": "6cec5602cccf66638c2e7e7e2c131757"}, {"line": 24557, "relation": "increases", "evidence": "APP intracellular domain (AICD) has been proposed as a transcriptional inductor that moves to the nucleus with the adaptor protein Fe65 and regulates transcription.", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 3563, "target": 794, "key": "a7ebf832147db5dfd3625bedf5f46575"}, {"line": 24553, "relation": "increases", "evidence": "c-Abl pmodulates AICD dependent cellular responses: transcriptional induction and apoptotic process.", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 3563, "target": 478, "key": "deb009c87b2a66405b6e2d21c98ff101"}, {"relation": "partOf", "source": 3563, "target": 1673, "key": "51a9c492ccec6c4c4c360e2ec1db3207"}, {"line": 24799, "relation": "association", "evidence": "Neprilysin has been proposed as a target gene for AICD.", "citation": {"db": "PubMed", "db_id": "19306298"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"Medium": true}}, "source": 3563, "target": 3057, "key": "046e5c930468de52fc26b513a8ccd146"}, {"line": 35604, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 3640, "key": "bae98293cc8767779635ef7403e387bc"}, {"line": 35614, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Species": {"10090": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3628, "key": "acf9ea2473fcaf5fe8f482e62d8a4c23"}, {"line": 37858, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2299, "key": "f3700e541571ca50b22d06921dc1a61a"}, {"line": 38434, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2299, "key": "91c48f59fbec5e9fda2a742c82167c4f"}, {"line": 38430, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2948, "key": "a31770a1455b9022faa73fcfb84b39c4"}, {"line": 38431, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2949, "key": "3b56b80f61d8a45b151776467a7dee20"}, {"line": 38432, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2950, "key": "5281533530e7a4926e729bf4784efdab"}, {"line": 38433, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2951, "key": "1f5918f7cb3f18daa539f322f3dade6f"}, {"line": 38435, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3354, "key": "8b9069c4a20744b3c95aaba2faeeebbe"}, {"line": 38436, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3356, "key": "4029e27013fb340d5cb1da6c1c11e0e8"}, {"line": 38437, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3358, "key": "0a93b0258bd572cffb1f4e2f7241fa91"}, {"line": 38438, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3360, "key": "f81b49f8fbb5f0620bb24f43a330e4c2"}, {"line": 38439, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3151, "key": "c0d38f784bd94807f5be96c9b47a3679"}, {"line": 38440, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2294, "key": "475f5525dc2f91ac8872e9ef0b4a9385"}, {"line": 38441, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2296, "key": "d8b1be53fa001890ec108e73ac58e66b"}, {"line": 38442, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2298, "key": "fddcfc3d69b96067df5cc32a3a52c6c6"}, {"line": 38443, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2535, "key": "bf7088e13c867da5344ff7123a1d8735"}, {"line": 38444, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2536, "key": "cde2441abeb2fa4dbffdf79f7218e542"}, {"line": 38445, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2537, "key": "84beef6edadc077dae5abf11282e21e1"}, {"line": 38446, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2616, "key": "cabfb000f9491df744934c6573ab72e8"}, {"relation": "partOf", "source": 3563, "target": 1510, "key": "9ebc12e19c5a31d981fff834953c89ac"}, {"relation": "partOf", "source": 3563, "target": 1511, "key": "0829a743f5583a13d09569f09a24d036"}, {"relation": "partOf", "source": 3563, "target": 1512, "key": "51753c3537ce15b159a2bb2744bc7b71"}, {"relation": "partOf", "source": 3563, "target": 1513, "key": "2bed17704446a0c90fd997b03b222c4c"}, {"relation": "partOf", "source": 3563, "target": 1629, "key": "eae0ada1d0c26e6d0155c23d37ad76d5"}, {"relation": "partOf", "source": 3563, "target": 1630, "key": "26441dd70ba0261b3b3799a4d693b392"}, {"relation": "partOf", "source": 3563, "target": 1631, "key": "51a68746148d63f36f10a54e13c04737"}, {"relation": "partOf", "source": 3563, "target": 1632, "key": "9ac63bc2f11398749700353f169f709f"}, {"relation": "partOf", "source": 3563, "target": 1600, "key": "ed974c441724b764a102ff2a1cc24e2c"}, {"relation": "partOf", "source": 3563, "target": 1079, "key": "764333e55bc151f3e3b62718c99495da"}, {"relation": "partOf", "source": 3563, "target": 1086, "key": "5b5a666e9c44b2416fd4df09503f2799"}, {"relation": "partOf", "source": 3563, "target": 1091, "key": "17da11579bc5fb2aedf569509f5a8e6c"}, {"relation": "partOf", "source": 3563, "target": 1356, "key": "b95c3c1ba38b6e701f09fe0361ebf583"}, {"relation": "partOf", "source": 3563, "target": 1357, "key": "064f5c75474e8b638d7d6c5a5e709cb6"}, {"relation": "partOf", "source": 3563, "target": 1358, "key": "027fc9e56c46bb792ab9a33cb2ec27a5"}, {"relation": "partOf", "source": 3563, "target": 1389, "key": "4339be6c5782face4da1d36933240540"}, {"line": 38474, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2352, "key": "c2c47f2714e98beb33778c4af06ab7aa"}, {"line": 38475, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3435, "key": "96f070aa8ca7657114e1ca146fb33c46"}, {"line": 38476, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3436, "key": "42c9fce256a2ee51d594edd8701104d3"}, {"line": 38477, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2954, "key": "ccb54ed555297199d10d9c79c6b1aa9b"}, {"line": 38478, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2681, "key": "43cc7fa67bf08b70bbd3b9af3f61d7e5"}, {"line": 38479, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2682, "key": "e708281776ffe8847e16f7a8232ee86c"}, {"line": 38480, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 1820, "key": "2184ab090ee6bf36ed7da4cdec618026"}, {"line": 38481, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3056, "key": "0b9eb910fc03b8905f2a7849cd331755"}, {"line": 38482, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 1873, "key": "3b10ab530d9661ed2170e44246755cb6"}, {"line": 38483, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3052, "key": "5cfeaa4ae16e3dfff31814020ff4de10"}, {"line": 38484, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3055, "key": "81609663f2d8b7482f5be87260a66db4"}, {"line": 38485, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2270, "key": "66b73aebe80f766c510e83a842cee0b5"}, {"line": 38486, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 1874, "key": "0912f2869068a6e9fe5e606a45de0239"}, {"line": 38487, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3054, "key": "6f3179c5e201f5a9b40711c66b313fe8"}, {"line": 38488, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 3053, "key": "eecea9b40090995da193befcfb651ed5"}, {"line": 38489, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3563, "target": 2953, "key": "31223d8d52753f462c83f69f5101c918"}, {"relation": "partOf", "source": 3563, "target": 1255, "key": "fceeed34ed26747a7fdf18321d73c7fb"}, {"relation": "partOf", "source": 3563, "target": 1634, "key": "8896f186716631e7fc6a4b3277f0ed4d"}, {"relation": "partOf", "source": 3563, "target": 1635, "key": "151d6eb99ca9dc5f8f0beb9712a651b9"}, {"relation": "partOf", "source": 3563, "target": 1515, "key": "60a662452e8d72a43d67782b6785061e"}, {"relation": "partOf", "source": 3563, "target": 1415, "key": "5ccea620d6ccca3b0468915689681233"}, {"relation": "partOf", "source": 3563, "target": 1416, "key": "d3e537d84d16f2ce2d966fce1ac19963"}, {"relation": "partOf", "source": 3563, "target": 1011, "key": "64ec3c6143f0e39b8685a1635d7e0320"}, {"relation": "partOf", "source": 3563, "target": 1575, "key": "89076e11ad7b1407c17e96a4f69163c2"}, {"relation": "partOf", "source": 3563, "target": 1012, "key": "b01633d10355a6b7c88bcdcbbbe286b7"}, {"relation": "partOf", "source": 3563, "target": 1571, "key": "29cbbee4d3f84dc569af445bd1c2fcf8"}, {"relation": "partOf", "source": 3563, "target": 1574, "key": "fb8be33f0be43a0dd1acf055b83ad276"}, {"relation": "partOf", "source": 3563, "target": 1064, "key": "97fdb3fa816cccf2910699a1b169ccda"}, {"relation": "partOf", "source": 3563, "target": 1013, "key": "39954f8d24b1de9b77b9e6c22fb3a37c"}, {"relation": "partOf", "source": 3563, "target": 1573, "key": "3b9ea2970313be73a64c96c83ddb4b85"}, {"relation": "partOf", "source": 3563, "target": 1572, "key": "f08326d7aece421871fa8a58e0147850"}, {"relation": "partOf", "source": 3563, "target": 1514, "key": "ce854a88153e5f6b037c0561b6df4e2f"}, {"relation": "hasVariant", "source": 3563, "target": 3564, "key": "872309284cf47f156d18c82708b1ffa6"}, {"relation": "partOf", "source": 3563, "target": 1098, "key": "aba702e2528eee42990a46f05274e8fa"}, {"line": 38548, "relation": "association", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 3563, "target": 1771, "key": "e8984c142411f81e6abda51637ad5f91"}, {"line": 38550, "relation": "association", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 3563, "target": 1877, "key": "5265f5335e74e40a2579fb03003ea217"}, {"line": 38555, "relation": "regulates", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 3563, "target": 1863, "key": "b54bd58883174cb6572f258dbed622e1"}, {"line": 38556, "relation": "regulates", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 3563, "target": 2003, "key": "b20c4a371d80a9cb5e38c18a037c5d25"}, {"line": 38565, "relation": "positiveCorrelation", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 3563, "target": 3563, "key": "e0476d2da816b91957e132a317e78550"}, {"line": 38565, "relation": "positiveCorrelation", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "object": {"modifier": "Activity"}, "source": 3563, "target": 3563, "key": "21f85b327a3692754fc1e23e0272c5ed"}, {"line": 38566, "relation": "regulates", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3563, "target": 3973, "key": "51754ea0c9dfece49cce1bac7d5c3fd7"}, {"line": 38567, "relation": "regulates", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3563, "target": 2172, "key": "b3e44486c5c3548d02e4c5c8264836ac"}, {"relation": "partOf", "source": 3563, "target": 1104, "key": "a34085c9d360c3d4f7ee45f34d7c0a9e"}, {"relation": "partOf", "source": 3563, "target": 1549, "key": "d76c52c3225e804c579582c8ccf1a40a"}, {"relation": "partOf", "source": 3563, "target": 1550, "key": "f27f823e6c8a0aeb8ba83491492712bc"}, {"relation": "partOf", "source": 3563, "target": 1551, "key": "d3a07ef3c5025686695360f726ee3eec"}, {"line": 166, "relation": "isA", "evidence": "We previously showed that beta-amyloid precursor protein (APP) is cleaved not only in the middle of the membrane (gamma-cleavage) but also at novel cleavage sites close to the membrane/cytoplasmic boundary (ε-cleavage), releasing APP intracellular domains (AICDs) 49−99 and 50−99. To learn more about the relationship between gamma- and ε-cleavage, C-terminally truncated carboxyl-terminal fragments (CTFs) of APP, especially CTFs1−48 and 1−49 (the postulated products that are generated by ε-cleavage), were transiently expressed in CHO cells. Most importantly, the cells expressing CTF1−49 secreted predominantly amyloid beta-protein (Abeta) 40, while those expressing CTF1−48 secreted preferentially Abeta42. This supports our assumption that ε-cleavage precedes Αbeta production and that preceding ε-cleavage determines the preference for the final Abeta species. The gamma-secretase inhibitors, L-685,458 and DAPT, suppressed Abeta production from CTF1−49. Regarding Abeta production from CTF1−48, L-685,458 suppressed it, but DAPT failed to do so. A dominant negative mutant of presenilin 1 suppressed the production of Abeta40 and 42 from both CTFs1−48 and 1−49. These data should shed significant light into the mechanism of Abeta production.", "citation": {"db": "PubMed", "db_id": "15491160"}, "annotations": {"TextLocation": {"Abstract": true}}, "source": 2326, "target": 3563, "key": "c2091bc061c93dca939c6ebe3637ad3e"}, {"line": 180, "relation": "increases", "evidence": "Processing of APP to produce Ab involves cleavage by b-site APP cleaving enzyme-1 (BACE1) and g-secretase that process APP at the N- and C-termini, respectively, of the Ab sequence.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "f301d045951fdc4cf457bf5825e98e05"}, {"line": 302, "relation": "increases", "evidence": "Amyloid plaques consist primarily of amyloid beta protein (Abeta), which is produced when APP is cleaved by beta-secretase and then cleaved again by gamma-secretase as part of the amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "22702962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "7e9d3550c00012c08b6326b5dac9010e"}, {"line": 464, "relation": "increases", "evidence": "Abeta is generated from APP by concerted proteolysis by Abeta-secretase, which generates carboxyl-terminal fragments (CTFs) of APP, and then by gamma-secretase.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "f7e9be7758c68857028ca2e491950454"}, {"line": 5170, "relation": "directlyIncreases", "evidence": "beta-Amyloid (Abeta) plays a central role in Alzheimer's disease (AD) pathogenesis. Neurons are major sources of Abeta in the brain. However, astrocytes outnumber neurons by at least five-fold. Thus, even a small level of astrocytic Abeta production could make a significant contribution to Abeta burden in AD. Moreover, activated astrocytes may increase Abeta generation. beta-Site APP cleaving enzyme 1 (BACE1) cleavage of amyloid precursor protein (APP) initiates Abeta production.", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "25f04d9821ecb58cb56dc257e66c596e"}, {"line": 6086, "relation": "increases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "6cdb7392821b9737d97781349ad77c30"}, {"line": 6960, "relation": "directlyIncreases", "evidence": "We then analyzed the levels of various APP metabolites including the cleavage products of alpha- and beta-secretases (Fig. 1C). Metformin reduced alpha-cleavage and promoted beta-cleavage, as evidenced by decreased sAPPα and increased APP C-terminal fragment, CTF-beta (the upper CTF band that resulted from cleavage by BACE1). No change in the levels of full-length PS1 (presenilin 1, the core component of gamma-secretase) or its N-terminal fragment was detected from total cell lysates.", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "bad7a38bfe22e41b4f4c915b7bcd8725"}, {"line": 8413, "relation": "directlyIncreases", "evidence": "Processing of APP to produce Ab involves cleavage by b-site APP cleaving enzyme-1 (BACE1) and g-secretase that process APP at the N- and C-termini, respectively, of the Ab sequence.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "556af514d649f9777b5f4a4fe555cead"}, {"line": 9184, "relation": "directlyIncreases", "evidence": "The amyloid-beta (Abeta) peptide is the derivative of amyloid precursor protein (APP) generated through sequential proteolytic processing by beta- and gamma-secretases. Excessive accumulation of Abeta, the main constituent of amyloid plaques, has been implicated in the etiology of Alzheimer disease (AD). ", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "b97d0c40ba37b927b1ed6d53f738f45e"}, {"line": 9212, "relation": "directlyIncreases", "evidence": "Such possibility is further corroborated by the observation that a significant decrease in miR-106b expression was found in sporadic AD patients.On the other hand, two miRNAs (miR-298 and miR-328) was found to regulate BACE mRNA translation, while BACE was responsible for APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "34d1cab890c8daa7ca51c3a076b7061f"}, {"line": 14311, "relation": "directlyIncreases", "evidence": "BACE1 cleaves beta-amyloid precursor protein (APP) to generate amyloid beta protein (Abeta), a central component of neuritic plaques in AD brains.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "7c47e83815cb50018672a482dee805ca"}, {"line": 27209, "relation": "directlyIncreases", "evidence": "We have recently demonstrated that bis(7)-Cognitin, a promising multifunctional anti-Alzheimer's dimer, can remarkably reduce the generation of amyloid beta peptide (Abeta) by inhibiting beta-secretase (BACE-1) and activating alpha-secretase activity.", "citation": {"db": "PubMed", "db_id": "19765582"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4096, "key": "89ab76572a8b5aecc9bb6cdcff9faae8"}, {"line": 1143, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in Abeta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "84c6b5dfb41cbe065227561c02b7f578"}, {"line": 3143, "relation": "increases", "evidence": "Ex vivo studies show that the levels of BACE1 are tightly regulated by the ATases. Specifically, up-regulation of ATase1 and ATase2 increases the levels of BACE1 and the generation of Abeta while siRNA-mediated down-regulation of either transferase achieves the opposite effects.", "citation": {"db": "PubMed", "db_id": "22267734"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Cell": {"microglial cell": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 80, "key": "cfd68a1fc47d5e3a27b46e19ae00e4b0"}, {"line": 4155, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2375, "target": 80, "key": "e0f186860e2c38271ed2ed2240e29057"}, {"line": 4420, "relation": "increases", "evidence": "The level of Abeta-site APP-cleaving enzyme 1 (BACE1) has been documented to increase in the brains of patients with Alzheimer's disease, which has resulted in elevation of Abeta-amyloid (Abeta) peptides.", "citation": {"db": "PubMed", "db_id": "22166376"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}}, "source": 2375, "target": 80, "key": "0744b3aa7c990ffbcb98b5faa63c3a0e"}, {"line": 8897, "relation": "increases", "evidence": "The role of miR-124 on the expression of beta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the beta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 80, "key": "6dab4d9de7231c0a79083ba3debc8ab2"}, {"line": 9323, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in beta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true, "Beta secretase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 2375, "target": 80, "key": "41d78cc00c336217cd332506af182f10"}, {"line": 26534, "relation": "increases", "evidence": "A pathogenic mutation at codons 670/671 in APP (APP Swedish) leads to enhanced cleavage at the beta-secretase scissile bond and increased Abeta formation.", "citation": {"db": "PubMed", "db_id": "10500121"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "10f0d66732be011565f04a379855c132"}, {"line": 26565, "relation": "increases", "evidence": "The novel transmembrane aspartic protease BACE (for Beta-site APP Cleaving Enzyme) is the beta-secretase that cleaves amyloid precursor protein to initiate beta-amyloid formation.", "citation": {"db": "PubMed", "db_id": "10956649"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "ca7fea7c56b4e6ecd23db98850b734bc"}, {"line": 26651, "relation": "increases", "evidence": "beta-Secretase, a beta-site amyloid precursor protein (APP) cleaving enzyme (BACE), participates in the secretion of beta-amyloid peptides (Abeta), the major components of the toxic amyloid plaques found in the brains of patients with Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "15218540"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2375, "target": 80, "key": "b008bf1979327125411041283467462e"}, {"line": 26680, "relation": "increases", "evidence": "Amyloid-beta (Abeta) the primary component of the senile plaques found in Alzheimer's disease (AD) is generated by the rate-limiting cleavage of amyloid precursor protein (APP) by beta-secretase followed by gamma-secretase cleavage.", "citation": {"db": "PubMed", "db_id": "15452128"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "b1f408536dddbea5d2723d34b7b56bb0"}, {"line": 26694, "relation": "increases", "evidence": "The generation of beta-amyloid peptides requires the enzymatic activity of the beta-site APP-cleaving enzyme 1 (BACE1).", "citation": {"db": "PubMed", "db_id": "15465276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "1879c56f15b7292495146bf68464a95e"}, {"line": 26710, "relation": "increases", "evidence": "The generation of beta-amyloid peptides by proteolytical processing of the amyloid precursor protein (APP) requires the enzymatic activity of the beta-site APP cleaving enzyme 1 (BACE1).", "citation": {"db": "PubMed", "db_id": "15663471"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "69c2091ad8fd80fa546a44bed0fb8887"}, {"line": 26761, "relation": "increases", "evidence": "Sequential processing of amyloid precursor protein (APP) by membrane-bound proteases, BACE1 and gamma-secretase, plays a crucial role in the pathogenesis of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "15824102"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "932e6ab9270e6109a40cee36a89300e6"}, {"line": 26768, "relation": "increases", "evidence": "gamma-Secretase is a membrane protein complex that cleaves the beta-amyloid precursor protein (APP) within the transmembrane region, after prior processing by beta-secretase, producing amyloid beta peptides Abeta(40) and Abeta(42).", "citation": {"db": "PubMed", "db_id": "15890777"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "347a696002005b31cf5d1171175c4603"}, {"line": 26778, "relation": "increases", "evidence": "Release of Abeta from the amyloid precursor protein (APP) requires proteolysis by the beta-site APP-cleaving enzyme (BACE1).", "citation": {"db": "PubMed", "db_id": "16027115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "33d1b640b829cf2eaf3ceef884acbd0b"}, {"line": 26811, "relation": "increases", "evidence": "Abeta is produced from beta-amyloid precursor protein (APP) by beta-secretase and gamma-secretase. beta-Secretase has been identified as beta-site APP cleaving enzyme1 (BACE1).", "citation": {"db": "PubMed", "db_id": "16290302"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "6c06b8946fc2a253810793cf25b2e9ed"}, {"line": 26826, "relation": "increases", "evidence": "The beta-amyloid peptide (Abeta) is a major component of the Alzheimer's disease (AD)-associated senile plaques and is generated by sequential cleavage of the beta-amyloid precursor protein (APP) by beta-secretase and gamma-secretase.", "citation": {"db": "PubMed", "db_id": "16888322"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "4670e55f322b8787bfb892dc93af5504"}, {"line": 26842, "relation": "increases", "evidence": "Amyloid beta-peptide (Abeta), which is a product of the proteolytic effect of beta-secretase (BACE) on an amyloid precursor protein, is closely associated with Alzheimer's disease (AD) pathogenesis.", "citation": {"db": "PubMed", "db_id": "17205046"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "95d9f478a004a05a5cd3a3290e1c375e"}, {"line": 26852, "relation": "increases", "evidence": "TNF-alpha directly stimulated beta-site APP-cleaving enzyme (BACE1) expression and enhanced beta-processing of APP in astrocytes.", "citation": {"db": "PubMed", "db_id": "17255335"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "277ccf00080f4bf7e95a46e8fbb23f1e"}, {"line": 26871, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with accumulation of the neurotoxic peptide amyloid-beta (Abeta), which is produced by sequential cleavage of amyloid precursor protein (APP) by the aspartyl protease beta-secretase and the presenilin-dependent protease gamma-secretase.", "citation": {"db": "PubMed", "db_id": "17360493"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "2164a92bf799d9f702f51efa37267f39"}, {"line": 26886, "relation": "increases", "evidence": "Such an approach limits APP processing by beta-secretase, mainly through the endocytic pathway, and overcomes some of the limitations of BACE inhibition.", "citation": {"db": "PubMed", "db_id": "17536186"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "daa8b59b3650428fbdb21d11a805bc2b"}, {"line": 26897, "relation": "increases", "evidence": "Proteolytic processing of the amyloid precursor protein (APP) by beta-secretase, beta-site APP cleaving enzyme (BACE1), is the initial step in the production of the amyloid beta (Abeta) peptide, which is involved in the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17573534"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "ec0b7a46b9e0891c3901604d87f72994"}, {"line": 26927, "relation": "increases", "evidence": "To address this issue, temporal neocortex of 27 AD and 21 non-demented control brains was examined to assess mRNA levels of APP isoforms (total APP, APP containing the Kunitz protease inhibitor domain [APP-KPI] and APP770) and APP metabolic enzymatic partners (the APP cleaving enzymes beta-secretase [BACE] and presenilin-1 [PS-1], and putative clearance molecules, low-density lipoprotein receptor protein [LRP] and apolipoprotein E [apoE]).", "citation": {"db": "PubMed", "db_id": "17586478"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "c54c89c5d4362adc6fc1b3ab65394816"}, {"line": 26937, "relation": "increases", "evidence": "Pathologically, AD is characterized by the deposition in the brain of amyloid-beta peptides derived from proteolysis of amyloid precursor protein (APP) by beta-site APP cleaving enzyme 1 (BACE1) and gamma-secretase.", "citation": {"db": "PubMed", "db_id": "17611486"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "00884e90d3bf56a38fc2860d3d2935c8"}, {"line": 26947, "relation": "increases", "evidence": "Abeta is generated by proteolytic processing of amyloid precursor protein (APP) via beta and gamma-secretases.", "citation": {"db": "PubMed", "db_id": "17908048"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "be5ade7aedeccb54fe9ecbe490cd53ff"}, {"line": 26986, "relation": "increases", "evidence": "Proteolytic cleavage of amyloid precursor protein by beta-secretase (BACE-1) and gamma-secretase leads to formation of beta-amyloid (A beta) a key component of amyloid plaques, which are considered the hallmark of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18162398"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "0dc5af8edc43a68a7ce83181f777533d"}, {"line": 26996, "relation": "increases", "evidence": "Here we describe that the two enzymatic activities responsible for Abeta production, beta-secretase and gamma-secretase, are inhibited in parallel by cholesterol reduction.", "citation": {"db": "PubMed", "db_id": "18308724"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "532c9f7fbdb07e9a6d4502cf853d1b7e"}, {"line": 27013, "relation": "increases", "evidence": "There was also an induced expression of the key APP processing enzyme i.e. beta-site APP cleaving enzyme 1 in both high fat/cholesterol-fed C57BL/6 and LDLR-/- mice accompanied by an increased generation of C-terminal fragments of APP.", "citation": {"db": "PubMed", "db_id": "18410513"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "77916e6203a5cfbf64df913a49c84c5f"}, {"line": 27026, "relation": "increases", "evidence": "The study provides new cell-based assays for the profiling of small molecule inhibitors of QC and points to conspicuous differences in processing of APP depending on sequence at the beta-secretase cleavage site.", "citation": {"db": "PubMed", "db_id": "18570439"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "9516108fa6cc5b9cbf10c6f051b100f8"}, {"line": 27037, "relation": "increases", "evidence": "The Abeta peptide results from cleavage of APP initially by BACE1 to produce the C99 fragment and releases soluble APPbeta (sAPPbeta); C99 is then further cleaved by gamma-secretase leading to the Abeta peptide.", "citation": {"db": "PubMed", "db_id": "18609117"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "2f88333ce58d2612a5419774594b6a61"}, {"line": 27048, "relation": "increases", "evidence": "Here, we show that lysines 587 and 595 of APP, which are immediately adjacent to the site of beta-secretase cleavage, are covalently pmodified by SUMO proteins in vivo.", "citation": {"db": "PubMed", "db_id": "18675254"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "a30e55746060b8032436610d520efd49"}, {"line": 27065, "relation": "increases", "evidence": "Specifically, hypoxia significantly increases beta-site APP cleaving enzyme (BACE1) gene transcription through the over-expression of hypoxia inducible factor 1alpha, resulting in increased BACE1 secretase activity and amyloid-beta production.", "citation": {"db": "PubMed", "db_id": "19196431"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Hypoxia response subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "85e228f46ffdca2d66ba151bea9e05e4"}, {"line": 27076, "relation": "increases", "evidence": "It is produced from amyloid precursor protein (APP) by proteolytic processing dependent on the beta-site APP-cleaving enzyme 1 (BACE1) and gamma-secretase, and is degraded by a broad range of proteases.", "citation": {"db": "PubMed", "db_id": "19199126"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "2da143a1d340ba3617d59f9b6168ae09"}, {"line": 27113, "relation": "increases", "evidence": "Three-dimensional quantitative structure-activity relationship (3D-QSAR) pmodels were developed based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), on a series of 43 hydroxyethylamine derivatives, acting as potent inhibitors of beta-site amyloid precursor protein (APP) cleavage enzyme (BACE-1).", "citation": {"db": "PubMed", "db_id": "19330459"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "593dd60d6f936a4d1fd40a0778bdd07a"}, {"line": 27135, "relation": "increases", "evidence": "beta-Site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1) is a membrane-bound protease that is essential for the production of beta-amyloid protein (Abeta).", "citation": {"db": "PubMed", "db_id": "19405102"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "5403dc018a66a8ef147f0bc9db7ed74e"}, {"line": 27151, "relation": "increases", "evidence": "Expression levels of the amyloid precursor protein (APP) and beta-site amyloid (Abeta) cleaving enzyme 1 (BACE1) have been implicated in Alzheimer disease (AD) progression.", "citation": {"db": "PubMed", "db_id": "19462468"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "897b91e5c1afd6e7d4253a98ee173283"}, {"line": 27162, "relation": "increases", "evidence": "The effect of nicotine (2 mg/(kg day)) on Abeta-induced spatial learning and memory impairments was assessed by evaluation of performance in the radial arm water maze (RAWM), in vivo electrophysiological recordings of early-phase long-term potentiation (E-LTP) in urethane-anesthetized rats, and immunoblot analysis to determine changes in the levels of beta-site amyloid precursor protein (APP)-cleaving enzyme (BACE), Abeta and memory-related protei", "citation": {"db": "PubMed", "db_id": "19464074"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "852a9ac4581296a1d5f05bf10c567528"}, {"line": 27172, "relation": "increases", "evidence": "Beta-site amyloid precursor protein cleaving enzyme (BACE1) is the rate-limiting enzyme for production of beta-amyloid peptides (Abeta), which are proposed to drive the pathological changes found in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "19669607"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "869d34d6764f364857af2670056215c6"}, {"line": 27182, "relation": "increases", "evidence": "Core CSF biomarkers include secreted Abeta and amyloid precursor protein (APP) isoforms, Abeta oligomers and beta-site APP-cleaving enzyme 1 (BACE1).", "citation": {"db": "PubMed", "db_id": "19698775"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "45481d37bea94908c526664cfbf09c66"}, {"line": 27195, "relation": "increases", "evidence": "In the amyloidogenic pathway, a small proportion of APP is cleaved by beta- and gamma-secretases, known as beta-site APP-cleaving enzyme 1 (BACE1) and presenilin, respectively, leading to the secretion of Abeta peptides.", "citation": {"db": "PubMed", "db_id": "19729516"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "ea59ac0de0443f12170eb6dccd3f6198"}, {"line": 27219, "relation": "increases", "evidence": "A beta is generated upon the sequential proteolytic cleavage of transmembrane amyloid precursor protein (APP) by two membrane-bound proteases, beta-secretase (BACE1) and the gamma-secretase complex comprising presenilin 1 (PS1), nicastrin, APH-1 and PEN-2.", "citation": {"db": "PubMed", "db_id": "19885829"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "552a2c687c328cf6580f372d87b42bbd"}, {"line": 27229, "relation": "increases", "evidence": "In Alzheimer disease, this feedback loop is disrupted, and the increased level of Abeta oligomers bind to PrP(C) and prevent it from regulating BACE1 activity. PrP(C) interacts with and inhibits the beta-secretase BACE1, the rate-limiting enzyme in the production of Abeta.", "citation": {"db": "PubMed", "db_id": "19887909"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "be3efdd838faf28734c01db585139734"}, {"line": 27245, "relation": "increases", "evidence": "The beta-site APP cleaving enzyme (BACE1) is responsible for the first step in the production of the beta-amyloid protein of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20067575"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "2a877b42e2f8a944becc2a4f70d5c26e"}, {"line": 27258, "relation": "increases", "evidence": "beta-Site amyloid precursor protein cleaving enzyme 1 (BACE1) initiates amyloid-beta (Abeta) generation that is central to the pathophysiology of Alzheimer's disease (AD). Therefore, lowering Abeta levels by BACE1 manipulations represents a key therapeutic strategy, but it remains unclear whether partial inhibition of BACE1, as expected for AD treatments, can improve memory deficits.", "citation": {"db": "PubMed", "db_id": "20089133"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "8d87a821fca68812832f88350df5c4f1"}, {"line": 27328, "relation": "increases", "evidence": "Abeta is generated from AbetaPP through an initial cleavage by the beta-secretase, BACE-1, which results in the generation of the soluble AbetaPPbeta fragment (sAbetaPPbeta) and the membrane bound C-terminal fragment beta (CTFbeta or C99). ", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "d3dcf5a51a9e964b8f853cd97a5e3146"}, {"line": 27337, "relation": "increases", "evidence": "We previously indicated that amyloid beta (Abeta) augments protein levels of beta-site amyloid precursor protein cleaving enzyme-1 (BACE-1) through oxidative stress.", "citation": {"db": "PubMed", "db_id": "20171164"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "ce4dd3750d9d68bfbf39f0ea77b04a3d"}, {"line": 27353, "relation": "increases", "evidence": "Abeta is derived from a type I transmembrane protein, amyloid precursor protein (APP), by the sequential proteolytic events mediated by beta-site APP cleaving enzyme 1 (BACE1) and gamma-secretase.", "citation": {"db": "PubMed", "db_id": "20303415"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "1db2f5ceeeaac9e7e581ef7bfc01d7dd"}, {"line": 27365, "relation": "increases", "evidence": "Medium", "citation": {"db": "PubMed", "db_id": "20409323"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "42e59cbffb81700e6e777691515187ad"}, {"line": 27380, "relation": "increases", "evidence": "Basic understanding of the activities of the amyloid beta peptide (Abeta) and associated proteins such as beta-site APP-cleaving enzyme 1 (BACE1) is necessary to develop effective medical responses to AD.", "citation": {"db": "PubMed", "db_id": "20451519"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "efaa9dffed48453738bc077bd2943708"}, {"line": 27477, "relation": "increases", "evidence": "Cleavage of APP by BACE1 is the first proteolytic step in the production of amyloid-beta (Abeta), which accumulates in senile plaques in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20685197"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "63f51820194c774cfe63bdbd8dd5e300"}, {"line": 27487, "relation": "increases", "evidence": "Abeta is generated from the beta-amyloid precursor protein (APP) through the proteolysis of beta-site APP cleaving enzyme 1 (BACE1) and gamma-secretase. ", "citation": {"db": "PubMed", "db_id": "20727383"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "8682fda32ebdbd0c0d8b7df3d2482bd3"}, {"line": 27499, "relation": "increases", "evidence": "The Swedish mutation (K595N/M596L) of amyloid precursor protein (APP-swe) has been known to increase abnormal cleavage of cellular APP by Beta-secretase (BACE), which causes tau protein hyperphosphorylation and early-onset Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "21034535"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "6b968b9edeb9338f545900e06fb67b79"}, {"line": 27521, "relation": "increases", "evidence": "Amyloid-beta peptide (Abeta) is generated by sequential cleavage of the amyloid precursor protein (APP) by beta-site amyloid precursor protein cleaving enzyme 1 (beta-secretase, or BACE1) and gamma-secretase. ", "citation": {"db": "PubMed", "db_id": "21433051"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "da8bfb7e0fb7ac3b842354b7972a3839"}, {"line": 27531, "relation": "increases", "evidence": "To release the amyloidogenic peptide A beta from the Alzheimer amyloid precursor protein (APP), two secretases act sequentially: first, beta-secretase cleaves close to the membrane within the ectodomain and then gamma-secretase cuts within the transmembrane domain.", "citation": {"db": "PubMed", "db_id": "9623986"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "8a863f4eac975adf9d04b7d8877efb57"}, {"line": 27541, "relation": "increases", "evidence": "Proteolytic processing of APP by beta-secretase, on the other hand, exposes the N-terminus of beta-amyloid, which is liberated after gamma-secretase cleavage at the variable amyloid C-terminus.", "citation": {"db": "PubMed", "db_id": "9775403"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "17042e41a24544685deba81ef69b606e"}, {"line": 27566, "relation": "increases", "evidence": "Pulse-chase experiments revealed beta-secretase cleavage from immature full-length amyloid precursor protein harboring the Swedish mutation.", "citation": {"db": "PubMed", "db_id": "10096041"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "08f5b7b8dfaf17a34732438708e2c25f"}, {"line": 27576, "relation": "increases", "evidence": "Solubilized Asp2 protein cleaves a synthetic APP peptide substrate at the beta-secretase site, and the rate of cleavage is increased tenfold by a mutation associated with early-onset Alzheimer's disease in Sweden.", "citation": {"db": "PubMed", "db_id": "10591213"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "bd2d0beef9fdf473122ba059e3c418ca"}, {"line": 27591, "relation": "increases", "evidence": "Recombinant memapsin 2 specifically hydrolyzed peptides derived from the beta-secretase site of both the wild-type and Swedish mutant beta-amyloid precursor protein (APP) with over 60-fold increase of catalytic efficiency for the latter. Human aspartic protease memapsin 2 cleaves the beta-secretase site of beta-amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "10677483"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "d677cb1dd04f0d4c931178bf2c4b869f"}, {"line": 27600, "relation": "increases", "evidence": "IFNgamma in combination with TNFalpha or IL-1beta seems to trigger Abeta production by supporting beta-secretase cleavage of the immature APP molecule.", "citation": {"db": "PubMed", "db_id": "11114266"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "101fbd635df854bffaba464695b51856"}, {"line": 27615, "relation": "increases", "evidence": "Amyloid precursor protein/P-selectin, which is sorted from early to late endosomes, undergoes significantly less alpha-secretase cleavage, and more beta-secretase cleavage, than amyloid precursor protein/P-selectin768A, a mutant that recycles more efficiently to the cell surface.", "citation": {"db": "PubMed", "db_id": "11737828"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "5793d0097fe70d75ccd9895d0f3de58f"}, {"line": 27624, "relation": "increases", "evidence": "Homeostatic APPsbetaswe levels with aging suggest that progressive amyloid deposition in brain results not from increased beta-secretase cleavage of APP but from impaired Abeta/amyloid clearance mechanisms.", "citation": {"db": "PubMed", "db_id": "11839594"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "6db0c9132e5a1232e260171a5d25c940"}, {"line": 27643, "relation": "increases", "evidence": "beta-Site APP-cleaving enzyme (BACE) initiates the processing of the amyloid precursor protein (APP) leading to the generation of beta-amyloid, the main component of Alzheimer's disease senile plaques.", "citation": {"db": "PubMed", "db_id": "11953458"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "f59fef834686914172521dd413fc757f"}, {"line": 27653, "relation": "increases", "evidence": "Beta-secretase cleavage represents the first step in the generation of Abeta polypeptides and initiates the amyloid cascade that leads to neurodegeneration in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "12112088"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "58a4cc1e9b1255166b56f547c5e1f7e5"}, {"line": 27676, "relation": "increases", "evidence": "Our recent knockout studies show that BACE1 is critical for Abeta generation, but the knockout mice show an otherwise normal phenotype, raising the possibility that therapeutic BACE1 inhibition could be accomplished without major mechanism based toxicity.", "citation": {"db": "PubMed", "db_id": "12470797"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "b9a535a24d07ff644644b9b540f42adc"}, {"line": 27686, "relation": "increases", "evidence": "BACE1 is a membrane-bound aspartic protease that cleaves the amyloid precursor protein (APP) at the beta-secretase site, a critical step in the Alzheimer disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "12473667"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "b3562be0387cc33c39986e4f03048672"}, {"line": 27697, "relation": "increases", "evidence": "APP is cleaved either by beta-secretase or by alpha-secretase to initiate amyloidogenic (release of A beta) or nonamyloidogenic processing of APP, respectively.", "citation": {"db": "PubMed", "db_id": "12515826"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "378aa84269fca4503175d7046e3c6b6c"}, {"line": 27707, "relation": "increases", "evidence": "Employing a superior BACE1 cleavage sequence to probe cellular APP processing.", "citation": {"db": "PubMed", "db_id": "12603825"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "24558c92da50d24f26bb4ab0b28d60ee"}, {"line": 27717, "relation": "increases", "evidence": "An integral membrane aspartyl protease, BACE, is responsible for beta-secretase processing of the beta-amyloid precursor protein (APP) to the large secreted sAPPbeta and membrane-bound CTFbeta of 99 residues.", "citation": {"db": "PubMed", "db_id": "14501002"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "b7bd29dd8db2269abb39f4515511ae9b"}, {"line": 27727, "relation": "increases", "evidence": "Here we report evidence that heparan sulfate (HS) interacts with beta-site APP-cleaving enzyme (BACE) 1 and regulates its cleavage of APP.", "citation": {"db": "PubMed", "db_id": "14530380"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "ff75281d3a96f8b8fc4d99da94b66050"}, {"line": 27740, "relation": "increases", "evidence": "Together, these results suggest that T668 phosphorylation may facilitate the BACE1 cleavage of APP to increase Abeta generation.", "citation": {"db": "PubMed", "db_id": "14557249"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "b2ebe6afd671ceda7d7f9afda0652599"}, {"line": 27752, "relation": "increases", "evidence": "Our results indicate that BACE1 siRNA specifically impacts on beta-cleavage of APP and may be a potential therapeutic approach for treating AD.", "citation": {"db": "PubMed", "db_id": "14600149"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "3ccee59fe82d24ab351d121f758558bb"}, {"line": 27762, "relation": "increases", "evidence": "The cerebral deposition of amyloid beta-peptide (Abeta) is a major factor in the etiology of Alzheimer's disease. beta-Secretase (BACE) initiates the generation of Abeta by cleaving the amyloid precursor protein at the beta-site and is therefore a prime target for therapeutic intervention.", "citation": {"db": "PubMed", "db_id": "14622952"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "4e6b711af12dc179f3ef56ea027761dd"}, {"line": 27770, "relation": "increases", "evidence": "BACE1, the major beta-secretase involved in cleaving APP, has been identified as a type 1 membrane-associated aspartyl protease.", "citation": {"db": "PubMed", "db_id": "14701757"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "488f5b00431a4763e46ba71b70ed9e5e"}, {"line": 27782, "relation": "increases", "evidence": "Medium", "citation": {"db": "PubMed", "db_id": "14715132"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "f31321da46efcf00d6770dfca0824af3"}, {"line": 27793, "relation": "increases", "evidence": "Alzheimer's beta-secretase (BACE1) is a membrane-bound protease that cleaves the amyloid precursor protein (APP) in the trans-Golgi network, an initial step in the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "14973371"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "ad646ea378f26900e63ba5749809f30e"}, {"line": 27808, "relation": "increases", "evidence": "The activity of BACE-1 as measured by the formation of the cleavage product of amyloid beta precursor protein, transiently increased up to 48 h after injury, but returned to basal level 7 days post injury.", "citation": {"db": "PubMed", "db_id": "15057522"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "f8a8a488479dde64e5c2497ecae4f67a"}, {"line": 27818, "relation": "increases", "evidence": "BACE is an aspartyl protease that cleaves the amyloid precursor protein (APP) at the beta-secretase cleavage site and is involved in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15080893"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "7ba0de224e7e957e0ce9ec96be0df40c"}, {"line": 27829, "relation": "increases", "evidence": "The beta-site APP-cleaving enzyme (BACE1) has been identified as the key enzyme leading to beta-amyloid formation, and cholinergic mechanisms have been shown to control APP processing.", "citation": {"db": "PubMed", "db_id": "15211591"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "bddc837d45a2acacc6a197c6775e96b1"}, {"line": 27848, "relation": "increases", "evidence": "Because alpha-secretase and BACE-1 cleave APP within the secretory pathway, it is likely that the two enzymes compete for the APP substrate.", "citation": {"db": "PubMed", "db_id": "15314262"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "212ba47720585c554e4a0183edc6acf6"}, {"line": 27858, "relation": "increases", "evidence": "Alzheimer's beta-secretase (BACE1) cleaves amyloid precursor protein to produce amyloid beta-peptide, which is a crucial initiation process of the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15467394"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "94482287b334f8840bb48c4aa26b4964"}, {"line": 27869, "relation": "increases", "evidence": "Mapping to the Down syndrome critical region (chromosome 21) and identified as a homologue of BACE1, BACE2 also cleaves amyloid precursor protein at the beta-site.", "citation": {"db": "PubMed", "db_id": "15473697"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "207540edf868297dd492f21e39ffa434"}, {"line": 27891, "relation": "increases", "evidence": "Although pmodest overexpression enhanced amyloid deposition, high BACE overexpression inhibited amyloid formation despite increased beta-cleavage of APP. BACE overexpression alters the subcellular processing of APP and inhibits Abeta deposition in vivo.", "citation": {"db": "PubMed", "db_id": "15642747"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "d599a1adc381419b45440eecfc181907"}, {"line": 27913, "relation": "increases", "evidence": "Medium", "citation": {"db": "PubMed", "db_id": "15857888"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 2375, "target": 80, "key": "b4559b2f67a8febea88304dab5be7670"}, {"line": 27935, "relation": "increases", "evidence": "Beta-secretase [beta-site amyloid precursor protein-cleaving enzyme 1 (BACE1)] is the key rate-limiting enzyme for the production of the beta-amyloid (Abeta) peptide involved in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "16306400"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "f23ae7c809544cc1247ebd8714cf87d8"}, {"line": 27943, "relation": "increases", "evidence": "This was associated with inefficient plasminogen binding and plasmin activation, the displacement of beta-secretase (BACE) from DRMs to APP-containing membrane fractions, increased beta-cleavage of APP and high levels of Abeta peptides.", "citation": {"db": "PubMed", "db_id": "16407971"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "2ff7e093a0a3bc9cc5f71dd9c7ef9b1a"}, {"line": 27953, "relation": "increases", "evidence": "Heparin can promote beta-secretase cleavage of APP in neuroblastoma cells.", "citation": {"db": "PubMed", "db_id": "16716081"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "626783a05617f107e756964e4e02317e"}, {"line": 27964, "relation": "increases", "evidence": "In AD, the specific inhibition of beta- or beta-secretase activities would decrease the production of Abeta from its precursor, in such a way that its relative concentration could be low enough to avoid the formation of aggregates.", "citation": {"db": "PubMed", "db_id": "16768248"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "c1a50c7bee6e4aaa72da2ba21c7a3107"}, {"line": 28011, "relation": "increases", "evidence": "BACE1 is a membrane-bound aspartyl protease that specifically cleaves amyloid precursor protein (APP) at the beta-secretase site.", "citation": {"db": "PubMed", "db_id": "16979658"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "d40b9b9db2adf04a6dfe8e2e61511adf"}, {"line": 28044, "relation": "increases", "evidence": "Hypoxia up-regulated beta-secretase cleavage of APP and amyloid-beta protein (Abeta) production by increasing BACE1 gene transcription and expression both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "17121991"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Hypoxia response subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "66e20dfb501f121fb5d3d0db9a55aa7b"}, {"line": 28053, "relation": "increases", "evidence": "Consequently, inhibition of BACE-1, a rate-limiting enzyme in the production of Abeta, is an attractive therapeutic approach for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17156133"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "2d7c003a7d2a0cd366fdcaad1b7e5412"}, {"line": 28063, "relation": "increases", "evidence": "In addition, we show that some NF-kappaB inhibitors decrease sAPPbeta and APP-CTFbeta suggesting that they reduce the beta-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "17223266"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "a72ac7a7ae91d0b7375de48c9f6dadb3"}, {"line": 28075, "relation": "increases", "evidence": "Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) (beta-secretase) initiates generation of beta-amyloid (Abeta), which plays an early role in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "17409228"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "2f91f441a2e91d9612990ca2c92ce444"}, {"line": 28085, "relation": "increases", "evidence": "Beta-secretase is a potential target for inhibitory drugs against Alzheimer's disease as it cleaves amyloid precursor protein (APP) to form insoluble amyloid plaques and vascular deposits in the brain.", "citation": {"db": "PubMed", "db_id": "17500040"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "9cda13fbfcb48fc623e8b692a915c957"}, {"line": 28101, "relation": "increases", "evidence": "Consequently, inhibition of BACE-1, a rate-limiting enzyme in the production of Abeta, is an attractive therapeutic approach to the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17541560"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "4b26cb7c00ce3c01226963a89b6ec224"}, {"line": 28122, "relation": "increases", "evidence": "Beta-site APP-cleaving enzyme (BACE) is required for production of the Alzheimer's disease (AD)-associated Abeta protein.", "citation": {"db": "PubMed", "db_id": "17553422"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "85ef3b42cac187d08d9286b579af1c30"}, {"line": 28132, "relation": "increases", "evidence": "The aspartyl protease beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) initiates processing of amyloid precursor protein (APP) into amyloid beta (Abeta) peptide, the major component of Alzheimer disease (AD) plaques.", "citation": {"db": "PubMed", "db_id": "17616527"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "f50641ebee95c66d4e4c7987c9db2817"}, {"line": 28142, "relation": "increases", "evidence": "The proteolytic enzyme beta-secretase (BACE-1) produces amyloid beta (Abeta) peptide, the primary constituent of neurofibrillary plaques, implicated in Alzheimer's disease, by cleavage of the amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "18068983"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "3ee31d0275707f4d0c34d4f271d9606c"}, {"line": 28159, "relation": "increases", "evidence": "Beta-amyloid (Abeta) peptides that accumulate in Alzheimer disease are generated from the beta-amyloid precursor protein (betaAPP) by cleavages by beta-secretase BACE1 and by presenilin-dependent gamma-secretase activities.", "citation": {"db": "PubMed", "db_id": "18263584"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "b1640d6d532f372a2b95dd56be3110da"}, {"line": 28169, "relation": "increases", "evidence": "Consequently, inhibition of BACE-1, a rate-limiting enzyme in the production of Abeta, is an attractive therapeutic approach for the treatment of AD.", "citation": {"db": "PubMed", "db_id": "18295609"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "3018d29e9f3e80b0df8838e347378f0d"}, {"line": 28182, "relation": "increases", "evidence": "In our opinion, albeit based on limited available data, a future potential therapeutic strategy is to mimic the mechanism by which the normal cellular form of the prion protein inhibits the beta-secretase beta-site amyloid precursor protein cleaving enzyme-1 (BACE1), and hence the production of Abeta.", "citation": {"db": "PubMed", "db_id": "18479216"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "58f819d6c1296ce20ba91e478e5d98da"}, {"line": 28191, "relation": "increases", "evidence": "BACE-1 cleavage is limiting for the production of Abeta, making it a particularly good drug target for the generation of inhibitors that lower Abeta.", "citation": {"db": "PubMed", "db_id": "18673212"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "1990b5b6884a229e8e3aa8e035294c22"}, {"line": 28201, "relation": "increases", "evidence": "One group described the poor kinetics of BACE 1 for cleaving the wild-type (WT) beta-secretase site of APP found in most AD patients.", "citation": {"db": "PubMed", "db_id": "18979625"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "2b431ec7d1f6a99d9503121364b6dd7a"}, {"line": 28208, "relation": "increases", "evidence": "These results indicate that post-translational S-palmitoylation of BACE1 is not required for APP processing, and that BACE1 can efficiently cleave APP in both raft and non-raft microdomains.", "citation": {"db": "PubMed", "db_id": "19074428"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "346fbfeea1fdf74d444c0b59f04bd4f0"}, {"line": 28218, "relation": "increases", "evidence": "Because beta-site APP-cleaving enzyme 1 (BACE1), an essential protease for Abeta production, is up-regulated in cells overexpressing RAGE and in RAGE-injected brains of Tg2576 mice, the molecular mechanisms underlying RAGE, BACE1 expression, and Abeta production were examined.", "citation": {"db": "PubMed", "db_id": "19332646"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "cccaa8e86de2932e45fd85003c605beb"}, {"line": 28231, "relation": "increases", "evidence": "However, unlike BRI2, the binding of BRI3 to the beta-secretase cleaved APP C-terminal fragment is negligible and BRI3 does not cause the massive accumulation of this APP fragment, suggesting that, unlike BRI2, BRI3 is a poor gamma-cleavage inhibitor.", "citation": {"db": "PubMed", "db_id": "19366692"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "0d11eacc1470788d83ce0bb12b6ff80c"}, {"line": 28241, "relation": "increases", "evidence": "BACE 1 (beta-site APP-cleaving enzyme 1 or beta-secretase), the key enzyme required for generating Abeta from the beta-amyloid precursor protein (APP), is regarded as an ideal target for AD therapeutic drug design.", "citation": {"db": "PubMed", "db_id": "19442147"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "16e7264490adb7a707af136ea34f8060"}, {"line": 28251, "relation": "increases", "evidence": "The beta-secretase, beta-site amyloid precursor protein cleaving enzyme (BACE1; also called Asp2, memapsin 2), is the enzyme responsible for initiating Abeta generation.", "citation": {"db": "PubMed", "db_id": "19828790"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "7fa6a0022cae7da9d2d25bf65bb6dac3"}, {"line": 28261, "relation": "increases", "evidence": "BACE1 initiates the amyloidogenic processing of APP; therefore, early or active amyloidogenic loci might exhibit site-specific BACE1 and Abeta elevation.", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "8719560b01b6bdc14b124cfd7ba22f0e"}, {"line": 28273, "relation": "increases", "evidence": "Taken together, ATXN1 functions as a genetic risk pmodifier that contributes to AD pathogenesis through a loss-of-function mechanism by regulating beta-secretase cleavage of APP and Abeta levels.", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "bb229be10a3e8572d95daca117dfd1f0"}, {"line": 28292, "relation": "increases", "evidence": "Three proteases that are involved in the processing of amyloid precursor protein-alpha-secretase, beta-secretase and gamma-secretase-are of particular interest as they are central to the generation and pmodulation of amyloid-beta peptide and can be targeted by small compounds in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "20139999"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "5902e5163f03222486fdf75dda7e8846"}, {"line": 28325, "relation": "increases", "evidence": "Although pmoderate decreases of either gamma-secretase or BACE1 are not associated with mechanism-based toxicities, they provide only pmodest benefits in reducing Abeta in the brains of APPswe/PS1DeltaE9 mice.", "citation": {"db": "PubMed", "db_id": "20371462"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "dee002132ac6c128a41ca023709ffe62"}, {"line": 28335, "relation": "increases", "evidence": "Reticulons are a group of membrane-bound proteins involved in diverse cellular functions, and are suggested to act as inhibitors of beta-secretase enzyme 1 (BACE1) activity that cleaves amyloid precursor protein. ", "citation": {"db": "PubMed", "db_id": "20374499"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "57aeb6d08f8f22f1a48b930d1d01c93c"}, {"line": 28345, "relation": "increases", "evidence": "Here we tested the possibility of targeting the cellular environment of beta-secretase cleavage instead of the beta-secretase enzyme itself. beta-Secretase has an acidic pH optimum and cleaves the amyloid precursor protein in the acidic endosomes.", "citation": {"db": "PubMed", "db_id": "20592218"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "40585434e0fe8a21a71677927bbd5695"}, {"line": 28355, "relation": "increases", "evidence": "ABCA2 expression promoted b-secretase (BACE1) cleavage of APP not at the common Asp1 amino acid site (beta-site) of Abeta in APP but at the Glu11 site (beta'-site) to increase C89 carboxyl-terminal fragment levels (beta'-CTF/C89). ", "citation": {"db": "PubMed", "db_id": "20704561"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "41a28a2664974fcaf5b52ba2002eb7c2"}, {"line": 28368, "relation": "increases", "evidence": "We discovered a nonpeptidic compound, TAK-070, that inhibited BACE1, a rate-limiting protease for the generation of Abeta peptides that are considered causative for Alzheimer's disease (AD), in a noncompetitive manner.", "citation": {"db": "PubMed", "db_id": "20720123"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "6297a05c8f727dce7b6e5de7a4ce6eac"}, {"line": 28378, "relation": "increases", "evidence": "Western blot analyses detected increased levels of BACE1 protein and beta-site-cleavage amyloid precursor protein C-terminal fragments in plaque-bearing human and monkey cortex relative to controls. ", "citation": {"db": "PubMed", "db_id": "20726888"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "8285f86e98ac3de794b2f4f9b6bb4d38"}, {"line": 28388, "relation": "increases", "evidence": "beta-Site APP-cleaving enzyme 1 (BACE1) initiates amyloid-beta (Abeta) generation and thus represents a prime therapeutic target in treating Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20886088"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "325b7d2d2c4d0746ffb35ff393b73587"}, {"line": 28398, "relation": "increases", "evidence": "Importantly, recent evidence reveals that expression and activity levels of the beta-site APP cleaving enzyme 1 (BACE1), which initiates amyloid-beta (Abeta) production, are elevated in AD brains.", "citation": {"db": "PubMed", "db_id": "21059265"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "658ae76dd2c812044b3e81b795e82a43"}, {"line": 28408, "relation": "increases", "evidence": "BACE1 (beta-site beta-amyloid precursor protein (APP)-cleaving enzyme 1) mediates the first proteolytic cleavage of APP, leading to amyloid beta-peptide (Abeta) production.", "citation": {"db": "PubMed", "db_id": "21245145"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "a712dde0df9b06b747c5bfad40445321"}, {"line": 28418, "relation": "increases", "evidence": "Increased beta-secretase cleavage of APP after introduction of the Swedish double mutation causes apical missorting of about 20% of beta-secretase-cleaved APP.", "citation": {"db": "PubMed", "db_id": "7876155"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "48bc2e9a8cb910e0f29dce81a5197704"}, {"line": 28428, "relation": "increases", "evidence": "Taken together, the data suggest that the processing pathway for betaPP is similar for both betaPP-wt and betaPP-sw cells and that increased Abeta production by betaPP-sw cells arises from enhanced cleavage of mutant betaPP by beta-secretase, the as-yet unidentified enzyme(s) that cleaves at the NH2 terminus of Abeta.", "citation": {"db": "PubMed", "db_id": "8621560"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "0a82f0f39e1ec8b2fd31166032e6bcc8"}, {"line": 28438, "relation": "increases", "evidence": "Heparin promotes beta-secretase cleavage of the Alzheimer's amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "9152995"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "9c3674c57b9837b8d2b06612f32b11e3"}, {"line": 28449, "relation": "increases", "evidence": "Sequential proteolytic cleavage of APP by beta-secretase and gamma-secretase liberates Abeta from APP.", "citation": {"db": "PubMed", "db_id": "20731541"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "ad1b343752503eab2424825a396e9453"}, {"line": 28858, "relation": "increases", "evidence": "beta-Secretase (BACE) carries out the first of two proteolysis steps to generate the amyloid-beta peptides that accumulate in the senile plaques in Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "15466887"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "876c29b7955fe5ca2f819b9b52d5e5c0"}, {"line": 28938, "relation": "increases", "evidence": "BACE1 is a membrane-bound aspartic protease that cleaves the amyloid precursor protein (APP) at the beta-secretase site, a critical step in the Alzheimer disease pathogenesis. ", "citation": {"db": "PubMed", "db_id": "12473667"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "183c598e04ac4b07026f141992b92544"}, {"line": 31386, "relation": "increases", "evidence": "LRRTM3 promotes processing of amyloid-precursor protein by BACE1 ", "citation": {"db": "PubMed", "db_id": "17098871"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "721db81394d4a38fa076df60c15e74d7"}, {"line": 31638, "relation": "increases", "evidence": "The complex mediates the intramembraneous proteolysis of beta-secretase cleaved beta-amyloid precursor protein (APP) leading to the secretion of the Alzheimer's disease-associated amyloid beta-peptide (Abeta).", "citation": {"db": "PubMed", "db_id": "15189355"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "51a4cd6f27294aa0d73f1e8ba926ddbe"}, {"line": 32618, "relation": "increases", "evidence": "The familial Alzheimer's disease gene product amyloid beta precursor protein (APP) is sequentially processed by beta- and gamma-secretases to generate the Abeta peptide.", "citation": {"db": "PubMed", "db_id": "11724784"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 80, "key": "be4a1adaad70520ef626968ae8b1f6da"}, {"line": 36604, "relation": "increases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 80, "key": "dcccce76d29ec9207b51414c3d19aefe"}, {"line": 44533, "relation": "increases", "evidence": "Down regulation of DNMT results in hypomethylation of BACE1 and APP which are involved in Abeta production and causes upregulation of their protein expression; in turn SP1 transcription factor increases which finally results in Abeta production.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 80, "key": "24578c6c26b1dd89f97134a9c1735cd9"}, {"line": 3106, "relation": "positiveCorrelation", "evidence": "BACE1 is primarily expressed by neurons and increased BACE1 protein concentrations and enzymatic activities have been reported in the brains of AD patients. However, there is accumulating evidence that, in addition to neurons, reactive astrocytes are capable of expressing BACE1 and, therefore, may contribute to beta-amyloid plaque formation. This suggests that conditions accompanied by chronic astrocyte activation may contribute to developing AD.", "citation": {"db": "PubMed", "db_id": "15465276"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2375, "target": 3823, "key": "451aa06fafd820416f2a3f08b8d21973"}, {"line": 14289, "relation": "association", "evidence": "Increased NF-κB signalling up-regulates BACE1 expression and its therapeutic potential in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 2375, "target": 3823, "key": "db8eeff0d15a5684d445ac20e02ba088"}, {"line": 14300, "relation": "positiveCorrelation", "evidence": "Elevated levels of beta-site APP cleaving enzyme 1 (BACE1) were found in the brain of some sporadic Alzheimer's disease (AD) patients; however, the underlying mechanism is unknown.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 2375, "target": 3823, "key": "502e274b879174f14b1e48b6526502ab"}, {"line": 14335, "relation": "positiveCorrelation", "evidence": "In this report we found that both BACE1 and NF-κB p65 levels were significantly increased in the brains of AD patients.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2375, "target": 3823, "key": "9298b01941b54907104acc196ea148c6"}, {"line": 17643, "relation": "association", "evidence": "beta-Secretase 1 (BACE-1) is an attractive therapeutic target for the treatment and prevention of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22984865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}}, "source": 2375, "target": 3823, "key": "7c885b340ed5e1667b086547e91b7152"}, {"line": 26683, "relation": "association", "evidence": "Our studies demonstrate, for the first time, that pmodulation of BACE1 activity may play a significant role in AD pathogenesis in vivo.", "citation": {"db": "PubMed", "db_id": "15452128"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 3823, "key": "4e8ed2b8a495ddafb0e01d25b3d4aae5"}, {"line": 26816, "relation": "increases", "evidence": "Taken together, these findings suggest that those neurons that survive in AD brains might generate more BACE1 than normal neurons in control brains, indicating that increased BACE1 activity could be one of the causes of AD. ", "citation": {"db": "PubMed", "db_id": "16290302"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 3823, "key": "80f862b23719471cbc5f0e4ea1962737"}, {"line": 27819, "relation": "association", "evidence": "BACE is an aspartyl protease that cleaves the amyloid precursor protein (APP) at the beta-secretase cleavage site and is involved in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15080893"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 3823, "key": "de171995476fec89b32f2bc7bc76acfc"}, {"line": 27859, "relation": "increases", "evidence": "Alzheimer's beta-secretase (BACE1) cleaves amyloid precursor protein to produce amyloid beta-peptide, which is a crucial initiation process of the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15467394"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 3823, "key": "56a761edd33c1db1c33a83e1c6856925"}, {"line": 32953, "relation": "association", "evidence": "BACE1 is a promising therapeutic and preventive target for Alzheimer's disease because it is essential for amyloid deposition. ", "citation": {"db": "PubMed", "db_id": "18413858"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}}, "source": 2375, "target": 3823, "key": "6ab8d25319218c8caed6eba81daf20a1"}, {"relation": "hasVariant", "source": 2375, "target": 2376, "key": "324898b35d27c732c5735a46397ec0da"}, {"relation": "hasVariant", "source": 2375, "target": 2378, "key": "4916f5c69e03642bb5dfe04cf3f84169"}, {"relation": "hasVariant", "source": 2375, "target": 2379, "key": "97e74e8b23ad88d37986bd0841a33df1"}, {"line": 3261, "relation": "positiveCorrelation", "evidence": "The role of miR-124 on the expression of Abeta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the Abeta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}}, "source": 2375, "target": 2375, "key": "6c9c86a091b2771472c0b58d54e47dd4"}, {"line": 3295, "relation": "increases", "evidence": "Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and Abeta production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "127e2b0a47111706b356f22d3df6b62a"}, {"line": 26679, "relation": "increases", "evidence": "Amyloid-beta (Abeta) the primary component of the senile plaques found in Alzheimer's disease (AD) is generated by the rate-limiting cleavage of amyloid precursor protein (APP) by beta-secretase followed by gamma-secretase cleavage.", "citation": {"db": "PubMed", "db_id": "15452128"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "c574cb6067a5a4566830b98d566aebd5"}, {"line": 26693, "relation": "increases", "evidence": "The generation of beta-amyloid peptides requires the enzymatic activity of the beta-site APP-cleaving enzyme 1 (BACE1).", "citation": {"db": "PubMed", "db_id": "15465276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "371714887f384883eefd242c1b05ff37"}, {"line": 26709, "relation": "increases", "evidence": "The generation of beta-amyloid peptides by proteolytical processing of the amyloid precursor protein (APP) requires the enzymatic activity of the beta-site APP cleaving enzyme 1 (BACE1).", "citation": {"db": "PubMed", "db_id": "15663471"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "2af68693bef6b789b3389761e3b2c6ad"}, {"line": 26760, "relation": "directlyIncreases", "evidence": "Sequential processing of amyloid precursor protein (APP) by membrane-bound proteases, BACE1 and gamma-secretase, plays a crucial role in the pathogenesis of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "15824102"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "81a67fdbb91439aab1cc4dc921307af3"}, {"line": 26777, "relation": "directlyIncreases", "evidence": "Release of Abeta from the amyloid precursor protein (APP) requires proteolysis by the beta-site APP-cleaving enzyme (BACE1).", "citation": {"db": "PubMed", "db_id": "16027115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "3c1b950718396e6246b4c9c5e4e424ff"}, {"line": 26810, "relation": "directlyIncreases", "evidence": "Abeta is produced from beta-amyloid precursor protein (APP) by beta-secretase and gamma-secretase. beta-Secretase has been identified as beta-site APP cleaving enzyme1 (BACE1).", "citation": {"db": "PubMed", "db_id": "16290302"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "6a8fe34463a1d402cc0d067d005eba3e"}, {"line": 26825, "relation": "directlyIncreases", "evidence": "The beta-amyloid peptide (Abeta) is a major component of the Alzheimer's disease (AD)-associated senile plaques and is generated by sequential cleavage of the beta-amyloid precursor protein (APP) by beta-secretase and gamma-secretase.", "citation": {"db": "PubMed", "db_id": "16888322"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "16dd3ba03c2219305045078bdd2bca04"}, {"line": 26841, "relation": "directlyIncreases", "evidence": "Amyloid beta-peptide (Abeta), which is a product of the proteolytic effect of beta-secretase (BACE) on an amyloid precursor protein, is closely associated with Alzheimer's disease (AD) pathogenesis.", "citation": {"db": "PubMed", "db_id": "17205046"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "f69800a0922cbecf4633cd8ec77ec827"}, {"line": 26896, "relation": "directlyIncreases", "evidence": "Proteolytic processing of the amyloid precursor protein (APP) by beta-secretase, beta-site APP cleaving enzyme (BACE1), is the initial step in the production of the amyloid beta (Abeta) peptide, which is involved in the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17573534"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "309ac66580d5faeadbb79fdff29f4d74"}, {"line": 26936, "relation": "directlyIncreases", "evidence": "Pathologically, AD is characterized by the deposition in the brain of amyloid-beta peptides derived from proteolysis of amyloid precursor protein (APP) by beta-site APP cleaving enzyme 1 (BACE1) and gamma-secretase.", "citation": {"db": "PubMed", "db_id": "17611486"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "d239e896681da6d1fd98b8b2e2bb8f9a"}, {"line": 26946, "relation": "directlyIncreases", "evidence": "Abeta is generated by proteolytic processing of amyloid precursor protein (APP) via beta and gamma-secretases.", "citation": {"db": "PubMed", "db_id": "17908048"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "8868f86b158c854a8e7ede303f57eae0"}, {"line": 26985, "relation": "directlyIncreases", "evidence": "Proteolytic cleavage of amyloid precursor protein by beta-secretase (BACE-1) and gamma-secretase leads to formation of beta-amyloid (A beta) a key component of amyloid plaques, which are considered the hallmark of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18162398"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "f8211ee3586f24e35000adc377ce4c8a"}, {"line": 27194, "relation": "directlyIncreases", "evidence": "In the amyloidogenic pathway, a small proportion of APP is cleaved by beta- and gamma-secretases, known as beta-site APP-cleaving enzyme 1 (BACE1) and presenilin, respectively, leading to the secretion of Abeta peptides.", "citation": {"db": "PubMed", "db_id": "19729516"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "502ebb9e76b7bc9d73f777d9ca681541"}, {"line": 27857, "relation": "directlyIncreases", "evidence": "Alzheimer's beta-secretase (BACE1) cleaves amyloid precursor protein to produce amyloid beta-peptide, which is a crucial initiation process of the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15467394"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "4c2491b6c1993205ddf0f0b7db338618"}, {"line": 27868, "relation": "directlyIncreases", "evidence": "Mapping to the Down syndrome critical region (chromosome 21) and identified as a homologue of BACE1, BACE2 also cleaves amyloid precursor protein at the beta-site.", "citation": {"db": "PubMed", "db_id": "15473697"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "d47f5c3277f9a8bab6021111eeb67e1a"}, {"line": 28010, "relation": "directlyIncreases", "evidence": "BACE1 is a membrane-bound aspartyl protease that specifically cleaves amyloid precursor protein (APP) at the beta-secretase site.", "citation": {"db": "PubMed", "db_id": "16979658"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "8c0bacae679fa9c4e77a2cbebcebc2c1"}, {"line": 28043, "relation": "directlyIncreases", "evidence": "Hypoxia up-regulated beta-secretase cleavage of APP and amyloid-beta protein (Abeta) production by increasing BACE1 gene transcription and expression both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "17121991"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Hypoxia response subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2375, "target": 2315, "key": "49c424f66a401794e9f428e24b659099"}, {"line": 3756, "relation": "association", "evidence": "overexpression of PrP results in increased cleavage of APP in contrast to previous suggestion suggesting a reduction. Our findings suggest that any relation between PrP and BACE-1 is indirect. Altered expression of PrP causes changes in the expression of many other proteins which may be as a result of altered copper metabolism.", "citation": {"db": "PubMed", "db_id": "22796214"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}}, "source": 2375, "target": 3254, "key": "c313a300c3f130bf7f7d055444d53ab0"}, {"relation": "partOf", "source": 2375, "target": 1003, "key": "94a23a22e826ddcc6215aefd32498249"}, {"line": 4502, "relation": "increases", "evidence": "beta- and gamma-secretase cleave the amyloid precursor protein (APP) to release the amyloidogenic beta-amyloid peptides (Abeta) and the APP intracellular domain (AICD).", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 4097, "key": "efccac47209f321c170c3a1f3d739652"}, {"line": 6092, "relation": "increases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 717, "key": "a7bf5693afeacc06cf5ee9fc0fef8f9b"}, {"line": 7726, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2375, "target": 2328, "key": "cd2d53e751ad630e9a63dd8fe59772a3"}, {"line": 17674, "relation": "decreases", "evidence": "In this study, we aimed to investigate the possibility of P-gp as a potential therapeutic target for Alzheimer's disease by examining the impact of P-gp up-regulation on the clearance of Abeta, a neuropathological hallmark of Alzheimer's disease.Uptake studies for-radiolabelled Abeta Approximately 10-35% decrease in Abeta intracellular accumulation was observed in cells treated with rifampicin, dexamethasone, caffeine, verapamil, hyperforin, beta-estradiol and pentylenetetrazole compared with control.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 2328, "key": "f6b3b444ba455a526e0cba84abeb4a5d"}, {"line": 25385, "relation": "increases", "evidence": "Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2375, "target": 2328, "key": "8dd89039c856b47729aad76510a0fde0"}, {"line": 25469, "relation": "increases", "evidence": "In human neural cells overexpressing beta-amyloid precursor protein (betaAPP), the lipid mediator suppressed Abeta42 shedding by downregulating beta-secretase (BACE1) while activating the alpha-secretase (ADAM10), thus shifting the alphaAPP cleavage from the noxious amyloidogenic pathway into a non-amyloidogenic, neurotrophic pathway.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2375, "target": 2328, "key": "39b32541daae4b4cd0df098f7107f41e"}, {"line": 26751, "relation": "increases", "evidence": "Using an assay that incorporates full-length recombinant APP as a substrate for beta-secretase (BACE), we have identified a series of compounds that inhibit APP processing, but do not affect the cleavage of peptide substrates by BACE1.", "citation": {"db": "PubMed", "db_id": "15737955"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "9e1bf441496b4a274cb219d794dbbc07"}, {"line": 26957, "relation": "increases", "evidence": "The PSEN1 AD mutations giving rise to CWP produce unusually high levels of the amyloid beta peptide (Abeta) ending at position 42 or 43, and the main component of CWP is amino-terminally truncated forms of amyloid beta peptide starting after the alternative beta-secretase cleavage site at position 11.", "citation": {"db": "PubMed", "db_id": "17995932"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "08f042bda411fe0a25b8641449054772"}, {"line": 27343, "relation": "increases", "evidence": "Familial Alzheimer's disease mutations in presenilin 1 do not alter levels of the secreted amyloid-beta protein precursor generated by beta-secretase cleavage.", "citation": {"db": "PubMed", "db_id": "20205669"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "1f80df494167965b1946fa3a131d2393"}, {"line": 27511, "relation": "increases", "evidence": "In human neural cells overexpressing beta-amyloid precursor protein (betaAPP), the lipid mediator suppressed Abeta42 shedding by downregulating beta-secretase (BACE1) while activating the alpha-secretase (ADAM10), thus shifting the betaAPP cleavage from the noxious amyloidogenic pathway into a non-amyloidogenic, neurotrophic pathway. ", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "b9305c54429f2079d088a6ea8d3cfcc0"}, {"line": 27633, "relation": "increases", "evidence": "ELISA analysis of beta-secretase cleavage of the Swedish amyloid precursor protein in the secretory and endocytic pathways.", "citation": {"db": "PubMed", "db_id": "11953452"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "80ae34d9629ef21a4c4fdb7ca64379d6"}, {"line": 27881, "relation": "increases", "evidence": "Preventing Abeta42 production with an M596I mutation (beta-), which blocks beta-secretase cleavage of APP, or by treatment with a gamma-secretase inhibitor increased the resistance of APP(FAD)-expressing cells to apoptotic process.", "citation": {"db": "PubMed", "db_id": "15584903"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "7a2ba6bc1767e4ff54a76751f554a141"}, {"line": 27901, "relation": "increases", "evidence": "Here, we established a novel approach to regulate production of Abeta based on intracellular expression of single chain antibodies (intrabodies) raised to an epitope adjacent to the beta-secretase cleavage site of human APP.", "citation": {"db": "PubMed", "db_id": "15767460"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "018978dfa6a68cc134593024ff800ed1"}, {"line": 27924, "relation": "increases", "evidence": "The aspartic protease beta-secretase (BACE) cleaves the amyloid precursor protein into a 42 residue beta-peptide, which is the principal biochemical marker of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16216580"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "ea57325e3970612d97cf38994425bf4a"}, {"line": 28021, "relation": "increases", "evidence": "Using high-throughput siRNA screening technology, we assessed 15,200 genes for their role in Abeta42 secretion and identified leucine-rich repeat transmembrane 3 (LRRTM3) as a neuronal gene that promotes APP processing by BACE1. siRNAs targeting LRRTM3 inhibit the secretion of Abeta40, Abeta42, and sAPPbeta, the N-terminal APP fragment produced by BACE1 cleavage, from cultured cells and primary neurons by up to 60%, whereas overexpression increases Abeta secretion.", "citation": {"db": "PubMed", "db_id": "17098871"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "65e2b456866f21ec41a1871972f9126c"}, {"line": 28149, "relation": "increases", "evidence": "After inhibitor treatment, the improved memory function was accompanied by reduced amyloid plaque load, decreased Abeta40 and Abeta42, and reduced C-terminal beta-secretase fragment derived from APP by beta-secretase.", "citation": {"db": "PubMed", "db_id": "18184658"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "9db57127ccaf9b6a09e1358533474d08"}, {"line": 28835, "relation": "increases", "evidence": "The novel transmembrane aspartic protease BACE (for Beta-site APP Cleaving Enzyme) is the beta-secretase that cleaves amyloid precursor protein to initiate beta-amyloid formation. As such, BACE is a prime therapeutic target for the treatment of Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "10956649"}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2328, "key": "b3c740edc8756776e131b7ea15eac5c6"}, {"line": 7727, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2375, "target": 2327, "key": "46c1c6202e2d5e5cb857d46548f8ec12"}, {"line": 8043, "relation": "association", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Sphingolipid metabolic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 228, "key": "829fae5fadda401b255faf483459e350"}, {"line": 8896, "relation": "association", "evidence": "The role of miR-124 on the expression of beta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the beta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 2082, "key": "a7f85fc34c4b54655cc864bd28749da9"}, {"line": 9228, "relation": "negativeCorrelation", "evidence": "The Expression of MicroRNA miR-107 Decreases Early in Alzheimer’s Disease and May Accelerate Disease Progression through Regulation of beta-Site Amyloid Precursor Protein-Cleaving Enzyme 1. BACE1 mRNA levels tended to increase as miR-107 levels decreased in the progression of AD. ", "citation": {"db": "PubMed", "db_id": "18234899"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2375, "target": 2092, "key": "10c5829993cb8f4ea4d9888068a74e30"}, {"line": 45906, "relation": "negativeCorrelation", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2375, "target": 2092, "key": "4e85f776d850e1104f5840b90de49608"}, {"line": 14363, "relation": "positiveCorrelation", "evidence": "We found that NF-κB p65 expression resulted in increased BACE1 promoter activity and BACE1 transcription, while disruption of NF-κB p65 decreased BACE1 gene expression in p65 knockout (RelA-knockout) cells.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 2375, "target": 3304, "key": "26e327e065c025a68ea486187c79c5de"}, {"line": 14396, "relation": "association", "evidence": "In addition, NF-κB p65 expression leads to up-regulated beta-secretase cleavage and Abeta production, while non-steroidal anti-inflammatory drugs (NSAIDs) inhibited BACE1 transcriptional activation induced by strong NF-κB activator tumour necrosis factor-alpha (TNF-α).", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 2375, "target": 3472, "key": "bfcf274f756e77193c6cc5056dadc968"}, {"relation": "partOf", "source": 2375, "target": 1144, "key": "b495e7f096c2925cf65f9580c8c0cdd9"}, {"line": 26605, "relation": "directlyIncreases", "evidence": "It may be inferred from these data that beta-secretase cleavage of FAD mutants of APP allows the appropriate caspase access to its site of action to produce C31, which directly causes neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11744168"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 478, "key": "d7cb58cc7820b3a061cf77ef0d5eb636"}, {"line": 26618, "relation": "increases", "evidence": "A beta is generated by the sequential intracellular cleavage of APP by beta-secretase to generate the N-terminal end of A beta, and intramembranous cleavage by gamma-secretase to generate the C-terminal end.", "citation": {"db": "PubMed", "db_id": "11813874"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2137, "key": "273d4e102799e983a5fe9a797b8c93b8"}, {"line": 26725, "relation": "association", "evidence": "These results suggest that PAR-4 may be directly involved in regulating the APP cleavage activity of BACE1.", "citation": {"db": "PubMed", "db_id": "15671026"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 3165, "key": "72fc508b12c81d7858b3df51247b972e"}, {"line": 26786, "relation": "increases", "evidence": "These results demonstrate that high levels of BACE1 activity are sufficient to elicit neurodegeneration and neurological decline in vivo.", "citation": {"db": "PubMed", "db_id": "16027115"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 648, "key": "9baa27a08ef11da2d941d29ee3aa2562"}, {"line": 26787, "relation": "decreases", "evidence": "These results demonstrate that high levels of BACE1 activity are sufficient to elicit neurodegeneration and neurological decline in vivo.", "citation": {"db": "PubMed", "db_id": "16027115"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 2375, "target": 431, "key": "e6aea291dbd6b453d0a3803ce4d78252"}, {"relation": "partOf", "source": 2375, "target": 1277, "key": "da413bb8e56004d841292fe7cb9da41d"}, {"relation": "partOf", "source": 2375, "target": 1265, "key": "c1b5fd2dff46a79de9fcf005e54d7e93"}, {"line": 26968, "relation": "increases", "evidence": "Quetiapine also decreased brain Abeta peptides, beta-secretase activity and expression, and the level of C99 (an APP C-terminal fragment following cleavage by beta-secretase) in the transgenic mice.", "citation": {"db": "PubMed", "db_id": "18079026"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2329, "key": "76ad64be0607429a95f17f6b6c4a28b9"}, {"line": 27663, "relation": "increases", "evidence": "We show that, in Sf9 cells, BACE performs the expected beta-secretase cleavage of APP, generating C99.", "citation": {"db": "PubMed", "db_id": "12423249"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2329, "key": "68a708de5e10353cb98e890fffe79716"}, {"relation": "partOf", "source": 2375, "target": 1275, "key": "b1c0fb8f3530a1f176dafe6d94b37b31"}, {"relation": "partOf", "source": 2375, "target": 1276, "key": "bc2ff6e5b865534a5aba6d5a83fbc659"}, {"relation": "partOf", "source": 2375, "target": 1145, "key": "70012e0ea6e7451769bb23df760b0dd6"}, {"relation": "partOf", "source": 2375, "target": 1273, "key": "19445186d0a99f1e93e77b84d6e45174"}, {"line": 27453, "relation": "increases", "evidence": "Interestingly, treatment of cultured primary neurons with amyloid-beta (Abeta) peptides caused an increase in the level of beta-site APP-cleaving enzyme 1 (BACE1), the key enzyme responsible for APP processing and Abeta production. This effect was inhibited by CAST overexpression. ", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Calpastatin-calpain subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2375, "target": 4101, "key": "72923a8b779c26e360b5ea57ed942265"}, {"relation": "partOf", "source": 2375, "target": 960, "key": "a1bbd436695cd3d25edfae4c54f29c1b"}, {"line": 27730, "relation": "association", "evidence": "Here we report evidence that heparan sulfate (HS) interacts with beta-site APP-cleaving enzyme (BACE) 1 and regulates its cleavage of APP.", "citation": {"db": "PubMed", "db_id": "14530380"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 128, "key": "cf929f5b7c5f18e5a9e986f66b54bb8e"}, {"relation": "partOf", "source": 2375, "target": 1278, "key": "c2dc67fdd8cb21aad526568f5fc50227"}, {"relation": "partOf", "source": 2375, "target": 1269, "key": "35de599b36ea1790be642f9cea42b26e"}, {"line": 28066, "relation": "association", "evidence": "In addition, we show that some NF-kappaB inhibitors decrease sAPPbeta and APP-CTFbeta suggesting that they reduce the beta-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "17223266"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 3112, "key": "c0fb8847e443bea88cdb23b4fec1243b"}, {"relation": "partOf", "source": 2375, "target": 977, "key": "a789ed64b5eb1b4a94d7e649bba9b7e3"}, {"line": 28219, "relation": "association", "evidence": "Because beta-site APP-cleaving enzyme 1 (BACE1), an essential protease for Abeta production, is up-regulated in cells overexpressing RAGE and in RAGE-injected brains of Tg2576 mice, the molecular mechanisms underlying RAGE, BACE1 expression, and Abeta production were examined.", "citation": {"db": "PubMed", "db_id": "19332646"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2271, "key": "e828ca96d2b5f8bb618674530a3b3895"}, {"relation": "partOf", "source": 2375, "target": 1267, "key": "60ce9d1bbde502671dc704b08291e457"}, {"line": 28271, "relation": "association", "evidence": "Taken together, ATXN1 functions as a genetic risk pmodifier that contributes to AD pathogenesis through a loss-of-function mechanism by regulating beta-secretase cleavage of APP and Abeta levels.", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 2371, "key": "af90734503af805909128cc52b5fb326"}, {"relation": "partOf", "source": 2375, "target": 1262, "key": "a0813f47b99dc6c5af0de3ac9f31880a"}, {"relation": "partOf", "source": 2375, "target": 1263, "key": "7091eec44ba079cf20fdddd206de36f0"}, {"relation": "hasVariant", "source": 2375, "target": 2380, "key": "101137ccda27848afa65abb9b088a053"}, {"relation": "partOf", "source": 2375, "target": 1264, "key": "73aefdb6afcc91f716c25ac060b391e3"}, {"relation": "hasVariant", "source": 2375, "target": 2377, "key": "b788ec67246c46648182f372a8070297"}, {"relation": "partOf", "source": 2375, "target": 1274, "key": "42f639b90a4174acd5684852ac757e5b"}, {"line": 28885, "relation": "increases", "evidence": "In this study, we revealed that BACE-1 is involved in the cleavage of membrane-bound prostaglandin E2 synthase-2 (mPGES-2) in its N-terminal portion, which, in turn, enhanced the generation of prostaglandin E2 (PGE2).", "citation": {"db": "PubMed", "db_id": "20171164"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 3276, "key": "2beef3cd299cdcb2d176a47857710984"}, {"line": 28949, "relation": "increases", "evidence": "We have recently found that when BACE1 was overexpressed in COS cells together with alpha2, 6-sialyltransferase (ST6Gal I), the secretion of ST6Gal I markedly increased, suggesting that BACE1 cleaves ST6Gal I as a physiological substrate.", "citation": {"db": "PubMed", "db_id": "14973371"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2375, "target": 3422, "key": "6bb254ef26788108a18e6caeb66fda2c"}, {"line": 28958, "relation": "increases", "evidence": "LEC rats exhibited simultaneous increases in BACE1 mRNA in the liver and in the E41 form of the ST6Gal I protein, the BACE1 product, in plasma as early as 6 weeks of age, again suggesting that BACE1 cleaves ST6Gal I in vivo and controls the secretion of the E41 form.", "citation": {"db": "PubMed", "db_id": "15364953"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2375, "target": 3422, "key": "3c524c85d6920647af7d793c04094e13"}, {"relation": "partOf", "source": 2375, "target": 1266, "key": "bd3b1ccf2e09bb41bea48bfcec6f77a6"}, {"relation": "partOf", "source": 2375, "target": 1268, "key": "3f4e1e6aeab3c5ea9e6837ff4d6a16e9"}, {"relation": "partOf", "source": 2375, "target": 1270, "key": "302614f8986655fb939ec73a3118a34b"}, {"relation": "partOf", "source": 2375, "target": 1271, "key": "700a02703ae819883a8a356dbb572f09"}, {"relation": "partOf", "source": 2375, "target": 1272, "key": "ccb124845644f2d4775e2ab00f1aff04"}, {"line": 44808, "relation": "orthologous", "evidence": "beta-secretase-1 (BACE1) elevation relative to Abeta accumulation and synaptic/neuritic alterations in the forebrain, using transgenic mice harboring familial AD (FAD) mutations (5XFAD and 2XFAD) as models", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "DiseaseState": {"Familial Alzheimers Disease": true}, "KnockoutMice": {"App transgenic": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2375, "target": 3593, "key": "181ded079d72fff6f45007e7a7545fb5"}, {"line": 45915, "relation": "association", "evidence": "Our studies showed that p300-HAT inhibitor curcumin abrogates H3 hyperacetylation of PS1 and BACE1, curcumin decreases PS1 activity", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2375, "target": 2803, "key": "46aac9ad43cf2e0263b644dba6e07c9c"}, {"relation": "hasReactant", "source": 4096, "target": 2315, "key": "f6584034d6ad52be058e530d292726f8"}, {"relation": "hasProduct", "source": 4096, "target": 80, "key": "a71d72ab8d060206d37ef98fb5ee33d4"}, {"line": 8958, "relation": "increases", "evidence": "Amyloid beta-peptide (Abeta) accumulating in the brain of Alzheimer disease (AD) patients is believed to be the main pathophysiologcal cause of the disease. Proteolytic processing of the amyloid precursor protein by alpha-secretase ADAM10 (a disintegrin and metalloprotease 10) protects the brain from the production of the Abeta. Meanwhile, dysregulation or aberrant expression of microRNAs (miRNAs) has been widely documented in AD patients. In this study, we demonstrated that overexpression of miR-144, which was previously reported to be increased in elderly primate brains and AD patients, significantly decreased activity of the luciferase reporter containing the ADAM10 3'-untranslated region (3'-UTR) and suppressed the ADAM10 protein level, whereas the miR-144 inhibitor led to an increase of the luciferase activity. The negative regulation caused by miR-144 was strictly dependent on the binding of the miRNA to its recognition element in the ADAM10 3'-UTR. Moreover, we also showed that activator protein-1 regulates the transcription of miR-144 and the up-regulation of miR-144 at least partially induces the suppression of the ADAM10 protein in the presence of Abeta. In addition, we found that miR-451, a miRNA processed from a single gene locus with miR-144, is also involved in the regulation of ADAM10 expression. Taken together, our data therefore demonstrate miR-144/451 is a negative regulator of the ADAM10 protein and suggest a mechanistic role for miR-144/451 in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "23546882"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 4096, "target": 3823, "key": "38dee3833ee636e815609c92c553ce8a"}, {"line": 189, "relation": "negativeCorrelation", "evidence": "Here, we show that siRNA-mediated loss of calsyntenin-1 in cultured neurons alters APP processing to increase production of Abeta. We also show that calsyntenin-1 is reduced in Alzheimer's disease brains and that the extent of this reduction correlates with increased Abeta levels.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Calsyntenin subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2532, "key": "740813eabed9d9d259c463a03383b1d8"}, {"line": 216, "relation": "negativeCorrelation", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 484, "key": "c023715bcefdd855b8fa38e4838d2a85"}, {"line": 237, "relation": "positiveCorrelation", "evidence": "gamma-Secretase comprises a molecular complex of four integral membrane proteins - presenilin, nicastrin, APH-1 and PEN-2 - and its molecular mechanism remains under extensive scrutiny. The ratio of Abeta(42) over Abeta(40) is increased by familial Alzheimer's disease mutations occurring in the presenilin genes or in APP, near the gamma-secretase cleavage site.", "citation": {"db": "PubMed", "db_id": "16696577"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2328, "key": "176f2e85dd3f28d4d3af3c5a26d9060b"}, {"line": 330, "relation": "positiveCorrelation", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 2328, "key": "42c37e30932e16f02ccb9e458509f7bd"}, {"line": 3625, "relation": "association", "evidence": "Amyloid beta-peptide 1-42 (Abeta(1-42)) and hyperphosphorylated tubulin associated unit (tau) isoforms appear to be the most sensitive and specific CSF biomarkers, the combination of these biomarkers depicting the best diagnosis value for AD.", "citation": {"db": "PubMed", "db_id": "18584921"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2328, "key": "572fe5e8b172c5e7d3d06474ea651ac9"}, {"line": 31989, "relation": "association", "evidence": "PrP(C) decreases amyloid-beta (Abeta) production, which is involved in AD pathogenesis, by inhibiting beta-secretase (BACE1) activity.", "citation": {"db": "PubMed", "db_id": "23577068"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2328, "key": "4407c9bc3abc3ffe44e1779dec328fd8"}, {"line": 238, "relation": "positiveCorrelation", "evidence": "gamma-Secretase comprises a molecular complex of four integral membrane proteins - presenilin, nicastrin, APH-1 and PEN-2 - and its molecular mechanism remains under extensive scrutiny. The ratio of Abeta(42) over Abeta(40) is increased by familial Alzheimer's disease mutations occurring in the presenilin genes or in APP, near the gamma-secretase cleavage site.", "citation": {"db": "PubMed", "db_id": "16696577"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2327, "key": "1fe86f2232468ec033b18763feeb8671"}, {"line": 267, "relation": "positiveCorrelation", "evidence": "In this issue of Nature Medicine, Thathiah et al.4 now provide provocative evidence that the adaptor protein beta mediates the Abeta-altering effects of these GPCRs by promoting Abeta generation. This newly uncovered function of beta-arrestin 2 suggests it could be targeted to decrease amyloid pathology in patients with Alzheimer's disease. Production of the amyloid-beta peptide in Alzheimer's disease by the gamma-secretase complex can be regulated by certain G protein coupled receptors. This regulation seems to be mediated by beta-arrestin-2, whose expression was found to be elevated in Alzheimer's disease brains.Recruitment of beta-arrestin 2 to a GPCR leads to interaction with the gamma-secretase complex via the Aph-1 subunit. Other members of the complex include presenilin-1 (PS-1), nicastrin (Nct) and Pen-2. The complex then moves laterally into lipid rafts, where gamma-secretase activation is enhanced. Internalization may also occur to localize gamma-secretase to late endosomes, where its activation is also increased. Cleavage of APP by beta-secretase (BACE1) to release soluble APP (sAPPb) followed by gamma-secretase produces Abeta and APP intracellular domain (AICD). Increased production and secretion of Abeta from cells can lead to extracellular Abeta aggregation in the form of plaques. Mutagenesis of GPR3 in regions of the protein that specifically interact with either G protein or beta-arrestin 2 further showed that beta-arrestin 2, not G protein, mediates the ability of GPR3 to increase Abeta levels.", "citation": {"db": "PubMed", "db_id": "23296004"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 2360, "key": "30fd3bb2eca009c0a896d608b219e435"}, {"line": 295, "relation": "positiveCorrelation", "evidence": "Alzheimer's disease (AD) is the most common form of dementia. Mutations in genes such as those encoding amyloid precursor protein (APP), presenilin 1 and presenilin 2, are responsible for early-onset familial AD. Case presentation In this study, we report a 275341 G > C (Val717Leu) mutation in the APP gene in a Japanese family with early onset AD by genetic screening. This mutation has previously been detected in European families", "citation": {"db": "PubMed", "db_id": "22702962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 1748, "key": "b968bcb63301e29106b20e69630d369b"}, {"line": 296, "relation": "positiveCorrelation", "evidence": "Alzheimer's disease (AD) is the most common form of dementia. Mutations in genes such as those encoding amyloid precursor protein (APP), presenilin 1 and presenilin 2, are responsible for early-onset familial AD. Case presentation In this study, we report a 275341 G > C (Val717Leu) mutation in the APP gene in a Japanese family with early onset AD by genetic screening. This mutation has previously been detected in European families", "citation": {"db": "PubMed", "db_id": "22702962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 2351, "key": "a8f1cfb43bcf068837db544a3e1e32b6"}, {"line": 317, "relation": "positiveCorrelation", "evidence": "Mutation analysis of the APP, PSEN1 and PSEN2 genes was performed. We herein report the case of a German EOAD patient with a family history of dementia and a missense mutation at codon 141 (N141I) of the PSEN2 gene. To our knowledge, this is the first German EOAD patient without a Volga-German ancestry and a positive family history for dementia carries the mutation PSEN-2 N141I", "citation": {"db": "PubMed", "db_id": "19073399"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 3271, "key": "d6ac894b541d9c48ec249a549fbac3b7"}, {"line": 665, "relation": "positiveCorrelation", "evidence": "polymorphisms in three other genes (among others), apolipoprotein E (apoE), alpha2-macroglobulin (alpham), and the low density lipoprotein receptor-related protein (LRP), are implicated to contribute to AD pathogenesis", "citation": {"db": "PubMed", "db_id": "10936878"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 3823, "target": 2313, "key": "a0760acbd7b9276e1ceb833f1115c085"}, {"line": 667, "relation": "positiveCorrelation", "evidence": "polymorphisms in three other genes (among others), apolipoprotein E (apoE), alpha2-macroglobulin (alpham), and the low density lipoprotein receptor-related protein (LRP), are implicated to contribute to AD pathogenesis", "citation": {"db": "PubMed", "db_id": "10936878"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Alpha 2 macroglobulin subgraph": true}}, "source": 3823, "target": 2228, "key": "6eaf25aa33ed941c0e213950776aefc9"}, {"line": 765, "relation": "association", "evidence": "Part of the inflammatory response in Alzheimer's disease (AD) is the upregulation of the inducible nitric oxide synthase (NOS2) resulting in increased NO production.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 577, "key": "a57a81292d8e51d994d84af2f0aac39d"}, {"line": 5006, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 577, "key": "9e69ec2df903ebfe1c0592c7de0a3b00"}, {"line": 43319, "relation": "association", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 577, "key": "06cf075502206ea378f1c6f6a85190eb"}, {"line": 43564, "relation": "positiveCorrelation", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3823, "target": 577, "key": "f94bcb4b5bed4693a951fe0ee61760d9"}, {"line": 774, "relation": "positiveCorrelation", "evidence": "Part of the inflammatory response in Alzheimer's disease (AD) is the upregulation of the inducible nitric oxide synthase (NOS2) resulting in increased NO production.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 3123, "key": "3448f734c2eb3b778b531b3690c4bf4d"}, {"line": 41875, "relation": "increases", "evidence": "Part of the inflammatory response in Alzheimer's disease (AD) is the upregulation of the inducible nitric oxide synthase (NOS2) resulting in increased NO production.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true, "Amyloidosis": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3123, "key": "4a85ac927c2e6b5ec43bbf811b44aa4b"}, {"line": 956, "relation": "negativeCorrelation", "evidence": "In an attempt to reverse the apoE deficit in AD, we identified and characterized several apoE inducer agents using a low throughput-screening assay. The most promising of these compounds is called probucol. Administration of probucol, an old cholesterol lowering drug, in mild to moderate sporadic AD led to significant increases in CSF apoE levels and a decrease of CSF beta amyloid 1-42 without significant modifications of CSF tau concentration or CSF lipid peroxides levels. These results are consistent with recent reports suggesting that the long term use of cholesterol lowering drugs that block 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) activity in the fourth and fifth decade of life may help reduce the risk of developing AD at later age.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 334, "key": "486aae2585c51aef06fe441db638669c"}, {"line": 970, "relation": "positiveCorrelation", "evidence": "It has been suggested that the C-->T (224Ala-->Val) transition within exon 2 of the cathepsin D gene (CTSD) might represent a risk factor for late onset AD.Possession of the CTSD T allele does not increase the risk of developing AD per se, but has a modulating effect on the pathogenesis of the disorder by increasing, in concert with the APOE e4 allele, the amount of Abeta deposited as senile plaques in the brain in the form of Abeta40.", "citation": {"db": "PubMed", "db_id": "16543533"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 3823, "target": 2594, "key": "d0511f59fe81805f8a8fb0d31a345acb"}, {"line": 988, "relation": "negativeCorrelation", "evidence": "Oxidative stress-mediated neuronal death may be initiated by a decrease in glutathione (GSH), whose levels are reduced in mitochondrial and synaptosomal fractions of specific CNS regions in Alzheimer disease (AD) patients", "citation": {"db": "PubMed", "db_id": "22326489"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Glutathione reductase subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2799, "key": "c9b27ac641f5adb940b866049aa53c15"}, {"line": 989, "relation": "increases", "evidence": "Oxidative stress-mediated neuronal death may be initiated by a decrease in glutathione (GSH), whose levels are reduced in mitochondrial and synaptosomal fractions of specific CNS regions in Alzheimer disease (AD) patients", "citation": {"db": "PubMed", "db_id": "22326489"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Glutathione reductase subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 584, "key": "f7962d15c99bec1b0708474bc84f596c"}, {"line": 5230, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 3823, "target": 584, "key": "59b8a020daf5da7019dbe46096f6c44e"}, {"line": 1017, "relation": "negativeCorrelation", "evidence": "In vitro and in vivo studies have consistently demonstrated a link between cholinergic activation and APP metabolism.Reduction in cholinergic neurotransmission--experimental or pathological, such as in AD--leads to amyloidogenic metabolism and contributes to the neuropathology and cognitive dysfunction", "citation": {"db": "PubMed", "db_id": "12675140"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 762, "key": "5b680ae377abfb2af2bca3ec9e214ba8"}, {"line": 12872, "relation": "decreases", "evidence": "In Alzheimerbetas disease (AD), the most common age-related primary dementing disorder, degeneration of the cholinergic neurons of the basal forebrain (Whitehouse et al., 1982) occurs. Additionally, cholinergic dysfunction may lead to endocrine abnormalities including altered plasma and cerebrospinal fluid (CSF) concentrations of various neuropeptides. Vasopressin, CRF and ACTH levels are reportedly reduced in the CSF of subjects with AD, whereas plasma cortisol levels are elevated in AD and are not suppressed by dexamethasone.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3823, "target": 762, "key": "265183cbcbd955fa77a471802d3837c9"}, {"line": 13580, "relation": "decreases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 762, "key": "911c47c7c0d92e778ec06a950c565360"}, {"line": 1040, "relation": "association", "evidence": "Sequestosome 1/p62 is gaining attention as it is involved in several diseases including Parkinson disease, Alzheimer disease, liver and breast cancer, Paget's disease of bone, obesity and insulin resistance", "citation": {"db": "PubMed", "db_id": "22296116"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3823, "target": 3415, "key": "4a5e9b17892b0d18ada76f1a05d74c1e"}, {"line": 1054, "relation": "positiveCorrelation", "evidence": "TNF-a-308 G/A gene polymorphism could affect cerebral inflammatory response and the risk of late-onset Alzheimer disease but -863 C/A polymorphism does not influence the risk of this disease", "citation": {"db": "PubMed", "db_id": "22279475"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3823, "target": 1999, "key": "31fcf4dc7bd5ce205eea6ee60209f523"}, {"line": 1075, "relation": "association", "evidence": "12/15-Lipoxygenase (12/15-LO) is an enzyme widely distributed in the central nervous system, and it has been involved in the neurobiology of Alzheimer disease (AD).12/15-Lipoxygenase (12/15-LO) is an enzyme widely distributed in the central nervous system, and it has been involved in the neurobiology of Alzheimer disease (AD)", "citation": {"db": "PubMed", "db_id": "22275252"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2286, "key": "317af0f61cb52f73d53400fcfe1043e9"}, {"line": 1076, "relation": "association", "evidence": "12/15-Lipoxygenase (12/15-LO) is an enzyme widely distributed in the central nervous system, and it has been involved in the neurobiology of Alzheimer disease (AD).12/15-Lipoxygenase (12/15-LO) is an enzyme widely distributed in the central nervous system, and it has been involved in the neurobiology of Alzheimer disease (AD)", "citation": {"db": "PubMed", "db_id": "22275252"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2287, "key": "bbdfcd09850a4fffc43dd1b257b796b2"}, {"line": 1195, "relation": "association", "evidence": "We identified a new genetic risk association of AD with rare coding CLU variations that is independent of the 5' common association signal identified in the GWA studies", "citation": {"db": "PubMed", "db_id": "22248099"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 3823, "target": 2538, "key": "b38182b864e8ea17dd88e73041e9d3b8"}, {"line": 36266, "relation": "association", "evidence": "Clusterin inhibits the aggregation of A beta, which might be neuroprotective according to the aggregation-toxicity hypothesis of A beta. However, clusterin enhanced the oxidative stress of A beta.", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "MeSHAnatomy": {"Blood": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2538, "key": "d7b092d93656fe24becba2ef1aa603de"}, {"line": 1349, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3823, "target": 2146, "key": "75b594c271d34113584f0fe6afd61ae0"}, {"line": 1389, "relation": "association", "evidence": "Cathepsin D, the most abundant lysosomal and endosomal aspartyl protease, shows beta and gamma secretase activity in vitro by cleaving the amyloid precursor protein (APP) into amyloid beta protein (Abeta). Polymorphism at position 224, C224T, on exon 2 of cathepsin D gene (CTSD) has been associated with an increased risk for Alzheimer's disease (AD) ", "citation": {"db": "PubMed", "db_id": "20597865"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 1791, "key": "a6592e154db5ef4217dfacf2c4748809"}, {"line": 1474, "relation": "negativeCorrelation", "evidence": "Alzheimer's disease is rapidly becoming one of the most prevalent human diseases. Inhibition of human acetylcholinestrase (hAChE) and butyrylcholinestrase (BChE) has been linked to amelioration of Alzheimer's symptoms and research into inhibitors is of critical importance", "citation": {"db": "PubMed", "db_id": "22445674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2392, "key": "2cf207821fbd27cb6db5d0a9003d3f1e"}, {"line": 9882, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2392, "key": "6b6277481212efba86334c8add5f40f9"}, {"line": 1475, "relation": "negativeCorrelation", "evidence": "Alzheimer's disease is rapidly becoming one of the most prevalent human diseases. Inhibition of human acetylcholinestrase (hAChE) and butyrylcholinestrase (BChE) has been linked to amelioration of Alzheimer's symptoms and research into inhibitors is of critical importance", "citation": {"db": "PubMed", "db_id": "22445674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 2244, "key": "a176fdec878a453f4e2e842f252e500d"}, {"line": 9898, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2244, "key": "1ab887e44e61a7a69914fd569f9b5061"}, {"line": 1490, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 2506, "key": "bcc6034a9ec08b831f8d0b99507a057a"}, {"line": 1491, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 2359, "key": "24970920e86e135de5a14d7632d9fea5"}, {"line": 1492, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 2409, "key": "d70c415fcfdb597acd72db21095188db"}, {"line": 1493, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 2505, "key": "92c194325a9ee098ce43e6cce1499b3b"}, {"line": 1494, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 2410, "key": "37b4c6d2d3d9c1e3a0bb4a5954526bf0"}, {"line": 1811, "relation": "association", "evidence": "Role of LPR8 activation in normal brain functioning and in neurodegeneration during AD.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2981, "key": "3b260a7df389ccdeec0048c4875f0475"}, {"line": 2125, "relation": "association", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2312, "key": "42fccc55d8a008edcf818661e9985175"}, {"line": 2233, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2312, "key": "1404e111e3cd5ceba3d0f35dfc9d5943"}, {"line": 3555, "relation": "positiveCorrelation", "evidence": "The implication that cholesterol plays an essential role in the pathogenesis of Alzheimer's disease (AD) is based on the 1993 finding that the presence of apolipoprotein E (apoE) allele epsilon;4 is a strong risk factor for developing AD. Since apoE is a regulator of lipid metabolism, it is reasonable to assume that lipids such as cholesterol are involved in the pathogenesis of AD", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2312, "key": "006847eb6eb9f1f5a952f7b4e3eb1170"}, {"line": 4136, "relation": "association", "evidence": "Apolipoprotein E is the main lipid carrier in the brain and the best-established risk factor for late-onset Alzheimer's disease. Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. Finally, compounds that modulate cholesterol metabolism affect amyloid-beta generation.", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2312, "key": "5808c20be450e5725ea0c6608920c723"}, {"line": 5133, "relation": "association", "evidence": "To date, the only established genetic risk factor for LOAD is apolipoprotein E ( APOE ) 4, which explains partially the risk of the disease and modifies the age of onset.", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true}}, "source": 3823, "target": 2312, "key": "22be922b297a32636d65812d55f686b8"}, {"line": 15691, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"APOE subgraph": true}}, "source": 3823, "target": 2312, "key": "0924afe67fd77e0863d0b1ad836f6d1e"}, {"line": 18608, "relation": "association", "evidence": "Epidemiological and molecular genetic studies have shown the existence of several genes associated with increased risk of AD, the major genetic susceptibility locus coding for apolipoprotein E (apoE).", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2312, "key": "04ddce5b2f0e72d98778d59a053da69b"}, {"line": 25830, "relation": "association", "evidence": "Apolipoprotein (apo) E and its polymorphism are linked to the pathogenesis of late-onset and sporadic Alzheimer's disease (AD)", "citation": {"db": "PubMed", "db_id": "11070505"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2312, "key": "dc3460c2ac3fea2b587755062926dcd6"}, {"line": 25922, "relation": "association", "evidence": "Of the three major isoforms of human apolipoprotein E (apoE), apoE4 is a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "12015813"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2312, "key": "ec633bcbf918de26c177cadac744dfaf"}, {"line": 25961, "relation": "association", "evidence": "Apolipoprotein E (ApoE) influences the risk of late onset Alzheimer's disease (AD) in an isoform-dependent manner, such that the presence of the apoE epsilon4 allele increases the risk of AD while the presence of the apoE epsilon2 allele appears to be protective. ", "citation": {"db": "PubMed", "db_id": "14501024"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2312, "key": "9b7d75ef3e57a212939dc90790a63632"}, {"line": 26214, "relation": "association", "evidence": "Late-onset Alzheimer's disease is linked to one isotype of apo E, apo E4.", "citation": {"db": "PubMed", "db_id": "7639323"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2312, "key": "0395cb6946ab077bdab86ee7984f097a"}, {"line": 26225, "relation": "association", "evidence": "Apolipoprotein E (ApoE) genotype is a significant risk factor for the development of Alzheimer disease (AD) and the ApoE protein is associated with senile plaques (SP) and neurofibrillary tangles (NFT)", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2312, "key": "590e4ddaf8079840a692de768e6dbcf5"}, {"line": 26251, "relation": "association", "evidence": "Late-onset and sporadic Alzheimer's disease are associated with the apolipoprotein E (apoE) type 4 allele expressing the protein isoform apoE4.", "citation": {"db": "PubMed", "db_id": "8040342"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2312, "key": "4340e79151c944d09c45c5845e2ef221"}, {"line": 26305, "relation": "association", "evidence": "Growing evidence indicates the involvement of apolipoprotein E (apoE) in the development of late-onset and sporadic forms of Alzheimer's disease, although its exact role remains unclear.", "citation": {"db": "PubMed", "db_id": "9886074"}, "annotations": {"Subgraph": {"APOE subgraph": true}}, "source": 3823, "target": 2312, "key": "6a419c41942b71bb567121edbd7bed9f"}, {"line": 33881, "relation": "association", "evidence": "Neuronal injury-induced glial apoE secretion is attenuated by the nuclear factor kappaB inhibitors, aspirin, Bay 11-7082 and MG-132, suggesting that this transcription factor is involved in both constitutive and induced glial apoE expression", "citation": {"db": "PubMed", "db_id": "11311545"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 3823, "target": 2312, "key": "9a6e3070aff6b9b1ebafb9a91f0ac74f"}, {"line": 33993, "relation": "association", "evidence": "The mechanism by which apolipoprotein E (ApoE) isoforms functionally influence the risk and progression of late-onset Alzheimer's disease (LOAD) remains hitherto unknown. ", "citation": {"db": "PubMed", "db_id": "15181248"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2312, "key": "b3a6bdd8b78f795d3f528973a269f1b5"}, {"line": 40433, "relation": "association", "evidence": "S1P/sphingosine ratio was 2.5-fold higher in hippocampus of ApoE2 carriers compared to ApoE4 carriers, and multivariate regression showed a significant association between APOE genotype and hippocampal S1P/sphingosine (p = 0.0495), suggesting a new link between APOE genotype and pre-disposition to AD.This study demonstrates loss of S1P and sphingosine kinase activity early in AD pathogenesis, and prior to AD diagnosis.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2312, "key": "83a1a46a1816a9ea3b401a45d165d33d"}, {"line": 45009, "relation": "positiveCorrelation", "evidence": "Homocysteine (upper tercile) was associated with AD risk, with an odds ratio of 2.8 (95% confidence interval (CI) 1.54-5.22, p=0.0008), which was increased 2.2- and 2.0-fold by MTHFR 677T (odds ratio 6.28, 95% CI 2.88-16.20, p < 0.0001) and APOE epsilon4 (odds ratio: 5.60, 95% CI 1.12-28.05, p=0.0361), respectively. In conclusion, association of homocysteine with AD was aggravated by MTHFR 677T and APOE epsilon4 alleles.", "citation": {"db": "PubMed", "db_id": "15073531"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 2312, "key": "5dfaad0775519e9792882119c973b100"}, {"line": 2174, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2484, "key": "10e58b87947a3e066439cca5a87ce6f0"}, {"line": 2175, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2486, "key": "da5a1630f97b7b7e35cc0df1889082c4"}, {"line": 2176, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2495, "key": "cdf8acd6f7243c427b3abe9e43bc7bc8"}, {"line": 8640, "relation": "negativeCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "miRNA subgraph": true}}, "source": 3823, "target": 2495, "key": "d42349b792e7033de6fd750df8ecbaec"}, {"line": 2177, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3051, "key": "e4349c8523c8df08052d4601c3b44f5c"}, {"line": 2178, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2461, "key": "894f71cd3554418b716d7dbf43aedc04"}, {"line": 2179, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2492, "key": "844d1098bb5b528d69bf6c1355ccd571"}, {"line": 2181, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cell cycle subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2133, "key": "581173a415548509464532613c9371b4"}, {"line": 2306, "relation": "association", "evidence": "Iron deposition in the brain is another important proposed mechanisms in the pathophysiology AD. Excessive iron can contribute to the formation of free radicals, leading to lipid peroxidation and neurotoxicity, which can result in cell membrane damage and cell death. Recently, it has been shown that iron concentration in AD patients brain was significantly higher than those of nondemented controls. In particular iron deposition in parietal cortex and hippocampus at the early stage of AD were positively correlated with the severity of patients cognitive impairment", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 135, "key": "5de4ee0ad6ae9bd296041007f3b85ab0"}, {"line": 2402, "relation": "decreases", "evidence": "Alzheimer's disease (AD), one of the major causes of disability and mortality in Western societies, is a progressive age-related neurodegenerative disorder. Increasing evidence suggests that the etiology of AD may involve disruptions of zinc (Zn) homeostasis. This review discusses current evidence supporting a potential role of Zn and zinc transporters (ZnTs) in processing of the amyloid beta protein precursor (APP) and amyloid beta (Abeta) peptide generation and aggregation.", "citation": {"db": "PubMed", "db_id": "22447723"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 805, "key": "02d5c44fd7b04ae2164b5c4431406efd"}, {"line": 2435, "relation": "association", "evidence": "The ubiquitin-proteasome pathway is a major protein degradation pathway whose dysfunction is now widely accepted as a cause of neurodegenerative diseases, including Alzheimer's disease. Here we demonstrate that the F-box and leucine rich repeat protein2 (FBL2), a component of the E3 ubiquitin ligase complex, regulates amyloid precursor protein (APP) metabolism through APP ubiquitination.", "citation": {"db": "PubMed", "db_id": "22399757"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 768, "key": "9ac7e4eb2199b4188836d8e4e7aa9f69"}, {"line": 2454, "relation": "negativeCorrelation", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3439, "key": "9b925b2baa98270eca5cd9fbf5cc782e"}, {"line": 2544, "relation": "association", "evidence": "The pathological importance of Abeta as the initiator of AD is well defined; however, there is a significant minority of people with high Abeta levels who are hardly affected by AD (45). This observation suggests that a critical turning point exists in the onset of AD after increased generation of Abeta. As discussed in this Forum issue, many novel factors, such as TEK/Tie2 (48), WAVE (49, 50), CRMP2 (49, 50), and HRD1, could play crucial roles in the onset or progression of AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 3439, "key": "28c7e0b0e30775b63461685692da8e58"}, {"line": 2461, "relation": "association", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3258, "key": "4335c134db7e02ef30d056dc72a8ecf6"}, {"line": 20391, "relation": "association", "evidence": "Mutations in the presenilin-1 (PS1) gene cause early onset familial Alzheimer's disease (FAD) by a mechanism believed to involve perturbed endoplasmic reticulum (ER) function and altered proteolytic processing of the amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "12390529"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Gamma secretase subgraph": true}}, "source": 3823, "target": 3258, "key": "4630b1ba9115061506ecbd4baf214a3b"}, {"line": 32006, "relation": "association", "evidence": "Mutations of presenilin-1, the gamma-secretase catalytic subunit, can affect amyloid-beta (Abeta) production and Alzheimer disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "17115048"}, "source": 3823, "target": 3258, "key": "871dbfa26eaece1936c9c13a118dad03"}, {"line": 32418, "relation": "association", "evidence": "The amyloid precursor protein (APP) and the presenilins 1 and 2 are genetically linked to the development of familial Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3258, "key": "03b31764be7f57168279fc70e0883b40"}, {"line": 32444, "relation": "association", "evidence": "Presenilin 1 (PS1) is linked to the pathogenesis of early onset familial Alzheimer's disease (FAD) and is localized at the synapse, where it binds N-cadherin and pmodulates its adhesive activity.", "citation": {"db": "PubMed", "db_id": "14515347"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 3823, "target": 3258, "key": "79cc45d4540fa73a74c3623844cfa89a"}, {"line": 34147, "relation": "association", "evidence": "Presenilin 1 (PS1) is linked with Alzheimer's disease but exhibits functional roles regulating growth and development.", "citation": {"db": "PubMed", "db_id": "11504726"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3258, "key": "e62b7dcd4dd56301496fa20cb1fe52bb"}, {"line": 46404, "relation": "association", "evidence": "Mutations in the presenilin 1 gene have been shown to result in Alzheimer's disease. Presenilin 1 is a multi-transmembrane protein with a large hydrophilic loop near the C-terminus. This region is required for known functions of presenilin 1. We have constrained this loop within the active site of the bacterial protein, thioredoxin, to mimic its native conformational state. This hybrid protein was used as bait in a yeast two hybrid screen in an attempt to identify presenilin binding proteins. By this method syntaxin 1A, a synaptic plasma membrane protein, was identified as a novel binding protein for presenilin 1. In vitro experiments confirm the two-hybrid results suggesting that PS1 binds syntaxin under physiological conditions.", "citation": {"db": "PubMed", "db_id": "10891589"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Gamma secretase subgraph": true}}, "source": 3823, "target": 3258, "key": "112131af3da6d9214eeae39ca71ffcbb"}, {"line": 2462, "relation": "association", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3268, "key": "ee1d2de0f7feb7dc0c0c355983b3fbbe"}, {"line": 17315, "relation": "association", "evidence": "Egr-1 upregulates the Alzheimer's disease presenilin-2 gene in neuronal cells.", "citation": {"db": "PubMed", "db_id": "14585504"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Published": {"Epilepsy comorbidity paper": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3268, "key": "742ab77b25345748d26c5ca2039b37e4"}, {"line": 17340, "relation": "association", "evidence": "Inherited Presenilin-2 mutations cause familial Alzheimer's disease, and its regulation may play a role in sporadic cases.", "citation": {"db": "PubMed", "db_id": "19573580"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3268, "key": "4b6a6fce2c19ed959dd720d9b90d4762"}, {"line": 32419, "relation": "association", "evidence": "The amyloid precursor protein (APP) and the presenilins 1 and 2 are genetically linked to the development of familial Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3268, "key": "1b7c35e9e3550da0386b9daa44788eb6"}, {"line": 2521, "relation": "positiveCorrelation", "evidence": "Two possible models for involvement of HRD1 in the pathogenesis of AD. Model 1 (cause of AD): Unknown stress initiates insolubilization of HRD1 protein, resulting in a decrease in the functional HRD1 protein in the ER membrane. Subsequently, APP accumulates in the ER and is processed into Abeta that induces hyperphosphorylation of tau protein (ptau). Finally, accumulated Abeta and/or p-tau causes neurodegeneration leading to AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "source": 3823, "target": 80, "key": "4136bd3c2f90082907575f4ac9749d5b"}, {"line": 5228, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 3823, "target": 80, "key": "40b5821b58aca1df625ba8b105dc5eec"}, {"line": 25987, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulation of beta-amyloid (Abeta).", "citation": {"db": "PubMed", "db_id": "15331417"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "da38378500c934c35e6430bfb64300d3"}, {"line": 26083, "relation": "association", "evidence": "Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "599204f097516ee66a223c473a3130e1"}, {"line": 26403, "relation": "association", "evidence": "Amyloid-beta peptide (Abeta) production and accumulation in the brain is a central event in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "17185504"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "a7585f3371260a3c33c496003573fbea"}, {"line": 26781, "relation": "association", "evidence": "Amyloid-beta peptides (Abeta) are widely presumed to play a causal role in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "16027115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "826babec765f9517ffe930712b4e1f26"}, {"line": 26843, "relation": "association", "evidence": "Amyloid beta-peptide (Abeta), which is a product of the proteolytic effect of beta-secretase (BACE) on an amyloid precursor protein, is closely associated with Alzheimer's disease (AD) pathogenesis.", "citation": {"db": "PubMed", "db_id": "17205046"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "0eebf34ccfab7b33250510800ec482fc"}, {"line": 26872, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulation of the neurotoxic peptide amyloid-beta (Abeta), which is produced by sequential cleavage of amyloid precursor protein (APP) by the aspartyl protease beta-secretase and the presenilin-dependent protease gamma-secretase.", "citation": {"db": "PubMed", "db_id": "17360493"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "2f88620611958299728d89c477a106e8"}, {"line": 27079, "relation": "association", "evidence": "Amyloid-beta (Abeta) is either directly involved in the pathogenesis of Alzheimer's disease (AD) or tightly correlated with other primary pathogenic factors.", "citation": {"db": "PubMed", "db_id": "19199126"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "6be723e8380aa7965a504b881925de9c"}, {"line": 27125, "relation": "association", "evidence": "Accumulation of beta-amyloid peptide (Abeta) in the brain is a primary influence driving Alzheimer's disease (AD) pathogenesis.", "citation": {"db": "PubMed", "db_id": "19355846"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "6fe3c049425b4d0edc1a8642bbed70ef"}, {"line": 27152, "relation": "association", "evidence": "Expression levels of the amyloid precursor protein (APP) and beta-site amyloid (Abeta) cleaving enzyme 1 (BACE1) have been implicated in Alzheimer disease (AD) progression.", "citation": {"db": "PubMed", "db_id": "19462468"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "fdc644f22aad2882871fe628f923842c"}, {"line": 27185, "relation": "association", "evidence": "Clearly, AD is associated with accumulation of amyloid beta (Abeta) in the brain. ", "citation": {"db": "PubMed", "db_id": "19698775"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "3153bfd3d87af0a03117966b072586af"}, {"line": 27291, "relation": "association", "evidence": "It has been suggested that cholesterol may pmodulate amyloid-beta (Abeta) formation, a causative factor of Alzheimer's disease (AD), by regulating distribution of the three key proteins in the pathogenesis of AD (beta-amyloid precursor protein (APP), beta-secretase (BACE1) and/or presenilin 1 (PS1)) within lipid rafts.", "citation": {"db": "PubMed", "db_id": "20138836"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "09452611093b861e7e45b257019fc99e"}, {"line": 27301, "relation": "association", "evidence": "Accumulation of amyloid-beta (Abeta) peptide and deposition of hyperphosphorylated tau protein are two major pathological hallmarks of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "3bfbc23ba4543589b63914e5d4cdd0d6"}, {"line": 27687, "relation": "association", "evidence": "BACE1 is a membrane-bound aspartic protease that cleaves the amyloid precursor protein (APP) at the beta-secretase site, a critical step in the Alzheimer disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "12473667"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "8d86bce622b095ad8fdf5c8e463b885a"}, {"line": 27763, "relation": "association", "evidence": "The cerebral deposition of amyloid beta-peptide (Abeta) is a major factor in the etiology of Alzheimer's disease. beta-Secretase (BACE) initiates the generation of Abeta by cleaving the amyloid precursor protein at the beta-site and is therefore a prime target for therapeutic intervention.", "citation": {"db": "PubMed", "db_id": "14622952"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "ee69f6e9240d3fa17a24acd659904604"}, {"line": 27936, "relation": "association", "evidence": "Beta-secretase [beta-site amyloid precursor protein-cleaving enzyme 1 (BACE1)] is the key rate-limiting enzyme for the production of the beta-amyloid (Abeta) peptide involved in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "16306400"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "b76b8c5aa7796b2c2ce0e7394d301634"}, {"line": 28094, "relation": "association", "evidence": "The amyloid beta (Abeta) peptide is responsible for toxic amyloid plaque formation and is central to the aetiology of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "17541560"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "cd6280f2706e19dba6b35dc510f913e3"}, {"line": 33125, "relation": "association", "evidence": "A beta is thought to play a role in the pathogenesis of Alzheimer's disease, and, hence, considerable effort has been invested in defining the means by which A beta is generated from the APPs.", "citation": {"db": "PubMed", "db_id": "8626687"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "8e33ed126a6f6029b1f42861a03965c4"}, {"line": 47058, "relation": "association", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3823, "target": 80, "key": "0d4b767f241c188cedbbbaf7a7c8a87a"}, {"line": 2522, "relation": "positiveCorrelation", "evidence": "Two possible models for involvement of HRD1 in the pathogenesis of AD. Model 1 (cause of AD): Unknown stress initiates insolubilization of HRD1 protein, resulting in a decrease in the functional HRD1 protein in the ER membrane. Subsequently, APP accumulates in the ER and is processed into Abeta that induces hyperphosphorylation of tau protein (ptau). Finally, accumulated Abeta and/or p-tau causes neurodegeneration leading to AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "source": 3823, "target": 3015, "key": "bed9aba22e8ad1fdf03e740d1a8f0813"}, {"line": 3628, "relation": "association", "evidence": "Amyloid beta-peptide 1-42 (Abeta(1-42)) and hyperphosphorylated tubulin associated unit (tau) isoforms appear to be the most sensitive and specific CSF biomarkers, the combination of these biomarkers depicting the best diagnosis value for AD.", "citation": {"db": "PubMed", "db_id": "18584921"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3015, "key": "4807239af2e8172ca956fb340deb50d8"}, {"line": 9002, "relation": "positiveCorrelation", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3823, "target": 3015, "key": "6eab1e49cfa04d86a13081e563e92fd8"}, {"line": 19149, "relation": "association", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 3823, "target": 3015, "key": "d09e4e21e79c7fe1f2c3cde74845085a"}, {"line": 27305, "relation": "association", "evidence": "Accumulation of amyloid-beta (Abeta) peptide and deposition of hyperphosphorylated tau protein are two major pathological hallmarks of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3015, "key": "26046e21c48cdb7f843341cb5236ce8f"}, {"line": 28990, "relation": "association", "evidence": "Intraneuronal accumulation of phosphorylated Tau protein is a molecular pathology found in many forms of dementia, including Alzheimer disease. Research into possible mechanisms leading to the accumulation of pmodified Tau protein and the possibility of removing Tau protein from the system have revealed that the chaperone protein system can interact with Tau and mediate its degradation. ", "citation": {"db": "PubMed", "db_id": "17954934"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3015, "key": "58cd712fe198f1cbe0f1225f28b8c9e7"}, {"line": 29650, "relation": "association", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 3823, "target": 3015, "key": "43adce36f367dbfd81675a92fad4e864"}, {"line": 29660, "relation": "positiveCorrelation", "evidence": "Microtubule associated protein tau is abnormally hyperphosphorylated in Alzheimer disease (AD) brain.", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3823, "target": 3015, "key": "93097c9fab078ef2c7090ba6dd5f0f1c"}, {"line": 29814, "relation": "association", "evidence": "One of the histopathological markers in Alzheimer's disease is the accumulation of hyperphosphorylated tau in neurons called neurofibrillary tangles (NFT) composing paired helical filaments (PHF). ", "citation": {"db": "PubMed", "db_id": "9565682"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3823, "target": 3015, "key": "334f885db7ed8f15a401166b6ea8b2e2"}, {"line": 31915, "relation": "association", "evidence": "Within the neurofibrillary tangles (NFTs) and dystrophic neurites (DNs) of Alzheimer's disease (AD), the cytoskeletal protein tau is abnormally hyperphosphorylated.", "citation": {"db": "PubMed", "db_id": "7533559"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3823, "target": 3015, "key": "ef12a26e89d8da7dc5067f43a542099b"}, {"line": 31927, "relation": "association", "evidence": "The microtubule-associated protein tau is more highly phosphorylated at certain residues in developing brain and in Alzheimer's disease paired helical filaments than in adult brain.", "citation": {"db": "PubMed", "db_id": "8730715"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3823, "target": 3015, "key": "d466b5f43816cd555595e4369fecd260"}, {"line": 32820, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3015, "key": "8cd552d7a1021c4ad8dcf92ce017cbda"}, {"line": 2530, "relation": "association", "evidence": "The pathological importance of Abeta as the initiator of AD is well defined; however, there is a significant minority of people with high Abeta levels who are hardly affected by AD (45). This observation suggests that a critical turning point exists in the onset of AD after increased generation of Abeta. As discussed in this Forum issue, many novel factors, such as TEK/Tie2 (48), WAVE (49, 50), CRMP2 (49, 50), and HRD1, could play crucial roles in the onset or progression of AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 3448, "key": "3f5b6957e36a25e13f2756ede8761ec3"}, {"line": 2534, "relation": "association", "evidence": "The pathological importance of Abeta as the initiator of AD is well defined; however, there is a significant minority of people with high Abeta levels who are hardly affected by AD (45). This observation suggests that a critical turning point exists in the onset of AD after increased generation of Abeta. As discussed in this Forum issue, many novel factors, such as TEK/Tie2 (48), WAVE (49, 50), CRMP2 (49, 50), and HRD1, could play crucial roles in the onset or progression of AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 1723, "key": "5d35379efd9b476a093d4caa3d550ed8"}, {"line": 2539, "relation": "association", "evidence": "The pathological importance of Abeta as the initiator of AD is well defined; however, there is a significant minority of people with high Abeta levels who are hardly affected by AD (45). This observation suggests that a critical turning point exists in the onset of AD after increased generation of Abeta. As discussed in this Forum issue, many novel factors, such as TEK/Tie2 (48), WAVE (49, 50), CRMP2 (49, 50), and HRD1, could play crucial roles in the onset or progression of AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal guidance subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 2641, "key": "63956f8a7150011f5574f6fa90c4673c"}, {"line": 2637, "relation": "association", "evidence": "Mint adaptor proteins bind to the amyloid precursor protein (APP) and regulate APP processing associated with Alzheimer's disease; however, the molecular mechanisms underlying Mint regulation in APP binding and processing remain unclear. Biochemical, biophysical, and cellular experiments now show that the Mint1 phosphotyrosine binding (PTB) domain that binds to APP is intramolecularly inhibited by the adjacent C-terminal linker region. The crystal structure of a C-terminally extended Mint1 PTB fragment reveals that the linker region forms a short a-helix that folds back onto the PTB domain and sterically hinders APP binding. This intramolecular interaction is disrupted by mutation of Tyr633 within the Mint1 autoinhibitory helix leading to enhanced APP binding and Abeta-amyloid production. Our findings suggest that an autoinhibitory mechanism in Mint1 is important for regulating APP processing and may provide novel therapies for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22355143"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "source": 3823, "target": 2294, "key": "68467e4721249a3a91ddc079174ba4d0"}, {"line": 2907, "relation": "association", "evidence": "Presenilin-1 (PS1) is the catalytic subunit of gamma-secretase and mutations in this protein cause familial Alzheimer Disease (FAD). ", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1925, "key": "ece84c9a7cc0ba5854e1f2aa12f6c167"}, {"line": 6559, "relation": "association", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1925, "key": "f4894c058fb50f92fe8af3585926a7cf"}, {"line": 2917, "relation": "association", "evidence": "Mutations in presenilin2 (PS2), a homolog of PS1, are also associated with FAD. While the precise mechanism on how these mutations cause AD is unknown, multiple theories have arisen to explain the role of PS1 and PS2 mutations on AD pathogenesis. These mutations lead to abnormal function of gamma- secretase, the beta-catenin pathway, calcium homoeostasis and the lysosomal/autophagy pathway as well as chaperones. Among these hypotheses, the effect of PS mutations on gamma-secretase has been extensively investigated. gamma-Secretase is composed of at least four subunits: PS, Nicastrin, Aph1 and Pen2; with a total of 19 putative transmembrane domains.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1928, "key": "f5f5287fdd1dabc89665aefe638a5360"}, {"line": 6560, "relation": "association", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1928, "key": "1ec7ede1e42690d3f07acd14da311ac9"}, {"line": 3106, "relation": "positiveCorrelation", "evidence": "BACE1 is primarily expressed by neurons and increased BACE1 protein concentrations and enzymatic activities have been reported in the brains of AD patients. However, there is accumulating evidence that, in addition to neurons, reactive astrocytes are capable of expressing BACE1 and, therefore, may contribute to beta-amyloid plaque formation. This suggests that conditions accompanied by chronic astrocyte activation may contribute to developing AD.", "citation": {"db": "PubMed", "db_id": "15465276"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2375, "key": "bec2d8e73b99818e0956e22d8434e4c5"}, {"line": 8440, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3823, "target": 2375, "key": "2a1a0b7db5f5a8ba09145f6051eee3c7"}, {"line": 14289, "relation": "association", "evidence": "Increased NF-κB signalling up-regulates BACE1 expression and its therapeutic potential in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2375, "key": "534300b5ebebd2a6876c0184e4663f49"}, {"line": 14300, "relation": "positiveCorrelation", "evidence": "Elevated levels of beta-site APP cleaving enzyme 1 (BACE1) were found in the brain of some sporadic Alzheimer's disease (AD) patients; however, the underlying mechanism is unknown.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2375, "key": "62f3b72137b8f35bcb64491fd6ff16aa"}, {"line": 14335, "relation": "positiveCorrelation", "evidence": "In this report we found that both BACE1 and NF-κB p65 levels were significantly increased in the brains of AD patients.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2375, "key": "122ddac389bc7701702105c887678b8f"}, {"line": 17643, "relation": "association", "evidence": "beta-Secretase 1 (BACE-1) is an attractive therapeutic target for the treatment and prevention of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22984865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}}, "source": 3823, "target": 2375, "key": "e530a7f523b04269150bb9a292bc7dfa"}, {"line": 26683, "relation": "association", "evidence": "Our studies demonstrate, for the first time, that pmodulation of BACE1 activity may play a significant role in AD pathogenesis in vivo.", "citation": {"db": "PubMed", "db_id": "15452128"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3823, "target": 2375, "key": "26104257b454663323103e24605f7c75"}, {"line": 27819, "relation": "association", "evidence": "BACE is an aspartyl protease that cleaves the amyloid precursor protein (APP) at the beta-secretase cleavage site and is involved in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15080893"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2375, "key": "021f4f62741c6c34d57bf7567bbdd5da"}, {"line": 32953, "relation": "association", "evidence": "BACE1 is a promising therapeutic and preventive target for Alzheimer's disease because it is essential for amyloid deposition. ", "citation": {"db": "PubMed", "db_id": "18413858"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}}, "source": 3823, "target": 2375, "key": "38bd2926a6b186c9019256ebc4a5fd04"}, {"line": 3129, "relation": "increases", "evidence": "The cellular levels of Abeta-site APP cleaving enzyme 1 (BACE1), the rate-limiting enzyme for the generation of the Alzheimer disease (AD) amyloid Abeta-peptide (Abeta), are tightly regulated by two ER-based acetyl-CoA:lysine acetyltransferases, ATase1 and ATase2. Here we report that both acetyltransferases are expressed in neurons and glial cells, and are up-regulated in the brain of AD patients. We also report the identification of first and second generation compounds that inhibit ATase1/ATase2 and down-regulate the expression levels as well as activity of BACE1. The mechanism of action involves competitive and non-competitive inhibition as well as generation of unstable intermediates of the ATases that undergo degradation.", "citation": {"db": "PubMed", "db_id": "22267734"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Cell": {"microglial cell": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3088, "key": "c031e8befad8f1a0b2285123ed9838d8"}, {"line": 3164, "relation": "association", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2747, "key": "876c203aa14d404a5a603ddae268191d"}, {"line": 3165, "relation": "association", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3399, "key": "5d8a1b0b93cddb510b41c464654c16dd"}, {"line": 3179, "relation": "association", "evidence": "AMP-activated protein kinase (AMPK), a master regulator of cellular energy homeostasis and a central player in glucose and lipid metabolism, is potentially implicated in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Autophagy signaling subgraph": true, "Sphingolipid metabolic subgraph": true, "Tau protein subgraph": true}}, "source": 3823, "target": 2210, "key": "64f90c2d26470f91f3199fc3ab4f07d0"}, {"line": 3188, "relation": "negativeCorrelation", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3823, "target": 2210, "key": "f50ffb4dc90625727ef9c1f2f4548e87"}, {"line": 3308, "relation": "association", "evidence": "After exclusion of loci already known to be involved in AD (APOE, BIN1 and CR1), 91 regions with suggestive haplotype effects were identified", "citation": {"db": "PubMed", "db_id": "22430674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 3823, "target": 1744, "key": "cd13c6d5a242e1980f869d9a22e13a37"}, {"line": 6562, "relation": "association", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1744, "key": "49a724cc4da0eae7acf6b313f1fed143"}, {"line": 26485, "relation": "association", "evidence": "The genes for both the beta-amyloid precursor protein and apolipoprotein E (ApoE) have been linked to Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9202294"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1744, "key": "114f9cad574c4cf1853b2056905b8f02"}, {"line": 3311, "relation": "association", "evidence": "After exclusion of loci already known to be involved in AD (APOE, BIN1 and CR1), 91 regions with suggestive haplotype effects were identified", "citation": {"db": "PubMed", "db_id": "22430674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Synapse assembly subgraph": true, "Caspase subgraph": true}}, "source": 3823, "target": 1760, "key": "9f3819e4a637a412c931d9be867c65de"}, {"line": 3347, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Caspase subgraph": true, "Synapse assembly subgraph": true}}, "source": 3823, "target": 1760, "key": "d733353131f123b31a542bf4f7e906e5"}, {"line": 3313, "relation": "association", "evidence": "After exclusion of loci already known to be involved in AD (APOE, BIN1 and CR1), 91 regions with suggestive haplotype effects were identified", "citation": {"db": "PubMed", "db_id": "22430674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 1782, "key": "8ae32db19600535ae8bab4dcd6b9a51f"}, {"line": 3350, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 3823, "target": 1782, "key": "40ee99c6537e973d27c2ad193ed8b774"}, {"line": 3319, "relation": "positiveCorrelation", "evidence": "In conclusion, combining both GWHA study and a conservative three-stage replication approach, we characterised FRMD4A as a new genetic risk factor of AD", "citation": {"db": "PubMed", "db_id": "22430674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3823, "target": 1826, "key": "1b3e5beef1d0d680b23739ea187c2f33"}, {"line": 3332, "relation": "association", "evidence": "Polymorphisms in CST3 and EXOC3L2 as well as the absence of APOE4 were associated with more aggressive disease courses. A trend was observed for BIN1", "citation": {"db": "PubMed", "db_id": "22414550"}, "annotations": {"Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 3823, "target": 1789, "key": "93872e2293ccc3cf1e2201b96933771b"}, {"line": 3333, "relation": "association", "evidence": "Polymorphisms in CST3 and EXOC3L2 as well as the absence of APOE4 were associated with more aggressive disease courses. A trend was observed for BIN1", "citation": {"db": "PubMed", "db_id": "22414550"}, "annotations": {"Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 3823, "target": 1819, "key": "bffdc1d2d3c208a7a3b7f648b678b962"}, {"line": 3336, "relation": "association", "evidence": "Polymorphisms in CST3 and EXOC3L2 as well as the absence of APOE4 were associated with more aggressive disease courses. A trend was observed for BIN1", "citation": {"db": "PubMed", "db_id": "22414550"}, "annotations": {"Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 3823, "target": 1762, "key": "563af78f3852025e97ffe6a03ac4365c"}, {"line": 3349, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 3823, "target": 1780, "key": "3e0eb2c88fb76d2b9f5a26d5a2ba491a"}, {"line": 3351, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 3823, "target": 1912, "key": "5ca1fa1a71b1b1d399cc1f94f644c636"}, {"line": 3352, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 3823, "target": 1879, "key": "25dbad8334e6b25db0ea373e87a4386a"}, {"line": 3358, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}}, "source": 3823, "target": 1731, "key": "83a33c029680ed143f6612944f3de66c"}, {"line": 45837, "relation": "association", "evidence": "cg02308560 in the ABCA7 locus is associated to AD pathology", "citation": {"db": "PubMed", "db_id": "25129075"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true}}, "source": 3823, "target": 1731, "key": "ddd764dcfba8d63c968e9c41be35279f"}, {"line": 3360, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 3823, "target": 1769, "key": "66c508a60f3f78afc121cbd6e10c4ea8"}, {"line": 3362, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"G-protein-mediated signaling": true}}, "source": 3823, "target": 1815, "key": "871ce00fa0efadd91381b4dd437ed860"}, {"line": 3392, "relation": "positiveCorrelation", "evidence": "Overall, this large, independent follow-up study for 15 of the top LOAD candidate genes provides support for GAB2 and LOC651924 (6q24.1) as risk modifiers of LOAD and novel associations between PGBD1 and EBF3 with age-at-onset.", "citation": {"db": "PubMed", "db_id": "21132329"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}}, "source": 3823, "target": 1829, "key": "8e22c85440e8c495f2fceba562d9b17a"}, {"line": 3396, "relation": "positiveCorrelation", "evidence": "Overall, this large, independent follow-up study for 15 of the top LOAD candidate genes provides support for GAB2 and LOC651924 (6q24.1) as risk modifiers of LOAD and novel associations between PGBD1 and EBF3 with age-at-onset.", "citation": {"db": "PubMed", "db_id": "21132329"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"T cells signaling": true}}, "source": 3823, "target": 1911, "key": "010bb4eaf17593c78230ce9502fd269e"}, {"line": 3397, "relation": "positiveCorrelation", "evidence": "Overall, this large, independent follow-up study for 15 of the top LOAD candidate genes provides support for GAB2 and LOC651924 (6q24.1) as risk modifiers of LOAD and novel associations between PGBD1 and EBF3 with age-at-onset.", "citation": {"db": "PubMed", "db_id": "21132329"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"T cells signaling": true}}, "source": 3823, "target": 1811, "key": "caedf83e5b86f9a09155cd6cd4f7e09e"}, {"line": 3443, "relation": "association", "evidence": "Binding to lipid and heparan sulfate proteoglycans (HSPG) induces apoE to adopt active conformations for binding to low-density lipoprotein receptor (LDLR) family. ApoE also interacts with beta amyloid peptide, manifests critical isoform-specific effects on Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21873229"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1127, "key": "eec4c45e8d7e68e54acb5d54e0b06faf"}, {"line": 3481, "relation": "positiveCorrelation", "evidence": "The CST3 Thr25 allele of CST3, which encodes cystatin C, leads to reduced cystatin C secretion and conveys susceptibility to Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "18026102"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 3823, "target": 2579, "key": "67e67a42f8cfa863cb9df2eb5964f614"}, {"line": 3496, "relation": "association", "evidence": "Two novel missense point mutations, Ser413Leu in the CHRNA4 gene and Gln397Pro in the CHRNB2 gene, were identified in two different AD cases but were not found in other AD cases and controls. These findings suggested that genetic polymorphisms of the neuronal nAChR genes might be related to the pathogenesis of sporadic AD.", "citation": {"db": "PubMed", "db_id": "12214130"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2519, "key": "134c21bce840394a34f01158ee88b507"}, {"line": 3500, "relation": "association", "evidence": "Two novel missense point mutations, Ser413Leu in the CHRNA4 gene and Gln397Pro in the CHRNB2 gene, were identified in two different AD cases but were not found in other AD cases and controls. These findings suggested that genetic polymorphisms of the neuronal nAChR genes might be related to the pathogenesis of sporadic AD.", "citation": {"db": "PubMed", "db_id": "12214130"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2525, "key": "22b669c13d43dd2b8a3e3dbe7db215e5"}, {"line": 3547, "relation": "association", "evidence": "The implication that cholesterol plays an essential role in the pathogenesis of Alzheimer's disease (AD) is based on the 1993 finding that the presence of apolipoprotein E (apoE) allele epsilon;4 is a strong risk factor for developing AD. Since apoE is a regulator of lipid metabolism, it is reasonable to assume that lipids such as cholesterol are involved in the pathogenesis of AD", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 231, "key": "fc639cc215b24b31b7821036962ecdb3"}, {"line": 27003, "relation": "association", "evidence": "In addition, growing evidence suggests a role of cholesterol in Alzheimer disease pathology and Abeta generation. ", "citation": {"db": "PubMed", "db_id": "18308724"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 231, "key": "48de769e78fd9a791ac7ad799bccea7d"}, {"line": 3660, "relation": "association", "evidence": "We obtained significant evidence of association with KIAA1033 (VEGAS p = 0.025), SNX1 (VEGAS p = 0.035), SNX3 (p = 0.0057), and RAB7A (VEGAS p = 0.018). Ten KIAA1033 SNPs were also significantly associated with AD in a group of African Americans (513 AD cases, 504 control subjects)", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3533, "key": "87b908045dfd80303bfa19a7ee0dc515"}, {"line": 3668, "relation": "association", "evidence": "We obtained significant evidence of association with KIAA1033 (VEGAS p = 0.025), SNX1 (VEGAS p = 0.035), SNX3 (p = 0.0057), and RAB7A (VEGAS p = 0.018). Ten KIAA1033 SNPs were also significantly associated with AD in a group of African Americans (513 AD cases, 504 control subjects)", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3388, "key": "8b6fd7252fbc51c15f561260190ca567"}, {"line": 3676, "relation": "association", "evidence": "We obtained significant evidence of association with KIAA1033 (VEGAS p = 0.025), SNX1 (VEGAS p = 0.035), SNX3 (p = 0.0057), and RAB7A (VEGAS p = 0.018). Ten KIAA1033 SNPs were also significantly associated with AD in a group of African Americans (513 AD cases, 504 control subjects)", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3289, "key": "79befcb5f0f6d2c7f6f663d700d5ce03"}, {"line": 3708, "relation": "positiveCorrelation", "evidence": "Insulin resistance, one of the major components of type 2 diabetes mellitus (T2DM), is a known risk factor for Alzheimer's disease (AD), which is characterized by an abnormal accumulation of intra- and extracellular amyloid beta peptide (Abeta).", "citation": {"db": "PubMed", "db_id": "22829447"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3823, "target": 3861, "key": "74b4e617684c3e11a5cb525a6ff690e3"}, {"line": 5982, "relation": "association", "evidence": "Evidence has suggested that insulin resistance (IR) or high levels of glucocorticoids (GCs) may be linked with the pathogenesis and/or progression of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3861, "key": "51d3d0772a06af80ac6fed368fccbada"}, {"line": 7799, "relation": "association", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"hyperinsulinism": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3861, "key": "3440a99f05117401ea3cf029e05fa606"}, {"line": 8236, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3823, "target": 3861, "key": "1cea782782e5eb622bd6ddb02be4d498"}, {"line": 20434, "relation": "association", "evidence": "Alzheimer's disease (AD) is linked to CNS insulin resistance, decreased expression of insulin and insulin receptor genes, and lower cerebrospinal insulin levels.", "citation": {"db": "PubMed", "db_id": "22142155"}, "annotations": {"MeSHDisease": {"Insulin Resistance": true, "Alzheimer Disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3861, "key": "23c92e602c415f080c05ae8fb873b469"}, {"line": 3769, "relation": "association", "evidence": "This report identifies ABCA2 as a key regulator of endogenous APP expression and processing and suggests a possible biochemical mechanism linking ABCA2 expression, APP processing and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20704561"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2230, "key": "8a122b99f189a788e5973d63bfcea955"}, {"line": 3781, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2885, "key": "d17229d14ac38636bc95b173e3061978"}, {"line": 17509, "relation": "causesNoChange", "evidence": "Sera from patients with Alzheimer disease and non-demented elderly subjects caused an increase in IL-2 and a decrease in IL-10 production by PBMC from middle-aged control subjects but did not affect IL-1beta, IL-6, and TNFalpha secretion, indicating alterations of the immune system related to aging.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Species": {"9606": true}}, "source": 3823, "target": 2885, "key": "1ca9526236e627664e96d71f79c4d823"}, {"line": 24470, "relation": "positiveCorrelation", "evidence": "IL-1 levels are elevated in Alzheimer brain, and overexpression of IL-1 is associated with beta-amyloid plaque progression.", "citation": {"db": "PubMed", "db_id": "10850859"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2885, "key": "32a7106a2aad9ead6cad8b11e6a0dae9"}, {"line": 25541, "relation": "association", "evidence": "The proinflammatory cytokine interleukin (IL)-1beta is up-regulated in microglial cells surrounding amyloid plaques, leading to the hypothesis that IL-1beta is a risk factor for Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2885, "key": "5ac0996de52bc1932303c92c62775e61"}, {"line": 46170, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2885, "key": "23965044a5e8f84817ed5e58c36f5119"}, {"line": 3782, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2894, "key": "59feed3473fd1fe21752754f80440b04"}, {"line": 8238, "relation": "positiveCorrelation", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3823, "target": 2894, "key": "1d416cace63494caa66cb7d825ae8a6e"}, {"line": 8387, "relation": "positiveCorrelation", "evidence": "Thus, laboratory and clinical findings are consistent with epidemiological reports of a reduced prevalence of Alzheimer's disease among persons who take nonsteroidal anti-inflammatory drugs (NSAIDs) for chronic pain.[125,126] Interleukin-6 (IL-6), an inflammatory cytokine, is one of the products that has been implicated in Alzheimer's disease. Elevated IL-6 immunoreactivity has been shown in human lumbar and ventricular CSF in patientswith Alzheimer's disease.[127] Furthermore, IL-6 has been found in senile plaques and may be involved in both the development of plaques and the development of dementia.[128]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2894, "key": "009256db9fb35df1c3c20ed157b7f638"}, {"line": 10775, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2894, "key": "c7570670cc8fc23df12f1dd0af163d78"}, {"line": 17510, "relation": "causesNoChange", "evidence": "Sera from patients with Alzheimer disease and non-demented elderly subjects caused an increase in IL-2 and a decrease in IL-10 production by PBMC from middle-aged control subjects but did not affect IL-1beta, IL-6, and TNFalpha secretion, indicating alterations of the immune system related to aging.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Species": {"9606": true}}, "source": 3823, "target": 2894, "key": "62f7d100ec333f7584b3cf71cb2c60d3"}, {"line": 38970, "relation": "positiveCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 2894, "key": "98ebaedacc8afb0c5a20863d6033aeae"}, {"line": 3783, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2606, "key": "b26a30633a5664cfd53479fbaff268db"}, {"line": 38971, "relation": "positiveCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 2606, "key": "afc0c5113597b587ded4ab64977b649e"}, {"line": 39810, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3823, "target": 2606, "key": "a800bd45fc2ce75a137882f339ab6b00"}, {"line": 3791, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3472, "key": "34671966b74f5ab1e4213211ca92dd83"}, {"line": 10783, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3472, "key": "1dd2aae65c432d708b4d7303488ae363"}, {"line": 11800, "relation": "positiveCorrelation", "evidence": "According to current scientific knowledge, excess tumour necrosis factor-alpha (TNF-alpha) and low insulin-like growth factor-I (IGF-I) are pathogenic-risk factors that constitute therapeutic targets for Alzheimer's disease (AD).At week 24, Cere reduced TNF-alpha and enhanced dissociable IGF-I with respect to placebo in a dose-related manner. Increases in total IGF-I were induced by 60 ml Cere", "citation": {"db": "PubMed", "db_id": "19531281"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3472, "key": "fa89585f987331ac7d6966ccdb915ad6"}, {"line": 17512, "relation": "causesNoChange", "evidence": "Sera from patients with Alzheimer disease and non-demented elderly subjects caused an increase in IL-2 and a decrease in IL-10 production by PBMC from middle-aged control subjects but did not affect IL-1beta, IL-6, and TNFalpha secretion, indicating alterations of the immune system related to aging.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Species": {"9606": true}}, "source": 3823, "target": 3472, "key": "234973909e4b52c9527953a3b8103c0c"}, {"line": 46176, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 3472, "key": "8cb248bfba685372016d7d546fe25c36"}, {"line": 3799, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3454, "key": "aaad64132e009cd938ccf716f0ed6ed9"}, {"line": 22678, "relation": "decreases", "evidence": "In Alzheimer's disease (AD), affected neurons accumulate beta amyloid protein, components of which can induce mouse microglia to express the high-output isoform of nitric oxide synthase (NOS2) in vitro. Products of NOS2 can be neurotoxic. In mice, NOS2 is normally suppressed by transforming growth factor beta 1 (TGF-beta 1). Expression of TGF-beta 1 is decreased in brains from AD patients, a situation that might be permissive for accumulation of NOS2.", "citation": {"db": "PubMed", "db_id": "8879214"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3454, "key": "8828fd635854704bea9aa9e6b1a5b1e9"}, {"line": 3800, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3455, "key": "8582e5aa0cfc21077c5cfeb68accd3f5"}, {"line": 3801, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3456, "key": "f79f8299f33c406d3ea5c0022060ebf8"}, {"line": 3808, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2464, "key": "253937cb22133f83aef2ffc81c841b56"}, {"line": 3817, "relation": "association", "evidence": "In addition, an increased risk of AD has been associated with several polymorphisms of proinflammatory genes, including IL-1 [26], IL-6 [27], TNF-α [28], and α1-antichymotrypsin [29].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"JAK-STAT signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3350, "key": "19b4f58bf58886ecd18e9baa3b08843e"}, {"line": 38985, "relation": "positiveCorrelation", "evidence": "Acute-phase proteins such as alpha 1-antichymotrypsin and c-reactive protein, elements of the complement / system, and activated microglial and astroglial cells are consistently found in brains of AD patients. Most importantly, / also cytokines such as interleukin-6 (IL-6) have been detected in the cortices of AD patients, indicating a local / activation of components of the unspecific inflammatory system.", "citation": {"db": "PubMed", "db_id": "8739396"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3823, "target": 3350, "key": "ad6c21d279ba8191a764e33ac1801b87"}, {"line": 4109, "relation": "association", "evidence": "It was clarified what molecules related to cell death are activated in the case of AD and we discovered that caspase-4 plays a key role in ER stress-induced apoptotic process. Caspase-4 also seems to act upstream of the beta-amyloid-induced ER stress pathway, suggesting that activation of caspase-4 might mediate neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true}}, "source": 3823, "target": 645, "key": "37fb23cd068f4a03b7b397d26aa378d6"}, {"line": 4846, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 645, "key": "e6b0657925c8075ebbcfa466af18541a"}, {"line": 4436, "relation": "decreases", "evidence": "Mitochondrial dysfunction is a prominent feature of Alzheimer's disease (AD) brain.Mitochondrial biogenesis is regulated by the peroxisome proliferator activator receptor gamma-coactivator 1a (PGC-1a)-nuclear respiratory factor (NRF)-mitochondrial transcription factor A pathway. Expression levels of PGC-1a, NRF 1, NRF 2, and mitochondrial transcription factor A were significantly decreased in both AD hippocampal tissues and APPswe M17 cells, suggesting a reduced mitochondrial biogenesis. Indeed, APPswe M17 cells demonstrated decreased mitochondrial DNA/nuclear DNA ratio, correlated with reduced ATP content, and decreased cytochrome C oxidase activity.", "citation": {"db": "PubMed", "db_id": "22077634"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 3823, "target": 621, "key": "8b5fcec0a726131c1a444b69ee789c70"}, {"line": 4443, "relation": "decreases", "evidence": "Mitochondrial dysfunction is a prominent feature of Alzheimer's disease (AD) brain.Mitochondrial biogenesis is regulated by the peroxisome proliferator activator receptor gamma-coactivator 1a (PGC-1a)-nuclear respiratory factor (NRF)-mitochondrial transcription factor A pathway. Expression levels of PGC-1a, NRF 1, NRF 2, and mitochondrial transcription factor A were significantly decreased in both AD hippocampal tissues and APPswe M17 cells, suggesting a reduced mitochondrial biogenesis. Indeed, APPswe M17 cells demonstrated decreased mitochondrial DNA/nuclear DNA ratio, correlated with reduced ATP content, and decreased cytochrome C oxidase activity.", "citation": {"db": "PubMed", "db_id": "22077634"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3212, "key": "55ceea587fd42606190aa8f6e70c8d30"}, {"line": 4444, "relation": "decreases", "evidence": "Mitochondrial dysfunction is a prominent feature of Alzheimer's disease (AD) brain.Mitochondrial biogenesis is regulated by the peroxisome proliferator activator receptor gamma-coactivator 1a (PGC-1a)-nuclear respiratory factor (NRF)-mitochondrial transcription factor A pathway. Expression levels of PGC-1a, NRF 1, NRF 2, and mitochondrial transcription factor A were significantly decreased in both AD hippocampal tissues and APPswe M17 cells, suggesting a reduced mitochondrial biogenesis. Indeed, APPswe M17 cells demonstrated decreased mitochondrial DNA/nuclear DNA ratio, correlated with reduced ATP content, and decreased cytochrome C oxidase activity.", "citation": {"db": "PubMed", "db_id": "22077634"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3139, "key": "54f88bfd1360339b62ecaf4190f59323"}, {"line": 4445, "relation": "decreases", "evidence": "Mitochondrial dysfunction is a prominent feature of Alzheimer's disease (AD) brain.Mitochondrial biogenesis is regulated by the peroxisome proliferator activator receptor gamma-coactivator 1a (PGC-1a)-nuclear respiratory factor (NRF)-mitochondrial transcription factor A pathway. Expression levels of PGC-1a, NRF 1, NRF 2, and mitochondrial transcription factor A were significantly decreased in both AD hippocampal tissues and APPswe M17 cells, suggesting a reduced mitochondrial biogenesis. Indeed, APPswe M17 cells demonstrated decreased mitochondrial DNA/nuclear DNA ratio, correlated with reduced ATP content, and decreased cytochrome C oxidase activity.", "citation": {"db": "PubMed", "db_id": "22077634"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3450, "key": "4f246376940a6dcb79f4cd8951bf3925"}, {"line": 4860, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2580, "key": "02ceb6bac69d93be35c391cd7bc460a7"}, {"line": 4861, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2794, "key": "897c84d1d3960828521f931ac5ced98d"}, {"line": 12493, "relation": "association", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 3823, "target": 2794, "key": "3e91dabe86f84611698f418daf525bc1"}, {"line": 29376, "relation": "association", "evidence": "Our findings indicate that the abnormal activation of glycogen synthase kinase 3beta can reduce neuronal viability and synaptic plasticity via modulating Presenilin 1/N-cadherin/beta-catenin interaction and thus have important implications in the pathophysiology of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2794, "key": "f04b08868aa4ab00f367383e8329dd00"}, {"line": 4970, "relation": "positiveCorrelation", "evidence": "Recent evidence suggests that mononuclear phagocyte accumulation in the AD brain is dependent on chemokines. CCL2, a major monocyte chemokine, is upregulated in the AD brain.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Complement system subgraph": true}}, "source": 3823, "target": 2455, "key": "add2d6704100ae6e101980ec11a3e465"}, {"line": 38968, "relation": "positiveCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 2455, "key": "2f473d809802d5c3eca2e45066487709"}, {"line": 5017, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 524, "key": "035ea69e97b0987ddd4e63aafde47b72"}, {"line": 5120, "relation": "association", "evidence": "In addition to an association of the PPARA L162V polymorphism with the AD risk, we observed four significant interactions between SNPs in PPARA and SNPs in IL1A, IL1B and IL10 affecting AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3211, "key": "fe7624e098476cdf5df823bf8ac203fb"}, {"line": 5148, "relation": "association", "evidence": "We examined the effect of the two previously reported variants of PPAR polymorphisms, the Pro12Ala and exon6 C478T, on the risk of LOAD and age of onset in a populati on- based f ol l ow- up sample of aged subj ects (125 LOAD patients and 462 non-demented controls). The genetic risk of AD was not significantly associated with the studied polymorphisms, but the PPARgamma Ala12-478T genotype carriers were significantly younger at the onset of dementia than the non-carriers (p = 0.026). These results suggest that the PPARgamma gene may modify the age of onset in LOAD", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3213, "key": "dad5a32fa633f07725b8d367ad621a66"}, {"line": 5229, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 3823, "target": 3901, "key": "89ce6283e135c2cb88e544622bd281ce"}, {"line": 10632, "relation": "association", "evidence": "Lysosomal beta-galactosidase and beta-hexosaminidase activities correlate with clinical stages of dementia associated with Alzheimer's disease and type 2 diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "CellStructure": {"Lysosomes": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3901, "key": "fb798188177205ec2cccd8c673432cdc"}, {"line": 15391, "relation": "isA", "evidence": "Alzheimer's disease (AD) is the most prevalent type of dementia.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 3823, "target": 3901, "key": "498dc97083ffddda99cf67a48eee6a0f"}, {"line": 18599, "relation": "association", "evidence": "Alzheimer's disease (AD) is the most common cause of dementia in the elderly.", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3901, "key": "99fbae1e308825fee85ebdea3b70e772"}, {"line": 19647, "relation": "association", "evidence": "Alzheimer's disease (AD) is the most common type of dementia accounting for 60-80% of the reported cases.", "citation": {"db": "PubMed", "db_id": "23871825"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 3823, "target": 3901, "key": "a6e9c55e086cd4fdd0d04621f1b56e45"}, {"line": 41198, "relation": "association", "evidence": "Among neurodegenerative disorders, Alzheimer's disease (AD) represents the most common cause of dementia in the elderly.", "citation": {"db": "PubMed", "db_id": "24860504"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true, "Dementia": true}}, "source": 3823, "target": 3901, "key": "8da57839eca2034bd3808826222843ec"}, {"line": 5231, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 3823, "target": 614, "key": "caca76152ce5a422e5efcefae63466f8"}, {"line": 5232, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 3823, "target": 648, "key": "23a353d9ad474ea6a6d2d351f39f1147"}, {"line": 15056, "relation": "association", "evidence": "Repair of oxidative DNA damage, cell-cycle regulation and neuronal death may influence the clinical manifestation of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24936870"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 648, "key": "5000b38eef21c3b32e9940ed3e36bc9c"}, {"line": 19151, "relation": "negativeCorrelation", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 3823, "target": 648, "key": "2f03749f7b863b0ee84f7d15c707e5ea"}, {"line": 30144, "relation": "association", "evidence": "The neuronal loss associated with Alzheimer's disease (AD) affects areas of the brain that are vital to cognition.", "citation": {"db": "PubMed", "db_id": "19458225"}, "source": 3823, "target": 648, "key": "6d996b8db5cee0f263da8a1fd3b4a4d4"}, {"line": 5405, "relation": "association", "evidence": "Memory mechanisms might be directly compromised by elevated ROS, which could explain the connection between AD and oxidative stress. The increase in oxidative damage exhibited by synaptic mitochondria will damage synapses, affect neurotransmission and might be ultimately responsible for cognitive decline in AD patients. Taken together these studies provide convincing evidence for the concept that mitochondria have a pivotal role in Abeta-induced synaptic dysfunction and neuronal stress. Improved function of mitochondria is an effective way of reducing effects of aging and may inhibit neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3823, "target": 842, "key": "3600844c7ae58823962410ca0913c6fc"}, {"line": 9688, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 842, "key": "b02cf8c8edceb770b7e2a837a4375fd7"}, {"line": 14082, "relation": "association", "evidence": "Oxidative stress has been suggested to play an important role in the pathogenesis of various neurodegenerative diseases including Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 842, "key": "0f2d73b16f2e3adacb3a83b31dc83294"}, {"line": 40108, "relation": "association", "evidence": "This is particularly the case with Alzheimer's disease, the most common age-related dementia associated with impairments in learning and memory accompanied by neuroinflammation, oxidative stress and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "24256258"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 3823, "target": 842, "key": "4bca6a715c7756807294ddf544b799b7"}, {"line": 5466, "relation": "decreases", "evidence": "Akt substrates such as mammalian target of rapamycin (mTOR; Ser2448) and decreased levels of cell-cycle inhibitors (p27kip1) are found in AD temporal cortex when compared to controls. GSK-3a has been implicated in the production of Abeta peptide while increased GSK-3beta activity has been implicated in tau hyperphosphorylation and neuronal cell death", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3076, "key": "9a0c07ad2b4ae57d3c3f21f582bed8ab"}, {"line": 5467, "relation": "decreases", "evidence": "Akt substrates such as mammalian target of rapamycin (mTOR; Ser2448) and decreased levels of cell-cycle inhibitors (p27kip1) are found in AD temporal cortex when compared to controls. GSK-3a has been implicated in the production of Abeta peptide while increased GSK-3beta activity has been implicated in tau hyperphosphorylation and neuronal cell death", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2958, "key": "3ff771fbf1af78c8940e076a606f4995"}, {"line": 5543, "relation": "decreases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 490, "key": "2ae6a19903da08e20e08525220f15467"}, {"line": 5545, "relation": "decreases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2164, "key": "0acac6fbe88ce74a427c89b8eab1d5e3"}, {"line": 5546, "relation": "decreases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3823, "target": 2162, "key": "7dfaf5c31cb8aa22f19a02dcb7e08e80"}, {"line": 5547, "relation": "decreases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2559, "key": "cc9ee53a497f11979306626d1ca629bd"}, {"line": 5557, "relation": "association", "evidence": "Disturbances of the cholesterol metabolism are associated with Alzheimer's disease (AD) risk and related cerebral pathology. Experimental studies found changing levels of cholesterol and its metabolites 24S-hydroxycholesterol (24S-OHC) and 27-hydroxycholesterol (27-OHC) to contribute to amyloidogenesis by increasing the production of soluble amyloid precursor protein (sAPP).The results suggest that high CSF concentrations of cholesterol, 24S-OHC, and 27-OHC are associated with increased production of both sAPP forms in AD.", "citation": {"db": "PubMed", "db_id": "22845771"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3823, "target": 529, "key": "9683fdfd3c518d145a8ec3b0a5cefafa"}, {"line": 5569, "relation": "association", "evidence": "Recent genome-wide association studies (GWAS) have identified common genetic variants that increase risk of LOAD. Two of the genes highlighted in these studies, CLU and CR1, suggest a role for the complement system in the aetiology of AD. In this review we analyse the evidence for an involvement of complement in AD. In particular we focus on one gene, CR1, and its role in the complement cascade. CR1 is a receptor for the complement fragments C3b and C4b and is expressed on many different cell types, particularly in the circulatory system.", "citation": {"db": "PubMed", "db_id": "21840620"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 534, "key": "f9d7d8bf5e633a37bd8feea574fd5cb9"}, {"line": 5983, "relation": "positiveCorrelation", "evidence": "Evidence has suggested that insulin resistance (IR) or high levels of glucocorticoids (GCs) may be linked with the pathogenesis and/or progression of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 263, "key": "73b39d6223184d2948740ba4f1b0f9b3"}, {"line": 19823, "relation": "positiveCorrelation", "evidence": "Increased circulating glucocorticoids are features of both aging and Alzheimer's disease (AD), and increased glucocorticoids accelerate the accumulation of AD pathologies.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 263, "key": "58c819b0969c24668502e8fd5e1c4ab8"}, {"line": 19824, "relation": "association", "evidence": "Increased circulating glucocorticoids are features of both aging and Alzheimer's disease (AD), and increased glucocorticoids accelerate the accumulation of AD pathologies.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 263, "key": "76eee998dbfb0ddd46594392b8068677"}, {"line": 6066, "relation": "association", "evidence": "a number of clinical and epidemiological studies have provided further direct evidence to strengthen the link between T2D and AD (1, 2)", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3850, "key": "5348735d89346055925af83481062494"}, {"line": 10415, "relation": "positiveCorrelation", "evidence": "ER stress contributes to the pathogenesis of obesity and diabetes, which are risk factors for Alzheimer's disease (AD) that accelerate the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3850, "key": "ccc5f596b6208fd3f86e5a412b2677ed"}, {"line": 20543, "relation": "association", "evidence": "In late-onset sporadic Alzheimer disease changes in the brain are similar to those caused by non-insulin-dependent diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3850, "key": "b92cb50618184cbf8faa9f2b465990fa"}, {"line": 6074, "relation": "negativeCorrelation", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 523, "key": "cf1a77f9a7cd44a91e55a3a2a14f13dc"}, {"line": 6124, "relation": "negativeCorrelation", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2899, "key": "2e2d206facd174375d104669fc1f0c2c"}, {"line": 6251, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2899, "key": "676bb1938e631b4f7fd4a2e14e88c4ea"}, {"line": 7842, "relation": "increases", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Plasma": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2899, "key": "024ba75444aa6fcc4b70eef36a96cd19"}, {"line": 7850, "relation": "decreases", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2899, "key": "836a6f4a139fe66af8868eaaf03b7e30"}, {"line": 8186, "relation": "positiveCorrelation", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2899, "key": "ba37edf6015b8f233579729432db33db"}, {"line": 8320, "relation": "negativeCorrelation", "evidence": "We found that patients with Alzheimer's disease had lower CSF insulin levels, higher plasma insulin levels and reduced insulin-mediated glucose disposal compared with healthy control individuals,[ 89,90] a pattern consistent with insulin resistance.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2899, "key": "0be2ddfda7f5383336ecaa6fa1c6a815"}, {"line": 8328, "relation": "positiveCorrelation", "evidence": "We found that patients with Alzheimer's disease had lower CSF insulin levels, higher plasma insulin levels and reduced insulin-mediated glucose disposal compared with healthy control individuals,[ 89,90] a pattern consistent with insulin resistance.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2899, "key": "bb3d888e792577cdacb7380f17360e11"}, {"line": 9717, "relation": "association", "evidence": "To test the hypothesis that polymorphic variation in insulin signalling genes may underlie the shared risk of dysfunctional insulin signalling and late onset Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2899, "key": "e04cef8925b989d86d03b04f1832ad07"}, {"line": 20435, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is linked to CNS insulin resistance, decreased expression of insulin and insulin receptor genes, and lower cerebrospinal insulin levels.", "citation": {"db": "PubMed", "db_id": "22142155"}, "annotations": {"MeSHDisease": {"Insulin Resistance": true, "Alzheimer Disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2899, "key": "f9574e8c5b699423e7924e188aa88bd5"}, {"line": 6130, "relation": "association", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 631, "key": "b9227ee5af060f19eee54147a3b47d65"}, {"line": 6252, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2871, "key": "a19d03d5083519104d6d3855cfcaab4a"}, {"line": 11808, "relation": "negativeCorrelation", "evidence": "According to current scientific knowledge, excess tumour necrosis factor-alpha (TNF-alpha) and low insulin-like growth factor-I (IGF-I) are pathogenic-risk factors that constitute therapeutic targets for Alzheimer's disease (AD).At week 24, Cere reduced TNF-alpha and enhanced dissociable IGF-I with respect to placebo in a dose-related manner. Increases in total IGF-I were induced by 60 ml Cere", "citation": {"db": "PubMed", "db_id": "19531281"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2871, "key": "ab2328bb319b64990fcfa2e3c3cd78db"}, {"line": 6254, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2872, "key": "7db67685e7605e922ebcf4f3aeb41611"}, {"line": 10894, "relation": "negativeCorrelation", "evidence": "According to this hypothesis, brains from AD patients showed substantially downregulated expression of the Insulin receptor (IR), the IGF-1 receptor (IGF-1R), and the insulin receptor substrate (IRS) proteins.", "citation": {"db": "PubMed", "db_id": "21916834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2872, "key": "1734424b54c04c0bb598a6bf98bb9ca7"}, {"line": 6261, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3823, "target": 3010, "key": "62f648a29942e85e573a3e72639b2b54"}, {"line": 29617, "relation": "association", "evidence": "Pathological alterations in the microtubule-associated protein (MAP) tau are well-established in a number of neurodegenerative disorders, including Alzheimer's Disease (AD), frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), and others. ", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3823, "target": 3010, "key": "7696a7a584822b6b73ffa8a14595bb93"}, {"line": 33382, "relation": "association", "evidence": "The beta-amyloid precursor protein APP and the microtubule-associated protein Tau play a crucial role in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3010, "key": "5b2947cf591362b1b1ff30cfdc219ce8"}, {"line": 6267, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Energy metabolic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 836, "key": "1a8326f395dfeba46ce5702f4f4b25ed"}, {"line": 6268, "relation": "positiveCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Energy metabolic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3812, "key": "c124a8f5a93809e841767d6b4495fa6e"}, {"line": 6414, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1778, "key": "fc773a63df8e32532ea5005e36960fa4"}, {"line": 6557, "relation": "association", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1746, "key": "2883a013cadce3dc5e0dccbbc70c73c0"}, {"line": 26484, "relation": "association", "evidence": "The genes for both the beta-amyloid precursor protein and apolipoprotein E (ApoE) have been linked to Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9202294"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1746, "key": "92af46c2c4ab3ab7ec45b70c5710e2b9"}, {"line": 6586, "relation": "association", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3889, "key": "4e1b9a3ef2a8432bf617433263202444"}, {"line": 6657, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1974, "key": "e2e0dcb3f19dd64493b9c330262f06fe"}, {"line": 10250, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1974, "key": "b6e6f586f53263d4df9dcfb21466d0c8"}, {"line": 10282, "relation": "association", "evidence": "In a replication study, we confirmed significant association of SNPs within three genes--PPARgamma, SOS2, and PCK1--with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1974, "key": "c5f7a1741a5a807a2dc460232b56848d"}, {"line": 6663, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"High": true}}, "source": 3823, "target": 1946, "key": "c5d97c5d3809b538751e4006b3b652e4"}, {"line": 6664, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"High": true}}, "source": 3823, "target": 1840, "key": "da17261604c7b9a5cb0fbce954bb83e3"}, {"line": 6665, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"High": true}}, "source": 3823, "target": 1922, "key": "ee3360eae9449d3de629254b37b2a254"}, {"line": 6671, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Energy metabolic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1908, "key": "a8f3048262cbe8575fb23be9684ffd38"}, {"line": 10264, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3823, "target": 1908, "key": "3979376ce85b83c9d27cd0effe9a441f"}, {"line": 10288, "relation": "association", "evidence": "In a replication study, we confirmed significant association of SNPs within three genes--PPARgamma, SOS2, and PCK1--with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1908, "key": "6c6a6272cf3596c9ce24899c6542ff7b"}, {"line": 6695, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1814, "key": "648f581e4c3d391b20dc9cff4897bb09"}, {"line": 6696, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1851, "key": "0fac765b868f000ffefbbe274bd5ee36"}, {"line": 6707, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 +ve": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1921, "key": "4a1ca43c26c7b30b86723e9fcb5ebae3"}, {"line": 10258, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1921, "key": "a5785fb4d688e19a3bdb2e05471cf27d"}, {"line": 10274, "relation": "association", "evidence": "In a replication study, we confirmed significant association of SNPs within three genes--PPARgamma, SOS2, and PCK1--with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1921, "key": "c4622603e1dc0b1d8c5fcff449c1c8de"}, {"line": 6708, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 +ve": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1849, "key": "1521a96892f8b9a947135c80716b35a8"}, {"line": 6716, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 +ve": true}, "Subgraph": {"Energy metabolic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1839, "key": "2e2418521c53bacde09b51e6d8c5cf27"}, {"line": 6724, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 +ve": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1896, "key": "5de9ee6046e078fdcc9e89562ea3a2e0"}, {"line": 6733, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Vitamin subgraph": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1830, "key": "85c1bf1cfe05182cf1f878542844409e"}, {"line": 6741, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 -ve": true}, "Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1736, "key": "7c47d600069bb2da8d6eae4d2bcc98d7"}, {"line": 6742, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 -ve": true}, "Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1777, "key": "6c6d7a70d74a05546500a25f7eb2e166"}, {"line": 6750, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 -ve": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1923, "key": "10a07f1f7eea52e0a6e257e231535ded"}, {"line": 7284, "relation": "negativeCorrelation", "evidence": "Overactivation and mislocalization of p35/CDK5 could translate the deleterious effect of a combination of different pathological signals, such as amylin deposition, high levels of plasma lipoproteins, and high glucose levels, as dysregulation of p35/CDK5 in the central nervous system has been associated with the pathological abnormalities found in Alzheimer's disease (2, 29) and amyotrophic lateral sclerosis (30). It is possible that glucose-induced dysregulation of the p35/CDK5 pathway is a pathophysiological mechanism involved in the beta-cell dysfunction and the predisposition to apoptotic cell death associated with the progression of type 2 diabetes", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 1340, "key": "41f7af942d87d040b67711977c58f946"}, {"line": 7553, "relation": "association", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2177, "key": "ccbc2da91231aabf809e1e79319c723c"}, {"line": 7760, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 580, "key": "1954a1dc422dc3e0d052c9ed77fb0a61"}, {"line": 9926, "relation": "decreases", "evidence": "Here we review the role of insulin signaling in brain aging and AD, concluding that the signaling pathways downstream to neurotrophic and insulin signaling are defective and coincident with aberrant phosphorylation and translocation of key components, notably AKT and GSK3beta, but also rac> PAK signaling.", "citation": {"db": "PubMed", "db_id": "17049785"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 580, "key": "9168d120295b40b93b9b9372148f2e24"}, {"line": 7761, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 583, "key": "82ed5a031331cef352703ad62cc9fb19"}, {"line": 7762, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3985, "key": "d47ebea82b531f2f0dfb34b74852a2d9"}, {"line": 7763, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3986, "key": "92eef1a803b17f6ff9df1077457bd216"}, {"line": 7764, "relation": "decreases", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3986, "key": "9656cb4eb0841713dfca73b71b4df296"}, {"line": 7769, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 3823, "target": 754, "key": "cde2e32af258f80fc4d334fa636ab7f5"}, {"line": 7775, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3981, "key": "0f5cd85496d9849035ff310e41ef2c9e"}, {"line": 7780, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2905, "key": "bee4efd3375ebc5bd0b22be2b3b261fc"}, {"line": 7781, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2913, "key": "80500a5d280a0ed2712cfc049ca70c88"}, {"line": 7893, "relation": "negativeCorrelation", "evidence": "Tau is a microtubule-binding protein that ligates tubulin and accounts for the stability of microtubules. If hyperphos­ phorylated, tau aggregates and interferes with intraneu ronal metabolism and transport lead ing to neurodegeneration [I 02]. Tau phosphorylation state is regulated by site-specific dephosphorylation through certain phosphatases and by kinases phosphorylating tau protein at specific sites. Protein phosphatase 2A (PP2A) is the major phosphatase with 70% tau phosphatase activity in human brains [I 03]. This implies a protective role of PP2A in neurodegeneration which is consistent with the finding that PP2A activity is reduced in AD brains [104, 105].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Wnt signaling subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3223, "key": "207e6b59ee3ac4cd1e63d5a888dfe990"}, {"line": 49492, "relation": "negativeCorrelation", "evidence": "There is a significant decrease in total PP2A activity measured in AD cortical and hippocampal brain homogenates.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3223, "key": "30215e2e4b200ad473bd108cafe2cec1"}, {"line": 8117, "relation": "positiveCorrelation", "evidence": "There are probably several mechanisms underlying the relationship between type 2 diabetes and the increased risk of AD. For example, the formation of advanced glycation end product (AGE) in diabetes has been shown to be aggregated with Abeta in plaques of AD brain", "citation": {"db": "PubMed", "db_id": "16399206"}, "source": 3823, "target": 74, "key": "278e35fdac8ddfdaf8873be99a462792"}, {"line": 8148, "relation": "negativeCorrelation", "evidence": "Abeta degrading activity by IDE was shown to be lower in AD brains than in the controls [71]. Moreover, the amount of hippocampal IDE protein was also found to reduce in AD brains as compared to the controls [15]. When the IDE gene was deleted in mouse model, Abeta levels in the brain were elevated [27] and [58], suggesting IDE activity is critical in determining the amount of brain Abeta in vivo. More significantly, enhanced IDE activity in the IDE and APP double transgenic mice decreased their brain Abeta levels, and prevented the formation of AD pathology [52].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2867, "key": "4389925099d80df67a61967c80cf0252"}, {"line": 30871, "relation": "negativeCorrelation", "evidence": "Insulin-degrading enzyme (IDE) is a protease that has been demonstrated to play a key role in degrading both Abeta and insulin and deficient in IDE function is associated with Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) pathology.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2867, "key": "11c61b7b86500838b5db060adb66d526"}, {"line": 8187, "relation": "positiveCorrelation", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2900, "key": "8535821d5dc128b479d4e4fbb89a4af5"}, {"line": 10895, "relation": "negativeCorrelation", "evidence": "According to this hypothesis, brains from AD patients showed substantially downregulated expression of the Insulin receptor (IR), the IGF-1 receptor (IGF-1R), and the insulin receptor substrate (IRS) proteins.", "citation": {"db": "PubMed", "db_id": "21916834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2900, "key": "cd917c2dfd0b279533029ed5c523bf9c"}, {"line": 20436, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is linked to CNS insulin resistance, decreased expression of insulin and insulin receptor genes, and lower cerebrospinal insulin levels.", "citation": {"db": "PubMed", "db_id": "22142155"}, "annotations": {"MeSHDisease": {"Insulin Resistance": true, "Alzheimer Disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2900, "key": "6616efa41b5d468b72ce255c9f999469"}, {"line": 8389, "relation": "positiveCorrelation", "evidence": "Thus, laboratory and clinical findings are consistent with epidemiological reports of a reduced prevalence of Alzheimer's disease among persons who take nonsteroidal anti-inflammatory drugs (NSAIDs) for chronic pain.[125,126] Interleukin-6 (IL-6), an inflammatory cytokine, is one of the products that has been implicated in Alzheimer's disease. Elevated IL-6 immunoreactivity has been shown in human lumbar and ventricular CSF in patientswith Alzheimer's disease.[127] Furthermore, IL-6 has been found in senile plaques and may be involved in both the development of plaques and the development of dementia.[128]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 393, "key": "5598c3ba92b8d88fde179f48298f9a18"}, {"line": 8425, "relation": "decreases", "evidence": "The expression of microRNA miR-107 decreases early in Alzheimer's disease and may accelerate disease progression through regulation of beta-site amyloid precursor protein-cleaving enzyme 1. Among the AD-related miRNA expression changes, miR-107 was exceptional because miR-107 levels decreased significantly even in patients with the earliest stages of pathology. In situ hybridization with cross-comparison to neuropathology demonstrated that particular cerebral cortical laminas involved by AD pathology exhibit diminished neuronal miR-107 expression. Computational analysis predicted that the 3'-untranslated region (UTR) of beta-site amyloid precursor protein-cleaving enzyme 1 (BACE1) mRNA is targeted multiply by miR-107.", "citation": {"db": "PubMed", "db_id": "18234899"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2092, "key": "611bf3141ac8b1e53523a597a631d96d"}, {"line": 8670, "relation": "decreases", "evidence": "MiR-107 is a microRNA (miRNA) that we reported previously to have decreased expression in the temporal cortical gray matter early in the progression of Alzheimer's disease (AD). Here we study a new group of well-characterized human temporal cortex samples (N=19). MiR-107 expression was assessed, normalized to miR-124 and let-7a. Correlation was observed between decreased miR-107 expression and increased neuritic plaque counts (P< 0.05) and neurofibrillary tangle counts (P< 0.02) in adjacent brain tissue. Adjusted miR-107 and BACE1 mRNA levels tended to correlate negatively (trend with regression P< 0.07). In sum, miR-107 expression tends to be lower relative to other miRNAs as AD progresses.", "citation": {"db": "PubMed", "db_id": "20413881"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3823, "target": 2092, "key": "87da6480f879f4d095adf539c423e9b5"}, {"line": 8716, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2092, "key": "ea44603d6071731d39845d35e7b91354"}, {"line": 9227, "relation": "negativeCorrelation", "evidence": "The Expression of MicroRNA miR-107 Decreases Early in Alzheimer’s Disease and May Accelerate Disease Progression through Regulation of beta-Site Amyloid Precursor Protein-Cleaving Enzyme 1. BACE1 mRNA levels tended to increase as miR-107 levels decreased in the progression of AD. ", "citation": {"db": "PubMed", "db_id": "18234899"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2092, "key": "92ac4c921ba39bee09f929caa28cd4f8"}, {"line": 45092, "relation": "decreases", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2092, "key": "a62129492af609522090fa1e1388b830"}, {"line": 45922, "relation": "negativeCorrelation", "evidence": "HAT activity of p300 stimulates the PS1 and BACE1 promoter histone hyperacetylation", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2092, "key": "6c1d4bd8fc513268fc1498e24889231c"}, {"line": 8438, "relation": "decreases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3823, "target": 2115, "key": "f5abc74b40b7a7e6ea6b20e4adae4ffc"}, {"line": 8618, "relation": "decreases", "evidence": "Aberrant microRNA expression in the brains of neurodegenerative diseases: miR-29a decreased in Alzheimer disease brains targets neurone navigator 3. However, we found significant down-regulation of miR-29a in Alzheimer disease (AD) brains. The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains. MiR-29a-mediated down-regulation of NAV3 was verified by the luciferase reporter assay. By immunohistochemistry, NAV3 expression was most evidently enhanced in degenerating pyramidal neurones in the cerebral cortex of AD. These observations suggest the hypothesis that underexpression of miR-29a affects neurodegenerative processes by enhancing neuronal NAV3 expression in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"miRNA subgraph": true, "Axonal guidance subgraph": true}}, "source": 3823, "target": 2115, "key": "2d047cfe13f276a50bff89401ac0eed8"}, {"line": 45925, "relation": "decreases", "evidence": "HAT activity of p300 stimulates the PS1 and BACE1 promoter histone hyperacetylation", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2115, "key": "74de5da87c500ffbf609aea0c8c3fbd9"}, {"line": 46193, "relation": "decreases", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2115, "key": "682f497c96a125bb36f4e5cd6cfbd530"}, {"line": 8452, "relation": "decreases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3823, "target": 2116, "key": "a1c5cf7f4a69391a9ab7455b0da43060"}, {"line": 45928, "relation": "decreases", "evidence": "HAT activity of p300 stimulates the PS1 and BACE1 promoter histone hyperacetylation", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2116, "key": "3473f65dbd0a48f3b446aba525f58040"}, {"line": 8507, "relation": "association", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2102, "key": "6a28a1fa71b9456b837fd02508156e7f"}, {"line": 46174, "relation": "association", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2102, "key": "15d4010b422f356ac07886f92e05073f"}, {"line": 8681, "relation": "positiveCorrelation", "evidence": "Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Notably, miR-144 that is highly conserved appeared to be associated with the aging progression. Moreover, miR-144 plays a central role in regulating the expression of ataxin 1 (ATXN1), the disease-causing gene for the development spinocerebellar ataxia type 1 (SCA1). miRNA activity, including miR-144, -101 and -130 processing, was increased in the cerebellum and cortex of SCA1 Alzheimer patients relative to healthy aged brains. Importantly, miR-144 and -101 inhibition increased ATXN1 levels in human cells. Thus, the activation of miRNA expression in the aging brain may serve to reduce the cytotoxic effect of polyglutamine expanded ATXN1 and the deregulation of miRNA expression may be a risk factor for disease development.", "citation": {"db": "PubMed", "db_id": "20451302 "}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3823, "target": 2100, "key": "a72f7c616f507a27d4cee7ff05dca75e"}, {"line": 8959, "relation": "positiveCorrelation", "evidence": "Amyloid beta-peptide (Abeta) accumulating in the brain of Alzheimer disease (AD) patients is believed to be the main pathophysiologcal cause of the disease. Proteolytic processing of the amyloid precursor protein by alpha-secretase ADAM10 (a disintegrin and metalloprotease 10) protects the brain from the production of the Abeta. Meanwhile, dysregulation or aberrant expression of microRNAs (miRNAs) has been widely documented in AD patients. In this study, we demonstrated that overexpression of miR-144, which was previously reported to be increased in elderly primate brains and AD patients, significantly decreased activity of the luciferase reporter containing the ADAM10 3'-untranslated region (3'-UTR) and suppressed the ADAM10 protein level, whereas the miR-144 inhibitor led to an increase of the luciferase activity. The negative regulation caused by miR-144 was strictly dependent on the binding of the miRNA to its recognition element in the ADAM10 3'-UTR. Moreover, we also showed that activator protein-1 regulates the transcription of miR-144 and the up-regulation of miR-144 at least partially induces the suppression of the ADAM10 protein in the presence of Abeta. In addition, we found that miR-451, a miRNA processed from a single gene locus with miR-144, is also involved in the regulation of ADAM10 expression. Taken together, our data therefore demonstrate miR-144/451 is a negative regulator of the ADAM10 protein and suggest a mechanistic role for miR-144/451 in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "23546882"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2100, "key": "4352b0647a6cc723c4c7b79784ae3ed6"}, {"line": 8682, "relation": "positiveCorrelation", "evidence": "Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Notably, miR-144 that is highly conserved appeared to be associated with the aging progression. Moreover, miR-144 plays a central role in regulating the expression of ataxin 1 (ATXN1), the disease-causing gene for the development spinocerebellar ataxia type 1 (SCA1). miRNA activity, including miR-144, -101 and -130 processing, was increased in the cerebellum and cortex of SCA1 Alzheimer patients relative to healthy aged brains. Importantly, miR-144 and -101 inhibition increased ATXN1 levels in human cells. Thus, the activation of miRNA expression in the aging brain may serve to reduce the cytotoxic effect of polyglutamine expanded ATXN1 and the deregulation of miRNA expression may be a risk factor for disease development.", "citation": {"db": "PubMed", "db_id": "20451302 "}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3823, "target": 2081, "key": "5a5b02ddb581f606b7da70df192d627d"}, {"line": 8785, "relation": "negativeCorrelation", "evidence": "MicroRNA-101 downregulates Alzheimer's amyloid-beta precursor protein levels in human cell cultures and is differentially expressed.Several bioinformatic algorithms predicted miR-101 target sites within the APP 3'-untranslated region (3'-UTR). Using reporter assays, we confirmed that, in human cell cultures, miR-101 significantly reduced the expression of a reporter under control of APP 3'-UTR. Mutation of predicted site 1, but not site 2, eliminated this reporter response. Delivery of miR-101 directly to human HeLa cells significantly reduced APP levels and this effect was eliminated by co-transfection with a miR-101 antisense inhibitor. Delivery of a specific target protector designed to blockade the interaction between miR-101 and its functional target site within APP 3'-UTR enhanced APP levels in HeLa. Therefore, endogenous miR-101 regulates expression of APP in human cells via a specific site located within its 3'-UTR. Finally, we demonstrate that, across a series of human cell lines, highest expression of miR-101 levels was observed in model NT2 neurons.", "citation": {"db": "PubMed", "db_id": "21172309"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2081, "key": "53a791194cfc8989e68d315be0fe70ea"}, {"line": 45280, "relation": "negativeCorrelation", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"Low": true}}, "source": 3823, "target": 2081, "key": "d13f4dc87b2aecc8b69a2b94a1794099"}, {"line": 8683, "relation": "positiveCorrelation", "evidence": "Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Notably, miR-144 that is highly conserved appeared to be associated with the aging progression. Moreover, miR-144 plays a central role in regulating the expression of ataxin 1 (ATXN1), the disease-causing gene for the development spinocerebellar ataxia type 1 (SCA1). miRNA activity, including miR-144, -101 and -130 processing, was increased in the cerebellum and cortex of SCA1 Alzheimer patients relative to healthy aged brains. Importantly, miR-144 and -101 inhibition increased ATXN1 levels in human cells. Thus, the activation of miRNA expression in the aging brain may serve to reduce the cytotoxic effect of polyglutamine expanded ATXN1 and the deregulation of miRNA expression may be a risk factor for disease development.", "citation": {"db": "PubMed", "db_id": "20451302 "}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3823, "target": 2096, "key": "bc6a3b19926ee3a5696e2e855c4e9ebc"}, {"line": 8698, "relation": "association", "evidence": "ATXN1 functions as a genetic risk modifier that contributes to AD pathogenesis through a loss-of-function mechanism by regulating beta-secretase cleavage of APP and Abeta levels.", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Akt subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1754, "key": "d5445556bc4e34b579e671cb6fea91a6"}, {"line": 8728, "relation": "association", "evidence": "We report here that BACE1-antisense prevents miRNA-induced repression of BACE1 mRNA by masking the binding site for miR-485-5p. Indeed, miR-485-5p and BACE1-antisense compete for binding within the same region in the open reading frame of the BACE1 mRNA. We observed opposing effects of BACE1-antisense and miR-485-5p on BACE1 protein in vitro and showed that Locked Nucleic Acid-antimiR mediated knockdown of miR-485-5p as well as BACE1-antisense over-expression can prevent the miRNA-induced BACE1 suppression. We found that the expression of BACE1-antisense as well as miR-485-5p are dysregulated in RNA samples from Alzheimer's disease subjects compared to control individuals.", "citation": {"db": "PubMed", "db_id": "20507594"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3823, "target": 2101, "key": "f0371f1756d67abe4e8f6bb2f7d500ee"}, {"line": 8743, "relation": "negativeCorrelation", "evidence": "Interestingly, neuronal degeneration coincides with the hyperphosphorylation of endogenous tau at several epitopes previously associated with neurofibrillary pathology. Transcriptome analysis of enzymes involved in tau phosphorylation identified ERK1 as one of the candidate kinases responsible for this event in vivo. We further demonstrate that miRNAs belonging to the miR-15 family are potent regulators of ERK1 expression in mouse neuronal cells and co-expressed with ERK1/2 in vivo. Finally, we show that miR-15a is specifically downregulated in Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "20660113"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}}, "source": 3823, "target": 2105, "key": "3124d34e4603a069e294df7f2e522c52"}, {"line": 8773, "relation": "decreases", "evidence": "Here, we present evidence that, besides APP expression regulation, miRNAs are equally involved in the regulation of neuronal APP mRNA alternative splicing. Lack of miRNAs in post-mitotic neurons in vivo is associated with APP exons 7 and 8 inclusion, while ectopic expression of miR-124, an abundant neuronal-specific miRNA, reversed these effects in cultured neurons. Similar results were obtained by depletion of endogenous polypyrimidine tract binding protein 1 (PTBP1) in cells, a recognized miR-124 target gene. Furthermore, PTBP1 levels correlate with the presence of APP exons 7 and 8, while PTBP2 levels correlate with the skipping of these exons during neuronal differentiation. Finally, we show that miR-124 is down-regulated in AD brain. In sum, our results suggest that specific miRNAs are involved in the fine-tuning of APP alternative splicing in neurons. Since abnormal neuronal splicing of APP affects beta-amyloid peptide production, these results could contribute to the understanding of the implication of miRNAs in brain health and disease.", "citation": {"db": "PubMed", "db_id": "21062284"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2082, "key": "3c80a4525b109ea11e3b94dfadb16e2d"}, {"line": 8797, "relation": "negativeCorrelation", "evidence": "Decreased relative expression levels of hsa-miR-590-3p was observed in patients with AD versus controls (0.685 ± °0.080 versus 0.931 ± °0.111, p = 0.079), and correlated negatively with hnRNP-A1 mRNA levels (r = -0.615, p = 0.0237). According to these findings, hnRNP-A1 and its transcription regulatory factor miR-590-3p are disregulated in patients with AD, and the hnRNP-A1 rs7967622 C/C genotype is likely a risk factor for FTLD in male populations.", "citation": {"db": "PubMed", "db_id": "21548758"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3823, "target": 2124, "key": "a7da33bc5fd03e8b38c40af214070fba"}, {"line": 8819, "relation": "increases", "evidence": "MicroRNA-146a (miRNA-146a) is an inducible, 22 nucleotide, small RNA over-expressed in Alzheimer's disease (AD) brain. Up-regulated miRNA-146a targets several inflammation-related and membrane-associated messenger RNAs (mRNAs), including those encoding complement factor-H (CFH) and the interleukin-1 receptor associated kinase-1 (IRAK-1), resulting in significant decreases in their expression (p<0.05, ANOVA). In this study we assayed miRNA-146a, CFH, IRAK-1 and tetraspanin-12 (TSPAN12), abundances in primary human neuronal-glial (HNG) co-cultures, in human astroglial (HAG) and microglial (HMG) cells stressed with Abeta42 peptide and tumor necrosis factor alpha (TNFalpha). The results indicate a consistent inverse relationship between miRNA-146a and CFH, IRAK-1 and TSPAN12 expression levels, and indicate that HNG, HAG and HMG cell types each respond differently to Abeta42-peptide+TNFalpha-triggered stress. While the strongest miRNA-146a-IRAK-1 response was found in HAG cells, the largest miRNA-146a-TSPAN12 response was found in HNG cells, and the most significant miRNA-146a-CFH changes were found in HMG cells, the 'resident scavenging macrophages' of the brain.", "citation": {"db": "PubMed", "db_id": "21640790"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2124, "key": "1b8e7b2af6813d78d382fcff9a246e47"}, {"line": 8799, "relation": "negativeCorrelation", "evidence": "Decreased relative expression levels of hsa-miR-590-3p was observed in patients with AD versus controls (0.685 ± °0.080 versus 0.931 ± °0.111, p = 0.079), and correlated negatively with hnRNP-A1 mRNA levels (r = -0.615, p = 0.0237). According to these findings, hnRNP-A1 and its transcription regulatory factor miR-590-3p are disregulated in patients with AD, and the hnRNP-A1 rs7967622 C/C genotype is likely a risk factor for FTLD in male populations.", "citation": {"db": "PubMed", "db_id": "21548758"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3823, "target": 2840, "key": "8b4d0633dfbc7fc4ac9e013dbbd28fb8"}, {"line": 9116, "relation": "negativeCorrelation", "evidence": "HnRNP A1 plays several key roles in neuronal functioning and its depletion, either due to debilitated cholinergic neurotransmission or under autoimmune reactions causes drastic changes in RNA metabolism. Consequently, hnRNP A1 decline contributes to the severity of symptoms in several neurodegenerative diseases, including Alzheimer's disease (AD), ", "citation": {"db": "PubMed", "db_id": "23247072"}, "annotations": {"Confidence": {"High": true}}, "source": 3823, "target": 2840, "key": "79a861891f717860057ed61a1d59f69e"}, {"line": 8854, "relation": "positiveCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 228, "key": "05d3a9c818c6c40614190e13fb622c99"}, {"line": 8863, "relation": "increases", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3414, "key": "e228c4d67d8befd90f5de153f9f79ac2"}, {"line": 8947, "relation": "negativeCorrelation", "evidence": "Delivery of a miR-153 antisense inhibitor to human fetal brain cultures significantly elevated APP expression. miR-153 delivery also reduced expression of the APP paralog APLP2. High functional redundancy between APP and APLP2 suggests that miR-153 may target biological pathways in which they both function. Interestingly, in a subset of human AD brain specimens with moderate AD pathology, miR-153 levels were reduced.", "citation": {"db": "PubMed", "db_id": "22733824"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2084, "key": "b9ca19e47628adc8f7cdd71f4b59cf2e"}, {"line": 8977, "relation": "positiveCorrelation", "evidence": "We reported that tumor necrosis factor receptor I (TNFRI) is required for neuronal death induced by amyloid-beta protein in the Alzheimer's disease (AD) brain. However, whether TNF receptor subtypes are expressed and activated differentially in AD brains compared to non-demented brains remains unclear. Our studies on Western blot and ELISA measurements demonstrated that TNFRI levels are increased whereas TNFRII levels are decreased in AD brains compared to non-demented brains (p <0.05).", "citation": {"db": "PubMed", "db_id": "20110607"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3823, "target": 3476, "key": "afc81656b39cb324ab8d020f589a3697"}, {"line": 8978, "relation": "negativeCorrelation", "evidence": "We reported that tumor necrosis factor receptor I (TNFRI) is required for neuronal death induced by amyloid-beta protein in the Alzheimer's disease (AD) brain. However, whether TNF receptor subtypes are expressed and activated differentially in AD brains compared to non-demented brains remains unclear. Our studies on Western blot and ELISA measurements demonstrated that TNFRI levels are increased whereas TNFRII levels are decreased in AD brains compared to non-demented brains (p <0.05).", "citation": {"db": "PubMed", "db_id": "20110607"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3823, "target": 3477, "key": "76bf6a8d24629b4aa8ad07119afdd9d7"}, {"line": 8987, "relation": "negativeCorrelation", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 3823, "target": 2633, "key": "981f0da62890bb44bd20da7a12aa87f0"}, {"line": 8989, "relation": "negativeCorrelation", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 3823, "target": 2632, "key": "ef60839611d78cff606cfdf2d85fd9e6"}, {"line": 8992, "relation": "negativeCorrelation", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 3823, "target": 452, "key": "3cecdb2111d6d721542df241f7873a35"}, {"line": 9099, "relation": "positiveCorrelation", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3939, "key": "851e1fd6b1ca8926abe18a8d75361391"}, {"line": 39577, "relation": "increases", "evidence": "In AD, an increased ApoE mRNA was reported in the hippocampus. The risk for AD has been reported to correlate with transcriptional activity of the ApoE gene. Binding sites for putative transcriptional factors (TF), such as AP-1, AP-2 and NF-kappaB, are present in the ApoE promoter. The promoter also contains sites for the inflammatory response transcription factors IL-6 RE-BP, MED1, STAT1 and STAT2. A functional peroxisome-proliferator-activated receptor gamma (PPARgamma) has been detected in the ApoE/ApoCI intergenic region.", "citation": {"db": "PubMed", "db_id": "15181251"}, "annotations": {"Subgraph": {"APOE subgraph": true}}, "source": 3823, "target": 3939, "key": "e0b1c9168eaf35dbe65e22e14d4ff1d9"}, {"line": 45112, "relation": "positiveCorrelation", "evidence": "APOE ε4 mRNA level is increased in AD compared to controls.The APOE gene was found to be of bimodal structure, with a hypomethylated CpG-poor promoter and a fully methylated 3′-CpG-island", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Low": true}}, "source": 3823, "target": 3939, "key": "66e48e357a76bd312fafefb4d3c705a8"}, {"line": 9100, "relation": "positiveCorrelation", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2970, "key": "86fee960715d3076145377502f3b69dd"}, {"line": 9193, "relation": "positiveCorrelation", "evidence": "It was reported that there was an upregulation of miR-9, miR-125b and miR-128 in hippocampus of AD affected post-mortem brain samples", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2086, "key": "d90ea02fbf6801ff0306e310700b87ff"}, {"line": 9194, "relation": "positiveCorrelation", "evidence": "It was reported that there was an upregulation of miR-9, miR-125b and miR-128 in hippocampus of AD affected post-mortem brain samples", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2093, "key": "8b53ee0da4aad9c0ab851b90c5b8f547"}, {"line": 9195, "relation": "positiveCorrelation", "evidence": "It was reported that there was an upregulation of miR-9, miR-125b and miR-128 in hippocampus of AD affected post-mortem brain samples", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2083, "key": "be96491eb764bc4f9087fe1695bef2d0"}, {"line": 9256, "relation": "positiveCorrelation", "evidence": "MiR-34a over-expression induces endothelial cell senescence and also suppresses cell proliferation by inhibiting cell cycle progression. Searching for how miR-34a affects senescence, we discovered that SIRT1 is a target of miR-34a. Over-expressing miR-34a inhibits SIRT1 protein expression, and knocking down miR-34a enhances SIRT1 expression. MiR-34a triggers endothelial senescence in part through SIRT1, since forced expression of SIRT1 blocks the ability of miR-34a to induce senescence. Our data suggest that miR-34a contributes to endothelial senescence through suppression of SIRT1.", "citation": {"db": "PubMed", "db_id": "20627091"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3823, "target": 520, "key": "cc3d7928f8c8978df999aac4c2f51d22"}, {"line": 9303, "relation": "association", "evidence": "Hyperphosphorylated tau protein is the basic structural component of the neurofibrillary tangle, a histopathological hallmark of Alzheimer's disease. The formation of hyperphosphorylated tau protein may impair learning and the synaptic plasticity of neurons. Tau is a protein that is associated with and stabilizes microtubules; hyperphosphorylated tau protein is unable to perform this stabilization function.", "citation": {"db": "PubMed", "db_id": "16504486"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3823, "target": 889, "key": "fbd65459b831851be886e3aa2dcc927c"}, {"line": 9422, "relation": "decreases", "evidence": "Cleavage of APP by alpha-secretase precludes Abeta generation as the cleavage site is within the Abeta domain (at the Lys16-Leu17 bond), and releases a large soluble ectodomain of APP called sAPPalpha. Early studies suggested that alpha-secretase is a membrane-bound endoprotease which cleaves APP primarily at the plasma membrane. Using proteinase inhibitor profiling, it was determined that alpha-secretase is a zinc metalloproteinase. Several members of the ADAM (a disintegrin and metalloproteinase) family possess alpha-secretase-like activity and three of them have been suggested as the alpha-secretase: ADAM9, ADAM10, and ADAM17. Like APP, they are also type-I transmembrane proteins.A dramatically reduced ADAM10 protein level in the platelets of sporadic AD patients was also found to correlate with the significantly decreased sAPPalpha levels found in their platlets and cerebrospinal fluid and the reduced aclpha-secretase activity in the temporal cortex homogenates of AD patients . These studies strongly suggest that ADAM10 is the constitutive alpha-secretase that is active at the cell surface, though there may be some functional redundancy in alpha-cleavage among the ADAM family.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2249, "key": "e5ea9f9133537534eaa857e89a691d2d"}, {"line": 12386, "relation": "negativeCorrelation", "evidence": "The proteolytic cleavage of the amyloid precursor protein (APP) through the alpha-secretase pathway decreases in AD, concurrent with cognitive impairment. This APP cleavage occurs within the beta-amyloid peptide (Abeta) sequence, precluding formation of amyloidogenic peptides and leading to the release of the soluble N-terminal APP fragment (sAPPalpha) which is neurotrophic and procognitive.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3823, "target": 2249, "key": "127080ad547555ee28a3abf2809dc262"}, {"line": 20574, "relation": "decreases", "evidence": "In Alzheimer's disease (AD), disturbed homeostasis of the proteases competing for amyloid precursor protein processing has been reported: a disintegrin and metalloproteinase 10 (ADAM10), the physiological α-secretase, is decreased in favor of the amyloid-beta-generating enzyme BACE-1.", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"ADAM Metallopeptidase subgraph": true}}, "source": 3823, "target": 2249, "key": "289d2bd9d703feeccc00366c90eaf960"}, {"line": 9684, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 170, "key": "7974126003dc0220185642671e748cd3"}, {"line": 42510, "relation": "positiveCorrelation", "evidence": "The cellular generation of reactive oxygen species (ROS) has been implicated in contributing to the pathology of human neurological disorders including Alzheimer's disease (AD) and Parkinson's disease (PD).", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"nervous system disease": true, "Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 170, "key": "d14919ef29c57de5e520479697810d88"}, {"line": 42558, "relation": "association", "evidence": "These findings have mechanistic implications for ROS-triggered inflammatory gene expression programs that may contribute to AD and PD neuropathologic mechanisms.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 170, "key": "fa6c378215ffacd4b28a1ae46d027131"}, {"line": 49444, "relation": "association", "evidence": "Antioxidants scavenge free radicals and other reactive oxygen species that damage cellular membranes, organelles, and macromolecules. Accumulation of reactive oxygen species may overwhelm the protective reserves of antioxidants in cells (oxidative stress). In neurons, which are especially vulnerable to free radical–mediated damage, these processes may be important in aging of the brain and the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "annotations": {"Cell": {"neuron": true}}, "source": 3823, "target": 170, "key": "3e217b93849199a1df9ab3a2973c95d1"}, {"line": 9692, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 430, "key": "0cbe43da6001cd9468d9bb6232b6bcab"}, {"line": 9734, "relation": "association", "evidence": "The p85alpha subunit of phosphatidyl inositol 3 kinase (PIK3R1) and the regulatory subunit 3 of protein phosphatase 1 (PPP1R3) were selected as candidate genes because both encode key proteins involved in insulin signalling and because polymorphisms in these genes have been previously implicated in insulin resistance or type II diabetes.Analysis of the Met326Ile PIK3R1 and the Asp905Tyr PPP1R3 polymorphisms in 202 patients with late onset AD and 160 or 170 age matched normal subjects.Logistic regression analysis using the recessive genetic model showed significant differences in genotype and allelic frequencies between the AD group and normal controls (genotypes: odds ratio (OR) 2.09, 95% confidence interval (CI) 1.17 to 3.74, p = 0.01; alleles: OR 1.99, 95% CI 1.17 to 3.40, p = 0.01) for the Met326Ile PIK3R1 polymorphism that were female specific.", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3189, "key": "8d4556b74836ce46812413d52c304bc0"}, {"line": 9744, "relation": "association", "evidence": "Additionally, in the dominant genetic model a marginally significant association in genotype frequencies between the Asp905Tyr PPP1R3 polymorphism and AD was observed (genotypes: OR 1.85, 95% CI 1.03 to 3.30, p = 0.04; alleles: OR 1.68, 95% CI 0.98 to 2.88, p = 0.06).", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Confidence": {"High": true}}, "source": 3823, "target": 3221, "key": "f07cfafbb564ee30c78d30b633c3ac40"}, {"line": 9810, "relation": "negativeCorrelation", "evidence": "The deficiency of insulin-PI3K-AKT signalling was more severe in individuals with both T2DM and AD (T2DM-AD).", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 579, "key": "fb11873a82115be7a8beaf793cd09d1e"}, {"line": 9981, "relation": "association", "evidence": "We have recently identified in vitro a high affinity interaction between beta-amyloid peptide (Abeta) of AD and islet amyloid polypeptide (IAPP) of T2D which results in the formation of non-fibrillar and non-cytotoxic Abeta-IAPP hetero-oligomers.", "citation": {"db": "PubMed", "db_id": "23713771"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 928, "key": "f3e08177393f9e496748d067944a3251"}, {"line": 10065, "relation": "association", "evidence": "The catalytic domain of insulin-degrading enzyme forms a denaturant-resistant complex with amyloid beta peptide: implications for Alzheimer disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "18411275"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1230, "key": "94cb0d5ec03c8d66922311ff43fe69ef"}, {"line": 10157, "relation": "negativeCorrelation", "evidence": "Moreover, reduction of beta-cell replication capabilities results in reduction of beta-cell mass in mammals, simultaneously with impaired glucose tolerance.", "citation": {"db": "PubMed", "db_id": "21537460"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 3823, "target": 797, "key": "a8c467470bc6e96f604b1fb209e963af"}, {"line": 10173, "relation": "association", "evidence": "Adiponectin as a new paradigm for approaching Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2259, "key": "8f4827f82d356b6d712b252c6e05bb4c"}, {"line": 40390, "relation": "positiveCorrelation", "evidence": "Here, we aim to summarize recent studies that suggest the potential correlation between adiponectin and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Low": true}}, "source": 3823, "target": 2259, "key": "3af21b5b3d4600bde7027dc75003ca42"}, {"line": 10414, "relation": "positiveCorrelation", "evidence": "ER stress contributes to the pathogenesis of obesity and diabetes, which are risk factors for Alzheimer's disease (AD) that accelerate the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3925, "key": "a4884c14e6098c2946b2846c66ed50ee"}, {"line": 10626, "relation": "association", "evidence": "Lysosomal beta-galactosidase and beta-hexosaminidase activities correlate with clinical stages of dementia associated with Alzheimer's disease and type 2 diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "CellStructure": {"Lysosomes": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2829, "key": "65ea9c80320b39e9fec76601191c073f"}, {"line": 10643, "relation": "association", "evidence": "In this regard, the activity of lysosomal glycohydrolases may of use, in light of the implication of these enzymes in early events that underlie AD pathology and an overt correlation, in diabetes, between altered metabolic homeostasis, abnormal glycohydrolase secretion in body fluids, and occurrence of diabetic complications.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"CellStructure": {"Lysosomes": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3163, "key": "ed9e51ddb790c62d887e50512832e4c9"}, {"line": 10769, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2564, "key": "dd5e3eb816412b5befcd2da7a4fe680e"}, {"line": 38987, "relation": "positiveCorrelation", "evidence": "Acute-phase proteins such as alpha 1-antichymotrypsin and c-reactive protein, elements of the complement / system, and activated microglial and astroglial cells are consistently found in brains of AD patients. Most importantly, / also cytokines such as interleukin-6 (IL-6) have been detected in the cortices of AD patients, indicating a local / activation of components of the unspecific inflammatory system.", "citation": {"db": "PubMed", "db_id": "8739396"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 2564, "key": "988d364a25e591cbc00a1e8d3d691cb7"}, {"line": 10789, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 3823, "target": 605, "key": "47777a598ac1ffe06e94cf99a33714d2"}, {"line": 43276, "relation": "increases", "evidence": "Increases in free fatty acids, eicosanoids, and products of lipid peroxidation are known to occur in various)/ conditions of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's diseasens/ of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 605, "key": "a2f39d78a453c1c4bad8ca24b1aebbcf"}, {"line": 10824, "relation": "negativeCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 156, "key": "dcb5a10176652c41e8b397b9722bf5b1"}, {"line": 16713, "relation": "association", "evidence": "The endothelial nitric oxide synthase (NOS3) gene encodes endothelial NOS, an enzyme that regulates the production of the vasodilatory nitric oxide associated with the cerebral small vessel pathology observed in early AD.", "citation": {"db": "PubMed", "db_id": "15016421"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 156, "key": "5c075a9e573fde2a9677c0cf82d51066"}, {"line": 21252, "relation": "positiveCorrelation", "evidence": "On the other hand, an overproduction of NO is related with several disorders as Alzheimer's disease, Huntington's disease and the amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Disease": {"Huntington's disease": true, "Alzheimer's disease": true, "amyotrophic lateral sclerosis": true}, "Confidence": {"High": true}}, "source": 3823, "target": 156, "key": "0e1f741bbbc3e8f6af47e8854b78a150"}, {"line": 10896, "relation": "negativeCorrelation", "evidence": "According to this hypothesis, brains from AD patients showed substantially downregulated expression of the Insulin receptor (IR), the IGF-1 receptor (IGF-1R), and the insulin receptor substrate (IRS) proteins.", "citation": {"db": "PubMed", "db_id": "21916834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2185, "key": "04621913062b2c969f1fd501e0072725"}, {"line": 11162, "relation": "association", "evidence": "Galanin (GAL) and GAL receptors (GALR) are overexpressed in degenerating brain regions associated with cognitive decline in Alzheimer's disease (AD). The functional consequences of GAL plasticity in AD are unclear. GAL inhibits cholinergic transmission in the hippocampus and impairs spatial memory in rodent models, suggesting that GAL overexpression exacerbates cognitive impairment in AD. By contrast, gene expression profiling of individual cholinergic basal forebrain (CBF) neurons aspirated from AD tissue revealed that GAL hyperinnervation positively regulates mRNAs that promote CBF neuronal function and survival. GAL also exerts neuroprotective effects in rodent models of neurotoxicity. These data support the growing concept that GAL overexpression preserves CBF neuron function, which may in turn delay the onset of symptoms of AD. Further elucidation of GAL activity in selectively vulnerable brain regions will help gauge the therapeutic potential of GALR ligands in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "21299067"}, "annotations": {"Subgraph": {"Galanin subgraph": true}}, "source": 3823, "target": 2737, "key": "a3af65cb96fdb58c8811a9d395e51672"}, {"line": 11163, "relation": "association", "evidence": "Galanin (GAL) and GAL receptors (GALR) are overexpressed in degenerating brain regions associated with cognitive decline in Alzheimer's disease (AD). The functional consequences of GAL plasticity in AD are unclear. GAL inhibits cholinergic transmission in the hippocampus and impairs spatial memory in rodent models, suggesting that GAL overexpression exacerbates cognitive impairment in AD. By contrast, gene expression profiling of individual cholinergic basal forebrain (CBF) neurons aspirated from AD tissue revealed that GAL hyperinnervation positively regulates mRNAs that promote CBF neuronal function and survival. GAL also exerts neuroprotective effects in rodent models of neurotoxicity. These data support the growing concept that GAL overexpression preserves CBF neuron function, which may in turn delay the onset of symptoms of AD. Further elucidation of GAL activity in selectively vulnerable brain regions will help gauge the therapeutic potential of GALR ligands in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "21299067"}, "annotations": {"Subgraph": {"Galanin subgraph": true}}, "source": 3823, "target": 2738, "key": "945547e2f158db86a579930f4412e540"}, {"line": 11164, "relation": "association", "evidence": "Galanin (GAL) and GAL receptors (GALR) are overexpressed in degenerating brain regions associated with cognitive decline in Alzheimer's disease (AD). The functional consequences of GAL plasticity in AD are unclear. GAL inhibits cholinergic transmission in the hippocampus and impairs spatial memory in rodent models, suggesting that GAL overexpression exacerbates cognitive impairment in AD. By contrast, gene expression profiling of individual cholinergic basal forebrain (CBF) neurons aspirated from AD tissue revealed that GAL hyperinnervation positively regulates mRNAs that promote CBF neuronal function and survival. GAL also exerts neuroprotective effects in rodent models of neurotoxicity. These data support the growing concept that GAL overexpression preserves CBF neuron function, which may in turn delay the onset of symptoms of AD. Further elucidation of GAL activity in selectively vulnerable brain regions will help gauge the therapeutic potential of GALR ligands in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "21299067"}, "annotations": {"Subgraph": {"Galanin subgraph": true}}, "source": 3823, "target": 2739, "key": "2d75a33422a81d635ae5f9f7ac10627f"}, {"line": 11195, "relation": "negativeCorrelation", "evidence": "It has become of particular interest in the pathogenesis of Alzheimer's disease (AD) because of the report that the activity of the gene coding for the enzyme DHCR24, which metabolizes desmosterol to cholesterol, is selectively reduced in the affected areas of the brain. Any change in the pattern of C27 sterol intermediates in cholesterol synthesis merits investigation with respect to the pathogenesis of AD, since neurosteroids such as progesterone can modulate the tissue levels", "citation": {"db": "PubMed", "db_id": "23042211"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3823, "target": 2627, "key": "c3ca444b823ba58ff928655c8bbe06bc"}, {"line": 46225, "relation": "negativeCorrelation", "evidence": "reduced expression of DHCR24 is found in the temporal cortex of Alzheimer's disease patients", "citation": {"db": "PubMed", "db_id": "20568014"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}}, "source": 3823, "target": 2627, "key": "72a8c5a3dcfce09ebfcfd1ca050cfae0"}, {"line": 11259, "relation": "positiveCorrelation", "evidence": "Here we show that bleomycin hydrolase, known to be induced in an oxidative environment, is specifically increased in neurons marked for degeneration in AD. These findings support a key proximal role for bleomycin hydrolase, and oxidative stress in AD.", "citation": {"db": "PubMed", "db_id": "10363952"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2401, "key": "fd74cd9cb1d4b353ed0126d924c3e325"}, {"line": 11287, "relation": "association", "evidence": "The interaction of Cu(2+) with the first 16 residues of the Alzheimer's amyliod beta peptide, Abeta(1-16), and human serum albumin (HSA) were studied in vitro by isothermal titration calorimetry at pH 7.2 and 310 K in aqueous solution. The solvation parameters recovered from the extended solvation model indicate that HSA is involved in the transport of copper ion. Complexes between Abeta(1-16) and copper ions have been proposed to be an aberrant interaction in the development of Alzheimer's disease, where Cu(2+) is involved in Abeta(1-16) aggregation. The indexes of stability indicate that HSA removed Cu(2+) from Abeta(1-16), rapidly, decreased Cu-induced aggregation of Abeta(1-16), and reduced the toxicity of Abeta(1-16) + Cu(2+) significantly.", "citation": {"db": "PubMed", "db_id": "22844264"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 953, "key": "6875753fe337139e2d2d44635f18fb8e"}, {"line": 11392, "relation": "positiveCorrelation", "evidence": "We demonstrate that astrocytic expression of calpain-10 is up-regulated, and CamKIIα down-regulated with increasing Braak stage. Using immunohistochemistry we confirm protein expression of calpain-10 in astrocytes throughout the temporal cortex and demonstrate that calpain-10 immunoreactivity is correlated with both local and global measures of Alzheimer-type pathology.", "citation": {"db": "PubMed", "db_id": "23421725"}, "annotations": {"Subgraph": {"Calpastatin-calpain subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "source": 3823, "target": 2429, "key": "973e5ae6b1bf03817e255d366f2ceee2"}, {"line": 11393, "relation": "negativeCorrelation", "evidence": "We demonstrate that astrocytic expression of calpain-10 is up-regulated, and CamKIIα down-regulated with increasing Braak stage. Using immunohistochemistry we confirm protein expression of calpain-10 in astrocytes throughout the temporal cortex and demonstrate that calpain-10 immunoreactivity is correlated with both local and global measures of Alzheimer-type pathology.", "citation": {"db": "PubMed", "db_id": "23421725"}, "annotations": {"Subgraph": {"Calpastatin-calpain subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "source": 3823, "target": 2425, "key": "61881732573e39a8add0da345b0bd82c"}, {"line": 11427, "relation": "negativeCorrelation", "evidence": "Double labeling immunofluorescence and confocal microscopy revealed reduced hemoglobin α-chain and beta-chain in practically all neurons with small amounts of granular or punctuate hyperphosphorylated tau deposits and in neurons with tangles in the hippocampus and frontal cortex in AD", "citation": {"db": "PubMed", "db_id": "21157025"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}}, "source": 3823, "target": 2812, "key": "292e74c36398971f078ee43db299ea7d"}, {"line": 11428, "relation": "negativeCorrelation", "evidence": "Double labeling immunofluorescence and confocal microscopy revealed reduced hemoglobin α-chain and beta-chain in practically all neurons with small amounts of granular or punctuate hyperphosphorylated tau deposits and in neurons with tangles in the hippocampus and frontal cortex in AD", "citation": {"db": "PubMed", "db_id": "21157025"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}}, "source": 3823, "target": 2813, "key": "a6e0ecf9199b937263b0d16201dfaa1a"}, {"line": 11453, "relation": "positiveCorrelation", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "annotations": {"Subgraph": {"T cells signaling": true, "Wnt signaling subgraph": true}}, "source": 3823, "target": 4017, "key": "376cfb7285f6298c6020de1848db5708"}, {"line": 11455, "relation": "positiveCorrelation", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "source": 3823, "target": 3996, "key": "0d159c739ec6fad4ac6b86d0caeda065"}, {"line": 11458, "relation": "positiveCorrelation", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Wnt signaling subgraph": true, "Axonal guidance subgraph": true}}, "source": 3823, "target": 3965, "key": "9590b04e2d26a3b3ba9aceb7bf7819a9"}, {"line": 11472, "relation": "positiveCorrelation", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 3823, "target": 2961, "key": "31e220936aced8752a62da7064ea9270"}, {"line": 11478, "relation": "negativeCorrelation", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 2962, "key": "31e5fdfa9d56b54e05334c99abaf0983"}, {"line": 11479, "relation": "negativeCorrelation", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 589, "key": "fdd412a73023407b41bfa0fb15c1e943"}, {"line": 11481, "relation": "positiveCorrelation", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 1006, "key": "a6d06813baee75b2cd84e4845e6a4859"}, {"line": 11533, "relation": "positiveCorrelation", "evidence": "Lipopigment, identifiable in the fluorescence microscope, is thought to be cellular debris partly derived from free-radical-induced peroxidation of cellular constituents. The volume of neuronal lipopigment has been positively correlated with advancing age, Alzheimer dementia, and the neuronal ceroidoses. Chronic administration of agents which can be correlated with decreased neuronal lipopigment in animal models might protect neuronal function against any adverse effects associated with (but not necessarily resulting from) lipopigment accumulation in normal ageing, anoxia, or certain degenerative diseases.", "citation": {"db": "PubMed", "db_id": "2690998"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}}, "source": 3823, "target": 436, "key": "e3f5504d741e291e590123eb83ba4ea9"}, {"line": 11774, "relation": "negativeCorrelation", "evidence": "Glutamate receptor subunit 1 (GluR1) is one of the four possible subunits of the AMPA-type glutamate receptor. The integrity of this receptor is crucial for learning processes. However, reductions of GluR1 are noticeable in the hippocampal formation of patients suffering from Alzheimer's disease. Such degradations presumably result in an impaired synaptic communication and might be causally linked to the neurodegenerative process in this cognitive disorder.", "citation": {"db": "PubMed", "db_id": "12197668"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 3823, "target": 2770, "key": "71abd106c113de47542ea93d41f2db73"}, {"line": 30228, "relation": "association", "evidence": "These findings indicate that abnormal expressions of the AMPA receptor and its interacting PSD molecule are associated with Alzheimer's disease and implicated in pathophysiology of this disease.", "citation": {"db": "PubMed", "db_id": "10588576"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 3823, "target": 2770, "key": "e25bd403ffa3d9d968d1ebc893f43a43"}, {"line": 11880, "relation": "decreases", "evidence": "Deficit in central cholinergic neurotransmission is a consistent change associated with Alzheimer's disease (AD). Donepezil hydrochloride exhibits selective inhibition of acetylcholinesterase (AChE) and is widely used for the treatment of AD.", "citation": {"db": "PubMed", "db_id": "18070217"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 789, "key": "ecbf7e0f395ccf90320236f2bdd69d27"}, {"line": 13560, "relation": "decreases", "evidence": "The nefiracetam-induced increase in the frequency of mEPSCs and mIPSCs over and above the level achieved by ACh alone would contribute to the therapeutic effect of nefiracetam as the cholinergic system is known to be downregulated in the brain of Alzheimer's disease patients.The therapeutic effects of galantamine are ascribed to the modest potentiation of nACh receptor and NMDA receptor activities in addition to the modest inhibition of acetylcholinesterase.", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 789, "key": "a1cbe1dfdf839ac1050ad8c24cdd1752"}, {"line": 12439, "relation": "positiveCorrelation", "evidence": "Depression is one of the most frequent neuropsychiatric symptoms in Alzheimer's disease (AD). plasminogen activator inhibitor-1 (PAI-1) is involved in the pathogenesis of both AD and depression. This suggests a potential role of the PAI-1 gene SERPINE1 in the development of AD-related depression and its response to antidepressant treatment.", "citation": {"db": "PubMed", "db_id": "22503724"}, "source": 3823, "target": 3902, "key": "4d712eaaf464b60cbad0b26d244e18a5"}, {"line": 14938, "relation": "association", "evidence": "Neuropsychiatric disorders such as depression are frequently associated with Alzheimer's disease (AD) and the degeneration of cholinergic basal forebrain neurons and reductions in acetylcholine that occur in AD have been identified as potential mediators of these secondary neuropsychiatric symptomologies.", "citation": {"db": "PubMed", "db_id": "21723926"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Prosencephalon": true, "Neurons": true}}, "source": 3823, "target": 3902, "key": "00f7a9d30c05d7e15e9c8be78d168d90"}, {"line": 12440, "relation": "association", "evidence": "Depression is one of the most frequent neuropsychiatric symptoms in Alzheimer's disease (AD). plasminogen activator inhibitor-1 (PAI-1) is involved in the pathogenesis of both AD and depression. This suggests a potential role of the PAI-1 gene SERPINE1 in the development of AD-related depression and its response to antidepressant treatment.", "citation": {"db": "PubMed", "db_id": "22503724"}, "source": 3823, "target": 3351, "key": "f6fab20e8770c6d17d267b0b0c8643d6"}, {"line": 12495, "relation": "positiveCorrelation", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 3823, "target": 2796, "key": "93b0168d55e3105dd0e378bedd41a5e8"}, {"line": 33710, "relation": "positiveCorrelation", "evidence": "In concordance, significant increases in the levels of phosphorylation of total Akt substrates, including: GSK3beta(Ser9), tau(Ser214), mTOR(Ser2448), and decreased levels of the Akt target, p27(kip1), were found in AD temporal cortex compared with controls.", "citation": {"db": "PubMed", "db_id": "15773910"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2796, "key": "73e88a23dd619415882dbf4761459a7c"}, {"line": 12496, "relation": "positiveCorrelation", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 3823, "target": 2581, "key": "3dffa2a86eec2058c1960d2fae486a1b"}, {"line": 12503, "relation": "positiveCorrelation", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2642, "key": "59fc1d1f5cd94e11d2a7ae90ccdeab99"}, {"line": 12784, "relation": "decreases", "evidence": "The mechanisms by which arecoline improves cognitive function are not known. We have demonstrated that arecoline can differentially influence cerebral blood flow and metabolism with both acute and chronic administration. Studies of cerebral glucose metabolism in patients with early or late onset of AD demonstrate glucose metabolic deficits in the temporal and parietal cortex. In advanced cases of AD, cerebral blood flow also can be reduced throughout the cortex.", "citation": {"db": "PubMed", "db_id": "8019853"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 566, "key": "8bc6094c1fafcda9b33d595534ff5ee7"}, {"line": 40384, "relation": "association", "evidence": "Previous studies demonstrated that adiponectin modulates memory and cognitive impairment and contributes to the deregulated glucose metabolism and mitochondrial dysfunction observed in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 566, "key": "686cc9b1b3e67aac69b88ffb3567ab4a"}, {"line": 12785, "relation": "decreases", "evidence": "The mechanisms by which arecoline improves cognitive function are not known. We have demonstrated that arecoline can differentially influence cerebral blood flow and metabolism with both acute and chronic administration. Studies of cerebral glucose metabolism in patients with early or late onset of AD demonstrate glucose metabolic deficits in the temporal and parietal cortex. In advanced cases of AD, cerebral blood flow also can be reduced throughout the cortex.", "citation": {"db": "PubMed", "db_id": "8019853"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 485, "key": "874af98d485fe1ecce6e91610912de64"}, {"line": 12878, "relation": "decreases", "evidence": "In Alzheimerbetas disease (AD), the most common age-related primary dementing disorder, degeneration of the cholinergic neurons of the basal forebrain (Whitehouse et al., 1982) occurs. Additionally, cholinergic dysfunction may lead to endocrine abnormalities including altered plasma and cerebrospinal fluid (CSF) concentrations of various neuropeptides. Vasopressin, CRF and ACTH levels are reportedly reduced in the CSF of subjects with AD, whereas plasma cortisol levels are elevated in AD and are not suppressed by dexamethasone.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}}, "source": 3823, "target": 366, "key": "719be43922ca89023700a3db3dcd63c1"}, {"line": 12879, "relation": "decreases", "evidence": "In Alzheimerbetas disease (AD), the most common age-related primary dementing disorder, degeneration of the cholinergic neurons of the basal forebrain (Whitehouse et al., 1982) occurs. Additionally, cholinergic dysfunction may lead to endocrine abnormalities including altered plasma and cerebrospinal fluid (CSF) concentrations of various neuropeptides. Vasopressin, CRF and ACTH levels are reportedly reduced in the CSF of subjects with AD, whereas plasma cortisol levels are elevated in AD and are not suppressed by dexamethasone.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}}, "source": 3823, "target": 204, "key": "6853bc90fb74e935166e7b43b1b437ed"}, {"line": 12880, "relation": "decreases", "evidence": "In Alzheimerbetas disease (AD), the most common age-related primary dementing disorder, degeneration of the cholinergic neurons of the basal forebrain (Whitehouse et al., 1982) occurs. Additionally, cholinergic dysfunction may lead to endocrine abnormalities including altered plasma and cerebrospinal fluid (CSF) concentrations of various neuropeptides. Vasopressin, CRF and ACTH levels are reportedly reduced in the CSF of subjects with AD, whereas plasma cortisol levels are elevated in AD and are not suppressed by dexamethasone.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}}, "source": 3823, "target": 102, "key": "646947f0b1e000ac9d6e049c6d78772e"}, {"line": 12889, "relation": "increases", "evidence": "In Alzheimerbetas disease (AD), the most common age-related primary dementing disorder, degeneration of the cholinergic neurons of the basal forebrain (Whitehouse et al., 1982) occurs. Additionally, cholinergic dysfunction may lead to endocrine abnormalities including altered plasma and cerebrospinal fluid (CSF) concentrations of various neuropeptides. Vasopressin, CRF and ACTH levels are reportedly reduced in the CSF of subjects with AD, whereas plasma cortisol levels are elevated in AD and are not suppressed by dexamethasone.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cortisol subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 237, "key": "8f853b1e40a670ab5023673a08b5f91e"}, {"line": 15682, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cortisol subgraph": true}}, "source": 3823, "target": 237, "key": "f2449cc5b14acf31d5000ba7c14addca"}, {"line": 17472, "relation": "association", "evidence": "Patients with Alzheimer's disease (AD) are characterized by an altered sensitivity to cortisol-mediated modulation of circulating lymphocytes.", "citation": {"db": "PubMed", "db_id": "17597922"}, "annotations": {"Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"Cortisol subgraph": true}}, "source": 3823, "target": 237, "key": "924670db682ece9ea4b3c8de64f16901"}, {"line": 13596, "relation": "increases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 639, "key": "427c0f0259c8836fa61968a31d7a6aaf"}, {"line": 13727, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2516, "key": "e6a8f2fcc883b910c57e2aa85f4aab38"}, {"line": 13728, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2517, "key": "4ea7270a11ca6d582a31b0e9eb63d682"}, {"line": 13729, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2518, "key": "90dc4533b5e163032916eba048988a43"}, {"line": 13730, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2520, "key": "25c50cd2e1505d68df39b78835c27e77"}, {"line": 13731, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2521, "key": "57a4ef80c427ccbc0331843124060dfe"}, {"line": 13732, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2522, "key": "85a13bc7f158ade45e46cf1c25cd38c1"}, {"line": 13733, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2523, "key": "218fa542f0e6cebcca6de3c980d402ec"}, {"line": 13734, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2515, "key": "5a883d9568bdea951f10d265087a7fc9"}, {"line": 13735, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2524, "key": "3a2a4127a5550074dce86b20f18a6f20"}, {"line": 46384, "relation": "negativeCorrelation", "evidence": "Significant bilateral reductions in nicotinic receptor binding were identified in frontal (left, p = 0.004; right, p = 0.002), striatal (left, p = 0.004; right, p = 0.003), right medial temporal (p = 0.04) and pons (p<0.001) in patients with AD compared to controls.Using 123I-5IA-85380 SPECT we found changes consistent with significant reductions in the nicotinic alpha4beta2 receptor in cortical and striatal brain regions.", "citation": {"db": "PubMed", "db_id": "17135460"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true, "Corpus Striatum": true}}, "source": 3823, "target": 2524, "key": "113fccf64264a80b2fd2a9704a4012d6"}, {"line": 13736, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2526, "key": "af554f53bfa6fd78d22a828f79a6bfe5"}, {"line": 13737, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2527, "key": "4fcd208ee84d2377895660e7b558e40a"}, {"line": 13738, "relation": "decreases", "evidence": "In the brain of Alzheimer's patients, both neuronal nicotinic acetylcholine (nACh) receptors and NMDA receptors are known to be down-regulated.", "citation": {"db": "PubMed", "db_id": "14607256"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2779, "key": "99c8e1752550684e0c77d40fb3edc33b"}, {"line": 13756, "relation": "positiveCorrelation", "evidence": "The expression level of the NFKB1(p105/50Kd) gene was significantly higher in AD with respect to adult age-matched controls (AA) and was related to the Mini-Mental State Examination (MMSE) score of the same patients.", "citation": {"db": "PubMed", "db_id": "21592054"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3823, "target": 3112, "key": "c8aa34a60314adf430ac2c4697e39ea0"}, {"line": 13761, "relation": "increases", "evidence": "In addition, the expression of various NF-κB target genes and of both NF-κBp50 and NF-κBp65 DNA-binding activity were increased in PBMC from AD patients in comparison with those from AA.", "citation": {"db": "PubMed", "db_id": "21592054"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3823, "target": 704, "key": "084ac82452783f02c1e6b0ce8552d42a"}, {"line": 14343, "relation": "positiveCorrelation", "evidence": "In this report we found that both BACE1 and NF-κB p65 levels were significantly increased in the brains of AD patients.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3304, "key": "e475e221f794730fdce23d36789c5e48"}, {"line": 14599, "relation": "association", "evidence": "p21 also up-regulates multiple genes that have been associated with senescence or implicated in age-related diseases, including atherosclerosis, Alzheimer's disease, amyloidosis, and arthritis.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true, "Arthritis": true, "Atherosclerosis": true, "Amyloidosis": true}}, "source": 3823, "target": 2493, "key": "4e48100dbcb9df3bdb8c8c03605f8dd1"}, {"line": 14722, "relation": "increases", "evidence": "Hippocampal neurons are vulnerable to injury induced by Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18687381"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Hippocampus": true, "Neurons": true}}, "source": 3823, "target": 3888, "key": "62b5228cc21e410d67ff1ca0de38a0dc"}, {"line": 14723, "relation": "association", "evidence": "Hippocampal neurons are vulnerable to injury induced by Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18687381"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Hippocampus": true, "Neurons": true}}, "source": 3823, "target": 3888, "key": "8307f5e73155cca05ae5b9459b6852b9"}, {"line": 15016, "relation": "negativeCorrelation", "evidence": "The CSF concentrations of MMPs and TIMPs were determined with ELISAs.CSF concentrations of MMP-9 were significantly lower, and the concentrations of MMP-3 significantly higher in AD patients compared to the controls.", "citation": {"db": "PubMed", "db_id": "24448781"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 3062, "key": "f680846a799bc7a660a51f6074133bfa"}, {"line": 15017, "relation": "positiveCorrelation", "evidence": "The CSF concentrations of MMPs and TIMPs were determined with ELISAs.CSF concentrations of MMP-9 were significantly lower, and the concentrations of MMP-3 significantly higher in AD patients compared to the controls.", "citation": {"db": "PubMed", "db_id": "24448781"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 3060, "key": "8b5eb98df92d5d9ef13a577b8ed2d606"}, {"line": 18695, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 3060, "key": "74e613ec7541d3d7a77ddcd862f8da3e"}, {"line": 15032, "relation": "association", "evidence": "The present findings are in line with the previous studies showing tau products cleaved by caspase-3, as recognized by s pecific tau-cleaved antibodies, in Alzheimer's disease and other tauopathies.", "citation": {"db": "PubMed", "db_id": "16496165"}, "annotations": {"MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2444, "key": "3887b440a066cd388456f11b40491787"}, {"line": 15063, "relation": "association", "evidence": "Alzheimer's disease (AD) is characterized by progressive cognitive decline associated with a featured neuropathology (neuritic plaques and neurofibrillary tangles).", "citation": {"db": "PubMed", "db_id": "24936870"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Plaque, Amyloid": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}}, "source": 3823, "target": 3881, "key": "d6d1b22c7f678145c1d543427274345d"}, {"line": 15100, "relation": "association", "evidence": "The excess vascular endothelial growth factor (VEGF) produced in the Alzheimer's disease (AD) brain can harm neurons, blood vessels, and other components of the neurovascular units (NVUs).", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3519, "key": "482d2cba6095cdd759acb62b1f203e03"}, {"line": 39816, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 3823, "target": 3519, "key": "3e90f2e3f990dcddf0e2ab572ebf6c32"}, {"line": 15157, "relation": "association", "evidence": "Downregulation of extracellular signal-regulated kinase 1/2 activity by calmodulin KII modulates p21Cip1 levels and survival of immortalized lymphocytes from Alzheimer's disease patients.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2173, "key": "076eb247bad8eee8cae74359264215b5"}, {"line": 15179, "relation": "increases", "evidence": "Quantitative reverse transcription polymerase chain reaction analysis demonstrated increased p21 messenger RNA (mRNA) levels in AD cells.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3823, "target": 3954, "key": "151639734265814f61bb0931949ba9d5"}, {"line": 15201, "relation": "increases", "evidence": "Upregulation of p21 transcription in AD cells appears to be the consequence of increased activity of forkhead box O3a (FOXO3a) as the result of diminished ERK1/2-mediated phosphorylation of this transcription factor, which in turn facilitates its nuclear accumulation.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Ubiquitin degradation subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2703, "key": "de52f9cc15838219253a70fe5323c893"}, {"line": 15210, "relation": "decreases", "evidence": "Murine double minute 2 (MDM2) protein levels were decreased in AD cells relative to control lymphoblasts, suggesting an impairment of FOXO3a degradation.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3823, "target": 2703, "key": "d0591d138ecbf65138fe781504bb2e20"}, {"line": 15211, "relation": "decreases", "evidence": "Murine double minute 2 (MDM2) protein levels were decreased in AD cells relative to control lymphoblasts, suggesting an impairment of FOXO3a degradation.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3047, "key": "fd8b5b8a905c1ec9892df609d8e5ac9b"}, {"line": 15225, "relation": "negativeCorrelation", "evidence": "Results showed a significant decrease in the intake of vitamins C (p < .001)", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 186, "key": "e750a7f8005f78c44cbe9ed466a006f8"}, {"line": 15237, "relation": "negativeCorrelation", "evidence": "The blood levels of catalase but not superoxide dismutase and glutathione were significantly decreased in the patients with severe AD when compared to controls (p < .01),", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2453, "key": "e0ff71eac5502f8118c015b652f7cd2f"}, {"line": 15245, "relation": "causesNoChange", "evidence": "The blood levels of catalase but not superoxide dismutase and glutathione were significantly decreased in the patients with severe AD when compared to controls (p < .01),", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 265, "key": "9131336feda6638a38f25d52c87dfc90"}, {"line": 15246, "relation": "causesNoChange", "evidence": "The blood levels of catalase but not superoxide dismutase and glutathione were significantly decreased in the patients with severe AD when compared to controls (p < .01),", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3391, "key": "e6b153e4c8a96f5ae7294c101ec695d6"}, {"line": 15688, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 3391, "key": "57ad9189c3293f164a62a12bee35ae42"}, {"line": 15335, "relation": "association", "evidence": "Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer.", "citation": {"db": "PubMed", "db_id": "19882751"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neoplasms": true, "Vascular Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 2194, "key": "06c478109f9858db0a0eb29eb4af407d"}, {"line": 18754, "relation": "association", "evidence": "A growing amount of evidence indicates that matrix metalloproteinases (MMPs) may play an important role in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "10672313"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2194, "key": "f5de876369e5910868f2873d990982e3"}, {"line": 15380, "relation": "decreases", "evidence": "Matrix metalloproteinase-2 and epidermal growth factor are decreased in platelets of Alzheimer patients.Our data show a significant decrease in the levels of epidermal growth factor (EGF) and of MMP-2 in platelets of AD patients and decreased levels of MMP-2 in MCI.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"MeSHAnatomy": {"Blood Platelets": true}, "Species": {"9606": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 3059, "key": "249f8b26d6f2c3b1c72ea4b22c688811"}, {"line": 15382, "relation": "negativeCorrelation", "evidence": "Matrix metalloproteinase-2 and epidermal growth factor are decreased in platelets of Alzheimer patients.Our data show a significant decrease in the levels of epidermal growth factor (EGF) and of MMP-2 in platelets of AD patients and decreased levels of MMP-2 in MCI.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"MeSHAnatomy": {"Blood Platelets": true}, "Species": {"9606": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 3059, "key": "cf46f31dacc8f79fe74fd5cb0360e82f"}, {"line": 15455, "relation": "decreases", "evidence": "There was a significant 1.5-fold decrease in MMP-2 activity in the AD group compared to HC (p < 0.001) and a 1.4-fold decrease compared to MCI (p < 0.01).", "citation": {"db": "PubMed", "db_id": "21694463"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3059, "key": "684bb11d022e565166ea2dcc578fb8fe"}, {"line": 15381, "relation": "decreases", "evidence": "Matrix metalloproteinase-2 and epidermal growth factor are decreased in platelets of Alzheimer patients.Our data show a significant decrease in the levels of epidermal growth factor (EGF) and of MMP-2 in platelets of AD patients and decreased levels of MMP-2 in MCI.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"MeSHAnatomy": {"Blood Platelets": true}, "Species": {"9606": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 2656, "key": "b891d93b5e2c377d735e3d62dc7fa03b"}, {"line": 15383, "relation": "negativeCorrelation", "evidence": "Matrix metalloproteinase-2 and epidermal growth factor are decreased in platelets of Alzheimer patients.Our data show a significant decrease in the levels of epidermal growth factor (EGF) and of MMP-2 in platelets of AD patients and decreased levels of MMP-2 in MCI.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"MeSHAnatomy": {"Blood Platelets": true}, "Species": {"9606": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 2656, "key": "70c01516d4eb05f28b8a3e51907639be"}, {"line": 15398, "relation": "causesNoChange", "evidence": "The APP ratio was slightly but not significantly decreased in AD patients, whereas CD40L and serotonin were unchanged.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 3823, "target": 350, "key": "825c1a27290d4ed4702ac7ea49552e53"}, {"line": 15399, "relation": "causesNoChange", "evidence": "The APP ratio was slightly but not significantly decreased in AD patients, whereas CD40L and serotonin were unchanged.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 3823, "target": 2475, "key": "7625b48c7ed94e047eb9c60d3436ec07"}, {"line": 15499, "relation": "increases", "evidence": "However, in a widely used mouse model for AD, immunohistochemistry demonstrated an increase of MMP2 expression in astrocytes surrounding senile plaques in APP/PS1 transgenic mice brains.", "citation": {"db": "PubMed", "db_id": "21376707"}, "annotations": {"CellStructure": {"Extracellular Matrix": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Species": {"10090": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 3679, "key": "e75fd5cc72544da76de2df51cdcdaeb3"}, {"line": 16307, "relation": "positiveCorrelation", "evidence": "Brain sections from AD and control mice showed that HIF-1α, Ang-2, MMP2 and caspase 3 are elevated and Bcl-xL decreased in the microvasculature of AD mice.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3823, "target": 3679, "key": "be18916aa17ffb05dd6ced049eb3dbe5"}, {"line": 15615, "relation": "positiveCorrelation", "evidence": "Plasma sICAM-1 and sPECAM-1 were higher and CSF sVCAM-1 were lower in AD and DLB patients than in controls (p<0.001).", "citation": {"db": "PubMed", "db_id": "17270454"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 2863, "key": "bb1b2c8f31223a45aa5a1ff4930408dd"}, {"line": 15616, "relation": "positiveCorrelation", "evidence": "Plasma sICAM-1 and sPECAM-1 were higher and CSF sVCAM-1 were lower in AD and DLB patients than in controls (p<0.001).", "citation": {"db": "PubMed", "db_id": "17270454"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 3182, "key": "ab9755d4f1c9bb9ef4f43ea987910b41"}, {"line": 15623, "relation": "negativeCorrelation", "evidence": "Plasma sICAM-1 and sPECAM-1 were higher and CSF sVCAM-1 were lower in AD and DLB patients than in controls (p<0.001).", "citation": {"db": "PubMed", "db_id": "17270454"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Cell adhesion subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 3823, "target": 3516, "key": "9960e04accb831300eb7eb80787928cb"}, {"line": 15680, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 3516, "key": "881feec82b8b4797e42b6c60b8f6691e"}, {"line": 15684, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 2875, "key": "21710fec6986fcc2a2d68a2631951e37"}, {"line": 40689, "relation": "positiveCorrelation", "evidence": "The levels of IGF-II and IGFBP-2 were significantly elevated in the CSF from patients with AD.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Serotonergic subgraph": true, "Insulin signal transduction": true}}, "source": 3823, "target": 2875, "key": "0dceaf062848e26eea519745cae97ae0"}, {"line": 15685, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 2374, "key": "adae84382b268a7475286f275081c76c"}, {"line": 15686, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 2474, "key": "e28f76f4ea3bb894be1707a59272916d"}, {"line": 15687, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 2457, "key": "938b36cf79986a4d2bf655a4c62ecf6b"}, {"line": 15689, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 275, "key": "a47011c3b99e318f05df9d8251356aac"}, {"line": 39747, "relation": "positiveCorrelation", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Response to oxidative stress": true}}, "source": 3823, "target": 275, "key": "0fcbb718cd05081f695ba1687cd14af4"}, {"line": 44878, "relation": "positiveCorrelation", "evidence": "AD individuals are characterized by decreased plasma folate values, as well as increased plasma homocysteine (Hcy) levels, and there is indication of impaired S-adenosylmethionine (SAM) levels in AD brains. ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 275, "key": "3162f61d524a34417a59e6e01cba9751"}, {"line": 44889, "relation": "positiveCorrelation", "evidence": "the majority of the studies agree that plasma Hcy values are increased in AD subjects ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 275, "key": "cb805ebc9c51487a169930ec14419145"}, {"line": 44900, "relation": "positiveCorrelation", "evidence": "There is also some indication that Hcy levels are increased in the cerebrospinal fluid (CSF) of AD patients, respect to controls", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 3823, "target": 275, "key": "725dea563454049216359a8c5890cc96"}, {"line": 44987, "relation": "positiveCorrelation", "evidence": "Our data show that patients with AD have higher levels of plasma tHcy", "citation": {"db": "PubMed", "db_id": "12784029"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 275, "key": "613df5a75381fd692a1c2dfa08110c5e"}, {"line": 15693, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 3823, "target": 2657, "key": "da6cd9c7aa01c37b08c3fd39f490ef90"}, {"line": 15695, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}}, "source": 3823, "target": 273, "key": "665a003d17459aae4374d481affd5e08"}, {"line": 15696, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}}, "source": 3823, "target": 94, "key": "972f5b537fad9a49a9ae889744bd2ab5"}, {"line": 15697, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}}, "source": 3823, "target": 189, "key": "c1cd98be7c2ddf411da8f9269247af0f"}, {"line": 15700, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Albumin subgraph": true}}, "source": 3823, "target": 2882, "key": "6a537a055ac413993494381d19f670eb"}, {"line": 15759, "relation": "association", "evidence": "Furthermore, recent studies have demonstrated that age-related androgen depletion results in accumulation of beta-amyloid protein and thereby acts as a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 209, "key": "73d10c8bb5f630fee096e79c61353412"}, {"line": 15783, "relation": "association", "evidence": "These results indicate that SARM is efficacious for the treatment of not only osteoporosis and sarcopenia, but also Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true, "Alzheimer Disease": true}}, "source": 3823, "target": 3929, "key": "e682eec6e6094564eaeb00c6d6ff4a9a"}, {"line": 15916, "relation": "association", "evidence": "Caffeine based measures of CYP1A2 activity correlate with oral clearance of tacrine in patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9764962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}}, "source": 3823, "target": 222, "key": "e2305a6730f055565f7a21740d893b6c"}, {"line": 15942, "relation": "association", "evidence": "Cytosolic phospholipase A2α (cPLA2α) plays a key role in the pathogenesis of many inflammatory diseases, such as rheumatoid arthritis, atopic dermatitis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24120545"}, "annotations": {"MeSHDisease": {"Dermatitis, Atopic": true, "Arthritis, Rheumatoid": true, "Alzheimer Disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3823, "target": 3198, "key": "0fa93eb0f159e233cc4905bb9e9e1d79"}, {"line": 15958, "relation": "association", "evidence": "Identification of cytochrome P450 1A2 as enzyme involved in the microsomal metabolism of Huperzine A. Huperzine A is a reversible and selective cholinesterase inhibitor approved for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "12586202"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3823, "target": 130, "key": "499f02b6bdd1c86601495f545761087a"}, {"line": 16159, "relation": "association", "evidence": "These results demonstrate that hyperoside can protect Abeta-induced primary cultured cortical neurons via PI3K/Akt/Bad/Bcl(XL)-regulated mitochondrial apoptotic pathway, and they raise the possibility that hyperoside could be developed into a clinically valuable treatment for Alzheimer's disease and other neuronal degenerative diseases associated with mitochondrial dysfunction.", "citation": {"db": "PubMed", "db_id": "21978835"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 3823, "target": 167, "key": "859b7e00a205e4089f1e8d9f4cf518cd"}, {"line": 16258, "relation": "association", "evidence": "Hypoxia inducible factor 1-alpha (HIF-1α), a key regulator of cellular responses to hypoxia, is elevated in the microcirculation of AD patients.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"MeSHDisease": {"Hypoxia": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Hypoxia response subgraph": true}}, "source": 3823, "target": 2830, "key": "90e4c4149341f442a2b2d77c8702291c"}, {"line": 16305, "relation": "positiveCorrelation", "evidence": "Brain sections from AD and control mice showed that HIF-1α, Ang-2, MMP2 and caspase 3 are elevated and Bcl-xL decreased in the microvasculature of AD mice.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Hypoxia response subgraph": true}}, "source": 3823, "target": 3582, "key": "58b6d853b76ef56896b0f34ed65af245"}, {"line": 16309, "relation": "positiveCorrelation", "evidence": "Brain sections from AD and control mice showed that HIF-1α, Ang-2, MMP2 and caspase 3 are elevated and Bcl-xL decreased in the microvasculature of AD mice.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Caspase subgraph": true}}, "source": 3823, "target": 3600, "key": "c43bf7151682b090b43a569cbf18953d"}, {"line": 16313, "relation": "negativeCorrelation", "evidence": "Brain sections from AD and control mice showed that HIF-1α, Ang-2, MMP2 and caspase 3 are elevated and Bcl-xL decreased in the microvasculature of AD mice.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Bcl-2 subgraph": true}}, "source": 3823, "target": 3598, "key": "3f84fc7b3b27fe03fc57cfa066087e78"}, {"line": 16335, "relation": "negativeCorrelation", "evidence": "Knockdown of phosphotyrosyl phosphatase activator induces apoptosis via mitochondrial pathway and the attenuation by simultaneous tau hyperphosphorylation. Phosphotyrosyl phosphatase activator (PTPA) is decreased in the brains of Alzheimer's disease (AD) and the AD transgenic mouse models.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3282, "key": "307c4973d6859a2cd9e1b2c2021b81a4"}, {"line": 16387, "relation": "positiveCorrelation", "evidence": "Osteopontin is increased in the cerebrospinal fluid of patients with Alzheimer's disease and its levels correlate with cognitive decline.", "citation": {"db": "PubMed", "db_id": "20308780"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3409, "key": "1bfbe8f74c83c2886f1148ba854bb0c3"}, {"line": 16424, "relation": "positiveCorrelation", "evidence": "In MCI converters individuals tested longitudinally, both plasma and CSF OPN concentrations were significantly elevated when they received a diagnosis of AD during followup.", "citation": {"db": "PubMed", "db_id": "23576854"}, "annotations": {"MeSHAnatomy": {"Cerebrospinal Fluid": true, "Plasma": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3823, "target": 3409, "key": "2454d3ce3fa5000a47fd20ad4e0e04a3"}, {"line": 16534, "relation": "positiveCorrelation", "evidence": "Increased expression of the remodeling- and tumorigenic-associated factor osteopontin in pyramidal neurons of the Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3409, "key": "c94a27c1a7c97bb1ec7225fb467ac372"}, {"line": 16403, "relation": "association", "evidence": "Inflammation is believed to play a role in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20308780"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3920, "key": "89168fd575d0448f50ae68f84e1aae43"}, {"line": 18551, "relation": "association", "evidence": "The role of inflammation in Alzheimer's disease, Parkinson's disease, and multiple sclerosis has recently come under increased scrutiny.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Parkinson Disease": true, "Inflammation": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3920, "key": "6ebcf4a24ba8c25d6ac6eeadcd59a7e4"}, {"line": 41590, "relation": "association", "evidence": "Neuroinflammation affects the pathobiology of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true, "Amyloidosis": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3823, "target": 3920, "key": "a902df9c7dbd485d5f1f5c997c3838d9"}, {"line": 46186, "relation": "association", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 3920, "key": "e0e4cfa2e4141bf3b079fb09f01b379d"}, {"line": 16452, "relation": "increases", "evidence": "Neuronal expression of myeloperoxidase is increased in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3066, "key": "736f6e31535e89b8d7895eb1bc2c1f2e"}, {"line": 16453, "relation": "positiveCorrelation", "evidence": "Neuronal expression of myeloperoxidase is increased in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3066, "key": "c9fc2e85d1fefb8e0c334c8376e92cf6"}, {"line": 16482, "relation": "association", "evidence": "Consistent with expression in phagocytic cells, myeloperoxidase immunoreactivity was present in some activated microglia in Alzheimer brains.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Cell": {"phagocyte": true, "microglial cell": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3066, "key": "28ae25c4b8212f9e13a39791d8870992"}, {"line": 18421, "relation": "increases", "evidence": "Increased myeloperoxidase plasma levels in patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24217274"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3066, "key": "f6e3afc950ae16b7fddfb237dce981f1"}, {"line": 18435, "relation": "increases", "evidence": "ResULTS: AD patients showed significantly higher plasma concentrations of MPO in comparison to healthy elderly controls (AD versus healthy elderly controls (mean ± SD): 132.8 ± 114.8 ng/mL versus 55.0 ± 42.6 ng/mL; p = 0.002).", "citation": {"db": "PubMed", "db_id": "24217274"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3066, "key": "9c9c3ad64452364a8622010c530560c6"}, {"line": 18446, "relation": "positiveCorrelation", "evidence": "In a binary logistic regression model, plasma MPO concentrations were independently associated with the presence of AD (p = 0.014).AD patients showed significantly increased plasma levels of MPO, which could be an important molecular link between atherosclerosis and AD.", "citation": {"db": "PubMed", "db_id": "24217274"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Alzheimer Disease": true, "Atherosclerosis": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3066, "key": "5c144052eb6e3b4c9658df51d4d7cc96"}, {"line": 18492, "relation": "increases", "evidence": "MPO is similarly expressed in astrocytes in human AD tissue.", "citation": {"db": "PubMed", "db_id": "19059911"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3066, "key": "35012fa821743ad1c57a8dcf569d1d07"}, {"line": 18616, "relation": "association", "evidence": "In females, we found a significant association between MPO genotype and AD (P=0.034),", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3066, "key": "be5c8437f13ecb456baf72b400e6dee5"}, {"line": 16514, "relation": "association", "evidence": "In the neurosciences, it has led to the discoveries of osteopontin in multiple sclerosis and SORL1/LR11 in Alzheimer's, and recent studies indicate its potential for identifying neurogenomic biomarkers.", "citation": {"db": "PubMed", "db_id": "19285134"}, "annotations": {"Disease": {"multiple sclerosis": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3823, "target": 3397, "key": "2ae900c2ccf39d6c396b25a9ef73c5d8"}, {"line": 45846, "relation": "negativeCorrelation", "evidence": "Our results showed that SORL1 gene is lower expressed in the brain than in blood leukocytes for AD patients", "citation": {"db": "PubMed", "db_id": "22836009"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 3397, "key": "35d68b5e7cece60a6ace9f05ec0e5677"}, {"line": 45850, "relation": "positiveCorrelation", "evidence": "Our results showed that SORL1 gene is lower expressed in the brain than in blood leukocytes for AD patients", "citation": {"db": "PubMed", "db_id": "22836009"}, "annotations": {"Cell": {"leukocyte": true, "blood cell": true}}, "source": 3823, "target": 3397, "key": "e93fe03b5e9e17fa029ea52670673048"}, {"line": 16668, "relation": "association", "evidence": "Genetic association between endothelial nitric oxide synthase and Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "16813604"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3124, "key": "53cfa09f32045e3e5b8159ca79f949c4"}, {"line": 16681, "relation": "association", "evidence": "The Glu/Glu genotype at the Glu298Asp variant of the endothelial nitric oxide synthase (NOS3) gene has been tested for association with AD in several Caucasian and Asian populations, with conflicting results.", "citation": {"db": "PubMed", "db_id": "16813604"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Endothelium": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3125, "key": "2b53bb52c2c4b34f154943885be5609f"}, {"line": 16692, "relation": "association", "evidence": "Finally, we compiled results of previous studies of Glu298Asp using meta-analysis, to determine whether the aggregate studies support an association between Glu298Asp and AD. None of the additional SNPs were associated with AD in the Caucasians, whereas two showed evidence for association in the African Americans.The meta-analysis showed a small effect of the Glu298Asp GG genotype on AD risk across all studies (summary odds ratio = 1.15, 95% confidence interval: 0.97-1.35)", "citation": {"db": "PubMed", "db_id": "16813604"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3125, "key": "2bd444f055d7d55ab918b8b89262b29c"}, {"line": 16823, "relation": "association", "evidence": "Pin1, endothelial nitric oxide synthase, and amyloid-beta form a feedback signaling loop involved in the pathogenesis of Alzheimer's disease, hypertension, and cerebral amyloid angiopathy.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1660, "key": "3eabdfee361e3aa7b5492c7c0035e26b"}, {"line": 16838, "relation": "positiveCorrelation", "evidence": "Although the molecular mechanism has not yet been clarified until now, it is very interesting that Alzheimer's disease (AD), hypertension (HTN), and cerebral amyloid angiopathy (CAA) often occur synchronously and possess many similar pathological characteristics.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3835, "key": "d2e5ceb5c258892ce045822d1033dec9"}, {"line": 20906, "relation": "association", "evidence": "The accumulation of fibrillar amyloid-beta protein (A beta) in cerebral blood vessels, a condition known as cerebral amyloid angiopathy (CAA), is a key pathological feature of Alzheimer's disease and certain related disorders and is intimately associated with cerebrovascular cell death both in vivo and in vitro.", "citation": {"db": "PubMed", "db_id": "12754271"}, "annotations": {"MeSHDisease": {"Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3835, "key": "cb0107f2c1079adc6ee3125e4a5f8f20"}, {"line": 16840, "relation": "positiveCorrelation", "evidence": "Although the molecular mechanism has not yet been clarified until now, it is very interesting that Alzheimer's disease (AD), hypertension (HTN), and cerebral amyloid angiopathy (CAA) often occur synchronously and possess many similar pathological characteristics.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3916, "key": "d8c4865ab362e1b250ef05367b5db741"}, {"line": 17012, "relation": "decreases", "evidence": "On the other hand, protein levels of EGR1 and ARC, SYN2, STX6 and PICALM are significantly lower in the brain of adult APP mice than in age-matched wild type animals.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 3823, "target": 3589, "key": "2230b251deaab1f1cf1df3154cdfe47a"}, {"line": 17014, "relation": "decreases", "evidence": "On the other hand, protein levels of EGR1 and ARC, SYN2, STX6 and PICALM are significantly lower in the brain of adult APP mice than in age-matched wild type animals.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 3734, "key": "16a2ea55b7df4a8e974b82c5ab5f3e5f"}, {"line": 17016, "relation": "decreases", "evidence": "On the other hand, protein levels of EGR1 and ARC, SYN2, STX6 and PICALM are significantly lower in the brain of adult APP mice than in age-matched wild type animals.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Gamma secretase subgraph": true}}, "source": 3823, "target": 3630, "key": "fc9fe8053ee524459c8656ddbe93390c"}, {"line": 17018, "relation": "decreases", "evidence": "On the other hand, protein levels of EGR1 and ARC, SYN2, STX6 and PICALM are significantly lower in the brain of adult APP mice than in age-matched wild type animals.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 3823, "target": 3695, "key": "d70a8ffba3ada43e978f94a48e7335d0"}, {"line": 17020, "relation": "decreases", "evidence": "On the other hand, protein levels of EGR1 and ARC, SYN2, STX6 and PICALM are significantly lower in the brain of adult APP mice than in age-matched wild type animals.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 3728, "key": "50fdaa6347ce4424b76fbe1260848229"}, {"line": 17039, "relation": "association", "evidence": "The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}}, "source": 3823, "target": 2658, "key": "d0693d1ac80e34e22858f74bfaa54bde"}, {"line": 17252, "relation": "association", "evidence": "In AD-Tg mice, a significant increase in hippocampal EGR1 protein levels was also found in response to GA immunization.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2658, "key": "71d005f441d3b08958f2854b888601bf"}, {"line": 17311, "relation": "association", "evidence": "Egr-1 upregulates the Alzheimer's disease presenilin-2 gene in neuronal cells.", "citation": {"db": "PubMed", "db_id": "14585504"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Published": {"Epilepsy comorbidity paper": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2658, "key": "1cde7544ecd4c7827ace6886d7310c86"}, {"line": 17366, "relation": "increases", "evidence": "Early growth response 1 (Egr-1) is a transcription factor that is significantly up-regulated in AD brain.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2658, "key": "f467e35b2f4433c65b9d405f4af0fa9b"}, {"line": 47844, "relation": "positiveCorrelation", "evidence": "Hippocampal expression levels of Egr1 have been shown to positively correlate with disease progression in AD, whereas the overexpression of EGR1 in rat brain induces tau phosphorylation via its target, and regulator of cdk5, p35.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Gamma secretase subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2658, "key": "b5facfc55bc78eb5b6b1f071945b7e23"}, {"line": 48798, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2658, "key": "830c3cff6cc0096dfbb0e00821b37f9b"}, {"line": 48921, "relation": "decreases", "evidence": "Importantly, expression of the CRE-driven immediate early gene, Egr-1 (Zif268) is decreased in the CA1 region of the hippocampus.", "citation": {"db": "PubMed", "db_id": "26682682"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Anatomy": {"CA1 field of hippocampus": true}}, "source": 3823, "target": 2658, "key": "d8023403e1c3e18146a4dfc3278217ba"}, {"line": 17454, "relation": "association", "evidence": "Alterations of IL-2-mediated NK cytotoxicity may therefore support the neuroimmune hypothesis of AD.", "citation": {"db": "PubMed", "db_id": "8915041"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3823, "target": 2888, "key": "6b8f47fc92e09eca92acaff715099188"}, {"line": 17507, "relation": "association", "evidence": "Sera from patients with Alzheimer disease and non-demented elderly subjects caused an increase in IL-2 and a decrease in IL-10 production by PBMC from middle-aged control subjects but did not affect IL-1beta, IL-6, and TNFalpha secretion, indicating alterations of the immune system related to aging.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Species": {"9606": true}}, "source": 3823, "target": 2888, "key": "68562e0911a690ec18481406f056db17"}, {"line": 17546, "relation": "decreases", "evidence": "The production of superoxide anions was increased only by monocytes from the elderly groups.The results suggest that although the impaired immune function in patients with Alzheimer's disease is related to the aging process, the significant low IL-2 production in these patients may play a role in their increased susceptibility to infections.", "citation": {"db": "PubMed", "db_id": "11961364"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2888, "key": "03ea5388c12c74009c20db924988971d"}, {"line": 17464, "relation": "association", "evidence": "Physiologic modulation of natural killer cell activity as an index of Alzheimer's disease progression.", "citation": {"db": "PubMed", "db_id": "17597922"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 624, "key": "7817e782ba9edfb144639d89db009066"}, {"line": 17494, "relation": "increases", "evidence": "One of the reasons for the increased susceptibility to infections in patients with Alzheimer disease may be enhanced apoptotic death of their peripheral leukocytes.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Cell": {"leukocyte": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3823, "target": 679, "key": "0585d035371cd1fda345f40ece3616eb"}, {"line": 17495, "relation": "decreases", "evidence": "One of the reasons for the increased susceptibility to infections in patients with Alzheimer disease may be enhanced apoptotic death of their peripheral leukocytes.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Cell": {"leukocyte": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3823, "target": 590, "key": "4d15a0b86d24f611ddae425df6f8d1c1"}, {"line": 17496, "relation": "increases", "evidence": "One of the reasons for the increased susceptibility to infections in patients with Alzheimer disease may be enhanced apoptotic death of their peripheral leukocytes.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Cell": {"leukocyte": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3823, "target": 3919, "key": "208c8b598e503281976f1f44371f35b0"}, {"line": 17547, "relation": "increases", "evidence": "The production of superoxide anions was increased only by monocytes from the elderly groups.The results suggest that although the impaired immune function in patients with Alzheimer's disease is related to the aging process, the significant low IL-2 production in these patients may play a role in their increased susceptibility to infections.", "citation": {"db": "PubMed", "db_id": "11961364"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3919, "key": "fc3c1bb0d74f8c25a627f06f3ce32b05"}, {"line": 17508, "relation": "decreases", "evidence": "Sera from patients with Alzheimer disease and non-demented elderly subjects caused an increase in IL-2 and a decrease in IL-10 production by PBMC from middle-aged control subjects but did not affect IL-1beta, IL-6, and TNFalpha secretion, indicating alterations of the immune system related to aging.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Species": {"9606": true}}, "source": 3823, "target": 2878, "key": "396f93ed2929b4b20839574512399876"}, {"line": 17566, "relation": "association", "evidence": "The Multi Drug Resistance (ABCB1) gene, encoding for P-gp, is highly polymorphic and this may result in a changed function of P-gp and may possibly interfere with the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16999857"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}}, "source": 3823, "target": 2232, "key": "9b730bf1796956dd19c29f91d37589cb"}, {"line": 17631, "relation": "association", "evidence": "The ABCB1 gene, coding for the efflux transporter P-glycoprotein (PGP), is a candidate gene for Alzheimer disease (AD).", "citation": {"db": "PubMed", "db_id": "21478475"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}}, "source": 3823, "target": 2232, "key": "97a7c07e9c47d6cfb29b7836a63a5e3a"}, {"line": 20730, "relation": "association", "evidence": "Recent studies have unraveled important roles of ABC transporters including ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistant protein, BCRP), ABCC1 (multidrug resistance protein 1, MRP1), and the cholesterol transporter ABCA1 in the pathogenesis of AD and Abeta peptides deposition inside the brain.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2232, "key": "e5c16c1e2c0cfebbb8f93c01c83f16fb"}, {"line": 20751, "relation": "association", "evidence": "These findings support the validity of increasing Abeta clearance via ABCB1 up-regulation as a therapeutic approach to slowing or halting the progression of AD.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2232, "key": "4395ca4faed82d2e24b20aca54ba1f2f"}, {"line": 17588, "relation": "association", "evidence": "P-glycoprotein is a blood-brain barrier efflux transporter involved in the clearance of amyloid-beta from the brain and, as such, might be involved in the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Blood-Brain Barrier": true}, "Confidence": {"High": true}}, "source": 3823, "target": 570, "key": "6fff80753d69d13f0968efcac1f8645e"}, {"line": 17727, "relation": "association", "evidence": "The therapeutic potential of screening for markers of renin-angiotensin abnormality for the prediction of Alzheimer's disease is considered, as is the potential use of agents known to influence the renin-angiotensin system in the treatment or prevention of the disease.", "citation": {"db": "PubMed", "db_id": "15853619"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3823, "target": 2274, "key": "f55fa85fcddfcd785d9da26e99e60224"}, {"line": 17764, "relation": "association", "evidence": "Here, we discuss the role of angiotensin II in cognitive impairment and AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2274, "key": "ae1e8f99abf82760db489b9d72de4fc1"}, {"line": 17728, "relation": "association", "evidence": "The therapeutic potential of screening for markers of renin-angiotensin abnormality for the prediction of Alzheimer's disease is considered, as is the potential use of agents known to influence the renin-angiotensin system in the treatment or prevention of the disease.", "citation": {"db": "PubMed", "db_id": "15853619"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3823, "target": 210, "key": "a1e3a1a6e6f885d896369f4d69273933"}, {"line": 17809, "relation": "association", "evidence": "Angiotensin as a target for the treatment of Alzheimer's disease, anxiety and depression.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3823, "target": 210, "key": "c7acf883db6d2c4bf8beb80787ee45d3"}, {"line": 17763, "relation": "association", "evidence": "Here, we discuss the role of angiotensin II in cognitive impairment and AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 81, "key": "f6fdc59db04ea5827760be6f05f69c3c"}, {"line": 17868, "relation": "association", "evidence": "These observations prompt the testable hypothesis for future study that CCL4 overexpression, regulated in part by hypomethylation of the ATF3 gene, may contribute to neuropathologic progression associated with amyloid deposition in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "24607962"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2458, "key": "316e2f0a7f901d89c695d5a53ea3cf17"}, {"line": 18088, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3110, "key": "418f77b195ad17ec9ebb479ddad4e562"}, {"line": 18089, "relation": "negativeCorrelation", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3110, "key": "c4ca46d7d1f3c5446de3b0192882b023"}, {"line": 18173, "relation": "association", "evidence": "Results warrant further exploration of the Nrf2-ARE pathway for treatment of AD and suggest that the Nrf2-ARE pathway may represent a potential therapeutic strategy to pursue in AD in humans, particularly in view of the multiple mechanisms by which Nrf2 can exert its protective effects.", "citation": {"db": "PubMed", "db_id": "19805328"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3110, "key": "7906ff723d299e51d0c79902cc90cbaa"}, {"line": 18192, "relation": "association", "evidence": "Nrf2-encoding NFE2L2 haplotypes influence disease progression but not risk in Alzheimer's disease and age-related cataract.", "citation": {"db": "PubMed", "db_id": "20064547"}, "annotations": {"MeSHDisease": {"Cataract": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3110, "key": "4f5eaee9f8c80ba5b397b0a6973f6419"}, {"line": 18217, "relation": "association", "evidence": "However, one haplotype allele of NFE2L2 was associated with 2 years earlier age at AD onset (p(c)=0.013)", "citation": {"db": "PubMed", "db_id": "20064547"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3110, "key": "2927e2ce58b504ef4575fa78d14c3982"}, {"line": 39947, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3110, "key": "26370ba55f995a1eababb7a6684efc09"}, {"line": 18313, "relation": "increases", "evidence": "Neurons in both AD brain and Abeta-treated cultures exhibited FasL upregulation and changes in immunoreactivity for Fas receptor.", "citation": {"db": "PubMed", "db_id": "12742739"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3823, "target": 2690, "key": "3effa079e31ec2d5b48ec66d2d24bc23"}, {"line": 18481, "relation": "positiveCorrelation", "evidence": "A functional -463G/A MPO promoter polymorphism has been associated with AD risk through as yet unidentified mechanisms.", "citation": {"db": "PubMed", "db_id": "19059911"}, "source": 3823, "target": 3067, "key": "41a43ecb8e0fd715fa7dd3a92e8cd6d7"}, {"line": 18624, "relation": "association", "evidence": "In conclusion, the G-463A polymorphism of MPO was statistically associated with AD in a gender-specific manner. However, given the low significance of P value we suggest no causal effect of the MPO gene in AD, as also evidenced in a recent meta-analysis.", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3067, "key": "e19b2d2316e88d26043705df53397f2f"}, {"line": 18815, "relation": "association", "evidence": "Herein, we conducted a meta-analysis to clarify the association between ESR1 polymorphisms and the occurrence of AD.", "citation": {"db": "PubMed", "db_id": "24857745"}, "annotations": {"Subgraph": {"Estrogen subgraph": true}}, "source": 3823, "target": 2680, "key": "2bd9c0d7b5fb0ff1844979e9d90b43f5"}, {"line": 49391, "relation": "association", "evidence": "Moreover, we have shown that vitamin D likely interacts with the estrogen receptor, Esr1, to regulate molecular pathways relevant to AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2680, "key": "07a443ccc660c99405b863753db8fd32"}, {"line": 18873, "relation": "association", "evidence": "Tissue plasminogen activator arrests Alzheimer's disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "24126163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Plasminogen activator subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3200, "key": "6dcf0c7f5a07c15d32e7481a92a3ea2b"}, {"line": 18927, "relation": "decreases", "evidence": "Decreased tPA activity was detected in the cortex and subcortex of AD mice, whereas increased tPA activity was found in the cerebellum of SCA1 mice.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Cerebral Cortex": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3200, "key": "b85ae7fa5820ef7a49fdd9ae688d6278"}, {"line": 18936, "relation": "negativeCorrelation", "evidence": "These findings extend the existing hypotheses that low tPA activity promotes AD, whereas increased tPA activity contributes to cerebellar degeneration.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3200, "key": "fe46e7a5a3357c68582df2858452c4f8"}, {"line": 18957, "relation": "association", "evidence": "Although conventionally associated with fibrin clot degradation, recent work has uncovered new functions for the tissue plasminogen activator (tPA)/plasminogen cascade in central nervous system physiology and pathology. This extracellular proteolytic cascade has been shown to have roles in learning and memory, stress, neuronal degeneration, addiction and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15841309"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3823, "target": 3200, "key": "5e608a69626ad4c16671432d44e791d7"}, {"line": 19035, "relation": "decreases", "evidence": "In this study, zymography, immunocapture, and ELISAs were utilized to show that tissue plasminogen activator activity in frontal cortex tissue of Alzheimer patients is dramatically reduced compared with age-matched controls, while tissue plasminogen activator and plasminogen protein levels are unchanged; suggesting that plasminogen activator activity is inhibited in the Alzheimer brain.", "citation": {"db": "PubMed", "db_id": "19222708"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Subgraph": {"Plasminogen activator subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3200, "key": "dcaec1ca439bfc0707309abd4028a925"}, {"line": 19017, "relation": "increases", "evidence": "Plasminogen activator activity is inhibited while neuroserpin is up-regulated in the Alzheimer disease brain.", "citation": {"db": "PubMed", "db_id": "19222708"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3352, "key": "20bad1fef823827ed35249fd775770ff"}, {"line": 19048, "relation": "increases", "evidence": "Furthermore, elevated amounts of tissue plasminogen activator-neuroserpin complexes are seen in the Alzheimer brain, and immunohistochemical studies demonstrate that both tissue plasminogen activator and neuroserpin are associated with amyloid-beta plaques in Alzheimer brain tissue.", "citation": {"db": "PubMed", "db_id": "19222708"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1608, "key": "be09a3da15bccd06ded7691037ce01b2"}, {"line": 19150, "relation": "negativeCorrelation", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 3823, "target": 718, "key": "727a7830f5f3fed729fa0bfd7eeda383"}, {"line": 19227, "relation": "positiveCorrelation", "evidence": "In Alzheimer's disease (AD) brain, increased levels of cyclooxygenase-2 (COX-2), cell cycle markers, and p38 MAP kinase (MAPK) can be detected in neuronal cells.", "citation": {"db": "PubMed", "db_id": "15056456"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Prostaglandin subgraph": true, "JAK-STAT signaling subgraph": true}}, "source": 3823, "target": 3278, "key": "4babe75951155f17c82d579d054aaf2d"}, {"line": 19615, "relation": "positiveCorrelation", "evidence": "In AD brains, COX-1-positive microglial cells were primarily associated with amyloid beta plaques, while the number of COX-2-positive neurons was increased compared to that in control brains.", "citation": {"db": "PubMed", "db_id": "11194936"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Neurons": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3278, "key": "a48f203ce9c7b34daf271ef7b7b0746c"}, {"line": 39566, "relation": "positiveCorrelation", "evidence": "Epidemiological studies, indicating that non-steroidal anti-inflammatory drugs (NSAIDs) decrease the risk of developing AD, have encouraged the study on the role of inflammation in AD. The best-characterized action of most NSAIDs is the inhibition of cyclooxygenase (COX). The expression of the constitutively expressed COX-1 and the inflammatory induced COX-2 has been intensively investigated in AD brain and different disease models for AD. Despite these studies, clinical trials with NSAIDs or selective COX-2 inhibitors showed little or no effect on clinical progression of AD. The expression levels of COX-1 and COX-2 change in the different stages of AD pathology. In an early stage, when low-fibrillar Abeta deposits are present and only very few neurofibrillary tangles are observed in the cortical areas, COX-2 is increased in neurons. The increased neuronal COX-2 expression parallels and colocalizes with the expression of cell cycle proteins. COX-1 is primarily expressed in microglia, which are associated with fibrillar Abeta deposits. This suggests that in AD brain COX-1 and COX-2 are involved in inflammatory and regenerating pathways respectively. In this review we will discuss the role of COX-1 and COX-2 in the different stages of AD pathology.", "citation": {"db": "PubMed", "db_id": "18537664"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3823, "target": 3278, "key": "88d416a5f4ce94f36545828498a7b6af"}, {"line": 19230, "relation": "positiveCorrelation", "evidence": "In Alzheimer's disease (AD) brain, increased levels of cyclooxygenase-2 (COX-2), cell cycle markers, and p38 MAP kinase (MAPK) can be detected in neuronal cells.", "citation": {"db": "PubMed", "db_id": "15056456"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Prostaglandin subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2998, "key": "31e817462ed3d8b9c13e47497937d997"}, {"line": 19271, "relation": "positiveCorrelation", "evidence": "Interestingly, in recent years increased cdk5/p25 expression has been demonstrated in the brains of patients with Alzheimer's and Parkinson's diseases.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2489, "key": "37f8c990c54d1e55d54ea655338ca4d2"}, {"line": 19482, "relation": "positiveCorrelation", "evidence": "In addition, aggressive patients showed a greater mean PRL increase (% baseline) (215 +/- 60, n = 11) than nonaggressive subjects (123 +/- 54, n = 11) (p =.01, 2-tailed t-test).", "citation": {"db": "PubMed", "db_id": "12377401"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}}, "source": 3823, "target": 3253, "key": "5f721ec2fc373b6ac5d782301d55dc4a"}, {"line": 19504, "relation": "association", "evidence": "Glutathione S-transferase P1 *C allelic variant increases susceptibility for late-onset Alzheimer disease: association study and relationship with apolipoprotein E epsilon4 allele.", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 3823, "target": 2800, "key": "8346601f630ac7f087d3c990006eb204"}, {"line": 19509, "relation": "association", "evidence": "GSTM1 and GSTT1 genotypes were studied by conventional PCR, whereas GSTP1 and ApoE genotypes were determined by real-time PCR on the LightCycler. We found a significant association between LOAD and the GSTP1*C allelic variant [odds ratio (OR) = 1.9;", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Glutathione reductase subgraph": true}}, "source": 3823, "target": 2800, "key": "f519bbd3d04d2276dbaea928d600913e"}, {"line": 19585, "relation": "positiveCorrelation", "evidence": "However, COX-1 immunopositive microglia were found in association with Abeta plaques, and the density of COX-1 immunopositive microglia in AD fusiform cortex was increased.", "citation": {"db": "PubMed", "db_id": "10560656"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3277, "key": "639250bd12c411a7f3d1cc65456d4250"}, {"line": 19595, "relation": "increases", "evidence": "This pattern suggests an overall increase of COX-1 expression in AD. The present study shows that COX-1 is widely expressed in human brain, and raises the possibility that COX-1 may contribute to CNS pathology.", "citation": {"db": "PubMed", "db_id": "10560656"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3277, "key": "c01e2ceb5d620dcbfff39749bc87a874"}, {"line": 19596, "relation": "positiveCorrelation", "evidence": "This pattern suggests an overall increase of COX-1 expression in AD. The present study shows that COX-1 is widely expressed in human brain, and raises the possibility that COX-1 may contribute to CNS pathology.", "citation": {"db": "PubMed", "db_id": "10560656"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3277, "key": "f60f6318b24736c8fd39cff75363358a"}, {"line": 39567, "relation": "positiveCorrelation", "evidence": "Epidemiological studies, indicating that non-steroidal anti-inflammatory drugs (NSAIDs) decrease the risk of developing AD, have encouraged the study on the role of inflammation in AD. The best-characterized action of most NSAIDs is the inhibition of cyclooxygenase (COX). The expression of the constitutively expressed COX-1 and the inflammatory induced COX-2 has been intensively investigated in AD brain and different disease models for AD. Despite these studies, clinical trials with NSAIDs or selective COX-2 inhibitors showed little or no effect on clinical progression of AD. The expression levels of COX-1 and COX-2 change in the different stages of AD pathology. In an early stage, when low-fibrillar Abeta deposits are present and only very few neurofibrillary tangles are observed in the cortical areas, COX-2 is increased in neurons. The increased neuronal COX-2 expression parallels and colocalizes with the expression of cell cycle proteins. COX-1 is primarily expressed in microglia, which are associated with fibrillar Abeta deposits. This suggests that in AD brain COX-1 and COX-2 are involved in inflammatory and regenerating pathways respectively. In this review we will discuss the role of COX-1 and COX-2 in the different stages of AD pathology.", "citation": {"db": "PubMed", "db_id": "18537664"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3823, "target": 3277, "key": "27d32fe4d1e20fe0fb611c4f796ed95a"}, {"line": 19705, "relation": "association", "evidence": "The decline in melatonin production in aged individuals has been suggested as one of the primary contributing factors for the development of age-associated neurodegenerative diseases, e.g., Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Confidence": {"High": true}}, "source": 3823, "target": 299, "key": "86e467ea17b6ce0b20d982d753c78e38"}, {"line": 39084, "relation": "negativeCorrelation", "evidence": "Recent studies showed that melatonin, an indoleamine secreted by the pineal gland, may play an important/ role in aging and AD as an antioxidant and neuroprotector. Melatonin decreases during aging and patients with AD have a/ more profound reduction in this hormone.Melatonin efficiently protects neuronal cells from Abeta-mediated toxicity via/ antioxidant and anti-amyloid properties: it not only inhibits Abeta generation, but also arrests the formation of amyloid/ fibrils by a structure-dependent interaction with Abeta. Our recent studies have demonstrated that melatonin efficiently/ attenuates Alzheimer-like tau hyperphosphorylation. Although the exact mechanism is still not fully understood, a direct/ regulatory influence of melatonin on the activities of protein kinases and protein phosphatases is proposed. Additionally,/ melatonin also plays a role in protecting cholinergic neurons and in anti-inflammation.", "citation": {"db": "PubMed", "db_id": "16364209"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 299, "key": "9f0716af24b49d3416111dbbb9b18835"}, {"line": 19777, "relation": "association", "evidence": "It is becoming evident that chronic exposure to stress not only might result in insulin resistance or cognitive deficits, but may also be considered a risk factor for pathologies such as depression or Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24090692"}, "annotations": {"MeSHDisease": {"Insulin Resistance": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 845, "key": "82e22a1d41bb9a932c2023c16b93229b"}, {"line": 19788, "relation": "negativeCorrelation", "evidence": "Hypercortisolemia and glucocorticoid receptor-signaling insufficiency in Alzheimer's disease initiation and development.", "citation": {"db": "PubMed", "db_id": "23906001"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 562, "key": "de4ada9a8855b2dddd686b34cbad0e03"}, {"line": 19794, "relation": "decreases", "evidence": "Given the capacity of glucocorticoids and corticotropin-releasing hormone to induce AD-associated pathologies, I suggest a role for circadian cortisol hypersecretion in the initiation of sporadic AD; and propose a temporal mechanism for AD development featuring neuroinflammation- mediated suppression of central glucocorticoid receptor (GR) signaling.", "citation": {"db": "PubMed", "db_id": "23906001"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 562, "key": "7eb3c20ef451c343dd02370d70b8b90e"}, {"line": 19789, "relation": "negativeCorrelation", "evidence": "Hypercortisolemia and glucocorticoid receptor-signaling insufficiency in Alzheimer's disease initiation and development.", "citation": {"db": "PubMed", "db_id": "23906001"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3135, "key": "c2046d312d923ef854d7ce0d40b95e11"}, {"line": 19795, "relation": "decreases", "evidence": "Given the capacity of glucocorticoids and corticotropin-releasing hormone to induce AD-associated pathologies, I suggest a role for circadian cortisol hypersecretion in the initiation of sporadic AD; and propose a temporal mechanism for AD development featuring neuroinflammation- mediated suppression of central glucocorticoid receptor (GR) signaling.", "citation": {"db": "PubMed", "db_id": "23906001"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3135, "key": "18f6ae3c864e7d0828f9218e36ed5faf"}, {"line": 19856, "relation": "decreases", "evidence": "In Alzheimer's disease (AD), the hypothalamic-pituitary-adrenal (HPA) axis is hyperactive and the sensitivity to dexamethasone is decreased, suggesting a possible involvement of glucocorticoid receptor alpha (GRalpha) defects in the aetiopathology of the disease.We, therefore, searched for the presence of mutations in the human GRalpha (hGRalpha) gene, focusing on the hormone-binding domain due to its importance in mediating glucocorticoids' effects.", "citation": {"db": "PubMed", "db_id": "15469870"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hypothalamus": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3135, "key": "b538825c00ca19bf9c385458d201c83c"}, {"line": 19939, "relation": "positiveCorrelation", "evidence": "We previously showed ECE-2 and ET-1 to be elevated in postmortem temporal cortex from AD patients, and ECE-2 expression and ET-1 release to be upregulated by Abeta42 in vitro.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Species": {"9606": true}}, "source": 3823, "target": 2653, "key": "8e5a5d138f9b04d85cc6b546bf5ca9ad"}, {"line": 19949, "relation": "increases", "evidence": "We have now studied isolated leptomeningeal blood vessels from postmortem brains and found that although ECE-1 level is reduced, ECE-1 activity and ET-1 level are significantly elevated in AD vessels.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Anatomy": {"brain blood vessel": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2653, "key": "0751729223f41156b4ea195167f159e0"}, {"line": 19987, "relation": "increases", "evidence": "Endothelin-1 is elevated in Alzheimer's disease brain microvessels and is neuroprotective.", "citation": {"db": "PubMed", "db_id": "20634595"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Anatomy": {"brain blood vessel": true}, "Subgraph": {"Neuroprotection subgraph": true, "Endothelin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2653, "key": "7f9fe9dbba4a35dce597783b0cbebdfc"}, {"line": 20016, "relation": "positiveCorrelation", "evidence": "Western blot analysis indicates a significantly higher level of ET-1 in AD vessels compared to vessels from age-matched controls.", "citation": {"db": "PubMed", "db_id": "20634595"}, "source": 3823, "target": 2653, "key": "7ae28ad032386f0949d5bc0c188ddffd"}, {"line": 20058, "relation": "positiveCorrelation", "evidence": "ET-1 and ECE-2 are also elevated in AD, making it likely that upregulation of the ECE-2-ET-1 axis by Abeta42 contributes to the chronic reduction of CBF in AD.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2653, "key": "a93bbd7787db36fc2617fa042321f45f"}, {"line": 20084, "relation": "positiveCorrelation", "evidence": "ET-1 mRNA measured in the temporal neocortex was significantly elevated in AD when normalized for expression of GAPDH (p = 0.0256) or the neuronal marker neuron-specific enolase (NSE, p = 0.0001). ET-1 protein was also significantly higher in AD than in control tissue, when adjusted for neuronal content by measurement of NSE (p = 0.0275).", "citation": {"db": "PubMed", "db_id": "22330820"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neocortex": true, "Neurons": true}}, "source": 3823, "target": 2653, "key": "cbfafd2a807962867411f0b8c15a8c27"}, {"line": 19940, "relation": "positiveCorrelation", "evidence": "We previously showed ECE-2 and ET-1 to be elevated in postmortem temporal cortex from AD patients, and ECE-2 expression and ET-1 release to be upregulated by Abeta42 in vitro.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Species": {"9606": true}}, "source": 3823, "target": 2651, "key": "0361a339a4b56d93969c3db2d9970f6f"}, {"line": 19950, "relation": "increases", "evidence": "We have now studied isolated leptomeningeal blood vessels from postmortem brains and found that although ECE-1 level is reduced, ECE-1 activity and ET-1 level are significantly elevated in AD vessels.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Anatomy": {"brain blood vessel": true}}, "source": 3823, "target": 2651, "key": "639aa8a4a9d21af0a3aa267ea99c2a64"}, {"line": 20059, "relation": "positiveCorrelation", "evidence": "ET-1 and ECE-2 are also elevated in AD, making it likely that upregulation of the ECE-2-ET-1 axis by Abeta42 contributes to the chronic reduction of CBF in AD.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2652, "key": "96cd7ff017b2fefc9718191dc1a3b818"}, {"line": 20071, "relation": "association", "evidence": "It has already been demonstrated that the endothelin receptor antagonist bosentan, preserves aortic and carotid endothelial function in Tg2576 mice, and our findings suggest that endothelin receptor antagonists may be beneficial in maintaining CBF in AD.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 220, "key": "21f48862af3e756aeec8c3e2c7420f89"}, {"line": 20096, "relation": "association", "evidence": "For example, the occurrence of stroke increases with age and has been linked to neurodegenerative disorders like Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "17561312"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Stroke": true, "Alzheimer Disease": true}}, "source": 3823, "target": 3930, "key": "fec9eb9bc133e1458a53ac133ff9c21e"}, {"line": 20199, "relation": "decreases", "evidence": "However, lymphoblasts derived from AD patients showed reduced levels of the Cdk inhibitor p27(kip1), which were restored after anti-calmodulin treatment of the cultures.", "citation": {"db": "PubMed", "db_id": "12901840"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3823, "target": 2494, "key": "22aa28205101f2cb59c1d13957035893"}, {"line": 33719, "relation": "negativeCorrelation", "evidence": "In concordance, significant increases in the levels of phosphorylation of total Akt substrates, including: GSK3beta(Ser9), tau(Ser214), mTOR(Ser2448), and decreased levels of the Akt target, p27(kip1), were found in AD temporal cortex compared with controls.", "citation": {"db": "PubMed", "db_id": "15773910"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Tau protein subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2494, "key": "99f92b776b5e017ce407289c70946607"}, {"line": 20219, "relation": "association", "evidence": "Recent data suggest that functional inactivation of TSC proteins might also be involved in the development of other diseases not associated with TSC, such as sporadic bladder cancer, breast cancer, ovarian carcinoma, gall bladder carcinoma, non-small-cell carcinoma of the lung, and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16713332"}, "annotations": {"MeSHDisease": {"Urinary Bladder Neoplasms": true, "Carcinoma, Non-Small-Cell Lung": true, "Tuberous Sclerosis": true, "Carcinoma": true, "Alzheimer Disease": true, "Breast Neoplasms": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3823, "target": 1639, "key": "f484ffee7cc9b6fb83258afd5e56b33c"}, {"line": 20313, "relation": "association", "evidence": "Endoplasmic reticulum (ER) stress is suggested to play a key role in the pathogenesis of neurodegenerative diseases including Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22046282"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 771, "key": "26962ddcd25e9d7aa665e8c507767bd7"}, {"line": 20363, "relation": "positiveCorrelation", "evidence": "Our results show an upregulation of gene expression in AD patients for c-Fos and BAK.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Bcl-2 subgraph": true}}, "source": 3823, "target": 2388, "key": "893a906ad9e962483cd0df464a4a5ae1"}, {"line": 20366, "relation": "positiveCorrelation", "evidence": "Our results show an upregulation of gene expression in AD patients for c-Fos and BAK.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}}, "source": 3823, "target": 2699, "key": "22bead9f901d3f4fdb0301b578976066"}, {"line": 20373, "relation": "positiveCorrelation", "evidence": "ICE-beta, c-Jun, Bax-alpha, Bcl-x(L), p53, and GADD153 were found to be upregulated in some AD samples but were not detected or downregulated in other AD or normal samples.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 3823, "target": 2442, "key": "2c63378151d5775b0472f5a08f094f78"}, {"line": 20375, "relation": "positiveCorrelation", "evidence": "ICE-beta, c-Jun, Bax-alpha, Bcl-x(L), p53, and GADD153 were found to be upregulated in some AD samples but were not detected or downregulated in other AD or normal samples.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Subgraph": {"Response DNA damage": true}}, "source": 3823, "target": 2622, "key": "53377c0e95f129dae69a0f8bdb5acdbf"}, {"line": 20377, "relation": "positiveCorrelation", "evidence": "ICE-beta, c-Jun, Bax-alpha, Bcl-x(L), p53, and GADD153 were found to be upregulated in some AD samples but were not detected or downregulated in other AD or normal samples.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true}}, "source": 3823, "target": 3482, "key": "2b2e693cdd3a36ece3d8dcc8321131a5"}, {"line": 44700, "relation": "positiveCorrelation", "evidence": "the p53 protein levels in each stage of AD development remained statistically nonsignificantly higher compared to the controls", "citation": {"db": "PubMed", "db_id": "21845541"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3482, "key": "7c24ee08e7bf0e3a372ad33bcb2f44e4"}, {"line": 20379, "relation": "positiveCorrelation", "evidence": "ICE-beta, c-Jun, Bax-alpha, Bcl-x(L), p53, and GADD153 were found to be upregulated in some AD samples but were not detected or downregulated in other AD or normal samples.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true}}, "source": 3823, "target": 2936, "key": "a4ff2daaca2ad284299d3f52784b7307"}, {"line": 20608, "relation": "positiveCorrelation", "evidence": "Polymorphism -116C/G of human X-box-binding protein 1 promoter is associated with risk of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "23421912"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 3823, "target": 3538, "key": "63c333a4d2fa2e59e2c65fd3b3fa0a49"}, {"line": 20731, "relation": "association", "evidence": "Recent studies have unraveled important roles of ABC transporters including ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistant protein, BCRP), ABCC1 (multidrug resistance protein 1, MRP1), and the cholesterol transporter ABCA1 in the pathogenesis of AD and Abeta peptides deposition inside the brain.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2239, "key": "d1e87661c60d94825c109b8a50e24f23"}, {"line": 20732, "relation": "association", "evidence": "Recent studies have unraveled important roles of ABC transporters including ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistant protein, BCRP), ABCC1 (multidrug resistance protein 1, MRP1), and the cholesterol transporter ABCA1 in the pathogenesis of AD and Abeta peptides deposition inside the brain.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2236, "key": "c10e6e4c2f24997dc90c13349eb20562"}, {"line": 20733, "relation": "association", "evidence": "Recent studies have unraveled important roles of ABC transporters including ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistant protein, BCRP), ABCC1 (multidrug resistance protein 1, MRP1), and the cholesterol transporter ABCA1 in the pathogenesis of AD and Abeta peptides deposition inside the brain.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2229, "key": "23e9626f2cd3dc52a9705b64d6d40670"}, {"line": 20782, "relation": "association", "evidence": "Several lines of evidence also implicate lipid transporters of the A-branch of ABC transporters in pathogenesis.", "citation": {"db": "PubMed", "db_id": "23789959"}, "annotations": {"Confidence": {"High": true}}, "source": 3823, "target": 594, "key": "d303cab67b4b6dd755f1cce09b96ec56"}, {"line": 20818, "relation": "association", "evidence": "Qualitative and quantitative changes in the expressions of uPAR and of its canonical ligand uPA have been observed in a large variety of epileptic disorders, either in human or in animal models, as well as in other brain diseases (stroke and brain trauma, multiple sclerosis, Alzheimer's disease, cerebral malaria, HIV-associated leukoencephalopathy and encephalitis).", "citation": {"db": "PubMed", "db_id": "21711233"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Brain Diseases": true, "Stroke": true, "Brain Injuries": true, "Malaria, Cerebral": true, "Encephalitis": true, "Leukoencephalopathies": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Cerebrum": true}, "Species": {"9606": true}}, "source": 3823, "target": 1917, "key": "f4a0948550bff643b1961e1aed5a13d1"}, {"line": 20870, "relation": "positiveCorrelation", "evidence": "UPAR protein levels were significantly increased in human brain tissues from the hippocampus, superior frontal gyrus and inferior temporal gyrus of AD cases compared with similar tissues from non-demented cases.", "citation": {"db": "PubMed", "db_id": "11814408"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"9606": true}}, "source": 3823, "target": 3202, "key": "ee027b50e03e19838a4071d67771b18b"}, {"line": 20877, "relation": "causesNoChange", "evidence": "Increased uPAR expression was not demonstrated in AD cerebellum.", "citation": {"db": "PubMed", "db_id": "11814408"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebellum": true}}, "source": 3823, "target": 3202, "key": "63d9cc1430386d44b75a9f03eea46ef8"}, {"line": 21080, "relation": "association", "evidence": "The association of angiotensin-converting enzyme with biomarkers for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2243, "key": "78e8737b6eb2afabc075b30e4752cbb9"}, {"line": 21090, "relation": "negativeCorrelation", "evidence": "Lower angiotensin-converting enzyme (ACE) activity could increase the risk of Alzheimer's disease (AD) as ACE functions to degrade amyloid-beta (Abeta).", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2243, "key": "b04fb5e41466becd369e497598bd584a"}, {"line": 21103, "relation": "negativeCorrelation", "evidence": "We measured ACE protein levels (ng/ml) and activity (RFU) in CSF and serum, and amyloid beta1-42, tau and ptau (pg/ml) in CSF. Cross-sectional regression analyses showed that ACE protein level and activity in CSF and serum were lower in patients with AD compared to controls.", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true, "Cerebrospinal Fluid": true}, "Species": {"9606": true}, "Subgraph": {"Renin-angiotensin subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2243, "key": "36e1a3b30b5f2cffcf9eee150c36e0d5"}, {"line": 21104, "relation": "negativeCorrelation", "evidence": "We measured ACE protein levels (ng/ml) and activity (RFU) in CSF and serum, and amyloid beta1-42, tau and ptau (pg/ml) in CSF. Cross-sectional regression analyses showed that ACE protein level and activity in CSF and serum were lower in patients with AD compared to controls.", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true, "Cerebrospinal Fluid": true}, "Species": {"9606": true}, "Subgraph": {"Renin-angiotensin subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2243, "key": "0cbce333c7abb3f54506e1b96c850492"}, {"line": 21137, "relation": "negativeCorrelation", "evidence": "Plasma ACE was lower in the AD subjects as compared to the controls both at baseline (p = 0.072) and after two years (p = 0.05).", "citation": {"db": "PubMed", "db_id": "19276555"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3823, "target": 2243, "key": "503ce717814d0c9e0d181d5edf3f6a25"}, {"line": 24636, "relation": "positiveCorrelation", "evidence": "AIMS: Several observations point to the involvement of angiotensin-converting enzyme-1 (ACE-1) in Alzheimer's disease (AD): ACE-1 cleaves amyloid-beta peptide (Abeta) in vitro, the level and activity of ACE-1 are reportedly increased in AD, and variations in the ACE-1 gene are associated with AD.", "citation": {"db": "PubMed", "db_id": "17973905"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 3823, "target": 2243, "key": "be604f9b4db7be18347dce8115d2d952"}, {"line": 24637, "relation": "positiveCorrelation", "evidence": "AIMS: Several observations point to the involvement of angiotensin-converting enzyme-1 (ACE-1) in Alzheimer's disease (AD): ACE-1 cleaves amyloid-beta peptide (Abeta) in vitro, the level and activity of ACE-1 are reportedly increased in AD, and variations in the ACE-1 gene are associated with AD.", "citation": {"db": "PubMed", "db_id": "17973905"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2243, "key": "98d0275ecc1b2fa395edfbc719a97ac8"}, {"line": 21310, "relation": "negativeCorrelation", "evidence": "Reduced serotonin 5-HT1A receptor binding in the temporal cortex correlates with aggressive behavior in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "12742626"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 986, "key": "51c547788e2c7864147322451e222488"}, {"line": 21311, "relation": "negativeCorrelation", "evidence": "Reduced serotonin 5-HT1A receptor binding in the temporal cortex correlates with aggressive behavior in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "12742626"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2857, "key": "59afbe74cfe7ba390f7f05be4617ae9e"}, {"line": 21452, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 3823, "target": 868, "key": "d3a24e2de4e811896070f660d8137ca8"}, {"line": 21453, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 3823, "target": 2206, "key": "37f1462e4a78ae49c8c89bfdf93fe80c"}, {"line": 21454, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 3823, "target": 471, "key": "78adafd089b6de7bbb3ea978d9cabb2b"}, {"line": 21710, "relation": "negativeCorrelation", "evidence": "The decline of emotional memory in AD patients is reflected by the decrease of p70S6k levels.", "citation": {"db": "PubMed", "db_id": "17101223"}, "source": 3823, "target": 3327, "key": "07b1dde627ddb3495dff1abec4c3250f"}, {"line": 21791, "relation": "increases", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2288, "key": "55e0514618e579ff3282fb1546169235"}, {"line": 21843, "relation": "positiveCorrelation", "evidence": "The proinflammatory enzyme 5-lipoxygenase (5-LOX) is upregulated in Alzheimer's disease (AD), but its localization and association with the hallmark lesions of the disease, beta-amyloid (Abeta) plaques and neurofibrillary tangles (NFTs), is unknown.", "citation": {"db": "PubMed", "db_id": "18678882"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Subgraph": {"Eicosanoids signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2288, "key": "bf2b29ed5f2dcade814ccc7e151ff61f"}, {"line": 45770, "relation": "positiveCorrelation", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product. In addition, a consistent reduction in DNA methylation at 5-LOX gene promoter was documented in AD versus healthy subjects.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Cell": {"blood cell": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2288, "key": "5920795434dc020ef62c1cd3a0e60db9"}, {"line": 21798, "relation": "decreases", "evidence": "In addition, a consistent reduction in DNA methylation at 5-LOX gene promoter was documented in AD versus healthy subjects.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2289, "key": "113168f12f88c21c9729292af581d845"}, {"line": 21812, "relation": "association", "evidence": "More recent data indicate that these enzymes and the biologically active lipid molecules they generate could influence the functioning of the central nervous system and the pathobiology of neurodegenerative disorders such as AD via mechanisms different from classical inflammation.", "citation": {"db": "PubMed", "db_id": "20691748"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Inflammation": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}}, "source": 3823, "target": 290, "key": "fc0797cdc0ca7cbd907115c267bc2538"}, {"line": 21986, "relation": "positiveCorrelation", "evidence": "In this study, we demonstrated for the first time an increased CD44 gene expression in lymphocytes derived from Alzheimer's disease (AD) patients in comparison with healthy subjects.", "citation": {"db": "PubMed", "db_id": "20197694"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3823, "target": 2476, "key": "a7bff355ca1cde6de4fb1182ccd363c6"}, {"line": 22343, "relation": "positiveCorrelation", "evidence": "Glimepiride released CD14 from RAW 264 cells and microglial cells. Pre-treatment with glimepiride significantly reduced TNF, IL-1 and IL-6 secretion from RAW 264 and microglial cells incubated with LPS, Abeta42, alphaSN and PrP82-146. More recently, the concentrations of soluble CD14 were found to be elevated in AD and PD patients.", "citation": {"db": "PubMed", "db_id": "24952384"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 3823, "target": 2468, "key": "e2ef12acea2d9aa036f55acaa01f25fa"}, {"line": 22785, "relation": "positiveCorrelation", "evidence": "The highly vulnerable CA1 pyramidal neurons were characterized by age- and disease-unrelated increases in PRCKB levels and by age- and disease-related increases in MAPK1 levels. In contrast, low PRKCB levels were found in CA2 pyramidal neurons, and MAPK1 levels were elevated in controls and intermediate AD stages. Both PRKCB and MAPK1 were increased in the late AD stages. MAPK1 and PRKCB levels were low in the brainstem and cerebellum. We propose that alterations in the expression of these two genes occur early in the pathogenesis of AD in a region-specific manner.", "citation": {"db": "PubMed", "db_id": "24334724"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"CA2 Region, Hippocampal": true, "Pyramidal Cells": true}}, "source": 3823, "target": 3990, "key": "f06176ce93c90402923b69649167c0e8"}, {"line": 22792, "relation": "negativeCorrelation", "evidence": "The highly vulnerable CA1 pyramidal neurons were characterized by age- and disease-unrelated increases in PRCKB levels and by age- and disease-related increases in MAPK1 levels. In contrast, low PRKCB levels were found in CA2 pyramidal neurons, and MAPK1 levels were elevated in controls and intermediate AD stages. Both PRKCB and MAPK1 were increased in the late AD stages. MAPK1 and PRKCB levels were low in the brainstem and cerebellum. We propose that alterations in the expression of these two genes occur early in the pathogenesis of AD in a region-specific manner.", "citation": {"db": "PubMed", "db_id": "24334724"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"Cerebellum": true, "Brain Stem": true}}, "source": 3823, "target": 3990, "key": "e75643116e88e37915769a4a5ad171a0"}, {"line": 22786, "relation": "positiveCorrelation", "evidence": "The highly vulnerable CA1 pyramidal neurons were characterized by age- and disease-unrelated increases in PRCKB levels and by age- and disease-related increases in MAPK1 levels. In contrast, low PRKCB levels were found in CA2 pyramidal neurons, and MAPK1 levels were elevated in controls and intermediate AD stages. Both PRKCB and MAPK1 were increased in the late AD stages. MAPK1 and PRKCB levels were low in the brainstem and cerebellum. We propose that alterations in the expression of these two genes occur early in the pathogenesis of AD in a region-specific manner.", "citation": {"db": "PubMed", "db_id": "24334724"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"CA2 Region, Hippocampal": true, "Pyramidal Cells": true}}, "source": 3823, "target": 4005, "key": "c405f9a166d1d2b9d511ff8a1262ef30"}, {"line": 22793, "relation": "negativeCorrelation", "evidence": "The highly vulnerable CA1 pyramidal neurons were characterized by age- and disease-unrelated increases in PRCKB levels and by age- and disease-related increases in MAPK1 levels. In contrast, low PRKCB levels were found in CA2 pyramidal neurons, and MAPK1 levels were elevated in controls and intermediate AD stages. Both PRKCB and MAPK1 were increased in the late AD stages. MAPK1 and PRKCB levels were low in the brainstem and cerebellum. We propose that alterations in the expression of these two genes occur early in the pathogenesis of AD in a region-specific manner.", "citation": {"db": "PubMed", "db_id": "24334724"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"Cerebellum": true, "Brain Stem": true}}, "source": 3823, "target": 4005, "key": "770ce7e37d865c8bf26f4247cb711536"}, {"line": 22858, "relation": "positiveCorrelation", "evidence": "Apoptosis plays a significant role in cell loss during neurodegenerative disorders such as Alzheimer's disease (AD) (Loh et al., 2006). A cascade of events like activation of caspases and aspartate-specific cysteine proteases has been proposed to play a key role in apoptosis (Nicholson and Thornberry ,1997). The major apoptotic pathway is characterized by mitochondrial dysfunction with the release of cytochrome c, activation of caspase-9, and subsequently of caspase-3. It has been suggested that caspase-3 is an ultimate effectors caspase whose activation leads to switch on the apoptotic cascade. Evidences of caspase-3 activation were also found in postmortem study conducted on the brain of AD patient (Engidawork et al., 2001).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3755, "key": "4e09497507f996b6254d5c9d2018e454"}, {"line": 24354, "relation": "increases", "evidence": "Calcineurin triggers reactive/inflammatory processes in astrocytes and is upregulated in aging and Alzheimer's models. This effect was blocked by the calcineurin inhibitor cyclosporin A.", "citation": {"db": "PubMed", "db_id": "15872113"}, "source": 3823, "target": 878, "key": "7725823094946cd3b5738c17b8d31e1e"}, {"line": 24355, "relation": "positiveCorrelation", "evidence": "Calcineurin triggers reactive/inflammatory processes in astrocytes and is upregulated in aging and Alzheimer's models. This effect was blocked by the calcineurin inhibitor cyclosporin A.", "citation": {"db": "PubMed", "db_id": "15872113"}, "source": 3823, "target": 878, "key": "a366feb6b8716925635e3bc28f60d359"}, {"line": 24457, "relation": "positiveCorrelation", "evidence": "In the present study, we investigated whether alpha2-macroglobulin (alpha2M), a protein present in neuritic plaques and elevated in Alzheimer's disease brain, is a potential regulatory factor for A beta fibril formation", "citation": {"db": "PubMed", "db_id": "9489740"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2227, "key": "df1163fe8fb422a2277c299dddbcc9e0"}, {"line": 25153, "relation": "association", "evidence": "Role of apoe/Abeta interactions in the pathogenesis of Alzheimer's disease and cerebral amyloid angiopathy.", "citation": {"db": "PubMed", "db_id": "11816788"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 914, "key": "5144d81a3b990dfc15c2e9f16604b9c1"}, {"line": 25186, "relation": "association", "evidence": "Different apoE isoforms may alter AD pathogenesis via their interactions with the amyloid beta-peptide (Abeta).", "citation": {"db": "PubMed", "db_id": "16207708"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 914, "key": "c767a0c6a3488d90f5fdc0d2b369b0a4"}, {"line": 25251, "relation": "association", "evidence": "Apolipoprotein E binds avidly to beta amyloid (A beta) peptide, a major component of senile plaque of Alzheimer's disease, in an isoform-specific manner.", "citation": {"db": "PubMed", "db_id": "8040342"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 914, "key": "71e19fc23966673509ddbfa38278a4e6"}, {"line": 25270, "relation": "association", "evidence": "It appears that the efficiency of binding between each of three main apoE isoforms and Abeta correlates inversely with the risk of developing late-onset familial AD and may indicate possible involvement of apoE in the binding and clearance of Abeta in vivo.", "citation": {"db": "PubMed", "db_id": "9265639"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 914, "key": "eb9780305478dad54355810cc740e741"}, {"line": 25910, "relation": "association", "evidence": "Role of apoE/Abeta interactions in Alzheimer's disease: insights from transgenic mouse pmodels.", "citation": {"db": "PubMed", "db_id": "11840304"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 914, "key": "03a3bb26b84b1100e47290f1e8bffbbf"}, {"line": 25975, "relation": "association", "evidence": "We will continue to investigate the effect of apoE isoform and Abeta conformation on the structural and functional interactions between these two proteins in relation to the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "15181252"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 914, "key": "d3815aa57b5c77a50f9e8ccb2834e88a"}, {"line": 26260, "relation": "association", "evidence": "How apoE is involved in the pathogenesis of AD is unclear; however, evidence exists for a direct apoE/A beta interaction", "citation": {"db": "PubMed", "db_id": "8631862"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 914, "key": "3e8d1d1c7686822f6e6c23a91ddc582c"}, {"line": 26270, "relation": "association", "evidence": "To explore whether the genetic linkage between apolipoprotein E (ApoE) alleles and susceptibility to Alzheimer's disease might be attributable to a direct molecular interaction between ApoE and the amyloid peptide A beta, we have produced ApoE variants in Escherichia coli and studied their interactions with A beta under native conditions.", "citation": {"db": "PubMed", "db_id": "8679539"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 914, "key": "b1aea5ea8b6cb5249b5b8a93dffab166"}, {"line": 25218, "relation": "association", "evidence": "APOE interacts with APP in human Alzheimer's brain", "citation": {"db": "PubMed", "db_id": "21297948"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1125, "key": "a6d357c680979c732ea3e93360bacdbc"}, {"line": 25406, "relation": "negativeCorrelation", "evidence": "Neuroprotectin D1 (NPD1) is a stereoselective mediator derived from the omega-3 essential fatty acid docosahexaenoic acid (DHA) with potent inflammatory resolving and neuroprotective bioactivity. NPD1 reduces Abeta42 peptide release from aging human brain cells and is severely depleted in Alzheimer's disease (AD) brain.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 166, "key": "da508cf56ad43b253dc65125a8bc6137"}, {"line": 25607, "relation": "association", "evidence": "ADAM19 is tightly associated with constitutive Alzheimer's disease APP alpha-secretase in A172 cells", "citation": {"db": "PubMed", "db_id": "17112471"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2251, "key": "983ea840049d206a3221be065109f3fe"}, {"line": 25776, "relation": "association", "evidence": "Particularly, it has been shown that agrin is associated with the pathological lesions of Alzheimer's disease (AD) and may contribute to the formation of beta-amyloid (Abeta) plaques in AD", "citation": {"db": "PubMed", "db_id": "16037493"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}}, "source": 3823, "target": 2273, "key": "0cf9c7dd475ad23a992a2df57133853d"}, {"line": 26172, "relation": "association", "evidence": "Recently, we have demonstrated that sulfatides are substantially and specifically depleted at the very early stage of AD", "citation": {"db": "PubMed", "db_id": "20052565"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 116, "key": "9da42c9c0cb6ab8d229952008aedf6e5"}, {"line": 26716, "relation": "association", "evidence": "This would suggest that the mechanism for astrocyte activation plays a role in the development of AD and that therapeutic strategies that target astrocyte activation in brain may be beneficial for the treatment of AD.", "citation": {"db": "PubMed", "db_id": "15663471"}, "source": 3823, "target": 480, "key": "c495e81442a2d3c27d175f676032c994"}, {"line": 43321, "relation": "positiveCorrelation", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 480, "key": "02facec72896a03b30d7105ce012689f"}, {"line": 26802, "relation": "association", "evidence": "Our findings identify EP2 receptor signaling as a novel proinflammatory and proamyloidogenic pathway in this pmodel of AD, and suggest a rationale for development of therapeutics targeting the EP2 receptor in neuroinflammatory diseases such as AD.", "citation": {"db": "PubMed", "db_id": "16267225"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3275, "key": "c0dd45c1e2f34f7924a2dedcd01c2c49"}, {"line": 27153, "relation": "association", "evidence": "Expression levels of the amyloid precursor protein (APP) and beta-site amyloid (Abeta) cleaving enzyme 1 (BACE1) have been implicated in Alzheimer disease (AD) progression.", "citation": {"db": "PubMed", "db_id": "19462468"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2315, "key": "353bfad05d74250d3f6f644522b8434d"}, {"line": 31403, "relation": "association", "evidence": "Amyloid precursor protein (APP) is a widely expressed transmembrane protein of unknown function that is involved in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "16797788"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2315, "key": "aabb9f4f780a9feb58f490bd98f7ccbd"}, {"line": 32417, "relation": "association", "evidence": "The amyloid precursor protein (APP) and the presenilins 1 and 2 are genetically linked to the development of familial Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2315, "key": "bb7ffec8a8a6a157a64713fa01276152"}, {"line": 33377, "relation": "association", "evidence": "The beta-amyloid precursor protein APP and the microtubule-associated protein Tau play a crucial role in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2315, "key": "0c5ebca9fd3669ba5218df5fc8e5ac89"}, {"line": 27235, "relation": "association", "evidence": "Recent evidence indicates that PrP(C) may play a critical role in the pathogenesis of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "19887909"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3254, "key": "bc8f1365fb9a07565cdcf80d4aaa93fc"}, {"line": 31980, "relation": "association", "evidence": "The cellular prion protein (PrP(C)) has been implicated in the development of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "23577068"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3254, "key": "d11bb79ecb590bc593d4cf4471732159"}, {"line": 27276, "relation": "association", "evidence": "Interestingly, addition of the dominant-negative mutant of Rab5, a small G-protein Rab5 involved in the endocytic process, inhibits the aging-related APP-BACE1 interaction and Abeta production, suggesting that endocytosis contributes to AD progression.", "citation": {"db": "PubMed", "db_id": "20127045"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Low": true}}, "source": 3823, "target": 813, "key": "8b67cf524c0f882dedbb390d20b4cd9a"}, {"line": 28270, "relation": "association", "evidence": "Taken together, ATXN1 functions as a genetic risk pmodifier that contributes to AD pathogenesis through a loss-of-function mechanism by regulating beta-secretase cleavage of APP and Abeta levels.", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2371, "key": "a55e403d55e902466c3e8f5ebf444f76"}, {"line": 28735, "relation": "association", "evidence": "These results suggest that the breakdown of HRD1-mediated ERAD causes Abeta generation and ER stress, possibly linked to AD.", "citation": {"db": "PubMed", "db_id": "20237263"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 835, "key": "a49ac1c936e9346215527050f8641c06"}, {"line": 29111, "relation": "association", "evidence": "Fetal Alz-50 clone 1 (FAC1) protein interacts with the Myc-associated zinc finger protein (ZF87/MAZ) and alters its transcriptional activity.", "citation": {"db": "PubMed", "db_id": "10727212"}, "source": 3823, "target": 1298, "key": "14a62359c1c43f1677dd7e9219261abc"}, {"line": 29213, "relation": "association", "evidence": "Colocalization of cyclin C and its preferred binding partner, Cdk8, was only observed in astrocytes but not in neurons.", "citation": {"db": "PubMed", "db_id": "12600719"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3823, "target": 1321, "key": "9e5fdaf7385a4e54d9e86e4a343d7287"}, {"line": 29382, "relation": "negativeCorrelation", "evidence": "Synaptic loss, which strongly correlates with the decline of cognitive function, is one of the pathological hallmarks of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21177868"}, "source": 3823, "target": 432, "key": "586429720c65625085a57160f07aa521"}, {"line": 29552, "relation": "increases", "evidence": "Cyclin-dependent kinase 5 (Cdk5) activity is significantly increased in AD and contributes to all three hallmarks: neurotoxic amyloid-beta (Abeta), neurofibrillary tangles (NFT), and extensive cell death.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3823, "target": 2487, "key": "72f7d7ff5317c4f1df9950d8e9dbe182"}, {"line": 29648, "relation": "association", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3823, "target": 2487, "key": "5c357c20d64b05ab034085c3802781fa"}, {"line": 29783, "relation": "association", "evidence": "Hyperphosphorylated tau is an integral part of the neurofibrillary tangles that form within neuronal cell bodies, and tau protein kinase II is reported to play a role in the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2487, "key": "bb843abd45809bb2294a3c8d30e1f9c0"}, {"line": 29914, "relation": "association", "evidence": "The EF-hand calcium binding protein Calmyrin (also called CIB-1) was shown to interact with presenilin-2 (PS-2), suggesting that this interaction might play a role in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "15885068"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 3823, "target": 1353, "key": "ce681c1dd32c4ba7e1796f1616de3d2c"}, {"line": 29934, "relation": "association", "evidence": "The interaction between the EF-hand Ca(2+)-binding protein calmyrin and presenilin 2 (PS2) has been suggested to play a role in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "16257512"}, "source": 3823, "target": 1353, "key": "54d9550ca0a6737c133a3cfc0ac58f4f"}, {"line": 30020, "relation": "association", "evidence": "These findings raise the intriguing possibility that PS1-beta-catenin interactions and subsequent activities may be consequential for the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "10341227"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1373, "key": "b0b633ffc2c12b5fea7c115f150a6e90"}, {"line": 30307, "relation": "association", "evidence": "It's possible that dUTPase is one of the proteins interacting with GIF in Alzheimer's disease human brain extracts.", "citation": {"db": "PubMed", "db_id": "15468912"}, "source": 3823, "target": 1399, "key": "42a2bf073357a5285c16b820d709bd02"}, {"line": 31342, "relation": "association", "evidence": "Pin1 regulates the conformation and function of certain phosphorylated proteins and plays an important role in cell cycle regulation , oncogenesis , and Alzheimer 's disease.", "citation": {"db": "PubMed", "db_id": "12388558"}, "source": 3823, "target": 3192, "key": "b17056c8226b72576575e6b370a6adc9"}, {"line": 31455, "relation": "association", "evidence": "vitro studies and mouse pmodels of AD suggest that PrP may be involved in AD pathogenesis through a highly specific interaction with amyloidbeta (Abeta42) oligomers.", "citation": {"db": "PubMed", "db_id": "21393248"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 940, "key": "12eca7ca08fa8d9c57837aea042cd149"}, {"line": 31810, "relation": "association", "evidence": "NGF, the principal neurotrophic factor for basal forebrain cholinergic neurons (BFCNs), has been correlated to Alzheimer's disease (AD) because of the selective vulnerability of BFCNs in AD.", "citation": {"db": "PubMed", "db_id": "20566851"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3823, "target": 3116, "key": "8fc8561310cb55aee73ecd6a08512abd"}, {"line": 32097, "relation": "association", "evidence": "Thus, the interaction between PS1 and APP is central to the molecular mechanism of AD.", "citation": {"db": "PubMed", "db_id": "11489281"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1202, "key": "45ded6dd53faa4d5221b60add3257316"}, {"line": 32942, "relation": "association", "evidence": "NQO1 binds STUB1 via the Hsc70-interacting domain (tetratricopeptide repeat domain) and undergoes ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "21220432"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 3823, "target": 3131, "key": "4d555b8f2a15418242141649241d6393"}, {"line": 33277, "relation": "association", "evidence": "This suggests a role for FE65 in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "11337355"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2299, "key": "3ac689662cbc2a0571463e9b439ee810"}, {"line": 33512, "relation": "association", "evidence": "The dysregulation of glycogen synthase kinase-3 (GSK3) has been implicated in Alzheimer disease (AD) pathogenesis and in Abeta-induced neurotoxicity, leading us to investigate it as a therapeutic target in an intracerebroventricular Abeta infusion pmodel. ", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2178, "key": "99f1a28e4ab8459363f9df646543dac9"}, {"line": 33715, "relation": "positiveCorrelation", "evidence": "In concordance, significant increases in the levels of phosphorylation of total Akt substrates, including: GSK3beta(Ser9), tau(Ser214), mTOR(Ser2448), and decreased levels of the Akt target, p27(kip1), were found in AD temporal cortex compared with controls.", "citation": {"db": "PubMed", "db_id": "15773910"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Tau protein subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3021, "key": "fcb5fc763add76dc63450b2ac1f5c263"}, {"line": 33725, "relation": "positiveCorrelation", "evidence": "In concordance, significant increases in the levels of phosphorylation of total Akt substrates, including: GSK3beta(Ser9), tau(Ser214), mTOR(Ser2448), and decreased levels of the Akt target, p27(kip1), were found in AD temporal cortex compared with controls.", "citation": {"db": "PubMed", "db_id": "15773910"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3078, "key": "84233cad9486c28c398ef99afe5a4588"}, {"line": 34652, "relation": "positiveCorrelation", "evidence": "We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3823, "target": 3610, "key": "f6b8172f4cfa9a727b3d39b5ebb439d4"}, {"line": 34653, "relation": "positiveCorrelation", "evidence": "We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3823, "target": 3729, "key": "e405915ba7bc4600c6054c040d8245fa"}, {"line": 34654, "relation": "positiveCorrelation", "evidence": "We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3823, "target": 3727, "key": "1fc9bb46ff4c87a4183596581589002e"}, {"line": 34731, "relation": "association", "evidence": "Recently, Notch receptors have been hypothesized to play a role in neurodegeneration and in particular in Alzheimer's disease (Notch1) and CADASIL (Notch3). Here we show that another family member (Notch2) is constitutively expressed in adult mouse hippocampus in DG and not in CA1 and CA3 neurons. Treatment with kainic acid resulted in marked Notch2 induction in pyramidal neurons of CA1 and in a subpopulation of CA3 neurons surviving the lesion and protein expression was still detectable 6 weeks after drug treatment. These results suggest Notch2 involvement in the response of postmitotic neurons to excitotoxic stimuli.", "citation": {"db": "PubMed", "db_id": "12802175"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3128, "key": "e08dd4c9f624cb09a9a48a7233e31bcc"}, {"line": 34770, "relation": "association", "evidence": "Furthermore, treatment with liquiritigenin inhibited astrocytosis in the hippocampus, and it may through its inhibitory activities on Notch-2, an important molecular regulating neural proliferation and differentiation. These findings provide evidence for beneficial activity of liquiritigenin in a mouse model of Alzheimer's disease and support the continued investigation of Notch signaling pathway as a target for treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21872584"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3128, "key": "a806c50d4f2eb300cd41d06df508ea81"}, {"line": 34783, "relation": "association", "evidence": "Our results show that liquiritigenin treatment improves the behavioral performance of the model rats and attenuates neuronal loss in the brain. More importantly, liquiritigenin treatment decreases mRNA levels and protein expression of Notch-2, an effect that could promote the generation of new neurons. These findings provide evidence for the beneficial activity of liquiritigenin in a brain-injured rat model and support the continued investigation of SERMs such as liquiritigenin as an alternative to estrogen-based hormone therapy in reducing the risk of neurodegenerative diseases such as Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20117143"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3128, "key": "970f3200911e7c7f2b7651b05e80d027"}, {"line": 34802, "relation": "association", "evidence": "The result showed that liquiritin significantly promotes the neurite outgrowth stimulated by NGF in PC12 cells in dose dependant manners whereas the liquiritin alone did not induce neurite outgrowth. Oligo microarray and RT-PCR analysis further clarified that the neurotrophic effect of liquiritin was related to the overexpression of neural related genes such as neurogenin 3, neurofibromatosis 1, notch gene homolog 2, neuromedin U receptor 2 and neurotrophin 5. Thus, liquiritin may be a good candidate for treatment of various neurodegenerative diseases such as Alzheimer's disease or Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "19789989"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3128, "key": "b325a05a0503e504724634b68b4a6484"}, {"line": 34830, "relation": "positiveCorrelation", "evidence": "Autopsy brain hippocampal tissues were obtained from controls and patients with AD and Western blots were performed using antibodies against mTOR signaling molecules and RagC, an upstream component of mTOR complex 1 (mTORC1) signaling. We found that expression of mTOR/p-mTOR and its downstream targets S6/p-S6 and Raptor/p-Raptor were expressed in the control and AD hippocampus. The expression levels of these signaling molecules were significantly increased in the hippocampus at the severe stages of AD, compared to controls and other stages of AD. Interestingly, Rictor expression level was unaltered. In addition, RagC was increased in the hippocampus at the early, moderate, and severe stages of AD. Our data indicate that mTORC1, but not mTORC2, was activated in the AD brains and that the level of mTOR signaling activation was correlated with cognitive severity of AD patients.", "citation": {"db": "PubMed", "db_id": "23979023"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "DiseaseState": {"Moderate AD": true, "Early-onset AD": true, "Late-onset AD": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3823, "target": 3329, "key": "964ac364f1d8454944fd913ee80847d3"}, {"line": 34872, "relation": "negativeCorrelation", "evidence": "Humanin (HN), a 24-amino acid peptide encoded by the mitochondrial 16S rRNA gene, was discovered by screening a cDNA library from the occipital cortex of a patient with Alzheimer's disease (AD) for a protection factor against AD-relevant insults. In the present work, we further confirmed interaction of HN with MPP8 in co-immunoprecipitation experiments and localized an MPP8-binding site in the region between 5 and 12 aa. of HN. We have also shown that an MPP8 fragment (residues 431-560) is sufficient to bind HN.", "citation": {"db": "PubMed", "db_id": "23532874"}, "annotations": {"MeSHAnatomy": {"Occipital Lobe": true}}, "source": 3823, "target": 1729, "key": "a3920d5dc0d6baf2a67c4a6256029c38"}, {"line": 34883, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3823, "target": 3994, "key": "d9e0ef1b3e690368f254c14138dd7c12"}, {"line": 34885, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}, "Subgraph": {"Gap junctions subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 3823, "target": 3934, "key": "c6bc83c34efc41d3551447dc29a6b223"}, {"line": 34887, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3823, "target": 3969, "key": "e7e557fc6c0f83a13952789acb868645"}, {"line": 34888, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3823, "target": 3970, "key": "3c2dd20440c35ce2b93972851968dbd5"}, {"line": 34889, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3823, "target": 3974, "key": "92577cb177f3245383b5eef6c4da0639"}, {"line": 34890, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3823, "target": 4001, "key": "a01da341ed7ca3e1a6be0c62f8135dfe"}, {"line": 34891, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3823, "target": 4004, "key": "8dcf58030ee7c989da1428a8b303c041"}, {"line": 34892, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3823, "target": 4029, "key": "e6c5c8fd7c306b08c97e92daf96bc74d"}, {"line": 34893, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3823, "target": 4031, "key": "cce51d206b84a1caa9589694fc182696"}, {"line": 34904, "relation": "positiveCorrelation", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 478, "key": "527a775a0e87425913839fd244caf696"}, {"line": 34912, "relation": "positiveCorrelation", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"DNA synthesis": true}, "Confidence": {"High": true}}, "source": 3823, "target": 447, "key": "91a371df7aca4a820e341462ac7344f4"}, {"line": 34934, "relation": "negativeCorrelation", "evidence": "alpha-Ketoglutarate dehydrogenase (E1k), also designated oxoglutarate dehydrogenase (OGDH; EC 1.2.4.2), is a component of the enzyme complex that catalyzes the conversion of alpha-ketogluterate to succinyl coenzyme A, a critical step in the Krebs tricarboxylic acid cycle. Deficiencies in the activity of this enzyme complex have been observed in brain and peripheral cells of patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "8020988"}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3152, "key": "3fa1fd19599a8fdc0cb77cd915bdb055"}, {"line": 34997, "relation": "decreases", "evidence": "Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3823, "target": 3513, "key": "e031d80c7aaf51b42cee1cf3d34e2073"}, {"line": 35135, "relation": "negativeCorrelation", "evidence": "Utilizing a novel microfluidic culture chamber, we demonstrate that Abeta oligomers compromise BDNF-mediated retrograde transport by impairing endosomal vesicle velocities, resulting in impaired downstream signaling driven by BDNF/TrkB, including ERK5 activation, and CREB-dependent gene regulation. Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3823, "target": 4030, "key": "198a5519e099a55ebacf896a33668c1f"}, {"line": 35481, "relation": "increases", "evidence": "The pathogenic correlation between Shc/Grb2 binding to AbetaPP during AD development is supported by the observation that the complexes AbetaPP (or CTFs)/ShcA or Grb2 are significantly increased in AD brain as compared to controls [55]. The increased phosphorylation/activation of ERK1/2, often described in AD brain, is also observed in thrombin-activated astrocytes, suggesting that, in this model, ERK1/2 may be activated by AbetaPP through ShcA. These data give prominence to the biological importance of AbetaPP phosphorylation for its functions and the regulation of intracellular adaptor binding as events responsible for the induction of glial-associated mitogenic pathway. Furthermore, ERK1/2, activated by Abeta in vitro, plays a role in AbetaPP processing and phosphorylates Tau in a PHF-Tau similar manner. However, it is conceivable that a different signaling Abeta-independent might as well activate tau phosphorylation by ERK1/2 via the intracellular signaling regulated by the AbetaPP/CTFs-Shc-Grb2 pathway", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "source": 3823, "target": 1173, "key": "621f99da873a63e771b6f301a62da7e8"}, {"line": 38857, "relation": "positiveCorrelation", "evidence": "CEBPD is upregulated in the astrocytes of AD patients. Therefore, we asked if activation of astrocytic/ CEBPD could contribute to AD pathogenesis. In this report, a novel role of CEBPD in attenuating macrophage-mediated/ phagocytosis of damaged neuron cells was found. By global gene expression profiling, we identified the inflammatory/ marker pentraxin-3 (PTX3, TNFAIP5, TSG-14) as a CEBPD target in astrocytes. Furthermore, we demonstrate that PTX3/ participates in the attenuation of macrophage-mediated phagocytosis of damaged neuron cells. This study provides the/ first demonstration of a role for astrocytic CEBPD and the CEBPD-regulated molecule PTX3 in the accumulation of damaged/ neurons, which is a hallmark of AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "21112127"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Innate immune system subgraph": true}}, "source": 3823, "target": 2501, "key": "a13dbbf4dd8ac7353648105c2a20131a"}, {"line": 38893, "relation": "positiveCorrelation", "evidence": "Unlike ho-2, the ho-1 gene in neural (and many systemic) tissues is exquisitely sensitive to upregulation/ by a host of pro-oxidant and other noxious stimuli. In Alzheimer disease, HO-1 immunoreactivity is significantly augmented/ in neurons and astrocytes of the hippocampus and cerebral cortex relative to age-matched, nondemented controls and/ colocalizes to senile plaques, neurofibrillary tangles, and corpora amylacea", "citation": {"db": "PubMed", "db_id": "15544918"}, "source": 3823, "target": 2839, "key": "d6799011ad3d6fb21eef152aa92b9cf7"}, {"line": 38940, "relation": "positiveCorrelation", "evidence": "In the central nervous system (CNS), prostaglandin (PG) and other bioactive lipids regulate vital aspects/ of neural membrane biology, including protein-lipid interactions, trans-membrane and trans-synaptic signaling. However, / a series of highly reactive PGs, free fatty acids, lysophospolipids, eicosanoids, platelet-activating factor, and reactive / oxygen species (ROS), all generated by enhanced phospholipase A2 (PLA2) activity and arachidonic acid (AA) release, / participate in cellular injury, particularly in neurodegeneration. PLA2 activation and PG production are among the earliest / initiating events in triggering brain-damage pathways, which can lead to long-term neurologic deficits. Altered / membrane-associated PLA2 activities have been correlated with several forms of acute and chronic brain injury, including / cerebral trauma, ischemic damage, induced seizures in the brain and epilepsy, schizophrenia, and in particular, Alzheimer's / disease (AD). Moreover, the expression of both COX-2 and PLA2 appears to be strongly activated / during Alzheimer's disease (AD), indicating the importance of inflammatory gene pathways as a response to brain injury./ This review addresses some current ideas concerning how brain PLA2 and brain PGs are early and key players in acute neural / trauma and in brain-cell damage associated with chronic neurodegenerative diseases such as AD.", "citation": {"db": "PubMed", "db_id": "12432919"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3823, "target": 2225, "key": "10eb45a42ce377070653f018f1b3b7c1"}, {"line": 38941, "relation": "positiveCorrelation", "evidence": "In the central nervous system (CNS), prostaglandin (PG) and other bioactive lipids regulate vital aspects/ of neural membrane biology, including protein-lipid interactions, trans-membrane and trans-synaptic signaling. However, / a series of highly reactive PGs, free fatty acids, lysophospolipids, eicosanoids, platelet-activating factor, and reactive / oxygen species (ROS), all generated by enhanced phospholipase A2 (PLA2) activity and arachidonic acid (AA) release, / participate in cellular injury, particularly in neurodegeneration. PLA2 activation and PG production are among the earliest / initiating events in triggering brain-damage pathways, which can lead to long-term neurologic deficits. Altered / membrane-associated PLA2 activities have been correlated with several forms of acute and chronic brain injury, including / cerebral trauma, ischemic damage, induced seizures in the brain and epilepsy, schizophrenia, and in particular, Alzheimer's / disease (AD). Moreover, the expression of both COX-2 and PLA2 appears to be strongly activated / during Alzheimer's disease (AD), indicating the importance of inflammatory gene pathways as a response to brain injury./ This review addresses some current ideas concerning how brain PLA2 and brain PGs are early and key players in acute neural / trauma and in brain-cell damage associated with chronic neurodegenerative diseases such as AD.", "citation": {"db": "PubMed", "db_id": "12432919"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3823, "target": 335, "key": "520359a1a58cd4c2fcecd2dff8da3a56"}, {"line": 39004, "relation": "positiveCorrelation", "evidence": "Astrocyte is the most abundant type of glial cells in the central nervous system (CNS) and appears to be/ involved in the induction of neuroinflammation. Under stress and injury, astrocytes become astrogliotic leading to an / upregulation of the expression of proinflammatory cytokines and chemokines, which are associated with the pathogenesis of AD. ", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3823, "target": 537, "key": "1a0fd966778634fb53eb7991c25fa657"}, {"line": 43565, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3823, "target": 537, "key": "29afa6d934edf79adeb0acc8cac0130f"}, {"line": 43567, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3823, "target": 537, "key": "1a49e381c0d0da905b1363710da1b96b"}, {"line": 39069, "relation": "positiveCorrelation", "evidence": "Analysis of RAGE expression in non-demented and Alzheimer's disease (AD) brains indicated that increases in/ RAGE protein and percentage of RAGE-expressing microglia paralleled the severity of disease. Ligands for RAGE in AD / include amyloid beta peptide (Abeta), S100/calgranulins, advanced glycation endproduct-modified proteins, and amphoterin.", "citation": {"db": "PubMed", "db_id": "15975028"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2271, "key": "6436339ecdb6c14c659d01ade872817b"}, {"line": 39309, "relation": "positiveCorrelation", "evidence": "In addition, the presence of KP1, CR3/43 and GFAP decreases significantly with increasing age in AD.", "citation": {"db": "PubMed", "db_id": "22152162"}, "source": 3823, "target": 2478, "key": "e847d74f7c749ee8e5c4e648b9340857"}, {"line": 39311, "relation": "positiveCorrelation", "evidence": "In addition, the presence of KP1, CR3/43 and GFAP decreases significantly with increasing age in AD.", "citation": {"db": "PubMed", "db_id": "22152162"}, "annotations": {"Subgraph": {"Complement system subgraph": true}}, "source": 3823, "target": 2925, "key": "51a3d07057559e6f75abf9481103ef90"}, {"line": 39313, "relation": "positiveCorrelation", "evidence": "In addition, the presence of KP1, CR3/43 and GFAP decreases significantly with increasing age in AD.", "citation": {"db": "PubMed", "db_id": "22152162"}, "source": 3823, "target": 2746, "key": "d6e00489477986a9f99dbcbaae4bd2b2"}, {"line": 39499, "relation": "positiveCorrelation", "evidence": "Activation of innate immune mechanisms leading to pro-inflammatory cytokine up-regulation is involved in devastating and disabling human brain illnesses, as Alzheimer's disease (AD), a progressive neurodegenerative disease that causes dementia in the elderly. Emerging data indicates that the cytokine Interleukin (IL)-18, one of the key mediator of inflammation and immune response, has relevance in the physiopathological processes of the brain, by ultimately influencing the integrity of neurons and putatively contributing to AD.", "citation": {"db": "PubMed", "db_id": "21184660"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3823, "target": 2883, "key": "97d0236a0900f3324b882feb2134b2ec"}, {"line": 39549, "relation": "positiveCorrelation", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 3823, "target": 270, "key": "8835af46fedd4733225e23623af30290"}, {"line": 39550, "relation": "positiveCorrelation", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 3823, "target": 303, "key": "429b5fbec97d6c286085ac3f9fe3d2d7"}, {"line": 39554, "relation": "positiveCorrelation", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 2751, "key": "c0f51b62c1e0574febd120ba2b6b259c"}, {"line": 39616, "relation": "negativeCorrelation", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 2469, "key": "75e4515f8dcb1d09abcd2bbc59efd294"}, {"line": 39617, "relation": "negativeCorrelation", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3951, "key": "5db542bd2391a094f799517dbf205534"}, {"line": 39618, "relation": "negativeCorrelation", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 2470, "key": "2bf28b17891e0e18c1cd08c18d1ebbe3"}, {"line": 39619, "relation": "negativeCorrelation", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3952, "key": "49ad5a29973f051138fb1353e3d9e629"}, {"line": 39658, "relation": "association", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3823, "target": 1456, "key": "a87126b7fc0e4919e3c19bdd02cd8c9f"}, {"line": 39659, "relation": "association", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3823, "target": 1454, "key": "9a8f1db8e8696313f803f12fe72afdb8"}, {"line": 39660, "relation": "association", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3823, "target": 1455, "key": "adb2a869d4fc0705bf848141eef484a6"}, {"line": 39736, "relation": "positiveCorrelation", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3823, "target": 23, "key": "c2e08bea00a3ba57ac4fe41756a6016a"}, {"line": 39737, "relation": "positiveCorrelation", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3823, "target": 30, "key": "5391d354e854c669b7c2faed8736a290"}, {"line": 39738, "relation": "positiveCorrelation", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3823, "target": 286, "key": "e81826db3c5e8762a9c76bd406893edf"}, {"line": 39812, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true}}, "source": 3823, "target": 2870, "key": "97b4cc0e872073387e9fe221881ae287"}, {"line": 39814, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3823, "target": 2465, "key": "d4888c0df638a60c534e9e38a6b85359"}, {"line": 39817, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 3823, "target": 3520, "key": "4293d4724ba373e6668a8064401d7c17"}, {"line": 39818, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 3823, "target": 3521, "key": "b70dd4c9004b16c7575e2028cf6da548"}, {"line": 39820, "relation": "negativeCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "source": 3823, "target": 3348, "key": "7474477c90ce34b8730dd41074282f69"}, {"line": 39821, "relation": "negativeCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "source": 3823, "target": 3347, "key": "c1f1f2c6b83149edff1125782b2b30ae"}, {"line": 39849, "relation": "positiveCorrelation", "evidence": "Some of them might increase steadily during disease progression or temporarily at the time of MCI to AD conversion.", "citation": {"db": "PubMed", "db_id": "24567119"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Cognitive Dysfunction": true, "Alzheimer Disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3823, "target": 420, "key": "b9b784ff505cc5556b3639fa8ebc3e0f"}, {"line": 40135, "relation": "positiveCorrelation", "evidence": "Elevated levels of several proinflammatory factors including cytokines, peptides, pathogenic structures, and peroxidants in the central nervous system (CNS) have been detected in patients with neurodegenerative diseases such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 420, "key": "8da339e38d9142a9560e685a631a8827"}, {"line": 39876, "relation": "positiveCorrelation", "evidence": "In 3 areas examined (hippocampus, frontal, and entorhinal cortex), a marked increase in neuronal ASS and iNOS expression was observed in AD brains.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Entorhinal Cortex": true, "Brain": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1753, "key": "cfd5cb32fa1513defdb584ae2d43ed44"}, {"line": 39877, "relation": "positiveCorrelation", "evidence": "In 3 areas examined (hippocampus, frontal, and entorhinal cortex), a marked increase in neuronal ASS and iNOS expression was observed in AD brains.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Entorhinal Cortex": true, "Brain": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1895, "key": "79618b3a0abd9cf6fb8cd0c8ae0b563d"}, {"line": 39891, "relation": "positiveCorrelation", "evidence": "GFAP-positive astrocytes expressing ASS were not increased in AD brains versus controls, whereas the number of iNOS expressing GFAP-positive astrocytes was significantly higher in AD brains.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1895, "key": "6dc612e4afcbff33979eed803729c146"}, {"line": 39887, "relation": "positiveCorrelation", "evidence": "GFAP-positive astrocytes expressing ASS were not increased in AD brains versus controls, whereas the number of iNOS expressing GFAP-positive astrocytes was significantly higher in AD brains.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"Low": true}}, "source": 3823, "target": 1831, "key": "8c1d93b8928c7dcb7dcd9f6c42190310"}, {"line": 40048, "relation": "association", "evidence": "Microglia activation and neuroinflammation have been associated with the pathogenesis of neurodegenerative disorders such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 609, "key": "34da36f9aff4de619ebdae31b2ae40c2"}, {"line": 43320, "relation": "positiveCorrelation", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 609, "key": "379b9b908a4e1cd02a6663d6990bfc38"}, {"line": 40094, "relation": "association", "evidence": "These data suggest that individuals at genetic risk for AD should be targeted for increased levels of PA as a means of reducing atrophy in a brain region critical for the formation of episodic memories.", "citation": {"db": "PubMed", "db_id": "24795624"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Atrophy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 849, "key": "b4d45023f8e208160fc4914a1714fa06"}, {"line": 40134, "relation": "positiveCorrelation", "evidence": "Elevated levels of several proinflammatory factors including cytokines, peptides, pathogenic structures, and peroxidants in the central nervous system (CNS) have been detected in patients with neurodegenerative diseases such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 325, "key": "0125bc1b0f04b2fc4d698af7a4479dc6"}, {"line": 40327, "relation": "association", "evidence": "Neuroinflammation plays a critical role in the pathogenesis of Alzheimer's disease (AD) and involves activation of the innate immune response via recognition of diverse stimuli by pattern recognition receptors (PRRs).", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3815, "key": "3c2aa76fb68326c659c27e1fb16dbe94"}, {"line": 41243, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3815, "key": "e76ba6f7b26efaeb477a44d421638825"}, {"line": 40688, "relation": "positiveCorrelation", "evidence": "The levels of IGF-II and IGFBP-2 were significantly elevated in the CSF from patients with AD.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Serotonergic subgraph": true, "Insulin signal transduction": true}}, "source": 3823, "target": 2874, "key": "00aff1b17f5fc14e0711b80a255dcf5f"}, {"line": 40698, "relation": "association", "evidence": "We also found correlations between established CSF biomarkers for AD (tau and P-tau) and components of the IGF system.CSF and blood plasma levels of IGF-II and some of its binding proteins are changed in patients with AD.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2874, "key": "974b3719d6d4f79c709aa7f9d16c8f33"}, {"line": 40782, "relation": "association", "evidence": "We speculate that higher affinity between Abeta and PLA2 has the therapeutic potential of decreasing the Abeta-Abeta interaction, thereby reducing the amyloid aggregation and plaque formation in AD.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3196, "key": "c1df0a82c17c7c8d5f50e74943df3211"}, {"line": 40784, "relation": "association", "evidence": "We speculate that higher affinity between Abeta and PLA2 has the therapeutic potential of decreasing the Abeta-Abeta interaction, thereby reducing the amyloid aggregation and plaque formation in AD.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 377, "key": "2ba5d75492b3c2a1ecad8a09df972f12"}, {"line": 42296, "relation": "association", "evidence": "Beta-amyloid (Abeta) aggregates have a pivotal role in pathological processing of Alzheimer's disease (AD).", "citation": {"db": "PubMed Central", "db_id": "PMC3981768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3823, "target": 377, "key": "6f799f4e0bfe15ced03064db9fae0309"}, {"line": 40804, "relation": "positiveCorrelation", "evidence": "We found that microglial/macrophage MMP-14 expression was upregulated in Alzheimer's disease tissue, in active lesions of multiple sclerosis, and in tissue from stage II stroke as well as in the corresponding mouse models for the human diseases.", "citation": {"db": "PubMed", "db_id": "24323769"}, "annotations": {"MeSHAnatomy": {"Macrophages": true, "Tissues": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Alzheimer Disease": true}}, "source": 3823, "target": 3993, "key": "1e784ff249232e318f34d1aa91954d2a"}, {"line": 40816, "relation": "positiveCorrelation", "evidence": "Antimicrobial peptide beta-defensin-1 expression is upregulated in Alzheimer's brain.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 83, "key": "53be78c50842b12e75ac002e68a863ad"}, {"line": 40853, "relation": "association", "evidence": "A higher level of hBD-1 was also seen in the choroid plexus of AD brain in comparison to age-matched control tissue.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Choroid Plexus": true, "Tissues": true}}, "source": 3823, "target": 2626, "key": "b64cfd943400d6a9c2d4150709512d03"}, {"line": 40874, "relation": "association", "evidence": "We also demonstrate that increased iron deposition in AD may contribute to the elevated expression of hBD-1 within the choroid plexus.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Choroid Plexus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 136, "key": "bdd65f3a43fec74844a14a97b7ba7d79"}, {"line": 41532, "relation": "positiveCorrelation", "evidence": "Our previous study presented evidence that the inflammation-related S100A9 gene is significantly upregulated in the brains of Alzheimer's disease (AD) animal models and human AD patients.", "citation": {"db": "PubMed", "db_id": "24586443"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Inflammation": true, "Plaque, Amyloid": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3334, "key": "5dbf99b1e37d2475d60ddbfdcf28d9a8"}, {"line": 43715, "relation": "positiveCorrelation", "evidence": "Interestingly, S100A9/Mrp14 expression was also increased in the brains of AD mice and patients with AD (Chang et al., 2012) and contributes to cause inflammation, which then affects the neuropathology including amyloid plaques burden and impairs cognitive function (Ha et al., 2010).", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3334, "key": "faa856c2ca18dfc673537645c85f8a73"}, {"line": 41656, "relation": "association", "evidence": "We hope that a more comprehensive understanding of the role that Cr1 played in AD may lead to the development of novel therapeutics for the prevention and treatment of AD.", "citation": {"db": "PubMed", "db_id": "24794147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3614, "key": "08a45210b1f90c7d6965941534f99e0f"}, {"line": 41859, "relation": "positiveCorrelation", "evidence": "The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3623, "key": "75283afb29354febff419772bc154e81"}, {"line": 41914, "relation": "association", "evidence": "Effects of Naproxen on Immune Responses in a Colchicine-Induced Rat Model of Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 310, "key": "693eb8c0cee7bca284d0b88a4ff6f6b5"}, {"line": 41916, "relation": "association", "evidence": "Effects of Naproxen on Immune Responses in a Colchicine-Induced Rat Model of Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 234, "key": "d4af81ae8df09a0d8433ad6462495dff"}, {"line": 41924, "relation": "association", "evidence": "Background: The components of the immune system have been indicated to be linked with the neurotoxicity in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Alzheimer Disease": true}}, "source": 3823, "target": 576, "key": "68d40c904cebb85696d7a7fa2aab6445"}, {"line": 41979, "relation": "association", "evidence": "Cannabinoid receptor subtype 2 (CB2) has been shown to be up-regulated in activated microglia and therefore plays an important role in neuroinflammatory and neurodegenerative diseases such as multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neurodegenerative Diseases": true, "Amyotrophic Lateral Sclerosis": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3613, "key": "1a1c09dbd1e2e0fd9aab15d0f48cfe36"}, {"line": 42011, "relation": "association", "evidence": "Beyond cognitive decline, Alzheimer's disease (AD) is characterized by numerous neuropathological changes in the brain.", "citation": {"db": "PubMed", "db_id": "24886182"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3872, "key": "ccdb3c73541d96fa77041987fbb42c1e"}, {"line": 42035, "relation": "association", "evidence": "Specifically, we demonstrate that microgliosis and astrocytosis are prominent aspects of this AD mouse model.", "citation": {"db": "PubMed", "db_id": "24886182"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Gliosis": true, "Plaque, Amyloid": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3912, "key": "27ba1d54945f41da40b4ab2f43d8bada"}, {"line": 42119, "relation": "positiveCorrelation", "evidence": "Particularly, the proinflammatory cytokine interleukin-1 beta (IL-1beta) is upregulated in human AD and believed to promote amyloid plaque deposition.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Species": {"9606": true}, "Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}}, "source": 3823, "target": 3661, "key": "6d4e56d0bd36bd5621257bbb808e6c07"}, {"line": 42329, "relation": "association", "evidence": "Our findings may provide a novel strategy for AD treatment by activating SR-A.", "citation": {"db": "PubMed Central", "db_id": "PMC3981768"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3823, "target": 3680, "key": "ff78ec4ca61167c208099396836e5c3f"}, {"line": 42490, "relation": "association", "evidence": "In this review we discussed the role of PET and MRI in evaluating the effect of GLP1 analogs in disease progression in both Alzheimer's and Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "24529526"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3635, "key": "38bb00e623c6a39aa1238985f9fd5b33"}, {"line": 42639, "relation": "association", "evidence": "Evidence of trem2 variant associated with triple risk of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24663666"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3745, "key": "b62af82bbd8f4c2974a1c921bef598d4"}, {"line": 42945, "relation": "association", "evidence": "These results indicated that rutin is a promising agent for AD treatment because of its antioxidant, anti-inflammatory, and reducing Abeta oligomer activities.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 75, "key": "89cc3b69b69595d1cd8396a85944ba2e"}, {"line": 43057, "relation": "positiveCorrelation", "evidence": "TTBK1 protein expression is significantly elevated in Alzheimer's disease (AD) brains, and genetic variations of the TTBK1 gene are associated with late-onset Alzheimer's disease in two cohorts of Chinese and Spanish populations.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3747, "key": "2f475f06857fe38b7c359d5f77cda417"}, {"line": 43089, "relation": "positiveCorrelation", "evidence": "These studies suggest that TTBK1 is an important molecule for the inflammatory axonal degeneration, which may be relevant to the pathobiology of tauopathy including AD.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Axons": true}, "MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3747, "key": "84ec097d7db0e26b6a61e726c34f12e0"}, {"line": 43260, "relation": "increases", "evidence": "Increases in free fatty acids, eicosanoids, and products of lipid peroxidation are known to occur in various)/ conditions of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's diseasens/ of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Beta-Oxidation of Fatty Acids": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 114, "key": "642aa448cabbcd94218fe69ae4baefc2"}, {"line": 43268, "relation": "increases", "evidence": "Increases in free fatty acids, eicosanoids, and products of lipid peroxidation are known to occur in various)/ conditions of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's diseasens/ of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 280, "key": "1d37870ea5d42694c232fff3ae951be4"}, {"line": 43389, "relation": "negativeCorrelation", "evidence": "Experimental evidence suggests that cortical noradrenaline (NA) depletion due to degeneration of the locus/ ceruleus (LC) - a pathological hallmark of AD - plays a permissive role in the development of inflammation in AD. Our/ results indicate for the first time that PPARgamma expression can be modulated by the cAMP signalling pathway, and/ suggest that the anti-inflammatory effects of NA on brain cells may be partly mediated by increasing PPARgamma levels./ Conversely, decreased NA due to LC cell death in AD may reduce endogenous PPARgamma expression and therefore potentiate/ neuroinflammatory processes.", "citation": {"db": "PubMed", "db_id": "12887689"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 3823, "target": 317, "key": "85a6ee5caa19571526d15385a5d22856"}, {"line": 43573, "relation": "positiveCorrelation", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3823, "target": 3666, "key": "844308012ee845c24f857481882d2602"}, {"line": 44255, "relation": "positiveCorrelation", "evidence": "Microglia manage immunosurveillance and mediate inflammation, both suggested to be important in Alzheimer's disease (AD). The aim of this study was to investigate if microglial markers could differentiate, firstly between AD and controls, and secondly between stable mild cognitive impairment (MCI) and those progressing to AD and vascular dementia (VaD). Furthermore, we investigated if these markers were sufficiently stable to be used in clinical trials. We quantified YKL-40 and sCD14 in cerebrospinal fluid (CSF) from 96 AD patients, 65 healthy controls, and 170 patients with MCI from baseline and over 5.7 years. For the stability analysis, two CSF samples were collected from 52 AD patients with a six-month interval in between. YKL-40, but not sCD14, was significantly elevated in AD compared with healthy controls (p = 0.003). Furthermore, YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and ABeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 x 10-25; r = 0.77, p = 2.0 x 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 2509, "key": "c1ae1912e78a25db8288cd634f5778fe"}, {"line": 44306, "relation": "increases", "evidence": "Discovery and validation cohorts, showed higher mean CSF YKL-40 in very mild and mild AD-type dementia (Clinical Dementia Rating [CDR] 0.5 and 1) versus control subjects (CDR 0) and PSP subjects. Importantly, CSF YKL-40/Abeta42 ratio predicted risk of developing cognitive impairment (CDR 0 to CDR > 0 conversion), as well as the best CSF biomarkers identified to date, tau/Abeta42 and p-tau 181/Abeta42. Mean plasma YKL-40 was higher in CDR 0.5 and 1 versus CDR 0, and correlated with CSF levels. YKL-40 immunoreactivity labeled astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Abeta deposition. CONCLUSIONS: These data demonstrate that YKL-40, a putative indicator of neuroinflammation, is elevated in AD and, together with Abeta42, has potential prognostic utility as a biomarker for preclinical AD.", "citation": {"db": "PubMed", "db_id": "21035623"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 2509, "key": "79a4689e259c9b77c166e183c6026209"}, {"line": 44275, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3941, "key": "aadb71e85c6311db9d00ee62e8e1bbff"}, {"line": 44276, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3995, "key": "e55ed711adea873b7828a14f341ef9ad"}, {"line": 44277, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 4025, "key": "768bde0fb7acfc5fa3a4cb78add35284"}, {"line": 46177, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 4025, "key": "9c00d69930d5ba61fc02fb365beb928b"}, {"line": 44278, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3957, "key": "eec89b70e41c0841434c9800784a0149"}, {"line": 44279, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3958, "key": "3c241b7300a6dd348b93ca380d79aa3c"}, {"line": 44416, "relation": "association", "evidence": "Caffeine is a widely consumed psychoactive drug, which is emerging as a protective agent against AD progression and in aging associated deficits. This occurs mainly through the blockade of adenosine A2A receptors, whose expression and function become aberrant throughout aging and in age-related pathologies.", "citation": {"db": "PubMed", "db_id": "21427489"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 709, "key": "70af62e75562219e51f070588c0d4944"}, {"line": 44722, "relation": "association", "evidence": "Two additional OGG1 mutations, A53T and A288V, were also identified in AD patients and both were found to reduce 8-oxoG glycosylase activity", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3155, "key": "9fb6991845094e2b28a5578a875aaf3f"}, {"line": 44727, "relation": "association", "evidence": "Two additional OGG1 mutations, A53T and A288V, were also identified in AD patients and both were found to reduce 8-oxoG glycosylase activity", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3154, "key": "7e3dc5226b272e16f0cad3ecb71e5de5"}, {"line": 44857, "relation": "negativeCorrelation", "evidence": "A trend towards a decreased SAT-alpha DNA methylation was observed in patients with AD as compared with healthy volunteers", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 1950, "key": "0a7eb82eddbc3ea0da36511ea5b075d0"}, {"line": 44877, "relation": "negativeCorrelation", "evidence": "AD individuals are characterized by decreased plasma folate values, as well as increased plasma homocysteine (Hcy) levels, and there is indication of impaired S-adenosylmethionine (SAM) levels in AD brains. ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 115, "key": "88a631e9a5f658796a9aba12f7df1332"}, {"line": 44879, "relation": "negativeCorrelation", "evidence": "AD individuals are characterized by decreased plasma folate values, as well as increased plasma homocysteine (Hcy) levels, and there is indication of impaired S-adenosylmethionine (SAM) levels in AD brains. ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 68, "key": "26154dd0fa0657e54864628e76c6e783"}, {"line": 44892, "relation": "negativeCorrelation", "evidence": "some authors observed significantly decreased levels of vitamin B12 in plasma of AD subjects respect to controls", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 233, "key": "8a0fe3f93c3ade70cc9971099c5f0c23"}, {"line": 44994, "relation": "negativeCorrelation", "evidence": "we additionally found that the AD population had significantly lower levels of vitamin B12 ", "citation": {"db": "PubMed", "db_id": "12784029"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "Race": {"Swedish": true}}, "source": 3823, "target": 233, "key": "147f129f4b8a68cba72d99677265f4de"}, {"line": 44908, "relation": "negativeCorrelation", "evidence": "mean SAM(S-Adenosylmethionine) and SAH(S-Adenosylhomocysteine) levels were significantly reduced in all the areas of AD brains examined ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 67, "key": "127a43a71299e1e71c440eb546b01edb"}, {"line": 45378, "relation": "negativeCorrelation", "evidence": "In all brain areas of AD patients (cerebral cortex subdivisions, hippocampus, and putamen) decreased levels of SAM and SAH(S-adenosylhomocysteine) were observed", "citation": {"db": "PubMed", "db_id": "16040194"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 67, "key": "7892f0ab624aeea4171b17dd8e4b8386"}, {"line": 44930, "relation": "positiveCorrelation", "evidence": "It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects. the MTHFR 677C>T polymorphism as a candidate AD risk factor", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Serum": true}}, "source": 3823, "target": 1882, "key": "260cd1d0da346e8e5c042a744601f5ab"}, {"line": 44954, "relation": "positiveCorrelation", "evidence": "observed association between the MTR 2756AA genotype and increased AD risk", "citation": {"db": "PubMed", "db_id": "21119889"}, "source": 3823, "target": 1885, "key": "743ba52b55c688fa09bcb95867ec56c4"}, {"line": 44978, "relation": "positiveCorrelation", "evidence": "Significant associations of reduced folate carrier gene(RFC1) A80G G allele and GG genotype with SAD(sporadic AD) were found", "citation": {"db": "PubMed", "db_id": "18258338"}, "annotations": {"Race": {"Chinese": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3823, "target": 1940, "key": "c0103afc814d1a60ba241620888d31a5"}, {"line": 45051, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}}, "source": 3823, "target": 1747, "key": "91d4c05aa9ec34c3577817298cfb4c9b"}, {"line": 45339, "relation": "negativeCorrelation", "evidence": "hypomethylation at the APP gene promoter as a possible risk factor for AD", "citation": {"db": "PubMed", "db_id": "21419233"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 1747, "key": "e702c9602252df4c67c58642e78739aa"}, {"line": 45452, "relation": "positiveCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 3823, "target": 1747, "key": "39ff3abd1a4b8dc2c7582b96de8ba3f0"}, {"line": 45732, "relation": "negativeCorrelation", "evidence": "significant demethylation of beta-amyloid precursor protein (APP) gene was observed in AD patients,", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 3823, "target": 1747, "key": "4a875186456ab121990a11b34bce50f6"}, {"line": 45056, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Gamma secretase subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1889, "key": "e91da533986e830bddf6516b99668646"}, {"line": 45061, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1756, "key": "78c03909f27335cace55dd0e7ae11afe"}, {"line": 45066, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1959, "key": "85b25d4b1b5ac0b7c318bd3c1e6ff977"}, {"line": 45074, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Gamma secretase subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1742, "key": "f9d2375d2df8f710744a0df740d3059e"}, {"line": 45080, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Response DNA damage": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1854, "key": "c421d0d0a9ecbc0772b33e28c18c6be4"}, {"line": 45086, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1808, "key": "6a9ea932db40b7416bba14277dd52fd5"}, {"line": 45120, "relation": "association", "evidence": "APOE ε4 mRNA level is increased in AD compared to controls.The APOE gene was found to be of bimodal structure, with a hypomethylated CpG-poor promoter and a fully methylated 3′-CpG-island", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 1745, "key": "2c0025cd8d4a2264a1eab5511632c206"}, {"line": 45129, "relation": "positiveCorrelation", "evidence": "APOE ε4 mRNA level is increased in AD compared to controls.The APOE gene was found to be of bimodal structure, with a hypomethylated CpG-poor promoter and a fully methylated 3′-CpG-island", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Epigenetic modification subgraph": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 3933, "key": "6dc67eb24606094ab631cb27e3e958ca"}, {"line": 45209, "relation": "association", "evidence": "Both exercised SAMR1 and SAMP8 mice showed significantly increased IGF1 plasma levels compared with their corresponding sedentary group ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Physical exercised group": true}}, "source": 3823, "target": 3655, "key": "12601d117a25af5216b9890ea58d0b86"}, {"line": 45346, "relation": "negativeCorrelation", "evidence": "drebrin expression is decreased in AD brains ", "citation": {"db": "PubMed", "db_id": "25058791"}, "source": 3823, "target": 2620, "key": "b49ce1b7c74bdec7a004b4ab5aa9044c"}, {"line": 45349, "relation": "positiveCorrelation", "evidence": "histone deacetylase (HDAC) activity is elevated in the AD model", "citation": {"db": "PubMed", "db_id": "25058791"}, "object": {"modifier": "Activity"}, "source": 3823, "target": 2819, "key": "4ff3f9d970e0cf145723018d8acaf289"}, {"line": 45377, "relation": "negativeCorrelation", "evidence": "In all brain areas of AD patients (cerebral cortex subdivisions, hippocampus, and putamen) decreased levels of SAM and SAH(S-adenosylhomocysteine) were observed", "citation": {"db": "PubMed", "db_id": "16040194"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 8, "key": "1f58d0334188870667b93aab65703993"}, {"line": 45387, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 3823, "target": 1943, "key": "5ed4cd124b7382acdbd046d83657d035"}, {"line": 45823, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "source": 3823, "target": 1943, "key": "f78c982585ccf648031f2b02ed526918"}, {"line": 45389, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 3823, "target": 1945, "key": "31b43246d9dc6b19bf91dd1fe719ef1b"}, {"line": 45824, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "source": 3823, "target": 1945, "key": "173ff211220891c6a916cb77679d1086"}, {"line": 45392, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 3823, "target": 2014, "key": "96616b600e1d2e28a668b304f2605539"}, {"line": 45394, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 3823, "target": 1773, "key": "d3b3b5965c2555be4f165c9edec86143"}, {"line": 45396, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 3823, "target": 1740, "key": "704435b39515870b3c27276251c9c8a6"}, {"line": 45821, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "source": 3823, "target": 1740, "key": "53b742159f43dc354818d0725b65873e"}, {"line": 45404, "relation": "association", "evidence": "RHDBF2 was found to be part of the same network of interacting proteins as the Alzheimer's risk gene PTK2B, suggesting a potential role in microglia and macrophage activity.", "citation": {"db": "PubMed", "db_id": "25157507"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 3823, "target": 1933, "key": "03b6c63d3e69316662225d9de6642230"}, {"line": 45428, "relation": "positiveCorrelation", "evidence": "we confirmed the mRNA OTC over-expression in AD", "citation": {"db": "PubMed", "db_id": "17893704"}, "annotations": {"Species": {"9606": true}, "Race": {"Caucasian": true}, "Gender": {"Male": true}}, "source": 3823, "target": 3999, "key": "ce088dcd1cac54d2b44706c41e3d58d7"}, {"line": 45432, "relation": "positiveCorrelation", "evidence": "BRCA1 gene which have been recently described to be over-expressed in AD", "citation": {"db": "PubMed", "db_id": "17893704"}, "annotations": {"Species": {"9606": true}, "Race": {"Caucasian": true}, "Gender": {"Male": true}}, "source": 3823, "target": 2405, "key": "d7d566079f1a7cc5f3ea0fab6d72df54"}, {"line": 45441, "relation": "negativeCorrelation", "evidence": "OTC promoter conversely to the rare -389 G/A and -241 A/G haplotype, increasing the risk of developing AD and potentially associated with a lower level of methylation.", "citation": {"db": "PubMed", "db_id": "17893704"}, "annotations": {"Species": {"9606": true}, "Race": {"Caucasian": true}, "Gender": {"Male": true}}, "source": 3823, "target": 1904, "key": "7f3f26439e477d50c99bfdd96e7cbce1"}, {"line": 45455, "relation": "negativeCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 3823, "target": 1871, "key": "722ac1e23a3705838040fc48f786b3d4"}, {"line": 45462, "relation": "negativeCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 3823, "target": 1835, "key": "fed6aabb64b6b0090959e6019c64d53d"}, {"line": 45475, "relation": "positiveCorrelation", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 3823, "target": 1920, "key": "b1725b8ff03df2d1e33fe5f8d87a7a31"}, {"line": 45477, "relation": "positiveCorrelation", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 3823, "target": 1842, "key": "3f11122f028f5c1bdfec6d13aa3d58b6"}, {"line": 45489, "relation": "negativeCorrelation", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 3823, "target": 1891, "key": "47bae5c180f8a35515b28eca3f436172"}, {"line": 45493, "relation": "positiveCorrelation", "evidence": "One of the two sites corresponding to EPHA1 was associated with hypermethylation with age (p = 0.029; cg02376703). One of the two sites associated with PSEN2 was associated with hypomethylation with age (p = 0.030; cg25514304) in LOAD case", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 3823, "target": 1816, "key": "162928b86b501e16b803e93e5cd66da5"}, {"line": 45494, "relation": "negativeCorrelation", "evidence": "One of the two sites corresponding to EPHA1 was associated with hypermethylation with age (p = 0.029; cg02376703). One of the two sites associated with PSEN2 was associated with hypomethylation with age (p = 0.030; cg25514304) in LOAD case", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 3823, "target": 1929, "key": "b7f0da81a0718dce97a79e91bd8b8795"}, {"line": 45499, "relation": "positiveCorrelation", "evidence": "One of the sites in the Differentially Methylated Regions (DMRs) for DIRAS3 was more highly methylated in AD cases (43.4%) than controls (38.5%) (p = 0.024; cg21808053). One of the sites in the DMR for GNAS was hypomethylated with age among controls (p = 0.012; cg21625881) and the site for KCNQ1 was hypermethylated with age (p = 0.023; cg27119222).", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 3823, "target": 1801, "key": "59dc5cf5ba24a0e2b77206d7c65ad3ca"}, {"line": 45500, "relation": "negativeCorrelation", "evidence": "One of the sites in the Differentially Methylated Regions (DMRs) for DIRAS3 was more highly methylated in AD cases (43.4%) than controls (38.5%) (p = 0.024; cg21808053). One of the sites in the DMR for GNAS was hypomethylated with age among controls (p = 0.012; cg21625881) and the site for KCNQ1 was hypermethylated with age (p = 0.023; cg27119222).", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 3823, "target": 1833, "key": "98b7317275b4c63b11f2e688dfee12fa"}, {"line": 45505, "relation": "negativeCorrelation", "evidence": "AD cases had 7.3% lower methylation at TMEM59 than controls.DNA methylation and RNA expression were negatively correlated at TMEM59 LOAD cases had lower methylation and higher expression of TMEM59 than control samples", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 3823, "target": 1996, "key": "e784e8b1dfdb52869b9b32fdb063af45"}, {"line": 45512, "relation": "negativeCorrelation", "evidence": "A notable exception was PSEN1, which was modestly hypomethylated in LOAD cases LOAD cases had reduced DNA methylation that was associated with increased PSEN1 gene expression, suggesting the DNA methylation change may be functional at this site", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 3823, "target": 1926, "key": "97ee31f6da8a18f77b8bc571fbb873ba"}, {"line": 45526, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2042, "key": "af065e97e340031b3ac6e68bab5d68be"}, {"line": 45530, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2040, "key": "d6eacb7886346ecad3fdc1c2627be0ba"}, {"line": 45536, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2072, "key": "1b5c2597c91f2dd0bb879076a8afac75"}, {"line": 45540, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2028, "key": "02815a2a0054de4824f60a4b61c15752"}, {"line": 45544, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2057, "key": "fefd4313479e6605362233beee7e55b5"}, {"line": 45548, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2034, "key": "16262aad3411736bf0a339cbfd18941a"}, {"line": 45552, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2050, "key": "6a4ffd18b482f943ca73eaf86e0ca3b9"}, {"line": 45556, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2059, "key": "a4cb6d6370508aadbffa645ac8949ca1"}, {"line": 45561, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2046, "key": "eac538118d597ff2a4cd48655d6dbfae"}, {"line": 45565, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2026, "key": "8c64b96239427ba1701deea5df8c5fa3"}, {"line": 45569, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2063, "key": "fc718134d74d1255e6367cbd9bfe877b"}, {"line": 45574, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2044, "key": "d52c17a6f1e433ffc70f04b98528e563"}, {"line": 45578, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2075, "key": "427b8d04b1e6837379be8cb54de35987"}, {"line": 45583, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2055, "key": "0d63a085a6be90a59bfb0c76fad96161"}, {"line": 45587, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2038, "key": "bb7ca517023f4b2ec4f518de8e439fac"}, {"line": 45591, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2079, "key": "c37b26b9a25667ada1f2cde746a3bd68"}, {"line": 45595, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2077, "key": "d8d7a0fe49b2bc2d65168c5e3fac0cc5"}, {"line": 45600, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2070, "key": "91c29f6a7f946b2e9b4adfe415f993f4"}, {"line": 45604, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2068, "key": "a900fe27b8fa70ee9763917df692ab5e"}, {"line": 45609, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2048, "key": "19e1577ff3fb8703a06bb00e81f1644a"}, {"line": 45614, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2030, "key": "5052cf5158e1443151672e2166783420"}, {"line": 45618, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2022, "key": "01aba4d6347315460dbd3bfc97651df1"}, {"line": 45622, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2053, "key": "3414c95f6d23eb45ce56262b9ea01bb3"}, {"line": 45628, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2032, "key": "13888abbcfed3d44908c6131b63c7f95"}, {"line": 45633, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Subgraph": {"Axonal guidance subgraph": true}}, "source": 3823, "target": 2024, "key": "84b93ded214d9ddc35cb27a203b9cbfb"}, {"line": 45638, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2019, "key": "5a5aba4e483897cebd0db21ec0846bf6"}, {"line": 45642, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2066, "key": "a20d70de17ae2f9bb8b56fe3a2321cb8"}, {"line": 45646, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2061, "key": "dda83bcfcab8fd44d02d10d14f464c81"}, {"line": 45650, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 3823, "target": 2036, "key": "c737329b242b18f0080a80082c9d01d9"}, {"line": 45660, "relation": "positiveCorrelation", "evidence": "we observed a higher HTERT methylation frequency in AD compared with elderly controls. ", "citation": {"db": "PubMed", "db_id": "18376059"}, "source": 3823, "target": 1993, "key": "a245915c0678566d8236700108219697"}, {"line": 45668, "relation": "negativeCorrelation", "evidence": "The electropherograms showed a very low degree of methylation at all CpG sites within the SST and absence of methylation in the SSTR4 CGI in AD patients", "citation": {"db": "PubMed", "db_id": "24602981"}, "annotations": {"Cell": {"blood cell": true}}, "source": 3823, "target": 1980, "key": "cf648034b6873f48b1151a5c61e627f6"}, {"line": 45670, "relation": "negativeCorrelation", "evidence": "The electropherograms showed a very low degree of methylation at all CpG sites within the SST and absence of methylation in the SSTR4 CGI in AD patients", "citation": {"db": "PubMed", "db_id": "24602981"}, "annotations": {"Cell": {"blood cell": true}}, "source": 3823, "target": 1982, "key": "a5a27e6f11baa5db729a26d583b99d0b"}, {"line": 45679, "relation": "negativeCorrelation", "evidence": "we present further evidence in a limited number of cortex samples that epigenetic alterations in the brain are associated with AD. CpG No. 10 of TNF-alpha protein was significantly hypomethylated and further CpGs were modestly hypomethylated in AD patients in comparison to controls.", "citation": {"db": "PubMed", "db_id": "24556805"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 1998, "key": "763476957dbd5a923b3a77cb21e1171e"}, {"line": 45697, "relation": "association", "evidence": "Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 was associated with pathological AD", "citation": {"db": "PubMed", "db_id": "25365775"}, "source": 3823, "target": 1973, "key": "877c64fa873f8ae5d3186480c9522138"}, {"line": 45699, "relation": "association", "evidence": "Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 was associated with pathological AD", "citation": {"db": "PubMed", "db_id": "25365775"}, "source": 3823, "target": 1732, "key": "8e45859a6d83146c08f52cc6014ac443"}, {"line": 45701, "relation": "association", "evidence": "Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 was associated with pathological AD", "citation": {"db": "PubMed", "db_id": "25365775"}, "source": 3823, "target": 1965, "key": "80b905147ebbd7acd96aa7c7da319b24"}, {"line": 45703, "relation": "association", "evidence": "Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 was associated with pathological AD", "citation": {"db": "PubMed", "db_id": "25365775"}, "source": 3823, "target": 1761, "key": "ba2d5fb7075bc244a778961005b1b771"}, {"line": 45721, "relation": "positiveCorrelation", "evidence": "the transforming growth factor-beta1 (TGF-beta1) signaling pathway has been demonstrated to be hypermethylated in the AD brain ", "citation": {"db": "PubMed", "db_id": "24347181"}, "source": 3823, "target": 1869, "key": "30534cb6bb3ab2aee60644b5e4481eb7"}, {"line": 45727, "relation": "positiveCorrelation", "evidence": "Results showed significant hypermethylation of mammalian orthologue of Sir2 (SIRT1) gene in AD patients ", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 3823, "target": 1961, "key": "552840468d527543177ab943ff1bf8ea"}, {"line": 45751, "relation": "positiveCorrelation", "evidence": "Our results show that IGFBP3 promoter CpGs (25 out of 32) within the CpG island were hypermethylated in H4-sw cells", "citation": {"db": "PubMed", "db_id": "24964199"}, "annotations": {"Race": {"Swedish": true}, "Species": {"10090": true}, "UserdefinedCellLine": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 1847, "key": "b980956534442a234858ac6bd44683c4"}, {"line": 45771, "relation": "positiveCorrelation", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product. In addition, a consistent reduction in DNA methylation at 5-LOX gene promoter was documented in AD versus healthy subjects.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Cell": {"blood cell": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2984, "key": "4b923a1d2806d91e93aa9a6fc0a5a96c"}, {"line": 45772, "relation": "negativeCorrelation", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product. In addition, a consistent reduction in DNA methylation at 5-LOX gene promoter was documented in AD versus healthy subjects.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Cell": {"blood cell": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1738, "key": "62c4df50ed8ebd083b2cb2f480ff540c"}, {"line": 45793, "relation": "positiveCorrelation", "evidence": "Our results showed that BDNF methylation was significantly higher in AD cases than in the controls", "citation": {"db": "PubMed", "db_id": "25364831"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}}, "source": 3823, "target": 1759, "key": "7475061cf8fa7cd97cfdfc0e6cfa8b0e"}, {"line": 46158, "relation": "positiveCorrelation", "evidence": "AD brains showed a significantly increased methylation state of the promoter region of the BDNF gene, There was a significant decrease in BDNF mRNA in the AD brain", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 1759, "key": "d44ca61019098e6624aff36b975db60c"}, {"line": 45802, "relation": "negativeCorrelation", "evidence": "The results showed that, the promoter of DR4 was hypomethylated in AD patients ", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2001, "key": "da18cadb34069299b6433181f82b7f5c"}, {"line": 45808, "relation": "positiveCorrelation", "evidence": "DNMT1 and DNMT3a mRNA level were elevated (P < 0.05) in AD patients and folate deficient medium cultured cells compared with controls (P < 0.05), together with lower folate concentration in AD", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 3963, "key": "1cd0bc9734aaf00aaf92e641b0c28538"}, {"line": 45809, "relation": "positiveCorrelation", "evidence": "DNMT1 and DNMT3a mRNA level were elevated (P < 0.05) in AD patients and folate deficient medium cultured cells compared with controls (P < 0.05), together with lower folate concentration in AD", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 3964, "key": "2ed4e8e23ff894b7ff14fd5decc7af5f"}, {"line": 45822, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "source": 3823, "target": 1799, "key": "454586eb192d45c690beeda92a96fadf"}, {"line": 45827, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3823, "target": 1955, "key": "0e773e0f27537a8d340210a310a636a4"}, {"line": 45828, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3823, "target": 1957, "key": "f4c053cbf531e2ec46088ae2f81028c9"}, {"line": 45860, "relation": "positiveCorrelation", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 3784, "key": "c93c4fe5915e0269a2bdb1631c275cea"}, {"line": 45870, "relation": "negativeCorrelation", "evidence": "Further chromatin immunoprecipitation and bisulfite sequencing studies illustrated the decreased cytosine methylation in the Cdk5 promoter region in hippocampal CA1 in the rodent model of AD", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3823, "target": 1775, "key": "16e8b444dc66231b1908072005305ff1"}, {"line": 45884, "relation": "negativeCorrelation", "evidence": "methylation status of androgen receptor promoter,may show us any deviation from the 50 : 50% X inactivation status in peripheral blood lymphocytes of women with AD", "citation": {"db": "PubMed", "db_id": "25159673"}, "annotations": {"Gender": {"Female": true}, "Cell": {"lymphocyte": true, "blood cell": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3823, "target": 1752, "key": "f0ab8150e466ee69a0fc3f4256040b5f"}, {"line": 45890, "relation": "positiveCorrelation", "evidence": "These findings suggest that four mechanisms may participate in the regulation of the PAD gene: the stress-related heat shock; the AP-1/Fos binding; the GC-rich element, and the possible methylation of the CpG region. PAD gene regulation could be of relevance for the progression of amyloid deposition in Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "2690103"}, "source": 3823, "target": 1906, "key": "3fd43ee9475bde37829a224b73dcf4b1"}, {"line": 45897, "relation": "positiveCorrelation", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2804, "key": "bdf63f5f866b2fb560ab287e25114802"}, {"line": 45995, "relation": "decreases", "evidence": "The levels of DNA methylation in promoters of APP, BACE1, and PS1 genes are decreased after anisomycin treatment.", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2091, "key": "bc46da8a68c47d4e307d912141e17b51"}, {"line": 46004, "relation": "positiveCorrelation", "evidence": "We found an association between the gain in hypermethylation of TBXA2R, SORBS3 and SPTBN4 in the frontal cortex of the patients with Alzheimer's disease with a reduction of the corresponding RNA transcripts (Fig. 3B) and proteins", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 3823, "target": 1989, "key": "ac0103622a38ccd8e9da65f8f5d986ce"}, {"line": 46006, "relation": "positiveCorrelation", "evidence": "We found an association between the gain in hypermethylation of TBXA2R, SORBS3 and SPTBN4 in the frontal cortex of the patients with Alzheimer's disease with a reduction of the corresponding RNA transcripts (Fig. 3B) and proteins", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 3823, "target": 1970, "key": "eb17f8aa0c57fdd3c5021cca491c0e3a"}, {"line": 46008, "relation": "positiveCorrelation", "evidence": "We found an association between the gain in hypermethylation of TBXA2R, SORBS3 and SPTBN4 in the frontal cortex of the patients with Alzheimer's disease with a reduction of the corresponding RNA transcripts (Fig. 3B) and proteins", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 3823, "target": 1976, "key": "4015415bd7ea080e8037fbf86a66035c"}, {"line": 46019, "relation": "positiveCorrelation", "evidence": "we found a similar trend for F2RL2 DNA methylation and RNA expression although the great variability of expression among samples precluded a definitive conclusion for this gene. ", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 3823, "target": 1822, "key": "0caf4559a911f6ff17f756013543fce9"}, {"line": 46110, "relation": "positiveCorrelation", "evidence": "we have identified the presence of promoter hypermethylation of the dual-specificity phosphatase 22 (DUSP22) gene in AD", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 1810, "key": "a8f5c486d546d153d7a3f13a10622247"}, {"line": 46128, "relation": "negativeCorrelation", "evidence": "studies reporting a decrease of CREB phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2808, "key": "4c5982837757dd58cdc96f61293aaef4"}, {"line": 46181, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significant increases in H3 phosphorylation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 2808, "key": "9fd708ba1f5f949322cd099ade6eec30"}, {"line": 46150, "relation": "positiveCorrelation", "evidence": "Only the AD brain showed hyper- and hypomethylated CpG islands in promoter regions for cAMP response element-binding protein and nuclear transcription factor kappa B genes, respectively", "citation": {"db": "PubMed", "db_id": "22760556"}, "source": 3823, "target": 1784, "key": "9e0fb49e38002c4648cadcb2343b4a81"}, {"line": 46151, "relation": "negativeCorrelation", "evidence": "Only the AD brain showed hyper- and hypomethylated CpG islands in promoter regions for cAMP response element-binding protein and nuclear transcription factor kappa B genes, respectively", "citation": {"db": "PubMed", "db_id": "22760556"}, "source": 3823, "target": 1893, "key": "d71ede2d4e0a49d0908dad0433609ec9"}, {"line": 46154, "relation": "negativeCorrelation", "evidence": "the COX-2 promoter CpG region showed decreased methylation in AD", "citation": {"db": "PubMed", "db_id": "22760556"}, "source": 3823, "target": 1932, "key": "8df4ab4b02fec9e52ab6fa5ab7a214d6"}, {"line": 46163, "relation": "positiveCorrelation", "evidence": "There was a significant increase in DNA methylation at the promoter region of synaptophysin in the AD", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 1987, "key": "5cb19cdfedf74428bf62c45ae6a54375"}, {"line": 46171, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 3982, "key": "f0f72c4fc20787c5b7d2b0746d09e65d"}, {"line": 46190, "relation": "association", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 761, "key": "db57eef31e4ec0ef7a35d3b3d519a591"}, {"line": 46252, "relation": "negativeCorrelation", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 655, "key": "0453506296874386f3587ffb4135b426"}, {"line": 46253, "relation": "negativeCorrelation", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3823, "target": 820, "key": "b55511dd4a10f3a65e7bae175240123e"}, {"line": 46333, "relation": "association", "evidence": "PEBP plays a pivotal modulatory role in several signal transduction pathways. PEBP inhibits the MAPK pathway through interacting with Raf-1, so it's also known as Raf-1 kinase inhibitor protein (RKIP). PEBP is involved in the regulation of PKC, G-protein-coupled receptor and NF-kappaB signaling pathway as well. In clinical researches, it was found that as the precursor of hippocampal cholinergic neurostimulating peptide (HCNP), PEBP has an important effect on the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19803424"}, "source": 3823, "target": 451, "key": "efe3472dc6e4a0e350325b6971050deb"}, {"line": 46335, "relation": "association", "evidence": "PEBP plays a pivotal modulatory role in several signal transduction pathways. PEBP inhibits the MAPK pathway through interacting with Raf-1, so it's also known as Raf-1 kinase inhibitor protein (RKIP). PEBP is involved in the regulation of PKC, G-protein-coupled receptor and NF-kappaB signaling pathway as well. In clinical researches, it was found that as the precursor of hippocampal cholinergic neurostimulating peptide (HCNP), PEBP has an important effect on the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19803424"}, "source": 3823, "target": 3181, "key": "d83ce0807f0d633f32bee0e23597f1f4"}, {"line": 46395, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is characterized by progressive cognitive decline. Recent studies have shown that synaptic loss in the cortex is the major correlate of cognitive decline in AD. In the present study we assessed synaptic proteins such as synaptobrevin, synaptophysin, synaptotagmin, synaptosomal-associated protein 25 (SNAP-25), and syntaxin1/HPC-1 in control and AD brains to determine whether synaptic proteins are equally or differentially affected in AD. Western analysis showed that in AD levels of synaptobrevin and synaptophysin were decreased by some 30% from amounts in controls, while those of synaptotagmin, SNAP-25, and syntaxin 1/HPC-1 were decreased by only about 10%. As synaptobrevin and synaptophysin are localized mainly in transmitter-containing synaptic vesicles while SNAP-25 and syntaxin 1/HPC-1 are found in presynaptic plasma membranes, these results suggest differential involvement of synaptic components in AD.", "citation": {"db": "PubMed", "db_id": "9240416"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Synapse assembly subgraph": true}}, "source": 3823, "target": 3431, "key": "b95a89e4460c38211c63620537dd05f9"}, {"line": 46435, "relation": "increases", "evidence": "Dual-specificity tyrosine(Y)-phosphorylation-regulated kinase 1A (Dyrk1A) is a protein kinase that might be responsible for mental retardation and early onset of Alzheimer's disease in Down's syndrome patients. Dyrk1A plays a role in many cellular pathways through phosphorylation of diverse substrate proteins; however, its role in synaptic vesicle exocytosis is poorly understood. Munc18-1, a central regulator of neurotransmitter release, interacts with Syntaxin 1 and X11-alpha. Syntaxin 1 is a key soluble N-ethylmaleimide-sensitive factor attachment protein receptor protein involved in synaptic vesicle docking/fusion events, and X11-alpha modulates amyloid precursor protein processing and beta amyloid generation. In this study, we demonstrate that Dyrk1A interacts with and phosphorylates Munc18-1 at the Thr(479) residue. The phosphorylation of Munc18-1 at Thr(479) by Dyrk1A stimulated binding of Munc18-1 to Syntaxin 1 and X11-alpha. ", "citation": {"db": "PubMed", "db_id": "22765017"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "DYRK1A subgraph": true}}, "source": 3823, "target": 2648, "key": "c685d13c403d92e92bf67a052d9012f3"}, {"line": 46448, "relation": "association", "evidence": "The p53-family member TAp73 is a transcription factor that plays a key role in many biological processes. Here, we show that p73 drives the expression of microRNA (miR)-34a, but not miR-34b and -c, by acting on specific binding sites on the miR-34a promoter. Expression of miR-34a is modulated in parallel with that of TAp73 during in vitro differentiation of neuroblastoma cells and cortical neurons. Retinoid-driven neuroblastoma differentiation is inhibited by knockdown of either p73 or miR-34a. Transcript expression of miR-34a is significantly reduced in vivo both in the cortex and hippocampus of p73(-/-) mice; miR-34a and TAp73 expression also increase during postnatal development of the brain and cerebellum when synaptogenesis occurs. Accordingly, overexpression or silencing of miR-34a inversely modulates expression of synaptic targets, including synaptotagmin-1 and syntaxin-1A. Notably, the axis TAp73/miR-34a/synaptotagmin-1 is conserved in brains from Alzheimer's patients. These data reinforce a role for TAp73 in neuronal development.", "citation": {"db": "PubMed", "db_id": "22160687"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3823, "target": 3486, "key": "bf150cd0ce2df38fedde018ec482347a"}, {"line": 46518, "relation": "decreases", "evidence": "We used a proteomics approach to identify binding partners and show that heat shock protein 60 (HSP60), a molecular chaperone localized to mitochondria and the plasma membrane, specifically associates with APP. We next generated stable neural cell lines expressing human wild-type or Swedish APP, and provide corroborating in vitro evidence that HSP60 mediates translocation of APP to the mitochondria. Viral-mediated shRNA knockdown of HSP60 attenuates APP and Abeta mislocalization to the mitochondria.", "citation": {"db": "PubMed", "db_id": "22753410"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3823, "target": 1179, "key": "5a2ea35921038fbd1927edbe8e3ac8e0"}, {"line": 46599, "relation": "negativeCorrelation", "evidence": "In this study, we found that expression levels of HSP60, -70, and -90 were downregulated in the cerebella of rats with AD. ", "citation": {"db": "PubMed", "db_id": "23665061"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true}}, "source": 3823, "target": 3787, "key": "9af4b1bf0fa1ef66b0d648171482fa03"}, {"line": 46600, "relation": "negativeCorrelation", "evidence": "In this study, we found that expression levels of HSP60, -70, and -90 were downregulated in the cerebella of rats with AD. ", "citation": {"db": "PubMed", "db_id": "23665061"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true}}, "source": 3823, "target": 3785, "key": "84df06fe9608a696b5a93a8c7602b6f5"}, {"line": 46619, "relation": "positiveCorrelation", "evidence": "The presence of misfolded proteins in the endoplasmic reticulum (ER) triggers a cellular stress response called the unfolded protein response (UPR) that may protect the cell against the toxic buildup of misfolded proteins. In this study we investigated the activation of the UPR in AD. Protein levels of BiP/GRP78, a molecular chaperone which is up-regulated during the UPR, was found to be increased in AD temporal cortex and hippocampus as determined by Western blot analysis.", "citation": {"db": "PubMed", "db_id": "15973543"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Chaperone subgraph": true, "Unfolded protein response subgraph": true}}, "source": 3823, "target": 550, "key": "f16e0e328a7122a25bd8fc2d1e85f909"}, {"line": 46754, "relation": "positiveCorrelation", "evidence": "Nevertheless, the CSF level of neurogranin is selectively increased in AD dementia", "citation": {"db": "PubMed", "db_id": "26783546"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3141, "key": "2baeef25465e278dda3573bb672e13ff"}, {"line": 46905, "relation": "positiveCorrelation", "evidence": "The rare variant discovered is a missense mutation in the loop region of exon 2 of Trem2 (rs75932628-T, Arg47His). Evidence of trem2 variant associated with triple risk of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24663666"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3494, "key": "1ba6e5cdd237a3c31997758b974173c9"}, {"line": 46915, "relation": "association", "evidence": "Late-onset Alzheimer's disease (AD) is a sporadic disorder with increasing prevalence in aging. The ɛ4 allele of Apolipoprotein E(ApoEɛ4) was the only known major risk factor for late onset AD. Recently, two groups of investigators independently identified variants of the TREM2 gene, encoding triggering receptor expressed on myeloid cells 2 as causing increased susceptibility to late onset AD with an odds ratio similar to that of ApoEɛ4. TREM2 is a receptor expressed on innate immune cells.", "citation": {"db": "PubMed", "db_id": "24355566"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3823, "target": 3493, "key": "9e10cbb86aff729570d33e33ff8ad2f8"}, {"line": 47478, "relation": "association", "evidence": "overexpression of CRF or exposure to chronic stress in rodents can induce phosphorylation and solubility changes in the microtubule-associated protein, tau, a process that is reliant on CRFR1. Furthermore, exposing rodents to chronic emotional stress results in increased phosphorylation and decreased solubility of the tau protein; changes that are also strictly dependent on CRFR1 signaling. In addition to work on tau, several reports demonstrate that CRF or stress exposure can impact Abeta production and accumulation in AD models and that stress-induced Abeta plaque formation in adult AD mice can be reduced by CRFR1 antagonism . In particular, our recently published work demonstrates that genetic ablation of CRFR1 greatly reduces the production of APP CTFs and accumulation of Abeta in the brains of AD mice. ", "citation": {"db": "PubMed", "db_id": "26555315"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3798, "key": "47ef4703a83fb8d53ce03de8f8a78f70"}, {"line": 47494, "relation": "association", "evidence": "Stress exposure or increased levels of corticotropin-releasing factor (CRF) induce hippocampal tau phosphorylation (tau-P) in rodent models, a process that is dependent on the type-1 CRF receptor (CRFR1).", "citation": {"db": "PubMed", "db_id": "26790099"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3798, "key": "a330ac7fad18ec956510ac768c230b9c"}, {"line": 47538, "relation": "association", "evidence": "We found that both PSAPP-R1(+/-) and PSAPP-R1(-/-) had significantly reduced Abeta burden in the hippocampus, insular, rhinal, and retrosplenial cortices. Accordingly, we observed dramatic reductions in Abeta peptides and AbetaPP-CTFs, providing support for a direct relationship between CRFR1 and Abeta production pathways. In summary, our results suggest that interference of CRFR1 in an AD model is tolerable and is efficacious in impacting Abeta neuropathology.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 3773, "key": "4667457dc52c579bbd0654504eee90fe"}, {"line": 47626, "relation": "decreases", "evidence": "Hypersecretion of CRF in the brain may contribute to the symptomatology seen in neuropsychiatric disorders, such as depression, anxiety-related disorders and anorexia nervosa. Furthermore, overproduction of CRF at peripheral inflammatory sites, such as synovial joints may contribute to autoimmune diseases such as rheumatoid arthritis. In contrast, deficits in brain CRF are apparent in neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease and Huntington's disease, as they relate to dysfunction of CRF neurons in the brain areas affected in the particular disorder. Strategies directed at developing CRF-related agents may hold promise for novel therapies for the treatment of these various disorders.", "citation": {"db": "PubMed", "db_id": "8834089"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2560, "key": "af2d184dfb30517e67f9c6c23f2bf633"}, {"line": 47673, "relation": "decreases", "evidence": "CRH-IR is significantly reduced in the cerebral cortex of individuals with AD, PD and PSP. Furthermore, we report that the decreases in CRH-IR in AD are accompanied by reciprocal increases in CRH receptors in affected cortical areas. The changes in pre- and postsynaptic markers for CRH are significantly correlated with decrements in ChAT activity. The demonstration of an up regulation of CRH receptors following a decrease in CRH-IR indicates a physiological relevance of the receptor site and is consistent with the concept that CRH acts as a neurotransmitter in normal cortical functions and that disease of this peptidergic systems may be important in certain clinical manifestations of dementia. While the clinical consequences of the changes in CRH in these various disorders are unclear, future therapies directed at increasing CRH levels in brain may prove useful for treatment.", "citation": {"db": "PubMed", "db_id": "3502064"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 2560, "key": "56519dcee60aec0ece3b1361fab444b7"}, {"line": 47669, "relation": "increases", "evidence": "CRH-IR is significantly reduced in the cerebral cortex of individuals with AD, PD and PSP. Furthermore, we report that the decreases in CRH-IR in AD are accompanied by reciprocal increases in CRH receptors in affected cortical areas. The changes in pre- and postsynaptic markers for CRH are significantly correlated with decrements in ChAT activity. The demonstration of an up regulation of CRH receptors following a decrease in CRH-IR indicates a physiological relevance of the receptor site and is consistent with the concept that CRH acts as a neurotransmitter in normal cortical functions and that disease of this peptidergic systems may be important in certain clinical manifestations of dementia. While the clinical consequences of the changes in CRH in these various disorders are unclear, future therapies directed at increasing CRH levels in brain may prove useful for treatment.", "citation": {"db": "PubMed", "db_id": "3502064"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 3823, "target": 2562, "key": "74422744134254aaf16d4a4261ad11ae"}, {"line": 47888, "relation": "association", "evidence": "Therefore, we hypothesize that Dkk1 may play a role in both osteoporosis and AD.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 3823, "target": 2629, "key": "e104c10c78d483ced0872f578b5beed6"}, {"line": 48178, "relation": "positiveCorrelation", "evidence": "More importantly, persistent activation of Wnt signaling through Wnt ligands, or inhibition of negative regulators of Wnt signaling, such as Dickkopf-1 (DKK-1) and glycogen synthase kinase-3 beta (GSK-3 beta ) that are hyperactive in the disease state, is able to protect against Abeta toxicity and ameliorate cognitive performance in AD.", "citation": {"db": "PubMed", "db_id": "24883305"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2629, "key": "57949e9442883358b5dc6c99debfb33a"}, {"line": 48262, "relation": "association", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 2629, "key": "c48ca797d11c0ee353285d53306e1968"}, {"line": 48470, "relation": "association", "evidence": "However, the importance of genes within chromosomal 8p region for neuropsychiatric disorders and cancer is well established.... Molecular genetics and developmental studies have identified 21 genes in this region (ADRA1A, ARHGEF10, CHRNA2, CHRNA6, CHRNB3, DKK4, DPYSL2, EGR3, FGF17, FGF20, FGFR1, FZD3, LDL, NAT2, NEF3, NRG1, PCM1, PLAT, PPP3CC, SFRP1 and VMAT1/SLC18A1) that are most likely to contribute to neuropsychiatric disorders (schizophrenia, autism, bipolar disorder and depression), neurodegenerative disorders (Parkinson's and Alzheimer's disease) and cancer. ", "citation": {"db": "PubMed", "db_id": "19204725"}, "source": 3823, "target": 2659, "key": "78239e9368222f189c44204bba052d27"}, {"line": 48575, "relation": "association", "evidence": "Accumulation of the amyloid beta (Abeta) peptide derived from the amyloid precursor protein (APP) plays a central role in the pathogenesis of Alzheimer's disease (AD). We previously reported that the scaffolding protein RanBP9 is markedly increased in AD brains and promotes Abeta generation by scaffolding APP/BACE1/LRP complexes together and accelerating APP endocytosis.", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3823, "target": 441, "key": "a72a1f335d9f56bb52d025d35075eb6f"}, {"line": 48581, "relation": "positiveCorrelation", "evidence": "Accumulation of the amyloid beta (Abeta) peptide derived from the amyloid precursor protein (APP) plays a central role in the pathogenesis of Alzheimer's disease (AD). We previously reported that the scaffolding protein RanBP9 is markedly increased in AD brains and promotes Abeta generation by scaffolding APP/BACE1/LRP complexes together and accelerating APP endocytosis.", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3823, "target": 3295, "key": "42cfb35ad4f7db1b51fa1bb155de8a7b"}, {"line": 48591, "relation": "association", "evidence": "Recent studies have implicated the filamentous actin (F-actin) severing protein, Cofilin, in synaptic remodeling, mitochondrial dysfunction, and AD pathogenesis", "citation": {"db": "PubMed", "db_id": "25698445"}, "source": 3823, "target": 2507, "key": "becfbf5e4d1d2720122835fa4123b1db"}, {"line": 48742, "relation": "association", "evidence": "The chemokine/chemokine receptor CCL2/CCR2 axis was impaired in BDMs from AD and MCI patients, causing a deficit in cell migration. Changes were also observed in MDM-mediated phagocytosis of Abeta fibrils, correlating with alterations in the expression and processing of the triggering receptor expressed on myeloid cells 2 (TREM2). Finally, immune-related microRNAs (miRNAs), including miR-155, -154, -200b, -27b, and -128, were found to be differentially expressed in these cells.", "citation": {"db": "PubMed", "db_id": "4879648"}, "annotations": {"Cell": {"monocyte": true}}, "source": 3823, "target": 2112, "key": "1db4a5436e8cc65ba09159adcf7b009b"}, {"line": 48743, "relation": "decreases", "evidence": "The chemokine/chemokine receptor CCL2/CCR2 axis was impaired in BDMs from AD and MCI patients, causing a deficit in cell migration. Changes were also observed in MDM-mediated phagocytosis of Abeta fibrils, correlating with alterations in the expression and processing of the triggering receptor expressed on myeloid cells 2 (TREM2). Finally, immune-related microRNAs (miRNAs), including miR-155, -154, -200b, -27b, and -128, were found to be differentially expressed in these cells.", "citation": {"db": "PubMed", "db_id": "4879648"}, "annotations": {"Cell": {"monocyte": true}}, "source": 3823, "target": 509, "key": "ebcdb95d8e19ad65bec070df9ccdfbbf"}, {"line": 48751, "relation": "negativeCorrelation", "evidence": "This work provides evidence that chemotaxis and phagocytosis, two crucial innate immune functions, are impaired in AD and MCI patients. Correlations with miRNA levels suggest an epigenetic contribution to systemic immune dysfunction in AD.", "citation": {"db": "PubMed", "db_id": "4879648"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3823, "target": 811, "key": "71a765a5fa6c67e70e682eff73799816"}, {"line": 48752, "relation": "negativeCorrelation", "evidence": "This work provides evidence that chemotaxis and phagocytosis, two crucial innate immune functions, are impaired in AD and MCI patients. Correlations with miRNA levels suggest an epigenetic contribution to systemic immune dysfunction in AD.", "citation": {"db": "PubMed", "db_id": "4879648"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3823, "target": 823, "key": "b3005eb223bfe253428df5d4e54c85fc"}, {"line": 48768, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Confidence": {"High": true}}, "source": 3823, "target": 3109, "key": "138da607e5276e1c29683da6202e1a0b"}, {"line": 48827, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "source": 3823, "target": 3514, "key": "4e9e35cd0b44e82085d4a790914bf79f"}, {"line": 48850, "relation": "association", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "source": 3823, "target": 2097, "key": "830b9d03a74728dc3ac54951d200bc0f"}, {"line": 49147, "relation": "positiveCorrelation", "evidence": "In this regard we find high levels of the tissue inhibitor of matrix metalloproteinases-1 (TIMP-1) in AD. Furthermore, we explore the ability of thrombin, previously shown to be present in AD microvessels, to affect TIMP expression in cultured brain endothelial cells and find that thrombin causes up regulation of TIMP-1", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 3463, "key": "300358cbd33ccb0944e160e71b6fc5e9"}, {"line": 49278, "relation": "positiveCorrelation", "evidence": "Levels of TIMP-1 were significantly elevated in CSF samples from all disease groups. TIMP-2 was significantly increased in CSF of AD and HD patients. MMP-2 levels did not differ significantly between groups. These findings show that TIMPs are elevated in the CSF of patients with neurodegenerative diseases suggesting a potential role of these endogenous inhibitors of matrix metalloproteinases in neurodegenerative diseases.", "citation": {"db": "PubMed", "db_id": " 12614934"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 3823, "target": 3464, "key": "2353edf13dfdd68f9e51fa5e0211f682"}, {"line": 49326, "relation": "negativeCorrelation", "evidence": "However, the frequency for allele A of ApaI was higher in the control group, which was later associated with a 30% lower risk of AD in Polish and British populations study.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 3823, "target": 1728, "key": "9880967455f34de984b3c30282776743"}, {"line": 49443, "relation": "association", "evidence": "Antioxidants scavenge free radicals and other reactive oxygen species that damage cellular membranes, organelles, and macromolecules. Accumulation of reactive oxygen species may overwhelm the protective reserves of antioxidants in cells (oxidative stress). In neurons, which are especially vulnerable to free radical–mediated damage, these processes may be important in aging of the brain and the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "annotations": {"Cell": {"neuron": true}}, "source": 3823, "target": 400, "key": "51b683f946d40fd1f7c851a2b4799d07"}, {"line": 189, "relation": "negativeCorrelation", "evidence": "Here, we show that siRNA-mediated loss of calsyntenin-1 in cultured neurons alters APP processing to increase production of Abeta. We also show that calsyntenin-1 is reduced in Alzheimer's disease brains and that the extent of this reduction correlates with increased Abeta levels.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Calsyntenin subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2532, "target": 3823, "key": "673aa4ecc468ddc39e2c775b9c7aaccb"}, {"line": 190, "relation": "decreases", "evidence": "Here, we show that siRNA-mediated loss of calsyntenin-1 in cultured neurons alters APP processing to increase production of Abeta. We also show that calsyntenin-1 is reduced in Alzheimer's disease brains and that the extent of this reduction correlates with increased Abeta levels.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Calsyntenin subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2532, "target": 80, "key": "bd57601782262cb163248a82bf767e3a"}, {"line": 191, "relation": "positiveCorrelation", "evidence": "Here, we show that siRNA-mediated loss of calsyntenin-1 in cultured neurons alters APP processing to increase production of Abeta. We also show that calsyntenin-1 is reduced in Alzheimer's disease brains and that the extent of this reduction correlates with increased Abeta levels.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Calsyntenin subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Degradation"}, "source": 2532, "target": 80, "key": "dcf27c69d8be488893491a84bb316210"}, {"line": 36071, "relation": "decreases", "evidence": "Understanding the mechanisms that control processing of the amyloid precursor protein (APP) to produce amyloid-ß (ABeta¸) peptide represents a key area of Alzheimer's disease research. We also show that calsyntenin-1 is reduced in Alzheimer's disease brains and that the extent of this reduction correlates with increased ABeta¸ levels. Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Calsyntenin subgraph": true}, "MeSHAnatomy": {"Axons": true}, "Confidence": {"Medium": true}}, "source": 2532, "target": 80, "key": "eb48929f38276b0eec27b99e5189559e"}, {"relation": "partOf", "source": 2532, "target": 1355, "key": "120999b8163cfcfca36fbc5eb4f02fd2"}, {"line": 202, "relation": "association", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2532, "target": 2315, "key": "a5194f6945c9bb36ce322323482cf1fd"}, {"line": 36111, "relation": "increases", "evidence": "Together, these results indicate a role for calsyntenin-1 in Kinesin-1-dependent TGN exit and post-Golgi transport of APP-containing organelles and further suggest that distinct intracellular routes may exhibit different capacities for proteolytic processing of APP.", "citation": {"db": "PubMed", "db_id": "19192245"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2532, "target": 2315, "key": "d3484295fe9f7e0863106f8da2ac9982"}, {"relation": "partOf", "source": 2532, "target": 1151, "key": "3a0b8bbe6155c56dc260b0457cd96a1a"}, {"line": 36092, "relation": "increases", "evidence": "Loss of calsyntenin-1 in the cultured neurons was associated with alterations to APP processing involving increased cleavage at the BACE1 sites and decreased cleavage at the a-secretase site.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Matrix metalloproteinase subgraph": true, "Beta secretase subgraph": true, "Calsyntenin subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2532, "target": 2249, "key": "092c7fdfd4b038ae381b9bf4c3fbfbd6"}, {"line": 36100, "relation": "decreases", "evidence": "Loss of calsyntenin-1 in the cultured neurons was associated with alterations to APP processing involving increased cleavage at the BACE1 sites and decreased cleavage at the a-secretase site.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Matrix metalloproteinase subgraph": true, "Beta secretase subgraph": true, "Calsyntenin subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2532, "target": 2375, "key": "528d5130fca949362b88313998dee351"}, {"line": 200, "relation": "increases", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 1355, "target": 2315, "key": "6ece21004ef265d0c711ec9b57b65430"}, {"line": 206, "relation": "decreases", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Degradation"}, "source": 1355, "target": 80, "key": "e6b923b5f7f0d4f06c310efb618673e7"}, {"line": 36076, "relation": "increases", "evidence": "Understanding the mechanisms that control processing of the amyloid precursor protein (APP) to produce amyloid-ß (ABeta¸) peptide represents a key area of Alzheimer's disease research. We also show that calsyntenin-1 is reduced in Alzheimer's disease brains and that the extent of this reduction correlates with increased ABeta¸ levels. Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Innate immune system subgraph": true, "Calsyntenin subgraph": true}, "MeSHAnatomy": {"Axons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 1355, "target": 2948, "key": "a26c380593b556c481f9b2166a686821"}, {"relation": "partOf", "source": 2948, "target": 1355, "key": "68ce96c920cfc06d59ccf95f3fe05b70"}, {"line": 201, "relation": "association", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2948, "target": 2315, "key": "a75756be9270c2dfef35feb4a59c4548"}, {"line": 36077, "relation": "increases", "evidence": "Understanding the mechanisms that control processing of the amyloid precursor protein (APP) to produce amyloid-ß (ABeta¸) peptide represents a key area of Alzheimer's disease research. We also show that calsyntenin-1 is reduced in Alzheimer's disease brains and that the extent of this reduction correlates with increased ABeta¸ levels. Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Innate immune system subgraph": true, "Calsyntenin subgraph": true}, "MeSHAnatomy": {"Axons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Axons"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 2948, "target": 2315, "key": "017ea96b9bf4238c6d2e7633b8f946c5"}, {"line": 48387, "relation": "association", "evidence": "The transport of amyloid precursor protein is mediated through its interaction with kinesin light-chain 1 (KNS2).", "citation": {"db": "PubMed", "db_id": "15364413"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Innate immune system subgraph": true}, "Confidence": {"High": true}}, "source": 2948, "target": 2315, "key": "e4ba483e4c35b2cdb87b0fcd77cf6225"}, {"line": 48414, "relation": "association", "evidence": "Second, reduced transport of APP by KLC1vE triggers an ER stress response that activates the amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "25394182"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Innate immune system subgraph": true}, "Confidence": {"Medium": true}}, "source": 2948, "target": 2315, "key": "23438b1ea6f5f72b7383f88cb721e0d0"}, {"line": 1089, "relation": "increases", "evidence": "Differentiated neural cultures derived from KLC1-suppressed hESC contained neural rosettes but further differentiation revealed obvious morphological changes along with reduced levels of microtubule-associated neural proteins, including Tau and less secreted Abeta, supporting the previously established connection between KLC1, Tau and Abeta", "citation": {"db": "PubMed", "db_id": "22272245"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2948, "target": 3010, "key": "e970daa07d1849b56dcd1fa1f1d2554e"}, {"line": 48397, "relation": "association", "evidence": "The virus [HSV-1] is transported to the nucleus via the dynein and kinesin (KNS2) motors associated with the microtubule network (MAPT)... A viral protein is also able to delete mitochondrial DNA, a situation prevalent in Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "18164103"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true, "Innate immune system subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2948, "target": 3010, "key": "cf25380516d5f9e1223744f6c2bca486"}, {"line": 48423, "relation": "increases", "evidence": "Differentiated neural cultures derived from KLC1-suppressed hESC contained neural rosettes but further differentiation revealed obvious morphological changes along with reduced levels of microtubule-associated neural proteins, including Tau and less secreted Abeta, supporting the previously established connection between KLC1, Tau and Abeta. ", "citation": {"db": "PubMed", "db_id": "22272245"}, "annotations": {"Cell": {"neuron": true}, "Subgraph": {"Axonal transport subgraph": true, "Innate immune system subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2948, "target": 3010, "key": "dc920991de20c0eb71078bd5a8ea4962"}, {"line": 1090, "relation": "increases", "evidence": "Differentiated neural cultures derived from KLC1-suppressed hESC contained neural rosettes but further differentiation revealed obvious morphological changes along with reduced levels of microtubule-associated neural proteins, including Tau and less secreted Abeta, supporting the previously established connection between KLC1, Tau and Abeta", "citation": {"db": "PubMed", "db_id": "22272245"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2948, "target": 80, "key": "041a0e330cf593dabddd11c2e1a39601"}, {"line": 48406, "relation": "increases", "evidence": "Using a novel, unbiased genetic screen, Morihara et al. identified kinesin light chain-1 splice variant E (KLC1vE) as a modifier of Abeta accumulation...First, KLC1vE reduces APP transport, leading to Abeta accumulation.", "citation": {"db": "PubMed", "db_id": "25394182"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "Innate immune system subgraph": true}}, "source": 2948, "target": 80, "key": "b6b0d37f091ab79656939e2ef73e2128"}, {"relation": "partOf", "source": 2948, "target": 1099, "key": "31253b4fdfc239bac1b33d81e3ab3023"}, {"relation": "partOf", "source": 2948, "target": 1181, "key": "54424356614807d9a9ec330e4fde8382"}, {"relation": "partOf", "source": 2948, "target": 1093, "key": "249daa64b646ce96038fa9cc533b6567"}, {"line": 38430, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2948, "target": 3563, "key": "ff529f09de59d0c32a4f54ff16ad5e7a"}, {"relation": "partOf", "source": 2948, "target": 1510, "key": "13d26dfba71edca3854118cac986b09a"}, {"line": 207, "relation": "association", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Calsyntenin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 484, "target": 80, "key": "6275882550c737bfad4d85006cc9af1c"}, {"line": 2652, "relation": "association", "evidence": "Although there are numerous studies regarding Alzheimer's disease (AD), the cause and progression of AD are still not well understood. The researches in the past decade implicated amyloid-beta (Abeta) overproduction as a causative event in disease pathogenesis, but still failed to clarify the mechanism of pathology from Abeta production to central neural system defects in AD. The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.For this hypothesis, the factors related with the initiation of AD pathology are not only limited to the neurons per se but also expanded to the microenvironment around neurons, such as the secretion of BDNF from astrocytes. The modification of the origin in this pathway may contribute to slow down the disease progression of AD.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 484, "target": 80, "key": "604e15e7719daab1c3d12f17d3551574"}, {"line": 216, "relation": "negativeCorrelation", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 484, "target": 3823, "key": "b3583d800b8f577373e81e1ef59ffbd7"}, {"line": 217, "relation": "association", "evidence": "Calsyntenin-1 is a ligand for kinesin-1 light chains and APP is transported through axons on kinesin-1 molecular motors. Defects in axonal transport are an early pathological feature in Alzheimer's disease and defective APP transport is known to increase Abeta production. We show that calsyntenin-1 and APP are co-transported through axons and that siRNA-induced loss of calsyntenin-1 markedly disrupts axonal transport of APP. Thus, perturbation to axonal transport of APP on calsyntenin-1 containing carriers induces alterations to APP processing that increase production of Abeta. Together, our findings suggest that disruption of calsyntenin-1-associated axonal transport of APP is a pathogenic mechanism in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 484, "target": 2315, "key": "c5c2e5af3be6e67ca17d8281be1234ed"}, {"line": 2653, "relation": "association", "evidence": "Although there are numerous studies regarding Alzheimer's disease (AD), the cause and progression of AD are still not well understood. The researches in the past decade implicated amyloid-beta (Abeta) overproduction as a causative event in disease pathogenesis, but still failed to clarify the mechanism of pathology from Abeta production to central neural system defects in AD. The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.For this hypothesis, the factors related with the initiation of AD pathology are not only limited to the neurons per se but also expanded to the microenvironment around neurons, such as the secretion of BDNF from astrocytes. The modification of the origin in this pathway may contribute to slow down the disease progression of AD.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 484, "target": 2397, "key": "49fa7d4e42d40d534a82e98a10bbd804"}, {"relation": "partOf", "source": 3258, "target": 868, "key": "758aed7d682baa50144e383698c92ebb"}, {"line": 282, "relation": "increases", "evidence": "Moreover, we find that PLD1 also regulates PS1 trafficking and that PLD1 overexpression promotes cell surface accumulation of PS1 in an APP-independent manner. Our results clearly elucidate a physiological function of APP in regulating protein trafficking and suggest that intracellular trafficking of PS1/gamma-secretase is regulated by multiple factors, including APP and PLD1.", "citation": {"db": "PubMed", "db_id": "19276086"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3258, "target": 868, "key": "f0ea7198c75829d12be69dfba842d239"}, {"line": 2941, "relation": "increases", "evidence": "Furthermore, we have demonstrated that proteoliposomes containing PS1 mutants alone are sufficient to alter the specificity of gamma-secretase for the production of Abeta40 and Abeta42", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 868, "key": "7fc78b21a1f9d872c5441b0a3c75559b"}, {"relation": "isA", "source": 3258, "target": 868, "key": "cf52dd7b8ad2d45d3ed807f06ef99de0"}, {"line": 35726, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3258, "target": 868, "key": "ad8a432f6b8805064a07eee1e724b33c"}, {"relation": "hasVariant", "source": 3258, "target": 3263, "key": "3e5068d0ba1fa332230d92950383a6f0"}, {"line": 866, "relation": "increases", "evidence": "PS1 mutations cause abnormalities in ER calcium homoeostasis, enhancing the calcium responses to stimuli that activate IP3- and ryanodine-sensitive ER calcium pools.", "citation": {"db": "PubMed", "db_id": "11447832"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2931, "key": "f4b18262fc9b88ceb81a8e0033544d14"}, {"line": 2336, "relation": "regulates", "evidence": "Mechanistic studies have shown that FAD mutants of presenilin can affect the intracellular calcium levels by affecting the ER calcium stores. A function for presenilins as ER calcium leak channels has been established and studies show that presenilins affect ER calcium load through an effect on IP(3) receptors, ryanodine receptors, or SERCA pumps. Even in the absence of an active gamma-secretase complex, presenilins seem to affect calcium homeostasis suggesting that these two functions of presenilins are independent of each other.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2931, "key": "abccdab0fedf2eaba2241f0758be8148"}, {"line": 867, "relation": "increases", "evidence": "PS1 mutations cause abnormalities in ER calcium homoeostasis, enhancing the calcium responses to stimuli that activate IP3- and ryanodine-sensitive ER calcium pools.", "citation": {"db": "PubMed", "db_id": "11447832"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 3333, "key": "9de5f6e4c5911ec376fa50fbf0db3415"}, {"line": 2337, "relation": "regulates", "evidence": "Mechanistic studies have shown that FAD mutants of presenilin can affect the intracellular calcium levels by affecting the ER calcium stores. A function for presenilins as ER calcium leak channels has been established and studies show that presenilins affect ER calcium load through an effect on IP(3) receptors, ryanodine receptors, or SERCA pumps. Even in the absence of an active gamma-secretase complex, presenilins seem to affect calcium homeostasis suggesting that these two functions of presenilins are independent of each other.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3258, "target": 3333, "key": "6ae2fd13b7163d0aec1b4ce59c1d1634"}, {"line": 4000, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3258, "target": 3333, "key": "2513993f2b376b5a8ff5fe054b9cc0af"}, {"line": 868, "relation": "increases", "evidence": "PS1 mutations cause abnormalities in ER calcium homoeostasis, enhancing the calcium responses to stimuli that activate IP3- and ryanodine-sensitive ER calcium pools.", "citation": {"db": "PubMed", "db_id": "11447832"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "source": 3258, "target": 757, "key": "50e49fa489749f3a45b56f73b1dfee4b"}, {"line": 3999, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3258, "target": 757, "key": "2fb519052f85426d5efe5cd892e8079f"}, {"line": 869, "relation": "increases", "evidence": "PS1 mutations cause abnormalities in ER calcium homoeostasis, enhancing the calcium responses to stimuli that activate IP3- and ryanodine-sensitive ER calcium pools.", "citation": {"db": "PubMed", "db_id": "11447832"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "source": 3258, "target": 733, "key": "bf0a55f15d2333df886f33d5d65df034"}, {"relation": "partOf", "source": 3258, "target": 1686, "key": "7d4b86d51879dc922e9cd6c1913a3ac0"}, {"relation": "partOf", "source": 3258, "target": 1171, "key": "b08a5d6c4e91e03b1f4e1146d4b86b21"}, {"relation": "partOf", "source": 3258, "target": 1716, "key": "b87a82466e5510c2c65c11f6d953faa3"}, {"line": 1965, "relation": "increases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3258, "target": 2580, "key": "b5d8b4e019e12b63937b5be3a89a103e"}, {"line": 34154, "relation": "regulates", "evidence": "For instance, PS1 binds to beta-catenin and pmodulates beta-catenin signaling.", "citation": {"db": "PubMed", "db_id": "11504726"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 2580, "key": "88e7817ba42293e7bab1044a8d83cfbe"}, {"line": 35507, "relation": "decreases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 2580, "key": "3e5d255d2ec931a9de9b0e4c24bac137"}, {"line": 1975, "relation": "regulates", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2794, "key": "62483ae09948425445b02a2eccc8a764"}, {"line": 30744, "relation": "increases", "evidence": "Previously we described presenilin-1 (PS1) as a GSK-3beta substrate.", "citation": {"db": "PubMed", "db_id": "16814287"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 2794, "key": "393ff578b3e13bb7341aa946f0ab4bfa"}, {"line": 32512, "relation": "association", "evidence": "PS1 directly binds tau and a tau kinase , glycogen synthase kinase 3beta", "citation": {"db": "PubMed Central", "db_id": "PMC21391"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 2794, "key": "2a298d7084164bb062c2f3497c409c20"}, {"line": 35509, "relation": "regulates", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2794, "key": "af22f43cca711f6122aafa2d360ea27a"}, {"line": 1976, "relation": "regulates", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2487, "key": "55b2874dee01e8ae362a79e3b969efeb"}, {"line": 35510, "relation": "regulates", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2487, "key": "208002eac5e86805bdedcb97882ab750"}, {"line": 2057, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 2393, "key": "c69919635fa0f39509c97b33104a195f"}, {"line": 20400, "relation": "decreases", "evidence": "We report that PS1 mutations cause a marked increase in basal protein levels of the pro-apoptotic transcription factor Gadd153. PS1 mutations increase Gadd153 protein translation without affecting mRNA levels, while decreasing levels of the anti-apoptotic protein Bcl-2.", "citation": {"db": "PubMed", "db_id": "12390529"}, "annotations": {"Subgraph": {"Bcl-2 subgraph": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 2393, "key": "2122c2515e134698bfe93859e46e670f"}, {"line": 2075, "relation": "increases", "evidence": "PS1 is also essential for efficient N-cadherin trafficking from ER to plasma membrane. Cadherins, including E-cadherin and neuronal cadherin (N-cadherin), are a family of type I transmembrane proteins that mediate Ca2+-dependent cell-cell adhesion, and recognition. PS1-mediated delivery of N-cadherin to the plasma membrane is important to exert its physiological function, including the control of the state of cell-cell contact.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Calcium-dependent signal transduction": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endoplasmic Reticulum"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 3258, "target": 2483, "key": "dfe8517352f65e90f10411d3d5fc61fe"}, {"line": 2095, "relation": "increases", "evidence": "PS1 is involved in the intramembrane cleavage of CD44, a cell surface adhesion molecule for the extracellular matrix components which is implicated in a wide variety of physiological and pathological processes including the regulation of tumor cell growth and metastasis.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2476, "key": "d06b665fa18df7646b69bba3e0aa72af"}, {"line": 2132, "relation": "association", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2173, "key": "20e61a1ddd41f5288ec3914ddb42bfd6"}, {"line": 35530, "relation": "regulates", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2173, "key": "2b49ddad1e7122e6a50ccbe33c17b0a6"}, {"line": 2162, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DNA synthesis": true, "Notch signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 531, "key": "312e3db4f3e96a6534902a9be0fca37d"}, {"line": 2169, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 3852, "key": "9cffa37ef448c0978fae9f794aabfd9e"}, {"line": 2218, "relation": "increases", "evidence": "As previously discussed, AbetaPP regulates ERK1/2 levels, its phosphorylation/translocation to the centrosome, and cell proliferation rate.Additionally, in the same study, we showed that also PS1 interacts with Grb2 in the centrosomes and modulates ERK1/2 signaling.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 2769, "key": "c9727baae2115564d379c17fa5009cae"}, {"line": 2320, "relation": "increases", "evidence": "Mechanistic studies have shown that FAD mutants of presenilin can affect the intracellular calcium levels by affecting the ER calcium stores. A function for presenilins as ER calcium leak channels has been established and studies show that presenilins affect ER calcium load through an effect on IP(3) receptors, ryanodine receptors, or SERCA pumps. Even in the absence of an active gamma-secretase complex, presenilins seem to affect calcium homeostasis suggesting that these two functions of presenilins are independent of each other.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endoplasmic Reticulum"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 3258, "target": 94, "key": "56217bb0282f9583c58b56e65f854798"}, {"line": 3982, "relation": "increases", "evidence": "Studies of the presenilins (PS) have implicated ER Ca2+ mishandling in AD. PS functions as an ER Ca2+ leak channel and FAD PS mutations impair this Ca2+ leak channel function resulting in excessive accumulation of Ca2+ in the ER and, as a consequence, enhances Ca2+ release through ryanodine receptor (RyR) and IP3 receptor (IP3R) channels", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endoplasmic Reticulum"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 3258, "target": 94, "key": "0516eb5491f056c0b069750e9138a5c0"}, {"line": 2338, "relation": "regulates", "evidence": "Mechanistic studies have shown that FAD mutants of presenilin can affect the intracellular calcium levels by affecting the ER calcium stores. A function for presenilins as ER calcium leak channels has been established and studies show that presenilins affect ER calcium load through an effect on IP(3) receptors, ryanodine receptors, or SERCA pumps. Even in the absence of an active gamma-secretase complex, presenilins seem to affect calcium homeostasis suggesting that these two functions of presenilins are independent of each other.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2369, "key": "6a7db5280f88ec20232f86f03e49aa86"}, {"line": 4002, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3258, "target": 2369, "key": "f394eb4d5ba36fb5d8018556c5a8a319"}, {"line": 2346, "relation": "decreases", "evidence": "Mechanistic studies have shown that FAD mutants of presenilin can affect the intracellular calcium levels by affecting the ER calcium stores. A function for presenilins as ER calcium leak channels has been established and studies show that presenilins affect ER calcium load through an effect on IP(3) receptors, ryanodine receptors, or SERCA pumps. Even in the absence of an active gamma-secretase complex, presenilins seem to affect calcium homeostasis suggesting that these two functions of presenilins are independent of each other.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3258, "target": 549, "key": "a56bfa8b7f2241782e0602f1a4d239b2"}, {"line": 2357, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 491, "key": "26f1805a9c07f53c86dd8501b6d55543"}, {"line": 2362, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 495, "key": "69e2befd839b662e8dc779662e6c564c"}, {"line": 2373, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 846, "key": "dba9e1be70535cf91c5b26b3bec99ce9"}, {"line": 2461, "relation": "association", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 3823, "key": "5749fe7f1b843791acf48b6570e238fa"}, {"line": 9070, "relation": "increases", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 3823, "key": "d243a82a970c7d646718618a963f2a5b"}, {"line": 20391, "relation": "association", "evidence": "Mutations in the presenilin-1 (PS1) gene cause early onset familial Alzheimer's disease (FAD) by a mechanism believed to involve perturbed endoplasmic reticulum (ER) function and altered proteolytic processing of the amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "12390529"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Gamma secretase subgraph": true}}, "source": 3258, "target": 3823, "key": "a7d9c99dfcc66cee7f5fb5e3537d04ce"}, {"line": 29335, "relation": "increases", "evidence": "Presenilin 1, a causative gene product of familial Alzheimer disease, has been reported to be localized mainly in the endoplasmic reticulum and Golgi membranes.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 3823, "key": "5204cdfc36b75ce3108514854fa558f9"}, {"line": 32006, "relation": "association", "evidence": "Mutations of presenilin-1, the gamma-secretase catalytic subunit, can affect amyloid-beta (Abeta) production and Alzheimer disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "17115048"}, "source": 3258, "target": 3823, "key": "8c84bd91c48192bf96497d4f1961dbc1"}, {"line": 32418, "relation": "association", "evidence": "The amyloid precursor protein (APP) and the presenilins 1 and 2 are genetically linked to the development of familial Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 3823, "key": "1a3bbcfe5469bd0e200627525a192b20"}, {"line": 32444, "relation": "association", "evidence": "Presenilin 1 (PS1) is linked to the pathogenesis of early onset familial Alzheimer's disease (FAD) and is localized at the synapse, where it binds N-cadherin and pmodulates its adhesive activity.", "citation": {"db": "PubMed", "db_id": "14515347"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 3258, "target": 3823, "key": "574bd6170243a9283ba2543f7e60203d"}, {"line": 34147, "relation": "association", "evidence": "Presenilin 1 (PS1) is linked with Alzheimer's disease but exhibits functional roles regulating growth and development.", "citation": {"db": "PubMed", "db_id": "11504726"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 3823, "key": "9cf0cdb021beb5268d8c036b9063014e"}, {"line": 46404, "relation": "association", "evidence": "Mutations in the presenilin 1 gene have been shown to result in Alzheimer's disease. Presenilin 1 is a multi-transmembrane protein with a large hydrophilic loop near the C-terminus. This region is required for known functions of presenilin 1. We have constrained this loop within the active site of the bacterial protein, thioredoxin, to mimic its native conformational state. This hybrid protein was used as bait in a yeast two hybrid screen in an attempt to identify presenilin binding proteins. By this method syntaxin 1A, a synaptic plasma membrane protein, was identified as a novel binding protein for presenilin 1. In vitro experiments confirm the two-hybrid results suggesting that PS1 binds syntaxin under physiological conditions.", "citation": {"db": "PubMed", "db_id": "10891589"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 3823, "key": "449bcca88110f5ad6f8e59193c1121c7"}, {"line": 2468, "relation": "decreases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Beta secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 880, "key": "0259788aaf1c0304682d608d272ea883"}, {"line": 2470, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Beta secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2678, "key": "20d1856c844fa1cf1037bb2bf583d9a6"}, {"line": 2480, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 771, "key": "ee1fd77de3b5b04a352f9d6355867e5e"}, {"line": 2481, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2367, "key": "99975f37edffde88357e04b895015a0c"}, {"line": 2484, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2664, "key": "e20b251c31f89e48608254b7ebb49ffc"}, {"line": 2710, "relation": "increases", "evidence": "In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. ", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3258, "target": 2315, "key": "96d8f3c605b20fa09ef06f54a79033dc"}, {"line": 27983, "relation": "directlyIncreases", "evidence": "Gamma-secretase (PSEN1, PSEN2, NCSTN) cleaves LRP1 and LRP8 as well as APP and their degradation products control transcription factor TFCP2, which regulates thymidylate synthase (TS) and GSK3B expression. ", "citation": {"db": "PubMed", "db_id": "16973241"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3258, "target": 2315, "key": "753d0a30c9a4c22a4ec786ec59d56ea5"}, {"line": 32122, "relation": "increases", "evidence": "Presenilin-1 (PS1) and presenilin 2 (PS2) are proposed to be transmembrane aspartyl proteases that cleave amyloid precursor protein and Notch.", "citation": {"db": "PubMed", "db_id": "12684521"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3258, "target": 2315, "key": "0598e583a1614bce3672e71949d1b1c3"}, {"line": 2713, "relation": "increases", "evidence": "In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. ", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3258, "target": 684, "key": "023683006d02394209756c1e8509c347"}, {"line": 35733, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3258, "target": 684, "key": "2dd71e6d425bf1a074b8fb4489b1f847"}, {"relation": "partOf", "source": 3258, "target": 1395, "key": "3591a8f062e3ffcbf9850ce65d10fd1b"}, {"line": 2951, "relation": "decreases", "evidence": "Our present study demonstrated that the PS1 mutants M146L, E280A and H163R directly affect gamma-secretase activity, which leads to a reduction in the rate of Notch1 cleavage.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 3126, "key": "fa1ba1873cad689921e0a3b2a0688e13"}, {"relation": "hasVariant", "source": 3258, "target": 3266, "key": "7d80cf0b685a5bf002c65ec61f363160"}, {"relation": "hasVariant", "source": 3258, "target": 3264, "key": "c4515724e1c95f1ad1e25f8616c7bf03"}, {"relation": "partOf", "source": 3258, "target": 1700, "key": "f39beee808a6e09b6c37ca617474c7c3"}, {"line": 4758, "relation": "increases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 3258, "target": 2654, "key": "9f215188c2fb817d4e1fbe78511fa65d"}, {"line": 4762, "relation": "increases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2654, "key": "d0fad0a9063d2e282c0e282836509d6d"}, {"line": 4760, "relation": "decreases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3258, "target": 2571, "key": "b23a24a7e1d9f62adba7a654087e97a4"}, {"line": 35928, "relation": "decreases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "object": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3258, "target": 2571, "key": "b47a747cf940f8f89812d1fc1c00bdc9"}, {"line": 4763, "relation": "decreases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 645, "key": "1a7d7e85dcd4224cfa4b8f75bb7898fb"}, {"line": 4847, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3258, "target": 2328, "key": "5188fb4f7c004763df89280248808a0c"}, {"line": 32486, "relation": "association", "evidence": "Presenilin 1 (PS1) plays a critical role in the gamma-secretase processing of the amyloid precursor protein to generate the beta-amyloid peptide, which accumulates in plaques in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "18299393"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 2328, "key": "c6bc53ac1959a93718bf30d11b26e91a"}, {"line": 45368, "relation": "increases", "evidence": "hypomethylation of the promoter of the presenilin 1 (PS1) gene, which will lead to overexpression of presenilin 1 and, consequently, to increased Abeta(1-42) (Abeta42) formation ", "citation": {"db": "PubMed", "db_id": "16040194"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3258, "target": 2328, "key": "64469c971b0882c6d0d8b2698a81ba42"}, {"relation": "partOf", "source": 3258, "target": 1373, "key": "4bc347a9df32c92afc89f27fddf1f730"}, {"line": 4848, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 3258, "target": 1373, "key": "932089f7d9901c20ae5039cee9330083"}, {"relation": "partOf", "source": 3258, "target": 1449, "key": "c89daad152d129951bdfc8872d7955b3"}, {"line": 4849, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 3258, "target": 1449, "key": "94b52091a339498941045eae875f28e0"}, {"line": 4851, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 3258, "target": 1375, "key": "16ee3ccba692b46b0768192d5de85c77"}, {"line": 4852, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 3258, "target": 1450, "key": "124f77d3383368b10ef300691b9c2bda"}, {"line": 4871, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 453, "key": "8862306368e2580576cfe7b8b26bc8fe"}, {"relation": "partOf", "source": 3258, "target": 1256, "key": "9c71e1b744b5e21b5ec8aaba0554b4fd"}, {"line": 9064, "relation": "association", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 3000, "key": "f782d31bede7a922e26b1e80680cbc53"}, {"line": 9065, "relation": "association", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2990, "key": "319f447229005a8afc2bed1d8ef820e5"}, {"line": 20398, "relation": "increases", "evidence": "We report that PS1 mutations cause a marked increase in basal protein levels of the pro-apoptotic transcription factor Gadd153. PS1 mutations increase Gadd153 protein translation without affecting mRNA levels, while decreasing levels of the anti-apoptotic protein Bcl-2.", "citation": {"db": "PubMed", "db_id": "12390529"}, "annotations": {"Subgraph": {"Response DNA damage": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 2622, "key": "50f588ce8d3ae241ca24db8eb535e88e"}, {"relation": "partOf", "source": 3258, "target": 1113, "key": "a9f1f47dce1a019fbaeb6804fd7c2838"}, {"relation": "partOf", "source": 3258, "target": 1249, "key": "bef44b94e042a8124d463ecb6a3aa31f"}, {"relation": "partOf", "source": 3258, "target": 1524, "key": "3f9a05612076150a0d8cb8378e5fa9cc"}, {"relation": "partOf", "source": 3258, "target": 1530, "key": "2337b6f4a8a008e9a67458ad4afb5c9b"}, {"relation": "partOf", "source": 3258, "target": 1331, "key": "d41bb8520ae52c61f5792f7559ce508e"}, {"line": 29345, "relation": "increases", "evidence": "Presenilin 1 interacts with N-cadherin/beta-catenin to form a trimeric complex at the synaptic site through its loop domain, whose serine residues (serine 353 and 357) can be phosphorylated by glycogen synthase kinase 3beta.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3258, "target": 1331, "key": "dd245016b581a514def41bd0eac05699"}, {"relation": "hasVariant", "source": 3258, "target": 3260, "key": "5274d9368a528e391c7d9fae3ff81aa6"}, {"relation": "hasVariant", "source": 3258, "target": 3261, "key": "11ba35669292e0ab4e66176c6854555a"}, {"relation": "partOf", "source": 3258, "target": 1335, "key": "c109b24176554e94a45f384946063912"}, {"line": 31469, "relation": "positiveCorrelation", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "cell surface"}}}, "source": 3258, "target": 1335, "key": "3507217adf7efb112d911502f83a72ad"}, {"relation": "hasVariant", "source": 3258, "target": 3259, "key": "4c78447b7e206ccdaad2205ef52ef235"}, {"relation": "partOf", "source": 3258, "target": 1350, "key": "b21c9b147cc334a3d71b7d5be66b9652"}, {"relation": "hasVariant", "source": 3258, "target": 3262, "key": "213919a7df60ef47047d56af4c91bbd3"}, {"line": 29927, "relation": "decreases", "evidence": "When PS and calpain were separately expressed in COS cells by cDNA transfection and then combined in vitro, or both were co-transfected to be co-expressed in vivo in COS cells, PS1 and PS2 reduced the casein proteolysis activity of m-calpain but not that of mu-calpain. ", "citation": {"db": "PubMed", "db_id": "10677567"}, "annotations": {"Subgraph": {"Calpastatin-calpain subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3258, "target": 2435, "key": "103d96d1d13187b8938453b912df270b"}, {"relation": "partOf", "source": 3258, "target": 1352, "key": "10cf75463bb697a1ae483d070905245f"}, {"line": 29949, "relation": "association", "evidence": "Presenilin (PS) proteins facilitate endoproteolysis of selected type I transmembrane proteins such as the Alzheimer's disease (AD) associated beta-Amyloid precursor protein (beta APP) and Notch.", "citation": {"db": "PubMed", "db_id": "11493036"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3258, "target": 2332, "key": "0aa8870e5d82855e6e13cabbc2db9605"}, {"line": 29950, "relation": "association", "evidence": "Presenilin (PS) proteins facilitate endoproteolysis of selected type I transmembrane proteins such as the Alzheimer's disease (AD) associated beta-Amyloid precursor protein (beta APP) and Notch.", "citation": {"db": "PubMed", "db_id": "11493036"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3258, "target": 3127, "key": "66138e70a5fe5d66fb82e680622a352e"}, {"relation": "hasVariant", "source": 3258, "target": 3267, "key": "0ef6b14435950c10331098b0d35a2203"}, {"relation": "hasVariant", "source": 3258, "target": 3265, "key": "c543023060ceea8d53350b6ba4fb06bd"}, {"relation": "partOf", "source": 3258, "target": 1382, "key": "4f311c2610a0653fb0e7d8788693ac7a"}, {"line": 32281, "relation": "increases", "evidence": "The effects of PS-1 on endogenous delta-catenin processing were confirmed in hippocampal neurons overexpressing PS-1, as well as in the transgenic mice expressing the disease-causing mutant PS-1 (M146V). ", "citation": {"db": "PubMed", "db_id": "17097608"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 1382, "key": "53e42b8a2aa297ce38831b707add321b"}, {"relation": "partOf", "source": 3258, "target": 1402, "key": "566bfc8f35806e19128e762999ce53df"}, {"relation": "partOf", "source": 3258, "target": 1506, "key": "27976ccf99bd969845252acf19ea0a49"}, {"relation": "partOf", "source": 3258, "target": 1582, "key": "cb67577e3c2247f63fb2d2d18b1d068b"}, {"relation": "partOf", "source": 3258, "target": 1115, "key": "08c699025403616877affb5b8eed555f"}, {"relation": "partOf", "source": 3258, "target": 1595, "key": "e362ebed4321626ba57a625073f918e8"}, {"relation": "partOf", "source": 3258, "target": 1202, "key": "48e0e41a97ca590ad0d3d597e8230c4a"}, {"line": 31861, "relation": "increases", "evidence": "This cleavage is similar to the PS-dependent gamma-secretase cleavage of the beta-amyloid precursor protein (betaAPP). ", "citation": {"db": "PubMed", "db_id": "12374741"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3258, "target": 80, "key": "a5fbc8a1503498d9e78a4d7db03845f8"}, {"line": 31999, "relation": "increases", "evidence": "Presenilin-1 (PS1) is intimately involved in cleavage of amyloid precursor protein to form beta-amyloid peptides, certain forms of which aggregate in the brains of patients with Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "11814648"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "d54ebe5e6d5c1cdfc0923e4502a73dd9"}, {"line": 32012, "relation": "increases", "evidence": "This enhancement involved the association of beta(2)-AR with presenilin-1 and required agonist-induced endocytosis of beta(2)-AR and subsequent trafficking of gamma-secretase to late endosomes and lysosomes, where Abeta production was elevated.", "citation": {"db": "PubMed", "db_id": "17115048"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "ab4737ed3902c08569fd3339c245b2e8"}, {"line": 32032, "relation": "increases", "evidence": "These findings suggest that PS1 may mediate the shuttling of APP fragments and/or facilitate their presentation for gamma-secretase cleavage through a direct interaction.", "citation": {"db": "PubMed", "db_id": "10801777"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "6071da06854566c9f25c83eef643115e"}, {"line": 32043, "relation": "increases", "evidence": "It was hypothesized that PS1 might directly cleave APP.", "citation": {"db": "PubMed", "db_id": "10670705"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "da3435681ef88d01225c3311fcb43864"}, {"line": 32055, "relation": "increases", "evidence": "Therefore, our results show that PS1 binds to APP directly and suggest that the PS1 protein itself is involved in the metabolism of beta-amyloid peptide.", "citation": {"db": "PubMed", "db_id": "9344855"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "d15869683e889af4a18630b16db2ed02"}, {"line": 32066, "relation": "increases", "evidence": "Our results indicate PS1 and APP can interact in the ER and Golgi, where PS1 is required for proper gamma-secretase processing of APP CTFs, and that PS1 mutations augment Abeta42 levels principally in Golgi-like vesicles.", "citation": {"db": "PubMed", "db_id": "9843412"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "9e6d1ee0f33b295e7d52bf3262e1b9da"}, {"line": 32077, "relation": "increases", "evidence": "Instead, membrane associated carboxyterminal fragments generated by (alpha- and beta-secretase accumulated suggesting that PS-1 is involved in the gamma-secretase activity cleaving the transmembrane domain of APP after alpha- and beta-secretase cleavage has occured.", "citation": {"db": "PubMed", "db_id": "10653282"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "9af4af1142b1d215c31aff80a6e2f6dc"}, {"line": 32137, "relation": "increases", "evidence": "Presenilin 1 (PS1) plays a critical role in cleaving amyloid precursor protein (APP) to produce amyloid-beta (Abeta), the primary proteinaceous component of the senile plaques associated with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "14980721"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "9a0b6981ab167812507ff301d17e4928"}, {"line": 32148, "relation": "increases", "evidence": "PS1 regulates the intramembranous proteolysis of a 99-amino-acid C-terminal fragment of the amyloid precursor protein (APP-C99), a cleavage event that releases Abeta following a reaction catalyzed by an enzyme termed 'gamma-secretase'.", "citation": {"db": "PubMed", "db_id": "14993906"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "f13a25b5154a5f03da143dbffb5e230c"}, {"line": 32266, "relation": "increases", "evidence": "Missense substitutions in the presenilin 1 (PS1) and presenilin 2 (PS2) proteins are associated with early-onset familial Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "10037471"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "c5be803e58834fcf7e325c5a7ad568dc"}, {"line": 32372, "relation": "increases", "evidence": "Amyloid beta-peptide (Abeta) is generated by the consecutive cleavages of beta- and gamma-secretase.", "citation": {"db": "PubMed", "db_id": "12147673"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "e265bdfc77864d487f821b22c6db1bb3"}, {"line": 32394, "relation": "increases", "evidence": "Abundant biochemical and genetic evidence suggests that presenilins are catalytic components of gamma-secretase, the protease responsible for generating the Alzheimer amyloid beta-protein.", "citation": {"db": "PubMed", "db_id": "12603837"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3258, "target": 80, "key": "7ae497466b3855b7b49869cf0f9846f0"}, {"line": 45145, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3258, "target": 80, "key": "49d6c252562dcc2a4ce4156cfe345667"}, {"relation": "partOf", "source": 3258, "target": 1605, "key": "bd4a09e2c90410c643672bba9c9f4aad"}, {"relation": "partOf", "source": 3258, "target": 1330, "key": "1c0b7a3a3296a7c8bec6833b60b51140"}, {"relation": "partOf", "source": 3258, "target": 1379, "key": "468eb62a52a99aa00b100b45e078ee8a"}, {"relation": "partOf", "source": 3258, "target": 1328, "key": "539a3cf930c8e97c0c710e2a9c85c9b2"}, {"relation": "partOf", "source": 3258, "target": 1377, "key": "b5cf1a84dabdbf84a6a19e0944387bdd"}, {"line": 32349, "relation": "decreases", "evidence": "Whereas non-processed PS1 inhibits beta-catenin.Tcf-4 activity through a mechanism independent of gamma-secretase and associated with the interaction of this protein with plakoglobin and Tcf-4, the effect of processed PS1 is prevented by gamma-secretase inhibitors, and requires its interaction with E- or N-cadherin and the generation of cytosolic terminal fragments of these two cadherins, which in turn destabilize the beta-catenin transcriptional cofactor CBP.", "citation": {"db": "PubMed", "db_id": "19114997"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 2586, "key": "1f2518f4051523c840c434b6ccbea283"}, {"line": 34069, "relation": "decreases", "evidence": "Alzheimer disease-linked Presenilin-1 (PS1) is a negative pmodulator of beta-catenin/Tcf-4 activity.", "citation": {"db": "PubMed", "db_id": "16306047"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3258, "target": 2586, "key": "155233a3196bd3000d121c11e25a76e9"}, {"relation": "partOf", "source": 3258, "target": 1292, "key": "2f7971fdd809b43c0b5cd7a6ee7b14ed"}, {"relation": "partOf", "source": 3258, "target": 1440, "key": "e3da76abcd7304c94a9db57d4b508cc0"}, {"line": 32434, "relation": "increases", "evidence": "First, we show that PS1 is phosphorylated by the Dyrk1A at Thr(354) and that this phosphorylation increases beta-secretase activity.", "citation": {"db": "PubMed", "db_id": "20456003"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3258, "target": 2649, "key": "94e513fed6b05386fd5bbc3b126ddaea"}, {"relation": "partOf", "source": 3258, "target": 1333, "key": "6c6e229f0fb79f593b1fbd6a54765824"}, {"relation": "partOf", "source": 3258, "target": 1048, "key": "91ffa11857b9ec9ee1fd0ddff4bcbea7"}, {"relation": "partOf", "source": 3258, "target": 1557, "key": "75effc4c23f371ddf30f4b8def0dd93e"}, {"line": 32507, "relation": "association", "evidence": "PS1 directly binds tau and a tau kinase , glycogen synthase kinase 3beta", "citation": {"db": "PubMed Central", "db_id": "PMC21391"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 3010, "key": "9d5f3a236f2c2a1d6edf938e403a9644"}, {"relation": "partOf", "source": 3258, "target": 1258, "key": "cc5d4a215d68e64aabdcbef87eac5e75"}, {"relation": "partOf", "source": 3258, "target": 1414, "key": "0a48435116bdd97836a29d9e4b0e8176"}, {"line": 33085, "relation": "increases", "evidence": "We have previously reported that familial Alzheimer's disease linked presenilin-1 variants downregulate the signaling pathway of the UPR by affecting the phosphorylation of Ire1 alpha.", "citation": {"db": "PubMed", "db_id": "11146108"}, "annotations": {"Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3258, "target": 2679, "key": "05c0a9a9a2ebc1546063f1962d0a0a9e"}, {"line": 33093, "relation": "decreases", "evidence": "Recent studies have shown independently that presenilin-1 (PS1) null mutants and familial Alzheimer's disease (FAD)-linked mutants should both down-regulate signaling of the unfolded protein response (UPR)", "citation": {"db": "PubMed", "db_id": "11551913"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 777, "key": "bdfd0accf25077808e68c366f4a80b2f"}, {"relation": "partOf", "source": 3258, "target": 1078, "key": "61829b07662b10dfb36cb4f2450c802a"}, {"relation": "partOf", "source": 3258, "target": 1505, "key": "1690f9f45f8332da1cd4a5d1fe64577f"}, {"relation": "partOf", "source": 3258, "target": 1621, "key": "483f8bfb4bff68bb8ec4dc692cecf77e"}, {"line": 34065, "relation": "increases", "evidence": "Presenilin-1 interacts with plakoglobin and enhances plakoglobin-Tcf-4 association.", "citation": {"db": "PubMed", "db_id": "16306047"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Gamma secretase subgraph": true}}, "source": 3258, "target": 1621, "key": "f085f0ecafbf83b4df67449b4bd5a8c8"}, {"relation": "partOf", "source": 3258, "target": 1576, "key": "e40fac26af9eeac94c0d874f111c9906"}, {"relation": "partOf", "source": 3258, "target": 1619, "key": "c4656b96fb3e2b0c8b262cf640add87d"}, {"line": 34133, "relation": "increases", "evidence": "Strikingly, in the absence of PS1 and PS1/PS2, PEN-2 levels are strongly reduced. ", "citation": {"db": "PubMed", "db_id": "12198112"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 3258, "target": 3272, "key": "1b9dc0ce431d48b2348c5b6c7bbb3dac"}, {"relation": "partOf", "source": 3258, "target": 1622, "key": "9564e986300b36057d1778048d134fc9"}, {"relation": "partOf", "source": 3258, "target": 1623, "key": "35c1e078d6fa81a2998498ece89d8131"}, {"relation": "partOf", "source": 3258, "target": 1692, "key": "fee78a0a1ca851f89cbfdae81fc5207f"}, {"relation": "partOf", "source": 3258, "target": 1687, "key": "1ab5253d6c737d053086f8044c1401b5"}, {"relation": "partOf", "source": 3258, "target": 1172, "key": "e5be40224bec863d11908bcced120307"}, {"relation": "partOf", "source": 3258, "target": 1374, "key": "45aff1df783397e628f3f82baa2cbbd8"}, {"relation": "partOf", "source": 3258, "target": 1717, "key": "f71d32f38ee64a8d73ecdbf07b1cc290"}, {"line": 45367, "relation": "negativeCorrelation", "evidence": "hypomethylation of the promoter of the presenilin 1 (PS1) gene, which will lead to overexpression of presenilin 1 and, consequently, to increased Abeta(1-42) (Abeta42) formation ", "citation": {"db": "PubMed", "db_id": "16040194"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3258, "target": 1926, "key": "7f31e75af3f8fe0af0403c54c1c3777a"}, {"line": 45514, "relation": "negativeCorrelation", "evidence": "A notable exception was PSEN1, which was modestly hypomethylated in LOAD cases LOAD cases had reduced DNA methylation that was associated with increased PSEN1 gene expression, suggesting the DNA methylation change may be functional at this site", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 3258, "target": 1926, "key": "008f99b30638b651fd87298af3f1d5ad"}, {"line": 46097, "relation": "negativeCorrelation", "evidence": "impaired DNA methylation resulting from a deficiency in S-adenosylmethionine (SAM, which is rapidly depleted following folate deprivation) leads to PS-1 overexpression, and that direct supplementation with SAM attenuates PS-1 overexpression. We determined that apple juice concentrate (AJC)contained levels of SAM comparable to those capable of suppressing PS-1 overexpression, suggesting that the SAM content of AJC represents a potential mechanism for preventing PS-1 overexpression, and further highlighting the possibility that AJC provides neuroprotection by mechanisms in addition to its antioxidant potential.", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 3258, "target": 1926, "key": "a3aaccccfe2b5672732bd436fa3cad3d"}, {"line": 45914, "relation": "association", "evidence": "Our studies showed that p300-HAT inhibitor curcumin abrogates H3 hyperacetylation of PS1 and BACE1, curcumin decreases PS1 activity", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3258, "target": 2803, "key": "48b2837a9a42bb259b204dbd15ac5a0a"}, {"line": 46086, "relation": "increases", "evidence": "presenilin-1 (PS-1) promote Alzheimer's disease (AD) by increasing reactive oxygen species, at least part of which is derived by an accompanying increase in generation of amyloid-beta (Abeta).", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 3258, "target": 170, "key": "99cda08f40da5aec07d540a402567dd4"}, {"line": 46090, "relation": "negativeCorrelation", "evidence": "folate deficiency has been shown to increase PS-1 expression.", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 3258, "target": 115, "key": "42ac4c140722eeda4f641e92f0fdfb76"}, {"line": 46093, "relation": "negativeCorrelation", "evidence": "we demonstrate that dietary deficiency in folate and vitamin E increased PS-1 expression", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 3258, "target": 188, "key": "027e3dd05e3166074406902f5afd8955"}, {"relation": "partOf", "source": 3258, "target": 1620, "key": "45b9f58a335788b53b5dd4da0955bb04"}, {"relation": "partOf", "source": 3094, "target": 868, "key": "7d20581187e7940a3feab0f5cda1cc2b"}, {"relation": "isA", "source": 3094, "target": 868, "key": "9d81149d8bdce04bb7ec4f6f66002008"}, {"relation": "partOf", "source": 3094, "target": 1113, "key": "be06476306c94d5ff3322b4955cef09f"}, {"relation": "partOf", "source": 3094, "target": 1522, "key": "e4603295caa80bf2669c1959a2f82b8e"}, {"relation": "partOf", "source": 3094, "target": 1529, "key": "a1cb8ea182e175bfac91fbd2ad76875e"}, {"line": 27995, "relation": "directlyIncreases", "evidence": "Gamma-secretase (PSEN1, PSEN2, NCSTN) cleaves LRP1 and LRP8 as well as APP and their degradation products control transcription factor TFCP2, which regulates thymidylate synthase (TS) and GSK3B expression. ", "citation": {"db": "PubMed", "db_id": "16973241"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3094, "target": 2315, "key": "df985aed47617ecad3fafb22335fdbcd"}, {"relation": "partOf", "source": 3094, "target": 1193, "key": "579b1432ae96f89926eaa94b57db7b3e"}, {"line": 31589, "relation": "association", "evidence": "Nicastrin also binds carboxy-terminal derivatives of beta-amyloid precursor protein ( betaAPP ) , and pmodulates the production of the amyloid beta-peptide ( A beta ) from these derivatives.", "citation": {"db": "PubMed", "db_id": "10993067"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3094, "target": 80, "key": "7d48dd2344b206deb485e13429b36d65"}, {"line": 31599, "relation": "association", "evidence": "Increasing evidences have shown that nicastrin (NCSTN) plays a crucial role in gamma-cleavage of the amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "16423463"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3094, "target": 80, "key": "8de53afdfbe1a4ebf9f30d5083563009"}, {"line": 31610, "relation": "association", "evidence": "Increasing evidences have shown that nicastrin (NCSTN) plays a crucial role in gamma-cleavage of the amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "19394408"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3094, "target": 80, "key": "74d4c537132390869a1a576451634590"}, {"line": 31691, "relation": "increases", "evidence": "Nicastrin also binds carboxy-terminal derivatives of beta-amyloid precursor protein (betaAPP), and pmodulates the production of the amyloid beta-peptide (A beta) from these derivatives.", "citation": {"db": "PubMed", "db_id": "10993067"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3094, "target": 80, "key": "f7359f82160cbf59f9d00926829e1db5"}, {"relation": "partOf", "source": 3094, "target": 1582, "key": "a750ab1e08509519fe348eec2ddb59c1"}, {"relation": "partOf", "source": 3094, "target": 1581, "key": "29197e0f5addc73eab5f6b91b489c12b"}, {"relation": "partOf", "source": 3094, "target": 1112, "key": "dd3d6c584ec00042daf452608be3b458"}, {"relation": "partOf", "source": 3094, "target": 1583, "key": "4f4e0ee25d37fd5e426b50d630be0039"}, {"relation": "partOf", "source": 2304, "target": 868, "key": "140db25cda4547438fc251bee2527dc5"}, {"relation": "partOf", "source": 2304, "target": 1111, "key": "dead0cf382537df9ba19d17e134623fb"}, {"relation": "partOf", "source": 2304, "target": 1113, "key": "d537ecea7730dffb54000bda3e876055"}, {"relation": "partOf", "source": 2304, "target": 1112, "key": "7383b6e111809d9b6caae8733bb799f4"}, {"relation": "partOf", "source": 2304, "target": 1115, "key": "d5cfe4a060f1f712348b972535b6af1f"}, {"relation": "partOf", "source": 2304, "target": 1114, "key": "57eb0749a60b8b1cc7e1f798d2a95ec2"}, {"line": 31659, "relation": "association", "evidence": "APH-1 interacts with mature and immature forms of presenilins and nicastrin and may play a role in maturation of presenilin.nicastrin complexes.", "citation": {"db": "PubMed", "db_id": "12471034"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2304, "target": 1595, "key": "878625623f405bcd293dde4eb59a6366"}, {"line": 34104, "relation": "increases", "evidence": "Similar to the loss of presenilin or nicastrin, the inactivation of endogenous mAPH-1 using small interfering RNAs results in the decrease of presenilin levels, accumulation of gamma-secretase substrates (APP carboxyl-terminal fragments), and reduction of gamma-secretase products (amyloid-beta peptides and the intracellular domains of APP and Notch). ", "citation": {"db": "PubMed", "db_id": "12297508"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2304, "target": 3258, "key": "e7ef0ac016242edf9f05f85320a55096"}, {"line": 34105, "relation": "increases", "evidence": "Similar to the loss of presenilin or nicastrin, the inactivation of endogenous mAPH-1 using small interfering RNAs results in the decrease of presenilin levels, accumulation of gamma-secretase substrates (APP carboxyl-terminal fragments), and reduction of gamma-secretase products (amyloid-beta peptides and the intracellular domains of APP and Notch). ", "citation": {"db": "PubMed", "db_id": "12297508"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2304, "target": 2315, "key": "8f2fd52f2a53e5654fbfdccfba6296f7"}, {"line": 34106, "relation": "increases", "evidence": "Similar to the loss of presenilin or nicastrin, the inactivation of endogenous mAPH-1 using small interfering RNAs results in the decrease of presenilin levels, accumulation of gamma-secretase substrates (APP carboxyl-terminal fragments), and reduction of gamma-secretase products (amyloid-beta peptides and the intracellular domains of APP and Notch). ", "citation": {"db": "PubMed", "db_id": "12297508"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2304, "target": 3126, "key": "9c79bee98a578b5fd1687883b1af0c1d"}, {"line": 34107, "relation": "increases", "evidence": "Similar to the loss of presenilin or nicastrin, the inactivation of endogenous mAPH-1 using small interfering RNAs results in the decrease of presenilin levels, accumulation of gamma-secretase substrates (APP carboxyl-terminal fragments), and reduction of gamma-secretase products (amyloid-beta peptides and the intracellular domains of APP and Notch). ", "citation": {"db": "PubMed", "db_id": "12297508"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2304, "target": 80, "key": "e3ae6acee424639376ae9a01e02a7d2b"}, {"relation": "partOf", "source": 3268, "target": 868, "key": "208cca47658cb2e24114b060221ee578"}, {"relation": "isA", "source": 3268, "target": 868, "key": "000fa905a2e54a45651b7e09b36a3dec"}, {"line": 35727, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3268, "target": 868, "key": "afb13f6e0c1220e15b49a517af22785d"}, {"relation": "hasVariant", "source": 3268, "target": 3271, "key": "c98097c9c1ecf3269ca5cde8e1a2c347"}, {"relation": "partOf", "source": 3268, "target": 1716, "key": "fb76ce5e4427b2c21dcc54e96632a6ca"}, {"line": 2321, "relation": "increases", "evidence": "Mechanistic studies have shown that FAD mutants of presenilin can affect the intracellular calcium levels by affecting the ER calcium stores. A function for presenilins as ER calcium leak channels has been established and studies show that presenilins affect ER calcium load through an effect on IP(3) receptors, ryanodine receptors, or SERCA pumps. Even in the absence of an active gamma-secretase complex, presenilins seem to affect calcium homeostasis suggesting that these two functions of presenilins are independent of each other.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endoplasmic Reticulum"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 3268, "target": 94, "key": "8849698fce517bfacdaaab2463410217"}, {"line": 3983, "relation": "increases", "evidence": "Studies of the presenilins (PS) have implicated ER Ca2+ mishandling in AD. PS functions as an ER Ca2+ leak channel and FAD PS mutations impair this Ca2+ leak channel function resulting in excessive accumulation of Ca2+ in the ER and, as a consequence, enhances Ca2+ release through ryanodine receptor (RyR) and IP3 receptor (IP3R) channels", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endoplasmic Reticulum"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 3268, "target": 94, "key": "1c244a704ac2a37acc82a69981a89112"}, {"line": 2347, "relation": "decreases", "evidence": "Mechanistic studies have shown that FAD mutants of presenilin can affect the intracellular calcium levels by affecting the ER calcium stores. A function for presenilins as ER calcium leak channels has been established and studies show that presenilins affect ER calcium load through an effect on IP(3) receptors, ryanodine receptors, or SERCA pumps. Even in the absence of an active gamma-secretase complex, presenilins seem to affect calcium homeostasis suggesting that these two functions of presenilins are independent of each other.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3268, "target": 549, "key": "8fd89e2f7ea895c2f76bd9cca4e1766e"}, {"line": 2358, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3268, "target": 491, "key": "d21d5800d975810afd7afc3ec979e6a4"}, {"line": 2363, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3268, "target": 495, "key": "3d19e137999226fc4bc0ba113a2aeb97"}, {"line": 2374, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3268, "target": 846, "key": "0d95afb9bf1bee6ad3128d673fd9f1e1"}, {"line": 2462, "relation": "association", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3268, "target": 3823, "key": "4541bc38b158ad008c6ac35ad083a055"}, {"line": 17315, "relation": "association", "evidence": "Egr-1 upregulates the Alzheimer's disease presenilin-2 gene in neuronal cells.", "citation": {"db": "PubMed", "db_id": "14585504"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Published": {"Epilepsy comorbidity paper": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3268, "target": 3823, "key": "2f842de5e60b68ce64a7742c4a285c4b"}, {"line": 17340, "relation": "association", "evidence": "Inherited Presenilin-2 mutations cause familial Alzheimer's disease, and its regulation may play a role in sporadic cases.", "citation": {"db": "PubMed", "db_id": "19573580"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3268, "target": 3823, "key": "53c5830fd9ddd537fd8b0998dc4ed7d5"}, {"line": 32419, "relation": "association", "evidence": "The amyloid precursor protein (APP) and the presenilins 1 and 2 are genetically linked to the development of familial Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3268, "target": 3823, "key": "12759828297b8895fd067f006c988eb2"}, {"line": 2469, "relation": "decreases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Beta secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3268, "target": 880, "key": "25dd10d00610c13999c7956ff5452354"}, {"line": 2471, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Beta secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3268, "target": 2678, "key": "bb60265e778eedd27b0206f7298ae94c"}, {"line": 2482, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3268, "target": 2367, "key": "ebbee4c57576057d527738963d6faaf0"}, {"line": 2483, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3268, "target": 771, "key": "414acea059476f8ac17f4f0bbe487d87"}, {"line": 2486, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3268, "target": 2664, "key": "a450f95dfdedd2d6e1b90387629a94ad"}, {"line": 2711, "relation": "increases", "evidence": "In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. ", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3268, "target": 2315, "key": "ff66d1fb2e90e7216d3cc29bba7a391c"}, {"line": 27989, "relation": "directlyIncreases", "evidence": "Gamma-secretase (PSEN1, PSEN2, NCSTN) cleaves LRP1 and LRP8 as well as APP and their degradation products control transcription factor TFCP2, which regulates thymidylate synthase (TS) and GSK3B expression. ", "citation": {"db": "PubMed", "db_id": "16973241"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3268, "target": 2315, "key": "ace3f8aaa0bc0efa5c3d76b202b35e24"}, {"line": 32123, "relation": "increases", "evidence": "Presenilin-1 (PS1) and presenilin 2 (PS2) are proposed to be transmembrane aspartyl proteases that cleave amyloid precursor protein and Notch.", "citation": {"db": "PubMed", "db_id": "12684521"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3268, "target": 2315, "key": "d71e24628f2fba20bd6e913fd748d388"}, {"line": 2714, "relation": "increases", "evidence": "In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. ", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3268, "target": 684, "key": "426464cd68fe79bef1856f7e9fee13ea"}, {"line": 35734, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3268, "target": 684, "key": "4f1e40442f5b0148985ebeb279136538"}, {"relation": "partOf", "source": 3268, "target": 1396, "key": "ae1fc429185799b45b5324af6a85f8e0"}, {"line": 4004, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3268, "target": 757, "key": "41804f276f1a10d82714d51b6d0e0d77"}, {"line": 4005, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3268, "target": 3333, "key": "1172eaab46f37d1b070bac358403fe5e"}, {"line": 4007, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3268, "target": 2369, "key": "b01645c88152c34dbf18a40f5e58c0da"}, {"line": 4850, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3268, "target": 2328, "key": "ee57e452b44fe8b3958962c0e3e53bb2"}, {"relation": "partOf", "source": 3268, "target": 1375, "key": "4056287a2802d1b3b6ac5fa2dead74ae"}, {"relation": "partOf", "source": 3268, "target": 1450, "key": "0b125d3a6e962ed4706e84738e2eadab"}, {"line": 4870, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3268, "target": 453, "key": "b8c7580bc1e2e04eb610fea11cbc7a09"}, {"relation": "partOf", "source": 3268, "target": 1408, "key": "744be19c25bb936b463c4eded9eec6c3"}, {"relation": "partOf", "source": 3268, "target": 1525, "key": "dc9710b11abda2cad85fd14030141783"}, {"relation": "partOf", "source": 3268, "target": 1531, "key": "fb905b93e0e0fac2b36ba09a4aa8552a"}, {"relation": "partOf", "source": 3268, "target": 1203, "key": "84d83dab34b700e522b56632039cf5a6"}, {"relation": "partOf", "source": 3268, "target": 1353, "key": "671aa3586fe2b7b310d3e833190d3e77"}, {"relation": "partOf", "source": 3268, "target": 1302, "key": "b07ba7ee33191d8b6eea00dd92d1990a"}, {"line": 29928, "relation": "decreases", "evidence": "When PS and calpain were separately expressed in COS cells by cDNA transfection and then combined in vitro, or both were co-transfected to be co-expressed in vivo in COS cells, PS1 and PS2 reduced the casein proteolysis activity of m-calpain but not that of mu-calpain. ", "citation": {"db": "PubMed", "db_id": "10677567"}, "annotations": {"Subgraph": {"Calpastatin-calpain subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3268, "target": 2435, "key": "7022ca5a215d45bc31247bb940391d1a"}, {"relation": "partOf", "source": 3268, "target": 1259, "key": "2e492a85bfb95c94a56d42336b77627f"}, {"relation": "partOf", "source": 3268, "target": 1420, "key": "6ed3707a5629f52186f8af1d53b6203a"}, {"relation": "partOf", "source": 3268, "target": 1507, "key": "3e139374293d06a35535abbed5944833"}, {"relation": "partOf", "source": 3268, "target": 1583, "key": "b5af5146f07f766c8fd3f501dab486e3"}, {"relation": "hasVariant", "source": 3268, "target": 3269, "key": "6c4418c17318d6d8bd085ba33c9b67fb"}, {"relation": "partOf", "source": 3268, "target": 1596, "key": "4f452cd9f3a84036443360e8bc7faa64"}, {"relation": "partOf", "source": 3268, "target": 1606, "key": "2265105bb7ded22294a32fffd1e6a587"}, {"relation": "partOf", "source": 3268, "target": 1383, "key": "bdaef655e55f5caa5d0b7fef7c299ed2"}, {"line": 32267, "relation": "increases", "evidence": "Missense substitutions in the presenilin 1 (PS1) and presenilin 2 (PS2) proteins are associated with early-onset familial Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "10037471"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3268, "target": 80, "key": "4028562776c4d003e6e5d431f405be34"}, {"line": 32373, "relation": "increases", "evidence": "Amyloid beta-peptide (Abeta) is generated by the consecutive cleavages of beta- and gamma-secretase.", "citation": {"db": "PubMed", "db_id": "12147673"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3268, "target": 80, "key": "a44f8b058ff903c9c956613b9a9dd485"}, {"line": 32395, "relation": "increases", "evidence": "Abundant biochemical and genetic evidence suggests that presenilins are catalytic components of gamma-secretase, the protease responsible for generating the Alzheimer amyloid beta-protein.", "citation": {"db": "PubMed", "db_id": "12603837"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3268, "target": 80, "key": "99cdbb2591e3b4ba4d17dd7d34b1f6b7"}, {"relation": "hasVariant", "source": 3268, "target": 3270, "key": "42bf553705be97551c646c1df88bae82"}, {"relation": "partOf", "source": 3268, "target": 1367, "key": "70f491e7d3108b46540b4239f4566847"}, {"relation": "partOf", "source": 3268, "target": 1368, "key": "fd597654d5c47c33563617d91b55cccb"}, {"relation": "partOf", "source": 3268, "target": 1624, "key": "f11f0ab2109b7c833b143fb3389157b5"}, {"line": 34134, "relation": "increases", "evidence": "Strikingly, in the absence of PS1 and PS1/PS2, PEN-2 levels are strongly reduced. ", "citation": {"db": "PubMed", "db_id": "12198112"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 3268, "target": 3272, "key": "7f52f9b0b57dd8e5f95e2be0e3e45980"}, {"relation": "partOf", "source": 3268, "target": 1378, "key": "74ca9fe7650fc62804fac596d2eadb57"}, {"relation": "partOf", "source": 3268, "target": 1625, "key": "c4fe909d0620b5459b0c4bd3fbecb549"}, {"relation": "partOf", "source": 3268, "target": 1692, "key": "be48acba94c6891591ddfbc792b5c568"}, {"relation": "partOf", "source": 3268, "target": 1687, "key": "8a1f08d4395691318726fd7119caba8f"}, {"relation": "partOf", "source": 3268, "target": 1172, "key": "4ee3a367748e7d9643ef042efb687236"}, {"relation": "partOf", "source": 3268, "target": 1374, "key": "6d8d681f33fab18339f8174714045197"}, {"relation": "partOf", "source": 3268, "target": 1718, "key": "a5de681d4ee00be08e59761ea8cff15b"}, {"line": 250, "relation": "decreases", "evidence": "Adding metalloprotease inhibitors to the reaction showed that \\\"5-chloro-7-iodoquinolin-8-ol\\\", phosphoramidon, and zinc metalloprotease inhibitors had no significant effect on gamma-secretase activity. In contrast, phenanthroline, EDTA, and EGTA markedly decreased gamma-secretase activity that could be restored by adding back calcium and magnesium ions. Mg(2+) stabilized a 1,000kDa presenilin 1 complex through blue native gel electrophoresis and size-exclusion chromatography. Data suggest that Ca(2+) and Mg(2+) stabilize gamma-secretase and enhance its activity", "citation": {"db": "PubMed", "db_id": "21253550"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 329, "target": 868, "key": "204a659d51a3469bb8bed5bc76fab06b"}, {"line": 251, "relation": "decreases", "evidence": "Adding metalloprotease inhibitors to the reaction showed that \\\"5-chloro-7-iodoquinolin-8-ol\\\", phosphoramidon, and zinc metalloprotease inhibitors had no significant effect on gamma-secretase activity. In contrast, phenanthroline, EDTA, and EGTA markedly decreased gamma-secretase activity that could be restored by adding back calcium and magnesium ions. Mg(2+) stabilized a 1,000kDa presenilin 1 complex through blue native gel electrophoresis and size-exclusion chromatography. Data suggest that Ca(2+) and Mg(2+) stabilize gamma-secretase and enhance its activity", "citation": {"db": "PubMed", "db_id": "21253550"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 112, "target": 868, "key": "8f802ffcfd7e3ced3d06b6ad8e540c7a"}, {"line": 252, "relation": "decreases", "evidence": "Adding metalloprotease inhibitors to the reaction showed that \\\"5-chloro-7-iodoquinolin-8-ol\\\", phosphoramidon, and zinc metalloprotease inhibitors had no significant effect on gamma-secretase activity. In contrast, phenanthroline, EDTA, and EGTA markedly decreased gamma-secretase activity that could be restored by adding back calcium and magnesium ions. Mg(2+) stabilized a 1,000kDa presenilin 1 complex through blue native gel electrophoresis and size-exclusion chromatography. Data suggest that Ca(2+) and Mg(2+) stabilize gamma-secretase and enhance its activity", "citation": {"db": "PubMed", "db_id": "21253550"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 111, "target": 868, "key": "6846e42e98a1a20c147844ce1d8c4a37"}, {"line": 256, "relation": "increases", "evidence": "Adding metalloprotease inhibitors to the reaction showed that \\\"5-chloro-7-iodoquinolin-8-ol\\\", phosphoramidon, and zinc metalloprotease inhibitors had no significant effect on gamma-secretase activity. In contrast, phenanthroline, EDTA, and EGTA markedly decreased gamma-secretase activity that could be restored by adding back calcium and magnesium ions. Mg(2+) stabilized a 1,000kDa presenilin 1 complex through blue native gel electrophoresis and size-exclusion chromatography. Data suggest that Ca(2+) and Mg(2+) stabilize gamma-secretase and enhance its activity", "citation": {"db": "PubMed", "db_id": "21253550"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 94, "target": 868, "key": "b3c6bd0849ed8ab7a6e92cba97e8e545"}, {"line": 598, "relation": "increases", "evidence": "addition of calcium can stimulate cleavage of p35 to p25", "citation": {"db": "PubMed", "db_id": "10830966"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Calcium-dependent signal transduction": true, "Cyclin-CDK subgraph": true}}, "source": 94, "target": 4105, "key": "cefae0298f16b1e967577da5ae70fc33"}, {"line": 1323, "relation": "increases", "evidence": "Mechanistically, glutamate-induced cholesterol loss requires high levels of intracellular Ca(2+), a functional stromal interaction molecule 2 (STIM2) and mobilization of CYP46A1 towards the plasma membrane. This study underscores the key role of excitatory neurotransmission in the control of membrane lipid composition, and consequently in neuronal membrane organization and function.", "citation": {"db": "PubMed", "db_id": "22343944"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Calcium-dependent signal transduction": true, "Glutamatergic subgraph": true}}, "source": 94, "target": 690, "key": "77abd8efb5c9b842e3e5cfac8d53b1d7"}, {"line": 3205, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 94, "target": 2159, "key": "c1bd1d1f71675fd53b0d4c40db542f03"}, {"relation": "partOf", "source": 94, "target": 950, "key": "7602fb7f53113f19c1018a2348d809ef"}, {"line": 3889, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 94, "target": 739, "key": "451d5befeebc44790ae0b50e4180c8f3"}, {"line": 3937, "relation": "increases", "evidence": "The interaction of Abeta with the plasma membrane may be facilitated by binding to phosphatidylserine (PtdS); age/AD-related mitochondrial impairment (ATP depletion) may trigger flipping of PtdS from the inner portion of the plasma membrane to the cell surface. The PtdS flipping may also result from Ca2+ influx or release from the endoplasmic reticulum (ER) or mitochondria which can activate a phospholipid scramblase (PLSCR1)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}, "CellStructure": {"Cell Membrane": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}}}, "source": 94, "target": 160, "key": "61c616d654a7d493f57fce9c7ef57524"}, {"line": 3938, "relation": "directlyIncreases", "evidence": "The interaction of Abeta with the plasma membrane may be facilitated by binding to phosphatidylserine (PtdS); age/AD-related mitochondrial impairment (ATP depletion) may trigger flipping of PtdS from the inner portion of the plasma membrane to the cell surface. The PtdS flipping may also result from Ca2+ influx or release from the endoplasmic reticulum (ER) or mitochondria which can activate a phospholipid scramblase (PLSCR1)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}, "CellStructure": {"Cell Membrane": true}}, "object": {"modifier": "Activity"}, "source": 94, "target": 3207, "key": "8fc0bb99004aead699e767f368c7fdf2"}, {"line": 4066, "relation": "increases", "evidence": "Our findings indicate that Cd elevates [Ca+2](i), which induces ROS and activates MAPK and mTOR pathways, leading to neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 94, "target": 2173, "key": "ecc6e7ce67ee0ab40db661c2986a7b58"}, {"line": 4067, "relation": "increases", "evidence": "Our findings indicate that Cd elevates [Ca+2](i), which induces ROS and activates MAPK and mTOR pathways, leading to neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 94, "target": 2187, "key": "6b4f4fc7513caa32dbb308e19722206c"}, {"line": 37240, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 94, "target": 2187, "key": "32b0af477a237425d52d8a469d3e3eab"}, {"line": 4068, "relation": "increases", "evidence": "Our findings indicate that Cd elevates [Ca+2](i), which induces ROS and activates MAPK and mTOR pathways, leading to neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 94, "target": 2222, "key": "10ff380792447b3f51985f495fc10b5d"}, {"line": 4070, "relation": "increases", "evidence": "Our findings indicate that Cd elevates [Ca+2](i), which induces ROS and activates MAPK and mTOR pathways, leading to neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 94, "target": 460, "key": "8cad1c8e635a9d46c71276671774e7af"}, {"line": 4075, "relation": "increases", "evidence": "Our findings indicate that Cd elevates [Ca+2](i), which induces ROS and activates MAPK and mTOR pathways, leading to neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 94, "target": 170, "key": "143d03009ed4c829755f831e7b3c00bc"}, {"line": 4788, "relation": "increases", "evidence": "Moreover, we demonstrate that neuronal activity upregulates CRP1 expression in hippocampal neurons via Ca²+ influx after depolarization. Ca²+/calmodulin-dependent protein kinase IV (CaMKIV) and cAMP response element binding protein mediate the Ca²+-induced upregulation of CRP1 expression. Furthermore, CRP1 is required for the dendritic growth induced by Ca+? influx or CaMKIV. Together, these data are the first to demonstrate a role for CRP1 in dendritic growth.", "citation": {"db": "PubMed", "db_id": "22090504"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Calcium-dependent signal transduction": true}}, "source": 94, "target": 492, "key": "d4498c2279bf2a26eb21cc1c7df25ca0"}, {"line": 5541, "relation": "decreases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 94, "target": 688, "key": "49f4db09437ac3a4989d8e525c021eaf"}, {"line": 10510, "relation": "association", "evidence": "We also found that human islet amylin and the prion protein fragment (PrP106-126), peptides that acquire beta-pleated sheet conformation in water solutions and have been reported to form ion channels across planar bilayer membranes, also increase cytosolic free calcium in GT1-7 neurons.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"Subgraph": {"Amylin subgraph": true}}, "source": 94, "target": 3254, "key": "898644e22c4d38df7220edc657799c13"}, {"line": 10517, "relation": "association", "evidence": "These results suggest that unregulated Ca(2+) entry across amyloid channels may be a common mechanism causing cell death, not only in diseases of the third age, including Alzheimer's disease and type 2 diabetes mellitus, but also in prion-induced diseases.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}}, "source": 94, "target": 505, "key": "8cce80d2c214ca89c07bc13f15d47d6d"}, {"line": 15696, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}}, "source": 94, "target": 3823, "key": "bd07cf8cb7aa9e30f659a862f024cdc6"}, {"line": 30187, "relation": "increases", "evidence": "Chelation of intracellular Ca(2+) markedly suppressed APP-induced activation of caspase-3. ", "citation": {"db": "PubMed", "db_id": "12425945"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 94, "target": 2444, "key": "af8e404f16df42936aef7d06d90b4203"}, {"line": 35940, "relation": "increases", "evidence": "The induction of long-term potentiation at CA3-CA1 synapses is caused by an N-methyl-d-aspartate (NMDA) receptordependent accumulation of intracellular Ca(2+), followed by Src family kinase activation and a positive feedback enhancement of NMDA receptors (NMDARs). Nevertheless, the amplitude of baseline transmission remains remarkably constant even though low frequency stimulation is also associated with an NMDAR-dependent influx of Ca(2+) into dendritic spines. We show here that an interaction between C-terminal Src kinase (Csk) and NMDARs controls the Src-dependent regulation of NMDAR activity. Csk associates with the NMDAR signaling complex in the adult brain, inhibiting the Src-dependent potentiation of NMDARs in CA1 neurons and attenuating the Src-dependent induction of long-term potentiation. Csk associates directly with Src-phosphorylated NR2 subunits in vitro. An inhibitory antibody for Csk disrupts this physical association, potentiates NMDAR mediated excitatory postsynaptic currents, and induces long-term potentiation at CA3-CA1 synapses. Thus, Csk serves to maintain the constancy of baseline excitatory synaptic transmission by inhibiting Src kinase-dependent synaptic plasticity in the hippocampus", "citation": {"db": "PubMed", "db_id": "18445593"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 94, "target": 3366, "key": "9a19a673b17a21669ed48e84861f9cd1"}, {"line": 36943, "relation": "increases", "evidence": "Protein kinase C: PKC is part of a multigene family of serine-threonine kinases central to many signal transduction pathways [138] with a prominent role in memory [139]. It is likely that ABeta¸-induced increases in cytosolic Ca2+ signals are transmitted to PKC for PKC-mediated transcriptional activation. In addition, PKC activates ERK by interacting with Ras or Raf-1 [140] to initiate CREB phosphorylation. While PKC levels decline in AD [141], their activation restores K+ channel function in cells from AD patients [142]. In addition, activation of PKC directly or indirectly enhances the a-processing cleavage of APP [143].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 94, "target": 3236, "key": "21148a63d14e9d145c8ea0580d2b706f"}, {"line": 36955, "relation": "increases", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 94, "target": 760, "key": "0559234f87c3a2b85aaf1ddb9d74187c"}, {"line": 37044, "relation": "increases", "evidence": "Dysregulation of intracellular calcium signaling has been implicated in the pathogenesis of Alzheimer’s disease [150]. ABeta¸ is known to act through multiple targets [151] including Ca2+ channels and various receptors in membranes. Synthetic ABeta¸ binds to the calcium permeable nAChRs with high affinity [152]. ABeta¸42 administered in the low picomolar range activates nAChRs at presynaptic nerve endings of synaptosomes [83, 153]. Under normal conditions, activation of nAChRs is necessary for the ABeta¸-induced increase in synaptic plasticity and memory [23]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the peptide with the nAChR. In addition, ABeta¸ enhances transmitter release by transient increase of glutamate release from the presynaptic terminal that results from brief periods of high frequency stimulation with Ca2+ buildup within the terminal that triggers mechanisms of short-term synaptic plasticity [154].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 94, "target": 758, "key": "6a80b1c50a551d4758675f0227f19dfd"}, {"line": 37052, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 94, "target": 635, "key": "8b6d5cdb7395d4f23e07511a30cfefe4"}, {"line": 40483, "relation": "increases", "evidence": "Collectively, Evo induced an influx of extracellular calcium, which led to JNK-mediated protective autophagy, and this provides a new option for ischemic stroke treatment.", "citation": {"db": "PubMed", "db_id": "24454492"}, "annotations": {"MeSHDisease": {"Stroke": true}, "Subgraph": {"Autophagy signaling subgraph": true}, "Confidence": {"High": true}}, "source": 94, "target": 808, "key": "6ad9314b19f34e45456682ee78c5d44c"}, {"line": 257, "relation": "increases", "evidence": "Adding metalloprotease inhibitors to the reaction showed that \\\"5-chloro-7-iodoquinolin-8-ol\\\", phosphoramidon, and zinc metalloprotease inhibitors had no significant effect on gamma-secretase activity. In contrast, phenanthroline, EDTA, and EGTA markedly decreased gamma-secretase activity that could be restored by adding back calcium and magnesium ions. Mg(2+) stabilized a 1,000kDa presenilin 1 complex through blue native gel electrophoresis and size-exclusion chromatography. Data suggest that Ca(2+) and Mg(2+) stabilize gamma-secretase and enhance its activity", "citation": {"db": "PubMed", "db_id": "21253550"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 148, "target": 868, "key": "af684ba8179d02cad082d3d7270013c8"}, {"line": 267, "relation": "positiveCorrelation", "evidence": "In this issue of Nature Medicine, Thathiah et al.4 now provide provocative evidence that the adaptor protein beta mediates the Abeta-altering effects of these GPCRs by promoting Abeta generation. This newly uncovered function of beta-arrestin 2 suggests it could be targeted to decrease amyloid pathology in patients with Alzheimer's disease. Production of the amyloid-beta peptide in Alzheimer's disease by the gamma-secretase complex can be regulated by certain G protein coupled receptors. This regulation seems to be mediated by beta-arrestin-2, whose expression was found to be elevated in Alzheimer's disease brains.Recruitment of beta-arrestin 2 to a GPCR leads to interaction with the gamma-secretase complex via the Aph-1 subunit. Other members of the complex include presenilin-1 (PS-1), nicastrin (Nct) and Pen-2. The complex then moves laterally into lipid rafts, where gamma-secretase activation is enhanced. Internalization may also occur to localize gamma-secretase to late endosomes, where its activation is also increased. Cleavage of APP by beta-secretase (BACE1) to release soluble APP (sAPPb) followed by gamma-secretase produces Abeta and APP intracellular domain (AICD). Increased production and secretion of Abeta from cells can lead to extracellular Abeta aggregation in the form of plaques. Mutagenesis of GPR3 in regions of the protein that specifically interact with either G protein or beta-arrestin 2 further showed that beta-arrestin 2, not G protein, mediates the ability of GPR3 to increase Abeta levels.", "citation": {"db": "PubMed", "db_id": "23296004"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"Very High": true}}, "source": 2360, "target": 3823, "key": "1f1230a3a62ca6022b832786546845ab"}, {"line": 268, "relation": "increases", "evidence": "In this issue of Nature Medicine, Thathiah et al.4 now provide provocative evidence that the adaptor protein beta mediates the Abeta-altering effects of these GPCRs by promoting Abeta generation. This newly uncovered function of beta-arrestin 2 suggests it could be targeted to decrease amyloid pathology in patients with Alzheimer's disease. Production of the amyloid-beta peptide in Alzheimer's disease by the gamma-secretase complex can be regulated by certain G protein coupled receptors. This regulation seems to be mediated by beta-arrestin-2, whose expression was found to be elevated in Alzheimer's disease brains.Recruitment of beta-arrestin 2 to a GPCR leads to interaction with the gamma-secretase complex via the Aph-1 subunit. Other members of the complex include presenilin-1 (PS-1), nicastrin (Nct) and Pen-2. The complex then moves laterally into lipid rafts, where gamma-secretase activation is enhanced. Internalization may also occur to localize gamma-secretase to late endosomes, where its activation is also increased. Cleavage of APP by beta-secretase (BACE1) to release soluble APP (sAPPb) followed by gamma-secretase produces Abeta and APP intracellular domain (AICD). Increased production and secretion of Abeta from cells can lead to extracellular Abeta aggregation in the form of plaques. Mutagenesis of GPR3 in regions of the protein that specifically interact with either G protein or beta-arrestin 2 further showed that beta-arrestin 2, not G protein, mediates the ability of GPR3 to increase Abeta levels.", "citation": {"db": "PubMed", "db_id": "23296004"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"Very High": true}}, "source": 2360, "target": 2767, "key": "3d54681c5c437837bcc0a98e6e99efbe"}, {"relation": "partOf", "source": 2360, "target": 1111, "key": "af4ecc27362432c1e34efaa4fe687a7f"}, {"line": 269, "relation": "increases", "evidence": "In this issue of Nature Medicine, Thathiah et al.4 now provide provocative evidence that the adaptor protein beta mediates the Abeta-altering effects of these GPCRs by promoting Abeta generation. This newly uncovered function of beta-arrestin 2 suggests it could be targeted to decrease amyloid pathology in patients with Alzheimer's disease. Production of the amyloid-beta peptide in Alzheimer's disease by the gamma-secretase complex can be regulated by certain G protein coupled receptors. This regulation seems to be mediated by beta-arrestin-2, whose expression was found to be elevated in Alzheimer's disease brains.Recruitment of beta-arrestin 2 to a GPCR leads to interaction with the gamma-secretase complex via the Aph-1 subunit. Other members of the complex include presenilin-1 (PS-1), nicastrin (Nct) and Pen-2. The complex then moves laterally into lipid rafts, where gamma-secretase activation is enhanced. Internalization may also occur to localize gamma-secretase to late endosomes, where its activation is also increased. Cleavage of APP by beta-secretase (BACE1) to release soluble APP (sAPPb) followed by gamma-secretase produces Abeta and APP intracellular domain (AICD). Increased production and secretion of Abeta from cells can lead to extracellular Abeta aggregation in the form of plaques. Mutagenesis of GPR3 in regions of the protein that specifically interact with either G protein or beta-arrestin 2 further showed that beta-arrestin 2, not G protein, mediates the ability of GPR3 to increase Abeta levels.", "citation": {"db": "PubMed", "db_id": "23296004"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"Very High": true}}, "source": 2767, "target": 80, "key": "6ba5e919d0968366d5e176d78907ff90"}, {"line": 270, "relation": "increases", "evidence": "In this issue of Nature Medicine, Thathiah et al.4 now provide provocative evidence that the adaptor protein beta mediates the Abeta-altering effects of these GPCRs by promoting Abeta generation. This newly uncovered function of beta-arrestin 2 suggests it could be targeted to decrease amyloid pathology in patients with Alzheimer's disease. Production of the amyloid-beta peptide in Alzheimer's disease by the gamma-secretase complex can be regulated by certain G protein coupled receptors. This regulation seems to be mediated by beta-arrestin-2, whose expression was found to be elevated in Alzheimer's disease brains.Recruitment of beta-arrestin 2 to a GPCR leads to interaction with the gamma-secretase complex via the Aph-1 subunit. Other members of the complex include presenilin-1 (PS-1), nicastrin (Nct) and Pen-2. The complex then moves laterally into lipid rafts, where gamma-secretase activation is enhanced. Internalization may also occur to localize gamma-secretase to late endosomes, where its activation is also increased. Cleavage of APP by beta-secretase (BACE1) to release soluble APP (sAPPb) followed by gamma-secretase produces Abeta and APP intracellular domain (AICD). Increased production and secretion of Abeta from cells can lead to extracellular Abeta aggregation in the form of plaques. Mutagenesis of GPR3 in regions of the protein that specifically interact with either G protein or beta-arrestin 2 further showed that beta-arrestin 2, not G protein, mediates the ability of GPR3 to increase Abeta levels.", "citation": {"db": "PubMed", "db_id": "23296004"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2767, "target": 868, "key": "ebcc56e7fbde3b3b94415a72245cec36"}, {"line": 271, "relation": "association", "evidence": "In this issue of Nature Medicine, Thathiah et al.4 now provide provocative evidence that the adaptor protein beta mediates the Abeta-altering effects of these GPCRs by promoting Abeta generation. This newly uncovered function of beta-arrestin 2 suggests it could be targeted to decrease amyloid pathology in patients with Alzheimer's disease. Production of the amyloid-beta peptide in Alzheimer's disease by the gamma-secretase complex can be regulated by certain G protein coupled receptors. This regulation seems to be mediated by beta-arrestin-2, whose expression was found to be elevated in Alzheimer's disease brains.Recruitment of beta-arrestin 2 to a GPCR leads to interaction with the gamma-secretase complex via the Aph-1 subunit. Other members of the complex include presenilin-1 (PS-1), nicastrin (Nct) and Pen-2. The complex then moves laterally into lipid rafts, where gamma-secretase activation is enhanced. Internalization may also occur to localize gamma-secretase to late endosomes, where its activation is also increased. Cleavage of APP by beta-secretase (BACE1) to release soluble APP (sAPPb) followed by gamma-secretase produces Abeta and APP intracellular domain (AICD). Increased production and secretion of Abeta from cells can lead to extracellular Abeta aggregation in the form of plaques. Mutagenesis of GPR3 in regions of the protein that specifically interact with either G protein or beta-arrestin 2 further showed that beta-arrestin 2, not G protein, mediates the ability of GPR3 to increase Abeta levels.", "citation": {"db": "PubMed", "db_id": "23296004"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Amyloidogenic subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 1111, "target": 868, "key": "d21f5d15a1d08c39e4a7679e0b9ee02e"}, {"line": 281, "relation": "increases", "evidence": "Moreover, we find that PLD1 also regulates PS1 trafficking and that PLD1 overexpression promotes cell surface accumulation of PS1 in an APP-independent manner. Our results clearly elucidate a physiological function of APP in regulating protein trafficking and suggest that intracellular trafficking of PS1/gamma-secretase is regulated by multiple factors, including APP and PLD1.", "citation": {"db": "PubMed", "db_id": "19276086"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3203, "target": 3258, "key": "ec8a8f389e519bf4eae835ac1eed2443"}, {"relation": "partOf", "source": 3203, "target": 1317, "key": "7c517b0f754ce141ef57b00160663f56"}, {"relation": "partOf", "source": 3203, "target": 1201, "key": "475a5dfbd4e557b6d9f52a330d7f5018"}, {"line": 295, "relation": "positiveCorrelation", "evidence": "Alzheimer's disease (AD) is the most common form of dementia. Mutations in genes such as those encoding amyloid precursor protein (APP), presenilin 1 and presenilin 2, are responsible for early-onset familial AD. Case presentation In this study, we report a 275341 G > C (Val717Leu) mutation in the APP gene in a Japanese family with early onset AD by genetic screening. This mutation has previously been detected in European families", "citation": {"db": "PubMed", "db_id": "22702962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1748, "target": 3823, "key": "c94decd86b6f7a8a3b99dd61a013c9fc"}, {"line": 332, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1748, "target": 2328, "key": "2b1c055d39dc5f43cc38c361fc3ea9ec"}, {"line": 333, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1748, "target": 80, "key": "9fa29ce9ce895a7ec401996fddec5369"}, {"relation": "hasVariant", "source": 1746, "target": 1748, "key": "ab0871fc52163a5161c4ca2d2fc7c28f"}, {"relation": "hasVariant", "source": 1746, "target": 1750, "key": "dc81f7d115d99b3eac009dafdea323b1"}, {"relation": "hasVariant", "source": 1746, "target": 1747, "key": "198118b026ade9897ba36186493661a8"}, {"line": 6377, "relation": "positiveCorrelation", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1746, "target": 842, "key": "7d944b952e1c14a4d711a56a6d9756fc"}, {"line": 6557, "relation": "association", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1746, "target": 3823, "key": "2e44448f7848f210a89659bfba5eabef"}, {"line": 26484, "relation": "association", "evidence": "The genes for both the beta-amyloid precursor protein and apolipoprotein E (ApoE) have been linked to Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9202294"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 1746, "target": 3823, "key": "041ce02aff08e36a4c47fb4eeb364a5a"}, {"line": 6565, "relation": "increases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1746, "target": 80, "key": "8f4fb1247ef8ba6ff59d966b49ea6313"}, {"line": 8771, "relation": "positiveCorrelation", "evidence": "Here, we present evidence that, besides APP expression regulation, miRNAs are equally involved in the regulation of neuronal APP mRNA alternative splicing. Lack of miRNAs in post-mitotic neurons in vivo is associated with APP exons 7 and 8 inclusion, while ectopic expression of miR-124, an abundant neuronal-specific miRNA, reversed these effects in cultured neurons. Similar results were obtained by depletion of endogenous polypyrimidine tract binding protein 1 (PTBP1) in cells, a recognized miR-124 target gene. Furthermore, PTBP1 levels correlate with the presence of APP exons 7 and 8, while PTBP2 levels correlate with the skipping of these exons during neuronal differentiation. Finally, we show that miR-124 is down-regulated in AD brain. In sum, our results suggest that specific miRNAs are involved in the fine-tuning of APP alternative splicing in neurons. Since abnormal neuronal splicing of APP affects beta-amyloid peptide production, these results could contribute to the understanding of the implication of miRNAs in brain health and disease.", "citation": {"db": "PubMed", "db_id": "21062284"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 1746, "target": 4007, "key": "76d70329d728a663b83f48344dd5af89"}, {"line": 8772, "relation": "increases", "evidence": "Here, we present evidence that, besides APP expression regulation, miRNAs are equally involved in the regulation of neuronal APP mRNA alternative splicing. Lack of miRNAs in post-mitotic neurons in vivo is associated with APP exons 7 and 8 inclusion, while ectopic expression of miR-124, an abundant neuronal-specific miRNA, reversed these effects in cultured neurons. Similar results were obtained by depletion of endogenous polypyrimidine tract binding protein 1 (PTBP1) in cells, a recognized miR-124 target gene. Furthermore, PTBP1 levels correlate with the presence of APP exons 7 and 8, while PTBP2 levels correlate with the skipping of these exons during neuronal differentiation. Finally, we show that miR-124 is down-regulated in AD brain. In sum, our results suggest that specific miRNAs are involved in the fine-tuning of APP alternative splicing in neurons. Since abnormal neuronal splicing of APP affects beta-amyloid peptide production, these results could contribute to the understanding of the implication of miRNAs in brain health and disease.", "citation": {"db": "PubMed", "db_id": "21062284"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 1746, "target": 2315, "key": "1f4ac68647fff6f416297a37dd233d50"}, {"line": 45340, "relation": "increases", "evidence": "hypomethylation at the APP gene promoter as a possible risk factor for AD", "citation": {"db": "PubMed", "db_id": "21419233"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1746, "target": 2315, "key": "4a12b75282084e1efed86fe1600f1db0"}, {"line": 9384, "relation": "increases", "evidence": "Utilizing human cell lines, we demonstrate that miRNAs hsa-mir-106a and hsa-mir-520c bind to their predicted target sequences in the APP 3'UTR and negatively regulate reporter gene expression. Over-expression of these miRNAs, but not control miRNAs, results in translational repression of APP mRNA and significantly reduces APP protein levels. These results are the first to demonstrate that levels of human APP can be regulated by miRNAs.", "citation": {"db": "PubMed", "db_id": "18684319"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 1746, "target": 3940, "key": "173c716ef7804f28b6438f945c4b61ef"}, {"relation": "hasVariant", "source": 1746, "target": 1749, "key": "5e71d27dc9c0953ca24ab155ebe65b35"}, {"line": 45276, "relation": "association", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 1746, "target": 2832, "key": "b9f6713deb2ad9a96bacba32f98d4878"}, {"line": 46268, "relation": "association", "evidence": "show that T3 treatment decreases both histone H3 acetylation and histone H3 lysine 4 methylation at the APP promoter and that chemical inhibitors of histone deacetylases and histone lysine demethylase abrogate T3-dependent APP silencing.", "citation": {"db": "PubMed", "db_id": "21458529"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1746, "target": 2803, "key": "4f158ae60070567566a3acbd7fd9098f"}, {"line": 296, "relation": "positiveCorrelation", "evidence": "Alzheimer's disease (AD) is the most common form of dementia. Mutations in genes such as those encoding amyloid precursor protein (APP), presenilin 1 and presenilin 2, are responsible for early-onset familial AD. Case presentation In this study, we report a 275341 G > C (Val717Leu) mutation in the APP gene in a Japanese family with early onset AD by genetic screening. This mutation has previously been detected in European families", "citation": {"db": "PubMed", "db_id": "22702962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2351, "target": 3823, "key": "2cd985e2728be5d7a054214b3cab8a58"}, {"line": 317, "relation": "positiveCorrelation", "evidence": "Mutation analysis of the APP, PSEN1 and PSEN2 genes was performed. We herein report the case of a German EOAD patient with a family history of dementia and a missense mutation at codon 141 (N141I) of the PSEN2 gene. To our knowledge, this is the first German EOAD patient without a Volga-German ancestry and a positive family history for dementia carries the mutation PSEN-2 N141I", "citation": {"db": "PubMed", "db_id": "19073399"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 3271, "target": 3823, "key": "a87fc4d63eeadef46e670c04a7052503"}, {"line": 341, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 3271, "target": 2328, "key": "aa041db33e5e28f148264bf1cf6b8b30"}, {"line": 342, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 3271, "target": 80, "key": "6e60662929879690201596d5bd6012d3"}, {"line": 3986, "relation": "decreases", "evidence": "Studies of the presenilins (PS) have implicated ER Ca2+ mishandling in AD. PS functions as an ER Ca2+ leak channel and FAD PS mutations impair this Ca2+ leak channel function resulting in excessive accumulation of Ca2+ in the ER and, as a consequence, enhances Ca2+ release through ryanodine receptor (RyR) and IP3 receptor (IP3R) channels", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endoplasmic Reticulum"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 3271, "target": 94, "key": "746e28ae5a23b87371738f533db68fc7"}, {"line": 3993, "relation": "increases", "evidence": "Studies of the presenilins (PS) have implicated ER Ca2+ mishandling in AD. PS functions as an ER Ca2+ leak channel and FAD PS mutations impair this Ca2+ leak channel function resulting in excessive accumulation of Ca2+ in the ER and, as a consequence, enhances Ca2+ release through ryanodine receptor (RyR) and IP3 receptor (IP3R) channels", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3271, "target": 757, "key": "ac96ee377007d12c18110ad44d4189d1"}, {"line": 3994, "relation": "increases", "evidence": "Studies of the presenilins (PS) have implicated ER Ca2+ mishandling in AD. PS functions as an ER Ca2+ leak channel and FAD PS mutations impair this Ca2+ leak channel function resulting in excessive accumulation of Ca2+ in the ER and, as a consequence, enhances Ca2+ release through ryanodine receptor (RyR) and IP3 receptor (IP3R) channels", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3271, "target": 733, "key": "2be96e336ef9c190485f75426d37ebd7"}, {"line": 4093, "relation": "decreases", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3271, "target": 2678, "key": "111f6263ce6a4f14f139eacf886c1964"}, {"line": 4094, "relation": "decreases", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3271, "target": 2664, "key": "27cac90e809702af792eac3e600c3586"}, {"line": 4095, "relation": "decreases", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3271, "target": 2367, "key": "0a5f5e8b3b973a5b0509ec997c566a22"}, {"line": 335, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1750, "target": 2328, "key": "dc8393012dc07eba2fac54b633e865d6"}, {"line": 336, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1750, "target": 80, "key": "91499c8b68027027758cfde03c57c459"}, {"line": 344, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 3263, "target": 2328, "key": "9ce22b2604ba95c6417fca9930f4d9f8"}, {"line": 345, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 3263, "target": 80, "key": "2c84ac3526182ec1d86f112b88d6393c"}, {"line": 2956, "relation": "decreases", "evidence": "Our present study demonstrated that the PS1 mutants M146L, E280A and H163R directly affect gamma-secretase activity, which leads to a reduction in the rate of Notch1 cleavage.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3263, "target": 868, "key": "f9f9a8caa637b29dd13d66cb94f29b78"}, {"line": 3985, "relation": "decreases", "evidence": "Studies of the presenilins (PS) have implicated ER Ca2+ mishandling in AD. PS functions as an ER Ca2+ leak channel and FAD PS mutations impair this Ca2+ leak channel function resulting in excessive accumulation of Ca2+ in the ER and, as a consequence, enhances Ca2+ release through ryanodine receptor (RyR) and IP3 receptor (IP3R) channels", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endoplasmic Reticulum"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 3263, "target": 94, "key": "36528fba4d5575b6c81e5ef4b13a25e0"}, {"line": 3990, "relation": "increases", "evidence": "Studies of the presenilins (PS) have implicated ER Ca2+ mishandling in AD. PS functions as an ER Ca2+ leak channel and FAD PS mutations impair this Ca2+ leak channel function resulting in excessive accumulation of Ca2+ in the ER and, as a consequence, enhances Ca2+ release through ryanodine receptor (RyR) and IP3 receptor (IP3R) channels", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3263, "target": 757, "key": "a515824c66588dd1b89cb674c62b2341"}, {"line": 3991, "relation": "increases", "evidence": "Studies of the presenilins (PS) have implicated ER Ca2+ mishandling in AD. PS functions as an ER Ca2+ leak channel and FAD PS mutations impair this Ca2+ leak channel function resulting in excessive accumulation of Ca2+ in the ER and, as a consequence, enhances Ca2+ release through ryanodine receptor (RyR) and IP3 receptor (IP3R) channels", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3263, "target": 733, "key": "b032d94dd3d8a543a1f3ea90f5a7bb6c"}, {"line": 4090, "relation": "decreases", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3263, "target": 2678, "key": "a22ddb3dfcdb13b6f672f69d4b6ae9e2"}, {"line": 4091, "relation": "decreases", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3263, "target": 2664, "key": "494e8361baac81401f2f1e5f14b96a2e"}, {"line": 4092, "relation": "decreases", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3263, "target": 2367, "key": "25e8a86553503a33405c4e3ad0f57043"}, {"line": 349, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Very High": true}}, "source": 2313, "target": 2328, "key": "97c3829ef208d1add28094e4ba292f70"}, {"line": 350, "relation": "increases", "evidence": "The main factors responsible for Abeta formation are mutation of APP or PS1 and PS2 genes or ApoE gene. All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specifically the more amyloidogenic form, Abeta42.", "citation": {"db": "PubMed", "db_id": "15177383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Very High": true}}, "source": 2313, "target": 80, "key": "4f8683a73d48c9d3ef098b31e409c87a"}, {"line": 665, "relation": "positiveCorrelation", "evidence": "polymorphisms in three other genes (among others), apolipoprotein E (apoE), alpha2-macroglobulin (alpham), and the low density lipoprotein receptor-related protein (LRP), are implicated to contribute to AD pathogenesis", "citation": {"db": "PubMed", "db_id": "10936878"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2313, "target": 3823, "key": "aea3bfdf0c7afd609994fef8cca072cc"}, {"relation": "hasVariant", "source": 2312, "target": 2313, "key": "259a568b7ba64f50ff6321da0e67a4d6"}, {"line": 920, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "APOE subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2312, "target": 2838, "key": "acded6f626a4f9d6960bdb0a535aae0e"}, {"line": 921, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "APOE subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 2312, "target": 530, "key": "1d237836e0b1415f69f0be15829e63da"}, {"line": 37117, "relation": "association", "evidence": "Cholesterol transport: High cholesterol levels have been linked to overproduction of ABeta¸ and are a risk factor for AD. One of the physiological functions of ABeta¸ has been suggested to control cholesterol transport [167]. Prevalence of AD is reduced among people treated with inhibitors of cholesterol biosynthesis, statins [168, 169] and animal studies support these results [170]. In vitro and in vivo studies have shown that cholesterol modulates APP processing and affects APP mRNA expression [171]. Another mechanism is the increased binding of ABeta¸ to ApoE4 over non-E4 alleles. ApoE is a lipid and cholesterol transport protein responsible for the efflux of cholesterol from neurons to form stable complexes both in vitro and in vivo [172]. Allele ApoE4 is a major risk factor in AD [173]. This relationship might promote synaptogenesis, since in vitro studies have demonstrated that cholesterol released by astroglia increases synaptogenesis [174, 175] with resulting modulation of spike rates [176]. Together, this evidence indicates that one of the physiological functions of APP might be to control cholesterol movement across neuronal membranes [167].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 530, "key": "1d2074de93e6c1eda26aa8957b7d72a5"}, {"line": 922, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "APOE subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 2312, "target": 594, "key": "696c64ab14607521978bb6cfa563fb48"}, {"line": 969, "relation": "association", "evidence": "It has been suggested that the C-->T (224Ala-->Val) transition within exon 2 of the cathepsin D gene (CTSD) might represent a risk factor for late onset AD.Possession of the CTSD T allele does not increase the risk of developing AD per se, but has a modulating effect on the pathogenesis of the disorder by increasing, in concert with the APOE e4 allele, the amount of Abeta deposited as senile plaques in the brain in the form of Abeta40.", "citation": {"db": "PubMed", "db_id": "16543533"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2312, "target": 2594, "key": "63b407c4cdc558c41839f219563ef307"}, {"relation": "partOf", "source": 2312, "target": 1679, "key": "cffb19fa0015a8b0213448db909b80b7"}, {"line": 2121, "relation": "increases", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 1712, "key": "60cca529400048603d44b9d9ddcff499"}, {"line": 2252, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Very High": true}}, "source": 2312, "target": 1712, "key": "3c3d8e7d40e3ca214fc87a0541961dd2"}, {"line": 2125, "relation": "association", "evidence": "Recently, also the low-density receptor-related protein (LRP) has been shown to be cleaved by a gamma-secretase-like activity. It is important to note that LRPs receptors are activated by apolipoprotein E, a well-known risk factor for the developing of late onset AD in carriers of the e4 alleles.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2312, "target": 3823, "key": "a6932cf394ceff2febbb2222594253d9"}, {"line": 2233, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2312, "target": 3823, "key": "a22c1f8fc75fcfc8718e371e12d07e29"}, {"line": 3555, "relation": "positiveCorrelation", "evidence": "The implication that cholesterol plays an essential role in the pathogenesis of Alzheimer's disease (AD) is based on the 1993 finding that the presence of apolipoprotein E (apoE) allele epsilon;4 is a strong risk factor for developing AD. Since apoE is a regulator of lipid metabolism, it is reasonable to assume that lipids such as cholesterol are involved in the pathogenesis of AD", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 3823, "key": "769a5c07ce26b4b6a294c637e23306ea"}, {"line": 4136, "relation": "association", "evidence": "Apolipoprotein E is the main lipid carrier in the brain and the best-established risk factor for late-onset Alzheimer's disease. Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. Finally, compounds that modulate cholesterol metabolism affect amyloid-beta generation.", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2312, "target": 3823, "key": "864893160c78fa7f1f3a097be805ab32"}, {"line": 5133, "relation": "association", "evidence": "To date, the only established genetic risk factor for LOAD is apolipoprotein E ( APOE ) 4, which explains partially the risk of the disease and modifies the age of onset.", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2312, "target": 3823, "key": "e50232be3c88bc14b27702dc0a80786f"}, {"line": 15691, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2312, "target": 3823, "key": "14801d31fbdd4e5f02af8dc41f59b510"}, {"line": 18608, "relation": "association", "evidence": "Epidemiological and molecular genetic studies have shown the existence of several genes associated with increased risk of AD, the major genetic susceptibility locus coding for apolipoprotein E (apoE).", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 3823, "key": "f9cfbac42a469bdae9e91af9127ba277"}, {"line": 25830, "relation": "association", "evidence": "Apolipoprotein (apo) E and its polymorphism are linked to the pathogenesis of late-onset and sporadic Alzheimer's disease (AD)", "citation": {"db": "PubMed", "db_id": "11070505"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3823, "key": "df4e3f970ffe3fadeec1008e2fe1be86"}, {"line": 25922, "relation": "association", "evidence": "Of the three major isoforms of human apolipoprotein E (apoE), apoE4 is a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "12015813"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3823, "key": "5cf7b522e5a275eff9b75119eb8af911"}, {"line": 25961, "relation": "association", "evidence": "Apolipoprotein E (ApoE) influences the risk of late onset Alzheimer's disease (AD) in an isoform-dependent manner, such that the presence of the apoE epsilon4 allele increases the risk of AD while the presence of the apoE epsilon2 allele appears to be protective. ", "citation": {"db": "PubMed", "db_id": "14501024"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3823, "key": "5d795591cd067555c60e494b67263cd6"}, {"line": 26214, "relation": "association", "evidence": "Late-onset Alzheimer's disease is linked to one isotype of apo E, apo E4.", "citation": {"db": "PubMed", "db_id": "7639323"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3823, "key": "fc16c874b22a3086a9582644a1cf3205"}, {"line": 26225, "relation": "association", "evidence": "Apolipoprotein E (ApoE) genotype is a significant risk factor for the development of Alzheimer disease (AD) and the ApoE protein is associated with senile plaques (SP) and neurofibrillary tangles (NFT)", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 3823, "key": "c85e0a1643e6fb75ff1ff7c9a90fc48c"}, {"line": 26251, "relation": "association", "evidence": "Late-onset and sporadic Alzheimer's disease are associated with the apolipoprotein E (apoE) type 4 allele expressing the protein isoform apoE4.", "citation": {"db": "PubMed", "db_id": "8040342"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3823, "key": "1ee249565c6ad18302b276a9c4f1d6c1"}, {"line": 26305, "relation": "association", "evidence": "Growing evidence indicates the involvement of apolipoprotein E (apoE) in the development of late-onset and sporadic forms of Alzheimer's disease, although its exact role remains unclear.", "citation": {"db": "PubMed", "db_id": "9886074"}, "annotations": {"Subgraph": {"APOE subgraph": true}}, "source": 2312, "target": 3823, "key": "ac8ff33aec2b490d95663fe6323c4cd7"}, {"line": 33881, "relation": "association", "evidence": "Neuronal injury-induced glial apoE secretion is attenuated by the nuclear factor kappaB inhibitors, aspirin, Bay 11-7082 and MG-132, suggesting that this transcription factor is involved in both constitutive and induced glial apoE expression", "citation": {"db": "PubMed", "db_id": "11311545"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 2312, "target": 3823, "key": "93564e7e69cc31d2eebdedd23b3e6f14"}, {"line": 33993, "relation": "association", "evidence": "The mechanism by which apolipoprotein E (ApoE) isoforms functionally influence the risk and progression of late-onset Alzheimer's disease (LOAD) remains hitherto unknown. ", "citation": {"db": "PubMed", "db_id": "15181248"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 3823, "key": "b227a95c8263cb5c0a931c9ed1fc7962"}, {"line": 40433, "relation": "association", "evidence": "S1P/sphingosine ratio was 2.5-fold higher in hippocampus of ApoE2 carriers compared to ApoE4 carriers, and multivariate regression showed a significant association between APOE genotype and hippocampal S1P/sphingosine (p = 0.0495), suggesting a new link between APOE genotype and pre-disposition to AD.This study demonstrates loss of S1P and sphingosine kinase activity early in AD pathogenesis, and prior to AD diagnosis.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2312, "target": 3823, "key": "af91a2419e6a583364dd5f4077441f31"}, {"line": 45009, "relation": "positiveCorrelation", "evidence": "Homocysteine (upper tercile) was associated with AD risk, with an odds ratio of 2.8 (95% confidence interval (CI) 1.54-5.22, p=0.0008), which was increased 2.2- and 2.0-fold by MTHFR 677T (odds ratio 6.28, 95% CI 2.88-16.20, p < 0.0001) and APOE epsilon4 (odds ratio: 5.60, 95% CI 1.12-28.05, p=0.0361), respectively. In conclusion, association of homocysteine with AD was aggravated by MTHFR 677T and APOE epsilon4 alleles.", "citation": {"db": "PubMed", "db_id": "15073531"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 2312, "target": 3823, "key": "f2ba4821d455ee412db9fcf6c8d24fa1"}, {"line": 2228, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 3526, "key": "b8103251ad960195dbf56c16695bc902"}, {"line": 2229, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 2821, "key": "1f85ec5cd7d41e25d7834255c0d9f87a"}, {"line": 2234, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2312, "target": 80, "key": "326c278972fe6558d71d1777de86b04a"}, {"line": 3609, "relation": "increases", "evidence": "Recently, many amyloid PET-positive and cognitively normal subjects were found in PiB-PET studies. PiB-PET studies on healthy subjects have also shown that apolipoprotein (APO) E4 boosts the accumulation of amyloid-beta and may consequently accelerate the pathogenesis of AD", "citation": {"db": "PubMed", "db_id": "20675880"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 80, "key": "5f18d9b9aeb502b1bff47b98aacf0cd2"}, {"line": 25834, "relation": "increases", "evidence": "ApoE facilitates the deposition and fibrillogenesis of beta-amyloid (Abeta), and may participate in Abeta clearance", "citation": {"db": "PubMed", "db_id": "11070505"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 80, "key": "8688dd17255724dffb5cffef6742c32b"}, {"line": 25856, "relation": "increases", "evidence": "In this study, we examined which apoE fragments are most strongly associated with amyloid deposits and whether apoE receptor binding domains were present.", "citation": {"db": "PubMed", "db_id": "11305869"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 80, "key": "cda1c5846a8a574ffbc833c032282fca"}, {"line": 25949, "relation": "increases", "evidence": "he E4 isoform of apolipoprotein E (apoE) has been associated with poor clearance of Abeta under in vitro conditions. This is thought to be due to its poor ability to bind Abeta compared with the other common isoforms, apoE2 and apoE3. ", "citation": {"db": "PubMed", "db_id": "12590160"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 80, "key": "fd207fbe0cb010be2b8386b73de402cf"}, {"line": 26021, "relation": "association", "evidence": "Moreover, apoE-positive newly formed plaques were seen more frequently in APOE epsilon4/4 cases than in non-APOE epsilon4/4 individuals, thereby underlining the potentially crucial role of apoE for the development of Abeta deposits.", "citation": {"db": "PubMed", "db_id": "16195918"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 80, "key": "4ead3d54422e9a0258052844e0b4b381"}, {"line": 26127, "relation": "decreases", "evidence": "ApoE was not protective, but was injurious, as deletion of ApoE delayed the neurodegeneration caused by alpha-synuclein and suppressed the accumulation of Abeta. ", "citation": {"db": "PubMed", "db_id": "18297066"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Synuclein subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 80, "key": "9b515a3476b6ecfa8484de44763550b8"}, {"line": 43979, "relation": "decreases", "evidence": "Alzheimer's disease is associated with impaired clearance of Abeta-amyloid from the brain, a process normally facilitated by apolipoprotein E (ApoE)", "citation": {"db": "PubMed", "db_id": "22323736"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2312, "target": 80, "key": "a8e2c40cadb0e225beed7e6e49ccb75d"}, {"line": 45146, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2312, "target": 80, "key": "59fbf16ec23d800b3a998772111333b3"}, {"line": 2247, "relation": "increases", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"APOE subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2312, "target": 2617, "key": "198037a4359f9d44f93bfb41e9b4f56a"}, {"line": 2249, "relation": "increases", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "APOE subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 2312, "target": 2173, "key": "3e5fe4c7da82081f7743cdc5afa4244c"}, {"line": 2250, "relation": "decreases", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "APOE subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 2312, "target": 2187, "key": "c715786a9f3b42b997ea5a5e87459545"}, {"relation": "partOf", "source": 2312, "target": 981, "key": "a8002d2db5a8c6f5e24ea32a7f1c5c2d"}, {"relation": "partOf", "source": 2312, "target": 1131, "key": "6446a0acc47219d136bb65bd15b73804"}, {"relation": "partOf", "source": 2312, "target": 1127, "key": "b3584ba6d78a5131dae0d2ebddff8244"}, {"line": 26135, "relation": "increases", "evidence": "Importantly, ApoE binds to Abeta and this, too, is influenced by its lipidation status", "citation": {"db": "PubMed", "db_id": "18549781"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"APOE subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2312, "target": 1127, "key": "68d97fdf7882aabdf08f742caace956f"}, {"line": 3556, "relation": "association", "evidence": "The implication that cholesterol plays an essential role in the pathogenesis of Alzheimer's disease (AD) is based on the 1993 finding that the presence of apolipoprotein E (apoE) allele epsilon;4 is a strong risk factor for developing AD. Since apoE is a regulator of lipid metabolism, it is reasonable to assume that lipids such as cholesterol are involved in the pathogenesis of AD", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 592, "key": "05116bf03d91664baf1fd5b6214f76a3"}, {"line": 4016, "relation": "decreases", "evidence": "Interaction of the protein reelin with the apolipoprotein E receptor (ApoER2) enhances Ca2+ influx through NMDA receptor channels by a mechanism involving a src family tyrosine kinsase (SFk); ApoE can block this effect of reelin", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2312, "target": 1532, "key": "0c25b375e60fbc94b4549e910115e5f7"}, {"line": 4126, "relation": "directlyIncreases", "evidence": "Apolipoprotein E is the main lipid carrier in the brain and the best-established risk factor for late-onset Alzheimer's disease. Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. Finally, compounds that modulate cholesterol metabolism affect amyloid-beta generation.", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 231, "key": "0b876d3979d9049229b52588f525db5d"}, {"line": 4139, "relation": "increases", "evidence": "Apolipoprotein E is the main lipid carrier in the brain and the best-established risk factor for late-onset Alzheimer's disease. Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. Finally, compounds that modulate cholesterol metabolism affect amyloid-beta generation.", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Low": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2312, "target": 2328, "key": "813d0ef88276e86d2ab4bfc95b580981"}, {"line": 9104, "relation": "association", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 2328, "key": "48517e687b249e30da803811806b541f"}, {"line": 26137, "relation": "association", "evidence": "Importantly, ApoE binds to Abeta and this, too, is influenced by its lipidation status", "citation": {"db": "PubMed", "db_id": "18549781"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"APOE subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2312, "target": 2328, "key": "a8807f02026898c79fbf5296109d636d"}, {"line": 26143, "relation": "decreases", "evidence": "We report that ApoE plays a role in facilitating the proteolytic clearance of soluble Abeta from the brain. ", "citation": {"db": "PubMed", "db_id": "18549781"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 2328, "key": "be6f7c0215f169b9250e58aa664a2e62"}, {"line": 39597, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2312, "target": 2328, "key": "5a81ee7a5f546c36e2ea67d92672a9ad"}, {"line": 43211, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 2328, "key": "f08f969bf51469b2e5718df058063b81"}, {"relation": "partOf", "source": 2312, "target": 1134, "key": "5eee4aa7540fd9ba35458a6230ee9cfc"}, {"relation": "partOf", "source": 2312, "target": 915, "key": "ae5426397bcf17bc5a1bacd7b7c3bace"}, {"line": 9095, "relation": "association", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 2970, "key": "fe13a569d7fa0bf7914cfc0ea07b3671"}, {"line": 31093, "relation": "association", "evidence": "Low density lipoprotein receptor-related protein (LRP) participates in the uptake and degradation of several ligands implicated in neuronal pathophysiology including apolipoprotein E (apoE), activated alpha(2) -macroglobulin (alpha(2)M*) and beta-amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "10797543"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2312, "target": 2970, "key": "a6efbdb755f4a583652db98e4ce04ac2"}, {"line": 9103, "relation": "association", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 527, "key": "712bb853aa2dc40ec2c0255e1a9d5efe"}, {"line": 19503, "relation": "association", "evidence": "Glutathione S-transferase P1 *C allelic variant increases susceptibility for late-onset Alzheimer disease: association study and relationship with apolipoprotein E epsilon4 allele.", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 2312, "target": 2800, "key": "591c97dae2e23a0cd83975ac9750a802"}, {"relation": "partOf", "source": 2312, "target": 1680, "key": "0d8d0aa6d7fd6e3f306638dee1313289"}, {"line": 19520, "relation": "association", "evidence": "P < 0.0001).The GSTP1*C allelic variant should be considered a candidate for LOAD, particularly in persons having the ApoE epsilon4 allelic variant, because the GSTP1 and ApoE gene products are implicated in oxidative stress and apoptosis processes leading to beta-amyloid-mediated neurodegeneration.", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 2312, "target": 478, "key": "189b0a902a6ff232c3919e8b85b4763b"}, {"relation": "partOf", "source": 2312, "target": 1130, "key": "b05a7109fcfad683196a7c19ac93153b"}, {"relation": "partOf", "source": 2312, "target": 1034, "key": "923c08049da6e3bfaa0132e9edb99ed9"}, {"relation": "partOf", "source": 2312, "target": 914, "key": "1e0dbea8333037f8ee3c7b1aebc251fd"}, {"relation": "partOf", "source": 2312, "target": 1125, "key": "4af07370243ca392595b2778491fbd68"}, {"line": 25794, "relation": "increases", "evidence": "These data demonstrate that ApoE facilitates the formation of both neuritic and cerebrovascular plaques", "citation": {"db": "PubMed", "db_id": "10852539"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3881, "key": "4fc4a8ae4dbbac591e24b3870c713e00"}, {"line": 25899, "relation": "association", "evidence": "Recent studies on the effect of murine and human apoE in APP transgenic mice provide direct evidence that apoE is critically involved in the in vivo converstion of Abeta into forms which contain high beta-sheet content and associated cellular toxicity (neuritic plaques and CAA). ", "citation": {"db": "PubMed", "db_id": "11816788"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3881, "key": "761aec6a0d3dbc56bfecbaba437a1cb2"}, {"line": 26226, "relation": "association", "evidence": "Apolipoprotein E (ApoE) genotype is a significant risk factor for the development of Alzheimer disease (AD) and the ApoE protein is associated with senile plaques (SP) and neurofibrillary tangles (NFT)", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 3881, "key": "ab629863330a2d21355341a10cc1683b"}, {"line": 45326, "relation": "increases", "evidence": "neprilysin (NPE) is an enzyme known to break down the beta-amyloid protein and aid in preventing formation of plaques", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 2312, "target": 3881, "key": "921fd6f11f89fa5e820b0d9e7f6cd7de"}, {"relation": "partOf", "source": 2312, "target": 1136, "key": "d487046993f7d2a797f9c666ac44c7be"}, {"line": 25900, "relation": "association", "evidence": "Recent studies on the effect of murine and human apoE in APP transgenic mice provide direct evidence that apoE is critically involved in the in vivo converstion of Abeta into forms which contain high beta-sheet content and associated cellular toxicity (neuritic plaques and CAA). ", "citation": {"db": "PubMed", "db_id": "11816788"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3835, "key": "cf5b4e99aa14193245563ecd0eefb27c"}, {"relation": "partOf", "source": 2312, "target": 1126, "key": "9d6124061f301830f4c6c56282977f18"}, {"line": 25935, "relation": "increases", "evidence": "As LRP ligands associated with Abeta deposits in AD brain may play an important role in inducing levels of LRP in both neurons and astrocytes, our findings support the idea that apoE might be involved in upregulation of LRP (present in fine astrocytic processes) and act as a local scaffolding protein for LRP and Abeta.", "citation": {"db": "PubMed", "db_id": "12117549"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 2973, "key": "e1204e5a8b171c17d23b9cf88a5efb84"}, {"relation": "partOf", "source": 2312, "target": 1124, "key": "c8989e7e43ab91f1647fbbd3e18a7242"}, {"line": 26125, "relation": "positiveCorrelation", "evidence": "ApoE was not protective, but was injurious, as deletion of ApoE delayed the neurodegeneration caused by alpha-synuclein and suppressed the accumulation of Abeta. ", "citation": {"db": "PubMed", "db_id": "18297066"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Synuclein subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2312, "target": 431, "key": "13c6b493c77940b8f2141c4f35dce589"}, {"line": 26159, "relation": "association", "evidence": "The sialic acid levels in the CSF apoE-containing lipoprotein fractions were 5.3 +/- 1.3% of the total CSF sialic acid, and were correlated with the CSF apoE concentrations.", "citation": {"db": "PubMed", "db_id": "19522249"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 172, "key": "fda8b52941792389d21e2284f629cac1"}, {"line": 26178, "relation": "decreases", "evidence": "Moreover, our recent studies further demonstrated that (1) apoE mediates sulfatide depletion in amyloid-beta precursor protein transgenic mice; (2) sulfatides enhance amyloid beta (Abeta) peptides binding to apoE-associated particles; (3) Abeta42 content notably correlates with sulfatide content in CSF;(4) sulfatides markedly enhance the uptake of Abeta peptides; and (5) abnormal sulfatide-facilitated Abeta uptake results in the accumulation of Abeta in lysosomes.", "citation": {"db": "PubMed", "db_id": "20052565"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 116, "key": "f0a8ce0f26fe068aace20c19d52702b7"}, {"line": 26227, "relation": "association", "evidence": "Apolipoprotein E (ApoE) genotype is a significant risk factor for the development of Alzheimer disease (AD) and the ApoE protein is associated with senile plaques (SP) and neurofibrillary tangles (NFT)", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 889, "key": "84d865fba8c8ef46b1133a54b7c8ad48"}, {"line": 26283, "relation": "decreases", "evidence": "ApoE3 can also inhibit fibril formation by A beta (1-42).", "citation": {"db": "PubMed", "db_id": "8823200"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 474, "key": "f2e357c85584006193ff9ac1accb715e"}, {"relation": "hasVariant", "source": 2312, "target": 2314, "key": "9f1bdf57f97767c5b5715bc1cf6806cd"}, {"line": 26346, "relation": "increases", "evidence": "We conclude that astrocytes: (i) strongly regulate neuronal APP expression in primary neurons, and (ii) promote the amyloidogenic pathway in an apoE4-dependent manner. Thus, apoE and astrocytic factor(s) may pmodulate the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11553277"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 864, "key": "8f9105949b8b38117bb05282be7d031f"}, {"line": 26440, "relation": "association", "evidence": "It has been shown that apoE binds and promotes the fibrillogenesis in vitro of Alzheimer's amyloid beta-peptide, suggesting an important role for apoE in the pmodulation of amyloidogenesis.", "citation": {"db": "PubMed", "db_id": "7672107"}, "source": 2312, "target": 863, "key": "f472c105bb210018eda5f11d5b4b1902"}, {"relation": "partOf", "source": 2312, "target": 1139, "key": "c2aaed805fea1e7e3de12313f8cb1980"}, {"line": 28282, "relation": "association", "evidence": "Four genes have been established to either cause familial early onset AD (APP, PSEN1, and PSEN2) or to increase susceptibility for late onset AD (APOE). ", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 3820, "key": "e82a070468f786b28367bb2a7937e0ef"}, {"relation": "partOf", "source": 2312, "target": 1140, "key": "371007e70e9efb54dddf45f93e6e67b8"}, {"line": 28718, "relation": "association", "evidence": "The beta-amyloid precursor protein (APP) shares intracellular and extracellular-binding partners with the family of receptors for apolipoprotein E (apoE). ", "citation": {"db": "PubMed", "db_id": "18415033"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 2315, "key": "95e3e5829bd59a962279c7ad864eafd7"}, {"line": 33929, "relation": "decreases", "evidence": "Further, Western blot analyses from mouse apoE(-/-) and AD brains showed statistically higher protein levels of APP, pAPP and increased APP association with the tyrosine kinase, Src.", "citation": {"db": "PubMed", "db_id": "19058878"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 2315, "key": "3e6f19a32ee63bc020f2c3311f518bd5"}, {"relation": "partOf", "source": 2312, "target": 1133, "key": "e9599b2f481295c3eadf0fc0a4112fd2"}, {"relation": "partOf", "source": 2312, "target": 1141, "key": "0fc9bbf3664fa22ecd261b00601261e7"}, {"relation": "partOf", "source": 2312, "target": 1132, "key": "3e9ba1d6f7fa9fb852de2a27b767fc0b"}, {"relation": "partOf", "source": 2312, "target": 1129, "key": "e4bd821ff8a3351516eed7987c092157"}, {"relation": "partOf", "source": 2312, "target": 1128, "key": "9079f224306b2746c9b9bb8c90595814"}, {"line": 33806, "relation": "association", "evidence": "Apolipoprotein E (ApoE) peptide regulates tau phosphorylation via two different signaling pathways.", "citation": {"db": "PubMed", "db_id": "9512010"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"APOE subgraph": true, "Tau protein subgraph": true}}, "source": 2312, "target": 3015, "key": "35aca4b5634a8da1cfcd9a7ddb7d1761"}, {"relation": "partOf", "source": 2312, "target": 1137, "key": "65cfd556438f2920a4c0c28d43043a0d"}, {"relation": "partOf", "source": 2312, "target": 1135, "key": "1310cf1e2fb35c254fb289c214c274cd"}, {"line": 33930, "relation": "decreases", "evidence": "Further, Western blot analyses from mouse apoE(-/-) and AD brains showed statistically higher protein levels of APP, pAPP and increased APP association with the tyrosine kinase, Src.", "citation": {"db": "PubMed", "db_id": "19058878"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 2334, "key": "99bf860b96bc7470fce41c7bb70a2a57"}, {"line": 33931, "relation": "decreases", "evidence": "Further, Western blot analyses from mouse apoE(-/-) and AD brains showed statistically higher protein levels of APP, pAPP and increased APP association with the tyrosine kinase, Src.", "citation": {"db": "PubMed", "db_id": "19058878"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 1216, "key": "8d82323f1c4654d7710ce6cfeb1d6f34"}, {"line": 33966, "relation": "increases", "evidence": "The receptor activation assays revealed that apoE as well as beta amyloid activated the CASR and that the level of activation appeared to be isoform dependent for apoE.", "citation": {"db": "PubMed", "db_id": "19035514"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2312, "target": 2451, "key": "c4d5ca72d0bf177e31728eb307884653"}, {"relation": "partOf", "source": 2312, "target": 1138, "key": "9a4998e0b5a284de0d1d9d73b4eeea1e"}, {"relation": "partOf", "source": 2312, "target": 1025, "key": "bf07890658065f3befd8baf5ac7d12dc"}, {"line": 39452, "relation": "increases", "evidence": "It has been shown that apoE increased the production of nitric oxide (NO) from human monocyte-derived macrophages (MDM); this effect could represent an important link between tissue redox balance and inflammation, since inflammation and oxidative stress are involved in chronic neurodegenerative disorders. Moreover, it has been evidenced that an overproduction of NO in the central nervous system (CNS) may play a key role in aging and that the glial cells (microglials cells and probably astrocytes) are able to form consistent amounts of NO through the induction of a nitric oxide synthase (iNOS) isoform so-called inducible or inflammatory.We observed a decreased NO production after incubation with both LDL and HDL and an increased peroxynitrite production. As it concerns NOS expression, densitometric analysis of bands indicated that iNOS protein levels were significantly higher in the cells incubated with both AD lipoproteins and offspring lipoproteins compared to cells incubated with control lipoproteins. These findings suggest the possibility to identify in NO pathway a precocious marker of AD.", "citation": {"db": "PubMed", "db_id": "16054114"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 2312, "target": 156, "key": "dfcf51495db678fa69e182cf451958ce"}, {"line": 39598, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2312, "target": 480, "key": "e47702ec7cd8652adfdd44807d6d4c01"}, {"line": 43212, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 480, "key": "5d3567986d475b45d246d8da780a82f7"}, {"line": 39599, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2312, "target": 609, "key": "c904f64ac25b4b49e19dc580e80846e6"}, {"line": 43213, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 2312, "target": 609, "key": "579b6d7567ff2a19b1948f60ef3ca7c1"}, {"line": 40400, "relation": "association", "evidence": "ApoE regulates secretion of the potent neuroprotective signaling lipid Sphingosine 1-phosphate (S1P).", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Sphingolipid metabolic subgraph": true}}, "source": 2312, "target": 177, "key": "4262519d16b51a6c016e65b502a2cd42"}, {"line": 40432, "relation": "association", "evidence": "S1P/sphingosine ratio was 2.5-fold higher in hippocampus of ApoE2 carriers compared to ApoE4 carriers, and multivariate regression showed a significant association between APOE genotype and hippocampal S1P/sphingosine (p = 0.0495), suggesting a new link between APOE genotype and pre-disposition to AD.This study demonstrates loss of S1P and sphingosine kinase activity early in AD pathogenesis, and prior to AD diagnosis.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2312, "target": 177, "key": "17c8d565101b805bb61be28fb0b8c6d1"}, {"line": 40431, "relation": "association", "evidence": "S1P/sphingosine ratio was 2.5-fold higher in hippocampus of ApoE2 carriers compared to ApoE4 carriers, and multivariate regression showed a significant association between APOE genotype and hippocampal S1P/sphingosine (p = 0.0495), suggesting a new link between APOE genotype and pre-disposition to AD.This study demonstrates loss of S1P and sphingosine kinase activity early in AD pathogenesis, and prior to AD diagnosis.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2312, "target": 354, "key": "0b9431c85a9470762309c8a74e32801b"}, {"line": 47008, "relation": "positiveCorrelation", "evidence": "Furthermore, in AD brains, mitochondrially associated APP formed stable ∼480 kDa complexes with the translocase of the outer mitochondrial membrane 40 (TOM40) import channel and a super complex of ∼620 kDa with both mitochondrial TOM40 and the translocase of the inner mitochondrial membrane 23 (TIM23) import channel TIM23 in an “Nin mitochondria–Cout cytoplasm” orientation. Accumulation of APP across mitochondrial import channels, which varied with the severity of AD, inhibited the entry of nuclear-encoded cytochrome c oxidase subunits IV and Vb proteins, which was associated with decreased cytochrome c oxidase activity and increased levels of H2O2", "citation": {"db": "PubMed", "db_id": "16943564"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true, "APOE subgraph": true}}, "source": 2312, "target": 3481, "key": "6c470f1285b045c9a70944edacd1550a"}, {"relation": "hasReactant", "source": 4090, "target": 137, "key": "afadc5557483fca53281b469ec4a3d50"}, {"relation": "hasReactant", "source": 4090, "target": 2328, "key": "5b78af11663ab18d4a3eccd09c95592b"}, {"relation": "hasProduct", "source": 4090, "target": 131, "key": "380b67eb60cbeb105ac5f8aa5fec32e0"}, {"relation": "partOf", "source": 137, "target": 961, "key": "cb173921da81a32d43cf73186b46bce5"}, {"line": 11125, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}}, "source": 137, "target": 2711, "key": "8183687438bb03866c4f5801d77f860a"}, {"line": 11127, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 137, "target": 868, "key": "48b941a4d154311bfd54735698486107"}, {"line": 11128, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 137, "target": 3272, "key": "04d876c3fc6381aff7ba8d90d15574ca"}, {"line": 11130, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Gamma secretase subgraph": true}}, "source": 137, "target": 1422, "key": "e7456b0a171be9f2e7ee63b6d1e1bb91"}, {"relation": "partOf", "source": 131, "target": 961, "key": "aa29289a238bc88b00da274ad61bbb22"}, {"relation": "partOf", "source": 131, "target": 951, "key": "d4f21a32cf55d8c738e06f26b18a2380"}, {"line": 438, "relation": "directlyIncreases", "evidence": "An alternative pathway is shown on the right side of the figure whereby superoxide or hydrogen peroxide (H2O2) oxidize lipids such as prostaglandins forming F2a-isoprostanes. Both H2O2 and F2a-isoprostanes are known to accelerate Abeta aggregation", "citation": {"db": "PubMed", "db_id": "15864339"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 131, "target": 286, "key": "cf8b91c36a25940dd731ae320c5665da"}, {"line": 448, "relation": "increases", "evidence": "An alternative pathway is shown on the right side of the figure whereby superoxide or hydrogen peroxide (H2O2) oxidize lipids such as prostaglandins forming F2a-isoprostanes. Both H2O2 and F2a-isoprostanes are known to accelerate Abeta aggregation", "citation": {"db": "PubMed", "db_id": "15864339"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 131, "target": 80, "key": "0a9331022a7442f508cd7b23827a270d"}, {"line": 2977, "relation": "directlyIncreases", "evidence": "Acetylcholinesterase (AChE) is thought to play an important role during apoptotic process. Our results showed that H2O2 induced AChE activity, a functional marker in apoptotic process, increases in neuronal-like PC12 cells. Glutathione, which is involved in cellular redox homeostasis, inhibited the increase of AChE activity, suggesting that reactive oxygen species (ROS) play a key role in this process. Further investigation showed that the elevation of AChE was observed after the degradation of Akt, release of cytochrome c from mitochondria into the cytosol, and activation of caspase family members. When nerve growth factor (NGF) was present, with the maintenance of Akt level, the elevation of AChE, the cytochrome c diffusion, as well as apoptotic process were markedly attenuated in H2O2-treated PC12 cells", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 131, "target": 2244, "key": "a7904f6a07e1bd782fdc92293b1b51a8"}, {"line": 3093, "relation": "negativeCorrelation", "evidence": "AChE activity was reported to be enhanced by low concentrations of H2O2. explanation is that mitochondrial efflux H2O2 modifies membrane structure through lipid peroxidation, and therefore contributes to modify the activity of the membrane-bound protein AChE [31]. AChE is synthesized as an inactive precursor and then matures into an active subunit in the endoplasmic reticulum [32]. Thus, the other explanation was that AChE could be exposed to cytoplasm when endoplasmic reticulum was destroyed by H2O2. At least one protein kinase (protein kinase A , PKA), was reported to increase the AChE activity by phosphorylation at non-consensus sites of this enzyme [33]. The apoptotic stimuli also enhanced the mRNA and protein levels of AChE in cells without background AChE expression, which were mediated by calcium signalling or the c-Jun kinase pathway", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 131, "target": 2244, "key": "20b0d459d23b3d9d200a406a5c98a7d3"}, {"line": 3045, "relation": "directlyDecreases", "evidence": "Hydrogen peroxide was reported to initiate the apoptotic cascade by perturbing the intracellular redox balance. GSH is the most abundant low molecular weight thiol that maintains cellular redox homeostasis", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 131, "target": 511, "key": "5ffc17ea1a2f2f453588b7582747b404"}, {"line": 3049, "relation": "increases", "evidence": "Hydrogen peroxide was reported to initiate the apoptotic cascade by perturbing the intracellular redox balance. GSH is the most abundant low molecular weight thiol that maintains cellular redox homeostasis", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 131, "target": 645, "key": "68fc9cc1f4959d4208090b62a3d32a01"}, {"line": 13126, "relation": "increases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Inflammatory response subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 131, "target": 645, "key": "d02f3963ce6a3be9d29c747b6f0a1ab3"}, {"line": 14098, "relation": "increases", "evidence": "Hydrogen peroxide (H2O2), one of the main reactive oxygen species, is converted into the highly toxic ·OH radical in the presence of redox-active transition metals, which then oxidises nucleic acids, lipids and proteins, leading to neurodegeneration and cell death.", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 131, "target": 645, "key": "4b7695680692156942fc0d1453bf913c"}, {"line": 3954, "relation": "increases", "evidence": "Abeta can also interact with Fe2+ and Cu+ to generate hydrogen peroxide and hydroxyl radical (OH.) resulting in membrane lipid peroxidation which generates toxic aldehydes that impair the function of membrane ion-motive ATPases (Na+ and Ca2+ pumps)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "CellStructure": {"Cell Membrane": true}, "Confidence": {"High": true}}, "source": 131, "target": 605, "key": "72b8646c93e4d8c537658dd113a6c15f"}, {"line": 14094, "relation": "isA", "evidence": "Hydrogen peroxide (H2O2), one of the main reactive oxygen species, is converted into the highly toxic ·OH radical in the presence of redox-active transition metals, which then oxidises nucleic acids, lipids and proteins, leading to neurodegeneration and cell death.", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 131, "target": 170, "key": "5bb91af9851d0385d2fff1184caab8e2"}, {"line": 14672, "relation": "increases", "evidence": "In vitro studies revealed that (1) exposure of neural stem cells (NSCs) from the hippocampus to STZ strikingly increased intracellular reactive oxygen species (ROS) levels, induced cell death and perturbed cell proliferation and differentiation, (2) hydrogen peroxide induced similar cellular activities as STZ, (3) pre-incubation of STZ-treated NSCs with catalase, an antioxidant, suppressed all these cellular activities induced by STZ, and (4) likewise, pre-incubation of STZ-treated NSCs with salidroside, also an antioxidant, suppressed all these activities as catalase: reduction of ROS levels and NSC death with simultaneous increases in proliferation and differentiation.", "citation": {"db": "PubMed", "db_id": "22235318"}, "annotations": {"Cell": {"neuronal stem cell": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 131, "target": 170, "key": "3bde79b777e475a5c9ee21ea50baf7e9"}, {"line": 14676, "relation": "increases", "evidence": "In vitro studies revealed that (1) exposure of neural stem cells (NSCs) from the hippocampus to STZ strikingly increased intracellular reactive oxygen species (ROS) levels, induced cell death and perturbed cell proliferation and differentiation, (2) hydrogen peroxide induced similar cellular activities as STZ, (3) pre-incubation of STZ-treated NSCs with catalase, an antioxidant, suppressed all these cellular activities induced by STZ, and (4) likewise, pre-incubation of STZ-treated NSCs with salidroside, also an antioxidant, suppressed all these activities as catalase: reduction of ROS levels and NSC death with simultaneous increases in proliferation and differentiation.", "citation": {"db": "PubMed", "db_id": "22235318"}, "annotations": {"Cell": {"neuronal stem cell": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 131, "target": 505, "key": "1e26d8d21acfca2b30ff2bec2b6f13ee"}, {"line": 16022, "relation": "increases", "evidence": "Bcl-xL protein was also up-regulated during oxidative stress induced by exposure to hydrogen peroxide (3-100microM) or ferric ions (1-10microM).", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Subgraph": {"Hydrogen peroxide subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 131, "target": 842, "key": "8876385b18747e3fc12b5454aa6a60ba"}, {"line": 16048, "relation": "increases", "evidence": "In contrast, Bcl-xL overexpression only conferred a mild protection against oxidative injury induced by hydrogen peroxide.", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Subgraph": {"Hydrogen peroxide subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 131, "target": 842, "key": "677cc846ef7f37835b51ec1b9f0158f5"}, {"line": 16180, "relation": "increases", "evidence": "Piceatannol attenuates hydrogen-peroxide- and peroxynitrite-induced apoptosis of PC12 cells by blocking down-regulation of Bcl-XL and activation of JNK.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"CellLine": {"PC-12 cell": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 131, "target": 478, "key": "53ba958684e08d0af1ec82b0bda77e4e"}, {"line": 16218, "relation": "decreases", "evidence": "Treatment of PC12 cells with hydrogen peroxide or SIN-1 led to down-regulation of Bcl-X(L) and activation of caspase-3 and -8, which were also inhibited by piceatannol treatment.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Caspase subgraph": true}, "CellLine": {"PC-12 cell": true}, "Confidence": {"High": true}}, "source": 131, "target": 2394, "key": "593a25250f130936b54ccac2330f1da4"}, {"line": 16222, "relation": "increases", "evidence": "Treatment of PC12 cells with hydrogen peroxide or SIN-1 led to down-regulation of Bcl-X(L) and activation of caspase-3 and -8, which were also inhibited by piceatannol treatment.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Caspase subgraph": true}, "CellLine": {"PC-12 cell": true}, "Confidence": {"High": true}}, "source": 131, "target": 2444, "key": "c7c9039765a0cdb3031c3b8f2fc99831"}, {"line": 16226, "relation": "increases", "evidence": "Treatment of PC12 cells with hydrogen peroxide or SIN-1 led to down-regulation of Bcl-X(L) and activation of caspase-3 and -8, which were also inhibited by piceatannol treatment.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Caspase subgraph": true}, "CellLine": {"PC-12 cell": true}, "Confidence": {"High": true}}, "source": 131, "target": 2448, "key": "7177e5f4e4525200158b786ed6946744"}, {"line": 16238, "relation": "increases", "evidence": "Hydrogen peroxide or SIN-1 treatment induced phosphorylation of the c-Jun-N-terminal kinase (JNK), which was inhibited by piceatannol treatment.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 131, "target": 3003, "key": "34645fccbca1ab9055cd0c3ee39896e5"}, {"line": 47609, "relation": "decreases", "evidence": "Hydrogen Peroxide results in decreased expression of CRHR1 mRNA", "citation": {"db": "PubMed", "db_id": "12419474"}, "annotations": {"Species": {"9606": true}}, "source": 131, "target": 2562, "key": "e5d7c4f68ba70c3c3b2d8d336f89fc17"}, {"relation": "hasReactant", "source": 4088, "target": 100, "key": "aa2036bbf746e6b32ecc4ca5cdf0965a"}, {"relation": "hasReactant", "source": 4088, "target": 2328, "key": "4bbf370dd3276ca96ce394a0380662d6"}, {"relation": "hasProduct", "source": 4088, "target": 131, "key": "ee1b22874fcaa4c6a09a97262fcecd24"}, {"relation": "partOf", "source": 100, "target": 951, "key": "415dfed321a3eb241bf9c4e8761f2808"}, {"line": 1158, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in Abeta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 775, "target": 2375, "key": "0498a28ef047d29d6d2dd66edd0ec7da"}, {"line": 9331, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in beta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 775, "target": 2375, "key": "675e5de1b9f01eac8faebe9bc46850c6"}, {"line": 1166, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in Abeta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Response to oxidative stress": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 775, "target": 2173, "key": "68d97130ddfa5c7274e92d5e403aa9cb"}, {"line": 9045, "relation": "increases", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 775, "target": 3384, "key": "c15458790b187768348836239f322205"}, {"line": 9047, "relation": "increases", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 775, "target": 3015, "key": "a0a307cc1682cc78b1bf8af5c8b1390b"}, {"line": 9333, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in beta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 775, "target": 3000, "key": "4bc03da2891f30ba1aa73224d33e181c"}, {"line": 33192, "relation": "decreases", "evidence": "Studies from our laboratory have shown significant inhibition of GAPDH dehydrogenase activity in Alzheimer's disease (AD) brain due to oxidative modification.Although oxidative stress and damage is a common phenomenon in the AD brain, it would seem that inhibition of glycolytic enzyme activity is merely one avenue in which AD pathology affects neuronal cell development and survival, as oxidative modification can also impart a toxic gain-of-function to many proteins, including GAPDH. I", "citation": {"db": "PubMed", "db_id": "20164570"}, "annotations": {"Subgraph": {"Glycolysis subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 775, "target": 2741, "key": "83e4fbfa8a0d546b9f82ed6d31ffd305"}, {"line": 35673, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Response to oxidative stress": true}}, "object": {"modifier": "Activity"}, "source": 775, "target": 2989, "key": "49c79873560a273ac939cc755ed0534b"}, {"line": 39740, "relation": "association", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Response to oxidative stress": true}}, "source": 775, "target": 23, "key": "ad278bfbe73cfd034b864b79e01667e0"}, {"line": 46414, "relation": "decreases", "evidence": "Neurons in the brain are considered to be particularly vulnerable to oxidative stress, leading to neuronal oxidative damage and neurodegenerative disorders such as Alzheimer's disease (AD) and senile dementia. The process of fusing synaptic plasma membranes and synaptic vesicles involves particular proteins, such as the soluble NSF (N-ethylmaleimide-sensitive factor) attachment protein receptor (SNARE) proteins for docking both membranes, and is integral to neurotransmission. To elucidate whether oxidative stress induces denaturation of SNARE proteins, and whether vitamin E can counteract this process, changes in the expression of synaptobrevin, synaptotagmin, SNAP-25, and syntaxin-1 in rat brain nerve terminals were analyzed using an immunoblotting method. The results showed that oxidative stress induced significant reductions in the levels synaptobrevin and synaptotagmin in synaptic vesicles. Similarly, marked decreases in the levels of SNAP-25 and syntaxin-1 in pre-synaptic plasma membranes were also observed. In the absence of oxidative stress, vitamin E-deficient rats exhibited similar decreases in these proteins.", "citation": {"db": "PubMed", "db_id": "21971407"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Response to oxidative stress": true}}, "source": 775, "target": 3431, "key": "43189b54af305947d29e7df6615a1df5"}, {"line": 46415, "relation": "increases", "evidence": "Neurons in the brain are considered to be particularly vulnerable to oxidative stress, leading to neuronal oxidative damage and neurodegenerative disorders such as Alzheimer's disease (AD) and senile dementia. The process of fusing synaptic plasma membranes and synaptic vesicles involves particular proteins, such as the soluble NSF (N-ethylmaleimide-sensitive factor) attachment protein receptor (SNARE) proteins for docking both membranes, and is integral to neurotransmission. To elucidate whether oxidative stress induces denaturation of SNARE proteins, and whether vitamin E can counteract this process, changes in the expression of synaptobrevin, synaptotagmin, SNAP-25, and syntaxin-1 in rat brain nerve terminals were analyzed using an immunoblotting method. The results showed that oxidative stress induced significant reductions in the levels synaptobrevin and synaptotagmin in synaptic vesicles. Similarly, marked decreases in the levels of SNAP-25 and syntaxin-1 in pre-synaptic plasma membranes were also observed. In the absence of oxidative stress, vitamin E-deficient rats exhibited similar decreases in these proteins.", "citation": {"db": "PubMed", "db_id": "21971407"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Response to oxidative stress": true}}, "source": 775, "target": 3874, "key": "7999a65b19e99adf1de5534f60eac98f"}, {"line": 405, "relation": "directlyIncreases", "evidence": "Superoxide is converted to H2O2 by the activity of superoxide dismutases (SOD) and superoxide can also interact with nitric oxide (NO) to produce peroxynitrite (ONOO*)", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 359, "target": 131, "key": "c18ea2d4b368f1d7b229a25a64ad5bca"}, {"line": 434, "relation": "increases", "evidence": "An alternative pathway is shown on the right side of the figure whereby superoxide or hydrogen peroxide (H2O2) oxidize lipids such as prostaglandins forming F2a-isoprostanes. Both H2O2 and F2a-isoprostanes are known to accelerate Abeta aggregation", "citation": {"db": "PubMed", "db_id": "15864339"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 359, "target": 286, "key": "0b0cdb9a53bca1871581555075add2b0"}, {"relation": "partOf", "source": 359, "target": 970, "key": "1e78e9bd98979951a07ebde4e8b570cb"}, {"line": 3207, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 190, "target": 3429, "key": "c3d145c2d1d12a956d33ad034b725e89"}, {"relation": "partOf", "source": 190, "target": 972, "key": "541588489882b1035a067e800b34d2de"}, {"relation": "partOf", "source": 190, "target": 973, "key": "10d194975526c88a7edc42b33d296eae"}, {"relation": "hasReactant", "source": 4091, "target": 156, "key": "c8155f1474db1d2a747eceed710b9999"}, {"relation": "hasReactant", "source": 4091, "target": 359, "key": "1b98313bd6bf465793545ca156ee6943"}, {"relation": "hasProduct", "source": 4091, "target": 327, "key": "52cac3c0567f3a68b172cf1567a2831d"}, {"line": 503, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true, "Nitric oxide subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 156, "target": 645, "key": "b1aba564b3a5c7fc2053c870a8ca940f"}, {"line": 795, "relation": "association", "evidence": "NO contributes to cell signaling by inducing posttranslational protein modifications. Under pathological conditions there is a shift from the signal transducing actions to the formation of protein tyrosine nitration by secondary products like peroxynitrite and nitrogen dioxide. We identified amyloid Abeta (Abeta) as an NO target, which is nitrated at tyrosine 10 (3NTyr(10)-Abeta). Nitration of Abeta accelerated its aggregation and was detected in the core of Abeta plaques of APP/PS1 mice and AD brains.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 156, "target": 656, "key": "bc7a2e508a4eeb22397f371cba2de105"}, {"line": 4832, "relation": "regulates", "evidence": "NGF increases APP levels through enhanced translation rate and that NO, which modulates the NGF-induced increase in APP protein, also regulates APP mRNA levels and could play a role in APP processing Interestingly, we also found that this inhibition of NOS only partially attenuated the increase in APP promoter activation mediated by NGF [7] suggesting that NGF- signal transduction pathways and NO may be influencing the rate of APP mRNA or protein synthesis or degradation in addition to altering gene transcription.", "citation": {"db": "PubMed", "db_id": "22550546"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 156, "target": 2315, "key": "0ab09eb272b56f536a19dbc2030c3596"}, {"line": 10820, "relation": "negativeCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 156, "target": 3850, "key": "999107ff5df8f770d13ba30171e16a2e"}, {"line": 10824, "relation": "negativeCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 156, "target": 3823, "key": "0555af9af242be98e9f746ddbf02ba6b"}, {"line": 16713, "relation": "association", "evidence": "The endothelial nitric oxide synthase (NOS3) gene encodes endothelial NOS, an enzyme that regulates the production of the vasodilatory nitric oxide associated with the cerebral small vessel pathology observed in early AD.", "citation": {"db": "PubMed", "db_id": "15016421"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 156, "target": 3823, "key": "c37ce8e6ffdafe78df8b3a19409c66b8"}, {"line": 21252, "relation": "positiveCorrelation", "evidence": "On the other hand, an overproduction of NO is related with several disorders as Alzheimer's disease, Huntington's disease and the amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Disease": {"Huntington's disease": true, "Alzheimer's disease": true, "amyotrophic lateral sclerosis": true}, "Confidence": {"High": true}}, "source": 156, "target": 3823, "key": "aab87b188eb108ec2a45209c312817cd"}, {"line": 11603, "relation": "increases", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 156, "target": 825, "key": "5ef47103c2f82ee083b85d7590dc607f"}, {"line": 15121, "relation": "association", "evidence": "We have shown with cultured cerebral cortical normal (i.e., untransformed) adult human astrocytes (NAHAs) that exogenous amyloid-beta peptides (Abetas) stimulate the astrocytes to make and secrete large amounts of Abetas and nitric oxide by a mechanism mediated through the calcium-sensing receptor (CaSR).", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true, "Cerebral Cortex": true}, "Species": {"9606": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 156, "target": 2451, "key": "02b014742d6ba932cba66d26c515bd87"}, {"relation": "partOf", "source": 156, "target": 966, "key": "1b1e5c2731851f9afa86ad334fe17c7b"}, {"line": 21242, "relation": "association", "evidence": "Nitric oxide (NO), which is produced by oxidation of L-arginine to L-citrulline in a process catalyzed by different isoforms of nitric oxide synthase (NOS), exhibits diverse roles in several physiological processes, including neurotransmission, blood pressure regulation and immunological defense mechanisms.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 156, "target": 846, "key": "bd461723b3e2858c266190d3d3c4b541"}, {"line": 21245, "relation": "association", "evidence": "Nitric oxide (NO), which is produced by oxidation of L-arginine to L-citrulline in a process catalyzed by different isoforms of nitric oxide synthase (NOS), exhibits diverse roles in several physiological processes, including neurotransmission, blood pressure regulation and immunological defense mechanisms.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 156, "target": 712, "key": "5546606e2364629e2c50be168890d3f4"}, {"line": 21253, "relation": "positiveCorrelation", "evidence": "On the other hand, an overproduction of NO is related with several disorders as Alzheimer's disease, Huntington's disease and the amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Disease": {"Huntington's disease": true, "Alzheimer's disease": true, "amyotrophic lateral sclerosis": true}, "Confidence": {"High": true}}, "source": 156, "target": 3858, "key": "3ee68f825c123ccb8a50ed151689ddd6"}, {"line": 21254, "relation": "positiveCorrelation", "evidence": "On the other hand, an overproduction of NO is related with several disorders as Alzheimer's disease, Huntington's disease and the amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Disease": {"Huntington's disease": true, "Alzheimer's disease": true, "amyotrophic lateral sclerosis": true}, "Confidence": {"High": true}}, "source": 156, "target": 3825, "key": "2864edc3f91ea771b9154ca978e2e0e5"}, {"line": 22717, "relation": "increases", "evidence": "NO is produced from L-arginine by different isoforms of NOS and takes part in many normal physiological functions, such as promoting vasodilation of blood vessels and mediating cell communication within the brain. In addition to its physiological actions, the free radical activity of NO may cause cellular damage through a phenomenon known as nitrosative stress", "citation": {"db": "PubMed", "db_id": "24817841"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 156, "target": 498, "key": "880b3021bf377954e6439d5029804ac7"}, {"line": 22721, "relation": "increases", "evidence": "NO is produced from L-arginine by different isoforms of NOS and takes part in many normal physiological functions, such as promoting vasodilation of blood vessels and mediating cell communication within the brain. In addition to its physiological actions, the free radical activity of NO may cause cellular damage through a phenomenon known as nitrosative stress", "citation": {"db": "PubMed", "db_id": "24817841"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 156, "target": 518, "key": "be5b7ed559a29d85c66c204ffe01edfb"}, {"relation": "partOf", "source": 156, "target": 965, "key": "c6d92ed9095e871c5809bcbffe5263c4"}, {"line": 39458, "relation": "increases", "evidence": "It has been shown that apoE increased the production of nitric oxide (NO) from human monocyte-derived macrophages (MDM); this effect could represent an important link between tissue redox balance and inflammation, since inflammation and oxidative stress are involved in chronic neurodegenerative disorders. Moreover, it has been evidenced that an overproduction of NO in the central nervous system (CNS) may play a key role in aging and that the glial cells (microglials cells and probably astrocytes) are able to form consistent amounts of NO through the induction of a nitric oxide synthase (iNOS) isoform so-called inducible or inflammatory.We observed a decreased NO production after incubation with both LDL and HDL and an increased peroxynitrite production. As it concerns NOS expression, densitometric analysis of bands indicated that iNOS protein levels were significantly higher in the cells incubated with both AD lipoproteins and offspring lipoproteins compared to cells incubated with control lipoproteins. These findings suggest the possibility to identify in NO pathway a precocious marker of AD.", "citation": {"db": "PubMed", "db_id": "16054114"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 156, "target": 3821, "key": "e4468f33806b7511db68854b66a9f600"}, {"relation": "partOf", "source": 156, "target": 964, "key": "c4b06054c3e6ab1f0cf16b669ac413ac"}, {"line": 47449, "relation": "increases", "evidence": "Nitric Oxide results in increased expression of CRHR1 mRNA", "citation": {"db": "PubMed", "db_id": "10460269"}, "annotations": {"Species": {"10116": true}}, "source": 156, "target": 3773, "key": "b3ebd1566dd45956901306323b79f63f"}, {"line": 420, "relation": "directlyIncreases", "evidence": "Interaction of H2O2 with Fe2+ or Cu+ generates hydroxyl radical (OH*) a highly reactive oxyradical and potent inducer of MAOS", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 961, "target": 132, "key": "ad17bbd7ad0de6fedcc45f915787f611"}, {"line": 421, "relation": "directlyIncreases", "evidence": "Interaction of H2O2 with Fe2+ or Cu+ generates hydroxyl radical (OH*) a highly reactive oxyradical and potent inducer of MAOS", "citation": {"db": "PubMed", "db_id": "15295589"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 951, "target": 132, "key": "02b5fbfe541de87f7774b092136941b7"}, {"line": 449, "relation": "increases", "evidence": "An alternative pathway is shown on the right side of the figure whereby superoxide or hydrogen peroxide (H2O2) oxidize lipids such as prostaglandins forming F2a-isoprostanes. Both H2O2 and F2a-isoprostanes are known to accelerate Abeta aggregation", "citation": {"db": "PubMed", "db_id": "15864339"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 286, "target": 80, "key": "858e3dca78826783ea9127d6f0609713"}, {"line": 39738, "relation": "positiveCorrelation", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 286, "target": 3823, "key": "d16f95a14a7eef06c95d5d4dd0e52277"}, {"line": 465, "relation": "increases", "evidence": "Abeta is generated from APP by concerted proteolysis by Abeta-secretase, which generates carboxyl-terminal fragments (CTFs) of APP, and then by gamma-secretase.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2381, "target": 4096, "key": "a16c8e1028987ff13dfdabf3a579c519"}, {"line": 6087, "relation": "increases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2381, "target": 4096, "key": "18c324cf357167b7205d55e6a9c010ea"}, {"line": 4156, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2381, "target": 80, "key": "4d6650f734af786192b927eecfc3de38"}, {"line": 26555, "relation": "increases", "evidence": "These data suggest that BACE2 contributes to Abeta production in individuals bearing the Flemish mutation, and that selective inhibition of these highly similar proteases may be feasible and therapeutically advantageous.", "citation": {"db": "PubMed", "db_id": "10931940"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2381, "target": 80, "key": "ee05f51a9ea468eca368e605f23bbed8"}, {"line": 26628, "relation": "increases", "evidence": "BACE2 cleaves the amyloid precursor protein at the beta-secretase site and is thought to contribute to amyloid beta protein production.", "citation": {"db": "PubMed", "db_id": "12052539"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2381, "target": 80, "key": "e4b1715aa75dbb8550735b8d320adc79"}, {"line": 27871, "relation": "increases", "evidence": "Mapping to the Down syndrome critical region (chromosome 21) and identified as a homologue of BACE1, BACE2 also cleaves amyloid precursor protein at the beta-site.", "citation": {"db": "PubMed", "db_id": "15473697"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2381, "target": 80, "key": "b8597bb1ac17421fe9821310076f2935"}, {"line": 36606, "relation": "increases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2381, "target": 80, "key": "c98716d84bbdfcd1e21978183a1797d1"}, {"line": 6093, "relation": "increases", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2381, "target": 717, "key": "edcec4920c75ba9e7d8144f43a3d5f77"}, {"relation": "partOf", "source": 2381, "target": 1146, "key": "e2c35bfb602922575be7abf6564dc80e"}, {"line": 26631, "relation": "association", "evidence": "This suggests the possibility that the elevated expression of BACE2 is involved in the Alzheimer-type neuropathology of DS", "citation": {"db": "PubMed", "db_id": "12052539"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2381, "target": 3852, "key": "d68035730f8221eeea11361d5849e9b9"}, {"line": 27870, "relation": "directlyIncreases", "evidence": "Mapping to the Down syndrome critical region (chromosome 21) and identified as a homologue of BACE1, BACE2 also cleaves amyloid precursor protein at the beta-site.", "citation": {"db": "PubMed", "db_id": "15473697"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2381, "target": 2315, "key": "5e0430faaf4598e94094b0785eed896d"}, {"line": 45179, "relation": "orthologous", "evidence": "BACE1 and BACE2, are involved in the development of Alzheimer's disease by producing Abeta", "citation": {"db": "PubMed", "db_id": "22166205"}, "annotations": {"Species": {"10090": true}}, "source": 2381, "target": 3594, "key": "6d23379c39cf47d2460bcc2ebae63fc7"}, {"line": 545, "relation": "increases", "evidence": "One of the consequences of caspase activation is cleavage of tau.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"namespace": "bel", "name": "pep"}}, "source": 2445, "target": 3012, "key": "c144654ff2814baf91b6aee9b0f5b8e9"}, {"line": 4105, "relation": "increases", "evidence": "It was clarified what molecules related to cell death are activated in the case of AD and we discovered that caspase-4 plays a key role in ER stress-induced apoptotic process. Caspase-4 also seems to act upstream of the beta-amyloid-induced ER stress pathway, suggesting that activation of caspase-4 might mediate neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Caspase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2445, "target": 771, "key": "2328d4ef8d8df2911ae0c90d11eb71a5"}, {"line": 4108, "relation": "increases", "evidence": "It was clarified what molecules related to cell death are activated in the case of AD and we discovered that caspase-4 plays a key role in ER stress-induced apoptotic process. Caspase-4 also seems to act upstream of the beta-amyloid-induced ER stress pathway, suggesting that activation of caspase-4 might mediate neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2445, "target": 645, "key": "def53a40d0b14754b67f2f424504d08c"}, {"line": 484, "relation": "association", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 577, "target": 3875, "key": "126c0333d58180c506203ff0d3d5cfa1"}, {"line": 765, "relation": "association", "evidence": "Part of the inflammatory response in Alzheimer's disease (AD) is the upregulation of the inducible nitric oxide synthase (NOS2) resulting in increased NO production.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 577, "target": 3823, "key": "aa4cf62637e178502c28e32268e64b94"}, {"line": 5006, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 3823, "key": "b718cfc4827c06dad909f61b24d6469a"}, {"line": 9135, "relation": "increases", "evidence": "Recently, there have been increasing evidences that microRNA-146 (miR-146) is related to up-regulated immune and inflammatory signaling through its target genes, such as IRAK1 and TRAF6. Additionally, abundant data continue to support the hypothesis that progressive up-regulation of inflammatory gene expression and elevated inflammatory signaling facilitate the development and progression of Alzheimer's disease (AD). This review focuses on the recent findings regarding the role of miR-146 in modulating immune response and its subsequent effects in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22209051"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 3823, "key": "a983f3aec8f96960b4b51c8aec480c2a"}, {"line": 41174, "relation": "increases", "evidence": "Our study demonstrated that TLR2 is a primary receptor for Abeta to trigger neuroinflammatory activation and suggested that inhibition of TLR2 in microglia could be beneficial in Alzheimer's disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 577, "target": 3823, "key": "8106269f21d4095f8516f932440e50c5"}, {"line": 43319, "relation": "association", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 577, "target": 3823, "key": "1eefa8dd5c9b8eec21674114fb3d040c"}, {"line": 43564, "relation": "positiveCorrelation", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 577, "target": 3823, "key": "86ae333f3f00a2884e6732778106f3e5"}, {"line": 770, "relation": "increases", "evidence": "Part of the inflammatory response in Alzheimer's disease (AD) is the upregulation of the inducible nitric oxide synthase (NOS2) resulting in increased NO production.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Nitric oxide subgraph": true}}, "source": 577, "target": 3123, "key": "28d473e0dc1771096d8ba377b9d0d483"}, {"line": 1061, "relation": "association", "evidence": "TNF-a-308 G/A gene polymorphism could affect cerebral inflammatory response and the risk of late-onset Alzheimer disease but -863 C/A polymorphism does not influence the risk of this disease", "citation": {"db": "PubMed", "db_id": "22279475"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 577, "target": 1999, "key": "424cd2c31a1eb5abbf4ee897202f63a7"}, {"line": 4730, "relation": "association", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 577, "target": 2746, "key": "e673562d0be7e28605bfc125f34ce9b7"}, {"line": 4731, "relation": "association", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 577, "target": 3472, "key": "532cb943334307920c749379d7d8b3dd"}, {"line": 14445, "relation": "increases", "evidence": "The serum IL-1beta and TNF-α level were significantly increased, and the expressions of TLR4 and NF-κB p65 mRNA and protein in the brain were up-regulated, indicating inflammation response was initiated following administration of Abeta1-42.", "citation": {"db": "PubMed", "db_id": "21515354"}, "annotations": {"MeSHAnatomy": {"Serum": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 3472, "key": "e522ed61c700da2c4d38d1035693c882"}, {"line": 4732, "relation": "association", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 577, "target": 2894, "key": "aaaa7842bc0cba8e0f4ed0f87e8b77d3"}, {"line": 5111, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2894, "key": "0be97cb637265fbfe34d88169f69b39e"}, {"line": 5007, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 608, "key": "3abc3520eb186ce08e797420d341916a"}, {"line": 5106, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 3210, "key": "71b9e53df227aaa39e133df43bef243b"}, {"line": 5109, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2884, "key": "e8726f28ad73da56ff3a17cf736b725c"}, {"line": 5110, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2885, "key": "54c4dde57d32346cb7791d6ce55665a8"}, {"line": 9370, "relation": "negativeCorrelation", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 577, "target": 2885, "key": "faee022973410574ecd59b544702c1c2"}, {"line": 14437, "relation": "increases", "evidence": "The serum IL-1beta and TNF-α level were significantly increased, and the expressions of TLR4 and NF-κB p65 mRNA and protein in the brain were up-regulated, indicating inflammation response was initiated following administration of Abeta1-42.", "citation": {"db": "PubMed", "db_id": "21515354"}, "annotations": {"MeSHAnatomy": {"Serum": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2885, "key": "a6511ae2ddf55f28f79f8269c976b364"}, {"line": 5112, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2878, "key": "310376da4f391b43b44d5fe2ff912a2f"}, {"line": 5141, "relation": "association", "evidence": "Peroxisome proliferator-activated receptor gamma (PPAR ) regulates the transcription of BACE1 as well as inflammatory responses in the brain and atherosclerotic risk factors known to be involved also in AD.", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}}, "source": 577, "target": 3212, "key": "8d9a40fde43766ede9476f9d49eab77d"}, {"line": 33686, "relation": "association", "evidence": "Peroxisome proliferator-activated receptor gamma (PPARgamma) regulates the transcription of BACE1 as well as inflammatory responses in the brain and atherosclerotic risk factors known to be involved also in AD.", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 577, "target": 3212, "key": "271e33fcb7c361a5b4e311416e6b3191"}, {"line": 8508, "relation": "association", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2102, "key": "cbaf4749a09b0e0b11886cb34f591ab5"}, {"line": 9131, "relation": "positiveCorrelation", "evidence": "Recently, there have been increasing evidences that microRNA-146 (miR-146) is related to up-regulated immune and inflammatory signaling through its target genes, such as IRAK1 and TRAF6. Additionally, abundant data continue to support the hypothesis that progressive up-regulation of inflammatory gene expression and elevated inflammatory signaling facilitate the development and progression of Alzheimer's disease (AD). This review focuses on the recent findings regarding the role of miR-146 in modulating immune response and its subsequent effects in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22209051"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2102, "key": "3b6ee128cf9e25b102868ece8070b253"}, {"line": 8713, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2790, "key": "6182c91e3c9a8b221b762abd986f70bc"}, {"line": 9288, "relation": "association", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2104, "key": "3fe3716f3c8692eb5129a8a6607c3332"}, {"line": 9367, "relation": "negativeCorrelation", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}}, "source": 577, "target": 3468, "key": "a806333fbe59caec9943abd85028ce97"}, {"line": 14453, "relation": "increases", "evidence": "The serum IL-1beta and TNF-α level were significantly increased, and the expressions of TLR4 and NF-κB p65 mRNA and protein in the brain were up-regulated, indicating inflammation response was initiated following administration of Abeta1-42.", "citation": {"db": "PubMed", "db_id": "21515354"}, "annotations": {"MeSHAnatomy": {"Serum": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 3468, "key": "e600a3a6da359c7a5a4a6a4244829d98"}, {"line": 9368, "relation": "negativeCorrelation", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}}, "source": 577, "target": 3467, "key": "ce6251f5071de955657258f1737dfe68"}, {"line": 39296, "relation": "association", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 577, "target": 3467, "key": "40f6b9531b03dd1b4c05d757a9928bc7"}, {"line": 9372, "relation": "negativeCorrelation", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 577, "target": 3440, "key": "a748084fc81f66a9becd540fe12fcddc"}, {"line": 14461, "relation": "increases", "evidence": "The serum IL-1beta and TNF-α level were significantly increased, and the expressions of TLR4 and NF-κB p65 mRNA and protein in the brain were up-regulated, indicating inflammation response was initiated following administration of Abeta1-42.", "citation": {"db": "PubMed", "db_id": "21515354"}, "annotations": {"MeSHAnatomy": {"Serum": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 3304, "key": "4c5f7c7c4ee65774249c29f1fc3cd91e"}, {"line": 18105, "relation": "association", "evidence": "New findings have also linked activation of the NRF2 system to anti-inflammatory effects via interactions with NF-κB.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 577, "target": 3110, "key": "ca941fb30b5af4fc020ba351c1d4dbf5"}, {"line": 19424, "relation": "association", "evidence": "The TSP1/CD36/CD47-complex is involved in T cell expansion and inflammatory responses to beta-amyloid, both relevant to IBM.", "citation": {"db": "PubMed", "db_id": "17572512"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 1323, "key": "ec46753c2df2f81c6ab8801c73d8070c"}, {"line": 33237, "relation": "association", "evidence": "Cytokines such as TGF beta 1 and interleukin 1 enhance the expression of clusterin, which may link clusterin to inflammatory mechanisms in AD.", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 2538, "key": "bf822b3cb52ec540dca61e119af0a178"}, {"line": 38952, "relation": "increases", "evidence": "Neuro-inflammation induced by lipopolysaccharide causes cognitive impairment through enhancement of beta-amyloid generation.", "citation": {"db": "PubMed", "db_id": "18759972"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 577, "target": 80, "key": "1f8a045033e190de36356cf212ca42e4"}, {"line": 38953, "relation": "decreases", "evidence": "Neuro-inflammation induced by lipopolysaccharide causes cognitive impairment through enhancement of beta-amyloid generation.", "citation": {"db": "PubMed", "db_id": "18759972"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 577, "target": 812, "key": "fa95e2eb62321d97c07583b91db743b4"}, {"line": 38965, "relation": "negativeCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 577, "target": 812, "key": "547e45ac285c677a518b85450744203b"}, {"line": 38966, "relation": "negativeCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 577, "target": 3822, "key": "35850557f72ec600b3f0f87f71b42efb"}, {"line": 39292, "relation": "association", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 577, "target": 2468, "key": "81de26849d97f44a080706b664d4afca"}, {"line": 39294, "relation": "association", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 577, "target": 3084, "key": "854ac9cb115da91c0b67a516646b9f95"}, {"line": 41370, "relation": "increases", "evidence": "Inflammatory responses were achieved by injection of aggregated Abeta1-42 peptide and IL-1beta into frontal cortex, which induced neuronal inducible nitric oxide synthase (iNOS) and microglial IL-1beta expression.", "citation": {"db": "PubMed", "db_id": "12675915"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 4051, "key": "f0a6fe664b943f25058ea076bed849d0"}, {"line": 41374, "relation": "increases", "evidence": "Inflammatory responses were achieved by injection of aggregated Abeta1-42 peptide and IL-1beta into frontal cortex, which induced neuronal inducible nitric oxide synthase (iNOS) and microglial IL-1beta expression.", "citation": {"db": "PubMed", "db_id": "12675915"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 4061, "key": "673955031d403cd6ae4ba0811f20f675"}, {"line": 43306, "relation": "increases", "evidence": "The presence of cytosolic phospholipase A(2) does not merely overlap with reactive astroglia, as reactive / astrocytes were observed that did not exhibit cytosolic phospholipase A(2) immunoreactivity. In most conditions evaluated,/ inflammatory processes have been postulated to play a pivotal role and may even participate in neuronal cell death.", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 648, "key": "8c49add740c46a5dc1337eee3791c549"}, {"line": 43318, "relation": "association", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 577, "target": 418, "key": "c21b93ecc004e2e60b53df14d740cacd"}, {"line": 46946, "relation": "association", "evidence": "TREM2 could suppress inflammatory response by repression of microglia-mediated cytokine production and secretion, which may prevent inflammation-induced bystander damage of neurons. TREM2 also participates in the regulation of phagocytic pathways that are responsible for the removal of neuronal debris.", "citation": {"db": "PubMed", "db_id": "23407992"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 538, "key": "f9dab61ceb9ecf1ab33957ac39be2893"}, {"line": 46947, "relation": "association", "evidence": "TREM2 could suppress inflammatory response by repression of microglia-mediated cytokine production and secretion, which may prevent inflammation-induced bystander damage of neurons. TREM2 also participates in the regulation of phagocytic pathways that are responsible for the removal of neuronal debris.", "citation": {"db": "PubMed", "db_id": "23407992"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 577, "target": 539, "key": "be93050c8a45170d0be748110a50235c"}, {"line": 484, "relation": "association", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3875, "target": 577, "key": "8d3c790dc98ec101e83290e65a84e2e4"}, {"line": 5098, "relation": "isA", "evidence": "Neuroinflammation contributes to the pathogenesis of sporadic Alzheimer's disease (AD)", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3875, "target": 577, "key": "fd9af39d7ece8541dd3d62da22d15261"}, {"line": 4224, "relation": "association", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 3875, "target": 645, "key": "78e549639fc99735f5f487c81207afe3"}, {"line": 4270, "relation": "decreases", "evidence": "Chronic neuroinflammation is a hallmark of several neurological disorders associated with cognitive loss. Activated microglia and secreted factors such as tumor necrosis factor (TNF)-a are key mediators of neuroinflammation and may contribute to neuronal dysfunction", "citation": {"db": "PubMed", "db_id": "22277195"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3875, "target": 812, "key": "beed54f2b2844900c675c8aa9a81e0dc"}, {"line": 5097, "relation": "increases", "evidence": "Neuroinflammation contributes to the pathogenesis of sporadic Alzheimer's disease (AD)", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3875, "target": 3823, "key": "933ead8712336109407bebf9192272a3"}, {"line": 519, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 170, "target": 645, "key": "3f5a6c1fa3ccff01cccbd0bfd30dbad5"}, {"line": 1683, "relation": "increases", "evidence": "Increased ROS levels act at multiple levels to impair mitochondrial function: they induce mtDNA mutations that consequently negatively influence mitochondrial function, enhance amyloid-beta production by guiding APP cleavage pathway toward the amyloidogenesis, increase lipid peroxidation, activate mitophagy, leading to a reduced mitochondrial number, and augment tau hyperphosphorylation and NFT formation impairing organelle trafficking and neuronal function finally leading to apoptotic process.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 645, "key": "10a92da4948066bbf9473806c7c2fe3a"}, {"line": 5285, "relation": "increases", "evidence": "Mitochondrial Abeta-binding alcohol dehydrogenase (ABAD): ABAD is a member of the short chain dehydrogenase reductase family in mitochondria that binds Abeta. Binding of Abeta to ABAD distorts the enzymebetas structure, rendering it inactive. In neurons, ABAD is predominately localized to mitochondria. Upon binding ABAD, Abeta triggers events leading to neuronal apoptosis through a mitochondrial pathway.Interestingly, mitochondrial ABAD is upregulated in neurons from AD patients. The ABAD-Abeta complex has been hypothesized to induce oxidant stress and mitochondrial dysfunction. Increased expression of ABAD exacerbates Abeta-mediated mitochondrial and neuronal stress. Abeta binding to ABAD causes free radical production and neuronal apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 645, "key": "4daa5dbb5a1dd4e5ddc1e33b9b774c5f"}, {"line": 1550, "relation": "decreases", "evidence": "Biochemical and morphological alterations of mitochondria may play an important role in the pathogenesis of Alzheimer's disease (AD). Particularly, mitochondrial dysfunction is a hallmark of amyloid-beta-induced neuronal toxicity in Alzheimer's disease. The recent emphasis on the intracellular biology of amyloid-beta and its precursor protein (APP) has led researchers to consider the possibility that mitochondria-associated and mitochondrial amyloid-beta may directly cause neurotoxicity. Both proteins are known to localize to mitochondrial membranes, block the transport of nuclear-encoded mitochondrial proteins to mitochondria, interact with mitochondrial proteins, disrupt the electron transport chain, increase reactive oxygen species production, cause mitochondrial damage, and prevent neurons from functioning normally. ", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "source": 170, "target": 614, "key": "63d99209e4802d05f6ecb85763c4b4b2"}, {"line": 1665, "relation": "increases", "evidence": "Increased ROS levels act at multiple levels to impair mitochondrial function: they induce mtDNA mutations that consequently negatively influence mitochondrial function, enhance amyloid-beta production by guiding APP cleavage pathway toward the amyloidogenesis, increase lipid peroxidation, activate mitophagy, leading to a reduced mitochondrial number, and augment tau hyperphosphorylation and NFT formation impairing organelle trafficking and neuronal function finally leading to apoptotic process.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 2328, "key": "3d6a4acee3159bf35ca4072bac0d16d1"}, {"line": 1671, "relation": "increases", "evidence": "Increased ROS levels act at multiple levels to impair mitochondrial function: they induce mtDNA mutations that consequently negatively influence mitochondrial function, enhance amyloid-beta production by guiding APP cleavage pathway toward the amyloidogenesis, increase lipid peroxidation, activate mitophagy, leading to a reduced mitochondrial number, and augment tau hyperphosphorylation and NFT formation impairing organelle trafficking and neuronal function finally leading to apoptotic process.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Reactive oxygen species subgraph": true}}, "source": 170, "target": 593, "key": "08d372d170d3fe68c60e4c4b4ee41964"}, {"line": 2304, "relation": "increases", "evidence": "Iron deposition in the brain is another important proposed mechanisms in the pathophysiology AD. Excessive iron can contribute to the formation of free radicals, leading to lipid peroxidation and neurotoxicity, which can result in cell membrane damage and cell death. Recently, it has been shown that iron concentration in AD patients brain was significantly higher than those of nondemented controls. In particular iron deposition in parietal cortex and hippocampus at the early stage of AD were positively correlated with the severity of patients cognitive impairment", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 170, "target": 593, "key": "579914ad93ae26ce28068a1850fff71a"}, {"line": 1675, "relation": "increases", "evidence": "Increased ROS levels act at multiple levels to impair mitochondrial function: they induce mtDNA mutations that consequently negatively influence mitochondrial function, enhance amyloid-beta production by guiding APP cleavage pathway toward the amyloidogenesis, increase lipid peroxidation, activate mitophagy, leading to a reduced mitochondrial number, and augment tau hyperphosphorylation and NFT formation impairing organelle trafficking and neuronal function finally leading to apoptotic process.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 620, "key": "bf621805798b60590d99447cb6a0543d"}, {"line": 1679, "relation": "increases", "evidence": "Increased ROS levels act at multiple levels to impair mitochondrial function: they induce mtDNA mutations that consequently negatively influence mitochondrial function, enhance amyloid-beta production by guiding APP cleavage pathway toward the amyloidogenesis, increase lipid peroxidation, activate mitophagy, leading to a reduced mitochondrial number, and augment tau hyperphosphorylation and NFT formation impairing organelle trafficking and neuronal function finally leading to apoptotic process.", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 3015, "key": "bbaae1889e42b37af8910d96674ad8d3"}, {"line": 5311, "relation": "decreases", "evidence": "Lower levels of ROS are required for synaptic signaling with ROS acting as messenger molecules in the process of LTP. However, high levels of ROS have been implicated as damaging toxic molecules in the age-related impairments of LTP. Our previous work shows that ROS levels increase with age of neurons in parallel with an age-related decline in transmembrane potential. As mitochondrial transmembrane potential is a driving force for cellular production of ATP, its decline in neurons will have a long term effect in many important energy driven reactions.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 597, "key": "2ef246fc072b6355539aa0081a617d87"}, {"line": 5401, "relation": "association", "evidence": "Memory mechanisms might be directly compromised by elevated ROS, which could explain the connection between AD and oxidative stress. The increase in oxidative damage exhibited by synaptic mitochondria will damage synapses, affect neurotransmission and might be ultimately responsible for cognitive decline in AD patients. Taken together these studies provide convincing evidence for the concept that mitochondria have a pivotal role in Abeta-induced synaptic dysfunction and neuronal stress. Improved function of mitochondria is an effective way of reducing effects of aging and may inhibit neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 170, "target": 3866, "key": "55a8bb35437ead31304d6ba347f32394"}, {"line": 5430, "relation": "increases", "evidence": "The interaction of CypD with Abeta causes functional modification of this protein leading to MPTP opening. Abeta also binds with another mitochondrial protein, ABAD to distort the enzymebetas structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 170, "target": 478, "key": "f4e5da04fff95dd7d89fdd4a45701ce0"}, {"line": 22090, "relation": "positiveCorrelation", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 478, "key": "d974010cddc39382d252498b5bd7b8ef"}, {"line": 9684, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 3823, "key": "dfa6bc6d66aba0510258e66265f7bb5b"}, {"line": 42510, "relation": "positiveCorrelation", "evidence": "The cellular generation of reactive oxygen species (ROS) has been implicated in contributing to the pathology of human neurological disorders including Alzheimer's disease (AD) and Parkinson's disease (PD).", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"nervous system disease": true, "Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "source": 170, "target": 3823, "key": "9ed5e6ced67860a77a1f196d898f7734"}, {"line": 42558, "relation": "association", "evidence": "These findings have mechanistic implications for ROS-triggered inflammatory gene expression programs that may contribute to AD and PD neuropathologic mechanisms.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 170, "target": 3823, "key": "b5ba7cf55aa85e4f7abac326fec1a1f6"}, {"line": 46087, "relation": "increases", "evidence": "presenilin-1 (PS-1) promote Alzheimer's disease (AD) by increasing reactive oxygen species, at least part of which is derived by an accompanying increase in generation of amyloid-beta (Abeta).", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 170, "target": 3823, "key": "1d9257ca96407cc91554c00c4dda89a7"}, {"line": 49444, "relation": "association", "evidence": "Antioxidants scavenge free radicals and other reactive oxygen species that damage cellular membranes, organelles, and macromolecules. Accumulation of reactive oxygen species may overwhelm the protective reserves of antioxidants in cells (oxidative stress). In neurons, which are especially vulnerable to free radical–mediated damage, these processes may be important in aging of the brain and the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "annotations": {"Cell": {"neuron": true}}, "source": 170, "target": 3823, "key": "3d727f62e9bdb2b09ac8993e9356ca1f"}, {"line": 49449, "relation": "increases", "evidence": "Sufficient levels of vitamin E may reduce the oxidative stress–related damage associated with pathological changes of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "source": 170, "target": 3823, "key": "df2f14cbf6de2ad4409f2f16dead2f37"}, {"line": 18563, "relation": "association", "evidence": "Associated with these inflammatory responses are tumor necrosis factor-alpha (TNF-alpha) and reactive oxygen species (ROS), both believed to be derived from brain microglia.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true}}, "source": 170, "target": 3920, "key": "26f8e56e96c15adc5655530d20617336"}, {"line": 18579, "relation": "association", "evidence": "However, the possible role of MPO and enzymatically inactive MPO (iMPO) as the choreographers of the destruction done by TNF-alpha and ROS is not generally recognized.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"Subgraph": {"Myeloperoxidase subgraph": true, "Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 3066, "key": "c88ad45f97d79b912d375dda196d617a"}, {"line": 22971, "relation": "increases", "evidence": "Recently, we have reported that intracerebroventricular (ICV) administration of okadaic acid (OKA) in rats induces memory impairment that was associated with increased oxidative stress. Besides memory deficit, OKA caused impairment in mitochondrial function as revealed by alterations in calcium ion, reactive oxygen species (ROS) generation, mitochondrial membrane potential (MMP), SDH activity and ATP level in the brain regions. Further, in histopathological study it was observed that donepezil and memantine reduced the cell loss and neurodegeneration in hippocampus and periventricular cortex regions in OKA treated rats.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 842, "key": "309eb0dde8ddbde6f7272e0559f27a50"}, {"line": 23096, "relation": "association", "evidence": "Moreover, the scavengers of reactive oxygen species (ROS) including Trolox and N-acetylcysteine (NAC) abrogated the SOD1(G93A)-induced increase in ASK1 activity (Supplementary Figure 2), suggesting that SOD1(G93A) induces ASK1 activation in a ROS-dependent manner.", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 170, "target": 3720, "key": "9ba3c7b36fc910d208155b0c55859c41"}, {"line": 23097, "relation": "association", "evidence": "Moreover, the scavengers of reactive oxygen species (ROS) including Trolox and N-acetylcysteine (NAC) abrogated the SOD1(G93A)-induced increase in ASK1 activity (Supplementary Figure 2), suggesting that SOD1(G93A) induces ASK1 activation in a ROS-dependent manner.", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 170, "target": 3668, "key": "c7d024ce1625b280d27b04aae3fbf44c"}, {"line": 23112, "relation": "increases", "evidence": "We also examined a possible effect of CIIA on TRAF2-ASK1 interaction, because the recruitment of TRAF2 to ASK1 is an integral part of the mechanism for ROS-induced ASK1 activation. SOD1(G93A) induced the binding of TRAF2 to ASK1 in NSC34 cells and this binding was potentiated in those expressing a CIIA siRNA (Figure 2C, upper panel), suggesting that CIIA suppresses the SOD1(G93A)-induced TRAF2-ASK1 interaction.", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 170, "target": 3668, "key": "c5306f9b531082ca62fc2c25149fa1cc"}, {"line": 23149, "relation": "increases", "evidence": "Furthermore, our data with ROS scavengers suggest that intracellular ROS mediates the SOD1(G93A)-induced activation of MST1, although the origin of ROS generation induced by SOD1(G93A) remains unclear (25).", "citation": {"db": "PubMed", "db_id": "23818595"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 170, "target": 3726, "key": "311557e36539b9eda303a593471f0621"}, {"line": 23252, "relation": "decreases", "evidence": "Reactive oxygen species (ROS), produced within mitochondria, inhibit the function of EAAT2, the main glial glutamate transporter protein, responsible for most of the reuptake of synaptically released glutamate. Glutamate excess increases intracellular calcium, which enhances oxidative stress and mitochondrial damage.", "citation": {"db": "PubMed", "db_id": "17015226"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 170, "target": 3369, "key": "6f78129f3cc6b5fa30836bc6c1907a37"}, {"line": 23359, "relation": "increases", "evidence": "Similarly, high glucose levels inhibit AMPK activity and increase ROS generation. This leads to the upregulation of Nox4 and the activation of p53-induced apoptosis in glomerular epithelial cells (podocytes), the loss of which may contribute to albuminuria and diabetic kidney disease. The reactivation of AMPK by AICAR in this context leads to a reduction in Nox4 levels, resulting in a suppression of p53 (Eid et al., 2010).", "citation": {"db": "PubMed", "db_id": "23954639"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "source": 170, "target": 3129, "key": "7bf57e81d16155f341311a6e817db028"}, {"line": 23377, "relation": "increases", "evidence": "Taken together, these results suggest that glucose deprivation leads to p53 activation through a pathway that involves elevated ROS levels and ATM activation in the absence of DNA double-strand breaks. In this paper, we report that glucose deprivation results in ROS production and in the activation of endogenous p53 through an ATM-dependent mechanism.", "citation": {"db": "PubMed", "db_id": "22055193"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 170, "target": 2368, "key": "ea295551c41680c9b42ce81e9c77eb1f"}, {"line": 23385, "relation": "increases", "evidence": "Taken together, these results suggest that glucose deprivation leads to p53 activation through a pathway that involves elevated ROS levels and ATM activation in the absence of DNA double-strand breaks. In this paper, we report that glucose deprivation results in ROS production and in the activation of endogenous p53 through an ATM-dependent mechanism.", "citation": {"db": "PubMed", "db_id": "22055193"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 170, "target": 3482, "key": "15577f30a65f803acef71630891423bc"}, {"line": 29543, "relation": "increases", "evidence": "In the second phase, Cdk5 activates c-Jun via ROS-mediated activation of JNK. Rapid c-Jun activation is supported by in vivo data showing c-Jun phosphorylation in cerebral cortex upon p25 induction in transgenic mice.", "citation": {"db": "PubMed", "db_id": "19776350"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 170, "target": 3002, "key": "73d31ce0d7a81af0a373b89fdee53f73"}, {"line": 40123, "relation": "positiveCorrelation", "evidence": "Under pathological conditions, increasing ROS production can regulate the expression of diverse inflammatory mediators during brain injury.", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Brain Injuries": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 170, "target": 3829, "key": "a3e2c65434cacdc18190699a5b711cdf"}, {"line": 42509, "relation": "positiveCorrelation", "evidence": "The cellular generation of reactive oxygen species (ROS) has been implicated in contributing to the pathology of human neurological disorders including Alzheimer's disease (AD) and Parkinson's disease (PD).", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"nervous system disease": true, "Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "source": 170, "target": 3878, "key": "d7487ca5b0eb4ae38ca12ae883cedca6"}, {"line": 42559, "relation": "association", "evidence": "These findings have mechanistic implications for ROS-triggered inflammatory gene expression programs that may contribute to AD and PD neuropathologic mechanisms.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 170, "target": 3878, "key": "5cc68e107b4010987343f8a4fa1235fb"}, {"line": 42513, "relation": "positiveCorrelation", "evidence": "The cellular generation of reactive oxygen species (ROS) has been implicated in contributing to the pathology of human neurological disorders including Alzheimer's disease (AD) and Parkinson's disease (PD).", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"nervous system disease": true, "Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 170, "target": 3873, "key": "764af80ffc951542b552014e12e0016c"}, {"relation": "partOf", "source": 170, "target": 968, "key": "4cea221277fa63ee7a24b787d3156c24"}, {"relation": "partOf", "source": 170, "target": 970, "key": "356fc770935114eac966529d5c984494"}, {"relation": "partOf", "source": 170, "target": 967, "key": "61559cefe2d25d65150a312c6549a93b"}, {"relation": "partOf", "source": 170, "target": 969, "key": "01197345135b93a740cc01edd4524050"}, {"line": 42534, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 170, "target": 73, "key": "ed5729a72c4d94459ed7f88caf781bcd"}, {"line": 42546, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 170, "target": 351, "key": "18cce8108e63e9b00348382c31368e77"}, {"line": 44566, "relation": "increases", "evidence": "hypomethylation of the APP promoter for example can increase the ceiling of expression of the APP gene in response to aging processes driving overproduction of APP and Abeta levels. The increased Abeta levels then facilitate ROS production with their pro-oxidant properties, damaging the DNA. ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 170, "target": 3842, "key": "e00a4f98298ac7a5ad2240fdd5d5c353"}, {"line": 499, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true, "Nitric oxide subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 700, "target": 645, "key": "ef7ca9b8fbdb285be5be2d92219f3bfe"}, {"line": 537, "relation": "isA", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 645, "target": 478, "key": "257659aa0d291f480c09bdaa2784ec68"}, {"line": 1755, "relation": "association", "evidence": "Alzheimer disease (AD) is a heterogeneous neurodegenerative disorder characterized by (1) progressive loss of synapses and neurons, (2) intracellular neurofibrillary tangles, composed of hyperphosphorylated Tau protein, and (3) amyloid plaques. Genetically, AD is linked to mutations in few proteins amyloid precursor protein (APP) and presenilin 1 and 2 (PS1 and PS2). The molecular mechanisms underlying neurodegeneration in AD as well as the physiological function of APP are not yet known. A recent theory has proposed that APP and PS1 modulate intracellular signals to induce cell-cycle abnormalities responsible for neuronal death and possibly amyloid deposition.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"Very High": true}}, "source": 645, "target": 503, "key": "97f024d432e5b0f747ba3c308b972d3a"}, {"line": 1806, "relation": "association", "evidence": "Schematic representation of the intracellular pathway by which AbetaPP and PS1 control the activation of the MAPK/ERK1/2 cascade and their final biological effects. In the figure is specified the interaction between APP intracellular domain and PS1 C-terminus, with the adaptor protein Grb2. Grb2 can bind simultaneously to APP and PS1 (as measured in FRET experiments) leading to the MAPK ERK1/2 cascade activation. In AD an aberrant activation of ERK1/2 induced by APP and/or PS1 can determine the tentative activation of the cell cycle that, in postmitotic neurons, may induce cells to undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 645, "target": 2173, "key": "fdc84627286aec4fb6337abd9f30050b"}, {"line": 4109, "relation": "association", "evidence": "It was clarified what molecules related to cell death are activated in the case of AD and we discovered that caspase-4 plays a key role in ER stress-induced apoptotic process. Caspase-4 also seems to act upstream of the beta-amyloid-induced ER stress pathway, suggesting that activation of caspase-4 might mediate neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true}}, "source": 645, "target": 3823, "key": "7803bd68cbeee7873cc5840677d5d291"}, {"line": 4846, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 645, "target": 3823, "key": "b43d74d02c8726fc7881ce27632a3201"}, {"line": 4223, "relation": "association", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 645, "target": 609, "key": "714f1c874c9fefd7c451d5a4e345b5f7"}, {"line": 4224, "relation": "association", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 645, "target": 3875, "key": "b228ee441f36c0f314de8fa30a2d89da"}, {"line": 5010, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 645, "target": 524, "key": "d96c4dd83f65d603fffa02dbf443f782"}, {"line": 5331, "relation": "decreases", "evidence": "Increased oxidative stress, coupled with dysregulation of calcium homeostasis and resulting apoptosis of vulnerable neuronal populations, are proposed to underlie the loss of synaptic activity and associated cognitive decline. From these deficiencies emerges the concept of synaptic energy exhaustion in AD, both phosphorylative (ATP) and redox (NAD[P]H) energies. Our previous work shows that hippocampal NAD(P)H and glutathione (GSH) decline with age in association with increased susceptibility to glutamate toxicity in neurons of old-age. Thus, an age-related decline in neuronal reducing currency (NAD[P]H) and reducing buffer (GSH) will surely promote oxidative stress and excess ROS. It is noteworthy that in the early stages of AD, there is already a reduction in the number of mitochondria and the activities of tricarboxylic acid cycle enzymes and cytochrome C oxidase. However, how ROS are produced at the synapse in response to Abeta oligomers is not fully known.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 645, "target": 760, "key": "004474cc57d4aebd9ea0edd29c871daa"}, {"line": 11683, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 645, "target": 434, "key": "28b010979a37a544e53b13bc7e3dd43f"}, {"line": 14551, "relation": "association", "evidence": "Consequently, we tested here the hypothesis that, in the PS1 FAD brain, cyclin D1 accumulation may occur and lead to neuronal apoptosis secondary to an abortive entry into the cell cycle.", "citation": {"db": "PubMed", "db_id": "18239458"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 645, "target": 2463, "key": "2f6d7b846d21f25a1a6002241beb6839"}, {"line": 36874, "relation": "positiveCorrelation", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 645, "target": 2794, "key": "13df7aef5377d40cbca89cf34e80fb11"}, {"line": 531, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3472, "target": 645, "key": "4ace2feb5db050026bed6fb44414eb38"}, {"line": 4265, "relation": "increases", "evidence": "Chronic neuroinflammation is a hallmark of several neurological disorders associated with cognitive loss. Activated microglia and secreted factors such as tumor necrosis factor (TNF)-a are key mediators of neuroinflammation and may contribute to neuronal dysfunction", "citation": {"db": "PubMed", "db_id": "22277195"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3472, "target": 645, "key": "04e32fbc13fa9450f1369a8cf6f4afcb"}, {"line": 1412, "relation": "biomarkerFor", "evidence": "A panel of five markers (CCL5, CSF1, ICAM1, IL8, TNF) with detectable expression levels in all individuals differed between AD patients", "citation": {"db": "PubMed", "db_id": "21942811"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3472, "target": 3823, "key": "b8e1edfe0a5d0151162a533901bb10c1"}, {"line": 3791, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3823, "key": "347b386b019fe0a111c68f2fb23022a4"}, {"line": 10783, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3823, "key": "a2ae76eeb8955bbdc8825fbfe4412b0e"}, {"line": 11800, "relation": "positiveCorrelation", "evidence": "According to current scientific knowledge, excess tumour necrosis factor-alpha (TNF-alpha) and low insulin-like growth factor-I (IGF-I) are pathogenic-risk factors that constitute therapeutic targets for Alzheimer's disease (AD).At week 24, Cere reduced TNF-alpha and enhanced dissociable IGF-I with respect to placebo in a dose-related manner. Increases in total IGF-I were induced by 60 ml Cere", "citation": {"db": "PubMed", "db_id": "19531281"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3823, "key": "c4cd22d2684c9b7f593c445bac79dd5e"}, {"line": 46176, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3472, "target": 3823, "key": "d53eeb10a8e889b210e4cb126b5c24a3"}, {"line": 4264, "relation": "increases", "evidence": "Chronic neuroinflammation is a hallmark of several neurological disorders associated with cognitive loss. Activated microglia and secreted factors such as tumor necrosis factor (TNF)-a are key mediators of neuroinflammation and may contribute to neuronal dysfunction", "citation": {"db": "PubMed", "db_id": "22277195"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3472, "target": 693, "key": "736ceeee258caea4384d953d13477d70"}, {"line": 8586, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3472, "target": 693, "key": "edb6618a8f40220599f4dd104fd9a47e"}, {"line": 39792, "relation": "increases", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}}, "source": 3472, "target": 693, "key": "5deee7c21cf88f0064ced5ada6dc7627"}, {"line": 4267, "relation": "increases", "evidence": "Chronic neuroinflammation is a hallmark of several neurological disorders associated with cognitive loss. Activated microglia and secreted factors such as tumor necrosis factor (TNF)-a are key mediators of neuroinflammation and may contribute to neuronal dysfunction", "citation": {"db": "PubMed", "db_id": "22277195"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3472, "target": 3875, "key": "8c074744aa2cb2017942dfadc67f39b8"}, {"line": 4402, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-a. TNF-a signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2).", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 575, "key": "617369b1250cf8611fc8d5ea8b5fa4a2"}, {"line": 4406, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-a. TNF-a signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2).", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2219, "key": "54dd65128bbeca953f700474db125902"}, {"line": 4731, "relation": "association", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 577, "key": "158d6a433d6d04e549826ebf086a36d9"}, {"line": 5014, "relation": "association", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 608, "key": "54496a01fed42955c5937857114b0641"}, {"line": 8561, "relation": "increases", "evidence": "A significant role of a pathological glial cell activation in the pathogenesis of Alzheimer's disease is supported by the growing evidence that inflammatory proteins, which are produced by reactive astrocytes, promote the transformation of diffuse beta-amyloid deposits into the filamentous, neurotoxic form. A number of vicious circles, driven by the release of TNF-a and free oxygen radicals from microglial cells, may cause an upregulated microglial activation and their production of interleukin-1 which triggers, secondarily, the crucial activation of astrocytes.", "citation": {"db": "PubMed", "db_id": "9850925"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3472, "target": 608, "key": "9b6d8f94e1754049be83833e825ba164"}, {"relation": "partOf", "source": 3472, "target": 1708, "key": "8972ae292d82e02df028a01b4bbb9a57"}, {"line": 6324, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3472, "target": 580, "key": "e6e0f40a9e010f28590693efe5d0fa80"}, {"line": 6325, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3472, "target": 583, "key": "25dcd86f60098df855c491c56ebf1bbe"}, {"line": 8532, "relation": "increases", "evidence": "Truncated phospholipids were essential elements of TNFalpha-induced apoptosis because overexpression of PAFAH2 (a phospholipase A(2) that selectively hydrolyzes truncated phospholipids) blocked TNFalpha-induced Az-PC accumulation without affecting phospholipid peroxidation. PAFAH2 also abolished apoptotic process. Thus, phospholipid oxidation and truncation to apoptotic phospholipids comprise an essential element connecting TNFalpha receptor signaling to mitochondrial damage and apoptotic death", "citation": {"db": "PubMed", "db_id": "22433871"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 394, "key": "a06c5a2a3f789a521f75856c3d9661df"}, {"line": 8538, "relation": "increases", "evidence": "Truncated phospholipids were essential elements of TNFalpha-induced apoptosis because overexpression of PAFAH2 (a phospholipase A(2) that selectively hydrolyzes truncated phospholipids) blocked TNFalpha-induced Az-PC accumulation without affecting phospholipid peroxidation. PAFAH2 also abolished apoptotic process. Thus, phospholipid oxidation and truncation to apoptotic phospholipids comprise an essential element connecting TNFalpha receptor signaling to mitochondrial damage and apoptotic death", "citation": {"db": "PubMed", "db_id": "22433871"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 478, "key": "716690fee33ecc96dbc1e9996f64bb4e"}, {"line": 10804, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3850, "key": "4fd10a996bbbfbecbb1f6e9c1dcd9ef9"}, {"line": 14388, "relation": "association", "evidence": "In addition, NF-κB p65 expression leads to up-regulated beta-secretase cleavage and Abeta production, while non-steroidal anti-inflammatory drugs (NSAIDs) inhibited BACE1 transcriptional activation induced by strong NF-κB activator tumour necrosis factor-alpha (TNF-α).", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3304, "key": "023344f845226237f398642442ebf9a7"}, {"line": 14396, "relation": "association", "evidence": "In addition, NF-κB p65 expression leads to up-regulated beta-secretase cleavage and Abeta production, while non-steroidal anti-inflammatory drugs (NSAIDs) inhibited BACE1 transcriptional activation induced by strong NF-κB activator tumour necrosis factor-alpha (TNF-α).", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2375, "key": "e74b5419dfcd03512779594aa0298b93"}, {"line": 18567, "relation": "association", "evidence": "Associated with these inflammatory responses are tumor necrosis factor-alpha (TNF-alpha) and reactive oxygen species (ROS), both believed to be derived from brain microglia.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3920, "key": "a531e48899111bf0125e4d3fd77af0d7"}, {"line": 22354, "relation": "increases", "evidence": "Cytokines, particularly tumor necrosis factor α (TNF-α) and interleukin 1beta (IL-1beta), can induce chronic inflammation that may promote the loss of synapses, cognitive dysfunction, and eventually neuronal death [19] and [20].", "citation": {"db": "PubMed", "db_id": "24960578"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3920, "key": "38b7985c5fa508340c7171986c21697e"}, {"line": 24160, "relation": "increases", "evidence": "TNF-α and IL-1beta play vital roles in joint inflammation and bone destruction.", "citation": {"db": "PubMed", "db_id": "21157520"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3472, "target": 3920, "key": "09065516aa89d53fb7287675ecc62a7f"}, {"line": 18583, "relation": "association", "evidence": "However, the possible role of MPO and enzymatically inactive MPO (iMPO) as the choreographers of the destruction done by TNF-alpha and ROS is not generally recognized.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"Subgraph": {"Myeloperoxidase subgraph": true, "Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3066, "key": "1d22909f2c6b4c3873a5ced9db736f53"}, {"line": 19443, "relation": "increases", "evidence": "Further, TNF-alpha upregulated the production of TSP1 and CD47 by myoblasts.", "citation": {"db": "PubMed", "db_id": "17572512"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2477, "key": "d0aa8897e22652ea8f8f5191c9377ef5"}, {"line": 19451, "relation": "increases", "evidence": "Further, TNF-alpha upregulated the production of TSP1 and CD47 by myoblasts.", "citation": {"db": "PubMed", "db_id": "17572512"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3459, "key": "fd6239d7bec1988c80feb28bd384d792"}, {"line": 22384, "relation": "increases", "evidence": "In addition, pericytes respond to the pro-inflammatory cytokines tumor necrosis factor-α and Interferon-gamma by inducing the expression of the CYP27B1 gene which is involved in 1,25D synthesis.", "citation": {"db": "PubMed", "db_id": "24934545"}, "annotations": {"Subgraph": {"Vitamin subgraph": true, "Tumor necrosis factor subgraph": true, "Metabolism of steroid hormones subgraph": true}}, "source": 3472, "target": 2610, "key": "52f1fd2738c4bc4cc3739ae81aad53ec"}, {"line": 22440, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 505, "key": "ed08344de1ad6de2c4138debc2269e48"}, {"line": 22448, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3567, "key": "bdce3d809f19d4222f97901caf77f931"}, {"line": 22452, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3568, "key": "a911557277c3a0b495bff7a9e3459007"}, {"line": 22460, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3569, "key": "e73301a2f1e219200ecfa9e8e8c2870c"}, {"line": 22464, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3570, "key": "5373b9ef47f3459856f6aa99934435da"}, {"line": 22468, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3571, "key": "4e24f7cc057c6051b0fadddfd69d89d8"}, {"line": 22472, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3572, "key": "9975f87e7af4d98aa31427524b4ef133"}, {"line": 22476, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3573, "key": "345edc045effaae9f32837dd4d78818f"}, {"line": 22480, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3574, "key": "77ee57fef4fc5009baab05931231b084"}, {"line": 22484, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 874, "key": "f3e289f44e9ebce337041ed806c5b458"}, {"line": 22488, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3472, "target": 3577, "key": "f0725387b70a9ad951d90c7555b0d495"}, {"line": 22508, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2770, "key": "6e386070ea5dfa04923c7130b8ba487c"}, {"line": 22509, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2779, "key": "6f07b0ef6bc3800d053e527d891ef8d1"}, {"line": 22517, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2721, "key": "658ff2ba2b6ebfb062668fcc9437b4de"}, {"line": 22521, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2722, "key": "b1e393ba93379a213d2b556fe5a02a60"}, {"line": 22525, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2723, "key": "4e03e040adfb5cc5451e8980aa40c02f"}, {"line": 22529, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2724, "key": "5cb4d09d52fe45b1fe5bae89e92727ee"}, {"line": 22533, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2725, "key": "4fc76baa5e103984f330260977334517"}, {"line": 22537, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2726, "key": "9dae2076fe94c3512c1516d28555276f"}, {"line": 22541, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2727, "key": "ebc9a6e8c46e5b1fab18fcb5363c747a"}, {"line": 22545, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2728, "key": "8ba623a6333a1c4738d31106187c3626"}, {"line": 22549, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2729, "key": "b42938f37ee3f90e4754144011dc7517"}, {"line": 22553, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2732, "key": "fd752e9d5c79137deac49483f9ae6d10"}, {"line": 22557, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2733, "key": "eec3d115625939606fb534f0a223351f"}, {"line": 22561, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2734, "key": "2476e7c189a0cf7f43f6d0e3b0148466"}, {"line": 22565, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2730, "key": "487cf48533051454247b92fbdfad9346"}, {"line": 22569, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2731, "key": "b77e9e6feae15798c8b4c470ae083e02"}, {"line": 22573, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2735, "key": "5caf392f3c08c6b9d321593a2af318f8"}, {"line": 22577, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2736, "key": "d2117f94e02dabbaf76bed24034deb36"}, {"line": 22581, "relation": "decreases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2719, "key": "deb80e53dbd5eda5c22cf16c5f4762be"}, {"line": 23837, "relation": "decreases", "evidence": "There is a physiological decline of the growth hormone (GH)/insulin-like growth factor-I (IGF-I) axis with ageing and the possibility that the GH/ IGF-I axis is involved in cognitive deficits has been recognized for several years. The IGF-I is a potent neurotrophic as well neuroprotective factor found in the brain with a wide range of actions in both central and peripheral nervous system. IGF-I is a critical promoter of brain development and neuronal survival and plays a role in neuronal rescue during degenerative diseases.When a cholinesterase inhibitor as rivastigmine, a drug for AD, is acutely administered the area under the curve of the GH response to GHRH doubled, showing that rivastigmine is a powerful drug to enhance GH release. TNFα production may promote neurodegeneration not through direct killing of neurons but rather through inhibition of IGF-I survival signalling", "citation": {"db": "PubMed", "db_id": "22524398"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3472, "target": 2871, "key": "a76020a1c0e3df326e248ffb4735af9f"}, {"line": 23863, "relation": "negativeCorrelation", "evidence": "our results indicate that IGF-I is neuroprotective at least in part, by abolishing the interaction induced by TNFα in astrocytes between calcineurin and Foxo3.", "citation": {"db": "PubMed", "db_id": "22005929"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3472, "target": 2871, "key": "91296c3e1bfcd8d46f4c95656e8726b9"}, {"line": 24114, "relation": "decreases", "evidence": "The effect of IGF-I in beta amyloid clearance is mediated by enhancing the transport of the beta amyloid carrier proteins, albumin and transthyretin into the brain through the choroid plexus, with increased levels of beta amyloid in the cerebrospinal fluid and this process is blocked by TNFα", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Albumin subgraph": true, "Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3472, "target": 2871, "key": "e7f99e7d416c690bebbef789fadf933f"}, {"line": 24083, "relation": "decreases", "evidence": "The moderating effect of rivastigmine on the endotoxin-induced suppression of GnRH/LH secretion may result from the inhibition of pro-inflammatory cytokines released through the cholinergic anti-inflammatory pathway. AChE inhibitors lead to an increase in the concentration of ACh and activate the cholinergic anti-inflammatory pathway (Borovikova et al., 2000). These inhibitors attenuate the cytokine release, including that of IL-1beta, IL-6 and TNFα, which have been previously described both in vitro and in vivo ( Borovikova et al., 2000 and Pollak et al., 2005). The ability of rivastigmine to reduce the inflammatory action within the brain could have a profound effect on GnRH secretion, as numerous studies have reported that centrally acting pro-inflammatory cytokines, especially IL-1beta but also IL-1α and TNFα, may be primarily responsible for the inhibition of GnRH/LH secretion", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3472, "target": 2756, "key": "d41a3e19fb83ec86bbfb582e2297a9dc"}, {"relation": "partOf", "source": 3472, "target": 1707, "key": "171dfc8e2d5f7fd917a091128f4fdefe"}, {"relation": "partOf", "source": 3472, "target": 1477, "key": "2a0bb885115daee88392820f4c4f4a7f"}, {"line": 39391, "relation": "association", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3472, "target": 2392, "key": "73b8f9cfd9523619e56759a34ed261f8"}, {"line": 39787, "relation": "positiveCorrelation", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}}, "source": 3472, "target": 3815, "key": "781c5626617b04cc4e7ee3e23e48bc9c"}, {"line": 40751, "relation": "association", "evidence": "This study involves the reductionist fragment-based approach to understand the structure adopted by N-terminal fragment of Alzheimer's Abeta peptide in its complex with PLA2.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3472, "target": 3196, "key": "2f0d768775a2383b30c154ba165aec4d"}, {"line": 46842, "relation": "increases", "evidence": "Both IL-1 and TNF are known to trigger a classical IκB kinase (IKK)gamma-dependent activation of NF-κB,", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3112, "key": "6f66d567c011755b84c9cb948ea11469"}, {"line": 46843, "relation": "increases", "evidence": "Both IL-1 and TNF are known to trigger a classical IκB kinase (IKK)gamma-dependent activation of NF-κB,", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3472, "target": 3113, "key": "024c0ae377cf631aa23a6ae0a7a6fdc4"}, {"line": 536, "relation": "increases", "evidence": "The extracellular amyloid deposits in senile plaques also trigger reactive glial changes and neuroinflammation that can also contribute to neuronal loss through production of reactive oxygen species (ROS), NO, and proinflammatory cytokines such as TNF-a and IL-1Abeta.", "citation": {"db": "PubMed", "db_id": "15232608"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2885, "target": 645, "key": "727a08d2eecaf0ed05809271009712cd"}, {"line": 1337, "relation": "increases", "evidence": "a-Synuclein potentiates interleukin-1Abeta-induced CXCL10 expression in human A172 astrocytoma cells. we investigated the in vitro effects of interleukin-1Abeta (IL-1Abeta) and a-synuclein on astroglial expression of interferon-gamma inducible protein-10 (CXCL10), a proinflammatory and neurotoxic chemokine. IL-1Abeta-induced CXCL10 protein expression was potentiated by co-exposure to a-synuclein. a-Synuclein did not significantly affect IL-1Abeta-induced CXCL10 mRNA expression, but did mediate increased CXCL10 mRNA stability, which may explain, in part, the increased levels of secreted CXCL10 protein. Future investigations are warranted to more fully define the mechanism by which a-synuclein enhances IL-1Abeta-induced astroglial CXCL10 expression", "citation": {"db": "PubMed", "db_id": "22178859"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "subject": {"modifier": "Activity"}, "source": 2885, "target": 2603, "key": "4135ed7230b23769325a5ba36dbc9f51"}, {"line": 1376, "relation": "increases", "evidence": "A temporal sequence was observed whereby Abeta accumulation is followed by expression of IL-1Abeta and eventually, of CXCL1, in the hippocampus and olfactory bulb but not the cortex.", "citation": {"db": "PubMed", "db_id": "21295112"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2885, "target": 2602, "key": "0ad5b2df4e29a01ecce813016df669c2"}, {"line": 3781, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 3823, "key": "bca4b4c4db8118e3a37842ec67361c57"}, {"line": 24470, "relation": "positiveCorrelation", "evidence": "IL-1 levels are elevated in Alzheimer brain, and overexpression of IL-1 is associated with beta-amyloid plaque progression.", "citation": {"db": "PubMed", "db_id": "10850859"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 3823, "key": "fc3fee08eaba0460a3e212faa1beddf7"}, {"line": 24939, "relation": "decreases", "evidence": "IL-1Ra, a physiological antagonist for the IL-1 receptor, reversed the effects of IL-1beta, suggesting that the IL-1beta-dependent up-regulation of alpha-cleavage is mediated by the IL-1 receptor. IL-1beta also induced this concomitant increase in alpha-cleavage and decrease in beta-cleavage in mouse primary cultured neurons. Taken together we conclude that IL-1beta is an anti-amyloidogenic factor, and that enhancement of its signaling or inhibition of IL-1Ra activity could represent potential therapeutic strategies against Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 3823, "key": "84175a76b4bae0f0e92d1f9394ffcf66"}, {"line": 25541, "relation": "association", "evidence": "The proinflammatory cytokine interleukin (IL)-1beta is up-regulated in microglial cells surrounding amyloid plaques, leading to the hypothesis that IL-1beta is a risk factor for Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 3823, "key": "10c328674a75755bf05aa88ada086dd2"}, {"line": 46170, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2885, "target": 3823, "key": "ad34e3d5eccd581e4c72d399b2c52780"}, {"line": 5021, "relation": "association", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 608, "key": "f0bb1a2fecf35704a9f66da7a58b6a46"}, {"relation": "partOf", "source": 2885, "target": 1709, "key": "3b2807e2c6fddfde1056804492ae784a"}, {"line": 5110, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 577, "key": "a086882306fde5c22787c6f0d2aa4683"}, {"line": 9370, "relation": "negativeCorrelation", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2885, "target": 577, "key": "4b3e05426e42765777384b4189182770"}, {"line": 39015, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 577, "key": "0612a8ad85951c5d0a53f3539404d1a7"}, {"line": 39761, "relation": "increases", "evidence": "The innate immunity mediators in the brain, namely microglia and astrocytes, express certain Pattern Recognition Receptors (PRRs), which are always on 'high-alert' for pathogens or other inflammatory triggers and participate in the assembly and activation of the inflammasome. The inflammasome orchestrates the activation of the precursors of proinflammatory caspases, which in turn, cleave the precursor forms of interleukin-1beta, IL-18 and IL-33 into their active forms; the secretion of which leads to a potent inflammatory response, and/or influences the release of toxins from glial and endothelial cells. Altered expression of inflammasome mediators can either promote or inhibit neurodegenerative processes.", "citation": {"db": "PubMed", "db_id": "20127816"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2885, "target": 577, "key": "f550b378092bc1be472b2238ec249d4b"}, {"line": 5158, "relation": "increases", "evidence": "IL-1beta also increased ApoE expression in neuronal cultures.", "citation": {"db": "PubMed", "db_id": "22171672"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "APOE subgraph": true}}, "source": 2885, "target": 2312, "key": "c6f63d2977cda78107e8cf0c451a8375"}, {"line": 8563, "relation": "increases", "evidence": "A significant role of a pathological glial cell activation in the pathogenesis of Alzheimer's disease is supported by the growing evidence that inflammatory proteins, which are produced by reactive astrocytes, promote the transformation of diffuse beta-amyloid deposits into the filamentous, neurotoxic form. A number of vicious circles, driven by the release of TNF-a and free oxygen radicals from microglial cells, may cause an upregulated microglial activation and their production of interleukin-1 which triggers, secondarily, the crucial activation of astrocytes.", "citation": {"db": "PubMed", "db_id": "9850925"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 2885, "target": 480, "key": "25ad28858cce474c7174513105edf69f"}, {"line": 9362, "relation": "association", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}}, "source": 2885, "target": 2104, "key": "77496fe7a5658d70ff576d08b22a19c6"}, {"line": 9528, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}}, "source": 2885, "target": 2102, "key": "cba14f22f3fb6656f2aa03c5b5c73ead"}, {"line": 22362, "relation": "increases", "evidence": "Cytokines, particularly tumor necrosis factor α (TNF-α) and interleukin 1beta (IL-1beta), can induce chronic inflammation that may promote the loss of synapses, cognitive dysfunction, and eventually neuronal death [19] and [20].", "citation": {"db": "PubMed", "db_id": "24960578"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 3920, "key": "76419ada4dff38029b30a668975ff800"}, {"line": 24158, "relation": "increases", "evidence": "TNF-α and IL-1beta play vital roles in joint inflammation and bone destruction.", "citation": {"db": "PubMed", "db_id": "21157520"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2885, "target": 3920, "key": "687a656731b4e78db328befc0e021e8b"}, {"line": 24074, "relation": "decreases", "evidence": "The moderating effect of rivastigmine on the endotoxin-induced suppression of GnRH/LH secretion may result from the inhibition of pro-inflammatory cytokines released through the cholinergic anti-inflammatory pathway. AChE inhibitors lead to an increase in the concentration of ACh and activate the cholinergic anti-inflammatory pathway (Borovikova et al., 2000). These inhibitors attenuate the cytokine release, including that of IL-1beta, IL-6 and TNFα, which have been previously described both in vitro and in vivo ( Borovikova et al., 2000 and Pollak et al., 2005). The ability of rivastigmine to reduce the inflammatory action within the brain could have a profound effect on GnRH secretion, as numerous studies have reported that centrally acting pro-inflammatory cytokines, especially IL-1beta but also IL-1α and TNFα, may be primarily responsible for the inhibition of GnRH/LH secretion", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2885, "target": 2756, "key": "4af6db35e1624062f119557dec9a7178"}, {"line": 24177, "relation": "decreases", "evidence": "Activated microglia release a combination of bioactive agents including interleukin-6 (IL-6), tumor necrosis factor alpha (TNFα), and insulin-like growth factor 1 (IGF-1). These bioactive agents have both protective and detrimental consequences for the surrounding brain tissue. We found that, while mitochondrial toxins did not affect LPS-induced activation, as measured by release of tumor necrosis factor alpha (TNF-alpha), interleukin-6 (IL-6) and interleukin-1beta (IL-1beta), they did inhibit part of the IL-4-induced alternative activation, as measured by arginase activity and expression, induction of insulin-like growth factor 1 (IGF-1) and the counteraction of the LPS induced cytokine release.", "citation": {"db": "PubMed", "db_id": "20701773"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 2893, "key": "d2c4bc79b50c2e57690755bcbd37404a"}, {"relation": "partOf", "source": 2885, "target": 1029, "key": "51660841a9e009cd2d32c6a84255b6aa"}, {"line": 24471, "relation": "increases", "evidence": "IL-1 levels are elevated in Alzheimer brain, and overexpression of IL-1 is associated with beta-amyloid plaque progression.", "citation": {"db": "PubMed", "db_id": "10850859"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 80, "key": "78212b1b58f7ab303ea274d4ee89d939"}, {"line": 24905, "relation": "positiveCorrelation", "evidence": "The proinflammatory cytokine interleukin (IL)-1beta is up-regulated in microglial cells surrounding amyloid plaques, leading to the hypothesis that IL-1beta is a risk factor for Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 80, "key": "66c4485e43c015962891fb1ea283cffe"}, {"relation": "partOf", "source": 2885, "target": 1130, "key": "46004f80f0a18e1f0cd93d8cf2d55133"}, {"relation": "partOf", "source": 2885, "target": 1481, "key": "4b308a2e0a28de9af2d6dc844c191b7d"}, {"relation": "partOf", "source": 2885, "target": 930, "key": "823cf22dad5a0cd19f19f6c2b3e4520c"}, {"line": 24917, "relation": "increases", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2885, "target": 2249, "key": "aeb9b4a730aa6e01fcc5ea037cde3d6e"}, {"line": 24918, "relation": "increases", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 2137, "key": "5c376fe6cc827b06692d7b10ce24f20a"}, {"line": 24919, "relation": "decreases", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 2138, "key": "a2be25622710a47afd6dccf2c8509c4b"}, {"line": 24920, "relation": "decreases", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 2327, "key": "05e349ab7d536775d41d27a698c0d9ee"}, {"line": 24921, "relation": "decreases", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 2328, "key": "383a84dbb841f973eda4962ca73b1fd9"}, {"line": 24926, "relation": "decreases", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 3943, "key": "c0295ef08e05fbf44af4e1cbd487330b"}, {"line": 25566, "relation": "causesNoChange", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 3943, "key": "5670ce28d9741775c3569e923b501841"}, {"line": 24931, "relation": "increases", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 3936, "key": "d2f2c5cc5bd08b155af7d107cd56c78d"}, {"line": 24946, "relation": "increases", "evidence": "Interleukin-1 beta up-regulates TACE to enhance alpha-cleavage of APP in neurons: resulting decrease in Abeta production.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2885, "target": 2250, "key": "e0062ef5f857a74da6331d9da67a00ba"}, {"line": 25588, "relation": "increases", "evidence": "IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10,but up-regulated that of TACE by threefold. The proform and mature form of TACE protein were also significantly up-regulated.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 2250, "key": "67004383f6e66606af5441c63466793e"}, {"line": 25564, "relation": "causesNoChange", "evidence": "we unexpectedly found that IL-1beta significantly enhanced alpha-cleavage, indicated by increases in sAPPalpha and C83, but reduced beta-cleavage, indicated by decreases in sAPPbeta and Abeta40/42, in human neuroblastoma SK-N-SH cells. IL-1beta did not significantly alter the mRNA levels of BACE1, ADAM-9, and ADAM-10, but up-regulated that of TACE by threefold.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 3935, "key": "d82220223190ce09ac1eaef501b6abac"}, {"relation": "partOf", "source": 2885, "target": 1479, "key": "cf4635af37751b2bb4e4d780d51c7de6"}, {"relation": "partOf", "source": 2885, "target": 1706, "key": "73ed49b42e91f2896bbc8b041c0cf029"}, {"relation": "partOf", "source": 2885, "target": 1480, "key": "edac19cefeb384bbd960fbd157f9d67f"}, {"line": 33230, "relation": "increases", "evidence": "Cytokines such as TGF beta 1 and interleukin 1 enhance the expression of clusterin, which may link clusterin to inflammatory mechanisms in AD.", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 2538, "key": "9f4b5a06a7713ac00d185b3d3807ca43"}, {"line": 36250, "relation": "increases", "evidence": "Cytokines such as TGF beta 1 and interleukin 1 enhance the expression of clusterin, which may link clusterin to inflammatory mechanisms in AD", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2885, "target": 2538, "key": "ad4536157f8c9631be7e217468af5706"}, {"relation": "partOf", "source": 2885, "target": 1707, "key": "4247a409bddae9d512dc479e9c33d111"}, {"relation": "partOf", "source": 2885, "target": 1475, "key": "7f531d2a78d33e000f3ef8969e6279d5"}, {"line": 39385, "relation": "association", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2885, "target": 2392, "key": "8b603231d11e6cfb467650d48b1ee28d"}, {"line": 39636, "relation": "increases", "evidence": "The amyloid precursor protein (APP) has been associated with Alzheimer's disease (AD) because APP is processed into the beta-peptide that accumulates in amyloid plaques, and APP gene mutations can cause early onset AD. Inflammation is also associated with AD as exemplified by increased expression of interleukin-1 (IL-1) in microglia in affected areas of the AD brain. Here we demonstrate that IL-1alpha and IL-1beta increase APP synthesis by up to 6-fold in primary human astrocytes and by 15-fold in human astrocytoma cells without changing the steady-state levels of APP mRNA. A 90-nucleotide sequence in the APP gene 5'-untranslated region (5'-UTR) conferred translational regulation by IL-1alpha and IL-1beta to a chloramphenicol acetyltransferase (CAT) reporter gene. Steady-state levels of transfected APP(5'-UTR)/CAT mRNAs were unchanged, whereas both base-line and IL-1-dependent CAT protein synthesis were increased. This APP mRNA translational enhancer maps from +55 to +144 nucleotides from the 5'-cap site and is homologous to related translational control elements in the 5'-UTR of the light and and heavy ferritin genes. Enhanced translation of APP mRNA provides a mechanism by which IL-1 influences the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "10037734"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "source": 2885, "target": 3815, "key": "c4a03116113449aee1e3adb77a8f545b"}, {"line": 39645, "relation": "increases", "evidence": "The amyloid precursor protein (APP) has been associated with Alzheimer's disease (AD) because APP is processed into the beta-peptide that accumulates in amyloid plaques, and APP gene mutations can cause early onset AD. Inflammation is also associated with AD as exemplified by increased expression of interleukin-1 (IL-1) in microglia in affected areas of the AD brain. Here we demonstrate that IL-1alpha and IL-1beta increase APP synthesis by up to 6-fold in primary human astrocytes and by 15-fold in human astrocytoma cells without changing the steady-state levels of APP mRNA. A 90-nucleotide sequence in the APP gene 5'-untranslated region (5'-UTR) conferred translational regulation by IL-1alpha and IL-1beta to a chloramphenicol acetyltransferase (CAT) reporter gene. Steady-state levels of transfected APP(5'-UTR)/CAT mRNAs were unchanged, whereas both base-line and IL-1-dependent CAT protein synthesis were increased. This APP mRNA translational enhancer maps from +55 to +144 nucleotides from the 5'-cap site and is homologous to related translational control elements in the 5'-UTR of the light and and heavy ferritin genes. Enhanced translation of APP mRNA provides a mechanism by which IL-1 influences the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "10037734"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"High": true}}, "source": 2885, "target": 2315, "key": "b4199dc3e602303273a3cff8885c465f"}, {"line": 39715, "relation": "decreases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chaperone subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2885, "target": 2397, "key": "09581feb129fbcb24829c69465de4ead"}, {"line": 39718, "relation": "increases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2885, "target": 2881, "key": "5afe366d04ef8a043e9837c5bb426257"}, {"line": 44291, "relation": "positiveCorrelation", "evidence": "The secreted protein, YKL-40, has been proposed as a biomarker of a variety of human diseases characterized by ongoing inflammation, including chronic neurologic pathologies such as multiple sclerosis and Alzheimer's disease. However, inflammatory mediators and the molecular mechanism responsible for enhanced expression of YKL-40 remained elusive. Using several mouse models of inflammation, we now show that YKL-40 expression correlated with increased expression of both IL-1 and IL-6. Furthermore, IL-1 together with IL-6 or the IL-6 family cytokine, oncostatin M, synergistically upregulated YKL-40 expression in both primary human and mouse astrocytes in vitro. The robust cytokine-driven expression of YKL-40 in astrocytes required both STAT3 and NF-kB binding elements of the YKL-40 promoter. In addition, YKL-40 expression was enhanced by constitutively active STAT3 and inhibited by dominant-negative IkBalpha. Surprisingly, cytokine-driven expression of YKL-40 in astrocytes was independent of the p65 subunit of NF-kB and instead required subunits RelB and p50. Mechanistically, we show that IL-1-induced RelB/p50 complex formation was further promoted by oncostatin M and that these complexes directly bound to the YKL-40 promoter. Moreover, we found that expression of RelB was strongly upregulated during inflammation in vivo and by IL-1 in astrocytes in vitro. We propose that IL-1 and the IL-6 family of cytokines regulate YKL-40 expression during sterile inflammation via both STAT3 and RelB/p50 complexes. These results suggest that IL-1 may regulate the expression of specific anti-inflammatory genes in nonlymphoid tissues via the canonical activation of the RelB/p50 complexes.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 2509, "key": "c05bdbcd6fa948cf96e770cf22883298"}, {"line": 46785, "relation": "positiveCorrelation", "evidence": "In addition, IL-6 and OSM moderately upregulate YKL-40 expression in human astrocytes [...] demonstrate that YKL-40 expression correlates with the expression of both IL-1beta and IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2885, "target": 2509, "key": "74d73ecd67d5654b3848551d62893196"}, {"relation": "partOf", "source": 2885, "target": 1710, "key": "9d70efd76a9852a748c866964c7e55c5"}, {"relation": "partOf", "source": 2885, "target": 1711, "key": "7ab90f6fc6057437b9c19af9cfc21bdb"}, {"line": 46845, "relation": "increases", "evidence": "Both IL-1 and TNF are known to trigger a classical IκB kinase (IKK)gamma-dependent activation of NF-κB,", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 3113, "key": "d0f2a0918e04f6d8d76850d02a3a5b90"}, {"line": 46849, "relation": "increases", "evidence": "Both IL-1 and TNF are known to trigger a classical IκB kinase (IKK)gamma-dependent activation of NF-κB,", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 2885, "target": 3112, "key": "d49eab0ce0640654265e43432e3fcfa0"}, {"line": 49170, "relation": "increases", "evidence": "Moreover, IL-1beta increased astrocytic production of pro-inflammatory chemokines such as CCL2, CCL20, and CXCL2, which induce immune cell migration and exacerbate BBB disruption and neuroinflammation. Our findings suggest that astrocytic SHH is a potential therapeutic target that could be used to restore disrupted BBB in patients with neurologic diseases.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2885, "target": 2455, "key": "c636fb835c649b1f0b80f1d4f98d0ff7"}, {"line": 49171, "relation": "increases", "evidence": "Moreover, IL-1beta increased astrocytic production of pro-inflammatory chemokines such as CCL2, CCL20, and CXCL2, which induce immune cell migration and exacerbate BBB disruption and neuroinflammation. Our findings suggest that astrocytic SHH is a potential therapeutic target that could be used to restore disrupted BBB in patients with neurologic diseases.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2885, "target": 2456, "key": "14bdcc9d167ba94942a08673fb3cbecd"}, {"line": 49172, "relation": "increases", "evidence": "Moreover, IL-1beta increased astrocytic production of pro-inflammatory chemokines such as CCL2, CCL20, and CXCL2, which induce immune cell migration and exacerbate BBB disruption and neuroinflammation. Our findings suggest that astrocytic SHH is a potential therapeutic target that could be used to restore disrupted BBB in patients with neurologic diseases.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2885, "target": 2605, "key": "e42c1f2ad531dd0423c878d25fdc4fe3"}, {"line": 2028, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 453, "key": "72ad20a0fdff06f85bd2c58d096e51a6"}, {"line": 2049, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 1716, "key": "a911ca1e26a37ca207dc08a5c8b42453"}, {"line": 2143, "relation": "association", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 478, "target": 2173, "key": "742899b41bff0476a1644341d19709c3"}, {"line": 3260, "relation": "positiveCorrelation", "evidence": "The role of miR-124 on the expression of Abeta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the Abeta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}}, "source": 478, "target": 80, "key": "8b35b4dae30f1e52b4678f4e0a6d79a7"}, {"line": 5828, "relation": "association", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Apoptosis signaling subgraph": true}}, "source": 478, "target": 428, "key": "700434f00bf2989eaedd094eb889eeb5"}, {"line": 7328, "relation": "negativeCorrelation", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "object": {"modifier": "Activity"}, "source": 478, "target": 3116, "key": "e5230fa722b39c9d9572a486595a0621"}, {"line": 8900, "relation": "association", "evidence": "The role of miR-124 on the expression of beta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the beta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 478, "target": 2082, "key": "f32ed12aa0592a3695c5f4ee1cf57634"}, {"line": 16146, "relation": "association", "evidence": "Furthermore, hyperoside inhibited mitochondria-dependent downstream caspase-mediated apoptotic pathway, such as that involving caspase-9, caspase-3, and poly ADP-ribose polymerase (PARP).", "citation": {"db": "PubMed", "db_id": "21978835"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 1305, "key": "298fc95c0769c60eaad4bea0381dd914"}, {"line": 16552, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 3409, "key": "7541c9046655520f2f2a6762b0a0ee64"}, {"line": 16596, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 3874, "key": "1ecf17b7e686b8a343526b5180017d89"}, {"line": 18256, "relation": "association", "evidence": "At age 10 weeks the rats were subjected to neurobehavioral testing, then sacrificed for measurement of AMPK, beta-amyloid and Fas ligand in the hippocampus. Oral and subcutaneous MSG both induced a lowering of hippocampal AMPK by 43% and 31% respectively (P<0.05 for both) and >2-fold increase in hippocampal Fas ligand, a mediator of apoptosis (P<0.001 for both).", "citation": {"db": "PubMed", "db_id": "24769037"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 2690, "key": "3e0c68ae4bc97dbf3fb7b0068a7ef631"}, {"line": 19470, "relation": "association", "evidence": "The tilted peptides of human prolactin and human growth hormone induce endothelial cell apoptotic process, inhibit endothelial cell proliferation, and inhibit capillary formation both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "16973751"}, "annotations": {"Species": {"9606": true}}, "source": 478, "target": 3253, "key": "a6907bb0094ba16d7c0443a6d7f99b69"}, {"line": 19519, "relation": "association", "evidence": "P < 0.0001).The GSTP1*C allelic variant should be considered a candidate for LOAD, particularly in persons having the ApoE epsilon4 allelic variant, because the GSTP1 and ApoE gene products are implicated in oxidative stress and apoptosis processes leading to beta-amyloid-mediated neurodegeneration.", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 478, "target": 2800, "key": "b73b71cfce2a0088a2bff310c01a3971"}, {"line": 19520, "relation": "association", "evidence": "P < 0.0001).The GSTP1*C allelic variant should be considered a candidate for LOAD, particularly in persons having the ApoE epsilon4 allelic variant, because the GSTP1 and ApoE gene products are implicated in oxidative stress and apoptosis processes leading to beta-amyloid-mediated neurodegeneration.", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 478, "target": 2312, "key": "e4945b64695f2425b76aeb4244f7cbbc"}, {"line": 21498, "relation": "increases", "evidence": "It is suggested that fragmentation of protein kinase C delta during the process of apoptosis results in the phosphorylation and the inactivation of GSK-3.", "citation": {"db": "PubMed", "db_id": "10850726"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Caspase subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 478, "target": 3239, "key": "e3a8f42c34defa8db04efb153afde3d8"}, {"line": 22088, "relation": "positiveCorrelation", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 2389, "key": "97aec17ca961381d4f852d2ceee7032b"}, {"line": 22089, "relation": "negativeCorrelation", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 2393, "key": "d06d975856a2ffc0d2c6b77968a4da11"}, {"line": 22090, "relation": "positiveCorrelation", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 170, "key": "e88a03a5d91adcde1c2a45f5ca389cec"}, {"line": 22284, "relation": "negativeCorrelation", "evidence": "knockdown of PTPA induced cell apoptosis in HEK293 and N2a cell lines. PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 3282, "key": "c8d184f6dc6ca338423662bdc9c836d7"}, {"line": 22301, "relation": "positiveCorrelation", "evidence": "This evidence led the authors to suggest that the observed reduction could be due to increased apoptosis of progenitor cells. However, analysis of the apoptotic marker caspase-3 demonstrated that increased caspase-3-dependant apoptosis only took place in the hippocampi of GD 17 exposed animals, yet another indication of the time-dependant variation in the response to an immune challenge.", "citation": {"db": "PubMed", "db_id": "24891958"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Species": {"10116": true}}, "object": {"modifier": "Activity"}, "source": 478, "target": 3600, "key": "28db485a2c9bd63ec2acb5d49e5422ed"}, {"line": 22855, "relation": "increases", "evidence": "Apoptosis plays a significant role in cell loss during neurodegenerative disorders such as Alzheimer's disease (AD) (Loh et al., 2006). A cascade of events like activation of caspases and aspartate-specific cysteine proteases has been proposed to play a key role in apoptosis (Nicholson and Thornberry ,1997). The major apoptotic pathway is characterized by mitochondrial dysfunction with the release of cytochrome c, activation of caspase-9, and subsequently of caspase-3. It has been suggested that caspase-3 is an ultimate effectors caspase whose activation leads to switch on the apoptotic cascade. Evidences of caspase-3 activation were also found in postmortem study conducted on the brain of AD patient (Engidawork et al., 2001).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 648, "key": "3db12cd987fa1d9e36ed02c6297f28d1"}, {"line": 28623, "relation": "association", "evidence": "The cytoplasmic tail of APP interacts with phosphotyrosine binding (PTB) domain containing proteins (Fe65, X11, mDab-1, and JIP-1) and may pmodulate gene expression and apoptotic process.", "citation": {"db": "PubMed", "db_id": "11877420"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 478, "target": 1092, "key": "f3dba1bdcf9a7554fb8b1c83a812f3d3"}, {"line": 28624, "relation": "association", "evidence": "The cytoplasmic tail of APP interacts with phosphotyrosine binding (PTB) domain containing proteins (Fe65, X11, mDab-1, and JIP-1) and may pmodulate gene expression and apoptotic process.", "citation": {"db": "PubMed", "db_id": "11877420"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 478, "target": 1075, "key": "b8760ed9b6e82efbb6ffefcd8806e9e5"}, {"line": 28625, "relation": "association", "evidence": "The cytoplasmic tail of APP interacts with phosphotyrosine binding (PTB) domain containing proteins (Fe65, X11, mDab-1, and JIP-1) and may pmodulate gene expression and apoptotic process.", "citation": {"db": "PubMed", "db_id": "11877420"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 478, "target": 1187, "key": "b5271a32c4563262d44a299c084e4b6e"}, {"line": 30051, "relation": "association", "evidence": "Here, we show that cells overexpressing tau exhibit marked resistance to apoptosis induced by various apoptotic stimuli, which also causes correlated tau hyperphosphorylation and glycogen synthase kinase 3 (GSK-3) activation. ", "citation": {"db": "PubMed", "db_id": "17360687"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 2581, "key": "df1b5586d56632004c3849d973c67d56"}, {"line": 30052, "relation": "association", "evidence": "Here, we show that cells overexpressing tau exhibit marked resistance to apoptosis induced by various apoptotic stimuli, which also causes correlated tau hyperphosphorylation and glycogen synthase kinase 3 (GSK-3) activation. ", "citation": {"db": "PubMed", "db_id": "17360687"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 478, "target": 2794, "key": "7937b1fdaf637932893053f8fb836249"}, {"line": 31577, "relation": "association", "evidence": "We show here that exogenous expression of a familial AD (FAD) mutant of APP or of the APP binding protein APP-BP1 in neurons causes enlargement of early endosomes, increased receptor-mediated endocytosis via a pathway dependent on APP-BP1 binding to APP, and apoptotic process.", "citation": {"db": "PubMed", "db_id": "17611268"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Gamma secretase subgraph": true}}, "source": 478, "target": 1192, "key": "b1969647331746b6b8e8451be26fcb3b"}, {"line": 33420, "relation": "association", "evidence": "Among downstream factors of PKR, the Fas-associated protein with a death domain (FADD) and subsequent activated caspase-8 are responsible for PKR-induced apoptosis in recombinant virus-infected cells.", "citation": {"db": "PubMed", "db_id": "19889624"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Caspase subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 2448, "key": "55be1ad401219e6db3acb59d856fa4f1"}, {"line": 34300, "relation": "association", "evidence": "The intracellular amyloid beta-peptide (A beta) binding protein, ERAB, a member of the short-chain dehydrogenase/reductase (SDR) family, is known to mediate apoptosis in different cell lines and to be a class II hydroxyacyl-CoA dehydrogenase.", "citation": {"db": "PubMed", "db_id": "10371197"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 2843, "key": "4957ebc127ac691454f2cc468632d715"}, {"line": 34904, "relation": "positiveCorrelation", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 3823, "key": "2802e1b6d944b06878ac7d493896c01d"}, {"line": 34920, "relation": "association", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 3162, "key": "0c78c50887ce61749998af5471593788"}, {"line": 34922, "relation": "association", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 478, "target": 1199, "key": "d628c632fd3c8900819a04b9e2595d96"}, {"line": 41744, "relation": "association", "evidence": "Furthermore, inhibition of Bax expression by specific antisense oligonucleotides protected glioma cells against PPARgamma-mediated apoptotic process, indicating an essential role of Bax in PPARgamma-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Bcl-2 subgraph": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"Medium": true}}, "source": 478, "target": 4036, "key": "da10b6e11cd762e3b56d51ac57d37ff3"}, {"relation": "hasVariant", "source": 3010, "target": 3012, "key": "fa42bd7475868449f89b55de5e5dc7a4"}, {"relation": "hasVariant", "source": 3010, "target": 3015, "key": "c3644c1d7b9687227b92f83689de76ab"}, {"relation": "partOf", "source": 3010, "target": 991, "key": "de76293dd691e254242789ed40f35f1f"}, {"relation": "hasVariant", "source": 3010, "target": 3023, "key": "4a8ad78cd28fa8bd34f4d1111a26ea72"}, {"relation": "hasVariant", "source": 3010, "target": 3035, "key": "5f4a328f509140c5a8917fc3a58645b7"}, {"relation": "hasVariant", "source": 3010, "target": 3026, "key": "7e8cdadd8f02aab4b7c437c774315dd9"}, {"relation": "hasVariant", "source": 3010, "target": 3027, "key": "4217fc06ad73c5120453dab60b74b776"}, {"line": 3592, "relation": "biomarkerFor", "evidence": "Our findings support the notion that CSF tau and Abeta(1-42) may be useful biomarkers in the early identification of AD in MCI subjects.", "citation": {"db": "PubMed", "db_id": "14699432"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3010, "target": 3823, "key": "00ecf740a4af9f378bf784ced894bfbb"}, {"line": 6261, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3010, "target": 3823, "key": "fdd3e5ec810e78a92fb4ae2aadc9ada2"}, {"line": 29617, "relation": "association", "evidence": "Pathological alterations in the microtubule-associated protein (MAP) tau are well-established in a number of neurodegenerative disorders, including Alzheimer's Disease (AD), frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), and others. ", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3010, "target": 3823, "key": "e76feccfda4900dbaad864cc465be2da"}, {"line": 33382, "relation": "association", "evidence": "The beta-amyloid precursor protein APP and the microtubule-associated protein Tau play a crucial role in the pathogenesis of Alzheimer's disease (AD). ", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3010, "target": 3823, "key": "a2dbd37e82f44c052cfecc7fdb3c3a91"}, {"line": 3875, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Tau protein subgraph": true}}, "source": 3010, "target": 739, "key": "05f4ece00926861757428519115bcd18"}, {"relation": "hasVariant", "source": 3010, "target": 3017, "key": "28ed25f0855bf43f16e1eb7573f3d140"}, {"relation": "hasVariant", "source": 3010, "target": 3030, "key": "1ff62c1812b2f8adf297f803d489de57"}, {"relation": "hasVariant", "source": 3010, "target": 3032, "key": "dfb8316be133288f5c612cd78f715de4"}, {"relation": "hasVariant", "source": 3010, "target": 3020, "key": "b465363fa247b92667ac2e13691c272d"}, {"relation": "hasVariant", "source": 3010, "target": 3022, "key": "6f6cfdd5961daaafede1830a07d056e0"}, {"relation": "partOf", "source": 3010, "target": 1446, "key": "8f381644e60aa712045a11d24b68cc9f"}, {"relation": "hasVariant", "source": 3010, "target": 3014, "key": "1fa952191246803f071909042813f7b5"}, {"line": 11179, "relation": "negativeCorrelation", "evidence": "CSF levels of total but not free IgG autoAbs against galanin were increased in AD, resulting in increased percentage of galanin autoAbs present as immune complexes. CSF levels of galanin total autoAbs and α-MSH free autoAbs correlated negatively with the severity of cognitive impairment as measured by MMSE. Both total and free autoAbs against galanin and α-MSH in CSF correlated negatively with age in AD patients but not in controls. CSF levels of galanin autoAbs and free α-MSH AutoAbs negatively correlated with CSF levels of t-Tau, p-Tau and ratios of t-Tau/Abeta42 or p-Tau/Abeta42 in AD patients but not in controls.", "citation": {"db": "PubMed", "db_id": "22078238"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}, "Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Tau protein subgraph": true}}, "source": 3010, "target": 2737, "key": "9c09c608664e9561b0fc0525ebdc5c7c"}, {"relation": "hasVariant", "source": 3010, "target": 3018, "key": "9ce547ff8f66f9cdf1018081bc4bb993"}, {"relation": "hasVariant", "source": 3010, "target": 3019, "key": "c003cab2e7cf5a092f385ac3de1efb1c"}, {"relation": "hasVariant", "source": 3010, "target": 3021, "key": "4dea1708d91e2f6a4fefd42d2289ac4b"}, {"relation": "hasVariant", "source": 3010, "target": 3033, "key": "4e179a668a77bb3ccc0095a97109935f"}, {"relation": "partOf", "source": 3010, "target": 1043, "key": "e47ba82235b18b744abfe1f533c870f0"}, {"relation": "partOf", "source": 3010, "target": 1136, "key": "1e2e11c86baa641338dc0cde391af0b9"}, {"relation": "partOf", "source": 3010, "target": 1020, "key": "b886cda57c9b06161f652e698b5148fe"}, {"relation": "partOf", "source": 3010, "target": 1281, "key": "c297c601d65c6b89fa04e8cdea4671bd"}, {"relation": "partOf", "source": 3010, "target": 1280, "key": "f20bd32585dc2825c2675c8d275fde46"}, {"relation": "partOf", "source": 3010, "target": 1556, "key": "d127c328c1b43e265c3893fcb9b1fdd9"}, {"line": 29075, "relation": "decreases", "evidence": "Finally, both tau and Bcl-2 were co-immunoprecipitated with PP2Ac, but the binding level of Bcl-2 with PP2Ac decreased prominently when tau was co-expressed.", "citation": {"db": "PubMed", "db_id": "20157251"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Bcl-2 subgraph": true}}, "source": 3010, "target": 1291, "key": "2b2bc5d9e2556dabddc0b185bcb1badb"}, {"relation": "partOf", "source": 3010, "target": 1309, "key": "410c3400c9c40f485c76f5da69024466"}, {"relation": "partOf", "source": 3010, "target": 1312, "key": "392557daa70fa76f312b0e2e2cd65d1b"}, {"relation": "partOf", "source": 3010, "target": 1325, "key": "3cd043e5138b757735e69c2a204f58a9"}, {"line": 29298, "relation": "association", "evidence": "The microtubule-associated protein tau, which becomes hyperphosphorylated and pathologically aggregates in a number of these diseases, is extremely sensitive to manipulations of chaperone signaling. ", "citation": {"db": "PubMed", "db_id": "21367866"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3010, "target": 865, "key": "e82b62e92a30c140a740bbad7a1130e6"}, {"relation": "partOf", "source": 3010, "target": 1348, "key": "9b326241f2809a98c2dbbdb1510ea893"}, {"relation": "hasVariant", "source": 3010, "target": 3031, "key": "589f0b2ec217f2cbe52d7a9a6bc44c88"}, {"relation": "hasVariant", "source": 3010, "target": 3034, "key": "72ea98eb2624aecf1fea6d61f17c6812"}, {"relation": "hasVariant", "source": 3010, "target": 3013, "key": "43b6aeddc8b89b4a574cacba80f1c8c0"}, {"line": 29618, "relation": "association", "evidence": "Pathological alterations in the microtubule-associated protein (MAP) tau are well-established in a number of neurodegenerative disorders, including Alzheimer's Disease (AD), frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), and others. ", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3010, "target": 3855, "key": "b0a15602bddc1365f02a97bd8c146c90"}, {"line": 29619, "relation": "association", "evidence": "Pathological alterations in the microtubule-associated protein (MAP) tau are well-established in a number of neurodegenerative disorders, including Alzheimer's Disease (AD), frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), and others. ", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3010, "target": 3886, "key": "06fe5ebf5671475f04baf3a366e0855f"}, {"relation": "hasVariant", "source": 3010, "target": 3028, "key": "cfaefc629670d47966f98e2d6af997a6"}, {"line": 30047, "relation": "decreases", "evidence": "Here, we show that cells overexpressing tau exhibit marked resistance to apoptosis induced by various apoptotic stimuli, which also causes correlated tau hyperphosphorylation and glycogen synthase kinase 3 (GSK-3) activation. ", "citation": {"db": "PubMed", "db_id": "17360687"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3010, "target": 478, "key": "29f5304eafd1140cb1176fd70fea7633"}, {"relation": "partOf", "source": 3010, "target": 1401, "key": "0e0e2b48c25857c373313cf4d3588841"}, {"relation": "partOf", "source": 3010, "target": 1417, "key": "3506aabbe28ea9bf4c3ab29bf1a47458"}, {"relation": "partOf", "source": 3010, "target": 1424, "key": "b0c39de5e0573d220caed19a17b245fb"}, {"relation": "hasVariant", "source": 3010, "target": 3036, "key": "3c537e0e931cf677ccded1070d3f4b90"}, {"relation": "hasVariant", "source": 3010, "target": 3011, "key": "74985ceb47ff553218d0a72a42133109"}, {"relation": "partOf", "source": 3010, "target": 1451, "key": "07b023b2fa80f73ce361f75741e59e07"}, {"relation": "partOf", "source": 3010, "target": 1555, "key": "88de85bd6891474a332730d1634c2178"}, {"relation": "hasVariant", "source": 3010, "target": 3016, "key": "c9c903ad6db0279c08fea5df9f33697d"}, {"relation": "partOf", "source": 3010, "target": 1559, "key": "5687310ea97a7723215c5e53e50203a5"}, {"relation": "partOf", "source": 3010, "target": 1553, "key": "00551e815e845e26016d7d960d43aaa6"}, {"line": 31878, "relation": "association", "evidence": "These results are consistent with observations that PSA pmodulates tau levels in vivo and suggest that this enzyme may be involved in tau degradation in human brain.", "citation": {"db": "PubMed", "db_id": "17154549"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Degradation"}, "source": 3010, "target": 3130, "key": "91d264466567eec4d39335732770ab42"}, {"relation": "partOf", "source": 3010, "target": 1554, "key": "0a1ead2af1f27bf4de48272e4ab2935f"}, {"relation": "partOf", "source": 3010, "target": 1557, "key": "d36bc6a3d74fd205c0b11ce0a1198d79"}, {"line": 32507, "relation": "association", "evidence": "PS1 directly binds tau and a tau kinase , glycogen synthase kinase 3beta", "citation": {"db": "PubMed Central", "db_id": "PMC21391"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}}, "source": 3010, "target": 3258, "key": "f7191dd8c29fdac47c1a4fef7592a628"}, {"relation": "partOf", "source": 3010, "target": 1539, "key": "a587893b9203225fbfcf65f0ec8d5b96"}, {"relation": "hasVariant", "source": 3010, "target": 3029, "key": "d0d3a6b42d272df202b1ecb7c2702444"}, {"relation": "partOf", "source": 3010, "target": 1541, "key": "f45dfee42e37f5b63864d2791dc53946"}, {"relation": "partOf", "source": 3010, "target": 1546, "key": "36312aba8f7253c6af9ae9671a8109a0"}, {"relation": "partOf", "source": 3010, "target": 1445, "key": "85a75096a23b21538108ba957b03ad5d"}, {"relation": "partOf", "source": 3010, "target": 1538, "key": "fab93e6750213971f3b59854148b889e"}, {"relation": "partOf", "source": 3010, "target": 1552, "key": "53c5036b76294e605a4edac93010e942"}, {"relation": "partOf", "source": 3010, "target": 1072, "key": "2760242b592f75fd0b14ea889effed2a"}, {"relation": "partOf", "source": 3010, "target": 1235, "key": "eb275d04587ef4b988b598d4a7881f44"}, {"relation": "partOf", "source": 3010, "target": 935, "key": "8481106936131e37925286ee26aa7704"}, {"relation": "partOf", "source": 3010, "target": 1560, "key": "231009e35e0091851dd972a1ca3c50af"}, {"relation": "partOf", "source": 3010, "target": 1103, "key": "200de58f58f5b86a27da38bbc72a82be"}, {"relation": "partOf", "source": 3010, "target": 1097, "key": "a6817e67bc5f73cff0fa0208b79d6003"}, {"relation": "hasVariant", "source": 3010, "target": 3038, "key": "5e81e40ca13f80537be21204e55d2d8a"}, {"relation": "partOf", "source": 3010, "target": 1561, "key": "dd1c47d237998446bc0d403748550702"}, {"relation": "hasVariant", "source": 3010, "target": 3040, "key": "7b76ae77ae7bbd367aba03d53a62ead0"}, {"relation": "hasVariant", "source": 3010, "target": 3039, "key": "315ec95dd1ac471f61f6d2aec2210a63"}, {"relation": "hasVariant", "source": 3010, "target": 3025, "key": "09f9a5c8ba2ffd695672cfafb5f98611"}, {"relation": "partOf", "source": 3010, "target": 1558, "key": "82a39333fface027c68b09238650fba0"}, {"relation": "hasVariant", "source": 3010, "target": 3024, "key": "f58069ee50baa89c2655cc71d508d775"}, {"line": 38718, "relation": "negativeCorrelation", "evidence": "Elevated levels of T-tau, P-tau (S396), IL-6 and · OH in CSF are significantly correlated with cognitive impairment in PD patients.", "citation": {"db": "PubMed", "db_id": "24884485"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tau protein subgraph": true}}, "source": 3010, "target": 812, "key": "beaef0552afd5737f17fc83000359b9a"}, {"relation": "hasVariant", "source": 3010, "target": 3037, "key": "afad7b1c4f9d037843d5ef171fbda93c"}, {"line": 45456, "relation": "negativeCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 3010, "target": 1871, "key": "e7864a3e40f4f6f0b793751e3bf7d36b"}, {"line": 47348, "relation": "isA", "evidence": "Binding to HSPGs requires a heparin/heparan sulfate-binding domain consisting of a stretch of positively charged lysines or arginines on the ligand. Prion protein, beta-amyloid, tau, and alpha-synuclein all have putative heparin-binding domains(25, 44–46).", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3010, "target": 49, "key": "82a9b7cc54e2e5d7380fe0e9b9e2eb5a"}, {"line": 48397, "relation": "association", "evidence": "The virus [HSV-1] is transported to the nucleus via the dynein and kinesin (KNS2) motors associated with the microtubule network (MAPT)... A viral protein is also able to delete mitochondrial DNA, a situation prevalent in Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "18164103"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true, "Innate immune system subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3010, "target": 2948, "key": "90b084db1f603a787c96a59abc970f6f"}, {"line": 49107, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3010, "target": 3463, "key": "3f61c355df9971c5bbbe37508d9dbdc9"}, {"line": 49121, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3010, "target": 2194, "key": "7c59f794cc2f1dfce1fa8a246eb0114f"}, {"line": 562, "relation": "decreases", "evidence": "Our results show that oligomeric Abeta specifically induces CaN activity and promotes CaN-dependent CREB and Bcl-2 Asociated death Protein (BAD) dephosphorylation and cell death", "citation": {"db": "PubMed", "db_id": "18782350"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Interleukin signaling subgraph": true, "Calcium-dependent signal transduction": true, "Bcl-2 subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 872, "target": 2383, "key": "5c28d6d6a35035d47c03ff8c545ec769"}, {"line": 570, "relation": "increases", "evidence": "Our results show that oligomeric Abeta specifically induces CaN activity and promotes CaN-dependent CREB and Bcl-2 Asociated death Protein (BAD) dephosphorylation and cell death", "citation": {"db": "PubMed", "db_id": "18782350"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Apoptosis signaling subgraph": true, "CREB subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 872, "target": 505, "key": "65449255727612ea0637bbc16c7b6f6d"}, {"line": 571, "relation": "decreases", "evidence": "Our results show that oligomeric Abeta specifically induces CaN activity and promotes CaN-dependent CREB and Bcl-2 Asociated death Protein (BAD) dephosphorylation and cell death", "citation": {"db": "PubMed", "db_id": "18782350"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Apoptosis signaling subgraph": true, "CREB subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 872, "target": 2163, "key": "ebaa6e3b566a524487d6ff2574b24213"}, {"line": 1443, "relation": "decreases", "evidence": "In mammals,CREB-regulated transcriptional coactivators (CRTCs) are a family of cofactors involved in diverse physiological processes including energy homeostasis, cancer and endoplasmic reticulum stress. Here we show that both AMPK and calcineurin modulate longevity exclusively through post-translational modification of CRTC-1, the sole C. elegans CRTC. We demonstrate that CRTC-1 is a direct AMPK target, and interacts with the CREB homologue-1 (CRH-1) transcription factor in vivo. The pro-longevity effects of activating AMPK or deactivating calcineurin decrease CRTC-1 and CRH-1 activity and induce transcriptional responses similar to those of CRH-1 null worms. Downregulation of crtc-1 increases lifespan in a crh-1-dependent manner and directly reducing crh-1 expression increases longevity, substantiating a role for CRTCs and CREB in ageing. Together, these findings indicate a novel role for CRTCs and CREB in determining lifespan downstream of AMPK and calcineurin, and illustrate the molecular mechanisms by which an evolutionarily conserved pathway responds to low energy to increase longevity.", "citation": {"db": "PubMed", "db_id": "21331044"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "CREB subgraph": true}}, "object": {"modifier": "Activity"}, "source": 872, "target": 2162, "key": "82cce0885ca5b369f95fa446adb92d11"}, {"line": 7541, "relation": "decreases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2383, "target": 2382, "key": "037f2fcab37c2f46349836c99698f85d"}, {"line": 7542, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2383, "target": 478, "key": "6782fa6b8eff5dd168ed112ecd35f2fd"}, {"line": 36423, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2383, "target": 392, "key": "8d245131e0f907e1b2316bb4db8df13b"}, {"relation": "hasVariant", "source": 2382, "target": 2383, "key": "1ecfdc53a63dd6d279f3a91da1e9bd78"}, {"line": 6207, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2382, "target": 478, "key": "789eeefbd822a11a7268c0d530f82edb"}, {"relation": "partOf", "source": 2382, "target": 1279, "key": "bc4fac5d65366b6b806f6692fe9437d2"}, {"line": 23336, "relation": "association", "evidence": "Activation of the mitochondrial pathway of apoptosis is one attractive explanation for the transcription-independent portion of p53-influenced apoptosis (Chen et al., 1996b; Haupt et al., 1995). Mitochondrial translocation of p53 following DNA damage (Mihara et al., 2003) and its ability to engage BCL-2 family proteins to regulate cytochrome c release have been noted (Chipuk et al., 2004).", "citation": {"db": "PubMed", "db_id": "14744432"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"Medium": true}}, "source": 2382, "target": 3482, "key": "cfb3e8ac60574600fa031cfa6459235c"}, {"line": 10517, "relation": "association", "evidence": "These results suggest that unregulated Ca(2+) entry across amyloid channels may be a common mechanism causing cell death, not only in diseases of the third age, including Alzheimer's disease and type 2 diabetes mellitus, but also in prion-induced diseases.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}}, "source": 505, "target": 94, "key": "e7dc7c2bae8ebaa90590248ed765cca8"}, {"line": 13926, "relation": "association", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"MeSHAnatomy": {"Nerve Tissue": true, "Neurons": true}, "CellStructure": {"Cytoplasm": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 2493, "key": "4d1a5e7c774798a4a2291280c8305e6b"}, {"line": 15482, "relation": "association", "evidence": "Our findings suggest that increased expression and activation of MMP-2 may contribute to HCSM cell death in response to pathogenic A beta.", "citation": {"db": "PubMed", "db_id": "12753080"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Species": {"9606": true}, "Cell": {"regular cardiac myocyte": true}}, "source": 505, "target": 3059, "key": "23f86930e7dac49aeb029542da1ad8f2"}, {"line": 17957, "relation": "association", "evidence": "In the chronic phase, concurrent activation of WOX1, CREB, and NF-kappaB occurs in small neurons just prior to apoptotic process.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 3553, "key": "426f838ad4ce61e67f2355f1ecf29248"}, {"line": 17958, "relation": "association", "evidence": "In the chronic phase, concurrent activation of WOX1, CREB, and NF-kappaB occurs in small neurons just prior to apoptotic process.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 3536, "key": "ac4315338b6a060d29eb668a4a0c0d87"}, {"line": 17966, "relation": "association", "evidence": "In the chronic phase, concurrent activation of WOX1, CREB, and NF-kappaB occurs in small neurons just prior to apoptotic process.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 2162, "key": "ad5bcf7cf83a64b6cb89b3d1dcf4af34"}, {"line": 18026, "relation": "association", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 3874, "key": "5c1edb071c7ebabcd24a0903bfedde42"}, {"line": 18030, "relation": "association", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 3878, "key": "d23a7918c0df69d8d31b1a28fb83ab67"}, {"line": 18034, "relation": "association", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 3858, "key": "0e5a5c6b1eaeaf3dbe94f24f20f15918"}, {"line": 18038, "relation": "association", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 3825, "key": "f60b5a01c734692b7b2f40c5263b80a3"}, {"line": 21010, "relation": "association", "evidence": "Transnitrosylation of XIAP regulates caspase-dependent neuronal cell death.", "citation": {"db": "PubMed", "db_id": "20670888"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "XIAP subgraph": true}, "Confidence": {"High": true}}, "source": 505, "target": 665, "key": "0f31cbf0747e47b347c8b24e258cf146"}, {"line": 21361, "relation": "association", "evidence": "The proteolytic activation of PKCdelta plays a key role in promoting apoptotic cell death in various cell types, including neuronal cells.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 505, "target": 3238, "key": "e7bd7d86e4ab129ccce24014e3fecd9e"}, {"line": 29555, "relation": "association", "evidence": "Cyclin-dependent kinase 5 (Cdk5) activity is significantly increased in AD and contributes to all three hallmarks: neurotoxic amyloid-beta (Abeta), neurofibrillary tangles (NFT), and extensive cell death.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 505, "target": 2487, "key": "e2000c0b3da2acb6a7ba14b7021dfb7c"}, {"line": 29905, "relation": "association", "evidence": "Functionally, calmyrin and PS2 increase cell death when cotransfected into HeLa cells. ", "citation": {"db": "PubMed", "db_id": "10366599"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 505, "target": 1353, "key": "a8b4b12290377bd3b107879e86dc03a7"}, {"line": 572, "relation": "decreases", "evidence": "Our results show that oligomeric Abeta specifically induces CaN activity and promotes CaN-dependent CREB and Bcl-2 Asociated death Protein (BAD) dephosphorylation and cell death", "citation": {"db": "PubMed", "db_id": "18782350"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Apoptosis signaling subgraph": true, "CREB subgraph": true}, "Confidence": {"Very High": true}}, "source": 2163, "target": 505, "key": "d42da82008041e4137e902e8847eaca6"}, {"line": 36946, "relation": "increases", "evidence": "Protein kinase C: PKC is part of a multigene family of serine-threonine kinases central to many signal transduction pathways [138] with a prominent role in memory [139]. It is likely that ABeta¸-induced increases in cytosolic Ca2+ signals are transmitted to PKC for PKC-mediated transcriptional activation. In addition, PKC activates ERK by interacting with Ras or Raf-1 [140] to initiate CREB phosphorylation. While PKC levels decline in AD [141], their activation restores K+ channel function in cells from AD patients [142]. In addition, activation of PKC directly or indirectly enhances the a-processing cleavage of APP [143].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2163, "target": 669, "key": "2c6797265101bc2750d8d70ecd11e652"}, {"line": 37249, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2163, "target": 2162, "key": "e6e19e98baabdab034eef0219bd37e82"}, {"relation": "hasVariant", "source": 2162, "target": 2163, "key": "ee9066f4ac74f79116720103ae187f88"}, {"line": 1441, "relation": "association", "evidence": "In mammals,CREB-regulated transcriptional coactivators (CRTCs) are a family of cofactors involved in diverse physiological processes including energy homeostasis, cancer and endoplasmic reticulum stress. Here we show that both AMPK and calcineurin modulate longevity exclusively through post-translational modification of CRTC-1, the sole C. elegans CRTC. We demonstrate that CRTC-1 is a direct AMPK target, and interacts with the CREB homologue-1 (CRH-1) transcription factor in vivo. The pro-longevity effects of activating AMPK or deactivating calcineurin decrease CRTC-1 and CRH-1 activity and induce transcriptional responses similar to those of CRH-1 null worms. Downregulation of crtc-1 increases lifespan in a crh-1-dependent manner and directly reducing crh-1 expression increases longevity, substantiating a role for CRTCs and CREB in ageing. Together, these findings indicate a novel role for CRTCs and CREB in determining lifespan downstream of AMPK and calcineurin, and illustrate the molecular mechanisms by which an evolutionarily conserved pathway responds to low energy to increase longevity.", "citation": {"db": "PubMed", "db_id": "21331044"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "CREB subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2162, "target": 771, "key": "428de0ef8c8820352fc86b62c11704cc"}, {"line": 4791, "relation": "increases", "evidence": "Moreover, we demonstrate that neuronal activity upregulates CRP1 expression in hippocampal neurons via Ca²+ influx after depolarization. Ca²+/calmodulin-dependent protein kinase IV (CaMKIV) and cAMP response element binding protein mediate the Ca²+-induced upregulation of CRP1 expression. Furthermore, CRP1 is required for the dendritic growth induced by Ca+? influx or CaMKIV. Together, these data are the first to demonstrate a role for CRP1 in dendritic growth.", "citation": {"db": "PubMed", "db_id": "22090504"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Calcium-dependent signal transduction": true}}, "source": 2162, "target": 492, "key": "a109f56c4d35b17f97810d883f6cb4e4"}, {"relation": "hasVariant", "source": 2162, "target": 2164, "key": "70fd0215148790d44e58e217a3a39471"}, {"line": 5517, "relation": "association", "evidence": "CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 2162, "target": 595, "key": "b108353b6b5edec5ddc325e0d4d42723"}, {"line": 5518, "relation": "association", "evidence": "CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 2162, "target": 559, "key": "85f11e3b1208a09045de6c794570ae67"}, {"line": 5519, "relation": "association", "evidence": "CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 2162, "target": 787, "key": "5bb3b51cb2d80e776580714918f07115"}, {"line": 5531, "relation": "increases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 2162, "target": 877, "key": "dcffeba2364a563239c638d262133793"}, {"line": 17937, "relation": "association", "evidence": "WOX1 physically interacted with CREB most strongly in the nuclei as determined by FRET analysis.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "source": 2162, "target": 3536, "key": "ec37c2267d61fa5b658f55b8b71a56c1"}, {"relation": "partOf", "source": 2162, "target": 1017, "key": "64c86d946cf64db00525697795c419d0"}, {"line": 17966, "relation": "association", "evidence": "In the chronic phase, concurrent activation of WOX1, CREB, and NF-kappaB occurs in small neurons just prior to apoptotic process.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 2162, "target": 505, "key": "82c7688d773ff26bf28a2bf3da842bee"}, {"line": 22253, "relation": "positiveCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 2162, "target": 859, "key": "32894f8f8322d032d4836f70d8340425"}, {"line": 35577, "relation": "positiveCorrelation", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2162, "target": 3946, "key": "dd6429ed2ad53d7eb501a2e7777e52dc"}, {"line": 35578, "relation": "negativeCorrelation", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2162, "target": 3640, "key": "209df4fd5478f5402d1c7766095e290e"}, {"line": 36503, "relation": "increases", "evidence": "Increased cytosolic calcium concentrations initiate the activation of several kinase-dependent signalling cascades including activation of PKC leading to CREB activation and phosphorylation at Ser133, a process critical for protein synthesis-dependent synaptic plasticity and LTP. PKC-a also activates ERK by interacting with Ras or Raf-1.Mitochondria are critical targets of intracellular ABeta¸. ABeta¸ interacts with CypD, a protein component of the membrane permeability transition pore (MPTP). The interaction of CypD with ABeta¸ causes functional modification of this protein leading to MPTP opening. ABeta¸ also binds with another mitochondrial protein, ABAD to distort the enzyme’s structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2162, "target": 761, "key": "23d2ee781e42e55aea5a92695915c793"}, {"line": 36508, "relation": "increases", "evidence": "Increased cytosolic calcium concentrations initiate the activation of several kinase-dependent signalling cascades including activation of PKC leading to CREB activation and phosphorylation at Ser133, a process critical for protein synthesis-dependent synaptic plasticity and LTP. PKC-a also activates ERK by interacting with Ras or Raf-1.Mitochondria are critical targets of intracellular ABeta¸. ABeta¸ interacts with CypD, a protein component of the membrane permeability transition pore (MPTP). The interaction of CypD with ABeta¸ causes functional modification of this protein leading to MPTP opening. ABeta¸ also binds with another mitochondrial protein, ABAD to distort the enzyme’s structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2162, "target": 738, "key": "84ca4bad4ae7bab8f2b572c25e58f966"}, {"line": 37251, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2162, "target": 820, "key": "8157346f843c6a9a70ded719121e025f"}, {"line": 48922, "relation": "association", "evidence": "Importantly, expression of the CRE-driven immediate early gene, Egr-1 (Zif268) is decreased in the CA1 region of the hippocampus.", "citation": {"db": "PubMed", "db_id": "26682682"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Anatomy": {"CA1 field of hippocampus": true}}, "source": 2162, "target": 2658, "key": "c6fe74929fe1dad15fc7ed2ee4294eaa"}, {"relation": "hasReactant", "source": 4105, "target": 2489, "key": "bfaa108886dd11c54b9172a921cbaa1b"}, {"relation": "hasProduct", "source": 4105, "target": 2133, "key": "d4d87652d29c222a864f5fbe585cae0c"}, {"line": 605, "relation": "increases", "evidence": "Conversion of p35 to p25 causes prolonged activation and mislocalization of cdk5. Consequently, the p25/cdk5 kinase hyperphosphorylates tau, disrupts the cytoskeleton and promotes the death (apoptotic process) of primary neurons.", "citation": {"db": "PubMed", "db_id": "10830966"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 4105, "target": 2487, "key": "0e87c352c19e3d1963dcc1ef6fefd1e4"}, {"relation": "partOf", "source": 2489, "target": 1341, "key": "784158be7e4c2ab3570bea8c3b91cd99"}, {"line": 7071, "relation": "increases", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2489, "target": 2487, "key": "e8b6fbeb88cc704701e71404ed6069d5"}, {"line": 19258, "relation": "increases", "evidence": "In addition, while cdk5 has important physiological functions related to brain development, the breakdown of cdk5/p35 into cdk5/p25 increases its kinase activity and neurotoxicity.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2489, "target": 2487, "key": "cc067329ff80b5a71795577f3196b15b"}, {"line": 7079, "relation": "increases", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2489, "target": 2133, "key": "330d1e7d09d513fc5907c3548de5ca81"}, {"line": 7139, "relation": "positiveCorrelation", "evidence": "To determine whether the p35 and CDK5 proteins detected in pancreatic islets interact with one another and form a functional complex, we immunoprecipitated the complex from human islets and determined its protein kinase activity as previously described (16). Immunoprecipitation with a p35 antibody, followed by kinase activity determination in the immunoprecipitate using histone H1 as a substrate, demonstrate that p35 and CDK5 form a functional complex capable of phosphorylating histone H1 (Fig. 1D). No kinase activity was detected in the absence of antibody or in the presence of an unrelated control antibody (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Islets of Langerhans": true, "Pancreas": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 2489, "target": 388, "key": "2671da9064c435983a29f86fa4e0be6c"}, {"line": 7157, "relation": "positiveCorrelation", "evidence": "Additional experiments were designed to investigate the presence of p35, CDK5, and p35/CDK5 kinase activity in beta-cell lines. Biochemical characterization of CDK5 kinase activity was also investigated. Among the different cell lines tested, only INS-1 cells, an insulin-producing beta-cell line, showed expression of p35 (Fig. 2A). These cells also contain protein kinase activity that can be immunoprecipitated with a p35-specific antibody (Fig. 2B). Both p35 expression and p35/CDK5 activity were absent in other cell lines, such as HeLa and NIH-3T3 (Fig. 2, A and B), although these cell lines expressed CDK5 protein (Fig. 2A). We then investigated whether the kinase activity immunoprecipitated by the p35 antibody was due to its association with CDK5. We performed experiments with roscovitine, a relatively specific inhibitor of CDK5 activity (17). The p35/CDK5 activity in INS-1 cells was inhibited by roscovitine, but not by other protein kinase inhibitors, such as H89 (protein kinase A) and SB202190 (p38MAPK; Fig. 2C). Also, the inhibition by roscovitine was dose dependent (Fig. 2D), with a 50% inhibitory concentration of 128 nm, within the range of that previously reported for CDK5 (18).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"INS-1 cells": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 2489, "target": 388, "key": "896c219c2124b35ffd20d0ce08c34a5c"}, {"relation": "partOf", "source": 2489, "target": 1340, "key": "5f6981ba676d424221d68f3a3cf036aa"}, {"line": 19271, "relation": "positiveCorrelation", "evidence": "Interestingly, in recent years increased cdk5/p25 expression has been demonstrated in the brains of patients with Alzheimer's and Parkinson's diseases.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2489, "target": 3823, "key": "d8ff6de1cb9c2457336747994712189d"}, {"line": 19272, "relation": "positiveCorrelation", "evidence": "Interestingly, in recent years increased cdk5/p25 expression has been demonstrated in the brains of patients with Alzheimer's and Parkinson's diseases.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2489, "target": 3878, "key": "c6648f5d6b70ec2dbd37875b5f373595"}, {"line": 19283, "relation": "increases", "evidence": "Cdk5/p25 subsequently phosphorylates the nuclear transcription factor myocyte enhancer factor (MEF2), thereby inhibiting its prosurvival activity.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2489, "target": 2192, "key": "166455c0b970f03b1039f0d51cb92403"}, {"line": 19297, "relation": "increases", "evidence": "However, cdk5/p25 could phosphorylate other substrates such as tau and p53, as well as the retinoblastoma protein pRb.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"p53 stabilization subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2489, "target": 3483, "key": "2939b4e4396769cc5f14ded02a4cc0e4"}, {"line": 19305, "relation": "increases", "evidence": "However, cdk5/p25 could phosphorylate other substrates such as tau and p53, as well as the retinoblastoma protein pRb.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2489, "target": 3300, "key": "a699f35de694117cc7a166e4fec393df"}, {"line": 19312, "relation": "increases", "evidence": "However, cdk5/p25 could phosphorylate other substrates such as tau and p53, as well as the retinoblastoma protein pRb.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2489, "target": 3015, "key": "6e948dfa6425dae78a5c487284aa6f36"}, {"line": 23049, "relation": "isA", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 2489, "target": 44, "key": "bb2adcc3458ebfd638777e0aaccbb24f"}, {"line": 23050, "relation": "causesNoChange", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 2489, "target": 3825, "key": "762d27fb36a1d4842f53e0cf51396241"}, {"line": 23051, "relation": "causesNoChange", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "object": {"modifier": "Activity"}, "source": 2489, "target": 3601, "key": "189075844ed8213c26dd86d29354d951"}, {"line": 23052, "relation": "decreases", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "object": {"modifier": "Activity"}, "source": 2489, "target": 3600, "key": "ab79e712071efe658d06235f5f08fe85"}, {"line": 23053, "relation": "decreases", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 2489, "target": 3600, "key": "a3e66120a38f65616372cd352554c8ca"}, {"relation": "partOf", "source": 2489, "target": 1261, "key": "1dcfe60c5a3ce11805d8e8b82b20e1dc"}, {"line": 2181, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cell cycle subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2133, "target": 3823, "key": "3feb69327da6108ffe365145b31eb82e"}, {"relation": "partOf", "source": 2133, "target": 1668, "key": "1e0fcc50076eb2898c1e37a82a17958b"}, {"relation": "partOf", "source": 2133, "target": 1014, "key": "46845e0a22199a4052c0ba29b5b6dc27"}, {"line": 7080, "relation": "negativeCorrelation", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 2133, "target": 2487, "key": "5b2bb2a3c3e074672a5f2f4cbc055b49"}, {"line": 35822, "relation": "increases", "evidence": "The state of tau phosphorylation and proteolysis can be regulated by calcium-dependent mechanisms. CaMKII can phosphorylate tau [189]. Cyclin-dependent kinase 5 (cdk5), another kinase involved in tau phosphorylation [190], is indirectly activated by the calcium-activated protease calpain. Indeed, cdk5 has to be associated with its regulatory subunit, p35 to be activated. Conversion of p35 to p25 deregulates cdk5 activity, resulting in an increased cdk5 kinase activity [191]. Calpain cleaves p35 into p25, and thus controls cdk5 activation [192]. Furthermore, tau is dephosphorylated by the calcium/calmodulin-dependent phosphatase, calcineurin [193]. Calpain was also proposed to directly participate in tau proteolysis and degradation", "citation": {"db": "PubMed", "db_id": "19419557"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Calpastatin-calpain subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2133, "target": 2487, "key": "995857e42ab8caa0820de2b6348f791f"}, {"line": 7089, "relation": "association", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2133, "target": 391, "key": "600bb3b35db33c20d43916135a2ef991"}, {"line": 7091, "relation": "increases", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2133, "target": 478, "key": "b497158ea3471a4f86c3034715d142c7"}, {"line": 29544, "relation": "increases", "evidence": "In the second phase, Cdk5 activates c-Jun via ROS-mediated activation of JNK. Rapid c-Jun activation is supported by in vivo data showing c-Jun phosphorylation in cerebral cortex upon p25 induction in transgenic mice.", "citation": {"db": "PubMed", "db_id": "19776350"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2133, "target": 2937, "key": "c48eadfea4081326892e607a74cb9d0b"}, {"line": 593, "relation": "increases", "evidence": "calpain directly cleaves p35 to release a fragment with relative molecular mass 25,000. The sequence of the calpain cleavage product corresponds precisely to that of p25.", "citation": {"db": "PubMed", "db_id": "10830966"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2160, "target": 4105, "key": "dbd035e053812f7c01b40affd11aef06"}, {"line": 35821, "relation": "increases", "evidence": "The state of tau phosphorylation and proteolysis can be regulated by calcium-dependent mechanisms. CaMKII can phosphorylate tau [189]. Cyclin-dependent kinase 5 (cdk5), another kinase involved in tau phosphorylation [190], is indirectly activated by the calcium-activated protease calpain. Indeed, cdk5 has to be associated with its regulatory subunit, p35 to be activated. Conversion of p35 to p25 deregulates cdk5 activity, resulting in an increased cdk5 kinase activity [191]. Calpain cleaves p35 into p25, and thus controls cdk5 activation [192]. Furthermore, tau is dephosphorylated by the calcium/calmodulin-dependent phosphatase, calcineurin [193]. Calpain was also proposed to directly participate in tau proteolysis and degradation", "citation": {"db": "PubMed", "db_id": "19419557"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Calpastatin-calpain subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2160, "target": 4105, "key": "a4a88b26f7c0a8a6828174a7dc5865e5"}, {"line": 35820, "relation": "increases", "evidence": "The state of tau phosphorylation and proteolysis can be regulated by calcium-dependent mechanisms. CaMKII can phosphorylate tau [189]. Cyclin-dependent kinase 5 (cdk5), another kinase involved in tau phosphorylation [190], is indirectly activated by the calcium-activated protease calpain. Indeed, cdk5 has to be associated with its regulatory subunit, p35 to be activated. Conversion of p35 to p25 deregulates cdk5 activity, resulting in an increased cdk5 kinase activity [191]. Calpain cleaves p35 into p25, and thus controls cdk5 activation [192]. Furthermore, tau is dephosphorylated by the calcium/calmodulin-dependent phosphatase, calcineurin [193]. Calpain was also proposed to directly participate in tau proteolysis and degradation", "citation": {"db": "PubMed", "db_id": "19419557"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Calpastatin-calpain subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2160, "target": 2487, "key": "43455b6692613ed814a10fcf3bd5900c"}, {"line": 46053, "relation": "negativeCorrelation", "evidence": "The activity of calpains is regulated by the inhibitor calpastatin, and increased activity of calpains and decreased calpastastin are often found in AD", "citation": {"db": "PubMed", "db_id": "24200051"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 2160, "target": 2452, "key": "11e02bae8b32df37a1ea0ed189626398"}, {"line": 606, "relation": "directlyIncreases", "evidence": "Conversion of p35 to p25 causes prolonged activation and mislocalization of cdk5. Consequently, the p25/cdk5 kinase hyperphosphorylates tau, disrupts the cytoskeleton and promotes the death (apoptotic process) of primary neurons.", "citation": {"db": "PubMed", "db_id": "10830966"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "545ce3b780228e3cd6517eb6c36d91b5"}, {"line": 636, "relation": "directlyIncreases", "evidence": "Two main protein kinases have been shown to be involved in anomalous tau phosphorylations: the cyclin-dependent kinase Cdk5 and glycogen synthase kinase GSK3beta", "citation": {"db": "PubMed", "db_id": "11578751"}, "annotations": {"Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "84586b575e270db187007ca38c3f40f8"}, {"line": 4899, "relation": "increases", "evidence": "Phosphorylation of the neurofilament proteins of high and medium relative molecular mass, as well as of the Alzheimer's tau protein, is thought to be catalysed by a protein kinase with Cdc2-like substrate specificity. We have purified a novel Cdc2-like kinase from bovine brain capable of phosphorylating both the neurofilament proteins and tau. The purified enzyme is a heterodimer of cyclin-dependent kinase 5 (Cdk5) and a novel regulatory subunit, p25. When overexpressed and purified from Escherichia coli, p25 can activate Cdk5 in vitro. Unlike Cdk5, which is ubiquitously expressed in human tissue, the p25 transcript is expressed only in brain. A full-length complementary DNA clone showed that p25 is a truncated form of a larger protein precursor, p35, which seems to be the predominant form of the protein in crude brain extract. Cdk5/p35 is the first example of a Cdc2-like kinase with neuronal function.", "citation": {"db": "PubMed", "db_id": "8090222"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Axonal transport subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "b217d20f7aa434c9846982e8f4461a30"}, {"line": 7070, "relation": "increases", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 3015, "key": "1576f21d604a7669a97389da0e1a887f"}, {"line": 7902, "relation": "increases", "evidence": "The phosphorylation of tau is mainly promoted by GSK-3and cyclin-dependent kinase 5 (Cdk5). Besides these kinases, activated c-Jun N-terminal kinases (JNK) and ERK-1 /-2 signaling lead to an increase in tau phosphorylation and th erefore might be of importance in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "9c26ce348a923b5ce5739b14340030b3"}, {"line": 29626, "relation": "increases", "evidence": "A large body of biochemical, genetic, and cell biological evidence implicate two major serine-threonine protein kinases, glycogen synthase kinase 3 (GSK-3) and cyclin-dependent kinase 5 (CDK5) as major kinases responsible for both normal and pathological phosphorylation of tau protein in vivo.", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "cef9f3b22223fd6529018583974443b0"}, {"line": 29647, "relation": "increases", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "eee6c7eb525d02691fd942fe66d65762"}, {"line": 29692, "relation": "increases", "evidence": "These studies suggest that PKA, cdk5, CaM Kinase II and GSK-3 are involved in the regulation of phosphorylation of tau and that AD-type phosphorylation of tau is probably a product of the synergistic action of two or more of these kinases.", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "dbbce96df189fe2cf7090155669dfc4e"}, {"line": 29707, "relation": "increases", "evidence": "Indirubins inhibit glycogen synthase kinase-3 beta and CDK5/p25, two protein kinases involved in abnormal tau phosphorylation in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11013232"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "d650922f885f8a81e92a50f410683abb"}, {"line": 29720, "relation": "increases", "evidence": "A number of kinases, including mitogen-activated protein (MAP) kinase, glycogen synthase kinase (GSK)-3 alpha, GSK-3 beta and cyclin-dependent kinase-5, phosphorylate recombinant tau in vitro so that it resembles PHF-tau as judged by its reactivity with a panel of antibodies capable of discriminating between normal tau and PHF-tau, and by a reduced electrophoretic mobility that is characteristic of PHF-tau.", "citation": {"db": "PubMed", "db_id": "7704571"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "53f61b190365193329a9fa0fd56b0592"}, {"line": 29733, "relation": "increases", "evidence": "Cyclin-dependent kinase 5 (Cdk5), a proline-directed serine/Threonine kinase, is considered to have a major tau-phosphorylating function in the brain, with pathological relevance in AD.", "citation": {"db": "PubMed", "db_id": "17965932"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "b09f7d8bf9d0dbd27c2759ba9e53a731"}, {"line": 29743, "relation": "increases", "evidence": "Cyclin dependent kinase 5 (Cdk5) phosphorylates tau protein, a microtubule-associated protein, at pathological sites in vitro as well as in Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "7669983"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "6fbda661b3e2aed5ca72fdaf1dfb5673"}, {"line": 29753, "relation": "increases", "evidence": "Phosphorylation of human tau protein by microtubule-associated kinases: GSK3beta and cdk5 are key participants.", "citation": {"db": "PubMed", "db_id": "11054815"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "f09a7fceaada13d587657760793ff2e3"}, {"line": 29764, "relation": "increases", "evidence": "Phosphorylation of tau protein is regulated by several kinases, especially glycogen synthase kinase 3beta (GSK-3beta), cyclin-dependent protein kinase 5 (cdk5) and cAMP-dependent protein kinase (PKA).", "citation": {"db": "PubMed", "db_id": "17078951"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "f63e64414bfa02daa184496671e496b1"}, {"line": 29777, "relation": "increases", "evidence": "In the studies reported here, a combination of mass spectrometric techniques was used to study the phosphorylation of human recombinant tau by recombinant tau protein kinase II (cdk5/p20) in vitro.", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "0a8e36b8e168ff6c4fd7ba3b6610a17c"}, {"line": 29804, "relation": "increases", "evidence": "Abnormal Alzheimer-like phosphorylation of tau-protein by cyclin-dependent kinases cdk2 and cdk5.", "citation": {"db": "PubMed", "db_id": "8282104"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "8afafd81a47ca61c8ebd51757ed95ef2"}, {"line": 29821, "relation": "increases", "evidence": "Combined tau protein kinase II (TPK II), which consists of CDK5 and its activator (p23), and glycogen synthase kinase-3beta (GSK-3beta) phosphorylate tau to the PHF-form in vitro.", "citation": {"db": "PubMed", "db_id": "9565682"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "5ad6da5a8759fade42b778dfd568d44b"}, {"line": 30617, "relation": "increases", "evidence": "Indirubins inhibit glycogen synthase kinase-3 beta and CDK5/p25, two protein kinases involved in abnormal tau phosphorylation in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11013232"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 3015, "key": "81a6c52682a031f8d9b1234877e15d18"}, {"line": 30659, "relation": "increases", "evidence": "Since prior phosphorylation of tau by TPKII strongly enhanced the action of TPKI, it was thought that TPKII was involved in the formation of PHF-tau in concert with TPKI.", "citation": {"db": "PubMed", "db_id": "9089387"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "7ace2d95bbd8edc400ef5269c5306f73"}, {"line": 30687, "relation": "increases", "evidence": "Phosphorylation of tau protein is regulated by several kinases, especially glycogen synthase kinase 3beta (GSK-3beta), cyclin-dependent protein kinase 5 (cdk5) and cAMP-dependent protein kinase (PKA).", "citation": {"db": "PubMed", "db_id": "17078951"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 3015, "key": "3ddf46c70aed62c0956f2bf8e02cedb1"}, {"line": 30699, "relation": "increases", "evidence": "Two protein kinases, tau protein kinase I (TPK I or GSK 3beta) and tau protein kinase II (TPK II; cdk5/p20), have been isolated from bovine brain microtubules", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Confidence": {"High": true}, "Species": {"9913": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 3015, "key": "aa79643ddadecdc181f5cdde7d9f7c2c"}, {"line": 32875, "relation": "increases", "evidence": "Direct interaction of soluble human recombinant tau protein with Abeta 1-42 results in tau aggregation and hyperphosphorylation by tau protein kinase II.", "citation": {"db": "PubMed", "db_id": "11943163"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "37f5d3566ed7f64025e7ffdb0cd4d294"}, {"line": 33108, "relation": "increases", "evidence": "Glycogen synthase kinase-3beta (GSK-3beta) and cyclin-dependent kinase 5 (CDK5) have been implicated as two major protein kinases involved in the abnormal hyperphosphorylation of tau in Alzheimer's disease (AD) brain, and the development of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19154537"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 3015, "key": "2259483dd34f01f803601418f5daedf8"}, {"line": 33400, "relation": "increases", "evidence": "This interaction is regulated by the phosphorylation of Tau at selected sites, by glycogen synthase kinase-3beta (GSK3beta) and cyclin-dependent kinase 5 (Cdk5), and requires an intact microtubule network.", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "03068241a547f861561b13fddf3672e8"}, {"line": 35819, "relation": "increases", "evidence": "The state of tau phosphorylation and proteolysis can be regulated by calcium-dependent mechanisms. CaMKII can phosphorylate tau [189]. Cyclin-dependent kinase 5 (cdk5), another kinase involved in tau phosphorylation [190], is indirectly activated by the calcium-activated protease calpain. Indeed, cdk5 has to be associated with its regulatory subunit, p35 to be activated. Conversion of p35 to p25 deregulates cdk5 activity, resulting in an increased cdk5 kinase activity [191]. Calpain cleaves p35 into p25, and thus controls cdk5 activation [192]. Furthermore, tau is dephosphorylated by the calcium/calmodulin-dependent phosphatase, calcineurin [193]. Calpain was also proposed to directly participate in tau proteolysis and degradation", "citation": {"db": "PubMed", "db_id": "19419557"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Calpastatin-calpain subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3015, "key": "2a82e320750c70efc1f2b061af931e0f"}, {"line": 607, "relation": "decreases", "evidence": "Conversion of p35 to p25 causes prolonged activation and mislocalization of cdk5. Consequently, the p25/cdk5 kinase hyperphosphorylates tau, disrupts the cytoskeleton and promotes the death (apoptotic process) of primary neurons.", "citation": {"db": "PubMed", "db_id": "10830966"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 540, "key": "c9d14c3715848f8bad3c7a24fdab47ca"}, {"line": 608, "relation": "increases", "evidence": "Conversion of p35 to p25 causes prolonged activation and mislocalization of cdk5. Consequently, the p25/cdk5 kinase hyperphosphorylates tau, disrupts the cytoskeleton and promotes the death (apoptotic process) of primary neurons.", "citation": {"db": "PubMed", "db_id": "10830966"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 645, "key": "2e79e27f69557a77b91492eeedf23a24"}, {"line": 4915, "relation": "decreases", "evidence": "Cyclin-dependent kinase 5 (cdk5) is a serine/threonine kinase activated by associating with its neuron-specific activators p35 and p39. Here, we show that cdk5 directly phosphorylates c-Jun N-terminal kinase 3 (JNK3) on Thr131 and inhibits its kinase activity, leading to reduced c-Jun phosphorylation. These effects can be restored by expression of a catalytically inactive mutant form of cdk5. Moreover, cdk5-deficient cultured cortical neurons exhibit increased sensitivity to apoptotic stimuli, as well as elevated JNK3 activity and c-Jun phosphorylation. Taken together, these findings show that cdk5 may exert its role as a key element by negatively regulating the c-Jun N-terminal kinase/stress-activated protein kinase signaling pathway during neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11823425"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 645, "key": "e28208fd7900343f0e8a0e913f3b1364"}, {"line": 1990, "relation": "increases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 2581, "key": "09450e7dde0feddd65167fc290928e34"}, {"line": 35522, "relation": "increases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 2581, "key": "f88cd8017285abed2a62d48dc6f5a50e"}, {"relation": "partOf", "source": 2487, "target": 1014, "key": "a97bc2b068df31f2dbd7fc9c8335321b"}, {"line": 4900, "relation": "increases", "evidence": "Phosphorylation of the neurofilament proteins of high and medium relative molecular mass, as well as of the Alzheimer's tau protein, is thought to be catalysed by a protein kinase with Cdc2-like substrate specificity. We have purified a novel Cdc2-like kinase from bovine brain capable of phosphorylating both the neurofilament proteins and tau. The purified enzyme is a heterodimer of cyclin-dependent kinase 5 (Cdk5) and a novel regulatory subunit, p25. When overexpressed and purified from Escherichia coli, p25 can activate Cdk5 in vitro. Unlike Cdk5, which is ubiquitously expressed in human tissue, the p25 transcript is expressed only in brain. A full-length complementary DNA clone showed that p25 is a truncated form of a larger protein precursor, p35, which seems to be the predominant form of the protein in crude brain extract. Cdk5/p35 is the first example of a Cdc2-like kinase with neuronal function.", "citation": {"db": "PubMed", "db_id": "8090222"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Axonal transport subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3102, "key": "e0b9d7e5641787e06b95241b1c862883"}, {"relation": "partOf", "source": 2487, "target": 1341, "key": "5954b2c0658802dcf844144df848d5b4"}, {"line": 4912, "relation": "decreases", "evidence": "Cyclin-dependent kinase 5 (cdk5) is a serine/threonine kinase activated by associating with its neuron-specific activators p35 and p39. Here, we show that cdk5 directly phosphorylates c-Jun N-terminal kinase 3 (JNK3) on Thr131 and inhibits its kinase activity, leading to reduced c-Jun phosphorylation. These effects can be restored by expression of a catalytically inactive mutant form of cdk5. Moreover, cdk5-deficient cultured cortical neurons exhibit increased sensitivity to apoptotic stimuli, as well as elevated JNK3 activity and c-Jun phosphorylation. Taken together, these findings show that cdk5 may exert its role as a key element by negatively regulating the c-Jun N-terminal kinase/stress-activated protein kinase signaling pathway during neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11823425"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2993, "key": "4a8380f8b0486912b0525bee3943d6dc"}, {"line": 4916, "relation": "decreases", "evidence": "Cyclin-dependent kinase 5 (cdk5) is a serine/threonine kinase activated by associating with its neuron-specific activators p35 and p39. Here, we show that cdk5 directly phosphorylates c-Jun N-terminal kinase 3 (JNK3) on Thr131 and inhibits its kinase activity, leading to reduced c-Jun phosphorylation. These effects can be restored by expression of a catalytically inactive mutant form of cdk5. Moreover, cdk5-deficient cultured cortical neurons exhibit increased sensitivity to apoptotic stimuli, as well as elevated JNK3 activity and c-Jun phosphorylation. Taken together, these findings show that cdk5 may exert its role as a key element by negatively regulating the c-Jun N-terminal kinase/stress-activated protein kinase signaling pathway during neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11823425"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 786, "key": "ff97df867b002c0a0038934c583d1bb3"}, {"line": 7068, "relation": "association", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 3872, "key": "fc7dfd1bf322faca575f7143758e6457"}, {"line": 7080, "relation": "negativeCorrelation", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation"}, "source": 2487, "target": 2133, "key": "a1c489a34606b978f76cb91a13cee686"}, {"line": 7138, "relation": "positiveCorrelation", "evidence": "To determine whether the p35 and CDK5 proteins detected in pancreatic islets interact with one another and form a functional complex, we immunoprecipitated the complex from human islets and determined its protein kinase activity as previously described (16). Immunoprecipitation with a p35 antibody, followed by kinase activity determination in the immunoprecipitate using histone H1 as a substrate, demonstrate that p35 and CDK5 form a functional complex capable of phosphorylating histone H1 (Fig. 1D). No kinase activity was detected in the absence of antibody or in the presence of an unrelated control antibody (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Islets of Langerhans": true, "Pancreas": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 2487, "target": 388, "key": "251d77f9e1518bd2b8bc9a5c6c0b3c05"}, {"relation": "partOf", "source": 2487, "target": 1340, "key": "eb2b31348df70bf1b4a289f682365781"}, {"line": 7204, "relation": "causesNoChange", "evidence": "Initially, the expression of p35 was believed to be restricted to the central nervous system, the lens (22), and recently in developing muscle, where it forms a p35/CDK5 active complex that regulates the expression of the acetylcholine receptor gene (23). Despite its name CDK5 does not affect the cell division cycle; it is expressed postmitotically, and its function is related to cytoskeletal dynamics, cell migration, cell differentiation, and exocytosis (14) instead of cellular proliferation. Recently, Pho-85, a yeast ortholog of CDK5, was shown to be involved in metabolic control by regulating different steps of glycogen and phosphate metabolism (13). Expression of CDK5 in insulin-producing cells is not surprising because widespread expression of this kinase has been described. CDK5 expression in beta-cells has also been reported (24).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2487, "target": 503, "key": "5486e9f046f7201c7a3160e59078be22"}, {"line": 7205, "relation": "association", "evidence": "Initially, the expression of p35 was believed to be restricted to the central nervous system, the lens (22), and recently in developing muscle, where it forms a p35/CDK5 active complex that regulates the expression of the acetylcholine receptor gene (23). Despite its name CDK5 does not affect the cell division cycle; it is expressed postmitotically, and its function is related to cytoskeletal dynamics, cell migration, cell differentiation, and exocytosis (14) instead of cellular proliferation. Recently, Pho-85, a yeast ortholog of CDK5, was shown to be involved in metabolic control by regulating different steps of glycogen and phosphate metabolism (13). Expression of CDK5 in insulin-producing cells is not surprising because widespread expression of this kinase has been described. CDK5 expression in beta-cells has also been reported (24).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2487, "target": 509, "key": "df9cb80c4c669dbf38939e69494aefb0"}, {"line": 7206, "relation": "association", "evidence": "Initially, the expression of p35 was believed to be restricted to the central nervous system, the lens (22), and recently in developing muscle, where it forms a p35/CDK5 active complex that regulates the expression of the acetylcholine receptor gene (23). Despite its name CDK5 does not affect the cell division cycle; it is expressed postmitotically, and its function is related to cytoskeletal dynamics, cell migration, cell differentiation, and exocytosis (14) instead of cellular proliferation. Recently, Pho-85, a yeast ortholog of CDK5, was shown to be involved in metabolic control by regulating different steps of glycogen and phosphate metabolism (13). Expression of CDK5 in insulin-producing cells is not surprising because widespread expression of this kinase has been described. CDK5 expression in beta-cells has also been reported (24).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2487, "target": 506, "key": "289699ad1ba33d42ed0558d9f606ca5e"}, {"line": 7207, "relation": "association", "evidence": "Initially, the expression of p35 was believed to be restricted to the central nervous system, the lens (22), and recently in developing muscle, where it forms a p35/CDK5 active complex that regulates the expression of the acetylcholine receptor gene (23). Despite its name CDK5 does not affect the cell division cycle; it is expressed postmitotically, and its function is related to cytoskeletal dynamics, cell migration, cell differentiation, and exocytosis (14) instead of cellular proliferation. Recently, Pho-85, a yeast ortholog of CDK5, was shown to be involved in metabolic control by regulating different steps of glycogen and phosphate metabolism (13). Expression of CDK5 in insulin-producing cells is not surprising because widespread expression of this kinase has been described. CDK5 expression in beta-cells has also been reported (24).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2487, "target": 814, "key": "22f74911fa43a5a32e6e6df0dfe6013b"}, {"line": 9012, "relation": "increases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3017, "key": "c82ac7a05a9f30f2c28aec001a922486"}, {"line": 9020, "relation": "increases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3030, "key": "bb18cb8d82bea0ab07e9211a12ede652"}, {"line": 17400, "relation": "increases", "evidence": "In addition, by phosphorylating and inactivating PP1, Cdk5 promotes tau phosphorylation at Sep(262) indirectly.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2487, "target": 3217, "key": "0f7162cd69ff46e06d3296f0c850bd34"}, {"line": 17403, "relation": "increases", "evidence": "In addition, by phosphorylating and inactivating PP1, Cdk5 promotes tau phosphorylation at Sep(262) indirectly.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2487, "target": 3023, "key": "dae2e1a156429107f6e8845aad7056a9"}, {"line": 19253, "relation": "association", "evidence": "In addition, while cdk5 has important physiological functions related to brain development, the breakdown of cdk5/p35 into cdk5/p25 increases its kinase activity and neurotoxicity.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2487, "target": 488, "key": "16cb59dba3a39441aa9baaac370c8197"}, {"line": 19254, "relation": "increases", "evidence": "In addition, while cdk5 has important physiological functions related to brain development, the breakdown of cdk5/p35 into cdk5/p25 increases its kinase activity and neurotoxicity.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2487, "target": 2489, "key": "25055818aaad5d793229703b0d7d1e34"}, {"line": 21193, "relation": "association", "evidence": "S-nitrosylation of Cdk5: potential implications in amyloid-beta-related neurotoxicity in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "22874667"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 2487, "target": 665, "key": "1b451d7d121820cf1b341449e6fa8eb1"}, {"line": 21223, "relation": "association", "evidence": "Here, we demonstrate that neuronal nitric oxide synthase (NOS1) interacts with Cdk5 and that the close proximity of the two proteins facilitates the formation of SNO-Cdk5.", "citation": {"db": "PubMed", "db_id": "22874667"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2487, "target": 665, "key": "bcc5949b00769b6a2cfdd310aa9661e3"}, {"line": 21204, "relation": "increases", "evidence": "We recently reported that S-nitrosylation of Cdk5 (forming SNO-Cdk5) at specific cysteine residues results in excessive activation of Cdk5, contributing to mitochondrial dysfunction, synaptic damage, and neuronal cell death in models of AD.", "citation": {"db": "PubMed", "db_id": "22874667"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 826, "key": "f2e38e50afd8d611deae4ee9de919fed"}, {"line": 21208, "relation": "increases", "evidence": "We recently reported that S-nitrosylation of Cdk5 (forming SNO-Cdk5) at specific cysteine residues results in excessive activation of Cdk5, contributing to mitochondrial dysfunction, synaptic damage, and neuronal cell death in models of AD.", "citation": {"db": "PubMed", "db_id": "22874667"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 505, "key": "4f50c1aed4058eb65d46fb3d322486cb"}, {"line": 29555, "relation": "association", "evidence": "Cyclin-dependent kinase 5 (Cdk5) activity is significantly increased in AD and contributes to all three hallmarks: neurotoxic amyloid-beta (Abeta), neurofibrillary tangles (NFT), and extensive cell death.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 505, "key": "f1343fb61d39d1c4198de3376f6cc39f"}, {"relation": "partOf", "source": 2487, "target": 1349, "key": "73d765fcf209fd2b6a9d17d38382671c"}, {"line": 21229, "relation": "increases", "evidence": "Interestingly, as a negative feedback mechanism, Cdk5 phosphorylates and suppresses NOS1 activity.", "citation": {"db": "PubMed", "db_id": "22874667"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2487, "target": 3122, "key": "77dc2f874f3aa0c10ff5364e9b4b69ca"}, {"line": 21230, "relation": "decreases", "evidence": "Interestingly, as a negative feedback mechanism, Cdk5 phosphorylates and suppresses NOS1 activity.", "citation": {"db": "PubMed", "db_id": "22874667"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2487, "target": 3121, "key": "3fe3f6519b4e2eff1cf53a1f65eb3dd7"}, {"relation": "partOf", "source": 2487, "target": 1261, "key": "86a3ed690fab9fb4f119385b8a975997"}, {"relation": "partOf", "source": 2487, "target": 1260, "key": "ee87eb12ff67c899de9014b3f567e73c"}, {"relation": "partOf", "source": 2487, "target": 1343, "key": "9565b40474ee9de94572bbb092e0b648"}, {"line": 29446, "relation": "increases", "evidence": "These results indicate that Cdk5 primarily phosphorylates CRMP2 at Ser522 and GSK3beta secondarily phosphorylates at Thr509.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2643, "key": "dfdbb0facb633b3c2d0dc44f482a4601"}, {"line": 30263, "relation": "increases", "evidence": "Ser-522 prephosphorylated by Cdk5 is required for subsequent GSK-3alpha-mediated phosphorylation of CRMP-2 in vitro.", "citation": {"db": "PubMed", "db_id": "17902168"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 2643, "key": "faa32d6622ed643d00ff56247be4f911"}, {"line": 29447, "relation": "increases", "evidence": "These results indicate that Cdk5 primarily phosphorylates CRMP2 at Ser522 and GSK3beta secondarily phosphorylates at Thr509.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2644, "key": "5e6ee3513dc31e17a5c30a20f22dd1d3"}, {"relation": "partOf", "source": 2487, "target": 1344, "key": "58e0a3a68a4732a7b409145c90f3e563"}, {"line": 29486, "relation": "increases", "evidence": "In addition, the sequential phosphorylation of CRMP2 by Cdk5 and GSK3beta is an important process of Sema3A signaling.", "citation": {"db": "PubMed", "db_id": "16866215"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2642, "key": "65cb73f540bc5a1177325cf23fde1522"}, {"line": 29510, "relation": "increases", "evidence": "This function of CRMP2 is regulated by phosphorylation by glycogen synthase kinase 3 (GSK3) and cyclin-dependent kinase 5 (Cdk5).", "citation": {"db": "PubMed", "db_id": "17683481"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2642, "key": "dae762351e17c2f458908249d60a3c33"}, {"line": 30279, "relation": "increases", "evidence": "Phosphorylation by GSK3beta was exclusively observed in Cdk5-phosphorylated CRMP2, but barely in CRMP2T509A.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "GSK3 subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 2642, "key": "2e99237a54ec174cbb9ff452a0c72924"}, {"line": 29502, "relation": "regulates", "evidence": "Collapsin response mediator protein 2 (CRMP2) is an abundant brain-enriched protein that can regulate microtubule assembly in neurons.", "citation": {"db": "PubMed", "db_id": "17683481"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2487, "target": 612, "key": "3193941ca3b06c7b9d9657590f954391"}, {"relation": "partOf", "source": 2487, "target": 1345, "key": "31a174519935fcadae75532e242360a5"}, {"line": 29526, "relation": "increases", "evidence": "Identification of the Cdk5 phosphorylation site on GM130 suggested a mechanism by which Cdk5 may cause Golgi fragmentation upon deregulation in AD.", "citation": {"db": "PubMed", "db_id": "18480410"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2758, "key": "141175852b232b60e9c8697b5bd85df7"}, {"relation": "partOf", "source": 2487, "target": 1346, "key": "3bbcaac1a58ebb2f5e77fcd4775ecf9a"}, {"line": 29542, "relation": "increases", "evidence": "In the second phase, Cdk5 activates c-Jun via ROS-mediated activation of JNK. Rapid c-Jun activation is supported by in vivo data showing c-Jun phosphorylation in cerebral cortex upon p25 induction in transgenic mice.", "citation": {"db": "PubMed", "db_id": "19776350"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2487, "target": 2936, "key": "0368b1d1c542c4e506ea8787da4d12db"}, {"line": 29553, "relation": "association", "evidence": "Cyclin-dependent kinase 5 (Cdk5) activity is significantly increased in AD and contributes to all three hallmarks: neurotoxic amyloid-beta (Abeta), neurofibrillary tangles (NFT), and extensive cell death.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 80, "key": "e9f28962f9009a1b0708df0bb4a70524"}, {"line": 29554, "relation": "association", "evidence": "Cyclin-dependent kinase 5 (Cdk5) activity is significantly increased in AD and contributes to all three hallmarks: neurotoxic amyloid-beta (Abeta), neurofibrillary tangles (NFT), and extensive cell death.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 889, "key": "23b74d37df61783fa05a565047456ed7"}, {"relation": "partOf", "source": 2487, "target": 1347, "key": "6b4ffa4699eadedfc011676172517ff3"}, {"line": 29561, "relation": "increases", "evidence": "Using Abeta and glutamate as the neurotoxic stimuli, we show that deregulated Cdk5 induces nuclear lamina dispersion by direct phosphorylation of lamin A and lamin B1 in neuronal cells and primary cortical neurons.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2965, "key": "0c3058baec618c9e7a493887b0dd11a2"}, {"line": 29562, "relation": "increases", "evidence": "Using Abeta and glutamate as the neurotoxic stimuli, we show that deregulated Cdk5 induces nuclear lamina dispersion by direct phosphorylation of lamin A and lamin B1 in neuronal cells and primary cortical neurons.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2967, "key": "53ed17a73b83e85fe50ea592716b15fe"}, {"relation": "partOf", "source": 2487, "target": 1348, "key": "7090261f73a4d9e08c5e35bbd26ce533"}, {"line": 29571, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3019, "key": "be2a7d36c6aa992f5f0285ce5c0d9c01"}, {"line": 29572, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3020, "key": "a3a6f3e44ec30abc271273f7007ed191"}, {"line": 29573, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3021, "key": "eb9efc41947dfd3033883eb37120beca"}, {"line": 29574, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3022, "key": "e159dc11b1783c0a88dd89507c2b0c05"}, {"line": 29575, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3026, "key": "cab72577f9220aa9b2a59da8fdfb5df0"}, {"line": 29789, "relation": "increases", "evidence": "we conclude that tau protein kinase II (cdk5/p20) can phosphorylate human tau at Thr(181), Thr(205), Thr(212), Thr(217), Ser(396) and Ser(404).", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3026, "key": "7a73de8720f63e910609b17d6a7c02d7"}, {"line": 29576, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3027, "key": "937ffa2f05a1ad788f0de3b0f5d57497"}, {"line": 29790, "relation": "increases", "evidence": "we conclude that tau protein kinase II (cdk5/p20) can phosphorylate human tau at Thr(181), Thr(205), Thr(212), Thr(217), Ser(396) and Ser(404).", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3027, "key": "db4020ca604a1a1ec75913abc68e2b17"}, {"line": 29577, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3031, "key": "8359ece8fa5b47b6cd5905f88841ccc2"}, {"line": 29791, "relation": "increases", "evidence": "we conclude that tau protein kinase II (cdk5/p20) can phosphorylate human tau at Thr(181), Thr(205), Thr(212), Thr(217), Ser(396) and Ser(404).", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3031, "key": "d26bf10ba694fbd50b3fd41eb3f63205"}, {"line": 29578, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3032, "key": "7bb2c85089855b60e94bb219abb1a1a4"}, {"line": 29792, "relation": "increases", "evidence": "we conclude that tau protein kinase II (cdk5/p20) can phosphorylate human tau at Thr(181), Thr(205), Thr(212), Thr(217), Ser(396) and Ser(404).", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3032, "key": "172f5bee24b08ab55a35bdb00c84ba6a"}, {"line": 29579, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3033, "key": "5334f761858573c86540eb0e3e0bc814"}, {"line": 29793, "relation": "increases", "evidence": "we conclude that tau protein kinase II (cdk5/p20) can phosphorylate human tau at Thr(181), Thr(205), Thr(212), Thr(217), Ser(396) and Ser(404).", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3033, "key": "11d7264be03046dd69e3edc915f5aa2d"}, {"line": 29580, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3034, "key": "dc72c4e14d6c7faff3eb824141e9853e"}, {"line": 29794, "relation": "increases", "evidence": "we conclude that tau protein kinase II (cdk5/p20) can phosphorylate human tau at Thr(181), Thr(205), Thr(212), Thr(217), Ser(396) and Ser(404).", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3034, "key": "15a918ea7fbdd7b5cd51bc882b9e2ccc"}, {"line": 29581, "relation": "increases", "evidence": "We found that cdk5 phosphorylated tau(441) at Thr-181, Ser-199, Ser-202, Thr-205, Thr-212, Ser-214, Thr-217, Thr-231, Ser-235, Ser-396, and Ser-404, but not at Ser-262, Ser-400, Thr-403, Ser-409, Ser-413, or Ser-422.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3035, "key": "534333f23dc4da66ddd26c5fc6a646f9"}, {"line": 29637, "relation": "regulates", "evidence": "Interestingly, some of these kinase and phosphatase activities have recently merged as key regulators of fast axonal transport (FAT). Specifically, CDK5 and GSK-3 have been recently shown to regulate kinesin-driven motility. ", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 484, "key": "e0b6e2fa17dca3b3516f9f8d3cee7af4"}, {"line": 29648, "relation": "association", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3823, "key": "3cd91d0de0cb2a2cafa8636312ea74ae"}, {"line": 29783, "relation": "association", "evidence": "Hyperphosphorylated tau is an integral part of the neurofibrillary tangles that form within neuronal cell bodies, and tau protein kinase II is reported to play a role in the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2487, "target": 3823, "key": "50713b53123df7277067a6cd07f77ff9"}, {"line": 29649, "relation": "association", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3825, "key": "592843340a1152416049f88c814462f4"}, {"line": 29870, "relation": "negativeCorrelation", "evidence": "We show that alsterpaullone is able to inhibit the in vivo phosphorylation of tau at AD-specific sites by GSK-3beta and the in vivo phosphorylation of DARPP-32 in isolated striatum slices by CDK5. ", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 207, "key": "00a6d77066dac3788aafc0c7c874470d"}, {"line": 29871, "relation": "increases", "evidence": "We show that alsterpaullone is able to inhibit the in vivo phosphorylation of tau at AD-specific sites by GSK-3beta and the in vivo phosphorylation of DARPP-32 in isolated striatum slices by CDK5. ", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2487, "target": 3219, "key": "48f3579b38cf7b9302e33436b9c34d67"}, {"relation": "partOf", "source": 2487, "target": 1350, "key": "2e1f3a5f0bab6248cbee96e8453ac2fe"}, {"line": 29883, "relation": "increases", "evidence": "Here we demonstrate that cyclin dependent kinase-5/p35 (cdk5/p35) phosphorylates PS1 on threonine(354) within C-PS1 both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "12056836"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 3262, "key": "aeadaa4070355d1510caae7d5405d2f4"}, {"relation": "partOf", "source": 2487, "target": 1342, "key": "b82041d2a4785ca5956649d980cfaca0"}, {"line": 32182, "relation": "increases", "evidence": "p35/cdk5 binds and phosphorylates beta-catenin and regulates beta-catenin/presenilin-1 interaction.", "citation": {"db": "PubMed", "db_id": "11168528"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2487, "target": 1373, "key": "a91eea797e0a8b945e58ce164bae8ace"}, {"line": 33401, "relation": "association", "evidence": "This interaction is regulated by the phosphorylation of Tau at selected sites, by glycogen synthase kinase-3beta (GSK3beta) and cyclin-dependent kinase 5 (Cdk5), and requires an intact microtubule network.", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 1103, "key": "f88d752f5a7a34a5f833be15abb5fc7b"}, {"relation": "partOf", "source": 2487, "target": 1150, "key": "ad25620da1a113fdac7fe31059cb7d51"}, {"line": 34456, "relation": "increases", "evidence": "Neuron-specific phosphorylation of Alzheimer's beta-amyloid precursor protein by cyclin-dependent kinase 5.", "citation": {"db": "PubMed", "db_id": "10936190"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2487, "target": 2334, "key": "5eaa989b8a2c7f68c5b5180267152750"}, {"relation": "hasVariant", "source": 2487, "target": 2488, "key": "005d5b270710dff63024571d07a42b5a"}, {"line": 1117, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interferon signaling subgraph": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 3007, "key": "1ec5b1b220e60284f2bf225781dd5561"}, {"line": 1120, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Bcl-2 subgraph": true, "Interferon signaling subgraph": true, "Tau protein subgraph": true, "Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 3002, "key": "89d6f22e46ee0de01f80a59b041ea496"}, {"line": 1123, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "MAPK-JNK subgraph": true, "Matrix metalloproteinase subgraph": true, "Interferon signaling subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 2998, "key": "571e8c3aaf26fe69cfad5305ab2d27a3"}, {"line": 1355, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Tau protein subgraph": true, "Chemokine signaling subgraph": true}}, "source": 3015, "target": 2455, "key": "89fe0af1488cadd2441d092efe5bc8ff"}, {"line": 1423, "relation": "positiveCorrelation", "evidence": "Thus, our results indicate that hyperphosphorylation of tau protein induced by stress may represent the pathogenic event upstream of tau protein misfolding, which leads to progression or eventually initiation of neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22222439"}, "annotations": {"Confidence": {"Very High": true}}, "source": 3015, "target": 3872, "key": "a2a9e2dd8526dfa9c3e2ae99dca4cc8f"}, {"line": 8740, "relation": "association", "evidence": "Interestingly, neuronal degeneration coincides with the hyperphosphorylation of endogenous tau at several epitopes previously associated with neurofibrillary pathology. Transcriptome analysis of enzymes involved in tau phosphorylation identified ERK1 as one of the candidate kinases responsible for this event in vivo. We further demonstrate that miRNAs belonging to the miR-15 family are potent regulators of ERK1 expression in mouse neuronal cells and co-expressed with ERK1/2 in vivo. Finally, we show that miR-15a is specifically downregulated in Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "20660113"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}}, "source": 3015, "target": 3872, "key": "d44c2b25ef67e554d5501886d716d25e"}, {"line": 1431, "relation": "association", "evidence": "The data show that CRH plays an important role in stress induced hyperphosphorylation of tau protein, which might be either a direct effect of CRH innervations in the brain or an effect mediated via the hypothalamo-pituitary-adrenal axis.", "citation": {"db": "PubMed", "db_id": "22222439"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 2560, "key": "2b36747754fbb3d2f8ed668b0469f457"}, {"line": 1900, "relation": "increases", "evidence": "The pathogenic correlation between Shc/Grb2 binding to AbetaPP during AD development is supported by the observation that the complexes AbetaPP (or CTFs)/ShcA or Grb2 are significantly increased in AD brain as compared to controls [55]. The increased phosphorylation/activation of ERK1/2, often described in AD brain, is also observed in thrombin-activated astrocytes, suggesting that, in this model, ERK1/2 may be activated by AbetaPP through ShcA. These data give prominence to the biological importance of AbetaPP phosphorylation for its functions and the regulation of intracellular adaptor binding as events responsible for the induction of glial-associated mitogenic pathway. Furthermore, ERK1/2, activated by Abetain vitro, plays a role in AbetaPP processing and phosphorylates Tau in a PHF-Tau similar manner. However, it is conceivable that a different signaling Abeta-independent might as well activate tau phosphorylation by ERK1/2 via the intracellular signaling regulated by the AbetaPP/CTFs-Shc-Grb2 pathway", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "6dd546e9f683906d5027d63754345a44"}, {"line": 2522, "relation": "positiveCorrelation", "evidence": "Two possible models for involvement of HRD1 in the pathogenesis of AD. Model 1 (cause of AD): Unknown stress initiates insolubilization of HRD1 protein, resulting in a decrease in the functional HRD1 protein in the ER membrane. Subsequently, APP accumulates in the ER and is processed into Abeta that induces hyperphosphorylation of tau protein (ptau). Finally, accumulated Abeta and/or p-tau causes neurodegeneration leading to AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "ec0bbc20fb532ca3bc0ea1ef629e8b41"}, {"line": 3628, "relation": "association", "evidence": "Amyloid beta-peptide 1-42 (Abeta(1-42)) and hyperphosphorylated tubulin associated unit (tau) isoforms appear to be the most sensitive and specific CSF biomarkers, the combination of these biomarkers depicting the best diagnosis value for AD.", "citation": {"db": "PubMed", "db_id": "18584921"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3823, "key": "a59dfc21fb46248a5e2b9ae340870f0d"}, {"line": 3901, "relation": "increases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "a72c14732c93d29526e52725479e1441"}, {"line": 6004, "relation": "increases", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3015, "target": 3823, "key": "0f29bd5ac54f1703a27841be9cc47abd"}, {"line": 9002, "relation": "positiveCorrelation", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "213d6504ba9f3ff67873ebf494580ed6"}, {"line": 9146, "relation": "increases", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3015, "target": 3823, "key": "6f148540378b1e06827df60ecbe0d47d"}, {"line": 19149, "relation": "association", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "06a1d002f86774a1a4be8d3d649e0caf"}, {"line": 27305, "relation": "association", "evidence": "Accumulation of amyloid-beta (Abeta) peptide and deposition of hyperphosphorylated tau protein are two major pathological hallmarks of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3823, "key": "965a605dad7c596ca5de449c1a17eac6"}, {"line": 28990, "relation": "association", "evidence": "Intraneuronal accumulation of phosphorylated Tau protein is a molecular pathology found in many forms of dementia, including Alzheimer disease. Research into possible mechanisms leading to the accumulation of pmodified Tau protein and the possibility of removing Tau protein from the system have revealed that the chaperone protein system can interact with Tau and mediate its degradation. ", "citation": {"db": "PubMed", "db_id": "17954934"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3823, "key": "1b55204da489f5dbb75381d650f32a0f"}, {"line": 29650, "relation": "association", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 3015, "target": 3823, "key": "113594bd4c2da492b5d76af3a45d4aa1"}, {"line": 29660, "relation": "positiveCorrelation", "evidence": "Microtubule associated protein tau is abnormally hyperphosphorylated in Alzheimer disease (AD) brain.", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "8b1ee750c308211298174bcd4481f17a"}, {"line": 29814, "relation": "association", "evidence": "One of the histopathological markers in Alzheimer's disease is the accumulation of hyperphosphorylated tau in neurons called neurofibrillary tangles (NFT) composing paired helical filaments (PHF). ", "citation": {"db": "PubMed", "db_id": "9565682"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "50bae60b9708ae7538f1fe651123829a"}, {"line": 31915, "relation": "association", "evidence": "Within the neurofibrillary tangles (NFTs) and dystrophic neurites (DNs) of Alzheimer's disease (AD), the cytoskeletal protein tau is abnormally hyperphosphorylated.", "citation": {"db": "PubMed", "db_id": "7533559"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "d0eae16c7cee40f924bf8799e34aa3e4"}, {"line": 31927, "relation": "association", "evidence": "The microtubule-associated protein tau is more highly phosphorylated at certain residues in developing brain and in Alzheimer's disease paired helical filaments than in adult brain.", "citation": {"db": "PubMed", "db_id": "8730715"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 3823, "key": "29eff81cc4e4707264625e27913925a0"}, {"line": 32820, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3823, "key": "b044585089c5611a6a9ce3d7974036ad"}, {"line": 2193, "relation": "decreases", "evidence": "Increasing observations suggest that aberrant activation of cell cycle may affect the formation of neurofibrillary tangles with hyperphosphorylation of Tau protein in AD brain. It is well known that p25/cdk5 complex hyperphosphorylates Tau and reduces its ability to associate with microtubules.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 991, "key": "38acea175bed86f96cebf8b1cda6a977"}, {"line": 3578, "relation": "association", "evidence": "Yet several studies have demonstrated that oligomeric Abeta affects the cellular cholesterol level, which in turn has a variety of effects on AD related pathologies, including modulation of tau phosphorylation, synapse formation and maintenance of its function, and the neurodegenerative process.", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 231, "key": "f35aea970178d587b831d6028f13beef"}, {"line": 3903, "relation": "increases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Cell Body": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 889, "key": "68b26c3c8992ce6fa40fbfc5212357d5"}, {"line": 7069, "relation": "increases", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 3015, "target": 889, "key": "51c10fa2ec7dc102b0d7a397a3eefd91"}, {"line": 8201, "relation": "increases", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3015, "target": 889, "key": "265cca7c55cc12e3d0b1f381773cc77b"}, {"line": 9003, "relation": "increases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 889, "key": "98635d1751883ae0b662ded04e97f440"}, {"line": 9302, "relation": "association", "evidence": "Hyperphosphorylated tau protein is the basic structural component of the neurofibrillary tangle, a histopathological hallmark of Alzheimer's disease. The formation of hyperphosphorylated tau protein may impair learning and the synaptic plasticity of neurons. Tau is a protein that is associated with and stabilizes microtubules; hyperphosphorylated tau protein is unable to perform this stabilization function.", "citation": {"db": "PubMed", "db_id": "16504486"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 889, "key": "14654aabf4f2b0138915c43b064d8ffa"}, {"line": 33110, "relation": "increases", "evidence": "Glycogen synthase kinase-3beta (GSK-3beta) and cyclin-dependent kinase 5 (CDK5) have been implicated as two major protein kinases involved in the abnormal hyperphosphorylation of tau in Alzheimer's disease (AD) brain, and the development of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19154537"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 3015, "target": 889, "key": "aaa649226cbcdf4525487c6bcfe5d0a1"}, {"line": 39190, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Tau protein subgraph": true}, "Confidence": {"Very High": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "source": 3015, "target": 889, "key": "a8f820ac168966b83b835ae1bd064aa9"}, {"line": 3909, "relation": "increases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}, "MeSHAnatomy": {"Synapses": true}}, "source": 3015, "target": 2328, "key": "cea2724d75952b3f9534b805fcd3c3b4"}, {"line": 3910, "relation": "increases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}, "MeSHAnatomy": {"Synapses": true}}, "source": 3015, "target": 2327, "key": "10dcc820eaa839ea43cddaa42badcc06"}, {"line": 3912, "relation": "directlyDecreases", "evidence": "During the course of AD tau becomes hyperphosphorylated and dissociates from microtubules which then depolymerize. The hyperphosphorylated tau self-aggregates and accumulates in the cell body where it forms paired-helical filaments (neurofibrillary tangles). As a consequence of accumulation of Abeta at synapses, Ca2+ regulation is impaired", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Tau protein subgraph": true}, "MeSHAnatomy": {"Synapses": true}}, "source": 3015, "target": 739, "key": "21943a44ef55de032a51c09da674b4b5"}, {"line": 6005, "relation": "association", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3015, "target": 3861, "key": "3c0d97e4111a9a1c25c3af309bd63d9d"}, {"line": 8240, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3015, "target": 3861, "key": "06fa4b1319f9e465e6c2b1963df83ef3"}, {"line": 6006, "relation": "association", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3015, "target": 263, "key": "0bcc6eab9c84dc72c766acad93a55024"}, {"line": 6041, "relation": "increases", "evidence": "We employed two cell lines, wild-type HEK293 cells and HEK293 cells stably expressing the longest human tau isoform (tau-441; HEK293/tau441 cells). We examined whether DEX, a synthetic GCs, induces tau phosphorylation and mu-calpain activation. If so, we examined whether the DEX-induced tau phosphorylation and mu-calpain activation mediate the DEX-induced inhibition on the insulin-stimulated Akt phosphorylation. The results showed that DEX increased tau phosphorylation and induced tau-mediated mu-calpain activation.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "CellLine": {"HEK293": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 2428, "key": "15711e0bb88550ca9bd1f094edd13d5e"}, {"line": 6054, "relation": "regulates", "evidence": "Finally, both LiCl pre-treatment and calpain inhibition prevented the DEX-induced inhibition on the insulin-stimulated Akt phosphorylation. In conclusion, our study suggests that the tau phosphorylation and calpain activation mediate the EX-induced inhibition on the insulin-stimulated Akt phosphorylation.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 2154, "key": "0f0b5986142fb52d72f79a1208181e8b"}, {"line": 7681, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 1467, "key": "497a86d9b973d8aaaecbecc3518692f0"}, {"line": 7707, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 3223, "key": "afab29c663a5665b3ccb19b630593c37"}, {"line": 7708, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 2794, "key": "efa919f0a7dd63e6c586507015a6aa9e"}, {"line": 30660, "relation": "increases", "evidence": "Since prior phosphorylation of tau by TPKII strongly enhanced the action of TPKI, it was thought that TPKII was involved in the formation of PHF-tau in concert with TPKI.", "citation": {"db": "PubMed", "db_id": "9089387"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 2794, "key": "1090c8db3198d97671f85b324a6384b7"}, {"line": 7989, "relation": "association", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 1448, "key": "950090f28473424d3c8890fea1636acb"}, {"line": 8202, "relation": "association", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3015, "target": 629, "key": "dae98791fa7c993543724e01e1f1a22c"}, {"line": 8243, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3015, "target": 3915, "key": "bd01bd7bd07ba93d1eaffdeba7505b91"}, {"line": 9147, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3015, "target": 2085, "key": "36a98f72f1afdbe950fd0987180a44a5"}, {"line": 9149, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3015, "target": 2097, "key": "cb31160bd0f590d8793a19568f9adea4"}, {"line": 9304, "relation": "decreases", "evidence": "Hyperphosphorylated tau protein is the basic structural component of the neurofibrillary tangle, a histopathological hallmark of Alzheimer's disease. The formation of hyperphosphorylated tau protein may impair learning and the synaptic plasticity of neurons. Tau is a protein that is associated with and stabilizes microtubules; hyperphosphorylated tau protein is unable to perform this stabilization function.", "citation": {"db": "PubMed", "db_id": "16504486"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 818, "key": "c20d976b60688dcd6d201c43b21952a8"}, {"line": 9305, "relation": "decreases", "evidence": "Hyperphosphorylated tau protein is the basic structural component of the neurofibrillary tangle, a histopathological hallmark of Alzheimer's disease. The formation of hyperphosphorylated tau protein may impair learning and the synaptic plasticity of neurons. Tau is a protein that is associated with and stabilizes microtubules; hyperphosphorylated tau protein is unable to perform this stabilization function.", "citation": {"db": "PubMed", "db_id": "16504486"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 761, "key": "1394cb8ae93dc858de48f5bdda5d71ca"}, {"line": 9306, "relation": "increases", "evidence": "Hyperphosphorylated tau protein is the basic structural component of the neurofibrillary tangle, a histopathological hallmark of Alzheimer's disease. The formation of hyperphosphorylated tau protein may impair learning and the synaptic plasticity of neurons. Tau is a protein that is associated with and stabilizes microtubules; hyperphosphorylated tau protein is unable to perform this stabilization function.", "citation": {"db": "PubMed", "db_id": "16504486"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 632, "key": "fdaf7aac7ff5896b9374abc6975d7d71"}, {"line": 9825, "relation": "association", "evidence": "This decrease in insulin-PI3K-AKT signalling could lead to activation of glycogen synthase kinase-3beta, the major tau kinase.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}}, "source": 3015, "target": 764, "key": "99257a2e1d21580fadffab0dc706c223"}, {"line": 9843, "relation": "negativeCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3015, "target": 2279, "key": "242d7a34534d658b0491f7a3c735d4be"}, {"line": 9844, "relation": "negativeCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3015, "target": 890, "key": "7c76cd0464efef1a7b363bf2d83bab1d"}, {"line": 9845, "relation": "negativeCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3015, "target": 2899, "key": "8b1b520006f7c622e71d1a9af6b29eb6"}, {"line": 10120, "relation": "association", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 3015, "target": 2899, "key": "f0c0e3be7bfc96337eebceb6bd917de2"}, {"line": 10985, "relation": "association", "evidence": "Phosphorylation of tau at some of the AD abnormal hyperphosphorylation sites was increased in T2DM brain.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3850, "key": "e123117c820bd078f0612defa2b5a48f"}, {"line": 10997, "relation": "positiveCorrelation", "evidence": "These results suggest that T2DM may contribute to the increased risk for AD by impairing brain glucose uptake/metabolism and, consequently, down-regulation of O-GlcNAcylation, which facilitates abnormal hyperphosphorylation of tau.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3015, "target": 3850, "key": "dfd6260380452533e462320c54e35b09"}, {"line": 11180, "relation": "negativeCorrelation", "evidence": "CSF levels of total but not free IgG autoAbs against galanin were increased in AD, resulting in increased percentage of galanin autoAbs present as immune complexes. CSF levels of galanin total autoAbs and α-MSH free autoAbs correlated negatively with the severity of cognitive impairment as measured by MMSE. Both total and free autoAbs against galanin and α-MSH in CSF correlated negatively with age in AD patients but not in controls. CSF levels of galanin autoAbs and free α-MSH AutoAbs negatively correlated with CSF levels of t-Tau, p-Tau and ratios of t-Tau/Abeta42 or p-Tau/Abeta42 in AD patients but not in controls.", "citation": {"db": "PubMed", "db_id": "22078238"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}, "Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Tau protein subgraph": true}}, "source": 3015, "target": 2737, "key": "8836661ce105ac0a99cde67ae27e8a71"}, {"line": 13876, "relation": "association", "evidence": "Both inhibitors accumulated in the cytoplasm of nerve cells, the majority of which contained inclusions made of hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 3015, "target": 2493, "key": "5aba8919be12436d5340420a649943a2"}, {"line": 13877, "relation": "association", "evidence": "Both inhibitors accumulated in the cytoplasm of nerve cells, the majority of which contained inclusions made of hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 3015, "target": 2494, "key": "682166eeac4a2cb4bfdffcca04c56a55"}, {"line": 17356, "relation": "association", "evidence": "Early growth response 1 (Egr-1) regulates phosphorylation of microtubule-associated protein tau in mammalian brain.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 2658, "key": "f9e827704cfa6861a0f0746c8b851450"}, {"line": 19148, "relation": "association", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 3015, "target": 3300, "key": "9f64b02523e58e7e405ee2e8ea815bcd"}, {"line": 25106, "relation": "association", "evidence": "Our studies indicate that GRK2 is a novel component of neuronal and glial fibrillary tau deposits with no preference in tau isoform binding. GRK2 may play a role in hyperphosphorylation of tau in tauopathies.", "citation": {"db": "PubMed", "db_id": "17146290"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 2786, "key": "76d00ade077af4057ee310a7db15db71"}, {"line": 25764, "relation": "association", "evidence": "Our studies indicate that GRK2 is a novel component of neuronal and glial fibrillary tau deposits with no preference in tau isoform binding. GRK2 may play a role in hyperphosphorylation of tau in tauopathies", "citation": {"db": "PubMed", "db_id": "17146290"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 2786, "key": "b0e199f8239eb0e35ef4d120d05f9ce1"}, {"line": 27320, "relation": "association", "evidence": "Leptin, an adipocytokine involved in cell survival and in learning, has been demonstrated to regulate Abeta production and tau hyperphosphorylation in transgenic mice for AD. ", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 2961, "key": "8ff02ff902ab80acc30c7057424963b8"}, {"line": 29252, "relation": "association", "evidence": "We have shown that interaction of CD40 with CD40L enables microglial activation in response to amyloid-beta peptide (Abeta), which is associated with Alzheimer's disease (AD)-like neuronal tau hyperphosphorylation in vivo.", "citation": {"db": "PubMed", "db_id": "12402041"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true, "Tau protein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3015, "target": 80, "key": "b5da08e5f76404880a7e36a906aae0f3"}, {"line": 33502, "relation": "positiveCorrelation", "evidence": "Compared to vehicle, Abeta increased GSK3 activity, and was associated with elevations in levels of ptau, caspase-3, the tau kinase phospho-c-jun N-terminal kinase (pJNK), neuronal DNA fragmentation, and gliosis.", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 80, "key": "d632bbb18c8595218d0adbf34c416aaa"}, {"line": 29651, "relation": "association", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 3015, "target": 3825, "key": "107caa9327e431061b69a900404c956a"}, {"line": 30030, "relation": "decreases", "evidence": "These results reveal an antiapoptotic function of tau hyperphosphorylation, which likely inhibits competitively phosphorylation of beta-catenin by GSK-3beta and hence facilitates the function of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360687"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3015, "target": 478, "key": "2e49f7ed0cff368a813b16700d8ae1fc"}, {"line": 30040, "relation": "decreases", "evidence": "These results reveal an antiapoptotic function of tau hyperphosphorylation, which likely inhibits competitively phosphorylation of beta-catenin by GSK-3beta and hence facilitates the function of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360687"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 2581, "key": "3595c262554831a51ed2eb1d9f17de74"}, {"line": 30427, "relation": "decreases", "evidence": "We previously showed that thrombin proteolyses the microtubule-associated protein tau and that phosphorylation of tau inhibits this process.", "citation": {"db": "PubMed", "db_id": "16410745"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3015, "target": 2683, "key": "fd8f9e62659c97e15c2128a15cb81fe0"}, {"relation": "partOf", "source": 3015, "target": 1431, "key": "fd1f6cf1b7c1950265edf6042525ebbf"}, {"relation": "partOf", "source": 3015, "target": 1563, "key": "cef6aa8f410465ff1c8cf3fc85830f97"}, {"relation": "partOf", "source": 3015, "target": 1562, "key": "ff2ce45b608a9d030af331a0c197f4b7"}, {"relation": "partOf", "source": 3015, "target": 1548, "key": "455ace3955afeb26b417c6e25541ef98"}, {"line": 32821, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3877, "key": "1397c208f7025495097a31238c332712"}, {"line": 32822, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3818, "key": "4aefc91dc3b8c7767d45154a40bb16bc"}, {"line": 32823, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3811, "key": "96336a4b69d731c4ee1e2049bba766ba"}, {"line": 32824, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3878, "key": "651b7a56ebe2f5c0f83f496ed6a412a6"}, {"line": 33395, "relation": "association", "evidence": "This interaction is regulated by the phosphorylation of Tau at selected sites, by glycogen synthase kinase-3beta (GSK3beta) and cyclin-dependent kinase 5 (Cdk5), and requires an intact microtubule network.", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 1103, "key": "f32ed51e69f1e2b3d2640aecd747730c"}, {"line": 33806, "relation": "association", "evidence": "Apolipoprotein E (ApoE) peptide regulates tau phosphorylation via two different signaling pathways.", "citation": {"db": "PubMed", "db_id": "9512010"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"APOE subgraph": true, "Tau protein subgraph": true}}, "source": 3015, "target": 2312, "key": "5753b5c8d072b6e2c969315fc44d4eb0"}, {"relation": "partOf", "source": 3015, "target": 1564, "key": "d3dc3e2dea6539fbcd29f5d16a19d57e"}, {"line": 34555, "relation": "decreases", "evidence": "Abnormally phosphorylated tau extracted from AD tissue displayed a dramatically reduced capacity to bind S100beta, which was restored by pretreatment with alkaline phosphatase. ", "citation": {"db": "PubMed", "db_id": "11264299"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "source": 3015, "target": 1558, "key": "13be2892d6d628f3adf8568fe8590f6f"}, {"line": 35637, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 645, "key": "2b49aaffb09ac64d643b55e393f8729a"}, {"line": 35644, "relation": "decreases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 820, "key": "fc874cae20e93b7d192fd6bc5a658118"}, {"line": 35815, "relation": "association", "evidence": "The state of tau phosphorylation and proteolysis can be regulated by calcium-dependent mechanisms. CaMKII can phosphorylate tau [189]. Cyclin-dependent kinase 5 (cdk5), another kinase involved in tau phosphorylation [190], is indirectly activated by the calcium-activated protease calpain. Indeed, cdk5 has to be associated with its regulatory subunit, p35 to be activated. Conversion of p35 to p25 deregulates cdk5 activity, resulting in an increased cdk5 kinase activity [191]. Calpain cleaves p35 into p25, and thus controls cdk5 activation [192]. Furthermore, tau is dephosphorylated by the calcium/calmodulin-dependent phosphatase, calcineurin [193]. Calpain was also proposed to directly participate in tau proteolysis and degradation", "citation": {"db": "PubMed", "db_id": "19419557"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Calcium-dependent signal transduction": true, "Tau protein subgraph": true}}, "source": 3015, "target": 495, "key": "66af837fcc4d82e35f0426de4e7d052f"}, {"line": 38788, "relation": "negativeCorrelation", "evidence": "Quantitative measures of ERK2 mRNA reveal that NFT-bearing neurons contain approximately 15% less ERK2 mRNA than nearest neighbors that do not contain NFT. NFT-bearing neurons contain approximately 25% less polyA mRNA, suggesting a relative preservation of ERK2 mRNA even in metabolically compromised cells.", "citation": {"db": "PubMed", "db_id": "8129042"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}}, "source": 3015, "target": 3990, "key": "b9e93edddf3566cb0aa15c16e36321cc"}, {"line": 39167, "relation": "negativeCorrelation", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Very High": true}}, "source": 3015, "target": 2580, "key": "99d4dfbc71a671eb31f4f81587c741fc"}, {"line": 49108, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 3463, "key": "eb2203c493299d89ea2280127a3d5abf"}, {"line": 49122, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3015, "target": 2194, "key": "b8edf2d1d19232275df52302968a8bcf"}, {"line": 49473, "relation": "association", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3015, "target": 3224, "key": "d730d650aa03c1ad93916e4e16632447"}, {"line": 49478, "relation": "biomarkerFor", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "source": 3015, "target": 3874, "key": "adabfbd6205a12d608b2d7ffc50e98b9"}, {"line": 9544, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 540, "target": 3522, "key": "83cb58a18641f6a3f2930172dfa3248c"}, {"line": 9545, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 540, "target": 2746, "key": "aa71156f3f6ecc66e93e47e710475c12"}, {"line": 9546, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 540, "target": 3488, "key": "9ebc625e898af1c80bf0a35e28678838"}, {"line": 9554, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"Medium": true}}, "source": 540, "target": 2641, "key": "1cd23aac018eab93823f3150ed0c4f14"}, {"line": 9562, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 540, "target": 3336, "key": "7b9cd4e89fc1a5866c82e9684d2aaf56"}, {"line": 9596, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 540, "target": 2292, "key": "36e9395187d6c0d645aa8a6dda54e4b8"}, {"line": 9600, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 540, "target": 2898, "key": "7e2a5343b58dfb999a392346491eb330"}, {"line": 9603, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Synuclein subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 540, "target": 3384, "key": "7d2c0e59341b5e1acd6bd2104fbd540e"}, {"line": 9604, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Synuclein subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 540, "target": 3387, "key": "49f599db5c2ba9c034dda1e1b3fd7c00"}, {"line": 9606, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 540, "target": 2565, "key": "62b951d8003ff59893ac1de75e931eb0"}, {"line": 9609, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 540, "target": 2599, "key": "de1de528a8485b390e46fadc8a2b885c"}, {"line": 9610, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 540, "target": 3513, "key": "66f63360d56f2b200eb2f64571280999"}, {"line": 9611, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 540, "target": 3471, "key": "2ce3fdfa91f4f2a612a8e56f8daf01a5"}, {"line": 622, "relation": "directlyIncreases", "evidence": "In Alzheimer disease, it has been proposed that the peptide beta amyloid promotes GSK3 activation, resulting in tau phosphorylation", "citation": {"db": "PubMed", "db_id": "19782073"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Tau protein subgraph": true, "Amyloidogenic subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2178, "target": 3015, "key": "736e30dfa4cc2fc863984c1752bfcc1f"}, {"line": 21771, "relation": "increases", "evidence": "Recent findings from our and other groups have suggested glycogen synthase kinase 3 and p70 S6 kinase as main tau kinases and protein phosphatase 2A as the main tau phosphatase involved in the formation of these processes.", "citation": {"db": "PubMed", "db_id": "18852562"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2178, "target": 3015, "key": "33c6b11c6253bc657093e7cb9e9016ab"}, {"line": 29629, "relation": "increases", "evidence": "A large body of biochemical, genetic, and cell biological evidence implicate two major serine-threonine protein kinases, glycogen synthase kinase 3 (GSK-3) and cyclin-dependent kinase 5 (CDK5) as major kinases responsible for both normal and pathological phosphorylation of tau protein in vivo.", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2178, "target": 3015, "key": "33ca80c8250ed04179faecf958c9688b"}, {"line": 4630, "relation": "regulates", "evidence": "The availability of megalin at the cell surface is controlled by several regulatory mechanisms, including the phosphorylation of its cytoplasmic domain by GSK3, the proteolysis of the extracellular domain at the cell surface (shedding), the subsequent intramembrane proteolysis of the transmembrane domain by the gamma-secretase complex, and exosome secretion", "citation": {"db": "PubMed", "db_id": "21720686"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2178, "target": 2975, "key": "6bcdfb4926a258e51c3b9bdac92979fa"}, {"line": 10925, "relation": "increases", "evidence": "As a constitutively active kinase, glycogen synthase kinase 3 (GSK3) is a kinase which regulates body metabolism by phosphorylation of glycogen synthase (GS) and other substrates.", "citation": {"db": "PubMed", "db_id": "22718609"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2178, "target": 2802, "key": "763e4a85b53c77b8954c42a6c1f9f592"}, {"line": 10950, "relation": "association", "evidence": "The neuroprotective effects of novel drugs developed to treat T2DM, glucagon-like peptide 1 (GLP-1) and its long-lasting analogs, have a possible link to GSK3 modification.", "citation": {"db": "PubMed", "db_id": "22718609"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Glucagon subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2178, "target": 2742, "key": "2f0a7f0b21d20ce361b0de32025187a4"}, {"relation": "hasVariant", "source": 2178, "target": 2179, "key": "c4c6e57bae79387e0120f8e3cc61c451"}, {"line": 29636, "relation": "regulates", "evidence": "Interestingly, some of these kinase and phosphatase activities have recently merged as key regulators of fast axonal transport (FAT). Specifically, CDK5 and GSK-3 have been recently shown to regulate kinesin-driven motility. ", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2178, "target": 484, "key": "9aac434b0910927c33d59985a47d3e38"}, {"line": 33512, "relation": "association", "evidence": "The dysregulation of glycogen synthase kinase-3 (GSK3) has been implicated in Alzheimer disease (AD) pathogenesis and in Abeta-induced neurotoxicity, leading us to investigate it as a therapeutic target in an intracerebroventricular Abeta infusion pmodel. ", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 2178, "target": 3823, "key": "902c9172f1c065915489ccc170a290c2"}, {"line": 33516, "relation": "increases", "evidence": "The dysregulation of glycogen synthase kinase-3 (GSK3) has been implicated in Alzheimer disease (AD) pathogenesis and in Abeta-induced neurotoxicity, leading us to investigate it as a therapeutic target in an intracerebroventricular Abeta infusion pmodel. ", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2178, "target": 2316, "key": "b47e049f933560d4f7ce6a55c6358d39"}, {"line": 639, "relation": "directlyIncreases", "evidence": "Two main protein kinases have been shown to be involved in anomalous tau phosphorylations: the cyclin-dependent kinase Cdk5 and glycogen synthase kinase GSK3beta", "citation": {"db": "PubMed", "db_id": "11578751"}, "annotations": {"Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "b2db8b3a557f796ef38b6851c2e14da8"}, {"line": 2811, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-b, may also contribute to AICD-related cytotoxicity by up-regulating tau hyperphosphorylation. GSK-3b activation and collapsin response mediator protein 2 (CRMP2) phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "85f76ba383d816dd25e9864f3aa523de"}, {"line": 5474, "relation": "increases", "evidence": "Akt substrates such as mammalian target of rapamycin (mTOR; Ser2448) and decreased levels of cell-cycle inhibitors (p27kip1) are found in AD temporal cortex when compared to controls. GSK-3a has been implicated in the production of Abeta peptide while increased GSK-3beta activity has been implicated in tau hyperphosphorylation and neuronal cell death", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "4219ca8bef101ed4da4796f7e159395f"}, {"line": 7708, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "0c8921f416ca029ef19f2437dce502d0"}, {"line": 7900, "relation": "increases", "evidence": "The phosphorylation of tau is mainly promoted by GSK-3and cyclin-dependent kinase 5 (Cdk5). Besides these kinases, activated c-Jun N-terminal kinases (JNK) and ERK-1 /-2 signaling lead to an increase in tau phosphorylation and th erefore might be of importance in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "04006fd5065a823cd32d06ea27cc2b65"}, {"line": 9041, "relation": "directlyIncreases", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "36ce4b5565c70a9a3d3d3bf38fb0ac5e"}, {"line": 10933, "relation": "increases", "evidence": "The inhibition of this kinase can prevent the aggregation of beta-amyloid (Abeta) and hyperphosphorylation of tau protein.", "citation": {"db": "PubMed", "db_id": "22718609"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "b4b1745ad522dcf89d85a1fa9fe028d6"}, {"line": 11509, "relation": "increases", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Leptin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2794, "target": 3015, "key": "f489dafc5b51a143f0c7d3b5ef7c7226"}, {"line": 12492, "relation": "increases", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2794, "target": 3015, "key": "8016114c904d01074f2b9d6ee9c88ae1"}, {"line": 12645, "relation": "increases", "evidence": "Here we describe results that show that although MAP kinase can hyperphosphorylate tau in vitro, activation of MAP kinase in transformed fibroblasts does not result in hyperphosphorylation of transfected tau, whereas glycogen synthase kinase-3 beta (GSK-3 beta) when co-transfected with tau does result in tau hyperphosphorylation. The findings imply that GSK-3 beta may be a stronger candidate than MAP kinase for inducing tau hyperphosphorylation in vivo.", "citation": {"db": "PubMed", "db_id": "7774712"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2794, "target": 3015, "key": "8a9277b71f36740b970d22943b4efe41"}, {"line": 28000, "relation": "directlyIncreases", "evidence": "GSK3B is known to phosphorylate the microtubule protein tau (MAPT).", "citation": {"db": "PubMed", "db_id": "16973241"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "c732a710316b0dc5d2cfd1657f04db2a"}, {"line": 29694, "relation": "increases", "evidence": "These studies suggest that PKA, cdk5, CaM Kinase II and GSK-3 are involved in the regulation of phosphorylation of tau and that AD-type phosphorylation of tau is probably a product of the synergistic action of two or more of these kinases.", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "a9c002de24b9b0a46e2ca0d220524ba9"}, {"line": 29710, "relation": "increases", "evidence": "Indirubins inhibit glycogen synthase kinase-3 beta and CDK5/p25, two protein kinases involved in abnormal tau phosphorylation in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11013232"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "202753aeee5591f8bd4b4efb0743875f"}, {"line": 29723, "relation": "increases", "evidence": "A number of kinases, including mitogen-activated protein (MAP) kinase, glycogen synthase kinase (GSK)-3 alpha, GSK-3 beta and cyclin-dependent kinase-5, phosphorylate recombinant tau in vitro so that it resembles PHF-tau as judged by its reactivity with a panel of antibodies capable of discriminating between normal tau and PHF-tau, and by a reduced electrophoretic mobility that is characteristic of PHF-tau.", "citation": {"db": "PubMed", "db_id": "7704571"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "5faca85e859bb5ad3eac0c5c24afae33"}, {"line": 29754, "relation": "increases", "evidence": "Phosphorylation of human tau protein by microtubule-associated kinases: GSK3beta and cdk5 are key participants.", "citation": {"db": "PubMed", "db_id": "11054815"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "e61619b2a15697f9ed279d7299a320e8"}, {"line": 29766, "relation": "increases", "evidence": "Phosphorylation of tau protein is regulated by several kinases, especially glycogen synthase kinase 3beta (GSK-3beta), cyclin-dependent protein kinase 5 (cdk5) and cAMP-dependent protein kinase (PKA).", "citation": {"db": "PubMed", "db_id": "17078951"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "009b05da71959565116eb98900fbddb3"}, {"line": 29822, "relation": "increases", "evidence": "Combined tau protein kinase II (TPK II), which consists of CDK5 and its activator (p23), and glycogen synthase kinase-3beta (GSK-3beta) phosphorylate tau to the PHF-form in vitro.", "citation": {"db": "PubMed", "db_id": "9565682"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "7fbf82925bb734aea858d2cf34b9ef61"}, {"line": 29872, "relation": "increases", "evidence": "We show that alsterpaullone is able to inhibit the in vivo phosphorylation of tau at AD-specific sites by GSK-3beta and the in vivo phosphorylation of DARPP-32 in isolated striatum slices by CDK5. ", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "b945e826e5dca9db21303ae53ff47621"}, {"line": 30073, "relation": "increases", "evidence": "Thirdly, GSK-3beta has many substrates, such as beta-catenin, APC, Axin, Tau protein, etc.", "citation": {"db": "PubMed", "db_id": "21352912"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "4ab739e86c8323037e3162b26ae53c32"}, {"line": 30579, "relation": "increases", "evidence": "In experimental pmodels of AD, GSK3B has been shown to hyperphosphorylate Tau, leading to microtubule disassembly and loss of function", "citation": {"db": "PubMed", "db_id": "20576277"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "d66f5ee0065ff7093e916de3334b3bcc"}, {"line": 30588, "relation": "increases", "evidence": "Glycogen synthase kinase 3beta (GSK3beta) is a proline-directed kinase generally considered as one of the major players that (hyper)phosphorylates Tau.", "citation": {"db": "PubMed", "db_id": "20679343"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "740cd51f31068169b89771fcf5098920"}, {"line": 30597, "relation": "increases", "evidence": "The results provide initial evidence that TPKI/GSK-3 beta/FA after heparin potentiation may represent one of the most potent systems possibly involved in the abnormal phosphorylation of PHF-tau and neuronal degeneration in Alzheimer disease brains.", "citation": {"db": "PubMed", "db_id": "7786411"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "caa4200f2aa866131ad3ac8e4d456162"}, {"line": 30606, "relation": "increases", "evidence": "A mixture of recombinant human tau isoforms phosphorylated by GSK-3 beta gave similar results to those obtained with control human brain tau.", "citation": {"db": "PubMed", "db_id": "8050597"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "dc3b3833723e9bd81d829d9790416037"}, {"line": 30638, "relation": "increases", "evidence": "Glycogen synthase kinase-3beta phosphorylates protein tau and rescues the axonopathy in the central nervous system of human four-repeat tau transgenic mice.", "citation": {"db": "PubMed", "db_id": "11007782"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "5ca5bddd96be161d7f35d36dcc6171b5"}, {"line": 30647, "relation": "increases", "evidence": "Recent studies have shown that GSK-3beta phosphorylates the microtubule-associated protein tau in vitro and in cell culture.", "citation": {"db": "PubMed", "db_id": "10486203"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "3433d452fa1e164ec594fdbcc2311c36"}, {"line": 30678, "relation": "increases", "evidence": "Colocalization of these epitopes suggests that tau protein kinase I/glycogen synthase kinase-3 beta abnormally phosphorylates tau and is in a position to disrupt neuronal metabolism in anatomical areas vulnerable to Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "8930358"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "69329b0832f51377329b226d189e5645"}, {"line": 30700, "relation": "increases", "evidence": "Two protein kinases, tau protein kinase I (TPK I or GSK 3beta) and tau protein kinase II (TPK II; cdk5/p20), have been isolated from bovine brain microtubules", "citation": {"db": "PubMed", "db_id": "11181841"}, "annotations": {"Confidence": {"High": true}, "Species": {"9913": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "b18e6844922c8f9bd9a7f6a584f8e333"}, {"line": 30711, "relation": "increases", "evidence": "In situ, FRAT-2 significantly increased GSK3 beta-mediated phosphorylation of tau at a primed epitope while not significantly affecting the phosphorylation of unprimed sites.", "citation": {"db": "PubMed", "db_id": "15522877"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "66c9458af8ed6c56dd2c3544b061a31d"}, {"line": 32737, "relation": "increases", "evidence": "Several kinases, such as glycogen synthase kinase 3 beta (GSK3beta) and c-Jun N-terminal kinase (JNK), phosphorylate tau at sites that are phosphorylated in PHF.", "citation": {"db": "PubMed", "db_id": "11803455"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "e339e6c671d82626cde56e588dbc8186"}, {"line": 33040, "relation": "increases", "evidence": "Aberrant tau phosphorylation by glycogen synthase kinase-3beta and JNK3 induces oligomeric tau fibrils in COS-7 cells.", "citation": {"db": "PubMed", "db_id": "12191990"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "5cbd84c3ac315c4fdc427580e32ff41f"}, {"line": 33051, "relation": "increases", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "b57830737650ca08dd0db24dd0bdbfcf"}, {"line": 33109, "relation": "increases", "evidence": "Glycogen synthase kinase-3beta (GSK-3beta) and cyclin-dependent kinase 5 (CDK5) have been implicated as two major protein kinases involved in the abnormal hyperphosphorylation of tau in Alzheimer's disease (AD) brain, and the development of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19154537"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "a4f130af126d30f12afe746230665fa3"}, {"line": 33397, "relation": "increases", "evidence": "This interaction is regulated by the phosphorylation of Tau at selected sites, by glycogen synthase kinase-3beta (GSK3beta) and cyclin-dependent kinase 5 (Cdk5), and requires an intact microtubule network.", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "1f55d70645addf839770aa7d994c5a0f"}, {"line": 33740, "relation": "increases", "evidence": "We found that betaE2 could attenuate tau hyperphosphorylation at multiple AD-related sites, including Ser396/404, Thr231, Thr205, and Ser199/202, induced by Wort/GFX or transient overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Estrogen subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3015, "key": "7fc7d2809ca409a776671615b9c30302"}, {"relation": "partOf", "source": 2794, "target": 1016, "key": "b32c36ca1a35126ef555e6ca6abbbb96"}, {"line": 4518, "relation": "increases", "evidence": "In this study, we found the AICD to strongly inhibit Wnt-induced transcriptional reporter activity, and to counteract Wnt-induced c-Myc expression. Loss of the AICD resulted in an increased responsiveness to Wnt/beta-catenin-mediated transcription. Mechanically, the AICD was found to interact with glycogen synthase kinase 3 beta (GSK3beta) and promote its kinase activity. The subsequent AICD-strengthened Axin-GSK3beta complex potentiates beta-catenin poly-ubiquitination.", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 1016, "key": "5b38a1fc35be1446d5cfee1755dcc26f"}, {"line": 1955, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2581, "key": "c6fb99e896b89a9693402bd23f7809e3"}, {"line": 1982, "relation": "increases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2581, "key": "6ed3404961ed9f31e04967e9cd038fb2"}, {"line": 30006, "relation": "increases", "evidence": "It was shown recently that axin negatively regulates beta-catenin by bridging beta-catenin and GSK-3beta together in the same complex and thereby facilitating the phosphorylation of beta-catenin by GSK-3beta ", "citation": {"db": "PubMed", "db_id": "10341227"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2581, "key": "f857bcdab9683fa954169967905b7e8f"}, {"line": 30035, "relation": "increases", "evidence": "These results reveal an antiapoptotic function of tau hyperphosphorylation, which likely inhibits competitively phosphorylation of beta-catenin by GSK-3beta and hence facilitates the function of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360687"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2581, "key": "30c1c30a68892c619973189120069a28"}, {"line": 30074, "relation": "increases", "evidence": "Thirdly, GSK-3beta has many substrates, such as beta-catenin, APC, Axin, Tau protein, etc.", "citation": {"db": "PubMed", "db_id": "21352912"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2581, "key": "3c0113b96c332e65d570656396572d6c"}, {"line": 30111, "relation": "increases", "evidence": "Notable among the signaling proteins regulated by GSK3beta are the many transcription factors, including activator protein-1, cyclic AMP response element binding protein, heat shock factor-1, nuclear factor of activated T cells, Myc, beta-catenin, CCAAT/enhancer binding protein, and NFkappaB. ", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2581, "key": "ab9de67bb69c3c9c83785e951c7386a3"}, {"line": 30152, "relation": "increases", "evidence": "Interestingly, GPCs from AD patients exhibited elevated levels of glycogen synthase kinase 3beta (GSK-3beta, an enzyme known to phosphorylate beta-catenin), accompanied by an increase in phosphorylated beta-catenin and a decrease in nonphosphorylated beta-catenin compared with HC counterparts.", "citation": {"db": "PubMed", "db_id": "19458225"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2794, "target": 2581, "key": "83704e9fa9ef4b58aef63d67b3800ba6"}, {"line": 33660, "relation": "increases", "evidence": "In the absence of an activating signal, phosphorylation of beta-catenin by glycogen synthase kinase 3 (GSK3) acting in conjunction with adenomatous polyposis coli and axin/conductin causes beta-catenin to interact with the beta-transducin repeat-containing protein which results in its ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "11212302"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2581, "key": "c520209b00504cb61b43dee3d2eeb291"}, {"line": 34161, "relation": "increases", "evidence": "This pool of beta-catenin exists in the cytosol and is normally rapidly degraded through phosphorylation by GSK 3beta, leading to its subsequent degradation. ", "citation": {"db": "PubMed", "db_id": "11504726"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2794, "target": 2581, "key": "9b9c437426e0e04e2e2b4f4986f066f5"}, {"line": 35516, "relation": "increases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2581, "key": "09a1273d447a398240f33b585730b00f"}, {"relation": "hasVariant", "source": 2794, "target": 2797, "key": "e6bf1435c757132ac5b4e36689c9ba59"}, {"line": 2780, "relation": "increases", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 645, "key": "39f8bc735d43cf2fd6b83d28dace81fc"}, {"line": 35629, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 645, "key": "4cc159f9ad8568da496af713acf76999"}, {"line": 36874, "relation": "positiveCorrelation", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 645, "key": "e16b6fc3d6ab10d70d3ef6f2754f87e7"}, {"line": 2782, "relation": "decreases", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2794, "target": 3565, "key": "a1515ffe083c1add21ca01d068767214"}, {"line": 2817, "relation": "decreases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-b, may also contribute to AICD-related cytotoxicity by up-regulating tau hyperphosphorylation. GSK-3b activation and collapsin response mediator protein 2 (CRMP2) phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 559, "key": "4c823cd61a87437e20bd691e87813903"}, {"line": 2818, "relation": "decreases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-b, may also contribute to AICD-related cytotoxicity by up-regulating tau hyperphosphorylation. GSK-3b activation and collapsin response mediator protein 2 (CRMP2) phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 820, "key": "134115d0a9a938c9fccbc0cdde149415"}, {"line": 33055, "relation": "decreases", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2794, "target": 820, "key": "bdf2f483e621dcf686ec0e87b14d8aa0"}, {"relation": "partOf", "source": 2794, "target": 1449, "key": "69fe7f1d9a0835be3d28b8468523859d"}, {"relation": "partOf", "source": 2794, "target": 1450, "key": "f2d24e350f9604a851eec1107662257e"}, {"line": 4861, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2794, "target": 3823, "key": "e1fbf0a86d70d954258938d132a3d534"}, {"line": 12493, "relation": "association", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2794, "target": 3823, "key": "a2bf017d91f8a3a421450a55ea31108d"}, {"line": 29376, "relation": "association", "evidence": "Our findings indicate that the abnormal activation of glycogen synthase kinase 3beta can reduce neuronal viability and synaptic plasticity via modulating Presenilin 1/N-cadherin/beta-catenin interaction and thus have important implications in the pathophysiology of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3823, "key": "a5250016609ea219cf6ef9507398025d"}, {"line": 5065, "relation": "increases", "evidence": "Inhibition of glycogen synthase kinase 3beta (GSK3beta) decreases inflammatory responses in brain endothelial cells.", "citation": {"db": "PubMed", "db_id": "20056834"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 577, "key": "c3f002f0e328cfd7b9a89e8b396ea72d"}, {"line": 5071, "relation": "increases", "evidence": "Genes known to contribute to neuroinflammation that were most negatively affected by GSK3beta inactivation included IP-10/CXCL10, MCP-1/CCL2, IL-8/CXCL8, RANTES/CCL5, and Groalpha/CXCL1. Interactions of monocytes with TNFalpha-activated BMVECs led to barrier disruption, and GSK3beta suppression in the endothelium restored barrier integrity. GSK3beta inhibition in vivo substantially decreased leukocyte adhesion to brain endothelium under inflammatory conditions. In summary, inhibition of GSK3beta emerges as an important target for stabilization of the blood-brain barrier in neuroinflammation.", "citation": {"db": "PubMed", "db_id": "20056834"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 577, "key": "b34206ecc632c668b0209bcd2f740e5d"}, {"line": 5073, "relation": "increases", "evidence": "Genes known to contribute to neuroinflammation that were most negatively affected by GSK3beta inactivation included IP-10/CXCL10, MCP-1/CCL2, IL-8/CXCL8, RANTES/CCL5, and Groalpha/CXCL1. Interactions of monocytes with TNFalpha-activated BMVECs led to barrier disruption, and GSK3beta suppression in the endothelium restored barrier integrity. GSK3beta inhibition in vivo substantially decreased leukocyte adhesion to brain endothelium under inflammatory conditions. In summary, inhibition of GSK3beta emerges as an important target for stabilization of the blood-brain barrier in neuroinflammation.", "citation": {"db": "PubMed", "db_id": "20056834"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2603, "key": "5e239879be3b57509b9aa06255b2e65e"}, {"line": 5074, "relation": "increases", "evidence": "Genes known to contribute to neuroinflammation that were most negatively affected by GSK3beta inactivation included IP-10/CXCL10, MCP-1/CCL2, IL-8/CXCL8, RANTES/CCL5, and Groalpha/CXCL1. Interactions of monocytes with TNFalpha-activated BMVECs led to barrier disruption, and GSK3beta suppression in the endothelium restored barrier integrity. GSK3beta inhibition in vivo substantially decreased leukocyte adhesion to brain endothelium under inflammatory conditions. In summary, inhibition of GSK3beta emerges as an important target for stabilization of the blood-brain barrier in neuroinflammation.", "citation": {"db": "PubMed", "db_id": "20056834"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2455, "key": "51e643132aea2990084018a176e301cf"}, {"line": 5076, "relation": "increases", "evidence": "Genes known to contribute to neuroinflammation that were most negatively affected by GSK3beta inactivation included IP-10/CXCL10, MCP-1/CCL2, IL-8/CXCL8, RANTES/CCL5, and Groalpha/CXCL1. Interactions of monocytes with TNFalpha-activated BMVECs led to barrier disruption, and GSK3beta suppression in the endothelium restored barrier integrity. GSK3beta inhibition in vivo substantially decreased leukocyte adhesion to brain endothelium under inflammatory conditions. In summary, inhibition of GSK3beta emerges as an important target for stabilization of the blood-brain barrier in neuroinflammation.", "citation": {"db": "PubMed", "db_id": "20056834"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2606, "key": "83f2d5ee9a3823e3264f61c4aae23882"}, {"line": 5078, "relation": "increases", "evidence": "Genes known to contribute to neuroinflammation that were most negatively affected by GSK3beta inactivation included IP-10/CXCL10, MCP-1/CCL2, IL-8/CXCL8, RANTES/CCL5, and Groalpha/CXCL1. Interactions of monocytes with TNFalpha-activated BMVECs led to barrier disruption, and GSK3beta suppression in the endothelium restored barrier integrity. GSK3beta inhibition in vivo substantially decreased leukocyte adhesion to brain endothelium under inflammatory conditions. In summary, inhibition of GSK3beta emerges as an important target for stabilization of the blood-brain barrier in neuroinflammation.", "citation": {"db": "PubMed", "db_id": "20056834"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2459, "key": "bc9907bc233537be34703c87ff9be896"}, {"line": 5079, "relation": "increases", "evidence": "Genes known to contribute to neuroinflammation that were most negatively affected by GSK3beta inactivation included IP-10/CXCL10, MCP-1/CCL2, IL-8/CXCL8, RANTES/CCL5, and Groalpha/CXCL1. Interactions of monocytes with TNFalpha-activated BMVECs led to barrier disruption, and GSK3beta suppression in the endothelium restored barrier integrity. GSK3beta inhibition in vivo substantially decreased leukocyte adhesion to brain endothelium under inflammatory conditions. In summary, inhibition of GSK3beta emerges as an important target for stabilization of the blood-brain barrier in neuroinflammation.", "citation": {"db": "PubMed", "db_id": "20056834"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2602, "key": "0d2c60bb33626f616248f8760ca6231f"}, {"line": 5085, "relation": "decreases", "evidence": "Genes known to contribute to neuroinflammation that were most negatively affected by GSK3beta inactivation included IP-10/CXCL10, MCP-1/CCL2, IL-8/CXCL8, RANTES/CCL5, and Groalpha/CXCL1. Interactions of monocytes with TNFalpha-activated BMVECs led to barrier disruption, and GSK3beta suppression in the endothelium restored barrier integrity. GSK3beta inhibition in vivo substantially decreased leukocyte adhesion to brain endothelium under inflammatory conditions. In summary, inhibition of GSK3beta emerges as an important target for stabilization of the blood-brain barrier in neuroinflammation.", "citation": {"db": "PubMed", "db_id": "20056834"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 601, "key": "570a1c50018da082e3b9c8769dfb8479"}, {"line": 5480, "relation": "increases", "evidence": "Akt substrates such as mammalian target of rapamycin (mTOR; Ser2448) and decreased levels of cell-cycle inhibitors (p27kip1) are found in AD temporal cortex when compared to controls. GSK-3a has been implicated in the production of Abeta peptide while increased GSK-3beta activity has been implicated in tau hyperphosphorylation and neuronal cell death", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "GSK3 subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2794, "target": 478, "key": "3a523550264d60e8e8da4d5b7ff3daa0"}, {"line": 30052, "relation": "association", "evidence": "Here, we show that cells overexpressing tau exhibit marked resistance to apoptosis induced by various apoptotic stimuli, which also causes correlated tau hyperphosphorylation and glycogen synthase kinase 3 (GSK-3) activation. ", "citation": {"db": "PubMed", "db_id": "17360687"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 478, "key": "4b96567938e0930085443fa10cb9902a"}, {"relation": "hasVariant", "source": 2794, "target": 2796, "key": "da307ab0643f9d727f4ab07862ba31e3"}, {"line": 6213, "relation": "regulates", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 836, "key": "70eb1b17068e2c0354dab64df2b0b9f7"}, {"relation": "hasVariant", "source": 2794, "target": 2795, "key": "3f28263814496b292c494f99c530bc31"}, {"line": 7687, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2794, "target": 1467, "key": "a9c6da8a0ea0973f764cf4f8ab0927eb"}, {"line": 7700, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 460, "key": "04f9dcc8ee579c657fe6b187aa1fdc09"}, {"relation": "partOf", "source": 2794, "target": 1448, "key": "a101d5e9d0edf321546a0ec50983f14b"}, {"line": 7987, "relation": "isA", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 1448, "key": "6f480956abfe79c17d671e9a1ac4a0d9"}, {"line": 7985, "relation": "negativeCorrelation", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3186, "key": "66d51a78bfee2d280d39683e59716105"}, {"line": 9008, "relation": "increases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3017, "key": "7599425fc555bf20c27b8460f8b83865"}, {"line": 9016, "relation": "increases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3030, "key": "6802e1609ea9749a004bf85f01fbf109"}, {"relation": "partOf", "source": 2794, "target": 1446, "key": "f7484bf9395966b6f1a4cf6e8f6ac097"}, {"line": 9044, "relation": "positiveCorrelation", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3384, "key": "524f03890231adc9a27711e0b74b302d"}, {"line": 9823, "relation": "negativeCorrelation", "evidence": "This decrease in insulin-PI3K-AKT signalling could lead to activation of glycogen synthase kinase-3beta, the major tau kinase.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 579, "key": "360bee79c0e0c26fa0f1a58c73635645"}, {"line": 9824, "relation": "increases", "evidence": "This decrease in insulin-PI3K-AKT signalling could lead to activation of glycogen synthase kinase-3beta, the major tau kinase.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 764, "key": "aeefc516e934499483ab7203f9d79f72"}, {"line": 10941, "relation": "increases", "evidence": "The inhibition of this kinase can prevent the aggregation of beta-amyloid (Abeta) and hyperphosphorylation of tau protein.", "citation": {"db": "PubMed", "db_id": "22718609"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 377, "key": "21a6d8e9ccbb29be404e34bf838ac3ef"}, {"line": 18116, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3920, "key": "31d72801a7cd335a912ee460f928b565"}, {"line": 18128, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}, "Subgraph": {"Inflammatory response subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2794, "target": 3110, "key": "42e18fc344aca4548fe490bf3c03b018"}, {"line": 39965, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}, "Subgraph": {"GSK3 subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 2794, "target": 3110, "key": "aa004766eba6ace94695507e73e25d86"}, {"line": 19320, "relation": "association", "evidence": "In addition, cdk5/p25 might also interact with other pathways such as glycogen synthetase kinase 3beta (GSK3beta) and c-JUN kinase.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2794, "target": 1340, "key": "7f85cd47714abb90f331100d415fd4e4"}, {"line": 24272, "relation": "increases", "evidence": "Activation of PI3K after CsA treatment appeared to trigger opposite effects. First, CsA induced PI3K-dependent activation of Akt, which mediated cellular responses against cell injury. Akt activation led to transient phosphorylation and inhibition of the pro-apoptotic GSK3beta and Bad, thus preventing GSK3beta-mediated phosphorylation and activation of the pro-apoptotic Bax, and Bad-sequestering of Bcl-2.", "citation": {"db": "PubMed", "db_id": "16316932"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2390, "key": "c4bcbd1f099e0200410bd2c1680cf255"}, {"line": 24368, "relation": "increases", "evidence": "Here, we show that GSK-3beta phosphorylates and regulates the activity of Bax, a pro-apoptotic Bcl-2 family member that stimulates the intrinsic (mitochondrial) death pathway by eliciting cytochrome c release from mitochondria.", "citation": {"db": "PubMed", "db_id": "15525785"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2794, "target": 2390, "key": "8aac840b0bf05743e838d2dac31d426b"}, {"line": 24372, "relation": "regulates", "evidence": "Here, we show that GSK-3beta phosphorylates and regulates the activity of Bax, a pro-apoptotic Bcl-2 family member that stimulates the intrinsic (mitochondrial) death pathway by eliciting cytochrome c release from mitochondria.", "citation": {"db": "PubMed", "db_id": "15525785"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2794, "target": 2389, "key": "04e6b921a97518322e54eb60c7b92151"}, {"line": 29346, "relation": "increases", "evidence": "Presenilin 1 interacts with N-cadherin/beta-catenin to form a trimeric complex at the synaptic site through its loop domain, whose serine residues (serine 353 and 357) can be phosphorylated by glycogen synthase kinase 3beta.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3260, "key": "38b921e202ab7c622fc33dc55153dc42"}, {"line": 30728, "relation": "increases", "evidence": "The physiological relevance of the association between PS1 and beta-catenin remains controversial. In this study, we report the identification and functional characterization of a highly conserved glycogen synthase kinase-3beta consensus phosphorylation site within the hydrophilic loop domain of PS1. Site-directed mutagenesis, together with in vitro and in vivo phosphorylation assays, indicates that PS1 residues Ser(353) and Ser(357) are glycogen synthase kinase-3beta targets.", "citation": {"db": "PubMed", "db_id": "11104755"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3260, "key": "499aede25bc7a1c7b1e9a9ed0846475b"}, {"line": 30754, "relation": "increases", "evidence": "We demonstrate that phosphorylation of serines 353 and 357 by glycogen synthase kinase-3beta (GSK3beta) induces a structural change of the hydrophilic loop of PS1 that can also be mimicked by substitution of the phosphorylation sites by negatively charged amino acids in vitro and in cultured cells. The structural change of PS1 reduces the interaction with beta-catenin leading to decreased phosphorylation and ubiquitination of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360711"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3260, "key": "3a1a04c73ec40e02ab5844c0ce4ce671"}, {"line": 32207, "relation": "increases", "evidence": "Site-directed mutagenesis, together with in vitro and in vivo phosphorylation assays, indicates that PS1 residues Ser(353) and Ser(357) are glycogen synthase kinase-3beta targets.", "citation": {"db": "PubMed", "db_id": "11104755"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3260, "key": "8f161a8870382bae2081c61bf624ea53"}, {"line": 29347, "relation": "increases", "evidence": "Presenilin 1 interacts with N-cadherin/beta-catenin to form a trimeric complex at the synaptic site through its loop domain, whose serine residues (serine 353 and 357) can be phosphorylated by glycogen synthase kinase 3beta.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3261, "key": "571f182093b47a6b8896aca726be88b8"}, {"line": 30729, "relation": "increases", "evidence": "The physiological relevance of the association between PS1 and beta-catenin remains controversial. In this study, we report the identification and functional characterization of a highly conserved glycogen synthase kinase-3beta consensus phosphorylation site within the hydrophilic loop domain of PS1. Site-directed mutagenesis, together with in vitro and in vivo phosphorylation assays, indicates that PS1 residues Ser(353) and Ser(357) are glycogen synthase kinase-3beta targets.", "citation": {"db": "PubMed", "db_id": "11104755"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3261, "key": "abbd27b3546b8364529eaf3c3c33f099"}, {"line": 30755, "relation": "increases", "evidence": "We demonstrate that phosphorylation of serines 353 and 357 by glycogen synthase kinase-3beta (GSK3beta) induces a structural change of the hydrophilic loop of PS1 that can also be mimicked by substitution of the phosphorylation sites by negatively charged amino acids in vitro and in cultured cells. The structural change of PS1 reduces the interaction with beta-catenin leading to decreased phosphorylation and ubiquitination of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360711"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3261, "key": "77519811a5a155c0e4c1c661da952f46"}, {"line": 32208, "relation": "increases", "evidence": "Site-directed mutagenesis, together with in vitro and in vivo phosphorylation assays, indicates that PS1 residues Ser(353) and Ser(357) are glycogen synthase kinase-3beta targets.", "citation": {"db": "PubMed", "db_id": "11104755"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3261, "key": "26dd8d25b886ea940bd70c9bd36f25b0"}, {"line": 29367, "relation": "increases", "evidence": "Our findings indicate that the abnormal activation of glycogen synthase kinase 3beta can reduce neuronal viability and synaptic plasticity via modulating Presenilin 1/N-cadherin/beta-catenin interaction and thus have important implications in the pathophysiology of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 648, "key": "c9dee1b1909592651c702b3c7d2845d7"}, {"line": 33053, "relation": "association", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2794, "target": 648, "key": "a713d6a718ad4ea9d5a52073ef3b9fc9"}, {"line": 29368, "relation": "decreases", "evidence": "Our findings indicate that the abnormal activation of glycogen synthase kinase 3beta can reduce neuronal viability and synaptic plasticity via modulating Presenilin 1/N-cadherin/beta-catenin interaction and thus have important implications in the pathophysiology of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 761, "key": "cc915d21e0e74f7015ad9725c73da99e"}, {"line": 29372, "relation": "regulates", "evidence": "Our findings indicate that the abnormal activation of glycogen synthase kinase 3beta can reduce neuronal viability and synaptic plasticity via modulating Presenilin 1/N-cadherin/beta-catenin interaction and thus have important implications in the pathophysiology of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 1331, "key": "fb059a9a004006abd85bcd9ec10163b0"}, {"line": 29492, "relation": "increases", "evidence": "In addition, the sequential phosphorylation of CRMP2 by Cdk5 and GSK3beta is an important process of Sema3A signaling.", "citation": {"db": "PubMed", "db_id": "16866215"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "GSK3 subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2642, "key": "f3df9a7b92b777de7a13fdb088c7af92"}, {"line": 29516, "relation": "increases", "evidence": "This function of CRMP2 is regulated by phosphorylation by glycogen synthase kinase 3 (GSK3) and cyclin-dependent kinase 5 (Cdk5).", "citation": {"db": "PubMed", "db_id": "17683481"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "GSK3 subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2642, "key": "8127b9c83f21c6a64924e41d250423a5"}, {"line": 30252, "relation": "increases", "evidence": "Both GSK-3alpha and 3beta phosphorylate purified pig brain CRMP-2 and significantly alter its mobility in SDS-gels, resembling the CRMP-2 pmodification observed in AD brain.", "citation": {"db": "PubMed", "db_id": "17902168"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "GSK3 subgraph": true, "Axonal guidance subgraph": true}, "Species": {"9823": true}, "Confidence": {"High": true}}, "source": 2794, "target": 2642, "key": "fa245a2b9fb6133a5db0079bbd378ce0"}, {"line": 30280, "relation": "positiveCorrelation", "evidence": "Phosphorylation by GSK3beta was exclusively observed in Cdk5-phosphorylated CRMP2, but barely in CRMP2T509A.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "GSK3 subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2642, "key": "6c24a9171a69e0c2347dad01fea0e6a3"}, {"line": 30281, "relation": "increases", "evidence": "Phosphorylation by GSK3beta was exclusively observed in Cdk5-phosphorylated CRMP2, but barely in CRMP2T509A.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "GSK3 subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2642, "key": "bc363a339b227527a48a3c1db5c846fb"}, {"line": 29828, "relation": "increases", "evidence": "GSK-3beta transfection showed the phosphorylation at Ser-199, Thr-231, Ser-396, and Ser-413.", "citation": {"db": "PubMed", "db_id": "9565682"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3019, "key": "2697eb36b2992c5931c71e1fe94478fe"}, {"line": 29829, "relation": "increases", "evidence": "GSK-3beta transfection showed the phosphorylation at Ser-199, Thr-231, Ser-396, and Ser-413.", "citation": {"db": "PubMed", "db_id": "9565682"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3035, "key": "563018bf77c83b4a9703f6371fb51f71"}, {"line": 29830, "relation": "increases", "evidence": "GSK-3beta transfection showed the phosphorylation at Ser-199, Thr-231, Ser-396, and Ser-413.", "citation": {"db": "PubMed", "db_id": "9565682"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3026, "key": "2210f56a32f3893bd885036538d5a1ce"}, {"line": 29831, "relation": "increases", "evidence": "GSK-3beta transfection showed the phosphorylation at Ser-199, Thr-231, Ser-396, and Ser-413.", "citation": {"db": "PubMed", "db_id": "9565682"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3028, "key": "e6a4c063abf95d4a48208b06143b2c03"}, {"line": 29869, "relation": "negativeCorrelation", "evidence": "We show that alsterpaullone is able to inhibit the in vivo phosphorylation of tau at AD-specific sites by GSK-3beta and the in vivo phosphorylation of DARPP-32 in isolated striatum slices by CDK5. ", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 207, "key": "9c89e1691c45253986ccc362cc23b17d"}, {"relation": "partOf", "source": 2794, "target": 1370, "key": "cfc40b91229455844142eba6d72fe0ac"}, {"line": 30075, "relation": "association", "evidence": "Thirdly, GSK-3beta has many substrates, such as beta-catenin, APC, Axin, Tau protein, etc.", "citation": {"db": "PubMed", "db_id": "21352912"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2157, "key": "293c6f3c792b473bbbe52514f7669a7b"}, {"line": 30076, "relation": "association", "evidence": "Thirdly, GSK-3beta has many substrates, such as beta-catenin, APC, Axin, Tau protein, etc.", "citation": {"db": "PubMed", "db_id": "21352912"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2303, "key": "293e9b8b331b8d0148976c0b546040ec"}, {"line": 30086, "relation": "increases", "evidence": "Notable among the signaling proteins regulated by GSK3beta are the many transcription factors, including activator protein-1, cyclic AMP response element binding protein, heat shock factor-1, nuclear factor of activated T cells, Myc, beta-catenin, CCAAT/enhancer binding protein, and NFkappaB. ", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 870, "key": "25567c70b8b13209a0a745035f9fe8c5"}, {"line": 30092, "relation": "increases", "evidence": "Notable among the signaling proteins regulated by GSK3beta are the many transcription factors, including activator protein-1, cyclic AMP response element binding protein, heat shock factor-1, nuclear factor of activated T cells, Myc, beta-catenin, CCAAT/enhancer binding protein, and NFkappaB. ", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"CREB subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2557, "key": "14e6b4800ae2174354405f24d792e87d"}, {"line": 30098, "relation": "increases", "evidence": "Notable among the signaling proteins regulated by GSK3beta are the many transcription factors, including activator protein-1, cyclic AMP response element binding protein, heat shock factor-1, nuclear factor of activated T cells, Myc, beta-catenin, CCAAT/enhancer binding protein, and NFkappaB. ", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2845, "key": "57efcc1db00f5c6f87ad4cd86a129ade"}, {"line": 30099, "relation": "increases", "evidence": "Notable among the signaling proteins regulated by GSK3beta are the many transcription factors, including activator protein-1, cyclic AMP response element binding protein, heat shock factor-1, nuclear factor of activated T cells, Myc, beta-catenin, CCAAT/enhancer binding protein, and NFkappaB. ", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3083, "key": "80245ad9c97dceee95821eb9064578d1"}, {"line": 30105, "relation": "increases", "evidence": "Notable among the signaling proteins regulated by GSK3beta are the many transcription factors, including activator protein-1, cyclic AMP response element binding protein, heat shock factor-1, nuclear factor of activated T cells, Myc, beta-catenin, CCAAT/enhancer binding protein, and NFkappaB. ", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"T cells signaling": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3108, "key": "c655e661fddf7df0e85af7b26b3a85df"}, {"line": 30115, "relation": "increases", "evidence": "Notable among the signaling proteins regulated by GSK3beta are the many transcription factors, including activator protein-1, cyclic AMP response element binding protein, heat shock factor-1, nuclear factor of activated T cells, Myc, beta-catenin, CCAAT/enhancer binding protein, and NFkappaB. ", "citation": {"db": "PubMed", "db_id": "11527574"}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2500, "key": "d6bf6ade483e07ba2b07dd218d12e47a"}, {"line": 30119, "relation": "regulates", "evidence": "Notable among the signaling proteins regulated by GSK3beta are the many transcription factors, including activator protein-1, cyclic AMP response element binding protein, heat shock factor-1, nuclear factor of activated T cells, Myc, beta-catenin, CCAAT/enhancer binding protein, and NFkappaB. ", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 871, "key": "461fa9842eac49033b8f5b3d807a42d9"}, {"line": 30127, "relation": "decreases", "evidence": "This mechanism was shown to allow local inhibition of GSK3beta, leading to stabilization of beta-catenin levels due to reduced GSK3beta-facilitated degradation of beta-catenin, a necessary step in embryo development", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2794, "target": 2580, "key": "c6e9edf3ece507b5b2a0eeff6eac462c"}, {"line": 36883, "relation": "negativeCorrelation", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2580, "key": "296586acf222f8dbb008bbaed4caf24b"}, {"line": 30570, "relation": "increases", "evidence": "In vitro kinase reaction revealed that recombinant GSK-3beta indeed phosphorylates MARK2, whereas it failed to phosphorylate Ser-262 of tau.", "citation": {"db": "PubMed", "db_id": "16257959"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3044, "key": "83c5fcefaf188d27f8411290611f9ce4"}, {"line": 30627, "relation": "increases", "evidence": "These results, respectively, indicated that GSK-3beta is responsible for phosphorylating Ser-262 of tau through phosphorylation and activation of MARK2 and that the phosphorylation of tau at this particular site is predominantly mediated by a GSK-3beta-MARK2 pathway.", "citation": {"db": "PubMed", "db_id": "16257959"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3044, "key": "4532c95cdfaac5ee05b66c30672419ac"}, {"line": 30626, "relation": "increases", "evidence": "These results, respectively, indicated that GSK-3beta is responsible for phosphorylating Ser-262 of tau through phosphorylation and activation of MARK2 and that the phosphorylation of tau at this particular site is predominantly mediated by a GSK-3beta-MARK2 pathway.", "citation": {"db": "PubMed", "db_id": "16257959"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3023, "key": "9d0c24a3ab5ce6a7d98f2bacf010ac76"}, {"line": 30669, "relation": "increases", "evidence": "Treatment of PSer262 peptide or GSK 3 beta phosphorylated tau with alkaline phosphatase increased Ab262 immunoreactivity, indicating that Ab262 is a reagent useful for studying tau phosphorylation at the Ser262 residue.", "citation": {"db": "PubMed", "db_id": "8769876"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3023, "key": "494ff0ad92160d611cc5706175d0d7f1"}, {"line": 30740, "relation": "increases", "evidence": "Previously we described presenilin-1 (PS1) as a GSK-3beta substrate.", "citation": {"db": "PubMed", "db_id": "16814287"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3259, "key": "c430360ccef0081c4e0362720fdea95f"}, {"line": 31467, "relation": "increases", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3259, "key": "dc72fc1bdf77056436225c6b5c73452c"}, {"line": 31466, "relation": "decreases", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 1335, "key": "75a68f457e4ff398ba1e9699d206c09b"}, {"line": 31470, "relation": "decreases", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "cell surface"}}}, "source": 2794, "target": 3258, "key": "591e85b90b387e576d132f2e13123231"}, {"line": 32512, "relation": "association", "evidence": "PS1 directly binds tau and a tau kinase , glycogen synthase kinase 3beta", "citation": {"db": "PubMed Central", "db_id": "PMC21391"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2794, "target": 3258, "key": "9f53cfa87c982b5ce3042bac8ffb1f07"}, {"relation": "partOf", "source": 2794, "target": 1445, "key": "03c11a1a44f19bb8bc44c2711827147f"}, {"line": 33052, "relation": "increases", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2794, "target": 889, "key": "73a1742b386f45032fb16fb6b659d4b6"}, {"line": 33054, "relation": "negativeCorrelation", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2794, "target": 432, "key": "0172d5f6b68a630ec60ba13f5e92a2fc"}, {"line": 33065, "relation": "increases", "evidence": "In vitro phosphorylation of the cytoplasmic domain of the amyloid precursor protein by glycogen synthase kinase-3beta.", "citation": {"db": "PubMed", "db_id": "8764598"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2334, "key": "cb9d81ed27deb87fb46ebcc9f6f316a4"}, {"line": 33398, "relation": "association", "evidence": "This interaction is regulated by the phosphorylation of Tau at selected sites, by glycogen synthase kinase-3beta (GSK3beta) and cyclin-dependent kinase 5 (Cdk5), and requires an intact microtubule network.", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 1103, "key": "b4364dc89a326a93b586cb8df5e491a6"}, {"line": 35495, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 2587, "key": "98c485eb55c4abeacc50111421d4bc2a"}, {"line": 35576, "relation": "decreases", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2794, "target": 3946, "key": "0226409384f700eab40092e6130d6cc2"}, {"relation": "partOf", "source": 2794, "target": 1447, "key": "778185829a3ddffcd59475575d10d9c2"}, {"line": 45465, "relation": "negativeCorrelation", "evidence": "considering the position of GSK3B 78–82, we speculate that hypermethylation may act as a gene expression suppressor", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 2794, "target": 1835, "key": "02784dfafc86fcaac9a3afe738a5c888"}, {"line": 47924, "relation": "regulates", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2794, "target": 2583, "key": "384c9a1bef3967b9148a0ae0f4aa0473"}, {"line": 47925, "relation": "regulates", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2794, "target": 2582, "key": "2ddd2c2b73fa335d8cb2c9146bae8f8d"}, {"line": 48066, "relation": "association", "evidence": "In silico molecular target prediction docking studies suggest that ETH interacts with Akt, Dkk-1, and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "26420483"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 2794, "target": 6, "key": "23156b43549d3a81f01d4816c8d4ec8d"}, {"line": 652, "relation": "increases", "evidence": "Our data suggests that the elevated levels of APP-KPI in AD brain may attenuate the clearance of Abeta, the proteins own amyloidogenic catabolic product.", "citation": {"db": "PubMed", "db_id": "15974929"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Very High": true}}, "source": 1183, "target": 80, "key": "0102b46626b3236ae06ae7d471a8cb6d"}, {"relation": "partOf", "source": 2968, "target": 1183, "key": "7f077211c94f8ebe2bcc54ac79bce5a1"}, {"line": 667, "relation": "positiveCorrelation", "evidence": "polymorphisms in three other genes (among others), apolipoprotein E (apoE), alpha2-macroglobulin (alpham), and the low density lipoprotein receptor-related protein (LRP), are implicated to contribute to AD pathogenesis", "citation": {"db": "PubMed", "db_id": "10936878"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Alpha 2 macroglobulin subgraph": true}}, "source": 2228, "target": 3823, "key": "0f89a8a8254ad3c9fd9a7b8a617b30a4"}, {"relation": "hasVariant", "source": 2227, "target": 2228, "key": "6de984cf8c831798f6e01b0aefe8ad2f"}, {"relation": "partOf", "source": 2227, "target": 909, "key": "40be609d2fd1f8713e8e7b858df2a1b5"}, {"relation": "partOf", "source": 2227, "target": 1026, "key": "840c17302070c0bfc7a1676b83674afb"}, {"relation": "partOf", "source": 2227, "target": 1029, "key": "d147aec21377e32fa8807df1622fdc57"}, {"relation": "partOf", "source": 2227, "target": 1027, "key": "9f2a0543f33b250bac05f7d72eade6f0"}, {"line": 24432, "relation": "regulates", "evidence": "alpha(2)M also regulates proteinase and growth factor activities", "citation": {"db": "PubMed", "db_id": "14678766"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2227, "target": 381, "key": "02b91120d49d11544351c1b8c7fc51ef"}, {"line": 24433, "relation": "association", "evidence": "alpha(2)M also regulates proteinase and growth factor activities", "citation": {"db": "PubMed", "db_id": "14678766"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true}, "Confidence": {"Medium": true}}, "source": 2227, "target": 748, "key": "2952debf0e7cdfdff60eae47547b4af2"}, {"relation": "partOf", "source": 2227, "target": 1033, "key": "73470e202ba017da05153077aa067f0d"}, {"relation": "partOf", "source": 2227, "target": 908, "key": "37d404be0e21d00bbc5015998838e901"}, {"line": 24457, "relation": "positiveCorrelation", "evidence": "In the present study, we investigated whether alpha2-macroglobulin (alpha2M), a protein present in neuritic plaques and elevated in Alzheimer's disease brain, is a potential regulatory factor for A beta fibril formation", "citation": {"db": "PubMed", "db_id": "9489740"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true}, "Confidence": {"Medium": true}}, "source": 2227, "target": 3823, "key": "53e0bcb55dcd35fe532edced658fdc13"}, {"line": 24458, "relation": "increases", "evidence": "In the present study, we investigated whether alpha2-macroglobulin (alpha2M), a protein present in neuritic plaques and elevated in Alzheimer's disease brain, is a potential regulatory factor for A beta fibril formation", "citation": {"db": "PubMed", "db_id": "9489740"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true}, "Confidence": {"Medium": true}}, "source": 2227, "target": 474, "key": "d8d6e7d7ccbb09a41406265260540933"}, {"line": 24721, "relation": "decreases", "evidence": "alpha(2)-Macroglobulin (alpha(2)M) is an abundant plasma/extracellular space protein implicated in clearance of amyloid beta (Abeta)", "citation": {"db": "PubMed", "db_id": "14678766"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2227, "target": 2328, "key": "3d5b1a5be7395d4d42b3430a71fdd7b9"}, {"line": 24722, "relation": "decreases", "evidence": "alpha(2)-Macroglobulin (alpha(2)M) is an abundant plasma/extracellular space protein implicated in clearance of amyloid beta (Abeta)", "citation": {"db": "PubMed", "db_id": "14678766"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2227, "target": 80, "key": "2ba58d11667c49c6cdeba08f78ff3748"}, {"line": 24728, "relation": "regulates", "evidence": "alpha(2)M also regulates proteinase and growth factor activities", "citation": {"db": "PubMed", "db_id": "14678766"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true}, "Confidence": {"Medium": true}}, "source": 2227, "target": 755, "key": "6a48ccaf8d8cccbb720dc1fe30f7c2d5"}, {"relation": "partOf", "source": 2227, "target": 1031, "key": "693085cc6ce55ef002aa3ef4b5055c57"}, {"relation": "partOf", "source": 2227, "target": 1032, "key": "bb81e1dce113b83837a9be8c0f0e1b38"}, {"line": 31085, "relation": "association", "evidence": "Low density lipoprotein receptor-related protein (LRP) participates in the uptake and degradation of several ligands implicated in neuronal pathophysiology including apolipoprotein E (apoE), activated alpha(2) -macroglobulin (alpha(2)M*) and beta-amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "10797543"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2227, "target": 2970, "key": "60ab1300eadf856cd2246b96daae4bdf"}, {"relation": "partOf", "source": 2227, "target": 1030, "key": "c721c752232854c66ff8300598b9ba97"}, {"relation": "partOf", "source": 2227, "target": 1028, "key": "368ca26b755c8345cf131784c938e8b8"}, {"relation": "partOf", "source": 2227, "target": 1025, "key": "d219b54b25858242fd1005ccb30a4346"}, {"line": 669, "relation": "increases", "evidence": "polymorphisms in three other genes (among others), apolipoprotein E (apoE), alpha2-macroglobulin (alpham), and the low density lipoprotein receptor-related protein (LRP), are implicated to contribute to AD pathogenesis", "citation": {"db": "PubMed", "db_id": "10936878"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Low density lipoprotein subgraph": true}}, "source": 2981, "target": 3823, "key": "c0d4211d2168d93892951b79ff07040f"}, {"line": 1811, "relation": "association", "evidence": "Role of LPR8 activation in normal brain functioning and in neurodegeneration during AD.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2981, "target": 3823, "key": "b5e61ff0d370cdd141245ba7fe921d23"}, {"relation": "isA", "source": 2981, "target": 3551, "key": "79ed37895fee00b44a344076f3acee23"}, {"relation": "partOf", "source": 2981, "target": 1712, "key": "db325149a75a77ef765ef24cc3b05d84"}, {"line": 2266, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Degradation"}, "source": 2981, "target": 2315, "key": "2c3d3bd1de14bd37ff431b5767f84921"}, {"line": 2272, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2981, "target": 3306, "key": "4e23a2d1e4534b4fe28305ff5b3c84c8"}, {"line": 2292, "relation": "decreases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2981, "target": 3306, "key": "52763416bd5d512d137848d71cd69311"}, {"line": 2274, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2981, "target": 2779, "key": "cf63a29d5bcac87d67d0c80b6d5db05c"}, {"line": 2275, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2981, "target": 2776, "key": "17f1490a62f0114c170fa88481df86c1"}, {"line": 2276, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2981, "target": 738, "key": "10d88a91b6b221a08774187c36a0ecd9"}, {"line": 2294, "relation": "decreases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2981, "target": 702, "key": "b0eb6c9935c836e20c612bfa06b5a553"}, {"relation": "partOf", "source": 2981, "target": 1102, "key": "fba4a8089458137029843bb97f22e1f1"}, {"line": 2418, "relation": "association", "evidence": "Several studies found that FE65, a cytoplasmic adaptor protein, interacts with APP and LRP1, altering the trafficking and processing of APP. We have previously shown that FE65 interacts with the ApoE receptor, ApoER2, altering its trafficking and processing. Interestingly, it has been shown that FE65 can act as a linker between APP and LRP1 or ApoER2.", "citation": {"db": "PubMed", "db_id": "22429478"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2981, "target": 1102, "key": "7447b181c260d943fac38101ae762e5c"}, {"relation": "partOf", "source": 2981, "target": 1532, "key": "b7f48ba7b28871dc71353b2cb1aee76a"}, {"relation": "partOf", "source": 2981, "target": 1134, "key": "5c10220450c2c9375498967b378a4325"}, {"relation": "partOf", "source": 2981, "target": 1076, "key": "0ff15b7f4be13826d64fd0c9f7b1fb79"}, {"relation": "partOf", "source": 2981, "target": 1082, "key": "96a7376041809e25ff8c2111872e7b34"}, {"relation": "partOf", "source": 2981, "target": 1088, "key": "aa33f91ac324cc588d2197e4ce2c9fb1"}, {"relation": "partOf", "source": 2981, "target": 1530, "key": "bce2b680de1f061cccc559e46b44828c"}, {"relation": "partOf", "source": 2981, "target": 1531, "key": "8372bc77d8eaacd2193c7c7581f62122"}, {"relation": "partOf", "source": 2981, "target": 1529, "key": "56bbe3e56acc592995cf26b3d1370d2d"}, {"relation": "partOf", "source": 2981, "target": 1528, "key": "3af09235d38c60b4a30bb09a8976c525"}, {"relation": "hasVariant", "source": 2981, "target": 2982, "key": "f97aa72c36e62ac266febc6a701a96e1"}, {"relation": "partOf", "source": 2981, "target": 1096, "key": "81bcb29b93aa9d148e348a88d4ff5003"}, {"line": 682, "relation": "increases", "evidence": "the apoptotic pathway activated by beta-amyloid is similar to the pathway activated by the Fas/TNFR family of death receptors, which requires caspase-8 activity and adaptor proteins such as FADD. We demonstrate that the selective caspase-8 inhibitor IETD-fmk blocks neuronal death induced by beta-amyloid.", "citation": {"db": "PubMed", "db_id": "10527810"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2689, "target": 478, "key": "a241da73761e3d2331e9154f460c264c"}, {"line": 5784, "relation": "increases", "evidence": "Under either physiological or pathological conditions, apoptosis is mostly driven by interactions among several families of proteins, i.e. caspases, Bcl-2 family proteins, and inhibitor of apoptosis proteins [10]. Besides the caspases, lysosomal proteases such as cathepsins D, B, and L have been shown to act as mediators of apoptosis in a number of cell systems [11–14]. Increased expression or activity of cathepsin D has been observed in apoptotic cells after activation of Fas/APO-12 and after exposure to oxidative stress or adriamycin ", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2689, "target": 2593, "key": "0ee020ef65546265832ef5727d878d0d"}, {"line": 6542, "relation": "increases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2689, "target": 879, "key": "27fe76b82b99fb6cfa0897d21af85079"}, {"line": 13915, "relation": "association", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2689, "target": 648, "key": "ebae78223fbd9b59dc0718964e2e9a00"}, {"relation": "partOf", "source": 2689, "target": 1418, "key": "3e036755784dda2327de827e725792c2"}, {"line": 18302, "relation": "association", "evidence": "Fas and Fas ligand are associated with neuritic degeneration in the AD brain and participate in beta-amyloid-induced neuronal death.", "citation": {"db": "PubMed", "db_id": "12742739"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 2689, "target": 3872, "key": "0bf31df036cd52b49f0c27c8c8d98efb"}, {"line": 686, "relation": "increases", "evidence": "the apoptotic pathway activated by beta-amyloid is similar to the pathway activated by the Fas/TNFR family of death receptors, which requires caspase-8 activity and adaptor proteins such as FADD. We demonstrate that the selective caspase-8 inhibitor IETD-fmk blocks neuronal death induced by beta-amyloid.", "citation": {"db": "PubMed", "db_id": "10527810"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2448, "target": 478, "key": "afe980059c65cde6fb71ac4a568ceb4c"}, {"line": 5736, "relation": "increases", "evidence": "Apoptosis is the most common form of physiological cell death in multicellular organisms. Apoptosis signaling is classically composed of two principle pathways. One is a direct pathway from death receptor (CD95, TNF-R1, and TRAIL-R1/TRAIL-R2 [9]) ligation to caspase cascade activation and cell death. Death receptor ligation triggers recruitment of the precursor form of caspase-8 to a death-inducing complex, through the adaptor protein FADD, which leads to caspase-8 activation. The other pathway triggered by stimuli such as drugs, radiation, infectious agents, and reactive oxygen species is initiated in mitochondria. After cytochrome c is released into the cytosol from the mitochondria, it binds to Apaf1 and ATP, which then activate caspase-9.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2448, "target": 478, "key": "6eadc80cb253d11fb1859c0beaa5a326"}, {"line": 33420, "relation": "association", "evidence": "Among downstream factors of PKR, the Fas-associated protein with a death domain (FADD) and subsequent activated caspase-8 are responsible for PKR-induced apoptosis in recombinant virus-infected cells.", "citation": {"db": "PubMed", "db_id": "19889624"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Caspase subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 2448, "target": 478, "key": "6d98e5188446e40735a0de72c9bc1e1f"}, {"relation": "partOf", "source": 2448, "target": 1658, "key": "32b5da5c335fb3236c7b8021db7e6147"}, {"relation": "partOf", "source": 2448, "target": 1315, "key": "c6208ac71335e0b192a333b14b982a3f"}, {"relation": "partOf", "source": 2448, "target": 1316, "key": "bd667afecf02dd28df5e6f5bc3ceffb9"}, {"relation": "partOf", "source": 2448, "target": 1304, "key": "7f0eeca8fd33fa48cae92d7a12df06d3"}, {"line": 34209, "relation": "increases", "evidence": "In an attempt to reconcile these two hypotheses, we investigated APP processing during apoptosis and found that APP is processed by the cell death proteases caspase-6 and -8.", "citation": {"db": "PubMed", "db_id": "10409650"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2448, "target": 2315, "key": "3c7873b2e443d16996de564f7a5247f9"}, {"line": 34219, "relation": "increases", "evidence": "Two human proteases, caspase-3 and caspase-8, were identified and confirmed to act by a mechanism that involved proteolysis at the site in the APP-Gal4 chimera that corresponded to the natural caspase cleavage site in APP, thus linking a readily scorable phenotype to proteolytic processing of APP.", "citation": {"db": "PubMed", "db_id": "10911620"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2448, "target": 2315, "key": "ccdb1e9ee4f11d0ca7659bea01b7342f"}, {"line": 34237, "relation": "increases", "evidence": "In the present study, we demonstrate that APP phosphorylated at Thr668 is less vulnerable to cytoplasmic cleavage by caspase-3 and caspase-8.", "citation": {"db": "PubMed", "db_id": "15178331"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2448, "target": 2315, "key": "a706bfee7552a0912b0c7d244c922447"}, {"line": 38190, "relation": "decreases", "evidence": "Impaired degradation of amyloid beta (Abeta) peptides could lead to Abeta accumulation, an early trigger of Alzheimer's disease (AD). How Abeta-degrading enzymes are regulated remains largely unknown. Cystatin C (CysC, CST3) is an endogenous inhibitor of cysteine proteases, including cathepsin B (CatB), a recently discovered Abeta-degrading enzyme", "citation": {"db": "PubMed", "db_id": "18957217"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2448, "target": 2328, "key": "6b14b90c8759fd6c724f8a7ed1817f0d"}, {"line": 39022, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2448, "target": 645, "key": "af93f88131455ae5301a9bea3b2f9f96"}, {"line": 691, "relation": "increases", "evidence": "the apoptotic pathway activated by beta-amyloid is similar to the pathway activated by the Fas/TNFR family of death receptors, which requires caspase-8 activity and adaptor proteins such as FADD. We demonstrate that the selective caspase-8 inhibitor IETD-fmk blocks neuronal death induced by beta-amyloid.", "citation": {"db": "PubMed", "db_id": "10527810"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2687, "target": 478, "key": "957f6a0e197b862da96d33618d827ff9"}, {"relation": "partOf", "source": 2687, "target": 1658, "key": "902e969717dec6643be75d49b60818e6"}, {"relation": "partOf", "source": 2687, "target": 1315, "key": "19b2a558a4cce27a41f56701d1d5247b"}, {"relation": "partOf", "source": 2687, "target": 1316, "key": "67cc0323d1a49266c58d61c57f55c1a6"}, {"relation": "partOf", "source": 2687, "target": 1409, "key": "a01826ffff35211e251c8eb03738a91f"}, {"relation": "hasVariant", "source": 2687, "target": 2688, "key": "f2ac9a4f2bc30d85d42a937d0b275226"}, {"line": 695, "relation": "increases", "evidence": "the apoptotic pathway activated by beta-amyloid is similar to the pathway activated by the Fas/TNFR family of death receptors, which requires caspase-8 activity and adaptor proteins such as FADD. We demonstrate that the selective caspase-8 inhibitor IETD-fmk blocks neuronal death induced by beta-amyloid.", "citation": {"db": "PubMed", "db_id": "10527810"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true, "Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 1658, "target": 645, "key": "c93d6b6619500f81709333efdfd1c8b7"}, {"line": 711, "relation": "increases", "evidence": "Caspase-2 mediates neuronal cell death induced by beta-amyloid", "citation": {"db": "PubMed", "db_id": "10662829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2443, "target": 645, "key": "a02c10d84d4129ffff3d1d55de39552a"}, {"line": 725, "relation": "increases", "evidence": "In all of the neuronal populations studied here (hippocampal neurons, sympathetic neurons, and PC12 cells), cell death was blocked by the broad spectrum caspase inhibitor N-benzyloxycarbonyl-val-ala-asp-fluoromethyl ketone and more specifically by the downregulation of caspase-2 with antisense oligonucleotides.", "citation": {"db": "PubMed", "db_id": "10662829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2443, "target": 645, "key": "a4ef3b1e162f8dc5b560e0da97690fc9"}, {"line": 723, "relation": "decreases", "evidence": "In all of the neuronal populations studied here (hippocampal neurons, sympathetic neurons, and PC12 cells), cell death was blocked by the broad spectrum caspase inhibitor N-benzyloxycarbonyl-val-ala-asp-fluoromethyl ketone and more specifically by the downregulation of caspase-2 with antisense oligonucleotides.", "citation": {"db": "PubMed", "db_id": "10662829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 72, "target": 645, "key": "efc9da2e2b327806bd46b739370e7592"}, {"line": 724, "relation": "decreases", "evidence": "In all of the neuronal populations studied here (hippocampal neurons, sympathetic neurons, and PC12 cells), cell death was blocked by the broad spectrum caspase inhibitor N-benzyloxycarbonyl-val-ala-asp-fluoromethyl ketone and more specifically by the downregulation of caspase-2 with antisense oligonucleotides.", "citation": {"db": "PubMed", "db_id": "10662829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 72, "target": 2443, "key": "e444729c87141c04a5266bb9d628b5bf"}, {"line": 736, "relation": "decreases", "evidence": "Humanin (HN) is a short neuroprotective peptide abolishing Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true}, "Confidence": {"Very High": true}}, "source": 3079, "target": 80, "key": "87c63046fa3ff8bc4a9d4ac4608b03a8"}, {"line": 38092, "relation": "decreases", "evidence": "Humanin (HN) is a short neuroprotective peptide abolishing Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3079, "target": 2328, "key": "bdabe2f8cade34be5b4f938532860a8f"}, {"line": 748, "relation": "increases", "evidence": "Abeta-dependent inactivation of the JAK2/STAT3 axis causes memory loss through cholinergic dysfunction.", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2933, "target": 820, "key": "c56c717498da98ec82e0ffcd6ee4388d"}, {"line": 749, "relation": "association", "evidence": "Abeta-dependent inactivation of the JAK2/STAT3 axis causes memory loss through cholinergic dysfunction.", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2933, "target": 762, "key": "73360f17844950befb9e850dde9ffb70"}, {"relation": "partOf", "source": 2933, "target": 1500, "key": "3e3460f0eed9bc2dc8ff3a04df11927f"}, {"line": 1294, "relation": "association", "evidence": "Here we show that the buildup of Abeta increases the mammalian target of rapamycin (mTOR) signaling, whereas decreasing mTOR signaling reduces Abeta levels, thereby highlighting an interrelation between mTOR signaling and Abeta. The mTOR pathway plays a central role in controlling protein homeostasis and hence, neuronal functions; indeed mTOR signaling regulates different forms of learning and memory.", "citation": {"db": "PubMed", "db_id": "20178983"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"mTOR signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 820, "target": 3076, "key": "2ee8f7e0b2b7a5cee9294320a7ec0691"}, {"line": 8843, "relation": "association", "evidence": "Age-related impairment of visual recognition memory correlates with impaired synaptic distribution of GluA2 and protein kinase Mζ in the dentate gyrus.", "citation": {"db": "PubMed", "db_id": "22985047"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 820, "target": 3971, "key": "80d575315b08ca6c7f312acf358ef652"}, {"line": 17778, "relation": "association", "evidence": "The functional involvements of the cerebral angiotensin IV in what concerns its possible participation in the normal neurochemical processes of memory and in the neurodegenerative processes of Alzheimer disease will be exposed, together with the vasodilating effects of angiotensin (1-7) as counteracting factor for the constricting effects of angiotensin II.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 820, "target": 2274, "key": "06bda9a20e1498068a26ec8ce554a17d"}, {"line": 18955, "relation": "association", "evidence": "Although conventionally associated with fibrin clot degradation, recent work has uncovered new functions for the tissue plasminogen activator (tPA)/plasminogen cascade in central nervous system physiology and pathology. This extracellular proteolytic cascade has been shown to have roles in learning and memory, stress, neuronal degeneration, addiction and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15841309"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 820, "target": 3200, "key": "005c697e6e5cea2e6a134e15800195c3"}, {"line": 21704, "relation": "positiveCorrelation", "evidence": "In a previous study, we have shown that peripheral p70S6k level is correlated with the decline of cognitive and memory functions in patients with AD.", "citation": {"db": "PubMed", "db_id": "17101223"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}}, "source": 820, "target": 3327, "key": "f12ecf8ed30f25122b7413fd4667ffad"}, {"line": 21711, "relation": "positiveCorrelation", "evidence": "The decline of emotional memory in AD patients is reflected by the decrease of p70S6k levels.", "citation": {"db": "PubMed", "db_id": "17101223"}, "source": 820, "target": 3327, "key": "ed20125c0b1db1e9b070bdd07e36e206"}, {"line": 22808, "relation": "positiveCorrelation", "evidence": "The ERK2 isoform of the ERK pathway was less activated in the hippocampal dentate gyrus of Tg mice in basal conditions. Furthermore activation of the ERK pathway by ex vivo cholinergic stimulation with carbachol caused significantly higher activation of ERK in the hippocampus of Wt mice than in Tg mice. These findings may pose a molecular basis for the memory disruption of Alzheimer's disease, since proper functioning of the basal forebrain cholinergic neurons and of ERK2 is critical for memory formation.", "citation": {"db": "PubMed", "db_id": "8129042"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 820, "target": 2990, "key": "e0cdaea6aa9c6338e0f026a8fb78d23a"}, {"line": 26372, "relation": "negativeCorrelation", "evidence": "Coupled with recent studies showing that synthetic and naturally occurring Abeta oligomers can inhibit hippocampal long-term potentiation, the in vivo age-dependent accumulation of SDS-soluble Abeta dimers in lipid rafts at the time when memory impairment begins in Tg2576 mice provides strong evidence linking Abeta oligomers to memory impairment.", "citation": {"db": "PubMed", "db_id": "15084661"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 820, "target": 80, "key": "9b3d03e41540031ee8afe932788e9b5f"}, {"line": 30224, "relation": "association", "evidence": "Glutamate receptors play crucial roles in cognition and memory.", "citation": {"db": "PubMed", "db_id": "10588576"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 820, "target": 2773, "key": "d95ebd504452470cf20a60b75c47daab"}, {"line": 40381, "relation": "association", "evidence": "Previous studies demonstrated that adiponectin modulates memory and cognitive impairment and contributes to the deregulated glucose metabolism and mitochondrial dysfunction observed in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 820, "target": 2259, "key": "de965fdc91fa29bbe5b617fe1392c5c2"}, {"line": 46253, "relation": "negativeCorrelation", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 820, "target": 3823, "key": "9cc9c55b13f9e3a336a002026d5a8ecd"}, {"line": 749, "relation": "association", "evidence": "Abeta-dependent inactivation of the JAK2/STAT3 axis causes memory loss through cholinergic dysfunction.", "citation": {"db": "PubMed", "db_id": "18813209"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 762, "target": 2933, "key": "621e7b030c15255a69697a87cb78ed5b"}, {"line": 1017, "relation": "negativeCorrelation", "evidence": "In vitro and in vivo studies have consistently demonstrated a link between cholinergic activation and APP metabolism.Reduction in cholinergic neurotransmission--experimental or pathological, such as in AD--leads to amyloidogenic metabolism and contributes to the neuropathology and cognitive dysfunction", "citation": {"db": "PubMed", "db_id": "12675140"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 762, "target": 3823, "key": "14f72cb167471190934102d816e9049e"}, {"line": 1021, "relation": "increases", "evidence": "In vitro and in vivo studies have consistently demonstrated a link between cholinergic activation and APP metabolism.Reduction in cholinergic neurotransmission--experimental or pathological, such as in AD--leads to amyloidogenic metabolism and contributes to the neuropathology and cognitive dysfunction", "citation": {"db": "PubMed", "db_id": "12675140"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 762, "target": 80, "key": "15bef09ffbcecfb80d8564dca807f109"}, {"line": 1025, "relation": "increases", "evidence": "In vitro and in vivo studies have consistently demonstrated a link between cholinergic activation and APP metabolism.Reduction in cholinergic neurotransmission--experimental or pathological, such as in AD--leads to amyloidogenic metabolism and contributes to the neuropathology and cognitive dysfunction", "citation": {"db": "PubMed", "db_id": "12675140"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 762, "target": 812, "key": "c731139dbcf2b88f2b9885fc4cac664c"}, {"line": 13584, "relation": "increases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 762, "target": 812, "key": "d385173cdbedbe18bc614ecde53531f0"}, {"line": 13588, "relation": "increases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 762, "target": 818, "key": "8158ea5c6106981adc5748e08e917296"}, {"line": 13592, "relation": "increases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 762, "target": 820, "key": "8b5cd87995a9d27cea0363e23ed2ef3d"}, {"line": 774, "relation": "positiveCorrelation", "evidence": "Part of the inflammatory response in Alzheimer's disease (AD) is the upregulation of the inducible nitric oxide synthase (NOS2) resulting in increased NO production.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 3123, "target": 3823, "key": "703023f814e00f4d2185fc0b0af3bb0c"}, {"line": 778, "relation": "increases", "evidence": "Part of the inflammatory response in Alzheimer's disease (AD) is the upregulation of the inducible nitric oxide synthase (NOS2) resulting in increased NO production.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 3123, "target": 156, "key": "ed4b5ff26a9e55b0b67e2388d90463e1"}, {"line": 39044, "relation": "increases", "evidence": "Recently, it was proposed that some NSAIDs might activate the peroxisome proliferator-activated / receptor-gamma (PPAR-gamma). PPAR-gamma belongs to a family of nuclear receptors that are able to regulate the / transcription of pro-inflammatory molecules, such as iNOS. ", "citation": {"db": "PubMed", "db_id": "16472958"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 3123, "target": 156, "key": "b91db1c335d709ea096b5705f5207e64"}, {"line": 39462, "relation": "increases", "evidence": "It has been shown that apoE increased the production of nitric oxide (NO) from human monocyte-derived macrophages (MDM); this effect could represent an important link between tissue redox balance and inflammation, since inflammation and oxidative stress are involved in chronic neurodegenerative disorders. Moreover, it has been evidenced that an overproduction of NO in the central nervous system (CNS) may play a key role in aging and that the glial cells (microglials cells and probably astrocytes) are able to form consistent amounts of NO through the induction of a nitric oxide synthase (iNOS) isoform so-called inducible or inflammatory.We observed a decreased NO production after incubation with both LDL and HDL and an increased peroxynitrite production. As it concerns NOS expression, densitometric analysis of bands indicated that iNOS protein levels were significantly higher in the cells incubated with both AD lipoproteins and offspring lipoproteins compared to cells incubated with control lipoproteins. These findings suggest the possibility to identify in NO pathway a precocious marker of AD.", "citation": {"db": "PubMed", "db_id": "16054114"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3123, "target": 156, "key": "c35405f12da13f6a493c959a6a7e5bfc"}, {"line": 22691, "relation": "negativeCorrelation", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3123, "target": 2867, "key": "a4c898acce83f66c43089b9a05a6f140"}, {"line": 38759, "relation": "decreases", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 3123, "target": 2867, "key": "6952407c5bbb4bbc3eea337bedd26f09"}, {"line": 22692, "relation": "positiveCorrelation", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3123, "target": 80, "key": "d540b37c52ed66734b25030284e6d1ef"}, {"line": 22700, "relation": "negativeCorrelation", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3123, "target": 80, "key": "e0425f05bb25a888721308afc07081d0"}, {"line": 22745, "relation": "decreases", "evidence": "Viral infection often activates the interferon (IFN)-gamma-inducible gene, nitric oxide synthase 2 (NOS2). Expression of NOS2 can limit viral growth but may also suppress the immune system and damage tissue.", "citation": {"db": "PubMed", "db_id": "9782132"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3123, "target": 769, "key": "df301d17b609b3950d5e1f9d1fb2ac26"}, {"line": 22749, "relation": "decreases", "evidence": "Viral infection often activates the interferon (IFN)-gamma-inducible gene, nitric oxide synthase 2 (NOS2). Expression of NOS2 can limit viral growth but may also suppress the immune system and damage tissue.", "citation": {"db": "PubMed", "db_id": "9782132"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3123, "target": 576, "key": "705fcfbc804e315af85c8158c14d6d01"}, {"line": 38763, "relation": "increases", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Degradation"}, "source": 3123, "target": 2328, "key": "398c8f56577e35adad1efc9ca2fcbacf"}, {"line": 38767, "relation": "negativeCorrelation", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3123, "target": 2328, "key": "22a12c236a50d5f274053f701462b25d"}, {"relation": "partOf", "source": 3123, "target": 901, "key": "a1dd13eb7ac8e95d48e2f4b69e6830c1"}, {"line": 787, "relation": "increases", "evidence": "NO contributes to cell signaling by inducing posttranslational protein modifications. Under pathological conditions there is a shift from the signal transducing actions to the formation of protein tyrosine nitration by secondary products like peroxynitrite and nitrogen dioxide. We identified amyloid Abeta (Abeta) as an NO target, which is nitrated at tyrosine 10 (3NTyr(10)-Abeta). Nitration of Abeta accelerated its aggregation and was detected in the core of Abeta plaques of APP/PS1 mice and AD brains.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 157, "target": 2343, "key": "ec9a0f976e14ea3ed690168f1dcd4f84"}, {"line": 802, "relation": "increases", "evidence": "NO contributes to cell signaling by inducing posttranslational protein modifications. Under pathological conditions there is a shift from the signal transducing actions to the formation of protein tyrosine nitration by secondary products like peroxynitrite and nitrogen dioxide. We identified amyloid Abeta (Abeta) as an NO target, which is nitrated at tyrosine 10 (3NTyr(10)-Abeta). Nitration of Abeta accelerated its aggregation and was detected in the core of Abeta plaques of APP/PS1 mice and AD brains.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}}, "source": 2343, "target": 2328, "key": "4f294e523880b46f85578dc51b8ab9e3"}, {"line": 803, "relation": "increases", "evidence": "NO contributes to cell signaling by inducing posttranslational protein modifications. Under pathological conditions there is a shift from the signal transducing actions to the formation of protein tyrosine nitration by secondary products like peroxynitrite and nitrogen dioxide. We identified amyloid Abeta (Abeta) as an NO target, which is nitrated at tyrosine 10 (3NTyr(10)-Abeta). Nitration of Abeta accelerated its aggregation and was detected in the core of Abeta plaques of APP/PS1 mice and AD brains.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}}, "source": 2343, "target": 3823, "key": "bdcb2aea327cb2045cad49f26b011e77"}, {"line": 41898, "relation": "increases", "evidence": "Further, injection of 3NTyr(10)-Abeta into the brain of young APP/PS1 mice induced beta-amyloidosis.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Confidence": {"High": true}, "Species": {"10090": true}, "Disease": {"amyloidosis": true}, "MeSHAnatomy": {"Brain": true}}, "source": 2343, "target": 3889, "key": "393b44a7ce1ae83f3c0423727af7266b"}, {"line": 791, "relation": "increases", "evidence": "NO contributes to cell signaling by inducing posttranslational protein modifications. Under pathological conditions there is a shift from the signal transducing actions to the formation of protein tyrosine nitration by secondary products like peroxynitrite and nitrogen dioxide. We identified amyloid Abeta (Abeta) as an NO target, which is nitrated at tyrosine 10 (3NTyr(10)-Abeta). Nitration of Abeta accelerated its aggregation and was detected in the core of Abeta plaques of APP/PS1 mice and AD brains.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 328, "target": 2343, "key": "1e3ef29fbc2f5dd104b976065a043a0b"}, {"line": 16188, "relation": "increases", "evidence": "Piceatannol attenuates hydrogen-peroxide- and peroxynitrite-induced apoptosis of PC12 cells by blocking down-regulation of Bcl-XL and activation of JNK.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"CellLine": {"PC-12 cell": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 328, "target": 478, "key": "b4d8728b39bca478ebe004fff29d6b43"}, {"line": 795, "relation": "association", "evidence": "NO contributes to cell signaling by inducing posttranslational protein modifications. Under pathological conditions there is a shift from the signal transducing actions to the formation of protein tyrosine nitration by secondary products like peroxynitrite and nitrogen dioxide. We identified amyloid Abeta (Abeta) as an NO target, which is nitrated at tyrosine 10 (3NTyr(10)-Abeta). Nitration of Abeta accelerated its aggregation and was detected in the core of Abeta plaques of APP/PS1 mice and AD brains.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "source": 656, "target": 156, "key": "6616fa0cc905782ab743d854655484ea"}, {"line": 819, "relation": "association", "evidence": "Alpha synuclein also contributes to the intracellular inclusions of multiple system atrophy, and a fragment has been found in senile plaques in Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "10491577"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synuclein subgraph": true}, "Confidence": {"Very High": true}}, "source": 3384, "target": 2328, "key": "b341ed47456ca619b91702bcfdea4b04"}, {"line": 1333, "relation": "increases", "evidence": "a-Synuclein potentiates interleukin-1Abeta-induced CXCL10 expression in human A172 astrocytoma cells. we investigated the in vitro effects of interleukin-1Abeta (IL-1Abeta) and a-synuclein on astroglial expression of interferon-gamma inducible protein-10 (CXCL10), a proinflammatory and neurotoxic chemokine. IL-1Abeta-induced CXCL10 protein expression was potentiated by co-exposure to a-synuclein. a-Synuclein did not significantly affect IL-1Abeta-induced CXCL10 mRNA expression, but did mediate increased CXCL10 mRNA stability, which may explain, in part, the increased levels of secreted CXCL10 protein. Future investigations are warranted to more fully define the mechanism by which a-synuclein enhances IL-1Abeta-induced astroglial CXCL10 expression", "citation": {"db": "PubMed", "db_id": "22178859"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Synuclein subgraph": true, "Caspase subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3384, "target": 2885, "key": "d4f7c6ac85f6642a586159870430e709"}, {"line": 4461, "relation": "decreases", "evidence": "A large body of literature has suggested an important role for a number of factors including oxidative stress, mitochondrial DNA mutations, imbalance in calcium homeostasis and aging in the dysfunction of mitochondrial complexes (28-34, 40, 46, 47). In addition, recent studies have also implicated a role for targeting and accumulation of plasma membrane APP and cytosolic alpha synuclein to mitochondria in the pathogenesis of mitochondrial dysfunction in Alzheimer's and Parkinson’s diseases, respectively.", "citation": {"db": "PubMed", "db_id": "19619643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Mitochondria": true}, "Subgraph": {"Synuclein subgraph": true}, "Confidence": {"High": true}}, "source": 3384, "target": 614, "key": "a50352f7dd25264f88d0b59ffe6e04cd"}, {"line": 6592, "relation": "association", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Synuclein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3384, "target": 80, "key": "6c47a30858ef376023db0815ae840e9a"}, {"relation": "partOf", "source": 3384, "target": 1446, "key": "a7a989f6c0a167504c0ca9a412f5c6d1"}, {"line": 9044, "relation": "positiveCorrelation", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3384, "target": 2794, "key": "7b067252e5ecaf92c9e8d5d303746e66"}, {"line": 9046, "relation": "directlyIncreases", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 3384, "target": 3015, "key": "aaff4635504cc9013ac4404c25dc5e13"}, {"line": 9577, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "Synuclein subgraph": true, "Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 3384, "target": 420, "key": "19b94bb1124b0b0c0dadb8510b9f8346"}, {"line": 9603, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Synuclein subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 3384, "target": 540, "key": "b1c0a62966a853cf34826f95b41910ee"}, {"relation": "hasVariant", "source": 3384, "target": 3386, "key": "579ffa88e9c5161fefa8aaea81c60a54"}, {"relation": "partOf", "source": 3384, "target": 1071, "key": "366bdb1186b9a3385643812139073b36"}, {"relation": "partOf", "source": 3384, "target": 1443, "key": "b47c9a417050a872d8f30cb7112ed30a"}, {"line": 26118, "relation": "increases", "evidence": "Pathogenic aggregates of alpha-synuclein are thought to contribute to the development of Parkinson's disease. ", "citation": {"db": "PubMed", "db_id": "18297066"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3384, "target": 3878, "key": "268ebfa8af63bb0242de564c787a37b2"}, {"line": 26126, "relation": "decreases", "evidence": "ApoE was not protective, but was injurious, as deletion of ApoE delayed the neurodegeneration caused by alpha-synuclein and suppressed the accumulation of Abeta. ", "citation": {"db": "PubMed", "db_id": "18297066"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Synuclein subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3384, "target": 431, "key": "2de5cfb0aab462d26d9776ed4d791626"}, {"relation": "hasVariant", "source": 3384, "target": 3385, "key": "1b5a87e401637c61badffcc4986e8c3f"}, {"line": 47349, "relation": "isA", "evidence": "Binding to HSPGs requires a heparin/heparan sulfate-binding domain consisting of a stretch of positively charged lysines or arginines on the ligand. Prion protein, beta-amyloid, tau, and alpha-synuclein all have putative heparin-binding domains(25, 44–46).", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3384, "target": 49, "key": "869b399f17e87715ac1edea86691ca6d"}, {"line": 833, "relation": "increases", "evidence": "Normal cells respond to ER stress by increasing transcription of genes encoding ER-resident chaperones such as GRP78/BiP, GRP94 and protein disulfide isomerase to facilitate protein folding. This induction system is termed the unfolded protein response. Familial Alzheimer's disease-linked presenilin-1 (PS1) mutation downregulates the unfolded protein response and leads to vulnerability to ER stress. ", "citation": {"db": "PubMed", "db_id": "11406343"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "source": 771, "target": 3978, "key": "dc76341ecbc57e019deb10d06bafb009"}, {"line": 834, "relation": "increases", "evidence": "Normal cells respond to ER stress by increasing transcription of genes encoding ER-resident chaperones such as GRP78/BiP, GRP94 and protein disulfide isomerase to facilitate protein folding. This induction system is termed the unfolded protein response. Familial Alzheimer's disease-linked presenilin-1 (PS1) mutation downregulates the unfolded protein response and leads to vulnerability to ER stress. ", "citation": {"db": "PubMed", "db_id": "11406343"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}}, "source": 771, "target": 3977, "key": "fd03639783fbf8b09a07903d7cfefa76"}, {"line": 1441, "relation": "association", "evidence": "In mammals,CREB-regulated transcriptional coactivators (CRTCs) are a family of cofactors involved in diverse physiological processes including energy homeostasis, cancer and endoplasmic reticulum stress. Here we show that both AMPK and calcineurin modulate longevity exclusively through post-translational modification of CRTC-1, the sole C. elegans CRTC. We demonstrate that CRTC-1 is a direct AMPK target, and interacts with the CREB homologue-1 (CRH-1) transcription factor in vivo. The pro-longevity effects of activating AMPK or deactivating calcineurin decrease CRTC-1 and CRH-1 activity and induce transcriptional responses similar to those of CRH-1 null worms. Downregulation of crtc-1 increases lifespan in a crh-1-dependent manner and directly reducing crh-1 expression increases longevity, substantiating a role for CRTCs and CREB in ageing. Together, these findings indicate a novel role for CRTCs and CREB in determining lifespan downstream of AMPK and calcineurin, and illustrate the molecular mechanisms by which an evolutionarily conserved pathway responds to low energy to increase longevity.", "citation": {"db": "PubMed", "db_id": "21331044"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "CREB subgraph": true}}, "object": {"modifier": "Activity"}, "source": 771, "target": 2162, "key": "22a3677625b08cad3e8bdb0028e1a7a7"}, {"line": 2487, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 771, "target": 3823, "key": "6921409542dd8161e2366084372c4386"}, {"line": 20313, "relation": "association", "evidence": "Endoplasmic reticulum (ER) stress is suggested to play a key role in the pathogenesis of neurodegenerative diseases including Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22046282"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"Medium": true}}, "source": 771, "target": 3823, "key": "ea3d14108c27f49912599c5b66bb0632"}, {"line": 4098, "relation": "association", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 771, "target": 2678, "key": "c8ec6eb3fe444e8da5615f3533a782f3"}, {"line": 4099, "relation": "association", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 771, "target": 2664, "key": "29525c9a8892bb33139dd73d18704d52"}, {"line": 4100, "relation": "association", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 771, "target": 2367, "key": "986084b120cc67c05e83066b518d2c68"}, {"line": 4106, "relation": "increases", "evidence": "It was clarified what molecules related to cell death are activated in the case of AD and we discovered that caspase-4 plays a key role in ER stress-induced apoptotic process. Caspase-4 also seems to act upstream of the beta-amyloid-induced ER stress pathway, suggesting that activation of caspase-4 might mediate neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Caspase subgraph": true}}, "source": 771, "target": 645, "key": "791153032edaaeb2d1d77831d591189e"}, {"line": 4111, "relation": "increases", "evidence": "It was clarified what molecules related to cell death are activated in the case of AD and we discovered that caspase-4 plays a key role in ER stress-induced apoptotic process. Caspase-4 also seems to act upstream of the beta-amyloid-induced ER stress pathway, suggesting that activation of caspase-4 might mediate neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Caspase subgraph": true}}, "source": 771, "target": 478, "key": "e2c2fcd77d87f255c27a26f4fc6c34f4"}, {"line": 10412, "relation": "increases", "evidence": "ER stress contributes to the pathogenesis of obesity and diabetes, which are risk factors for Alzheimer's disease (AD) that accelerate the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 771, "target": 3925, "key": "8d429cb643129f5586a0a2c47f1661da"}, {"line": 10413, "relation": "increases", "evidence": "ER stress contributes to the pathogenesis of obesity and diabetes, which are risk factors for Alzheimer's disease (AD) that accelerate the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 771, "target": 3850, "key": "db5fad1f00f259fbf14ac360dbf823d3"}, {"line": 10423, "relation": "increases", "evidence": "In this study, we demonstrate that ER stress induces presenilin-1 expression through activating transcription factor 4 (ATF4), resulting in increased amyloid-beta (Abeta) secretion by gamma-secretase activity, which is suppressed by quercetin by modifying UPR signaling.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "MeSHAnatomy": {"Bodily Secretions": true}, "Confidence": {"Medium": true}}, "source": 771, "target": 3258, "key": "f4f873619a27df7541c34c5865b0c3de"}, {"line": 10424, "relation": "increases", "evidence": "In this study, we demonstrate that ER stress induces presenilin-1 expression through activating transcription factor 4 (ATF4), resulting in increased amyloid-beta (Abeta) secretion by gamma-secretase activity, which is suppressed by quercetin by modifying UPR signaling.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "MeSHAnatomy": {"Bodily Secretions": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 771, "target": 2366, "key": "f76d7490d70631096580595eb77df774"}, {"line": 10426, "relation": "increases", "evidence": "In this study, we demonstrate that ER stress induces presenilin-1 expression through activating transcription factor 4 (ATF4), resulting in increased amyloid-beta (Abeta) secretion by gamma-secretase activity, which is suppressed by quercetin by modifying UPR signaling.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "MeSHAnatomy": {"Bodily Secretions": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 771, "target": 868, "key": "7cf498b54b6213b4f47de1049b049ac5"}, {"line": 20262, "relation": "increases", "evidence": "CHOP potentially co-operates with FOXO3a in neuronal cells to regulate PUMA and BIM expression in response to ER stress.", "citation": {"db": "PubMed", "db_id": "22761832"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Response DNA damage": true}}, "source": 771, "target": 2622, "key": "c91dadc425c91631607f665e7f887999"}, {"line": 20275, "relation": "increases", "evidence": "Consistent with previous studies, we show that both PUMA and BIM are induced in response to ER stress in neuronal cells and that transcriptional induction of PUMA regulates ER stress-induced cell death, independent of p53.", "citation": {"db": "PubMed", "db_id": "22761832"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Bcl-2 subgraph": true}}, "source": 771, "target": 2391, "key": "4907791964caf6eeb82aa6427d779347"}, {"line": 20276, "relation": "increases", "evidence": "Consistent with previous studies, we show that both PUMA and BIM are induced in response to ER stress in neuronal cells and that transcriptional induction of PUMA regulates ER stress-induced cell death, independent of p53.", "citation": {"db": "PubMed", "db_id": "22761832"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Bcl-2 subgraph": true}}, "source": 771, "target": 2396, "key": "54c6f37e0f18c21db640bc7ed5639a5a"}, {"line": 28734, "relation": "association", "evidence": "These results suggest that the breakdown of HRD1-mediated ERAD causes Abeta generation and ER stress, possibly linked to AD.", "citation": {"db": "PubMed", "db_id": "20237263"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 771, "target": 835, "key": "9a48c58432800462609aab949effe9d8"}, {"line": 35675, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 771, "target": 2989, "key": "9bcfbe89fc8c21f76d8c880a4ddff65c"}, {"line": 841, "relation": "decreases", "evidence": "Normal cells respond to ER stress by increasing transcription of genes encoding ER-resident chaperones such as GRP78/BiP, GRP94 and protein disulfide isomerase to facilitate protein folding. This induction system is termed the unfolded protein response. Familial Alzheimer's disease-linked presenilin-1 (PS1) mutation downregulates the unfolded protein response and leads to vulnerability to ER stress. ", "citation": {"db": "PubMed", "db_id": "11406343"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 1927, "target": 777, "key": "e48f8961795b19160cc4b1970cd6d984"}, {"line": 842, "relation": "decreases", "evidence": "Normal cells respond to ER stress by increasing transcription of genes encoding ER-resident chaperones such as GRP78/BiP, GRP94 and protein disulfide isomerase to facilitate protein folding. This induction system is termed the unfolded protein response. Familial Alzheimer's disease-linked presenilin-1 (PS1) mutation downregulates the unfolded protein response and leads to vulnerability to ER stress. ", "citation": {"db": "PubMed", "db_id": "11406343"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 1927, "target": 771, "key": "4dc3ea606403ec141ad613e7d8ec2a1c"}, {"line": 857, "relation": "decreases", "evidence": "PS1 mutations cause abnormalities in ER calcium homoeostasis, enhancing the calcium responses to stimuli that activate IP3- and ryanodine-sensitive ER calcium pools.", "citation": {"db": "PubMed", "db_id": "11447832"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 1927, "target": 549, "key": "64850f88135bcecd003cbd386b015692"}, {"line": 858, "relation": "decreases", "evidence": "PS1 mutations cause abnormalities in ER calcium homoeostasis, enhancing the calcium responses to stimuli that activate IP3- and ryanodine-sensitive ER calcium pools.", "citation": {"db": "PubMed", "db_id": "11447832"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 1927, "target": 491, "key": "0f20af9fa10592140388a8486cbabca3"}, {"relation": "hasVariant", "source": 1925, "target": 1927, "key": "e56dfd78acc9bf960b314179eca708e1"}, {"line": 2907, "relation": "association", "evidence": "Presenilin-1 (PS1) is the catalytic subunit of gamma-secretase and mutations in this protein cause familial Alzheimer Disease (FAD). ", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1925, "target": 3823, "key": "cf2483d50d0db25b0153d8b2e1033547"}, {"line": 6559, "relation": "association", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1925, "target": 3823, "key": "292928a012a1b775266d5b0e9e4f1392"}, {"relation": "partOf", "source": 1925, "target": 1667, "key": "9d3dc91125ec1431e31e50daa93e34c6"}, {"line": 6568, "relation": "increases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1925, "target": 80, "key": "53e3eb3bc49133bc790bae1d58376587"}, {"line": 6571, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1925, "target": 777, "key": "6bb87b665d395e158b0c4d653d708267"}, {"line": 26958, "relation": "increases", "evidence": "The PSEN1 AD mutations giving rise to CWP produce unusually high levels of the amyloid beta peptide (Abeta) ending at position 42 or 43, and the main component of CWP is amino-terminally truncated forms of amyloid beta peptide starting after the alternative beta-secretase cleavage site at position 11.", "citation": {"db": "PubMed", "db_id": "17995932"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1925, "target": 2328, "key": "2dab6f6920eb183eede15bcda8fa2fa7"}, {"relation": "hasVariant", "source": 1925, "target": 1926, "key": "25ef541bdf75d53d76d61230d960e4c7"}, {"line": 45898, "relation": "association", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1925, "target": 2803, "key": "204fa4ed5d3ef9f611c83670aa82c013"}, {"line": 46310, "relation": "orthologous", "evidence": "the demethylation of Presenilin1 gene promoter in nutritionally-induced hyperhomocysteinemia in a transgenic mouse model clearly demonstrated that Presenilin1 is regulated by DNA methylation.", "citation": {"db": "PubMed", "db_id": "22272624"}, "annotations": {"Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1925, "target": 2064, "key": "b6d0bb5aaf5b956a7dc1832755adb653"}, {"line": 6572, "relation": "increases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 777, "target": 771, "key": "130100388930fd70b7c984197e0e62ef"}, {"line": 21563, "relation": "decreases", "evidence": "Albumin prevents mitochondrial depolarization and apoptosis elicited by endoplasmic reticulum calcium depletion of neuroblastoma cells.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"Medium": true}}, "source": 549, "target": 616, "key": "4004fc8602d2fa6c88243360c6f07b39"}, {"line": 21564, "relation": "decreases", "evidence": "Albumin prevents mitochondrial depolarization and apoptosis elicited by endoplasmic reticulum calcium depletion of neuroblastoma cells.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"Medium": true}}, "source": 549, "target": 478, "key": "38ac58cbf4e9b8a56afd0be744d42b3d"}, {"line": 2370, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 491, "target": 80, "key": "cfdde4b5a21755eaf5544da3575ff560"}, {"line": 2849, "relation": "association", "evidence": "APP-binding protein 1 reportedly interacts with AICD and activates the neddylation pathway, further down-regulating the level of b-catenin and potentially resulting in apoptotic process. In addition, cellular Ca2+ homeostasis appears to be modulated by AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 491, "target": 3563, "key": "150b1c7aff7b0a28ad21ea456111a469"}, {"line": 3962, "relation": "negativeCorrelation", "evidence": "Abeta can also interact with Fe2+ and Cu+ to generate hydrogen peroxide and hydroxyl radical (OH.) resulting in membrane lipid peroxidation which generates toxic aldehydes that impair the function of membrane ion-motive ATPases (Na+ and Ca2+ pumps)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "CellStructure": {"Cell Membrane": true}, "Confidence": {"High": true}}, "source": 491, "target": 605, "key": "ceceb833047f40ee89bfaffa45c3232e"}, {"line": 5327, "relation": "increases", "evidence": "Increased oxidative stress, coupled with dysregulation of calcium homeostasis and resulting apoptosis of vulnerable neuronal populations, are proposed to underlie the loss of synaptic activity and associated cognitive decline. From these deficiencies emerges the concept of synaptic energy exhaustion in AD, both phosphorylative (ATP) and redox (NAD[P]H) energies. Our previous work shows that hippocampal NAD(P)H and glutathione (GSH) decline with age in association with increased susceptibility to glutamate toxicity in neurons of old-age. Thus, an age-related decline in neuronal reducing currency (NAD[P]H) and reducing buffer (GSH) will surely promote oxidative stress and excess ROS. It is noteworthy that in the early stages of AD, there is already a reduction in the number of mitochondria and the activities of tricarboxylic acid cycle enzymes and cytochrome C oxidase. However, how ROS are produced at the synapse in response to Abeta oligomers is not fully known.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 491, "target": 760, "key": "dfec46ded8a2d2605db4c3d19d83edc1"}, {"line": 5381, "relation": "association", "evidence": "Many studies suggest the possible involvement of oxidative stress and calcium dysfunction in Abeta toxicity.The question as to why brain synaptic ROS levels increase with age is uncertain, but may involve lack of use followed by acute overstimulation of excitatory NMDARs that leads to excessive ROS, related to excess Ca2+ entry into mitochondria. Dysregulation of NMDAR function induced by Abeta binding to neuronal synapses may lead to synaptic mitochondrial dysfunction and excessive ROS formation.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Calcium-dependent signal transduction": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 491, "target": 79, "key": "5bfcb9d887517976d155a302c81cb414"}, {"line": 10504, "relation": "association", "evidence": "A growing number of reports suggest that elevated levels of extracellular Alzheimer's beta-amyloid protein alter the homeostasis of free [Ca(2+)](i) in different cell types of the mammalian brain.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}}, "source": 491, "target": 2328, "key": "aabae467fea7a2209c816367b1c428cd"}, {"line": 864, "relation": "increases", "evidence": "PS1 mutations cause abnormalities in ER calcium homoeostasis, enhancing the calcium responses to stimuli that activate IP3- and ryanodine-sensitive ER calcium pools.", "citation": {"db": "PubMed", "db_id": "11447832"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 3333, "target": 757, "key": "5c90934f94f402c56609c99b523e8ab6"}, {"line": 2322, "relation": "increases", "evidence": "Mechanistic studies have shown that FAD mutants of presenilin can affect the intracellular calcium levels by affecting the ER calcium stores. A function for presenilins as ER calcium leak channels has been established and studies show that presenilins affect ER calcium load through an effect on IP(3) receptors, ryanodine receptors, or SERCA pumps. Even in the absence of an active gamma-secretase complex, presenilins seem to affect calcium homeostasis suggesting that these two functions of presenilins are independent of each other.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3333, "target": 757, "key": "23e211138eedea21b596ee4982586525"}, {"line": 4001, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3333, "target": 757, "key": "012e9c72a1e7b059bef437a08f791931"}, {"line": 4000, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3333, "target": 3258, "key": "7624c2fd73416e5aab27caddfb9b823e"}, {"line": 4005, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 3333, "target": 3268, "key": "c66dd2dd290100a1b7f56b9bfc74200d"}, {"line": 3999, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 757, "target": 3258, "key": "1c4ae8c9d480862750e5e515f942eda3"}, {"line": 4001, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 757, "target": 3333, "key": "2c5e957d6b53307824787c3c8033fedf"}, {"line": 4004, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 757, "target": 3268, "key": "dfba973b77befe18bc11830727cf0237"}, {"line": 865, "relation": "increases", "evidence": "PS1 mutations cause abnormalities in ER calcium homoeostasis, enhancing the calcium responses to stimuli that activate IP3- and ryanodine-sensitive ER calcium pools.", "citation": {"db": "PubMed", "db_id": "11447832"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 2931, "target": 733, "key": "63232272d3041ebeb15a92b9acea2566"}, {"relation": "partOf", "source": 2931, "target": 1497, "key": "e1103676e3c8eed151a4eac4f1cfb037"}, {"line": 883, "relation": "association", "evidence": "beta APs enhanced both kainate and NMDA neurotoxicity, indicating that the effect was not specific for a particular subtype of glutamate receptor.", "citation": {"db": "PubMed", "db_id": "1346802"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 3548, "target": 80, "key": "0db0faf623f4c14e94819d3b2f496931"}, {"line": 47067, "relation": "increases", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "source": 3548, "target": 80, "key": "b225900801435ad141c1d7040e1131bf"}, {"line": 47070, "relation": "association", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 80, "key": "9371c696221757d0ed8005dc1df1d69c"}, {"line": 4019, "relation": "directlyIncreases", "evidence": "Interaction of the protein reelin with the apolipoprotein E receptor (ApoER2) enhances Ca2+ influx through NMDA receptor channels by a mechanism involving a src family tyrosine kinsase (SFk); ApoE can block this effect of reelin", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 492, "key": "82850628523fe5df9f3c09374ab1b26c"}, {"line": 5385, "relation": "increases", "evidence": "Many studies suggest the possible involvement of oxidative stress and calcium dysfunction in Abeta toxicity.The question as to why brain synaptic ROS levels increase with age is uncertain, but may involve lack of use followed by acute overstimulation of excitatory NMDARs that leads to excessive ROS, related to excess Ca2+ entry into mitochondria. Dysregulation of NMDAR function induced by Abeta binding to neuronal synapses may lead to synaptic mitochondrial dysfunction and excessive ROS formation.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Calcium-dependent signal transduction": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 170, "key": "a91b3f6038f782843760751c3ddd16d0"}, {"line": 40979, "relation": "increases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Neurons": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 170, "key": "d7726883b1cf0f439f30e86f1e5218b9"}, {"line": 5389, "relation": "decreases", "evidence": "Many studies suggest the possible involvement of oxidative stress and calcium dysfunction in Abeta toxicity.The question as to why brain synaptic ROS levels increase with age is uncertain, but may involve lack of use followed by acute overstimulation of excitatory NMDARs that leads to excessive ROS, related to excess Ca2+ entry into mitochondria. Dysregulation of NMDAR function induced by Abeta binding to neuronal synapses may lead to synaptic mitochondrial dysfunction and excessive ROS formation.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Calcium-dependent signal transduction": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3548, "target": 430, "key": "a3b9a4d85c9910473c567975886635db"}, {"line": 5438, "relation": "decreases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "ADAM Metallopeptidase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3548, "target": 2252, "key": "da4b1a14aa815a6ea175f6ae56b692a4"}, {"line": 5439, "relation": "decreases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "ADAM Metallopeptidase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3548, "target": 2249, "key": "4d72e56ebdea56db7856fb414dff51a7"}, {"line": 5440, "relation": "decreases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "ADAM Metallopeptidase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3548, "target": 2250, "key": "e36748fb82c9d0452c85a80683bad42c"}, {"line": 17114, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 3548, "target": 761, "key": "0163edd21573bfe3aaf67db670848075"}, {"line": 47047, "relation": "association", "evidence": "The hippocampus, with its high density of glutamate receptors and in particular NMDA receptors, is known to be extremely important for some forms of learning and memory. Glutamatergic synapses can show pronounced plasticity in terms of the number and strength of individual synapses and are also characterized by their ability to express LTP – a long-lasting strengthening of synaptic transmission ", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Hippocampus": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 761, "key": "5b4e63114429ca5860f9c87d9586edc5"}, {"line": 47036, "relation": "association", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 2328, "key": "759ac3c519a4c6f511605ff2bc2a90dd"}, {"line": 47038, "relation": "association", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 588, "key": "d58b2a3a089fddbf76a0e436c0c6e1e2"}, {"line": 47048, "relation": "association", "evidence": "The hippocampus, with its high density of glutamate receptors and in particular NMDA receptors, is known to be extremely important for some forms of learning and memory. Glutamatergic synapses can show pronounced plasticity in terms of the number and strength of individual synapses and are also characterized by their ability to express LTP – a long-lasting strengthening of synaptic transmission ", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Hippocampus": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 588, "key": "54e69c0ae458ef47cfb505d633a619fe"}, {"line": 47049, "relation": "association", "evidence": "The hippocampus, with its high density of glutamate receptors and in particular NMDA receptors, is known to be extremely important for some forms of learning and memory. Glutamatergic synapses can show pronounced plasticity in terms of the number and strength of individual synapses and are also characterized by their ability to express LTP – a long-lasting strengthening of synaptic transmission ", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Hippocampus": true}}, "subject": {"modifier": "Activity"}, "source": 3548, "target": 596, "key": "7541de42b3024a4dfc992db8d18312ac"}, {"line": 884, "relation": "association", "evidence": "beta APs enhanced both kainate and NMDA neurotoxicity, indicating that the effect was not specific for a particular subtype of glutamate receptor.", "citation": {"db": "PubMed", "db_id": "1346802"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 736, "target": 80, "key": "0c1ca433b89fa92bfadfe7b05c2acf81"}, {"line": 905, "relation": "increases", "evidence": "Creatine kinase(CK) and beta-actin have increased carbonyl groups, an index of protein oxidation, and Glt-1, the principal glutamate transporter, has increased binding of the lipid peroxidation product, 4-hydroxy-2-nonenal (HNE). Abeta inhibits CK and causes lipid peroxidation, leading to HNE formation.", "citation": {"db": "PubMed", "db_id": "12607822"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2530, "target": 838, "key": "f3cc4b9c1d5986d1e03639c6ab20f286"}, {"line": 906, "relation": "increases", "evidence": "Creatine kinase(CK) and beta-actin have increased carbonyl groups, an index of protein oxidation, and Glt-1, the principal glutamate transporter, has increased binding of the lipid peroxidation product, 4-hydroxy-2-nonenal (HNE). Abeta inhibits CK and causes lipid peroxidation, leading to HNE formation.", "citation": {"db": "PubMed", "db_id": "12607822"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 838, "target": 24, "key": "a92d5926b8b74714a7affb8a3c7eee8e"}, {"line": 42025, "relation": "association", "evidence": "The hippocampal lipid peroxidation correlated strongly with the increase of LOC positive fiber load, whereas neocortical TBARS levels were unrelated to amyloidosis. These data illustrate for the first time the progression of major AD related neuropathological features other than plaque load in the APPSL mouse model.", "citation": {"db": "PubMed", "db_id": "24886182"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Amyloidosis": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true, "Neocortex": true}, "Confidence": {"Medium": true}}, "source": 838, "target": 3889, "key": "b28c0cda79f3bcbbe31c1ebc8a1946bf"}, {"line": 920, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "APOE subgraph": true, "Cholesterol metabolism subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2838, "target": 2312, "key": "7d3a1c984e651bc80303767f43e65908"}, {"line": 925, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Cholesterol metabolism subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2838, "target": 722, "key": "2cff68fcd97a886e1ca4bc3d7a6edfb1"}, {"line": 926, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Cholesterol metabolism subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2838, "target": 527, "key": "c261ff83b866261c8fc91b9bf36dc056"}, {"line": 938, "relation": "regulates", "evidence": "HMG-CoA reductase (3-hydroxy-3-methyl-glutaryl-coenzyme A reductase, officially abbreviated HMGCR) is the rate-controlling enzyme (NADH-dependent, EC 1.1.1.88; NADPH-dependent, EC 1.1.1.34) of the mevalonate pathway, the metabolic pathway that produces cholesterol and other isoprenoids. Statins, also known as HMG-CoA reductase inhibitors, are a class of lipid-lowering medications. ", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 2838, "target": 527, "key": "8f4db9aedf9f84b62938d6e1f436a97c"}, {"line": 937, "relation": "increases", "evidence": "HMG-CoA reductase (3-hydroxy-3-methyl-glutaryl-coenzyme A reductase, officially abbreviated HMGCR) is the rate-controlling enzyme (NADH-dependent, EC 1.1.1.88; NADPH-dependent, EC 1.1.1.34) of the mevalonate pathway, the metabolic pathway that produces cholesterol and other isoprenoids. Statins, also known as HMG-CoA reductase inhibitors, are a class of lipid-lowering medications. ", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 2838, "target": 231, "key": "def7c10b1b878eaebc4c4de540f5e7ee"}, {"line": 948, "relation": "increases", "evidence": "In an attempt to reverse the apoE deficit in AD, we identified and characterized several apoE inducer agents using a low throughput-screening assay. The most promising of these compounds is called probucol. Administration of probucol, an old cholesterol lowering drug, in mild to moderate sporadic AD led to significant increases in CSF apoE levels and a decrease of CSF beta amyloid 1-42 without significant modifications of CSF tau concentration or CSF lipid peroxides levels. These results are consistent with recent reports suggesting that the long term use of cholesterol lowering drugs that block 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) activity in the fourth and fifth decade of life may help reduce the risk of developing AD at later age.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2838, "target": 231, "key": "2fda574c0a625c84db8a270315b2165b"}, {"line": 921, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "APOE subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 530, "target": 2312, "key": "05e0484a98ff3dfd924dd88c04f4b5c5"}, {"line": 37117, "relation": "association", "evidence": "Cholesterol transport: High cholesterol levels have been linked to overproduction of ABeta¸ and are a risk factor for AD. One of the physiological functions of ABeta¸ has been suggested to control cholesterol transport [167]. Prevalence of AD is reduced among people treated with inhibitors of cholesterol biosynthesis, statins [168, 169] and animal studies support these results [170]. In vitro and in vivo studies have shown that cholesterol modulates APP processing and affects APP mRNA expression [171]. Another mechanism is the increased binding of ABeta¸ to ApoE4 over non-E4 alleles. ApoE is a lipid and cholesterol transport protein responsible for the efflux of cholesterol from neurons to form stable complexes both in vitro and in vivo [172]. Allele ApoE4 is a major risk factor in AD [173]. This relationship might promote synaptogenesis, since in vitro studies have demonstrated that cholesterol released by astroglia increases synaptogenesis [174, 175] with resulting modulation of spike rates [176]. Together, this evidence indicates that one of the physiological functions of APP might be to control cholesterol movement across neuronal membranes [167].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 530, "target": 2312, "key": "e6888e706c892a3aa91c03941eaf357b"}, {"line": 37114, "relation": "association", "evidence": "Cholesterol transport: High cholesterol levels have been linked to overproduction of ABeta¸ and are a risk factor for AD. One of the physiological functions of ABeta¸ has been suggested to control cholesterol transport [167]. Prevalence of AD is reduced among people treated with inhibitors of cholesterol biosynthesis, statins [168, 169] and animal studies support these results [170]. In vitro and in vivo studies have shown that cholesterol modulates APP processing and affects APP mRNA expression [171]. Another mechanism is the increased binding of ABeta¸ to ApoE4 over non-E4 alleles. ApoE is a lipid and cholesterol transport protein responsible for the efflux of cholesterol from neurons to form stable complexes both in vitro and in vivo [172]. Allele ApoE4 is a major risk factor in AD [173]. This relationship might promote synaptogenesis, since in vitro studies have demonstrated that cholesterol released by astroglia increases synaptogenesis [174, 175] with resulting modulation of spike rates [176]. Together, this evidence indicates that one of the physiological functions of APP might be to control cholesterol movement across neuronal membranes [167].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 530, "target": 2328, "key": "d8de3c8e59d9ff9fae3a9435fee8ecab"}, {"line": 922, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "APOE subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 594, "target": 2312, "key": "eeb2d51d40a348b257db74e5836ae8cf"}, {"line": 20782, "relation": "association", "evidence": "Several lines of evidence also implicate lipid transporters of the A-branch of ABC transporters in pathogenesis.", "citation": {"db": "PubMed", "db_id": "23789959"}, "annotations": {"Confidence": {"High": true}}, "source": 594, "target": 3823, "key": "11de3546a5b5e4072eaf7263e3843ef6"}, {"line": 925, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 722, "target": 2838, "key": "c8e5a32d045f0e44822a63382ba10f4d"}, {"line": 926, "relation": "association", "evidence": "in addition to lipid transport mediated by apoE, cholesterol homeostasis in the brain is markedly altered in response to changes in the HMGR pathway; suggesting a possible explanation for the potentially beneficial effect of statins in common AD.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 527, "target": 2838, "key": "068155f15751556d52780ab83f32f838"}, {"line": 4660, "relation": "association", "evidence": "Low-density lipoprotein receptor (LDLR) is a major apolipoprotein E (APOE) receptor and thereby is critical to cholesterol homeostasis.We interpret these results as suggesting that SFRS13A regulates LDLR splicing efficiency and may therefore emerge as a modulator of cholesterol homeostasis.", "citation": {"db": "PubMed", "db_id": "20232416"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 527, "target": 2960, "key": "257fc3c02004715bb7ffda8965cba70d"}, {"line": 9103, "relation": "association", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 527, "target": 2312, "key": "e529e47bff477d84bd226d4d0f5c308f"}, {"line": 47037, "relation": "association", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 527, "target": 2328, "key": "d3e16e8e2930e2bd353f924189424ecd"}, {"line": 3547, "relation": "association", "evidence": "The implication that cholesterol plays an essential role in the pathogenesis of Alzheimer's disease (AD) is based on the 1993 finding that the presence of apolipoprotein E (apoE) allele epsilon;4 is a strong risk factor for developing AD. Since apoE is a regulator of lipid metabolism, it is reasonable to assume that lipids such as cholesterol are involved in the pathogenesis of AD", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 231, "target": 3823, "key": "2207b790794a5079d13ce4b4db14cf97"}, {"line": 27003, "relation": "association", "evidence": "In addition, growing evidence suggests a role of cholesterol in Alzheimer disease pathology and Abeta generation. ", "citation": {"db": "PubMed", "db_id": "18308724"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 3823, "key": "853a50d370e07cb5b7cb0f814c9245c4"}, {"line": 3566, "relation": "regulates", "evidence": "Recent epidemiological and biochemical studies have strengthened this assumption by demonstrating the association between cholesterol and AD, and by proving that the cellular cholesterol level regulates synthesis of amyloid beta-protein (Abeta).", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 80, "key": "c487f7dd97264004731840731c7116a3"}, {"line": 3575, "relation": "association", "evidence": "Yet several studies have demonstrated that oligomeric Abeta affects the cellular cholesterol level, which in turn has a variety of effects on AD related pathologies, including modulation of tau phosphorylation, synapse formation and maintenance of its function, and the neurodegenerative process.", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 80, "key": "4e312d70bdcc70c71883297c324a66e4"}, {"line": 4133, "relation": "association", "evidence": "Apolipoprotein E is the main lipid carrier in the brain and the best-established risk factor for late-onset Alzheimer's disease. Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. Finally, compounds that modulate cholesterol metabolism affect amyloid-beta generation.", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 80, "key": "6462c03a7fe7c068ae7847e0e0cbc03c"}, {"line": 26082, "relation": "association", "evidence": "Intracellular cholesterol levels influence the generation of amyloid-beta peptides, the toxic species thought to be a primary cause of Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "17495608"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 80, "key": "1e369863027ef022b6cada20b4054303"}, {"line": 27002, "relation": "association", "evidence": "In addition, growing evidence suggests a role of cholesterol in Alzheimer disease pathology and Abeta generation. ", "citation": {"db": "PubMed", "db_id": "18308724"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 80, "key": "a703e10de842b9b818a24623233f0fda"}, {"line": 27292, "relation": "association", "evidence": "It has been suggested that cholesterol may pmodulate amyloid-beta (Abeta) formation, a causative factor of Alzheimer's disease (AD), by regulating distribution of the three key proteins in the pathogenesis of AD (beta-amyloid precursor protein (APP), beta-secretase (BACE1) and/or presenilin 1 (PS1)) within lipid rafts.", "citation": {"db": "PubMed", "db_id": "20138836"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 80, "key": "663bc5fe34bebbe23988b6013d806d6a"}, {"line": 3578, "relation": "association", "evidence": "Yet several studies have demonstrated that oligomeric Abeta affects the cellular cholesterol level, which in turn has a variety of effects on AD related pathologies, including modulation of tau phosphorylation, synapse formation and maintenance of its function, and the neurodegenerative process.", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 231, "target": 3015, "key": "f4279595423b8ae6f583b5c4fb52dd4f"}, {"line": 3579, "relation": "association", "evidence": "Yet several studies have demonstrated that oligomeric Abeta affects the cellular cholesterol level, which in turn has a variety of effects on AD related pathologies, including modulation of tau phosphorylation, synapse formation and maintenance of its function, and the neurodegenerative process.", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 231, "target": 788, "key": "d8a5caf0479fd3c7f3fc4d53627ac754"}, {"line": 3580, "relation": "association", "evidence": "Yet several studies have demonstrated that oligomeric Abeta affects the cellular cholesterol level, which in turn has a variety of effects on AD related pathologies, including modulation of tau phosphorylation, synapse formation and maintenance of its function, and the neurodegenerative process.", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 231, "target": 3872, "key": "31eff669f51eadb3242f86d108f8d237"}, {"line": 4558, "relation": "association", "evidence": "cholesterol efflux plays a major role in the atheroprotective effects of HDL and cholesterol homeostasis.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "subject": {"modifier": "Translocation"}, "source": 231, "target": 526, "key": "dbebb661c5e26b60d4484cc8ab3d5ecc"}, {"relation": "partOf", "source": 231, "target": 1655, "key": "c3e42a04867dec3c6876b7a63fb27bb0"}, {"line": 5584, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 231, "target": 1779, "key": "aa96948897a60d21af2e9bef7c4d4893"}, {"line": 5586, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 231, "target": 533, "key": "c37e4366254dfefdbd8f4f998e9c3808"}, {"line": 5589, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 231, "target": 1806, "key": "ac6661c3dedc69cc9e7a117d40eec1b3"}, {"line": 5591, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 231, "target": 676, "key": "566a7193e8524cd95256cb19773f3fb2"}, {"line": 5594, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 231, "target": 1817, "key": "778fcbf05a0bfb1c8562e9d1b3e45d34"}, {"line": 5597, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 231, "target": 1935, "key": "748a5b14bc3ffe0098054a01d890d9b1"}, {"line": 9771, "relation": "positiveCorrelation", "evidence": "Abeta autoantibody levels in the T2DM group were positively correlated with the levels of cholesterol (p=0.011),", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"High": true}}, "source": 231, "target": 2328, "key": "33f32266ad9ecc9e44c03c281214d90c"}, {"line": 10032, "relation": "increases", "evidence": "Cholesterol may also be directly involved in beta-amyloid aggregation: abnormal oxidative metabolites such as cholesterol-derived aldehydes can modify beta-amyloid, firstly promoting Schiff bas formation, then accelerating the early stages of amyloidogenesis.", "citation": {"db": "PubMed", "db_id": "21352095"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 377, "key": "c073ce187f1c9682fb4c3e4f4c4a010b"}, {"line": 10033, "relation": "association", "evidence": "Cholesterol may also be directly involved in beta-amyloid aggregation: abnormal oxidative metabolites such as cholesterol-derived aldehydes can modify beta-amyloid, firstly promoting Schiff bas formation, then accelerating the early stages of amyloidogenesis.", "citation": {"db": "PubMed", "db_id": "21352095"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 231, "target": 441, "key": "995099094ec6ca72e9d2b1e73f44d872"}, {"line": 11320, "relation": "association", "evidence": "Treatment of neurons with ApoB-containing LDL cholesterol increased endolysosome accumulation of cholesterol, enlarged endolysosomes, and elevated endolysosome pH. In addition, ApoB-containing LDL cholesterol increased endolysosome accumulation of BACE-1, enhanced BACE-1 activity, increased Abeta levels, increased levels of phosphorylated tau, and decreased levels of synaptophysin.", "citation": {"db": "PubMed", "db_id": "22580286"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "UserdefinedCellLine": {"primary neuron": true}, "Confidence": {"High": true}}, "source": 231, "target": 390, "key": "21966efa833fce2a16aea39e259491f1"}, {"line": 37115, "relation": "increases", "evidence": "Cholesterol transport: High cholesterol levels have been linked to overproduction of ABeta¸ and are a risk factor for AD. One of the physiological functions of ABeta¸ has been suggested to control cholesterol transport [167]. Prevalence of AD is reduced among people treated with inhibitors of cholesterol biosynthesis, statins [168, 169] and animal studies support these results [170]. In vitro and in vivo studies have shown that cholesterol modulates APP processing and affects APP mRNA expression [171]. Another mechanism is the increased binding of ABeta¸ to ApoE4 over non-E4 alleles. ApoE is a lipid and cholesterol transport protein responsible for the efflux of cholesterol from neurons to form stable complexes both in vitro and in vivo [172]. Allele ApoE4 is a major risk factor in AD [173]. This relationship might promote synaptogenesis, since in vitro studies have demonstrated that cholesterol released by astroglia increases synaptogenesis [174, 175] with resulting modulation of spike rates [176]. Together, this evidence indicates that one of the physiological functions of APP might be to control cholesterol movement across neuronal membranes [167].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 231, "target": 2315, "key": "385c645bc1d8c4643becd2b478d475b5"}, {"line": 37116, "relation": "increases", "evidence": "Cholesterol transport: High cholesterol levels have been linked to overproduction of ABeta¸ and are a risk factor for AD. One of the physiological functions of ABeta¸ has been suggested to control cholesterol transport [167]. Prevalence of AD is reduced among people treated with inhibitors of cholesterol biosynthesis, statins [168, 169] and animal studies support these results [170]. In vitro and in vivo studies have shown that cholesterol modulates APP processing and affects APP mRNA expression [171]. Another mechanism is the increased binding of ABeta¸ to ApoE4 over non-E4 alleles. ApoE is a lipid and cholesterol transport protein responsible for the efflux of cholesterol from neurons to form stable complexes both in vitro and in vivo [172]. Allele ApoE4 is a major risk factor in AD [173]. This relationship might promote synaptogenesis, since in vitro studies have demonstrated that cholesterol released by astroglia increases synaptogenesis [174, 175] with resulting modulation of spike rates [176]. Together, this evidence indicates that one of the physiological functions of APP might be to control cholesterol movement across neuronal membranes [167].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 231, "target": 3940, "key": "ae1a01f0a28db7b3cee57975ab2e7704"}, {"line": 939, "relation": "directlyDecreases", "evidence": "HMG-CoA reductase (3-hydroxy-3-methyl-glutaryl-coenzyme A reductase, officially abbreviated HMGCR) is the rate-controlling enzyme (NADH-dependent, EC 1.1.1.88; NADPH-dependent, EC 1.1.1.34) of the mevalonate pathway, the metabolic pathway that produces cholesterol and other isoprenoids. Statins, also known as HMG-CoA reductase inhibitors, are a class of lipid-lowering medications. ", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 355, "target": 2838, "key": "70b4d61bda33dab7d353c780bdcb1f19"}, {"line": 10014, "relation": "association", "evidence": "In addition, excitotoxicity from the overstimulation of glutamate receptors is considered a major cause of neuron death in AD and statins may be promising agents for protecting against memory loss.", "citation": {"db": "PubMed", "db_id": "21352095"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 355, "target": 588, "key": "74a4f13d3adc3db27e420973f75d210c"}, {"line": 16745, "relation": "decreases", "evidence": "Statin treatment has also considerable effect in prevention of ischemic stroke.", "citation": {"db": "PubMed", "db_id": "12218642"}, "annotations": {"MeSHDisease": {"Stroke": true}, "Confidence": {"High": true}}, "source": 355, "target": 3930, "key": "555696e59e30cb376745d152a729961b"}, {"line": 16755, "relation": "increases", "evidence": "In animal models of ischemic stroke, statins have proven to reduce infarct size through up-regulation of endothelial nitric oxide synthases.", "citation": {"db": "PubMed", "db_id": "12218642"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 355, "target": 3124, "key": "50ab7068340baf6555a0f1d7ec0abc31"}, {"line": 16769, "relation": "decreases", "evidence": "Data from recent observational studies have revealed a potential role for statins in prevention of AD.", "citation": {"db": "PubMed", "db_id": "12218642"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 355, "target": 3823, "key": "b25ed3db7d7be1d292a15910690cfcfe"}, {"line": 29284, "relation": "association", "evidence": "In addition, we examine potential therapeutic strategies such as statins, flavonoids, and human umbilical cord blood transplantation, all of which have been shown to pmodulate CD40-CD40L interaction in mouse pmodels of AD.", "citation": {"db": "PubMed", "db_id": "20205645"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 355, "target": 1324, "key": "c757f5c73b6f3c0a32ce8d92762b7844"}, {"line": 947, "relation": "directlyDecreases", "evidence": "In an attempt to reverse the apoE deficit in AD, we identified and characterized several apoE inducer agents using a low throughput-screening assay. The most promising of these compounds is called probucol. Administration of probucol, an old cholesterol lowering drug, in mild to moderate sporadic AD led to significant increases in CSF apoE levels and a decrease of CSF beta amyloid 1-42 without significant modifications of CSF tau concentration or CSF lipid peroxides levels. These results are consistent with recent reports suggesting that the long term use of cholesterol lowering drugs that block 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) activity in the fourth and fifth decade of life may help reduce the risk of developing AD at later age.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 334, "target": 231, "key": "6fa37df45653c324bc5256f6c7985715"}, {"line": 950, "relation": "increases", "evidence": "In an attempt to reverse the apoE deficit in AD, we identified and characterized several apoE inducer agents using a low throughput-screening assay. The most promising of these compounds is called probucol. Administration of probucol, an old cholesterol lowering drug, in mild to moderate sporadic AD led to significant increases in CSF apoE levels and a decrease of CSF beta amyloid 1-42 without significant modifications of CSF tau concentration or CSF lipid peroxides levels. These results are consistent with recent reports suggesting that the long term use of cholesterol lowering drugs that block 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) activity in the fourth and fifth decade of life may help reduce the risk of developing AD at later age.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebrospinal fluid": true}}, "source": 334, "target": 2312, "key": "b00916b8a0c08b7435529392252608c4"}, {"line": 951, "relation": "decreases", "evidence": "In an attempt to reverse the apoE deficit in AD, we identified and characterized several apoE inducer agents using a low throughput-screening assay. The most promising of these compounds is called probucol. Administration of probucol, an old cholesterol lowering drug, in mild to moderate sporadic AD led to significant increases in CSF apoE levels and a decrease of CSF beta amyloid 1-42 without significant modifications of CSF tau concentration or CSF lipid peroxides levels. These results are consistent with recent reports suggesting that the long term use of cholesterol lowering drugs that block 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) activity in the fourth and fifth decade of life may help reduce the risk of developing AD at later age.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebrospinal fluid": true}}, "source": 334, "target": 2328, "key": "cdc666cfb2c2fe2bc6b530db155fd791"}, {"line": 956, "relation": "negativeCorrelation", "evidence": "In an attempt to reverse the apoE deficit in AD, we identified and characterized several apoE inducer agents using a low throughput-screening assay. The most promising of these compounds is called probucol. Administration of probucol, an old cholesterol lowering drug, in mild to moderate sporadic AD led to significant increases in CSF apoE levels and a decrease of CSF beta amyloid 1-42 without significant modifications of CSF tau concentration or CSF lipid peroxides levels. These results are consistent with recent reports suggesting that the long term use of cholesterol lowering drugs that block 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) activity in the fourth and fifth decade of life may help reduce the risk of developing AD at later age.", "citation": {"db": "PubMed", "db_id": "15639314"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 334, "target": 3823, "key": "f71228d078697b2fec1577c765a771f4"}, {"line": 969, "relation": "association", "evidence": "It has been suggested that the C-->T (224Ala-->Val) transition within exon 2 of the cathepsin D gene (CTSD) might represent a risk factor for late onset AD.Possession of the CTSD T allele does not increase the risk of developing AD per se, but has a modulating effect on the pathogenesis of the disorder by increasing, in concert with the APOE e4 allele, the amount of Abeta deposited as senile plaques in the brain in the form of Abeta40.", "citation": {"db": "PubMed", "db_id": "16543533"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2594, "target": 2312, "key": "c08ee971f40e0abdbc75235d9872632a"}, {"line": 970, "relation": "positiveCorrelation", "evidence": "It has been suggested that the C-->T (224Ala-->Val) transition within exon 2 of the cathepsin D gene (CTSD) might represent a risk factor for late onset AD.Possession of the CTSD T allele does not increase the risk of developing AD per se, but has a modulating effect on the pathogenesis of the disorder by increasing, in concert with the APOE e4 allele, the amount of Abeta deposited as senile plaques in the brain in the form of Abeta40.", "citation": {"db": "PubMed", "db_id": "16543533"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 2594, "target": 3823, "key": "5ddfb8927229c4fec88df967a00a3453"}, {"relation": "partOf", "source": 2594, "target": 1679, "key": "8c88c676fd3f3e612238565ff33d1414"}, {"relation": "hasVariant", "source": 2593, "target": 2594, "key": "47e3767c16c19a394e84e4a324bb919b"}, {"line": 5696, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2593, "target": 428, "key": "b33c3bb1f79cc332332c83e81bf0b129"}, {"line": 5776, "relation": "increases", "evidence": "Under either physiological or pathological conditions, apoptosis is mostly driven by interactions among several families of proteins, i.e. caspases, Bcl-2 family proteins, and inhibitor of apoptosis proteins [10]. Besides the caspases, lysosomal proteases such as cathepsins D, B, and L have been shown to act as mediators of apoptosis in a number of cell systems [11–14]. Increased expression or activity of cathepsin D has been observed in apoptotic cells after activation of Fas/APO-12 and after exposure to oxidative stress or adriamycin ", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2593, "target": 478, "key": "c46a1ce18502a965ac7e8875147c4280"}, {"relation": "partOf", "source": 2593, "target": 1385, "key": "09278ef966d82dd26fadc9a2c07c31cd"}, {"line": 5848, "relation": "increases", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2593, "target": 2400, "key": "6f20f65b7dbdc5784ffc06eeadbc2d3b"}, {"line": 5861, "relation": "increases", "evidence": "Lysosomal dysfunction may be the earliest histological change in AD. Amyloid plaques are full of active lysosomal hydrolases, implying that plaques may originate from lysosomal rupture. Cathepsins D and E (intracellular aspartyl proteases) are considered to influence Abeta peptides generation within the endosomal–lysosomal pathway because they exhibit beta- and gamma-secretase like-activity [32]. For this reason, the endosomal–lysosomal pathway is a site for cleavage of the APP into smaller beta-amyloid-containing peptides, which are then degraded by cathepsins. Inhibition of cathepsins activity causes a rapid and pronounced build-up of potentially amyloidogenic protein fragments [33]. On the other hand, a failure to degrade aggregated Abeta_42 in late endosomes or secondary lysosomes was a mechanism that contributed to intracellular accumulation of Abeta in AD. The cysteine protease cathepsin B in lysosomes degrades A peptides, especially the aggregation-prone species Abeta_42.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Degradation"}, "source": 2593, "target": 2315, "key": "2c415acb0553b6fd27120ee2b347f1e3"}, {"line": 34355, "relation": "increases", "evidence": "In vitro processing of amyloid precursor protein by cathepsin D.", "citation": {"db": "PubMed", "db_id": "10605825"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2593, "target": 2315, "key": "4fbf935f75840d6f35ddc5edf0f54eb6"}, {"line": 34367, "relation": "increases", "evidence": "Several lines of evidence suggest that cathepsin D, the major lysosomal/endosomal aspartic protease, may be involved in this process. ", "citation": {"db": "PubMed", "db_id": "10605825"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2593, "target": 2315, "key": "97facd9463336f037007a0b31e48d1fe"}, {"line": 34377, "relation": "increases", "evidence": "Processing of beta-amyloid precursor protein by cathepsin D.", "citation": {"db": "PubMed", "db_id": "8943232"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2593, "target": 2315, "key": "72e0fde2986f575443e793ae8a42b832"}, {"line": 34386, "relation": "increases", "evidence": "Processing of the pre-beta-amyloid protein by cathepsin D is enhanced by a familial Alzheimer's disease mutation.", "citation": {"db": "PubMed", "db_id": "7523115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2593, "target": 2315, "key": "18aaf0d0a7dd45ae58fd61c7dd9d4293"}, {"line": 34392, "relation": "association", "evidence": "A major pre-beta-amyloid protein695 (APP695) processing activity from Alzheimer's disease brain extracts was identified and found to be indistinguishable from the activity of cathepsin D", "citation": {"db": "PubMed", "db_id": "7523115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2593, "target": 2315, "key": "2fadee53ef0cdda560752113deb654bc"}, {"line": 34402, "relation": "increases", "evidence": "Degradation of Alzheimer's beta-amyloid protein by human cathepsin D.", "citation": {"db": "PubMed", "db_id": "8930981"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2593, "target": 2315, "key": "d7e310651f44a1cfa3d1524de8bd7a77"}, {"line": 5863, "relation": "increases", "evidence": "Lysosomal dysfunction may be the earliest histological change in AD. Amyloid plaques are full of active lysosomal hydrolases, implying that plaques may originate from lysosomal rupture. Cathepsins D and E (intracellular aspartyl proteases) are considered to influence Abeta peptides generation within the endosomal–lysosomal pathway because they exhibit beta- and gamma-secretase like-activity [32]. For this reason, the endosomal–lysosomal pathway is a site for cleavage of the APP into smaller beta-amyloid-containing peptides, which are then degraded by cathepsins. Inhibition of cathepsins activity causes a rapid and pronounced build-up of potentially amyloidogenic protein fragments [33]. On the other hand, a failure to degrade aggregated Abeta_42 in late endosomes or secondary lysosomes was a mechanism that contributed to intracellular accumulation of Abeta in AD. The cysteine protease cathepsin B in lysosomes degrades A peptides, especially the aggregation-prone species Abeta_42.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2593, "target": 79, "key": "7dc73c410873abeb461d11c3b09c726a"}, {"relation": "partOf", "source": 2593, "target": 1162, "key": "ba36c208ab2ed55639bb286514d045fa"}, {"line": 973, "relation": "increases", "evidence": "It has been suggested that the C-->T (224Ala-->Val) transition within exon 2 of the cathepsin D gene (CTSD) might represent a risk factor for late onset AD.Possession of the CTSD T allele does not increase the risk of developing AD per se, but has a modulating effect on the pathogenesis of the disorder by increasing, in concert with the APOE e4 allele, the amount of Abeta deposited as senile plaques in the brain in the form of Abeta40.", "citation": {"db": "PubMed", "db_id": "16543533"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 1679, "target": 78, "key": "4607b98d9ad379a47da81ce744ba0437"}, {"line": 985, "relation": "increases", "evidence": "Oxidative stress-mediated neuronal death may be initiated by a decrease in glutathione (GSH), whose levels are reduced in mitochondrial and synaptosomal fractions of specific CNS regions in Alzheimer disease (AD) patients", "citation": {"db": "PubMed", "db_id": "22326489"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Glutathione reductase subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2799, "target": 265, "key": "b75d702b614bc3d8d54755b383605bf7"}, {"line": 3057, "relation": "directlyIncreases", "evidence": "Hydrogen peroxide was reported to initiate the apoptotic cascade by perturbing the intracellular redox balance. GSH is the most abundant low molecular weight thiol that maintains cellular redox homeostasis", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 2799, "target": 265, "key": "b6e7b7f714c9b75bb64e7d6a4b3deb61"}, {"line": 988, "relation": "negativeCorrelation", "evidence": "Oxidative stress-mediated neuronal death may be initiated by a decrease in glutathione (GSH), whose levels are reduced in mitochondrial and synaptosomal fractions of specific CNS regions in Alzheimer disease (AD) patients", "citation": {"db": "PubMed", "db_id": "22326489"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Glutathione reductase subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 2799, "target": 3823, "key": "038b060fade10277834d3d9658c6fc35"}, {"line": 3058, "relation": "directlyIncreases", "evidence": "Hydrogen peroxide was reported to initiate the apoptotic cascade by perturbing the intracellular redox balance. GSH is the most abundant low molecular weight thiol that maintains cellular redox homeostasis", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 2799, "target": 511, "key": "6648b9612844db0dbdb157df7044a9f7"}, {"line": 3069, "relation": "directlyDecreases", "evidence": "As indicated in Fig. 2, the presence of GSH protected the cells against H2O2-induced apoptosis and the increase in AChE activity. Therefore, the redox imbalance resulted in mitochondrial dysfunction and enhanced mitochondrial reactive oxygen species (ROS) generation.", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 2799, "target": 645, "key": "c9cab6885b01b13889b4b1b7e315ae54"}, {"line": 3073, "relation": "directlyDecreases", "evidence": "As indicated in Fig. 2, the presence of GSH protected the cells against H2O2-induced apoptosis and the increase in AChE activity. Therefore, the redox imbalance resulted in mitochondrial dysfunction and enhanced mitochondrial reactive oxygen species (ROS) generation.", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2799, "target": 2244, "key": "b1b60fa0558a9d6fc09db4e20eb4b2dc"}, {"line": 986, "relation": "decreases", "evidence": "Oxidative stress-mediated neuronal death may be initiated by a decrease in glutathione (GSH), whose levels are reduced in mitochondrial and synaptosomal fractions of specific CNS regions in Alzheimer disease (AD) patients", "citation": {"db": "PubMed", "db_id": "22326489"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Glutathione reductase subgraph": true}, "Confidence": {"Very High": true}}, "source": 265, "target": 584, "key": "8ca61509a0fa5f7aaa277841f9c44089"}, {"line": 2981, "relation": "directlyIncreases", "evidence": "Acetylcholinesterase (AChE) is thought to play an important role during apoptotic process. Our results showed that H2O2 induced AChE activity, a functional marker in apoptotic process, increases in neuronal-like PC12 cells. Glutathione, which is involved in cellular redox homeostasis, inhibited the increase of AChE activity, suggesting that reactive oxygen species (ROS) play a key role in this process. Further investigation showed that the elevation of AChE was observed after the degradation of Akt, release of cytochrome c from mitochondria into the cytosol, and activation of caspase family members. When nerve growth factor (NGF) was present, with the maintenance of Akt level, the elevation of AChE, the cytochrome c diffusion, as well as apoptotic process were markedly attenuated in H2O2-treated PC12 cells", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 265, "target": 511, "key": "ac46faeb89a32e2c6db1aacc30802520"}, {"line": 2988, "relation": "directlyDecreases", "evidence": "Acetylcholinesterase (AChE) is thought to play an important role during apoptotic process. Our results showed that H2O2 induced AChE activity, a functional marker in apoptotic process, increases in neuronal-like PC12 cells. Glutathione, which is involved in cellular redox homeostasis, inhibited the increase of AChE activity, suggesting that reactive oxygen species (ROS) play a key role in this process. Further investigation showed that the elevation of AChE was observed after the degradation of Akt, release of cytochrome c from mitochondria into the cytosol, and activation of caspase family members. When nerve growth factor (NGF) was present, with the maintenance of Akt level, the elevation of AChE, the cytochrome c diffusion, as well as apoptotic process were markedly attenuated in H2O2-treated PC12 cells", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 265, "target": 2244, "key": "461e0df2173fc096d4a59151d5fd1daa"}, {"line": 5337, "relation": "decreases", "evidence": "Increased oxidative stress, coupled with dysregulation of calcium homeostasis and resulting apoptosis of vulnerable neuronal populations, are proposed to underlie the loss of synaptic activity and associated cognitive decline. From these deficiencies emerges the concept of synaptic energy exhaustion in AD, both phosphorylative (ATP) and redox (NAD[P]H) energies. Our previous work shows that hippocampal NAD(P)H and glutathione (GSH) decline with age in association with increased susceptibility to glutamate toxicity in neurons of old-age. Thus, an age-related decline in neuronal reducing currency (NAD[P]H) and reducing buffer (GSH) will surely promote oxidative stress and excess ROS. It is noteworthy that in the early stages of AD, there is already a reduction in the number of mitochondria and the activities of tricarboxylic acid cycle enzymes and cytochrome C oxidase. However, how ROS are produced at the synapse in response to Abeta oligomers is not fully known.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Glutathione reductase subgraph": true}, "Confidence": {"Medium": true}}, "source": 265, "target": 842, "key": "954ed88a60cf51de5674c668ae553ebe"}, {"line": 5341, "relation": "decreases", "evidence": "Increased oxidative stress, coupled with dysregulation of calcium homeostasis and resulting apoptosis of vulnerable neuronal populations, are proposed to underlie the loss of synaptic activity and associated cognitive decline. From these deficiencies emerges the concept of synaptic energy exhaustion in AD, both phosphorylative (ATP) and redox (NAD[P]H) energies. Our previous work shows that hippocampal NAD(P)H and glutathione (GSH) decline with age in association with increased susceptibility to glutamate toxicity in neurons of old-age. Thus, an age-related decline in neuronal reducing currency (NAD[P]H) and reducing buffer (GSH) will surely promote oxidative stress and excess ROS. It is noteworthy that in the early stages of AD, there is already a reduction in the number of mitochondria and the activities of tricarboxylic acid cycle enzymes and cytochrome C oxidase. However, how ROS are produced at the synapse in response to Abeta oligomers is not fully known.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Glutathione reductase subgraph": true}, "Confidence": {"Medium": true}}, "source": 265, "target": 170, "key": "7908f8e9516bfa871f8d8b88a82382db"}, {"line": 14109, "relation": "association", "evidence": "Using combinations of catalase-, glutathione synthesis- and glutathione peroxidase-inhibitors it was shown that the increased resistance of Neuro2a-HR cells is not solely based on an increased activity of catalase or the glutathione system, suggesting that their resistance might be based on yet unknown, novel defence mechanisms.", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Low": true}}, "source": 265, "target": 716, "key": "d3445a3471785b3efa1dfd56d47dbdf9"}, {"line": 19681, "relation": "increases", "evidence": "Melatonin also enhances the antioxidant potential of the cell by stimulating the synthesis of antioxidant enzymes like superoxide dismutase, glutathione peroxidase and glutathione reductase, and by augmenting glutathione levels.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 265, "target": 2216, "key": "a37b1998899480faa7f3a116176934ca"}, {"line": 19682, "relation": "increases", "evidence": "Melatonin also enhances the antioxidant potential of the cell by stimulating the synthesis of antioxidant enzymes like superoxide dismutase, glutathione peroxidase and glutathione reductase, and by augmenting glutathione levels.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 265, "target": 2140, "key": "ec5c97ae685d8ead8357b8b771e583a7"}, {"line": 19690, "relation": "increases", "evidence": "Melatonin also enhances the antioxidant potential of the cell by stimulating the synthesis of antioxidant enzymes like superoxide dismutase, glutathione peroxidase and glutathione reductase, and by augmenting glutathione levels.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Confidence": {"Very High": true}}, "source": 265, "target": 2139, "key": "25ef909b9dc23e1a579ccb7bb5ead69d"}, {"line": 19695, "relation": "increases", "evidence": "Melatonin also enhances the antioxidant potential of the cell by stimulating the synthesis of antioxidant enzymes like superoxide dismutase, glutathione peroxidase and glutathione reductase, and by augmenting glutathione levels.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Very High": true}}, "source": 265, "target": 2798, "key": "f2b15f4ea9eb247d9d96c343ef88631c"}, {"line": 1136, "relation": "directlyIncreases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in Abeta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 584, "target": 2375, "key": "f98666bc1b25be48e4786d1a1aaade4a"}, {"line": 9319, "relation": "directlyIncreases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in beta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true, "Beta secretase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 584, "target": 2375, "key": "39994bd28ce4c09afcd9d9ea107a1651"}, {"line": 5230, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 584, "target": 3823, "key": "5b0befdda0d16e79717528d85cc28bab"}, {"line": 1002, "relation": "biomarkerFor", "evidence": "As a single marker, chitinase activity was most powerful for distinguishing patients with AD from no dementia patients with an accuracy of 85.8% using a single threshold", "citation": {"db": "PubMed", "db_id": "22323746"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Innate immune system subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2510, "target": 3823, "key": "328ee8c336025d78c25ebddf885c229a"}, {"line": 1295, "relation": "association", "evidence": "Here we show that the buildup of Abeta increases the mammalian target of rapamycin (mTOR) signaling, whereas decreasing mTOR signaling reduces Abeta levels, thereby highlighting an interrelation between mTOR signaling and Abeta. The mTOR pathway plays a central role in controlling protein homeostasis and hence, neuronal functions; indeed mTOR signaling regulates different forms of learning and memory.", "citation": {"db": "PubMed", "db_id": "20178983"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"mTOR signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 812, "target": 3076, "key": "2a835bf0ebc52d16b0ba076c779b6e41"}, {"line": 6115, "relation": "negativeCorrelation", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 812, "target": 3850, "key": "c575af1f92c767920035062a8b3a8a9e"}, {"line": 6511, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 812, "target": 596, "key": "99e65d03671d0a51a0a2e005646a7ed0"}, {"line": 6515, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 812, "target": 841, "key": "f27c32bb24c8a8785257bd741b2886fd"}, {"line": 6519, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 812, "target": 466, "key": "c5f1caefa87b5f0318bf6cb52eb7545f"}, {"line": 11686, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 812, "target": 434, "key": "9cd97180669043ffbb3d8cd2f8323de7"}, {"line": 13016, "relation": "positiveCorrelation", "evidence": "Results from a phase 2 study of tarenflurbil, a compound that inhibits gamma-secretase and has positive effects on cognition in animals, seemed promising in slowing decline in Alzheimer's disease assessment scale-cognitive (ADAS-cog) scores in patients with mild Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20170836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 812, "target": 7, "key": "510a2a73b948cfe5603af33c304819f7"}, {"line": 16388, "relation": "negativeCorrelation", "evidence": "Osteopontin is increased in the cerebrospinal fluid of patients with Alzheimer's disease and its levels correlate with cognitive decline.", "citation": {"db": "PubMed", "db_id": "20308780"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 812, "target": 3409, "key": "c053af2de8e06784cf4738bce17d32bf"}, {"line": 17755, "relation": "association", "evidence": "Moreover, basic experiments suggest a role of brain angiotensin II in neural injury, neuroinflammation, and cognitive function and that RAS blockade attenuates cognitive impairment in rodent dementia models of AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}}, "source": 812, "target": 2274, "key": "f929a269ea786b9557356a801c32330f"}, {"line": 17820, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS), which is comprised of a variety of peptides including angiotensin II, angiotensin III and angiotensin IV acting on AT1, AT2 and AT4 receptors, is important in cognition and anxiety.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 812, "target": 844, "key": "2bc2272e1a8fabd4c90d99ff1a6ced61"}, {"line": 17826, "relation": "negativeCorrelation", "evidence": "Perturbation of the RAS improves basal cognition and reverses age-, scopolamine-, ethanol- and diabetes-induced deficits.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 812, "target": 844, "key": "b2b10c1bcd85a9f091e50ac91898b4e9"}, {"line": 22642, "relation": "negativeCorrelation", "evidence": "Elevated levels of T-tau, P-tau (S396), IL-6 and · OH in CSF are significantly correlated with cognitive impairment in PD patients.", "citation": {"db": "PubMed", "db_id": "24884485"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 812, "target": 2894, "key": "b87fcae95fb22087c779cffb4fb8713b"}, {"line": 38720, "relation": "negativeCorrelation", "evidence": "Elevated levels of T-tau, P-tau (S396), IL-6 and · OH in CSF are significantly correlated with cognitive impairment in PD patients.", "citation": {"db": "PubMed", "db_id": "24884485"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tau protein subgraph": true}}, "source": 812, "target": 2894, "key": "3c7b6424aa44970d2dfe6f00433eb0f2"}, {"line": 30145, "relation": "negativeCorrelation", "evidence": "The neuronal loss associated with Alzheimer's disease (AD) affects areas of the brain that are vital to cognition.", "citation": {"db": "PubMed", "db_id": "19458225"}, "source": 812, "target": 648, "key": "68a397ae4a5d70baee11ed1e1b3b3757"}, {"line": 30223, "relation": "association", "evidence": "Glutamate receptors play crucial roles in cognition and memory.", "citation": {"db": "PubMed", "db_id": "10588576"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 812, "target": 2773, "key": "3d9cd2a8b6febaf2e9b3afda6b2e9b34"}, {"line": 34832, "relation": "association", "evidence": "Autopsy brain hippocampal tissues were obtained from controls and patients with AD and Western blots were performed using antibodies against mTOR signaling molecules and RagC, an upstream component of mTOR complex 1 (mTORC1) signaling. We found that expression of mTOR/p-mTOR and its downstream targets S6/p-S6 and Raptor/p-Raptor were expressed in the control and AD hippocampus. The expression levels of these signaling molecules were significantly increased in the hippocampus at the severe stages of AD, compared to controls and other stages of AD. Interestingly, Rictor expression level was unaltered. In addition, RagC was increased in the hippocampus at the early, moderate, and severe stages of AD. Our data indicate that mTORC1, but not mTORC2, was activated in the AD brains and that the level of mTOR signaling activation was correlated with cognitive severity of AD patients.", "citation": {"db": "PubMed", "db_id": "23979023"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "DiseaseState": {"Moderate AD": true, "Early-onset AD": true, "Late-onset AD": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 812, "target": 460, "key": "da5295a7c081ae238de8bca2d118427d"}, {"line": 37640, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "source": 812, "target": 2315, "key": "446c8eb0ddeb591d6e707c35870c646d"}, {"line": 37641, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "object": {"modifier": "Translocation"}, "source": 812, "target": 2781, "key": "6d0ebf71ce686db8e02858eac63fc67d"}, {"line": 38718, "relation": "negativeCorrelation", "evidence": "Elevated levels of T-tau, P-tau (S396), IL-6 and · OH in CSF are significantly correlated with cognitive impairment in PD patients.", "citation": {"db": "PubMed", "db_id": "24884485"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tau protein subgraph": true}}, "source": 812, "target": 3010, "key": "9cc043edd58f40d2967d340e46390961"}, {"line": 38719, "relation": "negativeCorrelation", "evidence": "Elevated levels of T-tau, P-tau (S396), IL-6 and · OH in CSF are significantly correlated with cognitive impairment in PD patients.", "citation": {"db": "PubMed", "db_id": "24884485"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tau protein subgraph": true}}, "source": 812, "target": 3026, "key": "07e1435886b200297ee9ae6d5f22fd64"}, {"line": 38965, "relation": "negativeCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 812, "target": 577, "key": "00428dcadd6bb0de9cb016935dea7e25"}, {"line": 40932, "relation": "negativeCorrelation", "evidence": "The epidemic and experimental studies have confirmed that the obesity can lead to neuroinflammation, neurodegenerative diseases and adversely affect cognition.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Obesity": true}}, "source": 812, "target": 3925, "key": "1923e4107292b3638c4e512ef967b914"}, {"line": 1040, "relation": "association", "evidence": "Sequestosome 1/p62 is gaining attention as it is involved in several diseases including Parkinson disease, Alzheimer disease, liver and breast cancer, Paget's disease of bone, obesity and insulin resistance", "citation": {"db": "PubMed", "db_id": "22296116"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3415, "target": 3823, "key": "7fccd046ed3ba59b2e9c96a91dcd1ff7"}, {"line": 1041, "relation": "association", "evidence": "Sequestosome 1/p62 is gaining attention as it is involved in several diseases including Parkinson disease, Alzheimer disease, liver and breast cancer, Paget's disease of bone, obesity and insulin resistance", "citation": {"db": "PubMed", "db_id": "22296116"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3415, "target": 3878, "key": "fd06af0880d8a80be0a0dccec1161882"}, {"line": 35206, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3415, "target": 3116, "key": "766312e17acc1d51337fc2313005722d"}, {"line": 38666, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3415, "target": 3116, "key": "c4778c9f1b7e57bf3c1d6d54b80e05ff"}, {"line": 1041, "relation": "association", "evidence": "Sequestosome 1/p62 is gaining attention as it is involved in several diseases including Parkinson disease, Alzheimer disease, liver and breast cancer, Paget's disease of bone, obesity and insulin resistance", "citation": {"db": "PubMed", "db_id": "22296116"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3878, "target": 3415, "key": "e6ca88ad4acd6f5d43985606dabceabe"}, {"line": 6587, "relation": "association", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3878, "target": 3889, "key": "d5dda98f38d0e82a248402bbb947f407"}, {"line": 18030, "relation": "association", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3878, "target": 505, "key": "270a2f6c65ca3820eab94c92889d4961"}, {"line": 18090, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 3878, "target": 3110, "key": "8adf7826a5f951e0083f809db9dd4311"}, {"line": 18091, "relation": "negativeCorrelation", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 3878, "target": 3110, "key": "4bb720b14693c01f8af4c82c093f8bde"}, {"line": 39948, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3878, "target": 3110, "key": "49ef5b00a09a6baf9395e5c752b87db1"}, {"line": 18552, "relation": "association", "evidence": "The role of inflammation in Alzheimer's disease, Parkinson's disease, and multiple sclerosis has recently come under increased scrutiny.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Parkinson Disease": true, "Inflammation": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3878, "target": 3920, "key": "b74e78e05c694b1f20abc297ec69347b"}, {"line": 18693, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3878, "target": 3060, "key": "f0accf17517ead832983060918364c87"}, {"line": 18833, "relation": "association", "evidence": "Positive association between an estrogen receptor gene polymorphism and Parkinson's disease with dementia.", "citation": {"db": "PubMed", "db_id": "10362895"}, "annotations": {"Disease": {"dementia": true, "Parkinson's disease": true}, "Subgraph": {"Estrogen subgraph": true}, "Species": {"9606": true}}, "source": 3878, "target": 2680, "key": "7ad9157e39bae79597006d08f1bc15f2"}, {"line": 19272, "relation": "positiveCorrelation", "evidence": "Interestingly, in recent years increased cdk5/p25 expression has been demonstrated in the brains of patients with Alzheimer's and Parkinson's diseases.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3878, "target": 2489, "key": "b47690d018790a80ec2919c2c1f10cef"}, {"line": 32824, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3878, "target": 3015, "key": "8538743d3f866e8fe374d6275ea1ec0f"}, {"line": 41244, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3878, "target": 3815, "key": "fb9b08403b6b437e298d47b5aa23bb43"}, {"line": 42489, "relation": "association", "evidence": "In this review we discussed the role of PET and MRI in evaluating the effect of GLP1 analogs in disease progression in both Alzheimer's and Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "24529526"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3878, "target": 3635, "key": "287e348ea33fd4ee3a5153c36c8bb023"}, {"line": 42509, "relation": "positiveCorrelation", "evidence": "The cellular generation of reactive oxygen species (ROS) has been implicated in contributing to the pathology of human neurological disorders including Alzheimer's disease (AD) and Parkinson's disease (PD).", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"nervous system disease": true, "Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "source": 3878, "target": 170, "key": "91d4f1203fd82a07bddb3b3e1d9d4fe3"}, {"line": 42559, "relation": "association", "evidence": "These findings have mechanistic implications for ROS-triggered inflammatory gene expression programs that may contribute to AD and PD neuropathologic mechanisms.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3878, "target": 170, "key": "fcbf12d29fe741e6703b332410164489"}, {"line": 47629, "relation": "decreases", "evidence": "Hypersecretion of CRF in the brain may contribute to the symptomatology seen in neuropsychiatric disorders, such as depression, anxiety-related disorders and anorexia nervosa. Furthermore, overproduction of CRF at peripheral inflammatory sites, such as synovial joints may contribute to autoimmune diseases such as rheumatoid arthritis. In contrast, deficits in brain CRF are apparent in neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease and Huntington's disease, as they relate to dysfunction of CRF neurons in the brain areas affected in the particular disorder. Strategies directed at developing CRF-related agents may hold promise for novel therapies for the treatment of these various disorders.", "citation": {"db": "PubMed", "db_id": "8834089"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"CRH subgraph": true}}, "source": 3878, "target": 2560, "key": "d295c53837fde332c1f6af578a209dbe"}, {"line": 48266, "relation": "association", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3878, "target": 2629, "key": "dc49888182405451c723dc8da6f19d9d"}, {"line": 1054, "relation": "positiveCorrelation", "evidence": "TNF-a-308 G/A gene polymorphism could affect cerebral inflammatory response and the risk of late-onset Alzheimer disease but -863 C/A polymorphism does not influence the risk of this disease", "citation": {"db": "PubMed", "db_id": "22279475"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 1999, "target": 3823, "key": "e3a66dfff9ab958b053e2742aca81f6b"}, {"line": 1061, "relation": "association", "evidence": "TNF-a-308 G/A gene polymorphism could affect cerebral inflammatory response and the risk of late-onset Alzheimer disease but -863 C/A polymorphism does not influence the risk of this disease", "citation": {"db": "PubMed", "db_id": "22279475"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 1999, "target": 577, "key": "ad844d4e5ee75de96399b7e291a44b20"}, {"relation": "hasVariant", "source": 1997, "target": 1999, "key": "f1d71171b3a33e84b08fbbe876c1bcaa"}, {"relation": "hasVariant", "source": 1997, "target": 1998, "key": "5e14519a7a97b62aef05ba45e0c64d27"}, {"line": 45691, "relation": "increases", "evidence": "in this study we found a hypomethylation of CpG Nos. 9 and 10 in the AD probes. These data suggest that the TNF-alpha promoter in the brain of AD patients is in a less restricted state than in healthy individuals.TNF-alpha induces neuronal apoptosis ", "citation": {"db": "PubMed", "db_id": "24556805"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1997, "target": 478, "key": "20da7675a842f7d8220a035669149ffc"}, {"line": 1075, "relation": "association", "evidence": "12/15-Lipoxygenase (12/15-LO) is an enzyme widely distributed in the central nervous system, and it has been involved in the neurobiology of Alzheimer disease (AD).12/15-Lipoxygenase (12/15-LO) is an enzyme widely distributed in the central nervous system, and it has been involved in the neurobiology of Alzheimer disease (AD)", "citation": {"db": "PubMed", "db_id": "22275252"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2286, "target": 3823, "key": "67be761fd40f7e4662df2f384a2c45ca"}, {"line": 1076, "relation": "association", "evidence": "12/15-Lipoxygenase (12/15-LO) is an enzyme widely distributed in the central nervous system, and it has been involved in the neurobiology of Alzheimer disease (AD).12/15-Lipoxygenase (12/15-LO) is an enzyme widely distributed in the central nervous system, and it has been involved in the neurobiology of Alzheimer disease (AD)", "citation": {"db": "PubMed", "db_id": "22275252"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2287, "target": 3823, "key": "25ea6b5b52b80d011f1911373fc8f8ba"}, {"line": 1103, "relation": "increases", "evidence": "Acetylcholinesterase (AChE) enzyme inhibition is an important target for the management of Alzheimer disease (AD) and AChE inhibitors are the main stay drugs for its management. Coumarins are the phytochemicals with wide range of biological activities including AChE inhibition", "citation": {"db": "PubMed", "db_id": "22257528"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2244, "target": 3823, "key": "0981d6d0e616bc6fa3fccedd6de083dd"}, {"line": 1475, "relation": "negativeCorrelation", "evidence": "Alzheimer's disease is rapidly becoming one of the most prevalent human diseases. Inhibition of human acetylcholinestrase (hAChE) and butyrylcholinestrase (BChE) has been linked to amelioration of Alzheimer's symptoms and research into inhibitors is of critical importance", "citation": {"db": "PubMed", "db_id": "22445674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2244, "target": 3823, "key": "103c6ad3ae4c6d212b3ec57b2c973ea8"}, {"line": 9898, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2244, "target": 3823, "key": "e0f52893143db148cf5d18ba4cb8a9ae"}, {"relation": "partOf", "source": 2244, "target": 1047, "key": "19e396de4ddee9dae1d3b5b6740afedb"}, {"line": 25325, "relation": "increases", "evidence": "Our results suggest that such amyloid-AChE complexes are formed when AChE interacts with the growing amyloid fibrils and accelerates the assembly of Abeta peptides", "citation": {"db": "PubMed", "db_id": "9325095"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2244, "target": 1047, "key": "10a8ffdd7113aef72164164dedefaba3"}, {"line": 2971, "relation": "regulates", "evidence": "Acetylcholinesterase (AChE) is thought to play an important role during apoptotic process. Our results showed that H2O2 induced AChE activity, a functional marker in apoptotic process, increases in neuronal-like PC12 cells. Glutathione, which is involved in cellular redox homeostasis, inhibited the increase of AChE activity, suggesting that reactive oxygen species (ROS) play a key role in this process. Further investigation showed that the elevation of AChE was observed after the degradation of Akt, release of cytochrome c from mitochondria into the cytosol, and activation of caspase family members. When nerve growth factor (NGF) was present, with the maintenance of Akt level, the elevation of AChE, the cytochrome c diffusion, as well as apoptotic process were markedly attenuated in H2O2-treated PC12 cells", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2244, "target": 645, "key": "0a726aeb3a638c62690d696189c2513e"}, {"line": 3093, "relation": "negativeCorrelation", "evidence": "AChE activity was reported to be enhanced by low concentrations of H2O2. explanation is that mitochondrial efflux H2O2 modifies membrane structure through lipid peroxidation, and therefore contributes to modify the activity of the membrane-bound protein AChE [31]. AChE is synthesized as an inactive precursor and then matures into an active subunit in the endoplasmic reticulum [32]. Thus, the other explanation was that AChE could be exposed to cytoplasm when endoplasmic reticulum was destroyed by H2O2. At least one protein kinase (protein kinase A , PKA), was reported to increase the AChE activity by phosphorylation at non-consensus sites of this enzyme [33]. The apoptotic stimuli also enhanced the mRNA and protein levels of AChE in cells without background AChE expression, which were mediated by calcium signalling or the c-Jun kinase pathway", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2244, "target": 131, "key": "6384f555879ed9a84efeb7881a68a899"}, {"line": 9902, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2244, "target": 3850, "key": "4c44d0a72c6968f8e3a92cffa81b9803"}, {"line": 9906, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2244, "target": 3861, "key": "95f74c09d8fdc86c01d85159d9320b0b"}, {"line": 9910, "relation": "regulates", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2244, "target": 592, "key": "017c7d2ac4a862be19bebee1c8a684c4"}, {"line": 10850, "relation": "decreases", "evidence": "Hence, elevated butyrylcholinesterase and acetylcholinesterase concentrations will lead to a decrease in the levels of acetylcholine that could trigger the onset of low-grade systemic inflammation seen in type 2 diabetes and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2244, "target": 204, "key": "a845cfda54a8827d9bc8fb60c2bbd207"}, {"line": 24856, "relation": "association", "evidence": "Acetylcholinesterase (AChE), an enzyme involved in the hydrolysis of the neurotransmitter acetylcholine, consistently colocalizes with the amyloid deposits characteristic of Alzheimer's disease and may contribute to the generation of amyloid proteins and/or physically affect fibril assembly.", "citation": {"db": "PubMed", "db_id": "9325095"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2244, "target": 204, "key": "3f6f83da81ddd54e7f09b892e32f4c4a"}, {"line": 16809, "relation": "association", "evidence": "Concurrently, Abeta alters erythrocyte cell morphology, decreases nitrites and nitrates levels, and affects membrane acetylcholinesterase activity.", "citation": {"db": "PubMed", "db_id": "22431227"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Cell": {"erythrocyte": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2244, "target": 2328, "key": "041c10428f49d08e0f7ef729cbfdf3dc"}, {"line": 24844, "relation": "association", "evidence": "At this point, inhibitors able to interact at the peripheral binding site are of particular relevance, as they might disrupt the interactions between the enzyme acetylcholinesterase and the beta-amyloid peptide. ", "citation": {"db": "PubMed", "db_id": "15544503"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2244, "target": 2328, "key": "8b2e3e108798864a46c7eeaef0352b68"}, {"line": 25288, "relation": "increases", "evidence": "The fact that acetylcholinesterase accelerates amyloid formation and the effect is sensitive to peripherical anionic site blockers of the enzyme, suggests that specific and new EC 3.1.1.7 (acetylcholinesterase) inhibitors may well provide an attractive possibility for treating Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "18205831"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2244, "target": 2328, "key": "9e87971287482d3a7271c59c3b95125a"}, {"line": 23733, "relation": "increases", "evidence": "The finding that acetylcholinesterase (AChE) colocalizes with beta-amyloid (Abeta) and promotes and accelerates Abeta aggregation has renewed an intense interest in developing new multifunctional AChE inhibitors as potential disease-modifying drugs for Alzheimer's therapy.", "citation": {"db": "PubMed", "db_id": "22778810"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2244, "target": 80, "key": "6f2b4ae78e2fd9331b07b409fe32bd20"}, {"line": 24709, "relation": "increases", "evidence": "These results, together with binding assays, have suggested that AChE may contribute to the generation of amyloid deposits and/or physically affects fibril assembly. Moreover, it has also been shown that the neurotoxicity of Abeta peptide aggregates depends on the amount of AChE bound to the complexes, suggesting that AChE may play a key role in the neurodegeneration observed in an AD patients brain.", "citation": {"db": "PubMed", "db_id": "17681794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2244, "target": 80, "key": "e4553517942c12d36a45683b5d7fae92"}, {"line": 24858, "relation": "increases", "evidence": "Acetylcholinesterase (AChE), an enzyme involved in the hydrolysis of the neurotransmitter acetylcholine, consistently colocalizes with the amyloid deposits characteristic of Alzheimer's disease and may contribute to the generation of amyloid proteins and/or physically affect fibril assembly.", "citation": {"db": "PubMed", "db_id": "9325095"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2244, "target": 80, "key": "77e1890c1d920e3773db62f7f061d2d3"}, {"relation": "partOf", "source": 2244, "target": 910, "key": "63015157a65c3651a63727251d94a765"}, {"relation": "partOf", "source": 2244, "target": 1046, "key": "8694446c2d1f50f23f481e024e2dd62c"}, {"line": 25294, "relation": "increases", "evidence": "Recent studies also indicate that acetylcholinesterase induces the aggregation of prion protein with a similar dependence on the peripherical anionic site", "citation": {"db": "PubMed", "db_id": "18205831"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2244, "target": 3254, "key": "a689b37ce0706a3b83530924624105d1"}, {"relation": "partOf", "source": 2244, "target": 1045, "key": "13180e34c767687b998c58992fc15abe"}, {"relation": "partOf", "source": 2244, "target": 1048, "key": "9d0463537dfc049cfb99ba410ec23555"}, {"line": 1117, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interferon signaling subgraph": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3007, "target": 3015, "key": "901c32ff21c18881f885dcc7d43fef85"}, {"line": 32808, "relation": "increases", "evidence": "Moreover, SAPK/JNK- and p38-immunoprecipitated sub-cellular fractions enriched in abnormal hyperphosphorylated tau have the capacity to phosphorylate recombinat tau and c-Jun and ATF-2 which are specific substrates of SAPK/JNK and p38 in AD and PiD.", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DNA synthesis": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}, "MeSHDisease": {"Niemann-Pick Diseases": true, "Alzheimer Disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3007, "target": 3015, "key": "ebb057ef6bdc21171bbe531951f03b43"}, {"line": 1118, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interferon signaling subgraph": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3007, "target": 3931, "key": "478f6aefde1ec1ddfc013a7b54fb719e"}, {"relation": "hasVariant", "source": 3007, "target": 3008, "key": "366aeb7d042ff218f0b867d81f7863b1"}, {"relation": "partOf", "source": 3007, "target": 1503, "key": "53db5836115916f423432a803ac33d0e"}, {"relation": "partOf", "source": 3007, "target": 1552, "key": "cb0efe434e207bd9fbb4b7f909ac2f9c"}, {"line": 32810, "relation": "increases", "evidence": "Moreover, SAPK/JNK- and p38-immunoprecipitated sub-cellular fractions enriched in abnormal hyperphosphorylated tau have the capacity to phosphorylate recombinat tau and c-Jun and ATF-2 which are specific substrates of SAPK/JNK and p38 in AD and PiD.", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DNA synthesis": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}, "MeSHDisease": {"Niemann-Pick Diseases": true, "Alzheimer Disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3007, "target": 2937, "key": "7595ee9b3991de4bc0621b29cb6b81a2"}, {"line": 32812, "relation": "increases", "evidence": "Moreover, SAPK/JNK- and p38-immunoprecipitated sub-cellular fractions enriched in abnormal hyperphosphorylated tau have the capacity to phosphorylate recombinat tau and c-Jun and ATF-2 which are specific substrates of SAPK/JNK and p38 in AD and PiD.", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DNA synthesis": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}, "MeSHDisease": {"Niemann-Pick Diseases": true, "Alzheimer Disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3007, "target": 2364, "key": "a263de9a77e2976bc662fb8ee63ef93b"}, {"line": 32834, "relation": "increases", "evidence": "The mitogen-activated protein (MAP) kinase SAPK/JNK phosphorylates tau protein at many of its proline-directed serine/threonine residues in vitro and is a likely candidate kinase to phosphorylate the pathologically relevant S422 site on tau.", "citation": {"db": "PubMed", "db_id": "16772869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3007, "target": 3029, "key": "b3974c6d1f1f5c69d59f06557f7fd06c"}, {"line": 37849, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "MAPK-JNK subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3007, "target": 2136, "key": "5a99768241e83197b65964030711e01f"}, {"line": 1118, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interferon signaling subgraph": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3931, "target": 3007, "key": "7e26c6066397a61685e72527eaa79260"}, {"line": 1121, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Bcl-2 subgraph": true, "Interferon signaling subgraph": true, "Tau protein subgraph": true, "Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3931, "target": 3002, "key": "9fee2e0de3682dd80ffcb0bf238e9b9c"}, {"line": 1124, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "MAPK-JNK subgraph": true, "Matrix metalloproteinase subgraph": true, "Interferon signaling subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3931, "target": 2998, "key": "f310da9a80be73a650646e4c1107e778"}, {"line": 15033, "relation": "association", "evidence": "The present findings are in line with the previous studies showing tau products cleaved by caspase-3, as recognized by s pecific tau-cleaved antibodies, in Alzheimer's disease and other tauopathies.", "citation": {"db": "PubMed", "db_id": "16496165"}, "annotations": {"MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 3931, "target": 2444, "key": "e64b547cb53eca433608e8fffcb378ee"}, {"line": 41645, "relation": "association", "evidence": "Finally, we discussed the possible impact of Cr1 on the pathogenesis of AD including amyloid-beta pathology, tauopathy, immune dysfunction and glial-mediated neuroinflammation.", "citation": {"db": "PubMed", "db_id": "24794147"}, "annotations": {"MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Neuroglia": true}, "Confidence": {"Medium": true}}, "source": 3931, "target": 3614, "key": "6331c07a682c4bf4be9a92530769d833"}, {"line": 43090, "relation": "positiveCorrelation", "evidence": "These studies suggest that TTBK1 is an important molecule for the inflammatory axonal degeneration, which may be relevant to the pathobiology of tauopathy including AD.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Axons": true}, "MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 3931, "target": 3747, "key": "286d90c358761d986efec857b3c55e58"}, {"line": 48163, "relation": "positiveCorrelation", "evidence": "Thus, we have identified a pathway whereby Abeta induces a clusterin/p53/Dkk1/wnt-PCP-JNK pathway, which drives the upregulation of several genes that mediate the development of AD-like neuropathologies, thereby providing new mechanistic insights into the action of Abeta in neurodegenerative diseases.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3931, "target": 463, "key": "59ad1e7df97c3c38de724f72143e64e6"}, {"line": 1120, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Bcl-2 subgraph": true, "Interferon signaling subgraph": true, "Tau protein subgraph": true, "Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3002, "target": 3015, "key": "0e652e219d9d40adeaba57af29072bf4"}, {"line": 32736, "relation": "increases", "evidence": "Several kinases, such as glycogen synthase kinase 3 beta (GSK3beta) and c-Jun N-terminal kinase (JNK), phosphorylate tau at sites that are phosphorylated in PHF.", "citation": {"db": "PubMed", "db_id": "11803455"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3002, "target": 3015, "key": "ff174037f5fa89c127a95bad05fe8e11"}, {"line": 32747, "relation": "increases", "evidence": "We have previously reported that the phosphorylated form of stress-activated protein kinase/c-Jun N-terminal kinase (p-SAPK/JNK) accumulates in granules within hippocampal pyramidal cell bodies in AD tissue at the time that hyperphosphorylated tau begins to aggregate into early-stage NFTs.", "citation": {"db": "PubMed", "db_id": "17089132"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3002, "target": 3015, "key": "0ce5a4a32fe01cfe929f60b6ad2f3fe2"}, {"line": 33074, "relation": "increases", "evidence": "Several kinases, such as glycogen synthase kinase 3 beta (GSK3beta) and c-Jun N-terminal kinase (JNK), phosphorylate tau at sites that are phosphorylated in PHF.", "citation": {"db": "PubMed", "db_id": "11803455"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3002, "target": 3015, "key": "ca3c237cb69064ec88530d30023b03a6"}, {"line": 33453, "relation": "increases", "evidence": "Moreover, SAPK/JNK- and p38-immunoprecipitated sub-cellular fractions enriched in abnormal hyperphosphorylated tau have the capacity to phosphorylate recombinant tau and c-Jun and ATF-2 which are specific substrates of SAPK/JNK and p38 in AD and PiD.", "citation": {"db": "PubMed", "db_id": "15977985"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3002, "target": 3015, "key": "c3182c6a110b00e375f8a6f72ea39de4"}, {"line": 34668, "relation": "increases", "evidence": "Finally, CaM kinase II is present in neurons but not in glial cells, thus suggesting no role of CaM kinase II in tau phosphorylation of glial cells. These observations, together with previous results of in vitro studies, support the idea that several MAPK/ERK, SAPK/JNK, p38 and CaM kinase II may participate in tau phosphorylation in tauopathies", "citation": {"db": "PubMed", "db_id": "11810404"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3002, "target": 3015, "key": "3a00e32bc3d6947e29ef2796ba79a14d"}, {"line": 1121, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Bcl-2 subgraph": true, "Interferon signaling subgraph": true, "Tau protein subgraph": true, "Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3002, "target": 3931, "key": "4bc8f0fff4008e02e24a5a2ae1d299a6"}, {"relation": "partOf", "source": 3002, "target": 1713, "key": "7817350502d5cffe967e8054033a64e4"}, {"line": 14002, "relation": "increases", "evidence": "Furthermore, Pin1 binds c-Jun that is phosphorylated on Ser63/73-Pro motifs by activated JNK or oncogenic Ras.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Subgraph": {"MAPK-ERK subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3002, "target": 2937, "key": "a77ada7e8d171dae18c37eb4a31e58ab"}, {"line": 14011, "relation": "association", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "source": 3002, "target": 3192, "key": "0851f244bd4abef7e635683967a81e86"}, {"line": 14014, "relation": "association", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3002, "target": 2936, "key": "e6bcdd930c4c4e5fffbba6f306394692"}, {"relation": "hasVariant", "source": 3002, "target": 3003, "key": "03b7066066cc2b276fa63c0cc632a636"}, {"line": 18373, "relation": "increases", "evidence": "We found that in cortical neurons exposed to Abeta, activated c-Jun N-terminal kinase (JNK) is required for the phosphorylation and activation of the c-Jun transcription factor, which in turn stimulates the transcription of several key target genes, including the death inducer Fas ligand.", "citation": {"db": "PubMed", "db_id": "11567045"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3002, "target": 2690, "key": "c388745ea02eceae9458cd4afa32717d"}, {"line": 22111, "relation": "association", "evidence": "Over-expression of hsp70 was found to reduce PQ-induced oxidative stress along with JNK and caspase-3 mediated dopaminergic neuronal cell death in exposed organism.", "citation": {"db": "PubMed", "db_id": "24887138"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Chaperone subgraph": true}, "MeSHAnatomy": {"Dopaminergic Neurons": true}}, "source": 3002, "target": 648, "key": "cdf5bed442ee692c25be8825c16bf058"}, {"relation": "partOf", "source": 3002, "target": 1186, "key": "ffded3fa08df4575bc2c4e25f55cfc1d"}, {"line": 32600, "relation": "increases", "evidence": "Amyloid beta protein precursor is phosphorylated by JNK-1 independent of, yet facilitated by, JNK-interacting protein (JIP)-1.", "citation": {"db": "PubMed", "db_id": "12917434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3002, "target": 2334, "key": "be83404739bbb78be3c2846c4536df88"}, {"relation": "partOf", "source": 3002, "target": 1485, "key": "1a022796f4726827365dc9892e94a49c"}, {"line": 32681, "relation": "increases", "evidence": "With insulin resistance in diabetics and pmodels, IRS-1 is phosphorylated at Ser312 by insulin-stimulated or stress-activated kinases, including c-Jun N-terminal kinase (JNK), which uncouples IRS-1 (Aguirre et al., 2002) and triggers rapid IRS-1 degradation (Sun et al., 1999), yielding a deficient signal transduction response (Pederson et al., 2001;Rui et al., 2001).", "citation": {"db": "PubMed", "db_id": "19605645"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3002, "target": 2908, "key": "f2d5f8c9e2fffd9af64e3ee49e6d72b4"}, {"line": 32683, "relation": "increases", "evidence": "With insulin resistance in diabetics and pmodels, IRS-1 is phosphorylated at Ser312 by insulin-stimulated or stress-activated kinases, including c-Jun N-terminal kinase (JNK), which uncouples IRS-1 (Aguirre et al., 2002) and triggers rapid IRS-1 degradation (Sun et al., 1999), yielding a deficient signal transduction response (Pederson et al., 2001;Rui et al., 2001).", "citation": {"db": "PubMed", "db_id": "19605645"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 3002, "target": 2905, "key": "7a869d70472f9eead1df7f3dd43107fb"}, {"relation": "partOf", "source": 3002, "target": 1545, "key": "f2b67ecc783d30fc99af97dfebc2d1c9"}, {"relation": "partOf", "source": 3002, "target": 1546, "key": "18ba95ae0cd19bb050720bdeb7271485"}, {"relation": "partOf", "source": 3002, "target": 1547, "key": "9a6cb9f1342df159ab054937e1bba8eb"}, {"relation": "partOf", "source": 3002, "target": 1502, "key": "8a7c393538389ca3b01a86e053638833"}, {"line": 37848, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "MAPK-JNK subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3002, "target": 2136, "key": "c17024e6d40fd14a1a3999c8150586fc"}, {"line": 39154, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3002, "target": 3335, "key": "ab0df3ba73b2066f8c9575053a164b52"}, {"line": 40471, "relation": "decreases", "evidence": "Finally, Evo-induced c-Jun N-terminal kinases (JNK) activation was reduced by a TRPV1 antagonist, indicating that Evo-induced autophagy may occur through a calcium/c-Jun N-terminal kinase (JNK) pathway.", "citation": {"db": "PubMed", "db_id": "24454492"}, "annotations": {"Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3002, "target": 3496, "key": "b881ddb85451c2ea8179e4fcaf92d4be"}, {"line": 1123, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "MAPK-JNK subgraph": true, "Matrix metalloproteinase subgraph": true, "Interferon signaling subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2998, "target": 3015, "key": "c609c950514f799b322dc6b736bd53d6"}, {"line": 32556, "relation": "increases", "evidence": "To further delineate this relationship, we investigated the role of p38, a stress activated protein kinase which phosphorylates tau protein at sites found in the NFT of AD", "citation": {"db": "PubMed", "db_id": "11079778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3015, "key": "8f509e545fd77b6b883d5b235994aeb9"}, {"line": 32566, "relation": "increases", "evidence": "Reactivating kinase/p38 phosphorylates tau protein in vitro.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3015, "key": "a30eecfee37fac6d3bbb8d5a308fa109"}, {"line": 32809, "relation": "increases", "evidence": "Moreover, SAPK/JNK- and p38-immunoprecipitated sub-cellular fractions enriched in abnormal hyperphosphorylated tau have the capacity to phosphorylate recombinat tau and c-Jun and ATF-2 which are specific substrates of SAPK/JNK and p38 in AD and PiD.", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DNA synthesis": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}, "MeSHDisease": {"Niemann-Pick Diseases": true, "Alzheimer Disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3015, "key": "3865a0f45c867d3ffcc2d45ef3df940c"}, {"line": 33454, "relation": "increases", "evidence": "Moreover, SAPK/JNK- and p38-immunoprecipitated sub-cellular fractions enriched in abnormal hyperphosphorylated tau have the capacity to phosphorylate recombinant tau and c-Jun and ATF-2 which are specific substrates of SAPK/JNK and p38 in AD and PiD.", "citation": {"db": "PubMed", "db_id": "15977985"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3015, "key": "baf156d176d16ceabe284120679c73c2"}, {"line": 34669, "relation": "increases", "evidence": "Finally, CaM kinase II is present in neurons but not in glial cells, thus suggesting no role of CaM kinase II in tau phosphorylation of glial cells. These observations, together with previous results of in vitro studies, support the idea that several MAPK/ERK, SAPK/JNK, p38 and CaM kinase II may participate in tau phosphorylation in tauopathies", "citation": {"db": "PubMed", "db_id": "11810404"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2998, "target": 3015, "key": "78c9cd56fb7c8f2b75e5012b0de264df"}, {"line": 1124, "relation": "positiveCorrelation", "evidence": "Recent studies have shown increased expression of select active kinases, including stress-activated kinase, c-Jun N-terminal kinase (SAPK/JNK) and kinase p38 in brain homogenates in all the tauopathies. Strong active SAPK/JNK and p38 immunoreactivity has been observed restricted to neurons and glial cells containing hyperphosphorylated tau, as well as in dystrophic neurites of senile plaques in AD", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "MAPK-JNK subgraph": true, "Matrix metalloproteinase subgraph": true, "Interferon signaling subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2998, "target": 3931, "key": "00a0b3b99061203231560c8f2e365a61"}, {"line": 18113, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}}, "subject": {"modifier": "Activity"}, "source": 2998, "target": 3920, "key": "0d889f33fbcae43cd9f0d047f21490f6"}, {"line": 18122, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2998, "target": 3110, "key": "fa792bd5b169407fa583865d6f59d602"}, {"line": 39972, "relation": "decreases", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}, "Subgraph": {"Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 2998, "target": 3110, "key": "feaf1a99006a388a45854ae8753d2aa5"}, {"line": 19230, "relation": "positiveCorrelation", "evidence": "In Alzheimer's disease (AD) brain, increased levels of cyclooxygenase-2 (COX-2), cell cycle markers, and p38 MAP kinase (MAPK) can be detected in neuronal cells.", "citation": {"db": "PubMed", "db_id": "15056456"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Prostaglandin subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2998, "target": 3823, "key": "888c60b15c9cfa86e10e83564f71deac"}, {"relation": "isA", "source": 2998, "target": 2173, "key": "75168a7c629af10dd88f610f7393500f"}, {"relation": "hasVariant", "source": 2998, "target": 2999, "key": "9d9fc7664badd564c5390a44255dece8"}, {"relation": "partOf", "source": 2998, "target": 1334, "key": "bf71b4b17802e581165ff374abb6fd29"}, {"relation": "partOf", "source": 2998, "target": 1542, "key": "5a2d811fd31fd917d3da4365a2af9531"}, {"relation": "partOf", "source": 2998, "target": 1535, "key": "b6479ac6dda05cc5c46845733c0e9f3f"}, {"relation": "partOf", "source": 2998, "target": 1541, "key": "31e6822dd38c48e49b00f1d9d3c8230c"}, {"line": 32759, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3031, "key": "85ab3a310b9188ab996410e99e667b1a"}, {"line": 32760, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3032, "key": "f068333ba2471fc2eefee0974c549848"}, {"line": 32761, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3020, "key": "8ee5ed32adb43b87ba6cc9872628fddd"}, {"line": 32762, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3026, "key": "3a046244cc6e15e2b11d24897452fb60"}, {"line": 32763, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 3029, "key": "1277418fe2484314c6a009b0a65d4b79"}, {"line": 32811, "relation": "increases", "evidence": "Moreover, SAPK/JNK- and p38-immunoprecipitated sub-cellular fractions enriched in abnormal hyperphosphorylated tau have the capacity to phosphorylate recombinat tau and c-Jun and ATF-2 which are specific substrates of SAPK/JNK and p38 in AD and PiD.", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DNA synthesis": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}, "MeSHDisease": {"Niemann-Pick Diseases": true, "Alzheimer Disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 2937, "key": "00797d493462352c4144e0abc16ecf8e"}, {"line": 32813, "relation": "increases", "evidence": "Moreover, SAPK/JNK- and p38-immunoprecipitated sub-cellular fractions enriched in abnormal hyperphosphorylated tau have the capacity to phosphorylate recombinat tau and c-Jun and ATF-2 which are specific substrates of SAPK/JNK and p38 in AD and PiD.", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DNA synthesis": true, "MAPK-JNK subgraph": true, "Tau protein subgraph": true}, "MeSHDisease": {"Niemann-Pick Diseases": true, "Alzheimer Disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2998, "target": 2364, "key": "5eea311ed1fbb820b044c3b11a8e43a5"}, {"relation": "partOf", "source": 2998, "target": 1540, "key": "6f81adf59e436791ebe3c19fbe550f67"}, {"relation": "partOf", "source": 2998, "target": 1257, "key": "86fc9c46ab3ea6c1794ecf9d22149d09"}, {"line": 35271, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2998, "target": 715, "key": "d42125f3c71d83371e628551d1d4bb30"}, {"relation": "partOf", "source": 2998, "target": 1669, "key": "d440a7e04104cff9c3ffe722abfa7b3d"}, {"line": 1150, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in Abeta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Response to oxidative stress": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2173, "target": 775, "key": "74833aeda19c9a647170e69609306522"}, {"line": 1167, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in Abeta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Response to oxidative stress": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 2375, "key": "1d4096cc6fd968a5bd8a1e0bb1bbd4d6"}, {"line": 1806, "relation": "association", "evidence": "Schematic representation of the intracellular pathway by which AbetaPP and PS1 control the activation of the MAPK/ERK1/2 cascade and their final biological effects. In the figure is specified the interaction between APP intracellular domain and PS1 C-terminus, with the adaptor protein Grb2. Grb2 can bind simultaneously to APP and PS1 (as measured in FRET experiments) leading to the MAPK ERK1/2 cascade activation. In AD an aberrant activation of ERK1/2 induced by APP and/or PS1 can determine the tentative activation of the cell cycle that, in postmitotic neurons, may induce cells to undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 645, "key": "d49f5698c720c4c434dbd5d432d5d123"}, {"line": 4058, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 645, "key": "c4ecf5cbc8a3b2f89f6cabf5d5f8f368"}, {"line": 35447, "relation": "increases", "evidence": "Schematic representation of the intracellular pathway by which AbetaPP and PS1 control the activation of the MAPK/ERK1/2 cascade and their final biological effects. In the figure is specified the interaction between APP intracellular domain and PS1 C-terminus, with the adaptor protein Grb2. Grb2 can bind simultaneously to APP and PS1 (as measured in FRET experiments) leading to the MAPK ERK1/2 cascade activation. In AD an aberrant activation of ERK1/2 induced by APP and/or PS1 can determine the tentative activation of the cell cycle that, in postmitotic neurons, may induce cells to undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "22496686"}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 645, "key": "cdae631b63791e20d850d15a8729fcd6"}, {"line": 1877, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "CREB subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 478, "key": "666c8503049f746eee3d7ce90d75dfbc"}, {"line": 2143, "relation": "association", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 478, "key": "9ab9974a3cebc7b3b0ae34295f72999f"}, {"line": 35464, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade. It is worth noting that ERK1/2 activity is increased in AD brains and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 478, "key": "37bacf7ea01a60042f6f0fad09f636a5"}, {"line": 35538, "relation": "increases", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 478, "key": "a1cd7275b0d426df0efde21e436a2f5f"}, {"line": 1884, "relation": "increases", "evidence": "It is worth noting that ERK1/2 activity is increased in AD brains and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 3015, "key": "a76bcba4e6071c450f2e3cdea8dd096c"}, {"line": 1899, "relation": "increases", "evidence": "The pathogenic correlation between Shc/Grb2 binding to AbetaPP during AD development is supported by the observation that the complexes AbetaPP (or CTFs)/ShcA or Grb2 are significantly increased in AD brain as compared to controls [55]. The increased phosphorylation/activation of ERK1/2, often described in AD brain, is also observed in thrombin-activated astrocytes, suggesting that, in this model, ERK1/2 may be activated by AbetaPP through ShcA. These data give prominence to the biological importance of AbetaPP phosphorylation for its functions and the regulation of intracellular adaptor binding as events responsible for the induction of glial-associated mitogenic pathway. Furthermore, ERK1/2, activated by Abetain vitro, plays a role in AbetaPP processing and phosphorylates Tau in a PHF-Tau similar manner. However, it is conceivable that a different signaling Abeta-independent might as well activate tau phosphorylation by ERK1/2 via the intracellular signaling regulated by the AbetaPP/CTFs-Shc-Grb2 pathway", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 3015, "key": "0dcbd50beb9f8997842a8ffdaf4b4709"}, {"line": 34667, "relation": "increases", "evidence": "Finally, CaM kinase II is present in neurons but not in glial cells, thus suggesting no role of CaM kinase II in tau phosphorylation of glial cells. These observations, together with previous results of in vitro studies, support the idea that several MAPK/ERK, SAPK/JNK, p38 and CaM kinase II may participate in tau phosphorylation in tauopathies", "citation": {"db": "PubMed", "db_id": "11810404"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2173, "target": 3015, "key": "d4e4f7a5e4b079c13cd7e00eea46cd5e"}, {"line": 35472, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade. It is worth noting that ERK1/2 activity is increased in AD brains and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 3015, "key": "717ce467fda4ab239b8d570b073741a8"}, {"line": 35483, "relation": "increases", "evidence": "The pathogenic correlation between Shc/Grb2 binding to AbetaPP during AD development is supported by the observation that the complexes AbetaPP (or CTFs)/ShcA or Grb2 are significantly increased in AD brain as compared to controls [55]. The increased phosphorylation/activation of ERK1/2, often described in AD brain, is also observed in thrombin-activated astrocytes, suggesting that, in this model, ERK1/2 may be activated by AbetaPP through ShcA. These data give prominence to the biological importance of AbetaPP phosphorylation for its functions and the regulation of intracellular adaptor binding as events responsible for the induction of glial-associated mitogenic pathway. Furthermore, ERK1/2, activated by Abeta in vitro, plays a role in AbetaPP processing and phosphorylates Tau in a PHF-Tau similar manner. However, it is conceivable that a different signaling Abeta-independent might as well activate tau phosphorylation by ERK1/2 via the intracellular signaling regulated by the AbetaPP/CTFs-Shc-Grb2 pathway", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 3015, "key": "e44cba005b917ca989f5fd257c73bb37"}, {"line": 2132, "relation": "association", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 3258, "key": "7d88ee7626185e927d16f197799f0bb0"}, {"line": 2138, "relation": "association", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2173, "target": 420, "key": "9b477a63b21cfca9c3fd85de93ea8d82"}, {"line": 35537, "relation": "increases", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2173, "target": 420, "key": "2848733aba6c0d8b45c7d47189ac4fda"}, {"line": 2215, "relation": "association", "evidence": "As previously discussed, AbetaPP regulates ERK1/2 levels, its phosphorylation/translocation to the centrosome, and cell proliferation rate.Additionally, in the same study, we showed that also PS1 interacts with Grb2 in the centrosomes and modulates ERK1/2 signaling.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2173, "target": 2315, "key": "ebd142424b13abd20058d677aa61ba81"}, {"line": 35539, "relation": "association", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 2315, "key": "600c4e721a4e936aff6657de58bf50a1"}, {"relation": "hasVariant", "source": 2173, "target": 2174, "key": "07f8a0d2e2ff070133a72135f6860005"}, {"line": 2262, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 492, "key": "32c66f09773d41e211c4f8d947d1008e"}, {"line": 5492, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"CREB subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2173, "target": 2164, "key": "81f2178d3956df1543deff2225fa6fb3"}, {"line": 35836, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2173, "target": 2164, "key": "b09fb393c3d224e15b042d1d696c22e4"}, {"line": 7718, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 80, "key": "5b1e5d8cda66b2af0529b4fd4abca456"}, {"line": 15156, "relation": "association", "evidence": "Downregulation of extracellular signal-regulated kinase 1/2 activity by calmodulin KII modulates p21Cip1 levels and survival of immortalized lymphocytes from Alzheimer's disease patients.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2173, "target": 2493, "key": "42df4ef9ed5ee5daf6b658ee69438cee"}, {"line": 15157, "relation": "association", "evidence": "Downregulation of extracellular signal-regulated kinase 1/2 activity by calmodulin KII modulates p21Cip1 levels and survival of immortalized lymphocytes from Alzheimer's disease patients.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2173, "target": 3823, "key": "34ff886b9b8ec9f7dadc302bdcde7065"}, {"line": 35272, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2173, "target": 715, "key": "a4a7decfa12278f25906b72b31991b9d"}, {"relation": "hasVariant", "source": 2173, "target": 2176, "key": "99c8ade769179eb901bc04a75e14c2bd"}, {"line": 36475, "relation": "increases", "evidence": "For example, ERK activates pro-survival transcription factor CREB, by activating both p90RSK and MSK1/2.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2173, "target": 2162, "key": "20d8173a621ed3494d5e0406866bdb93"}, {"line": 36476, "relation": "increases", "evidence": "For example, ERK activates pro-survival transcription factor CREB, by activating both p90RSK and MSK1/2.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 1719, "key": "b2a1c1faba218011725482ca31d5ed26"}, {"relation": "hasVariant", "source": 2173, "target": 2175, "key": "dbeb7eac1f19ef874f70afc90c0f4c2c"}, {"line": 36893, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 818, "key": "437794247b4418adfefe3f165624ee5a"}, {"line": 36895, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 3083, "key": "114d9faff3d149526515fa684e0cf332"}, {"line": 36896, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 2700, "key": "78ca2b10160b7a7f01aa44c961185f6e"}, {"line": 36897, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 2937, "key": "59e25dbf17722c7ab26bb483e5d2edfa"}, {"line": 36898, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 2672, "key": "283a5908d96dbe3f7503d02814b08a09"}, {"line": 36900, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2173, "target": 3322, "key": "b4bb1317942689a5a67de738bdd6704b"}, {"line": 36901, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2173, "target": 3323, "key": "c11affcf9aa90cf43e6087384f13bb3f"}, {"line": 36945, "relation": "increases", "evidence": "Protein kinase C: PKC is part of a multigene family of serine-threonine kinases central to many signal transduction pathways [138] with a prominent role in memory [139]. It is likely that ABeta¸-induced increases in cytosolic Ca2+ signals are transmitted to PKC for PKC-mediated transcriptional activation. In addition, PKC activates ERK by interacting with Ras or Raf-1 [140] to initiate CREB phosphorylation. While PKC levels decline in AD [141], their activation restores K+ channel function in cells from AD patients [142]. In addition, activation of PKC directly or indirectly enhances the a-processing cleavage of APP [143].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 2163, "key": "bdb49ae71a83296ac095a3b4ed5bce06"}, {"line": 36961, "relation": "association", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2173, "target": 495, "key": "5b3a0e358908faf0117dbecd017b55f8"}, {"line": 37101, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157]. In contrast to CaMKs, ERKs cannot directly phosphorylate CREB. Two related RSKs and mitogen- and stress-activated protein kinases (MSKs) transmit the signal from activated ERKs to CREB [158]. CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis [159, 160]. Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity [161]. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP) [162]. There is no direct evidence indicating how lower levels of ABeta¸ might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2173, "target": 2555, "key": "4bcecb14c8ea5e07a5489f28a7540e55"}, {"line": 1181, "relation": "increases", "evidence": "The neurotoxicity induced by AChE-Abeta complexes indicated that they trigger more neurodegeneration than those of the Abeta peptide alone.The fact that AChE is able to accelerate amyloid formation", "citation": {"db": "PubMed", "db_id": "15709485"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 1047, "target": 648, "key": "b1b737ffa71ed2e4cb4736eb9227aab9"}, {"line": 24865, "relation": "increases", "evidence": "Our results suggest that such amyloid-AChE complexes are formed when AChE interacts with the growing amyloid fibrils and accelerates the assembly of Abeta peptides", "citation": {"db": "PubMed", "db_id": "9325095"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1047, "target": 2328, "key": "6f1d3752447fbc59cc65384934e00309"}, {"line": 2032, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 648, "target": 453, "key": "dc25a1d51ee8fa03fdbc25d0a4347a6f"}, {"line": 5232, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 648, "target": 3823, "key": "d7fbd599ebe7aa08e29bea4ca36816bf"}, {"line": 15056, "relation": "association", "evidence": "Repair of oxidative DNA damage, cell-cycle regulation and neuronal death may influence the clinical manifestation of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24936870"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 648, "target": 3823, "key": "9e6d8e2d2944d7c8bb779138962a2162"}, {"line": 19151, "relation": "negativeCorrelation", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 648, "target": 3823, "key": "9747ed05c09cf8bdf85345fc24c7a561"}, {"line": 30144, "relation": "association", "evidence": "The neuronal loss associated with Alzheimer's disease (AD) affects areas of the brain that are vital to cognition.", "citation": {"db": "PubMed", "db_id": "19458225"}, "source": 648, "target": 3823, "key": "87963ecbe34b8aa5bf1da010f5d8174a"}, {"line": 46200, "relation": "increases", "evidence": "Mn neurotoxicity is also known to contribute to the development of multiple neurodegenerative disorders including AD,", "citation": {"db": "PubMed", "db_id": "25064045"}, "source": 648, "target": 3823, "key": "4baef4b23cfa4b679c42c3197845a649"}, {"line": 12399, "relation": "association", "evidence": "In this study, we show that at nanomolar-low micromolar concentrations, etazolate, a selective GABA(A) receptor modulator, stimulates sAPPalpha production in rat cortical neurons and in guinea pig brains. Etazolate (20 nM-2 microM) dose-dependently protected rat cortical neurons against Abeta-induced toxicity. The neuroprotective effects of etazolate were fully blocked by GABA(A) receptor antagonists indicating that this neuroprotection was due to GABA(A) receptor signalling. This indicating that etazolate exerts its neuroprotective effect via sAPPalpha induction.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 648, "target": 2328, "key": "ca620aa3b147436f611d52de033ae71a"}, {"line": 47035, "relation": "association", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 648, "target": 2328, "key": "c6295dee4551db57e27e08d068eaac10"}, {"line": 13915, "relation": "association", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 648, "target": 2689, "key": "5255962d1671e39768e4b45f71c1f1b8"}, {"line": 20830, "relation": "negativeCorrelation", "evidence": "In the mouse, the knock-out of the Upar-encoding gene (Plaur) leads to significant and nearly complete loss in parvalbumin-containing interneurons during brain development.", "citation": {"db": "PubMed", "db_id": "21711233"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Interneurons": true}}, "source": 648, "target": 3698, "key": "d93f9bab1b1f903ddd0a94cdfb55208c"}, {"line": 22110, "relation": "association", "evidence": "Over-expression of hsp70 was found to reduce PQ-induced oxidative stress along with JNK and caspase-3 mediated dopaminergic neuronal cell death in exposed organism.", "citation": {"db": "PubMed", "db_id": "24887138"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Chaperone subgraph": true}, "MeSHAnatomy": {"Dopaminergic Neurons": true}}, "source": 648, "target": 2444, "key": "0255336494cb1cb23ff01f96fe5dd368"}, {"line": 40582, "relation": "association", "evidence": "In addition, Pls also inhibited primary mouse hippocampal neuronal cell death induced by nutrient deprivation, which was associated with the inhibition of caspase-9 and caspase-3 cleavages.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 648, "target": 2444, "key": "f677539c1aab56613d1cd0c218eb9551"}, {"line": 22111, "relation": "association", "evidence": "Over-expression of hsp70 was found to reduce PQ-induced oxidative stress along with JNK and caspase-3 mediated dopaminergic neuronal cell death in exposed organism.", "citation": {"db": "PubMed", "db_id": "24887138"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Chaperone subgraph": true}, "MeSHAnatomy": {"Dopaminergic Neurons": true}}, "source": 648, "target": 3002, "key": "a18cbe3d279172b94de98ba6bb29a43d"}, {"line": 24092, "relation": "negativeCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 648, "target": 2871, "key": "fabe37062af730c70b45562d1f0d342c"}, {"line": 24095, "relation": "positiveCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 648, "target": 3861, "key": "dd71b8b8f786939b9ef625929a8ac399"}, {"line": 30145, "relation": "negativeCorrelation", "evidence": "The neuronal loss associated with Alzheimer's disease (AD) affects areas of the brain that are vital to cognition.", "citation": {"db": "PubMed", "db_id": "19458225"}, "source": 648, "target": 812, "key": "995b6ffaab089df0002a4a6f7fe7cb89"}, {"line": 33053, "relation": "association", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 648, "target": 2794, "key": "2344d5a5fe54f49aa43e5db4e24ae203"}, {"line": 40580, "relation": "association", "evidence": "In addition, Pls also inhibited primary mouse hippocampal neuronal cell death induced by nutrient deprivation, which was associated with the inhibition of caspase-9 and caspase-3 cleavages.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 648, "target": 2449, "key": "893b8d0eb9d103ac8b976ad7be056641"}, {"line": 43293, "relation": "association", "evidence": "We show that in every condition evaluated, cytosolic phospholipase A(2) is present in reactive glial cells/ within the precise region of neuron loss. In conditions where neurons did not degenerate or are protected from death,/ cytosolic phospholipase A(2) is not observed. Both astrocytes and microglial cells are immunoreactive for cytosolic/ phospholipase A(2) following injury, with astrocytes being the most consistent cell type expressing cytosolic/ phospholipase A(2).", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 648, "target": 3697, "key": "03ae9b3ac46eba1ab02890b44f7d214f"}, {"line": 43393, "relation": "decreases", "evidence": "Experimental evidence suggests that cortical noradrenaline (NA) depletion due to degeneration of the locus/ ceruleus (LC) - a pathological hallmark of AD - plays a permissive role in the development of inflammation in AD. Our/ results indicate for the first time that PPARgamma expression can be modulated by the cAMP signalling pathway, and/ suggest that the anti-inflammatory effects of NA on brain cells may be partly mediated by increasing PPARgamma levels./ Conversely, decreased NA due to LC cell death in AD may reduce endogenous PPARgamma expression and therefore potentiate/ neuroinflammatory processes.", "citation": {"db": "PubMed", "db_id": "12887689"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 648, "target": 317, "key": "16fff84321bf9952f4d5a77427e6ea5e"}, {"line": 43394, "relation": "decreases", "evidence": "Experimental evidence suggests that cortical noradrenaline (NA) depletion due to degeneration of the locus/ ceruleus (LC) - a pathological hallmark of AD - plays a permissive role in the development of inflammation in AD. Our/ results indicate for the first time that PPARgamma expression can be modulated by the cAMP signalling pathway, and/ suggest that the anti-inflammatory effects of NA on brain cells may be partly mediated by increasing PPARgamma levels./ Conversely, decreased NA due to LC cell death in AD may reduce endogenous PPARgamma expression and therefore potentiate/ neuroinflammatory processes.", "citation": {"db": "PubMed", "db_id": "12887689"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 648, "target": 3699, "key": "a0f09c8812d0b89b85f8c6945db9c1ad"}, {"line": 1195, "relation": "association", "evidence": "We identified a new genetic risk association of AD with rare coding CLU variations that is independent of the 5' common association signal identified in the GWA studies", "citation": {"db": "PubMed", "db_id": "22248099"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 2538, "target": 3823, "key": "c4389e100d10aa8990d2452727bc774c"}, {"line": 36266, "relation": "association", "evidence": "Clusterin inhibits the aggregation of A beta, which might be neuroprotective according to the aggregation-toxicity hypothesis of A beta. However, clusterin enhanced the oxidative stress of A beta.", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "MeSHAnatomy": {"Blood": true}, "Confidence": {"High": true}}, "source": 2538, "target": 3823, "key": "fc9c668270b1d8128ee6c8920746a921"}, {"line": 5571, "relation": "association", "evidence": "Recent genome-wide association studies (GWAS) have identified common genetic variants that increase risk of LOAD. Two of the genes highlighted in these studies, CLU and CR1, suggest a role for the complement system in the aetiology of AD. In this review we analyse the evidence for an involvement of complement in AD. In particular we focus on one gene, CR1, and its role in the complement cascade. CR1 is a receptor for the complement fragments C3b and C4b and is expressed on many different cell types, particularly in the circulatory system.", "citation": {"db": "PubMed", "db_id": "21840620"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 2538, "target": 534, "key": "bf9fabda9c98e3f3f9a7c6dfc8d9a48d"}, {"relation": "partOf", "source": 2538, "target": 1227, "key": "1342cd156242b8da2f84c179f40d6ef6"}, {"relation": "partOf", "source": 2538, "target": 922, "key": "41a2180baa4f7ecabec9fff8dbab5f4f"}, {"relation": "partOf", "source": 2538, "target": 1288, "key": "464a696f7dba1c679463209c723b3ee3"}, {"relation": "partOf", "source": 2538, "target": 1360, "key": "840c6ec809754bfaf5a9915a554041b7"}, {"relation": "partOf", "source": 2538, "target": 1361, "key": "2b98e8ec19f94e381c7603a401a76502"}, {"relation": "partOf", "source": 2538, "target": 1359, "key": "c9519cf272d2657b9705d5c8d2f0af1f"}, {"line": 33237, "relation": "association", "evidence": "Cytokines such as TGF beta 1 and interleukin 1 enhance the expression of clusterin, which may link clusterin to inflammatory mechanisms in AD.", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 2538, "target": 577, "key": "ca97679e6423f1fdef8c87b38863ae35"}, {"relation": "partOf", "source": 2538, "target": 1157, "key": "574cea45d9cdaa39939e3a269cf2c101"}, {"line": 36258, "relation": "decreases", "evidence": "Clusterin inhibits the aggregation of A beta, which might be neuroprotective according to the aggregation-toxicity hypothesis of A beta. However, clusterin enhanced the oxidative stress of A beta.", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2538, "target": 2328, "key": "588475761d0090e16b8351b91849dab1"}, {"line": 36292, "relation": "decreases", "evidence": "We have previously proposed that CLU-client complexes serve as vehicles to dispose of damaged misfolded extracellular proteins in vivo via receptor-mediated endocytosis", "citation": {"db": "PubMed", "db_id": "19996109"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "chap", "namespace": "bel"}}, "source": 2538, "target": 2328, "key": "f2e2c558bfba3bd60cbc63e6526a0906"}, {"line": 47758, "relation": "positiveCorrelation", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2538, "target": 2328, "key": "f17507833a51e7eb9cfa2223558b7c54"}, {"line": 36259, "relation": "increases", "evidence": "Clusterin inhibits the aggregation of A beta, which might be neuroprotective according to the aggregation-toxicity hypothesis of A beta. However, clusterin enhanced the oxidative stress of A beta.", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2538, "target": 774, "key": "d115b1686627f752cdae1f99228d45cb"}, {"line": 36277, "relation": "increases", "evidence": "CLU plays a key role in an extracellular proteostasis system that recognizes, keeps soluble, and then rapidly mediates the disposal of misfolded proteins", "citation": {"db": "PubMed", "db_id": "21505792"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2538, "target": 543, "key": "1edf24c51927a8a0029713103f1562eb"}, {"line": 36288, "relation": "directlyIncreases", "evidence": "We have previously proposed that CLU-client complexes serve as vehicles to dispose of damaged misfolded extracellular proteins in vivo via receptor-mediated endocytosis", "citation": {"db": "PubMed", "db_id": "19996109"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "chap", "namespace": "bel"}}, "source": 2538, "target": 701, "key": "d3b45a8886575e4cace8b26895c3553e"}, {"line": 47762, "relation": "positiveCorrelation", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2538, "target": 2629, "key": "516033cff36eb7915237b5b7e1d12ff2"}, {"line": 48120, "relation": "decreases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2538, "target": 2629, "key": "780fca3cc19021ecb3403868102cdef3"}, {"line": 47778, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "subject": {"location": {"namespace": "MESH", "name": "Cytosol"}}, "source": 2538, "target": 866, "key": "62c9fccafb0733637d93e96656808b79"}, {"line": 47782, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2538, "target": 866, "key": "cdbb1ea628fb21b1fa4a5eada1acce19"}, {"line": 48137, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"location": {"namespace": "MESH", "name": "Cytosol"}}, "source": 2538, "target": 866, "key": "87cff2824d427591df73ed7ca31dd83c"}, {"line": 48142, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2538, "target": 866, "key": "52d7fd289e7affb5e49a56dce83de9cb"}, {"line": 48666, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2538, "target": 3482, "key": "c7e1002fe6f9645ec906f26830809aaf"}, {"line": 1215, "relation": "increases", "evidence": "Our results suggest that SRs play a role on inflammatory activation, inducing production of NO and IL1Abeta, and show potentiation by Abeta. Potentiation of the inflammatory response of Abeta could be meaningful for the activation of glia observed in AD.We propose that scavenger receptors (SR) participate in the activation of glia by Abeta.", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Nitric oxide subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3069, "target": 156, "key": "e869001791ec87f46b77e337974f5792"}, {"line": 1234, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3069, "target": 2885, "key": "f98229c393d45cb2c515d3556d9fc65e"}, {"line": 39124, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3069, "target": 577, "key": "87b860172ef3fec23ae08f2b46152193"}, {"line": 1226, "relation": "increases", "evidence": "However, co-stimulation with Ab and fucoidan induced a statistically significant increase on JNK activation", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 979, "target": 670, "key": "fee54d7246ec427547838f39a32514a2"}, {"relation": "partOf", "source": 258, "target": 979, "key": "891b89965ddd144a3911e526dc07511b"}, {"line": 1236, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"High": true}}, "source": 2870, "target": 2885, "key": "39ca21b9fecacb73fc43b356d3e6554d"}, {"relation": "partOf", "source": 2870, "target": 1708, "key": "6a0f9bcdbe3f8b772e95d8d182a7a80b"}, {"line": 13783, "relation": "increases", "evidence": "Activation of nuclear factor-kappa B by beta-amyloid peptides and interferon-gamma in murine microglia.", "citation": {"db": "PubMed", "db_id": "9209268"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Interferon signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2870, "target": 871, "key": "933939531db3c5fb62bb2aa775932714"}, {"relation": "partOf", "source": 2870, "target": 1690, "key": "73b558fe9ad413848041978184a9639c"}, {"line": 22386, "relation": "increases", "evidence": "In addition, pericytes respond to the pro-inflammatory cytokines tumor necrosis factor-α and Interferon-gamma by inducing the expression of the CYP27B1 gene which is involved in 1,25D synthesis.", "citation": {"db": "PubMed", "db_id": "24934545"}, "annotations": {"Subgraph": {"Vitamin subgraph": true, "Interferon signaling subgraph": true, "Metabolism of steroid hormones subgraph": true}}, "source": 2870, "target": 2610, "key": "5981377524b1853ce22cbb6b85fa4d29"}, {"line": 26860, "relation": "increases", "evidence": "Aggregated Abeta induced IFN-gamma production from co-culture of astrocytes and microglia, and IFN-gamma elicited tumor necrosis factor (TNF)-alpha secretion in wild type (WT) but not GRKO microglia co-cultured with astrocytes. ", "citation": {"db": "PubMed", "db_id": "17255335"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 2870, "target": 3472, "key": "49a39b8fd411f76e1616c8d4bf6957dc"}, {"relation": "partOf", "source": 2870, "target": 1706, "key": "cdb4ad9390db200deb684d917ee8db4d"}, {"relation": "partOf", "source": 2870, "target": 1707, "key": "d570056866e54eba2a63afcf4e51af53"}, {"line": 39712, "relation": "increases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 2870, "target": 1232, "key": "c679b774ce908cb408b41fdc8b46c73e"}, {"line": 39714, "relation": "decreases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chaperone subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2870, "target": 2397, "key": "16b49964080d7b19411357ccb5a31b58"}, {"line": 39717, "relation": "increases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2870, "target": 2881, "key": "234e1cc2b911d5abcf73ae9e75341b2a"}, {"line": 39812, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true}}, "source": 2870, "target": 3823, "key": "ff2e7554cf37fef5f160a08da24d7da1"}, {"line": 44363, "relation": "increases", "evidence": "JAK-STAT signaling as an anti-inflammatory target. JAK-STAT signaling mediates the brain inflammation induced by LPS, IFN-gamma, ganglioside and thrombin. Curcumin activates SH2-containing phosphatase 2 (SHP2), while rosiglitazone and 15d-PGJ2 increase the expressions of SOCS1 and SOCS3. SHP2 and the SOCS proteins are typical negative feedback molecules of the JAK-STAT pathway.", "citation": {"db": "PubMed", "db_id": "26113788"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2870, "target": 3815, "key": "187834387ba842e23b4b31e5a3919e0c"}, {"line": 1243, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3341, "target": 577, "key": "779ed19583a49e5573ffff591a0d5fdb"}, {"line": 1250, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 3341, "target": 156, "key": "a12a3c9bba15489e38372dc279bfc8bd"}, {"line": 1263, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 3341, "target": 2885, "key": "956ff102b00608cd996959d64f03eecf"}, {"line": 1274, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Innate immune system subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 3341, "target": 608, "key": "f2e68aff293708fe38bfd6920f0b325c"}, {"line": 1244, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3340, "target": 577, "key": "cf160e3a47e20c9644ffe57b3be4c224"}, {"line": 39126, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3340, "target": 577, "key": "b25a7577b25228a1e349ed20b8518732"}, {"line": 1254, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 3340, "target": 156, "key": "86ed24d361708ed74b5ad8958e370fa5"}, {"line": 1264, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 3340, "target": 2885, "key": "5d2216577921c78448c034e7900a17df"}, {"line": 1275, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Innate immune system subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 3340, "target": 608, "key": "37b71a79e5ce0265039f6aa57758509d"}, {"relation": "partOf", "source": 3340, "target": 945, "key": "93ece32702055aa3f224e888d8b29285"}, {"line": 1245, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3339, "target": 577, "key": "d710a7e20abafc5fd57fc61fb908dfb8"}, {"line": 1258, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 3339, "target": 156, "key": "012fd175fb75c5559be3007b31f9f7cb"}, {"line": 1265, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 3339, "target": 2885, "key": "3cc7f2461ac094935aff79cd7dac6b38"}, {"line": 1276, "relation": "increases", "evidence": "Thus, Ab, SR ligands and LI were all able to induce production of pro-IL1b and IL1b by astrocytes", "citation": {"db": "PubMed", "db_id": "22237943"}, "annotations": {"Subgraph": {"Innate immune system subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true}, "Confidence": {"Low": true}}, "source": 3339, "target": 608, "key": "dca1a8c9add58bc825048ce691c6f56b"}, {"line": 4222, "relation": "association", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 608, "target": 609, "key": "b7be827bed3db4e39947b3373e3218ab"}, {"line": 4236, "relation": "increases", "evidence": "Microglia are activated in response to a number of different pathological states within the CNS including injury, ischemia, and infection. Microglial activation results in their production of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-a. While release of these factors is typically intended to prevent further damage to CNS tissue, they may also be toxic to neurons and other glial cells. Mounting evidence indicates that chronic microglial activation may also contribute to the development and progression of neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "22024597"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 2885, "key": "5328eef3e923f469002d34ba574b2f9f"}, {"line": 5021, "relation": "association", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 608, "target": 2885, "key": "0aa24bdb9336d6ee2cf03b3aaa6e1eb8"}, {"line": 8562, "relation": "increases", "evidence": "A significant role of a pathological glial cell activation in the pathogenesis of Alzheimer's disease is supported by the growing evidence that inflammatory proteins, which are produced by reactive astrocytes, promote the transformation of diffuse beta-amyloid deposits into the filamentous, neurotoxic form. A number of vicious circles, driven by the release of TNF-a and free oxygen radicals from microglial cells, may cause an upregulated microglial activation and their production of interleukin-1 which triggers, secondarily, the crucial activation of astrocytes.", "citation": {"db": "PubMed", "db_id": "9850925"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 608, "target": 2885, "key": "c1d206141cee713ba0d0a1bbdafea28c"}, {"line": 4237, "relation": "increases", "evidence": "Microglia are activated in response to a number of different pathological states within the CNS including injury, ischemia, and infection. Microglial activation results in their production of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-a. While release of these factors is typically intended to prevent further damage to CNS tissue, they may also be toxic to neurons and other glial cells. Mounting evidence indicates that chronic microglial activation may also contribute to the development and progression of neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "22024597"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 2894, "key": "65b8d1537e2244be67b6462a4c1cb8eb"}, {"line": 4245, "relation": "increases", "evidence": "Microglia are activated in response to a number of different pathological states within the CNS including injury, ischemia, and infection. Microglial activation results in their production of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-a. While release of these factors is typically intended to prevent further damage to CNS tissue, they may also be toxic to neurons and other glial cells. Mounting evidence indicates that chronic microglial activation may also contribute to the development and progression of neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "22024597"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 3472, "key": "8568bf78a4dd51de133814dfe964aaf2"}, {"line": 5014, "relation": "association", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 3472, "key": "14df4c6817f5cdd5d218920940d2acb7"}, {"line": 4252, "relation": "increases", "evidence": "Microglia are activated in response to a number of different pathological states within the CNS including injury, ischemia, and infection. Microglial activation results in their production of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-a. While release of these factors is typically intended to prevent further damage to CNS tissue, they may also be toxic to neurons and other glial cells. Mounting evidence indicates that chronic microglial activation may also contribute to the development and progression of neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "22024597"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 608, "target": 532, "key": "4a4762c7f6eaeca8e5cffccd712f7004"}, {"line": 4254, "relation": "increases", "evidence": "Microglia are activated in response to a number of different pathological states within the CNS including injury, ischemia, and infection. Microglial activation results in their production of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-a. While release of these factors is typically intended to prevent further damage to CNS tissue, they may also be toxic to neurons and other glial cells. Mounting evidence indicates that chronic microglial activation may also contribute to the development and progression of neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "22024597"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 608, "target": 3874, "key": "8e52d6c0d29c902b4c1b3e6b3af7bd8b"}, {"line": 5007, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 577, "key": "dfaa7842ec2c31e12ccba88b551fdd56"}, {"line": 5011, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 524, "key": "e52baeed89ae5496a77b31aa2f448f70"}, {"line": 5020, "relation": "association", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 608, "target": 2884, "key": "37a7e83f0d96e6f5441d69a116e261d5"}, {"line": 5025, "relation": "increases", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 693, "key": "b69f813762111f9e61d73cde11b2c016"}, {"line": 5028, "relation": "increases", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 2625, "key": "d35206248507ede20ad921d773e3e967"}, {"line": 38221, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is associated with a significant neuroinflammatory component. Mononuclear phagocytes including monocytes and microglia are the principal cells involved, and they accumulate at perivascular sites of beta-amyloid (Abeta) deposition and in senile plaques. Recent evidence suggests that mononuclear phagocyte accumulation in the AD brain is dependent on chemokines. CCL2, a major monocyte chemokine, is upregulated in the AD brain. Interaction of CCL2 with its receptor CCR2 regulates mononuclear phagocyte accumulation in a mouse model of AD. CCR2 deficiency leads to lower mononuclear phagocyte accumulation and is associated with higher brain Abeta levels, specifically around blood vessels, suggesting that monocytes accumulate at sites of Abeta deposition in an initial attempt to clear these deposits and stop or delay their neurotoxic effects. Indeed, enhancing mononuclear phagocyte accumulation delays progression of AD.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 608, "target": 2328, "key": "dbe162c872e164fbd39f65c272f4b0f1"}, {"line": 1294, "relation": "association", "evidence": "Here we show that the buildup of Abeta increases the mammalian target of rapamycin (mTOR) signaling, whereas decreasing mTOR signaling reduces Abeta levels, thereby highlighting an interrelation between mTOR signaling and Abeta. The mTOR pathway plays a central role in controlling protein homeostasis and hence, neuronal functions; indeed mTOR signaling regulates different forms of learning and memory.", "citation": {"db": "PubMed", "db_id": "20178983"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"mTOR signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3076, "target": 820, "key": "ae9fcb908f795f5d3a8a6e2790e0e331"}, {"line": 1295, "relation": "association", "evidence": "Here we show that the buildup of Abeta increases the mammalian target of rapamycin (mTOR) signaling, whereas decreasing mTOR signaling reduces Abeta levels, thereby highlighting an interrelation between mTOR signaling and Abeta. The mTOR pathway plays a central role in controlling protein homeostasis and hence, neuronal functions; indeed mTOR signaling regulates different forms of learning and memory.", "citation": {"db": "PubMed", "db_id": "20178983"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"mTOR signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3076, "target": 812, "key": "e9856967ea4a15e0d93c7d8bca59b795"}, {"line": 21628, "relation": "positiveCorrelation", "evidence": "mTOR Hyperactivation in Down Syndrome Hippocampus Appears Early During Development.", "citation": {"db": "PubMed", "db_id": "24918639"}, "annotations": {"MeSHDisease": {"Down Syndrome": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3076, "target": 3852, "key": "086136acfc50a8644d927545de4f906f"}, {"relation": "hasVariant", "source": 3076, "target": 3077, "key": "fb66bd93b83ba8c17ef34ff38fbfd6b6"}, {"relation": "hasVariant", "source": 3076, "target": 3078, "key": "f67a2232158b982933f05f9e52415ec1"}, {"line": 34831, "relation": "increases", "evidence": "Autopsy brain hippocampal tissues were obtained from controls and patients with AD and Western blots were performed using antibodies against mTOR signaling molecules and RagC, an upstream component of mTOR complex 1 (mTORC1) signaling. We found that expression of mTOR/p-mTOR and its downstream targets S6/p-S6 and Raptor/p-Raptor were expressed in the control and AD hippocampus. The expression levels of these signaling molecules were significantly increased in the hippocampus at the severe stages of AD, compared to controls and other stages of AD. Interestingly, Rictor expression level was unaltered. In addition, RagC was increased in the hippocampus at the early, moderate, and severe stages of AD. Our data indicate that mTORC1, but not mTORC2, was activated in the AD brains and that the level of mTOR signaling activation was correlated with cognitive severity of AD patients.", "citation": {"db": "PubMed", "db_id": "23979023"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "DiseaseState": {"Moderate AD": true, "Early-onset AD": true, "Late-onset AD": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3076, "target": 460, "key": "a7e9178e14d67bcc112d719d68c104e1"}, {"line": 1307, "relation": "increases", "evidence": "The deregulation of brain cholesterol metabolism is typical in acute neuronal injury (such as stroke, brain trauma and epileptic seizures) and chronic neurodegenerative diseases (Alzheimer's disease). We show that a short (30 min) stimulation of glutamatergic neurotransmission induces a small but significant loss of membrane cholesterol, which is paralleled by release to the extracellular milieu of the metabolite 24S-hydroxycholesterol.", "citation": {"db": "PubMed", "db_id": "22343944"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Glutamatergic subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Extracellular Space"}}}, "source": 690, "target": 231, "key": "91a4a7f2ac49d7d6acb68a90d66e3c46"}, {"line": 1308, "relation": "increases", "evidence": "The deregulation of brain cholesterol metabolism is typical in acute neuronal injury (such as stroke, brain trauma and epileptic seizures) and chronic neurodegenerative diseases (Alzheimer's disease). We show that a short (30 min) stimulation of glutamatergic neurotransmission induces a small but significant loss of membrane cholesterol, which is paralleled by release to the extracellular milieu of the metabolite 24S-hydroxycholesterol.", "citation": {"db": "PubMed", "db_id": "22343944"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Glutamatergic subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 690, "target": 19, "key": "237668d48eb3490fb93ac37600537e7d"}, {"line": 5558, "relation": "increases", "evidence": "Disturbances of the cholesterol metabolism are associated with Alzheimer's disease (AD) risk and related cerebral pathology. Experimental studies found changing levels of cholesterol and its metabolites 24S-hydroxycholesterol (24S-OHC) and 27-hydroxycholesterol (27-OHC) to contribute to amyloidogenesis by increasing the production of soluble amyloid precursor protein (sAPP).The results suggest that high CSF concentrations of cholesterol, 24S-OHC, and 27-OHC are associated with increased production of both sAPP forms in AD.", "citation": {"db": "PubMed", "db_id": "22845771"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 19, "target": 2137, "key": "6ba8a2bdf2b3fe561e0dbf51683db333"}, {"relation": "partOf", "source": 19, "target": 1655, "key": "facb0bddca35ac6485901b4d3d34342b"}, {"line": 1315, "relation": "increases", "evidence": "Consistent with a cause-effect relationship, knockdown of the enzyme cholesterol 24-hydroxylase (CYP46A1) prevented glutamate-mediated cholesterol loss. Functionally, the loss of cholesterol modulates the magnitude of the depolarization-evoked calcium response.", "citation": {"db": "PubMed", "db_id": "22343944"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Extracellular Space"}}}, "source": 2614, "target": 231, "key": "9a9a6787e1c14fe81d451dceb0125848"}, {"line": 1320, "relation": "increases", "evidence": "Mechanistically, glutamate-induced cholesterol loss requires high levels of intracellular Ca(2+), a functional stromal interaction molecule 2 (STIM2) and mobilization of CYP46A1 towards the plasma membrane. This study underscores the key role of excitatory neurotransmission in the control of membrane lipid composition, and consequently in neuronal membrane organization and function.", "citation": {"db": "PubMed", "db_id": "22343944"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Intracellular Space"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 2614, "target": 690, "key": "6557eb8a873b814dd2af5d040c62a899"}, {"line": 1322, "relation": "increases", "evidence": "Mechanistically, glutamate-induced cholesterol loss requires high levels of intracellular Ca(2+), a functional stromal interaction molecule 2 (STIM2) and mobilization of CYP46A1 towards the plasma membrane. This study underscores the key role of excitatory neurotransmission in the control of membrane lipid composition, and consequently in neuronal membrane organization and function.", "citation": {"db": "PubMed", "db_id": "22343944"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Calcium-dependent signal transduction": true, "Glutamatergic subgraph": true}}, "source": 3428, "target": 690, "key": "49f420b9f740ed8e08410a959123e919"}, {"line": 1361, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2603, "target": 80, "key": "610de8b485784225c782e5a0cb341196"}, {"relation": "partOf", "source": 2603, "target": 1386, "key": "a23e916f3fe2a8300c94bef0e82247d2"}, {"line": 40602, "relation": "association", "evidence": "The chemokine Interferon gamma-induced protein 10 (IP-10) and human leukocyte antigen (HLA) are widely used indicators of glial activation and neuroinflammation and are up-regulated in many brain disorders.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHDisease": {"Brain Diseases": true}, "MeSHAnatomy": {"Brain": true, "Leukocytes": true, "Neuroglia": true}, "Species": {"9606": true}}, "source": 2603, "target": 3815, "key": "a23033380fa287934d1a15e4fe7db16e"}, {"line": 40618, "relation": "association", "evidence": "In contrast, TGFbeta1 did not block the IFNgamma-induced increase in IP-10 in pericytes and meningeal fibroblasts.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Pericytes": true, "Meninges": true, "Fibroblasts": true}, "Species": {"9606": true}}, "source": 2603, "target": 3, "key": "458abf2d05be1d52ceca57741e7effb9"}, {"line": 40619, "relation": "association", "evidence": "In contrast, TGFbeta1 did not block the IFNgamma-induced increase in IP-10 in pericytes and meningeal fibroblasts.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Pericytes": true, "Meninges": true, "Fibroblasts": true}, "Species": {"9606": true}}, "source": 2603, "target": 2869, "key": "5ed9b59771974a485ea791a434d1e792"}, {"line": 1349, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2146, "target": 3823, "key": "b3d3e88478a9b033910a7b472b3be35d"}, {"line": 1350, "relation": "biomarkerFor", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2146, "target": 3823, "key": "ce5bb1de0da5186ab47c57c2f816bbec"}, {"line": 1351, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2455, "target": 2328, "key": "e4f6b3cbbe082b1ccf23ebaeab70f928"}, {"line": 1355, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Tau protein subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2455, "target": 3015, "key": "08c5958790c135c5a6fd744de9bd13b1"}, {"line": 1362, "relation": "biomarkerFor", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2455, "target": 3823, "key": "acce5d43f6d8b1c37914a551c5cc8c67"}, {"line": 4970, "relation": "positiveCorrelation", "evidence": "Recent evidence suggests that mononuclear phagocyte accumulation in the AD brain is dependent on chemokines. CCL2, a major monocyte chemokine, is upregulated in the AD brain.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Complement system subgraph": true}}, "source": 2455, "target": 3823, "key": "c4723cc0ffe9d3b51c69ad9b5b444cca"}, {"line": 38968, "relation": "positiveCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2455, "target": 3823, "key": "4379d36fd288c3b243b973e79a5f971f"}, {"line": 4973, "relation": "positiveCorrelation", "evidence": "Recent evidence suggests that mononuclear phagocyte accumulation in the AD brain is dependent on chemokines. CCL2, a major monocyte chemokine, is upregulated in the AD brain.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2455, "target": 622, "key": "02a3712943d10d20fe2edb044940db61"}, {"relation": "partOf", "source": 2455, "target": 1318, "key": "7df7aa22c5963e563a9355d57ef9f341"}, {"line": 4985, "relation": "directlyIncreases", "evidence": "Interaction of CCL2 with its receptor CCR2 regulates mononuclear phagocyte accumulation. CCR2 deficiency leads to lower mononuclear phagocyte accumulation and is associated with higher brain Abeta levels, specifically around blood vessels, suggesting that monocytes accumulate at sites of Abeta deposition in an initial attempt to clear these deposits and stop or delay their neurotoxic effects.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Complement system subgraph": true, "Chemokine signaling subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2455, "target": 2465, "key": "c440e093cb0a3801933d8f83d644ed39"}, {"relation": "partOf", "source": 2455, "target": 1319, "key": "0f4f7d204089ed5547932f6c99e66926"}, {"line": 49176, "relation": "increases", "evidence": "Moreover, IL-1beta increased astrocytic production of pro-inflammatory chemokines such as CCL2, CCL20, and CXCL2, which induce immune cell migration and exacerbate BBB disruption and neuroinflammation. Our findings suggest that astrocytic SHH is a potential therapeutic target that could be used to restore disrupted BBB in patients with neurologic diseases.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2455, "target": 509, "key": "613d1f8e9bddef43787286b12e1cb56d"}, {"line": 1360, "relation": "positiveCorrelation", "evidence": "Some studies have linked the presence of chemokines to the early stages of Alzheimer's disease (AD). Then, the identification of these mediators may contribute to diagnosis. Our objective was to evaluate the levels of beta-amyloid (BA), tau, phospho-tau (p-tau) and chemokines (CCL2, CXCL8 and CXCL10) in the cerebrospinal fluid (CSF) of patients with AD and healthy controls. The correlation of these markers with clinical parameters was also evaluated. The levels of p-tau were higher in AD compared to controls, while the tau/p-tau ratio was decreased. The expression of CCL2 was increased in AD. A positive correlation was observed between BA levels and all chemokines studied, and between CCL2 and p-tau levels. Our results suggest that levels of CCL2 in CSF are involved in the pathogenesis of AD and it may be an additional useful biomarker for monitoring disease progression.", "citation": {"db": "PubMed", "db_id": "21755121"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2606, "target": 80, "key": "344a5d52120245ec65e95ff175efcdb5"}, {"line": 1410, "relation": "biomarkerFor", "evidence": "A panel of five markers (CCL5, CSF1, ICAM1, IL8, TNF) with detectable expression levels in all individuals differed between AD patients", "citation": {"db": "PubMed", "db_id": "21942811"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2606, "target": 3823, "key": "b1f8377aad35a0da3c2a54d047180732"}, {"line": 3783, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2606, "target": 3823, "key": "3f1f2455950285c0bbd1a161379ffe37"}, {"line": 38971, "relation": "positiveCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2606, "target": 3823, "key": "1923d094fe79be92bf5b568e5e5b8cf3"}, {"line": 39810, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2606, "target": 3823, "key": "7d87ec237fb1c348a64d28f5a1f5a1a7"}, {"line": 22617, "relation": "increases", "evidence": "Furthermore, also some studies demonstrated that probiotics decreased the synthesis of pro-inflammatory cytokines which are upregulated in the elderly, such as interleukin (IL)-8, IL-6 or tumour necrosis factor ?, among others, and they increased the levels of activated lymphocytes, natural killer cells, phagocytic activity and even showed a greater response to influenza vaccination.", "citation": {"db": "PubMed", "db_id": "24889891"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2606, "target": 598, "key": "d3b06e0ae0e3db6e74bfc42f25487453"}, {"line": 22620, "relation": "increases", "evidence": "Furthermore, also some studies demonstrated that probiotics decreased the synthesis of pro-inflammatory cytokines which are upregulated in the elderly, such as interleukin (IL)-8, IL-6 or tumour necrosis factor ?, among others, and they increased the levels of activated lymphocytes, natural killer cells, phagocytic activity and even showed a greater response to influenza vaccination.", "citation": {"db": "PubMed", "db_id": "24889891"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2606, "target": 624, "key": "493e9e86a3a93f8396b6644e59bcaad8"}, {"line": 22623, "relation": "increases", "evidence": "Furthermore, also some studies demonstrated that probiotics decreased the synthesis of pro-inflammatory cytokines which are upregulated in the elderly, such as interleukin (IL)-8, IL-6 or tumour necrosis factor ?, among others, and they increased the levels of activated lymphocytes, natural killer cells, phagocytic activity and even showed a greater response to influenza vaccination.", "citation": {"db": "PubMed", "db_id": "24889891"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2606, "target": 823, "key": "c710c4d10435c28861fa92e6f7b6816d"}, {"relation": "hasReactant", "source": 4104, "target": 2315, "key": "dfad56b1f056f4b30f9edccfcc8f7434"}, {"relation": "hasReactant", "source": 4104, "target": 2593, "key": "1dcbb1abd0d03ac4eaf95ae2b14cd49a"}, {"relation": "hasProduct", "source": 4104, "target": 80, "key": "333a16b0804274f6a6c31b657fb7e6db"}, {"line": 1388, "relation": "positiveCorrelation", "evidence": "Cathepsin D, the most abundant lysosomal and endosomal aspartyl protease, shows beta and gamma secretase activity in vitro by cleaving the amyloid precursor protein (APP) into amyloid beta protein (Abeta). Polymorphism at position 224, C224T, on exon 2 of cathepsin D gene (CTSD) has been associated with an increased risk for Alzheimer's disease (AD) ", "citation": {"db": "PubMed", "db_id": "20597865"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1792, "target": 2328, "key": "de8ee5e8e0ecfabcd9e3e8002ec870b9"}, {"relation": "hasVariant", "source": 1791, "target": 1792, "key": "78ce2fbe1b929e5f061d55d4747dacb1"}, {"line": 1389, "relation": "association", "evidence": "Cathepsin D, the most abundant lysosomal and endosomal aspartyl protease, shows beta and gamma secretase activity in vitro by cleaving the amyloid precursor protein (APP) into amyloid beta protein (Abeta). Polymorphism at position 224, C224T, on exon 2 of cathepsin D gene (CTSD) has been associated with an increased risk for Alzheimer's disease (AD) ", "citation": {"db": "PubMed", "db_id": "20597865"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1791, "target": 3823, "key": "e81c4b01196384e8ce44f171c3a1f829"}, {"line": 1402, "relation": "biomarkerFor", "evidence": "A panel of five markers (CCL5, CSF1, ICAM1, IL8, TNF) with detectable expression levels in all individuals differed between AD patients", "citation": {"db": "PubMed", "db_id": "21942811"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2459, "target": 3823, "key": "4fddee7c6b9a59e354477f8ae1d6eaaf"}, {"line": 1405, "relation": "biomarkerFor", "evidence": "A panel of five markers (CCL5, CSF1, ICAM1, IL8, TNF) with detectable expression levels in all individuals differed between AD patients", "citation": {"db": "PubMed", "db_id": "21942811"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2566, "target": 3823, "key": "a801467575546037b827d4a91a7e93b7"}, {"relation": "partOf", "source": 2566, "target": 1659, "key": "b8fdc9587aea517cc4fd91d436f435cb"}, {"relation": "partOf", "source": 2566, "target": 1365, "key": "46852fe4a58914f24737809f66a0f759"}, {"line": 1408, "relation": "biomarkerFor", "evidence": "A panel of five markers (CCL5, CSF1, ICAM1, IL8, TNF) with detectable expression levels in all individuals differed between AD patients", "citation": {"db": "PubMed", "db_id": "21942811"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Cell adhesion subgraph": true, "Inflammatory response subgraph": true, "Low density lipoprotein subgraph": true}}, "source": 2863, "target": 3823, "key": "b8093bb5218d683c530e998219ec1dbb"}, {"line": 15615, "relation": "positiveCorrelation", "evidence": "Plasma sICAM-1 and sPECAM-1 were higher and CSF sVCAM-1 were lower in AD and DLB patients than in controls (p<0.001).", "citation": {"db": "PubMed", "db_id": "17270454"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 2863, "target": 3823, "key": "6010d048dbefeb82d77fe1283184985d"}, {"line": 15643, "relation": "association", "evidence": "Adhesion molecules, particularly intracellular adhesion molecule (ICAM)-1, vascular cell adhesion molecule (VCAM)-1, and E-selectin, have been associated with cardiovascular disease.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"MeSHDisease": {"Cardiovascular Diseases": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 2863, "target": 3834, "key": "85977b0c168bba984f5831159bd9abda"}, {"line": 15666, "relation": "association", "evidence": "High-fat load and glucose alone produced an increase of nitrotyrosine, ICAM-1, VCAM-1, and E-selectin plasma levels in normal and diabetic subjects.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 2863, "target": 264, "key": "dd33a68d5af6f1885b43848aabd60910"}, {"relation": "partOf", "source": 2863, "target": 1462, "key": "09deb7c9274115587191a8726e262e8d"}, {"relation": "partOf", "source": 2863, "target": 993, "key": "a535dfb972811666159c3983ee87d8da"}, {"relation": "partOf", "source": 2863, "target": 1456, "key": "92c8e43fc3c0e50ba8b436c9296b4d88"}, {"relation": "partOf", "source": 2863, "target": 1454, "key": "3b7060c4966092da838397aa839aa1bd"}, {"relation": "partOf", "source": 2863, "target": 1455, "key": "3626b2b6798f5b50c419dff629d67f35"}, {"line": 1422, "relation": "increases", "evidence": "Thus, our results indicate that hyperphosphorylation of tau protein induced by stress may represent the pathogenic event upstream of tau protein misfolding, which leads to progression or eventually initiation of neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22222439"}, "annotations": {"Confidence": {"Very High": true}}, "source": 776, "target": 3015, "key": "b7c4d7f0a9f6f5ab6ca242d65163317d"}, {"line": 1430, "relation": "increases", "evidence": "The data show that CRH plays an important role in stress induced hyperphosphorylation of tau protein, which might be either a direct effect of CRH innervations in the brain or an effect mediated via the hypothalamo-pituitary-adrenal axis.", "citation": {"db": "PubMed", "db_id": "22222439"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 776, "target": 3015, "key": "33bc1855a734650133ea4e15123ed1d5"}, {"line": 1429, "relation": "increases", "evidence": "The data show that CRH plays an important role in stress induced hyperphosphorylation of tau protein, which might be either a direct effect of CRH innervations in the brain or an effect mediated via the hypothalamo-pituitary-adrenal axis.", "citation": {"db": "PubMed", "db_id": "22222439"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Tau protein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 776, "target": 2560, "key": "7447b83145bd88c5f5a1a5ad4f4df212"}, {"line": 2516, "relation": "decreases", "evidence": "Two possible models for involvement of HRD1 in the pathogenesis of AD. Model 1 (cause of AD): Unknown stress initiates insolubilization of HRD1 protein, resulting in a decrease in the functional HRD1 protein in the ER membrane. Subsequently, APP accumulates in the ER and is processed into Abeta that induces hyperphosphorylation of tau protein (ptau). Finally, accumulated Abeta and/or p-tau causes neurodegeneration leading to AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Confidence": {"Very High": true}, "Subgraph": {"Ubiquitin degradation subgraph": true, "Unfolded protein response subgraph": true}}, "source": 776, "target": 3439, "key": "1167c7c57f8edf982d232404908e29aa"}, {"line": 2519, "relation": "increases", "evidence": "Two possible models for involvement of HRD1 in the pathogenesis of AD. Model 1 (cause of AD): Unknown stress initiates insolubilization of HRD1 protein, resulting in a decrease in the functional HRD1 protein in the ER membrane. Subsequently, APP accumulates in the ER and is processed into Abeta that induces hyperphosphorylation of tau protein (ptau). Finally, accumulated Abeta and/or p-tau causes neurodegeneration leading to AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "source": 776, "target": 80, "key": "af6df6f44c2dab82be580df9e75ec2c2"}, {"line": 46636, "relation": "decreases", "evidence": "CRS significantly increased the expression of PKCα, CHOP and MANF, and decreased that of GRP78 in the frontal cortex and hippocampus.", "citation": {"db": "PubMed", "db_id": " 25482165"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Frontal Lobe": true}, "Subgraph": {"Chaperone subgraph": true, "Unfolded protein response subgraph": true}}, "source": 776, "target": 3650, "key": "5ddca4a349c21d06e7572449e650a254"}, {"line": 1423, "relation": "positiveCorrelation", "evidence": "Thus, our results indicate that hyperphosphorylation of tau protein induced by stress may represent the pathogenic event upstream of tau protein misfolding, which leads to progression or eventually initiation of neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22222439"}, "annotations": {"Confidence": {"Very High": true}}, "source": 3872, "target": 3015, "key": "23d81eea38da5705f58d760a8a52b9ab"}, {"line": 8740, "relation": "association", "evidence": "Interestingly, neuronal degeneration coincides with the hyperphosphorylation of endogenous tau at several epitopes previously associated with neurofibrillary pathology. Transcriptome analysis of enzymes involved in tau phosphorylation identified ERK1 as one of the candidate kinases responsible for this event in vivo. We further demonstrate that miRNAs belonging to the miR-15 family are potent regulators of ERK1 expression in mouse neuronal cells and co-expressed with ERK1/2 in vivo. Finally, we show that miR-15a is specifically downregulated in Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "20660113"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}}, "source": 3872, "target": 3015, "key": "f73e210d6f95f44e52d7ad4171e1050d"}, {"line": 3580, "relation": "association", "evidence": "Yet several studies have demonstrated that oligomeric Abeta affects the cellular cholesterol level, which in turn has a variety of effects on AD related pathologies, including modulation of tau phosphorylation, synapse formation and maintenance of its function, and the neurodegenerative process.", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3872, "target": 231, "key": "3186681c88d399e24f85b7317bff3115"}, {"line": 7068, "relation": "association", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3872, "target": 2487, "key": "1fb38ec128d248561a69c631d4e6b769"}, {"line": 11684, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3872, "target": 434, "key": "b45d85b09d502015bf0e1def7b6092bc"}, {"line": 18301, "relation": "association", "evidence": "Fas and Fas ligand are associated with neuritic degeneration in the AD brain and participate in beta-amyloid-induced neuronal death.", "citation": {"db": "PubMed", "db_id": "12742739"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3872, "target": 1418, "key": "1e478ee52f76d3a89ca2a0de4e3c9e2c"}, {"line": 18326, "relation": "association", "evidence": "These findings suggest that Fas-FasL interactions may contribute to mechanisms of neuronal loss and neuritic degeneration in AD.", "citation": {"db": "PubMed", "db_id": "12742739"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3872, "target": 1418, "key": "5205e511813fb379518353a3543e7fdf"}, {"line": 18302, "relation": "association", "evidence": "Fas and Fas ligand are associated with neuritic degeneration in the AD brain and participate in beta-amyloid-induced neuronal death.", "citation": {"db": "PubMed", "db_id": "12742739"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3872, "target": 2689, "key": "ad330bf64d1e5905f441ef1d5d334d1a"}, {"line": 18303, "relation": "association", "evidence": "Fas and Fas ligand are associated with neuritic degeneration in the AD brain and participate in beta-amyloid-induced neuronal death.", "citation": {"db": "PubMed", "db_id": "12742739"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3872, "target": 2690, "key": "c67d25c32fc236afc811d41c7e92ca2f"}, {"line": 33473, "relation": "association", "evidence": "Phospho-c-Jun (Ser73) was found to be strongly associated with neurofibrillary tangles and granulovacuolar degeneration (GVD) in addition to the nuclei in neurons in the hippocampal regions of the AD brain, but was virtually absent in most controls.", "citation": {"db": "PubMed", "db_id": "17455299"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}}, "source": 3872, "target": 2938, "key": "39cb47f59919b09a9dfe5a7cbdf15081"}, {"line": 42011, "relation": "association", "evidence": "Beyond cognitive decline, Alzheimer's disease (AD) is characterized by numerous neuropathological changes in the brain.", "citation": {"db": "PubMed", "db_id": "24886182"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3872, "target": 3823, "key": "17ddfbef8e504205777e317009438844"}, {"line": 42758, "relation": "association", "evidence": "For many years, research has been focused on Abeta accumulation in senile plaques, as these aggregations were perceived as the main cause of the neurodegeneration found in AD.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Alzheimer Disease": true}, "Confidence": {"Medium": true}}, "source": 3872, "target": 377, "key": "939a90b7601268098c14ead598ae4697"}, {"line": 1431, "relation": "association", "evidence": "The data show that CRH plays an important role in stress induced hyperphosphorylation of tau protein, which might be either a direct effect of CRH innervations in the brain or an effect mediated via the hypothalamo-pituitary-adrenal axis.", "citation": {"db": "PubMed", "db_id": "22222439"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2560, "target": 3015, "key": "7bb9d00b1d0e6c2040a0e81726ea860c"}, {"relation": "partOf", "source": 2560, "target": 1364, "key": "55707c54373323f5e045fec057d4fc23"}, {"line": 47622, "relation": "increases", "evidence": "Hypersecretion of CRF in the brain may contribute to the symptomatology seen in neuropsychiatric disorders, such as depression, anxiety-related disorders and anorexia nervosa. Furthermore, overproduction of CRF at peripheral inflammatory sites, such as synovial joints may contribute to autoimmune diseases such as rheumatoid arthritis. In contrast, deficits in brain CRF are apparent in neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease and Huntington's disease, as they relate to dysfunction of CRF neurons in the brain areas affected in the particular disorder. Strategies directed at developing CRF-related agents may hold promise for novel therapies for the treatment of these various disorders.", "citation": {"db": "PubMed", "db_id": "8834089"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"CRH subgraph": true}}, "source": 2560, "target": 3827, "key": "fbbdb051676f93e8df38557643a571cb"}, {"line": 47623, "relation": "increases", "evidence": "Hypersecretion of CRF in the brain may contribute to the symptomatology seen in neuropsychiatric disorders, such as depression, anxiety-related disorders and anorexia nervosa. Furthermore, overproduction of CRF at peripheral inflammatory sites, such as synovial joints may contribute to autoimmune diseases such as rheumatoid arthritis. In contrast, deficits in brain CRF are apparent in neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease and Huntington's disease, as they relate to dysfunction of CRF neurons in the brain areas affected in the particular disorder. Strategies directed at developing CRF-related agents may hold promise for novel therapies for the treatment of these various disorders.", "citation": {"db": "PubMed", "db_id": "8834089"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"CRH subgraph": true}}, "source": 2560, "target": 3826, "key": "40a8a32e30ffa8cea182b55d55bce4df"}, {"line": 47642, "relation": "increases", "evidence": "In cells, CRF treatment increases Abeta production and triggers CRF receptor 1 (CRFR1) and gamma-secretase internalization. Co-immunoprecipitation studies establish that gamma-secretase associates with CRFR1; this is mediated by beta-arrestin binding motifs. ", "citation": {"db": "PubMed", "db_id": "25964433"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2560, "target": 2328, "key": "9380e8ed354d92d97dc0d0651470c77b"}, {"line": 47674, "relation": "association", "evidence": "CRH-IR is significantly reduced in the cerebral cortex of individuals with AD, PD and PSP. Furthermore, we report that the decreases in CRH-IR in AD are accompanied by reciprocal increases in CRH receptors in affected cortical areas. The changes in pre- and postsynaptic markers for CRH are significantly correlated with decrements in ChAT activity. The demonstration of an up regulation of CRH receptors following a decrease in CRH-IR indicates a physiological relevance of the receptor site and is consistent with the concept that CRH acts as a neurotransmitter in normal cortical functions and that disease of this peptidergic systems may be important in certain clinical manifestations of dementia. While the clinical consequences of the changes in CRH in these various disorders are unclear, future therapies directed at increasing CRH levels in brain may prove useful for treatment.", "citation": {"db": "PubMed", "db_id": "3502064"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2560, "target": 2508, "key": "a96cd2b281d42875c87c82d86fe0403a"}, {"line": 47699, "relation": "increases", "evidence": "Thus, behavioral stressors can rapidly increase ISF Abeta through neuronal activity in a CRF-dependent manner, and the results suggest a mechanism by which behavioral stress may affect Alzheimer's disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "17551018"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Anatomy": {"interstitial fluid": true}, "Confidence": {"Medium": true}}, "source": 2560, "target": 80, "key": "c124681641893a880b1836a4e3a6fd14"}, {"line": 1442, "relation": "decreases", "evidence": "In mammals,CREB-regulated transcriptional coactivators (CRTCs) are a family of cofactors involved in diverse physiological processes including energy homeostasis, cancer and endoplasmic reticulum stress. Here we show that both AMPK and calcineurin modulate longevity exclusively through post-translational modification of CRTC-1, the sole C. elegans CRTC. We demonstrate that CRTC-1 is a direct AMPK target, and interacts with the CREB homologue-1 (CRH-1) transcription factor in vivo. The pro-longevity effects of activating AMPK or deactivating calcineurin decrease CRTC-1 and CRH-1 activity and induce transcriptional responses similar to those of CRH-1 null worms. Downregulation of crtc-1 increases lifespan in a crh-1-dependent manner and directly reducing crh-1 expression increases longevity, substantiating a role for CRTCs and CREB in ageing. Together, these findings indicate a novel role for CRTCs and CREB in determining lifespan downstream of AMPK and calcineurin, and illustrate the molecular mechanisms by which an evolutionarily conserved pathway responds to low energy to increase longevity.", "citation": {"db": "PubMed", "db_id": "21331044"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "CREB subgraph": true}}, "object": {"modifier": "Activity"}, "source": 869, "target": 2162, "key": "1bd667a99f097185021e78adc7bf9246"}, {"line": 1454, "relation": "increases", "evidence": "HSPB8 is a small heat shock protein that forms a complex with the co-chaperone BAG3. Overexpression of the HSPB8-BAG3 complex in cells stimulates autophagy and facilitates the clearance of mutated aggregation-prone proteins, whose accumulation is a hallmark of many neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "21696420"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1283, "target": 808, "key": "486bbab5bab5f0e1b2bc571fcb124586"}, {"line": 1458, "relation": "decreases", "evidence": "HSPB8 is a small heat shock protein that forms a complex with the co-chaperone BAG3. Overexpression of the HSPB8-BAG3 complex in cells stimulates autophagy and facilitates the clearance of mutated aggregation-prone proteins, whose accumulation is a hallmark of many neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "21696420"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1283, "target": 377, "key": "b8da72cb1638522789f253066a76d5c7"}, {"relation": "partOf", "source": 2386, "target": 1283, "key": "307c6ab680336a97d93fc8f793dcfca2"}, {"relation": "partOf", "source": 2386, "target": 1282, "key": "ba7e1aa35673b653f592880188489f0e"}, {"relation": "hasVariant", "source": 2386, "target": 2387, "key": "7f2716a581f657da19d25bce2ec5d0a3"}, {"relation": "partOf", "source": 2854, "target": 1283, "key": "42c4c1740619329dc9873b5cca4bd0ec"}, {"relation": "partOf", "source": 2854, "target": 1457, "key": "61be4fd3aa6bf23c801633db909b7a39"}, {"relation": "partOf", "source": 2854, "target": 1284, "key": "700b1f4c8da8380648a17ecf62fae65e"}, {"line": 39656, "relation": "increases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Chaperone subgraph": true}}, "source": 2854, "target": 2328, "key": "f488c4000f0e25ed903060d07a48167e"}, {"line": 38233, "relation": "decreases", "evidence": "The pharmacological blockage of autophagy resulted in a dramatic increase of mutant SOD1 aggregates. Immunoprecipitation studies, performed during autophagic flux blockage, demonstrated that mutant SOD1 interacts with the HspB8/Bag3/Hsc70/CHIP multiheteromeric complex, known to selectively activate autophagic removal of misfolded proteins. ", "citation": {"db": "PubMed", "db_id": "20570967"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 808, "target": 3392, "key": "484043f004159c97ae56d78287187672"}, {"line": 40484, "relation": "increases", "evidence": "Collectively, Evo induced an influx of extracellular calcium, which led to JNK-mediated protective autophagy, and this provides a new option for ischemic stroke treatment.", "citation": {"db": "PubMed", "db_id": "24454492"}, "annotations": {"MeSHDisease": {"Stroke": true}, "Subgraph": {"Autophagy signaling subgraph": true}, "Confidence": {"High": true}}, "source": 808, "target": 3930, "key": "55fbd51c3276109eb7707ea504fd2b7e"}, {"line": 40056, "relation": "association", "evidence": "The expression of inflammatory mediators, TLR4 and NF-κB and the activity of NF-κB were measured. The results showed that Gen could attenuate the cytotoxicity and inflammatory damage induced by Abeta25-35.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "source": 377, "target": 261, "key": "4fe86922a79afaccd11950095576ac2a"}, {"line": 40783, "relation": "association", "evidence": "We speculate that higher affinity between Abeta and PLA2 has the therapeutic potential of decreasing the Abeta-Abeta interaction, thereby reducing the amyloid aggregation and plaque formation in AD.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 377, "target": 3196, "key": "31ce4b97cfc1b7a15c18ce89366b7edc"}, {"line": 40784, "relation": "association", "evidence": "We speculate that higher affinity between Abeta and PLA2 has the therapeutic potential of decreasing the Abeta-Abeta interaction, thereby reducing the amyloid aggregation and plaque formation in AD.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 377, "target": 3823, "key": "d7b3cbd59588132d9723281ab7912fdd"}, {"line": 42296, "relation": "association", "evidence": "Beta-amyloid (Abeta) aggregates have a pivotal role in pathological processing of Alzheimer's disease (AD).", "citation": {"db": "PubMed Central", "db_id": "PMC3981768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 377, "target": 3823, "key": "f45c0258c40a8787431f0a700b28b96e"}, {"line": 42758, "relation": "association", "evidence": "For many years, research has been focused on Abeta accumulation in senile plaques, as these aggregations were perceived as the main cause of the neurodegeneration found in AD.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Alzheimer Disease": true}, "Confidence": {"Medium": true}}, "source": 377, "target": 3872, "key": "92bb9c4f2a63e97697a394b40b6fa459"}, {"line": 1474, "relation": "negativeCorrelation", "evidence": "Alzheimer's disease is rapidly becoming one of the most prevalent human diseases. Inhibition of human acetylcholinestrase (hAChE) and butyrylcholinestrase (BChE) has been linked to amelioration of Alzheimer's symptoms and research into inhibitors is of critical importance", "citation": {"db": "PubMed", "db_id": "22445674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2392, "target": 3823, "key": "abf44d99e852c0be8f443b2c93bbb450"}, {"line": 9882, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2392, "target": 3823, "key": "6073691bff5850e49b1c8b1bb2237b74"}, {"line": 9886, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2392, "target": 3850, "key": "592fb94dc5d64976d2fcb9f6cc63632d"}, {"line": 9890, "relation": "regulates", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2392, "target": 3861, "key": "85451963437fe60115944ffc302276ef"}, {"line": 9894, "relation": "regulates", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2392, "target": 592, "key": "c9dbf0b560887f642ffd7a70d32c5f09"}, {"line": 10846, "relation": "decreases", "evidence": "Hence, elevated butyrylcholinesterase and acetylcholinesterase concentrations will lead to a decrease in the levels of acetylcholine that could trigger the onset of low-grade systemic inflammation seen in type 2 diabetes and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2392, "target": 204, "key": "2d74742f02a796bc34ca18495d8ad674"}, {"line": 39369, "relation": "positiveCorrelation", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2392, "target": 418, "key": "1c6a56e39fed27989ca83c757b091b72"}, {"line": 39373, "relation": "association", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2392, "target": 2746, "key": "ca6d84792a5554036c50f7bfc04e3852"}, {"line": 39379, "relation": "association", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}}, "source": 2392, "target": 3335, "key": "22fc725031c91fe2aaf14ef336f7a127"}, {"line": 39385, "relation": "association", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2392, "target": 2885, "key": "2ae5f318708e86568d70195f3b6248b2"}, {"line": 39391, "relation": "association", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2392, "target": 3472, "key": "05a3d5e7b4f90e4192bece316dd4a38b"}, {"line": 1490, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 2506, "target": 3823, "key": "b57b72b9f0ab5f7e584d209685ed0561"}, {"line": 8472, "relation": "decreases", "evidence": "Here we provide evidence in AD brains of a specific up-regulation of an NF-kappaB-sensitive miRNA-146a highly complementary to the 3'-untranslated region of complement factor H (CFH), an important repressor of the inflammatory response of the brain.Transfection of HN cells using an NF-kappaB-containing pre-miRNA-146a promoter-luciferase reporter construct in stressed HN cells showed significant up-regulation of luciferase activity that paralleled decreases in CFH gene expression. Up-regulation of miRNA-146a coupled to down-regulation of CFH was observed in AD brain and in interleukin-1beta, Abeta42, and/or oxidatively stressed human neural (HN) cells in primary culture.", "citation": {"db": "PubMed", "db_id": "18801740"}, "annotations": {"Subgraph": {"Complement system subgraph": true}}, "source": 2506, "target": 577, "key": "a81fa104111a264fc8e444459309502f"}, {"line": 8497, "relation": "association", "evidence": "Here we provide evidence in human neural (HN) cells of an aluminum-sulfate- and reactive oxygen species (ROS)-mediated up-regulation of an NF-kappaB-sensitive miRNA-146a that down-regulates the expression of complement factor H (CFH), an important repressor of inflammation. This NF-kappaB-miRNA-146a-CFH signaling circuit is known to be similarly affected by Abeta42 peptides and in AD brain. These aluminum-sulfate-inducible events were not observed in parallel experiments using iron-, magnesium-, or zinc-sulfate-stressed HN cells. An NF-kappaB-containing miRNA-146a-promoter-luciferase reporter construct transfected into HN cells showed significant up-regulation of miRNA-146a after aluminum-sulfate treatment that corresponded to decreased CFH gene expression. These data suggest that (1) as in AD brain, NF-kappaB-sensitive, miRNA-146a-mediated, modulation of CFH gene expression may contribute to inflammatory responses in aluminum-stressed HN cells, and (2) underscores the potential of nanomolar aluminum to drive genotoxic mechanisms characteristic of neurodegenerative disease processes.", "citation": {"db": "PubMed", "db_id": "19540598"}, "annotations": {"Subgraph": {"Complement system subgraph": true}}, "source": 2506, "target": 532, "key": "165d11f5224f298e1a12ffe8b3732bb0"}, {"line": 1491, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 2359, "target": 3823, "key": "176dcb887b0f754ca6f527d40b6d1df6"}, {"line": 1492, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 2409, "target": 3823, "key": "ed18e4c25a5331b594b1e6d97884608d"}, {"line": 1493, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 2505, "target": 3823, "key": "6da31db48771defbddec582ff3f61761"}, {"line": 1494, "relation": "association", "evidence": "A large cohort of AD (n = 3898) patients and controls were genotyped for single nucleotide polymorphisms (SNPs) in the complement factor H (CFH), the Age-related maculopathy susceptibility protein 2 (ARMS2) the complement component 2 (C2), the complement factor B (CFB), and the complement component 3 (C3) genes. While significant but modest associations were identified between the complement factor H, the age-related maculopathy susceptibility protein 2, and the complement component 3 single nucleotide polymorphisms and AD, these were different in direction or genetic model to that observed in AMD. In addition the multilocus genetic model that predicts around a half of the sibling risk for AMD does not predict risk for AD", "citation": {"db": "PubMed", "db_id": "22300950"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 2410, "target": 3823, "key": "e4404f085c41a71582207534d5d9741f"}, {"line": 1508, "relation": "decreases", "evidence": "AICD was shown to induce the expression of genes having functional roles in actin organization and dynamics, including transgelin (SM22) and alpha2-actin, resulting in a loss of organized filamentous actin structures within the cell. In fact, overexpression of transgelin, a proposed AICD target gene, causes destabilization of actin filaments, depolarization of mitochondrial membrane potential (DeltaPsim), and significant alteration of mitochondrial distribution and morphology in human SHEP neuroblastoma cells and primary neurons. These data demonstrate that induction of AICD/APP significantly alters cytoskeletal dynamics and mitochondrial function in neuronal cells by interacting with JIP1b or Fe65.", "citation": {"db": "PubMed", "db_id": "21034527"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3441, "target": 708, "key": "d217ba8ee6b44bb926ef30ff46b6e50b"}, {"line": 1512, "relation": "decreases", "evidence": "AICD was shown to induce the expression of genes having functional roles in actin organization and dynamics, including transgelin (SM22) and alpha2-actin, resulting in a loss of organized filamentous actin structures within the cell. In fact, overexpression of transgelin, a proposed AICD target gene, causes destabilization of actin filaments, depolarization of mitochondrial membrane potential (DeltaPsim), and significant alteration of mitochondrial distribution and morphology in human SHEP neuroblastoma cells and primary neurons. These data demonstrate that induction of AICD/APP significantly alters cytoskeletal dynamics and mitochondrial function in neuronal cells by interacting with JIP1b or Fe65.", "citation": {"db": "PubMed", "db_id": "21034527"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3441, "target": 740, "key": "7929b15c90594f70bad933ab5bd0528f"}, {"line": 1511, "relation": "decreases", "evidence": "AICD was shown to induce the expression of genes having functional roles in actin organization and dynamics, including transgelin (SM22) and alpha2-actin, resulting in a loss of organized filamentous actin structures within the cell. In fact, overexpression of transgelin, a proposed AICD target gene, causes destabilization of actin filaments, depolarization of mitochondrial membrane potential (DeltaPsim), and significant alteration of mitochondrial distribution and morphology in human SHEP neuroblastoma cells and primary neurons. These data demonstrate that induction of AICD/APP significantly alters cytoskeletal dynamics and mitochondrial function in neuronal cells by interacting with JIP1b or Fe65.", "citation": {"db": "PubMed", "db_id": "21034527"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2246, "target": 708, "key": "77adc413f4eba63227739d243b8b7db9"}, {"line": 1513, "relation": "decreases", "evidence": "AICD was shown to induce the expression of genes having functional roles in actin organization and dynamics, including transgelin (SM22) and alpha2-actin, resulting in a loss of organized filamentous actin structures within the cell. In fact, overexpression of transgelin, a proposed AICD target gene, causes destabilization of actin filaments, depolarization of mitochondrial membrane potential (DeltaPsim), and significant alteration of mitochondrial distribution and morphology in human SHEP neuroblastoma cells and primary neurons. These data demonstrate that induction of AICD/APP significantly alters cytoskeletal dynamics and mitochondrial function in neuronal cells by interacting with JIP1b or Fe65.", "citation": {"db": "PubMed", "db_id": "21034527"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "source": 1677, "target": 708, "key": "4b78309c439b04a13ccffdc2e5cc0bdf"}, {"line": 1515, "relation": "decreases", "evidence": "AICD was shown to induce the expression of genes having functional roles in actin organization and dynamics, including transgelin (SM22) and alpha2-actin, resulting in a loss of organized filamentous actin structures within the cell. In fact, overexpression of transgelin, a proposed AICD target gene, causes destabilization of actin filaments, depolarization of mitochondrial membrane potential (DeltaPsim), and significant alteration of mitochondrial distribution and morphology in human SHEP neuroblastoma cells and primary neurons. These data demonstrate that induction of AICD/APP significantly alters cytoskeletal dynamics and mitochondrial function in neuronal cells by interacting with JIP1b or Fe65.", "citation": {"db": "PubMed", "db_id": "21034527"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "source": 1677, "target": 740, "key": "00684a1999f1a6f219a349048382ec56"}, {"relation": "partOf", "source": 2299, "target": 1677, "key": "127f938a71068b999546b8e766b26c8f"}, {"relation": "partOf", "source": 2299, "target": 1092, "key": "91668a6d96a277336462bae29238f612"}, {"line": 1850, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2299, "target": 1678, "key": "029c9c734d5cb52ab78b0f99e4408360"}, {"relation": "partOf", "source": 2299, "target": 1094, "key": "8c8754943320acb5e37bac6bd7643240"}, {"relation": "partOf", "source": 2299, "target": 1102, "key": "1a8131d34ee85f308cc00d74f1497dfc"}, {"line": 2424, "relation": "association", "evidence": "In the present study, we tested whether FE65 can interact with another ApoE receptor, VLDLR, thereby altering its trafficking and processing, and whether FE65 can serve as a linker between APP and VLDLR", "citation": {"db": "PubMed", "db_id": "22429478"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2299, "target": 3526, "key": "e9172f64ebcbb1d6c1088e1f7532f97b"}, {"relation": "partOf", "source": 2299, "target": 1099, "key": "46216136ae68f2ef219c283e17d8028b"}, {"relation": "partOf", "source": 2299, "target": 1676, "key": "04ec40badede9e913b71aac92fa3dfdb"}, {"relation": "partOf", "source": 2299, "target": 1105, "key": "890ea4d5d9378c9a2be784a2887afcdf"}, {"relation": "partOf", "source": 2299, "target": 1041, "key": "c0929c03e4e00f075fc0e28bc7fad1f9"}, {"relation": "hasVariant", "source": 2299, "target": 2300, "key": "0ae75c9d319578e06aac4d5465d7b46c"}, {"relation": "partOf", "source": 2299, "target": 1040, "key": "f905db40bb0ea4fada9f867481293899"}, {"line": 31837, "relation": "regulates", "evidence": "APP-dependent transcription mediated by Fe65 is blocked by p75(NTR)", "citation": {"db": "PubMed", "db_id": "19334058"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2299, "target": 2315, "key": "1a296771ca276afe8adb07ff3eeb0aa8"}, {"line": 33277, "relation": "association", "evidence": "This suggests a role for FE65 in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "11337355"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2299, "target": 3823, "key": "c8e34cb0c94d7d852da4f9a15488ea4a"}, {"line": 33294, "relation": "decreases", "evidence": "We report here that (i) a single amino acid mutation at the Thr-668 residue of APP695, located 14 amino acids toward the amino-terminal end from the (682)YENPTY(687) motif, reduced the interaction between members of the Fe65 family of proteins and APP, whereas interaction of APP with the phosphotyrosine interaction domain of other APP binders such as X11-like and mammalian disabled-1 was not influenced by this mutation; (ii) the phosphorylation of APP at Thr-668 diminished the interaction of APP with Fe65 by causing a conformational change in the cytoplasmic domain that contains the Fe65-binding motif, YENPTY; and (iii) the expression of Fe65 slightly suppressed maturation of APP and decreased production of beta-amyloid (Abeta).", "citation": {"db": "PubMed", "db_id": "11517218"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2299, "target": 80, "key": "0f8bd8c79d1131258c957137a834515f"}, {"relation": "partOf", "source": 2299, "target": 1101, "key": "b8dab0ac417a3f908622ab175e8b3d9c"}, {"relation": "partOf", "source": 2299, "target": 1103, "key": "d4c87f803d9c5ce8d52f98c73cfb2b09"}, {"relation": "partOf", "source": 2299, "target": 1097, "key": "b640215a99a05e5ea8a376cdf0731661"}, {"relation": "partOf", "source": 2299, "target": 1100, "key": "ab83a4f3ad4e017f73c45999b216b79d"}, {"relation": "partOf", "source": 2299, "target": 1093, "key": "04883527e952bfbd0711c4d46334d141"}, {"line": 37438, "relation": "increases", "evidence": "Although the requirements for binding of Fe65 and X11 to the GYENPTY motif differ slightly, they are sufficiently overlapping to make it conceivable that these proteins compete for binding to APP. Interestingly, opposite effects on APP processing have been observed after co-expression of APP with either Fe65 or X11. Fe65 overexpression increased the amount of cell-surface-associated APP, as well as the processing of APP to APPs and Ab peptide. By contrast, X11 decreased processing and cellular retention of APP. Thus, the functional balance between the two proteins is important for regulation of APP metabolism and possibly APP function. This balance could be further modified by a third protein, mDAB1 (the mammalian homologue of Disabled), that binds to the same region", "citation": {"db": "PubMed", "db_id": "10806097"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2299, "target": 4101, "key": "3d79a228248c6b9c4210151dd3d7389d"}, {"relation": "partOf", "source": 2299, "target": 1096, "key": "9e0d6e726e382fca97e45cd8b2c2b455"}, {"line": 37456, "relation": "increases", "evidence": "Coimmunoprecipitation studies indicated that there was also a complex formed between APP and VLDLR, which is increased in the presence of FE65 in vitro and in vivo. This data suggests that FE65 acts as a linker between VLDLR and APP. Moreover, we found that these interactions modulate APP and VLDLR trafficking and processing.", "citation": {"db": "PubMed", "db_id": "22429478"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2299, "target": 1223, "key": "9e3142d2f099c62ef7953aca267f6f0f"}, {"line": 37834, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2299, "target": 2136, "key": "d85589859a2c9b6b1a47faa20ef672e2"}, {"relation": "partOf", "source": 2299, "target": 1095, "key": "3a034a5fa754c01fdcd1fc50b03772c6"}, {"line": 37858, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2299, "target": 3563, "key": "dabcfa6bd87459427f2f02f08fdc4dce"}, {"line": 38434, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2299, "target": 3563, "key": "9de20bd7b36ca51094a0aa86907af21e"}, {"line": 38346, "relation": "increases", "evidence": "In the brain, megalin is expressed in brain capillaries, ependymal cells and choroid plexus, where it participates in the clearance of brain amyloid beta-peptide (Abeta) complex.Additionally, given that FE65 mediates the interaction between the low density lipoprotein receptor-related protein-1 and the amyloid precursor protein (APP) to modulate the rate of APP internalization from the cell surface, we hypothesize that megalin could also interact with APP in neurons.", "citation": {"db": "PubMed", "db_id": "20637285"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 2299, "target": 1184, "key": "7fdea7f808f177317de7853e805616e9"}, {"relation": "partOf", "source": 2299, "target": 1098, "key": "01d170518449951304edd540a4529f0a"}, {"relation": "partOf", "source": 2299, "target": 1104, "key": "d5dc27b4f40b4fe9e1988908288d16d7"}, {"line": 1525, "relation": "association", "evidence": "These data indicate that PICALM, an adaptor protein involved in clathrin-dependent endocytosis, regulates APP internalization and subsequent Abeta generation. PICALM contributes to amyloid plaque load in brain likely via its effect on Abeta metabolism.", "citation": {"db": "PubMed", "db_id": "22539346"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Very High": true}}, "source": 3184, "target": 533, "key": "9fb2702a87a595c50099c57628b7eabd"}, {"line": 1526, "relation": "regulates", "evidence": "These data indicate that PICALM, an adaptor protein involved in clathrin-dependent endocytosis, regulates APP internalization and subsequent Abeta generation. PICALM contributes to amyloid plaque load in brain likely via its effect on Abeta metabolism.", "citation": {"db": "PubMed", "db_id": "22539346"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Degradation"}, "source": 3184, "target": 2315, "key": "891aba1fc55fb1d9bfc1f0479efe6b89"}, {"line": 1527, "relation": "increases", "evidence": "These data indicate that PICALM, an adaptor protein involved in clathrin-dependent endocytosis, regulates APP internalization and subsequent Abeta generation. PICALM contributes to amyloid plaque load in brain likely via its effect on Abeta metabolism.", "citation": {"db": "PubMed", "db_id": "22539346"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Very High": true}}, "source": 3184, "target": 80, "key": "2c3f9027c99f8c32db49a3ce8451111e"}, {"relation": "partOf", "source": 3184, "target": 1603, "key": "2c8fc594521ceabfb2d9554eecfb8497"}, {"line": 1525, "relation": "association", "evidence": "These data indicate that PICALM, an adaptor protein involved in clathrin-dependent endocytosis, regulates APP internalization and subsequent Abeta generation. PICALM contributes to amyloid plaque load in brain likely via its effect on Abeta metabolism.", "citation": {"db": "PubMed", "db_id": "22539346"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Very High": true}}, "source": 533, "target": 3184, "key": "3dda8e17bac3ae0649b2f225cc0cacec"}, {"line": 5587, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 533, "target": 80, "key": "7de32936539be4357fecec00b48f7100"}, {"line": 5681, "relation": "decreases", "evidence": "Picalm is a key component of clathrin-dependent endocytosis. It recruits clathrin and adaptor protein 2 (AP-2) to the plasma membrane and, along with, AP-2 recognizes target proteins. The attached clathrin triskelions cause membrane deformation around the target proteins enclosing them within clathrin-coated vesicles to be processed in lysosomes or endosomes.The transport of Abeta across vessel walls and into the bloodstream is a major pathway of Abeta removal from the brain and picalm is ideally situated within endothelial cells to participate in this process", "citation": {"db": "PubMed", "db_id": "20838239"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 533, "target": 2328, "key": "7bcf7638ec104baf30d402f33317159f"}, {"line": 16995, "relation": "association", "evidence": "Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Gamma secretase subgraph": true}}, "source": 533, "target": 2658, "key": "e6a173ab6f7eb65de1f7728d01746d1e"}, {"line": 17030, "relation": "association", "evidence": "The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Gamma secretase subgraph": true}}, "source": 533, "target": 2658, "key": "467f17c8d0bdc3bae0370b1b9617546e"}, {"line": 48933, "relation": "association", "evidence": "Early growth response gene 1 (Egr1) is a member of the immediate early gene (IEG) family of transcription factors and plays a role in memory formation. The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 533, "target": 2658, "key": "e4ab2f084276d50e1cf15a0a45a71f60"}, {"line": 1568, "relation": "decreases", "evidence": "Interestingly, in brain tissues of AD-affected subjects, APP localized with mitochondria fraction, associated to TOM40 and TIM23, in a translocation-arrested manner, that may prevent import of de novo synthesised nuclear-encoded mitochondrial protein,such as subunits of the electron transport chain", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"High": true}}, "source": 1219, "target": 696, "key": "91515040af666483d95871322fc1e46e"}, {"line": 1569, "relation": "decreases", "evidence": "Interestingly, in brain tissues of AD-affected subjects, APP localized with mitochondria fraction, associated to TOM40 and TIM23, in a translocation-arrested manner, that may prevent import of de novo synthesised nuclear-encoded mitochondrial protein,such as subunits of the electron transport chain", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"High": true}}, "source": 1219, "target": 546, "key": "ec92230f2b1b9a4485895769cdd52781"}, {"line": 47010, "relation": "increases", "evidence": "Furthermore, in AD brains, mitochondrially associated APP formed stable ∼480 kDa complexes with the translocase of the outer mitochondrial membrane 40 (TOM40) import channel and a super complex of ∼620 kDa with both mitochondrial TOM40 and the translocase of the inner mitochondrial membrane 23 (TIM23) import channel TIM23 in an “Nin mitochondria–Cout cytoplasm” orientation. Accumulation of APP across mitochondrial import channels, which varied with the severity of AD, inhibited the entry of nuclear-encoded cytochrome c oxidase subunits IV and Vb proteins, which was associated with decreased cytochrome c oxidase activity and increased levels of H2O2", "citation": {"db": "PubMed", "db_id": "16943564"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true}}, "source": 1219, "target": 3867, "key": "4b602523adfb7109e4a8d46fff9f6396"}, {"line": 47011, "relation": "increases", "evidence": "Furthermore, in AD brains, mitochondrially associated APP formed stable ∼480 kDa complexes with the translocase of the outer mitochondrial membrane 40 (TOM40) import channel and a super complex of ∼620 kDa with both mitochondrial TOM40 and the translocase of the inner mitochondrial membrane 23 (TIM23) import channel TIM23 in an “Nin mitochondria–Cout cytoplasm” orientation. Accumulation of APP across mitochondrial import channels, which varied with the severity of AD, inhibited the entry of nuclear-encoded cytochrome c oxidase subunits IV and Vb proteins, which was associated with decreased cytochrome c oxidase activity and increased levels of H2O2", "citation": {"db": "PubMed", "db_id": "16943564"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true}}, "source": 1219, "target": 3881, "key": "fd8ea7bf42b055db8a67a3f76d8c331e"}, {"relation": "partOf", "source": 3462, "target": 1219, "key": "5ddd8c8885c94662865d26e436f805cb"}, {"relation": "partOf", "source": 3481, "target": 1219, "key": "c8da779c05ed495ec8c4d11cbc9764f6"}, {"relation": "partOf", "source": 3481, "target": 1221, "key": "e282264e5ae3802d9dfe368bce6227ee"}, {"line": 47008, "relation": "positiveCorrelation", "evidence": "Furthermore, in AD brains, mitochondrially associated APP formed stable ∼480 kDa complexes with the translocase of the outer mitochondrial membrane 40 (TOM40) import channel and a super complex of ∼620 kDa with both mitochondrial TOM40 and the translocase of the inner mitochondrial membrane 23 (TIM23) import channel TIM23 in an “Nin mitochondria–Cout cytoplasm” orientation. Accumulation of APP across mitochondrial import channels, which varied with the severity of AD, inhibited the entry of nuclear-encoded cytochrome c oxidase subunits IV and Vb proteins, which was associated with decreased cytochrome c oxidase activity and increased levels of H2O2", "citation": {"db": "PubMed", "db_id": "16943564"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true, "APOE subgraph": true}}, "source": 3481, "target": 2312, "key": "59b3139ca28cf4a5a59cccd3efbc7648"}, {"line": 1615, "relation": "decreases", "evidence": "In support of the hypothesis that APP and amyloid-beta enter mitochondria, several studies have found APP and its derivatives (monomeric and oligomeric forms of amyloid-beta) in mitochondrial membranes. Amyloid-beta normally interact with the mitochondrial matrix protein, amyloid-beta-binding alcohol dehydrogenase (ABAD), leading to mitochondrial dysfunction", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 927, "target": 621, "key": "2f7d58161beaeb56749460aaea7df269"}, {"relation": "partOf", "source": 2843, "target": 927, "key": "d398f668db0ebadb640b5b541d93c8d1"}, {"line": 5257, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 2843, "target": 2328, "key": "dfbe2d63d97013bb4f3aac283b7878ad"}, {"line": 37149, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2843, "target": 2328, "key": "44ee7354007a842ce5e33d0b4007690b"}, {"relation": "partOf", "source": 2843, "target": 1229, "key": "7db8669021108f7b7a793a0f273a450c"}, {"relation": "partOf", "source": 2843, "target": 1178, "key": "d76954323750b41002d57324d6fb2767"}, {"line": 34300, "relation": "association", "evidence": "The intracellular amyloid beta-peptide (A beta) binding protein, ERAB, a member of the short-chain dehydrogenase/reductase (SDR) family, is known to mediate apoptosis in different cell lines and to be a class II hydroxyacyl-CoA dehydrogenase.", "citation": {"db": "PubMed", "db_id": "10371197"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2843, "target": 478, "key": "1be259f50734c381e5803c169cb55c5d"}, {"line": 37169, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2843, "target": 384, "key": "a22aa8202c760719a5f836f5cdb12fd0"}, {"line": 37170, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2843, "target": 399, "key": "6082e72f77e62d1679a9e99446151e83"}, {"line": 1632, "relation": "directlyDecreases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "GO", "name": "mitochondrion"}}}, "source": 2323, "target": 2545, "key": "15f1ee869793739493843db230b3e150"}, {"line": 1634, "relation": "directlyDecreases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "GO", "name": "mitochondrion"}}}, "source": 2323, "target": 2546, "key": "ab0014e0de7a41080b9cb2c53aa5e3c2"}, {"line": 1636, "relation": "directlyDecreases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "GO", "name": "mitochondrion"}}}, "source": 2323, "target": 2548, "key": "71f6e0dd3ea15f1405b51693fa2cbed8"}, {"line": 1639, "relation": "negativeCorrelation", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2323, "target": 894, "key": "a3811a039add6f503171800a4fd5aeef"}, {"line": 1644, "relation": "positiveCorrelation", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2323, "target": 340, "key": "7a39c4cb90a271eb38f08d61d1b37947"}, {"line": 1649, "relation": "increases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2545, "target": 894, "key": "d01ebaf7ad8ccccea077d846997bc26f"}, {"line": 1633, "relation": "directlyDecreases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "GO", "name": "mitochondrion"}}}, "source": 2325, "target": 2545, "key": "9fada903a45c5969d37b7d00194fc65a"}, {"line": 1635, "relation": "directlyDecreases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "GO", "name": "mitochondrion"}}}, "source": 2325, "target": 2546, "key": "d1ee39451a4840866593b7ab02e0b909"}, {"line": 1637, "relation": "directlyDecreases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "GO", "name": "mitochondrion"}}}, "source": 2325, "target": 2548, "key": "62f063e1636fd8b449742beeb9800689"}, {"line": 1640, "relation": "negativeCorrelation", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2325, "target": 894, "key": "81d5bcddbbbadbd9cf645ecf783a3850"}, {"line": 1645, "relation": "negativeCorrelation", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2325, "target": 340, "key": "fc550d1bb5b65b144a1f77624349c03f"}, {"line": 1650, "relation": "increases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2546, "target": 894, "key": "0611ba70e9bed8992a0f25a2f95d596e"}, {"line": 1651, "relation": "increases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2548, "target": 894, "key": "1e3325890878cfdfee85d6c6efe2a9e6"}, {"line": 1639, "relation": "negativeCorrelation", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 894, "target": 2323, "key": "b5f92be81c5a68c7dd5dad390cd5c05a"}, {"line": 1640, "relation": "negativeCorrelation", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 894, "target": 2325, "key": "d9e2aae097310725e2c4caf1fd754253"}, {"line": 1642, "relation": "isA", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 894, "target": 2141, "key": "ca0058f9b4588c85dc1869952546641e"}, {"line": 1644, "relation": "positiveCorrelation", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 340, "target": 2323, "key": "49febe2ef5852a2692bf28e9121e091e"}, {"line": 1645, "relation": "negativeCorrelation", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 340, "target": 2325, "key": "b22f3a383a1d77458b1e4fa726e7810d"}, {"line": 19730, "relation": "increases", "evidence": "Melatonin's efficacy in combating free radical damage in the brain suggests that it may be a valuable therapeutic agent in the treatment of cerebral edema after traumatic brain injury.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"MeSHDisease": {"Brain Edema": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Cerebrum": true}, "Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 340, "target": 3828, "key": "11e6cf297fbfea569f19836e9f0ebd5c"}, {"line": 1653, "relation": "decreases", "evidence": "The accumulation of APP across mitochondrial import channels inhibited the entry of nuclear-encoded cytochrome c-oxidase subunits IV and Vb proteins and was associated with decreased cytochrome oxidase and increased free radical production", "citation": {"db": "PubMed", "db_id": "22523685"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Electron transport chain": true, "Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 770, "target": 340, "key": "b3ac9943338b053b2aa400fdc24fd0d3"}, {"line": 2305, "relation": "increases", "evidence": "Iron deposition in the brain is another important proposed mechanisms in the pathophysiology AD. Excessive iron can contribute to the formation of free radicals, leading to lipid peroxidation and neurotoxicity, which can result in cell membrane damage and cell death. Recently, it has been shown that iron concentration in AD patients brain was significantly higher than those of nondemented controls. In particular iron deposition in parietal cortex and hippocampus at the early stage of AD were positively correlated with the severity of patients cognitive impairment", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 593, "target": 505, "key": "a13922dc26a9ebb039d7b5ce68cd1b73"}, {"line": 1727, "relation": "increases", "evidence": "Amyloid precursor protein (APP) mutations associated with familial Alzheimer's disease (AD) usually lead to increases in amyloid Abeta-protein (Abeta) levels or aggregation. Here, we identified a novel APP mutation, located within the Abeta sequence (Abeta(D7H)), in a Taiwanese family with early onset AD and explored the pathogenicity of this mutation. Cellular and biochemical analysis reveal that this mutation increased Abeta production, Abeta42/40 ratio and prolonged Abeta42 oligomer state with higher neurotoxicity. Because the D7H mutant Abeta has an additional metal ion-coordinating residue, histidine, we speculate that this mutation may promote susceptibility of Abeta to ion. When co-incubated with Zn(2+) or Cu(2+), Abeta(D7H) aggregated into low molecular weight oligomers. Together, the D7H mutation could contribute to AD pathology through a double punch effect on elevating both Abeta production and oligomerization. Although the pathogenic nature of this mutation needs further confirmation, our findings suggest that the Abeta N-terminal region potentially modulates APP processing and Abeta aggregation, and further provides a genetic indication of the importance of Zn(2+) and Cu(2+) in the etiology of AD.Our result indicates that the D7H mutation promotes Abeta40 interaction with Zn2+ and Cu2+, where the Abeta interaction with Zn2+ is especially enhanced by the mutation", "citation": {"db": "PubMed", "db_id": "22558227"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2345, "target": 952, "key": "7d9685ef78a5601271d4808f29441fd0"}, {"relation": "partOf", "source": 101, "target": 952, "key": "f3615d3733c1826103d058963b9a6fb8"}, {"relation": "partOf", "source": 101, "target": 953, "key": "de4ccb15636f0fb7659ff790298497bd"}, {"line": 11288, "relation": "increases", "evidence": "The interaction of Cu(2+) with the first 16 residues of the Alzheimer's amyliod beta peptide, Abeta(1-16), and human serum albumin (HSA) were studied in vitro by isothermal titration calorimetry at pH 7.2 and 310 K in aqueous solution. The solvation parameters recovered from the extended solvation model indicate that HSA is involved in the transport of copper ion. Complexes between Abeta(1-16) and copper ions have been proposed to be an aberrant interaction in the development of Alzheimer's disease, where Cu(2+) is involved in Abeta(1-16) aggregation. The indexes of stability indicate that HSA removed Cu(2+) from Abeta(1-16), rapidly, decreased Cu-induced aggregation of Abeta(1-16), and reduced the toxicity of Abeta(1-16) + Cu(2+) significantly.", "citation": {"db": "PubMed", "db_id": "22844264"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 101, "target": 2328, "key": "2699bb1aa1202d8c2829b9d52716587c"}, {"line": 15833, "relation": "association", "evidence": "Tumor growth and metastasis depend on angiogenesis that requires the cofactor copper.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Neoplasms": true, "Neoplasm Metastasis": true}, "Confidence": {"High": true}}, "source": 101, "target": 807, "key": "ceef3a1a9162e9707ca089bdd31e1c85"}, {"line": 15835, "relation": "association", "evidence": "Tumor growth and metastasis depend on angiogenesis that requires the cofactor copper.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Neoplasms": true, "Neoplasm Metastasis": true}, "Confidence": {"High": true}}, "source": 101, "target": 3871, "key": "860f6a999e6a928c10840deb1ac69f40"}, {"relation": "partOf", "source": 101, "target": 899, "key": "1ce2863d01cd1cb777c79dd6344d2e32"}, {"relation": "partOf", "source": 189, "target": 952, "key": "610cba9f4549bd3f35fa29cfd0a49024"}, {"line": 10748, "relation": "association", "evidence": "Our results suggest that Zn2+-dependent regulation of FOXOs and gluconeogenesis may contribute to the therapeutic properties of this drug.", "citation": {"db": "PubMed", "db_id": "22248233"}, "object": {"modifier": "Activity"}, "source": 189, "target": 2701, "key": "b89e539ba2b54c45bce48acdc5ad8c8c"}, {"line": 10749, "relation": "association", "evidence": "Our results suggest that Zn2+-dependent regulation of FOXOs and gluconeogenesis may contribute to the therapeutic properties of this drug.", "citation": {"db": "PubMed", "db_id": "22248233"}, "source": 189, "target": 727, "key": "f28ff4936a47d792de8f09b8601915bf"}, {"line": 15697, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}}, "source": 189, "target": 3823, "key": "a2bc9359e24835d4f6cbd60b98add77f"}, {"line": 21734, "relation": "increases", "evidence": "Zinc-induced p70S6K activation could only upregulate translation of total S6 and tau but not global proteins in SH-SY5Y cells.", "citation": {"db": "PubMed", "db_id": "16364302"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 189, "target": 3327, "key": "8653d7ca98b8c9866c7b4c8b86193d54"}, {"line": 34549, "relation": "increases", "evidence": "S100beta interaction with tau is promoted by zinc and inhibited by hyperphosphorylation in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11264299"}, "source": 189, "target": 1558, "key": "5a433346974dcc2e63f455de87894724"}, {"relation": "partOf", "source": 189, "target": 971, "key": "f0db83821bddad31e9d5926e17d07404"}, {"line": 1740, "relation": "association", "evidence": "The K16N mutation is located exactly at the a-secretase cleavage site and influences both APP and Abeta. First, due to the K16N mutation APP secretion is affected and a higher amount of Abeta peptides is being produced.", "citation": {"db": "PubMed", "db_id": "22514144"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2348, "target": 80, "key": "17ceb88d46a0972a3801080476902703"}, {"line": 1741, "relation": "increases", "evidence": "The K16N mutation is located exactly at the a-secretase cleavage site and influences both APP and Abeta. First, due to the K16N mutation APP secretion is affected and a higher amount of Abeta peptides is being produced.", "citation": {"db": "PubMed", "db_id": "22514144"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2348, "target": 80, "key": "bdff00f4ae622683781f630c44dc58a9"}, {"line": 1754, "relation": "regulates", "evidence": "Alzheimer disease (AD) is a heterogeneous neurodegenerative disorder characterized by (1) progressive loss of synapses and neurons, (2) intracellular neurofibrillary tangles, composed of hyperphosphorylated Tau protein, and (3) amyloid plaques. Genetically, AD is linked to mutations in few proteins amyloid precursor protein (APP) and presenilin 1 and 2 (PS1 and PS2). The molecular mechanisms underlying neurodegeneration in AD as well as the physiological function of APP are not yet known. A recent theory has proposed that APP and PS1 modulate intracellular signals to induce cell-cycle abnormalities responsible for neuronal death and possibly amyloid deposition.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"Very High": true}}, "source": 1686, "target": 503, "key": "bde81b880c555215032af874831f0c2d"}, {"line": 1765, "relation": "association", "evidence": "Moreover, recent findings have also suggested that AbetaPP, through an NPTY motif located in its cytodomain, and PSs form functional complexes with different signaling protein, supporting the hypothesis that AbetaPP and PS1 are at the centre of a complex network of interactions, likely involved in multiple cell-signaling events which are still unknown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell-cell communication subgraph": true}, "Confidence": {"Medium": true}}, "source": 1686, "target": 498, "key": "adca74873361fc436d2e7af85e30a224"}, {"line": 1755, "relation": "association", "evidence": "Alzheimer disease (AD) is a heterogeneous neurodegenerative disorder characterized by (1) progressive loss of synapses and neurons, (2) intracellular neurofibrillary tangles, composed of hyperphosphorylated Tau protein, and (3) amyloid plaques. Genetically, AD is linked to mutations in few proteins amyloid precursor protein (APP) and presenilin 1 and 2 (PS1 and PS2). The molecular mechanisms underlying neurodegeneration in AD as well as the physiological function of APP are not yet known. A recent theory has proposed that APP and PS1 modulate intracellular signals to induce cell-cycle abnormalities responsible for neuronal death and possibly amyloid deposition.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"Very High": true}}, "source": 503, "target": 645, "key": "7588b286604c8b3a82350a1223de12ae"}, {"line": 2189, "relation": "increases", "evidence": "Increasing observations suggest that aberrant activation of cell cycle may affect the formation of neurofibrillary tangles with hyperphosphorylation of Tau protein in AD brain. It is well known that p25/cdk5 complex hyperphosphorylates Tau and reduces its ability to associate with microtubules.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tau protein subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"High": true}}, "source": 503, "target": 3015, "key": "acbb97ba3a4fbe55e42907723cf5fe20"}, {"line": 2201, "relation": "increases", "evidence": "On the other hand, cell-cycle activation can lead to apoptotic process, and several studies showed the activation of caspases in AD brain", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Caspase subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"High": true}}, "source": 503, "target": 478, "key": "8e5701b072a71febcd8f0111a5b632bc"}, {"line": 2202, "relation": "increases", "evidence": "On the other hand, cell-cycle activation can lead to apoptotic process, and several studies showed the activation of caspases in AD brain", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Caspase subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 503, "target": 2167, "key": "1472b2ed197c13cce505505b1d0ce5dd"}, {"line": 16643, "relation": "association", "evidence": "Given that the induction of OPN expression (and amyloid-beta generation) is associated with remodeling and tumorigenesis, our results suggest that OPN may play a role in the aberrant re-entry of neurons into the cell cycle and/or neuronal remyelination in AD.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"MeSHDisease": {"Cell Transformation, Neoplastic": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 503, "target": 3409, "key": "3f742041f907f7f2672dcbc719a69934"}, {"line": 35328, "relation": "decreases", "evidence": "Genetically, AD is linked to mutations in few proteins amyloid precursor protein (APP) and presenilin 1 and 2 (PS1 and PS2). The molecular mechanisms underlying neurodegeneration in AD as well as the physiological function of APP are not yet known. A recent theory has proposed that APP and PS1 modulate intracellular signals to induce cell-cycle abnormalities responsible for neuronal death and possibly amyloid deposition.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 503, "target": 648, "key": "f5c5d0a6e2a805c1fb6f60a10a6cac3d"}, {"line": 1765, "relation": "association", "evidence": "Moreover, recent findings have also suggested that AbetaPP, through an NPTY motif located in its cytodomain, and PSs form functional complexes with different signaling protein, supporting the hypothesis that AbetaPP and PS1 are at the centre of a complex network of interactions, likely involved in multiple cell-signaling events which are still unknown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell-cell communication subgraph": true}, "Confidence": {"Medium": true}}, "source": 498, "target": 1686, "key": "a29d6883c652d6fdbda6c5f037433109"}, {"line": 1777, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1253, "target": 3400, "key": "b7a617700aff618ca9fe5fda2601f92b"}, {"line": 1778, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1253, "target": 3401, "key": "75de66d7fe241499dd7fa450ff947126"}, {"line": 1779, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1253, "target": 2213, "key": "8e8098db78ef4956531111e012feedee"}, {"line": 1780, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1253, "target": 2212, "key": "e56993f05ed232f11d04a56fdae5dab2"}, {"line": 1781, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1253, "target": 2193, "key": "9acb1a4431ed6b929fed56e536a59fa4"}, {"relation": "partOf", "source": 2339, "target": 1253, "key": "916009174bb1130ffbdd43d9cbdc8ebb"}, {"line": 1842, "relation": "increases", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2339, "target": 1208, "key": "ab95baaa71a09586d7cf2c29ebacef22"}, {"line": 1843, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2339, "target": 1170, "key": "d720a10e89dbba22d2f472715b5ec392"}, {"line": 35455, "relation": "association", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade. It is worth noting that ERK1/2 activity is increased in AD brains and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2339, "target": 1170, "key": "ff6255ec74e6b59a5deea8937f5516bb"}, {"line": 28619, "relation": "increases", "evidence": "Tyrosine phosphorylation of the beta-amyloid precursor protein cytoplasmic tail promotes interaction with Shc.", "citation": {"db": "PubMed", "db_id": "11877420"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2339, "target": 1207, "key": "9dc995d72b2767f60274edb9009cfcd8"}, {"line": 35454, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade. It is worth noting that ERK1/2 activity is increased in AD brains and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2339, "target": 1207, "key": "481aed02107a4eee4f5c11128d1a856e"}, {"relation": "partOf", "source": 2339, "target": 1254, "key": "bbc372fbe3a2869dca9c7bd61e61b5f9"}, {"relation": "partOf", "source": 2339, "target": 1252, "key": "46a2b91ad566085c7262bdda75afe238"}, {"line": 35377, "relation": "increases", "evidence": "Schematic representation of AbetaPP processing, the adaptor proteins interacting with its intracellular domain and the pathway leading to ERK1/2 activation. In the left panels is reported the transmembrane protein APP, before and after ITS sequential beta secretase (BACE) and gamma secretase cleavage, with its final products, AICD, APP ectodomain, and beta amyloid peptide (1–40/1–42). In the right part of the figure are indicated the protein interacting with APP intracellular domain, upon or independently from tyrosine phosphorylation. The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2339, "target": 1174, "key": "a717c3a8d6fbc9a9142202ef6c5793ee"}, {"relation": "partOf", "source": 2769, "target": 1253, "key": "2ea6953789d65ac8bf54c5b62828a89e"}, {"relation": "partOf", "source": 2769, "target": 1171, "key": "33fe9cf1243f73cecce852c599543675"}, {"relation": "partOf", "source": 2769, "target": 1170, "key": "3333929960bdc86c5309bccd49949b61"}, {"relation": "partOf", "source": 2769, "target": 1173, "key": "8067906b89c577e8b934364feb309cfb"}, {"relation": "partOf", "source": 2769, "target": 1022, "key": "664b86d61d79f539bd1e326bb46fbf97"}, {"line": 2219, "relation": "increases", "evidence": "As previously discussed, AbetaPP regulates ERK1/2 levels, its phosphorylation/translocation to the centrosome, and cell proliferation rate.Additionally, in the same study, we showed that also PS1 interacts with Grb2 in the centrosomes and modulates ERK1/2 signaling.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2769, "target": 448, "key": "37864282344d4f33db71090c464e5587"}, {"relation": "partOf", "source": 2769, "target": 1438, "key": "abf4f2ceb66efc10833076d31eeea7c5"}, {"relation": "partOf", "source": 2769, "target": 1437, "key": "232eea5dc42cd46fd097bf46216ed660"}, {"relation": "partOf", "source": 2769, "target": 1436, "key": "cfb89e4ec6a3f2150edd2311a32f81e2"}, {"relation": "partOf", "source": 2769, "target": 1021, "key": "6cccdeb7867c002143e1c3a77bbea917"}, {"relation": "partOf", "source": 2769, "target": 1426, "key": "8a9e9b3a8c89f2938359a283b477f4ca"}, {"relation": "partOf", "source": 2769, "target": 1427, "key": "36af88b052f07a69afe85f99045c2b0f"}, {"relation": "partOf", "source": 2769, "target": 1441, "key": "551ab9fff061e485fc92c0ee88906037"}, {"relation": "partOf", "source": 2769, "target": 1251, "key": "4cbc5fcef5cd52fb92cd01d85c380d53"}, {"relation": "partOf", "source": 2769, "target": 1440, "key": "e6b13d55215fa9b89b158d4e7b3cc130"}, {"relation": "partOf", "source": 2769, "target": 1439, "key": "7a0a66a30f79753642b3572c8e4593b4"}, {"relation": "partOf", "source": 2769, "target": 1252, "key": "169ce1502335fdf4e6f31f215b10706c"}, {"relation": "partOf", "source": 2769, "target": 1174, "key": "79721a1aaef29d5a9da9c67b7dd55f90"}, {"relation": "partOf", "source": 2769, "target": 1442, "key": "96e5a32d9cbc073acc106f18c2a28e4c"}, {"relation": "partOf", "source": 2769, "target": 1172, "key": "75b570bf5a930f06e022db23d960816a"}, {"line": 36696, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 2910, "key": "29b1cca80321a9a3e5a0e9511d015fc1"}, {"line": 36697, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 3178, "key": "30b6715880c3efdcac881be38ab4035b"}, {"line": 36698, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 2915, "key": "3c17cd13c1fdbb463ac2d16fad9f9466"}, {"line": 36707, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 3355, "key": "f6ed6c00f0fe949e54998557854f1d25"}, {"line": 36708, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 3357, "key": "5a6715e5c7214f8254e801de06270709"}, {"line": 36709, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 3359, "key": "6a6923dc86968dd742927fd6eef227c9"}, {"line": 36710, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 3361, "key": "ec0f92ebf97a4e5b2743dc5ebfb03e25"}, {"line": 36728, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 2794, "key": "0ed227cdae43247f0f1196d4e4ed42e1"}, {"line": 36733, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 2792, "key": "6a377ed238e6e14d0bc7c976bc1fde3b"}, {"line": 36769, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2769, "target": 2187, "key": "00d326cee2a89e995ff2bb2ddb8b3dc8"}, {"line": 37999, "relation": "association", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of ABeta¸PP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of ABeta¸PP cytodomain [36, 49]. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptosis and neuronal development [50, 51]. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade (Figures ?(Figures11 and ?and2)2) [50, 52–54]. It is worth noting that ERK1/2 activity is increased in AD brains [55–57] and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD ", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2769, "target": 2340, "key": "9e3268cc0e29ff19bf608e000bcbf4ff"}, {"relation": "partOf", "source": 3356, "target": 1253, "key": "e5ec16210130b7fe82e8215f0c729fad"}, {"relation": "partOf", "source": 3356, "target": 1208, "key": "41f6b7855817a5667a2fbe6f76d7a759"}, {"relation": "partOf", "source": 3356, "target": 1254, "key": "5b8845a06af8603031af1a0022e8fa22"}, {"relation": "partOf", "source": 3356, "target": 1174, "key": "ca84b42d939e7399cba9b42491a7ee54"}, {"relation": "partOf", "source": 3356, "target": 1442, "key": "7dacb74b2d6bbfb60a7f910dcad9f32e"}, {"relation": "hasVariant", "source": 3356, "target": 3357, "key": "ed7369ab606d6c7ba2a434e176671108"}, {"line": 38436, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3356, "target": 3563, "key": "f060e3e2ad394301105234d32f012bb8"}, {"relation": "partOf", "source": 3356, "target": 1630, "key": "30cab0bbbb962604d68440d8956e320b"}, {"line": 1789, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3400, "target": 2173, "key": "a4f4a1998dec8359ba9cb870b0c42770"}, {"line": 35073, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3400, "target": 2173, "key": "cb2182c9b9679bff897ed0033aca0072"}, {"relation": "partOf", "source": 3400, "target": 1427, "key": "ef6d3bc1e5476f3bf0943c7e3225f8fa"}, {"line": 35380, "relation": "increases", "evidence": "Schematic representation of AbetaPP processing, the adaptor proteins interacting with its intracellular domain and the pathway leading to ERK1/2 activation. In the left panels is reported the transmembrane protein APP, before and after ITS sequential beta secretase (BACE) and gamma secretase cleavage, with its final products, AICD, APP ectodomain, and beta amyloid peptide (1–40/1–42). In the right part of the figure are indicated the protein interacting with APP intracellular domain, upon or independently from tyrosine phosphorylation. The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3400, "target": 2990, "key": "5a9edede9bab47e11cff0b84c2f5c6bf"}, {"line": 35382, "relation": "increases", "evidence": "Schematic representation of AbetaPP processing, the adaptor proteins interacting with its intracellular domain and the pathway leading to ERK1/2 activation. In the left panels is reported the transmembrane protein APP, before and after ITS sequential beta secretase (BACE) and gamma secretase cleavage, with its final products, AICD, APP ectodomain, and beta amyloid peptide (1–40/1–42). In the right part of the figure are indicated the protein interacting with APP intracellular domain, upon or independently from tyrosine phosphorylation. The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3400, "target": 3000, "key": "e5c34eb35b6816ac42f242eceec879cb"}, {"line": 1790, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3401, "target": 2173, "key": "6b57dab70ca13c0e1732656896679015"}, {"line": 35074, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3401, "target": 2173, "key": "2be92f03a10f5c4c8d6fb79e56fe55eb"}, {"line": 6683, "relation": "association", "evidence": "SOS2 codes for the homolog of the SOS1 gene, which is a guanine nucleotide exchange factor. These proteins are involved in signal transduction pathways, including insulin signaling. There are no previous reports of the association of SOS2 variants with T2DM, and this study is the first to report an association of polymorphisms in this gene with LOAD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3401, "target": 580, "key": "79195180c9c3ed9aa54023da2cd09db2"}, {"line": 35381, "relation": "increases", "evidence": "Schematic representation of AbetaPP processing, the adaptor proteins interacting with its intracellular domain and the pathway leading to ERK1/2 activation. In the left panels is reported the transmembrane protein APP, before and after ITS sequential beta secretase (BACE) and gamma secretase cleavage, with its final products, AICD, APP ectodomain, and beta amyloid peptide (1–40/1–42). In the right part of the figure are indicated the protein interacting with APP intracellular domain, upon or independently from tyrosine phosphorylation. The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3401, "target": 2990, "key": "129aa111157880d8cd0879a4ab5fe3e5"}, {"line": 35383, "relation": "increases", "evidence": "Schematic representation of AbetaPP processing, the adaptor proteins interacting with its intracellular domain and the pathway leading to ERK1/2 activation. In the left panels is reported the transmembrane protein APP, before and after ITS sequential beta secretase (BACE) and gamma secretase cleavage, with its final products, AICD, APP ectodomain, and beta amyloid peptide (1–40/1–42). In the right part of the figure are indicated the protein interacting with APP intracellular domain, upon or independently from tyrosine phosphorylation. The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3401, "target": 3000, "key": "e32118f0b12eecad597806685a587693"}, {"line": 1792, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2213, "target": 2173, "key": "306f69058eedbbf41f9bf7855998b69a"}, {"line": 35075, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "gtp", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 2213, "target": 2173, "key": "c6ed853ed6f879cf07fd50de10f0e6d9"}, {"line": 13956, "relation": "association", "evidence": "Pin1 is overexpressed in breast cancer and cooperates with Ras signaling in increasing the transcriptional activity of c-Jun towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Breast Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2213, "target": 3192, "key": "ba4769e4bd33106287e0bb34611dfc3e"}, {"line": 14012, "relation": "association", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "source": 2213, "target": 3192, "key": "28bdc20e604ac73507f2c9c0711aa3c3"}, {"line": 14001, "relation": "increases", "evidence": "Furthermore, Pin1 binds c-Jun that is phosphorylated on Ser63/73-Pro motifs by activated JNK or oncogenic Ras.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Subgraph": {"MAPK-ERK subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2213, "target": 2937, "key": "e9f59d86863e5b697522bdc2c70dde78"}, {"line": 14015, "relation": "association", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2213, "target": 2936, "key": "e39f9eb14baa295e33a1a9c86e63053d"}, {"relation": "partOf", "source": 2213, "target": 1671, "key": "88e5f35184cc39c690072110c8c63bb4"}, {"line": 1793, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2212, "target": 2173, "key": "2f5d46ecf2d325131624adc19948dfd0"}, {"line": 35076, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2212, "target": 2173, "key": "a80dda965ec8ac4e725790bc490ff274"}, {"line": 1794, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras,Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2193, "target": 2173, "key": "22cc9b8471057750b866afb7801e0899"}, {"line": 2135, "relation": "increases", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2193, "target": 2173, "key": "b21adf2d21c952226fcc156c44bb8753"}, {"line": 35077, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2193, "target": 2173, "key": "8be9e4bf23dce1ea65429963cdeb7aad"}, {"relation": "partOf", "source": 2193, "target": 1022, "key": "3d2b55f74a0ab22878fd599facb6ef5d"}, {"line": 36453, "relation": "increases", "evidence": "ERK1/2 are activated by upstream MAPKK, such as MEK1/2, and MAPKKK, such as c-Raf. MEK1/2 induce ERK1/2 activation via dual phosphorylation on threonine 202 and tyrosine 204 residues.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2193, "target": 2176, "key": "97188dec078fc8c623acda3a3fe38693"}, {"line": 1805, "relation": "increases", "evidence": "Schematic representation of the intracellular pathway by which AbetaPP and PS1 control the activation of the MAPK/ERK1/2 cascade and their final biological effects. In the figure is specified the interaction between APP intracellular domain and PS1 C-terminus, with the adaptor protein Grb2. Grb2 can bind simultaneously to APP and PS1 (as measured in FRET experiments) leading to the MAPK ERK1/2 cascade activation. In AD an aberrant activation of ERK1/2 induced by APP and/or PS1 can determine the tentative activation of the cell cycle that, in postmitotic neurons, may induce cells to undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1171, "target": 2173, "key": "dbbf66350c490cad545559078d1d0392"}, {"relation": "hasVariant", "source": 2942, "target": 2943, "key": "364769eed4566ab90dd4986535e55c4d"}, {"relation": "partOf", "source": 2942, "target": 1098, "key": "77590407cbe133d4bf09f47cdf780157"}, {"line": 45304, "relation": "increases", "evidence": "H4K5 and H4K12 are known to be acetylated by Tip60 ", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2942, "target": 2834, "key": "44e0ef5181b54c272abcd05bbcd55581"}, {"line": 45305, "relation": "increases", "evidence": "H4K5 and H4K12 are known to be acetylated by Tip60 ", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2942, "target": 2833, "key": "a3cbb69195a0b108de276d8800f0d2b4"}, {"line": 1823, "relation": "association", "evidence": "Recent data have demonstrated that AbetaPP may signal to the nucleus also using a Abeta-secretase-independent mechanism that involves membrane sequestration and phosphorylation of Tip60.More recently, Stante et al. have suggested that the presence of Fe65 into the nucleus may have a protective role, and that its translocation depends on AbetaPP. They propose that DNA repair defects could significantly contribute to the neurodysfunction and neurodegeneration observed in AD, and that an involvement of the Fe65-APP complex in the response of the cells to DNA damage and in the DNA repair machinery could represent a possible mechanism contributing to neuronal degeneration observed in AD pathology", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Response DNA damage": true}, "Confidence": {"Medium": true}}, "source": 1092, "target": 517, "key": "fefd528f5be9b5bd4963e51513315ace"}, {"line": 1832, "relation": "increases", "evidence": "Recent data have demonstrated that AbetaPP may signal to the nucleus also using a Abeta-secretase-independent mechanism that involves membrane sequestration and phosphorylation of Tip60.More recently, Stante et al. have suggested that the presence of Fe65 into the nucleus may have a protective role, and that its translocation depends on AbetaPP. They propose that DNA repair defects could significantly contribute to the neurodysfunction and neurodegeneration observed in AD, and that an involvement of the Fe65-APP complex in the response of the cells to DNA damage and in the DNA repair machinery could represent a possible mechanism contributing to neuronal degeneration observed in AD pathology", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1092, "target": 645, "key": "abb9100d7230f993755efcf7ed454bc0"}, {"line": 28623, "relation": "association", "evidence": "The cytoplasmic tail of APP interacts with phosphotyrosine binding (PTB) domain containing proteins (Fe65, X11, mDab-1, and JIP-1) and may pmodulate gene expression and apoptotic process.", "citation": {"db": "PubMed", "db_id": "11877420"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1092, "target": 478, "key": "10ed40f5ec66726fdb606a93a2bc8f12"}, {"line": 33250, "relation": "increases", "evidence": "The interaction of the APP C terminus with the adaptor protein Fe65 mediates APP trafficking and signalling, and is thought to regulate APP processing and Abeta generation.", "citation": {"db": "PubMed", "db_id": "18833287"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1092, "target": 80, "key": "f0a9ac83041e3eeb03c0697c51870b3e"}, {"line": 1823, "relation": "association", "evidence": "Recent data have demonstrated that AbetaPP may signal to the nucleus also using a Abeta-secretase-independent mechanism that involves membrane sequestration and phosphorylation of Tip60.More recently, Stante et al. have suggested that the presence of Fe65 into the nucleus may have a protective role, and that its translocation depends on AbetaPP. They propose that DNA repair defects could significantly contribute to the neurodysfunction and neurodegeneration observed in AD, and that an involvement of the Fe65-APP complex in the response of the cells to DNA damage and in the DNA repair machinery could represent a possible mechanism contributing to neuronal degeneration observed in AD pathology", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Response DNA damage": true}, "Confidence": {"Medium": true}}, "source": 517, "target": 1092, "key": "3dcf457820ef7fb873816ed0eecfb8f8"}, {"line": 30417, "relation": "association", "evidence": "In addition, a physical interaction between XPB and OGG1 may account for a potential mechanism involving these DNA repair responses.", "citation": {"db": "PubMed", "db_id": "19765189"}, "annotations": {"Subgraph": {"Response DNA damage": true}}, "source": 517, "target": 1413, "key": "59dcea701db3d58ef2e32567729739ab"}, {"line": 1843, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1170, "target": 2339, "key": "d0ec1cf678836a0f5e3d58da45eb5318"}, {"line": 35455, "relation": "association", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade. It is worth noting that ERK1/2 activity is increased in AD brains and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1170, "target": 2339, "key": "130da44baaea43cc886fd4b10eeea081"}, {"line": 28579, "relation": "association", "evidence": "Signal transduction through tyrosine-phosphorylated C-terminal fragments of amyloid precursor protein via an enhanced interaction with Shc/Grb2 adaptor proteins in reactive astrocytes of Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "12084708"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1170, "target": 780, "key": "a03b90e21a399d2969d366811816f7c1"}, {"line": 34324, "relation": "regulates", "evidence": "Amyloid precursor protein and Presenilin1 interact with the adaptor GRB2 and modulate ERK 1,2 signaling.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1170, "target": 448, "key": "e15e75ca58d6f6a56badc1eddb7e97f7"}, {"line": 35393, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1170, "target": 3298, "key": "e24b9921c1098a2994806bed05024ae4"}, {"line": 35403, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 1170, "target": 3291, "key": "65a4ca5561c3bdd89de8ac764183d3db"}, {"line": 35413, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 1170, "target": 2959, "key": "f5aa0fe3971fc956f6f03f5acd0527e2"}, {"line": 1846, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1678, "target": 2294, "key": "707bad3608d4dc95f706c3dbdb1b000b"}, {"line": 1848, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1678, "target": 2296, "key": "d5eb7a5d390a871bf7f76933d4a32aaf"}, {"line": 1849, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1678, "target": 2298, "key": "a0b7d134e5a255313ac86fdd4917118a"}, {"line": 1850, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1678, "target": 2299, "key": "6cd746690aea550ca7a841fb71239acd"}, {"line": 1851, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1678, "target": 2618, "key": "b179daeb7cbe35da01260685db1c8ae5"}, {"line": 1853, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1678, "target": 2240, "key": "cbe1fa38fbf8fb574b11d5f002b4fb6f"}, {"line": 1857, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 1678, "target": 3004, "key": "c097d1294e09efd36121355ef75c9da7"}, {"line": 1861, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1678, "target": 3151, "key": "fb29139e88a73e897daca3ab2c820562"}, {"relation": "partOf", "source": 2306, "target": 1678, "key": "a9325cca05784cfb1cc947217cbfde08"}, {"relation": "partOf", "source": 2306, "target": 1116, "key": "0ffc13a0ec0f892ac3af8acd2c0feb6f"}, {"relation": "partOf", "source": 2306, "target": 1118, "key": "21d61c88492e8d69597abcbb0aa20ac7"}, {"relation": "partOf", "source": 2306, "target": 1117, "key": "895d33a8a35b97011181b35da479e709"}, {"relation": "partOf", "source": 2306, "target": 1109, "key": "057ca233b97fe0288edbb680bcc902ee"}, {"line": 37467, "relation": "association", "evidence": "APP/APLP expression is up-regulated during neuronal maturation and differentiation, undergoes rapid anterograde transport, and is targeted in vesicles distinct from synaptophysin transport vesicles to synaptic sites", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2306, "target": 649, "key": "64075ce24c9bdc116e031d4e2d865f79"}, {"line": 37549, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2306, "target": 822, "key": "4bab0919b3c236c533374160c2e678d3"}, {"line": 37552, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2306, "target": 743, "key": "ffe411003c68570d34ac7202b3f3f2b7"}, {"line": 37555, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2306, "target": 652, "key": "5ad8b767d7860fea4852b0db42a9562a"}, {"line": 37558, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2306, "target": 646, "key": "e5ae6fc4b110b24f5d3748c79591a16c"}, {"line": 37565, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2306, "target": 787, "key": "bbe17de093f2ad9f4dbb3f3258a5a754"}, {"line": 37568, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2306, "target": 737, "key": "492934d1cc6385a6bcd95567cb1e146a"}, {"line": 37571, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2306, "target": 758, "key": "b41b93aab5f0f2273a73dd6ee26b00df"}, {"relation": "partOf", "source": 2307, "target": 1678, "key": "784dce3231a0d4024414c42c5d603a12"}, {"relation": "partOf", "source": 2307, "target": 1119, "key": "fd943c8e7da6d52ac6b8e88a77a3e377"}, {"relation": "partOf", "source": 2307, "target": 1120, "key": "1f57f45fe39654efe8588feb01f03f8e"}, {"relation": "partOf", "source": 2307, "target": 1106, "key": "22b45f7d209b9145567b60a7da322ca6"}, {"line": 37529, "relation": "association", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2307, "target": 2656, "key": "321bf602f65c8d73ecba53ca5e7867a7"}, {"line": 37550, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2307, "target": 822, "key": "87b2ac612cc2d5dc321aac7b3d4528cf"}, {"line": 37553, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2307, "target": 743, "key": "72ef4adf032ecf9242be4f9a9952c23f"}, {"line": 37556, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2307, "target": 652, "key": "4d51f73153e00649b22bc71151746ad4"}, {"line": 37559, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2307, "target": 646, "key": "22a3c2982eae46ac07cf2b3397bbebde"}, {"line": 37566, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2307, "target": 787, "key": "62166adfd21a109a032d3fdffce7d215"}, {"line": 37569, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2307, "target": 737, "key": "56141710688ec4c5ce7eec22d8b59e94"}, {"line": 37572, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2307, "target": 758, "key": "703030f404a8a3f9fd740c174949b934"}, {"line": 1846, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2294, "target": 1678, "key": "e85a2518be22f0971cb423d7f25afac2"}, {"relation": "hasVariant", "source": 2294, "target": 2295, "key": "42fbc0109da6be464f2e0380096afab1"}, {"line": 2637, "relation": "association", "evidence": "Mint adaptor proteins bind to the amyloid precursor protein (APP) and regulate APP processing associated with Alzheimer's disease; however, the molecular mechanisms underlying Mint regulation in APP binding and processing remain unclear. Biochemical, biophysical, and cellular experiments now show that the Mint1 phosphotyrosine binding (PTB) domain that binds to APP is intramolecularly inhibited by the adjacent C-terminal linker region. The crystal structure of a C-terminally extended Mint1 PTB fragment reveals that the linker region forms a short a-helix that folds back onto the PTB domain and sterically hinders APP binding. This intramolecular interaction is disrupted by mutation of Tyr633 within the Mint1 autoinhibitory helix leading to enhanced APP binding and Abeta-amyloid production. Our findings suggest that an autoinhibitory mechanism in Mint1 is important for regulating APP processing and may provide novel therapies for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22355143"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "source": 2294, "target": 3823, "key": "70d03dc4ff01be4f5fa169e40f07f327"}, {"relation": "partOf", "source": 2294, "target": 1076, "key": "078ba516334da1e276fa80884a779b31"}, {"relation": "partOf", "source": 2294, "target": 1675, "key": "14ff8763b64cefc252e3a73e2f0874d0"}, {"relation": "partOf", "source": 2294, "target": 1674, "key": "08c35bfd50ab2657ab4d4d6c24ffddf8"}, {"relation": "partOf", "source": 2294, "target": 1075, "key": "0a689f9d82773e2cde85eca20521844e"}, {"relation": "partOf", "source": 2294, "target": 1078, "key": "2fb51730281b27c231e101ef366ad95e"}, {"line": 37439, "relation": "decreases", "evidence": "Although the requirements for binding of Fe65 and X11 to the GYENPTY motif differ slightly, they are sufficiently overlapping to make it conceivable that these proteins compete for binding to APP. Interestingly, opposite effects on APP processing have been observed after co-expression of APP with either Fe65 or X11. Fe65 overexpression increased the amount of cell-surface-associated APP, as well as the processing of APP to APPs and Ab peptide. By contrast, X11 decreased processing and cellular retention of APP. Thus, the functional balance between the two proteins is important for regulation of APP metabolism and possibly APP function. This balance could be further modified by a third protein, mDAB1 (the mammalian homologue of Disabled), that binds to the same region", "citation": {"db": "PubMed", "db_id": "10806097"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2294, "target": 4101, "key": "9cd6477e0ba750e504735c0855f28376"}, {"line": 37837, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2294, "target": 2136, "key": "1d4128fc8a750bbf816a581d749aecf1"}, {"relation": "partOf", "source": 2294, "target": 1077, "key": "6925544cb82cc5b72dd450f549703fff"}, {"line": 37983, "relation": "decreases", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2294, "target": 868, "key": "d9e89025ed3f8c30c4e6d68d79e77991"}, {"line": 37984, "relation": "decreases", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2294, "target": 2328, "key": "687fa9257930e4f7d725ddf057908900"}, {"line": 38440, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2294, "target": 3563, "key": "51f79466d11b89407d2b08c45b94d2a3"}, {"relation": "partOf", "source": 2294, "target": 1079, "key": "a331ad167ff2a3c8ac58adf5334d98bd"}, {"line": 1848, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2296, "target": 1678, "key": "7209fd32111ddcb5804ad6506274bec2"}, {"relation": "partOf", "source": 2296, "target": 1082, "key": "549694621e198bc586a5e34be54c738f"}, {"relation": "partOf", "source": 2296, "target": 1675, "key": "bd19d6961cc3bddfb63c30df862bdcba"}, {"relation": "partOf", "source": 2296, "target": 1674, "key": "ca6a2babd5425315e18ad88a86bf32f6"}, {"relation": "hasVariant", "source": 2296, "target": 2297, "key": "5da079069eb2367313f00c7d764c50d1"}, {"relation": "partOf", "source": 2296, "target": 1084, "key": "9e8563babd2b94aa1a291a69f7d19f9a"}, {"relation": "partOf", "source": 2296, "target": 1081, "key": "23d000abda3463f974fbeb8da8c802e7"}, {"relation": "partOf", "source": 2296, "target": 1085, "key": "8df8caaeaf0037324851b869a5997dea"}, {"line": 37838, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2296, "target": 2136, "key": "ab27860e1b4cb28bf00dac8312b21975"}, {"relation": "partOf", "source": 2296, "target": 1083, "key": "21d41470f0db4d262f26bfc97ff0871e"}, {"line": 37985, "relation": "decreases", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2296, "target": 868, "key": "3c5d467b89b511f060e2de318171476b"}, {"line": 37986, "relation": "decreases", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2296, "target": 2328, "key": "f4a2f8b4531fd30f9d26b1e2d7f4615c"}, {"line": 45418, "relation": "decreases", "evidence": "Overexpression of neuronal adaptor protein X11beta has been shown to decrease the production of amyloid-beta", "citation": {"db": "PubMed", "db_id": "22222501"}, "source": 2296, "target": 2328, "key": "9f5028eb5e2aa8d511b2355ca272aa73"}, {"line": 38441, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2296, "target": 3563, "key": "eb210fe834c2ff1d6779ddfdcee572e0"}, {"relation": "partOf", "source": 2296, "target": 1086, "key": "46bf9764d84812709c8107b7ce276a87"}, {"line": 1849, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2298, "target": 1678, "key": "575e78d65e3ef9b4494c849cf4e8bb34"}, {"relation": "partOf", "source": 2298, "target": 1088, "key": "70cf953ff5b74c127a0297fea49e94ed"}, {"relation": "partOf", "source": 2298, "target": 1675, "key": "70964692f4cd654e5e8d0d930ecda477"}, {"relation": "partOf", "source": 2298, "target": 1674, "key": "33ce0f1a4fe7f5bf6ab30456aeebd59b"}, {"relation": "partOf", "source": 2298, "target": 1087, "key": "64a042cb7439f4b5684639c79e51fd96"}, {"relation": "partOf", "source": 2298, "target": 1090, "key": "22c02b89882f4f95650d4d48ff75a92e"}, {"line": 37839, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2298, "target": 2136, "key": "4ac4677d5ff6b40f3544cb503fd1eed9"}, {"relation": "partOf", "source": 2298, "target": 1089, "key": "e0faa36eee8846ada0ce9fb3b625126f"}, {"line": 37987, "relation": "decreases", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2298, "target": 868, "key": "71338320ec85064a5029103df8e20bbd"}, {"line": 37988, "relation": "decreases", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2298, "target": 2328, "key": "5b181e11bd9e549831d892b7a1fb313f"}, {"line": 38442, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2298, "target": 3563, "key": "4460f1e86dc4eb1b4f455b8df1bb8935"}, {"relation": "partOf", "source": 2298, "target": 1091, "key": "84fa06d27009799fc728fd06b78606df"}, {"line": 1851, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2618, "target": 1678, "key": "7867fe08b2486079cd7142913fe5cfce"}, {"relation": "partOf", "source": 2618, "target": 1164, "key": "1b1abeac9cfb96f678b55cb68481fb9a"}, {"line": 1853, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2240, "target": 1678, "key": "b35babdab1dc90c8558e9a92958fd7f4"}, {"relation": "partOf", "source": 2240, "target": 1041, "key": "722384191f41bd2a4fa91ad0dee0dcb8"}, {"line": 24551, "relation": "association", "evidence": "c-Abl pmodulates AICD dependent cellular responses: transcriptional induction and apoptotic process.", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 2240, "target": 3563, "key": "43c6f487bf0e14f8a3ec3fd8b807c254"}, {"line": 24578, "relation": "association", "evidence": "Our results show that c-Abl pmodulates AICD dependent cellular responses, transcriptional induction as well as the apoptotic response, which could participate in the onset and progression of the neurodegenerative pathology, observed in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 2240, "target": 3563, "key": "379627b098112a841d22a3542e878af3"}, {"line": 24563, "relation": "increases", "evidence": "The two proteins, APP and Fe65, can be phosphorylated by c-Abl kinase.", "citation": {"db": "PubMed", "db_id": "19306298"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2240, "target": 2334, "key": "cab70aa75b259cce28fcb0bfb324e3a8"}, {"line": 24564, "relation": "increases", "evidence": "The two proteins, APP and Fe65, can be phosphorylated by c-Abl kinase.", "citation": {"db": "PubMed", "db_id": "19306298"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2240, "target": 2300, "key": "733721998689d6553537191ac49d3170"}, {"line": 24570, "relation": "association", "evidence": "Neprilysin has been proposed as a target gene for AICD.", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 2240, "target": 3057, "key": "ac1d9d225f7746b5f4b4c7e24da6e118"}, {"relation": "partOf", "source": 2240, "target": 1673, "key": "30e7c58a1b2badd26f966bfce3de918d"}, {"relation": "partOf", "source": 2240, "target": 1040, "key": "5eba50e9967a781249c45212cd719168"}, {"relation": "partOf", "source": 2240, "target": 1042, "key": "6f6110ed974574814a10f87c376002fb"}, {"relation": "partOf", "source": 2240, "target": 1043, "key": "f56e174302115f78fa3edff018d2b2a2"}, {"line": 24609, "relation": "directlyIncreases", "evidence": "In vitro, Abl phosphorylated tau directly.", "citation": {"db": "PubMed", "db_id": "16014719"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Tau protein subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2240, "target": 3015, "key": "4bdc59885ee7dc1bb01761231d21d6e4"}, {"relation": "partOf", "source": 2240, "target": 1672, "key": "4e410425e5805f3ed1598b30e8ac519f"}, {"line": 1857, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 3004, "target": 1678, "key": "8203a71a1ca15856963bac36b4c694e6"}, {"relation": "partOf", "source": 3004, "target": 1187, "key": "fa2bcffec5b92d8ec365d3634ad4c00c"}, {"relation": "partOf", "source": 3004, "target": 1536, "key": "f31d44284f8d1e671211de72ab3bba4c"}, {"line": 32601, "relation": "increases", "evidence": "Amyloid beta protein precursor is phosphorylated by JNK-1 independent of, yet facilitated by, JNK-interacting protein (JIP)-1.", "citation": {"db": "PubMed", "db_id": "12917434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "source": 3004, "target": 2334, "key": "59b981114244f235103c804965173ab9"}, {"relation": "partOf", "source": 3004, "target": 1508, "key": "02f02394a26f545d054702558989ccd8"}, {"relation": "partOf", "source": 3004, "target": 1548, "key": "f636f79715115a5753e27a9da67270b6"}, {"relation": "partOf", "source": 3004, "target": 1545, "key": "c91e6749acaf17b9733f4987fce8b7ca"}, {"relation": "partOf", "source": 3004, "target": 1528, "key": "74f3fbc5d144b0d8ff21ef215342e652"}, {"relation": "partOf", "source": 3004, "target": 1509, "key": "585cb31f6524eab9660474aca8f9633c"}, {"relation": "partOf", "source": 3004, "target": 1521, "key": "14db4dd4cdb0845469e490c37e667796"}, {"relation": "partOf", "source": 3004, "target": 1549, "key": "373d678279d7975c32738ca6c4517701"}, {"line": 1861, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3151, "target": 1678, "key": "3f91d55cd7d94319da36c99d1d204439"}, {"line": 38439, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3151, "target": 3563, "key": "f04b0d96f7f93cc9e96adcbf6af723b5"}, {"relation": "partOf", "source": 3151, "target": 1600, "key": "f3e359748d943c580277f9f0f8471022"}, {"line": 1865, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gap junctions subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1164, "target": 3306, "key": "60154d4728e215ef91e1a5c7ac74c715"}, {"line": 1866, "relation": "increases", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gap junctions subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1164, "target": 2327, "key": "02af0f051ba238f69267c64bb3fa72f3"}, {"line": 35442, "relation": "increases", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "source": 1164, "target": 2328, "key": "c451578dc0805de9238179d7da0b22a2"}, {"line": 1865, "relation": "association", "evidence": "In particular,in receptor TK, tyrosine residue can be phosphorylated to generate the NPXpY motif, which represents a docking sitefor several intracellular adaptor proteins through the phosphortyrosine-binding domain (PTB). Similarly, the adaptor proteins Shc and Grb2 can bind AbetaPP (or its CTFs) in the presence of phosphorylated tyrosine in this motif. However, AbetaPP (or its CTFs) and the AbetaPP-related proteins, APLP1 and APLP2, can also interact with several other signalling proteins, including X11, Fe65, mDab, c-Abl, JIP-1, and Numb, independently of the phosphorylation of the tyrosine residue within the YENPTY motif. From a functional point of view, the interaction between the neuron-specific adaptor protein Fe65 and AbetaPP via the second PTB domain of Fe65 was shown to modulate AbetaPP processing, favoring the generation of Abeta and AbetaPP trafficking, in several cell lines. Another adaptor that binds to AbetaPP is mDAB. It is a protein related to the Reelin subgraph and interacting with YENPTY motif through a PTB domain. mDAB is active during embryogenesis, where it regulates the position of neurons in the brain laminar structure, and mDAB binding increases the amounts of mature AbetaPP and Abeta formation. On the contrary, X11 stabilizes AbetaPP conformation in membrane, inhibiting Abeta secretion in cultured cells, likely impairing AbetaPP trafficking to sites containing active gamma-secretase complexes. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of AbetaPP. The expression of JIP1b stabilizes immature AbetaPP and decreases the AbetaPP ectodomain, Abeta40/Abeta42 and CTFs abundance.All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gap junctions subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3306, "target": 1164, "key": "8b06621906b051e08c5def41832e32a2"}, {"line": 2272, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3306, "target": 2981, "key": "f8802277386c63a943c2e18c0399f1b1"}, {"line": 2273, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 3306, "target": 641, "key": "0a2e0afd315a4a31d1ed8ae93abca196"}, {"relation": "partOf", "source": 3306, "target": 1205, "key": "77f6870ecdbb2e683d10c502258f0e08"}, {"line": 2282, "relation": "decreases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3306, "target": 3823, "key": "55061f38a4da3d1f08949f945fbb3a7d"}, {"line": 2286, "relation": "increases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "NMDA receptor": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3306, "target": 2777, "key": "ab47f4b17e763eac3e240800ff70b1dd"}, {"line": 2287, "relation": "increases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "NMDA receptor": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3306, "target": 2780, "key": "b4d1ed74815f52873880a96077124d06"}, {"line": 2293, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3306, "target": 702, "key": "4cbf6c0c292400b7b38060eab8159b23"}, {"relation": "partOf", "source": 3306, "target": 1532, "key": "324dfbc4826784d7c8d72eaf442a58d4"}, {"relation": "partOf", "source": 3306, "target": 1516, "key": "aab7e71f6e5ebafc4abc70d4681c3bb6"}, {"relation": "partOf", "source": 3306, "target": 1626, "key": "2931742ae03ea291562f3516dbbe6d79"}, {"line": 37272, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3306, "target": 2315, "key": "ed7268c22afe5aafc8c02840c8a2390a"}, {"line": 37273, "relation": "increases", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3306, "target": 652, "key": "65efc2b1c9d575b788692b690faef3b6"}, {"line": 37275, "relation": "increases", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3306, "target": 2617, "key": "db0e99d0d2599f80f9512e8d1c9ad8b4"}, {"line": 37303, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3306, "target": 2617, "key": "d26e9e8815b58c2f779745f9974e9191"}, {"line": 37276, "relation": "increases", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3306, "target": 743, "key": "6bc2e5f9e4c97f81306ef075042fd943"}, {"line": 37304, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3306, "target": 743, "key": "d86be6370d39864f9da4da442edc9165"}, {"line": 37279, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3306, "target": 2926, "key": "20872c3e028682606b7153bd3abedbae"}, {"line": 37312, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 3306, "target": 2778, "key": "c3bba076dc42c71c836e110fc98235bd"}, {"line": 37313, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 3306, "target": 738, "key": "918e1a0022b8cb9e9a0b75dd6cfaf861"}, {"line": 37315, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 3306, "target": 2773, "key": "4fb4235e74d207529fbd133a518346a4"}, {"line": 37316, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 3306, "target": 763, "key": "529b8368fc3f4103cd5c3917122ee39c"}, {"relation": "partOf", "source": 3306, "target": 1688, "key": "60d7ca735fb740b9d074259e86849f6d"}, {"line": 1872, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "CREB subgraph": true}, "Confidence": {"Medium": true}}, "source": 2340, "target": 1207, "key": "4f1a3701825363c22c275a3543aa9b22"}, {"line": 1873, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "CREB subgraph": true}, "Confidence": {"Medium": true}}, "source": 2340, "target": 1170, "key": "03b076bee687c5b68743ec656ab3a8ec"}, {"line": 37997, "relation": "association", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of ABeta¸PP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of ABeta¸PP cytodomain [36, 49]. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptosis and neuronal development [50, 51]. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade (Figures ?(Figures11 and ?and2)2) [50, 52–54]. It is worth noting that ERK1/2 activity is increased in AD brains [55–57] and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD ", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2340, "target": 3354, "key": "a38d7b28399445f21957c33239d9a4f2"}, {"line": 37998, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of ABeta¸PP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of ABeta¸PP cytodomain [36, 49]. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptosis and neuronal development [50, 51]. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade (Figures ?(Figures11 and ?and2)2) [50, 52–54]. It is worth noting that ERK1/2 activity is increased in AD brains [55–57] and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD ", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2340, "target": 649, "key": "d3885da352b80931cc4d43ca07b2d3f3"}, {"line": 37999, "relation": "association", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of ABeta¸PP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of ABeta¸PP cytodomain [36, 49]. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptosis and neuronal development [50, 51]. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade (Figures ?(Figures11 and ?and2)2) [50, 52–54]. It is worth noting that ERK1/2 activity is increased in AD brains [55–57] and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD ", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2340, "target": 2769, "key": "8cc92f96a7e6ebf0a099615cf105a74d"}, {"line": 38397, "relation": "increases", "evidence": "We find that NGF induces tyrosine phosphorylation of APP, and that APP interacts with TrkA and this interaction requires Y(682). Unpredictably, we also uncover that APP, and specifically Y(682), regulates activation of the NGF/TrkA signaling pathway in vivo, the subcellular distribution of TrkA and the sensitivity of neurons to the trophic action of NGF.", "citation": {"db": "PubMed", "db_id": "21849536"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2340, "target": 1197, "key": "4ed5ffc7b9f5f1bc527a156065e64f50"}, {"line": 38403, "relation": "regulates", "evidence": "We find that NGF induces tyrosine phosphorylation of APP, and that APP interacts with TrkA and this interaction requires Y(682). Unpredictably, we also uncover that APP, and specifically Y(682), regulates activation of the NGF/TrkA signaling pathway in vivo, the subcellular distribution of TrkA and the sensitivity of neurons to the trophic action of NGF.", "citation": {"db": "PubMed", "db_id": "21849536"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2340, "target": 3146, "key": "200cbe09d7434c539c17ffaab5f2cc20"}, {"line": 1874, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "CREB subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1207, "target": 2173, "key": "d4d42f8e2d904bf10752c7613bc5dccd"}, {"line": 35456, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade. It is worth noting that ERK1/2 activity is increased in AD brains and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1207, "target": 2173, "key": "ac040d8f96758eebea156e7961308a65"}, {"line": 28578, "relation": "association", "evidence": "Signal transduction through tyrosine-phosphorylated C-terminal fragments of amyloid precursor protein via an enhanced interaction with Shc/Grb2 adaptor proteins in reactive astrocytes of Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "12084708"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1207, "target": 780, "key": "0fb3d366407c5d2a902ab31a168e3019"}, {"relation": "partOf", "source": 3354, "target": 1207, "key": "fdbbb78de784aaed474f22dba4c8b376"}, {"relation": "partOf", "source": 3354, "target": 1173, "key": "da7fda6470ffc89432dff73d4ec49ce1"}, {"relation": "partOf", "source": 3354, "target": 1441, "key": "373f17052b6697e49d94ae0c6723ec19"}, {"relation": "partOf", "source": 3354, "target": 1251, "key": "38f4a22684b2c4a8ec0defa9be9959ab"}, {"relation": "partOf", "source": 3354, "target": 1527, "key": "ef0d5ff231648e967826a87db63dec95"}, {"relation": "partOf", "source": 3354, "target": 1439, "key": "c7a1cfe3c912e65978d39317cce1e83f"}, {"relation": "hasVariant", "source": 3354, "target": 3355, "key": "902f5ab85dd049dbf38f3ddf51ed53c3"}, {"line": 37997, "relation": "association", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of ABeta¸PP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of ABeta¸PP cytodomain [36, 49]. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptosis and neuronal development [50, 51]. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade (Figures ?(Figures11 and ?and2)2) [50, 52–54]. It is worth noting that ERK1/2 activity is increased in AD brains [55–57] and that activated MAPKs have been involved in the abnormal hyperphosphorylation of Tau in AD ", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 3354, "target": 2340, "key": "5e1341336f5d74c1713c2fee062e8f31"}, {"line": 38435, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3354, "target": 3563, "key": "e3c7611e9128fa630bf4c05446f22ced"}, {"relation": "partOf", "source": 3354, "target": 1629, "key": "b92b14734344dcf65c79e2197ec2df64"}, {"line": 1875, "relation": "increases", "evidence": "In this context, it was reported that other two adaptor proteins, which have been involved in the regulation of the amyloidogenic pathway, ShcA and growth factor receptor-bound protein 2 (Grb2) are able to interact with the cytodomain of AbetaPP in the presence of specific tyrosine 682 phosphorylation in the YENPTY motif of AbetaPP cytodomain. ShcA (or ShcC) adaptors connect growth factor receptors to specific signaling pathways (typically Ras/ERK1/2 pathway but also PI3K/Akt signalling) and are involved in cell proliferation differentiation and apoptotic process and neuronal development. Also the role of Grb2 in Ras-signaling pathway is well known as well as its involvement in the activation of the mitogen-activated protein kinase (MAPK) pathways cascade.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "CREB subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1209, "target": 2173, "key": "bf8a6c039515cfbe30598f6c398d9a72"}, {"relation": "partOf", "source": 3358, "target": 1209, "key": "68ca246a1939f3ee9bcf9167d456a697"}, {"relation": "hasVariant", "source": 3358, "target": 3359, "key": "4bbfccfe651ea6b2a30f724e43d05a69"}, {"line": 38437, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3358, "target": 3563, "key": "be935bb26fe7352d5161c2ea7c687a4f"}, {"relation": "partOf", "source": 3358, "target": 1631, "key": "b08c5c9d4c7a427b5e59cb1a60a9e332"}, {"line": 1892, "relation": "increases", "evidence": "The pathogenic correlation between Shc/Grb2 binding to AbetaPP during AD development is supported by the observation that the complexes AbetaPP (or CTFs)/ShcA or Grb2 are significantly increased in AD brain as compared to controls [55]. The increased phosphorylation/activation of ERK1/2, often described in AD brain, is also observed in thrombin-activated astrocytes, suggesting that, in this model, ERK1/2 may be activated by AbetaPP through ShcA. These data give prominence to the biological importance of AbetaPP phosphorylation for its functions and the regulation of intracellular adaptor binding as events responsible for the induction of glial-associated mitogenic pathway. Furthermore, ERK1/2, activated by Abetain vitro, plays a role in AbetaPP processing and phosphorylates Tau in a PHF-Tau similar manner. However, it is conceivable that a different signaling Abeta-independent might as well activate tau phosphorylation by ERK1/2 via the intracellular signaling regulated by the AbetaPP/CTFs-Shc-Grb2 pathway", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1173, "target": 2173, "key": "9aaad4d6ccbda2dccc540de11128b3c1"}, {"line": 35482, "relation": "increases", "evidence": "The pathogenic correlation between Shc/Grb2 binding to AbetaPP during AD development is supported by the observation that the complexes AbetaPP (or CTFs)/ShcA or Grb2 are significantly increased in AD brain as compared to controls [55]. The increased phosphorylation/activation of ERK1/2, often described in AD brain, is also observed in thrombin-activated astrocytes, suggesting that, in this model, ERK1/2 may be activated by AbetaPP through ShcA. These data give prominence to the biological importance of AbetaPP phosphorylation for its functions and the regulation of intracellular adaptor binding as events responsible for the induction of glial-associated mitogenic pathway. Furthermore, ERK1/2, activated by Abeta in vitro, plays a role in AbetaPP processing and phosphorylates Tau in a PHF-Tau similar manner. However, it is conceivable that a different signaling Abeta-independent might as well activate tau phosphorylation by ERK1/2 via the intracellular signaling regulated by the AbetaPP/CTFs-Shc-Grb2 pathway", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "object": {"modifier": "Activity"}, "source": 1173, "target": 2173, "key": "b0bc0f6f1b004a8bd7938436083362f4"}, {"line": 1910, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1716, "target": 2201, "key": "50bcc6ead17e0b3640765f6bbd86db74"}, {"line": 1911, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1716, "target": 2676, "key": "bdf040c52718379c8b5af2987c5d98b9"}, {"line": 1912, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1716, "target": 2713, "key": "fd2654a1db59b6190a045222775453b4"}, {"line": 1919, "relation": "regulates", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 1716, "target": 2970, "key": "0b1a9943fb7de84dca343f7650cd3889"}, {"line": 1927, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1716, "target": 2483, "key": "2bbb6c2d91492e7829efe720a4f5e016"}, {"line": 1936, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1716, "target": 2221, "key": "658a4dc8085a6f36d6d6be17b5a9b1f0"}, {"line": 2000, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1716, "target": 2200, "key": "00648dad47cc8009f4f4a91517439dcb"}, {"line": 34757, "relation": "association", "evidence": "The amyloid precursor protein is a ubiquitously expressed transmembrane protein that has been long implicated in the pathogenesis of Alzheimer's disease but its normal biological function has remained elusive despite extensive effort. We have previously reported the identification of Notch2 as an amyloid precursor protein interacting protein in E18 rat neurons. Here, we sought to reveal the physiologic consequences of this interaction. We report a functional relationship between amyloid precursor protein and Notch1, which does not affect Delta ligand binding. First, we observed interactions between the amyloid precursor protein and Notch in mouse embryonic stem cells lacking both presenilin 1 and presenilin 2, the active proteolytic components of the gamma-secretase complex, suggesting that these two transmembrane proteins can interact in the absence of presenilin. Next, we demonstrated that the amyloid precursor protein affects Notch signaling by using Notch-dependent luciferase assays in two cell lines, the human embryonic kidney 293 and the monkey kidney, COS7. We found that the amyloid precursor protein exerts opposing effects on Notch signaling in human embryonic kidney 293 vs. COS7 cells. Finally, we show that more Notch Intracellular Domain is found in the nucleus in the presence of exogenous amyloid precursor protein or its intracellular domain, suggesting the mechanism by which the amyloid precursor protein affects Notch signaling in certain cells. Our results provide evidence of potentially important communications between the amyloid precursor protein and Notch.", "citation": {"db": "PubMed", "db_id": "20089128"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1716, "target": 2200, "key": "97ea42e982dac92fa5ad4cc35ddf7a1b"}, {"line": 2049, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1716, "target": 478, "key": "99bdbcc5a1cc2bb7f8fb03787ca802ea"}, {"line": 1910, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2201, "target": 1716, "key": "946a6bb237714fe9fa89e7e2e348140e"}, {"relation": "hasVariant", "source": 2200, "target": 2201, "key": "e34d1a098b4a165e14faf9d003699962"}, {"line": 2000, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2200, "target": 1716, "key": "3a7952be22ee18f8e258eae2fff838b8"}, {"line": 34757, "relation": "association", "evidence": "The amyloid precursor protein is a ubiquitously expressed transmembrane protein that has been long implicated in the pathogenesis of Alzheimer's disease but its normal biological function has remained elusive despite extensive effort. We have previously reported the identification of Notch2 as an amyloid precursor protein interacting protein in E18 rat neurons. Here, we sought to reveal the physiologic consequences of this interaction. We report a functional relationship between amyloid precursor protein and Notch1, which does not affect Delta ligand binding. First, we observed interactions between the amyloid precursor protein and Notch in mouse embryonic stem cells lacking both presenilin 1 and presenilin 2, the active proteolytic components of the gamma-secretase complex, suggesting that these two transmembrane proteins can interact in the absence of presenilin. Next, we demonstrated that the amyloid precursor protein affects Notch signaling by using Notch-dependent luciferase assays in two cell lines, the human embryonic kidney 293 and the monkey kidney, COS7. We found that the amyloid precursor protein exerts opposing effects on Notch signaling in human embryonic kidney 293 vs. COS7 cells. Finally, we show that more Notch Intracellular Domain is found in the nucleus in the presence of exogenous amyloid precursor protein or its intracellular domain, suggesting the mechanism by which the amyloid precursor protein affects Notch signaling in certain cells. Our results provide evidence of potentially important communications between the amyloid precursor protein and Notch.", "citation": {"db": "PubMed", "db_id": "20089128"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2200, "target": 1716, "key": "11917d4ae139d6a58a0afa9c9575b34a"}, {"line": 2008, "relation": "increases", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2200, "target": 453, "key": "be34d45548f5c02fc1a4a78376066a0a"}, {"line": 34758, "relation": "increases", "evidence": "The amyloid precursor protein is a ubiquitously expressed transmembrane protein that has been long implicated in the pathogenesis of Alzheimer's disease but its normal biological function has remained elusive despite extensive effort. We have previously reported the identification of Notch2 as an amyloid precursor protein interacting protein in E18 rat neurons. Here, we sought to reveal the physiologic consequences of this interaction. We report a functional relationship between amyloid precursor protein and Notch1, which does not affect Delta ligand binding. First, we observed interactions between the amyloid precursor protein and Notch in mouse embryonic stem cells lacking both presenilin 1 and presenilin 2, the active proteolytic components of the gamma-secretase complex, suggesting that these two transmembrane proteins can interact in the absence of presenilin. Next, we demonstrated that the amyloid precursor protein affects Notch signaling by using Notch-dependent luciferase assays in two cell lines, the human embryonic kidney 293 and the monkey kidney, COS7. We found that the amyloid precursor protein exerts opposing effects on Notch signaling in human embryonic kidney 293 vs. COS7 cells. Finally, we show that more Notch Intracellular Domain is found in the nucleus in the presence of exogenous amyloid precursor protein or its intracellular domain, suggesting the mechanism by which the amyloid precursor protein affects Notch signaling in certain cells. Our results provide evidence of potentially important communications between the amyloid precursor protein and Notch.", "citation": {"db": "PubMed", "db_id": "20089128"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2200, "target": 453, "key": "f6d9d544974180c783508fd27d26036e"}, {"line": 2036, "relation": "increases", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "object": {"modifier": "Activity"}, "source": 2200, "target": 3303, "key": "60dd8d49a0431d8e771dcf5b854b34f2"}, {"line": 37729, "relation": "increases", "evidence": "These results suggest that sAPP-induced glial differentiation is mediated through the gamma-secretase dependent Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2200, "target": 2137, "key": "4c37ad351dce4a863463dd12bd1df02c"}, {"line": 37731, "relation": "increases", "evidence": "These results suggest that sAPP-induced glial differentiation is mediated through the gamma-secretase dependent Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2200, "target": 561, "key": "f25c2e5053feac886195c62b338204d2"}, {"line": 37784, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2200, "target": 2894, "key": "4ba9b4ee9e09de694797068364f84d1d"}, {"line": 1911, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2676, "target": 1716, "key": "f3a5ac99ca83fcc5d19447ab2fc16a27"}, {"relation": "hasVariant", "source": 2675, "target": 2676, "key": "51606b52111a2522c7c0294b9edfb31d"}, {"line": 1912, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2713, "target": 1716, "key": "e98405785ac00e0dcb39b4eeb27d302e"}, {"relation": "hasVariant", "source": 2712, "target": 2713, "key": "d4ea1af4d6d836e833338613873b5997"}, {"relation": "isA", "source": 2970, "target": 3551, "key": "c895a7526ca3be92d1f383d0f1ba7486"}, {"relation": "partOf", "source": 2970, "target": 1712, "key": "e1b10d24d7e9be716efb6f6b310f828e"}, {"relation": "partOf", "source": 2970, "target": 1094, "key": "3944060dae644643c5e71dca5984ab4d"}, {"relation": "partOf", "source": 2970, "target": 1184, "key": "cccfcdc38edd08f99ba4c12353b3f96f"}, {"line": 37411, "relation": "increases", "evidence": "The large, promiscuous LRP receptor is thus far the only known receptor that binds to APPs containing the Kunitz-type proteinase inhibitor sequence (Kounnas et al., 1995; Knauer et al., 1996). LRP is also responsible for the clearance of other protease-protease-inhibitor complexes, such as a2- macroglobulin and tissue plaminogen activator inhibitor type 1", "citation": {"db": "PubMed", "db_id": "10806097"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2970, "target": 1184, "key": "05dda67644839270cd2a6c2cf09987fb"}, {"relation": "partOf", "source": 2970, "target": 934, "key": "74dc886a844ab8c59d2bda1d27d8eb63"}, {"relation": "partOf", "source": 2970, "target": 915, "key": "1c521fcf0f30719adc43c98e50d45f50"}, {"relation": "partOf", "source": 2970, "target": 909, "key": "086d2f760b5fa73ab55e8de7e2a4ee33"}, {"line": 9081, "relation": "increases", "evidence": "APP (amyloid precursor protein) and LRP1 (low-density lipoprotein receptor-related protein 1) have been implicated in the pathogenesis of AD (Alzheimer's disease). They are functionally linked by Fe65, a PTB (phosphotyrosine-binding)-domain-containing adaptor protein that binds to intracellular NPxY-motifs of APP and LRP1, thereby influencing expression levels, cellular trafficking and processing. Additionally, Fe65 has been reported to mediate nuclear signalling in combination with intracellular domains of APP and LRP1. We have previously identified another adaptor protein, GULP1 (engulfment adaptor PTB-domain-containing 1). In the present study we characterize and compare nuclear trafficking and transactivation of GULP1 and Fe65 together with APP and LRP1 and report differential nuclear trafficking of adaptors when APP or LRP1 are co-expressed. The observed effects were additionally supported by a reporter-plasmid-based transactivation assay. The results from the present study indicate that Fe65 might have signalling properties together with APP and LRP1, whereas GULP1 only mediates LRP1 transactivation.", "citation": {"db": "PubMed", "db_id": "23167255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2970, "target": 3823, "key": "4c3765e2d551253af2d3a3cf58c05405"}, {"line": 9100, "relation": "positiveCorrelation", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2970, "target": 3823, "key": "ac03dc7507fbfff82043ced2a21a4b49"}, {"line": 9095, "relation": "association", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2970, "target": 2312, "key": "78430feef347b2c4e47c4709aebe3087"}, {"line": 31093, "relation": "association", "evidence": "Low density lipoprotein receptor-related protein (LRP) participates in the uptake and degradation of several ligands implicated in neuronal pathophysiology including apolipoprotein E (apoE), activated alpha(2) -macroglobulin (alpha(2)M*) and beta-amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "10797543"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2970, "target": 2312, "key": "cf980b73c12b0ffff391ecd884264809"}, {"line": 9499, "relation": "association", "evidence": "The distribution of LRP in the central nervous system is consistent with the potential function of this receptor in the regulation of proteinase activity, cytokine activity, and cholesterol metabolism.", "citation": {"db": "PubMed", "db_id": "1632469 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2970, "target": 748, "key": "1f966fc89882aebe771d9919eb6f1b24"}, {"line": 9507, "relation": "association", "evidence": "The distribution of LRP in the central nervous system is consistent with the potential function of this receptor in the regulation of proteinase activity, cytokine activity, and cholesterol metabolism.", "citation": {"db": "PubMed", "db_id": "1632469 "}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2970, "target": 420, "key": "bec0cbbdffbb1d63330aa6170ef3a1af"}, {"line": 9515, "relation": "association", "evidence": "The distribution of LRP in the central nervous system is consistent with the potential function of this receptor in the regulation of proteinase activity, cytokine activity, and cholesterol metabolism.", "citation": {"db": "PubMed", "db_id": "1632469 "}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2970, "target": 529, "key": "2c144e25210cb1f6c6817376ec0d51d8"}, {"relation": "partOf", "source": 2970, "target": 1145, "key": "6fe44a629f8b51b83a1fcc823e64769a"}, {"relation": "partOf", "source": 2970, "target": 1269, "key": "d7caa596c3b8c22d65aaefaf8fc7f269"}, {"relation": "partOf", "source": 2970, "target": 1524, "key": "10b8e450aacc3ac17fcf5f9405955ee7"}, {"relation": "partOf", "source": 2970, "target": 1525, "key": "7628686e5f5788af5047a15d5524e3dc"}, {"relation": "partOf", "source": 2970, "target": 1522, "key": "5aac59038a7270fb92a56d3eda7154a7"}, {"line": 31077, "relation": "association", "evidence": "Low density lipoprotein receptor-related protein (LRP) participates in the uptake and degradation of several ligands implicated in neuronal pathophysiology including apolipoprotein E (apoE), activated alpha(2) -macroglobulin (alpha(2)M*) and beta-amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "10797543"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2970, "target": 2315, "key": "76bf73a86714700f382bc22c758895a4"}, {"line": 31153, "relation": "association", "evidence": "Increasing evidence suggests that the low density lipoprotein receptor-related protein ( LRP ) affects the processing of amyloid precursor protein ( APP ) and amyloid beta ( Abeta ) protein production as well as mediates the clearance of Abeta from the brain.", "citation": {"db": "PubMed", "db_id": "15772078"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2970, "target": 2315, "key": "472cbd38f777a856a178a3cf6370d294"}, {"line": 31085, "relation": "association", "evidence": "Low density lipoprotein receptor-related protein (LRP) participates in the uptake and degradation of several ligands implicated in neuronal pathophysiology including apolipoprotein E (apoE), activated alpha(2) -macroglobulin (alpha(2)M*) and beta-amyloid precursor protein (APP).", "citation": {"db": "PubMed", "db_id": "10797543"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2970, "target": 2227, "key": "df776501027e09055829c96c558dde64"}, {"line": 37419, "relation": "increases", "evidence": "The large, promiscuous LRP receptor is thus far the only known receptor that binds to APPs containing the Kunitz-type proteinase inhibitor sequence (Kounnas et al., 1995; Knauer et al., 1996). LRP is also responsible for the clearance of other protease-protease-inhibitor complexes, such as a2- macroglobulin and tissue plaminogen activator inhibitor type 1", "citation": {"db": "PubMed", "db_id": "10806097"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation"}, "source": 2970, "target": 2227, "key": "a238a9a7dac0b567d927e350893d68d9"}, {"relation": "partOf", "source": 2970, "target": 1133, "key": "90bbf50979c79cba5c5ed5f3d293be32"}, {"line": 31119, "relation": "increases", "evidence": "Our results support the hypothesis that LRP binds and endocytoses Abeta42 both directly and via apoE but that endocytosed Abeta42 is not completely degraded and accumulates in intraneuronal lysosomes.", "citation": {"db": "PubMed", "db_id": "17012232"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2970, "target": 1133, "key": "6adfb0151fd559a10e17c8b43c8a8894"}, {"relation": "partOf", "source": 2970, "target": 1234, "key": "d858087f4dd69105bebd5e5f18bf79c5"}, {"line": 31127, "relation": "increases", "evidence": "Our results support the hypothesis that LRP binds and endocytoses Abeta42 both directly and via apoE but that endocytosed Abeta42 is not completely degraded and accumulates in intraneuronal lysosomes.", "citation": {"db": "PubMed", "db_id": "17012232"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2970, "target": 1234, "key": "dbf695f65c15c2182cb410925c1d05bb"}, {"line": 36204, "relation": "directlyIncreases", "evidence": "it has been demonstrated that LRP1 might bind to APP independent of the KPI domain in APP. This APP - LRP1 interaction is facilitated through a trimeric complex of APP-FE65-LRP1, which has a functional role in APP processing. Along with LRP1, APP is transported from the early secretory compartments to the cell surface and subsequently internalised into the endosomal / lysosomal compartments. Recent investigations indicate that ApoER2 and SorLA fulfil a similar role in shifting APP localisation in the cell, which affects APP processing and the production of the APP derived amyloid beta-peptide (Abeta)", "citation": {"db": "PubMed", "db_id": "18288927"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2970, "target": 1234, "key": "39135164bdf147c4c59c2470b7c4a488"}, {"line": 31154, "relation": "association", "evidence": "Increasing evidence suggests that the low density lipoprotein receptor-related protein ( LRP ) affects the processing of amyloid precursor protein ( APP ) and amyloid beta ( Abeta ) protein production as well as mediates the clearance of Abeta from the brain.", "citation": {"db": "PubMed", "db_id": "15772078"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2970, "target": 80, "key": "0036b73b94388bce501e44d9b86bd25f"}, {"line": 31168, "relation": "association", "evidence": "Recombinant LRP cluster IV ( LRP-IV ) bound Abeta in plasma in mice and Alzheimer 's disease-affected humans with compromised sLRP-mediated AAbetabinding , and reduced Abeta-related pathology and dysfunction in a mouse pmodel of Alzheimer disease , suggesting that LRP-IV can effectively replace native sLRP and clear AAbeta", "citation": {"db": "PubMed", "db_id": "17694066"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2970, "target": 2328, "key": "57da7c2c8373bcdef0fdf87d25009446"}, {"line": 36205, "relation": "decreases", "evidence": "it has been demonstrated that LRP1 might bind to APP independent of the KPI domain in APP. This APP - LRP1 interaction is facilitated through a trimeric complex of APP-FE65-LRP1, which has a functional role in APP processing. Along with LRP1, APP is transported from the early secretory compartments to the cell surface and subsequently internalised into the endosomal / lysosomal compartments. Recent investigations indicate that ApoER2 and SorLA fulfil a similar role in shifting APP localisation in the cell, which affects APP processing and the production of the APP derived amyloid beta-peptide (Abeta)", "citation": {"db": "PubMed", "db_id": "18288927"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2970, "target": 2328, "key": "2c910a345622f20732d762ef3f997bd6"}, {"relation": "partOf", "source": 2970, "target": 1030, "key": "7db4378c8e4f902fbfa59932f355e677"}, {"relation": "partOf", "source": 2970, "target": 1523, "key": "27791048ab7e39ef8bb98dae871fa6d0"}, {"relation": "partOf", "source": 2970, "target": 1526, "key": "db53b06d9e236e4cf5ff2c0e4cace2cf"}, {"relation": "partOf", "source": 2970, "target": 1519, "key": "16d5e9986da70aa2f4f6a8d9ef7749a4"}, {"relation": "partOf", "source": 2970, "target": 1520, "key": "30a8244e30bba04e1ed82a7e97330f64"}, {"relation": "partOf", "source": 2970, "target": 1527, "key": "eaa3487a9606bbb5279b912d235b1501"}, {"relation": "partOf", "source": 2970, "target": 1521, "key": "214b17dd03b1e4ac53996700ef64b29d"}, {"relation": "partOf", "source": 2970, "target": 1101, "key": "03b43a3ba672d93ddd8b8eb8428fc5e7"}, {"relation": "partOf", "source": 2970, "target": 1388, "key": "81483fa2f527b2a39b58504cec55dcb9"}, {"line": 37428, "relation": "increases", "evidence": "The large, promiscuous LRP receptor is thus far the only known receptor that binds to APPs containing the Kunitz-type proteinase inhibitor sequence (Kounnas et al., 1995; Knauer et al., 1996). LRP is also responsible for the clearance of other protease-protease-inhibitor complexes, such as a2- macroglobulin and tissue plaminogen activator inhibitor type 1", "citation": {"db": "PubMed", "db_id": "10806097"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation"}, "source": 2970, "target": 3351, "key": "d3d1ad0f6b7560a229af3eea1b65a9df"}, {"line": 37450, "relation": "decreases", "evidence": "The PTB1 domain of FE65 interacts with ApoE receptors, including LRP1 and ApoER2, via the ApoE receptor’s NPXY motif. Moreover, FE65 acts as a functional linker between LRP1 and APP.In a recent study, we have shown that a similar tripartite complex is formed between APP, FE65, and ApoER2 and that LRP1 may be competing with ApoER2 for FE65 binding sites. This complex results in altered processing of both APP and ApoER2. Overexpression of FE65 led to a significant increase in secreted ApoER2, secreted ApoER2 CTF, and cell surface levels of ApoER2 in COS7 cells. Whether FE65 can interact with other ApoE receptors, affecting receptor trafficking and processing, is unknown.", "citation": {"db": "PubMed", "db_id": "22429478"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2970, "target": 1096, "key": "d69e7e49c8e7659e023a75bb7d704168"}, {"line": 1927, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2483, "target": 1716, "key": "e547552fd77c06ae8126939dfae81a26"}, {"line": 2082, "relation": "increases", "evidence": "PS1 is also essential for efficient N-cadherin trafficking from ER to plasma membrane. Cadherins, including E-cadherin and neuronal cadherin (N-cadherin), are a family of type I transmembrane proteins that mediate Ca2+-dependent cell-cell adhesion, and recognition. PS1-mediated delivery of N-cadherin to the plasma membrane is important to exert its physiological function, including the control of the state of cell-cell contact.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2483, "target": 494, "key": "adbb235d0e49ff2cfc7f908e68a57bc4"}, {"line": 2083, "relation": "association", "evidence": "PS1 is also essential for efficient N-cadherin trafficking from ER to plasma membrane. Cadherins, including E-cadherin and neuronal cadherin (N-cadherin), are a family of type I transmembrane proteins that mediate Ca2+-dependent cell-cell adhesion, and recognition. PS1-mediated delivery of N-cadherin to the plasma membrane is important to exert its physiological function, including the control of the state of cell-cell contact.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2483, "target": 513, "key": "30d76b8cea13d882ca9b889f29c10bf6"}, {"line": 2085, "relation": "increases", "evidence": "PS1 is also essential for efficient N-cadherin trafficking from ER to plasma membrane. Cadherins, including E-cadherin and neuronal cadherin (N-cadherin), are a family of type I transmembrane proteins that mediate Ca2+-dependent cell-cell adhesion, and recognition. PS1-mediated delivery of N-cadherin to the plasma membrane is important to exert its physiological function, including the control of the state of cell-cell contact.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endoplasmic Reticulum"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 2483, "target": 720, "key": "7ea9035cb25bd5939a6182cda66a07ce"}, {"relation": "partOf", "source": 2483, "target": 1331, "key": "6bfcaf83dc72b74120b1a0cfc4a82e63"}, {"relation": "partOf", "source": 2483, "target": 1335, "key": "cefc2b1f27c7655c2348aa9d4b086fe5"}, {"relation": "partOf", "source": 2483, "target": 1336, "key": "574c31e62a8420d6b1ab3d9ad958771b"}, {"relation": "partOf", "source": 2483, "target": 1334, "key": "cb3cdd6179918845418e4c2e117f0399"}, {"line": 29404, "relation": "decreases", "evidence": "We demonstrated that N-cadherin expression had an inhibitory effect on JLP-mediated p38 MAPK signal activation by decreasing the interaction between JLP and p38 MAPK in COS7 cells.", "citation": {"db": "PubMed", "db_id": "21177868"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2483, "target": 1542, "key": "418b72cce609e668210c8ce5cc5a7ca0"}, {"relation": "partOf", "source": 2483, "target": 1333, "key": "c66c4b247eee54a0c44322c3061a0f40"}, {"relation": "partOf", "source": 2483, "target": 1332, "key": "02dea078d25abc90f9fbb665feefeeb3"}, {"line": 1936, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2221, "target": 1716, "key": "8bc092ebb0148e1fe1adc9e788ba6a0a"}, {"line": 1937, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2221, "target": 547, "key": "9a8531cf4647a9eae116fde38d69aec8"}, {"line": 1938, "relation": "increases", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2221, "target": 2586, "key": "0da56cac631f0619d709ae68b2d5a094"}, {"line": 47938, "relation": "increases", "evidence": "Wnt signaling requires both Fz and LRP6 (or LRP5), likely through a Wnt-induced Fz-LRP6 complex (Figure 1). Wnt-induced LRP6 phosphorylation is a key event in receptor activation (Tamai et al., 2004). LRP6, LRP5 and Arrow each have five reiterated PPPSPxS motifs (P, proline; S, serine or threonine, x, a variable residue), which are essential for LRP6 function and are each transferrable to a heterologous receptor to result in constitutive beta-catenin signaling (MacDonald et al., 2008;Tamai et al., 2004;Zeng et al., 2005).", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2221, "target": 2980, "key": "4af1dcef2ab66fb61b89cc32dab409cb"}, {"line": 1937, "relation": "association", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 547, "target": 2221, "key": "0f7330045e590988e8391017c0a28c19"}, {"line": 2226, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 547, "target": 2960, "key": "a949af2bbf7bed354b4a06503f18c59e"}, {"relation": "partOf", "source": 2586, "target": 1015, "key": "6e67d2a34ab64c6d4391d270fada5348"}, {"relation": "partOf", "source": 2586, "target": 1377, "key": "18b14bbe2fd6e67d71f59cdd59270292"}, {"relation": "partOf", "source": 2586, "target": 1333, "key": "96807f2c8d84e5c803ead0e76236775a"}, {"relation": "partOf", "source": 2586, "target": 1332, "key": "58759310d126b73a04464611d4eee77e"}, {"relation": "partOf", "source": 2586, "target": 1376, "key": "36084f46c408770f796326bdf3fcfa6c"}, {"relation": "partOf", "source": 2586, "target": 1369, "key": "7ef912c906d0055931a8f6b936f3a137"}, {"relation": "partOf", "source": 2586, "target": 1378, "key": "e6b801e7133b0e40b3615fb87e79b72b"}, {"relation": "hasVariant", "source": 2586, "target": 2587, "key": "08153a4f368e1ec2aeac3cae9e40fc4f"}, {"line": 35496, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2586, "target": 2586, "key": "6c65faa5f560ef9cd8c08fe86030d738"}, {"line": 1940, "relation": "increases", "evidence": "Besides their involvement in Abeta formation, PSs regulate the cleavage of other signaling receptors and transducers such as Notch-1, ErbB4, DC44, and LDL-receptor-related proteins and cadherins. PSs also affect different other signaling molecules, such as wingless-type MMTV integration site family (Wnt) signal transduction pathway, which is evolutionary conserved and controls many events during the embryogenesis . At cellular level, this pathway regulates morphology, proliferation, and motility of the cell. Wnt signaling pathway plays a central role during tumorigenesis, and the inappropriate activation of this pathway has been observed in several human cancers. It has been shown that Wnt-ligand-mediated signaling leads to the accumulation of cytosolic beta-catenin. Cytosolic beta-catenin will then translocate into the nucleus to bind to members of the T-cell factor (Tcf)/lymphoid-enhancing factor (Lef) family of DNA-binding proteins leading to the transcription of Wnt target genes.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1015, "target": 2221, "key": "3c7dc7cc6135b6e03ab776e272b0a08a"}, {"relation": "partOf", "source": 2150, "target": 1015, "key": "16bd9d88d3dffa11d7b2de67b7ada3f2"}, {"relation": "partOf", "source": 2157, "target": 1016, "key": "8aeb782a341644d715a6cd3656ac3e04"}, {"line": 1951, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2157, "target": 1016, "key": "6a42e6718fc75ca65c6200a25d6f996f"}, {"line": 33647, "relation": "increases", "evidence": "In the absence of an activating signal, phosphorylation of beta-catenin by glycogen synthase kinase 3 (GSK3) acting in conjunction with adenomatous polyposis coli and axin/conductin causes beta-catenin to interact with the beta-transducin repeat-containing protein which results in its ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "11212302"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2157, "target": 1016, "key": "155cbd933528f56db4b22bf28859a8ee"}, {"line": 30000, "relation": "decreases", "evidence": "It was shown recently that axin negatively regulates beta-catenin by bridging beta-catenin and GSK-3beta together in the same complex and thereby facilitating the phosphorylation of beta-catenin by GSK-3beta ", "citation": {"db": "PubMed", "db_id": "10341227"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2157, "target": 2580, "key": "3d524f36591add8079e3fb5baf5a2757"}, {"line": 30001, "relation": "increases", "evidence": "It was shown recently that axin negatively regulates beta-catenin by bridging beta-catenin and GSK-3beta together in the same complex and thereby facilitating the phosphorylation of beta-catenin by GSK-3beta ", "citation": {"db": "PubMed", "db_id": "10341227"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2157, "target": 1370, "key": "243eef990f7063d87a82a1a99f2f7be2"}, {"line": 30005, "relation": "increases", "evidence": "It was shown recently that axin negatively regulates beta-catenin by bridging beta-catenin and GSK-3beta together in the same complex and thereby facilitating the phosphorylation of beta-catenin by GSK-3beta ", "citation": {"db": "PubMed", "db_id": "10341227"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2157, "target": 2794, "key": "78f4bfdbf91a667d5b98b1fa1e979df6"}, {"line": 30075, "relation": "association", "evidence": "Thirdly, GSK-3beta has many substrates, such as beta-catenin, APC, Axin, Tau protein, etc.", "citation": {"db": "PubMed", "db_id": "21352912"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2157, "target": 2794, "key": "d8a573979aedb560396e3dfed575ab65"}, {"line": 1954, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1016, "target": 2794, "key": "19e154b6a6446d565cb90891a9f76a23"}, {"line": 33659, "relation": "increases", "evidence": "In the absence of an activating signal, phosphorylation of beta-catenin by glycogen synthase kinase 3 (GSK3) acting in conjunction with adenomatous polyposis coli and axin/conductin causes beta-catenin to interact with the beta-transducin repeat-containing protein which results in its ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "11212302"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1016, "target": 2794, "key": "80fb39630cb57e91cae553dfdc5735a8"}, {"line": 4520, "relation": "increases", "evidence": "In this study, we found the AICD to strongly inhibit Wnt-induced transcriptional reporter activity, and to counteract Wnt-induced c-Myc expression. Loss of the AICD resulted in an increased responsiveness to Wnt/beta-catenin-mediated transcription. Mechanically, the AICD was found to interact with glycogen synthase kinase 3 beta (GSK3beta) and promote its kinase activity. The subsequent AICD-strengthened Axin-GSK3beta complex potentiates beta-catenin poly-ubiquitination.", "citation": {"db": "PubMed", "db_id": "22613765"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}}, "source": 1016, "target": 2585, "key": "5b13e211abc23a404568a40b7ad142e3"}, {"relation": "partOf", "source": 2303, "target": 1016, "key": "30c7784e1788d133fd4e0c579af38668"}, {"line": 30076, "relation": "association", "evidence": "Thirdly, GSK-3beta has many substrates, such as beta-catenin, APC, Axin, Tau protein, etc.", "citation": {"db": "PubMed", "db_id": "21352912"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2303, "target": 2794, "key": "79b2dc17eaa60ecef66ad3c08772545e"}, {"relation": "partOf", "source": 2572, "target": 1016, "key": "0eaa464dc72c0fb35f7a909ee207be93"}, {"line": 1952, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2572, "target": 2581, "key": "55e34397c64acd069dfb8c1c5432c5e9"}, {"line": 33653, "relation": "increases", "evidence": "In the absence of an activating signal, phosphorylation of beta-catenin by glycogen synthase kinase 3 (GSK3) acting in conjunction with adenomatous polyposis coli and axin/conductin causes beta-catenin to interact with the beta-transducin repeat-containing protein which results in its ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "11212302"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2572, "target": 2581, "key": "2699445190345da20c9b14fcd140c2e2"}, {"line": 26876, "relation": "increases", "evidence": "Overexpression of constitutively active CK1epsilon, one of the CK1 isoforms expressed in brain, leads to an increase in Abeta peptide production. ", "citation": {"db": "PubMed", "db_id": "17360493"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2572, "target": 80, "key": "3994f155a23b63e80ff578d68040afbe"}, {"line": 47923, "relation": "regulates", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2572, "target": 2584, "key": "bc361de5482a500852c2d4951a856bba"}, {"relation": "partOf", "source": 2580, "target": 1016, "key": "84064d03080eaaa1e2073fb909f75587"}, {"relation": "hasVariant", "source": 2580, "target": 2581, "key": "c106d28a269ae45947308182dd31cbc5"}, {"relation": "hasVariant", "source": 2580, "target": 2585, "key": "5cb9c85e1ea91f7abddfc3feec578c7c"}, {"relation": "partOf", "source": 2580, "target": 1373, "key": "a8e016f88b01dbcaacba5ebcc4cdd240"}, {"relation": "partOf", "source": 2580, "target": 1375, "key": "f769f07161282e221e6c3fdb987f82ec"}, {"line": 4860, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2580, "target": 3823, "key": "76a3671af37b03c4a9c8edf5c1f95fdd"}, {"line": 6820, "relation": "association", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2580, "target": 462, "key": "7609dde8e12ff4dc837cce056ce1cfbf"}, {"line": 39182, "relation": "positiveCorrelation", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Calcium-dependent signal transduction": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2580, "target": 462, "key": "05da749379b7c87e6e10968b7163f277"}, {"relation": "partOf", "source": 2580, "target": 1331, "key": "1fedf827772976a4122d9abefd7dec69"}, {"relation": "partOf", "source": 2580, "target": 1370, "key": "665f261dd12520a623765bc189c9f21d"}, {"relation": "partOf", "source": 2580, "target": 1160, "key": "fc1f4af5a1f04f1fba6b1e68af681d3c"}, {"relation": "partOf", "source": 2580, "target": 1372, "key": "297298ea549d744aeeb010323d775241"}, {"relation": "partOf", "source": 2580, "target": 1342, "key": "3b578e131a888a5e61a10a50a7da3215"}, {"relation": "partOf", "source": 2580, "target": 1371, "key": "62de35f08cfaacf98692d468439181dd"}, {"relation": "partOf", "source": 2580, "target": 1326, "key": "81a55153161db5d0a85443fecad025d9"}, {"relation": "partOf", "source": 2580, "target": 1369, "key": "28c51c0f8234894f8d4f46db410ba092"}, {"relation": "partOf", "source": 2580, "target": 1374, "key": "5158a9283cc26e94d34273dad50587eb"}, {"line": 35506, "relation": "directlyIncreases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2580, "target": 1374, "key": "37445de70be5b37d6a337ba2caa79ed8"}, {"line": 36883, "relation": "negativeCorrelation", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2580, "target": 2794, "key": "ec132e8257324067b08cc05a42b44749"}, {"line": 39167, "relation": "negativeCorrelation", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Very High": true}}, "source": 2580, "target": 3015, "key": "36537c6bc539152684cb9760ad6a1e8e"}, {"relation": "hasVariant", "source": 2580, "target": 2584, "key": "5d07c190cae710bb41fea05e9ffb5d05"}, {"relation": "hasVariant", "source": 2580, "target": 2583, "key": "dd2f590c44dd4057f22041e35a67be39"}, {"relation": "hasVariant", "source": 2580, "target": 2582, "key": "a977c1474c46427382e85cd018072f55"}, {"line": 1956, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2581, "target": 2406, "key": "9b6246b092b5fd7b2e4afe63faed5e4b"}, {"line": 33661, "relation": "increases", "evidence": "In the absence of an activating signal, phosphorylation of beta-catenin by glycogen synthase kinase 3 (GSK3) acting in conjunction with adenomatous polyposis coli and axin/conductin causes beta-catenin to interact with the beta-transducin repeat-containing protein which results in its ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "11212302"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2581, "target": 2406, "key": "67333a19a6b91e8dfba817a5574a5a97"}, {"line": 1969, "relation": "increases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2581, "target": 2580, "key": "6fe4d1be6b266d08af5860dfe6e6274e"}, {"line": 30007, "relation": "decreases", "evidence": "It was shown recently that axin negatively regulates beta-catenin by bridging beta-catenin and GSK-3beta together in the same complex and thereby facilitating the phosphorylation of beta-catenin by GSK-3beta ", "citation": {"db": "PubMed", "db_id": "10341227"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2581, "target": 2580, "key": "8d02f3e35c879e9e057b8fd15c51fb0a"}, {"line": 34162, "relation": "decreases", "evidence": "This pool of beta-catenin exists in the cytosol and is normally rapidly degraded through phosphorylation by GSK 3beta, leading to its subsequent degradation. ", "citation": {"db": "PubMed", "db_id": "11504726"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2581, "target": 2580, "key": "0aeaad905a3a3994ca416a1b31c7e0c8"}, {"line": 35508, "relation": "decreases", "evidence": "It was reported that beta-catenin interacts with PSs, and that PS1 promotes beta-catenin degradation regulating phosphorylation by cyclin-dependent kinase 5 (CDK5) and GSK-3beta. Importantly, GSK-3beta was implicated in various neurological disorders, including AD", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2581, "target": 2580, "key": "45e06b1b431c1a279780e0fe585a118f"}, {"line": 12496, "relation": "positiveCorrelation", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2581, "target": 3823, "key": "003072d8685a07a16d1183f4cfa676fc"}, {"line": 30051, "relation": "association", "evidence": "Here, we show that cells overexpressing tau exhibit marked resistance to apoptosis induced by various apoptotic stimuli, which also causes correlated tau hyperphosphorylation and glycogen synthase kinase 3 (GSK-3) activation. ", "citation": {"db": "PubMed", "db_id": "17360687"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2581, "target": 478, "key": "ab2e5364afab193117182eb98306d2f4"}, {"line": 30758, "relation": "positiveCorrelation", "evidence": "We demonstrate that phosphorylation of serines 353 and 357 by glycogen synthase kinase-3beta (GSK3beta) induces a structural change of the hydrophilic loop of PS1 that can also be mimicked by substitution of the phosphorylation sites by negatively charged amino acids in vitro and in cultured cells. The structural change of PS1 reduces the interaction with beta-catenin leading to decreased phosphorylation and ubiquitination of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360711"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}}, "source": 2581, "target": 1373, "key": "c80229a17203160e740a800681e48ca0"}, {"line": 1957, "relation": "decreases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2406, "target": 2580, "key": "616d559fdf9372d08db9c1221ccea5cd"}, {"line": 33662, "relation": "decreases", "evidence": "In the absence of an activating signal, phosphorylation of beta-catenin by glycogen synthase kinase 3 (GSK3) acting in conjunction with adenomatous polyposis coli and axin/conductin causes beta-catenin to interact with the beta-transducin repeat-containing protein which results in its ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "11212302"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2406, "target": 2580, "key": "1e2d779b13f551569dfbc594a676f867"}, {"line": 35497, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2406, "target": 2586, "key": "abfdbd5f1076efadaccc862a3c9d1faa"}, {"line": 35498, "relation": "decreases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2406, "target": 2586, "key": "23888fc71a5c25a536793b48f76baff7"}, {"line": 2012, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 453, "target": 719, "key": "db4c122d6cfcdd7b0c00f5d40cbc75d8"}, {"line": 2020, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 453, "target": 506, "key": "690fcbeace64129cb9b580acbeb48292"}, {"line": 2028, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 453, "target": 478, "key": "408b0bb72fe84f27d9e86d1b82331345"}, {"line": 2032, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 453, "target": 648, "key": "b72552d17f1de1918206ee3e3ee0d3a8"}, {"line": 4870, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 453, "target": 3268, "key": "501099fb06f572d2a6fbfa75a536d321"}, {"line": 4871, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 453, "target": 3258, "key": "e8f34b0644aac66ee279037227d19524"}, {"line": 4880, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 453, "target": 488, "key": "1808805b3ae8697207c673faa78b8139"}, {"line": 11104, "relation": "positiveCorrelation", "evidence": "PEN-2 is an integral membrane protein that is a necessary component of the gamma-secretase complex, which is central in the pathogenesis of Alzheimer's disease and is also required for Notch signaling. In the absence of PEN-2, Notch signaling fails to guide normal development in Caenorhabditis elegans, and amyloid beta peptide is not generated from the amyloid precursor protein", "citation": {"db": "PubMed", "db_id": "12639958"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 453, "target": 3272, "key": "dd904becca1e56a679183df3bdcb9b7a"}, {"line": 48512, "relation": "increases", "evidence": "Notch signaling has a tumor suppressor role in neuroendocrine tumnors;effect of thiocoraline on the activation of the Notch pathway; In order to assess if thiocoraline treatment functionally activated the Notch pathway, real-time PCR was conducted to assess the relative induction or reduction in mRNA levels of HES1, HES2, HES6, and HEY1. Analysis revealed that expression levels of HES1, HES2, and HEY1 increased", "citation": {"db": "PubMed Central", "db_id": "PMC3892805"}, "annotations": {"MeSHDisease": {"Thyroid Neoplasms": true}, "Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 453, "target": 3975, "key": "b156e3eb6e3c855b3844026aa4b25c3b"}, {"line": 2012, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 719, "target": 453, "key": "a18aa03b2c3c0be1fb848dcb846e75db"}, {"line": 2020, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 506, "target": 453, "key": "f85eb4f94f0fe6f7a5a5adb46377d764"}, {"line": 7206, "relation": "association", "evidence": "Initially, the expression of p35 was believed to be restricted to the central nervous system, the lens (22), and recently in developing muscle, where it forms a p35/CDK5 active complex that regulates the expression of the acetylcholine receptor gene (23). Despite its name CDK5 does not affect the cell division cycle; it is expressed postmitotically, and its function is related to cytoskeletal dynamics, cell migration, cell differentiation, and exocytosis (14) instead of cellular proliferation. Recently, Pho-85, a yeast ortholog of CDK5, was shown to be involved in metabolic control by regulating different steps of glycogen and phosphate metabolism (13). Expression of CDK5 in insulin-producing cells is not surprising because widespread expression of this kinase has been described. CDK5 expression in beta-cells has also been reported (24).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 506, "target": 2487, "key": "ec3851312d5b20e465f4615ffb262dd1"}, {"line": 49239, "relation": "association", "evidence": "Overall, KLF10 has been implicated in cell differentiation, as a target gene for a variety of signaling pathways", "citation": {"db": "PubMed", "db_id": "20087894"}, "source": 506, "target": 2952, "key": "c179fb21b950a3c463eefab5da947461"}, {"relation": "partOf", "source": 3303, "target": 988, "key": "9accf2e6a9eee69b2c94a3843f02f621"}, {"line": 2057, "relation": "association", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2393, "target": 3258, "key": "66e9889e28e3a2a75d62b5d92cdf93e0"}, {"line": 2065, "relation": "decreases", "evidence": "Another relevant role for PSs is Notch processing. Notch signaling is involved in cell fate regulation, cell differentiation, proliferation, and apoptosis as well as neurodegeneration. Notch is a membrane receptor whose C-terminal domain (NICD), upon interaction with appropriate ligands, translocates into the nucleus where it activates the CSL family of transcription factors. NICD formation depends on gamma-secretase complex as the AICD fragment of AbetaPP. PSs play a role in apoptotic process, since FAD mutants cause cell death or induce secondary events that may lead to apoptotic process .Animals, in which PS1 and PS2 genes are deleted, show deficit in learning, memory, synaptic function and neuronal death. The processes beneath these effects are unknown, but the findings that PS1 interacts with antiapoptotic member of Bcl-2 family might indicate a possible mechanism.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2393, "target": 478, "key": "dde363b8e7d2292419d15a64ba81e05c"}, {"line": 16083, "relation": "decreases", "evidence": "GDNF protects against aluminum-induced apoptosis in rabbits by upregulating Bcl-2 and Bcl-XL and inhibiting mitochondrial Bax translocation.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2393, "target": 478, "key": "f36152aec7cb9cb24ecd303215e25f78"}, {"line": 22089, "relation": "negativeCorrelation", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2393, "target": 478, "key": "55ecf6697638e130abd62a8fadf344e5"}, {"line": 23240, "relation": "increases", "evidence": "Furthermore, mitochondrial ceramide generation induces intrinsic apoptosis mediated by cytochrome c release. However, all these apoptotic events can be prevented by overexpression of Bcl-2s ", "citation": {"db": "PubMed", "db_id": "23455468"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 2393, "target": 478, "key": "0bd08cda675220c3d866437a541116b3"}, {"line": 23239, "relation": "increases", "evidence": "Furthermore, mitochondrial ceramide generation induces intrinsic apoptosis mediated by cytochrome c release. However, all these apoptotic events can be prevented by overexpression of Bcl-2s ", "citation": {"db": "PubMed", "db_id": "23455468"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 2393, "target": 2608, "key": "d3b63bd271db1fd2d0adecbdc99ae489"}, {"relation": "partOf", "source": 2393, "target": 1286, "key": "63b677fdbefc921dbc6cc75563610bc9"}, {"relation": "partOf", "source": 2393, "target": 1291, "key": "953bb2b04bbca6f8ffdf53a729d2868a"}, {"relation": "partOf", "source": 2393, "target": 1292, "key": "71b5ff54c95f7cd55cee8dd484494e8e"}, {"relation": "partOf", "source": 2393, "target": 1290, "key": "ad0a8fecb72916d8e9c82cbf55418d67"}, {"line": 2084, "relation": "association", "evidence": "PS1 is also essential for efficient N-cadherin trafficking from ER to plasma membrane. Cadherins, including E-cadherin and neuronal cadherin (N-cadherin), are a family of type I transmembrane proteins that mediate Ca2+-dependent cell-cell adhesion, and recognition. PS1-mediated delivery of N-cadherin to the plasma membrane is important to exert its physiological function, including the control of the state of cell-cell contact.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 494, "target": 513, "key": "d78b537c76e5e915cdac746ee9704e79"}, {"line": 2083, "relation": "association", "evidence": "PS1 is also essential for efficient N-cadherin trafficking from ER to plasma membrane. Cadherins, including E-cadherin and neuronal cadherin (N-cadherin), are a family of type I transmembrane proteins that mediate Ca2+-dependent cell-cell adhesion, and recognition. PS1-mediated delivery of N-cadherin to the plasma membrane is important to exert its physiological function, including the control of the state of cell-cell contact.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 513, "target": 2483, "key": "29a10d91c467b467b31d25224956bf83"}, {"line": 2084, "relation": "association", "evidence": "PS1 is also essential for efficient N-cadherin trafficking from ER to plasma membrane. Cadherins, including E-cadherin and neuronal cadherin (N-cadherin), are a family of type I transmembrane proteins that mediate Ca2+-dependent cell-cell adhesion, and recognition. PS1-mediated delivery of N-cadherin to the plasma membrane is important to exert its physiological function, including the control of the state of cell-cell contact.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 513, "target": 494, "key": "18afec968b05eea8aa623415ccaa73a1"}, {"line": 2102, "relation": "association", "evidence": "PS1 is involved in the intramembrane cleavage of CD44, a cell surface adhesion molecule for the extracellular matrix components which is implicated in a wide variety of physiological and pathological processes including the regulation of tumor cell growth and metastasis.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2476, "target": 574, "key": "f98d113dd3e2b9b4bc28f445065b74b9"}, {"line": 21986, "relation": "positiveCorrelation", "evidence": "In this study, we demonstrated for the first time an increased CD44 gene expression in lymphocytes derived from Alzheimer's disease (AD) patients in comparison with healthy subjects.", "citation": {"db": "PubMed", "db_id": "20197694"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 2476, "target": 3823, "key": "19d5544c40f074c3b935eaf76359128b"}, {"line": 21993, "relation": "association", "evidence": "We also found that lymphocytes of the same patients expressed significant levels of unfolded p53 isoform, confirming what we already demonstrated in fibroblasts and lymphocytes derived from other cohorts of AD patients.", "citation": {"db": "PubMed", "db_id": "20197694"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell adhesion subgraph": true, "p53 stabilization subgraph": true}, "Cell": {"lymphocyte": true, "fibroblast": true}}, "source": 2476, "target": 3482, "key": "a077c1581f460becb43809c29374a112"}, {"line": 22013, "relation": "association", "evidence": "Several of the Abeta42/43 -increasing mutants severely impaired the cleavages of Notch1 and CD44 substrates, which were not affected by any other L383 mutation.", "citation": {"db": "PubMed", "db_id": "23237322"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2476, "target": 2328, "key": "8889c6ce7525f7a53f30c09292b06800"}, {"line": 2102, "relation": "association", "evidence": "PS1 is involved in the intramembrane cleavage of CD44, a cell surface adhesion molecule for the extracellular matrix components which is implicated in a wide variety of physiological and pathological processes including the regulation of tumor cell growth and metastasis.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 574, "target": 2476, "key": "8b87d7c89a25cab0f2e08bac142de033"}, {"relation": "isA", "source": 2974, "target": 3551, "key": "4a3d008fee0489fdecbf5157bc689bf2"}, {"relation": "partOf", "source": 2974, "target": 1712, "key": "512643c0d0fdc16fba19d6edc7ede737"}, {"relation": "hasVariant", "source": 2974, "target": 2975, "key": "857c0d16c901fb34dd31817732322ee5"}, {"line": 25209, "relation": "increases", "evidence": "We hypothesized LRP2 may be involved in efflux of apoJ out of the CNS, and Abeta binding to apoJ may enhance clearance of highly pathogenic Abeta42. Our data show that both RAP and LRP2-specific antibody block apoJ clearance, indicating LRP2 is required for apoJ efflux at the BBB. ", "citation": {"db": "PubMed", "db_id": "17077814"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2974, "target": 2538, "key": "56501ee3184a5c7deb2343bf167e4a00"}, {"relation": "partOf", "source": 2974, "target": 1468, "key": "0760718940f0c1a6c4a24d44fc1eb927"}, {"line": 30891, "relation": "association", "evidence": "We have now analyzed this process in greater detail and found that the IGF-I receptor interacts with the transmembrane region of megalin, whereas the perimembrane domain of megalin is required for IGF-I internalization.", "citation": {"db": "PubMed", "db_id": "20351102"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2974, "target": 2871, "key": "f5ada63b99678ef7ecc30a3f32e936ef"}, {"relation": "partOf", "source": 2974, "target": 1518, "key": "d10c8de9bc5d251cc9c31d60a9d5bf49"}, {"relation": "partOf", "source": 2974, "target": 1095, "key": "ae15b27ac65a315475eb8e4530391182"}, {"line": 38342, "relation": "increases", "evidence": "In the brain, megalin is expressed in brain capillaries, ependymal cells and choroid plexus, where it participates in the clearance of brain amyloid beta-peptide (Abeta) complex.Additionally, given that FE65 mediates the interaction between the low density lipoprotein receptor-related protein-1 and the amyloid precursor protein (APP) to modulate the rate of APP internalization from the cell surface, we hypothesize that megalin could also interact with APP in neurons.", "citation": {"db": "PubMed", "db_id": "20637285"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2974, "target": 80, "key": "23c09b8c9c2425b3d555a6123fcb3a8b"}, {"relation": "isA", "source": 2976, "target": 3551, "key": "37daae6e2d964be2022c7e967b32466a"}, {"relation": "partOf", "source": 2976, "target": 1712, "key": "3be1f4d26927893f4470dda862efb43b"}, {"relation": "isA", "source": 2977, "target": 3551, "key": "a1c1a3ccdf85dac1006739019a3f7f52"}, {"relation": "partOf", "source": 2977, "target": 1712, "key": "5b860f0bee60c66f60823c5d19d447e2"}, {"relation": "partOf", "source": 2977, "target": 1070, "key": "5eeefe257b88888ffb1a725b6e74e8fc"}, {"relation": "isA", "source": 2979, "target": 3551, "key": "58b9aef343580c57e5621b5e444a6eef"}, {"relation": "partOf", "source": 2979, "target": 1712, "key": "39cdd74449eb3f3d25029ddf7e6a9d51"}, {"relation": "hasVariant", "source": 2979, "target": 2980, "key": "6abf7fd6da00106d33b50391b5f57300"}, {"relation": "isA", "source": 2971, "target": 3551, "key": "400c73b1ccc291410534357b101cfabd"}, {"relation": "partOf", "source": 2971, "target": 1712, "key": "fac31a844fc75c5efafbf0963a7cd647"}, {"relation": "isA", "source": 2972, "target": 3551, "key": "ae76da7b0a66a75c0da7d299958822bb"}, {"relation": "partOf", "source": 2972, "target": 1712, "key": "285a6f91ca5bd20ce1eacc39fa03e128"}, {"relation": "isA", "source": 2978, "target": 3551, "key": "5ff7528c3709782a54ead1b917e9ca25"}, {"relation": "partOf", "source": 2978, "target": 1712, "key": "7ce57e9d1476e2c25de4b1f779cf3926"}, {"line": 2252, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Very High": true}}, "source": 1712, "target": 2312, "key": "b804e6f01e327772f36d43dd19c1f2fd"}, {"line": 2256, "relation": "increases", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 1712, "target": 2173, "key": "430fe628eaba2fb6d7c5022f7c1ff0df"}, {"line": 2257, "relation": "decreases", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 1712, "target": 2187, "key": "6d7fef678e98785477d9e124132ce135"}, {"line": 2134, "relation": "increases", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1022, "target": 2193, "key": "d8f62d2636997ed83130be6044ef59a7"}, {"line": 2138, "relation": "association", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 420, "target": 2173, "key": "0280cf3034347d2576725c43b55c29b5"}, {"line": 6333, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Chemokine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 580, "key": "d9985a735bf2011384c3bf206010dc37"}, {"line": 6334, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Chemokine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 583, "key": "c9465ec7d0114e2e92fe8376e1b3bacd"}, {"line": 9507, "relation": "association", "evidence": "The distribution of LRP in the central nervous system is consistent with the potential function of this receptor in the regulation of proteinase activity, cytokine activity, and cholesterol metabolism.", "citation": {"db": "PubMed", "db_id": "1632469 "}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 2970, "key": "e849ce717641af904dc7f8c618c1dd6e"}, {"line": 9569, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 2292, "key": "b67f85811ca56246787515c260666531"}, {"line": 9572, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3336, "key": "5b745a95dd8290f64861632b4b436a38"}, {"line": 9574, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 2898, "key": "3d94b01768145d618835d1d8472f7df2"}, {"line": 9577, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "Synuclein subgraph": true, "Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3384, "key": "2633d2bc4872cdf2d775755ebf3f9d36"}, {"line": 9578, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "Synuclein subgraph": true, "Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3387, "key": "6906c2b764eb6af65131dbf20ab466d4"}, {"line": 9580, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 2565, "key": "75114159914d3bd21cfe010d7d6b0901"}, {"line": 9582, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3522, "key": "a20664c406e0dbd3e781eee8d70a5872"}, {"line": 9583, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 2599, "key": "b8e6da3ba93fc1147859ab0f4fdb0731"}, {"line": 9584, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3513, "key": "9aa0d790cebea0e64a241a29f7bcfa18"}, {"line": 9585, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3471, "key": "f53cfa6acd745f9b9d5d50fa86b8014c"}, {"line": 36648, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 2910, "key": "3e9b655bcecca52ac0588badfed56ef5"}, {"line": 36650, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 2915, "key": "43e49a6678a79037ea11c9401ee80685"}, {"line": 36652, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3355, "key": "97c86508ecd2d85bcfacd9f9b5e89942"}, {"line": 36654, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3357, "key": "0657c1e9b840a5e051958ca307c5daae"}, {"line": 36656, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3359, "key": "da2a56c268c10ff43df293f7f6c149a6"}, {"line": 36658, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 420, "target": 3361, "key": "749e5f25a33778ac1bcd8ddaeb60f5f4"}, {"relation": "partOf", "source": 420, "target": 1665, "key": "7ca1334475cd6986d9505168bd6f5d6a"}, {"line": 39838, "relation": "association", "evidence": "Several cytokines are evidently regulated in (neuro-) inflammatory processes associated with neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "24567119"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 420, "target": 3874, "key": "2da23afc87e40765efee0a44ff96abec"}, {"line": 40137, "relation": "positiveCorrelation", "evidence": "Elevated levels of several proinflammatory factors including cytokines, peptides, pathogenic structures, and peroxidants in the central nervous system (CNS) have been detected in patients with neurodegenerative diseases such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 420, "target": 3874, "key": "e15a05379e369a36a8d8594d3097f04f"}, {"line": 39846, "relation": "positiveCorrelation", "evidence": "Some of them might increase steadily during disease progression or temporarily at the time of MCI to AD conversion.", "citation": {"db": "PubMed", "db_id": "24567119"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Cognitive Dysfunction": true, "Alzheimer Disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 420, "target": 3839, "key": "cd7037d9e4cc5e32b4dca3dbe2ffb25b"}, {"line": 39849, "relation": "positiveCorrelation", "evidence": "Some of them might increase steadily during disease progression or temporarily at the time of MCI to AD conversion.", "citation": {"db": "PubMed", "db_id": "24567119"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Cognitive Dysfunction": true, "Alzheimer Disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 420, "target": 3823, "key": "274de416f8b0292e61fb976ade85a8c1"}, {"line": 40135, "relation": "positiveCorrelation", "evidence": "Elevated levels of several proinflammatory factors including cytokines, peptides, pathogenic structures, and peroxidants in the central nervous system (CNS) have been detected in patients with neurodegenerative diseases such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 420, "target": 3823, "key": "09d56c2a884041483ae9c06eb27869eb"}, {"line": 40305, "relation": "association", "evidence": "These results indicated that cultured human astrocytes express a distinct set of NF-kB-target cytokines and chemokines in resting and activated conditions, suggesting that the NF-kB signaling pathway differentially regulates gene expression of cytokines and chemokines in human astrocytes under physiological and inflammatory conditions.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Cytokine signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 420, "target": 3553, "key": "3ea15029bee7515944ff1b6992ba5847"}, {"line": 42723, "relation": "positiveCorrelation", "evidence": "These results suggest that IL-32α can prevent cerebral ischemia damage via upregulation of anti-neuroinflammatory cytokine expression and STAT3 activation, but downregulation of neuroinflammatory cytokines and NF-κB activation.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Brain Ischemia": true}, "Confidence": {"High": true}}, "source": 420, "target": 2890, "key": "10671aa41bfa71aff4beae605aa0a5e6"}, {"line": 42724, "relation": "decreases", "evidence": "These results suggest that IL-32α can prevent cerebral ischemia damage via upregulation of anti-neuroinflammatory cytokine expression and STAT3 activation, but downregulation of neuroinflammatory cytokines and NF-κB activation.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Brain Ischemia": true}, "Confidence": {"High": true}}, "source": 420, "target": 3831, "key": "f2f59734dfc0747f3d86d2dacf24b747"}, {"line": 2161, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DNA synthesis": true, "Notch signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 531, "target": 2315, "key": "a2a64c7952fc120374080224444938d3"}, {"line": 2162, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DNA synthesis": true, "Notch signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 531, "target": 3258, "key": "ce9e8e162a2eb6aef4d7025e8fa89012"}, {"line": 2166, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3852, "target": 2315, "key": "96ac55c16bdc168aa049a71f062f294d"}, {"line": 2169, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3852, "target": 3258, "key": "e60b2ca5ae546514c45c8e6d3687495c"}, {"line": 8647, "relation": "association", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3852, "target": 3912, "key": "74a4182f3bc971e1804ace8550ca63c3"}, {"line": 21628, "relation": "positiveCorrelation", "evidence": "mTOR Hyperactivation in Down Syndrome Hippocampus Appears Early During Development.", "citation": {"db": "PubMed", "db_id": "24918639"}, "annotations": {"MeSHDisease": {"Down Syndrome": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3852, "target": 3076, "key": "b2b92299b1dba5b12133dc09eb2a585a"}, {"line": 21634, "relation": "positiveCorrelation", "evidence": "Increased expression of phosphorylated S6, phosphorylated S6 kinase, phosphorylated eukaryotic initiation factor 4E binding protein 1, and phosphorylated mTOR was observed in DS hippocampus compared with controls.", "citation": {"db": "PubMed", "db_id": "24918639"}, "annotations": {"MeSHDisease": {"Down Syndrome": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Interferon signaling subgraph": true}}, "source": 3852, "target": 3328, "key": "ca88f1e00e951b6d7e8914696457bf57"}, {"line": 21635, "relation": "positiveCorrelation", "evidence": "Increased expression of phosphorylated S6, phosphorylated S6 kinase, phosphorylated eukaryotic initiation factor 4E binding protein 1, and phosphorylated mTOR was observed in DS hippocampus compared with controls.", "citation": {"db": "PubMed", "db_id": "24918639"}, "annotations": {"MeSHDisease": {"Down Syndrome": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Interferon signaling subgraph": true}}, "source": 3852, "target": 2669, "key": "e272da401269c72b2e60407be57ce1ba"}, {"line": 21637, "relation": "positiveCorrelation", "evidence": "Increased expression of phosphorylated S6, phosphorylated S6 kinase, phosphorylated eukaryotic initiation factor 4E binding protein 1, and phosphorylated mTOR was observed in DS hippocampus compared with controls.", "citation": {"db": "PubMed", "db_id": "24918639"}, "annotations": {"MeSHDisease": {"Down Syndrome": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3852, "target": 3077, "key": "24bb3b1ced6ff20b6c25f869ad7d8118"}, {"line": 26631, "relation": "association", "evidence": "This suggests the possibility that the elevated expression of BACE2 is involved in the Alzheimer-type neuropathology of DS", "citation": {"db": "PubMed", "db_id": "12052539"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3852, "target": 2381, "key": "d5cad68f80a199925f34569ba5f3a95c"}, {"line": 2174, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2484, "target": 3823, "key": "9e3059f3190d8ac8438ce0be2e7f8c3c"}, {"relation": "partOf", "source": 2484, "target": 1668, "key": "10722f0c7cc5b06421c517bc63a9636d"}, {"line": 2175, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2486, "target": 3823, "key": "b3d78a59f83f09326f34350d95e92365"}, {"relation": "partOf", "source": 2486, "target": 1668, "key": "754981c5f606d0de8bebe8ec5d8b3fc7"}, {"relation": "partOf", "source": 2486, "target": 1337, "key": "31cffef607c2e2feb718bdde7d126353"}, {"relation": "partOf", "source": 2486, "target": 1338, "key": "9adb3f6cfb5a3e0861749953fdbf7e7f"}, {"relation": "partOf", "source": 2486, "target": 1339, "key": "c77fe0096edafe290a98270020e71a86"}, {"line": 33535, "relation": "positiveCorrelation", "evidence": "Moreover, neurons that overexpress Bim in AD brains also show elevated levels of the cell cycle-related proteins cdk4 and phospho-Rb.", "citation": {"db": "PubMed", "db_id": "17251431"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2486, "target": 2396, "key": "ff7c38c077b8139a8d8e171458649486"}, {"line": 2176, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2495, "target": 3823, "key": "d1c403912c11e2dc8b26a60df5d728ab"}, {"line": 8640, "relation": "negativeCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "miRNA subgraph": true}}, "source": 2495, "target": 3823, "key": "175dad1f3af5c7f75b82c6b26f6f8b35"}, {"relation": "partOf", "source": 2495, "target": 1668, "key": "6ebd5ee4d45f4e0b5aadd92db9defe60"}, {"line": 8638, "relation": "negativeCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "miRNA subgraph": true}}, "source": 2495, "target": 2093, "key": "ea1dc416ab115278486f8edef6663037"}, {"line": 8639, "relation": "negativeCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "miRNA subgraph": true}}, "source": 2495, "target": 2094, "key": "fb662b45f8a4bce3f381d594e917b68c"}, {"line": 2177, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3051, "target": 3823, "key": "b67c78fb07e6d7f94ed43716470a719a"}, {"relation": "partOf", "source": 3051, "target": 1668, "key": "0494dee27c2409b6b382fb01562cee57"}, {"line": 2178, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2461, "target": 3823, "key": "d4b8263412124a32740b7539dfa636e7"}, {"relation": "partOf", "source": 2461, "target": 1668, "key": "e3bcf57b2707ec6e39649182646c50e4"}, {"line": 2179, "relation": "association", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2492, "target": 3823, "key": "c005f66a993a47f72a161de2feaa57a1"}, {"relation": "partOf", "source": 2492, "target": 1668, "key": "c99f212416bd183c7e36604ffd699143"}, {"line": 2184, "relation": "increases", "evidence": "Chromosome missegregation and trisomy 21 mosaicism have been associated with mutations in AbetaPP and PSs. Aberrant expression of cell-cycle proteins and tetraploidy in neurons from AD patients have been described. In AD brains, the activation of several cell-cycle components has been detected, including cdc2, cdk4, p16, Ki-67, cyclin B1 and cyclin D, p25 (the regulatory subunit of cdk5), as well as the increased expression activity of genes encoding for cell-cycle proteins. It was observed that hippocampal pyramidal and basal forebrain neurons, in AD brain show markers of DNA replication, and it was speculated that the state of tetraploidy is lethal to neurons", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1668, "target": 718, "key": "556b0e7febe128e05e89b9fd1488526b"}, {"line": 19150, "relation": "negativeCorrelation", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 718, "target": 3823, "key": "f2b867d195094ded4b272ad5f6d9a0f8"}, {"line": 19152, "relation": "association", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 718, "target": 3300, "key": "1187a229819522ab0c1d9a69653aa704"}, {"line": 31340, "relation": "association", "evidence": "Pin1 regulates the conformation and function of certain phosphorylated proteins and plays an important role in cell cycle regulation , oncogenesis , and Alzheimer 's disease.", "citation": {"db": "PubMed", "db_id": "12388558"}, "source": 718, "target": 3192, "key": "a8aa8aef58461320fe93dd4ab947b27f"}, {"line": 2191, "relation": "increases", "evidence": "Increasing observations suggest that aberrant activation of cell cycle may affect the formation of neurofibrillary tangles with hyperphosphorylation of Tau protein in AD brain. It is well known that p25/cdk5 complex hyperphosphorylates Tau and reduces its ability to associate with microtubules.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1014, "target": 3015, "key": "d770be3eeac4877a1f20474bfddc4428"}, {"line": 2192, "relation": "decreases", "evidence": "Increasing observations suggest that aberrant activation of cell cycle may affect the formation of neurofibrillary tangles with hyperphosphorylation of Tau protein in AD brain. It is well known that p25/cdk5 complex hyperphosphorylates Tau and reduces its ability to associate with microtubules.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1014, "target": 991, "key": "70c6cbb9c2f304d334f28030fa101ef8"}, {"relation": "partOf", "source": 391, "target": 991, "key": "1474f5cf6850234d2dbba43301ff9c47"}, {"line": 3461, "relation": "association", "evidence": "The low-density lipoprotein receptor (LDLR) has the highest affinity for apoE and plays an important role in brain cholesterol metabolism.These data suggest that increased APP expression and Abeta exposure alters microtubule function, leading to reduced transport of LDLR to the plasma membrane. Consequent deleterious effects on apoE uptake and function will have implications for AD pathogenesis and/or progression", "citation": {"db": "PubMed", "db_id": "20049331"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 391, "target": 80, "key": "09b2093ca201ac39cf783433ebebc4d1"}, {"line": 7089, "relation": "association", "evidence": "Alzheimer's disease is an age-related form of dementia due to a progressive loss of neuronal functioning, also characterized by the deposition of extracellular amyloid throughout the brain (amyloid plaques) and a significant decrease in brain mass. An overactivity of the cyclin-dependent protein kinase CDK5 in the brain has been implicated in neuronal degeneration and the formation of neurofibrillary tangles due to hyperphosphorylation of essential neuronal proteins such as MAP and Tau (2). CDK5 is regulated by the neuron-specific and cyclin-related protein p35 (also known as p35Ncdk5a). During the development of Alzheimer's disease, p35 is processed to a smaller protein, p25, which is a constitutive activator of CDK5. The generation of p25 disrupts CDK5 localization and substrate preferences (2). In addition, the expression of p25 in neurons results in a collapse of microtubules, neurite retraction, and apoptosis (2).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity"}, "source": 391, "target": 2133, "key": "f5446e7065da4bd74298b5be8995a704"}, {"line": 3004, "relation": "directlyIncreases", "evidence": "Acetylcholinesterase (AChE) is thought to play an important role during apoptotic process. Our results showed that H2O2 induced AChE activity, a functional marker in apoptotic process, increases in neuronal-like PC12 cells. Glutathione, which is involved in cellular redox homeostasis, inhibited the increase of AChE activity, suggesting that reactive oxygen species (ROS) play a key role in this process. Further investigation showed that the elevation of AChE was observed after the degradation of Akt, release of cytochrome c from mitochondria into the cytosol, and activation of caspase family members. When nerve growth factor (NGF) was present, with the maintenance of Akt level, the elevation of AChE, the cytochrome c diffusion, as well as apoptotic process were markedly attenuated in H2O2-treated PC12 cells", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2167, "target": 2244, "key": "df5be7ed123a193df334fa339b2d5442"}, {"line": 21029, "relation": "association", "evidence": "XIAP also functions as an E3 ubiquitin ligase, targeting caspases for degradation.", "citation": {"db": "PubMed", "db_id": "20670888"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2167, "target": 3539, "key": "a7e35ba2433fc2575d669975e85c754a"}, {"line": 2216, "relation": "association", "evidence": "As previously discussed, AbetaPP regulates ERK1/2 levels, its phosphorylation/translocation to the centrosome, and cell proliferation rate.Additionally, in the same study, we showed that also PS1 interacts with Grb2 in the centrosomes and modulates ERK1/2 signaling.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2174, "target": 2315, "key": "95400261efaad07c39721e7c5996a573"}, {"line": 36466, "relation": "increases", "evidence": "Phosphorylation of ERK leads to the activation of a number of transcription factors, important in controlling differentiation, neuronal survival, learning and memory plasticity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2174, "target": 820, "key": "dc1280f45313991d6d575719ade9e918"}, {"line": 36467, "relation": "increases", "evidence": "Phosphorylation of ERK leads to the activation of a number of transcription factors, important in controlling differentiation, neuronal survival, learning and memory plasticity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2174, "target": 812, "key": "d40328e2b0e8b51684043edf60049eb7"}, {"line": 7647, "relation": "association", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 448, "target": 841, "key": "d2bf19ac6440033fa4b51f3d8b937d51"}, {"line": 7648, "relation": "association", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 448, "target": 839, "key": "ab9ae07a6b0e71a8a2e539e2e2a20880"}, {"line": 7656, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Apoptosis signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 448, "target": 584, "key": "2f4dd6158c73226196afa7f08d85f9d9"}, {"line": 7906, "relation": "increases", "evidence": "The phosphorylation of tau is mainly promoted by GSK-3and cyclin-dependent kinase 5 (Cdk5). Besides these kinases, activated c-Jun N-terminal kinases (JNK) and ERK-1 /-2 signaling lead to an increase in tau phosphorylation and th erefore might be of importance in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 448, "target": 3015, "key": "360172625b7158609a98b9f103bb2f42"}, {"line": 36927, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 448, "target": 2173, "key": "b642aaa950d3aadaa3e5194d4decea9c"}, {"line": 36931, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 448, "target": 669, "key": "c1234e5fd32bf75e4f2fc6b1c3f7f016"}, {"line": 41062, "relation": "increases", "evidence": "SF-E's action on microglial cells appears to be mediated through inhibition of the IFNgamma-induced p-ERK1/2 signaling pathway which is central to regulating a number of intracellular metabolic processes including enhancing Stat1α phosphorylation and filopodia formation.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 448, "target": 3724, "key": "24f6b992b72773af1008679881474208"}, {"line": 2224, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2960, "target": 528, "key": "98882cb6b4dde3edb5a8ab376bd8fc38"}, {"line": 2225, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2960, "target": 651, "key": "8a20c19edcf3c4a6c17333f968da3d62"}, {"line": 2226, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2960, "target": 547, "key": "d5cc03602a735ce05102ac388835594d"}, {"relation": "partOf", "source": 2960, "target": 1131, "key": "9984fe62596b49d04ae7098f889ee2d6"}, {"line": 33846, "relation": "increases", "evidence": "LDLR, a member of the LDL receptor family, binds to apoE, yet its potential role in AD pathogenesis remains unclear.", "citation": {"db": "PubMed", "db_id": "20005821"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2960, "target": 1131, "key": "5e1e4504dd9bde9a113e67cea9f25bfd"}, {"line": 3460, "relation": "association", "evidence": "The low-density lipoprotein receptor (LDLR) has the highest affinity for apoE and plays an important role in brain cholesterol metabolism.These data suggest that increased APP expression and Abeta exposure alters microtubule function, leading to reduced transport of LDLR to the plasma membrane. Consequent deleterious effects on apoE uptake and function will have implications for AD pathogenesis and/or progression", "citation": {"db": "PubMed", "db_id": "20049331"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 2960, "target": 529, "key": "a44c2e4265a61354c3e5684d5537ab0c"}, {"line": 4660, "relation": "association", "evidence": "Low-density lipoprotein receptor (LDLR) is a major apolipoprotein E (APOE) receptor and thereby is critical to cholesterol homeostasis.We interpret these results as suggesting that SFRS13A regulates LDLR splicing efficiency and may therefore emerge as a modulator of cholesterol homeostasis.", "citation": {"db": "PubMed", "db_id": "20232416"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2960, "target": 527, "key": "c7b8fd4a2c0f0953e049950bdbd653c8"}, {"relation": "partOf", "source": 2960, "target": 933, "key": "48c64a0f51a0711d726c631ed0ec1fc1"}, {"line": 26413, "relation": "increases", "evidence": "Our in vivo studies using transgenic mice have shown that overexpression of LRP in central nervous system (CNS) neurons increases soluble brain Abeta and this increase correlates with deficits in memory.", "citation": {"db": "PubMed", "db_id": "17185504"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2960, "target": 80, "key": "6445f3731dcdc48541a8e875f74ef0d8"}, {"line": 36305, "relation": "increases", "evidence": "Cellular uptake and degradation by glial cells is one means by which ABeta¸ may be cleared from the brain. In the current study, we demonstrate that modulating levels of the low-density lipoprotein receptor (LDLR), a cell surface receptor that regulates the amount of apolipoprotein E (apoE) in the brain, altered both the uptake and degradation of ABeta¸ by astrocytes. Deletion of LDLR caused a decrease in ABeta¸ uptake, while increasing LDLR levels significantly enhanced both the uptake and clearance of ABeta¸. Increasing LDLR levels also enhanced the cellular degradation of ABeta¸ and facilitated the vesicular transport of ABeta¸ to lysosomes. Despite the fact that LDLR regulated the uptake of apoE by astrocytes, we found that the effect of LDLR on ABeta¸ uptake and clearance occurred in the absence of apoE. Finally, we provide evidence that ABeta¸ can directly bind to LDLR, suggesting an interaction between LDLR and ABeta¸ could be responsible for LDLR-mediated ABeta¸ uptake. Therefore, these results identify LDLR as a receptor that mediates ABeta¸ uptake and clearance by astrocytes, and provide evidence that increasing glial LDLR levels may promote ABeta¸ degradation within the brain", "citation": {"db": "PubMed", "db_id": "22383525"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2960, "target": 80, "key": "5e92ac35790d425ca29ac0b3d3ff1f8f"}, {"relation": "partOf", "source": 2960, "target": 1516, "key": "a98fab7c36722f33bf2990cf0c2e2603"}, {"line": 33856, "relation": "decreases", "evidence": "Furthermore, LDLR overexpression dramatically reduced A beta aggregation and enhanced A beta clearance from the brain extracellular space. ", "citation": {"db": "PubMed", "db_id": "20005821"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2960, "target": 377, "key": "8aca05f6f93edde2b14bdf222a60c1ef"}, {"relation": "partOf", "source": 2960, "target": 1387, "key": "26cdf67f6db8b2c196598e04253a1054"}, {"relation": "partOf", "source": 2960, "target": 1100, "key": "b211e1d8dceb147feb236d2469de9cb1"}, {"line": 36304, "relation": "regulates", "evidence": "Cellular uptake and degradation by glial cells is one means by which ABeta¸ may be cleared from the brain. In the current study, we demonstrate that modulating levels of the low-density lipoprotein receptor (LDLR), a cell surface receptor that regulates the amount of apolipoprotein E (apoE) in the brain, altered both the uptake and degradation of ABeta¸ by astrocytes. Deletion of LDLR caused a decrease in ABeta¸ uptake, while increasing LDLR levels significantly enhanced both the uptake and clearance of ABeta¸. Increasing LDLR levels also enhanced the cellular degradation of ABeta¸ and facilitated the vesicular transport of ABeta¸ to lysosomes. Despite the fact that LDLR regulated the uptake of apoE by astrocytes, we found that the effect of LDLR on ABeta¸ uptake and clearance occurred in the absence of apoE. Finally, we provide evidence that ABeta¸ can directly bind to LDLR, suggesting an interaction between LDLR and ABeta¸ could be responsible for LDLR-mediated ABeta¸ uptake. Therefore, these results identify LDLR as a receptor that mediates ABeta¸ uptake and clearance by astrocytes, and provide evidence that increasing glial LDLR levels may promote ABeta¸ degradation within the brain", "citation": {"db": "PubMed", "db_id": "22383525"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2960, "target": 2312, "key": "593144c76f2b766424c59be152e2af05"}, {"line": 36316, "relation": "increases", "evidence": "Cellular uptake and degradation by glial cells is one means by which ABeta¸ may be cleared from the brain. In the current study, we demonstrate that modulating levels of the low-density lipoprotein receptor (LDLR), a cell surface receptor that regulates the amount of apolipoprotein E (apoE) in the brain, altered both the uptake and degradation of ABeta¸ by astrocytes. Deletion of LDLR caused a decrease in ABeta¸ uptake, while increasing LDLR levels significantly enhanced both the uptake and clearance of ABeta¸. Increasing LDLR levels also enhanced the cellular degradation of ABeta¸ and facilitated the vesicular transport of ABeta¸ to lysosomes. Despite the fact that LDLR regulated the uptake of apoE by astrocytes, we found that the effect of LDLR on ABeta¸ uptake and clearance occurred in the absence of apoE. Finally, we provide evidence that ABeta¸ can directly bind to LDLR, suggesting an interaction between LDLR and ABeta¸ could be responsible for LDLR-mediated ABeta¸ uptake. Therefore, these results identify LDLR as a receptor that mediates ABeta¸ uptake and clearance by astrocytes, and provide evidence that increasing glial LDLR levels may promote ABeta¸ degradation within the brain", "citation": {"db": "PubMed", "db_id": "22383525"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "COP-Coated Vesicles"}, "toLoc": {"namespace": "MESH", "name": "Lysosomes"}}}, "source": 2960, "target": 2328, "key": "cb6da499e6bf4a2c4704d2c2af8c3aa2"}, {"relation": "partOf", "source": 2960, "target": 1233, "key": "8ea630fa84b7b0c832576e2b3dc9f479"}, {"relation": "partOf", "source": 2960, "target": 1517, "key": "7b302bc539226d837169b1dee0cd4f19"}, {"line": 2224, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 528, "target": 2960, "key": "37cd11bb89ea499aa42e7cf46cf834f2"}, {"line": 2225, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 651, "target": 2960, "key": "e176f305ba8646785f487eefc0246d6a"}, {"line": 18704, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 651, "target": 3060, "key": "814033bdd22076d33828b3ede320329b"}, {"line": 2228, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3526, "target": 2312, "key": "3b6e1bda10398e7a82164c011deaa56a"}, {"line": 2424, "relation": "association", "evidence": "In the present study, we tested whether FE65 can interact with another ApoE receptor, VLDLR, thereby altering its trafficking and processing, and whether FE65 can serve as a linker between APP and VLDLR", "citation": {"db": "PubMed", "db_id": "22429478"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3526, "target": 2299, "key": "42581531f5746e183760f49ffc6d1b8f"}, {"relation": "partOf", "source": 3526, "target": 1141, "key": "7925ff54e87a598921fb859b1df68292"}, {"relation": "partOf", "source": 3526, "target": 1626, "key": "bdf07402b36c0b18a8edf424764931a0"}, {"relation": "hasVariant", "source": 3526, "target": 3527, "key": "7f46ccc4c8cb7ab26d1f0596168f7c32"}, {"relation": "partOf", "source": 3526, "target": 1223, "key": "c64b28d34961398c98bee1a03789fb0a"}, {"line": 2229, "relation": "association", "evidence": "Low-density lipoprotein receptors (LDLRs) are type I integral membrane proteins currently composed of 10 members. LDLR possesses a wide array of ligands with different functions from cellular cholesterol uptake in the liver to cell specification and neuronal positioning during embryogenesis. ApoE, complexed in HDL and VLDL, is the major ligand for these receptors, and, being the e4 allele of APOE gene, the most relevant risk for the development of late-onset AD, several studies support a role for these receptors in the pathogenesis of AD. Although the molecular mechanisms underlying the association between ApoE alleles and AD development have not yet been completely elucidated, ApoE, along with its receptor-LDLR and LDL-receptors related protein (LRP), was reported to modulate Abeta production and clearance.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2821, "target": 2312, "key": "d06c625d74dfc4acc0a0e32a32631727"}, {"line": 37277, "relation": "increases", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2617, "target": 743, "key": "5bda45269e078dd42b92d16f084431b1"}, {"line": 37305, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2617, "target": 743, "key": "d278f63827033ef10c3c5645dbb600a4"}, {"relation": "hasVariant", "source": 2616, "target": 2617, "key": "5fe7b7b31799453f2d0b397caf851d83"}, {"line": 3831, "relation": "decreases", "evidence": "Dab1 significantly decreased the amount of APP bound to LRP and the level of secreted APP and APP-CTF in LRP expressing cells, unlike Fe65. It implies that overexpression of Dab1 diminish LRP-APP complex formation, resulting in altered APP processing.", "citation": {"db": "PubMed", "db_id": "20568118"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2616, "target": 1184, "key": "81eb863e987956f43be634ed7d22d98c"}, {"relation": "partOf", "source": 2616, "target": 1117, "key": "5081aca35144721cfb8559fb489020a5"}, {"relation": "partOf", "source": 2616, "target": 1163, "key": "25ec20a601327017df15c67e0a6bc7bc"}, {"relation": "partOf", "source": 2616, "target": 1388, "key": "3a7597afd587121481e22381583a2fe1"}, {"relation": "partOf", "source": 2616, "target": 1387, "key": "456158fb00c87469f6b30797ee1b6934"}, {"line": 37278, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2616, "target": 2315, "key": "ba22bfeb2fe14352eb95a057729ce397"}, {"line": 37840, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2616, "target": 2136, "key": "80de56011de49b51249f5bba402f3a76"}, {"line": 38446, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2616, "target": 3563, "key": "80ede4e9ae18b3c2e7d8fb5a275e3832"}, {"relation": "partOf", "source": 2616, "target": 1389, "key": "009c5b900bb61f5a6ecb9d4c70033e49"}, {"line": 2264, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Calcium-dependent signal transduction": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2187, "target": 492, "key": "76876ea252aa61434cc8ca04bf82c9a8"}, {"line": 2690, "relation": "increases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER. This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2187, "target": 2338, "key": "3a4e1d71c44d5026d9d7750a775659d3"}, {"line": 2693, "relation": "decreases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER. This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2187, "target": 1099, "key": "284b19d1b4f1b1eb86ee788374a8781a"}, {"line": 2694, "relation": "increases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER. This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2187, "target": 2328, "key": "a30ad7efea163cc17916ed27dd1c71b9"}, {"line": 4059, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2187, "target": 645, "key": "7ab0b797992fd34e736a2de04743d78f"}, {"line": 4480, "relation": "decreases", "evidence": "Soluble Abeta oligomers can rapidly disrupt synaptic memory mechanisms at extremely low concentrations via stress-activated kinases and oxidative/nitrosative stress mediators.", "citation": {"db": "PubMed", "db_id": "17956317"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2187, "target": 820, "key": "7c339c4ac8f26f8c5814aa9b4f1208b2"}, {"line": 18395, "relation": "increases", "evidence": "These findings raise the possibility that the JNK pathway may also contribute to Abeta-dependent death in AD patients.", "citation": {"db": "PubMed", "db_id": "11567045"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2187, "target": 80, "key": "1344c3c9923746b62dd1cf7c722c2328"}, {"line": 35273, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2187, "target": 715, "key": "2861cee974f7e282d45cc6476dadf1ca"}, {"relation": "hasVariant", "source": 2187, "target": 2188, "key": "0c47463f75d024ccd5560e0262472dfe"}, {"line": 37246, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2187, "target": 2672, "key": "6ed135885803570128f791a594374da6"}, {"line": 37247, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2187, "target": 2163, "key": "a93233a4c8689a303a310c6db918208c"}, {"line": 2259, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 876, "target": 492, "key": "e9e20aa77029fc69e6a047cf0d92a4bb"}, {"line": 8338, "relation": "increases", "evidence": "Other insulin-related mechanisms have been implicated in normal hippocampal functioning in addition to insulin-receptormodulation. For example, insulin may modulate long-term potentiation (LTP), a molecular model of learning. LTP can be induced by N-methyl-D-aspartate (NMDA) receptor activation, thus increasing neuronal Ca2+ influx. Elevated intracellular Ca2+ level presumably activates α-calcium-calmodulin-dependent kinase II (αCaMKII) and other Ca2+-dependent enzymes, eventuating in stronger synaptic associations between neurons. Insulin may influence several constituents of this LTP cascade. For example, insulin promoted the cellmembrane expression of NMDA receptors,[60] which may increase the likelihood of LTP induction.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 876, "target": 839, "key": "e7b1a7bc27a07e027e18c7eecbca5188"}, {"line": 2259, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 492, "target": 876, "key": "331d21ae2e87c2d9b6386e1d3b04631d"}, {"line": 2262, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 492, "target": 2173, "key": "c9afc7d2cc2e426251958bc6dda5920b"}, {"line": 2264, "relation": "association", "evidence": "ApoE was reported to induce Dab1 phosphorylation and ERK1/2 activation and JNK inhibition via LRPs. This pathway depends on the presence of Ca++ influx through the NMDA receptor, but it is independent of Dab1.Overall these data indicate a likely involvement of LRP8 as modulator of AbetaPP processing, by affecting its endocytic trafficking and the proportion of AbetaPP present in lipid rafts. These events may have consequence on the gamma-secretase-mediated cleavage of AbetaPP and on its neurodegeneration-related signaling activity.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Calcium-dependent signal transduction": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 492, "target": 2187, "key": "f3810be1dcf37441b5218aa75754f65e"}, {"line": 3888, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 492, "target": 760, "key": "3a2ec08eb1f4301ba702a087f6c6d591"}, {"line": 3890, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 492, "target": 739, "key": "60b5195d52aa795ceb1e1645e0c8afbe"}, {"line": 4776, "relation": "increases", "evidence": "The induction of long-term potentiation at CA3-CA1 synapses is caused by an N-methyl-d-aspartate (NMDA) receptordependent accumulation of intracellular Ca(2+), followed by Src family kinase activation and a positive feedback enhancement of NMDA receptors (NMDARs). Nevertheless, the amplitude of baseline transmission remains remarkably constant even though low frequency stimulation is also associated with an NMDAR-dependent influx of Ca(2+) into dendritic spines. We show here that an interaction between C-terminal Src kinase (Csk) and NMDARs controls the Src-dependent regulation of NMDAR activity. Csk associates with the NMDAR signaling complex in the adult brain, inhibiting the Src-dependent potentiation of NMDARs in CA1 neurons and attenuating the Src-dependent induction of long-term potentiation. Csk associates directly with Src-phosphorylated NR2 subunits in vitro. An inhibitory antibody for Csk disrupts this physical association, potentiates NMDAR mediated excitatory postsynaptic currents, and induces long-term potentiation at CA3-CA1 synapses. Thus, Csk serves to maintain the constancy of baseline excitatory synaptic transmission by inhibiting Src kinase-dependent synaptic plasticity in the hippocampus", "citation": {"db": "PubMed", "db_id": "18445593"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 492, "target": 2785, "key": "08affd7033c62d8698c20a373d08539d"}, {"line": 4789, "relation": "directlyIncreases", "evidence": "Moreover, we demonstrate that neuronal activity upregulates CRP1 expression in hippocampal neurons via Ca²+ influx after depolarization. Ca²+/calmodulin-dependent protein kinase IV (CaMKIV) and cAMP response element binding protein mediate the Ca²+-induced upregulation of CRP1 expression. Furthermore, CRP1 is required for the dendritic growth induced by Ca+? influx or CaMKIV. Together, these data are the first to demonstrate a role for CRP1 in dendritic growth.", "citation": {"db": "PubMed", "db_id": "22090504"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Calcium-dependent signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 492, "target": 2502, "key": "f7fc439ba5544fb5e0e49e6ea5a76812"}, {"line": 8347, "relation": "increases", "evidence": "Other insulin-related mechanisms have been implicated in normal hippocampal functioning in addition to insulin-receptormodulation. For example, insulin may modulate long-term potentiation (LTP), a molecular model of learning. LTP can be induced by N-methyl-D-aspartate (NMDA) receptor activation, thus increasing neuronal Ca2+ influx. Elevated intracellular Ca2+ level presumably activates α-calcium-calmodulin-dependent kinase II (αCaMKII) and other Ca2+-dependent enzymes, eventuating in stronger synaptic associations between neurons. Insulin may influence several constituents of this LTP cascade. For example, insulin promoted the cellmembrane expression of NMDA receptors,[60] which may increase the likelihood of LTP induction.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 492, "target": 2425, "key": "2e24e316156f4e470c075c42c9311ddd"}, {"line": 8350, "relation": "association", "evidence": "Other insulin-related mechanisms have been implicated in normal hippocampal functioning in addition to insulin-receptormodulation. For example, insulin may modulate long-term potentiation (LTP), a molecular model of learning. LTP can be induced by N-methyl-D-aspartate (NMDA) receptor activation, thus increasing neuronal Ca2+ influx. Elevated intracellular Ca2+ level presumably activates α-calcium-calmodulin-dependent kinase II (αCaMKII) and other Ca2+-dependent enzymes, eventuating in stronger synaptic associations between neurons. Insulin may influence several constituents of this LTP cascade. For example, insulin promoted the cellmembrane expression of NMDA receptors,[60] which may increase the likelihood of LTP induction.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 492, "target": 523, "key": "10b8173d1565cbed5a05f98d4c63d493"}, {"line": 37238, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 492, "target": 94, "key": "021539ec7fa8d473c218751892db1e46"}, {"line": 2273, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 641, "target": 3306, "key": "02f4ec6f600af7e115dea89d9393c0f4"}, {"line": 2274, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2779, "target": 2981, "key": "9ccb169bb6299736c469235175da2670"}, {"relation": "hasVariant", "source": 2779, "target": 2780, "key": "879e8d6c0c550ab261c3cde57b07cdfb"}, {"relation": "isA", "source": 2779, "target": 2779, "key": "212198246d5e0890568078883505849f"}, {"line": 13641, "relation": "increases", "evidence": "In conclusion, nefiracetam enhances NMDA-receptor function through stimulation of its glycine binding site and nefiracetam-induced CaMKII activation likely contributes to improvement of cognition, learning, and memory.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2779, "target": 812, "key": "441a619cf1a52da21c6a581c66598be1"}, {"line": 13645, "relation": "increases", "evidence": "In conclusion, nefiracetam enhances NMDA-receptor function through stimulation of its glycine binding site and nefiracetam-induced CaMKII activation likely contributes to improvement of cognition, learning, and memory.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2779, "target": 818, "key": "6b119b9f52d2236108fe22af9a1e76f3"}, {"line": 13649, "relation": "increases", "evidence": "In conclusion, nefiracetam enhances NMDA-receptor function through stimulation of its glycine binding site and nefiracetam-induced CaMKII activation likely contributes to improvement of cognition, learning, and memory.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2779, "target": 820, "key": "921d2ce0ad517a4f2daf9b2a26f88129"}, {"line": 36536, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2779, "target": 2252, "key": "7ed052adb4f1c7314a7430c1bb3d6200"}, {"line": 36537, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2779, "target": 2249, "key": "80094fb099434da99be102db24a9106f"}, {"line": 36538, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2779, "target": 2250, "key": "1efdd55f0a838f0d172ab0b27a5be585"}, {"line": 36560, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2779, "target": 80, "key": "59e6a30ac641cfd0f0fc557147963cb8"}, {"line": 36588, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2779, "target": 80, "key": "9d884ac690a7b55b7dac8ae8ee5b2d3b"}, {"line": 36621, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2779, "target": 384, "key": "4110ad9aca10f1b67d2d8253db617490"}, {"line": 37159, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2779, "target": 384, "key": "dbd19b8b89459cd3e14fc01cb7aa7bb2"}, {"line": 37143, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2779, "target": 2328, "key": "984ca865d93d67b7062e6c738e8bb36e"}, {"line": 37160, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2779, "target": 399, "key": "c5e6fb096eae47d19d13f30cdfa2f705"}, {"line": 37603, "relation": "association", "evidence": "APP is expressed pre- and postsynaptically and promotes synapse formation via trans-synaptic interactions of its extracellular domains. Full-length APP also may promote dendritic spine formation as well as surface expression of GluA2-containing AMPA receptors and GluN2B-containing NMDA receptors. Enhanced synaptic activity drives APP processing via the amyloidogenic ß -secretase pathway, leading to subsequent spine loss and downregulation of glutamate receptors in a negative feedback loop.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2779, "target": 787, "key": "420fd8a3f51fee7ac6421672c9a4c5c6"}, {"line": 2275, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2776, "target": 2981, "key": "d6613659f30aca8160c42f1ce22cca69"}, {"relation": "hasVariant", "source": 2776, "target": 2777, "key": "0e37717446d011b58bf626262496bf29"}, {"relation": "isA", "source": 2776, "target": 2779, "key": "95c1feaaafede0ad94a68c035d654b4d"}, {"relation": "partOf", "source": 2776, "target": 1393, "key": "8e92bf660468e8dd263a1738f2c61695"}, {"line": 36532, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2776, "target": 2252, "key": "2b58e652e348fae373dcfcbe14971b8c"}, {"line": 36533, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2776, "target": 2249, "key": "0ad13d958daca4223c76f0c351ccb0f6"}, {"line": 36534, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2776, "target": 2250, "key": "2d366c623459a3f134a1e0dace225b7f"}, {"line": 36559, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2776, "target": 80, "key": "8c47a68e4cd592bd26be9ad3b3a49a37"}, {"line": 36587, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2776, "target": 80, "key": "2ba4349ff703992dc8382ef0a32be6d6"}, {"line": 36619, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2776, "target": 384, "key": "55312e3b8e09c26db648a8182f73cd71"}, {"line": 37157, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2776, "target": 384, "key": "3cc4cd5d103d953b8002028b9ceb6554"}, {"line": 37142, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2776, "target": 2328, "key": "d1dc02a22132f1ff08a593680f9e44bc"}, {"line": 37158, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2776, "target": 399, "key": "098bf3c3d7780b1596e81297959ca0c8"}, {"relation": "hasVariant", "source": 2776, "target": 2778, "key": "10b00429107a268a9bfbe51d723910b4"}, {"relation": "partOf", "source": 2776, "target": 1682, "key": "dee1918ff2bb94415d57b32acbc8ebfa"}, {"line": 2276, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 738, "target": 2981, "key": "60e2cf14080b535d02ccc9d419beec87"}, {"line": 37518, "relation": "association", "evidence": "Moreover, gain- or loss-of-function studies with either intraventricular APPsa infusion, down-regulation by antibody infusion or pharmacological inhibition of a-secretase coherently showed a function for APPsa in spatial memory and for LTP", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 738, "target": 2315, "key": "aa69cfacb31df1095d45a1aae1f48fd7"}, {"line": 2279, "relation": "increases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 1205, "target": 2315, "key": "5a3c1920249696c42d707ddd0ce89719"}, {"line": 2293, "relation": "association", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 702, "target": 3306, "key": "5fffcbe6d20661d14b5135110711df4a"}, {"line": 2291, "relation": "increases", "evidence": "Upon binding, LRP8 transduces reelin signaling during neuronal development, and recent evidence has indicated that it interacts with the NR2A and NR2B subunits of NMDA receptor, being involved in neuronal functions such as maturation of NMDA receptor composition in the hippocampus, and the regulation of long-term potentiation. Subsequently, a novel interaction between reelin and AbetaPP was discovered, leading to increase in the cell surface levels of AbetaPP and affecting AbetaPP processing and Abeta production. It was shown that reelin signaling in excitatory synapses can restore normal synaptic plasticity, which is impaired by oligomeric Abeta peptides at concentrations within the range detectable in the brains of AD patients. At high concentrations of Abeta peptides, reelin can no longer overcome the Abeta-induced functional suppression, and this condition coincides with a complete blockade of the reelin-dependent phosphorylation of NR2 subunits in NMDA receptors. This reversal requires the LRP receptor-dependent activation of tyrosine kinases of the Src family.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2195, "target": 2981, "key": "de5888b89669ed3d756a527d42604f37"}, {"line": 2302, "relation": "increases", "evidence": "Iron deposition in the brain is another important proposed mechanisms in the pathophysiology AD. Excessive iron can contribute to the formation of free radicals, leading to lipid peroxidation and neurotoxicity, which can result in cell membrane damage and cell death. Recently, it has been shown that iron concentration in AD patients brain was significantly higher than those of nondemented controls. In particular iron deposition in parietal cortex and hippocampus at the early stage of AD were positively correlated with the severity of patients cognitive impairment", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 135, "target": 170, "key": "432fb4079003991ba18aec47d63c3d30"}, {"line": 2306, "relation": "association", "evidence": "Iron deposition in the brain is another important proposed mechanisms in the pathophysiology AD. Excessive iron can contribute to the formation of free radicals, leading to lipid peroxidation and neurotoxicity, which can result in cell membrane damage and cell death. Recently, it has been shown that iron concentration in AD patients brain was significantly higher than those of nondemented controls. In particular iron deposition in parietal cortex and hippocampus at the early stage of AD were positively correlated with the severity of patients cognitive impairment", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"CellStructure": {"Centrosome": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 135, "target": 3823, "key": "bad6d1bbd423ccb9c9d94129ff6f15d9"}, {"line": 43509, "relation": "increases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 135, "target": 3812, "key": "75655558f105c93188396973d04a3978"}, {"line": 4002, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 2369, "target": 3258, "key": "11b2fc0c11344cb170276a23c3d2278d"}, {"line": 4007, "relation": "association", "evidence": "There is also evidence that PS can interact directly or indirectly with RyR and SERCA (smooth endoplasmic reticulum Ca2+-ATPase) to alter ER Ca2+ release and uptake", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Calcium-dependent signal transduction": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}, "CellStructure": {"Endoplasmic Reticulum": true}}, "source": 2369, "target": 3268, "key": "5578256aac741ee17505efebb35df3ad"}, {"line": 12278, "relation": "association", "evidence": "The endoplasmic reticulum protein sigma-1 receptors play a key role in Ca2+ signalling and cell survival, and have been shown to regulate a number of neurotransmitter systems in the brain. The selective serotonin reuptake inhibitor (SSRI) fluvoxamine is a very potent agonist at sigma-1 receptors, which are also implicated in cognition and the pathophysiology of neuropsychiatric diseases. A study using the selective sigma-1 receptor agonist [11C]-SA4503 and positron emission tomography demonstrated that fluvoxamine binds to sigma-1 receptors in living human brain at therapeutic doses, suggesting that sigma-1 receptors might play a role in the mechanism of action of fluvoxamine ", "citation": {"db": "PubMed", "db_id": "20148109"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "source": 495, "target": 3363, "key": "626f25a8c9c4321294f18becf54c38b4"}, {"line": 35815, "relation": "association", "evidence": "The state of tau phosphorylation and proteolysis can be regulated by calcium-dependent mechanisms. CaMKII can phosphorylate tau [189]. Cyclin-dependent kinase 5 (cdk5), another kinase involved in tau phosphorylation [190], is indirectly activated by the calcium-activated protease calpain. Indeed, cdk5 has to be associated with its regulatory subunit, p35 to be activated. Conversion of p35 to p25 deregulates cdk5 activity, resulting in an increased cdk5 kinase activity [191]. Calpain cleaves p35 into p25, and thus controls cdk5 activation [192]. Furthermore, tau is dephosphorylated by the calcium/calmodulin-dependent phosphatase, calcineurin [193]. Calpain was also proposed to directly participate in tau proteolysis and degradation", "citation": {"db": "PubMed", "db_id": "19419557"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Calcium-dependent signal transduction": true, "Tau protein subgraph": true}}, "source": 495, "target": 3015, "key": "87f0c3a18923d38141ae99fddf0ed540"}, {"line": 36960, "relation": "association", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 495, "target": 2159, "key": "9e803fccdab85b92e4f75bf54a9a9eaa"}, {"line": 36961, "relation": "association", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 495, "target": 2173, "key": "31c6fddef8647a9db51a3fa1fa134c68"}, {"line": 36967, "relation": "decreases", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 495, "target": 478, "key": "caf5e002882b93aea161bfb2b40e02f6"}, {"line": 36968, "relation": "increases", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 495, "target": 635, "key": "712f82f302ada786a89b2acce4f1efc5"}, {"line": 36976, "relation": "increases", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 495, "target": 2149, "key": "dc15c670b1a4507c1135adfd68b16c2d"}, {"line": 36977, "relation": "increases", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 495, "target": 2153, "key": "fb5b1430b1f508f68573d933299d0440"}, {"line": 2371, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 628, "target": 80, "key": "cd4cda69a87c87d35cb6fe98d341ffd6"}, {"line": 2378, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 628, "target": 94, "key": "eb9d54dc523f277e66dfc553fa535ffe"}, {"line": 2379, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 628, "target": 2208, "key": "88fdf891d71bab85b2bb965943abc817"}, {"line": 2380, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 628, "target": 2209, "key": "4f81748a54cf08b2d0a8d40a9f36ac90"}, {"line": 2383, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 628, "target": 2145, "key": "1c5ff248786de20744785979444d2b74"}, {"line": 2384, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 628, "target": 2998, "key": "10d2da521900f2cfca3eeb32a7b1aaca"}, {"line": 2389, "relation": "increases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 628, "target": 636, "key": "4e3540e16e664245749a1d4b5cb80c4a"}, {"line": 21242, "relation": "association", "evidence": "Nitric oxide (NO), which is produced by oxidation of L-arginine to L-citrulline in a process catalyzed by different isoforms of nitric oxide synthase (NOS), exhibits diverse roles in several physiological processes, including neurotransmission, blood pressure regulation and immunological defense mechanisms.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 846, "target": 156, "key": "2d4c9e6d10fe12d7a7cae0484752be9d"}, {"line": 15155, "relation": "decreases", "evidence": "Downregulation of extracellular signal-regulated kinase 1/2 activity by calmodulin KII modulates p21Cip1 levels and survival of immortalized lymphocytes from Alzheimer's disease patients.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2145, "target": 2173, "key": "fd87582dbd86182162d8059f8de4de34"}, {"line": 24128, "relation": "increases", "evidence": "Taken together, the stimulation of CaMKII activity in the hippocampus is essential for rivastigmine-induced memory improvement in OBX mice.", "citation": {"db": "PubMed", "db_id": "24164423"}, "subject": {"modifier": "Activity"}, "source": 2145, "target": 820, "key": "271971c65168ea4754b19d1e0a57f77d"}, {"line": 35270, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2145, "target": 715, "key": "832c630f7d0867c28b289784eb2f7286"}, {"relation": "partOf", "source": 2145, "target": 1669, "key": "33521b0e4c3efd97c81f3d5af4437802"}, {"line": 2390, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease. This chapter gives an overview of calcium signaling in different systems, specifically neurons, the functioning of pre- and post-synaptic signaling, and how their deregulation influences pathology development in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 636, "target": 820, "key": "a6f94bfc2c87500e44e8d9e90cba6333"}, {"line": 2403, "relation": "association", "evidence": "Alzheimer's disease (AD), one of the major causes of disability and mortality in Western societies, is a progressive age-related neurodegenerative disorder. Increasing evidence suggests that the etiology of AD may involve disruptions of zinc (Zn) homeostasis. This review discusses current evidence supporting a potential role of Zn and zinc transporters (ZnTs) in processing of the amyloid beta protein precursor (APP) and amyloid beta (Abeta) peptide generation and aggregation.", "citation": {"db": "PubMed", "db_id": "22447723"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Degradation"}, "source": 3378, "target": 2315, "key": "fdf0e7b0016a6e9668709ef55dc80702"}, {"line": 2416, "relation": "association", "evidence": "Several studies found that FE65, a cytoplasmic adaptor protein, interacts with APP and LRP1, altering the trafficking and processing of APP. We have previously shown that FE65 interacts with the ApoE receptor, ApoER2, altering its trafficking and processing. Interestingly, it has been shown that FE65 can act as a linker between APP and LRP1 or ApoER2.", "citation": {"db": "PubMed", "db_id": "22429478"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 1094, "target": 2315, "key": "24ffddd1517d0ae2623b11960f5aae44"}, {"line": 9082, "relation": "increases", "evidence": "APP (amyloid precursor protein) and LRP1 (low-density lipoprotein receptor-related protein 1) have been implicated in the pathogenesis of AD (Alzheimer's disease). They are functionally linked by Fe65, a PTB (phosphotyrosine-binding)-domain-containing adaptor protein that binds to intracellular NPxY-motifs of APP and LRP1, thereby influencing expression levels, cellular trafficking and processing. Additionally, Fe65 has been reported to mediate nuclear signalling in combination with intracellular domains of APP and LRP1. We have previously identified another adaptor protein, GULP1 (engulfment adaptor PTB-domain-containing 1). In the present study we characterize and compare nuclear trafficking and transactivation of GULP1 and Fe65 together with APP and LRP1 and report differential nuclear trafficking of adaptors when APP or LRP1 are co-expressed. The observed effects were additionally supported by a reporter-plasmid-based transactivation assay. The results from the present study indicate that Fe65 might have signalling properties together with APP and LRP1, whereas GULP1 only mediates LRP1 transactivation.", "citation": {"db": "PubMed", "db_id": "23167255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1094, "target": 2315, "key": "1bd0f6672059ffc14527df8d671ef41f"}, {"line": 37856, "relation": "increases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 1094, "target": 2315, "key": "10f1b407880635df9c8fedeabfb2698d"}, {"line": 4179, "relation": "increases", "evidence": "Neuronal Fe65 is a central adapter for the intracellular protein network of Alzheimer's disease related amyloid precursor protein (APP). It contains a unique tandem array of phosphotyrosine-binding (PTB) domains that recognize NPXY internalization motifs present in the intracellular domains of APP (AICD) and the low-density lipoprotein receptor-related protein LRP1 (LICD). The ternary APP/Fe65/LRP1 complex is an important mediator of APP processing and affects Abeta-amyloid peptide production. Here we dissect by biochemical and biophysical methods the direct interactions within the ternary complex and reveal a phosphorylation-dependent insulin receptor substrate (IRS-) like interaction of the distal NPVY(4507) motif of LICD with Fe65-PTB1.", "citation": {"db": "PubMed", "db_id": "21968187"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 1094, "target": 80, "key": "5ac513c99f6bf474bbe52322d945329f"}, {"line": 4180, "relation": "increases", "evidence": "Neuronal Fe65 is a central adapter for the intracellular protein network of Alzheimer's disease related amyloid precursor protein (APP). It contains a unique tandem array of phosphotyrosine-binding (PTB) domains that recognize NPXY internalization motifs present in the intracellular domains of APP (AICD) and the low-density lipoprotein receptor-related protein LRP1 (LICD). The ternary APP/Fe65/LRP1 complex is an important mediator of APP processing and affects Abeta-amyloid peptide production. Here we dissect by biochemical and biophysical methods the direct interactions within the ternary complex and reveal a phosphorylation-dependent insulin receptor substrate (IRS-) like interaction of the distal NPVY(4507) motif of LICD with Fe65-PTB1.", "citation": {"db": "PubMed", "db_id": "21968187"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 1094, "target": 4096, "key": "053f72b912d81b16affd2ca614483762"}, {"line": 2418, "relation": "association", "evidence": "Several studies found that FE65, a cytoplasmic adaptor protein, interacts with APP and LRP1, altering the trafficking and processing of APP. We have previously shown that FE65 interacts with the ApoE receptor, ApoER2, altering its trafficking and processing. Interestingly, it has been shown that FE65 can act as a linker between APP and LRP1 or ApoER2.", "citation": {"db": "PubMed", "db_id": "22429478"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 1102, "target": 2981, "key": "d43407855ae78e890bc1b99c739ea9e6"}, {"line": 2435, "relation": "association", "evidence": "The ubiquitin-proteasome pathway is a major protein degradation pathway whose dysfunction is now widely accepted as a cause of neurodegenerative diseases, including Alzheimer's disease. Here we demonstrate that the F-box and leucine rich repeat protein2 (FBL2), a component of the E3 ubiquitin ligase complex, regulates amyloid precursor protein (APP) metabolism through APP ubiquitination.", "citation": {"db": "PubMed", "db_id": "22399757"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 768, "target": 3823, "key": "0463922e651617d8baefe564312b1ea1"}, {"line": 2437, "relation": "increases", "evidence": "The ubiquitin-proteasome pathway is a major protein degradation pathway whose dysfunction is now widely accepted as a cause of neurodegenerative diseases, including Alzheimer's disease. Here we demonstrate that the F-box and leucine rich repeat protein2 (FBL2), a component of the E3 ubiquitin ligase complex, regulates amyloid precursor protein (APP) metabolism through APP ubiquitination.", "citation": {"db": "PubMed", "db_id": "22399757"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2691, "target": 2341, "key": "d97832e4bafec799f611c1e519f378df"}, {"line": 2439, "relation": "increases", "evidence": "The ubiquitin-proteasome pathway is a major protein degradation pathway whose dysfunction is now widely accepted as a cause of neurodegenerative diseases, including Alzheimer's disease. Here we demonstrate that the F-box and leucine rich repeat protein2 (FBL2), a component of the E3 ubiquitin ligase complex, regulates amyloid precursor protein (APP) metabolism through APP ubiquitination.", "citation": {"db": "PubMed", "db_id": "22399757"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2341, "target": 2315, "key": "ce09e1b501e472657517f34c57572879"}, {"line": 2450, "relation": "positiveCorrelation", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2341, "target": 2315, "key": "245745eafba4137b1a56e29b4fd54288"}, {"line": 2452, "relation": "decreases", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2341, "target": 2328, "key": "635ceed98524c875013df9223c4e51b1"}, {"line": 38270, "relation": "decreases", "evidence": "the evidence that PPARgamma stimulates the ubiquitination of APP supports the fact that the Abeta-lowering effect of PPARgamma is due to the proteasome-mediated degradation of APP. Another issue in the present study is the finding that PPARgamma, by decreasing Abeta secretion, protects the cells against H2O2-mediated necrosis", "citation": {"db": "PubMed", "db_id": "15946122"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2341, "target": 2328, "key": "da3671d23da708d411b5d39743ad711a"}, {"line": 2505, "relation": "increases", "evidence": "A possible model for involvement of HRD1 in Alzheimer's disease (AD). Suppression of AD: Upregulation of HRD1 promotes the ubiquitination and degradation of APP in the ERAD pathway, resulting in decreased Abeta generation.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2341, "target": 80, "key": "3cab072294e9d6de654097afe8c1ec4e"}, {"line": 2448, "relation": "increases", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 3439, "target": 705, "key": "afc6cd8acfc73955ea8eeecdee4151b9"}, {"line": 2449, "relation": "increases", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 3439, "target": 2341, "key": "5b744a5bcb0d63dd554713e793517dbb"}, {"line": 2500, "relation": "increases", "evidence": "A possible model for involvement of HRD1 in Alzheimer's disease (AD). Suppression of AD: Upregulation of HRD1 promotes the ubiquitination and degradation of APP in the ERAD pathway, resulting in decreased Abeta generation.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3439, "target": 2341, "key": "230619cebc5f1f85e5f3e9b331e29e61"}, {"line": 2454, "relation": "negativeCorrelation", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3439, "target": 3823, "key": "4c7e364485fe466e43987bce23595c81"}, {"line": 2544, "relation": "association", "evidence": "The pathological importance of Abeta as the initiator of AD is well defined; however, there is a significant minority of people with high Abeta levels who are hardly affected by AD (45). This observation suggests that a critical turning point exists in the onset of AD after increased generation of Abeta. As discussed in this Forum issue, many novel factors, such as TEK/Tie2 (48), WAVE (49, 50), CRMP2 (49, 50), and HRD1, could play crucial roles in the onset or progression of AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3439, "target": 3823, "key": "4dc63f0f5a2199f57bebda38fc377634"}, {"line": 2455, "relation": "negativeCorrelation", "evidence": "Endoplasmic reticulum (ER)-associated degradation (ERAD) is a protective mechanism against ER stress in which unfolded proteins accumulated in the ER are selectively transported to the cytosol for degradation by the ubiquitin-proteasome system. We cloned the novel ubiquitin ligase HRD1, which is involved in ERAD, and showed that HRD1 promoted amyloid precursor protein (APP) ubiquitination and degradation, resulting in decreased generation of amyloid Abeta (Abeta). In addition, suppression of HRD1 expression caused APP accumulation and promoted Abeta generation associated with ER stress and apoptotic process. Interestingly, HRD1 levels were significantly decreased in the cerebral cortex of patients with Alzheimer's disease (AD), and the brains of these patients experienced ER stress. Our recent study revealed that this decrease in HRD1 was due to its insolubilization; however, controversy persists about whether the decrease in HRD1 protein promotes Abeta generation or whether Abeta neurotoxicity causes the decrease in HRD1 protein levels. Here, we review current findings on the mechanism of HRD1 protein loss in the AD brain and the involvement of HRD1 in the pathogenesis of AD. Furthermore, we propose that HRD1 may be a target for novel AD therapeutics.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3439, "target": 2328, "key": "54821fc5837a63109e35d505601ce267"}, {"relation": "partOf", "source": 3439, "target": 1217, "key": "bf4a9d95cbf9c076369e218eea9c8bcf"}, {"line": 28732, "relation": "increases", "evidence": "These results suggest that the breakdown of HRD1-mediated ERAD causes Abeta generation and ER stress, possibly linked to AD.", "citation": {"db": "PubMed", "db_id": "20237263"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 3439, "target": 835, "key": "ddeb5c70aec81b724d7b1e44e2152d4b"}, {"line": 4098, "relation": "association", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 2678, "target": 771, "key": "eb3edca2f9c05b44fa1494a3b96369b3"}, {"relation": "partOf", "source": 2678, "target": 1414, "key": "340df0dcf163ba84f024f055b1605b46"}, {"relation": "hasVariant", "source": 2678, "target": 2679, "key": "8a9e24c1f20926940e6dfa1ce331c79a"}, {"line": 4100, "relation": "association", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 2367, "target": 771, "key": "b704fa7de12a7bb98e22f12557ea3f17"}, {"relation": "partOf", "source": 2367, "target": 1259, "key": "b517eefc216da7f53b0a544b059bc6e7"}, {"relation": "partOf", "source": 2367, "target": 1258, "key": "de9d7ceb1e3994a4754f1c4390741905"}, {"line": 2488, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2664, "target": 2661, "key": "3e1c923827ddb8418fbfc08e942dd348"}, {"line": 4099, "relation": "association", "evidence": "The mechanisms by which mutant PS1 affects the ER stress response are attributed to the inhibited activation of ER stress transducers such as IRE1, PERK and ATF6", "citation": {"db": "PubMed", "db_id": "15363492"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 2664, "target": 771, "key": "0d8cdba47f88ea45c4f93bfc70ea004a"}, {"relation": "hasVariant", "source": 2664, "target": 2665, "key": "931806fab41d8b7a8e29892e5366b1a1"}, {"relation": "partOf", "source": 2664, "target": 1411, "key": "0761976abedc14d55e6cc2f3c81a24ce"}, {"line": 2489, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2661, "target": 2375, "key": "c1d24f562360e3960efcb3933e5c940b"}, {"line": 2490, "relation": "increases", "evidence": "gene mutation of presenilin, which is a component of the gamma-secretase complex and is associated with familial AD, results in an attenuation of ER chaperone expression during ER stress. Mutations in presenilin inhibit activation of IRE1, ATF6, and PERK, all of which act as signal transducers of ER stress in the ER membrane (for details, see the article by Hosoi and Ozawa in this Forum Minireview series: Ref. 14), thereby making neurons vulnerable to ER stress. In addition, nitric oxide–induced S-nitrosylation of ER chaperone protein disulfide isomerase (PDI) inhibits its enzymatic activity, leading to ER stress–induced neuronal death. Interestingly, S-nitrosylation of PDI has been observed in the brain of sporadic AD and PD patients. ER stress can also cause AD onset. For example, eIF2a phosphorylation, which is induced by eIF2a kinase PERK, elevates BACE1 (Abeta-secretase) levels and causes increased Abeta production (17). Furthermore, Abeta has been reported to induce ER stress, leading to activation of ER stress–specific initiator caspases, including mouse caspase- 12 and human caspase-4. In conclusion, because postmortem AD patient brain samples exhibit activated UPR markers, including phosphorylated PERK, eIF2a, and IRE1, ER stress probably plays a key role in AD pathogenesis as a cause or consequence", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2661, "target": 80, "key": "67d7da7d4772f778ed61e14f208b6fdd"}, {"line": 21881, "relation": "negativeCorrelation", "evidence": "5-LOX inhibition induces eIF2α and PERK (protein kinase R-like extracellular signal-regulated kinase) phosphorylation, and HSP90 and ATF4 levels.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Subgraph": {"Eicosanoids signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2661, "target": 2288, "key": "2346eeb0149b28d4ed3b9038ea696d9b"}, {"relation": "hasVariant", "source": 2660, "target": 2661, "key": "fb0ca23c5eb91f1dc1f35560286cc90e"}, {"line": 2530, "relation": "association", "evidence": "The pathological importance of Abeta as the initiator of AD is well defined; however, there is a significant minority of people with high Abeta levels who are hardly affected by AD (45). This observation suggests that a critical turning point exists in the onset of AD after increased generation of Abeta. As discussed in this Forum issue, many novel factors, such as TEK/Tie2 (48), WAVE (49, 50), CRMP2 (49, 50), and HRD1, could play crucial roles in the onset or progression of AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3448, "target": 3823, "key": "0dff7011e52bf47f69f56a56cfe5f94d"}, {"line": 2534, "relation": "association", "evidence": "The pathological importance of Abeta as the initiator of AD is well defined; however, there is a significant minority of people with high Abeta levels who are hardly affected by AD (45). This observation suggests that a critical turning point exists in the onset of AD after increased generation of Abeta. As discussed in this Forum issue, many novel factors, such as TEK/Tie2 (48), WAVE (49, 50), CRMP2 (49, 50), and HRD1, could play crucial roles in the onset or progression of AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Very High": true}}, "source": 1723, "target": 3823, "key": "7d1f05a8b66a8c31ab1e9b04d2a56c26"}, {"relation": "partOf", "source": 3531, "target": 1723, "key": "dca93cd9d95278cacf2adf5e595f0c60"}, {"relation": "partOf", "source": 3532, "target": 1723, "key": "45a1d60702422b1cea6935a46118164e"}, {"line": 2539, "relation": "association", "evidence": "The pathological importance of Abeta as the initiator of AD is well defined; however, there is a significant minority of people with high Abeta levels who are hardly affected by AD (45). This observation suggests that a critical turning point exists in the onset of AD after increased generation of Abeta. As discussed in this Forum issue, many novel factors, such as TEK/Tie2 (48), WAVE (49, 50), CRMP2 (49, 50), and HRD1, could play crucial roles in the onset or progression of AD.", "citation": {"db": "PubMed", "db_id": "22382662"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal guidance subgraph": true}, "Confidence": {"Very High": true}}, "source": 2641, "target": 3823, "key": "7b43c38444640fbfccef37f485532e29"}, {"relation": "hasVariant", "source": 2641, "target": 2642, "key": "371f871659234955a1ec33f012a168e6"}, {"line": 9554, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"Medium": true}}, "source": 2641, "target": 540, "key": "5795d02027cd5008157763bb3d7da237"}, {"relation": "partOf", "source": 2641, "target": 1343, "key": "0120c74a9f1e401c0964f4b14f8307ea"}, {"relation": "hasVariant", "source": 2641, "target": 2643, "key": "ee3d693e1d6b05ea8347e94bbaedc655"}, {"relation": "hasVariant", "source": 2641, "target": 2644, "key": "6c2a19d7f002e90944d050811ffd09e4"}, {"relation": "partOf", "source": 2641, "target": 1398, "key": "91318fdea977d2fa516d79bc44303a10"}, {"line": 2557, "relation": "increases", "evidence": "Here we find that a mitochondrial solute carrier family protein, appoptosin, induces reactive oxygen species release and intrinsic caspase-dependent apoptotic process", "citation": {"db": "PubMed Central", "db_id": "PMC3287608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3370, "target": 170, "key": "ecfad67bb708609128b249e7a13ad25c"}, {"line": 2558, "relation": "increases", "evidence": "Here we find that a mitochondrial solute carrier family protein, appoptosin, induces reactive oxygen species release and intrinsic caspase-dependent apoptotic process", "citation": {"db": "PubMed Central", "db_id": "PMC3287608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3370, "target": 3557, "key": "5c9846e25afc6e763df905d9b3039a4a"}, {"line": 2565, "relation": "increases", "evidence": "The physiological function of appoptosin is to transport/exchange glycine/5-amino-levulinic acid across the mitochondrial membrane for heme synthesis.", "citation": {"db": "PubMed Central", "db_id": "PMC3287608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "cytoplasm"}, "toLoc": {"namespace": "GO", "name": "mitochondrion"}}}, "source": 3370, "target": 267, "key": "6752a70482813fad112db727472dd643"}, {"line": 2566, "relation": "increases", "evidence": "The physiological function of appoptosin is to transport/exchange glycine/5-amino-levulinic acid across the mitochondrial membrane for heme synthesis.", "citation": {"db": "PubMed Central", "db_id": "PMC3287608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "cytoplasm"}, "toLoc": {"namespace": "GO", "name": "mitochondrion"}}}, "source": 3370, "target": 25, "key": "648561aa20f178f147c2145cb4785945"}, {"relation": "partOf", "source": 3370, "target": 1210, "key": "b974918aad8f049edf85fead38b9ec89"}, {"line": 2589, "relation": "positiveCorrelation", "evidence": "Levels of appoptosin are upregulated in brain samples from Alzheimer's disease and infarct patients and in rodent stroke models, as well as in cells treated with Abeta-amyloid (Abeta) and glutamate. Downregulation of appoptosin prevents the cell death and caspase activation caused by glutamate or Abeta insults.", "citation": {"db": "PubMed Central", "db_id": "PMC3287608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "source": 3370, "target": 1657, "key": "94c9eda50d9b1dfa86ff62cc74e2cd7e"}, {"line": 2559, "relation": "increases", "evidence": "Here we find that a mitochondrial solute carrier family protein, appoptosin, induces reactive oxygen species release and intrinsic caspase-dependent apoptotic process", "citation": {"db": "PubMed Central", "db_id": "PMC3287608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "source": 3557, "target": 478, "key": "08ad645610cecb850cac908e54057963"}, {"line": 2572, "relation": "regulates", "evidence": "Alzheimer's Abeta-amyloid precursor protein interacts with appoptosin and modulates appoptosin-induced apoptotic process.", "citation": {"db": "PubMed Central", "db_id": "PMC3287608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "source": 1210, "target": 478, "key": "4ff2f91eff2bb5e8321888969ed36e95"}, {"line": 2589, "relation": "positiveCorrelation", "evidence": "Levels of appoptosin are upregulated in brain samples from Alzheimer's disease and infarct patients and in rodent stroke models, as well as in cells treated with Abeta-amyloid (Abeta) and glutamate. Downregulation of appoptosin prevents the cell death and caspase activation caused by glutamate or Abeta insults.", "citation": {"db": "PubMed Central", "db_id": "PMC3287608"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "source": 1657, "target": 3370, "key": "1e6202f512c86ec7d886920441c4d78e"}, {"relation": "partOf", "source": 57, "target": 1657, "key": "d516c85401db9eb279cbc42f7e170d4a"}, {"line": 23318, "relation": "increases", "evidence": "Another component of neuronal degeneration in many neurodegenerative disorders is excessive glutamate-induced stimulation of postsynaptic glutamate receptors. This activates massive calcium influxes that are potentially detrimental through calcium-activated processes and molecules (for example, proteases, nucleases and lipases). There is considerable evidence in support of this view, such as the observed threefold increase in glutamate levels in the cerebrospinal fluid of patients with ALS134, 135, 136 and the benefits in ALS of the anti-glutamate drug riluzole. EAATs are present at most synapses in the CNS, and transport glutamate from the synaptic space into astrocytes after glutamate release during neurotransmission137, 138, 139. ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 57, "target": 2788, "key": "0fad4c3ad61a1ca1183e73acde266db1"}, {"line": 23319, "relation": "increases", "evidence": "Another component of neuronal degeneration in many neurodegenerative disorders is excessive glutamate-induced stimulation of postsynaptic glutamate receptors. This activates massive calcium influxes that are potentially detrimental through calcium-activated processes and molecules (for example, proteases, nucleases and lipases). There is considerable evidence in support of this view, such as the observed threefold increase in glutamate levels in the cerebrospinal fluid of patients with ALS134, 135, 136 and the benefits in ALS of the anti-glutamate drug riluzole. EAATs are present at most synapses in the CNS, and transport glutamate from the synaptic space into astrocytes after glutamate release during neurotransmission137, 138, 139. ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 57, "target": 2789, "key": "902dba9109bb7a944a144392d406a5be"}, {"line": 23322, "relation": "positiveCorrelation", "evidence": "Another component of neuronal degeneration in many neurodegenerative disorders is excessive glutamate-induced stimulation of postsynaptic glutamate receptors. This activates massive calcium influxes that are potentially detrimental through calcium-activated processes and molecules (for example, proteases, nucleases and lipases). There is considerable evidence in support of this view, such as the observed threefold increase in glutamate levels in the cerebrospinal fluid of patients with ALS134, 135, 136 and the benefits in ALS of the anti-glutamate drug riluzole. EAATs are present at most synapses in the CNS, and transport glutamate from the synaptic space into astrocytes after glutamate release during neurotransmission137, 138, 139. ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 57, "target": 3825, "key": "14b8403a3a34cb34f84dfc73cbdf9cd2"}, {"line": 2604, "relation": "increases", "evidence": "We recently reported increased mitochondrial fission and decreased fusion, increased amyloid beta (Abeta) interaction with the mitochondrial fission protein Drp1, increased mitochondrial fragmentation, impaired axonal transport of mitochondria and synaptic degeneration in neurons affected by AD. In the present study, we extended our previous investigations to determine whether phosphorylated tau interacts with Drp1 and to elucidate mitochondrial damage in the progression of AD.", "citation": {"db": "PubMed", "db_id": "22367970"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Very High": true}}, "source": 617, "target": 924, "key": "82fbd83d915a8931386dd1079c17ece9"}, {"relation": "partOf", "source": 2697, "target": 924, "key": "8c47d915b4be1ef073f4d83a6695fdc6"}, {"relation": "partOf", "source": 2697, "target": 1228, "key": "102bf43a2daac5b4dc8b736e5cdd4017"}, {"line": 2613, "relation": "decreases", "evidence": "We recently reported increased mitochondrial fission and decreased fusion, increased amyloid beta (Abeta) interaction with the mitochondrial fission protein Drp1, increased mitochondrial fragmentation, impaired axonal transport of mitochondria and synaptic degeneration in neurons affected by AD. In the present study, we extended our previous investigations to determine whether phosphorylated tau interacts with Drp1 and to elucidate mitochondrial damage in the progression of AD.", "citation": {"db": "PubMed", "db_id": "22367970"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true}, "Confidence": {"High": true}}, "source": 1228, "target": 484, "key": "35e7f2f6b9e458f206b6882fe52a5158"}, {"line": 2634, "relation": "increases", "evidence": "Mint adaptor proteins bind to the amyloid precursor protein (APP) and regulate APP processing associated with Alzheimer's disease; however, the molecular mechanisms underlying Mint regulation in APP binding and processing remain unclear. Biochemical, biophysical, and cellular experiments now show that the Mint1 phosphotyrosine binding (PTB) domain that binds to APP is intramolecularly inhibited by the adjacent C-terminal linker region. The crystal structure of a C-terminally extended Mint1 PTB fragment reveals that the linker region forms a short a-helix that folds back onto the PTB domain and sterically hinders APP binding. This intramolecular interaction is disrupted by mutation of Tyr633 within the Mint1 autoinhibitory helix leading to enhanced APP binding and Abeta-amyloid production. Our findings suggest that an autoinhibitory mechanism in Mint1 is important for regulating APP processing and may provide novel therapies for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22355143"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "source": 1080, "target": 80, "key": "0a42ec6db60f362b98dfcd85ff88cab9"}, {"relation": "partOf", "source": 2295, "target": 1080, "key": "a4c993b9c96ca748d11e04057e1c8314"}, {"relation": "isA", "source": 3147, "target": 2199, "key": "44eb15950faf2bb9beac75f5dec389ad"}, {"relation": "partOf", "source": 3147, "target": 1594, "key": "395dfee004eaf8c94d35863bebd62fae"}, {"relation": "partOf", "source": 3147, "target": 1295, "key": "28b93304ef34dd3aef34a52f92a7c560"}, {"line": 35111, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"High": true}}, "source": 3147, "target": 649, "key": "f449ad19bcf469027f8ddc96bcc9797d"}, {"line": 38629, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by ABeta¸ and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and ABeta¸ production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3147, "target": 649, "key": "9389e8108e92ce7fee5b6f0419a78838"}, {"line": 2653, "relation": "association", "evidence": "Although there are numerous studies regarding Alzheimer's disease (AD), the cause and progression of AD are still not well understood. The researches in the past decade implicated amyloid-beta (Abeta) overproduction as a causative event in disease pathogenesis, but still failed to clarify the mechanism of pathology from Abeta production to central neural system defects in AD. The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.For this hypothesis, the factors related with the initiation of AD pathology are not only limited to the neurons per se but also expanded to the microenvironment around neurons, such as the secretion of BDNF from astrocytes. The modification of the origin in this pathway may contribute to slow down the disease progression of AD.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2397, "target": 484, "key": "b94d8b2fdcb17565469bcd4ae9a05a4c"}, {"line": 34985, "relation": "increases", "evidence": "The neurotrophin, brain-derived neurotrophic factor (BDNF), is essential for synaptic function, plasticity and neuronal survival. At the axon terminal, when BDNF binds to its receptor, tropomyosin-related kinase B (TrkB), the signal is propagated along the axon to the cell body, via retrograde transport, regulating gene expression and neuronal function. Alzheimer disease (AD) is characterized by early impairments in synaptic function that may result in part from neurotrophin signaling deficits. Growing evidence suggests that soluble beta-amyloid (Abeta) assemblies cause synaptic dysfunction by disrupting both neurotransmitter and neurotrophin signaling. Furthermore, Abeta oligomers alone impair BDNF retrograde transport. Thus, Abeta reduces BDNF signaling by impairing axonal transport and this may underlie the synaptic dysfunction observed in AD.", "citation": {"db": "PubMed", "db_id": "19540623"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2397, "target": 484, "key": "9572aa4ece236972387d0668aa78c28e"}, {"line": 35031, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2397, "target": 484, "key": "7663378f6fba174607c1ec60f01b4693"}, {"line": 38625, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by ABeta¸ and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and ABeta¸ production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2397, "target": 484, "key": "09f8bdfb86932f75c479d1c5d328fe61"}, {"line": 4804, "relation": "increases", "evidence": "In addition to the interaction with cytoplasmic polyadenylation element binding protein-1 (CPEB-1), depolarization enhanced CPEB-1 recruitment to the activity-dependent targeting element. These results suggest that CPE-like sequences are involved in the activity-dependent as well as constitutive dendritic targeting of BDNF mRNA.", "citation": {"db": "PubMed", "db_id": "20603120"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Cell cycle subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2397, "target": 726, "key": "f280aaff0992c69862e6574dc3e60d70"}, {"line": 35953, "relation": "increases", "evidence": "In addition to the interaction with cytoplasmic polyadenylation element binding protein-1 (CPEB-1), depolarization enhanced CPEB-1 recruitment to the activity-dependent targeting element. These results suggest that CPE-like sequences are involved in the activity-dependent as well as constitutive dendritic targeting of BDNF mRNA.", "citation": {"db": "PubMed", "db_id": "20603120"}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2397, "target": 726, "key": "254d79d935397ace438d2a1742a34e43"}, {"relation": "isA", "source": 2397, "target": 434, "key": "da8fa3dfdc9ed1006290cc6da863ef82"}, {"relation": "partOf", "source": 2397, "target": 1295, "key": "f57af54dea63dc4591aa0f3538fc53f9"}, {"line": 35142, "relation": "increases", "evidence": "Utilizing a novel microfluidic culture chamber, we demonstrate that Abeta oligomers compromise BDNF-mediated retrograde transport by impairing endosomal vesicle velocities, resulting in impaired downstream signaling driven by BDNF/TrkB, including ERK5 activation, and CREB-dependent gene regulation. Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2397, "target": 745, "key": "664971aca249741dd599d85fb464686f"}, {"line": 35144, "relation": "increases", "evidence": "Utilizing a novel microfluidic culture chamber, we demonstrate that Abeta oligomers compromise BDNF-mediated retrograde transport by impairing endosomal vesicle velocities, resulting in impaired downstream signaling driven by BDNF/TrkB, including ERK5 activation, and CREB-dependent gene regulation. Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2397, "target": 649, "key": "d84e0dd5b261440a299b110f58be9f4d"}, {"line": 38639, "relation": "increases", "evidence": "The neurotrophin, brain-derived neurotrophic factor (BDNF), is essential for synaptic function, plasticity and neuronal survival. At the axon terminal, when BDNF binds to its receptor, tropomyosin-related kinase B (TrkB), the signal is propagated along the axon to the cell body, via retrograde transport, regulating gene expression and neuronal function. Alzheimer disease (AD) is characterized by early impairments in synaptic function that may result in part from neurotrophin signaling deficits. Growing evidence suggests that soluble beta-amyloid (Abeta) assemblies cause synaptic dysfunction by disrupting both neurotransmitter and neurotrophin signaling. Furthermore, Abeta oligomers alone impair BDNF retrograde transport. Thus, Abeta reduces BDNF signaling by impairing axonal transport and this may underlie the synaptic dysfunction observed in AD.", "citation": {"db": "PubMed", "db_id": "19540623"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2397, "target": 756, "key": "444807be03c87dc73af5ca51ef933679"}, {"line": 40652, "relation": "association", "evidence": "We found that frontal CC regions were preserved with respect to the posterior ones in aMCI; in these individuals significant correlations were seen between DTI-derived metrics in frontal-parietal CC areas and Abeta42-stimulated BDNF-producing CD4+ T lymphocytes and PDL-1-expressing CD14+ cells.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"MeSHAnatomy": {"T-Lymphocytes": true}, "Confidence": {"High": true}}, "source": 2397, "target": 2471, "key": "3ceaec598ab95244384009ec57403873"}, {"line": 43834, "relation": "increases", "evidence": "We found that frontal CC regions were preserved with respect to the posterior ones in aMCI; in these individuals significant correlations were seen between DTI-derived metrics in frontal-parietal CC areas and Abeta42-stimulated BDNF-producing CD4+ T lymphocytes and PDL-1-expressing CD14+ cells.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"MeSHAnatomy": {"T-Lymphocytes": true}, "Subgraph": {"T cells signaling": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2397, "target": 2471, "key": "73587b50aefaaf783799612be78869a5"}, {"line": 40653, "relation": "association", "evidence": "We found that frontal CC regions were preserved with respect to the posterior ones in aMCI; in these individuals significant correlations were seen between DTI-derived metrics in frontal-parietal CC areas and Abeta42-stimulated BDNF-producing CD4+ T lymphocytes and PDL-1-expressing CD14+ cells.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"MeSHAnatomy": {"T-Lymphocytes": true}, "Confidence": {"High": true}}, "source": 2397, "target": 181, "key": "52c87e1d426be85ac1057795570ad451"}, {"line": 45217, "relation": "orthologous", "evidence": "In the hippocampus, Bdnf gene was underexpressed in sedentary mice and both Bdnf and its receptor TrkB were significantly upregulated in response to the exercise intervention", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}}, "source": 2397, "target": 3599, "key": "d50ab42a309d0707c6195804f43518dd"}, {"line": 45287, "relation": "positiveCorrelation", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2397, "target": 2832, "key": "3ead0300b4c037b918d1782f930ea089"}, {"line": 46249, "relation": "positiveCorrelation", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2397, "target": 2804, "key": "9cf1aaf614fc3901c416203ad288ad69"}, {"line": 46250, "relation": "decreases", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2397, "target": 655, "key": "97706acf758bd06c678a9a72b6f55e16"}, {"line": 46251, "relation": "decreases", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2397, "target": 820, "key": "85e827837f74df0df24460d428f50c0b"}, {"line": 2665, "relation": "decreases", "evidence": "Alzheimer disease (AD) is characterized by the presence of senile plaques of amyloid-beta (Abeta) peptides derived from amyloid precursor protein (APP) and neurofibrillary tangles made of hyperphosphorylated Tau. Increasing APP gene dosage or expression has been shown to cause familial early-onset AD. However, whether and how protein stability of APP is regulated is unclear. The prolyl isomerase Pin1 and glycogen synthase kinase-3beta (GSK3beta) have been shown to have the opposite effects on APP processing and Tau hyperphosphorylation, relevant to the pathogenesis of AD. However, nothing is known about their relationship. In this study, we found that Pin1 binds to the pT330-P motif in GSK3beta to inhibit its kinase activity. Furthermore, Pin1 promotes protein turnover of APP by inhibiting GSK3beta activity. A point mutation either at Thr-330, the Pin1-binding site in GSK3beta, or at Thr-668, the GSK3beta phosphorylation site in APP, abolished the regulation of GSK3beta activity, Thr-668 phosphorylation, and APP stability by Pin1, resulting in reduced non-amyloidogenic APP processing and increased APP levels. These results uncover a novel role of Pin1 in inhibiting GSK3beta kinase activity to reduce APP protein levels, providing a previously unrecognized mechanism by which Pin1 protects against Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "22184106"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3192, "target": 2794, "key": "39a3eefc2a2f7e6da056126a39369318"}, {"line": 2666, "relation": "increases", "evidence": "Alzheimer disease (AD) is characterized by the presence of senile plaques of amyloid-beta (Abeta) peptides derived from amyloid precursor protein (APP) and neurofibrillary tangles made of hyperphosphorylated Tau. Increasing APP gene dosage or expression has been shown to cause familial early-onset AD. However, whether and how protein stability of APP is regulated is unclear. The prolyl isomerase Pin1 and glycogen synthase kinase-3beta (GSK3beta) have been shown to have the opposite effects on APP processing and Tau hyperphosphorylation, relevant to the pathogenesis of AD. However, nothing is known about their relationship. In this study, we found that Pin1 binds to the pT330-P motif in GSK3beta to inhibit its kinase activity. Furthermore, Pin1 promotes protein turnover of APP by inhibiting GSK3beta activity. A point mutation either at Thr-330, the Pin1-binding site in GSK3beta, or at Thr-668, the GSK3beta phosphorylation site in APP, abolished the regulation of GSK3beta activity, Thr-668 phosphorylation, and APP stability by Pin1, resulting in reduced non-amyloidogenic APP processing and increased APP levels. These results uncover a novel role of Pin1 in inhibiting GSK3beta kinase activity to reduce APP protein levels, providing a previously unrecognized mechanism by which Pin1 protects against Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "22184106"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3192, "target": 2315, "key": "8b2b8333b49d4ec10dc110d7c298369b"}, {"line": 2667, "relation": "increases", "evidence": "Alzheimer disease (AD) is characterized by the presence of senile plaques of amyloid-beta (Abeta) peptides derived from amyloid precursor protein (APP) and neurofibrillary tangles made of hyperphosphorylated Tau. Increasing APP gene dosage or expression has been shown to cause familial early-onset AD. However, whether and how protein stability of APP is regulated is unclear. The prolyl isomerase Pin1 and glycogen synthase kinase-3beta (GSK3beta) have been shown to have the opposite effects on APP processing and Tau hyperphosphorylation, relevant to the pathogenesis of AD. However, nothing is known about their relationship. In this study, we found that Pin1 binds to the pT330-P motif in GSK3beta to inhibit its kinase activity. Furthermore, Pin1 promotes protein turnover of APP by inhibiting GSK3beta activity. A point mutation either at Thr-330, the Pin1-binding site in GSK3beta, or at Thr-668, the GSK3beta phosphorylation site in APP, abolished the regulation of GSK3beta activity, Thr-668 phosphorylation, and APP stability by Pin1, resulting in reduced non-amyloidogenic APP processing and increased APP levels. These results uncover a novel role of Pin1 in inhibiting GSK3beta kinase activity to reduce APP protein levels, providing a previously unrecognized mechanism by which Pin1 protects against Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "22184106"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3192, "target": 80, "key": "a5b3841af377d406cfd0357275c260e1"}, {"line": 16869, "relation": "decreases", "evidence": "First, Pin1 inhibits the production of Abeta, and enhances the activity of eNOS. Second, Abeta and eNOS form a mutual inhibition system. Third, the well-balanced feedback signaling loop avoids the development of AD, HTN, and CAA by inhibiting the frequent pathological characteristics of these diseases, including Abeta deposition in cerebral microvessels and cerebral microbleeds.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"Medium": true}}, "source": 3192, "target": 80, "key": "0b7ca5e15b0410e5a9de94cfb17de4be"}, {"line": 9021, "relation": "decreases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 3017, "key": "4b601bb0b642a5dfc99391ee4cdc04f9"}, {"line": 9022, "relation": "decreases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3192, "target": 2487, "key": "a0433074b3942652f13a5d9c6a3f0212"}, {"line": 9023, "relation": "decreases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 3030, "key": "1151c70be8fae42b136d558edd90ab57"}, {"line": 9028, "relation": "increases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3192, "target": 3223, "key": "0e709819b260a8a91be23a509a15c553"}, {"line": 13947, "relation": "increases", "evidence": "Pin1 is overexpressed in breast cancer and cooperates with Ras signaling in increasing the transcriptional activity of c-Jun towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Breast Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3192, "target": 2936, "key": "377f01d9e6d73064de27f192b7b89d94"}, {"line": 14013, "relation": "increases", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3192, "target": 2936, "key": "e6256360980a1c777a12078020afe19e"}, {"line": 13948, "relation": "association", "evidence": "Pin1 is overexpressed in breast cancer and cooperates with Ras signaling in increasing the transcriptional activity of c-Jun towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Breast Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 2463, "key": "a4171fdc98eba36e152b45c106f6eca0"}, {"line": 13989, "relation": "increases", "evidence": "Overexpression of Pin1 increases cellular cyclin D1 protein and activates its promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "source": 3192, "target": 2463, "key": "79513d3c9908a7df7445ba05fb97bd12"}, {"line": 13956, "relation": "association", "evidence": "Pin1 is overexpressed in breast cancer and cooperates with Ras signaling in increasing the transcriptional activity of c-Jun towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Breast Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 2213, "key": "b0ded9b9eb0d577c0f44c7029e428e7f"}, {"line": 14012, "relation": "association", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "source": 3192, "target": 2213, "key": "17918d15c5279019d089e53022f83005"}, {"line": 13957, "relation": "association", "evidence": "Pin1 is overexpressed in breast cancer and cooperates with Ras signaling in increasing the transcriptional activity of c-Jun towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Breast Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 3833, "key": "f5b189b4e272decd1df8a3e6c1cc5ddd"}, {"line": 13972, "relation": "decreases", "evidence": "Inhibition of Pin1 induces apoptosis and may also contribute to neuronal death in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 478, "key": "03470621aead312c5e987512d7969789"}, {"line": 13973, "relation": "decreases", "evidence": "Inhibition of Pin1 induces apoptosis and may also contribute to neuronal death in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 648, "key": "64b789579c1d67606432136d7da58b7f"}, {"line": 13983, "relation": "association", "evidence": "However, little is known about the role of Pin1 in cancer or in modulating transcription factor activity.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "source": 3192, "target": 3923, "key": "d51c3a12fb63dc4288db6afc2d7f4d8c"}, {"line": 14027, "relation": "positiveCorrelation", "evidence": "Thus, Pin1 is up-regulated in human tumors and cooperates with Ras signaling in increasing c-Jun transcriptional activity towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 3923, "key": "d06bc55e8ce8c620e71bc038f63d6a78"}, {"relation": "partOf", "source": 3192, "target": 1504, "key": "62a278c7e4a2fe122e8ae197958f8188"}, {"line": 13997, "relation": "increases", "evidence": "Furthermore, Pin1 binds c-Jun that is phosphorylated on Ser63/73-Pro motifs by activated JNK or oncogenic Ras.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Subgraph": {"MAPK-ERK subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "source": 3192, "target": 1504, "key": "7b6b85f450411ffc876430ec3a4d1e56"}, {"line": 14011, "relation": "association", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "source": 3192, "target": 3002, "key": "8a191b8f5c67c830ce3eaf33be81c9dc"}, {"relation": "partOf", "source": 3192, "target": 1671, "key": "d3a8e0c2710b225b52f876f2ae51e41e"}, {"relation": "partOf", "source": 3192, "target": 1660, "key": "947fc8bb59722bbdb7922507c3ecc03a"}, {"line": 16851, "relation": "association", "evidence": "Herein, we hypothesize that a feedback signaling loop, consisted of Pin1, endothelial nitric oxide synthase (eNOS), and amyloid-beta (Abeta), may contribute to the interesting pathological phenomenon.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 3192, "target": 3124, "key": "944f269ffe58f918874206394ab53d5c"}, {"line": 16873, "relation": "increases", "evidence": "First, Pin1 inhibits the production of Abeta, and enhances the activity of eNOS. Second, Abeta and eNOS form a mutual inhibition system. Third, the well-balanced feedback signaling loop avoids the development of AD, HTN, and CAA by inhibiting the frequent pathological characteristics of these diseases, including Abeta deposition in cerebral microvessels and cerebral microbleeds.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3192, "target": 3124, "key": "8ca5fc602575f4b2f70fd3c99cfc5376"}, {"line": 16859, "relation": "association", "evidence": "Herein, we hypothesize that a feedback signaling loop, consisted of Pin1, endothelial nitric oxide synthase (eNOS), and amyloid-beta (Abeta), may contribute to the interesting pathological phenomenon.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 3192, "target": 2328, "key": "246ec30632f986d5d567eaf2164405c0"}, {"line": 16924, "relation": "decreases", "evidence": "To explore the molecular mechanism underlying AD, HTN, and CAA, we hypothesize a feedback signaling loop consisted of Pin1, eNOS, and Abeta. Pin1 and eNOS mainly inhibit Abeta deposition in cerebral vessels, cerebral microbleeds, and elevation of blood pressure, preventing the development of AD, HTN, and CAA, however, Abeta plays an opposite role and aggravates these diseases.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3192, "target": 2328, "key": "21a7d43350b63c3d22722d61918ba56a"}, {"relation": "partOf", "source": 3192, "target": 1714, "key": "056e48e02c7b889e37efe3558465685b"}, {"relation": "partOf", "source": 3192, "target": 1555, "key": "57c2938ddc867e66a2a25d43d3df0c25"}, {"relation": "partOf", "source": 3192, "target": 1569, "key": "7ea91e73a707be00a3d27e8aaf511f3a"}, {"relation": "partOf", "source": 3192, "target": 1563, "key": "24dd4f274c07e72c7a90dc296515081f"}, {"relation": "partOf", "source": 3192, "target": 1568, "key": "830a4e1b5b84e77831b06b797fe45e89"}, {"relation": "partOf", "source": 3192, "target": 1565, "key": "913420780709bbe11c1d1050ff188910"}, {"relation": "partOf", "source": 3192, "target": 1604, "key": "18195b8035e5b65e10daa194dd9a8dfb"}, {"line": 31340, "relation": "association", "evidence": "Pin1 regulates the conformation and function of certain phosphorylated proteins and plays an important role in cell cycle regulation , oncogenesis , and Alzheimer 's disease.", "citation": {"db": "PubMed", "db_id": "12388558"}, "source": 3192, "target": 718, "key": "f32dc98414af61300d6bc617c141f451"}, {"line": 31341, "relation": "association", "evidence": "Pin1 regulates the conformation and function of certain phosphorylated proteins and plays an important role in cell cycle regulation , oncogenesis , and Alzheimer 's disease.", "citation": {"db": "PubMed", "db_id": "12388558"}, "source": 3192, "target": 3898, "key": "0638c29b23583ab10f1dd6f93a36aeff"}, {"line": 31342, "relation": "association", "evidence": "Pin1 regulates the conformation and function of certain phosphorylated proteins and plays an important role in cell cycle regulation , oncogenesis , and Alzheimer 's disease.", "citation": {"db": "PubMed", "db_id": "12388558"}, "source": 3192, "target": 3823, "key": "a3d4680b85216e8ecb7030c4f8cb7628"}, {"relation": "hasVariant", "source": 3192, "target": 3193, "key": "04611225d704aa7d3f7efe3a46560275"}, {"line": 46688, "relation": "association", "evidence": "Taken together, these studies indicate that SULT4A1 stability is regulated by post-translational modification that involves the ERK pathway and PP2A. The phosphorylation of SULT4A1 allows interaction with Pin1, which then promotes degradation of the sulfotransferase.", "citation": {"db": "PubMed", "db_id": "20920535"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "source": 3192, "target": 3696, "key": "d5bda46d1c29d84e27df290705849dc5"}, {"line": 2668, "relation": "increases", "evidence": "Alzheimer disease (AD) is characterized by the presence of senile plaques of amyloid-beta (Abeta) peptides derived from amyloid precursor protein (APP) and neurofibrillary tangles made of hyperphosphorylated Tau. Increasing APP gene dosage or expression has been shown to cause familial early-onset AD. However, whether and how protein stability of APP is regulated is unclear. The prolyl isomerase Pin1 and glycogen synthase kinase-3beta (GSK3beta) have been shown to have the opposite effects on APP processing and Tau hyperphosphorylation, relevant to the pathogenesis of AD. However, nothing is known about their relationship. In this study, we found that Pin1 binds to the pT330-P motif in GSK3beta to inhibit its kinase activity. Furthermore, Pin1 promotes protein turnover of APP by inhibiting GSK3beta activity. A point mutation either at Thr-330, the Pin1-binding site in GSK3beta, or at Thr-668, the GSK3beta phosphorylation site in APP, abolished the regulation of GSK3beta activity, Thr-668 phosphorylation, and APP stability by Pin1, resulting in reduced non-amyloidogenic APP processing and increased APP levels. These results uncover a novel role of Pin1 in inhibiting GSK3beta kinase activity to reduce APP protein levels, providing a previously unrecognized mechanism by which Pin1 protects against Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "22184106"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2797, "target": 2315, "key": "27d2797c806045ca05dc86c86c7d8c04"}, {"line": 2681, "relation": "increases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER. This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation"}, "source": 1099, "target": 2315, "key": "5340a343e9b53659a05654b7efcdd2ce"}, {"line": 35980, "relation": "increases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER.This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation"}, "source": 1099, "target": 2315, "key": "9add9d47a26cda451de342514a29a7fd"}, {"line": 2691, "relation": "increases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER. This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 2338, "target": 2328, "key": "2a4a3046f8aa87cf67ac2d16abda0745"}, {"line": 3292, "relation": "decreases", "evidence": "Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and Abeta production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 2338, "target": 1092, "key": "2f748af9984612fd45cbeab16de5c0bd"}, {"line": 33293, "relation": "decreases", "evidence": "We report here that (i) a single amino acid mutation at the Thr-668 residue of APP695, located 14 amino acids toward the amino-terminal end from the (682)YENPTY(687) motif, reduced the interaction between members of the Fe65 family of proteins and APP, whereas interaction of APP with the phosphotyrosine interaction domain of other APP binders such as X11-like and mammalian disabled-1 was not influenced by this mutation; (ii) the phosphorylation of APP at Thr-668 diminished the interaction of APP with Fe65 by causing a conformational change in the cytoplasmic domain that contains the Fe65-binding motif, YENPTY; and (iii) the expression of Fe65 slightly suppressed maturation of APP and decreased production of beta-amyloid (Abeta).", "citation": {"db": "PubMed", "db_id": "11517218"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2338, "target": 1092, "key": "86864b662c2960e58a6a350c00991e61"}, {"line": 3293, "relation": "increases", "evidence": "Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and Abeta production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2338, "target": 2375, "key": "1ccc886e5f116633e5e746114585f1a4"}, {"line": 27741, "relation": "increases", "evidence": "Together, these results suggest that T668 phosphorylation may facilitate the BACE1 cleavage of APP to increase Abeta generation.", "citation": {"db": "PubMed", "db_id": "14557249"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2338, "target": 2375, "key": "fe20765d919b4b306ae4677ec79d8ce7"}, {"line": 3294, "relation": "increases", "evidence": "Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and Abeta production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 2338, "target": 868, "key": "864cb1a9dce3a6fd2737a8290569238f"}, {"line": 27742, "relation": "increases", "evidence": "Together, these results suggest that T668 phosphorylation may facilitate the BACE1 cleavage of APP to increase Abeta generation.", "citation": {"db": "PubMed", "db_id": "14557249"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2338, "target": 80, "key": "1918af76cd89c572d39c33159d98e38e"}, {"line": 35984, "relation": "increases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER.This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}}, "source": 2338, "target": 80, "key": "c08d211f787f85c9489f70ae9ee048e4"}, {"line": 34239, "relation": "decreases", "evidence": "In the present study, we demonstrate that APP phosphorylated at Thr668 is less vulnerable to cytoplasmic cleavage by caspase-3 and caspase-8.", "citation": {"db": "PubMed", "db_id": "15178331"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2338, "target": 2315, "key": "7533176e332df0cc743eaab08942c43b"}, {"line": 37647, "relation": "increases", "evidence": "Another study shows that APP, when phosphorylated at the Thr668 residue, is distributed in neuronal growth cones, and that the phosphorylated form of APP regulates neurite outgrowth in PC12 cells [52]. In addition, human APP and Drosophila APPL promoted postdevelopmental axonal arborization, depending on the interaction between the C-terminus of APP and Abelson (Abl) tyrosine kinase, suggesting a potential role for APP in axonal outgrowth following traumatic brain injury [4]. Furthermore, secreted sAPPa promoted axonal and dendritic growth and induced neurite outgrowth in neural stem cell-derived neurons through MAP kinase signaling", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2338, "target": 2990, "key": "bb4e308e0d9e461a54d14df39fcf49eb"}, {"line": 37648, "relation": "increases", "evidence": "Another study shows that APP, when phosphorylated at the Thr668 residue, is distributed in neuronal growth cones, and that the phosphorylated form of APP regulates neurite outgrowth in PC12 cells [52]. In addition, human APP and Drosophila APPL promoted postdevelopmental axonal arborization, depending on the interaction between the C-terminus of APP and Abelson (Abl) tyrosine kinase, suggesting a potential role for APP in axonal outgrowth following traumatic brain injury [4]. Furthermore, secreted sAPPa promoted axonal and dendritic growth and induced neurite outgrowth in neural stem cell-derived neurons through MAP kinase signaling", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 2338, "target": 652, "key": "b4dcbf76d1806309aebf6a0e2a563c15"}, {"line": 2719, "relation": "increases", "evidence": "MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1)", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1395, "target": 684, "key": "28c2aca11c42c6ec9d598e371c6eedc7"}, {"relation": "partOf", "source": 2639, "target": 1395, "key": "091bef51f607d74cbab1e3c12f66792b"}, {"relation": "partOf", "source": 2639, "target": 1396, "key": "be36d6c69c1240a4693ce930e727ef3f"}, {"relation": "partOf", "source": 2639, "target": 1397, "key": "5a959bb91ebb72419d610fbb28acc318"}, {"line": 2723, "relation": "increases", "evidence": "MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1)", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2639, "target": 684, "key": "50bb82122de8a216d4178ee65e6a8e80"}, {"line": 35744, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2639, "target": 684, "key": "0df1d80925b83010dc25a729227add22"}, {"relation": "partOf", "source": 2639, "target": 1394, "key": "71221c90a1dcea4fa9a775d87dc605bb"}, {"relation": "partOf", "source": 2639, "target": 1702, "key": "c2b941b096077f152051e66aed006e68"}, {"line": 2720, "relation": "increases", "evidence": "MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1)", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1396, "target": 684, "key": "b0557acfd840e1a3717204803aa7910b"}, {"line": 2722, "relation": "increases", "evidence": "MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1)", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1397, "target": 684, "key": "97676c7393b4d91df417e1be2f6f7a71"}, {"relation": "partOf", "source": 3290, "target": 1397, "key": "1d767307902a713f24b8397a34fed90b"}, {"relation": "partOf", "source": 3290, "target": 1394, "key": "1fe521937bd509a802fa510d4084a3e6"}, {"relation": "partOf", "source": 3290, "target": 1713, "key": "64a563d18088aa143167c384aae8e93e"}, {"line": 32651, "relation": "increases", "evidence": "This processing is activated through a pathway involving the PDGF receptor, Src, and Rac1. ", "citation": {"db": "PubMed", "db_id": "14766758"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3290, "target": 2315, "key": "8cd589da6c68c93113e02caf34a956ea"}, {"relation": "partOf", "source": 3290, "target": 1717, "key": "5872abdfd94e1d77763946a72f945380"}, {"relation": "partOf", "source": 3290, "target": 1718, "key": "138d910c1f490ff402a75aaa676fc686"}, {"relation": "partOf", "source": 3290, "target": 1702, "key": "0d05c16331472d706909850bc5ebb962"}, {"line": 2726, "relation": "increases", "evidence": "MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1)", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"DNA synthesis": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1394, "target": 684, "key": "96dd191f4d2d7b2c098d0815c31d3603"}, {"line": 2728, "relation": "increases", "evidence": "MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1)", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"DNA synthesis": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1394, "target": 3258, "key": "b3975b631850ee4620707c09c187522d"}, {"line": 2729, "relation": "increases", "evidence": "MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1)", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"DNA synthesis": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1394, "target": 3268, "key": "e9eda6da450ba4462e745fd9a80b23f5"}, {"relation": "partOf", "source": 2989, "target": 1394, "key": "dee43dd0637a27f67ec3c30f5faeb4d3"}, {"relation": "partOf", "source": 2989, "target": 1435, "key": "ca0079b8b2897856d6c1189479fc0e72"}, {"relation": "partOf", "source": 2989, "target": 1537, "key": "eff38679d579499cc13ed1f8c9516097"}, {"relation": "partOf", "source": 2989, "target": 1536, "key": "e82862b460700e549c3ad2dd490dd375"}, {"relation": "partOf", "source": 2989, "target": 1535, "key": "1b8e26627f7a3bf5fddc0b38b2cc96a4"}, {"line": 35670, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2989, "target": 478, "key": "7bfeb2f24b527f7e55c3849692c5be43"}, {"line": 35677, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2989, "target": 2223, "key": "6c1eb3ed8bb007b280ad4898306337a0"}, {"line": 35679, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2989, "target": 2188, "key": "984908ce5576967d041df3ff2269b68c"}, {"line": 35702, "relation": "increases", "evidence": "Dual-specificity tyrosine (Y)-phosphorylation-regulated protein kinase 1A (Dyrk1A) is the mammalian homologue of Drosophila melanogaster minibrain and its human gene is mapped to the Down syndrome critical region of chromosome 21. Dyrk1A phosphorylates several transcription factors, including NFAT and CREB and a number of cytosolic proteins such as APP, tau, and a-synuclein. Although Dyrk1A is involved in the control of cell growth and postembryonic neurogenesis, its potential role during cell death and signaling pathway is not clearly understood. In the present study, we show that Dyrk1A is activated under the condition of apoptotic cell death. In addition, Dyrk1A is coupled to JNK1 activation, and directly interacts with apoptosis signal-regulating kinase 1 (ASK1). Moreover, Dyrk1A positively regulates ASK1-mediated JNK1-signaling, and appears to directly phosphorylate ASK1. These data indicate that Dyrk1A regulates cell death through facilitating ASK1-mediated signaling events.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2989, "target": 684, "key": "9e4aa2b23dd19a392b26851c43c0eb61"}, {"relation": "partOf", "source": 2989, "target": 1702, "key": "b3d3f250368114cee8aae6e44d1b2c37"}, {"line": 2742, "relation": "increases", "evidence": "Membrane ceramides are not only the major component of lipid rafts but they also contribute to AD pathology by facilitating the mislocation of BACE1 and gamma-secretase to lipid rafts, and thereby promoting amyloid beta (Abeta) formation (Lee et al., 1998; Vetrivel et al., 2005). Inhibiting de novo ceramide synthesis has been shown to decrease the production of Abeta while exogenous addition of ceramide increased Abeta production (Puglielli et al., 2003; Patil et al., 2007). Numerous studies suggest a connection between ceramides and Abeta, and indicate increased ceramide levels may be an important risk factor for sporadic AD", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Sphingolipid metabolic subgraph": true}, "Confidence": {"Very High": true}}, "source": 228, "target": 80, "key": "5e1058e80ef2f4c4e5b7d0285739d323"}, {"line": 2758, "relation": "increases", "evidence": "SPT is the first rate limiting enzyme in the de novo ceramide synthesis pathway (Hannun and Obeid, 2008). Activation of SPT elevates ceramide levels (Perry et al., 2000) and inhibition of SPT decreases ceramide levels (Hojjati et al., 2005; Patil et al., 2007) and neuronal cell death by Abeta (Cutler et al., 2004), supporting SPT as an important regulator of ceramide. SPT is a heterodimer composed of serine palmitoyltransferase long chain 1 (SPTLC1) and either serine palmitoyltransferase long chain 2 (SPTLC2) or serine palmitoyltransferase long chain 3 (SPTLC3) (Rotthier et al., 2010). In the brain, SPTLC3 is lowly expressed while SPTLC1 and SPTLC2 are the major subunits (Hornemann et al., 2006). However, the regulation of these subunits and in turn SPT is not well understood. Cell culture studies demonstrate that SPT activity increases in response to various stimuli (i.e. etoposide or resveratrol), but without concomitant changes in SPTLC1 and SPTLC2 mRNA levels, which have led researchers to hypothesize that SPT may be post-transcriptionally regulated.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Sphingolipid metabolic subgraph": true}, "Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 228, "target": 2328, "key": "1d6024af4edf93d521c725d9bf260fc1"}, {"line": 43456, "relation": "increases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 228, "target": 2328, "key": "4f67429169b862dd1a6f796b968dda7a"}, {"line": 8043, "relation": "association", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Sphingolipid metabolic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 228, "target": 2375, "key": "b0cd57c7644786d5e180a63151395e92"}, {"line": 8854, "relation": "positiveCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"Medium": true}}, "source": 228, "target": 3823, "key": "a3f62fdfddd9fa68c42a9dfdc65a4d34"}, {"line": 43457, "relation": "increases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 228, "target": 3676, "key": "210b75d42d595e3e8408f7822dcfa72f"}, {"line": 43460, "relation": "association", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 228, "target": 566, "key": "b693a9a9af49b3664c07cb689c74f612"}, {"line": 2750, "relation": "increases", "evidence": "SPT is the first rate limiting enzyme in the de novo ceramide synthesis pathway (Hannun and Obeid, 2008). Activation of SPT elevates ceramide levels (Perry et al., 2000) and inhibition of SPT decreases ceramide levels (Hojjati et al., 2005; Patil et al., 2007) and neuronal cell death by Abeta (Cutler et al., 2004), supporting SPT as an important regulator of ceramide. SPT is a heterodimer composed of serine palmitoyltransferase long chain 1 (SPTLC1) and either serine palmitoyltransferase long chain 2 (SPTLC2) or serine palmitoyltransferase long chain 3 (SPTLC3) (Rotthier et al., 2010). In the brain, SPTLC3 is lowly expressed while SPTLC1 and SPTLC2 are the major subunits (Hornemann et al., 2006). However, the regulation of these subunits and in turn SPT is not well understood. Cell culture studies demonstrate that SPT activity increases in response to various stimuli (i.e. etoposide or resveratrol), but without concomitant changes in SPTLC1 and SPTLC2 mRNA levels, which have led researchers to hypothesize that SPT may be post-transcriptionally regulated.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3412, "target": 228, "key": "2f32e6d68d199e71a72a1ba36717ac54"}, {"line": 2751, "relation": "increases", "evidence": "SPT is the first rate limiting enzyme in the de novo ceramide synthesis pathway (Hannun and Obeid, 2008). Activation of SPT elevates ceramide levels (Perry et al., 2000) and inhibition of SPT decreases ceramide levels (Hojjati et al., 2005; Patil et al., 2007) and neuronal cell death by Abeta (Cutler et al., 2004), supporting SPT as an important regulator of ceramide. SPT is a heterodimer composed of serine palmitoyltransferase long chain 1 (SPTLC1) and either serine palmitoyltransferase long chain 2 (SPTLC2) or serine palmitoyltransferase long chain 3 (SPTLC3) (Rotthier et al., 2010). In the brain, SPTLC3 is lowly expressed while SPTLC1 and SPTLC2 are the major subunits (Hornemann et al., 2006). However, the regulation of these subunits and in turn SPT is not well understood. Cell culture studies demonstrate that SPT activity increases in response to various stimuli (i.e. etoposide or resveratrol), but without concomitant changes in SPTLC1 and SPTLC2 mRNA levels, which have led researchers to hypothesize that SPT may be post-transcriptionally regulated.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3413, "target": 228, "key": "f7529a9510711ba927b2ad2c1483c6da"}, {"line": 2752, "relation": "increases", "evidence": "SPT is the first rate limiting enzyme in the de novo ceramide synthesis pathway (Hannun and Obeid, 2008). Activation of SPT elevates ceramide levels (Perry et al., 2000) and inhibition of SPT decreases ceramide levels (Hojjati et al., 2005; Patil et al., 2007) and neuronal cell death by Abeta (Cutler et al., 2004), supporting SPT as an important regulator of ceramide. SPT is a heterodimer composed of serine palmitoyltransferase long chain 1 (SPTLC1) and either serine palmitoyltransferase long chain 2 (SPTLC2) or serine palmitoyltransferase long chain 3 (SPTLC3) (Rotthier et al., 2010). In the brain, SPTLC3 is lowly expressed while SPTLC1 and SPTLC2 are the major subunits (Hornemann et al., 2006). However, the regulation of these subunits and in turn SPT is not well understood. Cell culture studies demonstrate that SPT activity increases in response to various stimuli (i.e. etoposide or resveratrol), but without concomitant changes in SPTLC1 and SPTLC2 mRNA levels, which have led researchers to hypothesize that SPT may be post-transcriptionally regulated.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3414, "target": 228, "key": "3e6574239887a2cbc56ceb7859b647a3"}, {"line": 8858, "relation": "rateLimitingStepOf", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 3414, "target": 522, "key": "fb118c9126aa30ef92cd5472aa93acde"}, {"line": 2803, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-b, may also contribute to AICD-related cytotoxicity by up-regulating tau hyperphosphorylation. GSK-3b activation and collapsin response mediator protein 2 (CRMP2) phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3482, "target": 478, "key": "085ae1f98fccd483abcb7c591eef0175"}, {"line": 35597, "relation": "regulates", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3482, "target": 478, "key": "33f0b73a098029775a6b3c4a63225536"}, {"relation": "hasVariant", "source": 3482, "target": 3484, "key": "3c1a24c6f258827d12a9dd662476e44e"}, {"relation": "partOf", "source": 3482, "target": 1385, "key": "1fb59b9146b62f81b99f7743a4fe67e4"}, {"line": 5795, "relation": "increases", "evidence": "Results show that p53 has two binding sites located at the cathepsin D promoter gene and that cathepsin D participates in p53-dependent apoptotic process. Cathepsin D showed augmented activity soon after it was released and that was accompanied by increased levels of p53 protein, a cathepsin D transcription factor [16]. Therefore, the mechanism responsible for increase in cathepsin D activity might be an effect of increased synthesis regulated by p53. Cathepsin B has also been implicated in the activation of the pro-inflammatory caspases-1 and -11, and the cleavage of Bcl-2 family member Bid which may lead to cytochrome c release from the mitochondria and subsequent caspase activation", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 3482, "target": 2593, "key": "05b072ffdce93f1a6e78d6def0b95b5c"}, {"relation": "hasVariant", "source": 3482, "target": 3483, "key": "bbe0d0e4a62612bad77ddf204465dda5"}, {"line": 20377, "relation": "positiveCorrelation", "evidence": "ICE-beta, c-Jun, Bax-alpha, Bcl-x(L), p53, and GADD153 were found to be upregulated in some AD samples but were not detected or downregulated in other AD or normal samples.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true}}, "source": 3482, "target": 3823, "key": "f1aa4718e6456c66050aeeda70654531"}, {"line": 44700, "relation": "positiveCorrelation", "evidence": "the p53 protein levels in each stage of AD development remained statistically nonsignificantly higher compared to the controls", "citation": {"db": "PubMed", "db_id": "21845541"}, "annotations": {"Subgraph": {"p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "source": 3482, "target": 3823, "key": "8fc89f4d6782f0fddce74815c2ca31b7"}, {"line": 21993, "relation": "association", "evidence": "We also found that lymphocytes of the same patients expressed significant levels of unfolded p53 isoform, confirming what we already demonstrated in fibroblasts and lymphocytes derived from other cohorts of AD patients.", "citation": {"db": "PubMed", "db_id": "20197694"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell adhesion subgraph": true, "p53 stabilization subgraph": true}, "Cell": {"lymphocyte": true, "fibroblast": true}}, "source": 3482, "target": 2476, "key": "5442d1d88a61b1cbb9b62ddc07e8f6aa"}, {"line": 23334, "relation": "association", "evidence": "Activation of the mitochondrial pathway of apoptosis is one attractive explanation for the transcription-independent portion of p53-influenced apoptosis (Chen et al., 1996b; Haupt et al., 1995). Mitochondrial translocation of p53 following DNA damage (Mihara et al., 2003) and its ability to engage BCL-2 family proteins to regulate cytochrome c release have been noted (Chipuk et al., 2004).", "citation": {"db": "PubMed", "db_id": "14744432"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"Medium": true}}, "source": 3482, "target": 2608, "key": "c6906450d755bb94539f95dfa44ff1d2"}, {"line": 23335, "relation": "association", "evidence": "Activation of the mitochondrial pathway of apoptosis is one attractive explanation for the transcription-independent portion of p53-influenced apoptosis (Chen et al., 1996b; Haupt et al., 1995). Mitochondrial translocation of p53 following DNA damage (Mihara et al., 2003) and its ability to engage BCL-2 family proteins to regulate cytochrome c release have been noted (Chipuk et al., 2004).", "citation": {"db": "PubMed", "db_id": "14744432"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"Medium": true}}, "source": 3482, "target": 2389, "key": "38875783cee7b93534865f81a4c465d2"}, {"line": 23336, "relation": "association", "evidence": "Activation of the mitochondrial pathway of apoptosis is one attractive explanation for the transcription-independent portion of p53-influenced apoptosis (Chen et al., 1996b; Haupt et al., 1995). Mitochondrial translocation of p53 following DNA damage (Mihara et al., 2003) and its ability to engage BCL-2 family proteins to regulate cytochrome c release have been noted (Chipuk et al., 2004).", "citation": {"db": "PubMed", "db_id": "14744432"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"Medium": true}}, "source": 3482, "target": 2382, "key": "5aec5fecf47cb769d5879acb3bd7ce13"}, {"relation": "partOf", "source": 3482, "target": 1410, "key": "c3cab787d4a33de213937b2b271666c0"}, {"relation": "partOf", "source": 3482, "target": 1412, "key": "14a537d228a7d0d1e4e77530b8d3b60e"}, {"relation": "partOf", "source": 3482, "target": 1604, "key": "d98ee50dd9b2ecf3fe15a655eabb3c4d"}, {"relation": "partOf", "source": 3482, "target": 1547, "key": "67cc67a7282011d9deefadaa3b59e31a"}, {"line": 48672, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3482, "target": 3961, "key": "b2447710318de0d324e59d46f7923cc9"}, {"line": 12503, "relation": "positiveCorrelation", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 2642, "target": 3823, "key": "efdb7f97acbd5f24d37f2b41180bccfa"}, {"line": 30280, "relation": "positiveCorrelation", "evidence": "Phosphorylation by GSK3beta was exclusively observed in Cdk5-phosphorylated CRMP2, but barely in CRMP2T509A.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "GSK3 subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2642, "target": 2794, "key": "cc3833d86ffc86bf50b7884da7994411"}, {"line": 35621, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2642, "target": 645, "key": "965dd00bd53699be5b8838f0d6d9e838"}, {"line": 5518, "relation": "association", "evidence": "CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 559, "target": 2162, "key": "bfff41d1b7455db3f8671c63c0f89de0"}, {"line": 2826, "relation": "association", "evidence": "However, C31, a short form of AICD generated by caspase cleavage, has been reported to directly activate caspase 3 in the tumor cell death process.C31 also appears to induce a caspase-independent toxicity by selectively increasing Ab42", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Complement system subgraph": true}, "Confidence": {"Medium": true}}, "source": 2330, "target": 3563, "key": "8ae52f7bca01d21b9bdfa41db6d3aa62"}, {"line": 2830, "relation": "increases", "evidence": "However, C31, a short form of AICD generated by caspase cleavage, has been reported to directly activate caspase 3 in the tumor cell death process.C31 also appears to induce a caspase-independent toxicity by selectively increasing Ab42", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Complement system subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2330, "target": 2444, "key": "0034ae1f5cff36b9640f1b8d6363d14f"}, {"line": 2831, "relation": "increases", "evidence": "However, C31, a short form of AICD generated by caspase cleavage, has been reported to directly activate caspase 3 in the tumor cell death process.C31 also appears to induce a caspase-independent toxicity by selectively increasing Ab42", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Complement system subgraph": true}, "Confidence": {"High": true}}, "source": 2330, "target": 2328, "key": "05ce35a383dc12844d0c0eafce272ab0"}, {"line": 15032, "relation": "association", "evidence": "The present findings are in line with the previous studies showing tau products cleaved by caspase-3, as recognized by s pecific tau-cleaved antibodies, in Alzheimer's disease and other tauopathies.", "citation": {"db": "PubMed", "db_id": "16496165"}, "annotations": {"MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 2444, "target": 3823, "key": "87807c57f287a136de1c2c2c650f9e32"}, {"line": 15033, "relation": "association", "evidence": "The present findings are in line with the previous studies showing tau products cleaved by caspase-3, as recognized by s pecific tau-cleaved antibodies, in Alzheimer's disease and other tauopathies.", "citation": {"db": "PubMed", "db_id": "16496165"}, "annotations": {"MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 2444, "target": 3931, "key": "dc11029c629eef348a654ef6a286b3e7"}, {"line": 15038, "relation": "increases", "evidence": "The present findings are in line with the previous studies showing tau products cleaved by caspase-3, as recognized by s pecific tau-cleaved antibodies, in Alzheimer's disease and other tauopathies.", "citation": {"db": "PubMed", "db_id": "16496165"}, "annotations": {"MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Subgraph": {"Caspase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2444, "target": 3012, "key": "51d9955afa38a9874a16717969daf25d"}, {"line": 29147, "relation": "increases", "evidence": "In primary cortical neurons, minocycline prevents beta-amyloid-induced neuronal death, reduces caspase-3 activation, and lowers generation of caspase-3-cleaved tau fragments.", "citation": {"db": "PubMed", "db_id": "19001528"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Caspase subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2444, "target": 3012, "key": "a12864e435b2d5bd9a12c2d0877fec68"}, {"line": 29157, "relation": "increases", "evidence": "Using an in vitro translation assay to screen a human brain cDNA library, we isolated the microtubule-associated protein Tau and determined it to be a caspase-3 substrate whose C-terminal cleavage occurred during neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11162250"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Caspase subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2444, "target": 3012, "key": "dae09d4250debe02ed9c03d0518c84ec"}, {"line": 29167, "relation": "increases", "evidence": "The neuronal microtubule-associated protein tau is a substrate for caspase-3 and an effector of apoptotic process", "citation": {"db": "PubMed", "db_id": "10899937"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Caspase subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2444, "target": 3012, "key": "230ffb3aba93044bfbb7a5864e7bd601"}, {"relation": "partOf", "source": 2444, "target": 1305, "key": "b58e507741b25c9f09055b2b58b1c7a6"}, {"relation": "partOf", "source": 2444, "target": 1303, "key": "ca8c368962cc9787b08339743d5116c5"}, {"line": 21349, "relation": "increases", "evidence": "During proteolysis, caspase-3 cleaves the native PKCdelta (72-74 kDa) into 41-kDa catalytically active and 38-kDa regulatory fragments to persistently activate the kinase.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2444, "target": 1715, "key": "a955fe5042e02ca008c69a64bedfa746"}, {"line": 22110, "relation": "association", "evidence": "Over-expression of hsp70 was found to reduce PQ-induced oxidative stress along with JNK and caspase-3 mediated dopaminergic neuronal cell death in exposed organism.", "citation": {"db": "PubMed", "db_id": "24887138"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Chaperone subgraph": true}, "MeSHAnatomy": {"Dopaminergic Neurons": true}}, "source": 2444, "target": 648, "key": "691bf729995e315636568fe3d41137aa"}, {"line": 40582, "relation": "association", "evidence": "In addition, Pls also inhibited primary mouse hippocampal neuronal cell death induced by nutrient deprivation, which was associated with the inhibition of caspase-9 and caspase-3 cleavages.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2444, "target": 648, "key": "251686914786ae71a12db5c8bd0d389d"}, {"line": 22311, "relation": "increases", "evidence": "We also performed immunolabeling for activated caspase-3, which plays a central role in the execution phase of cell apoptotic process.", "citation": {"db": "PubMed", "db_id": "24804730"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2444, "target": 478, "key": "2ba92a774ac9a47decb3da623e7d64d3"}, {"line": 23169, "relation": "increases", "evidence": "We showed that the localization of mutant SOD1 in the mitochondria triggered the release of mitochondrial cytochrome c followed by the activation of caspase cascade and induced neuronal cell death without cytoplasmic mutant SOD1 aggregate formation.", "citation": {"db": "PubMed", "db_id": "12393885"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2444, "target": 478, "key": "cd4b0543d9f383075fd8ec80f06e6cb6"}, {"relation": "partOf", "source": 2444, "target": 1307, "key": "5752abbca02a16d21f42830fd44ff9da"}, {"relation": "partOf", "source": 2444, "target": 1309, "key": "5e3f8b57c998a1693bd2b071f254f51f"}, {"relation": "partOf", "source": 2444, "target": 1310, "key": "430c0eb2043e1961f58e907e5f26904a"}, {"relation": "partOf", "source": 2444, "target": 1308, "key": "e796b6cbd2bf42e21404bd9c94797068"}, {"relation": "partOf", "source": 2444, "target": 1304, "key": "33667a91cac17084dcc3005b85075308"}, {"relation": "partOf", "source": 2444, "target": 1306, "key": "be3650312df04dca5e8907329beeccc5"}, {"line": 34220, "relation": "increases", "evidence": "Two human proteases, caspase-3 and caspase-8, were identified and confirmed to act by a mechanism that involved proteolysis at the site in the APP-Gal4 chimera that corresponded to the natural caspase cleavage site in APP, thus linking a readily scorable phenotype to proteolytic processing of APP.", "citation": {"db": "PubMed", "db_id": "10911620"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2444, "target": 2315, "key": "1e59d8db6ce01e0b6f8969179bf82037"}, {"line": 34238, "relation": "increases", "evidence": "In the present study, we demonstrate that APP phosphorylated at Thr668 is less vulnerable to cytoplasmic cleavage by caspase-3 and caspase-8.", "citation": {"db": "PubMed", "db_id": "15178331"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2444, "target": 2315, "key": "eedaa3fab222bcce557c21346c72aa53"}, {"line": 2841, "relation": "association", "evidence": "APP-binding protein 1 reportedly interacts with AICD and activates the neddylation pathway, further down-regulating the level of b-catenin and potentially resulting in apoptotic process. In addition, cellular Ca2+ homeostasis appears to be modulated by AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2301, "target": 3563, "key": "6588bd625c20bb8069357651c02d6d37"}, {"relation": "partOf", "source": 2301, "target": 1108, "key": "e2be6a52409f1d4f6e8b293d9798a5ae"}, {"relation": "partOf", "source": 2301, "target": 1107, "key": "d81f794f70b6ae66b41bc67251cb489b"}, {"relation": "partOf", "source": 2301, "target": 1106, "key": "47c74c0bf13f9a17558a1995e121663c"}, {"line": 37835, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2301, "target": 2136, "key": "c15ff858bd3073268ed8c99d00240a11"}, {"line": 2846, "relation": "increases", "evidence": "APP-binding protein 1 reportedly interacts with AICD and activates the neddylation pathway, further down-regulating the level of b-catenin and potentially resulting in apoptotic process. In addition, cellular Ca2+ homeostasis appears to be modulated by AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1108, "target": 685, "key": "4577d6fa2825defb0b07a090dcf64b3a"}, {"line": 2847, "relation": "decreases", "evidence": "APP-binding protein 1 reportedly interacts with AICD and activates the neddylation pathway, further down-regulating the level of b-catenin and potentially resulting in apoptotic process. In addition, cellular Ca2+ homeostasis appears to be modulated by AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1108, "target": 2580, "key": "2062dc815a02be00a9a026be0229f40d"}, {"line": 2848, "relation": "increases", "evidence": "APP-binding protein 1 reportedly interacts with AICD and activates the neddylation pathway, further down-regulating the level of b-catenin and potentially resulting in apoptotic process. In addition, cellular Ca2+ homeostasis appears to be modulated by AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1108, "target": 478, "key": "9d60d84a25377e927cb65086689b3d64"}, {"line": 2895, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 685, "target": 645, "key": "dbbdc18f62d33f91c8dd94e9f307941f"}, {"line": 2876, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 1192, "target": 1579, "key": "569673f595c483eeb3b885c46035d1c7"}, {"line": 31577, "relation": "association", "evidence": "We show here that exogenous expression of a familial AD (FAD) mutant of APP or of the APP binding protein APP-BP1 in neurons causes enlargement of early endosomes, increased receptor-mediated endocytosis via a pathway dependent on APP-BP1 binding to APP, and apoptotic process.", "citation": {"db": "PubMed", "db_id": "17611268"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Gamma secretase subgraph": true}}, "source": 1192, "target": 478, "key": "0084ca3ed8bb95e9a968bec3ef6f8995"}, {"relation": "partOf", "source": 3087, "target": 1192, "key": "fc7bd2e405d2d308ba1fe23218827d59"}, {"relation": "partOf", "source": 3087, "target": 1579, "key": "0be1ee2049ef4521c3f82a7fea0de998"}, {"relation": "partOf", "source": 3087, "target": 1578, "key": "c54eb92a12f622282756833d421923b2"}, {"line": 33142, "relation": "increases", "evidence": "We have shown that APP-BP1 drives the S- to M-phase transition in dividing cells, and causes apoptosis in neurons.", "citation": {"db": "PubMed", "db_id": "14557245"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3087, "target": 478, "key": "44e5b886a7e52465793f00480787154c"}, {"relation": "partOf", "source": 3087, "target": 1577, "key": "d14cfa3ce045e6d6a5860e652616ab8f"}, {"line": 38009, "relation": "association", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3087, "target": 2315, "key": "b794458c5e35ece68d58f552eadd270c"}, {"line": 38010, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3087, "target": 445, "key": "46a0e0b6a42c76c7aeed875a8aa75e23"}, {"line": 38017, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3087, "target": 645, "key": "a20e739908f2cf304e4f1d8ee1e09d9b"}, {"line": 38021, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "subject": {"modifier": "Activity"}, "source": 3087, "target": 502, "key": "20bb36acfcc0caa7d472fdd4c00fbd3f"}, {"line": 2878, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1579, "target": 3098, "key": "affbc83c933239d7895a855ee1dfe804"}, {"relation": "partOf", "source": 3508, "target": 1579, "key": "71a6a8d87f7647d5b8175b1864e45ff4"}, {"relation": "partOf", "source": 3098, "target": 1585, "key": "0e6922edf219e4d8250692144f0e1970"}, {"line": 2880, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3098, "target": 1585, "key": "2670e8325a9a42deab7a59ea55c9374d"}, {"line": 2884, "relation": "association", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 3098, "target": 753, "key": "b26c1ee585213b05443b34be2dfeea92"}, {"line": 2885, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 3098, "target": 3048, "key": "8306cf5abbc66cda78146a586f463402"}, {"line": 2886, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 3098, "target": 3484, "key": "b64bc52dbd758b36ac14aa45506cef83"}, {"line": 2887, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 3098, "target": 2166, "key": "55ec6675f77d6b67f35413d2d3e5721d"}, {"line": 2889, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 3098, "target": 718, "key": "6f28c4c0aaa759b2d74bc52692771f37"}, {"line": 2890, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 3098, "target": 695, "key": "06a8bb504dae213f6c9657bc3fafc751"}, {"line": 2892, "relation": "association", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3098, "target": 3149, "key": "775bb9d6c646ca9c20be8e63fb83e3f6"}, {"line": 2893, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 3098, "target": 685, "key": "006f438f30029d3343e199974f80f298"}, {"line": 2894, "relation": "increases", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 3098, "target": 645, "key": "f7a689e2a812172d0b57b982fcd41903"}, {"relation": "partOf", "source": 3098, "target": 1584, "key": "5c0b4c8c3feb75b34185e76afc5566f1"}, {"relation": "partOf", "source": 3098, "target": 1577, "key": "f27667891d6206338f26253023234659"}, {"line": 2882, "relation": "association", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 1585, "target": 2479, "key": "c7fb69ac8f8db01e815551b47d515dac"}, {"relation": "partOf", "source": 3511, "target": 1585, "key": "a61f7cda82b32af3440c31315bbacf2f"}, {"line": 2882, "relation": "association", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 2479, "target": 1585, "key": "3b25a2ba6b69a19f4fe85e741fdccd6a"}, {"line": 2884, "relation": "association", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 753, "target": 3098, "key": "185f4c2263fafff7e75fcb826ba17650"}, {"relation": "hasVariant", "source": 3047, "target": 3048, "key": "3896c70abf5fa0e45963a0050a0e0a5d"}, {"line": 15212, "relation": "increases", "evidence": "Murine double minute 2 (MDM2) protein levels were decreased in AD cells relative to control lymphoblasts, suggesting an impairment of FOXO3a degradation.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3047, "target": 2703, "key": "7e4c42a03858e2a80894f0d14103034c"}, {"relation": "hasVariant", "source": 2165, "target": 2166, "key": "c96c4712f86ff8eaca9d5e475148b43f"}, {"line": 2892, "relation": "association", "evidence": "The interaction of APP with APP-BP1 activates a pathway leading to the conjugation of NEDD8, a ubiquitin-like protein, to its target (Fig. 1, ref. 44). APP-BP1, together with hUba3, is functionally analogous to the ubiquitin activating enzyme E1, with hUba3 containing the active cysteine and ATP binding site. When NEDD8 is activated by the APP-BP1/hUba3 complex, it forms a thiol ester bond with hUbc12, which has a function parallel to that of ubiquitin-conjugating enzyme Cdc32. Subsequently, NEDD8 is covalently coupled to lysine residues in its target proteins (46). So far, the proteins known to be neddylated via this pathway are a family of proteins called cullins (47) and the Mdm2 oncogene product, which in turn regulates neddylation of the cell cycle protein p53. Cullins are scaffold proteins for the E3 ubiquitin ligase complex, and neddylation of cullin enhances its ability to promote ubiquitination (49,50). Indeed, NEDD8 has been found in ubiquitinated neurofibrillary tangles in AD brain (51). NEDD8 signalling has been shown to regulate protein degradation pathways participating in cell cycle progression (52–55). The discovery of a novel protein, NUB1, which recruits NEDD8-conjugates to the proteasome for degradation, provides a direct link between these 2 systems (56,57). Inhibition of the neddylation pathway in neurons by expression of a dominant negative mutant of hUbc12 prevents FAD APP-mediated cell cycle entry and apoptotic process (44,45). Thus, elements of this pathway are attractive targets for potential therapies aimed at preventing neurons in AD brain from entering the cell cycle.", "citation": {"db": "PubMed", "db_id": "17113271"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3149, "target": 3098, "key": "b2f1706b2af559d6c7ffd9ad909408ad"}, {"line": 2917, "relation": "association", "evidence": "Mutations in presenilin2 (PS2), a homolog of PS1, are also associated with FAD. While the precise mechanism on how these mutations cause AD is unknown, multiple theories have arisen to explain the role of PS1 and PS2 mutations on AD pathogenesis. These mutations lead to abnormal function of gamma- secretase, the beta-catenin pathway, calcium homoeostasis and the lysosomal/autophagy pathway as well as chaperones. Among these hypotheses, the effect of PS mutations on gamma-secretase has been extensively investigated. gamma-Secretase is composed of at least four subunits: PS, Nicastrin, Aph1 and Pen2; with a total of 19 putative transmembrane domains.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1928, "target": 3823, "key": "ef99f0da8ee71d66e5ce02b1a2dfbf71"}, {"line": 6560, "relation": "association", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1928, "target": 3823, "key": "55ba09886fac644d340ec32fa2e07b49"}, {"relation": "partOf", "source": 1928, "target": 1667, "key": "794229da2461d57dcbf747d6fb476e13"}, {"line": 6569, "relation": "increases", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1928, "target": 80, "key": "e34c73b4003d2ff0669d0c7b9d37b34c"}, {"relation": "hasVariant", "source": 1928, "target": 1929, "key": "f730922d0cd3d434114a5db025f68a39"}, {"line": 2918, "relation": "association", "evidence": "Mutations in presenilin2 (PS2), a homolog of PS1, are also associated with FAD. While the precise mechanism on how these mutations cause AD is unknown, multiple theories have arisen to explain the role of PS1 and PS2 mutations on AD pathogenesis. These mutations lead to abnormal function of gamma- secretase, the beta-catenin pathway, calcium homoeostasis and the lysosomal/autophagy pathway as well as chaperones. Among these hypotheses, the effect of PS mutations on gamma-secretase has been extensively investigated. gamma-Secretase is composed of at least four subunits: PS, Nicastrin, Aph1 and Pen2; with a total of 19 putative transmembrane domains.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1667, "target": 868, "key": "29b630a0aba294d4c20092dc8221c4d5"}, {"line": 29949, "relation": "association", "evidence": "Presenilin (PS) proteins facilitate endoproteolysis of selected type I transmembrane proteins such as the Alzheimer's disease (AD) associated beta-Amyloid precursor protein (beta APP) and Notch.", "citation": {"db": "PubMed", "db_id": "11493036"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2332, "target": 3258, "key": "3fbaeb371f4f3f437d1b9d67ee2a23db"}, {"line": 38042, "relation": "association", "evidence": "Two recent studies (Lauren et al., 2009; Nikolaev et al., 2009) now connect the physiological and pathological functions of APP processing products. Lauren et al. show that ABeta¸42 binds to the cellular prion protein (PrP), which itself can cause neuropathology when misfolded. In a separate study, Nikolaev et al. report that the N-terminal fragment of APP (N-APP) interacts with death receptor 6 (DR6), resulting in pruning of axons and neurons during development of the central nervous system (CNS).These studies suggest that APP processing constitutes a complex signaling center that serves multiple physiological functions that could trigger pathological events when deregulated during disease.", "citation": {"db": "PubMed", "db_id": "19524503"}, "source": 2332, "target": 3478, "key": "1724f6937cc6c2dddb235e0cbad1d9e7"}, {"line": 38043, "relation": "increases", "evidence": "Two recent studies (Lauren et al., 2009; Nikolaev et al., 2009) now connect the physiological and pathological functions of APP processing products. Lauren et al. show that ABeta¸42 binds to the cellular prion protein (PrP), which itself can cause neuropathology when misfolded. In a separate study, Nikolaev et al. report that the N-terminal fragment of APP (N-APP) interacts with death receptor 6 (DR6), resulting in pruning of axons and neurons during development of the central nervous system (CNS).These studies suggest that APP processing constitutes a complex signaling center that serves multiple physiological functions that could trigger pathological events when deregulated during disease.", "citation": {"db": "PubMed", "db_id": "19524503"}, "source": 2332, "target": 654, "key": "ce30c7142374365a043eb207513fb3f2"}, {"relation": "partOf", "source": 2332, "target": 1250, "key": "2ac84a21b5072d26d69c13b3fd52c35e"}, {"relation": "hasVariant", "source": 3126, "target": 3127, "key": "80918d560b418aed607669c33c7a77a2"}, {"line": 22021, "relation": "association", "evidence": "Several of the Abeta42/43 -increasing mutants severely impaired the cleavages of Notch1 and CD44 substrates, which were not affected by any other L383 mutation.", "citation": {"db": "PubMed", "db_id": "23237322"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3126, "target": 2328, "key": "358033dad45ee3ff359b30718f675f86"}, {"relation": "partOf", "source": 3126, "target": 1581, "key": "31a7bba8e8290c4a17fe08c67c0a2a2a"}, {"relation": "partOf", "source": 3126, "target": 1114, "key": "5396f9748caadca01c3a6e5e5efb4452"}, {"relation": "partOf", "source": 3126, "target": 1595, "key": "4c27a273717e767746ad6a5b61fab25a"}, {"relation": "partOf", "source": 3126, "target": 1596, "key": "a5a152061db3dd2d857d7e2d96c162ef"}, {"relation": "partOf", "source": 3126, "target": 1372, "key": "aef06c5f7d06f971c7da07dfab5acb78"}, {"line": 32310, "relation": "association", "evidence": "The mammalian homologue of lin-12, Notch1, is a transmembrane receptor that plays an important role in cell fate decisions during development, including neurogenesis, but does not have a known function in fully differentiated cells. ", "citation": {"db": "PubMed", "db_id": "10366748"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3126, "target": 822, "key": "14316cf517b05b93b7c0df0eb8cbbf7e"}, {"relation": "partOf", "source": 3126, "target": 1498, "key": "67599b078762478cec09d7095967b463"}, {"relation": "partOf", "source": 3126, "target": 1194, "key": "59d0732e0fe3bfacd999efd8381c222a"}, {"line": 2955, "relation": "decreases", "evidence": "Our present study demonstrated that the PS1 mutants M146L, E280A and H163R directly affect gamma-secretase activity, which leads to a reduction in the rate of Notch1 cleavage.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3266, "target": 868, "key": "272aa8be52f1f6e2103801ec1fad09d2"}, {"line": 2957, "relation": "decreases", "evidence": "Our present study demonstrated that the PS1 mutants M146L, E280A and H163R directly affect gamma-secretase activity, which leads to a reduction in the rate of Notch1 cleavage.", "citation": {"db": "PubMed", "db_id": "22461631"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3264, "target": 868, "key": "7443e9047cecd55d390dd0d6dc1570cf"}, {"line": 2994, "relation": "directlyIncreases", "evidence": "Acetylcholinesterase (AChE) is thought to play an important role during apoptotic process. Our results showed that H2O2 induced AChE activity, a functional marker in apoptotic process, increases in neuronal-like PC12 cells. Glutathione, which is involved in cellular redox homeostasis, inhibited the increase of AChE activity, suggesting that reactive oxygen species (ROS) play a key role in this process. Further investigation showed that the elevation of AChE was observed after the degradation of Akt, release of cytochrome c from mitochondria into the cytosol, and activation of caspase family members. When nerve growth factor (NGF) was present, with the maintenance of Akt level, the elevation of AChE, the cytochrome c diffusion, as well as apoptotic process were markedly attenuated in H2O2-treated PC12 cells", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2153, "target": 2244, "key": "771de671f4574cf49c5311e4b5766eb1"}, {"line": 3030, "relation": "decreases", "evidence": "The overexpression of constitutively activated Akt, which is a downstream signalling element of the NGF receptor TrkA, delayed mitochondrial collapse and inhibited elevation of AChE activity. Thus, NGF prevented apoptosis and elevation of AChE activity by activating the Akt pathway and stabilizing the function of mitochondria", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2153, "target": 2244, "key": "47c909846d83c5eedbdc58a1000a7557"}, {"relation": "hasVariant", "source": 2153, "target": 2154, "key": "d3aa3d2203c49b985a98952d39660ef9"}, {"line": 6204, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2153, "target": 2796, "key": "52e98baf0764b28335f444836547bbf2"}, {"line": 36853, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2153, "target": 2796, "key": "8ecd982173dd824faff2b54c69baaa3b"}, {"line": 6205, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2153, "target": 2793, "key": "feed9c96e3ea1ae80134832dc9e0079f"}, {"line": 36866, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2153, "target": 2793, "key": "8b1a75561be6186422b2cd76de9f4ce4"}, {"line": 6206, "relation": "decreases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2153, "target": 2382, "key": "da076b5454cd560809e0d7805d0b9049"}, {"line": 24275, "relation": "decreases", "evidence": "Activation of PI3K after CsA treatment appeared to trigger opposite effects. First, CsA induced PI3K-dependent activation of Akt, which mediated cellular responses against cell injury. Akt activation led to transient phosphorylation and inhibition of the pro-apoptotic GSK3beta and Bad, thus preventing GSK3beta-mediated phosphorylation and activation of the pro-apoptotic Bax, and Bad-sequestering of Bcl-2.", "citation": {"db": "PubMed", "db_id": "16316932"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2153, "target": 2382, "key": "61d4d7f74beb940161ec203d51320ec5"}, {"line": 36860, "relation": "decreases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2153, "target": 2382, "key": "3df4f7ccfcc2592926f72206dec3dfe2"}, {"line": 6208, "relation": "decreases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2153, "target": 478, "key": "87d1e4b202734908462c46b29b1846b0"}, {"line": 36445, "relation": "decreases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2153, "target": 478, "key": "53d2296145045bbec0401f75bd6d28c3"}, {"line": 24271, "relation": "decreases", "evidence": "Activation of PI3K after CsA treatment appeared to trigger opposite effects. First, CsA induced PI3K-dependent activation of Akt, which mediated cellular responses against cell injury. Akt activation led to transient phosphorylation and inhibition of the pro-apoptotic GSK3beta and Bad, thus preventing GSK3beta-mediated phosphorylation and activation of the pro-apoptotic Bax, and Bad-sequestering of Bcl-2.", "citation": {"db": "PubMed", "db_id": "16316932"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2153, "target": 2794, "key": "3b65b0195eb19bdea5f9f2d96ec612f7"}, {"line": 36852, "relation": "decreases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2153, "target": 2794, "key": "19436dba00b1b2119394a57f98414b42"}, {"line": 24274, "relation": "decreases", "evidence": "Activation of PI3K after CsA treatment appeared to trigger opposite effects. First, CsA induced PI3K-dependent activation of Akt, which mediated cellular responses against cell injury. Akt activation led to transient phosphorylation and inhibition of the pro-apoptotic GSK3beta and Bad, thus preventing GSK3beta-mediated phosphorylation and activation of the pro-apoptotic Bax, and Bad-sequestering of Bcl-2.", "citation": {"db": "PubMed", "db_id": "16316932"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2153, "target": 2389, "key": "6a01ded72fcb012415b70977703f67dc"}, {"line": 36402, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "cytoplasm"}, "toLoc": {"namespace": "GO", "name": "nucleus"}}}, "source": 2153, "target": 2163, "key": "5c9c5a05c28853627ce812c2a5380b72"}, {"line": 36417, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2153, "target": 2383, "key": "c4c8ad172df761ebea278f25414871ca"}, {"line": 36428, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2153, "target": 2450, "key": "84d9a013db3c459f7986024f4e8c25ce"}, {"line": 36816, "relation": "association", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 2153, "target": 635, "key": "59b113f25f7a4fbbe71040ab6ecf4595"}, {"line": 36820, "relation": "association", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 2153, "target": 688, "key": "8fc87921ffa9c143f9671045ea65194d"}, {"relation": "partOf", "source": 2153, "target": 1670, "key": "5ce247b8f37772fbae2a1f90ad6a5aa1"}, {"relation": "hasVariant", "source": 2153, "target": 2155, "key": "6a7dfa30cc41c4473b0cc877dde86469"}, {"line": 36839, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2153, "target": 2153, "key": "dd087bb85e14ab86ba4f9c98ced7f7ac"}, {"relation": "hasVariant", "source": 2153, "target": 2156, "key": "86a845aeeb8f1c122c3cc45c6e3ccc2b"}, {"line": 36847, "relation": "decreases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2153, "target": 2177, "key": "766f7bcc5bac422004c822ea9dce4fd8"}, {"line": 36865, "relation": "decreases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2153, "target": 2792, "key": "1025c6cc9f0b1c9f2e9e79e791bf54ef"}, {"line": 40569, "relation": "association", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2153, "target": 65, "key": "7c74900efccde55e028faa8950ede93c"}, {"line": 3008, "relation": "directlyDecreases", "evidence": "Acetylcholinesterase (AChE) is thought to play an important role during apoptotic process. Our results showed that H2O2 induced AChE activity, a functional marker in apoptotic process, increases in neuronal-like PC12 cells. Glutathione, which is involved in cellular redox homeostasis, inhibited the increase of AChE activity, suggesting that reactive oxygen species (ROS) play a key role in this process. Further investigation showed that the elevation of AChE was observed after the degradation of Akt, release of cytochrome c from mitochondria into the cytosol, and activation of caspase family members. When nerve growth factor (NGF) was present, with the maintenance of Akt level, the elevation of AChE, the cytochrome c diffusion, as well as apoptotic process were markedly attenuated in H2O2-treated PC12 cells", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3116, "target": 2244, "key": "6b983674f8c4d881485afdd51ffa8c32"}, {"line": 3012, "relation": "directlyDecreases", "evidence": "Acetylcholinesterase (AChE) is thought to play an important role during apoptotic process. Our results showed that H2O2 induced AChE activity, a functional marker in apoptotic process, increases in neuronal-like PC12 cells. Glutathione, which is involved in cellular redox homeostasis, inhibited the increase of AChE activity, suggesting that reactive oxygen species (ROS) play a key role in this process. Further investigation showed that the elevation of AChE was observed after the degradation of Akt, release of cytochrome c from mitochondria into the cytosol, and activation of caspase family members. When nerve growth factor (NGF) was present, with the maintenance of Akt level, the elevation of AChE, the cytochrome c diffusion, as well as apoptotic process were markedly attenuated in H2O2-treated PC12 cells", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3116, "target": 645, "key": "21dc9e09732cf18ab54acc76d2e0aa66"}, {"line": 3034, "relation": "increases", "evidence": "The overexpression of constitutively activated Akt, which is a downstream signalling element of the NGF receptor TrkA, delayed mitochondrial collapse and inhibited elevation of AChE activity. Thus, NGF prevented apoptosis and elevation of AChE activity by activating the Akt pathway and stabilizing the function of mitochondria", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3116, "target": 3146, "key": "bbde0b8cd85a70769cad177d8397441c"}, {"line": 3081, "relation": "increases", "evidence": "The NGF protected neuronal cells from oxidative stress by activating the PI3K and Akt signaling pathways.", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3116, "target": 751, "key": "a1ef55586a1a5745b0db00f5fe58a385"}, {"line": 3082, "relation": "increases", "evidence": "The NGF protected neuronal cells from oxidative stress by activating the PI3K and Akt signaling pathways.", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3116, "target": 698, "key": "715b17c9b011c557002dd5d9675b0043"}, {"line": 4826, "relation": "increases", "evidence": "NGF increases APP levels through enhanced translation rate and that NO, which modulates the NGF-induced increase in APP protein, also regulates APP mRNA levels and could play a role in APP processing Interestingly, we also found that this inhibition of NOS only partially attenuated the increase in APP promoter activation mediated by NGF [7] suggesting that NGF- signal transduction pathways and NO may be influencing the rate of APP mRNA or protein synthesis or degradation in addition to altering gene transcription.", "citation": {"db": "PubMed", "db_id": "22550546"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3116, "target": 2315, "key": "c803f39f86887e0eb9fab08731c2d24a"}, {"line": 7325, "relation": "negativeCorrelation", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3116, "target": 2396, "key": "fe109090b7eacb7b56044e5c3a175cd9"}, {"line": 7328, "relation": "negativeCorrelation", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "source": 3116, "target": 478, "key": "c596b8b397da7d95452bc34ecb13a1f3"}, {"relation": "isA", "source": 3116, "target": 434, "key": "890e7ca8c535f646c58c7b9272d23534"}, {"relation": "partOf", "source": 3116, "target": 1592, "key": "748f29d794ea92a1a2a5cf6b412ae4a7"}, {"line": 35198, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3116, "target": 1592, "key": "78265cf3c1b0b84abdd1556373546924"}, {"relation": "partOf", "source": 3116, "target": 1589, "key": "f91ea1230bcbe89f1db076004f5a05cd"}, {"relation": "partOf", "source": 3116, "target": 1593, "key": "87d82fef8cb1ddb0a47df4a0045904fb"}, {"line": 31810, "relation": "association", "evidence": "NGF, the principal neurotrophic factor for basal forebrain cholinergic neurons (BFCNs), has been correlated to Alzheimer's disease (AD) because of the selective vulnerability of BFCNs in AD.", "citation": {"db": "PubMed", "db_id": "20566851"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3116, "target": 3823, "key": "35ac360c3e0b564bd8c9e998a576e67e"}, {"relation": "partOf", "source": 3116, "target": 1594, "key": "6c2022734348da9ad6ab92f4afcf858d"}, {"line": 35175, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3116, "target": 3118, "key": "b977f39fa1c09fc4a03eaae5ba8dd5d4"}, {"line": 38660, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3116, "target": 3118, "key": "87322a1f12336238860c799752cb8fa2"}, {"line": 38395, "relation": "increases", "evidence": "We find that NGF induces tyrosine phosphorylation of APP, and that APP interacts with TrkA and this interaction requires Y(682). Unpredictably, we also uncover that APP, and specifically Y(682), regulates activation of the NGF/TrkA signaling pathway in vivo, the subcellular distribution of TrkA and the sensitivity of neurons to the trophic action of NGF.", "citation": {"db": "PubMed", "db_id": "21849536"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3116, "target": 2340, "key": "9be039272b543c5351bb184c260fc3cc"}, {"line": 38396, "relation": "increases", "evidence": "We find that NGF induces tyrosine phosphorylation of APP, and that APP interacts with TrkA and this interaction requires Y(682). Unpredictably, we also uncover that APP, and specifically Y(682), regulates activation of the NGF/TrkA signaling pathway in vivo, the subcellular distribution of TrkA and the sensitivity of neurons to the trophic action of NGF.", "citation": {"db": "PubMed", "db_id": "21849536"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3116, "target": 1197, "key": "4869d101865c5e382b80b7100b3885fd"}, {"line": 49413, "relation": "increases", "evidence": "Second, vitamin D stimulates the synthesis of NGF within the hippocampus, leading to an enhanced neurite outgrowth and a reduced cellular proliferation.", "citation": {"db": "PubMed", "db_id": "29318446"}, "annotations": {"Confidence": {"Medium": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 3116, "target": 652, "key": "88899be4a1d5474e363ce77215198fdf"}, {"line": 3024, "relation": "directlyIncreases", "evidence": "The overexpression of constitutively activated Akt, which is a downstream signalling element of the NGF receptor TrkA, delayed mitochondrial collapse and inhibited elevation of AChE activity. Thus, NGF prevented apoptosis and elevation of AChE activity by activating the Akt pathway and stabilizing the function of mitochondria", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3146, "target": 2153, "key": "e25e275884be78d238477d07a0a6d0a1"}, {"line": 3028, "relation": "decreases", "evidence": "The overexpression of constitutively activated Akt, which is a downstream signalling element of the NGF receptor TrkA, delayed mitochondrial collapse and inhibited elevation of AChE activity. Thus, NGF prevented apoptosis and elevation of AChE activity by activating the Akt pathway and stabilizing the function of mitochondria", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3146, "target": 2244, "key": "f6442faa27220c648867faa26abf844d"}, {"line": 3035, "relation": "decreases", "evidence": "The overexpression of constitutively activated Akt, which is a downstream signalling element of the NGF receptor TrkA, delayed mitochondrial collapse and inhibited elevation of AChE activity. Thus, NGF prevented apoptosis and elevation of AChE activity by activating the Akt pathway and stabilizing the function of mitochondria", "citation": {"db": "PubMed", "db_id": "17213958"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3146, "target": 645, "key": "f3aab133e5a0e0c13c3bd3041e0c7fc6"}, {"line": 8008, "relation": "association", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "Confidence": {"High": true}}, "source": 3146, "target": 806, "key": "dcb88b3236e8976bd3982361c22eded8"}, {"relation": "partOf", "source": 3146, "target": 1593, "key": "2bbc882e9d908d2abb9cb7b87a1a02b1"}, {"relation": "isA", "source": 3146, "target": 2199, "key": "dced433c6b5ae905f43e40d74c6a8515"}, {"relation": "partOf", "source": 3146, "target": 1599, "key": "3a4d88fe7714919b5e0f3085c598083d"}, {"relation": "partOf", "source": 3146, "target": 1197, "key": "b4264b4db5e3f66efa23fd80cdb27b6e"}, {"line": 3108, "relation": "increases", "evidence": "BACE1 is primarily expressed by neurons and increased BACE1 protein concentrations and enzymatic activities have been reported in the brains of AD patients. However, there is accumulating evidence that, in addition to neurons, reactive astrocytes are capable of expressing BACE1 and, therefore, may contribute to beta-amyloid plaque formation. This suggests that conditions accompanied by chronic astrocyte activation may contribute to developing AD.", "citation": {"db": "PubMed", "db_id": "15465276"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 2375, "key": "57ac00cad0f6bf98eda72c60e6ed4566"}, {"line": 3524, "relation": "increases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 80, "key": "7afcc1f01c64ecf8711f450a68c9e823"}, {"line": 5169, "relation": "increases", "evidence": "beta-Amyloid (Abeta) plays a central role in Alzheimer's disease (AD) pathogenesis. Neurons are major sources of Abeta in the brain. However, astrocytes outnumber neurons by at least five-fold. Thus, even a small level of astrocytic Abeta production could make a significant contribution to Abeta burden in AD. Moreover, activated astrocytes may increase Abeta generation. beta-Site APP cleaving enzyme 1 (BACE1) cleavage of amyloid precursor protein (APP) initiates Abeta production.", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}}, "source": 418, "target": 80, "key": "18e51901eea06ea8827ced31c449e5f4"}, {"line": 40347, "relation": "increases", "evidence": "In addition, primary human astrocytes stimulated with the RIG-1 ligand 5'ppp RNA showed increased expression of amyloid precursor protein (APP) and amyloid-beta (Abeta), supporting the idea that RIG-1 is involved in the pathology of MCI associated with early progression to AD.These findings suggest that RIG-1 may play a critical role in incipient AD.", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "MeSHDisease": {"Cognitive Dysfunction": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}}, "source": 418, "target": 80, "key": "5c8fce58448dd68bba05206896ed243b"}, {"line": 43408, "relation": "increases", "evidence": "Cytokines including TNF-α+IFN-gamma increase levels of endogenous BACE1, APP, and Abeta and stimulate amyloidogenic/ APP processing in astrocytes. Oligomeric and fibrillar Abeta42 also increase levels of astrocytic BACE1, APP, and beta-secretase/ processing. Together, our results suggest a cytokine- and Abeta42-driven feed-forward mechanism that promotes astrocytic Abeta/ production. Given that astrocytes greatly outnumber neurons, activated astrocytes may represent significant sources of Abeta/ during neuroinflammation in AD", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 80, "key": "aa3485b7778bd6ab2722e28fc4c059fe"}, {"line": 4194, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 418, "target": 524, "key": "0a0640cb4f9454599bb36d0c28f3aa2d"}, {"line": 4196, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 418, "target": 2879, "key": "404781d90d34ca892de5c9ae7902cd28"}, {"line": 4198, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 418, "target": 2603, "key": "d13c9337f179eb23e667f3013b3f5f7c"}, {"line": 40250, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2603, "key": "88b479b408c0701348fc31f1e78317dd"}, {"line": 4199, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 418, "target": 2455, "key": "b6bb1a7cc7301415f09e157d960e11b4"}, {"line": 40222, "relation": "increases", "evidence": "Non-stimulated human astrocytes in culture expressed eight cytokines, including G-CSF, GM-CSF, GROα (CXCL1), IL-6, IL-8 (CXCL8), MCP-1 (CCL2), MIF and Serpin E1.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2455, "key": "71cc3a083b860b852499b2f0d75133b7"}, {"line": 4200, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 418, "target": 2457, "key": "f1c75662da8bd0d8db42e7103d172195"}, {"line": 40251, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2457, "key": "f008db4e19932b3819d70bf445111488"}, {"line": 4201, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 418, "target": 2459, "key": "bca65e76be2ec6e0c7815b75731d4976"}, {"line": 40252, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2459, "key": "335a8ba5c1fb6157ee6d9f95bea88e4b"}, {"line": 4202, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 418, "target": 2602, "key": "02fd747001321916da40bdae0f082318"}, {"line": 40226, "relation": "increases", "evidence": "Non-stimulated human astrocytes in culture expressed eight cytokines, including G-CSF, GM-CSF, GROα (CXCL1), IL-6, IL-8 (CXCL8), MCP-1 (CCL2), MIF and Serpin E1.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2602, "key": "3e7fa55f8b7813a758461f6ec5596552"}, {"line": 4203, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 418, "target": 2570, "key": "4d3cfa56fe5d80297ea081e2535a197e"}, {"line": 40203, "relation": "increases", "evidence": "Non-stimulated human astrocytes in culture expressed eight cytokines, including G-CSF, GM-CSF, GROα (CXCL1), IL-6, IL-8 (CXCL8), MCP-1 (CCL2), MIF and Serpin E1.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2570, "key": "eb46d26152ffd0da314ef91acf3ee2a2"}, {"line": 4205, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 418, "target": 2885, "key": "daca913b3e28fd51d6aa57cf3cd1bf63"}, {"line": 40238, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2885, "key": "f780fcdae2e0dc53a10082210d0176ab"}, {"line": 4206, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 418, "target": 2894, "key": "395c3178d919af6299d7927ffb756437"}, {"line": 40215, "relation": "increases", "evidence": "Non-stimulated human astrocytes in culture expressed eight cytokines, including G-CSF, GM-CSF, GROα (CXCL1), IL-6, IL-8 (CXCL8), MCP-1 (CCL2), MIF and Serpin E1.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2894, "key": "e0fd43ce34230cbc4941daa35cd3ad25"}, {"line": 4207, "relation": "increases", "evidence": "Microglia produced only IL-12p40 and CXCL10, whereas astroglia produced these cytokines plus CCL2, CCL3, CCL5, CXCL1, G-CSF, IL-1beta, IL-6, IL-12p70, and IL-13.", "citation": {"db": "PubMed", "db_id": "19710140"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 418, "target": 2880, "key": "fc965819e92adce83d976338143212dc"}, {"line": 6350, "relation": "negativeCorrelation", "evidence": "Correspondingly, the reduced expression of neuronal and oligodendroglial specific genes and the increased expression of astrocytic and microglial inflammatory genes in AD were attributed to progressive brain insulin/IGF deficiency and resistance.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 2899, "key": "db7935b077df59e8cc9147e1ce398078"}, {"line": 6353, "relation": "negativeCorrelation", "evidence": "Correspondingly, the reduced expression of neuronal and oligodendroglial specific genes and the increased expression of astrocytic and microglial inflammatory genes in AD were attributed to progressive brain insulin/IGF deficiency and resistance.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 2871, "key": "328c7b899d3e9965cfcd1586f62585e1"}, {"line": 6354, "relation": "negativeCorrelation", "evidence": "Correspondingly, the reduced expression of neuronal and oligodendroglial specific genes and the increased expression of astrocytic and microglial inflammatory genes in AD were attributed to progressive brain insulin/IGF deficiency and resistance.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 2874, "key": "5ab8980388c244e3ec268e7f97ad0a20"}, {"line": 6358, "relation": "increases", "evidence": "Correspondingly, the reduced expression of neuronal and oligodendroglial specific genes and the increased expression of astrocytic and microglial inflammatory genes in AD were attributed to progressive brain insulin/IGF deficiency and resistance.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 3861, "key": "5d43adb4560c1a972a4adf4b7495a766"}, {"line": 6369, "relation": "association", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 418, "target": 3920, "key": "0ce8ed4dadd45b97c67e32685222966a"}, {"line": 26345, "relation": "association", "evidence": "We conclude that astrocytes: (i) strongly regulate neuronal APP expression in primary neurons, and (ii) promote the amyloidogenic pathway in an apoE4-dependent manner. Thus, apoE and astrocytic factor(s) may pmodulate the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11553277"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 418, "target": 2315, "key": "875ab4b475590224b3d314c4b6806ba9"}, {"line": 40346, "relation": "increases", "evidence": "In addition, primary human astrocytes stimulated with the RIG-1 ligand 5'ppp RNA showed increased expression of amyloid precursor protein (APP) and amyloid-beta (Abeta), supporting the idea that RIG-1 is involved in the pathology of MCI associated with early progression to AD.These findings suggest that RIG-1 may play a critical role in incipient AD.", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "MeSHDisease": {"Cognitive Dysfunction": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}}, "source": 418, "target": 2315, "key": "5fa56646c6140c63535627a3685d6003"}, {"line": 26347, "relation": "increases", "evidence": "We conclude that astrocytes: (i) strongly regulate neuronal APP expression in primary neurons, and (ii) promote the amyloidogenic pathway in an apoE4-dependent manner. Thus, apoE and astrocytic factor(s) may pmodulate the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11553277"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 418, "target": 864, "key": "aa048d86d4303d4160be609cbb4881c4"}, {"line": 36310, "relation": "increases", "evidence": "Cellular uptake and degradation by glial cells is one means by which ABeta¸ may be cleared from the brain. In the current study, we demonstrate that modulating levels of the low-density lipoprotein receptor (LDLR), a cell surface receptor that regulates the amount of apolipoprotein E (apoE) in the brain, altered both the uptake and degradation of ABeta¸ by astrocytes. Deletion of LDLR caused a decrease in ABeta¸ uptake, while increasing LDLR levels significantly enhanced both the uptake and clearance of ABeta¸. Increasing LDLR levels also enhanced the cellular degradation of ABeta¸ and facilitated the vesicular transport of ABeta¸ to lysosomes. Despite the fact that LDLR regulated the uptake of apoE by astrocytes, we found that the effect of LDLR on ABeta¸ uptake and clearance occurred in the absence of apoE. Finally, we provide evidence that ABeta¸ can directly bind to LDLR, suggesting an interaction between LDLR and ABeta¸ could be responsible for LDLR-mediated ABeta¸ uptake. Therefore, these results identify LDLR as a receptor that mediates ABeta¸ uptake and clearance by astrocytes, and provide evidence that increasing glial LDLR levels may promote ABeta¸ degradation within the brain", "citation": {"db": "PubMed", "db_id": "22383525"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 418, "target": 2328, "key": "6fbfed7081cce0e3ec1b2ff76148bc59"}, {"line": 39002, "relation": "increases", "evidence": "Astrocyte is the most abundant type of glial cells in the central nervous system (CNS) and appears to be/ involved in the induction of neuroinflammation. Under stress and injury, astrocytes become astrogliotic leading to an / upregulation of the expression of proinflammatory cytokines and chemokines, which are associated with the pathogenesis of AD. ", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 418, "target": 577, "key": "81981f09a3ebe5d59564c7089425074c"}, {"line": 43318, "relation": "association", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 418, "target": 577, "key": "a9d2d8e225c7c71fd8ae7502eb55e417"}, {"line": 39324, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 418, "target": 3815, "key": "73dc58551a364ea8a09239002f2ccf32"}, {"line": 39369, "relation": "positiveCorrelation", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 418, "target": 2392, "key": "c45dd96d45c8a76201dcd7fb88afd209"}, {"line": 40209, "relation": "increases", "evidence": "Non-stimulated human astrocytes in culture expressed eight cytokines, including G-CSF, GM-CSF, GROα (CXCL1), IL-6, IL-8 (CXCL8), MCP-1 (CCL2), MIF and Serpin E1.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2569, "key": "759e050f9358bd7ea4c5ac9f79e95f78"}, {"line": 40216, "relation": "increases", "evidence": "Non-stimulated human astrocytes in culture expressed eight cytokines, including G-CSF, GM-CSF, GROα (CXCL1), IL-6, IL-8 (CXCL8), MCP-1 (CCL2), MIF and Serpin E1.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2606, "key": "69c6364f061e5316b2447b8b460e30d6"}, {"line": 40224, "relation": "increases", "evidence": "Non-stimulated human astrocytes in culture expressed eight cytokines, including G-CSF, GM-CSF, GROα (CXCL1), IL-6, IL-8 (CXCL8), MCP-1 (CCL2), MIF and Serpin E1.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 3050, "key": "59fcf3e9de8fad0d7c1524fb38b574c1"}, {"line": 40225, "relation": "increases", "evidence": "Non-stimulated human astrocytes in culture expressed eight cytokines, including G-CSF, GM-CSF, GROα (CXCL1), IL-6, IL-8 (CXCL8), MCP-1 (CCL2), MIF and Serpin E1.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 3351, "key": "d1e9cf0160b5e5c65d57a1c33dfdf24d"}, {"line": 40239, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2887, "key": "323b8ac12d891d3810b1e2cfa34cb1dd"}, {"line": 40245, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 3472, "key": "81eea562610122f7ef2d10307d933cba"}, {"line": 40257, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2863, "key": "01ba3ff8f58deeeb9e17aaa0f96cd5de"}, {"line": 40264, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Complement system subgraph": true}, "Confidence": {"High": true}}, "source": 418, "target": 2411, "key": "b4c7ae6b635d3e17b54cf510530bf988"}, {"line": 40348, "relation": "association", "evidence": "In addition, primary human astrocytes stimulated with the RIG-1 ligand 5'ppp RNA showed increased expression of amyloid precursor protein (APP) and amyloid-beta (Abeta), supporting the idea that RIG-1 is involved in the pathology of MCI associated with early progression to AD.These findings suggest that RIG-1 may play a critical role in incipient AD.", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "MeSHDisease": {"Cognitive Dysfunction": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}}, "source": 418, "target": 3839, "key": "772d6d20df77be6894fd00eefbbbf6d8"}, {"line": 43350, "relation": "increases", "evidence": "Here we report that the expression of COX-2 and glial fibrillary acidic protein were enhanced and that of/ peroxisome proliferator-activated receptor gamma (PPARgamma) was decreased in Abeta(25-35)-treated astrocytes. In line/ with these results, nuclear factor-kappaB translocation was increased in the presence of Abeta.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Prostaglandin subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 3636, "key": "af05e8afb9bd9406c72a7f62777ee3e4"}, {"line": 43362, "relation": "decreases", "evidence": "Here we report that the expression of COX-2 and glial fibrillary acidic protein were enhanced and that of/ peroxisome proliferator-activated receptor gamma (PPARgamma) was decreased in Abeta(25-35)-treated astrocytes. In line/ with these results, nuclear factor-kappaB translocation was increased in the presence of Abeta.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Prostaglandin subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 3699, "key": "c8066089907aeb496ad939dcd91057c8"}, {"line": 43370, "relation": "increases", "evidence": "Here we report that the expression of COX-2 and glial fibrillary acidic protein were enhanced and that of/ peroxisome proliferator-activated receptor gamma (PPARgamma) was decreased in Abeta(25-35)-treated astrocytes. In line/ with these results, nuclear factor-kappaB translocation was increased in the presence of Abeta.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Prostaglandin subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 418, "target": 3706, "key": "a138c1c60098dba7ff09815dbeab660f"}, {"line": 43463, "relation": "increases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 418, "target": 565, "key": "af6dbcc661580173fe0768279de5123a"}, {"line": 43465, "relation": "increases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 418, "target": 587, "key": "8d4551fae555fe5ec7a071fe9a7f7daf"}, {"line": 3110, "relation": "increases", "evidence": "BACE1 is primarily expressed by neurons and increased BACE1 protein concentrations and enzymatic activities have been reported in the brains of AD patients. However, there is accumulating evidence that, in addition to neurons, reactive astrocytes are capable of expressing BACE1 and, therefore, may contribute to beta-amyloid plaque formation. This suggests that conditions accompanied by chronic astrocyte activation may contribute to developing AD.", "citation": {"db": "PubMed", "db_id": "15465276"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 480, "target": 3823, "key": "5ae326d46ef5ce6d97937b3fa8df33c3"}, {"line": 26699, "relation": "increases", "evidence": "This suggests that conditions accompanied by chronic astrocyte activation may contribute to developing AD.", "citation": {"db": "PubMed", "db_id": "15465276"}, "annotations": {"Confidence": {"Medium": true}}, "source": 480, "target": 3823, "key": "ff3fc7a2a6afb2d09ac145980318e4d5"}, {"line": 26716, "relation": "association", "evidence": "This would suggest that the mechanism for astrocyte activation plays a role in the development of AD and that therapeutic strategies that target astrocyte activation in brain may be beneficial for the treatment of AD.", "citation": {"db": "PubMed", "db_id": "15663471"}, "source": 480, "target": 3823, "key": "adb7b271508ba998cba36d02818d5d29"}, {"line": 43321, "relation": "positiveCorrelation", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 480, "target": 3823, "key": "b199849341f54313424a035b8bedfad8"}, {"line": 39598, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}}, "source": 480, "target": 2312, "key": "d5131a2a33240238acf975c0d0781e80"}, {"line": 43212, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 480, "target": 2312, "key": "897c0e4b7a52b9631d14ec7950235789"}, {"line": 39603, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 480, "target": 3556, "key": "e2894be5736806e65873db9ba1c2f73b"}, {"line": 43220, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Confidence": {"Medium": true}}, "source": 480, "target": 3350, "key": "594d4df939843974c95c7ba088362b52"}, {"line": 3125, "relation": "regulates", "evidence": "The cellular levels of Abeta-site APP cleaving enzyme 1 (BACE1), the rate-limiting enzyme for the generation of the Alzheimer disease (AD) amyloid Abeta-peptide (Abeta), are tightly regulated by two ER-based acetyl-CoA:lysine acetyltransferases, ATase1 and ATase2. Here we report that both acetyltransferases are expressed in neurons and glial cells, and are up-regulated in the brain of AD patients. We also report the identification of first and second generation compounds that inhibit ATase1/ATase2 and down-regulate the expression levels as well as activity of BACE1. The mechanism of action involves competitive and non-competitive inhibition as well as generation of unstable intermediates of the ATases that undergo degradation.", "citation": {"db": "PubMed", "db_id": "22267734"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Cell": {"microglial cell": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3089, "target": 2376, "key": "87191f497be984e2a631a494c743642b"}, {"line": 3139, "relation": "increases", "evidence": "Ex vivo studies show that the levels of BACE1 are tightly regulated by the ATases. Specifically, up-regulation of ATase1 and ATase2 increases the levels of BACE1 and the generation of Abeta while siRNA-mediated down-regulation of either transferase achieves the opposite effects.", "citation": {"db": "PubMed", "db_id": "22267734"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Cell": {"microglial cell": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3089, "target": 2375, "key": "564f9b967e424601a2e565fbc2f29cde"}, {"line": 3140, "relation": "increases", "evidence": "Ex vivo studies show that the levels of BACE1 are tightly regulated by the ATases. Specifically, up-regulation of ATase1 and ATase2 increases the levels of BACE1 and the generation of Abeta while siRNA-mediated down-regulation of either transferase achieves the opposite effects.", "citation": {"db": "PubMed", "db_id": "22267734"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Cell": {"microglial cell": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3089, "target": 80, "key": "dba214f565ee360835ea6f6c172b7e69"}, {"line": 3127, "relation": "regulates", "evidence": "The cellular levels of Abeta-site APP cleaving enzyme 1 (BACE1), the rate-limiting enzyme for the generation of the Alzheimer disease (AD) amyloid Abeta-peptide (Abeta), are tightly regulated by two ER-based acetyl-CoA:lysine acetyltransferases, ATase1 and ATase2. Here we report that both acetyltransferases are expressed in neurons and glial cells, and are up-regulated in the brain of AD patients. We also report the identification of first and second generation compounds that inhibit ATase1/ATase2 and down-regulate the expression levels as well as activity of BACE1. The mechanism of action involves competitive and non-competitive inhibition as well as generation of unstable intermediates of the ATases that undergo degradation.", "citation": {"db": "PubMed", "db_id": "22267734"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Cell": {"microglial cell": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3088, "target": 2376, "key": "ee899a1c3012cf7d4a9d621cc7336d86"}, {"line": 3142, "relation": "increases", "evidence": "Ex vivo studies show that the levels of BACE1 are tightly regulated by the ATases. Specifically, up-regulation of ATase1 and ATase2 increases the levels of BACE1 and the generation of Abeta while siRNA-mediated down-regulation of either transferase achieves the opposite effects.", "citation": {"db": "PubMed", "db_id": "22267734"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Cell": {"microglial cell": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3088, "target": 2375, "key": "7837456c7f40720a0c856e9c7c482867"}, {"line": 3155, "relation": "increases", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "GO", "name": "trans-Golgi network"}}}, "source": 2747, "target": 2375, "key": "a417498cd14f63a502c8da4d837268fb"}, {"line": 3158, "relation": "increases", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2747, "target": 2375, "key": "7df502224914c01e96f7292cfc27736e"}, {"line": 3164, "relation": "association", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2747, "target": 3823, "key": "188cb86f0bc1b1e2dcac303cfb4560be"}, {"relation": "partOf", "source": 2747, "target": 1265, "key": "81a78676f1026c3dace4ac08b0b3c861"}, {"relation": "partOf", "source": 2747, "target": 1703, "key": "30633fba2dd3d3de7c1c4cf71e75af67"}, {"line": 3156, "relation": "increases", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2378, "target": 2747, "key": "20ba63d64b8d4802f0bebc756909a16b"}, {"line": 3159, "relation": "increases", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2378, "target": 2375, "key": "7f238bcbca467d75fca957799eda4afa"}, {"line": 3160, "relation": "increases", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2379, "target": 2749, "key": "2cc91b042a1ee93b4ce8f59196d12b98"}, {"line": 3162, "relation": "increases", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Lysosomes"}}}, "source": 2379, "target": 2375, "key": "935343549e0b50d3db45f463724985ac"}, {"line": 3161, "relation": "increases", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Lysosomes"}}}, "source": 2749, "target": 2375, "key": "58182b60d409fa1648891ce7568c211b"}, {"line": 28110, "relation": "decreases", "evidence": "RNAi silencing of GGA3 also elevated levels of BACE and Abeta.", "citation": {"db": "PubMed", "db_id": "17553422"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2749, "target": 2375, "key": "047b885acb2db2747390ca16c8fe9525"}, {"line": 28115, "relation": "decreases", "evidence": "RNAi silencing of GGA3 also elevated levels of BACE and Abeta.", "citation": {"db": "PubMed", "db_id": "17553422"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2749, "target": 80, "key": "bc12ac300100b97abd95c2eb3ff825d5"}, {"relation": "partOf", "source": 2749, "target": 1307, "key": "05dc2f37be214f61ad30b7b41eed4b28"}, {"relation": "partOf", "source": 2749, "target": 1705, "key": "a9928c75be62bef86e66f4e77e44ff49"}, {"line": 3165, "relation": "association", "evidence": "Abeta-Site APP-cleaving enzyme (BACE1) cleaves the amyloid precursor protein (APP) at the Abeta-secretase site to initiate the production of Abeta peptides. These accumulate to form toxic oligomers and the amyloid plaques associated with Alzheimer's disease (AD). An increase of BACE1 levels in the brain of AD patients has been mostly attributed to alterations of its intracellular trafficking. Golgi-associated adaptor proteins, GGA sort BACE1 for export to the endosomal compartment, which is the major cellular site of BACE1 activity. BACE1 undergoes recycling between endosome, trans-Golgi network (TGN), and the plasma membrane, from where it is endocytosed and either further recycled or retrieved to the endosome. Phosphorylation of Ser498 facilitates BACE1 recognition by GGA1 for retrieval to the endosome. Ubiquitination of BACE1 C-terminal Lys501 signals GGA3 for exporting BACE1 to the lysosome for degradation. In addition, the retromer mediates the retrograde transport of BACE1 from endosome to TGN. Decreased levels of GGA proteins and increased levels of retromer-associated sortilin have been associated with AD. Both would promote the co-localization of BACE1 and the amyloid precursor protein in the TGN and endosomes. Decreased levels of GGA3 also impair BACE1 degradation. Further understanding of BACE1 trafficking and its regulation may offer new therapeutic approaches for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22171895"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3399, "target": 3823, "key": "a2c1888a9126327d656b642d7cba924f"}, {"line": 3177, "relation": "regulates", "evidence": "AMP-activated protein kinase (AMPK), a master regulator of cellular energy homeostasis and a central player in glucose and lipid metabolism, is potentially implicated in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Autophagy signaling subgraph": true, "Sphingolipid metabolic subgraph": true, "Tau protein subgraph": true}}, "source": 2210, "target": 566, "key": "62ad566b0c5c20062bb22845052dcc99"}, {"line": 3178, "relation": "regulates", "evidence": "AMP-activated protein kinase (AMPK), a master regulator of cellular energy homeostasis and a central player in glucose and lipid metabolism, is potentially implicated in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Autophagy signaling subgraph": true, "Sphingolipid metabolic subgraph": true, "Tau protein subgraph": true}}, "source": 2210, "target": 592, "key": "f3e5d30f56ddff539f02c90d08f33990"}, {"line": 3179, "relation": "association", "evidence": "AMP-activated protein kinase (AMPK), a master regulator of cellular energy homeostasis and a central player in glucose and lipid metabolism, is potentially implicated in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Autophagy signaling subgraph": true, "Sphingolipid metabolic subgraph": true, "Tau protein subgraph": true}}, "source": 2210, "target": 3823, "key": "f960c15209032e6e162a8e8d5a04f887"}, {"line": 3188, "relation": "negativeCorrelation", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 3823, "key": "a74615eaa3f2abe1ed7c49e3a6a954b5"}, {"line": 3189, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 80, "key": "b221aa2034c4f656a7f80675e3ed98b0"}, {"line": 3228, "relation": "increases", "evidence": "Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 80, "key": "b81224321196df3f1246e9f586e78803"}, {"line": 3193, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 3015, "key": "dfac5e5d7f6a0526b610e496d1ed1515"}, {"line": 3232, "relation": "increases", "evidence": "Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 3015, "key": "8841725c300adf9715e02c5a4d284fa8"}, {"line": 3199, "relation": "regulates", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2210, "target": 176, "key": "5c23e9f241ccf98ecaa9e25468129085"}, {"line": 3200, "relation": "regulates", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2210, "target": 231, "key": "f2c348a0066767d5e2b7277417640179"}, {"line": 3201, "relation": "regulates", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane Structures"}}}, "source": 2210, "target": 2315, "key": "2aa2d90d2666d77cd24be70a80c9f7bb"}, {"relation": "hasVariant", "source": 2210, "target": 2211, "key": "bdd66b4be20a81d7975e0ab5ec5f0bde"}, {"line": 3211, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 3023, "key": "112314b7cd29425f29639561a97e0c6d"}, {"line": 3213, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 3035, "key": "c2938c12b4333becef5e6c03598c4958"}, {"line": 3215, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 3026, "key": "6a7c898c9b716ec5b670fb976d53c6da"}, {"line": 3227, "relation": "decreases", "evidence": "Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 460, "key": "5f7d73b28745f2cddbecf305a2db45f4"}, {"line": 3235, "relation": "increases", "evidence": "Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Autophagy signaling subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2210, "target": 808, "key": "077ea3d1c1fe131fe2662bcfb2f8ea96"}, {"line": 3239, "relation": "increases", "evidence": "Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 2210, "target": 428, "key": "3c4a53e71e6a4a8a661b022eace12059"}, {"line": 9667, "relation": "negativeCorrelation", "evidence": "It has been argued that in late-onset Alzheimer's disease a disturbance in the control of neuronal glucose metabolism consequent to impaired insulin signalling strongly resembles the pathophysiology of type 2 diabetes in non-neural tissue.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Tissues": true}, "Confidence": {"Low": true}}, "source": 566, "target": 734, "key": "62daaa472b94e473993eb90aca00aac6"}, {"line": 10212, "relation": "negativeCorrelation", "evidence": "Previous studies demonstrated that adiponectin modulates memory and cognitive impairment and contributes to the deregulated glucose metabolism and mitochondrial dysfunction observed in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 566, "target": 2259, "key": "b6198a58e3d349cde1ec3e481d771df9"}, {"line": 40384, "relation": "association", "evidence": "Previous studies demonstrated that adiponectin modulates memory and cognitive impairment and contributes to the deregulated glucose metabolism and mitochondrial dysfunction observed in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 566, "target": 3823, "key": "44770e5201c7a6f8502172b093559b71"}, {"line": 43460, "relation": "association", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 566, "target": 228, "key": "ded3be2202f006aa39306602c9b9a89a"}, {"line": 3556, "relation": "association", "evidence": "The implication that cholesterol plays an essential role in the pathogenesis of Alzheimer's disease (AD) is based on the 1993 finding that the presence of apolipoprotein E (apoE) allele epsilon;4 is a strong risk factor for developing AD. Since apoE is a regulator of lipid metabolism, it is reasonable to assume that lipids such as cholesterol are involved in the pathogenesis of AD", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"High": true}}, "source": 592, "target": 2312, "key": "e24155fbba52dd4191e9a746c39e13c9"}, {"line": 3206, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2159, "target": 3429, "key": "8d2da047809eb548395007dbd762cd86"}, {"line": 5496, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"CREB subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2159, "target": 2164, "key": "e98812cbdf376fd0431be9624c69f971"}, {"line": 35843, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2159, "target": 2164, "key": "2909e2165df2014e8a561984e32ace40"}, {"line": 29128, "relation": "increases", "evidence": "Calcium/calpmodulin (CaM)-dependent protein kinases isolated from bovine and rat brains phosphorylate the microtubule-associated tau protein in the pmode that shifts the mobility of tau in sodium dodecyl sulfate-polyacrylamide gel electrophoresis (pmode I).", "citation": {"db": "PubMed", "db_id": "3121601"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2159, "target": 3015, "key": "0134bc63652033d2e4e6214285ad9659"}, {"line": 36960, "relation": "association", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2159, "target": 495, "key": "63b20f4cdd08936b3a5f35e340c98f7d"}, {"line": 37105, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157]. In contrast to CaMKs, ERKs cannot directly phosphorylate CREB. Two related RSKs and mitogen- and stress-activated protein kinases (MSKs) transmit the signal from activated ERKs to CREB [158]. CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis [159, 160]. Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity [161]. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP) [162]. There is no direct evidence indicating how lower levels of ABeta¸ might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2159, "target": 2555, "key": "bd1a8fd0be9675f7528498d0939487b9"}, {"line": 38536, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 2159, "target": 2336, "key": "6e620dcbb0e6e3aebde74d05783702b5"}, {"line": 38537, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 2159, "target": 2338, "key": "18871fe330b444a4debcd0454ec6de81"}, {"line": 38538, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 2159, "target": 2337, "key": "ecd8b7620d62454db54b6e05719e3b36"}, {"line": 3208, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3429, "target": 2211, "key": "f1f3ec9f8cb07fd182f4f6e4cb75cd67"}, {"line": 3209, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3429, "target": 2328, "key": "856948383740883ebe7d57eae2cdd11d"}, {"line": 3210, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2211, "target": 2328, "key": "f1f217783c373ec5f2c5e3d7aeb0110e"}, {"line": 3212, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3023, "target": 2328, "key": "0d6af78242eaadf3c9274829679bf06d"}, {"line": 30629, "relation": "positiveCorrelation", "evidence": "These results, respectively, indicated that GSK-3beta is responsible for phosphorylating Ser-262 of tau through phosphorylation and activation of MARK2 and that the phosphorylation of tau at this particular site is predominantly mediated by a GSK-3beta-MARK2 pathway.", "citation": {"db": "PubMed", "db_id": "16257959"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3023, "target": 3043, "key": "0dfabb8920ec8ff6e3987be2401aa883"}, {"line": 3214, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3035, "target": 2328, "key": "2079d21e75357352fdf764e7782bfafa"}, {"relation": "partOf", "source": 3035, "target": 1569, "key": "3b8a12aa16af291fd5cc2d57705e9eec"}, {"line": 3216, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3026, "target": 2328, "key": "eb2930b4dfb09c195fe467944230b6b4"}, {"relation": "partOf", "source": 3026, "target": 1566, "key": "be51e8642b9a6c725364660eef551bb1"}, {"line": 38719, "relation": "negativeCorrelation", "evidence": "Elevated levels of T-tau, P-tau (S396), IL-6 and · OH in CSF are significantly correlated with cognitive impairment in PD patients.", "citation": {"db": "PubMed", "db_id": "24884485"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tau protein subgraph": true}}, "source": 3026, "target": 812, "key": "ff32dfac2b058356380326ea40214d4f"}, {"line": 3217, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3228, "target": 3027, "key": "331266ed96d69240d2a4b76191187f89"}, {"relation": "isA", "source": 3228, "target": 2143, "key": "c1579f5c78271f774b2014bb8787f548"}, {"relation": "partOf", "source": 3228, "target": 1614, "key": "18e0acb1141ca1c79fd960320ee8b728"}, {"line": 3218, "relation": "increases", "evidence": "AMPK activity decreases in AD brain, indicating decreased mitochondrial biogenesis and function. Emerging evidence demonstrates that AMPK activation is a potential target for improving perturbed brain energy metabolism that is involved in the pathogenesis of AD. The roles of AMPK in the pathogenesis of AD include Abeta-amyloid protein (Abeta) generation and tau phosphorylation. In particular, AMPK may regulate Abeta generation through modulating neuronal cholesterol and sphingomyelin levels and through regulating APP distribution in the lipid rafts. AMPK is activated by phosphorylation of Thr-172 by LKB1 complex in response to increase in the AMP/ATP ratio and by calmodulin-dependent protein kinase kinase-beta in response to elevated Ca(2+) levels, which contributes to regulating Abeta generation. AMPK is a physiological tau kinase and can increase the phosphorylation of tau at Ser-262. AMPK can also directly phosphorylate tau at Thr-231 and Ser-396/404. Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD.", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Sphingolipid metabolic subgraph": true, "Akt subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3027, "target": 2328, "key": "5b02c30cff7985f6cb0fee9a0f73059e"}, {"relation": "partOf", "source": 3027, "target": 1567, "key": "8fc698dd0cba821a8e15ef282a19dc58"}, {"line": 4057, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 460, "target": 645, "key": "9759410dc1e12f46b35f20de6111828d"}, {"line": 7694, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 460, "target": 3223, "key": "c4e2de927768af224189345cbdd17e9a"}, {"line": 7700, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 460, "target": 2794, "key": "912c0d8d00e38fa02214442ea42dddc8"}, {"line": 25501, "relation": "decreases", "evidence": "The mediator neuroprotectin D1 (NPD1) is an enzymatic derivative of the omega-3 essential fatty acid docosahexaenoic acid. NPD1 stereoselectively and specifically binds to human retinal pigment epithelium (RPE) cells and neutrophils. In turn, this lipid mediator induces dephosphorylation of Bcl-x(L) in a PP2A-dependent manner and induces PI3K/Akt and mTOR/p70S6K pathways leading to RPE cell survival during oxidative stress-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Response to oxidative stress": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 460, "target": 504, "key": "26b09b57d10bf281a8ea5b7ffb60174e"}, {"line": 34832, "relation": "association", "evidence": "Autopsy brain hippocampal tissues were obtained from controls and patients with AD and Western blots were performed using antibodies against mTOR signaling molecules and RagC, an upstream component of mTOR complex 1 (mTORC1) signaling. We found that expression of mTOR/p-mTOR and its downstream targets S6/p-S6 and Raptor/p-Raptor were expressed in the control and AD hippocampus. The expression levels of these signaling molecules were significantly increased in the hippocampus at the severe stages of AD, compared to controls and other stages of AD. Interestingly, Rictor expression level was unaltered. In addition, RagC was increased in the hippocampus at the early, moderate, and severe stages of AD. Our data indicate that mTORC1, but not mTORC2, was activated in the AD brains and that the level of mTOR signaling activation was correlated with cognitive severity of AD patients.", "citation": {"db": "PubMed", "db_id": "23979023"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "DiseaseState": {"Moderate AD": true, "Early-onset AD": true, "Late-onset AD": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 460, "target": 812, "key": "b396327dec5fdafe1b510340017843f1"}, {"line": 3240, "relation": "increases", "evidence": "Furthermore, AMPK activation decreases mTOR signaling activity to facilitate autophagy and promotes lysosomal degradation of Abeta. However, AMPK activation has non-neuroprotective property and may lead to detrimental outcomes, including Abeta generation and tau phosphorylation. Therefore, it is still unclear whether AMPK could serve a potential therapeutic target for AD, and hence, further studies will be needed to clarify the role of AMPK in AD", "citation": {"db": "PubMed", "db_id": "22367557"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 428, "target": 2328, "key": "099d6aafb2ec08dd84cce5e58528e599"}, {"line": 39029, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 428, "target": 2328, "key": "5012731677f8d200bb99500689edbf8b"}, {"line": 5693, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 2590, "key": "880796b7657bc6c88a902da59a92f03a"}, {"line": 5694, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 2591, "key": "bf1e42f1f9029fc27f818bf03f48dea2"}, {"line": 5695, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 2592, "key": "e4d797445b2ee93691a9d0a063eef2c7"}, {"line": 5696, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 2593, "key": "10d3563ea02e28597e2bbe91c5bbf3cb"}, {"line": 5697, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 2595, "key": "6028b5ff4e48068a1e9f439de3f5004a"}, {"line": 5698, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 2596, "key": "ec4e5ce3f943cc2b5c7a675068a53be6"}, {"line": 5699, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 2597, "key": "367548e2a098e34d36dabb74b815057e"}, {"line": 5700, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 2598, "key": "e5ee23de9378246c1683ad9631454d3e"}, {"line": 5706, "relation": "increases", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 808, "key": "55ff5cf695602b9b6db3e628fc3a6678"}, {"line": 5714, "relation": "increases", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 428, "target": 813, "key": "a6fe23663fc07c1cc079dac9243d3c94"}, {"line": 5721, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 428, "target": 823, "key": "1e80d3b296ab16b49859bbf0ab80ee4f"}, {"line": 5828, "relation": "association", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Apoptosis signaling subgraph": true}}, "source": 428, "target": 478, "key": "dfcb2937493c5d0bb2978204dd539b09"}, {"line": 3257, "relation": "decreases", "evidence": "The role of miR-124 on the expression of Abeta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the Abeta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Very High": true}, "Subgraph": {"Beta secretase subgraph": true}}, "source": 2082, "target": 3943, "key": "77020b568c9d28746f110e2c82f6fcf2"}, {"line": 8896, "relation": "association", "evidence": "The role of miR-124 on the expression of beta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the beta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2082, "target": 2375, "key": "b47d067ea9dfe4491555dd13410a9f35"}, {"line": 45903, "relation": "increases", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2082, "target": 2375, "key": "5649088d2f0e3f0542b80b7ac5bca5b3"}, {"line": 8898, "relation": "association", "evidence": "The role of miR-124 on the expression of beta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the beta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2082, "target": 80, "key": "8b0f3c017e89b0498ba23e100817d5b9"}, {"line": 8900, "relation": "association", "evidence": "The role of miR-124 on the expression of beta-site APP cleaving enzyme 1 (BACE1), an important cleavager of amyloid precursor protein that plays a pivotal role in the beta-amyloid production, was studied in this paper using cellular models for Alzheimer' disease (AD) of cultured PC12 cell lines and primary cultured hippocampal neurons. The aim of the present study was to uncover novel potential miR-124 targets and shed light on its function in the cellular AD model. MiR-124 expression was steadily altered when its mimic and inhibitor were transfected in vitro. The results showed the expression of BACE1, one of the potential functional downstream targets of miR-124, was well correlated with cell death induced by Abeta neurotoxicity, and its expression level could be up- and down-regulated by suppression or over expression of miR-124 level respectively. These findings suggest that miR-124 may work as a basilic regulating factor to alleviate cell death in the process of AD by targeting BACE1, play an essential role in the control of BACE1 gene expression, and might be considered as a novel therapeutic target in treating AD.", "citation": {"db": "PubMed", "db_id": "22178568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2082, "target": 478, "key": "0db10aa22bfa028e95678b2531b5c0aa"}, {"line": 9266, "relation": "negativeCorrelation", "evidence": "In P19 cells, miR-124 suppresses SCP1 expression and induces neurogenesis, and SCP1 counteracts this proneural activity of miR-124. Our results suggest that, during CNS development, timely down-regulation of SCP1 is critical for inducing neurogenesis, and miR-124 contributes to this process at least in part by down-regulating SCP1 expression.", "citation": {"db": "PubMed", "db_id": "17403776"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2082, "target": 3437, "key": "0afb6deb936d6d9e5a194134daf7800d"}, {"line": 9267, "relation": "association", "evidence": "In P19 cells, miR-124 suppresses SCP1 expression and induces neurogenesis, and SCP1 counteracts this proneural activity of miR-124. Our results suggest that, during CNS development, timely down-regulation of SCP1 is critical for inducing neurogenesis, and miR-124 contributes to this process at least in part by down-regulating SCP1 expression.", "citation": {"db": "PubMed", "db_id": "17403776"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2082, "target": 822, "key": "73ec062e6e42639f568ec58cc6f21a8f"}, {"line": 9470, "relation": "association", "evidence": "The MicroRNA miR-124 Promotes Neuronal Differentiation by Triggering Brain-Specific Alternative Pre-mRNA Splicing. When this exon is skipped, PTBP2 mRNA is subject to nonsense-mediated decay (NMD). During neuronal differentiation, miR-124 reduces PTBP1 levels, leading to the accumulation of correctly spliced PTBP2 mRNA and a dramatic increase in PTBP2 protein.", "citation": {"db": "PubMed", "db_id": "17679093"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2082, "target": 3273, "key": "77cbd8352f87e718a56780bc3a68982f"}, {"line": 9640, "relation": "negativeCorrelation", "evidence": "We found that miRs 18 and 124a reduced GR-mediated events in addition to decreasing GR protein levels. miR reporter assays revealed binding of miR-124a to the 3' untranslated region of GR. In correspondence, the activation of the GR-responsive gene glucocorticoid-induced leucine zipper was strongly impaired by miR-124a and -18 overexpression. Although miR-18 is expressed widely throughout the body, expression of miR-124a is restricted to the brain. Endogenous miR-124a up-regulation during neuronal differentiation of P19 cells was associated with a decreasing amount of GR protein levels and reduced activity of luciferase reporter constructs bearing GR 3' untranslated regions. Furthermore, we show that miR-124a expression varies over time during the stress hyporesponsive period, a neonatal period when GC signaling is modulated. Our findings demonstrate a potential role for miRs in the regulation of cell type-specific responsiveness to GCs, as may occur during critical periods of neuronal development.", "citation": {"db": "PubMed", "db_id": "19131573"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true, "miRNA subgraph": true}}, "source": 2082, "target": 2798, "key": "284e1af65e53ed1eae3ccc058c4d2917"}, {"line": 9642, "relation": "association", "evidence": "We found that miRs 18 and 124a reduced GR-mediated events in addition to decreasing GR protein levels. miR reporter assays revealed binding of miR-124a to the 3' untranslated region of GR. In correspondence, the activation of the GR-responsive gene glucocorticoid-induced leucine zipper was strongly impaired by miR-124a and -18 overexpression. Although miR-18 is expressed widely throughout the body, expression of miR-124a is restricted to the brain. Endogenous miR-124a up-regulation during neuronal differentiation of P19 cells was associated with a decreasing amount of GR protein levels and reduced activity of luciferase reporter constructs bearing GR 3' untranslated regions. Furthermore, we show that miR-124a expression varies over time during the stress hyporesponsive period, a neonatal period when GC signaling is modulated. Our findings demonstrate a potential role for miRs in the regulation of cell type-specific responsiveness to GCs, as may occur during critical periods of neuronal development.", "citation": {"db": "PubMed", "db_id": "19131573"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true, "miRNA subgraph": true}}, "source": 2082, "target": 649, "key": "216a1f39e4d560d64c803726b778de02"}, {"line": 45904, "relation": "increases", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2082, "target": 2328, "key": "46d82d5ab54101e067d9d6f7e867b305"}, {"line": 5140, "relation": "association", "evidence": "Peroxisome proliferator-activated receptor gamma (PPAR ) regulates the transcription of BACE1 as well as inflammatory responses in the brain and atherosclerotic risk factors known to be involved also in AD.", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3943, "target": 3212, "key": "f0d494e3fa88e29cbb41a516679a4b28"}, {"line": 8441, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3943, "target": 2375, "key": "eb6655f66763a79b6837b29332c379b6"}, {"line": 8730, "relation": "increases", "evidence": "We report here that BACE1-antisense prevents miRNA-induced repression of BACE1 mRNA by masking the binding site for miR-485-5p. Indeed, miR-485-5p and BACE1-antisense compete for binding within the same region in the open reading frame of the BACE1 mRNA. We observed opposing effects of BACE1-antisense and miR-485-5p on BACE1 protein in vitro and showed that Locked Nucleic Acid-antimiR mediated knockdown of miR-485-5p as well as BACE1-antisense over-expression can prevent the miRNA-induced BACE1 suppression. We found that the expression of BACE1-antisense as well as miR-485-5p are dysregulated in RNA samples from Alzheimer's disease subjects compared to control individuals.", "citation": {"db": "PubMed", "db_id": "20507594"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3943, "target": 2375, "key": "8665ad13ff93fbf64d585572424682af"}, {"line": 45101, "relation": "increases", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true, "miRNA subgraph": true}, "Confidence": {"Low": true}}, "source": 3943, "target": 2375, "key": "be559be8611f3a96560ac7571f97479b"}, {"line": 8671, "relation": "negativeCorrelation", "evidence": "MiR-107 is a microRNA (miRNA) that we reported previously to have decreased expression in the temporal cortical gray matter early in the progression of Alzheimer's disease (AD). Here we study a new group of well-characterized human temporal cortex samples (N=19). MiR-107 expression was assessed, normalized to miR-124 and let-7a. Correlation was observed between decreased miR-107 expression and increased neuritic plaque counts (P< 0.05) and neurofibrillary tangle counts (P< 0.02) in adjacent brain tissue. Adjusted miR-107 and BACE1 mRNA levels tended to correlate negatively (trend with regression P< 0.07). In sum, miR-107 expression tends to be lower relative to other miRNAs as AD progresses.", "citation": {"db": "PubMed", "db_id": "20413881"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3943, "target": 2092, "key": "bdca786fd74600415d930aa3430f18cc"}, {"line": 45096, "relation": "negativeCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true, "miRNA subgraph": true}, "Confidence": {"Low": true}}, "source": 3943, "target": 2092, "key": "0b091061e1f2a24a0436aef24d9e2838"}, {"line": 45972, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3943, "target": 1756, "key": "c89791ba5025f6fa95ec9795b0370640"}, {"line": 3275, "relation": "decreases", "evidence": "Another study found that disrupting the interaction between APP and FE65 in hippocampal neurons increases neurite branching without affecting total neurite outgrowth, suggesting that APP negatively regulates neurite branching via an interaction with FE65 during early neuronal development", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1676, "target": 652, "key": "0be9c95aa1dd4299475380c7f3ba4dfd"}, {"line": 37666, "relation": "increases", "evidence": "Recent studies have also shown that APP is involved in cell motility. For example, we and others found that APP accelerates wound healing and that the interaction between APP and FE65 in MDCK cells further accelerates wound healing [59,60]. These results suggest that the cooperative interaction between APP and FE65 is involved in regulating cell motility.", "citation": {"db": "PubMed", "db_id": "21199446"}, "source": 1676, "target": 803, "key": "0eb3e53edbccc2fb96fc93e436328adf"}, {"line": 37667, "relation": "increases", "evidence": "Recent studies have also shown that APP is involved in cell motility. For example, we and others found that APP accelerates wound healing and that the interaction between APP and FE65 in MDCK cells further accelerates wound healing [59,60]. These results suggest that the cooperative interaction between APP and FE65 is involved in regulating cell motility.", "citation": {"db": "PubMed", "db_id": "21199446"}, "source": 1676, "target": 510, "key": "834eeb44715e7eeb2c95162110b657b3"}, {"line": 18705, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 652, "target": 3060, "key": "ceb721186908d1f68c01b093b5b4d6cb"}, {"line": 34842, "relation": "association", "evidence": "Abnormal neuritic sprouting is a prominent feature of Alzheimer's disease (AD), and the Thy-1 glycoprotein has a role in neurite growth in culture. We therefore investigated the distribution of Thy-1 immunoreactivity in the hippocampus of normal elderly patients and of AD patients. Some Thy-1-immunoreactive dystrophic neurites entered senile plaques. The data confirm that there is extensive growth of abnormal neurites in AD and suggest that Thy-1 is involved in this process.", "citation": {"db": "PubMed", "db_id": "1347079"}, "annotations": {"Subgraph": {"Cell-cell communication subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 652, "target": 3461, "key": "a7021ff73a8b11b98a429e147841fa13"}, {"line": 37285, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 652, "target": 2315, "key": "68271f918607556194bce1cd527c02ff"}, {"line": 37554, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 652, "target": 2315, "key": "2549295191423c3fbd51ed2ff4916033"}, {"line": 37293, "relation": "association", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 652, "target": 2186, "key": "37b506827616a333e1d4d970fe5af3de"}, {"line": 37555, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 652, "target": 2306, "key": "773ad3c04506a5c23023d9f98cd3e6bd"}, {"line": 37556, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 652, "target": 2307, "key": "819e5ed851130e9882cb0ca3bea3eed3"}, {"line": 3286, "relation": "decreases", "evidence": "Abeta42 oligomers bind to PrP with high affinity and specificity. Abeta42 either did not bind to RAGE or the a7 nicotinic acetylcholine receptor or bound with very low affinity. Nanomolar concentrations of oligomeric Abeta42 potently inhibited LTP, but this effect could be abrogated either in the absence of PrP or by blocking the Abeta42-PrP interaction with antibodies against the Abeta42-binding region. Thus, the authors provide compelling evidence that PrP is a specific binding partner for Abeta42 oligomers and mediates Abeta42’s inhibitory effect on synaptic plasticity. This finding fits well with previous reports that independently implicate Abeta42 and PrP in synaptic function", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Synapse assembly subgraph": true}, "Confidence": {"Very High": true}}, "source": 1245, "target": 761, "key": "5b4b3eb8d3f96028f1ca3f89f485caf4"}, {"relation": "partOf", "source": 3254, "target": 1245, "key": "ac73fd7cc722ab0f62af27b3e669e5db"}, {"line": 3755, "relation": "increases", "evidence": "overexpression of PrP results in increased cleavage of APP in contrast to previous suggestion suggesting a reduction. Our findings suggest that any relation between PrP and BACE-1 is indirect. Altered expression of PrP causes changes in the expression of many other proteins which may be as a result of altered copper metabolism.", "citation": {"db": "PubMed", "db_id": "22796214"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 3254, "target": 2315, "key": "22242edec38063dc0838f5be8e4dd424"}, {"line": 3756, "relation": "association", "evidence": "overexpression of PrP results in increased cleavage of APP in contrast to previous suggestion suggesting a reduction. Our findings suggest that any relation between PrP and BACE-1 is indirect. Altered expression of PrP causes changes in the expression of many other proteins which may be as a result of altered copper metabolism.", "citation": {"db": "PubMed", "db_id": "22796214"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true}}, "source": 3254, "target": 2375, "key": "1bc84778454b81feac8c7e247160b551"}, {"line": 26916, "relation": "decreases", "evidence": "Cellular overexpression of PrP(C) inhibited the beta-secretase cleavage of APP and reduced Abeta formation. ", "citation": {"db": "PubMed", "db_id": "17573534"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3254, "target": 2375, "key": "a19a8a3ca0eb28042c1a26a6ed13e9e3"}, {"line": 27231, "relation": "decreases", "evidence": "In Alzheimer disease, this feedback loop is disrupted, and the increased level of Abeta oligomers bind to PrP(C) and prevent it from regulating BACE1 activity. PrP(C) interacts with and inhibits the beta-secretase BACE1, the rate-limiting enzyme in the production of Abeta.", "citation": {"db": "PubMed", "db_id": "19887909"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3254, "target": 2375, "key": "128b3bab36c5b8bea3f8a56781c6bb25"}, {"line": 28183, "relation": "decreases", "evidence": "In our opinion, albeit based on limited available data, a future potential therapeutic strategy is to mimic the mechanism by which the normal cellular form of the prion protein inhibits the beta-secretase beta-site amyloid precursor protein cleaving enzyme-1 (BACE1), and hence the production of Abeta.", "citation": {"db": "PubMed", "db_id": "18479216"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3254, "target": 2375, "key": "1437b8eceb65e3a6f496d38ecec2f59f"}, {"line": 31951, "relation": "regulates", "evidence": "Cellular prion protein regulates beta-secretase cleavage of the Alzheimer's amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "17573534"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3254, "target": 2375, "key": "e907b7b43d89603d6cc00bc65adf4f24"}, {"line": 31963, "relation": "decreases", "evidence": "Recent evidence indicates that PrP(C) may play a critical role in the pathogenesis of Alzheimer disease. PrP(C) interacts with and inhibits the beta-secretase BACE1", "citation": {"db": "PubMed", "db_id": "19887909"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3254, "target": 2375, "key": "b03934a4aa39efa85bfa580df62d667c"}, {"line": 31987, "relation": "decreases", "evidence": "PrP(C) decreases amyloid-beta (Abeta) production, which is involved in AD pathogenesis, by inhibiting beta-secretase (BACE1) activity.", "citation": {"db": "PubMed", "db_id": "23577068"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3254, "target": 2375, "key": "fcf95ff2403366d8317ef4b1b967187f"}, {"line": 10494, "relation": "increases", "evidence": "Alzheimer's beta-amyloid, human islet amylin, and prion protein fragment evoke intracellular free calcium elevations by a common mechanism in a hypothalamic GnRH neuronal cell line.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"MeSHAnatomy": {"Hypothalamus": true}, "Subgraph": {"Calcium-dependent signal transduction": true}}, "source": 3254, "target": 94, "key": "d5eb6e262704e371af7985b573673f8b"}, {"line": 10510, "relation": "association", "evidence": "We also found that human islet amylin and the prion protein fragment (PrP106-126), peptides that acquire beta-pleated sheet conformation in water solutions and have been reported to form ion channels across planar bilayer membranes, also increase cytosolic free calcium in GT1-7 neurons.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"Subgraph": {"Amylin subgraph": true}}, "source": 3254, "target": 94, "key": "9092c5a54e982f31b13c128187b8c957"}, {"line": 26903, "relation": "increases", "evidence": "The normal cellular function of the prion protein (PrP(C)), the causative agent of the transmissible spongiform encephalopathies such as Creutzfeldt-Jakob disease in humans, remains enigmatic. ", "citation": {"db": "PubMed", "db_id": "17573534"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3254, "target": 3841, "key": "bbc10a18b584719dae24dc5046d6340e"}, {"line": 26911, "relation": "decreases", "evidence": "Cellular overexpression of PrP(C) inhibited the beta-secretase cleavage of APP and reduced Abeta formation. ", "citation": {"db": "PubMed", "db_id": "17573534"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3254, "target": 2328, "key": "b3bcb639526984824bcbd8ef1826c9cf"}, {"line": 31952, "relation": "regulates", "evidence": "Cellular prion protein regulates beta-secretase cleavage of the Alzheimer's amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "17573534"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3254, "target": 2328, "key": "068a63d857a5e70953768aa51bd5d1ac"}, {"line": 31988, "relation": "decreases", "evidence": "PrP(C) decreases amyloid-beta (Abeta) production, which is involved in AD pathogenesis, by inhibiting beta-secretase (BACE1) activity.", "citation": {"db": "PubMed", "db_id": "23577068"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3254, "target": 2328, "key": "1f04f27087cafed43ccba4483a3ad7fd"}, {"line": 38045, "relation": "association", "evidence": "Two recent studies (Lauren et al., 2009; Nikolaev et al., 2009) now connect the physiological and pathological functions of APP processing products. Lauren et al. show that ABeta¸42 binds to the cellular prion protein (PrP), which itself can cause neuropathology when misfolded. In a separate study, Nikolaev et al. report that the N-terminal fragment of APP (N-APP) interacts with death receptor 6 (DR6), resulting in pruning of axons and neurons during development of the central nervous system (CNS).These studies suggest that APP processing constitutes a complex signaling center that serves multiple physiological functions that could trigger pathological events when deregulated during disease.", "citation": {"db": "PubMed", "db_id": "19524503"}, "source": 3254, "target": 2328, "key": "72bb755caf7fce8f5209c2469f86c4ef"}, {"relation": "partOf", "source": 3254, "target": 940, "key": "3abcf0cc954f7bf4b3a0c36033cba82d"}, {"relation": "partOf", "source": 3254, "target": 1273, "key": "dd4fa3cc6dd04e911e1c0fa3be0fd8ce"}, {"line": 27235, "relation": "association", "evidence": "Recent evidence indicates that PrP(C) may play a critical role in the pathogenesis of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "19887909"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3254, "target": 3823, "key": "7837f934966b2a95f9f1a8ca3604841b"}, {"line": 31980, "relation": "association", "evidence": "The cellular prion protein (PrP(C)) has been implicated in the development of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "23577068"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3254, "target": 3823, "key": "84f3dd32326b00a3bee9f6d208fedeff"}, {"line": 28184, "relation": "decreases", "evidence": "In our opinion, albeit based on limited available data, a future potential therapeutic strategy is to mimic the mechanism by which the normal cellular form of the prion protein inhibits the beta-secretase beta-site amyloid precursor protein cleaving enzyme-1 (BACE1), and hence the production of Abeta.", "citation": {"db": "PubMed", "db_id": "18479216"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3254, "target": 80, "key": "92527724c0922464c94e6909dce728cb"}, {"line": 31446, "relation": "increases", "evidence": "The codistribution of plaques and CJD-associated changes suggests that PrP plays a central role in Abeta formation and that Abeta pathology and prion disease likely in fluence each other.", "citation": {"db": "PubMed", "db_id": "19822779"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3254, "target": 80, "key": "7ed4d2727667e5f5a6e9bfa3af8e8406"}, {"relation": "partOf", "source": 3254, "target": 1618, "key": "360cf9ec744c833a31f1732bb95b0038"}, {"line": 47346, "relation": "isA", "evidence": "Binding to HSPGs requires a heparin/heparan sulfate-binding domain consisting of a stretch of positively charged lysines or arginines on the ligand. Prion protein, beta-amyloid, tau, and alpha-synuclein all have putative heparin-binding domains(25, 44–46).", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3254, "target": 49, "key": "28394c9f54728d1097cd8e3a4370c37d"}, {"line": 10719, "relation": "association", "evidence": "Furthermore, GLP-1 peptides are not only effective in modulating insulin-release and achieving glycaemic control in type 2 diabetes, but are also effective in modulating synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "20035739"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 761, "target": 2742, "key": "17986b5e94eca336105c64c10f026689"}, {"line": 17050, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2721, "key": "580cb58c5f7a23f250fbb0f3ef26cf3a"}, {"line": 17054, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2722, "key": "a7384156a6ff544f524e25daf2a1cc53"}, {"line": 17058, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2723, "key": "f0fea6cb7bde9d55793fb9a948bc59b2"}, {"line": 17062, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2724, "key": "f35419965c62172238dbf0ccc1eb9202"}, {"line": 17066, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2725, "key": "12abfc2bb35507a6dd31224f1cbfbf12"}, {"line": 17070, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2726, "key": "c5e45271456b08a3f1ff4a673fac13e9"}, {"line": 17074, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2727, "key": "a891391946f8f60d7920c0173190e4d8"}, {"line": 17078, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2728, "key": "3734d01ec2e7c11ca3b84a6d6452449e"}, {"line": 17082, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2729, "key": "418b04b797a91d104e350d363549a000"}, {"line": 17086, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2732, "key": "0db74afde8ae2240708c0e3609d57903"}, {"line": 17090, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2733, "key": "9b4d1f1675ed187b43a353de00b5bd9d"}, {"line": 17094, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2734, "key": "a3520793c7533440224da422e22521d5"}, {"line": 17098, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2730, "key": "cf7d7791ff496e18c3739d94aa06f43d"}, {"line": 17102, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2731, "key": "bef9a0b7c2de7ae77b328f18a19d1d6b"}, {"line": 17106, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2735, "key": "a9836465ab5fcad37cb8536428835c6d"}, {"line": 17110, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 2736, "key": "81e829955a606a19529b9ff692351e9e"}, {"line": 17114, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 761, "target": 3548, "key": "4cf8bcc7abdb6d9a825c04214c0993f5"}, {"line": 47047, "relation": "association", "evidence": "The hippocampus, with its high density of glutamate receptors and in particular NMDA receptors, is known to be extremely important for some forms of learning and memory. Glutamatergic synapses can show pronounced plasticity in terms of the number and strength of individual synapses and are also characterized by their ability to express LTP – a long-lasting strengthening of synaptic transmission ", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Hippocampus": true}}, "object": {"modifier": "Activity"}, "source": 761, "target": 3548, "key": "a3a9c72989ff74cbe998fb7a3f2f01ba"}, {"line": 18708, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 761, "target": 3060, "key": "c8464aa27c1db1d11dec200a4375516a"}, {"line": 37637, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "source": 761, "target": 2315, "key": "324cca9a51265e9fda1dd535e7b87378"}, {"line": 37638, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "object": {"modifier": "Translocation"}, "source": 761, "target": 2781, "key": "231c29a0363b99eb8203ab5e6472ea60"}, {"line": 46188, "relation": "negativeCorrelation", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 761, "target": 3438, "key": "e080d9a2dfc1d4cccc37a2fa0eb98ed6"}, {"line": 46189, "relation": "negativeCorrelation", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 761, "target": 4015, "key": "df001b1e3d2e983b7e26332217fcf761"}, {"line": 46190, "relation": "association", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 761, "target": 3823, "key": "ca10a677ce01fb31e363f75b44de3d0c"}, {"line": 3308, "relation": "association", "evidence": "After exclusion of loci already known to be involved in AD (APOE, BIN1 and CR1), 91 regions with suggestive haplotype effects were identified", "citation": {"db": "PubMed", "db_id": "22430674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"APOE subgraph": true}}, "source": 1744, "target": 3823, "key": "b783a9a17f784f4d63e01ae8479f3f6c"}, {"line": 6562, "relation": "association", "evidence": "Alzheimer's disease (AD) is a chronic disorder that slowly destroys neurons and causes serious cognitive disability. AD is associated with senile plaques and neurofibrillary tangles (NFTs). Amyloid-beta (Abeta), a major component of senile plaques, has various pathological effects on cell and organelle function. The extracellular Abeta oligomers may activate caspases through activation of cell surface death receptors. Alternatively, intracellular Abeta may contribute to pathology by facilitating tau hyper-phosphorylation, disrupting mitochondria function, and triggering calcium dysfunction. To date genetic studies have revealed four genes that may be linked to autosomal dominant or familial early onset AD (FAD). These four genes include: amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2) and apolipoprotein E (ApoE). All mutations associated with APP and PS proteins can lead to an increase in the production of Abeta peptides, specfically the more amyloidogenic form, Abeta42. FAD-linked PS1 mutation downregulates the unfolded protein response and leads to vulnerability to ER stress.", "citation": {"db": "Online Resource", "db_id": "hsa05010"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 1744, "target": 3823, "key": "2890158dc14a3db67eece7672d6e9c94"}, {"line": 26485, "relation": "association", "evidence": "The genes for both the beta-amyloid precursor protein and apolipoprotein E (ApoE) have been linked to Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9202294"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 1744, "target": 3823, "key": "df1f517f753125fe800a8302608849e8"}, {"line": 39578, "relation": "increases", "evidence": "In AD, an increased ApoE mRNA was reported in the hippocampus. The risk for AD has been reported to correlate with transcriptional activity of the ApoE gene. Binding sites for putative transcriptional factors (TF), such as AP-1, AP-2 and NF-kappaB, are present in the ApoE promoter. The promoter also contains sites for the inflammatory response transcription factors IL-6 RE-BP, MED1, STAT1 and STAT2. A functional peroxisome-proliferator-activated receptor gamma (PPARgamma) has been detected in the ApoE/ApoCI intergenic region.", "citation": {"db": "PubMed", "db_id": "15181251"}, "annotations": {"Subgraph": {"APOE subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 1744, "target": 3823, "key": "ec51283436fe864c11c140a2dd49c024"}, {"line": 4571, "relation": "increases", "evidence": "Alzheimer's disease is associated with impaired clearance of Abeta-amyloid from the brain, a process normally facilitated by apolipoprotein E (ApoE)", "citation": {"db": "PubMed", "db_id": "22323736"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 1744, "target": 80, "key": "07e4fa0ef98eefe4ff039b1103efb0c8"}, {"line": 6632, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 1744, "target": 3861, "key": "31b064a0c0be4e41a009de9504f9de05"}, {"line": 6633, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 1744, "target": 3915, "key": "47aa94b337701fc7187c626b7e106155"}, {"line": 6645, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 1744, "target": 3847, "key": "51a9edcdb409054351131489e7151ce8"}, {"line": 6646, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 1744, "target": 3901, "key": "cb5b602badfdf7f7138c978e1b1a81c2"}, {"relation": "hasVariant", "source": 1744, "target": 1745, "key": "16e033fc50db1913f3b0e5b45f9d4fbc"}, {"line": 3311, "relation": "association", "evidence": "After exclusion of loci already known to be involved in AD (APOE, BIN1 and CR1), 91 regions with suggestive haplotype effects were identified", "citation": {"db": "PubMed", "db_id": "22430674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Synapse assembly subgraph": true, "Caspase subgraph": true}}, "source": 1760, "target": 3823, "key": "c4de428c575760ae99da8a5b0b62bdc8"}, {"line": 3347, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Caspase subgraph": true, "Synapse assembly subgraph": true}}, "source": 1760, "target": 3823, "key": "fa661fabd8925b68b19477a79d7671e3"}, {"line": 3374, "relation": "directlyIncreases", "evidence": "These studies combined include data from over 43000 independent individuals and provide compelling evidence that variants in four novel susceptibility genes (CLU, PICALM, CR1, BIN1) are associated with disease risk.", "citation": {"db": "PubMed", "db_id": "20957767"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 1760, "target": 3823, "key": "9a4b2cec207399f36aa87243f14278cf"}, {"relation": "hasVariant", "source": 1760, "target": 1762, "key": "3374b11571a3e84ba39fd918670ac936"}, {"relation": "hasVariant", "source": 1760, "target": 1761, "key": "c437cafeefea67b66bda9117f9609840"}, {"line": 3313, "relation": "association", "evidence": "After exclusion of loci already known to be involved in AD (APOE, BIN1 and CR1), 91 regions with suggestive haplotype effects were identified", "citation": {"db": "PubMed", "db_id": "22430674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 1782, "target": 3823, "key": "44ae77caf42f1842c251443ecdce5a04"}, {"line": 3350, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1782, "target": 3823, "key": "c3014c3790fa3d39261fe5abce14f527"}, {"line": 3378, "relation": "directlyIncreases", "evidence": "These studies combined include data from over 43000 independent individuals and provide compelling evidence that variants in four novel susceptibility genes (CLU, PICALM, CR1, BIN1) are associated with disease risk.", "citation": {"db": "PubMed", "db_id": "20957767"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 1782, "target": 3823, "key": "9487bd7d0a707ab83b05b6e6895266d4"}, {"line": 3319, "relation": "positiveCorrelation", "evidence": "In conclusion, combining both GWHA study and a conservative three-stage replication approach, we characterised FRMD4A as a new genetic risk factor of AD", "citation": {"db": "PubMed", "db_id": "22430674"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 1826, "target": 3823, "key": "302febdd5f0ba2b500c27e3be09dee0f"}, {"line": 3332, "relation": "association", "evidence": "Polymorphisms in CST3 and EXOC3L2 as well as the absence of APOE4 were associated with more aggressive disease courses. A trend was observed for BIN1", "citation": {"db": "PubMed", "db_id": "22414550"}, "annotations": {"Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 1789, "target": 3823, "key": "0118a2f808816d164eadd8460e894946"}, {"relation": "hasVariant", "source": 1788, "target": 1789, "key": "f2a036e7eae678d92f5bf2e5bedb03c9"}, {"line": 3333, "relation": "association", "evidence": "Polymorphisms in CST3 and EXOC3L2 as well as the absence of APOE4 were associated with more aggressive disease courses. A trend was observed for BIN1", "citation": {"db": "PubMed", "db_id": "22414550"}, "annotations": {"Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 1819, "target": 3823, "key": "92af4a0761354199a546bea080e9f25b"}, {"relation": "hasVariant", "source": 1818, "target": 1819, "key": "886dd2d9ebb9f5d79e61040867e9b989"}, {"line": 3336, "relation": "association", "evidence": "Polymorphisms in CST3 and EXOC3L2 as well as the absence of APOE4 were associated with more aggressive disease courses. A trend was observed for BIN1", "citation": {"db": "PubMed", "db_id": "22414550"}, "annotations": {"Confidence": {"Very High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 1762, "target": 3823, "key": "d39fc70d58d20a2bac69a241a7e960ba"}, {"line": 3349, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1780, "target": 3823, "key": "c3b877c3ce9dfd7cb0a7406ed716b7cb"}, {"line": 3376, "relation": "directlyIncreases", "evidence": "These studies combined include data from over 43000 independent individuals and provide compelling evidence that variants in four novel susceptibility genes (CLU, PICALM, CR1, BIN1) are associated with disease risk.", "citation": {"db": "PubMed", "db_id": "20957767"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1780, "target": 3823, "key": "70aac65e471c9b56607c0b2c506596b8"}, {"line": 3351, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1912, "target": 3823, "key": "7977d771029e8e214584e65a5e8960d0"}, {"line": 3380, "relation": "directlyIncreases", "evidence": "These studies combined include data from over 43000 independent individuals and provide compelling evidence that variants in four novel susceptibility genes (CLU, PICALM, CR1, BIN1) are associated with disease risk.", "citation": {"db": "PubMed", "db_id": "20957767"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1912, "target": 3823, "key": "760e56252c0915d16377dd0117a7ea3b"}, {"relation": "partOf", "source": 1912, "target": 1666, "key": "5c14dcf713a698e611e9944d23e9d4b9"}, {"line": 5666, "relation": "directlyIncreases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 1912, "target": 533, "key": "775256bd664174eb4a8943306890b84e"}, {"line": 5680, "relation": "increases", "evidence": "Picalm is a key component of clathrin-dependent endocytosis. It recruits clathrin and adaptor protein 2 (AP-2) to the plasma membrane and, along with, AP-2 recognizes target proteins. The attached clathrin triskelions cause membrane deformation around the target proteins enclosing them within clathrin-coated vesicles to be processed in lysosomes or endosomes.The transport of Abeta across vessel walls and into the bloodstream is a major pathway of Abeta removal from the brain and picalm is ideally situated within endothelial cells to participate in this process", "citation": {"db": "PubMed", "db_id": "20838239"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1912, "target": 533, "key": "a487fba000a2ab6917717d529242b1e6"}, {"line": 5678, "relation": "increases", "evidence": "Picalm is a key component of clathrin-dependent endocytosis. It recruits clathrin and adaptor protein 2 (AP-2) to the plasma membrane and, along with, AP-2 recognizes target proteins. The attached clathrin triskelions cause membrane deformation around the target proteins enclosing them within clathrin-coated vesicles to be processed in lysosomes or endosomes.The transport of Abeta across vessel walls and into the bloodstream is a major pathway of Abeta removal from the brain and picalm is ideally situated within endothelial cells to participate in this process", "citation": {"db": "PubMed", "db_id": "20838239"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1912, "target": 1010, "key": "8e02e852e9fb352be26aebe725d6c60b"}, {"line": 3352, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1879, "target": 3823, "key": "6057cf26feb7a759f86e0728de29e048"}, {"line": 3355, "relation": "increases", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Immunoglobulin subgraph": true}}, "source": 1770, "target": 3823, "key": "a4496578fe567a5f92d6996c27ccd3fb"}, {"line": 3358, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}}, "source": 1731, "target": 3823, "key": "9349bdacee668e1cc896e2330223b616"}, {"line": 45837, "relation": "association", "evidence": "cg02308560 in the ABCA7 locus is associated to AD pathology", "citation": {"db": "PubMed", "db_id": "25129075"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true}}, "source": 1731, "target": 3823, "key": "b2f0a779e7474bc514e7c550f7fade28"}, {"relation": "hasVariant", "source": 1731, "target": 1732, "key": "f427bf481475510109550674e0b4b6ac"}, {"line": 3360, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 1769, "target": 3823, "key": "1327f97f8adbfe8988dfec19fa0f0fc4"}, {"line": 3362, "relation": "positiveCorrelation", "evidence": "Recently genome-wide association studies have identified significant association between Alzheimer's disease (AD) and variations in CLU, PICALM, BIN1, CR1, MS4A4/MS4A6E, CD2AP, CD33, EPHA1, and ABCA7", "citation": {"db": "PubMed", "db_id": "22384383"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"G-protein-mediated signaling": true}}, "source": 1815, "target": 3823, "key": "73b52903a0dcf68020446dd109faeb7c"}, {"relation": "hasVariant", "source": 1815, "target": 1816, "key": "db0ff80b788861ef7f4182bff9d9e88e"}, {"line": 3392, "relation": "positiveCorrelation", "evidence": "Overall, this large, independent follow-up study for 15 of the top LOAD candidate genes provides support for GAB2 and LOC651924 (6q24.1) as risk modifiers of LOAD and novel associations between PGBD1 and EBF3 with age-at-onset.", "citation": {"db": "PubMed", "db_id": "21132329"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}}, "source": 1829, "target": 3823, "key": "4d962c7d829f7117d8d065297b0402de"}, {"line": 3396, "relation": "positiveCorrelation", "evidence": "Overall, this large, independent follow-up study for 15 of the top LOAD candidate genes provides support for GAB2 and LOC651924 (6q24.1) as risk modifiers of LOAD and novel associations between PGBD1 and EBF3 with age-at-onset.", "citation": {"db": "PubMed", "db_id": "21132329"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"T cells signaling": true}}, "source": 1911, "target": 3823, "key": "a8c55d16ba4e1fe498d4e208ea561b49"}, {"line": 3397, "relation": "positiveCorrelation", "evidence": "Overall, this large, independent follow-up study for 15 of the top LOAD candidate genes provides support for GAB2 and LOC651924 (6q24.1) as risk modifiers of LOAD and novel associations between PGBD1 and EBF3 with age-at-onset.", "citation": {"db": "PubMed", "db_id": "21132329"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"T cells signaling": true}}, "source": 1811, "target": 3823, "key": "8084e970305a7ba98d9aa872fa61292f"}, {"line": 3410, "relation": "association", "evidence": "Gangliosides are a family of sialic acid containing glycosphingolipids, highly enriched in neuronal and glial membranes, where they play important roles for development, proliferation, differentiation and maintenance of neuronal tissues and cells. The ganglioside GM3 serves as a common precursor for the a- and b-series gangliosides. The GD3-synthase (GD3S) catalyzes the synthesis of GD3 by adding sialic acid to GM3, segregating the a- and b-series of gangliosides and therefore controlling the levels of the major brain gangliosides GM1, GD1a, GD1b and GT1b.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 260, "target": 650, "key": "f7a1beb266e3974ae4c084adaee7d0c8"}, {"line": 44364, "relation": "increases", "evidence": "JAK-STAT signaling as an anti-inflammatory target. JAK-STAT signaling mediates the brain inflammation induced by LPS, IFN-gamma, ganglioside and thrombin. Curcumin activates SH2-containing phosphatase 2 (SHP2), while rosiglitazone and 15d-PGJ2 increase the expressions of SOCS1 and SOCS3. SHP2 and the SOCS proteins are typical negative feedback molecules of the JAK-STAT pathway.", "citation": {"db": "PubMed", "db_id": "26113788"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 260, "target": 3815, "key": "f17a64df22a606b9a8023a5d52703050"}, {"line": 3410, "relation": "association", "evidence": "Gangliosides are a family of sialic acid containing glycosphingolipids, highly enriched in neuronal and glial membranes, where they play important roles for development, proliferation, differentiation and maintenance of neuronal tissues and cells. The ganglioside GM3 serves as a common precursor for the a- and b-series gangliosides. The GD3-synthase (GD3S) catalyzes the synthesis of GD3 by adding sialic acid to GM3, segregating the a- and b-series of gangliosides and therefore controlling the levels of the major brain gangliosides GM1, GD1a, GD1b and GT1b.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 650, "target": 260, "key": "098650d1663c7e6686f809ac68d8112c"}, {"line": 9471, "relation": "association", "evidence": "The MicroRNA miR-124 Promotes Neuronal Differentiation by Triggering Brain-Specific Alternative Pre-mRNA Splicing. When this exon is skipped, PTBP2 mRNA is subject to nonsense-mediated decay (NMD). During neuronal differentiation, miR-124 reduces PTBP1 levels, leading to the accumulation of correctly spliced PTBP2 mRNA and a dramatic increase in PTBP2 protein.", "citation": {"db": "PubMed", "db_id": "17679093"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 650, "target": 3274, "key": "a87a463b38f9c13150e080981c52f08e"}, {"line": 3411, "relation": "directlyIncreases", "evidence": "Gangliosides are a family of sialic acid containing glycosphingolipids, highly enriched in neuronal and glial membranes, where they play important roles for development, proliferation, differentiation and maintenance of neuronal tissues and cells. The ganglioside GM3 serves as a common precursor for the a- and b-series gangliosides. The GD3-synthase (GD3S) catalyzes the synthesis of GD3 by adding sialic acid to GM3, segregating the a- and b-series of gangliosides and therefore controlling the levels of the major brain gangliosides GM1, GD1a, GD1b and GT1b.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 3423, "target": 4092, "key": "cc4ad57d6614beb93d7359e315d48189"}, {"line": 3412, "relation": "increases", "evidence": "Gangliosides are a family of sialic acid containing glycosphingolipids, highly enriched in neuronal and glial membranes, where they play important roles for development, proliferation, differentiation and maintenance of neuronal tissues and cells. The ganglioside GM3 serves as a common precursor for the a- and b-series gangliosides. The GD3-synthase (GD3S) catalyzes the synthesis of GD3 by adding sialic acid to GM3, segregating the a- and b-series of gangliosides and therefore controlling the levels of the major brain gangliosides GM1, GD1a, GD1b and GT1b.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 3423, "target": 260, "key": "54fc0766d94bc17a1259fd0e85643990"}, {"relation": "hasReactant", "source": 4092, "target": 172, "key": "1a26ad29bfbf5e523bd49677335239fb"}, {"relation": "hasProduct", "source": 4092, "target": 76, "key": "0a7e74eb787549064b4dab6fbf5aeaf6"}, {"line": 3413, "relation": "increases", "evidence": "Gangliosides are a family of sialic acid containing glycosphingolipids, highly enriched in neuronal and glial membranes, where they play important roles for development, proliferation, differentiation and maintenance of neuronal tissues and cells. The ganglioside GM3 serves as a common precursor for the a- and b-series gangliosides. The GD3-synthase (GD3S) catalyzes the synthesis of GD3 by adding sialic acid to GM3, segregating the a- and b-series of gangliosides and therefore controlling the levels of the major brain gangliosides GM1, GD1a, GD1b and GT1b.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 4092, "target": 260, "key": "9823e89500ccd84763887cfab7b8490e"}, {"line": 26159, "relation": "association", "evidence": "The sialic acid levels in the CSF apoE-containing lipoprotein fractions were 5.3 +/- 1.3% of the total CSF sialic acid, and were correlated with the CSF apoE concentrations.", "citation": {"db": "PubMed", "db_id": "19522249"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 172, "target": 2312, "key": "d9904278dce822038143c40a9dd5ef8d"}, {"relation": "isA", "source": 119, "target": 260, "key": "820f0763e9628dd039b301a11d7a3f47"}, {"relation": "isA", "source": 87, "target": 260, "key": "d6c80b682a48e1e2a3c4de6746735ddb"}, {"relation": "isA", "source": 89, "target": 260, "key": "c3e6bef9a06a6919198d99b251e9c1c4"}, {"relation": "isA", "source": 120, "target": 260, "key": "c2129e900d5b1efc88c7569890686d98"}, {"line": 3422, "relation": "decreases", "evidence": "Molecular mechanisms of APP cleavage products in the regulation of GD3S enzyme activity. (A) In absence of Ab peptides a-series ganglioside GM3 binds to GD3S and is converted to the b-series ganglioside GD3. In presence of Ab, Ab binds ganglioside GM3, forming an Ab-GM3 complex. This complex still binds to GD3S, but cannot be converted to GD3. (B) Dual function of Ab and AICD in GD3S regulation. Ab reduces enzyme activity of GD3S by forming an Ab-GM3 complex, resulting in reduced turnover of GM3 to GD3. AICD binds the adaptor protein Fe65 and reduces GD3S gene transcription, which also results in reduced turnover of GM3 to GD3.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 902, "target": 4092, "key": "cc7946c32df1a2eb392074c9f6d1d0b4"}, {"line": 3423, "relation": "decreases", "evidence": "Molecular mechanisms of APP cleavage products in the regulation of GD3S enzyme activity. (A) In absence of Ab peptides a-series ganglioside GM3 binds to GD3S and is converted to the b-series ganglioside GD3. In presence of Ab, Ab binds ganglioside GM3, forming an Ab-GM3 complex. This complex still binds to GD3S, but cannot be converted to GD3. (B) Dual function of Ab and AICD in GD3S regulation. Ab reduces enzyme activity of GD3S by forming an Ab-GM3 complex, resulting in reduced turnover of GM3 to GD3. AICD binds the adaptor protein Fe65 and reduces GD3S gene transcription, which also results in reduced turnover of GM3 to GD3.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 902, "target": 260, "key": "7db2c63604ac0f2560997496c647ee37"}, {"relation": "partOf", "source": 60, "target": 902, "key": "77e51e4b1df1e55da215272dd4b1189e"}, {"line": 3424, "relation": "decreases", "evidence": "Molecular mechanisms of APP cleavage products in the regulation of GD3S enzyme activity. (A) In absence of Ab peptides a-series ganglioside GM3 binds to GD3S and is converted to the b-series ganglioside GD3. In presence of Ab, Ab binds ganglioside GM3, forming an Ab-GM3 complex. This complex still binds to GD3S, but cannot be converted to GD3. (B) Dual function of Ab and AICD in GD3S regulation. Ab reduces enzyme activity of GD3S by forming an Ab-GM3 complex, resulting in reduced turnover of GM3 to GD3. AICD binds the adaptor protein Fe65 and reduces GD3S gene transcription, which also results in reduced turnover of GM3 to GD3.", "citation": {"db": "PubMed", "db_id": "22470521"}, "annotations": {"MeSHAnatomy": {"Autonomic Fibers, Postganglionic": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1105, "target": 3423, "key": "20fb0a119166d72cd8388be8ac9aeef7"}, {"line": 24558, "relation": "increases", "evidence": "APP intracellular domain (AICD) has been proposed as a transcriptional inductor that moves to the nucleus with the adaptor protein Fe65 and regulates transcription.", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 1105, "target": 794, "key": "e3ce66bbcc282f22e543fa6d0bd0c771"}, {"line": 3439, "relation": "increases", "evidence": "Binding to lipid and heparan sulfate proteoglycans (HSPG) induces apoE to adopt active conformations for binding to low-density lipoprotein receptor (LDLR) family. ApoE also interacts with beta amyloid peptide, manifests critical isoform-specific effects on Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21873229"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 981, "target": 1131, "key": "f44bda9a76b2120d8b6935aec9c41f7f"}, {"relation": "partOf", "source": 290, "target": 981, "key": "d0f59778f26dd8b8027c55d9b89a9f40"}, {"line": 21811, "relation": "association", "evidence": "More recent data indicate that these enzymes and the biologically active lipid molecules they generate could influence the functioning of the central nervous system and the pathobiology of neurodegenerative disorders such as AD via mechanisms different from classical inflammation.", "citation": {"db": "PubMed", "db_id": "20691748"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Inflammation": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}}, "source": 290, "target": 3874, "key": "30e23b6b57fbea09a3873067ee05c189"}, {"line": 21812, "relation": "association", "evidence": "More recent data indicate that these enzymes and the biologically active lipid molecules they generate could influence the functioning of the central nervous system and the pathobiology of neurodegenerative disorders such as AD via mechanisms different from classical inflammation.", "citation": {"db": "PubMed", "db_id": "20691748"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Inflammation": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}}, "source": 290, "target": 3823, "key": "b9c44ed462b3f004ca98954551dc4b0f"}, {"relation": "partOf", "source": 290, "target": 905, "key": "b1f0136915fbcf396f91ba6ee0ccf3b1"}, {"relation": "partOf", "source": 2856, "target": 981, "key": "725494126952dea9bc15e3c8a7578dab"}, {"line": 3443, "relation": "association", "evidence": "Binding to lipid and heparan sulfate proteoglycans (HSPG) induces apoE to adopt active conformations for binding to low-density lipoprotein receptor (LDLR) family. ApoE also interacts with beta amyloid peptide, manifests critical isoform-specific effects on Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21873229"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 1127, "target": 3823, "key": "b43a46aadf993659d1b533d0ca017e1d"}, {"line": 25260, "relation": "increases", "evidence": "We have found the fundamental activity of apolipoprotein E in its Abeta interactions in vitro is to retard fibril growth by inhibiting seeding.", "citation": {"db": "PubMed", "db_id": "8823200"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 1127, "target": 889, "key": "5aa67162a990789994eff69b1cd14322"}, {"line": 26041, "relation": "increases", "evidence": "Medium", "citation": {"db": "PubMed", "db_id": "17077814"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 1127, "target": 2328, "key": "63f325fd2197bb667b2f20055062838e"}, {"line": 37121, "relation": "increases", "evidence": "Cholesterol transport: High cholesterol levels have been linked to overproduction of ABeta¸ and are a risk factor for AD. One of the physiological functions of ABeta¸ has been suggested to control cholesterol transport [167]. Prevalence of AD is reduced among people treated with inhibitors of cholesterol biosynthesis, statins [168, 169] and animal studies support these results [170]. In vitro and in vivo studies have shown that cholesterol modulates APP processing and affects APP mRNA expression [171]. Another mechanism is the increased binding of ABeta¸ to ApoE4 over non-E4 alleles. ApoE is a lipid and cholesterol transport protein responsible for the efflux of cholesterol from neurons to form stable complexes both in vitro and in vivo [172]. Allele ApoE4 is a major risk factor in AD [173]. This relationship might promote synaptogenesis, since in vitro studies have demonstrated that cholesterol released by astroglia increases synaptogenesis [174, 175] with resulting modulation of spike rates [176]. Together, this evidence indicates that one of the physiological functions of APP might be to control cholesterol movement across neuronal membranes [167].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1127, "target": 530, "key": "a711eecc76185e2c7b167947c8f2467d"}, {"line": 37122, "relation": "increases", "evidence": "Cholesterol transport: High cholesterol levels have been linked to overproduction of ABeta¸ and are a risk factor for AD. One of the physiological functions of ABeta¸ has been suggested to control cholesterol transport [167]. Prevalence of AD is reduced among people treated with inhibitors of cholesterol biosynthesis, statins [168, 169] and animal studies support these results [170]. In vitro and in vivo studies have shown that cholesterol modulates APP processing and affects APP mRNA expression [171]. Another mechanism is the increased binding of ABeta¸ to ApoE4 over non-E4 alleles. ApoE is a lipid and cholesterol transport protein responsible for the efflux of cholesterol from neurons to form stable complexes both in vitro and in vivo [172]. Allele ApoE4 is a major risk factor in AD [173]. This relationship might promote synaptogenesis, since in vitro studies have demonstrated that cholesterol released by astroglia increases synaptogenesis [174, 175] with resulting modulation of spike rates [176]. Together, this evidence indicates that one of the physiological functions of APP might be to control cholesterol movement across neuronal membranes [167].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1127, "target": 787, "key": "57c32946f83bf612827bc8d7ac9e6281"}, {"line": 3460, "relation": "association", "evidence": "The low-density lipoprotein receptor (LDLR) has the highest affinity for apoE and plays an important role in brain cholesterol metabolism.These data suggest that increased APP expression and Abeta exposure alters microtubule function, leading to reduced transport of LDLR to the plasma membrane. Consequent deleterious effects on apoE uptake and function will have implications for AD pathogenesis and/or progression", "citation": {"db": "PubMed", "db_id": "20049331"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 529, "target": 2960, "key": "310d283817cf844445c858e3ab4461a7"}, {"line": 5557, "relation": "association", "evidence": "Disturbances of the cholesterol metabolism are associated with Alzheimer's disease (AD) risk and related cerebral pathology. Experimental studies found changing levels of cholesterol and its metabolites 24S-hydroxycholesterol (24S-OHC) and 27-hydroxycholesterol (27-OHC) to contribute to amyloidogenesis by increasing the production of soluble amyloid precursor protein (sAPP).The results suggest that high CSF concentrations of cholesterol, 24S-OHC, and 27-OHC are associated with increased production of both sAPP forms in AD.", "citation": {"db": "PubMed", "db_id": "22845771"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 529, "target": 3823, "key": "544ebaca569b08a872593f4e3b2a7e64"}, {"line": 9515, "relation": "association", "evidence": "The distribution of LRP in the central nervous system is consistent with the potential function of this receptor in the regulation of proteinase activity, cytokine activity, and cholesterol metabolism.", "citation": {"db": "PubMed", "db_id": "1632469 "}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 529, "target": 2970, "key": "6fd5f8ed001f649c115d8710cd3960ab"}, {"line": 3480, "relation": "decreases", "evidence": "The CST3 Thr25 allele of CST3, which encodes cystatin C, leads to reduced cystatin C secretion and conveys susceptibility to Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "18026102"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 2579, "target": 2578, "key": "0afab032db93e235b10fa3f2d9b03a29"}, {"line": 3481, "relation": "positiveCorrelation", "evidence": "The CST3 Thr25 allele of CST3, which encodes cystatin C, leads to reduced cystatin C secretion and conveys susceptibility to Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "18026102"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 2579, "target": 3823, "key": "f1c270dc4de29d58c1bfa4f582f236cd"}, {"relation": "hasVariant", "source": 2578, "target": 2579, "key": "599194f10af81c151ab64f646928df44"}, {"line": 38189, "relation": "decreases", "evidence": "Impaired degradation of amyloid beta (Abeta) peptides could lead to Abeta accumulation, an early trigger of Alzheimer's disease (AD). How Abeta-degrading enzymes are regulated remains largely unknown. Cystatin C (CysC, CST3) is an endogenous inhibitor of cysteine proteases, including cathepsin B (CatB), a recently discovered Abeta-degrading enzyme", "citation": {"db": "PubMed", "db_id": "18957217"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2578, "target": 2448, "key": "6ffb955cfbcc989f232e2112278a18ea"}, {"line": 38191, "relation": "decreases", "evidence": "Impaired degradation of amyloid beta (Abeta) peptides could lead to Abeta accumulation, an early trigger of Alzheimer's disease (AD). How Abeta-degrading enzymes are regulated remains largely unknown. Cystatin C (CysC, CST3) is an endogenous inhibitor of cysteine proteases, including cathepsin B (CatB), a recently discovered Abeta-degrading enzyme", "citation": {"db": "PubMed", "db_id": "18957217"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2578, "target": 2591, "key": "a5161d87480caed0da6ed306a668bd9d"}, {"line": 38193, "relation": "decreases", "evidence": "Impaired degradation of amyloid beta (Abeta) peptides could lead to Abeta accumulation, an early trigger of Alzheimer's disease (AD). How Abeta-degrading enzymes are regulated remains largely unknown. Cystatin C (CysC, CST3) is an endogenous inhibitor of cysteine proteases, including cathepsin B (CatB), a recently discovered Abeta-degrading enzyme", "citation": {"db": "PubMed", "db_id": "18957217"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2578, "target": 2328, "key": "93a73f5f929acd00e0835574210c2f29"}, {"line": 3496, "relation": "association", "evidence": "Two novel missense point mutations, Ser413Leu in the CHRNA4 gene and Gln397Pro in the CHRNB2 gene, were identified in two different AD cases but were not found in other AD cases and controls. These findings suggested that genetic polymorphisms of the neuronal nAChR genes might be related to the pathogenesis of sporadic AD.", "citation": {"db": "PubMed", "db_id": "12214130"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2519, "target": 3823, "key": "b128bc0d556b6a4aec22d0e09db2de24"}, {"relation": "hasVariant", "source": 2518, "target": 2519, "key": "920ea543137bf4f6d1dedb332532f86b"}, {"line": 13477, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2518, "target": 117, "key": "dcfe5c94bb67dec93a6afcabb61d2781"}, {"line": 13521, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2518, "target": 122, "key": "f9e1acd62a8397837997f75f3af57088"}, {"line": 14206, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2518, "target": 204, "key": "198038adbc332aecac3cbe1829d0bfd0"}, {"line": 37203, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2518, "target": 492, "key": "f9eef485c578c1e98fff50ae551af7d8"}, {"line": 39347, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2518, "target": 3815, "key": "ff93b64e8df7b42fc425971e49d4a1fc"}, {"line": 3500, "relation": "association", "evidence": "Two novel missense point mutations, Ser413Leu in the CHRNA4 gene and Gln397Pro in the CHRNB2 gene, were identified in two different AD cases but were not found in other AD cases and controls. These findings suggested that genetic polymorphisms of the neuronal nAChR genes might be related to the pathogenesis of sporadic AD.", "citation": {"db": "PubMed", "db_id": "12214130"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2525, "target": 3823, "key": "525f8d2779f70962d270359d189404c5"}, {"relation": "hasVariant", "source": 2524, "target": 2525, "key": "8c9805a1213fc1e50942fb0eebc6f47b"}, {"line": 13501, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2524, "target": 122, "key": "4654f80e29a773a652d2834498fa1ee7"}, {"line": 14230, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2524, "target": 204, "key": "34d9b512a5de1eb82898db94b26590ca"}, {"line": 37182, "relation": "regulates", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2524, "target": 2328, "key": "69fd00af3b92e53c5e629fd794304db4"}, {"line": 37227, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2524, "target": 492, "key": "2c19f16c0e0643ec60f03568ed9a184d"}, {"line": 39351, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2524, "target": 3815, "key": "29a28bef2f3dde33149a2c53fc6a83c8"}, {"line": 46384, "relation": "negativeCorrelation", "evidence": "Significant bilateral reductions in nicotinic receptor binding were identified in frontal (left, p = 0.004; right, p = 0.002), striatal (left, p = 0.004; right, p = 0.003), right medial temporal (p = 0.04) and pons (p<0.001) in patients with AD compared to controls.Using 123I-5IA-85380 SPECT we found changes consistent with significant reductions in the nicotinic alpha4beta2 receptor in cortical and striatal brain regions.", "citation": {"db": "PubMed", "db_id": "17135460"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true, "Corpus Striatum": true}}, "source": 2524, "target": 3823, "key": "c8887b0632bb260bdc8dc4287b79e31e"}, {"line": 3513, "relation": "decreases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Low": true}}, "source": 2466, "target": 608, "key": "4d37a33e607ee0bd63fa90856f3858e6"}, {"line": 3522, "relation": "decreases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2466, "target": 2465, "key": "e8508e5da1d1df1b79c3c93acadf10ba"}, {"line": 3527, "relation": "decreases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2466, "target": 3823, "key": "dfcac0dc062a7a2f00a6a4a653554e94"}, {"relation": "partOf", "source": 2466, "target": 1320, "key": "a65ce51e2d6324f542d2a3c2c1ec3f59"}, {"line": 3514, "relation": "increases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Low": true}}, "source": 2465, "target": 609, "key": "b2c5d61e38be7fe3cfb62a9c5c265a70"}, {"line": 3520, "relation": "increases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2465, "target": 80, "key": "6a92c24d50481aad4d1f5d1c5a45568a"}, {"line": 3521, "relation": "decreases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2465, "target": 820, "key": "54727bac467b6bde988abaf24c6596ea"}, {"line": 3523, "relation": "increases", "evidence": "These findings suggest that the absence of CCR5 increases expression of CCR2, which leads to the activation of astrocytes causing Abeta deposit, and thereby impairs memory function. These results suggest that CCR5 may be a critical suppressor of the development and progression of AD pathology.", "citation": {"db": "PubMed", "db_id": "19394434"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2465, "target": 418, "key": "f078105704820fa98465667f8c201635"}, {"relation": "partOf", "source": 2465, "target": 1318, "key": "2e4e6c54214c886d5d1e0cd13f2db55f"}, {"line": 4986, "relation": "increases", "evidence": "Interaction of CCL2 with its receptor CCR2 regulates mononuclear phagocyte accumulation. CCR2 deficiency leads to lower mononuclear phagocyte accumulation and is associated with higher brain Abeta levels, specifically around blood vessels, suggesting that monocytes accumulate at sites of Abeta deposition in an initial attempt to clear these deposits and stop or delay their neurotoxic effects.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Complement system subgraph": true, "Chemokine signaling subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2465, "target": 622, "key": "d3e886602c1ca8de36fb5d640c71a66b"}, {"line": 39814, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2465, "target": 3823, "key": "92998612dda10f7138fbd1838a959dd9"}, {"line": 4222, "relation": "association", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 609, "target": 608, "key": "1a9c9055fae9d27c81df85dd60e69565"}, {"line": 4223, "relation": "association", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 609, "target": 645, "key": "7b7164eb82a66ddf0a1e816bd1b53439"}, {"line": 4226, "relation": "increases", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 609, "target": 532, "key": "97cc5bc0eb4a0cd89c21591fcee5c282"}, {"line": 5046, "relation": "increases", "evidence": "Neuritic plaques in the brain of Alzheimer's disease patients are characterized by beta-amyloid deposits associated with a glia-mediated inflammatory response.", "citation": {"db": "PubMed", "db_id": "15817521"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 532, "key": "5c0121623bd279e1d268250cb7ca5f0a"}, {"line": 4269, "relation": "increases", "evidence": "Chronic neuroinflammation is a hallmark of several neurological disorders associated with cognitive loss. Activated microglia and secreted factors such as tumor necrosis factor (TNF)-a are key mediators of neuroinflammation and may contribute to neuronal dysfunction", "citation": {"db": "PubMed", "db_id": "22277195"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 609, "target": 3875, "key": "aceaa0b2d70b60605285a82897174549"}, {"line": 6376, "relation": "increases", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 80, "key": "756457f3862741c1e276c115a401919a"}, {"line": 40188, "relation": "association", "evidence": "Activated microglia play a critical role in amyloid clearance, but chronic deregulation of CNS inflammatory pathways results in secretion of neurotoxic mediators that ultimately contribute to neurodegeneration in AD.", "citation": {"db": "PubMed", "db_id": "24369524"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Microglia": true, "Bodily Secretions": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 80, "key": "3a4de4c6dcc6deaba7dbe8d49802b810"}, {"line": 42416, "relation": "decreases", "evidence": "PPARgamma/RXRα-induced and CD36-mediated microglial amyloid-beta phagocytosis results in cognitive improvement in amyloid precursor protein/presenilin 1 mice.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 80, "key": "3f0a4748f34a8b195dc9e186fd6a5aa4"}, {"line": 39521, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "source": 609, "target": 3815, "key": "d03032aa44228e41fd1220664271b352"}, {"line": 39525, "relation": "decreases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 609, "target": 431, "key": "291b8a3e6b25ddf3b6c07102299429ff"}, {"line": 39599, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}}, "source": 609, "target": 2312, "key": "a9aaf28ddf6e507475abf62fd27b7b05"}, {"line": 43213, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 2312, "key": "eefc775ccc43ce7324bcfa28f03873ff"}, {"line": 39604, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 609, "target": 3556, "key": "3f569e37a52c6189075a2138277f326f"}, {"line": 39708, "relation": "increases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 609, "target": 3823, "key": "a366633b465746fa520fbdcab3c94ced"}, {"line": 40048, "relation": "association", "evidence": "Microglia activation and neuroinflammation have been associated with the pathogenesis of neurodegenerative disorders such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 609, "target": 3823, "key": "021e0fa1cb77cf93ec9cd162bbd4091b"}, {"line": 43109, "relation": "increases", "evidence": "Microglia are resident mononuclear phagocytes of the brain that become activated in response to insults including neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and prion disease.", "citation": {"db": "PubMed", "db_id": "24655482"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Parkinson Disease": true, "Prion Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Phagocytes": true}, "Species": {"9606": true}}, "source": 609, "target": 3823, "key": "5722951e8958c515fb2e01d031fc6f74"}, {"line": 43320, "relation": "positiveCorrelation", "evidence": "Accumulating data indicate that astrocytes play an important role in the neuroinflammation related to the/ pathogenesis of AD. It has been shown that microglia and astrocytes are activated in AD brain and amyloid-beta (Abeta)/ can increase the expression of cyclooxygenase 2 (COX-2), interleukin-1, and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 3823, "key": "3a4ac09c383003d26ccff7ae58008778"}, {"line": 39897, "relation": "association", "evidence": "Occasionally, both ASS-and iNOS expression was detectable in CD 68-positive activated microglia cells in close proximity to senile plaques.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 609, "target": 1753, "key": "79c4d5dfef673cdcdb4ac3d003efae74"}, {"line": 39898, "relation": "association", "evidence": "Occasionally, both ASS-and iNOS expression was detectable in CD 68-positive activated microglia cells in close proximity to senile plaques.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 609, "target": 1895, "key": "d68d907f6dd7a8b4af0258eca02b0913"}, {"line": 41608, "relation": "association", "evidence": "One important control for such cell activation is through the CC-chemokine ligand 2 (Ccl2) and its receptor, the CC-chemokine receptor 2. Both affect microglia and peripheral macrophage immune responses and for the latter, cell ingress across the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Macrophages": true, "Blood-Brain Barrier": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 1641, "key": "8b2cf3492f24f33e405660bf5da42ff3"}, {"line": 43110, "relation": "increases", "evidence": "Microglia are resident mononuclear phagocytes of the brain that become activated in response to insults including neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and prion disease.", "citation": {"db": "PubMed", "db_id": "24655482"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Parkinson Disease": true, "Prion Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Phagocytes": true}, "Species": {"9606": true}}, "source": 609, "target": 3874, "key": "821bc64e2df414cb61f7044fedb6c83b"}, {"line": 43111, "relation": "increases", "evidence": "Microglia are resident mononuclear phagocytes of the brain that become activated in response to insults including neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and prion disease.", "citation": {"db": "PubMed", "db_id": "24655482"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Parkinson Disease": true, "Prion Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Phagocytes": true}, "Species": {"9606": true}}, "source": 609, "target": 3878, "key": "50704b7dd69c81338ba2d5187e8c81f3"}, {"line": 43112, "relation": "increases", "evidence": "Microglia are resident mononuclear phagocytes of the brain that become activated in response to insults including neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and prion disease.", "citation": {"db": "PubMed", "db_id": "24655482"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Parkinson Disease": true, "Prion Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Phagocytes": true}, "Species": {"9606": true}}, "source": 609, "target": 3882, "key": "5797ea72366762236a95f7b5caefe5be"}, {"line": 43126, "relation": "association", "evidence": "Cx3cl1/Cx3cr1 signalling is thought to maintain microglia in their resting state and disrupting this equilibrium may allow microglia to become activated.", "citation": {"db": "PubMed", "db_id": "24655482"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 609, "target": 1643, "key": "6bdf793785af2e112d80117fc993b58c"}, {"line": 43221, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 3350, "key": "844942f5304ee25d95a9820aacac5c92"}, {"line": 43428, "relation": "increases", "evidence": "Chronic neuroinflammatory processes including glial activation may play a role in the pathogenesis of/ Alzheimer's disease (AD). The immune and inflammatory mediator CD40 ligand (CD40L) can augment the activation of/ cultured microglia by amyloid beta-protein (Abeta) and promote neuron death. We investigated whether CD40L is/ increased in AD and in animal models of AD and neuroinflammation. These findings indicate that astrocytes are / the predominant source of CD40L in brain, and are consistent with the proposed role of CD40L-mediated neurotoxic/ inflammation in AD.", "citation": {"db": "PubMed", "db_id": "11755016"}, "annotations": {"Cell": {"microglial cell": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 609, "target": 648, "key": "7bbd16789401e7fbb4cb2f3b6ce8c870"}, {"line": 44258, "relation": "positiveCorrelation", "evidence": "Microglia manage immunosurveillance and mediate inflammation, both suggested to be important in Alzheimer's disease (AD). The aim of this study was to investigate if microglial markers could differentiate, firstly between AD and controls, and secondly between stable mild cognitive impairment (MCI) and those progressing to AD and vascular dementia (VaD). Furthermore, we investigated if these markers were sufficiently stable to be used in clinical trials. We quantified YKL-40 and sCD14 in cerebrospinal fluid (CSF) from 96 AD patients, 65 healthy controls, and 170 patients with MCI from baseline and over 5.7 years. For the stability analysis, two CSF samples were collected from 52 AD patients with a six-month interval in between. YKL-40, but not sCD14, was significantly elevated in AD compared with healthy controls (p = 0.003). Furthermore, YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and ABeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 x 10-25; r = 0.77, p = 2.0 x 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 609, "target": 2509, "key": "e629d0edd85a0086e811629da41d22b5"}, {"line": 46866, "relation": "association", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 609, "target": 3493, "key": "1d673b9ad60a2b5f1e5f990ee85f0951"}, {"line": 3579, "relation": "association", "evidence": "Yet several studies have demonstrated that oligomeric Abeta affects the cellular cholesterol level, which in turn has a variety of effects on AD related pathologies, including modulation of tau phosphorylation, synapse formation and maintenance of its function, and the neurodegenerative process.", "citation": {"db": "PubMed", "db_id": "12668899"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 788, "target": 231, "key": "51f05691045d5a30d66225266f7c28ba"}, {"line": 49475, "relation": "association", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 788, "target": 3224, "key": "f244774eb6c0ae9b73e70664a59f71cb"}, {"line": 49482, "relation": "negativeCorrelation", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 788, "target": 3874, "key": "7a1d4d1a0a4b575469b986909a6a1e2f"}, {"line": 3641, "relation": "directlyIncreases", "evidence": "The retromer complex is a conserved protein complex required for endosome-to-Golgi retrieval of a number of physiologically important membrane proteins including SorL1", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}}}, "source": 895, "target": 3397, "key": "9defc2e546c80f6e5a4dce22cc83ee21"}, {"line": 3649, "relation": "association", "evidence": "The retromer complex is a conserved protein complex required for endosome-to-Golgi retrieval of a number of physiologically important membrane proteins including SorL1", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}}}, "source": 3397, "target": 3397, "key": "2bfaca96e0bbd80c54477209576db1f7"}, {"line": 3649, "relation": "association", "evidence": "The retromer complex is a conserved protein complex required for endosome-to-Golgi retrieval of a number of physiologically important membrane proteins including SorL1", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 3397, "target": 3397, "key": "870729c4dd8cbaec0f4036d74a251a61"}, {"line": 16514, "relation": "association", "evidence": "In the neurosciences, it has led to the discoveries of osteopontin in multiple sclerosis and SORL1/LR11 in Alzheimer's, and recent studies indicate its potential for identifying neurogenomic biomarkers.", "citation": {"db": "PubMed", "db_id": "19285134"}, "annotations": {"Disease": {"multiple sclerosis": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3397, "target": 3823, "key": "619dc9d0fc615b8268d197dd6d3650aa"}, {"line": 45846, "relation": "negativeCorrelation", "evidence": "Our results showed that SORL1 gene is lower expressed in the brain than in blood leukocytes for AD patients", "citation": {"db": "PubMed", "db_id": "22836009"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 3397, "target": 3823, "key": "78db98a2a662f0af4a03b711b775343c"}, {"line": 45850, "relation": "positiveCorrelation", "evidence": "Our results showed that SORL1 gene is lower expressed in the brain than in blood leukocytes for AD patients", "citation": {"db": "PubMed", "db_id": "22836009"}, "annotations": {"Cell": {"leukocyte": true, "blood cell": true}}, "source": 3397, "target": 3823, "key": "fb801d71d0e2bb941008afcf9783663f"}, {"relation": "partOf", "source": 3397, "target": 1212, "key": "39dcd68ab26738a70af3c84f398d41be"}, {"relation": "partOf", "source": 3397, "target": 1140, "key": "8947fd11ea2ce9f68e671749dfa93c79"}, {"line": 33788, "relation": "association", "evidence": "SorLA has been shown to be down regulated in Alzheimer's disease brains, interact with ApoE, and pmodulate Abeta production", "citation": {"db": "PubMed", "db_id": "16930450"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3397, "target": 80, "key": "c5f69e60654495436e4e37070331367a"}, {"line": 36122, "relation": "decreases", "evidence": "SORLA is proposed to act as a retention factor for APP in the TGN, preventing the release of precursor molecules into the processing pathways. Consequently, over-expression of SORLA in neurons prevents the targeting of APP from TGN to the cell surface and to endosomes and reduces the production of Abeta peptides [9-11]. The importance of SORLA for AD is further supported by low levels of receptor expression seen in patients suffering from the disease", "citation": {"db": "PubMed", "db_id": "22727043"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 3397, "target": 2315, "key": "b4f3107e4f4be1a471212ae5038c732a"}, {"line": 36123, "relation": "decreases", "evidence": "SORLA is proposed to act as a retention factor for APP in the TGN, preventing the release of precursor molecules into the processing pathways. Consequently, over-expression of SORLA in neurons prevents the targeting of APP from TGN to the cell surface and to endosomes and reduces the production of Abeta peptides [9-11]. The importance of SORLA for AD is further supported by low levels of receptor expression seen in patients suffering from the disease", "citation": {"db": "PubMed", "db_id": "22727043"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 3397, "target": 2315, "key": "b5c414d85a1db8f5d5f555b2ec18536a"}, {"line": 36124, "relation": "directlyDecreases", "evidence": "SORLA is proposed to act as a retention factor for APP in the TGN, preventing the release of precursor molecules into the processing pathways. Consequently, over-expression of SORLA in neurons prevents the targeting of APP from TGN to the cell surface and to endosomes and reduces the production of Abeta peptides [9-11]. The importance of SORLA for AD is further supported by low levels of receptor expression seen in patients suffering from the disease", "citation": {"db": "PubMed", "db_id": "22727043"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 3397, "target": 2328, "key": "05e4f90fb90b04ea14a065a04e406df2"}, {"line": 36166, "relation": "decreases", "evidence": "SORL1 encodes the Sortilin-related receptor LR11/SorLA, a protein involved in the control of amyloid beta peptide production", "citation": {"db": "PubMed", "db_id": "22472873"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3397, "target": 2328, "key": "cb41b72d5a187640c849abfc9e1b3879"}, {"relation": "hasVariant", "source": 3397, "target": 3398, "key": "91b8cecba9ee3cc53ff8f775cc48b316"}, {"relation": "partOf", "source": 3397, "target": 1627, "key": "220b3528c24f3901c0ba1a07ab921302"}, {"relation": "partOf", "source": 3397, "target": 1703, "key": "21408de6c7fe9ba43deac5cd43312045"}, {"relation": "partOf", "source": 3397, "target": 1704, "key": "88909b6bd81c159f68d3c677a6e4ec65"}, {"relation": "partOf", "source": 3397, "target": 1705, "key": "ee17927c76bb87cf0ade27e97b052e9d"}, {"line": 3660, "relation": "association", "evidence": "We obtained significant evidence of association with KIAA1033 (VEGAS p = 0.025), SNX1 (VEGAS p = 0.035), SNX3 (p = 0.0057), and RAB7A (VEGAS p = 0.018). Ten KIAA1033 SNPs were also significantly associated with AD in a group of African Americans (513 AD cases, 504 control subjects)", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}, "Confidence": {"High": true}}, "source": 3533, "target": 3823, "key": "975d8b1461a8e2b29a84d20ad519d1ab"}, {"line": 3668, "relation": "association", "evidence": "We obtained significant evidence of association with KIAA1033 (VEGAS p = 0.025), SNX1 (VEGAS p = 0.035), SNX3 (p = 0.0057), and RAB7A (VEGAS p = 0.018). Ten KIAA1033 SNPs were also significantly associated with AD in a group of African Americans (513 AD cases, 504 control subjects)", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "source": 3388, "target": 3823, "key": "2010199a34782cf57836f80b0da23e97"}, {"line": 3676, "relation": "association", "evidence": "We obtained significant evidence of association with KIAA1033 (VEGAS p = 0.025), SNX1 (VEGAS p = 0.035), SNX3 (p = 0.0057), and RAB7A (VEGAS p = 0.018). Ten KIAA1033 SNPs were also significantly associated with AD in a group of African Americans (513 AD cases, 504 control subjects)", "citation": {"db": "PubMed", "db_id": "22673115"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 3289, "target": 3823, "key": "85b1f15fb7d95cfee499adb6a93acda7"}, {"line": 3692, "relation": "increases", "evidence": "Pb may exert neurotoxic effects through mechanisms that alter the global and promoter methylation patterns of APP gene.", "citation": {"db": "PubMed", "db_id": "22764079"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 140, "target": 1747, "key": "f9660fba324c754b026913c4ac1c2a35"}, {"line": 44481, "relation": "increases", "evidence": "We found that specificity protein 1(Sp1) expression exhibited a high level of induction after Pb exposure; we have shown that exposure to Pb increases the expression of the APP gene and the activity of Sp1", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 140, "target": 3721, "key": "973a6bb7050bb39a7fe5ce33e6dce8c2"}, {"line": 44483, "relation": "increases", "evidence": "We found that specificity protein 1(Sp1) expression exhibited a high level of induction after Pb exposure; we have shown that exposure to Pb increases the expression of the APP gene and the activity of Sp1", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 140, "target": 3584, "key": "d051dfe8f583c64fbfe9b5aea0a7860d"}, {"line": 44532, "relation": "decreases", "evidence": "Down regulation of DNMT results in hypomethylation of BACE1 and APP which are involved in Abeta production and causes upregulation of their protein expression; in turn SP1 transcription factor increases which finally results in Abeta production.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 1747, "target": 2315, "key": "c968b08c311f2c388e081434aa3b9be4"}, {"line": 44605, "relation": "decreases", "evidence": "hypomethylated APP, individuals, which in turn produces more APP, which is further cleaved to build up Abeta levels", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 1747, "target": 2315, "key": "369152aeab16b739af3a043f5b3f78b1"}, {"line": 45453, "relation": "positiveCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 1747, "target": 2315, "key": "972fda9dd47afdefd357e1356bc6993d"}, {"line": 46141, "relation": "negativeCorrelation", "evidence": "APP gene sequence data that suggests there are multiple potential sites for CpG methylation both within and around the APP gene, and that at least one of these sites is hypomethylated in brain tissue from an AD patient. That results in Increased levels of APP proteins and mRNA ", "citation": {"db": "PubMed", "db_id": "8746452"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1747, "target": 2315, "key": "d9ed0f27f1d569d38169acbcf03ce402"}, {"line": 46290, "relation": "negativeCorrelation", "evidence": "The Pb exposure acted to inhibit DNA methylation patterns, thus setting the responsiveness of the APP promoter and the expression of the APP gene at a higher level. ", "citation": {"db": "PubMed", "db_id": "22764079"}, "source": 1747, "target": 2315, "key": "8308e11e91e1afd4baee47c801532d1d"}, {"line": 44554, "relation": "negativeCorrelation", "evidence": "hypomethylation of the APP promoter for example can increase the ceiling of expression of the APP gene in response to aging processes driving overproduction of APP and Abeta levels. The increased Abeta levels then facilitate ROS production with their pro-oxidant properties, damaging the DNA. ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 1747, "target": 4033, "key": "f41bafce22990f253031c7317fe5c481"}, {"line": 44555, "relation": "negativeCorrelation", "evidence": "hypomethylation of the APP promoter for example can increase the ceiling of expression of the APP gene in response to aging processes driving overproduction of APP and Abeta levels. The increased Abeta levels then facilitate ROS production with their pro-oxidant properties, damaging the DNA. ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 1747, "target": 3584, "key": "94f27ab8c46d1947fbf6035a00345216"}, {"line": 44556, "relation": "negativeCorrelation", "evidence": "hypomethylation of the APP promoter for example can increase the ceiling of expression of the APP gene in response to aging processes driving overproduction of APP and Abeta levels. The increased Abeta levels then facilitate ROS production with their pro-oxidant properties, damaging the DNA. ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 1747, "target": 2328, "key": "84e2689903fc217feabecc41f66962f0"}, {"line": 45967, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1747, "target": 2328, "key": "8fc7534afb6d2f7388f6600d934ca56b"}, {"line": 45051, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}}, "source": 1747, "target": 3823, "key": "bc137890a26aef3edb44753e9ecaeb08"}, {"line": 45339, "relation": "negativeCorrelation", "evidence": "hypomethylation at the APP gene promoter as a possible risk factor for AD", "citation": {"db": "PubMed", "db_id": "21419233"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1747, "target": 3823, "key": "41aa1252a51e0146d688bc96da1b0e86"}, {"line": 45452, "relation": "positiveCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 1747, "target": 3823, "key": "a01a64f19693dcbf0a2df9c305dffa6b"}, {"line": 45732, "relation": "negativeCorrelation", "evidence": "significant demethylation of beta-amyloid precursor protein (APP) gene was observed in AD patients,", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 1747, "target": 3823, "key": "ab01694d1728631ca59aa6647fb8edcc"}, {"line": 45736, "relation": "positiveCorrelation", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 1747, "target": 3940, "key": "050d203d09bfd229d8dc57f4b68de28e"}, {"line": 45966, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1747, "target": 3940, "key": "6e3f100d65f04a549871528fa6af2aa1"}, {"line": 46142, "relation": "negativeCorrelation", "evidence": "APP gene sequence data that suggests there are multiple potential sites for CpG methylation both within and around the APP gene, and that at least one of these sites is hypomethylated in brain tissue from an AD patient. That results in Increased levels of APP proteins and mRNA ", "citation": {"db": "PubMed", "db_id": "8746452"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1747, "target": 3940, "key": "cdcf246657ba4da713a8699fcdd90267"}, {"line": 3696, "relation": "increases", "evidence": "It has been suggested that lead (Pb) exposure in early life may increase amyloid precursor protein (APP) expression and promote the pathogenesis of Alzheimer's disease in old age", "citation": {"db": "PubMed", "db_id": "22764079"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 2333, "target": 3823, "key": "c4cbb2fb6bbc45a3abe15222c9e678dd"}, {"line": 3708, "relation": "positiveCorrelation", "evidence": "Insulin resistance, one of the major components of type 2 diabetes mellitus (T2DM), is a known risk factor for Alzheimer's disease (AD), which is characterized by an abnormal accumulation of intra- and extracellular amyloid beta peptide (Abeta).", "citation": {"db": "PubMed", "db_id": "22829447"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3861, "target": 3823, "key": "972e6ef78b5da61da784f230a6984d37"}, {"line": 5982, "relation": "association", "evidence": "Evidence has suggested that insulin resistance (IR) or high levels of glucocorticoids (GCs) may be linked with the pathogenesis and/or progression of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 3823, "key": "7ec27e2c634e9da3c24c6bd26eed148a"}, {"line": 7799, "relation": "association", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"hyperinsulinism": true}, "Confidence": {"High": true}}, "source": 3861, "target": 3823, "key": "a22361a4d3df9415507171ada36d8428"}, {"line": 8236, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3861, "target": 3823, "key": "35688a1b3bc2c74eae0c623c2e832355"}, {"line": 20434, "relation": "association", "evidence": "Alzheimer's disease (AD) is linked to CNS insulin resistance, decreased expression of insulin and insulin receptor genes, and lower cerebrospinal insulin levels.", "citation": {"db": "PubMed", "db_id": "22142155"}, "annotations": {"MeSHDisease": {"Insulin Resistance": true, "Alzheimer Disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 3823, "key": "c401808785b231b3ce505447bd7a21d9"}, {"line": 3709, "relation": "increases", "evidence": "Insulin resistance, one of the major components of type 2 diabetes mellitus (T2DM), is a known risk factor for Alzheimer's disease (AD), which is characterized by an abnormal accumulation of intra- and extracellular amyloid beta peptide (Abeta).", "citation": {"db": "PubMed", "db_id": "22829447"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3861, "target": 80, "key": "98600f11f823fbb562945ba0dec48059"}, {"line": 5993, "relation": "positiveCorrelation", "evidence": "Although studies have shown that a high level of GCs results in IR, little is known about the molecular details that link GCs and IR in the context of AD", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 263, "key": "f3d8f5c277e19fcfd8cd72d99338582c"}, {"line": 6005, "relation": "association", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3861, "target": 3015, "key": "11850b594f7ebb3e7c92467f8c2793ed"}, {"line": 8240, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3861, "target": 3015, "key": "6258a7cc56383d01e32d1e9383cd3e58"}, {"line": 6015, "relation": "association", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Calpastatin-calpain subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3861, "target": 2428, "key": "d95b6391a6efe6eaaa9af6a3e39592fa"}, {"line": 6107, "relation": "positiveCorrelation", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 3850, "key": "7553472d14a1d0449f6421dc1381ad1b"}, {"line": 8237, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3861, "target": 3850, "key": "1b3c75f8fd419b20c4b0d2f9de8feeff"}, {"line": 6153, "relation": "negativeCorrelation", "evidence": "Insulin resistance in T2DM is partly mediated by reduced insulin receptor expression, insulin receptor tyrosine kinase activity, insulin receptor substrate (IRS) type 1 expression, and/or phosphatidyl-inositol-3 (PI3) kinase activation in skeletal muscle and adipocytes.15 Gestational diabetes is pregnancy associated and caused by insulin deficiency and hyperglycemia. ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"High": true}}, "source": 3861, "target": 2900, "key": "2e7c4c9689e38f35a9dc568ef207a9ff"}, {"line": 6154, "relation": "negativeCorrelation", "evidence": "Insulin resistance in T2DM is partly mediated by reduced insulin receptor expression, insulin receptor tyrosine kinase activity, insulin receptor substrate (IRS) type 1 expression, and/or phosphatidyl-inositol-3 (PI3) kinase activation in skeletal muscle and adipocytes.15 Gestational diabetes is pregnancy associated and caused by insulin deficiency and hyperglycemia. ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3861, "target": 2900, "key": "8bc187369f8cbb1e0cb738def14fb237"}, {"line": 6155, "relation": "negativeCorrelation", "evidence": "Insulin resistance in T2DM is partly mediated by reduced insulin receptor expression, insulin receptor tyrosine kinase activity, insulin receptor substrate (IRS) type 1 expression, and/or phosphatidyl-inositol-3 (PI3) kinase activation in skeletal muscle and adipocytes.15 Gestational diabetes is pregnancy associated and caused by insulin deficiency and hyperglycemia. ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"High": true}}, "source": 3861, "target": 2905, "key": "fcca8286e0333f9f01acb32fec8e7894"}, {"line": 6165, "relation": "negativeCorrelation", "evidence": "Insulin resistance in T2DM is partly mediated by reduced insulin receptor expression, insulin receptor tyrosine kinase activity, insulin receptor substrate (IRS) type 1 expression, and/or phosphatidyl-inositol-3 (PI3) kinase activation in skeletal muscle and adipocytes.15 Gestational diabetes is pregnancy associated and caused by insulin deficiency and hyperglycemia. ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Cell": {"skeletal muscle fiber": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 579, "key": "39a11847ae0affb52919e60b2498eacb"}, {"line": 6452, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3861, "target": 389, "key": "5bccbfc246968c598d71c5ea3a2137aa"}, {"line": 6473, "relation": "increases", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 596, "key": "ba9fb18f20ec1a2a135234fa3bb34c4e"}, {"line": 6483, "relation": "decreases", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 841, "key": "51ddda8abfaed7aefa81d0299ecc4227"}, {"line": 6499, "relation": "decreases", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 466, "key": "7b887053723300d09f96015a26b23103"}, {"line": 6632, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 3861, "target": 1744, "key": "82699377b3cdd61801d1907581111784"}, {"line": 7797, "relation": "decreases", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"hyperinsulinism": true}, "Confidence": {"High": true}}, "source": 3861, "target": 812, "key": "623f31e2be83f9806ce15ffd6e0e0371"}, {"line": 7798, "relation": "decreases", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"hyperinsulinism": true}, "Confidence": {"High": true}}, "source": 3861, "target": 820, "key": "dedb6fa154c11a898b9a75e6a2e071b2"}, {"line": 7862, "relation": "positiveCorrelation", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Confidence": {"Medium": true}}, "source": 3861, "target": 855, "key": "a5d3eacb1ba80708bc5baffde7ebed96"}, {"line": 7875, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Confidence": {"High": true}}, "source": 3861, "target": 2912, "key": "9400e498288e07207b8218a821c75246"}, {"line": 8301, "relation": "association", "evidence": "Another consequence of insulin resistance may be impaired regulation of the hypothalamicpituitary- adrenal (HPA) axis. Insulin and cortisol, a primary HPA axis hormone, are counter-regulatory, and a change in the level of either hormone influences the level of the other.[29,30] Thus, glucocorticoids can induce insulin resistance in healthy humans,[ 31,32] and hyperinsulinaemia resulting from insulin resistance can produce hypercortisolaemia. An animal study showed that rats with type 2 diabetes had higher levels of adrenocorticotrophic hormone than control individuals, consistent with chronic activation of the HPA axis.[33] In humans, plasma cortisol levels were elevated in patients with type 2 diabetes,[34] and patientswith both poorly and well controlled type 2 diabetes showed abnormal cortisol responses to hypoglycaemia.[35,36] In metabolic studies, insulin administration has been shown to increase HPA axis activity, indexed by a rise in cortisol levels", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3861, "target": 237, "key": "1f8c9388ffa584323a38765b86d34946"}, {"line": 9906, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3861, "target": 2244, "key": "fbef34db820e0d57fc73b7b299fd5f17"}, {"line": 19770, "relation": "positiveCorrelation", "evidence": "Stress contributes to the development of central insulin resistance during aging: implications for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24090692"}, "annotations": {"MeSHDisease": {"Insulin Resistance": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3861, "target": 845, "key": "a5a5301fcd58816d2a8614e042e43382"}, {"line": 24095, "relation": "positiveCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3861, "target": 648, "key": "b3d6c4d68be6fdb9d909be1d89ac6d85"}, {"line": 24096, "relation": "positiveCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3861, "target": 2328, "key": "6bca335cd4767626ef5fed4e74a882de"}, {"line": 24097, "relation": "positiveCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3861, "target": 889, "key": "7068261a7d436059584d8ef4dc12adcb"}, {"line": 3714, "relation": "decreases", "evidence": "Defects in insulin signal transduction affect autophagic flux by inhibiting the mammalian target of rapamycin pathway, resulting in altered APP processing in these cell culture systems.", "citation": {"db": "PubMed", "db_id": "22829447"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "object": {"modifier": "Activity"}, "source": 133, "target": 3551, "key": "8155b108d2cd4394847d03168154e947"}, {"line": 3715, "relation": "decreases", "evidence": "Defects in insulin signal transduction affect autophagic flux by inhibiting the mammalian target of rapamycin pathway, resulting in altered APP processing in these cell culture systems.", "citation": {"db": "PubMed", "db_id": "22829447"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 133, "target": 460, "key": "73a42b569936b28fee906e380ee23b19"}, {"line": 6051, "relation": "increases", "evidence": "Finally, both LiCl pre-treatment and calpain inhibition prevented the DEX-induced inhibition on the insulin-stimulated Akt phosphorylation. In conclusion, our study suggests that the tau phosphorylation and calpain activation mediate the EX-induced inhibition on the insulin-stimulated Akt phosphorylation.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 133, "target": 2154, "key": "da1ce9808574834cfa80fe6cd0535936"}, {"line": 22130, "relation": "decreases", "evidence": "This study disclosed that intrahippocampal insulin treatment averts not only Abeta-induced memory deterioration but also hippocampal caspase-3, ERK and P38 activation.", "citation": {"db": "PubMed", "db_id": "24881967"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Electron transport chain": true, "Caspase subgraph": true, "Insulin signal transduction": true}, "Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 133, "target": 3866, "key": "850b88bb7521c3e58809fb29be1a07ed"}, {"line": 22131, "relation": "decreases", "evidence": "This study disclosed that intrahippocampal insulin treatment averts not only Abeta-induced memory deterioration but also hippocampal caspase-3, ERK and P38 activation.", "citation": {"db": "PubMed", "db_id": "24881967"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Electron transport chain": true, "Caspase subgraph": true, "Insulin signal transduction": true}, "Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 133, "target": 3755, "key": "203121d9a09eb3b370c9c9038f3677d7"}, {"line": 22132, "relation": "decreases", "evidence": "This study disclosed that intrahippocampal insulin treatment averts not only Abeta-induced memory deterioration but also hippocampal caspase-3, ERK and P38 activation.", "citation": {"db": "PubMed", "db_id": "24881967"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Electron transport chain": true, "Caspase subgraph": true, "Insulin signal transduction": true}, "Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 133, "target": 2173, "key": "efa27722fbacbd7d5d21931a4bb4eaa0"}, {"line": 32864, "relation": "association", "evidence": "We have reported recently that the microtubule-associated protein tau is phosphorylated in vitro by Akt , an important kinase in anti-apoptotic signaling regulated by insulin and growth factors.", "citation": {"db": "PubMed", "db_id": "15283963"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Akt subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 133, "target": 2279, "key": "50f4fd958b06d2b95237d910e37e6870"}, {"line": 3729, "relation": "increases", "evidence": "Depletion of Hrs and Tsg101, acting early in the multivesicular body pathway, retained APP in early endosomes and reduced Abeta(40) production. Conversely, depletion of CHMP6 and VPS4, acting late in the pathway, rerouted endosomal APP to the TGN for enhanced APP processing. We found that VPS35 (retromer)-mediated APP recycling to the TGN was required for efficient Abeta(40) production.", "citation": {"db": "PubMed", "db_id": "22711829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}}}, "source": 1720, "target": 2315, "key": "7b8e41c0ae97b2767577a66cb66d7ec5"}, {"line": 3731, "relation": "increases", "evidence": "Depletion of Hrs and Tsg101, acting early in the multivesicular body pathway, retained APP in early endosomes and reduced Abeta(40) production. Conversely, depletion of CHMP6 and VPS4, acting late in the pathway, rerouted endosomal APP to the TGN for enhanced APP processing. We found that VPS35 (retromer)-mediated APP recycling to the TGN was required for efficient Abeta(40) production.", "citation": {"db": "PubMed", "db_id": "22711829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 1720, "target": 2328, "key": "83a2ee5c41b458905f0b96a1940af9dc"}, {"relation": "partOf", "source": 3421, "target": 1720, "key": "b2ae628cb7ac789e64a79742bcf45c7c"}, {"relation": "partOf", "source": 3500, "target": 1720, "key": "9e3bdfeaada0991326a42c1112262740"}, {"line": 3732, "relation": "decreases", "evidence": "Depletion of Hrs and Tsg101, acting early in the multivesicular body pathway, retained APP in early endosomes and reduced Abeta(40) production. Conversely, depletion of CHMP6 and VPS4, acting late in the pathway, rerouted endosomal APP to the TGN for enhanced APP processing. We found that VPS35 (retromer)-mediated APP recycling to the TGN was required for efficient Abeta(40) production.", "citation": {"db": "PubMed", "db_id": "22711829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}}}, "source": 1698, "target": 2315, "key": "22206db9b4e34ebf990769b568ab110c"}, {"relation": "partOf", "source": 2511, "target": 1698, "key": "c1d3a8a1ab6b024b9a6cd4008e200b49"}, {"relation": "partOf", "source": 3529, "target": 1698, "key": "6018e243f64fbeac9f1de5640a9239c8"}, {"relation": "partOf", "source": 3530, "target": 1698, "key": "796a5eeb707c9030aeef8a3f33ab3a75"}, {"line": 3734, "relation": "increases", "evidence": "Depletion of Hrs and Tsg101, acting early in the multivesicular body pathway, retained APP in early endosomes and reduced Abeta(40) production. Conversely, depletion of CHMP6 and VPS4, acting late in the pathway, rerouted endosomal APP to the TGN for enhanced APP processing. We found that VPS35 (retromer)-mediated APP recycling to the TGN was required for efficient Abeta(40) production.", "citation": {"db": "PubMed", "db_id": "22711829"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Endosomes"}, "toLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}}}, "source": 3528, "target": 2315, "key": "cca1641858c61da344a3f00d40e851fd"}, {"line": 3767, "relation": "increases", "evidence": "This report identifies ABCA2 as a key regulator of endogenous APP expression and processing and suggests a possible biochemical mechanism linking ABCA2 expression, APP processing and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20704561"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2230, "target": 2315, "key": "96de21ceb15b2a7c38fb90a99e477a4a"}, {"line": 3768, "relation": "regulates", "evidence": "This report identifies ABCA2 as a key regulator of endogenous APP expression and processing and suggests a possible biochemical mechanism linking ABCA2 expression, APP processing and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20704561"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 2230, "target": 2315, "key": "f9c1139c3c2b8cf25cd5a7cd2820f94a"}, {"line": 3769, "relation": "association", "evidence": "This report identifies ABCA2 as a key regulator of endogenous APP expression and processing and suggests a possible biochemical mechanism linking ABCA2 expression, APP processing and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20704561"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 2230, "target": 3823, "key": "68bf7649a1193849ec045c0d877c0607"}, {"line": 28357, "relation": "increases", "evidence": "ABCA2 expression promoted b-secretase (BACE1) cleavage of APP not at the common Asp1 amino acid site (beta-site) of Abeta in APP but at the Glu11 site (beta'-site) to increase C89 carboxyl-terminal fragment levels (beta'-CTF/C89). ", "citation": {"db": "PubMed", "db_id": "20704561"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2230, "target": 2375, "key": "be86267fe12d302ab219a674eb291dc4"}, {"line": 3782, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2894, "target": 3823, "key": "73a62330b1ce54229de13140082322b2"}, {"line": 8238, "relation": "positiveCorrelation", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 2894, "target": 3823, "key": "846f45a30c120557d45cb915ba1e382b"}, {"line": 8387, "relation": "positiveCorrelation", "evidence": "Thus, laboratory and clinical findings are consistent with epidemiological reports of a reduced prevalence of Alzheimer's disease among persons who take nonsteroidal anti-inflammatory drugs (NSAIDs) for chronic pain.[125,126] Interleukin-6 (IL-6), an inflammatory cytokine, is one of the products that has been implicated in Alzheimer's disease. Elevated IL-6 immunoreactivity has been shown in human lumbar and ventricular CSF in patientswith Alzheimer's disease.[127] Furthermore, IL-6 has been found in senile plaques and may be involved in both the development of plaques and the development of dementia.[128]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2894, "target": 3823, "key": "2eacdc43f67300d4936373d2d67c9352"}, {"line": 10775, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2894, "target": 3823, "key": "b8fe4a2f1d9b0eef0d61ade3332c6bb5"}, {"line": 38970, "relation": "positiveCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2894, "target": 3823, "key": "066ee613bd21715dbe3366ab06c9d444"}, {"line": 4732, "relation": "association", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2894, "target": 577, "key": "8bf0dab94b3702c5ced13040298f4237"}, {"line": 5111, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2894, "target": 577, "key": "338ffa10d09f10b445796387b4837007"}, {"line": 8239, "relation": "positiveCorrelation", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 2894, "target": 3850, "key": "cd0606fe314c0c20c6b270342f1e995a"}, {"line": 10796, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2894, "target": 3850, "key": "942477a8104b03636124c841cd49d8c3"}, {"line": 8388, "relation": "association", "evidence": "Thus, laboratory and clinical findings are consistent with epidemiological reports of a reduced prevalence of Alzheimer's disease among persons who take nonsteroidal anti-inflammatory drugs (NSAIDs) for chronic pain.[125,126] Interleukin-6 (IL-6), an inflammatory cytokine, is one of the products that has been implicated in Alzheimer's disease. Elevated IL-6 immunoreactivity has been shown in human lumbar and ventricular CSF in patientswith Alzheimer's disease.[127] Furthermore, IL-6 has been found in senile plaques and may be involved in both the development of plaques and the development of dementia.[128]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2894, "target": 393, "key": "2d06afbb3ab3ac0db0f47031d8b51fa7"}, {"line": 8589, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2894, "target": 693, "key": "b6825513b824f9fdb6bb0f0f5f1a21ab"}, {"line": 39790, "relation": "increases", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "source": 2894, "target": 693, "key": "358cd10dab0c0ff3ee576a04ba9d0b8a"}, {"line": 22616, "relation": "increases", "evidence": "Furthermore, also some studies demonstrated that probiotics decreased the synthesis of pro-inflammatory cytokines which are upregulated in the elderly, such as interleukin (IL)-8, IL-6 or tumour necrosis factor ?, among others, and they increased the levels of activated lymphocytes, natural killer cells, phagocytic activity and even showed a greater response to influenza vaccination.", "citation": {"db": "PubMed", "db_id": "24889891"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2894, "target": 598, "key": "698131f664907c398b9fd6e79798e86c"}, {"line": 22619, "relation": "increases", "evidence": "Furthermore, also some studies demonstrated that probiotics decreased the synthesis of pro-inflammatory cytokines which are upregulated in the elderly, such as interleukin (IL)-8, IL-6 or tumour necrosis factor ?, among others, and they increased the levels of activated lymphocytes, natural killer cells, phagocytic activity and even showed a greater response to influenza vaccination.", "citation": {"db": "PubMed", "db_id": "24889891"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2894, "target": 624, "key": "36eca444cebb4facfc793e2f04e56718"}, {"line": 22622, "relation": "increases", "evidence": "Furthermore, also some studies demonstrated that probiotics decreased the synthesis of pro-inflammatory cytokines which are upregulated in the elderly, such as interleukin (IL)-8, IL-6 or tumour necrosis factor ?, among others, and they increased the levels of activated lymphocytes, natural killer cells, phagocytic activity and even showed a greater response to influenza vaccination.", "citation": {"db": "PubMed", "db_id": "24889891"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2894, "target": 823, "key": "2471c097637401f1a75c115b8e4ef731"}, {"line": 22642, "relation": "negativeCorrelation", "evidence": "Elevated levels of T-tau, P-tau (S396), IL-6 and · OH in CSF are significantly correlated with cognitive impairment in PD patients.", "citation": {"db": "PubMed", "db_id": "24884485"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2894, "target": 812, "key": "ed62791a059616361e44645d1b81f1eb"}, {"line": 38720, "relation": "negativeCorrelation", "evidence": "Elevated levels of T-tau, P-tau (S396), IL-6 and · OH in CSF are significantly correlated with cognitive impairment in PD patients.", "citation": {"db": "PubMed", "db_id": "24884485"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Confidence": {"Very High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tau protein subgraph": true}}, "source": 2894, "target": 812, "key": "adca232c160361dec546b838bf248953"}, {"line": 24075, "relation": "decreases", "evidence": "The moderating effect of rivastigmine on the endotoxin-induced suppression of GnRH/LH secretion may result from the inhibition of pro-inflammatory cytokines released through the cholinergic anti-inflammatory pathway. AChE inhibitors lead to an increase in the concentration of ACh and activate the cholinergic anti-inflammatory pathway (Borovikova et al., 2000). These inhibitors attenuate the cytokine release, including that of IL-1beta, IL-6 and TNFα, which have been previously described both in vitro and in vivo ( Borovikova et al., 2000 and Pollak et al., 2005). The ability of rivastigmine to reduce the inflammatory action within the brain could have a profound effect on GnRH secretion, as numerous studies have reported that centrally acting pro-inflammatory cytokines, especially IL-1beta but also IL-1α and TNFα, may be primarily responsible for the inhibition of GnRH/LH secretion", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2894, "target": 2756, "key": "2c0084f794c92651bb2771346adc07bd"}, {"line": 24176, "relation": "decreases", "evidence": "Activated microglia release a combination of bioactive agents including interleukin-6 (IL-6), tumor necrosis factor alpha (TNFα), and insulin-like growth factor 1 (IGF-1). These bioactive agents have both protective and detrimental consequences for the surrounding brain tissue. We found that, while mitochondrial toxins did not affect LPS-induced activation, as measured by release of tumor necrosis factor alpha (TNF-alpha), interleukin-6 (IL-6) and interleukin-1beta (IL-1beta), they did inhibit part of the IL-4-induced alternative activation, as measured by arginase activity and expression, induction of insulin-like growth factor 1 (IGF-1) and the counteraction of the LPS induced cytokine release.", "citation": {"db": "PubMed", "db_id": "20701773"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2894, "target": 2893, "key": "961a666fe395644337c17899e741ad0d"}, {"relation": "partOf", "source": 2894, "target": 1482, "key": "284bc3fd35a537e51fd4315854b81a0a"}, {"relation": "partOf", "source": 2894, "target": 1476, "key": "2b3859e571333ed4679e2e7976bc33d0"}, {"line": 37788, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2894, "target": 815, "key": "c35b003a1e5d5edcaa946ab82eef4828"}, {"line": 37794, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2894, "target": 2897, "key": "e2667ae14bd85aab3eaf8aa0c6c1bc0c"}, {"line": 37801, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2894, "target": 2934, "key": "78482a19f6992a3b8f5a57e8b97d21c6"}, {"line": 37802, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2894, "target": 3426, "key": "98d47ec0b9075cb16796e073dcd5ee92"}, {"line": 37808, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2894, "target": 1434, "key": "fa73ff78e1ef292b243af84d8dc1d0bf"}, {"line": 39785, "relation": "positiveCorrelation", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "source": 2894, "target": 3815, "key": "c4b5f41f9febbc94f4923fdc95fa5118"}, {"line": 44292, "relation": "positiveCorrelation", "evidence": "The secreted protein, YKL-40, has been proposed as a biomarker of a variety of human diseases characterized by ongoing inflammation, including chronic neurologic pathologies such as multiple sclerosis and Alzheimer's disease. However, inflammatory mediators and the molecular mechanism responsible for enhanced expression of YKL-40 remained elusive. Using several mouse models of inflammation, we now show that YKL-40 expression correlated with increased expression of both IL-1 and IL-6. Furthermore, IL-1 together with IL-6 or the IL-6 family cytokine, oncostatin M, synergistically upregulated YKL-40 expression in both primary human and mouse astrocytes in vitro. The robust cytokine-driven expression of YKL-40 in astrocytes required both STAT3 and NF-kB binding elements of the YKL-40 promoter. In addition, YKL-40 expression was enhanced by constitutively active STAT3 and inhibited by dominant-negative IkBalpha. Surprisingly, cytokine-driven expression of YKL-40 in astrocytes was independent of the p65 subunit of NF-kB and instead required subunits RelB and p50. Mechanistically, we show that IL-1-induced RelB/p50 complex formation was further promoted by oncostatin M and that these complexes directly bound to the YKL-40 promoter. Moreover, we found that expression of RelB was strongly upregulated during inflammation in vivo and by IL-1 in astrocytes in vitro. We propose that IL-1 and the IL-6 family of cytokines regulate YKL-40 expression during sterile inflammation via both STAT3 and RelB/p50 complexes. These results suggest that IL-1 may regulate the expression of specific anti-inflammatory genes in nonlymphoid tissues via the canonical activation of the RelB/p50 complexes.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2894, "target": 2509, "key": "6a0573882a1d252b3932307841e4c83e"}, {"line": 46783, "relation": "increases", "evidence": "In addition, IL-6 and OSM moderately upregulate YKL-40 expression in human astrocytes [...] demonstrate that YKL-40 expression correlates with the expression of both IL-1beta and IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2894, "target": 2509, "key": "b0f954eee0b3580f65db809500b11ef1"}, {"relation": "partOf", "source": 2894, "target": 1710, "key": "1f87aaf1784db6faeb96f710caa0930b"}, {"line": 3799, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 3454, "target": 3823, "key": "3db257692ce2e2580a1f6bf030fea8bb"}, {"relation": "partOf", "source": 3454, "target": 1721, "key": "d3a486cf994976e613b7774d04c8dde2"}, {"line": 9280, "relation": "increases", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"Smad subgraph": true, "TGF-Beta subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3454, "target": 3382, "key": "6b3fb72b78fa583cdab2d524cb2d3d79"}, {"relation": "partOf", "source": 3454, "target": 1033, "key": "15ea88b5f874f163edb880bab57c4c93"}, {"line": 33224, "relation": "increases", "evidence": "Cytokines such as TGF beta 1 and interleukin 1 enhance the expression of clusterin, which may link clusterin to inflammatory mechanisms in AD.", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 3454, "target": 2538, "key": "017bae7ca20ee3e5a7afa2ae21a2f287"}, {"line": 36243, "relation": "increases", "evidence": "Cytokines such as TGF beta 1 and interleukin 1 enhance the expression of clusterin, which may link clusterin to inflammatory mechanisms in AD", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 3454, "target": 2538, "key": "679b135b119c0103a46bb52b94b78ef9"}, {"relation": "partOf", "source": 3454, "target": 1224, "key": "fc60f0c7f38aa39ae9073422e26bb867"}, {"line": 39023, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3454, "target": 2328, "key": "a7fb4e77c8f32cfb745c03f9bca1fc52"}, {"line": 49224, "relation": "increases", "evidence": "KLF10 has been shown to be rapidly induced by TGFbeta1, 2, 3, E2, epidermal growth factor, and bone morphogenetic protein-2.", "citation": {"db": "PubMed", "db_id": "20087894"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 3454, "target": 1857, "key": "3a05cb2f1c07105ed6041857ee77f372"}, {"line": 49249, "relation": "association", "evidence": "KLF10 has been shown to play a major role in the TGFbeta inhibition of cell proliferation and inflammation and induction of apoptosis ", "citation": {"db": "PubMed", "db_id": "20087894"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 3454, "target": 2952, "key": "1f4ae3c5c195d6bd0e52ff485de80311"}, {"line": 49251, "relation": "decreases", "evidence": "KLF10 has been shown to play a major role in the TGFbeta inhibition of cell proliferation and inflammation and induction of apoptosis ", "citation": {"db": "PubMed", "db_id": "20087894"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 3454, "target": 3920, "key": "a77860c9213629b393909e52ab7a2c08"}, {"line": 49252, "relation": "increases", "evidence": "KLF10 has been shown to play a major role in the TGFbeta inhibition of cell proliferation and inflammation and induction of apoptosis ", "citation": {"db": "PubMed", "db_id": "20087894"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 3454, "target": 848, "key": "1e835ecd5f0ed63652d88aa61acc8df8"}, {"line": 3800, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 3455, "target": 3823, "key": "65282f1927dcb582bce68cb8093c92bd"}, {"relation": "partOf", "source": 3455, "target": 1721, "key": "4401cf3cff7b87e7fcb03d4c97abda61"}, {"relation": "partOf", "source": 3455, "target": 1225, "key": "c74e9e5cde604162abab4caaac944fcd"}, {"line": 39027, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"Medium": true}}, "source": 3455, "target": 2328, "key": "eb9634789cfa033713cf1804c797846d"}, {"line": 39028, "relation": "decreases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"Medium": true}}, "source": 3455, "target": 428, "key": "9ac7528267cdd62410406f1b820502a2"}, {"line": 49225, "relation": "increases", "evidence": "KLF10 has been shown to be rapidly induced by TGFbeta1, 2, 3, E2, epidermal growth factor, and bone morphogenetic protein-2.", "citation": {"db": "PubMed", "db_id": "20087894"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 3455, "target": 1857, "key": "9a5ae3b3eec45772c4f43c9985cb7f14"}, {"line": 3801, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"TGF-Beta subgraph": true}, "Confidence": {"High": true}}, "source": 3456, "target": 3823, "key": "e1c5f2fe460239cbbb7aca7f70d6475c"}, {"relation": "partOf", "source": 3456, "target": 1721, "key": "c167be707e60ba5d97a34659cbd3a42d"}, {"line": 49226, "relation": "increases", "evidence": "KLF10 has been shown to be rapidly induced by TGFbeta1, 2, 3, E2, epidermal growth factor, and bone morphogenetic protein-2.", "citation": {"db": "PubMed", "db_id": "20087894"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 3456, "target": 1857, "key": "92d1df4ccf1d5be897d2b700f00a4c35"}, {"line": 3808, "relation": "association", "evidence": "Since then, many of the cytokines and chemokines that have been studied in AD, including beta, IL-6, TNF-α, IL-8, TGF-beta and macrophage inflammatory protein-1α (MIP-1α) have been found to have altered expression compared with control individuals [22].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2464, "target": 3823, "key": "1fc8ebf3de3b5438aab386b14db6be14"}, {"line": 3817, "relation": "association", "evidence": "In addition, an increased risk of AD has been associated with several polymorphisms of proinflammatory genes, including IL-1 [26], IL-6 [27], TNF-α [28], and α1-antichymotrypsin [29].", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"JAK-STAT signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3350, "target": 3823, "key": "c0d73cecd9b20c9b53f560f44dd55cb8"}, {"line": 38985, "relation": "positiveCorrelation", "evidence": "Acute-phase proteins such as alpha 1-antichymotrypsin and c-reactive protein, elements of the complement / system, and activated microglial and astroglial cells are consistently found in brains of AD patients. Most importantly, / also cytokines such as interleukin-6 (IL-6) have been detected in the cortices of AD patients, indicating a local / activation of components of the unspecific inflammatory system.", "citation": {"db": "PubMed", "db_id": "8739396"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3350, "target": 3823, "key": "c9c5cd27a7965e7c5c0cc8c192bd43c4"}, {"relation": "partOf", "source": 3350, "target": 1481, "key": "c4ed0ad8b70fe0e69158724a3082e3ef"}, {"relation": "partOf", "source": 3350, "target": 1478, "key": "7e34a54872208094868e352a1f8a9586"}, {"relation": "partOf", "source": 3350, "target": 946, "key": "fb2f36c9113f4173cb8799db5317459a"}, {"line": 33941, "relation": "increases", "evidence": "Like APOE protein, ACT binds to beta-amyloid peptide (A beta P) with high affinity in the filamentous deposits found in the AD brain and serves as a strong stimulatory factor in the polymerization of A beta P into amyloid filaments.", "citation": {"db": "PubMed", "db_id": "7670501"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3350, "target": 474, "key": "f90d4d7dcb20b5edfe3216fae29d79e0"}, {"line": 33956, "relation": "association", "evidence": "Our recent studies demonstrated that alpha 1-antichymotrypsin (ACT), a serine protease inhibitor, was associated with the beta-protein in the brain amyloid deposits of Alzheimer's disease, aged human controls and aged monkeys, suggesting a role for the inhibitor in the amyloid deposition.", "citation": {"db": "PubMed", "db_id": "2190106"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3350, "target": 2328, "key": "55f46668338fed9993de8fdcc95e8e23"}, {"line": 43219, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Confidence": {"Medium": true}}, "source": 3350, "target": 2328, "key": "dc99532f11cae15a0a7131a64ecd1443"}, {"line": 39744, "relation": "biomarkerFor", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 3350, "target": 3872, "key": "e66a3eca240fa8520fa5e93ff9555eb0"}, {"line": 43220, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Confidence": {"Medium": true}}, "source": 3350, "target": 480, "key": "9e6bdb25a65af7f1f0ed7f0727b8336a"}, {"line": 43221, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": "9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Confidence": {"Medium": true}}, "source": 3350, "target": 609, "key": "96c863ad73284a7e19a672e9f0129941"}, {"line": 31107, "relation": "regulates", "evidence": "LDL receptor-related protein , a multifunctional ApoE receptor , binds secreted beta-amyloid precursor protein and mediates its degradation.", "citation": {"db": "PubMed", "db_id": "12212791"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 1184, "target": 2315, "key": "d23384ac1dc1e374e809309f3884657f"}, {"line": 38350, "relation": "regulates", "evidence": "In the brain, megalin is expressed in brain capillaries, ependymal cells and choroid plexus, where it participates in the clearance of brain amyloid beta-peptide (Abeta) complex.Additionally, given that FE65 mediates the interaction between the low density lipoprotein receptor-related protein-1 and the amyloid precursor protein (APP) to modulate the rate of APP internalization from the cell surface, we hypothesize that megalin could also interact with APP in neurons.", "citation": {"db": "PubMed", "db_id": "20637285"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "cell surface"}, "toLoc": {"namespace": "GO", "name": "intracellular"}}}, "source": 1184, "target": 2315, "key": "b231fdcbf8de262109786effc4ff5ed8"}, {"line": 3841, "relation": "directlyIncreases", "evidence": "It is believed that amyloid-beta peptide (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD). Thus, the process of amyloid precursor protein (APP) cleavage is a key event and has raised much attention in the field of AD research. It is proposed that APP, beta- and gamma-secretases are all located on the lipid raft, and the meeting of them is an indispensable step for Abeta generation. Endocytosis can lead to clustering of APP, beta- and gamma-secretases from separate smaller lipid rafts into a larger one. On the other hand, for myristoylated alanine-rich C kinase substrate (MARCKS), phosphorylation by protein kinase C (PKC) or interaction with Ca(2+) can lead to its release from membrane into cytoplasm. This process induces the release of actins and phosphatidylinositol 4, 5-bisphosphate (PIP2), which are important factors for endocytosis. Thus, the present review proposes that MARCKS may be implicated in Abeta generation, by modulating free PIP2 level and actin movement, causing endocytosis", "citation": {"db": "PubMed", "db_id": "20651816"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1003, "target": 80, "key": "9b7056d170c4d98ff7a24627737328e0"}, {"line": 3842, "relation": "increases", "evidence": "It is believed that amyloid-beta peptide (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD). Thus, the process of amyloid precursor protein (APP) cleavage is a key event and has raised much attention in the field of AD research. It is proposed that APP, beta- and gamma-secretases are all located on the lipid raft, and the meeting of them is an indispensable step for Abeta generation. Endocytosis can lead to clustering of APP, beta- and gamma-secretases from separate smaller lipid rafts into a larger one. On the other hand, for myristoylated alanine-rich C kinase substrate (MARCKS), phosphorylation by protein kinase C (PKC) or interaction with Ca(2+) can lead to its release from membrane into cytoplasm. This process induces the release of actins and phosphatidylinositol 4, 5-bisphosphate (PIP2), which are important factors for endocytosis. Thus, the present review proposes that MARCKS may be implicated in Abeta generation, by modulating free PIP2 level and actin movement, causing endocytosis", "citation": {"db": "PubMed", "db_id": "20651816"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 813, "target": 1003, "key": "c75575d0cedae4eaeaa3f732a75e78bf"}, {"line": 5459, "relation": "increases", "evidence": "Cirrito et al. [26] also show that synaptic activity-induced increase in endocytosis drives more APP into the endocytic compartment, ultimately resulting in increased Abeta production and release. Abeta produced in the endocytic pathway is then brought to the cell surface where it is released into the extracellular fluid [70]. Inhibition of endocytosis reduces APP internalization and reduces Abeta production and release in cell lines", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 813, "target": 2315, "key": "729dad5be47936a0a45a08533600209a"}, {"line": 27273, "relation": "association", "evidence": "Interestingly, addition of the dominant-negative mutant of Rab5, a small G-protein Rab5 involved in the endocytic process, inhibits the aging-related APP-BACE1 interaction and Abeta production, suggesting that endocytosis contributes to AD progression.", "citation": {"db": "PubMed", "db_id": "20127045"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Low": true}}, "source": 813, "target": 3288, "key": "74a4b2fe511d11f4f9ab6c291d6d042a"}, {"line": 27276, "relation": "association", "evidence": "Interestingly, addition of the dominant-negative mutant of Rab5, a small G-protein Rab5 involved in the endocytic process, inhibits the aging-related APP-BACE1 interaction and Abeta production, suggesting that endocytosis contributes to AD progression.", "citation": {"db": "PubMed", "db_id": "20127045"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Low": true}}, "source": 813, "target": 3823, "key": "fc433f721caeff043d42ef44588cbd32"}, {"line": 3843, "relation": "increases", "evidence": "It is believed that amyloid-beta peptide (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD). Thus, the process of amyloid precursor protein (APP) cleavage is a key event and has raised much attention in the field of AD research. It is proposed that APP, beta- and gamma-secretases are all located on the lipid raft, and the meeting of them is an indispensable step for Abeta generation. Endocytosis can lead to clustering of APP, beta- and gamma-secretases from separate smaller lipid rafts into a larger one. On the other hand, for myristoylated alanine-rich C kinase substrate (MARCKS), phosphorylation by protein kinase C (PKC) or interaction with Ca(2+) can lead to its release from membrane into cytoplasm. This process induces the release of actins and phosphatidylinositol 4, 5-bisphosphate (PIP2), which are important factors for endocytosis. Thus, the present review proposes that MARCKS may be implicated in Abeta generation, by modulating free PIP2 level and actin movement, causing endocytosis", "citation": {"db": "PubMed", "db_id": "20651816"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 950, "target": 3041, "key": "6b9daadec1aa89bb8677176d7c897172"}, {"relation": "partOf", "source": 3041, "target": 950, "key": "9c356c098e77727ef6bc3a4fecbbd430"}, {"line": 3845, "relation": "increases", "evidence": "It is believed that amyloid-beta peptide (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD). Thus, the process of amyloid precursor protein (APP) cleavage is a key event and has raised much attention in the field of AD research. It is proposed that APP, beta- and gamma-secretases are all located on the lipid raft, and the meeting of them is an indispensable step for Abeta generation. Endocytosis can lead to clustering of APP, beta- and gamma-secretases from separate smaller lipid rafts into a larger one. On the other hand, for myristoylated alanine-rich C kinase substrate (MARCKS), phosphorylation by protein kinase C (PKC) or interaction with Ca(2+) can lead to its release from membrane into cytoplasm. This process induces the release of actins and phosphatidylinositol 4, 5-bisphosphate (PIP2), which are important factors for endocytosis. Thus, the present review proposes that MARCKS may be implicated in Abeta generation, by modulating free PIP2 level and actin movement, causing endocytosis", "citation": {"db": "PubMed", "db_id": "20651816"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Cytoplasm"}}}, "source": 3041, "target": 161, "key": "c64e3a74ed3ee110131dfa80649886f0"}, {"line": 3847, "relation": "increases", "evidence": "It is believed that amyloid-beta peptide (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD). Thus, the process of amyloid precursor protein (APP) cleavage is a key event and has raised much attention in the field of AD research. It is proposed that APP, beta- and gamma-secretases are all located on the lipid raft, and the meeting of them is an indispensable step for Abeta generation. Endocytosis can lead to clustering of APP, beta- and gamma-secretases from separate smaller lipid rafts into a larger one. On the other hand, for myristoylated alanine-rich C kinase substrate (MARCKS), phosphorylation by protein kinase C (PKC) or interaction with Ca(2+) can lead to its release from membrane into cytoplasm. This process induces the release of actins and phosphatidylinositol 4, 5-bisphosphate (PIP2), which are important factors for endocytosis. Thus, the present review proposes that MARCKS may be implicated in Abeta generation, by modulating free PIP2 level and actin movement, causing endocytosis", "citation": {"db": "PubMed", "db_id": "20651816"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3041, "target": 161, "key": "f6ada76e2ea36ac04c4bf85703d7c6c5"}, {"relation": "hasVariant", "source": 3041, "target": 3042, "key": "32c58f26734b0bd4bd38427b5179b8e3"}, {"line": 3849, "relation": "increases", "evidence": "It is believed that amyloid-beta peptide (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD). Thus, the process of amyloid precursor protein (APP) cleavage is a key event and has raised much attention in the field of AD research. It is proposed that APP, beta- and gamma-secretases are all located on the lipid raft, and the meeting of them is an indispensable step for Abeta generation. Endocytosis can lead to clustering of APP, beta- and gamma-secretases from separate smaller lipid rafts into a larger one. On the other hand, for myristoylated alanine-rich C kinase substrate (MARCKS), phosphorylation by protein kinase C (PKC) or interaction with Ca(2+) can lead to its release from membrane into cytoplasm. This process induces the release of actins and phosphatidylinositol 4, 5-bisphosphate (PIP2), which are important factors for endocytosis. Thus, the present review proposes that MARCKS may be implicated in Abeta generation, by modulating free PIP2 level and actin movement, causing endocytosis", "citation": {"db": "PubMed", "db_id": "20651816"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3041, "target": 2328, "key": "5b8fb2a4b36e2ac1fc283dfcf165d69b"}, {"relation": "partOf", "source": 3041, "target": 1570, "key": "3f4a04c31739feec151a16701b32bbfa"}, {"line": 3848, "relation": "increases", "evidence": "It is believed that amyloid-beta peptide (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD). Thus, the process of amyloid precursor protein (APP) cleavage is a key event and has raised much attention in the field of AD research. It is proposed that APP, beta- and gamma-secretases are all located on the lipid raft, and the meeting of them is an indispensable step for Abeta generation. Endocytosis can lead to clustering of APP, beta- and gamma-secretases from separate smaller lipid rafts into a larger one. On the other hand, for myristoylated alanine-rich C kinase substrate (MARCKS), phosphorylation by protein kinase C (PKC) or interaction with Ca(2+) can lead to its release from membrane into cytoplasm. This process induces the release of actins and phosphatidylinositol 4, 5-bisphosphate (PIP2), which are important factors for endocytosis. Thus, the present review proposes that MARCKS may be implicated in Abeta generation, by modulating free PIP2 level and actin movement, causing endocytosis", "citation": {"db": "PubMed", "db_id": "20651816"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 161, "target": 813, "key": "767d92985a04dbed79001101a1308c9d"}, {"line": 3846, "relation": "increases", "evidence": "It is believed that amyloid-beta peptide (Abeta) plays a central role in the pathogenesis of Alzheimer's disease (AD). Thus, the process of amyloid precursor protein (APP) cleavage is a key event and has raised much attention in the field of AD research. It is proposed that APP, beta- and gamma-secretases are all located on the lipid raft, and the meeting of them is an indispensable step for Abeta generation. Endocytosis can lead to clustering of APP, beta- and gamma-secretases from separate smaller lipid rafts into a larger one. On the other hand, for myristoylated alanine-rich C kinase substrate (MARCKS), phosphorylation by protein kinase C (PKC) or interaction with Ca(2+) can lead to its release from membrane into cytoplasm. This process induces the release of actins and phosphatidylinositol 4, 5-bisphosphate (PIP2), which are important factors for endocytosis. Thus, the present review proposes that MARCKS may be implicated in Abeta generation, by modulating free PIP2 level and actin movement, causing endocytosis", "citation": {"db": "PubMed", "db_id": "20651816"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2205, "target": 3042, "key": "8dcd59693507d8ca3ecdd74a0b8943c3"}, {"line": 23541, "relation": "increases", "evidence": "These findings show that riluzole maintains altered oxidant-antioxidant balance. Consistently, previous studies have shown the antioxidant effect of riluzole [19, 20 and 21]. In the study of Koh et al. [ 19], riluzole, besides preventing the excitotoxic neuronal damage, was also effective against FeCl3 induced nonexcitotoxic injury in cortical neuron cultures. In another study, riluzole was shown to protect the dopaminergic neurons against oxidative stress by reducing lipid peroxidation and adenosine triphosphate consumption [ 21]. It has been suggested that the mechanism involved in the protective effects in nonexcitotoxic oxidant damage was inhibition of PLA2, thereby reducing arachidonic acid and its metabolites, and further inhibition of protein kinase C [ 43].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2205, "target": 842, "key": "4ed765fd231b92b929e3bd7a73eeefbc"}, {"relation": "partOf", "source": 2205, "target": 1023, "key": "7ed1971501d903afb6df27d73e4f90ca"}, {"line": 47157, "relation": "increases", "evidence": "Protein kinase C is also recruited into the large multiprotein complex forming at the adhesion point where it regulates the associations. Previous studies have underlined the involvement of PKC in HSPG-dependent phagocytosis in HeLa cells ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellLine": {"HeLa cell": true}}, "source": 2205, "target": 3344, "key": "51b9a65bd70ebbfd7e4cf40d9992ce04"}, {"line": 47173, "relation": "increases", "evidence": "In the present study, we examined the possibility that the cellular receptors of PEI/DNA complexes might also be subjected to PKC regulation. To test if PKC might have a role in internalization of PEI/DNA complexes, we treated HeLa cells with staurosporine and tested the effects of this PKC inhibitor on transfection efï¬betaciency using a luciferase assay. As shown in Figure 1, staurosporine inhibited transfection in a concentration-dependent manner. The effects were observed at concentrations similar to those used to interfere with bacterial invasion [17] and uptake of HSPG-ligating beads [15] into HeLa cells.", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellLine": {"HeLa cell": true}, "Confidence": {"Medium": true}}, "source": 2205, "target": 955, "key": "0cdef6e8f22c79beb481cf639ff66eeb"}, {"line": 47175, "relation": "increases", "evidence": "In the present study, we examined the possibility that the cellular receptors of PEI/DNA complexes might also be subjected to PKC regulation. To test if PKC might have a role in internalization of PEI/DNA complexes, we treated HeLa cells with staurosporine and tested the effects of this PKC inhibitor on transfection efï¬betaciency using a luciferase assay. As shown in Figure 1, staurosporine inhibited transfection in a concentration-dependent manner. The effects were observed at concentrations similar to those used to interfere with bacterial invasion [17] and uptake of HSPG-ligating beads [15] into HeLa cells.", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellLine": {"HeLa cell": true}, "Confidence": {"Medium": true}}, "source": 2205, "target": 856, "key": "6fb4115eae410a63adeff100255a3ec5"}, {"line": 3860, "relation": "increases", "evidence": "Nuclear factor-kappaB activation regulates cyclooxygenase-2 induction in human astrocytes in response to CXCL12: role in neuronal toxicity", "citation": {"db": "PubMed", "db_id": "20180883"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Prostaglandin subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3114, "target": 3278, "key": "0920769e6120c200b40b31976fd3af38"}, {"line": 3863, "relation": "increases", "evidence": "Nuclear factor-kappaB activation regulates cyclooxygenase-2 induction in human astrocytes in response to CXCL12: role in neuronal toxicity", "citation": {"db": "PubMed", "db_id": "20180883"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Chemokine signaling subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3114, "target": 645, "key": "2ffb148ee2d8298b7998a34b8476aa3d"}, {"line": 8549, "relation": "association", "evidence": "recent findings have demonstrated that cyclooxygenase (COX)-2 is of primary importance in the inflammatory response and may have a role in neurodegeneration. Therefore, selective COX-2 inhibitors (coxibs) may have an advantage over traditional NSAIDs as potential therapeutic agents in AD.", "citation": {"db": "PubMed", "db_id": "11992749"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3278, "target": 532, "key": "64cba68f56467bb277b645bb58c8506c"}, {"line": 19227, "relation": "positiveCorrelation", "evidence": "In Alzheimer's disease (AD) brain, increased levels of cyclooxygenase-2 (COX-2), cell cycle markers, and p38 MAP kinase (MAPK) can be detected in neuronal cells.", "citation": {"db": "PubMed", "db_id": "15056456"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Prostaglandin subgraph": true, "JAK-STAT signaling subgraph": true}}, "source": 3278, "target": 3823, "key": "9aac1e4b21da6dc75e6490490cf5454f"}, {"line": 19615, "relation": "positiveCorrelation", "evidence": "In AD brains, COX-1-positive microglial cells were primarily associated with amyloid beta plaques, while the number of COX-2-positive neurons was increased compared to that in control brains.", "citation": {"db": "PubMed", "db_id": "11194936"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Neurons": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3278, "target": 3823, "key": "9fc7c6550f48142639f09f319a5fb1fd"}, {"line": 39566, "relation": "positiveCorrelation", "evidence": "Epidemiological studies, indicating that non-steroidal anti-inflammatory drugs (NSAIDs) decrease the risk of developing AD, have encouraged the study on the role of inflammation in AD. The best-characterized action of most NSAIDs is the inhibition of cyclooxygenase (COX). The expression of the constitutively expressed COX-1 and the inflammatory induced COX-2 has been intensively investigated in AD brain and different disease models for AD. Despite these studies, clinical trials with NSAIDs or selective COX-2 inhibitors showed little or no effect on clinical progression of AD. The expression levels of COX-1 and COX-2 change in the different stages of AD pathology. In an early stage, when low-fibrillar Abeta deposits are present and only very few neurofibrillary tangles are observed in the cortical areas, COX-2 is increased in neurons. The increased neuronal COX-2 expression parallels and colocalizes with the expression of cell cycle proteins. COX-1 is primarily expressed in microglia, which are associated with fibrillar Abeta deposits. This suggests that in AD brain COX-1 and COX-2 are involved in inflammatory and regenerating pathways respectively. In this review we will discuss the role of COX-1 and COX-2 in the different stages of AD pathology.", "citation": {"db": "PubMed", "db_id": "18537664"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3278, "target": 3823, "key": "b8ab843dc9855089bd7d77c87b5b2b18"}, {"line": 19240, "relation": "association", "evidence": "Besides mediating COX-2 expression, p38 MAPK is suggested to mediate cell cycle progression through phosphorylation of the retinoblastoma protein (pRb).", "citation": {"db": "PubMed", "db_id": "15056456"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "JAK-STAT signaling subgraph": true}}, "source": 3278, "target": 2222, "key": "e8470881d4afa978fa07009cb2854be7"}, {"line": 38922, "relation": "increases", "evidence": "For example, when the brain is injured, microglia become activated by Abeta deposits and recruit astrocytes / by secreting acute-phase proteins such as complement factors and cytokines. Reactive microglia and astrocytes additionally / generate proinflammatory mediators, including cytokines, chemokines, prostaglandins, neurotoxic secretory products, / reactive oxygen species, and nitric oxide (Griffin et al., 1998; Tuppo and Arias, 2005). Cytokines and chemokines, / in turn, stimulate the synthesis of other enzymes, such as COXs and prostaglandin synthases. In AD, the expression / of COX-2, the inducible isoform, increases in response to inflammatory agents in neurons and glial cells (Pasinetti and / Aisen, 1998; Sairanen et al., 1998). Because COX is the rate-limiting enzyme in the production of prostaglandins (O'Banion,/ 1999; Smith et al., 1991), the increase in COX activity leads to an increase in prostaglandin production (Consilvio et al.,/ 2004). ", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3278, "target": 335, "key": "f50ef3c021022a99a8167643f8608c1d"}, {"line": 39564, "relation": "increases", "evidence": "Epidemiological studies, indicating that non-steroidal anti-inflammatory drugs (NSAIDs) decrease the risk of developing AD, have encouraged the study on the role of inflammation in AD. The best-characterized action of most NSAIDs is the inhibition of cyclooxygenase (COX). The expression of the constitutively expressed COX-1 and the inflammatory induced COX-2 has been intensively investigated in AD brain and different disease models for AD. Despite these studies, clinical trials with NSAIDs or selective COX-2 inhibitors showed little or no effect on clinical progression of AD. The expression levels of COX-1 and COX-2 change in the different stages of AD pathology. In an early stage, when low-fibrillar Abeta deposits are present and only very few neurofibrillary tangles are observed in the cortical areas, COX-2 is increased in neurons. The increased neuronal COX-2 expression parallels and colocalizes with the expression of cell cycle proteins. COX-1 is primarily expressed in microglia, which are associated with fibrillar Abeta deposits. This suggests that in AD brain COX-1 and COX-2 are involved in inflammatory and regenerating pathways respectively. In this review we will discuss the role of COX-1 and COX-2 in the different stages of AD pathology.", "citation": {"db": "PubMed", "db_id": "18537664"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3278, "target": 3815, "key": "4ebcb8e53d25efe4b7b4568e4c34f2a6"}, {"line": 40149, "relation": "increases", "evidence": "These proinflammatory factors act as potent stimuli in brain inflammation through upregulation of diverse inflammatory genes, including matrix metalloproteinases (MMPs), cytosolic phospholipase A2 (cPLA2), cyclooxygenase-2 (COX-2), and adhesion molecules.", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Encephalitis": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Prostaglandin subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3278, "target": 577, "key": "d54519e00de27cd40cf03e40901079ac"}, {"line": 3862, "relation": "increases", "evidence": "Nuclear factor-kappaB activation regulates cyclooxygenase-2 induction in human astrocytes in response to CXCL12: role in neuronal toxicity", "citation": {"db": "PubMed", "db_id": "20180883"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Chemokine signaling subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2604, "target": 3114, "key": "2101e6ceaa22bd910b14bfc7ed0e1829"}, {"line": 3875, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Tau protein subgraph": true}}, "source": 739, "target": 3010, "key": "4b7fe413ea00683a567c8e95adcaf0ee"}, {"line": 3889, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 739, "target": 94, "key": "2e9aacc777163e941f9a90234d55240c"}, {"line": 3890, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 739, "target": 492, "key": "7c6a866064d6feb2a547af7ad218dc58"}, {"line": 3891, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 739, "target": 468, "key": "c5c9c86b8c4c5d6c37c504b478bb33f2"}, {"line": 3888, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 760, "target": 492, "key": "cac7fae96451512bc28695bdac0bf86d"}, {"line": 5458, "relation": "increases", "evidence": "Cirrito et al. [26] also show that synaptic activity-induced increase in endocytosis drives more APP into the endocytic compartment, ultimately resulting in increased Abeta production and release. Abeta produced in the endocytic pathway is then brought to the cell surface where it is released into the extracellular fluid [70]. Inhibition of endocytosis reduces APP internalization and reduces Abeta production and release in cell lines", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 760, "target": 813, "key": "8aa598e493c5d9c368b00c754b631ba4"}, {"line": 36580, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 760, "target": 2328, "key": "2898c0cec4d014bcf4bed8cd134b3a67"}, {"line": 36956, "relation": "increases", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 760, "target": 635, "key": "4cc3e5ff2a6c2abbf506775ce79b28e7"}, {"line": 3891, "relation": "association", "evidence": "In healthy neurons the axon contains relatively high amounts of microtubules which are stabilized by the protein tau. Microtubule dynamics in axons play pivotal roles in organellar (mitochondria, for example) and protein transport to presynaptic axon terminals. Dendrites receive synaptic inputs in postsynaptic structures called spines whose shape is controlled by actin filaments and various scaffolding proteins. Ca2+ influx during synaptic activity modifies the dynamics of actin and microtubules in ways that allow the neuron to adapt to environmental demands.", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 468, "target": 739, "key": "ed19754a4e97fea934e83713a89e0f15"}, {"line": 8262, "relation": "positiveCorrelation", "evidence": "A separate analysis using just the DM2+ cases broken down by ApoE e4 carrier status was carried out (Table 2). There were 22 ApoE e4 carriers and 17 ApoE e4 non-carriers (n=39) as ApoE data was not available for 1 case. This analysis found that individuals carrying the e4 allele had significantly greater plaque and tangle pathology across all cortical areas except for tangle counts in the parietal and entorhinal areas. AD-DM2+ ApoE e4 carriers had a significantly lower age of death than AD-DM2+ ApoE e4 noncarriers", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 889, "target": 3850, "key": "fd87aed1fe6699d69847d3ad06b3c825"}, {"line": 9004, "relation": "increases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 889, "target": 3823, "key": "2e6f371287b995d47b41064251a3ff82"}, {"line": 9303, "relation": "association", "evidence": "Hyperphosphorylated tau protein is the basic structural component of the neurofibrillary tangle, a histopathological hallmark of Alzheimer's disease. The formation of hyperphosphorylated tau protein may impair learning and the synaptic plasticity of neurons. Tau is a protein that is associated with and stabilizes microtubules; hyperphosphorylated tau protein is unable to perform this stabilization function.", "citation": {"db": "PubMed", "db_id": "16504486"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 889, "target": 3823, "key": "f7171495663b13a3b641777f1dbd67ae"}, {"line": 9302, "relation": "association", "evidence": "Hyperphosphorylated tau protein is the basic structural component of the neurofibrillary tangle, a histopathological hallmark of Alzheimer's disease. The formation of hyperphosphorylated tau protein may impair learning and the synaptic plasticity of neurons. Tau is a protein that is associated with and stabilizes microtubules; hyperphosphorylated tau protein is unable to perform this stabilization function.", "citation": {"db": "PubMed", "db_id": "16504486"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 889, "target": 3015, "key": "9c57789deffa3bcdeb3d8b250c96a370"}, {"line": 11304, "relation": "positiveCorrelation", "evidence": "Human serum albumin (HSA) binds 95% of Abeta in blood plasma and is thought to inhibit plaque formation in peripheral tissue. However, the role of albumin in binding Abeta in the cerebrospinal fluid has been largely overlooked. Here we investigate the effect of HSA on both Abeta(1-40) and Abeta(1-42) fibril growth. We show that at micromolar cerebrospinal fluid levels, HSA inhibits the kinetics of Abeta fibrillization, significantly increasing the lag time and decreasing the total amount of fibrils produced", "citation": {"db": "PubMed", "db_id": "22718756"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Albumin subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 889, "target": 644, "key": "c5e590af0e798f21283df4bbd951dc72"}, {"relation": "partOf", "source": 889, "target": 1006, "key": "2c4f956632bc71d6ee3f412d5ac2a046"}, {"line": 22772, "relation": "negativeCorrelation", "evidence": "Quantitative measures of ERK2 mRNA reveal that NFT-bearing neurons contain approximately 15% less ERK2 mRNA than nearest neighbors that do not contain NFT. NFT-bearing neurons contain approximately 25% less polyA mRNA, suggesting a relative preservation of ERK2 mRNA even in metabolically compromised cells.", "citation": {"db": "PubMed", "db_id": "8129042"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}}, "source": 889, "target": 3990, "key": "46d1363ee096c7f5be6a8b7baa039e67"}, {"line": 24094, "relation": "negativeCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 889, "target": 2871, "key": "6e98be34cb6d153108de9b63ff7313da"}, {"line": 24097, "relation": "positiveCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 889, "target": 3861, "key": "ce2db4c9501cd462111f87f8d048bc37"}, {"line": 26227, "relation": "association", "evidence": "Apolipoprotein E (ApoE) genotype is a significant risk factor for the development of Alzheimer disease (AD) and the ApoE protein is associated with senile plaques (SP) and neurofibrillary tangles (NFT)", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 889, "target": 2312, "key": "8fe1728dbc92a13d1cdb92b0993c624a"}, {"line": 26233, "relation": "association", "evidence": "These findings suggest that the interaction of ApoE with tau and amyloid-beta proteins in AD could play a important role in the formation of NFT and SP, respectively, contributing to the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 889, "target": 1136, "key": "da723a41090838009f95f51b6dff7c73"}, {"line": 29554, "relation": "association", "evidence": "Cyclin-dependent kinase 5 (Cdk5) activity is significantly increased in AD and contributes to all three hallmarks: neurotoxic amyloid-beta (Abeta), neurofibrillary tangles (NFT), and extensive cell death.", "citation": {"db": "PubMed", "db_id": "21389115"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 889, "target": 2487, "key": "3290442f66f17358c132a45796eb0dcf"}, {"line": 39191, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Tau protein subgraph": true}, "Confidence": {"Very High": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "source": 889, "target": 577, "key": "d0b0252c26af154a25a2a3031feae7b2"}, {"relation": "hasReactant", "source": 4094, "target": 868, "key": "870efd5ea498b6c5ac9f2d22213eb815"}, {"relation": "hasReactant", "source": 4094, "target": 2315, "key": "631b3ce8d67e47ecb21f22c25e8be1d6"}, {"relation": "hasReactant", "source": 4094, "target": 2375, "key": "fdd30396bbd154416589904b9fa6be60"}, {"relation": "hasReactant", "source": 4094, "target": 2381, "key": "01a42e6bfc5ae9e22b3c03cb7f42f04b"}, {"relation": "hasProduct", "source": 4094, "target": 80, "key": "c2026428575747cddb1daf9029908499"}, {"line": 3958, "relation": "negativeCorrelation", "evidence": "Abeta can also interact with Fe2+ and Cu+ to generate hydrogen peroxide and hydroxyl radical (OH.) resulting in membrane lipid peroxidation which generates toxic aldehydes that impair the function of membrane ion-motive ATPases (Na+ and Ca2+ pumps)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "CellStructure": {"Cell Membrane": true}, "Confidence": {"High": true}}, "source": 781, "target": 605, "key": "76ad67d1cd5e30d5d63539fa05aee2ff"}, {"relation": "hasReactant", "source": 4087, "target": 100, "key": "248d5ab761f3e371a40890934ab60b78"}, {"relation": "hasReactant", "source": 4087, "target": 137, "key": "6d87bc390da38724ac099880e90c758e"}, {"relation": "hasReactant", "source": 4087, "target": 2328, "key": "742bb74383ac0bbd41e65f953a904786"}, {"relation": "hasProduct", "source": 4087, "target": 131, "key": "ad573344029bdb3cfdcd9e04419cd13a"}, {"relation": "hasProduct", "source": 4087, "target": 277, "key": "723e91362376cb84fa403cb54061d9c4"}, {"line": 3958, "relation": "negativeCorrelation", "evidence": "Abeta can also interact with Fe2+ and Cu+ to generate hydrogen peroxide and hydroxyl radical (OH.) resulting in membrane lipid peroxidation which generates toxic aldehydes that impair the function of membrane ion-motive ATPases (Na+ and Ca2+ pumps)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "CellStructure": {"Cell Membrane": true}, "Confidence": {"High": true}}, "source": 605, "target": 781, "key": "b1b0b1749675c4d9c597c5a393ed071d"}, {"line": 3962, "relation": "negativeCorrelation", "evidence": "Abeta can also interact with Fe2+ and Cu+ to generate hydrogen peroxide and hydroxyl radical (OH.) resulting in membrane lipid peroxidation which generates toxic aldehydes that impair the function of membrane ion-motive ATPases (Na+ and Ca2+ pumps)", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Hydrogen peroxide subgraph": true}, "CellStructure": {"Cell Membrane": true}, "Confidence": {"High": true}}, "source": 605, "target": 491, "key": "1c5dc9de8f7aa86e029c61366ebab705"}, {"line": 10789, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 605, "target": 3823, "key": "c29e569100d3df88aea4fedc452a05a3"}, {"line": 10810, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 605, "target": 3850, "key": "c061facdd062eb7fcb3f7c6f77eb30a3"}, {"relation": "hasReactant", "source": 4103, "target": 2315, "key": "929a23e27b9b6962cd3e62beeb63c467"}, {"relation": "hasProduct", "source": 4103, "target": 3563, "key": "9d8f731466d1a4f9a7e54f483fc02d97"}, {"line": 3974, "relation": "association", "evidence": "Amyloidogenic processing also generates an intracellular APP domain (AICD) which can translocate to the nucleus and modify gene transcription in ways that perturb Ca2+ homeostasis", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 766, "target": 3563, "key": "742ac78b23d65361ac9409e3f01a291d"}, {"line": 4017, "relation": "directlyIncreases", "evidence": "Interaction of the protein reelin with the apolipoprotein E receptor (ApoER2) enhances Ca2+ influx through NMDA receptor channels by a mechanism involving a src family tyrosine kinsase (SFk); ApoE can block this effect of reelin", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 1532, "target": 2217, "key": "192276fe28946dc3df5d1c8c808ffcaa"}, {"line": 37298, "relation": "association", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1532, "target": 743, "key": "a3f87c7ce901dcc7d7ed9f13aed85244"}, {"line": 4018, "relation": "directlyIncreases", "evidence": "Interaction of the protein reelin with the apolipoprotein E receptor (ApoER2) enhances Ca2+ influx through NMDA receptor channels by a mechanism involving a src family tyrosine kinsase (SFk); ApoE can block this effect of reelin", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2217, "target": 3548, "key": "e30cab6e74f6729bfa0f3767560ce371"}, {"line": 4026, "relation": "increases", "evidence": "Amyloidogenic APP processing may prevent a-secretase (a) cleavage of APP which would otherwise generate a secreted form of APP (sAPPa).", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2315, "key": "ae5e1f3452c1f7ffe7146226f2c0dbef"}, {"line": 5448, "relation": "increases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2315, "key": "34a06870b3d5422639e7c2e08260bf9c"}, {"line": 25355, "relation": "positiveCorrelation", "evidence": "Elevation of active ADAM10 correlates with increased alpha-CTF cleavage, and elevated sAPP-alpha.", "citation": {"db": "PubMed", "db_id": "16624814"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2315, "key": "08d962a1b104673d45c775acda01b680"}, {"line": 25368, "relation": "increases", "evidence": "Our laboratory has previously shown that EGCG can increase non-amyloidogenic processing of APP through promotion of the beta-secretase ADAM10, which consequently reduced Abeta deposition and improved cognition in AD mice", "citation": {"db": "PubMed", "db_id": "20849853"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2315, "key": "8934ded3ef5ce1076c1a8ac76d73d1c8"}, {"line": 4027, "relation": "increases", "evidence": "Amyloidogenic APP processing may prevent a-secretase (a) cleavage of APP which would otherwise generate a secreted form of APP (sAPPa).", "citation": {"db": "PubMed", "db_id": "18675468"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 2137, "key": "d7028abf861deec0a6dca632bbfa9890"}, {"line": 12385, "relation": "increases", "evidence": "The proteolytic cleavage of the amyloid precursor protein (APP) through the alpha-secretase pathway decreases in AD, concurrent with cognitive impairment. This APP cleavage occurs within the beta-amyloid peptide (Abeta) sequence, precluding formation of amyloidogenic peptides and leading to the release of the soluble N-terminal APP fragment (sAPPalpha) which is neurotrophic and procognitive.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 2137, "key": "b4e4c3754ce7ce921452b21fd290ed24"}, {"line": 23808, "relation": "increases", "evidence": "Rivastigmine shifts APP processing toward the α-secretase pathway. Together, these results suggest that rivastigmine alters the activities of the α- and beta-secretase pathways in favor of sAPPα production. ", "citation": {"db": "PubMed", "db_id": "21799757"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2249, "target": 2137, "key": "9057a9231ea87eefe340492a79bb81c5"}, {"line": 23848, "relation": "increases", "evidence": "Some authors show that IGF-I increases α-secretase processing of endogenous amyloid precursor protein and the amyloid precursor-like proteins 1 and 2 [36, 97-100].", "citation": {"db": "PubMed", "db_id": "22524398"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"ADAM Metallopeptidase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2249, "target": 2137, "key": "b3c29d507347a43834369ca4fccdb7e8"}, {"line": 25356, "relation": "positiveCorrelation", "evidence": "Elevation of active ADAM10 correlates with increased alpha-CTF cleavage, and elevated sAPP-alpha.", "citation": {"db": "PubMed", "db_id": "16624814"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "source": 2249, "target": 2137, "key": "24690628ea61d11d4ce8aeea39517fde"}, {"line": 25391, "relation": "increases", "evidence": "Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2249, "target": 2137, "key": "b2ac9cdaeb677c92ed142596dc509901"}, {"line": 25478, "relation": "increases", "evidence": "In human neural cells overexpressing beta-amyloid precursor protein (betaAPP), the lipid mediator suppressed Abeta42 shedding by downregulating beta-secretase (BACE1) while activating the alpha-secretase (ADAM10), thus shifting the alphaAPP cleavage from the noxious amyloidogenic pathway into a non-amyloidogenic, neurotrophic pathway.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2249, "target": 2137, "key": "36a5bde876f9ad9089d9672e0a7b9a50"}, {"line": 5451, "relation": "increases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2249, "target": 80, "key": "b04b2a93a47d86f7a127f30c54971e28"}, {"line": 25370, "relation": "decreases", "evidence": "Our laboratory has previously shown that EGCG can increase non-amyloidogenic processing of APP through promotion of the beta-secretase ADAM10, which consequently reduced Abeta deposition and improved cognition in AD mice", "citation": {"db": "PubMed", "db_id": "20849853"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2249, "target": 80, "key": "f2f1edfc7d23f1e78f2809dbda38e72d"}, {"line": 6963, "relation": "directlyIncreases", "evidence": "We then analyzed the levels of various APP metabolites including the cleavage products of alpha- and beta-secretases (Fig. 1C). Metformin reduced alpha-cleavage and promoted beta-cleavage, as evidenced by decreased sAPPα and increased APP C-terminal fragment, CTF-beta (the upper CTF band that resulted from cleavage by BACE1). No change in the levels of full-length PS1 (presenilin 1, the core component of gamma-secretase) or its N-terminal fragment was detected from total cell lysates.", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4096, "key": "234f33ce1a35e4933c60140dc54380d6"}, {"line": 8957, "relation": "decreases", "evidence": "Amyloid beta-peptide (Abeta) accumulating in the brain of Alzheimer disease (AD) patients is believed to be the main pathophysiologcal cause of the disease. Proteolytic processing of the amyloid precursor protein by alpha-secretase ADAM10 (a disintegrin and metalloprotease 10) protects the brain from the production of the Abeta. Meanwhile, dysregulation or aberrant expression of microRNAs (miRNAs) has been widely documented in AD patients. In this study, we demonstrated that overexpression of miR-144, which was previously reported to be increased in elderly primate brains and AD patients, significantly decreased activity of the luciferase reporter containing the ADAM10 3'-untranslated region (3'-UTR) and suppressed the ADAM10 protein level, whereas the miR-144 inhibitor led to an increase of the luciferase activity. The negative regulation caused by miR-144 was strictly dependent on the binding of the miRNA to its recognition element in the ADAM10 3'-UTR. Moreover, we also showed that activator protein-1 regulates the transcription of miR-144 and the up-regulation of miR-144 at least partially induces the suppression of the ADAM10 protein in the presence of Abeta. In addition, we found that miR-451, a miRNA processed from a single gene locus with miR-144, is also involved in the regulation of ADAM10 expression. Taken together, our data therefore demonstrate miR-144/451 is a negative regulator of the ADAM10 protein and suggest a mechanistic role for miR-144/451 in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "23546882"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2249, "target": 4096, "key": "3e84bd25656b271510a022ceddca1d16"}, {"line": 36056, "relation": "increases", "evidence": "In addition, APP can be cleaved by a- and g-secretases and this precludes Ab production since a-secretase cleaves APP within the Ab sequence", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4096, "key": "ac22ba4a18cd1bb654f94cbfee3bcfc6"}, {"line": 7735, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2249, "target": 750, "key": "c0d2864fc25d8714a2aaa1cb30c42bef"}, {"line": 9426, "relation": "decreases", "evidence": "Cleavage of APP by alpha-secretase precludes Abeta generation as the cleavage site is within the Abeta domain (at the Lys16-Leu17 bond), and releases a large soluble ectodomain of APP called sAPPalpha. Early studies suggested that alpha-secretase is a membrane-bound endoprotease which cleaves APP primarily at the plasma membrane. Using proteinase inhibitor profiling, it was determined that alpha-secretase is a zinc metalloproteinase. Several members of the ADAM (a disintegrin and metalloproteinase) family possess alpha-secretase-like activity and three of them have been suggested as the alpha-secretase: ADAM9, ADAM10, and ADAM17. Like APP, they are also type-I transmembrane proteins.A dramatically reduced ADAM10 protein level in the platelets of sporadic AD patients was also found to correlate with the significantly decreased sAPPalpha levels found in their platlets and cerebrospinal fluid and the reduced aclpha-secretase activity in the temporal cortex homogenates of AD patients . These studies strongly suggest that ADAM10 is the constitutive alpha-secretase that is active at the cell surface, though there may be some functional redundancy in alpha-cleavage among the ADAM family.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4101, "key": "27e163611fed202adba9080a63c93fc8"}, {"line": 34972, "relation": "decreases", "evidence": "Cleavage of APP by alpha-secretase precludes Abeta generation as the cleavage site is within the Abeta domain (at the Lys16-Leu17 bond), and releases a large soluble ectodomain of APP called sAPPalpha. Early studies suggested that alpha-secretase is a membrane-bound endoprotease which cleaves APP primarily at the plasma membrane. Using proteinase inhibitor profiling, it was determined that alpha-secretase is a zinc metalloproteinase. Several members of the ADAM (a disintegrin and metalloproteinase) family possess alpha-secretase-like activity and three of them have been suggested as the alpha-secretase: ADAM9, ADAM10, and ADAM17. Like APP, they are also type-I transmembrane proteins.A dramatically reduced ADAM10 protein level in the platelets of sporadic AD patients was also found to correlate with the significantly decreased sAPPalpha levels found in their platlets and cerebrospinal fluid and the reduced alpha-secretase activity in the temporal cortex homogenates of AD patients . These studies strongly suggest that ADAM10 is the constitutive alpha-secretase that is active at the cell surface, though there may be some functional redundancy in alpha-cleavage among the ADAM family.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4101, "key": "a748be77c036696d2685214ec195f323"}, {"line": 36096, "relation": "decreases", "evidence": "Loss of calsyntenin-1 in the cultured neurons was associated with alterations to APP processing involving increased cleavage at the BACE1 sites and decreased cleavage at the a-secretase site.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Matrix metalloproteinase subgraph": true, "Beta secretase subgraph": true, "Calsyntenin subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4101, "key": "1e313de18a3ab58c9bb11b3846f512d3"}, {"line": 9427, "relation": "increases", "evidence": "Cleavage of APP by alpha-secretase precludes Abeta generation as the cleavage site is within the Abeta domain (at the Lys16-Leu17 bond), and releases a large soluble ectodomain of APP called sAPPalpha. Early studies suggested that alpha-secretase is a membrane-bound endoprotease which cleaves APP primarily at the plasma membrane. Using proteinase inhibitor profiling, it was determined that alpha-secretase is a zinc metalloproteinase. Several members of the ADAM (a disintegrin and metalloproteinase) family possess alpha-secretase-like activity and three of them have been suggested as the alpha-secretase: ADAM9, ADAM10, and ADAM17. Like APP, they are also type-I transmembrane proteins.A dramatically reduced ADAM10 protein level in the platelets of sporadic AD patients was also found to correlate with the significantly decreased sAPPalpha levels found in their platlets and cerebrospinal fluid and the reduced aclpha-secretase activity in the temporal cortex homogenates of AD patients . These studies strongly suggest that ADAM10 is the constitutive alpha-secretase that is active at the cell surface, though there may be some functional redundancy in alpha-cleavage among the ADAM family.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4099, "key": "d97ef5a2485b017651b334f03197b7d0"}, {"line": 12383, "relation": "increases", "evidence": "The proteolytic cleavage of the amyloid precursor protein (APP) through the alpha-secretase pathway decreases in AD, concurrent with cognitive impairment. This APP cleavage occurs within the beta-amyloid peptide (Abeta) sequence, precluding formation of amyloidogenic peptides and leading to the release of the soluble N-terminal APP fragment (sAPPalpha) which is neurotrophic and procognitive.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4099, "key": "fdaeebbd78a81860bd328251d23afcf4"}, {"line": 34973, "relation": "increases", "evidence": "Cleavage of APP by alpha-secretase precludes Abeta generation as the cleavage site is within the Abeta domain (at the Lys16-Leu17 bond), and releases a large soluble ectodomain of APP called sAPPalpha. Early studies suggested that alpha-secretase is a membrane-bound endoprotease which cleaves APP primarily at the plasma membrane. Using proteinase inhibitor profiling, it was determined that alpha-secretase is a zinc metalloproteinase. Several members of the ADAM (a disintegrin and metalloproteinase) family possess alpha-secretase-like activity and three of them have been suggested as the alpha-secretase: ADAM9, ADAM10, and ADAM17. Like APP, they are also type-I transmembrane proteins.A dramatically reduced ADAM10 protein level in the platelets of sporadic AD patients was also found to correlate with the significantly decreased sAPPalpha levels found in their platlets and cerebrospinal fluid and the reduced alpha-secretase activity in the temporal cortex homogenates of AD patients . These studies strongly suggest that ADAM10 is the constitutive alpha-secretase that is active at the cell surface, though there may be some functional redundancy in alpha-cleavage among the ADAM family.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4099, "key": "a46f8b81f59b0ee246d0b14a4644f17f"}, {"line": 36024, "relation": "increases", "evidence": "APP is synthesized in the endoplasmic reticulum (ER) and then transported through the Golgi apparatus to the trans-Golgi-network (TGN) where the highest concentration of APP is found in neurons at steady state. Abeta is generated in the ER and Golgi/TGN. From the TGN, APP can be transported in TGN-derived secretory vesicles to the cell surface where it is either cleaved by alpha-secretase to produce a soluble molecule, sAPPalpha [37], or re-internalized via an endosomal/lysosomal degradation pathway", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 4099, "key": "4b641d4049e671a040f9c2f54a2fd906"}, {"line": 9458, "relation": "increases", "evidence": "SIRT1 directly activates the transcription of the gene encoding the alpha-secretase, ADAM10. SIRT1 deacetylates and coactivates the retinoic acid receptor beta, a known regulator of ADAM10 transcription. ADAM10 activation by SIRT1 also induces the Notch signaling pathway, which is known to repair neuronal damage in the brain.", "citation": {"db": "PubMed", "db_id": "20655472"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2249, "target": 2200, "key": "06b467cfeefabf9d3f9aa6d5d89f05bd"}, {"line": 37719, "relation": "increases", "evidence": "SIRT1 directly activates the transcription of the gene encoding the alpha-secretase, ADAM10. SIRT1 deacetylates and coactivates the retinoic acid receptor beta, a known regulator of ADAM10 transcription. ADAM10 activation by SIRT1 also induces the Notch signaling pathway, which is known to repair neuronal damage in the brain.", "citation": {"db": "PubMed", "db_id": "20655472"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2249, "target": 2200, "key": "e6a1f85db962c20d0642c309b79bc738"}, {"line": 12386, "relation": "negativeCorrelation", "evidence": "The proteolytic cleavage of the amyloid precursor protein (APP) through the alpha-secretase pathway decreases in AD, concurrent with cognitive impairment. This APP cleavage occurs within the beta-amyloid peptide (Abeta) sequence, precluding formation of amyloidogenic peptides and leading to the release of the soluble N-terminal APP fragment (sAPPalpha) which is neurotrophic and procognitive.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2249, "target": 3823, "key": "6d2b4d8bf53527fcca011177926c31da"}, {"line": 20569, "relation": "association", "evidence": "Unfolded protein response signaling by transcription factor XBP-1 regulates ADAM10 and is affected in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 2249, "target": 3537, "key": "3e9b4cf8b1e9fa01de2e9731aac64610"}, {"line": 20582, "relation": "association", "evidence": "One selective inducer of ADAM10 gene expression is the X-box binding protein-1 (XBP-1).", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 2249, "target": 677, "key": "afb2471471882d0c8fb4d2e74067566d"}, {"line": 20595, "relation": "association", "evidence": "Our results demonstrate that XBP-1 is a driver of ADAM10 gene expression and that disturbance of this pathway might contribute to development or progression of AD.", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 2249, "target": 677, "key": "830b461ce29a0cfbe1dc5b661f2c3e62"}, {"line": 24891, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs). ", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Wrong": true}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2534, "key": "9f13dbfb7bcf8e8c3207a5f506b04ef3"}, {"line": 25513, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs).", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2534, "key": "0864ea75d77f7aa731076d9273a22731"}, {"line": 24892, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs). ", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Wrong": true}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2533, "key": "6a44ce201b52611b655e6510b4283df1"}, {"line": 25514, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs).", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2533, "key": "5280f12f726c927d20c5d341ac280d15"}, {"line": 24893, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs). ", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Wrong": true}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2532, "key": "e7d8430636b4bdfb0ad7b77429871d93"}, {"line": 25515, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs).", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2249, "target": 2532, "key": "8c3f663fa5602c2d47fa5a66ebc914ca"}, {"line": 24966, "relation": "association", "evidence": "ADAM19 is tightly associated with constitutive Alzheimer's disease APP alpha-secretase in A172 cells", "citation": {"db": "PubMed", "db_id": "17112471"}, "annotations": {"Confidence": {"High": true}}, "source": 2249, "target": 2251, "key": "2fef70c30f12392d47523de137e595d1"}, {"relation": "partOf", "source": 2249, "target": 1052, "key": "77fa91b4f80417406dee34d667d46ca2"}, {"line": 25371, "relation": "increases", "evidence": "Our laboratory has previously shown that EGCG can increase non-amyloidogenic processing of APP through promotion of the beta-secretase ADAM10, which consequently reduced Abeta deposition and improved cognition in AD mice", "citation": {"db": "PubMed", "db_id": "20849853"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2249, "target": 812, "key": "c4c06dfe349a8714fc20cb3de78695f5"}, {"relation": "partOf", "source": 2249, "target": 1053, "key": "b8fe078f3c3130db0beff166ddebcd55"}, {"relation": "partOf", "source": 2249, "target": 1054, "key": "748d8bb7ff8c4f7080f8768fa0384525"}, {"line": 9428, "relation": "equivalentTo", "evidence": "Cleavage of APP by alpha-secretase precludes Abeta generation as the cleavage site is within the Abeta domain (at the Lys16-Leu17 bond), and releases a large soluble ectodomain of APP called sAPPalpha. Early studies suggested that alpha-secretase is a membrane-bound endoprotease which cleaves APP primarily at the plasma membrane. Using proteinase inhibitor profiling, it was determined that alpha-secretase is a zinc metalloproteinase. Several members of the ADAM (a disintegrin and metalloproteinase) family possess alpha-secretase-like activity and three of them have been suggested as the alpha-secretase: ADAM9, ADAM10, and ADAM17. Like APP, they are also type-I transmembrane proteins.A dramatically reduced ADAM10 protein level in the platelets of sporadic AD patients was also found to correlate with the significantly decreased sAPPalpha levels found in their platlets and cerebrospinal fluid and the reduced aclpha-secretase activity in the temporal cortex homogenates of AD patients . These studies strongly suggest that ADAM10 is the constitutive alpha-secretase that is active at the cell surface, though there may be some functional redundancy in alpha-cleavage among the ADAM family.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2137, "target": 410, "key": "59b8ded1b65c1622bc720613d05b77a6"}, {"line": 12400, "relation": "decreases", "evidence": "In this study, we show that at nanomolar-low micromolar concentrations, etazolate, a selective GABA(A) receptor modulator, stimulates sAPPalpha production in rat cortical neurons and in guinea pig brains. Etazolate (20 nM-2 microM) dose-dependently protected rat cortical neurons against Abeta-induced toxicity. The neuroprotective effects of etazolate were fully blocked by GABA(A) receptor antagonists indicating that this neuroprotection was due to GABA(A) receptor signalling. This indicating that etazolate exerts its neuroprotective effect via sAPPalpha induction.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2137, "target": 2328, "key": "435a57ff260747896b579422509a282a"}, {"line": 13244, "relation": "increases", "evidence": "Furthermore, both pharmacological alpha-secretase pathway inhibition and sAPPalpha immunoneutralization approaches prevented etazolate neuroprotection against Abeta, indicating that etazolate exerts its neuroprotective effect via sAPPalpha induction.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2137, "target": 854, "key": "e941b6ba1b59abdbf7e98f8b5705379c"}, {"line": 25356, "relation": "positiveCorrelation", "evidence": "Elevation of active ADAM10 correlates with increased alpha-CTF cleavage, and elevated sAPP-alpha.", "citation": {"db": "PubMed", "db_id": "16624814"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"High": true}}, "source": 2137, "target": 2249, "key": "fbd667c2a0acb1158c5638eacb7ab51d"}, {"line": 36033, "relation": "increases", "evidence": "In contrast to Abeta, sAPPalpha has an important role in neuronal plasticity/survival and is protective against excitotoxicity [42,43]. sAPPalpha also regulates neural stem cell proliferation and is important for early CNS development [57,58]. We and others have also found that sAPPalpha can inhibit stress-induced CDK5 activation and participate in various neuroprotective reagent-mediated excitoprotection.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2137, "target": 745, "key": "8c297d06dde5f3ea46cab511ba031045"}, {"line": 36034, "relation": "increases", "evidence": "In contrast to Abeta, sAPPalpha has an important role in neuronal plasticity/survival and is protective against excitotoxicity [42,43]. sAPPalpha also regulates neural stem cell proliferation and is important for early CNS development [57,58]. We and others have also found that sAPPalpha can inhibit stress-induced CDK5 activation and participate in various neuroprotective reagent-mediated excitoprotection.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2137, "target": 783, "key": "0a36102ea2d5d4abc906358adc8efddd"}, {"line": 36035, "relation": "increases", "evidence": "In contrast to Abeta, sAPPalpha has an important role in neuronal plasticity/survival and is protective against excitotoxicity [42,43]. sAPPalpha also regulates neural stem cell proliferation and is important for early CNS development [57,58]. We and others have also found that sAPPalpha can inhibit stress-induced CDK5 activation and participate in various neuroprotective reagent-mediated excitoprotection.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2137, "target": 521, "key": "449be4e3f1381161c41edd19cd4600f1"}, {"line": 36036, "relation": "decreases", "evidence": "In contrast to Abeta, sAPPalpha has an important role in neuronal plasticity/survival and is protective against excitotoxicity [42,43]. sAPPalpha also regulates neural stem cell proliferation and is important for early CNS development [57,58]. We and others have also found that sAPPalpha can inhibit stress-induced CDK5 activation and participate in various neuroprotective reagent-mediated excitoprotection.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2137, "target": 2487, "key": "cf144a41dc3b9835c1fdfac1227ac942"}, {"line": 36040, "relation": "increases", "evidence": "In contrast to Abeta, sAPPalpha has an important role in neuronal plasticity/survival and is protective against excitotoxicity [42,43]. sAPPalpha also regulates neural stem cell proliferation and is important for early CNS development [57,58]. We and others have also found that sAPPalpha can inhibit stress-induced CDK5 activation and participate in various neuroprotective reagent-mediated excitoprotection.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2137, "target": 431, "key": "8e6edfb73aff4a9ef0a589133b3dbda7"}, {"line": 37676, "relation": "increases", "evidence": "beta-Amyloid precursor protein is axonally transported and accumulates in presynaptic terminals and growth cones. A secreted form of beta-APP (sAPP alpha) is released from neurons in response to electrical activity and may function in modulation of neuronal excitability, synaptic plasticity, neurite outgrowth, synaptogenesis, and cell survival. A signaling pathway involving guanosine 3',5'-cyclic monophosphate is activated by sAPP alpha and modulates the activities of potassium channels, N-methyl-D-aspartate receptors, and the transcription factor NF kappa B. Additional functions of beta-APP may include modulation of cell adhesion and regulation of proliferation of nonneuronal cells.", "citation": {"db": "PubMed", "db_id": "9354812"}, "source": 2137, "target": 21, "key": "1395a9cac33f1ecdf1b49afdd9bbc550"}, {"line": 37678, "relation": "association", "evidence": "beta-Amyloid precursor protein is axonally transported and accumulates in presynaptic terminals and growth cones. A secreted form of beta-APP (sAPP alpha) is released from neurons in response to electrical activity and may function in modulation of neuronal excitability, synaptic plasticity, neurite outgrowth, synaptogenesis, and cell survival. A signaling pathway involving guanosine 3',5'-cyclic monophosphate is activated by sAPP alpha and modulates the activities of potassium channels, N-methyl-D-aspartate receptors, and the transcription factor NF kappa B. Additional functions of beta-APP may include modulation of cell adhesion and regulation of proliferation of nonneuronal cells.", "citation": {"db": "PubMed", "db_id": "9354812"}, "object": {"modifier": "Activity"}, "source": 2137, "target": 2781, "key": "4e1588afd1443d0e5ffa1d6c6f65a487"}, {"line": 37679, "relation": "association", "evidence": "beta-Amyloid precursor protein is axonally transported and accumulates in presynaptic terminals and growth cones. A secreted form of beta-APP (sAPP alpha) is released from neurons in response to electrical activity and may function in modulation of neuronal excitability, synaptic plasticity, neurite outgrowth, synaptogenesis, and cell survival. A signaling pathway involving guanosine 3',5'-cyclic monophosphate is activated by sAPP alpha and modulates the activities of potassium channels, N-methyl-D-aspartate receptors, and the transcription factor NF kappa B. Additional functions of beta-APP may include modulation of cell adhesion and regulation of proliferation of nonneuronal cells.", "citation": {"db": "PubMed", "db_id": "9354812"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2137, "target": 3115, "key": "d3f6b00d3a0a23a3e48e0ebd93d89711"}, {"line": 37691, "relation": "increases", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2137, "target": 656, "key": "a6230c95682dafc03ecb5bba216deea4"}, {"line": 37692, "relation": "increases", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2137, "target": 763, "key": "be3ad7ebb57428f401924ea70337215c"}, {"line": 37693, "relation": "increases", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2137, "target": 761, "key": "74da7abaf3e37abe355b51671020304a"}, {"line": 37699, "relation": "association", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2137, "target": 3112, "key": "c45b9763ab3309998b56ae4960bd85ae"}, {"line": 37730, "relation": "increases", "evidence": "These results suggest that sAPP-induced glial differentiation is mediated through the gamma-secretase dependent Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2137, "target": 561, "key": "bdfe21d23610577b2c2e7d39f1a013a0"}, {"line": 37738, "relation": "increases", "evidence": "Recently, He et al. proposed that the Jak/STAT pathway is central to the gliogenic machinery and postulated a framework for understanding the control of gliogenesis during development (He et al., 2005). Treatment with sAPP increased phosphorylation of STAT3, which was suppressed when treated with L-685,458 (Fig 5C), indicating existence of crosstalk between the Notch and Jak/STAT pathway in APP-induced glial differentiation.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2137, "target": 3427, "key": "b9cde10361187a63f741b42156405b54"}, {"line": 37743, "relation": "increases", "evidence": "Recently, He et al. proposed that the Jak/STAT pathway is central to the gliogenic machinery and postulated a framework for understanding the control of gliogenesis during development (He et al., 2005). Treatment with sAPP increased phosphorylation of STAT3, which was suppressed when treated with L-685,458 (Fig 5C), indicating existence of crosstalk between the Notch and Jak/STAT pathway in APP-induced glial differentiation.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2137, "target": 2200, "key": "843bf12e842682971bf6e383471a2020"}, {"relation": "hasReactant", "source": 4095, "target": 2249, "key": "c18a62252e781301405bb631d02f6510"}, {"relation": "hasReactant", "source": 4095, "target": 2315, "key": "e796651005abad60e0d1d28ea8b25074"}, {"relation": "hasProduct", "source": 4095, "target": 2137, "key": "afe83bc3d68b66187ddfa2994b68b119"}, {"line": 4042, "relation": "increases", "evidence": "Cadmium (Cd), a toxic environmental contaminant, induces oxidative stress, leading to neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Response to oxidative stress": true}}, "source": 93, "target": 775, "key": "efa7e89f7b48a59f5c6cf81de1e66a4e"}, {"line": 4050, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 93, "target": 645, "key": "5474948969d66af01b3fec9d99bb38d2"}, {"line": 4051, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 93, "target": 2173, "key": "7417da313b12cef9c087efec77b93d5d"}, {"line": 4052, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 93, "target": 2187, "key": "049d85f749e28f38f908e650808909de"}, {"line": 4053, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity"}, "source": 93, "target": 2222, "key": "1007c87a432de585ea2fae4f9ececb23"}, {"line": 4056, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 93, "target": 460, "key": "c1374892933793d5a209ba44b52ae027"}, {"line": 4065, "relation": "increases", "evidence": "Our findings indicate that Cd elevates [Ca+2](i), which induces ROS and activates MAPK and mTOR pathways, leading to neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "source": 93, "target": 94, "key": "b5f7300a78626604dc3aee8af09ac288"}, {"line": 44515, "relation": "decreases", "evidence": "More recently, it was reported that subchronic exposure to Cd inhibited DNA-methyltransferase activity in cultured cells, while chronic exposure enhanced the activity of the DNA-methyltransferase.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}, "Duration_of_Chemical_Exposure": {"Subchronic": true}}, "source": 93, "target": 3626, "key": "e9a3ad0510612a5460d28010a2d48d2a"}, {"line": 44521, "relation": "increases", "evidence": "More recently, it was reported that subchronic exposure to Cd inhibited DNA-methyltransferase activity in cultured cells, while chronic exposure enhanced the activity of the DNA-methyltransferase.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}, "Duration_of_Chemical_Exposure": {"Chronic": true}}, "source": 93, "target": 3626, "key": "f3d36e07dde2d4557ffd7f8ffedea8bc"}, {"line": 4060, "relation": "increases", "evidence": "Recently we have demonstrated that Cd induces neuronal apoptosis in part by activation of the mitogen-activated protein kineses (MAPK) and mammalian target of rapamycin (mTOR) pathways.", "citation": {"db": "PubMed", "db_id": "21544200"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2222, "target": 645, "key": "0943d705945eda8537dfb3cfc3dcc251"}, {"line": 19238, "relation": "increases", "evidence": "Besides mediating COX-2 expression, p38 MAPK is suggested to mediate cell cycle progression through phosphorylation of the retinoblastoma protein (pRb).", "citation": {"db": "PubMed", "db_id": "15056456"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "JAK-STAT signaling subgraph": true}}, "source": 2222, "target": 3300, "key": "26da1e5ec90c9966a2a879328e5310d8"}, {"line": 19240, "relation": "association", "evidence": "Besides mediating COX-2 expression, p38 MAPK is suggested to mediate cell cycle progression through phosphorylation of the retinoblastoma protein (pRb).", "citation": {"db": "PubMed", "db_id": "15056456"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "JAK-STAT signaling subgraph": true}}, "source": 2222, "target": 3278, "key": "db6ce8c2f95d96840c83d168cf83767b"}, {"line": 23137, "relation": "increases", "evidence": "Activation of p38 and its upstream kinase apoptosis signal–regulated kinase 1 () was apparent in the lumbar spinal cord of 8-wk-old SOD1(G93A) mice, and this activation was abolished in SOD1(G93A)/MST1−/− mice. Collectively, these results suggested that SOD1(G93A) induces MST1 activation, which in turn mediates activation of the p38 signaling pathway as well as that of caspase-9 and -3 in the lumbar spinal cord of ALS mice. This suggesting that the p38 pathway mediates SOD1(G93A)-induced caspase activation.", "citation": {"db": "PubMed", "db_id": "23818595"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2222, "target": 3601, "key": "c97e935f45083e0d51ccdfa6a6903ca0"}, {"line": 23138, "relation": "increases", "evidence": "Activation of p38 and its upstream kinase apoptosis signal–regulated kinase 1 () was apparent in the lumbar spinal cord of 8-wk-old SOD1(G93A) mice, and this activation was abolished in SOD1(G93A)/MST1−/− mice. Collectively, these results suggested that SOD1(G93A) induces MST1 activation, which in turn mediates activation of the p38 signaling pathway as well as that of caspase-9 and -3 in the lumbar spinal cord of ALS mice. This suggesting that the p38 pathway mediates SOD1(G93A)-induced caspase activation.", "citation": {"db": "PubMed", "db_id": "23818595"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2222, "target": 3600, "key": "e56294a5751c528dd4b90966e05f4215"}, {"line": 35274, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2222, "target": 715, "key": "bdcb49a21cf1e3be820ee8fcd9f28b26"}, {"relation": "hasVariant", "source": 2222, "target": 2223, "key": "c131613e79ece1c096a109a2e1d9f930"}, {"line": 4160, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 1134, "target": 2315, "key": "35e1fe8c8e7730b4e1468c8c5d0f8280"}, {"line": 4161, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 1134, "target": 2375, "key": "92b4ff9297d858a550102b5b6b5de268"}, {"line": 4162, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 1134, "target": 2381, "key": "a4d75f9587e9f90a5018da647143c197"}, {"line": 4163, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 1134, "target": 2981, "key": "cbb247c7914aef967db5622c733fce8d"}, {"line": 4167, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 1076, "target": 1134, "key": "3bb2cb4cd9acceeb94123f8800df63dc"}, {"line": 4168, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 1082, "target": 1134, "key": "87fd6a3c1b2b714e91a8eaa42da2b7f9"}, {"line": 4169, "relation": "increases", "evidence": "Abeta is a fragment of amyloid-beta precursor protein (APP) generated in neurons by two proteases, beta- and gamma-secretases. APP and beta-secretase, both present on cell surface, are endocytosed into endosomes to produce Abeta. The molecular mechanism by which neurons trigger the production of Abeta is poorly understood. We describe here evidence that the binding of lipid-carrying apolipoprotein E (ApoE) to receptor apolipoprotein E receptor 2 (ApoER2) triggers the endocytosis of APP, beta-secretase, and ApoER2 in neuroblastoma cells, leading to the production of Abeta. This mechanism, mediated by adaptor protein X11alpha or X11beta (X11alpha/beta), whose PTB (phosphotyrosine-binding) domain binds to APP and a newly recognized motif in the cytosolic domain of ApoER2. Isomorphic form ApoE4 triggers the production of more Abeta than by ApoE2 or ApoE3; thus, it may play a role in the genetic risk of ApoE4 for the sporadic AD.", "citation": {"db": "PubMed", "db_id": "17428983"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 1088, "target": 1134, "key": "4960757b6de57c3fab3c1998ff1882e4"}, {"line": 5010, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 524, "target": 645, "key": "550561923166c12405275c37ffed783e"}, {"line": 5011, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 524, "target": 608, "key": "ebb6edef8dcc97429f180e8e5269c7a8"}, {"line": 5017, "relation": "positiveCorrelation", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 524, "target": 3823, "key": "dd3cc56a64400b35e251771eadfd8c9b"}, {"line": 5031, "relation": "increases", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 524, "target": 3472, "key": "43701f89729f18ae2501697dedafaf5c"}, {"line": 5032, "relation": "increases", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 524, "target": 1709, "key": "22da4a89bccb3768d341a4d464fcf450"}, {"line": 39281, "relation": "increases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 2879, "target": 577, "key": "cfd10f8936f87502719593a668be3872"}, {"line": 15687, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 2457, "target": 3823, "key": "7167f4c43c7cf10cedf563a87ad2691a"}, {"relation": "partOf", "source": 2457, "target": 1320, "key": "8d007a20cdcbfdb99ffa4bd84efbb43d"}, {"line": 39621, "relation": "increases", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2880, "target": 2470, "key": "f6e575a5e56f8a15bb8b56a605ded895"}, {"line": 46833, "relation": "increases", "evidence": "In fact YKL-40 induces the interaction of αvbeta3 integrins with syndecan-1 in endothelial cells (27), it activates ERK, AKT, and Wnt/beta-catenin signaling in macrophages via IL-13 receptor alpha 2-dependent mechanism (55),", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2880, "target": 462, "key": "6323492e4f4126bb7772e9ca326519e1"}, {"line": 46834, "relation": "increases", "evidence": "In fact YKL-40 induces the interaction of αvbeta3 integrins with syndecan-1 in endothelial cells (27), it activates ERK, AKT, and Wnt/beta-catenin signaling in macrophages via IL-13 receptor alpha 2-dependent mechanism (55),", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2880, "target": 698, "key": "5e061b3a845460efd7faed09256316eb"}, {"line": 46835, "relation": "increases", "evidence": "In fact YKL-40 induces the interaction of αvbeta3 integrins with syndecan-1 in endothelial cells (27), it activates ERK, AKT, and Wnt/beta-catenin signaling in macrophages via IL-13 receptor alpha 2-dependent mechanism (55),", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2880, "target": 448, "key": "271b51b99b5b464d0c87a2e2fd245ba5"}, {"line": 4219, "relation": "association", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2600, "target": 2601, "key": "4dfefbf502293eece587008cf50e9e00"}, {"line": 4220, "relation": "decreases", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2600, "target": 608, "key": "f13bb40ab8c37558d947ee17f35732c3"}, {"line": 4221, "relation": "decreases", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2600, "target": 609, "key": "02ce39f9ae42751e7d171d2acb92588f"}, {"line": 4604, "relation": "increases", "evidence": "Fractalkine/CX(3)CL1 is a transmembrane chemokine abundantly expressed in the brain by neurons, where it modulates glutamatergic transmission and long-term plasticity processes regulating the intercellular communication between glia and neurons, being its specific receptor CX(3)CR1 expressed by microglia", "citation": {"db": "PubMed", "db_id": "22025910"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2600, "target": 763, "key": "552785a371c4ce6befe0853ce81117ca"}, {"line": 4219, "relation": "association", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2601, "target": 2600, "key": "062e39b3bdbcbacc3c40818c40a7b87b"}, {"line": 46322, "relation": "decreases", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2601, "target": 812, "key": "c332ad2bf5252cb2df78858f9d0129d0"}, {"line": 46323, "relation": "decreases", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2601, "target": 820, "key": "448075e74db5189bc11ca99c44e971de"}, {"line": 46324, "relation": "increases", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2601, "target": 577, "key": "076907a4f965439e52f20dbe6e40cb20"}, {"line": 46325, "relation": "increases", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2601, "target": 648, "key": "a6999d20b68cade8e41729528ffe58db"}, {"line": 4225, "relation": "increases", "evidence": "Microglia have neuroprotective capacities, yet chronic activation can promote neurotoxic inflammation. Neuronal fractalkine (FKN), acting on CX(3)CR1, has been shown to suppress excessive microglia activation.", "citation": {"db": "PubMed", "db_id": "20018408"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 532, "target": 645, "key": "aae8b2a13e2f0b0a29deea537742f4d9"}, {"line": 4253, "relation": "increases", "evidence": "Microglia are activated in response to a number of different pathological states within the CNS including injury, ischemia, and infection. Microglial activation results in their production of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-a. While release of these factors is typically intended to prevent further damage to CNS tissue, they may also be toxic to neurons and other glial cells. Mounting evidence indicates that chronic microglial activation may also contribute to the development and progression of neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "22024597"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 532, "target": 645, "key": "209a0fb40803a60769229b679a85d66a"}, {"line": 4404, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-a. TNF-a signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2).", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 532, "target": 693, "key": "ac3df835df0a75fd141a0c7e3845ebd2"}, {"line": 4405, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-a. TNF-a signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2).", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 532, "target": 3472, "key": "b063b2ed16149753071404fb9130ab46"}, {"line": 8497, "relation": "association", "evidence": "Here we provide evidence in human neural (HN) cells of an aluminum-sulfate- and reactive oxygen species (ROS)-mediated up-regulation of an NF-kappaB-sensitive miRNA-146a that down-regulates the expression of complement factor H (CFH), an important repressor of inflammation. This NF-kappaB-miRNA-146a-CFH signaling circuit is known to be similarly affected by Abeta42 peptides and in AD brain. These aluminum-sulfate-inducible events were not observed in parallel experiments using iron-, magnesium-, or zinc-sulfate-stressed HN cells. An NF-kappaB-containing miRNA-146a-promoter-luciferase reporter construct transfected into HN cells showed significant up-regulation of miRNA-146a after aluminum-sulfate treatment that corresponded to decreased CFH gene expression. These data suggest that (1) as in AD brain, NF-kappaB-sensitive, miRNA-146a-mediated, modulation of CFH gene expression may contribute to inflammatory responses in aluminum-stressed HN cells, and (2) underscores the potential of nanomolar aluminum to drive genotoxic mechanisms characteristic of neurodegenerative disease processes.", "citation": {"db": "PubMed", "db_id": "19540598"}, "annotations": {"Subgraph": {"Complement system subgraph": true}}, "source": 532, "target": 2506, "key": "830be74b8e4355726092692cbd14e358"}, {"line": 8549, "relation": "association", "evidence": "recent findings have demonstrated that cyclooxygenase (COX)-2 is of primary importance in the inflammatory response and may have a role in neurodegeneration. Therefore, selective COX-2 inhibitors (coxibs) may have an advantage over traditional NSAIDs as potential therapeutic agents in AD.", "citation": {"db": "PubMed", "db_id": "11992749"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 532, "target": 3278, "key": "646405a2c41a5c0a0658fbbe80679724"}, {"line": 9629, "relation": "association", "evidence": "PPARgamma activation leads to the inhibition of microglial activation and the expression of a broad range of proinflammatory molecules. The newly appreciated anti-inflammatory actions of PPARgamma agonists may allow novel therapies for AD and other CNS indications with an inflammatory component.", "citation": {"db": "PubMed", "db_id": "11755002"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 532, "target": 3212, "key": "4f8150e3fbf669b1bf1c3085365dc8ef"}, {"line": 13845, "relation": "association", "evidence": "Specific polymorphisms within the vascular endothelial growth factor (VEGF) gene promoter region are of particular interest: VEGF variability has been associated with increased risk of developing a wide variety of disorders from diabetes to neurodegenerative diseases, suggesting functions not confined to its originally described vascular effects.", "citation": {"db": "PubMed", "db_id": "19272614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}}, "source": 3874, "target": 3519, "key": "f66041f2ca1d9635d654b9527b8d06a5"}, {"line": 14078, "relation": "association", "evidence": "Oxidative stress has been suggested to play an important role in the pathogenesis of various neurodegenerative diseases including Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 842, "key": "855434d1f35995be95c10c7ade23315c"}, {"line": 16611, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 842, "key": "339d0315c8fc389d5334fd02f0dcbadb"}, {"line": 18530, "relation": "association", "evidence": "The increase in neuronal myeloperoxidase expression we observed in Alzheimer disease brains raises the possibility that the enzyme contributes to the oxidative stress implicated in the pathogenesis of the neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"Disease": {"neurodegenerative disease": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 842, "key": "6b3a82a704a20edbaeea7ea5c5b07407"}, {"line": 15010, "relation": "association", "evidence": "A growing body of evidence shows the involvement of matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) in neurodegeneration processes.", "citation": {"db": "PubMed", "db_id": "24448781"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3874, "target": 2194, "key": "d8c8993577dc9f72ef198dceb8ae8125"}, {"line": 16590, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 3874, "target": 807, "key": "fe078141c132d13fcb806410d36280c6"}, {"line": 16596, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 478, "key": "6516182ccba8b5bb6bf2f913c5cdeb17"}, {"line": 16603, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 3920, "key": "e60941fe16f6235180e8e46139137305"}, {"line": 16617, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 3874, "target": 803, "key": "ce323a2fd4d21467ff6f9650f5a16504"}, {"line": 16618, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 3874, "target": 821, "key": "0b85044f5665e997ad056cd7f7b83ff8"}, {"line": 16619, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 3874, "target": 487, "key": "b37b83c04d10481d85b0378c0986c8e0"}, {"line": 16620, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 3874, "target": 509, "key": "38e7c089010000acc048c0cead35d37a"}, {"line": 17743, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS) has been highlighted as having a pathological role in stroke, dementia, and neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Stroke": true, "Dementia": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3874, "target": 844, "key": "177756eb331ffca574b4f46eef98ede7"}, {"line": 18026, "relation": "association", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 505, "key": "e8272e99dc48dc41a47c2a210644d5ce"}, {"line": 18694, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3874, "target": 3060, "key": "80a9d6b1a0730346490f1e8f321f3a12"}, {"line": 19704, "relation": "association", "evidence": "The decline in melatonin production in aged individuals has been suggested as one of the primary contributing factors for the development of age-associated neurodegenerative diseases, e.g., Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Confidence": {"High": true}}, "source": 3874, "target": 299, "key": "7cee30816b5ec77260e3c888dbcce848"}, {"line": 21811, "relation": "association", "evidence": "More recent data indicate that these enzymes and the biologically active lipid molecules they generate could influence the functioning of the central nervous system and the pathobiology of neurodegenerative disorders such as AD via mechanisms different from classical inflammation.", "citation": {"db": "PubMed", "db_id": "20691748"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Inflammation": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}}, "source": 3874, "target": 290, "key": "b8fc016719bf460996156bc75f1904f3"}, {"line": 34744, "relation": "association", "evidence": "To investigate potential homodimeric and heterodimeric interactions of APP and Notch2 (N2), we have visualized the subcellular localization of the APP/N2 complexes formed in living cells using bimolecular fluorescence complementation (BiFC) analysis. BiFC was accomplished by fusing the N-terminal fragment or the C-terminal fragment of yellow fluorescent protein (YFP) to APP, N2, and a C-terminally truncated form of N2.When expressed in COS-7 cells, these tagged proteins alone did not produce a fluorescent signal. The tagged APP homodimer produced a weak fluorescent signal, while neither full-length N2, nor a truncated N2 alone, produced a visible signal, suggesting that N2 receptors do not form homodimers. The strongest fluorescent signal was obtained with co-expression of the C-terminal fragment of YFP fused to APP and the N-terminal fragment of YFP fused to the truncated form of N2. This heterodimer localized to plasma membrane, endoplasmic reticulum (ER), Golgi and other compartments. The results were confirmed and quantified by flow cytometry. The BiFC method of specifically visualizing APP/Notch interactions can be applied to study APP and Notch signaling during development, aging and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "16515557"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 3128, "key": "d19d16921b0d298a2179d45bd6853a18"}, {"line": 39838, "relation": "association", "evidence": "Several cytokines are evidently regulated in (neuro-) inflammatory processes associated with neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "24567119"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3874, "target": 420, "key": "c195ebdf310bd3354fa6ce73809ccba1"}, {"line": 40137, "relation": "positiveCorrelation", "evidence": "Elevated levels of several proinflammatory factors including cytokines, peptides, pathogenic structures, and peroxidants in the central nervous system (CNS) have been detected in patients with neurodegenerative diseases such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 420, "key": "7c30dd2147c57e85c27c71c4becec560"}, {"line": 40136, "relation": "positiveCorrelation", "evidence": "Elevated levels of several proinflammatory factors including cytokines, peptides, pathogenic structures, and peroxidants in the central nervous system (CNS) have been detected in patients with neurodegenerative diseases such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 325, "key": "386a369650d13081b81533d64e394e8f"}, {"line": 40365, "relation": "association", "evidence": "Adiponectin is an adipocytokine released by the adipose tissue and has multiple roles in the immune system and in the metabolic syndromes such as cardiovascular disease, Type 2 diabetes, obesity and also in the neurodegenerative disorders including Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "MeSHAnatomy": {"Adipose Tissue": true}, "Confidence": {"High": true}}, "source": 3874, "target": 2259, "key": "726ed24b83228f8a9470724788787899"}, {"line": 40589, "relation": "association", "evidence": "Further studies on precise mechanisms of Pls-mediated protection against cell death may lead us to establish a novel therapeutic approach to cure neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Confidence": {"High": true}}, "source": 3874, "target": 65, "key": "874e12d507a0fe26230cebc0aae5b3ae"}, {"line": 40930, "relation": "positiveCorrelation", "evidence": "The epidemic and experimental studies have confirmed that the obesity can lead to neuroinflammation, neurodegenerative diseases and adversely affect cognition.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Obesity": true}}, "source": 3874, "target": 3925, "key": "28d2a79c1de2aff2d529a4c7584c3ee8"}, {"line": 41197, "relation": "association", "evidence": "Among neurodegenerative disorders, Alzheimer's disease (AD) represents the most common cause of dementia in the elderly.", "citation": {"db": "PubMed", "db_id": "24860504"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true, "Dementia": true}}, "source": 3874, "target": 3901, "key": "de2c443ea9e2a4a1b911849f9b1aea82"}, {"line": 41978, "relation": "association", "evidence": "Cannabinoid receptor subtype 2 (CB2) has been shown to be up-regulated in activated microglia and therefore plays an important role in neuroinflammatory and neurodegenerative diseases such as multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neurodegenerative Diseases": true, "Amyotrophic Lateral Sclerosis": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3874, "target": 3613, "key": "99b6fd3fec75b9e8d5a6f92cde57182d"}, {"line": 46424, "relation": "decreases", "evidence": "We measured the concentration of two presynaptic proteins, synaptophysin and syntaxin 1, and also postsynaptic density-95 (PSD95), in superior temporal cortex from 42 AD and 160 normal brains, and determined the APOE genotypes. The concentration of both presynaptic proteins was approximately two-thirds lower in AD than normal brains and that of PSD95 one-third lower. ", "citation": {"db": "PubMed", "db_id": "15979210"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 3874, "target": 3431, "key": "d5250d4509c516a1e5fb6883c5b0d67f"}, {"line": 46425, "relation": "decreases", "evidence": "We measured the concentration of two presynaptic proteins, synaptophysin and syntaxin 1, and also postsynaptic density-95 (PSD95), in superior temporal cortex from 42 AD and 160 normal brains, and determined the APOE genotypes. The concentration of both presynaptic proteins was approximately two-thirds lower in AD than normal brains and that of PSD95 one-third lower. ", "citation": {"db": "PubMed", "db_id": "15979210"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 3874, "target": 3438, "key": "454c590af7cf53a5957b5c648a600d88"}, {"line": 46426, "relation": "decreases", "evidence": "We measured the concentration of two presynaptic proteins, synaptophysin and syntaxin 1, and also postsynaptic density-95 (PSD95), in superior temporal cortex from 42 AD and 160 normal brains, and determined the APOE genotypes. The concentration of both presynaptic proteins was approximately two-thirds lower in AD than normal brains and that of PSD95 one-third lower. ", "citation": {"db": "PubMed", "db_id": "15979210"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 3874, "target": 2633, "key": "2a37050bad944426ee3bee1f070f555d"}, {"line": 48254, "relation": "positiveCorrelation", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3874, "target": 2629, "key": "1dbcc5e434b100c84b6ba2fcb54679c8"}, {"line": 49482, "relation": "negativeCorrelation", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3874, "target": 788, "key": "c57f3190754c59717f5aba5bf0413f95"}, {"line": 4266, "relation": "increases", "evidence": "Chronic neuroinflammation is a hallmark of several neurological disorders associated with cognitive loss. Activated microglia and secreted factors such as tumor necrosis factor (TNF)-a are key mediators of neuroinflammation and may contribute to neuronal dysfunction", "citation": {"db": "PubMed", "db_id": "22277195"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 693, "target": 645, "key": "05e1477f8b3d291b7a45a6c406706eda"}, {"line": 4268, "relation": "increases", "evidence": "Chronic neuroinflammation is a hallmark of several neurological disorders associated with cognitive loss. Activated microglia and secreted factors such as tumor necrosis factor (TNF)-a are key mediators of neuroinflammation and may contribute to neuronal dysfunction", "citation": {"db": "PubMed", "db_id": "22277195"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 693, "target": 3875, "key": "5a77d72617d9d81471996f3c4badf3f9"}, {"line": 4729, "relation": "decreases", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 693, "target": 812, "key": "74ea198fd55df9c7f6fb39a77b6868b2"}, {"line": 8591, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 693, "target": 532, "key": "db1430c1499b68a23c5dddcf3ccf1ed9"}, {"line": 4301, "relation": "increases", "evidence": "We observed that the neuronal cells primarily produce IL-34 and that microglia express its receptor, colony-stimulating factor 1 receptor. IL-34 promoted microglial proliferation and clearance of soluble oligomeric amyloid-beta (oAbeta), which mediates synaptic dysfunction and neuronal damage in AD.", "citation": {"db": "PubMed", "db_id": "21872563"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2892, "target": 2567, "key": "c989efc1c63c8256f11bdb580e97fbac"}, {"line": 4304, "relation": "decreases", "evidence": "We observed that the neuronal cells primarily produce IL-34 and that microglia express its receptor, colony-stimulating factor 1 receptor. IL-34 promoted microglial proliferation and clearance of soluble oligomeric amyloid-beta (oAbeta), which mediates synaptic dysfunction and neuronal damage in AD.", "citation": {"db": "PubMed", "db_id": "21872563"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2892, "target": 2328, "key": "fe5b96fcffb81933e1ce6cbf9b9aafd5"}, {"line": 4315, "relation": "increases", "evidence": "IL-34 increased the expression of insulin-degrading enzyme, aiding the clearance of oAbeta, and induced the antioxidant enzyme heme oxygenase-1 in microglia to reduce oxidative stress, without producing neurotoxic molecules.", "citation": {"db": "PubMed", "db_id": "21872563"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"High": true}}, "source": 2892, "target": 2867, "key": "f9404613bfacc119fc1157c6232bccf2"}, {"line": 4316, "relation": "increases", "evidence": "IL-34 increased the expression of insulin-degrading enzyme, aiding the clearance of oAbeta, and induced the antioxidant enzyme heme oxygenase-1 in microglia to reduce oxidative stress, without producing neurotoxic molecules.", "citation": {"db": "PubMed", "db_id": "21872563"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"High": true}}, "source": 2892, "target": 2839, "key": "a3965f2e4477f83a3bff692382a5a65d"}, {"line": 4317, "relation": "decreases", "evidence": "IL-34 increased the expression of insulin-degrading enzyme, aiding the clearance of oAbeta, and induced the antioxidant enzyme heme oxygenase-1 in microglia to reduce oxidative stress, without producing neurotoxic molecules.", "citation": {"db": "PubMed", "db_id": "21872563"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"High": true}}, "source": 2892, "target": 775, "key": "5ebdf01ef8ba3439a0f2a45cfdec2da6"}, {"line": 4335, "relation": "increases", "evidence": "In this study, we found that IL-34 dose-dependently induces TGF-beta in microglia, and that TGF-beta attenuates oAbeta neurotoxicity in neuron microglial co-cultures. The TGF-beta 1 receptor kinase inhibitor SD208 enhances microglial proliferation by IL-34 and suppresses the neuroprotective effect of IL-34-treated microglia. These findings suggest that TGF-beta produced by IL-34-treated microglia is a negative regulator of microglial proliferation and enhances the neuroprotective property of microglia.", "citation": {"db": "PubMed", "db_id": "22985514"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "TGF-Beta subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2892, "target": 3454, "key": "5aa97abbe34a0e06236419808b18d5ef"}, {"line": 4336, "relation": "increases", "evidence": "In this study, we found that IL-34 dose-dependently induces TGF-beta in microglia, and that TGF-beta attenuates oAbeta neurotoxicity in neuron microglial co-cultures. The TGF-beta 1 receptor kinase inhibitor SD208 enhances microglial proliferation by IL-34 and suppresses the neuroprotective effect of IL-34-treated microglia. These findings suggest that TGF-beta produced by IL-34-treated microglia is a negative regulator of microglial proliferation and enhances the neuroprotective property of microglia.", "citation": {"db": "PubMed", "db_id": "22985514"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "TGF-Beta subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2892, "target": 3455, "key": "4f441071bf510811fc4c11fe07533589"}, {"line": 4337, "relation": "increases", "evidence": "In this study, we found that IL-34 dose-dependently induces TGF-beta in microglia, and that TGF-beta attenuates oAbeta neurotoxicity in neuron microglial co-cultures. The TGF-beta 1 receptor kinase inhibitor SD208 enhances microglial proliferation by IL-34 and suppresses the neuroprotective effect of IL-34-treated microglia. These findings suggest that TGF-beta produced by IL-34-treated microglia is a negative regulator of microglial proliferation and enhances the neuroprotective property of microglia.", "citation": {"db": "PubMed", "db_id": "22985514"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "TGF-Beta subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2892, "target": 3456, "key": "d0261c44512d3dfc67e181f4ff223608"}, {"line": 4339, "relation": "increases", "evidence": "In this study, we found that IL-34 dose-dependently induces TGF-beta in microglia, and that TGF-beta attenuates oAbeta neurotoxicity in neuron microglial co-cultures. The TGF-beta 1 receptor kinase inhibitor SD208 enhances microglial proliferation by IL-34 and suppresses the neuroprotective effect of IL-34-treated microglia. These findings suggest that TGF-beta produced by IL-34-treated microglia is a negative regulator of microglial proliferation and enhances the neuroprotective property of microglia.", "citation": {"db": "PubMed", "db_id": "22985514"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "TGF-Beta subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2892, "target": 2218, "key": "91967b7183618e6d0a05d79721c5c049"}, {"relation": "partOf", "source": 2567, "target": 1365, "key": "60e6a1c1fa1bd43f1d3f0f32c76ac5fa"}, {"line": 8093, "relation": "increases", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 2328, "key": "c32db37ec5e596f686eb13b03850472c"}, {"line": 8135, "relation": "increases", "evidence": "Since Abeta is secreted extracellularly, and deposits outside the neuronal cells in the AD brain, we took an approach to screen any secreted protease(s) in neuronal and non-neuronal cell culture media for the ability to degrade Abeta. Among all secreted proteases from the cells, only IDE degraded Abeta. We found that under physiological conditions IDE is secreted at high levels from the microglial cells, and degrades Abeta extracellularly [73]. Purified IDE from rat liver and brain was shown to degrade Abeta effectively. IDE is present in the soluble fractions from human brains, and binds and degrades Abeta specifically [57] and [71]. Primary cultured neurons were also shown to clear Abeta via extracellular IDE as well as IDE on the cell surface [90]. IDE from brain homogenates degrades different forms of Abeta: Abeta40, Abeta42 and an Abeta mutant in one type of AD (Dutch Variant 1-40 Q) [61] and [71]. Abeta42 is the longer form of Abeta and more abundant in the AD brain.", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 2328, "key": "e9dd01ff6dbc86f93387e164a83ec0f6"}, {"line": 8155, "relation": "negativeCorrelation", "evidence": "Abeta degrading activity by IDE was shown to be lower in AD brains than in the controls [71]. Moreover, the amount of hippocampal IDE protein was also found to reduce in AD brains as compared to the controls [15]. When the IDE gene was deleted in mouse model, Abeta levels in the brain were elevated [27] and [58], suggesting IDE activity is critical in determining the amount of brain Abeta in vivo. More significantly, enhanced IDE activity in the IDE and APP double transgenic mice decreased their brain Abeta levels, and prevented the formation of AD pathology [52].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2867, "target": 2328, "key": "fe30e2c1ddb3d6505a0327663e82bccc"}, {"line": 10077, "relation": "association", "evidence": "Insulin-degrading enzyme (IDE) is central to the turnover of insulin and degrades amyloid beta (Abeta) in the mammalian brain.", "citation": {"db": "PubMed", "db_id": "18411275"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 2328, "key": "5501526266f2120f584e272fb782ede5"}, {"line": 22696, "relation": "increases", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 2328, "key": "dfa8c264337dfec005cbc76fd31d31e4"}, {"line": 30870, "relation": "increases", "evidence": "Insulin-degrading enzyme (IDE) is a protease that has been demonstrated to play a key role in degrading both Abeta and insulin and deficient in IDE function is associated with Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) pathology.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 2328, "key": "d6d9fa9d526fd0efce56b609ade81d3b"}, {"line": 38123, "relation": "decreases", "evidence": "Lower-expression of PS1 and over-expression of IDE or NEP may be helpful in potentially lowering brain Abeta levels in subjects with AD", "citation": {"db": "PubMed", "db_id": "19355846"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2867, "target": 2328, "key": "5422382d89bdd273a8a3c0079317e859"}, {"relation": "hasVariant", "source": 2867, "target": 2868, "key": "893dde4245ca11ec7e49917a1b9154c1"}, {"line": 8127, "relation": "negativeCorrelation", "evidence": "Further, IDE expression is affected by aging, with IDE activity significantly decreased in both the muscles and liver of old animals as compared to young animals [76].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2867, "target": 806, "key": "4873e78629bf0ecb0b7eb4c09e93420b"}, {"line": 8148, "relation": "negativeCorrelation", "evidence": "Abeta degrading activity by IDE was shown to be lower in AD brains than in the controls [71]. Moreover, the amount of hippocampal IDE protein was also found to reduce in AD brains as compared to the controls [15]. When the IDE gene was deleted in mouse model, Abeta levels in the brain were elevated [27] and [58], suggesting IDE activity is critical in determining the amount of brain Abeta in vivo. More significantly, enhanced IDE activity in the IDE and APP double transgenic mice decreased their brain Abeta levels, and prevented the formation of AD pathology [52].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2867, "target": 3823, "key": "19f3984e39638bd5c56b9e404f95703b"}, {"line": 30871, "relation": "negativeCorrelation", "evidence": "Insulin-degrading enzyme (IDE) is a protease that has been demonstrated to play a key role in degrading both Abeta and insulin and deficient in IDE function is associated with Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) pathology.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2867, "target": 3823, "key": "72999895b4258f62c3bab341b113661e"}, {"line": 8169, "relation": "increases", "evidence": "Indeed, adding increasing amounts of insulin, a substrate of IDE with low Km (Km = ∼0.1 μM), specifically inhibited enzyme activity for degradation of Abeta (Km >2 μM) [74] in the cell culture model for secreted IDE. Therefore, if the insulin level increases in the brain, it would inhibit IDE to degrade Abeta effectively, which could cause Abeta neurotoxicity, and then AD.", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 80, "key": "2b43085c965c924d919f7c99aebd8a5e"}, {"relation": "partOf", "source": 2867, "target": 1230, "key": "1c70d2ae64c42f579c2f320993e162a3"}, {"line": 10078, "relation": "association", "evidence": "Insulin-degrading enzyme (IDE) is central to the turnover of insulin and degrades amyloid beta (Abeta) in the mammalian brain.", "citation": {"db": "PubMed", "db_id": "18411275"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 2899, "key": "058430ee1e2dda4c18b69fabcde58943"}, {"line": 30832, "relation": "increases", "evidence": "Because one of the main functions of IDE is to degrade insulin, we hypothesized that there is a negative feedback mechanism whereby stimulation of insulin receptor-mediated signaling upregulates IDE to prevent chronic activation of the pathway.", "citation": {"db": "PubMed", "db_id": "15590928"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 2899, "key": "f0930d80b18b892fb09ae215f9f650d7"}, {"line": 30869, "relation": "increases", "evidence": "Insulin-degrading enzyme (IDE) is a protease that has been demonstrated to play a key role in degrading both Abeta and insulin and deficient in IDE function is associated with Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) pathology.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2867, "target": 2899, "key": "5fdd5432f3b2f54bff3f8b7ab6b0f5c8"}, {"line": 22691, "relation": "negativeCorrelation", "evidence": "These data suggest that NOS2 upregulation impairs amyloid beta degradation through negative regulation of IDE activity and thus loss of NOS2 activity will positively influence amyloid beta clearance.", "citation": {"db": "PubMed", "db_id": "22227962"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2867, "target": 3123, "key": "b50a71552176dca7e7afc2a78e6d328d"}, {"relation": "partOf", "source": 2867, "target": 1464, "key": "bf02dc33ded6a7746293d4d66ea01ec0"}, {"line": 30858, "relation": "positiveCorrelation", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 2867, "target": 2893, "key": "a13581d25d5008f6acf1d236fe9053de"}, {"line": 30872, "relation": "negativeCorrelation", "evidence": "Insulin-degrading enzyme (IDE) is a protease that has been demonstrated to play a key role in degrading both Abeta and insulin and deficient in IDE function is associated with Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) pathology.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2867, "target": 3850, "key": "5bc568d03b443c4e8daca3e493a43324"}, {"relation": "partOf", "source": 2867, "target": 1465, "key": "424cb602eb03b0bbfeab8761d0cb6b65"}, {"line": 30880, "relation": "directlyIncreases", "evidence": "IDE is known to bind the cytoplasmic intermediate filament protein nestin with high affinity.", "citation": {"db": "PubMed", "db_id": "21185309"}, "annotations": {"Confidence": {"Medium": true}}, "source": 2867, "target": 1465, "key": "26d9c44e99f6ffb333b163cba3411e6f"}, {"line": 30881, "relation": "association", "evidence": "IDE is known to bind the cytoplasmic intermediate filament protein nestin with high affinity.", "citation": {"db": "PubMed", "db_id": "21185309"}, "annotations": {"Confidence": {"Medium": true}}, "source": 2867, "target": 3104, "key": "995403775b653824f8fe33e8a2d30d57"}, {"line": 4318, "relation": "decreases", "evidence": "IL-34 increased the expression of insulin-degrading enzyme, aiding the clearance of oAbeta, and induced the antioxidant enzyme heme oxygenase-1 in microglia to reduce oxidative stress, without producing neurotoxic molecules.", "citation": {"db": "PubMed", "db_id": "21872563"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"High": true}}, "source": 2839, "target": 775, "key": "ce8546be4eecddb2eaf9c7d6f7c81177"}, {"line": 38893, "relation": "positiveCorrelation", "evidence": "Unlike ho-2, the ho-1 gene in neural (and many systemic) tissues is exquisitely sensitive to upregulation/ by a host of pro-oxidant and other noxious stimuli. In Alzheimer disease, HO-1 immunoreactivity is significantly augmented/ in neurons and astrocytes of the hippocampus and cerebral cortex relative to age-matched, nondemented controls and/ colocalizes to senile plaques, neurofibrillary tangles, and corpora amylacea", "citation": {"db": "PubMed", "db_id": "15544918"}, "source": 2839, "target": 3823, "key": "e28fd7bc485df508ed9b028553d8738f"}, {"line": 38901, "relation": "increases", "evidence": "The heme oxygenases (HOs), responsible for the degradation of heme to biliverdin/bilirubin, free iron and CO,/ have been heavily implicated in mammalian CNS aging and disease. In Alzheimer disease and mild cognitive impairment, / immunoreactive HO-1 protein is over-expressed in neurons and astrocytes of the cerebral cortex and hippocampus relative to / age-matched, cognitively intact controls and co-localizes to senile plaques, neurofibrillary tangles, and corpora amylacea./ In 'stressed' astroglia, HO-1 hyperactivity promotes mitochondrial sequestration of non-transferrin iron and macroautophagy/ and may thereby contribute to the pathological iron deposition and bioenergetic failure amply documented in Alzheimer/ disease, Parkinson disease and other aging-related neurodegenerative disorders. Glial HO-1 expression may also impact cell/ survival and neuroplasticity in these conditions by modulating brain sterol metabolism and proteosomal degradation of/ neurotoxic protein aggregates.", "citation": {"db": "PubMed", "db_id": "19457088"}, "object": {"modifier": "Degradation"}, "source": 2839, "target": 272, "key": "dccdc8b66752646e93b5eedb48bb3173"}, {"line": 38902, "relation": "directlyIncreases", "evidence": "The heme oxygenases (HOs), responsible for the degradation of heme to biliverdin/bilirubin, free iron and CO,/ have been heavily implicated in mammalian CNS aging and disease. In Alzheimer disease and mild cognitive impairment, / immunoreactive HO-1 protein is over-expressed in neurons and astrocytes of the cerebral cortex and hippocampus relative to / age-matched, cognitively intact controls and co-localizes to senile plaques, neurofibrillary tangles, and corpora amylacea./ In 'stressed' astroglia, HO-1 hyperactivity promotes mitochondrial sequestration of non-transferrin iron and macroautophagy/ and may thereby contribute to the pathological iron deposition and bioenergetic failure amply documented in Alzheimer/ disease, Parkinson disease and other aging-related neurodegenerative disorders. Glial HO-1 expression may also impact cell/ survival and neuroplasticity in these conditions by modulating brain sterol metabolism and proteosomal degradation of/ neurotoxic protein aggregates.", "citation": {"db": "PubMed", "db_id": "19457088"}, "source": 2839, "target": 4093, "key": "86ece6ca510efe9f7c681efdae38e1cd"}, {"line": 38904, "relation": "increases", "evidence": "The heme oxygenases (HOs), responsible for the degradation of heme to biliverdin/bilirubin, free iron and CO,/ have been heavily implicated in mammalian CNS aging and disease. In Alzheimer disease and mild cognitive impairment, / immunoreactive HO-1 protein is over-expressed in neurons and astrocytes of the cerebral cortex and hippocampus relative to / age-matched, cognitively intact controls and co-localizes to senile plaques, neurofibrillary tangles, and corpora amylacea./ In 'stressed' astroglia, HO-1 hyperactivity promotes mitochondrial sequestration of non-transferrin iron and macroautophagy/ and may thereby contribute to the pathological iron deposition and bioenergetic failure amply documented in Alzheimer/ disease, Parkinson disease and other aging-related neurodegenerative disorders. Glial HO-1 expression may also impact cell/ survival and neuroplasticity in these conditions by modulating brain sterol metabolism and proteosomal degradation of/ neurotoxic protein aggregates.", "citation": {"db": "PubMed", "db_id": "19457088"}, "source": 2839, "target": 135, "key": "315b39e0158699283f94e199a5781ecb"}, {"line": 4338, "relation": "decreases", "evidence": "In this study, we found that IL-34 dose-dependently induces TGF-beta in microglia, and that TGF-beta attenuates oAbeta neurotoxicity in neuron microglial co-cultures. The TGF-beta 1 receptor kinase inhibitor SD208 enhances microglial proliferation by IL-34 and suppresses the neuroprotective effect of IL-34-treated microglia. These findings suggest that TGF-beta produced by IL-34-treated microglia is a negative regulator of microglial proliferation and enhances the neuroprotective property of microglia.", "citation": {"db": "PubMed", "db_id": "22985514"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "TGF-Beta subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 1721, "target": 2328, "key": "8f6d874392632ebcff767a3ff881931c"}, {"line": 4340, "relation": "decreases", "evidence": "In this study, we found that IL-34 dose-dependently induces TGF-beta in microglia, and that TGF-beta attenuates oAbeta neurotoxicity in neuron microglial co-cultures. The TGF-beta 1 receptor kinase inhibitor SD208 enhances microglial proliferation by IL-34 and suppresses the neuroprotective effect of IL-34-treated microglia. These findings suggest that TGF-beta produced by IL-34-treated microglia is a negative regulator of microglial proliferation and enhances the neuroprotective property of microglia.", "citation": {"db": "PubMed", "db_id": "22985514"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "TGF-Beta subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2218, "target": 2328, "key": "53274ea84e35303cd08df65cd9691263"}, {"line": 4341, "relation": "decreases", "evidence": "In this study, we found that IL-34 dose-dependently induces TGF-beta in microglia, and that TGF-beta attenuates oAbeta neurotoxicity in neuron microglial co-cultures. The TGF-beta 1 receptor kinase inhibitor SD208 enhances microglial proliferation by IL-34 and suppresses the neuroprotective effect of IL-34-treated microglia. These findings suggest that TGF-beta produced by IL-34-treated microglia is a negative regulator of microglial proliferation and enhances the neuroprotective property of microglia.", "citation": {"db": "PubMed", "db_id": "22985514"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "TGF-Beta subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2218, "target": 622, "key": "311f70d80a9ee914f136d9189a0954ba"}, {"line": 4973, "relation": "positiveCorrelation", "evidence": "Recent evidence suggests that mononuclear phagocyte accumulation in the AD brain is dependent on chemokines. CCL2, a major monocyte chemokine, is upregulated in the AD brain.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 622, "target": 2455, "key": "9ad1b54efdf2518e41261545a9719207"}, {"line": 4354, "relation": "increases", "evidence": "Abeta(1-42) peptides in combination with C1q or C1q and SAP increased microglial interleukin (IL)-6 secretion four- and eightfold, respectively. Tumor necrosis factor (TNF)-alpha, as well as intracellular IL-1alpha and IL-1beta levels, also increased upon exposure of microglia to Abeta(1-42)-SAP-C1q complexes.", "citation": {"db": "PubMed", "db_id": "12536224"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Complement system subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 1689, "target": 2894, "key": "70429529a3f89944454b2932b12d88c5"}, {"line": 4355, "relation": "increases", "evidence": "Abeta(1-42) peptides in combination with C1q or C1q and SAP increased microglial interleukin (IL)-6 secretion four- and eightfold, respectively. Tumor necrosis factor (TNF)-alpha, as well as intracellular IL-1alpha and IL-1beta levels, also increased upon exposure of microglia to Abeta(1-42)-SAP-C1q complexes.", "citation": {"db": "PubMed", "db_id": "12536224"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Complement system subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 1689, "target": 3472, "key": "39d97920f2f49d0bf64214a083c15d75"}, {"line": 4356, "relation": "increases", "evidence": "Abeta(1-42) peptides in combination with C1q or C1q and SAP increased microglial interleukin (IL)-6 secretion four- and eightfold, respectively. Tumor necrosis factor (TNF)-alpha, as well as intracellular IL-1alpha and IL-1beta levels, also increased upon exposure of microglia to Abeta(1-42)-SAP-C1q complexes.", "citation": {"db": "PubMed", "db_id": "12536224"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Complement system subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 1689, "target": 2885, "key": "72147cbf475ecb36c161493e0066de5f"}, {"line": 4357, "relation": "increases", "evidence": "Abeta(1-42) peptides in combination with C1q or C1q and SAP increased microglial interleukin (IL)-6 secretion four- and eightfold, respectively. Tumor necrosis factor (TNF)-alpha, as well as intracellular IL-1alpha and IL-1beta levels, also increased upon exposure of microglia to Abeta(1-42)-SAP-C1q complexes.", "citation": {"db": "PubMed", "db_id": "12536224"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Complement system subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 1689, "target": 2884, "key": "da99ea675c464fab1ac083bffb8eca49"}, {"relation": "partOf", "source": 2408, "target": 1689, "key": "a1e07d469f53f86bf1d94d2dc050b5d1"}, {"line": 5020, "relation": "association", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2884, "target": 608, "key": "b9e881d9c94d7bc7358269adfc7585db"}, {"relation": "partOf", "source": 2884, "target": 1709, "key": "ef3d789dbafad2cef3224713c2571765"}, {"line": 5109, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2884, "target": 577, "key": "01374d211d8b6a0f9b8c71954681b115"}, {"line": 9529, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}}, "source": 2884, "target": 2102, "key": "d580276545b5f90ef4c06fd3f928c589"}, {"relation": "partOf", "source": 2884, "target": 929, "key": "a9887212284be8a09f0953d7db3ab635"}, {"relation": "partOf", "source": 2884, "target": 1474, "key": "eb54883cbbf71d11e4e8c4c6b2d994d6"}, {"relation": "partOf", "source": 2884, "target": 1129, "key": "dc845ebedc7ceb0db685852c78e14762"}, {"relation": "partOf", "source": 2884, "target": 1478, "key": "18ca6ca893b8ac112e69bfa65cb1107d"}, {"relation": "partOf", "source": 2884, "target": 1028, "key": "4c5cd5bdf575790fc36d70253d8a936c"}, {"line": 36249, "relation": "increases", "evidence": "Cytokines such as TGF beta 1 and interleukin 1 enhance the expression of clusterin, which may link clusterin to inflammatory mechanisms in AD", "citation": {"db": "PubMed", "db_id": "8892344"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2884, "target": 2538, "key": "c72f51fbe4d08cfedb9da2dc5382468f"}, {"line": 39489, "relation": "increases", "evidence": "Polymorphisms within IL-1A influence the degree of brain microglial cell activation, especially in bearers of APOE epsilon4 allele, reinforcing the importance of neuroinflammatory processes in the pathogenesis of AD, and supporting the rationale for treating the disease with inflammation modulating drugs.", "citation": {"db": "PubMed", "db_id": "15377701"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2884, "target": 609, "key": "7f5969dd9cf38eccac46888e2e22b74b"}, {"line": 39635, "relation": "increases", "evidence": "The amyloid precursor protein (APP) has been associated with Alzheimer's disease (AD) because APP is processed into the beta-peptide that accumulates in amyloid plaques, and APP gene mutations can cause early onset AD. Inflammation is also associated with AD as exemplified by increased expression of interleukin-1 (IL-1) in microglia in affected areas of the AD brain. Here we demonstrate that IL-1alpha and IL-1beta increase APP synthesis by up to 6-fold in primary human astrocytes and by 15-fold in human astrocytoma cells without changing the steady-state levels of APP mRNA. A 90-nucleotide sequence in the APP gene 5'-untranslated region (5'-UTR) conferred translational regulation by IL-1alpha and IL-1beta to a chloramphenicol acetyltransferase (CAT) reporter gene. Steady-state levels of transfected APP(5'-UTR)/CAT mRNAs were unchanged, whereas both base-line and IL-1-dependent CAT protein synthesis were increased. This APP mRNA translational enhancer maps from +55 to +144 nucleotides from the 5'-cap site and is homologous to related translational control elements in the 5'-UTR of the light and and heavy ferritin genes. Enhanced translation of APP mRNA provides a mechanism by which IL-1 influences the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "10037734"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "source": 2884, "target": 3815, "key": "842767d56295f9a2304fb3c4f54ffed2"}, {"line": 39784, "relation": "positiveCorrelation", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "source": 2884, "target": 3815, "key": "0f76df0bd52a2dab138c7bd4bae1339e"}, {"line": 39644, "relation": "increases", "evidence": "The amyloid precursor protein (APP) has been associated with Alzheimer's disease (AD) because APP is processed into the beta-peptide that accumulates in amyloid plaques, and APP gene mutations can cause early onset AD. Inflammation is also associated with AD as exemplified by increased expression of interleukin-1 (IL-1) in microglia in affected areas of the AD brain. Here we demonstrate that IL-1alpha and IL-1beta increase APP synthesis by up to 6-fold in primary human astrocytes and by 15-fold in human astrocytoma cells without changing the steady-state levels of APP mRNA. A 90-nucleotide sequence in the APP gene 5'-untranslated region (5'-UTR) conferred translational regulation by IL-1alpha and IL-1beta to a chloramphenicol acetyltransferase (CAT) reporter gene. Steady-state levels of transfected APP(5'-UTR)/CAT mRNAs were unchanged, whereas both base-line and IL-1-dependent CAT protein synthesis were increased. This APP mRNA translational enhancer maps from +55 to +144 nucleotides from the 5'-cap site and is homologous to related translational control elements in the 5'-UTR of the light and and heavy ferritin genes. Enhanced translation of APP mRNA provides a mechanism by which IL-1 influences the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "10037734"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"High": true}}, "source": 2884, "target": 2315, "key": "0a182a849eb2f47e8b2ee5aa240c7e82"}, {"line": 39789, "relation": "increases", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "source": 2884, "target": 693, "key": "d30325bba7c6b9319e2f39bb89d9bf5c"}, {"line": 46809, "relation": "increases", "evidence": "It is generally accepted that IL-1 triggers a classical NF-κB pathway in many cell types, which leads to the induction of p65/p50 target genes, including those encoding proinflammatory cytokines such as IL-8 and IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 2884, "target": 3112, "key": "8614dca16bbd008970674bd4bfb093a8"}, {"line": 46846, "relation": "increases", "evidence": "Both IL-1 and TNF are known to trigger a classical IκB kinase (IKK)gamma-dependent activation of NF-κB,", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 2884, "target": 3112, "key": "79092d40216648823947675a605ef271"}, {"line": 46810, "relation": "increases", "evidence": "It is generally accepted that IL-1 triggers a classical NF-κB pathway in many cell types, which leads to the induction of p65/p50 target genes, including those encoding proinflammatory cytokines such as IL-8 and IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 2884, "target": 3113, "key": "f1c6ab253b2f2b33dbe1171515a61da2"}, {"line": 46848, "relation": "increases", "evidence": "Both IL-1 and TNF are known to trigger a classical IκB kinase (IKK)gamma-dependent activation of NF-κB,", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 2884, "target": 3113, "key": "d6b7ec82b08e2a1e1d705d7e4c1da486"}, {"line": 4370, "relation": "decreases", "evidence": "The peroxisome proliferator-activated receptor-gamma (PPAR-gamma) is a ligand-inducible transcription factor that suppresses microglial inflammatory responses and inhibits amyloid beta (Abeta) production through promoting cholesterol efflux from glial cells. PPAR-gamma agonists have been advanced as a new disease altering approach to Alzheimer's disease (AD), with rosiglitazone therapy having improved cognition in those AD patients that did not possess an Apolipoprotein E (APOE) ε4 allele", "citation": {"db": "PubMed", "db_id": "19660836"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 608, "key": "c1c8ea4f7ac3eef5a78e8479358512b5"}, {"line": 9627, "relation": "decreases", "evidence": "PPARgamma activation leads to the inhibition of microglial activation and the expression of a broad range of proinflammatory molecules. The newly appreciated anti-inflammatory actions of PPARgamma agonists may allow novel therapies for AD and other CNS indications with an inflammatory component.", "citation": {"db": "PubMed", "db_id": "11755002"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3212, "target": 608, "key": "9699dd87af116bf946fcba8be0e6c1ea"}, {"line": 4378, "relation": "decreases", "evidence": "The peroxisome proliferator-activated receptor-gamma (PPAR-gamma) is a ligand-inducible transcription factor that suppresses microglial inflammatory responses and inhibits amyloid beta (Abeta) production through promoting cholesterol efflux from glial cells. PPAR-gamma agonists have been advanced as a new disease altering approach to Alzheimer's disease (AD), with rosiglitazone therapy having improved cognition in those AD patients that did not possess an Apolipoprotein E (APOE) ε4 allele", "citation": {"db": "PubMed", "db_id": "19660836"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 2328, "key": "7c7103b406f0074db63d08c1fdec6134"}, {"line": 33675, "relation": "decreases", "evidence": "PPARgamma activity decreases Abeta production by promoting harmless catabolism of amyloid precursor protein while blocking the up-regulatory impact of cytokines on beta-secretase expression.", "citation": {"db": "PubMed", "db_id": "16828233"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 2328, "key": "e8e106212f7dce7b5f64d3bad01636b1"}, {"line": 38329, "relation": "decreases", "evidence": "these findings suggest the existence of a down-regulation of PPARgamma under inflammatory conditions, which would result in an increase in BACE1 transcription and Abeta generation. ", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 2328, "key": "78de6fdaa0335ed9aa39f358b76da635"}, {"line": 4386, "relation": "increases", "evidence": "The peroxisome proliferator-activated receptor-gamma (PPAR-gamma) is a ligand-inducible transcription factor that suppresses microglial inflammatory responses and inhibits amyloid beta (Abeta) production through promoting cholesterol efflux from glial cells. PPAR-gamma agonists have been advanced as a new disease altering approach to Alzheimer's disease (AD), with rosiglitazone therapy having improved cognition in those AD patients that did not possess an Apolipoprotein E (APOE) ε4 allele", "citation": {"db": "PubMed", "db_id": "19660836"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 526, "key": "bb0b7edc96215ebfce92cea9e15e2943"}, {"line": 4425, "relation": "increases", "evidence": "As a transcription factor binding site of the BACE1 promoter, peroxisome proliferator-activated receptor-gamma (PPARgamma) response element regulates the activity of the BACE1 promoter activity, indicating that PPAR? may become a potential target for Alzheimer's disease treatment.", "citation": {"db": "PubMed", "db_id": "22166376"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Beta secretase subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3212, "target": 3943, "key": "4e2eb1029d2bb4ad993da6e5db88e03b"}, {"line": 5140, "relation": "association", "evidence": "Peroxisome proliferator-activated receptor gamma (PPAR ) regulates the transcription of BACE1 as well as inflammatory responses in the brain and atherosclerotic risk factors known to be involved also in AD.", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3212, "target": 3943, "key": "357c9306748f2fe4922d0554b64b5815"}, {"line": 33699, "relation": "increases", "evidence": "Here we show that PPARgamma depletion potentiates beta-secretase [beta-site amyloid precursor protein cleaving enzyme (BACE1)] mRNA levels by increasing BACE1 gene promoter activity. ", "citation": {"db": "PubMed", "db_id": "16407166"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}}, "source": 3212, "target": 3943, "key": "c8a16a87da5ee0267ed926584fcf8c2a"}, {"line": 38328, "relation": "decreases", "evidence": "these findings suggest the existence of a down-regulation of PPARgamma under inflammatory conditions, which would result in an increase in BACE1 transcription and Abeta generation. ", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 3943, "key": "f80dbad0cf04d5ab3f905ce5da2e04a0"}, {"line": 4438, "relation": "increases", "evidence": "Mitochondrial dysfunction is a prominent feature of Alzheimer's disease (AD) brain.Mitochondrial biogenesis is regulated by the peroxisome proliferator activator receptor gamma-coactivator 1a (PGC-1a)-nuclear respiratory factor (NRF)-mitochondrial transcription factor A pathway. Expression levels of PGC-1a, NRF 1, NRF 2, and mitochondrial transcription factor A were significantly decreased in both AD hippocampal tissues and APPswe M17 cells, suggesting a reduced mitochondrial biogenesis. Indeed, APPswe M17 cells demonstrated decreased mitochondrial DNA/nuclear DNA ratio, correlated with reduced ATP content, and decreased cytochrome C oxidase activity.", "citation": {"db": "PubMed", "db_id": "22077634"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 3212, "target": 621, "key": "244d1839a0f969a0f295e7209df5e5a4"}, {"line": 4644, "relation": "directlyIncreases", "evidence": "PPARa/gamma and their agonists positively control megalin expression", "citation": {"db": "PubMed", "db_id": "21311715"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 1864, "key": "3c512c8b4df272bf9770e8744a54aedd"}, {"line": 5141, "relation": "association", "evidence": "Peroxisome proliferator-activated receptor gamma (PPAR ) regulates the transcription of BACE1 as well as inflammatory responses in the brain and atherosclerotic risk factors known to be involved also in AD.", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}}, "source": 3212, "target": 577, "key": "724ed670284a4e628c763f3a94c51aaa"}, {"line": 33686, "relation": "association", "evidence": "Peroxisome proliferator-activated receptor gamma (PPARgamma) regulates the transcription of BACE1 as well as inflammatory responses in the brain and atherosclerotic risk factors known to be involved also in AD.", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 3212, "target": 577, "key": "3b9e4ec6675092027eddd04c83a297e5"}, {"relation": "hasVariant", "source": 3212, "target": 3213, "key": "a33283df3320f1c8d77846153cc67a1e"}, {"line": 6807, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "UserdefinedCellLine": {"App transgenic": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3212, "target": 565, "key": "4baf5fae237629e0f923b9e00fc03492"}, {"line": 6842, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta-Catenin subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3212, "target": 2580, "key": "3eb056ed18ff1bf23dac31cc278647b4"}, {"line": 6860, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}}, "subject": {"modifier": "Activity"}, "source": 3212, "target": 820, "key": "71dcbf485b7e8c31972d74cc7ccd2a5d"}, {"line": 6865, "relation": "decreases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3212, "target": 80, "key": "cb5065cb106aad658b742351dc533d49"}, {"line": 6867, "relation": "decreases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3212, "target": 80, "key": "158e2921d140647fedadec9da330333e"}, {"line": 44118, "relation": "decreases", "evidence": "PPARgamma activity decreases Abeta production by promoting harmless catabolism of amyloid precursor protein while blocking the up-regulatory impact of cytokines on beta-secretase expression.", "citation": {"db": "PubMed", "db_id": "16828233"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3212, "target": 80, "key": "fcb05730e23f695dd5eca97e6f0a2095"}, {"line": 6866, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3212, "target": 2341, "key": "0c456ccfb46d2b10245fee786fd83cea"}, {"line": 38269, "relation": "increases", "evidence": "the evidence that PPARgamma stimulates the ubiquitination of APP supports the fact that the Abeta-lowering effect of PPARgamma is due to the proteasome-mediated degradation of APP. Another issue in the present study is the finding that PPARgamma, by decreasing Abeta secretion, protects the cells against H2O2-mediated necrosis", "citation": {"db": "PubMed", "db_id": "15946122"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 2341, "key": "cacca71f222e8a40f963e4567f7bbff1"}, {"line": 6869, "relation": "decreases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3212, "target": 573, "key": "5e3d238d25346eea15ee376a29aa82cb"}, {"line": 6877, "relation": "decreases", "evidence": "Additionally, recent work has shown that PPARGgamma is a potential repressor of BACE1 (Gene ID 23621) expression [Sastre et al., 2006], indicating a process by which the G allele could additionally act as a risk factor", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3212, "target": 1755, "key": "b4aa57fe0df2a60f6e6b4b3acbe44d31"}, {"line": 7053, "relation": "regulates", "evidence": "BACE1 transcription has recently been reported to be regulated by the PPARgamma pathway (36). We now demonstrate that the diabetes drug metformin can also modulate BACE1 transcription, likely independently of the PPARgamma pathway despite the presence of several PPAR/RXR binding sites in the promoter (31, 32). Metformin-mediated transcriptional activation of BACE1 appears to depended on a pathway involving AMPK.", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3212, "target": 1755, "key": "e9543af38520d4658a5e70c6e0f3dfe2"}, {"line": 8576, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3212, "target": 3472, "key": "7bfef14c0922eea70ae00fc315acf817"}, {"line": 44080, "relation": "decreases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3212, "target": 3472, "key": "10711229d6c5ab4018395aee0878217e"}, {"line": 8578, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}}, "source": 3212, "target": 2894, "key": "5a8f123fca43492371b09606af960157"}, {"line": 44082, "relation": "decreases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}}, "source": 3212, "target": 2894, "key": "b94bfe17ded1eeb0a15ddfbf05b5cb2b"}, {"line": 8599, "relation": "decreases", "evidence": "During differentiation of macrophages primarily the promoter 3 and to a certain extent promoter 1 is activated. Consequently macrophages mainly express PPARgamma1 (10). In macrophages PPARgamma represses inducible nitric-oxide (NO) synthase induction as well as concomitant NO production (11) and attenuates the oxidative burst (13, 14). Moreover, inhibiting nuclear factor κB (NFκB) decreases expression of inflammatory cytokines such as interleukin (IL)-1, tumor necrosis factor α (TNFα) or IL-6 (12). Thus, PPARgamma is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 3212, "target": 3123, "key": "3d1abbb73026dd58ecd264b796f69e3a"}, {"line": 39040, "relation": "regulates", "evidence": "Recently, it was proposed that some NSAIDs might activate the peroxisome proliferator-activated / receptor-gamma (PPAR-gamma). PPAR-gamma belongs to a family of nuclear receptors that are able to regulate the / transcription of pro-inflammatory molecules, such as iNOS. ", "citation": {"db": "PubMed", "db_id": "16472958"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3212, "target": 3123, "key": "3148839ecf93f06a1ae76e4f3d122d60"}, {"line": 9628, "relation": "increases", "evidence": "PPARgamma activation leads to the inhibition of microglial activation and the expression of a broad range of proinflammatory molecules. The newly appreciated anti-inflammatory actions of PPARgamma agonists may allow novel therapies for AD and other CNS indications with an inflammatory component.", "citation": {"db": "PubMed", "db_id": "11755002"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3212, "target": 693, "key": "15f6f51b67019949543ad4cbd02f9b20"}, {"line": 9629, "relation": "association", "evidence": "PPARgamma activation leads to the inhibition of microglial activation and the expression of a broad range of proinflammatory molecules. The newly appreciated anti-inflammatory actions of PPARgamma agonists may allow novel therapies for AD and other CNS indications with an inflammatory component.", "citation": {"db": "PubMed", "db_id": "11755002"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3212, "target": 532, "key": "1b7c80016145c4254c57c30347e7a2c6"}, {"relation": "partOf", "source": 3212, "target": 1242, "key": "51d2f70112cf9497abefd28662a3e400"}, {"relation": "partOf", "source": 3212, "target": 1272, "key": "84f7149cf101e2c2abeec0f69be05ecb"}, {"line": 38271, "relation": "directlyDecreases", "evidence": "the evidence that PPARgamma stimulates the ubiquitination of APP supports the fact that the Abeta-lowering effect of PPARgamma is due to the proteasome-mediated degradation of APP. Another issue in the present study is the finding that PPARgamma, by decreasing Abeta secretion, protects the cells against H2O2-mediated necrosis", "citation": {"db": "PubMed", "db_id": "15946122"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 626, "key": "7445b4d7670bf3ca939ceae0bf7f95cd"}, {"line": 38297, "relation": "association", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3212, "target": 3112, "key": "2c5e1256d4b3cfacdb2abf4bf8c0a8d0"}, {"line": 38298, "relation": "association", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3212, "target": 3113, "key": "06180865b402fa01d334ed88b023d8e0"}, {"line": 38322, "relation": "decreases", "evidence": "PPARgamma is a transcription factor that is involved in the regulation of the metabolism of glucose and lipids, in cellular differentiation as well as in the control of transcription of a wide range of inflammatory genes.Furthermore, lack of PPARgamma led to an increase of BACE1 promoter activity [90], which suggested that PPARgamma could be a repressor of BACE1. ", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3212, "target": 2375, "key": "3c6d9b7e41bdf7ba63e8320b260d1cb9"}, {"line": 38364, "relation": "directlyDecreases", "evidence": "The study also revealed that the nuclear receptor peroxisome proliferators activated receptor gamma (PPARgamma) played an important role in the CLA-induced intracellular BACE1 decrease, as well as the extracellular sAPPalpha increase through knockdown of PPARgamma transcription using siRNA. We hypothesize that CLA acts as an agonist or ligand, which binds with PPARgamma and leads to the increase in APP cleavage via alpha-secretase-mediated pathway and the decrease in the deposition of Abeta.", "citation": {"db": "PubMed", "db_id": "21800078"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3212, "target": 2375, "key": "2ef68a854563881a28286ad1db990840"}, {"relation": "partOf", "source": 3212, "target": 962, "key": "73ebae336e406c0ad840db14476c2e23"}, {"line": 39286, "relation": "decreases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3212, "target": 2879, "key": "030ee76ea57daaf806c1fba8238a7a4c"}, {"line": 39287, "relation": "decreases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3212, "target": 2889, "key": "135c52b1c662af9f2b468021231eef71"}, {"line": 39298, "relation": "decreases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3212, "target": 2468, "key": "7419eb15862fdb992b2159cb8eef4caf"}, {"line": 39299, "relation": "decreases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3212, "target": 3084, "key": "58e29e6f9b88fdc2d63da0fb635c08d5"}, {"line": 39300, "relation": "decreases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3212, "target": 3467, "key": "6a0a3d90acb3038137b2f01167d5525c"}, {"line": 39355, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3212, "target": 3815, "key": "389e4e4d949de151e03b6e5761783d63"}, {"line": 42415, "relation": "increases", "evidence": "PPARgamma/RXRα-induced and CD36-mediated microglial amyloid-beta phagocytosis results in cognitive improvement in amyloid precursor protein/presenilin 1 mice.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3212, "target": 609, "key": "f86d438acd7d8edb9f2c95c90443fcf7"}, {"relation": "partOf", "source": 3212, "target": 980, "key": "fd123c5e925ff4fdd7f26a204cf5a9c4"}, {"line": 4558, "relation": "association", "evidence": "cholesterol efflux plays a major role in the atheroprotective effects of HDL and cholesterol homeostasis.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 526, "target": 231, "key": "9eecb8e4d49e88ac9f04a28439753dd1"}, {"line": 4559, "relation": "decreases", "evidence": "cholesterol efflux plays a major role in the atheroprotective effects of HDL and cholesterol homeostasis.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 526, "target": 527, "key": "a36e21f27d134a3654f68fd582c743b3"}, {"line": 38140, "relation": "decreases", "evidence": "These studies indicate that ABCA7 has the capacity to stimulate cellular cholesterol efflux to apoE discs and regulate APP processing resulting in an inhibition of Abeta production", "citation": {"db": "PubMed", "db_id": "18429932"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Low": true}}, "source": 526, "target": 2328, "key": "43cb7d2ba7b7d20bb9fa2adad9641619"}, {"line": 4403, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-a. TNF-a signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2).", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 575, "target": 532, "key": "20497c182dc0400c6df2148a7c371169"}, {"line": 29264, "relation": "association", "evidence": "We show that ligation of CD40 by CD40L pmodulates Abeta-induced innate immune responses in microglia, including decreased microglia phagocytosis of exogenous Abeta(1-42) and increased production of pro-inflammatory cytokines.", "citation": {"db": "PubMed", "db_id": "15688347"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 575, "target": 1324, "key": "e56aa283539fc092af714eaf7f84f8fc"}, {"line": 4406, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-a. TNF-a signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2).", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 2219, "target": 3472, "key": "9091311674497ca1de41d90dc2eaa6c6"}, {"line": 4440, "relation": "increases", "evidence": "Mitochondrial dysfunction is a prominent feature of Alzheimer's disease (AD) brain.Mitochondrial biogenesis is regulated by the peroxisome proliferator activator receptor gamma-coactivator 1a (PGC-1a)-nuclear respiratory factor (NRF)-mitochondrial transcription factor A pathway. Expression levels of PGC-1a, NRF 1, NRF 2, and mitochondrial transcription factor A were significantly decreased in both AD hippocampal tissues and APPswe M17 cells, suggesting a reduced mitochondrial biogenesis. Indeed, APPswe M17 cells demonstrated decreased mitochondrial DNA/nuclear DNA ratio, correlated with reduced ATP content, and decreased cytochrome C oxidase activity.", "citation": {"db": "PubMed", "db_id": "22077634"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 3139, "target": 621, "key": "bb2ea5c770bd70c858a88aea825a9e1e"}, {"line": 4441, "relation": "increases", "evidence": "Mitochondrial dysfunction is a prominent feature of Alzheimer's disease (AD) brain.Mitochondrial biogenesis is regulated by the peroxisome proliferator activator receptor gamma-coactivator 1a (PGC-1a)-nuclear respiratory factor (NRF)-mitochondrial transcription factor A pathway. Expression levels of PGC-1a, NRF 1, NRF 2, and mitochondrial transcription factor A were significantly decreased in both AD hippocampal tissues and APPswe M17 cells, suggesting a reduced mitochondrial biogenesis. Indeed, APPswe M17 cells demonstrated decreased mitochondrial DNA/nuclear DNA ratio, correlated with reduced ATP content, and decreased cytochrome C oxidase activity.", "citation": {"db": "PubMed", "db_id": "22077634"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 3450, "target": 621, "key": "86b187716df4eaf3a1b36a4598b587e4"}, {"line": 4468, "relation": "decreases", "evidence": "Nevertheless, the precise relationship between mitochondrial accumulation of APP and ApoE genotyping in the pathogenesis of AD subjects is not known. However, it is noteworthy to mention that recent studies have shown mutations on TOM40 gene, which is located on the chromosome (19q) in close proximity to upstream of ApoE, as a possible risk factor in the genesis of AD (118,119). Based on this, one can speculate that mutations on TOM40 gene may result in the dysfunction of general import pore TOM40 protein, which may in turn accentuate the mitochondrial translocational arrest of APP and associated mitochondrial dysfunction. However, it remains to be seen whether mutations render impairment of TOM40 functions during the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "19619643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"High": true}}, "source": 2002, "target": 614, "key": "90cd3172859679c4bd2d0a372004c58e"}, {"line": 4486, "relation": "decreases", "evidence": "Here, we summarize experiments that investigated whether certain putative receptors for Abeta, the alphav integrin extracellular cell matrix-binding protein and the cytokine TNFalpha (tumour necrosis factor alpha) type-1 death receptor mediate Abeta oligomer-induced inhibition of LTP (long-term potentiation).", "citation": {"db": "PubMed", "db_id": "17956317"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1691, "target": 597, "key": "f8a96c699114664dc60c82a3633bd424"}, {"relation": "partOf", "source": 2921, "target": 1691, "key": "7830f6400e26bd94d6493cc51f508510"}, {"line": 4778, "relation": "association", "evidence": "The induction of long-term potentiation at CA3-CA1 synapses is caused by an N-methyl-d-aspartate (NMDA) receptordependent accumulation of intracellular Ca(2+), followed by Src family kinase activation and a positive feedback enhancement of NMDA receptors (NMDARs). Nevertheless, the amplitude of baseline transmission remains remarkably constant even though low frequency stimulation is also associated with an NMDAR-dependent influx of Ca(2+) into dendritic spines. We show here that an interaction between C-terminal Src kinase (Csk) and NMDARs controls the Src-dependent regulation of NMDAR activity. Csk associates with the NMDAR signaling complex in the adult brain, inhibiting the Src-dependent potentiation of NMDARs in CA1 neurons and attenuating the Src-dependent induction of long-term potentiation. Csk associates directly with Src-phosphorylated NR2 subunits in vitro. An inhibitory antibody for Csk disrupts this physical association, potentiates NMDAR mediated excitatory postsynaptic currents, and induces long-term potentiation at CA3-CA1 synapses. Thus, Csk serves to maintain the constancy of baseline excitatory synaptic transmission by inhibiting Src kinase-dependent synaptic plasticity in the hippocampus", "citation": {"db": "PubMed", "db_id": "18445593"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 597, "target": 1366, "key": "9e895d63e117d214b00ca53a87bd78bc"}, {"line": 4489, "relation": "decreases", "evidence": "Here, we summarize experiments that investigated whether certain putative receptors for Abeta, the alphav integrin extracellular cell matrix-binding protein and the cytokine TNFalpha (tumour necrosis factor alpha) type-1 death receptor mediate Abeta oligomer-induced inhibition of LTP (long-term potentiation).", "citation": {"db": "PubMed", "db_id": "17956317"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1694, "target": 597, "key": "8fc4ba5083c4f05587c44ea08b861e2e"}, {"relation": "partOf", "source": 3476, "target": 1694, "key": "308cd70cc08e9f2eb56d5575fe471ee9"}, {"line": 8976, "relation": "increases", "evidence": "We reported that tumor necrosis factor receptor I (TNFRI) is required for neuronal death induced by amyloid-beta protein in the Alzheimer's disease (AD) brain. However, whether TNF receptor subtypes are expressed and activated differentially in AD brains compared to non-demented brains remains unclear. Our studies on Western blot and ELISA measurements demonstrated that TNFRI levels are increased whereas TNFRII levels are decreased in AD brains compared to non-demented brains (p <0.05).", "citation": {"db": "PubMed", "db_id": "20110607"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3476, "target": 672, "key": "0ccf8fe7934baf4e3cf0c4d997daf6e7"}, {"line": 8977, "relation": "positiveCorrelation", "evidence": "We reported that tumor necrosis factor receptor I (TNFRI) is required for neuronal death induced by amyloid-beta protein in the Alzheimer's disease (AD) brain. However, whether TNF receptor subtypes are expressed and activated differentially in AD brains compared to non-demented brains remains unclear. Our studies on Western blot and ELISA measurements demonstrated that TNFRI levels are increased whereas TNFRII levels are decreased in AD brains compared to non-demented brains (p <0.05).", "citation": {"db": "PubMed", "db_id": "20110607"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3476, "target": 3823, "key": "1e1f9e9ad14423a2ebe208c135c855f0"}, {"relation": "partOf", "source": 3476, "target": 1534, "key": "4ed709ca2cd8750c52977fc98c688375"}, {"relation": "hasReactant", "source": 4097, "target": 2315, "key": "6802f1bc229260555c93f10e292779bd"}, {"relation": "hasProduct", "source": 4097, "target": 80, "key": "7a00a34ac8ca34a3943a5b242dc68b82"}, {"relation": "hasProduct", "source": 4097, "target": 3563, "key": "c6e511286c1770766bf82eec884d10a7"}, {"line": 30759, "relation": "positiveCorrelation", "evidence": "We demonstrate that phosphorylation of serines 353 and 357 by glycogen synthase kinase-3beta (GSK3beta) induces a structural change of the hydrophilic loop of PS1 that can also be mimicked by substitution of the phosphorylation sites by negatively charged amino acids in vitro and in cultured cells. The structural change of PS1 reduces the interaction with beta-catenin leading to decreased phosphorylation and ubiquitination of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360711"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}}, "source": 2585, "target": 1373, "key": "f9908f8916e4f469b1ed2568ab9c17c0"}, {"line": 47957, "relation": "increases", "evidence": "In the Wnt/beta-catenin cascade, signaling events converge on the regulation of ubiquitin-mediated degradation of the crucial transcriptional regulator beta-catenin.", "citation": {"db": "PubMed", "db_id": "20930545"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2585, "target": 2580, "key": "f5bd6beb0de6799828909579ef78bd72"}, {"line": 4879, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 462, "target": 2169, "key": "273645addf07c81133b0e88ce71ff8b0"}, {"line": 4881, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 462, "target": 488, "key": "adc57d394f2553f2a5c2238c10413386"}, {"line": 47898, "relation": "association", "evidence": "The Wnt signaling pathway plays a crucial role in the proper development and maintenance of brain and bone structure and function.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 488, "key": "165afd99a5387ce972148b5fb1efab7c"}, {"line": 47908, "relation": "association", "evidence": "Growing evidence indicates that wingless-type (Wnt) signaling plays an important role in neuronal development, synapse formation and synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "26032671"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 462, "target": 488, "key": "2c16de226aea93ef3287d56d70ee28e6"}, {"line": 4886, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 3823, "key": "fd50b796b0ec0689ecf8d3716aeeded9"}, {"line": 6819, "relation": "association", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 345, "key": "8093f40f081ef35c60e4f434c17e73f9"}, {"line": 6820, "relation": "association", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 2580, "key": "29c8cc3e348a48ce910dcfa8abe4af82"}, {"line": 39182, "relation": "positiveCorrelation", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Calcium-dependent signal transduction": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 2580, "key": "5f910e9e1c174ccdad8784db2777a6c6"}, {"line": 11459, "relation": "association", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Wnt signaling subgraph": true, "Axonal guidance subgraph": true}}, "source": 462, "target": 822, "key": "4be4f4f2e953ae797ce34a7cab881949"}, {"line": 11460, "relation": "positiveCorrelation", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Wnt signaling subgraph": true, "Axonal guidance subgraph": true}}, "source": 462, "target": 3965, "key": "c44d0c6e0822c2cb23ac7632b929ea2b"}, {"line": 47899, "relation": "association", "evidence": "The Wnt signaling pathway plays a crucial role in the proper development and maintenance of brain and bone structure and function.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 713, "key": "e3887c52a76ac15bd7d90ad5f2481d99"}, {"line": 47955, "relation": "regulates", "evidence": "In the Wnt/beta-catenin cascade, signaling events converge on the regulation of ubiquitin-mediated degradation of the crucial transcriptional regulator beta-catenin.", "citation": {"db": "PubMed", "db_id": "20930545"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 462, "target": 2585, "key": "ae65ad086b68a89c91fe25c2fa3a4db8"}, {"line": 48197, "relation": "negativeCorrelation", "evidence": "Here, we report that the Wnt antagonist Dkk-1 selectively increases tau phosphorylation in the hippocampus of aged rats at Ser199/202, Ser396/404, and Ser214 sites", "citation": {"db": "PubMed", "db_id": "24270208"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 3776, "key": "ae8362d4701a872482a2128d82a250cb"}, {"line": 48712, "relation": "increases", "evidence": "Only one of these common genes, CCND1 (cyclin D1), is a known canonical wnt target,32 with the remaining four encoding transcription factors: EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2), KLF10 (Krüppel-like factor-10) and FOS (FBJ murine osteosarcoma viral oncogene homologue). We confirmed the induction of these genes by both Abeta25-35 and by Dkk1 using qRT-PCR", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 1886, "key": "ab80d7436589f4be729fea373a434a15"}, {"line": 48716, "relation": "increases", "evidence": "Only one of these common genes, CCND1 (cyclin D1), is a known canonical wnt target,32 with the remaining four encoding transcription factors: EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2), KLF10 (Krüppel-like factor-10) and FOS (FBJ murine osteosarcoma viral oncogene homologue). We confirmed the induction of these genes by both Abeta25-35 and by Dkk1 using qRT-PCR", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 1812, "key": "ec353ff4a6ae1aab7aa3551a9f5f4afc"}, {"line": 48720, "relation": "increases", "evidence": "Only one of these common genes, CCND1 (cyclin D1), is a known canonical wnt target,32 with the remaining four encoding transcription factors: EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2), KLF10 (Krüppel-like factor-10) and FOS (FBJ murine osteosarcoma viral oncogene homologue). We confirmed the induction of these genes by both Abeta25-35 and by Dkk1 using qRT-PCR", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 1857, "key": "9db3c9ac8f9c39ad435447fb4d98897c"}, {"line": 48724, "relation": "increases", "evidence": "Only one of these common genes, CCND1 (cyclin D1), is a known canonical wnt target,32 with the remaining four encoding transcription factors: EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2), KLF10 (Krüppel-like factor-10) and FOS (FBJ murine osteosarcoma viral oncogene homologue). We confirmed the induction of these genes by both Abeta25-35 and by Dkk1 using qRT-PCR", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 462, "target": 1825, "key": "4909c1a7529261ba762fbb4597a81bf1"}, {"line": 4535, "relation": "increases", "evidence": "These studies have further identified the scavenger receptor type B-I (SR-BI), the adenosine triphosphate (ATP)-binding cassette transporters ATP-binding cassette subfamily A1 (ABCA1), ATP-binding cassette subfamily G1 (ABCG1) and ABCG4, the liver X receptor/retinoid X receptor (LXR/RXR) and peroxisome proliferator-activated receptorgamma(PPAR gamma) transcription factors, the HDL components apolipoprotein A-I (apoA-I), lecithin-cholesterol acyltransferase (LCAT), and phospholipids as additional mediators of cholesterol transport.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1951, "target": 231, "key": "8274bfc5afb7e60d696dfc26094c5de7"}, {"line": 4537, "relation": "increases", "evidence": "These studies have further identified the scavenger receptor type B-I (SR-BI), the adenosine triphosphate (ATP)-binding cassette transporters ATP-binding cassette subfamily A1 (ABCA1), ATP-binding cassette subfamily G1 (ABCG1) and ABCG4, the liver X receptor/retinoid X receptor (LXR/RXR) and peroxisome proliferator-activated receptorgamma(PPAR gamma) transcription factors, the HDL components apolipoprotein A-I (apoA-I), lecithin-cholesterol acyltransferase (LCAT), and phospholipids as additional mediators of cholesterol transport.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1730, "target": 231, "key": "b8507db8430499d535d1ef3e539c2147"}, {"line": 4538, "relation": "increases", "evidence": "These studies have further identified the scavenger receptor type B-I (SR-BI), the adenosine triphosphate (ATP)-binding cassette transporters ATP-binding cassette subfamily A1 (ABCA1), ATP-binding cassette subfamily G1 (ABCG1) and ABCG4, the liver X receptor/retinoid X receptor (LXR/RXR) and peroxisome proliferator-activated receptorgamma(PPAR gamma) transcription factors, the HDL components apolipoprotein A-I (apoA-I), lecithin-cholesterol acyltransferase (LCAT), and phospholipids as additional mediators of cholesterol transport.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1733, "target": 231, "key": "ac05a41863de1a542a536659dccff1d6"}, {"line": 4539, "relation": "increases", "evidence": "These studies have further identified the scavenger receptor type B-I (SR-BI), the adenosine triphosphate (ATP)-binding cassette transporters ATP-binding cassette subfamily A1 (ABCA1), ATP-binding cassette subfamily G1 (ABCG1) and ABCG4, the liver X receptor/retinoid X receptor (LXR/RXR) and peroxisome proliferator-activated receptorgamma(PPAR gamma) transcription factors, the HDL components apolipoprotein A-I (apoA-I), lecithin-cholesterol acyltransferase (LCAT), and phospholipids as additional mediators of cholesterol transport.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1734, "target": 231, "key": "edf019ee7e62763092b235b7eff82f09"}, {"line": 4541, "relation": "increases", "evidence": "These studies have further identified the scavenger receptor type B-I (SR-BI), the adenosine triphosphate (ATP)-binding cassette transporters ATP-binding cassette subfamily A1 (ABCA1), ATP-binding cassette subfamily G1 (ABCG1) and ABCG4, the liver X receptor/retinoid X receptor (LXR/RXR) and peroxisome proliferator-activated receptorgamma(PPAR gamma) transcription factors, the HDL components apolipoprotein A-I (apoA-I), lecithin-cholesterol acyltransferase (LCAT), and phospholipids as additional mediators of cholesterol transport.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1948, "target": 231, "key": "0ae604f35c4af831970d055fb3b7d093"}, {"line": 4579, "relation": "increases", "evidence": "ApoE expression is transcriptionally induced through the action of the nuclear receptors peroxisome proliferator activated receptor (PPARgamma) and liver X receptors (LXR) in coordination with retinoid X receptors (RXR)", "citation": {"db": "PubMed", "db_id": "22323736"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 1948, "target": 1744, "key": "9e3db65aeda894ab50d846a807cc910c"}, {"line": 4543, "relation": "increases", "evidence": "These studies have further identified the scavenger receptor type B-I (SR-BI), the adenosine triphosphate (ATP)-binding cassette transporters ATP-binding cassette subfamily A1 (ABCA1), ATP-binding cassette subfamily G1 (ABCG1) and ABCG4, the liver X receptor/retinoid X receptor (LXR/RXR) and peroxisome proliferator-activated receptorgamma(PPAR gamma) transcription factors, the HDL components apolipoprotein A-I (apoA-I), lecithin-cholesterol acyltransferase (LCAT), and phospholipids as additional mediators of cholesterol transport.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1861, "target": 231, "key": "c98d6251af5e4e0e4db41635f862986a"}, {"line": 4546, "relation": "increases", "evidence": "These studies have further identified the scavenger receptor type B-I (SR-BI), the adenosine triphosphate (ATP)-binding cassette transporters ATP-binding cassette subfamily A1 (ABCA1), ATP-binding cassette subfamily G1 (ABCG1) and ABCG4, the liver X receptor/retinoid X receptor (LXR/RXR) and peroxisome proliferator-activated receptorgamma(PPAR gamma) transcription factors, the HDL components apolipoprotein A-I (apoA-I), lecithin-cholesterol acyltransferase (LCAT), and phospholipids as additional mediators of cholesterol transport.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1921, "target": 231, "key": "d830d9578cf348af9a7b03ae2d0d9bc0"}, {"line": 4577, "relation": "increases", "evidence": "ApoE expression is transcriptionally induced through the action of the nuclear receptors peroxisome proliferator activated receptor (PPARgamma) and liver X receptors (LXR) in coordination with retinoid X receptors (RXR)", "citation": {"db": "PubMed", "db_id": "22323736"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 1921, "target": 1744, "key": "7f68a8c4be32e60e67e0c4632e0fa5c0"}, {"line": 5055, "relation": "directlyDecreases", "evidence": "the nuclear receptor peroxisome proliferator-activated receptor-gamma (PPARgamma) which acts to inhibit the expression of proinflammatory genes, this receptor appears a good candidate to mediate the observed anti-inflammatory effects", "citation": {"db": "PubMed", "db_id": "15817521"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 1921, "target": 532, "key": "12e1f83457702f2978c4d175c43a984b"}, {"line": 6707, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 +ve": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 1921, "target": 3823, "key": "94388ab408a01c3c13dba06680efb4a9"}, {"line": 10258, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 1921, "target": 3823, "key": "ab0d5545a6968c2ce887d061e0b3640a"}, {"line": 10274, "relation": "association", "evidence": "In a replication study, we confirmed significant association of SNPs within three genes--PPARgamma, SOS2, and PCK1--with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 1921, "target": 3823, "key": "9e1bdfd83dfeffe26ed3dac4fe1bf508"}, {"line": 6758, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 1921, "target": 843, "key": "33ed55a2678f529609866ecee8e4904c"}, {"line": 8597, "relation": "increases", "evidence": "During differentiation of macrophages primarily the promoter 3 and to a certain extent promoter 1 is activated. Consequently macrophages mainly express PPARgamma1 (10). In macrophages PPARgamma represses inducible nitric-oxide (NO) synthase induction as well as concomitant NO production (11) and attenuates the oxidative burst (13, 14). Moreover, inhibiting nuclear factor κB (NFκB) decreases expression of inflammatory cytokines such as interleukin (IL)-1, tumor necrosis factor α (TNFα) or IL-6 (12). Thus, PPARgamma is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 1921, "target": 4003, "key": "ae7d0cd2b8e5e934b9b47ed67f527c62"}, {"line": 10226, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1921, "target": 580, "key": "c1e7a17de7645bde784e2e2d45baa72d"}, {"line": 44011, "relation": "decreases", "evidence": "the nuclear receptor peroxisome proliferator-activated receptor-gamma (PPARgamma) which acts to inhibit the expression of proinflammatory genes, this receptor appears a good candidate to mediate the observed anti-inflammatory effects", "citation": {"db": "PubMed", "db_id": "15817521"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}}, "source": 1921, "target": 577, "key": "00168d6110e1bcd5e09825ba287db486"}, {"line": 44055, "relation": "decreases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1921, "target": 2328, "key": "68c75ca465d745561cbee3100ad8d267"}, {"line": 44056, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 1921, "target": 2341, "key": "a1354bec5d0530361fd2653ccf2d6ece"}, {"line": 4547, "relation": "increases", "evidence": "These studies have further identified the scavenger receptor type B-I (SR-BI), the adenosine triphosphate (ATP)-binding cassette transporters ATP-binding cassette subfamily A1 (ABCA1), ATP-binding cassette subfamily G1 (ABCG1) and ABCG4, the liver X receptor/retinoid X receptor (LXR/RXR) and peroxisome proliferator-activated receptorgamma(PPAR gamma) transcription factors, the HDL components apolipoprotein A-I (apoA-I), lecithin-cholesterol acyltransferase (LCAT), and phospholipids as additional mediators of cholesterol transport.", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1887, "target": 231, "key": "e15a2e531163f292b03bd99dfb27477d"}, {"line": 4552, "relation": "increases", "evidence": "Although HDL-mediated cholesterol efflux is apoA-I-dependent, recent studies have suggested an involvement of the enzyme paraoxonase 1 (PON1)", "citation": {"db": "PubMed", "db_id": "19405812"}, "annotations": {"Subgraph": {"Paroxetine subgraph": true, "Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 1918, "target": 231, "key": "aac813e2e5f42bc437597a65f5525f8a"}, {"line": 4578, "relation": "increases", "evidence": "ApoE expression is transcriptionally induced through the action of the nuclear receptors peroxisome proliferator activated receptor (PPARgamma) and liver X receptors (LXR) in coordination with retinoid X receptors (RXR)", "citation": {"db": "PubMed", "db_id": "22323736"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 1897, "target": 1744, "key": "b6252a8b816503a94f1939f7e3df9778"}, {"line": 4593, "relation": "decreases", "evidence": "protein phosphatase 2A, PP2A, is a principal tau dephosphorylating enzyme in the brain", "citation": {"db": "PubMed", "db_id": "22299660"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3282, "target": 3015, "key": "5e254d912e1c9b093dc512e63d239b63"}, {"line": 34489, "relation": "decreases", "evidence": "Aberrant glycosylation pmodulates phosphorylation of tau by protein kinase A and dephosphorylation of tau by protein phosphatase 2A and 5.", "citation": {"db": "PubMed", "db_id": "12435421"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3282, "target": 3015, "key": "c4b2f2b72e187404bb46449ad055fcf9"}, {"line": 16331, "relation": "decreases", "evidence": "Knockdown of phosphotyrosyl phosphatase activator induces apoptosis via mitochondrial pathway and the attenuation by simultaneous tau hyperphosphorylation. Phosphotyrosyl phosphatase activator (PTPA) is decreased in the brains of Alzheimer's disease (AD) and the AD transgenic mouse models.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3282, "target": 478, "key": "a10eb892793c18c4415c8a9dffd7cd02"}, {"line": 22284, "relation": "negativeCorrelation", "evidence": "knockdown of PTPA induced cell apoptosis in HEK293 and N2a cell lines. PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 3282, "target": 478, "key": "8d471a635d6adb3d855664a1c1ba0d94"}, {"line": 16335, "relation": "negativeCorrelation", "evidence": "Knockdown of phosphotyrosyl phosphatase activator induces apoptosis via mitochondrial pathway and the attenuation by simultaneous tau hyperphosphorylation. Phosphotyrosyl phosphatase activator (PTPA) is decreased in the brains of Alzheimer's disease (AD) and the AD transgenic mouse models.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3282, "target": 3823, "key": "f3485a32a0923f71142bb4d0e9447df3"}, {"line": 16351, "relation": "decreases", "evidence": "PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3282, "target": 2444, "key": "79f35e16e4f65f6e0f5776bd5209c3b0"}, {"line": 16359, "relation": "increases", "evidence": "PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Subgraph": {"Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3282, "target": 2394, "key": "f7f9b167e2f2b8643aef0ecc0e47d351"}, {"line": 16360, "relation": "increases", "evidence": "PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Subgraph": {"Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3282, "target": 2393, "key": "2070e8aa842cae3d86f94f543937d44e"}, {"line": 22285, "relation": "positiveCorrelation", "evidence": "knockdown of PTPA induced cell apoptosis in HEK293 and N2a cell lines. PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 3282, "target": 682, "key": "319612c3dd9a5c5d95aabfd7890f8bf6"}, {"line": 22286, "relation": "negativeCorrelation", "evidence": "knockdown of PTPA induced cell apoptosis in HEK293 and N2a cell lines. PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3282, "target": 3600, "key": "ccbdb3d0b140400ab7ba75fb91531cc9"}, {"line": 22287, "relation": "positiveCorrelation", "evidence": "knockdown of PTPA induced cell apoptosis in HEK293 and N2a cell lines. PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 3282, "target": 3597, "key": "5dad021ca5c7d87cc8494bfa4317af68"}, {"line": 4615, "relation": "directlyIncreases", "evidence": "Neuromuscular synapse formation requires an exchange of signals between motor neurons and muscle. Agrin, supplied by motor neurons, binds to Lrp4 in muscle, stimulating phosphorylation of MuSK and recruitment of a signaling complex essential for synapse-specific transcription and anchoring of key proteins in the postsynaptic membrane. Lrp4, like the LDLR and other Lrp-family members, contains an intracellular region with motifs that can regulate receptor trafficking, as well as assembly of an intracellular signaling complex.", "citation": {"db": "PubMed", "db_id": "22038977"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 1070, "target": 3081, "key": "f02b8c0b72d7a346aafa1f40c93cf7ec"}, {"relation": "partOf", "source": 2273, "target": 1070, "key": "970e030f0e44fdcbd103fa58226f0d51"}, {"relation": "partOf", "source": 2273, "target": 1071, "key": "fbc712e57fbf0fef144d00b47dfd213c"}, {"relation": "partOf", "source": 2273, "target": 913, "key": "81bddd569fa73100edf326e1fef6b7cb"}, {"line": 25776, "relation": "association", "evidence": "Particularly, it has been shown that agrin is associated with the pathological lesions of Alzheimer's disease (AD) and may contribute to the formation of beta-amyloid (Abeta) plaques in AD", "citation": {"db": "PubMed", "db_id": "16037493"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}}, "source": 2273, "target": 3823, "key": "4cef7a8cd1e31f09cd9fab308d5815b6"}, {"line": 25777, "relation": "association", "evidence": "Particularly, it has been shown that agrin is associated with the pathological lesions of Alzheimer's disease (AD) and may contribute to the formation of beta-amyloid (Abeta) plaques in AD", "citation": {"db": "PubMed", "db_id": "16037493"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}}, "source": 2273, "target": 2328, "key": "d164f71e813c665090a84a0167943cc7"}, {"line": 25782, "relation": "increases", "evidence": "These studies indicate that agrin is capable of accelerating the formation of insoluble protein fibrils in a second common neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "16037493"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}}, "source": 2273, "target": 3384, "key": "86017dc7fabc43c9b06403134b7cee94"}, {"line": 4616, "relation": "increases", "evidence": "Neuromuscular synapse formation requires an exchange of signals between motor neurons and muscle. Agrin, supplied by motor neurons, binds to Lrp4 in muscle, stimulating phosphorylation of MuSK and recruitment of a signaling complex essential for synapse-specific transcription and anchoring of key proteins in the postsynaptic membrane. Lrp4, like the LDLR and other Lrp-family members, contains an intracellular region with motifs that can regulate receptor trafficking, as well as assembly of an intracellular signaling complex.", "citation": {"db": "PubMed", "db_id": "22038977"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3081, "target": 795, "key": "73c03b58619438810503e14ff73488f7"}, {"relation": "hasVariant", "source": 3080, "target": 3081, "key": "708a46a4603650532532e1c3ac18af18"}, {"line": 37915, "relation": "association", "evidence": "We found that APP was present in the postsynaptic density of central excitatory synapses and coimmunoprecipitated with N-methyl-d-aspartate receptors (NMDARs). The presence of APP in the postsynaptic density was supported by the observation that NMDARs regulated trafficking and processing of APP; overexpression of the NR1 subunit increased surface levels of APP, whereas activation of NMDARs decreased surface APP and promoted production of ABeta¸. We transfected APP or APP RNA interference into primary neurons and used electrophysiological techniques to explore the effects of APP on postsynaptic function. Reduction of APP decreased (and overexpression of APP increased) NMDAR whole cell current density and peak amplitude of spontaneous miniature excitatory postsynaptic currents. The increase in NMDAR current by APP was due to specific recruitment of additional NR2B-containing receptors. Consistent with these findings, immunohistochemical experiments demonstrated that APP increased the surface levels and decreased internalization of NR2B subunits. These results demonstrate a novel physiological role of postsynaptic APP in enhancing NMDAR function.", "citation": {"db": "PubMed", "db_id": "19164281"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "source": 795, "target": 1682, "key": "933e165dc5108d5762a618030eb911f2"}, {"line": 4643, "relation": "directlyIncreases", "evidence": "PPARa/gamma and their agonists positively control megalin expression", "citation": {"db": "PubMed", "db_id": "21311715"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3210, "target": 1864, "key": "0bc6b27df8def33364d7a8df3c346293"}, {"line": 5106, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3210, "target": 577, "key": "f07264b7866fa6e0c8ad6a8e9a328c06"}, {"relation": "hasVariant", "source": 3210, "target": 3211, "key": "09c6d187bf981f13adf3cf613de98e5f"}, {"relation": "hasVariant", "source": 1864, "target": 1865, "key": "1daefb649768b1a2b4a4e693d6c435af"}, {"line": 4659, "relation": "regulates", "evidence": "Low-density lipoprotein receptor (LDLR) is a major apolipoprotein E (APOE) receptor and thereby is critical to cholesterol homeostasis.We interpret these results as suggesting that SFRS13A regulates LDLR splicing efficiency and may therefore emerge as a modulator of cholesterol homeostasis.", "citation": {"db": "PubMed", "db_id": "20232416"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3420, "target": 2960, "key": "1af19734ad1e0ffe8b0b851a540b47cb"}, {"line": 4671, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2717, "target": 795, "key": "4ef0d6a2f73e17a9dfbd8ecfa9725f35"}, {"relation": "partOf", "source": 2717, "target": 1428, "key": "1fb788fd2d0cf563860d87f5bcfd5cf7"}, {"relation": "partOf", "source": 2717, "target": 1429, "key": "89b1317ca07ddec8ce31527beb40733a"}, {"relation": "hasVariant", "source": 2717, "target": 2718, "key": "3f516c6e8cd157742cec96006bd798dc"}, {"line": 4672, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 1428, "target": 2717, "key": "729a4a0f395f8b99feaeb13340d295d2"}, {"relation": "partOf", "source": 3279, "target": 1428, "key": "804488181f73ec94a75f29c30303357e"}, {"line": 4674, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Nerve growth factor subgraph": true}}, "source": 3279, "target": 2718, "key": "dec02091a20765cef6c61dad30895019"}, {"line": 35866, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3279, "target": 2718, "key": "b7787d7614945cf327d3354919dabf3f"}, {"line": 4673, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 1429, "target": 2717, "key": "0c9d64379cc67a1493a9f6b022e64d2e"}, {"relation": "partOf", "source": 3281, "target": 1429, "key": "b56d9f8f00d73120c2b8c136f85cfe1b"}, {"line": 4677, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Nerve growth factor subgraph": true}}, "source": 3281, "target": 2718, "key": "31e71f887dc0ca00fc20730cf99f15c7"}, {"line": 35869, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3281, "target": 2718, "key": "07d829849431da5a83affd894955c6e8"}, {"line": 4675, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Nerve growth factor subgraph": true}}, "source": 2718, "target": 3190, "key": "c7e0b74e0fdabf257e714edf0f7d9d98"}, {"line": 4680, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Nerve growth factor subgraph": true}}, "source": 2718, "target": 3365, "key": "c0526c6df2ff621416ed2d285be3fede"}, {"line": 4686, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 2718, "target": 2563, "key": "ec2716e701f1cd067cfb61727670c952"}, {"relation": "partOf", "source": 3190, "target": 1699, "key": "a9262adf20d0e7dfb1b7d932f0f99175"}, {"relation": "partOf", "source": 3190, "target": 1713, "key": "43cd4669a7f300c36e4efda093f2b4c1"}, {"relation": "partOf", "source": 3190, "target": 1493, "key": "615aed4c9f6b9464436678f0c76cd88d"}, {"relation": "partOf", "source": 3190, "target": 1490, "key": "bf5442b4816a1eab4e3b1159f54e3cda"}, {"relation": "partOf", "source": 3190, "target": 1487, "key": "7447f53d82ce51a4c979a626d96531ee"}, {"relation": "partOf", "source": 3365, "target": 1699, "key": "cf2666fcf692a0d86af68a89da3fd2a6"}, {"relation": "partOf", "source": 2563, "target": 1699, "key": "828d59a170160d0af6070007789fc432"}, {"line": 4689, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 1699, "target": 508, "key": "5ccbd24dba45d03923a9df049a2cb76d"}, {"line": 4690, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1699, "target": 420, "key": "cd2119401c40c929d6800418e1d06ace"}, {"line": 4691, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 1699, "target": 506, "key": "032d7ee06e4e0728bc48fb9c4d4fd731"}, {"line": 4692, "relation": "increases", "evidence": "The growth factor receptor-bound protein 2 (Grb2)-associated binder (Gab) proteins are intracellular scaffolding/docking molecules, and participate in multiple signaling pathways, usually acting as the downstream effector of protein-tyrosine kinases (PTKs)-triggered signal transduction pathway. When phosphorylated by PTKs, Gab proteins can recruit several signaling molecules (p85, SHP2, and Crk), and subsequently activate multiple transmitting signals that are critical for cell growth, survival, differentiation and apoptotic process. Recently, it has been reported that Gab2 polymorphism is associated with the increase in the risk of Alzheimer's disease (AD) and is involved in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "20502503"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 1699, "target": 741, "key": "41f1cc29cfce3ca5812493745b93b50e"}, {"line": 4704, "relation": "increases", "evidence": "S100B protein stimulates microglia migration via RAGE-dependent up-regulation of chemokine expression and release.We show here that high S100B stimulates murine microglia migration in Boyden chambers via RAGE-dependent activation of Src kinase, Ras, PI3K, MEK/ERK1/2, RhoA/ROCK, Rac1/JNK/AP-1, Rac1/NF-kB, and, to a lesser extent, p38 MAPK. Recruitment of the adaptor protein, diaphanous-1, a member of the formin protein family, is also required for S100B/RAGE-induced migration of microglia. The S100B/RAGE-dependent activation of diaphanous-1/Rac1/JNK/AP-1, Ras/Rac1/NF-kB and Src/Ras/PI3K/RhoA/diaphanous-1 results in the up-regulation of expression of the chemokines, CCL3, CCL5, and CXCL12, whose release and activity are required for S100B to stimulate microglia migration. Lastly, RAGE engagement by S100B in microglia results in up-regulation of the chemokine receptors, CCR1 and CCR5. These results suggests that S100B might participate in the pathophysiology of brain inflammatory disorders via RAGE-dependent regulation of several inflammation-related events including activation and migration of microglia.", "citation": {"db": "PubMed", "db_id": "21209080"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 3335, "target": 793, "key": "e6fe6aed6d5240d88c7f27ac7b5f826e"}, {"line": 35292, "relation": "increases", "evidence": "S100B protein stimulates microglia migration via RAGE-dependent up-regulation of chemokine expression and release. Recruitment of the adaptor protein, diaphanous-1, a member of the formin protein family, is also required for S100B/RAGE-induced migration of microglia. The S100B/RAGE-dependent activation of diaphanous-1/Rac1/JNK/AP-1, Ras/Rac1/NF-kB and Src/Ras/PI3K/RhoA/diaphanous-1 results in the up-regulation of expression of the chemokines, CCL3, CCL5, and CXCL12, whose release and activity are required for S100B to stimulate microglia migration. Lastly, RAGE engagement by S100B in microglia results in up-regulation of the chemokine receptors, CCR1 and CCR5. These results suggests that S100B might participate in the pathophysiology of brain inflammatory disorders via RAGE-dependent regulation of several inflammation-related events including activation and migration of microglia.", "citation": {"db": "PubMed", "db_id": "21209080"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3335, "target": 793, "key": "69725950b95a7cdf414d9e147bc39367"}, {"line": 4705, "relation": "increases", "evidence": "S100B protein stimulates microglia migration via RAGE-dependent up-regulation of chemokine expression and release.We show here that high S100B stimulates murine microglia migration in Boyden chambers via RAGE-dependent activation of Src kinase, Ras, PI3K, MEK/ERK1/2, RhoA/ROCK, Rac1/JNK/AP-1, Rac1/NF-kB, and, to a lesser extent, p38 MAPK. Recruitment of the adaptor protein, diaphanous-1, a member of the formin protein family, is also required for S100B/RAGE-induced migration of microglia. The S100B/RAGE-dependent activation of diaphanous-1/Rac1/JNK/AP-1, Ras/Rac1/NF-kB and Src/Ras/PI3K/RhoA/diaphanous-1 results in the up-regulation of expression of the chemokines, CCL3, CCL5, and CXCL12, whose release and activity are required for S100B to stimulate microglia migration. Lastly, RAGE engagement by S100B in microglia results in up-regulation of the chemokine receptors, CCR1 and CCR5. These results suggests that S100B might participate in the pathophysiology of brain inflammatory disorders via RAGE-dependent regulation of several inflammation-related events including activation and migration of microglia.", "citation": {"db": "PubMed", "db_id": "21209080"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3335, "target": 3064, "key": "f7ea44d1132ccf8d6200091a92646a10"}, {"line": 35293, "relation": "increases", "evidence": "S100B protein stimulates microglia migration via RAGE-dependent up-regulation of chemokine expression and release. Recruitment of the adaptor protein, diaphanous-1, a member of the formin protein family, is also required for S100B/RAGE-induced migration of microglia. The S100B/RAGE-dependent activation of diaphanous-1/Rac1/JNK/AP-1, Ras/Rac1/NF-kB and Src/Ras/PI3K/RhoA/diaphanous-1 results in the up-regulation of expression of the chemokines, CCL3, CCL5, and CXCL12, whose release and activity are required for S100B to stimulate microglia migration. Lastly, RAGE engagement by S100B in microglia results in up-regulation of the chemokine receptors, CCR1 and CCR5. These results suggests that S100B might participate in the pathophysiology of brain inflammatory disorders via RAGE-dependent regulation of several inflammation-related events including activation and migration of microglia.", "citation": {"db": "PubMed", "db_id": "21209080"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3335, "target": 3064, "key": "307f27b64862bbc89dce68da9434a5f8"}, {"relation": "partOf", "source": 3335, "target": 1391, "key": "38ef84ccbafbc219fbac28dc59b165d0"}, {"relation": "partOf", "source": 3335, "target": 1069, "key": "e79e52f4a40de32e0220c621f9ac619a"}, {"relation": "partOf", "source": 3335, "target": 1558, "key": "a7e8ab9f3031b2c6766a2bbd838d5827"}, {"line": 39014, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3335, "target": 577, "key": "f16d3f97b82eb488fe77769f72eb5e43"}, {"line": 39141, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3335, "target": 2271, "key": "f5073d8dda5cbd9b752a50d2c92f3900"}, {"line": 39159, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Calcium-dependent signal transduction": true, "GSK3 subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3335, "target": 2795, "key": "2ca10cf036af71cd1d3bdf7cfc4639bc"}, {"line": 39160, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Calcium-dependent signal transduction": true, "GSK3 subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3335, "target": 2629, "key": "b413052c3d7d0488bc09621ae1b69408"}, {"line": 39176, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3335, "target": 2580, "key": "8b3c2f7705d0aa2960b405ebafbc340f"}, {"line": 39188, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Tau protein subgraph": true}, "Confidence": {"Very High": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "source": 3335, "target": 889, "key": "d012dbd7f8b295078c22cdb0458ff49c"}, {"line": 39189, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true, "Tau protein subgraph": true}, "Confidence": {"Very High": true}, "MeSHDisease": {"Alzheimer Disease": true}}, "source": 3335, "target": 3015, "key": "74b61af543fa22c23c991731a3ab9894"}, {"line": 39379, "relation": "association", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Very High": true}}, "source": 3335, "target": 2392, "key": "4d8907fccd172c46fe9403d48bac4cdf"}, {"line": 4713, "relation": "increases", "evidence": "S100B protein stimulates microglia migration via RAGE-dependent up-regulation of chemokine expression and release.We show here that high S100B stimulates murine microglia migration in Boyden chambers via RAGE-dependent activation of Src kinase, Ras, PI3K, MEK/ERK1/2, RhoA/ROCK, Rac1/JNK/AP-1, Rac1/NF-kB, and, to a lesser extent, p38 MAPK. Recruitment of the adaptor protein, diaphanous-1, a member of the formin protein family, is also required for S100B/RAGE-induced migration of microglia. The S100B/RAGE-dependent activation of diaphanous-1/Rac1/JNK/AP-1, Ras/Rac1/NF-kB and Src/Ras/PI3K/RhoA/diaphanous-1 results in the up-regulation of expression of the chemokines, CCL3, CCL5, and CXCL12, whose release and activity are required for S100B to stimulate microglia migration. Lastly, RAGE engagement by S100B in microglia results in up-regulation of the chemokine receptors, CCR1 and CCR5. These results suggests that S100B might participate in the pathophysiology of brain inflammatory disorders via RAGE-dependent regulation of several inflammation-related events including activation and migration of microglia.", "citation": {"db": "PubMed", "db_id": "21209080"}, "annotations": {"Subgraph": {"Innate immune system subgraph": true, "RhoA subgraph": true, "Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3064, "target": 1713, "key": "b54fab050d794bc1d4969d9195f7f0a4"}, {"line": 35294, "relation": "increases", "evidence": "S100B protein stimulates microglia migration via RAGE-dependent up-regulation of chemokine expression and release. Recruitment of the adaptor protein, diaphanous-1, a member of the formin protein family, is also required for S100B/RAGE-induced migration of microglia. The S100B/RAGE-dependent activation of diaphanous-1/Rac1/JNK/AP-1, Ras/Rac1/NF-kB and Src/Ras/PI3K/RhoA/diaphanous-1 results in the up-regulation of expression of the chemokines, CCL3, CCL5, and CXCL12, whose release and activity are required for S100B to stimulate microglia migration. Lastly, RAGE engagement by S100B in microglia results in up-regulation of the chemokine receptors, CCR1 and CCR5. These results suggests that S100B might participate in the pathophysiology of brain inflammatory disorders via RAGE-dependent regulation of several inflammation-related events including activation and migration of microglia.", "citation": {"db": "PubMed", "db_id": "21209080"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3064, "target": 1713, "key": "9553854b2a4f3019be3562a6597adf96"}, {"relation": "partOf", "source": 3064, "target": 1391, "key": "3369b22f64b7cea862b06c3ade138694"}, {"line": 25691, "relation": "increases", "evidence": "In each case, the Abstimulation of M-CSF secretion was significantly blocked by treatment of cultures with anti-RAGE F(ab')2.", "citation": {"db": "PubMed", "db_id": "15882940"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3064, "target": 2566, "key": "0e3e34ee4564b0348d99ffd1df2c4b79"}, {"relation": "partOf", "source": 2990, "target": 1713, "key": "cdf50aa965885a25328a31ed35dbe549"}, {"line": 9062, "relation": "association", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2990, "target": 2315, "key": "04c02e7d1cdb5095e0428049e5047389"}, {"line": 9065, "relation": "association", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2990, "target": 3258, "key": "c583bcf12517729df039924b88fe0bd8"}, {"relation": "isA", "source": 2990, "target": 2173, "key": "8f3fe6054539f96d51171506a22804a8"}, {"line": 22808, "relation": "positiveCorrelation", "evidence": "The ERK2 isoform of the ERK pathway was less activated in the hippocampal dentate gyrus of Tg mice in basal conditions. Furthermore activation of the ERK pathway by ex vivo cholinergic stimulation with carbachol caused significantly higher activation of ERK in the hippocampus of Wt mice than in Tg mice. These findings may pose a molecular basis for the memory disruption of Alzheimer's disease, since proper functioning of the basal forebrain cholinergic neurons and of ERK2 is critical for memory formation.", "citation": {"db": "PubMed", "db_id": "8129042"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2990, "target": 820, "key": "e3c9474a1f5686dbdd399c455d8f391e"}, {"relation": "partOf", "source": 2990, "target": 1538, "key": "6470aa48b7fedb92317056ae092b4cac"}, {"line": 32765, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2990, "target": 3031, "key": "0350ae13647f3f410f5cf2fa2edb9062"}, {"line": 32766, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2990, "target": 3032, "key": "6247b0297cf9189fea49933547f57552"}, {"line": 32767, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2990, "target": 3020, "key": "8a6aceb4c5266865e8490cfd5530102d"}, {"line": 32768, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2990, "target": 3026, "key": "2ebb93b5a99536bbb6f88f291a2fca91"}, {"line": 32769, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2990, "target": 3029, "key": "46416f21ebdc8cb49c2af40c63759f45"}, {"line": 35983, "relation": "increases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER.This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2990, "target": 2338, "key": "63323e56cf51853f9a5c9ad048763849"}, {"relation": "hasVariant", "source": 2990, "target": 2991, "key": "c3e445409436ee32b0bfb1ecc06ca8c8"}, {"line": 37649, "relation": "increases", "evidence": "Another study shows that APP, when phosphorylated at the Thr668 residue, is distributed in neuronal growth cones, and that the phosphorylated form of APP regulates neurite outgrowth in PC12 cells [52]. In addition, human APP and Drosophila APPL promoted postdevelopmental axonal arborization, depending on the interaction between the C-terminus of APP and Abelson (Abl) tyrosine kinase, suggesting a potential role for APP in axonal outgrowth following traumatic brain injury [4]. Furthermore, secreted sAPPa promoted axonal and dendritic growth and induced neurite outgrowth in neural stem cell-derived neurons through MAP kinase signaling", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2990, "target": 652, "key": "8a7218c41b2be23e2bf3a475a0c378c2"}, {"relation": "partOf", "source": 3191, "target": 1713, "key": "baee32298e39fd3c6766966c04cde6a0"}, {"line": 36408, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 3177, "key": "4e054f1b7d10cdac88723ca68f0bcd76"}, {"line": 36410, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3191, "target": 2153, "key": "1d5a8ccf5fe2659c6baca484e486d45f"}, {"line": 36667, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 2910, "key": "1af605b68b4af7fd866cd73b01179196"}, {"line": 36672, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 3178, "key": "0ecc841425a6b22decbfa5bceab70c08"}, {"line": 36678, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 2915, "key": "b1b5a608123261db3d0163c1e2eed1a5"}, {"line": 36686, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 3355, "key": "a56cd717d827016a3de3bebba99015ae"}, {"line": 36688, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 3357, "key": "16b3aa65d826acd88a267788499a5474"}, {"line": 36689, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 3359, "key": "5971738ce25fa4a85445d55780ac7c0c"}, {"line": 36690, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 3361, "key": "bb1f7d94fa2540a12334ce68b3c0b6de"}, {"line": 36716, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 2792, "key": "3bc24443473cbd414a60412a8e73470c"}, {"line": 36718, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 2794, "key": "c6cd3e6a6d434773e05637f012bb373d"}, {"line": 36755, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 2187, "key": "f0900674fc2955f84b463fe9460f4e29"}, {"line": 36775, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 3137, "key": "c17965fb215ffde9e2c7d394e2c059c2"}, {"line": 36781, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3191, "target": 3291, "key": "634c322961f2419d7c8eea1c6a647f91"}, {"relation": "partOf", "source": 3191, "target": 1444, "key": "cbb1ca245dc82dafa62dbee79c860640"}, {"relation": "partOf", "source": 3191, "target": 1447, "key": "dd570a32a96580342affc0ebed5699d8"}, {"relation": "partOf", "source": 3298, "target": 1713, "key": "16ec9871e6fa7b8d9752090f982b242d"}, {"line": 35398, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3298, "target": 2990, "key": "7c76a991371d95757547af8ecf22d85d"}, {"line": 35399, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3298, "target": 3000, "key": "f47b0efe08f24a571f9ebd15577ce62c"}, {"relation": "partOf", "source": 3310, "target": 1713, "key": "2f79c46d22480678335424844d5e2ca7"}, {"relation": "partOf", "source": 3367, "target": 1713, "key": "01e59cc5107eb5ae2a242be7e876b9e5"}, {"line": 4714, "relation": "increases", "evidence": "S100B protein stimulates microglia migration via RAGE-dependent up-regulation of chemokine expression and release.We show here that high S100B stimulates murine microglia migration in Boyden chambers via RAGE-dependent activation of Src kinase, Ras, PI3K, MEK/ERK1/2, RhoA/ROCK, Rac1/JNK/AP-1, Rac1/NF-kB, and, to a lesser extent, p38 MAPK. Recruitment of the adaptor protein, diaphanous-1, a member of the formin protein family, is also required for S100B/RAGE-induced migration of microglia. The S100B/RAGE-dependent activation of diaphanous-1/Rac1/JNK/AP-1, Ras/Rac1/NF-kB and Src/Ras/PI3K/RhoA/diaphanous-1 results in the up-regulation of expression of the chemokines, CCL3, CCL5, and CXCL12, whose release and activity are required for S100B to stimulate microglia migration. Lastly, RAGE engagement by S100B in microglia results in up-regulation of the chemokine receptors, CCR1 and CCR5. These results suggests that S100B might participate in the pathophysiology of brain inflammatory disorders via RAGE-dependent regulation of several inflammation-related events including activation and migration of microglia.", "citation": {"db": "PubMed", "db_id": "21209080"}, "annotations": {"Subgraph": {"Innate immune system subgraph": true, "RhoA subgraph": true, "Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 1391, "target": 793, "key": "76e0491a020c3a713a58f0c70ae2d9c2"}, {"line": 35295, "relation": "increases", "evidence": "S100B protein stimulates microglia migration via RAGE-dependent up-regulation of chemokine expression and release. Recruitment of the adaptor protein, diaphanous-1, a member of the formin protein family, is also required for S100B/RAGE-induced migration of microglia. The S100B/RAGE-dependent activation of diaphanous-1/Rac1/JNK/AP-1, Ras/Rac1/NF-kB and Src/Ras/PI3K/RhoA/diaphanous-1 results in the up-regulation of expression of the chemokines, CCL3, CCL5, and CXCL12, whose release and activity are required for S100B to stimulate microglia migration. Lastly, RAGE engagement by S100B in microglia results in up-regulation of the chemokine receptors, CCR1 and CCR5. These results suggests that S100B might participate in the pathophysiology of brain inflammatory disorders via RAGE-dependent regulation of several inflammation-related events including activation and migration of microglia.", "citation": {"db": "PubMed", "db_id": "21209080"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 1391, "target": 793, "key": "b20a61fe87978e71ee2549a0036e22b5"}, {"relation": "partOf", "source": 2628, "target": 1391, "key": "173e59216e5ce513a5a33d1fec5a5002"}, {"line": 4730, "relation": "association", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2746, "target": 577, "key": "a5452d416a64bdedbb948148824d9baf"}, {"line": 8631, "relation": "positiveCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "miRNA subgraph": true}}, "source": 2746, "target": 2093, "key": "b98dc8f54a1c46134c98442cca382aac"}, {"line": 8632, "relation": "positiveCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "miRNA subgraph": true}}, "source": 2746, "target": 2094, "key": "0ffc88d2dcd7e6d3426fc32197418f74"}, {"line": 8660, "relation": "association", "evidence": "the activation of caspases and cleavage of cellular proteins such as GFAP may contribute to astrocyte injury and damage in the AD brain.", "citation": {"db": "PubMed", "db_id": "16507909"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 2746, "target": 3912, "key": "656f0bedc31a869cb93a595f5b1acfcc"}, {"line": 9545, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 2746, "target": 540, "key": "4185e8ad316185cdc67726364fbd16fd"}, {"line": 14566, "relation": "association", "evidence": "MMP-9 and GFAP expression may play an important role in excess Abeta deposition, which is caused by an imbalance between the protein's synthesis and removal.", "citation": {"db": "PubMed", "db_id": "24962158"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 2746, "target": 3566, "key": "f61193fea703339e5acdb6c9b9758871"}, {"relation": "partOf", "source": 2746, "target": 1433, "key": "453c5700c8d8523a66d52da64bba9fea"}, {"relation": "partOf", "source": 2746, "target": 1434, "key": "0a746d8e62b58ec2e7ce2fb8200d1709"}, {"line": 37810, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2746, "target": 561, "key": "2bc80f1c5f75f61a45211d9647932226"}, {"line": 39313, "relation": "positiveCorrelation", "evidence": "In addition, the presence of KP1, CR3/43 and GFAP decreases significantly with increasing age in AD.", "citation": {"db": "PubMed", "db_id": "22152162"}, "source": 2746, "target": 3823, "key": "f3def202af974e0e9f835190563f69ac"}, {"line": 39373, "relation": "association", "evidence": "Butyrylcholinesterase (BuChE) activity is associated with activated astrocytes in Alzheimer's disease brain. BuChE genotype was linked with differential CSF levels of glial fibrillary acidic protein, S100B, interleukin-1beta, and tumor necrosis factor (TNF)-alpha. BCHE-K noncarriers displayed 100%-150% higher glial fibrillary acidic protein and 64%-110% higher S100B than BCHE-K carriers, who, in contrast, had 40%-80% higher interleukin-1beta and 21%-27% higher TNF-alpha compared with noncarriers. A high level of CSF BuChE enzymatic phenotype also significantly correlated with higher CSF levels of astroglial markers and several factors of the innate complement system, but lower levels of proinflammatory cytokines. These individuals also displayed beneficial paraclinical and clinical findings, such as high cerebral glucose utilization, low beta-amyloid load, and less severe progression of clinical symptoms.", "citation": {"db": "PubMed", "db_id": "23759148"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Very High": true}}, "source": 2746, "target": 2392, "key": "e5ad61050daa6372685c8d90997d1c14"}, {"line": 39733, "relation": "biomarkerFor", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}, "Confidence": {"Medium": true}}, "source": 2746, "target": 3872, "key": "f4ae937d240cc71a545585a20f230467"}, {"line": 4733, "relation": "decreases", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 305, "target": 693, "key": "7a3fa9fb182798dc13c6bd1bf79c8b00"}, {"line": 4734, "relation": "decreases", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 305, "target": 2746, "key": "f17354d911a00b06b02a26f85fde9690"}, {"line": 4735, "relation": "decreases", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 305, "target": 3472, "key": "de7ac9325cb8106941f971baa4ecf236"}, {"line": 4736, "relation": "decreases", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 305, "target": 2894, "key": "d23c48ccad9baf34c09390250298016d"}, {"line": 4737, "relation": "increases", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 305, "target": 2602, "key": "2959df8cfe3de3451386478ad4df3b42"}, {"line": 4738, "relation": "increases", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 305, "target": 2457, "key": "dd2e98f7be3792e413dd103b39654d0f"}, {"line": 4739, "relation": "decreases", "evidence": "Cognitive decline in Alzheimer's disease (AD) occurs as a result of the buildup of pathological proteins and downstream events including an elevated and altered inflammatory response. Inflammation has previously been linked to increased abnormal phosphorylation of tau protein.To determine if endogenous amyloid-beta (Abeta)-induced neuroinflammation drives tau phosphorylation in vivo, we treated 8-month-old 3xTg-AD with minocycline, an anti-inflammatory agent, to assess how it influenced cognitive decline and development of pathology. 4 months of treatment restored cognition to non-transgenic performance. Inflammatory profiling revealed a marked decrease in GFAP, TNFalpha, and IL6 and an increase in the CXCL1 chemokines KC and MIP1a. Minocycline also reduced levels of insoluble Abeta and soluble fibrils. Despite reducing levels of the tau kinase cdk5 coactivator p25, minocycline did not have wide effects on tau pathology with only one phospho-epitope showing reduction with treatment (S212/S214). The sum of these findings shows that reduction of the inflammatory events in an AD mouse model prevents cognitive deficits associated with pathology, but that endogenous Abeta-derived neuroinflammation does not contribute significantly to the development of tau pathology.", "citation": {"db": "PubMed", "db_id": "20555131"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 305, "target": 80, "key": "ea25e719d48854e6affbc969f932148e"}, {"line": 29145, "relation": "decreases", "evidence": "In primary cortical neurons, minocycline prevents beta-amyloid-induced neuronal death, reduces caspase-3 activation, and lowers generation of caspase-3-cleaved tau fragments.", "citation": {"db": "PubMed", "db_id": "19001528"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Caspase subgraph": true, "Tau protein subgraph": true}}, "source": 305, "target": 3012, "key": "d954377ab8f25fcb3942a13c2c7f0393"}, {"line": 4753, "relation": "increases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "source": 1406, "target": 2655, "key": "33fc042c0a1eb17e8da21abdbfd403e6"}, {"relation": "partOf", "source": 2654, "target": 1406, "key": "4d694016806c35f86db84d534639d1b1"}, {"relation": "hasVariant", "source": 2654, "target": 2655, "key": "c1b0a0355f6e1c5b87234f3688eca2da"}, {"line": 4759, "relation": "decreases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2654, "target": 2571, "key": "91b3d66cb4d6d4aa6fdaedaadd3d790a"}, {"line": 35927, "relation": "decreases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 2654, "target": 2571, "key": "28ad0cf239933c6cfa0fad2a6d960042"}, {"relation": "partOf", "source": 2674, "target": 1406, "key": "19a5eb8224bc3eaf629a9e40b6611bb8"}, {"line": 4754, "relation": "increases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "source": 2655, "target": 787, "key": "6a0bdeab10ed972939c3e38ed81130ab"}, {"line": 4756, "relation": "increases", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "source": 2655, "target": 745, "key": "25ec7da74ae5f4ed4a05ae9b02910238"}, {"line": 4761, "relation": "association", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "source": 2655, "target": 2571, "key": "dbca385af761baa1934fc5d83b4ae7c3"}, {"line": 5519, "relation": "association", "evidence": "CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 787, "target": 2162, "key": "a2963c6ddf0a2f1ed5d587274c96b1a5"}, {"line": 19384, "relation": "association", "evidence": "Thrombospondins are extracellular matrix proteins that, in the CNS, are predominantly produced by astrocytes and have been implicated in synaptogenesis.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}}, "source": 787, "target": 3459, "key": "24569963e108b63c6f224fab5c505098"}, {"line": 37564, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 787, "target": 2315, "key": "9816c636286bf7952cc3a72a572945dd"}, {"line": 37565, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 787, "target": 2306, "key": "000608a38ed88614f66d3369d21a07b7"}, {"line": 37566, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 787, "target": 2307, "key": "007d8bf64edfe8aadb9e33c1232d3ca8"}, {"line": 37601, "relation": "association", "evidence": "APP is expressed pre- and postsynaptically and promotes synapse formation via trans-synaptic interactions of its extracellular domains. Full-length APP also may promote dendritic spine formation as well as surface expression of GluA2-containing AMPA receptors and GluN2B-containing NMDA receptors. Enhanced synaptic activity drives APP processing via the amyloidogenic ß -secretase pathway, leading to subsequent spine loss and downregulation of glutamate receptors in a negative feedback loop.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 787, "target": 2772, "key": "acf5805cfb0267aea713bf8e9dae3730"}, {"line": 37603, "relation": "association", "evidence": "APP is expressed pre- and postsynaptically and promotes synapse formation via trans-synaptic interactions of its extracellular domains. Full-length APP also may promote dendritic spine formation as well as surface expression of GluA2-containing AMPA receptors and GluN2B-containing NMDA receptors. Enhanced synaptic activity drives APP processing via the amyloidogenic ß -secretase pathway, leading to subsequent spine loss and downregulation of glutamate receptors in a negative feedback loop.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 787, "target": 2779, "key": "62e8e632ceadf13aeae50ef8e580610d"}, {"line": 17120, "relation": "association", "evidence": "Most forms of neuronal plasticity are associated with the induction of the transcription factor zif268 (egr1).", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 745, "target": 2658, "key": "e13501237350834477624c67bcd86803"}, {"line": 4757, "relation": "association", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1700, "target": 3366, "key": "4093d05d8ad70438f48c02606a9a114b"}, {"relation": "partOf", "source": 2571, "target": 1700, "key": "d5db498cb86b1462838a0f7fbd3c3e61"}, {"line": 4761, "relation": "association", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "source": 2571, "target": 2655, "key": "c46b5a4a4ecebea587de3e3589757325"}, {"relation": "partOf", "source": 2571, "target": 1366, "key": "dadb988ec0546df36e503e331231b88e"}, {"line": 4757, "relation": "association", "evidence": "Reverse signaling through the ephrinB ligands is important for several morphogenetic events, such as axon guidance, neuronal plasticity, spine maturation, and synaptogenesis. Signaling is initiated by binding of EphB receptors to ephrinB ligands, stimulating their tyrosine phosphorylation via an unclear mechanism. Here we show that this mechanism involves presenilin1 (PS1)/gamma-secretase regulation of phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk binding protein (PAG/Cbp), an adaptor protein that controls the activity of Src kinases.Using immunoprecipitation and Western blot of mouse primary neuronal and human embryonic kidney (HEK293) cell extracts overexpressing PAG/Cbp, we show that EphB2 induces tyrosine dephosphorylation of PAG/Cbp in a gamma-secretase-dependent manner. In these cells, PAG/Cbp dephosphorylation is promoted by the PS1/gamma-secretase-produced fragment of ephrinB2 cleavage (ephrinB2/CTF2), which forms complexes with PAG/Cbp when introduced exogenously. EphB2-induced tyrosine phosphorylation of ephrinB2 depends on PAG/Cbp because EphB2 cannot increase ephrinB2 phosphorylation in cells treated with anti-PAG siRNA or in PAG/Cbp-knockout (KO) cells. Furthermore, in contrast to WT PS1, familial Alzheimer disease (FAD) PS1 mutants expressed in PS1-KO mouse embryonic fibroblasts inhibited both the EphB2-induced dephosphorylation of PAG/Cbp and the phosphorylation of ephrinB2. PS1 FAD mutations may thus inhibit the function of ephrinB in the brain, promoting neurodegeneration in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21746865"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cell cycle subgraph": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3366, "target": 1700, "key": "301671d3e6e179aaa1e28f72d6937ef7"}, {"line": 4775, "relation": "increases", "evidence": "The induction of long-term potentiation at CA3-CA1 synapses is caused by an N-methyl-d-aspartate (NMDA) receptordependent accumulation of intracellular Ca(2+), followed by Src family kinase activation and a positive feedback enhancement of NMDA receptors (NMDARs). Nevertheless, the amplitude of baseline transmission remains remarkably constant even though low frequency stimulation is also associated with an NMDAR-dependent influx of Ca(2+) into dendritic spines. We show here that an interaction between C-terminal Src kinase (Csk) and NMDARs controls the Src-dependent regulation of NMDAR activity. Csk associates with the NMDAR signaling complex in the adult brain, inhibiting the Src-dependent potentiation of NMDARs in CA1 neurons and attenuating the Src-dependent induction of long-term potentiation. Csk associates directly with Src-phosphorylated NR2 subunits in vitro. An inhibitory antibody for Csk disrupts this physical association, potentiates NMDAR mediated excitatory postsynaptic currents, and induces long-term potentiation at CA3-CA1 synapses. Thus, Csk serves to maintain the constancy of baseline excitatory synaptic transmission by inhibiting Src kinase-dependent synaptic plasticity in the hippocampus", "citation": {"db": "PubMed", "db_id": "18445593"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3366, "target": 2785, "key": "9ae2fcfb9b26c10af3639eb571ab1bb5"}, {"line": 4777, "relation": "increases", "evidence": "The induction of long-term potentiation at CA3-CA1 synapses is caused by an N-methyl-d-aspartate (NMDA) receptordependent accumulation of intracellular Ca(2+), followed by Src family kinase activation and a positive feedback enhancement of NMDA receptors (NMDARs). Nevertheless, the amplitude of baseline transmission remains remarkably constant even though low frequency stimulation is also associated with an NMDAR-dependent influx of Ca(2+) into dendritic spines. We show here that an interaction between C-terminal Src kinase (Csk) and NMDARs controls the Src-dependent regulation of NMDAR activity. Csk associates with the NMDAR signaling complex in the adult brain, inhibiting the Src-dependent potentiation of NMDARs in CA1 neurons and attenuating the Src-dependent induction of long-term potentiation. Csk associates directly with Src-phosphorylated NR2 subunits in vitro. An inhibitory antibody for Csk disrupts this physical association, potentiates NMDAR mediated excitatory postsynaptic currents, and induces long-term potentiation at CA3-CA1 synapses. Thus, Csk serves to maintain the constancy of baseline excitatory synaptic transmission by inhibiting Src kinase-dependent synaptic plasticity in the hippocampus", "citation": {"db": "PubMed", "db_id": "18445593"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2785, "target": 597, "key": "65813a6a71ec2730c2887ffec9e4094c"}, {"relation": "partOf", "source": 2785, "target": 1366, "key": "18bb9414b08dbb4958d833438985bec6"}, {"line": 4778, "relation": "association", "evidence": "The induction of long-term potentiation at CA3-CA1 synapses is caused by an N-methyl-d-aspartate (NMDA) receptordependent accumulation of intracellular Ca(2+), followed by Src family kinase activation and a positive feedback enhancement of NMDA receptors (NMDARs). Nevertheless, the amplitude of baseline transmission remains remarkably constant even though low frequency stimulation is also associated with an NMDAR-dependent influx of Ca(2+) into dendritic spines. We show here that an interaction between C-terminal Src kinase (Csk) and NMDARs controls the Src-dependent regulation of NMDAR activity. Csk associates with the NMDAR signaling complex in the adult brain, inhibiting the Src-dependent potentiation of NMDARs in CA1 neurons and attenuating the Src-dependent induction of long-term potentiation. Csk associates directly with Src-phosphorylated NR2 subunits in vitro. An inhibitory antibody for Csk disrupts this physical association, potentiates NMDAR mediated excitatory postsynaptic currents, and induces long-term potentiation at CA3-CA1 synapses. Thus, Csk serves to maintain the constancy of baseline excitatory synaptic transmission by inhibiting Src kinase-dependent synaptic plasticity in the hippocampus", "citation": {"db": "PubMed", "db_id": "18445593"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 1366, "target": 597, "key": "00b29fc9c4946f1fb9cdecfafc7f6fcd"}, {"line": 4792, "relation": "increases", "evidence": "Moreover, we demonstrate that neuronal activity upregulates CRP1 expression in hippocampal neurons via Ca²+ influx after depolarization. Ca²+/calmodulin-dependent protein kinase IV (CaMKIV) and cAMP response element binding protein mediate the Ca²+-induced upregulation of CRP1 expression. Furthermore, CRP1 is required for the dendritic growth induced by Ca+? influx or CaMKIV. Together, these data are the first to demonstrate a role for CRP1 in dendritic growth.", "citation": {"db": "PubMed", "db_id": "22090504"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Calcium-dependent signal transduction": true}}, "subject": {"modifier": "Activity"}, "source": 2502, "target": 541, "key": "942c36ac7cd55913b27822eed07a7b44"}, {"line": 4790, "relation": "increases", "evidence": "Moreover, we demonstrate that neuronal activity upregulates CRP1 expression in hippocampal neurons via Ca²+ influx after depolarization. Ca²+/calmodulin-dependent protein kinase IV (CaMKIV) and cAMP response element binding protein mediate the Ca²+-induced upregulation of CRP1 expression. Furthermore, CRP1 is required for the dendritic growth induced by Ca+? influx or CaMKIV. Together, these data are the first to demonstrate a role for CRP1 in dendritic growth.", "citation": {"db": "PubMed", "db_id": "22090504"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Calcium-dependent signal transduction": true}}, "source": 2427, "target": 492, "key": "4f323acb352361e648b04e024aa535b8"}, {"line": 4793, "relation": "association", "evidence": "Moreover, we demonstrate that neuronal activity upregulates CRP1 expression in hippocampal neurons via Ca²+ influx after depolarization. Ca²+/calmodulin-dependent protein kinase IV (CaMKIV) and cAMP response element binding protein mediate the Ca²+-induced upregulation of CRP1 expression. Furthermore, CRP1 is required for the dendritic growth induced by Ca+? influx or CaMKIV. Together, these data are the first to demonstrate a role for CRP1 in dendritic growth.", "citation": {"db": "PubMed", "db_id": "22090504"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Calcium-dependent signal transduction": true}}, "source": 541, "target": 726, "key": "2345eb55e8a46a30d61c5549be2f1e5b"}, {"line": 4793, "relation": "association", "evidence": "Moreover, we demonstrate that neuronal activity upregulates CRP1 expression in hippocampal neurons via Ca²+ influx after depolarization. Ca²+/calmodulin-dependent protein kinase IV (CaMKIV) and cAMP response element binding protein mediate the Ca²+-induced upregulation of CRP1 expression. Furthermore, CRP1 is required for the dendritic growth induced by Ca+? influx or CaMKIV. Together, these data are the first to demonstrate a role for CRP1 in dendritic growth.", "citation": {"db": "PubMed", "db_id": "22090504"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Calcium-dependent signal transduction": true}}, "source": 726, "target": 541, "key": "3677f98f8e00876369da6c3ae38150b8"}, {"line": 37634, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "object": {"modifier": "Translocation"}, "source": 726, "target": 2781, "key": "6beb822ec650276a0e7d57ba1fb87a3a"}, {"line": 37635, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "source": 726, "target": 2315, "key": "552f60075e22356fa051b1cf58422abb"}, {"line": 4803, "relation": "increases", "evidence": "In addition to the interaction with cytoplasmic polyadenylation element binding protein-1 (CPEB-1), depolarization enhanced CPEB-1 recruitment to the activity-dependent targeting element. These results suggest that CPE-like sequences are involved in the activity-dependent as well as constitutive dendritic targeting of BDNF mRNA.", "citation": {"db": "PubMed", "db_id": "20603120"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Cell cycle subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2551, "target": 2397, "key": "5b9c567c509b89ca5b02a794b78d7cab"}, {"line": 35952, "relation": "increases", "evidence": "In addition to the interaction with cytoplasmic polyadenylation element binding protein-1 (CPEB-1), depolarization enhanced CPEB-1 recruitment to the activity-dependent targeting element. These results suggest that CPE-like sequences are involved in the activity-dependent as well as constitutive dendritic targeting of BDNF mRNA.", "citation": {"db": "PubMed", "db_id": "20603120"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2551, "target": 2397, "key": "dfbed458f9893555c55b86aab3e52996"}, {"line": 4813, "relation": "decreases", "evidence": "absence of CPE leads to degeneration of the CA3 neurons and perturbation of the cytoarchitecture of the hippocampus. Ex vivo studies showed that overexpression of CPE in cultured hippocampal neurons protected them against H(2)O(2) oxidative-stress induced cell death. These findings taken together indicate that CPE is essential for the survival of adult hippocampal CA3 neurons to maintain normal cognitive function.", "citation": {"db": "PubMed", "db_id": "18570185"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Protein biosynthesis subgraph": true}}, "source": 2550, "target": 584, "key": "e04e98818a5a4452ed4c6c002a994d56"}, {"line": 4815, "relation": "directlyIncreases", "evidence": "absence of CPE leads to degeneration of the CA3 neurons and perturbation of the cytoarchitecture of the hippocampus. Ex vivo studies showed that overexpression of CPE in cultured hippocampal neurons protected them against H(2)O(2) oxidative-stress induced cell death. These findings taken together indicate that CPE is essential for the survival of adult hippocampal CA3 neurons to maintain normal cognitive function.", "citation": {"db": "PubMed", "db_id": "18570185"}, "annotations": {"Subgraph": {"Protein biosynthesis subgraph": true}}, "source": 2550, "target": 812, "key": "fd965ed068461f30fc412e0ad347a7a2"}, {"line": 4848, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 1373, "target": 3258, "key": "335fc9a4cc3405146831ace65a45760d"}, {"line": 30020, "relation": "association", "evidence": "These findings raise the intriguing possibility that PS1-beta-catenin interactions and subsequent activities may be consequential for the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "10341227"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1373, "target": 3823, "key": "b8162cf9500010dc6350428dd1d74c55"}, {"line": 30758, "relation": "positiveCorrelation", "evidence": "We demonstrate that phosphorylation of serines 353 and 357 by glycogen synthase kinase-3beta (GSK3beta) induces a structural change of the hydrophilic loop of PS1 that can also be mimicked by substitution of the phosphorylation sites by negatively charged amino acids in vitro and in cultured cells. The structural change of PS1 reduces the interaction with beta-catenin leading to decreased phosphorylation and ubiquitination of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360711"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}}, "source": 1373, "target": 2581, "key": "f7d0c504b6db59b471b732ed125a7707"}, {"line": 30759, "relation": "positiveCorrelation", "evidence": "We demonstrate that phosphorylation of serines 353 and 357 by glycogen synthase kinase-3beta (GSK3beta) induces a structural change of the hydrophilic loop of PS1 that can also be mimicked by substitution of the phosphorylation sites by negatively charged amino acids in vitro and in cultured cells. The structural change of PS1 reduces the interaction with beta-catenin leading to decreased phosphorylation and ubiquitination of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360711"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}}, "source": 1373, "target": 2585, "key": "510eb83e49b0245e87b73409ece6c30f"}, {"line": 4849, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 1449, "target": 3258, "key": "00ba12f550f2395bca90c12f2f78125f"}, {"line": 4851, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 1375, "target": 3258, "key": "16a3e50ec0d00ba60b0e564a78110b7e"}, {"line": 4852, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 1450, "target": 3258, "key": "9b2d9526390654b73de9b279a2fb5f58"}, {"line": 4879, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2169, "target": 462, "key": "92f1801085d25a716f9fed9e4bb86bff"}, {"relation": "hasVariant", "source": 2169, "target": 2170, "key": "b2d9fddd465f5d193298d81b73bf99f9"}, {"line": 4880, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 488, "target": 453, "key": "176463a511b77cb167c1452a3a60b9f1"}, {"line": 4881, "relation": "association", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 488, "target": 462, "key": "044284204f1901390967bd4da2ccac89"}, {"line": 47898, "relation": "association", "evidence": "The Wnt signaling pathway plays a crucial role in the proper development and maintenance of brain and bone structure and function.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 488, "target": 462, "key": "edb11f55c2fbd47215cd98b4f5b98a91"}, {"line": 47908, "relation": "association", "evidence": "Growing evidence indicates that wingless-type (Wnt) signaling plays an important role in neuronal development, synapse formation and synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "26032671"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 488, "target": 462, "key": "5443a8a8ee92a2dea0bb0b979d413e66"}, {"line": 4885, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is a neurodegenerative disease with progressive dementia accompanied by three main structural changes in the brain: diffuse loss of neurons; intracellular protein deposits termed neurofibrillary tangles (NFT) and extracellular protein deposits termed amyloid or senile plaques, surrounded by dystrophic neurites. Recent studies have suggested that the trafficking process of membrane associated proteins is modulated by the FAD-linked presenilin (PS) proteins, and that amyloid beta-peptide deposition may be initiated intracellularly, through the secretory pathway. Current hypotheses concerning presenilin function are based upon its cellular localization and its putative interaction as macromolecular complexes with the cell-adhesion/signaling beta-catenin molecule and the glycogen synthase kinase 3beta (GSK-3beta) enzyme. Developmental studies have shown that PS proteins function as components in the Notch signal transduction cascade and that beta-catenin and GSK-3beta are transducers of the Wnt signaling pathway. Both pathways are thought to have an important role in brain development, and they have been connected through Dishevelled (Dvl) protein, a known transducer of the Wnt signaling pathway. In addition to a review of the current state of research on the subject, we present a cell signaling model in which a sustained loss of function of Wnt signaling components would trigger a series of misrecognition events, determining the onset and development of AD.", "citation": {"db": "PubMed", "db_id": "10967351"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 488, "target": 645, "key": "8953eaa9d28dbb003445c4789c091b6e"}, {"line": 19253, "relation": "association", "evidence": "In addition, while cdk5 has important physiological functions related to brain development, the breakdown of cdk5/p35 into cdk5/p25 increases its kinase activity and neurotoxicity.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 488, "target": 2487, "key": "6ab35438f814cc18c4712d7014e7fa73"}, {"relation": "hasVariant", "source": 3101, "target": 3102, "key": "7adbb6769aa99caee1991b0e51d60f97"}, {"line": 4911, "relation": "increases", "evidence": "Cyclin-dependent kinase 5 (cdk5) is a serine/threonine kinase activated by associating with its neuron-specific activators p35 and p39. Here, we show that cdk5 directly phosphorylates c-Jun N-terminal kinase 3 (JNK3) on Thr131 and inhibits its kinase activity, leading to reduced c-Jun phosphorylation. These effects can be restored by expression of a catalytically inactive mutant form of cdk5. Moreover, cdk5-deficient cultured cortical neurons exhibit increased sensitivity to apoptotic stimuli, as well as elevated JNK3 activity and c-Jun phosphorylation. Taken together, these findings show that cdk5 may exert its role as a key element by negatively regulating the c-Jun N-terminal kinase/stress-activated protein kinase signaling pathway during neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11823425"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 1341, "target": 2487, "key": "ef8f08ddf13401a88ef82092604caf98"}, {"relation": "partOf", "source": 2490, "target": 1341, "key": "5c4866d907b5b7ce11b7616acd95279c"}, {"relation": "hasVariant", "source": 2992, "target": 2993, "key": "818402b8ab97a9591610a4ca6c331e23"}, {"relation": "hasVariant", "source": 2992, "target": 2994, "key": "eac7db7d2a2b94807996194538fee103"}, {"relation": "partOf", "source": 2992, "target": 1533, "key": "c03f0af3f0007225a5ee536a265821bf"}, {"relation": "partOf", "source": 2992, "target": 1539, "key": "d5b7fe5a8b0763f9681c6d60f205dd06"}, {"line": 32540, "relation": "increases", "evidence": "Furthermore, JNK has been shown to phosphorylate tau at Ser422(20), a site that is specifically phosphorylated in AD brains", "citation": {"db": "PubMed", "db_id": "12191990"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2992, "target": 3029, "key": "4a44e17a2f46432a28427c45834c2736"}, {"line": 32775, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2992, "target": 3029, "key": "ae637f1dbf2dbb804522b5cb0af27dc8"}, {"line": 32771, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2992, "target": 3031, "key": "4412ea23775783dd46dca3cf2afb5fef"}, {"line": 32772, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2992, "target": 3032, "key": "233e843e456781088c1ef6a1342effb1"}, {"line": 32773, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2992, "target": 3020, "key": "6c1456a3510699d0bd70c4b8b1360428"}, {"line": 32774, "relation": "increases", "evidence": "Western blots with phosphorylation-sensitive antibodies showed that p38, like ERK2 and SAP kinase-beta/JNK, phosphorylated tau at sites found phosphorylated physiologically (Thr181, Ser202, Thr205, and Ser396) and also at Ser422, which is phosphorylated in neurofibrillary tangles but not in normal adult or foetal brain.", "citation": {"db": "PubMed", "db_id": "9202310"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2992, "target": 3026, "key": "100863977b4140be564ff29083424219"}, {"line": 33039, "relation": "increases", "evidence": "Aberrant tau phosphorylation by glycogen synthase kinase-3beta and JNK3 induces oligomeric tau fibrils in COS-7 cells.", "citation": {"db": "PubMed", "db_id": "12191990"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2992, "target": 3015, "key": "04e6296b19ce07d35d0d58215fe66647"}, {"line": 37850, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "MAPK-JNK subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2992, "target": 2136, "key": "2ef25d64619ce9ab8e7bdd2610bb47c2"}, {"line": 4913, "relation": "decreases", "evidence": "Cyclin-dependent kinase 5 (cdk5) is a serine/threonine kinase activated by associating with its neuron-specific activators p35 and p39. Here, we show that cdk5 directly phosphorylates c-Jun N-terminal kinase 3 (JNK3) on Thr131 and inhibits its kinase activity, leading to reduced c-Jun phosphorylation. These effects can be restored by expression of a catalytically inactive mutant form of cdk5. Moreover, cdk5-deficient cultured cortical neurons exhibit increased sensitivity to apoptotic stimuli, as well as elevated JNK3 activity and c-Jun phosphorylation. Taken together, these findings show that cdk5 may exert its role as a key element by negatively regulating the c-Jun N-terminal kinase/stress-activated protein kinase signaling pathway during neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11823425"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2994, "target": 2992, "key": "890425e2e4f3a0dbd6b059e8a63c54dc"}, {"line": 4914, "relation": "decreases", "evidence": "Cyclin-dependent kinase 5 (cdk5) is a serine/threonine kinase activated by associating with its neuron-specific activators p35 and p39. Here, we show that cdk5 directly phosphorylates c-Jun N-terminal kinase 3 (JNK3) on Thr131 and inhibits its kinase activity, leading to reduced c-Jun phosphorylation. These effects can be restored by expression of a catalytically inactive mutant form of cdk5. Moreover, cdk5-deficient cultured cortical neurons exhibit increased sensitivity to apoptotic stimuli, as well as elevated JNK3 activity and c-Jun phosphorylation. Taken together, these findings show that cdk5 may exert its role as a key element by negatively regulating the c-Jun N-terminal kinase/stress-activated protein kinase signaling pathway during neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11823425"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2994, "target": 3008, "key": "584a6404dd6fe2c648b5b48dbd9e0be4"}, {"line": 4917, "relation": "increases", "evidence": "Cyclin-dependent kinase 5 (cdk5) is a serine/threonine kinase activated by associating with its neuron-specific activators p35 and p39. Here, we show that cdk5 directly phosphorylates c-Jun N-terminal kinase 3 (JNK3) on Thr131 and inhibits its kinase activity, leading to reduced c-Jun phosphorylation. These effects can be restored by expression of a catalytically inactive mutant form of cdk5. Moreover, cdk5-deficient cultured cortical neurons exhibit increased sensitivity to apoptotic stimuli, as well as elevated JNK3 activity and c-Jun phosphorylation. Taken together, these findings show that cdk5 may exert its role as a key element by negatively regulating the c-Jun N-terminal kinase/stress-activated protein kinase signaling pathway during neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "11823425"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 786, "target": 645, "key": "2768b106c66c42f083c1808f18759b12"}, {"line": 4979, "relation": "increases", "evidence": "Interaction of CCL2 with its receptor CCR2 regulates mononuclear phagocyte accumulation. CCR2 deficiency leads to lower mononuclear phagocyte accumulation and is associated with higher brain Abeta levels, specifically around blood vessels, suggesting that monocytes accumulate at sites of Abeta deposition in an initial attempt to clear these deposits and stop or delay their neurotoxic effects.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Complement system subgraph": true, "Chemokine signaling subgraph": true}, "Cell": {"microglial cell": true}}, "source": 1318, "target": 388, "key": "bd2121e93ebc3ecd1e72de4f1ba490ab"}, {"line": 4991, "relation": "negativeCorrelation", "evidence": "Interaction of CCL2 with its receptor CCR2 regulates mononuclear phagocyte accumulation. CCR2 deficiency leads to lower mononuclear phagocyte accumulation and is associated with higher brain Abeta levels, specifically around blood vessels, suggesting that monocytes accumulate at sites of Abeta deposition in an initial attempt to clear these deposits and stop or delay their neurotoxic effects.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Chemokine signaling subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 1318, "target": 80, "key": "71463ab789ec77b9269da09b05b08f0b"}, {"line": 7111, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Nervous System": true}, "Confidence": {"Low": true}}, "source": 388, "target": 1813, "key": "cd4c0ffb555a1e3a60085e5f2aeed371"}, {"line": 7112, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Nervous System": true}, "Confidence": {"Low": true}}, "source": 388, "target": 3515, "key": "222d5c7c4d71dbc57246acd5e0dfa27d"}, {"line": 7121, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 388, "target": 1850, "key": "a2d7e2e0afa5ca729373503abeab7e1e"}, {"line": 7122, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 388, "target": 1910, "key": "0fd1da1b68e581893dd9496e82c69cf6"}, {"line": 7124, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 388, "target": 1852, "key": "24038c4da8dbe30278fa9342591a08f2"}, {"line": 7138, "relation": "positiveCorrelation", "evidence": "To determine whether the p35 and CDK5 proteins detected in pancreatic islets interact with one another and form a functional complex, we immunoprecipitated the complex from human islets and determined its protein kinase activity as previously described (16). Immunoprecipitation with a p35 antibody, followed by kinase activity determination in the immunoprecipitate using histone H1 as a substrate, demonstrate that p35 and CDK5 form a functional complex capable of phosphorylating histone H1 (Fig. 1D). No kinase activity was detected in the absence of antibody or in the presence of an unrelated control antibody (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Islets of Langerhans": true, "Pancreas": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 388, "target": 2487, "key": "d12957bdaf501f3084320153dd7318ad"}, {"line": 7139, "relation": "positiveCorrelation", "evidence": "To determine whether the p35 and CDK5 proteins detected in pancreatic islets interact with one another and form a functional complex, we immunoprecipitated the complex from human islets and determined its protein kinase activity as previously described (16). Immunoprecipitation with a p35 antibody, followed by kinase activity determination in the immunoprecipitate using histone H1 as a substrate, demonstrate that p35 and CDK5 form a functional complex capable of phosphorylating histone H1 (Fig. 1D). No kinase activity was detected in the absence of antibody or in the presence of an unrelated control antibody (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Islets of Langerhans": true, "Pancreas": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 388, "target": 2489, "key": "864dd0f85514a90aaeb3475af21a69eb"}, {"line": 7157, "relation": "positiveCorrelation", "evidence": "Additional experiments were designed to investigate the presence of p35, CDK5, and p35/CDK5 kinase activity in beta-cell lines. Biochemical characterization of CDK5 kinase activity was also investigated. Among the different cell lines tested, only INS-1 cells, an insulin-producing beta-cell line, showed expression of p35 (Fig. 2A). These cells also contain protein kinase activity that can be immunoprecipitated with a p35-specific antibody (Fig. 2B). Both p35 expression and p35/CDK5 activity were absent in other cell lines, such as HeLa and NIH-3T3 (Fig. 2, A and B), although these cell lines expressed CDK5 protein (Fig. 2A). We then investigated whether the kinase activity immunoprecipitated by the p35 antibody was due to its association with CDK5. We performed experiments with roscovitine, a relatively specific inhibitor of CDK5 activity (17). The p35/CDK5 activity in INS-1 cells was inhibited by roscovitine, but not by other protein kinase inhibitors, such as H89 (protein kinase A) and SB202190 (p38MAPK; Fig. 2C). Also, the inhibition by roscovitine was dose dependent (Fig. 2D), with a 50% inhibitory concentration of 128 nm, within the range of that previously reported for CDK5 (18).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"INS-1 cells": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 388, "target": 2489, "key": "95876a192ed899fa27f53b376f93c13b"}, {"line": 7158, "relation": "positiveCorrelation", "evidence": "Additional experiments were designed to investigate the presence of p35, CDK5, and p35/CDK5 kinase activity in beta-cell lines. Biochemical characterization of CDK5 kinase activity was also investigated. Among the different cell lines tested, only INS-1 cells, an insulin-producing beta-cell line, showed expression of p35 (Fig. 2A). These cells also contain protein kinase activity that can be immunoprecipitated with a p35-specific antibody (Fig. 2B). Both p35 expression and p35/CDK5 activity were absent in other cell lines, such as HeLa and NIH-3T3 (Fig. 2, A and B), although these cell lines expressed CDK5 protein (Fig. 2A). We then investigated whether the kinase activity immunoprecipitated by the p35 antibody was due to its association with CDK5. We performed experiments with roscovitine, a relatively specific inhibitor of CDK5 activity (17). The p35/CDK5 activity in INS-1 cells was inhibited by roscovitine, but not by other protein kinase inhibitors, such as H89 (protein kinase A) and SB202190 (p38MAPK; Fig. 2C). Also, the inhibition by roscovitine was dose dependent (Fig. 2D), with a 50% inhibitory concentration of 128 nm, within the range of that previously reported for CDK5 (18).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"INS-1 cells": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 388, "target": 1340, "key": "a954f074189a97a054db29457c697b9e"}, {"line": 5026, "relation": "increases", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2625, "target": 693, "key": "ad2b061151d2e308b77ade72d37aa738"}, {"line": 5027, "relation": "increases", "evidence": "Inflammatory changes are a prominent feature of brains affected by Alzheimer's disease (AD) Activated glial cells release inflammatory cytokines which modulate the neurodegenerative process.These cytokines are encoded by genes representing several interleukins and TNFA, which are associated with AD. The gene coding for HLA-B associated transcript 1 (BAT1) lies adjacent to TNFA in the central major histocompatibility complex (MHC) BAT1, a member of the DEAD-box family of RNA helicases, appears to regulate the production of inflammatory cytokines associated with AD pathology.", "citation": {"db": "PubMed", "db_id": "18715507"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2625, "target": 524, "key": "cecba32a20baf2224838757b9de3a128"}, {"line": 7362, "relation": "decreases", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 601, "target": 2315, "key": "332ee2e6b98293b5eef71df1b98d67fc"}, {"line": 7363, "relation": "decreases", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 601, "target": 80, "key": "e2dcf68b114dadbaf8e00847e425cbde"}, {"line": 8105, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 601, "target": 80, "key": "a1b91421346bae1239a8263a6aebb638"}, {"line": 7747, "relation": "association", "evidence": "Taken together, the BBB is an important interface between the blood and the CNS compartment regu­ lating uptake of insulin and IGF-1 into the bra in. However, the molecul ar mech anisms by which different conditions like aging or AD decrease insulin 's transport to the brain are not known yet. Wh eth er these mech anisms contribute to th e pathogenesis of AD and cognitive decline is still unclear.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 601, "target": 2899, "key": "260e401d5d550267b48e8187df5b3ae6"}, {"line": 7858, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Confidence": {"High": true}}, "source": 601, "target": 2899, "key": "d2997638e088b82b0ed04c0299d1c15d"}, {"line": 7748, "relation": "association", "evidence": "Taken together, the BBB is an important interface between the blood and the CNS compartment regu­ lating uptake of insulin and IGF-1 into the bra in. However, the molecul ar mech anisms by which different conditions like aging or AD decrease insulin 's transport to the brain are not known yet. Wh eth er these mech anisms contribute to th e pathogenesis of AD and cognitive decline is still unclear.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 601, "target": 2871, "key": "c0209ca9b9c53f1de2ed07d3dcda01a9"}, {"line": 42397, "relation": "association", "evidence": "Exogenous application of VEGF can increase the permeability of the BBB without causing brain edema.", "citation": {"db": "PubMed", "db_id": "24551038"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Brain Edema": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 601, "target": 3751, "key": "c1e7a9ac795233c27ece9f0af390b050"}, {"line": 5112, "relation": "association", "evidence": "We investigated whether genes involved in inflammation, i.e. PPAR-α, interleukins (IL) IL- 1α, IL-1beta, IL-6, and IL-10 may interact to increase AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2878, "target": 577, "key": "3e31ec18062cab655a7ed11a6fb5cf68"}, {"relation": "partOf", "source": 2878, "target": 1476, "key": "c632cda163e7b7e95cdf4d3b70490227"}, {"relation": "partOf", "source": 2878, "target": 1474, "key": "00e50d9c244f00388855fe5d642133fa"}, {"relation": "partOf", "source": 2878, "target": 1475, "key": "24d670380c75291db08c7ee27744c7c0"}, {"relation": "partOf", "source": 2878, "target": 1477, "key": "410fe6e43c5732d8aa175275b918d516"}, {"relation": "partOf", "source": 2878, "target": 1319, "key": "022b5a9b8adf8c7d12a7ff3efbd9798c"}, {"line": 33616, "relation": "increases", "evidence": "IL-10 was found to suppress all A beta and LPS-induced inflammatory proteins measured (IL-1 alpha, IL-1 beta, IL-6, TNF-alpha and MCP-1) in both cell types with the exception of LPS-induced MCP-1 in THP-1 cells where no change was observed.", "citation": {"db": "PubMed", "db_id": "11137576"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2878, "target": 2884, "key": "c7d9e2ab3fcbc466c12ad6c924e9decb"}, {"line": 33617, "relation": "increases", "evidence": "IL-10 was found to suppress all A beta and LPS-induced inflammatory proteins measured (IL-1 alpha, IL-1 beta, IL-6, TNF-alpha and MCP-1) in both cell types with the exception of LPS-induced MCP-1 in THP-1 cells where no change was observed.", "citation": {"db": "PubMed", "db_id": "11137576"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2878, "target": 2885, "key": "cd1cf06c2bc1cdf066fb360676be4a7b"}, {"line": 33618, "relation": "increases", "evidence": "IL-10 was found to suppress all A beta and LPS-induced inflammatory proteins measured (IL-1 alpha, IL-1 beta, IL-6, TNF-alpha and MCP-1) in both cell types with the exception of LPS-induced MCP-1 in THP-1 cells where no change was observed.", "citation": {"db": "PubMed", "db_id": "11137576"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2878, "target": 2894, "key": "b93ed7d586a531be75c4bda70c6f5699"}, {"line": 33620, "relation": "increases", "evidence": "IL-10 was found to suppress all A beta and LPS-induced inflammatory proteins measured (IL-1 alpha, IL-1 beta, IL-6, TNF-alpha and MCP-1) in both cell types with the exception of LPS-induced MCP-1 in THP-1 cells where no change was observed.", "citation": {"db": "PubMed", "db_id": "11137576"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 2878, "target": 3472, "key": "2597960c27717a950be53c8f66cca4c6"}, {"line": 33622, "relation": "increases", "evidence": "IL-10 was found to suppress all A beta and LPS-induced inflammatory proteins measured (IL-1 alpha, IL-1 beta, IL-6, TNF-alpha and MCP-1) in both cell types with the exception of LPS-induced MCP-1 in THP-1 cells where no change was observed.", "citation": {"db": "PubMed", "db_id": "11137576"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2878, "target": 2455, "key": "8cff2a849777cb8dc62e54bb3b346be9"}, {"line": 5120, "relation": "association", "evidence": "In addition to an association of the PPARA L162V polymorphism with the AD risk, we observed four significant interactions between SNPs in PPARA and SNPs in IL1A, IL1B and IL10 affecting AD risk.", "citation": {"db": "PubMed", "db_id": "22493750"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3211, "target": 3823, "key": "a35bdf6e430373c77a968aade2e9f7ae"}, {"line": 5148, "relation": "association", "evidence": "We examined the effect of the two previously reported variants of PPAR polymorphisms, the Pro12Ala and exon6 C478T, on the risk of LOAD and age of onset in a populati on- based f ol l ow- up sample of aged subj ects (125 LOAD patients and 462 non-demented controls). The genetic risk of AD was not significantly associated with the studied polymorphisms, but the PPARgamma Ala12-478T genotype carriers were significantly younger at the onset of dementia than the non-carriers (p = 0.026). These results suggest that the PPARgamma gene may modify the age of onset in LOAD", "citation": {"db": "PubMed", "db_id": "16988505"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3213, "target": 3823, "key": "5075a8d419c6e49ebc28d3e39e139beb"}, {"line": 5177, "relation": "increases", "evidence": "TNF-α+IFN-gamma stimulation significantly increased levels of astrocytic BACE1, APP, and secreted Abeta40", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Beta secretase subgraph": true, "Tumor necrosis factor subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "source": 1708, "target": 2375, "key": "8b4419fde238ce9028748a18d2695123"}, {"line": 5180, "relation": "increases", "evidence": "TNF-α+IFN-gamma stimulation significantly increased levels of astrocytic BACE1, APP, and secreted Abeta40", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "source": 1708, "target": 2315, "key": "600df456ae65e85f08bb6fd51a6cc6cd"}, {"line": 5181, "relation": "increases", "evidence": "TNF-α+IFN-gamma stimulation significantly increased levels of astrocytic BACE1, APP, and secreted Abeta40", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 1708, "target": 2327, "key": "2c77fe99c992fdc4bffd5699b589417c"}, {"line": 27603, "relation": "increases", "evidence": "IFNgamma in combination with TNFalpha or IL-1beta seems to trigger Abeta production by supporting beta-secretase cleavage of the immature APP molecule.", "citation": {"db": "PubMed", "db_id": "11114266"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 1708, "target": 80, "key": "779dfb6a7edb23561caea4d30aa804ce"}, {"line": 5207, "relation": "increases", "evidence": "Higher APP expression and elevated Abeta levels cause greater than required Cu export, leading to increased Cu in cerebrospinal fluid (CSF) and serum, and an intracellular (IC) Cu deficiency in the brain. Cu-deficient superoxide dismutase (SOD1) contributes to the reduced antioxidant capacity of the brain, allowing further oxidative stress.", "citation": {"db": "PubMed", "db_id": "15910549"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 3391, "target": 775, "key": "1d78a88436383dee34411cc70aaeacb6"}, {"line": 5210, "relation": "increases", "evidence": "Higher APP expression and elevated Abeta levels cause greater than required Cu export, leading to increased Cu in cerebrospinal fluid (CSF) and serum, and an intracellular (IC) Cu deficiency in the brain. Cu-deficient superoxide dismutase (SOD1) contributes to the reduced antioxidant capacity of the brain, allowing further oxidative stress.", "citation": {"db": "PubMed", "db_id": "15910549"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 3391, "target": 519, "key": "08e6e10140f0e1f14cf221e7b7513c61"}, {"line": 7567, "relation": "positiveCorrelation", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Species": {"7227": true, "6239": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3391, "target": 850, "key": "e48ea98ef224970b98a6f0edef202538"}, {"line": 15688, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3391, "target": 3823, "key": "97205a3456e9c767927a962589b42aa3"}, {"relation": "partOf", "source": 3391, "target": 1693, "key": "f08a9bd8a5728c6e94379a5911e97ff6"}, {"relation": "hasVariant", "source": 3391, "target": 3393, "key": "78eb98b8ef43e97527002d137bab5f0c"}, {"relation": "partOf", "source": 3391, "target": 1277, "key": "5802c03ac05c9ab1452be6c76e2678e6"}, {"relation": "hasVariant", "source": 3391, "target": 3392, "key": "77fd56af9ce0d778699f355002bb9bad"}, {"line": 38238, "relation": "increases", "evidence": "The pharmacological blockage of autophagy resulted in a dramatic increase of mutant SOD1 aggregates. Immunoprecipitation studies, performed during autophagic flux blockage, demonstrated that mutant SOD1 interacts with the HspB8/Bag3/Hsc70/CHIP multiheteromeric complex, known to selectively activate autophagic removal of misfolded proteins. ", "citation": {"db": "PubMed", "db_id": "20570967"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "source": 3391, "target": 2328, "key": "c7c8ec7920c889075a43e69d7eebe9ce"}, {"line": 5229, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "source": 3901, "target": 3823, "key": "980e122efe061cef195c892b48c4e932"}, {"line": 10632, "relation": "association", "evidence": "Lysosomal beta-galactosidase and beta-hexosaminidase activities correlate with clinical stages of dementia associated with Alzheimer's disease and type 2 diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "CellStructure": {"Lysosomes": true}, "Confidence": {"High": true}}, "source": 3901, "target": 3823, "key": "49adfefb45ce87e5dc3dfbae698dd40c"}, {"line": 18599, "relation": "association", "evidence": "Alzheimer's disease (AD) is the most common cause of dementia in the elderly.", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3901, "target": 3823, "key": "da685813e2f082f808ef22c5a797ffc4"}, {"line": 19647, "relation": "association", "evidence": "Alzheimer's disease (AD) is the most common type of dementia accounting for 60-80% of the reported cases.", "citation": {"db": "PubMed", "db_id": "23871825"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 3901, "target": 3823, "key": "99b32755f4162b8acac2644839bda560"}, {"line": 41198, "relation": "association", "evidence": "Among neurodegenerative disorders, Alzheimer's disease (AD) represents the most common cause of dementia in the elderly.", "citation": {"db": "PubMed", "db_id": "24860504"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true, "Dementia": true}}, "source": 3901, "target": 3823, "key": "e6025d4a98b47f2fc245e154194f5f93"}, {"line": 6279, "relation": "negativeCorrelation", "evidence": "This study carries additional significance because it established that, like all other pancreatic and intestinal polypeptide genes, the insulin gene was also expressed in the adult human brain. Moreover, the results taught us that endogenous brain deficiencies in insulin, IGF-1, IGF-2, and their corresponding receptors, in the absence of T2DM or obesity, could be linked to the most common form of dementia-associated neurodegeneration in the Western hemisphere.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3901, "target": 2899, "key": "3ab65c2388fe5f1056e3adb3b37f72af"}, {"line": 6280, "relation": "negativeCorrelation", "evidence": "This study carries additional significance because it established that, like all other pancreatic and intestinal polypeptide genes, the insulin gene was also expressed in the adult human brain. Moreover, the results taught us that endogenous brain deficiencies in insulin, IGF-1, IGF-2, and their corresponding receptors, in the absence of T2DM or obesity, could be linked to the most common form of dementia-associated neurodegeneration in the Western hemisphere.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3901, "target": 2871, "key": "8a1368eabfbfd6161dd8a8e4f317af76"}, {"line": 6281, "relation": "negativeCorrelation", "evidence": "This study carries additional significance because it established that, like all other pancreatic and intestinal polypeptide genes, the insulin gene was also expressed in the adult human brain. Moreover, the results taught us that endogenous brain deficiencies in insulin, IGF-1, IGF-2, and their corresponding receptors, in the absence of T2DM or obesity, could be linked to the most common form of dementia-associated neurodegeneration in the Western hemisphere.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3901, "target": 2874, "key": "6bb39928a791d5acb1056ca3db310d67"}, {"line": 6644, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 3901, "target": 3847, "key": "6118eca86e419b8e63cb12d2577bc625"}, {"line": 6646, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 3901, "target": 1744, "key": "d3cfe1121c3a99f84f53104727813bdf"}, {"line": 10627, "relation": "association", "evidence": "Lysosomal beta-galactosidase and beta-hexosaminidase activities correlate with clinical stages of dementia associated with Alzheimer's disease and type 2 diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "CellStructure": {"Lysosomes": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3901, "target": 2829, "key": "aa68ec62ae730f5c4bdc958d4ebb98e3"}, {"line": 10631, "relation": "association", "evidence": "Lysosomal beta-galactosidase and beta-hexosaminidase activities correlate with clinical stages of dementia associated with Alzheimer's disease and type 2 diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "CellStructure": {"Lysosomes": true}, "Confidence": {"High": true}}, "source": 3901, "target": 3850, "key": "7107ca6bf9d3119478cb9760767d1478"}, {"line": 17526, "relation": "decreases", "evidence": "The secretion of IL-2 was markedly low in the demented patients, compared with both elderly and middle-aged subjects.", "citation": {"db": "PubMed", "db_id": "11961364"}, "annotations": {"Disease": {"dementia": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3901, "target": 2888, "key": "319b8c8a5d6eb99556454390475e0a3a"}, {"line": 17574, "relation": "association", "evidence": "ABCB1 genotypes are presently not useful as a biomarker for dementia, as they were not significantly different between demented patients and age-matched control subjects.", "citation": {"db": "PubMed", "db_id": "16999857"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true}, "Disease": {"dementia": true}, "Species": {"9606": true}}, "source": 3901, "target": 2232, "key": "cdf997ebaf202d041e9f55f6174b5977"}, {"line": 17742, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS) has been highlighted as having a pathological role in stroke, dementia, and neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Stroke": true, "Dementia": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3901, "target": 844, "key": "8cdaedd351a46ef1e3ccabb23d642f29"}, {"line": 18797, "relation": "association", "evidence": "Our findings support the hypothesis that MMPs may influence the risk of dementia.", "citation": {"db": "PubMed", "db_id": "16822591"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3901, "target": 2194, "key": "5f5e2453a6547e3007ca6d8022fe3fd5"}, {"line": 41197, "relation": "association", "evidence": "Among neurodegenerative disorders, Alzheimer's disease (AD) represents the most common cause of dementia in the elderly.", "citation": {"db": "PubMed", "db_id": "24860504"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true, "Dementia": true}}, "source": 3901, "target": 3874, "key": "007aaa249b4dbd66558c901636d7d4e1"}, {"line": 5234, "relation": "directlyDecreases", "evidence": "Alzheimer's disease (AD) is associated with accumulations of amyloid-beta (Abeta) peptides, oxidative damage, mitochondrial dysfunction, neurodegeneration, and dementia", "citation": {"db": "PubMed", "db_id": "16687508"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response to oxidative stress": true}}, "subject": {"modifier": "Activity"}, "source": 3394, "target": 3823, "key": "3c53fd332fa19bf721d30a6ed1aaeee7"}, {"line": 5250, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 3374, "target": 80, "key": "ecad5c35a617288afdb9715967f83efb"}, {"relation": "partOf", "source": 3374, "target": 1613, "key": "adb0437d0e3124b4e7ab7de69f5eb1cd"}, {"line": 5251, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 3372, "target": 80, "key": "ec656de27073677621ac4851e4553ba0"}, {"relation": "partOf", "source": 3372, "target": 1611, "key": "68798484f146f1b9190e3bf4856fd7d0"}, {"line": 5252, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 3373, "target": 80, "key": "3bf6d0452dd8acb3054f214707e37f85"}, {"relation": "partOf", "source": 3373, "target": 1612, "key": "ac20e3b232258ff29c3594886a7ade38"}, {"line": 5253, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 3371, "target": 80, "key": "fbf326cc63ed0a20f2cb77f6d9b35352"}, {"relation": "partOf", "source": 3371, "target": 1610, "key": "ba4e39edd9410639926853883c7d44e7"}, {"line": 5260, "relation": "association", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 3215, "target": 2328, "key": "0c658b637a9a8dc76e8cd6aa97614504"}, {"relation": "partOf", "source": 3215, "target": 939, "key": "2159149bd436d3243758dde5feaacc7f"}, {"line": 5263, "relation": "increases", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 1243, "target": 584, "key": "f871d6f7f92d8404f6c8d870db885ad3"}, {"relation": "partOf", "source": 3214, "target": 1243, "key": "ee74cee6a482f0969e533d17246c5e89"}, {"relation": "partOf", "source": 3214, "target": 1613, "key": "4e74b6cee97dccafa4da4bf747bc8895"}, {"relation": "partOf", "source": 3214, "target": 1612, "key": "29a2f029a586e2074a22ef9309958ef9"}, {"relation": "partOf", "source": 3214, "target": 1611, "key": "3533486869676941a18e092e53fc819e"}, {"relation": "partOf", "source": 3214, "target": 1610, "key": "857030efdfb3c2b00baec57382721697"}, {"line": 37148, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3214, "target": 2328, "key": "abb2e4d223514d6b315334bfef0492cc"}, {"line": 5265, "relation": "increases", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 1613, "target": 633, "key": "a99a5d718141b330b887cfe91a729d11"}, {"line": 5269, "relation": "increases", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 633, "target": 478, "key": "ce4daa26eaaa70cb2eec9927bc5844e8"}, {"line": 5266, "relation": "increases", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 1612, "target": 633, "key": "f09e8f6aa2647668a77e2c983e2b8e27"}, {"line": 5267, "relation": "increases", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 1611, "target": 633, "key": "3864e0c1034fcc2973a9b2134f033c6d"}, {"line": 5268, "relation": "increases", "evidence": "Mitochondrial cyclophilin D: Once inside the mitochondria, Abeta is able to interact with a number of targets, including the mitochondrial proteins ABAD and cyclophilin-D (CypD). Opening the mitochondrial permeability transition pore (MPTP) to depolarize mitochondria and release cytochrome C may be central to mitochondrial and neuronal malfunction in AD patients. CypD, an integral part of the MPTP, whose opening leads to cell death, interacts with Abeta peptide within the mitochondria of AD patients and a Tg mouse model of AD. MPTP causes mitochondrial swelling, outer membrane rupture, release of cell death mediators and enhances production of reactive oxygen species (ROS). Computer simulation studies show that Abeta interacts with both ANT and CypD. CypD/Abeta interaction causes an oxidative stress and increased MPTP opening that triggers neurodegeneration. CypD-deficient cortical mitochondria are resistant to Abeta- and Ca2+-induced mitochondrial swelling and MPTP opening. Adenine nucleotide translocase (ANT) is a transport protein for ADP and ATP and component of MPTP that binds directly to CypD. This interaction may facilitate its anchoring in the inner membrane and disturbance of the mitochondrial membrane potential, mitochondrial swelling and cell death. Interestingly, the MPTP also requires the participation of members of the Bcl-2 family proteins but a clear understanding of the interaction of Abeta with CypD together with both proapoptotic or antiapoptotic Bcl-2 family proteins in AD has not been made. The ability of CypD to protect neurons from Abeta- and oxidative stress-induced cell death and its role in improvement of synaptic and cognitive functions has been suggested to provide a new therapeutic approach for the treatment of conditions associated with AD. Together these studies provide new mechanisms for Abeta targets that link the MPTP to synaptic stress and the neurodegeneration seen in AD.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 1610, "target": 633, "key": "77ebd22e9cb4af5e766ed62841bfc5d6"}, {"line": 5279, "relation": "increases", "evidence": "Mitochondrial Abeta-binding alcohol dehydrogenase (ABAD): ABAD is a member of the short chain dehydrogenase reductase family in mitochondria that binds Abeta. Binding of Abeta to ABAD distorts the enzymebetas structure, rendering it inactive. In neurons, ABAD is predominately localized to mitochondria. Upon binding ABAD, Abeta triggers events leading to neuronal apoptosis through a mitochondrial pathway.Interestingly, mitochondrial ABAD is upregulated in neurons from AD patients. The ABAD-Abeta complex has been hypothesized to induce oxidant stress and mitochondrial dysfunction. Increased expression of ABAD exacerbates Abeta-mediated mitochondrial and neuronal stress. Abeta binding to ABAD causes free radical production and neuronal apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true, "Free radical formation subgraph": true, "Metabolism of steroid hormones subgraph": true}, "Confidence": {"High": true}}, "source": 1229, "target": 400, "key": "b1bac395c14e9e5e862582a82cbb54a7"}, {"line": 5293, "relation": "decreases", "evidence": "Mitochondrial Abeta-binding alcohol dehydrogenase (ABAD): ABAD is a member of the short chain dehydrogenase reductase family in mitochondria that binds Abeta. Binding of Abeta to ABAD distorts the enzymebetas structure, rendering it inactive. In neurons, ABAD is predominately localized to mitochondria. Upon binding ABAD, Abeta triggers events leading to neuronal apoptosis through a mitochondrial pathway.Interestingly, mitochondrial ABAD is upregulated in neurons from AD patients. The ABAD-Abeta complex has been hypothesized to induce oxidant stress and mitochondrial dysfunction. Increased expression of ABAD exacerbates Abeta-mediated mitochondrial and neuronal stress. Abeta binding to ABAD causes free radical production and neuronal apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true, "Metabolism of steroid hormones subgraph": true}, "Confidence": {"High": true}}, "source": 1229, "target": 614, "key": "01ea5a39f00598633e2cacce1d703393"}, {"line": 5301, "relation": "increases", "evidence": "Mitochondrial Abeta-binding alcohol dehydrogenase (ABAD): ABAD is a member of the short chain dehydrogenase reductase family in mitochondria that binds Abeta. Binding of Abeta to ABAD distorts the enzymebetas structure, rendering it inactive. In neurons, ABAD is predominately localized to mitochondria. Upon binding ABAD, Abeta triggers events leading to neuronal apoptosis through a mitochondrial pathway.Interestingly, mitochondrial ABAD is upregulated in neurons from AD patients. The ABAD-Abeta complex has been hypothesized to induce oxidant stress and mitochondrial dysfunction. Increased expression of ABAD exacerbates Abeta-mediated mitochondrial and neuronal stress. Abeta binding to ABAD causes free radical production and neuronal apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true, "Metabolism of steroid hormones subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 1229, "target": 519, "key": "741410ac8457fe9662c7a42b1ac1292c"}, {"line": 5422, "relation": "increases", "evidence": "The interaction of CypD with Abeta causes functional modification of this protein leading to MPTP opening. Abeta also binds with another mitochondrial protein, ABAD to distort the enzymebetas structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 1229, "target": 170, "key": "d01e80684fd5cefc7d0715216cda7a3a"}, {"line": 5426, "relation": "increases", "evidence": "The interaction of CypD with Abeta causes functional modification of this protein leading to MPTP opening. Abeta also binds with another mitochondrial protein, ABAD to distort the enzymebetas structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 1229, "target": 478, "key": "5e1c18f1725b215168bca95ab5657963"}, {"line": 34287, "relation": "association", "evidence": "By interacting with intracellular amyloid-beta, ERAB may therefore contribute to the neuronal dysfunction associated with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9338779"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1229, "target": 431, "key": "6067cddacc573d0c4ef8f8e61dde9d12"}, {"line": 49443, "relation": "association", "evidence": "Antioxidants scavenge free radicals and other reactive oxygen species that damage cellular membranes, organelles, and macromolecules. Accumulation of reactive oxygen species may overwhelm the protective reserves of antioxidants in cells (oxidative stress). In neurons, which are especially vulnerable to free radical–mediated damage, these processes may be important in aging of the brain and the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "annotations": {"Cell": {"neuron": true}}, "source": 400, "target": 3823, "key": "2dc1b9a6d207e65cd60aaf276b4d5ae8"}, {"line": 5315, "relation": "increases", "evidence": "Lower levels of ROS are required for synaptic signaling with ROS acting as messenger molecules in the process of LTP. However, high levels of ROS have been implicated as damaging toxic molecules in the age-related impairments of LTP. Our previous work shows that ROS levels increase with age of neurons in parallel with an age-related decline in transmembrane potential. As mitochondrial transmembrane potential is a driving force for cellular production of ATP, its decline in neurons will have a long term effect in many important energy driven reactions.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 682, "target": 190, "key": "84dee6e5219cb4ce61bb8d179c1cbc0f"}, {"line": 22076, "relation": "association", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 682, "target": 86, "key": "3e7e1c0f61391f5f8881caff13e358a7"}, {"line": 22285, "relation": "positiveCorrelation", "evidence": "knockdown of PTPA induced cell apoptosis in HEK293 and N2a cell lines. PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 682, "target": 3282, "key": "ec100330e3a2d55273050af80ec62dd6"}, {"line": 5323, "relation": "decreases", "evidence": "Increased oxidative stress, coupled with dysregulation of calcium homeostasis and resulting apoptosis of vulnerable neuronal populations, are proposed to underlie the loss of synaptic activity and associated cognitive decline. From these deficiencies emerges the concept of synaptic energy exhaustion in AD, both phosphorylative (ATP) and redox (NAD[P]H) energies. Our previous work shows that hippocampal NAD(P)H and glutathione (GSH) decline with age in association with increased susceptibility to glutamate toxicity in neurons of old-age. Thus, an age-related decline in neuronal reducing currency (NAD[P]H) and reducing buffer (GSH) will surely promote oxidative stress and excess ROS. It is noteworthy that in the early stages of AD, there is already a reduction in the number of mitochondria and the activities of tricarboxylic acid cycle enzymes and cytochrome C oxidase. However, how ROS are produced at the synapse in response to Abeta oligomers is not fully known.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 842, "target": 760, "key": "dc74496aaf8a80f3ac699d135a3d78aa"}, {"line": 5393, "relation": "association", "evidence": "Many studies suggest the possible involvement of oxidative stress and calcium dysfunction in Abeta toxicity.The question as to why brain synaptic ROS levels increase with age is uncertain, but may involve lack of use followed by acute overstimulation of excitatory NMDARs that leads to excessive ROS, related to excess Ca2+ entry into mitochondria. Dysregulation of NMDAR function induced by Abeta binding to neuronal synapses may lead to synaptic mitochondrial dysfunction and excessive ROS formation.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Calcium-dependent signal transduction": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 842, "target": 79, "key": "99796d100c51090a6b49e4c762544ffe"}, {"line": 5405, "relation": "association", "evidence": "Memory mechanisms might be directly compromised by elevated ROS, which could explain the connection between AD and oxidative stress. The increase in oxidative damage exhibited by synaptic mitochondria will damage synapses, affect neurotransmission and might be ultimately responsible for cognitive decline in AD patients. Taken together these studies provide convincing evidence for the concept that mitochondria have a pivotal role in Abeta-induced synaptic dysfunction and neuronal stress. Improved function of mitochondria is an effective way of reducing effects of aging and may inhibit neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 842, "target": 3823, "key": "5d3d9f4fa852b32f58768819287b1189"}, {"line": 9688, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3823, "key": "bac59877307426fa5c231424a6cd5990"}, {"line": 14082, "relation": "association", "evidence": "Oxidative stress has been suggested to play an important role in the pathogenesis of various neurodegenerative diseases including Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3823, "key": "54c9b0c41500c19e4ea86f99b3342ec9"}, {"line": 40108, "relation": "association", "evidence": "This is particularly the case with Alzheimer's disease, the most common age-related dementia associated with impairments in learning and memory accompanied by neuroinflammation, oxidative stress and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "24256258"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 842, "target": 3823, "key": "d2608172abeed283774b02b836a2819e"}, {"line": 6111, "relation": "positiveCorrelation", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 842, "target": 3850, "key": "b3bd8e6a3fc69d21587bb57a4c055ca0"}, {"line": 9696, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3850, "key": "e5198f7a2a5737daa8871f778e7c7b8c"}, {"line": 6312, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 842, "target": 580, "key": "7a1c97dfa5b9aa6179f365e0c87b37cf"}, {"line": 6316, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 842, "target": 583, "key": "7365526f7a2e3c94afdc44e2c8b8988d"}, {"line": 6377, "relation": "positiveCorrelation", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 842, "target": 1746, "key": "16c35a7856f94f643497c879914c67f4"}, {"line": 6400, "relation": "increases", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 1746, "key": "dee92efbd75d3d5258f81985fc0a33a2"}, {"line": 6378, "relation": "positiveCorrelation", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 842, "target": 2315, "key": "675f7678e6f5267491326d4cfec9fc38"}, {"line": 6404, "relation": "increases", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 4101, "key": "909706a877dbe1cfdc28dea9a70c1147"}, {"line": 11673, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 434, "key": "1d8ce192601d1da23a5b1d16d7d08f47"}, {"line": 14078, "relation": "association", "evidence": "Oxidative stress has been suggested to play an important role in the pathogenesis of various neurodegenerative diseases including Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3874, "key": "928ca2a02df6e9a3672c89cba0dbb03e"}, {"line": 16611, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3874, "key": "f1e61b5955c87c5d18736ecbe1302436"}, {"line": 18530, "relation": "association", "evidence": "The increase in neuronal myeloperoxidase expression we observed in Alzheimer disease brains raises the possibility that the enzyme contributes to the oxidative stress implicated in the pathogenesis of the neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"Disease": {"neurodegenerative disease": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3874, "key": "162b9ee6ce0a824ae46d97d9c22512eb"}, {"line": 14907, "relation": "positiveCorrelation", "evidence": "Stress of the endoplasmic reticulum and oxidative stress play critical roles in the pathogenesis of Fuchs Endothelial Corneal Dystrophy (FECD).", "citation": {"db": "PubMed", "db_id": "22956607"}, "annotations": {"Disease": {"Fuchs' endothelial dystrophy": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3856, "key": "d07b3d7d99e893d21d82627aa1fc5058"}, {"line": 16026, "relation": "increases", "evidence": "Bcl-xL protein was also up-regulated during oxidative stress induced by exposure to hydrogen peroxide (3-100microM) or ferric ions (1-10microM).", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Subgraph": {"Hydrogen peroxide subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 2394, "key": "3125c73ecce41ff38874a9d53d11772f"}, {"line": 16496, "relation": "association", "evidence": "The increase in neuronal myeloperoxidase expression we observed in Alzheimer disease brains raises the possibility that the enzyme contributes to the oxidative stress implicated in the pathogenesis of the neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"Disease": {"neurodegenerative disease": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3066, "key": "b723e91399ba1319e6006528081bbc0e"}, {"line": 16567, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3409, "key": "566bb790730fcb8cf602115c236ee6dc"}, {"line": 17790, "relation": "association", "evidence": "The data concerning the bioactive fragments of angiotensin II will be accompanied by those regarding its implication in the cardiovascular modeling and the induction of oxidative stress, inflammation, atherogenesis, etc.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHDisease": {"Inflammation": true, "Atherosclerosis": true}}, "source": 842, "target": 81, "key": "d4aae476fa357b61502c9e88adddbd44"}, {"line": 18022, "relation": "increases", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 505, "key": "b4aed19dad5246ba3996f2eec4f3fb4b"}, {"line": 18059, "relation": "increases", "evidence": "However, in response to oxidative stress, NRF2 translocates to the nucleus and binds to specific DNA sites termed anti-oxidant response elements or electrophile response elements to initiate transcription of cytoprotective genes.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 842, "target": 3110, "key": "7b7468a7c3ba68906943b38d0c774d81"}, {"line": 18135, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}, "Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 842, "target": 3110, "key": "d3819d012d95efc6a3df1f7b4c3c3b06"}, {"line": 18204, "relation": "association", "evidence": "We hypothesize that common variants of NFE2L2 and KEAP1, the genes encoding the main regulators of the Nrf2 system, an important defence system against oxidative stress, may influence risk of AD and/or age-related cataract.", "citation": {"db": "PubMed", "db_id": "20064547"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3110, "key": "df15228fbe5b519883aef324c48db141"}, {"line": 18208, "relation": "association", "evidence": "We hypothesize that common variants of NFE2L2 and KEAP1, the genes encoding the main regulators of the Nrf2 system, an important defence system against oxidative stress, may influence risk of AD and/or age-related cataract.", "citation": {"db": "PubMed", "db_id": "20064547"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 2945, "key": "2b41a42b27e709cfaeac56326cd1acf7"}, {"line": 19390, "relation": "association", "evidence": "Because Abeta is known to induce oxidative stress in astrocytes, we examined the effects of the antioxidants tempol and apocynin on astrocytic TSP-1 levels and release.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Astrocytes": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 842, "target": 3459, "key": "9b905babba444ec956991770a8a4a154"}, {"line": 20862, "relation": "increases", "evidence": "Induction of uPAR surface expression by microglia was inhibited by the antioxidant N-acetyl-cysteine, indicating that this gene may be induced as a result of oxidative stress-related mechanisms.", "citation": {"db": "PubMed", "db_id": "11814408"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 842, "target": 3202, "key": "4e76da1ac4707a9c9438c531f66f2041"}, {"line": 21337, "relation": "increases", "evidence": "Oxidative stress activates the PKCdelta kinase by translocation, tyrosine phosphorylation, or proteolysis.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 842, "target": 3238, "key": "e1fec4871e7f2488eb5424a9deb65a02"}, {"line": 21340, "relation": "increases", "evidence": "Oxidative stress activates the PKCdelta kinase by translocation, tyrosine phosphorylation, or proteolysis.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 842, "target": 3238, "key": "b1e42806c0fe805490d2de58a1d51c5e"}, {"line": 21342, "relation": "increases", "evidence": "Oxidative stress activates the PKCdelta kinase by translocation, tyrosine phosphorylation, or proteolysis.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 842, "target": 3238, "key": "c63148362383e04dfd1bcf56e18771a3"}, {"line": 21338, "relation": "increases", "evidence": "Oxidative stress activates the PKCdelta kinase by translocation, tyrosine phosphorylation, or proteolysis.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 842, "target": 3242, "key": "e204502a54aa5311f98a286fa49a78b1"}, {"line": 22963, "relation": "decreases", "evidence": "Recently, we have reported that intracerebroventricular (ICV) administration of okadaic acid (OKA) in rats induces memory impairment that was associated with increased oxidative stress. Besides memory deficit, OKA caused impairment in mitochondrial function as revealed by alterations in calcium ion, reactive oxygen species (ROS) generation, mitochondrial membrane potential (MMP), SDH activity and ATP level in the brain regions. Further, in histopathological study it was observed that donepezil and memantine reduced the cell loss and neurodegeneration in hippocampus and periventricular cortex regions in OKA treated rats.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 820, "key": "fa7148580d0462df5a9f57aa05b5151f"}, {"line": 23473, "relation": "association", "evidence": "As a step in searching for possible antioxidative mechanisms of riluzole, we tested in test-tube conditions its effects on the activity of PLA2, an enzyme that is linked to oxidative injury via the AA cascades (Janssen-Timmen et al., 1994 ; Katsuki and Okuda, 1995). Riluzole (10-100 μM), in a concentration-dependent manner, attenuated the activity of cPLA2, but not that of group I and group II PLA2", "citation": {"db": "PubMed", "db_id": "9930745"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 842, "target": 3198, "key": "b6a05d8c89b995034676765d67161642"}, {"line": 23529, "relation": "association", "evidence": "These findings show that riluzole maintains altered oxidant-antioxidant balance. Consistently, previous studies have shown the antioxidant effect of riluzole [19, 20 and 21]. In the study of Koh et al. [ 19], riluzole, besides preventing the excitotoxic neuronal damage, was also effective against FeCl3 induced nonexcitotoxic injury in cortical neuron cultures. In another study, riluzole was shown to protect the dopaminergic neurons against oxidative stress by reducing lipid peroxidation and adenosine triphosphate consumption [ 21]. It has been suggested that the mechanism involved in the protective effects in nonexcitotoxic oxidant damage was inhibition of PLA2, thereby reducing arachidonic acid and its metabolites, and further inhibition of protein kinase C [ 43].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 3198, "key": "479d8fe4a24f6064ab54da1a1a72cce6"}, {"line": 46459, "relation": "increases", "evidence": "We have examined this by using oxidative stress to induce apoptosis in a mouse hippocampal neuronal cell line (HT-22). Oxidatively modified proteins were measured by high-resolution two-dimensional gel electrophoresis coupled with oxidation-specific immunostains.Under these conditions the oxidatively stressed cells undergo apoptotic process, and specific proteins are oxidized. The three proteins that appeared to be most susceptible to oxidation were identified by mass spectrometry. Those oxidized proteins are heat shock protein 60 and vimentin, both believed to function as antiapoptotic proteins, and a third protein with sequence homology to hemoglobin alpha-chain. When the cells were pretreated with vitamin E, these proteins were not oxidized and the cells did not undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "12548636"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 842, "target": 478, "key": "5eee0de9f86433b91e8495ae355f89f8"}, {"line": 5347, "relation": "decreases", "evidence": "Increased oxidative stress, coupled with dysregulation of calcium homeostasis and resulting apoptosis of vulnerable neuronal populations, are proposed to underlie the loss of synaptic activity and associated cognitive decline. From these deficiencies emerges the concept of synaptic energy exhaustion in AD, both phosphorylative (ATP) and redox (NAD[P]H) energies. Our previous work shows that hippocampal NAD(P)H and glutathione (GSH) decline with age in association with increased susceptibility to glutamate toxicity in neurons of old-age. Thus, an age-related decline in neuronal reducing currency (NAD[P]H) and reducing buffer (GSH) will surely promote oxidative stress and excess ROS. It is noteworthy that in the early stages of AD, there is already a reduction in the number of mitochondria and the activities of tricarboxylic acid cycle enzymes and cytochrome C oxidase. However, how ROS are produced at the synapse in response to Abeta oligomers is not fully known.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 198, "target": 170, "key": "9cfa9054bb95fab3450f86a34c7f9066"}, {"line": 5351, "relation": "decreases", "evidence": "Increased oxidative stress, coupled with dysregulation of calcium homeostasis and resulting apoptosis of vulnerable neuronal populations, are proposed to underlie the loss of synaptic activity and associated cognitive decline. From these deficiencies emerges the concept of synaptic energy exhaustion in AD, both phosphorylative (ATP) and redox (NAD[P]H) energies. Our previous work shows that hippocampal NAD(P)H and glutathione (GSH) decline with age in association with increased susceptibility to glutamate toxicity in neurons of old-age. Thus, an age-related decline in neuronal reducing currency (NAD[P]H) and reducing buffer (GSH) will surely promote oxidative stress and excess ROS. It is noteworthy that in the early stages of AD, there is already a reduction in the number of mitochondria and the activities of tricarboxylic acid cycle enzymes and cytochrome C oxidase. However, how ROS are produced at the synapse in response to Abeta oligomers is not fully known.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 198, "target": 842, "key": "0a3c74ef055123aa3177ad34873930e4"}, {"line": 5367, "relation": "decreases", "evidence": "Excessive ROS are locally generated in response to synaptic Abeta oligomer binding. This ROS formation can be totally blocked by the mitochondrial uncoupler, 2,4-dinitrophenol which suggests a central role of mitochondria in Abeta-induced oxidative stress.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 16, "target": 842, "key": "3ebfa69569dc3711e53525a288c04b34"}, {"line": 9692, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 430, "target": 3823, "key": "dade8b0d5cf8400dcc4f4327e45cb62d"}, {"line": 9700, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 430, "target": 3850, "key": "481fb7cff0fbf4e9696da72bfe27fd8e"}, {"line": 25643, "relation": "association", "evidence": "Abeta progressively accumulates in mitochondria and mediates mitochondrial toxicity", "citation": {"db": "PubMed", "db_id": "17424907"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 430, "target": 80, "key": "311ea097563a4ea32f5799d70b821316"}, {"line": 30786, "relation": "negativeCorrelation", "evidence": "The levels of hexokinase I (HXKI), which interacts with VDAC1 and affects its function, were decreased in mitochondrial samples from AD pmodels.", "citation": {"db": "PubMed", "db_id": "20930307"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 430, "target": 1452, "key": "3dae0a825b2a472581ae635669f280e8"}, {"line": 5401, "relation": "association", "evidence": "Memory mechanisms might be directly compromised by elevated ROS, which could explain the connection between AD and oxidative stress. The increase in oxidative damage exhibited by synaptic mitochondria will damage synapses, affect neurotransmission and might be ultimately responsible for cognitive decline in AD patients. Taken together these studies provide convincing evidence for the concept that mitochondria have a pivotal role in Abeta-induced synaptic dysfunction and neuronal stress. Improved function of mitochondria is an effective way of reducing effects of aging and may inhibit neuronal cell death in AD", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3866, "target": 170, "key": "2aaf52eaa16745a99b622085747fde4b"}, {"line": 5413, "relation": "increases", "evidence": "The interaction of CypD with Abeta causes functional modification of this protein leading to MPTP opening. Abeta also binds with another mitochondrial protein, ABAD to distort the enzymebetas structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true}, "Confidence": {"Medium": true}}, "source": 939, "target": 888, "key": "734f5e30c4fdfcbb4c34c4ac80bf7010"}, {"line": 31425, "relation": "increases", "evidence": "Here we show that interaction of CypD with mitochondrial amyloid-beta protein (Abeta) potentiates mitochondrial, neuronal and synaptic stress.", "citation": {"db": "PubMed", "db_id": "18806802"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 939, "target": 845, "key": "aff9a4f24e1ca085e8226eb85bdcf9ce"}, {"line": 24334, "relation": "increases", "evidence": "A major distinction between apoptosis and necrosis is that the former requires ATP whilst the latter occurs in its absence [24,25]. Only if the MPTP opens sufficiently to cause cytochrome c release but then closes again to ensure that cellular ATP concentrations are maintained will the cell undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "14962470"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 888, "target": 2608, "key": "2032c0fc312718e43b17cc2bda7b5a47"}, {"line": 5447, "relation": "increases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2252, "target": 2315, "key": "5c6f4363b1f9d1808b0e62c0c4781b83"}, {"line": 5450, "relation": "increases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2252, "target": 80, "key": "7058b9aaf1971699a2df3ca15912f2b3"}, {"line": 5449, "relation": "increases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2250, "target": 2315, "key": "474802b232487313c6e3068dad7ae0c6"}, {"line": 5452, "relation": "increases", "evidence": "Activation of NMDARs decreases a-secretase cleavage, consequently increasing Abeta levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2250, "target": 80, "key": "bf29d1ece4a4383dc9bdfd1eefe06cc2"}, {"line": 24894, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs). ", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Wrong": true}}, "object": {"modifier": "Degradation"}, "source": 2250, "target": 2534, "key": "6bebbbf6a31ab19a530d4dae909f77e7"}, {"line": 25516, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs).", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2250, "target": 2534, "key": "e5b12d33a2bc732188580a10e2e7f5ce"}, {"line": 24947, "relation": "decreases", "evidence": "Interleukin-1 beta up-regulates TACE to enhance alpha-cleavage of APP in neurons: resulting decrease in Abeta production.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2250, "target": 4101, "key": "b2e6a645ca432098e034c0cd3a83a874"}, {"line": 25517, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs).", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2250, "target": 2533, "key": "89cbec58877d165c4265a7ecaa127a75"}, {"line": 25518, "relation": "increases", "evidence": "We found that APP alpha-secretases ADAM 10 and ADAM 17 primarily cleave Alc proteins and trigger the subsequent secondary intramembranous cleavage of Alc C-terminal fragments by a presenilin-dependent gamma-secretase complex, thereby generating APP p3-like and non-aggregative Alc peptides (p3-Alcs).", "citation": {"db": "PubMed", "db_id": "19864413"}, "annotations": {"Subgraph": {"Calsyntenin subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2250, "target": 2532, "key": "783ad62ecf577061107f3ad32335758a"}, {"line": 5470, "relation": "increases", "evidence": "Akt substrates such as mammalian target of rapamycin (mTOR; Ser2448) and decreased levels of cell-cycle inhibitors (p27kip1) are found in AD temporal cortex when compared to controls. GSK-3a has been implicated in the production of Abeta peptide while increased GSK-3beta activity has been implicated in tau hyperphosphorylation and neuronal cell death", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 2792, "target": 80, "key": "8920b04fbe735dc4563d19651962056e"}, {"line": 33056, "relation": "increases", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2792, "target": 80, "key": "38ce7728f94c0ed41442d009457cde7f"}, {"relation": "hasVariant", "source": 2792, "target": 2793, "key": "30fd880f737fab8c5006df0a80cae56e"}, {"line": 30251, "relation": "increases", "evidence": "Both GSK-3alpha and 3beta phosphorylate purified pig brain CRMP-2 and significantly alter its mobility in SDS-gels, resembling the CRMP-2 pmodification observed in AD brain.", "citation": {"db": "PubMed", "db_id": "17902168"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "GSK3 subgraph": true, "Axonal guidance subgraph": true}, "Species": {"9823": true}, "Confidence": {"High": true}}, "source": 2792, "target": 2642, "key": "903749c466d1c9c5657b0aa24da47252"}, {"line": 30270, "relation": "increases", "evidence": "Ser-522 prephosphorylated by Cdk5 is required for subsequent GSK-3alpha-mediated phosphorylation of CRMP-2 in vitro.", "citation": {"db": "PubMed", "db_id": "17902168"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2792, "target": 2642, "key": "6311d7aa189d7c53213a90a25f27fc18"}, {"line": 30269, "relation": "positiveCorrelation", "evidence": "Ser-522 prephosphorylated by Cdk5 is required for subsequent GSK-3alpha-mediated phosphorylation of CRMP-2 in vitro.", "citation": {"db": "PubMed", "db_id": "17902168"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2792, "target": 2643, "key": "998ed4bb731778ee0a0a83dac32cc1f6"}, {"line": 30550, "relation": "increases", "evidence": "In sharp contrast, when Ser-10 of this peptide was replaced by a phosphoserine, the phosphopeptide fragment (VAVVRTPPKS(p)PSSAK) became an excellent substrate for kinase FA/GSK-3 alpha.", "citation": {"db": "PubMed", "db_id": "7505567"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2792, "target": 3011, "key": "618613f0f5dce01be0870d7233236f5a"}, {"line": 30559, "relation": "increases", "evidence": "Previously, we identified protein kinase FA/glycogen synthase kinase-3 alpha (GSK-3 alpha) as a brain microtubule-associated tau kinase that phosphorylates Ser235 and Ser404 of tau and causes its electrophoretic mobility shift in gels, a unique property characteristic of paired helical filament-associated pathological tau (PHF-tau) in Alzheimer's disease brains.", "citation": {"db": "PubMed", "db_id": "7931292"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2792, "target": 3022, "key": "fc4c6698fa8d091500d2cc7e84a9cafb"}, {"line": 30560, "relation": "increases", "evidence": "Previously, we identified protein kinase FA/glycogen synthase kinase-3 alpha (GSK-3 alpha) as a brain microtubule-associated tau kinase that phosphorylates Ser235 and Ser404 of tau and causes its electrophoretic mobility shift in gels, a unique property characteristic of paired helical filament-associated pathological tau (PHF-tau) in Alzheimer's disease brains.", "citation": {"db": "PubMed", "db_id": "7931292"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2792, "target": 3027, "key": "4e25c1e70208049a19a007a245e0027a"}, {"relation": "partOf", "source": 2792, "target": 1444, "key": "f4d14f6c654876c21277f028f2bac922"}, {"line": 5491, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"CREB subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3232, "target": 2164, "key": "21a7349f177a8536cf9b97823e7f0b7c"}, {"line": 35835, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3232, "target": 2164, "key": "a513af870e4640ad8ca49566fd6d30d2"}, {"line": 27838, "relation": "decreases", "evidence": "In contrast, BACE1 expression was suppressed by stimulation of M2-mediated pathways via selective M2-agonist binding or direct activation of adenylate cyclase with forskolin, an effect that was prevented by inhibiting protein kinase A.", "citation": {"db": "PubMed", "db_id": "15211591"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 3232, "target": 2254, "key": "740154c9104d8b291179c287ad064e23"}, {"line": 29695, "relation": "increases", "evidence": "These studies suggest that PKA, cdk5, CaM Kinase II and GSK-3 are involved in the regulation of phosphorylation of tau and that AD-type phosphorylation of tau is probably a product of the synergistic action of two or more of these kinases.", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3232, "target": 3015, "key": "6c2226405ad3e99ef6cd8afd2817088a"}, {"line": 29767, "relation": "increases", "evidence": "Phosphorylation of tau protein is regulated by several kinases, especially glycogen synthase kinase 3beta (GSK-3beta), cyclin-dependent protein kinase 5 (cdk5) and cAMP-dependent protein kinase (PKA).", "citation": {"db": "PubMed", "db_id": "17078951"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3232, "target": 3015, "key": "a93d227011efc8cae15eef103701df04"}, {"line": 34488, "relation": "increases", "evidence": "Aberrant glycosylation pmodulates phosphorylation of tau by protein kinase A and dephosphorylation of tau by protein phosphatase 2A and 5.", "citation": {"db": "PubMed", "db_id": "12435421"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3232, "target": 3015, "key": "e54c05670b55b335a125de8b578d356b"}, {"relation": "partOf", "source": 3232, "target": 1615, "key": "a824e42fc77ffa7e1d46f7b62a598d58"}, {"line": 36906, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3232, "target": 2163, "key": "d20019961f0b913cdd75676da0cb0d8d"}, {"line": 37099, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157]. In contrast to CaMKs, ERKs cannot directly phosphorylate CREB. Two related RSKs and mitogen- and stress-activated protein kinases (MSKs) transmit the signal from activated ERKs to CREB [158]. CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis [159, 160]. Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity [161]. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP) [162]. There is no direct evidence indicating how lower levels of ABeta¸ might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3232, "target": 2555, "key": "cab2c6aab2d271da90a143c1fa469bfb"}, {"line": 5504, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2164, "target": 2162, "key": "7eaf803b24867c870a078b623bf2b539"}, {"line": 35850, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2164, "target": 2162, "key": "8b88cbf8222e059f76be810a2354bcf3"}, {"line": 36502, "relation": "increases", "evidence": "Increased cytosolic calcium concentrations initiate the activation of several kinase-dependent signalling cascades including activation of PKC leading to CREB activation and phosphorylation at Ser133, a process critical for protein synthesis-dependent synaptic plasticity and LTP. PKC-a also activates ERK by interacting with Ras or Raf-1.Mitochondria are critical targets of intracellular ABeta¸. ABeta¸ interacts with CypD, a protein component of the membrane permeability transition pore (MPTP). The interaction of CypD with ABeta¸ causes functional modification of this protein leading to MPTP opening. ABeta¸ also binds with another mitochondrial protein, ABAD to distort the enzyme’s structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2164, "target": 2162, "key": "90f1b96099fdac9c635cbfae88468fd5"}, {"line": 5505, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 2164, "target": 669, "key": "73b568a018db57e96ce24964ae81b9e9"}, {"line": 35851, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 2164, "target": 669, "key": "5bd93a43afff19b8b07242e142801543"}, {"line": 5493, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"CREB subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3322, "target": 2164, "key": "266d545d64de1c53df24155f34844f37"}, {"line": 35837, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3322, "target": 2164, "key": "58935535f89c885a483ba96fdd0c3b09"}, {"relation": "partOf", "source": 3322, "target": 1719, "key": "9a7205aa793fb56436699abd886c8d8d"}, {"line": 37102, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157]. In contrast to CaMKs, ERKs cannot directly phosphorylate CREB. Two related RSKs and mitogen- and stress-activated protein kinases (MSKs) transmit the signal from activated ERKs to CREB [158]. CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis [159, 160]. Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity [161]. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP) [162]. There is no direct evidence indicating how lower levels of ABeta¸ might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3322, "target": 2555, "key": "9b813ae0eaedb4ccac7b5b052fd2c18d"}, {"line": 5495, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"CREB subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3324, "target": 2164, "key": "78211f86b7a7d3706afdbbe5382844d8"}, {"line": 35842, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3324, "target": 2164, "key": "9a5bcfb5d9dfb5573e3a51251a389384"}, {"line": 37103, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157]. In contrast to CaMKs, ERKs cannot directly phosphorylate CREB. Two related RSKs and mitogen- and stress-activated protein kinases (MSKs) transmit the signal from activated ERKs to CREB [158]. CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis [159, 160]. Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity [161]. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP) [162]. There is no direct evidence indicating how lower levels of ABeta¸ might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3324, "target": 2555, "key": "efa33ec34694e1382ffc630ff15fdd17"}, {"line": 5517, "relation": "association", "evidence": "CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 595, "target": 2162, "key": "d26e640101b31fd50b8f3481408bc431"}, {"line": 5532, "relation": "increases", "evidence": "Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP). There is no direct evidence indicating how lower levels of Abeta might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways. However, exceeding physiological levels of Abeta could deregulate Ca2+ signaling mechanism by excessive accumulation of Ca2+ in the cytoplasm and cytoplasmic organelles such as mitochondria. Since hippocampal neuronal calcium is one of the most potent signals in neuronal gene expression [149], Abeta-induced Ca2+ deregulation may lead to compromised synaptic function. Consistence with this hypothesis, AD has been associated with impaired cAMP signaling which may contribute to the pathophysiology of the disease. Levels of the activated (i.e. phosphorylated) form of CREB are reduced in AD compared to that of an age-matched healthy control group [164]. Calcium signaling to the cell nucleus is the key inducer of CREB phosphorylation on its activator site serine 133 [165]. Experiments in aged neurons show altered calcium signaling at the level of either calcium signal generation and/or calcium signal propagation [166]. These studies indicate a critical role of calcium in Abeta-induced synaptic activity and memory formation by regulating specific signal transduction pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2558, "target": 877, "key": "986510f3d8c9a156f8b2aeb1800c8c96"}, {"relation": "hasVariant", "source": 2558, "target": 2559, "key": "6c5f40bdbb37aba83dd3ee212c69347f"}, {"relation": "partOf", "source": 2558, "target": 1362, "key": "758ee612f91c7bd96751672fe9101c7e"}, {"line": 36818, "relation": "association", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "source": 688, "target": 2149, "key": "491dd2db4c8dd1368b538fb971416355"}, {"line": 36820, "relation": "association", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 688, "target": 2153, "key": "3da261e40ad0dc0c07ba217ee71d07f5"}, {"line": 48851, "relation": "association", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "source": 688, "target": 2097, "key": "ca21d4a3c627e8cc7046196155e2411b"}, {"line": 48867, "relation": "association", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 688, "target": 2658, "key": "7db23a5518a078a0a50971d9efbaafba"}, {"line": 43390, "relation": "association", "evidence": "Experimental evidence suggests that cortical noradrenaline (NA) depletion due to degeneration of the locus/ ceruleus (LC) - a pathological hallmark of AD - plays a permissive role in the development of inflammation in AD. Our/ results indicate for the first time that PPARgamma expression can be modulated by the cAMP signalling pathway, and/ suggest that the anti-inflammatory effects of NA on brain cells may be partly mediated by increasing PPARgamma levels./ Conversely, decreased NA due to LC cell death in AD may reduce endogenous PPARgamma expression and therefore potentiate/ neuroinflammatory processes.", "citation": {"db": "PubMed", "db_id": "12887689"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 490, "target": 3699, "key": "736b1f5658b8b959bc9f59e97e158899"}, {"line": 5559, "relation": "increases", "evidence": "Disturbances of the cholesterol metabolism are associated with Alzheimer's disease (AD) risk and related cerebral pathology. Experimental studies found changing levels of cholesterol and its metabolites 24S-hydroxycholesterol (24S-OHC) and 27-hydroxycholesterol (27-OHC) to contribute to amyloidogenesis by increasing the production of soluble amyloid precursor protein (sAPP).The results suggest that high CSF concentrations of cholesterol, 24S-OHC, and 27-OHC are associated with increased production of both sAPP forms in AD.", "citation": {"db": "PubMed", "db_id": "22845771"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 395, "target": 2137, "key": "f51195e77a44291296b8bf25c1d2548a"}, {"relation": "partOf", "source": 395, "target": 1655, "key": "d1308a74194789f2484852c04c58ac49"}, {"line": 5560, "relation": "increases", "evidence": "Disturbances of the cholesterol metabolism are associated with Alzheimer's disease (AD) risk and related cerebral pathology. Experimental studies found changing levels of cholesterol and its metabolites 24S-hydroxycholesterol (24S-OHC) and 27-hydroxycholesterol (27-OHC) to contribute to amyloidogenesis by increasing the production of soluble amyloid precursor protein (sAPP).The results suggest that high CSF concentrations of cholesterol, 24S-OHC, and 27-OHC are associated with increased production of both sAPP forms in AD.", "citation": {"db": "PubMed", "db_id": "22845771"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 1655, "target": 2137, "key": "fea88967212a9d825ce30ddc309619ee"}, {"line": 5569, "relation": "association", "evidence": "Recent genome-wide association studies (GWAS) have identified common genetic variants that increase risk of LOAD. Two of the genes highlighted in these studies, CLU and CR1, suggest a role for the complement system in the aetiology of AD. In this review we analyse the evidence for an involvement of complement in AD. In particular we focus on one gene, CR1, and its role in the complement cascade. CR1 is a receptor for the complement fragments C3b and C4b and is expressed on many different cell types, particularly in the circulatory system.", "citation": {"db": "PubMed", "db_id": "21840620"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 534, "target": 3823, "key": "fd2be47f7bdf35c0ae7624699cf67509"}, {"line": 5571, "relation": "association", "evidence": "Recent genome-wide association studies (GWAS) have identified common genetic variants that increase risk of LOAD. Two of the genes highlighted in these studies, CLU and CR1, suggest a role for the complement system in the aetiology of AD. In this review we analyse the evidence for an involvement of complement in AD. In particular we focus on one gene, CR1, and its role in the complement cascade. CR1 is a receptor for the complement fragments C3b and C4b and is expressed on many different cell types, particularly in the circulatory system.", "citation": {"db": "PubMed", "db_id": "21840620"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 534, "target": 2538, "key": "1c3d20ea636a262aa61fa52ce8e21bb8"}, {"line": 5573, "relation": "association", "evidence": "Recent genome-wide association studies (GWAS) have identified common genetic variants that increase risk of LOAD. Two of the genes highlighted in these studies, CLU and CR1, suggest a role for the complement system in the aetiology of AD. In this review we analyse the evidence for an involvement of complement in AD. In particular we focus on one gene, CR1, and its role in the complement cascade. CR1 is a receptor for the complement fragments C3b and C4b and is expressed on many different cell types, particularly in the circulatory system.", "citation": {"db": "PubMed", "db_id": "21840620"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 534, "target": 2553, "key": "1b523cc850f95d17d1afa958e8d974a7"}, {"line": 5573, "relation": "association", "evidence": "Recent genome-wide association studies (GWAS) have identified common genetic variants that increase risk of LOAD. Two of the genes highlighted in these studies, CLU and CR1, suggest a role for the complement system in the aetiology of AD. In this review we analyse the evidence for an involvement of complement in AD. In particular we focus on one gene, CR1, and its role in the complement cascade. CR1 is a receptor for the complement fragments C3b and C4b and is expressed on many different cell types, particularly in the circulatory system.", "citation": {"db": "PubMed", "db_id": "21840620"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 2553, "target": 534, "key": "28e2cc7b07a66973291f8e3e82eca8a2"}, {"line": 41634, "relation": "biomarkerFor", "evidence": "The complement component receptor 1 gene (Cr1), which encodes a type-I transmembrane glycoprotein, has recently been identified as one of the most important risk genes for late-onset Alzheimer's disease (LOAD).", "citation": {"db": "PubMed", "db_id": "24794147"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 2553, "target": 3823, "key": "f5441cdc439ba0e013da3812f75a0e8f"}, {"line": 5585, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 1779, "target": 533, "key": "f1c301bb690ce4b17a8ebc1eadc8cbd6"}, {"relation": "partOf", "source": 1779, "target": 1666, "key": "0da0a3ad7c163ac60047833f1620b165"}, {"relation": "partOf", "source": 1779, "target": 1010, "key": "b9ca07f4d88694614dcc0a6d3074f768"}, {"line": 5592, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 1806, "target": 676, "key": "7b8928762e57ed88ca3cb7ce5cd7cc31"}, {"relation": "partOf", "source": 1806, "target": 1666, "key": "753e1ff174ba0e3867b45bc01ca5f306"}, {"line": 5590, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 676, "target": 80, "key": "3951aa4be953f56ea81af667e003dc7a"}, {"line": 5595, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 1817, "target": 676, "key": "337b08f756f727de5c5d75aaa10ebb3e"}, {"line": 5598, "relation": "directlyIncreases", "evidence": "In conclusion clathrin-dependent APP endocytosis appears to be very sensitive to the levels of membrane cholesterol. These results suggest that cholesterol increase in AD could be responsible for the enhanced internalization of clathrin-, dynamin2-, Eps15- and Rab5-dependent endocytosis of APP and the ensuing overproduction of Abeta", "citation": {"db": "PubMed", "db_id": "20580937"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Cholesterol metabolism subgraph": true}}, "source": 1935, "target": 676, "key": "3a2a5cfc5a12922a048bd183632a36a9"}, {"line": 5609, "relation": "increases", "evidence": "We identified significant increases in the levels of clathrin, dynamin and PICALM, all proteins intimately involved with the clathrin-mediated endocytic pathway, in the transgenic animals", "citation": {"db": "PubMed", "db_id": "22079091"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 1666, "target": 533, "key": "8c28d3105f9101fe9ef311e51434da05"}, {"line": 5621, "relation": "increases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Subgraph": {"Neurotransmitter release subgraph": true}, "Confidence": {"Medium": true}}, "source": 2017, "target": 668, "key": "32fe08a747408a30587d0d0128e87bc4"}, {"line": 5625, "relation": "increases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Subgraph": {"Neurotransmitter release subgraph": true}, "Confidence": {"Medium": true}}, "source": 2017, "target": 533, "key": "bdf576626a878f7709033a5cf2f7abe9"}, {"line": 5629, "relation": "increases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Subgraph": {"Neurotransmitter release subgraph": true}, "Confidence": {"High": true}}, "source": 668, "target": 533, "key": "513c292ea75f465580ec3b1dd97d1e35"}, {"line": 5635, "relation": "directlyIncreases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Confidence": {"High": true}}, "source": 1953, "target": 533, "key": "ff080de965954ab6edf0053d4e2718a0"}, {"line": 5641, "relation": "directlyIncreases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}, "Confidence": {"High": true}}, "source": 2007, "target": 533, "key": "82d2897dc83530369a3c101df2d86b28"}, {"line": 5642, "relation": "directlyIncreases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}, "Confidence": {"High": true}}, "source": 2006, "target": 533, "key": "8aa2bd757dc72ab962b5a3097395fb81"}, {"line": 5650, "relation": "directlyIncreases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2005, "target": 533, "key": "a4e8171c80791869f4f561343995f322"}, {"line": 5658, "relation": "directlyIncreases", "evidence": "SNAREs provide a large part of the specificity and energy needed for membrane fusion and, to do so, must be localized to their correct membranes. Here, we show that the R-SNAREs VAMP8, VAMP3, and VAMP2, which cycle between the plasma membrane and endosomes, bind directly to the ubiquitously expressed, PtdIns4,5P(2)-binding, endocytic clathrin adaptor CALM/PICALM. X-ray crystallography shows that the N-terminal halves of their SNARE motifs bind the CALM(ANTH) domain as helices in a manner that mimics SNARE complex formation. Mutation of residues in the CALM:SNARE interface inhibits binding in vitro and prevents R-SNARE endocytosis in vivo. Thus, CALM:R-SNARE interactions ensure that R-SNAREs, required for the fusion of endocytic clathrin-coated vesicles with endosomes and also for subsequent postendosomal trafficking, are sorted into endocytic vesicles. CALM's role in directing the endocytosis of small R-SNAREs may provide insight into the association of CALM/PICALM mutations with growth retardation, cognitive defects, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22118466"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 1915, "target": 533, "key": "984f5bbf4fbdf0d25bb62ed2723d47c0"}, {"line": 5679, "relation": "increases", "evidence": "Picalm is a key component of clathrin-dependent endocytosis. It recruits clathrin and adaptor protein 2 (AP-2) to the plasma membrane and, along with, AP-2 recognizes target proteins. The attached clathrin triskelions cause membrane deformation around the target proteins enclosing them within clathrin-coated vesicles to be processed in lysosomes or endosomes.The transport of Abeta across vessel walls and into the bloodstream is a major pathway of Abeta removal from the brain and picalm is ideally situated within endothelial cells to participate in this process", "citation": {"db": "PubMed", "db_id": "20838239"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}}, "source": 1010, "target": 533, "key": "51205748416e7b65e2de9a13a941380e"}, {"line": 5693, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2590, "target": 428, "key": "5d770243cc2a8d313c65c22e83d84855"}, {"line": 5694, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2591, "target": 428, "key": "186fde9471c724575cecdad91e95250a"}, {"line": 5775, "relation": "increases", "evidence": "Under either physiological or pathological conditions, apoptosis is mostly driven by interactions among several families of proteins, i.e. caspases, Bcl-2 family proteins, and inhibitor of apoptosis proteins [10]. Besides the caspases, lysosomal proteases such as cathepsins D, B, and L have been shown to act as mediators of apoptosis in a number of cell systems [11–14]. Increased expression or activity of cathepsin D has been observed in apoptotic cells after activation of Fas/APO-12 and after exposure to oxidative stress or adriamycin ", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2591, "target": 478, "key": "d76281288b3564964ef01505a1e55a45"}, {"line": 5803, "relation": "increases", "evidence": "Results show that p53 has two binding sites located at the cathepsin D promoter gene and that cathepsin D participates in p53-dependent apoptotic process. Cathepsin D showed augmented activity soon after it was released and that was accompanied by increased levels of p53 protein, a cathepsin D transcription factor [16]. Therefore, the mechanism responsible for increase in cathepsin D activity might be an effect of increased synthesis regulated by p53. Cathepsin B has also been implicated in the activation of the pro-inflammatory caspases-1 and -11, and the cleavage of Bcl-2 family member Bid which may lead to cytochrome c release from the mitochondria and subsequent caspase activation", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2591, "target": 2442, "key": "abb9fdb00858c89860370a3e20df43a6"}, {"line": 5804, "relation": "increases", "evidence": "Results show that p53 has two binding sites located at the cathepsin D promoter gene and that cathepsin D participates in p53-dependent apoptotic process. Cathepsin D showed augmented activity soon after it was released and that was accompanied by increased levels of p53 protein, a cathepsin D transcription factor [16]. Therefore, the mechanism responsible for increase in cathepsin D activity might be an effect of increased synthesis regulated by p53. Cathepsin B has also been implicated in the activation of the pro-inflammatory caspases-1 and -11, and the cleavage of Bcl-2 family member Bid which may lead to cytochrome c release from the mitochondria and subsequent caspase activation", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2591, "target": 3337, "key": "44c461ecfc5acaa7d7473fcede019218"}, {"line": 5812, "relation": "increases", "evidence": "Results show that p53 has two binding sites located at the cathepsin D promoter gene and that cathepsin D participates in p53-dependent apoptotic process. Cathepsin D showed augmented activity soon after it was released and that was accompanied by increased levels of p53 protein, a cathepsin D transcription factor [16]. Therefore, the mechanism responsible for increase in cathepsin D activity might be an effect of increased synthesis regulated by p53. Cathepsin B has also been implicated in the activation of the pro-inflammatory caspases-1 and -11, and the cleavage of Bcl-2 family member Bid which may lead to cytochrome c release from the mitochondria and subsequent caspase activation", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true, "XIAP subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2591, "target": 2400, "key": "54ff9c54ee3b74e25fa42129ab663810"}, {"line": 5847, "relation": "increases", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2591, "target": 2400, "key": "664214e8291fc7be43cb669bfa205497"}, {"line": 5865, "relation": "increases", "evidence": "Lysosomal dysfunction may be the earliest histological change in AD. Amyloid plaques are full of active lysosomal hydrolases, implying that plaques may originate from lysosomal rupture. Cathepsins D and E (intracellular aspartyl proteases) are considered to influence Abeta peptides generation within the endosomal–lysosomal pathway because they exhibit beta- and gamma-secretase like-activity [32]. For this reason, the endosomal–lysosomal pathway is a site for cleavage of the APP into smaller beta-amyloid-containing peptides, which are then degraded by cathepsins. Inhibition of cathepsins activity causes a rapid and pronounced build-up of potentially amyloidogenic protein fragments [33]. On the other hand, a failure to degrade aggregated Abeta_42 in late endosomes or secondary lysosomes was a mechanism that contributed to intracellular accumulation of Abeta in AD. The cysteine protease cathepsin B in lysosomes degrades A peptides, especially the aggregation-prone species Abeta_42.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Degradation"}, "source": 2591, "target": 79, "key": "47389427f90b2efc8e8a4555ecb581d9"}, {"relation": "partOf", "source": 2591, "target": 1384, "key": "efe635802befb38c3af821fde47dc09a"}, {"relation": "partOf", "source": 2591, "target": 1161, "key": "48471476b4b553014201d0995f75cfcb"}, {"line": 30207, "relation": "increases", "evidence": "Cathepsin B cleaves the wild-type beta-secretase site sequence in AbetaPP to produce Abeta, and cathepsin B inhibitors administered to animal pmodels expressing AbetaPP containing the wild-type beta-secretase site sequence reduce brain Abeta in a manner consistent with beta-secretase inhibition. ", "citation": {"db": "PubMed", "db_id": "21613740"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2591, "target": 80, "key": "c3382a3ef166ba06a36acacb7ec64381"}, {"line": 38192, "relation": "decreases", "evidence": "Impaired degradation of amyloid beta (Abeta) peptides could lead to Abeta accumulation, an early trigger of Alzheimer's disease (AD). How Abeta-degrading enzymes are regulated remains largely unknown. Cystatin C (CysC, CST3) is an endogenous inhibitor of cysteine proteases, including cathepsin B (CatB), a recently discovered Abeta-degrading enzyme", "citation": {"db": "PubMed", "db_id": "18957217"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2591, "target": 2328, "key": "05e233528976fbb40fe8938b71a096cc"}, {"line": 5695, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2592, "target": 428, "key": "24c91ea58b6f6fdb0076ce6ff3da7770"}, {"line": 5697, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2595, "target": 428, "key": "daed714eed95d4c7b8e8625e9da11d2c"}, {"line": 5862, "relation": "increases", "evidence": "Lysosomal dysfunction may be the earliest histological change in AD. Amyloid plaques are full of active lysosomal hydrolases, implying that plaques may originate from lysosomal rupture. Cathepsins D and E (intracellular aspartyl proteases) are considered to influence Abeta peptides generation within the endosomal–lysosomal pathway because they exhibit beta- and gamma-secretase like-activity [32]. For this reason, the endosomal–lysosomal pathway is a site for cleavage of the APP into smaller beta-amyloid-containing peptides, which are then degraded by cathepsins. Inhibition of cathepsins activity causes a rapid and pronounced build-up of potentially amyloidogenic protein fragments [33]. On the other hand, a failure to degrade aggregated Abeta_42 in late endosomes or secondary lysosomes was a mechanism that contributed to intracellular accumulation of Abeta in AD. The cysteine protease cathepsin B in lysosomes degrades A peptides, especially the aggregation-prone species Abeta_42.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Degradation"}, "source": 2595, "target": 2315, "key": "bdfbbb72f4860fce0ae685a7ad9c1b77"}, {"line": 5864, "relation": "increases", "evidence": "Lysosomal dysfunction may be the earliest histological change in AD. Amyloid plaques are full of active lysosomal hydrolases, implying that plaques may originate from lysosomal rupture. Cathepsins D and E (intracellular aspartyl proteases) are considered to influence Abeta peptides generation within the endosomal–lysosomal pathway because they exhibit beta- and gamma-secretase like-activity [32]. For this reason, the endosomal–lysosomal pathway is a site for cleavage of the APP into smaller beta-amyloid-containing peptides, which are then degraded by cathepsins. Inhibition of cathepsins activity causes a rapid and pronounced build-up of potentially amyloidogenic protein fragments [33]. On the other hand, a failure to degrade aggregated Abeta_42 in late endosomes or secondary lysosomes was a mechanism that contributed to intracellular accumulation of Abeta in AD. The cysteine protease cathepsin B in lysosomes degrades A peptides, especially the aggregation-prone species Abeta_42.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2595, "target": 79, "key": "5cf1da75e828046b0cfe5ecba4198690"}, {"line": 5698, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2596, "target": 428, "key": "25a9423aaf2ee77c87c92052a67da4a9"}, {"line": 5699, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2597, "target": 428, "key": "75278b8cd702b9c2a7887fc19c0afee1"}, {"line": 5700, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true}, "Confidence": {"High": true}}, "source": 2598, "target": 428, "key": "0890cc455fde399e2c39f9a036113047"}, {"line": 5777, "relation": "increases", "evidence": "Under either physiological or pathological conditions, apoptosis is mostly driven by interactions among several families of proteins, i.e. caspases, Bcl-2 family proteins, and inhibitor of apoptosis proteins [10]. Besides the caspases, lysosomal proteases such as cathepsins D, B, and L have been shown to act as mediators of apoptosis in a number of cell systems [11–14]. Increased expression or activity of cathepsin D has been observed in apoptotic cells after activation of Fas/APO-12 and after exposure to oxidative stress or adriamycin ", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2598, "target": 478, "key": "514e2f1899299886288b91d8ec57b7ff"}, {"line": 5821, "relation": "increases", "evidence": "Ishisaka et al. [13] revealed the participation of cathepsin L in a direct activation of caspase-3", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Caspase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2598, "target": 2444, "key": "d40d5f5919e178ad722d28a2a5d9f3aa"}, {"line": 5721, "relation": "association", "evidence": "Lysosomes are responsible for the degradation of macromolecules derived from the extracellular space through endocytosis or phagocytosis, as well as from the cytoplasm through autophagy.The main class of lysosomal proteases is represented by the cathepsin which is derived from the Greek term meaning ‘to digest’. Cathepsins are subdivided into three subgroups according to the amino acids of their active sites that confer catalytic activity: cysteine (cathepsins B, C, F, H, K, L, N, O, S, T, U, W, and X), aspartyl (cathepsins D and E), and serine cathepsins (cathepsins A and G).", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 823, "target": 428, "key": "a3f7e40b03d0419e228f00df70da2262"}, {"line": 46868, "relation": "decreases", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 823, "target": 2328, "key": "0ceee6857b14dad4a51e878257070260"}, {"line": 48752, "relation": "negativeCorrelation", "evidence": "This work provides evidence that chemotaxis and phagocytosis, two crucial innate immune functions, are impaired in AD and MCI patients. Correlations with miRNA levels suggest an epigenetic contribution to systemic immune dysfunction in AD.", "citation": {"db": "PubMed", "db_id": "4879648"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 823, "target": 3823, "key": "63dc1aada41ecf0e018fad2dafac3f48"}, {"line": 5728, "relation": "increases", "evidence": "Apoptosis is the most common form of physiological cell death in multicellular organisms. Apoptosis signaling is classically composed of two principle pathways. One is a direct pathway from death receptor (CD95, TNF-R1, and TRAIL-R1/TRAIL-R2 [9]) ligation to caspase cascade activation and cell death. Death receptor ligation triggers recruitment of the precursor form of caspase-8 to a death-inducing complex, through the adaptor protein FADD, which leads to caspase-8 activation. The other pathway triggered by stimuli such as drugs, radiation, infectious agents, and reactive oxygen species is initiated in mitochondria. After cytochrome c is released into the cytosol from the mitochondria, it binds to Apaf1 and ATP, which then activate caspase-9.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1315, "target": 2448, "key": "88565fae3ace4c7d99192c5dacd297c5"}, {"relation": "partOf", "source": 3284, "target": 1315, "key": "c0d3d845f10cf710c8b6a854bd27067f"}, {"line": 5732, "relation": "increases", "evidence": "Apoptosis is the most common form of physiological cell death in multicellular organisms. Apoptosis signaling is classically composed of two principle pathways. One is a direct pathway from death receptor (CD95, TNF-R1, and TRAIL-R1/TRAIL-R2 [9]) ligation to caspase cascade activation and cell death. Death receptor ligation triggers recruitment of the precursor form of caspase-8 to a death-inducing complex, through the adaptor protein FADD, which leads to caspase-8 activation. The other pathway triggered by stimuli such as drugs, radiation, infectious agents, and reactive oxygen species is initiated in mitochondria. After cytochrome c is released into the cytosol from the mitochondria, it binds to Apaf1 and ATP, which then activate caspase-9.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1316, "target": 2448, "key": "bb556c4645e1790bd419c1b20fa7c48e"}, {"relation": "partOf", "source": 3489, "target": 1316, "key": "5229f48cc4da3a06cca3eadb5006c4bd"}, {"line": 5757, "relation": "increases", "evidence": "Apoptosis is the most common form of physiological cell death in multicellular organisms. Apoptosis signaling is classically composed of two principle pathways. One is a direct pathway from death receptor (CD95, TNF-R1, and TRAIL-R1/TRAIL-R2 [9]) ligation to caspase cascade activation and cell death. Death receptor ligation triggers recruitment of the precursor form of caspase-8 to a death-inducing complex, through the adaptor protein FADD, which leads to caspase-8 activation. The other pathway triggered by stimuli such as drugs, radiation, infectious agents, and reactive oxygen species is initiated in mitochondria. After cytochrome c is released into the cytosol from the mitochondria, it binds to Apaf1 and ATP, which then activate caspase-9.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 972, "target": 2449, "key": "59f957198f645ccda7adb7d16b5656bd"}, {"relation": "partOf", "source": 2293, "target": 972, "key": "fa13b70abe00de8a0d2a7b225aa9fda6"}, {"relation": "partOf", "source": 2293, "target": 1074, "key": "3ecb7e75942bbbd704a8cb1287247838"}, {"relation": "partOf", "source": 2608, "target": 972, "key": "791eb3c3d32df697fdb6c049700830a6"}, {"line": 23166, "relation": "increases", "evidence": "We showed that the localization of mutant SOD1 in the mitochondria triggered the release of mitochondrial cytochrome c followed by the activation of caspase cascade and induced neuronal cell death without cytoplasmic mutant SOD1 aggregate formation.", "citation": {"db": "PubMed", "db_id": "12393885"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2608, "target": 2449, "key": "a211fbdd691bdf5b35a34421493904ce"}, {"line": 23167, "relation": "increases", "evidence": "We showed that the localization of mutant SOD1 in the mitochondria triggered the release of mitochondrial cytochrome c followed by the activation of caspase cascade and induced neuronal cell death without cytoplasmic mutant SOD1 aggregate formation.", "citation": {"db": "PubMed", "db_id": "12393885"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2608, "target": 2444, "key": "b5215e62ffc36e93386ab0da142d784b"}, {"relation": "partOf", "source": 2608, "target": 1074, "key": "298a1f1c4a7941d1ca6a1d7342d303c7"}, {"line": 23179, "relation": "increases", "evidence": "Once released from the mitochondria, cytochrome c interacts in the cytosol with Apaf-1, forming an ATP-dependent complex that activates caspase-9 (Liu et al., 1996; Li et al., 1997; Zou et al., 1997; Hu et al., 1999; Saleh et al., 1999), which is instrumental in the mitochondrial-dependent activation of downstream effector caspases such as caspase-3 and caspase-7 (Slee et al., 1999)", "citation": {"db": "PubMed", "db_id": "11517246"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 2608, "target": 1074, "key": "bcbbcc8ea8023f205de4f5aa14b04efc"}, {"line": 23238, "relation": "increases", "evidence": "Furthermore, mitochondrial ceramide generation induces intrinsic apoptosis mediated by cytochrome c release. However, all these apoptotic events can be prevented by overexpression of Bcl-2s ", "citation": {"db": "PubMed", "db_id": "23455468"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 2608, "target": 478, "key": "80ce0c384b70c045200e5be6bae1c461"}, {"line": 23334, "relation": "association", "evidence": "Activation of the mitochondrial pathway of apoptosis is one attractive explanation for the transcription-independent portion of p53-influenced apoptosis (Chen et al., 1996b; Haupt et al., 1995). Mitochondrial translocation of p53 following DNA damage (Mihara et al., 2003) and its ability to engage BCL-2 family proteins to regulate cytochrome c release have been noted (Chipuk et al., 2004).", "citation": {"db": "PubMed", "db_id": "14744432"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"Medium": true}}, "source": 2608, "target": 3482, "key": "597776d8d3e89562a5027cfad608d7f5"}, {"line": 5765, "relation": "increases", "evidence": "Apoptosis is the most common form of physiological cell death in multicellular organisms. Apoptosis signaling is classically composed of two principle pathways. One is a direct pathway from death receptor (CD95, TNF-R1, and TRAIL-R1/TRAIL-R2 [9]) ligation to caspase cascade activation and cell death. Death receptor ligation triggers recruitment of the precursor form of caspase-8 to a death-inducing complex, through the adaptor protein FADD, which leads to caspase-8 activation. The other pathway triggered by stimuli such as drugs, radiation, infectious agents, and reactive oxygen species is initiated in mitochondria. After cytochrome c is released into the cytosol from the mitochondria, it binds to Apaf1 and ATP, which then activate caspase-9.", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2449, "target": 478, "key": "d015cb312fc24cf04e99e320e3eca2d9"}, {"line": 23168, "relation": "increases", "evidence": "We showed that the localization of mutant SOD1 in the mitochondria triggered the release of mitochondrial cytochrome c followed by the activation of caspase cascade and induced neuronal cell death without cytoplasmic mutant SOD1 aggregate formation.", "citation": {"db": "PubMed", "db_id": "12393885"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2449, "target": 478, "key": "7afda78b9e2e43cb8be60b1c6e0f6f60"}, {"relation": "partOf", "source": 2449, "target": 1305, "key": "5d489834667bd6e4b3dd6f21bea67463"}, {"relation": "partOf", "source": 2449, "target": 1303, "key": "607d40fc94232d89ba4e6e49a5d0e586"}, {"line": 23029, "relation": "positiveCorrelation", "evidence": "To examine possible caspase-9-activation in human sporadic ALS, we performed immunohistochemistry using anti-active caspase-9 antibody on post mortem human samples. Four of the eight ALS spinal cords showed obvious caspase-9 activation in the motor neurons studied, but this was not seen in any of the controls (Figure 5A); this suggests that caspase-9 may play an instrumental role in some forms of human sporadic ALS", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 2449, "target": 3825, "key": "29a848896997f579f3efcce9ca74cb73"}, {"relation": "hasVariant", "source": 2449, "target": 2450, "key": "412e5ae09548c87d6aa1d714ecbcc087"}, {"line": 40580, "relation": "association", "evidence": "In addition, Pls also inhibited primary mouse hippocampal neuronal cell death induced by nutrient deprivation, which was associated with the inhibition of caspase-9 and caspase-3 cleavages.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2449, "target": 648, "key": "e6a16b33b9aed2605b35741ed01f4083"}, {"line": 46564, "relation": "increases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2449, "target": 80, "key": "13c8bbf461195d8a19ecb616530debc0"}, {"line": 5791, "relation": "increases", "evidence": "Results show that p53 has two binding sites located at the cathepsin D promoter gene and that cathepsin D participates in p53-dependent apoptotic process. Cathepsin D showed augmented activity soon after it was released and that was accompanied by increased levels of p53 protein, a cathepsin D transcription factor [16]. Therefore, the mechanism responsible for increase in cathepsin D activity might be an effect of increased synthesis regulated by p53. Cathepsin B has also been implicated in the activation of the pro-inflammatory caspases-1 and -11, and the cleavage of Bcl-2 family member Bid which may lead to cytochrome c release from the mitochondria and subsequent caspase activation", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "source": 1385, "target": 478, "key": "9b6d3e00684ca4d6331640aa03c35f29"}, {"relation": "partOf", "source": 2442, "target": 1697, "key": "d0cf6863e7667b24fa2b2145490b6370"}, {"line": 20373, "relation": "positiveCorrelation", "evidence": "ICE-beta, c-Jun, Bax-alpha, Bcl-x(L), p53, and GADD153 were found to be upregulated in some AD samples but were not detected or downregulated in other AD or normal samples.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 2442, "target": 3823, "key": "8b3ae2481278e08abf067d32cf6ae140"}, {"line": 35774, "relation": "increases", "evidence": "Results show that p53 has two binding sites located at the cathepsin D promoter gene and that cathepsin D participates in p53-dependent apoptotic process. Cathepsin D showed augmented activity soon after it was released and that was accompanied by increased levels of p53 protein, a cathepsin D transcription factor [16]. Therefore, the mechanism responsible for increase in cathepsin D activity might be an effect of increased synthesis regulated by p53. Cathepsin B has also been implicated in the activation of the pro-inflammatory caspases-1 and -11, and the cleavage of Bcl-2 family member Bid which may lead to cytochrome c release from the mitochondria and subsequent caspase activation", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2442, "target": 577, "key": "77c4ac08ff110c5bcabd28074217d37b"}, {"line": 40515, "relation": "increases", "evidence": "The processing is induced by an increase in activity of caspase-1 and NOD-like receptor family, pyrin domain containing 3 (NLRP3) via mitochondrial reactive oxygen species (ROS) and partially via NADPH oxidase-induced ROS.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2442, "target": 2885, "key": "49be72079343b29cddbcadde166b0ee0"}, {"line": 40519, "relation": "increases", "evidence": "The processing is induced by an increase in activity of caspase-1 and NOD-like receptor family, pyrin domain containing 3 (NLRP3) via mitochondrial reactive oxygen species (ROS) and partially via NADPH oxidase-induced ROS.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 2442, "target": 170, "key": "38d971943511ad01d84cdf53960dd7a0"}, {"relation": "partOf", "source": 3337, "target": 1697, "key": "8b5fe50b67327aafc790f96d670fee67"}, {"line": 35775, "relation": "increases", "evidence": "Results show that p53 has two binding sites located at the cathepsin D promoter gene and that cathepsin D participates in p53-dependent apoptotic process. Cathepsin D showed augmented activity soon after it was released and that was accompanied by increased levels of p53 protein, a cathepsin D transcription factor [16]. Therefore, the mechanism responsible for increase in cathepsin D activity might be an effect of increased synthesis regulated by p53. Cathepsin B has also been implicated in the activation of the pro-inflammatory caspases-1 and -11, and the cleavage of Bcl-2 family member Bid which may lead to cytochrome c release from the mitochondria and subsequent caspase activation", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3337, "target": 577, "key": "52dd38fdd8603ef36fee71a4bb36d2ee"}, {"line": 5805, "relation": "increases", "evidence": "Results show that p53 has two binding sites located at the cathepsin D promoter gene and that cathepsin D participates in p53-dependent apoptotic process. Cathepsin D showed augmented activity soon after it was released and that was accompanied by increased levels of p53 protein, a cathepsin D transcription factor [16]. Therefore, the mechanism responsible for increase in cathepsin D activity might be an effect of increased synthesis regulated by p53. Cathepsin B has also been implicated in the activation of the pro-inflammatory caspases-1 and -11, and the cleavage of Bcl-2 family member Bid which may lead to cytochrome c release from the mitochondria and subsequent caspase activation", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 1697, "target": 577, "key": "6b907593ea1561364c1ff627d661037b"}, {"line": 5849, "relation": "increases", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity"}, "source": 2400, "target": 2400, "key": "8ca979a74590633833bfcf39375f5b2b"}, {"line": 5832, "relation": "increases", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 680, "target": 478, "key": "41cc6e8f91b2e52e486ed1f5dfabf2b3"}, {"line": 5836, "relation": "increases", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Lysosomes"}, "toLoc": {"namespace": "MESH", "name": "Cytosol"}}}, "source": 680, "target": 3558, "key": "8b6fe733171fd76923be15a1f0a1813c"}, {"line": 5840, "relation": "increases", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 680, "target": 2400, "key": "d4cba659a1f1cd3b9b660a850dfa5e51"}, {"line": 5852, "relation": "increases", "evidence": "It is known that lysosome is involved not only in apoptosis but also in other types of cell death. The permeabilization of the lysosome has been shown to initiate a cell death pathway in specific circumstances. Lysosomal membrane permeabilization (LMP) causes the release of cathepsins and other hydrolases from the lysosomal lumen to the cytosol. LMP is a potentially lethal event because the ectopic presence of lysosomal proteases in the cytosol causes digestion of vital proteins and the activation of additional hydrolases including caspases. This latter process is usually mediated indirectly, through a cascade in which LMP causes the proteolytic activation of Bid (which is cleaved by the two lysosomal cathepsins B and D). The Bid activation then induces mitochondrial outer membrane permeabilization, resulting in cytochrome c release and apoptosome-dependent caspase activation [19]. However, massive LMP often results in cell death without caspase activation, which may adopt a subapoptotic or necrotic appearance. Moreover, the pro-apoptotic Bcl-2 family member Bax can translocate from the cytosol to lysosomal membranes and induce LMP", "citation": {"db": "PubMed", "db_id": "19499146"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytosol"}, "toLoc": {"namespace": "MESH", "name": "Lysosomes"}}}, "source": 2389, "target": 680, "key": "8d91f870cff48cf7e0d0f87d64249259"}, {"line": 22088, "relation": "positiveCorrelation", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2389, "target": 478, "key": "9eac9bf03a1703b2caa7fc7abec01f81"}, {"line": 24376, "relation": "increases", "evidence": "Here, we show that GSK-3beta phosphorylates and regulates the activity of Bax, a pro-apoptotic Bcl-2 family member that stimulates the intrinsic (mitochondrial) death pathway by eliciting cytochrome c release from mitochondria.", "citation": {"db": "PubMed", "db_id": "15525785"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2389, "target": 478, "key": "ffcb00472d6350afb483aa132d083496"}, {"line": 23335, "relation": "association", "evidence": "Activation of the mitochondrial pathway of apoptosis is one attractive explanation for the transcription-independent portion of p53-influenced apoptosis (Chen et al., 1996b; Haupt et al., 1995). Mitochondrial translocation of p53 following DNA damage (Mihara et al., 2003) and its ability to engage BCL-2 family proteins to regulate cytochrome c release have been noted (Chipuk et al., 2004).", "citation": {"db": "PubMed", "db_id": "14744432"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Apoptosis signaling subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"Medium": true}}, "source": 2389, "target": 3482, "key": "2a65601be211d5f14386780f518f95dd"}, {"relation": "hasVariant", "source": 2389, "target": 2390, "key": "b439f9266cabc48b3eb43dbab05feb71"}, {"relation": "partOf", "source": 2389, "target": 1286, "key": "cf6077bdb51158d5d4a49b763e6ddfca"}, {"relation": "partOf", "source": 2389, "target": 1287, "key": "5f9db694fa758e7edc2b0ec93e810e67"}, {"relation": "partOf", "source": 2389, "target": 1285, "key": "6a50988debdfe8f121ddb78a5914e443"}, {"relation": "partOf", "source": 2389, "target": 1288, "key": "550b3186043854243a17d8fc3f8e046e"}, {"relation": "partOf", "source": 2389, "target": 1289, "key": "1a94f36393f199b90367f08c06f48eb4"}, {"line": 5878, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2355, "target": 3353, "key": "399742cfa8cf25993ae26abe035d7f2a"}, {"line": 5887, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2355, "target": 2634, "key": "e139798e8b73c80efd5028264ca2b898"}, {"line": 5888, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2355, "target": 2635, "key": "399c6eb92329af4b943dc511db444786"}, {"line": 5898, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2355, "target": 2375, "key": "08ec3641cef9014019499f06056ec836"}, {"line": 5905, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Membrane"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2355, "target": 2315, "key": "b06c2c1c5c3460db615cec6d1ef20efd"}, {"relation": "partOf", "source": 2355, "target": 1256, "key": "457a7955ae1fdf52d3a58661b303a63a"}, {"line": 49265, "relation": "positiveCorrelation", "evidence": "Reductions in Arc and alpha-actinin-2 correlated tightly with reductions in Fos and calbindin", "citation": {"db": "PubMed", "db_id": "16237173"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2355, "target": 2699, "key": "0c706467ecf2111984e765b0c136190c"}, {"line": 49266, "relation": "positiveCorrelation", "evidence": "Reductions in Arc and alpha-actinin-2 correlated tightly with reductions in Fos and calbindin", "citation": {"db": "PubMed", "db_id": "16237173"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2355, "target": 2421, "key": "71608eff0abeec750124bfa62b9bb920"}, {"line": 5879, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 3353, "target": 2770, "key": "cdcab6a79042fad90112a400ad8783da"}, {"relation": "isA", "source": 2770, "target": 2770, "key": "20fb7211b6f5087f8bc86a1c03a4bdb0"}, {"line": 11773, "relation": "positiveCorrelation", "evidence": "Glutamate receptor subunit 1 (GluR1) is one of the four possible subunits of the AMPA-type glutamate receptor. The integrity of this receptor is crucial for learning processes. However, reductions of GluR1 are noticeable in the hippocampal formation of patients suffering from Alzheimer's disease. Such degradations presumably result in an impaired synaptic communication and might be causally linked to the neurodegenerative process in this cognitive disorder.", "citation": {"db": "PubMed", "db_id": "12197668"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2770, "target": 818, "key": "5a8e1098829af039ccdceae983f9ad0f"}, {"line": 11774, "relation": "negativeCorrelation", "evidence": "Glutamate receptor subunit 1 (GluR1) is one of the four possible subunits of the AMPA-type glutamate receptor. The integrity of this receptor is crucial for learning processes. However, reductions of GluR1 are noticeable in the hippocampal formation of patients suffering from Alzheimer's disease. Such degradations presumably result in an impaired synaptic communication and might be causally linked to the neurodegenerative process in this cognitive disorder.", "citation": {"db": "PubMed", "db_id": "12197668"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2770, "target": 3823, "key": "03140ca9942f869e1fd0191d01d01fa6"}, {"line": 30228, "relation": "association", "evidence": "These findings indicate that abnormal expressions of the AMPA receptor and its interacting PSD molecule are associated with Alzheimer's disease and implicated in pathophysiology of this disease.", "citation": {"db": "PubMed", "db_id": "10588576"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2770, "target": 3823, "key": "7f6edbff3d7897577d8e16648103f26c"}, {"line": 11776, "relation": "increases", "evidence": "Glutamate receptor subunit 1 (GluR1) is one of the four possible subunits of the AMPA-type glutamate receptor. The integrity of this receptor is crucial for learning processes. However, reductions of GluR1 are noticeable in the hippocampal formation of patients suffering from Alzheimer's disease. Such degradations presumably result in an impaired synaptic communication and might be causally linked to the neurodegenerative process in this cognitive disorder.", "citation": {"db": "PubMed", "db_id": "12197668"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2770, "target": 523, "key": "53b1fcc252f7f9728774749157c307e6"}, {"relation": "hasVariant", "source": 2770, "target": 2771, "key": "2671abdf972eb904e9279a1f76f8a08a"}, {"relation": "partOf", "source": 2770, "target": 1392, "key": "19bb41d0d9f886f690f93b68c7f78450"}, {"line": 36610, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2770, "target": 384, "key": "6d639d4a9890599c9a652b2d35b04b6d"}, {"line": 5889, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 2634, "target": 2770, "key": "e8209b54668c99d11471ae767e180d89"}, {"line": 5890, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 2635, "target": 2770, "key": "b1543525b476f7727f782c4b3f19e2fe"}, {"line": 5912, "relation": "increases", "evidence": "Assemblies of Abeta-amyloid (Abeta) peptides are pathological mediators of Alzheimer's Disease (AD) and are produced by the sequential cleavages of amyloid precursor protein (APP) by Abeta-secretase (BACE1) and gamma-secretase. The generation of Abeta is coupled to neuronal activity, but the molecular basis is unknown. Here, we report that the immediate early gene Arc is required for activity-dependent generation of Abeta. Arc is a postsynaptic protein that recruits endophilin2/3 and dynamin to early/recycling endosomes that traffic AMPA receptors to reduce synaptic strength in both hebbian and non-hebbian forms of plasticity. The Arc-endosome also traffics APP and BACE1, and Arc physically associates with presenilin1 (PS1) to regulate gamma-secretase trafficking and confer activity dependence.", "citation": {"db": "PubMed", "db_id": "22036569"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 1256, "target": 868, "key": "f7fc833e22a2dd920e9cf211b6e18e7e"}, {"line": 5922, "relation": "increases", "evidence": "Mint adaptor proteins bind to the membrane-bound amyloid precursor protein (APP) and affect the production of pathogenic amyloid-beta (Abeta) peptides related to Alzheimer's disease (AD)", "citation": {"db": "PubMed", "db_id": "22787047"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1675, "target": 80, "key": "e8bfbb64553b10ba4f8ce9175d574fc1"}, {"line": 5928, "relation": "increases", "evidence": "Because Abeta generation involves the internalization of membrane-bound APP via endosomes and Mints bind directly to the endocytic motif of APP, we proposed that Mints are involved in APP intracellular trafficking, which in turn, affects Abeta generation.", "citation": {"db": "PubMed", "db_id": "22787047"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 1674, "target": 2315, "key": "60f969ae8dd27e11af20347a8c19274d"}, {"line": 5934, "relation": "increases", "evidence": "These results demonstrate that Src-mediated phosphorylation of Mint2 regulates the APP endocytic sorting pathway, providing a mechanism for regulating Abeta secretion.", "citation": {"db": "PubMed", "db_id": "22787047"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3416, "target": 2297, "key": "64f1d746344274e42d6f5f04d869809b"}, {"line": 32645, "relation": "increases", "evidence": "This processing is activated through a pathway involving the PDGF receptor, Src, and Rac1. ", "citation": {"db": "PubMed", "db_id": "14766758"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3416, "target": 2315, "key": "4bfe8ef20cdae25d04a433378581215d"}, {"relation": "partOf", "source": 3416, "target": 1216, "key": "417f7c8c1c3086b084f458753ee8c7ea"}, {"line": 5935, "relation": "association", "evidence": "These results demonstrate that Src-mediated phosphorylation of Mint2 regulates the APP endocytic sorting pathway, providing a mechanism for regulating Abeta secretion.", "citation": {"db": "PubMed", "db_id": "22787047"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "trans-Golgi Network"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 2297, "target": 2315, "key": "531535a3208c3700a1ce47717aa2c5d4"}, {"line": 5958, "relation": "increases", "evidence": "For example, proteins like calreticulin (Johnson et al, 2001) or clathrin (Nordstedt et al, 1993) are reported to bind APP, but are also known to be quite generally involved in protein maturation or endocytosis, respectively.", "citation": {"db": "PubMed", "db_id": "16252002"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Synaptic vesicle endocytosis subgraph": true}, "Confidence": {"High": true}}, "source": 1148, "target": 813, "key": "077a01ec77b22ef6a883c13865ea97a2"}, {"relation": "partOf", "source": 2424, "target": 1148, "key": "bc72bf4968cf84ae94d3956d2c85d9d6"}, {"relation": "partOf", "source": 2424, "target": 918, "key": "6089f67a2d69f4c775e89551677f9a27"}, {"line": 5959, "relation": "increases", "evidence": "For example, proteins like calreticulin (Johnson et al, 2001) or clathrin (Nordstedt et al, 1993) are reported to bind APP, but are also known to be quite generally involved in protein maturation or endocytosis, respectively.", "citation": {"db": "PubMed", "db_id": "16252002"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Synaptic vesicle endocytosis subgraph": true}, "Confidence": {"High": true}}, "source": 1154, "target": 813, "key": "c4e237cd3b13dc0984b1812e570f3eaf"}, {"relation": "partOf", "source": 2535, "target": 1154, "key": "94e777983b476b34f06dc13db7572a8a"}, {"line": 38443, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2535, "target": 3563, "key": "67c0840a745eb1d464950dddad1d470d"}, {"relation": "partOf", "source": 2535, "target": 1356, "key": "38033af000678ac48a72afa2ce756920"}, {"line": 5960, "relation": "increases", "evidence": "For example, proteins like calreticulin (Johnson et al, 2001) or clathrin (Nordstedt et al, 1993) are reported to bind APP, but are also known to be quite generally involved in protein maturation or endocytosis, respectively.", "citation": {"db": "PubMed", "db_id": "16252002"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Synaptic vesicle endocytosis subgraph": true}, "Confidence": {"High": true}}, "source": 1155, "target": 813, "key": "41181fbc23fdc9104d317b63750a5848"}, {"relation": "partOf", "source": 2536, "target": 1155, "key": "bb284b7833b7e32d9e3384a7f6f21ab0"}, {"line": 38444, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2536, "target": 3563, "key": "986fafd4b7660cc47e71bd90d5a89f14"}, {"relation": "partOf", "source": 2536, "target": 1357, "key": "58c0b2b7e8eb13095dd4d975f022c28a"}, {"line": 5961, "relation": "increases", "evidence": "For example, proteins like calreticulin (Johnson et al, 2001) or clathrin (Nordstedt et al, 1993) are reported to bind APP, but are also known to be quite generally involved in protein maturation or endocytosis, respectively.", "citation": {"db": "PubMed", "db_id": "16252002"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Synaptic vesicle endocytosis subgraph": true}, "Confidence": {"High": true}}, "source": 1156, "target": 813, "key": "d240200e62a0cdcf57c3b07e3df12635"}, {"relation": "partOf", "source": 2537, "target": 1156, "key": "568aa02ecc0f3e484516c82fd65eb45d"}, {"line": 38445, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2537, "target": 3563, "key": "ab97b7eafb6859c5e8a622087b11126e"}, {"relation": "partOf", "source": 2537, "target": 1358, "key": "0e89a9bcb3b6589ceff032ccdd8cc99c"}, {"line": 5983, "relation": "positiveCorrelation", "evidence": "Evidence has suggested that insulin resistance (IR) or high levels of glucocorticoids (GCs) may be linked with the pathogenesis and/or progression of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 263, "target": 3823, "key": "a318bb7986274aa49ecf1a45e60d36b7"}, {"line": 19823, "relation": "positiveCorrelation", "evidence": "Increased circulating glucocorticoids are features of both aging and Alzheimer's disease (AD), and increased glucocorticoids accelerate the accumulation of AD pathologies.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 263, "target": 3823, "key": "40d94fa554194003bbdfbbf31942725a"}, {"line": 19824, "relation": "association", "evidence": "Increased circulating glucocorticoids are features of both aging and Alzheimer's disease (AD), and increased glucocorticoids accelerate the accumulation of AD pathologies.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 263, "target": 3823, "key": "4365916164b76ca05a7172a15e5c0065"}, {"line": 5993, "relation": "positiveCorrelation", "evidence": "Although studies have shown that a high level of GCs results in IR, little is known about the molecular details that link GCs and IR in the context of AD", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 263, "target": 3861, "key": "a9c08e942f1283416b69c2a6a3aa7a1d"}, {"line": 8302, "relation": "increases", "evidence": "Another consequence of insulin resistance may be impaired regulation of the hypothalamicpituitary- adrenal (HPA) axis. Insulin and cortisol, a primary HPA axis hormone, are counter-regulatory, and a change in the level of either hormone influences the level of the other.[29,30] Thus, glucocorticoids can induce insulin resistance in healthy humans,[ 31,32] and hyperinsulinaemia resulting from insulin resistance can produce hypercortisolaemia. An animal study showed that rats with type 2 diabetes had higher levels of adrenocorticotrophic hormone than control individuals, consistent with chronic activation of the HPA axis.[33] In humans, plasma cortisol levels were elevated in patients with type 2 diabetes,[34] and patientswith both poorly and well controlled type 2 diabetes showed abnormal cortisol responses to hypoglycaemia.[35,36] In metabolic studies, insulin administration has been shown to increase HPA axis activity, indexed by a rise in cortisol levels", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 263, "target": 3861, "key": "608578e0786262e063b775cc15fae4ee"}, {"line": 6006, "relation": "association", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 263, "target": 3015, "key": "39005f5bc61c6525a5dd82957b8d772d"}, {"line": 6016, "relation": "association", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Calpastatin-calpain subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 263, "target": 2428, "key": "378f12efcb85e1807d0c570862d51f1d"}, {"line": 6014, "relation": "increases", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Calpastatin-calpain subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2428, "target": 3823, "key": "e183e184597edce92af4626b2a1500eb"}, {"line": 6015, "relation": "association", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Calpastatin-calpain subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2428, "target": 3861, "key": "9edce54a164917478b3412badefd2364"}, {"line": 6016, "relation": "association", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Calpastatin-calpain subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2428, "target": 263, "key": "08d9e8cc0be6901e7872641706cbcca2"}, {"line": 6053, "relation": "decreases", "evidence": "Finally, both LiCl pre-treatment and calpain inhibition prevented the DEX-induced inhibition on the insulin-stimulated Akt phosphorylation. In conclusion, our study suggests that the tau phosphorylation and calpain activation mediate the EX-induced inhibition on the insulin-stimulated Akt phosphorylation.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2428, "target": 2154, "key": "9bd6a32dcb4d95a92df28b3ee05f1638"}, {"line": 9861, "relation": "negativeCorrelation", "evidence": "The decrease in brain insulin-PI3K-AKT signalling also correlated with the activation of calpain I in the brain, suggesting that the decrease might be caused by calpain over-activation.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Beta secretase subgraph": true, "Calpastatin-calpain subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2428, "target": 579, "key": "b560f52fd53e3b096788d819f8348322"}, {"line": 9862, "relation": "decreases", "evidence": "The decrease in brain insulin-PI3K-AKT signalling also correlated with the activation of calpain I in the brain, suggesting that the decrease might be caused by calpain over-activation.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Beta secretase subgraph": true, "Calpastatin-calpain subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 2428, "target": 579, "key": "c26b2075ee0907a325bbd219eb4b716a"}, {"relation": "isA", "source": 2428, "target": 2160, "key": "9f4d101eaca01bcfa0b376dea36d7e4a"}, {"line": 6024, "relation": "biomarkerFor", "evidence": "Abnormal phosphorylation of tau and activation of mu-calpain are two key events in the pathology of AD. Importantly, these two events are also related with GCs and IR. We therefore speculate that tau phosphorylation and mu-calpain activation may mediate the GCs-induced IR. Akt phosphorylation at Ser-473 (pAkt) is commonly used as a marker for assessing IR.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Akt subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2281, "target": 3861, "key": "8ebc8efcf24eb9b32c83e0ed3feaada3"}, {"relation": "hasVariant", "source": 2279, "target": 2281, "key": "5998a2aa05ad0808db3116c7f40453c5"}, {"line": 7550, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2279, "target": 2704, "key": "3094df298c50a9719c00b6f8e01824bd"}, {"line": 7579, "relation": "increases", "evidence": "Furthermore, AKT phosphorylates tuberin (TSC2), which mh1b1.ts 1ts AP activity (guanosine triphosphatase­ assocrated protem) towards the small G protein RHEB (RAS homolog enriched in brain), which causes an accumulation of the RHEB-GTP complex that activates mammalian target ofrapamycin (mTOR) [36].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 2279, "target": 3499, "key": "bc598a1cf1540561d8a0f6cb9fcf40c0"}, {"line": 7931, "relation": "increases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2279, "target": 2796, "key": "fbafc26df3ed52d09c1624f06547dc5a"}, {"line": 9811, "relation": "association", "evidence": "The deficiency of insulin-PI3K-AKT signalling was more severe in individuals with both T2DM and AD (T2DM-AD).", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 2279, "target": 579, "key": "b5fbbbe654f6d469105dfd1d7da0b817"}, {"line": 9843, "relation": "negativeCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2279, "target": 3015, "key": "4bd4377db12c2120a42103e2491d4282"}, {"line": 32853, "relation": "increases", "evidence": "Our data suggest that phosphorylation of tau by Akt may play specific role(s) in Akt-mediated anti-apoptotic signaling, particularly relevant to AD and other neurodegenerations.", "citation": {"db": "PubMed", "db_id": "14636947"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Akt subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2279, "target": 3015, "key": "a48d15370adbaa9d8f3b2b7875b6a60d"}, {"line": 32863, "relation": "increases", "evidence": "We have reported recently that the microtubule-associated protein tau is phosphorylated in vitro by Akt , an important kinase in anti-apoptotic signaling regulated by insulin and growth factors.", "citation": {"db": "PubMed", "db_id": "15283963"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Akt subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2279, "target": 3015, "key": "7f4a3af79f9a8f52c1cc577aee468a7c"}, {"line": 9846, "relation": "positiveCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 2279, "target": 3156, "key": "135f084e0cc01b9a6283b97addc18bff"}, {"relation": "hasVariant", "source": 2279, "target": 2280, "key": "0973a6edc279bc50c322c6d6fe94f2e8"}, {"relation": "partOf", "source": 2279, "target": 1072, "key": "0f6c8eaffe454c09bfdee3af8db414fd"}, {"line": 32864, "relation": "association", "evidence": "We have reported recently that the microtubule-associated protein tau is phosphorylated in vitro by Akt , an important kinase in anti-apoptotic signaling regulated by insulin and growth factors.", "citation": {"db": "PubMed", "db_id": "15283963"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Akt subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2279, "target": 133, "key": "2c9b2d88db8f8f15e983d1a6961362c1"}, {"line": 48065, "relation": "association", "evidence": "In silico molecular target prediction docking studies suggest that ETH interacts with Akt, Dkk-1, and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "26420483"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 2279, "target": 6, "key": "357fb2ce7eeec27bea149dd3a3253427"}, {"line": 6039, "relation": "increases", "evidence": "We employed two cell lines, wild-type HEK293 cells and HEK293 cells stably expressing the longest human tau isoform (tau-441; HEK293/tau441 cells). We examined whether DEX, a synthetic GCs, induces tau phosphorylation and mu-calpain activation. If so, we examined whether the DEX-induced tau phosphorylation and mu-calpain activation mediate the DEX-induced inhibition on the insulin-stimulated Akt phosphorylation. The results showed that DEX increased tau phosphorylation and induced tau-mediated mu-calpain activation.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "CellLine": {"HEK293": true}}, "source": 241, "target": 3015, "key": "ec87be16718bed2aedc0b552e749bbcf"}, {"line": 6040, "relation": "increases", "evidence": "We employed two cell lines, wild-type HEK293 cells and HEK293 cells stably expressing the longest human tau isoform (tau-441; HEK293/tau441 cells). We examined whether DEX, a synthetic GCs, induces tau phosphorylation and mu-calpain activation. If so, we examined whether the DEX-induced tau phosphorylation and mu-calpain activation mediate the DEX-induced inhibition on the insulin-stimulated Akt phosphorylation. The results showed that DEX increased tau phosphorylation and induced tau-mediated mu-calpain activation.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "CellLine": {"HEK293": true}}, "object": {"modifier": "Activity"}, "source": 241, "target": 2428, "key": "c851164a81e297a03f4a7744d0ae636b"}, {"line": 6050, "relation": "decreases", "evidence": "Finally, both LiCl pre-treatment and calpain inhibition prevented the DEX-induced inhibition on the insulin-stimulated Akt phosphorylation. In conclusion, our study suggests that the tau phosphorylation and calpain activation mediate the EX-induced inhibition on the insulin-stimulated Akt phosphorylation.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 241, "target": 2154, "key": "5b2f557a571dd5aefbe426c946050297"}, {"line": 17676, "relation": "decreases", "evidence": "In this study, we aimed to investigate the possibility of P-gp as a potential therapeutic target for Alzheimer's disease by examining the impact of P-gp up-regulation on the clearance of Abeta, a neuropathological hallmark of Alzheimer's disease.Uptake studies for-radiolabelled Abeta Approximately 10-35% decrease in Abeta intracellular accumulation was observed in cells treated with rifampicin, dexamethasone, caffeine, verapamil, hyperforin, beta-estradiol and pentylenetetrazole compared with control.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 241, "target": 2328, "key": "df1a2bc1354d54f4e7179a1bdbdf28ea"}, {"line": 6052, "relation": "increases", "evidence": "Finally, both LiCl pre-treatment and calpain inhibition prevented the DEX-induced inhibition on the insulin-stimulated Akt phosphorylation. In conclusion, our study suggests that the tau phosphorylation and calpain activation mediate the EX-induced inhibition on the insulin-stimulated Akt phosphorylation.", "citation": {"db": "PubMed", "db_id": "22536436"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 146, "target": 2154, "key": "ae48d34dd24544bc497fb77c54971053"}, {"line": 12550, "relation": "isA", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 146, "target": 37, "key": "5643fe0e2b95e432973f5b6d5c403061"}, {"line": 12556, "relation": "decreases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 146, "target": 159, "key": "a7a24016deee54c6677c67d294f8b35f"}, {"line": 12561, "relation": "decreases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"GSK3 subgraph": true}}, "source": 146, "target": 2794, "key": "020bc899489f93ee6dbedc145a4a8b0c"}, {"line": 12569, "relation": "decreases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 146, "target": 3015, "key": "e9ed8a3657b26cae5ac38b2aa50ebcfc"}, {"line": 12575, "relation": "increases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 146, "target": 854, "key": "ffe2e9c004c8687383fcdc4fbd5c6a0f"}, {"line": 12581, "relation": "decreases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 146, "target": 648, "key": "6ce42cde2bd848413e68db8277065982"}, {"line": 6066, "relation": "association", "evidence": "a number of clinical and epidemiological studies have provided further direct evidence to strengthen the link between T2D and AD (1, 2)", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3823, "key": "4fff64e04d9df9e392cb959259a0112e"}, {"line": 10415, "relation": "positiveCorrelation", "evidence": "ER stress contributes to the pathogenesis of obesity and diabetes, which are risk factors for Alzheimer's disease (AD) that accelerate the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 3823, "key": "4705e639dca961d8be5e75568a3a3fce"}, {"line": 20543, "relation": "association", "evidence": "In late-onset sporadic Alzheimer disease changes in the brain are similar to those caused by non-insulin-dependent diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3823, "key": "198a9810a85e7050caeabb3129ce0382"}, {"line": 6107, "relation": "positiveCorrelation", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3861, "key": "619aa925574360d3107610163c231b26"}, {"line": 8237, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3850, "target": 3861, "key": "7a89e2dc6cd86a99091d6bdec79eb11f"}, {"line": 6111, "relation": "positiveCorrelation", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 842, "key": "d619812e29518b1cd5c9d1ae9e78a215"}, {"line": 9696, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3850, "target": 842, "key": "78f0f525e4b10e1d12bedf1a277153be"}, {"line": 6115, "relation": "negativeCorrelation", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 812, "key": "37c42b7f89c956f1880eb2a63ed545fd"}, {"line": 6228, "relation": "increases", "evidence": "Mechanistically, the increased risk of dementia in T2DM and obesity could be linked to chronic hyperglycemia, peripheral insulin resistance, oxidative stress, accumulation of advanced glycation end products, increased production of pro-inflammatory cytokines, and/orcerebral microvascular disease.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 2183, "key": "6e4015d8912b43d3553d7b56b9a38eda"}, {"line": 6237, "relation": "increases", "evidence": "Mechanistically, the increased risk of dementia in T2DM and obesity could be linked to chronic hyperglycemia, peripheral insulin resistance, oxidative stress, accumulation of advanced glycation end products, increased production of pro-inflammatory cytokines, and/orcerebral microvascular disease.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3472, "key": "85195a014a4b9ad1dee39ba4679868f6"}, {"line": 10804, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3472, "key": "61ef036446639547b2006127c5d9c3b7"}, {"line": 6239, "relation": "positiveCorrelation", "evidence": "Mechanistically, the increased risk of dementia in T2DM and obesity could be linked to chronic hyperglycemia, peripheral insulin resistance, oxidative stress, accumulation of advanced glycation end products, increased production of pro-inflammatory cytokines, and/orcerebral microvascular disease.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3837, "key": "92634e849df83c4a92aaa66a32858e23"}, {"line": 7354, "relation": "association", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3857, "key": "cc59d49ebbe0411bc40f1d5ae6c1f367"}, {"line": 7885, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 2912, "key": "fe4c8e83f8060208f74c9ad957cca9bf"}, {"line": 8239, "relation": "positiveCorrelation", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3850, "target": 2894, "key": "a99ed32403198de364dd14613183c91e"}, {"line": 10796, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3850, "target": 2894, "key": "4020831d81cfc9070d22a59b567a3d5d"}, {"line": 8262, "relation": "positiveCorrelation", "evidence": "A separate analysis using just the DM2+ cases broken down by ApoE e4 carrier status was carried out (Table 2). There were 22 ApoE e4 carriers and 17 ApoE e4 non-carriers (n=39) as ApoE data was not available for 1 case. This analysis found that individuals carrying the e4 allele had significantly greater plaque and tangle pathology across all cortical areas except for tangle counts in the parietal and entorhinal areas. AD-DM2+ ApoE e4 carriers had a significantly lower age of death than AD-DM2+ ApoE e4 noncarriers", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 3850, "target": 889, "key": "5e441760ac1e15d5f5a34476c5c624ef"}, {"line": 8306, "relation": "positiveCorrelation", "evidence": "Another consequence of insulin resistance may be impaired regulation of the hypothalamicpituitary- adrenal (HPA) axis. Insulin and cortisol, a primary HPA axis hormone, are counter-regulatory, and a change in the level of either hormone influences the level of the other.[29,30] Thus, glucocorticoids can induce insulin resistance in healthy humans,[ 31,32] and hyperinsulinaemia resulting from insulin resistance can produce hypercortisolaemia. An animal study showed that rats with type 2 diabetes had higher levels of adrenocorticotrophic hormone than control individuals, consistent with chronic activation of the HPA axis.[33] In humans, plasma cortisol levels were elevated in patients with type 2 diabetes,[34] and patientswith both poorly and well controlled type 2 diabetes showed abnormal cortisol responses to hypoglycaemia.[35,36] In metabolic studies, insulin administration has been shown to increase HPA axis activity, indexed by a rise in cortisol levels", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"10116": true}}, "source": 3850, "target": 3805, "key": "a0f63d79440a4103880bae99bd1fb9ea"}, {"line": 8310, "relation": "positiveCorrelation", "evidence": "Another consequence of insulin resistance may be impaired regulation of the hypothalamicpituitary- adrenal (HPA) axis. Insulin and cortisol, a primary HPA axis hormone, are counter-regulatory, and a change in the level of either hormone influences the level of the other.[29,30] Thus, glucocorticoids can induce insulin resistance in healthy humans,[ 31,32] and hyperinsulinaemia resulting from insulin resistance can produce hypercortisolaemia. An animal study showed that rats with type 2 diabetes had higher levels of adrenocorticotrophic hormone than control individuals, consistent with chronic activation of the HPA axis.[33] In humans, plasma cortisol levels were elevated in patients with type 2 diabetes,[34] and patientswith both poorly and well controlled type 2 diabetes showed abnormal cortisol responses to hypoglycaemia.[35,36] In metabolic studies, insulin administration has been shown to increase HPA axis activity, indexed by a rise in cortisol levels", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 3850, "target": 237, "key": "f2b5e89095094a7d5ba8d1773dda516b"}, {"line": 9700, "relation": "association", "evidence": "The fact that mitochondria are the major generators and direct targets of reactive oxygen species led several investigators to foster the idea that oxidative stress and damage in mitochondria are contributory factors to several disorders including Alzheimer's disease and diabetes.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3850, "target": 430, "key": "f6b4592fc2357e0d2fdfd69c2bbbe951"}, {"line": 9761, "relation": "association", "evidence": "Abeta autoantibody levels were increased in T2DM compared with age-matched controls by 45.4 +/- 8.1% (p< 0.001).", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 2328, "key": "811b17bd3de360cf3be2b42a4b4d8bda"}, {"line": 9809, "relation": "negativeCorrelation", "evidence": "The deficiency of insulin-PI3K-AKT signalling was more severe in individuals with both T2DM and AD (T2DM-AD).", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 3850, "target": 579, "key": "0dfe7e49f24e19fbff61daac9515839a"}, {"line": 9886, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 2392, "key": "54461d622cd0021e2fc8fddee2a29f70"}, {"line": 9902, "relation": "association", "evidence": "Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.", "citation": {"db": "PubMed", "db_id": "17096857"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 2244, "key": "470b8bf8cfc1fadddcd24f60210c20d0"}, {"line": 9980, "relation": "association", "evidence": "We have recently identified in vitro a high affinity interaction between beta-amyloid peptide (Abeta) of AD and islet amyloid polypeptide (IAPP) of T2D which results in the formation of non-fibrillar and non-cytotoxic Abeta-IAPP hetero-oligomers.", "citation": {"db": "PubMed", "db_id": "23713771"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 928, "key": "79ed2e5cdada28a0037baafa33999932"}, {"line": 10158, "relation": "negativeCorrelation", "evidence": "Moreover, reduction of beta-cell replication capabilities results in reduction of beta-cell mass in mammals, simultaneously with impaired glucose tolerance.", "citation": {"db": "PubMed", "db_id": "21537460"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 3850, "target": 797, "key": "9c380669d6d411e380500a6d04465ac0"}, {"line": 10184, "relation": "association", "evidence": "Adiponectin is an adipocytokine released by the adipose tissue and has multiple roles in the immune system and in the metabolic syndromes such as cardiovascular disease, Type 2 diabetes, obesity and also in the neurodegenerative disorders including Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}}, "source": 3850, "target": 2259, "key": "09e910195d31740e018a4584b7c864a9"}, {"line": 10625, "relation": "association", "evidence": "Lysosomal beta-galactosidase and beta-hexosaminidase activities correlate with clinical stages of dementia associated with Alzheimer's disease and type 2 diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "CellStructure": {"Lysosomes": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3850, "target": 2829, "key": "772313d6cda82efd6db77edf1316f402"}, {"line": 10657, "relation": "increases", "evidence": "In particular, plasma beta-Galactosidase and beta-Hexosaminidase levels were higher in patients with AD-T2DM compared to those with T2DM, suggesting different mechanisms leading to enzyme secretion.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"CellStructure": {"Lysosomes": true}, "MeSHAnatomy": {"Bodily Secretions": true, "Plasma": true}, "MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 2829, "key": "742f32c4f411546812905f93e27a0481"}, {"line": 10631, "relation": "association", "evidence": "Lysosomal beta-galactosidase and beta-hexosaminidase activities correlate with clinical stages of dementia associated with Alzheimer's disease and type 2 diabetes mellitus.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "CellStructure": {"Lysosomes": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3901, "key": "75f1e29929ce191e3becc0f055d63abf"}, {"line": 10656, "relation": "increases", "evidence": "In particular, plasma beta-Galactosidase and beta-Hexosaminidase levels were higher in patients with AD-T2DM compared to those with T2DM, suggesting different mechanisms leading to enzyme secretion.", "citation": {"db": "PubMed", "db_id": "21321400"}, "annotations": {"CellStructure": {"Lysosomes": true}, "MeSHAnatomy": {"Bodily Secretions": true, "Plasma": true}, "MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 2750, "key": "0f575c006abb3095c171910e8f629769"}, {"line": 10692, "relation": "negativeCorrelation", "evidence": "The median (18)F-FDG ratio was lower in diabetic individuals than in nondiabetic individuals in the AD signature meta-ROI (1.32 vs. 1.40,", "citation": {"db": "PubMed", "db_id": "24652830"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Patient": {"AD T2DM -ve": true}}, "source": 3850, "target": 17, "key": "1fe1ea1571985f3b1e9041e13cf57424"}, {"line": 10699, "relation": "positiveCorrelation", "evidence": "The median (18)F-FDG ratio was lower in diabetic individuals than in nondiabetic individuals in the AD signature meta-ROI (1.32 vs. 1.40,", "citation": {"db": "PubMed", "db_id": "24652830"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Patient": {"AD T2DM +ve": true}}, "source": 3850, "target": 17, "key": "4e2d3405b7ec05b486c85608462da249"}, {"line": 10790, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 3850, "target": 2564, "key": "0266745ed4ebc3218d021d72f7116f49"}, {"line": 10810, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 3850, "target": 605, "key": "5635883634393d6bfeecb2a483135ba7"}, {"line": 10820, "relation": "negativeCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3850, "target": 156, "key": "8a8d084fddfe3b143f7c2b053ad17346"}, {"line": 10869, "relation": "association", "evidence": "Hyperamylinemia, a common pancreatic disorder in obese and insulin-resistant patients, is known to cause amylin oligomerization and cytotoxicity in pancreatic islets, leading to beta-cell mass depletion and development of type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "23794448"}, "source": 3850, "target": 3813, "key": "7c62952f6120b8720b7997516b4e216e"}, {"line": 10966, "relation": "negativeCorrelation", "evidence": "We found that the neuronal glucose transporter 3 was decreased to a bigger extent in T2DM brain than in AD brain.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3375, "key": "36f0052e983ff9575a0bc0fff786215b"}, {"line": 10974, "relation": "negativeCorrelation", "evidence": "The O-GlcNAcylation levels of global proteins and of tau were also decreased in T2DM brain as seen in AD brain.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3850, "target": 3156, "key": "224a4bdc63dc9a47a82224034ceaf359"}, {"line": 10994, "relation": "decreases", "evidence": "These results suggest that T2DM may contribute to the increased risk for AD by impairing brain glucose uptake/metabolism and, consequently, down-regulation of O-GlcNAcylation, which facilitates abnormal hyperphosphorylation of tau.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3850, "target": 3156, "key": "7914d1d5467048751bfb00174135d897"}, {"line": 10979, "relation": "negativeCorrelation", "evidence": "The O-GlcNAcylation levels of global proteins and of tau were also decreased in T2DM brain as seen in AD brain.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 3014, "key": "852dcccf51e916a1a17cb09a12306ffa"}, {"line": 10985, "relation": "association", "evidence": "Phosphorylation of tau at some of the AD abnormal hyperphosphorylation sites was increased in T2DM brain.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3850, "target": 3015, "key": "347c6ad0f0a159ccb5dc3d374d80e3a6"}, {"line": 10997, "relation": "positiveCorrelation", "evidence": "These results suggest that T2DM may contribute to the increased risk for AD by impairing brain glucose uptake/metabolism and, consequently, down-regulation of O-GlcNAcylation, which facilitates abnormal hyperphosphorylation of tau.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 3015, "key": "d760947f84454ff591c4c33aaac89367"}, {"line": 10992, "relation": "decreases", "evidence": "These results suggest that T2DM may contribute to the increased risk for AD by impairing brain glucose uptake/metabolism and, consequently, down-regulation of O-GlcNAcylation, which facilitates abnormal hyperphosphorylation of tau.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 565, "key": "d9ef5d4c90f1d1254f823f02dabd834d"}, {"line": 10993, "relation": "decreases", "evidence": "These results suggest that T2DM may contribute to the increased risk for AD by impairing brain glucose uptake/metabolism and, consequently, down-regulation of O-GlcNAcylation, which facilitates abnormal hyperphosphorylation of tau.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 566, "key": "89896419369639e3d7f7a923c144c09d"}, {"line": 21858, "relation": "association", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 3850, "target": 289, "key": "c8736b58e8e37d72ebfc1245a7a8b695"}, {"line": 28669, "relation": "association", "evidence": "SORCS1 is also genetically associated with types 1 and 2 diabetes mellitus (T1DM, T2DM).", "citation": {"db": "PubMed", "db_id": "20881129"}, "source": 3850, "target": 1972, "key": "6e890a6dc2f49ddaacb75048d5e04152"}, {"line": 30872, "relation": "negativeCorrelation", "evidence": "Insulin-degrading enzyme (IDE) is a protease that has been demonstrated to play a key role in degrading both Abeta and insulin and deficient in IDE function is associated with Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) pathology.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 2867, "key": "1cd4a3f0e58632edc7c7afa79a61affa"}, {"line": 42434, "relation": "association", "evidence": "The PPARgamma agonist pioglitazone and a novel selective PPARα/gamma modulator, DSP-8658, currently in clinical development for the treatment of type 2 diabetes, enhanced the microglial uptake of Abeta in a PPARgamma-dependent manner.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"Medium": true}}, "source": 3850, "target": 414, "key": "87e245c9212c027988eaaf3d992096bd"}, {"line": 6074, "relation": "negativeCorrelation", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 523, "target": 3823, "key": "4dc67166f05e661974c24b94c5016f74"}, {"line": 6075, "relation": "negativeCorrelation", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 523, "target": 80, "key": "bb727c29cee7186bb894789dddc9cc33"}, {"line": 8350, "relation": "association", "evidence": "Other insulin-related mechanisms have been implicated in normal hippocampal functioning in addition to insulin-receptormodulation. For example, insulin may modulate long-term potentiation (LTP), a molecular model of learning. LTP can be induced by N-methyl-D-aspartate (NMDA) receptor activation, thus increasing neuronal Ca2+ influx. Elevated intracellular Ca2+ level presumably activates α-calcium-calmodulin-dependent kinase II (αCaMKII) and other Ca2+-dependent enzymes, eventuating in stronger synaptic associations between neurons. Insulin may influence several constituents of this LTP cascade. For example, insulin promoted the cellmembrane expression of NMDA receptors,[60] which may increase the likelihood of LTP induction.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 523, "target": 492, "key": "11c7dfc5815cf32cda1a1b805fdfc049"}, {"line": 11674, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 523, "target": 434, "key": "997e73b9547a2883349525709877de1b"}, {"line": 36371, "relation": "increases", "evidence": "ABeta¸ was proposed as a regulator of ion channel function [27] and as essential for neuronal health . ABeta¸ is secreted from neurons in response to synaptic activity and that ABeta¸, in turn, down regulates synaptic transmission [29]. This negative feedback loop could operate as a physiological homeostatic mechanism to limit levels of neuronal activity", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 523, "target": 647, "key": "58524510e9071294d6930c30f9110d7f"}, {"line": 36603, "relation": "increases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 523, "target": 2375, "key": "c0a005e5d381800b972cf00753be612c"}, {"line": 36605, "relation": "increases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 523, "target": 2381, "key": "a56b80079736bd1ba6eb97b810f70258"}, {"line": 6079, "relation": "association", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 839, "target": 588, "key": "79ae991fcd645578bc15fbdc2b8eaab8"}, {"line": 7648, "relation": "association", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 839, "target": 448, "key": "33b889e8ee6215b78ade4903d309e032"}, {"line": 8337, "relation": "association", "evidence": "Other insulin-related mechanisms have been implicated in normal hippocampal functioning in addition to insulin-receptormodulation. For example, insulin may modulate long-term potentiation (LTP), a molecular model of learning. LTP can be induced by N-methyl-D-aspartate (NMDA) receptor activation, thus increasing neuronal Ca2+ influx. Elevated intracellular Ca2+ level presumably activates α-calcium-calmodulin-dependent kinase II (αCaMKII) and other Ca2+-dependent enzymes, eventuating in stronger synaptic associations between neurons. Insulin may influence several constituents of this LTP cascade. For example, insulin promoted the cellmembrane expression of NMDA receptors,[60] which may increase the likelihood of LTP induction.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 839, "target": 2899, "key": "3dfd1970cbb05adec18bcbaaf3cf258e"}, {"line": 8344, "relation": "increases", "evidence": "Other insulin-related mechanisms have been implicated in normal hippocampal functioning in addition to insulin-receptormodulation. For example, insulin may modulate long-term potentiation (LTP), a molecular model of learning. LTP can be induced by N-methyl-D-aspartate (NMDA) receptor activation, thus increasing neuronal Ca2+ influx. Elevated intracellular Ca2+ level presumably activates α-calcium-calmodulin-dependent kinase II (αCaMKII) and other Ca2+-dependent enzymes, eventuating in stronger synaptic associations between neurons. Insulin may influence several constituents of this LTP cascade. For example, insulin promoted the cellmembrane expression of NMDA receptors,[60] which may increase the likelihood of LTP induction.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 839, "target": 492, "key": "a6f882e2d09d4c7e52673041c1855923"}, {"line": 12228, "relation": "positiveCorrelation", "evidence": "After the treatment, memantine-treated mice had restored cognition and significantly reduced the levels of insoluble amyloid-beta (Abeta), Abeta dodecamers (Abeta*56), prefibrillar soluble oligomers, and fibrillar oligomers. The effects on pathology were stronger in older, more impaired animals. Memantine treatment also was associated with a decline in the levels of total tau and hyperphosphorylated tau. Finally, memantine pre-incubation prevented Abeta-induced inhibition of long-term potentiation in hippocampal slices of cognitively normal mice.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"NMDA receptor": true, "Non-amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "source": 839, "target": 300, "key": "4a16e1dbb89830ebf23bb2f3222eca01"}, {"line": 6079, "relation": "association", "evidence": "The pathogenesis of AD begins with impaired synaptic function, which may result from the accumulation of amyloid-beta (Abeta) peptide (5â€8). For many years, researchers have focused on the insoluble deposits of amyloid fibrils as the leading cause of memory loss and as the culprit of AD. More recent findings, however, suggest soluble Abeta oligomers may be the cause of memory loss, especially in the early stages of AD, because Abeta oligomers inhibit long-term potentiation in neurons, a well-adopted experimental paradigm for learning and memory (6, 9). Abeta is produced from amyloid precursor protein (APP) by serial proteolytic reactions catalyzed by beta-site of APP cleaving enzyme (BACE) and a multiprotein complex gamma-secretase, in which presenilin is likely the catalytic component ", "citation": {"db": "PubMed", "db_id": "20385830"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 588, "target": 839, "key": "47a8e6329e97b001997fac8dfcb8d7a7"}, {"line": 6608, "relation": "positiveCorrelation", "evidence": "Glucose transport within the brain is mostly insulin independent, suggesting that the primary role of brain insulin might be in signal transduction pathways involved in cognitive processes [Schulingkamp et al., 2000]. Indeed, studies have shown that insulin and insulin receptors are abundantly but selectively distributed in brain areas that are important for learning and memory [Schulingkamp et al., 2000], and treatment of both normal healthy individuals and patients with mild AD with insulin has been shown to improve cognition [Watson and Craft, 2004].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 588, "target": 2899, "key": "86f89a5dc5805d70ad8edfe969f5b04b"}, {"line": 7410, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 588, "target": 2899, "key": "dd682c7e2fb03914ff19f4aa5aee7d8b"}, {"line": 7411, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 588, "target": 2871, "key": "85ddd272b6ad4c2596f105a6581194a4"}, {"line": 10014, "relation": "association", "evidence": "In addition, excitotoxicity from the overstimulation of glutamate receptors is considered a major cause of neuron death in AD and statins may be promising agents for protecting against memory loss.", "citation": {"db": "PubMed", "db_id": "21352095"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 588, "target": 355, "key": "c0fe34390dea4971b7f9f20d2a1e0de8"}, {"line": 10211, "relation": "negativeCorrelation", "evidence": "Previous studies demonstrated that adiponectin modulates memory and cognitive impairment and contributes to the deregulated glucose metabolism and mitochondrial dysfunction observed in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 588, "target": 2259, "key": "587463f43ebc9805992968a858ad25de"}, {"line": 11150, "relation": "association", "evidence": "In the central nervous system, galanin alters the release of several neurotransmitters. In particular the ability of galanin to inhibit acetylcholine release, along with the observation of hyperinervation of galanin fibres in the human basal forebrain of Alzheimer's disease patients, suggest a possible role for galanin in the cholinergic dysfunction, characteristic of the disease. Moreover, galanin has been suggested to be involved in other neuronal functions, such as learning and memory, epileptic activity, nociception, spinal reflexes and feeding. Galanin has also been shown to increase the levels of growth hormone, prolactin and luteinizing hormone, to inhibit glucose induced insulin release and to affect gastrointestinal motility.", "citation": {"db": "PubMed", "db_id": "12769595"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 588, "target": 2737, "key": "a47d6c5bda55960a5f5880edb6ce96aa"}, {"line": 47038, "relation": "association", "evidence": "Abeta is widely accepted to be one of the major pathomechanisms underlying Alzheimer's disease (AD), although there is presently lively debate regarding the relative roles of particular species/forms of this peptide. Most recent evidence indicates that soluble oligomers rather than plaques are the major cause of synaptic dysfunction and ultimately neurodegeneration. Soluble oligomeric Abeta has been shown to interact with several proteins, for example glutamatergic receptors of the NMDA type and proteins responsible for maintaining glutamate homeostasis such as uptake and release. As NMDA receptors are critically involved in neuronal plasticity including learning and memory, we felt that it would be valuable to provide an up to date review of the evidence connecting Abeta to these receptors and related neuronal plasticity. Strong support for the clinical relevance of such interactions is provided by the NMDA receptor antagonist memantine. This substance is the only NMDA receptor antagonist used clinically in the treatment of AD and therefore offers an excellent tool to facilitate translational extrapolations from in vitro studies through in vivo animal experiments to its ultimate clinical utility", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 588, "target": 3548, "key": "6b33e5d15517f7bd02aaff1298020e40"}, {"line": 47048, "relation": "association", "evidence": "The hippocampus, with its high density of glutamate receptors and in particular NMDA receptors, is known to be extremely important for some forms of learning and memory. Glutamatergic synapses can show pronounced plasticity in terms of the number and strength of individual synapses and are also characterized by their ability to express LTP – a long-lasting strengthening of synaptic transmission ", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Hippocampus": true}}, "object": {"modifier": "Activity"}, "source": 588, "target": 3548, "key": "bf2d362e8375b21fa9c416d72629f95f"}, {"line": 48932, "relation": "association", "evidence": "Early growth response gene 1 (Egr1) is a member of the immediate early gene (IEG) family of transcription factors and plays a role in memory formation. The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 588, "target": 2658, "key": "ce3c2c9eada70b7310cc363091f25ff2"}, {"relation": "hasReactant", "source": 4101, "target": 2315, "key": "f71e9d5c26599293f8fae5991e186a79"}, {"relation": "hasProduct", "source": 4101, "target": 2328, "key": "972d689c331d3084ea2876ace689360e"}, {"line": 6124, "relation": "negativeCorrelation", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 2899, "target": 3823, "key": "6fd1c118942c0877230abeb2a50234fb"}, {"line": 6251, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2899, "target": 3823, "key": "a68bec4499520501584e5b0ca8b27bf5"}, {"line": 8186, "relation": "positiveCorrelation", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 2899, "target": 3823, "key": "86f2e692ce4bd737e905efc97e28325b"}, {"line": 8320, "relation": "negativeCorrelation", "evidence": "We found that patients with Alzheimer's disease had lower CSF insulin levels, higher plasma insulin levels and reduced insulin-mediated glucose disposal compared with healthy control individuals,[ 89,90] a pattern consistent with insulin resistance.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}}, "source": 2899, "target": 3823, "key": "b75f7e1c74685dc4f73081886c1bd52a"}, {"line": 8328, "relation": "positiveCorrelation", "evidence": "We found that patients with Alzheimer's disease had lower CSF insulin levels, higher plasma insulin levels and reduced insulin-mediated glucose disposal compared with healthy control individuals,[ 89,90] a pattern consistent with insulin resistance.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 2899, "target": 3823, "key": "310131f3986ded9142ae8d9f95c4d9c6"}, {"line": 9717, "relation": "association", "evidence": "To test the hypothesis that polymorphic variation in insulin signalling genes may underlie the shared risk of dysfunctional insulin signalling and late onset Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 3823, "key": "bdd8eb9717868028b09fb8b600c69854"}, {"line": 6179, "relation": "increases", "evidence": "since around 2005, this field literally exploded with new information and a new concept, i.e., that primary brain insulin resistance and insulin deficiency mediate cognitive impairment and AD. This idea was fueled by evidence that tau gene expression and phosphorylation are regulated through insulin and insulin-like growth factor (IGF) signaling cascades.23,24", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 2899, "target": 3991, "key": "22808ce43aef392a87b05c3616c4bdc0"}, {"line": 6434, "relation": "regulates", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 3991, "key": "0ce996f8151f7d8665dbf24f3e381b2e"}, {"line": 6180, "relation": "increases", "evidence": "since around 2005, this field literally exploded with new information and a new concept, i.e., that primary brain insulin resistance and insulin deficiency mediate cognitive impairment and AD. This idea was fueled by evidence that tau gene expression and phosphorylation are regulated through insulin and insulin-like growth factor (IGF) signaling cascades.23,24", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 2899, "target": 3015, "key": "65b92954ce870ee5fb9580f354a34908"}, {"line": 7950, "relation": "increases", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}, "CellLine": {"SH-SY5Y": true}}, "source": 2899, "target": 3015, "key": "4aa9fa2eda26c72b73f1b9eb63e9c617"}, {"line": 7965, "relation": "regulates", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"NT2N cells": true}, "Confidence": {"Medium": true}}, "source": 2899, "target": 3015, "key": "c0491da743343937f889bff48c671041"}, {"line": 8200, "relation": "increases", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 2899, "target": 3015, "key": "d70d4d79059a6ae3e90fdf3f88a2f925"}, {"line": 9845, "relation": "negativeCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2899, "target": 3015, "key": "f46f608e27c296eff077a26bd14a514b"}, {"line": 10120, "relation": "association", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 3015, "key": "e79efc17fcddc898f3074a4f54f03911"}, {"line": 6190, "relation": "directlyIncreases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2899, "target": 2900, "key": "252fb991315b21d4791f4d7520254466"}, {"line": 10463, "relation": "positiveCorrelation", "evidence": "Most significantly, this loss of surface IRs, and ADDL-induced oxidative stress and synaptic spine deterioration, could be completely prevented by insulin.", "citation": {"db": "PubMed", "db_id": "19188609"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "cell surface"}}}, "source": 2899, "target": 2900, "key": "870d97a60c38a6bd2be0a92e6f04ba83"}, {"line": 36642, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2899, "target": 2900, "key": "7de2200f7225ca579b87bc5d2f5c8a1f"}, {"line": 6279, "relation": "negativeCorrelation", "evidence": "This study carries additional significance because it established that, like all other pancreatic and intestinal polypeptide genes, the insulin gene was also expressed in the adult human brain. Moreover, the results taught us that endogenous brain deficiencies in insulin, IGF-1, IGF-2, and their corresponding receptors, in the absence of T2DM or obesity, could be linked to the most common form of dementia-associated neurodegeneration in the Western hemisphere.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2899, "target": 3901, "key": "6d5345158e3dff85c4a2acc2b116f64e"}, {"line": 6350, "relation": "negativeCorrelation", "evidence": "Correspondingly, the reduced expression of neuronal and oligodendroglial specific genes and the increased expression of astrocytic and microglial inflammatory genes in AD were attributed to progressive brain insulin/IGF deficiency and resistance.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2899, "target": 418, "key": "f3ee2a1f47641a0bc38c0ccd1e863c89"}, {"line": 6440, "relation": "increases", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 3956, "key": "5a353191db86ddb67de6b39527c97f83"}, {"line": 6450, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 2899, "target": 389, "key": "2026a579d127f3823774ff2f3af49e30"}, {"line": 6470, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2899, "target": 596, "key": "2954b7d98972df4a2349ec3ebb4e3c7a"}, {"line": 6481, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2899, "target": 841, "key": "e3fe2a748ee586ac6b8ceed3a424dc3b"}, {"line": 6491, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2899, "target": 466, "key": "d6b2c652f3179f4f837a4e5e14f3de5d"}, {"line": 6608, "relation": "positiveCorrelation", "evidence": "Glucose transport within the brain is mostly insulin independent, suggesting that the primary role of brain insulin might be in signal transduction pathways involved in cognitive processes [Schulingkamp et al., 2000]. Indeed, studies have shown that insulin and insulin receptors are abundantly but selectively distributed in brain areas that are important for learning and memory [Schulingkamp et al., 2000], and treatment of both normal healthy individuals and patients with mild AD with insulin has been shown to improve cognition [Watson and Craft, 2004].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 2899, "target": 588, "key": "c84b2519cca269b447a4c80843835005"}, {"line": 7410, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 588, "key": "4c8e83513897c0a275483c47b45e7752"}, {"line": 6612, "relation": "regulates", "evidence": "Glucose transport within the brain is mostly insulin independent, suggesting that the primary role of brain insulin might be in signal transduction pathways involved in cognitive processes [Schulingkamp et al., 2000]. Indeed, studies have shown that insulin and insulin receptors are abundantly but selectively distributed in brain areas that are important for learning and memory [Schulingkamp et al., 2000], and treatment of both normal healthy individuals and patients with mild AD with insulin has been shown to improve cognition [Watson and Craft, 2004].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 2899, "target": 812, "key": "7cb2b255258d0f6aeeebd14d9aaebc9f"}, {"line": 6620, "relation": "increases", "evidence": "Glucose transport within the brain is mostly insulin independent, suggesting that the primary role of brain insulin might be in signal transduction pathways involved in cognitive processes [Schulingkamp et al., 2000]. Indeed, studies have shown that insulin and insulin receptors are abundantly but selectively distributed in brain areas that are important for learning and memory [Schulingkamp et al., 2000], and treatment of both normal healthy individuals and patients with mild AD with insulin has been shown to improve cognition [Watson and Craft, 2004].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2899, "target": 812, "key": "10d829a0af65b8d887f8e1fa9e49b0e6"}, {"line": 7807, "relation": "increases", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Gender": {"Female": true}, "Confidence": {"High": true}}, "source": 2899, "target": 812, "key": "78b6c395e3e46f5420a58d6ae7e35adc"}, {"line": 6631, "relation": "increases", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 2899, "target": 820, "key": "8747199e96483dea9f62694a6e153cae"}, {"line": 7808, "relation": "increases", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Gender": {"Female": true}, "Confidence": {"High": true}}, "source": 2899, "target": 820, "key": "a78249b6b9786db79d042c54bb1e29b0"}, {"line": 6891, "relation": "regulates", "evidence": "Insulin modulates metabolism of beta-amyloid precursor protein (APP) in neurons, decreasing the intracellular accumulation of beta-amyloid (Abeta) peptides, which are pivotal in AD pathogenesis. The present study investigates whether the widely prescribed insulin-sensitizing drug, metformin (GlucophageR), affects APP metabolism and Abeta generation in various cell models. We demonstrate that metformin, at doses that lead to activation of the AMP-activated protein kinase (AMPK), significantly increases the generation of both intracellular and extracellular Abeta species", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 2899, "target": 2328, "key": "0ef131561a6b805aeede02f801cf2f10"}, {"line": 8193, "relation": "increases", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2899, "target": 2328, "key": "79ca537cd230f9e4af8d26ada13c2428"}, {"line": 33200, "relation": "increases", "evidence": "Insulin and IGF-I may pmodulate brain levels of insulin degrading enzyme, which would also lead to an accumulation of Abeta amyloid.", "citation": {"db": "PubMed", "db_id": "16444902"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2899, "target": 2328, "key": "fb3ad967195a7c717762281a282c4e33"}, {"relation": "partOf", "source": 2899, "target": 1664, "key": "6530e980698ad5308cdb6477cffd9aef"}, {"line": 7377, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 565, "key": "d96408a7274cca11c870e5df6852c054"}, {"line": 7386, "relation": "decreases", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Liver": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 566, "key": "679b73b86e606ac131f4b7de03099939"}, {"line": 9657, "relation": "increases", "evidence": "It has been argued that in late-onset Alzheimer's disease a disturbance in the control of neuronal glucose metabolism consequent to impaired insulin signalling strongly resembles the pathophysiology of type 2 diabetes in non-neural tissue.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 566, "key": "844320665777dc4a5710e43e46d00288"}, {"line": 7396, "relation": "positiveCorrelation", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 552, "key": "efdb13f704a5182581e73576b304c019"}, {"line": 7403, "relation": "positiveCorrelation", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 830, "key": "3eb7d70327033d30de2f4d4999e4d310"}, {"line": 7412, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 850, "key": "7e2723e7563ed7d27f526fef05a2f3ab"}, {"relation": "partOf", "source": 2899, "target": 1470, "key": "94623e90b72c9304d1109560eec88980"}, {"line": 7610, "relation": "regulates", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2899, "target": 451, "key": "c4edf3684d13498e5680f269b6aef71f"}, {"line": 7619, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 1021, "key": "aef8e783f81bdc1c3b20abf28b4d0f99"}, {"line": 7621, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 1426, "key": "8d94f5bf05802f35eb9c8431e9298b92"}, {"line": 7747, "relation": "association", "evidence": "Taken together, the BBB is an important interface between the blood and the CNS compartment regu­ lating uptake of insulin and IGF-1 into the bra in. However, the molecul ar mech anisms by which different conditions like aging or AD decrease insulin 's transport to the brain are not known yet. Wh eth er these mech anisms contribute to th e pathogenesis of AD and cognitive decline is still unclear.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 601, "key": "7b39c082a8c52d9a4a656e0ee9b0c3f6"}, {"line": 7858, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Confidence": {"High": true}}, "source": 2899, "target": 601, "key": "735876b7036d1277cf18648d9c345838"}, {"line": 7814, "relation": "causesNoChange", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Gender": {"Female": true}, "MeSHAnatomy": {"Blood": true}, "Confidence": {"High": true}}, "source": 2899, "target": 264, "key": "ff87988c3999247343bb6c3577713ebc"}, {"line": 7975, "relation": "increases", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}, "UserdefinedCellLine": {"primary cortical neuron": true}}, "source": 2899, "target": 1448, "key": "21eeef77de1444e4284d06bf7c193101"}, {"line": 8165, "relation": "decreases", "evidence": "Indeed, adding increasing amounts of insulin, a substrate of IDE with low Km (Km = ∼0.1 μM), specifically inhibited enzyme activity for degradation of Abeta (Km >2 μM) [74] in the cell culture model for secreted IDE. Therefore, if the insulin level increases in the brain, it would inhibit IDE to degrade Abeta effectively, which could cause Abeta neurotoxicity, and then AD.", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2899, "target": 2867, "key": "31c1989254fe3de878238cf495b4ab89"}, {"line": 10078, "relation": "association", "evidence": "Insulin-degrading enzyme (IDE) is central to the turnover of insulin and degrades amyloid beta (Abeta) in the mammalian brain.", "citation": {"db": "PubMed", "db_id": "18411275"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2899, "target": 2867, "key": "42d6d9aa8d552144deab0c3904e523f6"}, {"line": 30855, "relation": "decreases", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "source": 2899, "target": 2867, "key": "bb0f3c309af1ca48db5c21d76d44a871"}, {"line": 8208, "relation": "association", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2899, "target": 475, "key": "48faff11307cb1c33b48cca7896b61d9"}, {"relation": "partOf", "source": 2899, "target": 1483, "key": "04a5f34ff94326cf4ffd6eb8f0c66199"}, {"line": 8244, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 2899, "target": 710, "key": "afd1176481f2a9ca7c855904a29145b0"}, {"line": 8285, "relation": "increases", "evidence": "In contrast, GLUT 4 and 8 (or X1) are insulin-sensitive transporters,[20,21] which are expressed in intracellular compartments of adipocytes andmuscle cells and are translocated to membranes in response to the presence of insulin.[ 22] Translocation allows muscle and adipose tissue to increase glucose uptake 10- to 40-fold within a matter of minutes.[22]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Insulin signal transduction": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Intracellular Space"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 2899, "target": 3376, "key": "443e590684f7216f1277dbe11871ddb8"}, {"line": 8287, "relation": "increases", "evidence": "In contrast, GLUT 4 and 8 (or X1) are insulin-sensitive transporters,[20,21] which are expressed in intracellular compartments of adipocytes andmuscle cells and are translocated to membranes in response to the presence of insulin.[ 22] Translocation allows muscle and adipose tissue to increase glucose uptake 10- to 40-fold within a matter of minutes.[22]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Insulin signal transduction": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Intracellular Space"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 2899, "target": 3377, "key": "d2ed033b1358bedba5a0e6300b9fba01"}, {"line": 8312, "relation": "increases", "evidence": "Another consequence of insulin resistance may be impaired regulation of the hypothalamicpituitary- adrenal (HPA) axis. Insulin and cortisol, a primary HPA axis hormone, are counter-regulatory, and a change in the level of either hormone influences the level of the other.[29,30] Thus, glucocorticoids can induce insulin resistance in healthy humans,[ 31,32] and hyperinsulinaemia resulting from insulin resistance can produce hypercortisolaemia. An animal study showed that rats with type 2 diabetes had higher levels of adrenocorticotrophic hormone than control individuals, consistent with chronic activation of the HPA axis.[33] In humans, plasma cortisol levels were elevated in patients with type 2 diabetes,[34] and patientswith both poorly and well controlled type 2 diabetes showed abnormal cortisol responses to hypoglycaemia.[35,36] In metabolic studies, insulin administration has been shown to increase HPA axis activity, indexed by a rise in cortisol levels", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2899, "target": 237, "key": "445fb7de262ebf5653241e6377931245"}, {"line": 8337, "relation": "association", "evidence": "Other insulin-related mechanisms have been implicated in normal hippocampal functioning in addition to insulin-receptormodulation. For example, insulin may modulate long-term potentiation (LTP), a molecular model of learning. LTP can be induced by N-methyl-D-aspartate (NMDA) receptor activation, thus increasing neuronal Ca2+ influx. Elevated intracellular Ca2+ level presumably activates α-calcium-calmodulin-dependent kinase II (αCaMKII) and other Ca2+-dependent enzymes, eventuating in stronger synaptic associations between neurons. Insulin may influence several constituents of this LTP cascade. For example, insulin promoted the cellmembrane expression of NMDA receptors,[60] which may increase the likelihood of LTP induction.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 2899, "target": 839, "key": "bdeff38f11ec518090c8dded2dc69732"}, {"line": 8351, "relation": "increases", "evidence": "Other insulin-related mechanisms have been implicated in normal hippocampal functioning in addition to insulin-receptormodulation. For example, insulin may modulate long-term potentiation (LTP), a molecular model of learning. LTP can be induced by N-methyl-D-aspartate (NMDA) receptor activation, thus increasing neuronal Ca2+ influx. Elevated intracellular Ca2+ level presumably activates α-calcium-calmodulin-dependent kinase II (αCaMKII) and other Ca2+-dependent enzymes, eventuating in stronger synaptic associations between neurons. Insulin may influence several constituents of this LTP cascade. For example, insulin promoted the cellmembrane expression of NMDA receptors,[60] which may increase the likelihood of LTP induction.", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 2899, "target": 3548, "key": "ffdeda5d5aabccc90732a79f12f45d98"}, {"line": 8361, "relation": "decreases", "evidence": "We found that insulin administration reduced plasma APP for patients with Alzheimer's disease with an APOE ε4 allele but raised APP for patients without an ε4 allele", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Plasma": true}, "Disease": {"Alzheimer's disease": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 2899, "target": 2315, "key": "47d6a724bd3ffbab07a789f898f1eca8"}, {"line": 8373, "relation": "increases", "evidence": "We found that insulin administration reduced plasma APP for patients with Alzheimer's disease with an APOE ε4 allele but raised APP for patients without an ε4 allele", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Plasma": true}, "Disease": {"Alzheimer's disease": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 2899, "target": 2315, "key": "b664c5b65d55c4e719687097c5ab8d19"}, {"line": 9847, "relation": "positiveCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 2899, "target": 3156, "key": "80591da5d3b885d7be6b3f1f38b6228b"}, {"line": 10098, "relation": "regulates", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 564, "key": "1384b6874e6d1d7ea8cc0407f0546732"}, {"line": 10106, "relation": "regulates", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 465, "key": "954edd332aac6523255df6f02c81f6b5"}, {"line": 10114, "relation": "regulates", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 231, "key": "5b039283890ab7d2aadf4100fa729493"}, {"line": 10127, "relation": "association", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 80, "key": "19482f247aa9adfc4faa09b208d8b7b5"}, {"line": 10142, "relation": "negativeCorrelation", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2899, "target": 80, "key": "34f8a55fb081b5433555b936a4dc110b"}, {"line": 10135, "relation": "association", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2899, "target": 703, "key": "f898d3fcdd3e5a351b2f633e5bfeeb87"}, {"line": 10469, "relation": "decreases", "evidence": "The mechanism of insulin protection entailed a marked reduction in pathogenic ADDL binding.", "citation": {"db": "PubMed", "db_id": "19188609"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2899, "target": 378, "key": "7bcc6b975dfeeafb5245cb3680f890dd"}, {"relation": "partOf", "source": 2899, "target": 1062, "key": "7e82143ae9cc3a95b0a9397f6425b744"}, {"relation": "partOf", "source": 2899, "target": 1484, "key": "084bc64377935f4fdeb71fc4c4313bd5"}, {"relation": "partOf", "source": 2899, "target": 1464, "key": "8baee8c4bb886b8f5cc75362e39a9bfb"}, {"line": 30857, "relation": "decreases", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 2899, "target": 2893, "key": "d4d952eae47233f9f9356236d9a7d986"}, {"line": 6130, "relation": "association", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 631, "target": 3823, "key": "13a979c8457e1ef4d575c3ebd993f7de"}, {"line": 7996, "relation": "increases", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 631, "target": 3015, "key": "0fc51d3ab61072aa90f4e31c8a21b7d2"}, {"line": 6131, "relation": "increases", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 357, "target": 3848, "key": "0e72a2c11926a167308975c989e4b7f5"}, {"line": 11041, "relation": "increases", "evidence": "The present study examines the efficacy of Saxagliptin, a DPP-4 inhibitor in a streptozotocin (STZ) induced rat model of AD. Three months following induction of AD by intracerebral administration of streptozotocin, animals were orally administered Saxagliptin (0.25, 0.5 and 1 mg/kg) for 60 days. The effect of the DPP-4 inhibitor on hippocampal GLP-1 levels, Abeta burden, tau phosphorylation, inflammatory markers and memory retention were evaluated. The results reveal an attenuation of Abeta, tau phosphorylation and inflammatory markers and an improvement in hippocampal GLP-1 and memory retention following treatment.", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "source": 357, "target": 3823, "key": "cfb72b341a43512612a0eaba9f41647c"}, {"line": 14729, "relation": "increases", "evidence": "The rat model of Alzheimer's disease used in the present study was induced by the intracerebroventricular (ICV) injection of streptozotocin (STZ) using a stereotaxic instrument.", "citation": {"db": "PubMed", "db_id": "18687381"}, "source": 357, "target": 3823, "key": "d280ac51036eca84be33dc96a189fe6b"}, {"line": 14656, "relation": "increases", "evidence": "In vitro studies revealed that (1) exposure of neural stem cells (NSCs) from the hippocampus to STZ strikingly increased intracellular reactive oxygen species (ROS) levels, induced cell death and perturbed cell proliferation and differentiation, (2) hydrogen peroxide induced similar cellular activities as STZ, (3) pre-incubation of STZ-treated NSCs with catalase, an antioxidant, suppressed all these cellular activities induced by STZ, and (4) likewise, pre-incubation of STZ-treated NSCs with salidroside, also an antioxidant, suppressed all these activities as catalase: reduction of ROS levels and NSC death with simultaneous increases in proliferation and differentiation.", "citation": {"db": "PubMed", "db_id": "22235318"}, "annotations": {"Cell": {"neuronal stem cell": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 357, "target": 170, "key": "f2869ab174a39fe6e3d6509279d413ff"}, {"line": 14664, "relation": "increases", "evidence": "In vitro studies revealed that (1) exposure of neural stem cells (NSCs) from the hippocampus to STZ strikingly increased intracellular reactive oxygen species (ROS) levels, induced cell death and perturbed cell proliferation and differentiation, (2) hydrogen peroxide induced similar cellular activities as STZ, (3) pre-incubation of STZ-treated NSCs with catalase, an antioxidant, suppressed all these cellular activities induced by STZ, and (4) likewise, pre-incubation of STZ-treated NSCs with salidroside, also an antioxidant, suppressed all these activities as catalase: reduction of ROS levels and NSC death with simultaneous increases in proliferation and differentiation.", "citation": {"db": "PubMed", "db_id": "22235318"}, "annotations": {"Cell": {"neuronal stem cell": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 357, "target": 505, "key": "9e4c2451e6961bde4d65c29ebdf5e7df"}, {"line": 14734, "relation": "decreases", "evidence": "The results of the present study showed that ICV injection of STZ impaired long-term memory capacity and decreased the number of c-Fos-positive cells in several regions of the rat hippocampus.", "citation": {"db": "PubMed", "db_id": "18687381"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}}, "source": 357, "target": 2699, "key": "5fe8a2da4af14c436319eca5c64fdf2d"}, {"line": 14735, "relation": "decreases", "evidence": "The results of the present study showed that ICV injection of STZ impaired long-term memory capacity and decreased the number of c-Fos-positive cells in several regions of the rat hippocampus.", "citation": {"db": "PubMed", "db_id": "18687381"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}}, "source": 357, "target": 595, "key": "d5bbbef46de8282deb11eabfff7e1e26"}, {"line": 6139, "relation": "decreases", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 134, "target": 3848, "key": "694a0223d0693cd07620f8ded235ddff"}, {"line": 6140, "relation": "decreases", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 134, "target": 3850, "key": "044e9b2bee7c04e85849ef0e905c4bbc"}, {"line": 6141, "relation": "decreases", "evidence": "Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 134, "target": 3861, "key": "7071ff5eedcaa1900edddcf4670c805b"}, {"line": 6153, "relation": "negativeCorrelation", "evidence": "Insulin resistance in T2DM is partly mediated by reduced insulin receptor expression, insulin receptor tyrosine kinase activity, insulin receptor substrate (IRS) type 1 expression, and/or phosphatidyl-inositol-3 (PI3) kinase activation in skeletal muscle and adipocytes.15 Gestational diabetes is pregnancy associated and caused by insulin deficiency and hyperglycemia. ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"High": true}}, "source": 2900, "target": 3861, "key": "448217574e28d7c142e8f0cbd05c499a"}, {"line": 6154, "relation": "negativeCorrelation", "evidence": "Insulin resistance in T2DM is partly mediated by reduced insulin receptor expression, insulin receptor tyrosine kinase activity, insulin receptor substrate (IRS) type 1 expression, and/or phosphatidyl-inositol-3 (PI3) kinase activation in skeletal muscle and adipocytes.15 Gestational diabetes is pregnancy associated and caused by insulin deficiency and hyperglycemia. ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2900, "target": 3861, "key": "e6eddf5104520225fb271d9508671bd7"}, {"line": 6191, "relation": "positiveCorrelation", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2900, "target": 580, "key": "8418623e795bdc1d22ca3acedc7ded4c"}, {"line": 6195, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2900, "target": 2906, "key": "a70cf2d2bc579d78f22514a336412ce9"}, {"line": 6197, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2900, "target": 2914, "key": "9d51ae0923406b66c9c4df99c4c53c35"}, {"line": 6199, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2900, "target": 2918, "key": "77bd9cf88540f3b2100cd19cadcb57ba"}, {"relation": "partOf", "source": 2900, "target": 1471, "key": "23bc4e26f7991d4cc3f784426d51ded6"}, {"relation": "partOf", "source": 2900, "target": 1466, "key": "5d683e4474c3ebf79747fa53227a6abb"}, {"relation": "partOf", "source": 2900, "target": 1469, "key": "839796fefa3a2dc76b2fd06a1b3bd86d"}, {"relation": "partOf", "source": 2900, "target": 1470, "key": "8ccc2d821429da911e9a847ef87624b2"}, {"relation": "partOf", "source": 2900, "target": 1472, "key": "8e0d26084006f66db8e117980ffdb53e"}, {"relation": "partOf", "source": 2900, "target": 1467, "key": "365cbc46fa606f785757477cb37ea3f2"}, {"line": 8187, "relation": "positiveCorrelation", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 2900, "target": 3823, "key": "ed5c739a456d101624be283fb73f9980"}, {"line": 10895, "relation": "negativeCorrelation", "evidence": "According to this hypothesis, brains from AD patients showed substantially downregulated expression of the Insulin receptor (IR), the IGF-1 receptor (IGF-1R), and the insulin receptor substrate (IRS) proteins.", "citation": {"db": "PubMed", "db_id": "21916834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2900, "target": 3823, "key": "e339183efa05c0a78c5c9b5128db965e"}, {"relation": "partOf", "source": 2900, "target": 1483, "key": "5e94b76337daf93e853d3c621befc71e"}, {"relation": "partOf", "source": 2900, "target": 931, "key": "bdd7cb62fefacf32f41b1c9f04a7ce4c"}, {"line": 10349, "relation": "negativeCorrelation", "evidence": "As far as the metabolism of amyloid precursor protein (APP) in late-onset sporadic Alzheimer disease is concerned, neuronal insulin receptor dysfunction may result in the intracellular accumulation of Abeta and in subsequent cellular damage.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2900, "target": 80, "key": "b038db217e09a081ecab9db78c4452eb"}, {"line": 10463, "relation": "positiveCorrelation", "evidence": "Most significantly, this loss of surface IRs, and ADDL-induced oxidative stress and synaptic spine deterioration, could be completely prevented by insulin.", "citation": {"db": "PubMed", "db_id": "19188609"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "cell surface"}}}, "object": {"modifier": "Activity"}, "source": 2900, "target": 2899, "key": "08e200360948e9ef3d986751e9d999df"}, {"relation": "partOf", "source": 2900, "target": 989, "key": "aac8e189109947b95d7e448eefa3ab50"}, {"relation": "partOf", "source": 2900, "target": 1063, "key": "2c8a6a9d52e3bc2e1282869b5a69e312"}, {"line": 10471, "relation": "negativeCorrelation", "evidence": "The mechanism of insulin protection entailed a marked reduction in pathogenic ADDL binding.", "citation": {"db": "PubMed", "db_id": "19188609"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "cell surface"}}}, "source": 2900, "target": 1063, "key": "a24b04f3db689522a21e4521b754835e"}, {"line": 10475, "relation": "association", "evidence": "Surprisingly, insulin failed to block ADDL binding when IR tyrosine kinase activity was inhibited; in fact, a significant increase in binding was caused by IR inhibition.", "citation": {"db": "PubMed", "db_id": "19188609"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 2900, "target": 754, "key": "214b92822758a181efaeef9d66f8ad9f"}, {"line": 10478, "relation": "negativeCorrelation", "evidence": "Surprisingly, insulin failed to block ADDL binding when IR tyrosine kinase activity was inhibited; in fact, a significant increase in binding was caused by IR inhibition.", "citation": {"db": "PubMed", "db_id": "19188609"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 2900, "target": 2257, "key": "83f21b0db62d294793bc88833bc2938d"}, {"relation": "partOf", "source": 2900, "target": 1231, "key": "089bb05eef3a09dbb208d5007a235acc"}, {"line": 21679, "relation": "negativeCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2900, "target": 3851, "key": "26f810276015ca226079897771f2e1bf"}, {"line": 36646, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2900, "target": 688, "key": "24b59aac5f3d42e918dcb4e5b68722df"}, {"line": 6155, "relation": "negativeCorrelation", "evidence": "Insulin resistance in T2DM is partly mediated by reduced insulin receptor expression, insulin receptor tyrosine kinase activity, insulin receptor substrate (IRS) type 1 expression, and/or phosphatidyl-inositol-3 (PI3) kinase activation in skeletal muscle and adipocytes.15 Gestational diabetes is pregnancy associated and caused by insulin deficiency and hyperglycemia. ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"High": true}}, "source": 2905, "target": 3861, "key": "51d4808d12bac1025851e410e3de4156"}, {"relation": "hasVariant", "source": 2905, "target": 2906, "key": "f7a02f746bb624e10f83f2689a87623b"}, {"line": 6201, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2905, "target": 860, "key": "c774bef3761675a400583ae6b5cd8665"}, {"line": 6209, "relation": "decreases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2905, "target": 842, "key": "bd3b52f53600969a6b20ca60f02098b3"}, {"line": 6216, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2905, "target": 861, "key": "04ba1b831c14734449dfa8e769456a51"}, {"relation": "hasVariant", "source": 2905, "target": 2911, "key": "e088a73b62e033e791b366d6f85226c5"}, {"relation": "isA", "source": 2905, "target": 2185, "key": "1300681de8a361e2661a9fc3f1677ce8"}, {"line": 7780, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2905, "target": 3823, "key": "5b07a1ac4184b4d2ef668c1d30a822c5"}, {"relation": "hasVariant", "source": 2905, "target": 2908, "key": "5801e23134608b44b6fa6afbb0dfce95"}, {"line": 32682, "relation": "positiveCorrelation", "evidence": "With insulin resistance in diabetics and pmodels, IRS-1 is phosphorylated at Ser312 by insulin-stimulated or stress-activated kinases, including c-Jun N-terminal kinase (JNK), which uncouples IRS-1 (Aguirre et al., 2002) and triggers rapid IRS-1 degradation (Sun et al., 1999), yielding a deficient signal transduction response (Pederson et al., 2001;Rui et al., 2001).", "citation": {"db": "PubMed", "db_id": "19605645"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 2905, "target": 2908, "key": "206fdd939f7f607c274c2deacfdd3033"}, {"relation": "hasVariant", "source": 2905, "target": 2909, "key": "da43a49cdbc279e065cd9737a2026df6"}, {"line": 7784, "relation": "positiveCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2905, "target": 580, "key": "8e9fd386aeeda40a96a42638e40cdb27"}, {"relation": "hasVariant", "source": 2905, "target": 2912, "key": "a35e0ef18a2910851241b04ed3dfb29e"}, {"line": 8084, "relation": "association", "evidence": "Furthermore, in C. elegans the DAF-2 pathway is pro­ posed to control longevity [I 37]. However, decreased DAF- 2 signaling causes a considerable lifespan extension [137, 138]. The longevity in DAF-2 mutant animals is negatively influenced by mutations in DAF-16, indicating that DAF-16 is inhibited by DAF-2 and is a major downstream effector. Similar findings were seen in Drosophila melanogaster where insulin signaling is mediated via chico the ortholog of human IRS. If either the lR or chico is mutated, lifespan of these flies is prolonged [139, 140]. Also, overexpression of dFoxO, the ortholog of human FOXO, decreases mortality and increases I ifespan in Drosophila [ 141].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"7227": true}}, "source": 2905, "target": 850, "key": "7c9554c92f970b17789ab6acc76f524b"}, {"line": 21656, "relation": "association", "evidence": "Chronically increased S6K1 is associated with impaired IRS1 signaling in skeletal muscle of GDM women with impaired glucose tolerance postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "MeSHAnatomy": {"Muscle, Skeletal": true}, "Species": {"9606": true}, "Subgraph": {"Interferon signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2905, "target": 3327, "key": "35ed03d31e72d1d86a66ad388d4a23d3"}, {"line": 21670, "relation": "negativeCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2905, "target": 3851, "key": "76858a8580135b07c53d3691647463d7"}, {"line": 21671, "relation": "negativeCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2905, "target": 3851, "key": "d98dd59ecc5915fbbcfca23c6df06a7e"}, {"relation": "hasVariant", "source": 2905, "target": 2907, "key": "d278bf2e6e05383185fd2da5c2d1a2af"}, {"relation": "partOf", "source": 2905, "target": 1485, "key": "4adb40772767cc3c39568a7bde4e4c62"}, {"relation": "hasVariant", "source": 2905, "target": 2910, "key": "102cc0b69e57dc6ccf90113e32a8144e"}, {"line": 6165, "relation": "negativeCorrelation", "evidence": "Insulin resistance in T2DM is partly mediated by reduced insulin receptor expression, insulin receptor tyrosine kinase activity, insulin receptor substrate (IRS) type 1 expression, and/or phosphatidyl-inositol-3 (PI3) kinase activation in skeletal muscle and adipocytes.15 Gestational diabetes is pregnancy associated and caused by insulin deficiency and hyperglycemia. ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Cell": {"skeletal muscle fiber": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 579, "target": 3861, "key": "e67209ca96c661bdc20fdecb799acff3"}, {"line": 9809, "relation": "negativeCorrelation", "evidence": "The deficiency of insulin-PI3K-AKT signalling was more severe in individuals with both T2DM and AD (T2DM-AD).", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 579, "target": 3850, "key": "a3748a970ba44ee0f51e22a6626e45b7"}, {"line": 9810, "relation": "negativeCorrelation", "evidence": "The deficiency of insulin-PI3K-AKT signalling was more severe in individuals with both T2DM and AD (T2DM-AD).", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 579, "target": 3823, "key": "433f67ca285ac167b412bc1f634b626b"}, {"line": 9811, "relation": "association", "evidence": "The deficiency of insulin-PI3K-AKT signalling was more severe in individuals with both T2DM and AD (T2DM-AD).", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 579, "target": 2279, "key": "e2062002c8ad20fd7730b8b9375b2817"}, {"line": 9823, "relation": "negativeCorrelation", "evidence": "This decrease in insulin-PI3K-AKT signalling could lead to activation of glycogen synthase kinase-3beta, the major tau kinase.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 579, "target": 2794, "key": "c1d59d335000af6003f336e142f4cabb"}, {"line": 9834, "relation": "association", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 579, "target": 890, "key": "bf5492dfbeefd96ddbdc19636c48ce90"}, {"line": 9861, "relation": "negativeCorrelation", "evidence": "The decrease in brain insulin-PI3K-AKT signalling also correlated with the activation of calpain I in the brain, suggesting that the decrease might be caused by calpain over-activation.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Beta secretase subgraph": true, "Calpastatin-calpain subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 579, "target": 2428, "key": "04ea74156fdb4b75aa7cdc1d654b4c1a"}, {"line": 6424, "relation": "association", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "object": {"modifier": "Activity"}, "source": 3991, "target": 2871, "key": "1c781b2a18bbda9f5bef8bba61749087"}, {"line": 9148, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3991, "target": 2085, "key": "522e227a41ab199eaa94bf3dc96dceb8"}, {"line": 45458, "relation": "association", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 3991, "target": 2085, "key": "30b7d97c72e6ac095c0f5f1b50fe4354"}, {"line": 45460, "relation": "increases", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 3991, "target": 3010, "key": "1dadece42e1a11e347163c78fe75a3c2"}, {"line": 6181, "relation": "regulates", "evidence": "since around 2005, this field literally exploded with new information and a new concept, i.e., that primary brain insulin resistance and insulin deficiency mediate cognitive impairment and AD. This idea was fueled by evidence that tau gene expression and phosphorylation are regulated through insulin and insulin-like growth factor (IGF) signaling cascades.23,24", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 583, "target": 3991, "key": "9e449799a8fb36503b8231bf2c1f7c5e"}, {"line": 6182, "relation": "regulates", "evidence": "since around 2005, this field literally exploded with new information and a new concept, i.e., that primary brain insulin resistance and insulin deficiency mediate cognitive impairment and AD. This idea was fueled by evidence that tau gene expression and phosphorylation are regulated through insulin and insulin-like growth factor (IGF) signaling cascades.23,24", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 583, "target": 3015, "key": "431bf9ef4ae3b2d478b2707cc1c5b660"}, {"line": 7939, "relation": "increases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 583, "target": 3015, "key": "7e08068368264096eb7a007372fabbad"}, {"line": 6194, "relation": "positiveCorrelation", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 583, "target": 2872, "key": "62d24c7760fd6e0b5d47a0736d633dea"}, {"line": 6295, "relation": "positiveCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Confidence": {"High": true}}, "source": 583, "target": 830, "key": "77130dc5cbd69997a92e9dd4402beaa9"}, {"line": 6304, "relation": "association", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 583, "target": 836, "key": "3d5c0c2fa718c16b24b689e4eec88c66"}, {"line": 6316, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 583, "target": 842, "key": "2f135b7bb50d539d6692a2a649e892e6"}, {"line": 6325, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 583, "target": 3472, "key": "169dd3bf2fa7d63d12ded1318eac064a"}, {"line": 6334, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Chemokine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 583, "target": 420, "key": "9e4e9abe5ece4333f3f841c156c425f3"}, {"line": 6342, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "source": 583, "target": 3940, "key": "f123f6c8b5be38b90bf6dfa7ecb321bf"}, {"line": 6390, "relation": "negativeCorrelation", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 583, "target": 3812, "key": "bfb2472c402a83efe3a9494c2197d50f"}, {"line": 7761, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 583, "target": 3823, "key": "bb1a825a474b86b66636179cc24a245e"}, {"line": 7787, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 583, "target": 2908, "key": "4967186eb23b3a3067a61a7fdb47fb43"}, {"line": 7788, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 583, "target": 2909, "key": "e271ceae89d6648e3f1cfd2eeb576478"}, {"line": 7914, "relation": "decreases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 583, "target": 2794, "key": "cf7d411adf975a627fcea922c415efc8"}, {"line": 7941, "relation": "increases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 583, "target": 889, "key": "61190b791e6756d6cdaa4d8a7e495aac"}, {"line": 8071, "relation": "increases", "evidence": "Experiments in Caenoi-habditis elegans revealed new insights into the role oflR/IGF-IR signaling in A1 -42 toxic­ ity, and Ametabolism. Cohen and coworkers could show that knocking down the DAF-2 pathway in C. elegans, which is orthologous to the mammalian insulin and IGF-l signaling cascade, reduces Al31-42 toxicity [35]. Furthermore, this effect was mediated by the two downstream transcrip­ tion factors, DAF-16 and HSF-l (heat shock transcription factor-!) [132}. DAF-\\6 encodes a forkhead transcription factor [133, 134], which translocates into the nucleus [135], and modulates transcription when DAF-2 signaling is abro­ gated . The mammalian DAF-16 orthologs are Foxol, 3, and 4 [136). In the mammalian system the IR/IGF-1 R induces phosphorylation of Foxo I and triggers its translocation from the nucleus. The DAF-2 pathway reduces A ,_42 toxicity by two possible mechanisms of detoxification [35]: The first detoxification route leads to disaggregation of the toxic oli­ gomer that is positively regulated by HSF-1 and degradation of the amyloidogenic peptides. The second mechanism mediates the formation of low toxic, high molecular weight aggregates from high toxic low molecular weight aggregates, which is positively regulated by DAF-!6. Both detoxifica­ tion mechanisms are negatively regulated by DAF-2 signal­ ing.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 583, "target": 2702, "key": "4c554583fc367a7daee199d4b6f175d2"}, {"line": 8072, "relation": "association", "evidence": "Experiments in Caenoi-habditis elegans revealed new insights into the role oflR/IGF-IR signaling in A1 -42 toxic­ ity, and Ametabolism. Cohen and coworkers could show that knocking down the DAF-2 pathway in C. elegans, which is orthologous to the mammalian insulin and IGF-l signaling cascade, reduces Al31-42 toxicity [35]. Furthermore, this effect was mediated by the two downstream transcrip­ tion factors, DAF-16 and HSF-l (heat shock transcription factor-!) [132}. DAF-\\6 encodes a forkhead transcription factor [133, 134], which translocates into the nucleus [135], and modulates transcription when DAF-2 signaling is abro­ gated . The mammalian DAF-16 orthologs are Foxol, 3, and 4 [136). In the mammalian system the IR/IGF-1 R induces phosphorylation of Foxo I and triggers its translocation from the nucleus. The DAF-2 pathway reduces A ,_42 toxicity by two possible mechanisms of detoxification [35]: The first detoxification route leads to disaggregation of the toxic oli­ gomer that is positively regulated by HSF-1 and degradation of the amyloidogenic peptides. The second mechanism mediates the formation of low toxic, high molecular weight aggregates from high toxic low molecular weight aggregates, which is positively regulated by DAF-!6. Both detoxifica­ tion mechanisms are negatively regulated by DAF-2 signal­ ing.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "intracellular"}, "toLoc": {"namespace": "GO", "name": "nucleus"}}}, "source": 583, "target": 2702, "key": "2e1f3d7b6331a323cad1311096bdfce6"}, {"line": 6191, "relation": "positiveCorrelation", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 580, "target": 2900, "key": "57e2e31babbfb8b23eed1a545f9dad89"}, {"line": 6294, "relation": "positiveCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Confidence": {"High": true}}, "source": 580, "target": 830, "key": "5d02570b78f4a7c05598a83c1a1d92d1"}, {"line": 6303, "relation": "association", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 580, "target": 836, "key": "e463d141160f7455184b3163b7d04e32"}, {"line": 6312, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 580, "target": 842, "key": "28941e5c0752fa256e85c9ffd031d617"}, {"line": 6324, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 580, "target": 3472, "key": "65f81f38549d1c5a494560f86f460fd9"}, {"line": 6333, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Chemokine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 580, "target": 420, "key": "bb698bc8b5f3d3b941374d54cb75e8d4"}, {"line": 6341, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "source": 580, "target": 3940, "key": "29c3498e91d9f8e1d2a807057ac10f52"}, {"line": 6382, "relation": "decreases", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 580, "target": 3812, "key": "387c7cc0f0d617d6b347917acca984ba"}, {"line": 6683, "relation": "association", "evidence": "SOS2 codes for the homolog of the SOS1 gene, which is a guanine nucleotide exchange factor. These proteins are involved in signal transduction pathways, including insulin signaling. There are no previous reports of the association of SOS2 variants with T2DM, and this study is the first to report an association of polymorphisms in this gene with LOAD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 580, "target": 3401, "key": "4b3264a5bf563cdffef654a880f5fe8b"}, {"line": 7760, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 580, "target": 3823, "key": "e24a850b76109af2e025a4842e81471a"}, {"line": 7784, "relation": "positiveCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 580, "target": 2905, "key": "f5935335e479e7a21b8fff450d6d9ea0"}, {"line": 7785, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 580, "target": 2908, "key": "4be5736e3e77b49b59dcb87369e7f272"}, {"line": 7915, "relation": "decreases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 580, "target": 2794, "key": "e3869aafbdc24da006e4c10a14baf8eb"}, {"line": 7923, "relation": "increases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 580, "target": 2279, "key": "cfd1258b8c38d80f07c5f84a5db978b9"}, {"line": 7940, "relation": "increases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 580, "target": 3015, "key": "220b361a3e1c8f292a3c04209ae390a2"}, {"line": 10336, "relation": "increases", "evidence": "The consequences of the inhibition of neuronal insulin signal transduction may be largely identical to those of disturbances of oxidative energy metabolism and related metabolism, and of hyperphosphorylation of tau-protein.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 580, "target": 3015, "key": "34b7429071e377313bc0ed63f7dd3179"}, {"line": 7942, "relation": "increases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 580, "target": 889, "key": "5c9f94e2c64fb6f7c2a3dcbb12e77767"}, {"line": 8063, "relation": "decreases", "evidence": "Experiments in Caenoi-habditis elegans revealed new insights into the role oflR/IGF-IR signaling in A1 -42 toxic­ ity, and Ametabolism. Cohen and coworkers could show that knocking down the DAF-2 pathway in C. elegans, which is orthologous to the mammalian insulin and IGF-l signaling cascade, reduces Al31-42 toxicity [35]. Furthermore, this effect was mediated by the two downstream transcrip­ tion factors, DAF-16 and HSF-l (heat shock transcription factor-!) [132}. DAF-\\6 encodes a forkhead transcription factor [133, 134], which translocates into the nucleus [135], and modulates transcription when DAF-2 signaling is abro­ gated . The mammalian DAF-16 orthologs are Foxol, 3, and 4 [136). In the mammalian system the IR/IGF-1 R induces phosphorylation of Foxo I and triggers its translocation from the nucleus. The DAF-2 pathway reduces A ,_42 toxicity by two possible mechanisms of detoxification [35]: The first detoxification route leads to disaggregation of the toxic oli­ gomer that is positively regulated by HSF-1 and degradation of the amyloidogenic peptides. The second mechanism mediates the formation of low toxic, high molecular weight aggregates from high toxic low molecular weight aggregates, which is positively regulated by DAF-!6. Both detoxifica­ tion mechanisms are negatively regulated by DAF-2 signal­ ing.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "object": {"modifier": "Activity"}, "source": 580, "target": 2328, "key": "c76856d25da2d44d9ee019dea415db08"}, {"line": 8078, "relation": "negativeCorrelation", "evidence": "Furthermore, in C. elegans the DAF-2 pathway is pro­ posed to control longevity [I 37]. However, decreased DAF- 2 signaling causes a considerable lifespan extension [137, 138]. The longevity in DAF-2 mutant animals is negatively influenced by mutations in DAF-16, indicating that DAF-16 is inhibited by DAF-2 and is a major downstream effector. Similar findings were seen in Drosophila melanogaster where insulin signaling is mediated via chico the ortholog of human IRS. If either the lR or chico is mutated, lifespan of these flies is prolonged [139, 140]. Also, overexpression of dFoxO, the ortholog of human FOXO, decreases mortality and increases I ifespan in Drosophila [ 141].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 580, "target": 850, "key": "1ac2d11382f6f109a649e62c31b4056c"}, {"line": 8110, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 580, "target": 3979, "key": "158432d4f3e24e7c4602c89b3a667055"}, {"line": 9927, "relation": "increases", "evidence": "Here we review the role of insulin signaling in brain aging and AD, concluding that the signaling pathways downstream to neurotrophic and insulin signaling are defective and coincident with aberrant phosphorylation and translocation of key components, notably AKT and GSK3beta, but also rac> PAK signaling.", "citation": {"db": "PubMed", "db_id": "17049785"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 580, "target": 2795, "key": "9a423867af6e1c7d63339576d735e8c7"}, {"line": 9935, "relation": "association", "evidence": "Here we review the role of insulin signaling in brain aging and AD, concluding that the signaling pathways downstream to neurotrophic and insulin signaling are defective and coincident with aberrant phosphorylation and translocation of key components, notably AKT and GSK3beta, but also rac> PAK signaling.", "citation": {"db": "PubMed", "db_id": "17049785"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Akt subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 580, "target": 2280, "key": "1d9a6dd4461f451771a8b85ae0eb2751"}, {"line": 9943, "relation": "association", "evidence": "Here we review the role of insulin signaling in brain aging and AD, concluding that the signaling pathways downstream to neurotrophic and insulin signaling are defective and coincident with aberrant phosphorylation and translocation of key components, notably AKT and GSK3beta, but also rac> PAK signaling.", "citation": {"db": "PubMed", "db_id": "17049785"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 580, "target": 2203, "key": "482b6779ea66320fd230f8737de83980"}, {"line": 9965, "relation": "association", "evidence": "In the CA1 region of hippocampus of mice, several of the insulin signaling-related proteins we had chosen are co-located with ChAT, and most double immunoreactive positive cells were pyramidal cells.", "citation": {"db": "PubMed", "db_id": "19013138"}, "annotations": {"Confidence": {"Medium": true}, "MeSHAnatomy": {"CA1 Region, Hippocampal": true}, "Subgraph": {"Insulin signal transduction": true}, "Species": {"10090": true}}, "source": 580, "target": 3611, "key": "1fe742f915dba0e211bdd4c2e6a8245e"}, {"line": 10048, "relation": "negativeCorrelation", "evidence": "Interestingly, brain inflammation has recently been proposed to underlie defective neuronal insulin signaling in AD [14] Several pathological features, including impaired insulin signaling and inflammation, appear to be shared by patients with diabetes and patients with AD.", "citation": {"db": "PubMed", "db_id": "24529528"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}}, "source": 580, "target": 3920, "key": "55b7c2f3e15bb7ce4e64e1a4ea20ae53"}, {"line": 10226, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 580, "target": 1921, "key": "465873e36bcb2c558634cebb1c162d66"}, {"line": 10234, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 580, "target": 1974, "key": "f11723c545285c6426f0b79aad641272"}, {"line": 10242, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 580, "target": 1908, "key": "f74021e5eafbec323684978e577f80de"}, {"line": 10337, "relation": "positiveCorrelation", "evidence": "The consequences of the inhibition of neuronal insulin signal transduction may be largely identical to those of disturbances of oxidative energy metabolism and related metabolism, and of hyperphosphorylation of tau-protein.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 580, "target": 552, "key": "1a3f6239f6394722e60eef5d216d03ee"}, {"line": 6192, "relation": "directlyIncreases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2871, "target": 2872, "key": "b033b28679857a2daee8f1ba73d78132"}, {"line": 6252, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 3823, "key": "1223b2269a61d6e12e325a43ff77a912"}, {"line": 11808, "relation": "negativeCorrelation", "evidence": "According to current scientific knowledge, excess tumour necrosis factor-alpha (TNF-alpha) and low insulin-like growth factor-I (IGF-I) are pathogenic-risk factors that constitute therapeutic targets for Alzheimer's disease (AD).At week 24, Cere reduced TNF-alpha and enhanced dissociable IGF-I with respect to placebo in a dose-related manner. Increases in total IGF-I were induced by 60 ml Cere", "citation": {"db": "PubMed", "db_id": "19531281"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 3823, "key": "de04f41ceef7afb4271ad59e681080bd"}, {"line": 6280, "relation": "negativeCorrelation", "evidence": "This study carries additional significance because it established that, like all other pancreatic and intestinal polypeptide genes, the insulin gene was also expressed in the adult human brain. Moreover, the results taught us that endogenous brain deficiencies in insulin, IGF-1, IGF-2, and their corresponding receptors, in the absence of T2DM or obesity, could be linked to the most common form of dementia-associated neurodegeneration in the Western hemisphere.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 3901, "key": "4e2a0dfdd17ef94562ecd9552ae8c0e0"}, {"line": 6353, "relation": "negativeCorrelation", "evidence": "Correspondingly, the reduced expression of neuronal and oligodendroglial specific genes and the increased expression of astrocytic and microglial inflammatory genes in AD were attributed to progressive brain insulin/IGF deficiency and resistance.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2871, "target": 418, "key": "7d1f9aab8cbc7e3d34922952438fd8bb"}, {"line": 6424, "relation": "association", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 3991, "key": "248e452bcb4ebc7cd59d183007306d61"}, {"line": 6428, "relation": "regulates", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Tau protein subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 1778, "key": "d03f6c80ffd77408c231b9a7cc6b05d6"}, {"line": 6451, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 2871, "target": 389, "key": "fae0ba1accfb1ff8cca3959e807f1f14"}, {"line": 6472, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 596, "key": "dc4d9a9df8d29f9c781b3e55a02c8a66"}, {"line": 6482, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 841, "key": "4a6ba8d6dd2d49dfeb5063a3a3e3f98b"}, {"line": 6495, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 466, "key": "46897e81ff0f1ef7b1c7ed8c601b4814"}, {"line": 7378, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 565, "key": "d350e77edbf77a5c605773d2a3ff3cb2"}, {"line": 7387, "relation": "decreases", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Liver": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 566, "key": "934f10cf4843b300462025077e3daae0"}, {"line": 36804, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "source": 2871, "target": 566, "key": "007068bea4359abd9e236f12d202a55d"}, {"line": 7397, "relation": "positiveCorrelation", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 552, "key": "10242eb61d7b87a82ab7addd14deaa68"}, {"line": 7404, "relation": "positiveCorrelation", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 830, "key": "dacdc14993da7ef57d33f198d0c892c4"}, {"line": 7411, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 588, "key": "c5d478f881096ad941fb798d312a7503"}, {"line": 7413, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 850, "key": "3b7f0a9f5fa5f8246c37a49c3264ee0c"}, {"relation": "partOf", "source": 2871, "target": 1466, "key": "55dece5465d4098065590fb77d735d91"}, {"line": 7611, "relation": "regulates", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2871, "target": 451, "key": "2dfca690e2a9d9836c2f1674db24ab2c"}, {"line": 7620, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 1021, "key": "3feaae8b4c00ee471b51982c4cc5ffb3"}, {"line": 7622, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 1426, "key": "a9bf0f1610c5d3207b308d6abc12bdef"}, {"relation": "partOf", "source": 2871, "target": 1467, "key": "4148ab25c7d843f0f3cdbd95ba57293c"}, {"line": 7720, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2871, "target": 1894, "key": "1d4dc77d1eabfeadbc95547df0ddccca"}, {"line": 7748, "relation": "association", "evidence": "Taken together, the BBB is an important interface between the blood and the CNS compartment regu­ lating uptake of insulin and IGF-1 into the bra in. However, the molecul ar mech anisms by which different conditions like aging or AD decrease insulin 's transport to the brain are not known yet. Wh eth er these mech anisms contribute to th e pathogenesis of AD and cognitive decline is still unclear.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 601, "key": "a843fdf1ff8aca168afd9bd2bd200224"}, {"line": 7966, "relation": "regulates", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"NT2N cells": true}, "Confidence": {"Medium": true}}, "source": 2871, "target": 3015, "key": "71e66cb7182074eef93169d48c248c85"}, {"line": 8023, "relation": "increases", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"CellLine": {"SH-SY5Y": true}, "UserdefinedCellLine": {"primary cortical neuron": true}, "Subgraph": {"Insulin signal transduction": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2871, "target": 3998, "key": "63c03934d555f294dfc6d73ea27aefd0"}, {"line": 23828, "relation": "increases", "evidence": "There is a physiological decline of the growth hormone (GH)/insulin-like growth factor-I (IGF-I) axis with ageing and the possibility that the GH/ IGF-I axis is involved in cognitive deficits has been recognized for several years. The IGF-I is a potent neurotrophic as well neuroprotective factor found in the brain with a wide range of actions in both central and peripheral nervous system. IGF-I is a critical promoter of brain development and neuronal survival and plays a role in neuronal rescue during degenerative diseases.When a cholinesterase inhibitor as rivastigmine, a drug for AD, is acutely administered the area under the curve of the GH response to GHRH doubled, showing that rivastigmine is a powerful drug to enhance GH release. TNFα production may promote neurodegeneration not through direct killing of neurons but rather through inhibition of IGF-I survival signalling", "citation": {"db": "PubMed", "db_id": "22524398"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Neuroprotection subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 488, "key": "b1d75d7262219fd6dba8ee93eb0876af"}, {"line": 23847, "relation": "increases", "evidence": "Some authors show that IGF-I increases α-secretase processing of endogenous amyloid precursor protein and the amyloid precursor-like proteins 1 and 2 [36, 97-100].", "citation": {"db": "PubMed", "db_id": "22524398"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"ADAM Metallopeptidase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 2249, "key": "70ebab598bcbbc67ac588667dc09647b"}, {"line": 23863, "relation": "negativeCorrelation", "evidence": "our results indicate that IGF-I is neuroprotective at least in part, by abolishing the interaction induced by TNFα in astrocytes between calcineurin and Foxo3.", "citation": {"db": "PubMed", "db_id": "22005929"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2871, "target": 3472, "key": "a92795a70ba492887e7593ca0c348e3a"}, {"line": 24092, "relation": "negativeCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 648, "key": "d8d29a2822a2440796cbd87bd74c2cb6"}, {"line": 24093, "relation": "negativeCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 2328, "key": "2908e3826569a8e775301a1dfd3322fb"}, {"line": 33201, "relation": "increases", "evidence": "Insulin and IGF-I may pmodulate brain levels of insulin degrading enzyme, which would also lead to an accumulation of Abeta amyloid.", "citation": {"db": "PubMed", "db_id": "16444902"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2871, "target": 2328, "key": "89ac4d86798a91ad942d063f79451f88"}, {"line": 24094, "relation": "negativeCorrelation", "evidence": "Insulin not only regulates blood sugar concentrations but also acts as a growth factor on all cell including neurons in the central nervous system [38]. Brain resistance to insulin/IGF-I accounts for neuronal atrophy and death, tangle formation and brain amyloidosis typical of AD pathology [47].", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 889, "key": "ca4a2ba5c84dd9bff198661fa4c78af7"}, {"line": 24107, "relation": "increases", "evidence": "The effect of IGF-I in beta amyloid clearance is mediated by enhancing the transport of the beta amyloid carrier proteins, albumin and transthyretin into the brain through the choroid plexus, with increased levels of beta amyloid in the cerebrospinal fluid and this process is blocked by TNFα", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Albumin subgraph": true, "Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2871, "target": 2284, "key": "12c632b9697c6b3d68437594d117799c"}, {"line": 24108, "relation": "increases", "evidence": "The effect of IGF-I in beta amyloid clearance is mediated by enhancing the transport of the beta amyloid carrier proteins, albumin and transthyretin into the brain through the choroid plexus, with increased levels of beta amyloid in the cerebrospinal fluid and this process is blocked by TNFα", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Albumin subgraph": true, "Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2871, "target": 3502, "key": "c1922719bc6229aa752f68e56e33f24a"}, {"relation": "partOf", "source": 2871, "target": 1468, "key": "0771e2db18525b0101709fa80be473d1"}, {"line": 30890, "relation": "directlyIncreases", "evidence": "We have now analyzed this process in greater detail and found that the IGF-I receptor interacts with the transmembrane region of megalin, whereas the perimembrane domain of megalin is required for IGF-I internalization.", "citation": {"db": "PubMed", "db_id": "20351102"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2871, "target": 1468, "key": "235a484736763e59ffec01e92a3703fc"}, {"line": 30891, "relation": "association", "evidence": "We have now analyzed this process in greater detail and found that the IGF-I receptor interacts with the transmembrane region of megalin, whereas the perimembrane domain of megalin is required for IGF-I internalization.", "citation": {"db": "PubMed", "db_id": "20351102"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2871, "target": 2974, "key": "ce60bc722d4a9fc04938a65bb238f277"}, {"line": 33208, "relation": "association", "evidence": "Peripheral levels of Insulin Growth Factor-1 (IGF-I) are associated with glucose regulation and influence glucose disposal.", "citation": {"db": "PubMed", "db_id": "16444902"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 564, "key": "d22a337fec36a8fd2115b9c4b1f3e06e"}, {"line": 33209, "relation": "association", "evidence": "Peripheral levels of Insulin Growth Factor-1 (IGF-I) are associated with glucose regulation and influence glucose disposal.", "citation": {"db": "PubMed", "db_id": "16444902"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2871, "target": 563, "key": "6135980cf589b50d52e49252074cb4fb"}, {"line": 36787, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "G-protein-mediated signaling": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2871, "target": 1444, "key": "f0a6b5ca149d9eb8e5b5c971f36cac04"}, {"line": 36789, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "G-protein-mediated signaling": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2871, "target": 1447, "key": "cb31ae94f7931ef0bcd5a99458be4ae5"}, {"line": 36805, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "source": 2871, "target": 553, "key": "04f9000f214905689d89308486dc5a14"}, {"line": 36806, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "source": 2871, "target": 635, "key": "f6ce04b30abad2a704773d63a480e36c"}, {"line": 36827, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2871, "target": 2149, "key": "945a4176a532f0b75246a79f2c2b9e61"}, {"line": 36829, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 2910, "key": "b5ff253ba795546b41b89cbcf821890c"}, {"line": 36831, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2871, "target": 2915, "key": "09465551162ca14307bea47ce506d7f0"}, {"line": 45208, "relation": "orthologous", "evidence": "Both exercised SAMR1 and SAMP8 mice showed significantly increased IGF1 plasma levels compared with their corresponding sedentary group ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Physical exercised group": true}}, "source": 2871, "target": 3655, "key": "d61af1bef48a9a533b5d50f7735bd91c"}, {"line": 6193, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2872, "target": 2918, "key": "21228646cbc41b4ca4177fc17c5f38d0"}, {"line": 6194, "relation": "positiveCorrelation", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2872, "target": 583, "key": "061143285638ec028515af42dfd0e35a"}, {"line": 6254, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2872, "target": 3823, "key": "65cfb1f2c689a28ea28cea0cc5e38767"}, {"line": 10894, "relation": "negativeCorrelation", "evidence": "According to this hypothesis, brains from AD patients showed substantially downregulated expression of the Insulin receptor (IR), the IGF-1 receptor (IGF-1R), and the insulin receptor substrate (IRS) proteins.", "citation": {"db": "PubMed", "db_id": "21916834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2872, "target": 3823, "key": "e932c8351549b7f88b59942f8f8fd3bf"}, {"relation": "partOf", "source": 2872, "target": 1471, "key": "fa20655d784557f63c2207e63afb7e3f"}, {"relation": "partOf", "source": 2872, "target": 1466, "key": "ddce2caa998c7dfda8723d33ee94be12"}, {"relation": "partOf", "source": 2872, "target": 1469, "key": "f1b04fb3821e4393bef4bd3bebd854b1"}, {"relation": "partOf", "source": 2872, "target": 1470, "key": "e6d601c2b28acda0001a604979b989a1"}, {"relation": "hasVariant", "source": 2872, "target": 2873, "key": "4d31377ef5bec645c97da6be4fb8f9e6"}, {"line": 6200, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2918, "target": 2917, "key": "dec7f99a7798bbdc6222f29dec1a4e2c"}, {"relation": "hasVariant", "source": 2917, "target": 2918, "key": "5d53d024b92ccc8f1d0f404d36880b53"}, {"line": 6211, "relation": "decreases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2917, "target": 842, "key": "d3ec64b23f00c880ce505b3ef05a61fa"}, {"line": 6214, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2917, "target": 2769, "key": "c54d2d7178f498f645bc614090bc8420"}, {"line": 6215, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2917, "target": 861, "key": "ea76696236ceaaa7257c6a43c1d8d4e3"}, {"relation": "hasVariant", "source": 2917, "target": 2919, "key": "0390f593020d88e1d292fff092070ea8"}, {"relation": "isA", "source": 2917, "target": 2185, "key": "c1f37ba0edccafb17c0d714e23f7c91b"}, {"line": 6196, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2906, "target": 2905, "key": "ec7d1799106a08ecc8f52916f9b85cfc"}, {"line": 6198, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2914, "target": 2913, "key": "3a5c43ba25df2c96ebcf4354159f195c"}, {"relation": "hasVariant", "source": 2913, "target": 2914, "key": "ded59fafc7c8d48714815ccf51934ef8"}, {"line": 6202, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2913, "target": 860, "key": "90ae74d92e95996aabdb905a63412672"}, {"line": 6210, "relation": "decreases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2913, "target": 842, "key": "a725f127db110058b9dd4c5993ba35e4"}, {"line": 6217, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2913, "target": 861, "key": "8b58edf1d7081c3f6ee3202b944b8f07"}, {"relation": "hasVariant", "source": 2913, "target": 2916, "key": "a4cd0f5245eb32c9494e544584cdd834"}, {"relation": "isA", "source": 2913, "target": 2185, "key": "6707d557f37ee8d0456089aef65d7b46"}, {"line": 7781, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2913, "target": 3823, "key": "97dd4f782b5af8ee1455a29866a0067c"}, {"relation": "hasVariant", "source": 2913, "target": 2915, "key": "18f0f70982fc4ec00118cf00710a1fe7"}, {"line": 6203, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 860, "target": 2153, "key": "53acace08bacf465d283f5621100a675"}, {"line": 6212, "relation": "decreases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2796, "target": 2794, "key": "070c3ff791c826c488eedd429f0b4f98"}, {"line": 7932, "relation": "decreases", "evidence": "GSK-3 is a serine/threonine kinase, modulated by insulin/IGF-1 signaling. When the IRIIGF-IR path­ way is activated, GSK-3is phosphorylated by AKT at Scr 9 leading to its inactivation [I 06-109]. However, PP2A dephosphorylates GSK-3(review in [II 0]), which then phosphorylates tau at several sites leading to an equilibrium of phosphorylation and dephosphorylation of tau (Overview : see Fig. 1). Thus, impaired IRIIGF-1 R signaling might lead to hyperphosphory lation of tau protein and an in creased for­ mation of neurofibrillary tangles.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2796, "target": 2794, "key": "633ec4b2ed65a9038d7d0d00bff8362e"}, {"line": 12494, "relation": "decreases", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2796, "target": 2794, "key": "81d9a4427ff9c1d1e9f5b3be7dd7b2c3"}, {"line": 33748, "relation": "decreases", "evidence": "Simultaneously, it increased the level of Ser9-phosphorylated (inactive) GSK-3beta", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2796, "target": 2794, "key": "bf75da133497cbf90bdbd6f58e809814"}, {"line": 36854, "relation": "decreases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2796, "target": 2794, "key": "5dd0e54f885d5ff00f853665c7c1defb"}, {"line": 12495, "relation": "positiveCorrelation", "evidence": "Glycogen synthase kinase-3beta (GSK3beta) is recognized as one of major kinases to phosphorylate tau in Alzheimer's disease (AD), thus lots of AD drug discoveries target GSK3beta. However, the inactive form of GSK3beta which is phosphorylated at serine-9 is increased in AD brains. This is also inconsistent with phosphorylation status of other GSK3beta substrates, such as beta-catenin and collapsin response mediator protein-2 (CRMP2) since their phosphorylation is all increased in AD brains. ", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2796, "target": 3823, "key": "01583459aaeb8ead68bd099f61dfcf83"}, {"line": 33710, "relation": "positiveCorrelation", "evidence": "In concordance, significant increases in the levels of phosphorylation of total Akt substrates, including: GSK3beta(Ser9), tau(Ser214), mTOR(Ser2448), and decreased levels of the Akt target, p27(kip1), were found in AD temporal cortex compared with controls.", "citation": {"db": "PubMed", "db_id": "15773910"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2796, "target": 3823, "key": "cbe6831fcd386084414f7caad5ffb00f"}, {"line": 36872, "relation": "decreases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2796, "target": 3015, "key": "aee00767771d58d21550d9bdc70a8697"}, {"line": 36873, "relation": "decreases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2796, "target": 645, "key": "f60a07defc696ecf5e4ed752aa88e3a3"}, {"line": 36867, "relation": "decreases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2793, "target": 2792, "key": "c9977c3d29fe9035e47be35c4f6006f1"}, {"line": 6267, "relation": "negativeCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Energy metabolic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 836, "target": 3823, "key": "c46a6fdf0c1393bbcb1610e0c38bbda1"}, {"line": 6303, "relation": "association", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 836, "target": 580, "key": "49deb713de042a60574f8ff903aac762"}, {"line": 6304, "relation": "association", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 836, "target": 583, "key": "76868068c5f264d013c6ea78d5b3d367"}, {"line": 6218, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 861, "target": 508, "key": "4647e77a8a64d7bb44bbc85e055d6e7c"}, {"line": 6219, "relation": "increases", "evidence": "By way of review, insulin and IGF-1 mediate their effects by activating complex intracellular signaling pathways starting with ligand binding to cell surface receptors, followed by autophosphorylation and activation of the intrinsic receptor tyrosine kinases.34–36 Insulin/IGF-1 receptor tyrosine kinases phosphorylate IRS molecules,34,37–39 which transmit signals downstream by activating the extracellular signal-related kinase/mitogen-activated protein kinase (ERK/MAPK) and PI3 kinase/Akt pathways, and inhibit glycogen synthase kinase 3beta (GSK-3beta). Major biological responses to signaling through IRS molecules include increased cell growth; survival, energy metabolism, and cholinergic gene expression; and inhibition of oxidative stress and apoptotic process.39–46 ", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"MAPK-ERK subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 861, "target": 830, "key": "043622b1b17dd9f371f33c770bb3e86b"}, {"line": 6294, "relation": "positiveCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Confidence": {"High": true}}, "source": 830, "target": 580, "key": "d4081726a2357c8afc81fda4245f3836"}, {"line": 6295, "relation": "positiveCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Confidence": {"High": true}}, "source": 830, "target": 583, "key": "c9bbb6abeb3ea2b7ea02ecb5d22a0b0b"}, {"line": 7403, "relation": "positiveCorrelation", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 830, "target": 2899, "key": "d8ec27d32afa6d49f22a4f5ced7457db"}, {"line": 7404, "relation": "positiveCorrelation", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 830, "target": 2871, "key": "0cf4da50458532a0cead924f623819ef"}, {"line": 9150, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 830, "target": 2085, "key": "9e567ae331141fa057d945435579013f"}, {"line": 9151, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 830, "target": 2097, "key": "e4e610d18d331330f13c0cd4387199b9"}, {"line": 27311, "relation": "association", "evidence": "Leptin, an adipocytokine involved in cell survival and in learning, has been demonstrated to regulate Abeta production and tau hyperphosphorylation in transgenic mice for AD. ", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 830, "target": 2961, "key": "4e594aef1f7e70fd9890d8abaffeefda"}, {"line": 42137, "relation": "association", "evidence": "Four weeks after rAAV2-IL-1beta transduction, we found significant reductions in 6E10 and Congo red staining of amyloid plaques that was confirmed by decreased levels of insoluble Abeta1-42 and Abeta1-40 in the inflamed hippocampus.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 2183, "target": 3881, "key": "fa7038077c96bee9770abbae5d6dfb2c"}, {"line": 42148, "relation": "decreases", "evidence": "Bone marrow chimeric studies confirmed the presence of infiltrating immune cells following IL-1beta overexpression and revealed that dramatic reduction of CCR2(+) peripheral mononuclear cell recruitment to the inflamed hippocampus did not prevent the ability of IL-1beta to induce amyloid plaque clearance.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Bone Marrow": true}, "Confidence": {"Medium": true}}, "source": 2183, "target": 3881, "key": "42057250fa8337ab91542054a8d09aca"}, {"line": 42138, "relation": "association", "evidence": "Four weeks after rAAV2-IL-1beta transduction, we found significant reductions in 6E10 and Congo red staining of amyloid plaques that was confirmed by decreased levels of insoluble Abeta1-42 and Abeta1-40 in the inflamed hippocampus.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 2183, "target": 22, "key": "61dcf65e6cb2dab6cd908a891e96ac77"}, {"line": 42358, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2183, "target": 369, "key": "01e21332bd8de27308633ee4105e4130"}, {"line": 42375, "relation": "association", "evidence": "Blockade of CysLT1R by repeated treatment with montelukast (1 or 2 mg/kg, ig, 4 weeks) reduced Abeta1-42-induced CysLT1R expression and also suppressed Abeta1-42-induced increments of NF-κB p65, TNF-α, IL-1beta and caspase-3 activation, and Bcl-2 downregulation in the hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Bcl-2 subgraph": true}}, "source": 2183, "target": 3597, "key": "f5695771bc3fdcd6c71ac53a27ed8405"}, {"line": 43194, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 2183, "target": 1645, "key": "930e7e475fe0fadeeffe3c3fae9d9baa"}, {"line": 6229, "relation": "increases", "evidence": "Mechanistically, the increased risk of dementia in T2DM and obesity could be linked to chronic hyperglycemia, peripheral insulin resistance, oxidative stress, accumulation of advanced glycation end products, increased production of pro-inflammatory cytokines, and/orcerebral microvascular disease.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3925, "target": 2183, "key": "ceab937b4f42e886b8c4b01f22cc91f2"}, {"line": 6238, "relation": "increases", "evidence": "Mechanistically, the increased risk of dementia in T2DM and obesity could be linked to chronic hyperglycemia, peripheral insulin resistance, oxidative stress, accumulation of advanced glycation end products, increased production of pro-inflammatory cytokines, and/orcerebral microvascular disease.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3925, "target": 3472, "key": "109b2ef562877cc1d19e2a59b5146e69"}, {"line": 6240, "relation": "positiveCorrelation", "evidence": "Mechanistically, the increased risk of dementia in T2DM and obesity could be linked to chronic hyperglycemia, peripheral insulin resistance, oxidative stress, accumulation of advanced glycation end products, increased production of pro-inflammatory cytokines, and/orcerebral microvascular disease.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3925, "target": 3837, "key": "8e6172dbfd315d09e21650b4f3674141"}, {"line": 10183, "relation": "association", "evidence": "Adiponectin is an adipocytokine released by the adipose tissue and has multiple roles in the immune system and in the metabolic syndromes such as cardiovascular disease, Type 2 diabetes, obesity and also in the neurodegenerative disorders including Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}}, "source": 3925, "target": 2259, "key": "183c343cfdb8768c0a1766e45c9c14e5"}, {"line": 10414, "relation": "positiveCorrelation", "evidence": "ER stress contributes to the pathogenesis of obesity and diabetes, which are risk factors for Alzheimer's disease (AD) that accelerate the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22115781"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3925, "target": 3823, "key": "b1dd9fffafc19d9b9333688d0c75e2d9"}, {"line": 21859, "relation": "association", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 3925, "target": 289, "key": "8e62456bad390b44d264463eba825f42"}, {"line": 40920, "relation": "association", "evidence": "Luteolin protects against high fat diet-induced cognitive deficits in obesity mice.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Obesity": true}, "Species": {"10090": true}}, "source": 3925, "target": 297, "key": "fc0b5ac51ff61495149d6a51dbf7812e"}, {"line": 40921, "relation": "decreases", "evidence": "Luteolin protects against high fat diet-induced cognitive deficits in obesity mice.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Obesity": true}, "Species": {"10090": true}}, "source": 3925, "target": 812, "key": "719ff796fe5fa74d0f13f1c4bd00eedd"}, {"line": 40932, "relation": "negativeCorrelation", "evidence": "The epidemic and experimental studies have confirmed that the obesity can lead to neuroinflammation, neurodegenerative diseases and adversely affect cognition.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Obesity": true}}, "source": 3925, "target": 812, "key": "71cb5b2513d79dbe5ea2c7b9e0a2bb02"}, {"line": 40936, "relation": "decreases", "evidence": "Despite the numerous elucidations on the impact of obesity on cognition decline, the contributors to the impairments in obesity remain unclear.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Obesity": true}}, "source": 3925, "target": 812, "key": "f6b6d6507ed927131ffe95f37eb9b986"}, {"line": 40930, "relation": "positiveCorrelation", "evidence": "The epidemic and experimental studies have confirmed that the obesity can lead to neuroinflammation, neurodegenerative diseases and adversely affect cognition.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Obesity": true}}, "source": 3925, "target": 3874, "key": "a0ef50e83e5758f1b57e99f7cbddd428"}, {"line": 40931, "relation": "positiveCorrelation", "evidence": "The epidemic and experimental studies have confirmed that the obesity can lead to neuroinflammation, neurodegenerative diseases and adversely affect cognition.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Obesity": true}}, "source": 3925, "target": 3920, "key": "1d586382afe5cb7a146d092eee015c2b"}, {"line": 6239, "relation": "positiveCorrelation", "evidence": "Mechanistically, the increased risk of dementia in T2DM and obesity could be linked to chronic hyperglycemia, peripheral insulin resistance, oxidative stress, accumulation of advanced glycation end products, increased production of pro-inflammatory cytokines, and/orcerebral microvascular disease.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3837, "target": 3850, "key": "8b8baae5beafe43625e2c14bfb46afd1"}, {"line": 6240, "relation": "positiveCorrelation", "evidence": "Mechanistically, the increased risk of dementia in T2DM and obesity could be linked to chronic hyperglycemia, peripheral insulin resistance, oxidative stress, accumulation of advanced glycation end products, increased production of pro-inflammatory cytokines, and/orcerebral microvascular disease.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3837, "target": 3925, "key": "6e6daccaf3eca8026c57a88274389732"}, {"line": 6268, "relation": "positiveCorrelation", "evidence": "In that study, we demonstrated advanced AD to be associated with strikingly reduced levels of insulin and IGF-1 polypeptide and receptor genes in the brain (Figure 1). In addition, all the signaling pathways that mediate insulin and IGF-1-stimulated neuronal survival, tau expression, energy metabolism, and mitochondrial function were perturbed in AD.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Adipocytes": true}, "Subgraph": {"Energy metabolic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3812, "target": 3823, "key": "0705aee0fe103fd3f4f90b7117bcce52"}, {"line": 6390, "relation": "negativeCorrelation", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3812, "target": 583, "key": "caad59660afb2c75aa768025f32419fe"}, {"line": 6391, "relation": "increases", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3812, "target": 1746, "key": "11074979737f4751a04789c90c323b9f"}, {"line": 6392, "relation": "increases", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3812, "target": 4101, "key": "5cad72ef8a6260c3c6be2b9aec4ef012"}, {"line": 6281, "relation": "negativeCorrelation", "evidence": "This study carries additional significance because it established that, like all other pancreatic and intestinal polypeptide genes, the insulin gene was also expressed in the adult human brain. Moreover, the results taught us that endogenous brain deficiencies in insulin, IGF-1, IGF-2, and their corresponding receptors, in the absence of T2DM or obesity, could be linked to the most common form of dementia-associated neurodegeneration in the Western hemisphere.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2874, "target": 3901, "key": "0865ce72f82cc0d96652105565b8629e"}, {"line": 6354, "relation": "negativeCorrelation", "evidence": "Correspondingly, the reduced expression of neuronal and oligodendroglial specific genes and the increased expression of astrocytic and microglial inflammatory genes in AD were attributed to progressive brain insulin/IGF deficiency and resistance.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2874, "target": 418, "key": "7966883906021cb7f95e730f73313cd4"}, {"relation": "partOf", "source": 2874, "target": 1469, "key": "73d537545c76ffda9aa0b3b446e02a94"}, {"line": 40688, "relation": "positiveCorrelation", "evidence": "The levels of IGF-II and IGFBP-2 were significantly elevated in the CSF from patients with AD.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Serotonergic subgraph": true, "Insulin signal transduction": true}}, "source": 2874, "target": 3823, "key": "9435cd26ae70aee173882abc288aa3da"}, {"line": 40698, "relation": "association", "evidence": "We also found correlations between established CSF biomarkers for AD (tau and P-tau) and components of the IGF system.CSF and blood plasma levels of IGF-II and some of its binding proteins are changed in patients with AD.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 2874, "target": 3823, "key": "ce4a34ae26edbbecd4870787d2da9b5b"}, {"line": 6341, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "source": 3940, "target": 580, "key": "2439f9d811c8aac8a5b4a3aaae7516d1"}, {"line": 6342, "relation": "negativeCorrelation", "evidence": "we have been able to draw the conclusion that neuronal and oligodendroglial cell survival and function are integrally related to the integrity of insulin and IGF signaling mechanisms in the brain.10,28,29,31,33,88,89 Similarly, impairments in insulin/IGF signaling lead to deficits in energy metabolism with attendant increased oxidative stress, mitochondrial dysfunction, proinflammatory cytokine activation, and APP expression.4", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Oligodendroglia": true, "Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "source": 3940, "target": 583, "key": "19631fc8b55e88833cfa45c83aa0836f"}, {"line": 6371, "relation": "association", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3940, "target": 3920, "key": "758bfb0a046e64960dc1d9898570d900"}, {"line": 8944, "relation": "negativeCorrelation", "evidence": "Delivery of a miR-153 antisense inhibitor to human fetal brain cultures significantly elevated APP expression. miR-153 delivery also reduced expression of the APP paralog APLP2. High functional redundancy between APP and APLP2 suggests that miR-153 may target biological pathways in which they both function. Interestingly, in a subset of human AD brain specimens with moderate AD pathology, miR-153 levels were reduced.", "citation": {"db": "PubMed", "db_id": "22733824"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3940, "target": 2084, "key": "e60e85968b8ba5e7f69c5687a2da5e64"}, {"line": 45738, "relation": "negativeCorrelation", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 3940, "target": 2084, "key": "60bf0bc363c1d0622a1bedbeb1d623c1"}, {"line": 8945, "relation": "increases", "evidence": "Delivery of a miR-153 antisense inhibitor to human fetal brain cultures significantly elevated APP expression. miR-153 delivery also reduced expression of the APP paralog APLP2. High functional redundancy between APP and APLP2 suggests that miR-153 may target biological pathways in which they both function. Interestingly, in a subset of human AD brain specimens with moderate AD pathology, miR-153 levels were reduced.", "citation": {"db": "PubMed", "db_id": "22733824"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3940, "target": 2315, "key": "37462c5b5bbec19e2d5e7001a9d25258"}, {"line": 9385, "relation": "increases", "evidence": "Utilizing human cell lines, we demonstrate that miRNAs hsa-mir-106a and hsa-mir-520c bind to their predicted target sequences in the APP 3'UTR and negatively regulate reporter gene expression. Over-expression of these miRNAs, but not control miRNAs, results in translational repression of APP mRNA and significantly reduces APP protein levels. These results are the first to demonstrate that levels of human APP can be regulated by miRNAs.", "citation": {"db": "PubMed", "db_id": "18684319"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 3940, "target": 2315, "key": "63704dfce5a731b0afbc5099f075fe5f"}, {"relation": "partOf", "source": 3940, "target": 1049, "key": "b86cb776487ff943c5949ef6386b0ad6"}, {"relation": "partOf", "source": 3940, "target": 1453, "key": "5650b5111bf2714a109fb860d7889854"}, {"relation": "partOf", "source": 3940, "target": 1580, "key": "575e544dc17f9fac92e60102cac29283"}, {"line": 45736, "relation": "positiveCorrelation", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 3940, "target": 1747, "key": "c9278f19b99c152a840d716598f77e08"}, {"line": 45966, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3940, "target": 1747, "key": "b228b2d2546ccdb820b1179537a80fa5"}, {"line": 46142, "relation": "negativeCorrelation", "evidence": "APP gene sequence data that suggests there are multiple potential sites for CpG methylation both within and around the APP gene, and that at least one of these sites is hypomethylated in brain tissue from an AD patient. That results in Increased levels of APP proteins and mRNA ", "citation": {"db": "PubMed", "db_id": "8746452"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3940, "target": 1747, "key": "767871ed52cbf95e6e1660bf570b3ae9"}, {"line": 6369, "relation": "association", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3920, "target": 418, "key": "69b122111c9046ed3bdd227a5a2e8665"}, {"line": 6371, "relation": "association", "evidence": "Although this point requires the generation of experimental models to demonstrate proof of principle, the finding that microglial, astrocytic, and APP mRNA levels are all increased in the early stages of neurodegeneration supports the inflammatory hypothesis of AD.6Previous studies demonstrated that microglial activation promotes APP-Abeta accumulation90–92 and that APP gene expression and cleavage increase with oxidative stress.93 Therefore, the mechanism we propose is that impaired insulin/ IGF signaling leads to increased oxidative stress and mitochondrial dysfunction,32,94,95 which induces APP gene expression and cleavage.93 The attendant APP-Abeta accumulations cause local neurotoxicity96–98 and further increase in oxidative stress-induced APP expression and APP-Abeta deposition.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3920, "target": 3940, "key": "9e82e4f422e20a03cdc3ba7d08bf4437"}, {"line": 10048, "relation": "negativeCorrelation", "evidence": "Interestingly, brain inflammation has recently been proposed to underlie defective neuronal insulin signaling in AD [14] Several pathological features, including impaired insulin signaling and inflammation, appear to be shared by patients with diabetes and patients with AD.", "citation": {"db": "PubMed", "db_id": "24529528"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3920, "target": 580, "key": "5ef58f7efa767764702cdda50ab5ae9f"}, {"line": 10050, "relation": "decreases", "evidence": "Interestingly, brain inflammation has recently been proposed to underlie defective neuronal insulin signaling in AD [14] Several pathological features, including impaired insulin signaling and inflammation, appear to be shared by patients with diabetes and patients with AD.", "citation": {"db": "PubMed", "db_id": "24529528"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}}, "object": {"modifier": "Activity"}, "source": 3920, "target": 431, "key": "af4cdf0c94ea498134a409d69b8bf1a8"}, {"line": 10204, "relation": "association", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 2259, "key": "ad6cdf3b8aaaf30d98f83902e50b10b5"}, {"line": 10854, "relation": "negativeCorrelation", "evidence": "Hence, elevated butyrylcholinesterase and acetylcholinesterase concentrations will lead to a decrease in the levels of acetylcholine that could trigger the onset of low-grade systemic inflammation seen in type 2 diabetes and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 204, "key": "bc0fd658feb1a73cdee6459f732535c9"}, {"line": 11669, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"MeSHAnatomy": {"Nervous System": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 434, "key": "891826221ac95ca2b638c195cde678f2"}, {"line": 14321, "relation": "association", "evidence": "Nuclear factor-kappa B (NF-κB) signalling plays an important role in gene regulation and is implicated in inflammation, oxidative stress and apoptotic process.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 875, "key": "fa011a4d82b4c4c3442d13ee054a83fa"}, {"line": 14536, "relation": "association", "evidence": "This neuroinflammation is known to be substantially regulated by the transcription factor NF-κB, which is predominantly found in the form of heterodimer of p65 (RelA) and p50 subunit, with p50/p50 homodimers being also common.", "citation": {"db": "PubMed", "db_id": "24345324"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3920, "target": 3112, "key": "c37e232d4eac217b84b2fe9c16043dd3"}, {"line": 15530, "relation": "association", "evidence": "S-nitrosoglutathione (GSNO) is an endogenous nitric oxide carrier modulating endothelial function, inflammation, and neurotransmission.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 69, "key": "417a69d7a66283cb2499e7a403001505"}, {"line": 16403, "relation": "association", "evidence": "Inflammation is believed to play a role in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20308780"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3823, "key": "d0a9ec4871319c470b60ec134697f394"}, {"line": 18551, "relation": "association", "evidence": "The role of inflammation in Alzheimer's disease, Parkinson's disease, and multiple sclerosis has recently come under increased scrutiny.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Parkinson Disease": true, "Inflammation": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3823, "key": "220d89b1eb4355c6aa03ee2a98c238a6"}, {"line": 41590, "relation": "association", "evidence": "Neuroinflammation affects the pathobiology of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true, "Amyloidosis": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3920, "target": 3823, "key": "03349521b98ac04e11030892d4105cf9"}, {"line": 46186, "relation": "association", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3920, "target": 3823, "key": "4d2e97ce2c9620567f2f37134bfbaaa2"}, {"line": 16468, "relation": "association", "evidence": "Myeloperoxidase, a heme protein expressed by professional phagocytic cells, generates an array of oxidants which are proposed to contribute to tissue damage during inflammation.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Cell": {"phagocyte": true}, "Subgraph": {"Myeloperoxidase subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3066, "key": "08730d1925cc838a95ad3be5f60483a1"}, {"line": 16559, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3409, "key": "af4d076a41dedc7d27712b2d43555cfc"}, {"line": 16603, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3874, "key": "f43daf2a4839ee7066906f9596e60f0a"}, {"line": 17754, "relation": "association", "evidence": "Moreover, basic experiments suggest a role of brain angiotensin II in neural injury, neuroinflammation, and cognitive function and that RAS blockade attenuates cognitive impairment in rodent dementia models of AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}}, "source": 3920, "target": 2274, "key": "58efa44712ecce4f8ca381a5ab10ea87"}, {"line": 17788, "relation": "association", "evidence": "The data concerning the bioactive fragments of angiotensin II will be accompanied by those regarding its implication in the cardiovascular modeling and the induction of oxidative stress, inflammation, atherogenesis, etc.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHDisease": {"Inflammation": true, "Atherosclerosis": true}}, "source": 3920, "target": 81, "key": "029d3a3beec61ef1366260f1f8440fba"}, {"line": 18113, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}}, "object": {"modifier": "Activity"}, "source": 3920, "target": 2998, "key": "942cc7025ad1c19894df01bce2d227d7"}, {"line": 18116, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}}, "object": {"modifier": "Activity"}, "source": 3920, "target": 2794, "key": "fb05e6546b0a83d13b8d97d21743dfd7"}, {"line": 18121, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}}, "object": {"modifier": "Activity"}, "source": 3920, "target": 3110, "key": "43492200f9d4caafee061eb944412f82"}, {"line": 18550, "relation": "association", "evidence": "The role of inflammation in Alzheimer's disease, Parkinson's disease, and multiple sclerosis has recently come under increased scrutiny.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Parkinson Disease": true, "Inflammation": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3869, "key": "4fb4cfbc6b2739f9eb1718806cf2c6cb"}, {"line": 18552, "relation": "association", "evidence": "The role of inflammation in Alzheimer's disease, Parkinson's disease, and multiple sclerosis has recently come under increased scrutiny.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Parkinson Disease": true, "Inflammation": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3878, "key": "a8b35a188236daa04e8db11af0acb254"}, {"line": 18563, "relation": "association", "evidence": "Associated with these inflammatory responses are tumor necrosis factor-alpha (TNF-alpha) and reactive oxygen species (ROS), both believed to be derived from brain microglia.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true}}, "source": 3920, "target": 170, "key": "4820900e60712e6a5269ac8284597d7e"}, {"line": 18567, "relation": "association", "evidence": "Associated with these inflammatory responses are tumor necrosis factor-alpha (TNF-alpha) and reactive oxygen species (ROS), both believed to be derived from brain microglia.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3472, "key": "71ae7599a30170103cd91f418f413e51"}, {"line": 19553, "relation": "association", "evidence": "Cyclooxygenase-1 null mice show reduced neuroinflammation in response to beta-amyloid.", "citation": {"db": "PubMed", "db_id": "20157512"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3920, "target": 80, "key": "29e4942e8c3969cd7ce044626040d6dd"}, {"line": 20005, "relation": "association", "evidence": "The vasoactive protein endothelin-1 (ET-1) is produced by vascular endothelial cells and participates in the regulation of vascular inflammation.", "citation": {"db": "PubMed", "db_id": "20634595"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Cell": {"endothelial cell": true}, "Subgraph": {"Endothelin subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 2653, "key": "2d32a6dbe78f68d17b9febe9cfcc531c"}, {"line": 22370, "relation": "decreases", "evidence": "Cytokines, particularly tumor necrosis factor α (TNF-α) and interleukin 1beta (IL-1beta), can induce chronic inflammation that may promote the loss of synapses, cognitive dysfunction, and eventually neuronal death [19] and [20].", "citation": {"db": "PubMed", "db_id": "24960578"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 787, "key": "ed9528e9bffc19fd88eec0470e26200d"}, {"line": 22371, "relation": "decreases", "evidence": "Cytokines, particularly tumor necrosis factor α (TNF-α) and interleukin 1beta (IL-1beta), can induce chronic inflammation that may promote the loss of synapses, cognitive dysfunction, and eventually neuronal death [19] and [20].", "citation": {"db": "PubMed", "db_id": "24960578"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 812, "key": "5279b5053b9801a0659f60f0921f0f07"}, {"line": 22372, "relation": "increases", "evidence": "Cytokines, particularly tumor necrosis factor α (TNF-α) and interleukin 1beta (IL-1beta), can induce chronic inflammation that may promote the loss of synapses, cognitive dysfunction, and eventually neuronal death [19] and [20].", "citation": {"db": "PubMed", "db_id": "24960578"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 648, "key": "e85a335096b331128592a33837d68aea"}, {"line": 40176, "relation": "association", "evidence": "The presence of activated microglia and astrocytes in the vicinity of amyloid plaques in the brains of Alzheimer's disease (AD) patients and mouse models implicates inflammation as a contributor to AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24369524"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Plaque, Amyloid": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Astrocytes": true}, "Species": {"9606": true, "10090": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3881, "key": "71d4751784ed85a9730c40111817e185"}, {"line": 40931, "relation": "positiveCorrelation", "evidence": "The epidemic and experimental studies have confirmed that the obesity can lead to neuroinflammation, neurodegenerative diseases and adversely affect cognition.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Obesity": true}}, "source": 3920, "target": 3925, "key": "7e18b60fb78b37186c0be3f102bc6e93"}, {"line": 41344, "relation": "association", "evidence": "Interleukin-17A (IL-17A) is a key cytokine modulating the course of inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "23468966"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3920, "target": 3659, "key": "ddd7c36e8e12b1b90a050548841f5bc3"}, {"line": 42173, "relation": "association", "evidence": "Although neuropeptides such as bradykinin (BK), somatostatin (Sst), and endothelin (ET) are known to be important mediators of inflammation in the periphery, evidence of a similar function in brain is scarce.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3920, "target": 221, "key": "71ab240cd21f82ac6ea12e64f256b371"}, {"line": 42174, "relation": "association", "evidence": "Although neuropeptides such as bradykinin (BK), somatostatin (Sst), and endothelin (ET) are known to be important mediators of inflammation in the periphery, evidence of a similar function in brain is scarce.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3920, "target": 353, "key": "2befcc54207f80f227526bfdadbb28e1"}, {"line": 42175, "relation": "association", "evidence": "Although neuropeptides such as bradykinin (BK), somatostatin (Sst), and endothelin (ET) are known to be important mediators of inflammation in the periphery, evidence of a similar function in brain is scarce.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3920, "target": 3722, "key": "5e32ba6d7d56a050c7de587afc2d3d82"}, {"line": 42176, "relation": "association", "evidence": "Although neuropeptides such as bradykinin (BK), somatostatin (Sst), and endothelin (ET) are known to be important mediators of inflammation in the periphery, evidence of a similar function in brain is scarce.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3920, "target": 2171, "key": "1b59ebfbe39ac0bdd9a4165cc3f05379"}, {"line": 43147, "relation": "association", "evidence": "Opposing roles for CXCR3 signaling in central nervous system versus ocular inflammation mediated by the astrocyte-targeted production of IL-12.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Central Nervous System": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3920, "target": 3621, "key": "c2be54aa02461e5fbcf0b0632ff77bfd"}, {"line": 44309, "relation": "positiveCorrelation", "evidence": "Discovery and validation cohorts, showed higher mean CSF YKL-40 in very mild and mild AD-type dementia (Clinical Dementia Rating [CDR] 0.5 and 1) versus control subjects (CDR 0) and PSP subjects. Importantly, CSF YKL-40/Abeta42 ratio predicted risk of developing cognitive impairment (CDR 0 to CDR > 0 conversion), as well as the best CSF biomarkers identified to date, tau/Abeta42 and p-tau 181/Abeta42. Mean plasma YKL-40 was higher in CDR 0.5 and 1 versus CDR 0, and correlated with CSF levels. YKL-40 immunoreactivity labeled astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Abeta deposition. CONCLUSIONS: These data demonstrate that YKL-40, a putative indicator of neuroinflammation, is elevated in AD and, together with Abeta42, has potential prognostic utility as a biomarker for preclinical AD.", "citation": {"db": "PubMed", "db_id": "21035623"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3920, "target": 2509, "key": "51b5ea86e2ac986463e5f1e8860e3bac"}, {"line": 6414, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1778, "target": 3823, "key": "8ad13094ca44d49f728b639acfc770a9"}, {"line": 6450, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 389, "target": 2899, "key": "8b4fcc25aa618b71f4113aa27f86c922"}, {"line": 6451, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 389, "target": 2871, "key": "24ca7ecfca06e0f0c02355b70aa1bdee"}, {"line": 6452, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 389, "target": 3861, "key": "619ae7bb29593990fe0674597190ac60"}, {"line": 6470, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 596, "target": 2899, "key": "f76cc37fa329bd3dd59a5e66aaf0050a"}, {"line": 6472, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 596, "target": 2871, "key": "8b594b4cfb05190808e629e536f0da41"}, {"line": 6511, "relation": "negativeCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 596, "target": 812, "key": "e8f375ad1c67d19e5d0f61c343b6f699"}, {"line": 47049, "relation": "association", "evidence": "The hippocampus, with its high density of glutamate receptors and in particular NMDA receptors, is known to be extremely important for some forms of learning and memory. Glutamatergic synapses can show pronounced plasticity in terms of the number and strength of individual synapses and are also characterized by their ability to express LTP – a long-lasting strengthening of synaptic transmission ", "citation": {"db": "PubMed", "db_id": " 22646481 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Hippocampus": true}}, "object": {"modifier": "Activity"}, "source": 596, "target": 3548, "key": "b19721a25bc6922320d19954a081c723"}, {"line": 6481, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 841, "target": 2899, "key": "75b7cdfd3fb81fae09d3d9621d95373d"}, {"line": 6482, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 841, "target": 2871, "key": "e7b67621d4319c9ef20e3427e026dbea"}, {"line": 6515, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 841, "target": 812, "key": "b2a5ffd69d9148a8d91f71bb8d3bb649"}, {"line": 7647, "relation": "association", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 841, "target": 448, "key": "afb5ffa84dc48603eb06edf772aea40a"}, {"line": 11682, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 841, "target": 434, "key": "cfca07ff8861792e722f19deeaf7e58e"}, {"line": 6491, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 466, "target": 2899, "key": "50b916814754750e486bbff6c0866b24"}, {"line": 6495, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 466, "target": 2871, "key": "7e7a73bbc78f13f2ef3dde0936337a97"}, {"line": 6519, "relation": "positiveCorrelation", "evidence": "In addition, the studies demonstrated AD Braak stage-associated declines in choline acetyltransferase (ChAT) expression with reduced colocalization of ChAT with insulin or IGF-1 receptor immunoreactivity in cortical neurons. These results correspond with experimental data demonstrating that neuronal tau and ChAT gene expression are regulated by IGF-1 and insulin stimulation.88 Therefore, brain insulin and IGF deficiency and resistance could account for the cytoskeletal collapse, neurite retraction, synaptic disconnection, loss of neuronal plasticity, and deficiencies in acetylcholine production, all of which correlate with cognitive decline and dementia in AD", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 466, "target": 812, "key": "56e5e4c970a0016225a503d0fd960aa7"}, {"line": 18385, "relation": "increases", "evidence": "The binding of Fas ligand to its receptor Fas then induces a cascade of events that lead to caspase activation and ultimately cell death.", "citation": {"db": "PubMed", "db_id": "11567045"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 879, "target": 505, "key": "629be49cd175157cdc1f82444a03746c"}, {"line": 21019, "relation": "association", "evidence": "X-linked inhibitor of apoptosis (XIAP) is a potent antagonist of caspase apoptotic activity.", "citation": {"db": "PubMed", "db_id": "20670888"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "XIAP subgraph": true}, "Confidence": {"High": true}}, "source": 879, "target": 3539, "key": "df79629443505fa8844498ffa801338c"}, {"line": 6586, "relation": "association", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3889, "target": 3823, "key": "ec86732902a037f68cb214879ec6e7fb"}, {"line": 6587, "relation": "association", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3889, "target": 3878, "key": "b5f22dc7c2fba88984383a3f090599b0"}, {"line": 6588, "relation": "association", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3889, "target": 3858, "key": "2e810ca427ea457c63dd8202c17bd30d"}, {"line": 14598, "relation": "association", "evidence": "p21 also up-regulates multiple genes that have been associated with senescence or implicated in age-related diseases, including atherosclerosis, Alzheimer's disease, amyloidosis, and arthritis.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true, "Arthritis": true, "Atherosclerosis": true, "Amyloidosis": true}}, "source": 3889, "target": 2493, "key": "576f27323055e2538e877380c1d68649"}, {"line": 41579, "relation": "association", "evidence": "Ccl2 affects beta-amyloidosis and progressive neurocognitive dysfunction in a mouse model of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Alzheimer Disease": true}, "Species": {"10090": true}, "Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}}, "source": 3889, "target": 3602, "key": "15ab2a0e3989ff74223fdb1849defe8a"}, {"line": 42024, "relation": "causesNoChange", "evidence": "The hippocampal lipid peroxidation correlated strongly with the increase of LOC positive fiber load, whereas neocortical TBARS levels were unrelated to amyloidosis. These data illustrate for the first time the progression of major AD related neuropathological features other than plaque load in the APPSL mouse model.", "citation": {"db": "PubMed", "db_id": "24886182"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Amyloidosis": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true, "Neocortex": true}, "Confidence": {"Medium": true}}, "source": 3889, "target": 407, "key": "929b78a0a30280e51c8e46141628ebb0"}, {"line": 42025, "relation": "association", "evidence": "The hippocampal lipid peroxidation correlated strongly with the increase of LOC positive fiber load, whereas neocortical TBARS levels were unrelated to amyloidosis. These data illustrate for the first time the progression of major AD related neuropathological features other than plaque load in the APPSL mouse model.", "citation": {"db": "PubMed", "db_id": "24886182"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Amyloidosis": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true, "Neocortex": true}, "Confidence": {"Medium": true}}, "source": 3889, "target": 838, "key": "6b714cbf36599f2843b7986a50913108"}, {"line": 6588, "relation": "association", "evidence": "Amyloid is a term used to describe typically extracellular deposits of aggregated proteins, sometimes known as plaques. Abnormal accumulation of amyloid is Amyloidosis, a term associated with diseased organs and tissues, particularly neurodegenerative diseases such as Alzheimer's, Parkinson's and Huntingdon's. Amyloid deposits consist predominantly of amyloid fibrils, rigid, non-branching structures that form ordered assemblies, characteristically with a cross beta-sheet structure where the sheets run parallel to the direction of the fibril (Sawaya et al. 2007). Often the fibril has a left-handed twist (Nelson & Eisenberg 2006). At least 27 human proteins form amyloid fibrils (Sipe et al. 2010). Many of these proteins have non-pathological functions; the trigger that leads to abnormal aggregations differs between proteins and is not well understood but in many cases the peptides are abnormal fragments or mutant forms arising from polymorphisms, suggesting that the initial event may be aggregation of misfolded or unfolded peptides. Early studies of Amyloid-Beta assembly led to a widely accepted model that assembly was a nucleation-dependent polymerization reaction (Teplow 1998) but it is now understood to be more complex, with multiple 'off-pathway' events leading to a variety of oligomeric structures in addition to fibrils (Roychaudhuri et al. 2008). An increasing body of evidence suggests that these oligomeric forms are primarily responsible for the neurotoxic effects of Amyloid-beta (Roychaudhuri et al. 2008), alpha-synuclein (Winner et al. 2011) and tau (Dance & Strobel 2009, Meraz-Rios et al. 2010). Amyloid oligomers are believed to have a common structural motif that is independent of the protein involved and not present in fibrils (Kayed et al. 2003). Conformation dependent, aggregation specific antibodies suggest that there are 3 general classes of amyloid oligomer structures (Glabe 2009) including annular structures which may be responsible for the widely reported membrane permeabilization effect of amyloid oligomers. Toxicity of amyloid oligomers preceeds the appearance of plaques in mouse models (Ferretti et al. 2011). Fibrils are often associated with other molecules, notably heparan sulfate proteoglycans and Serum Amyloid P-component, which are universally associated and seem to stabilize fibrils, possibly by protecting them from degradation.", "citation": {"db": "Online Resource", "db_id": "REACT_75925.2"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3858, "target": 3889, "key": "bd50812a59f879ee04af15ff95ac8d2c"}, {"line": 18034, "relation": "association", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3858, "target": 505, "key": "e5046f9c3468ab89bdf571a9a56fd1e4"}, {"line": 21253, "relation": "positiveCorrelation", "evidence": "On the other hand, an overproduction of NO is related with several disorders as Alzheimer's disease, Huntington's disease and the amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Disease": {"Huntington's disease": true, "Alzheimer's disease": true, "amyotrophic lateral sclerosis": true}, "Confidence": {"High": true}}, "source": 3858, "target": 156, "key": "fd3a209201f1a1a74a7257b55c72ebc7"}, {"line": 47630, "relation": "decreases", "evidence": "Hypersecretion of CRF in the brain may contribute to the symptomatology seen in neuropsychiatric disorders, such as depression, anxiety-related disorders and anorexia nervosa. Furthermore, overproduction of CRF at peripheral inflammatory sites, such as synovial joints may contribute to autoimmune diseases such as rheumatoid arthritis. In contrast, deficits in brain CRF are apparent in neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease and Huntington's disease, as they relate to dysfunction of CRF neurons in the brain areas affected in the particular disorder. Strategies directed at developing CRF-related agents may hold promise for novel therapies for the treatment of these various disorders.", "citation": {"db": "PubMed", "db_id": "8834089"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"CRH subgraph": true}}, "source": 3858, "target": 2560, "key": "35f1960f8f6547f01a7c318680ae0a13"}, {"line": 6633, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 3915, "target": 1744, "key": "3fc7286c1b907951d80205a31f344d6b"}, {"line": 7368, "relation": "association", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Disease": {"type 2 diabetes mellitus": true}}, "source": 3915, "target": 80, "key": "2dfe5913937cde1e2c84feee51bbea67"}, {"line": 7868, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Peripheral Nervous System": true}, "Confidence": {"Low": true}}, "source": 3915, "target": 3817, "key": "f6f32f1438566b4c48cd3e2388fb287f"}, {"line": 8243, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 3915, "target": 3015, "key": "daa57c5be050a66d4055346da5620395"}, {"line": 6644, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 3847, "target": 3901, "key": "0df7d73d3a5d8199e766e464b364bd78"}, {"line": 6645, "relation": "association", "evidence": "Patients without an APOE ε4 allele require higher amounts of insulin to improve memory [Craft and Watson, 2004], consistent with the observation of insulin resistance and hyperinsulinemia in some studies of APOE ε4 allele negative individuals [Kuusisto et al., 1997]. Furthermore, the association between diabetes and dementia is particularly strong in APOE ε4 carriers [Peila et al., 2002].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 +ve": true}, "Confidence": {"High": true}}, "source": 3847, "target": 1744, "key": "0c358d1fce84af60608a1079baa02394"}, {"line": 13846, "relation": "association", "evidence": "Specific polymorphisms within the vascular endothelial growth factor (VEGF) gene promoter region are of particular interest: VEGF variability has been associated with increased risk of developing a wide variety of disorders from diabetes to neurodegenerative diseases, suggesting functions not confined to its originally described vascular effects.", "citation": {"db": "PubMed", "db_id": "19272614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}}, "source": 3847, "target": 3519, "key": "b277a8ff1c1765639db1339a45dfb82c"}, {"line": 6657, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 1974, "target": 3823, "key": "6a5b92f6c62ffe154f266c2d0b0d4d3a"}, {"line": 10250, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 1974, "target": 3823, "key": "e3193cbc70ee80f89c4d0dfe1690426a"}, {"line": 10282, "relation": "association", "evidence": "In a replication study, we confirmed significant association of SNPs within three genes--PPARgamma, SOS2, and PCK1--with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 1974, "target": 3823, "key": "f0c357e18b61c9e0a91faed76cb56b1f"}, {"line": 6760, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 1974, "target": 843, "key": "ecaa308419e1cfcc4f4c84dcaafa85c2"}, {"line": 10234, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1974, "target": 580, "key": "36bc19cf651418008df694db882722ea"}, {"line": 6663, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"High": true}}, "source": 1946, "target": 3823, "key": "c8c6e797a5d401c6f5339b8b46c53a2b"}, {"line": 6761, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 1946, "target": 843, "key": "81cc3d9ed63fe8da0631c05a975b29e2"}, {"line": 6664, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"High": true}}, "source": 1840, "target": 3823, "key": "c2427afadd4102450d06b7b8fdec3d00"}, {"line": 6762, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 1840, "target": 843, "key": "393a35b5649358d59488db25cf17e619"}, {"line": 6665, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"High": true}}, "source": 1922, "target": 3823, "key": "a0c191a378028baaf06d809cbc42b201"}, {"line": 6763, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 1922, "target": 843, "key": "70562eb141c47529c4f21a2af74ba9eb"}, {"line": 6671, "relation": "association", "evidence": "Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1.", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Energy metabolic subgraph": true}, "Confidence": {"High": true}}, "source": 1908, "target": 3823, "key": "e2b47151187541692467c8e51fa17c9e"}, {"line": 10264, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Confidence": {"Medium": true}}, "source": 1908, "target": 3823, "key": "eaba723d20d4223e3d0575c88e90cffb"}, {"line": 10288, "relation": "association", "evidence": "In a replication study, we confirmed significant association of SNPs within three genes--PPARgamma, SOS2, and PCK1--with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 1908, "target": 3823, "key": "713de52790ee3e61dae1e7c6cdb93a30"}, {"line": 6764, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 1908, "target": 843, "key": "2e0f56e86586a2bb924f72935e6752bd"}, {"line": 10242, "relation": "association", "evidence": "Candidate gene association study of insulin signaling genes and Alzheimer's disease: evidence for SOS2, PCK1, and PPARgamma as susceptibility loci.", "citation": {"db": "PubMed", "db_id": "17440948"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1908, "target": 580, "key": "87cee6b54428b91b294970c6ce6e192a"}, {"line": 6695, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 1814, "target": 3823, "key": "f165c228054e04ab6eb7214929adb7a9"}, {"line": 6765, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 1814, "target": 843, "key": "769dffc1fcb10a87a60708965ae461d8"}, {"line": 6696, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Insulin signal transduction": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 1851, "target": 3823, "key": "b8fd9f0d1ee77fb8a26c9d0d1477c66d"}, {"line": 6767, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Insulin signal transduction": true}}, "source": 1851, "target": 843, "key": "f856d33d4dee9c219abb92117e828b8c"}, {"line": 6708, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 +ve": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 1849, "target": 3823, "key": "1468d53cf3b6a102b9ee0d9a677c15db"}, {"line": 6768, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Insulin signal transduction": true}}, "source": 1849, "target": 843, "key": "728ffa16e4146a3d7ee79fbad639f44d"}, {"line": 6716, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 +ve": true}, "Subgraph": {"Energy metabolic subgraph": true}, "Confidence": {"High": true}}, "source": 1839, "target": 3823, "key": "bc9339a1c2af1fa35d0d5dbf59a440d5"}, {"line": 6769, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Insulin signal transduction": true}}, "source": 1839, "target": 843, "key": "598c265912a254b966f8119548f15292"}, {"line": 6724, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 +ve": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 1896, "target": 3823, "key": "acfd1b245666f4c8f81f226804f259dc"}, {"line": 6775, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 1896, "target": 843, "key": "53dde98c13d8e0955a24a714c1a20685"}, {"line": 6733, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Vitamin subgraph": true}, "Patient": {"APOE e4 -ve": true}, "Confidence": {"High": true}}, "source": 1830, "target": 3823, "key": "f3f2a3ac0aa311cc8c292cd1c97f58f8"}, {"line": 6780, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"Low": true}}, "source": 1830, "target": 843, "key": "2a56a99e6aff61f6d364e26a747e4753"}, {"line": 6741, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 -ve": true}, "Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 1736, "target": 3823, "key": "0a5f73c55fbc07b53066c09656aae268"}, {"line": 6782, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"Low": true}}, "source": 1736, "target": 843, "key": "1ab549467c0ba8fdf50f0db1e1a3942e"}, {"line": 6742, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 -ve": true}, "Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 1777, "target": 3823, "key": "515ac73259dc4a25991519c2f093ad38"}, {"line": 6783, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"Low": true}}, "source": 1777, "target": 843, "key": "aef18decc6e07d306a9ffa3508667106"}, {"line": 6750, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Patient": {"APOE e4 -ve": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1923, "target": 3823, "key": "83edce1698cd936fd6587079c046361a"}, {"line": 6784, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"Low": true}}, "source": 1923, "target": 843, "key": "67582ddffddef6cebf3f632eebf53621"}, {"line": 6758, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 843, "target": 1921, "key": "2e44b03832c62bd19a80e3d3e17083d3"}, {"line": 6760, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 843, "target": 1974, "key": "6c9edb2639b67e47f25b584611c0bf81"}, {"line": 6761, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 843, "target": 1946, "key": "2f1a74c36c257ecb8f49e1bbcf5d5b1e"}, {"line": 6762, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 843, "target": 1840, "key": "43193250aed9e9aaa823c6cbda46a0a0"}, {"line": 6763, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 843, "target": 1922, "key": "8ae774b3a756146fa82ee0817af52d54"}, {"line": 6764, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 843, "target": 1908, "key": "788de1e08eb11168e8e0ad94e3b17b46"}, {"line": 6765, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 843, "target": 1814, "key": "3064579789758b389476b2fa4986cad3"}, {"line": 6767, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Insulin signal transduction": true}}, "source": 843, "target": 1851, "key": "688371c5e2b33adf0612ddb1bd3b2ca2"}, {"line": 6768, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Insulin signal transduction": true}}, "source": 843, "target": 1849, "key": "43d2cc86697615740f7e461235a9aaf1"}, {"line": 6769, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Insulin signal transduction": true}}, "source": 843, "target": 1839, "key": "d549ca9a69ac8055c587d1ee302f423e"}, {"line": 6775, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 843, "target": 1896, "key": "4272f4ae0b611bf5abf1845aaa669e7f"}, {"line": 6780, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"Low": true}}, "source": 843, "target": 1830, "key": "5b27eb2660bb808f61f04ccec609abca"}, {"line": 6782, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"Low": true}}, "source": 843, "target": 1736, "key": "dbe8b341979f979fe77a5442f83747dd"}, {"line": 6783, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"Low": true}}, "source": 843, "target": 1777, "key": "731fa28c3d10f20baaf5bd99c6b174a9"}, {"line": 6784, "relation": "association", "evidence": "In the first stage of this study a total of 152 SNPs were screened (Table S6). All SNPs did not significantly deviate from Hardy–Weinberg equilibrium proportions in controls, 10 SNPs deviated in cases. 39 SNPs dropped out prior to analysis due to monomorphism (n = 8), genotyping failure (n = 21), not biallelic (n = 2) and a MAF < 0.05 (n = 8). A total of 113 SNPs were analyzed for association with disease status in the overall sample, and 6 SNPs in 5 different genes showed significant association with disease status under at least one genetic model (Table I). Both SNPs within SOS2 were significantly associated with disease status, with the remaining positive SNPs located within RPS6KA2 (Gene ID 6196), HNF4A (Gene ID 3172), PPP1CC (Gene ID 5501), and PCK1. Additional analysis of the data stratified by APOE genotype provided evidence of association for 15 SNPs in either the APOE ε4 positive or APOE ε4 negative sample sets (Table I). Two of these SNPs were associated in the overall Stage 1 sample; SNP 139 (HNF4A) and SNP 150 (PCK1), and it was observed that these SNPs had a greater effect when stratified by APOE genotype: OR 0.5 versus 0.63; OR 2.11 versus 1.54, respectively. Two further PCK1 SNPs (SNP 144 and SNP 149) showed marginal association in the APOE ε4 positive samples (P = 0.07, 0.09). In addition, there was evidence of positive association for both PPARgamma polymorphisms (SNP 65 and SNP 66) in the APOE ε4 positive sample set. Allele 2 of both of these SNPs (G and T, respectively) appeared to protect against disease (OR = 0.5, OR = 0.45). The result obtained with the major allele of SNP 52 within INSR (Gene ID 3643), allele A, was also suggestive of a protective factor (OR = 0.5; APOE ε4 negative samples). A further SNP from INSR, SNP 48, showed a trend toward association, but this result was observed in the APOE ε4 positive samples (P = 0.06). SNP 20 from the ENPP1 gene (Gene ID 5167) showed evidence for association in both the APOE ε4 positive and ε4 negative samples. A final 7 SNPs showed evidence of association in the APOE ε4 positive samples, SNP 34 (INPPL1 Gene ID 3636), SNP 62 (HK2 Gene ID 3099), SNP 93 (NOS3 Gene ID 4846), and in the APOE ε4 negative samples, SNP 72 (GC Gene ID 2638), SNP 94 (ADRB3 Gene ID 155), SNP 130 (CETP Gene ID 1071), and SNP 136 (PPP2R1A Gene ID 5518). Following correction using the False Discovery Rate Procedure (FDR) at a variety of levels, however, the majority of these results could be rejected as false discoveries (Table S8).", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true}, "Confidence": {"Low": true}}, "source": 843, "target": 1923, "key": "6b1d120b5d4e830ea0b2927ef31f10ee"}, {"line": 11079, "relation": "association", "evidence": "Recently, it has been shown that PEN-2 mutations could be involved in Alzheimer's disease (AD). We performed a mutational screening of all PEN-2 coding and promoter regions in a FAD cohort derived from Southern Italy. Four hundred and fifty-two subjects (FAD: 97; Controls: 355) were recruited for this study. We identified for the first time in a key region necessary for the promoter activity a novel 3 bp deletion in a subject with early-FAD. Our genetic data demonstrate that the mutant allele may influence the transcriptional activity of the PEN-2 gene.", "citation": {"db": "PubMed", "db_id": "22055974"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 843, "target": 3272, "key": "c5c8aad901f697f7eda005c8096e575f"}, {"line": 11080, "relation": "association", "evidence": "Recently, it has been shown that PEN-2 mutations could be involved in Alzheimer's disease (AD). We performed a mutational screening of all PEN-2 coding and promoter regions in a FAD cohort derived from Southern Italy. Four hundred and fifty-two subjects (FAD: 97; Controls: 355) were recruited for this study. We identified for the first time in a key region necessary for the promoter activity a novel 3 bp deletion in a subject with early-FAD. Our genetic data demonstrate that the mutant allele may influence the transcriptional activity of the PEN-2 gene.", "citation": {"db": "PubMed", "db_id": "22055974"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 843, "target": 1930, "key": "4b8c900542f4c702098ee1d5e0ca8cfc"}, {"line": 6800, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true}, "UserdefinedCellLine": {"App transgenic": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 345, "target": 812, "key": "31db37c2d5d79c7dd82cfa5a47ecf460"}, {"line": 6806, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "UserdefinedCellLine": {"App transgenic": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 345, "target": 3212, "key": "fd72ea9d475b7c4692f89716b9a2e4a5"}, {"line": 6810, "relation": "negativeCorrelation", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "UserdefinedCellLine": {"App transgenic": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 345, "target": 2328, "key": "6c1666aff269966cfcc8b5b2f335b9c4"}, {"line": 6819, "relation": "association", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 345, "target": 462, "key": "84d12cd42bf7c06b44e7e8e89b35a6f2"}, {"line": 6829, "relation": "decreases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 345, "target": 2794, "key": "31741f7ea40078f9c8c5890190ccee5d"}, {"line": 6841, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta-Catenin subgraph": true}, "Confidence": {"High": true}}, "source": 345, "target": 2580, "key": "6bb64b1e328bfef397037f7997cb1ec2"}, {"line": 6855, "relation": "isA", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 345, "target": 66, "key": "125e53a78874cded6d2a044731562493"}, {"line": 44372, "relation": "increases", "evidence": "JAK-STAT signaling as an anti-inflammatory target. JAK-STAT signaling mediates the brain inflammation induced by LPS, IFN-gamma, ganglioside and thrombin. Curcumin activates SH2-containing phosphatase 2 (SHP2), while rosiglitazone and 15d-PGJ2 increase the expressions of SOCS1 and SOCS3. SHP2 and the SOCS proteins are typical negative feedback molecules of the JAK-STAT pathway.", "citation": {"db": "PubMed", "db_id": "26113788"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 345, "target": 3389, "key": "f5950e5f86c11e2b2678d9143a111a80"}, {"line": 44373, "relation": "increases", "evidence": "JAK-STAT signaling as an anti-inflammatory target. JAK-STAT signaling mediates the brain inflammation induced by LPS, IFN-gamma, ganglioside and thrombin. Curcumin activates SH2-containing phosphatase 2 (SHP2), while rosiglitazone and 15d-PGJ2 increase the expressions of SOCS1 and SOCS3. SHP2 and the SOCS proteins are typical negative feedback molecules of the JAK-STAT pathway.", "citation": {"db": "PubMed", "db_id": "26113788"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 345, "target": 3390, "key": "903ce093630df87d0078227b7a992690"}, {"line": 7377, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 565, "target": 2899, "key": "d9461ee08800ae32e9dcb58312d9519a"}, {"line": 7378, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 565, "target": 2871, "key": "33b2f92a76bcb860151f6a848af5f00d"}, {"line": 10696, "relation": "positiveCorrelation", "evidence": "The median (18)F-FDG ratio was lower in diabetic individuals than in nondiabetic individuals in the AD signature meta-ROI (1.32 vs. 1.40,", "citation": {"db": "PubMed", "db_id": "24652830"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 565, "target": 17, "key": "48593e8a01e18205a99adf59c8825775"}, {"line": 6849, "relation": "decreases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 66, "target": 3920, "key": "092ae77c0a3524dc17937dc5312a56ce"}, {"line": 6853, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 66, "target": 3212, "key": "d02a58e2cb66d6d37326429498c0cc0e"}, {"line": 6859, "relation": "increases", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}}, "source": 66, "target": 820, "key": "ce0aadcf7951d64cc7bd524f8195776b"}, {"line": 9630, "relation": "decreases", "evidence": "PPARgamma activation leads to the inhibition of microglial activation and the expression of a broad range of proinflammatory molecules. The newly appreciated anti-inflammatory actions of PPARgamma agonists may allow novel therapies for AD and other CNS indications with an inflammatory component.", "citation": {"db": "PubMed", "db_id": "11755002"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 66, "target": 532, "key": "8f6a00f9db0eff466f19238a8bf5bbc0"}, {"line": 6868, "relation": "positiveCorrelation", "evidence": "Rosiglitazone is a TZD derivative that was recently observed to improve cognition in both APP transgenic mice and AD patients [Watson et al., 2005; Pedersen et al., 2006]. It activates PPARgamma leading to increased glucose disposal rates [Jung et al., 2005] and is also neuroprotective against Abeta neurotoxicity [Inestrosa et al., 2005]. Neuroprotection may result through modulation of Wnt signaling since an increase in beta-catenin and inhibition of GSK-3beta is observed upon exposure to rosiglitazone [Inestrosa et al., 2005], although a different study has shown that activation of PPARgamma by a potent ligand leads to beta-catenin degradation [Liu and Farmer 2004]. Nonetheless, in support of the role for PPARgamma activation in neuroprotection, PPARgamma agonists have potent anti-inflammatory effects [Luna-Medina et al., 2005], inhibit microglial activation [Bernardo et al., 2005; Heneka et al., 2005] and have been shown to improve verbal memory in AD patients with T2DM, possibly in a mechanism dependent on PPARgamma activation . A recent report demonstrates that overexpression of PPARgamma in cultured cells leads to a dramatic decrease in the production of Abeta, by increasing the rate of APP degradation via ubiquitination [D'Abramo et al., 2005]. This study also showed that by decreasing Abeta secretion, PPARgamma protects the cells against H2O2-mediated necrosis [D'Abramo et al., 2005].", "citation": {"db": "PubMed", "db_id": "19885299"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Peroxisome proliferator activated receptor subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 573, "target": 80, "key": "74a7271ac33c6fa733df2d1392261424"}, {"line": 7725, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 1755, "target": 2375, "key": "23fbe4c705ab0b71e5d9683d1698b9cd"}, {"line": 8445, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 1755, "target": 3943, "key": "8a09f061032ef61c3c3399edee019e8b"}, {"relation": "hasVariant", "source": 1755, "target": 1756, "key": "bbe1cc07a66adb54bfc17d955c130184"}, {"line": 45901, "relation": "association", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1755, "target": 2803, "key": "5ac2446974459a8c2b017410a86ec7ac"}, {"line": 45944, "relation": "orthologous", "evidence": "BACE1 mRNA levels were increased in aged 3xTg-AD mice as well as in AD PBMCs along with an increase in promoter accessibility and histone H3 acetylation, while the BACE1 promoter region was less accessible in PBMCs from MCI individuals", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1755, "target": 2020, "key": "aa7cb5a3a5bf80ac06a5e3d338baef69"}, {"line": 6899, "relation": "increases", "evidence": "Insulin modulates metabolism of beta-amyloid precursor protein (APP) in neurons, decreasing the intracellular accumulation of beta-amyloid (Abeta) peptides, which are pivotal in AD pathogenesis. The present study investigates whether the widely prescribed insulin-sensitizing drug, metformin (GlucophageR), affects APP metabolism and Abeta generation in various cell models. We demonstrate that metformin, at doses that lead to activation of the AMP-activated protein kinase (AMPK), significantly increases the generation of both intracellular and extracellular Abeta species", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Energy metabolic subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 301, "target": 2143, "key": "d7102c26e25eb5413c3fe0957ec59970"}, {"line": 7055, "relation": "association", "evidence": "BACE1 transcription has recently been reported to be regulated by the PPARgamma pathway (36). We now demonstrate that the diabetes drug metformin can also modulate BACE1 transcription, likely independently of the PPARgamma pathway despite the presence of several PPAR/RXR binding sites in the promoter (31, 32). Metformin-mediated transcriptional activation of BACE1 appears to depended on a pathway involving AMPK.", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 301, "target": 2143, "key": "c7685be41e704d0aa40d43754d444e85"}, {"line": 6901, "relation": "increases", "evidence": "Insulin modulates metabolism of beta-amyloid precursor protein (APP) in neurons, decreasing the intracellular accumulation of beta-amyloid (Abeta) peptides, which are pivotal in AD pathogenesis. The present study investigates whether the widely prescribed insulin-sensitizing drug, metformin (GlucophageR), affects APP metabolism and Abeta generation in various cell models. We demonstrate that metformin, at doses that lead to activation of the AMP-activated protein kinase (AMPK), significantly increases the generation of both intracellular and extracellular Abeta species", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Energy metabolic subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 301, "target": 80, "key": "288396dd0bc4119d499f16b07b155ffb"}, {"line": 6910, "relation": "increases", "evidence": "Furthermore, the effect of metformin on Abeta generation is mediated by transcriptional up-regulation of beta-secretase (BACE1), which results in an elevated protein level and increased enzymatic activity. Unlike insulin, metformin exerts no effect on Abeta degradation. In addition, we found that glucose deprivation and various tyrphostins, known inhibitors of insulin-like growth factors/insulin receptor tyrosine kinases, do not modulate the effect of metformin on Abeta. Finally, inhibition of AMP-activated protein kinase (AMPK) by the pharmacological inhibitor Compound C largely suppresses metformin's effect on Abeta generation and BACE1 transcription, suggesting an AMPK-dependent mechanism. Although insulin and metformin display opposing effects on Abeta generation, in combined use, metformin enhances insulin's effect in reducing Abeta levels", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 301, "target": 80, "key": "b7a1b4f1cad870ba463aafee96dd473c"}, {"line": 6911, "relation": "causesNoChange", "evidence": "Furthermore, the effect of metformin on Abeta generation is mediated by transcriptional up-regulation of beta-secretase (BACE1), which results in an elevated protein level and increased enzymatic activity. Unlike insulin, metformin exerts no effect on Abeta degradation. In addition, we found that glucose deprivation and various tyrphostins, known inhibitors of insulin-like growth factors/insulin receptor tyrosine kinases, do not modulate the effect of metformin on Abeta. Finally, inhibition of AMP-activated protein kinase (AMPK) by the pharmacological inhibitor Compound C largely suppresses metformin's effect on Abeta generation and BACE1 transcription, suggesting an AMPK-dependent mechanism. Although insulin and metformin display opposing effects on Abeta generation, in combined use, metformin enhances insulin's effect in reducing Abeta levels", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 301, "target": 80, "key": "00f65f6ae788df32433ed52d036e09a8"}, {"line": 7042, "relation": "increases", "evidence": "We examined whether metformin's Abeta-increasing effect depended on activation of AMPK, a known molecular target of metformin. Phosphorylation of AMPK at Thr-172 and its substrate, acetyl CoA carboxylase (ACC), were found to be both induced by metformin in a dose-dependent manner (Fig. 4 A and B). We also observed a significant inhibition of metformin-stimulated Abeta production by compound C, a specific AMPK inhibitor, in a dose-dependent manner. Compound C inhibited metformin's effect by 50% when used at a concentration (20 μM) that is known to guarantee its specificity for AMPK (32) (Fig. 4C). These results indicate an AMPK-dependent mechanism for metformin's effect on Abeta. Significantly, the antagonizing effect of compound C was largely attributed to suppression of BACE1 transcription because the mRNA was greatly reduced after treatment with the two drugs (Fig. 4D).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 301, "target": 80, "key": "dd4328a9e68e61b2bf47e6f04616090e"}, {"line": 6909, "relation": "increases", "evidence": "Furthermore, the effect of metformin on Abeta generation is mediated by transcriptional up-regulation of beta-secretase (BACE1), which results in an elevated protein level and increased enzymatic activity. Unlike insulin, metformin exerts no effect on Abeta degradation. In addition, we found that glucose deprivation and various tyrphostins, known inhibitors of insulin-like growth factors/insulin receptor tyrosine kinases, do not modulate the effect of metformin on Abeta. Finally, inhibition of AMP-activated protein kinase (AMPK) by the pharmacological inhibitor Compound C largely suppresses metformin's effect on Abeta generation and BACE1 transcription, suggesting an AMPK-dependent mechanism. Although insulin and metformin display opposing effects on Abeta generation, in combined use, metformin enhances insulin's effect in reducing Abeta levels", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 301, "target": 2375, "key": "ac8cf78e2db209ed9e77bc4e9678e4aa"}, {"line": 6957, "relation": "increases", "evidence": "We then analyzed the levels of various APP metabolites including the cleavage products of alpha- and beta-secretases (Fig. 1C). Metformin reduced alpha-cleavage and promoted beta-cleavage, as evidenced by decreased sAPPα and increased APP C-terminal fragment, CTF-beta (the upper CTF band that resulted from cleavage by BACE1). No change in the levels of full-length PS1 (presenilin 1, the core component of gamma-secretase) or its N-terminal fragment was detected from total cell lysates.", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 301, "target": 2375, "key": "e97b8f66d3badedb55c18ea0e16295ec"}, {"line": 6991, "relation": "increases", "evidence": "The surface levels of both APP and LRP1 (low-density lipoprotein receptor-related protein 1), which are known to comigrate during trafficking, were dramatically reduced after metformin treatment as detected by biotinylation assays, whereas their total protein levels remained unchanged (Fig. 1D). However, the surface and the total BACE1 were markedly increased by metformin. Through further subfractionation, using sucrose gradients, we showed that metformin treatment caused changes in the compartmentalization of APP, as evidenced by increased distribution in trans-Golgi network (TGN) vesicles (fraction 2), including those trafficks en route to early endosomes and TGN (fraction 3), and decreased distribution in membranes (fraction 5) (Fig. S1 B and C). BACE1 protein levels were found to be elevated in all 3 fractions: 2, 3, and 5, with an ≈2-fold increase of the total protein. The increased distribution of both APP and BACE1 in fractions 2 and 3 are expected to favor Abeta generation within TGN and/or endocytic compartments, the two compartments with mild acidic pH optimal for BACE1 activity (33, 34). Indeed, we detected increased immunofluorescent Abeta40 signals after metformin treatment in TGN (Fig. 1E).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"CellStructure": {"Endosomes": true}, "Subgraph": {"Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 301, "target": 2375, "key": "40a9c61244be182ce5952933fd1325cc"}, {"line": 7017, "relation": "increases", "evidence": "As correlated with its increased protein level (Fig. 1D), metformin increased the total BACE1 enzymatic activity by 2-fold (Fig. 2A). BACE1 mRNA was also increased by metformin in a time-dependent manner in both N2a695 and primary cortical neurons (Fig. 2B), as measured by semiquantitative RT-PCR", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 301, "target": 2375, "key": "1a6c6a8307d2e8735946de4eb6e45624"}, {"relation": "partOf", "source": 301, "target": 1664, "key": "287000ec77c97e66d9178d21ce85e10a"}, {"line": 6927, "relation": "increases", "evidence": "Metformin (GlucophageR, 1, 2-dimethylbiguanide hydrochloride; ≈36 million U.S. prescriptions in 2003) (30), is a biguanide that has pleiotropic effects on metabolism, including insulin-sensitization, increased glucose uptake, decreased hepatic glucose synthesis, activation of AMP activated protein kinase (AMPK, an enzyme involved in glucose and fatty acid metabolism), and mitochondria inhibition (31, 32).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 301, "target": 773, "key": "7b32bab10caaa0237d0b108485b6135e"}, {"line": 6928, "relation": "increases", "evidence": "Metformin (GlucophageR, 1, 2-dimethylbiguanide hydrochloride; ≈36 million U.S. prescriptions in 2003) (30), is a biguanide that has pleiotropic effects on metabolism, including insulin-sensitization, increased glucose uptake, decreased hepatic glucose synthesis, activation of AMP activated protein kinase (AMPK, an enzyme involved in glucose and fatty acid metabolism), and mitochondria inhibition (31, 32).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 301, "target": 565, "key": "c2cb1aa767e49353802536dcb0af83d7"}, {"line": 6931, "relation": "decreases", "evidence": "Metformin (GlucophageR, 1, 2-dimethylbiguanide hydrochloride; ≈36 million U.S. prescriptions in 2003) (30), is a biguanide that has pleiotropic effects on metabolism, including insulin-sensitization, increased glucose uptake, decreased hepatic glucose synthesis, activation of AMP activated protein kinase (AMPK, an enzyme involved in glucose and fatty acid metabolism), and mitochondria inhibition (31, 32).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Liver": true}}, "source": 301, "target": 566, "key": "326f667367584ffc2a4b6df269bb00bd"}, {"line": 7029, "relation": "causesNoChange", "evidence": "Taken together, these results indicate that metformin likely augments Abeta production through mechanisms independent of insulin signaling and glucose metabolism", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 301, "target": 566, "key": "595013c9a6f5ea5ae9a9f13e876df2d0"}, {"line": 6946, "relation": "increases", "evidence": "We treated N2a695 cells with metformin and found that metformin increased levels of both extracellular (Fig. 1A) and intracellular (data not shown) Abeta40/42 in dose-dependent manners, with the maximum effect (≈3-fold) seen after 24 h at 10 mM. Similar effects were seen in primary neurons at a much lower concentration of metformin (10 μM) (Fig. 1B). To ascertain that the intracellular Abeta measured from cell lysates did not include the secreted Abeta that is often associated with cell membranes, we briefly treated cells with trypsin and then with trypsin inhibitors before lysis and found no significant difference in the intracellular Abeta levels with or without trypsin cleavage", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"N2a695 cell": true}}, "source": 301, "target": 2328, "key": "2536286a124de674460398e7d709c88a"}, {"line": 6947, "relation": "increases", "evidence": "We treated N2a695 cells with metformin and found that metformin increased levels of both extracellular (Fig. 1A) and intracellular (data not shown) Abeta40/42 in dose-dependent manners, with the maximum effect (≈3-fold) seen after 24 h at 10 mM. Similar effects were seen in primary neurons at a much lower concentration of metformin (10 μM) (Fig. 1B). To ascertain that the intracellular Abeta measured from cell lysates did not include the secreted Abeta that is often associated with cell membranes, we briefly treated cells with trypsin and then with trypsin inhibitors before lysis and found no significant difference in the intracellular Abeta levels with or without trypsin cleavage", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"N2a695 cell": true}}, "source": 301, "target": 2327, "key": "2334fd3e9dd440dd486c043796f3f87b"}, {"line": 6955, "relation": "decreases", "evidence": "We then analyzed the levels of various APP metabolites including the cleavage products of alpha- and beta-secretases (Fig. 1C). Metformin reduced alpha-cleavage and promoted beta-cleavage, as evidenced by decreased sAPPα and increased APP C-terminal fragment, CTF-beta (the upper CTF band that resulted from cleavage by BACE1). No change in the levels of full-length PS1 (presenilin 1, the core component of gamma-secretase) or its N-terminal fragment was detected from total cell lysates.", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 301, "target": 2249, "key": "a215a43b9f820eed6ba2799fee991d72"}, {"line": 6970, "relation": "decreases", "evidence": "The surface levels of both APP and LRP1 (low-density lipoprotein receptor-related protein 1), which are known to comigrate during trafficking, were dramatically reduced after metformin treatment as detected by biotinylation assays, whereas their total protein levels remained unchanged (Fig. 1D). However, the surface and the total BACE1 were markedly increased by metformin. Through further subfractionation, using sucrose gradients, we showed that metformin treatment caused changes in the compartmentalization of APP, as evidenced by increased distribution in trans-Golgi network (TGN) vesicles (fraction 2), including those trafficks en route to early endosomes and TGN (fraction 3), and decreased distribution in membranes (fraction 5) (Fig. S1 B and C). BACE1 protein levels were found to be elevated in all 3 fractions: 2, 3, and 5, with an ≈2-fold increase of the total protein. The increased distribution of both APP and BACE1 in fractions 2 and 3 are expected to favor Abeta generation within TGN and/or endocytic compartments, the two compartments with mild acidic pH optimal for BACE1 activity (33, 34). Indeed, we detected increased immunofluorescent Abeta40 signals after metformin treatment in TGN (Fig. 1E).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "cell surface"}}}, "source": 301, "target": 2315, "key": "4a06a64a195f89fedbeacdfdad0924d4"}, {"line": 6980, "relation": "decreases", "evidence": "The surface levels of both APP and LRP1 (low-density lipoprotein receptor-related protein 1), which are known to comigrate during trafficking, were dramatically reduced after metformin treatment as detected by biotinylation assays, whereas their total protein levels remained unchanged (Fig. 1D). However, the surface and the total BACE1 were markedly increased by metformin. Through further subfractionation, using sucrose gradients, we showed that metformin treatment caused changes in the compartmentalization of APP, as evidenced by increased distribution in trans-Golgi network (TGN) vesicles (fraction 2), including those trafficks en route to early endosomes and TGN (fraction 3), and decreased distribution in membranes (fraction 5) (Fig. S1 B and C). BACE1 protein levels were found to be elevated in all 3 fractions: 2, 3, and 5, with an ≈2-fold increase of the total protein. The increased distribution of both APP and BACE1 in fractions 2 and 3 are expected to favor Abeta generation within TGN and/or endocytic compartments, the two compartments with mild acidic pH optimal for BACE1 activity (33, 34). Indeed, we detected increased immunofluorescent Abeta40 signals after metformin treatment in TGN (Fig. 1E).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "CellStructure": {"Cell Membrane": true}, "Confidence": {"High": true}}, "source": 301, "target": 2315, "key": "9f87d6443fee4d355af73cdcac39155c"}, {"line": 7002, "relation": "increases", "evidence": "The surface levels of both APP and LRP1 (low-density lipoprotein receptor-related protein 1), which are known to comigrate during trafficking, were dramatically reduced after metformin treatment as detected by biotinylation assays, whereas their total protein levels remained unchanged (Fig. 1D). However, the surface and the total BACE1 were markedly increased by metformin. Through further subfractionation, using sucrose gradients, we showed that metformin treatment caused changes in the compartmentalization of APP, as evidenced by increased distribution in trans-Golgi network (TGN) vesicles (fraction 2), including those trafficks en route to early endosomes and TGN (fraction 3), and decreased distribution in membranes (fraction 5) (Fig. S1 B and C). BACE1 protein levels were found to be elevated in all 3 fractions: 2, 3, and 5, with an ≈2-fold increase of the total protein. The increased distribution of both APP and BACE1 in fractions 2 and 3 are expected to favor Abeta generation within TGN and/or endocytic compartments, the two compartments with mild acidic pH optimal for BACE1 activity (33, 34). Indeed, we detected increased immunofluorescent Abeta40 signals after metformin treatment in TGN (Fig. 1E).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"CellStructure": {"Endosomes": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 301, "target": 2315, "key": "e7b588c370b31a668cad72d5067c5e4c"}, {"line": 6973, "relation": "decreases", "evidence": "The surface levels of both APP and LRP1 (low-density lipoprotein receptor-related protein 1), which are known to comigrate during trafficking, were dramatically reduced after metformin treatment as detected by biotinylation assays, whereas their total protein levels remained unchanged (Fig. 1D). However, the surface and the total BACE1 were markedly increased by metformin. Through further subfractionation, using sucrose gradients, we showed that metformin treatment caused changes in the compartmentalization of APP, as evidenced by increased distribution in trans-Golgi network (TGN) vesicles (fraction 2), including those trafficks en route to early endosomes and TGN (fraction 3), and decreased distribution in membranes (fraction 5) (Fig. S1 B and C). BACE1 protein levels were found to be elevated in all 3 fractions: 2, 3, and 5, with an ≈2-fold increase of the total protein. The increased distribution of both APP and BACE1 in fractions 2 and 3 are expected to favor Abeta generation within TGN and/or endocytic compartments, the two compartments with mild acidic pH optimal for BACE1 activity (33, 34). Indeed, we detected increased immunofluorescent Abeta40 signals after metformin treatment in TGN (Fig. 1E).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 301, "target": 2970, "key": "9b8edd8ff0caef48d5bed97e95580863"}, {"line": 7010, "relation": "causesNoChange", "evidence": "We next studied the potential effect of metformin on the levels of 2 enzymes known to degrade Abeta, neprilysin and insulin-degrading enzyme (IDE). Metformin had no effect on both enzymes, including protein levels and their activities (Fig. S2 A and B). Moreover, metformin had no effect on Abeta degradation as measured by pulse–chase assay (Fig. S2C). We also found that the increased Abeta production caused by metformin was not due to increased APP expression, because the total APP level was unaltered (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 301, "target": 2867, "key": "aab0b0d4bda0740802d86212f58b8dd2"}, {"line": 7011, "relation": "causesNoChange", "evidence": "We next studied the potential effect of metformin on the levels of 2 enzymes known to degrade Abeta, neprilysin and insulin-degrading enzyme (IDE). Metformin had no effect on both enzymes, including protein levels and their activities (Fig. S2 A and B). Moreover, metformin had no effect on Abeta degradation as measured by pulse–chase assay (Fig. S2C). We also found that the increased Abeta production caused by metformin was not due to increased APP expression, because the total APP level was unaltered (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 301, "target": 3057, "key": "45571130a30fee10ddc1e6b3323cbb88"}, {"line": 7021, "relation": "increases", "evidence": "As correlated with its increased protein level (Fig. 1D), metformin increased the total BACE1 enzymatic activity by 2-fold (Fig. 2A). BACE1 mRNA was also increased by metformin in a time-dependent manner in both N2a695 and primary cortical neurons (Fig. 2B), as measured by semiquantitative RT-PCR", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}, "UserdefinedCellLine": {"primary cortical neuron": true}}, "source": 301, "target": 3943, "key": "dd1e7e6facf0a14ae730e71f83be37fa"}, {"line": 7030, "relation": "causesNoChange", "evidence": "Taken together, these results indicate that metformin likely augments Abeta production through mechanisms independent of insulin signaling and glucose metabolism", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 301, "target": 580, "key": "6cff1d7693ed86678073ca858777658d"}, {"line": 7037, "relation": "increases", "evidence": "We examined whether metformin's Abeta-increasing effect depended on activation of AMPK, a known molecular target of metformin. Phosphorylation of AMPK at Thr-172 and its substrate, acetyl CoA carboxylase (ACC), were found to be both induced by metformin in a dose-dependent manner (Fig. 4 A and B). We also observed a significant inhibition of metformin-stimulated Abeta production by compound C, a specific AMPK inhibitor, in a dose-dependent manner. Compound C inhibited metformin's effect by 50% when used at a concentration (20 μM) that is known to guarantee its specificity for AMPK (32) (Fig. 4C). These results indicate an AMPK-dependent mechanism for metformin's effect on Abeta. Significantly, the antagonizing effect of compound C was largely attributed to suppression of BACE1 transcription because the mRNA was greatly reduced after treatment with the two drugs (Fig. 4D).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 301, "target": 2144, "key": "d728fa11ad4e922ec9f3f18529bacbab"}, {"line": 7038, "relation": "increases", "evidence": "We examined whether metformin's Abeta-increasing effect depended on activation of AMPK, a known molecular target of metformin. Phosphorylation of AMPK at Thr-172 and its substrate, acetyl CoA carboxylase (ACC), were found to be both induced by metformin in a dose-dependent manner (Fig. 4 A and B). We also observed a significant inhibition of metformin-stimulated Abeta production by compound C, a specific AMPK inhibitor, in a dose-dependent manner. Compound C inhibited metformin's effect by 50% when used at a concentration (20 μM) that is known to guarantee its specificity for AMPK (32) (Fig. 4C). These results indicate an AMPK-dependent mechanism for metformin's effect on Abeta. Significantly, the antagonizing effect of compound C was largely attributed to suppression of BACE1 transcription because the mRNA was greatly reduced after treatment with the two drugs (Fig. 4D).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 301, "target": 2242, "key": "0198a10236cd42925d0cef86e9d00956"}, {"line": 7054, "relation": "regulates", "evidence": "BACE1 transcription has recently been reported to be regulated by the PPARgamma pathway (36). We now demonstrate that the diabetes drug metformin can also modulate BACE1 transcription, likely independently of the PPARgamma pathway despite the presence of several PPAR/RXR binding sites in the promoter (31, 32). Metformin-mediated transcriptional activation of BACE1 appears to depended on a pathway involving AMPK.", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 301, "target": 1755, "key": "48db9587bdfe0a914c5ba557f2c5163c"}, {"line": 22321, "relation": "increases", "evidence": "Compared with the caspase-3 levels in the control cells, metformin treatment increased caspase-3 levels significantly at 50 and 100 mM.", "citation": {"db": "PubMed", "db_id": "24940426"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 301, "target": 2444, "key": "289c2a79fd86d0a24322f8f7eafe4f41"}, {"line": 6902, "relation": "increases", "evidence": "Insulin modulates metabolism of beta-amyloid precursor protein (APP) in neurons, decreasing the intracellular accumulation of beta-amyloid (Abeta) peptides, which are pivotal in AD pathogenesis. The present study investigates whether the widely prescribed insulin-sensitizing drug, metformin (GlucophageR), affects APP metabolism and Abeta generation in various cell models. We demonstrate that metformin, at doses that lead to activation of the AMP-activated protein kinase (AMPK), significantly increases the generation of both intracellular and extracellular Abeta species", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Energy metabolic subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2143, "target": 80, "key": "4178cecdeafee29a9878a53a28b0c9b0"}, {"relation": "hasVariant", "source": 2143, "target": 2144, "key": "a28ea930502c52db09af69d59ef8344a"}, {"line": 7055, "relation": "association", "evidence": "BACE1 transcription has recently been reported to be regulated by the PPARgamma pathway (36). We now demonstrate that the diabetes drug metformin can also modulate BACE1 transcription, likely independently of the PPARgamma pathway despite the presence of several PPAR/RXR binding sites in the promoter (31, 32). Metformin-mediated transcriptional activation of BACE1 appears to depended on a pathway involving AMPK.", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2143, "target": 301, "key": "3c9caf071a40bd55a8f48cfaf73d4d19"}, {"line": 6914, "relation": "isA", "evidence": "Furthermore, the effect of metformin on Abeta generation is mediated by transcriptional up-regulation of beta-secretase (BACE1), which results in an elevated protein level and increased enzymatic activity. Unlike insulin, metformin exerts no effect on Abeta degradation. In addition, we found that glucose deprivation and various tyrphostins, known inhibitors of insulin-like growth factors/insulin receptor tyrosine kinases, do not modulate the effect of metformin on Abeta. Finally, inhibition of AMP-activated protein kinase (AMPK) by the pharmacological inhibitor Compound C largely suppresses metformin's effect on Abeta generation and BACE1 transcription, suggesting an AMPK-dependent mechanism. Although insulin and metformin display opposing effects on Abeta generation, in combined use, metformin enhances insulin's effect in reducing Abeta levels", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 246, "target": 38, "key": "7dbc57f00eeb77c0d01895114c54f69d"}, {"line": 6915, "relation": "decreases", "evidence": "Furthermore, the effect of metformin on Abeta generation is mediated by transcriptional up-regulation of beta-secretase (BACE1), which results in an elevated protein level and increased enzymatic activity. Unlike insulin, metformin exerts no effect on Abeta degradation. In addition, we found that glucose deprivation and various tyrphostins, known inhibitors of insulin-like growth factors/insulin receptor tyrosine kinases, do not modulate the effect of metformin on Abeta. Finally, inhibition of AMP-activated protein kinase (AMPK) by the pharmacological inhibitor Compound C largely suppresses metformin's effect on Abeta generation and BACE1 transcription, suggesting an AMPK-dependent mechanism. Although insulin and metformin display opposing effects on Abeta generation, in combined use, metformin enhances insulin's effect in reducing Abeta levels", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 246, "target": 301, "key": "09f58643c61ac9d300c70ec5003223d7"}, {"line": 7041, "relation": "decreases", "evidence": "We examined whether metformin's Abeta-increasing effect depended on activation of AMPK, a known molecular target of metformin. Phosphorylation of AMPK at Thr-172 and its substrate, acetyl CoA carboxylase (ACC), were found to be both induced by metformin in a dose-dependent manner (Fig. 4 A and B). We also observed a significant inhibition of metformin-stimulated Abeta production by compound C, a specific AMPK inhibitor, in a dose-dependent manner. Compound C inhibited metformin's effect by 50% when used at a concentration (20 μM) that is known to guarantee its specificity for AMPK (32) (Fig. 4C). These results indicate an AMPK-dependent mechanism for metformin's effect on Abeta. Significantly, the antagonizing effect of compound C was largely attributed to suppression of BACE1 transcription because the mRNA was greatly reduced after treatment with the two drugs (Fig. 4D).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 246, "target": 301, "key": "20ba327cfef6090dc5388402b8dc7012"}, {"line": 6916, "relation": "decreases", "evidence": "Furthermore, the effect of metformin on Abeta generation is mediated by transcriptional up-regulation of beta-secretase (BACE1), which results in an elevated protein level and increased enzymatic activity. Unlike insulin, metformin exerts no effect on Abeta degradation. In addition, we found that glucose deprivation and various tyrphostins, known inhibitors of insulin-like growth factors/insulin receptor tyrosine kinases, do not modulate the effect of metformin on Abeta. Finally, inhibition of AMP-activated protein kinase (AMPK) by the pharmacological inhibitor Compound C largely suppresses metformin's effect on Abeta generation and BACE1 transcription, suggesting an AMPK-dependent mechanism. Although insulin and metformin display opposing effects on Abeta generation, in combined use, metformin enhances insulin's effect in reducing Abeta levels", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 246, "target": 80, "key": "971853853c8f8c45091f7e33a50cb3b0"}, {"line": 7043, "relation": "decreases", "evidence": "We examined whether metformin's Abeta-increasing effect depended on activation of AMPK, a known molecular target of metformin. Phosphorylation of AMPK at Thr-172 and its substrate, acetyl CoA carboxylase (ACC), were found to be both induced by metformin in a dose-dependent manner (Fig. 4 A and B). We also observed a significant inhibition of metformin-stimulated Abeta production by compound C, a specific AMPK inhibitor, in a dose-dependent manner. Compound C inhibited metformin's effect by 50% when used at a concentration (20 μM) that is known to guarantee its specificity for AMPK (32) (Fig. 4C). These results indicate an AMPK-dependent mechanism for metformin's effect on Abeta. Significantly, the antagonizing effect of compound C was largely attributed to suppression of BACE1 transcription because the mRNA was greatly reduced after treatment with the two drugs (Fig. 4D).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 246, "target": 80, "key": "d57502a67da14ce8baf53bdd71e9199e"}, {"line": 6917, "relation": "decreases", "evidence": "Furthermore, the effect of metformin on Abeta generation is mediated by transcriptional up-regulation of beta-secretase (BACE1), which results in an elevated protein level and increased enzymatic activity. Unlike insulin, metformin exerts no effect on Abeta degradation. In addition, we found that glucose deprivation and various tyrphostins, known inhibitors of insulin-like growth factors/insulin receptor tyrosine kinases, do not modulate the effect of metformin on Abeta. Finally, inhibition of AMP-activated protein kinase (AMPK) by the pharmacological inhibitor Compound C largely suppresses metformin's effect on Abeta generation and BACE1 transcription, suggesting an AMPK-dependent mechanism. Although insulin and metformin display opposing effects on Abeta generation, in combined use, metformin enhances insulin's effect in reducing Abeta levels", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 246, "target": 2375, "key": "76aae4dce02c0508f13bb7af33718e6c"}, {"line": 7045, "relation": "decreases", "evidence": "We examined whether metformin's Abeta-increasing effect depended on activation of AMPK, a known molecular target of metformin. Phosphorylation of AMPK at Thr-172 and its substrate, acetyl CoA carboxylase (ACC), were found to be both induced by metformin in a dose-dependent manner (Fig. 4 A and B). We also observed a significant inhibition of metformin-stimulated Abeta production by compound C, a specific AMPK inhibitor, in a dose-dependent manner. Compound C inhibited metformin's effect by 50% when used at a concentration (20 μM) that is known to guarantee its specificity for AMPK (32) (Fig. 4C). These results indicate an AMPK-dependent mechanism for metformin's effect on Abeta. Significantly, the antagonizing effect of compound C was largely attributed to suppression of BACE1 transcription because the mRNA was greatly reduced after treatment with the two drugs (Fig. 4D).", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 246, "target": 3943, "key": "1dc662772191874d2f898db3e90658d9"}, {"line": 6919, "relation": "decreases", "evidence": "Furthermore, the effect of metformin on Abeta generation is mediated by transcriptional up-regulation of beta-secretase (BACE1), which results in an elevated protein level and increased enzymatic activity. Unlike insulin, metformin exerts no effect on Abeta degradation. In addition, we found that glucose deprivation and various tyrphostins, known inhibitors of insulin-like growth factors/insulin receptor tyrosine kinases, do not modulate the effect of metformin on Abeta. Finally, inhibition of AMP-activated protein kinase (AMPK) by the pharmacological inhibitor Compound C largely suppresses metformin's effect on Abeta generation and BACE1 transcription, suggesting an AMPK-dependent mechanism. Although insulin and metformin display opposing effects on Abeta generation, in combined use, metformin enhances insulin's effect in reducing Abeta levels", "citation": {"db": "PubMed", "db_id": "19237574"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1664, "target": 2328, "key": "f83e1a0df39c65e1547f3ac01d457b10"}, {"line": 7828, "relation": "association", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 773, "target": 251, "key": "6f044b5bc2cf00d99d984063f70d2f4d"}, {"line": 24570, "relation": "association", "evidence": "Neprilysin has been proposed as a target gene for AICD.", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 3057, "target": 2240, "key": "7242a396aead193697a3fb49ee96a2e7"}, {"line": 24799, "relation": "association", "evidence": "Neprilysin has been proposed as a target gene for AICD.", "citation": {"db": "PubMed", "db_id": "19306298"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"Medium": true}}, "source": 3057, "target": 3563, "key": "e2f75abccacff3ace118f05eef100f68"}, {"line": 30859, "relation": "positiveCorrelation", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 3057, "target": 2893, "key": "b127405597a99baa626dabdb75501be6"}, {"line": 38124, "relation": "decreases", "evidence": "Lower-expression of PS1 and over-expression of IDE or NEP may be helpful in potentially lowering brain Abeta levels in subjects with AD", "citation": {"db": "PubMed", "db_id": "19355846"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3057, "target": 2328, "key": "fdf8d6b68c8eeb806a244c86c8e67578"}, {"line": 45324, "relation": "decreases", "evidence": "neprilysin (NPE) is an enzyme known to break down the beta-amyloid protein and aid in preventing formation of plaques", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 3057, "target": 1746, "key": "ccdebc6a928d738901251256e2809707"}, {"line": 45325, "relation": "decreases", "evidence": "neprilysin (NPE) is an enzyme known to break down the beta-amyloid protein and aid in preventing formation of plaques", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 3057, "target": 3881, "key": "dd9a19c6dd9d1ebb6043767943d87925"}, {"relation": "isA", "source": 3229, "target": 2143, "key": "75ecbc236605545e7dac6830ba741443"}, {"relation": "partOf", "source": 3229, "target": 1614, "key": "72899e38582296804b63257f1fb01830"}, {"line": 23354, "relation": "decreases", "evidence": "Similarly, high glucose levels inhibit AMPK activity and increase ROS generation. This leads to the upregulation of Nox4 and the activation of p53-induced apoptosis in glomerular epithelial cells (podocytes), the loss of which may contribute to albuminuria and diabetic kidney disease. The reactivation of AMPK by AICAR in this context leads to a reduction in Nox4 levels, resulting in a suppression of p53 (Eid et al., 2010).", "citation": {"db": "PubMed", "db_id": "23954639"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3229, "target": 170, "key": "20b22a385324bacd14115287c0d515b1"}, {"relation": "hasVariant", "source": 2241, "target": 2242, "key": "abb1ca474ea16d124b7877b144ba62a2"}, {"line": 7111, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Nervous System": true}, "Confidence": {"Low": true}}, "source": 1813, "target": 388, "key": "95342c48a9f2b6dde0546f7ae302404d"}, {"line": 7112, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Nervous System": true}, "Confidence": {"Low": true}}, "source": 3515, "target": 388, "key": "8807889cb9fda72fe21751b5d04b9ad6"}, {"line": 7121, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 1850, "target": 388, "key": "7f13aca9754eb776ad937f1a959d2726"}, {"line": 7122, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 1910, "target": 388, "key": "e30c1a74824a4cd9811878723cd059fc"}, {"line": 7124, "relation": "positiveCorrelation", "evidence": "Genes such as enolase, tetanus toxin receptor, and the A2B5 antigen initially found in the nervous system were later found to be expressed in beta-cells (3). Another group of genes normally expressed in islet of Langerhans has been shown to be present in the nervous system. Some of these genes are only temporally expressed in the developing brain, such as insulin and the transcription factor IDX-1 (islet and duodenum homeobox-1) [PDX-1 (pancreatic and duodenal homeobox-1)] (4, 5, 6, 7), whereas other genes are permanently expressed, such as the genes for ATP-sensitive K+ channel (8, 9) and the LIM (Lin-11, Isl-1, and Mec-3) homeodomain protein islet-1 (10).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 1852, "target": 388, "key": "1f0015aa28202e269235c59dd5a754d8"}, {"line": 7143, "relation": "increases", "evidence": "To determine whether the p35 and CDK5 proteins detected in pancreatic islets interact with one another and form a functional complex, we immunoprecipitated the complex from human islets and determined its protein kinase activity as previously described (16). Immunoprecipitation with a p35 antibody, followed by kinase activity determination in the immunoprecipitate using histone H1 as a substrate, demonstrate that p35 and CDK5 form a functional complex capable of phosphorylating histone H1 (Fig. 1D). No kinase activity was detected in the absence of antibody or in the presence of an unrelated control antibody (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Islets of Langerhans": true, "Pancreas": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1340, "target": 2148, "key": "df2c6cca903eb036a2b5edaf7960125e"}, {"line": 7144, "relation": "association", "evidence": "To determine whether the p35 and CDK5 proteins detected in pancreatic islets interact with one another and form a functional complex, we immunoprecipitated the complex from human islets and determined its protein kinase activity as previously described (16). Immunoprecipitation with a p35 antibody, followed by kinase activity determination in the immunoprecipitate using histone H1 as a substrate, demonstrate that p35 and CDK5 form a functional complex capable of phosphorylating histone H1 (Fig. 1D). No kinase activity was detected in the absence of antibody or in the presence of an unrelated control antibody (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Islets of Langerhans": true, "Pancreas": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1340, "target": 472, "key": "e78ceb3fa9740905f8c90b64e5ebf207"}, {"line": 7158, "relation": "positiveCorrelation", "evidence": "Additional experiments were designed to investigate the presence of p35, CDK5, and p35/CDK5 kinase activity in beta-cell lines. Biochemical characterization of CDK5 kinase activity was also investigated. Among the different cell lines tested, only INS-1 cells, an insulin-producing beta-cell line, showed expression of p35 (Fig. 2A). These cells also contain protein kinase activity that can be immunoprecipitated with a p35-specific antibody (Fig. 2B). Both p35 expression and p35/CDK5 activity were absent in other cell lines, such as HeLa and NIH-3T3 (Fig. 2, A and B), although these cell lines expressed CDK5 protein (Fig. 2A). We then investigated whether the kinase activity immunoprecipitated by the p35 antibody was due to its association with CDK5. We performed experiments with roscovitine, a relatively specific inhibitor of CDK5 activity (17). The p35/CDK5 activity in INS-1 cells was inhibited by roscovitine, but not by other protein kinase inhibitors, such as H89 (protein kinase A) and SB202190 (p38MAPK; Fig. 2C). Also, the inhibition by roscovitine was dose dependent (Fig. 2D), with a 50% inhibitory concentration of 128 nm, within the range of that previously reported for CDK5 (18).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"INS-1 cells": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 388, "key": "6a666f92cd4892802867bfe4c97d8ad1"}, {"line": 7184, "relation": "increases", "evidence": "Signaling through p35/CDK5 results in transcriptional activation of the insulin gene", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "UserdefinedCellLine": {"INS-1 cells": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 3985, "key": "fa34ba386f742f45ebb56cac654b53a5"}, {"line": 7195, "relation": "regulates", "evidence": "Initially, the expression of p35 was believed to be restricted to the central nervous system, the lens (22), and recently in developing muscle, where it forms a p35/CDK5 active complex that regulates the expression of the acetylcholine receptor gene (23). Despite its name CDK5 does not affect the cell division cycle; it is expressed postmitotically, and its function is related to cytoskeletal dynamics, cell migration, cell differentiation, and exocytosis (14) instead of cellular proliferation. Recently, Pho-85, a yeast ortholog of CDK5, was shown to be involved in metabolic control by regulating different steps of glycogen and phosphate metabolism (13). Expression of CDK5 in insulin-producing cells is not surprising because widespread expression of this kinase has been described. CDK5 expression in beta-cells has also been reported (24).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHAnatomy": {"Muscles": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1340, "target": 2244, "key": "df75b1e9d3456a6f86bfeebf21b0b99e"}, {"line": 7247, "relation": "regulates", "evidence": "In neurons, p35/CDK5 regulates not only cytoskeletal dynamics and cell migration, but also the cAMP-dependent protein kinase A and the calcium-mediated signaling pathway (14). It also regulates dopamine signaling through phosphorylation of DARPP-32, which converts it into an inhibitor of protein kinase A (25). Therefore, we anticipate that in beta-cells the presence of p35/CDK5 could be involved in the regulation of additional functional pathways. Our findings also indicate that in insulin-producing beta-cells, CDK5 activity is predominantly regulated by variations in the expression of its activator p35, which, in turn, is regulated by extracellular glucose concentrations. During brain development, the expression of p35 seems to be constitutive, and regulation of CDK5 activity is dependent on its subcellular localization, phosphorylation by other protein kinases such as Abl, and association with other regulatory proteins (Dab1 and Cables) (14). However, up-regulation of p35 at the level of transcription can also occur, as shown after stimulation by laminin in cultured neurons (26), nerve growth factor in PC12 cells (27), neuroregulin in cultured myotubules (23), and chronic cocaine administration to rats (28).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 509, "key": "1ceca85a86573f30471ba08ee597c921"}, {"line": 7248, "relation": "increases", "evidence": "In neurons, p35/CDK5 regulates not only cytoskeletal dynamics and cell migration, but also the cAMP-dependent protein kinase A and the calcium-mediated signaling pathway (14). It also regulates dopamine signaling through phosphorylation of DARPP-32, which converts it into an inhibitor of protein kinase A (25). Therefore, we anticipate that in beta-cells the presence of p35/CDK5 could be involved in the regulation of additional functional pathways. Our findings also indicate that in insulin-producing beta-cells, CDK5 activity is predominantly regulated by variations in the expression of its activator p35, which, in turn, is regulated by extracellular glucose concentrations. During brain development, the expression of p35 seems to be constitutive, and regulation of CDK5 activity is dependent on its subcellular localization, phosphorylation by other protein kinases such as Abl, and association with other regulatory proteins (Dab1 and Cables) (14). However, up-regulation of p35 at the level of transcription can also occur, as shown after stimulation by laminin in cultured neurons (26), nerve growth factor in PC12 cells (27), neuroregulin in cultured myotubules (23), and chronic cocaine administration to rats (28).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 714, "key": "e9d42e19f6544074194d723276719a39"}, {"line": 7249, "relation": "regulates", "evidence": "In neurons, p35/CDK5 regulates not only cytoskeletal dynamics and cell migration, but also the cAMP-dependent protein kinase A and the calcium-mediated signaling pathway (14). It also regulates dopamine signaling through phosphorylation of DARPP-32, which converts it into an inhibitor of protein kinase A (25). Therefore, we anticipate that in beta-cells the presence of p35/CDK5 could be involved in the regulation of additional functional pathways. Our findings also indicate that in insulin-producing beta-cells, CDK5 activity is predominantly regulated by variations in the expression of its activator p35, which, in turn, is regulated by extracellular glucose concentrations. During brain development, the expression of p35 seems to be constitutive, and regulation of CDK5 activity is dependent on its subcellular localization, phosphorylation by other protein kinases such as Abl, and association with other regulatory proteins (Dab1 and Cables) (14). However, up-regulation of p35 at the level of transcription can also occur, as shown after stimulation by laminin in cultured neurons (26), nerve growth factor in PC12 cells (27), neuroregulin in cultured myotubules (23), and chronic cocaine administration to rats (28).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 495, "key": "0992a8c9d074c87eb21d1820742e9f67"}, {"line": 7257, "relation": "increases", "evidence": "In neurons, p35/CDK5 regulates not only cytoskeletal dynamics and cell migration, but also the cAMP-dependent protein kinase A and the calcium-mediated signaling pathway (14). It also regulates dopamine signaling through phosphorylation of DARPP-32, which converts it into an inhibitor of protein kinase A (25). Therefore, we anticipate that in beta-cells the presence of p35/CDK5 could be involved in the regulation of additional functional pathways. Our findings also indicate that in insulin-producing beta-cells, CDK5 activity is predominantly regulated by variations in the expression of its activator p35, which, in turn, is regulated by extracellular glucose concentrations. During brain development, the expression of p35 seems to be constitutive, and regulation of CDK5 activity is dependent on its subcellular localization, phosphorylation by other protein kinases such as Abl, and association with other regulatory proteins (Dab1 and Cables) (14). However, up-regulation of p35 at the level of transcription can also occur, as shown after stimulation by laminin in cultured neurons (26), nerve growth factor in PC12 cells (27), neuroregulin in cultured myotubules (23), and chronic cocaine administration to rats (28).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Dopaminergic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 3219, "key": "3c8965950d9a165c134df9a2c6d08cb6"}, {"line": 7269, "relation": "increases", "evidence": "Overactivation and mislocalization of p35/CDK5 could translate the deleterious effect of a combination of different pathological signals, such as amylin deposition, high levels of plasma lipoproteins, and high glucose levels, as dysregulation of p35/CDK5 in the central nervous system has been associated with the pathological abnormalities found in Alzheimer's disease (2, 29) and amyotrophic lateral sclerosis (30). It is possible that glucose-induced dysregulation of the p35/CDK5 pathway is a pathophysiological mechanism involved in the beta-cell dysfunction and the predisposition to apoptotic cell death associated with the progression of type 2 diabetes", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 1340, "target": 2862, "key": "65f54b560599a794e5faab248e93eb64"}, {"line": 7271, "relation": "increases", "evidence": "Overactivation and mislocalization of p35/CDK5 could translate the deleterious effect of a combination of different pathological signals, such as amylin deposition, high levels of plasma lipoproteins, and high glucose levels, as dysregulation of p35/CDK5 in the central nervous system has been associated with the pathological abnormalities found in Alzheimer's disease (2, 29) and amyotrophic lateral sclerosis (30). It is possible that glucose-induced dysregulation of the p35/CDK5 pathway is a pathophysiological mechanism involved in the beta-cell dysfunction and the predisposition to apoptotic cell death associated with the progression of type 2 diabetes", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 2862, "key": "3e245e872222cacb6804b51181f4c383"}, {"line": 7272, "relation": "increases", "evidence": "Overactivation and mislocalization of p35/CDK5 could translate the deleterious effect of a combination of different pathological signals, such as amylin deposition, high levels of plasma lipoproteins, and high glucose levels, as dysregulation of p35/CDK5 in the central nervous system has been associated with the pathological abnormalities found in Alzheimer's disease (2, 29) and amyotrophic lateral sclerosis (30). It is possible that glucose-induced dysregulation of the p35/CDK5 pathway is a pathophysiological mechanism involved in the beta-cell dysfunction and the predisposition to apoptotic cell death associated with the progression of type 2 diabetes", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 1340, "target": 891, "key": "dec628bc299216ca0f9ecda2f562f5e0"}, {"line": 7273, "relation": "increases", "evidence": "Overactivation and mislocalization of p35/CDK5 could translate the deleterious effect of a combination of different pathological signals, such as amylin deposition, high levels of plasma lipoproteins, and high glucose levels, as dysregulation of p35/CDK5 in the central nervous system has been associated with the pathological abnormalities found in Alzheimer's disease (2, 29) and amyotrophic lateral sclerosis (30). It is possible that glucose-induced dysregulation of the p35/CDK5 pathway is a pathophysiological mechanism involved in the beta-cell dysfunction and the predisposition to apoptotic cell death associated with the progression of type 2 diabetes", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 891, "key": "d3c0b6a2cd7015ff6426f9820eb493e0"}, {"line": 7277, "relation": "increases", "evidence": "Overactivation and mislocalization of p35/CDK5 could translate the deleterious effect of a combination of different pathological signals, such as amylin deposition, high levels of plasma lipoproteins, and high glucose levels, as dysregulation of p35/CDK5 in the central nervous system has been associated with the pathological abnormalities found in Alzheimer's disease (2, 29) and amyotrophic lateral sclerosis (30). It is possible that glucose-induced dysregulation of the p35/CDK5 pathway is a pathophysiological mechanism involved in the beta-cell dysfunction and the predisposition to apoptotic cell death associated with the progression of type 2 diabetes", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Low": true}, "MeSHAnatomy": {"Blood": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 264, "key": "0dfb6eedaf363732d9128d78c4e84811"}, {"line": 7284, "relation": "negativeCorrelation", "evidence": "Overactivation and mislocalization of p35/CDK5 could translate the deleterious effect of a combination of different pathological signals, such as amylin deposition, high levels of plasma lipoproteins, and high glucose levels, as dysregulation of p35/CDK5 in the central nervous system has been associated with the pathological abnormalities found in Alzheimer's disease (2, 29) and amyotrophic lateral sclerosis (30). It is possible that glucose-induced dysregulation of the p35/CDK5 pathway is a pathophysiological mechanism involved in the beta-cell dysfunction and the predisposition to apoptotic cell death associated with the progression of type 2 diabetes", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1340, "target": 3823, "key": "ec25e8fdf545b2ddbd39b5b4103da817"}, {"line": 19320, "relation": "association", "evidence": "In addition, cdk5/p25 might also interact with other pathways such as glycogen synthetase kinase 3beta (GSK3beta) and c-JUN kinase.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1340, "target": 2794, "key": "bc8e5cf7a83337b6a3be64adea5502fa"}, {"line": 19321, "relation": "association", "evidence": "In addition, cdk5/p25 might also interact with other pathways such as glycogen synthetase kinase 3beta (GSK3beta) and c-JUN kinase.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1340, "target": 2936, "key": "c9e1289ddede7ee0aa94b62e7935b574"}, {"relation": "hasVariant", "source": 2147, "target": 2148, "key": "582778e56cb21b313aa5c0cc07febb36"}, {"line": 7144, "relation": "association", "evidence": "To determine whether the p35 and CDK5 proteins detected in pancreatic islets interact with one another and form a functional complex, we immunoprecipitated the complex from human islets and determined its protein kinase activity as previously described (16). Immunoprecipitation with a p35 antibody, followed by kinase activity determination in the immunoprecipitate using histone H1 as a substrate, demonstrate that p35 and CDK5 form a functional complex capable of phosphorylating histone H1 (Fig. 1D). No kinase activity was detected in the absence of antibody or in the presence of an unrelated control antibody (Fig. 1D).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Islets of Langerhans": true, "Pancreas": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 472, "target": 1340, "key": "911d8f1b8f9da97565ed606b15d857e5"}, {"line": 7162, "relation": "directlyDecreases", "evidence": "Additional experiments were designed to investigate the presence of p35, CDK5, and p35/CDK5 kinase activity in beta-cell lines. Biochemical characterization of CDK5 kinase activity was also investigated. Among the different cell lines tested, only INS-1 cells, an insulin-producing beta-cell line, showed expression of p35 (Fig. 2A). These cells also contain protein kinase activity that can be immunoprecipitated with a p35-specific antibody (Fig. 2B). Both p35 expression and p35/CDK5 activity were absent in other cell lines, such as HeLa and NIH-3T3 (Fig. 2, A and B), although these cell lines expressed CDK5 protein (Fig. 2A). We then investigated whether the kinase activity immunoprecipitated by the p35 antibody was due to its association with CDK5. We performed experiments with roscovitine, a relatively specific inhibitor of CDK5 activity (17). The p35/CDK5 activity in INS-1 cells was inhibited by roscovitine, but not by other protein kinase inhibitors, such as H89 (protein kinase A) and SB202190 (p38MAPK; Fig. 2C). Also, the inhibition by roscovitine was dose dependent (Fig. 2D), with a 50% inhibitory concentration of 128 nm, within the range of that previously reported for CDK5 (18).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"INS-1 cells": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 349, "target": 2487, "key": "3f3976263748fe672cccdf9bb0c66701"}, {"line": 7166, "relation": "decreases", "evidence": "Additional experiments were designed to investigate the presence of p35, CDK5, and p35/CDK5 kinase activity in beta-cell lines. Biochemical characterization of CDK5 kinase activity was also investigated. Among the different cell lines tested, only INS-1 cells, an insulin-producing beta-cell line, showed expression of p35 (Fig. 2A). These cells also contain protein kinase activity that can be immunoprecipitated with a p35-specific antibody (Fig. 2B). Both p35 expression and p35/CDK5 activity were absent in other cell lines, such as HeLa and NIH-3T3 (Fig. 2, A and B), although these cell lines expressed CDK5 protein (Fig. 2A). We then investigated whether the kinase activity immunoprecipitated by the p35 antibody was due to its association with CDK5. We performed experiments with roscovitine, a relatively specific inhibitor of CDK5 activity (17). The p35/CDK5 activity in INS-1 cells was inhibited by roscovitine, but not by other protein kinase inhibitors, such as H89 (protein kinase A) and SB202190 (p38MAPK; Fig. 2C). Also, the inhibition by roscovitine was dose dependent (Fig. 2D), with a 50% inhibitory concentration of 128 nm, within the range of that previously reported for CDK5 (18).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"INS-1 cells": true}, "Confidence": {"Medium": true}}, "source": 349, "target": 1340, "key": "f65d3b862d888551531a0fdabc993f90"}, {"line": 19330, "relation": "isA", "evidence": "Drugs like roscovitine, flavopiridol, calpain inhibitors, kenpaullone and induribins, which inhibit cdk5/p25 formation, constitute potential drugs for the treatment of neurological disorders.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"Medium": true}}, "source": 349, "target": 36, "key": "710516bb1137b6878dfb57a2bf3476b3"}, {"line": 19332, "relation": "directlyDecreases", "evidence": "Drugs like roscovitine, flavopiridol, calpain inhibitors, kenpaullone and induribins, which inhibit cdk5/p25 formation, constitute potential drugs for the treatment of neurological disorders.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 349, "target": 2142, "key": "09b21c51900aa6dfef800e6f52be1c04"}, {"line": 19334, "relation": "decreases", "evidence": "Drugs like roscovitine, flavopiridol, calpain inhibitors, kenpaullone and induribins, which inhibit cdk5/p25 formation, constitute potential drugs for the treatment of neurological disorders.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"Medium": true}}, "source": 349, "target": 1014, "key": "099a0da105dfe4e6a7df00e8796ded3c"}, {"line": 7174, "relation": "increases", "evidence": "The concentration of glucose in the culture medium regulates p35 expression and p35/CDK5 kinase activity in INS-1 cells", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"INS-1 cells": true}}, "object": {"modifier": "Activity"}, "source": 264, "target": 1776, "key": "5d67f17220af90592b1df14853efb712"}, {"line": 7175, "relation": "increases", "evidence": "The concentration of glucose in the culture medium regulates p35 expression and p35/CDK5 kinase activity in INS-1 cells", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "Insulin signal transduction": true}, "UserdefinedCellLine": {"INS-1 cells": true}}, "source": 264, "target": 1340, "key": "d58b6883b35786a63ddca884a3b53e45"}, {"line": 7215, "relation": "regulates", "evidence": "It is known that the expression of genes essential for the function of beta-cells, such as insulin, PDX-1 (pancreatic and duodenal homeobox-1), amylin, and glucose transporter 2, are closely regulated by extracellular concentrations of glucose. This circumstance suggests that the p35/CDK5 protein kinase pathway may play an important role in beta-cell function. In fact, our initial functional studies indicate that p35/CDK5 is involved in the expression of the insulin gene", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 264, "target": 1850, "key": "8140a26fd5fc0ec6991118e5607d29a6"}, {"line": 7216, "relation": "regulates", "evidence": "It is known that the expression of genes essential for the function of beta-cells, such as insulin, PDX-1 (pancreatic and duodenal homeobox-1), amylin, and glucose transporter 2, are closely regulated by extracellular concentrations of glucose. This circumstance suggests that the p35/CDK5 protein kinase pathway may play an important role in beta-cell function. In fact, our initial functional studies indicate that p35/CDK5 is involved in the expression of the insulin gene", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 264, "target": 1910, "key": "810e584779bce4f4f76245ac63602eba"}, {"line": 7218, "relation": "regulates", "evidence": "It is known that the expression of genes essential for the function of beta-cells, such as insulin, PDX-1 (pancreatic and duodenal homeobox-1), amylin, and glucose transporter 2, are closely regulated by extracellular concentrations of glucose. This circumstance suggests that the p35/CDK5 protein kinase pathway may play an important role in beta-cell function. In fact, our initial functional studies indicate that p35/CDK5 is involved in the expression of the insulin gene", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amylin subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 264, "target": 1844, "key": "5b3b5582419e5dff02c00b44bef6719e"}, {"line": 7220, "relation": "regulates", "evidence": "It is known that the expression of genes essential for the function of beta-cells, such as insulin, PDX-1 (pancreatic and duodenal homeobox-1), amylin, and glucose transporter 2, are closely regulated by extracellular concentrations of glucose. This circumstance suggests that the p35/CDK5 protein kinase pathway may play an important role in beta-cell function. In fact, our initial functional studies indicate that p35/CDK5 is involved in the expression of the insulin gene", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amylin subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 264, "target": 1966, "key": "3984bdcff77db87d739124597e2169db"}, {"line": 10023, "relation": "association", "evidence": "Possible pathophysiologic mechanisms common to both T2DM and AD are glucose toxicity and a direct effect of insulin on amyloid metabolism.", "citation": {"db": "PubMed", "db_id": "21352095"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 264, "target": 476, "key": "fafd279e113216e6888e986ee1e5d92f"}, {"line": 15665, "relation": "association", "evidence": "High-fat load and glucose alone produced an increase of nitrotyrosine, ICAM-1, VCAM-1, and E-selectin plasma levels in normal and diabetic subjects.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 264, "target": 3516, "key": "2ef509c10fc8a9507702102ef8a64494"}, {"line": 15666, "relation": "association", "evidence": "High-fat load and glucose alone produced an increase of nitrotyrosine, ICAM-1, VCAM-1, and E-selectin plasma levels in normal and diabetic subjects.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 264, "target": 2863, "key": "24311444ab6614246634b09e99e662f3"}, {"line": 23350, "relation": "decreases", "evidence": "Similarly, high glucose levels inhibit AMPK activity and increase ROS generation. This leads to the upregulation of Nox4 and the activation of p53-induced apoptosis in glomerular epithelial cells (podocytes), the loss of which may contribute to albuminuria and diabetic kidney disease. The reactivation of AMPK by AICAR in this context leads to a reduction in Nox4 levels, resulting in a suppression of p53 (Eid et al., 2010).", "citation": {"db": "PubMed", "db_id": "23954639"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 264, "target": 3229, "key": "94d58124d5178376dbdb08a91b234d54"}, {"line": 40946, "relation": "association", "evidence": "We showed that adding luteolin in high-fat diet can significantly reduce body weight gain, food intake and plasma cytokines as well as improving glucose metabolism of mice on HFD.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Species": {"10090": true}}, "source": 264, "target": 297, "key": "109f7797e1ec9b75618d843f6f0f78ca"}, {"line": 7762, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3985, "target": 3823, "key": "d0b3207cae317541e46d69bc928c43b8"}, {"line": 7205, "relation": "association", "evidence": "Initially, the expression of p35 was believed to be restricted to the central nervous system, the lens (22), and recently in developing muscle, where it forms a p35/CDK5 active complex that regulates the expression of the acetylcholine receptor gene (23). Despite its name CDK5 does not affect the cell division cycle; it is expressed postmitotically, and its function is related to cytoskeletal dynamics, cell migration, cell differentiation, and exocytosis (14) instead of cellular proliferation. Recently, Pho-85, a yeast ortholog of CDK5, was shown to be involved in metabolic control by regulating different steps of glycogen and phosphate metabolism (13). Expression of CDK5 in insulin-producing cells is not surprising because widespread expression of this kinase has been described. CDK5 expression in beta-cells has also been reported (24).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 509, "target": 2487, "key": "9f4115d6d59bd59ec46779a1ae464218"}, {"line": 16578, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 509, "target": 3409, "key": "d31641719d4b72cc472ef538823f0e60"}, {"line": 16620, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 509, "target": 3874, "key": "5f675e2a35005472caa8751b86de4366"}, {"line": 7207, "relation": "association", "evidence": "Initially, the expression of p35 was believed to be restricted to the central nervous system, the lens (22), and recently in developing muscle, where it forms a p35/CDK5 active complex that regulates the expression of the acetylcholine receptor gene (23). Despite its name CDK5 does not affect the cell division cycle; it is expressed postmitotically, and its function is related to cytoskeletal dynamics, cell migration, cell differentiation, and exocytosis (14) instead of cellular proliferation. Recently, Pho-85, a yeast ortholog of CDK5, was shown to be involved in metabolic control by regulating different steps of glycogen and phosphate metabolism (13). Expression of CDK5 in insulin-producing cells is not surprising because widespread expression of this kinase has been described. CDK5 expression in beta-cells has also been reported (24).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 814, "target": 2487, "key": "2e5dd9bb3a43c438d29479c6726da63b"}, {"line": 7260, "relation": "association", "evidence": "In neurons, p35/CDK5 regulates not only cytoskeletal dynamics and cell migration, but also the cAMP-dependent protein kinase A and the calcium-mediated signaling pathway (14). It also regulates dopamine signaling through phosphorylation of DARPP-32, which converts it into an inhibitor of protein kinase A (25). Therefore, we anticipate that in beta-cells the presence of p35/CDK5 could be involved in the regulation of additional functional pathways. Our findings also indicate that in insulin-producing beta-cells, CDK5 activity is predominantly regulated by variations in the expression of its activator p35, which, in turn, is regulated by extracellular glucose concentrations. During brain development, the expression of p35 seems to be constitutive, and regulation of CDK5 activity is dependent on its subcellular localization, phosphorylation by other protein kinases such as Abl, and association with other regulatory proteins (Dab1 and Cables) (14). However, up-regulation of p35 at the level of transcription can also occur, as shown after stimulation by laminin in cultured neurons (26), nerve growth factor in PC12 cells (27), neuroregulin in cultured myotubules (23), and chronic cocaine administration to rats (28).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Dopaminergic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 714, "target": 3219, "key": "36e1f655be0c4a82eb170b503b5a7e81"}, {"line": 11501, "relation": "decreases", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Leptin subgraph": true, "Tau protein subgraph": true}}, "source": 714, "target": 3015, "key": "4cde1265da306927a23e017cc41c5ccd"}, {"line": 11502, "relation": "decreases", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Leptin subgraph": true, "Tau protein subgraph": true}}, "source": 714, "target": 2328, "key": "411cd96a40b0f23b70557508a15bf0b7"}, {"line": 11503, "relation": "decreases", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Leptin subgraph": true, "Tau protein subgraph": true}}, "source": 714, "target": 2327, "key": "3b72cfee014807ec04dcca6e7c3a773d"}, {"line": 11508, "relation": "association", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Leptin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 714, "target": 2961, "key": "8e482a4544c26fe37dc6def98cc7669d"}, {"line": 7258, "relation": "regulates", "evidence": "In neurons, p35/CDK5 regulates not only cytoskeletal dynamics and cell migration, but also the cAMP-dependent protein kinase A and the calcium-mediated signaling pathway (14). It also regulates dopamine signaling through phosphorylation of DARPP-32, which converts it into an inhibitor of protein kinase A (25). Therefore, we anticipate that in beta-cells the presence of p35/CDK5 could be involved in the regulation of additional functional pathways. Our findings also indicate that in insulin-producing beta-cells, CDK5 activity is predominantly regulated by variations in the expression of its activator p35, which, in turn, is regulated by extracellular glucose concentrations. During brain development, the expression of p35 seems to be constitutive, and regulation of CDK5 activity is dependent on its subcellular localization, phosphorylation by other protein kinases such as Abl, and association with other regulatory proteins (Dab1 and Cables) (14). However, up-regulation of p35 at the level of transcription can also occur, as shown after stimulation by laminin in cultured neurons (26), nerve growth factor in PC12 cells (27), neuroregulin in cultured myotubules (23), and chronic cocaine administration to rats (28).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Dopaminergic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 3219, "target": 544, "key": "b36fd47533b0264bb6526cb9bcb72c62"}, {"line": 7260, "relation": "association", "evidence": "In neurons, p35/CDK5 regulates not only cytoskeletal dynamics and cell migration, but also the cAMP-dependent protein kinase A and the calcium-mediated signaling pathway (14). It also regulates dopamine signaling through phosphorylation of DARPP-32, which converts it into an inhibitor of protein kinase A (25). Therefore, we anticipate that in beta-cells the presence of p35/CDK5 could be involved in the regulation of additional functional pathways. Our findings also indicate that in insulin-producing beta-cells, CDK5 activity is predominantly regulated by variations in the expression of its activator p35, which, in turn, is regulated by extracellular glucose concentrations. During brain development, the expression of p35 seems to be constitutive, and regulation of CDK5 activity is dependent on its subcellular localization, phosphorylation by other protein kinases such as Abl, and association with other regulatory proteins (Dab1 and Cables) (14). However, up-regulation of p35 at the level of transcription can also occur, as shown after stimulation by laminin in cultured neurons (26), nerve growth factor in PC12 cells (27), neuroregulin in cultured myotubules (23), and chronic cocaine administration to rats (28).", "citation": {"db": "PubMed", "db_id": "14976144"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Dopaminergic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 3219, "target": 714, "key": "ebd6cac457eb5ad4ec5ee5e8d980abb1"}, {"relation": "hasVariant", "source": 3218, "target": 3219, "key": "21bda2c47fa56dfe5224f0f9941e3c83"}, {"relation": "partOf", "source": 2862, "target": 928, "key": "1656b59b09890730c3d3d339f35bc22e"}, {"line": 10493, "relation": "increases", "evidence": "Alzheimer's beta-amyloid, human islet amylin, and prion protein fragment evoke intracellular free calcium elevations by a common mechanism in a hypothalamic GnRH neuronal cell line.", "citation": {"db": "PubMed", "db_id": "10799482"}, "annotations": {"MeSHAnatomy": {"Hypothalamus": true}, "Subgraph": {"Calcium-dependent signal transduction": true}}, "source": 2862, "target": 94, "key": "fea8e55146a05e7d7d29f755a6723012"}, {"relation": "partOf", "source": 2862, "target": 1459, "key": "cef553f8a2f856f1e58fe08e3808345f"}, {"relation": "partOf", "source": 2862, "target": 1460, "key": "d65ce41cce6b0bf180892425c996bbf3"}, {"line": 10606, "relation": "increases", "evidence": "Amylin receptor antagonist AC253 blocks increases in intracellular Ca(2+), activation of protein kinase A, MAPK, Akt, cFos, and cell death, which occur upon AMY3 activation with hAmylin, Abeta1-42, or their co-application.", "citation": {"db": "PubMed", "db_id": "22500019"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Amylin subgraph": true}, "Confidence": {"Medium": true}}, "source": 2862, "target": 505, "key": "407553d008bd9e715442fc0685206d88"}, {"relation": "partOf", "source": 2862, "target": 1461, "key": "8aa00f8fc43085e61900bee8d633e9cf"}, {"line": 7296, "relation": "decreases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2742, "target": 3874, "key": "ef6616245445ccb3c5fc1f3fda16a137"}, {"line": 7299, "relation": "increases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}, "Disease": {"type 2 diabetes mellitus": true}}, "source": 2742, "target": 581, "key": "d324398708a0ec720181e87ebd9947cc"}, {"line": 7302, "relation": "decreases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}, "Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Blood": true}}, "source": 2742, "target": 264, "key": "610f49cf6b76ed16d4288ed95d16b721"}, {"line": 7320, "relation": "decreases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2742, "target": 139, "key": "23f7b388f138830098549de83a676a2d"}, {"line": 7322, "relation": "decreases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2742, "target": 3116, "key": "d53b1fe235356fcd0e64814d171e0472"}, {"line": 7336, "relation": "decreases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Neurons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2742, "target": 478, "key": "b61629675062ce929d8ea14652b67858"}, {"line": 9995, "relation": "decreases", "evidence": "GLP-1 possesses neurotropic properties and can reduce amyloid protein levels in the brain.", "citation": {"db": "PubMed", "db_id": "17592527"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}}, "subject": {"modifier": "Activity"}, "source": 2742, "target": 2315, "key": "bc04610117ba3b19ee074ba4fed0b1d4"}, {"line": 10715, "relation": "regulates", "evidence": "Furthermore, GLP-1 peptides are not only effective in modulating insulin-release and achieving glycaemic control in type 2 diabetes, but are also effective in modulating synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "20035739"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2742, "target": 582, "key": "ea2560e18cb2501377a0028638f3f727"}, {"line": 10719, "relation": "association", "evidence": "Furthermore, GLP-1 peptides are not only effective in modulating insulin-release and achieving glycaemic control in type 2 diabetes, but are also effective in modulating synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "20035739"}, "annotations": {"Disease": {"type 2 diabetes mellitus": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2742, "target": 761, "key": "498e3cc887c67acf9ba54577c1f11688"}, {"line": 10950, "relation": "association", "evidence": "The neuroprotective effects of novel drugs developed to treat T2DM, glucagon-like peptide 1 (GLP-1) and its long-lasting analogs, have a possible link to GSK3 modification.", "citation": {"db": "PubMed", "db_id": "22718609"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Glucagon subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2742, "target": 2178, "key": "fdfcf3baa520aa0326effd260f2ea5ae"}, {"line": 11019, "relation": "negativeCorrelation", "evidence": "Pharmacological agents, such as dipeptidyl peptidase-4 (DPP-4) inhibitors, which increase the level of glucagon-like peptide-1 (GLP-1) and ameliorate T2D, have become valuable candidates as disease modifying agents in the treatment of AD. In addition, endogenous GLP-1 levels decrease amyloid beta (Abeta) peptide and tau phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}}, "source": 2742, "target": 2640, "key": "b64e8448bf0b46be3d6dbec82cb15ea2"}, {"line": 11020, "relation": "decreases", "evidence": "Pharmacological agents, such as dipeptidyl peptidase-4 (DPP-4) inhibitors, which increase the level of glucagon-like peptide-1 (GLP-1) and ameliorate T2D, have become valuable candidates as disease modifying agents in the treatment of AD. In addition, endogenous GLP-1 levels decrease amyloid beta (Abeta) peptide and tau phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}}, "source": 2742, "target": 3850, "key": "2c5a0d942b70e20f4d03a8fbea37141a"}, {"line": 11025, "relation": "decreases", "evidence": "Pharmacological agents, such as dipeptidyl peptidase-4 (DPP-4) inhibitors, which increase the level of glucagon-like peptide-1 (GLP-1) and ameliorate T2D, have become valuable candidates as disease modifying agents in the treatment of AD. In addition, endogenous GLP-1 levels decrease amyloid beta (Abeta) peptide and tau phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "Glucagon subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2742, "target": 3015, "key": "0ec80c3fae7bc2479b8ae6a08519aa09"}, {"line": 11026, "relation": "decreases", "evidence": "Pharmacological agents, such as dipeptidyl peptidase-4 (DPP-4) inhibitors, which increase the level of glucagon-like peptide-1 (GLP-1) and ameliorate T2D, have become valuable candidates as disease modifying agents in the treatment of AD. In addition, endogenous GLP-1 levels decrease amyloid beta (Abeta) peptide and tau phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "Glucagon subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2742, "target": 80, "key": "0e1ad733907512c3371a0637d12c236a"}, {"relation": "partOf", "source": 2742, "target": 1432, "key": "3978f08f7d96f2c4a81fabfc439d70b1"}, {"line": 7306, "relation": "isA", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 113, "target": 121, "key": "bf7b9f9284d8c32849e444a449525619"}, {"line": 7316, "relation": "decreases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 113, "target": 3850, "key": "c1b4db4e7f563e295002d1d814a96e91"}, {"line": 10366, "relation": "decreases", "evidence": "Liraglutide (Victoza) and exenatide (Byetta) are novel long-lasting analogues of the GLP-1 incretin hormone and are currently available to treat diabetes.", "citation": {"db": "PubMed", "db_id": "22443187"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 113, "target": 3850, "key": "462d3f8b6ba7b120e070d5f0b001d492"}, {"line": 10378, "relation": "increases", "evidence": "They facilitate insulin signalling via the GLP-1 receptor (GLP-1R).", "citation": {"db": "PubMed", "db_id": "22443187"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"Medium": true}}, "source": 113, "target": 580, "key": "cb7a5ccb260bbf11cc8d9b28875ff32d"}, {"line": 7315, "relation": "directlyIncreases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 121, "target": 2752, "key": "9155974fff80b7f25cf1277ef512ecd5"}, {"relation": "partOf", "source": 2752, "target": 1432, "key": "6725296ade9ddb113c227777c6cba102"}, {"line": 7321, "relation": "increases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "source": 139, "target": 3876, "key": "0a9ccfd1a58257d0b8dacfe3db778661"}, {"line": 23465, "relation": "increases", "evidence": "In the present study, we confirmed that riluzole at ≤30 μM protects against excitotoxic neuronal injury induced by NMDA or kainate in mouse cortical cultures. The protective concentration range of riluzole is comparable with those in the previous reports (Malgouris et al., 1994 ; Estevez et al., 1995 ; Mary et al., 1995). However, unlike other potent direct glutamate receptor antagonist such as MK-801 and CNQX, riluzole protected against excitotoxic death only at modest levels of injury.", "citation": {"db": "PubMed", "db_id": "9930745"}, "subject": {"modifier": "Activity"}, "source": 139, "target": 648, "key": "8ce2b763f30cff86ae2d1697e51bcd68"}, {"line": 47441, "relation": "increases", "evidence": "Kainic Acid results in increased expression of CRHR1 mRNA", "citation": {"db": "PubMed", "db_id": "12093084"}, "annotations": {"Species": {"10116": true}}, "source": 139, "target": 3773, "key": "39ad8c145baae3b9f88ddd090b07b778"}, {"line": 33517, "relation": "association", "evidence": "The dysregulation of glycogen synthase kinase-3 (GSK3) has been implicated in Alzheimer disease (AD) pathogenesis and in Abeta-induced neurotoxicity, leading us to investigate it as a therapeutic target in an intracerebroventricular Abeta infusion pmodel. ", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3876, "target": 2316, "key": "2c82fa3b1969e8c3e2f3732c008c5991"}, {"line": 7325, "relation": "negativeCorrelation", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2396, "target": 3116, "key": "cee924388bc2d746def2d09e4602123f"}, {"line": 7327, "relation": "increases", "evidence": "One promising intervention is the use of the incretin hormone glucagon-like peptide-1 (GLP-1) as a treatment for neurodegenerative diseases [25] and [34]. GLP-1 enhances glucose-dependent insulin secretion and lowers blood glucose in subjects with T2DM [15]. Currently, the GLP-1 receptor agonist exenatide (Byetta) is approved for the treatment of T2DM, and many other GLP-1 analogues are in late stage clinical trials. GLP-1 also plays important roles in the brain. The GLP-1 receptor is expressed in neurons [19], and GLP-1 has growth factor-like properties and protects neurons from kainate-induced neurotoxicity [13] and [33], and of the effects of reduced NGF levels [3]. GLP-1 also reduces the induction of apoptosis of hippocampal neurons [13]", "citation": {"db": "PubMed", "db_id": "19573562"}, "annotations": {"Subgraph": {"Glucagon subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "source": 2396, "target": 478, "key": "27f33bd93602e974383293a0b27a2052"}, {"line": 33535, "relation": "positiveCorrelation", "evidence": "Moreover, neurons that overexpress Bim in AD brains also show elevated levels of the cell cycle-related proteins cdk4 and phospho-Rb.", "citation": {"db": "PubMed", "db_id": "17251431"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2396, "target": 2486, "key": "8a8d045d10be03fd50cfea99937f4b08"}, {"line": 33541, "relation": "positiveCorrelation", "evidence": "Moreover, neurons that overexpress Bim in AD brains also show elevated levels of the cell cycle-related proteins cdk4 and phospho-Rb.", "citation": {"db": "PubMed", "db_id": "17251431"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2396, "target": 3299, "key": "fca4b0ed0775b5266f15ece71264a5f2"}, {"line": 33548, "relation": "association", "evidence": "Our observations indicate that Bim is a proapoptotic effector of Abeta and of dysregulated cell cycle proteins in AD and identify both Bim and cell cycle elements as potential therapeutic targets.", "citation": {"db": "PubMed", "db_id": "17251431"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2396, "target": 80, "key": "ae56399756baa25101006022dd2d9fc9"}, {"line": 7352, "relation": "negativeCorrelation", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 318, "target": 3857, "key": "7b3468e92d2dd9fb48f8f01433d4fd40"}, {"line": 7352, "relation": "negativeCorrelation", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3857, "target": 318, "key": "04ff423416983948e79e65d8e6b22b14"}, {"line": 7353, "relation": "positiveCorrelation", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3857, "target": 2328, "key": "564ba97f87d5abd4a20069e37b0ff31d"}, {"line": 7354, "relation": "association", "evidence": "To some extent vascular complications of type 2 diabetes or glucose intolerance might be leading to neurodegeneration due to insufficient neuronal nutrient supply as well as im­ paired -amyloid (A) clearance from the brain [12] . Fur­thermore, disturbed cerebral perfusion due to endothelial dysfunction and alteration of the blood brain barrier results in upregulation of amyloid precursor protein (APP) expres­ sion and Adeposition [13]. Also, hyperinsulinemia as it is present in type 2 diabetes may play an important role in for­ mation of senile plaques (14].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3857, "target": 3850, "key": "9857945d467b304a86b40c237afeb382"}, {"line": 7396, "relation": "positiveCorrelation", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 552, "target": 2899, "key": "26fb045fb43ed454b02fb9ab75f153b4"}, {"line": 7397, "relation": "positiveCorrelation", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 552, "target": 2871, "key": "2b0ec667cbd97b71edf363f5b5a3dc55"}, {"line": 10337, "relation": "positiveCorrelation", "evidence": "The consequences of the inhibition of neuronal insulin signal transduction may be largely identical to those of disturbances of oxidative energy metabolism and related metabolism, and of hyperphosphorylation of tau-protein.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 552, "target": 580, "key": "0db0e1210a5989ac9312f390231003b6"}, {"line": 7412, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 850, "target": 2899, "key": "1cf9e3a7cded9faf30c313d1917528ea"}, {"line": 7413, "relation": "association", "evidence": "The signaling mechanisms and the biological effects of insulin and IGF-l have been studied mainly in classical insulin target tissues, such as skeletal muscle, fat and liver, with' respect to glucose uptake, regulation of cell proliferation, gene. expression and the suppression of hepatic glucose pro­ duction. Over the past few years, it has become clear that insulin and IGF-1 also have profound effects in the central nervous system (CNS), regulating key processes such as energy homeostasis, neuronal survival, and longevity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 850, "target": 2871, "key": "bee994b4e7acab60e22b98f5a7464328"}, {"line": 7563, "relation": "positiveCorrelation", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Species": {"7227": true, "6239": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 850, "target": 2453, "key": "9affcb5dcecd9c88bcc47b6cb14934a5"}, {"line": 7567, "relation": "positiveCorrelation", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Species": {"7227": true, "6239": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 850, "target": 3391, "key": "7003c411af904da50fc89b258323fad5"}, {"line": 8078, "relation": "negativeCorrelation", "evidence": "Furthermore, in C. elegans the DAF-2 pathway is pro­ posed to control longevity [I 37]. However, decreased DAF- 2 signaling causes a considerable lifespan extension [137, 138]. The longevity in DAF-2 mutant animals is negatively influenced by mutations in DAF-16, indicating that DAF-16 is inhibited by DAF-2 and is a major downstream effector. Similar findings were seen in Drosophila melanogaster where insulin signaling is mediated via chico the ortholog of human IRS. If either the lR or chico is mutated, lifespan of these flies is prolonged [139, 140]. Also, overexpression of dFoxO, the ortholog of human FOXO, decreases mortality and increases I ifespan in Drosophila [ 141].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 850, "target": 580, "key": "287d1445c480da3085dfa581476f573f"}, {"line": 8084, "relation": "association", "evidence": "Furthermore, in C. elegans the DAF-2 pathway is pro­ posed to control longevity [I 37]. However, decreased DAF- 2 signaling causes a considerable lifespan extension [137, 138]. The longevity in DAF-2 mutant animals is negatively influenced by mutations in DAF-16, indicating that DAF-16 is inhibited by DAF-2 and is a major downstream effector. Similar findings were seen in Drosophila melanogaster where insulin signaling is mediated via chico the ortholog of human IRS. If either the lR or chico is mutated, lifespan of these flies is prolonged [139, 140]. Also, overexpression of dFoxO, the ortholog of human FOXO, decreases mortality and increases I ifespan in Drosophila [ 141].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"7227": true}}, "source": 850, "target": 2905, "key": "d4ac1ac497d1f1fcc102018094e07c65"}, {"line": 7422, "relation": "increases", "evidence": "The IR and the IGF-1 R are heterotetrameric receptor tyrosine kinases consisting of two a- and P-subunits linked by disulfide bonds (Fig. 1). The a-subunits are located ex­ clusively extracellularly [16-18], the transmembrane and intracellu Jar parts of the P-subunits contain an insulin­ r gulate? .tyrosine-specific protein kinase (19, 20]. Alterna­ tive sphcmg of exon-11 leads to synthesis of two insulin rece tor isoforms (IRa and IRb). IRb binds insulin with high affin ty, wheres IRa binds insulin or IGF-2 with comparable affin1ty. Hybnd receptors composed of ap-heterodimers from the IGF-1 R and the !Rb selectively bind IGF-1 , whereas hybrid receptors composed of IGF-I R and IRa bind IGFs and insulin with similar affinities [2I ].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 1471, "target": 1466, "key": "8a69cc094bba6a71a0ee6b58a4caaa8b"}, {"line": 7423, "relation": "increases", "evidence": "The IR and the IGF-1 R are heterotetrameric receptor tyrosine kinases consisting of two a- and P-subunits linked by disulfide bonds (Fig. 1). The a-subunits are located ex­ clusively extracellularly [16-18], the transmembrane and intracellu Jar parts of the P-subunits contain an insulin­ r gulate? .tyrosine-specific protein kinase (19, 20]. Alterna­ tive sphcmg of exon-11 leads to synthesis of two insulin rece tor isoforms (IRa and IRb). IRb binds insulin with high affin ty, wheres IRa binds insulin or IGF-2 with comparable affin1ty. Hybnd receptors composed of ap-heterodimers from the IGF-1 R and the !Rb selectively bind IGF-1 , whereas hybrid receptors composed of IGF-I R and IRa bind IGFs and insulin with similar affinities [2I ].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 1471, "target": 1469, "key": "4d08936698c92165e5019be56842f420"}, {"line": 7424, "relation": "increases", "evidence": "The IR and the IGF-1 R are heterotetrameric receptor tyrosine kinases consisting of two a- and P-subunits linked by disulfide bonds (Fig. 1). The a-subunits are located ex­ clusively extracellularly [16-18], the transmembrane and intracellu Jar parts of the P-subunits contain an insulin­ r gulate? .tyrosine-specific protein kinase (19, 20]. Alterna­ tive sphcmg of exon-11 leads to synthesis of two insulin rece tor isoforms (IRa and IRb). IRb binds insulin with high affin ty, wheres IRa binds insulin or IGF-2 with comparable affin1ty. Hybnd receptors composed of ap-heterodimers from the IGF-1 R and the !Rb selectively bind IGF-1 , whereas hybrid receptors composed of IGF-I R and IRa bind IGFs and insulin with similar affinities [2I ].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 1471, "target": 1470, "key": "78947d2762aef03431c0c99edb74beb4"}, {"line": 7434, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1466, "target": 754, "key": "e276fc7378a731ab9cd4b328969109b8"}, {"line": 7435, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1470, "target": 754, "key": "8d3cc0905780632822d9908e630226cb"}, {"line": 7769, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 754, "target": 3823, "key": "558a33dbcffb711c5745fa952baf693a"}, {"line": 10475, "relation": "association", "evidence": "Surprisingly, insulin failed to block ADDL binding when IR tyrosine kinase activity was inhibited; in fact, a significant increase in binding was caused by IR inhibition.", "citation": {"db": "PubMed", "db_id": "19188609"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 754, "target": 2900, "key": "6276b391f685484e93ee29e6aacde9e8"}, {"line": 10477, "relation": "increases", "evidence": "Surprisingly, insulin failed to block ADDL binding when IR tyrosine kinase activity was inhibited; in fact, a significant increase in binding was caused by IR inhibition.", "citation": {"db": "PubMed", "db_id": "19188609"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 754, "target": 2899, "key": "02b496285b860aa3c68c163661dfa613"}, {"line": 7439, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1472, "target": 2911, "key": "78dfe09df97541b8aead10280bcc5e0c"}, {"line": 7440, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1472, "target": 2916, "key": "28bcaf7cd39b2da88f6ad43af359e90d"}, {"line": 7441, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1472, "target": 2919, "key": "976265c73cdc901879dfc7a8a2a257c5"}, {"relation": "partOf", "source": 2873, "target": 1472, "key": "d072994c6f0237ba848c16997d964029"}, {"relation": "partOf", "source": 2911, "target": 1486, "key": "0109a52d49747ecbf747aa42b8088bc5"}, {"line": 7453, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2911, "target": 1486, "key": "7b55d7b82b7fa7a23a7ee551e3a9b430"}, {"relation": "partOf", "source": 2911, "target": 1487, "key": "d9fe20169441801ad460f1d8763ac859"}, {"line": 7454, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2911, "target": 1487, "key": "a6a4b37dae45387dd31f0fa79fdf5766"}, {"relation": "partOf", "source": 2911, "target": 1436, "key": "bcff2d4254f08cdd2882287dfd53ed90"}, {"line": 7470, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2911, "target": 1436, "key": "ae307aaa735cd28ac5b598e0af968630"}, {"relation": "partOf", "source": 2911, "target": 1488, "key": "2bb9c2b3f140e3e2e23caef6c55d9417"}, {"line": 7474, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2911, "target": 1488, "key": "ad1db8bcdd8c529a0df1c9c8763dd84e"}, {"relation": "partOf", "source": 2916, "target": 1489, "key": "d1522688bd7664084f34ce5f540503d3"}, {"line": 7451, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2916, "target": 1489, "key": "6d42980029f577b07e43c9003e581d79"}, {"relation": "partOf", "source": 2916, "target": 1490, "key": "525f2968265eb3699d08274263fb32ba"}, {"line": 7452, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2916, "target": 1490, "key": "bf161edaaae5075263e3a5907d08233b"}, {"relation": "partOf", "source": 2916, "target": 1437, "key": "7bb92b4465fe40b3cf6a7e02560fe5fb"}, {"line": 7469, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2916, "target": 1437, "key": "ffc830e0ca1af909cc7629bfcf8e28c5"}, {"relation": "partOf", "source": 2916, "target": 1491, "key": "423817b7e43b7767e3d9694c76485254"}, {"line": 7473, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2916, "target": 1491, "key": "d09280017bbbb02db07d9af720a70be5"}, {"relation": "partOf", "source": 2919, "target": 1492, "key": "9bf6c35d4521f3d82248f65d294ad16d"}, {"line": 7449, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2919, "target": 1492, "key": "889efd12a00daa854ccd14fddef68165"}, {"relation": "partOf", "source": 2919, "target": 1493, "key": "f9105d2abd55bc6a86f4edf358bbbcdc"}, {"line": 7450, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2919, "target": 1493, "key": "bf8ef9581cb9a9c3a15809b8cda271ec"}, {"relation": "partOf", "source": 2919, "target": 1438, "key": "92a0d142509875a255eb16a3829fa3bb"}, {"line": 7468, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2919, "target": 1438, "key": "ea8385241e206f01b45881edcee8dbb0"}, {"relation": "partOf", "source": 2919, "target": 1494, "key": "ce6b9f8f1cf9899b74751a2c6a3c671f"}, {"line": 7472, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2919, "target": 1494, "key": "83b53a89a6ed28008a48f38cb4925742"}, {"relation": "partOf", "source": 3188, "target": 1492, "key": "eae9c23dc047dbb1afa39e443e998d81"}, {"relation": "partOf", "source": 3188, "target": 1489, "key": "2241a4d0265cfb5bc353ac23376cc43d"}, {"relation": "partOf", "source": 3188, "target": 1486, "key": "d5d25fec302d0c19aaa2e00c475dea23"}, {"line": 7461, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3188, "target": 3186, "key": "9e469bc1bed865a87b3ed6a639e2aeb9"}, {"line": 7462, "relation": "increases", "evidence": "Binding of insulin or IGF-1 induces a conformational chan.ge of the receptor and activates tyrosine kinase activity leadmg to autophosphorylation of the intracellular P-subunit [22]. Tyrosine-phosphorylated IRIIG F-1 R P-subunits recruit and subsequently phosphorylate tyrosine residues of the in­ trac llular insulin receptor substrates (IRS). The IRS protein family has at least four members, IRS-1 to -4 [23-25]. These proteins are homolog in structure and function but show dis­ tinct tissue distribution. IRS- I and IRS-2 are widely distrib­ utd throughout different tissues and the brain, whereas IRS- 3 IS only expressed in rodent adipose tissue, and IRS-4 is predominantly localized in hypothalamus, thymus, skeletal muscle, heart, kidney, and liver [26-28]. Upon its activation the IRS proteins bind several Src homology(SH)2 domain­ containing cellular signaling proteins, such as p85, the regu­ latory subunit of phosphatidylinositide(PI)3-kinase, growth factor receptor binding protein(G RB)2 and SH2-Phosphatase (SHP)2.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3188, "target": 3187, "key": "37ac8bb5c398d713bb8d995b280cdb62"}, {"line": 9729, "relation": "association", "evidence": "The p85alpha subunit of phosphatidyl inositol 3 kinase (PIK3R1) and the regulatory subunit 3 of protein phosphatase 1 (PPP1R3) were selected as candidate genes because both encode key proteins involved in insulin signalling and because polymorphisms in these genes have been previously implicated in insulin resistance or type II diabetes.Analysis of the Met326Ile PIK3R1 and the Asp905Tyr PPP1R3 polymorphisms in 202 patients with late onset AD and 160 or 170 age matched normal subjects.Logistic regression analysis using the recessive genetic model showed significant differences in genotype and allelic frequencies between the AD group and normal controls (genotypes: odds ratio (OR) 2.09, 95% confidence interval (CI) 1.17 to 3.74, p = 0.01; alleles: OR 1.99, 95% CI 1.17 to 3.40, p = 0.01) for the Met326Ile PIK3R1 polymorphism that were female specific.", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3188, "target": 2184, "key": "997541bc80eecd96b495bcbb062a8edc"}, {"relation": "hasVariant", "source": 3188, "target": 3189, "key": "6e8c61da5f2a1807998c32960e0ac625"}, {"line": 32576, "relation": "increases", "evidence": "Ser727 of STAT1 can be phosphorylated by diverse kinases, such as phosphatidylinositol 3-kinase/Akt, calcium/calpmodulin-dependent kinase II, protein kinase C, and MAPKs.", "citation": {"db": "PubMed", "db_id": "17091494"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "JAK-STAT signaling subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3188, "target": 3425, "key": "79db089b71204ea9c2bfeaaa141fe431"}, {"line": 7502, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3186, "target": 10, "key": "a04de8552dd66e1db5457813043c7612"}, {"line": 7985, "relation": "negativeCorrelation", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3186, "target": 2794, "key": "a01ce494e586c04461e2fa9393b11906"}, {"line": 7986, "relation": "negativeCorrelation", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3186, "target": 3223, "key": "77b60ef5903cc89f5f524d413223c51f"}, {"relation": "partOf", "source": 3283, "target": 1494, "key": "033b287acacf9f8237f40b2baa13e274"}, {"relation": "partOf", "source": 3283, "target": 1491, "key": "9ac961ba00f06591ef2222d80d44a93d"}, {"relation": "partOf", "source": 3283, "target": 1488, "key": "ba8aa024362cbf9c861054f0c958c687"}, {"line": 7504, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 10, "target": 3178, "key": "69a3bee1ab4cb334fab0e1b5b394cdac"}, {"line": 7506, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 10, "target": 2279, "key": "4e1f352a785d8b83edc9f313418e9ac9"}, {"line": 7515, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 10, "target": 3327, "key": "2f388d90a436a0632569c047272a3f2a"}, {"line": 7516, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 10, "target": 2794, "key": "bd24cc4df2a96617252f30f297449ad1"}, {"line": 7524, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 10, "target": 2382, "key": "744c80313ec2a8f5731199a50fed789b"}, {"relation": "partOf", "source": 3178, "target": 1444, "key": "60a20a861fb526ac60e07041ffaf97d6"}, {"relation": "partOf", "source": 3178, "target": 1447, "key": "79982594756ef27b32465ad4067b2586"}, {"relation": "hasVariant", "source": 3327, "target": 3328, "key": "bc3fe8509b91f9da5d35b2f5eb62fe99"}, {"line": 21656, "relation": "association", "evidence": "Chronically increased S6K1 is associated with impaired IRS1 signaling in skeletal muscle of GDM women with impaired glucose tolerance postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "MeSHAnatomy": {"Muscle, Skeletal": true}, "Species": {"9606": true}, "Subgraph": {"Interferon signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3327, "target": 2905, "key": "e1ce79bcdb3d3aac699d380fdf718a23"}, {"line": 21687, "relation": "positiveCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3327, "target": 3851, "key": "4765309ff2475dc17f6f68715592144c"}, {"line": 21704, "relation": "positiveCorrelation", "evidence": "In a previous study, we have shown that peripheral p70S6k level is correlated with the decline of cognitive and memory functions in patients with AD.", "citation": {"db": "PubMed", "db_id": "17101223"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}}, "source": 3327, "target": 820, "key": "824e9fde918a6e2b3de907895939c02d"}, {"line": 21711, "relation": "positiveCorrelation", "evidence": "The decline of emotional memory in AD patients is reflected by the decrease of p70S6k levels.", "citation": {"db": "PubMed", "db_id": "17101223"}, "source": 3327, "target": 820, "key": "dc5e82077ec2d8ac19e63cd5cc879b03"}, {"line": 21710, "relation": "negativeCorrelation", "evidence": "The decline of emotional memory in AD patients is reflected by the decrease of p70S6k levels.", "citation": {"db": "PubMed", "db_id": "17101223"}, "source": 3327, "target": 3823, "key": "f04c60c94548334c759cb06b994463cc"}, {"line": 21722, "relation": "directlyIncreases", "evidence": "Currently, we found that the 70-kDa p70 S6 kinase (p70S6K) directly phosphorylates tau at S262, S214, and T212 sites in vitro.", "citation": {"db": "PubMed", "db_id": "16364302"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "mTOR signaling subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3327, "target": 3023, "key": "f89d75a58689d1ecfb3f6768efcebd20"}, {"line": 21723, "relation": "directlyIncreases", "evidence": "Currently, we found that the 70-kDa p70 S6 kinase (p70S6K) directly phosphorylates tau at S262, S214, and T212 sites in vitro.", "citation": {"db": "PubMed", "db_id": "16364302"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "mTOR signaling subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3327, "target": 3021, "key": "cbb46a7bd916936133424beeae6cf587"}, {"line": 21724, "relation": "directlyIncreases", "evidence": "Currently, we found that the 70-kDa p70 S6 kinase (p70S6K) directly phosphorylates tau at S262, S214, and T212 sites in vitro.", "citation": {"db": "PubMed", "db_id": "16364302"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "mTOR signaling subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3327, "target": 3033, "key": "0a2c786d78c8e5889aeda1f0b261b826"}, {"line": 21753, "relation": "decreases", "evidence": "The extracellular amyloid-beta deposition in AD brains could be a causative factor that activates p70S6K. We hypothesized that amyloid-beta deposition activates p70S6K whose anti-apoptotic property subsequently keeps neurons from entering into the apoptotic process.", "citation": {"db": "PubMed", "db_id": "18688088"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3327, "target": 478, "key": "3db73d7d22f72a959bfb52de1ef69dfe"}, {"line": 21767, "relation": "increases", "evidence": "Recent findings from our and other groups have suggested glycogen synthase kinase 3 and p70 S6 kinase as main tau kinases and protein phosphatase 2A as the main tau phosphatase involved in the formation of these processes.", "citation": {"db": "PubMed", "db_id": "18852562"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "mTOR signaling subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3327, "target": 3015, "key": "d4ce9b1616412fd28cecdbefd685e99c"}, {"line": 7532, "relation": "decreases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2795, "target": 2794, "key": "f37a8410dba18fea9d8e979e0bc5594d"}, {"line": 7533, "relation": "increases", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2795, "target": 478, "key": "1932606cab7898959256143a2db5cf0a"}, {"line": 39161, "relation": "positiveCorrelation", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Calcium-dependent signal transduction": true, "GSK3 subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2795, "target": 2629, "key": "b4376bb223797400c85859e4d851d819"}, {"line": 39166, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Very High": true}}, "source": 2795, "target": 3015, "key": "d326de9a5a43aadb487b6e6ef4c4e53a"}, {"relation": "hasVariant", "source": 2703, "target": 2704, "key": "88c44544fa2974f30762f363b6e83265"}, {"line": 8065, "relation": "increases", "evidence": "Experiments in Caenoi-habditis elegans revealed new insights into the role oflR/IGF-IR signaling in A1 -42 toxic­ ity, and Ametabolism. Cohen and coworkers could show that knocking down the DAF-2 pathway in C. elegans, which is orthologous to the mammalian insulin and IGF-l signaling cascade, reduces Al31-42 toxicity [35]. Furthermore, this effect was mediated by the two downstream transcrip­ tion factors, DAF-16 and HSF-l (heat shock transcription factor-!) [132}. DAF-\\6 encodes a forkhead transcription factor [133, 134], which translocates into the nucleus [135], and modulates transcription when DAF-2 signaling is abro­ gated . The mammalian DAF-16 orthologs are Foxol, 3, and 4 [136). In the mammalian system the IR/IGF-1 R induces phosphorylation of Foxo I and triggers its translocation from the nucleus. The DAF-2 pathway reduces A ,_42 toxicity by two possible mechanisms of detoxification [35]: The first detoxification route leads to disaggregation of the toxic oli­ gomer that is positively regulated by HSF-1 and degradation of the amyloidogenic peptides. The second mechanism mediates the formation of low toxic, high molecular weight aggregates from high toxic low molecular weight aggregates, which is positively regulated by DAF-!6. Both detoxifica­ tion mechanisms are negatively regulated by DAF-2 signal­ ing.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 2703, "target": 580, "key": "8d6af9a27c9322cfa15b948876965bd1"}, {"line": 8080, "relation": "decreases", "evidence": "Furthermore, in C. elegans the DAF-2 pathway is pro­ posed to control longevity [I 37]. However, decreased DAF- 2 signaling causes a considerable lifespan extension [137, 138]. The longevity in DAF-2 mutant animals is negatively influenced by mutations in DAF-16, indicating that DAF-16 is inhibited by DAF-2 and is a major downstream effector. Similar findings were seen in Drosophila melanogaster where insulin signaling is mediated via chico the ortholog of human IRS. If either the lR or chico is mutated, lifespan of these flies is prolonged [139, 140]. Also, overexpression of dFoxO, the ortholog of human FOXO, decreases mortality and increases I ifespan in Drosophila [ 141].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 2703, "target": 2871, "key": "ca882aa6fc163bf83db14de9e39a7931"}, {"line": 20261, "relation": "association", "evidence": "CHOP potentially co-operates with FOXO3a in neuronal cells to regulate PUMA and BIM expression in response to ER stress.", "citation": {"db": "PubMed", "db_id": "22761832"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Response DNA damage": true}}, "source": 2703, "target": 2622, "key": "674915fcb61adcfca57afc91b74883cf"}, {"relation": "partOf", "source": 2703, "target": 1390, "key": "795580ad5e68af1962498ea1dda6cfd0"}, {"line": 7553, "relation": "association", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 2177, "target": 3823, "key": "dfe184ac968329333cb849a35d2fbd74"}, {"line": 8085, "relation": "decreases", "evidence": "Furthermore, in C. elegans the DAF-2 pathway is pro­ posed to control longevity [I 37]. However, decreased DAF- 2 signaling causes a considerable lifespan extension [137, 138]. The longevity in DAF-2 mutant animals is negatively influenced by mutations in DAF-16, indicating that DAF-16 is inhibited by DAF-2 and is a major downstream effector. Similar findings were seen in Drosophila melanogaster where insulin signaling is mediated via chico the ortholog of human IRS. If either the lR or chico is mutated, lifespan of these flies is prolonged [139, 140]. Also, overexpression of dFoxO, the ortholog of human FOXO, decreases mortality and increases I ifespan in Drosophila [ 141].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"7227": true}}, "source": 2177, "target": 505, "key": "c9f240acbfca61f869922932f8a70320"}, {"line": 8086, "relation": "increases", "evidence": "Furthermore, in C. elegans the DAF-2 pathway is pro­ posed to control longevity [I 37]. However, decreased DAF- 2 signaling causes a considerable lifespan extension [137, 138]. The longevity in DAF-2 mutant animals is negatively influenced by mutations in DAF-16, indicating that DAF-16 is inhibited by DAF-2 and is a major downstream effector. Similar findings were seen in Drosophila melanogaster where insulin signaling is mediated via chico the ortholog of human IRS. If either the lR or chico is mutated, lifespan of these flies is prolonged [139, 140]. Also, overexpression of dFoxO, the ortholog of human FOXO, decreases mortality and increases I ifespan in Drosophila [ 141].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"7227": true}}, "source": 2177, "target": 850, "key": "61cd2ae7c8cc4c598809ebd15a5e5738"}, {"line": 7563, "relation": "positiveCorrelation", "evidence": "Activation of the catalytic subunit of the PI3-kinase results in phosphorylation of phosphatidylinositide-diphosphate (PI4,5P) to generate phosphatidylinositide-triphosphate (PI3,4,5P). This leads to activation of several downstream targets, such as phosphoinositide-dependent protein kinase (PDK)-1, protein kinase B (PKB, AKT), p70S6kinase, glycogen synthase kinase(GSK)-3 and BAD a proapoptotic member of the Bcl-2 family. Phosphorylation of GSK-3 and BAD inactivates these proteins and thereby inhibits further signaling leading to apoptosis. Activated AKT phosphorylates the forkhead transcription factor Foxo, which triggers its nuclear exclusion and thereby regulates transcription of genes involved in development, growth, stress resistance, apoptosis, metabolism and aging [29-31]. Several Foxo target genes might play an important role for the pathogenetics of AD (review in [32]): Catalase and MnSOD (manganese superoxid dismutase) are crucial in promoting longevity by possibly acting as antioxidants due to reduced insulin-like signaling in various species such as drosophila [33] and C. elegans [34]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Species": {"7227": true, "6239": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 2453, "target": 850, "key": "5dcc51014c5777158d8fb94ef0651513"}, {"line": 15237, "relation": "negativeCorrelation", "evidence": "The blood levels of catalase but not superoxide dismutase and glutathione were significantly decreased in the patients with severe AD when compared to controls (p < .01),", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 2453, "target": 3823, "key": "c199162c0bb3d4fbf0a2a0b0ef345e2c"}, {"line": 15259, "relation": "positiveCorrelation", "evidence": "The blood catalase levels of dementia patients, as a whole, were significantly and positively associated with the intake of vitamins A (p < .05),", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Disease": {"dementia": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 2453, "target": 184, "key": "60b86e758fc118fff373dbf405b4a030"}, {"line": 15275, "relation": "association", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 2453, "target": 184, "key": "8907d8cd8f866a8f08bc276a0fc43970"}, {"line": 15276, "relation": "association", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 2453, "target": 186, "key": "d8431b1669aeb7cc681650852e847902"}, {"line": 15277, "relation": "association", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 2453, "target": 188, "key": "18f6fb6f983bd48100d5728e26b576af"}, {"line": 7580, "relation": "decreases", "evidence": "Furthermore, AKT phosphorylates tuberin (TSC2), which mh1b1.ts 1ts AP activity (guanosine triphosphatase­ assocrated protem) towards the small G protein RHEB (RAS homolog enriched in brain), which causes an accumulation of the RHEB-GTP complex that activates mammalian target ofrapamycin (mTOR) [36].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 3499, "target": 469, "key": "4f0dec956269e562631c42f4c5b349a4"}, {"relation": "hasVariant", "source": 3498, "target": 3499, "key": "8856dc251a65208c8cb7a0cefae23a9f"}, {"line": 7581, "relation": "association", "evidence": "Furthermore, AKT phosphorylates tuberin (TSC2), which mh1b1.ts 1ts AP activity (guanosine triphosphatase­ assocrated protem) towards the small G protein RHEB (RAS homolog enriched in brain), which causes an accumulation of the RHEB-GTP complex that activates mammalian target ofrapamycin (mTOR) [36].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 469, "target": 3309, "key": "7a8bf951516ad68dccf77186cafa4431"}, {"line": 7581, "relation": "association", "evidence": "Furthermore, AKT phosphorylates tuberin (TSC2), which mh1b1.ts 1ts AP activity (guanosine triphosphatase­ assocrated protem) towards the small G protein RHEB (RAS homolog enriched in brain), which causes an accumulation of the RHEB-GTP complex that activates mammalian target ofrapamycin (mTOR) [36].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 3309, "target": 469, "key": "a00667a7902199090f33b76601d37fb4"}, {"relation": "partOf", "source": 3309, "target": 974, "key": "d03b3a76a471ee5bf780899973f76359"}, {"line": 7589, "relation": "increases", "evidence": "Furthermore, AKT phosphorylates tuberin (TSC2), which mh1b1.ts 1ts AP activity (guanosine triphosphatase­ assocrated protem) towards the small G protein RHEB (RAS homolog enriched in brain), which causes an accumulation of the RHEB-GTP complex that activates mammalian target ofrapamycin (mTOR) [36].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 974, "target": 3076, "key": "9bda09bddecc717c02c5ffa8d9bb46bd"}, {"relation": "partOf", "source": 193, "target": 974, "key": "9209067f8b9af0015807c662877cd165"}, {"line": 46333, "relation": "association", "evidence": "PEBP plays a pivotal modulatory role in several signal transduction pathways. PEBP inhibits the MAPK pathway through interacting with Raf-1, so it's also known as Raf-1 kinase inhibitor protein (RKIP). PEBP is involved in the regulation of PKC, G-protein-coupled receptor and NF-kappaB signaling pathway as well. In clinical researches, it was found that as the precursor of hippocampal cholinergic neurostimulating peptide (HCNP), PEBP has an important effect on the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19803424"}, "source": 451, "target": 3823, "key": "2eb6eac7f37200c6f79518cfdb07473d"}, {"line": 46685, "relation": "association", "evidence": "Taken together, these studies indicate that SULT4A1 stability is regulated by post-translational modification that involves the ERK pathway and PP2A. The phosphorylation of SULT4A1 allows interaction with Pin1, which then promotes degradation of the sulfotransferase.", "citation": {"db": "PubMed", "db_id": "20920535"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "source": 451, "target": 3731, "key": "da048575c9dd77b5a747d2278b95cace"}, {"relation": "partOf", "source": 2185, "target": 1021, "key": "df56793bfa69c4f28cfe9e81930fbe98"}, {"line": 7666, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2185, "target": 890, "key": "983a9aa508f5ac2a5e4e9ed36351fec9"}, {"line": 7674, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2185, "target": 2173, "key": "d538ccdff7f5153bedf00d347c089976"}, {"line": 10896, "relation": "negativeCorrelation", "evidence": "According to this hypothesis, brains from AD patients showed substantially downregulated expression of the Insulin receptor (IR), the IGF-1 receptor (IGF-1R), and the insulin receptor substrate (IRS) proteins.", "citation": {"db": "PubMed", "db_id": "21916834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2185, "target": 3823, "key": "1e3a2b5a44f2214bd15b3fbfac17abf4"}, {"line": 7630, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1021, "target": 1427, "key": "508d85e2e0dd08ef685af581d7e3a087"}, {"line": 7631, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1426, "target": 1427, "key": "c9fd3b7a3ab8ec5de1f1c188aa18c9a5"}, {"relation": "partOf", "source": 2716, "target": 1426, "key": "4af5608626bb1bb9b3b73b2c8f46fd6a"}, {"relation": "partOf", "source": 2716, "target": 1427, "key": "6048b380a9a8fc828e5b004f3932b5bd"}, {"line": 7635, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"namespace": "bel", "name": "gtp"}}, "source": 1427, "target": 2213, "key": "c9177b14698bcf2db54afce8005fe618"}, {"line": 7638, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "cytosol"}, "toLoc": {"namespace": "GO", "name": "membrane"}}}, "source": 1427, "target": 3291, "key": "1b3a3236bb294313a0279b97cbe035da"}, {"line": 7644, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3291, "target": 470, "key": "e6d951aca56ebaf8c140f4ee9eec1bef"}, {"line": 35408, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3291, "target": 2990, "key": "b5c59e4d8d9b7039fad62bb2add98527"}, {"line": 35409, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3291, "target": 3000, "key": "9eb4f46cdc0cf48429ce96c167ecd2c9"}, {"line": 36460, "relation": "increases", "evidence": "ERK1/2 are activated by upstream MAPKK, such as MEK1/2, and MAPKKK, such as c-Raf. MEK1/2 induce ERK1/2 activation via dual phosphorylation on threonine 202 and tyrosine 204 residues.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3291, "target": 2173, "key": "ecdeeaeed4e7557862bdb2ccda50f02d"}, {"line": 36514, "relation": "association", "evidence": "Increased cytosolic calcium concentrations initiate the activation of several kinase-dependent signalling cascades including activation of PKC leading to CREB activation and phosphorylation at Ser133, a process critical for protein synthesis-dependent synaptic plasticity and LTP. PKC-a also activates ERK by interacting with Ras or Raf-1.Mitochondria are critical targets of intracellular ABeta¸. ABeta¸ interacts with CypD, a protein component of the membrane permeability transition pore (MPTP). The interaction of CypD with ABeta¸ causes functional modification of this protein leading to MPTP opening. ABeta¸ also binds with another mitochondrial protein, ABAD to distort the enzyme’s structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3291, "target": 3236, "key": "ce5c368d3350f2b6cc0c3ac6e595f630"}, {"relation": "partOf", "source": 3291, "target": 1616, "key": "58a8c70a3f945e6a9033b74e66b64ee2"}, {"line": 7646, "relation": "increases", "evidence": "Another major physiological role of insulin and IGF-1 is the regulation of gene transcription via the MAP kinase cas­ cade. Following insulin and IGF-1 stimulation, IRS prote ins and GAB(GRB-2 associated binder)- I bind to the Sl-12 do­ mains of several small adaptor proteins such as GRB-2. These proteins then interact with the GDP/GTP exchanoe factor SOS (son of sevenless) leading to activation of the small G-protein RAS and subsequently to the recruitment of CRAF to the membrane. Activated CRAF activates MEK which then activates extracellular signal-regulated kinase (ERK)-11-2 (37]. ERK-1/-2 mediated signals are involved in I?n -lasting neuronal plasticity, including long-term poten­ tiatiOn and memory consolidation (review in [38]). In con­ trast, (over-)activation of ERK-1 /-2 also leads to apoptotic eeldeath i.n cae of oxidative stress or growth factor depri­ vation (review m [39]). However, ERKs seems to be an es­ sential gene since animals lacking ERK-2 die in early em­ bryonic development (40].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 470, "target": 448, "key": "3ba79900da40cd621853cc818306cbaa"}, {"line": 9834, "relation": "association", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 890, "target": 579, "key": "2944cf88383902ccee9bf91ef40b9702"}, {"line": 9844, "relation": "negativeCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "source": 890, "target": 3015, "key": "aa1fb9ef83c0ca9ffee7312a3745c6dc"}, {"line": 9848, "relation": "positiveCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 890, "target": 3156, "key": "abb4fc2573eed8fbdd89fea2d4cb02f8"}, {"line": 40564, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 890, "target": 2153, "key": "7516e8a91dfd5b6063b5fe9782318dc7"}, {"line": 40565, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 890, "target": 890, "key": "887b27b5a23187573e157e922ecd2063"}, {"line": 40566, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 890, "target": 3552, "key": "82dd4100059cc9303ccd8aaafe163043"}, {"line": 40567, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 890, "target": 3001, "key": "920e115d4541bd5c5b38503546e6cd60"}, {"line": 40568, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 890, "target": 2154, "key": "0e3f94a886acac92f8cf47903f2942ce"}, {"line": 40570, "relation": "association", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 890, "target": 65, "key": "04a3e3804ca401fb1e6c7ebda7e5ad86"}, {"line": 7681, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "subject": {"modifier": "Activity"}, "source": 1467, "target": 3015, "key": "061a33ca4edd05abbbcd4285b0178866"}, {"line": 36647, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1467, "target": 2910, "key": "a2739d718f6dc9bda2baaa57414c1faf"}, {"line": 36649, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1467, "target": 2915, "key": "8ad702a7e10748dbad374c247832d047"}, {"line": 36651, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1467, "target": 3355, "key": "1379ce2c99345a49e437ddcdac932613"}, {"line": 36653, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1467, "target": 3357, "key": "9373977a3696a70dd4555f34b01d4f2a"}, {"line": 36655, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1467, "target": 3359, "key": "86db3adc74f393ed8e9bb29cba7f5fab"}, {"line": 36657, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1467, "target": 3361, "key": "01161e6464de5f5961962b66ca34eb28"}, {"line": 7688, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3223, "target": 1467, "key": "e48d471989adabbf2ff913ec2c41a921"}, {"line": 7694, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 460, "key": "f4a59b642408be2baa8fe12698e5d78d"}, {"line": 7707, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 3015, "key": "1ef3d437d8d4801b944a068eb7f3822c"}, {"line": 7894, "relation": "decreases", "evidence": "Tau is a microtubule-binding protein that ligates tubulin and accounts for the stability of microtubules. If hyperphos­ phorylated, tau aggregates and interferes with intraneu ronal metabolism and transport lead ing to neurodegeneration [I 02]. Tau phosphorylation state is regulated by site-specific dephosphorylation through certain phosphatases and by kinases phosphorylating tau protein at specific sites. Protein phosphatase 2A (PP2A) is the major phosphatase with 70% tau phosphatase activity in human brains [I 03]. This implies a protective role of PP2A in neurodegeneration which is consistent with the finding that PP2A activity is reduced in AD brains [104, 105].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Wnt signaling subgraph": true, "Tau protein subgraph": true}}, "source": 3223, "target": 3015, "key": "d60f45b7d11b8f5ebc65af3b3d21d472"}, {"line": 31939, "relation": "decreases", "evidence": "More significantly, these and other studies may be interpreted to suggest that the abnormal phosphorylation of PHFtau may result from the failure of protein phosphatases (i.e., PP2A and 2B) to dephosphorylate PHFtau.", "citation": {"db": "PubMed", "db_id": "8529836"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3223, "target": 3015, "key": "d6e5c82f68064de3ea7248380efe54cb"}, {"line": 7893, "relation": "negativeCorrelation", "evidence": "Tau is a microtubule-binding protein that ligates tubulin and accounts for the stability of microtubules. If hyperphos­ phorylated, tau aggregates and interferes with intraneu ronal metabolism and transport lead ing to neurodegeneration [I 02]. Tau phosphorylation state is regulated by site-specific dephosphorylation through certain phosphatases and by kinases phosphorylating tau protein at specific sites. Protein phosphatase 2A (PP2A) is the major phosphatase with 70% tau phosphatase activity in human brains [I 03]. This implies a protective role of PP2A in neurodegeneration which is consistent with the finding that PP2A activity is reduced in AD brains [104, 105].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Wnt signaling subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 3823, "key": "b11c2edafc95f9722e716d9ac71da83f"}, {"line": 49492, "relation": "negativeCorrelation", "evidence": "There is a significant decrease in total PP2A activity measured in AD cortical and hippocampal brain homogenates.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 3823, "key": "dd5b13f27327e430ab5bfb0f451e5aee"}, {"relation": "partOf", "source": 3223, "target": 1448, "key": "29739c7efc9b82c59c08dc18d239e2a8"}, {"line": 7988, "relation": "isA", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 1448, "key": "342c15b2dae8947c7fe942e08e2b38c4"}, {"line": 7986, "relation": "negativeCorrelation", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 3186, "key": "0db0a09cb94a586f32fbe502e4ec980c"}, {"line": 9024, "relation": "decreases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3223, "target": 3032, "key": "4529ba01bd1fa5c7002f4edf8617538b"}, {"line": 9025, "relation": "decreases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3223, "target": 3020, "key": "6639173c9daa52effa83e682a2d838a7"}, {"line": 9026, "relation": "decreases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3223, "target": 3022, "key": "045ffdfb3c1171ae2d2585a3717ece88"}, {"line": 9027, "relation": "decreases", "evidence": "Alzheimer disease, the most common tauopathy, is characterized by neurofibrillary tangles that are mainly composed of abnormally phosphorylated Tau. Tau phosphorylation occurs mainly at proline-directed Ser/Thr sites, which are targeted by protein kinases such as GSK3beta and Cdk5. We reported previously that dephosphorylation of Tau at Cdk5-mediated sites was enhanced by Pin1, a peptidyl-prolyl isomerase that stimulates dephosphorylation at proline-directed sites by protein phosphatase 2A. Pin1 deficiency is suggested to cause Tau hyperphosphorylation in Alzheimer disease. Up to the present, Pin1 binding was only shown for two Tau phosphorylation sites (Thr-212 and Thr-231) despite the presence of many more hyperphosphorylated sites. Here, we analyzed the interaction of Pin1 with Tau phosphorylated by Cdk5-p25 using a GST pulldown assay and Biacore approach. We found that Pin1 binds and stimulates dephosphorylation of Tau at all Cdk5-mediated sites (Ser-202, Thr-205, Ser-235, and Ser-404). Furthermore, FTDP-17 mutant Tau (P301L or R406W) showed slightly weaker Pin1 binding than non-mutated Tau, suggesting that FTDP-17 mutations induce hyperphosphorylation by reducing the interaction between Pin1 and Tau.", "citation": {"db": "PubMed", "db_id": "23362255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3223, "target": 3027, "key": "bfae91300e5d3c1503e7281e97eae807"}, {"relation": "partOf", "source": 3223, "target": 1663, "key": "edcac25a6836f20298c98220852b66d7"}, {"relation": "partOf", "source": 3223, "target": 1291, "key": "9a4a9ddb4a63138d8158b9bc6469952d"}, {"relation": "partOf", "source": 3223, "target": 1556, "key": "420c96659d4a9e4c912802e9afbf2bec"}, {"relation": "hasVariant", "source": 3223, "target": 3224, "key": "7cbe3536f0fe59bc3ba109d8f0746945"}, {"line": 49487, "relation": "increases", "evidence": "Deregulation of PP2A enzymes also affects the activity of many Ser/Thr protein kinases implicated in AD.", "citation": {"db": "PubMed", "db_id": "24653673"}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 686, "key": "cf3f6755a0b32e3c86fcd4a460878078"}, {"line": 49496, "relation": "increases", "evidence": "Specific PP2A inhibition has been proven to lead to in vivo deregulation of many major brain Ser/Thr kinases implicated in AD, including GSK3beta (Wang et al., 2010; Louis et al., 2011), cdk5 (Louis et al., 2011; Kimura et al., 2013), extracellular signal-regulated kinase (ERK) and JNK (Kins et al., 2003).", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 2794, "key": "ab80fc5cf8f3c1cc3336d5198dc797ed"}, {"line": 49497, "relation": "increases", "evidence": "Specific PP2A inhibition has been proven to lead to in vivo deregulation of many major brain Ser/Thr kinases implicated in AD, including GSK3beta (Wang et al., 2010; Louis et al., 2011), cdk5 (Louis et al., 2011; Kimura et al., 2013), extracellular signal-regulated kinase (ERK) and JNK (Kins et al., 2003).", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 2487, "key": "23da5c777a187a225993bc6ffbbefdfe"}, {"line": 49498, "relation": "increases", "evidence": "Specific PP2A inhibition has been proven to lead to in vivo deregulation of many major brain Ser/Thr kinases implicated in AD, including GSK3beta (Wang et al., 2010; Louis et al., 2011), cdk5 (Louis et al., 2011; Kimura et al., 2013), extracellular signal-regulated kinase (ERK) and JNK (Kins et al., 2003).", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 2990, "key": "58fcdcb024281849c0bc3c1649f0d1ca"}, {"line": 49499, "relation": "increases", "evidence": "Specific PP2A inhibition has been proven to lead to in vivo deregulation of many major brain Ser/Thr kinases implicated in AD, including GSK3beta (Wang et al., 2010; Louis et al., 2011), cdk5 (Louis et al., 2011; Kimura et al., 2013), extracellular signal-regulated kinase (ERK) and JNK (Kins et al., 2003).", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}}, "subject": {"modifier": "Activity"}, "source": 3223, "target": 3002, "key": "e730631babfcb20d22f2d2427fc29435"}, {"line": 7715, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 750, "target": 80, "key": "0f906c93c3c5413f44cecbe100f095ca"}, {"line": 7735, "relation": "association", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 750, "target": 2249, "key": "d55608602fcc5d19fa0898476f055aaa"}, {"line": 7724, "relation": "increases", "evidence": "CerebraiiR/IGF-1 signaling in APP metabolism. Binding of insulin and IGF-1 to their receptors leads to autophosphorylation of the -subunit of the IR/IGF-IR and recruitment of IRS-1 /-2. IRS proteins activate mainly two pathways, the PI3 kinase pathway, and the RAS-RAF-MAP kinase cascade. Tau phosphorylation via IR/IGF-1 signaling is influenced by GSK-3 and the tau phosphatase PP2A. Inter­ estingly, mTOR signaling couples the activity ofPP2A and GSK-3in a way that the activities of both enzymes change always in the same direction. suggesting that only a dysregulation of either PP2A or GSK-3induces tau hyperphosphorylation. The PI3K pathway as well as the RAS-RAF-MAPK cascade regulate different transcription factors involved in transcriptional regulation of metabolism and clearance of ­ amyloid. APP is cleaved by a.-, -secretase (BACE-1) and y-secretase (presenilin). - and subsequent y-cleavage of APP leads to generation of -amyloid 1 -4o/ t-42• IGF-1 signaling promotes a switch lrom TrkA to p75NTR expression leading to increased -secretase activity due to an upregulation of BACE-1 expression. Recent data suggest that not only BACE-1 is int1uenced by IRIIGF- IR mediated signals but also a.­ secretase activity is stimulated by the PI3K pathway.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 1894, "target": 1755, "key": "74821053eb001423f56538b260c0d179"}, {"line": 7763, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3986, "target": 3823, "key": "a9bf74d2178e5f87fc3ee6cee1f3a8af"}, {"line": 7775, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3981, "target": 3823, "key": "5b711ea7b964b81c374cd94c4b6f1813"}, {"line": 7782, "relation": "decreases", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2908, "target": 2905, "key": "451b4b0f3d1b55df0e58a69cd1edfd0a"}, {"line": 32682, "relation": "positiveCorrelation", "evidence": "With insulin resistance in diabetics and pmodels, IRS-1 is phosphorylated at Ser312 by insulin-stimulated or stress-activated kinases, including c-Jun N-terminal kinase (JNK), which uncouples IRS-1 (Aguirre et al., 2002) and triggers rapid IRS-1 degradation (Sun et al., 1999), yielding a deficient signal transduction response (Pederson et al., 2001;Rui et al., 2001).", "citation": {"db": "PubMed", "db_id": "19605645"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2908, "target": 2905, "key": "0de73f9fcc4c2878dca191469d22f60f"}, {"line": 7785, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2908, "target": 580, "key": "53c8efa5fcb5b80c535d1a4e1df35ada"}, {"line": 7787, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2908, "target": 583, "key": "1150046ae7341e57505900800d989492"}, {"line": 7783, "relation": "decreases", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2909, "target": 2905, "key": "964ae2d87d331fa2198aa2c659234892"}, {"line": 7788, "relation": "negativeCorrelation", "evidence": "IR and IGF-1 R sign a ling is markedly disturbed in the CNS of AD patients (54, 76, 77]. Postmortem studies have show n that mRNA levels of insulin and its receptor decrease w ith a n a lm ost 80% reduced 1R expression in severe AD [15, 78]. Accordingly, Frolich et a/. [54] found that neuronal ty­ ros ine kinase activity is decreased in AD patients compared to age-matched controls. The overall expression of IGF- I R is redu ced in AD brains d ependent on the severity of the disease. However, in some cases IGF-1 R density was found to be increased i n n eurons next to amyloid plaques [77, 78]. Bra in IGF-1 mRNA levels diminish in severe AD, w hereas I GF-1 serum leve ls a re increased in early stages of disease, suggesting that IGF-1 resistance plays a role in the patho­ genesis of AD [78, 79]. IRS-1 /-2 protei n ex pression is re­ duced in AD brains and in activatin g Serine-phosphorylation oflRS-l at Ser3 12 and Ser616 is increased lead ing to impaired IR and IGF-1 R signa ling [77]. Thus, lRIIGF-1 R downstream sig n al transd uction is impaired in A D brains, leading to the hypoth es is that cerebral insulin/IG F-1 resistance might be involved in the path ogen esis of AD.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2909, "target": 583, "key": "44651953381cb79438dba5a16ead12f1"}, {"line": 7828, "relation": "association", "evidence": "Clinical tr ials in health y hum ans under hyper insulin emic euglycemic clamp conditions showed a n egative shi ft in transcortica l direct current potentials, indicati ng that circ ulat­ ing insulin can rapidly act on brain activity independent from its systemic effects [91 ]. Longitudinal studies revealed th at insulin resistance with persisting hyperi nsu linem i a comes along w ith an elevated risk for disturbed cognition, impa ired memory and A D (10, 92, 93]. In AD patients as well as i n h ealthy subjects hyperinsulinemic eug lycemic clamp studies revea led an i mproving effect of.insu li n on cognitive functio n (94, 95]. Intranasa l app l ication of insu lin in healthy hum ans directly entered th e CSF and improved memory function and cogn it ive capacity especi a lly in women with out in fluencing per i phera l blood gl ucose levels (96-98]. These gender spe­ cific findings suggest an influence of sex hormon es. Accord ­ ingly, Cl egg el a!. d emonstrated th a t i nsul in sensitiv i ty in ra t brains differ, dependin g on estrogen levels (99].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 251, "target": 773, "key": "3daafa6a8a5c16e6cd3dbe01127aebe8"}, {"line": 15409, "relation": "increases", "evidence": "Estrogen activates matrix metalloproteinases-2 and -9 to increase beta amyloid degradation.", "citation": {"db": "PubMed", "db_id": "22402435"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 251, "target": 3059, "key": "1597f9df986a53c3d3e94cd61a04ccd7"}, {"line": 15411, "relation": "increases", "evidence": "Estrogen activates matrix metalloproteinases-2 and -9 to increase beta amyloid degradation.", "citation": {"db": "PubMed", "db_id": "22402435"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 251, "target": 3062, "key": "6c63eae01eab03bbc8c4b0b47267a22a"}, {"line": 48220, "relation": "decreases", "evidence": "Along these lines, one mechanism through which E2 protects the hippocampus from cerebral ischemia is by preventing the post-ischemic elevation of Dkk1, a neurodegenerative factor that serves as an antagonist of the canonical Wnt signaling pathway, and simultaneously inducing pro-survival Wnt/beta-Catenin signaling in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 251, "target": 2629, "key": "89c0a18a21f5dcbf2ade4c7712f3c115"}, {"line": 48224, "relation": "decreases", "evidence": "Along these lines, one mechanism through which E2 protects the hippocampus from cerebral ischemia is by preventing the post-ischemic elevation of Dkk1, a neurodegenerative factor that serves as an antagonist of the canonical Wnt signaling pathway, and simultaneously inducing pro-survival Wnt/beta-Catenin signaling in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 251, "target": 3831, "key": "1ef993a0b6e8108b923e3d022535e695"}, {"line": 48231, "relation": "increases", "evidence": "Along these lines, one mechanism through which E2 protects the hippocampus from cerebral ischemia is by preventing the post-ischemic elevation of Dkk1, a neurodegenerative factor that serves as an antagonist of the canonical Wnt signaling pathway, and simultaneously inducing pro-survival Wnt/beta-Catenin signaling in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Wnt signaling subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 251, "target": 462, "key": "6e896e2aa6881d34cd3df7b83e675a4e"}, {"line": 7862, "relation": "positiveCorrelation", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Confidence": {"Medium": true}}, "source": 855, "target": 3861, "key": "90c49b9da005fb731dfd8d4ac0f2e3e8"}, {"line": 7868, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Peripheral Nervous System": true}, "Confidence": {"Low": true}}, "source": 3817, "target": 3915, "key": "34fb3828695c6580d2f6a262ff06230a"}, {"line": 7875, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Confidence": {"High": true}}, "source": 2912, "target": 3861, "key": "910a0c3cd8a1400ba6a4f5965d4efc36"}, {"line": 7876, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Confidence": {"High": true}}, "source": 2912, "target": 1727, "key": "8f5302b7621858b1eea37e94aceff663"}, {"line": 7885, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2912, "target": 3850, "key": "f76092214fded82f4f6b314535bfe854"}, {"line": 7876, "relation": "association", "evidence": "Craft et a!. found that AD patients have higher plasma insulin but lower CSF insulin levels [71]. A possible exp l a­ nation could be that cen tra l hypoinsulin emia is caused by reduced transport via the BBB or that peri pheral h yper insulinemia might be react ive to central hypoinsulinem ia, medi­ ated by so far non distinctiv.e pathways. V ery recent data suggest that intranasal insu lin administration in AD patients im proves mem ory as well, providin g a possible therapeutic option ( l 00]. Tschritter et a/. (101] used a magnetoencephalography approach during a two-step hyperinsulinemic euglycemic clamp to assess cerebrocortical insulin effects in humans. In lean humans, stimulated cortical activity in­ creased with insulin infusion relative to saline. In obese hu­ mans, these effects were suppressed, suggesting cerebral insulin resistance in these patients. Moreover, cerebrocortical insulin resistance was found in individuals carrying the Gly972Arg polymorphism of IRS-I, which is considered to elevate the risk to develop type 2 diabetes [I 0 I].", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Confidence": {"High": true}}, "source": 1727, "target": 2912, "key": "73075d76f2ed665532c9b037f8fd76c8"}, {"line": 7904, "relation": "increases", "evidence": "The phosphorylation of tau is mainly promoted by GSK-3and cyclin-dependent kinase 5 (Cdk5). Besides these kinases, activated c-Jun N-terminal kinases (JNK) and ERK-1 /-2 signaling lead to an increase in tau phosphorylation and th erefore might be of importance in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"High": true}}, "source": 450, "target": 3015, "key": "7b40d91cd3621e2c18e16879ce055135"}, {"line": 7989, "relation": "association", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 1448, "target": 3015, "key": "56b6fab67ea3f07262c08f00861ebb78"}, {"line": 7997, "relation": "increases", "evidence": "In SHSY5Y cells, a human neuroblastoma cell line, as well as in primary cultu res of rat cortical neurons insulin administration leads to tau hyperphosphorylation (111-113]. In contrast, insulin and IGF- 1 administration in NT2N cells, cultured human neurons, decreases tau phosphorylation [114]. In primary cortical neuron cultures, M esk e et a/ . (115J found that insulin treatment causes a regulatory interaction between PP2A and GSK-3. Inhibition of Pl3-kinase leads to activat ion of GSK-3and PP2A. Enzyme activity of both enzymes al ways changed in the same direction. This bal­ anced response seemed to induce a steady state in tau phos­ phorylation at GSK-3/PP2A-dependent sites (115]. Thus, on ly a dysbalance of insulin/IGF-1 regulated tau kinases and phosphatases might lead to tau hyperphosphorylation, partially explaining the different resu lts obtained under different conditions.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 629, "target": 3015, "key": "df40cbf0649e2860cba870f7477c1f46"}, {"line": 8202, "relation": "association", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 629, "target": 3015, "key": "7ae145b41fff27b3d261a8df4c3801a1"}, {"line": 8008, "relation": "association", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "Confidence": {"High": true}}, "source": 806, "target": 3146, "key": "ab7bb58cf328a1590be7db280bd75036"}, {"line": 8010, "relation": "association", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "Confidence": {"High": true}}, "source": 806, "target": 3117, "key": "83c3b485a39bff2fada0b11458afef38"}, {"line": 8127, "relation": "negativeCorrelation", "evidence": "Further, IDE expression is affected by aging, with IDE activity significantly decreased in both the muscles and liver of old animals as compared to young animals [76].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 806, "target": 2867, "key": "b6894faac05df392e06392d56bf721ee"}, {"line": 10320, "relation": "positiveCorrelation", "evidence": "In late-onset sporadic Alzheimer disease, the neuronal insulin receptor may be desensitized by inhibition of receptor function at different sites by noradrenaline and/or cortisol, the levels of which both increase with increasing age.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Subgraph": {"Cortisol subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 806, "target": 237, "key": "c5be2f6a72aeb518d6ea08b5a370551b"}, {"line": 10321, "relation": "positiveCorrelation", "evidence": "In late-onset sporadic Alzheimer disease, the neuronal insulin receptor may be desensitized by inhibition of receptor function at different sites by noradrenaline and/or cortisol, the levels of which both increase with increasing age.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Subgraph": {"Cortisol subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 806, "target": 317, "key": "53800d760a884ab75ef4fdec9e6d46d3"}, {"line": 15748, "relation": "decreases", "evidence": "Age-related androgen depletion is known to be a risk factor for various diseases, such as osteoporosis and sarcopenia.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true}, "Subgraph": {"Androgen subgraph": true}}, "source": 806, "target": 209, "key": "48761cb8da26d8940b88c8cbd7e67e7b"}, {"line": 15757, "relation": "decreases", "evidence": "Furthermore, recent studies have demonstrated that age-related androgen depletion results in accumulation of beta-amyloid protein and thereby acts as a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 806, "target": 209, "key": "a6757d7009d2960713db94c048e1f1f9"}, {"line": 15799, "relation": "decreases", "evidence": "In both brain areas of male and female patients over the age of 56 nuclear staining had almost disappeared and cytoplasmic AR expression was decreased.", "citation": {"db": "PubMed", "db_id": "12093089"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "CellStructure": {"Cytoplasm": true}, "Species": {"9606": true}, "Subgraph": {"Androgen subgraph": true}}, "source": 806, "target": 2354, "key": "a21acac42a334a39387c9422fbef17a0"}, {"line": 16958, "relation": "decreases", "evidence": "Although minimizing these detrimental factors is the best course of action, nonetheless chronological age steadily impairs endothelial function through reduced endothelial nitric oxide synthase (eNOS) expression/action, accelerated nitric oxide (NO) degradation, increased phosphodiesterase activity, inhibition of NOS activity by endogenous NOS inhibitors, increased production of reactive oxygen species, inflammatory reactions, decreased endothelial progenitor cell number and function, and impaired telomerase activity or telomere shortening.", "citation": {"db": "PubMed", "db_id": "22079549"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 806, "target": 3124, "key": "717018d92617230aa3c161ce4a0623f9"}, {"line": 16962, "relation": "increases", "evidence": "Although minimizing these detrimental factors is the best course of action, nonetheless chronological age steadily impairs endothelial function through reduced endothelial nitric oxide synthase (eNOS) expression/action, accelerated nitric oxide (NO) degradation, increased phosphodiesterase activity, inhibition of NOS activity by endogenous NOS inhibitors, increased production of reactive oxygen species, inflammatory reactions, decreased endothelial progenitor cell number and function, and impaired telomerase activity or telomere shortening.", "citation": {"db": "PubMed", "db_id": "22079549"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 806, "target": 156, "key": "b8dc5ab880b5322d7d8bf1a40c6b135f"}, {"line": 16966, "relation": "decreases", "evidence": "Although minimizing these detrimental factors is the best course of action, nonetheless chronological age steadily impairs endothelial function through reduced endothelial nitric oxide synthase (eNOS) expression/action, accelerated nitric oxide (NO) degradation, increased phosphodiesterase activity, inhibition of NOS activity by endogenous NOS inhibitors, increased production of reactive oxygen species, inflammatory reactions, decreased endothelial progenitor cell number and function, and impaired telomerase activity or telomere shortening.", "citation": {"db": "PubMed", "db_id": "22079549"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 806, "target": 747, "key": "85f669b55c0f462efecd12cdb2d3e5e1"}, {"line": 16970, "relation": "increases", "evidence": "Although minimizing these detrimental factors is the best course of action, nonetheless chronological age steadily impairs endothelial function through reduced endothelial nitric oxide synthase (eNOS) expression/action, accelerated nitric oxide (NO) degradation, increased phosphodiesterase activity, inhibition of NOS activity by endogenous NOS inhibitors, increased production of reactive oxygen species, inflammatory reactions, decreased endothelial progenitor cell number and function, and impaired telomerase activity or telomere shortening.", "citation": {"db": "PubMed", "db_id": "22079549"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 806, "target": 170, "key": "796fd9078dde608cefea4f59e7c25f3e"}, {"line": 16974, "relation": "decreases", "evidence": "Although minimizing these detrimental factors is the best course of action, nonetheless chronological age steadily impairs endothelial function through reduced endothelial nitric oxide synthase (eNOS) expression/action, accelerated nitric oxide (NO) degradation, increased phosphodiesterase activity, inhibition of NOS activity by endogenous NOS inhibitors, increased production of reactive oxygen species, inflammatory reactions, decreased endothelial progenitor cell number and function, and impaired telomerase activity or telomere shortening.", "citation": {"db": "PubMed", "db_id": "22079549"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 806, "target": 765, "key": "d68b6f0e5c0ecf4d0f49723f14afe075"}, {"line": 17536, "relation": "decreases", "evidence": "The production of IL-6 and IL-10 was significantly lower when compared to that of the middle-aged, but did not differ between the elderly patients with and without dementia.", "citation": {"db": "PubMed", "db_id": "11961364"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Species": {"9606": true}, "Disease": {"dementia": true}}, "source": 806, "target": 2894, "key": "b458987e7a9a26b62160176b7d793c06"}, {"line": 17537, "relation": "decreases", "evidence": "The production of IL-6 and IL-10 was significantly lower when compared to that of the middle-aged, but did not differ between the elderly patients with and without dementia.", "citation": {"db": "PubMed", "db_id": "11961364"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Species": {"9606": true}, "Disease": {"dementia": true}}, "source": 806, "target": 2878, "key": "0af5fbf422605eb0980b50c4922e6f32"}, {"line": 17575, "relation": "association", "evidence": "ABCB1 genotypes are presently not useful as a biomarker for dementia, as they were not significantly different between demented patients and age-matched control subjects.", "citation": {"db": "PubMed", "db_id": "16999857"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true}, "Disease": {"dementia": true}, "Species": {"9606": true}}, "source": 806, "target": 2232, "key": "6c5c7e599786fd630e7cf4250b366e68"}, {"line": 21829, "relation": "increases", "evidence": "We recently reported that aging is associated with a significant increase in neuronal 5-LOX gene expression and with increased, 5-LOX inhibitor-sensitive, vulnerability of neurons to degeneration.", "citation": {"db": "PubMed", "db_id": "10790729"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 806, "target": 2288, "key": "08e00248b23b21ddce20e675c41d443a"}, {"line": 23826, "relation": "decreases", "evidence": "There is a physiological decline of the growth hormone (GH)/insulin-like growth factor-I (IGF-I) axis with ageing and the possibility that the GH/ IGF-I axis is involved in cognitive deficits has been recognized for several years. The IGF-I is a potent neurotrophic as well neuroprotective factor found in the brain with a wide range of actions in both central and peripheral nervous system. IGF-I is a critical promoter of brain development and neuronal survival and plays a role in neuronal rescue during degenerative diseases.When a cholinesterase inhibitor as rivastigmine, a drug for AD, is acutely administered the area under the curve of the GH response to GHRH doubled, showing that rivastigmine is a powerful drug to enhance GH release. TNFα production may promote neurodegeneration not through direct killing of neurons but rather through inhibition of IGF-I survival signalling", "citation": {"db": "PubMed", "db_id": "22524398"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Neuroprotection subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 806, "target": 2871, "key": "82d42db7dd434cc992632fe22df9b7aa"}, {"line": 48077, "relation": "increases", "evidence": "Our results showed increased Dickkopf-1 protein levels in SAMP8 with age, in addition to GSK-3 α/beta activation and hyperphosphorylated tau", "citation": {"db": "PubMed", "db_id": "25443287"}, "annotations": {"Species": {"10090": true}}, "source": 806, "target": 3624, "key": "1e345883cac9d87e2aaaa9fe4b073d49"}, {"line": 48078, "relation": "increases", "evidence": "Our results showed increased Dickkopf-1 protein levels in SAMP8 with age, in addition to GSK-3 α/beta activation and hyperphosphorylated tau", "citation": {"db": "PubMed", "db_id": "25443287"}, "annotations": {"Species": {"10090": true}}, "object": {"modifier": "Activity"}, "source": 806, "target": 3640, "key": "b3c768f313beec52636c437ee33a18dd"}, {"line": 48079, "relation": "increases", "evidence": "Our results showed increased Dickkopf-1 protein levels in SAMP8 with age, in addition to GSK-3 α/beta activation and hyperphosphorylated tau", "citation": {"db": "PubMed", "db_id": "25443287"}, "annotations": {"Species": {"10090": true}}, "source": 806, "target": 3676, "key": "bb59b8400e3a4aa24d3fc2ee1edbe513"}, {"line": 8010, "relation": "association", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "Confidence": {"High": true}}, "source": 3117, "target": 806, "key": "3d80a35603a00a2de5dbfe99be3f3a08"}, {"relation": "partOf", "source": 3117, "target": 1592, "key": "c9bba5b3532f6c471c1a0c0e051fad5b"}, {"relation": "partOf", "source": 3117, "target": 936, "key": "5df169644d31ab59d047e4d1e19fcf7a"}, {"line": 31836, "relation": "decreases", "evidence": "APP-dependent transcription mediated by Fe65 is blocked by p75(NTR)", "citation": {"db": "PubMed", "db_id": "19334058"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3117, "target": 2299, "key": "cd10e2eae9666d61e8e5d90d9121f8eb"}, {"line": 33561, "relation": "association", "evidence": "A possible cell surface target for Abetas is the p75 neurotrophin receptor (p75(NTR)).", "citation": {"db": "PubMed", "db_id": "17385278"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3117, "target": 80, "key": "dc852b8e5c0da824b8af23fe50e289e8"}, {"line": 33576, "relation": "increases", "evidence": "Soluble Abeta40, the major amyloid precursor protein cleavage product, by itself stimulates astrocytes to express NOS-2 and make NO, possibly by activating p75(NTR) receptors, which they share with neurons, and can considerably amplify NOS-2 expression by the pro-inflammatory cytokine trio. ", "citation": {"db": "PubMed", "db_id": "17385278"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3117, "target": 3123, "key": "0024534f0dbcb09431095723c58d805a"}, {"relation": "partOf", "source": 3117, "target": 1238, "key": "a1e47fe5725fe39e630b02869b822618"}, {"relation": "hasVariant", "source": 3117, "target": 3118, "key": "3cb20f32f74f37d5263dcb07c89350cd"}, {"relation": "partOf", "source": 3117, "target": 1473, "key": "09261f00c91149514e0d29d45bee1168"}, {"line": 35186, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3117, "target": 3112, "key": "8e0febabb4d120d46a1263faf85550fe"}, {"line": 38668, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3117, "target": 3112, "key": "24480e091259edc7a5b2324c919b55c6"}, {"line": 8031, "relation": "increases", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"CellLine": {"SH-SY5Y": true}, "UserdefinedCellLine": {"primary cortical neuron": true}, "Subgraph": {"Beta secretase subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3998, "target": 2375, "key": "fb29f4f6e076cdff795ce4fced9b6de8"}, {"line": 8052, "relation": "association", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 735, "target": 2328, "key": "f896b11afdfe809988662386b4fef46a"}, {"line": 8053, "relation": "increases", "evidence": "During aging changes in the cerebral expression levels of the neurotrophin receptors, TrkA (tyrosine kinase receptor A) and p75l'lTR (p75 neurotrophin receptor) have been de­ scribed. In the human neuroblastoma cell line SHSY5Y as well as in primary cultured neurons chronic treatment with IGF-1 leads to a switch from TrkA to p75NTR expression as seen in aging brains [128]. This switch causes increased 13- secretase activity indirectly by activation of neuronal sphin­ gomyelinase which is responsible for hydrolysis of sphin­ gomyelin and the active liberation of the second messenger ceramide (review in (129]). Ceramide is responsible for the molecular stabilization of BACE-1, the 13-secretase which is rate-limiting for generation of A[130]. This process leads to accumulation of Ap, connecting IGF-1 R signaling to neu­ rotrophin action (Fig. l). ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 735, "target": 658, "key": "b2e948309c3630f64161a9845f171b5c"}, {"line": 8066, "relation": "increases", "evidence": "Experiments in Caenoi-habditis elegans revealed new insights into the role oflR/IGF-IR signaling in A1 -42 toxic­ ity, and Ametabolism. Cohen and coworkers could show that knocking down the DAF-2 pathway in C. elegans, which is orthologous to the mammalian insulin and IGF-l signaling cascade, reduces Al31-42 toxicity [35]. Furthermore, this effect was mediated by the two downstream transcrip­ tion factors, DAF-16 and HSF-l (heat shock transcription factor-!) [132}. DAF-\\6 encodes a forkhead transcription factor [133, 134], which translocates into the nucleus [135], and modulates transcription when DAF-2 signaling is abro­ gated . The mammalian DAF-16 orthologs are Foxol, 3, and 4 [136). In the mammalian system the IR/IGF-1 R induces phosphorylation of Foxo I and triggers its translocation from the nucleus. The DAF-2 pathway reduces A ,_42 toxicity by two possible mechanisms of detoxification [35]: The first detoxification route leads to disaggregation of the toxic oli­ gomer that is positively regulated by HSF-1 and degradation of the amyloidogenic peptides. The second mechanism mediates the formation of low toxic, high molecular weight aggregates from high toxic low molecular weight aggregates, which is positively regulated by DAF-!6. Both detoxifica­ tion mechanisms are negatively regulated by DAF-2 signal­ ing.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 2701, "target": 580, "key": "b1cadad47d1203ea133bb0f9dc59512a"}, {"relation": "hasVariant", "source": 2701, "target": 2702, "key": "ba6f62d5c04fd6f13b643adfaeaffd46"}, {"line": 10730, "relation": "association", "evidence": "The anti-neurodegenerative agent ", "citation": {"db": "PubMed", "db_id": "22248233"}, "source": 2701, "target": 27, "key": "f91653cde1040a317432e6fafd3d7b68"}, {"line": 10744, "relation": "association", "evidence": "Comparative investigation of the signalling properties of a panel of these compounds demonstrates that CQ alone exhibits FOXO1a regulation without diabetogenicity.", "citation": {"db": "PubMed", "db_id": "22248233"}, "subject": {"modifier": "Activity"}, "source": 2701, "target": 27, "key": "221936d6ab8451d186b067ed0d827a9c"}, {"line": 10748, "relation": "association", "evidence": "Our results suggest that Zn2+-dependent regulation of FOXOs and gluconeogenesis may contribute to the therapeutic properties of this drug.", "citation": {"db": "PubMed", "db_id": "22248233"}, "subject": {"modifier": "Activity"}, "source": 2701, "target": 189, "key": "8c7ff1b33dd8b0355bf375b8e387ee96"}, {"line": 8067, "relation": "increases", "evidence": "Experiments in Caenoi-habditis elegans revealed new insights into the role oflR/IGF-IR signaling in A1 -42 toxic­ ity, and Ametabolism. Cohen and coworkers could show that knocking down the DAF-2 pathway in C. elegans, which is orthologous to the mammalian insulin and IGF-l signaling cascade, reduces Al31-42 toxicity [35]. Furthermore, this effect was mediated by the two downstream transcrip­ tion factors, DAF-16 and HSF-l (heat shock transcription factor-!) [132}. DAF-\\6 encodes a forkhead transcription factor [133, 134], which translocates into the nucleus [135], and modulates transcription when DAF-2 signaling is abro­ gated . The mammalian DAF-16 orthologs are Foxol, 3, and 4 [136). In the mammalian system the IR/IGF-1 R induces phosphorylation of Foxo I and triggers its translocation from the nucleus. The DAF-2 pathway reduces A ,_42 toxicity by two possible mechanisms of detoxification [35]: The first detoxification route leads to disaggregation of the toxic oli­ gomer that is positively regulated by HSF-1 and degradation of the amyloidogenic peptides. The second mechanism mediates the formation of low toxic, high molecular weight aggregates from high toxic low molecular weight aggregates, which is positively regulated by DAF-!6. Both detoxifica­ tion mechanisms are negatively regulated by DAF-2 signal­ ing.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 2705, "target": 580, "key": "c74a80c627811c3e4a20a7b8cca91d91"}, {"line": 8068, "relation": "increases", "evidence": "Experiments in Caenoi-habditis elegans revealed new insights into the role oflR/IGF-IR signaling in A1 -42 toxic­ ity, and Ametabolism. Cohen and coworkers could show that knocking down the DAF-2 pathway in C. elegans, which is orthologous to the mammalian insulin and IGF-l signaling cascade, reduces Al31-42 toxicity [35]. Furthermore, this effect was mediated by the two downstream transcrip­ tion factors, DAF-16 and HSF-l (heat shock transcription factor-!) [132}. DAF-\\6 encodes a forkhead transcription factor [133, 134], which translocates into the nucleus [135], and modulates transcription when DAF-2 signaling is abro­ gated . The mammalian DAF-16 orthologs are Foxol, 3, and 4 [136). In the mammalian system the IR/IGF-1 R induces phosphorylation of Foxo I and triggers its translocation from the nucleus. The DAF-2 pathway reduces A ,_42 toxicity by two possible mechanisms of detoxification [35]: The first detoxification route leads to disaggregation of the toxic oli­ gomer that is positively regulated by HSF-1 and degradation of the amyloidogenic peptides. The second mechanism mediates the formation of low toxic, high molecular weight aggregates from high toxic low molecular weight aggregates, which is positively regulated by DAF-!6. Both detoxifica­ tion mechanisms are negatively regulated by DAF-2 signal­ ing.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 2844, "target": 580, "key": "37eb2d0b0811af44f819c065ba720293"}, {"relation": "hasVariant", "source": 2844, "target": 2845, "key": "51aee863a583e7b02a62d83686cec189"}, {"line": 8072, "relation": "association", "evidence": "Experiments in Caenoi-habditis elegans revealed new insights into the role oflR/IGF-IR signaling in A1 -42 toxic­ ity, and Ametabolism. Cohen and coworkers could show that knocking down the DAF-2 pathway in C. elegans, which is orthologous to the mammalian insulin and IGF-l signaling cascade, reduces Al31-42 toxicity [35]. Furthermore, this effect was mediated by the two downstream transcrip­ tion factors, DAF-16 and HSF-l (heat shock transcription factor-!) [132}. DAF-\\6 encodes a forkhead transcription factor [133, 134], which translocates into the nucleus [135], and modulates transcription when DAF-2 signaling is abro­ gated . The mammalian DAF-16 orthologs are Foxol, 3, and 4 [136). In the mammalian system the IR/IGF-1 R induces phosphorylation of Foxo I and triggers its translocation from the nucleus. The DAF-2 pathway reduces A ,_42 toxicity by two possible mechanisms of detoxification [35]: The first detoxification route leads to disaggregation of the toxic oli­ gomer that is positively regulated by HSF-1 and degradation of the amyloidogenic peptides. The second mechanism mediates the formation of low toxic, high molecular weight aggregates from high toxic low molecular weight aggregates, which is positively regulated by DAF-!6. Both detoxifica­ tion mechanisms are negatively regulated by DAF-2 signal­ ing.", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "intracellular"}, "toLoc": {"namespace": "GO", "name": "nucleus"}}}, "source": 2702, "target": 583, "key": "0a1311044ace9fcf020cc06b5ffc01ae"}, {"line": 8097, "relation": "increases", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2651, "target": 2328, "key": "4a12adba99b01738af5342fa4ec87cae"}, {"line": 19931, "relation": "increases", "evidence": "In the brain, endothelin-1 (ET-1) is a locally acting vasoconstrictor, produced in neurons by endothelin-converting enzyme (ECE)-2 and in endothelial cells by ECE-1.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endothelin subgraph": true}}, "source": 2651, "target": 2653, "key": "4f410ec49def5bedf5b33266939e9437"}, {"line": 30356, "relation": "increases", "evidence": "Endothelin-converting enzyme-1 and 2 (ECE-1 and ECE-2) are expressed in endothelial cells and neurones, respectively, and both cleave 'big endothelin' to produce the vasoconstrictor endothelin-1 (ET-1).", "citation": {"db": "PubMed", "db_id": "20345647"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2651, "target": 2653, "key": "c4124d0c6ad5c4470d623d4fc1161ab1"}, {"line": 19940, "relation": "positiveCorrelation", "evidence": "We previously showed ECE-2 and ET-1 to be elevated in postmortem temporal cortex from AD patients, and ECE-2 expression and ET-1 release to be upregulated by Abeta42 in vitro.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Species": {"9606": true}}, "source": 2651, "target": 3823, "key": "d842fd55bad1fe8b93d526f45cfab4a4"}, {"line": 19972, "relation": "increases", "evidence": "Our findings indicate that cerebral vasoconstriction induced by Abeta results in part from a free radical-mediated increase in ECE-1 activity and ET-1 production.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 2651, "target": 824, "key": "cf72b706f1e56945c2336d279a263da4"}, {"relation": "partOf", "source": 2651, "target": 1404, "key": "2275ef8c561e2d9a5f4316d7a4ecfe3c"}, {"line": 8100, "relation": "increases", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2243, "target": 80, "key": "793b1a15f4dfe4566c208ecb0938d3b2"}, {"line": 21080, "relation": "association", "evidence": "The association of angiotensin-converting enzyme with biomarkers for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}, "Confidence": {"High": true}}, "source": 2243, "target": 3823, "key": "eecdfb888dea266353f9e204645c6c49"}, {"line": 21090, "relation": "negativeCorrelation", "evidence": "Lower angiotensin-converting enzyme (ACE) activity could increase the risk of Alzheimer's disease (AD) as ACE functions to degrade amyloid-beta (Abeta).", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2243, "target": 3823, "key": "85cda5fad9f28f628f598baf3f77a000"}, {"line": 21103, "relation": "negativeCorrelation", "evidence": "We measured ACE protein levels (ng/ml) and activity (RFU) in CSF and serum, and amyloid beta1-42, tau and ptau (pg/ml) in CSF. Cross-sectional regression analyses showed that ACE protein level and activity in CSF and serum were lower in patients with AD compared to controls.", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true, "Cerebrospinal Fluid": true}, "Species": {"9606": true}, "Subgraph": {"Renin-angiotensin subgraph": true}, "Confidence": {"High": true}}, "source": 2243, "target": 3823, "key": "bec5c89f3af4c43ef68ffd620a274c55"}, {"line": 21104, "relation": "negativeCorrelation", "evidence": "We measured ACE protein levels (ng/ml) and activity (RFU) in CSF and serum, and amyloid beta1-42, tau and ptau (pg/ml) in CSF. Cross-sectional regression analyses showed that ACE protein level and activity in CSF and serum were lower in patients with AD compared to controls.", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true, "Cerebrospinal Fluid": true}, "Species": {"9606": true}, "Subgraph": {"Renin-angiotensin subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2243, "target": 3823, "key": "6b7fb85f64a8487a77d4aad10dffe2eb"}, {"line": 21137, "relation": "negativeCorrelation", "evidence": "Plasma ACE was lower in the AD subjects as compared to the controls both at baseline (p = 0.072) and after two years (p = 0.05).", "citation": {"db": "PubMed", "db_id": "19276555"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 2243, "target": 3823, "key": "2ecaf7c54fdaeaa335e9bb2cf95a2d07"}, {"line": 24636, "relation": "positiveCorrelation", "evidence": "AIMS: Several observations point to the involvement of angiotensin-converting enzyme-1 (ACE-1) in Alzheimer's disease (AD): ACE-1 cleaves amyloid-beta peptide (Abeta) in vitro, the level and activity of ACE-1 are reportedly increased in AD, and variations in the ACE-1 gene are associated with AD.", "citation": {"db": "PubMed", "db_id": "17973905"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2243, "target": 3823, "key": "9e9e2f65be4df8911120995a94be93e1"}, {"line": 24637, "relation": "positiveCorrelation", "evidence": "AIMS: Several observations point to the involvement of angiotensin-converting enzyme-1 (ACE-1) in Alzheimer's disease (AD): ACE-1 cleaves amyloid-beta peptide (Abeta) in vitro, the level and activity of ACE-1 are reportedly increased in AD, and variations in the ACE-1 gene are associated with AD.", "citation": {"db": "PubMed", "db_id": "17973905"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2243, "target": 3823, "key": "857cb400d07556abf5dbe8f2c2258a0c"}, {"line": 21091, "relation": "increases", "evidence": "Lower angiotensin-converting enzyme (ACE) activity could increase the risk of Alzheimer's disease (AD) as ACE functions to degrade amyloid-beta (Abeta).", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2243, "target": 2328, "key": "1604ecbb761372d524babac587d4103f"}, {"line": 21118, "relation": "negativeCorrelation", "evidence": "Lower CSF ACE protein level, and to a lesser extent serum ACE protein level and CSF ACE activity, were associated with lower CSF Abeta, indicating more brain Abeta pathology; adjusted regression coefficients (B) (95% CI) per SD increase were 0.09 (0.04; 0.15), 0.06 (0.00; 0.12) and 0.05 (0.00; 0.11), respectively. Further, lower CSF ACE protein level was associated with lower CSF tau and ptau levels; adjusted B's (95% CI) per SD increase were 0.15 (0.06; 0.25) and 0.17 (0.10; 0.25), respectively.These results strengthen the hypothesis that ACE degrades Abeta.", "citation": {"db": "PubMed", "db_id": "24987467"}, "annotations": {"Anatomy": {"cerebrospinal fluid": true, "serum": true}, "Confidence": {"Medium": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2243, "target": 2328, "key": "4b5c343844a2c4e53317db0c3a475d5f"}, {"line": 24623, "relation": "increases", "evidence": "These findings led to the hypothesis that ACE may affect susceptibility to AD by degrading A beta and preventing the accumulation of amyloid plaques in vivo.", "citation": {"db": "PubMed", "db_id": "11604391"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2243, "target": 2328, "key": "1274875d608c372cdfb13016216303e2"}, {"line": 24627, "relation": "increases", "evidence": "The most striking fact was that ACE degraded A beta by cleaving A beta-(1-40) at the site Asp(7)-Ser(8).", "citation": {"db": "PubMed", "db_id": "11604391"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2243, "target": 2327, "key": "f379bc4222e026c29d1bce83ff41c53c"}, {"relation": "partOf", "source": 2243, "target": 1044, "key": "4a6eb0e91f7a87bf0735940a725f220e"}, {"line": 24821, "relation": "decreases", "evidence": "ACE was found to significantly inhibit A beta aggregation in a dose response manner.", "citation": {"db": "PubMed", "db_id": "11604391"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2243, "target": 377, "key": "e1b604139a1a6e48c7b50ff25cfc2ab5"}, {"line": 8104, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 934, "target": 80, "key": "ea3b13ff0ec391702a33a54b010d889c"}, {"line": 8106, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 915, "target": 80, "key": "35eb4ad3738282db89a4eecb764644a4"}, {"line": 8107, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Alpha 2 macroglobulin subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 909, "target": 80, "key": "5179dc9f6eb6af814e499bd9b329953c"}, {"line": 8110, "relation": "association", "evidence": "The amyloid hypothesis of AD suggests that -amyloid accumulation is the critical event in development of disease (I 49, I 50]. Lots of research has been done on the formation and accumulation of Al3, however, in the last years the mechanism' of amyloid clearance came into focus. For Al3 clearance several mechanisms are known (Fig. 2): i) Enzy­ matic degradation by activated microglia or by insulin de­ grading enzyme (IDE), neprilysin, endothelin converting enzyme (ECE), and angiotensin converting enzyme (ACE); ii) Receptor-mediated transport across the blood brain barrier (BBB) by binding to the low-density lipoprotein receptor­ related protein (LRP) either directly or after binding to apol­ ipoprotein E (ApoE) and/or a2-macroglobulin (a2M) to be delivered to peripheral sites of degradation, e.g., liver and kidney. (Review in [151 ]). Concerning insulin resistance it has been shown that IDE expression is stimulated by the IR/lGF-1 R cascade (152]. ", "citation": {"db": "PubMed", "db_id": "19519303"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3979, "target": 580, "key": "f2f7ff96ffa11368d5adade5abce87e6"}, {"line": 8117, "relation": "positiveCorrelation", "evidence": "There are probably several mechanisms underlying the relationship between type 2 diabetes and the increased risk of AD. For example, the formation of advanced glycation end product (AGE) in diabetes has been shown to be aggregated with Abeta in plaques of AD brain", "citation": {"db": "PubMed", "db_id": "16399206"}, "source": 74, "target": 3823, "key": "3f5c09995774e7427ed44a57ad494528"}, {"line": 48355, "relation": "association", "evidence": "Cyclin D1 positive neurons are colocalized with AGEs [Advanced glycation end products] or directly surrounded by extracellular AGE deposits in AD brain.", "citation": {"db": "PubMed", "db_id": "25448604"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "Cell cycle subgraph": true}}, "source": 74, "target": 2463, "key": "372392f7e6243379955910e0d0a29227"}, {"line": 8123, "relation": "decreases", "evidence": "In addition, ubiquitin forms a complex with, and inhibits the activity of, IDE within the cells [77].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2868, "target": 2867, "key": "af5dc3f98aac783f0c7c559e61c929c0"}, {"line": 8208, "relation": "association", "evidence": "Insulin and insulin receptors were shown to decrease in a normal brain with aging, but increase in AD brains [29]. Several basic science studies have explored and shown the relationship between the increased insulin and AD pathology in the aspects other than Abeta degradation alone. For example, insulin increases the secretion of Abeta into extracellular space [31], stimulates tau phosphorylation to form neurofibrillary tangles, and impairs insulin signal transduction [32] and [40] (reviewed by Gasparini and Hoyer). Insulin also affected APP processing in vivo, a critical molecular step in generating Abeta, to secrete sAPP [13], [16] and [81]. In addition, Abeta reduces insulin binding to insulin receptors [95].", "citation": {"db": "PubMed", "db_id": "16399206"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 475, "target": 2899, "key": "33a44e6527f52b8ec85c25a9f1da7dec"}, {"line": 8244, "relation": "association", "evidence": "The underlying mechanisms of the association between AD neuropathology and DM2 emanate from several factors, however cortical insulin resistance and inflammatory path­ ways appear to be key components of this association. Son­ nen eta/. [14] demonstrated the AD cases with DM2 had higher levels of cortical IL-6 and greater frequency of mi­ crovascular infarcts when compared to AD cases without DM2. Further research by Freude et al. [15] has linked hy-perinsulinemia to tau hyperphosphorylation which is an im­ portant component in the process underlying AD pathology. Schubert eta!. [16] demonstrated that impaired cortical insu­ lin resistance is also linked to tau hyperphosphorylation. Martins et al. [17] also describe several studies implicating neuronal insulin resistance as a precursor to increased amy­ loid deposition. Additional work by Kulstad et al. [18] sug­ gested that insulin levels are associated with abnormal regu­ lation of AP clearance which may also play a mechanistic role in the formation of AD pathology.", "citation": {"db": "PubMed", "db_id": "23627755"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}}, "source": 710, "target": 2899, "key": "bf4cca993eb674fb8c0890068ecf2afc"}, {"line": 8292, "relation": "increases", "evidence": "In contrast, GLUT 4 and 8 (or X1) are insulin-sensitive transporters,[20,21] which are expressed in intracellular compartments of adipocytes andmuscle cells and are translocated to membranes in response to the presence of insulin.[ 22] Translocation allows muscle and adipose tissue to increase glucose uptake 10- to 40-fold within a matter of minutes.[22]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Adipose Tissue": true, "Muscles": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Intracellular Space"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 3376, "target": 565, "key": "69c5e290b27e4a90cbcd97a1f07b8a65"}, {"line": 8293, "relation": "increases", "evidence": "In contrast, GLUT 4 and 8 (or X1) are insulin-sensitive transporters,[20,21] which are expressed in intracellular compartments of adipocytes andmuscle cells and are translocated to membranes in response to the presence of insulin.[ 22] Translocation allows muscle and adipose tissue to increase glucose uptake 10- to 40-fold within a matter of minutes.[22]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Insulin signal transduction": true}, "MeSHAnatomy": {"Adipose Tissue": true, "Muscles": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Intracellular Space"}, "toLoc": {"namespace": "MESH", "name": "Cell Membrane"}}}, "source": 3377, "target": 565, "key": "ec47d68dba781fdfd8ef50dd55624e41"}, {"line": 8301, "relation": "association", "evidence": "Another consequence of insulin resistance may be impaired regulation of the hypothalamicpituitary- adrenal (HPA) axis. Insulin and cortisol, a primary HPA axis hormone, are counter-regulatory, and a change in the level of either hormone influences the level of the other.[29,30] Thus, glucocorticoids can induce insulin resistance in healthy humans,[ 31,32] and hyperinsulinaemia resulting from insulin resistance can produce hypercortisolaemia. An animal study showed that rats with type 2 diabetes had higher levels of adrenocorticotrophic hormone than control individuals, consistent with chronic activation of the HPA axis.[33] In humans, plasma cortisol levels were elevated in patients with type 2 diabetes,[34] and patientswith both poorly and well controlled type 2 diabetes showed abnormal cortisol responses to hypoglycaemia.[35,36] In metabolic studies, insulin administration has been shown to increase HPA axis activity, indexed by a rise in cortisol levels", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 237, "target": 3861, "key": "9af6414836686839662692fcae6e61de"}, {"line": 8310, "relation": "positiveCorrelation", "evidence": "Another consequence of insulin resistance may be impaired regulation of the hypothalamicpituitary- adrenal (HPA) axis. Insulin and cortisol, a primary HPA axis hormone, are counter-regulatory, and a change in the level of either hormone influences the level of the other.[29,30] Thus, glucocorticoids can induce insulin resistance in healthy humans,[ 31,32] and hyperinsulinaemia resulting from insulin resistance can produce hypercortisolaemia. An animal study showed that rats with type 2 diabetes had higher levels of adrenocorticotrophic hormone than control individuals, consistent with chronic activation of the HPA axis.[33] In humans, plasma cortisol levels were elevated in patients with type 2 diabetes,[34] and patientswith both poorly and well controlled type 2 diabetes showed abnormal cortisol responses to hypoglycaemia.[35,36] In metabolic studies, insulin administration has been shown to increase HPA axis activity, indexed by a rise in cortisol levels", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 237, "target": 3850, "key": "e8b87567edd5a1d7e5093bf9331532bd"}, {"line": 10315, "relation": "decreases", "evidence": "In late-onset sporadic Alzheimer disease, the neuronal insulin receptor may be desensitized by inhibition of receptor function at different sites by noradrenaline and/or cortisol, the levels of which both increase with increasing age.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Subgraph": {"Cortisol subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 237, "target": 2900, "key": "ac86d2fee8e76ff5e108cce2156f2882"}, {"line": 10320, "relation": "positiveCorrelation", "evidence": "In late-onset sporadic Alzheimer disease, the neuronal insulin receptor may be desensitized by inhibition of receptor function at different sites by noradrenaline and/or cortisol, the levels of which both increase with increasing age.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Subgraph": {"Cortisol subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 237, "target": 806, "key": "a6eb8831e61dd125ac5f1ada217e2ddf"}, {"line": 12923, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 237, "target": 655, "key": "398535ee09cc8c3f53b419da483f5df1"}, {"line": 12943, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 237, "target": 812, "key": "1ddc398060ee95fd4914360c41c7f984"}, {"line": 15682, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cortisol subgraph": true}}, "source": 237, "target": 3823, "key": "b053e9d2a6f83e74952f2d08bbb7acbb"}, {"line": 17472, "relation": "association", "evidence": "Patients with Alzheimer's disease (AD) are characterized by an altered sensitivity to cortisol-mediated modulation of circulating lymphocytes.", "citation": {"db": "PubMed", "db_id": "17597922"}, "annotations": {"Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"Cortisol subgraph": true}}, "source": 237, "target": 3823, "key": "df700293ae87f7d103002dce838d454c"}, {"line": 8306, "relation": "positiveCorrelation", "evidence": "Another consequence of insulin resistance may be impaired regulation of the hypothalamicpituitary- adrenal (HPA) axis. Insulin and cortisol, a primary HPA axis hormone, are counter-regulatory, and a change in the level of either hormone influences the level of the other.[29,30] Thus, glucocorticoids can induce insulin resistance in healthy humans,[ 31,32] and hyperinsulinaemia resulting from insulin resistance can produce hypercortisolaemia. An animal study showed that rats with type 2 diabetes had higher levels of adrenocorticotrophic hormone than control individuals, consistent with chronic activation of the HPA axis.[33] In humans, plasma cortisol levels were elevated in patients with type 2 diabetes,[34] and patientswith both poorly and well controlled type 2 diabetes showed abnormal cortisol responses to hypoglycaemia.[35,36] In metabolic studies, insulin administration has been shown to increase HPA axis activity, indexed by a rise in cortisol levels", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}, "Species": {"10116": true}}, "source": 3805, "target": 3850, "key": "22377e1b9e5e9dc8b2207e7e5ca2d2a2"}, {"line": 11393, "relation": "negativeCorrelation", "evidence": "We demonstrate that astrocytic expression of calpain-10 is up-regulated, and CamKIIα down-regulated with increasing Braak stage. Using immunohistochemistry we confirm protein expression of calpain-10 in astrocytes throughout the temporal cortex and demonstrate that calpain-10 immunoreactivity is correlated with both local and global measures of Alzheimer-type pathology.", "citation": {"db": "PubMed", "db_id": "23421725"}, "annotations": {"Subgraph": {"Calpastatin-calpain subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "source": 2425, "target": 3823, "key": "6082f810301acf5206c00b91e2ce9f35"}, {"line": 29697, "relation": "increases", "evidence": "These studies suggest that PKA, cdk5, CaM Kinase II and GSK-3 are involved in the regulation of phosphorylation of tau and that AD-type phosphorylation of tau is probably a product of the synergistic action of two or more of these kinases.", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2425, "target": 3015, "key": "fc30b07bcb1946b926a443115a1beae6"}, {"line": 32577, "relation": "increases", "evidence": "Ser727 of STAT1 can be phosphorylated by diverse kinases, such as phosphatidylinositol 3-kinase/Akt, calcium/calpmodulin-dependent kinase II, protein kinase C, and MAPKs.", "citation": {"db": "PubMed", "db_id": "17091494"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "JAK-STAT signaling subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2425, "target": 3425, "key": "f69388a623ba2ddc7faf7ad9ab03b29f"}, {"relation": "partOf", "source": 2425, "target": 1696, "key": "85bf69d28bfc066465b5cc141d9daf56"}, {"line": 36911, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2425, "target": 2163, "key": "f096f46117752459e410c1b66b0c1ec0"}, {"line": 8385, "relation": "decreases", "evidence": "Thus, laboratory and clinical findings are consistent with epidemiological reports of a reduced prevalence of Alzheimer's disease among persons who take nonsteroidal anti-inflammatory drugs (NSAIDs) for chronic pain.[125,126] Interleukin-6 (IL-6), an inflammatory cytokine, is one of the products that has been implicated in Alzheimer's disease. Elevated IL-6 immunoreactivity has been shown in human lumbar and ventricular CSF in patientswith Alzheimer's disease.[127] Furthermore, IL-6 has been found in senile plaques and may be involved in both the development of plaques and the development of dementia.[128]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 158, "target": 3823, "key": "5677f708d440eadb70b3a8b1a9265657"}, {"line": 8388, "relation": "association", "evidence": "Thus, laboratory and clinical findings are consistent with epidemiological reports of a reduced prevalence of Alzheimer's disease among persons who take nonsteroidal anti-inflammatory drugs (NSAIDs) for chronic pain.[125,126] Interleukin-6 (IL-6), an inflammatory cytokine, is one of the products that has been implicated in Alzheimer's disease. Elevated IL-6 immunoreactivity has been shown in human lumbar and ventricular CSF in patientswith Alzheimer's disease.[127] Furthermore, IL-6 has been found in senile plaques and may be involved in both the development of plaques and the development of dementia.[128]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 393, "target": 2894, "key": "9d70ce9040ea3631064338f52a60fe9a"}, {"line": 8389, "relation": "positiveCorrelation", "evidence": "Thus, laboratory and clinical findings are consistent with epidemiological reports of a reduced prevalence of Alzheimer's disease among persons who take nonsteroidal anti-inflammatory drugs (NSAIDs) for chronic pain.[125,126] Interleukin-6 (IL-6), an inflammatory cytokine, is one of the products that has been implicated in Alzheimer's disease. Elevated IL-6 immunoreactivity has been shown in human lumbar and ventricular CSF in patientswith Alzheimer's disease.[127] Furthermore, IL-6 has been found in senile plaques and may be involved in both the development of plaques and the development of dementia.[128]", "citation": {"db": "PubMed", "db_id": "19383491"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 393, "target": 3823, "key": "b45dc889e07c32f43a9244fe58e9cff7"}, {"line": 8426, "relation": "regulates", "evidence": "The expression of microRNA miR-107 decreases early in Alzheimer's disease and may accelerate disease progression through regulation of beta-site amyloid precursor protein-cleaving enzyme 1. Among the AD-related miRNA expression changes, miR-107 was exceptional because miR-107 levels decreased significantly even in patients with the earliest stages of pathology. In situ hybridization with cross-comparison to neuropathology demonstrated that particular cerebral cortical laminas involved by AD pathology exhibit diminished neuronal miR-107 expression. Computational analysis predicted that the 3'-untranslated region (UTR) of beta-site amyloid precursor protein-cleaving enzyme 1 (BACE1) mRNA is targeted multiply by miR-107.", "citation": {"db": "PubMed", "db_id": "18234899"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2092, "target": 2375, "key": "7f4346a325c8054b11ab12ad9265149b"}, {"line": 9228, "relation": "negativeCorrelation", "evidence": "The Expression of MicroRNA miR-107 Decreases Early in Alzheimer’s Disease and May Accelerate Disease Progression through Regulation of beta-Site Amyloid Precursor Protein-Cleaving Enzyme 1. BACE1 mRNA levels tended to increase as miR-107 levels decreased in the progression of AD. ", "citation": {"db": "PubMed", "db_id": "18234899"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2092, "target": 2375, "key": "7e96eb4a7805af264886ee35251b7e49"}, {"line": 45906, "relation": "negativeCorrelation", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2092, "target": 2375, "key": "888b1d7fc945c1dbecfeefc51b0c10b1"}, {"line": 45923, "relation": "increases", "evidence": "HAT activity of p300 stimulates the PS1 and BACE1 promoter histone hyperacetylation", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2092, "target": 2375, "key": "c7ef9476f814ccbafa7a0d6c87f29bfa"}, {"line": 8671, "relation": "negativeCorrelation", "evidence": "MiR-107 is a microRNA (miRNA) that we reported previously to have decreased expression in the temporal cortical gray matter early in the progression of Alzheimer's disease (AD). Here we study a new group of well-characterized human temporal cortex samples (N=19). MiR-107 expression was assessed, normalized to miR-124 and let-7a. Correlation was observed between decreased miR-107 expression and increased neuritic plaque counts (P< 0.05) and neurofibrillary tangle counts (P< 0.02) in adjacent brain tissue. Adjusted miR-107 and BACE1 mRNA levels tended to correlate negatively (trend with regression P< 0.07). In sum, miR-107 expression tends to be lower relative to other miRNAs as AD progresses.", "citation": {"db": "PubMed", "db_id": "20413881"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2092, "target": 3943, "key": "6cb0fce407f3a6adec3440ff8fa8c5c8"}, {"line": 45096, "relation": "negativeCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true, "miRNA subgraph": true}, "Confidence": {"Low": true}}, "source": 2092, "target": 3943, "key": "11da7595db4d1a3b7b27a5b09de16f4e"}, {"line": 8716, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2092, "target": 3823, "key": "fef5973567ec4ad37194bbeb5e3c8eb2"}, {"line": 9227, "relation": "negativeCorrelation", "evidence": "The Expression of MicroRNA miR-107 Decreases Early in Alzheimer’s Disease and May Accelerate Disease Progression through Regulation of beta-Site Amyloid Precursor Protein-Cleaving Enzyme 1. BACE1 mRNA levels tended to increase as miR-107 levels decreased in the progression of AD. ", "citation": {"db": "PubMed", "db_id": "18234899"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2092, "target": 3823, "key": "e98ddba08cbd0d4c718f43846da38fd3"}, {"line": 45922, "relation": "negativeCorrelation", "evidence": "HAT activity of p300 stimulates the PS1 and BACE1 promoter histone hyperacetylation", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2092, "target": 3823, "key": "69bbf92a90f3bee78f4d85eeaf770d83"}, {"line": 8717, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2092, "target": 3972, "key": "773429f4113f3f8128d68e547e806699"}, {"line": 8920, "relation": "decreases", "evidence": "Eleven miRNAs were selected, which have evolutionary conserved binding sites. Three of them (miR-103, miR-107, miR-1306) were further analysed as they are linked to AD and most strictly conserved between different species. Predicted target genes of miR-103 (p-value = 0.0065) and miR-107 (p-value = 0.0009) showed significant overlap with the AlzGene database except for miR-1306. Interactions between miR-103 and miR-107 to genes were revealed playing a role in processes leading to AD. ADAM10 expression in the reporter assay was reduced by miR-1306 (28%), miR-103 (45%) and miR-107 (52%).", "citation": {"db": "PubMed", "db_id": "22594617"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "miRNA subgraph": true}}, "source": 2092, "target": 2249, "key": "b1fba96c9c67c6abb6b146772fd5485d"}, {"line": 8439, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2115, "target": 2375, "key": "d66b8dd174709cbe38d741fd4594d1f2"}, {"line": 8442, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2115, "target": 2375, "key": "1111b99b5ba05ba862aae50bc048fd4c"}, {"line": 45926, "relation": "increases", "evidence": "HAT activity of p300 stimulates the PS1 and BACE1 promoter histone hyperacetylation", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2115, "target": 2375, "key": "f9f130c8a7e79cdbf061c6caa0aebf59"}, {"line": 8443, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2115, "target": 3943, "key": "3933966df52514130c1e5b87ac2ab1bc"}, {"line": 8444, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2115, "target": 1755, "key": "bc075b3a4a341c40726a0cf6a753cffa"}, {"line": 8617, "relation": "association", "evidence": "Aberrant microRNA expression in the brains of neurodegenerative diseases: miR-29a decreased in Alzheimer disease brains targets neurone navigator 3. However, we found significant down-regulation of miR-29a in Alzheimer disease (AD) brains. The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains. MiR-29a-mediated down-regulation of NAV3 was verified by the luciferase reporter assay. By immunohistochemistry, NAV3 expression was most evidently enhanced in degenerating pyramidal neurones in the cerebral cortex of AD. These observations suggest the hypothesis that underexpression of miR-29a affects neurodegenerative processes by enhancing neuronal NAV3 expression in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"miRNA subgraph": true, "Axonal guidance subgraph": true}}, "source": 2115, "target": 3997, "key": "e775b6b1849e832c12991bd85170934f"}, {"line": 8620, "relation": "increases", "evidence": "Aberrant microRNA expression in the brains of neurodegenerative diseases: miR-29a decreased in Alzheimer disease brains targets neurone navigator 3. However, we found significant down-regulation of miR-29a in Alzheimer disease (AD) brains. The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains. MiR-29a-mediated down-regulation of NAV3 was verified by the luciferase reporter assay. By immunohistochemistry, NAV3 expression was most evidently enhanced in degenerating pyramidal neurones in the cerebral cortex of AD. These observations suggest the hypothesis that underexpression of miR-29a affects neurodegenerative processes by enhancing neuronal NAV3 expression in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"miRNA subgraph": true, "Axonal guidance subgraph": true}}, "source": 2115, "target": 3997, "key": "031e83a8bf6ee5050171fe227e488c1a"}, {"line": 8884, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2115, "target": 4013, "key": "19e6db6553c0a0ed19f32671c659a71b"}, {"line": 8885, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2115, "target": 2328, "key": "029f2221a4ce9f68cc4496d25b556c0d"}, {"line": 8447, "relation": "decreases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3820, "target": 2115, "key": "a8641f9be30686a3ac5f2ed40cfb1604"}, {"line": 8448, "relation": "decreases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 3820, "target": 2116, "key": "05272724cf5a13a85faab05b17fbe9e8"}, {"line": 8485, "relation": "decreases", "evidence": "We show that miRNAs belonging to the miR-20a family (that is, miR-20a, miR-17-5p and miR-106b) could regulate APP expression in vitro and at the endogenous level in neuronal cell lines. A tight correlation between these miRNAs and APP was found during brain development and in differentiating neurons. We thus identify miRNAs as novel endogenous regulators of APP expression, suggesting that variations in miRNA expression could contribute to changes in APP expression in the brain during development and disease. This possibility is further corroborated by the observation that a statistically significant decrease in miR-106b expression was found in sporadic AD patients.", "citation": {"db": "PubMed", "db_id": "19110058"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3820, "target": 2091, "key": "db5c18db71e79e159520830500a48424"}, {"line": 9210, "relation": "negativeCorrelation", "evidence": "Such possibility is further corroborated by the observation that a significant decrease in miR-106b expression was found in sporadic AD patients.On the other hand, two miRNAs (miR-298 and miR-328) was found to regulate BACE mRNA translation, while BACE was responsible for APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 3820, "target": 2091, "key": "7f00174af0615363e1155c2b0a3823a8"}, {"line": 28282, "relation": "association", "evidence": "Four genes have been established to either cause familial early onset AD (APP, PSEN1, and PSEN2) or to increase susceptibility for late onset AD (APOE). ", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3820, "target": 2312, "key": "635a3b5fd28c665de8bf6d86d5cb233a"}, {"line": 45032, "relation": "positiveCorrelation", "evidence": "Two genes were increased in LOAD (C10orf105 and RARRES3),while three genes were decreased in LOAD", "citation": {"db": "PubMed", "db_id": "25380588"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3820, "target": 2407, "key": "15e86e159da86fb7332a195c18763dbe"}, {"line": 45033, "relation": "positiveCorrelation", "evidence": "Two genes were increased in LOAD (C10orf105 and RARRES3),while three genes were decreased in LOAD", "citation": {"db": "PubMed", "db_id": "25380588"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3820, "target": 3297, "key": "cfc4625f039c762413b76a9b122812ae"}, {"line": 45039, "relation": "negativeCorrelation", "evidence": "Two networks involved in myelination and innate immune response specifically correlated to LOAD. FRMD4B and ST18, hub genes within the myelination network, were previously implicated in LOAD.Overall hypomethylation was observed across the genome in LOAD when compared to both controls", "citation": {"db": "PubMed", "db_id": "25380588"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3820, "target": 1828, "key": "e3a2f3c9e166d979e7e146127e1b8745"}, {"line": 45040, "relation": "negativeCorrelation", "evidence": "Two networks involved in myelination and innate immune response specifically correlated to LOAD. FRMD4B and ST18, hub genes within the myelination network, were previously implicated in LOAD.Overall hypomethylation was observed across the genome in LOAD when compared to both controls", "citation": {"db": "PubMed", "db_id": "25380588"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3820, "target": 1984, "key": "2086947629df264ededc0f9788deb929"}, {"line": 45138, "relation": "association", "evidence": "LOAD patients were usually correlated with a further demethylation of the PSEN1 and TFAM promoters.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3820, "target": 1926, "key": "689de00864be677e13fd8bd314bdf382"}, {"line": 45159, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3820, "target": 571, "key": "056e1e58b110ed18f43af4444cbbe25c"}, {"line": 45711, "relation": "negativeCorrelation", "evidence": "In LOAD subjects, there was a statistically significant reduction in Ser(16) phosphorylation (-30%; p = 0.041) and promoter methylation (-8%; p = 0.001), whereas Pin1 expression was significantly increased", "citation": {"db": "PubMed", "db_id": "22261503"}, "annotations": {"Cell": {"blood cell": true}}, "source": 3820, "target": 3193, "key": "36c09f6f7e8fdff5d474fb12e6edb45d"}, {"line": 45714, "relation": "negativeCorrelation", "evidence": "In LOAD subjects, there was a statistically significant reduction in Ser(16) phosphorylation (-30%; p = 0.041) and promoter methylation (-8%; p = 0.001), whereas Pin1 expression was significantly increased", "citation": {"db": "PubMed", "db_id": "22261503"}, "annotations": {"Cell": {"blood cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3820, "target": 1914, "key": "d477e4dda816773b4935f8b04f5c7b5e"}, {"line": 45782, "relation": "negativeCorrelation", "evidence": "we also observed in LOAD subjects an increase in FAAH protein levels and activity , as well as a reduction in DNA methylation at faah gene promoter", "citation": {"db": "PubMed", "db_id": "22720070"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3820, "target": 1824, "key": "cec1adb7d543914ab68f14a66fe1d1e2"}, {"line": 8453, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2116, "target": 2375, "key": "4324326cb1d26c02891088ea02e5bcda"}, {"line": 8454, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2116, "target": 2375, "key": "a81876ba73d093a184fc9a450e19e5ed"}, {"line": 45929, "relation": "increases", "evidence": "HAT activity of p300 stimulates the PS1 and BACE1 promoter histone hyperacetylation", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2116, "target": 2375, "key": "c94e60c98a36d773797baae631a978d4"}, {"line": 8455, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2116, "target": 3943, "key": "420489c791b134f92c9a5b1a325d0ded"}, {"line": 8456, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2116, "target": 1755, "key": "e10331b1a645a639cf171363a8c43d16"}, {"line": 8460, "relation": "increases", "evidence": "The miR-29a/b-1 cluster was significantly (and AD-dementia-specific) decreased in AD patients displaying abnormally high BACE1 protein. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.We show here that miR-29a, -29b-1, and -9 can regulate BACE1 expression in vitro.", "citation": {"db": "PubMed", "db_id": "18434550"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2086, "target": 2375, "key": "9d2d95581a76998220909b59b3fcd68c"}, {"line": 8881, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2086, "target": 4013, "key": "694d861d100099044fe704b9a1e50d1e"}, {"line": 8882, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2086, "target": 2328, "key": "bfb4d4257e9a8493afc288fcfd774531"}, {"line": 9193, "relation": "positiveCorrelation", "evidence": "It was reported that there was an upregulation of miR-9, miR-125b and miR-128 in hippocampus of AD affected post-mortem brain samples", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2086, "target": 3823, "key": "a7826d782f10677f762b9e3005ad6875"}, {"line": 45482, "relation": "increases", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "Confidence": {"Medium": true}}, "source": 2086, "target": 3823, "key": "b6fd3414ada775a7a708ca51c1a3853c"}, {"line": 48289, "relation": "association", "evidence": ". IL-6, AP3B1, TC10, ONECUT2, IGF2BP1, MYO1D, and ANXA2 were confirmed to be miR-9 targets in HCC.", "citation": {"db": "PubMed", "db_id": "26547929"}, "source": 2086, "target": 3317, "key": "69e8b3a3396d2505be9d46412263491f"}, {"line": 8470, "relation": "decreases", "evidence": "Here we provide evidence in AD brains of a specific up-regulation of an NF-kappaB-sensitive miRNA-146a highly complementary to the 3'-untranslated region of complement factor H (CFH), an important repressor of the inflammatory response of the brain.Transfection of HN cells using an NF-kappaB-containing pre-miRNA-146a promoter-luciferase reporter construct in stressed HN cells showed significant up-regulation of luciferase activity that paralleled decreases in CFH gene expression. Up-regulation of miRNA-146a coupled to down-regulation of CFH was observed in AD brain and in interleukin-1beta, Abeta42, and/or oxidatively stressed human neural (HN) cells in primary culture.", "citation": {"db": "PubMed", "db_id": "18801740"}, "annotations": {"Subgraph": {"Complement system subgraph": true, "miRNA subgraph": true}}, "source": 2102, "target": 2506, "key": "ff9f789559bdc771d79a8c1ca6e90aed"}, {"line": 8820, "relation": "decreases", "evidence": "MicroRNA-146a (miRNA-146a) is an inducible, 22 nucleotide, small RNA over-expressed in Alzheimer's disease (AD) brain. Up-regulated miRNA-146a targets several inflammation-related and membrane-associated messenger RNAs (mRNAs), including those encoding complement factor-H (CFH) and the interleukin-1 receptor associated kinase-1 (IRAK-1), resulting in significant decreases in their expression (p<0.05, ANOVA). In this study we assayed miRNA-146a, CFH, IRAK-1 and tetraspanin-12 (TSPAN12), abundances in primary human neuronal-glial (HNG) co-cultures, in human astroglial (HAG) and microglial (HMG) cells stressed with Abeta42 peptide and tumor necrosis factor alpha (TNFalpha). The results indicate a consistent inverse relationship between miRNA-146a and CFH, IRAK-1 and TSPAN12 expression levels, and indicate that HNG, HAG and HMG cell types each respond differently to Abeta42-peptide+TNFalpha-triggered stress. While the strongest miRNA-146a-IRAK-1 response was found in HAG cells, the largest miRNA-146a-TSPAN12 response was found in HNG cells, and the most significant miRNA-146a-CFH changes were found in HMG cells, the 'resident scavenging macrophages' of the brain.", "citation": {"db": "PubMed", "db_id": "21640790"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 2506, "key": "d5404cda9ec73f77f664a355bdef3782"}, {"line": 8494, "relation": "decreases", "evidence": "Here we provide evidence in human neural (HN) cells of an aluminum-sulfate- and reactive oxygen species (ROS)-mediated up-regulation of an NF-kappaB-sensitive miRNA-146a that down-regulates the expression of complement factor H (CFH), an important repressor of inflammation. This NF-kappaB-miRNA-146a-CFH signaling circuit is known to be similarly affected by Abeta42 peptides and in AD brain. These aluminum-sulfate-inducible events were not observed in parallel experiments using iron-, magnesium-, or zinc-sulfate-stressed HN cells. An NF-kappaB-containing miRNA-146a-promoter-luciferase reporter construct transfected into HN cells showed significant up-regulation of miRNA-146a after aluminum-sulfate treatment that corresponded to decreased CFH gene expression. These data suggest that (1) as in AD brain, NF-kappaB-sensitive, miRNA-146a-mediated, modulation of CFH gene expression may contribute to inflammatory responses in aluminum-stressed HN cells, and (2) underscores the potential of nanomolar aluminum to drive genotoxic mechanisms characteristic of neurodegenerative disease processes.", "citation": {"db": "PubMed", "db_id": "19540598"}, "annotations": {"Subgraph": {"Complement system subgraph": true, "miRNA subgraph": true}}, "source": 2102, "target": 3955, "key": "cf71c1533f79687c5cbd5638162fa1c5"}, {"line": 8518, "relation": "decreases", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Complement system subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 3955, "key": "6959071c88c1c7e93b61f9ba6835bc8c"}, {"line": 8507, "relation": "association", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 3823, "key": "4575d174b8d4ecd99f80f259dced200a"}, {"line": 46174, "relation": "association", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2102, "target": 3823, "key": "39db00dbae041e8e58c2bc41f3932675"}, {"line": 8508, "relation": "association", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 577, "key": "fb9d807dc66ed81f03a583ecbd15282f"}, {"line": 9131, "relation": "positiveCorrelation", "evidence": "Recently, there have been increasing evidences that microRNA-146 (miR-146) is related to up-regulated immune and inflammatory signaling through its target genes, such as IRAK1 and TRAF6. Additionally, abundant data continue to support the hypothesis that progressive up-regulation of inflammatory gene expression and elevated inflammatory signaling facilitate the development and progression of Alzheimer's disease (AD). This review focuses on the recent findings regarding the role of miR-146 in modulating immune response and its subsequent effects in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22209051"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 577, "key": "9df1f6879e395e8328c691bc7ccfb345"}, {"line": 8509, "relation": "increases", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 4000, "key": "d2d33dac143ea4669739626709972031"}, {"line": 8512, "relation": "increases", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 4008, "key": "39dfda666c224958ff30cc48d6a58740"}, {"line": 8515, "relation": "increases", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 3982, "key": "94e28a5b6a5e7456d2983c5ff1dabb39"}, {"line": 46173, "relation": "increases", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2102, "target": 3982, "key": "2f56f8207b4d6d6060b5b641f25b2761"}, {"line": 8755, "relation": "decreases", "evidence": "Differential regulation of interleukin-1 receptor-associated kinase-1 (IRAK-1) and IRAK-2 by microRNA-146a and NF-kappaB in stressed human astroglial cells and in Alzheimer disease.In monocytes, increased expression of an NF-κB-regulated miRNA-146a down-regulates expression of the interleukin-1 receptor-associated kinase-1 (IRAK-1), an essential component of Toll-like/IL-1 receptor signaling. Here we extend those observations to the hippocampus and neocortex of Alzheimer disease (AD) brain and to stressed human astroglial (HAG) cells in primary culture. In 66 control and AD samples we note a significant up-regulation of miRNA-146a coupled to down-regulation of IRAK-1 and a compensatory up-regulation of IRAK-2. Using miRNA-146a-, IRAK-1-, or IRAK-2 promoter-luciferase reporter constructs, we observe decreases in IRAK-1 and increases in miRNA-146a and IRAK-2 expression in interleukin-1beta (IL-1beta) and amyloid-beta-42 (Abeta42) peptide-stressed HAG cells. NF-κB-mediated transcriptional control of human IRAK-2 was localized to between -119 and +12 bp of the immediate IRAK-2 promoter. The NF-κB inhibitors curcumin, pyrrolidine dithiocarbamate or CAY10512 abrogated both IRAK-2 and miRNA-146a expression, whereas IRAK-1 was up-regulated. Incubation of a protected antisense miRNA-146a was found to inhibit miRNA-146a and restore IRAK-1, whereas IRAK-2 remained unaffected. These data suggest a significantly independent regulation of IRAK-1 and IRAK-2 in AD and in IL-1beta+Abeta42 peptide-stressed HAG cells and that an inducible, NF-κB-sensitive, miRNA-146a-mediated down-regulation of IRAK-1 coupled to an NF-κB-induced up-regulation of IRAK-2 expression drives an extensively sustained inflammatory response. The interactive signaling of NF-κB and miRNA-146a further illustrate interplay between inducible transcription factors and pro-inflammatory miRNAs that regulate brain IRAK expression. The combinatorial use of NF-κB inhibitors with miRNA-146a or antisense miRNA-146a may have potential as a bi-pronged therapeutic strategy directed against IRAK-2-driven pathogenic signaling.", "citation": {"db": "PubMed", "db_id": "20937840"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 3987, "key": "71edc6e65dbe8f0a24993e51c572dc3f"}, {"line": 9132, "relation": "association", "evidence": "Recently, there have been increasing evidences that microRNA-146 (miR-146) is related to up-regulated immune and inflammatory signaling through its target genes, such as IRAK1 and TRAF6. Additionally, abundant data continue to support the hypothesis that progressive up-regulation of inflammatory gene expression and elevated inflammatory signaling facilitate the development and progression of Alzheimer's disease (AD). This review focuses on the recent findings regarding the role of miR-146 in modulating immune response and its subsequent effects in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22209051"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 3987, "key": "d52d026939e0c17475d6a0220472df7a"}, {"line": 8757, "relation": "increases", "evidence": "Differential regulation of interleukin-1 receptor-associated kinase-1 (IRAK-1) and IRAK-2 by microRNA-146a and NF-kappaB in stressed human astroglial cells and in Alzheimer disease.In monocytes, increased expression of an NF-κB-regulated miRNA-146a down-regulates expression of the interleukin-1 receptor-associated kinase-1 (IRAK-1), an essential component of Toll-like/IL-1 receptor signaling. Here we extend those observations to the hippocampus and neocortex of Alzheimer disease (AD) brain and to stressed human astroglial (HAG) cells in primary culture. In 66 control and AD samples we note a significant up-regulation of miRNA-146a coupled to down-regulation of IRAK-1 and a compensatory up-regulation of IRAK-2. Using miRNA-146a-, IRAK-1-, or IRAK-2 promoter-luciferase reporter constructs, we observe decreases in IRAK-1 and increases in miRNA-146a and IRAK-2 expression in interleukin-1beta (IL-1beta) and amyloid-beta-42 (Abeta42) peptide-stressed HAG cells. NF-κB-mediated transcriptional control of human IRAK-2 was localized to between -119 and +12 bp of the immediate IRAK-2 promoter. The NF-κB inhibitors curcumin, pyrrolidine dithiocarbamate or CAY10512 abrogated both IRAK-2 and miRNA-146a expression, whereas IRAK-1 was up-regulated. Incubation of a protected antisense miRNA-146a was found to inhibit miRNA-146a and restore IRAK-1, whereas IRAK-2 remained unaffected. These data suggest a significantly independent regulation of IRAK-1 and IRAK-2 in AD and in IL-1beta+Abeta42 peptide-stressed HAG cells and that an inducible, NF-κB-sensitive, miRNA-146a-mediated down-regulation of IRAK-1 coupled to an NF-κB-induced up-regulation of IRAK-2 expression drives an extensively sustained inflammatory response. The interactive signaling of NF-κB and miRNA-146a further illustrate interplay between inducible transcription factors and pro-inflammatory miRNAs that regulate brain IRAK expression. The combinatorial use of NF-κB inhibitors with miRNA-146a or antisense miRNA-146a may have potential as a bi-pronged therapeutic strategy directed against IRAK-2-driven pathogenic signaling.", "citation": {"db": "PubMed", "db_id": "20937840"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 3988, "key": "e081b2797f2e8d7f242211ea22b2ee52"}, {"line": 8821, "relation": "decreases", "evidence": "MicroRNA-146a (miRNA-146a) is an inducible, 22 nucleotide, small RNA over-expressed in Alzheimer's disease (AD) brain. Up-regulated miRNA-146a targets several inflammation-related and membrane-associated messenger RNAs (mRNAs), including those encoding complement factor-H (CFH) and the interleukin-1 receptor associated kinase-1 (IRAK-1), resulting in significant decreases in their expression (p<0.05, ANOVA). In this study we assayed miRNA-146a, CFH, IRAK-1 and tetraspanin-12 (TSPAN12), abundances in primary human neuronal-glial (HNG) co-cultures, in human astroglial (HAG) and microglial (HMG) cells stressed with Abeta42 peptide and tumor necrosis factor alpha (TNFalpha). The results indicate a consistent inverse relationship between miRNA-146a and CFH, IRAK-1 and TSPAN12 expression levels, and indicate that HNG, HAG and HMG cell types each respond differently to Abeta42-peptide+TNFalpha-triggered stress. While the strongest miRNA-146a-IRAK-1 response was found in HAG cells, the largest miRNA-146a-TSPAN12 response was found in HNG cells, and the most significant miRNA-146a-CFH changes were found in HMG cells, the 'resident scavenging macrophages' of the brain.", "citation": {"db": "PubMed", "db_id": "21640790"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 2904, "key": "e5f62dfcfd99c79c5eefe1da5a679210"}, {"line": 9532, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 2102, "target": 2904, "key": "7df7ed5d00ff56d07486bd12da1792e5"}, {"line": 8822, "relation": "decreases", "evidence": "MicroRNA-146a (miRNA-146a) is an inducible, 22 nucleotide, small RNA over-expressed in Alzheimer's disease (AD) brain. Up-regulated miRNA-146a targets several inflammation-related and membrane-associated messenger RNAs (mRNAs), including those encoding complement factor-H (CFH) and the interleukin-1 receptor associated kinase-1 (IRAK-1), resulting in significant decreases in their expression (p<0.05, ANOVA). In this study we assayed miRNA-146a, CFH, IRAK-1 and tetraspanin-12 (TSPAN12), abundances in primary human neuronal-glial (HNG) co-cultures, in human astroglial (HAG) and microglial (HMG) cells stressed with Abeta42 peptide and tumor necrosis factor alpha (TNFalpha). The results indicate a consistent inverse relationship between miRNA-146a and CFH, IRAK-1 and TSPAN12 expression levels, and indicate that HNG, HAG and HMG cell types each respond differently to Abeta42-peptide+TNFalpha-triggered stress. While the strongest miRNA-146a-IRAK-1 response was found in HAG cells, the largest miRNA-146a-TSPAN12 response was found in HNG cells, and the most significant miRNA-146a-CFH changes were found in HMG cells, the 'resident scavenging macrophages' of the brain.", "citation": {"db": "PubMed", "db_id": "21640790"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 3501, "key": "18ad67642780b11bb75c18a7af0e2548"}, {"line": 9133, "relation": "association", "evidence": "Recently, there have been increasing evidences that microRNA-146 (miR-146) is related to up-regulated immune and inflammatory signaling through its target genes, such as IRAK1 and TRAF6. Additionally, abundant data continue to support the hypothesis that progressive up-regulation of inflammatory gene expression and elevated inflammatory signaling facilitate the development and progression of Alzheimer's disease (AD). This review focuses on the recent findings regarding the role of miR-146 in modulating immune response and its subsequent effects in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22209051"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2102, "target": 4027, "key": "09aa00cf5b719835a0f8cd5ba3d6ad42"}, {"line": 9525, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 2102, "target": 3468, "key": "5f0fa1a4b30606b69595ebf1e184c3c2"}, {"line": 9526, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 2102, "target": 3467, "key": "9000fcaccfe0f5e1c7973e9ff7902020"}, {"line": 9528, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}}, "source": 2102, "target": 2885, "key": "fda2f4b827acfde4033cedacc2b85433"}, {"line": 9529, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}}, "source": 2102, "target": 2884, "key": "e922a3a13b269959532d2ac306c4d6ba"}, {"line": 9531, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 2102, "target": 3491, "key": "c20e4b49af7a53586a55d4f392e8b910"}, {"relation": "isA", "source": 2106, "target": 2111, "key": "d3b5dc2f7421c49c0736b42a4f22d630"}, {"relation": "isA", "source": 2091, "target": 2111, "key": "58aa177f43b8933c3daed82c4aa9166f"}, {"line": 8484, "relation": "association", "evidence": "We show that miRNAs belonging to the miR-20a family (that is, miR-20a, miR-17-5p and miR-106b) could regulate APP expression in vitro and at the endogenous level in neuronal cell lines. A tight correlation between these miRNAs and APP was found during brain development and in differentiating neurons. We thus identify miRNAs as novel endogenous regulators of APP expression, suggesting that variations in miRNA expression could contribute to changes in APP expression in the brain during development and disease. This possibility is further corroborated by the observation that a statistically significant decrease in miR-106b expression was found in sporadic AD patients.", "citation": {"db": "PubMed", "db_id": "19110058"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2091, "target": 2315, "key": "b88aa74dc1554456ad90966dd1d0ecab"}, {"line": 45992, "relation": "association", "evidence": "The levels of DNA methylation in promoters of APP, BACE1, and PS1 genes are decreased after anisomycin treatment.", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2091, "target": 2315, "key": "336e51f343b71a8aec1d678985f228f2"}, {"line": 9210, "relation": "negativeCorrelation", "evidence": "Such possibility is further corroborated by the observation that a significant decrease in miR-106b expression was found in sporadic AD patients.On the other hand, two miRNAs (miR-298 and miR-328) was found to regulate BACE mRNA translation, while BACE was responsible for APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2091, "target": 3820, "key": "67a0744ad1894c62d92f577bb7b88005"}, {"line": 8496, "relation": "increases", "evidence": "Here we provide evidence in human neural (HN) cells of an aluminum-sulfate- and reactive oxygen species (ROS)-mediated up-regulation of an NF-kappaB-sensitive miRNA-146a that down-regulates the expression of complement factor H (CFH), an important repressor of inflammation. This NF-kappaB-miRNA-146a-CFH signaling circuit is known to be similarly affected by Abeta42 peptides and in AD brain. These aluminum-sulfate-inducible events were not observed in parallel experiments using iron-, magnesium-, or zinc-sulfate-stressed HN cells. An NF-kappaB-containing miRNA-146a-promoter-luciferase reporter construct transfected into HN cells showed significant up-regulation of miRNA-146a after aluminum-sulfate treatment that corresponded to decreased CFH gene expression. These data suggest that (1) as in AD brain, NF-kappaB-sensitive, miRNA-146a-mediated, modulation of CFH gene expression may contribute to inflammatory responses in aluminum-stressed HN cells, and (2) underscores the potential of nanomolar aluminum to drive genotoxic mechanisms characteristic of neurodegenerative disease processes.", "citation": {"db": "PubMed", "db_id": "19540598"}, "annotations": {"Subgraph": {"Complement system subgraph": true}}, "source": 3955, "target": 2506, "key": "0615cf3378a5b5df2a6dc7fd015ce9c8"}, {"line": 8908, "relation": "negativeCorrelation", "evidence": "Down's syndrome brain is typified by activated microglia, increases in inflammatory signaling, and an aberrant immune system. In these studies, a screening of micro-RNA (miRNA) from Down's syndrome brain and peripheral tissues indicated an upregulation of a chromosome 21-encoded miRNA-155 and a decrease in the abundance of the miRNA-155 mRNA target complement factor H (CFH), an important repressor of the innate immune response. Stressed primary human neuronal-glial cells indicated both miRNA-155 increase and CFH downregulation, an effect that was reversed using anti-miRNA-155. These findings suggest that immunopathological deficits associated with Down's syndrome can, in part, be explained by a generalized miRNA-155-mediated downregulation of CFH that may contribute to both brain and systemic immune pathology.", "citation": {"db": "PubMed", "db_id": "22182977"}, "annotations": {"Subgraph": {"Complement system subgraph": true, "miRNA subgraph": true}}, "source": 3955, "target": 2104, "key": "da807c4601610403464b2c67f33cbbff"}, {"line": 9397, "relation": "increases", "evidence": "Truncated phospholipids were essential elements of TNFalpha-induced apoptosis because overexpression of PAFAH2 (a phospholipase A(2) that selectively hydrolyzes truncated phospholipids) blocked TNFalpha-induced Az-PC accumulation without affecting phospholipid peroxidation. PAFAH2 also abolished apoptotic process. Thus, phospholipid oxidation and truncation to apoptotic phospholipids comprise an essential element connecting TNFalpha receptor signaling to mitochondrial damage and apoptotic death", "citation": {"db": "PubMed", "db_id": "22433871"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 4000, "target": 3161, "key": "a736dfbcfe3959f141eed65a5f9912c8"}, {"line": 8520, "relation": "increases", "evidence": "We report that infection of human primary neural cells with a high phenotypic reactivator HSV-1 (17syn+) induces upregulation of a brain-enriched microRNA (miRNA)-146a that is associated with proinflammatory signaling in stressed brain cells and Alzheimer's disease. Expression of cytoplasmic phospholipase A2, the inducible prostaglandin synthase cyclooxygenase-2, and the neuroinflammatory cytokine interleukin-1beta were each upregulated. A known miRNA-146a target in the brain, complement factor H, was downregulated. These data suggest a role for HSV-1-induced miRNA-146a in the evasion of HSV-1 from the complement system, and the activation of key elements of the arachidonic acid cascade known to contribute to Alzheimer-type neuropathological change.", "citation": {"db": "PubMed", "db_id": "19801956"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "Confidence": {"High": true}}, "source": 4008, "target": 3278, "key": "12e1c1b46d9cfc39192f37bb1b4fb396"}, {"line": 8559, "relation": "increases", "evidence": "A significant role of a pathological glial cell activation in the pathogenesis of Alzheimer's disease is supported by the growing evidence that inflammatory proteins, which are produced by reactive astrocytes, promote the transformation of diffuse beta-amyloid deposits into the filamentous, neurotoxic form. A number of vicious circles, driven by the release of TNF-a and free oxygen radicals from microglial cells, may cause an upregulated microglial activation and their production of interleukin-1 which triggers, secondarily, the crucial activation of astrocytes.", "citation": {"db": "PubMed", "db_id": "9850925"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3982, "target": 2885, "key": "413dbfd76e12f3326ff279573779369f"}, {"line": 46171, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3982, "target": 3823, "key": "9923507b97892fc161dbf1d18b652299"}, {"line": 8533, "relation": "decreases", "evidence": "Truncated phospholipids were essential elements of TNFalpha-induced apoptosis because overexpression of PAFAH2 (a phospholipase A(2) that selectively hydrolyzes truncated phospholipids) blocked TNFalpha-induced Az-PC accumulation without affecting phospholipid peroxidation. PAFAH2 also abolished apoptotic process. Thus, phospholipid oxidation and truncation to apoptotic phospholipids comprise an essential element connecting TNFalpha receptor signaling to mitochondrial damage and apoptotic death", "citation": {"db": "PubMed", "db_id": "22433871"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3161, "target": 394, "key": "79a8a8e08549d159fbb6b167c1b0fce5"}, {"line": 8534, "relation": "decreases", "evidence": "Truncated phospholipids were essential elements of TNFalpha-induced apoptosis because overexpression of PAFAH2 (a phospholipase A(2) that selectively hydrolyzes truncated phospholipids) blocked TNFalpha-induced Az-PC accumulation without affecting phospholipid peroxidation. PAFAH2 also abolished apoptotic process. Thus, phospholipid oxidation and truncation to apoptotic phospholipids comprise an essential element connecting TNFalpha receptor signaling to mitochondrial damage and apoptotic death", "citation": {"db": "PubMed", "db_id": "22433871"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3161, "target": 478, "key": "f643499e6e203ecf8474ae0fc708ab82"}, {"line": 9401, "relation": "increases", "evidence": "Truncated phospholipids were essential elements of TNFalpha-induced apoptosis because overexpression of PAFAH2 (a phospholipase A(2) that selectively hydrolyzes truncated phospholipids) blocked TNFalpha-induced Az-PC accumulation without affecting phospholipid peroxidation. PAFAH2 also abolished apoptotic process. Thus, phospholipid oxidation and truncation to apoptotic phospholipids comprise an essential element connecting TNFalpha receptor signaling to mitochondrial damage and apoptotic death", "citation": {"db": "PubMed", "db_id": "22433871"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3161, "target": 330, "key": "39a7ac6f92783104094eb61ec60ef08d"}, {"line": 9405, "relation": "decreases", "evidence": "Truncated phospholipids were essential elements of TNFalpha-induced apoptosis because overexpression of PAFAH2 (a phospholipase A(2) that selectively hydrolyzes truncated phospholipids) blocked TNFalpha-induced Az-PC accumulation without affecting phospholipid peroxidation. PAFAH2 also abolished apoptotic process. Thus, phospholipid oxidation and truncation to apoptotic phospholipids comprise an essential element connecting TNFalpha receptor signaling to mitochondrial damage and apoptotic death", "citation": {"db": "PubMed", "db_id": "22433871"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 3161, "target": 3472, "key": "72afa2f6819815101df6112b023ec59b"}, {"line": 8574, "relation": "decreases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 2112, "target": 3212, "key": "5c228c6fa084c2d19e70ba3540e218bc"}, {"line": 8580, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 2112, "target": 4025, "key": "d69cdb98e0bb6d6b5c61641d7518fa1d"}, {"line": 44084, "relation": "decreases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 2112, "target": 4025, "key": "ccbfd0db48ed0ff5f950c417cd65302a"}, {"line": 46178, "relation": "increases", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2112, "target": 4025, "key": "d8b465a6581ed7ab8b90544f05d02115"}, {"line": 8582, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2112, "target": 3984, "key": "02a310f882500f4d559cd9b7f9c5d3b4"}, {"line": 48742, "relation": "association", "evidence": "The chemokine/chemokine receptor CCL2/CCR2 axis was impaired in BDMs from AD and MCI patients, causing a deficit in cell migration. Changes were also observed in MDM-mediated phagocytosis of Abeta fibrils, correlating with alterations in the expression and processing of the triggering receptor expressed on myeloid cells 2 (TREM2). Finally, immune-related microRNAs (miRNAs), including miR-155, -154, -200b, -27b, and -128, were found to be differentially expressed in these cells.", "citation": {"db": "PubMed", "db_id": "4879648"}, "annotations": {"Cell": {"monocyte": true}}, "source": 2112, "target": 3823, "key": "967f2008a3d6cfa70403f89a26190290"}, {"line": 8587, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 4025, "target": 3472, "key": "90417b8321930bd6e826db7eac1b6081"}, {"line": 45686, "relation": "negativeCorrelation", "evidence": "In blood monocytes from our AD patients, no aberrant methylation of the TNF-alpha promoter was detectable, suggesting that the upregulation of TNF-alpha protein levels in the blood", "citation": {"db": "PubMed", "db_id": "24556805"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Cell": {"blood cell": true}, "Disease": {"Alzheimer's disease": true}}, "source": 4025, "target": 1998, "key": "4e313d2001eb33417558e9eefbbd1a44"}, {"line": 46177, "relation": "positiveCorrelation", "evidence": "AD brains exhibit significantly increased mRNA and protein levels of neuroinflammatory markers such as IL-1B and TNF-alpha, and of markers of astrocytic and microglial activation", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 4025, "target": 3823, "key": "daa5c91185f7c368e09e4e9a863c049a"}, {"line": 8583, "relation": "increases", "evidence": "As RNA stability is often regulated via 3'-untranslated regions (UTRs), we analyzed the impact of the PPARgamma-3'-UTR by reporter assays using specific constructs. LPS significantly reduced luciferase activity of the pGL3-PPARgamma-3'-UTR, suggesting that PPARgamma1 mRNA is destabilized. Deletion or mutation of a potential microRNA-27a/b (miR-27a/b) binding site within the 3'-UTR restored luciferase activity. Moreover, inhibition of miR-27b, which was induced upon LPS exposure, partially reversed PPARgamma1 mRNA decay, whereas miR-27b overexpression decreased PPARgamma1 mRNA content. In addition, LPS further reduced this decay. The functional relevance of miR-27b-dependent PPARgamma1 decrease was proven by inhibition or overexpression of miR-27b, which affected LPS-induced expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and interleukin (IL)-6. We provide evidence that LPS-induced miR-27b contributes to destabilization of PPARgamma1 mRNA. Understanding molecular mechanisms decreasing PPARgamma might help to better appreciate inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 3984, "target": 2894, "key": "69a6f3e2245a9a1def64fbd89e00f754"}, {"line": 8596, "relation": "increases", "evidence": "During differentiation of macrophages primarily the promoter 3 and to a certain extent promoter 1 is activated. Consequently macrophages mainly express PPARgamma1 (10). In macrophages PPARgamma represses inducible nitric-oxide (NO) synthase induction as well as concomitant NO production (11) and attenuates the oxidative burst (13, 14). Moreover, inhibiting nuclear factor κB (NFκB) decreases expression of inflammatory cytokines such as interleukin (IL)-1, tumor necrosis factor α (TNFα) or IL-6 (12). Thus, PPARgamma is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 599, "target": 3212, "key": "104cb934785dbb578980fe24d6f7dd1d"}, {"line": 46894, "relation": "increases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 599, "target": 577, "key": "feee2c0a67d6cd2dad8c028cfc271263"}, {"line": 8598, "relation": "increases", "evidence": "During differentiation of macrophages primarily the promoter 3 and to a certain extent promoter 1 is activated. Consequently macrophages mainly express PPARgamma1 (10). In macrophages PPARgamma represses inducible nitric-oxide (NO) synthase induction as well as concomitant NO production (11) and attenuates the oxidative burst (13, 14). Moreover, inhibiting nuclear factor κB (NFκB) decreases expression of inflammatory cytokines such as interleukin (IL)-1, tumor necrosis factor α (TNFα) or IL-6 (12). Thus, PPARgamma is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 4003, "target": 3212, "key": "cc92b686bd238e3c6fb4385260cf9e66"}, {"line": 8603, "relation": "increases", "evidence": "During differentiation of macrophages primarily the promoter 3 and to a certain extent promoter 1 is activated. Consequently macrophages mainly express PPARgamma1 (10). In macrophages PPARgamma represses inducible nitric-oxide (NO) synthase induction as well as concomitant NO production (11) and attenuates the oxidative burst (13, 14). Moreover, inhibiting nuclear factor κB (NFκB) decreases expression of inflammatory cytokines such as interleukin (IL)-1, tumor necrosis factor α (TNFα) or IL-6 (12). Thus, PPARgamma is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 3113, "target": 2884, "key": "f682631046e497876c64e3287433cb51"}, {"line": 8604, "relation": "increases", "evidence": "During differentiation of macrophages primarily the promoter 3 and to a certain extent promoter 1 is activated. Consequently macrophages mainly express PPARgamma1 (10). In macrophages PPARgamma represses inducible nitric-oxide (NO) synthase induction as well as concomitant NO production (11) and attenuates the oxidative burst (13, 14). Moreover, inhibiting nuclear factor κB (NFκB) decreases expression of inflammatory cytokines such as interleukin (IL)-1, tumor necrosis factor α (TNFα) or IL-6 (12). Thus, PPARgamma is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 3113, "target": 2885, "key": "0a011f6487206842484920e3fa3df573"}, {"line": 8607, "relation": "increases", "evidence": "During differentiation of macrophages primarily the promoter 3 and to a certain extent promoter 1 is activated. Consequently macrophages mainly express PPARgamma1 (10). In macrophages PPARgamma represses inducible nitric-oxide (NO) synthase induction as well as concomitant NO production (11) and attenuates the oxidative burst (13, 14). Moreover, inhibiting nuclear factor κB (NFκB) decreases expression of inflammatory cytokines such as interleukin (IL)-1, tumor necrosis factor α (TNFα) or IL-6 (12). Thus, PPARgamma is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 3113, "target": 3472, "key": "c59bec52989b2cf1fab25863f09f25c2"}, {"line": 8608, "relation": "increases", "evidence": "During differentiation of macrophages primarily the promoter 3 and to a certain extent promoter 1 is activated. Consequently macrophages mainly express PPARgamma1 (10). In macrophages PPARgamma represses inducible nitric-oxide (NO) synthase induction as well as concomitant NO production (11) and attenuates the oxidative burst (13, 14). Moreover, inhibiting nuclear factor κB (NFκB) decreases expression of inflammatory cytokines such as interleukin (IL)-1, tumor necrosis factor α (TNFα) or IL-6 (12). Thus, PPARgamma is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation", "citation": {"db": "PubMed", "db_id": "20164187"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 3113, "target": 2894, "key": "8bd19e482b63eb9d79c4c7d8698f09d2"}, {"relation": "partOf", "source": 3113, "target": 1588, "key": "704debf928f9c1ae5ce5510deedc7eb2"}, {"line": 18104, "relation": "association", "evidence": "New findings have also linked activation of the NRF2 system to anti-inflammatory effects via interactions with NF-κB.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3113, "target": 3110, "key": "bd0b5c7c1f1daaa3ee5c8e0cf2c8880b"}, {"relation": "isA", "source": 3113, "target": 2198, "key": "ddf07437ba8dbd228ac2383b9e283703"}, {"line": 38292, "relation": "increases", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3113, "target": 2375, "key": "346b29b78c6a98be8f630ba58c640098"}, {"line": 38298, "relation": "association", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3113, "target": 3212, "key": "a0836a5b4b57dcfacbd678f98b848723"}, {"line": 38305, "relation": "increases", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3113, "target": 2315, "key": "0db5b6803ac1e48484ab181087d6b0fb"}, {"line": 38312, "relation": "increases", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3113, "target": 3258, "key": "2dc0b55650357e7694fdec260447ad7d"}, {"line": 38314, "relation": "increases", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3113, "target": 3268, "key": "c2ea9c42b19ade62197d97224e071b1a"}, {"line": 46818, "relation": "increases", "evidence": "It is also accepted that RelB is activated in lymphoid cells, such as dendritic cells, by a noncanonical NF-κB pathway and generation of RelB/p52 complexes that are important for proper dendritic cell functions ", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3113, "target": 3305, "key": "f837b8d88ca3bdac4efd87085608c2f8"}, {"relation": "partOf", "source": 3113, "target": 1591, "key": "62fdea47f026a809cbe618568d73affc"}, {"line": 8617, "relation": "association", "evidence": "Aberrant microRNA expression in the brains of neurodegenerative diseases: miR-29a decreased in Alzheimer disease brains targets neurone navigator 3. However, we found significant down-regulation of miR-29a in Alzheimer disease (AD) brains. The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains. MiR-29a-mediated down-regulation of NAV3 was verified by the luciferase reporter assay. By immunohistochemistry, NAV3 expression was most evidently enhanced in degenerating pyramidal neurones in the cerebral cortex of AD. These observations suggest the hypothesis that underexpression of miR-29a affects neurodegenerative processes by enhancing neuronal NAV3 expression in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"miRNA subgraph": true, "Axonal guidance subgraph": true}}, "source": 3997, "target": 2115, "key": "a696f29bbb41ab2228a43c866dbbad4d"}, {"line": 9349, "relation": "increases", "evidence": "The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true}}, "source": 3997, "target": 3090, "key": "a9280678cfd6f25928d14de066490ea4"}, {"line": 8619, "relation": "association", "evidence": "Aberrant microRNA expression in the brains of neurodegenerative diseases: miR-29a decreased in Alzheimer disease brains targets neurone navigator 3. However, we found significant down-regulation of miR-29a in Alzheimer disease (AD) brains. The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains. MiR-29a-mediated down-regulation of NAV3 was verified by the luciferase reporter assay. By immunohistochemistry, NAV3 expression was most evidently enhanced in degenerating pyramidal neurones in the cerebral cortex of AD. These observations suggest the hypothesis that underexpression of miR-29a affects neurodegenerative processes by enhancing neuronal NAV3 expression in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"miRNA subgraph": true, "Axonal guidance subgraph": true}}, "source": 3090, "target": 3814, "key": "fb22d03e50235ca8f238fef16516da1b"}, {"line": 9350, "relation": "association", "evidence": "The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true}}, "source": 3090, "target": 481, "key": "038902792a2616ffc8a4080e9ccb4beb"}, {"line": 8619, "relation": "association", "evidence": "Aberrant microRNA expression in the brains of neurodegenerative diseases: miR-29a decreased in Alzheimer disease brains targets neurone navigator 3. However, we found significant down-regulation of miR-29a in Alzheimer disease (AD) brains. The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains. MiR-29a-mediated down-regulation of NAV3 was verified by the luciferase reporter assay. By immunohistochemistry, NAV3 expression was most evidently enhanced in degenerating pyramidal neurones in the cerebral cortex of AD. These observations suggest the hypothesis that underexpression of miR-29a affects neurodegenerative processes by enhancing neuronal NAV3 expression in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"miRNA subgraph": true, "Axonal guidance subgraph": true}}, "source": 3814, "target": 3090, "key": "60109a62de2de3bd64ddea51210a924e"}, {"line": 29605, "relation": "association", "evidence": "These data suggest that aberrant glycosylation of tau in AD might be involved in neurofibrillary degeneration by promoting abnormal hyperphosphorylation by cdk5 and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}}, "source": 3814, "target": 3013, "key": "8154669d2dc95dd6f1eebf4d402311d7"}, {"line": 8631, "relation": "positiveCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "miRNA subgraph": true}}, "source": 2093, "target": 2746, "key": "16b8e3019899e1b3c9790e8b41eb1c29"}, {"line": 8634, "relation": "positiveCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "miRNA subgraph": true}}, "source": 2093, "target": 3522, "key": "e58285b22617e5b96cb10cc95efc4dbb"}, {"line": 8638, "relation": "negativeCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "miRNA subgraph": true}}, "source": 2093, "target": 2495, "key": "64c7010010b5a6b61f0414db12b689d2"}, {"line": 8643, "relation": "increases", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2093, "target": 3912, "key": "f62874a3b2739d54f877150b62f477f2"}, {"line": 8650, "relation": "decreases", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "miRNA subgraph": true}}, "source": 2093, "target": 503, "key": "24c38d3cb53b5639e9515100310f27fe"}, {"line": 9194, "relation": "positiveCorrelation", "evidence": "It was reported that there was an upregulation of miR-9, miR-125b and miR-128 in hippocampus of AD affected post-mortem brain samples", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2093, "target": 3823, "key": "47620298aff58c45c3b310a22c96d2e4"}, {"line": 8632, "relation": "positiveCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "miRNA subgraph": true}}, "source": 2094, "target": 2746, "key": "72c7836b146c9210eb0c766ab023a36f"}, {"line": 8635, "relation": "positiveCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "miRNA subgraph": true}}, "source": 2094, "target": 3522, "key": "a0c4241cbcbfbea75e0083b2f67383d7"}, {"line": 8639, "relation": "negativeCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true, "miRNA subgraph": true}}, "source": 2094, "target": 2495, "key": "75dc102bf6f4279d953333405457ccd3"}, {"line": 8644, "relation": "increases", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2094, "target": 3912, "key": "73d8a12c0184613624e9b8e803bdb732"}, {"line": 8651, "relation": "decreases", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "miRNA subgraph": true}}, "source": 2094, "target": 503, "key": "94646345d49f0d912bdff458639bec57"}, {"line": 8634, "relation": "positiveCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "miRNA subgraph": true}}, "source": 3522, "target": 2093, "key": "377aa87cc35c955505584c297524adf6"}, {"line": 8635, "relation": "positiveCorrelation", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "miRNA subgraph": true}}, "source": 3522, "target": 2094, "key": "2d7b5decd17453b85e95eda1e0a5354f"}, {"line": 9544, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 3522, "target": 540, "key": "b0e2e0c75c31a45b8dbf6fbef537c11d"}, {"line": 9582, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 3522, "target": 420, "key": "16f9548bed39977e2a55dd7defd29abe"}, {"line": 8647, "relation": "association", "evidence": "Here we report that the levels of a human brain-enriched miRNA-125b are up-regulated in interleukin-6 (IL-6)-stressed normal human astrocytes (NHA), a treatment known to induce astrogliosis. In vitro, anti-miRNA-125b added exogenously to IL-6-stressed NHA cultures attenuated both glial cell proliferation and increased the expression of the cyclin-dependent kinase inhibitor 2A (CDKN2A), a miRNA-125b target and negative regulator of cell growth. A strong positive correlation between miRNA-125b abundance and the glial cell markers glial fibrillary acidic protein (GFAP) and vimentin, and CDKN2A down-regulation was noted in advanced Alzheimer's disease (AD) and in Down's syndrome (DS) brain, chronic neurological disorders associated with astrogliosis. The results suggest that miRNA-125b up-regulation contributes to astrogliosis and to defects in the cell cycle that are characteristic of degenerating brain tissues.", "citation": {"db": "PubMed", "db_id": "20347935"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3912, "target": 3852, "key": "694f1f94b05221d91fc3844ed909bd41"}, {"line": 8660, "relation": "association", "evidence": "the activation of caspases and cleavage of cellular proteins such as GFAP may contribute to astrocyte injury and damage in the AD brain.", "citation": {"db": "PubMed", "db_id": "16507909"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 3912, "target": 2746, "key": "137ff345e9cc68e37628f762e0be6803"}, {"line": 38881, "relation": "increases", "evidence": "Here we present evidence demonstrating that astrocytes are an alternative source of BACE1 and therefore/ may contribute to beta-amyloid plaque formation. While resting astroyctes in brain do not express BACE1 at detectable / levels, cultured astrocytes display BACE1 promoter activity and express BACE1 mRNA and enzymatically active BACE1 protein./ Additionally, in animal models of chronic gliosis and in brains of AD patients, there is BACE1 expression in reactive/ astrocytes. This would suggest that the mechanism for astrocyte activation plays a role in the development of AD and/ that therapeutic strategies that target astrocyte activation in brain may be beneficial for the treatment of AD", "citation": {"db": "PubMed", "db_id": "15663471"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3912, "target": 2375, "key": "2763b641e1d04828a7057ccc2e997513"}, {"line": 39003, "relation": "association", "evidence": "Astrocyte is the most abundant type of glial cells in the central nervous system (CNS) and appears to be/ involved in the induction of neuroinflammation. Under stress and injury, astrocytes become astrogliotic leading to an / upregulation of the expression of proinflammatory cytokines and chemokines, which are associated with the pathogenesis of AD. ", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3912, "target": 537, "key": "587803cb260e3ee79f869937e8bacd48"}, {"line": 39016, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3912, "target": 3480, "key": "9f4dbcc7755e95016c5d90019896c2f6"}, {"line": 41192, "relation": "association", "evidence": "Sirtuin modulators control reactive gliosis in an in vitro model of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24860504"}, "annotations": {"MeSHDisease": {"Gliosis": true, "Alzheimer Disease": true}}, "source": 3912, "target": 3559, "key": "ab556d81bdc7ddd259d1fbeb60440968"}, {"line": 42035, "relation": "association", "evidence": "Specifically, we demonstrate that microgliosis and astrocytosis are prominent aspects of this AD mouse model.", "citation": {"db": "PubMed", "db_id": "24886182"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Gliosis": true, "Plaque, Amyloid": true}, "Confidence": {"Medium": true}}, "source": 3912, "target": 3823, "key": "8118b92eca2a14ae4314461b3228d5b1"}, {"line": 43585, "relation": "decreases", "evidence": "beta-Amyloid of Alzheimer's disease induces reactive gliosis that inhibits axonal outgrowth.", "citation": {"db": "PubMed", "db_id": "8287928"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Autophagy signaling subgraph": true, "Amyloidogenic subgraph": true}}, "source": 3912, "target": 809, "key": "0f952aa73a4e49d520c8ac98a5e9e5ec"}, {"line": 8681, "relation": "positiveCorrelation", "evidence": "Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Notably, miR-144 that is highly conserved appeared to be associated with the aging progression. Moreover, miR-144 plays a central role in regulating the expression of ataxin 1 (ATXN1), the disease-causing gene for the development spinocerebellar ataxia type 1 (SCA1). miRNA activity, including miR-144, -101 and -130 processing, was increased in the cerebellum and cortex of SCA1 Alzheimer patients relative to healthy aged brains. Importantly, miR-144 and -101 inhibition increased ATXN1 levels in human cells. Thus, the activation of miRNA expression in the aging brain may serve to reduce the cytotoxic effect of polyglutamine expanded ATXN1 and the deregulation of miRNA expression may be a risk factor for disease development.", "citation": {"db": "PubMed", "db_id": "20451302 "}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2100, "target": 3823, "key": "ce8aa6659367b5fc43b71aff48053a91"}, {"line": 8959, "relation": "positiveCorrelation", "evidence": "Amyloid beta-peptide (Abeta) accumulating in the brain of Alzheimer disease (AD) patients is believed to be the main pathophysiologcal cause of the disease. Proteolytic processing of the amyloid precursor protein by alpha-secretase ADAM10 (a disintegrin and metalloprotease 10) protects the brain from the production of the Abeta. Meanwhile, dysregulation or aberrant expression of microRNAs (miRNAs) has been widely documented in AD patients. In this study, we demonstrated that overexpression of miR-144, which was previously reported to be increased in elderly primate brains and AD patients, significantly decreased activity of the luciferase reporter containing the ADAM10 3'-untranslated region (3'-UTR) and suppressed the ADAM10 protein level, whereas the miR-144 inhibitor led to an increase of the luciferase activity. The negative regulation caused by miR-144 was strictly dependent on the binding of the miRNA to its recognition element in the ADAM10 3'-UTR. Moreover, we also showed that activator protein-1 regulates the transcription of miR-144 and the up-regulation of miR-144 at least partially induces the suppression of the ADAM10 protein in the presence of Abeta. In addition, we found that miR-451, a miRNA processed from a single gene locus with miR-144, is also involved in the regulation of ADAM10 expression. Taken together, our data therefore demonstrate miR-144/451 is a negative regulator of the ADAM10 protein and suggest a mechanistic role for miR-144/451 in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "23546882"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2100, "target": 3823, "key": "1d2a7d88b416071c51c1deb68e713364"}, {"line": 8686, "relation": "decreases", "evidence": "Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Notably, miR-144 that is highly conserved appeared to be associated with the aging progression. Moreover, miR-144 plays a central role in regulating the expression of ataxin 1 (ATXN1), the disease-causing gene for the development spinocerebellar ataxia type 1 (SCA1). miRNA activity, including miR-144, -101 and -130 processing, was increased in the cerebellum and cortex of SCA1 Alzheimer patients relative to healthy aged brains. Importantly, miR-144 and -101 inhibition increased ATXN1 levels in human cells. Thus, the activation of miRNA expression in the aging brain may serve to reduce the cytotoxic effect of polyglutamine expanded ATXN1 and the deregulation of miRNA expression may be a risk factor for disease development.", "citation": {"db": "PubMed", "db_id": "20451302 "}, "annotations": {"Subgraph": {"Akt subgraph": true}}, "source": 2100, "target": 3942, "key": "b2408448a6a615efc12028a4c215d35c"}, {"line": 8961, "relation": "decreases", "evidence": "Amyloid beta-peptide (Abeta) accumulating in the brain of Alzheimer disease (AD) patients is believed to be the main pathophysiologcal cause of the disease. Proteolytic processing of the amyloid precursor protein by alpha-secretase ADAM10 (a disintegrin and metalloprotease 10) protects the brain from the production of the Abeta. Meanwhile, dysregulation or aberrant expression of microRNAs (miRNAs) has been widely documented in AD patients. In this study, we demonstrated that overexpression of miR-144, which was previously reported to be increased in elderly primate brains and AD patients, significantly decreased activity of the luciferase reporter containing the ADAM10 3'-untranslated region (3'-UTR) and suppressed the ADAM10 protein level, whereas the miR-144 inhibitor led to an increase of the luciferase activity. The negative regulation caused by miR-144 was strictly dependent on the binding of the miRNA to its recognition element in the ADAM10 3'-UTR. Moreover, we also showed that activator protein-1 regulates the transcription of miR-144 and the up-regulation of miR-144 at least partially induces the suppression of the ADAM10 protein in the presence of Abeta. In addition, we found that miR-451, a miRNA processed from a single gene locus with miR-144, is also involved in the regulation of ADAM10 expression. Taken together, our data therefore demonstrate miR-144/451 is a negative regulator of the ADAM10 protein and suggest a mechanistic role for miR-144/451 in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "23546882"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2100, "target": 2249, "key": "ea3eb962e8baee63c40fff01809c259b"}, {"line": 8682, "relation": "positiveCorrelation", "evidence": "Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Notably, miR-144 that is highly conserved appeared to be associated with the aging progression. Moreover, miR-144 plays a central role in regulating the expression of ataxin 1 (ATXN1), the disease-causing gene for the development spinocerebellar ataxia type 1 (SCA1). miRNA activity, including miR-144, -101 and -130 processing, was increased in the cerebellum and cortex of SCA1 Alzheimer patients relative to healthy aged brains. Importantly, miR-144 and -101 inhibition increased ATXN1 levels in human cells. Thus, the activation of miRNA expression in the aging brain may serve to reduce the cytotoxic effect of polyglutamine expanded ATXN1 and the deregulation of miRNA expression may be a risk factor for disease development.", "citation": {"db": "PubMed", "db_id": "20451302 "}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2081, "target": 3823, "key": "07bc4e3f6cc60b81792c5273deaddd32"}, {"line": 8785, "relation": "negativeCorrelation", "evidence": "MicroRNA-101 downregulates Alzheimer's amyloid-beta precursor protein levels in human cell cultures and is differentially expressed.Several bioinformatic algorithms predicted miR-101 target sites within the APP 3'-untranslated region (3'-UTR). Using reporter assays, we confirmed that, in human cell cultures, miR-101 significantly reduced the expression of a reporter under control of APP 3'-UTR. Mutation of predicted site 1, but not site 2, eliminated this reporter response. Delivery of miR-101 directly to human HeLa cells significantly reduced APP levels and this effect was eliminated by co-transfection with a miR-101 antisense inhibitor. Delivery of a specific target protector designed to blockade the interaction between miR-101 and its functional target site within APP 3'-UTR enhanced APP levels in HeLa. Therefore, endogenous miR-101 regulates expression of APP in human cells via a specific site located within its 3'-UTR. Finally, we demonstrate that, across a series of human cell lines, highest expression of miR-101 levels was observed in model NT2 neurons.", "citation": {"db": "PubMed", "db_id": "21172309"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2081, "target": 3823, "key": "85a097fe11a51a7a9c8c9f5612656f78"}, {"line": 45280, "relation": "negativeCorrelation", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"Low": true}}, "source": 2081, "target": 3823, "key": "c1da1018c92150b155298f5606a25e99"}, {"line": 8687, "relation": "decreases", "evidence": "Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Notably, miR-144 that is highly conserved appeared to be associated with the aging progression. Moreover, miR-144 plays a central role in regulating the expression of ataxin 1 (ATXN1), the disease-causing gene for the development spinocerebellar ataxia type 1 (SCA1). miRNA activity, including miR-144, -101 and -130 processing, was increased in the cerebellum and cortex of SCA1 Alzheimer patients relative to healthy aged brains. Importantly, miR-144 and -101 inhibition increased ATXN1 levels in human cells. Thus, the activation of miRNA expression in the aging brain may serve to reduce the cytotoxic effect of polyglutamine expanded ATXN1 and the deregulation of miRNA expression may be a risk factor for disease development.", "citation": {"db": "PubMed", "db_id": "20451302 "}, "annotations": {"Subgraph": {"Akt subgraph": true}}, "source": 2081, "target": 3942, "key": "035499f50b2df45ff4e4f23bd9c03b75"}, {"line": 8786, "relation": "decreases", "evidence": "MicroRNA-101 downregulates Alzheimer's amyloid-beta precursor protein levels in human cell cultures and is differentially expressed.Several bioinformatic algorithms predicted miR-101 target sites within the APP 3'-untranslated region (3'-UTR). Using reporter assays, we confirmed that, in human cell cultures, miR-101 significantly reduced the expression of a reporter under control of APP 3'-UTR. Mutation of predicted site 1, but not site 2, eliminated this reporter response. Delivery of miR-101 directly to human HeLa cells significantly reduced APP levels and this effect was eliminated by co-transfection with a miR-101 antisense inhibitor. Delivery of a specific target protector designed to blockade the interaction between miR-101 and its functional target site within APP 3'-UTR enhanced APP levels in HeLa. Therefore, endogenous miR-101 regulates expression of APP in human cells via a specific site located within its 3'-UTR. Finally, we demonstrate that, across a series of human cell lines, highest expression of miR-101 levels was observed in model NT2 neurons.", "citation": {"db": "PubMed", "db_id": "21172309"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2081, "target": 2315, "key": "6e546f7725a15913bd79fe6d12efbc8c"}, {"line": 45281, "relation": "decreases", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"Low": true}}, "source": 2081, "target": 2315, "key": "9a8df8c3f885489df0d2b1596ee875d5"}, {"line": 9217, "relation": "increases", "evidence": "Another recent study showed that miR-101 is a negative regulator of APP expression and could affect the accumulation of Abeta, suggesting a possible role for miR-101 in neuropathological conditions.", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2081, "target": 4101, "key": "410c002a5b44747fd77b1c5ade71299a"}, {"line": 8683, "relation": "positiveCorrelation", "evidence": "Genome-wide analysis of miRNA expression reveals a potential role for miR-144 in brain aging and spinocerebellar ataxia pathogenesis. Notably, miR-144 that is highly conserved appeared to be associated with the aging progression. Moreover, miR-144 plays a central role in regulating the expression of ataxin 1 (ATXN1), the disease-causing gene for the development spinocerebellar ataxia type 1 (SCA1). miRNA activity, including miR-144, -101 and -130 processing, was increased in the cerebellum and cortex of SCA1 Alzheimer patients relative to healthy aged brains. Importantly, miR-144 and -101 inhibition increased ATXN1 levels in human cells. Thus, the activation of miRNA expression in the aging brain may serve to reduce the cytotoxic effect of polyglutamine expanded ATXN1 and the deregulation of miRNA expression may be a risk factor for disease development.", "citation": {"db": "PubMed", "db_id": "20451302 "}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2096, "target": 3823, "key": "f9d23d493a4f9f635907ba74795925fb"}, {"line": 8698, "relation": "association", "evidence": "ATXN1 functions as a genetic risk modifier that contributes to AD pathogenesis through a loss-of-function mechanism by regulating beta-secretase cleavage of APP and Abeta levels.", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Akt subgraph": true}, "Confidence": {"Medium": true}}, "source": 1754, "target": 3823, "key": "54fdefe9aec8ba114457c0dcbd759fc8"}, {"line": 8699, "relation": "regulates", "evidence": "ATXN1 functions as a genetic risk modifier that contributes to AD pathogenesis through a loss-of-function mechanism by regulating beta-secretase cleavage of APP and Abeta levels.", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Akt subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2371, "target": 2375, "key": "c1b1ac6e5fa04d4ecd10c3129ce77eca"}, {"line": 28271, "relation": "association", "evidence": "Taken together, ATXN1 functions as a genetic risk pmodifier that contributes to AD pathogenesis through a loss-of-function mechanism by regulating beta-secretase cleavage of APP and Abeta levels.", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2371, "target": 2375, "key": "1d2df09ee2eb04107681984335dfc978"}, {"line": 28312, "relation": "regulates", "evidence": "Regarding the underlying molecular mechanism, we show that the effect of ATXN1 expression on Abeta levels is pmodulated via beta-secretase cleavage of APP. ", "citation": {"db": "PubMed", "db_id": "20139999"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Akt subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2371, "target": 2375, "key": "56d8fecc6c26f8266ce2ae05c2a5844d"}, {"line": 28270, "relation": "association", "evidence": "Taken together, ATXN1 functions as a genetic risk pmodifier that contributes to AD pathogenesis through a loss-of-function mechanism by regulating beta-secretase cleavage of APP and Abeta levels.", "citation": {"db": "PubMed", "db_id": "20097758"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2371, "target": 3823, "key": "1adb680834c8e2bc8a8f4bfdb1dc646e"}, {"line": 28297, "relation": "association", "evidence": "Ataxin 1 (ATXN1) is one of these four AD candidate genes and has been indicated to be the disease gene for spinocerebellar ataxia type 1, which is also a neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "20139999"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"Medium": true}}, "source": 2371, "target": 3884, "key": "3b0cdebfb06c1bbb95b91aad9461c1f2"}, {"line": 28303, "relation": "decreases", "evidence": "We show that knock-down of ATXN1 significantly increases the levels of both Abeta40 and Abeta42. This effect could be rescued with concurrent overexpression of ATXN1. Moreover, overexpression of ATXN1 decreased Abeta levels. ", "citation": {"db": "PubMed", "db_id": "20139999"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2371, "target": 2328, "key": "0071a158def5099d2c8bbcc7ba784f7b"}, {"line": 28304, "relation": "negativeCorrelation", "evidence": "We show that knock-down of ATXN1 significantly increases the levels of both Abeta40 and Abeta42. This effect could be rescued with concurrent overexpression of ATXN1. Moreover, overexpression of ATXN1 decreased Abeta levels. ", "citation": {"db": "PubMed", "db_id": "20139999"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2371, "target": 2327, "key": "a55337b3bea3ad89227d538173565a78"}, {"line": 8711, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2088, "target": 2272, "key": "46507d3a9750f123c3d888c721f0bcdf"}, {"line": 8711, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2272, "target": 2088, "key": "98a673a2b6b253c98dbfe08b294a6b8d"}, {"line": 8712, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2790, "target": 803, "key": "38024377471218e64b03191023b8344e"}, {"line": 8713, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2790, "target": 577, "key": "b9aa9516d06958fb3e5b0ef8d824d222"}, {"line": 8714, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2790, "target": 3923, "key": "ed0fa74be219f34292155af19efd331e"}, {"line": 8715, "relation": "increases", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2790, "target": 3823, "key": "d2fe54f6b71107bf8bb6eef3ed932fca"}, {"line": 8712, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 803, "target": 2790, "key": "f40393b4c87a7a48c92f14eed2d4eda8"}, {"line": 16575, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 803, "target": 3409, "key": "12a2921ba56f5dc5aa96a16929412c4f"}, {"line": 16617, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 803, "target": 3874, "key": "c290e9a02bf819a674fdfbcfb269e0ef"}, {"line": 8714, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3923, "target": 2790, "key": "00a5f1c1628e9cfca558b5a88353c3dd"}, {"line": 13812, "relation": "association", "evidence": "Importantly, miR-16 inhibition decreased animal survival in a xenograft model of MM by increasing tumor load and host angiogenesis.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"MeSHDisease": {"Multiple Myeloma": true, "Neoplasms": true}}, "source": 3923, "target": 3868, "key": "7bb57a08a3964425a8b3fd0ead5af485"}, {"line": 13983, "relation": "association", "evidence": "However, little is known about the role of Pin1 in cancer or in modulating transcription factor activity.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "source": 3923, "target": 3192, "key": "93d68a8cc0fd34586a68278872272fa7"}, {"line": 14027, "relation": "positiveCorrelation", "evidence": "Thus, Pin1 is up-regulated in human tumors and cooperates with Ras signaling in increasing c-Jun transcriptional activity towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3923, "target": 3192, "key": "dfb12f9d8968c207b1ac979c9bb13b94"}, {"line": 15333, "relation": "association", "evidence": "Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer.", "citation": {"db": "PubMed", "db_id": "19882751"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neoplasms": true, "Vascular Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3923, "target": 2194, "key": "450bb6b223c9b8912100e99551206549"}, {"line": 18690, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3923, "target": 3060, "key": "3fcb61120c92bf049d7818b3a7794f35"}, {"line": 8717, "relation": "association", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3972, "target": 2092, "key": "c7d7001bb042979534fd6ae9427bc231"}, {"line": 8718, "relation": "increases", "evidence": "Granulin (GRN, or progranulin) is a protein involved in wound repair, inflammation, and neoplasia. GRN has also been directly implicated in frontotemporal dementia and may contribute to Alzheimer's disease pathogenesis. However, GRN regulation expression is poorly understood. A high-throughput experimental microRNA assay showed that GRN is the strongest target for miR-107 in human H4 neuroglioma cells. miR-107 has been implicated in Alzheimer's disease pathogenesis, and sequence elements in the open reading frame-rather than the 3' untranslated region-of GRN mRNA are recognized by miR-107 and are highly conserved among vertebrate species. To better understand the mechanism of this interaction, FLAG-tagged Argonaute constructs were used following miR-107 transfection. GRN mRNA interacts preferentially with Argonaute 2. In vitro and in vivo studies indicate that regulation of GRN by miR-107 may be functionally important. Glucose supplementation in cultured cells that leads to increased miR-107 levels also results in decreased GRN expression, including changes in cell compartmentation and decreased secretion of GRN protein. This effect was eliminated following miR-107 transfection. We also tested a mouse model where miR-107 has been shown to be down-regulated. In brain tissue subjacent to 1.0 mm depth controlled cortical impact, surviving hippocampal neurons show decreased miR-107 with augmentation of neuronal GRN expression. These findings indicate that miR-107 contributes to GRN expression regulation with implications for brain disorders.", "citation": {"db": "PubMed", "db_id": "20489155"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3972, "target": 2790, "key": "1c343478bebde7a4e0088c2bfcc70984"}, {"line": 8728, "relation": "association", "evidence": "We report here that BACE1-antisense prevents miRNA-induced repression of BACE1 mRNA by masking the binding site for miR-485-5p. Indeed, miR-485-5p and BACE1-antisense compete for binding within the same region in the open reading frame of the BACE1 mRNA. We observed opposing effects of BACE1-antisense and miR-485-5p on BACE1 protein in vitro and showed that Locked Nucleic Acid-antimiR mediated knockdown of miR-485-5p as well as BACE1-antisense over-expression can prevent the miRNA-induced BACE1 suppression. We found that the expression of BACE1-antisense as well as miR-485-5p are dysregulated in RNA samples from Alzheimer's disease subjects compared to control individuals.", "citation": {"db": "PubMed", "db_id": "20507594"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2101, "target": 3823, "key": "c74559c0118ec591603820903c613f89"}, {"line": 8729, "relation": "decreases", "evidence": "We report here that BACE1-antisense prevents miRNA-induced repression of BACE1 mRNA by masking the binding site for miR-485-5p. Indeed, miR-485-5p and BACE1-antisense compete for binding within the same region in the open reading frame of the BACE1 mRNA. We observed opposing effects of BACE1-antisense and miR-485-5p on BACE1 protein in vitro and showed that Locked Nucleic Acid-antimiR mediated knockdown of miR-485-5p as well as BACE1-antisense over-expression can prevent the miRNA-induced BACE1 suppression. We found that the expression of BACE1-antisense as well as miR-485-5p are dysregulated in RNA samples from Alzheimer's disease subjects compared to control individuals.", "citation": {"db": "PubMed", "db_id": "20507594"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2101, "target": 3943, "key": "0127474fcbfabd86045950003c8e73a2"}, {"line": 45100, "relation": "decreases", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true, "miRNA subgraph": true}, "Confidence": {"Low": true}}, "source": 2101, "target": 3943, "key": "2d99defb3710a994ccc74a0512175938"}, {"line": 9238, "relation": "association", "evidence": "These predictions were tested using miRNA luciferase reporter vectors, with Robo2 and srGAP2 evaluated as the potential targets of miR-145 and miR-214, respectively. The role of miR-145 in cultured primary neurons was also investigated, and the result found that miR-145 miR-145 inhibited neurite growth and down-regulated Robo2 expression.", "citation": {"db": "PubMed", "db_id": "21276775"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true, "miRNA subgraph": true}}, "source": 2101, "target": 3319, "key": "dbd6e9dccfca3e921676bba787a38fde"}, {"line": 9239, "relation": "association", "evidence": "These predictions were tested using miRNA luciferase reporter vectors, with Robo2 and srGAP2 evaluated as the potential targets of miR-145 and miR-214, respectively. The role of miR-145 in cultured primary neurons was also investigated, and the result found that miR-145 miR-145 inhibited neurite growth and down-regulated Robo2 expression.", "citation": {"db": "PubMed", "db_id": "21276775"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true, "miRNA subgraph": true}}, "source": 2101, "target": 3417, "key": "ffbc4d6338ad5729305177f5f9deae92"}, {"line": 9240, "relation": "decreases", "evidence": "These predictions were tested using miRNA luciferase reporter vectors, with Robo2 and srGAP2 evaluated as the potential targets of miR-145 and miR-214, respectively. The role of miR-145 in cultured primary neurons was also investigated, and the result found that miR-145 miR-145 inhibited neurite growth and down-regulated Robo2 expression.", "citation": {"db": "PubMed", "db_id": "21276775"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true, "miRNA subgraph": true}}, "source": 2101, "target": 653, "key": "b44aeb13425c22164fbae7369a4bc97a"}, {"line": 8741, "relation": "increases", "evidence": "Interestingly, neuronal degeneration coincides with the hyperphosphorylation of endogenous tau at several epitopes previously associated with neurofibrillary pathology. Transcriptome analysis of enzymes involved in tau phosphorylation identified ERK1 as one of the candidate kinases responsible for this event in vivo. We further demonstrate that miRNAs belonging to the miR-15 family are potent regulators of ERK1 expression in mouse neuronal cells and co-expressed with ERK1/2 in vivo. Finally, we show that miR-15a is specifically downregulated in Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "20660113"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3000, "target": 3015, "key": "5230627aba34bb018344487d856983f6"}, {"line": 9061, "relation": "association", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3000, "target": 2315, "key": "27201866671a24e95cc0e24be0f7761b"}, {"line": 9064, "relation": "association", "evidence": "Among the different APP-interacting proteins, we focused our interest on the GRB2 adaptor protein, which connects cell surface receptors to intracellular signaling pathways. In this study we provide evidence by co-immunoprecipitation experiments, confocal and electron microscopy, and by fluorescence resonance energy transfer experiments that both APP and presenilin1 interact with GRB2 in vesicular structures at the centrosome of the cell. The final target for these interactions is ERK1,2, which is activated in mitotic centrosomes in a PS1- and APP-dependent manner. These data suggest that both APP and presenilin1 can be part of a common signaling pathway that regulates ERK1,2 and the cell cycle.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3000, "target": 3258, "key": "f501562f066afa818d14ea5c8f33ac84"}, {"line": 9330, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in beta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3000, "target": 775, "key": "ef1b2626a761238b15fcab6af9c13eb0"}, {"line": 9334, "relation": "increases", "evidence": "oxidative stress-mediated ERK activation contributes to increases in beta-secretase and, thus, an increase of Abeta generation in neuronal cells expressing mutant PS2", "citation": {"db": "PubMed", "db_id": "22249458"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3000, "target": 2375, "key": "1ff22ee53c8964a6f2c12a30845bf0c8"}, {"relation": "isA", "source": 3000, "target": 2173, "key": "16ca2fcc76945273f203d153a3cd1352"}, {"relation": "partOf", "source": 3000, "target": 1543, "key": "942f9737bfabbc0bd424aba0d05e145c"}, {"relation": "partOf", "source": 3000, "target": 1544, "key": "16ead9393fb40c7c267d852ac058e9ac"}, {"relation": "hasVariant", "source": 3000, "target": 3001, "key": "53429b4f8c8cc3da45e48c85f162b8a6"}, {"line": 8742, "relation": "increases", "evidence": "Interestingly, neuronal degeneration coincides with the hyperphosphorylation of endogenous tau at several epitopes previously associated with neurofibrillary pathology. Transcriptome analysis of enzymes involved in tau phosphorylation identified ERK1 as one of the candidate kinases responsible for this event in vivo. We further demonstrate that miRNAs belonging to the miR-15 family are potent regulators of ERK1 expression in mouse neuronal cells and co-expressed with ERK1/2 in vivo. Finally, we show that miR-15a is specifically downregulated in Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "20660113"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}}, "source": 2105, "target": 3000, "key": "8cf2d1844ee58d8c0d360f4e6126960a"}, {"line": 8743, "relation": "negativeCorrelation", "evidence": "Interestingly, neuronal degeneration coincides with the hyperphosphorylation of endogenous tau at several epitopes previously associated with neurofibrillary pathology. Transcriptome analysis of enzymes involved in tau phosphorylation identified ERK1 as one of the candidate kinases responsible for this event in vivo. We further demonstrate that miRNAs belonging to the miR-15 family are potent regulators of ERK1 expression in mouse neuronal cells and co-expressed with ERK1/2 in vivo. Finally, we show that miR-15a is specifically downregulated in Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "20660113"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}}, "source": 2105, "target": 3823, "key": "f41115eba7362cb54ecc880fb74ba190"}, {"line": 8756, "relation": "decreases", "evidence": "Differential regulation of interleukin-1 receptor-associated kinase-1 (IRAK-1) and IRAK-2 by microRNA-146a and NF-kappaB in stressed human astroglial cells and in Alzheimer disease.In monocytes, increased expression of an NF-κB-regulated miRNA-146a down-regulates expression of the interleukin-1 receptor-associated kinase-1 (IRAK-1), an essential component of Toll-like/IL-1 receptor signaling. Here we extend those observations to the hippocampus and neocortex of Alzheimer disease (AD) brain and to stressed human astroglial (HAG) cells in primary culture. In 66 control and AD samples we note a significant up-regulation of miRNA-146a coupled to down-regulation of IRAK-1 and a compensatory up-regulation of IRAK-2. Using miRNA-146a-, IRAK-1-, or IRAK-2 promoter-luciferase reporter constructs, we observe decreases in IRAK-1 and increases in miRNA-146a and IRAK-2 expression in interleukin-1beta (IL-1beta) and amyloid-beta-42 (Abeta42) peptide-stressed HAG cells. NF-κB-mediated transcriptional control of human IRAK-2 was localized to between -119 and +12 bp of the immediate IRAK-2 promoter. The NF-κB inhibitors curcumin, pyrrolidine dithiocarbamate or CAY10512 abrogated both IRAK-2 and miRNA-146a expression, whereas IRAK-1 was up-regulated. Incubation of a protected antisense miRNA-146a was found to inhibit miRNA-146a and restore IRAK-1, whereas IRAK-2 remained unaffected. These data suggest a significantly independent regulation of IRAK-1 and IRAK-2 in AD and in IL-1beta+Abeta42 peptide-stressed HAG cells and that an inducible, NF-κB-sensitive, miRNA-146a-mediated down-regulation of IRAK-1 coupled to an NF-κB-induced up-regulation of IRAK-2 expression drives an extensively sustained inflammatory response. The interactive signaling of NF-κB and miRNA-146a further illustrate interplay between inducible transcription factors and pro-inflammatory miRNAs that regulate brain IRAK expression. The combinatorial use of NF-κB inhibitors with miRNA-146a or antisense miRNA-146a may have potential as a bi-pronged therapeutic strategy directed against IRAK-2-driven pathogenic signaling.", "citation": {"db": "PubMed", "db_id": "20937840"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3987, "target": 577, "key": "4689612e931b7f2a7cd7376098f91918"}, {"line": 9132, "relation": "association", "evidence": "Recently, there have been increasing evidences that microRNA-146 (miR-146) is related to up-regulated immune and inflammatory signaling through its target genes, such as IRAK1 and TRAF6. Additionally, abundant data continue to support the hypothesis that progressive up-regulation of inflammatory gene expression and elevated inflammatory signaling facilitate the development and progression of Alzheimer's disease (AD). This review focuses on the recent findings regarding the role of miR-146 in modulating immune response and its subsequent effects in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22209051"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3987, "target": 2102, "key": "6a8bf0c034153261635f193ecb4e728e"}, {"line": 9533, "relation": "increases", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 3987, "target": 2904, "key": "b03b5678c9df2103092e440a8b20be1a"}, {"line": 8758, "relation": "increases", "evidence": "Differential regulation of interleukin-1 receptor-associated kinase-1 (IRAK-1) and IRAK-2 by microRNA-146a and NF-kappaB in stressed human astroglial cells and in Alzheimer disease.In monocytes, increased expression of an NF-κB-regulated miRNA-146a down-regulates expression of the interleukin-1 receptor-associated kinase-1 (IRAK-1), an essential component of Toll-like/IL-1 receptor signaling. Here we extend those observations to the hippocampus and neocortex of Alzheimer disease (AD) brain and to stressed human astroglial (HAG) cells in primary culture. In 66 control and AD samples we note a significant up-regulation of miRNA-146a coupled to down-regulation of IRAK-1 and a compensatory up-regulation of IRAK-2. Using miRNA-146a-, IRAK-1-, or IRAK-2 promoter-luciferase reporter constructs, we observe decreases in IRAK-1 and increases in miRNA-146a and IRAK-2 expression in interleukin-1beta (IL-1beta) and amyloid-beta-42 (Abeta42) peptide-stressed HAG cells. NF-κB-mediated transcriptional control of human IRAK-2 was localized to between -119 and +12 bp of the immediate IRAK-2 promoter. The NF-κB inhibitors curcumin, pyrrolidine dithiocarbamate or CAY10512 abrogated both IRAK-2 and miRNA-146a expression, whereas IRAK-1 was up-regulated. Incubation of a protected antisense miRNA-146a was found to inhibit miRNA-146a and restore IRAK-1, whereas IRAK-2 remained unaffected. These data suggest a significantly independent regulation of IRAK-1 and IRAK-2 in AD and in IL-1beta+Abeta42 peptide-stressed HAG cells and that an inducible, NF-κB-sensitive, miRNA-146a-mediated down-regulation of IRAK-1 coupled to an NF-κB-induced up-regulation of IRAK-2 expression drives an extensively sustained inflammatory response. The interactive signaling of NF-κB and miRNA-146a further illustrate interplay between inducible transcription factors and pro-inflammatory miRNAs that regulate brain IRAK expression. The combinatorial use of NF-κB inhibitors with miRNA-146a or antisense miRNA-146a may have potential as a bi-pronged therapeutic strategy directed against IRAK-2-driven pathogenic signaling.", "citation": {"db": "PubMed", "db_id": "20937840"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3988, "target": 577, "key": "2e43746850d58f54b8bfaf8f4a925ae6"}, {"line": 8771, "relation": "positiveCorrelation", "evidence": "Here, we present evidence that, besides APP expression regulation, miRNAs are equally involved in the regulation of neuronal APP mRNA alternative splicing. Lack of miRNAs in post-mitotic neurons in vivo is associated with APP exons 7 and 8 inclusion, while ectopic expression of miR-124, an abundant neuronal-specific miRNA, reversed these effects in cultured neurons. Similar results were obtained by depletion of endogenous polypyrimidine tract binding protein 1 (PTBP1) in cells, a recognized miR-124 target gene. Furthermore, PTBP1 levels correlate with the presence of APP exons 7 and 8, while PTBP2 levels correlate with the skipping of these exons during neuronal differentiation. Finally, we show that miR-124 is down-regulated in AD brain. In sum, our results suggest that specific miRNAs are involved in the fine-tuning of APP alternative splicing in neurons. Since abnormal neuronal splicing of APP affects beta-amyloid peptide production, these results could contribute to the understanding of the implication of miRNAs in brain health and disease.", "citation": {"db": "PubMed", "db_id": "21062284"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4007, "target": 1746, "key": "2321ce261b87eaa00208c5ac2b352329"}, {"line": 8774, "relation": "increases", "evidence": "Here, we present evidence that, besides APP expression regulation, miRNAs are equally involved in the regulation of neuronal APP mRNA alternative splicing. Lack of miRNAs in post-mitotic neurons in vivo is associated with APP exons 7 and 8 inclusion, while ectopic expression of miR-124, an abundant neuronal-specific miRNA, reversed these effects in cultured neurons. Similar results were obtained by depletion of endogenous polypyrimidine tract binding protein 1 (PTBP1) in cells, a recognized miR-124 target gene. Furthermore, PTBP1 levels correlate with the presence of APP exons 7 and 8, while PTBP2 levels correlate with the skipping of these exons during neuronal differentiation. Finally, we show that miR-124 is down-regulated in AD brain. In sum, our results suggest that specific miRNAs are involved in the fine-tuning of APP alternative splicing in neurons. Since abnormal neuronal splicing of APP affects beta-amyloid peptide production, these results could contribute to the understanding of the implication of miRNAs in brain health and disease.", "citation": {"db": "PubMed", "db_id": "21062284"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4007, "target": 3273, "key": "da9c0eb3fcb65233502927c956f6add9"}, {"line": 9470, "relation": "association", "evidence": "The MicroRNA miR-124 Promotes Neuronal Differentiation by Triggering Brain-Specific Alternative Pre-mRNA Splicing. When this exon is skipped, PTBP2 mRNA is subject to nonsense-mediated decay (NMD). During neuronal differentiation, miR-124 reduces PTBP1 levels, leading to the accumulation of correctly spliced PTBP2 mRNA and a dramatic increase in PTBP2 protein.", "citation": {"db": "PubMed", "db_id": "17679093"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3273, "target": 2082, "key": "90e4a2ca4afa5eb98043968d1d9c63c6"}, {"line": 8797, "relation": "negativeCorrelation", "evidence": "Decreased relative expression levels of hsa-miR-590-3p was observed in patients with AD versus controls (0.685 ± °0.080 versus 0.931 ± °0.111, p = 0.079), and correlated negatively with hnRNP-A1 mRNA levels (r = -0.615, p = 0.0237). According to these findings, hnRNP-A1 and its transcription regulatory factor miR-590-3p are disregulated in patients with AD, and the hnRNP-A1 rs7967622 C/C genotype is likely a risk factor for FTLD in male populations.", "citation": {"db": "PubMed", "db_id": "21548758"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2124, "target": 3823, "key": "c6f017fb8606e89b2ecb8c2cc1b42496"}, {"line": 8798, "relation": "association", "evidence": "Decreased relative expression levels of hsa-miR-590-3p was observed in patients with AD versus controls (0.685 ± °0.080 versus 0.931 ± °0.111, p = 0.079), and correlated negatively with hnRNP-A1 mRNA levels (r = -0.615, p = 0.0237). According to these findings, hnRNP-A1 and its transcription regulatory factor miR-590-3p are disregulated in patients with AD, and the hnRNP-A1 rs7967622 C/C genotype is likely a risk factor for FTLD in male populations.", "citation": {"db": "PubMed", "db_id": "21548758"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2124, "target": 2840, "key": "172ab1bbd03b362f5f7290915ea2944e"}, {"line": 8798, "relation": "association", "evidence": "Decreased relative expression levels of hsa-miR-590-3p was observed in patients with AD versus controls (0.685 ± °0.080 versus 0.931 ± °0.111, p = 0.079), and correlated negatively with hnRNP-A1 mRNA levels (r = -0.615, p = 0.0237). According to these findings, hnRNP-A1 and its transcription regulatory factor miR-590-3p are disregulated in patients with AD, and the hnRNP-A1 rs7967622 C/C genotype is likely a risk factor for FTLD in male populations.", "citation": {"db": "PubMed", "db_id": "21548758"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2840, "target": 2124, "key": "14f0c0719f7062ef5e04d34cf4a8b4ce"}, {"line": 8799, "relation": "negativeCorrelation", "evidence": "Decreased relative expression levels of hsa-miR-590-3p was observed in patients with AD versus controls (0.685 ± °0.080 versus 0.931 ± °0.111, p = 0.079), and correlated negatively with hnRNP-A1 mRNA levels (r = -0.615, p = 0.0237). According to these findings, hnRNP-A1 and its transcription regulatory factor miR-590-3p are disregulated in patients with AD, and the hnRNP-A1 rs7967622 C/C genotype is likely a risk factor for FTLD in male populations.", "citation": {"db": "PubMed", "db_id": "21548758"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2840, "target": 3823, "key": "fb1278f7e6ab533d24a3a734deee26de"}, {"line": 9116, "relation": "negativeCorrelation", "evidence": "HnRNP A1 plays several key roles in neuronal functioning and its depletion, either due to debilitated cholinergic neurotransmission or under autoimmune reactions causes drastic changes in RNA metabolism. Consequently, hnRNP A1 decline contributes to the severity of symptoms in several neurodegenerative diseases, including Alzheimer's disease (AD), ", "citation": {"db": "PubMed", "db_id": "23247072"}, "annotations": {"Confidence": {"High": true}}, "source": 2840, "target": 3823, "key": "b28f5ea8f3ecfce00bfca331057d7a27"}, {"line": 8807, "relation": "decreases", "evidence": "HnRNP A1 plays several key roles in neuronal functioning and its depletion, either due to debilitated cholinergic neurotransmission or under autoimmune reactions causes drastic changes in RNA metabolism. ", "citation": {"db": "PubMed", "db_id": "23247072"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2840, "target": 762, "key": "4f653eb62529a19804eb22272e45f5dc"}, {"line": 8808, "relation": "decreases", "evidence": "HnRNP A1 plays several key roles in neuronal functioning and its depletion, either due to debilitated cholinergic neurotransmission or under autoimmune reactions causes drastic changes in RNA metabolism. ", "citation": {"db": "PubMed", "db_id": "23247072"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2840, "target": 789, "key": "69a922302c535d158ea5b1b55515c7d6"}, {"line": 8809, "relation": "decreases", "evidence": "HnRNP A1 plays several key roles in neuronal functioning and its depletion, either due to debilitated cholinergic neurotransmission or under autoimmune reactions causes drastic changes in RNA metabolism. ", "citation": {"db": "PubMed", "db_id": "23247072"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2840, "target": 454, "key": "d032175aac272a4be3f684efd9a6f1c1"}, {"line": 9120, "relation": "regulates", "evidence": "HnRNP A1 plays several key roles in neuronal functioning and its depletion, either due to debilitated cholinergic neurotransmission or under autoimmune reactions causes drastic changes in RNA metabolism. Consequently, hnRNP A1 decline contributes to the severity of symptoms in several neurodegenerative diseases, including Alzheimer's disease (AD), ", "citation": {"db": "PubMed", "db_id": "23247072"}, "annotations": {"Confidence": {"Medium": true}}, "source": 2840, "target": 454, "key": "7c141ff199773d538055d86885cfba5c"}, {"line": 9532, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 2904, "target": 2102, "key": "44ac2dee4b7fd4ecf122672142cdfec8"}, {"line": 8830, "relation": "association", "evidence": "Using a bioinformatics screen to identify sequence motifs enriched in the 3'UTR of rapidly destabilized mRNAs, we identified a developmentally and activity-regulated miRNA (miR-485) that controls dendritic spine number and synapse formation in an activity-dependent homeostatic manner. We find that many plasticity-associated genes contain predicted miR-485 binding sites and further identify the presynaptic protein SV2A as a target of miR-485. miR-485 negatively regulated dendritic spine density, postsynaptic density 95 (PSD-95) clustering, and surface expression of GluR2. Furthermore, miR-485 overexpression reduced spontaneous synaptic responses and transmitter release, as measured by miniature excitatory postsynaptic current (EPSC) analysis and FM 1-43 staining. SV2A knockdown mimicked the effects of miR-485, and these effects were reversed by SV2A overexpression. Moreover, 5 d of increased synaptic activity induced homeostatic changes in synaptic specializations that were blocked by a miR-485 inhibitor. Our findings reveal a role for this previously uncharacterized miRNA and the presynaptic protein SV2A in homeostatic plasticity and nervous system development, with possible implications in neurological disorders (e.g., Huntington and Alzheimer's disease), where miR-485 has been found to be dysregulated.", "citation": {"db": "PubMed", "db_id": "21697510"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "miRNA subgraph": true}}, "source": 2121, "target": 4014, "key": "828de4c57963f230e32c28f655a6e198"}, {"line": 8832, "relation": "decreases", "evidence": "Using a bioinformatics screen to identify sequence motifs enriched in the 3'UTR of rapidly destabilized mRNAs, we identified a developmentally and activity-regulated miRNA (miR-485) that controls dendritic spine number and synapse formation in an activity-dependent homeostatic manner. We find that many plasticity-associated genes contain predicted miR-485 binding sites and further identify the presynaptic protein SV2A as a target of miR-485. miR-485 negatively regulated dendritic spine density, postsynaptic density 95 (PSD-95) clustering, and surface expression of GluR2. Furthermore, miR-485 overexpression reduced spontaneous synaptic responses and transmitter release, as measured by miniature excitatory postsynaptic current (EPSC) analysis and FM 1-43 staining. SV2A knockdown mimicked the effects of miR-485, and these effects were reversed by SV2A overexpression. Moreover, 5 d of increased synaptic activity induced homeostatic changes in synaptic specializations that were blocked by a miR-485 inhibitor. Our findings reveal a role for this previously uncharacterized miRNA and the presynaptic protein SV2A in homeostatic plasticity and nervous system development, with possible implications in neurological disorders (e.g., Huntington and Alzheimer's disease), where miR-485 has been found to be dysregulated.", "citation": {"db": "PubMed", "db_id": "21697510"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true, "miRNA subgraph": true}}, "source": 2121, "target": 3971, "key": "8bf2a8ec14bcf4371e1fc66b40a92428"}, {"line": 8833, "relation": "decreases", "evidence": "Using a bioinformatics screen to identify sequence motifs enriched in the 3'UTR of rapidly destabilized mRNAs, we identified a developmentally and activity-regulated miRNA (miR-485) that controls dendritic spine number and synapse formation in an activity-dependent homeostatic manner. We find that many plasticity-associated genes contain predicted miR-485 binding sites and further identify the presynaptic protein SV2A as a target of miR-485. miR-485 negatively regulated dendritic spine density, postsynaptic density 95 (PSD-95) clustering, and surface expression of GluR2. Furthermore, miR-485 overexpression reduced spontaneous synaptic responses and transmitter release, as measured by miniature excitatory postsynaptic current (EPSC) analysis and FM 1-43 staining. SV2A knockdown mimicked the effects of miR-485, and these effects were reversed by SV2A overexpression. Moreover, 5 d of increased synaptic activity induced homeostatic changes in synaptic specializations that were blocked by a miR-485 inhibitor. Our findings reveal a role for this previously uncharacterized miRNA and the presynaptic protein SV2A in homeostatic plasticity and nervous system development, with possible implications in neurological disorders (e.g., Huntington and Alzheimer's disease), where miR-485 has been found to be dysregulated.", "citation": {"db": "PubMed", "db_id": "21697510"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true, "miRNA subgraph": true}}, "source": 2121, "target": 3962, "key": "e8e72334a31fc1f9b914d0c8b9cbde4f"}, {"line": 8834, "relation": "decreases", "evidence": "Using a bioinformatics screen to identify sequence motifs enriched in the 3'UTR of rapidly destabilized mRNAs, we identified a developmentally and activity-regulated miRNA (miR-485) that controls dendritic spine number and synapse formation in an activity-dependent homeostatic manner. We find that many plasticity-associated genes contain predicted miR-485 binding sites and further identify the presynaptic protein SV2A as a target of miR-485. miR-485 negatively regulated dendritic spine density, postsynaptic density 95 (PSD-95) clustering, and surface expression of GluR2. Furthermore, miR-485 overexpression reduced spontaneous synaptic responses and transmitter release, as measured by miniature excitatory postsynaptic current (EPSC) analysis and FM 1-43 staining. SV2A knockdown mimicked the effects of miR-485, and these effects were reversed by SV2A overexpression. Moreover, 5 d of increased synaptic activity induced homeostatic changes in synaptic specializations that were blocked by a miR-485 inhibitor. Our findings reveal a role for this previously uncharacterized miRNA and the presynaptic protein SV2A in homeostatic plasticity and nervous system development, with possible implications in neurological disorders (e.g., Huntington and Alzheimer's disease), where miR-485 has been found to be dysregulated.", "citation": {"db": "PubMed", "db_id": "21697510"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true, "miRNA subgraph": true}}, "source": 2121, "target": 761, "key": "ebe9b1a94ba46b8897474eaf10b2d43d"}, {"line": 8830, "relation": "association", "evidence": "Using a bioinformatics screen to identify sequence motifs enriched in the 3'UTR of rapidly destabilized mRNAs, we identified a developmentally and activity-regulated miRNA (miR-485) that controls dendritic spine number and synapse formation in an activity-dependent homeostatic manner. We find that many plasticity-associated genes contain predicted miR-485 binding sites and further identify the presynaptic protein SV2A as a target of miR-485. miR-485 negatively regulated dendritic spine density, postsynaptic density 95 (PSD-95) clustering, and surface expression of GluR2. Furthermore, miR-485 overexpression reduced spontaneous synaptic responses and transmitter release, as measured by miniature excitatory postsynaptic current (EPSC) analysis and FM 1-43 staining. SV2A knockdown mimicked the effects of miR-485, and these effects were reversed by SV2A overexpression. Moreover, 5 d of increased synaptic activity induced homeostatic changes in synaptic specializations that were blocked by a miR-485 inhibitor. Our findings reveal a role for this previously uncharacterized miRNA and the presynaptic protein SV2A in homeostatic plasticity and nervous system development, with possible implications in neurological disorders (e.g., Huntington and Alzheimer's disease), where miR-485 has been found to be dysregulated.", "citation": {"db": "PubMed", "db_id": "21697510"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "miRNA subgraph": true}}, "source": 4014, "target": 2121, "key": "5d6b8c3d564109451603b78c2e793ca4"}, {"line": 8843, "relation": "association", "evidence": "Age-related impairment of visual recognition memory correlates with impaired synaptic distribution of GluA2 and protein kinase Mζ in the dentate gyrus.", "citation": {"db": "PubMed", "db_id": "22985047"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 3971, "target": 820, "key": "3d9bbe7b3552063a9a801b3f7a5d1177"}, {"line": 8988, "relation": "increases", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 3962, "target": 2633, "key": "15dd8a7985a33ed1c9aeac54d41a1ad5"}, {"line": 8859, "relation": "increases", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"High": true}}, "source": 522, "target": 228, "key": "cd61ccbe320cef554bb5435c1e95f092"}, {"line": 43453, "relation": "increases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"Medium": true}}, "source": 522, "target": 228, "key": "97b9f450d946d4e1f82d8f3ce523847d"}, {"line": 8867, "relation": "increases", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"High": true}}, "source": 4013, "target": 3414, "key": "194712af4b0a5c39e88cb4bb9dbbc20d"}, {"line": 8875, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4013, "target": 2098, "key": "83742ab54994e86037aeb8db47f3fca9"}, {"line": 8878, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4013, "target": 2107, "key": "16dd274a64cb5c469d905232030c7148"}, {"line": 8881, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4013, "target": 2086, "key": "65189f13905318ccb49aeacc8c29c599"}, {"line": 8884, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4013, "target": 2115, "key": "6317c04dc4ac37e6df387b245ec71376"}, {"line": 8875, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2098, "target": 4013, "key": "8cee7f141cf52c8bd94b793911c387cf"}, {"line": 8876, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2098, "target": 2328, "key": "c58194c806cacebbb3d2626e0cebcf71"}, {"line": 8878, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2107, "target": 4013, "key": "c954d49160857c3d66b006d58dafdede"}, {"line": 8879, "relation": "negativeCorrelation", "evidence": "Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate-limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be posttranscriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Abeta) generation. We show that SPT is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9, and 29a/b-1 increases SPT and in turn Abeta levels, and provides a mechanism for the elevated risk of AD associated with age, high-saturated-fat diet, and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.", "citation": {"db": "PubMed", "db_id": "21994399"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true, "Non-amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2107, "target": 2328, "key": "e0dacea5272144b4e9911e7ad51534c4"}, {"line": 9168, "relation": "decreases", "evidence": "Target gene repression mediated by miRNAs miR-181c and miR-9 both of which are down-regulated by amyloid-beta. MicroRNAs (miRNAs) are small non-coding RNA regulators of protein synthesis that are essential for normal brain development and function. Their profiles are significantly altered in neurodegenerative diseases such as Alzheimer's disease (AD) that is characterized by amyloid-beta (Abeta) and tau deposition in brain. How deregulated miRNAs contribute to AD is not understood, as their dysfunction could be both a cause and a consequence of disease. To address this question we had previously profiled miRNAs in models of AD. This identified miR-9 and -181c as being down-regulated by Abeta in hippocampal cultures. Interestingly, there was a remarkable overlap with those miRNAs that are deregulated in Abeta-depositing APP23 transgenic mice and in human AD tissue. While the Abeta precursor protein APP itself is a target of miRNA regulation, the challenge resides in identifying further targets. Here, we expand the repertoire of miRNA target genes by identifying the 3' untranslated regions (3' UTRs) of TGFBI, TRIM2, SIRT1 and BTBD3 as being repressed by miR-9 and -181c, either alone or in combination. Taken together, our study identifies putative target genes of miRNAs miR-9 and 181c, which may function in brain homeostasis and disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "21720722"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2107, "target": 4021, "key": "5005c8168011927db5a894cab174936b"}, {"line": 9170, "relation": "decreases", "evidence": "Target gene repression mediated by miRNAs miR-181c and miR-9 both of which are down-regulated by amyloid-beta. MicroRNAs (miRNAs) are small non-coding RNA regulators of protein synthesis that are essential for normal brain development and function. Their profiles are significantly altered in neurodegenerative diseases such as Alzheimer's disease (AD) that is characterized by amyloid-beta (Abeta) and tau deposition in brain. How deregulated miRNAs contribute to AD is not understood, as their dysfunction could be both a cause and a consequence of disease. To address this question we had previously profiled miRNAs in models of AD. This identified miR-9 and -181c as being down-regulated by Abeta in hippocampal cultures. Interestingly, there was a remarkable overlap with those miRNAs that are deregulated in Abeta-depositing APP23 transgenic mice and in human AD tissue. While the Abeta precursor protein APP itself is a target of miRNA regulation, the challenge resides in identifying further targets. Here, we expand the repertoire of miRNA target genes by identifying the 3' untranslated regions (3' UTRs) of TGFBI, TRIM2, SIRT1 and BTBD3 as being repressed by miR-9 and -181c, either alone or in combination. Taken together, our study identifies putative target genes of miRNAs miR-9 and 181c, which may function in brain homeostasis and disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "21720722"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2107, "target": 4028, "key": "b0dbda0b853a20858767f65def7d8a48"}, {"line": 9171, "relation": "decreases", "evidence": "Target gene repression mediated by miRNAs miR-181c and miR-9 both of which are down-regulated by amyloid-beta. MicroRNAs (miRNAs) are small non-coding RNA regulators of protein synthesis that are essential for normal brain development and function. Their profiles are significantly altered in neurodegenerative diseases such as Alzheimer's disease (AD) that is characterized by amyloid-beta (Abeta) and tau deposition in brain. How deregulated miRNAs contribute to AD is not understood, as their dysfunction could be both a cause and a consequence of disease. To address this question we had previously profiled miRNAs in models of AD. This identified miR-9 and -181c as being down-regulated by Abeta in hippocampal cultures. Interestingly, there was a remarkable overlap with those miRNAs that are deregulated in Abeta-depositing APP23 transgenic mice and in human AD tissue. While the Abeta precursor protein APP itself is a target of miRNA regulation, the challenge resides in identifying further targets. Here, we expand the repertoire of miRNA target genes by identifying the 3' untranslated regions (3' UTRs) of TGFBI, TRIM2, SIRT1 and BTBD3 as being repressed by miR-9 and -181c, either alone or in combination. Taken together, our study identifies putative target genes of miRNAs miR-9 and 181c, which may function in brain homeostasis and disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "21720722"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2107, "target": 4010, "key": "7bcc2f0de1cf7bda952d9315fb699717"}, {"line": 45729, "relation": "decreases", "evidence": "Results showed significant hypermethylation of mammalian orthologue of Sir2 (SIRT1) gene in AD patients ", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 2107, "target": 4010, "key": "9c470bbe0140847f60d59cfe018cb055"}, {"line": 9172, "relation": "decreases", "evidence": "Target gene repression mediated by miRNAs miR-181c and miR-9 both of which are down-regulated by amyloid-beta. MicroRNAs (miRNAs) are small non-coding RNA regulators of protein synthesis that are essential for normal brain development and function. Their profiles are significantly altered in neurodegenerative diseases such as Alzheimer's disease (AD) that is characterized by amyloid-beta (Abeta) and tau deposition in brain. How deregulated miRNAs contribute to AD is not understood, as their dysfunction could be both a cause and a consequence of disease. To address this question we had previously profiled miRNAs in models of AD. This identified miR-9 and -181c as being down-regulated by Abeta in hippocampal cultures. Interestingly, there was a remarkable overlap with those miRNAs that are deregulated in Abeta-depositing APP23 transgenic mice and in human AD tissue. While the Abeta precursor protein APP itself is a target of miRNA regulation, the challenge resides in identifying further targets. Here, we expand the repertoire of miRNA target genes by identifying the 3' untranslated regions (3' UTRs) of TGFBI, TRIM2, SIRT1 and BTBD3 as being repressed by miR-9 and -181c, either alone or in combination. Taken together, our study identifies putative target genes of miRNAs miR-9 and 181c, which may function in brain homeostasis and disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "21720722"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2107, "target": 3947, "key": "b77414161185fec85594b9290fd44c6d"}, {"line": 8908, "relation": "negativeCorrelation", "evidence": "Down's syndrome brain is typified by activated microglia, increases in inflammatory signaling, and an aberrant immune system. In these studies, a screening of micro-RNA (miRNA) from Down's syndrome brain and peripheral tissues indicated an upregulation of a chromosome 21-encoded miRNA-155 and a decrease in the abundance of the miRNA-155 mRNA target complement factor H (CFH), an important repressor of the innate immune response. Stressed primary human neuronal-glial cells indicated both miRNA-155 increase and CFH downregulation, an effect that was reversed using anti-miRNA-155. These findings suggest that immunopathological deficits associated with Down's syndrome can, in part, be explained by a generalized miRNA-155-mediated downregulation of CFH that may contribute to both brain and systemic immune pathology.", "citation": {"db": "PubMed", "db_id": "22182977"}, "annotations": {"Subgraph": {"Complement system subgraph": true, "miRNA subgraph": true}}, "source": 2104, "target": 3955, "key": "d4419b08ba825cd276939a6bd107b3d3"}, {"line": 9279, "relation": "decreases", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"Smad subgraph": true, "TGF-Beta subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2104, "target": 3454, "key": "2804f2653ae1e3e8337c689fee793204"}, {"line": 9288, "relation": "association", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2104, "target": 577, "key": "956489ac22f64ceb8c56fce5e375d0dd"}, {"line": 9290, "relation": "association", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2104, "target": 3909, "key": "77da79c0e4350dbf20b559a9a051c432"}, {"line": 9291, "relation": "association", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2104, "target": 711, "key": "1fe4d2a586313508b33483b2fa501cc8"}, {"line": 9359, "relation": "association", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 2104, "target": 3468, "key": "afeb12aa8b321371a6db32a5e135e831"}, {"line": 9360, "relation": "association", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 2104, "target": 3467, "key": "a082021e1e33a21ed611339964f8520d"}, {"line": 9362, "relation": "association", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}}, "source": 2104, "target": 2885, "key": "590e98e0b39893dccc83fdb5b48b9d7e"}, {"line": 9364, "relation": "association", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true, "miRNA subgraph": true}}, "source": 2104, "target": 3440, "key": "14c59a34b3770282aba5851e5ff9d4de"}, {"line": 8918, "relation": "decreases", "evidence": "Eleven miRNAs were selected, which have evolutionary conserved binding sites. Three of them (miR-103, miR-107, miR-1306) were further analysed as they are linked to AD and most strictly conserved between different species. Predicted target genes of miR-103 (p-value = 0.0065) and miR-107 (p-value = 0.0009) showed significant overlap with the AlzGene database except for miR-1306. Interactions between miR-103 and miR-107 to genes were revealed playing a role in processes leading to AD. ADAM10 expression in the reporter assay was reduced by miR-1306 (28%), miR-103 (45%) and miR-107 (52%).", "citation": {"db": "PubMed", "db_id": "22594617"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "miRNA subgraph": true}}, "source": 2095, "target": 2249, "key": "aa9586252b2f25b9d2c59feb849f412a"}, {"line": 8919, "relation": "decreases", "evidence": "Eleven miRNAs were selected, which have evolutionary conserved binding sites. Three of them (miR-103, miR-107, miR-1306) were further analysed as they are linked to AD and most strictly conserved between different species. Predicted target genes of miR-103 (p-value = 0.0065) and miR-107 (p-value = 0.0009) showed significant overlap with the AlzGene database except for miR-1306. Interactions between miR-103 and miR-107 to genes were revealed playing a role in processes leading to AD. ADAM10 expression in the reporter assay was reduced by miR-1306 (28%), miR-103 (45%) and miR-107 (52%).", "citation": {"db": "PubMed", "db_id": "22594617"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "miRNA subgraph": true}}, "source": 2089, "target": 2249, "key": "729033df0b82a0f6738176ce3777bee6"}, {"line": 8921, "relation": "increases", "evidence": "Eleven miRNAs were selected, which have evolutionary conserved binding sites. Three of them (miR-103, miR-107, miR-1306) were further analysed as they are linked to AD and most strictly conserved between different species. Predicted target genes of miR-103 (p-value = 0.0065) and miR-107 (p-value = 0.0009) showed significant overlap with the AlzGene database except for miR-1306. Interactions between miR-103 and miR-107 to genes were revealed playing a role in processes leading to AD. ADAM10 expression in the reporter assay was reduced by miR-1306 (28%), miR-103 (45%) and miR-107 (52%).", "citation": {"db": "PubMed", "db_id": "22594617"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "miRNA subgraph": true}}, "source": 3935, "target": 2249, "key": "f3e5ee3493446db92be2ff8283413373"}, {"line": 8931, "relation": "increases", "evidence": "We uncovered an unconventional role for the microRNA let-7, a highly abundant regulator of gene expression in the CNS, in which extracellular let-7 activates the RNA-sensing Toll-like receptor (TLR) 7 and induces neurodegeneration through neuronal TLR7.", "citation": {"db": "PubMed", "db_id": "22610069"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 2126, "target": 4023, "key": "29c7bb7dc2de94718671ba08bcd18ef1"}, {"line": 8932, "relation": "increases", "evidence": "We uncovered an unconventional role for the microRNA let-7, a highly abundant regulator of gene expression in the CNS, in which extracellular let-7 activates the RNA-sensing Toll-like receptor (TLR) 7 and induces neurodegeneration through neuronal TLR7.", "citation": {"db": "PubMed", "db_id": "22610069"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 4023, "target": 3469, "key": "26eecbee3642da54fcb75e3a3db1320a"}, {"line": 8944, "relation": "negativeCorrelation", "evidence": "Delivery of a miR-153 antisense inhibitor to human fetal brain cultures significantly elevated APP expression. miR-153 delivery also reduced expression of the APP paralog APLP2. High functional redundancy between APP and APLP2 suggests that miR-153 may target biological pathways in which they both function. Interestingly, in a subset of human AD brain specimens with moderate AD pathology, miR-153 levels were reduced.", "citation": {"db": "PubMed", "db_id": "22733824"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2084, "target": 3940, "key": "f4ac5fb2b7a48a742c16677d9dd9d853"}, {"line": 45738, "relation": "negativeCorrelation", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 2084, "target": 3940, "key": "12ff0a2531b0ca86fca57c701237ff6e"}, {"line": 8946, "relation": "negativeCorrelation", "evidence": "Delivery of a miR-153 antisense inhibitor to human fetal brain cultures significantly elevated APP expression. miR-153 delivery also reduced expression of the APP paralog APLP2. High functional redundancy between APP and APLP2 suggests that miR-153 may target biological pathways in which they both function. Interestingly, in a subset of human AD brain specimens with moderate AD pathology, miR-153 levels were reduced.", "citation": {"db": "PubMed", "db_id": "22733824"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2084, "target": 3938, "key": "c1fa650a6cd9799964644174aed42acc"}, {"line": 8947, "relation": "negativeCorrelation", "evidence": "Delivery of a miR-153 antisense inhibitor to human fetal brain cultures significantly elevated APP expression. miR-153 delivery also reduced expression of the APP paralog APLP2. High functional redundancy between APP and APLP2 suggests that miR-153 may target biological pathways in which they both function. Interestingly, in a subset of human AD brain specimens with moderate AD pathology, miR-153 levels were reduced.", "citation": {"db": "PubMed", "db_id": "22733824"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2084, "target": 3823, "key": "fa18eb11586261978315ea92c000cc5d"}, {"line": 8946, "relation": "negativeCorrelation", "evidence": "Delivery of a miR-153 antisense inhibitor to human fetal brain cultures significantly elevated APP expression. miR-153 delivery also reduced expression of the APP paralog APLP2. High functional redundancy between APP and APLP2 suggests that miR-153 may target biological pathways in which they both function. Interestingly, in a subset of human AD brain specimens with moderate AD pathology, miR-153 levels were reduced.", "citation": {"db": "PubMed", "db_id": "22733824"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3938, "target": 2084, "key": "d8168a2bad70b1591ddbb71a55de4ac6"}, {"line": 8963, "relation": "increases", "evidence": "Amyloid beta-peptide (Abeta) accumulating in the brain of Alzheimer disease (AD) patients is believed to be the main pathophysiologcal cause of the disease. Proteolytic processing of the amyloid precursor protein by alpha-secretase ADAM10 (a disintegrin and metalloprotease 10) protects the brain from the production of the Abeta. Meanwhile, dysregulation or aberrant expression of microRNAs (miRNAs) has been widely documented in AD patients. In this study, we demonstrated that overexpression of miR-144, which was previously reported to be increased in elderly primate brains and AD patients, significantly decreased activity of the luciferase reporter containing the ADAM10 3'-untranslated region (3'-UTR) and suppressed the ADAM10 protein level, whereas the miR-144 inhibitor led to an increase of the luciferase activity. The negative regulation caused by miR-144 was strictly dependent on the binding of the miRNA to its recognition element in the ADAM10 3'-UTR. Moreover, we also showed that activator protein-1 regulates the transcription of miR-144 and the up-regulation of miR-144 at least partially induces the suppression of the ADAM10 protein in the presence of Abeta. In addition, we found that miR-451, a miRNA processed from a single gene locus with miR-144, is also involved in the regulation of ADAM10 expression. Taken together, our data therefore demonstrate miR-144/451 is a negative regulator of the ADAM10 protein and suggest a mechanistic role for miR-144/451 in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "23546882"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "ADAM Metallopeptidase subgraph": true, "MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2936, "target": 2100, "key": "74167b606ed5cf12d5c451bdd1f5505f"}, {"line": 8964, "relation": "decreases", "evidence": "Amyloid beta-peptide (Abeta) accumulating in the brain of Alzheimer disease (AD) patients is believed to be the main pathophysiologcal cause of the disease. Proteolytic processing of the amyloid precursor protein by alpha-secretase ADAM10 (a disintegrin and metalloprotease 10) protects the brain from the production of the Abeta. Meanwhile, dysregulation or aberrant expression of microRNAs (miRNAs) has been widely documented in AD patients. In this study, we demonstrated that overexpression of miR-144, which was previously reported to be increased in elderly primate brains and AD patients, significantly decreased activity of the luciferase reporter containing the ADAM10 3'-untranslated region (3'-UTR) and suppressed the ADAM10 protein level, whereas the miR-144 inhibitor led to an increase of the luciferase activity. The negative regulation caused by miR-144 was strictly dependent on the binding of the miRNA to its recognition element in the ADAM10 3'-UTR. Moreover, we also showed that activator protein-1 regulates the transcription of miR-144 and the up-regulation of miR-144 at least partially induces the suppression of the ADAM10 protein in the presence of Abeta. In addition, we found that miR-451, a miRNA processed from a single gene locus with miR-144, is also involved in the regulation of ADAM10 expression. Taken together, our data therefore demonstrate miR-144/451 is a negative regulator of the ADAM10 protein and suggest a mechanistic role for miR-144/451 in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "23546882"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "ADAM Metallopeptidase subgraph": true, "MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2936, "target": 2249, "key": "176a61efa8a18824a40197ce198da2ee"}, {"line": 13823, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true}}, "source": 2936, "target": 2744, "key": "c736ed71c6198cf8cdb9b0826c25fe0f"}, {"relation": "hasVariant", "source": 2936, "target": 2937, "key": "ed191a7a0bda5b0033c044fe47820b71"}, {"line": 14014, "relation": "association", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2936, "target": 3002, "key": "13a38fc488a2013f0dd2856f9968a330"}, {"line": 14015, "relation": "association", "evidence": "Moreover, Pin1 cooperates with either activated Ras or JNK to increase transcriptional activity of c-Jun towards the cyclin D1 promoter.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2936, "target": 2213, "key": "a0aad5823c95d612e66e6fc15721faa9"}, {"line": 14029, "relation": "increases", "evidence": "Thus, Pin1 is up-regulated in human tumors and cooperates with Ras signaling in increasing c-Jun transcriptional activity towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2936, "target": 3950, "key": "6d6959bbcde1e00b839565e61de80dae"}, {"relation": "partOf", "source": 2936, "target": 1017, "key": "92998aadc0563071f1b02f2ebeb5e379"}, {"line": 19321, "relation": "association", "evidence": "In addition, cdk5/p25 might also interact with other pathways such as glycogen synthetase kinase 3beta (GSK3beta) and c-JUN kinase.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2936, "target": 1340, "key": "79ed24acbe654f12f28606f6ce8e6b9a"}, {"line": 20379, "relation": "positiveCorrelation", "evidence": "ICE-beta, c-Jun, Bax-alpha, Bcl-x(L), p53, and GADD153 were found to be upregulated in some AD samples but were not detected or downregulated in other AD or normal samples.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true}}, "source": 2936, "target": 3823, "key": "7f4f1889d543c0ba5da9f973ecb93c41"}, {"line": 29534, "relation": "increases", "evidence": "Inhibition of c-Jun prevents neuronal cell death in in vivo AD pmodels, highlighting it as a major JNK effector.", "citation": {"db": "PubMed", "db_id": "19776350"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "source": 2936, "target": 648, "key": "38373ffa35d1324f3563ad38608c0cfc"}, {"relation": "partOf", "source": 2936, "target": 1346, "key": "2483e6759539f367059c42e03eea2cfa"}, {"relation": "partOf", "source": 2936, "target": 1501, "key": "2ec639926fb7c5109207c75c54da8520"}, {"relation": "partOf", "source": 2936, "target": 1419, "key": "1bdfa925ce9da9cde39b08b3a8560e6d"}, {"relation": "partOf", "source": 2936, "target": 1503, "key": "550737dc3238de545349a5f73d9801de"}, {"relation": "partOf", "source": 2936, "target": 1502, "key": "aa75327e70dcaa35a0afb9218d93d53b"}, {"relation": "hasVariant", "source": 2936, "target": 2938, "key": "2929310e12927ba419eed48992127ba1"}, {"line": 8966, "relation": "decreases", "evidence": "Amyloid beta-peptide (Abeta) accumulating in the brain of Alzheimer disease (AD) patients is believed to be the main pathophysiologcal cause of the disease. Proteolytic processing of the amyloid precursor protein by alpha-secretase ADAM10 (a disintegrin and metalloprotease 10) protects the brain from the production of the Abeta. Meanwhile, dysregulation or aberrant expression of microRNAs (miRNAs) has been widely documented in AD patients. In this study, we demonstrated that overexpression of miR-144, which was previously reported to be increased in elderly primate brains and AD patients, significantly decreased activity of the luciferase reporter containing the ADAM10 3'-untranslated region (3'-UTR) and suppressed the ADAM10 protein level, whereas the miR-144 inhibitor led to an increase of the luciferase activity. The negative regulation caused by miR-144 was strictly dependent on the binding of the miRNA to its recognition element in the ADAM10 3'-UTR. Moreover, we also showed that activator protein-1 regulates the transcription of miR-144 and the up-regulation of miR-144 at least partially induces the suppression of the ADAM10 protein in the presence of Abeta. In addition, we found that miR-451, a miRNA processed from a single gene locus with miR-144, is also involved in the regulation of ADAM10 expression. Taken together, our data therefore demonstrate miR-144/451 is a negative regulator of the ADAM10 protein and suggest a mechanistic role for miR-144/451 in AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "23546882"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2120, "target": 2249, "key": "534bc79b3622808985ddfc5d38f19d6f"}, {"line": 8978, "relation": "negativeCorrelation", "evidence": "We reported that tumor necrosis factor receptor I (TNFRI) is required for neuronal death induced by amyloid-beta protein in the Alzheimer's disease (AD) brain. However, whether TNF receptor subtypes are expressed and activated differentially in AD brains compared to non-demented brains remains unclear. Our studies on Western blot and ELISA measurements demonstrated that TNFRI levels are increased whereas TNFRII levels are decreased in AD brains compared to non-demented brains (p <0.05).", "citation": {"db": "PubMed", "db_id": "20110607"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3477, "target": 3823, "key": "cd2757706d07463f51a206b1ac00f44e"}, {"line": 8987, "relation": "negativeCorrelation", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 2633, "target": 3823, "key": "86d6f137b06cf0b8afcf2bbb7f724272"}, {"line": 8990, "relation": "increases", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 2633, "target": 452, "key": "15cb1da72cf7a7cfe053d704a13290eb"}, {"relation": "partOf", "source": 2633, "target": 1393, "key": "29f092d9371cf270f84e7152d1ea4f66"}, {"relation": "partOf", "source": 2633, "target": 1380, "key": "9cafa905e260561b31e165b3e401eb89"}, {"line": 8989, "relation": "negativeCorrelation", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 2632, "target": 3823, "key": "a00e2de52dd323ac28e45be543ac72ff"}, {"line": 8991, "relation": "increases", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 2632, "target": 452, "key": "0a9bfd3f4fa66db3c8d6784132bdff9b"}, {"line": 8992, "relation": "negativeCorrelation", "evidence": "PSD-95 and SAP-102 protein expression was markedly reduced in the AD inferior temporal cortex. Both mRNA and protein levels were reduced according to disease severity. SAP102 protein levels were significantly reduced in AD subjects carrying a copy of the APOEε4 allele. This is the first study to investigate SAP-102 in the aging human brain and suggest a possible mechanism for NMDA receptor expression aberrations in AD.", "citation": {"db": "PubMed", "db_id": "20634587"}, "annotations": {"Subgraph": {"NMDA receptor": true, "APOE subgraph": true}}, "source": 452, "target": 3823, "key": "0def5963abb3f976391a4bc7507f800c"}, {"line": 10010, "relation": "increases", "evidence": "In addition, excitotoxicity from the overstimulation of glutamate receptors is considered a major cause of neuron death in AD and statins may be promising agents for protecting against memory loss.", "citation": {"db": "PubMed", "db_id": "21352095"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 452, "target": 505, "key": "fe2b925732484f80082d4768a58d61d5"}, {"line": 9042, "relation": "directlyIncreases", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 1446, "target": 3015, "key": "e65977b43aa6997b96c8638bb08b04c1"}, {"line": 9043, "relation": "increases", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1446, "target": 2794, "key": "e608e232eb5ff3ce65effa46598123be"}, {"line": 9048, "relation": "decreases", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2182, "target": 3015, "key": "534edbb3d3c294107a6f8048691f904e"}, {"line": 9049, "relation": "directlyDecreases", "evidence": "In this in vitro study, we found that: (a) alpha-SN directly stimulates the phosphorylation of tau by glycogen synthase kinase-3beta (GSK-3beta), (b) alpha-SN forms a heterotrimeric complex with tau and GSK-3beta, and (c) the nonamyloid beta component (NAC) domain and an acidic region of alpha-SN are responsible for the stimulation of GSK-3beta-mediated tau phosphorylation. Thus, it is concluded that alpha-SN functions as a connecting mediator for tau and GSK-3beta, resulting in GSK-3beta-mediated tau phosphorylation. Because the expression of alpha-SN is promoted by oxidative stress, the accumulation of alpha-SN induced by such stress may directly induce the hyperphosphorylation of tau by GSK-3beta. Furthermore, we found that heat shock protein 70 (Hsp70) suppresses the alpha-SN-induced phosphorylation of tau by GSK-3beta through its direct binding to alpha-SN, suggesting that Hsp70 acts as a physiological suppressor of alpha-SN-mediated tau hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "21985244"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Synuclein subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 2182, "target": 3384, "key": "332c149e0d6d4d43367c53ed6b0547bd"}, {"line": 22107, "relation": "decreases", "evidence": "Over-expression of hsp70 was found to reduce PQ-induced oxidative stress along with JNK and caspase-3 mediated dopaminergic neuronal cell death in exposed organism.", "citation": {"db": "PubMed", "db_id": "24887138"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Chaperone subgraph": true}, "MeSHAnatomy": {"Dopaminergic Neurons": true}}, "source": 2182, "target": 321, "key": "c71d90e37d2602bb53de78c3cddc88b1"}, {"line": 22109, "relation": "decreases", "evidence": "Over-expression of hsp70 was found to reduce PQ-induced oxidative stress along with JNK and caspase-3 mediated dopaminergic neuronal cell death in exposed organism.", "citation": {"db": "PubMed", "db_id": "24887138"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Chaperone subgraph": true}, "MeSHAnatomy": {"Dopaminergic Neurons": true}}, "source": 2182, "target": 648, "key": "4a17d09793e9d7c728b6e79c23a16b71"}, {"relation": "partOf", "source": 2182, "target": 1018, "key": "14eeeb9ca4be4468de3fc3235e653925"}, {"relation": "partOf", "source": 2182, "target": 1020, "key": "29581dfcf57fe9b66cf38a96b71cb7b6"}, {"line": 28998, "relation": "increases", "evidence": "Hsp70/Hsc70, a member of the chaperone protein family, interacts with Tau protein and mediates proper folding of Tau and can promote degradation of Tau protein under certain circumstances. ", "citation": {"db": "PubMed", "db_id": "17954934"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2182, "target": 3010, "key": "0233e3975b1eb41c305345780f85a993"}, {"line": 30804, "relation": "increases", "evidence": "Hsp70/Hsc70, a member of the chaperone protein family, interacts with Tau protein and mediates proper folding of Tau and can promote degradation of Tau protein under certain circumstances.", "citation": {"db": "PubMed", "db_id": "17954934"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Chaperone subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2182, "target": 3010, "key": "731dd3ebc1121301c467834a40cbfbb3"}, {"relation": "partOf", "source": 2182, "target": 1019, "key": "472d11fdc10626e8634393c72ca9dcdf"}, {"line": 9084, "relation": "increases", "evidence": "APP (amyloid precursor protein) and LRP1 (low-density lipoprotein receptor-related protein 1) have been implicated in the pathogenesis of AD (Alzheimer's disease). They are functionally linked by Fe65, a PTB (phosphotyrosine-binding)-domain-containing adaptor protein that binds to intracellular NPxY-motifs of APP and LRP1, thereby influencing expression levels, cellular trafficking and processing. Additionally, Fe65 has been reported to mediate nuclear signalling in combination with intracellular domains of APP and LRP1. We have previously identified another adaptor protein, GULP1 (engulfment adaptor PTB-domain-containing 1). In the present study we characterize and compare nuclear trafficking and transactivation of GULP1 and Fe65 together with APP and LRP1 and report differential nuclear trafficking of adaptors when APP or LRP1 are co-expressed. The observed effects were additionally supported by a reporter-plasmid-based transactivation assay. The results from the present study indicate that Fe65 might have signalling properties together with APP and LRP1, whereas GULP1 only mediates LRP1 transactivation.", "citation": {"db": "PubMed", "db_id": "23167255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3989, "target": 2970, "key": "2f8757aef7fb19e93f07361ede594b59"}, {"line": 9101, "relation": "positiveCorrelation", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3989, "target": 3939, "key": "25272c2c3255d54b2dc68c66a62a2e8d"}, {"line": 9485, "relation": "association", "evidence": "Low-density lipoprotein receptor-related protein 1 (LRP1) is a multifunctional endocytic receptor that plays critical roles in the pathogenesis of several human diseases including tumor metastasis and Alzheimer's disease. However, mechanisms that regulate LRP1 expression under physiological and pathophysiological conditions are not unclear. In human cell lines, we found that miR-205 down-regulates the expression of LRP1 by targeting sequences in the 3'UTR of LRP1 mRNA. This effect was abolished by deleting the miR-205 seed site in the 3'UTR of LRP1. The ectopic expression of miR-205 also significantly mitigated migration of both U87 and SK-LU-1 cells. These results, for the first time, demonstrate that expression of human LRP1 is regulated in part by a specific miRNA, leading to decreased tumor cell migration.", "citation": {"db": "PubMed", "db_id": "19665999"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3989, "target": 2110, "key": "49f37c90fba2dd5543c4449647865368"}, {"line": 9099, "relation": "positiveCorrelation", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3939, "target": 3823, "key": "4cc368dac2885e00235578c29580ad1d"}, {"line": 45112, "relation": "positiveCorrelation", "evidence": "APOE ε4 mRNA level is increased in AD compared to controls.The APOE gene was found to be of bimodal structure, with a hypomethylated CpG-poor promoter and a fully methylated 3′-CpG-island", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Low": true}}, "source": 3939, "target": 3823, "key": "fb5e8cbd51ea84286f255a8c4bd9e968"}, {"line": 9101, "relation": "positiveCorrelation", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3939, "target": 3989, "key": "b4ab5bd787b49c49dc9bafadddd44e82"}, {"line": 9102, "relation": "increases", "evidence": "Inheritance of the ε4 allele of apolipoprotein E (ApoE) is the only confirmed and consistently replicated risk factor for late onset Alzheimer's disease (AD). ApoE is also a key ligand for low-density lipoprotein (LDL) receptor-related protein (LRP), a major neuronal low-density lipoprotein receptor. ApoE and LRP mRNA expression was significantly elevated in the postmortem inferior temporal gyrus (area 20) and the hippocampus from individuals with dementia compared with those with intact cognition. In addition to their strong association with the progression of cognitive dysfunction, LRP and ApoE mRNA levels were also positively correlated with increasing neuropathological hallmarks of AD. Additionally, Western blot analysis of ApoE protein expression in the hippocampus showed that the differential expression observed at the transcriptional level is also reflected at the protein level. Given the critical role played by LRP and ApoE in amyloid beta (Abeta) and cholesterol trafficking, increased expression of LRP and ApoE may not only disrupt cholesterol homeostasis but may also contribute to some of the neurobiological features of AD, including plaque deposition.", "citation": {"db": "PubMed", "db_id": "21676498"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3939, "target": 2312, "key": "d0f23163cc9ee1442f2d78eef2c19345"}, {"line": 9133, "relation": "association", "evidence": "Recently, there have been increasing evidences that microRNA-146 (miR-146) is related to up-regulated immune and inflammatory signaling through its target genes, such as IRAK1 and TRAF6. Additionally, abundant data continue to support the hypothesis that progressive up-regulation of inflammatory gene expression and elevated inflammatory signaling facilitate the development and progression of Alzheimer's disease (AD). This review focuses on the recent findings regarding the role of miR-146 in modulating immune response and its subsequent effects in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22209051"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4027, "target": 2102, "key": "a2a49c6b1ebb56a2d1616e3f2f07a87f"}, {"line": 9134, "relation": "increases", "evidence": "Recently, there have been increasing evidences that microRNA-146 (miR-146) is related to up-regulated immune and inflammatory signaling through its target genes, such as IRAK1 and TRAF6. Additionally, abundant data continue to support the hypothesis that progressive up-regulation of inflammatory gene expression and elevated inflammatory signaling facilitate the development and progression of Alzheimer's disease (AD). This review focuses on the recent findings regarding the role of miR-146 in modulating immune response and its subsequent effects in the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "22209051"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4027, "target": 3491, "key": "2dc12d8e2f25366c0455bde22b34ee4a"}, {"line": 9531, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 3491, "target": 2102, "key": "8943f176c13a4bd1af2cb1ef0968d9dc"}, {"line": 35205, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3491, "target": 3116, "key": "7fdd7129ed78283efe66f8d59b5e7dd7"}, {"line": 38665, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3491, "target": 3116, "key": "cc37384f557cbb12616df683d6adf657"}, {"line": 35207, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3491, "target": 3118, "key": "135bcc8d4ebb409ce42932dc30a7a372"}, {"line": 38667, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3491, "target": 3118, "key": "8eb85f1bc5498aa5a7ca120818c2bd19"}, {"line": 35682, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3491, "target": 2989, "key": "da7f8444b77be5bf05cfe0c7d2846692"}, {"line": 9147, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2085, "target": 3015, "key": "bc2ebacc90985c91291980b7b7996837"}, {"line": 9148, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2085, "target": 3991, "key": "d06dd68d616c3705cd6938fdd62bc3ce"}, {"line": 45458, "relation": "association", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 2085, "target": 3991, "key": "f68ba4666a1298b1ff132829e1fde67d"}, {"line": 9150, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2085, "target": 830, "key": "bf00d37826c14625bbe7b8816266bd8a"}, {"line": 9152, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2085, "target": 828, "key": "d6be1639e2451ab688cc236ff0c30f23"}, {"line": 9149, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2097, "target": 3015, "key": "b3f5edbac5517225eb270c41cb6e5c03"}, {"line": 9151, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2097, "target": 830, "key": "1a211ae10bf00825558c67a0f6318b02"}, {"line": 9153, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2097, "target": 828, "key": "03d294795a770ef3540440268072c9d8"}, {"line": 45459, "relation": "association", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 2097, "target": 1871, "key": "ce236996356c2bc6b4427d378c618a2e"}, {"line": 48850, "relation": "association", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "source": 2097, "target": 3823, "key": "6c96e6c6aeb787a54b83eb57576e8e1c"}, {"line": 48851, "relation": "association", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "source": 2097, "target": 688, "key": "daf334b0f148cc0ca03c6a4686bf4d8b"}, {"line": 48852, "relation": "increases", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "source": 2097, "target": 760, "key": "dbea97800a25ab53d9b765d78bd75bc6"}, {"line": 48857, "relation": "increases", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2097, "target": 789, "key": "4827c27aa149472e5afd9e083a1cf344"}, {"line": 48861, "relation": "positiveCorrelation", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2097, "target": 2508, "key": "6662f8266e00b51086e668b19c28adad"}, {"line": 9152, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 828, "target": 2085, "key": "34066a267c372a458c1866c1faa6d45c"}, {"line": 9153, "relation": "association", "evidence": "Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.", "citation": {"db": "PubMed", "db_id": "22720189"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "miRNA subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 828, "target": 2097, "key": "c2dcc5aa3f8684ce5aeac3bc2d65a14b"}, {"line": 9173, "relation": "increases", "evidence": "Target gene repression mediated by miRNAs miR-181c and miR-9 both of which are down-regulated by amyloid-beta. MicroRNAs (miRNAs) are small non-coding RNA regulators of protein synthesis that are essential for normal brain development and function. Their profiles are significantly altered in neurodegenerative diseases such as Alzheimer's disease (AD) that is characterized by amyloid-beta (Abeta) and tau deposition in brain. How deregulated miRNAs contribute to AD is not understood, as their dysfunction could be both a cause and a consequence of disease. To address this question we had previously profiled miRNAs in models of AD. This identified miR-9 and -181c as being down-regulated by Abeta in hippocampal cultures. Interestingly, there was a remarkable overlap with those miRNAs that are deregulated in Abeta-depositing APP23 transgenic mice and in human AD tissue. While the Abeta precursor protein APP itself is a target of miRNA regulation, the challenge resides in identifying further targets. Here, we expand the repertoire of miRNA target genes by identifying the 3' untranslated regions (3' UTRs) of TGFBI, TRIM2, SIRT1 and BTBD3 as being repressed by miR-9 and -181c, either alone or in combination. Taken together, our study identifies putative target genes of miRNAs miR-9 and 181c, which may function in brain homeostasis and disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "21720722"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 4010, "target": 3364, "key": "226a5f5c61413ead1376b774fd50fac1"}, {"line": 45735, "relation": "negativeCorrelation", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 4010, "target": 1961, "key": "be7d0292e4598ff23872eb0037c76987"}, {"line": 9254, "relation": "decreases", "evidence": "MiR-34a over-expression induces endothelial cell senescence and also suppresses cell proliferation by inhibiting cell cycle progression. Searching for how miR-34a affects senescence, we discovered that SIRT1 is a target of miR-34a. Over-expressing miR-34a inhibits SIRT1 protein expression, and knocking down miR-34a enhances SIRT1 expression. MiR-34a triggers endothelial senescence in part through SIRT1, since forced expression of SIRT1 blocks the ability of miR-34a to induce senescence. Our data suggest that miR-34a contributes to endothelial senescence through suppression of SIRT1.", "citation": {"db": "PubMed", "db_id": "20627091"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3364, "target": 520, "key": "686324b36143b6894cb0d89337a4b2f0"}, {"line": 9441, "relation": "increases", "evidence": "SIRT1 directly activates the transcription of the gene encoding the alpha-secretase, ADAM10. SIRT1 deacetylates and coactivates the retinoic acid receptor beta, a known regulator of ADAM10 transcription. ADAM10 activation by SIRT1 also induces the Notch signaling pathway, which is known to repair neuronal damage in the brain.", "citation": {"db": "PubMed", "db_id": "20655472"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3364, "target": 3296, "key": "a851b5d39c962c5355a7710140e8c6bb"}, {"line": 37711, "relation": "increases", "evidence": "SIRT1 directly activates the transcription of the gene encoding the alpha-secretase, ADAM10. SIRT1 deacetylates and coactivates the retinoic acid receptor beta, a known regulator of ADAM10 transcription. ADAM10 activation by SIRT1 also induces the Notch signaling pathway, which is known to repair neuronal damage in the brain.", "citation": {"db": "PubMed", "db_id": "20655472"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3364, "target": 3296, "key": "8230495905ac341b296eb9f8faa332d0"}, {"line": 9450, "relation": "increases", "evidence": "SIRT1 directly activates the transcription of the gene encoding the alpha-secretase, ADAM10. SIRT1 deacetylates and coactivates the retinoic acid receptor beta, a known regulator of ADAM10 transcription. ADAM10 activation by SIRT1 also induces the Notch signaling pathway, which is known to repair neuronal damage in the brain.", "citation": {"db": "PubMed", "db_id": "20655472"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3364, "target": 2249, "key": "f0d342dfc2257dd8b9033da9bcaaeb31"}, {"line": 9195, "relation": "positiveCorrelation", "evidence": "It was reported that there was an upregulation of miR-9, miR-125b and miR-128 in hippocampus of AD affected post-mortem brain samples", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2083, "target": 3823, "key": "9c6c5b3ba8dbe731d9d6c17eaee2d369"}, {"line": 9201, "relation": "association", "evidence": "It was found that miRNAs hsa-mir-106a and hsa-mir-520c could bind to their predicted target sequences in the APP 3′UTR and negatively regulate APP expression.94 Another recent study showed that miR-101 is a negative regulator of APP expression and could affect the accumulation of Abeta, suggesting a possible role for miR-101 in neuropathological conditions.", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2090, "target": 2315, "key": "da731c02bdfa5d6130c93d76a9406647"}, {"line": 45740, "relation": "association", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 2090, "target": 2315, "key": "227203b3112001a57e7797c973fdd4c0"}, {"line": 45969, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2090, "target": 2315, "key": "2020337245f252bf1f6e8ef3f0bba911"}, {"line": 9204, "relation": "increases", "evidence": "It was found that miRNAs hsa-mir-106a and hsa-mir-520c could bind to their predicted target sequences in the APP 3′UTR and negatively regulate APP expression.94 Another recent study showed that miR-101 is a negative regulator of APP expression and could affect the accumulation of Abeta, suggesting a possible role for miR-101 in neuropathological conditions.", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2090, "target": 4096, "key": "c7b7129921df67dd9ac1d8a410adfacb"}, {"line": 9382, "relation": "decreases", "evidence": "Utilizing human cell lines, we demonstrate that miRNAs hsa-mir-106a and hsa-mir-520c bind to their predicted target sequences in the APP 3'UTR and negatively regulate reporter gene expression. Over-expression of these miRNAs, but not control miRNAs, results in translational repression of APP mRNA and significantly reduces APP protein levels. These results are the first to demonstrate that levels of human APP can be regulated by miRNAs.", "citation": {"db": "PubMed", "db_id": "18684319"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2090, "target": 3940, "key": "3ea322f0ed714fa67047b3d2e9884c74"}, {"line": 9202, "relation": "association", "evidence": "It was found that miRNAs hsa-mir-106a and hsa-mir-520c could bind to their predicted target sequences in the APP 3′UTR and negatively regulate APP expression.94 Another recent study showed that miR-101 is a negative regulator of APP expression and could affect the accumulation of Abeta, suggesting a possible role for miR-101 in neuropathological conditions.", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2123, "target": 2315, "key": "9cb69e8b2cd8aad296c1800cc106076f"}, {"line": 45741, "relation": "association", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 2123, "target": 2315, "key": "7c15ec2e66e1ef33990ffaafa1ac5f09"}, {"line": 45970, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2123, "target": 2315, "key": "1ed9bfa93da54a7546205ec4a683c62e"}, {"line": 9383, "relation": "decreases", "evidence": "Utilizing human cell lines, we demonstrate that miRNAs hsa-mir-106a and hsa-mir-520c bind to their predicted target sequences in the APP 3'UTR and negatively regulate reporter gene expression. Over-expression of these miRNAs, but not control miRNAs, results in translational repression of APP mRNA and significantly reduces APP protein levels. These results are the first to demonstrate that levels of human APP can be regulated by miRNAs.", "citation": {"db": "PubMed", "db_id": "18684319"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2123, "target": 3940, "key": "42fba3c262f61bbb7f5ac1da18ac8693"}, {"line": 9211, "relation": "increases", "evidence": "Such possibility is further corroborated by the observation that a significant decrease in miR-106b expression was found in sporadic AD patients.On the other hand, two miRNAs (miR-298 and miR-328) was found to regulate BACE mRNA translation, while BACE was responsible for APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2114, "target": 2375, "key": "39869b846b23f4c35b4edfdd1d4154f5"}, {"line": 9213, "relation": "increases", "evidence": "Such possibility is further corroborated by the observation that a significant decrease in miR-106b expression was found in sporadic AD patients.On the other hand, two miRNAs (miR-298 and miR-328) was found to regulate BACE mRNA translation, while BACE was responsible for APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "21785276"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2118, "target": 2375, "key": "710b54d3da5a50c5f2ca1404d9033fb0"}, {"line": 9238, "relation": "association", "evidence": "These predictions were tested using miRNA luciferase reporter vectors, with Robo2 and srGAP2 evaluated as the potential targets of miR-145 and miR-214, respectively. The role of miR-145 in cultured primary neurons was also investigated, and the result found that miR-145 miR-145 inhibited neurite growth and down-regulated Robo2 expression.", "citation": {"db": "PubMed", "db_id": "21276775"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true, "miRNA subgraph": true}}, "source": 3319, "target": 2101, "key": "aa09290d65850990a0563aeed529898e"}, {"line": 9239, "relation": "association", "evidence": "These predictions were tested using miRNA luciferase reporter vectors, with Robo2 and srGAP2 evaluated as the potential targets of miR-145 and miR-214, respectively. The role of miR-145 in cultured primary neurons was also investigated, and the result found that miR-145 miR-145 inhibited neurite growth and down-regulated Robo2 expression.", "citation": {"db": "PubMed", "db_id": "21276775"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true, "miRNA subgraph": true}}, "source": 3417, "target": 2101, "key": "b0b0ecb6c465bd068478ff291f93ad69"}, {"line": 9241, "relation": "decreases", "evidence": "These predictions were tested using miRNA luciferase reporter vectors, with Robo2 and srGAP2 evaluated as the potential targets of miR-145 and miR-214, respectively. The role of miR-145 in cultured primary neurons was also investigated, and the result found that miR-145 miR-145 inhibited neurite growth and down-regulated Robo2 expression.", "citation": {"db": "PubMed", "db_id": "21276775"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true, "miRNA subgraph": true}}, "source": 653, "target": 3319, "key": "6bf5b93b8e8d3756cdd00fe463cafe77"}, {"line": 9250, "relation": "increases", "evidence": "MiR-34a over-expression induces endothelial cell senescence and also suppresses cell proliferation by inhibiting cell cycle progression. Searching for how miR-34a affects senescence, we discovered that SIRT1 is a target of miR-34a. Over-expressing miR-34a inhibits SIRT1 protein expression, and knocking down miR-34a enhances SIRT1 expression. MiR-34a triggers endothelial senescence in part through SIRT1, since forced expression of SIRT1 blocks the ability of miR-34a to induce senescence. Our data suggest that miR-34a contributes to endothelial senescence through suppression of SIRT1.", "citation": {"db": "PubMed", "db_id": "20627091"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2119, "target": 520, "key": "0f44a1739d075c81837f72adc810a82b"}, {"line": 9252, "relation": "decreases", "evidence": "MiR-34a over-expression induces endothelial cell senescence and also suppresses cell proliferation by inhibiting cell cycle progression. Searching for how miR-34a affects senescence, we discovered that SIRT1 is a target of miR-34a. Over-expressing miR-34a inhibits SIRT1 protein expression, and knocking down miR-34a enhances SIRT1 expression. MiR-34a triggers endothelial senescence in part through SIRT1, since forced expression of SIRT1 blocks the ability of miR-34a to induce senescence. Our data suggest that miR-34a contributes to endothelial senescence through suppression of SIRT1.", "citation": {"db": "PubMed", "db_id": "20627091"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2119, "target": 503, "key": "5444b123536cff9cc0f2b272d95659ef"}, {"line": 9253, "relation": "decreases", "evidence": "MiR-34a over-expression induces endothelial cell senescence and also suppresses cell proliferation by inhibiting cell cycle progression. Searching for how miR-34a affects senescence, we discovered that SIRT1 is a target of miR-34a. Over-expressing miR-34a inhibits SIRT1 protein expression, and knocking down miR-34a enhances SIRT1 expression. MiR-34a triggers endothelial senescence in part through SIRT1, since forced expression of SIRT1 blocks the ability of miR-34a to induce senescence. Our data suggest that miR-34a contributes to endothelial senescence through suppression of SIRT1.", "citation": {"db": "PubMed", "db_id": "20627091"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2119, "target": 3364, "key": "2dc218dfc24b1336a98469d74bcaac21"}, {"line": 46447, "relation": "negativeCorrelation", "evidence": "The p53-family member TAp73 is a transcription factor that plays a key role in many biological processes. Here, we show that p73 drives the expression of microRNA (miR)-34a, but not miR-34b and -c, by acting on specific binding sites on the miR-34a promoter. Expression of miR-34a is modulated in parallel with that of TAp73 during in vitro differentiation of neuroblastoma cells and cortical neurons. Retinoid-driven neuroblastoma differentiation is inhibited by knockdown of either p73 or miR-34a. Transcript expression of miR-34a is significantly reduced in vivo both in the cortex and hippocampus of p73(-/-) mice; miR-34a and TAp73 expression also increase during postnatal development of the brain and cerebellum when synaptogenesis occurs. Accordingly, overexpression or silencing of miR-34a inversely modulates expression of synaptic targets, including synaptotagmin-1 and syntaxin-1A. Notably, the axis TAp73/miR-34a/synaptotagmin-1 is conserved in brains from Alzheimer's patients. These data reinforce a role for TAp73 in neuronal development.", "citation": {"db": "PubMed", "db_id": "22160687"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 2119, "target": 3431, "key": "f29a2ebcbac592625d05b9dfbdf6c49e"}, {"line": 46886, "relation": "decreases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2119, "target": 3493, "key": "abfb27e3b12c444f50a7d63a8de9db29"}, {"line": 46887, "relation": "increases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 2119, "target": 3494, "key": "bbdfbf9caf695eaee0c58b2a306bf362"}, {"line": 9256, "relation": "positiveCorrelation", "evidence": "MiR-34a over-expression induces endothelial cell senescence and also suppresses cell proliferation by inhibiting cell cycle progression. Searching for how miR-34a affects senescence, we discovered that SIRT1 is a target of miR-34a. Over-expressing miR-34a inhibits SIRT1 protein expression, and knocking down miR-34a enhances SIRT1 expression. MiR-34a triggers endothelial senescence in part through SIRT1, since forced expression of SIRT1 blocks the ability of miR-34a to induce senescence. Our data suggest that miR-34a contributes to endothelial senescence through suppression of SIRT1.", "citation": {"db": "PubMed", "db_id": "20627091"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 520, "target": 3823, "key": "8906c7f9fa133b3677b4d14076c60e94"}, {"line": 14922, "relation": "association", "evidence": "Human FECD endothelium exhibited increased levels of nuclear p21 protein.Our results identify endothelial Cdkn1a (p21) upregulation in a mouse model of early-onset FECD, confirm overexpression of p21 in late-onset human FECD endothelium, and suggest a role for premature senescence in FECD.", "citation": {"db": "PubMed", "db_id": "22956607"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 520, "target": 2493, "key": "338fe5b8628c6978e80e3ada9780fbe4"}, {"line": 9266, "relation": "negativeCorrelation", "evidence": "In P19 cells, miR-124 suppresses SCP1 expression and induces neurogenesis, and SCP1 counteracts this proneural activity of miR-124. Our results suggest that, during CNS development, timely down-regulation of SCP1 is critical for inducing neurogenesis, and miR-124 contributes to this process at least in part by down-regulating SCP1 expression.", "citation": {"db": "PubMed", "db_id": "17403776"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3437, "target": 2082, "key": "3f9ea215295263c68062aa04b3558534"}, {"line": 9268, "relation": "negativeCorrelation", "evidence": "In P19 cells, miR-124 suppresses SCP1 expression and induces neurogenesis, and SCP1 counteracts this proneural activity of miR-124. Our results suggest that, during CNS development, timely down-regulation of SCP1 is critical for inducing neurogenesis, and miR-124 contributes to this process at least in part by down-regulating SCP1 expression.", "citation": {"db": "PubMed", "db_id": "17403776"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3437, "target": 822, "key": "e870198bff18f9a16d69bdb60a8a33f3"}, {"line": 9267, "relation": "association", "evidence": "In P19 cells, miR-124 suppresses SCP1 expression and induces neurogenesis, and SCP1 counteracts this proneural activity of miR-124. Our results suggest that, during CNS development, timely down-regulation of SCP1 is critical for inducing neurogenesis, and miR-124 contributes to this process at least in part by down-regulating SCP1 expression.", "citation": {"db": "PubMed", "db_id": "17403776"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 822, "target": 2082, "key": "3d9db7fc895f635d066c8c31947df568"}, {"line": 9268, "relation": "negativeCorrelation", "evidence": "In P19 cells, miR-124 suppresses SCP1 expression and induces neurogenesis, and SCP1 counteracts this proneural activity of miR-124. Our results suggest that, during CNS development, timely down-regulation of SCP1 is critical for inducing neurogenesis, and miR-124 contributes to this process at least in part by down-regulating SCP1 expression.", "citation": {"db": "PubMed", "db_id": "17403776"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 822, "target": 3437, "key": "774ae35f87a272082ac20eb2a2f5620f"}, {"line": 11459, "relation": "association", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Wnt signaling subgraph": true, "Axonal guidance subgraph": true}}, "source": 822, "target": 462, "key": "26d06c953da68df88a7995f7f0407d3c"}, {"line": 11685, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 822, "target": 434, "key": "a11133c8b864b6ea0679a433b3627237"}, {"line": 32310, "relation": "association", "evidence": "The mammalian homologue of lin-12, Notch1, is a transmembrane receptor that plays an important role in cell fate decisions during development, including neurogenesis, but does not have a known function in fully differentiated cells. ", "citation": {"db": "PubMed", "db_id": "10366748"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 822, "target": 3126, "key": "62821a01e609f74362b9539cba64f113"}, {"line": 37548, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 822, "target": 2315, "key": "4feb305d67b12ecc43bde44563d95ac0"}, {"line": 37549, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 822, "target": 2306, "key": "66a9fbddaafea5240319c273b4a6edd5"}, {"line": 37550, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 822, "target": 2307, "key": "ca82a84ae2d56a81c6ab1e2dfad674d9"}, {"relation": "hasVariant", "source": 3381, "target": 3382, "key": "97178fdc8e91f5f87e4ffc436dc67a59"}, {"relation": "partOf", "source": 3381, "target": 1360, "key": "0ec36197b21539a1a46262e5ea602a1e"}, {"line": 9281, "relation": "increases", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"Smad subgraph": true, "TGF-Beta subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4020, "target": 3454, "key": "9785c06e3b6921a3f5593bb11b713a2f"}, {"line": 9290, "relation": "association", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3909, "target": 2104, "key": "c2d86b0f6c32bfcda94057a085a51d88"}, {"line": 9291, "relation": "association", "evidence": "Here, we provide direct evidence of binding of miR-155 to a predicted binding site and the ability of miR-155 to repress SMAD2 protein expression. We employed a lentivirally transduced monocyte cell line (THP1-155) containing an inducible miR-155 transgene to show that endogenous levels of SMAD2 protein were decreased after sustained overexpression of miR-155. This decrease in SMAD2 led to a reduction in both TGF-beta-induced SMAD-2 phosphorylation and SMAD-2-dependent activation of the expression of the CAGA(12)LUC reporter plasmid. Overexpression of miR-155 altered the cellular responses to TGF-beta by changing the expression of a set of genes that is involved in inflammation, fibrosis, and angiogenesis. Our study provides firm evidence of a role for miR-155 in directly repressing SMAD2 expression, and our results demonstrate the relevance of one of the two predicted target sites in SMAD2 3'-UTR. Altogether, our data uncover an important role for miR-155 in modulating the cellular response to TGF-beta with possible implications in several human diseases where homeostasis of TGF-beta might be altered.", "citation": {"db": "PubMed", "db_id": "21036908"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 711, "target": 2104, "key": "aff7565bff5d4208b6e843f4c5539f52"}, {"line": 11773, "relation": "positiveCorrelation", "evidence": "Glutamate receptor subunit 1 (GluR1) is one of the four possible subunits of the AMPA-type glutamate receptor. The integrity of this receptor is crucial for learning processes. However, reductions of GluR1 are noticeable in the hippocampal formation of patients suffering from Alzheimer's disease. Such degradations presumably result in an impaired synaptic communication and might be causally linked to the neurodegenerative process in this cognitive disorder.", "citation": {"db": "PubMed", "db_id": "12197668"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 818, "target": 2770, "key": "edf890a8f328e3d53878da5b708bb181"}, {"line": 18709, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 818, "target": 3060, "key": "c6cb1eb806b058525f60d1d8cd2e6bc7"}, {"line": 18954, "relation": "association", "evidence": "Although conventionally associated with fibrin clot degradation, recent work has uncovered new functions for the tissue plasminogen activator (tPA)/plasminogen cascade in central nervous system physiology and pathology. This extracellular proteolytic cascade has been shown to have roles in learning and memory, stress, neuronal degeneration, addiction and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15841309"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 818, "target": 3200, "key": "e9edd0e9187c8deade64dec288263851"}, {"line": 9350, "relation": "association", "evidence": "The database search on TargetScan, PicTar and miRBase Target identified neurone navigator 3 (NAV3), a regulator of axon guidance, as a principal target of miR-29a, and actually NAV3 mRNA levels were elevated in AD brains.", "citation": {"db": "PubMed", "db_id": "20202123"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true}}, "source": 481, "target": 3090, "key": "612bb10f93197f0662be55a91d93a6f1"}, {"line": 9359, "relation": "association", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 3468, "target": 2104, "key": "00bc7d36174cd6f4b4e625e1e4e18c82"}, {"line": 9367, "relation": "negativeCorrelation", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}}, "source": 3468, "target": 577, "key": "0d2f39bfaf3e3cb93c60a105f88c05bc"}, {"line": 39108, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3468, "target": 577, "key": "bf27e2f9db567bffa7d3c1d07aa92e58"}, {"line": 9525, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 3468, "target": 2102, "key": "a8ebb09ce3fcc3b0b1bed22c2cbeea65"}, {"line": 40017, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3468, "target": 261, "key": "28ee8aeb75056f4b9d895b25081f8135"}, {"line": 40068, "relation": "association", "evidence": "Gen also significantly reversed Abeta25-35-induced up-regulation of TLR4 and NF-κB expression and the DNA binding and transcriptional activities of NF-κB.These results indicated that Gen could alleviate the inflammation caused by Abeta25-35 treatment, which might be associated with the regulation of the TLR4/NF-κB signal pathway.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3468, "target": 261, "key": "3bbae8fb4e094276cba8e5dd0d10145a"}, {"line": 40040, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3468, "target": 2315, "key": "adb82a6fc33549075e8f1f1c398ed0ac"}, {"line": 46883, "relation": "increases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Toll like receptor subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3468, "target": 3112, "key": "0740ac81da2a3d86a766b7b88bf85a87"}, {"line": 9360, "relation": "association", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 3467, "target": 2104, "key": "d69fae09e7656747080eb71b673c0720"}, {"line": 9368, "relation": "negativeCorrelation", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}}, "source": 3467, "target": 577, "key": "377c1adabfbcc6a7ef53daa7ab453a2c"}, {"line": 39106, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3467, "target": 577, "key": "e3dcb201e5a54e0f924796f6fffd3248"}, {"line": 39296, "relation": "association", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3467, "target": 577, "key": "09d174e75646610f2131749980a478a7"}, {"line": 9526, "relation": "negativeCorrelation", "evidence": "Expression profiling of 200 microRNAs in human monocytes revealed that several of them (miR-146a/b, miR-132, and miR-155) are endotoxin-responsive genes. Analysis of miR-146a and miR-146b gene expression unveiled a pattern of induction in response to a variety of microbial components and proinflammatory cytokines. By means of promoter analysis, miR-146a was found to be a NF-kappaB-dependent gene. Importantly, miR-146a/b were predicted to base-pair with sequences in the 3' UTRs of the TNF receptor-associated factor 6 and IL-1 receptor-associated kinase 1 genes, and we found that these UTRs inhibit expression of a linked reporter gene. These genes encode two key adapter molecules downstream of Toll-like and cytokine receptors. Thus, we propose a role for miR-146 in control of Toll-like receptor and cytokine signaling through a negative feedback regulation loop involving down-regulation of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels.", "citation": {"db": "PubMed", "db_id": "16885212"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true, "miRNA subgraph": true}}, "source": 3467, "target": 2102, "key": "302f2b324701b5650f1c8e9a82ab16a0"}, {"line": 9364, "relation": "association", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true, "miRNA subgraph": true}}, "source": 3440, "target": 2104, "key": "752123fa4c704167d72514474be7f1c1"}, {"line": 9372, "relation": "negativeCorrelation", "evidence": "In response to inflammatory stimulation, dendritic cells (DCs) have a remarkable pattern of differentiation (maturation) that exhibits specific mechanisms to control immunity. Here, we show that in response to Lipopolysaccharides (LPS), several microRNAs (miRNAs) are regulated in human monocyte-derived dendritic cells. Among these miRNAs, miR-155 is highly up-regulated during maturation. Using LNA silencing combined to microarray technology, we have identified the Toll-like receptor/interleukin-1 (TLR/IL-1) inflammatory pathway as a general target of miR-155. We further demonstrate that miR-155 directly controls the level of TAB2, an important signal transduction molecule. Our observations suggest, therefore, that in mature human DCs, miR-155 is part of a negative feedback loop, which down-modulates inflammatory cytokine production in response to microbial stimuli.", "citation": {"db": "PubMed", "db_id": "19193853"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 3440, "target": 577, "key": "698c1c501cea286742adda5a52086cea"}, {"relation": "hasReactant", "source": 4099, "target": 2315, "key": "622d95ec6682618f09bb2ee604b69349"}, {"relation": "hasProduct", "source": 4099, "target": 2137, "key": "736e6fc8983a1b881d4eb5b53d15367f"}, {"line": 9428, "relation": "equivalentTo", "evidence": "Cleavage of APP by alpha-secretase precludes Abeta generation as the cleavage site is within the Abeta domain (at the Lys16-Leu17 bond), and releases a large soluble ectodomain of APP called sAPPalpha. Early studies suggested that alpha-secretase is a membrane-bound endoprotease which cleaves APP primarily at the plasma membrane. Using proteinase inhibitor profiling, it was determined that alpha-secretase is a zinc metalloproteinase. Several members of the ADAM (a disintegrin and metalloproteinase) family possess alpha-secretase-like activity and three of them have been suggested as the alpha-secretase: ADAM9, ADAM10, and ADAM17. Like APP, they are also type-I transmembrane proteins.A dramatically reduced ADAM10 protein level in the platelets of sporadic AD patients was also found to correlate with the significantly decreased sAPPalpha levels found in their platlets and cerebrospinal fluid and the reduced aclpha-secretase activity in the temporal cortex homogenates of AD patients . These studies strongly suggest that ADAM10 is the constitutive alpha-secretase that is active at the cell surface, though there may be some functional redundancy in alpha-cleavage among the ADAM family.", "citation": {"db": "PubMed", "db_id": "21214928"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 410, "target": 2137, "key": "a0e59065b712ff076ca4ccc21257350d"}, {"line": 9449, "relation": "regulates", "evidence": "SIRT1 directly activates the transcription of the gene encoding the alpha-secretase, ADAM10. SIRT1 deacetylates and coactivates the retinoic acid receptor beta, a known regulator of ADAM10 transcription. ADAM10 activation by SIRT1 also induces the Notch signaling pathway, which is known to repair neuronal damage in the brain.", "citation": {"db": "PubMed", "db_id": "20655472"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3296, "target": 3935, "key": "f30f4a7f9b63fbb3d25d394bc55bcb2b"}, {"line": 37712, "relation": "increases", "evidence": "SIRT1 directly activates the transcription of the gene encoding the alpha-secretase, ADAM10. SIRT1 deacetylates and coactivates the retinoic acid receptor beta, a known regulator of ADAM10 transcription. ADAM10 activation by SIRT1 also induces the Notch signaling pathway, which is known to repair neuronal damage in the brain.", "citation": {"db": "PubMed", "db_id": "20655472"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3296, "target": 3935, "key": "a391e2f9c8d726a18c33d2aaff5f177b"}, {"line": 9471, "relation": "association", "evidence": "The MicroRNA miR-124 Promotes Neuronal Differentiation by Triggering Brain-Specific Alternative Pre-mRNA Splicing. When this exon is skipped, PTBP2 mRNA is subject to nonsense-mediated decay (NMD). During neuronal differentiation, miR-124 reduces PTBP1 levels, leading to the accumulation of correctly spliced PTBP2 mRNA and a dramatic increase in PTBP2 protein.", "citation": {"db": "PubMed", "db_id": "17679093"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3274, "target": 650, "key": "4e9f6fe895e60b82de6b36a15034b2cd"}, {"line": 9484, "relation": "decreases", "evidence": "Low-density lipoprotein receptor-related protein 1 (LRP1) is a multifunctional endocytic receptor that plays critical roles in the pathogenesis of several human diseases including tumor metastasis and Alzheimer's disease. However, mechanisms that regulate LRP1 expression under physiological and pathophysiological conditions are not unclear. In human cell lines, we found that miR-205 down-regulates the expression of LRP1 by targeting sequences in the 3'UTR of LRP1 mRNA. This effect was abolished by deleting the miR-205 seed site in the 3'UTR of LRP1. The ectopic expression of miR-205 also significantly mitigated migration of both U87 and SK-LU-1 cells. These results, for the first time, demonstrate that expression of human LRP1 is regulated in part by a specific miRNA, leading to decreased tumor cell migration.", "citation": {"db": "PubMed", "db_id": "19665999"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2110, "target": 3989, "key": "9e018998300084aa67f4e44f9b4f6a2e"}, {"line": 9485, "relation": "association", "evidence": "Low-density lipoprotein receptor-related protein 1 (LRP1) is a multifunctional endocytic receptor that plays critical roles in the pathogenesis of several human diseases including tumor metastasis and Alzheimer's disease. However, mechanisms that regulate LRP1 expression under physiological and pathophysiological conditions are not unclear. In human cell lines, we found that miR-205 down-regulates the expression of LRP1 by targeting sequences in the 3'UTR of LRP1 mRNA. This effect was abolished by deleting the miR-205 seed site in the 3'UTR of LRP1. The ectopic expression of miR-205 also significantly mitigated migration of both U87 and SK-LU-1 cells. These results, for the first time, demonstrate that expression of human LRP1 is regulated in part by a specific miRNA, leading to decreased tumor cell migration.", "citation": {"db": "PubMed", "db_id": "19665999"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2110, "target": 3989, "key": "aa54d6a998e61c6981e0dff8c0435a12"}, {"line": 9499, "relation": "association", "evidence": "The distribution of LRP in the central nervous system is consistent with the potential function of this receptor in the regulation of proteinase activity, cytokine activity, and cholesterol metabolism.", "citation": {"db": "PubMed", "db_id": "1632469 "}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 748, "target": 2970, "key": "5f577921bc40cad0f03ba4008485e54c"}, {"line": 18733, "relation": "association", "evidence": "Matrix metalloproteinase-3 (MMP-3) is a member of the class of zinc-dependent proteases known to degrade the extracellular matrix.", "citation": {"db": "PubMed", "db_id": "21044079"}, "annotations": {"CellStructure": {"Extracellular Matrix": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 748, "target": 3060, "key": "20cec772f43437148dbe2eeaaa061ca1"}, {"line": 24433, "relation": "association", "evidence": "alpha(2)M also regulates proteinase and growth factor activities", "citation": {"db": "PubMed", "db_id": "14678766"}, "annotations": {"Subgraph": {"Alpha 2 macroglobulin subgraph": true}, "Confidence": {"Medium": true}}, "source": 748, "target": 2227, "key": "3f7635a233b568ab8b597dcb47c8d2ee"}, {"line": 9546, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 3488, "target": 540, "key": "2587b561c3c783874af384e8d3ed34cc"}, {"line": 9562, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 3336, "target": 540, "key": "d08198c2dde53d8f9ab62c58bd82c0b6"}, {"line": 9572, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 3336, "target": 420, "key": "05522bf05ac4a2cca3c1743c1124e830"}, {"line": 9569, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 2292, "target": 420, "key": "74265dd50e4450aef4b9c4ce5b75eb14"}, {"line": 9596, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2292, "target": 540, "key": "47c048fac62bd294cb8638b5b14854ba"}, {"line": 9574, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 2898, "target": 420, "key": "74e4c139111943015c99d93f490aeda1"}, {"line": 9600, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2898, "target": 540, "key": "49c5fd7b6fccf441fabf4bfb92600c35"}, {"line": 9578, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "Synuclein subgraph": true, "Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 3387, "target": 420, "key": "786e9a40242b5022af329879523b9c97"}, {"line": 9604, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Synuclein subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 3387, "target": 540, "key": "d97b2e3493eb44e94f20612ec99a9aa5"}, {"line": 9580, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 2565, "target": 420, "key": "c527b721fad6a28ab1e52704706de117"}, {"line": 9606, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2565, "target": 540, "key": "d4e00fbe1055b1df753ddc99fe65ae40"}, {"line": 17422, "relation": "association", "evidence": "In 2001 we noted that aB crystallin (cryab) was the most abundant transcript found in MS lesions, but not in healthy brains.", "citation": {"db": "PubMed", "db_id": "24711007"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 2565, "target": 3869, "key": "7b8c44b2f2662223819409e091a64d25"}, {"line": 17430, "relation": "decreases", "evidence": "Cryab can reverse paralysis and attenuate inflammation in several models of inflammation including experimental autoimmune encephalomyelitis (EAE), and various models of ischemia.", "citation": {"db": "PubMed", "db_id": "24711007"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}, "MeSHDisease": {"Paralysis": true, "Inflammation": true, "Ischemia": true, "Encephalomyelitis, Autoimmune, Experimental": true}}, "source": 2565, "target": 3927, "key": "747790a59b2046c8e48a77aee7c51a87"}, {"line": 17431, "relation": "decreases", "evidence": "Cryab can reverse paralysis and attenuate inflammation in several models of inflammation including experimental autoimmune encephalomyelitis (EAE), and various models of ischemia.", "citation": {"db": "PubMed", "db_id": "24711007"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}, "MeSHDisease": {"Paralysis": true, "Inflammation": true, "Ischemia": true, "Encephalomyelitis, Autoimmune, Experimental": true}}, "source": 2565, "target": 3920, "key": "3ef35ab90472fe2a9f5a460bbfe7ea27"}, {"line": 17432, "relation": "decreases", "evidence": "Cryab can reverse paralysis and attenuate inflammation in several models of inflammation including experimental autoimmune encephalomyelitis (EAE), and various models of ischemia.", "citation": {"db": "PubMed", "db_id": "24711007"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}, "MeSHDisease": {"Paralysis": true, "Inflammation": true, "Ischemia": true, "Encephalomyelitis, Autoimmune, Experimental": true}}, "source": 2565, "target": 3921, "key": "7a0bb1a00519efd464af4f7a6055af2c"}, {"line": 17433, "relation": "decreases", "evidence": "Cryab can reverse paralysis and attenuate inflammation in several models of inflammation including experimental autoimmune encephalomyelitis (EAE), and various models of ischemia.", "citation": {"db": "PubMed", "db_id": "24711007"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}, "MeSHDisease": {"Paralysis": true, "Inflammation": true, "Ischemia": true, "Encephalomyelitis, Autoimmune, Experimental": true}}, "source": 2565, "target": 3853, "key": "7766fe492bfbb4c062d8cde355e0b2c4"}, {"relation": "partOf", "source": 2565, "target": 923, "key": "798846116c51b5843b5fb9d99bfecb0a"}, {"line": 34416, "relation": "increases", "evidence": "Instead of preventing the cell from the neurotoxicity of Abeta, alphaB-crystallin induces an increased neurotoxicity.", "citation": {"db": "PubMed", "db_id": "17046756"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2565, "target": 3876, "key": "4fbce3c6febbfd0acb4261615b88616a"}, {"relation": "partOf", "source": 2565, "target": 1159, "key": "ff5add13a0d23c97eeb8ff48349524da"}, {"line": 9583, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 2599, "target": 420, "key": "ddaf5d714e02d3e4ed8fe3ad75f972ed"}, {"line": 9609, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2599, "target": 540, "key": "763e2b515d15ca01691e867eaad3f77f"}, {"line": 9584, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 3513, "target": 420, "key": "0ac5514cc8e1b5406c267cd7a1ffe051"}, {"line": 9610, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 3513, "target": 540, "key": "8d6fe39cca18ddb3c177da5c98593342"}, {"line": 34996, "relation": "increases", "evidence": "Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3513, "target": 2397, "key": "95ffd57c5f4d2055a790c8fb5261f094"}, {"line": 9585, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Low": true}}, "object": {"modifier": "Activity"}, "source": 3471, "target": 420, "key": "4d6804f798372fd27b198447f6a33740"}, {"line": 9611, "relation": "association", "evidence": "Molecules related to cytoskeleton maintenance, calcium metabolism and cellular survival such as glial fibrillary acidic protein, vimentin, tropomyosin, collapsin response mediator protein-2, calmodulin, S100-P, annexin A1, alpha-internexin, alpha- and beta-synuclein, alpha-B-crystalline, fascin-1, ubiquitin carboxyl-terminal esterase and thymosine were altered between AD and NDC pools. Our experiments suggest that WM activities become globally impaired during the course of AD with significant morphological, biochemical and functional consequential implications for gray matter function and cognitive deficits.", "citation": {"db": "PubMed", "db_id": "23231993"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 3471, "target": 540, "key": "fff7a33a975b3e733970773372d65fc4"}, {"line": 9639, "relation": "negativeCorrelation", "evidence": "We found that miRs 18 and 124a reduced GR-mediated events in addition to decreasing GR protein levels. miR reporter assays revealed binding of miR-124a to the 3' untranslated region of GR. In correspondence, the activation of the GR-responsive gene glucocorticoid-induced leucine zipper was strongly impaired by miR-124a and -18 overexpression. Although miR-18 is expressed widely throughout the body, expression of miR-124a is restricted to the brain. Endogenous miR-124a up-regulation during neuronal differentiation of P19 cells was associated with a decreasing amount of GR protein levels and reduced activity of luciferase reporter constructs bearing GR 3' untranslated regions. Furthermore, we show that miR-124a expression varies over time during the stress hyporesponsive period, a neonatal period when GC signaling is modulated. Our findings demonstrate a potential role for miRs in the regulation of cell type-specific responsiveness to GCs, as may occur during critical periods of neuronal development.", "citation": {"db": "PubMed", "db_id": "19131573"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true, "miRNA subgraph": true}}, "source": 2108, "target": 2798, "key": "ea123827fac1778c1451aaf7a6fafb95"}, {"line": 9641, "relation": "association", "evidence": "We found that miRs 18 and 124a reduced GR-mediated events in addition to decreasing GR protein levels. miR reporter assays revealed binding of miR-124a to the 3' untranslated region of GR. In correspondence, the activation of the GR-responsive gene glucocorticoid-induced leucine zipper was strongly impaired by miR-124a and -18 overexpression. Although miR-18 is expressed widely throughout the body, expression of miR-124a is restricted to the brain. Endogenous miR-124a up-regulation during neuronal differentiation of P19 cells was associated with a decreasing amount of GR protein levels and reduced activity of luciferase reporter constructs bearing GR 3' untranslated regions. Furthermore, we show that miR-124a expression varies over time during the stress hyporesponsive period, a neonatal period when GC signaling is modulated. Our findings demonstrate a potential role for miRs in the regulation of cell type-specific responsiveness to GCs, as may occur during critical periods of neuronal development.", "citation": {"db": "PubMed", "db_id": "19131573"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true, "miRNA subgraph": true}}, "source": 2108, "target": 649, "key": "58aadd11166dba2e3b1f23f16b8197a1"}, {"line": 9639, "relation": "negativeCorrelation", "evidence": "We found that miRs 18 and 124a reduced GR-mediated events in addition to decreasing GR protein levels. miR reporter assays revealed binding of miR-124a to the 3' untranslated region of GR. In correspondence, the activation of the GR-responsive gene glucocorticoid-induced leucine zipper was strongly impaired by miR-124a and -18 overexpression. Although miR-18 is expressed widely throughout the body, expression of miR-124a is restricted to the brain. Endogenous miR-124a up-regulation during neuronal differentiation of P19 cells was associated with a decreasing amount of GR protein levels and reduced activity of luciferase reporter constructs bearing GR 3' untranslated regions. Furthermore, we show that miR-124a expression varies over time during the stress hyporesponsive period, a neonatal period when GC signaling is modulated. Our findings demonstrate a potential role for miRs in the regulation of cell type-specific responsiveness to GCs, as may occur during critical periods of neuronal development.", "citation": {"db": "PubMed", "db_id": "19131573"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true, "miRNA subgraph": true}}, "source": 2798, "target": 2108, "key": "df86fbac8ebce9d4fa773086a1dcbdb1"}, {"line": 9640, "relation": "negativeCorrelation", "evidence": "We found that miRs 18 and 124a reduced GR-mediated events in addition to decreasing GR protein levels. miR reporter assays revealed binding of miR-124a to the 3' untranslated region of GR. In correspondence, the activation of the GR-responsive gene glucocorticoid-induced leucine zipper was strongly impaired by miR-124a and -18 overexpression. Although miR-18 is expressed widely throughout the body, expression of miR-124a is restricted to the brain. Endogenous miR-124a up-regulation during neuronal differentiation of P19 cells was associated with a decreasing amount of GR protein levels and reduced activity of luciferase reporter constructs bearing GR 3' untranslated regions. Furthermore, we show that miR-124a expression varies over time during the stress hyporesponsive period, a neonatal period when GC signaling is modulated. Our findings demonstrate a potential role for miRs in the regulation of cell type-specific responsiveness to GCs, as may occur during critical periods of neuronal development.", "citation": {"db": "PubMed", "db_id": "19131573"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true, "miRNA subgraph": true}}, "source": 2798, "target": 2082, "key": "7e84a1bc6cb34e99290382a78363b0fd"}, {"line": 9641, "relation": "association", "evidence": "We found that miRs 18 and 124a reduced GR-mediated events in addition to decreasing GR protein levels. miR reporter assays revealed binding of miR-124a to the 3' untranslated region of GR. In correspondence, the activation of the GR-responsive gene glucocorticoid-induced leucine zipper was strongly impaired by miR-124a and -18 overexpression. Although miR-18 is expressed widely throughout the body, expression of miR-124a is restricted to the brain. Endogenous miR-124a up-regulation during neuronal differentiation of P19 cells was associated with a decreasing amount of GR protein levels and reduced activity of luciferase reporter constructs bearing GR 3' untranslated regions. Furthermore, we show that miR-124a expression varies over time during the stress hyporesponsive period, a neonatal period when GC signaling is modulated. Our findings demonstrate a potential role for miRs in the regulation of cell type-specific responsiveness to GCs, as may occur during critical periods of neuronal development.", "citation": {"db": "PubMed", "db_id": "19131573"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true, "miRNA subgraph": true}}, "source": 649, "target": 2108, "key": "cae8b6561cd1058f86e1fc0c7ae67750"}, {"line": 9642, "relation": "association", "evidence": "We found that miRs 18 and 124a reduced GR-mediated events in addition to decreasing GR protein levels. miR reporter assays revealed binding of miR-124a to the 3' untranslated region of GR. In correspondence, the activation of the GR-responsive gene glucocorticoid-induced leucine zipper was strongly impaired by miR-124a and -18 overexpression. Although miR-18 is expressed widely throughout the body, expression of miR-124a is restricted to the brain. Endogenous miR-124a up-regulation during neuronal differentiation of P19 cells was associated with a decreasing amount of GR protein levels and reduced activity of luciferase reporter constructs bearing GR 3' untranslated regions. Furthermore, we show that miR-124a expression varies over time during the stress hyporesponsive period, a neonatal period when GC signaling is modulated. Our findings demonstrate a potential role for miRs in the regulation of cell type-specific responsiveness to GCs, as may occur during critical periods of neuronal development.", "citation": {"db": "PubMed", "db_id": "19131573"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true, "miRNA subgraph": true}}, "source": 649, "target": 2082, "key": "83e5f8e3f91deb7bacedadf34a978ce4"}, {"line": 37465, "relation": "association", "evidence": "APP/APLP expression is up-regulated during neuronal maturation and differentiation, undergoes rapid anterograde transport, and is targeted in vesicles distinct from synaptophysin transport vesicles to synaptic sites", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 649, "target": 2315, "key": "83d1d71aaa4a7755b211f8b8a8420dd5"}, {"line": 37467, "relation": "association", "evidence": "APP/APLP expression is up-regulated during neuronal maturation and differentiation, undergoes rapid anterograde transport, and is targeted in vesicles distinct from synaptophysin transport vesicles to synaptic sites", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 649, "target": 2306, "key": "7fb07ea59bd1eed8280065b63895b716"}, {"line": 48250, "relation": "positiveCorrelation", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 649, "target": 2629, "key": "a1c764f7a76b1f0ed4d6b679e8cea7af"}, {"line": 9667, "relation": "negativeCorrelation", "evidence": "It has been argued that in late-onset Alzheimer's disease a disturbance in the control of neuronal glucose metabolism consequent to impaired insulin signalling strongly resembles the pathophysiology of type 2 diabetes in non-neural tissue.", "citation": {"db": "PubMed", "db_id": "17316694"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Disease": {"type 2 diabetes mellitus": true}, "MeSHAnatomy": {"Tissues": true}, "Confidence": {"Low": true}}, "source": 734, "target": 566, "key": "d2454a87ad3abc17fe3add9a2a6ca22c"}, {"line": 9728, "relation": "association", "evidence": "The p85alpha subunit of phosphatidyl inositol 3 kinase (PIK3R1) and the regulatory subunit 3 of protein phosphatase 1 (PPP1R3) were selected as candidate genes because both encode key proteins involved in insulin signalling and because polymorphisms in these genes have been previously implicated in insulin resistance or type II diabetes.Analysis of the Met326Ile PIK3R1 and the Asp905Tyr PPP1R3 polymorphisms in 202 patients with late onset AD and 160 or 170 age matched normal subjects.Logistic regression analysis using the recessive genetic model showed significant differences in genotype and allelic frequencies between the AD group and normal controls (genotypes: odds ratio (OR) 2.09, 95% confidence interval (CI) 1.17 to 3.74, p = 0.01; alleles: OR 1.99, 95% CI 1.17 to 3.40, p = 0.01) for the Met326Ile PIK3R1 polymorphism that were female specific.", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2184, "target": 3220, "key": "ad869d1ed61513303e8a6b41e8a15e4d"}, {"line": 9729, "relation": "association", "evidence": "The p85alpha subunit of phosphatidyl inositol 3 kinase (PIK3R1) and the regulatory subunit 3 of protein phosphatase 1 (PPP1R3) were selected as candidate genes because both encode key proteins involved in insulin signalling and because polymorphisms in these genes have been previously implicated in insulin resistance or type II diabetes.Analysis of the Met326Ile PIK3R1 and the Asp905Tyr PPP1R3 polymorphisms in 202 patients with late onset AD and 160 or 170 age matched normal subjects.Logistic regression analysis using the recessive genetic model showed significant differences in genotype and allelic frequencies between the AD group and normal controls (genotypes: odds ratio (OR) 2.09, 95% confidence interval (CI) 1.17 to 3.74, p = 0.01; alleles: OR 1.99, 95% CI 1.17 to 3.40, p = 0.01) for the Met326Ile PIK3R1 polymorphism that were female specific.", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2184, "target": 3188, "key": "2bb7bae28a1019a21e0a3e64b6cf9a20"}, {"line": 40373, "relation": "association", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "source": 2184, "target": 2259, "key": "39e55dbcdf485ebeb57a868ce69e2195"}, {"line": 9728, "relation": "association", "evidence": "The p85alpha subunit of phosphatidyl inositol 3 kinase (PIK3R1) and the regulatory subunit 3 of protein phosphatase 1 (PPP1R3) were selected as candidate genes because both encode key proteins involved in insulin signalling and because polymorphisms in these genes have been previously implicated in insulin resistance or type II diabetes.Analysis of the Met326Ile PIK3R1 and the Asp905Tyr PPP1R3 polymorphisms in 202 patients with late onset AD and 160 or 170 age matched normal subjects.Logistic regression analysis using the recessive genetic model showed significant differences in genotype and allelic frequencies between the AD group and normal controls (genotypes: odds ratio (OR) 2.09, 95% confidence interval (CI) 1.17 to 3.74, p = 0.01; alleles: OR 1.99, 95% CI 1.17 to 3.40, p = 0.01) for the Met326Ile PIK3R1 polymorphism that were female specific.", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3220, "target": 2184, "key": "6bdf24c9d1ae1696cc19c5c80e8c4917"}, {"relation": "hasVariant", "source": 3220, "target": 3221, "key": "24ddb7e8bbc57a0654d6e9024dd7c7f8"}, {"line": 9734, "relation": "association", "evidence": "The p85alpha subunit of phosphatidyl inositol 3 kinase (PIK3R1) and the regulatory subunit 3 of protein phosphatase 1 (PPP1R3) were selected as candidate genes because both encode key proteins involved in insulin signalling and because polymorphisms in these genes have been previously implicated in insulin resistance or type II diabetes.Analysis of the Met326Ile PIK3R1 and the Asp905Tyr PPP1R3 polymorphisms in 202 patients with late onset AD and 160 or 170 age matched normal subjects.Logistic regression analysis using the recessive genetic model showed significant differences in genotype and allelic frequencies between the AD group and normal controls (genotypes: odds ratio (OR) 2.09, 95% confidence interval (CI) 1.17 to 3.74, p = 0.01; alleles: OR 1.99, 95% CI 1.17 to 3.40, p = 0.01) for the Met326Ile PIK3R1 polymorphism that were female specific.", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3189, "target": 3823, "key": "b76e0f03454d2fee06af9c966e6ccaa2"}, {"line": 9735, "relation": "increases", "evidence": "The p85alpha subunit of phosphatidyl inositol 3 kinase (PIK3R1) and the regulatory subunit 3 of protein phosphatase 1 (PPP1R3) were selected as candidate genes because both encode key proteins involved in insulin signalling and because polymorphisms in these genes have been previously implicated in insulin resistance or type II diabetes.Analysis of the Met326Ile PIK3R1 and the Asp905Tyr PPP1R3 polymorphisms in 202 patients with late onset AD and 160 or 170 age matched normal subjects.Logistic regression analysis using the recessive genetic model showed significant differences in genotype and allelic frequencies between the AD group and normal controls (genotypes: odds ratio (OR) 2.09, 95% confidence interval (CI) 1.17 to 3.74, p = 0.01; alleles: OR 1.99, 95% CI 1.17 to 3.40, p = 0.01) for the Met326Ile PIK3R1 polymorphism that were female specific.", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3189, "target": 3861, "key": "9c295e4f05e6c57558befd58c5a1e300"}, {"line": 9736, "relation": "increases", "evidence": "The p85alpha subunit of phosphatidyl inositol 3 kinase (PIK3R1) and the regulatory subunit 3 of protein phosphatase 1 (PPP1R3) were selected as candidate genes because both encode key proteins involved in insulin signalling and because polymorphisms in these genes have been previously implicated in insulin resistance or type II diabetes.Analysis of the Met326Ile PIK3R1 and the Asp905Tyr PPP1R3 polymorphisms in 202 patients with late onset AD and 160 or 170 age matched normal subjects.Logistic regression analysis using the recessive genetic model showed significant differences in genotype and allelic frequencies between the AD group and normal controls (genotypes: odds ratio (OR) 2.09, 95% confidence interval (CI) 1.17 to 3.74, p = 0.01; alleles: OR 1.99, 95% CI 1.17 to 3.40, p = 0.01) for the Met326Ile PIK3R1 polymorphism that were female specific.", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3221, "target": 3861, "key": "904c3301c977b99cbaafafeff573b3bb"}, {"line": 9744, "relation": "association", "evidence": "Additionally, in the dominant genetic model a marginally significant association in genotype frequencies between the Asp905Tyr PPP1R3 polymorphism and AD was observed (genotypes: OR 1.85, 95% CI 1.03 to 3.30, p = 0.04; alleles: OR 1.68, 95% CI 0.98 to 2.88, p = 0.06).", "citation": {"db": "PubMed", "db_id": "12185156"}, "annotations": {"Confidence": {"High": true}}, "source": 3221, "target": 3823, "key": "67a47a7bcad3aa35c8faa83637a966f6"}, {"line": 9782, "relation": "positiveCorrelation", "evidence": "low density lipoprotein cholesterol (p=0.020), and triglycerides (p=0.039).", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"Medium": true}}, "source": 405, "target": 2328, "key": "6cce716ba6fa39aa5f826c07c7662fbe"}, {"relation": "partOf", "source": 405, "target": 997, "key": "0ff2dadb1e5b0cca63dce2d5fb9e2fe0"}, {"line": 9783, "relation": "positiveCorrelation", "evidence": "low density lipoprotein cholesterol (p=0.020), and triglycerides (p=0.039).", "citation": {"db": "PubMed", "db_id": "20061608"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"Medium": true}}, "source": 365, "target": 2328, "key": "b26fcd4e0c7a7406ff370d04eaca4763"}, {"line": 9825, "relation": "association", "evidence": "This decrease in insulin-PI3K-AKT signalling could lead to activation of glycogen synthase kinase-3beta, the major tau kinase.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true, "Insulin signal transduction": true}}, "source": 764, "target": 3015, "key": "5c7ca4f75a4a363b41228abcd05624c0"}, {"line": 9846, "relation": "positiveCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 3156, "target": 2279, "key": "eae190f949abd8ce8ca9834c36fdd830"}, {"line": 9847, "relation": "positiveCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 3156, "target": 2899, "key": "8003ba8eccb0aa05e33fc7589ea39dcc"}, {"line": 9848, "relation": "positiveCorrelation", "evidence": "The levels and the activation of the insulin-PI3K-AKT signalling components correlated negatively with the level of tau phosphorylation and positively with protein O-GlcNAcylation, suggesting that impaired insulin-PI3K-AKT signalling might contribute to neurodegeneration in AD through down-regulation of O-GlcNAcylation and the consequent promotion of abnormal tau hyperphosphorylation and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "21598254"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Tau protein subgraph": true, "Akt subgraph": true, "Insulin signal transduction": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 3156, "target": 890, "key": "9a03621fe9546982f5a105b733e982f8"}, {"line": 10974, "relation": "negativeCorrelation", "evidence": "The O-GlcNAcylation levels of global proteins and of tau were also decreased in T2DM brain as seen in AD brain.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3156, "target": 3850, "key": "fc9356fb72e0bfd4d297f1a41605c772"}, {"line": 10995, "relation": "increases", "evidence": "These results suggest that T2DM may contribute to the increased risk for AD by impairing brain glucose uptake/metabolism and, consequently, down-regulation of O-GlcNAcylation, which facilitates abnormal hyperphosphorylation of tau.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3156, "target": 3014, "key": "7dd5b9a4c97901205ffef3f5d641e5b3"}, {"relation": "partOf", "source": 3156, "target": 1586, "key": "6fcf80a822f9427a24eb266867923275"}, {"line": 9935, "relation": "association", "evidence": "Here we review the role of insulin signaling in brain aging and AD, concluding that the signaling pathways downstream to neurotrophic and insulin signaling are defective and coincident with aberrant phosphorylation and translocation of key components, notably AKT and GSK3beta, but also rac> PAK signaling.", "citation": {"db": "PubMed", "db_id": "17049785"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Akt subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2280, "target": 580, "key": "876db01cfa96f24e982e19110c5cd636"}, {"line": 48376, "relation": "decreases", "evidence": "Furthermore, Western blot showed that FLZ inhibited phosphorylation of Akt and retinoblastoma protein (Rb), down-regulated the expressions of cyclin D1, cyclin E, cyclin-dependent kinase 2 (CDK2), and enhanced the expression of CDK inhibitor p27(kip1), while did not affect CDK4 expression.", "citation": {"db": "PubMed", "db_id": "21835169"}, "annotations": {"Subgraph": {"Akt subgraph": true, "Cell cycle subgraph": true}}, "source": 2280, "target": 2463, "key": "64e241553345440cee412896859c94ab"}, {"line": 9943, "relation": "association", "evidence": "Here we review the role of insulin signaling in brain aging and AD, concluding that the signaling pathways downstream to neurotrophic and insulin signaling are defective and coincident with aberrant phosphorylation and translocation of key components, notably AKT and GSK3beta, but also rac> PAK signaling.", "citation": {"db": "PubMed", "db_id": "17049785"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2203, "target": 580, "key": "0f16f3827ce5bdb6deef5165992c8d6b"}, {"relation": "hasVariant", "source": 2202, "target": 2203, "key": "3f4d8d91b0a70a694fb7527812473496"}, {"line": 9965, "relation": "association", "evidence": "In the CA1 region of hippocampus of mice, several of the insulin signaling-related proteins we had chosen are co-located with ChAT, and most double immunoreactive positive cells were pyramidal cells.", "citation": {"db": "PubMed", "db_id": "19013138"}, "annotations": {"Confidence": {"Medium": true}, "MeSHAnatomy": {"CA1 Region, Hippocampal": true}, "Subgraph": {"Insulin signal transduction": true}, "Species": {"10090": true}}, "source": 3611, "target": 580, "key": "849c894007f0d8f8122974e2f089dee4"}, {"line": 9980, "relation": "association", "evidence": "We have recently identified in vitro a high affinity interaction between beta-amyloid peptide (Abeta) of AD and islet amyloid polypeptide (IAPP) of T2D which results in the formation of non-fibrillar and non-cytotoxic Abeta-IAPP hetero-oligomers.", "citation": {"db": "PubMed", "db_id": "23713771"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 928, "target": 3850, "key": "5553558002e957092ec85ab35f9bba63"}, {"line": 9981, "relation": "association", "evidence": "We have recently identified in vitro a high affinity interaction between beta-amyloid peptide (Abeta) of AD and islet amyloid polypeptide (IAPP) of T2D which results in the formation of non-fibrillar and non-cytotoxic Abeta-IAPP hetero-oligomers.", "citation": {"db": "PubMed", "db_id": "23713771"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 928, "target": 3823, "key": "452f50de51a3468d7bc7569e70b10cee"}, {"line": 10023, "relation": "association", "evidence": "Possible pathophysiologic mechanisms common to both T2DM and AD are glucose toxicity and a direct effect of insulin on amyloid metabolism.", "citation": {"db": "PubMed", "db_id": "21352095"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 476, "target": 264, "key": "592bd7d808247f8c5d199dbfead208f3"}, {"line": 10033, "relation": "association", "evidence": "Cholesterol may also be directly involved in beta-amyloid aggregation: abnormal oxidative metabolites such as cholesterol-derived aldehydes can modify beta-amyloid, firstly promoting Schiff bas formation, then accelerating the early stages of amyloidogenesis.", "citation": {"db": "PubMed", "db_id": "21352095"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 441, "target": 231, "key": "7decfb9bf52fc1bebac9105c9eda5cbd"}, {"line": 48575, "relation": "association", "evidence": "Accumulation of the amyloid beta (Abeta) peptide derived from the amyloid precursor protein (APP) plays a central role in the pathogenesis of Alzheimer's disease (AD). We previously reported that the scaffolding protein RanBP9 is markedly increased in AD brains and promotes Abeta generation by scaffolding APP/BACE1/LRP complexes together and accelerating APP endocytosis.", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 441, "target": 3823, "key": "4f4dcb1297bee4466c88d29ecb7d2bb4"}, {"line": 49474, "relation": "association", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 441, "target": 3224, "key": "2328734a19f4d34499f39dfc3080cc46"}, {"line": 49481, "relation": "biomarkerFor", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 441, "target": 3874, "key": "8d7a3f9e4cdd8652bc72f54f8176edf2"}, {"line": 26125, "relation": "positiveCorrelation", "evidence": "ApoE was not protective, but was injurious, as deletion of ApoE delayed the neurodegeneration caused by alpha-synuclein and suppressed the accumulation of Abeta. ", "citation": {"db": "PubMed", "db_id": "18297066"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Synuclein subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 431, "target": 2312, "key": "31e2a165fc00cc26a61bc6d84c9dc3cb"}, {"line": 34287, "relation": "association", "evidence": "By interacting with intracellular amyloid-beta, ERAB may therefore contribute to the neuronal dysfunction associated with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9338779"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 431, "target": 1229, "key": "427093d74270915a42ecb5159a8e5f41"}, {"line": 40164, "relation": "decreases", "evidence": "Neurodegenerative diseases involve the progressive loss of neurons, and a pathological hallmark is the presence of abnormal inclusions containing misfolded proteins.", "citation": {"db": "PubMed", "db_id": "24348565"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 431, "target": 3874, "key": "0ee5ccd44979b6f630f10123c06fae65"}, {"line": 10065, "relation": "association", "evidence": "The catalytic domain of insulin-degrading enzyme forms a denaturant-resistant complex with amyloid beta peptide: implications for Alzheimer disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "18411275"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 1230, "target": 3823, "key": "86f612d4a13f4004962be5685208fdf0"}, {"line": 33208, "relation": "association", "evidence": "Peripheral levels of Insulin Growth Factor-1 (IGF-I) are associated with glucose regulation and influence glucose disposal.", "citation": {"db": "PubMed", "db_id": "16444902"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 564, "target": 2871, "key": "eefcbee1dbb0feb08d6c517fdf359d08"}, {"line": 40374, "relation": "association", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "source": 564, "target": 2259, "key": "879f1a9d6d69350adf58caf84661bb38"}, {"line": 10135, "relation": "association", "evidence": "This abnormality along with a reduction in brain insulin concentration is assumed to induce a cascade-like process of disturbances including cellular glucose, acetylcholine, cholesterol, and ATP associated with abnormalities in membrane pathology and the formation of both amyloidogenic derivatives and hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "11956956"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 703, "target": 2899, "key": "919d1294cfdaba780a318405c5d47e46"}, {"line": 10157, "relation": "negativeCorrelation", "evidence": "Moreover, reduction of beta-cell replication capabilities results in reduction of beta-cell mass in mammals, simultaneously with impaired glucose tolerance.", "citation": {"db": "PubMed", "db_id": "21537460"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 797, "target": 3823, "key": "03b8afd1307b4254db6462a30de74f61"}, {"line": 10158, "relation": "negativeCorrelation", "evidence": "Moreover, reduction of beta-cell replication capabilities results in reduction of beta-cell mass in mammals, simultaneously with impaired glucose tolerance.", "citation": {"db": "PubMed", "db_id": "21537460"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"Low": true}}, "source": 797, "target": 3850, "key": "e67a44422da2012ae74a5aa698816acb"}, {"line": 10173, "relation": "association", "evidence": "Adiponectin as a new paradigm for approaching Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}}, "source": 2259, "target": 3823, "key": "c5cefee0a86f81d18159e2bd4771d4db"}, {"line": 40390, "relation": "positiveCorrelation", "evidence": "Here, we aim to summarize recent studies that suggest the potential correlation between adiponectin and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Low": true}}, "source": 2259, "target": 3823, "key": "e321ccb9cf0af94a74b2e0fb6205787a"}, {"line": 10183, "relation": "association", "evidence": "Adiponectin is an adipocytokine released by the adipose tissue and has multiple roles in the immune system and in the metabolic syndromes such as cardiovascular disease, Type 2 diabetes, obesity and also in the neurodegenerative disorders including Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}}, "source": 2259, "target": 3925, "key": "a2369d11254db0508f80abb118b68d5d"}, {"line": 10184, "relation": "association", "evidence": "Adiponectin is an adipocytokine released by the adipose tissue and has multiple roles in the immune system and in the metabolic syndromes such as cardiovascular disease, Type 2 diabetes, obesity and also in the neurodegenerative disorders including Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}}, "source": 2259, "target": 3850, "key": "18e4f4246a20a2d8e37da2df4f85f8f7"}, {"line": 10185, "relation": "association", "evidence": "Adiponectin is an adipocytokine released by the adipose tissue and has multiple roles in the immune system and in the metabolic syndromes such as cardiovascular disease, Type 2 diabetes, obesity and also in the neurodegenerative disorders including Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}}, "source": 2259, "target": 3834, "key": "b1c6bf2fbf577bef16d7bad4ff505cc2"}, {"line": 10195, "relation": "regulates", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2259, "target": 2184, "key": "46aad351206bff31ac7a296a56e2533a"}, {"line": 40373, "relation": "association", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "source": 2259, "target": 2184, "key": "6a6c31b4129a2378d963abcb95050cc5"}, {"line": 10196, "relation": "regulates", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2259, "target": 556, "key": "c37fada236821c56246eb24e2b4ec176"}, {"line": 10197, "relation": "regulates", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 2259, "target": 564, "key": "5cbdad5acfe94c2fe64090f2d22cced8"}, {"line": 40374, "relation": "association", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "source": 2259, "target": 564, "key": "3daf4e108a0326c200a10621f3b03225"}, {"line": 10204, "relation": "association", "evidence": "Adiponectin regulates the sensitivity of insulin, fatty acid catabolism, glucose homeostasis and anti-inflammatory system through various mechanisms.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2259, "target": 3920, "key": "aa24a87598c8a0d90771e8b85c9c13a4"}, {"line": 10211, "relation": "negativeCorrelation", "evidence": "Previous studies demonstrated that adiponectin modulates memory and cognitive impairment and contributes to the deregulated glucose metabolism and mitochondrial dysfunction observed in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24386594"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Beta-Oxidation of Fatty Acids": true}, "Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 2259, "target": 588, "key": "1ebcebb24867ca06f39b5a4cbbb30333"}, {"line": 10212, "relation": "negativeCorrelation", "evidence": "Previous studies 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"22248233"}, "source": 27, "target": 3823, "key": "3422ec04b5a2397b0a5dad6a8879f42c"}, {"line": 10737, "relation": "decreases", "evidence": "A key transcriptional response to IIS is the inhibition of hepatic gluconeogenic gene expression, and, in rat liver cells, CQ represses expression of the key gluconeogenic regulatory enzymes PEPCK (phosphoenolpyruvate carboxykinase) and G6Pase (glucose-6-phosphatase).", "citation": {"db": "PubMed", "db_id": "22248233"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Liver": true}}, "source": 27, "target": 3166, "key": "053534103089244e4bd350fab06b2273"}, {"line": 10738, "relation": "decreases", "evidence": "A key transcriptional response to IIS is the inhibition of hepatic gluconeogenic gene expression, and, in rat liver cells, CQ represses expression of the key gluconeogenic regulatory enzymes PEPCK (phosphoenolpyruvate carboxykinase) and G6Pase (glucose-6-phosphatase).", "citation": {"db": "PubMed", "db_id": "22248233"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Liver": true}}, "source": 27, "target": 728, "key": "a457cfb3d19d43a5782e5df0a3e0a461"}, {"line": 15820, "relation": "decreases", "evidence": "5-chloro-7-iodoquinolin-8-ol, a therapeutic agent for Alzheimer's disease, has proteasome-inhibitory, androgen receptor-suppressing, apoptotic process-inducing, and antitumor activities in human prostate cancer cells and xenografts.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Prostatic Neoplasms": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Prostate": true}, "Species": {"9606": true}, "Subgraph": {"Androgen subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 27, "target": 2354, "key": "8aabafab25ab03e2fa793bf3db8ee2c8"}, {"line": 15878, "relation": "decreases", "evidence": "In addition, '5-chloro-7-iodoquinolin-8-ol' alone exhibits similar effects in prostate cancer cell lines with elevated copper at concentrations similar to those found in patients.Our study provides strong evidence that ", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Prostate": true}, "MeSHDisease": {"Prostatic Neoplasms": true}, "Subgraph": {"Androgen subgraph": true}, "Confidence": {"High": true}}, "source": 27, "target": 2354, "key": "c31420dfd871513492cc6d7bc5a8d130"}, {"line": 15821, "relation": "isA", "evidence": "5-chloro-7-iodoquinolin-8-ol, a therapeutic agent for Alzheimer's disease, has proteasome-inhibitory, androgen receptor-suppressing, apoptotic process-inducing, and antitumor activities in human prostate cancer cells and xenografts.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Prostatic Neoplasms": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Prostate": true}, "Species": {"9606": true}, "Subgraph": {"Androgen subgraph": true}, "Confidence": {"Medium": true}}, "source": 27, "target": 165, "key": "edbb1b0d59af51820d5ec86b3c102a98"}, {"line": 15822, "relation": "increases", "evidence": "5-chloro-7-iodoquinolin-8-ol, a therapeutic agent for Alzheimer's disease, has proteasome-inhibitory, androgen receptor-suppressing, apoptotic process-inducing, and antitumor activities in human prostate cancer cells and xenografts.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Prostatic Neoplasms": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Prostate": true}, "Species": {"9606": true}, "Subgraph": {"Androgen subgraph": true}, "Confidence": {"Medium": true}}, "source": 27, "target": 478, "key": "2ee3b4b35f3f18c65e5505d610e139b1"}, {"line": 15879, "relation": "increases", "evidence": "In addition, '5-chloro-7-iodoquinolin-8-ol' alone exhibits similar effects in prostate cancer cell lines with elevated copper at concentrations similar to those found in patients.Our study provides strong evidence that ", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Prostate": true}, "MeSHDisease": {"Prostatic Neoplasms": true}, "Subgraph": {"Androgen subgraph": true}, "Confidence": {"High": true}}, "source": 27, "target": 478, "key": "2c18a751247c49e776e249dee6412d77"}, {"relation": "partOf", "source": 27, "target": 899, "key": "fb605d1942efc803800a95d273f9ea8a"}, {"line": 15845, "relation": "increases", "evidence": "5-chloro-7-iodoquinolin-8-ol is capable of forming stable complexes with copper and currently used in clinics for treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Androgen subgraph": true}, "Confidence": {"Medium": true}}, "source": 27, "target": 899, "key": "49ba39a54616e93e260037a33f92d777"}, {"line": 15877, "relation": "increases", "evidence": "In addition, '5-chloro-7-iodoquinolin-8-ol' alone exhibits similar effects in prostate cancer cell lines with elevated copper at concentrations similar to those found in patients.Our study provides strong evidence that ", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Prostate": true}, "MeSHDisease": {"Prostatic Neoplasms": true}, "Subgraph": {"Androgen subgraph": true}, "Confidence": {"High": true}}, "source": 27, "target": 101, "key": "8967d9e170c0882b669534ae5a2451a1"}, {"line": 10749, "relation": "association", "evidence": "Our results suggest that Zn2+-dependent regulation of FOXOs and gluconeogenesis may contribute to the therapeutic properties of this drug.", "citation": {"db": "PubMed", "db_id": "22248233"}, "source": 727, "target": 189, "key": "b3296071857be5118ef52cae7f3b9b9d"}, {"line": 10769, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 2564, "target": 3823, "key": "6f8aa609d493e0889630fa820e4ef212"}, {"line": 38987, "relation": "positiveCorrelation", "evidence": "Acute-phase proteins such as alpha 1-antichymotrypsin and c-reactive protein, elements of the complement / system, and activated microglial and astroglial cells are consistently found in brains of AD patients. Most importantly, / also cytokines such as interleukin-6 (IL-6) have been detected in the cortices of AD patients, indicating a local / activation of components of the unspecific inflammatory system.", "citation": {"db": "PubMed", "db_id": "8739396"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Subgraph": {"Complement system subgraph": true}}, "source": 2564, "target": 3823, "key": "495edd68f378bb8aeee8977b30b402aa"}, {"line": 10790, "relation": "positiveCorrelation", "evidence": "Plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and lipid peroxides are elevated and concentrations of endothelial nitric oxide (eNO) decreased in type 2 diabetes mellitus and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Confidence": {"High": true}}, "source": 2564, "target": 3850, "key": "e52379aef94dc3b1454ffdb3093db310"}, {"line": 10854, "relation": "negativeCorrelation", "evidence": "Hence, elevated butyrylcholinesterase and acetylcholinesterase concentrations will lead to a decrease in the levels of acetylcholine that could trigger the onset of low-grade systemic inflammation seen in type 2 diabetes and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "17553629"}, "annotations": {"MeSHDisease": {"Diabetes Mellitus, Type 2": true, "Alzheimer Disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 3920, "key": "d6d4d1007294876065e97ce507f839d7"}, {"line": 12810, "relation": "increases", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Cortisol subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 237, "key": "f0f217a723408a2b08af4face6f7d955"}, {"line": 12818, "relation": "regulates", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 204, "target": 126, "key": "9cd8c6c80ad228a3caedc4d6d7127256"}, {"line": 12822, "relation": "regulates", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 204, "target": 58, "key": "21335d436f6fed5da917f9d69b1022a1"}, {"line": 12826, "relation": "regulates", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 204, "target": 366, "key": "008286a602f9c0284ba5fdeddd72c1ad"}, {"line": 12830, "relation": "negativeCorrelation", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 204, "target": 694, "key": "dab8d849113b600a13129490bc83ef3a"}, {"line": 14198, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 2516, "key": "b39911e1c8392e6b9144122bb2736e6d"}, {"line": 14202, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 2517, "key": "180cb51243cb0b15b314e849ae502441"}, {"line": 14206, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 2518, "key": "0ee9a10cde5aa39c3174b5357de2fcf2"}, {"line": 14210, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through 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"key": "81daebcf86aa4c0b3d000741c10c5476"}, {"line": 14230, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 2524, "key": "b89b408591fcd7a4461e6d0af3599b2f"}, {"line": 14234, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 2526, "key": "9501d99cc9346986cf6dc7e89912055c"}, {"line": 14238, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 2527, "key": "56af66eec277c57e2a02ffb56a3af59c"}, {"line": 14244, "relation": "increases", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 350, "key": "3b61f6c6e4b3430e62dcb2aa4eccef07"}, {"line": 14267, "relation": "association", "evidence": "5-HT depletion decreased ACh-induced c-Fos immunoreactivity in the dentate gyrus.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"MeSHAnatomy": {"Dentate Gyrus": true}, "Subgraph": {"Serotonergic subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 350, "key": "2098be80a4ecd55f7aa023980caa120e"}, {"line": 24045, "relation": "increases", "evidence": "Rivastigmine treatment prevented the inhibitory effects of LPS and resulted in an increase (P ≤ 0.01) in GnRH concentrations compared to control-treated ewes or concentrations prior to treatment. The stimulation of GnRH secretion by treatment with LPS and rivastigmine may result from ACh accumulation in the brain.", "citation": {"db": "PubMed", "db_id": "23557940"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 204, "target": 2756, "key": "4877aeae9796b8a7dcb9e4482f7c1bdd"}, {"line": 24856, "relation": "association", "evidence": "Acetylcholinesterase (AChE), an enzyme involved in the hydrolysis of the neurotransmitter acetylcholine, consistently colocalizes with the amyloid deposits characteristic of Alzheimer's disease and may contribute to the generation of amyloid proteins and/or physically affect fibril assembly.", "citation": {"db": "PubMed", "db_id": "9325095"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 204, "target": 2244, "key": "9e70b2eacd0cf4b7820b15024dee1649"}, {"line": 10869, "relation": "association", "evidence": "Hyperamylinemia, a common pancreatic disorder in obese and insulin-resistant patients, is known to cause amylin oligomerization and cytotoxicity in pancreatic islets, leading to beta-cell mass depletion and development of type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "23794448"}, "source": 3813, "target": 3850, "key": "ec79721f100cad7c6f1be4d183c572c8"}, {"relation": "hasVariant", "source": 2801, "target": 2802, "key": "9285e21458e39c28d8f5262e02c075a3"}, {"line": 10966, "relation": "negativeCorrelation", "evidence": "We found that the neuronal glucose transporter 3 was decreased to a bigger extent in T2DM brain than in AD brain.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3375, "target": 3850, "key": "d120d4bcc1c2b4b64a23098dc7849ae3"}, {"line": 10979, "relation": "negativeCorrelation", "evidence": "The O-GlcNAcylation levels of global proteins and of tau were also decreased in T2DM brain as seen in AD brain.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3014, "target": 3850, "key": "e9a11ae9e0462f9b5e7bda4d36e2fe17"}, {"line": 10996, "relation": "decreases", "evidence": "These results suggest that T2DM may contribute to the increased risk for AD by impairing brain glucose uptake/metabolism and, consequently, down-regulation of O-GlcNAcylation, which facilitates abnormal hyperphosphorylation of tau.", "citation": {"db": "PubMed", "db_id": "19659459"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3014, "target": 3015, "key": "a9d841bb6fe81c1032524897af66b446"}, {"line": 11019, "relation": "negativeCorrelation", "evidence": "Pharmacological agents, such as dipeptidyl peptidase-4 (DPP-4) inhibitors, which increase the level of glucagon-like peptide-1 (GLP-1) and ameliorate T2D, have become valuable candidates as disease modifying agents in the treatment of AD. In addition, endogenous GLP-1 levels decrease amyloid beta (Abeta) peptide and tau phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}}, "source": 2640, "target": 2742, "key": "a48c09c08682001b6e817090ac213f2b"}, {"line": 11060, "relation": "increases", "evidence": "The results of present in vitro research demonstrate for the first time the ability of DPPIV to truncate the commercial Aβ40 and Aβ42 peptides, to hinder the fibril formation by them and to participate in the disaggregation of preformed fibrils of these peptides", "citation": {"db": "PubMed", "db_id": "23579020"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2640, "target": 2328, "key": "cf8b7af8033d714eac49cbe50240f228"}, {"line": 11061, "relation": "increases", "evidence": "The results of present in vitro research demonstrate for the first time the ability of DPPIV to truncate the commercial Aβ40 and Aβ42 peptides, to hinder the fibril formation by them and to participate in the disaggregation of preformed fibrils of these peptides", "citation": {"db": "PubMed", "db_id": "23579020"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2640, "target": 2327, "key": "21ce9a160ca513faf6a404ec40297c75"}, {"line": 11062, "relation": "decreases", "evidence": "The results of present in vitro research demonstrate for the first time the ability of DPPIV to truncate the commercial Aβ40 and Aβ42 peptides, to hinder the fibril formation by them and to participate in the disaggregation of preformed fibrils of these peptides", "citation": {"db": "PubMed", "db_id": "23579020"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2640, "target": 889, "key": "e33d35d357e0b3732626acdbaf5c91f0"}, {"line": 11036, "relation": "decreases", "evidence": "The present study examines the efficacy of Saxagliptin, a DPP-4 inhibitor in a streptozotocin (STZ) induced rat model of AD. Three months following induction of AD by intracerebral administration of streptozotocin, animals were orally administered Saxagliptin (0.25, 0.5 and 1 mg/kg) for 60 days. The effect of the DPP-4 inhibitor on hippocampal GLP-1 levels, Abeta burden, tau phosphorylation, inflammatory markers and memory retention were evaluated. The results reveal an attenuation of Abeta, tau phosphorylation and inflammatory markers and an improvement in hippocampal GLP-1 and memory retention following treatment.", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Species": {"10116": true}, "Confidence": {"Medium": true}}, "source": 348, "target": 3823, "key": "2c3d2e3056d0503fa918411feccad577"}, {"line": 11040, "relation": "decreases", "evidence": "The present study examines the efficacy of Saxagliptin, a DPP-4 inhibitor in a streptozotocin (STZ) induced rat model of AD. Three months following induction of AD by intracerebral administration of streptozotocin, animals were orally administered Saxagliptin (0.25, 0.5 and 1 mg/kg) for 60 days. The effect of the DPP-4 inhibitor on hippocampal GLP-1 levels, Abeta burden, tau phosphorylation, inflammatory markers and memory retention were evaluated. The results reveal an attenuation of Abeta, tau phosphorylation and inflammatory markers and an improvement in hippocampal GLP-1 and memory retention following treatment.", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "source": 348, "target": 3777, "key": "5240277bfd7644a6dce263e3e1ac042d"}, {"line": 11047, "relation": "increases", "evidence": "The present study examines the efficacy of Saxagliptin, a DPP-4 inhibitor in a streptozotocin (STZ) induced rat model of AD. Three months following induction of AD by intracerebral administration of streptozotocin, animals were orally administered Saxagliptin (0.25, 0.5 and 1 mg/kg) for 60 days. The effect of the DPP-4 inhibitor on hippocampal GLP-1 levels, Abeta burden, tau phosphorylation, inflammatory markers and memory retention were evaluated. The results reveal an attenuation of Abeta, tau phosphorylation and inflammatory markers and an improvement in hippocampal GLP-1 and memory retention following treatment.", "citation": {"db": "PubMed", "db_id": "23603201"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Glucagon subgraph": true}, "Confidence": {"High": true}}, "source": 348, "target": 3779, "key": "dfe0eb36b1bf64e19ac606c13c4bf4fb"}, {"line": 11079, "relation": "association", "evidence": "Recently, it has been shown that PEN-2 mutations could be involved in Alzheimer's disease (AD). We performed a mutational screening of all PEN-2 coding and promoter regions in a FAD cohort derived from Southern Italy. Four hundred and fifty-two subjects (FAD: 97; Controls: 355) were recruited for this study. We identified for the first time in a key region necessary for the promoter activity a novel 3 bp deletion in a subject with early-FAD. Our genetic data demonstrate that the mutant allele may influence the transcriptional activity of the PEN-2 gene.", "citation": {"db": "PubMed", "db_id": "22055974"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3272, "target": 843, "key": "9f863b1e075502818655dc876d6ad9b6"}, {"relation": "hasMember", "source": 3272, "target": 868, "key": "3a85d7319849aee747f4f89baac6cf2e"}, {"relation": "isA", "source": 3272, "target": 868, "key": "52e9875af54dbce714dc0de94e9a891b"}, {"line": 11104, "relation": "positiveCorrelation", "evidence": "PEN-2 is an integral membrane protein that is a necessary component of the gamma-secretase complex, which is central in the pathogenesis of Alzheimer's disease and is also required for Notch signaling. In the absence of PEN-2, Notch signaling fails to guide normal development in Caenorhabditis elegans, and amyloid beta peptide is not generated from the amyloid precursor protein", "citation": {"db": "PubMed", "db_id": "12639958"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 3272, "target": 453, "key": "df4bae853b4cb6ea0247af60aac4de3d"}, {"line": 11105, "relation": "positiveCorrelation", "evidence": "PEN-2 is an integral membrane protein that is a necessary component of the gamma-secretase complex, which is central in the pathogenesis of Alzheimer's disease and is also required for Notch signaling. In the absence of PEN-2, Notch signaling fails to guide normal development in Caenorhabditis elegans, and amyloid beta peptide is not generated from the amyloid precursor protein", "citation": {"db": "PubMed", "db_id": "12639958"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}, "Species": {"6239": true}}, "source": 3272, "target": 2328, "key": "02487e10588a6c3bb680a929e9fc2271"}, {"relation": "partOf", "source": 3272, "target": 1422, "key": "583006512444c0aa80f35718376b3228"}, {"line": 11118, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 3272, "target": 1422, "key": "696408557479a8e8de2af9da75672e17"}, {"relation": "partOf", "source": 3272, "target": 1113, "key": "5a1d7c9cea03ee50c537b0ec1414c38b"}, {"relation": "partOf", "source": 3272, "target": 1619, "key": "32f8bea25b2796853340a4780c3c9721"}, {"relation": "partOf", "source": 3272, "target": 1624, "key": "0f3f21cefbaea79e77ff85c7f86d9975"}, {"line": 11080, "relation": "association", "evidence": "Recently, it has been shown that PEN-2 mutations could be involved in Alzheimer's disease (AD). We performed a mutational screening of all PEN-2 coding and promoter regions in a FAD cohort derived from Southern Italy. Four hundred and fifty-two subjects (FAD: 97; Controls: 355) were recruited for this study. We identified for the first time in a key region necessary for the promoter activity a novel 3 bp deletion in a subject with early-FAD. Our genetic data demonstrate that the mutant allele may influence the transcriptional activity of the PEN-2 gene.", "citation": {"db": "PubMed", "db_id": "22055974"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 1930, "target": 843, "key": "8ade7b294df994f60705f45b873bbfea"}, {"relation": "isA", "source": 2305, "target": 868, "key": "4ba59d10b0f777dc4103d4fe2ac44f4c"}, {"relation": "partOf", "source": 2711, "target": 1422, "key": "5ad334610d7d5028a3cd20829a29f5a8"}, {"line": 11120, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Gamma secretase subgraph": true}}, "source": 2711, "target": 3272, "key": "4b42693578dce39c0d4a0109e08c5e1f"}, {"line": 11121, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Gamma secretase subgraph": true}}, "source": 2711, "target": 3258, "key": "63b9beb2538da7e19f58e0be8e84159a"}, {"line": 11122, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Gamma secretase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2711, "target": 868, "key": "0b993783b8e8ab67cb892f85572cdb99"}, {"line": 11123, "relation": "increases", "evidence": "The gamma-secretase complex comprises presenilins (PS1 or PS2), nicastrin, APH-1, and PEN-2. Herein, we find that PEN-2 can interact with ferritin light chain (FTL), an important component of the iron storage protein ferritin. In addition, we show that overexpression of FTL increases the protein levels of PEN-2 and PS1 amino-terminal fragment (NTF) and promotes gamma-secretase activity for more production of Abeta and notch intracellular domain (NICD). Furthermore, iron treatments increase the levels of FTL, PEN-2 and PS1 NTF and promote gamma-secretase-mediated NICD production. Moreover, downregulation of FTL decreases the levels of PEN-2 and PS1 NTF. Together, our results suggest that iron can increase gamma-secretase activity through promoting the level of FTL that interacts with and stabilizes PEN-2, providing a new molecular link between iron, PEN-2/gamma-secretase and Abeta generation in AD.", "citation": {"db": "PubMed", "db_id": "23685131"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Gamma secretase subgraph": true}}, "source": 2711, "target": 2328, "key": "034ed0fdf277439296966508098fd92b"}, {"line": 11142, "relation": "decreases", "evidence": "In the central nervous system, galanin alters the release of several neurotransmitters. In particular the ability of galanin to inhibit acetylcholine release, along with the observation of hyperinervation of galanin fibres in the human basal forebrain of Alzheimer's disease patients, suggest a possible role for galanin in the cholinergic dysfunction, characteristic of the disease. Moreover, galanin has been suggested to be involved in other neuronal functions, such as learning and memory, epileptic activity, nociception, spinal reflexes and feeding. Galanin has also been shown to increase the levels of growth hormone, prolactin and luteinizing hormone, to inhibit glucose induced insulin release and to affect gastrointestinal motility.", "citation": {"db": "PubMed", "db_id": "12769595"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2737, "target": 466, "key": "ded092e54c1775ba22aad7c854b18788"}, {"line": 11146, "relation": "association", "evidence": "In the central nervous system, galanin alters the release of several neurotransmitters. In particular the ability of galanin to inhibit acetylcholine release, along with the observation of hyperinervation of galanin fibres in the human basal forebrain of Alzheimer's disease patients, suggest a possible role for galanin in the cholinergic dysfunction, characteristic of the disease. Moreover, galanin has been suggested to be involved in other neuronal functions, such as learning and memory, epileptic activity, nociception, spinal reflexes and feeding. Galanin has also been shown to increase the levels of growth hormone, prolactin and luteinizing hormone, to inhibit glucose induced insulin release and to affect gastrointestinal motility.", "citation": {"db": "PubMed", "db_id": "12769595"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2737, "target": 638, "key": "f951e6a71aff20086328fd71816c7a9a"}, {"line": 11150, "relation": "association", "evidence": "In the central nervous system, galanin alters the release of several neurotransmitters. In particular the ability of galanin to inhibit acetylcholine release, along with the observation of hyperinervation of galanin fibres in the human basal forebrain of Alzheimer's disease patients, suggest a possible role for galanin in the cholinergic dysfunction, characteristic of the disease. Moreover, galanin has been suggested to be involved in other neuronal functions, such as learning and memory, epileptic activity, nociception, spinal reflexes and feeding. Galanin has also been shown to increase the levels of growth hormone, prolactin and luteinizing hormone, to inhibit glucose induced insulin release and to affect gastrointestinal motility.", "citation": {"db": "PubMed", "db_id": "12769595"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2737, "target": 588, "key": "0a8214b8e76188a92c96b5a8129db523"}, {"line": 11166, "relation": "decreases", "evidence": "Galanin (GAL) and GAL receptors (GALR) are overexpressed in degenerating brain regions associated with cognitive decline in Alzheimer's disease (AD). The functional consequences of GAL plasticity in AD are unclear. GAL inhibits cholinergic transmission in the hippocampus and impairs spatial memory in rodent models, suggesting that GAL overexpression exacerbates cognitive impairment in AD. By contrast, gene expression profiling of individual cholinergic basal forebrain (CBF) neurons aspirated from AD tissue revealed that GAL hyperinnervation positively regulates mRNAs that promote CBF neuronal function and survival. GAL also exerts neuroprotective effects in rodent models of neurotoxicity. These data support the growing concept that GAL overexpression preserves CBF neuron function, which may in turn delay the onset of symptoms of AD. Further elucidation of GAL activity in selectively vulnerable brain regions will help gauge the therapeutic potential of GALR ligands in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "21299067"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true}}, "source": 2737, "target": 588, "key": "93ba396ab0a813d1f6527dfd32a70f66"}, {"line": 11154, "relation": "decreases", "evidence": "In the central nervous system, galanin alters the release of several neurotransmitters. In particular the ability of galanin to inhibit acetylcholine release, along with the observation of hyperinervation of galanin fibres in the human basal forebrain of Alzheimer's disease patients, suggest a possible role for galanin in the cholinergic dysfunction, characteristic of the disease. Moreover, galanin has been suggested to be involved in other neuronal functions, such as learning and memory, epileptic activity, nociception, spinal reflexes and feeding. Galanin has also been shown to increase the levels of growth hormone, prolactin and luteinizing hormone, to inhibit glucose induced insulin release and to affect gastrointestinal motility.", "citation": {"db": "PubMed", "db_id": "12769595"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2737, "target": 630, "key": "98052d1ada7a73eb327b95a35f6a8884"}, {"line": 11162, "relation": "association", "evidence": "Galanin (GAL) and GAL receptors (GALR) are overexpressed in degenerating brain regions associated with cognitive decline in Alzheimer's disease (AD). The functional consequences of GAL plasticity in AD are unclear. GAL inhibits cholinergic transmission in the hippocampus and impairs spatial memory in rodent models, suggesting that GAL overexpression exacerbates cognitive impairment in AD. By contrast, gene expression profiling of individual cholinergic basal forebrain (CBF) neurons aspirated from AD tissue revealed that GAL hyperinnervation positively regulates mRNAs that promote CBF neuronal function and survival. GAL also exerts neuroprotective effects in rodent models of neurotoxicity. These data support the growing concept that GAL overexpression preserves CBF neuron function, which may in turn delay the onset of symptoms of AD. Further elucidation of GAL activity in selectively vulnerable brain regions will help gauge the therapeutic potential of GALR ligands in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "21299067"}, "annotations": {"Subgraph": {"Galanin subgraph": true}}, "source": 2737, "target": 3823, "key": "4bd2d101f8bbae7e84313f8dd4a183f9"}, {"line": 11179, "relation": "negativeCorrelation", "evidence": "CSF levels of total but not free IgG autoAbs against galanin were increased in AD, resulting in increased percentage of galanin autoAbs present as immune complexes. CSF levels of galanin total autoAbs and α-MSH free autoAbs correlated negatively with the severity of cognitive impairment as measured by MMSE. Both total and free autoAbs against galanin and α-MSH in CSF correlated negatively with age in AD patients but not in controls. CSF levels of galanin autoAbs and free α-MSH AutoAbs negatively correlated with CSF levels of t-Tau, p-Tau and ratios of t-Tau/Abeta42 or p-Tau/Abeta42 in AD patients but not in controls.", "citation": {"db": "PubMed", "db_id": "22078238"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}, "Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Tau protein subgraph": true}}, "source": 2737, "target": 3010, "key": "cdbe06c5c437eba9ea37255c1e5896c5"}, {"line": 11180, "relation": "negativeCorrelation", "evidence": "CSF levels of total but not free IgG autoAbs against galanin were increased in AD, resulting in increased percentage of galanin autoAbs present as immune complexes. CSF levels of galanin total autoAbs and α-MSH free autoAbs correlated negatively with the severity of cognitive impairment as measured by MMSE. Both total and free autoAbs against galanin and α-MSH in CSF correlated negatively with age in AD patients but not in controls. CSF levels of galanin autoAbs and free α-MSH AutoAbs negatively correlated with CSF levels of t-Tau, p-Tau and ratios of t-Tau/Abeta42 or p-Tau/Abeta42 in AD patients but not in controls.", "citation": {"db": "PubMed", "db_id": "22078238"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}, "Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Tau protein subgraph": true}}, "source": 2737, "target": 3015, "key": "19aea98ba4e067a292619a3af5598ef9"}, {"line": 11181, "relation": "negativeCorrelation", "evidence": "CSF levels of total but not free IgG autoAbs against galanin were increased in AD, resulting in increased percentage of galanin autoAbs present as immune complexes. CSF levels of galanin total autoAbs and α-MSH free autoAbs correlated negatively with the severity of cognitive impairment as measured by MMSE. Both total and free autoAbs against galanin and α-MSH in CSF correlated negatively with age in AD patients but not in controls. CSF levels of galanin autoAbs and free α-MSH AutoAbs negatively correlated with CSF levels of t-Tau, p-Tau and ratios of t-Tau/Abeta42 or p-Tau/Abeta42 in AD patients but not in controls.", "citation": {"db": "PubMed", "db_id": "22078238"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}, "Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Tau protein subgraph": true}}, "source": 2737, "target": 2328, "key": "8228a12d14d66b7e686f6a46ba2b0f56"}, {"line": 11146, "relation": "association", "evidence": "In the central nervous system, galanin alters the release of several neurotransmitters. In particular the ability of galanin to inhibit acetylcholine release, along with the observation of hyperinervation of galanin fibres in the human basal forebrain of Alzheimer's disease patients, suggest a possible role for galanin in the cholinergic dysfunction, characteristic of the disease. Moreover, galanin has been suggested to be involved in other neuronal functions, such as learning and memory, epileptic activity, nociception, spinal reflexes and feeding. Galanin has also been shown to increase the levels of growth hormone, prolactin and luteinizing hormone, to inhibit glucose induced insulin release and to affect gastrointestinal motility.", "citation": {"db": "PubMed", "db_id": "12769595"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Galanin subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 638, "target": 2737, "key": "1d588bc084f352dc7cf47a58e8a01ce6"}, {"line": 11163, "relation": "association", "evidence": "Galanin (GAL) and GAL receptors (GALR) are overexpressed in degenerating brain regions associated with cognitive decline in Alzheimer's disease (AD). The functional consequences of GAL plasticity in AD are unclear. GAL inhibits cholinergic transmission in the hippocampus and impairs spatial memory in rodent models, suggesting that GAL overexpression exacerbates cognitive impairment in AD. By contrast, gene expression profiling of individual cholinergic basal forebrain (CBF) neurons aspirated from AD tissue revealed that GAL hyperinnervation positively regulates mRNAs that promote CBF neuronal function and survival. GAL also exerts neuroprotective effects in rodent models of neurotoxicity. These data support the growing concept that GAL overexpression preserves CBF neuron function, which may in turn delay the onset of symptoms of AD. Further elucidation of GAL activity in selectively vulnerable brain regions will help gauge the therapeutic potential of GALR ligands in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "21299067"}, "annotations": {"Subgraph": {"Galanin subgraph": true}}, "source": 2738, "target": 3823, "key": "e4b9100fd3fafc467223031b65a42f1d"}, {"line": 11164, "relation": "association", "evidence": "Galanin (GAL) and GAL receptors (GALR) are overexpressed in degenerating brain regions associated with cognitive decline in Alzheimer's disease (AD). The functional consequences of GAL plasticity in AD are unclear. GAL inhibits cholinergic transmission in the hippocampus and impairs spatial memory in rodent models, suggesting that GAL overexpression exacerbates cognitive impairment in AD. By contrast, gene expression profiling of individual cholinergic basal forebrain (CBF) neurons aspirated from AD tissue revealed that GAL hyperinnervation positively regulates mRNAs that promote CBF neuronal function and survival. GAL also exerts neuroprotective effects in rodent models of neurotoxicity. These data support the growing concept that GAL overexpression preserves CBF neuron function, which may in turn delay the onset of symptoms of AD. Further elucidation of GAL activity in selectively vulnerable brain regions will help gauge the therapeutic potential of GALR ligands in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "21299067"}, "annotations": {"Subgraph": {"Galanin subgraph": true}}, "source": 2739, "target": 3823, "key": "744b628a384cf1c1af735df62d8f9fb1"}, {"line": 11194, "relation": "increases", "evidence": "It has become of particular interest in the pathogenesis of Alzheimer's disease (AD) because of the report that the activity of the gene coding for the enzyme DHCR24, which metabolizes desmosterol to cholesterol, is selectively reduced in the affected areas of the brain. Any change in the pattern of C27 sterol intermediates in cholesterol synthesis merits investigation with respect to the pathogenesis of AD, since neurosteroids such as progesterone can modulate the tissue levels", "citation": {"db": "PubMed", "db_id": "23042211"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 2627, "target": 525, "key": "7c9191fdad6c5e3f1651d956469220d0"}, {"line": 11195, "relation": "negativeCorrelation", "evidence": "It has become of particular interest in the pathogenesis of Alzheimer's disease (AD) because of the report that the activity of the gene coding for the enzyme DHCR24, which metabolizes desmosterol to cholesterol, is selectively reduced in the affected areas of the brain. Any change in the pattern of C27 sterol intermediates in cholesterol synthesis merits investigation with respect to the pathogenesis of AD, since neurosteroids such as progesterone can modulate the tissue levels", "citation": {"db": "PubMed", "db_id": "23042211"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 2627, "target": 3823, "key": "1aa0a48a00b114c0f5d50aefabbae519"}, {"line": 46225, "relation": "negativeCorrelation", "evidence": "reduced expression of DHCR24 is found in the temporal cortex of Alzheimer's disease patients", "citation": {"db": "PubMed", "db_id": "20568014"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}}, "source": 2627, "target": 3823, "key": "e30fd5a13057d78f7c156df98ad05cd0"}, {"line": 11214, "relation": "decreases", "evidence": "Seladin-1 was considered a novel neuroprotective factor, because of its anti-apoptotic activity. Subsequently, it was demonstrated that seladin-1 has also enzymatic activity [3-beta-hydroxysterol delta-24-reductase, (DHCR24)], which catalyzes the synthesis of cholesterol from desmosterol. The amount of membrane cholesterol may play an important role both in protecting neuronal cells against toxic insults and in inhibiting the production of beta-amyloid. We demonstrated that seladin-1 overexpression increases the amount of membrane cholesterol and induces resistance against beta-amyloid aggregates in neuroblastoma cells, whereas a specific inhibitor of DHCR24 increased cell vulnerability. We also hypothesized that seladin-1 might be a mediator of the neuroprotective effects of estrogens. We first demonstrated that, in human fetal neuroepithelial cells (FNC), 17beta-estradiol, raloxifene, and tamoxifen exert protective effects against beta-amyloid toxicity and oxidative stress. In addition, these molecules significantly increased the expression of seladin-1 and the amount of cell cholesterol. Then, we showed that, upon seladin-1 silencing, the protective effects of estrogens were abolished, thus indicating this factor as a fundamental mediator of estrogen-mediated neuroprotection, at least in FNC cells. Furthermore, we detected the presence of functionally active half-palindromic estrogen responsive elements upstream the coding region of the seladin-1 gene. Overall, our results indicate that seladin-1 may be viewed as a multi-faceted protein, which conjugates both the neuroprotective properties of estrogens and the important functions of cholesterol in maintaining brain homeostasis", "citation": {"db": "PubMed", "db_id": "21396986"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2627, "target": 478, "key": "6020004904e559886eb0df228dda2549"}, {"line": 11221, "relation": "increases", "evidence": "Seladin-1 was considered a novel neuroprotective factor, because of its anti-apoptotic activity. Subsequently, it was demonstrated that seladin-1 has also enzymatic activity [3-beta-hydroxysterol delta-24-reductase, (DHCR24)], which catalyzes the synthesis of cholesterol from desmosterol. The amount of membrane cholesterol may play an important role both in protecting neuronal cells against toxic insults and in inhibiting the production of beta-amyloid. We demonstrated that seladin-1 overexpression increases the amount of membrane cholesterol and induces resistance against beta-amyloid aggregates in neuroblastoma cells, whereas a specific inhibitor of DHCR24 increased cell vulnerability. We also hypothesized that seladin-1 might be a mediator of the neuroprotective effects of estrogens. We first demonstrated that, in human fetal neuroepithelial cells (FNC), 17beta-estradiol, raloxifene, and tamoxifen exert protective effects against beta-amyloid toxicity and oxidative stress. In addition, these molecules significantly increased the expression of seladin-1 and the amount of cell cholesterol. Then, we showed that, upon seladin-1 silencing, the protective effects of estrogens were abolished, thus indicating this factor as a fundamental mediator of estrogen-mediated neuroprotection, at least in FNC cells. Furthermore, we detected the presence of functionally active half-palindromic estrogen responsive elements upstream the coding region of the seladin-1 gene. Overall, our results indicate that seladin-1 may be viewed as a multi-faceted protein, which conjugates both the neuroprotective properties of estrogens and the important functions of cholesterol in maintaining brain homeostasis", "citation": {"db": "PubMed", "db_id": "21396986"}, "annotations": {"Confidence": {"Medium": true}, "UserdefinedCellLine": {"Neuroblastoma cell": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2627, "target": 231, "key": "b5b915136cad652393d022be02107f4c"}, {"line": 11222, "relation": "decreases", "evidence": "Seladin-1 was considered a novel neuroprotective factor, because of its anti-apoptotic activity. Subsequently, it was demonstrated that seladin-1 has also enzymatic activity [3-beta-hydroxysterol delta-24-reductase, (DHCR24)], which catalyzes the synthesis of cholesterol from desmosterol. The amount of membrane cholesterol may play an important role both in protecting neuronal cells against toxic insults and in inhibiting the production of beta-amyloid. We demonstrated that seladin-1 overexpression increases the amount of membrane cholesterol and induces resistance against beta-amyloid aggregates in neuroblastoma cells, whereas a specific inhibitor of DHCR24 increased cell vulnerability. We also hypothesized that seladin-1 might be a mediator of the neuroprotective effects of estrogens. We first demonstrated that, in human fetal neuroepithelial cells (FNC), 17beta-estradiol, raloxifene, and tamoxifen exert protective effects against beta-amyloid toxicity and oxidative stress. In addition, these molecules significantly increased the expression of seladin-1 and the amount of cell cholesterol. Then, we showed that, upon seladin-1 silencing, the protective effects of estrogens were abolished, thus indicating this factor as a fundamental mediator of estrogen-mediated neuroprotection, at least in FNC cells. Furthermore, we detected the presence of functionally active half-palindromic estrogen responsive elements upstream the coding region of the seladin-1 gene. Overall, our results indicate that seladin-1 may be viewed as a multi-faceted protein, which conjugates both the neuroprotective properties of estrogens and the important functions of cholesterol in maintaining brain homeostasis", "citation": {"db": "PubMed", "db_id": "21396986"}, "annotations": {"Confidence": {"Medium": true}, "UserdefinedCellLine": {"Neuroblastoma cell": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2627, "target": 2328, "key": "0c3e55c36b6af864d644fa715560d08a"}, {"line": 11223, "relation": "association", "evidence": "Seladin-1 was considered a novel neuroprotective factor, because of its anti-apoptotic activity. Subsequently, it was demonstrated that seladin-1 has also enzymatic activity [3-beta-hydroxysterol delta-24-reductase, (DHCR24)], which catalyzes the synthesis of cholesterol from desmosterol. The amount of membrane cholesterol may play an important role both in protecting neuronal cells against toxic insults and in inhibiting the production of beta-amyloid. We demonstrated that seladin-1 overexpression increases the amount of membrane cholesterol and induces resistance against beta-amyloid aggregates in neuroblastoma cells, whereas a specific inhibitor of DHCR24 increased cell vulnerability. We also hypothesized that seladin-1 might be a mediator of the neuroprotective effects of estrogens. We first demonstrated that, in human fetal neuroepithelial cells (FNC), 17beta-estradiol, raloxifene, and tamoxifen exert protective effects against beta-amyloid toxicity and oxidative stress. In addition, these molecules significantly increased the expression of seladin-1 and the amount of cell cholesterol. Then, we showed that, upon seladin-1 silencing, the protective effects of estrogens were abolished, thus indicating this factor as a fundamental mediator of estrogen-mediated neuroprotection, at least in FNC cells. Furthermore, we detected the presence of functionally active half-palindromic estrogen responsive elements upstream the coding region of the seladin-1 gene. Overall, our results indicate that seladin-1 may be viewed as a multi-faceted protein, which conjugates both the neuroprotective properties of estrogens and the important functions of cholesterol in maintaining brain homeostasis", "citation": {"db": "PubMed", "db_id": "21396986"}, "annotations": {"Confidence": {"Medium": true}, "UserdefinedCellLine": {"Neuroblastoma cell": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2627, "target": 2328, "key": "5e376b4d88722a87d2bf1970afaae28e"}, {"line": 11225, "relation": "increases", "evidence": "Seladin-1 was considered a novel neuroprotective factor, because of its anti-apoptotic activity. Subsequently, it was demonstrated that seladin-1 has also enzymatic activity [3-beta-hydroxysterol delta-24-reductase, (DHCR24)], which catalyzes the synthesis of cholesterol from desmosterol. The amount of membrane cholesterol may play an important role both in protecting neuronal cells against toxic insults and in inhibiting the production of beta-amyloid. We demonstrated that seladin-1 overexpression increases the amount of membrane cholesterol and induces resistance against beta-amyloid aggregates in neuroblastoma cells, whereas a specific inhibitor of DHCR24 increased cell vulnerability. We also hypothesized that seladin-1 might be a mediator of the neuroprotective effects of estrogens. We first demonstrated that, in human fetal neuroepithelial cells (FNC), 17beta-estradiol, raloxifene, and tamoxifen exert protective effects against beta-amyloid toxicity and oxidative stress. In addition, these molecules significantly increased the expression of seladin-1 and the amount of cell cholesterol. Then, we showed that, upon seladin-1 silencing, the protective effects of estrogens were abolished, thus indicating this factor as a fundamental mediator of estrogen-mediated neuroprotection, at least in FNC cells. Furthermore, we detected the presence of functionally active half-palindromic estrogen responsive elements upstream the coding region of the seladin-1 gene. Overall, our results indicate that seladin-1 may be viewed as a multi-faceted protein, which conjugates both the neuroprotective properties of estrogens and the important functions of cholesterol in maintaining brain homeostasis", "citation": {"db": "PubMed", "db_id": "21396986"}, "annotations": {"Confidence": {"Medium": true}, "UserdefinedCellLine": {"Neuroblastoma cell": true}, "Subgraph": {"Cholesterol metabolism subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2627, "target": 251, "key": "90c22f4a8ec33bb16587c14e8792a503"}, {"line": 11239, "relation": "increases", "evidence": "Human bleomycin hydrolase (hBH) is a neutral cysteine protease genetically associated with increased risk for Alzheimer disease. We show here that ectopic expression of hBH in 293APPwt and CHOAPPsw cells altered the processing of amyloid precursor protein (APP) and increased significantly the release of its proteolytic fragment, beta amyloid (Abeta). We also found that hBH interacted and colocalized with APP as determined by subcellular fractionation, in vitro binding assay, and confocal immunolocalization. Metabolic labeling and pulse-chase experiments showed that ectopic hBH expression increased secretion of soluble APPalpha/beta products without changing the half-life of cellular APP. ", "citation": {"db": "PubMed", "db_id": "10973933"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "UserdefinedCellLine": {"293APPwt": true, "CHOAPPsw": true}}, "source": 2401, "target": 80, "key": "f833337eed355833fe2f293ee1de7fee"}, {"relation": "partOf", "source": 2401, "target": 1147, "key": "552f89f20262482453f8ae536dc6b9d8"}, {"line": 11253, "relation": "increases", "evidence": "Here we show that bleomycin hydrolase, known to be induced in an oxidative environment, is specifically increased in neurons marked for degeneration in AD. These findings support a key proximal role for bleomycin hydrolase, and oxidative stress in AD.", "citation": {"db": "PubMed", "db_id": "10363952"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2401, "target": 842, "key": "409ca307e5852835b3f081aed6c52e9a"}, {"line": 11259, "relation": "positiveCorrelation", "evidence": "Here we show that bleomycin hydrolase, known to be induced in an oxidative environment, is specifically increased in neurons marked for degeneration in AD. These findings support a key proximal role for bleomycin hydrolase, and oxidative stress in AD.", "citation": {"db": "PubMed", "db_id": "10363952"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 2401, "target": 3823, "key": "7ee8d18d08bb3d3690bb681c8fed1c0b"}, {"line": 11274, "relation": "decreases", "evidence": "This study investigated the anti-glycative activity of LSOPC in a bovine serum albumin (BSA)-glucose model. The level of glycation and conformational alterations were assessed by specific fluorescence, Congo red binding assay and circular dichroism. The results show that LSOPC has a significant anti-glycative activity in vitro and it can also effectively protect the secondary structure of BSA during glycation. LSOPC or catechin (a major constituent unit of LSOPC), were used to react with methylglyoxal. The structures of their carbonyl adducts were tentatively identified using HPLC-MS(2). Their capacity to scavenge methylglyoxal suggested carbonyl scavenging as a major mechanism of antiglycation. Therefore, LSOPC could be helpful to prevent AGEs-associated diseases, and with the potential to be used as functional food ingredients.", "citation": {"db": "PubMed", "db_id": "23411272"}, "annotations": {"Subgraph": {"Albumin subgraph": true}}, "source": 226, "target": 2285, "key": "d8a27d0b6cdcd71ce94d76c65bd3c798"}, {"line": 11276, "relation": "decreases", "evidence": "This study investigated the anti-glycative activity of LSOPC in a bovine serum albumin (BSA)-glucose model. The level of glycation and conformational alterations were assessed by specific fluorescence, Congo red binding assay and circular dichroism. The results show that LSOPC has a significant anti-glycative activity in vitro and it can also effectively protect the secondary structure of BSA during glycation. LSOPC or catechin (a major constituent unit of LSOPC), were used to react with methylglyoxal. The structures of their carbonyl adducts were tentatively identified using HPLC-MS(2). Their capacity to scavenge methylglyoxal suggested carbonyl scavenging as a major mechanism of antiglycation. Therefore, LSOPC could be helpful to prevent AGEs-associated diseases, and with the potential to be used as functional food ingredients.", "citation": {"db": "PubMed", "db_id": "23411272"}, "annotations": {"Subgraph": {"Albumin subgraph": true}}, "source": 226, "target": 74, "key": "3daaf76a9105422375420191a57d6fb3"}, {"line": 11275, "relation": "increases", "evidence": "This study investigated the anti-glycative activity of LSOPC in a bovine serum albumin (BSA)-glucose model. The level of glycation and conformational alterations were assessed by specific fluorescence, Congo red binding assay and circular dichroism. The results show that LSOPC has a significant anti-glycative activity in vitro and it can also effectively protect the secondary structure of BSA during glycation. LSOPC or catechin (a major constituent unit of LSOPC), were used to react with methylglyoxal. The structures of their carbonyl adducts were tentatively identified using HPLC-MS(2). Their capacity to scavenge methylglyoxal suggested carbonyl scavenging as a major mechanism of antiglycation. Therefore, LSOPC could be helpful to prevent AGEs-associated diseases, and with the potential to be used as functional food ingredients.", "citation": {"db": "PubMed", "db_id": "23411272"}, "annotations": {"Subgraph": {"Albumin subgraph": true}}, "source": 2285, "target": 74, "key": "1e0d07bfa5d7f2649cfff1348d367b23"}, {"relation": "hasVariant", "source": 2284, "target": 2285, "key": "695913dbc1675dcc3e78c977c629618f"}, {"line": 11289, "relation": "decreases", "evidence": "The interaction of Cu(2+) with the first 16 residues of the Alzheimer's amyliod beta peptide, Abeta(1-16), and human serum albumin (HSA) were studied in vitro by isothermal titration calorimetry at pH 7.2 and 310 K in aqueous solution. The solvation parameters recovered from the extended solvation model indicate that HSA is involved in the transport of copper ion. Complexes between Abeta(1-16) and copper ions have been proposed to be an aberrant interaction in the development of Alzheimer's disease, where Cu(2+) is involved in Abeta(1-16) aggregation. The indexes of stability indicate that HSA removed Cu(2+) from Abeta(1-16), rapidly, decreased Cu-induced aggregation of Abeta(1-16), and reduced the toxicity of Abeta(1-16) + Cu(2+) significantly.", "citation": {"db": "PubMed", "db_id": "22844264"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2284, "target": 953, "key": "7a29427b287410cd9342715981dcf88b"}, {"line": 11290, "relation": "decreases", "evidence": "The interaction of Cu(2+) with the first 16 residues of the Alzheimer's amyliod beta peptide, Abeta(1-16), and human serum albumin (HSA) were studied in vitro by isothermal titration calorimetry at pH 7.2 and 310 K in aqueous solution. The solvation parameters recovered from the extended solvation model indicate that HSA is involved in the transport of copper ion. Complexes between Abeta(1-16) and copper ions have been proposed to be an aberrant interaction in the development of Alzheimer's disease, where Cu(2+) is involved in Abeta(1-16) aggregation. The indexes of stability indicate that HSA removed Cu(2+) from Abeta(1-16), rapidly, decreased Cu-induced aggregation of Abeta(1-16), and reduced the toxicity of Abeta(1-16) + Cu(2+) significantly.", "citation": {"db": "PubMed", "db_id": "22844264"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2284, "target": 785, "key": "0dbf5a03b6b09199606611e00b22f54e"}, {"line": 11303, "relation": "decreases", "evidence": "Human serum albumin (HSA) binds 95% of Abeta in blood plasma and is thought to inhibit plaque formation in peripheral tissue. However, the role of albumin in binding Abeta in the cerebrospinal fluid has been largely overlooked. Here we investigate the effect of HSA on both Abeta(1-40) and Abeta(1-42) fibril growth. We show that at micromolar cerebrospinal fluid levels, HSA inhibits the kinetics of Abeta fibrillization, significantly increasing the lag time and decreasing the total amount of fibrils produced", "citation": {"db": "PubMed", "db_id": "22718756"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Albumin subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "subject": {"modifier": "Activity"}, "source": 2284, "target": 644, "key": "1368f374db306856ebdf9b0f5b62b06f"}, {"line": 21524, "relation": "decreases", "evidence": "Human serum albumin (HSA) is a potent inhibitor of Abeta self-association and this novel, to our knowledge, function of HSA is of potential therapeutic interest for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24094411"}, "annotations": {"MeSHAnatomy": {"Serum": true}, "Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Albumin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2284, "target": 80, "key": "33b84a81bed87b3bc1006fbc9e57a746"}, {"line": 21605, "relation": "regulates", "evidence": "Human serum albumin can regulate amyloid-beta peptide fiber growth in the brain interstitium: implications for Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "22718756"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Subgraph": {"Albumin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2284, "target": 80, "key": "a82529f9717f759ab4fa951827de1732"}, {"line": 21558, "relation": "decreases", "evidence": "Albumin prevents mitochondrial depolarization and apoptosis elicited by endoplasmic reticulum calcium depletion of neuroblastoma cells.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 2284, "target": 616, "key": "b0331ef850c44f4f32dde3ec0590cef4"}, {"line": 21559, "relation": "decreases", "evidence": "Albumin prevents mitochondrial depolarization and apoptosis elicited by endoplasmic reticulum calcium depletion of neuroblastoma cells.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 2284, "target": 478, "key": "7cb2373b8c46b40585ceb805c6e3eb14"}, {"line": 21583, "relation": "decreases", "evidence": "In serum-free medium, albumin (29 or 49 mg/ml) fully prevented the apoptotic effects of dotarizine, flunarizine and cyclopiazonic acid.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 2284, "target": 103, "key": "590dc936c8d9e7f2a8b89090e1790f28"}, {"line": 21585, "relation": "decreases", "evidence": "In serum-free medium, albumin (29 or 49 mg/ml) fully prevented the apoptotic effects of dotarizine, flunarizine and cyclopiazonic acid.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 2284, "target": 247, "key": "c909694cab0b8aff7a714a9179124c73"}, {"line": 21587, "relation": "decreases", "evidence": "In serum-free medium, albumin (29 or 49 mg/ml) fully prevented the apoptotic effects of dotarizine, flunarizine and cyclopiazonic acid.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 2284, "target": 47, "key": "270a337a2f636668bc3d798611d13a69"}, {"relation": "partOf", "source": 2284, "target": 1073, "key": "79046faa5c324398a7df03b27f932f82"}, {"line": 11287, "relation": "association", "evidence": "The interaction of Cu(2+) with the first 16 residues of the Alzheimer's amyliod beta peptide, Abeta(1-16), and human serum albumin (HSA) were studied in vitro by isothermal titration calorimetry at pH 7.2 and 310 K in aqueous solution. The solvation parameters recovered from the extended solvation model indicate that HSA is involved in the transport of copper ion. Complexes between Abeta(1-16) and copper ions have been proposed to be an aberrant interaction in the development of Alzheimer's disease, where Cu(2+) is involved in Abeta(1-16) aggregation. The indexes of stability indicate that HSA removed Cu(2+) from Abeta(1-16), rapidly, decreased Cu-induced aggregation of Abeta(1-16), and reduced the toxicity of Abeta(1-16) + Cu(2+) significantly.", "citation": {"db": "PubMed", "db_id": "22844264"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 953, "target": 3823, "key": "6658fac3fbd6537bd0f738c97b7700ad"}, {"line": 11304, "relation": "positiveCorrelation", "evidence": "Human serum albumin (HSA) binds 95% of Abeta in blood plasma and is thought to inhibit plaque formation in peripheral tissue. However, the role of albumin in binding Abeta in the cerebrospinal fluid has been largely overlooked. Here we investigate the effect of HSA on both Abeta(1-40) and Abeta(1-42) fibril growth. We show that at micromolar cerebrospinal fluid levels, HSA inhibits the kinetics of Abeta fibrillization, significantly increasing the lag time and decreasing the total amount of fibrils produced", "citation": {"db": "PubMed", "db_id": "22718756"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Albumin subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 644, "target": 889, "key": "1e3532b59d14f9212e63d72da2920952"}, {"line": 11319, "relation": "increases", "evidence": "Treatment of neurons with ApoB-containing LDL cholesterol increased endolysosome accumulation of cholesterol, enlarged endolysosomes, and elevated endolysosome pH. In addition, ApoB-containing LDL cholesterol increased endolysosome accumulation of BACE-1, enhanced BACE-1 activity, increased Abeta levels, increased levels of phosphorylated tau, and decreased levels of synaptophysin.", "citation": {"db": "PubMed", "db_id": "22580286"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "UserdefinedCellLine": {"primary neuron": true}, "Confidence": {"High": true}}, "source": 997, "target": 231, "key": "8e5d1788928d5ca76982fe98773c7f88"}, {"line": 11328, "relation": "increases", "evidence": "Treatment of neurons with ApoB-containing LDL cholesterol increased endolysosome accumulation of cholesterol, enlarged endolysosomes, and elevated endolysosome pH. In addition, ApoB-containing LDL cholesterol increased endolysosome accumulation of BACE-1, enhanced BACE-1 activity, increased Abeta levels, increased levels of phosphorylated tau, and decreased levels of synaptophysin.", "citation": {"db": "PubMed", "db_id": "22580286"}, "annotations": {"UserdefinedCellLine": {"primary neuron": true}, "Subgraph": {"Beta secretase subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 997, "target": 2375, "key": "ef58bb5bdccb5597ed4a1cb726434d29"}, {"line": 11329, "relation": "increases", "evidence": "Treatment of neurons with ApoB-containing LDL cholesterol increased endolysosome accumulation of cholesterol, enlarged endolysosomes, and elevated endolysosome pH. In addition, ApoB-containing LDL cholesterol increased endolysosome accumulation of BACE-1, enhanced BACE-1 activity, increased Abeta levels, increased levels of phosphorylated tau, and decreased levels of synaptophysin.", "citation": {"db": "PubMed", "db_id": "22580286"}, "annotations": {"UserdefinedCellLine": {"primary neuron": true}, "Subgraph": {"Beta secretase subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 997, "target": 2375, "key": "592dad946a8e65692e8e9dd56bf89a4a"}, {"line": 11336, "relation": "increases", "evidence": "Treatment of neurons with ApoB-containing LDL cholesterol increased endolysosome accumulation of cholesterol, enlarged endolysosomes, and elevated endolysosome pH. In addition, ApoB-containing LDL cholesterol increased endolysosome accumulation of BACE-1, enhanced BACE-1 activity, increased Abeta levels, increased levels of phosphorylated tau, and decreased levels of synaptophysin.", "citation": {"db": "PubMed", "db_id": "22580286"}, "annotations": {"UserdefinedCellLine": {"primary neuron": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 997, "target": 80, "key": "622c7231d285ab72ab0b37c31f955832"}, {"line": 11341, "relation": "increases", "evidence": "Treatment of neurons with ApoB-containing LDL cholesterol increased endolysosome accumulation of cholesterol, enlarged endolysosomes, and elevated endolysosome pH. In addition, ApoB-containing LDL cholesterol increased endolysosome accumulation of BACE-1, enhanced BACE-1 activity, increased Abeta levels, increased levels of phosphorylated tau, and decreased levels of synaptophysin.", "citation": {"db": "PubMed", "db_id": "22580286"}, "annotations": {"UserdefinedCellLine": {"primary neuron": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 997, "target": 3015, "key": "3d36406e943ca3f57c325c2eeb504f19"}, {"line": 11347, "relation": "decreases", "evidence": "Treatment of neurons with ApoB-containing LDL cholesterol increased endolysosome accumulation of cholesterol, enlarged endolysosomes, and elevated endolysosome pH. In addition, ApoB-containing LDL cholesterol increased endolysosome accumulation of BACE-1, enhanced BACE-1 activity, increased Abeta levels, increased levels of phosphorylated tau, and decreased levels of synaptophysin.", "citation": {"db": "PubMed", "db_id": "22580286"}, "annotations": {"UserdefinedCellLine": {"primary neuron": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 997, "target": 3438, "key": "406ff885ca22c1876a639a09f788b748"}, {"relation": "partOf", "source": 2310, "target": 997, "key": "5fd8d64a52783423291fed6bb10c11c8"}, {"line": 11320, "relation": "association", "evidence": "Treatment of neurons with ApoB-containing LDL cholesterol increased endolysosome accumulation of cholesterol, enlarged endolysosomes, and elevated endolysosome pH. In addition, ApoB-containing LDL cholesterol increased endolysosome accumulation of BACE-1, enhanced BACE-1 activity, increased Abeta levels, increased levels of phosphorylated tau, and decreased levels of synaptophysin.", "citation": {"db": "PubMed", "db_id": "22580286"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true}, "UserdefinedCellLine": {"primary neuron": true}, "Confidence": {"High": true}}, "source": 390, "target": 231, "key": "fddef2cd52fdec45ceb6b4739e954c64"}, {"line": 46164, "relation": "negativeCorrelation", "evidence": "There was a significant increase in DNA methylation at the promoter region of synaptophysin in the AD", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3438, "target": 1987, "key": "88a8340583c2d46d6a76bda59df7b811"}, {"line": 46188, "relation": "negativeCorrelation", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3438, "target": 761, "key": "d52e8a3098bc200e1753bd62de5234e0"}, {"line": 11375, "relation": "increases", "evidence": "We tested the effects of native GIP and the agonist N-AcGIP on synaptic plasticity [long-term potentiation (LTP)] in the hippocampus [15 nmol, administered intracerebroventricularly (icv)] and report for the first time that both peptides have enhancing effects on LTP. In contrast, the antagonist of GIP, Pro(3)GIP (15 nmol icv), reduced LTP. Injection of beta-amyloid(25-35) (100 nmol), a peptide that aggregates in brains of AD patients, also impaired LTP. The injection of N-AcGIP (15 nmol icv) 30 min prior to injection of amyloid(25-35) (100 nmol icv) fully reversed the impairment of LTP induced by beta-amyloid. The results demonstrate for the first time that GIP (particularly enzyme-resistant forms) not only directly modulates neurotransmitter release and LTP formation, but also protects synapses from the detrimental effects of beta-amyloid fragments on LTP formation. ", "citation": {"db": "PubMed", "db_id": "18234983"}, "annotations": {"Species": {"10116": true}}, "object": {"modifier": "Activity"}, "source": 372, "target": 3780, "key": "cb3beed10351c50f78289c0b068cba67"}, {"line": 11378, "relation": "increases", "evidence": "We tested the effects of native GIP and the agonist N-AcGIP on synaptic plasticity [long-term potentiation (LTP)] in the hippocampus [15 nmol, administered intracerebroventricularly (icv)] and report for the first time that both peptides have enhancing effects on LTP. In contrast, the antagonist of GIP, Pro(3)GIP (15 nmol icv), reduced LTP. Injection of beta-amyloid(25-35) (100 nmol), a peptide that aggregates in brains of AD patients, also impaired LTP. The injection of N-AcGIP (15 nmol icv) 30 min prior to injection of amyloid(25-35) (100 nmol icv) fully reversed the impairment of LTP induced by beta-amyloid. The results demonstrate for the first time that GIP (particularly enzyme-resistant forms) not only directly modulates neurotransmitter release and LTP formation, but also protects synapses from the detrimental effects of beta-amyloid fragments on LTP formation. ", "citation": {"db": "PubMed", "db_id": "18234983"}, "annotations": {"Species": {"10116": true}}, "source": 372, "target": 597, "key": "efd99706435c824393cabfefddff3941"}, {"line": 11377, "relation": "directlyIncreases", "evidence": "We tested the effects of native GIP and the agonist N-AcGIP on synaptic plasticity [long-term potentiation (LTP)] in the hippocampus [15 nmol, administered intracerebroventricularly (icv)] and report for the first time that both peptides have enhancing effects on LTP. In contrast, the antagonist of GIP, Pro(3)GIP (15 nmol icv), reduced LTP. Injection of beta-amyloid(25-35) (100 nmol), a peptide that aggregates in brains of AD patients, also impaired LTP. The injection of N-AcGIP (15 nmol icv) 30 min prior to injection of amyloid(25-35) (100 nmol icv) fully reversed the impairment of LTP induced by beta-amyloid. The results demonstrate for the first time that GIP (particularly enzyme-resistant forms) not only directly modulates neurotransmitter release and LTP formation, but also protects synapses from the detrimental effects of beta-amyloid fragments on LTP formation. ", "citation": {"db": "PubMed", "db_id": "18234983"}, "annotations": {"Species": {"10116": true}}, "subject": {"modifier": "Activity"}, "source": 3780, "target": 597, "key": "e21cb424c920e8a7e9c1462b5e4e78e8"}, {"line": 11380, "relation": "decreases", "evidence": "We tested the effects of native GIP and the agonist N-AcGIP on synaptic plasticity [long-term potentiation (LTP)] in the hippocampus [15 nmol, administered intracerebroventricularly (icv)] and report for the first time that both peptides have enhancing effects on LTP. In contrast, the antagonist of GIP, Pro(3)GIP (15 nmol icv), reduced LTP. Injection of beta-amyloid(25-35) (100 nmol), a peptide that aggregates in brains of AD patients, also impaired LTP. The injection of N-AcGIP (15 nmol icv) 30 min prior to injection of amyloid(25-35) (100 nmol icv) fully reversed the impairment of LTP induced by beta-amyloid. The results demonstrate for the first time that GIP (particularly enzyme-resistant forms) not only directly modulates neurotransmitter release and LTP formation, but also protects synapses from the detrimental effects of beta-amyloid fragments on LTP formation. ", "citation": {"db": "PubMed", "db_id": "18234983"}, "annotations": {"Species": {"10116": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3780, "target": 2328, "key": "ce5375a5fa581a36417e82635f658d16"}, {"line": 11381, "relation": "directlyIncreases", "evidence": "We tested the effects of native GIP and the agonist N-AcGIP on synaptic plasticity [long-term potentiation (LTP)] in the hippocampus [15 nmol, administered intracerebroventricularly (icv)] and report for the first time that both peptides have enhancing effects on LTP. In contrast, the antagonist of GIP, Pro(3)GIP (15 nmol icv), reduced LTP. Injection of beta-amyloid(25-35) (100 nmol), a peptide that aggregates in brains of AD patients, also impaired LTP. The injection of N-AcGIP (15 nmol icv) 30 min prior to injection of amyloid(25-35) (100 nmol icv) fully reversed the impairment of LTP induced by beta-amyloid. The results demonstrate for the first time that GIP (particularly enzyme-resistant forms) not only directly modulates neurotransmitter release and LTP formation, but also protects synapses from the detrimental effects of beta-amyloid fragments on LTP formation. ", "citation": {"db": "PubMed", "db_id": "18234983"}, "annotations": {"Species": {"10116": true}}, "subject": {"modifier": "Activity"}, "source": 3780, "target": 657, "key": "261a4ff9a68ad131eee425f4c7ba5917"}, {"line": 11376, "relation": "decreases", "evidence": "We tested the effects of native GIP and the agonist N-AcGIP on synaptic plasticity [long-term potentiation (LTP)] in the hippocampus [15 nmol, administered intracerebroventricularly (icv)] and report for the first time that both peptides have enhancing effects on LTP. In contrast, the antagonist of GIP, Pro(3)GIP (15 nmol icv), reduced LTP. Injection of beta-amyloid(25-35) (100 nmol), a peptide that aggregates in brains of AD patients, also impaired LTP. The injection of N-AcGIP (15 nmol icv) 30 min prior to injection of amyloid(25-35) (100 nmol icv) fully reversed the impairment of LTP induced by beta-amyloid. The results demonstrate for the first time that GIP (particularly enzyme-resistant forms) not only directly modulates neurotransmitter release and LTP formation, but also protects synapses from the detrimental effects of beta-amyloid fragments on LTP formation. ", "citation": {"db": "PubMed", "db_id": "18234983"}, "annotations": {"Species": {"10116": true}}, "object": {"modifier": "Activity"}, "source": 374, "target": 3780, "key": "17346917ec55f98dd6b1b2a6d76eb318"}, {"line": 11379, "relation": "decreases", "evidence": "We tested the effects of native GIP and the agonist N-AcGIP on synaptic plasticity [long-term potentiation (LTP)] in the hippocampus [15 nmol, administered intracerebroventricularly (icv)] and report for the first time that both peptides have enhancing effects on LTP. In contrast, the antagonist of GIP, Pro(3)GIP (15 nmol icv), reduced LTP. Injection of beta-amyloid(25-35) (100 nmol), a peptide that aggregates in brains of AD patients, also impaired LTP. The injection of N-AcGIP (15 nmol icv) 30 min prior to injection of amyloid(25-35) (100 nmol icv) fully reversed the impairment of LTP induced by beta-amyloid. The results demonstrate for the first time that GIP (particularly enzyme-resistant forms) not only directly modulates neurotransmitter release and LTP formation, but also protects synapses from the detrimental effects of beta-amyloid fragments on LTP formation. ", "citation": {"db": "PubMed", "db_id": "18234983"}, "annotations": {"Species": {"10116": true}}, "source": 374, "target": 597, "key": "60529b841c2249129f916b39dbc46fea"}, {"line": 11392, "relation": "positiveCorrelation", "evidence": "We demonstrate that astrocytic expression of calpain-10 is up-regulated, and CamKIIα down-regulated with increasing Braak stage. Using immunohistochemistry we confirm protein expression of calpain-10 in astrocytes throughout the temporal cortex and demonstrate that calpain-10 immunoreactivity is correlated with both local and global measures of Alzheimer-type pathology.", "citation": {"db": "PubMed", "db_id": "23421725"}, "annotations": {"Subgraph": {"Calpastatin-calpain subgraph": true}, "MeSHAnatomy": {"Astrocytes": true}}, "source": 2429, "target": 3823, "key": "61f0370b852f265cc13eda1c236cdfa3"}, {"relation": "isA", "source": 2429, "target": 2160, "key": "94ae2f24b51a5f6b498cbe2fb016b8c4"}, {"line": 11404, "relation": "increases", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3711, "target": 190, "key": "2253f85af7cd5cb2cf6ab0012d024b6f"}, {"line": 11405, "relation": "increases", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3711, "target": 840, "key": "66eeedfd415995d2acf255ddd2ab6edc"}, {"line": 11406, "relation": "decreases", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3711, "target": 848, "key": "ece9ddc6619c3db6e0720474d1097a04"}, {"line": 11407, "relation": "decreases", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3711, "target": 3596, "key": "09f81c1714a708efad5d59648dfeeb95"}, {"line": 11408, "relation": "decreases", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3711, "target": 3597, "key": "1edf97c35d5a379dfbf8ec17243a2210"}, {"line": 11409, "relation": "decreases", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3711, "target": 3600, "key": "b3a2c20414096025af094fd6b79f8a09"}, {"line": 11410, "relation": "decreases", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3711, "target": 3622, "key": "fc938addbd51fc56d40a19a97042740c"}, {"line": 11411, "relation": "association", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 848, "target": 3596, "key": "59ee708b46adf02492eac9ce015216dc"}, {"line": 11412, "relation": "association", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 848, "target": 3597, "key": "e410b3c6a977e1feb4f34b0ea07970a8"}, {"line": 11413, "relation": "association", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 848, "target": 3600, "key": "a1941d851d0e216ff6c6a8130da5d2a0"}, {"line": 11414, "relation": "association", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 848, "target": 3622, "key": "dd719eba7d465b01f64ddcb1f0658aab"}, {"line": 11411, "relation": "association", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3596, "target": 848, "key": "d530c44e1d91814db1a1930411e4bb80"}, {"line": 23197, "relation": "positiveCorrelation", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3596, "target": 3825, "key": "df2418164ccaa57f237effa508bd3fc8"}, {"line": 23207, "relation": "increases", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3596, "target": 478, "key": "f170829bde3d8bf4c3aee84462061dfe"}, {"line": 23224, "relation": "increases", "evidence": "Overexpression of Bax induces the release of cytochrome c, which activates Apaf-1, which is associated caspase-independent activity. Caspase-3 activity is an important signaling molecule in apoptosis and affects the function of mitochondria by ROS and NO. Over-expression of Bax protein, followed by up-regulated activity of caspase-3 induces cell death", "citation": {"db": "PubMed", "db_id": "25009706"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3596, "target": 3622, "key": "8174b5fc03d654f41a5318baf769f04e"}, {"line": 23226, "relation": "increases", "evidence": "Overexpression of Bax induces the release of cytochrome c, which activates Apaf-1, which is associated caspase-independent activity. Caspase-3 activity is an important signaling molecule in apoptosis and affects the function of mitochondria by ROS and NO. Over-expression of Bax protein, followed by up-regulated activity of caspase-3 induces cell death", "citation": {"db": "PubMed", "db_id": "25009706"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3596, "target": 3600, "key": "609cc54f6d73d0bc82de14167c69c944"}, {"line": 41731, "relation": "positiveCorrelation", "evidence": "The apoptotic death in the glioma cell lines treated with PPARgamma agonists was correlated with the transient up-regulation of Bax and Bad protein levels.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Bcl-2 subgraph": true}, "MeSHDisease": {"Glioma": true}, "MeSHAnatomy": {"Cell Line": true}, "Confidence": {"High": true}}, "source": 3596, "target": 3699, "key": "606c9b868a2bacf61c7aafdd96abe055"}, {"line": 41742, "relation": "increases", "evidence": "Furthermore, inhibition of Bax expression by specific antisense oligonucleotides protected glioma cells against PPARgamma-mediated apoptotic process, indicating an essential role of Bax in PPARgamma-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Bcl-2 subgraph": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"Medium": true}}, "source": 3596, "target": 3699, "key": "6d53f5fb355e7223353934c5ec12c445"}, {"line": 11412, "relation": "association", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3597, "target": 848, "key": "47d02c1c719ea1d9908d55eaf5aee0a5"}, {"line": 22287, "relation": "positiveCorrelation", "evidence": "knockdown of PTPA induced cell apoptosis in HEK293 and N2a cell lines. PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 3597, "target": 3282, "key": "c61d527a6231d50cec5b5bbf42ac6ae3"}, {"line": 23195, "relation": "negativeCorrelation", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3597, "target": 3825, "key": "9900c5a6b34bab4855470f1bd6ef7d9b"}, {"line": 23205, "relation": "decreases", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3597, "target": 478, "key": "6b896ce795e5c4f631f190c5eca8a8c2"}, {"line": 41832, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3597, "target": 4044, "key": "995afd19552834e2150277e4c56bf1c7"}, {"line": 42354, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Bcl-2 subgraph": true}}, "source": 3597, "target": 369, "key": "45ab48314522e5f1a1f19d593366dae5"}, {"line": 42362, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Bcl-2 subgraph": true}}, "source": 3597, "target": 3741, "key": "311dfcd945b09bfe92f9dce95d453d79"}, {"line": 42373, "relation": "association", "evidence": "Blockade of CysLT1R by repeated treatment with montelukast (1 or 2 mg/kg, ig, 4 weeks) reduced Abeta1-42-induced CysLT1R expression and also suppressed Abeta1-42-induced increments of NF-κB p65, TNF-α, IL-1beta and caspase-3 activation, and Bcl-2 downregulation in the hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Bcl-2 subgraph": true}}, "source": 3597, "target": 3741, "key": "4a31d2405c16c20a698163232515d79c"}, {"line": 42375, "relation": "association", "evidence": "Blockade of CysLT1R by repeated treatment with montelukast (1 or 2 mg/kg, ig, 4 weeks) reduced Abeta1-42-induced CysLT1R expression and also suppressed Abeta1-42-induced increments of NF-κB p65, TNF-α, IL-1beta and caspase-3 activation, and Bcl-2 downregulation in the hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Bcl-2 subgraph": true}}, "source": 3597, "target": 2183, "key": "e3ceea361836b464178ee7fed4222164"}, {"line": 42377, "relation": "association", "evidence": "Blockade of CysLT1R by repeated treatment with montelukast (1 or 2 mg/kg, ig, 4 weeks) reduced Abeta1-42-induced CysLT1R expression and also suppressed Abeta1-42-induced increments of NF-κB p65, TNF-α, IL-1beta and caspase-3 activation, and Bcl-2 downregulation in the hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3597, "target": 3600, "key": "19c42d0ac0c6a48d6230f0e7433eb8e1"}, {"line": 11413, "relation": "association", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3600, "target": 848, "key": "768cbc6631f3e8aaab3db0cdac1dadd1"}, {"line": 16309, "relation": "positiveCorrelation", "evidence": "Brain sections from AD and control mice showed that HIF-1α, Ang-2, MMP2 and caspase 3 are elevated and Bcl-xL decreased in the microvasculature of AD mice.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Caspase subgraph": true}}, "source": 3600, "target": 3823, "key": "68df99288b90eef7ec2f7471ba041b1d"}, {"line": 22286, "relation": "negativeCorrelation", "evidence": "knockdown of PTPA induced cell apoptosis in HEK293 and N2a cell lines. PTPA knockdown decreased mitochondrial membrane potential and induced Bax translocation into the mitochondria with a simultaneous release of Cyt C, activation of caspase-3, cleavage of poly (DNA ribose) polymerase (PARP), and decrease in Bcl-xl and Bcl-2 protein levels.", "citation": {"db": "PubMed", "db_id": "24821282"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3600, "target": 3282, "key": "341209b6454ddbbd3bba336a70916741"}, {"line": 22301, "relation": "positiveCorrelation", "evidence": "This evidence led the authors to suggest that the observed reduction could be due to increased apoptosis of progenitor cells. However, analysis of the apoptotic marker caspase-3 demonstrated that increased caspase-3-dependant apoptosis only took place in the hippocampi of GD 17 exposed animals, yet another indication of the time-dependant variation in the response to an immune challenge.", "citation": {"db": "PubMed", "db_id": "24891958"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Species": {"10116": true}}, "subject": {"modifier": "Activity"}, "source": 3600, "target": 478, "key": "26d1aac632dc0556cebd0195bd44ad8e"}, {"line": 23055, "relation": "increases", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 3600, "target": 505, "key": "c5803089497d6b43ff494ecfc3a20883"}, {"line": 23227, "relation": "increases", "evidence": "Overexpression of Bax induces the release of cytochrome c, which activates Apaf-1, which is associated caspase-independent activity. Caspase-3 activity is an important signaling molecule in apoptosis and affects the function of mitochondria by ROS and NO. Over-expression of Bax protein, followed by up-regulated activity of caspase-3 induces cell death", "citation": {"db": "PubMed", "db_id": "25009706"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3600, "target": 505, "key": "598f9998744b3d6fad1745614b09628b"}, {"line": 41831, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3600, "target": 4044, "key": "cbd016852b6c0e00c1d1d97ec434ad7d"}, {"line": 42356, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Caspase subgraph": true}}, "source": 3600, "target": 369, "key": "48de479812d41bf8407402782a3a9613"}, {"line": 42377, "relation": "association", "evidence": "Blockade of CysLT1R by repeated treatment with montelukast (1 or 2 mg/kg, ig, 4 weeks) reduced Abeta1-42-induced CysLT1R expression and also suppressed Abeta1-42-induced increments of NF-κB p65, TNF-α, IL-1beta and caspase-3 activation, and Bcl-2 downregulation in the hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3600, "target": 3597, "key": "f88d41b22e0beab5a712e7cce5e789fc"}, {"line": 11414, "relation": "association", "evidence": "We also found that resistin could improve mitochondrial function in N2a/D9 cells through increasing the level of ATP and mitochondrial membrane potential. MTT and LDH assay indicated that N2a/D9 cells show increased vulnerability to H2O2-induced insult, which could be ameliorated by resistin. Mechanically, we found that resistin prevented apoptosis signals through reducing the ratio of Bax/Bcl2, the level of cleaved caspase-3, and attenuating cytochrome C release.", "citation": {"db": "PubMed", "db_id": "23747409"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3622, "target": 848, "key": "558a28d47c45829ab14ec83c578f5620"}, {"line": 23002, "relation": "increases", "evidence": "Distinct from WT SOD1, mutant SOD1 induces morphological change and cytochrome c release in cultured neurons that resulted in apoptotic process. Two transgenic studies further indicated the involvement of mitochondria-mediated apoptotic process in mutant SOD1-linked ALS.", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 3622, "target": 478, "key": "7e8d5ab6086b5a76e4a4bbfe37224b78"}, {"line": 23062, "relation": "increases", "evidence": "Caspase-9 activation is mainly triggered by cytochrome c release from the mitochondria, which occurs at the asymptomatic stage prior to disease onset (Guebetagan et al., 2001). Cytochrome c, together with ATP/ADP, Apaf-1 and procaspase-9, forms a complex termed `apoptosome', in which caspase-9 is activated. Caspase-9 then cleaves procaspase-3 to generate active caspase-3. Both caspase-9 and caspase-3 cleave procaspase-9 to form an `amplication loop' of caspase-9 activation. XIAP does not inhibit `apoptosome' formation and its upstream events, but suppresses the downstream `amplibetacation loop' by inhibition of caspase-9 and -3. Therefore, the activation of caspase-9 preceded by cytochrome c release can occur in the presence of a potent caspase-9 inhibitor XIAP and be considered an indicator of disease onset, regardless of the level of caspase-9.", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "object": {"modifier": "Activity"}, "source": 3622, "target": 3601, "key": "865c02ecf0ef63b845aaaf779135be54"}, {"relation": "partOf", "source": 3622, "target": 973, "key": "ee0e734c77bb9c167086003005be327e"}, {"line": 23225, "relation": "increases", "evidence": "Overexpression of Bax induces the release of cytochrome c, which activates Apaf-1, which is associated caspase-independent activity. Caspase-3 activity is an important signaling molecule in apoptosis and affects the function of mitochondria by ROS and NO. Over-expression of Bax protein, followed by up-regulated activity of caspase-3 induces cell death", "citation": {"db": "PubMed", "db_id": "25009706"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3622, "target": 3581, "key": "f6f7a1a48e7dcc7e9af223fa7bead8c4"}, {"line": 11427, "relation": "negativeCorrelation", "evidence": "Double labeling immunofluorescence and confocal microscopy revealed reduced hemoglobin α-chain and beta-chain in practically all neurons with small amounts of granular or punctuate hyperphosphorylated tau deposits and in neurons with tangles in the hippocampus and frontal cortex in AD", "citation": {"db": "PubMed", "db_id": "21157025"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}}, "source": 2812, "target": 3823, "key": "fe13f28e06a607aef747376e51187918"}, {"relation": "partOf", "source": 2812, "target": 1176, "key": "abccfec1f9db3f61d4ebae38e1668482"}, {"line": 11428, "relation": "negativeCorrelation", "evidence": "Double labeling immunofluorescence and confocal microscopy revealed reduced hemoglobin α-chain and beta-chain in practically all neurons with small amounts of granular or punctuate hyperphosphorylated tau deposits and in neurons with tangles in the hippocampus and frontal cortex in AD", "citation": {"db": "PubMed", "db_id": "21157025"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true}}, "source": 2813, "target": 3823, "key": "9e910f3843179e434138e23afd15e4da"}, {"line": 11453, "relation": "positiveCorrelation", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "annotations": {"Subgraph": {"T cells signaling": true, "Wnt signaling subgraph": true}}, "source": 4017, "target": 3823, "key": "03d59ba78930f2ff773c8106ab3f99ee"}, {"line": 11455, "relation": "positiveCorrelation", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "source": 3996, "target": 3823, "key": "da549dd2b58ad638d8f7810f4f98fd42"}, {"line": 11458, "relation": "positiveCorrelation", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Wnt signaling subgraph": true, "Axonal guidance subgraph": true}}, "source": 3965, "target": 3823, "key": "382e49a6113002e07ba18a60207ae9fe"}, {"line": 11460, "relation": "positiveCorrelation", "evidence": "Several components in the Wnt signaling pathway, including beta-catenin and glycogen synthase kinase 3 beta, have been implied in AD pathogenesis. Here, mRNA brain levels from five-month-old tg-ArcSwe and nontransgenic mice were compared using Affymetrix microarray analysis. With surprisingly small overall changes, Wnt signaling was the most affected pathway with altered expression of nine genes in tg-ArcSwe mice. When analyzing mRNA levels of these genes in human brain, transcription factor 7-like 2 (TCF7L2) and v-myc myelocytomatosis viral oncogene homolog (MYC), were increased in Alzheimer's disease (AD) (P < .05). Furthermore, no clear differences in TCF7L2 and MYC mRNA were found in brains with frontotemporal lobar degeneration, suggesting that altered regulation of these Wnt-related genes could be specific to AD. Finally, mRNA levels of three neurogenesis markers were analyzed. Increased mRNA levels of dihydropyrimidinase-like 3 were observed in AD brain, suggesting that altered Wnt signaling pathway regulation may signify synaptic rearrangement or neurogenesis.", "citation": {"db": "PubMed", "db_id": "21234373"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Wnt signaling subgraph": true, "Axonal guidance subgraph": true}}, "source": 3965, "target": 462, "key": "218ad84304e88092ebda863e08723a0a"}, {"line": 11472, "relation": "positiveCorrelation", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 2961, "target": 3823, "key": "c8df4ff666c8ab10fe5cfd8caa932261"}, {"line": 11480, "relation": "association", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 2961, "target": 589, "key": "271273431ad9c91f2f71e61b8db2740a"}, {"relation": "partOf", "source": 2961, "target": 1006, "key": "1deca87a282b2ea23bce0114e9289a81"}, {"line": 11493, "relation": "decreases", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Subgraph": {"Leptin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2961, "target": 80, "key": "88436e32f4703050a76033b705d48b59"}, {"line": 27316, "relation": "association", "evidence": "Leptin, an adipocytokine involved in cell survival and in learning, has been demonstrated to regulate Abeta production and tau hyperphosphorylation in transgenic mice for AD. ", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2961, "target": 80, "key": "70cd265c9abef60569b5e1f42fb3860a"}, {"line": 31043, "relation": "decreases", "evidence": "Treatment with leptin reversed the 27-OHC-induced increase in Abeta and phosphorylated tau by decreasing the levels of BACE-1 and GSK-3beta respectively.", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2961, "target": 80, "key": "2ebd415e35debe9e631cf431f6b7bbe5"}, {"line": 11500, "relation": "decreases", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Leptin subgraph": true, "Tau protein subgraph": true}}, "source": 2961, "target": 3015, "key": "9f2afe8e2f18264e49de04eb7040e7e1"}, {"line": 27320, "relation": "association", "evidence": "Leptin, an adipocytokine involved in cell survival and in learning, has been demonstrated to regulate Abeta production and tau hyperphosphorylation in transgenic mice for AD. ", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2961, "target": 3015, "key": "e5ff42b564a935e85a4222e4b1773a7a"}, {"line": 31046, "relation": "decreases", "evidence": "Treatment with leptin reversed the 27-OHC-induced increase in Abeta and phosphorylated tau by decreasing the levels of BACE-1 and GSK-3beta respectively.", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2961, "target": 3015, "key": "7e6f91298e589a406d23e6b58d4a4f85"}, {"line": 11507, "relation": "decreases", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Leptin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2961, "target": 2794, "key": "8d91ba6e65934320429e62290891843b"}, {"line": 31051, "relation": "decreases", "evidence": "Treatment with leptin reversed the 27-OHC-induced increase in Abeta and phosphorylated tau by decreasing the levels of BACE-1 and GSK-3beta respectively.", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2961, "target": 2794, "key": "7a2e26dfa27f66f8a1f426060a553de6"}, {"line": 11508, "relation": "association", "evidence": "We have previously demonstrated that Leptin reduces extracellular amyloid beta (Abeta) protein both in vitro and in vivo, and intracellular tau phosphorylation in vitro. Further, we have shown that these effects are dependent on activation of AMP-activated protein kinase (AMPK) in vitro. Herein, we investigated downstream effectors of AMPK signaling directly linked to tau phosphorylation. One such target, of relevance to Alzheimer's disease (AD), may be GSK-3beta, which has been shown to be inactivated by Leptin. We therefore dissected the role of GSK-3beta in mediating Leptin's ability to reduce tau phosphorylation in neuronal cells. Our data suggest that Leptin regulates tau phosphorylation through a pathway involving both AMPK and GSK-3beta. This was based on the following: Leptin and the cell-permeable AMPK activator, 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR), reduced tau phosphorylation at AD-relevant sites similarly to the GSK-3beta inhibitor, lithium chloride (LiCl). Further, this reduction of tau phosphorylation was mimicked by the downregulation of GSK-3beta, achieved using siRNA technology and antagonized by the ectopic overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "19429119"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Leptin subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 2961, "target": 714, "key": "030d34f910763378e5b0d65028e6cfd3"}, {"line": 27311, "relation": "association", "evidence": "Leptin, an adipocytokine involved in cell survival and in learning, has been demonstrated to regulate Abeta production and tau hyperphosphorylation in transgenic mice for AD. ", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2961, "target": 830, "key": "07c23ab152e0ac9fbab99d5d1ca94fea"}, {"relation": "partOf", "source": 2961, "target": 1268, "key": "f26cc9bbcf2c18fc20fa3d3fde4489df"}, {"line": 31057, "relation": "decreases", "evidence": "Treatment with leptin reversed the 27-OHC-induced increase in Abeta and phosphorylated tau by decreasing the levels of BACE-1 and GSK-3beta respectively.", "citation": {"db": "PubMed", "db_id": "20157255"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2961, "target": 2375, "key": "919ce5513c24e4a6ece8a3aa770f92a6"}, {"relation": "partOf", "source": 2961, "target": 1518, "key": "5e29cbabc3acb135ee8f60216047a279"}, {"line": 11478, "relation": "negativeCorrelation", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 2962, "target": 3823, "key": "fab1078dd2f0ba763588c8b8f1fe2e12"}, {"line": 11479, "relation": "negativeCorrelation", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 589, "target": 3823, "key": "f83a5c5fb8331eaa386ec6f66612a203"}, {"line": 11480, "relation": "association", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 589, "target": 2961, "key": "3b1123945117ce16420ca3f0a6c25d18"}, {"line": 11481, "relation": "positiveCorrelation", "evidence": "We analyzed leptin levels in CSF, and the concentration and localization of leptin and leptin receptor in the hippocampus. Significant elevations in leptin levels in both CSF and hippocampal tissue of AD patients, compared with age-matched control cases, indicate a physiological up-regulation of leptin in AD. However, the level of leptin receptor mRNA decreased in AD brain and the leptin receptor protein was localized to neurofibrillary tangles, suggesting a severe discontinuity in the leptin signaling pathway. Collectively, our results suggest that leptin resistance in the hippocampus may play a role in the characteristic changes associated with the disease. These findings are the first to demonstrate such dysregulated leptin-signaling circuitry and provide novel insights into the possible role of aberrant leptin signaling in AD. In this study, increased leptin was found in CSF and hippocampus in Alzheimer disease indicating its physiological up-regulation, yet leptin receptor mRNA was decreased and leptin receptor protein was localized to neurofibrillary tangles, suggesting a discontinuity in the leptin signaling pathway. The lack of leptin signaling within degenerating neurons may represent a novel neuronal leptin resistance in Alzheimer disease. ", "citation": {"db": "PubMed", "db_id": "23895348 "}, "annotations": {"Subgraph": {"Leptin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 1006, "target": 3823, "key": "8c2e10f440d56e2bb5af7ca3c25a2309"}, {"line": 11531, "relation": "isA", "evidence": "Lipopigment, identifiable in the fluorescence microscope, is thought to be cellular debris partly derived from free-radical-induced peroxidation of cellular constituents. The volume of neuronal lipopigment has been positively correlated with advancing age, Alzheimer dementia, and the neuronal ceroidoses. Chronic administration of agents which can be correlated with decreased neuronal lipopigment in animal models might protect neuronal function against any adverse effects associated with (but not necessarily resulting from) lipopigment accumulation in normal ageing, anoxia, or certain degenerative diseases.", "citation": {"db": "PubMed", "db_id": "2690998"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}}, "source": 436, "target": 91, "key": "8f596dd12a7ca8a6a7806ecfe4ebe7e7"}, {"line": 11533, "relation": "positiveCorrelation", "evidence": "Lipopigment, identifiable in the fluorescence microscope, is thought to be cellular debris partly derived from free-radical-induced peroxidation of cellular constituents. The volume of neuronal lipopigment has been positively correlated with advancing age, Alzheimer dementia, and the neuronal ceroidoses. Chronic administration of agents which can be correlated with decreased neuronal lipopigment in animal models might protect neuronal function against any adverse effects associated with (but not necessarily resulting from) lipopigment accumulation in normal ageing, anoxia, or certain degenerative diseases.", "citation": {"db": "PubMed", "db_id": "2690998"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}}, "source": 436, "target": 3823, "key": "81df4ae1d48ec96e73508a934d4f513c"}, {"line": 11535, "relation": "decreases", "evidence": "Lipopigment, identifiable in the fluorescence microscope, is thought to be cellular debris partly derived from free-radical-induced peroxidation of cellular constituents. The volume of neuronal lipopigment has been positively correlated with advancing age, Alzheimer dementia, and the neuronal ceroidoses. Chronic administration of agents which can be correlated with decreased neuronal lipopigment in animal models might protect neuronal function against any adverse effects associated with (but not necessarily resulting from) lipopigment accumulation in normal ageing, anoxia, or certain degenerative diseases.", "citation": {"db": "PubMed", "db_id": "2690998"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}}, "source": 110, "target": 436, "key": "740d95d1ac7e59ae58422c89f05b90ba"}, {"line": 11545, "relation": "isA", "evidence": "Ergoloid mesylate is a dihydrogenated ergot (Claviceps purpurea) derivative alkaloid used as a vasodilator agent. Ergoloid Mesylate is the only vasodilator that has shown mild benefits in the treatment of vascular dementia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}}, "source": 110, "target": 183, "key": "47425e96a4f1d7d874401388541b0727"}, {"line": 11546, "relation": "decreases", "evidence": "Ergoloid mesylate is a dihydrogenated ergot (Claviceps purpurea) derivative alkaloid used as a vasodilator agent. Ergoloid Mesylate is the only vasodilator that has shown mild benefits in the treatment of vascular dementia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}}, "source": 110, "target": 3844, "key": "4aaaa6f72e0945ecadf63c94395adc82"}, {"line": 11553, "relation": "increases", "evidence": "Ergoloid Mesylate may increase cerebral metabolism and blood flow. The role of this medication in the therapy of dementia is controversial. A recent controlled study in patients with Alzheimer's disease found that there was no advantage to the use of ergoloid mesylates compared to placebo, suggesting that ergoloid mesylates may lower scores on some cognitive and behavioral rating scales. Further study is needed to determine the risk-benefit profile of ergoloid mesylates in the treatment of dementia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 606, "key": "2da61a00092048e51b8f9040bee897c2"}, {"line": 11554, "relation": "increases", "evidence": "Ergoloid Mesylate may increase cerebral metabolism and blood flow. The role of this medication in the therapy of dementia is controversial. A recent controlled study in patients with Alzheimer's disease found that there was no advantage to the use of ergoloid mesylates compared to placebo, suggesting that ergoloid mesylates may lower scores on some cognitive and behavioral rating scales. Further study is needed to determine the risk-benefit profile of ergoloid mesylates in the treatment of dementia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 485, "key": "2ab1cf2943a507128bb04fb4b94754b4"}, {"line": 11555, "relation": "causesNoChange", "evidence": "Ergoloid Mesylate may increase cerebral metabolism and blood flow. The role of this medication in the therapy of dementia is controversial. A recent controlled study in patients with Alzheimer's disease found that there was no advantage to the use of ergoloid mesylates compared to placebo, suggesting that ergoloid mesylates may lower scores on some cognitive and behavioral rating scales. Further study is needed to determine the risk-benefit profile of ergoloid mesylates in the treatment of dementia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 3823, "key": "217a48bbc6e55fed09c2692f99b61c3a"}, {"line": 11561, "relation": "increases", "evidence": "Ergoloid mesylates act centrally, decreasing vascular tone and slowing the heart rate, and acts peripherally to block alpha-receptors. One other possible mechanism is the effect of ergoloid mesylates on neuronal cell metabolism, resulting in improved oxygen uptake and cerebral metabolism, thereby normalizing depressed neurotransmitter levels.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 825, "key": "037ba45dc7cf722aec9880c0c73b7e7d"}, {"line": 11562, "relation": "increases", "evidence": "Ergoloid mesylates act centrally, decreasing vascular tone and slowing the heart rate, and acts peripherally to block alpha-receptors. One other possible mechanism is the effect of ergoloid mesylates on neuronal cell metabolism, resulting in improved oxygen uptake and cerebral metabolism, thereby normalizing depressed neurotransmitter levels.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 678, "key": "9cb2b2cba4c938c921a9c43f7588e02e"}, {"line": 11563, "relation": "isA", "evidence": "Ergoloid mesylates act centrally, decreasing vascular tone and slowing the heart rate, and acts peripherally to block alpha-receptors. One other possible mechanism is the effect of ergoloid mesylates on neuronal cell metabolism, resulting in improved oxygen uptake and cerebral metabolism, thereby normalizing depressed neurotransmitter levels.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 77, "key": "74961199974da1ea799b7462400d53c4"}, {"line": 11566, "relation": "decreases", "evidence": "Ergoloid mesylates act centrally, decreasing vascular tone and slowing the heart rate, and acts peripherally to block alpha-receptors. One other possible mechanism is the effect of ergoloid mesylates on neuronal cell metabolism, resulting in improved oxygen uptake and cerebral metabolism, thereby normalizing depressed neurotransmitter levels.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "object": {"modifier": "Activity"}, "source": 110, "target": 2152, "key": "9a790606d7a66e0e017c6cc11ff71af3"}, {"line": 11569, "relation": "decreases", "evidence": "Ergoloid mesylates act centrally, decreasing vascular tone and slowing the heart rate, and acts peripherally to block alpha-receptors. One other possible mechanism is the effect of ergoloid mesylates on neuronal cell metabolism, resulting in improved oxygen uptake and cerebral metabolism, thereby normalizing depressed neurotransmitter levels.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 473, "key": "5d5c5791500423c05cfad6cbe2796db6"}, {"line": 11570, "relation": "increases", "evidence": "Ergoloid mesylates act centrally, decreasing vascular tone and slowing the heart rate, and acts peripherally to block alpha-receptors. One other possible mechanism is the effect of ergoloid mesylates on neuronal cell metabolism, resulting in improved oxygen uptake and cerebral metabolism, thereby normalizing depressed neurotransmitter levels.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 663, "key": "0aeb0a4191c3a1e5a4aebc930294612f"}, {"line": 11571, "relation": "association", "evidence": "Ergoloid mesylates act centrally, decreasing vascular tone and slowing the heart rate, and acts peripherally to block alpha-receptors. One other possible mechanism is the effect of ergoloid mesylates on neuronal cell metabolism, resulting in improved oxygen uptake and cerebral metabolism, thereby normalizing depressed neurotransmitter levels.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 746, "key": "19cd62dbd2f82a68b88072da8028e493"}, {"line": 11577, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 3905, "key": "3141b873e19ecfdaa8729807ed9eb962"}, {"line": 11578, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 3918, "key": "853ab3deef4a897ee641e294f863ae00"}, {"line": 11579, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 3916, "key": "8ce6a4946df7431a0303d913f1b075a1"}, {"line": 11580, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 3900, "key": "6299c0d99da0235f2688e224a5f0737f"}, {"line": 11581, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 3922, "key": "467c836528a189e228fe774f028c7eb8"}, {"line": 11582, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 3932, "key": "9b6515b0c4a2fb471c7309893e2462d0"}, {"line": 11583, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 110, "target": 3897, "key": "8938fb78d50e036a18d0a4236f84d4c5"}, {"line": 15305, "relation": "association", "evidence": "Donepezil is metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "19300564"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 606, "target": 244, "key": "dfe7a5441dce5df95a3b66e551269a96"}, {"line": 18654, "relation": "association", "evidence": "Cytochrome P450 (CYP) 2D6 enzyme is the major responsible for the metabolism of donepezil, an inhibitor of acetyl cholinesterase currently used for the symptomatic treatment of mild-to-moderate Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20859244"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Paroxetine subgraph": true}}, "source": 606, "target": 244, "key": "f71fe9a2c5fbd4d417b3fbcc9a4edec9"}, {"line": 15968, "relation": "association", "evidence": "In conclusion, Huperzine A metabolism in rat liver microsomes is mediated primarily by CYP1A2, with a probable secondary contribution of CYP3A1/2.", "citation": {"db": "PubMed", "db_id": "12586202"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "Species": {"10116": true}}, "source": 606, "target": 130, "key": "548089ad7ef7d26d8887420e9a96b40e"}, {"line": 17779, "relation": "association", "evidence": "The functional involvements of the cerebral angiotensin IV in what concerns its possible participation in the normal neurochemical processes of memory and in the neurodegenerative processes of Alzheimer disease will be exposed, together with the vasodilating effects of angiotensin (1-7) as counteracting factor for the constricting effects of angiotensin II.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 825, "target": 2275, "key": "b38e0d3a853e8934b61a37eed674d7d3"}, {"relation": "isA", "source": 2261, "target": 2152, "key": "ba264e5b6a5977484bccb71f6ba23eaa"}, {"relation": "isA", "source": 2262, "target": 2152, "key": "ba27057d71cdc08f4417afb0258b4b47"}, {"relation": "isA", "source": 2263, "target": 2152, "key": "726a0626f9fea92cd426ab00ffe20183"}, {"relation": "isA", "source": 2264, "target": 2152, "key": "bf8e96e4343ec2b8b5514d2c4c78eb4d"}, {"relation": "isA", "source": 2265, "target": 2152, "key": "cfab37d73b95c087e075367b46126a8e"}, {"relation": "isA", "source": 2266, "target": 2152, "key": "ff0cbc4ff36d4d857776eb8f918066fa"}, {"line": 11571, "relation": "association", "evidence": "Ergoloid mesylates act centrally, decreasing vascular tone and slowing the heart rate, and acts peripherally to block alpha-receptors. One other possible mechanism is the effect of ergoloid mesylates on neuronal cell metabolism, resulting in improved oxygen uptake and cerebral metabolism, thereby normalizing depressed neurotransmitter levels.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 746, "target": 110, "key": "286d9b63a0356b0f60020b6632614d55"}, {"line": 11577, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3905, "target": 110, "key": "4216992f43592753f84f9a99f669e935"}, {"line": 11578, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3918, "target": 110, "key": "d3386dd9e40edfc57dfb701b33d97ca9"}, {"line": 11579, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3916, "target": 110, "key": "98bae9c9554af44f90e4c6d8f98a0df8"}, {"line": 16735, "relation": "increases", "evidence": "Vascular risk factors such as hypertension and hypercholesterolemia during midlife increase the risk for Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "12218642"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Hypercholesterolemia": true, "Alzheimer Disease": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3916, "target": 3823, "key": "2c9aa663f5f1681c06bcf1f1fbe2bd55"}, {"line": 16840, "relation": "positiveCorrelation", "evidence": "Although the molecular mechanism has not yet been clarified until now, it is very interesting that Alzheimer's disease (AD), hypertension (HTN), and cerebral amyloid angiopathy (CAA) often occur synchronously and possess many similar pathological characteristics.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3916, "target": 3823, "key": "848256d8c9c8ffb2ed2850da53b91e9a"}, {"line": 16824, "relation": "association", "evidence": "Pin1, endothelial nitric oxide synthase, and amyloid-beta form a feedback signaling loop involved in the pathogenesis of Alzheimer's disease, hypertension, and cerebral amyloid angiopathy.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3916, "target": 1660, "key": "51058c82f50dbb733fb446bee7ca5fc0"}, {"line": 16839, "relation": "positiveCorrelation", "evidence": "Although the molecular mechanism has not yet been clarified until now, it is very interesting that Alzheimer's disease (AD), hypertension (HTN), and cerebral amyloid angiopathy (CAA) often occur synchronously and possess many similar pathological characteristics.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3916, "target": 3835, "key": "305e8bbc2ea6fcb1b717e197fa9ba593"}, {"line": 42779, "relation": "association", "evidence": "Nicardipine is a calcium channel blocker that has been widely used to control blood pressure in severe hypertension following events such as ischemic stroke, traumatic brain injury, and intracerebral hemorrhage.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Stroke": true, "Cerebral Hemorrhage": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Blood": true}, "Confidence": {"High": true}}, "source": 3916, "target": 199, "key": "b6e442ec06782eaf3c982df8d5684003"}, {"line": 11580, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3900, "target": 110, "key": "f298edfb77dba9eb65924b826f12bd01"}, {"line": 11581, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3922, "target": 110, "key": "f6c4870a88f96e220ffea274d4fbc7d1"}, {"line": 11582, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3932, "target": 110, "key": "638085c9d09135c5d890698152df663b"}, {"line": 11583, "relation": "positiveCorrelation", "evidence": "Symptoms of overdose include dyspnea, hypotension or hypertension, rapid weak pulse, delirium, nausea, vomiting, and bradycardia.", "citation": {"db": "Online Resource", "db_id": "DB01049"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Dopaminergic subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3897, "target": 110, "key": "b1a88acfd3426edc2fd78eaf8946b872"}, {"line": 17797, "relation": "association", "evidence": "Biphasic, dose-dependent effects were observed for angiotensin (1-7), induced both through nitric oxide, kinins and prostaglandin release for counteracting the vasoconstricting effects of angiotensin II and the modulation of its own vasodilator action.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 824, "target": 81, "key": "d6fa17cc780f8eb7c7991eb8cfda2221"}, {"line": 11600, "relation": "isA", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 2653, "target": 182, "key": "5368c2134d064ef27dbf5f39ba21f679"}, {"line": 19930, "relation": "isA", "evidence": "In the brain, endothelin-1 (ET-1) is a locally acting vasoconstrictor, produced in neurons by endothelin-converting enzyme (ECE)-2 and in endothelial cells by ECE-1.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endothelin subgraph": true}}, "source": 2653, "target": 182, "key": "82526e013ff826a8628734212ddfa4ce"}, {"line": 11601, "relation": "increases", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 2653, "target": 824, "key": "ab76c121572b589eac0a3ea0a8215fab"}, {"line": 19973, "relation": "increases", "evidence": "Our findings indicate that cerebral vasoconstriction induced by Abeta results in part from a free radical-mediated increase in ECE-1 activity and ET-1 production.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 2653, "target": 824, "key": "fda34a32a63822bd8099657192ffe61a"}, {"line": 19939, "relation": "positiveCorrelation", "evidence": "We previously showed ECE-2 and ET-1 to be elevated in postmortem temporal cortex from AD patients, and ECE-2 expression and ET-1 release to be upregulated by Abeta42 in vitro.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebral Cortex": true}, "Species": {"9606": true}}, "source": 2653, "target": 3823, "key": "d39c6003d66837b715484a66b19c0030"}, {"line": 20016, "relation": "positiveCorrelation", "evidence": "Western blot analysis indicates a significantly higher level of ET-1 in AD vessels compared to vessels from age-matched controls.", "citation": {"db": "PubMed", "db_id": "20634595"}, "source": 2653, "target": 3823, "key": "eac5e01639baf2a941fe1b51ebabfca3"}, {"line": 20058, "relation": "positiveCorrelation", "evidence": "ET-1 and ECE-2 are also elevated in AD, making it likely that upregulation of the ECE-2-ET-1 axis by Abeta42 contributes to the chronic reduction of CBF in AD.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2653, "target": 3823, "key": "5b388de06d3138bff7b0949d531e9b84"}, {"line": 20084, "relation": "positiveCorrelation", "evidence": "ET-1 mRNA measured in the temporal neocortex was significantly elevated in AD when normalized for expression of GAPDH (p = 0.0256) or the neuronal marker neuron-specific enolase (NSE, p = 0.0001). ET-1 protein was also significantly higher in AD than in control tissue, when adjusted for neuronal content by measurement of NSE (p = 0.0275).", "citation": {"db": "PubMed", "db_id": "22330820"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neocortex": true, "Neurons": true}}, "source": 2653, "target": 3823, "key": "70147bd9aa48a54305dc1b5dd04d12e9"}, {"line": 20005, "relation": "association", "evidence": "The vasoactive protein endothelin-1 (ET-1) is produced by vascular endothelial cells and participates in the regulation of vascular inflammation.", "citation": {"db": "PubMed", "db_id": "20634595"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Cell": {"endothelial cell": true}, "Subgraph": {"Endothelin subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2653, "target": 3920, "key": "46e3eaf2f5c25b320f80cac9024bf1cd"}, {"relation": "partOf", "source": 2653, "target": 1404, "key": "7f4583e00c61b5d340e6dafe07c0a24f"}, {"relation": "partOf", "source": 2653, "target": 1405, "key": "ffccd48d9145cfd305b4ccc092bc7e24"}, {"line": 11606, "relation": "isA", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 41, "target": 41, "key": "ed96bfe32fd9bef76efd858c9281e332"}, {"line": 11607, "relation": "isA", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 243, "target": 41, "key": "0a7cca1f7a73c55c8f2d8a769a7c35f4"}, {"line": 11608, "relation": "decreases", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "source": 243, "target": 824, "key": "8d573a6353049ebc5e65b59f394eb413"}, {"line": 11609, "relation": "association", "evidence": "Our results show that freshly solubilized Abeta1†40 enhances the vasoconstriction induced by endothelin-1 (ET-1) and increases resistance to relaxation triggered by nitric oxide (NO), suggesting that Abeta may oppose the NO/cGMP pathway. Using specific inhibitors and activators of the NO/cGMP pathway, we show that Abeta vasoactivity is not due to a modulation of nitric oxide synthase (NOS) or soluble guanylyl cyclase (sGC). However, we find that a selective cGMP phosphodiesterase (cGMP-PDE) inhibitor (dipyridamole) is able to interactively block the enhanced vasoconstriction as well as the opposition to relaxation induced by Abeta, suggesting that Abeta could effect the activity of this enzyme. Cyclic GMP levels, but not cAMP concentrations, are reduced after Abeta treatment of rat aortic rings, further substantiating this hypothesis.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 243, "target": 2328, "key": "754d2a68e5f00130ffc4d7ed62259f07"}, {"line": 11630, "relation": "decreases", "evidence": "Moreover, in examination of this pathway in another cell type pertinent to AD, we find that Abeta induces a proinflammatory response in microglia as evidenced by increased leukotriene B4 release. We show that both dipyridamole and compounds which increase cGMP levels prevent Abeta-induced microglial inflammation. Our results suggest that therapeutic intervention aimed at reduction of microglial-mediated inflammation via inhibition of cGMP-PDE or elevation of cGMP may be beneficial in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 243, "target": 3920, "key": "550fd966c2e3a7672dfc4e8382a90ad1"}, {"line": 11631, "relation": "increases", "evidence": "Moreover, in examination of this pathway in another cell type pertinent to AD, we find that Abeta induces a proinflammatory response in microglia as evidenced by increased leukotriene B4 release. We show that both dipyridamole and compounds which increase cGMP levels prevent Abeta-induced microglial inflammation. Our results suggest that therapeutic intervention aimed at reduction of microglial-mediated inflammation via inhibition of cGMP-PDE or elevation of cGMP may be beneficial in the treatment of AD.", "citation": {"db": "PubMed", "db_id": "10222124"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Blood vessel dilation subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 243, "target": 21, "key": "fa67dc08fdb8df6f60f559876cbc8e47"}, {"line": 11661, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 2328, "key": "c14046170114c6b9f4654a3c5191c64a"}, {"line": 11848, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might have the ability of both reducing amyloid burden and improving synaptic plasticity in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "14628195"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 2328, "key": "1abf02ff4eea1e9d112117c6637b1e6c"}, {"line": 11669, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"MeSHAnatomy": {"Nervous System": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3920, "key": "1f12ca0ba678c5043f365dd4d70257ba"}, {"line": 11673, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 842, "key": "b8d4fd3a6858ec9cb2e293822bff5e84"}, {"line": 11674, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Response to oxidative stress": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 523, "key": "41aa3d3e310a3643411426a1248091e5"}, {"line": 11679, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Brain": true}}, "source": 434, "target": 851, "key": "4334d2fa4cbe85338a677c0c37d33937"}, {"line": 11682, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 841, "key": "508aed3511acc29f62de564ecdd22997"}, {"line": 11683, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 645, "key": "77b4512f45225dc2dd7642947cff9cfb"}, {"line": 11684, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3872, "key": "cb515ef8661c7235e68cd731c31bc992"}, {"line": 11685, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 822, "key": "43f08aa4a6b37b45d088ff1b9482efae"}, {"line": 11686, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 812, "key": "8e46a9712acaee01924d8513e04d1a53"}, {"line": 11762, "relation": "increases", "evidence": "This study indicates that Cerebrolysin is a safe drug that improves the cognitive deficits and global function in patients with mild to moderate AD.", "citation": {"db": "PubMed", "db_id": "11129744"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 434, "target": 812, "key": "968e9e391a19b9ad5c2130ca180f14de"}, {"line": 11691, "relation": "increases", "evidence": "These pleiotropic effects of Cerebrolysin on Alzheimer's disease-related pathogenic events are consistent with a neurotrophic-like mode of action, and seems to involve the activation of the phosphatidylinositol 3-kinase/Akt/glycogen synthase kinase-3 beta intracellular signaling pathway.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 860, "key": "22c56c9baa26f67bdcb268609be47610"}, {"line": 11692, "relation": "increases", "evidence": "These pleiotropic effects of Cerebrolysin on Alzheimer's disease-related pathogenic events are consistent with a neurotrophic-like mode of action, and seems to involve the activation of the phosphatidylinositol 3-kinase/Akt/glycogen synthase kinase-3 beta intracellular signaling pathway.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 434, "target": 2794, "key": "e0d272d8e8c581ea0ea6cd132c807a7b"}, {"line": 11703, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3823, "key": "727bf05704fb240667205ffde44b8a6a"}, {"line": 11712, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}}, "source": 434, "target": 3585, "key": "a3caba3114138c45ac747b8fe9342195"}, {"line": 11713, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}}, "source": 434, "target": 4033, "key": "df8f67f930e8b33fa30f1977753aa5b3"}, {"line": 11721, "relation": "causesNoChange", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Beta secretase subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3593, "key": "7c1c9531adefb3fc375257f469d44bbc"}, {"line": 11729, "relation": "causesNoChange", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Notch signaling subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3690, "key": "3e7d7ddbddae14def645eca114536d3e"}, {"line": 11730, "relation": "causesNoChange", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Notch signaling subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3678, "key": "3f5982a0c9526185bc94300dfe171db1"}, {"line": 11732, "relation": "causesNoChange", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Notch signaling subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3652, "key": "31504fdf1d13da56d04dc88844d548bf"}, {"line": 11740, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "GSK3 subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3610, "key": "3d694c52a2d8464b5e1f6a5190c267ea"}, {"line": 11741, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "GSK3 subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3640, "key": "a46aa49594855f7c4ba90f15c3f58026"}, {"line": 11744, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "GSK3 subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 3586, "key": "228881b5a2416aa1d4bd57232e8cc3b9"}, {"line": 11745, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "GSK3 subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cell Surface Extensions"}, "toLoc": {"namespace": "MESH", "name": "Endosomes"}}}, "source": 434, "target": 3584, "key": "56503546b58f6e7298a9fd730f830638"}, {"line": 11746, "relation": "decreases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "GSK3 subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 397, "key": "c985f4d2ca9485f8d252dfd6262ca4b0"}, {"line": 11782, "relation": "increases", "evidence": "Cerebrolysin and E021 increased GluR1 density in most measured regions of the hippocampal formation in a highly significant way. These results correlate with the behavioural outcome, revealing an improvement in learning and memory of these rats after treatment with Cerebrolysin and E021.", "citation": {"db": "PubMed", "db_id": "12197668"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}, "Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 434, "target": 3637, "key": "a21ed8d82243eb28483cfece621a9264"}, {"line": 11786, "relation": "increases", "evidence": "Cerebrolysin and E021 increased GluR1 density in most measured regions of the hippocampal formation in a highly significant way. These results correlate with the behavioural outcome, revealing an improvement in learning and memory of these rats after treatment with Cerebrolysin and E021.", "citation": {"db": "PubMed", "db_id": "12197668"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}, "Species": {"10116": true}}, "source": 434, "target": 588, "key": "35ca75ae1d655f050928e22bdadb3019"}, {"line": 11816, "relation": "decreases", "evidence": "According to current scientific knowledge, excess tumour necrosis factor-alpha (TNF-alpha) and low insulin-like growth factor-I (IGF-I) are pathogenic-risk factors that constitute therapeutic targets for Alzheimer's disease (AD).At week 24, Cere reduced TNF-alpha and enhanced dissociable IGF-I with respect to placebo in a dose-related manner. Increases in total IGF-I were induced by 60 ml Cere", "citation": {"db": "PubMed", "db_id": "19531281"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 434, "target": 3472, "key": "6f7e9a06a457a3a169fc1c1853c7d6c1"}, {"line": 11824, "relation": "increases", "evidence": "According to current scientific knowledge, excess tumour necrosis factor-alpha (TNF-alpha) and low insulin-like growth factor-I (IGF-I) are pathogenic-risk factors that constitute therapeutic targets for Alzheimer's disease (AD).At week 24, Cere reduced TNF-alpha and enhanced dissociable IGF-I with respect to placebo in a dose-related manner. Increases in total IGF-I were induced by 60 ml Cere", "citation": {"db": "PubMed", "db_id": "19531281"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 434, "target": 2871, "key": "1e64fc25d3d4e939f99c92eb50df49c3"}, {"line": 11836, "relation": "positiveCorrelation", "evidence": "Cerebrolysin was generally well tolerated in clinical trials, with dizziness (or vertigo) being the most frequently reported adverse event.", "citation": {"db": "PubMed", "db_id": "20155999"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 434, "target": 3904, "key": "ca287fc51be4dee72c24d18c1538fac8"}, {"line": 11849, "relation": "increases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might have the ability of both reducing amyloid burden and improving synaptic plasticity in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "14628195"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 434, "target": 688, "key": "b9c25cf3542b80ca9bc5849e3c6d68eb"}, {"line": 11864, "relation": "increases", "evidence": "Cerebrolysin (CBL) treated hAPP tg mice showed levels of pro-NGF comparable to control and increased levels of mature NGF.", "citation": {"db": "PubMed", "db_id": "23152192"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}}, "source": 434, "target": 3688, "key": "18fb8e3ac56be95a938225f980acaa7c"}, {"relation": "isA", "source": 2745, "target": 434, "key": "2c08b0a33fad85a2bf69f0b3320a63c5"}, {"line": 16082, "relation": "increases", "evidence": "GDNF protects against aluminum-induced apoptosis in rabbits by upregulating Bcl-2 and Bcl-XL and inhibiting mitochondrial Bax translocation.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2745, "target": 2393, "key": "9ea9a814410148154aa2a7fb8bf0209b"}, {"line": 33591, "relation": "decreases", "evidence": "Coadministration of glial cell neuronal-derived factor (GDNF) inhibits these Bcl-2 and Bax changes, upregulates Bcl-XL, and abolishes the caspase-3 activity.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2745, "target": 2393, "key": "72e463f2fc4615e1bd027c90e7f2a77b"}, {"line": 16087, "relation": "increases", "evidence": "GDNF protects against aluminum-induced apoptosis in rabbits by upregulating Bcl-2 and Bcl-XL and inhibiting mitochondrial Bax translocation.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2745, "target": 2394, "key": "058ba8dc61e232efd85cf506625350c4"}, {"line": 16103, "relation": "decreases", "evidence": "Coadministration of glial cell neuronal-derived factor (GDNF) inhibits these Bcl-2 and Bax changes, upregulates Bcl-XL, and abolishes the caspase-3 activity.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"MeSHAnatomy": {"Neuroglia": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2745, "target": 2394, "key": "7492134d88d35451d2254158ce831018"}, {"line": 33600, "relation": "increases", "evidence": "Coadministration of glial cell neuronal-derived factor (GDNF) inhibits these Bcl-2 and Bax changes, upregulates Bcl-XL, and abolishes the caspase-3 activity.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2745, "target": 2394, "key": "1c952e7cfa346c98c0e3f93de05e7cde"}, {"line": 16107, "relation": "decreases", "evidence": "Coadministration of glial cell neuronal-derived factor (GDNF) inhibits these Bcl-2 and Bax changes, upregulates Bcl-XL, and abolishes the caspase-3 activity.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"MeSHAnatomy": {"Neuroglia": true}, "Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2745, "target": 2444, "key": "299dd2a245bebe82060373dc8b52084f"}, {"line": 33596, "relation": "increases", "evidence": "Coadministration of glial cell neuronal-derived factor (GDNF) inhibits these Bcl-2 and Bax changes, upregulates Bcl-XL, and abolishes the caspase-3 activity.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2745, "target": 2444, "key": "67a6066178a7ca1d037ee896a8715f26"}, {"line": 16118, "relation": "decreases", "evidence": "Furthermore, treatment with GDNF dramatically inhibits apoptotic process, as assessed by the TUNEL technique for detecting DNA damage.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2745, "target": 478, "key": "903df08a274c8ac641beb630e084329e"}, {"relation": "partOf", "source": 2745, "target": 1293, "key": "424a38511db56c6eda8e617135a8f807"}, {"relation": "partOf", "source": 2745, "target": 1306, "key": "de92521a0c93c0944f19e2f7f3bcc92a"}, {"relation": "partOf", "source": 2745, "target": 1290, "key": "b9438e70aabc3c10e97a24ee01489852"}, {"relation": "partOf", "source": 2745, "target": 1289, "key": "de1151fe559fd6190bb831ceda93ff12"}, {"line": 33592, "relation": "decreases", "evidence": "Coadministration of glial cell neuronal-derived factor (GDNF) inhibits these Bcl-2 and Bax changes, upregulates Bcl-XL, and abolishes the caspase-3 activity.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Bcl-2 subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2745, "target": 2389, "key": "b3f6bc8e177d5b7225b3773e6b5d405a"}, {"line": 38284, "relation": "increases", "evidence": "Activated microglia can reduce Abeta accumulation by increasing its phagocytosis or extracellular degradation. Microglia also release trophic factors such as the glia-derived neurotrophic factor (GDNF), which is neuroprotective", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2745, "target": 431, "key": "98999a4469bb68d4772072833706c53b"}, {"line": 44166, "relation": "increases", "evidence": "Activated microglia can reduce Abeta accumulation by increasing its phagocytosis or extracellular degradation. Microglia also release trophic factors such as the glia-derived neurotrophic factor (GDNF), which is neuroprotective", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 2745, "target": 854, "key": "ae400de6a499ed415df7d035ce0b349a"}, {"relation": "isA", "source": 2541, "target": 434, "key": "a348305a3910a73e20c2c4f6a7138c06"}, {"line": 11679, "relation": "association", "evidence": "Cerebrolysin is a neuropeptide preparation mimicking the action of endogenous neurotrophic factors. Positive effects of Cerebrolysin on beta-amyloid- and tau-related pathologies, neuroinflammation, neurotrophic factors, oxidative stress, excitotoxicity, neurotransmission, brain metabolism, neuroplasticity, neuronal apoptosis and degeneration, neurogenesis and cognition were demonstrated in experimental conditions.", "citation": {"db": "PubMed", "db_id": "22013558"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}, "MeSHAnatomy": {"Brain": true}}, "source": 851, "target": 434, "key": "3ef2126ca9bb251ae5163b7061516371"}, {"relation": "hasVariant", "source": 3584, "target": 3585, "key": "8f4dd9c5919a0ec3d3af3a03a7a3caa4"}, {"relation": "hasVariant", "source": 3584, "target": 3586, "key": "8cf894f0096c82d27ef55cff954777f9"}, {"line": 41304, "relation": "association", "evidence": "Pan-PPAR Modulation Effectively Protects APP/PS1 Mice from Amyloid Deposition and Cognitive Deficits.", "citation": {"db": "PubMed", "db_id": "24838579"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3584, "target": 3703, "key": "430e25ec12d243547905db7e5c902623"}, {"line": 41892, "relation": "association", "evidence": "NOS2 deficiency or oral treatment with the NOS2 inhibitor L-NIL strongly decreased 3NTyr(10)-Abeta, overall Abeta deposition and cognitive dysfunction in APP/PS1 mice.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Confidence": {"High": true}, "Species": {"10090": true}}, "source": 3584, "target": 3703, "key": "33c7448f2c001ccd0b926e03299a8bd5"}, {"line": 41805, "relation": "association", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3584, "target": 3623, "key": "98ac7ad5d06faed755287332f8e77c76"}, {"relation": "hasVariant", "source": 3584, "target": 3588, "key": "619e50f624b55c399e0505d632f1d732"}, {"relation": "hasVariant", "source": 3584, "target": 3587, "key": "0370a82ffed6124f551a4dc6743b258b"}, {"line": 44430, "relation": "orthologous", "evidence": "developmental exposure of rodents to the heavy metal lead (Pb) increases APP (amyloid precursor protein) and Abeta production later in the aging brain", "citation": {"db": "PubMed", "db_id": "18157652"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3584, "target": 2315, "key": "dfdc39389b44510909bbb45cdde66d00"}, {"line": 44458, "relation": "positiveCorrelation", "evidence": "We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age.", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 3584, "target": 80, "key": "fd730f5c832fcf556386154a0245cf66"}, {"line": 44555, "relation": "negativeCorrelation", "evidence": "hypomethylation of the APP promoter for example can increase the ceiling of expression of the APP gene in response to aging processes driving overproduction of APP and Abeta levels. The increased Abeta levels then facilitate ROS production with their pro-oxidant properties, damaging the DNA. ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 3584, "target": 1747, "key": "353b04a058a9e42bf67116e00307515b"}, {"line": 44457, "relation": "positiveCorrelation", "evidence": "We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age.", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 4033, "target": 4077, "key": "95757ff960a46ac48fcc9646d1fae7ad"}, {"line": 44554, "relation": "negativeCorrelation", "evidence": "hypomethylation of the APP promoter for example can increase the ceiling of expression of the APP gene in response to aging processes driving overproduction of APP and Abeta levels. The increased Abeta levels then facilitate ROS production with their pro-oxidant properties, damaging the DNA. ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 4033, "target": 1747, "key": "375ead1f4415dac4db97cd8dbfaf00ef"}, {"line": 43245, "relation": "directlyIncreases", "evidence": "Processing of APP to produce Ab involves cleavage by b-site APP cleaving enzyme-1 (BACE1) and g-secretase that process APP at the N- and C-termini, respectively, of the Ab sequence.", "citation": {"db": "PubMed", "db_id": "22434822"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3593, "target": 4107, "key": "e6235a6c93f16bd1c9fa6ba51ef4eeb4"}, {"line": 44784, "relation": "increases", "evidence": "We showed in early-onset familial Alzheimer's disease(FAD) mouse models that compact plaque formation is associated with a progressive axonal pathology inherent with increased expression of beta-secretase (BACE1)", "citation": {"db": "PubMed", "db_id": "21725719"}, "annotations": {"Species": {"10090": true}, "DiseaseState": {"Familial Alzheimers Disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3593, "target": 3823, "key": "23ce4ee81cd27b136a18250107f69fa6"}, {"line": 44822, "relation": "increases", "evidence": "BACE1 protein and enzymatic activity are increased the brains of sporadic and familiar AD cases", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "KnockoutMice": {"App transgenic": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "DiseaseState": {"Sporadic Alzheimers Disease": true}, "Confidence": {"High": true}}, "source": 3593, "target": 3823, "key": "53a1fc7a11e8c710b24d83c9c15f462a"}, {"line": 44792, "relation": "positiveCorrelation", "evidence": "Levels of BACE1 protein, enzymatic activity and beta-CTF elevate with age in the cerebrum, suggesting a functional role of BACE1 in Abeta overproduction.", "citation": {"db": "PubMed", "db_id": "21725719"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3593, "target": 80, "key": "97da33758634ecc52e5f01b3be4299fd"}, {"line": 44806, "relation": "positiveCorrelation", "evidence": "beta-secretase-1 (BACE1) elevation relative to Abeta accumulation and synaptic/neuritic alterations in the forebrain, using transgenic mice harboring familial AD (FAD) mutations (5XFAD and 2XFAD) as models", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "DiseaseState": {"Familial Alzheimers Disease": true}, "KnockoutMice": {"App transgenic": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3593, "target": 80, "key": "0afef9bd59584794a9db53115b72cb22"}, {"line": 44815, "relation": "increases", "evidence": "BACE1 initiates the amyloidogenic processing of APP", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "KnockoutMice": {"App transgenic": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3593, "target": 80, "key": "cf87950f808c2718163d12f3fc53927e"}, {"line": 44807, "relation": "positiveCorrelation", "evidence": "beta-secretase-1 (BACE1) elevation relative to Abeta accumulation and synaptic/neuritic alterations in the forebrain, using transgenic mice harboring familial AD (FAD) mutations (5XFAD and 2XFAD) as models", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "DiseaseState": {"Familial Alzheimers Disease": true}, "KnockoutMice": {"App transgenic": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3593, "target": 3881, "key": "a8947a3560351081e94dc1674edeee1c"}, {"line": 44808, "relation": "orthologous", "evidence": "beta-secretase-1 (BACE1) elevation relative to Abeta accumulation and synaptic/neuritic alterations in the forebrain, using transgenic mice harboring familial AD (FAD) mutations (5XFAD and 2XFAD) as models", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "DiseaseState": {"Familial Alzheimers Disease": true}, "KnockoutMice": {"App transgenic": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3593, "target": 2375, "key": "047202fed1e47367344322c9a0c86aa1"}, {"line": 45177, "relation": "increases", "evidence": "BACE1 and BACE2, are involved in the development of Alzheimer's disease by producing Abeta", "citation": {"db": "PubMed", "db_id": "22166205"}, "annotations": {"Species": {"10090": true}}, "source": 3593, "target": 2328, "key": "124102061b6795dd80d16bc909a313e3"}, {"line": 11742, "relation": "increases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "GSK3 subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3610, "target": 3586, "key": "e8bde700636061b622eb052a2566bec1"}, {"line": 34646, "relation": "decreases", "evidence": "Cyclin-dependent kinase 5 and its activator p35 disrupt Munc18a-syntaxin 1 binding, thereby promoting synaptic vesicle fusion during exocytosis. We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3610, "target": 1650, "key": "3dae305118e915ba4cd9ed026d29b5be"}, {"line": 34648, "relation": "increases", "evidence": "Cyclin-dependent kinase 5 and its activator p35 disrupt Munc18a-syntaxin 1 binding, thereby promoting synaptic vesicle fusion during exocytosis. We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3610, "target": 791, "key": "9c1706b1417e84bfd953146b5fce26d0"}, {"line": 34652, "relation": "positiveCorrelation", "evidence": "We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3610, "target": 3823, "key": "fa631ca6f193230c74f7ff7ad38e984e"}, {"line": 11743, "relation": "increases", "evidence": "Cerebrolysin is a peptide mixture with neurotrophic effects that might reduce the neurodegenerative pathology in Alzheimer's disease (AD). Cerebrolysin might reduce amyloid deposition by regulating amyloid-beta (Abeta) degradation or by modulating APP expression, maturation, or processing. To investigate these possibilities, APP tg mice were treated for 6 months with Cerebrolysin and analyzed in the water maze, followed by RNA, immunoblot, and confocal microscopy analysis of full-length (FL) APP and its fragments, beta-secretase (BACE1), and Abeta-degrading enzymes [neprilysin (Nep) and insulin-degrading enzyme (IDE)]. Consistent with previous studies, Cerebrolysin ameliorated the performance deficits in the spatial learning portion of the water maze and reduced the synaptic pathology and amyloid burden in the brains of APP tg mice. These effects were associated with reduced levels of FL APP and APP C-terminal fragments, but levels of BACE1, Notch1, Nep, and IDE were unchanged. In contrast, levels of active cyclin-dependent kinase-5 (CDK5) and glycogen synthase kinase-3beta [GSK-3beta; but not stress-activated protein kinase-1 (SAPK1)], kinases that phosphorylate APP, were reduced. Furthermore, Cerebrolysin reduced the levels of phosphorylated APP and the accumulation of APP in the neuritic processes. Taken together, these results suggest that Cerebrolysin might reduce AD-like pathology in the APP tg mice by regulating APP maturation and transport to sites where Abeta protein is generated.", "citation": {"db": "PubMed", "db_id": "16511867"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Cyclin-CDK subgraph": true, "GSK3 subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3640, "target": 3586, "key": "eab3508c16770cf6c37ad3f95b1d5edc"}, {"line": 35578, "relation": "negativeCorrelation", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3640, "target": 2162, "key": "71c5097d3d7e3143cdb415afbfcee4cc"}, {"line": 35609, "relation": "increases", "evidence": "In addition, AICD-induced cytotoxicity may be mediated by its regulation targets. For example, P53 expression, as well as p53-mediated apoptotic process, can be enhanced by AICD. Another AICD target gene, GSK3-beta, may also contribute to AICD-related cytotoxicity by upregulating tau hyperphosphorylation. GSK-3beta activation and CRMP-2 phosphorylation, along with downstream tau hyper-phosphorylation/aggregation, neurodegeneration and memory loss, are observed in an AICD C59 transgenic mouse line in which Fe65 is co-expressed", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3640, "target": 3676, "key": "42a4c9c4e425a15a576cb885e792031a"}, {"relation": "isA", "source": 2772, "target": 2770, "key": "f61b8a2ae69d414e6d245145614221a0"}, {"line": 36612, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2772, "target": 384, "key": "c628444a8e5329a1ab13d04cceece250"}, {"line": 37593, "relation": "association", "evidence": "APP is expressed pre- and postsynaptically and promotes synapse formation via trans-synaptic interactions of its extracellular domains. Full-length APP also may promote dendritic spine formation as well as surface expression of GluA2-containing AMPA receptors and GluN2B-containing NMDA receptors. Enhanced synaptic activity drives APP processing via the amyloidogenic ß -secretase pathway, leading to subsequent spine loss and downregulation of glutamate receptors in a negative feedback loop.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2772, "target": 2315, "key": "b9128a888b5c57d1639e1fe8bfbde38e"}, {"line": 37601, "relation": "association", "evidence": "APP is expressed pre- and postsynaptically and promotes synapse formation via trans-synaptic interactions of its extracellular domains. Full-length APP also may promote dendritic spine formation as well as surface expression of GluA2-containing AMPA receptors and GluN2B-containing NMDA receptors. Enhanced synaptic activity drives APP processing via the amyloidogenic ß -secretase pathway, leading to subsequent spine loss and downregulation of glutamate receptors in a negative feedback loop.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 2772, "target": 787, "key": "38b46ae339eb29ac55605635914844a8"}, {"line": 37625, "relation": "increases", "evidence": "Interestingly, we found that APP also affects excitatory synaptic transmission by altering AMPA receptor (AMPAR) and NMDA receptor (NMDAR) trafficking. Recently, we demonstrated that APP increases cell surface levels of the GluA2 (or GluR2) subunit of AMPA receptors (or GluAs), but does not alter levels of GluA1 (or GluR1), suggesting that APP regulates certain AMPAR subunits, specifically GluA2. Considering that alterations in AMPAR subunit expression (particularly in the synaptic content of GluA2-containing AMPARs) can impact synaptic transmission and plasticity, these changes may also potentially alter the function of excitatory synapses. The increase in GluA2 levels is expected to enhance excitatory synaptic transmission, especially because it occurred in the absence of a decrease in GluA1, suggesting an overall increase in AMPAR number at synapses.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2772, "target": 790, "key": "d8e8ee22507411f440acd08b29940668"}, {"relation": "isA", "source": 2773, "target": 2770, "key": "1ccc12f9c1fae9e86748231b28bd276e"}, {"line": 30223, "relation": "association", "evidence": "Glutamate receptors play crucial roles in cognition and memory.", "citation": {"db": "PubMed", "db_id": "10588576"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2773, "target": 812, "key": "77bbf6bd3d91a639f2fae9b3f2b5cfbc"}, {"line": 30224, "relation": "association", "evidence": "Glutamate receptors play crucial roles in cognition and memory.", "citation": {"db": "PubMed", "db_id": "10588576"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 2773, "target": 820, "key": "dc07437228522582ac7ff2fc5979597c"}, {"line": 36614, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2773, "target": 384, "key": "ad97e09c4e1e99b0fdff56b4c87228a3"}, {"line": 37317, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 2773, "target": 763, "key": "46839c8af3771e568b255e4422aaed35"}, {"relation": "isA", "source": 2774, "target": 2770, "key": "5a599ffc8fb4ca40c46ef44140854187"}, {"line": 36616, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2774, "target": 384, "key": "a344fc32791b4f6737634af2cee325ff"}, {"line": 11836, "relation": "positiveCorrelation", "evidence": "Cerebrolysin was generally well tolerated in clinical trials, with dizziness (or vertigo) being the most frequently reported adverse event.", "citation": {"db": "PubMed", "db_id": "20155999"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3904, "target": 434, "key": "5f0b0527be976f33fa866bb1afa7c930"}, {"line": 11884, "relation": "isA", "evidence": "Deficit in central cholinergic neurotransmission is a consistent change associated with Alzheimer's disease (AD). Donepezil hydrochloride exhibits selective inhibition of acetylcholinesterase (AChE) and is widely used for the treatment of AD.", "citation": {"db": "PubMed", "db_id": "18070217"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 244, "target": 39, "key": "e0860f146f1296bec0285b9a7a59ac8e"}, {"line": 11950, "relation": "isA", "evidence": "Alzheimer disease (AD) is associated with a decreased APP forms ratio (APPr) between the three major forms. This study demonstrated that donepezil, a drug acting as an inhibitor of acetylcholinesterase, affects APP metabolism in Alzheimer disease, restoring the ratio of APP forms in platelets up to the normal range after 4 weeks of treatment.", "citation": {"db": "PubMed", "db_id": "12007670"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 244, "target": 39, "key": "c772186052585563e298f49b16fe2619"}, {"line": 11888, "relation": "decreases", "evidence": "Deficit in central cholinergic neurotransmission is a consistent change associated with Alzheimer's disease (AD). Donepezil hydrochloride exhibits selective inhibition of acetylcholinesterase (AChE) and is widely used for the treatment of AD.", "citation": {"db": "PubMed", "db_id": "18070217"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 244, "target": 2244, "key": "6adbee6d7e307b8a697becaa5c114e04"}, {"line": 11954, "relation": "directlyDecreases", "evidence": "Alzheimer disease (AD) is associated with a decreased APP forms ratio (APPr) between the three major forms. This study demonstrated that donepezil, a drug acting as an inhibitor of acetylcholinesterase, affects APP metabolism in Alzheimer disease, restoring the ratio of APP forms in platelets up to the normal range after 4 weeks of treatment.", "citation": {"db": "PubMed", "db_id": "12007670"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2244, "key": "b1364f2846bf9d0d4433c1816c2436a5"}, {"line": 11899, "relation": "increases", "evidence": "Donepezil (10 mg/d) increased cerebral blood flow velocity and MMSE score in our AD patients, but more extensive trials are recommended.", "citation": {"db": "PubMed", "db_id": "20831025"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}}, "source": 244, "target": 485, "key": "be10cd0f6567cf75a427af122bb774c2"}, {"line": 11912, "relation": "increases", "evidence": "Donepezil 5 and 10 mg/day significantly improved cognition and global clinical function compared with placebo in well designed short term trials (14 to 30 weeks) in 161 to 818 patients with mild to moderate Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18070217"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 244, "target": 812, "key": "e8419a110d5446effd41a363c54d6413"}, {"line": 11928, "relation": "increases", "evidence": "The donepezil group demonstrated a greater likelihood of increases in both non-declarative and declarative processes.", "citation": {"db": "PubMed", "db_id": "17627484"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 244, "target": 820, "key": "10a30bd38b46b99248053ec3d6876c96"}, {"line": 11946, "relation": "regulates", "evidence": "Alzheimer disease (AD) is associated with a decreased APP forms ratio (APPr) between the three major forms. This study demonstrated that donepezil, a drug acting as an inhibitor of acetylcholinesterase, affects APP metabolism in Alzheimer disease, restoring the ratio of APP forms in platelets up to the normal range after 4 weeks of treatment.", "citation": {"db": "PubMed", "db_id": "12007670"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 244, "target": 476, "key": "32abdf66d0deaac545422c8d14714789"}, {"line": 11974, "relation": "decreases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Tau protein subgraph": true}, "Confidence": {"Medium": true}}, "source": 244, "target": 2129, "key": "7ca6a20cb3d2bbd6fc7f783e1be6dc68"}, {"line": 11981, "relation": "decreases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2794, "key": "0c21483c6d393947ffcd7a888774ee6f"}, {"line": 11987, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2516, "key": "4187e13ba8938e55d42e5a056b87fdc5"}, {"line": 11991, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2517, "key": "e0cd3475a25a92333ea05f3183e46911"}, {"line": 11995, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2518, "key": "8d4fdf6e4f896104a17b2c7ebfcd4485"}, {"line": 11999, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2520, "key": "736e4cfcd4fcaf62c49ba57dfde8aec5"}, {"line": 12003, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2521, "key": "96c0981366e3295889e932585d9bdc79"}, {"line": 12007, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2522, "key": "89d457dba820855ba74016dfe7de6cfe"}, {"line": 12011, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2523, "key": "310a790d44ddb928583a83a6041f5912"}, {"line": 12015, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2515, "key": "c0a0e3ef4ef433154e4aa5ded07edcc1"}, {"line": 12019, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2524, "key": "80f739a3f29c6ac46421788cac44225f"}, {"line": 12023, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2526, "key": "2f95e0ffb17af730b8730048fcbbe6d7"}, {"line": 12027, "relation": "increases", "evidence": "Taken together, our results suggest that the neuroprotective effects of donepezil against Abeta42-induced neurotoxicity are mediated through activation of PP2A, but its additional mechanisms including regulation of GSK-3beta and nAChRs activity would partially contribute to its effects. This observation led us to assume that additional mechanisms of donepezil, including its inhibitory effect on GSK-3beta activity and/or the activation role of nicotinic acetylcholine receptors (nAChRs), might be involved. Donepezil increased neuronal viability with reduced p-tau by enhancing PP2A activity.", "citation": {"db": "PubMed", "db_id": "23711227"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 2527, "key": "3fe3f9769093faa98ed742a5bdaa7b4c"}, {"line": 15305, "relation": "association", "evidence": "Donepezil is metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "19300564"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 244, "target": 606, "key": "ce2c0a5ab03c2229fe866b2389fec07f"}, {"line": 18654, "relation": "association", "evidence": "Cytochrome P450 (CYP) 2D6 enzyme is the major responsible for the metabolism of donepezil, an inhibitor of acetyl cholinesterase currently used for the symptomatic treatment of mild-to-moderate Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20859244"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Paroxetine subgraph": true}}, "source": 244, "target": 606, "key": "065a74661ef7259be57229bb464f46b3"}, {"line": 18643, "relation": "association", "evidence": "Our preliminary data suggest that the CYP2D6 polymorphism influences both donepezil metabolism and therapeutic outcome and that a knowledge of a patient's CYP2D6 genotype together with donepezil concentration measurements might be useful in the context of improving the clinical efficacy of donepezil therapy.", "citation": {"db": "PubMed", "db_id": "16845507"}, "annotations": {"Subgraph": {"Paroxetine subgraph": true}}, "source": 244, "target": 2611, "key": "2fb602c6cd735323a0dcce787a78d671"}, {"relation": "partOf", "source": 244, "target": 976, "key": "e9c892a8dd28a41e62b16b2b9dfcb0ca"}, {"line": 22841, "relation": "decreases", "evidence": "There was a significant increase in lipid peroxidation and nitrite in synaptosomal preparations. Preventivetreatment daily for 13 days with antidementic drugs, donepezil (5 mg/kg, p.o) and memantine(10 mg/kg, p.o), significantly attenuated OKA induced mitochondrial dysfunction, apoptotic cell death, memory impairment and histological changes. Mitochondrial dysfunction appeared as a key factor in OKA induced memory impairment and apoptotic cell death. OKA also increases Ca2+ in hippocampal neuronal cell culture.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 244, "target": 159, "key": "6a27f3565b81a7ad3e2ebaa169ca5e8b"}, {"line": 22880, "relation": "decreases", "evidence": "There was a significant (Pb0.01) increase in Ca2+ in hippocampus, cortex, striatum and cerebellum of OKA 200 ng treated rats as compared to control and vehicle treated rats.Treatment with memantine and donepezil significantly (P<0.01) reduced amount of Ca2+ in OKA treated rat brain regions", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 244, "target": 94, "key": "2e367701b3e8164a7827e8b574385255"}, {"line": 22897, "relation": "causesNoChange", "evidence": "Production of reactive oxygen species (ROS) in brain regions was measured relative to control. There was a significant increase (P<0.01) in ROS level in cerebellum, hippocampus, cortex and striatum of OKA 200 ng treated rats as compared to control group. Treatment with memantine significantly (Pb0.05) reduced the amount of ROS whereas donepezil did not show significant (PN0.05) effect in any brain regions.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Cerebellum": true, "Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 244, "target": 170, "key": "1264a9f28c6c6638a241c9de5282917a"}, {"line": 22916, "relation": "increases", "evidence": "There was a significant (Pb0.05) decrease in MMP in hippocampus and cortex of OKA 200 ng treated rats as compared to control and vehicle treated rats.Treatment with memantine significantly (P<0.05) increased MMP in cortex and hippocampus whereas donepezil significantly (Pb0.05) increased MMP in cortex, hippocampus and striatum as compared to OKA 200 ng treated rat.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 244, "target": 682, "key": "cfdf77ca530702b8443ff5245cedc262"}, {"line": 22928, "relation": "decreases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-3 was observed in hippocampus, striatum and cortex of OKA treated rat brain in comparison to that of control and vehicle groups. Treatment with memantine and donepezil significantly (P<0.05) decreased caspase-3 activity and mRNA level in hippocampus, striatum and cortex of OKA 200 ng treated rat brain", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 3755, "key": "0694df455be4dcf3fc08e9c7e2fd4287"}, {"line": 22934, "relation": "decreases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-3 was observed in hippocampus, striatum and cortex of OKA treated rat brain in comparison to that of control and vehicle groups. Treatment with memantine and donepezil significantly (P<0.05) decreased caspase-3 activity and mRNA level in hippocampus, striatum and cortex of OKA 200 ng treated rat brain", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 244, "target": 4082, "key": "0f47a64ebe209320a265228c4642775f"}, {"line": 22946, "relation": "decreases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-9 was observed in hippocampus and cortex of OKA treated rats as compared to that of control and vehicle group. Treatment with memantine and donepezil significantly (P<0.01) decreased caspase-9 activity and mRNA expression in hippocampus and cortex as compared to OKA 200 ng treated group (Fig. 10A and B).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 244, "target": 3756, "key": "e6eb429f2410fb355b0edf88c49bad0b"}, {"line": 22952, "relation": "decreases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-9 was observed in hippocampus and cortex of OKA treated rats as compared to that of control and vehicle group. Treatment with memantine and donepezil significantly (P<0.01) decreased caspase-9 activity and mRNA expression in hippocampus and cortex as compared to OKA 200 ng treated group (Fig. 10A and B).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 244, "target": 4083, "key": "6dd6c859427218ee2b6a66a77fa440e1"}, {"line": 22979, "relation": "decreases", "evidence": "Recently, we have reported that intracerebroventricular (ICV) administration of okadaic acid (OKA) in rats induces memory impairment that was associated with increased oxidative stress. Besides memory deficit, OKA caused impairment in mitochondrial function as revealed by alterations in calcium ion, reactive oxygen species (ROS) generation, mitochondrial membrane potential (MMP), SDH activity and ATP level in the brain regions. Further, in histopathological study it was observed that donepezil and memantine reduced the cell loss and neurodegeneration in hippocampus and periventricular cortex regions in OKA treated rats.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 244, "target": 648, "key": "05186eefbb9e4eff4bb5a3231f7eec81"}, {"line": 23273, "relation": "increases", "evidence": "donepezil inhibits the reaction [Hydrogen Peroxide results in decreased expression of BCL2 protein]", "citation": {"db": "PubMed", "db_id": "19345205"}, "annotations": {"Subgraph": {"Bcl-2 subgraph": true}}, "source": 244, "target": 2393, "key": "7a220a4096f73267480ed6ba4371140c"}, {"line": 23278, "relation": "increases", "evidence": "donepezil inhibits the reaction [Hydrogen Peroxide results in decreased expression of XIAP protein]", "citation": {"db": "PubMed", "db_id": "19345205"}, "annotations": {"Subgraph": {"XIAP subgraph": true}}, "source": 244, "target": 3539, "key": "53ee3cd81123f46564cbdc2ee9c30dc6"}, {"line": 11961, "relation": "increases", "evidence": "in superfused rat cortical brain slices, AChEIs have been shown to increase sAPPalpha release with a pattern related to the AChEIs dose.", "citation": {"db": "PubMed", "db_id": "12007670"}, "source": 39, "target": 2137, "key": "7d985eb0d49c3373bd253b9525da7460"}, {"line": 13004, "relation": "increases", "evidence": "Currently available treatments for Alzheimer's disease (EC 3.1.1.7 (acetylcholinesterase) inhibitors and memantine) improve cognitive function, but do not slow the long-term progression of this disorder.", "citation": {"db": "PubMed", "db_id": "20170836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 39, "target": 812, "key": "879424b7e22d38edc5c8bac4bea439fb"}, {"line": 13469, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2516, "target": 117, "key": "2ba92bf9488077e41b3b8474b3860ae1"}, {"line": 13513, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2516, "target": 122, "key": "bf076a896b307978db06dbcc5b635ead"}, {"line": 14198, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2516, "target": 204, "key": "746c977f6db17edbeb4f214c4ae8d853"}, {"line": 37188, "relation": "regulates", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2516, "target": 2328, "key": "23ef820af4223ce4e0ee9bbb9036c1e5"}, {"line": 37211, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2516, "target": 492, "key": "43f6b279d799262aa23520986787f902"}, {"line": 13473, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2517, "target": 117, "key": "f92f305104db8cbea45a89c3a829a517"}, {"line": 13517, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2517, "target": 122, "key": "7d5e2f235bf69dada3f2e7b6b60f8f02"}, {"line": 14202, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2517, "target": 204, "key": "b7c1182dbb077f5ddb867e1212820607"}, {"line": 39339, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2517, "target": 3815, "key": "99b5a8982bab079277947d579173888c"}, {"line": 13481, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2520, "target": 117, "key": "0d512d6ce1ad13703c6d8bb5c3ce3d0d"}, {"line": 13525, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2520, "target": 122, "key": "9100101965a4be4d19292e00a41db3ef"}, {"line": 14210, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2520, "target": 204, "key": "f33bd00ec822910f9079e0307aecdf97"}, {"line": 37199, "relation": "regulates", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2520, "target": 2328, "key": "5c6c041932ec60c32531d192394b8788"}, {"line": 37223, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2520, "target": 492, "key": "224b9eedf8d23dc68a1e4319fc4ab22c"}, {"line": 39335, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2520, "target": 3815, "key": "d2970cfa430586d8e5580ba58539a90c"}, {"line": 13485, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2521, "target": 117, "key": "0168503fe214d26ad627a06d93e37ccd"}, {"line": 13529, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2521, "target": 122, "key": "cece1e24a8ae67d1ce191a050d758401"}, {"line": 14214, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2521, "target": 204, "key": "f22cac1562bd6b806cbe2e368e7f7d90"}, {"line": 37219, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2521, "target": 492, "key": "a509db1d61a8235f888226d74955938d"}, {"line": 13489, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2522, "target": 117, "key": "213b470d32e9104b54bbc010db0c1347"}, {"line": 13532, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2522, "target": 122, "key": "246f11cdad899656e04c59bd600d5cd8"}, {"line": 14218, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2522, "target": 204, "key": "c44033dcd0d09693f838ee69feb82038"}, {"relation": "partOf", "source": 2522, "target": 921, "key": "2f976c7e1821ff85809513702c85e745"}, {"line": 37207, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2522, "target": 492, "key": "fcdffc5e0cb0779d6e93fe41fa738b55"}, {"line": 39343, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2522, "target": 3815, "key": "c69bb443e20d36e5c8624722f7d2e95a"}, {"line": 13493, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2523, "target": 117, "key": "7fd839099e033aff139bd0c7762574a8"}, {"line": 13536, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2523, "target": 122, "key": "00af6f3275e59b7c122f5fd5a6339333"}, {"line": 14222, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2523, "target": 204, "key": "65a0b77b62938e0ec4425abfd7e29da9"}, {"line": 37192, "relation": "regulates", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2523, "target": 2328, "key": "565e2960db502e3863c5be77cffa474d"}, {"line": 37215, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2523, "target": 492, "key": "4221cfb28e32c340547c947d43e40ea4"}, {"line": 13497, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2515, "target": 117, "key": "490c2b60a9f32923b2ee7429a4ec3aee"}, {"line": 13540, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2515, "target": 122, "key": "16ba66985c2746d2d0a3b37d3525e7af"}, {"line": 14226, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2515, "target": 204, "key": "322f876b08b569d508c96e8be3b3af58"}, {"line": 13505, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2526, "target": 122, "key": "6775b4f8482969a66e181298484a5a7b"}, {"line": 14234, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2526, "target": 204, "key": "482cc30003d0ac860a638981422619c6"}, {"line": 37231, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2526, "target": 492, "key": "3ae9ae57ecc03220bb9ca6b9c68b2334"}, {"line": 13509, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2527, "target": 122, "key": "fd4b936f1ece3a2877229f551edeb407"}, {"line": 14238, "relation": "association", "evidence": "On a molecular level, studies suggest that acetylcholine (ACh) increases serotonin (5-HT) release through nicotinic receptors located at nerve terminals.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2527, "target": 204, "key": "79cbeb0c18a1cbcced06ae124c8d82da"}, {"line": 12040, "relation": "isA", "evidence": "Rivastigmine tartrate is a reversible cholinesterase inhibitor indicated for the symptomatic treatment of mild to moderate dementia. Rivastigmine has been shown to improve or maintain patients' performance in 3 major domains: cognitive function, global function (ADLs), and behavior.", "citation": {"db": "PubMed", "db_id": "12860489"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 39, "key": "117b815547b5fcdc7b7dece840aaf1d9"}, {"line": 12044, "relation": "increases", "evidence": "Rivastigmine tartrate is a reversible cholinesterase inhibitor indicated for the symptomatic treatment of mild to moderate dementia. Rivastigmine has been shown to improve or maintain patients' performance in 3 major domains: cognitive function, global function (ADLs), and behavior.", "citation": {"db": "PubMed", "db_id": "12860489"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 812, "key": "06ff75cdf5d0762574ce702bd5224470"}, {"line": 12048, "relation": "increases", "evidence": "Rivastigmine tartrate is a reversible cholinesterase inhibitor indicated for the symptomatic treatment of mild to moderate dementia. Rivastigmine has been shown to improve or maintain patients' performance in 3 major domains: cognitive function, global function (ADLs), and behavior.", "citation": {"db": "PubMed", "db_id": "12860489"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 810, "key": "9ecc17f4c848a7a9fd138140bb977173"}, {"line": 12061, "relation": "decreases", "evidence": "Carboxylesterases (CEs) are ubiquitously expressed proteins that are responsible for the detoxification of xenobiotics. Due to the conable structural similarity between cholinesterases (ChE) and CEs, we have assessed the ability of a series of ChE inhibitors to modulate the activity of the human liver (hCE1) and the human intestinal CE (hiCE) isoforms. For example, rivastigmine resulted in greater than 95% inhibition of hiCE that was irreversible under the conditions used. Hence, the administration of esterified drugs, in combination with these carbamates, may inadvertently result in decreased hydrolysis of the former, thereby limiting their efficacy. Given that hCE1 and the ChEs demonstrate considerable structural homology, we assessed the ability of panel of known AChE and BChE inhibitors to modulate CE activity. However, because carbamate-containing compounds can irreversibly inhibit esterases [3], we evaluated the ability of a selected panel of these molecules to inactivate the human CEs. All of the compounds, with the exception of donepezil, demonstrated activity toward hiCE (Table 3). Indeed for tolserine and rivastigmine, significant loss of enzyme activity was observed.", "citation": {"db": "PubMed", "db_id": "23123248"}, "object": {"modifier": "Activity"}, "source": 344, "target": 2503, "key": "ae7d3e6be8fc16e4988e41dd818fe504"}, {"line": 12062, "relation": "decreases", "evidence": "Carboxylesterases (CEs) are ubiquitously expressed proteins that are responsible for the detoxification of xenobiotics. Due to the conable structural similarity between cholinesterases (ChE) and CEs, we have assessed the ability of a series of ChE inhibitors to modulate the activity of the human liver (hCE1) and the human intestinal CE (hiCE) isoforms. For example, rivastigmine resulted in greater than 95% inhibition of hiCE that was irreversible under the conditions used. Hence, the administration of esterified drugs, in combination with these carbamates, may inadvertently result in decreased hydrolysis of the former, thereby limiting their efficacy. Given that hCE1 and the ChEs demonstrate considerable structural homology, we assessed the ability of panel of known AChE and BChE inhibitors to modulate CE activity. However, because carbamate-containing compounds can irreversibly inhibit esterases [3], we evaluated the ability of a selected panel of these molecules to inactivate the human CEs. All of the compounds, with the exception of donepezil, demonstrated activity toward hiCE (Table 3). Indeed for tolserine and rivastigmine, significant loss of enzyme activity was observed.", "citation": {"db": "PubMed", "db_id": "23123248"}, "object": {"modifier": "Activity"}, "source": 344, "target": 2504, "key": "ff449112b25763b819388bfe82a9a7e7"}, {"line": 14253, "relation": "increases", "evidence": "Rivastigmine significantly increased c-Fos immunoreactivity in medial prefrontal cortex and the hippocampus, but not in the septum and dorsal raphe nucleus.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Prefrontal Cortex": true, "Raphe Nuclei": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 2699, "key": "392617ea6576ed22aec3eed8a300651b"}, {"line": 14254, "relation": "association", "evidence": "Rivastigmine significantly increased c-Fos immunoreactivity in medial prefrontal cortex and the hippocampus, but not in the septum and dorsal raphe nucleus.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Prefrontal Cortex": true, "Raphe Nuclei": true}, "Confidence": {"High": true}}, "source": 344, "target": 2699, "key": "1a39c9a4257cb7ce2a0505212eb0eb3c"}, {"line": 19654, "relation": "decreases", "evidence": "Depending on the treatment received, a distinct inflammatory and oxidative stress profile was observed: in Rivastigmine-treated group, IL6 levels were 47% lower than the average value of the remaining AD patients; homocysteine and glutathione reductase were statistically unchanged in the Rivastigmine and Donepezil-Memantine, respectively Donepezil group.", "citation": {"db": "PubMed", "db_id": "23871825"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 344, "target": 2894, "key": "cca5223ec4569848617634be419b15ae"}, {"line": 24147, "relation": "decreases", "evidence": "Rivastigmine (1 mg/kg) also reduced myeloperoxidase activity and IL-6 by >60%, and the infiltration of CD11b expressing cells by 80%. These effects were accompanied by significantly greater ChE inhibition in cortex, brain stem, plasma and colon than that after 0.5 mg/kg.", "citation": {"db": "PubMed", "db_id": "23469045"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 2894, "key": "7ac35c363233030fcbfed3e0f549d2c1"}, {"line": 19657, "relation": "causesNoChange", "evidence": "Depending on the treatment received, a distinct inflammatory and oxidative stress profile was observed: in Rivastigmine-treated group, IL6 levels were 47% lower than the average value of the remaining AD patients; homocysteine and glutathione reductase were statistically unchanged in the Rivastigmine and Donepezil-Memantine, respectively Donepezil group.", "citation": {"db": "PubMed", "db_id": "23871825"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Glutathione reductase subgraph": true}}, "source": 344, "target": 2798, "key": "06740a16d1f7aa20eb1c3a923ffd2a35"}, {"line": 23678, "relation": "increases", "evidence": "We confirmed that higher initial disease severity (higher ADAS-Cog scores) and the increase in the con-centration of plasma Abeta(1-42) peptide following 2 weeks of treatment with an initial dose of rivastigmine", "citation": {"db": "PubMed", "db_id": "20054753"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2328, "key": "e1a4c5161c3225e0fcd6a8a37606c6b2"}, {"line": 23716, "relation": "increases", "evidence": "Over the last several years a number of reports have emerged suggesting that at least some ChEI might take part in betaAPP metabolism, influencing its secretion and Abeta differential cleavage. Moreover, we (Sob6w and Kloszewska 2005) and others (Basun et al. 2002, Zimmermann et al. 2005) have shown that the treatment with ChEI might influence BAPP metabolism in AD patients as measured by changes in plasma (including platelet-derived) metabolites. In our previous pilot study we have demonstrated that short-term treatment with ChEI rivastigmine exhibits a significant effect on plasma concentrations of Abeta-42 (mean increase after treatment reached 7.8 ± 8.4 pg/ml) with a negative correlation to patients age, while no changes in Abeta-40 levels were detected.", "citation": {"db": "PubMed", "db_id": "17691220"}, "annotations": {"Species": {"10116": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 344, "target": 2328, "key": "5d05e59f65a2a23f45ebe18dd70cc340"}, {"line": 23695, "relation": "decreases", "evidence": "In conclusion, inhibition of both BuChE and AChE with rivastigmine was improved the cognition without affecting on the peripheral IR in the elderly patients with LOAD by HOMA.", "citation": {"db": "PubMed", "db_id": "22323348"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2244, "key": "17edca01b08811cf06ee68dbc7b7a6a7"}, {"line": 23967, "relation": "decreases", "evidence": "Inhibition of acetylcholinesterase activity by rivastigmine decreases lipopolysaccharide-induced IL-1beta expression in the hypothalamus of ewes", "citation": {"db": "PubMed", "db_id": "23291013"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2244, "key": "6e4b6955aed5f1366e51406e11055889"}, {"line": 23699, "relation": "decreases", "evidence": "In conclusion, inhibition of both BuChE and AChE with rivastigmine was improved the cognition without affecting on the peripheral IR in the elderly patients with LOAD by HOMA.", "citation": {"db": "PubMed", "db_id": "22323348"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2392, "key": "7f7285d8e47fbdb99d0b3b23ac329da4"}, {"line": 23713, "relation": "association", "evidence": "Over the last several years a number of reports have emerged suggesting that at least some ChEI might take part in betaAPP metabolism, influencing its secretion and Abeta differential cleavage. Moreover, we (Sob6w and Kloszewska 2005) and others (Basun et al. 2002, Zimmermann et al. 2005) have shown that the treatment with ChEI might influence BAPP metabolism in AD patients as measured by changes in plasma (including platelet-derived) metabolites. In our previous pilot study we have demonstrated that short-term treatment with ChEI rivastigmine exhibits a significant effect on plasma concentrations of Abeta-42 (mean increase after treatment reached 7.8 ± 8.4 pg/ml) with a negative correlation to patients age, while no changes in Abeta-40 levels were detected.", "citation": {"db": "PubMed", "db_id": "17691220"}, "annotations": {"Species": {"10116": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 344, "target": 2315, "key": "6823eea31c655001f1ce18152ec80d35"}, {"line": 23770, "relation": "increases", "evidence": "Rivastigmine treatment induced an elevation in both metabolic activity and APP secretion, and differentially impacted two isoforms of sAPP. In our cultures, low molecular weight sAPP corresponded closely to neuronal viability, and high molecular weight sAPP corresponded with glial proliferation. Our results suggest that within the mixed culture system used, neurons are the primary source of sAPP, and rivastigmine's actions are mediated principally through neuronal, rather than glial, targets. Importantly, rivastigmine was found to increase the neurotrophic sAPPα and decrease Abeta secretion, suggesting a mechanism for the previously observed neuropreservation effects.", "citation": {"db": "PubMed", "db_id": "21799757"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 344, "target": 2315, "key": "080a2d793eee3f7b64cf257622fd4896"}, {"line": 23717, "relation": "causesNoChange", "evidence": "Over the last several years a number of reports have emerged suggesting that at least some ChEI might take part in betaAPP metabolism, influencing its secretion and Abeta differential cleavage. Moreover, we (Sob6w and Kloszewska 2005) and others (Basun et al. 2002, Zimmermann et al. 2005) have shown that the treatment with ChEI might influence BAPP metabolism in AD patients as measured by changes in plasma (including platelet-derived) metabolites. In our previous pilot study we have demonstrated that short-term treatment with ChEI rivastigmine exhibits a significant effect on plasma concentrations of Abeta-42 (mean increase after treatment reached 7.8 ± 8.4 pg/ml) with a negative correlation to patients age, while no changes in Abeta-40 levels were detected.", "citation": {"db": "PubMed", "db_id": "17691220"}, "annotations": {"Species": {"10116": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 344, "target": 2327, "key": "ee8e4c7a5710886fed88957d5c45546e"}, {"line": 23777, "relation": "increases", "evidence": "Rivastigmine treatment induced an elevation in both metabolic activity and APP secretion, and differentially impacted two isoforms of sAPP. In our cultures, low molecular weight sAPP corresponded closely to neuronal viability, and high molecular weight sAPP corresponded with glial proliferation. Our results suggest that within the mixed culture system used, neurons are the primary source of sAPP, and rivastigmine's actions are mediated principally through neuronal, rather than glial, targets. Importantly, rivastigmine was found to increase the neurotrophic sAPPα and decrease Abeta secretion, suggesting a mechanism for the previously observed neuropreservation effects.", "citation": {"db": "PubMed", "db_id": "21799757"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2137, "key": "b134e1c5dceade0848e0b82c27bfcd91"}, {"line": 23795, "relation": "increases", "evidence": "In a separate experiment, media samples were taken before the onset of neurodegeneration at day 12 and during the degenerating phase of the cell culture at day 16. The lower molecular weight neuronal form of sAPP declined significantly in untreated cells, but was rescued by 10 µM rivastigmine treatment. Rivastigmine treatment preserved neuronal structure.hus, rivastigmine treatment protected neurons from degeneration. Rivastigmine treatment increased the neuron-related low molecular weight form of sAPP. This is suggestive of a mechanism by which rivastigmine may protect neurons by enhancing sAPP production, which may protect neurons from neurite retraction [33] and apoptosis [30].", "citation": {"db": "PubMed", "db_id": "21799757"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2137, "key": "131d7d8ffdb1838329c607583925306c"}, {"line": 23785, "relation": "decreases", "evidence": "Rivastigmine treatment induced an elevation in both metabolic activity and APP secretion, and differentially impacted two isoforms of sAPP. In our cultures, low molecular weight sAPP corresponded closely to neuronal viability, and high molecular weight sAPP corresponded with glial proliferation. Our results suggest that within the mixed culture system used, neurons are the primary source of sAPP, and rivastigmine's actions are mediated principally through neuronal, rather than glial, targets. Importantly, rivastigmine was found to increase the neurotrophic sAPPα and decrease Abeta secretion, suggesting a mechanism for the previously observed neuropreservation effects.", "citation": {"db": "PubMed", "db_id": "21799757"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 344, "target": 80, "key": "eabd7dfc9efb86ec2e14681778394d77"}, {"line": 23807, "relation": "increases", "evidence": "Rivastigmine shifts APP processing toward the α-secretase pathway. Together, these results suggest that rivastigmine alters the activities of the α- and beta-secretase pathways in favor of sAPPα production. ", "citation": {"db": "PubMed", "db_id": "21799757"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 2249, "key": "343720c21f1c04f7b037dccc27873fda"}, {"line": 23829, "relation": "increases", "evidence": "There is a physiological decline of the growth hormone (GH)/insulin-like growth factor-I (IGF-I) axis with ageing and the possibility that the GH/ IGF-I axis is involved in cognitive deficits has been recognized for several years. The IGF-I is a potent neurotrophic as well neuroprotective factor found in the brain with a wide range of actions in both central and peripheral nervous system. IGF-I is a critical promoter of brain development and neuronal survival and plays a role in neuronal rescue during degenerative diseases.When a cholinesterase inhibitor as rivastigmine, a drug for AD, is acutely administered the area under the curve of the GH response to GHRH doubled, showing that rivastigmine is a powerful drug to enhance GH release. TNFα production may promote neurodegeneration not through direct killing of neurons but rather through inhibition of IGF-I survival signalling", "citation": {"db": "PubMed", "db_id": "22524398"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Neuroprotection subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 344, "target": 2871, "key": "f7eeeb4c1e376d53f1008c2623c937e4"}, {"line": 23874, "relation": "increases", "evidence": "Thus, we propose that elevated hippocampal ACh levels observed in OBX mice after rivastigmine treatment increase 5-HT levels and underlie neurogenesis via ERK and Akt activation in the DG. We observed inhibition of rivastigmine-induced phosphorylation of Akt (Ser-473) and ERK following WAY-100635 administration. We conclude that stimulation of the 5-HT1A receptor induced by rivastigmine accounts for hippocampal neurogenesis.", "citation": {"db": "PubMed", "db_id": "24797332"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 2990, "key": "90cda576f8d5d5f50530bfa3ca1b43c2"}, {"line": 23876, "relation": "increases", "evidence": "Thus, we propose that elevated hippocampal ACh levels observed in OBX mice after rivastigmine treatment increase 5-HT levels and underlie neurogenesis via ERK and Akt activation in the DG. We observed inhibition of rivastigmine-induced phosphorylation of Akt (Ser-473) and ERK following WAY-100635 administration. We conclude that stimulation of the 5-HT1A receptor induced by rivastigmine accounts for hippocampal neurogenesis.", "citation": {"db": "PubMed", "db_id": "24797332"}, "annotations": {"Subgraph": {"Akt subgraph": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 2279, "key": "5a913c835043492ecda1dd5d64c88535"}, {"line": 23878, "relation": "increases", "evidence": "Thus, we propose that elevated hippocampal ACh levels observed in OBX mice after rivastigmine treatment increase 5-HT levels and underlie neurogenesis via ERK and Akt activation in the DG. We observed inhibition of rivastigmine-induced phosphorylation of Akt (Ser-473) and ERK following WAY-100635 administration. We conclude that stimulation of the 5-HT1A receptor induced by rivastigmine accounts for hippocampal neurogenesis.", "citation": {"db": "PubMed", "db_id": "24797332"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 2857, "key": "9b16b724f2d521c0f320a732d5d2662a"}, {"line": 23889, "relation": "decreases", "evidence": "Rivastigmine decreased the reactivity of encephalitogenic T-cells and the production of pro-inflammatory cytokines (TNF-α, IFN-gamma and IL-17) without affecting IL-10 production.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 3472, "key": "1250a3b2579ab6e94018cd07ecda55e9"}, {"line": 23897, "relation": "decreases", "evidence": "Rivastigmine decreased the reactivity of encephalitogenic T-cells and the production of pro-inflammatory cytokines (TNF-α, IFN-gamma and IL-17) without affecting IL-10 production.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2870, "key": "bd05cf3ef1cc6718fb03dec82d1a4039"}, {"line": 23905, "relation": "decreases", "evidence": "Rivastigmine decreased the reactivity of encephalitogenic T-cells and the production of pro-inflammatory cytokines (TNF-α, IFN-gamma and IL-17) without affecting IL-10 production.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2882, "key": "cb3254ddcde68fd4365e8e4eeb793c80"}, {"line": 23906, "relation": "causesNoChange", "evidence": "Rivastigmine decreased the reactivity of encephalitogenic T-cells and the production of pro-inflammatory cytokines (TNF-α, IFN-gamma and IL-17) without affecting IL-10 production.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2878, "key": "a709dd9739bf9caee629d4072a25ec21"}, {"line": 23915, "relation": "decreases", "evidence": "Attenuation of CNS inflammation, demyelination and neuronal damage by rivastigmine treatment. In the rivastigmine-treated group only minimal inflammation was found. A comparison of the number of inflammatory infiltrates showed a 75% reduction in the treated group (from 42.4 ± 3.4 to 10.6 ± 7, p = 0.002, for the control and rivastigmine groups, respectively). Demyelination, the degree of activation and the number of microglia (from 8–15/10 high power fields in representative control untreated mice to 1–3 cells/10 high power fields in representative treated mice) were also reduced ( Fig. 2D, E). Cumulatively, these reduced parameters of inflammation culminated in decreased axonal loss and damage as manifested by neurofilament staining.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 3920, "key": "b6d864a563908530c087487f6b146f69"}, {"line": 23916, "relation": "decreases", "evidence": "Attenuation of CNS inflammation, demyelination and neuronal damage by rivastigmine treatment. In the rivastigmine-treated group only minimal inflammation was found. A comparison of the number of inflammatory infiltrates showed a 75% reduction in the treated group (from 42.4 ± 3.4 to 10.6 ± 7, p = 0.002, for the control and rivastigmine groups, respectively). Demyelination, the degree of activation and the number of microglia (from 8–15/10 high power fields in representative control untreated mice to 1–3 cells/10 high power fields in representative treated mice) were also reduced ( Fig. 2D, E). Cumulatively, these reduced parameters of inflammation culminated in decreased axonal loss and damage as manifested by neurofilament staining.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 634, "key": "4e8cf0f27521345b245cb262c536f030"}, {"line": 23924, "relation": "decreases", "evidence": "Attenuation of CNS inflammation, demyelination and neuronal damage by rivastigmine treatment. In the rivastigmine-treated group only minimal inflammation was found. A comparison of the number of inflammatory infiltrates showed a 75% reduction in the treated group (from 42.4 ± 3.4 to 10.6 ± 7, p = 0.002, for the control and rivastigmine groups, respectively). Demyelination, the degree of activation and the number of microglia (from 8–15/10 high power fields in representative control untreated mice to 1–3 cells/10 high power fields in representative treated mice) were also reduced ( Fig. 2D, E). Cumulatively, these reduced parameters of inflammation culminated in decreased axonal loss and damage as manifested by neurofilament staining.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 648, "key": "aab68111af432642b0ad7f0f942647f9"}, {"line": 23948, "relation": "increases", "evidence": "The cells were exposed to various concentrations of rivastigmine to determine whether the drug protected cells from toxic injury and induced the 1st phase of the cellular heat shock response. In all, 100-μmol/L rivastigmine decreases cell death by 40% compared with untreated cells. This concentration enhances Hsf1 activation by strengthening both its multimerization and its phosphorylation, which leads to increased messenger RNA (mRNA) for hsp70. Therefore, one of the putative neuroprotective mechanisms of rivastigmine seems to be mediated through the heat shock response.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 2844, "key": "e30aec7447287893ad5223b4e4c1ba5d"}, {"line": 23954, "relation": "increases", "evidence": "The cells were exposed to various concentrations of rivastigmine to determine whether the drug protected cells from toxic injury and induced the 1st phase of the cellular heat shock response. In all, 100-μmol/L rivastigmine decreases cell death by 40% compared with untreated cells. This concentration enhances Hsf1 activation by strengthening both its multimerization and its phosphorylation, which leads to increased messenger RNA (mRNA) for hsp70. Therefore, one of the putative neuroprotective mechanisms of rivastigmine seems to be mediated through the heat shock response.", "citation": {"db": "PubMed", "db_id": "18692909"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 772, "key": "27a54aba737389d717cff10d58c293c3"}, {"line": 23973, "relation": "decreases", "evidence": "Inhibition of acetylcholinesterase activity by rivastigmine decreases lipopolysaccharide-induced IL-1beta expression in the hypothalamus of ewes", "citation": {"db": "PubMed", "db_id": "23291013"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2885, "key": "2a2ac7204222054adc8280eb737b8e84"}, {"line": 23987, "relation": "decreases", "evidence": "Rivastigmine treatment decreased (P ≤ 0.01) LPS-induced elevation of IL-1beta and IL-1R1 mRNA expression in these structures ( Fig. 1). In contrast, we did not find any effect of immune stress or rivastigmine treatment on CHRNA7 gene expression in the hypothalamus. which showed that rivastigmine suppresses both the AChE activity and synthesis of IL-1beta in the hypothalamus", "citation": {"db": "PubMed", "db_id": "23291013"}, "annotations": {"MeSHAnatomy": {"Hypothalamus": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 2885, "key": "dfd26ebc1840bad37df5a4ef81fa5707"}, {"line": 23985, "relation": "decreases", "evidence": "Rivastigmine treatment decreased (P ≤ 0.01) LPS-induced elevation of IL-1beta and IL-1R1 mRNA expression in these structures ( Fig. 1). In contrast, we did not find any effect of immune stress or rivastigmine treatment on CHRNA7 gene expression in the hypothalamus. which showed that rivastigmine suppresses both the AChE activity and synthesis of IL-1beta in the hypothalamus", "citation": {"db": "PubMed", "db_id": "23291013"}, "annotations": {"MeSHAnatomy": {"Hypothalamus": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 3982, "key": "7be5e23ac8bdad71c6242409069f7a92"}, {"line": 23986, "relation": "decreases", "evidence": "Rivastigmine treatment decreased (P ≤ 0.01) LPS-induced elevation of IL-1beta and IL-1R1 mRNA expression in these structures ( Fig. 1). In contrast, we did not find any effect of immune stress or rivastigmine treatment on CHRNA7 gene expression in the hypothalamus. which showed that rivastigmine suppresses both the AChE activity and synthesis of IL-1beta in the hypothalamus", "citation": {"db": "PubMed", "db_id": "23291013"}, "annotations": {"MeSHAnatomy": {"Hypothalamus": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 3983, "key": "4601a3724eae818694cd10de9b27f6aa"}, {"line": 24004, "relation": "decreases", "evidence": "Rivastigmine (1 and 2 mg/kg, p.o.) administration attenuated lipid peroxidation, nitrite levels and restored the decrease levels of SOD and catalase activities in striatum, cortex and hippocampus areas of 3-NP treated rats as compared to vehicle treated rats", "citation": {"db": "PubMed", "db_id": "19445928"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 593, "key": "53cf7662203a08124a8c05a85026d3d7"}, {"line": 24010, "relation": "decreases", "evidence": "Rivastigmine (1 and 2 mg/kg, p.o.) administration attenuated lipid peroxidation, nitrite levels and restored the decrease levels of SOD and catalase activities in striatum, cortex and hippocampus areas of 3-NP treated rats as compared to vehicle treated rats", "citation": {"db": "PubMed", "db_id": "19445928"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "source": 344, "target": 315, "key": "7bb7a28ce8077c316a982c621d9b87f4"}, {"line": 24016, "relation": "increases", "evidence": "Rivastigmine (1 and 2 mg/kg, p.o.) administration attenuated lipid peroxidation, nitrite levels and restored the decrease levels of SOD and catalase activities in striatum, cortex and hippocampus areas of 3-NP treated rats as compared to vehicle treated rats", "citation": {"db": "PubMed", "db_id": "19445928"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 3806, "key": "a3f2f4d9233fcf42058d671cf1d00751"}, {"line": 24017, "relation": "increases", "evidence": "Rivastigmine (1 and 2 mg/kg, p.o.) administration attenuated lipid peroxidation, nitrite levels and restored the decrease levels of SOD and catalase activities in striatum, cortex and hippocampus areas of 3-NP treated rats as compared to vehicle treated rats", "citation": {"db": "PubMed", "db_id": "19445928"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 344, "target": 3807, "key": "48a728f1d0fab6681efa8b6b8e127ea7"}, {"line": 24027, "relation": "decreases", "evidence": "Whereas rivastigmine (1 and 2 mg/kg, p.o.) treatment significantly decreases lactate dehydrogenase activity in striatum, cortex and hippocampus regions of 3-NP treated rats ( Fig. 6) (P < 0.05).", "citation": {"db": "PubMed", "db_id": "19445928"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hypothalamus": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 3790, "key": "96583235cc6ab54e69c4de5d73713360"}, {"line": 24028, "relation": "decreases", "evidence": "Whereas rivastigmine (1 and 2 mg/kg, p.o.) treatment significantly decreases lactate dehydrogenase activity in striatum, cortex and hippocampus regions of 3-NP treated rats ( Fig. 6) (P < 0.05).", "citation": {"db": "PubMed", "db_id": "19445928"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hypothalamus": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 3791, "key": "d2d02d8da6f9507d30a007e46b6387db"}, {"line": 24029, "relation": "decreases", "evidence": "Whereas rivastigmine (1 and 2 mg/kg, p.o.) treatment significantly decreases lactate dehydrogenase activity in striatum, cortex and hippocampus regions of 3-NP treated rats ( Fig. 6) (P < 0.05).", "citation": {"db": "PubMed", "db_id": "19445928"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hypothalamus": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 3792, "key": "698424c0bd4789599b703998fcf44b46"}, {"line": 24127, "relation": "increases", "evidence": "Taken together, the stimulation of CaMKII activity in the hippocampus is essential for rivastigmine-induced memory improvement in OBX mice.", "citation": {"db": "PubMed", "db_id": "24164423"}, "object": {"modifier": "Activity"}, "source": 344, "target": 2145, "key": "fbd3994b118a9040e9c3aff18b9ede31"}, {"line": 24139, "relation": "decreases", "evidence": "Rivastigmine (1 mg/kg) also reduced myeloperoxidase activity and IL-6 by >60%, and the infiltration of CD11b expressing cells by 80%. These effects were accompanied by significantly greater ChE inhibition in cortex, brain stem, plasma and colon than that after 0.5 mg/kg.", "citation": {"db": "PubMed", "db_id": "23469045"}, "annotations": {"Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 344, "target": 3066, "key": "26e8dfdbec72cfe9bb7c490918af86ea"}, {"line": 12056, "relation": "decreases", "evidence": "Carboxylesterases (CEs) are ubiquitously expressed proteins that are responsible for the detoxification of xenobiotics. Due to the conable structural similarity between cholinesterases (ChE) and CEs, we have assessed the ability of a series of ChE inhibitors to modulate the activity of the human liver (hCE1) and the human intestinal CE (hiCE) isoforms. For example, rivastigmine resulted in greater than 95% inhibition of hiCE that was irreversible under the conditions used. Hence, the administration of esterified drugs, in combination with these carbamates, may inadvertently result in decreased hydrolysis of the former, thereby limiting their efficacy. Given that hCE1 and the ChEs demonstrate considerable structural homology, we assessed the ability of panel of known AChE and BChE inhibitors to modulate CE activity. However, because carbamate-containing compounds can irreversibly inhibit esterases [3], we evaluated the ability of a selected panel of these molecules to inactivate the human CEs. All of the compounds, with the exception of donepezil, demonstrated activity toward hiCE (Table 3). Indeed for tolserine and rivastigmine, significant loss of enzyme activity was observed.", "citation": {"db": "PubMed", "db_id": "23123248"}, "source": 2503, "target": 804, "key": "59807a7336c9a980e0e3d632799f4d80"}, {"relation": "partOf", "source": 2503, "target": 987, "key": "94278e85bf6730c01f3a59dabf2ffbc4"}, {"line": 12057, "relation": "decreases", "evidence": "Carboxylesterases (CEs) are ubiquitously expressed proteins that are responsible for the detoxification of xenobiotics. Due to the conable structural similarity between cholinesterases (ChE) and CEs, we have assessed the ability of a series of ChE inhibitors to modulate the activity of the human liver (hCE1) and the human intestinal CE (hiCE) isoforms. For example, rivastigmine resulted in greater than 95% inhibition of hiCE that was irreversible under the conditions used. Hence, the administration of esterified drugs, in combination with these carbamates, may inadvertently result in decreased hydrolysis of the former, thereby limiting their efficacy. Given that hCE1 and the ChEs demonstrate considerable structural homology, we assessed the ability of panel of known AChE and BChE inhibitors to modulate CE activity. However, because carbamate-containing compounds can irreversibly inhibit esterases [3], we evaluated the ability of a selected panel of these molecules to inactivate the human CEs. All of the compounds, with the exception of donepezil, demonstrated activity toward hiCE (Table 3). Indeed for tolserine and rivastigmine, significant loss of enzyme activity was observed.", "citation": {"db": "PubMed", "db_id": "23123248"}, "source": 2504, "target": 804, "key": "5fc089e86e284a8397545b196d5b47e0"}, {"line": 12089, "relation": "increases", "evidence": "This study found that galantamine effected significant benefits on the cognitive, functional, and behavioral symptoms of mild to moderate AD in this population of Korean patients.", "citation": {"db": "PubMed", "db_id": "15598477"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 259, "target": 812, "key": "3e75a7133835961b824349f41bb29bba"}, {"line": 12093, "relation": "increases", "evidence": "This study found that galantamine effected significant benefits on the cognitive, functional, and behavioral symptoms of mild to moderate AD in this population of Korean patients.", "citation": {"db": "PubMed", "db_id": "15598477"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 259, "target": 810, "key": "b4786dc5ae83ea17d293e9036efeac47"}, {"line": 12106, "relation": "isA", "evidence": "Galantamine is a phenanthrene alkaloid (similar to codeine) and acts as a reversible inhibitor of AChE with a competitive action. In addition, galantamine's nicotinic agonist properties may provide an additional therapeutic mechanism to AChE inhibition.", "citation": {"db": "PubMed", "db_id": "15530663"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 259, "target": 39, "key": "67233b3355b853a217c3ffceb9856cf3"}, {"line": 12110, "relation": "decreases", "evidence": "Galantamine is a phenanthrene alkaloid (similar to codeine) and acts as a reversible inhibitor of AChE with a competitive action. In addition, galantamine's nicotinic agonist properties may provide an additional therapeutic mechanism to AChE inhibition.", "citation": {"db": "PubMed", "db_id": "15530663"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2244, "key": "b7ec0c41c4c1f641203d60748a914b48"}, {"line": 12114, "relation": "isA", "evidence": "Galantamine is a phenanthrene alkaloid (similar to codeine) and acts as a reversible inhibitor of AChE with a competitive action. In addition, galantamine's nicotinic agonist properties may provide an additional therapeutic mechanism to AChE inhibition.", "citation": {"db": "PubMed", "db_id": "15530663"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 259, "target": 154, "key": "b9e061119569df9f16c56b3da13e8bec"}, {"line": 12126, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 259, "target": 204, "key": "7fe26ddfb4ee29e078bac51828751955"}, {"line": 12130, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 259, "target": 789, "key": "ff3746a218690be5c22ab06e7c9d82e0"}, {"line": 12134, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2516, "key": "edc3043ff3bbb6a19a7aa8c768d6998a"}, {"line": 13425, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2516, "key": "803470b8c62d282010e1ed238eceef3c"}, {"line": 12138, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2517, "key": "254ed8c0453d28fe28b8c975b3ef841f"}, {"line": 13429, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2517, "key": "023970e82def1a63ca70c7e1390ca120"}, {"line": 12142, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2518, "key": "937a793ecbc76980d4fcc8e4d5364ac6"}, {"line": 13433, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2518, "key": "e720bb9fc0a72a8c43c53f14d2df7b25"}, {"line": 12146, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2520, "key": "52cbe5be70c1f623316695bcd7895489"}, {"line": 13437, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2520, "key": "7e4664d6da3de1bd5b11f6fbb0b5aa7f"}, {"line": 12150, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2521, "key": "dda07426bcdc6c383aa7265d4a8283cb"}, {"line": 13441, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2521, "key": "efcf0854aa96d87b248f8fa68dbca5a6"}, {"line": 12154, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2522, "key": "3bf82bf42eae9d41e15f11fe6604a14b"}, {"line": 13445, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2522, "key": "481a57599d1aa9a91a720d9a7e57e0ef"}, {"line": 12158, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2523, "key": "59b7ae4ad6d8163b7e4df9b8677910e0"}, {"line": 13449, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2523, "key": "8190d36cec59659d255385b4ee1ee3a4"}, {"line": 12162, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2515, "key": "85be148dbbba35e62d8e81a0cecb219d"}, {"line": 13453, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2515, "key": "aa593e7b5aa6417030c7b90591437fa7"}, {"line": 12166, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2524, "key": "bca72caa90070894b61b8d965ba81356"}, {"line": 13457, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2524, "key": "fbc1ed79757488ee726103ae970cf6ad"}, {"line": 12170, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2526, "key": "ed1b557b74215612dc54be9ec9e87d74"}, {"line": 13461, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2526, "key": "c5ac4f25fdd1dc06d12d286a82c4a6bb"}, {"line": 12174, "relation": "increases", "evidence": "Galantamine increases the availability of ACh in the cholinergic synapse by competitively inhibiting AChE, the enzyme responsible for its breakdown. Galantamine also potentiates cholinergic neurotransmission by positively modulating the response of nAChRs to ACh.", "citation": {"db": "PubMed", "db_id": "11230880"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2527, "key": "881ad321e63d19723d3ee57c3588e493"}, {"line": 13465, "relation": "increases", "evidence": "Galantamine and nefiracetam have been shown to potentiate the phasic activity of nicotinic acetylcholine receptors (nAChRss) in the brain. Stimulation of nAChRss is also known to cause release of various neurotransmitters including glutamate and gamma-aminobutyric acid (GABA).", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 259, "target": 2527, "key": "152ccfe9668e424d7247e6f3545703d1"}, {"line": 12182, "relation": "decreases", "evidence": "Cognitive decline over 36 months of continuous galantamine treatment was substantially less than the predicted cognitive decline of untreated patients with mild to moderate dementia. These findings suggest that galantamine slows the clinical progression of AD.", "citation": {"db": "PubMed", "db_id": "14967774"}, "source": 259, "target": 3823, "key": "9112079a058267aae3c53260660c991d"}, {"line": 12192, "relation": "isA", "evidence": "Memantine (Ebixa, Namenda, Axura) is an uncompetitive NMDA receptor antagonist used in the management of patients with moderate-to-severe Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15748093"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "source": 300, "target": 64, "key": "1f2f8f69576ba5418e00e4dc69095557"}, {"line": 12204, "relation": "increases", "evidence": "After the treatment, memantine-treated mice had restored cognition and significantly reduced the levels of insoluble amyloid-beta (Abeta), Abeta dodecamers (Abeta*56), prefibrillar soluble oligomers, and fibrillar oligomers. The effects on pathology were stronger in older, more impaired animals. Memantine treatment also was associated with a decline in the levels of total tau and hyperphosphorylated tau. Finally, memantine pre-incubation prevented Abeta-induced inhibition of long-term potentiation in hippocampal slices of cognitively normal mice.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 300, "target": 812, "key": "8a316335d98495357d59372198a791ee"}, {"line": 13008, "relation": "increases", "evidence": "Currently available treatments for Alzheimer's disease (EC 3.1.1.7 (acetylcholinesterase) inhibitors and memantine) improve cognitive function, but do not slow the long-term progression of this disorder.", "citation": {"db": "PubMed", "db_id": "20170836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 300, "target": 812, "key": "456b1cf02db40cee086039fff8f22c59"}, {"line": 12212, "relation": "decreases", "evidence": "After the treatment, memantine-treated mice had restored cognition and significantly reduced the levels of insoluble amyloid-beta (Abeta), Abeta dodecamers (Abeta*56), prefibrillar soluble oligomers, and fibrillar oligomers. The effects on pathology were stronger in older, more impaired animals. Memantine treatment also was associated with a decline in the levels of total tau and hyperphosphorylated tau. Finally, memantine pre-incubation prevented Abeta-induced inhibition of long-term potentiation in hippocampal slices of cognitively normal mice.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"NMDA receptor": true, "Non-amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 380, "key": "7b1a309a5ed57c4d2f3df6a287274705"}, {"line": 12219, "relation": "decreases", "evidence": "After the treatment, memantine-treated mice had restored cognition and significantly reduced the levels of insoluble amyloid-beta (Abeta), Abeta dodecamers (Abeta*56), prefibrillar soluble oligomers, and fibrillar oligomers. The effects on pathology were stronger in older, more impaired animals. Memantine treatment also was associated with a decline in the levels of total tau and hyperphosphorylated tau. Finally, memantine pre-incubation prevented Abeta-induced inhibition of long-term potentiation in hippocampal slices of cognitively normal mice.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Confidence": {"High": true}, "Subgraph": {"NMDA receptor": true, "Tau protein subgraph": true, "Glutamatergic subgraph": true}}, "source": 300, "target": 3675, "key": "ac3409c71a3da6c0a9fd1820c0b24f91"}, {"line": 12220, "relation": "decreases", "evidence": "After the treatment, memantine-treated mice had restored cognition and significantly reduced the levels of insoluble amyloid-beta (Abeta), Abeta dodecamers (Abeta*56), prefibrillar soluble oligomers, and fibrillar oligomers. The effects on pathology were stronger in older, more impaired animals. Memantine treatment also was associated with a decline in the levels of total tau and hyperphosphorylated tau. Finally, memantine pre-incubation prevented Abeta-induced inhibition of long-term potentiation in hippocampal slices of cognitively normal mice.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Confidence": {"High": true}, "Subgraph": {"NMDA receptor": true, "Tau protein subgraph": true, "Glutamatergic subgraph": true}}, "source": 300, "target": 3676, "key": "ce352a62416442baa3595b8760032bf8"}, {"line": 12226, "relation": "decreases", "evidence": "After the treatment, memantine-treated mice had restored cognition and significantly reduced the levels of insoluble amyloid-beta (Abeta), Abeta dodecamers (Abeta*56), prefibrillar soluble oligomers, and fibrillar oligomers. The effects on pathology were stronger in older, more impaired animals. Memantine treatment also was associated with a decline in the levels of total tau and hyperphosphorylated tau. Finally, memantine pre-incubation prevented Abeta-induced inhibition of long-term potentiation in hippocampal slices of cognitively normal mice.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"NMDA receptor": true, "Non-amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 2328, "key": "3c9e23bd147496297deeb211404ebf23"}, {"line": 12228, "relation": "positiveCorrelation", "evidence": "After the treatment, memantine-treated mice had restored cognition and significantly reduced the levels of insoluble amyloid-beta (Abeta), Abeta dodecamers (Abeta*56), prefibrillar soluble oligomers, and fibrillar oligomers. The effects on pathology were stronger in older, more impaired animals. Memantine treatment also was associated with a decline in the levels of total tau and hyperphosphorylated tau. Finally, memantine pre-incubation prevented Abeta-induced inhibition of long-term potentiation in hippocampal slices of cognitively normal mice.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"NMDA receptor": true, "Non-amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 839, "key": "fc137e7c7c7e3c5035057485c82c17f6"}, {"line": 12237, "relation": "increases", "evidence": "In this study, we found that memantine improved both hippocampus- and amygdala-dependent memory impairments in 3xTg-AD mice of three different ages and degrees of pathology.", "citation": {"db": "PubMed", "db_id": "20042680"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 300, "target": 820, "key": "df839f29e31c89c906e6a398ca9538d3"}, {"line": 12249, "relation": "decreases", "evidence": "In addition to blocking the NMDA receptor, memantine also decreases the basal level of intracellular Ca(2+) and increases the sensitivity of cells to extracellular stimuli. All these effects may be of benefit in the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18769047"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "source": 300, "target": 2779, "key": "8a12c4483ca25dafa1c494842b1b834f"}, {"line": 12250, "relation": "decreases", "evidence": "In addition to blocking the NMDA receptor, memantine also decreases the basal level of intracellular Ca(2+) and increases the sensitivity of cells to extracellular stimuli. All these effects may be of benefit in the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18769047"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "source": 300, "target": 724, "key": "01495ae2ad2feb79fc2a18c439853438"}, {"relation": "partOf", "source": 300, "target": 976, "key": "d664d5f07292731249ee37f476f9d293"}, {"line": 22245, "relation": "decreases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 3585, "key": "d7c9ff323313f64e8025fcdad24e7459"}, {"line": 22246, "relation": "decreases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 3584, "key": "ebcdd437e090cb548de2f8097b2e42b6"}, {"line": 22248, "relation": "increases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 3688, "key": "3f0e5ef5f1d14d39cb79c8d80b544857"}, {"line": 22249, "relation": "increases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 3693, "key": "179b14e9bbde4eb6963e97f58ee585a9"}, {"line": 22250, "relation": "increases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 859, "key": "f8b9cbda47dd52b7390f58b8dbd270f6"}, {"line": 22254, "relation": "increases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 2162, "key": "498ac7b7fef2e83e21dccee8b1b0b2ec"}, {"line": 22256, "relation": "increases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 3709, "key": "dbe3af0f70bea2795418ff59cf7496e4"}, {"line": 22259, "relation": "increases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 2174, "key": "acd5fa4b861e6ee8c3860a06316820f8"}, {"line": 22260, "relation": "increases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 3674, "key": "9d082f15b2ab785589604ac46791f56f"}, {"line": 22263, "relation": "decreases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 300, "target": 1592, "key": "82a3300c96aa9657ade709c4d6385859"}, {"line": 22266, "relation": "decreases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 3600, "key": "e0dd46aa0e8180e283b7f35fb406cce6"}, {"line": 22268, "relation": "decreases", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"p53 stabilization subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 3746, "key": "3bb8df58e8db77f849ec3a9518134c2a"}, {"line": 22837, "relation": "decreases", "evidence": "There was a significant increase in lipid peroxidation and nitrite in synaptosomal preparations. Preventivetreatment daily for 13 days with antidementic drugs, donepezil (5 mg/kg, p.o) and memantine(10 mg/kg, p.o), significantly attenuated OKA induced mitochondrial dysfunction, apoptotic cell death, memory impairment and histological changes. Mitochondrial dysfunction appeared as a key factor in OKA induced memory impairment and apoptotic cell death. OKA also increases Ca2+ in hippocampal neuronal cell culture.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 159, "key": "669000852cad1d6f75ae6e9951780113"}, {"line": 22881, "relation": "decreases", "evidence": "There was a significant (Pb0.01) increase in Ca2+ in hippocampus, cortex, striatum and cerebellum of OKA 200 ng treated rats as compared to control and vehicle treated rats.Treatment with memantine and donepezil significantly (P<0.01) reduced amount of Ca2+ in OKA treated rat brain regions", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 300, "target": 94, "key": "c289eef9914eebe0c71248778f1559c2"}, {"line": 22901, "relation": "decreases", "evidence": "Production of reactive oxygen species (ROS) in brain regions was measured relative to control. There was a significant increase (P<0.01) in ROS level in cerebellum, hippocampus, cortex and striatum of OKA 200 ng treated rats as compared to control group. Treatment with memantine significantly (Pb0.05) reduced the amount of ROS whereas donepezil did not show significant (PN0.05) effect in any brain regions.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Cerebellum": true, "Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 300, "target": 170, "key": "9c66a4a72914357184a9cbdd71a2dd36"}, {"line": 22917, "relation": "increases", "evidence": "There was a significant (Pb0.05) decrease in MMP in hippocampus and cortex of OKA 200 ng treated rats as compared to control and vehicle treated rats.Treatment with memantine significantly (P<0.05) increased MMP in cortex and hippocampus whereas donepezil significantly (Pb0.05) increased MMP in cortex, hippocampus and striatum as compared to OKA 200 ng treated rat.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 300, "target": 682, "key": "78b3a39e6368b1c8de31f10f0bbd939a"}, {"line": 22929, "relation": "decreases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-3 was observed in hippocampus, striatum and cortex of OKA treated rat brain in comparison to that of control and vehicle groups. Treatment with memantine and donepezil significantly (P<0.05) decreased caspase-3 activity and mRNA level in hippocampus, striatum and cortex of OKA 200 ng treated rat brain", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 300, "target": 3755, "key": "21d58e559b8fad962a9c7a56088f0f6a"}, {"line": 22935, "relation": "decreases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-3 was observed in hippocampus, striatum and cortex of OKA treated rat brain in comparison to that of control and vehicle groups. Treatment with memantine and donepezil significantly (P<0.05) decreased caspase-3 activity and mRNA level in hippocampus, striatum and cortex of OKA 200 ng treated rat brain", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 4082, "key": "cceab7dc5d2bb90f304f0fb26c03359c"}, {"line": 22947, "relation": "decreases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-9 was observed in hippocampus and cortex of OKA treated rats as compared to that of control and vehicle group. Treatment with memantine and donepezil significantly (P<0.01) decreased caspase-9 activity and mRNA expression in hippocampus and cortex as compared to OKA 200 ng treated group (Fig. 10A and B).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 300, "target": 3756, "key": "ec6e0b839a0f40de3f67da150dbe1ada"}, {"line": 22953, "relation": "decreases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-9 was observed in hippocampus and cortex of OKA treated rats as compared to that of control and vehicle group. Treatment with memantine and donepezil significantly (P<0.01) decreased caspase-9 activity and mRNA expression in hippocampus and cortex as compared to OKA 200 ng treated group (Fig. 10A and B).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 300, "target": 4083, "key": "4bc0cbe696a3b38f5116091b2566c162"}, {"line": 22980, "relation": "decreases", "evidence": "Recently, we have reported that intracerebroventricular (ICV) administration of okadaic acid (OKA) in rats induces memory impairment that was associated with increased oxidative stress. Besides memory deficit, OKA caused impairment in mitochondrial function as revealed by alterations in calcium ion, reactive oxygen species (ROS) generation, mitochondrial membrane potential (MMP), SDH activity and ATP level in the brain regions. Further, in histopathological study it was observed that donepezil and memantine reduced the cell loss and neurodegeneration in hippocampus and periventricular cortex regions in OKA treated rats.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 300, "target": 648, "key": "17da974baa5c2b2e743259ddfa621387"}, {"line": 14997, "relation": "positiveCorrelation", "evidence": "In agreement with the altered sNG2 levels, we found decreased MMP-9 activity after fibrillar Abeta_42 exposure and a trend toward increased MMP-9 activity after oligomeric Abeta_42 exposure.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 380, "target": 3062, "key": "79868e497cd380d15bdf6a17127ed9a8"}, {"line": 45360, "relation": "decreases", "evidence": "Abeta-derived diffusible ligands, ADDLs reduce drebrin cluster density", "citation": {"db": "PubMed", "db_id": "25058791"}, "source": 380, "target": 2620, "key": "3f33ff13fb63acf5eddebf16fcf67621"}, {"relation": "hasVariant", "source": 3675, "target": 3676, "key": "8e34efeb45eb075a0688451a05848522"}, {"relation": "hasVariant", "source": 3675, "target": 3677, "key": "346bf23a86f29fa59e66c4d2c47221a6"}, {"line": 43068, "relation": "association", "evidence": "Our recent study shows that there is a striking switch in mononuclear phagocyte and activation phenotypes in the anterior horn of the spinal cord from alternatively activated (M2-skewed) microglia in P301L tau mutant mice to pro-inflammatory (M1-skewed) infiltrating peripheral monocytes by crossing the tau mice with TTBK1 transgenic mice.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Monocytes": true, "Spinal Cord": true, "Phagocytes": true, "Microglia": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "source": 3675, "target": 3747, "key": "7b3024210d1742d9198a7b0f8c4714c3"}, {"line": 43459, "relation": "increases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 3676, "target": 3823, "key": "53444849b709f29dd8e52682de2f5de7"}, {"line": 12261, "relation": "increases", "evidence": "Fluvoxamine was effective in controlling BPSD with AD. This finding shows that the pathophysiology of behavioral and psychological symptoms of dementia BPSD due to AD may occur because of a hyposerotonergic state in the brain.", "citation": {"db": "PubMed", "db_id": "17427765"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Species": {"9606": true}}, "source": 257, "target": 810, "key": "08376b4b66db5292ada0bea21d411b8a"}, {"line": 12272, "relation": "isA", "evidence": "We report on two cases in which monotherapy of the selective serotonin reuptake inhibitor and sigma-1 receptor agonist fluvoxamine was effective in ameliorating the delirium of patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20148109"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "source": 257, "target": 171, "key": "cd639c918cff1c219b7050c6297a90ae"}, {"line": 12273, "relation": "decreases", "evidence": "We report on two cases in which monotherapy of the selective serotonin reuptake inhibitor and sigma-1 receptor agonist fluvoxamine was effective in ameliorating the delirium of patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20148109"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "source": 257, "target": 3900, "key": "119cc85c029415afc55dd1771ff85b18"}, {"relation": "partOf", "source": 257, "target": 978, "key": "45ec487b72de37c3c8b13a9165318925"}, {"line": 12279, "relation": "association", "evidence": "The endoplasmic reticulum protein sigma-1 receptors play a key role in Ca2+ signalling and cell survival, and have been shown to regulate a number of neurotransmitter systems in the brain. The selective serotonin reuptake inhibitor (SSRI) fluvoxamine is a very potent agonist at sigma-1 receptors, which are also implicated in cognition and the pathophysiology of neuropsychiatric diseases. A study using the selective sigma-1 receptor agonist [11C]-SA4503 and positron emission tomography demonstrated that fluvoxamine binds to sigma-1 receptors in living human brain at therapeutic doses, suggesting that sigma-1 receptors might play a role in the mechanism of action of fluvoxamine ", "citation": {"db": "PubMed", "db_id": "20148109"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 257, "target": 978, "key": "c8a435bfe0dfc09a258cef92156d7297"}, {"line": 12280, "relation": "association", "evidence": "The endoplasmic reticulum protein sigma-1 receptors play a key role in Ca2+ signalling and cell survival, and have been shown to regulate a number of neurotransmitter systems in the brain. The selective serotonin reuptake inhibitor (SSRI) fluvoxamine is a very potent agonist at sigma-1 receptors, which are also implicated in cognition and the pathophysiology of neuropsychiatric diseases. A study using the selective sigma-1 receptor agonist [11C]-SA4503 and positron emission tomography demonstrated that fluvoxamine binds to sigma-1 receptors in living human brain at therapeutic doses, suggesting that sigma-1 receptors might play a role in the mechanism of action of fluvoxamine ", "citation": {"db": "PubMed", "db_id": "20148109"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 257, "target": 3363, "key": "ef5a92b5132a2ef7ce983ae9a788ac72"}, {"line": 12349, "relation": "increases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 257, "target": 2885, "key": "79ee0d5bf3154e1677a8656ee4f53102"}, {"line": 12366, "relation": "isA", "evidence": "The antidepressant fluvoxamine, the drug of abuse methamphetamine, and the neurosteroid progesterone were amongst the many ligands whose interactions with the sigma(1) receptor were confirmed with our screening assay.", "citation": {"db": "PubMed", "db_id": "17961544"}, "source": 257, "target": 212, "key": "72d6c1abc41b5ab6ed57279ef10deead"}, {"line": 12278, "relation": "association", "evidence": "The endoplasmic reticulum protein sigma-1 receptors play a key role in Ca2+ signalling and cell survival, and have been shown to regulate a number of neurotransmitter systems in the brain. The selective serotonin reuptake inhibitor (SSRI) fluvoxamine is a very potent agonist at sigma-1 receptors, which are also implicated in cognition and the pathophysiology of neuropsychiatric diseases. A study using the selective sigma-1 receptor agonist [11C]-SA4503 and positron emission tomography demonstrated that fluvoxamine binds to sigma-1 receptors in living human brain at therapeutic doses, suggesting that sigma-1 receptors might play a role in the mechanism of action of fluvoxamine ", "citation": {"db": "PubMed", "db_id": "20148109"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "source": 3363, "target": 495, "key": "69304718779d6eab37efd2f868c8cde8"}, {"relation": "partOf", "source": 3363, "target": 978, "key": "82a3eedfa429991bd8a011aad7974e62"}, {"line": 12280, "relation": "association", "evidence": "The endoplasmic reticulum protein sigma-1 receptors play a key role in Ca2+ signalling and cell survival, and have been shown to regulate a number of neurotransmitter systems in the brain. The selective serotonin reuptake inhibitor (SSRI) fluvoxamine is a very potent agonist at sigma-1 receptors, which are also implicated in cognition and the pathophysiology of neuropsychiatric diseases. A study using the selective sigma-1 receptor agonist [11C]-SA4503 and positron emission tomography demonstrated that fluvoxamine binds to sigma-1 receptors in living human brain at therapeutic doses, suggesting that sigma-1 receptors might play a role in the mechanism of action of fluvoxamine ", "citation": {"db": "PubMed", "db_id": "20148109"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3363, "target": 257, "key": "58ff81ca6ff0ae6bec0ed04a879e1362"}, {"line": 12370, "relation": "association", "evidence": "The affinity of some typical dopamine D4 receptor selective compounds for the sigma1 receptor was investigated, because of the historical tendency for drugs that bind dopamine D2 and D3 receptor subtypes to also bind to sigma1 receptors, and because an initial report hinted that a drug developed to be selective for the dopamine D4 receptor subtype might also bind to Sigma receptor subtypes.", "citation": {"db": "PubMed", "db_id": "17961544"}, "annotations": {"Subgraph": {"Dopaminergic subgraph": true}}, "source": 3363, "target": 2645, "key": "d299661fcf29b38852ae33711297140c"}, {"line": 12279, "relation": "association", "evidence": "The endoplasmic reticulum protein sigma-1 receptors play a key role in Ca2+ signalling and cell survival, and have been shown to regulate a number of neurotransmitter systems in the brain. The selective serotonin reuptake inhibitor (SSRI) fluvoxamine is a very potent agonist at sigma-1 receptors, which are also implicated in cognition and the pathophysiology of neuropsychiatric diseases. A study using the selective sigma-1 receptor agonist [11C]-SA4503 and positron emission tomography demonstrated that fluvoxamine binds to sigma-1 receptors in living human brain at therapeutic doses, suggesting that sigma-1 receptors might play a role in the mechanism of action of fluvoxamine ", "citation": {"db": "PubMed", "db_id": "20148109"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 978, "target": 257, "key": "7016c5e7dd8f01bc4ea6b33a1ea911eb"}, {"line": 12295, "relation": "decreases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Tumor necrosis factor subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 145, "target": 3472, "key": "ed3b2d9b5d1203a206463495fca334a0"}, {"relation": "partOf", "source": 145, "target": 1661, "key": "dff9b165d1c329f0d193ea81c89df279"}, {"line": 12303, "relation": "decreases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 343, "target": 2885, "key": "e8652c141a5b8dfc2ad44147b48df429"}, {"line": 12311, "relation": "increases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 319, "target": 3450, "key": "e3a337f4677c406221dae273a8f14646"}, {"line": 12352, "relation": "decreases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 319, "target": 3460, "key": "56b2f35ddd1b505a61f79f188a5bbd58"}, {"line": 12353, "relation": "decreases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 319, "target": 3254, "key": "00b662da1e95d6c707eb42a8ca557ab9"}, {"line": 12354, "relation": "increases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 319, "target": 2884, "key": "979192e288b4ee2a54361f39dbc396a5"}, {"line": 12319, "relation": "increases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Endosomal lysosomal subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 256, "target": 2538, "key": "d78060c3aa0f988d7125fecadb89fcb9"}, {"line": 12325, "relation": "increases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Low density lipoprotein subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 256, "target": 3396, "key": "5a41cc7560780f271bd3534f35509880"}, {"line": 12333, "relation": "increases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 256, "target": 3099, "key": "ef3df3473cf634e1a84046500c838ef2"}, {"line": 12340, "relation": "increases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Inflammatory response subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 256, "target": 2790, "key": "5ce90d2ec461474fd0ad0bb491222e8c"}, {"line": 12342, "relation": "increases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 256, "target": 2651, "key": "b144b6f7eafa75890274956374a16175"}, {"line": 36145, "relation": "directlyDecreases", "evidence": "Sorting mechanisms that cause the amyloid precursor protein (APP) and the ß-secretases and gamma-secretases to colocalize in the same compartment play an important role in the regulation of ABeta¸ production in Alzheimer's disease (AD). We and others have reported that genetic variants in the Sortilin-related receptor (SORL1) increased the risk of AD, that SORL1 is involved in trafficking of APP, and that underexpression of SORL1 leads to overproduction of ABeta¸. Here we explored the role of one of its homologs, the sortilin-related VPS10 domain containing receptor 1 (SORCS1), in AD.SorCS1 influenced APP processing. While overexpression of SorCS1 reduced gamma-secretase activity and ABeta¸ levels, the suppression of SorCS1 increased gamma-secretase processing of APP and the levels of ABeta¸.", "citation": {"db": "PubMed", "db_id": "21280075"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3396, "target": 868, "key": "24db82bcb78df931bc7bcf5857f6b71f"}, {"line": 36153, "relation": "directlyDecreases", "evidence": "Sorting mechanisms that cause the amyloid precursor protein (APP) and the ß-secretases and gamma-secretases to colocalize in the same compartment play an important role in the regulation of ABeta¸ production in Alzheimer's disease (AD). We and others have reported that genetic variants in the Sortilin-related receptor (SORL1) increased the risk of AD, that SORL1 is involved in trafficking of APP, and that underexpression of SORL1 leads to overproduction of ABeta¸. Here we explored the role of one of its homologs, the sortilin-related VPS10 domain containing receptor 1 (SORCS1), in AD.SorCS1 influenced APP processing. While overexpression of SorCS1 reduced gamma-secretase activity and ABeta¸ levels, the suppression of SorCS1 increased gamma-secretase processing of APP and the levels of ABeta¸.", "citation": {"db": "PubMed", "db_id": "21280075"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3396, "target": 2328, "key": "a46b8570e6eb5dc3eb0c2e2eb232479e"}, {"line": 12344, "relation": "decreases", "evidence": "AD risk may be attenuated by antipsychotics and lithium (down-regulate TNF), atypical antipsychotics (down-regulate TF), risperidone (down-regulates IL1B), olanzapine (up-regulates TFAM, down-regulates PRNP), fluoxetine (up-regulates CLU, SORCS1, NEDD9, GRN, and ECE1), and lithium coadministered with antipsychotics (down-regulates IL1B). ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 1661, "target": 2885, "key": "394db6b22ec868776d8e8bdbc21fee8c"}, {"relation": "partOf", "source": 84, "target": 1661, "key": "cfa1bc36f3340d9039dea53dc0ddf89d"}, {"line": 12350, "relation": "increases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 271, "target": 2885, "key": "fa44d27f290d0b8a31600ead129f0661"}, {"line": 12351, "relation": "increases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 271, "target": 2791, "key": "fa48e4a60ba73376545ce1ec0623c405"}, {"relation": "partOf", "source": 2791, "target": 1266, "key": "b9ec1dce11a40820c28961a0be4ffa9d"}, {"relation": "partOf", "source": 2791, "target": 1175, "key": "59da25454778c8cb48c538763dba0214"}, {"line": 31351, "relation": "increases", "evidence": "Here we report the discovery of a novel gamma-secretase activating protein (GSAP) that drastically and selectively increases amyloid-beta production through a mechanism involving its interactions with both gamma-secretase and its substrate, the amyloid precursor protein carboxy-terminal fragment (APP-CTF).", "citation": {"db": "PubMed", "db_id": "20811458"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 2791, "target": 80, "key": "ee93c4c9d58e9434170744898dd11530"}, {"line": 12355, "relation": "increases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 229, "target": 2885, "key": "1e2fe755a8c495b33717c86d26cac5a8"}, {"line": 12356, "relation": "increases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 281, "target": 2885, "key": "cb8a5a932443cc904719fa102d0e59f8"}, {"line": 12357, "relation": "increases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 242, "target": 2885, "key": "b4fbb17f8a0fc45eeb7f6bcdbb91e435"}, {"line": 12358, "relation": "increases", "evidence": "AD risk may be enhanced by neuroleptics (up-regulate TF), haloperidol (up-regulates IL1B and PION), olanzapine (down-regulates THRA and PRNP, up-regulates IL1A), and chlorpromazine, imipramine, maprotiline, fluvoxamine, and diazepam (up-regulate IL1B). There were no results for dextromethorphan-plus-quinidine. ", "citation": {"db": "PubMed", "db_id": "21399480"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 196, "target": 2885, "key": "3c5953c1bb2f98707f76917a3c1910d4"}, {"line": 12370, "relation": "association", "evidence": "The affinity of some typical dopamine D4 receptor selective compounds for the sigma1 receptor was investigated, because of the historical tendency for drugs that bind dopamine D2 and D3 receptor subtypes to also bind to sigma1 receptors, and because an initial report hinted that a drug developed to be selective for the dopamine D4 receptor subtype might also bind to Sigma receptor subtypes.", "citation": {"db": "PubMed", "db_id": "17961544"}, "annotations": {"Subgraph": {"Dopaminergic subgraph": true}}, "source": 2645, "target": 3363, "key": "4281c2e0aa57dbd01bf2b79ba22df607"}, {"line": 12397, "relation": "isA", "evidence": "In this study, we show that at nanomolar-low micromolar concentrations, etazolate, a selective GABA(A) receptor modulator, stimulates sAPPalpha production in rat cortical neurons and in guinea pig brains. Etazolate (20 nM-2 microM) dose-dependently protected rat cortical neurons against Abeta-induced toxicity. The neuroprotective effects of etazolate were fully blocked by GABA(A) receptor antagonists indicating that this neuroprotection was due to GABA(A) receptor signalling. This indicating that etazolate exerts its neuroprotective effect via sAPPalpha induction.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 252, "target": 48, "key": "6525f7675f35658ab91190d84943ff56"}, {"line": 13299, "relation": "isA", "evidence": "EHT0202 (etazolate hydrochloride) is a new compound exhibiting both potential disease-modifying and symptomatic treatment properties in Alzheimer's Disease increasing alpha-secretase activity and sAPP alpha secretion, as well as acting as a GABA-A receptor modulator and as a PDE-4 inhibitor.", "citation": {"db": "PubMed", "db_id": "21222604"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "Species": {"9606": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 252, "target": 48, "key": "dd5af202ec4f1a69ec32e9cd0a787a44"}, {"line": 12398, "relation": "increases", "evidence": "In this study, we show that at nanomolar-low micromolar concentrations, etazolate, a selective GABA(A) receptor modulator, stimulates sAPPalpha production in rat cortical neurons and in guinea pig brains. Etazolate (20 nM-2 microM) dose-dependently protected rat cortical neurons against Abeta-induced toxicity. The neuroprotective effects of etazolate were fully blocked by GABA(A) receptor antagonists indicating that this neuroprotection was due to GABA(A) receptor signalling. This indicating that etazolate exerts its neuroprotective effect via sAPPalpha induction.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 252, "target": 2137, "key": "9edf52523764178c7b4b1918d1f81986"}, {"line": 13236, "relation": "increases", "evidence": "Furthermore, both pharmacological alpha-secretase pathway inhibition and sAPPalpha immunoneutralization approaches prevented etazolate neuroprotection against Abeta, indicating that etazolate exerts its neuroprotective effect via sAPPalpha induction.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 252, "target": 2137, "key": "b634ecbf3d178d99fb5944942780129a"}, {"line": 13270, "relation": "increases", "evidence": "Etazolate is a phosphodiesterase 4 (PDE4) inhibitor and GABAA receptor modulator that also stimulates alpha-secretase activity and neurotrophic soluble amyloid precursor protein (sAPPalpha) production, currently developed as a possible Alzheimer's disease therapeutic.", "citation": {"db": "PubMed", "db_id": "20223232"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 252, "target": 2137, "key": "f486df1cf398a419401677d9730ddd22"}, {"line": 13310, "relation": "increases", "evidence": "EHT0202 (etazolate hydrochloride) is a new compound exhibiting both potential disease-modifying and symptomatic treatment properties in Alzheimer's Disease increasing alpha-secretase activity and sAPP alpha secretion, as well as acting as a GABA-A receptor modulator and as a PDE-4 inhibitor.", "citation": {"db": "PubMed", "db_id": "21222604"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "Species": {"9606": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 252, "target": 2137, "key": "d7c5e7a160fad77a14d39dd9b4bf2bc9"}, {"line": 13240, "relation": "increases", "evidence": "Furthermore, both pharmacological alpha-secretase pathway inhibition and sAPPalpha immunoneutralization approaches prevented etazolate neuroprotection against Abeta, indicating that etazolate exerts its neuroprotective effect via sAPPalpha induction.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 252, "target": 854, "key": "36fe1b1cc756aaec1fac6061ac229ddc"}, {"line": 13262, "relation": "isA", "evidence": "Etazolate is a phosphodiesterase 4 (PDE4) inhibitor and GABAA receptor modulator that also stimulates alpha-secretase activity and neurotrophic soluble amyloid precursor protein (sAPPalpha) production, currently developed as a possible Alzheimer's disease therapeutic.", "citation": {"db": "PubMed", "db_id": "20223232"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 252, "target": 41, "key": "3334a6ba8906ce4215938a7fc83ec396"}, {"line": 13302, "relation": "isA", "evidence": "EHT0202 (etazolate hydrochloride) is a new compound exhibiting both potential disease-modifying and symptomatic treatment properties in Alzheimer's Disease increasing alpha-secretase activity and sAPP alpha secretion, as well as acting as a GABA-A receptor modulator and as a PDE-4 inhibitor.", "citation": {"db": "PubMed", "db_id": "21222604"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "Species": {"9606": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 252, "target": 41, "key": "08a913aa56b9fdcc6a1592cb496c4b95"}, {"line": 13265, "relation": "increases", "evidence": "Etazolate is a phosphodiesterase 4 (PDE4) inhibitor and GABAA receptor modulator that also stimulates alpha-secretase activity and neurotrophic soluble amyloid precursor protein (sAPPalpha) production, currently developed as a possible Alzheimer's disease therapeutic.", "citation": {"db": "PubMed", "db_id": "20223232"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 252, "target": 2249, "key": "1434b57ad213afdf2aa8cb1b871f615d"}, {"line": 13305, "relation": "increases", "evidence": "EHT0202 (etazolate hydrochloride) is a new compound exhibiting both potential disease-modifying and symptomatic treatment properties in Alzheimer's Disease increasing alpha-secretase activity and sAPP alpha secretion, as well as acting as a GABA-A receptor modulator and as a PDE-4 inhibitor.", "citation": {"db": "PubMed", "db_id": "21222604"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "Species": {"9606": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 252, "target": 2249, "key": "99a17690e8cf6d89357fdfaf5e689b6f"}, {"line": 13280, "relation": "increases", "evidence": "The combined behavioral data demonstrate positive effects of etazolate on separate age-related cognitive deficits, using a complex task based on naturally occurring rodent behaviors.", "citation": {"db": "PubMed", "db_id": "20223232"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 252, "target": 588, "key": "cca7731540b70077a1f9a459bc966892"}, {"line": 12417, "relation": "positiveCorrelation", "evidence": "Significant relations were noted between the serum level of the drug and both serum prolactin(PRL) level and treatment response.", "citation": {"db": "PubMed", "db_id": "2676994"}, "annotations": {"Subgraph": {"Dopaminergic subgraph": true}}, "source": 197, "target": 3253, "key": "934af16009415924b2edc2c910240601"}, {"line": 12417, "relation": "positiveCorrelation", "evidence": "Significant relations were noted between the serum level of the drug and both serum prolactin(PRL) level and treatment response.", "citation": {"db": "PubMed", "db_id": "2676994"}, "annotations": {"Subgraph": {"Dopaminergic subgraph": true}}, "source": 3253, "target": 197, "key": "f4370525e46754a1e6f765eb7a5fc0bc"}, {"line": 19470, "relation": "association", "evidence": "The tilted peptides of human prolactin and human growth hormone induce endothelial cell apoptotic process, inhibit endothelial cell proliferation, and inhibit capillary formation both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "16973751"}, "annotations": {"Species": {"9606": true}}, "source": 3253, "target": 478, "key": "b699ea2aadb892936465ec004f523be5"}, {"line": 19471, "relation": "association", "evidence": "The tilted peptides of human prolactin and human growth hormone induce endothelial cell apoptotic process, inhibit endothelial cell proliferation, and inhibit capillary formation both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "16973751"}, "annotations": {"Species": {"9606": true}}, "source": 3253, "target": 551, "key": "c4b93adfa1d98878eee224ee451dc62a"}, {"line": 19482, "relation": "positiveCorrelation", "evidence": "In addition, aggressive patients showed a greater mean PRL increase (% baseline) (215 +/- 60, n = 11) than nonaggressive subjects (123 +/- 54, n = 11) (p =.01, 2-tailed t-test).", "citation": {"db": "PubMed", "db_id": "12377401"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}}, "source": 3253, "target": 3823, "key": "dea1445aea13446c359be2959407a3ed"}, {"line": 12429, "relation": "increases", "evidence": "Administration of paroxetine, a SSRI, to 3x transgenic mice for 5 months reversed the memory impairment as assessed by the spatial version of the Morris water maze as well as decreased the AD-like neuropathology characteristic of these transgenic mice (Nelson et al., this issue).", "citation": {"db": "PubMed", "db_id": "17662278"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true}, "Species": {"10090": true}}, "source": 322, "target": 588, "key": "133653e3c6037dbcf12a8e7ba5a0397a"}, {"line": 12430, "relation": "decreases", "evidence": "Administration of paroxetine, a SSRI, to 3x transgenic mice for 5 months reversed the memory impairment as assessed by the spatial version of the Morris water maze as well as decreased the AD-like neuropathology characteristic of these transgenic mice (Nelson et al., this issue).", "citation": {"db": "PubMed", "db_id": "17662278"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true}, "Species": {"10090": true}}, "source": 322, "target": 3823, "key": "fccfc809aa245eed2523827cf2ca9169"}, {"line": 12452, "relation": "increases", "evidence": "Paroxetine treatment ameliorated the spatial navigation deficit in 3xTgAD male and female mice, without affecting swim speed or distance traveled, suggesting a preservation of cognitive function. Levels of amyloid beta-peptide (Abeta) and numbers of Abeta immunoreactive neurons were significantly reduced in the hippocampus of male and female paroxetine-treated 3xTgAD mice compared to saline-treated 3xTgAD mice.", "citation": {"db": "PubMed", "db_id": "17368447"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Non-amyloidogenic subgraph": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "source": 322, "target": 812, "key": "9e0431a0c17155087350a8ce347aadab"}, {"line": 12456, "relation": "decreases", "evidence": "Paroxetine treatment ameliorated the spatial navigation deficit in 3xTgAD male and female mice, without affecting swim speed or distance traveled, suggesting a preservation of cognitive function. Levels of amyloid beta-peptide (Abeta) and numbers of Abeta immunoreactive neurons were significantly reduced in the hippocampus of male and female paroxetine-treated 3xTgAD mice compared to saline-treated 3xTgAD mice.", "citation": {"db": "PubMed", "db_id": "17368447"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Non-amyloidogenic subgraph": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 322, "target": 80, "key": "ec32ac9acd7094b03be31449e087b524"}, {"line": 12471, "relation": "association", "evidence": "Since APP holoprotein levels are proportionate to Abeta peptide output in many systems we tested the efficacy of paroxetine and dimercaptopropanol to limit Abeta secretion as measured by ELISA assays. Paroxetine and dimercaptopropanol limited Abeta peptide secretion from lens epithelial cells (B3 cells). Interestingly, paroxetine changed the steady-state levels of transferrin receptor mRNAs. These data suggested that this serotonin reuptake inhibitor (SSRI) provided extra pharmacological action to chelate interacellular iron or change the intracellular iron distribution. An altered iron distribution would be predicted to indirectly limit APP holoprotein expression and Abeta peptide secretion.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Serotonergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 322, "target": 4019, "key": "8e898d65c31f74c3d88cc4deddea833f"}, {"line": 12472, "relation": "association", "evidence": "Since APP holoprotein levels are proportionate to Abeta peptide output in many systems we tested the efficacy of paroxetine and dimercaptopropanol to limit Abeta secretion as measured by ELISA assays. Paroxetine and dimercaptopropanol limited Abeta peptide secretion from lens epithelial cells (B3 cells). Interestingly, paroxetine changed the steady-state levels of transferrin receptor mRNAs. These data suggested that this serotonin reuptake inhibitor (SSRI) provided extra pharmacological action to chelate interacellular iron or change the intracellular iron distribution. An altered iron distribution would be predicted to indirectly limit APP holoprotein expression and Abeta peptide secretion.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Serotonergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 322, "target": 4018, "key": "fb727c547734972639189ba4b0efb16a"}, {"line": 12473, "relation": "association", "evidence": "Since APP holoprotein levels are proportionate to Abeta peptide output in many systems we tested the efficacy of paroxetine and dimercaptopropanol to limit Abeta secretion as measured by ELISA assays. Paroxetine and dimercaptopropanol limited Abeta peptide secretion from lens epithelial cells (B3 cells). Interestingly, paroxetine changed the steady-state levels of transferrin receptor mRNAs. These data suggested that this serotonin reuptake inhibitor (SSRI) provided extra pharmacological action to chelate interacellular iron or change the intracellular iron distribution. An altered iron distribution would be predicted to indirectly limit APP holoprotein expression and Abeta peptide secretion.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Serotonergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 322, "target": 586, "key": "2cb9cadcb42ceec7692f0d22f0bb2a58"}, {"line": 12475, "relation": "decreases", "evidence": "Since APP holoprotein levels are proportionate to Abeta peptide output in many systems we tested the efficacy of paroxetine and dimercaptopropanol to limit Abeta secretion as measured by ELISA assays. Paroxetine and dimercaptopropanol limited Abeta peptide secretion from lens epithelial cells (B3 cells). Interestingly, paroxetine changed the steady-state levels of transferrin receptor mRNAs. These data suggested that this serotonin reuptake inhibitor (SSRI) provided extra pharmacological action to chelate interacellular iron or change the intracellular iron distribution. An altered iron distribution would be predicted to indirectly limit APP holoprotein expression and Abeta peptide secretion.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 322, "target": 2315, "key": "e9a8fe4c8b6253d3622353f1cb564cbb"}, {"line": 12476, "relation": "decreases", "evidence": "Since APP holoprotein levels are proportionate to Abeta peptide output in many systems we tested the efficacy of paroxetine and dimercaptopropanol to limit Abeta secretion as measured by ELISA assays. Paroxetine and dimercaptopropanol limited Abeta peptide secretion from lens epithelial cells (B3 cells). Interestingly, paroxetine changed the steady-state levels of transferrin receptor mRNAs. These data suggested that this serotonin reuptake inhibitor (SSRI) provided extra pharmacological action to chelate interacellular iron or change the intracellular iron distribution. An altered iron distribution would be predicted to indirectly limit APP holoprotein expression and Abeta peptide secretion.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Serotonergic subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 322, "target": 2328, "key": "9f14094059d9f353e42f26d2a2e6d776"}, {"line": 12439, "relation": "positiveCorrelation", "evidence": "Depression is one of the most frequent neuropsychiatric symptoms in Alzheimer's disease (AD). plasminogen activator inhibitor-1 (PAI-1) is involved in the pathogenesis of both AD and depression. This suggests a potential role of the PAI-1 gene SERPINE1 in the development of AD-related depression and its response to antidepressant treatment.", "citation": {"db": "PubMed", "db_id": "22503724"}, "source": 3902, "target": 3823, "key": "c8dd91546a141ca7d023e5709a04ba9e"}, {"line": 14938, "relation": "association", "evidence": "Neuropsychiatric disorders such as depression are frequently associated with Alzheimer's disease (AD) and the degeneration of cholinergic basal forebrain neurons and reductions in acetylcholine that occur in AD have been identified as potential mediators of these secondary neuropsychiatric symptomologies.", "citation": {"db": "PubMed", "db_id": "21723926"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Prosencephalon": true, "Neurons": true}}, "source": 3902, "target": 3823, "key": "77fac4b48c721b3a583e9af75a62fdb5"}, {"line": 17810, "relation": "association", "evidence": "Angiotensin as a target for the treatment of Alzheimer's disease, anxiety and depression.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3902, "target": 210, "key": "0c2bdbb300e3130e27cf1ff603d7029a"}, {"line": 12440, "relation": "association", "evidence": "Depression is one of the most frequent neuropsychiatric symptoms in Alzheimer's disease (AD). plasminogen activator inhibitor-1 (PAI-1) is involved in the pathogenesis of both AD and depression. This suggests a potential role of the PAI-1 gene SERPINE1 in the development of AD-related depression and its response to antidepressant treatment.", "citation": {"db": "PubMed", "db_id": "22503724"}, "source": 3351, "target": 3823, "key": "7667fe8d038cce601efe91293ed3ceed"}, {"relation": "partOf", "source": 3351, "target": 1526, "key": "f5d5360262433e74857aa0e65997f6e7"}, {"line": 12471, "relation": "association", "evidence": "Since APP holoprotein levels are proportionate to Abeta peptide output in many systems we tested the efficacy of paroxetine and dimercaptopropanol to limit Abeta secretion as measured by ELISA assays. Paroxetine and dimercaptopropanol limited Abeta peptide secretion from lens epithelial cells (B3 cells). Interestingly, paroxetine changed the steady-state levels of transferrin receptor mRNAs. These data suggested that this serotonin reuptake inhibitor (SSRI) provided extra pharmacological action to chelate interacellular iron or change the intracellular iron distribution. An altered iron distribution would be predicted to indirectly limit APP holoprotein expression and Abeta peptide secretion.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Serotonergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 4019, "target": 322, "key": "25f3dec41847ca38bbb2ac93283b1120"}, {"line": 12472, "relation": "association", "evidence": "Since APP holoprotein levels are proportionate to Abeta peptide output in many systems we tested the efficacy of paroxetine and dimercaptopropanol to limit Abeta secretion as measured by ELISA assays. Paroxetine and dimercaptopropanol limited Abeta peptide secretion from lens epithelial cells (B3 cells). Interestingly, paroxetine changed the steady-state levels of transferrin receptor mRNAs. These data suggested that this serotonin reuptake inhibitor (SSRI) provided extra pharmacological action to chelate interacellular iron or change the intracellular iron distribution. An altered iron distribution would be predicted to indirectly limit APP holoprotein expression and Abeta peptide secretion.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Serotonergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 4018, "target": 322, "key": "7b537d877e532493e19c463cc3307dbe"}, {"line": 12473, "relation": "association", "evidence": "Since APP holoprotein levels are proportionate to Abeta peptide output in many systems we tested the efficacy of paroxetine and dimercaptopropanol to limit Abeta secretion as measured by ELISA assays. Paroxetine and dimercaptopropanol limited Abeta peptide secretion from lens epithelial cells (B3 cells). Interestingly, paroxetine changed the steady-state levels of transferrin receptor mRNAs. These data suggested that this serotonin reuptake inhibitor (SSRI) provided extra pharmacological action to chelate interacellular iron or change the intracellular iron distribution. An altered iron distribution would be predicted to indirectly limit APP holoprotein expression and Abeta peptide secretion.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Serotonergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 586, "target": 322, "key": "a97b692c899540146d669ac4cae55add"}, {"line": 12516, "relation": "decreases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 159, "target": 3223, "key": "0d57fd5eaa8ff77743f64374bb282e45"}, {"line": 12517, "relation": "increases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 159, "target": 3015, "key": "61000a983a075f7332db76e6f649c9e7"}, {"line": 12518, "relation": "increases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 159, "target": 505, "key": "75ee2ccc471d5546dea1a94d18cfa530"}, {"line": 12528, "relation": "increases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 2796, "key": "50b379d2b89e3f3846c0315617dad2b5"}, {"line": 12541, "relation": "increases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta-Catenin subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 2581, "key": "5bba6aacd6ea5e17792389aafd1978e6"}, {"line": 12549, "relation": "increases", "evidence": "Thus, we addressed this paradoxical condition of AD in rat neurons treated with okadaic acid (OA) which inhibits protein phosphatase-2A (PP2A) and induces tau hyperphosphorylation and cell death. Interestingly, OA also induces phosphorylation of GSK3beta at serine-9 and other substrates including tau, beta-catenin and CRMP2 like in AD brains. In this context, we observed that GSK3beta inhibitors such as lithium chloride and 6-bromoindirubin-3'-monoxime (6-BIO) reversed those phosphorylation events and protected neurons. These data suggest that GSK3beta may still have its kinase activity despite increase of its phosphorylation at serine-9 in AD brains at least in PP2A-compromised conditions and that GSK3beta inhibitors could be a valuable drug candidate in AD.", "citation": {"db": "PubMed", "db_id": "15314261"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 2642, "key": "412b060a0bece49ba8e683a4c99ddc30"}, {"line": 22842, "relation": "increases", "evidence": "There was a significant increase in lipid peroxidation and nitrite in synaptosomal preparations. Preventivetreatment daily for 13 days with antidementic drugs, donepezil (5 mg/kg, p.o) and memantine(10 mg/kg, p.o), significantly attenuated OKA induced mitochondrial dysfunction, apoptotic cell death, memory impairment and histological changes. Mitochondrial dysfunction appeared as a key factor in OKA induced memory impairment and apoptotic cell death. OKA also increases Ca2+ in hippocampal neuronal cell culture.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 478, "key": "181401ff6fbc2a48c7a0e4ffb3cdf66c"}, {"line": 22843, "relation": "decreases", "evidence": "There was a significant increase in lipid peroxidation and nitrite in synaptosomal preparations. Preventivetreatment daily for 13 days with antidementic drugs, donepezil (5 mg/kg, p.o) and memantine(10 mg/kg, p.o), significantly attenuated OKA induced mitochondrial dysfunction, apoptotic cell death, memory impairment and histological changes. Mitochondrial dysfunction appeared as a key factor in OKA induced memory impairment and apoptotic cell death. OKA also increases Ca2+ in hippocampal neuronal cell culture.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 820, "key": "be390229449b45478ec5128409a8067d"}, {"line": 22844, "relation": "increases", "evidence": "There was a significant increase in lipid peroxidation and nitrite in synaptosomal preparations. Preventivetreatment daily for 13 days with antidementic drugs, donepezil (5 mg/kg, p.o) and memantine(10 mg/kg, p.o), significantly attenuated OKA induced mitochondrial dysfunction, apoptotic cell death, memory impairment and histological changes. Mitochondrial dysfunction appeared as a key factor in OKA induced memory impairment and apoptotic cell death. OKA also increases Ca2+ in hippocampal neuronal cell culture.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 94, "key": "e2d82411c531aee136a73aa93ffe31ce"}, {"line": 22879, "relation": "increases", "evidence": "There was a significant (Pb0.01) increase in Ca2+ in hippocampus, cortex, striatum and cerebellum of OKA 200 ng treated rats as compared to control and vehicle treated rats.Treatment with memantine and donepezil significantly (P<0.01) reduced amount of Ca2+ in OKA treated rat brain regions", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 159, "target": 94, "key": "ae9c311898cff1553b2fa5261b1a3d3c"}, {"line": 22845, "relation": "increases", "evidence": "There was a significant increase in lipid peroxidation and nitrite in synaptosomal preparations. Preventivetreatment daily for 13 days with antidementic drugs, donepezil (5 mg/kg, p.o) and memantine(10 mg/kg, p.o), significantly attenuated OKA induced mitochondrial dysfunction, apoptotic cell death, memory impairment and histological changes. Mitochondrial dysfunction appeared as a key factor in OKA induced memory impairment and apoptotic cell death. OKA also increases Ca2+ in hippocampal neuronal cell culture.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 593, "key": "47fb2dca6275a56a2f88fdf46def1128"}, {"line": 22893, "relation": "increases", "evidence": "Production of reactive oxygen species (ROS) in brain regions was measured relative to control. There was a significant increase (P<0.01) in ROS level in cerebellum, hippocampus, cortex and striatum of OKA 200 ng treated rats as compared to control group. Treatment with memantine significantly (Pb0.05) reduced the amount of ROS whereas donepezil did not show significant (PN0.05) effect in any brain regions.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Cerebellum": true, "Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 170, "key": "69e0fd3ee4bb709153b58c24b01a3159"}, {"line": 22967, "relation": "increases", "evidence": "Recently, we have reported that intracerebroventricular (ICV) administration of okadaic acid (OKA) in rats induces memory impairment that was associated with increased oxidative stress. Besides memory deficit, OKA caused impairment in mitochondrial function as revealed by alterations in calcium ion, reactive oxygen species (ROS) generation, mitochondrial membrane potential (MMP), SDH activity and ATP level in the brain regions. Further, in histopathological study it was observed that donepezil and memantine reduced the cell loss and neurodegeneration in hippocampus and periventricular cortex regions in OKA treated rats.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 170, "key": "cde4f4a49c300dcda009058add12d391"}, {"line": 22915, "relation": "decreases", "evidence": "There was a significant (Pb0.05) decrease in MMP in hippocampus and cortex of OKA 200 ng treated rats as compared to control and vehicle treated rats.Treatment with memantine significantly (P<0.05) increased MMP in cortex and hippocampus whereas donepezil significantly (Pb0.05) increased MMP in cortex, hippocampus and striatum as compared to OKA 200 ng treated rat.", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 159, "target": 682, "key": "2048579f20b7bf2b132d2183745654f9"}, {"line": 22927, "relation": "increases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-3 was observed in hippocampus, striatum and cortex of OKA treated rat brain in comparison to that of control and vehicle groups. Treatment with memantine and donepezil significantly (P<0.05) decreased caspase-3 activity and mRNA level in hippocampus, striatum and cortex of OKA 200 ng treated rat brain", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 159, "target": 3755, "key": "1a921711198dde8edb9a06ae44a40b12"}, {"line": 22933, "relation": "increases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-3 was observed in hippocampus, striatum and cortex of OKA treated rat brain in comparison to that of control and vehicle groups. Treatment with memantine and donepezil significantly (P<0.05) decreased caspase-3 activity and mRNA level in hippocampus, striatum and cortex of OKA 200 ng treated rat brain", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 159, "target": 4082, "key": "bd30101fdc0452d50411470bff75bd57"}, {"line": 22945, "relation": "increases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-9 was observed in hippocampus and cortex of OKA treated rats as compared to that of control and vehicle group. Treatment with memantine and donepezil significantly (P<0.01) decreased caspase-9 activity and mRNA expression in hippocampus and cortex as compared to OKA 200 ng treated group (Fig. 10A and B).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 159, "target": 3756, "key": "6e477f93262452d9c43b66696ee7d74a"}, {"line": 22951, "relation": "increases", "evidence": "A significant (Pb0.01) increase in activity and mRNA expression of caspase-9 was observed in hippocampus and cortex of OKA treated rats as compared to that of control and vehicle group. Treatment with memantine and donepezil significantly (P<0.01) decreased caspase-9 activity and mRNA expression in hippocampus and cortex as compared to OKA 200 ng treated group (Fig. 10A and B).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true, "Cerebral Cortex": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 159, "target": 4083, "key": "8ac267d08bed8d6cedd6f0091533c060"}, {"line": 12601, "relation": "decreases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 217, "target": 412, "key": "f63328ae0216b247e439b3b4826ad8a7"}, {"line": 12624, "relation": "decreases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 217, "target": 2794, "key": "09f2593c10351eab8868831c262303c9"}, {"line": 12625, "relation": "decreases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 217, "target": 298, "key": "44ef5895becc89e9263d5a6811d2e4d7"}, {"line": 12633, "relation": "increases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 217, "target": 3391, "key": "e14135c18e4ac3796f420a71a2296721"}, {"line": 12602, "relation": "increases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 412, "target": 3018, "key": "4eee3d2befe884145fdd916b2a981d1d"}, {"line": 12604, "relation": "increases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 412, "target": 3019, "key": "2852a531179dc36c2e0f2510b4c02029"}, {"line": 12606, "relation": "increases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 412, "target": 3020, "key": "7753b2e47c53b27db76dfd0bd7f9518c"}, {"line": 12608, "relation": "increases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 412, "target": 3026, "key": "e3008fabedefa8a750550584baeb9f33"}, {"line": 12610, "relation": "increases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 412, "target": 3027, "key": "6ca25402a914bb2998aad6dc379b3753"}, {"line": 12612, "relation": "increases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 412, "target": 3032, "key": "4bccfc10c986876f23fcbd2e42cd2fa6"}, {"line": 12614, "relation": "increases", "evidence": "We found a significant reduction of calyculin A-induced tau hyperphosphorylation at Ser198/199/202, Ser396, Ser404, Thr205, and Thr231 24 h after treatment with 20 μg/ml berberine. Berberine also restored protein phosphates 2A activity and reversed glycogen synthase kinase-3beta (GSK-3beta) activation, as determined by phosphatase activity assay and GSK-3beta phosphorylation at Tyr216 and Ser9, respectively. Furthermore, berberine reversed both the increase of malondialdehyde and the decrease of superoxide dismutase activity induced by calyculin A, indicating its role in anti-oxidative stress.", "citation": {"db": "PubMed", "db_id": "21297267"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 412, "target": 3035, "key": "a5bd261b52e23c072d77583f651da549"}, {"line": 41502, "relation": "association", "evidence": "The brains were then removed and malondialdehyde (MDA) and total thiol groups concentrations were measured.The time latency to enter the dark compartment by OVX-LPS group was shorter than that of OVX at both first and 24th hours after the shock (P < 0.05 - P < 0.001).", "citation": {"db": "PubMed", "db_id": "24829769"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 298, "target": 291, "key": "b56c6479a21d99ebf636dd403089aae6"}, {"line": 41508, "relation": "association", "evidence": "The hippocampal MDA concentration in OVX-LPS group was higher than Sham- LPS group (P < 0.01).Brain tissue oxidative damage contributed in deleterious effects of LPS on learning and memory.", "citation": {"db": "PubMed", "db_id": "24829769"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Brain": true, "Tissues": true}}, "source": 298, "target": 291, "key": "3e83dcbdf87ae6beb61f53c062d6efa3"}, {"line": 12661, "relation": "isA", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "source": 216, "target": 153, "key": "61c17a95612589824c9555eb18e4f3f5"}, {"line": 12673, "relation": "decreases", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 216, "target": 214, "key": "16208a19c48ffee3c2c5f9365d30ed08"}, {"line": 12689, "relation": "decreases", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 153, "target": 214, "key": "c786bf9903b11d6e8a5cb61554ba8fa9"}, {"line": 12665, "relation": "isA", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "source": 302, "target": 153, "key": "a3d14cc766fd5716653765fbf15f07d3"}, {"line": 12681, "relation": "decreases", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 302, "target": 214, "key": "10066866efbd6706d686fe8e81c8bdd3"}, {"line": 12669, "relation": "isA", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "source": 194, "target": 155, "key": "d72f639ddbc2609a0b22948e8da2f724"}, {"line": 12700, "relation": "causesNoChange", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}}, "object": {"modifier": "Activity"}, "source": 194, "target": 214, "key": "bf7405296fa3cda55be5a215e6c28f15"}, {"line": 41700, "relation": "association", "evidence": "We report the effect of three structurally different PPARgamma agonists inducing apoptosis in human (U87MG and A172) and rat (C6) glioma cells.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Species": {"9606": true, "10116": true}, "CellLine": {"A172": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"High": true}}, "source": 194, "target": 3699, "key": "e987052cf845c81c7b62495964d2ab49"}, {"line": 12698, "relation": "causesNoChange", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}}, "object": {"modifier": "Activity"}, "source": 155, "target": 214, "key": "6e294b856c4b0ffbc42d56fd9723cdaa"}, {"line": 12677, "relation": "increases", "evidence": "The action of arecoline on rat locus coeruleus neurons was studied by intracellular recording from the in vitro brain slice preparation. The arecoline-induced excitatory effects were antagonized by the muscarinic receptor antagonist, atropine, but not by the nicotinic receptor antagonist, hexamethonium. Methoctramine, a selective M2-muscarinic receptor antagonist, was also effective in reversing the arecoline-induced effects.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 214, "target": 779, "key": "09f9b1bc0d1d0d96bfe9b96091832eba"}, {"relation": "partOf", "source": 214, "target": 975, "key": "3d80fbac97c721411a23fa717d4389d9"}, {"line": 12706, "relation": "increases", "evidence": "These results therefore suggest that arecoline exerts its excitatory actions by binding to M2-muscarinic receptors on the cell membrane of neurons of the locus coeruleus.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 214, "target": 975, "key": "9d3f5e94162f43c980b7a52153225bc8"}, {"line": 12721, "relation": "isA", "evidence": "Physostigmine, an acetyl cholinesterase inhibitor, and arecoline, a muscarinic agonist, have been shown to improve Alzheimer presenile dementia in some patients when administered parenterally. Both of these compounds are ineffective orally due to first-pass metabolism.", "citation": {"db": "PubMed", "db_id": "1791534"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 214, "target": 152, "key": "8e12732938646d94b8adde989fc6c165"}, {"line": 12729, "relation": "decreases", "evidence": "Physostigmine, an acetyl cholinesterase inhibitor, and arecoline, a muscarinic agonist, have been shown to improve Alzheimer presenile dementia in some patients when administered parenterally. Both of these compounds are ineffective orally due to first-pass metabolism.", "citation": {"db": "PubMed", "db_id": "1791534"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 214, "target": 3823, "key": "430fd50781b5438fc1f9ccd8e9235052"}, {"line": 12748, "relation": "increases", "evidence": "In an attempt to improve cognitive function in AD, arecoline (a muscarinic cholinergic receptor agonist) was given to patients with probable or possible AD in a two-phase design, and verbal memory function was examined. Results indicate that some patients demonstrate reliable improvements of verbal memory during arecoline treatment.", "citation": {"db": "PubMed", "db_id": "1775605"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 214, "target": 820, "key": "d7e431dad559490f01ebe4d8598a1e29"}, {"line": 12764, "relation": "increases", "evidence": "Treatment with the muscarinic agonist arecoline improves memory retention in patients with Alzheimer's disease (AD). In animal models, arecoline selectively increases local cerebral glucose utilization (LCGU).We examined (1) whether these focal increases in metabolism were coupled to local cerebral blood flow (LCBF) and (2) whether the effect of arecoline on LCGU and LCBF was dependent upon duration of drug administration. In general, LCBF correlated closely with LCGU following arecoline 2 mg/kg administration, but heterogeneous regions were present.", "citation": {"db": "PubMed", "db_id": "8019853"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 214, "target": 820, "key": "918c237d4803bb8c86dd759c954d9eb3"}, {"line": 12768, "relation": "increases", "evidence": "Treatment with the muscarinic agonist arecoline improves memory retention in patients with Alzheimer's disease (AD). In animal models, arecoline selectively increases local cerebral glucose utilization (LCGU).We examined (1) whether these focal increases in metabolism were coupled to local cerebral blood flow (LCBF) and (2) whether the effect of arecoline on LCGU and LCBF was dependent upon duration of drug administration. In general, LCBF correlated closely with LCGU following arecoline 2 mg/kg administration, but heterogeneous regions were present.", "citation": {"db": "PubMed", "db_id": "8019853"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 214, "target": 566, "key": "020a684a8becf903fdd5bfb4d2d8d2b3"}, {"line": 12772, "relation": "increases", "evidence": "Treatment with the muscarinic agonist arecoline improves memory retention in patients with Alzheimer's disease (AD). In animal models, arecoline selectively increases local cerebral glucose utilization (LCGU).We examined (1) whether these focal increases in metabolism were coupled to local cerebral blood flow (LCBF) and (2) whether the effect of arecoline on LCGU and LCBF was dependent upon duration of drug administration. In general, LCBF correlated closely with LCGU following arecoline 2 mg/kg administration, but heterogeneous regions were present.", "citation": {"db": "PubMed", "db_id": "8019853"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 214, "target": 485, "key": "34f28d4a30d722b3f3eccfb6ea4cea16"}, {"line": 12783, "relation": "increases", "evidence": "The mechanisms by which arecoline improves cognitive function are not known. We have demonstrated that arecoline can differentially influence cerebral blood flow and metabolism with both acute and chronic administration. Studies of cerebral glucose metabolism in patients with early or late onset of AD demonstrate glucose metabolic deficits in the temporal and parietal cortex. In advanced cases of AD, cerebral blood flow also can be reduced throughout the cortex.", "citation": {"db": "PubMed", "db_id": "8019853"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 214, "target": 812, "key": "79c99c79139f56ffcf39f94de3bce32a"}, {"line": 12800, "relation": "increases", "evidence": "Acute arecoline administered to 14 subjects produced unpleasant side-effects (e.g. nausea, vomiting), mean adrenocorticotrophic hormone (p = .0006), cortisol (p = .0001) and beta-endorphin (p = .0001) levels were elevated. Thus, high-dose arecoline activates the hypothalamic-pituitary-adrenal (HPA) axis and may increase other anterior pituitary hormone levels, likely representing a 'stress response', but cognition-enhancing, low doses of arecoline do not produce a glucocorticoid response. Hence, arecoline-induced memory improvement is not due to the induction of 'stress' nor to the elevation of peripheral corticosteroid levels.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 214, "target": 236, "key": "fa85225c1645a6921fc2723f8b260c1a"}, {"line": 12839, "relation": "isA", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 214, "target": 97, "key": "5df4819f187225b8f265e5dae2a4c117"}, {"line": 12853, "relation": "increases", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 214, "target": 3545, "key": "c2ef0dfa24298947ee362cfc980c02c8"}, {"line": 12707, "relation": "increases", "evidence": "These results therefore suggest that arecoline exerts its excitatory actions by binding to M2-muscarinic receptors on the cell membrane of neurons of the locus coeruleus.", "citation": {"db": "PubMed", "db_id": "10857465"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 975, "target": 779, "key": "6026693c3157df2fa24ff3a4428f67b4"}, {"relation": "partOf", "source": 3760, "target": 975, "key": "bbaf04074c3d8d5fe009c12d0d911b7c"}, {"line": 12725, "relation": "isA", "evidence": "Physostigmine, an acetyl cholinesterase inhibitor, and arecoline, a muscarinic agonist, have been shown to improve Alzheimer presenile dementia in some patients when administered parenterally. Both of these compounds are ineffective orally due to first-pass metabolism.", "citation": {"db": "PubMed", "db_id": "1791534"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 331, "target": 40, "key": "27e8c812a8c282f25081713b029cf73f"}, {"line": 12733, "relation": "decreases", "evidence": "Physostigmine, an acetyl cholinesterase inhibitor, and arecoline, a muscarinic agonist, have been shown to improve Alzheimer presenile dementia in some patients when administered parenterally. Both of these compounds are ineffective orally due to first-pass metabolism.", "citation": {"db": "PubMed", "db_id": "1791534"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 331, "target": 3823, "key": "9db2b7a7bbfb69586725892215d80253"}, {"line": 12836, "relation": "isA", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 331, "target": 97, "key": "5cd466d6b9604bc300e02a4932ac8727"}, {"line": 12838, "relation": "increases", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 331, "target": 204, "key": "e6b2ef65b5b4d2430dfb78b73e3a88e1"}, {"line": 12919, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 236, "target": 655, "key": "80f5bebe821fdac1f7d96d913c23be4c"}, {"line": 12939, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 236, "target": 812, "key": "dd6572076428e078f9f0576fe5afd5de"}, {"line": 12927, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 366, "target": 655, "key": "d09f660512aac544d32b3f1b5b4998eb"}, {"line": 12947, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 366, "target": 812, "key": "8bea95f52dcf2eac7803c3b04e9c50f5"}, {"line": 12830, "relation": "negativeCorrelation", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 694, "target": 204, "key": "d70625e9086122ee01d28b59c8621723"}, {"line": 12837, "relation": "increases", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 97, "target": 204, "key": "4f084dddba31dbc25602d1aac8dbd0a7"}, {"line": 12845, "relation": "increases", "evidence": "The hypothalamic-pituitary-adrenal (HPA) axis is regulated by cholinergic mechanisms. Acetylcholine (ACh), the cholinergic neurotransmitter, has been reported to alter the levels of cortisol as well as those of prolactin, growth hormone, leutinizing hormone and vasopressin. Prolactin secretion, however, is suppressed by increased pure cholinergic activity. Central actions of cholinomimetic drugs that either increase the concentration of ACh in brain (e.g. physostigmine) or activate the postsynaptic muscarinic receptors (e.g. arecoline), can also activate the HPA axis in both animals and human subjects.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 97, "target": 3545, "key": "82ef8f54aa5bcd038565d82dc72d82c7"}, {"line": 21455, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3545, "target": 868, "key": "e663be1dc1bb8c62684ca1cf6e9bb9b1"}, {"line": 21456, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3545, "target": 2206, "key": "b84fdf9ff986b284a9025d4acc4e26a6"}, {"line": 21457, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3545, "target": 471, "key": "04f5d2d7c8277758fe0425df78edcdef"}, {"line": 12935, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 655, "target": 812, "key": "4a0638b624a80c5be8a7bae0bfcf939f"}, {"line": 17035, "relation": "association", "evidence": "The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 655, "target": 2658, "key": "ce73fa0b75c6c5a3bf8ee2fb708d205c"}, {"line": 48938, "relation": "association", "evidence": "Early growth response gene 1 (Egr1) is a member of the immediate early gene (IEG) family of transcription factors and plays a role in memory formation. The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 655, "target": 2658, "key": "c6ff6df15848dbf0e40f4e6e4d3fd723"}, {"line": 35279, "relation": "increases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 655, "target": 820, "key": "5f06330210cfbac8e479fd959b123840"}, {"line": 46252, "relation": "negativeCorrelation", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 655, "target": 3823, "key": "75d646f64ec071b1b9be6996478f6469"}, {"line": 46394, "relation": "association", "evidence": "Alzheimer's disease (AD) is characterized by progressive cognitive decline. Recent studies have shown that synaptic loss in the cortex is the major correlate of cognitive decline in AD. In the present study we assessed synaptic proteins such as synaptobrevin, synaptophysin, synaptotagmin, synaptosomal-associated protein 25 (SNAP-25), and syntaxin1/HPC-1 in control and AD brains to determine whether synaptic proteins are equally or differentially affected in AD. Western analysis showed that in AD levels of synaptobrevin and synaptophysin were decreased by some 30% from amounts in controls, while those of synaptotagmin, SNAP-25, and syntaxin 1/HPC-1 were decreased by only about 10%. As synaptobrevin and synaptophysin are localized mainly in transmitter-containing synaptic vesicles while SNAP-25 and syntaxin 1/HPC-1 are found in presynaptic plasma membranes, these results suggest differential involvement of synaptic components in AD.", "citation": {"db": "PubMed", "db_id": "9240416"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Synapse assembly subgraph": true}}, "source": 655, "target": 3431, "key": "4e8afa0a0ab8795d9a60ce2749cacc37"}, {"line": 12931, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 5, "target": 655, "key": "b8035889b7f8d0ff2a0d708f0e44eb51"}, {"line": 12951, "relation": "increases", "evidence": "Cumulative evidence from human and animal studies suggests that central activation of the HPA axis by cholinergic drugs can result in the elevation of plasma levels of several neuropeptides including adrenocorticotrophic hormone (ACTH), cortisol, b-endorphin, vasopressin, and epinephrine. Neuropeptides can modulate neurotransmission and can effect cognition. It has, therefore, been speculated that cognitive enhancement by cholinergic drugs could be mediated by the central activation of the HPA axis.", "citation": {"db": "PubMed", "db_id": "8584603"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 5, "target": 812, "key": "b51cce22ccf8a82cdce709a722d2f3a3"}, {"line": 12979, "relation": "decreases", "evidence": "Flurbiprofen, the NSAID of which tarenflurbil is the R-enantiomer, has substantial amyloid-reducing activity. Because the R-enantiomer retains this anti-amyloid effect but does not have the cyclo-oxygenase inhibitory effect of flurbiprofen, it can be given at relatively high doses to elderly patients. It is thus a promising candidate for treatment of AD.", "citation": {"db": "PubMed", "db_id": "18450518"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 80, "key": "66caaad22b63c4ed63954c2d9f1b23be"}, {"line": 13017, "relation": "decreases", "evidence": "Results from a phase 2 study of tarenflurbil, a compound that inhibits gamma-secretase and has positive effects on cognition in animals, seemed promising in slowing decline in Alzheimer's disease assessment scale-cognitive (ADAS-cog) scores in patients with mild Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20170836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 80, "key": "bca0acd818f77fbf11f417d04ee6387d"}, {"line": 12981, "relation": "causesNoChange", "evidence": "Flurbiprofen, the NSAID of which tarenflurbil is the R-enantiomer, has substantial amyloid-reducing activity. Because the R-enantiomer retains this anti-amyloid effect but does not have the cyclo-oxygenase inhibitory effect of flurbiprofen, it can be given at relatively high doses to elderly patients. It is thus a promising candidate for treatment of AD.", "citation": {"db": "PubMed", "db_id": "18450518"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 752, "key": "e3518566ca43786acf40097e156964d0"}, {"line": 12988, "relation": "causesNoChange", "evidence": "The phase II trial of tarenflurbil1 was designed to show slowing of cognitive and functional decline in mild to moderate AD, with enrolment of about 200 patients in a 1-year trial. The primary analysis failed: there was no overall effect on the primary outcomes. But planned analyses nonetheless suggested that the drug had an influence on the outcome measures: there was a significant interactive effect of treatment and baseline cognitive function on change in outcomes. Do these results prove the efficacy of tarenflurbil in slowing decline in mild AD? No—the data are consistent with a beneficial effect of tarenflurbil in mild AD, but are hardly conclusive.", "citation": {"db": "PubMed", "db_id": "18450518"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3823, "key": "cb6653fba906e8c7410be8da5cf6839e"}, {"line": 13023, "relation": "causesNoChange", "evidence": "A recent report by Green and colleagues3 presents the results of this phase 3 trial of tarenflurbil in patients with mild Alzheimer's disease, in which there were no significant effects on any of the primary endpoints", "citation": {"db": "PubMed", "db_id": "20170836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3823, "key": "33815ecaa6c24ef6087eaea79a539991"}, {"line": 13059, "relation": "causesNoChange", "evidence": "Tarenflurbil had no beneficial effect on the co-primary outcomes using an intent-to-treat analysis. No significant differences occurred in the secondary outcomes. The ADAS-Cog score decreased by 7.1 points over 18 months. The tarenflurbil group had a small increase in frequency of dizziness, anemia, and infections.", "citation": {"db": "PubMed", "db_id": "20009055"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3823, "key": "d4a7cbf77a848982098599e22274eb82"}, {"line": 13098, "relation": "causesNoChange", "evidence": "In patients with moderate AD, 800 mg tarenflurbil twice per day had no significant effects on ADCS-ADL and ADAS-cog and had a negative effect on CDR-sb (-52%, Cohen's d -1.08; p=0.003).The most common adverse events were diarrhoea (in seven, nine, and five patients in the 800 mg, 400 mg, and placebo groups, respectively), nausea (in seven, seven, and four patients), and dizziness (in five, nine, and four patients).", "citation": {"db": "PubMed", "db_id": "18450517"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3823, "key": "ec3604dc05519ab56f095152446fc9f4"}, {"line": 13015, "relation": "decreases", "evidence": "Results from a phase 2 study of tarenflurbil, a compound that inhibits gamma-secretase and has positive effects on cognition in animals, seemed promising in slowing decline in Alzheimer's disease assessment scale-cognitive (ADAS-cog) scores in patients with mild Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20170836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 868, "key": "9ae8740f1ce3b0f071286a3c3166f441"}, {"line": 13016, "relation": "positiveCorrelation", "evidence": "Results from a phase 2 study of tarenflurbil, a compound that inhibits gamma-secretase and has positive effects on cognition in animals, seemed promising in slowing decline in Alzheimer's disease assessment scale-cognitive (ADAS-cog) scores in patients with mild Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20170836"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 812, "key": "4b7fbdb73dba59555c5ae192cdca8e96"}, {"line": 13040, "relation": "causesNoChange", "evidence": "In conclusion, supposing that the Aβ hypothesis of AD is still correct, it is possible that the negative results obtained by tarenflurbil in mild AD patients in the recently completed Phase III study are due to the compound’s poor pharmacological profile as Aβ 1−42 lowering agent, its poor ability to penetrate the blood-brain barrier and to its residual anti-inflammatory activity.", "citation": {"db": "PubMed", "db_id": "19542625"}, "annotations": {"FDASTATUS": {"Phase 3": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 2328, "key": "9fcd2ffbd52461c914d50ebc8d4398c3"}, {"line": 13113, "relation": "decreases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 7, "target": 2328, "key": "5fe7433c84342c1570482ba9214a60f0"}, {"line": 13160, "relation": "decreases", "evidence": "Tarenflurbil binds to a gamma-secretase site other than the active/catalytic center of relevance to production of Abeta42, thereby altering the conformation of gamma-secretase and shifting production away from Abeta42, while avoiding interference with other physiologically essential gamma-secretase substrates. Tarenflurbil, which is the pure, R-enantiomer of flurbiprofen, shifts cleavage of APP away from Abeta42, thereby producing shorter, nontoxic fragments (e.g., Abeta38).", "citation": {"db": "PubMed", "db_id": "17599166"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 2328, "key": "df57e06945720e4a31f02e28f942c12d"}, {"line": 13060, "relation": "increases", "evidence": "Tarenflurbil had no beneficial effect on the co-primary outcomes using an intent-to-treat analysis. No significant differences occurred in the secondary outcomes. The ADAS-Cog score decreased by 7.1 points over 18 months. The tarenflurbil group had a small increase in frequency of dizziness, anemia, and infections.", "citation": {"db": "PubMed", "db_id": "20009055"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3904, "key": "92b3caa94c2bcb62cd6aca26cdf40a53"}, {"line": 13101, "relation": "increases", "evidence": "In patients with moderate AD, 800 mg tarenflurbil twice per day had no significant effects on ADCS-ADL and ADAS-cog and had a negative effect on CDR-sb (-52%, Cohen's d -1.08; p=0.003).The most common adverse events were diarrhoea (in seven, nine, and five patients in the 800 mg, 400 mg, and placebo groups, respectively), nausea (in seven, seven, and four patients), and dizziness (in five, nine, and four patients).", "citation": {"db": "PubMed", "db_id": "18450517"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3904, "key": "56e088b478587b213c9a50db4085291d"}, {"line": 13061, "relation": "increases", "evidence": "Tarenflurbil had no beneficial effect on the co-primary outcomes using an intent-to-treat analysis. No significant differences occurred in the secondary outcomes. The ADAS-Cog score decreased by 7.1 points over 18 months. The tarenflurbil group had a small increase in frequency of dizziness, anemia, and infections.", "citation": {"db": "PubMed", "db_id": "20009055"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3890, "key": "9cfce1564526c9db8a464e3e5941df50"}, {"line": 13169, "relation": "increases", "evidence": "Adverse events observed at a higher frequency in the treated groups compared with placebo included transient eosinophilia, mild anemia, blood pressure elevation, lower respiratory infection, and rash.", "citation": {"db": "PubMed", "db_id": "17599166"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3890, "key": "193571184b621e9e209abb7f018a2902"}, {"line": 13062, "relation": "increases", "evidence": "Tarenflurbil had no beneficial effect on the co-primary outcomes using an intent-to-treat analysis. No significant differences occurred in the secondary outcomes. The ADAS-Cog score decreased by 7.1 points over 18 months. The tarenflurbil group had a small increase in frequency of dizziness, anemia, and infections.", "citation": {"db": "PubMed", "db_id": "20009055"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3919, "key": "5c4fbec04a1583dc61e16c9cb607d02d"}, {"line": 13077, "relation": "decreases", "evidence": "The outcomes of the clinical trials of the gamma-secretase inhibitor Semagacestat (LY-450139) and the gamma-secretase modulator (GSM) Tarenflurbil were disappointing, but may not represent the end of the gamma-secretase era. gamma-Secretase modulators, by definition, only block the gamma-secretase cleavage of amyloid-beta protein precursor (AbetaPP) to generate the longer, 42-residue amyloid-beta (Abeta42) without changing the production of total Abeta. The first generation GSMs were shown to block Abeta42 generation while increasing Abeta38. The non-steroidal anti-inflammatory drug, Tarenflurbil, binds to AbetaPP and shifts the cleavage site from Abeta42 to Abeta38", "citation": {"db": "PubMed", "db_id": "22710916"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 4096, "key": "792223a15ad88797f964e4881d9336e0"}, {"line": 13078, "relation": "isA", "evidence": "The outcomes of the clinical trials of the gamma-secretase inhibitor Semagacestat (LY-450139) and the gamma-secretase modulator (GSM) Tarenflurbil were disappointing, but may not represent the end of the gamma-secretase era. gamma-Secretase modulators, by definition, only block the gamma-secretase cleavage of amyloid-beta protein precursor (AbetaPP) to generate the longer, 42-residue amyloid-beta (Abeta42) without changing the production of total Abeta. The first generation GSMs were shown to block Abeta42 generation while increasing Abeta38. The non-steroidal anti-inflammatory drug, Tarenflurbil, binds to AbetaPP and shifts the cleavage site from Abeta42 to Abeta38", "citation": {"db": "PubMed", "db_id": "22710916"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 118, "key": "d916f1c29a96afd1f3ffcbd3e361021c"}, {"line": 13099, "relation": "increases", "evidence": "In patients with moderate AD, 800 mg tarenflurbil twice per day had no significant effects on ADCS-ADL and ADAS-cog and had a negative effect on CDR-sb (-52%, Cohen's d -1.08; p=0.003).The most common adverse events were diarrhoea (in seven, nine, and five patients in the 800 mg, 400 mg, and placebo groups, respectively), nausea (in seven, seven, and four patients), and dizziness (in five, nine, and four patients).", "citation": {"db": "PubMed", "db_id": "18450517"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3903, "key": "aa79d9f235985788d576418628234dbe"}, {"line": 13100, "relation": "increases", "evidence": "In patients with moderate AD, 800 mg tarenflurbil twice per day had no significant effects on ADCS-ADL and ADAS-cog and had a negative effect on CDR-sb (-52%, Cohen's d -1.08; p=0.003).The most common adverse events were diarrhoea (in seven, nine, and five patients in the 800 mg, 400 mg, and placebo groups, respectively), nausea (in seven, seven, and four patients), and dizziness (in five, nine, and four patients).", "citation": {"db": "PubMed", "db_id": "18450517"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3922, "key": "01452f0915f41ba15ebf253304bcd027"}, {"line": 13122, "relation": "decreases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Inflammatory response subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 131, "key": "2a842017b1d030e45f39fa9620c3c542"}, {"line": 13130, "relation": "decreases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Inflammatory response subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 645, "key": "f786a3e6ce2de71a81b5d6bfbae7682e"}, {"line": 13137, "relation": "increases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 640, "key": "0cf11a465982971451a628bc94eef8d0"}, {"line": 13138, "relation": "increases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3116, "key": "40f3c359a979ae89e30776daa17e2470"}, {"line": 13140, "relation": "increases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 2397, "key": "1e0e9cea7ce164ea35024edc2ca5493f"}, {"line": 13161, "relation": "increases", "evidence": "Tarenflurbil binds to a gamma-secretase site other than the active/catalytic center of relevance to production of Abeta42, thereby altering the conformation of gamma-secretase and shifting production away from Abeta42, while avoiding interference with other physiologically essential gamma-secretase substrates. Tarenflurbil, which is the pure, R-enantiomer of flurbiprofen, shifts cleavage of APP away from Abeta42, thereby producing shorter, nontoxic fragments (e.g., Abeta38).", "citation": {"db": "PubMed", "db_id": "17599166"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 7, "target": 411, "key": "c50ab6c8d3c4cd27949a79e68b43d702"}, {"line": 13168, "relation": "increases", "evidence": "Adverse events observed at a higher frequency in the treated groups compared with placebo included transient eosinophilia, mild anemia, blood pressure elevation, lower respiratory infection, and rash.", "citation": {"db": "PubMed", "db_id": "17599166"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3907, "key": "c1ca2f4923c11ee71720a28d44940a85"}, {"line": 13170, "relation": "increases", "evidence": "Adverse events observed at a higher frequency in the treated groups compared with placebo included transient eosinophilia, mild anemia, blood pressure elevation, lower respiratory infection, and rash.", "citation": {"db": "PubMed", "db_id": "17599166"}, "annotations": {"FDASTATUS": {"Phase 2": true}, "DiseaseState": {"Mild AD": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 7, "target": 3883, "key": "0f998eef5172de2b5ead0fbe118a00ee"}, {"line": 13203, "relation": "decreases", "evidence": "We have found that chronic administration of R-flurbiprofen is able to attenuate spatial learning deficits if given prior to plaque deposition in Tg2576 mice.", "citation": {"db": "PubMed", "db_id": "17650315"}, "annotations": {"Species": {"10090": true}}, "source": 7, "target": 3863, "key": "f0b24ec13e753af5512908ffed1b60ad"}, {"line": 13139, "relation": "increases", "evidence": "The present investigation reports that clinically relevant concentrations of tarenflurbil (i.e., 1-5 microM) protect both cultured human neuroblastoma cell lines and primary neurons from cytotoxicity associated with exposure to Abeta_{42} or H_{2}O_{2}. In concert with this protection, there is an upregulation of neurotrophins [i.e., nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF)]. Furthermore, blocking exogenous NGF or BDNF by binding it to antibody prevents tarenflurbil from protecting human neuronal cells from Abeta_{42} and H_{2}O_{2} cytotoxicity. These findings suggest that up-regulation of neurotrophins might represent an underlying mechanism contributing to the beneficial effects seen with tarenflurbil in AD.", "citation": {"db": "PubMed", "db_id": "18997293"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 640, "target": 3116, "key": "1124706eb05e62b40c5dc8809e5c575c"}, {"relation": "hasReactant", "source": 4106, "target": 3126, "key": "54b8c9f59d97bdaef5387149652947d3"}, {"relation": "hasProduct", "source": 4106, "target": 3127, "key": "a78fc5c08594417f8b9199efccef77fb"}, {"line": 29950, "relation": "association", "evidence": "Presenilin (PS) proteins facilitate endoproteolysis of selected type I transmembrane proteins such as the Alzheimer's disease (AD) associated beta-Amyloid precursor protein (beta APP) and Notch.", "citation": {"db": "PubMed", "db_id": "11493036"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3127, "target": 3258, "key": "47c96b25e82772492810c345470f1a69"}, {"line": 13220, "relation": "isA", "evidence": "In this study, we show that at nanomolar-low micromolar concentrations, etazolate, a selective GABA(A) receptor modulator, stimulates sAPPalpha production in rat cortical neurons and in guinea pig brains. Etazolate (20 nM-2 microM) dose-dependently protected rat cortical neurons against Abeta-induced toxicity.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 422, "target": 48, "key": "126fbfffe73d2b669048ece59be1cab5"}, {"line": 13224, "relation": "increases", "evidence": "In this study, we show that at nanomolar-low micromolar concentrations, etazolate, a selective GABA(A) receptor modulator, stimulates sAPPalpha production in rat cortical neurons and in guinea pig brains. Etazolate (20 nM-2 microM) dose-dependently protected rat cortical neurons against Abeta-induced toxicity.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 422, "target": 2137, "key": "4ec337a55e88853d092fce82d4970279"}, {"line": 13274, "relation": "increases", "evidence": "Etazolate is a phosphodiesterase 4 (PDE4) inhibitor and GABAA receptor modulator that also stimulates alpha-secretase activity and neurotrophic soluble amyloid precursor protein (sAPPalpha) production, currently developed as a possible Alzheimer's disease therapeutic.", "citation": {"db": "PubMed", "db_id": "20223232"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 422, "target": 2137, "key": "7f7f8186d434ce0a5353517d89241773"}, {"line": 13228, "relation": "increases", "evidence": "In this study, we show that at nanomolar-low micromolar concentrations, etazolate, a selective GABA(A) receptor modulator, stimulates sAPPalpha production in rat cortical neurons and in guinea pig brains. Etazolate (20 nM-2 microM) dose-dependently protected rat cortical neurons against Abeta-induced toxicity.", "citation": {"db": "PubMed", "db_id": "18397369"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 422, "target": 854, "key": "55b07df37e69f0d3fb322f5e2bd4ee1c"}, {"line": 13326, "relation": "increases", "evidence": "Nefiracetam is undergoing preclinical and clinical tests as a cognition-enhancing drug in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "9201800"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 311, "target": 812, "key": "9c50c95714d82149b16f1c4b109a6853"}, {"line": 13337, "relation": "increases", "evidence": "The nootropic compound nefiracetam was evaluated in 88 older rabbits in a 750-ms delay paradigm of eyeblink classical conditioning (EBCC). Nefiracetam facilitated acquisition of EBCC in older rabbits. EBCC is performed poorly by older humans and is seriously impaired in Alzheimer's disease. These preclinical data in an animal model with clear parallels in humans suggest that nefiracetam may prove effective as a cognition enhancer in clinical trials.", "citation": {"db": "PubMed", "db_id": "7838908"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 311, "target": 812, "key": "10d827cb395700143c6e2685529ee382"}, {"line": 13607, "relation": "increases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 311, "target": 812, "key": "af16513f8716463f6579c616ca7a4dc3"}, {"line": 13717, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 311, "target": 812, "key": "f57aa437516842680beb658116ab3240"}, {"line": 13353, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2779, "key": "a7724371b1525b246a5cdb1dca95dcc7"}, {"line": 13633, "relation": "increases", "evidence": "In conclusion, nefiracetam enhances NMDA-receptor function through stimulation of its glycine binding site and nefiracetam-induced CaMKII activation likely contributes to improvement of cognition, learning, and memory.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2779, "key": "5a1ccb46c535532a124154c7139c0c8c"}, {"line": 13357, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2516, "key": "d9f1d8f8e3df327a4b040a1c22d6c175"}, {"line": 13361, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2517, "key": "eba288f14acc6887149a291e50533170"}, {"line": 13365, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2518, "key": "118363be6f09b031d3fb46d5251242f0"}, {"line": 13369, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2520, "key": "e334b567b291ab837ef5189393adcad3"}, {"line": 13373, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2521, "key": "c21e7cd78b20e2bff5d74ed685676b9e"}, {"line": 13377, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2522, "key": "a39fa65bb99da88d311dda236c3a54fd"}, {"line": 13381, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2523, "key": "2c78e1ae03e0a1e8b792d50074b71b49"}, {"line": 13385, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2515, "key": "fa89f1222171147038fcf7547eb8855a"}, {"line": 13389, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2524, "key": "bc3bd63c328056cd6689cf00fcc64d00"}, {"line": 13393, "relation": "increases", "evidence": "We have previously demonstrated that the nootropic drug nefiracetam potentiates the activity of both nicotinic acetylcholine and NMDA receptors and that nefiracetam modulates the glycine binding site of the NMDA receptor.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 2526, "key": "0e745b600820e4cc58d5156ae8d17286"}, {"line": 13403, "relation": "increases", "evidence": "In immunoblotting analysis, nefiracetam treatment increased the PKCalpha activity with a bell-shaped dose-response relationship peaking at 10 nM, thereby increasing phosphorylation of PKC substrate and NMDA receptor. Nefiracetam treatment did not affect the PKA activity.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"NMDA receptor": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 3236, "key": "4e0c9f53641060d0e1983a1a3cd2abc3"}, {"line": 13411, "relation": "causesNoChange", "evidence": "In immunoblotting analysis, nefiracetam treatment increased the PKCalpha activity with a bell-shaped dose-response relationship peaking at 10 nM, thereby increasing phosphorylation of PKC substrate and NMDA receptor. Nefiracetam treatment did not affect the PKA activity.", "citation": {"db": "PubMed", "db_id": "17095583"}, "annotations": {"Subgraph": {"NMDA receptor": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 311, "target": 697, "key": "c8958c657ff0cceef1ce3117894e4f38"}, {"line": 13564, "relation": "isA", "evidence": "The nefiracetam-induced increase in the frequency of mEPSCs and mIPSCs over and above the level achieved by ACh alone would contribute to the therapeutic effect of nefiracetam as the cholinergic system is known to be downregulated in the brain of Alzheimer's disease patients.The therapeutic effects of galantamine are ascribed to the modest potentiation of nACh receptor and NMDA receptor activities in addition to the modest inhibition of acetylcholinesterase.", "citation": {"db": "PubMed", "db_id": "19272425"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 311, "target": 39, "key": "bafc4e57d8a424dcad43a6d45bcd8b85"}, {"line": 13617, "relation": "increases", "evidence": "Nefiracetam treatment significantly enhanced long-term potentiation (LTP) with the same bell-shaped dose-response curve. Furthermore, nefiracetam-induced LTP enhancement was closely associated with calcium/calmodulin-dependent protein kinase II (CaMKII) activation with increase in phosphorylation of AMPA-type glutamate receptor subunit 1 (GluA1) (Ser-831) as a postsynaptic CaMKII substrate. ", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 311, "target": 597, "key": "f235ee38835b17c13148620ab169d9ec"}, {"line": 13621, "relation": "increases", "evidence": "Nefiracetam treatment significantly enhanced long-term potentiation (LTP) with the same bell-shaped dose-response curve. Furthermore, nefiracetam-induced LTP enhancement was closely associated with calcium/calmodulin-dependent protein kinase II (CaMKII) activation with increase in phosphorylation of AMPA-type glutamate receptor subunit 1 (GluA1) (Ser-831) as a postsynaptic CaMKII substrate. ", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 867, "key": "95394d2868e65773918b866d15378896"}, {"line": 13637, "relation": "increases", "evidence": "In conclusion, nefiracetam enhances NMDA-receptor function through stimulation of its glycine binding site and nefiracetam-induced CaMKII activation likely contributes to improvement of cognition, learning, and memory.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 311, "target": 867, "key": "4f48f5db2b54c76f71f6bfaa3a006b5c"}, {"relation": "partOf", "source": 311, "target": 982, "key": "396453ac5f655b9a25111b4114abf613"}, {"line": 13669, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "source": 311, "target": 982, "key": "0a2b476df7514a1b1abb0ef93bb057af"}, {"relation": "isA", "source": 2775, "target": 2779, "key": "39b1d4c0d90fe2e33eecdb8a23692417"}, {"relation": "partOf", "source": 2775, "target": 907, "key": "ec371c9c2b685260ca79a77091691263"}, {"relation": "isA", "source": 2781, "target": 2779, "key": "44338d9dfa9bd8628ef4023127465e41"}, {"line": 36540, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2781, "target": 2252, "key": "447e0ed906cdc949fd82ccb4bec65b43"}, {"line": 36541, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2781, "target": 2249, "key": "1c2f95a39fefe68db8de52e0c09a60f1"}, {"line": 36542, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2781, "target": 2250, "key": "fe58039ea75de4884ff1db1d2bd7cf91"}, {"line": 36561, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2781, "target": 80, "key": "e8fbc0dca23c522b682cc02f6ab2902c"}, {"line": 36589, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2781, "target": 80, "key": "d99c53f3d585a56b642c7ab27deb6373"}, {"line": 36623, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2781, "target": 384, "key": "15e10b6a8d8da928ad55c6388d92e4e0"}, {"line": 37161, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2781, "target": 384, "key": "2b436dfadb64fc6498c9d32eb7ba8515"}, {"line": 37144, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2781, "target": 2328, "key": "22d7c12657d85f5734990281e13dd7e3"}, {"line": 37162, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2781, "target": 399, "key": "eca415e5e7ca41c990e053882ebc9f60"}, {"line": 37634, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "subject": {"modifier": "Translocation"}, "source": 2781, "target": 726, "key": "913da3654a51d69e426266c624f2d3ae"}, {"line": 37638, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "subject": {"modifier": "Translocation"}, "source": 2781, "target": 761, "key": "bc41b941fc75649259cf0f691fe65da4"}, {"line": 37641, "relation": "association", "evidence": "Consistent with our cell biological studies implicating APP in regulation of dendritic spines and NMDAR trafficking, numerous behavioral studies suggest that APP influences synaptic plasticity as well as learning and memory.", "citation": {"db": "PubMed", "db_id": "21199446"}, "subject": {"modifier": "Translocation"}, "source": 2781, "target": 812, "key": "50cc465e777332ce1bac7bd075d985cf"}, {"line": 37678, "relation": "association", "evidence": "beta-Amyloid precursor protein is axonally transported and accumulates in presynaptic terminals and growth cones. A secreted form of beta-APP (sAPP alpha) is released from neurons in response to electrical activity and may function in modulation of neuronal excitability, synaptic plasticity, neurite outgrowth, synaptogenesis, and cell survival. A signaling pathway involving guanosine 3',5'-cyclic monophosphate is activated by sAPP alpha and modulates the activities of potassium channels, N-methyl-D-aspartate receptors, and the transcription factor NF kappa B. Additional functions of beta-APP may include modulation of cell adhesion and regulation of proliferation of nonneuronal cells.", "citation": {"db": "PubMed", "db_id": "9354812"}, "subject": {"modifier": "Activity"}, "source": 2781, "target": 2137, "key": "5bed18f3ed8c1fba10476801fdcce8bf"}, {"relation": "isA", "source": 2782, "target": 2779, "key": "e8059cdd02b6690f4ba4b6ff70e76fb0"}, {"relation": "partOf", "source": 2782, "target": 926, "key": "deed7df2ed04a390db4433e111377a6c"}, {"line": 36544, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2782, "target": 2252, "key": "97b7f17cc0315cc6bb50118338c549fe"}, {"line": 36545, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2782, "target": 2249, "key": "f8ff3f8cd62365b268367e175e607459"}, {"line": 36546, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2782, "target": 2250, "key": "32f456fba9c4a75ea391cdbd6bf43954"}, {"line": 36562, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2782, "target": 80, "key": "7b17eaae2c80f3c9644d0f52378560ea"}, {"line": 36590, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2782, "target": 80, "key": "313e9bd9abe7f8e0366c78b932930d4d"}, {"line": 36625, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2782, "target": 384, "key": "c05b585ad9309e67075bba45dbeb25bc"}, {"line": 37163, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2782, "target": 384, "key": "25b4e0cb48f3362dd8d47f6b5598f728"}, {"line": 37145, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2782, "target": 2328, "key": "3b060de5907697466362c96850435d1e"}, {"line": 37164, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2782, "target": 399, "key": "ff82e3496ae1a741a5d677f08449cace"}, {"relation": "isA", "source": 2783, "target": 2779, "key": "d897b95a5a9228c34b4f11c5f1cafe03"}, {"line": 36548, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2783, "target": 2252, "key": "7a0aaea675e6eeacb9cb6738c861a597"}, {"line": 36549, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2783, "target": 2249, "key": "8c06fc74d4a5876c61ff39d0e6a490c0"}, {"line": 36550, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2783, "target": 2250, "key": "f992455f143d66ab52f55326b2f2e902"}, {"line": 36563, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2783, "target": 80, "key": "163e8f7bdeac97a1e0d2457da58b6c2b"}, {"line": 36591, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2783, "target": 80, "key": "52b5f37eff9a4764d00cb443fe5e0fcd"}, {"line": 36627, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2783, "target": 384, "key": "35c6afcdebe6d71053d0da5574adb9c5"}, {"line": 37165, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2783, "target": 384, "key": "71b1ad5288de2f52bedcb10b50625ff1"}, {"line": 37146, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2783, "target": 2328, "key": "513da0fc76d491743cabde1de2d1dee4"}, {"line": 37166, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2783, "target": 399, "key": "3b00dbeb56b0eaf9029f7a7722d8f059"}, {"relation": "isA", "source": 2784, "target": 2779, "key": "7f4e77b6d50bba5a9d511dca94c2481a"}, {"line": 36552, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2784, "target": 2252, "key": "5680ab7a4dcd9594cd6adbe0c9ae752d"}, {"line": 36553, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2784, "target": 2249, "key": "000623a4bed2a15a2b948a0034d0163b"}, {"line": 36554, "relation": "decreases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2784, "target": 2250, "key": "f4993f3c91c495ccf0b1b59e57d8173f"}, {"line": 36564, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2784, "target": 80, "key": "26ef57933c58a70811d279cb8daa62aa"}, {"line": 36592, "relation": "increases", "evidence": "The lower concentrations of aged ABeta¸42used by Puzzo et al.[83] are close to those found in the normal brain and shown to enhance LTP and memory. Increase in synaptic activity will increase ABeta¸ production while lowering synaptic activity minimizes ABeta¸ production. Similarly specific stimulation of NMDA receptors promotes ABeta¸ production and ABeta¸ in turn depresses synaptic activity. Thus indirectly ABeta¸ also plays a role in suppressing excessive glutamate release. Interestingly, ABeta¸ may play an important role during synapse elimination and stimulatation of neurogenesis in the hippocampus during brain development [93]. These studies provide convincing evidence to show that physiological levels of ABeta¸ have a role in controlling synaptic activity.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2784, "target": 80, "key": "d61c2b2c26ce487f82101cb7cc5c155e"}, {"line": 36629, "relation": "decreases", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2784, "target": 384, "key": "0b1379d0c51f483c51ab3bc740697039"}, {"line": 37167, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2784, "target": 384, "key": "644692dd6c6f5c182a72cda4f35e4a17"}, {"line": 37147, "relation": "association", "evidence": "Molecular targets of ABeta¸ induced synaptic dysfunction: The search for a mechanism by which ABeta¸ impairs synaptic plasticity has led to the identification of the cell surface receptors and signaling pathways mediating ABeta¸-induced synaptoxicity. Cell surface interaction sites reported for ABeta¸ include receptor for Advanced Glycation End products (RAGE) and NMDAR [152, 209]. ABeta¸ has been variously reported to directly affect the activity of NMDAR, possibly by binding to nAChRs, or intracellular mitochondrial cyclophilin D (CypD), mitochondrial ABeta¸ alcohol dehydrogenase (ABAD) or certain protein kinases. Examination of the evidence for these multiple activities of ABeta¸ and their affinity constants may distinguish direct binding partners from downstream effectors.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2784, "target": 2328, "key": "ac2ac1a7d883fe638784313dc1295260"}, {"line": 37168, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 2784, "target": 399, "key": "af69bc2896e4fe9d8e7ef0b7e32eba9e"}, {"relation": "isA", "source": 3236, "target": 3236, "key": "47601fa94cb122a9217488557da517e9"}, {"relation": "partOf", "source": 3236, "target": 982, "key": "e6d2c17dd2835ed4d09dd08267188f5c"}, {"line": 23573, "relation": "increases", "evidence": "Of the signaling molecules downstream of VEGF receptors, PKC has been demonstrated to be critical in mediating endothelial cell proliferation.17 18 19 Although inhibition of PKC may seem a reasonable approach to curtail endothelial cell proliferation in proliferative retinopathies,20 21 PKC serves diverse essential normal roles in intracellular signaling, indicating broad-spectrum inhibition of all PKCs may have harmful side effects, including cell apoptotic process. Riluzole inhibited PKC activity in cortical cell cultures, and also inhibited the activity of purified PKC in vitro.", "citation": {"db": "PubMed", "db_id": "16303979"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3236, "target": 478, "key": "eeec5105d8ec5e400910639e552afef7"}, {"line": 27555, "relation": "increases", "evidence": "It has also been shown in animal pmodels that under conditions of reduced M1/M3 muscarinic acetylcholine receptor stimulation the secretory pathway of APP processing is inhibited and that constitutive upregulation of M1/M3-associated PKC increases APP secretion.", "citation": {"db": "PubMed", "db_id": "9775403"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3236, "target": 2315, "key": "e55682e39eddc4fa8567172b135a7056"}, {"line": 27834, "relation": "increases", "evidence": "Similar effects of BACE1 up-regulation were observed when protein kinase C was directly activated by phorbol esters.", "citation": {"db": "PubMed", "db_id": "15211591"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3236, "target": 2375, "key": "d4870be8e2357e981cae1ec242496f91"}, {"line": 32578, "relation": "increases", "evidence": "Ser727 of STAT1 can be phosphorylated by diverse kinases, such as phosphatidylinositol 3-kinase/Akt, calcium/calpmodulin-dependent kinase II, protein kinase C, and MAPKs.", "citation": {"db": "PubMed", "db_id": "17091494"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "JAK-STAT signaling subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3236, "target": 3425, "key": "19802cd7c4e47085354fa864a96d6269"}, {"line": 36178, "relation": "increases", "evidence": "Protein kinase C and rho activated coiled coil protein kinase 2 (ROCK2) modulate Alzheimer's APP metabolism and phosphorylation of the Vps10-domain protein, SorL1.Generation of the amyloid ß (ABeta¸) peptide of Alzheimer's disease (AD) is differentially regulated through the intracellular trafficking of the amyloid ß precursor protein (APP) within the secretory and endocytic pathways. Protein kinase C (PKC) and rho-activated coiled-coil kinases (ROCKs) are two third messenger signaling molecules that control the relative utilization of these two pathways.", "citation": {"db": "PubMed", "db_id": "21192821"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3236, "target": 3398, "key": "03337931d2bba1caeaecef72dd2454bd"}, {"line": 36501, "relation": "increases", "evidence": "Increased cytosolic calcium concentrations initiate the activation of several kinase-dependent signalling cascades including activation of PKC leading to CREB activation and phosphorylation at Ser133, a process critical for protein synthesis-dependent synaptic plasticity and LTP. PKC-a also activates ERK by interacting with Ras or Raf-1.Mitochondria are critical targets of intracellular ABeta¸. ABeta¸ interacts with CypD, a protein component of the membrane permeability transition pore (MPTP). The interaction of CypD with ABeta¸ causes functional modification of this protein leading to MPTP opening. ABeta¸ also binds with another mitochondrial protein, ABAD to distort the enzyme’s structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3236, "target": 2164, "key": "fb0fb9893ba82945d6b48a4f4b0e6aec"}, {"line": 36514, "relation": "association", "evidence": "Increased cytosolic calcium concentrations initiate the activation of several kinase-dependent signalling cascades including activation of PKC leading to CREB activation and phosphorylation at Ser133, a process critical for protein synthesis-dependent synaptic plasticity and LTP. PKC-a also activates ERK by interacting with Ras or Raf-1.Mitochondria are critical targets of intracellular ABeta¸. ABeta¸ interacts with CypD, a protein component of the membrane permeability transition pore (MPTP). The interaction of CypD with ABeta¸ causes functional modification of this protein leading to MPTP opening. ABeta¸ also binds with another mitochondrial protein, ABAD to distort the enzyme’s structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3236, "target": 3291, "key": "78c1fa3aad2ab5e876e4c2a005d94310"}, {"relation": "partOf", "source": 3236, "target": 1616, "key": "069e13c91e217cf3b6d9bb4bbdcd75dc"}, {"line": 36947, "relation": "increases", "evidence": "Protein kinase C: PKC is part of a multigene family of serine-threonine kinases central to many signal transduction pathways [138] with a prominent role in memory [139]. It is likely that ABeta¸-induced increases in cytosolic Ca2+ signals are transmitted to PKC for PKC-mediated transcriptional activation. In addition, PKC activates ERK by interacting with Ras or Raf-1 [140] to initiate CREB phosphorylation. While PKC levels decline in AD [141], their activation restores K+ channel function in cells from AD patients [142]. In addition, activation of PKC directly or indirectly enhances the a-processing cleavage of APP [143].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3236, "target": 2249, "key": "aa4e5158378c66a93b134421b391a7c8"}, {"relation": "partOf", "source": 3236, "target": 1617, "key": "672dcd20a75a39d1c669b9e4f8454317"}, {"line": 13604, "relation": "decreases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 639, "target": 812, "key": "38c94d30891e8607415a478c87493164"}, {"line": 13605, "relation": "decreases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 639, "target": 818, "key": "842a59ac049ec160f09a71ac81e42a38"}, {"line": 13606, "relation": "decreases", "evidence": "In the brain of Alzheimer's disease patients, down-regulation of both cholinergic and glutamatergic systems have been found and is thought to play an important role in impairment of cognition, learning, and memory. Nefiracetam is a pyrrolidine-related nootropic drug exhibiting various pharmacological actions such as a cognitive-enhancing effect.", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 639, "target": 820, "key": "d60a9e349e31e3ca8271502ec7318a30"}, {"line": 13625, "relation": "increases", "evidence": "Nefiracetam treatment significantly enhanced long-term potentiation (LTP) with the same bell-shaped dose-response curve. Furthermore, nefiracetam-induced LTP enhancement was closely associated with calcium/calmodulin-dependent protein kinase II (CaMKII) activation with increase in phosphorylation of AMPA-type glutamate receptor subunit 1 (GluA1) (Ser-831) as a postsynaptic CaMKII substrate. ", "citation": {"db": "PubMed", "db_id": "21821968"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 867, "target": 2771, "key": "f2b96ac5193cc9714a2d7064b464098d"}, {"relation": "isA", "source": 3237, "target": 3236, "key": "a0612066a845221e4cafd70830258fc5"}, {"relation": "isA", "source": 3238, "target": 3236, "key": "257cc802cd85e14fd8663a4339481317"}, {"relation": "hasVariant", "source": 3238, "target": 3242, "key": "f70d869cfa1af30b3aa79ac2a8198496"}, {"line": 21341, "relation": "increases", "evidence": "Oxidative stress activates the PKCdelta kinase by translocation, tyrosine phosphorylation, or proteolysis.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation"}, "object": {"modifier": "Activity"}, "source": 3238, "target": 3238, "key": "8276611924fd33d445742657e25ecdb9"}, {"line": 21343, "relation": "increases", "evidence": "Oxidative stress activates the PKCdelta kinase by translocation, tyrosine phosphorylation, or proteolysis.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity"}, "source": 3238, "target": 3238, "key": "009a8a3a9c0d5cff24d85e6e3bf2b851"}, {"relation": "hasVariant", "source": 3238, "target": 3240, "key": "edcecbd15954369d24484fd564a824fe"}, {"relation": "hasVariant", "source": 3238, "target": 3241, "key": "821344ba67c99c09827af9dee840f8fc"}, {"line": 21361, "relation": "association", "evidence": "The proteolytic activation of PKCdelta plays a key role in promoting apoptotic cell death in various cell types, including neuronal cells.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3238, "target": 505, "key": "660fa7e78d004caa0ad75637e0914813"}, {"line": 21371, "relation": "increases", "evidence": "PKCdelta may also amplify apoptotic signaling via positive feedback activation of the caspase cascade.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Apoptosis signaling subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 3238, "target": 479, "key": "5767a72e61e79a2f1d345348cc548ed8"}, {"line": 21389, "relation": "increases", "evidence": "Acrolein induces Hsp72 via both PKCdelta/JNK and calcium signaling pathways in human umbilical vein endothelial cells.", "citation": {"db": "PubMed", "db_id": "16036326"}, "annotations": {"Cell": {"endothelial cell of umbilical vein": true}, "Species": {"9606": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "JAK-STAT signaling subgraph": true}}, "source": 3238, "target": 2846, "key": "0e4af2544d9351b94cd86499f7554758"}, {"line": 21401, "relation": "increases", "evidence": "Here, we report that low concentrations of acrolein induce Hsp72 in human umbilical vein endothelial cells (HUVEC) and that both the PKCdelta/JNK pathway and calcium pathway were involved in the induction.", "citation": {"db": "PubMed", "db_id": "16036326"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "JAK-STAT signaling subgraph": true}, "Species": {"9606": true}, "Cell": {"endothelial cell of umbilical vein": true}}, "source": 3238, "target": 2846, "key": "c590c6bba73c20b6250170184e7a9626"}, {"relation": "partOf", "source": 3238, "target": 1570, "key": "3a77162d8a86d01871c5f231e566e2b0"}, {"relation": "hasVariant", "source": 3238, "target": 3239, "key": "34bcd75cca04e2d80bb0ea70c3aafd98"}, {"relation": "isA", "source": 3243, "target": 3236, "key": "9d33b09c40030d36ba12b635aa97020b"}, {"relation": "isA", "source": 3244, "target": 3236, "key": "65c2db44fe3e600da35648fd20c1b74d"}, {"relation": "isA", "source": 3245, "target": 3236, "key": "b95abbc97f245c48e56c6502daf43c4f"}, {"line": 36570, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Vascular endothelial growth factor subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3245, "target": 2252, "key": "7c5c9211593de89853245fdfd19687aa"}, {"line": 36571, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Vascular endothelial growth factor subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3245, "target": 2249, "key": "1c2b44d1ae832ba61d40d5736af3b487"}, {"line": 36572, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Vascular endothelial growth factor subgraph": true}, "Confidence": {"Low": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3245, "target": 2250, "key": "81a0fad6e4c9b554007cac2b38dec6e7"}, {"relation": "isA", "source": 3246, "target": 3236, "key": "c60a7b477be9b0cd84b8053fe4d7b29e"}, {"relation": "isA", "source": 3247, "target": 3236, "key": "84303158dc1669e108d3f1697ec4ae12"}, {"relation": "isA", "source": 3248, "target": 3236, "key": "fd12a5cc7616fdd4c4019044d6463217"}, {"line": 13673, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2516, "key": "7af8cc188c7253f3359e1cc273319d34"}, {"line": 13677, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2517, "key": "f22330956851ae0bdafda108b0cc3bb6"}, {"line": 13681, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2518, "key": "b68fc6adbc8862895ebc0153fb2df1ab"}, {"line": 13685, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2520, "key": "f76fa2eae93df0e470a899765a8f6ef2"}, {"line": 13689, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2521, "key": "dc1e5ec46c15bd2f3afac47ac8f16ab1"}, {"line": 13693, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2522, "key": "d6926fb4b91a111e9e6a50ef948de6c0"}, {"line": 13697, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2523, "key": "608f2bd0eb9c232b88c908c1bffaa3f3"}, {"line": 13701, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2515, "key": "53cad63ec30bfce1dc0771bd6204c5db"}, {"line": 13705, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2524, "key": "d35fb06f08931c80f0aa6ba61b2ed451"}, {"line": 13709, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2526, "key": "62fe195ac0e1badcb3013f6fedeac6e6"}, {"line": 13713, "relation": "increases", "evidence": "The results of the present study, thus, suggest that nefiracetam enhances activity of nicotinic ACh receptors by interacting with a PKC pathway, thereby increasing glutamate release from presynaptic terminals, and then leading to a sustained facilitation of hippocampal neurotransmission. This may represent a cellular mechanism underlying the cognition-enhancing action of nefiracetam. The results also provide the possibility that nefiracetam could be developed as a promising therapeutic drug for senile dementia or Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "11039729"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Acetylcholine signaling subgraph": true, "GABA subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 982, "target": 2527, "key": "89ce7f1eb5282d9de252291b13f0bbec"}, {"line": 13756, "relation": "positiveCorrelation", "evidence": "The expression level of the NFKB1(p105/50Kd) gene was significantly higher in AD with respect to adult age-matched controls (AA) and was related to the Mini-Mental State Examination (MMSE) score of the same patients.", "citation": {"db": "PubMed", "db_id": "21592054"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3112, "target": 3823, "key": "2dfb53d407d0c0ee37cbab4fd3e92e7b"}, {"relation": "isA", "source": 3112, "target": 871, "key": "c5146d48b2e3dcc8e55a700afdbcde8a"}, {"relation": "partOf", "source": 3112, "target": 1588, "key": "0aa504fe8aa88f885c094c35b2191b40"}, {"relation": "partOf", "source": 3112, "target": 1587, "key": "fe8fa9ccf9711469c72c1183c32a9ca0"}, {"relation": "isA", "source": 3112, "target": 3112, "key": "1a9fd6578f4e5b936da0397f0640e55b"}, {"line": 14536, "relation": "association", "evidence": "This neuroinflammation is known to be substantially regulated by the transcription factor NF-κB, which is predominantly found in the form of heterodimer of p65 (RelA) and p50 subunit, with p50/p50 homodimers being also common.", "citation": {"db": "PubMed", "db_id": "24345324"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3112, "target": 3920, "key": "e712edfa68f7f3a8f95dfb908000ff2f"}, {"line": 46185, "relation": "increases", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3112, "target": 3920, "key": "fb57820646d014b6bf33a308f75142a2"}, {"line": 14540, "relation": "association", "evidence": "These alterations in expression of inflammatory mediators in Nfkb1 deficient mice were associated with reduced expression of CD45.", "citation": {"db": "PubMed", "db_id": "24345324"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3112, "target": 3285, "key": "08a801943b546eb4d3dacb0de2bdc6f4"}, {"line": 18103, "relation": "association", "evidence": "New findings have also linked activation of the NRF2 system to anti-inflammatory effects via interactions with NF-κB.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3112, "target": 3110, "key": "a0c20cf7c3bafb35b6f6b521fe620ee0"}, {"line": 22205, "relation": "association", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3112, "target": 3623, "key": "047715c50919a2d952009a74ebc29345"}, {"relation": "isA", "source": 3112, "target": 2198, "key": "a55c63838a8e9f8d436da5955ba5960e"}, {"line": 28065, "relation": "increases", "evidence": "In addition, we show that some NF-kappaB inhibitors decrease sAPPbeta and APP-CTFbeta suggesting that they reduce the beta-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "17223266"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "source": 3112, "target": 80, "key": "4b05511fe9ec3362aa802847a6374581"}, {"line": 28066, "relation": "association", "evidence": "In addition, we show that some NF-kappaB inhibitors decrease sAPPbeta and APP-CTFbeta suggesting that they reduce the beta-secretase cleavage of APP.", "citation": {"db": "PubMed", "db_id": "17223266"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3112, "target": 2375, "key": "86de86da829dd3f0bcdaaade0d6cfb2e"}, {"line": 38070, "relation": "decreases", "evidence": "Under physiological conditions, NFkappaB lowers the transcriptional activity of the promoters of betaAPP, beta-secretase (beta-site APP-cleaving enzyme 1, BACE1), and of the four protein components (Aph-1, Pen-2, nicastrin, presenilin-1, or presenilin-2) of the gamma-secretase in HEK293 cells. This was accompanied by a reduction of both protein levels and enzymatic activities, thereby ultimately yielding lower amounts of Abeta and AICD (APP intracellular domain).", "citation": {"db": "PubMed", "db_id": "22654105"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3112, "target": 2375, "key": "e4dc3cc7f89f19b7039bcbaa60637ed5"}, {"line": 38291, "relation": "increases", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 2375, "key": "ada07a1f77d78b244156f390bab5456b"}, {"relation": "partOf", "source": 3112, "target": 1351, "key": "9268e817fe6af197afd9c99b4a3c736a"}, {"relation": "partOf", "source": 3112, "target": 1589, "key": "7f2e5edef7a27dd10deffe68ba90e5ed"}, {"relation": "partOf", "source": 3112, "target": 1085, "key": "e535fd0a059910ed883d419a9b7a0b48"}, {"relation": "partOf", "source": 3112, "target": 1137, "key": "fe8b09e0af2ccb4d4fd0a285b4fd15d3"}, {"line": 35187, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3112, "target": 648, "key": "7d391f39d547c3e6cc1408878ff0e611"}, {"line": 38664, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3112, "target": 648, "key": "59bed2c44205ef999a4ad7613885ed93"}, {"line": 37699, "relation": "association", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "object": {"modifier": "Activity"}, "source": 3112, "target": 2137, "key": "7b593a3369223bdac3d85c79dd57cdf5"}, {"line": 38075, "relation": "decreases", "evidence": "Under physiological conditions, NFkappaB lowers the transcriptional activity of the promoters of betaAPP, beta-secretase (beta-site APP-cleaving enzyme 1, BACE1), and of the four protein components (Aph-1, Pen-2, nicastrin, presenilin-1, or presenilin-2) of the gamma-secretase in HEK293 cells. This was accompanied by a reduction of both protein levels and enzymatic activities, thereby ultimately yielding lower amounts of Abeta and AICD (APP intracellular domain).", "citation": {"db": "PubMed", "db_id": "22654105"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3112, "target": 868, "key": "bbe8ff1cf49ac4b0d7cac7e5fec35657"}, {"line": 38081, "relation": "decreases", "evidence": "Under physiological conditions, NFkappaB lowers the transcriptional activity of the promoters of betaAPP, beta-secretase (beta-site APP-cleaving enzyme 1, BACE1), and of the four protein components (Aph-1, Pen-2, nicastrin, presenilin-1, or presenilin-2) of the gamma-secretase in HEK293 cells. This was accompanied by a reduction of both protein levels and enzymatic activities, thereby ultimately yielding lower amounts of Abeta and AICD (APP intracellular domain).", "citation": {"db": "PubMed", "db_id": "22654105"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3112, "target": 2328, "key": "13bd93fd8b8fcda1ecc43e3652ba232c"}, {"line": 38297, "relation": "association", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 3212, "key": "2e62d46f95bffbb2477f6c1114bcd15f"}, {"line": 38304, "relation": "increases", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 2315, "key": "70c7e3fe7af187268f060972973122de"}, {"line": 40041, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3112, "target": 2315, "key": "c53971712db762eec18d812aee64b868"}, {"line": 38311, "relation": "increases", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 3258, "key": "4ab781ff9ad694a4950467d9f5b5f12c"}, {"line": 38313, "relation": "increases", "evidence": "The effect of NFκB on BACE1 promoter could be direct or through changes in PPARgamma, because PPARgamma agonists can antagonize the activity of transcription factors such as NFκB.NFκB sites are present in the promoters of APP [86], presenilin and BACE1 [87]. In neurons exposed to soluble Abeta peptides and in TNFalpha-activated glial cells the mutation of the BACE1 promoter NFκB site led to significant decreases in promoter activity, indicating an activating role for NFκB in BACE1 expression in Abeta.", "citation": {"db": "PubMed", "db_id": "18564425"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 3268, "key": "7807e703b24c181b899c5a4ed8a3aa60"}, {"line": 40031, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3112, "target": 261, "key": "893d5ec129dc7a0e629b9ed3769afc8b"}, {"line": 40270, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 2885, "key": "5cb97d9fe2296e16fc2e09accfbfb4ff"}, {"line": 40271, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 2887, "key": "131d04b92bb05ed6997eace192e14481"}, {"line": 40277, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 3472, "key": "170ab69aa8cae2a68973a60344a382fd"}, {"line": 40283, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 2603, "key": "3d38d5295d3ebf5c26441ef0eb876b27"}, {"line": 40284, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 2457, "key": "fbff3e4262f125c586cb3dc586fc8b3f"}, {"line": 40285, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 2459, "key": "e4dc5dbdcc2dd882f9485f322c7c553f"}, {"line": 40295, "relation": "increases", "evidence": "Following stimulation with IL-1beta and TNF-α, activated astrocytes newly produced IL-1beta, IL-1ra, TNF-α, IP-10 (CXCL10), MIP-1α (CCL3) and RANTES (CCL5), in addition to the induction of sICAM-1 and complement component 5. Database search indicated that most of cytokines and chemokines produced by non-stimulated and activated astrocytes are direct targets of the transcription factor NF-kB.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Complement system subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3112, "target": 2411, "key": "9cc2a181cce13a0f00f8cfd358205ed7"}, {"line": 46184, "relation": "negativeCorrelation", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3112, "target": 1893, "key": "144f23d5086b755b9f61cb92d7ddd903"}, {"relation": "partOf", "source": 3112, "target": 1590, "key": "4e816698ee031cc0adc27bebe890f2a5"}, {"line": 46817, "relation": "increases", "evidence": "It is also accepted that RelB is activated in lymphoid cells, such as dendritic cells, by a noncanonical NF-κB pathway and generation of RelB/p52 complexes that are important for proper dendritic cell functions ", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3112, "target": 3305, "key": "1f37bcf1a196f5c2b92191972188c266"}, {"line": 46884, "relation": "increases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Toll like receptor subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3112, "target": 2119, "key": "9220310584efc59d224f6e339c901729"}, {"line": 13762, "relation": "association", "evidence": "In addition, the expression of various NF-κB target genes and of both NF-κBp50 and NF-κBp65 DNA-binding activity were increased in PBMC from AD patients in comparison with those from AA.", "citation": {"db": "PubMed", "db_id": "21592054"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 704, "target": 3304, "key": "7e1d7fc14325217402fde51c35376144"}, {"line": 13765, "relation": "association", "evidence": "In addition, the expression of various NF-κB target genes and of both NF-κBp50 and NF-κBp65 DNA-binding activity were increased in PBMC from AD patients in comparison with those from AA.", "citation": {"db": "PubMed", "db_id": "21592054"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 704, "target": 2474, "key": "ff0e922b56950c3eaaac6c56e107e605"}, {"line": 13799, "relation": "association", "evidence": "As expected, LPS strongly induced the formation of two NF-kappa B DNA-binding activities, one of which was identified as RelA/p50.", "citation": {"db": "PubMed", "db_id": "9209268"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 704, "target": 871, "key": "a221815041bfd3c6d6086066465d8971"}, {"line": 13762, "relation": "association", "evidence": "In addition, the expression of various NF-κB target genes and of both NF-κBp50 and NF-κBp65 DNA-binding activity were increased in PBMC from AD patients in comparison with those from AA.", "citation": {"db": "PubMed", "db_id": "21592054"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3304, "target": 704, "key": "8af05b37c2a66dd9537690cef83db6cf"}, {"relation": "isA", "source": 3304, "target": 871, "key": "8cb3c0bb609a386e4b5dd17ab189d469"}, {"line": 14343, "relation": "positiveCorrelation", "evidence": "In this report we found that both BACE1 and NF-κB p65 levels were significantly increased in the brains of AD patients.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3304, "target": 3823, "key": "4da79b7dd719bcaa96cb58fbaa92e19e"}, {"line": 14357, "relation": "increases", "evidence": "We found that NF-κB p65 expression resulted in increased BACE1 promoter activity and BACE1 transcription, while disruption of NF-κB p65 decreased BACE1 gene expression in p65 knockout (RelA-knockout) cells.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3304, "target": 1755, "key": "e2daf6bc9b9f990b9d0f8ced407cf27a"}, {"line": 14361, "relation": "increases", "evidence": "We found that NF-κB p65 expression resulted in increased BACE1 promoter activity and BACE1 transcription, while disruption of NF-κB p65 decreased BACE1 gene expression in p65 knockout (RelA-knockout) cells.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3304, "target": 3943, "key": "665905aa4d6554aeb93c1a3ff0f18625"}, {"line": 14362, "relation": "increases", "evidence": "We found that NF-κB p65 expression resulted in increased BACE1 promoter activity and BACE1 transcription, while disruption of NF-κB p65 decreased BACE1 gene expression in p65 knockout (RelA-knockout) cells.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3304, "target": 2375, "key": "ca059c69ecd5e8c94a7a87c8ce7277f1"}, {"line": 14363, "relation": "positiveCorrelation", "evidence": "We found that NF-κB p65 expression resulted in increased BACE1 promoter activity and BACE1 transcription, while disruption of NF-κB p65 decreased BACE1 gene expression in p65 knockout (RelA-knockout) cells.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3304, "target": 2375, "key": "4344e2da0a1c243a487e138236d69482"}, {"line": 14373, "relation": "increases", "evidence": "In addition, NF-κB p65 expression leads to up-regulated beta-secretase cleavage and Abeta production, while non-steroidal anti-inflammatory drugs (NSAIDs) inhibited BACE1 transcriptional activation induced by strong NF-κB activator tumour necrosis factor-alpha (TNF-α).", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3304, "target": 2375, "key": "9ded9ac1e2de367349b2695c3261f25d"}, {"line": 14380, "relation": "increases", "evidence": "In addition, NF-κB p65 expression leads to up-regulated beta-secretase cleavage and Abeta production, while non-steroidal anti-inflammatory drugs (NSAIDs) inhibited BACE1 transcriptional activation induced by strong NF-κB activator tumour necrosis factor-alpha (TNF-α).", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "source": 3304, "target": 80, "key": "3de74b278c0b8073e1220e989110a891"}, {"line": 14388, "relation": "association", "evidence": "In addition, NF-κB p65 expression leads to up-regulated beta-secretase cleavage and Abeta production, while non-steroidal anti-inflammatory drugs (NSAIDs) inhibited BACE1 transcriptional activation induced by strong NF-κB activator tumour necrosis factor-alpha (TNF-α).", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3304, "target": 3472, "key": "2f8b930b677184d92c785b13a7f95652"}, {"relation": "isA", "source": 3304, "target": 3112, "key": "df103c274f71aaa231113f3ca5487a9f"}, {"line": 13765, "relation": "association", "evidence": "In addition, the expression of various NF-κB target genes and of both NF-κBp50 and NF-κBp65 DNA-binding activity were increased in PBMC from AD patients in comparison with those from AA.", "citation": {"db": "PubMed", "db_id": "21592054"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 2474, "target": 704, "key": "bec44212215b0be5f215f4191dca1310"}, {"line": 15686, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 2474, "target": 3823, "key": "5d64270c03a73b9219828cb3dfd36ef7"}, {"relation": "partOf", "source": 2474, "target": 1324, "key": "823958df069b27d854b346b3dab99411"}, {"line": 13799, "relation": "association", "evidence": "As expected, LPS strongly induced the formation of two NF-kappa B DNA-binding activities, one of which was identified as RelA/p50.", "citation": {"db": "PubMed", "db_id": "9209268"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 871, "target": 704, "key": "54cb33726ed31596c2b5900d118d8687"}, {"line": 20743, "relation": "increases", "evidence": "In the brains of Abcg2 knockout mice, NF-kB activation as a result of Abcg2 deficiency increased Abeta deposition compared to controls. This result was further confirmed in vitro in N2a-695 cells where overexpression of ABCG2 significantly decreased the processing rate of APP and Abeta production as compared with controls.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 871, "target": 2328, "key": "3f96db767b8386b6d3137a745f4b8ef9"}, {"line": 13792, "relation": "increases", "evidence": "Accordingly, immunoblot experiments showed that amongst NF-kappa B/Rel proteins, RelA and p50 are mobilized to the nucleus following microglial cell stimulation with A beta(25-35) plus IFN gamma.", "citation": {"db": "PubMed", "db_id": "9209268"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytosol"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 1690, "target": 3304, "key": "bdc7af8be5b4e98e02acf1d6d985b895"}, {"line": 13798, "relation": "increases", "evidence": "As expected, LPS strongly induced the formation of two NF-kappa B DNA-binding activities, one of which was identified as RelA/p50.", "citation": {"db": "PubMed", "db_id": "9209268"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 291, "target": 871, "key": "903a1783bd66aaa80527a3c88e45982b"}, {"line": 38951, "relation": "increases", "evidence": "Neuro-inflammation induced by lipopolysaccharide causes cognitive impairment through enhancement of beta-amyloid generation.", "citation": {"db": "PubMed", "db_id": "18759972"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 291, "target": 577, "key": "84d1c704626bb20f76a3cc20f014b587"}, {"line": 41007, "relation": "increases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 291, "target": 170, "key": "233f99a76f0c92979d1c78e10a1709fc"}, {"line": 41014, "relation": "increases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 291, "target": 156, "key": "60defbc3e1297733a5295d0aaea2677b"}, {"line": 41502, "relation": "association", "evidence": "The brains were then removed and malondialdehyde (MDA) and total thiol groups concentrations were measured.The time latency to enter the dark compartment by OVX-LPS group was shorter than that of OVX at both first and 24th hours after the shock (P < 0.05 - P < 0.001).", "citation": {"db": "PubMed", "db_id": "24829769"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 291, "target": 298, "key": "ff36d93bb59d798c7a8d9c770a33e2c1"}, {"line": 41508, "relation": "association", "evidence": "The hippocampal MDA concentration in OVX-LPS group was higher than Sham- LPS group (P < 0.01).Brain tissue oxidative damage contributed in deleterious effects of LPS on learning and memory.", "citation": {"db": "PubMed", "db_id": "24829769"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Brain": true, "Tissues": true}}, "source": 291, "target": 298, "key": "4880fe46c046e9e8c0fa6c7a12a8643a"}, {"line": 42577, "relation": "increases", "evidence": "Norepinephrine enhances the LPS-induced expression of COX-2 and secretion of PGE2 in primary rat microglia.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Microglia": true, "Bodily Secretions": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 291, "target": 4068, "key": "68410564948259cce25c3f802f243117"}, {"line": 42615, "relation": "increases", "evidence": "Norepinephrine strongly enhanced COX-2 expression and PGE2 production induced by lipopolysaccharide (LPS).", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 291, "target": 4068, "key": "dbaea722c4b39a63e5195d6c7815f529"}, {"line": 42580, "relation": "association", "evidence": "Norepinephrine enhances the LPS-induced expression of COX-2 and secretion of PGE2 in primary rat microglia.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Microglia": true, "Bodily Secretions": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 291, "target": 317, "key": "0b36c7b993c8c0f639de0583b541de69"}, {"line": 42617, "relation": "association", "evidence": "Norepinephrine strongly enhanced COX-2 expression and PGE2 production induced by lipopolysaccharide (LPS).", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 291, "target": 317, "key": "dcabc85e6b2e6b601710bdab40d8f048"}, {"line": 42581, "relation": "association", "evidence": "Norepinephrine enhances the LPS-induced expression of COX-2 and secretion of PGE2 in primary rat microglia.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Microglia": true, "Bodily Secretions": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 291, "target": 3706, "key": "a0e5c8064698e94e2b1e31ed8a4b2724"}, {"line": 44362, "relation": "increases", "evidence": "JAK-STAT signaling as an anti-inflammatory target. JAK-STAT signaling mediates the brain inflammation induced by LPS, IFN-gamma, ganglioside and thrombin. Curcumin activates SH2-containing phosphatase 2 (SHP2), while rosiglitazone and 15d-PGJ2 increase the expressions of SOCS1 and SOCS3. SHP2 and the SOCS proteins are typical negative feedback molecules of the JAK-STAT pathway.", "citation": {"db": "PubMed", "db_id": "26113788"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 291, "target": 3815, "key": "845c4120773e0f936c6bc6d80997e2a5"}, {"line": 46881, "relation": "increases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 291, "target": 3468, "key": "7ac93e1bbe746326dc2a7f79286a4964"}, {"line": 13810, "relation": "increases", "evidence": "Importantly, miR-16 inhibition decreased animal survival in a xenograft model of MM by increasing tumor load and host angiogenesis.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"MeSHDisease": {"Multiple Myeloma": true, "Neoplasms": true}}, "source": 2744, "target": 807, "key": "39ac3c1d7702a5cff24697365dd72d9c"}, {"line": 13811, "relation": "increases", "evidence": "Importantly, miR-16 inhibition decreased animal survival in a xenograft model of MM by increasing tumor load and host angiogenesis.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"MeSHDisease": {"Multiple Myeloma": true, "Neoplasms": true}}, "source": 2744, "target": 3923, "key": "2d96b21fac28176c588ca0a14bba885b"}, {"line": 13819, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2744, "target": 2694, "key": "26bc5c96e9d7701cf9f587fe4397610d"}, {"line": 13821, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 2744, "target": 3519, "key": "0ad93e9dce774b611c59451b44ad3461"}, {"line": 13823, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true}}, "source": 2744, "target": 2936, "key": "8b0035e8cc45559aa122ac112e8d8b3b"}, {"line": 13825, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "source": 2744, "target": 2932, "key": "b2e5d4063a96df01ea8466cfc4c19089"}, {"line": 13826, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "source": 2744, "target": 3049, "key": "486234dfa9f16421b04ffd2865287b13"}, {"line": 14044, "relation": "association", "evidence": "Neural deletion of Tgfbr2 impairs angiogenesis through an altered secretome.", "citation": {"db": "PubMed", "db_id": "24990151"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 807, "target": 3458, "key": "c7b653a971538c3d2444299ee2185021"}, {"line": 14057, "relation": "negativeCorrelation", "evidence": "In this context, simultaneous activation of VEGF- and inhibition of TGFbeta-signalling leads to increased angiogenesis.", "citation": {"db": "PubMed", "db_id": "24990151"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "TGF-Beta subgraph": true}}, "source": 807, "target": 3458, "key": "2e550361ca940a59c1e7511b03ff4216"}, {"line": 14056, "relation": "positiveCorrelation", "evidence": "In this context, simultaneous activation of VEGF- and inhibition of TGFbeta-signalling leads to increased angiogenesis.", "citation": {"db": "PubMed", "db_id": "24990151"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "TGF-Beta subgraph": true}}, "source": 807, "target": 3519, "key": "6b8e28b723c8fc9c28ecfceaa644c2ec"}, {"line": 15833, "relation": "association", "evidence": "Tumor growth and metastasis depend on angiogenesis that requires the cofactor copper.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Neoplasms": true, "Neoplasm Metastasis": true}, "Confidence": {"High": true}}, "source": 807, "target": 101, "key": "a986a7e55133ce38a60dc97a66948c65"}, {"line": 15834, "relation": "association", "evidence": "Tumor growth and metastasis depend on angiogenesis that requires the cofactor copper.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Neoplasms": true, "Neoplasm Metastasis": true}, "Confidence": {"High": true}}, "source": 807, "target": 3871, "key": "8cb9a29a11feffba1932e65edac41431"}, {"line": 16270, "relation": "association", "evidence": "Cerebral hypoxia is a potent stimulus for vascular activation and angiogenesis.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Subgraph": {"Hypoxia response subgraph": true}, "MeSHDisease": {"Hypoxia, Brain": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 807, "target": 3859, "key": "89c02d61e323ed9e6b46552d9f747733"}, {"line": 16544, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 807, "target": 3409, "key": "a791d24b189e2c7b4fa7bcb9ad078f78"}, {"line": 16590, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 807, "target": 3874, "key": "8ab3fd5e0d7d5fa6cf558ba870845030"}, {"line": 13812, "relation": "association", "evidence": "Importantly, miR-16 inhibition decreased animal survival in a xenograft model of MM by increasing tumor load and host angiogenesis.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"MeSHDisease": {"Multiple Myeloma": true, "Neoplasms": true}}, "source": 3868, "target": 3923, "key": "2f71aa7eade016f5c14d8f7cd0afbb9a"}, {"line": 13819, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2694, "target": 2744, "key": "8680e4c5935d6fd9072742253ed693dd"}, {"line": 13821, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 3519, "target": 2744, "key": "d779d718f2f1a26d3b07ea42629a3d1c"}, {"line": 13837, "relation": "increases", "evidence": "VEGF genetic variability is associated with increased risk of developing Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19272614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 3519, "target": 3823, "key": "22bbbb7897aa9a592589c9faf4687449"}, {"line": 15100, "relation": "association", "evidence": "The excess vascular endothelial growth factor (VEGF) produced in the Alzheimer's disease (AD) brain can harm neurons, blood vessels, and other components of the neurovascular units (NVUs).", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3519, "target": 3823, "key": "e44bfe07d050d10a19ef818d15f27d06"}, {"line": 39816, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 3519, "target": 3823, "key": "6d67f2cdbe333b777c2607931d596d41"}, {"line": 13845, "relation": "association", "evidence": "Specific polymorphisms within the vascular endothelial growth factor (VEGF) gene promoter region are of particular interest: VEGF variability has been associated with increased risk of developing a wide variety of disorders from diabetes to neurodegenerative diseases, suggesting functions not confined to its originally described vascular effects.", "citation": {"db": "PubMed", "db_id": "19272614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}}, "source": 3519, "target": 3874, "key": "5e475fa4b7638933f451c952aa3c73aa"}, {"line": 13846, "relation": "association", "evidence": "Specific polymorphisms within the vascular endothelial growth factor (VEGF) gene promoter region are of particular interest: VEGF variability has been associated with increased risk of developing a wide variety of disorders from diabetes to neurodegenerative diseases, suggesting functions not confined to its originally described vascular effects.", "citation": {"db": "PubMed", "db_id": "19272614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}}, "source": 3519, "target": 3847, "key": "4252d0c91715bf6909d2e26c5f7959c8"}, {"line": 14050, "relation": "association", "evidence": "Supplementing CM of Tgfbr2-cKO with VEGFA rescued these defects, but application of TGFbeta aggravated them.", "citation": {"db": "PubMed", "db_id": "24990151"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "TGF-Beta subgraph": true}}, "source": 3519, "target": 3458, "key": "4132b118fe165080c10c063ccc0ec70e"}, {"line": 14056, "relation": "positiveCorrelation", "evidence": "In this context, simultaneous activation of VEGF- and inhibition of TGFbeta-signalling leads to increased angiogenesis.", "citation": {"db": "PubMed", "db_id": "24990151"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "TGF-Beta subgraph": true}}, "source": 3519, "target": 807, "key": "78fecaaea768f983e8ddac302c54aaeb"}, {"line": 15086, "relation": "association", "evidence": "The Abeta Peptides-Activated Calcium-Sensing Receptor Stimulates the Production and Secretion of Vascular Endothelial Growth Factor-A by Normoxic Adult Human Cortical Astrocytes.", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Bodily Secretions": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3519, "target": 2451, "key": "496de335faa4c345c3033a7aa75cb900"}, {"line": 15135, "relation": "association", "evidence": "Here, we report that exogenous Abetas stimulate the NAHAs to produce and secrete even VEGF-A through a CaSR-mediated mechanism.", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3519, "target": 2451, "key": "8cd9568f769ed803dfc3a0c0de9cf57e"}, {"line": 23561, "relation": "increases", "evidence": "Riluzole has been shown to inhibit vascular endothelial growth factor (VEGF)-stimulated protein kinase c (PKC), beta2 activation and cell proliferation in bovine retinal endothelial cell and human umbilical vein endothelial cell cultures (17). ", "citation": {"db": "PubMed", "db_id": "19528481"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3519, "target": 3237, "key": "27b47a253614d9f3b4615c847159547b"}, {"line": 23583, "relation": "increases", "evidence": "Of the signaling molecules downstream of VEGF receptors, PKC has been demonstrated to be critical in mediating endothelial cell proliferation.17 18 19 Although inhibition of PKC may seem a reasonable approach to curtail endothelial cell proliferation in proliferative retinopathies,20 21 PKC serves diverse essential normal roles in intracellular signaling, indicating broad-spectrum inhibition of all PKCs may have harmful side effects, including cell apoptotic process. Riluzole inhibited PKC activity in cortical cell cultures, and also inhibited the activity of purified PKC in vitro.", "citation": {"db": "PubMed", "db_id": "16303979"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3519, "target": 3237, "key": "088b57ceadcf56de9c4428a687d08fbc"}, {"relation": "partOf", "source": 3519, "target": 1421, "key": "c26da6d4f2d53fddefa200fbf7978b13"}, {"line": 13825, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "source": 2932, "target": 2744, "key": "72e9626111e56e92518503c9b9572eac"}, {"relation": "partOf", "source": 2932, "target": 1498, "key": "54130c04f41fd37209b058ec84503818"}, {"relation": "partOf", "source": 2932, "target": 1499, "key": "2ddeea091a39998b2b0b0a7e1860d2cd"}, {"line": 13826, "relation": "negativeCorrelation", "evidence": "Expression profiling analysis of miR-16-deficient cells identified a large number of downstream target genes including FGFR1, PI3KCa, MDM4, VEGFa, as well as secondary affected genes such as JUN and Jag1.", "citation": {"db": "PubMed", "db_id": "20962322"}, "source": 3049, "target": 2744, "key": "71b52f53d53ffa142381fc1de640e65d"}, {"line": 13865, "relation": "decreases", "evidence": "p21Cip1 and p27Kip1 are related proteins that inhibit cell-cycle progression by interacting with cyclin-CDK complexes in the nucleus.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2493, "target": 503, "key": "a0f0c8bd3bca9c18dd1e80d1a5404b4e"}, {"line": 13876, "relation": "association", "evidence": "Both inhibitors accumulated in the cytoplasm of nerve cells, the majority of which contained inclusions made of hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2493, "target": 3015, "key": "b895c5a320d1d7c6f679298fcd14e8e5"}, {"line": 13888, "relation": "decreases", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2493, "target": 786, "key": "714a92f1f8023497255637741710d79d"}, {"line": 13889, "relation": "decreases", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2493, "target": 2989, "key": "d5efab614707b6a9e7e597d6fd5fb3eb"}, {"line": 13898, "relation": "decreases", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2493, "target": 478, "key": "22630cbbfa795b93b283e8e82d72f889"}, {"line": 13906, "relation": "decreases", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2493, "target": 2444, "key": "5b87fdaa3e0f29e47cbce319e3570272"}, {"line": 13914, "relation": "decreases", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2493, "target": 2689, "key": "27d6a0cc24cbe3461957599c1d429c14"}, {"line": 13926, "relation": "association", "evidence": "Cytoplasmic p21Cip1 has been reported to interact with stress-activated protein kinases and ASK1 kinase and to inhibit their catalytic activities, thus preventing apoptotic process.59,60 p21Cip1 has also been shown to inhibit activation of caspase 3 and to resist Fas-mediated cell death.61 In the human P301S tau mice, nerve cell death occurs through a nonapoptotic mechanism,41 suggesting a possible link with the cytoplasmic expression of p21Cip1.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"MeSHAnatomy": {"Nerve Tissue": true, "Neurons": true}, "CellStructure": {"Cytoplasm": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2493, "target": 505, "key": "3db7fdba072886cbfa5b0e79838d6352"}, {"line": 14578, "relation": "decreases", "evidence": "Overexpression of p21 from an inducible promoter in a human cell line induces growth arrest and phenotypic features of senescence.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2493, "target": 508, "key": "44e7fd7f8215160b5b1f6c2482eedb26"}, {"line": 14579, "relation": "increases", "evidence": "Overexpression of p21 from an inducible promoter in a human cell line induces growth arrest and phenotypic features of senescence.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2493, "target": 520, "key": "6e544f37d1a88245ac068159a6524621"}, {"line": 14922, "relation": "association", "evidence": "Human FECD endothelium exhibited increased levels of nuclear p21 protein.Our results identify endothelial Cdkn1a (p21) upregulation in a mouse model of early-onset FECD, confirm overexpression of p21 in late-onset human FECD endothelium, and suggest a role for premature senescence in FECD.", "citation": {"db": "PubMed", "db_id": "22956607"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2493, "target": 520, "key": "e349a80725b86ad521ba76c223a3dfff"}, {"line": 14586, "relation": "association", "evidence": "cDNA array hybridization showed that p21 expression selectively inhibits a set of genes involved in mitosis, DNA replication, segregation, and repair.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2493, "target": 852, "key": "aa0fc86242f7dd968f412d7735cef984"}, {"line": 14587, "relation": "association", "evidence": "cDNA array hybridization showed that p21 expression selectively inhibits a set of genes involved in mitosis, DNA replication, segregation, and repair.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2493, "target": 627, "key": "a6d3d4bdb0c21700fcab8666b758d58e"}, {"line": 14592, "relation": "increases", "evidence": "The kinetics of inhibition of these genes on p21 induction parallels the onset of growth arrest, and their reexpression on release from p21 precedes the reentry of cells into cell cycle, indicating that inhibition of cell-cycle progression genes is a mechanism of p21-induced growth arrest.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2493, "target": 500, "key": "698f1f8e463180f1cc351637204b85be"}, {"line": 14597, "relation": "association", "evidence": "p21 also up-regulates multiple genes that have been associated with senescence or implicated in age-related diseases, including atherosclerosis, Alzheimer's disease, amyloidosis, and arthritis.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true, "Arthritis": true, "Atherosclerosis": true, "Amyloidosis": true}}, "source": 2493, "target": 3891, "key": "2b81eae5b56550424b486c48ed403c78"}, {"line": 14598, "relation": "association", "evidence": "p21 also up-regulates multiple genes that have been associated with senescence or implicated in age-related diseases, including atherosclerosis, Alzheimer's disease, amyloidosis, and arthritis.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true, "Arthritis": true, "Atherosclerosis": true, "Amyloidosis": true}}, "source": 2493, "target": 3889, "key": "1fc755d8c4bb7efc96eae5f4af8b9923"}, {"line": 14599, "relation": "association", "evidence": "p21 also up-regulates multiple genes that have been associated with senescence or implicated in age-related diseases, including atherosclerosis, Alzheimer's disease, amyloidosis, and arthritis.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true, "Arthritis": true, "Atherosclerosis": true, "Amyloidosis": true}}, "source": 2493, "target": 3823, "key": "0bcf97b41289546b400e0333bfa6399b"}, {"line": 14600, "relation": "association", "evidence": "p21 also up-regulates multiple genes that have been associated with senescence or implicated in age-related diseases, including atherosclerosis, Alzheimer's disease, amyloidosis, and arthritis.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true, "Arthritis": true, "Atherosclerosis": true, "Amyloidosis": true}}, "source": 2493, "target": 3895, "key": "1e7d2c3c477a9364a0a494c44f647dfb"}, {"line": 14951, "relation": "association", "evidence": "p21 is dispensable for AID-mediated class switch recombination and mutagenesis of immunoglobulin genes during somatic hypermutation.", "citation": {"db": "PubMed", "db_id": "21288574"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2493, "target": 2277, "key": "07900d7c6c01458c037c25cf68328943"}, {"line": 14957, "relation": "association", "evidence": "Regulation of PCNA ubiquitination by p21, also known as Cdkn1a and p21(Cip1/Waf1), is an important mechanism that controls mutation loads in mammalian cells.", "citation": {"db": "PubMed", "db_id": "21288574"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2493, "target": 3167, "key": "944631cbe3e7b999d5be34fecfce0282"}, {"line": 15156, "relation": "association", "evidence": "Downregulation of extracellular signal-regulated kinase 1/2 activity by calmodulin KII modulates p21Cip1 levels and survival of immortalized lymphocytes from Alzheimer's disease patients.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2493, "target": 2173, "key": "82ad6f49cb5def7132b66a5b3735f7e4"}, {"line": 13866, "relation": "decreases", "evidence": "p21Cip1 and p27Kip1 are related proteins that inhibit cell-cycle progression by interacting with cyclin-CDK complexes in the nucleus.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2494, "target": 503, "key": "5f665f4356f608a069772dd170907a90"}, {"line": 13877, "relation": "association", "evidence": "Both inhibitors accumulated in the cytoplasm of nerve cells, the majority of which contained inclusions made of hyperphosphorylated tau protein.", "citation": {"db": "PubMed", "db_id": "16507903"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2494, "target": 3015, "key": "32b037674268fe1e1b56dcdbf90eb7d8"}, {"line": 20128, "relation": "decreases", "evidence": "Insect peptide CopA3-induced protein degradation of p27Kip1 stimulates proliferation and protects neuronal cells from apoptotic process.", "citation": {"db": "PubMed", "db_id": "23791873"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2494, "target": 478, "key": "3a7c9effbb0ef522606f34455f638a36"}, {"line": 20156, "relation": "association", "evidence": "Enhanced proteasome-dependent degradation of the CDK inhibitor p27(kip1) in immortalized lymphocytes from Alzheimer's dementia patients.", "citation": {"db": "PubMed", "db_id": "17448572"}, "annotations": {"Disease": {"dementia": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"Ubiquitin degradation subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Degradation"}, "source": 2494, "target": 893, "key": "dcb3110712bc803f0b32e7e3f2ee5408"}, {"line": 20166, "relation": "association", "evidence": "We have recently reported the existence of a molecular link between decreased p27 levels and enhanced phosphorylation of pRb protein and proliferation of immortalized lymphocytes from Alzheimer's disease (AD) patients.", "citation": {"db": "PubMed", "db_id": "17448572"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}}, "subject": {"modifier": "Degradation"}, "source": 2494, "target": 3300, "key": "9e4145a7b6fef0d2a5c424ae4a7ad4f0"}, {"line": 20206, "relation": "increases", "evidence": "These observations suggest that in AD cells the enhanced rates of cell proliferation and phosphorylation of pRb and the intracellular content of p27(kip1) may be interrelated events controlled by a mechanism dependent on the Ca(2+)/calmodulin signaling pathway.", "citation": {"db": "PubMed", "db_id": "12901840"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 2494, "target": 3300, "key": "f9460c4e250849659d27a8ce15d123aa"}, {"line": 20225, "relation": "association", "evidence": "Furthermore, they are potent positive regulators of the cyclin-dependent kinase inhibitor p27, a major regulator of the mammalian cell cycle.", "citation": {"db": "PubMed", "db_id": "16713332"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2494, "target": 1639, "key": "c3d6ec94092c60727d665b50cfd2dc7f"}, {"line": 33719, "relation": "negativeCorrelation", "evidence": "In concordance, significant increases in the levels of phosphorylation of total Akt substrates, including: GSK3beta(Ser9), tau(Ser214), mTOR(Ser2448), and decreased levels of the Akt target, p27(kip1), were found in AD temporal cortex compared with controls.", "citation": {"db": "PubMed", "db_id": "15773910"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Tau protein subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 2494, "target": 3823, "key": "4032924340dd195a54d473b0fa15742a"}, {"line": 13946, "relation": "increases", "evidence": "Pin1 is overexpressed in breast cancer and cooperates with Ras signaling in increasing the transcriptional activity of c-Jun towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Breast Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 3833, "target": 3192, "key": "cb315b9f29d2bdd904fea17b146a70c1"}, {"line": 13957, "relation": "association", "evidence": "Pin1 is overexpressed in breast cancer and cooperates with Ras signaling in increasing the transcriptional activity of c-Jun towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Breast Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3833, "target": 3192, "key": "207a3fec068cdbb411ce1f753047fbb8"}, {"line": 13948, "relation": "association", "evidence": "Pin1 is overexpressed in breast cancer and cooperates with Ras signaling in increasing the transcriptional activity of c-Jun towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Breast Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2463, "target": 3192, "key": "a8a7cbaf9c7dd180d5092afe7e0a0fa1"}, {"line": 14551, "relation": "association", "evidence": "Consequently, we tested here the hypothesis that, in the PS1 FAD brain, cyclin D1 accumulation may occur and lead to neuronal apoptosis secondary to an abortive entry into the cell cycle.", "citation": {"db": "PubMed", "db_id": "18239458"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2463, "target": 645, "key": "c243057e68328f510d92596726844dd9"}, {"line": 48339, "relation": "increases", "evidence": "This cyclin forms a complex with and functions as a regulatory subunit of CDK4 or CDK6, whose activity is required for cell cycle G1/S transition", "citation": {"db": "PubMed", "db_id": "1826542"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Cell cycle subgraph": true}}, "source": 2463, "target": 499, "key": "2b70aea6fe09d8771b824003d85c8b1e"}, {"line": 48355, "relation": "association", "evidence": "Cyclin D1 positive neurons are colocalized with AGEs [Advanced glycation end products] or directly surrounded by extracellular AGE deposits in AD brain.", "citation": {"db": "PubMed", "db_id": "25448604"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "Cell cycle subgraph": true}}, "source": 2463, "target": 74, "key": "b3549170ac7427e22a13e1b786142958"}, {"relation": "partOf", "source": 2937, "target": 1504, "key": "b189640faf21ec74afd775f0121bf64f"}, {"line": 14028, "relation": "increases", "evidence": "Thus, Pin1 is up-regulated in human tumors and cooperates with Ras signaling in increasing c-Jun transcriptional activity towards cyclin D1.", "citation": {"db": "PubMed", "db_id": "11432833"}, "annotations": {"MeSHDisease": {"Neoplasms": true}, "Species": {"9606": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 1671, "target": 2936, "key": "41d02fde014fea3435afaf5be543655f"}, {"line": 14044, "relation": "association", "evidence": "Neural deletion of Tgfbr2 impairs angiogenesis through an altered secretome.", "citation": {"db": "PubMed", "db_id": "24990151"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 3458, "target": 807, "key": "a0796ef2f51140f51af5856a1f9272c4"}, {"line": 14057, "relation": "negativeCorrelation", "evidence": "In this context, simultaneous activation of VEGF- and inhibition of TGFbeta-signalling leads to increased angiogenesis.", "citation": {"db": "PubMed", "db_id": "24990151"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "TGF-Beta subgraph": true}}, "source": 3458, "target": 807, "key": "c89be9d00c58a9728e8e0cfb888db65e"}, {"line": 14050, "relation": "association", "evidence": "Supplementing CM of Tgfbr2-cKO with VEGFA rescued these defects, but application of TGFbeta aggravated them.", "citation": {"db": "PubMed", "db_id": "24990151"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "TGF-Beta subgraph": true}}, "source": 3458, "target": 3519, "key": "af1f0bacbf08a2012bccb24f8a134744"}, {"line": 14108, "relation": "association", "evidence": "Using combinations of catalase-, glutathione synthesis- and glutathione peroxidase-inhibitors it was shown that the increased resistance of Neuro2a-HR cells is not solely based on an increased activity of catalase or the glutathione system, suggesting that their resistance might be based on yet unknown, novel defence mechanisms.", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Low": true}}, "source": 2768, "target": 716, "key": "58111ad5ec7e1098f19f08cdab21e797"}, {"line": 14108, "relation": "association", "evidence": "Using combinations of catalase-, glutathione synthesis- and glutathione peroxidase-inhibitors it was shown that the increased resistance of Neuro2a-HR cells is not solely based on an increased activity of catalase or the glutathione system, suggesting that their resistance might be based on yet unknown, novel defence mechanisms.", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Low": true}}, "source": 716, "target": 2768, "key": "d7c3e83e0bbc2173be82ff20b87a017a"}, {"line": 14109, "relation": "association", "evidence": "Using combinations of catalase-, glutathione synthesis- and glutathione peroxidase-inhibitors it was shown that the increased resistance of Neuro2a-HR cells is not solely based on an increased activity of catalase or the glutathione system, suggesting that their resistance might be based on yet unknown, novel defence mechanisms.", "citation": {"db": "PubMed", "db_id": "23653222"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Low": true}}, "source": 716, "target": 265, "key": "3ed89053b9eb7f3d1a69f181ec05cff4"}, {"line": 15278, "relation": "association", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 716, "target": 213, "key": "d0219e9ce7cbf6a150b625dda2b01eac"}, {"line": 41828, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 4044, "target": 3685, "key": "a64a9ffe28f5aa642868c63740dee6d1"}, {"line": 41829, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 4044, "target": 3741, "key": "745347cd9e6f9be4ab655fc3c00165ae"}, {"line": 41830, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 4044, "target": 3661, "key": "1d0274f60a8a901c19c5c456463b8fc3"}, {"line": 41831, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 4044, "target": 3600, "key": "9b602c99fc8fc1db681bb0fc21e83540"}, {"line": 41832, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 4044, "target": 3597, "key": "e77ddea2e715925e888700d8ff4ec0d2"}, {"line": 14150, "relation": "decreases", "evidence": "The CysLT1R antagonist pranlukast not only reversed Abeta1-42-induced upregulation of CysLT1R, but also suppressed Abeta1-42-triggered neurotoxicity evidenced by enhanced nuclear factor-kappa B p65, activated caspase-3, decreased B-cell lymphoma-2 and cell viability and impaired memory.", "citation": {"db": "PubMed", "db_id": "24269024"}, "annotations": {"MeSHDisease": {"Lymphoma, B-Cell": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "G-protein-mediated signaling": true}, "Confidence": {"High": true}}, "source": 59, "target": 2615, "key": "91285e53ce7f355b6b34961ba98b1de7"}, {"line": 14158, "relation": "decreases", "evidence": "The CysLT1R antagonist pranlukast not only reversed Abeta1-42-induced upregulation of CysLT1R, but also suppressed Abeta1-42-triggered neurotoxicity evidenced by enhanced nuclear factor-kappa B p65, activated caspase-3, decreased B-cell lymphoma-2 and cell viability and impaired memory.", "citation": {"db": "PubMed", "db_id": "24269024"}, "annotations": {"MeSHDisease": {"Lymphoma, B-Cell": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 59, "target": 3304, "key": "129f01955e367048d717a5f71c492556"}, {"line": 14166, "relation": "decreases", "evidence": "The CysLT1R antagonist pranlukast not only reversed Abeta1-42-induced upregulation of CysLT1R, but also suppressed Abeta1-42-triggered neurotoxicity evidenced by enhanced nuclear factor-kappa B p65, activated caspase-3, decreased B-cell lymphoma-2 and cell viability and impaired memory.", "citation": {"db": "PubMed", "db_id": "24269024"}, "annotations": {"MeSHDisease": {"Lymphoma, B-Cell": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 59, "target": 2444, "key": "db2f29e189bf64d90b56b86a1badeef2"}, {"line": 14176, "relation": "increases", "evidence": "Furthermore, chronic treatment with pranlukast produced similar beneficial effects on memory behavior and hippocampal long-term potentiation to memantine or donepezil in intrahippocampal Abeta1-42-injected mice.", "citation": {"db": "PubMed", "db_id": "24269024"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 59, "target": 820, "key": "7628bb3e254cfa85eef238ebb59a583b"}, {"line": 14177, "relation": "increases", "evidence": "Furthermore, chronic treatment with pranlukast produced similar beneficial effects on memory behavior and hippocampal long-term potentiation to memantine or donepezil in intrahippocampal Abeta1-42-injected mice.", "citation": {"db": "PubMed", "db_id": "24269024"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 59, "target": 839, "key": "67e9bc707cc010ca66f1c183ba47e906"}, {"line": 14265, "relation": "positiveCorrelation", "evidence": "5-HT depletion decreased ACh-induced c-Fos immunoreactivity in the dentate gyrus.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"MeSHAnatomy": {"Dentate Gyrus": true}, "Subgraph": {"Serotonergic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 350, "target": 2699, "key": "4a06a26f12011899022db8a95eab5216"}, {"line": 14266, "relation": "association", "evidence": "5-HT depletion decreased ACh-induced c-Fos immunoreactivity in the dentate gyrus.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"MeSHAnatomy": {"Dentate Gyrus": true}, "Subgraph": {"Serotonergic subgraph": true}, "Confidence": {"High": true}}, "source": 350, "target": 2699, "key": "b68a28aede0f5a7b57d223e2128d46c2"}, {"line": 14267, "relation": "association", "evidence": "5-HT depletion decreased ACh-induced c-Fos immunoreactivity in the dentate gyrus.", "citation": {"db": "PubMed", "db_id": "16426583"}, "annotations": {"MeSHAnatomy": {"Dentate Gyrus": true}, "Subgraph": {"Serotonergic subgraph": true}, "Confidence": {"High": true}}, "source": 350, "target": 204, "key": "8aeaf69aa0f9333dd8fd20f4c07dbff9"}, {"line": 21288, "relation": "increases", "evidence": "Serotonin hyperpolarizes entorhinal neurons and inhibits the excitatory synaptic transmission via activation of 5-HT(1A) receptors but facilitates GABA release via activation of 5-HT(2A) receptors.", "citation": {"db": "PubMed", "db_id": "23320133"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Serotonergic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 350, "target": 2857, "key": "4a1f5eae5d1963e0092da0bd3298ff71"}, {"line": 21289, "relation": "increases", "evidence": "Serotonin hyperpolarizes entorhinal neurons and inhibits the excitatory synaptic transmission via activation of 5-HT(1A) receptors but facilitates GABA release via activation of 5-HT(2A) receptors.", "citation": {"db": "PubMed", "db_id": "23320133"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Serotonergic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 350, "target": 2858, "key": "68427580b9ee636ee09703bf50e951c6"}, {"relation": "partOf", "source": 350, "target": 986, "key": "8ac6438a3b1d82c8a2d93ed39d156fa4"}, {"line": 14288, "relation": "increases", "evidence": "Increased NF-κB signalling up-regulates BACE1 expression and its therapeutic potential in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1588, "target": 2375, "key": "6b11494380ab6adacdf1189d886496e7"}, {"line": 14407, "relation": "increases", "evidence": "Taken together, our results clearly demonstrate that NF-κB signalling facilitates BACE1 gene expression and APP processing, and increased BACE1 expression mediated by NF-κB signalling in the brain could be one of the novel molecular mechanisms underlying the development of AD in some sporadic cases.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Beta secretase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1588, "target": 2375, "key": "cea43c37c64896fb98394acfc5816b0f"}, {"line": 22206, "relation": "association", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 1588, "target": 3623, "key": "d702016d085db74a4dc1f922c7bf23f6"}, {"line": 22426, "relation": "association", "evidence": "In support of this mechanism, in vitro, rapamycin significantly inhibits the production of NO, TNF-α in BV2 microglials by modulating NF-κB signaling.", "citation": {"db": "PubMed", "db_id": "24923557"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1588, "target": 352, "key": "351ec9cb2a93f33a1d445c9f146bc036"}, {"line": 14321, "relation": "association", "evidence": "Nuclear factor-kappa B (NF-κB) signalling plays an important role in gene regulation and is implicated in inflammation, oxidative stress and apoptotic process.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 875, "target": 3920, "key": "7672f115c08bed425b4e9e8f922e79e3"}, {"line": 14322, "relation": "equivalentTo", "evidence": "Nuclear factor-kappa B (NF-κB) signalling plays an important role in gene regulation and is implicated in inflammation, oxidative stress and apoptotic process.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 875, "target": 1587, "key": "8ab1af24cfa2ee01c286eede1dff54d2"}, {"line": 14322, "relation": "equivalentTo", "evidence": "Nuclear factor-kappa B (NF-κB) signalling plays an important role in gene regulation and is implicated in inflammation, oxidative stress and apoptotic process.", "citation": {"db": "PubMed", "db_id": "21329555"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 1587, "target": 875, "key": "2a5fb38455adf93dd021901b8e33284f"}, {"line": 14423, "relation": "decreases", "evidence": "Soybean isoflavone alleviates beta-amyloid 1-42 induced inflammatory response to improve learning and memory ability by down regulation of Toll-like receptor 4 expression and nuclear factor-κB activity in rats.", "citation": {"db": "PubMed", "db_id": "21515354"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 284, "target": 3468, "key": "7c34c6597156d7596cb49e3223b5bffc"}, {"line": 14424, "relation": "increases", "evidence": "Soybean isoflavone alleviates beta-amyloid 1-42 induced inflammatory response to improve learning and memory ability by down regulation of Toll-like receptor 4 expression and nuclear factor-κB activity in rats.", "citation": {"db": "PubMed", "db_id": "21515354"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 284, "target": 820, "key": "f86f671a17a973480d457ed0f3a3a0b3"}, {"line": 14425, "relation": "increases", "evidence": "Soybean isoflavone alleviates beta-amyloid 1-42 induced inflammatory response to improve learning and memory ability by down regulation of Toll-like receptor 4 expression and nuclear factor-κB activity in rats.", "citation": {"db": "PubMed", "db_id": "21515354"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 284, "target": 818, "key": "d601cb650d6a5675a2be6fe1612bf06e"}, {"line": 14472, "relation": "decreases", "evidence": "After intragastric pre-treatment with SIF, inflammatory cytokines was significantly reduced and also SIF reversed the Abeta1-42 induced up-regulation of TLR4 and NF-κB p65 mRNA and protein expression in the brain and expression of NF-κB p65 in nuclei.", "citation": {"db": "PubMed", "db_id": "21515354"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 284, "target": 3304, "key": "eeab7affcf7ec1150ed2749ad0fea81d"}, {"line": 14500, "relation": "increases", "evidence": "Human Down's syndrome brains also exhibited elevated zymogenic activity of MMP9, the major NGF-degrading protease.", "citation": {"db": "PubMed", "db_id": "24519975"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3062, "target": 3116, "key": "5fdbb3212f0fb6d3b6ab5547d12117ec"}, {"line": 14510, "relation": "positiveCorrelation", "evidence": "In temporal cortex, analysis revealed a significant correlation between MMP9 activity and amyloid-beta42. In accordance with our analysis in adult brains, MMP9 activation positively correlated with amyloid-beta42 levels", "citation": {"db": "PubMed", "db_id": "24519975"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3062, "target": 80, "key": "57de40f13c616721984398d94bd4163a"}, {"line": 15412, "relation": "increases", "evidence": "Estrogen activates matrix metalloproteinases-2 and -9 to increase beta amyloid degradation.", "citation": {"db": "PubMed", "db_id": "22402435"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3062, "target": 80, "key": "8e06ec3fda2d99d44f46cd39735b8768"}, {"line": 31395, "relation": "increases", "evidence": "OBJECTIVE: Urinary-type plasminogen activator (uPA) binding to uPA receptor (uPAR) promotes the activation of matrix metalloproteinase-9 (MMP-9), which degrades amyloid beta protein (Abeta) in vitro.", "citation": {"db": "PubMed", "db_id": "11327298"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3062, "target": 80, "key": "a1496135ef0e60aa3eae0061deafcdd0"}, {"line": 14521, "relation": "positiveCorrelation", "evidence": "TIMP1 messenger RNA positively correlated with MMP9 activity in frontal cortex", "citation": {"db": "PubMed", "db_id": "24519975"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3062, "target": 4022, "key": "607ec2a1360a8e5bf5d485ef010cbd16"}, {"line": 14565, "relation": "association", "evidence": "MMP-9 and GFAP expression may play an important role in excess Abeta deposition, which is caused by an imbalance between the protein's synthesis and removal.", "citation": {"db": "PubMed", "db_id": "24962158"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3062, "target": 3566, "key": "5e14853b2b015fcaefc4f65ac372a111"}, {"line": 14889, "relation": "association", "evidence": "Plasma MMP-9 concentration measured 4 times, immediately before starting atorvastatin or placebo, immediately before surgery, 24 hours and two weeks after the surgery.", "citation": {"db": "PubMed", "db_id": "24397933"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3062, "target": 215, "key": "ebf79184469ec7958eb4ff64aef66556"}, {"line": 14974, "relation": "association", "evidence": "Involvement of Matrix Metalloproteinase-9 in Amyloid-beta 1-42-Induced Shedding of the Pericyte Proteoglycan NG2.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "source": 3062, "target": 2577, "key": "dafc7b74a7d9d186c5112bbeb9011ac2"}, {"line": 14989, "relation": "positiveCorrelation", "evidence": "There was also a trend toward increased MMP-9 activity observed after oligomeric Abeta1-42 exposure.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Confidence": {"High": true}}, "source": 3062, "target": 2328, "key": "87736c97e03c2c6f4183698c75e40781"}, {"line": 14996, "relation": "negativeCorrelation", "evidence": "In agreement with the altered sNG2 levels, we found decreased MMP-9 activity after fibrillar Abeta_42 exposure and a trend toward increased MMP-9 activity after oligomeric Abeta_42 exposure.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3062, "target": 2328, "key": "d0521717526af19e5e6668bd4edeb197"}, {"line": 31526, "relation": "increases", "evidence": "These findings suggest that MMP-9 can degrade fAbeta and may contribute to ongoing clearance of plaques from amyloid-laden brains.", "citation": {"db": "PubMed", "db_id": "16787929"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 3062, "target": 2328, "key": "720f24dd0c7a7bfd1f56abe6e9d90858"}, {"line": 14997, "relation": "positiveCorrelation", "evidence": "In agreement with the altered sNG2 levels, we found decreased MMP-9 activity after fibrillar Abeta_42 exposure and a trend toward increased MMP-9 activity after oligomeric Abeta_42 exposure.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3062, "target": 380, "key": "e768b117dca64dcc2e7a8ad2bd6ee79f"}, {"line": 15016, "relation": "negativeCorrelation", "evidence": "The CSF concentrations of MMPs and TIMPs were determined with ELISAs.CSF concentrations of MMP-9 were significantly lower, and the concentrations of MMP-3 significantly higher in AD patients compared to the controls.", "citation": {"db": "PubMed", "db_id": "24448781"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3062, "target": 3823, "key": "11ecd6471af1c52f60d0e899f95a24bf"}, {"line": 15420, "relation": "increases", "evidence": "In conclusion, the present study shows for the first time that MMP-2 and -9 give a main contribution to estrogen's neuroprotective effect.", "citation": {"db": "PubMed", "db_id": "22402435"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "source": 3062, "target": 251, "key": "48dcee6db823154ea71ff65aaa22e224"}, {"line": 15438, "relation": "association", "evidence": "MMP-2/MMP-9 plasma level and brain expression in cerebral amyloid angiopathy-associated hemorrhagic stroke.", "citation": {"db": "PubMed", "db_id": "21707819"}, "annotations": {"MeSHDisease": {"Stroke": true, "Cerebral Amyloid Angiopathy": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3062, "target": 3930, "key": "372f676c3deae21d95fe2496a6eb81cb"}, {"relation": "partOf", "source": 3062, "target": 1237, "key": "211c679bf9e1b6a30ea8cd605bf59fe0"}, {"line": 31544, "relation": "increases", "evidence": "Overexpression of MMP-9 or treatment of HEK/APP695 cells with activated recombinant MMP-9 resulted in enhanced secretion of soluble APP (sAPPalpha), a product of alpha-secretase cleavage, and reduction of Abeta release.", "citation": {"db": "PubMed", "db_id": "17761425"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3062, "target": 2137, "key": "42fda7ce36e18091b98b13e0c918d747"}, {"relation": "partOf", "source": 3062, "target": 1190, "key": "51e6aca46e6936855e2bcacfe2408c46"}, {"line": 14521, "relation": "positiveCorrelation", "evidence": "TIMP1 messenger RNA positively correlated with MMP9 activity in frontal cortex", "citation": {"db": "PubMed", "db_id": "24519975"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "source": 4022, "target": 3062, "key": "842290a707a8b972129942c4b91b3ada"}, {"line": 14540, "relation": "association", "evidence": "These alterations in expression of inflammatory mediators in Nfkb1 deficient mice were associated with reduced expression of CD45.", "citation": {"db": "PubMed", "db_id": "24345324"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3285, "target": 3112, "key": "e70218fd04df09933bd9e32a9c467813"}, {"line": 14565, "relation": "association", "evidence": "MMP-9 and GFAP expression may play an important role in excess Abeta deposition, which is caused by an imbalance between the protein's synthesis and removal.", "citation": {"db": "PubMed", "db_id": "24962158"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3566, "target": 3062, "key": "6054ef6b354933f8821e0ce66d732cb6"}, {"line": 14566, "relation": "association", "evidence": "MMP-9 and GFAP expression may play an important role in excess Abeta deposition, which is caused by an imbalance between the protein's synthesis and removal.", "citation": {"db": "PubMed", "db_id": "24962158"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3566, "target": 2746, "key": "f7c2e3a63a182d62e742c51ad0f72b75"}, {"line": 14586, "relation": "association", "evidence": "cDNA array hybridization showed that p21 expression selectively inhibits a set of genes involved in mitosis, DNA replication, segregation, and repair.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 852, "target": 2493, "key": "4bf332ab884759918222257d51ff459a"}, {"line": 14587, "relation": "association", "evidence": "cDNA array hybridization showed that p21 expression selectively inhibits a set of genes involved in mitosis, DNA replication, segregation, and repair.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 627, "target": 2493, "key": "80597eafa56114727b65812d426aeada"}, {"line": 14597, "relation": "association", "evidence": "p21 also up-regulates multiple genes that have been associated with senescence or implicated in age-related diseases, including atherosclerosis, Alzheimer's disease, amyloidosis, and arthritis.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true, "Arthritis": true, "Atherosclerosis": true, "Amyloidosis": true}}, "source": 3891, "target": 2493, "key": "3ca117e965a6cf279f8dcb0cc8e9bb4b"}, {"line": 14600, "relation": "association", "evidence": "p21 also up-regulates multiple genes that have been associated with senescence or implicated in age-related diseases, including atherosclerosis, Alzheimer's disease, amyloidosis, and arthritis.", "citation": {"db": "PubMed", "db_id": "10760295"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true, "Cyclin-CDK subgraph": true}, "MeSHDisease": {"Alzheimer Disease": true, "Arthritis": true, "Atherosclerosis": true, "Amyloidosis": true}}, "source": 3895, "target": 2493, "key": "53b967ebe7f8b08c73bbde4797aab72f"}, {"line": 17789, "relation": "association", "evidence": "The data concerning the bioactive fragments of angiotensin II will be accompanied by those regarding its implication in the cardiovascular modeling and the induction of oxidative stress, inflammation, atherogenesis, etc.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHDisease": {"Inflammation": true, "Atherosclerosis": true}}, "source": 3895, "target": 81, "key": "f1e037ad38915c7359f63f743fde1494"}, {"line": 18447, "relation": "positiveCorrelation", "evidence": "In a binary logistic regression model, plasma MPO concentrations were independently associated with the presence of AD (p = 0.014).AD patients showed significantly increased plasma levels of MPO, which could be an important molecular link between atherosclerosis and AD.", "citation": {"db": "PubMed", "db_id": "24217274"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Alzheimer Disease": true, "Atherosclerosis": true}, "Confidence": {"High": true}}, "source": 3895, "target": 3066, "key": "cf00c93756e4cc13f2b6ffe93ffad104"}, {"line": 14622, "relation": "decreases", "evidence": "Consistent with these results, α-iso-cubebene inhibited the expression of inducible nitric oxide synthase (iNOS), cyclooxygenase 2 (COX-2) and MMP-9 in amyloid beta-stimulated microglia.", "citation": {"db": "PubMed", "db_id": "24090820"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 409, "target": 3123, "key": "b302f46155b074908217946820fa258e"}, {"line": 14630, "relation": "decreases", "evidence": "Consistent with these results, α-iso-cubebene inhibited the expression of inducible nitric oxide synthase (iNOS), cyclooxygenase 2 (COX-2) and MMP-9 in amyloid beta-stimulated microglia.", "citation": {"db": "PubMed", "db_id": "24090820"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 409, "target": 3278, "key": "2c29dbccc0ea1dab31e937c02248174f"}, {"line": 14638, "relation": "decreases", "evidence": "Consistent with these results, α-iso-cubebene inhibited the expression of inducible nitric oxide synthase (iNOS), cyclooxygenase 2 (COX-2) and MMP-9 in amyloid beta-stimulated microglia.", "citation": {"db": "PubMed", "db_id": "24090820"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "source": 409, "target": 3062, "key": "79678470d2785bbfc8aee7016c5de9a3"}, {"line": 14692, "relation": "decreases", "evidence": "In vitro studies revealed that (1) exposure of neural stem cells (NSCs) from the hippocampus to STZ strikingly increased intracellular reactive oxygen species (ROS) levels, induced cell death and perturbed cell proliferation and differentiation, (2) hydrogen peroxide induced similar cellular activities as STZ, (3) pre-incubation of STZ-treated NSCs with catalase, an antioxidant, suppressed all these cellular activities induced by STZ, and (4) likewise, pre-incubation of STZ-treated NSCs with salidroside, also an antioxidant, suppressed all these activities as catalase: reduction of ROS levels and NSC death with simultaneous increases in proliferation and differentiation.", "citation": {"db": "PubMed", "db_id": "22235318"}, "annotations": {"Cell": {"neuronal stem cell": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 201, "target": 170, "key": "2ca960597d1b812152769f44a605d576"}, {"line": 14696, "relation": "decreases", "evidence": "In vitro studies revealed that (1) exposure of neural stem cells (NSCs) from the hippocampus to STZ strikingly increased intracellular reactive oxygen species (ROS) levels, induced cell death and perturbed cell proliferation and differentiation, (2) hydrogen peroxide induced similar cellular activities as STZ, (3) pre-incubation of STZ-treated NSCs with catalase, an antioxidant, suppressed all these cellular activities induced by STZ, and (4) likewise, pre-incubation of STZ-treated NSCs with salidroside, also an antioxidant, suppressed all these activities as catalase: reduction of ROS levels and NSC death with simultaneous increases in proliferation and differentiation.", "citation": {"db": "PubMed", "db_id": "22235318"}, "annotations": {"Cell": {"neuronal stem cell": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 201, "target": 505, "key": "12b998d04e17472884c82c65e74a6247"}, {"line": 14723, "relation": "association", "evidence": "Hippocampal neurons are vulnerable to injury induced by Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18687381"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Hippocampus": true, "Neurons": true}}, "source": 3888, "target": 3823, "key": "66c31040632e5463d15eede9770a2275"}, {"line": 14862, "relation": "association", "evidence": "Neurosurgical procedures such as craniotomy and brain tumor resection could potentially lead to unavoidable cerebral injuries.", "citation": {"db": "PubMed", "db_id": "24397933"}, "annotations": {"MeSHDisease": {"Brain Neoplasms": true, "Wounds and Injuries": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3888, "target": 3832, "key": "e6d0d9c823963885b873b01b197b55a4"}, {"line": 14870, "relation": "increases", "evidence": "Matrix metalloproteinase-9 (MMP-9) is up-regulated in neurological injuries.", "citation": {"db": "PubMed", "db_id": "24397933"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3888, "target": 3062, "key": "47f891d62c3cf99432e994ff57057a95"}, {"line": 17753, "relation": "association", "evidence": "Moreover, basic experiments suggest a role of brain angiotensin II in neural injury, neuroinflammation, and cognitive function and that RAS blockade attenuates cognitive impairment in rodent dementia models of AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}}, "source": 3888, "target": 2274, "key": "97d14017820b60770527d69ecfe27e62"}, {"line": 17884, "relation": "increases", "evidence": "Accumulation of p-WOX1, p-JNK1, p-CREB, p-c-Jun, NF-kappaB and ATF3 in the nuclei of injured neurons took place within hours or the first week of injury.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}, "Confidence": {"High": true}}, "source": 3888, "target": 2365, "key": "b83b8253f37555e720e8230e09dbf0e3"}, {"line": 17892, "relation": "increases", "evidence": "Accumulation of p-WOX1, p-JNK1, p-CREB, p-c-Jun, NF-kappaB and ATF3 in the nuclei of injured neurons took place within hours or the first week of injury.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3888, "target": 2937, "key": "4bacc13783eab9494acadcffeb87a467"}, {"line": 17900, "relation": "increases", "evidence": "Accumulation of p-WOX1, p-JNK1, p-CREB, p-c-Jun, NF-kappaB and ATF3 in the nuclei of injured neurons took place within hours or the first week of injury.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"CREB subgraph": true}, "Confidence": {"Medium": true}}, "source": 3888, "target": 2163, "key": "c83c6675ab0b57fa5edecead9c7f0ad8"}, {"line": 17908, "relation": "increases", "evidence": "Accumulation of p-WOX1, p-JNK1, p-CREB, p-c-Jun, NF-kappaB and ATF3 in the nuclei of injured neurons took place within hours or the first week of injury.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "source": 3888, "target": 3553, "key": "26de9ff5ba31b3e6eff0b6ba5aa8dc37"}, {"line": 17916, "relation": "increases", "evidence": "Accumulation of p-WOX1, p-JNK1, p-CREB, p-c-Jun, NF-kappaB and ATF3 in the nuclei of injured neurons took place within hours or the first week of injury.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3888, "target": 3002, "key": "6cb46b1eb3c1cbc358235dd4f3025370"}, {"line": 17924, "relation": "increases", "evidence": "Accumulation of p-WOX1, p-JNK1, p-CREB, p-c-Jun, NF-kappaB and ATF3 in the nuclei of injured neurons took place within hours or the first week of injury.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3888, "target": 3536, "key": "abbfca5d0650db213306ced3fed18afd"}, {"line": 17994, "relation": "association", "evidence": "Evidently, WOX1 is the potential target for drug intervention in mitigating symptoms associated with neuronal injury.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "MeSHDisease": {"Wounds and Injuries": true}, "Confidence": {"High": true}}, "source": 3888, "target": 3536, "key": "050afcb2dd314fd101508bea15ab2ac7"}, {"line": 18136, "relation": "association", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}, "Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3888, "target": 3110, "key": "15adc70d61c419149a357bb6ff0e7901"}, {"line": 40666, "relation": "association", "evidence": "Ligands that target PPARs (peroxisome proliferator-activated receptors), a group of ligand-activated transcription factors, are promising therapeutics for neurologic disease and CNS injury because their activation affects many, if not all, of these interrelated pathologic mechanisms.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"MeSHAnatomy": {"Peroxisomes": true}, "MeSHDisease": {"Wounds and Injuries": true}, "Confidence": {"High": true}}, "source": 3888, "target": 2207, "key": "c7bd3cb06c3ac78d8911f0d273c40be7"}, {"line": 14750, "relation": "increases", "evidence": "Isorhynchophylline improves learning and memory impairments induced by D-galactose in mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Species": {"10090": true}, "Confidence": {"High": true}}, "source": 195, "target": 818, "key": "6eb13b03c0cdef28fde51e91cd797571"}, {"line": 14751, "relation": "increases", "evidence": "Isorhynchophylline improves learning and memory impairments induced by D-galactose in mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Species": {"10090": true}, "Confidence": {"High": true}}, "source": 195, "target": 820, "key": "78cbc028305d314b0cb4053326bdd81a"}, {"line": 14762, "relation": "decreases", "evidence": "Isorhynchophylline (IRN), an alkaloid isolated from Uncaria rhynchophylla, has been reported to improve cognitive impairment induced by beta-amyloid in rats.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Confidence": {"Medium": true}, "Species": {"10116": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 195, "target": 80, "key": "41d15cdc0f3a727a463bfdab0e6fcefe"}, {"line": 14764, "relation": "increases", "evidence": "Isorhynchophylline (IRN), an alkaloid isolated from Uncaria rhynchophylla, has been reported to improve cognitive impairment induced by beta-amyloid in rats.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Confidence": {"Medium": true}, "Species": {"10116": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 195, "target": 812, "key": "49dd38dad54e897a7fcfd455cc3a1d69"}, {"line": 14777, "relation": "increases", "evidence": "In the mechanistic studies, IRN significantly increased the level of glutathione (GSH) and the activities of superoxide dismutase (SOD) and catalase (CAT), while decreased the level of malondialdehyde (MDA) in the brain tissues of the D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 195, "target": 265, "key": "ec151509afea7ffbb22699cb891c9083"}, {"line": 14785, "relation": "increases", "evidence": "In the mechanistic studies, IRN significantly increased the level of glutathione (GSH) and the activities of superoxide dismutase (SOD) and catalase (CAT), while decreased the level of malondialdehyde (MDA) in the brain tissues of the D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"Medium": true}}, "source": 195, "target": 759, "key": "f6a19896a6f3973e56a39333b3d163ad"}, {"line": 14793, "relation": "increases", "evidence": "In the mechanistic studies, IRN significantly increased the level of glutathione (GSH) and the activities of superoxide dismutase (SOD) and catalase (CAT), while decreased the level of malondialdehyde (MDA) in the brain tissues of the D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 195, "target": 2453, "key": "f604476341577d299543979659a3fcc0"}, {"line": 14799, "relation": "decreases", "evidence": "In the mechanistic studies, IRN significantly increased the level of glutathione (GSH) and the activities of superoxide dismutase (SOD) and catalase (CAT), while decreased the level of malondialdehyde (MDA) in the brain tissues of the D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 195, "target": 298, "key": "2259be79eaf6e0c836e15d333e99111e"}, {"line": 14810, "relation": "decreases", "evidence": "Moreover, IRN (20 or 40mg/kg) significantly inhibited the production of prostaglandin E 2 (PGE2) and nitric oxide (NO), and the mRNA expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS), as well as the activation of nuclear factor kappa B (NF-κB) in the brain tissues of D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "Confidence": {"High": true}}, "source": 195, "target": 163, "key": "7255da311f17fe8b14ca5819f21ef2e1"}, {"line": 14818, "relation": "decreases", "evidence": "Moreover, IRN (20 or 40mg/kg) significantly inhibited the production of prostaglandin E 2 (PGE2) and nitric oxide (NO), and the mRNA expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS), as well as the activation of nuclear factor kappa B (NF-κB) in the brain tissues of D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 195, "target": 156, "key": "87e0c5cd3d8e27fa62a9750f68b493b6"}, {"line": 14826, "relation": "decreases", "evidence": "Moreover, IRN (20 or 40mg/kg) significantly inhibited the production of prostaglandin E 2 (PGE2) and nitric oxide (NO), and the mRNA expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS), as well as the activation of nuclear factor kappa B (NF-κB) in the brain tissues of D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "Confidence": {"High": true}}, "source": 195, "target": 3278, "key": "f68a4626e1bd7150690b621f3f2a27d1"}, {"line": 14833, "relation": "decreases", "evidence": "Moreover, IRN (20 or 40mg/kg) significantly inhibited the production of prostaglandin E 2 (PGE2) and nitric oxide (NO), and the mRNA expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS), as well as the activation of nuclear factor kappa B (NF-κB) in the brain tissues of D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 195, "target": 3123, "key": "f25e6334588d38b979cc413e6acff763"}, {"line": 14840, "relation": "decreases", "evidence": "Moreover, IRN (20 or 40mg/kg) significantly inhibited the production of prostaglandin E 2 (PGE2) and nitric oxide (NO), and the mRNA expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS), as well as the activation of nuclear factor kappa B (NF-κB) in the brain tissues of D-gal-treated mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 195, "target": 875, "key": "38f3e756cafeab8e6a22a482722eadfc"}, {"line": 14752, "relation": "decreases", "evidence": "Isorhynchophylline improves learning and memory impairments induced by D-galactose in mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Species": {"10090": true}, "Confidence": {"High": true}}, "source": 34, "target": 818, "key": "f30d2ba6e8ff2fdfe3b2d08957fc6977"}, {"line": 14753, "relation": "decreases", "evidence": "Isorhynchophylline improves learning and memory impairments induced by D-galactose in mice.", "citation": {"db": "PubMed", "db_id": "24984171"}, "annotations": {"Species": {"10090": true}, "Confidence": {"High": true}}, "source": 34, "target": 820, "key": "4a3f7392f1286a1dfd12ebc2b0734a6e"}, {"line": 14862, "relation": "association", "evidence": "Neurosurgical procedures such as craniotomy and brain tumor resection could potentially lead to unavoidable cerebral injuries.", "citation": {"db": "PubMed", "db_id": "24397933"}, "annotations": {"MeSHDisease": {"Brain Neoplasms": true, "Wounds and Injuries": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3832, "target": 3888, "key": "9984376109488b5917471e53423af161"}, {"line": 41686, "relation": "association", "evidence": "Malignant astrocytomas are among the most common brain tumours and few therapeutic options exist.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "MeSHDisease": {"Astrocytoma": true, "Brain Neoplasms": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3832, "target": 3893, "key": "a666f832b9d3790f36629f9dcb310191"}, {"line": 14889, "relation": "association", "evidence": "Plasma MMP-9 concentration measured 4 times, immediately before starting atorvastatin or placebo, immediately before surgery, 24 hours and two weeks after the surgery.", "citation": {"db": "PubMed", "db_id": "24397933"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 215, "target": 3062, "key": "88d7f8d71f8200c0e45ffd9b52e81f8e"}, {"line": 14907, "relation": "positiveCorrelation", "evidence": "Stress of the endoplasmic reticulum and oxidative stress play critical roles in the pathogenesis of Fuchs Endothelial Corneal Dystrophy (FECD).", "citation": {"db": "PubMed", "db_id": "22956607"}, "annotations": {"Disease": {"Fuchs' endothelial dystrophy": true}, "CellStructure": {"Endoplasmic Reticulum": true}, "MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3856, "target": 842, "key": "41f20a63208b11843045caba768407cf"}, {"line": 14951, "relation": "association", "evidence": "p21 is dispensable for AID-mediated class switch recombination and mutagenesis of immunoglobulin genes during somatic hypermutation.", "citation": {"db": "PubMed", "db_id": "21288574"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2277, "target": 2493, "key": "d40b7c2b367e2850392dba40cd7413d8"}, {"line": 14963, "relation": "increases", "evidence": "We also show that p21 transcript levels are the same in both wildtype and AID-deficient B cells during B cell activation, and that AID-mediated class switch recombination (CSR) is not affected by p21 deficiency; thereby indicating that p21 regulation in B cells is not altered by AID-induced DNA damage and that p21 has no affect on AID-dependent Ig gene diversification.", "citation": {"db": "PubMed", "db_id": "21288574"}, "source": 2277, "target": 3842, "key": "0bc25b559497d1992f53606b1d0b8f20"}, {"line": 14957, "relation": "association", "evidence": "Regulation of PCNA ubiquitination by p21, also known as Cdkn1a and p21(Cip1/Waf1), is an important mechanism that controls mutation loads in mammalian cells.", "citation": {"db": "PubMed", "db_id": "21288574"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3167, "target": 2493, "key": "47678cd556b4f4aa892a501f276b11e8"}, {"line": 44615, "relation": "increases", "evidence": "Markers of DNA damage, particularly oxidative DNA damage, have been largely found in brain regions, peripheral tissues, and biological fluids of Alzheimer's disease (AD) patients.", "citation": {"db": "PubMed", "db_id": "19199873"}, "annotations": {"Subgraph": {"Response DNA damage": true, "Epigenetic modification subgraph": true}}, "source": 3842, "target": 3823, "key": "ebce8c34424d5a71007f45bd163a18b3"}, {"line": 14974, "relation": "association", "evidence": "Involvement of Matrix Metalloproteinase-9 in Amyloid-beta 1-42-Induced Shedding of the Pericyte Proteoglycan NG2.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "source": 2577, "target": 3062, "key": "1490584c4800036e24e1d6c430610987"}, {"line": 14983, "relation": "positiveCorrelation", "evidence": "Conversely, oligomer-enriched preparations of Abeta1-42 increased soluble NG2 levels in the supernatants.", "citation": {"db": "PubMed", "db_id": "24918635"}, "annotations": {"Confidence": {"High": true}}, "source": 2577, "target": 2328, "key": "cc742057ec17133b4564257e250a4764"}, {"line": 15010, "relation": "association", "evidence": "A growing body of evidence shows the involvement of matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) in neurodegeneration processes.", "citation": {"db": "PubMed", "db_id": "24448781"}, "annotations": {"Disease": {"dementia": true, "Alzheimer's disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 2194, "target": 3874, "key": "2362397ce9407caf2159c319495d5b9b"}, {"line": 15333, "relation": "association", "evidence": "Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer.", "citation": {"db": "PubMed", "db_id": "19882751"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neoplasms": true, "Vascular Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 2194, "target": 3923, "key": "4d1b00e17a421d72b2c095ec698a6c9e"}, {"line": 15335, "relation": "association", "evidence": "Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer.", "citation": {"db": "PubMed", "db_id": "19882751"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neoplasms": true, "Vascular Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 2194, "target": 3823, "key": "9d02ca82003e86b00e398758afdea7a7"}, {"line": 18754, "relation": "association", "evidence": "A growing amount of evidence indicates that matrix metalloproteinases (MMPs) may play an important role in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "10672313"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "source": 2194, "target": 3823, "key": "038d9fc735847680e7d9b5b3f51be416"}, {"line": 15336, "relation": "association", "evidence": "Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer.", "citation": {"db": "PubMed", "db_id": "19882751"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neoplasms": true, "Vascular Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 2194, "target": 3869, "key": "80da989389e1570faf263d4b290cdbd1"}, {"line": 15337, "relation": "association", "evidence": "Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer.", "citation": {"db": "PubMed", "db_id": "19882751"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neoplasms": true, "Vascular Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 2194, "target": 3887, "key": "671b4580b28870b7303822a3a3a7aee0"}, {"line": 18787, "relation": "increases", "evidence": "Several proteases were shown to hydrolyze Abeta in vitro or in cell-based assays, and are likely candidates for a role in Abeta clearance in brain. Previous reports suggest that matrix metalloproteinases (MMPs) could be involved in such a mechanism.", "citation": {"db": "PubMed", "db_id": "16822591"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2194, "target": 2328, "key": "4279983fd0e0f60f8c4c53dd9f09544d"}, {"line": 49117, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2194, "target": 2328, "key": "5e185704a8aa0d18666ca5808c79364a"}, {"line": 18797, "relation": "association", "evidence": "Our findings support the hypothesis that MMPs may influence the risk of dementia.", "citation": {"db": "PubMed", "db_id": "16822591"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2194, "target": 3901, "key": "bf91149e58eb7e421c61f6b46aeeecfa"}, {"line": 40147, "relation": "increases", "evidence": "These proinflammatory factors act as potent stimuli in brain inflammation through upregulation of diverse inflammatory genes, including matrix metalloproteinases (MMPs), cytosolic phospholipase A2 (cPLA2), cyclooxygenase-2 (COX-2), and adhesion molecules.", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Encephalitis": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2194, "target": 577, "key": "8734eef10cccd90ab4003a8da97b94c1"}, {"line": 47387, "relation": "increases", "evidence": "The ectodomains of all HS-chain-enriched snd-1 molecules are constitutively released (i.e. shed) from cell membranes as part of normal physiology (Bishop et al.,2007). Proteases, including matrix metalloproteinases (MMPs) and ADAM sheddases, mediate the ectodomain shedding of membrane-bound proteins (such as snd-1). Previously, we showed snd-1 shedding plays an active role in HPV16 infection in cultured HKs (Surviladze et al., 2012).", "citation": {"db": "PubMed", "db_id": "26289843"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 2194, "target": 3343, "key": "a5534fbfffed56c6269e7400500613ab"}, {"line": 49112, "relation": "positiveCorrelation", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2194, "target": 3463, "key": "b37cd2bd67eca68ca1c6fc54c90fc868"}, {"line": 49121, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2194, "target": 3010, "key": "15284af44eb0c576b36adeafe134c698"}, {"line": 49122, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2194, "target": 3015, "key": "a386f6eb33fd780f6dac667db6ecb644"}, {"line": 49136, "relation": "increases", "evidence": "However, MMPs can degrade both soluble and fibrillar forms of amyloid-beta (Abeta). It has also been shown that Abeta enhances the expression of MMPs in neuroglial cultures and induces the release of TIMP-1 by brain cell", "citation": {"db": "PubMed", "db_id": "23792694"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Microglia": true}, "Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2194, "target": 80, "key": "7997cc3f735a87e78a685fb98805928b"}, {"line": 15017, "relation": "positiveCorrelation", "evidence": "The CSF concentrations of MMPs and TIMPs were determined with ELISAs.CSF concentrations of MMP-9 were significantly lower, and the concentrations of MMP-3 significantly higher in AD patients compared to the controls.", "citation": {"db": "PubMed", "db_id": "24448781"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 3823, "key": "902cf5fbfe1c8e1937f7f76fd836f746"}, {"line": 18695, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 3823, "key": "df49c3045e8b0118e0685a69d19ad025"}, {"line": 18689, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 3826, "key": "44d75c61c8a7ae232ce3f0956e940221"}, {"line": 18690, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 3923, "key": "0642a47ac0184baed7ffeca0e159a2a3"}, {"line": 18691, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 3892, "key": "50d38591fa8fd8354b5b35525b1b11a4"}, {"line": 18692, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 3869, "key": "181f36724afb571784b0d9ee7a7552bc"}, {"line": 18693, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 3878, "key": "59d2275ef9806ca43f24fa77d3cabbca"}, {"line": 18694, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 3874, "key": "e8bdf0dbb10a3bc089b66364b3d958c9"}, {"line": 18704, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 3060, "target": 651, "key": "10e5f924d16c2a0f7d41541e60d4eafc"}, {"line": 18705, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 3060, "target": 652, "key": "b7c30fb3b01539fc82d362e2aaa3b456"}, {"line": 18707, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 3060, "target": 521, "key": "71ee83ca479595dd7d74c9967640fe9b"}, {"line": 18708, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 3060, "target": 761, "key": "d9a2ffc068e34a4f68d16e006c532e66"}, {"line": 18709, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 3060, "target": 818, "key": "c75db6a9ea32d319a3ad5adc77ae34d5"}, {"line": 18717, "relation": "association", "evidence": "Moreover, a strict spatiotemporal MMP-3 up-regulation in the injured or diseased CNS might support remyelination and neuroprotection, as well as genesis and migration of stem cells in the damaged brain.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Brain": true}, "MeSHDisease": {"Wounds and Injuries": true}}, "source": 3060, "target": 821, "key": "a2843cdbedb11bd5e07d1e63c03f85af"}, {"line": 18732, "relation": "increases", "evidence": "Matrix metalloproteinase-3 (MMP-3) is a member of the class of zinc-dependent proteases known to degrade the extracellular matrix.", "citation": {"db": "PubMed", "db_id": "21044079"}, "annotations": {"CellStructure": {"Extracellular Matrix": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 3060, "target": 385, "key": "1287e1a249e0f3459dda1e391b9a3a78"}, {"line": 18733, "relation": "association", "evidence": "Matrix metalloproteinase-3 (MMP-3) is a member of the class of zinc-dependent proteases known to degrade the extracellular matrix.", "citation": {"db": "PubMed", "db_id": "21044079"}, "annotations": {"CellStructure": {"Extracellular Matrix": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3060, "target": 748, "key": "a4b6931a06cb9133d39aa3da767445cb"}, {"line": 18740, "relation": "association", "evidence": "In neuronal cells, MMP-3 expression is increased in response to cell stress, and the cleaved, active MMP-3 participates in apoptotic signaling.", "citation": {"db": "PubMed", "db_id": "21044079"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity"}, "source": 3060, "target": 479, "key": "7239d5c00ebf62802eaea2f8cb551d62"}, {"line": 18762, "relation": "increases", "evidence": "Stromelysin-1 (MMP-3) plays a central role in activating latent-type MMPs, which are originally secreted as proenzymes.", "citation": {"db": "PubMed", "db_id": "10672313"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3060, "target": 2194, "key": "de74bd7d7aa11f70c9dc47a41109804e"}, {"line": 18772, "relation": "association", "evidence": "The selective distribution of MMP-3 in the human brain suggests that MMP-3 might play an important role in the pathogenesis of AD, especially in the degradation of beta-amyloid protein.", "citation": {"db": "PubMed", "db_id": "10672313"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3060, "target": 2328, "key": "52864059fc38a74e7b72af59083aabfc"}, {"line": 15063, "relation": "association", "evidence": "Alzheimer's disease (AD) is characterized by progressive cognitive decline associated with a featured neuropathology (neuritic plaques and neurofibrillary tangles).", "citation": {"db": "PubMed", "db_id": "24936870"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Plaque, Amyloid": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}}, "source": 3881, "target": 3823, "key": "118749cf3b8f0ba8de8879aa80e05edd"}, {"line": 17269, "relation": "negativeCorrelation", "evidence": "Further, EGR1 levels were negatively correlated with hippocampal amyloid-beta plaque burden.This study presents global gene expression profiles associated with GA immunization in a glaucoma rat model.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Species": {"10116": true}, "Disease": {"glaucoma": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Published": {"Epilepsy comorbidity paper": true}, "Confidence": {"Medium": true}}, "source": 3881, "target": 2658, "key": "7264032624d74ac8f5a20096438f141b"}, {"line": 19053, "relation": "association", "evidence": "Furthermore, elevated amounts of tissue plasminogen activator-neuroserpin complexes are seen in the Alzheimer brain, and immunohistochemical studies demonstrate that both tissue plasminogen activator and neuroserpin are associated with amyloid-beta plaques in Alzheimer brain tissue.", "citation": {"db": "PubMed", "db_id": "19222708"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3881, "target": 1608, "key": "7cda61a3ba8eb6fcdea3a374257324e9"}, {"line": 25899, "relation": "association", "evidence": "Recent studies on the effect of murine and human apoE in APP transgenic mice provide direct evidence that apoE is critically involved in the in vivo converstion of Abeta into forms which contain high beta-sheet content and associated cellular toxicity (neuritic plaques and CAA). ", "citation": {"db": "PubMed", "db_id": "11816788"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3881, "target": 2312, "key": "ca755e3c734fc2f7ceaa408692684f2f"}, {"line": 26226, "relation": "association", "evidence": "Apolipoprotein E (ApoE) genotype is a significant risk factor for the development of Alzheimer disease (AD) and the ApoE protein is associated with senile plaques (SP) and neurofibrillary tangles (NFT)", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 3881, "target": 2312, "key": "ae7f97c790415a100ba670ec1b667ccf"}, {"line": 26237, "relation": "association", "evidence": "These findings suggest that the interaction of ApoE with tau and amyloid-beta proteins in AD could play a important role in the formation of NFT and SP, respectively, contributing to the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3881, "target": 914, "key": "953dafd277355a1b5b9260f88683657d"}, {"line": 40176, "relation": "association", "evidence": "The presence of activated microglia and astrocytes in the vicinity of amyloid plaques in the brains of Alzheimer's disease (AD) patients and mouse models implicates inflammation as a contributor to AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24369524"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Plaque, Amyloid": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Astrocytes": true}, "Species": {"9606": true, "10090": true}, "Confidence": {"High": true}}, "source": 3881, "target": 3920, "key": "de61128c5c869d8880720132cfd68b5c"}, {"line": 41556, "relation": "association", "evidence": "In this study, we established a new transgenic animal model of AD by crossbreeding the Tg2576 mouse with the S100A9 knockout (KO) mouse.", "citation": {"db": "PubMed", "db_id": "24586443"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 3881, "target": 3714, "key": "f78b6ececd749e1a92f4bf386943877b"}, {"line": 41933, "relation": "association", "evidence": "The participation of the immune system in the neurodegeneration in a rat model of colchicine-induced AD has not been explored.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Species": {"10116": true}}, "source": 3881, "target": 234, "key": "95e5fb82fa77bc2d5a022cfb01667a73"}, {"line": 41942, "relation": "association", "evidence": "Methods: In the present study, hippocampal neurodegeneration along with reactive oxygen species (ROS), nitrite and TNF-α in the hippocampus and some systemic immune responses were measured after 15 and 21 days of intracerebroventricular colchicine injection in rats and again after oral administration of different doses of the anti-inflammatory drug naproxen in AD rats.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 3881, "target": 310, "key": "ef9f5a10ea28280c8df5d511f59ac35f"}, {"line": 42137, "relation": "association", "evidence": "Four weeks after rAAV2-IL-1beta transduction, we found significant reductions in 6E10 and Congo red staining of amyloid plaques that was confirmed by decreased levels of insoluble Abeta1-42 and Abeta1-40 in the inflamed hippocampus.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 3881, "target": 2183, "key": "f58ecee6feb4fecd5749828e4a578a5b"}, {"line": 42158, "relation": "regulates", "evidence": "These results suggest that infiltrating CCR2(+) monocytes do not contribute to IL-1beta-mediated amyloid plaque clearance.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Monocytes": true}, "Confidence": {"Medium": true}}, "source": 3881, "target": 2183, "key": "e03bfa77aa52302c2dfd424b05fa2561"}, {"line": 42139, "relation": "association", "evidence": "Four weeks after rAAV2-IL-1beta transduction, we found significant reductions in 6E10 and Congo red staining of amyloid plaques that was confirmed by decreased levels of insoluble Abeta1-42 and Abeta1-40 in the inflamed hippocampus.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 3881, "target": 22, "key": "13f1ef54e5c8c206eac12118c417f002"}, {"line": 44807, "relation": "positiveCorrelation", "evidence": "beta-secretase-1 (BACE1) elevation relative to Abeta accumulation and synaptic/neuritic alterations in the forebrain, using transgenic mice harboring familial AD (FAD) mutations (5XFAD and 2XFAD) as models", "citation": {"db": "PubMed", "db_id": "20092570"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "DiseaseState": {"Familial Alzheimers Disease": true}, "KnockoutMice": {"App transgenic": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3881, "target": 3593, "key": "b15db354573261e0cf1ba22ac3cc870c"}, {"line": 47012, "relation": "increases", "evidence": "Furthermore, in AD brains, mitochondrially associated APP formed stable ∼480 kDa complexes with the translocase of the outer mitochondrial membrane 40 (TOM40) import channel and a super complex of ∼620 kDa with both mitochondrial TOM40 and the translocase of the inner mitochondrial membrane 23 (TIM23) import channel TIM23 in an “Nin mitochondria–Cout cytoplasm” orientation. Accumulation of APP across mitochondrial import channels, which varied with the severity of AD, inhibited the entry of nuclear-encoded cytochrome c oxidase subunits IV and Vb proteins, which was associated with decreased cytochrome c oxidase activity and increased levels of H2O2", "citation": {"db": "PubMed", "db_id": "16943564"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true}}, "source": 3881, "target": 131, "key": "b3fabfea22db029142a27caf4a41a934"}, {"line": 47013, "relation": "decreases", "evidence": "Furthermore, in AD brains, mitochondrially associated APP formed stable ∼480 kDa complexes with the translocase of the outer mitochondrial membrane 40 (TOM40) import channel and a super complex of ∼620 kDa with both mitochondrial TOM40 and the translocase of the inner mitochondrial membrane 23 (TIM23) import channel TIM23 in an “Nin mitochondria–Cout cytoplasm” orientation. Accumulation of APP across mitochondrial import channels, which varied with the severity of AD, inhibited the entry of nuclear-encoded cytochrome c oxidase subunits IV and Vb proteins, which was associated with decreased cytochrome c oxidase activity and increased levels of H2O2", "citation": {"db": "PubMed", "db_id": "16943564"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true}}, "source": 3881, "target": 2547, "key": "018faa40af539b293369a2f5d2d2f8ac"}, {"line": 15085, "relation": "increases", "evidence": "The Abeta Peptides-Activated Calcium-Sensing Receptor Stimulates the Production and Secretion of Vascular Endothelial Growth Factor-A by Normoxic Adult Human Cortical Astrocytes.", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Bodily Secretions": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2451, "target": 3519, "key": "c09f06d591ffc2ffed58a9f9730af322"}, {"line": 15086, "relation": "association", "evidence": "The Abeta Peptides-Activated Calcium-Sensing Receptor Stimulates the Production and Secretion of Vascular Endothelial Growth Factor-A by Normoxic Adult Human Cortical Astrocytes.", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Bodily Secretions": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 2451, "target": 3519, "key": "bf739f87ad4226f953e0c6106ab8ea5e"}, {"line": 15135, "relation": "association", "evidence": "Here, we report that exogenous Abetas stimulate the NAHAs to produce and secrete even VEGF-A through a CaSR-mediated mechanism.", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2451, "target": 3519, "key": "41573bc7cdb1fc5bb35a3453e72d7fde"}, {"line": 15113, "relation": "association", "evidence": "We have shown with cultured cerebral cortical normal (i.e., untransformed) adult human astrocytes (NAHAs) that exogenous amyloid-beta peptides (Abetas) stimulate the astrocytes to make and secrete large amounts of Abetas and nitric oxide by a mechanism mediated through the calcium-sensing receptor (CaSR).", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true, "Cerebral Cortex": true}, "Species": {"9606": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2451, "target": 2328, "key": "a040b5ee63031e2762a5be6c1333008a"}, {"line": 15121, "relation": "association", "evidence": "We have shown with cultured cerebral cortical normal (i.e., untransformed) adult human astrocytes (NAHAs) that exogenous amyloid-beta peptides (Abetas) stimulate the astrocytes to make and secrete large amounts of Abetas and nitric oxide by a mechanism mediated through the calcium-sensing receptor (CaSR).", "citation": {"db": "PubMed", "db_id": "24948534"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true, "Cerebral Cortex": true}, "Species": {"9606": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 2451, "target": 156, "key": "c039b5cbaa763c18acfd90c9f537702b"}, {"line": 15170, "relation": "decreases", "evidence": "Previously, we reported a Ca(2+)/calmodulin (CaM)-dependent impairment of apoptosis induced by serum deprivation in Alzheimer's disease (AD) lymphoblasts.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2158, "target": 478, "key": "c14e70341661c857a0172bf353eb3c9d"}, {"line": 15188, "relation": "increases", "evidence": "The CaM antagonist, calmidazolium, and the CaMKII inhibitor, KN-62, normalized the survival pattern of AD lymphoblasts by augmenting ERK1/2 activation and reducing p21 mRNA and protein levels.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 433, "target": 2173, "key": "4d6746dc0774868511c0e63dbea8360d"}, {"line": 15190, "relation": "decreases", "evidence": "The CaM antagonist, calmidazolium, and the CaMKII inhibitor, KN-62, normalized the survival pattern of AD lymphoblasts by augmenting ERK1/2 activation and reducing p21 mRNA and protein levels.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 433, "target": 3954, "key": "c79a851aefa4cce00b656995f49877ac"}, {"line": 15189, "relation": "increases", "evidence": "The CaM antagonist, calmidazolium, and the CaMKII inhibitor, KN-62, normalized the survival pattern of AD lymphoblasts by augmenting ERK1/2 activation and reducing p21 mRNA and protein levels.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 404, "target": 2173, "key": "905850ddfe378a3efe230a865a75345f"}, {"line": 15191, "relation": "decreases", "evidence": "The CaM antagonist, calmidazolium, and the CaMKII inhibitor, KN-62, normalized the survival pattern of AD lymphoblasts by augmenting ERK1/2 activation and reducing p21 mRNA and protein levels.", "citation": {"db": "PubMed", "db_id": "23153928"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 404, "target": 3954, "key": "8e5d3a3294673bde8920ba9bf53f1a62"}, {"line": 15225, "relation": "negativeCorrelation", "evidence": "Results showed a significant decrease in the intake of vitamins C (p < .001)", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 186, "target": 3823, "key": "511f78cbad6d9f064d4e52e2f528aa1a"}, {"line": 49434, "relation": "decreases", "evidence": "Use of vitamin E and vitamin C supplements in combination is associated with reduced prevalence and incidence of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "source": 186, "target": 3823, "key": "fd36467995e3966bf04344d3a77999a7"}, {"line": 15273, "relation": "isA", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 186, "target": 213, "key": "f12e87c9250c2b931b1440eb074dc034"}, {"line": 15276, "relation": "association", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 186, "target": 2453, "key": "9f325665647adb0f20ebecadbb591cfa"}, {"line": 49432, "relation": "association", "evidence": "Use of vitamin E and vitamin C supplements in combination is associated with reduced prevalence and incidence of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "source": 186, "target": 188, "key": "3657a25984d99b486210cd713d8785da"}, {"line": 15259, "relation": "positiveCorrelation", "evidence": "The blood catalase levels of dementia patients, as a whole, were significantly and positively associated with the intake of vitamins A (p < .05),", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Disease": {"dementia": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 184, "target": 2453, "key": "0ee79dc364de4c5149ab81a07cfa3968"}, {"line": 15275, "relation": "association", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 184, "target": 2453, "key": "1f8c2e7f424f263adf8862c07dda5dac"}, {"line": 15272, "relation": "isA", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 184, "target": 213, "key": "68c2270c1acc4a94049b3c9a43cbeee8"}, {"line": 15278, "relation": "association", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 213, "target": 716, "key": "b01146007c449bc6b44557ede29eb2c4"}, {"relation": "partOf", "source": 213, "target": 904, "key": "ab809af664187d143c788170e6e08a53"}, {"line": 15274, "relation": "isA", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 188, "target": 213, "key": "8cc503492c7617b9d33b60413cf6eaa9"}, {"line": 15277, "relation": "association", "evidence": "The results indicated that dietary intake of vitamins A, C, and E may influence blood levels of catalase possibly through their antioxidant effects on free radicals.", "citation": {"db": "PubMed", "db_id": "12094909"}, "annotations": {"MeSHAnatomy": {"Blood": true}, "Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 188, "target": 2453, "key": "44d56c2134e481881e47f9a8a02005d5"}, {"line": 44748, "relation": "decreases", "evidence": "antioxidants such as vitamin E prevents Abeta-induced ROS production, oxidative damage and neurotoxicity in brain cells. ", "citation": {"db": "PubMed", "db_id": "10658956"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 188, "target": 170, "key": "1f9ab6bfaf6cfff127a9301eadd583c7"}, {"line": 49448, "relation": "decreases", "evidence": "Sufficient levels of vitamin E may reduce the oxidative stress–related damage associated with pathological changes of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "source": 188, "target": 170, "key": "387415f9e5bcc14f1b78f78cfd753a5d"}, {"line": 44752, "relation": "decreases", "evidence": "antioxidants such as vitamin E prevents Abeta-induced ROS production, oxidative damage and neurotoxicity in brain cells. ", "citation": {"db": "PubMed", "db_id": "10658956"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 188, "target": 842, "key": "e044e6b4d0f426270543170721025aee"}, {"line": 46093, "relation": "negativeCorrelation", "evidence": "we demonstrate that dietary deficiency in folate and vitamin E increased PS-1 expression", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 188, "target": 3258, "key": "3f888bf6cffbc18ebdfe385fd5ebe123"}, {"line": 46487, "relation": "decreases", "evidence": "We have examined this by using oxidative stress to induce apoptosis in a mouse hippocampal neuronal cell line (HT-22). Oxidatively modified proteins were measured by high-resolution two-dimensional gel electrophoresis coupled with oxidation-specific immunostains.Under these conditions the oxidatively stressed cells undergo apoptotic process, and specific proteins are oxidized. The three proteins that appeared to be most susceptible to oxidation were identified by mass spectrometry. Those oxidized proteins are heat shock protein 60 and vimentin, both believed to function as antiapoptotic proteins, and a third protein with sequence homology to hemoglobin alpha-chain. When the cells were pretreated with vitamin E, these proteins were not oxidized and the cells did not undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "12548636"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 188, "target": 478, "key": "7ad4adcf38f2c84c5dc9490c05135e66"}, {"line": 49432, "relation": "association", "evidence": "Use of vitamin E and vitamin C supplements in combination is associated with reduced prevalence and incidence of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "source": 188, "target": 186, "key": "79fca16d0b562d9a164a272ad3812ab2"}, {"line": 49433, "relation": "decreases", "evidence": "Use of vitamin E and vitamin C supplements in combination is associated with reduced prevalence and incidence of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "source": 188, "target": 3823, "key": "38a9fc7546e0ca5a4f4bfff6dcb4c549"}, {"line": 15300, "relation": "increases", "evidence": "Donepezil is metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "19300564"}, "annotations": {"Subgraph": {"Paroxetine subgraph": true}}, "source": 2611, "target": 606, "key": "ed8225c7a08f8f74262c1c48bb3dfa5d"}, {"line": 18653, "relation": "increases", "evidence": "Cytochrome P450 (CYP) 2D6 enzyme is the major responsible for the metabolism of donepezil, an inhibitor of acetyl cholinesterase currently used for the symptomatic treatment of mild-to-moderate Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "20859244"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Paroxetine subgraph": true}}, "source": 2611, "target": 606, "key": "dab7f55024417aec93bf5b4af9099bfa"}, {"line": 18643, "relation": "association", "evidence": "Our preliminary data suggest that the CYP2D6 polymorphism influences both donepezil metabolism and therapeutic outcome and that a knowledge of a patient's CYP2D6 genotype together with donepezil concentration measurements might be useful in the context of improving the clinical efficacy of donepezil therapy.", "citation": {"db": "PubMed", "db_id": "16845507"}, "annotations": {"Subgraph": {"Paroxetine subgraph": true}}, "source": 2611, "target": 244, "key": "c17177c939e6d445083d2bb9e73effec"}, {"line": 19100, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2611, "target": 244, "key": "28e4c5e90e2c7b8f43a5e7a433df3afc"}, {"line": 19101, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2611, "target": 360, "key": "456c237fda14df0a04b9d4ae808e7d6b"}, {"line": 19102, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2611, "target": 259, "key": "6d67b6fed18670aa6b036d8f49408fb0"}, {"line": 15302, "relation": "increases", "evidence": "Donepezil is metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "19300564"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "Paroxetine subgraph": true}}, "source": 2613, "target": 606, "key": "760f381dfb2650520e4f46d014355109"}, {"line": 19103, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2613, "target": 244, "key": "10cf51b8edeb46c11f4824642b1c59a4"}, {"line": 19104, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2613, "target": 360, "key": "44cd2d6211f65211785874795ad6af91"}, {"line": 19105, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2613, "target": 259, "key": "f66b431fcb07f959f899665981a6c932"}, {"line": 15304, "relation": "increases", "evidence": "Donepezil is metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "19300564"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 2609, "target": 606, "key": "39b79546da3bcf6967891b362b957f4d"}, {"line": 15967, "relation": "increases", "evidence": "In conclusion, Huperzine A metabolism in rat liver microsomes is mediated primarily by CYP1A2, with a probable secondary contribution of CYP3A1/2.", "citation": {"db": "PubMed", "db_id": "12586202"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "Species": {"10116": true}}, "source": 2609, "target": 606, "key": "fcdd2bc8201c6d485671383dfd7f288b"}, {"line": 15919, "relation": "association", "evidence": "Caffeine based measures of CYP1A2 activity correlate with oral clearance of tacrine in patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9764962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 2609, "target": 360, "key": "58ebc3a18f35060be9ce2ec4330b03d6"}, {"line": 15929, "relation": "increases", "evidence": "These observations support a central role for CYP1A2 in the in vivo disposition of tacrine and the potential for drug interactions when tacrine treated patients receive known inducers or inhibitors of this enzyme.", "citation": {"db": "PubMed", "db_id": "9764962"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "Species": {"9606": true}}, "source": 2609, "target": 360, "key": "7b226ec01c47a02165e799562d7ac2b9"}, {"line": 19107, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2609, "target": 360, "key": "2885e612de74fae971139ff823fb1c1d"}, {"line": 15957, "relation": "association", "evidence": "Identification of cytochrome P450 1A2 as enzyme involved in the microsomal metabolism of Huperzine A. Huperzine A is a reversible and selective cholinesterase inhibitor approved for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "12586202"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 2609, "target": 130, "key": "9965b16ea0e88c4bc66852c68c777c74"}, {"line": 19106, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2609, "target": 244, "key": "aa021257501e769ab006538843b0671c"}, {"line": 19108, "relation": "increases", "evidence": "Some cholinesterase inhibitors (tacrine, donepezil, galantamine) are metabolized via CYP-related enzymes, especially CYP2D6, CYP3A4, and CYP1A2.", "citation": {"db": "PubMed", "db_id": "17908053"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2609, "target": 259, "key": "de2fe274dd62009a7bfda1a5572f0b13"}, {"line": 15336, "relation": "association", "evidence": "Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer.", "citation": {"db": "PubMed", "db_id": "19882751"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neoplasms": true, "Vascular Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3869, "target": 2194, "key": "5f0dfbb3c450c595de1f313556bf820c"}, {"line": 16436, "relation": "association", "evidence": "In multiple sclerosis, the role of OPN has been studied in the inflammatory phase, where it was shown that the protein levels increase during disease relapses.", "citation": {"db": "PubMed", "db_id": "21358042"}, "annotations": {"Disease": {"multiple sclerosis": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3869, "target": 3409, "key": "20a14051b0fe3ec2c1d81246ecfe3f83"}, {"line": 16513, "relation": "association", "evidence": "In the neurosciences, it has led to the discoveries of osteopontin in multiple sclerosis and SORL1/LR11 in Alzheimer's, and recent studies indicate its potential for identifying neurogenomic biomarkers.", "citation": {"db": "PubMed", "db_id": "19285134"}, "annotations": {"Disease": {"multiple sclerosis": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3869, "target": 3409, "key": "60c433794b4a0bf23b78e1cde09a61ad"}, {"line": 17422, "relation": "association", "evidence": "In 2001 we noted that aB crystallin (cryab) was the most abundant transcript found in MS lesions, but not in healthy brains.", "citation": {"db": "PubMed", "db_id": "24711007"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 3869, "target": 2565, "key": "c87ea0e1ee18596ce55073aafc910b54"}, {"line": 18284, "relation": "association", "evidence": "FasL has also been implicated as a negative regulator for the inflammatory component of the demyelinating brain disorder multiple sclerosis (MS).", "citation": {"db": "PubMed", "db_id": "15031631"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Brain Diseases": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 3869, "target": 2690, "key": "82e349c228affbade9007874b8102360"}, {"line": 18550, "relation": "association", "evidence": "The role of inflammation in Alzheimer's disease, Parkinson's disease, and multiple sclerosis has recently come under increased scrutiny.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Parkinson Disease": true, "Inflammation": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3869, "target": 3920, "key": "6cd12f4242097638de1e3dd596ad565a"}, {"line": 18692, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3869, "target": 3060, "key": "3f28d92902399b73866af78cae139504"}, {"line": 20819, "relation": "association", "evidence": "Qualitative and quantitative changes in the expressions of uPAR and of its canonical ligand uPA have been observed in a large variety of epileptic disorders, either in human or in animal models, as well as in other brain diseases (stroke and brain trauma, multiple sclerosis, Alzheimer's disease, cerebral malaria, HIV-associated leukoencephalopathy and encephalitis).", "citation": {"db": "PubMed", "db_id": "21711233"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Brain Diseases": true, "Stroke": true, "Brain Injuries": true, "Malaria, Cerebral": true, "Encephalitis": true, "Leukoencephalopathies": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Cerebrum": true}, "Species": {"9606": true}}, "source": 3869, "target": 1917, "key": "6bda4a4c4db83032b9d0aaaeb6a47539"}, {"line": 41241, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3869, "target": 3815, "key": "d92d4f653dd93773486926cdc043b5da"}, {"line": 41980, "relation": "association", "evidence": "Cannabinoid receptor subtype 2 (CB2) has been shown to be up-regulated in activated microglia and therefore plays an important role in neuroinflammatory and neurodegenerative diseases such as multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neurodegenerative Diseases": true, "Amyotrophic Lateral Sclerosis": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3869, "target": 3613, "key": "b3132af59a168c76e4ade356893e162b"}, {"line": 15337, "relation": "association", "evidence": "Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer.", "citation": {"db": "PubMed", "db_id": "19882751"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neoplasms": true, "Vascular Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3887, "target": 2194, "key": "c77568651d9e0dfa5a42ee4012a34699"}, {"line": 15352, "relation": "increases", "evidence": "Our aim was to test if this enzyme could also degrade the insoluble 40-42 residues long A beta peptides purified from Alzheimer Disease brain. Our results indicate that MMP2 hydrolyzes A beta 1-40 and A beta 1-42 peptides at Lys 16-Leu 17, at Leu 34-Met 35, and Met 35-Val 36 peptide bonds.These results suggest that MMP2 has the ability of degrading A beta of AD in vitro.", "citation": {"db": "PubMed", "db_id": "7811262"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 3059, "target": 2328, "key": "0168c108bba8fba978914b6869be40ee"}, {"line": 15353, "relation": "increases", "evidence": "Our aim was to test if this enzyme could also degrade the insoluble 40-42 residues long A beta peptides purified from Alzheimer Disease brain. Our results indicate that MMP2 hydrolyzes A beta 1-40 and A beta 1-42 peptides at Lys 16-Leu 17, at Leu 34-Met 35, and Met 35-Val 36 peptide bonds.These results suggest that MMP2 has the ability of degrading A beta of AD in vitro.", "citation": {"db": "PubMed", "db_id": "7811262"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 3059, "target": 2327, "key": "bcb2ac207b0bdae447f461765991f171"}, {"line": 15364, "relation": "decreases", "evidence": "If this hydrolysis also occurs in the brain's extracellular matrix, the enzymatic action of gelatinase a could prevent the generation of amyloidogenic A beta 1-40(42).", "citation": {"db": "PubMed", "db_id": "7811262"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3059, "target": 80, "key": "a5e6ef74e928cade948af3d9b461a839"}, {"line": 15410, "relation": "increases", "evidence": "Estrogen activates matrix metalloproteinases-2 and -9 to increase beta amyloid degradation.", "citation": {"db": "PubMed", "db_id": "22402435"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3059, "target": 80, "key": "add7f672c87dd3830c89f1a9b06f8f97"}, {"line": 15382, "relation": "negativeCorrelation", "evidence": "Matrix metalloproteinase-2 and epidermal growth factor are decreased in platelets of Alzheimer patients.Our data show a significant decrease in the levels of epidermal growth factor (EGF) and of MMP-2 in platelets of AD patients and decreased levels of MMP-2 in MCI.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"MeSHAnatomy": {"Blood Platelets": true}, "Species": {"9606": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3059, "target": 3823, "key": "77dfac9c1f97bfba0766a426d41b1862"}, {"line": 31504, "relation": "increases", "evidence": "Gelatinase A is an enzyme capable of cleaving soluble beta-amyloid protein (beta AP), and may function as an alpha-secretase to produce secretory forms of amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "7538720"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3059, "target": 3823, "key": "4db653a697d0fdc9e77e76afbb9ac5b4"}, {"line": 15422, "relation": "increases", "evidence": "In conclusion, the present study shows for the first time that MMP-2 and -9 give a main contribution to estrogen's neuroprotective effect.", "citation": {"db": "PubMed", "db_id": "22402435"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "source": 3059, "target": 251, "key": "e3e307092440f5aa1ee36d428d1a8faf"}, {"line": 15437, "relation": "association", "evidence": "MMP-2/MMP-9 plasma level and brain expression in cerebral amyloid angiopathy-associated hemorrhagic stroke.", "citation": {"db": "PubMed", "db_id": "21707819"}, "annotations": {"MeSHDisease": {"Stroke": true, "Cerebral Amyloid Angiopathy": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3059, "target": 3930, "key": "3e9f4d1b4fca50edb910349f042797db"}, {"line": 15482, "relation": "association", "evidence": "Our findings suggest that increased expression and activation of MMP-2 may contribute to HCSM cell death in response to pathogenic A beta.", "citation": {"db": "PubMed", "db_id": "12753080"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Species": {"9606": true}, "Cell": {"regular cardiac myocyte": true}}, "source": 3059, "target": 505, "key": "6fe782a3526c044de6e8bf0931bd452d"}, {"relation": "partOf", "source": 3059, "target": 1189, "key": "149207d61588c7a5570fa8bb356dc9c0"}, {"line": 15383, "relation": "negativeCorrelation", "evidence": "Matrix metalloproteinase-2 and epidermal growth factor are decreased in platelets of Alzheimer patients.Our data show a significant decrease in the levels of epidermal growth factor (EGF) and of MMP-2 in platelets of AD patients and decreased levels of MMP-2 in MCI.", "citation": {"db": "PubMed", "db_id": "21875409"}, "annotations": {"MeSHAnatomy": {"Blood Platelets": true}, "Species": {"9606": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 2656, "target": 3823, "key": "b7f1430e7795ab58e24e474a6b01c87d"}, {"relation": "partOf", "source": 2656, "target": 1407, "key": "ffcb96240dc6dcf160b13a2f64261d09"}, {"line": 37528, "relation": "association", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2656, "target": 2315, "key": "276f573cd7303224bf603fbf6fc93b82"}, {"line": 37529, "relation": "association", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2656, "target": 2307, "key": "43aab6a8949a48b553d5cd2d767e97f3"}, {"line": 49229, "relation": "increases", "evidence": "KLF10 has been shown to be rapidly induced by TGFbeta1, 2, 3, E2, epidermal growth factor, and bone morphogenetic protein-2.", "citation": {"db": "PubMed", "db_id": "20087894"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2656, "target": 1857, "key": "9c9e05f721e1f9188981748658c85ad8"}, {"relation": "partOf", "source": 2475, "target": 1324, "key": "b26b034ca9cab01fafa3bd64970320c4"}, {"line": 15437, "relation": "association", "evidence": "MMP-2/MMP-9 plasma level and brain expression in cerebral amyloid angiopathy-associated hemorrhagic stroke.", "citation": {"db": "PubMed", "db_id": "21707819"}, "annotations": {"MeSHDisease": {"Stroke": true, "Cerebral Amyloid Angiopathy": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3930, "target": 3059, "key": "5779524f123a598492d0a6980301b5b1"}, {"line": 15438, "relation": "association", "evidence": "MMP-2/MMP-9 plasma level and brain expression in cerebral amyloid angiopathy-associated hemorrhagic stroke.", "citation": {"db": "PubMed", "db_id": "21707819"}, "annotations": {"MeSHDisease": {"Stroke": true, "Cerebral Amyloid Angiopathy": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3930, "target": 3062, "key": "8c86378e55aaf753fe4f3694dcb65d3b"}, {"line": 15439, "relation": "association", "evidence": "MMP-2/MMP-9 plasma level and brain expression in cerebral amyloid angiopathy-associated hemorrhagic stroke.", "citation": {"db": "PubMed", "db_id": "21707819"}, "annotations": {"MeSHDisease": {"Stroke": true, "Cerebral Amyloid Angiopathy": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3930, "target": 3835, "key": "4e1265f573b2d274cee45166fd85c2bb"}, {"line": 16759, "relation": "association", "evidence": "In animal models of ischemic stroke, statins have proven to reduce infarct size through up-regulation of endothelial nitric oxide synthases.", "citation": {"db": "PubMed", "db_id": "12218642"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3930, "target": 3124, "key": "b1811f13f285fe50be3bbbad7397abff"}, {"line": 17741, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS) has been highlighted as having a pathological role in stroke, dementia, and neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Stroke": true, "Dementia": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3930, "target": 844, "key": "60b024ab53d7159ee539d149c486fb35"}, {"line": 18072, "relation": "association", "evidence": "Acute oxidative stress to the brain, such as stroke and traumatic brain injury is increased in animals that are deficient in NRF2.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Stroke": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3930, "target": 3110, "key": "e9d84dac7d0629485edb488811791ed9"}, {"line": 18073, "relation": "negativeCorrelation", "evidence": "Acute oxidative stress to the brain, such as stroke and traumatic brain injury is increased in animals that are deficient in NRF2.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Stroke": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3930, "target": 3110, "key": "7812ed8fde3ab138d1f3807c9a86eb59"}, {"line": 18918, "relation": "association", "evidence": "In addition, we assessed changes in endogenous net tPA activity in WT mice following morphine administration, epileptic seizures, traumatic brain injury and ischaemic stroke-neurological settings in which tPA has a known functional role.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"MeSHDisease": {"Stroke": true, "Epilepsy": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3930, "target": 3200, "key": "b5f03c9087e934d098bb0e54dd7da61e"}, {"line": 20096, "relation": "association", "evidence": "For example, the occurrence of stroke increases with age and has been linked to neurodegenerative disorders like Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "17561312"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Stroke": true, "Alzheimer Disease": true}}, "source": 3930, "target": 3823, "key": "1d36206512be981fa5c9b8f3bb1a15f5"}, {"line": 20105, "relation": "association", "evidence": "The current experiments test the hypothesis that a vascular insult and aging are co-factors that contribute to dementia by evaluating the neuronal and functional integrity of the hippocampus following small, localized strokes induced by the potent vasoconstrictor, endothelin-1 (ET-1) in the rat model of hippocampal aging.", "citation": {"db": "PubMed", "db_id": "17561312"}, "annotations": {"MeSHDisease": {"Stroke": true, "Dementia": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10116": true}, "Subgraph": {"Endothelin subgraph": true}}, "source": 3930, "target": 3778, "key": "7367127966c43b28f69a4666f4f1e408"}, {"line": 21860, "relation": "association", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 3930, "target": 289, "key": "bc42ddfa7a5be4fae7f560e215e9ecfd"}, {"line": 41240, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3930, "target": 3815, "key": "37d78bac29c26ec53d4b9a2e337e66b1"}, {"line": 42659, "relation": "increases", "evidence": "Reducing Effect of IL-32α in the Development of Stroke Through Blocking of NF-κB, but Enhancement of STAT3 Pathways.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"MeSHDisease": {"Stroke": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3930, "target": 3725, "key": "cd46a6d8b0d8b21bf953ce8e71e48de1"}, {"line": 42778, "relation": "association", "evidence": "Nicardipine is a calcium channel blocker that has been widely used to control blood pressure in severe hypertension following events such as ischemic stroke, traumatic brain injury, and intracerebral hemorrhage.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Stroke": true, "Cerebral Hemorrhage": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Blood": true}, "Confidence": {"High": true}}, "source": 3930, "target": 199, "key": "0b913a732e27b17b8edabf5f61322c5e"}, {"line": 48258, "relation": "association", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3930, "target": 2629, "key": "a301d5b8e02474fec0920d8acb646978"}, {"line": 15439, "relation": "association", "evidence": "MMP-2/MMP-9 plasma level and brain expression in cerebral amyloid angiopathy-associated hemorrhagic stroke.", "citation": {"db": "PubMed", "db_id": "21707819"}, "annotations": {"MeSHDisease": {"Stroke": true, "Cerebral Amyloid Angiopathy": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3835, "target": 3930, "key": "a4d0f29c6fc7e4d26054dd343d6934d7"}, {"line": 16825, "relation": "association", "evidence": "Pin1, endothelial nitric oxide synthase, and amyloid-beta form a feedback signaling loop involved in the pathogenesis of Alzheimer's disease, hypertension, and cerebral amyloid angiopathy.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3835, "target": 1660, "key": "1e10d4e998741883fdcf0845f3432e34"}, {"line": 16838, "relation": "positiveCorrelation", "evidence": "Although the molecular mechanism has not yet been clarified until now, it is very interesting that Alzheimer's disease (AD), hypertension (HTN), and cerebral amyloid angiopathy (CAA) often occur synchronously and possess many similar pathological characteristics.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3835, "target": 3823, "key": "3d3a21816a9778aa87c831e1378305d4"}, {"line": 20906, "relation": "association", "evidence": "The accumulation of fibrillar amyloid-beta protein (A beta) in cerebral blood vessels, a condition known as cerebral amyloid angiopathy (CAA), is a key pathological feature of Alzheimer's disease and certain related disorders and is intimately associated with cerebrovascular cell death both in vivo and in vitro.", "citation": {"db": "PubMed", "db_id": "12754271"}, "annotations": {"MeSHDisease": {"Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"High": true}}, "source": 3835, "target": 3823, "key": "a3e1e8fd63414543f1fe0ea4644e4cb7"}, {"line": 16839, "relation": "positiveCorrelation", "evidence": "Although the molecular mechanism has not yet been clarified until now, it is very interesting that Alzheimer's disease (AD), hypertension (HTN), and cerebral amyloid angiopathy (CAA) often occur synchronously and possess many similar pathological characteristics.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Cerebrum": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3835, "target": 3916, "key": "69b3192c548032a62d33f5c7c71bf131"}, {"line": 20907, "relation": "increases", "evidence": "The accumulation of fibrillar amyloid-beta protein (A beta) in cerebral blood vessels, a condition known as cerebral amyloid angiopathy (CAA), is a key pathological feature of Alzheimer's disease and certain related disorders and is intimately associated with cerebrovascular cell death both in vivo and in vitro.", "citation": {"db": "PubMed", "db_id": "12754271"}, "annotations": {"MeSHDisease": {"Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"High": true}}, "source": 3835, "target": 505, "key": "39c5fbb9fb9217f0dcf595d437bd1639"}, {"line": 25900, "relation": "association", "evidence": "Recent studies on the effect of murine and human apoE in APP transgenic mice provide direct evidence that apoE is critically involved in the in vivo converstion of Abeta into forms which contain high beta-sheet content and associated cellular toxicity (neuritic plaques and CAA). ", "citation": {"db": "PubMed", "db_id": "11816788"}, "annotations": {"Subgraph": {"APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 3835, "target": 2312, "key": "8820906b7472412e399aa777c008ecd5"}, {"line": 15475, "relation": "increases", "evidence": "Furthermore, we demonstrate that the increase in MMP-2 activation is largely caused by increased expression of membrane type-1 (MT1)-MMP expression, the primary MMP-2 activator.", "citation": {"db": "PubMed", "db_id": "12753080"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Species": {"9606": true}}, "source": 3058, "target": 3059, "key": "44eda66287383c56e9d367ab81ffd576"}, {"line": 15476, "relation": "increases", "evidence": "Furthermore, we demonstrate that the increase in MMP-2 activation is largely caused by increased expression of membrane type-1 (MT1)-MMP expression, the primary MMP-2 activator.", "citation": {"db": "PubMed", "db_id": "12753080"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 3058, "target": 3059, "key": "90b99c43fb0f0f5ec92e665b51575ead"}, {"relation": "partOf", "source": 3058, "target": 1236, "key": "e2a7f9c843fbfd50934a9982dde1469e"}, {"line": 40797, "relation": "positiveCorrelation", "evidence": "Membrane-type 1 metalloproteinase is upregulated in microglia/brain macrophages in neurodegenerative and neuroinflammatory diseases.", "citation": {"db": "PubMed", "db_id": "24323769"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Membranes": true, "Macrophages": true}}, "source": 3058, "target": 3815, "key": "52fbd263683e4d9ddfa2d5993bb95020"}, {"line": 15507, "relation": "increases", "evidence": "In addition, elevated mRNA levels of MMP stimulating cytokines such as IL-1beta and TGFbeta were found in the brains of APP/PS1 mice.", "citation": {"db": "PubMed", "db_id": "21376707"}, "annotations": {"CellStructure": {"Extracellular Matrix": true}, "Species": {"10090": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Matrix metalloproteinase subgraph": true}}, "source": 3679, "target": 3661, "key": "278a8578b918becea3e4b0ba2af5de3a"}, {"line": 15510, "relation": "increases", "evidence": "In addition, elevated mRNA levels of MMP stimulating cytokines such as IL-1beta and TGFbeta were found in the brains of APP/PS1 mice.", "citation": {"db": "PubMed", "db_id": "21376707"}, "annotations": {"CellStructure": {"Extracellular Matrix": true}, "Species": {"10090": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "TGF-Beta subgraph": true}}, "source": 3679, "target": 2218, "key": "660afa0dea8b12605b0015b97804f385"}, {"line": 16307, "relation": "positiveCorrelation", "evidence": "Brain sections from AD and control mice showed that HIF-1α, Ang-2, MMP2 and caspase 3 are elevated and Bcl-xL decreased in the microvasculature of AD mice.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3679, "target": 3823, "key": "022ea50abdcdfaf48f4b0d2234ccc12b"}, {"line": 41272, "relation": "increases", "evidence": "Our data show that NRG1-beta decreased the levels of VCAM-1, E-selectin, and neutrophil adhesion to brain microvascular endothelial cells activated by IL1-beta.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHAnatomy": {"Neutrophils": true, "Brain": true, "Endothelial Cells": true}, "Confidence": {"High": true}}, "source": 3661, "target": 659, "key": "49cdda45bb38b9c29fd3e4f0231d036f"}, {"line": 41830, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3661, "target": 4044, "key": "b624dc4fccb10c8599bcd13a582533a3"}, {"line": 42119, "relation": "positiveCorrelation", "evidence": "Particularly, the proinflammatory cytokine interleukin-1 beta (IL-1beta) is upregulated in human AD and believed to promote amyloid plaque deposition.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Species": {"9606": true}, "Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}}, "source": 3661, "target": 3823, "key": "2591007d6bd2dd7599ce1d71f2956b0f"}, {"line": 42120, "relation": "increases", "evidence": "Particularly, the proinflammatory cytokine interleukin-1 beta (IL-1beta) is upregulated in human AD and believed to promote amyloid plaque deposition.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Species": {"9606": true}, "Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}}, "source": 3661, "target": 3881, "key": "4c2715180da59585d148a1afbd034c12"}, {"line": 42129, "relation": "increases", "evidence": "However, studies from our laboratory have shown that chronic IL-1beta overexpression in the APPswe/PSEN1dE9 (APP/PS1) mouse model of AD ameliorates amyloid pathology, increases plaque-associated microglia, and induces recruitment of peripheral immune cells to the brain parenchyma.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Species": {"10090": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true}, "Confidence": {"Medium": true}}, "source": 3661, "target": 3881, "key": "466793f03af079d08f6401684ad3ea13"}, {"line": 43504, "relation": "increases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "source": 3661, "target": 3649, "key": "61ddccfb9e7677ce4819892d9e4fb5dd"}, {"line": 43647, "relation": "increases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3661, "target": 577, "key": "423bef64f8459aeed5134d172c153f43"}, {"line": 15530, "relation": "association", "evidence": "S-nitrosoglutathione (GSNO) is an endogenous nitric oxide carrier modulating endothelial function, inflammation, and neurotransmission.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 69, "target": 3920, "key": "5f89f2a3942ebe9daebbc218a1631555"}, {"line": 15534, "relation": "regulates", "evidence": "S-nitrosoglutathione (GSNO) is an endogenous nitric oxide carrier modulating endothelial function, inflammation, and neurotransmission.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 69, "target": 3920, "key": "34d8412a7a9bc1faeddc5ec6d8743d23"}, {"line": 15548, "relation": "increases", "evidence": "GSNO treatment (50 μg/kg/day for 2 months) significantly improved learning and memory performance of BCCAO rats and reduced the Abeta levels and ICAM-1/VCAM-1 expression in the brain.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 69, "target": 820, "key": "1b09e2fad5ffd7c942811160ebeacc9c"}, {"line": 15552, "relation": "increases", "evidence": "GSNO treatment (50 μg/kg/day for 2 months) significantly improved learning and memory performance of BCCAO rats and reduced the Abeta levels and ICAM-1/VCAM-1 expression in the brain.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 69, "target": 818, "key": "58df2b20d7d2816c62c01123a2f9bdf7"}, {"line": 15558, "relation": "decreases", "evidence": "GSNO treatment (50 μg/kg/day for 2 months) significantly improved learning and memory performance of BCCAO rats and reduced the Abeta levels and ICAM-1/VCAM-1 expression in the brain.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Lipid metabolism subgraph": true, "Cell adhesion subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 69, "target": 3810, "key": "0313204d242f8dca7a8e618b5851f81d"}, {"line": 15564, "relation": "decreases", "evidence": "GSNO treatment (50 μg/kg/day for 2 months) significantly improved learning and memory performance of BCCAO rats and reduced the Abeta levels and ICAM-1/VCAM-1 expression in the brain.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Low density lipoprotein subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 69, "target": 3788, "key": "1703cdf62bb84d49cf2efcfbdc283cda"}, {"line": 15570, "relation": "decreases", "evidence": "GSNO treatment (50 μg/kg/day for 2 months) significantly improved learning and memory performance of BCCAO rats and reduced the Abeta levels and ICAM-1/VCAM-1 expression in the brain.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 69, "target": 2328, "key": "69108b601c52656718d1bce7da5d60cc"}, {"line": 15582, "relation": "decreases", "evidence": "Further, in in vitro cell culture studies, GSNO treatment also decreased the cytokine-induced proinflammatory responses, such as activations of NFκB and STAT3 and expression of ICAM-1 and VCAM-1 in endothelial cells.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Nitric oxide subgraph": true}, "Cell": {"endothelial cell": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 69, "target": 871, "key": "1b31481f35451f39163565a1703e3b46"}, {"line": 15588, "relation": "decreases", "evidence": "Further, in in vitro cell culture studies, GSNO treatment also decreased the cytokine-induced proinflammatory responses, such as activations of NFκB and STAT3 and expression of ICAM-1 and VCAM-1 in endothelial cells.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Cell": {"endothelial cell": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 69, "target": 3426, "key": "ab0b810da3eb64af24cddb67437d6349"}, {"line": 15594, "relation": "decreases", "evidence": "Further, in in vitro cell culture studies, GSNO treatment also decreased the cytokine-induced proinflammatory responses, such as activations of NFκB and STAT3 and expression of ICAM-1 and VCAM-1 in endothelial cells.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cell adhesion subgraph": true, "Nitric oxide subgraph": true}, "Cell": {"endothelial cell": true}, "Confidence": {"High": true}}, "source": 69, "target": 3516, "key": "9665d9c7cd06ff4d6e897ce288b078dd"}, {"line": 15598, "relation": "decreases", "evidence": "Further, in in vitro cell culture studies, GSNO treatment also decreased the cytokine-induced proinflammatory responses, such as activations of NFκB and STAT3 and expression of ICAM-1 and VCAM-1 in endothelial cells.", "citation": {"db": "PubMed", "db_id": "23254638"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Cell adhesion subgraph": true, "Nitric oxide subgraph": true}, "Cell": {"endothelial cell": true}, "Confidence": {"High": true}}, "source": 69, "target": 2863, "key": "76b84d1cc0db9e8e5837fd5ea411cb6a"}, {"relation": "partOf", "source": 3426, "target": 1500, "key": "6863600540b7cb8fde4e86df0976d20d"}, {"relation": "hasVariant", "source": 3426, "target": 3427, "key": "b9a00efb077f48f8703a7c0db74b77ce"}, {"relation": "partOf", "source": 3426, "target": 1434, "key": "2c78493409c95b2fe933451a2479fd73"}, {"line": 37809, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3426, "target": 1434, "key": "5592e8232bac7977e7d2703f2ba8ea40"}, {"line": 46772, "relation": "increases", "evidence": "We propose that IL-1 and the IL-6 family of cytokines regulate YKL-40 expression during sterile inflammation via both STAT3 and RelB/p50 complexes", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3426, "target": 2509, "key": "ea6ce96ed7c08679ed3c3fc1c8279ba5"}, {"line": 15623, "relation": "negativeCorrelation", "evidence": "Plasma sICAM-1 and sPECAM-1 were higher and CSF sVCAM-1 were lower in AD and DLB patients than in controls (p<0.001).", "citation": {"db": "PubMed", "db_id": "17270454"}, "annotations": {"Subgraph": {"Lipid metabolism subgraph": true, "Cell adhesion subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 3516, "target": 3823, "key": "b3e1e7a0cb0cf71994d1584c635ff652"}, {"line": 15680, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3516, "target": 3823, "key": "ad13de7d17d87994c9a4cd741c24d552"}, {"line": 15642, "relation": "association", "evidence": "Adhesion molecules, particularly intracellular adhesion molecule (ICAM)-1, vascular cell adhesion molecule (VCAM)-1, and E-selectin, have been associated with cardiovascular disease.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"MeSHDisease": {"Cardiovascular Diseases": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3516, "target": 3834, "key": "ea1ae1aaddf71d191dc5d60504f824c6"}, {"line": 15665, "relation": "association", "evidence": "High-fat load and glucose alone produced an increase of nitrotyrosine, ICAM-1, VCAM-1, and E-selectin plasma levels in normal and diabetic subjects.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 3516, "target": 264, "key": "b1e285a73c286514afac1e8f5c788bc3"}, {"line": 15616, "relation": "positiveCorrelation", "evidence": "Plasma sICAM-1 and sPECAM-1 were higher and CSF sVCAM-1 were lower in AD and DLB patients than in controls (p<0.001).", "citation": {"db": "PubMed", "db_id": "17270454"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 3182, "target": 3823, "key": "93d946afd0c54f5249e09581a75b250c"}, {"relation": "partOf", "source": 3182, "target": 995, "key": "61b657fc599f0c93b4468e311973f2dd"}, {"line": 15653, "relation": "increases", "evidence": "Postprandial hypertriglyceridemia and hyperglycemia are considered risk factors for cardiovascular disease, and evidence suggests that postprandial hypertriglyceridemia and hyperglycemia may induce an increase in circulating adhesion molecules.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "MeSHDisease": {"Hyperglycemia": true, "Hypertriglyceridemia": true, "Cardiovascular Diseases": true}}, "source": 3914, "target": 3516, "key": "95016bddac8b03f1efbf475cc7c17c58"}, {"line": 15655, "relation": "increases", "evidence": "Postprandial hypertriglyceridemia and hyperglycemia are considered risk factors for cardiovascular disease, and evidence suggests that postprandial hypertriglyceridemia and hyperglycemia may induce an increase in circulating adhesion molecules.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "MeSHDisease": {"Hyperglycemia": true, "Hypertriglyceridemia": true, "Cardiovascular Diseases": true}}, "source": 3914, "target": 2863, "key": "a24bd2da6b9ad7adabb751377befabc2"}, {"line": 15654, "relation": "increases", "evidence": "Postprandial hypertriglyceridemia and hyperglycemia are considered risk factors for cardiovascular disease, and evidence suggests that postprandial hypertriglyceridemia and hyperglycemia may induce an increase in circulating adhesion molecules.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "MeSHDisease": {"Hyperglycemia": true, "Hypertriglyceridemia": true, "Cardiovascular Diseases": true}}, "source": 3917, "target": 3516, "key": "577356c6dda64b1a1dbbc604d4b22918"}, {"line": 15656, "relation": "increases", "evidence": "Postprandial hypertriglyceridemia and hyperglycemia are considered risk factors for cardiovascular disease, and evidence suggests that postprandial hypertriglyceridemia and hyperglycemia may induce an increase in circulating adhesion molecules.", "citation": {"db": "PubMed", "db_id": "14988255"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "MeSHDisease": {"Hyperglycemia": true, "Hypertriglyceridemia": true, "Cardiovascular Diseases": true}}, "source": 3917, "target": 2863, "key": "b42d56dcf597306058db6b3b642a8596"}, {"line": 15684, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 2875, "target": 3823, "key": "baa52ff79a3b56b3f5ed22a87b166012"}, {"line": 40689, "relation": "positiveCorrelation", "evidence": "The levels of IGF-II and IGFBP-2 were significantly elevated in the CSF from patients with AD.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Serotonergic subgraph": true, "Insulin signal transduction": true}}, "source": 2875, "target": 3823, "key": "406ae7dc7df3e0684bd9b51468ee1dd5"}, {"line": 15685, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 2374, "target": 3823, "key": "44bcbe756d1c8b7637dcff965ae162fe"}, {"line": 34588, "relation": "decreases", "evidence": "Here we identify beta2-microglobulin (B2M), a component of major histocompatibility complex class 1 (MHC I) molecules, as a circulating factor that negatively regulates cognitive and regenerative function in the adult hippocampus in an age-dependent manner. B2M is elevated in the blood of aging humans and mice, and it is increased within the hippocampus of aged mice and young heterochronic parabionts. Exogenous B2M injected systemically, or locally in the hippocampus, impairs hippocampal-dependent cognitive function and neurogenesis in young mice. ", "citation": {"db": "PubMed", "db_id": "26147761"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}, "Confidence": {"High": true}}, "source": 2374, "target": 853, "key": "6555f8fce393e9dac0b39508b4e0df6f"}, {"line": 15689, "relation": "positiveCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Cell adhesion subgraph": true}}, "source": 275, "target": 3823, "key": "9c9564c7307afd8e3c07715d31bd66b5"}, {"line": 39747, "relation": "positiveCorrelation", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Response to oxidative stress": true}}, "source": 275, "target": 3823, "key": "4ea6ba2531e1e49564deee85dd98ee32"}, {"line": 44878, "relation": "positiveCorrelation", "evidence": "AD individuals are characterized by decreased plasma folate values, as well as increased plasma homocysteine (Hcy) levels, and there is indication of impaired S-adenosylmethionine (SAM) levels in AD brains. ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 275, "target": 3823, "key": "4868ba35fddf565e84ed7c0d628ff3fe"}, {"line": 44889, "relation": "positiveCorrelation", "evidence": "the majority of the studies agree that plasma Hcy values are increased in AD subjects ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 275, "target": 3823, "key": "006ee7a2ff5e41bd89e54e2929472a3d"}, {"line": 44900, "relation": "positiveCorrelation", "evidence": "There is also some indication that Hcy levels are increased in the cerebrospinal fluid (CSF) of AD patients, respect to controls", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 275, "target": 3823, "key": "56c0fa09989cedc2852d5f53aa2eea35"}, {"line": 44987, "relation": "positiveCorrelation", "evidence": "Our data show that patients with AD have higher levels of plasma tHcy", "citation": {"db": "PubMed", "db_id": "12784029"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 275, "target": 3823, "key": "cade36d768e06a92fb390617c041b74c"}, {"line": 44911, "relation": "negativeCorrelation", "evidence": "a significant decrease in Hcy levels was paralleled by a significant increase in MAT activity", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 275, "target": 3045, "key": "7699c039421f5ee4942714920cf1fd83"}, {"line": 44921, "relation": "increases", "evidence": "It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects. the MTHFR 677C>T polymorphism as a candidate AD risk factor", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 275, "target": 3074, "key": "bb105639d5457a590484f87b743ada18"}, {"line": 44922, "relation": "increases", "evidence": "It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects. the MTHFR 677C>T polymorphism as a candidate AD risk factor", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 275, "target": 3073, "key": "f39770ada06cf90afba7fcad96d9ed4d"}, {"line": 44938, "relation": "increases", "evidence": "MTHFR 677TT homozygous AD subjects had higher plasma tHcy values and/or decreased folate values compared to carriers of the MTHFR 677CT or 677CC genotypes", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 275, "target": 1883, "key": "464940e7d22c524aa314b8a91cbc986e"}, {"line": 44969, "relation": "increases", "evidence": "Studies showed that Hcy accumulation reduces cellular levels of SAM, stimulates glutamate excitotoxicity and increases oxidative damage. Hcy has been also associated to vascular disease in AD", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 275, "target": 68, "key": "d1219a56e084c69ccd94c2519d941773"}, {"line": 44970, "relation": "increases", "evidence": "Studies showed that Hcy accumulation reduces cellular levels of SAM, stimulates glutamate excitotoxicity and increases oxidative damage. Hcy has been also associated to vascular disease in AD", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 275, "target": 842, "key": "67e211a6ad8d4823bb364472903223fc"}, {"line": 46298, "relation": "increases", "evidence": "late-onset AD is the association of the disease with hyperhomocysteinemia, low B vitamins and impaired methylation", "citation": {"db": "PubMed", "db_id": "20573497"}, "source": 275, "target": 3820, "key": "4c07928eb19f2500cb3716aadad96419"}, {"line": 46309, "relation": "increases", "evidence": "the demethylation of Presenilin1 gene promoter in nutritionally-induced hyperhomocysteinemia in a transgenic mouse model clearly demonstrated that Presenilin1 is regulated by DNA methylation.", "citation": {"db": "PubMed", "db_id": "22272624"}, "annotations": {"Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 275, "target": 1926, "key": "aa313e6915569526556578e1997b22a5"}, {"line": 49455, "relation": "negativeCorrelation", "evidence": "There is intriguing evidence that homocysteine levels may be related to plasma levels of amyloid peptides in individuals with AD,12,13 and that reduction of homocysteine levels may lower amyloid levels.", "citation": {"db": "PubMed", "db_id": "18854539"}, "source": 275, "target": 80, "key": "7e02fac0e8b744b967bbad386f22bea1"}, {"line": 15693, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2657, "target": 3823, "key": "5769024a93a7b942371c20c931a0cf4b"}, {"relation": "partOf", "source": 2657, "target": 1407, "key": "3381dc26b33026d7cc846512337dcad8"}, {"line": 15695, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}}, "source": 273, "target": 3823, "key": "03361fd3fd1459e921ef513e658a1baa"}, {"line": 15700, "relation": "negativeCorrelation", "evidence": "To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, beta(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD.", "citation": {"db": "PubMed", "db_id": "22801742"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Albumin subgraph": true}}, "source": 2882, "target": 3823, "key": "c99f3a15a470a0b69e409c2f67f28b72"}, {"line": 15722, "relation": "increases", "evidence": "Testosterone-mediated neuroprotection through the androgen receptor in human primary neurons.", "citation": {"db": "PubMed", "db_id": "11389183"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Androgen subgraph": true}}, "source": 361, "target": 671, "key": "16d0d8d26bfc4a8fa79bd77520864119"}, {"line": 15749, "relation": "association", "evidence": "Age-related androgen depletion is known to be a risk factor for various diseases, such as osteoporosis and sarcopenia.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true}, "Subgraph": {"Androgen subgraph": true}}, "source": 209, "target": 3929, "key": "d27750bcc5da4515a0a8d91abdaeac82"}, {"line": 15768, "relation": "decreases", "evidence": "Supplemental androgen therapy has been shown to be efficacious in treating osteoporosis and sarcopenia.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true}}, "source": 209, "target": 3929, "key": "ef188dde23d29dfeaa270cace8efb000"}, {"line": 15750, "relation": "association", "evidence": "Age-related androgen depletion is known to be a risk factor for various diseases, such as osteoporosis and sarcopenia.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true}, "Subgraph": {"Androgen subgraph": true}}, "source": 209, "target": 3926, "key": "e4f9ed368c9aba2e8649f3bad0356f1a"}, {"line": 15769, "relation": "decreases", "evidence": "Supplemental androgen therapy has been shown to be efficacious in treating osteoporosis and sarcopenia.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true}}, "source": 209, "target": 3926, "key": "e5004b3b2d905b7938406562c057611d"}, {"line": 15758, "relation": "negativeCorrelation", "evidence": "Furthermore, recent studies have demonstrated that age-related androgen depletion results in accumulation of beta-amyloid protein and thereby acts as a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 209, "target": 2328, "key": "2b1e6dca5932ca3ced156312aed72e78"}, {"line": 15759, "relation": "association", "evidence": "Furthermore, recent studies have demonstrated that age-related androgen depletion results in accumulation of beta-amyloid protein and thereby acts as a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 209, "target": 3823, "key": "38c2e4844bef94d539e6c0ba204d4854"}, {"line": 15777, "relation": "decreases", "evidence": "In addition, studies in animals have demonstrated that androgens can play a protective role against Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 209, "target": 3823, "key": "91ba5d3cd6ea0747a06c94f14a4dece3"}, {"line": 15760, "relation": "association", "evidence": "Furthermore, recent studies have demonstrated that age-related androgen depletion results in accumulation of beta-amyloid protein and thereby acts as a risk factor for the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 209, "target": 2315, "key": "c0e1f78a86e8b417af661f91157a76fe"}, {"line": 15749, "relation": "association", "evidence": "Age-related androgen depletion is known to be a risk factor for various diseases, such as osteoporosis and sarcopenia.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true}, "Subgraph": {"Androgen subgraph": true}}, "source": 3929, "target": 209, "key": "c3ae6753602707a9d11187970680fa56"}, {"line": 15783, "relation": "association", "evidence": "These results indicate that SARM is efficacious for the treatment of not only osteoporosis and sarcopenia, but also Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"Subgraph": {"Androgen subgraph": true}, "MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true, "Alzheimer Disease": true}}, "source": 3929, "target": 3823, "key": "1eb126877f9ac2c93bf03f02cd94dd11"}, {"line": 15750, "relation": "association", "evidence": "Age-related androgen depletion is known to be a risk factor for various diseases, such as osteoporosis and sarcopenia.", "citation": {"db": "PubMed", "db_id": "24177288"}, "annotations": {"MeSHDisease": {"Sarcopenia": true, "Osteoporosis": true}, "Subgraph": {"Androgen subgraph": true}}, "source": 3926, "target": 209, "key": "fbf52991c8e4564e9a21ac0c47786047"}, {"line": 47889, "relation": "association", "evidence": "Therefore, we hypothesize that Dkk1 may play a role in both osteoporosis and AD.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 3926, "target": 2629, "key": "b495f1916813a1d3ac4732b551799b75"}, {"line": 48033, "relation": "association", "evidence": "Dickkopf-related protein 1 (Dkk1), a vital antagonist of the Wnt signaling, was reported to be closely associated with bone homeostasis and osteoporosis.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3926, "target": 2629, "key": "c6eee292d470c91106f71b20e4ce8b22"}, {"line": 15834, "relation": "association", "evidence": "Tumor growth and metastasis depend on angiogenesis that requires the cofactor copper.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Neoplasms": true, "Neoplasm Metastasis": true}, "Confidence": {"High": true}}, "source": 3871, "target": 807, "key": "24c47bf0c5ef9af9a09005634d593684"}, {"line": 15835, "relation": "association", "evidence": "Tumor growth and metastasis depend on angiogenesis that requires the cofactor copper.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"MeSHDisease": {"Neoplasms": true, "Neoplasm Metastasis": true}, "Confidence": {"High": true}}, "source": 3871, "target": 101, "key": "780433352346f560c15174d3fbd45140"}, {"line": 49000, "relation": "association", "evidence": "Previous studies have suggested that TNFRSF12A may serve a role in tumor growth and metastasis.", "citation": {"db": "PubMed", "db_id": "28138696"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3871, "target": 3475, "key": "2a748d5c815253430108480295c36387"}, {"line": 15859, "relation": "decreases", "evidence": "We report here that after binding to copper, '5-chloro-7-iodoquinolin-8-ol' can inhibit the proteasomal chymotrypsin-like activity, repress androgen receptor (AR) protein expression, and induce apoptotic cell death in human prostate cancer LNCaP and C4-2B cells.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "MeSHDisease": {"Prostatic Neoplasms": true}, "MeSHAnatomy": {"Prostate": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 899, "target": 2354, "key": "f68fa8476896a295f6ce55ffa42ec1f7"}, {"line": 15860, "relation": "increases", "evidence": "We report here that after binding to copper, '5-chloro-7-iodoquinolin-8-ol' can inhibit the proteasomal chymotrypsin-like activity, repress androgen receptor (AR) protein expression, and induce apoptotic cell death in human prostate cancer LNCaP and C4-2B cells.", "citation": {"db": "PubMed", "db_id": "17308104"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "MeSHDisease": {"Prostatic Neoplasms": true}, "MeSHAnatomy": {"Prostate": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 899, "target": 478, "key": "1183c20cabecf85452c374951068a20c"}, {"line": 15895, "relation": "increases", "evidence": "At the same time noradrenaline stimulation of beta3-AR receptors increases glucose uptake solely in astrocytes.", "citation": {"db": "PubMed", "db_id": "24810634"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}}, "source": 2269, "target": 565, "key": "f87375a2cfbcc819ece3690679d3cc62"}, {"relation": "partOf", "source": 2269, "target": 985, "key": "3f5ee9afa52906bc248ff5c981b6d52f"}, {"line": 15916, "relation": "association", "evidence": "Caffeine based measures of CYP1A2 activity correlate with oral clearance of tacrine in patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9764962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}}, "source": 222, "target": 3823, "key": "d979d71c5f1d422b2faccd9ae21e6114"}, {"line": 15918, "relation": "association", "evidence": "Caffeine based measures of CYP1A2 activity correlate with oral clearance of tacrine in patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9764962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 222, "target": 360, "key": "3080092e06ffba1ad0de97413e9d9b5c"}, {"line": 17677, "relation": "decreases", "evidence": "In this study, we aimed to investigate the possibility of P-gp as a potential therapeutic target for Alzheimer's disease by examining the impact of P-gp up-regulation on the clearance of Abeta, a neuropathological hallmark of Alzheimer's disease.Uptake studies for-radiolabelled Abeta Approximately 10-35% decrease in Abeta intracellular accumulation was observed in cells treated with rifampicin, dexamethasone, caffeine, verapamil, hyperforin, beta-estradiol and pentylenetetrazole compared with control.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 222, "target": 2328, "key": "86386ef9a3c92761afc7905122e07557"}, {"line": 44414, "relation": "decreases", "evidence": "Caffeine is a widely consumed psychoactive drug, which is emerging as a protective agent against AD progression and in aging associated deficits. This occurs mainly through the blockade of adenosine A2A receptors, whose expression and function become aberrant throughout aging and in age-related pathologies.", "citation": {"db": "PubMed", "db_id": "21427489"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 222, "target": 2260, "key": "252ae5dd29fb0411283eb1eb95168212"}, {"line": 15918, "relation": "association", "evidence": "Caffeine based measures of CYP1A2 activity correlate with oral clearance of tacrine in patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9764962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 360, "target": 222, "key": "9d551585a7ad5bed3027edb038711639"}, {"line": 15919, "relation": "association", "evidence": "Caffeine based measures of CYP1A2 activity correlate with oral clearance of tacrine in patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "9764962"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 360, "target": 2609, "key": "d71d0144bd49c99aeb1b4706c54df7e2"}, {"line": 15982, "relation": "decreases", "evidence": "Tacrine, a cholinesterase inhibitor, was approved for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15258105"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 360, "target": 3823, "key": "83bbeff2460ff77c3d7907383113ef44"}, {"line": 19088, "relation": "increases", "evidence": "NO donors coupled to the tacrine moiety may exert an additional beneficial effect on AD via an increased blood supply to the brain and by reducing inflammation.", "citation": {"db": "PubMed", "db_id": "20533758"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 360, "target": 485, "key": "d6b4edadaa0f1fc3ebbf1fd953e2c607"}, {"line": 19089, "relation": "decreases", "evidence": "NO donors coupled to the tacrine moiety may exert an additional beneficial effect on AD via an increased blood supply to the brain and by reducing inflammation.", "citation": {"db": "PubMed", "db_id": "20533758"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 360, "target": 3920, "key": "bf77b1e4abd58272af4b4d763dc94e55"}, {"relation": "partOf", "source": 360, "target": 987, "key": "2ed6b95bb0e4cbdc40b18bafd55517f6"}, {"line": 15941, "relation": "association", "evidence": "Cytosolic phospholipase A2α (cPLA2α) plays a key role in the pathogenesis of many inflammatory diseases, such as rheumatoid arthritis, atopic dermatitis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24120545"}, "annotations": {"MeSHDisease": {"Dermatitis, Atopic": true, "Arthritis, Rheumatoid": true, "Alzheimer Disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3826, "target": 3198, "key": "a8cc45ec0b950d16e56dc49b28ac5ea4"}, {"line": 18689, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3826, "target": 3060, "key": "b1eabcb2856999c10ca453081197b8b6"}, {"line": 15941, "relation": "association", "evidence": "Cytosolic phospholipase A2α (cPLA2α) plays a key role in the pathogenesis of many inflammatory diseases, such as rheumatoid arthritis, atopic dermatitis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24120545"}, "annotations": {"MeSHDisease": {"Dermatitis, Atopic": true, "Arthritis, Rheumatoid": true, "Alzheimer Disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3198, "target": 3826, "key": "f7c11c115c15eced19f402f1c2a8cb7c"}, {"line": 15942, "relation": "association", "evidence": "Cytosolic phospholipase A2α (cPLA2α) plays a key role in the pathogenesis of many inflammatory diseases, such as rheumatoid arthritis, atopic dermatitis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24120545"}, "annotations": {"MeSHDisease": {"Dermatitis, Atopic": true, "Arthritis, Rheumatoid": true, "Alzheimer Disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3198, "target": 3823, "key": "a160bba7f0325914ba58d0d4934acfe3"}, {"line": 15943, "relation": "association", "evidence": "Cytosolic phospholipase A2α (cPLA2α) plays a key role in the pathogenesis of many inflammatory diseases, such as rheumatoid arthritis, atopic dermatitis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24120545"}, "annotations": {"MeSHDisease": {"Dermatitis, Atopic": true, "Arthritis, Rheumatoid": true, "Alzheimer Disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3198, "target": 3845, "key": "e60fd1785dbd14da5c20f2ae3f603f3e"}, {"line": 23473, "relation": "association", "evidence": "As a step in searching for possible antioxidative mechanisms of riluzole, we tested in test-tube conditions its effects on the activity of PLA2, an enzyme that is linked to oxidative injury via the AA cascades (Janssen-Timmen et al., 1994 ; Katsuki and Okuda, 1995). Riluzole (10-100 μM), in a concentration-dependent manner, attenuated the activity of cPLA2, but not that of group I and group II PLA2", "citation": {"db": "PubMed", "db_id": "9930745"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 3198, "target": 842, "key": "822c1e67afb915bd3c88c06535dee26a"}, {"line": 23529, "relation": "association", "evidence": "These findings show that riluzole maintains altered oxidant-antioxidant balance. Consistently, previous studies have shown the antioxidant effect of riluzole [19, 20 and 21]. In the study of Koh et al. [ 19], riluzole, besides preventing the excitotoxic neuronal damage, was also effective against FeCl3 induced nonexcitotoxic injury in cortical neuron cultures. In another study, riluzole was shown to protect the dopaminergic neurons against oxidative stress by reducing lipid peroxidation and adenosine triphosphate consumption [ 21]. It has been suggested that the mechanism involved in the protective effects in nonexcitotoxic oxidant damage was inhibition of PLA2, thereby reducing arachidonic acid and its metabolites, and further inhibition of protein kinase C [ 43].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 3198, "target": 842, "key": "833e8367c19f52513c924f051c57ce4e"}, {"line": 40151, "relation": "increases", "evidence": "These proinflammatory factors act as potent stimuli in brain inflammation through upregulation of diverse inflammatory genes, including matrix metalloproteinases (MMPs), cytosolic phospholipase A2 (cPLA2), cyclooxygenase-2 (COX-2), and adhesion molecules.", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Encephalitis": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3198, "target": 577, "key": "640c9b3382267febd5ceeeee1045cd24"}, {"line": 15943, "relation": "association", "evidence": "Cytosolic phospholipase A2α (cPLA2α) plays a key role in the pathogenesis of many inflammatory diseases, such as rheumatoid arthritis, atopic dermatitis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24120545"}, "annotations": {"MeSHDisease": {"Dermatitis, Atopic": true, "Arthritis, Rheumatoid": true, "Alzheimer Disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 3845, "target": 3198, "key": "0e35e6737ea9b4773cb8950afd575ac5"}, {"line": 15956, "relation": "isA", "evidence": "Identification of cytochrome P450 1A2 as enzyme involved in the microsomal metabolism of Huperzine A. Huperzine A is a reversible and selective cholinesterase inhibitor approved for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "12586202"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 130, "target": 40, "key": "b5e59008d79a61998cc19086f3149acb"}, {"line": 15957, "relation": "association", "evidence": "Identification of cytochrome P450 1A2 as enzyme involved in the microsomal metabolism of Huperzine A. Huperzine A is a reversible and selective cholinesterase inhibitor approved for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "12586202"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 130, "target": 2609, "key": "80023d01085bcf8cf02cfc7385df3f15"}, {"line": 15958, "relation": "association", "evidence": "Identification of cytochrome P450 1A2 as enzyme involved in the microsomal metabolism of Huperzine A. Huperzine A is a reversible and selective cholinesterase inhibitor approved for the treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "12586202"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 130, "target": 3823, "key": "590a7008981a0d3650c3440a8727a20e"}, {"line": 15968, "relation": "association", "evidence": "In conclusion, Huperzine A metabolism in rat liver microsomes is mediated primarily by CYP1A2, with a probable secondary contribution of CYP3A1/2.", "citation": {"db": "PubMed", "db_id": "12586202"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "Species": {"10116": true}}, "source": 130, "target": 606, "key": "a44afc9f9577e10d8a9c6e4cad1f4dbd"}, {"line": 16034, "relation": "decreases", "evidence": "Cells overexpressing Bcl-xL were significantly protected from beta-amyloid neurotoxicity and staurosporine-induced apoptosis compared to vector-transfected controls.", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Bcl-2 subgraph": true}}, "source": 2394, "target": 80, "key": "1c6e28ba43a35af8b868046503fe5990"}, {"line": 16044, "relation": "decreases", "evidence": "In contrast, Bcl-xL overexpression only conferred a mild protection against oxidative injury induced by hydrogen peroxide.", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "Subgraph": {"Hydrogen peroxide subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2394, "target": 842, "key": "64d1e1a852cd1e7728ff2d2cfdd15e5d"}, {"line": 16060, "relation": "decreases", "evidence": "We conclude that up-regulation of Bcl-xL expression in response to subtoxic concentrations of beta-amyloid is a stress response that increases the resistance of neurons to beta-amyloid neurotoxicity primarily by inhibiting apoptotic processes.", "citation": {"db": "PubMed", "db_id": "11226677"}, "annotations": {"MeSHDisease": {"Wounds and Injuries": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2394, "target": 478, "key": "8b53fd8c660bbfc357d10f58b2f7746d"}, {"line": 16088, "relation": "decreases", "evidence": "GDNF protects against aluminum-induced apoptosis in rabbits by upregulating Bcl-2 and Bcl-XL and inhibiting mitochondrial Bax translocation.", "citation": {"db": "PubMed", "db_id": "11592846"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 2394, "target": 478, "key": "0627a22242eff704fb9984dcd92d2b23"}, {"relation": "partOf", "source": 2394, "target": 1279, "key": "24385b1ad83cdb1667cbdaee1ae79e9f"}, {"relation": "hasVariant", "source": 2394, "target": 2395, "key": "4f491d5f4d1e4f3762ddbf17a47225ab"}, {"relation": "partOf", "source": 2394, "target": 1287, "key": "f87455c8795e52c3a40fb2ace33efc5f"}, {"relation": "partOf", "source": 2394, "target": 1294, "key": "9aae157af5b22736e517e23311e7cb47"}, {"relation": "partOf", "source": 2394, "target": 1293, "key": "859d468902ed838eea158996409d24af"}, {"line": 16134, "relation": "decreases", "evidence": "Further study indicated that hyperoside can activate the PI3K/Akt signaling pathway, resulting in inhibition of the interaction between Bad and Bcl(XL), without effects on the interaction between Bad and Bcl-2.", "citation": {"db": "PubMed", "db_id": "21978835"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 167, "target": 1279, "key": "28e42635609176ec16b89e879527cb9f"}, {"line": 16145, "relation": "decreases", "evidence": "Furthermore, hyperoside inhibited mitochondria-dependent downstream caspase-mediated apoptotic pathway, such as that involving caspase-9, caspase-3, and poly ADP-ribose polymerase (PARP).", "citation": {"db": "PubMed", "db_id": "21978835"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 167, "target": 1305, "key": "7f3d926acd370c72685abb66eeae529c"}, {"line": 16159, "relation": "association", "evidence": "These results demonstrate that hyperoside can protect Abeta-induced primary cultured cortical neurons via PI3K/Akt/Bad/Bcl(XL)-regulated mitochondrial apoptotic pathway, and they raise the possibility that hyperoside could be developed into a clinically valuable treatment for Alzheimer's disease and other neuronal degenerative diseases associated with mitochondrial dysfunction.", "citation": {"db": "PubMed", "db_id": "21978835"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 167, "target": 3823, "key": "9e519f28b59921aeb7d3ed3217d075ae"}, {"line": 16146, "relation": "association", "evidence": "Furthermore, hyperoside inhibited mitochondria-dependent downstream caspase-mediated apoptotic pathway, such as that involving caspase-9, caspase-3, and poly ADP-ribose polymerase (PARP).", "citation": {"db": "PubMed", "db_id": "21978835"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 1305, "target": 478, "key": "ccecb753ba4afc58a97ec6bcdf45ab4a"}, {"line": 16148, "relation": "association", "evidence": "Furthermore, hyperoside inhibited mitochondria-dependent downstream caspase-mediated apoptotic pathway, such as that involving caspase-9, caspase-3, and poly ADP-ribose polymerase (PARP).", "citation": {"db": "PubMed", "db_id": "21978835"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 1305, "target": 479, "key": "799b9864e06a6cb405d98463ee278763"}, {"line": 16148, "relation": "association", "evidence": "Furthermore, hyperoside inhibited mitochondria-dependent downstream caspase-mediated apoptotic pathway, such as that involving caspase-9, caspase-3, and poly ADP-ribose polymerase (PARP).", "citation": {"db": "PubMed", "db_id": "21978835"}, "annotations": {"CellStructure": {"Mitochondria": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 479, "target": 1305, "key": "5d01e03648f6a4aaf7a33acc0fdc8443"}, {"line": 18740, "relation": "association", "evidence": "In neuronal cells, MMP-3 expression is increased in response to cell stress, and the cleaved, active MMP-3 participates in apoptotic signaling.", "citation": {"db": "PubMed", "db_id": "21044079"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "object": {"modifier": "Activity"}, "source": 479, "target": 3060, "key": "30993166a16e50b689a49610549e3c8e"}, {"line": 16176, "relation": "decreases", "evidence": "Piceatannol attenuates hydrogen-peroxide- and peroxynitrite-induced apoptosis of PC12 cells by blocking down-regulation of Bcl-XL and activation of JNK.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"CellLine": {"PC-12 cell": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 332, "target": 131, "key": "b6f632651fe331702170bca3af622713"}, {"line": 16184, "relation": "decreases", "evidence": "Piceatannol attenuates hydrogen-peroxide- and peroxynitrite-induced apoptosis of PC12 cells by blocking down-regulation of Bcl-XL and activation of JNK.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"CellLine": {"PC-12 cell": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"High": true}}, "source": 332, "target": 328, "key": "9946fbd7a1e1ca94fb009284041060b2"}, {"line": 16192, "relation": "increases", "evidence": "Piceatannol attenuates hydrogen-peroxide- and peroxynitrite-induced apoptosis of PC12 cells by blocking down-regulation of Bcl-XL and activation of JNK.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"CellLine": {"PC-12 cell": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 332, "target": 2394, "key": "de4f81cb4746d58ea0af19a23d3a6cb4"}, {"line": 16193, "relation": "decreases", "evidence": "Piceatannol attenuates hydrogen-peroxide- and peroxynitrite-induced apoptosis of PC12 cells by blocking down-regulation of Bcl-XL and activation of JNK.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"CellLine": {"PC-12 cell": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Hydrogen peroxide subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 332, "target": 3002, "key": "aaa03f29e0e9834961eeabeeb4d00caa"}, {"line": 16205, "relation": "decreases", "evidence": "Piceatannol treatment attenuated hydrogen-peroxide- and peroxynitrite-induced cytotoxicity, apoptotic features, PARP cleavage and intracellular ROS and RNS accumulation.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 332, "target": 170, "key": "7b0939c95c6c75243b92e99d25fa345a"}, {"line": 16209, "relation": "decreases", "evidence": "Piceatannol treatment attenuated hydrogen-peroxide- and peroxynitrite-induced cytotoxicity, apoptotic features, PARP cleavage and intracellular ROS and RNS accumulation.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 332, "target": 169, "key": "95ebc8e1bdbe2654858ae59f5da84f50"}, {"line": 16242, "relation": "decreases", "evidence": "Hydrogen peroxide or SIN-1 treatment induced phosphorylation of the c-Jun-N-terminal kinase (JNK), which was inhibited by piceatannol treatment.", "citation": {"db": "PubMed", "db_id": "17869087"}, "annotations": {"Subgraph": {"Hydrogen peroxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 332, "target": 3003, "key": "f5a6c4f395b52d07517ddecf3257f304"}, {"line": 39148, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3003, "target": 3002, "key": "7d9dc9300163529603d48a3f2c2a1bbe"}, {"line": 16258, "relation": "association", "evidence": "Hypoxia inducible factor 1-alpha (HIF-1α), a key regulator of cellular responses to hypoxia, is elevated in the microcirculation of AD patients.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"MeSHDisease": {"Hypoxia": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Hypoxia response subgraph": true}}, "source": 2830, "target": 3823, "key": "70cf26c89dadacffab7498151c7ee76e"}, {"line": 16259, "relation": "association", "evidence": "Hypoxia inducible factor 1-alpha (HIF-1α), a key regulator of cellular responses to hypoxia, is elevated in the microcirculation of AD patients.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"MeSHDisease": {"Hypoxia": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Hypoxia response subgraph": true}}, "source": 2830, "target": 3859, "key": "de7ce21dd525e6c302466a17a22c35a2"}, {"line": 27066, "relation": "increases", "evidence": "Specifically, hypoxia significantly increases beta-site APP cleaving enzyme (BACE1) gene transcription through the over-expression of hypoxia inducible factor 1alpha, resulting in increased BACE1 secretase activity and amyloid-beta production.", "citation": {"db": "PubMed", "db_id": "19196431"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true, "Hypoxia response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2830, "target": 2375, "key": "9f0c3d799df05b2eb35a9c4fdd7b23b9"}, {"line": 16259, "relation": "association", "evidence": "Hypoxia inducible factor 1-alpha (HIF-1α), a key regulator of cellular responses to hypoxia, is elevated in the microcirculation of AD patients.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"MeSHDisease": {"Hypoxia": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Subgraph": {"Hypoxia response subgraph": true}}, "source": 3859, "target": 2830, "key": "f350bfe2139c430f153ea85c1005ba63"}, {"line": 16282, "relation": "increases", "evidence": "Our results demonstrated that HIF-1α is induced in cultured brain endothelial cells exposed to hypoxia and that expression of Ang-2, MMP2 and caspase 3 was elevated and the anti-apoptotic protein Bcl-xL decreased.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Subgraph": {"Hypoxia response subgraph": true}, "MeSHDisease": {"Hypoxia": true}, "Cell": {"endothelial cell": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3859, "target": 2830, "key": "27d91c7f7e6db5db0b58adee0cb57907"}, {"line": 16270, "relation": "association", "evidence": "Cerebral hypoxia is a potent stimulus for vascular activation and angiogenesis.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Subgraph": {"Hypoxia response subgraph": true}, "MeSHDisease": {"Hypoxia, Brain": true}, "MeSHAnatomy": {"Cerebrum": true}}, "source": 3859, "target": 807, "key": "01ff7d263cbffb982f18e551162c7397"}, {"line": 16284, "relation": "increases", "evidence": "Our results demonstrated that HIF-1α is induced in cultured brain endothelial cells exposed to hypoxia and that expression of Ang-2, MMP2 and caspase 3 was elevated and the anti-apoptotic protein Bcl-xL decreased.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Hypoxia response subgraph": true}, "MeSHDisease": {"Hypoxia": true}, "Cell": {"endothelial cell": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3859, "target": 3059, "key": "d3d19a42df4c34c3c0227d611b86f40b"}, {"line": 16286, "relation": "increases", "evidence": "Our results demonstrated that HIF-1α is induced in cultured brain endothelial cells exposed to hypoxia and that expression of Ang-2, MMP2 and caspase 3 was elevated and the anti-apoptotic protein Bcl-xL decreased.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Hypoxia response subgraph": true}, "MeSHDisease": {"Hypoxia": true}, "Cell": {"endothelial cell": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3859, "target": 2444, "key": "d6f38b3189d8cfef85daf145aca122fd"}, {"line": 16288, "relation": "increases", "evidence": "Our results demonstrated that HIF-1α is induced in cultured brain endothelial cells exposed to hypoxia and that expression of Ang-2, MMP2 and caspase 3 was elevated and the anti-apoptotic protein Bcl-xL decreased.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Subgraph": {"Hypoxia response subgraph": true}, "MeSHDisease": {"Hypoxia": true}, "Cell": {"endothelial cell": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3859, "target": 2291, "key": "1a8e60ac8d7b7d7c3cc7bb209392eb87"}, {"line": 16290, "relation": "decreases", "evidence": "Our results demonstrated that HIF-1α is induced in cultured brain endothelial cells exposed to hypoxia and that expression of Ang-2, MMP2 and caspase 3 was elevated and the anti-apoptotic protein Bcl-xL decreased.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Subgraph": {"Hypoxia response subgraph": true, "Bcl-2 subgraph": true}, "MeSHDisease": {"Hypoxia": true}, "Cell": {"endothelial cell": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3859, "target": 2394, "key": "0fb53c56719c7ae612249f21440e4d55"}, {"line": 28032, "relation": "increases", "evidence": "Hypoxia up-regulated beta-secretase cleavage of APP and amyloid-beta protein (Abeta) production by increasing BACE1 gene transcription and expression both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "17121991"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Hypoxia response subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3859, "target": 2375, "key": "4f7507db4d5e5d079a263e5ab2fbe811"}, {"line": 28039, "relation": "increases", "evidence": "Hypoxia up-regulated beta-secretase cleavage of APP and amyloid-beta protein (Abeta) production by increasing BACE1 gene transcription and expression both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "17121991"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Hypoxia response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3859, "target": 80, "key": "914c26320c3ab7014cd21902e7d768f9"}, {"line": 16305, "relation": "positiveCorrelation", "evidence": "Brain sections from AD and control mice showed that HIF-1α, Ang-2, MMP2 and caspase 3 are elevated and Bcl-xL decreased in the microvasculature of AD mice.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Hypoxia response subgraph": true}}, "source": 3582, "target": 3823, "key": "8f8fbb78b42179523a837cdaadaef7c1"}, {"line": 16313, "relation": "negativeCorrelation", "evidence": "Brain sections from AD and control mice showed that HIF-1α, Ang-2, MMP2 and caspase 3 are elevated and Bcl-xL decreased in the microvasculature of AD mice.", "citation": {"db": "PubMed", "db_id": "21904637"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Bcl-2 subgraph": true}}, "source": 3598, "target": 3823, "key": "e5dcbaa32c02718f4ffcbee03f9b41dd"}, {"line": 23196, "relation": "negativeCorrelation", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3598, "target": 3825, "key": "df878782399159a94e5f50a5384a5cda"}, {"line": 23206, "relation": "decreases", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3598, "target": 478, "key": "4e4cd2101096bb2de3f7582c46cd1edf"}, {"line": 16387, "relation": "positiveCorrelation", "evidence": "Osteopontin is increased in the cerebrospinal fluid of patients with Alzheimer's disease and its levels correlate with cognitive decline.", "citation": {"db": "PubMed", "db_id": "20308780"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 3823, "key": "f2e608a221e2992e7443bfdd0b52f8cf"}, {"line": 16424, "relation": "positiveCorrelation", "evidence": "In MCI converters individuals tested longitudinally, both plasma and CSF OPN concentrations were significantly elevated when they received a diagnosis of AD during followup.", "citation": {"db": "PubMed", "db_id": "23576854"}, "annotations": {"MeSHAnatomy": {"Cerebrospinal Fluid": true, "Plasma": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3409, "target": 3823, "key": "855e177cf57cde04d63a4ebd5de6743b"}, {"line": 16534, "relation": "positiveCorrelation", "evidence": "Increased expression of the remodeling- and tumorigenic-associated factor osteopontin in pyramidal neurons of the Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Confidence": {"Medium": true}}, "source": 3409, "target": 3823, "key": "1ea86595f558a423c8d1596b0b18f0c6"}, {"line": 16388, "relation": "negativeCorrelation", "evidence": "Osteopontin is increased in the cerebrospinal fluid of patients with Alzheimer's disease and its levels correlate with cognitive decline.", "citation": {"db": "PubMed", "db_id": "20308780"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 812, "key": "143e31fe24a26a7eb49533ab7f78a28b"}, {"line": 16421, "relation": "positiveCorrelation", "evidence": "In MCI converters individuals tested longitudinally, both plasma and CSF OPN concentrations were significantly elevated when they received a diagnosis of AD during followup.", "citation": {"db": "PubMed", "db_id": "23576854"}, "annotations": {"MeSHAnatomy": {"Cerebrospinal Fluid": true, "Plasma": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3409, "target": 3838, "key": "c57754289704fc9b58388e166be83600"}, {"line": 16436, "relation": "association", "evidence": "In multiple sclerosis, the role of OPN has been studied in the inflammatory phase, where it was shown that the protein levels increase during disease relapses.", "citation": {"db": "PubMed", "db_id": "21358042"}, "annotations": {"Disease": {"multiple sclerosis": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3409, "target": 3869, "key": "a53dc308aaf665a92db4e4c430652e74"}, {"line": 16513, "relation": "association", "evidence": "In the neurosciences, it has led to the discoveries of osteopontin in multiple sclerosis and SORL1/LR11 in Alzheimer's, and recent studies indicate its potential for identifying neurogenomic biomarkers.", "citation": {"db": "PubMed", "db_id": "19285134"}, "annotations": {"Disease": {"multiple sclerosis": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3409, "target": 3869, "key": "57d932cf88aa63a1051d86026d86a69d"}, {"line": 16544, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 807, "key": "7fa1549ceb640e51805719413330b2a6"}, {"line": 16552, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 478, "key": "8f84a0888ac53452b463af10071ffc45"}, {"line": 16559, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 3920, "key": "1ecb3269f97c45469eac702430cd8b1d"}, {"line": 16567, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 842, "key": "080a31359dc0bdfc985db9bb448266ed"}, {"line": 16575, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 803, "key": "92baeda2ff249b406f8d2d63bf747dfb"}, {"line": 16576, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 821, "key": "5de967a331814b5b7c319e7d836d26f5"}, {"line": 16577, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 487, "key": "694689ba921c6be52f3b6e28e45333fb"}, {"line": 16578, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3409, "target": 509, "key": "5ef3ea377d925650269ec6f56c08865f"}, {"line": 16631, "relation": "positiveCorrelation", "evidence": "Additionally, there was a significant positive correlation between OPN staining intensity and both amyloid-beta load (p(2) = 0.25; P < 0.05; n = 20) and aging (p(2) = 0.32; P < 0.01; n = 20) among all control and AD subjects.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3409, "target": 2328, "key": "fff2b71682118e5ea01cfeba6451aa4e"}, {"line": 16643, "relation": "association", "evidence": "Given that the induction of OPN expression (and amyloid-beta generation) is associated with remodeling and tumorigenesis, our results suggest that OPN may play a role in the aberrant re-entry of neurons into the cell cycle and/or neuronal remyelination in AD.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"MeSHDisease": {"Cell Transformation, Neoplastic": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 3409, "target": 503, "key": "57739d5847a14961825b0c429fb2011d"}, {"line": 16421, "relation": "positiveCorrelation", "evidence": "In MCI converters individuals tested longitudinally, both plasma and CSF OPN concentrations were significantly elevated when they received a diagnosis of AD during followup.", "citation": {"db": "PubMed", "db_id": "23576854"}, "annotations": {"MeSHAnatomy": {"Cerebrospinal Fluid": true, "Plasma": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3838, "target": 3409, "key": "ebe229fa677f3b9bff6897fb8c631dbe"}, {"line": 16453, "relation": "positiveCorrelation", "evidence": "Neuronal expression of myeloperoxidase is increased in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3066, "target": 3823, "key": "25bf5e5ebb73df38c36ee9c048084e49"}, {"line": 16482, "relation": "association", "evidence": "Consistent with expression in phagocytic cells, myeloperoxidase immunoreactivity was present in some activated microglia in Alzheimer brains.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Cell": {"phagocyte": true, "microglial cell": true}, "Confidence": {"Medium": true}}, "source": 3066, "target": 3823, "key": "1825c4e148031bf22419b6c4adf580b3"}, {"line": 18446, "relation": "positiveCorrelation", "evidence": "In a binary logistic regression model, plasma MPO concentrations were independently associated with the presence of AD (p = 0.014).AD patients showed significantly increased plasma levels of MPO, which could be an important molecular link between atherosclerosis and AD.", "citation": {"db": "PubMed", "db_id": "24217274"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Alzheimer Disease": true, "Atherosclerosis": true}, "Confidence": {"High": true}}, "source": 3066, "target": 3823, "key": "9a78a1e99f564fd404fad8554b83a43e"}, {"line": 18616, "relation": "association", "evidence": "In females, we found a significant association between MPO genotype and AD (P=0.034),", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3066, "target": 3823, "key": "55a7ab6d2faa25c2cd9ff4aa646ea2a5"}, {"line": 18625, "relation": "causesNoChange", "evidence": "In conclusion, the G-463A polymorphism of MPO was statistically associated with AD in a gender-specific manner. However, given the low significance of P value we suggest no causal effect of the MPO gene in AD, as also evidenced in a recent meta-analysis.", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3066, "target": 3823, "key": "d3b7fc4f62dce641745182ea04293567"}, {"line": 16468, "relation": "association", "evidence": "Myeloperoxidase, a heme protein expressed by professional phagocytic cells, generates an array of oxidants which are proposed to contribute to tissue damage during inflammation.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "Cell": {"phagocyte": true}, "Subgraph": {"Myeloperoxidase subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3066, "target": 3920, "key": "6c64b38b408c2bc17d7d82b076b12baf"}, {"line": 16496, "relation": "association", "evidence": "The increase in neuronal myeloperoxidase expression we observed in Alzheimer disease brains raises the possibility that the enzyme contributes to the oxidative stress implicated in the pathogenesis of the neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"Disease": {"neurodegenerative disease": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3066, "target": 842, "key": "215d986e12f7fcfec588faab973552a2"}, {"line": 18529, "relation": "increases", "evidence": "The increase in neuronal myeloperoxidase expression we observed in Alzheimer disease brains raises the possibility that the enzyme contributes to the oxidative stress implicated in the pathogenesis of the neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "15255951"}, "annotations": {"Disease": {"neurodegenerative disease": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3066, "target": 842, "key": "a9d2b2fe0975f33dc225f72816915aac"}, {"line": 18447, "relation": "positiveCorrelation", "evidence": "In a binary logistic regression model, plasma MPO concentrations were independently associated with the presence of AD (p = 0.014).AD patients showed significantly increased plasma levels of MPO, which could be an important molecular link between atherosclerosis and AD.", "citation": {"db": "PubMed", "db_id": "24217274"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Alzheimer Disease": true, "Atherosclerosis": true}, "Confidence": {"High": true}}, "source": 3066, "target": 3895, "key": "8d19ebf63b6c348564bcb83f25fa50a7"}, {"line": 18470, "relation": "increases", "evidence": "Aberrant expression of myeloperoxidase in astrocytes promotes phospholipid oxidation and memory deficits in a mouse model of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "19059911"}, "annotations": {"MeSHDisease": {"Memory Disorders": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Species": {"10090": true}, "Subgraph": {"Myeloperoxidase subgraph": true}, "Confidence": {"High": true}}, "source": 3066, "target": 3866, "key": "5e0feb5abbbb2185c7f713eaca101d45"}, {"relation": "hasVariant", "source": 3066, "target": 3067, "key": "cf462784d2e3e9c5676a46e12199187a"}, {"line": 18579, "relation": "association", "evidence": "However, the possible role of MPO and enzymatically inactive MPO (iMPO) as the choreographers of the destruction done by TNF-alpha and ROS is not generally recognized.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"Subgraph": {"Myeloperoxidase subgraph": true, "Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3066, "target": 170, "key": "a3a1ed5cb570d55542accf238a3a7bd4"}, {"line": 18583, "relation": "association", "evidence": "However, the possible role of MPO and enzymatically inactive MPO (iMPO) as the choreographers of the destruction done by TNF-alpha and ROS is not generally recognized.", "citation": {"db": "PubMed", "db_id": "18554520"}, "annotations": {"Subgraph": {"Myeloperoxidase subgraph": true, "Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3066, "target": 3472, "key": "fc550931dd099f88ebad0008bb90c660"}, {"relation": "partOf", "source": 3066, "target": 1122, "key": "c9f8d24004864a564a70a2669dbf149f"}, {"line": 16576, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 821, "target": 3409, "key": "26470d20509d9e01611b236d6b18b90b"}, {"line": 16618, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 821, "target": 3874, "key": "cbba28c6670885cb9a28c2c711049b52"}, {"line": 18717, "relation": "association", "evidence": "Moreover, a strict spatiotemporal MMP-3 up-regulation in the injured or diseased CNS might support remyelination and neuroprotection, as well as genesis and migration of stem cells in the damaged brain.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Brain": true}, "MeSHDisease": {"Wounds and Injuries": true}}, "source": 821, "target": 3060, "key": "ccef9de1b62748454995ab12a12db906"}, {"line": 16577, "relation": "association", "evidence": "OPN is involved in a number of physiologic and pathologic events including angiogenesis, apoptotic process, inflammation, oxidative stress, remyelination, wound healing, bone remodeling, cell migration and tumorigenesis.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 487, "target": 3409, "key": "0f14ec15aa2eb1fa1b262d383c449536"}, {"line": 16619, "relation": "association", "evidence": "Since these functions of OPN, and the events that it regulates, are involved with neurodegeneration, we examined whether OPN was differentially expressed in the hippocampus of the Alzheimer's disease (AD) compared with age-matched (59-93 years) control brain.", "citation": {"db": "PubMed", "db_id": "17316167"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true}, "Confidence": {"High": true}}, "source": 487, "target": 3874, "key": "a1ebecbca17c878d13e27dfb03ee2878"}, {"line": 16668, "relation": "association", "evidence": "Genetic association between endothelial nitric oxide synthase and Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "16813604"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3124, "target": 3823, "key": "5ffb1dbaf7bb49807f89e61a8182f6fd"}, {"relation": "hasVariant", "source": 3124, "target": 3125, "key": "9e582ace1db2779e5d43a9f89cfa31a0"}, {"line": 16709, "relation": "regulates", "evidence": "The endothelial nitric oxide synthase (NOS3) gene encodes endothelial NOS, an enzyme that regulates the production of the vasodilatory nitric oxide associated with the cerebral small vessel pathology observed in early AD.", "citation": {"db": "PubMed", "db_id": "15016421"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3124, "target": 156, "key": "864fb1334200955c20cb9ee7cb959032"}, {"line": 16759, "relation": "association", "evidence": "In animal models of ischemic stroke, statins have proven to reduce infarct size through up-regulation of endothelial nitric oxide synthases.", "citation": {"db": "PubMed", "db_id": "12218642"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3124, "target": 3930, "key": "42bb614b3362b6c57978b3284c0e19c4"}, {"relation": "partOf", "source": 3124, "target": 1660, "key": "1eaf62317304bcf94bf6826d2cdaf443"}, {"line": 16851, "relation": "association", "evidence": "Herein, we hypothesize that a feedback signaling loop, consisted of Pin1, endothelial nitric oxide synthase (eNOS), and amyloid-beta (Abeta), may contribute to the interesting pathological phenomenon.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 3124, "target": 3192, "key": "d6bdcdd390bcadafac1f7195e42cdaaf"}, {"line": 16855, "relation": "association", "evidence": "Herein, we hypothesize that a feedback signaling loop, consisted of Pin1, endothelial nitric oxide synthase (eNOS), and amyloid-beta (Abeta), may contribute to the interesting pathological phenomenon.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 3124, "target": 2328, "key": "ced60057518080b602ddce1bc93892c3"}, {"line": 16877, "relation": "decreases", "evidence": "First, Pin1 inhibits the production of Abeta, and enhances the activity of eNOS. Second, Abeta and eNOS form a mutual inhibition system. Third, the well-balanced feedback signaling loop avoids the development of AD, HTN, and CAA by inhibiting the frequent pathological characteristics of these diseases, including Abeta deposition in cerebral microvessels and cerebral microbleeds.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"High": true}}, "source": 3124, "target": 2328, "key": "f382804316879cd47e67f5f68bd13fdd"}, {"line": 16928, "relation": "decreases", "evidence": "To explore the molecular mechanism underlying AD, HTN, and CAA, we hypothesize a feedback signaling loop consisted of Pin1, eNOS, and Abeta. Pin1 and eNOS mainly inhibit Abeta deposition in cerebral vessels, cerebral microbleeds, and elevation of blood pressure, preventing the development of AD, HTN, and CAA, however, Abeta plays an opposite role and aggravates these diseases.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3124, "target": 2328, "key": "c8d06121ca69f17732b879442e7ef32c"}, {"relation": "partOf", "source": 3124, "target": 1714, "key": "8ba1621106ba599ef96e3888becf5a47"}, {"line": 16681, "relation": "association", "evidence": "The Glu/Glu genotype at the Glu298Asp variant of the endothelial nitric oxide synthase (NOS3) gene has been tested for association with AD in several Caucasian and Asian populations, with conflicting results.", "citation": {"db": "PubMed", "db_id": "16813604"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Endothelium": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3125, "target": 3823, "key": "2b68de69e428f5ef90121e130ec58232"}, {"line": 16692, "relation": "association", "evidence": "Finally, we compiled results of previous studies of Glu298Asp using meta-analysis, to determine whether the aggregate studies support an association between Glu298Asp and AD. None of the additional SNPs were associated with AD in the Caucasians, whereas two showed evidence for association in the African Americans.The meta-analysis showed a small effect of the Glu298Asp GG genotype on AD risk across all studies (summary odds ratio = 1.15, 95% confidence interval: 0.97-1.35)", "citation": {"db": "PubMed", "db_id": "16813604"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3125, "target": 3823, "key": "a01933d6e449b6360d5989dd9a77c1c5"}, {"line": 16731, "relation": "increases", "evidence": "Vascular risk factors such as hypertension and hypercholesterolemia during midlife increase the risk for Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "12218642"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Hypercholesterolemia": true, "Alzheimer Disease": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3913, "target": 3823, "key": "41c89f5ddf3c714393154a0c78777b72"}, {"line": 27016, "relation": "association", "evidence": "The findings link hypercholesterolemia with cognitive dysfunction potentially mediated by increased neuroinflammation and APP processing in a non-transgenic mouse pmodel.", "citation": {"db": "PubMed", "db_id": "18410513"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3913, "target": 2315, "key": "380f2de1d26f7866a897ce4ec3cb4a69"}, {"line": 16823, "relation": "association", "evidence": "Pin1, endothelial nitric oxide synthase, and amyloid-beta form a feedback signaling loop involved in the pathogenesis of Alzheimer's disease, hypertension, and cerebral amyloid angiopathy.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 1660, "target": 3823, "key": "cbaf4960307978cd2c6f0977a5895e6b"}, {"line": 16824, "relation": "association", "evidence": "Pin1, endothelial nitric oxide synthase, and amyloid-beta form a feedback signaling loop involved in the pathogenesis of Alzheimer's disease, hypertension, and cerebral amyloid angiopathy.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 1660, "target": 3916, "key": "87b04e7031c10b8049ea1da0db6bb220"}, {"line": 16825, "relation": "association", "evidence": "Pin1, endothelial nitric oxide synthase, and amyloid-beta form a feedback signaling loop involved in the pathogenesis of Alzheimer's disease, hypertension, and cerebral amyloid angiopathy.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Cerebral Amyloid Angiopathy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Endothelium": true, "Cerebrum": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 1660, "target": 3835, "key": "17efb41d77a060435569759f5f7dc06a"}, {"line": 16885, "relation": "decreases", "evidence": "First, Pin1 inhibits the production of Abeta, and enhances the activity of eNOS. Second, Abeta and eNOS form a mutual inhibition system. Third, the well-balanced feedback signaling loop avoids the development of AD, HTN, and CAA by inhibiting the frequent pathological characteristics of these diseases, including Abeta deposition in cerebral microvessels and cerebral microbleeds.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Anatomy": {"cerebral blood vessel": true}, "Confidence": {"Low": true}}, "source": 1714, "target": 2328, "key": "382ecc08ec81223075fed0e28132c1d2"}, {"line": 16904, "relation": "decreases", "evidence": "On one hand, Pin1 and eNOS not only inhibit Abeta production but also accelerate Abeta clearance, preventing Abeta deposition in cerebral microvessels. On the other hand, Pin1 and eNOS promote vasodilatation and prevent the elevation of blood pressure in brain, alleviating the pathology of cerebral microbleeds. However, once the precise balance is disturbed, it may result in Abeta deposition, microbleeds, and elevated blood pressure, possibly leading to the synchronous occurrence of AD, HTN, and CAA.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Disease": {"cerebral amyloid angiopathy": true, "Alzheimer's disease": true, "hypertension": true}, "Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 1714, "target": 80, "key": "835f07a601fa4dcb452d498e432ea2f3"}, {"line": 16944, "relation": "decreases", "evidence": "To explore the molecular mechanism underlying AD, HTN, and CAA, we hypothesize a feedback signaling loop consisted of Pin1, eNOS, and Abeta. Pin1 and eNOS mainly inhibit Abeta deposition in cerebral vessels, cerebral microbleeds, and elevation of blood pressure, preventing the development of AD, HTN, and CAA, however, Abeta plays an opposite role and aggravates these diseases.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1714, "target": 80, "key": "4bd3d105af1350063e46ce8db82288f9"}, {"line": 16908, "relation": "increases", "evidence": "On one hand, Pin1 and eNOS not only inhibit Abeta production but also accelerate Abeta clearance, preventing Abeta deposition in cerebral microvessels. On the other hand, Pin1 and eNOS promote vasodilatation and prevent the elevation of blood pressure in brain, alleviating the pathology of cerebral microbleeds. However, once the precise balance is disturbed, it may result in Abeta deposition, microbleeds, and elevated blood pressure, possibly leading to the synchronous occurrence of AD, HTN, and CAA.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Disease": {"cerebral amyloid angiopathy": true, "Alzheimer's disease": true, "hypertension": true}, "Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 1714, "target": 825, "key": "bdcf7140af9e80185d34b933887b0642"}, {"line": 16912, "relation": "increases", "evidence": "On one hand, Pin1 and eNOS not only inhibit Abeta production but also accelerate Abeta clearance, preventing Abeta deposition in cerebral microvessels. On the other hand, Pin1 and eNOS promote vasodilatation and prevent the elevation of blood pressure in brain, alleviating the pathology of cerebral microbleeds. However, once the precise balance is disturbed, it may result in Abeta deposition, microbleeds, and elevated blood pressure, possibly leading to the synchronous occurrence of AD, HTN, and CAA.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Disease": {"cerebral amyloid angiopathy": true, "Alzheimer's disease": true, "hypertension": true}, "Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 1714, "target": 712, "key": "1489ce3fdfa5b174e6bdac72a946085a"}, {"line": 16932, "relation": "decreases", "evidence": "To explore the molecular mechanism underlying AD, HTN, and CAA, we hypothesize a feedback signaling loop consisted of Pin1, eNOS, and Abeta. Pin1 and eNOS mainly inhibit Abeta deposition in cerebral vessels, cerebral microbleeds, and elevation of blood pressure, preventing the development of AD, HTN, and CAA, however, Abeta plays an opposite role and aggravates these diseases.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1714, "target": 3823, "key": "cc21987a3f3cc19b8bb2aadbe94ef9c1"}, {"line": 16936, "relation": "decreases", "evidence": "To explore the molecular mechanism underlying AD, HTN, and CAA, we hypothesize a feedback signaling loop consisted of Pin1, eNOS, and Abeta. Pin1 and eNOS mainly inhibit Abeta deposition in cerebral vessels, cerebral microbleeds, and elevation of blood pressure, preventing the development of AD, HTN, and CAA, however, Abeta plays an opposite role and aggravates these diseases.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1714, "target": 3916, "key": "c431239215bed61790d1dca5c1f36eec"}, {"line": 16940, "relation": "decreases", "evidence": "To explore the molecular mechanism underlying AD, HTN, and CAA, we hypothesize a feedback signaling loop consisted of Pin1, eNOS, and Abeta. Pin1 and eNOS mainly inhibit Abeta deposition in cerebral vessels, cerebral microbleeds, and elevation of blood pressure, preventing the development of AD, HTN, and CAA, however, Abeta plays an opposite role and aggravates these diseases.", "citation": {"db": "PubMed", "db_id": "24332564"}, "annotations": {"Anatomy": {"cerebral blood vessel": true}, "Subgraph": {"Nitric oxide subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1714, "target": 3835, "key": "b220777220be745e8aff1880944854c0"}, {"line": 21245, "relation": "association", "evidence": "Nitric oxide (NO), which is produced by oxidation of L-arginine to L-citrulline in a process catalyzed by different isoforms of nitric oxide synthase (NOS), exhibits diverse roles in several physiological processes, including neurotransmission, blood pressure regulation and immunological defense mechanisms.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 712, "target": 156, "key": "3917787d425c15889a70820c33cac8fc"}, {"line": 16993, "relation": "association", "evidence": "Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Gamma secretase subgraph": true}}, "source": 2658, "target": 792, "key": "277f564a5da3ce0ba028ecc343c2c8d9"}, {"line": 17032, "relation": "association", "evidence": "The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Gamma secretase subgraph": true}}, "source": 2658, "target": 792, "key": "0f8950412a67855ee2538a2cf027794e"}, {"line": 16995, "relation": "association", "evidence": "Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Gamma secretase subgraph": true}}, "source": 2658, "target": 533, "key": "d2058784bd96119b1c4e6f27f709e1e8"}, {"line": 17030, "relation": "association", "evidence": "The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "Gamma secretase subgraph": true}}, "source": 2658, "target": 533, "key": "a14e8bfa5259682734a11ab950c68cd8"}, {"line": 48933, "relation": "association", "evidence": "Early growth response gene 1 (Egr1) is a member of the immediate early gene (IEG) family of transcription factors and plays a role in memory formation. The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 2658, "target": 533, "key": "18bda96b22f9c8a31b7cad61ddb64e7b"}, {"line": 16998, "relation": "association", "evidence": "Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 604, "key": "044eb56edfff0ae15e50972582a0517c"}, {"line": 17035, "relation": "association", "evidence": "The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 655, "key": "186518150e07731ff02e1666fec3099f"}, {"line": 48938, "relation": "association", "evidence": "Early growth response gene 1 (Egr1) is a member of the immediate early gene (IEG) family of transcription factors and plays a role in memory formation. The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 655, "key": "8b364b851b643f118ed6807582b39e85"}, {"line": 17039, "relation": "association", "evidence": "The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Gamma secretase subgraph": true}}, "source": 2658, "target": 3823, "key": "dfd35b478c0f9b9e9da3b6caf072a7fd"}, {"line": 17252, "relation": "association", "evidence": "In AD-Tg mice, a significant increase in hippocampal EGR1 protein levels was also found in response to GA immunization.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 3823, "key": "228c2ab44cce90ce6414e400dd302784"}, {"line": 17311, "relation": "association", "evidence": "Egr-1 upregulates the Alzheimer's disease presenilin-2 gene in neuronal cells.", "citation": {"db": "PubMed", "db_id": "14585504"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Published": {"Epilepsy comorbidity paper": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 3823, "key": "8ef3c42246c7a3f62a5ce8c4f3b9870d"}, {"line": 47844, "relation": "positiveCorrelation", "evidence": "Hippocampal expression levels of Egr1 have been shown to positively correlate with disease progression in AD, whereas the overexpression of EGR1 in rat brain induces tau phosphorylation via its target, and regulator of cdk5, p35.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Gamma secretase subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 3823, "key": "1df6c4f1e1c9997efa67af0b36bd6d4d"}, {"line": 48798, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 3823, "key": "f4a21dd4b9d03480f0e5ed06e60927cc"}, {"line": 17120, "relation": "association", "evidence": "Most forms of neuronal plasticity are associated with the induction of the transcription factor zif268 (egr1).", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 745, "key": "0336a7f906b6cd97c0d52765ea88f5a0"}, {"line": 17128, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2721, "key": "a135a614fbdc97d5b2beba2771a98fce"}, {"line": 17132, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2722, "key": "1e29e5515e868f9bbb55d47cf141ed1f"}, {"line": 17136, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2723, "key": "2a3fa7f595609c2b8be5f8994bf4a1fb"}, {"line": 17140, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2724, "key": "a401544f71331255b0ccd151b5702fd3"}, {"line": 17144, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2725, "key": "580167bfd633a17a8934baf21f910477"}, {"line": 17148, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2726, "key": "41525c3afb067443a0b08fd7d4b121d9"}, {"line": 17152, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2727, "key": "f951347b4feb46acec2b2bcff172bff0"}, {"line": 17156, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2728, "key": "19e07d902c9f77276da51358800b3df0"}, {"line": 17160, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2729, "key": "ba1d14dbcc016d2e3bd457076f95b412"}, {"line": 17164, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2732, "key": "ba7959c4976824be6e09ef835e386e5f"}, {"line": 17168, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2733, "key": "c3d9aba6be4dae2d9a8ff75abac2f112"}, {"line": 17172, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2734, "key": "a5040250e2c18afe7707a5f4c932a4c9"}, {"line": 17176, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2730, "key": "5f5713350035c45b50bc08acb893a7d0"}, {"line": 17179, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2731, "key": "4bd9046a700713b1f111ae0727e912d5"}, {"line": 17183, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2735, "key": "b06a911bf6a1ca6b4e325d58eee237ef"}, {"line": 17187, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 2736, "key": "ea656daa00b1dcc70db1c2f0ca12ab05"}, {"line": 17191, "relation": "regulates", "evidence": "Hence, it is predicted that zif268 may regulate transcription of genes associated with glutamate receptors and/or GABA(A)Rs.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 3548, "key": "624d4342c3d32a5d3a05398ecbd7b5b6"}, {"line": 17200, "relation": "regulates", "evidence": "Induction of zif268 in neurons leads to altered expression of proteasome subunit and proteasome-regulatory genes, thereby changing the capacity of the neuron to degrade synaptic proteins, including receptors and receptor subunits.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "GABA subgraph": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 893, "key": "8e20391faa49f0bd30e13d9069a76c73"}, {"line": 17209, "relation": "association", "evidence": "In addition, zif268 alters the transcription of genes associated with GABA(A)R expression and trafficking, such as ubiquilin and gephyrin.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 2766, "key": "6ba08a067db619b153980d9e51f77a55"}, {"line": 17269, "relation": "negativeCorrelation", "evidence": "Further, EGR1 levels were negatively correlated with hippocampal amyloid-beta plaque burden.This study presents global gene expression profiles associated with GA immunization in a glaucoma rat model.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Species": {"10116": true}, "Disease": {"glaucoma": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Published": {"Epilepsy comorbidity paper": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 3881, "key": "5bc17c511bf062be759a630fb371635f"}, {"line": 17275, "relation": "association", "evidence": "Further, EGR1 levels were negatively correlated with hippocampal amyloid-beta plaque burden.This study presents global gene expression profiles associated with GA immunization in a glaucoma rat model.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Species": {"10116": true}, "Disease": {"glaucoma": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 3910, "key": "f4ccf5bb90dcaf5080e08995c883c40f"}, {"line": 17286, "relation": "increases", "evidence": "Moreover, it identifies EGR1 transcription factor as a potential mediator for GA-induced neuroprotection in both glaucoma and AD.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true, "Glaucoma": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 401, "key": "a768200ee20a67cd764f400ba5ef8d72"}, {"line": 17307, "relation": "increases", "evidence": "Egr-1 upregulates the Alzheimer's disease presenilin-2 gene in neuronal cells.", "citation": {"db": "PubMed", "db_id": "14585504"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Published": {"Epilepsy comorbidity paper": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 3268, "key": "a9fc02467127ba729d581cb05b0cdf34"}, {"relation": "partOf", "source": 2658, "target": 1408, "key": "99af973111bca8251293cdaeedea2700"}, {"line": 17356, "relation": "association", "evidence": "Early growth response 1 (Egr-1) regulates phosphorylation of microtubule-associated protein tau in mammalian brain.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 2658, "target": 3015, "key": "1f2a91b22baf06f0d6380db6cb37e1f5"}, {"line": 47811, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 2658, "target": 3015, "key": "693db949b5f508360d2b005d7e60b181"}, {"line": 47862, "relation": "increases", "evidence": "Hippocampal expression levels of Egr1 have been shown to positively correlate with disease progression in AD, whereas the overexpression of EGR1 in rat brain induces tau phosphorylation via its target, and regulator of cdk5, p35.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10116": true}, "Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 3015, "key": "a55ab192224cd4add436dfbe2d059384"}, {"line": 49081, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 3015, "key": "56cfc637c2dd2601baf075661088ddbd"}, {"line": 17379, "relation": "increases", "evidence": "In this study, we found that lentivirus-mediated overexpression of Egr-1 in rat brain hippocampus and primary neurons in culture activates proline-directed kinase Cdk5, inactivates PP1, promotes tau phosphorylation at both proline-directed Sep(396/404) and non-proline-directed Sep(262) sites, and destabilizes microtubules.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Brain": true, "Neurons": true}, "Species": {"10116": true}, "Subgraph": {"Gamma secretase subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2658, "target": 2487, "key": "8a5bf8ecae412d5e6ad8ba3cde24b5b2"}, {"line": 17386, "relation": "increases", "evidence": "In this study, we found that lentivirus-mediated overexpression of Egr-1 in rat brain hippocampus and primary neurons in culture activates proline-directed kinase Cdk5, inactivates PP1, promotes tau phosphorylation at both proline-directed Sep(396/404) and non-proline-directed Sep(262) sites, and destabilizes microtubules.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Brain": true, "Neurons": true}, "Species": {"10116": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}}, "source": 2658, "target": 3026, "key": "ac3b51953611873aa4f606054a5da523"}, {"line": 17387, "relation": "increases", "evidence": "In this study, we found that lentivirus-mediated overexpression of Egr-1 in rat brain hippocampus and primary neurons in culture activates proline-directed kinase Cdk5, inactivates PP1, promotes tau phosphorylation at both proline-directed Sep(396/404) and non-proline-directed Sep(262) sites, and destabilizes microtubules.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Brain": true, "Neurons": true}, "Species": {"10116": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}}, "source": 2658, "target": 3027, "key": "0b452d080c304588dc1fcbe0b9ef877a"}, {"line": 17388, "relation": "increases", "evidence": "In this study, we found that lentivirus-mediated overexpression of Egr-1 in rat brain hippocampus and primary neurons in culture activates proline-directed kinase Cdk5, inactivates PP1, promotes tau phosphorylation at both proline-directed Sep(396/404) and non-proline-directed Sep(262) sites, and destabilizes microtubules.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Brain": true, "Neurons": true}, "Species": {"10116": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}}, "source": 2658, "target": 3023, "key": "9fada2a242c9797a27f5fef588d4407e"}, {"line": 17390, "relation": "increases", "evidence": "In this study, we found that lentivirus-mediated overexpression of Egr-1 in rat brain hippocampus and primary neurons in culture activates proline-directed kinase Cdk5, inactivates PP1, promotes tau phosphorylation at both proline-directed Sep(396/404) and non-proline-directed Sep(262) sites, and destabilizes microtubules.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Brain": true, "Neurons": true}, "Species": {"10116": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}}, "source": 2658, "target": 611, "key": "90f698339b7eecc8dc0ea46d90407588"}, {"line": 45285, "relation": "positiveCorrelation", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2658, "target": 2832, "key": "2716ccb95b5782fa9f2e850c8d6c12b3"}, {"line": 47806, "relation": "positiveCorrelation", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 2658, "target": 2328, "key": "c14cdf80ef87be90da5f4a317bc82082"}, {"line": 47857, "relation": "regulates", "evidence": "Hippocampal expression levels of Egr1 have been shown to positively correlate with disease progression in AD, whereas the overexpression of EGR1 in rat brain induces tau phosphorylation via its target, and regulator of cdk5, p35.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10116": true}, "Subgraph": {"Gamma secretase subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 1340, "key": "8bee51807e2abf780604bb0cedfdf250"}, {"line": 48698, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 648, "key": "85e63ae1d30b3d35ed83895196c7fc88"}, {"line": 48804, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 1797, "key": "28f3a64f0d0f4eb2c7d231c86ff90b57"}, {"line": 48809, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 1743, "key": "4428f76723ce53d5abd229f0f40b5f6c"}, {"line": 48814, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 1994, "key": "5dcc949587456bcc8b42693374594ae3"}, {"line": 48818, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 2009, "key": "41359a44f6f7e56b120075218f8eea8a"}, {"line": 48822, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 1985, "key": "79db5be8702ca771b4a9b3bc6d04a55c"}, {"line": 48867, "relation": "association", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 688, "key": "dd0b450dd1a27332427612f0c6551177"}, {"line": 48871, "relation": "increases", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 760, "key": "0af62c4731856b63b5c58ad2d2038329"}, {"line": 48877, "relation": "increases", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 789, "key": "1f9a893504da31802297cc7125ad892a"}, {"line": 48881, "relation": "positiveCorrelation", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2658, "target": 2508, "key": "fc70ce43f3239eb210208dcfe58b8cc8"}, {"line": 48922, "relation": "association", "evidence": "Importantly, expression of the CRE-driven immediate early gene, Egr-1 (Zif268) is decreased in the CA1 region of the hippocampus.", "citation": {"db": "PubMed", "db_id": "26682682"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Anatomy": {"CA1 field of hippocampus": true}}, "source": 2658, "target": 2162, "key": "18e799a77ff3d7e7fb61b3ce05265528"}, {"line": 48932, "relation": "association", "evidence": "Early growth response gene 1 (Egr1) is a member of the immediate early gene (IEG) family of transcription factors and plays a role in memory formation. The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 2658, "target": 588, "key": "aa215de9dc1e3baa45de3432d530c4fc"}, {"line": 48937, "relation": "association", "evidence": "Early growth response gene 1 (Egr1) is a member of the immediate early gene (IEG) family of transcription factors and plays a role in memory formation. The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2658, "target": 799, "key": "8ecc4e2a270edcd6dfd3e622a2e453fc"}, {"line": 16993, "relation": "association", "evidence": "Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Gamma secretase subgraph": true}}, "source": 792, "target": 2658, "key": "2db47b49c9b65f234c299b5fd5068b3c"}, {"line": 17032, "relation": "association", "evidence": "The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Gamma secretase subgraph": true}}, "source": 792, "target": 2658, "key": "83ec0066f612d432b6766436ce9a1ff7"}, {"line": 16998, "relation": "association", "evidence": "Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 604, "target": 2658, "key": "cc0bc3588da96beba647008e4a66361b"}, {"line": 17050, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2721, "target": 761, "key": "de960d828b95b116a856c1678fd98686"}, {"relation": "isA", "source": 2721, "target": 3547, "key": "6d749bf0bb5d58a86d4c6f8080caa7f4"}, {"line": 17054, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2722, "target": 761, "key": "c37b6b065b6afd05728bb61935be20bd"}, {"relation": "isA", "source": 2722, "target": 3547, "key": "97af03c2b55b76717ea829800fa4cf18"}, {"line": 17058, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2723, "target": 761, "key": "5cd344a2879f1cfbacfcd113bb14f2a1"}, {"relation": "isA", "source": 2723, "target": 3547, "key": "7f8d97a7b87aefa7399e6df380b477b2"}, {"line": 17062, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2724, "target": 761, "key": "bad91539302563fc729338aa56ddc61b"}, {"relation": "isA", "source": 2724, "target": 3547, "key": "2cd21e5799f4f7f05173838fd929fac5"}, {"line": 17066, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2725, "target": 761, "key": "c6c002ee651faa765cb3dbb7ab49dd43"}, {"relation": "isA", "source": 2725, "target": 3547, "key": "b5d715a2f56957f1a9ccb4f2549a24a3"}, {"line": 17070, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2726, "target": 761, "key": "d033976dda8fcded06ba9dbed22ae5b7"}, {"relation": "isA", "source": 2726, "target": 3547, "key": "eca4f11e6cef73e4671614fa9a682c8f"}, {"line": 17074, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2727, "target": 761, "key": "4456b10ea19303dab52dffd4d74cd7ac"}, {"relation": "isA", "source": 2727, "target": 3547, "key": "cecb19eea9f66914642f2359fd0ca070"}, {"line": 17078, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2728, "target": 761, "key": "d513c58cbb902131c47dd930a471b7a2"}, {"relation": "isA", "source": 2728, "target": 3547, "key": "e8285b290d910f36a5a3650d06f236c4"}, {"line": 34598, "relation": "increases", "evidence": "The stable expression of 17A in SHSY5Y neuroblastoma cells induces the synthesis of an alternative splicing isoform that abolish GABA B2 intracellular signaling (i.e., inhibition of cAMP accumulation and activation of K(+) channels). Indeed, 17A is expressed in human brain, and we report that it is upregulated in cerebral tissues derived from Alzheimer disease patients. We demonstrate that 17A expression in neuroblastoma cells enhances the secretion of amyloid beta peptide (Abeta) and the Abeta x-42/Αbeta x-40 peptide ratio and that its synthesis is induced in response to inflammatory stimuli.", "citation": {"db": "PubMed", "db_id": "26147761"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}, "CellLine": {"SH-SY5Y": true}}, "source": 2728, "target": 558, "key": "5184a09a5a3e15613d0a11e5575d099e"}, {"line": 17082, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2729, "target": 761, "key": "20c0301eb225bfb0e9d07b61a9fb15c6"}, {"relation": "isA", "source": 2729, "target": 3547, "key": "cfd8cb5fdf2cec16cace0972c2953f62"}, {"line": 17086, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2732, "target": 761, "key": "aa6433b6a715c12993fa190dad7a8518"}, {"relation": "isA", "source": 2732, "target": 3547, "key": "e0d45033c62afe2c390451e58d286164"}, {"line": 17090, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2733, "target": 761, "key": "b4951eff29f758b6437d7a0bc6c6cfca"}, {"relation": "isA", "source": 2733, "target": 3547, "key": "7c92c151272f9ecdf0266780e778f98c"}, {"line": 17094, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2734, "target": 761, "key": "cec5d3f6180b5bcf3f61cecf7a1fab40"}, {"relation": "isA", "source": 2734, "target": 3547, "key": "3120c5d93e531a78838c12ee8f943f44"}, {"line": 17098, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2730, "target": 761, "key": "8c16db2cd65307d388d9231c62d5c3ce"}, {"relation": "isA", "source": 2730, "target": 3547, "key": "0ccfab73e1bc5e5aad1d176134596bbc"}, {"line": 17102, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2731, "target": 761, "key": "23da8f79f87f0a820ec7706eb69384a7"}, {"relation": "isA", "source": 2731, "target": 3547, "key": "885302b83c01f656bf017e87c7bd1787"}, {"line": 17106, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2735, "target": 761, "key": "5faabdcb1344b51e88d7eba886a150bc"}, {"relation": "isA", "source": 2735, "target": 3547, "key": "11071893914806b10b68217b8a79c9a7"}, {"line": 17110, "relation": "association", "evidence": "The regulation of synaptic glutamate receptor and GABA(A)R (gamma-aminobutyric acid subtype A receptor) levels is a key component of synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2736, "target": 761, "key": "3dbcd62fbaf964e027cbc043e2804df9"}, {"relation": "isA", "source": 2736, "target": 3547, "key": "192074ee467ead30b4f32993144c8ec8"}, {"line": 20155, "relation": "increases", "evidence": "Enhanced proteasome-dependent degradation of the CDK inhibitor p27(kip1) in immortalized lymphocytes from Alzheimer's dementia patients.", "citation": {"db": "PubMed", "db_id": "17448572"}, "annotations": {"Disease": {"dementia": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"Ubiquitin degradation subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 893, "target": 2494, "key": "23d5667d93718792e30628ad9fd131c2"}, {"line": 20156, "relation": "association", "evidence": "Enhanced proteasome-dependent degradation of the CDK inhibitor p27(kip1) in immortalized lymphocytes from Alzheimer's dementia patients.", "citation": {"db": "PubMed", "db_id": "17448572"}, "annotations": {"Disease": {"dementia": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}, "Subgraph": {"Ubiquitin degradation subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 893, "target": 2494, "key": "00d903b1a39c825d789ea3823e1570f3"}, {"line": 41104, "relation": "association", "evidence": "Using the cerebral cortex of WT and TLR4-knockout mice with and without chronic ethanol treatment, we demonstrate that ethanol induces poly-ubiquitinated proteins accumulation and promotes immunoproteasome activation by inducing the expression of beta2i, beta5i and PA28α, although it decreases the 20S constitutive proteasome subunits (α2, beta5).", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Species": {"10090": true}}, "source": 893, "target": 253, "key": "dc260c2b0c36d67ce89a9ce3af929dea"}, {"line": 17209, "relation": "association", "evidence": "In addition, zif268 alters the transcription of genes associated with GABA(A)R expression and trafficking, such as ubiquilin and gephyrin.", "citation": {"db": "PubMed", "db_id": "19909279"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2766, "target": 2658, "key": "cd710fabde79e50d2bcbf4b83c2df26d"}, {"line": 17225, "relation": "increases", "evidence": "Egr1 expression is induced following glatiramer acetate immunotherapy in rodent models of glaucoma and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Alzheimer Disease": true, "Glaucoma": true}, "Subgraph": {"GABA subgraph": true}, "Confidence": {"Medium": true}}, "source": 401, "target": 3630, "key": "a98297f981abf9e2243c9c5b4f3db137"}, {"line": 17236, "relation": "decreases", "evidence": "Immunization with glatiramer acetate (GA) alleviates the neuropathology associated with glaucoma and Alzheimer's disease (AD) in rodent models.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Confidence": {"High": true}}, "source": 401, "target": 3823, "key": "809bbc3a521d38bbc77b3d94cbe38fa6"}, {"line": 17237, "relation": "decreases", "evidence": "Immunization with glatiramer acetate (GA) alleviates the neuropathology associated with glaucoma and Alzheimer's disease (AD) in rodent models.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Confidence": {"High": true}}, "source": 401, "target": 3910, "key": "7aa401dca0e6a4c947d6fdc5cffc3707"}, {"line": 17248, "relation": "increases", "evidence": "In AD-Tg mice, a significant increase in hippocampal EGR1 protein levels was also found in response to GA immunization.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"GABA subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 401, "target": 2658, "key": "daa7e9ef4d5a3ef86e799169932f88bd"}, {"line": 17275, "relation": "association", "evidence": "Further, EGR1 levels were negatively correlated with hippocampal amyloid-beta plaque burden.This study presents global gene expression profiles associated with GA immunization in a glaucoma rat model.", "citation": {"db": "PubMed", "db_id": "21969301"}, "annotations": {"Species": {"10116": true}, "Disease": {"glaucoma": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3910, "target": 2658, "key": "f999b6e0eb7fef605a4797fcc9a177cd"}, {"line": 17325, "relation": "increases", "evidence": "We show that Egr-1 binds to PSEN-P2, and that PSEN-P2 activity is increased threefold by overexpression of Egr-1, and by 12-O-tetradecanoylphorbol-13-acetate (TPA), which induces physiological Egr-1 levels.", "citation": {"db": "PubMed", "db_id": "14585504"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1408, "target": 3268, "key": "bac4ec0c9ead325d089b7326cf4bf69d"}, {"line": 17401, "relation": "decreases", "evidence": "In addition, by phosphorylating and inactivating PP1, Cdk5 promotes tau phosphorylation at Sep(262) indirectly.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3217, "target": 3216, "key": "5753c938fbc79d8591bc2e994e96a4ff"}, {"relation": "hasVariant", "source": 3216, "target": 3217, "key": "aeb37543d0b843e92e58d60977e529e5"}, {"line": 17402, "relation": "decreases", "evidence": "In addition, by phosphorylating and inactivating PP1, Cdk5 promotes tau phosphorylation at Sep(262) indirectly.", "citation": {"db": "PubMed", "db_id": "21489990"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3216, "target": 3023, "key": "304edd64e46032ad175f555c6302ba91"}, {"line": 42673, "relation": "positiveCorrelation", "evidence": "Middle cerebral artery occlusion (MCAO) induced development of ischemia, and ischemic neuronal cell death were reduced in IL-32α-overexpressing transgenic mice (IL-32α mice) brain through the decreased release of neuroinflammatory cytokines (IL-6, IL-1beta, TNF-α) and activation of astrocytes, but enhancement of anti- neuroinflammatory cytokines (IL-10).", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHDisease": {"Ischemia": true, "Infarction, Middle Cerebral Artery": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true, "Middle Cerebral Artery": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 3921, "target": 3860, "key": "9a573023a81f77e14fc95ee402714eae"}, {"line": 17446, "relation": "positiveCorrelation", "evidence": "Higher NK cytotoxicity (expressed as total lysis and percent increase) at different IL-2 concentrations (50 and 100 IU/ml/cells) was demonstrated in patients with SDAT than in healthy elderly subjects (p < 0.001) and MID patients (p < 0.001).", "citation": {"db": "PubMed", "db_id": "8915041"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2888, "target": 625, "key": "b0f80daf2771609904d91e664823e691"}, {"line": 17453, "relation": "positiveCorrelation", "evidence": "Alterations of IL-2-mediated NK cytotoxicity may therefore support the neuroimmune hypothesis of AD.", "citation": {"db": "PubMed", "db_id": "8915041"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2888, "target": 625, "key": "74209245e94810d8fe7abcbf55f32b31"}, {"line": 17454, "relation": "association", "evidence": "Alterations of IL-2-mediated NK cytotoxicity may therefore support the neuroimmune hypothesis of AD.", "citation": {"db": "PubMed", "db_id": "8915041"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2888, "target": 3823, "key": "f75561a5b177a0840f7a301219f9d6d7"}, {"line": 17507, "relation": "association", "evidence": "Sera from patients with Alzheimer disease and non-demented elderly subjects caused an increase in IL-2 and a decrease in IL-10 production by PBMC from middle-aged control subjects but did not affect IL-1beta, IL-6, and TNFalpha secretion, indicating alterations of the immune system related to aging.", "citation": {"db": "PubMed", "db_id": "12218646"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Species": {"9606": true}}, "source": 2888, "target": 3823, "key": "0f4c67604203fbf2b73bd39f7e650cbd"}, {"relation": "partOf", "source": 2888, "target": 1480, "key": "c586ac0148f6236d9ba8b7d7e1b31f96"}, {"line": 17446, "relation": "positiveCorrelation", "evidence": "Higher NK cytotoxicity (expressed as total lysis and percent increase) at different IL-2 concentrations (50 and 100 IU/ml/cells) was demonstrated in patients with SDAT than in healthy elderly subjects (p < 0.001) and MID patients (p < 0.001).", "citation": {"db": "PubMed", "db_id": "8915041"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 625, "target": 2888, "key": "ab5ca1e063b91ced2815bd9a2ed5aa62"}, {"line": 17453, "relation": "positiveCorrelation", "evidence": "Alterations of IL-2-mediated NK cytotoxicity may therefore support the neuroimmune hypothesis of AD.", "citation": {"db": "PubMed", "db_id": "8915041"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 625, "target": 2888, "key": "cae9d31fa25088b362672a1d38b74867"}, {"line": 17464, "relation": "association", "evidence": "Physiologic modulation of natural killer cell activity as an index of Alzheimer's disease progression.", "citation": {"db": "PubMed", "db_id": "17597922"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 624, "target": 3823, "key": "c251140a80b0a54c1112b6eb76560a67"}, {"line": 43159, "relation": "association", "evidence": "CXCR3 and its ligands are important for the trafficking of activated CD4(+) T(H)1 T cells, CD8(+) T cells, and natural killer cells during inflammation.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Central Nervous System": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 624, "target": 3621, "key": "1d0ad0933f6a724bc871b088c8231abb"}, {"line": 17566, "relation": "association", "evidence": "The Multi Drug Resistance (ABCB1) gene, encoding for P-gp, is highly polymorphic and this may result in a changed function of P-gp and may possibly interfere with the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16999857"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}}, "source": 2232, "target": 3823, "key": "bc0711e3c999d5c690c23a71c34a6bf4"}, {"line": 17631, "relation": "association", "evidence": "The ABCB1 gene, coding for the efflux transporter P-glycoprotein (PGP), is a candidate gene for Alzheimer disease (AD).", "citation": {"db": "PubMed", "db_id": "21478475"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}}, "source": 2232, "target": 3823, "key": "c949b3664a362efd8eb55016a36db108"}, {"line": 20730, "relation": "association", "evidence": "Recent studies have unraveled important roles of ABC transporters including ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistant protein, BCRP), ABCC1 (multidrug resistance protein 1, MRP1), and the cholesterol transporter ABCA1 in the pathogenesis of AD and Abeta peptides deposition inside the brain.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 2232, "target": 3823, "key": "9bdfd2781df89f30ef88e2b43f7ad1b4"}, {"line": 20751, "relation": "association", "evidence": "These findings support the validity of increasing Abeta clearance via ABCB1 up-regulation as a therapeutic approach to slowing or halting the progression of AD.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 2232, "target": 3823, "key": "f381dc34be8fe0e14f5c3d0c214a23b4"}, {"line": 17574, "relation": "association", "evidence": "ABCB1 genotypes are presently not useful as a biomarker for dementia, as they were not significantly different between demented patients and age-matched control subjects.", "citation": {"db": "PubMed", "db_id": "16999857"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true}, "Disease": {"dementia": true}, "Species": {"9606": true}}, "source": 2232, "target": 3901, "key": "6846505ad3efa1b3f44f9d6f6915ec62"}, {"line": 17575, "relation": "association", "evidence": "ABCB1 genotypes are presently not useful as a biomarker for dementia, as they were not significantly different between demented patients and age-matched control subjects.", "citation": {"db": "PubMed", "db_id": "16999857"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true}, "Disease": {"dementia": true}, "Species": {"9606": true}}, "source": 2232, "target": 806, "key": "542bab0f04cfc0051342957d0532f9e5"}, {"relation": "hasVariant", "source": 2232, "target": 2233, "key": "58a265787dab7792526fe67c5037e9d2"}, {"relation": "hasVariant", "source": 2232, "target": 2235, "key": "51a58b7d1f6252ac026a9f9d05b705d1"}, {"relation": "hasVariant", "source": 2232, "target": 2234, "key": "f28564e8810ad418f4ffa2b6bb847630"}, {"line": 17652, "relation": "association", "evidence": "Herein, we describe the discovery of a novel class of BACE-1 inhibitors represented by sulfamide 14g, using a medicinal chemistry strategy to optimize central nervous system (CNS) penetration by minimizing hydrogen bond donors (HBDs) and reducing P-glycoprotein (P-gp) mediated efflux.", "citation": {"db": "PubMed", "db_id": "22984865"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "ATP binding cassette transport subgraph": true}, "MeSHAnatomy": {"Central Nervous System": true}}, "source": 2232, "target": 570, "key": "fec89e4422a51630f0304722bc56c790"}, {"line": 17666, "relation": "negativeCorrelation", "evidence": "Up-regulation of P-glycoprotein reduces intracellular accumulation of beta amyloid: investigation of P-glycoprotein as a novel therapeutic target for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2232, "target": 2328, "key": "12193c57173ecfa52685917d5ffff813"}, {"line": 17687, "relation": "negativeCorrelation", "evidence": "Also, fluorescent micrographs showed an inverse relationship between levels of P-gp expression and 5-carboxyfluorescein labelled Abeta (FAM-Abeta42) intracellular accumulation.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2232, "target": 2328, "key": "5de3c46037c84b649006618eba6fa0e2"}, {"line": 20752, "relation": "decreases", "evidence": "These findings support the validity of increasing Abeta clearance via ABCB1 up-regulation as a therapeutic approach to slowing or halting the progression of AD.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 2232, "target": 80, "key": "f92054adb70b96fe0f448d54ec042e15"}, {"relation": "partOf", "source": 2232, "target": 1035, "key": "efbdba233aed79cc0a8cca4f05671634"}, {"relation": "partOf", "source": 2232, "target": 1037, "key": "f0d7d8ec362fe68bb8438d9a0b3fc3e3"}, {"relation": "partOf", "source": 2232, "target": 1036, "key": "8ead941b6b9f0a08b45bdda3383d566b"}, {"line": 17588, "relation": "association", "evidence": "P-glycoprotein is a blood-brain barrier efflux transporter involved in the clearance of amyloid-beta from the brain and, as such, might be involved in the pathogenesis of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Blood-Brain Barrier": true}, "Confidence": {"High": true}}, "source": 570, "target": 3823, "key": "8b00a2780ab7f515fdacc7e71ec80825"}, {"line": 17602, "relation": "association", "evidence": "In contrast, patients with Alzheimer's disease with one or more T in C1236T, G2677T and C3435T had significantly higher binding potential values than patients without a T. In addition, there was a relationship between binding potential and T dose in C1236T and G2677T.In Alzheimer's disease patients, C1236T, G2677T/A and C3435T single-nucleotide polymorphisms may be related to changes in P-glycoprotein function at the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Blood-Brain Barrier": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 570, "target": 2233, "key": "0ce31b2e9b3f41213582f5476411c95d"}, {"line": 17603, "relation": "association", "evidence": "In contrast, patients with Alzheimer's disease with one or more T in C1236T, G2677T and C3435T had significantly higher binding potential values than patients without a T. In addition, there was a relationship between binding potential and T dose in C1236T and G2677T.In Alzheimer's disease patients, C1236T, G2677T/A and C3435T single-nucleotide polymorphisms may be related to changes in P-glycoprotein function at the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Blood-Brain Barrier": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 570, "target": 2235, "key": "a603ae7ca13a073256e94215667deb15"}, {"line": 17604, "relation": "association", "evidence": "In contrast, patients with Alzheimer's disease with one or more T in C1236T, G2677T and C3435T had significantly higher binding potential values than patients without a T. In addition, there was a relationship between binding potential and T dose in C1236T and G2677T.In Alzheimer's disease patients, C1236T, G2677T/A and C3435T single-nucleotide polymorphisms may be related to changes in P-glycoprotein function at the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Blood-Brain Barrier": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 570, "target": 2234, "key": "d9f3a178eae752607f7c4cb7792ce1fc"}, {"line": 17652, "relation": "association", "evidence": "Herein, we describe the discovery of a novel class of BACE-1 inhibitors represented by sulfamide 14g, using a medicinal chemistry strategy to optimize central nervous system (CNS) penetration by minimizing hydrogen bond donors (HBDs) and reducing P-glycoprotein (P-gp) mediated efflux.", "citation": {"db": "PubMed", "db_id": "22984865"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "ATP binding cassette transport subgraph": true}, "MeSHAnatomy": {"Central Nervous System": true}}, "source": 570, "target": 2232, "key": "ba58afafc5dab25d5071b44b2f0ad979"}, {"line": 17602, "relation": "association", "evidence": "In contrast, patients with Alzheimer's disease with one or more T in C1236T, G2677T and C3435T had significantly higher binding potential values than patients without a T. In addition, there was a relationship between binding potential and T dose in C1236T and G2677T.In Alzheimer's disease patients, C1236T, G2677T/A and C3435T single-nucleotide polymorphisms may be related to changes in P-glycoprotein function at the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Blood-Brain Barrier": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 2233, "target": 570, "key": "484d94f53ee8bfe37ba76384fc52bf08"}, {"line": 17616, "relation": "increases", "evidence": "As such, genetic variations in ABCB1 might contribute to the progression of amyloid-beta deposition in the brain.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 2233, "target": 80, "key": "7210f49d91383be4599e5fcd505ae205"}, {"line": 17603, "relation": "association", "evidence": "In contrast, patients with Alzheimer's disease with one or more T in C1236T, G2677T and C3435T had significantly higher binding potential values than patients without a T. In addition, there was a relationship between binding potential and T dose in C1236T and G2677T.In Alzheimer's disease patients, C1236T, G2677T/A and C3435T single-nucleotide polymorphisms may be related to changes in P-glycoprotein function at the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Blood-Brain Barrier": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 2235, "target": 570, "key": "3ea807bf05abd1c461f692fc6e6854db"}, {"line": 17617, "relation": "increases", "evidence": "As such, genetic variations in ABCB1 might contribute to the progression of amyloid-beta deposition in the brain.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 2235, "target": 80, "key": "1e391c326bf8f2b591c86d4d1d17267b"}, {"line": 17604, "relation": "association", "evidence": "In contrast, patients with Alzheimer's disease with one or more T in C1236T, G2677T and C3435T had significantly higher binding potential values than patients without a T. In addition, there was a relationship between binding potential and T dose in C1236T and G2677T.In Alzheimer's disease patients, C1236T, G2677T/A and C3435T single-nucleotide polymorphisms may be related to changes in P-glycoprotein function at the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Blood-Brain Barrier": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 2234, "target": 570, "key": "9485ff748bdb16db5af74187f94fa72f"}, {"line": 17618, "relation": "increases", "evidence": "As such, genetic variations in ABCB1 might contribute to the progression of amyloid-beta deposition in the brain.", "citation": {"db": "PubMed", "db_id": "23067778"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "ATP binding cassette transport subgraph": true}, "Confidence": {"Medium": true}}, "source": 2234, "target": 80, "key": "034303e97129a84f509174db342e50b8"}, {"line": 17651, "relation": "decreases", "evidence": "Herein, we describe the discovery of a novel class of BACE-1 inhibitors represented by sulfamide 14g, using a medicinal chemistry strategy to optimize central nervous system (CNS) penetration by minimizing hydrogen bond donors (HBDs) and reducing P-glycoprotein (P-gp) mediated efflux.", "citation": {"db": "PubMed", "db_id": "22984865"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "ATP binding cassette transport subgraph": true}, "MeSHAnatomy": {"Central Nervous System": true}}, "source": 358, "target": 2232, "key": "a54988d77a07a47c0daf359569f6ebf7"}, {"line": 17653, "relation": "decreases", "evidence": "Herein, we describe the discovery of a novel class of BACE-1 inhibitors represented by sulfamide 14g, using a medicinal chemistry strategy to optimize central nervous system (CNS) penetration by minimizing hydrogen bond donors (HBDs) and reducing P-glycoprotein (P-gp) mediated efflux.", "citation": {"db": "PubMed", "db_id": "22984865"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "ATP binding cassette transport subgraph": true}, "MeSHAnatomy": {"Central Nervous System": true}}, "source": 358, "target": 2375, "key": "89ec29db55b9c21c056a21d61f2ef4a5"}, {"line": 17675, "relation": "decreases", "evidence": "In this study, we aimed to investigate the possibility of P-gp as a potential therapeutic target for Alzheimer's disease by examining the impact of P-gp up-regulation on the clearance of Abeta, a neuropathological hallmark of Alzheimer's disease.Uptake studies for-radiolabelled Abeta Approximately 10-35% decrease in Abeta intracellular accumulation was observed in cells treated with rifampicin, dexamethasone, caffeine, verapamil, hyperforin, beta-estradiol and pentylenetetrazole compared with control.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 342, "target": 2328, "key": "a63ff42ea47d16b86aff85fc5c40974f"}, {"line": 42208, "relation": "increases", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 342, "target": 3650, "key": "d3ba4d5c3aec5e2b254244d92fd2b9b7"}, {"line": 42212, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 342, "target": 3650, "key": "6582411b089d5fcb6c69c9cebdd42b46"}, {"line": 42241, "relation": "association", "evidence": "GRP78 functions cytoprotectively in stressed cells, therefore, we hypothesized that GRP78 mediated rifampicin-induced neuroprotection.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 342, "target": 3650, "key": "eea7916d9b7c2a972b959baf7a4f3687"}, {"line": 42273, "relation": "increases", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 342, "target": 3650, "key": "d8ad6c213357b990374f319604bf61e0"}, {"line": 42278, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 342, "target": 3650, "key": "2d1a74058592c50aa0b825f1bdb01c6e"}, {"line": 42209, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 342, "target": 3590, "key": "115f80615a35735b23edd1d38cb3ceef"}, {"line": 42275, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against 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{"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 342, "target": 881, "key": "051acb681b9a77b5139b570db0711f52"}, {"line": 42211, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 342, "target": 3634, "key": "331ee0eaa821999ba93f90760118ed8d"}, {"line": 42277, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 342, "target": 3634, "key": "6c75f54a7fbcbcff4bc3373116bdcf94"}, {"line": 42225, "relation": "decreases", "evidence": "Rifampicin has been proposed as a therapeutic candidate for Parkinson's disease (PD).", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Disease": {"Parkinson's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "source": 342, "target": 3878, "key": "f22313f8c533ba49835abd7db50ab96c"}, {"line": 42231, "relation": "decreases", "evidence": "We previously showed that rifampicin was neuroprotective in PD models in vivo and in vitro.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Disease": {"Parkinson's disease": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "source": 342, "target": 3878, "key": "3cf58d17c4b0f341bf7f4aa1d322bf2e"}, {"line": 42274, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 342, "target": 346, "key": "59423adb281f85fd9b9a0b2672031d61"}, {"line": 17678, "relation": "decreases", "evidence": "In this study, we aimed to investigate the possibility of P-gp as a potential therapeutic target for Alzheimer's disease by examining the impact of P-gp up-regulation on the clearance of Abeta, a neuropathological hallmark of Alzheimer's disease.Uptake studies for-radiolabelled Abeta Approximately 10-35% decrease in Abeta intracellular accumulation was observed in cells treated with rifampicin, dexamethasone, caffeine, verapamil, hyperforin, beta-estradiol and pentylenetetrazole compared with control.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 367, "target": 2328, "key": "3297dfb90631a334af9a58eadd6a8def"}, {"line": 17679, "relation": "decreases", "evidence": "In this study, we aimed to investigate the possibility of P-gp as a potential therapeutic target for Alzheimer's disease by examining the impact of P-gp up-regulation on the clearance of Abeta, a neuropathological hallmark of Alzheimer's disease.Uptake studies for-radiolabelled Abeta Approximately 10-35% decrease in Abeta intracellular accumulation was observed in cells treated with rifampicin, dexamethasone, caffeine, verapamil, hyperforin, beta-estradiol and pentylenetetrazole compared with control.", "citation": {"db": "PubMed", "db_id": "21718295"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Disease": {"Alzheimer's disease": 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"relation": "decreases", "evidence": "Ability of carbazole salts, inhibitors of Alzheimer beta-amyloid fibril formation, to cross cellular membranes. Several classes of molecules have been reported to inhibit beta-amyloid fibril formation and among them carbazoles.", "citation": {"db": "PubMed", "db_id": "17291491"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 225, "target": 379, "key": "97c1cbc678305823d81ee4d81025c93b"}, {"line": 17705, "relation": "decreases", "evidence": "Ability of carbazole salts, inhibitors of Alzheimer beta-amyloid fibril formation, to cross cellular membranes. Several classes of molecules have been reported to inhibit beta-amyloid fibril formation and among them carbazoles.", "citation": {"db": "PubMed", "db_id": "17291491"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 225, "target": 80, "key": "4a7793b7fbb465feb0d5f542e2ba51e8"}, {"line": 17727, "relation": "association", "evidence": "The therapeutic potential of screening for markers of renin-angiotensin abnormality for the prediction of Alzheimer's disease is considered, as is the potential use of agents known to influence the renin-angiotensin system in the treatment or prevention of the disease.", "citation": {"db": "PubMed", "db_id": "15853619"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 2274, "target": 3823, "key": "99b93e3c916441b77964301730ddf8ec"}, {"line": 17764, "relation": "association", "evidence": "Here, we discuss the role of angiotensin II in cognitive impairment and AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2274, "target": 3823, "key": "f9487fae0391a415e98cfa6a8d962907"}, {"line": 17753, "relation": "association", "evidence": "Moreover, basic experiments suggest a role of brain angiotensin II in neural injury, neuroinflammation, and cognitive function and that RAS blockade attenuates cognitive impairment in rodent dementia models of AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}}, "source": 2274, "target": 3888, "key": "5ca3f4ab34ab4f00df8fad40e948be3a"}, {"line": 17754, "relation": "association", "evidence": "Moreover, basic experiments suggest a role of brain angiotensin II in neural injury, neuroinflammation, and cognitive function and that RAS blockade attenuates cognitive impairment in rodent dementia models of AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}}, "source": 2274, "target": 3920, "key": "02464dc34804c8b009e365e32191a133"}, {"line": 17755, "relation": "association", "evidence": "Moreover, basic experiments suggest a role of brain angiotensin II in neural injury, neuroinflammation, and cognitive function and that RAS blockade attenuates cognitive impairment in rodent dementia models of AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}}, "source": 2274, "target": 812, "key": "ebbd62abaa655b2f49750b0e3d728f8f"}, {"line": 17778, "relation": "association", "evidence": "The functional involvements of the cerebral angiotensin IV in what concerns its possible participation in the normal neurochemical processes of memory and in the neurodegenerative processes of Alzheimer disease will be exposed, together with the vasodilating effects of angiotensin (1-7) as counteracting factor for the constricting effects of angiotensin II.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 2274, "target": 820, "key": "22a40f33a579217790e2ec052f935587"}, {"relation": "hasVariant", "source": 2274, "target": 2275, "key": "97f74e55931cb71f3ee37ca7f2ff1ce9"}, {"line": 17787, "relation": "increases", "evidence": "The data concerning the bioactive fragments of angiotensin II will be accompanied by those regarding its implication in the cardiovascular modeling and the induction of oxidative stress, inflammation, atherogenesis, etc.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHDisease": {"Inflammation": true, "Atherosclerosis": true}}, "source": 2274, "target": 81, "key": "73f668f8f80d2635272889c57bef530f"}, {"line": 17808, "relation": "increases", "evidence": "Angiotensin as a target for the treatment of Alzheimer's disease, anxiety and depression.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 2274, "target": 210, "key": "011f4c3ca173a285634b4a5a7c057b0f"}, {"line": 17728, "relation": "association", "evidence": "The therapeutic potential of screening for markers of renin-angiotensin abnormality for the prediction of Alzheimer's disease is considered, as is the potential use of agents known to influence the renin-angiotensin system in the treatment or prevention of the disease.", "citation": {"db": "PubMed", "db_id": "15853619"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 210, "target": 3823, "key": "8b8fd7122c945e9e7edefcdbaca0a91f"}, {"line": 17809, "relation": "association", "evidence": "Angiotensin as a target for the treatment of Alzheimer's disease, anxiety and depression.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 210, "target": 3823, "key": "dac7829b2584b5266b56df7728110da2"}, {"line": 17810, "relation": "association", "evidence": "Angiotensin as a target for the treatment of Alzheimer's disease, anxiety and depression.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Renin-angiotensin 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"Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 844, "target": 3930, "key": "8d459eea0f70e225534b012d05b9e61c"}, {"line": 17742, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS) has been highlighted as having a pathological role in stroke, dementia, and neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Stroke": true, "Dementia": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 844, "target": 3901, "key": "53ef6e3c43e997b64f9906f7db0e6679"}, {"line": 17743, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS) has been highlighted as having a pathological role in stroke, dementia, and neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Stroke": true, "Dementia": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 844, "target": 3874, "key": "af6a597f5b1aa00c9e82d4f1b4c60ac1"}, {"line": 17818, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS), which is comprised of a variety of peptides including angiotensin II, angiotensin III and angiotensin IV acting on AT1, AT2 and AT4 receptors, is important in cognition and anxiety.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 844, "target": 81, "key": "f06cf5cfaf1cae4a48f2808d219dbdc7"}, {"line": 17819, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS), which is comprised of a variety of peptides including angiotensin II, angiotensin III and angiotensin IV acting on AT1, AT2 and AT4 receptors, is important in cognition and anxiety.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 844, "target": 32, "key": "f4c13cde8b3b124a23989a964c613ea7"}, {"line": 17820, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS), which is comprised of a variety of peptides including angiotensin II, angiotensin III and angiotensin IV acting on AT1, AT2 and AT4 receptors, is important in cognition and anxiety.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 844, "target": 812, "key": "dc178c6321130fd27c67734dc2895e81"}, {"line": 17826, "relation": "negativeCorrelation", "evidence": "Perturbation of the RAS improves basal cognition and reverses age-, scopolamine-, ethanol- and diabetes-induced deficits.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 844, "target": 812, "key": "9b755e9658a54c4b0666607fdc2241ee"}, {"line": 17740, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS) has been highlighted as having a pathological role in stroke, dementia, and neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Stroke": true, "Dementia": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 3307, "target": 844, "key": "f165fde7279afe95b6ca3414a2052010"}, {"line": 17763, "relation": "association", "evidence": "Here, we discuss the role of angiotensin II in cognitive impairment and AD.", "citation": {"db": "PubMed", "db_id": "23304450"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 81, "target": 3823, "key": "48298c897f0a783ed280ad97b0e6acc2"}, {"line": 17788, "relation": "association", "evidence": "The data concerning the bioactive fragments of angiotensin II will be accompanied by those regarding its implication in the cardiovascular modeling and the induction of oxidative stress, inflammation, atherogenesis, etc.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHDisease": {"Inflammation": true, "Atherosclerosis": true}}, "source": 81, "target": 3920, "key": "0dbb725412efdcc8d3e7c8da6f6a8e06"}, {"line": 17789, "relation": "association", "evidence": "The data concerning the bioactive fragments of angiotensin II will be accompanied by those regarding its implication in the cardiovascular modeling and the induction of oxidative stress, inflammation, atherogenesis, etc.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHDisease": {"Inflammation": true, "Atherosclerosis": true}}, "source": 81, "target": 3895, "key": "e4a85619cf35b8cfa3b302d497b7362c"}, {"line": 17790, "relation": "association", "evidence": "The data concerning the bioactive fragments of angiotensin II will be accompanied by those regarding its implication in the cardiovascular modeling and the induction of oxidative stress, inflammation, atherogenesis, etc.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHDisease": {"Inflammation": true, "Atherosclerosis": true}}, "source": 81, "target": 842, "key": "0b7e1e58ac9ee7b66e252fef67131c7e"}, {"line": 17797, "relation": "association", "evidence": "Biphasic, dose-dependent effects were observed for angiotensin (1-7), induced both through nitric oxide, kinins and prostaglandin release for counteracting the vasoconstricting effects of angiotensin II and the modulation of its own vasodilator action.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 81, "target": 824, "key": "5858f31a6558476d89355ee31d89c029"}, {"line": 17818, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS), which is comprised of a variety of peptides including angiotensin II, angiotensin III and angiotensin IV acting on AT1, AT2 and AT4 receptors, is important in cognition and anxiety.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 81, "target": 844, "key": "c802545e41223de91f8b443deba5cdc5"}, {"line": 17779, "relation": "association", "evidence": "The functional involvements of the cerebral angiotensin IV in what concerns its possible participation in the normal neurochemical processes of memory and in the neurodegenerative processes of Alzheimer disease will be exposed, together with the vasodilating effects of angiotensin (1-7) as counteracting factor for the constricting effects of angiotensin II.", "citation": {"db": "PubMed", "db_id": "15529593"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Renin-angiotensin subgraph": true}}, "source": 2275, "target": 825, "key": "efd37f908d9b0124d37cf8509977f189"}, {"line": 17819, "relation": "association", "evidence": "The brain renin-angiotensin system (RAS), which is comprised of a variety of peptides including angiotensin II, angiotensin III and angiotensin IV acting on AT1, AT2 and AT4 receptors, is important in cognition and anxiety.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 32, "target": 844, "key": "d3d0c01f08040507d0167e1e40e2f870"}, {"line": 17832, "relation": "association", "evidence": "In studies of dementias and Alzheimer's disease (AD), some studies have shown that antihypertensive drugs, including angiotensin-converting enzyme inhibitors, have some moderate effects on cognitive decline, but that the angiotensin receptor antagonist losartan has a significantly beneficial effect.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 2276, "target": 296, "key": "cf9efa00ef43c41d40e5c62ce35714d2"}, {"line": 17832, "relation": "association", "evidence": "In studies of dementias and Alzheimer's disease (AD), some studies have shown that antihypertensive drugs, including angiotensin-converting enzyme inhibitors, have some moderate effects on cognitive decline, but that the angiotensin receptor antagonist losartan has a significantly beneficial effect.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 296, "target": 2276, "key": "a99a0afe7d7168e7a5644ca06b892497"}, {"line": 17834, "relation": "isA", "evidence": "In studies of dementias and Alzheimer's disease (AD), some studies have shown that antihypertensive drugs, including angiotensin-converting enzyme inhibitors, have some moderate effects on cognitive decline, but that the angiotensin receptor antagonist losartan has a significantly beneficial effect.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 296, "target": 82, "key": "54af7dc4ff6e2c67d95e804fa0affcd2"}, {"line": 17835, "relation": "increases", "evidence": "In studies of dementias and Alzheimer's disease (AD), some studies have shown that antihypertensive drugs, including angiotensin-converting enzyme inhibitors, have some moderate effects on cognitive decline, but that the angiotensin receptor antagonist losartan has a significantly beneficial effect.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 296, "target": 812, "key": "5d9f82043dea319a770d09ec217a610b"}, {"line": 17833, "relation": "increases", "evidence": "In studies of dementias and Alzheimer's disease (AD), some studies have shown that antihypertensive drugs, including angiotensin-converting enzyme inhibitors, have some moderate effects on cognitive decline, but that the angiotensin receptor antagonist losartan has a significantly beneficial effect.", "citation": {"db": "PubMed", "db_id": "14996614"}, "annotations": {"Subgraph": {"Renin-angiotensin subgraph": true}, "Disease": {"dementia": true, "Alzheimer's disease": true}}, "source": 42, "target": 812, "key": "10fa0bedf48301a09dc1caecb6dcc39e"}, {"line": 17855, "relation": "positiveCorrelation", "evidence": "Here, we show that CCL4 mRNA and protein are overexpressed in the brains of APPswe/PS1ΔE9 (APP/PS1) double-transgenic mice, a model of cerebral amyloid deposition; expression was minimal in brains from nontransgenic littermates or single-mutant controls.", "citation": {"db": "PubMed", "db_id": "24607962"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Cerebrum": true}, "Species": {"10090": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2458, "target": 2328, "key": "a4266048efa33af28fe5a1ef0fa446af"}, {"line": 17868, "relation": "association", "evidence": "These observations prompt the testable hypothesis for future study that CCL4 overexpression, regulated in part by hypomethylation of the ATF3 gene, may contribute to neuropathologic progression associated with amyloid deposition in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "24607962"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2458, "target": 3823, "key": "e2aa78355a5855422e661d0da63a389e"}, {"line": 17862, "relation": "association", "evidence": "Results from chromatin immunoprecipitation-quantitative polymerase chain reaction confirmed that ATF3 binds to the promoter region of the CCL4 gene, consistent with a potential role in regulating CCL4 transcription.", "citation": {"db": "PubMed", "db_id": "24607962"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"CREB subgraph": true, "Chemokine signaling subgraph": true}}, "source": 1009, "target": 1766, "key": "6bd3fbc4320d733a3710c993204f5fb3"}, {"relation": "partOf", "source": 1766, "target": 1009, "key": "73eee98c50524a380bed440da69d8ff7"}, {"line": 17862, "relation": "association", "evidence": "Results from chromatin immunoprecipitation-quantitative polymerase chain reaction confirmed that ATF3 binds to the promoter region of the CCL4 gene, consistent with a potential role in regulating CCL4 transcription.", "citation": {"db": "PubMed", "db_id": "24607962"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"CREB subgraph": true, "Chemokine signaling subgraph": true}}, "source": 1766, "target": 1009, "key": "f20dc67f1c1d8afb41318ad38d54dbd7"}, {"relation": "partOf", "source": 2365, "target": 1009, "key": "62acb07c084e6ef272e22e6e8ba29a8f"}, {"line": 17957, "relation": "association", "evidence": "In the chronic phase, concurrent activation of WOX1, CREB, and NF-kappaB occurs in small neurons just prior to apoptotic process.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3553, "target": 505, "key": "303632eb11a06009fff324cde10a93e5"}, {"line": 40305, "relation": "association", "evidence": "These results indicated that cultured human astrocytes express a distinct set of NF-kB-target cytokines and chemokines in resting and activated conditions, suggesting that the NF-kB signaling pathway differentially regulates gene expression of cytokines and chemokines in human astrocytes under physiological and inflammatory conditions.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Cytokine signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3553, "target": 420, "key": "b4007476a8d3691682e5b9881e18817a"}, {"line": 40311, "relation": "association", "evidence": "These results indicated that cultured human astrocytes express a distinct set of NF-kB-target cytokines and chemokines in resting and activated conditions, suggesting that the NF-kB signaling pathway differentially regulates gene expression of cytokines and chemokines in human astrocytes under physiological and inflammatory conditions.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3553, "target": 419, "key": "2e65f459dc312bc8ab9cd722bb1e160f"}, {"line": 17937, "relation": "association", "evidence": "WOX1 physically interacted with CREB most strongly in the nuclei as determined by FRET analysis.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "source": 3536, "target": 2162, "key": "7456e555056447c03344576f1c1d9f48"}, {"line": 17976, "relation": "decreases", "evidence": "Likely in vivo interactions are: 1) WOX1 inhibits the neuroprotective CREB, which leads to eventual neuronal death, and 2) WOX1 enhances NF-kappaB promoter activation (which turns to be proapoptotic).", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "source": 3536, "target": 2162, "key": "30eb3a12c9ce6d71a6db95c28708aafa"}, {"relation": "partOf", "source": 3536, "target": 1017, "key": "1a627ad7c58ac8a33488a42f4789e68e"}, {"line": 17958, "relation": "association", "evidence": "In the chronic phase, concurrent activation of WOX1, CREB, and NF-kappaB occurs in small neurons just prior to apoptotic process.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3536, "target": 505, "key": "b62721108fed51fd1ed83b2dd1d33f1a"}, {"line": 17977, "relation": "increases", "evidence": "Likely in vivo interactions are: 1) WOX1 inhibits the neuroprotective CREB, which leads to eventual neuronal death, and 2) WOX1 enhances NF-kappaB promoter activation (which turns to be proapoptotic).", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "source": 3536, "target": 505, "key": "19fc5353d284048166e3c6f6f59e2d56"}, {"line": 17985, "relation": "increases", "evidence": "Likely in vivo interactions are: 1) WOX1 inhibits the neuroprotective CREB, which leads to eventual neuronal death, and 2) WOX1 enhances NF-kappaB promoter activation (which turns to be proapoptotic).", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3536, "target": 2016, "key": "4c7b3df9f21efda99e84123596143dc6"}, {"line": 17994, "relation": "association", "evidence": "Evidently, WOX1 is the potential target for drug intervention in mitigating symptoms associated with neuronal injury.", "citation": {"db": "PubMed", "db_id": "19918364"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "MeSHDisease": {"Wounds and Injuries": true}, "Confidence": {"High": true}}, "source": 3536, "target": 3888, "key": "3be3af44d2bfce68fd324c42e56a0112"}, {"line": 18038, "relation": "association", "evidence": "Increased oxidative stress is associated with neuronal cell death during the pathogenesis of multiple chronic neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "19076431"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Neurons": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Huntington Disease": true, "Parkinson Disease": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3825, "target": 505, "key": "addc0e3fca1ea70dea0c6efafb3803cf"}, {"line": 18086, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 3825, "target": 3110, "key": "ad170ce41877d0ea76ff1a9a673cd6f8"}, {"line": 18087, "relation": "negativeCorrelation", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "object": {"modifier": "Activity"}, "source": 3825, "target": 3110, "key": "058d3f50ae14f1fe8070242b93a4c48f"}, {"line": 39946, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3825, "target": 3110, "key": "861241162a319ae38553549d8dd885dd"}, {"line": 21254, "relation": "positiveCorrelation", "evidence": "On the other hand, an overproduction of NO is related with several disorders as Alzheimer's disease, Huntington's disease and the amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Disease": {"Huntington's disease": true, "Alzheimer's disease": true, "amyotrophic lateral sclerosis": true}, "Confidence": {"High": true}}, "source": 3825, "target": 156, "key": "1f4e0b9212d0a3b0e22ab61b050e9464"}, {"line": 22999, "relation": "positiveCorrelation", "evidence": "Distinct from WT SOD1, mutant SOD1 induces morphological change and cytochrome c release in cultured neurons that resulted in apoptotic process. Two transgenic studies further indicated the involvement of mitochondria-mediated apoptotic process in mutant SOD1-linked ALS.", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 3825, "target": 3720, "key": "4cdf98992b18c5777adbef90de8ce51b"}, {"line": 23011, "relation": "association", "evidence": "Inoue et al. [37] demonstrated that suppressing caspase-9 by overexpressing XIAP in motor neurons effectively slowed the progression of ALS in G93A SOD1 Tg mice, while Reyes et al. documented that neuron-specific deletion of BCL-associated X protein (BAX) or BCL2-homologous antagonist/killer (BAK), which are both proapoptotic BCL-2 family proteins, delayed the onset and extended the longevity of disease in the same mice [38].", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "MeSHAnatomy": {"Motor Neurons": true}}, "source": 3825, "target": 3601, "key": "761b432a634290ab7e93deb3d8073a43"}, {"line": 23029, "relation": "positiveCorrelation", "evidence": "To examine possible caspase-9-activation in human sporadic ALS, we performed immunohistochemistry using anti-active caspase-9 antibody on post mortem human samples. Four of the eight ALS spinal cords showed obvious caspase-9 activation in the motor neurons studied, but this was not seen in any of the controls (Figure 5A); this suggests that caspase-9 may play an instrumental role in some forms of human sporadic ALS", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 3825, "target": 2449, "key": "649a750f2cd98532ea73666640617528"}, {"line": 23041, "relation": "negativeCorrelation", "evidence": "Hence, as XIAP levels decrease in spinal motor neurons of mutant SOD1 mice during disease progression (Ishigaki et al., 2002), caspase-9-initiated apoptosis may be promoted.", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"XIAP subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}}, "source": 3825, "target": 3753, "key": "d0943c9926a97de002d6fe66348a074a"}, {"line": 23195, "relation": "negativeCorrelation", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3825, "target": 3597, "key": "35c07338170278ecfa6220a44b00b6fa"}, {"line": 23196, "relation": "negativeCorrelation", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3825, "target": 3598, "key": "c36122c6120e4cce43564c7ad5661438"}, {"line": 23197, "relation": "positiveCorrelation", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3825, "target": 3596, "key": "5aa47962c55fdaaa8c70e50b1df580d0"}, {"line": 23198, "relation": "positiveCorrelation", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3825, "target": 3595, "key": "b58f0effa44c5d18f0f712a606c7d982"}, {"line": 23322, "relation": "positiveCorrelation", "evidence": "Another component of neuronal degeneration in many neurodegenerative disorders is excessive glutamate-induced stimulation of postsynaptic glutamate receptors. This activates massive calcium influxes that are potentially detrimental through calcium-activated processes and molecules (for example, proteases, nucleases and lipases). There is considerable evidence in support of this view, such as the observed threefold increase in glutamate levels in the cerebrospinal fluid of patients with ALS134, 135, 136 and the benefits in ALS of the anti-glutamate drug riluzole. EAATs are present at most synapses in the CNS, and transport glutamate from the synaptic space into astrocytes after glutamate release during neurotransmission137, 138, 139. ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 3825, "target": 57, "key": "a2a85b4638654acd710a5cebcb2b3c87"}, {"line": 29649, "relation": "association", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3825, "target": 2487, "key": "f4118b2203029d6e5dcd55ab19a56d61"}, {"line": 29651, "relation": "association", "evidence": "Both the hyperactivation of Cdk5 activity and subsequent hyperphosphorylation of neurofilaments and the microtubule-associated protein tau have been implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "14673212"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 3825, "target": 3015, "key": "03b48241982130dae1ec11cfff3a5161"}, {"line": 41981, "relation": "association", "evidence": "Cannabinoid receptor subtype 2 (CB2) has been shown to be up-regulated in activated microglia and therefore plays an important role in neuroinflammatory and neurodegenerative diseases such as multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neurodegenerative Diseases": true, "Amyotrophic Lateral Sclerosis": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3825, "target": 3613, "key": "bd12bddd34ed96397af5af5044fcad5d"}, {"line": 18070, "relation": "association", "evidence": "Acute oxidative stress to the brain, such as stroke and traumatic brain injury is increased in animals that are deficient in NRF2.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Stroke": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3830, "key": "a1625cccee299ab60ccb2eb272729c1e"}, {"line": 18071, "relation": "negativeCorrelation", "evidence": "Acute oxidative stress to the brain, such as stroke and traumatic brain injury is increased in animals that are deficient in NRF2.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Stroke": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3830, "key": "abf5bfcd04b23d9ef1d78ad22edf49c0"}, {"line": 18072, "relation": "association", "evidence": "Acute oxidative stress to the brain, such as stroke and traumatic brain injury is increased in animals that are deficient in NRF2.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Stroke": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3930, "key": "fe378e35defab0d8aa8ace2732b4f4f7"}, {"line": 18073, "relation": "negativeCorrelation", "evidence": "Acute oxidative stress to the brain, such as stroke and traumatic brain injury is increased in animals that are deficient in NRF2.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Stroke": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3930, "key": "3e3ce3136720c120ed2ddacc5d193322"}, {"line": 18086, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "subject": {"modifier": "Activity"}, "source": 3110, "target": 3825, "key": "6f712a637be5ff064803de112e4d6029"}, {"line": 18087, "relation": "negativeCorrelation", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "subject": {"modifier": "Activity"}, "source": 3110, "target": 3825, "key": "ad4ebe115e64139f2e06ba1b4a5b0253"}, {"line": 39946, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3825, "key": "ed46d61c8e28e2c630b0a4afa2081996"}, {"line": 18088, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "subject": {"modifier": "Activity"}, "source": 3110, "target": 3823, "key": "cef858b121801576dd5533ab7a389bb3"}, {"line": 18089, "relation": "negativeCorrelation", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}}, "subject": {"modifier": "Activity"}, "source": 3110, "target": 3823, "key": "cdb19574d68daec3d8d7cbb1409fbec0"}, {"line": 18173, "relation": "association", "evidence": "Results warrant further exploration of the Nrf2-ARE pathway for treatment of AD and suggest that the Nrf2-ARE pathway may represent a potential therapeutic strategy to pursue in AD in humans, particularly in view of the multiple mechanisms by which Nrf2 can exert its protective effects.", "citation": {"db": "PubMed", "db_id": "19805328"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3823, "key": "607f72afa8243c02f313cfb74fb1f57a"}, {"line": 18192, "relation": "association", "evidence": "Nrf2-encoding NFE2L2 haplotypes influence disease progression but not risk in Alzheimer's disease and age-related cataract.", "citation": {"db": "PubMed", "db_id": "20064547"}, "annotations": {"MeSHDisease": {"Cataract": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3823, "key": "354943599e623be7c29e5a8cb49d68b5"}, {"line": 18217, "relation": "association", "evidence": "However, one haplotype allele of NFE2L2 was associated with 2 years earlier age at AD onset (p(c)=0.013)", "citation": {"db": "PubMed", "db_id": "20064547"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3823, "key": "9411b30f993a3f8c40284a4847d445bf"}, {"line": 39947, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Amyotrophic Lateral Sclerosis": true, "Parkinson Disease": true, "Alzheimer Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3110, "target": 3823, "key": "9d98f5713d9fe2761ac319321b88123f"}, {"line": 18090, "relation": "association", "evidence": "Insufficient NRF2 activation in humans has been linked to chronic diseases such as Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Free radical formation subgraph": true}, "Confidence": {"High": true}, "MeSHDisease": 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"Activity"}, "object": {"modifier": "Activity"}, "source": 3550, "target": 2690, "key": "5e60a5e8c7dc8ae7e09504b9f01b6fbe"}, {"line": 18346, "relation": "regulates", "evidence": "Metalloproteinase shedding of Fas ligand regulates beta-amyloid neurotoxicity.", "citation": {"db": "PubMed", "db_id": "12372252"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3550, "target": 2328, "key": "4bd7356914be8142bd721d3426ac17aa"}, {"line": 18356, "relation": "decreases", "evidence": "In contrast, enhanced FasL shedding, by recombinant MMP-7, completely protected neurons from Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "12372252"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3061, "target": 2328, "key": "2ed5da750670f4e4a260bb2647dd2470"}, {"line": 18481, "relation": "positiveCorrelation", "evidence": "A functional -463G/A MPO promoter polymorphism has been associated with AD risk through as yet unidentified mechanisms.", "citation": {"db": "PubMed", "db_id": "19059911"}, "source": 3067, "target": 3823, "key": "fe2d92741b7528ca610a87a5fcb92b24"}, {"line": 18624, "relation": "association", "evidence": "In conclusion, the G-463A polymorphism of MPO was statistically associated with AD in a gender-specific manner. However, given the low significance of P value we suggest no causal effect of the MPO gene in AD, as also evidenced in a recent meta-analysis.", "citation": {"db": "PubMed", "db_id": "12946561"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3067, "target": 3823, "key": "cfe397ecc83fbbb7bd2587651131a58e"}, {"line": 18691, "relation": "association", "evidence": "Also matrix metalloproteinase-3 (MMP-3) or stromelysin-1 contributes to several pathologies, such as cancer, asthma and rheumatoid arthritis, and has also been associated with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Arthritis, Rheumatoid": true, "Asthma": true, "Parkinson Disease": true, "Neoplasms": true, "Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true}}, "source": 3892, "target": 3060, "key": "576097a391d94a34ef5dc4ad8a2025de"}, {"line": 21861, "relation": "association", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 3892, "target": 289, "key": "fae206b455deab1097725b0735c46088"}, {"line": 42343, "relation": "association", "evidence": "Montelukast, known as a cysteinyl leukotriene receptor 1 (CysLT1R) antagonist, is currently used for treatment of inflammatory diseases such as asthma.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHDisease": {"Asthma": true}}, "source": 3892, "target": 307, "key": "e0c2f943d0e543b766ed2c60f0b66b42"}, {"line": 42345, "relation": "association", "evidence": "Montelukast, known as a cysteinyl leukotriene receptor 1 (CysLT1R) antagonist, is currently used for treatment of inflammatory diseases such as asthma.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHDisease": {"Asthma": true}}, "source": 3892, "target": 3623, "key": "17d83e9ca71ee4650d8682553c8436c7"}, {"line": 18707, "relation": "association", "evidence": "As such, MMP-3 is correlated with neuronal migration and neurite outgrowth and guidance in the developing CNS and contributes to synaptic plasticity and learning in the adult CNS.", "citation": {"db": "PubMed", "db_id": "22862420"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurites": true, "Central Nervous System": true}}, "source": 521, "target": 3060, "key": "2ccb038c18dd26d3b2c49f94c848473a"}, {"line": 18739, "relation": "increases", "evidence": "In neuronal cells, MMP-3 expression is increased in response to cell stress, and the cleaved, active MMP-3 participates in apoptotic signaling.", "citation": {"db": "PubMed", "db_id": "21044079"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 845, "target": 3060, "key": "48344acf5c1acdddfea97931103df2dd"}, {"line": 18956, "relation": "association", "evidence": "Although conventionally associated with fibrin clot degradation, recent work has uncovered new functions for the tissue plasminogen activator (tPA)/plasminogen cascade in central nervous system physiology and pathology. This extracellular proteolytic cascade has been shown to have roles in learning and memory, stress, neuronal degeneration, addiction and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15841309"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 845, "target": 3200, "key": "508f7cc163a6fd1afea53a86a158a358"}, {"line": 19770, "relation": "positiveCorrelation", "evidence": "Stress contributes to the development of central insulin resistance during aging: implications for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24090692"}, "annotations": {"MeSHDisease": {"Insulin Resistance": true}, "Disease": {"Alzheimer's disease": true}}, "source": 845, "target": 3861, "key": "2d7953146476256b59589af66ed33cfc"}, {"line": 19777, "relation": "association", "evidence": "It is becoming evident that chronic exposure to stress not only might result in insulin resistance or cognitive deficits, but may also be considered a risk factor for pathologies such as depression or Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24090692"}, "annotations": {"MeSHDisease": {"Insulin Resistance": true}, "Disease": {"Alzheimer's disease": true}}, "source": 845, "target": 3823, "key": "27225c250e0b5c4376e491cbfb11517e"}, {"line": 18815, "relation": "association", "evidence": "Herein, we conducted a meta-analysis to clarify the association between ESR1 polymorphisms and the occurrence of AD.", "citation": {"db": "PubMed", "db_id": "24857745"}, "annotations": {"Subgraph": {"Estrogen subgraph": true}}, "source": 2680, "target": 3823, "key": "1933d83edc725912599f79e0942ba26f"}, {"line": 49391, "relation": "association", "evidence": "Moreover, we have shown that vitamin D likely interacts with the estrogen receptor, Esr1, to regulate molecular pathways relevant to AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 2680, "target": 3823, "key": "0a663c0a9e2c5ccd9d78480f1ab74611"}, {"line": 18833, "relation": "association", "evidence": "Positive association between an estrogen receptor gene polymorphism and Parkinson's disease with dementia.", "citation": {"db": "PubMed", "db_id": "10362895"}, "annotations": {"Disease": {"dementia": true, "Parkinson's disease": true}, "Subgraph": {"Estrogen subgraph": true}, "Species": {"9606": true}}, "source": 2680, "target": 3878, "key": "032edf99313505aba1867a171e59cef8"}, {"relation": "partOf", "source": 2680, "target": 1128, "key": "3af9e79db4eca8acdb850fc6ee8bba81"}, {"line": 49390, "relation": "association", "evidence": "Moreover, we have shown that vitamin D likely interacts with the estrogen receptor, Esr1, to regulate molecular pathways relevant to AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 2680, "target": 187, "key": "04d84e7c21d3e357dc4def2d881df53c"}, {"line": 18873, "relation": "association", "evidence": "Tissue plasminogen activator arrests Alzheimer's disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "24126163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Plasminogen activator subgraph": true}, "Confidence": {"High": true}}, "source": 3200, "target": 3823, "key": "0d50cd2e3f7965d36aa45f2f8b668829"}, {"line": 18874, "relation": "decreases", "evidence": "Tissue plasminogen activator arrests Alzheimer's disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "24126163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Plasminogen activator subgraph": true}, "Confidence": {"High": true}}, "source": 3200, "target": 3823, "key": "e96b3d9ba6def5fe8072249d43b061be"}, {"line": 18936, "relation": "negativeCorrelation", "evidence": "These findings extend the existing hypotheses that low tPA activity promotes AD, whereas increased tPA activity contributes to cerebellar degeneration.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 3200, "target": 3823, "key": "ab8419187511561b91bc8bdc94eacceb"}, {"line": 18957, "relation": "association", "evidence": "Although conventionally associated with fibrin clot degradation, recent work has uncovered new functions for the tissue plasminogen activator (tPA)/plasminogen cascade in central nervous system physiology and pathology. This extracellular proteolytic cascade has been shown to have roles in learning and memory, stress, neuronal degeneration, addiction and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15841309"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3200, "target": 3823, "key": "128dae2ce4d3b9a9b0633412abd9c720"}, {"line": 18887, "relation": "association", "evidence": "Although the complete loss of tPA was developmentally fatal to Tg2576 mice, tPA-heterozygous Tg2576 mice expressed the more severe degenerative phenotypes than tPA wild-type Tg2576 mice, including abnormal and unhealthy growth, shorter life spans, significantly enhanced Abeta levels, and the deposition of more and larger amyloid plaques in the brain.", "citation": {"db": "PubMed", "db_id": "24126163"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3200, "target": 817, "key": "f5b8fb8c3ab1c7ed2b19cec8268a93b7"}, {"line": 18888, "relation": "negativeCorrelation", "evidence": "Although the complete loss of tPA was developmentally fatal to Tg2576 mice, tPA-heterozygous Tg2576 mice expressed the more severe degenerative phenotypes than tPA wild-type Tg2576 mice, including abnormal and unhealthy growth, shorter life spans, significantly enhanced Abeta levels, and the deposition of more and larger amyloid plaques in the brain.", "citation": {"db": "PubMed", "db_id": "24126163"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3200, "target": 2328, "key": "b440507a34d599933e41b9fd2bc81785"}, {"line": 18900, "relation": "increases", "evidence": "Thus, endogenous tPA, preferentially its aggregate form, could degrade Abeta molecules and maintain low levels of brain Abeta, resulting in the delay of AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "24126163"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3200, "target": 2328, "key": "e5ac16eb36b57af6099b7e205f91d559"}, {"line": 31363, "relation": "increases", "evidence": "Additionally, plasmin (activated by tPA) attenuates Abeta neurotoxicity by degrading the peptide and rendering it inactive.", "citation": {"db": "PubMed", "db_id": "14501010"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3200, "target": 2328, "key": "756cf9658e9abb54433f1d36958ec3e6"}, {"line": 18915, "relation": "association", "evidence": "In addition, we assessed changes in endogenous net tPA activity in WT mice following morphine administration, epileptic seizures, traumatic brain injury and ischaemic stroke-neurological settings in which tPA has a known functional role.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"MeSHDisease": {"Stroke": true, "Epilepsy": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3200, "target": 3829, "key": "de7c80381fdacf7bf60d50d4284b8b06"}, {"line": 18916, "relation": "association", "evidence": "In addition, we assessed changes in endogenous net tPA activity in WT mice following morphine administration, epileptic seizures, traumatic brain injury and ischaemic stroke-neurological settings in which tPA has a known functional role.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"MeSHDisease": {"Stroke": true, "Epilepsy": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3200, "target": 3908, "key": "848809e2f53a57c5c093ebce66b1ae70"}, {"line": 18917, "relation": "association", "evidence": "In addition, we assessed changes in endogenous net tPA activity in WT mice following morphine administration, epileptic seizures, traumatic brain injury and ischaemic stroke-neurological settings in which tPA has a known functional role.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"MeSHDisease": {"Stroke": true, "Epilepsy": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3200, "target": 308, "key": "7af023dd616a1c3bc93f18481a949ad8"}, {"line": 18918, "relation": "association", "evidence": "In addition, we assessed changes in endogenous net tPA activity in WT mice following morphine administration, epileptic seizures, traumatic brain injury and ischaemic stroke-neurological settings in which tPA has a known functional role.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"MeSHDisease": {"Stroke": true, "Epilepsy": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3200, "target": 3930, "key": "49ffcd948a482fb9edbcb148d1c78d4f"}, {"line": 18937, "relation": "positiveCorrelation", "evidence": "These findings extend the existing hypotheses that low tPA activity promotes AD, whereas increased tPA activity contributes to cerebellar degeneration.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 3200, "target": 3885, "key": "3e617857ae0fe9e6f750987ea77f14e0"}, {"line": 18943, "relation": "association", "evidence": "On the basis of this evidence, we propose that alterations in tPA activity levels could be used as a biomarker for perturbations in brain homeostasis.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3200, "target": 219, "key": "d1372c403aebb2e26221e8433b1002bd"}, {"line": 18954, "relation": "association", "evidence": "Although conventionally associated with fibrin clot degradation, recent work has uncovered new functions for the tissue plasminogen activator (tPA)/plasminogen cascade in central nervous system physiology and pathology. This extracellular proteolytic cascade has been shown to have roles in learning and memory, stress, neuronal degeneration, addiction and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15841309"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3200, "target": 818, "key": "2cb5eaa6615c7646e796100ed0e88ce9"}, {"line": 18955, "relation": "association", "evidence": "Although conventionally associated with fibrin clot degradation, recent work has uncovered new functions for the tissue plasminogen activator (tPA)/plasminogen cascade in central nervous system physiology and pathology. This extracellular proteolytic cascade has been shown to have roles in learning and memory, stress, neuronal degeneration, addiction and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15841309"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3200, "target": 820, "key": "b22178c25961a6a3cf5227dec5e22985"}, {"line": 18956, "relation": "association", "evidence": "Although conventionally associated with fibrin clot degradation, recent work has uncovered new functions for the tissue plasminogen activator (tPA)/plasminogen cascade in central nervous system physiology and pathology. This extracellular proteolytic cascade has been shown to have roles in learning and memory, stress, neuronal degeneration, addiction and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "15841309"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3200, "target": 845, "key": "7c723aee3231f41bb4c145e0eb7fa3f1"}, {"line": 18980, "relation": "increases", "evidence": "Subsequent studies showed that it was plasmin, the product of tPA activation of plasminogen, that specifically cleaved A beta 1-40 in the amino terminal region between Arg5 and His6.", "citation": {"db": "PubMed", "db_id": "10471309"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3200, "target": 3205, "key": "7752b584b344474711d5b4f9f17e3322"}, {"line": 18982, "relation": "increases", "evidence": "Subsequent studies showed that it was plasmin, the product of tPA activation of plasminogen, that specifically cleaved A beta 1-40 in the amino terminal region between Arg5 and His6.", "citation": {"db": "PubMed", "db_id": "10471309"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3200, "target": 2327, "key": "89b9761b1df06df29d5c2554432c8544"}, {"relation": "partOf", "source": 3200, "target": 1241, "key": "d70710291647c3c2b6c9f83cd9e7ec46"}, {"relation": "partOf", "source": 3200, "target": 1608, "key": "33fe790921407c7046288a0600c57d9b"}, {"relation": "partOf", "source": 3200, "target": 1240, "key": "b34c9b668a26f64fe197fc7e6f73eb09"}, {"relation": "partOf", "source": 3200, "target": 1607, "key": "bc3c1cd59bbe938386f5af6a9eb178db"}, {"line": 18887, "relation": "association", "evidence": "Although the complete loss of tPA was developmentally fatal to Tg2576 mice, tPA-heterozygous Tg2576 mice expressed the more severe degenerative phenotypes than tPA wild-type Tg2576 mice, including abnormal and unhealthy growth, shorter life spans, significantly enhanced Abeta levels, and the deposition of more and larger amyloid plaques in the brain.", "citation": {"db": "PubMed", "db_id": "24126163"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 817, "target": 3200, "key": "4ade821fdfb667630dbe56b5e3c52b79"}, {"line": 18915, "relation": "association", "evidence": "In addition, we assessed changes in endogenous net tPA activity in WT mice following morphine administration, epileptic seizures, traumatic brain injury and ischaemic stroke-neurological settings in which tPA has a known functional role.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"MeSHDisease": {"Stroke": true, "Epilepsy": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3829, "target": 3200, "key": "4650af23b980bfcc04f1615131c98524"}, {"line": 20820, "relation": "association", "evidence": "Qualitative and quantitative changes in the expressions of uPAR and of its canonical ligand uPA have been observed in a large variety of epileptic disorders, either in human or in animal models, as well as in other brain diseases (stroke and brain trauma, multiple sclerosis, Alzheimer's disease, cerebral malaria, HIV-associated leukoencephalopathy and encephalitis).", "citation": {"db": "PubMed", "db_id": "21711233"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Brain Diseases": true, "Stroke": true, "Brain Injuries": true, "Malaria, Cerebral": true, "Encephalitis": true, "Leukoencephalopathies": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true, "Cerebrum": true}, "Species": {"9606": true}}, "source": 3829, "target": 1917, "key": "bec92d0465ddac72dd57967e8307121d"}, {"line": 40123, "relation": "positiveCorrelation", "evidence": "Under pathological conditions, increasing ROS production can regulate the expression of diverse inflammatory mediators during brain injury.", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Brain Injuries": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3829, "target": 170, "key": "6868f4cf095d4c91995d91deb6566841"}, {"line": 42777, "relation": "association", "evidence": "Nicardipine is a calcium channel blocker that has been widely used to control blood pressure in severe hypertension following events such as ischemic stroke, traumatic brain injury, and intracerebral hemorrhage.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Stroke": true, "Cerebral Hemorrhage": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Blood": true}, "Confidence": {"High": true}}, "source": 3829, "target": 199, "key": "aa81a6300766808df395b47b617c385f"}, {"line": 18916, "relation": "association", "evidence": "In addition, we assessed changes in endogenous net tPA activity in WT mice following morphine administration, epileptic seizures, traumatic brain injury and ischaemic stroke-neurological settings in which tPA has a known functional role.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"MeSHDisease": {"Stroke": true, "Epilepsy": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3908, "target": 3200, "key": "60e238b19063b2569a7f81f64f2564f4"}, {"line": 20806, "relation": "association", "evidence": "The role of the urokinase receptor in epilepsy, in disorders of language, cognition, communication and behavior, and in the central nervous system.", "citation": {"db": "PubMed", "db_id": "21711233"}, "annotations": {"MeSHDisease": {"Epilepsy": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3908, "target": 1917, "key": "7ee7076cb8bda6b0c065f023effeec17"}, {"line": 20807, "relation": "association", "evidence": "The role of the urokinase receptor in epilepsy, in disorders of language, cognition, communication and behavior, and in the central nervous system.", "citation": {"db": "PubMed", "db_id": "21711233"}, "annotations": {"MeSHDisease": {"Epilepsy": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 3908, "target": 798, "key": "62c213b6fa42bee4b37e21748a040005"}, {"line": 18917, "relation": "association", "evidence": "In addition, we assessed changes in endogenous net tPA activity in WT mice following morphine administration, epileptic seizures, traumatic brain injury and ischaemic stroke-neurological settings in which tPA has a known functional role.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"MeSHDisease": {"Stroke": true, "Epilepsy": true, "Brain Injuries, Traumatic": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}}, "source": 308, "target": 3200, "key": "f3b9950ad73e647630b4b7ba72a04740"}, {"line": 18937, "relation": "positiveCorrelation", "evidence": "These findings extend the existing hypotheses that low tPA activity promotes AD, whereas increased tPA activity contributes to cerebellar degeneration.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Activity"}, "source": 3885, "target": 3200, "key": "ecf791e9cec1084ef8f6159938bfff8f"}, {"line": 18943, "relation": "association", "evidence": "On the basis of this evidence, we propose that alterations in tPA activity levels could be used as a biomarker for perturbations in brain homeostasis.", "citation": {"db": "PubMed", "db_id": "21519332"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 219, "target": 3200, "key": "5f843d80c1d178e43d820449bf87a498"}, {"line": 18981, "relation": "increases", "evidence": "Subsequent studies showed that it was plasmin, the product of tPA activation of plasminogen, that specifically cleaved A beta 1-40 in the amino terminal region between Arg5 and His6.", "citation": {"db": "PubMed", "db_id": "10471309"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3205, "target": 2327, "key": "76af69ff9f8ee55fabc9bfeecc171a9a"}, {"relation": "partOf", "source": 3205, "target": 1241, "key": "c1ef4ac5d7ac67c0773ee9cfd2776240"}, {"line": 20919, "relation": "increases", "evidence": "In the presence of plasminogen there was robust degradation of A beta that was added to the HCSM cells resulting in restoration of cell viability.", "citation": {"db": "PubMed", "db_id": "12754271"}, "annotations": {"Species": {"9606": true}, "Cell": {"regular cardiac myocyte": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3205, "target": 2328, "key": "8390e55df1ffa343be8363591cce11d1"}, {"relation": "partOf", "source": 3205, "target": 1607, "key": "9f9df741422733845429648e30d8789b"}, {"line": 18999, "relation": "increases", "evidence": "These findings suggest that pathologic interactions between A beta, tPA, and plasmin in the cerebral vessel wall could result in excessive proteolysis contributing to intracerebral hemorrhages.", "citation": {"db": "PubMed", "db_id": "10471309"}, "annotations": {"MeSHDisease": {"Cerebral Hemorrhage": true}, "MeSHAnatomy": {"Cerebrum": true}, "Confidence": {"High": true}}, "source": 1241, "target": 3836, "key": "84d7745951fb00435270c8e3603391bc"}, {"line": 42776, "relation": "association", "evidence": "Nicardipine is a calcium channel blocker that has been widely used to control blood pressure in severe hypertension following events such as ischemic stroke, traumatic brain injury, and intracerebral hemorrhage.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Stroke": true, "Cerebral Hemorrhage": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Blood": true}, "Confidence": {"High": true}}, "source": 3836, "target": 199, "key": "d7b107580fe9e8720b42b30f37efd664"}, {"line": 19018, "relation": "decreases", "evidence": "Plasminogen activator activity is inhibited while neuroserpin is up-regulated in the Alzheimer disease brain.", "citation": {"db": "PubMed", "db_id": "19222708"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Plasminogen activator subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3352, "target": 3200, "key": "7e6253ff3e88b301c94be3caf1400a24"}, {"relation": "partOf", "source": 3352, "target": 1608, "key": "49f018eb0892b823b701d8a850edf3d3"}, {"line": 19064, "relation": "decreases", "evidence": "Thus, neuroserpin inhibition of tissue plasminogen activator activity leads to reduced plasmin and may be responsible for reduced clearance of amyloid-beta in the Alzheimer disease brain.", "citation": {"db": "PubMed", "db_id": "19222708"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3352, "target": 3205, "key": "79e2b3e90f3ebfc9630c3c98ef025202"}, {"line": 19065, "relation": "increases", "evidence": "Thus, neuroserpin inhibition of tissue plasminogen activator activity leads to reduced plasmin and may be responsible for reduced clearance of amyloid-beta in the Alzheimer disease brain.", "citation": {"db": "PubMed", "db_id": "19222708"}, "annotations": {"Species": {"9606": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3352, "target": 80, "key": "10b23191ee32c8370d9927e732f3a116"}, {"line": 19053, "relation": "association", "evidence": "Furthermore, elevated amounts of tissue plasminogen activator-neuroserpin complexes are seen in the Alzheimer brain, and immunohistochemical studies demonstrate that both tissue plasminogen activator and neuroserpin are associated with amyloid-beta plaques in Alzheimer brain tissue.", "citation": {"db": "PubMed", "db_id": "19222708"}, "annotations": {"Species": {"9606": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 1608, "target": 3881, "key": "64e7c033600e9f9a98c66077134e2294"}, {"line": 19126, "relation": "regulates", "evidence": "One critical function of pRb is the control of the G1-to-S phase checkpoint of the cell cycle.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"Medium": true}}, "source": 3299, "target": 501, "key": "f08cdb3786058be53053a4a7cc42311b"}, {"line": 19166, "relation": "decreases", "evidence": "pRb inhibits the transcription of cell cycle proteins in the nucleus of healthy cells by interaction and consequent blocking of the active site of E2F, dependent upon the phosphate stoichiometry and combination of the locations of their 16 potential phosphorylation sites on pRb.", "citation": {"db": "PubMed", "db_id": "18784806"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "Cell cycle subgraph": true}}, "source": 3299, "target": 501, "key": "d049b9d918e67d384f3fe8298a9e5786"}, {"relation": "hasVariant", "source": 3299, "target": 3300, "key": "112f6b7d91f649cdf080e4a1c976abce"}, {"relation": "hasVariant", "source": 3299, "target": 3301, "key": "9c25b4b02f69ad4a948e064e15c1f4b7"}, {"relation": "hasVariant", "source": 3299, "target": 3302, "key": "27f373ab25f9a044e59be72757d91798"}, {"line": 33541, "relation": "positiveCorrelation", "evidence": "Moreover, neurons that overexpress Bim in AD brains also show elevated levels of the cell cycle-related proteins cdk4 and phospho-Rb.", "citation": {"db": "PubMed", "db_id": "17251431"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3299, "target": 2396, "key": "cc80683259a1fa4b7ceb6e699241a24e"}, {"line": 48347, "relation": "increases", "evidence": "This protein has been shown to interact with tumor suppressor protein Rb and the expression of this gene is regulated positively by Rb.", "citation": {"db": "PubMed", "db_id": "1826542"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "Estrogen subgraph": true, "Cell cycle subgraph": true}}, "source": 3299, "target": 2463, "key": "74bfbceee853aed7477641821267369f"}, {"line": 19134, "relation": "increases", "evidence": "On phosphorylation, primarily by cyclin-dependent kinases, phosphorylated pRb dissociates from E2F and permits cell cycle progression.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3546, "target": 3300, "key": "a6f44052bbbd7a30553f33b581a159ca"}, {"line": 19138, "relation": "increases", "evidence": "On phosphorylation, primarily by cyclin-dependent kinases, phosphorylated pRb dissociates from E2F and permits cell cycle progression.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Confidence": {"High": true}}, "source": 3300, "target": 501, "key": "37f2d34ec92730eb8a3e922aab521829"}, {"line": 19172, "relation": "increases", "evidence": "Therefore, to determine whether pRb is involved in the aberrant cell cycle phenotype in AD neurons, a systematic immunocytochemical evaluation of the phosphorylation status of pRb protein using antibodies specific for multiple phosphorylation sites (i.e., pSpT249/252, pS612, pS795, pS807, pS811 and pT821) was carried out in the hippocampal regions of brains from AD patients. Increased levels of phospho-pRb (ppRb) for all these phosphorylation sites were noted in the brains of AD patients as compared to control cases.", "citation": {"db": "PubMed", "db_id": "18784806"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "Cell cycle subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true, "Brain": true, "Neurons": true}}, "source": 3300, "target": 501, "key": "44a5b6bfaa0a79fa692fd10fdf970177"}, {"line": 19148, "relation": "association", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 3300, "target": 3015, "key": "794ec701408b58896c5842ebfad71616"}, {"line": 19152, "relation": "association", "evidence": "We previously found phosphorylated pRb to be intimately associated with hyperphosphorylated tau-containing neurofibrillary tangles of Alzheimer disease (AD), the pathogenesis of which is believed to involve dysregulation of the cell cycle and marked neuronal death.", "citation": {"db": "PubMed", "db_id": "21666500"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}, "Subgraph": {"Retinoblastoma subgraph": true, "Tau protein subgraph": true}}, "source": 3300, "target": 718, "key": "6d48567ac0005cbb369a122cd82e7785"}, {"line": 20166, "relation": "association", "evidence": "We have recently reported the existence of a molecular link between decreased p27 levels and enhanced phosphorylation of pRb protein and proliferation of immortalized lymphocytes from Alzheimer's disease (AD) patients.", "citation": {"db": "PubMed", "db_id": "17448572"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Cell": {"lymphocyte": true}, "Species": {"9606": true}}, "object": {"modifier": "Degradation"}, "source": 3300, "target": 2494, "key": "770040ee64075fd75a2e6328415081e4"}, {"line": 48373, "relation": "decreases", "evidence": "Furthermore, Western blot showed that FLZ inhibited phosphorylation of Akt and retinoblastoma protein (Rb), down-regulated the expressions of cyclin D1, cyclin E, cyclin-dependent kinase 2 (CDK2), and enhanced the expression of CDK inhibitor p27(kip1), while did not affect CDK4 expression.", "citation": {"db": "PubMed", "db_id": "21835169"}, "annotations": {"Subgraph": {"Retinoblastoma subgraph": true, "Cell cycle subgraph": true}}, "source": 3300, "target": 2463, "key": "5fcdb4bdc3a9cefe219f2371f3996737"}, {"line": 19287, "relation": "decreases", "evidence": "Cdk5/p25 subsequently phosphorylates the nuclear transcription factor myocyte enhancer factor (MEF2), thereby inhibiting its prosurvival activity.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2192, "target": 2191, "key": "61e48794c838aaca0c174a11cde3cd98"}, {"relation": "hasVariant", "source": 2191, "target": 2192, "key": "437afaa40cdc44c71e6635fb24b1ab4a"}, {"line": 19331, "relation": "directlyDecreases", "evidence": "Drugs like roscovitine, flavopiridol, calpain inhibitors, kenpaullone and induribins, which inhibit cdk5/p25 formation, constitute potential drugs for the treatment of neurological disorders.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 36, "target": 2142, "key": "f81f1bd1d8df5006a5843fcf58fcb740"}, {"line": 19336, "relation": "decreases", "evidence": "Drugs like roscovitine, flavopiridol, calpain inhibitors, kenpaullone and induribins, which inhibit cdk5/p25 formation, constitute potential drugs for the treatment of neurological disorders.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"Medium": true}}, "source": 208, "target": 1014, "key": "d35164cbb90335322e9023b99f722166"}, {"line": 19337, "relation": "decreases", "evidence": "Drugs like roscovitine, flavopiridol, calpain inhibitors, kenpaullone and induribins, which inhibit cdk5/p25 formation, constitute potential drugs for the treatment of neurological disorders.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"Medium": true}}, "source": 95, "target": 1014, "key": "3532430e65bfeb393ab821f44c5d97ef"}, {"line": 19339, "relation": "decreases", "evidence": "Drugs like roscovitine, flavopiridol, calpain inhibitors, kenpaullone and induribins, which inhibit cdk5/p25 formation, constitute potential drugs for the treatment of neurological disorders.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"Medium": true}}, "source": 9, "target": 1014, "key": "89f8883f7c62903a4ba5fab2e862b630"}, {"line": 19340, "relation": "decreases", "evidence": "Drugs like roscovitine, flavopiridol, calpain inhibitors, kenpaullone and induribins, which inhibit cdk5/p25 formation, constitute potential drugs for the treatment of neurological disorders.", "citation": {"db": "PubMed", "db_id": "17160145"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"Medium": true}}, "source": 435, "target": 1014, "key": "64fdbabda8dc11483972da88ad09e1b2"}, {"line": 19384, "relation": "association", "evidence": "Thrombospondins are extracellular matrix proteins that, in the CNS, are predominantly produced by astrocytes and have been implicated in synaptogenesis.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}}, "source": 3459, "target": 787, "key": "4fbb3d0e2c35139658f73984ae2552e7"}, {"line": 19390, "relation": "association", "evidence": "Because Abeta is known to induce oxidative stress in astrocytes, we examined the effects of the antioxidants tempol and apocynin on astrocytic TSP-1 levels and release.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Astrocytes": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3459, "target": 842, "key": "cacdc7ee9a3584b1a8ce167ffea64c49"}, {"line": 19398, "relation": "decreases", "evidence": "These findings suggest that Abeta-mediated reduction in astrocytic TSP-1 release, possibly related to oxidative stress, contributes to the loss of synaptophysin in neurons.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3459, "target": 842, "key": "426ccd9beb6f963f181df224520b417b"}, {"line": 19403, "relation": "increases", "evidence": "These findings suggest that Abeta-mediated reduction in astrocytic TSP-1 release, possibly related to oxidative stress, contributes to the loss of synaptophysin in neurons.", "citation": {"db": "PubMed", "db_id": "23860027"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3459, "target": 3438, "key": "12d99bd800297176f155a9fbb1dee752"}, {"relation": "partOf", "source": 3459, "target": 1323, "key": "03f4f0472533d54d89ffc6dd2fea68a4"}, {"line": 19417, "relation": "association", "evidence": "The TSP1/CD36/CD47-complex is involved in T cell expansion and inflammatory responses to beta-amyloid, both relevant to IBM.", "citation": {"db": "PubMed", "db_id": "17572512"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "T cells signaling": true}, "Confidence": {"High": true}}, "source": 1323, "target": 458, "key": "de6f93b4b9def7f74af17c46aa5db674"}, {"line": 19424, "relation": "association", "evidence": "The TSP1/CD36/CD47-complex is involved in T cell expansion and inflammatory responses to beta-amyloid, both relevant to IBM.", "citation": {"db": "PubMed", "db_id": "17572512"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 1323, "target": 577, "key": "11759123ffec11f221bba6d182401690"}, {"relation": "partOf", "source": 2473, "target": 1323, "key": "a3ec33f2c1f8ba1d5a7a636786ba938a"}, {"relation": "partOf", "source": 2473, "target": 919, "key": "08d91233f9a1c387101325cc3a465bff"}, {"relation": "partOf", "source": 2473, "target": 920, "key": "271e9274ec50ad2729c9882a576a9780"}, {"relation": "partOf", "source": 2477, "target": 1323, "key": "0d96a1c39d65c0995ebc7c116da4d2f9"}, {"relation": "partOf", "source": 2477, "target": 920, "key": "3508c81e5a60a27ac6f60662698acfb6"}, {"line": 19417, "relation": "association", "evidence": "The TSP1/CD36/CD47-complex is involved in T cell expansion and inflammatory responses to beta-amyloid, both relevant to IBM.", "citation": {"db": "PubMed", "db_id": "17572512"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "T cells signaling": true}, "Confidence": {"High": true}}, "source": 458, "target": 1323, "key": "f03b50edf0f3023b17cd646a97e4b3de"}, {"line": 19433, "relation": "increases", "evidence": "The TSP1/CD36 /CD47 was upregulated in IBM.", "citation": {"db": "PubMed", "db_id": "17572512"}, "annotations": {"Disease": {"inclusion body myositis": true}, "Confidence": {"High": true}}, "source": 3870, "target": 1323, "key": "b96dc3b6744b7c62122c2aab81e1835f"}, {"line": 19471, "relation": "association", "evidence": "The tilted peptides of human prolactin and human growth hormone induce endothelial cell apoptotic process, inhibit endothelial cell proliferation, and inhibit capillary formation both in vitro and in vivo.", "citation": {"db": "PubMed", "db_id": "16973751"}, "annotations": {"Species": {"9606": true}}, "source": 551, "target": 3253, "key": "4e3a9e88639875cc376f780c56c7cb49"}, {"line": 19503, "relation": "association", "evidence": "Glutathione S-transferase P1 *C allelic variant increases susceptibility for late-onset Alzheimer disease: association study and relationship with apolipoprotein E epsilon4 allele.", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 2800, "target": 2312, "key": "d4f648edb1a333eb745f9c2ba9b1dd98"}, {"line": 19504, "relation": "association", "evidence": "Glutathione S-transferase P1 *C allelic variant increases susceptibility for late-onset Alzheimer disease: association study and relationship with apolipoprotein E epsilon4 allele.", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 2800, "target": 3823, "key": "138ec3e213a8ea03014989744457dfe5"}, {"line": 19509, "relation": "association", "evidence": "GSTM1 and GSTT1 genotypes were studied by conventional PCR, whereas GSTP1 and ApoE genotypes were determined by real-time PCR on the LightCycler. We found a significant association between LOAD and the GSTP1*C allelic variant [odds ratio (OR) = 1.9;", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Glutathione reductase subgraph": true}}, "source": 2800, "target": 3823, "key": "f772d551003565e650ade68fd05397cb"}, {"line": 19530, "relation": "causesNoChange", "evidence": "In conclusion, the involvement of GSTP1 alleles in individual susceptibility to AD was not confirmed as statistically significant in the tested Croatian Caucasian population.", "citation": {"db": "PubMed", "db_id": "15080568"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "source": 2800, "target": 3823, "key": "8b95a94157629d2f81859090165d2934"}, {"relation": "partOf", "source": 2800, "target": 1680, "key": "b2408056201becf4f0c1866c7676a7c2"}, {"line": 19519, "relation": "association", "evidence": "P < 0.0001).The GSTP1*C allelic variant should be considered a candidate for LOAD, particularly in persons having the ApoE epsilon4 allelic variant, because the GSTP1 and ApoE gene products are implicated in oxidative stress and apoptosis processes leading to beta-amyloid-mediated neurodegeneration.", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 2800, "target": 478, "key": "06253fdfade9613d95d9d6489d868694"}, {"line": 19514, "relation": "increases", "evidence": "In addition, a preliminary result suggested that carriers of both the GSTP1*C and ApoE epsilon4 allelic variants were at increased risk of LOAD (OR = 19.98;", "citation": {"db": "PubMed", "db_id": "15805147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"APOE subgraph": true, "Glutathione reductase subgraph": true}}, "source": 1680, "target": 3823, "key": "95e08aa1ec355117245637bc0dcb5265"}, {"line": 19552, "relation": "increases", "evidence": "Cyclooxygenase-1 null mice show reduced neuroinflammation in response to beta-amyloid.", "citation": {"db": "PubMed", "db_id": "20157512"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3277, "target": 3920, "key": "fcf071d94a0df4094473b9938d89da05"}, {"line": 19564, "relation": "increases", "evidence": "These results indicate that inhibition of COX-1 activity may be valid therapeutic strategy to reduce brain inflammatory response and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "20157512"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "Inflammatory response subgraph": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3277, "target": 3920, "key": "6999d11f7153f8ccda9928181228fd0f"}, {"line": 19565, "relation": "increases", "evidence": "These results indicate that inhibition of COX-1 activity may be valid therapeutic strategy to reduce brain inflammatory response and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "20157512"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true, "Inflammatory response subgraph": true}, "MeSHAnatomy": {"Brain": true, "Neurons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3277, "target": 3872, "key": "903e8cd338139e8342f36dcbd286fec4"}, {"line": 19584, "relation": "positiveCorrelation", "evidence": "However, COX-1 immunopositive microglia were found in association with Abeta plaques, and the density of COX-1 immunopositive microglia in AD fusiform cortex was increased.", "citation": {"db": "PubMed", "db_id": "10560656"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3277, "target": 80, "key": "3e5baa239a0cbcb23e9be7f7be2d3edd"}, {"line": 19614, "relation": "positiveCorrelation", "evidence": "In AD brains, COX-1-positive microglial cells were primarily associated with amyloid beta plaques, while the number of COX-2-positive neurons was increased compared to that in control brains.", "citation": {"db": "PubMed", "db_id": "11194936"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Microglia": true, "Neurons": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3277, "target": 80, "key": "b6f1544579456a4a788928f72737b00c"}, {"line": 19585, "relation": "positiveCorrelation", "evidence": "However, COX-1 immunopositive microglia were found in association with Abeta plaques, and the density of COX-1 immunopositive microglia in AD fusiform cortex was increased.", "citation": {"db": "PubMed", "db_id": "10560656"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 3277, "target": 3823, "key": "9f80ecacd156e0d221a8766da00b7547"}, {"line": 19596, "relation": "positiveCorrelation", "evidence": "This pattern suggests an overall increase of COX-1 expression in AD. The present study shows that COX-1 is widely expressed in human brain, and raises the possibility that COX-1 may contribute to CNS pathology.", "citation": {"db": "PubMed", "db_id": "10560656"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3277, "target": 3823, "key": "e93666adef4d1cbb36cfd9f90c8a275a"}, {"line": 39567, "relation": "positiveCorrelation", "evidence": "Epidemiological studies, indicating that non-steroidal anti-inflammatory drugs (NSAIDs) decrease the risk of developing AD, have encouraged the study on the role of inflammation in AD. The best-characterized action of most NSAIDs is the inhibition of cyclooxygenase (COX). The expression of the constitutively expressed COX-1 and the inflammatory induced COX-2 has been intensively investigated in AD brain and different disease models for AD. Despite these studies, clinical trials with NSAIDs or selective COX-2 inhibitors showed little or no effect on clinical progression of AD. The expression levels of COX-1 and COX-2 change in the different stages of AD pathology. In an early stage, when low-fibrillar Abeta deposits are present and only very few neurofibrillary tangles are observed in the cortical areas, COX-2 is increased in neurons. The increased neuronal COX-2 expression parallels and colocalizes with the expression of cell cycle proteins. COX-1 is primarily expressed in microglia, which are associated with fibrillar Abeta deposits. This suggests that in AD brain COX-1 and COX-2 are involved in inflammatory and regenerating pathways respectively. In this review we will discuss the role of COX-1 and COX-2 in the different stages of AD pathology.", "citation": {"db": "PubMed", "db_id": "18537664"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3277, "target": 3823, "key": "59864e28b53902d0cce8809db08618bc"}, {"line": 39565, "relation": "increases", "evidence": "Epidemiological studies, indicating that non-steroidal anti-inflammatory drugs (NSAIDs) decrease the risk of developing AD, have encouraged the study on the role of inflammation in AD. The best-characterized action of most NSAIDs is the inhibition of cyclooxygenase (COX). The expression of the constitutively expressed COX-1 and the inflammatory induced COX-2 has been intensively investigated in AD brain and different disease models for AD. Despite these studies, clinical trials with NSAIDs or selective COX-2 inhibitors showed little or no effect on clinical progression of AD. The expression levels of COX-1 and COX-2 change in the different stages of AD pathology. In an early stage, when low-fibrillar Abeta deposits are present and only very few neurofibrillary tangles are observed in the cortical areas, COX-2 is increased in neurons. The increased neuronal COX-2 expression parallels and colocalizes with the expression of cell cycle proteins. COX-1 is primarily expressed in microglia, which are associated with fibrillar Abeta deposits. This suggests that in AD brain COX-1 and COX-2 are involved in inflammatory and regenerating pathways respectively. In this review we will discuss the role of COX-1 and COX-2 in the different stages of AD pathology.", "citation": {"db": "PubMed", "db_id": "18537664"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3277, "target": 3815, "key": "8e68966db65eec11a4c69da3a175bf92"}, {"line": 39568, "relation": "positiveCorrelation", "evidence": "Epidemiological studies, indicating that non-steroidal anti-inflammatory drugs (NSAIDs) decrease the risk of developing AD, have encouraged the study on the role of inflammation in AD. The best-characterized action of most NSAIDs is the inhibition of cyclooxygenase (COX). The expression of the constitutively expressed COX-1 and the inflammatory induced COX-2 has been intensively investigated in AD brain and different disease models for AD. Despite these studies, clinical trials with NSAIDs or selective COX-2 inhibitors showed little or no effect on clinical progression of AD. The expression levels of COX-1 and COX-2 change in the different stages of AD pathology. In an early stage, when low-fibrillar Abeta deposits are present and only very few neurofibrillary tangles are observed in the cortical areas, COX-2 is increased in neurons. The increased neuronal COX-2 expression parallels and colocalizes with the expression of cell cycle proteins. COX-1 is primarily expressed in microglia, which are associated with fibrillar Abeta deposits. This suggests that in AD brain COX-1 and COX-2 are involved in inflammatory and regenerating pathways respectively. In this review we will discuss the role of COX-1 and COX-2 in the different stages of AD pathology.", "citation": {"db": "PubMed", "db_id": "18537664"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3277, "target": 2328, "key": "9a5e7b9fe2d05cd2650d816e0146ad16"}, {"line": 19634, "relation": "increases", "evidence": "The administration of exogenous neurohormone melatonin at pharmacological doses has been shown not only to be an effective scavenger of reactive oxygen and nitrogen species but also to enhance the levels of GSH and the expression and activities of the GSH-related enzymes including gamma-GCL, GPxs, and GSR.", "citation": {"db": "PubMed", "db_id": "20868358"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "source": 299, "target": 2798, "key": "d1370b83b0e848276af95483948f370d"}, {"line": 19635, "relation": "increases", "evidence": "The administration of exogenous neurohormone melatonin at pharmacological doses has been shown not only to be an effective scavenger of reactive oxygen and nitrogen species but also to enhance the levels of GSH and the expression and activities of the GSH-related enzymes including gamma-GCL, GPxs, and GSR.", "citation": {"db": "PubMed", "db_id": "20868358"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 299, "target": 2798, "key": "aedce34f2e24ed4e2df49ebcc5072092"}, {"line": 19636, "relation": "increases", "evidence": "The administration of exogenous neurohormone melatonin at pharmacological doses has been shown not only to be an effective scavenger of reactive oxygen and nitrogen species but also to enhance the levels of GSH and the expression and activities of the GSH-related enzymes including gamma-GCL, GPxs, and GSR.", "citation": {"db": "PubMed", "db_id": "20868358"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "source": 299, "target": 2743, "key": "9b6f545e0daaed1e8284f68b1d22e58c"}, {"line": 19637, "relation": "increases", "evidence": "The administration of exogenous neurohormone melatonin at pharmacological doses has been shown not only to be an effective scavenger of reactive oxygen and nitrogen species but also to enhance the levels of GSH and the expression and activities of the GSH-related enzymes including gamma-GCL, GPxs, and GSR.", "citation": {"db": "PubMed", "db_id": "20868358"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 299, "target": 2743, "key": "e7f349be49218ec2d6688886cccb5ed5"}, {"line": 19668, "relation": "isA", "evidence": "The pineal product melatonin has remarkable antioxidant properties.", "citation": {"db": "PubMed", "db_id": "16179266"}, "source": 299, "target": 213, "key": "edeea8eba57374aa423b7a2d9123bb1f"}, {"line": 19674, "relation": "increases", "evidence": "Melatonin also enhances the antioxidant potential of the cell by stimulating the synthesis of antioxidant enzymes like superoxide dismutase, glutathione peroxidase and glutathione reductase, and by augmenting glutathione levels.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true, "Glutathione reductase subgraph": true}, "Confidence": {"Very High": true}}, "source": 299, "target": 265, "key": "09b9b7628b43cd38ead2790dd70788b4"}, {"line": 19704, "relation": "association", "evidence": "The decline in melatonin production in aged individuals has been suggested as one of the primary contributing factors for the development of age-associated neurodegenerative diseases, e.g., Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Confidence": {"High": true}}, "source": 299, "target": 3874, "key": "69965712d06350b69b97134ce6fda36b"}, {"line": 19705, "relation": "association", "evidence": "The decline in melatonin production in aged individuals has been suggested as one of the primary contributing factors for the development of age-associated neurodegenerative diseases, e.g., Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Confidence": {"High": true}}, "source": 299, "target": 3823, "key": "996bc036693c84c87e5c90a8454f9983"}, {"line": 19716, "relation": "decreases", "evidence": "Therapeutic trials with melatonin have been effective in slowing the progression of Alzheimer's disease but not of Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 299, "target": 3823, "key": "280b0859fa7917425eb0454dd1eaaef6"}, {"line": 39084, "relation": "negativeCorrelation", "evidence": "Recent studies showed that melatonin, an indoleamine secreted by the pineal gland, may play an important/ role in aging and AD as an antioxidant and neuroprotector. Melatonin decreases during aging and patients with AD have a/ more profound reduction in this hormone.Melatonin efficiently protects neuronal cells from Abeta-mediated toxicity via/ antioxidant and anti-amyloid properties: it not only inhibits Abeta generation, but also arrests the formation of amyloid/ fibrils by a structure-dependent interaction with Abeta. Our recent studies have demonstrated that melatonin efficiently/ attenuates Alzheimer-like tau hyperphosphorylation. Although the exact mechanism is still not fully understood, a direct/ regulatory influence of melatonin on the activities of protein kinases and protein phosphatases is proposed. Additionally,/ melatonin also plays a role in protecting cholinergic neurons and in anti-inflammation.", "citation": {"db": "PubMed", "db_id": "16364209"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 299, "target": 3823, "key": "cc55584edda04fbd7f3a624fc1d457f7"}, {"line": 19715, "relation": "causesNoChange", "evidence": "Therapeutic trials with melatonin have been effective in slowing the progression of Alzheimer's disease but not of Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true}, "Disease": {"Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 299, "target": 3878, "key": "c4cde838f7d43b0ee1850a41d67a4aaf"}, {"line": 19729, "relation": "decreases", "evidence": "Melatonin's efficacy in combating free radical damage in the brain suggests that it may be a valuable therapeutic agent in the treatment of cerebral edema after traumatic brain injury.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"MeSHDisease": {"Brain Edema": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Cerebrum": true}, "Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 299, "target": 340, "key": "2fb2f2814bb0bf7e908e56f919d64057"}, {"line": 39085, "relation": "decreases", "evidence": "Recent studies showed that melatonin, an indoleamine secreted by the pineal gland, may play an important/ role in aging and AD as an antioxidant and neuroprotector. Melatonin decreases during aging and patients with AD have a/ more profound reduction in this hormone.Melatonin efficiently protects neuronal cells from Abeta-mediated toxicity via/ antioxidant and anti-amyloid properties: it not only inhibits Abeta generation, but also arrests the formation of amyloid/ fibrils by a structure-dependent interaction with Abeta. Our recent studies have demonstrated that melatonin efficiently/ attenuates Alzheimer-like tau hyperphosphorylation. Although the exact mechanism is still not fully understood, a direct/ regulatory influence of melatonin on the activities of protein kinases and protein phosphatases is proposed. Additionally,/ melatonin also plays a role in protecting cholinergic neurons and in anti-inflammation.", "citation": {"db": "PubMed", "db_id": "16364209"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 299, "target": 2328, "key": "d7536d85d78477308de05e1283b6f871"}, {"line": 39091, "relation": "decreases", "evidence": "Recent studies showed that melatonin, an indoleamine secreted by the pineal gland, may play an important/ role in aging and AD as an antioxidant and neuroprotector. Melatonin decreases during aging and patients with AD have a/ more profound reduction in this hormone.Melatonin efficiently protects neuronal cells from Abeta-mediated toxicity via/ antioxidant and anti-amyloid properties: it not only inhibits Abeta generation, but also arrests the formation of amyloid/ fibrils by a structure-dependent interaction with Abeta. Our recent studies have demonstrated that melatonin efficiently/ attenuates Alzheimer-like tau hyperphosphorylation. Although the exact mechanism is still not fully understood, a direct/ regulatory influence of melatonin on the activities of protein kinases and protein phosphatases is proposed. Additionally,/ melatonin also plays a role in protecting cholinergic neurons and in anti-inflammation.", "citation": {"db": "PubMed", "db_id": "16364209"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"Very High": true}}, "source": 299, "target": 3015, "key": "3d88aed2091397943c2b7946e7918885"}, {"line": 39095, "relation": "decreases", "evidence": "Recent studies showed that melatonin, an indoleamine secreted by the pineal gland, may play an important/ role in aging and AD as an antioxidant and neuroprotector. Melatonin decreases during aging and patients with AD have a/ more profound reduction in this hormone.Melatonin efficiently protects neuronal cells from Abeta-mediated toxicity via/ antioxidant and anti-amyloid properties: it not only inhibits Abeta generation, but also arrests the formation of amyloid/ fibrils by a structure-dependent interaction with Abeta. Our recent studies have demonstrated that melatonin efficiently/ attenuates Alzheimer-like tau hyperphosphorylation. Although the exact mechanism is still not fully understood, a direct/ regulatory influence of melatonin on the activities of protein kinases and protein phosphatases is proposed. Additionally,/ melatonin also plays a role in protecting cholinergic neurons and in anti-inflammation.", "citation": {"db": "PubMed", "db_id": "16364209"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 299, "target": 577, "key": "163b0d49f6e0cf5d8458deb09d15aef6"}, {"line": 19656, "relation": "causesNoChange", "evidence": "Depending on the treatment received, a distinct inflammatory and oxidative stress profile was observed: in Rivastigmine-treated group, IL6 levels were 47% lower than the average value of the remaining AD patients; homocysteine and glutathione reductase were statistically unchanged in the Rivastigmine and Donepezil-Memantine, respectively Donepezil group.", "citation": {"db": "PubMed", "db_id": "23871825"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Glutathione reductase subgraph": true}}, "source": 976, "target": 2798, "key": "4a9256535dd51d61bb22879c70b6037c"}, {"line": 19683, "relation": "equivalentTo", "evidence": "Melatonin also enhances the antioxidant potential of the cell by stimulating the synthesis of antioxidant enzymes like superoxide dismutase, glutathione peroxidase and glutathione reductase, and by augmenting glutathione levels.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 2216, "target": 2140, "key": "bacbefb6ac8822e01611ba7b4afdca35"}, {"line": 19683, "relation": "equivalentTo", "evidence": "Melatonin also enhances the antioxidant potential of the cell by stimulating the synthesis of antioxidant enzymes like superoxide dismutase, glutathione peroxidase and glutathione reductase, and by augmenting glutathione levels.", "citation": {"db": "PubMed", "db_id": "16179266"}, "annotations": {"Subgraph": {"Free radical formation subgraph": true}, "Confidence": {"Very High": true}}, "source": 2140, "target": 2216, "key": "c0bd89318eb98af41daa8b0bdc512db5"}, {"line": 19788, "relation": "negativeCorrelation", "evidence": "Hypercortisolemia and glucocorticoid receptor-signaling insufficiency in Alzheimer's disease initiation and development.", "citation": {"db": "PubMed", "db_id": "23906001"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 562, "target": 3823, "key": "8409ee4dd56bfd861c9a24a33878b21f"}, {"line": 19789, "relation": "negativeCorrelation", "evidence": "Hypercortisolemia and glucocorticoid receptor-signaling insufficiency in Alzheimer's disease initiation and development.", "citation": {"db": "PubMed", "db_id": "23906001"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 3135, "target": 3823, "key": "d1415f43ef848a795d91a50466393426"}, {"line": 19808, "relation": "association", "evidence": "Mifepristone alters amyloid precursor protein processing to preclude amyloid beta and also reduces tau pathology.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 304, "target": 2315, "key": "a3b5329575c32d3887a41a6c34580fcf"}, {"line": 19809, "relation": "decreases", "evidence": "Mifepristone alters amyloid precursor protein processing to preclude amyloid beta and also reduces tau pathology.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 304, "target": 2328, "key": "db1a4f945a9d6a378a07b5e216dbae8c"}, {"line": 19814, "relation": "decreases", "evidence": "Mifepristone alters amyloid precursor protein processing to preclude amyloid beta and also reduces tau pathology.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 304, "target": 3931, "key": "dc3d0bf407c804852a851238fc4a54e6"}, {"line": 19835, "relation": "decreases", "evidence": "Hence, mifepristone induces a novel C-terminal cleavage of APP that prevents it being cleaved by α- or beta-secretase, thereby precluding Abeta generation in the central nervous system; this cleavage and the production of the 17-kDa APP fragment was generated by a calcium-dependent cysteine protease.", "citation": {"db": "PubMed", "db_id": "23312564"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 304, "target": 4101, "key": "f0c5895847a572f082b99598f3431931"}, {"line": 19881, "relation": "association", "evidence": "Induction and recovery time course of rat brain CYP2E1 after nicotine treatment.", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Species": {"10116": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 312, "target": 2612, "key": "233e309b51f503787838959367aee60c"}, {"line": 19898, "relation": "increases", "evidence": "Nicotine from tobacco smoke may contribute to the enhanced hepatic CYP2E1 activity in smokers.", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 312, "target": 2612, "key": "1b9bc4bd784a0d4ac40e4b1a3a9b057e"}, {"line": 19912, "relation": "increases", "evidence": "In contrast, acute nicotine treatment did not induce CYP2E1 in frontal cortex and hippocampus but increased CYP2E1 in cerebellum 8 h after treatment (1.6-fold, p < 0.01).", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "MeSHAnatomy": {"Cerebellum": true}}, "source": 312, "target": 2612, "key": "e6e48f41bcf22dafc70d6eb68914c59d"}, {"line": 19904, "relation": "increases", "evidence": "We have previously shown that chronic nicotine treatment can increase CYP2E1 in rat liver and brain.", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10116": true}}, "source": 312, "target": 3775, "key": "2483445edded1f5c22c2582203aa5e84"}, {"line": 19881, "relation": "association", "evidence": "Induction and recovery time course of rat brain CYP2E1 after nicotine treatment.", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Species": {"10116": true}, "Subgraph": {"Cholesterol metabolism subgraph": true}}, "source": 2612, "target": 312, "key": "dd4cddb10a602a390036a111b8d5db47"}, {"line": 19889, "relation": "increases", "evidence": "CYP2E1, the primary ethanol-metabolizing cytochrome P450, metabolizes endogenous substrates (e.g., arachidonic acid) and drugs (e.g., acetaminophen, chlorzoxazone) and bioactivates procarcinogens (e.g., tobacco-specific nitrosamines) and toxins (e.g., carbon tetrachloride).", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2612, "target": 85, "key": "5a78134714f355cd41a8c9f5168e2aaf"}, {"line": 19890, "relation": "increases", "evidence": "CYP2E1, the primary ethanol-metabolizing cytochrome P450, metabolizes endogenous substrates (e.g., arachidonic acid) and drugs (e.g., acetaminophen, chlorzoxazone) and bioactivates procarcinogens (e.g., tobacco-specific nitrosamines) and toxins (e.g., carbon tetrachloride).", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2612, "target": 320, "key": "c538b1525c563a3a7841d0e5c53b4cd9"}, {"line": 19891, "relation": "increases", "evidence": "CYP2E1, the primary ethanol-metabolizing cytochrome P450, metabolizes endogenous substrates (e.g., arachidonic acid) and drugs (e.g., acetaminophen, chlorzoxazone) and bioactivates procarcinogens (e.g., tobacco-specific nitrosamines) and toxins (e.g., carbon tetrachloride).", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2612, "target": 230, "key": "8e9116c52e69be72169086c1a715880c"}, {"line": 19892, "relation": "increases", "evidence": "CYP2E1, the primary ethanol-metabolizing cytochrome P450, metabolizes endogenous substrates (e.g., arachidonic acid) and drugs (e.g., acetaminophen, chlorzoxazone) and bioactivates procarcinogens (e.g., tobacco-specific nitrosamines) and toxins (e.g., carbon tetrachloride).", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2612, "target": 316, "key": "054afc1584557d7671c2fbc5314105de"}, {"line": 19893, "relation": "increases", "evidence": "CYP2E1, the primary ethanol-metabolizing cytochrome P450, metabolizes endogenous substrates (e.g., arachidonic acid) and drugs (e.g., acetaminophen, chlorzoxazone) and bioactivates procarcinogens (e.g., tobacco-specific nitrosamines) and toxins (e.g., carbon tetrachloride).", "citation": {"db": "PubMed", "db_id": "16434548"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2612, "target": 362, "key": "1f7dea629e1db0cb918afbdb2e20e78d"}, {"line": 38939, "relation": "increases", "evidence": "In the central nervous system (CNS), prostaglandin (PG) and other bioactive lipids regulate vital aspects/ of neural membrane biology, including protein-lipid interactions, trans-membrane and trans-synaptic signaling. However, / a series of highly reactive PGs, free fatty acids, lysophospolipids, eicosanoids, platelet-activating factor, and reactive / oxygen species (ROS), all generated by enhanced phospholipase A2 (PLA2) activity and arachidonic acid (AA) release, / participate in cellular injury, particularly in neurodegeneration. PLA2 activation and PG production are among the earliest / initiating events in triggering brain-damage pathways, which can lead to long-term neurologic deficits. Altered / membrane-associated PLA2 activities have been correlated with several forms of acute and chronic brain injury, including / cerebral trauma, ischemic damage, induced seizures in the brain and epilepsy, schizophrenia, and in particular, Alzheimer's / disease (AD). Moreover, the expression of both COX-2 and PLA2 appears to be strongly activated / during Alzheimer's disease (AD), indicating the importance of inflammatory gene pathways as a response to brain injury./ This review addresses some current ideas concerning how brain PLA2 and brain PGs are early and key players in acute neural / trauma and in brain-cell damage associated with chronic neurodegenerative diseases such as AD.", "citation": {"db": "PubMed", "db_id": "12432919"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 85, "target": 1662, "key": "36d7b119467f6c7d0099db5a0af18868"}, {"line": 19967, "relation": "decreases", "evidence": "Addition of superoxide dismutase to cells exposed to Abeta40 prevented the increase in the concentration of ET-1.", "citation": {"db": "PubMed", "db_id": "23629587"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 1693, "target": 2653, "key": "7cd1ae5a90ac44460ebaf99bdfdf6642"}, {"line": 20024, "relation": "increases", "evidence": "ET-1 expression and secretion are both induced by the inflammatory and neurotoxic protein thrombin.", "citation": {"db": "PubMed", "db_id": "20634595"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Endothelin subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2683, "target": 2653, "key": "3cb3c00a1eeddfc501b499f1846fd115"}, {"line": 20025, "relation": "increases", "evidence": "ET-1 expression and secretion are both induced by the inflammatory and neurotoxic protein thrombin.", "citation": {"db": "PubMed", "db_id": "20634595"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Endothelin subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2683, "target": 2653, "key": "055d4a51146f83faed6f0da4359a6285"}, {"relation": "partOf", "source": 2683, "target": 1417, "key": "4a007f943b6c75b4ac77c9f48cffe6fe"}, {"line": 30426, "relation": "increases", "evidence": "We previously showed that thrombin proteolyses the microtubule-associated protein tau and that phosphorylation of tau inhibits this process.", "citation": {"db": "PubMed", "db_id": "16410745"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Plasminogen activator subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 2683, "target": 3010, "key": "f647785c88c516e909096af284631b8a"}, {"line": 49153, "relation": "increases", "evidence": "In this regard we find high levels of the tissue inhibitor of matrix metalloproteinases-1 (TIMP-1) in AD. Furthermore, we explore the ability of thrombin, previously shown to be present in AD microvessels, to affect TIMP expression in cultured brain endothelial cells and find that thrombin causes up regulation of TIMP-1", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true, "Matrix metalloproteinase subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2683, "target": 3463, "key": "d2e4692795b604d0d3b0574cd9528376"}, {"line": 20050, "relation": "increases", "evidence": "Abeta42 causes increased neuronal production and release of endothelin-1 (ET-1), a potent vasoconstrictor, and upregulation of endothelin-converting enzyme-2 (ECE-2), the enzyme which cleaves ET-1 from its inactive precursor.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2652, "target": 2653, "key": "b5882ac9eed51f5be0253e6ff9dcfea8"}, {"line": 30357, "relation": "increases", "evidence": "Endothelin-converting enzyme-1 and 2 (ECE-1 and ECE-2) are expressed in endothelial cells and neurones, respectively, and both cleave 'big endothelin' to produce the vasoconstrictor endothelin-1 (ET-1).", "citation": {"db": "PubMed", "db_id": "20345647"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2652, "target": 2653, "key": "893ff19ff8ca90504770b619ef4d08f6"}, {"line": 30366, "relation": "increases", "evidence": "Endothelin-converting enzyme-2 (ECE-2), which is expressed in neural tissues, cleaves 'big endothelin' to produce the vasoconstrictor endothelin-1.", "citation": {"db": "PubMed", "db_id": "19541930"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2652, "target": 2653, "key": "0227ebabc45e44145a2d38ec251bb557"}, {"line": 20059, "relation": "positiveCorrelation", "evidence": "ET-1 and ECE-2 are also elevated in AD, making it likely that upregulation of the ECE-2-ET-1 axis by Abeta42 contributes to the chronic reduction of CBF in AD.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2652, "target": 3823, "key": "1d35b1925240cdc338a88b77c3e9496d"}, {"relation": "partOf", "source": 2652, "target": 1405, "key": "47528f18dcefd5617cae668e0ea6d4ac"}, {"line": 20070, "relation": "isA", "evidence": "It has already been demonstrated that the endothelin receptor antagonist bosentan, preserves aortic and carotid endothelial function in Tg2576 mice, and our findings suggest that endothelin receptor antagonists may be beneficial in maintaining CBF in AD.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Confidence": {"High": true}}, "source": 220, "target": 109, "key": "05fe94b954a26a0997925e21edc3c7f8"}, {"line": 20071, "relation": "association", "evidence": "It has already been demonstrated that the endothelin receptor antagonist bosentan, preserves aortic and carotid endothelial function in Tg2576 mice, and our findings suggest that endothelin receptor antagonists may be beneficial in maintaining CBF in AD.", "citation": {"db": "PubMed", "db_id": "21193044"}, "annotations": {"Subgraph": {"Endothelin subgraph": true}, "Confidence": {"High": true}}, "source": 220, "target": 3823, "key": "9020010cbbb8533e55db13dca5b497a4"}, {"line": 20105, "relation": "association", "evidence": "The current experiments test the hypothesis that a vascular insult and aging are co-factors that contribute to dementia by evaluating the neuronal and functional integrity of the hippocampus following small, localized strokes induced by the potent vasoconstrictor, endothelin-1 (ET-1) in the rat model of hippocampal aging.", "citation": {"db": "PubMed", "db_id": "17561312"}, "annotations": {"MeSHDisease": {"Stroke": true, "Dementia": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10116": true}, "Subgraph": {"Endothelin subgraph": true}}, "source": 3778, "target": 3930, "key": "da80437883169bb82f14a06c4366e364"}, {"line": 20174, "relation": "increases", "evidence": "The increased phosphorylation of p27 at Thr187, rather than changes in the 26S proteasome activity, is likely responsible for the enhanced degradation of p27 in AD cells.", "citation": {"db": "PubMed", "db_id": "17448572"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Cyclin-CDK subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "object": {"modifier": "Degradation"}, "source": 3302, "target": 2494, "key": "5a8231a3491dcc7d7d556bd97be58131"}, {"line": 20219, "relation": "association", "evidence": "Recent data suggest that functional inactivation of TSC proteins might also be involved in the development of other diseases not associated with TSC, such as sporadic bladder cancer, breast cancer, ovarian carcinoma, gall bladder carcinoma, non-small-cell carcinoma of the lung, and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16713332"}, "annotations": {"MeSHDisease": {"Urinary Bladder Neoplasms": true, "Carcinoma, Non-Small-Cell Lung": true, "Tuberous Sclerosis": true, "Carcinoma": true, "Alzheimer Disease": true, "Breast Neoplasms": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 1639, "target": 3823, "key": "ce47667715b9d9ec38090a4a13a23c1f"}, {"line": 20225, "relation": "association", "evidence": "Furthermore, they are potent positive regulators of the cyclin-dependent kinase inhibitor p27, a major regulator of the mammalian cell cycle.", "citation": {"db": "PubMed", "db_id": "16713332"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 1639, "target": 2494, "key": "7785e93e9340e9be6a638289f77a2b86"}, {"relation": "partOf", "source": 3497, "target": 1639, "key": "4c9a7fe6034865273009b7a372586fe8"}, {"line": 20261, "relation": "association", "evidence": "CHOP potentially co-operates with FOXO3a in neuronal cells to regulate PUMA and BIM expression in response to ER stress.", "citation": {"db": "PubMed", "db_id": "22761832"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Response DNA damage": true}}, "source": 2622, "target": 2703, "key": "6c04ddf8f2f7569cdf9efb2f1e73af63"}, {"line": 20265, "relation": "increases", "evidence": "CHOP potentially co-operates with FOXO3a in neuronal cells to regulate PUMA and BIM expression in response to ER stress.", "citation": {"db": "PubMed", "db_id": "22761832"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Response DNA damage": true, "Bcl-2 subgraph": true}}, "source": 2622, "target": 2391, "key": "4ff119b5a6d9f6b44375377895ed5904"}, {"line": 20267, "relation": "increases", "evidence": "CHOP potentially co-operates with FOXO3a in neuronal cells to regulate PUMA and BIM expression in response to ER stress.", "citation": {"db": "PubMed", "db_id": "22761832"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Response DNA damage": true, "Bcl-2 subgraph": true}}, "source": 2622, "target": 2396, "key": "3602c052e79b699d839ee9b03159319d"}, {"relation": "partOf", "source": 2622, "target": 1390, "key": "175cc1dc1d90067e8fab1cb82ee88bcd"}, {"line": 20298, "relation": "decreases", "evidence": "Silencing GADD153/CHOP gene expression protects against Alzheimer's disease-like pathology induced by 27-hydroxycholesterol in rabbit hippocampus.", "citation": {"db": "PubMed", "db_id": "22046282"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Unfolded protein response subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 2622, "target": 3823, "key": "9d3899bb76a4bb150dde2de939ecbe9d"}, {"line": 20375, "relation": "positiveCorrelation", "evidence": "ICE-beta, c-Jun, Bax-alpha, Bcl-x(L), p53, and GADD153 were found to be upregulated in some AD samples but were not detected or downregulated in other AD or normal samples.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Subgraph": {"Response DNA damage": true}}, "source": 2622, "target": 3823, "key": "cd68190895b1a8e65a0eaba9181cbb1a"}, {"line": 20325, "relation": "increases", "evidence": "Activated gadd153 can generate oxidative damage and reactive oxygen species (ROS), increase beta-amyloid (Abeta) levels, disturb iron homeostasis and induce inflammation as well as cell death, which are all pathological hallmarks of AD.", "citation": {"db": "PubMed", "db_id": "22046282"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Alzheimer Disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Response DNA damage": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2622, "target": 170, "key": "b08209180e0a451c5fbb82507028e4ad"}, {"line": 20333, "relation": "increases", "evidence": "Activated gadd153 can generate oxidative damage and reactive oxygen species (ROS), increase beta-amyloid (Abeta) levels, disturb iron homeostasis and induce inflammation as well as cell death, which are all pathological hallmarks of AD.", "citation": {"db": "PubMed", "db_id": "22046282"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Alzheimer Disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "source": 2622, "target": 80, "key": "49d9b35a85c3e3f31904981a07c86043"}, {"line": 20334, "relation": "decreases", "evidence": "Activated gadd153 can generate oxidative damage and reactive oxygen species (ROS), increase beta-amyloid (Abeta) levels, disturb iron homeostasis and induce inflammation as well as cell death, which are all pathological hallmarks of AD.", "citation": {"db": "PubMed", "db_id": "22046282"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Alzheimer Disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "source": 2622, "target": 585, "key": "b053ed49068704e26160978b68201dcf"}, {"line": 20339, "relation": "increases", "evidence": "Activated gadd153 can generate oxidative damage and reactive oxygen species (ROS), increase beta-amyloid (Abeta) levels, disturb iron homeostasis and induce inflammation as well as cell death, which are all pathological hallmarks of AD.", "citation": {"db": "PubMed", "db_id": "22046282"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Alzheimer Disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "source": 2622, "target": 3920, "key": "356a0bd010e7055efeffa3091d1f63a4"}, {"line": 20345, "relation": "increases", "evidence": "Activated gadd153 can generate oxidative damage and reactive oxygen species (ROS), increase beta-amyloid (Abeta) levels, disturb iron homeostasis and induce inflammation as well as cell death, which are all pathological hallmarks of AD.", "citation": {"db": "PubMed", "db_id": "22046282"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Alzheimer Disease": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "source": 2622, "target": 505, "key": "3024d91d44de7b1daa9be920bfcf4d2e"}, {"line": 46623, "relation": "positiveCorrelation", "evidence": "The presence of misfolded proteins in the endoplasmic reticulum (ER) triggers a cellular stress response called the unfolded protein response (UPR) that may protect the cell against the toxic buildup of misfolded proteins. In this study we investigated the activation of the UPR in AD. Protein levels of BiP/GRP78, a molecular chaperone which is up-regulated during the UPR, was found to be increased in AD temporal cortex and hippocampus as determined by Western blot analysis.", "citation": {"db": "PubMed", "db_id": "15973543"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Chaperone subgraph": true, "Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Temporal Lobe": true}}, "source": 2849, "target": 550, "key": "e8cdeba1af3acf1cb319360c8424c102"}, {"line": 20278, "relation": "increases", "evidence": "Consistent with previous studies, we show that both PUMA and BIM are induced in response to ER stress in neuronal cells and that transcriptional induction of PUMA regulates ER stress-induced cell death, independent of p53.", "citation": {"db": "PubMed", "db_id": "22761832"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Bcl-2 subgraph": true}}, "source": 2391, "target": 505, "key": "6f1ff56cd75996ec0f9eecffbac605dc"}, {"line": 20363, "relation": "positiveCorrelation", "evidence": "Our results show an upregulation of gene expression in AD patients for c-Fos and BAK.", "citation": {"db": "PubMed", "db_id": "17712163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Bcl-2 subgraph": true}}, "source": 2388, "target": 3823, "key": "13eb09107bea9002ad6ac71bf07a7cfa"}, {"line": 20456, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 818, "key": "18bc1efd8ff7a10f452b7e0af00f4caa"}, {"line": 20460, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 820, "key": "df6d544a3438fe5880eb5b3c85d69e8e"}, {"line": 20464, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3762, "key": "a1136f4582623aab1f0d1ce7395fa3d2"}, {"line": 20468, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3763, "key": "6087abb61a1fc9327df162bc80cb13f6"}, {"line": 20472, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3764, "key": "1c2a0e877a9e436396b4ef04d6fe1415"}, {"line": 20476, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3765, "key": "0deec4632f4206f901a4825fd121c357"}, {"line": 20480, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3766, "key": "35231dc7c292469a64e4327d96791b7e"}, {"line": 20484, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3767, "key": "54bf5cf79f7ef74fb6476ca276079202"}, {"line": 20488, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3768, "key": "ba0f8f7b5ff56cca65c7ff47bc02ccff"}, {"line": 20492, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3761, "key": "9369759ad240c2337c3cf422e8843aba"}, {"line": 20496, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3769, "key": "bb28f4d94c911558732b1996c33c3f2f"}, {"line": 20500, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3770, "key": "9665321cd94402c32c9f9528d4af95ec"}, {"line": 20504, "relation": "increases", "evidence": "In the rats injected with insulin, it was found that their learning and memory abilities were improved significantly (P < 0.01) and that the expression of the nicotinic acetylcholine receptors were increased and GFAP positive astrocytes were decreased obviously (P < 0.05), as compared with the model rats. Insulin is able to enhance the learning and memory abilities of the Alzheimer's disease-like rats, possibly by improving the function of the acetylcholine system and decreasing the astrocytes proliferation in the brain.", "citation": {"db": "PubMed", "db_id": "21158163"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "Subgraph": {"Acetylcholine signaling subgraph": true, "Insulin signal transduction": true}, "Confidence": {"Medium": true}}, "source": 3789, "target": 3771, "key": "742256cdfd7add96c155cc1c86e4ebc5"}, {"line": 20522, "relation": "decreases", "evidence": "In early-onset familial Alzheimer disease, the inhibition of neuronal insulin receptor function may be due to competitive binding of amyloid beta (Abeta) to the insulin receptor.", "citation": {"db": "PubMed", "db_id": "15094078"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1231, "target": 2900, "key": "7cbcf1ebc6667a6b72d69ba4a9221ab4"}, {"line": 36385, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1231, "target": 3177, "key": "f382ba24d9c38ba785234ba07a72f6ef"}, {"line": 36392, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 1231, "target": 2282, "key": "e814a669a59efde9584e377720a970e3"}, {"line": 20569, "relation": "association", "evidence": "Unfolded protein response signaling by transcription factor XBP-1 regulates ADAM10 and is affected in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 3537, "target": 2249, "key": "df3b2d4af9f03d212c7837b008ab26ba"}, {"line": 20580, "relation": "increases", "evidence": "One selective inducer of ADAM10 gene expression is the X-box binding protein-1 (XBP-1).", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 3537, "target": 2249, "key": "bbad9f69df8c4a227fb11c2be25d2d24"}, {"line": 20581, "relation": "increases", "evidence": "One selective inducer of ADAM10 gene expression is the X-box binding protein-1 (XBP-1).", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 3537, "target": 677, "key": "5cfd904ec8c88a0382407e9786a09c45"}, {"line": 20594, "relation": "increases", "evidence": "Our results demonstrate that XBP-1 is a driver of ADAM10 gene expression and that disturbance of this pathway might contribute to development or progression of AD.", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 3537, "target": 677, "key": "6742555528ad7f9967e7242e852bbc76"}, {"relation": "partOf", "source": 3537, "target": 1484, "key": "79d33052a5fcc3c87b55e136e7c3d9c2"}, {"relation": "hasVariant", "source": 3537, "target": 3538, "key": "8578c87880d416be883e6ef02757eba6"}, {"line": 20639, "relation": "decreases", "evidence": "Trying to identify the mechanisms mediating XBP1s neuroprotection, we found that in PC12 cells treated with Abeta oligomers, XBP1s prevents the accumulation of free calcium (Ca(2+)) in the cytosol.", "citation": {"db": "PubMed", "db_id": "21389082"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "CellStructure": {"Cytosol": true}}, "source": 3537, "target": 94, "key": "07234b01312a935adf7a53aae30d5036"}, {"line": 20647, "relation": "decreases", "evidence": "This protective activity can be mediated by the downregulation of a specific isoform of the ryanodine Ca(2+) channel, RyR3.", "citation": {"db": "PubMed", "db_id": "21389082"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Calcium-dependent signal transduction": true}}, "source": 3537, "target": 3333, "key": "a73dfd333ee4b27aa8d609558790ab8f"}, {"line": 20582, "relation": "association", "evidence": "One selective inducer of ADAM10 gene expression is the X-box binding protein-1 (XBP-1).", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 677, "target": 2249, "key": "cb191af1927fc3784c2249e0bcfecf06"}, {"line": 20595, "relation": "association", "evidence": "Our results demonstrate that XBP-1 is a driver of ADAM10 gene expression and that disturbance of this pathway might contribute to development or progression of AD.", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 677, "target": 2249, "key": "9b53501b2d8e317f348f2c3869121a95"}, {"line": 20588, "relation": "increases", "evidence": "We demonstrate that particularly the spliced XBP-1 variant dose dependently regulates ADAM10 expression, which can be synergistically enhanced by 100 nM insulin.", "citation": {"db": "PubMed", "db_id": "24165480"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "source": 1484, "target": 2249, "key": "1a998492d5fd1d211eb1cf01cddf0b81"}, {"line": 20608, "relation": "positiveCorrelation", "evidence": "Polymorphism -116C/G of human X-box-binding protein 1 promoter is associated with risk of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "23421912"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"9606": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 3538, "target": 3823, "key": "c8d4b960863fd504c55d1ad4ee68ae24"}, {"line": 20658, "relation": "increases", "evidence": "We have shown previously that activation of PKR in Abeta-triggered apoptotic process.", "citation": {"db": "PubMed", "db_id": "16532272"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2662, "target": 478, "key": "184a4dae545fd70588a027a94b3ec140"}, {"relation": "partOf", "source": 2662, "target": 1409, "key": "215aa6ce9054735e0ce643431c48552f"}, {"relation": "hasVariant", "source": 2662, "target": 2663, "key": "5c80dc0700d5176d19064109acf9c646"}, {"relation": "partOf", "source": 2662, "target": 1410, "key": "e3ecd4d25728ac97ca176471ecc7a17e"}, {"line": 30394, "relation": "increases", "evidence": "Furthermore, statistical correlations between proteins and genes suggest that active PKR could phosphorylate p53 which could induce the transcription of Redd1 gene.", "citation": {"db": "PubMed", "db_id": "19210572"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Tumor necrosis factor subgraph": true, "p53 stabilization subgraph": true}}, "source": 2662, "target": 3483, "key": "df923f98154d768fb85920bd62409b01"}, {"line": 30403, "relation": "increases", "evidence": "PERK, an ER-resident transmembrane protein kinase, is also a sensor for the unfolded protein response (UPR), causing phosphorylation of eukaryotic initiation factor 2alpha (eIF2alpha) to inhibit translation initiation.", "citation": {"db": "PubMed", "db_id": "12163019"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 2662, "target": 2667, "key": "3c0aaf51e5ea92e8770b6845a52336e7"}, {"line": 33419, "relation": "directlyIncreases", "evidence": "Among downstream factors of PKR, the Fas-associated protein with a death domain (FADD) and subsequent activated caspase-8 are responsible for PKR-induced apoptosis in recombinant virus-infected cells.", "citation": {"db": "PubMed", "db_id": "19889624"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Caspase subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 2662, "target": 2448, "key": "df92d4011736006cf022daea94bb3fcc"}, {"line": 34612, "relation": "increases", "evidence": "We confirm effects of three kinases from this screen, the eukaryotic translation initiation factor 2 alpha kinase 2 (EIF2AK2), the dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A), and the A-kinase anchor protein 13 (AKAP13) on tau phosphorylation at the 12E8 epitope (serine 262/serine 356).", "citation": {"db": "PubMed", "db_id": "20067632"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "source": 2662, "target": 3023, "key": "0c9fdd36b5b20e7c907d10610945cb80"}, {"line": 34613, "relation": "increases", "evidence": "We confirm effects of three kinases from this screen, the eukaryotic translation initiation factor 2 alpha kinase 2 (EIF2AK2), the dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A), and the A-kinase anchor protein 13 (AKAP13) on tau phosphorylation at the 12E8 epitope (serine 262/serine 356).", "citation": {"db": "PubMed", "db_id": "20067632"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "source": 2662, "target": 3025, "key": "03517ee7bf447151406b7dddd9c8ca84"}, {"line": 20696, "relation": "negativeCorrelation", "evidence": "Deficiency of ABCC1 substantially increased cerebral Abeta levels without altering the expression of most enzymes that would favor the production of Abeta from the Abeta precursor protein.", "citation": {"db": "PubMed", "db_id": "21881209"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3579, "target": 80, "key": "350669cb5035b68919d0cc7a5abe971f"}, {"line": 20708, "relation": "decreases", "evidence": "In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced Abeta load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1.", "citation": {"db": "PubMed", "db_id": "21881209"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Nausea": true, "Vomiting": true, "Alzheimer Disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3579, "target": 2328, "key": "574dbefa36b6e7e8fdca36a8cc22e8a0"}, {"line": 20707, "relation": "increases", "evidence": "In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced Abeta load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1.", "citation": {"db": "PubMed", "db_id": "21881209"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Nausea": true, "Vomiting": true, "Alzheimer Disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 364, "target": 3579, "key": "67c861de53e0028f61bbb1c6f41706a1"}, {"line": 20709, "relation": "decreases", "evidence": "In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced Abeta load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1.", "citation": {"db": "PubMed", "db_id": "21881209"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Nausea": true, "Vomiting": true, "Alzheimer Disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 364, "target": 3922, "key": "24be50b60521346b40c5189f20dd55ac"}, {"line": 20710, "relation": "decreases", "evidence": "In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced Abeta load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1.", "citation": {"db": "PubMed", "db_id": "21881209"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Nausea": true, "Vomiting": true, "Alzheimer Disease": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 364, "target": 3932, "key": "e97db8e32396faecf450c93be1ab9cc6"}, {"line": 20731, "relation": "association", "evidence": "Recent studies have unraveled important roles of ABC transporters including ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistant protein, BCRP), ABCC1 (multidrug resistance protein 1, MRP1), and the cholesterol transporter ABCA1 in the pathogenesis of AD and Abeta peptides deposition inside the brain.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 2239, "target": 3823, "key": "731dbc89261bbc657092c3e5401ce58e"}, {"line": 20741, "relation": "negativeCorrelation", "evidence": "In the brains of Abcg2 knockout mice, NF-kB activation as a result of Abcg2 deficiency increased Abeta deposition compared to controls. This result was further confirmed in vitro in N2a-695 cells where overexpression of ABCG2 significantly decreased the processing rate of APP and Abeta production as compared with controls.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2239, "target": 2328, "key": "b9bf5142523f7f830f03169caf586ece"}, {"line": 20742, "relation": "decreases", "evidence": "In the brains of Abcg2 knockout mice, NF-kB activation as a result of Abcg2 deficiency increased Abeta deposition compared to controls. This result was further confirmed in vitro in N2a-695 cells where overexpression of ABCG2 significantly decreased the processing rate of APP and Abeta production as compared with controls.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}, "Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2239, "target": 871, "key": "fb2b9fcbc0e1e47a9becf5a0038ed2ad"}, {"relation": "partOf", "source": 2239, "target": 1038, "key": "2d1135e94482f07b4cd2158b875a32e7"}, {"relation": "partOf", "source": 2239, "target": 1039, "key": "cf4a32c2a96bf0d50846f4b32bd9cdde"}, {"line": 20732, "relation": "association", "evidence": "Recent studies have unraveled important roles of ABC transporters including ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistant protein, BCRP), ABCC1 (multidrug resistance protein 1, MRP1), and the cholesterol transporter ABCA1 in the pathogenesis of AD and Abeta peptides deposition inside the brain.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "source": 2236, "target": 3823, "key": "49f75d41e0131d069e01a4335c2c031d"}, {"line": 20733, "relation": "association", "evidence": "Recent studies have unraveled important roles of ABC transporters including ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistant protein, BCRP), ABCC1 (multidrug resistance protein 1, MRP1), and the cholesterol transporter ABCA1 in the pathogenesis of AD and Abeta peptides deposition inside the brain.", "citation": {"db": "PubMed", "db_id": "23181169"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"ATP binding cassette 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"effect": {"name": "tport", "namespace": "bel"}}, "source": 2229, "target": 2328, "key": "65dd9f7b2d6312a1227849fb3f3c8bac"}, {"line": 38151, "relation": "increases", "evidence": "These data indicate that ABCA1 and ABCG1 play a significant role in the regulation of neuronal cholesterol efflux to apoE discs and in suppression of APP processing to generate Abeta peptides.", "citation": {"db": "PubMed", "db_id": "17121837"}, "annotations": {"MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2229, "target": 526, "key": "1bab50e411a6a2daf265413cd7387816"}, {"line": 20806, "relation": "association", "evidence": "The role of the urokinase receptor in epilepsy, in disorders of language, cognition, communication and behavior, and in the central nervous system.", "citation": {"db": "PubMed", "db_id": "21711233"}, "annotations": {"MeSHDisease": {"Epilepsy": true}, "MeSHAnatomy": {"Central Nervous System": true}, 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"target": 914, "key": "f71ac067e0546360bbe7c626c2ae1692"}, {"line": 21172, "relation": "decreases", "evidence": "Gold and silver nanoparticles (average 4.0 nm) inhibited fibril formation when added to the induced fibrils from nNOS-Abeta incubation.", "citation": {"db": "PubMed", "db_id": "24293250"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 173, "target": 474, "key": "5e47becb815191214628697e2a99c132"}, {"line": 21173, "relation": "decreases", "evidence": "Gold and silver nanoparticles (average 4.0 nm) inhibited fibril formation when added to the induced fibrils from nNOS-Abeta incubation.", "citation": {"db": "PubMed", "db_id": "24293250"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 125, "target": 474, "key": "bbec4d94f8a13000ad1a8feefdeef1e7"}, {"line": 21219, "relation": "increases", "evidence": "Here, we demonstrate that neuronal nitric oxide synthase (NOS1) interacts with Cdk5 and that the close proximity of the two proteins facilitates the formation of SNO-Cdk5.", "citation": {"db": "PubMed", "db_id": "22874667"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1349, "target": 665, "key": "5fadc4870f1f2dd7c74010dcc9b44fa4"}, {"line": 21264, "relation": "decreases", "evidence": "Structural comparison between the three most representative families of compounds (kynurenines, kynurenamines and 4,5-dihydro-1H-pyrazole derivatives) allows the establishment of structure-activity relationships for the inhibition of nNOS, and a pharmacophore model that fulfills all of the observed SARs were developed.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 288, "target": 3121, "key": "e78465aad5eea8883caedb9d8bfec46e"}, {"line": 21268, "relation": "decreases", "evidence": "Structural comparison between the three most representative families of compounds (kynurenines, kynurenamines and 4,5-dihydro-1H-pyrazole derivatives) allows the establishment of structure-activity relationships for the inhibition of nNOS, and a pharmacophore model that fulfills all of the observed SARs were developed.", "citation": {"db": "PubMed", "db_id": "22512552"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 13, "target": 3121, "key": "a66c331b91e598804a4d1ede3220b19e"}, {"relation": "partOf", "source": 2857, "target": 986, "key": "4771eb18dd56cd2e651b6688a54023bb"}, {"line": 21311, "relation": "negativeCorrelation", "evidence": "Reduced serotonin 5-HT1A receptor binding in the temporal cortex correlates with aggressive behavior in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "12742626"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "subject": {"modifier": "Activity"}, "source": 2857, "target": 3823, "key": "088cd4660400d4115523497225701abc"}, {"line": 21310, "relation": "negativeCorrelation", "evidence": "Reduced serotonin 5-HT1A receptor binding in the temporal cortex correlates with aggressive behavior in Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "12742626"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 986, "target": 3823, "key": "b18016721efc772724746be4516d5316"}, {"line": 21339, "relation": "increases", "evidence": "Oxidative stress activates the PKCdelta kinase by translocation, tyrosine phosphorylation, or proteolysis.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3242, "target": 3238, "key": "9e747c68e4e744377c2089020e154cf2"}, {"line": 21419, "relation": "increases", "evidence": "We found that the kinase activity of PKC-delta but not that of PKC-alpha or -epsilon is increased by stimulation of microglia with Abeta, with a striking tyrosine phosphorylation of PKC-delta.", "citation": {"db": "PubMed", "db_id": "11290384"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "object": {"modifier": "Activity"}, "source": 3242, "target": 3238, "key": "9757cede0037b3b81b975e6e5193c6f4"}, {"line": 21350, "relation": "increases", "evidence": "During proteolysis, caspase-3 cleaves the native PKCdelta (72-74 kDa) into 41-kDa catalytically active and 38-kDa regulatory fragments to persistently activate the kinase.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1715, "target": 3238, "key": "c6d9c1ae5a652707a1f223ca047d48e6"}, {"line": 21357, "relation": "increases", "evidence": "The proteolytic activation of PKCdelta plays a key role in promoting apoptotic cell death in various cell types, including neuronal cells.", "citation": {"db": "PubMed", "db_id": "14580317"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Interferon signaling subgraph": true, "Response to oxidative stress": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1715, "target": 3238, "key": "5039a217ab0ff3cdb9a6ac0fe4635d13"}, {"relation": "partOf", "source": 3240, "target": 1715, "key": "c80e204ee0172112cd25d32cce88ecc9"}, {"relation": "partOf", "source": 3241, "target": 1715, "key": "b9d2cb1d3371e2f8e6c00409bde3f848"}, {"line": 21387, "relation": "increases", "evidence": "Acrolein induces Hsp72 via both PKCdelta/JNK and calcium signaling pathways in human umbilical vein endothelial cells.", "citation": {"db": "PubMed", "db_id": "16036326"}, "annotations": {"Cell": {"endothelial cell of umbilical vein": true}, "Species": {"9606": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "JAK-STAT signaling subgraph": true}}, "object": {"modifier": "Activity"}, "source": 206, "target": 2187, "key": "ec3547eeba51ecf6bd490a5df6089ad8"}, {"line": 21388, "relation": "increases", "evidence": "Acrolein induces Hsp72 via both PKCdelta/JNK and calcium signaling pathways in human umbilical vein endothelial cells.", "citation": {"db": "PubMed", "db_id": "16036326"}, "annotations": {"Cell": {"endothelial cell of umbilical vein": true}, "Species": {"9606": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true, "JAK-STAT signaling subgraph": true}}, "source": 206, "target": 3238, "key": "ad9f96973eb0ff251f268aa6fb8afd88"}, {"line": 21395, "relation": "increases", "evidence": "A number of studies have reported that acrolein evokes downstream signaling via an elevation in cellular oxidative stress.", "citation": {"db": "PubMed", "db_id": "16036326"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "JAK-STAT signaling subgraph": true}}, "source": 206, "target": 842, "key": "873d890c256fe1cd5131fb16fd9e9a31"}, {"relation": "isA", "source": 2846, "target": 2182, "key": "77bc09157a3784e7fca8afc9e9467b4c"}, {"line": 21453, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 2206, "target": 3823, "key": "219c1cc50675ca1bc7c9ef8cfd9178bb"}, {"line": 21456, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2206, "target": 3545, "key": "66d3a03b79343eed2fb3a0151548a450"}, {"line": 21467, "relation": "positiveCorrelation", "evidence": "To further evaluate the dependence of PLC on PSs we measured PLC activity and the activation of variant protein kinase C (PKC) isoforms in mouse embryonic fibroblasts (MEFs) lacking either PS1, PS2, or both. PLC activity and PKCalpha and PKCgamma activations were significantly lower in PS1 and PS2 double knockout MEFs after PLC stimulation. Protein levels of PKCalpha and PKCgamma were lower in PS1 and PS2 double knockout MEFs.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Cell": {"fibroblast": true}}, "subject": {"modifier": "Activity"}, "source": 2206, "target": 1724, "key": "bd96d567bf650fe66fc3dfa0936ea064"}, {"line": 21454, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 471, "target": 3823, "key": "2c6b47bc8b51841e2128cb4285109f80"}, {"line": 21457, "relation": "association", "evidence": "We previously showed that some familial Alzheimer's disease PS mutations cause increased basal and acetylcholine muscarinic receptor-stimulated phospholipase C (PLC) activity which was gamma-secretase dependent.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 471, "target": 3545, "key": "0647cdcda280f4761b6b1d7e70474a6b"}, {"line": 21465, "relation": "positiveCorrelation", "evidence": "To further evaluate the dependence of PLC on PSs we measured PLC activity and the activation of variant protein kinase C (PKC) isoforms in mouse embryonic fibroblasts (MEFs) lacking either PS1, PS2, or both. PLC activity and PKCalpha and PKCgamma activations were significantly lower in PS1 and PS2 double knockout MEFs after PLC stimulation. Protein levels of PKCalpha and PKCgamma were lower in PS1 and PS2 double knockout MEFs.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Cell": {"fibroblast": true}}, "object": {"modifier": "Activity"}, "source": 1724, "target": 3702, "key": "040316f06b05839d0f4e420574d75bc5"}, {"line": 21466, "relation": "positiveCorrelation", "evidence": "To further evaluate the dependence of PLC on PSs we measured PLC activity and the activation of variant protein kinase C (PKC) isoforms in mouse embryonic fibroblasts (MEFs) lacking either PS1, PS2, or both. PLC activity and PKCalpha and PKCgamma activations were significantly lower in PS1 and PS2 double knockout MEFs after PLC stimulation. Protein levels of PKCalpha and PKCgamma were lower in PS1 and PS2 double knockout MEFs.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Cell": {"fibroblast": true}}, "object": {"modifier": "Activity"}, "source": 1724, "target": 3700, "key": "0f024f37fe834f1d16b8b9ab1852f7a0"}, {"line": 21467, "relation": "positiveCorrelation", "evidence": "To further evaluate the dependence of PLC on PSs we measured PLC activity and the activation of variant protein kinase C (PKC) isoforms in mouse embryonic fibroblasts (MEFs) lacking either PS1, PS2, or both. PLC activity and PKCalpha and PKCgamma activations were significantly lower in PS1 and PS2 double knockout MEFs after PLC stimulation. Protein levels of PKCalpha and PKCgamma were lower in PS1 and PS2 double knockout MEFs.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Cell": {"fibroblast": true}}, "object": {"modifier": "Activity"}, "source": 1724, "target": 2206, "key": "04e3a212a8bfb160ca486556699f22ca"}, {"line": 21471, "relation": "negativeCorrelation", "evidence": "In contrast, PKCdelta levels were significantly elevated in PS1 and PS2 double knockout as well as in PS1 knockout MEFs. Also, PKCdelta levels were lowered after transfection of PS1 into PS1 knockout or PS double knockout MEFs.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Cell": {"fibroblast": true}}, "object": {"modifier": "Activity"}, "source": 1724, "target": 3701, "key": "f99c52c659a451f94bc0bbb83aa87325"}, {"relation": "partOf", "source": 3703, "target": 1724, "key": "f2ada3cf4e58bfc4c4686f0d1fdb0eed"}, {"line": 41304, "relation": "association", "evidence": "Pan-PPAR Modulation Effectively Protects APP/PS1 Mice from Amyloid Deposition and Cognitive Deficits.", "citation": {"db": "PubMed", "db_id": "24838579"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3703, "target": 3584, "key": "bcb8bf2e2ebaecedf315ccf899df8eca"}, {"line": 41892, "relation": "association", "evidence": "NOS2 deficiency or oral treatment with the NOS2 inhibitor L-NIL strongly decreased 3NTyr(10)-Abeta, overall Abeta deposition and cognitive dysfunction in APP/PS1 mice.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Confidence": {"High": true}, "Species": {"10090": true}}, "source": 3703, "target": 3584, "key": "862d74a7cd2d1ee8c66748eea53b4865"}, {"line": 41620, "relation": "association", "evidence": "To this end, we now report that Ccl2 deficiency influences behavioral abnormalities and disease progression in Abeta precursor protein/presenilin-1 double-transgenic mice.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3703, "target": 3602, "key": "3cb7606897e8f063b0e49ba86de15e4c"}, {"line": 42454, "relation": "association", "evidence": "Evaluation of DSP-8658 in the amyloid precursor protein/presenilin 1 mouse model confirmed an increased microglial Abeta phagocytosis in vivo, which subsequently resulted in a reduction of cortical and hippocampal Abeta levels.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 3703, "target": 414, "key": "86baaecbd5efb13c477ea1eab65a3992"}, {"relation": "partOf", "source": 3704, "target": 1724, "key": "abf80c22eb599b74d764e120f8a36089"}, {"line": 21465, "relation": "positiveCorrelation", "evidence": "To further evaluate the dependence of PLC on PSs we measured PLC activity and the activation of variant protein kinase C (PKC) isoforms in mouse embryonic fibroblasts (MEFs) lacking either PS1, PS2, or both. PLC activity and PKCalpha and PKCgamma activations were significantly lower in PS1 and PS2 double knockout MEFs after PLC stimulation. Protein levels of PKCalpha and PKCgamma were lower in PS1 and PS2 double knockout MEFs.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Cell": {"fibroblast": true}}, "subject": {"modifier": "Activity"}, "source": 3702, "target": 1724, "key": "c512377e7e0397c289f0b5c36701d5b8"}, {"line": 21466, "relation": "positiveCorrelation", "evidence": "To further evaluate the dependence of PLC on PSs we measured PLC activity and the activation of variant protein kinase C (PKC) isoforms in mouse embryonic fibroblasts (MEFs) lacking either PS1, PS2, or both. PLC activity and PKCalpha and PKCgamma activations were significantly lower in PS1 and PS2 double knockout MEFs after PLC stimulation. Protein levels of PKCalpha and PKCgamma were lower in PS1 and PS2 double knockout MEFs.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Cell": {"fibroblast": true}}, "subject": {"modifier": "Activity"}, "source": 3700, "target": 1724, "key": "9a1f2b6e63711a2b0fd910c053345e5b"}, {"line": 21471, "relation": "negativeCorrelation", "evidence": "In contrast, PKCdelta levels were significantly elevated in PS1 and PS2 double knockout as well as in PS1 knockout MEFs. Also, PKCdelta levels were lowered after transfection of PS1 into PS1 knockout or PS double knockout MEFs.", "citation": {"db": "PubMed", "db_id": "17437536"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Cell": {"fibroblast": true}}, "subject": {"modifier": "Activity"}, "source": 3701, "target": 1724, "key": "c4c4e5d4da74147a81c586925ce1d983"}, {"line": 21486, "relation": "increases", "evidence": "In cells treated with wortmannin, protein kinase C delta fragments were observed, and the protein kinase C activity increased after 3 hours, whereas treatment of cells with z-DEVD-fmk, an inhibitor of caspase-3, inhibited fragmentation of protein kinase C delta and induced continuous activation of GSK-3.", "citation": {"db": "PubMed", "db_id": "10850726"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Caspase subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 370, "target": 3238, "key": "5211c0c33b8bc9a7bea18d2593d9c279"}, {"line": 29678, "relation": "increases", "evidence": "The basal kinase activities of protein kinase-A (PKA), CaM Kinase II and GSK-3 were stimulated more than two-fold by isoproterenol, bradykinin and wortmannin, respectively. ", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 370, "target": 2794, "key": "812a8f4edb2aa8ee265aab3c43e19a1a"}, {"line": 21487, "relation": "decreases", "evidence": "In cells treated with wortmannin, protein kinase C delta fragments were observed, and the protein kinase C activity increased after 3 hours, whereas treatment of cells with z-DEVD-fmk, an inhibitor of caspase-3, inhibited fragmentation of protein kinase C delta and induced continuous activation of GSK-3.", "citation": {"db": "PubMed", "db_id": "10850726"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Caspase subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 71, "target": 2444, "key": "78d60703d43f89358017b69cfafe462e"}, {"line": 21488, "relation": "decreases", "evidence": "In cells treated with wortmannin, protein kinase C delta fragments were observed, and the protein kinase C activity increased after 3 hours, whereas treatment of cells with z-DEVD-fmk, an inhibitor of caspase-3, inhibited fragmentation of protein kinase C delta and induced continuous activation of GSK-3.", "citation": {"db": "PubMed", "db_id": "10850726"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Caspase subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "source": 71, "target": 3238, "key": "ef1ac5e7be76c7b0760dd45dfbd17e75"}, {"line": 21489, "relation": "increases", "evidence": "In cells treated with wortmannin, protein kinase C delta fragments were observed, and the protein kinase C activity increased after 3 hours, whereas treatment of cells with z-DEVD-fmk, an inhibitor of caspase-3, inhibited fragmentation of protein kinase C delta and induced continuous activation of GSK-3.", "citation": {"db": "PubMed", "db_id": "10850726"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Caspase subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 71, "target": 2178, "key": "2593436beb433044b3b2e5f52b7916c0"}, {"line": 21499, "relation": "increases", "evidence": "It is suggested that fragmentation of protein kinase C delta during the process of apoptosis results in the phosphorylation and the inactivation of GSK-3.", "citation": {"db": "PubMed", "db_id": "10850726"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Caspase subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3239, "target": 2179, "key": "dd25dfdcc0159013dc2092542d7c679b"}, {"line": 21500, "relation": "decreases", "evidence": "It is suggested that fragmentation of protein kinase C delta during the process of apoptosis results in the phosphorylation and the inactivation of GSK-3.", "citation": {"db": "PubMed", "db_id": "10850726"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Caspase subgraph": true, "GSK3 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3239, "target": 2178, "key": "88577ec2ae33207d0a5de4de00d88c10"}, {"line": 21541, "relation": "decreases", "evidence": "Recent studies have shown that not only albumin concentration but also albumin function is reduced in liver failure.", "citation": {"db": "PubMed", "db_id": "23423799"}, "annotations": {"MeSHDisease": {"Liver Failure": true}, "Subgraph": {"Albumin subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3864, "target": 2284, "key": "0037c0b141f4f598af0c60fbb5c9952a"}, {"line": 21542, "relation": "decreases", "evidence": "Recent studies have shown that not only albumin concentration but also albumin function is reduced in liver failure.", "citation": {"db": "PubMed", "db_id": "23423799"}, "annotations": {"MeSHDisease": {"Liver Failure": true}, "Subgraph": {"Albumin subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3864, "target": 2284, "key": "fab598192ff5705698fe4965f181b44a"}, {"line": 21573, "relation": "increases", "evidence": "Dotarizine (30--50 microM) enhanced basal apoptosis to 18--43%, flunarizine (30--50 microM) to 15%, thapsigargin (1--10 microM) to 21--35%, and cyclopiazonic acid (100 microM) to 10%.", "citation": {"db": "PubMed", "db_id": "16153637"}, "source": 363, "target": 478, "key": "5c8abaa63b78a0db0709e87a3d3c23b1"}, {"line": 21574, "relation": "increases", "evidence": "Dotarizine (30--50 microM) enhanced basal apoptosis to 18--43%, flunarizine (30--50 microM) to 15%, thapsigargin (1--10 microM) to 21--35%, and cyclopiazonic acid (100 microM) to 10%.", "citation": {"db": "PubMed", "db_id": "16153637"}, "source": 103, "target": 478, "key": "fe3f0df763fad3de0c08a25f3cb809cf"}, {"line": 21584, "relation": "increases", "evidence": "In serum-free medium, albumin (29 or 49 mg/ml) fully prevented the apoptotic effects of dotarizine, flunarizine and cyclopiazonic acid.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 103, "target": 478, "key": "6cc2153f14900844d1bd9b744ec5b464"}, {"line": 21575, "relation": "increases", "evidence": "Dotarizine (30--50 microM) enhanced basal apoptosis to 18--43%, flunarizine (30--50 microM) to 15%, thapsigargin (1--10 microM) to 21--35%, and cyclopiazonic acid (100 microM) to 10%.", "citation": {"db": "PubMed", "db_id": "16153637"}, "source": 247, "target": 478, "key": "ee975a134ad7452d39a15d465c8abd7c"}, {"line": 21586, "relation": "increases", "evidence": "In serum-free medium, albumin (29 or 49 mg/ml) fully prevented the apoptotic effects of dotarizine, flunarizine and cyclopiazonic acid.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 247, "target": 478, "key": "defa06e76e4a409c8ea4b7861364dd8a"}, {"line": 21576, "relation": "increases", "evidence": "Dotarizine (30--50 microM) enhanced basal apoptosis to 18--43%, flunarizine (30--50 microM) to 15%, thapsigargin (1--10 microM) to 21--35%, and cyclopiazonic acid (100 microM) to 10%.", "citation": {"db": "PubMed", "db_id": "16153637"}, "source": 47, "target": 478, "key": "178f4d693a0b569dd22959dbd416eaac"}, {"line": 21588, "relation": "increases", "evidence": "In serum-free medium, albumin (29 or 49 mg/ml) fully prevented the apoptotic effects of dotarizine, flunarizine and cyclopiazonic acid.", "citation": {"db": "PubMed", "db_id": "16153637"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Albumin subgraph": true}, "Confidence": {"High": true}}, "source": 47, "target": 478, "key": "8a740e7696950819680b80c3534058dc"}, {"line": 21634, "relation": "positiveCorrelation", "evidence": "Increased expression of phosphorylated S6, phosphorylated S6 kinase, phosphorylated eukaryotic initiation factor 4E binding protein 1, and phosphorylated mTOR was observed in DS hippocampus compared with controls.", "citation": {"db": "PubMed", "db_id": "24918639"}, "annotations": {"MeSHDisease": {"Down Syndrome": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Interferon signaling subgraph": true}}, "source": 3328, "target": 3852, "key": "fc8966e3eab41e9247a052e4fac09d13"}, {"line": 21635, "relation": "positiveCorrelation", "evidence": "Increased expression of phosphorylated S6, phosphorylated S6 kinase, phosphorylated eukaryotic initiation factor 4E binding protein 1, and phosphorylated mTOR was observed in DS hippocampus compared with controls.", "citation": {"db": "PubMed", "db_id": "24918639"}, "annotations": {"MeSHDisease": {"Down Syndrome": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Interferon signaling subgraph": true}}, "source": 2669, "target": 3852, "key": "60fac712d405ae375b2e8fa87bb7a19d"}, {"relation": "hasVariant", "source": 2668, "target": 2669, "key": "6a07d40631864fb23e2d946d463c18b6"}, {"line": 21637, "relation": "positiveCorrelation", "evidence": "Increased expression of phosphorylated S6, phosphorylated S6 kinase, phosphorylated eukaryotic initiation factor 4E binding protein 1, and phosphorylated mTOR was observed in DS hippocampus compared with controls.", "citation": {"db": "PubMed", "db_id": "24918639"}, "annotations": {"MeSHDisease": {"Down Syndrome": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3077, "target": 3852, "key": "9ff6b19ac16fa7f1ede153184cdf5345"}, {"line": 21670, "relation": "negativeCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3851, "target": 2905, "key": "0196ee2cee3d95f76b39ba2d1baa2365"}, {"line": 21671, "relation": "negativeCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3851, "target": 2905, "key": "c3a2912df56ef4dc7ea05a213efdc531"}, {"line": 21679, "relation": "negativeCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 3851, "target": 2900, "key": "62e3e3db60d81192ae692cd8598e2baa"}, {"line": 21687, "relation": "positiveCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3851, "target": 3327, "key": "8c34dbb8c8a24925b3ba7aec90e65413"}, {"line": 21688, "relation": "positiveCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3851, "target": 2907, "key": "a337aeaf6da41e074d9912f71e8e9ecf"}, {"line": 21688, "relation": "positiveCorrelation", "evidence": "Postpartum biopsies were collected in five weight-matched GDM women with IGT (GDM/IGT). GDM women had decreased skeletal muscle insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine activation and reduced IRS1, concomitant with increased basal IRS1 serine phosphorylation and basal p70 S6-kinase (S6K1) activation, which resolved postpartum.", "citation": {"db": "PubMed", "db_id": "21289241"}, "annotations": {"MeSHDisease": {"Diabetes, Gestational": true}, "Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2907, "target": 3851, "key": "24b5e042966a90225f8ce37e84f96ede"}, {"line": 33715, "relation": "positiveCorrelation", "evidence": "In concordance, significant increases in the levels of phosphorylation of total Akt substrates, including: GSK3beta(Ser9), tau(Ser214), mTOR(Ser2448), and decreased levels of the Akt target, p27(kip1), were found in AD temporal cortex compared with controls.", "citation": {"db": "PubMed", "db_id": "15773910"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Tau protein subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "source": 3021, "target": 3823, "key": "a5a8f364c0c2fc3d03aac300d8e27e9c"}, {"relation": "partOf", "source": 3033, "target": 1568, "key": "83f3587ad6b7e55abde6c1c5a022aa6d"}, {"line": 21792, "relation": "increases", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2288, "target": 873, "key": "a7d54f35170991f3c223c2c2c0503888"}, {"line": 21793, "relation": "increases", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2288, "target": 143, "key": "f2bf3d1709cf6d02f3a729ed47d441cc"}, {"relation": "hasVariant", "source": 2288, "target": 2289, "key": "0c91c9d030ead126906d28ce325d3943"}, {"line": 21824, "relation": "increases", "evidence": "The formation of endogenous inflammatory lipid mediators, leukotrienes, is initiated by 5-lipoxygenase (5-LOX), which is also expressed in neurons.", "citation": {"db": "PubMed", "db_id": "10790729"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 2288, "target": 289, "key": "9e64c67c8a2c72c63d1686a31c05aeb3"}, {"line": 21843, "relation": "positiveCorrelation", "evidence": "The proinflammatory enzyme 5-lipoxygenase (5-LOX) is upregulated in Alzheimer's disease (AD), but its localization and association with the hallmark lesions of the disease, beta-amyloid (Abeta) plaques and neurofibrillary tangles (NFTs), is unknown.", "citation": {"db": "PubMed", "db_id": "18678882"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Subgraph": {"Eicosanoids signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2288, "target": 3823, "key": "bd2486203acc4b4afc040fe8341be4db"}, {"line": 45770, "relation": "positiveCorrelation", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product. In addition, a consistent reduction in DNA methylation at 5-LOX gene promoter was documented in AD versus healthy subjects.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Cell": {"blood cell": true}, "Confidence": {"High": true}}, "source": 2288, "target": 3823, "key": "7d113a075149c635fb1d65b4dd8018b2"}, {"line": 21881, "relation": "negativeCorrelation", "evidence": "5-LOX inhibition induces eIF2α and PERK (protein kinase R-like extracellular signal-regulated kinase) phosphorylation, and HSP90 and ATF4 levels.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Subgraph": {"Eicosanoids signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2288, "target": 2661, "key": "82fa43b820fbb47b184e1a11563e3c20"}, {"line": 21889, "relation": "negativeCorrelation", "evidence": "5-LOX inhibition induces eIF2α and PERK (protein kinase R-like extracellular signal-regulated kinase) phosphorylation, and HSP90 and ATF4 levels.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2288, "target": 2665, "key": "81826afc9fd69bca7e22c5b61336f847"}, {"line": 21897, "relation": "negativeCorrelation", "evidence": "5-LOX inhibition induces eIF2α and PERK (protein kinase R-like extracellular signal-regulated kinase) phosphorylation, and HSP90 and ATF4 levels.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 2288, "target": 2366, "key": "34e31647b590d062c205fdf0d62ce20c"}, {"line": 21898, "relation": "negativeCorrelation", "evidence": "5-LOX inhibition induces eIF2α and PERK (protein kinase R-like extracellular signal-regulated kinase) phosphorylation, and HSP90 and ATF4 levels.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 2288, "target": 2181, "key": "d817a0a4bb0a3c93d15205ece87be851"}, {"line": 21946, "relation": "increases", "evidence": "Moreover, 5-LO targeted gene disruption or its in vivo selective pharmacological inhibition results in a significant reduction of Abeta, CREB and gamma-secretase levels. These data establish a novel functional role for 5-LO in regulating endogenous formation of Abeta levels in the central nervous system.", "citation": {"db": "PubMed", "db_id": "21280074"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2288, "target": 80, "key": "7b0498edbd2db4715a3ebcec6c004429"}, {"line": 21961, "relation": "association", "evidence": "Moreover, 5-LO targeted gene disruption or its in vivo selective pharmacological inhibition results in a significant reduction of Abeta, CREB and gamma-secretase levels. These data establish a novel functional role for 5-LO in regulating endogenous formation of Abeta levels in the central nervous system.", "citation": {"db": "PubMed", "db_id": "21280074"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Eicosanoids signaling subgraph": true}}, "source": 2288, "target": 80, "key": "de0cb1a81767fe6528594b7987e838a5"}, {"line": 21954, "relation": "increases", "evidence": "Moreover, 5-LO targeted gene disruption or its in vivo selective pharmacological inhibition results in a significant reduction of Abeta, CREB and gamma-secretase levels. These data establish a novel functional role for 5-LO in regulating endogenous formation of Abeta levels in the central nervous system.", "citation": {"db": "PubMed", "db_id": "21280074"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Eicosanoids signaling subgraph": true, "CREB subgraph": true}, "Confidence": {"Medium": true}}, "source": 2288, "target": 2162, "key": "833b97be4604e6f15b6c8d48a211eb39"}, {"line": 21857, "relation": "increases", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 289, "target": 577, "key": "bbd4b2bec50866c1a53a3ed9de6748df"}, {"line": 21858, "relation": "association", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 289, "target": 3850, "key": "cab145ca99e574aecaf99190e9c9ae0a"}, {"line": 21859, "relation": "association", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 289, "target": 3925, "key": "05b118d929fa655d7bbe50e111809354"}, {"line": 21860, "relation": "association", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 289, "target": 3930, "key": "d7605fdc0de9f92e2003282ec29a316d"}, {"line": 21861, "relation": "association", "evidence": "These leukotrienes are known to produce inflammatory responses in asthma and allergic reactions, to induce a reduction of tyrosine hydroxylase in brain, and are involved in the development of cardiac strokes, obesity and type 2 diabetes.", "citation": {"db": "PubMed", "db_id": "24059322"}, "annotations": {"MeSHDisease": {"Stroke": true, "Asthma": true, "Hypersensitivity": true, "Obesity": true, "Diabetes Mellitus, Type 2": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Eicosanoids signaling subgraph": true}}, "source": 289, "target": 3892, "key": "ae409d124a3a445d89bf377414eaa22f"}, {"line": 21873, "relation": "decreases", "evidence": "pyrazole, CNB-001 is a potent inhibitor of 5-lipoxygenase (5-LOX), decreases 5-LOX expression, and increases proteasome activity.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Confidence": {"High": true}}, "source": 337, "target": 2288, "key": "72f2c6431ae6ec2966debb943ac2e5bd"}, {"line": 21910, "relation": "increases", "evidence": "When fed to AD transgenic mice, CNB-001 also increases eIF2α phosphorylation and HSP90 and ATF4 levels, and limits the accumulation of soluble Abeta and ubiquitinated aggregated proteins.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Subgraph": {"Unfolded protein response subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 337, "target": 3632, "key": "c8e91cd3eac98eacd3592a9d8cb536c5"}, {"line": 21918, "relation": "increases", "evidence": "When fed to AD transgenic mice, CNB-001 also increases eIF2α phosphorylation and HSP90 and ATF4 levels, and limits the accumulation of soluble Abeta and ubiquitinated aggregated proteins.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 337, "target": 2181, "key": "8f1986d2ed0a80fac980025f7783ffb7"}, {"line": 21919, "relation": "increases", "evidence": "When fed to AD transgenic mice, CNB-001 also increases eIF2α phosphorylation and HSP90 and ATF4 levels, and limits the accumulation of soluble Abeta and ubiquitinated aggregated proteins.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 337, "target": 3590, "key": "1f6201b908d1141c300f8e72b0698b4c"}, {"line": 21927, "relation": "decreases", "evidence": "When fed to AD transgenic mice, CNB-001 also increases eIF2α phosphorylation and HSP90 and ATF4 levels, and limits the accumulation of soluble Abeta and ubiquitinated aggregated proteins.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 337, "target": 2328, "key": "b25eb5eaef1f9e9562da62cd52bbdaab"}, {"line": 21889, "relation": "negativeCorrelation", "evidence": "5-LOX inhibition induces eIF2α and PERK (protein kinase R-like extracellular signal-regulated kinase) phosphorylation, and HSP90 and ATF4 levels.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2665, "target": 2288, "key": "9af475104983370a3863b51c559e7948"}, {"line": 21898, "relation": "negativeCorrelation", "evidence": "5-LOX inhibition induces eIF2α and PERK (protein kinase R-like extracellular signal-regulated kinase) phosphorylation, and HSP90 and ATF4 levels.", "citation": {"db": "PubMed", "db_id": "23785163"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true, "Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 2181, "target": 2288, "key": "f25db3efc791507047914d7436a7f2f1"}, {"line": 29325, "relation": "increases", "evidence": "Cdc37 overexpression prevented whereas Cdc37 suppression potentiated tau clearance following Hsp90 inhibition.", "citation": {"db": "PubMed", "db_id": "21367866"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2181, "target": 3010, "key": "6be178cc3b8f3f15d3e9ec36892a84d5"}, {"line": 35685, "relation": "directlyDecreases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2181, "target": 2989, "key": "31aa0fbd3267e0e813dbec6bc61e7e5e"}, {"relation": "hasVariant", "source": 3631, "target": 3632, "key": "64b5239cbc87702c64b4da45563ee277"}, {"line": 42209, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 3590, "target": 342, "key": "b6b049a332a8ca22b2e94f48c2062711"}, {"line": 42254, "relation": "increases", "evidence": "Our results showed that PERK, eukaryotic initiation factor 2α (eIF2α), and activating transcription factor 4 (ATF4) were activated in rifampicin-treated PC12 cells.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}}, "source": 3590, "target": 342, "key": "b30eac5d61bef3887e0539ac4759b539"}, {"line": 42275, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3590, "target": 342, "key": "ffec7f846eebd85f7aca7a7378625327"}, {"line": 42213, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 3590, "target": 346, "key": "d446d074fda8c2f2be63efec7e02231b"}, {"line": 42262, "relation": "decreases", "evidence": "Silencing the ATF4 gene using RNAi inhibited GRP78 stimulation.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3590, "target": 3650, "key": "6b5b5e0b78f52a0dd26a2831806eda22"}, {"line": 22054, "relation": "decreases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 86, "target": 2328, "key": "d02838b7241d102b8efb96237a313504"}, {"line": 22058, "relation": "decreases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 86, "target": 681, "key": "1c637c37be72c7776de63ea697613cdc"}, {"line": 22074, "relation": "decreases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 86, "target": 478, "key": "4f91417dffd77101f3b7b03d4e8f6d06"}, {"line": 22075, "relation": "decreases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 86, "target": 170, "key": "22e7936c3d5a54a475e69f6a648e8e25"}, {"line": 22076, "relation": "association", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 86, "target": 682, "key": "018f1da0c1f1d9903d25849a53833391"}, {"line": 22077, "relation": "increases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 86, "target": 442, "key": "f16885a71b17aaf23507730d826b360a"}, {"line": 22078, "relation": "decreases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 86, "target": 2444, "key": "92c0f151c5f941cb2153ec91bce6d032"}, {"line": 22079, "relation": "decreases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 86, "target": 2444, "key": "b05293b27cb5487c3368b9bbbb07fde3"}, {"line": 22087, "relation": "decreases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 86, "target": 2389, "key": "97ecc06346328cb8048a94d61a923ab8"}, {"line": 22091, "relation": "increases", "evidence": "In this study, we investigated whether AS-IV could prevent Abeta1-42-induced neurotoxicity in SK-N-SH cells via inhibiting the mPTP opening. The results showed that pretreatment of AS-IV significantly increased the viability of neuronal cells, reduced apoptotic process, decreased the generation of intracellular reactive oxygen species (ROS) and decreased mitochondrial superoxide in the presence of Abeta1-42. In addition, pretreatment of AS-IV inhibited the mPTP opening, rescued mitochondrial membrane potential, enhanced ATP generation, improved the activity of cytochrome c oxidase and blocked cytochrome c release from mitochondria in Abeta1-42 rich milieu. Moreover, pretreatment of AS-IV reduced the expression of Bax and cleaved caspase-3 and increased the expression of Bcl-2 in an Abeta1-42 rich environment. These data indicate that AS-IV prevents Abeta1-42-induced SK-N-SH cell apoptosis via inhibiting the mPTP opening and ROS generation.", "citation": {"db": "PubMed", "db_id": "24905226"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 86, "target": 2393, "key": "1b001a5929f9b183b1011feb618f211c"}, {"relation": "isA", "source": 2847, "target": 2182, "key": "b2738955a60d2ec54a9297969f7e8857"}, {"relation": "partOf", "source": 2847, "target": 1282, "key": "78c92611f2b622c73a49366518f5201d"}, {"line": 30852, "relation": "increases", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Degradation"}, "source": 2847, "target": 2328, "key": "926579b6f91c29182f44f07666b86830"}, {"line": 46540, "relation": "increases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Low": true}, "CellStructure": {"Mitochondria": true}, "Subgraph": {"Chaperone subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2847, "target": 662, "key": "98383ba4170fcdb6af5edf0ea5c72618"}, {"line": 46552, "relation": "increases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 2847, "target": 442, "key": "3070a57ee9a94e921103fbee3c753501"}, {"line": 46558, "relation": "decreases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Chaperone subgraph": true, "Caspase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2847, "target": 2449, "key": "6b9ad1da276b7915c151c5799c22b13e"}, {"relation": "isA", "source": 2848, "target": 2182, "key": "bcc5788239ef5918578f0898f11a43d6"}, {"line": 22108, "relation": "increases", "evidence": "Over-expression of hsp70 was found to reduce PQ-induced oxidative stress along with JNK and caspase-3 mediated dopaminergic neuronal cell death in exposed organism.", "citation": {"db": "PubMed", "db_id": "24887138"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Chaperone subgraph": true}, "MeSHAnatomy": {"Dopaminergic Neurons": true}}, "source": 321, "target": 775, "key": "7c4d4baf9a205b471979850df7152ea5"}, {"relation": "isA", "source": 2995, "target": 2173, "key": "7e2c3f099facde993da7dd5c38ef99fd"}, {"relation": "isA", "source": 2996, "target": 2173, "key": "d5bb5443c3cc9ac9e347e9d9b59b436d"}, {"relation": "isA", "source": 2997, "target": 2173, "key": "3337f8bf57fc4c14f63289abe54e9e78"}, {"line": 22857, "relation": "increases", "evidence": "Apoptosis plays a significant role in cell loss during neurodegenerative disorders such as Alzheimer's disease (AD) (Loh et al., 2006). A cascade of events like activation of caspases and aspartate-specific cysteine proteases has been proposed to play a key role in apoptosis (Nicholson and Thornberry ,1997). The major apoptotic pathway is characterized by mitochondrial dysfunction with the release of cytochrome c, activation of caspase-9, and subsequently of caspase-3. It has been suggested that caspase-3 is an ultimate effectors caspase whose activation leads to switch on the apoptotic cascade. Evidences of caspase-3 activation were also found in postmortem study conducted on the brain of AD patient (Engidawork et al., 2001).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3755, "target": 478, "key": "c166d3f590fba462e7ecb98462df100d"}, {"line": 22858, "relation": "positiveCorrelation", "evidence": "Apoptosis plays a significant role in cell loss during neurodegenerative disorders such as Alzheimer's disease (AD) (Loh et al., 2006). A cascade of events like activation of caspases and aspartate-specific cysteine proteases has been proposed to play a key role in apoptosis (Nicholson and Thornberry ,1997). The major apoptotic pathway is characterized by mitochondrial dysfunction with the release of cytochrome c, activation of caspase-9, and subsequently of caspase-3. It has been suggested that caspase-3 is an ultimate effectors caspase whose activation leads to switch on the apoptotic cascade. Evidences of caspase-3 activation were also found in postmortem study conducted on the brain of AD patient (Engidawork et al., 2001).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3755, "target": 3823, "key": "30706944d5e334cfe3661a8a7aba98df"}, {"line": 22150, "relation": "increases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Species": {"10090": true}}, "subject": {"modifier": "Activity"}, "source": 3623, "target": 80, "key": "9cd1a078a539adbb8369c80a3645e802"}, {"line": 41857, "relation": "increases", "evidence": "The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3623, "target": 80, "key": "b7717b783ab0d1f4db19e71a9795e16b"}, {"line": 22159, "relation": "increases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "source": 3623, "target": 3710, "key": "8d97f2e7832310490c8f6ab8dece48cc"}, {"line": 22168, "relation": "increases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3623, "target": 3741, "key": "a3f2977f5885fc11fcced224585d4517"}, {"line": 22176, "relation": "increases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3623, "target": 3661, "key": "f7a8502bd7a24f3b97161b4338d73ba0"}, {"line": 22184, "relation": "increases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3623, "target": 3600, "key": "ce71367cdcc294133dd7a4e90b3bb4be"}, {"line": 22192, "relation": "decreases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3623, "target": 3597, "key": "d5aa863255308c0d8ff9f2131a051956"}, {"line": 22205, "relation": "association", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3623, "target": 3112, "key": "4d9a32b3af7a04cad34f20c0031d4c62"}, {"line": 22206, "relation": "association", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3623, "target": 1588, "key": "353a351ca5ce47d80dc0456bc0644f3b"}, {"line": 41798, "relation": "decreases", "evidence": "Montelukast, known as a cysteinyl leukotriene receptor 1 (CysLT1R) antagonist, is currently used for treatment of inflammatory diseases such as asthma.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Asthma": true}, "Confidence": {"Medium": true}}, "source": 3623, "target": 3892, "key": "c3e47c0524b9d4eb0cfc41f35341b06e"}, {"line": 42345, "relation": "association", "evidence": "Montelukast, known as a cysteinyl leukotriene receptor 1 (CysLT1R) antagonist, is currently used for treatment of inflammatory diseases such as asthma.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHDisease": {"Asthma": true}}, "source": 3623, "target": 3892, "key": "f5c80e745f2b2ab7275e097754a796e4"}, {"line": 41805, "relation": "association", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3623, "target": 3584, "key": "eff5a8e7e67773b4eddf6a0c95d54164"}, {"line": 41858, "relation": "increases", "evidence": "The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 3623, "target": 478, "key": "69b7621ed46ea6fff8089e174bc3cf2a"}, {"line": 41859, "relation": "positiveCorrelation", "evidence": "The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 3623, "target": 3823, "key": "0518e3789bc2479a918a07c35987eeae"}, {"line": 42344, "relation": "association", "evidence": "Montelukast, known as a cysteinyl leukotriene receptor 1 (CysLT1R) antagonist, is currently used for treatment of inflammatory diseases such as asthma.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHDisease": {"Asthma": true}}, "source": 3623, "target": 307, "key": "d9d7c3775020a94a91006c86735647f6"}, {"line": 42367, "relation": "association", "evidence": "Interestingly, this treatment resulted in upregulation of protein or mRNA of CysLT1R in both hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Bcl-2 subgraph": true}}, "source": 3623, "target": 150, "key": "11251d5a58704bd9a9b06d3acb99828c"}, {"line": 41829, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3741, "target": 4044, "key": "6d8833bb8356a47e0d5fa51e52100115"}, {"line": 41952, "relation": "increases", "evidence": "Results: Chromatolysis and amyloid plaques were found along with higher ROS, nitrite and TNF-α levels in the hippocampus of colchicine-induced AD rats, and these changes were prevented by naproxen in a dose-dependent manner.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3741, "target": 3881, "key": "2233af460756452dbe08641f7de83e73"}, {"line": 42360, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3741, "target": 369, "key": "1ae61ceb4390228e501d3bcea8e99267"}, {"line": 42362, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Bcl-2 subgraph": true}}, "source": 3741, "target": 3597, "key": "7fa82cb130fc05cdc17005d754e6acf0"}, {"line": 42373, "relation": "association", "evidence": "Blockade of CysLT1R by repeated treatment with montelukast (1 or 2 mg/kg, ig, 4 weeks) reduced Abeta1-42-induced CysLT1R expression and also suppressed Abeta1-42-induced increments of NF-κB p65, TNF-α, IL-1beta and caspase-3 activation, and Bcl-2 downregulation in the hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Bcl-2 subgraph": true}}, "source": 3741, "target": 3597, "key": "746daaadd35ab476ee03b5226b35bab8"}, {"line": 43192, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tumor necrosis factor subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 3741, "target": 1645, "key": "c20b34f29a2dde2310bb78a545d470ec"}, {"line": 43405, "relation": "increases", "evidence": "Cytokines including TNF-α+IFN-gamma increase levels of endogenous BACE1, APP, and Abeta and stimulate amyloidogenic/ APP processing in astrocytes. Oligomeric and fibrillar Abeta42 also increase levels of astrocytic BACE1, APP, and beta-secretase/ processing. Together, our results suggest a cytokine- and Abeta42-driven feed-forward mechanism that promotes astrocytic Abeta/ production. Given that astrocytes greatly outnumber neurons, activated astrocytes may represent significant sources of Abeta/ during neuroinflammation in AD", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3741, "target": 3593, "key": "261c13697e975482d59a58090c10ae0c"}, {"line": 43406, "relation": "increases", "evidence": "Cytokines including TNF-α+IFN-gamma increase levels of endogenous BACE1, APP, and Abeta and stimulate amyloidogenic/ APP processing in astrocytes. Oligomeric and fibrillar Abeta42 also increase levels of astrocytic BACE1, APP, and beta-secretase/ processing. Together, our results suggest a cytokine- and Abeta42-driven feed-forward mechanism that promotes astrocytic Abeta/ production. Given that astrocytes greatly outnumber neurons, activated astrocytes may represent significant sources of Abeta/ during neuroinflammation in AD", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3741, "target": 3584, "key": "824b7eabb79eeab836e48f64a6a0764b"}, {"line": 43407, "relation": "increases", "evidence": "Cytokines including TNF-α+IFN-gamma increase levels of endogenous BACE1, APP, and Abeta and stimulate amyloidogenic/ APP processing in astrocytes. Oligomeric and fibrillar Abeta42 also increase levels of astrocytic BACE1, APP, and beta-secretase/ processing. Together, our results suggest a cytokine- and Abeta42-driven feed-forward mechanism that promotes astrocytic Abeta/ production. Given that astrocytes greatly outnumber neurons, activated astrocytes may represent significant sources of Abeta/ during neuroinflammation in AD", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3741, "target": 80, "key": "75411048f516173eb6d10212dba20b57"}, {"line": 43505, "relation": "increases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "source": 3741, "target": 3649, "key": "184143da4806be8674436c479bb0bdab"}, {"line": 43566, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3741, "target": 537, "key": "dc5d2eecef496d1ff373a88e1442cdb4"}, {"line": 43568, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3741, "target": 537, "key": "11e71239295aa05027a0df18712164b2"}, {"relation": "partOf", "source": 3741, "target": 1652, "key": "d805de82df650ff79e658d9706927843"}, {"relation": "partOf", "source": 3741, "target": 1653, "key": "96f028044b1a7d18a7a3cb5702cc0fda"}, {"line": 43644, "relation": "increases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3741, "target": 577, "key": "a677ea94cfb9b8a39873d01112eb1297"}, {"line": 22196, "relation": "decreases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 2328, "key": "2e454afd08f430b5b35b365df3f7400b"}, {"line": 22219, "relation": "decreases", "evidence": "Montelukast also suppressed the Abeta_42-induced Bcl-2 decrease and Caspase-3 activation. Therefore, montelukast may exhibit a potent, anti-apoptotic effect, which contributes to the blockade of apoptotic responses induced by Abeta_42.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 2328, "key": "0d29ca2d13c1de41782cb229b70dc4d4"}, {"line": 41783, "relation": "decreases", "evidence": "Montelukast rescues primary neurons against Abeta1-42-induced toxicity through inhibiting CysLT1R-mediated NF-κB signaling.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"G-protein-mediated signaling": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 2328, "key": "14533463ddf54636ec79d970c3232700"}, {"line": 41812, "relation": "decreases", "evidence": "In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 2328, "key": "b4eeb69a7c3e50d91b8ec5cd43870c77"}, {"line": 22197, "relation": "decreases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 307, "target": 3623, "key": "cf2037445ee8a3f9334253a29fbe6dc4"}, {"line": 41782, "relation": "decreases", "evidence": "Montelukast rescues primary neurons against Abeta1-42-induced toxicity through inhibiting CysLT1R-mediated NF-κB signaling.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"G-protein-mediated signaling": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 3623, "key": "588f3188ba7bd16f3199bebe37d13dc5"}, {"line": 41856, "relation": "decreases", "evidence": "The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 3623, "key": "e50ab5fdcbd514080f2581bc57bfa5e2"}, {"line": 42344, "relation": "association", "evidence": "Montelukast, known as a cysteinyl leukotriene receptor 1 (CysLT1R) antagonist, is currently used for treatment of inflammatory diseases such as asthma.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHDisease": {"Asthma": true}}, "source": 307, "target": 3623, "key": "aef8648ebe281df0e9b14b3d4bb59576"}, {"line": 22207, "relation": "decreases", "evidence": "We have previously reported that CysLT1R activation is involved in Abeta generation. In this study, we investigated rescuing effect of CysLT1R antagonist montelukast on Abeta1-42-induced neurotoxicity in primary neurons. Our data showed that Abeta1-42 elicited a marked increase of CysLT1R expression in primary mouse neurons. This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction. This observation was confirmed with treatment of montelukast, a selective CysLT1R antagonist, which had significant effect on Abeta1-42-induced cytotoxicity. Moreover, blockade of CysLT1R with montelukast reversed Abeta1-42-mediated increase of CysLT1R expression, and concomitant changes of the pro-inflammatory factors and the apoptotic process-related proteins. The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 1588, "key": "0096fbd5df0c24ee320a67bfb6957ebc"}, {"line": 22229, "relation": "decreases", "evidence": "Montelukast also suppressed the Abeta_42-induced Bcl-2 decrease and Caspase-3 activation. Therefore, montelukast may exhibit a potent, anti-apoptotic effect, which contributes to the blockade of apoptotic responses induced by Abeta_42.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 3600, "key": "177f134867a241b08e0f8c649b3ed2cc"}, {"line": 22230, "relation": "decreases", "evidence": "Montelukast also suppressed the Abeta_42-induced Bcl-2 decrease and Caspase-3 activation. Therefore, montelukast may exhibit a potent, anti-apoptotic effect, which contributes to the blockade of apoptotic responses induced by Abeta_42.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 307, "target": 478, "key": "5b5794b95eadc9c9295ee1382ce8bef2"}, {"line": 41855, "relation": "decreases", "evidence": "The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 307, "target": 478, "key": "23916fc1f1d997cc8387da4eb0820521"}, {"line": 41853, "relation": "increases", "evidence": "The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "source": 307, "target": 431, "key": "688241a13971c7c0a1e666f64f47607c"}, {"line": 41854, "relation": "decreases", "evidence": "The results demonstrate that montelukast rescued neurons against Abeta1-42-induced neurotoxicity, neuroinflammation and apoptosis by down-regulating CysLT1R-mediated NF-κB signaling, suggesting that CysLT1R may be a potential target for AD, and its antagonist may have beneficial effects for treatment of AD.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 307, "target": 80, "key": "1731a784cda071687c1fd38aad6bb9b1"}, {"line": 42343, "relation": "association", "evidence": "Montelukast, known as a cysteinyl leukotriene receptor 1 (CysLT1R) antagonist, is currently used for treatment of inflammatory diseases such as asthma.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHDisease": {"Asthma": true}}, "source": 307, "target": 3892, "key": "fb5079075ded754ea28fccfa418f7fe5"}, {"line": 22251, "relation": "positiveCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3693, "target": 859, "key": "0eeef089f130e211a49fa9b1adf264fe"}, {"relation": "hasVariant", "source": 3692, "target": 3693, "key": "1544dc96220a6e1aff673dd09e4194b4"}, {"line": 22251, "relation": "positiveCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 859, "target": 3693, "key": "c17d8081eafc0f466889b618c96dc00e"}, {"line": 22252, "relation": "positiveCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 859, "target": 3709, "key": "35ec7d26f7772fdfe4718d0645fb34b5"}, {"line": 22253, "relation": "positiveCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 859, "target": 2162, "key": "dacfe0267db0e2acb82a0e47dcdcc6b9"}, {"line": 22252, "relation": "positiveCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3709, "target": 859, "key": "aabab5403a2cd667495809b23018076e"}, {"relation": "hasVariant", "source": 3708, "target": 3709, "key": "642789e67884db5cb0d38c6975eb6108"}, {"line": 22261, "relation": "negativeCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3674, "target": 1592, "key": "19423d950a44debcadc756c8a034634b"}, {"relation": "hasVariant", "source": 3673, "target": 3674, "key": "3dfdb1b37a5d5418a22ce4124aaf0db5"}, {"line": 22261, "relation": "negativeCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 1592, "target": 3674, "key": "5abf440bd62fd87f4b31542672e8ba5d"}, {"line": 22262, "relation": "positiveCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 1592, "target": 3746, "key": "59ac7382bb0cec36197e4adabd5daf17"}, {"line": 31822, "relation": "increases", "evidence": "Taken together , these results indicate that pro-NGF purified from AD human brains can induce apoptosis in neuronal cell cultures through its interaction with the p75NTR receptor.", "citation": {"db": "PubMed", "db_id": "15681836"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 1592, "target": 478, "key": "b57894a0c9ccdf82eff8fa08c12388ab"}, {"line": 35200, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 1592, "target": 3118, "key": "ce0d88e8e8c123559df73970bce5541d"}, {"line": 38659, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1592, "target": 3116, "key": "d52616e3338e4c081053cf2de2b1a688"}, {"line": 22262, "relation": "positiveCorrelation", "evidence": "The over-expressions of Abeta(1-42) and amyloid precursor protein (APP) were also decreased in the brains of APP/PS1 mice. Interestingly, MEM treatment improved the decreased NGF levels in APP/PS1 mice. Furthermore, NGF/TrkA signaling was activated by increasing the phosphorylation levels of tyrosine kinase (TrkA), proto-oncogene serine/threonine-protein kinase, Raf1 (c-Raf), extracellular regulated protein kinases (ERK)1/2 and cAMP-response element binding protein (CREB) after MEM treatment. Simultaneously, MEM also inhibited NGF/p75(NTR) signaling via decreasing the cleavage substrate of p75(NTR), increasing the JNK2 phosphorylation and decreasing the levels of p53 and cleaved-caspase 3.", "citation": {"db": "PubMed", "db_id": "24846616"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3746, "target": 1592, "key": "107896f9cadb26f0f900331204ccb694"}, {"line": 22336, "relation": "decreases", "evidence": "Glimepiride released CD14 from RAW 264 cells and microglial cells. Pre-treatment with glimepiride significantly reduced TNF, IL-1 and IL-6 secretion from RAW 264 and microglial cells incubated with LPS, Abeta42, alphaSN and PrP82-146. More recently, the concentrations of soluble CD14 were found to be elevated in AD and PD patients.", "citation": {"db": "PubMed", "db_id": "24952384"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 262, "target": 2468, "key": "b085cd65e1d5b62e75e4e88eba5c92bf"}, {"line": 22338, "relation": "decreases", "evidence": "Glimepiride released CD14 from RAW 264 cells and microglial cells. Pre-treatment with glimepiride significantly reduced TNF, IL-1 and IL-6 secretion from RAW 264 and microglial cells incubated with LPS, Abeta42, alphaSN and PrP82-146. More recently, the concentrations of soluble CD14 were found to be elevated in AD and PD patients.", "citation": {"db": "PubMed", "db_id": "24952384"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 262, "target": 3472, "key": "21b7681944b48176bb38b8fd4714ec3f"}, {"line": 22340, "relation": "decreases", "evidence": "Glimepiride released CD14 from RAW 264 cells and microglial cells. Pre-treatment with glimepiride significantly reduced TNF, IL-1 and IL-6 secretion from RAW 264 and microglial cells incubated with LPS, Abeta42, alphaSN and PrP82-146. More recently, the concentrations of soluble CD14 were found to be elevated in AD and PD patients.", "citation": {"db": "PubMed", "db_id": "24952384"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 262, "target": 2183, "key": "141984588e350072d9a75702d0ba781f"}, {"line": 22341, "relation": "decreases", "evidence": "Glimepiride released CD14 from RAW 264 cells and microglial cells. Pre-treatment with glimepiride significantly reduced TNF, IL-1 and IL-6 secretion from RAW 264 and microglial cells incubated with LPS, Abeta42, alphaSN and PrP82-146. More recently, the concentrations of soluble CD14 were found to be elevated in AD and PD patients.", "citation": {"db": "PubMed", "db_id": "24952384"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 262, "target": 2894, "key": "15f624cdc37318648f022f3a6899ded4"}, {"line": 22343, "relation": "positiveCorrelation", "evidence": "Glimepiride released CD14 from RAW 264 cells and microglial cells. Pre-treatment with glimepiride significantly reduced TNF, IL-1 and IL-6 secretion from RAW 264 and microglial cells incubated with LPS, Abeta42, alphaSN and PrP82-146. More recently, the concentrations of soluble CD14 were found to be elevated in AD and PD patients.", "citation": {"db": "PubMed", "db_id": "24952384"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 2468, "target": 3823, "key": "2311345f38a20f0e394f991473bdf068"}, {"line": 39292, "relation": "association", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 2468, "target": 577, "key": "da9f3ecab855c23ac46a1dade771e2c4"}, {"line": 39696, "relation": "positiveCorrelation", "evidence": "YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and Abeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 × 10-25; r = 0.77, p = 2.0 × 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 2468, "target": 3839, "key": "05d0d53185023bdc99f74280a747f2ee"}, {"line": 22387, "relation": "increases", "evidence": "In addition, pericytes respond to the pro-inflammatory cytokines tumor necrosis factor-α and Interferon-gamma by inducing the expression of the CYP27B1 gene which is involved in 1,25D synthesis.", "citation": {"db": "PubMed", "db_id": "24934545"}, "annotations": {"Subgraph": {"Vitamin subgraph": true, "Interferon signaling subgraph": true, "Metabolism of steroid hormones subgraph": true}}, "source": 2610, "target": 223, "key": "5f4249727055f193a47ac2858cd6a60b"}, {"line": 22422, "relation": "association", "evidence": "In support of this mechanism, in vitro, rapamycin significantly inhibits the production of NO, TNF-α in BV2 microglials by modulating NF-κB signaling.", "citation": {"db": "PubMed", "db_id": "24923557"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2198, "target": 352, "key": "82d5949afc3264bef5fca1074b18fb48"}, {"line": 22406, "relation": "decreases", "evidence": "In support of this mechanism, in vitro, rapamycin significantly inhibits the production of NO, TNF-α in BV2 microglials by modulating NF-κB signaling.", "citation": {"db": "PubMed", "db_id": "24923557"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "source": 352, "target": 156, "key": "ba1331bc1fcabb9d1d8f5513d8b0d43b"}, {"line": 22414, "relation": "decreases", "evidence": "In support of this mechanism, in vitro, rapamycin significantly inhibits the production of NO, TNF-α in BV2 microglials by modulating NF-κB signaling.", "citation": {"db": "PubMed", "db_id": "24923557"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "source": 352, "target": 3472, "key": "f6e1c4e603e25724891b740d7268b562"}, {"line": 22422, "relation": "association", "evidence": "In support of this mechanism, in vitro, rapamycin significantly inhibits the production of NO, TNF-α in BV2 microglials by modulating NF-κB signaling.", "citation": {"db": "PubMed", "db_id": "24923557"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 352, "target": 2198, "key": "34ce2e0efc88a9e248dd96c8af7a621d"}, {"line": 22426, "relation": "association", "evidence": "In support of this mechanism, in vitro, rapamycin significantly inhibits the production of NO, TNF-α in BV2 microglials by modulating NF-κB signaling.", "citation": {"db": "PubMed", "db_id": "24923557"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 352, "target": 1588, "key": "1e772a8066a9716477eb8bccdd1d1538"}, {"line": 22444, "relation": "increases", "evidence": "In addition, TNF-alpha can potentiate glutamate-mediated cytotoxicity by two complementary mechanisms: indirectly, by inhibiting glutamate transport on astrocytes, and directly, by rapidly triggering the surface expression of Ca+2 permeable-AMPA receptors and NMDA receptors, while decreasing inhibitory GABAA receptors on neurons.", "citation": {"db": "PubMed", "db_id": "24966471"}, "annotations": {"Subgraph": {"GABA subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 423, "target": 505, "key": "e43530989877fbde278a22462bfe671b"}, {"line": 22672, "relation": "negativeCorrelation", "evidence": "In Alzheimer's disease (AD), affected neurons accumulate beta amyloid protein, components of which can induce mouse microglia to express the high-output isoform of nitric oxide synthase (NOS2) in vitro. Products of NOS2 can be neurotoxic. In mice, NOS2 is normally suppressed by transforming growth factor beta 1 (TGF-beta 1). Expression of TGF-beta 1 is decreased in brains from AD patients, a situation that might be permissive for accumulation of NOS2.", "citation": {"db": "PubMed", "db_id": "8879214"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"TGF-Beta subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3689, "target": 3736, "key": "2dcc4ae4e5294869ff2c89232fadedd9"}, {"line": 41891, "relation": "increases", "evidence": "NOS2 deficiency or oral treatment with the NOS2 inhibitor L-NIL strongly decreased 3NTyr(10)-Abeta, overall Abeta deposition and cognitive dysfunction in APP/PS1 mice.", "citation": {"db": "PubMed", "db_id": "21903077"}, "annotations": {"Confidence": {"High": true}, "Species": {"10090": true}}, "source": 3689, "target": 2343, "key": "3c3446fda56d3afe3629011a87949738"}, {"line": 42692, "relation": "decreases", "evidence": "Reactive oxygen species generation and lipid peroxidation as well as expression of inducible nitric oxide and cyclooxygenase-2 were also reduced in the IL-32α mice brain.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Nitric oxide subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3689, "target": 2890, "key": "3df748d4dbd0722b681fada595f52453"}, {"line": 22672, "relation": "negativeCorrelation", "evidence": "In Alzheimer's disease (AD), affected neurons accumulate beta amyloid protein, components of which can induce mouse microglia to express the high-output isoform of nitric oxide synthase (NOS2) in vitro. Products of NOS2 can be neurotoxic. In mice, NOS2 is normally suppressed by transforming growth factor beta 1 (TGF-beta 1). Expression of TGF-beta 1 is decreased in brains from AD patients, a situation that might be permissive for accumulation of NOS2.", "citation": {"db": "PubMed", "db_id": "8879214"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"TGF-Beta subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3736, "target": 3689, "key": "21d69f8e2d39c869b64728b1cd5c9fb8"}, {"line": 38743, "relation": "decreases", "evidence": "In Alzheimer's disease (AD), affected neurons accumulate beta amyloid protein, components of which can induce mouse microglia to express the high-output isoform of nitric oxide synthase (NOS2) in vitro. Products of NOS2 can be neurotoxic. In mice, NOS2 is normally suppressed by transforming growth factor beta 1 (TGF-beta 1). Expression of TGF-beta 1 is decreased in brains from AD patients, a situation that might be permissive for accumulation of NOS2.", "citation": {"db": "PubMed", "db_id": "8879214"}, "annotations": {"Species": {"10116": true}, "Confidence": {"High": true}}, "source": 3736, "target": 3689, "key": "9103a28f5aa6168ce3ab06f138daeb75"}, {"line": 22733, "relation": "increases", "evidence": "Viral infection often activates the interferon (IFN)-gamma-inducible gene, nitric oxide synthase 2 (NOS2). Expression of NOS2 can limit viral growth but may also suppress the immune system and damage tissue.", "citation": {"db": "PubMed", "db_id": "9782132"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 769, "target": 2870, "key": "c7a4345adfaea3fe3b436d5c0beecf4e"}, {"line": 22741, "relation": "increases", "evidence": "Viral infection often activates the interferon (IFN)-gamma-inducible gene, nitric oxide synthase 2 (NOS2). Expression of NOS2 can limit viral growth but may also suppress the immune system and damage tissue.", "citation": {"db": "PubMed", "db_id": "9782132"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 801, "target": 3123, "key": "46c9644b5e480349eb607cc574fd958f"}, {"line": 41924, "relation": "association", "evidence": "Background: The components of the immune system have been indicated to be linked with the neurotoxicity in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Alzheimer Disease": true}}, "source": 576, "target": 3823, "key": "d1f7d969c1d46782f735db973e454124"}, {"line": 22772, "relation": "negativeCorrelation", "evidence": "Quantitative measures of ERK2 mRNA reveal that NFT-bearing neurons contain approximately 15% less ERK2 mRNA than nearest neighbors that do not contain NFT. NFT-bearing neurons contain approximately 25% less polyA mRNA, suggesting a relative preservation of ERK2 mRNA even in metabolically compromised cells.", "citation": {"db": "PubMed", "db_id": "8129042"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}}, "source": 3990, "target": 889, "key": "f2c400d5f1a3e0b07972d9a9f3387b41"}, {"line": 22785, "relation": "positiveCorrelation", "evidence": "The highly vulnerable CA1 pyramidal neurons were characterized by age- and disease-unrelated increases in PRCKB levels and by age- and disease-related increases in MAPK1 levels. In contrast, low PRKCB levels were found in CA2 pyramidal neurons, and MAPK1 levels were elevated in controls and intermediate AD stages. Both PRKCB and MAPK1 were increased in the late AD stages. MAPK1 and PRKCB levels were low in the brainstem and cerebellum. We propose that alterations in the expression of these two genes occur early in the pathogenesis of AD in a region-specific manner.", "citation": {"db": "PubMed", "db_id": "24334724"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"CA2 Region, Hippocampal": true, "Pyramidal Cells": true}}, "source": 3990, "target": 3823, "key": "5ff601ce22b37bb96ca4bbfc32ae03bc"}, {"line": 22792, "relation": "negativeCorrelation", "evidence": "The highly vulnerable CA1 pyramidal neurons were characterized by age- and disease-unrelated increases in PRCKB levels and by age- and disease-related increases in MAPK1 levels. In contrast, low PRKCB levels were found in CA2 pyramidal neurons, and MAPK1 levels were elevated in controls and intermediate AD stages. Both PRKCB and MAPK1 were increased in the late AD stages. MAPK1 and PRKCB levels were low in the brainstem and cerebellum. We propose that alterations in the expression of these two genes occur early in the pathogenesis of AD in a region-specific manner.", "citation": {"db": "PubMed", "db_id": "24334724"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"Cerebellum": true, "Brain Stem": true}}, "source": 3990, "target": 3823, "key": "2888ae443fb3814aca0fdd20ecaf1957"}, {"line": 38788, "relation": "negativeCorrelation", "evidence": "Quantitative measures of ERK2 mRNA reveal that NFT-bearing neurons contain approximately 15% less ERK2 mRNA than nearest neighbors that do not contain NFT. NFT-bearing neurons contain approximately 25% less polyA mRNA, suggesting a relative preservation of ERK2 mRNA even in metabolically compromised cells.", "citation": {"db": "PubMed", "db_id": "8129042"}, "annotations": {"Confidence": {"Very High": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Tau protein subgraph": true}}, "source": 3990, "target": 3015, "key": "c2c152d322fabb646a01631039befacf"}, {"line": 22786, "relation": "positiveCorrelation", "evidence": "The highly vulnerable CA1 pyramidal neurons were characterized by age- and disease-unrelated increases in PRCKB levels and by age- and disease-related increases in MAPK1 levels. In contrast, low PRKCB levels were found in CA2 pyramidal neurons, and MAPK1 levels were elevated in controls and intermediate AD stages. Both PRKCB and MAPK1 were increased in the late AD stages. MAPK1 and PRKCB levels were low in the brainstem and cerebellum. We propose that alterations in the expression of these two genes occur early in the pathogenesis of AD in a region-specific manner.", "citation": {"db": "PubMed", "db_id": "24334724"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"CA2 Region, Hippocampal": true, "Pyramidal Cells": true}}, "source": 4005, "target": 3823, "key": "2f34743f2c0fead8db4e55703e8f93f3"}, {"line": 22793, "relation": "negativeCorrelation", "evidence": "The highly vulnerable CA1 pyramidal neurons were characterized by age- and disease-unrelated increases in PRCKB levels and by age- and disease-related increases in MAPK1 levels. In contrast, low PRKCB levels were found in CA2 pyramidal neurons, and MAPK1 levels were elevated in controls and intermediate AD stages. Both PRKCB and MAPK1 were increased in the late AD stages. MAPK1 and PRKCB levels were low in the brainstem and cerebellum. We propose that alterations in the expression of these two genes occur early in the pathogenesis of AD in a region-specific manner.", "citation": {"db": "PubMed", "db_id": "24334724"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"Cerebellum": true, "Brain Stem": true}}, "source": 4005, "target": 3823, "key": "5889f4fc3c2ee183754cc926024088e3"}, {"line": 22856, "relation": "increases", "evidence": "Apoptosis plays a significant role in cell loss during neurodegenerative disorders such as Alzheimer's disease (AD) (Loh et al., 2006). A cascade of events like activation of caspases and aspartate-specific cysteine proteases has been proposed to play a key role in apoptosis (Nicholson and Thornberry ,1997). The major apoptotic pathway is characterized by mitochondrial dysfunction with the release of cytochrome c, activation of caspase-9, and subsequently of caspase-3. It has been suggested that caspase-3 is an ultimate effectors caspase whose activation leads to switch on the apoptotic cascade. Evidences of caspase-3 activation were also found in postmortem study conducted on the brain of AD patient (Engidawork et al., 2001).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3756, "target": 3755, "key": "5c3ba80d95511e8098c9c2a00e9cae9f"}, {"line": 22868, "relation": "increases", "evidence": "Activation of glutamate receptors is believed to play a major role in the neuronal cell death (Mattson, 1996). Activation of glutamate receptors causes massive calcium influx through NMDA, voltage-dependent calcium channel (Choi, 1994) and oxyradical production (Mattson, 1996).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 886, "target": 648, "key": "a0e2320180af258289784893111a84d9"}, {"line": 22869, "relation": "increases", "evidence": "Activation of glutamate receptors is believed to play a major role in the neuronal cell death (Mattson, 1996). Activation of glutamate receptors causes massive calcium influx through NMDA, voltage-dependent calcium channel (Choi, 1994) and oxyradical production (Mattson, 1996).", "citation": {"db": "PubMed", "db_id": "21893081"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 886, "target": 724, "key": "5fa8ea243375acfb327408115c15fea9"}, {"line": 22999, "relation": "positiveCorrelation", "evidence": "Distinct from WT SOD1, mutant SOD1 induces morphological change and cytochrome c release in cultured neurons that resulted in apoptotic process. Two transgenic studies further indicated the involvement of mitochondria-mediated apoptotic process in mutant SOD1-linked ALS.", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 3720, "target": 3825, "key": "3daf14673c10da3e76e82bbdf9870432"}, {"line": 23000, "relation": "increases", "evidence": "Distinct from WT SOD1, mutant SOD1 induces morphological change and cytochrome c release in cultured neurons that resulted in apoptotic process. Two transgenic studies further indicated the involvement of mitochondria-mediated apoptotic process in mutant SOD1-linked ALS.", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 3720, "target": 105, "key": "39da51fb74b2b5b8819d90217546c59e"}, {"line": 23001, "relation": "increases", "evidence": "Distinct from WT SOD1, mutant SOD1 induces morphological change and cytochrome c release in cultured neurons that resulted in apoptotic process. Two transgenic studies further indicated the involvement of mitochondria-mediated apoptotic process in mutant SOD1-linked ALS.", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 3720, "target": 3622, "key": "6716d1b06bdcfe45ae15113efe8400c6"}, {"line": 23040, "relation": "decreases", "evidence": "Hence, as XIAP levels decrease in spinal motor neurons of mutant SOD1 mice during disease progression (Ishigaki et al., 2002), caspase-9-initiated apoptosis may be promoted.", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"XIAP subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}}, "source": 3720, "target": 3753, "key": "365501b4e2027d10f103ee628ec8f38e"}, {"line": 23083, "relation": "increases", "evidence": "ASK1, a member of the MAP3K family, has been shown to mediate SOD1(G93A)-induced neurotoxicity in an ALS mouse model", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3720, "target": 3668, "key": "6b5a76ecd222b5dda12686048ee2e299"}, {"line": 23095, "relation": "increases", "evidence": "Moreover, the scavengers of reactive oxygen species (ROS) including Trolox and N-acetylcysteine (NAC) abrogated the SOD1(G93A)-induced increase in ASK1 activity (Supplementary Figure 2), suggesting that SOD1(G93A) induces ASK1 activation in a ROS-dependent manner.", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3720, "target": 3668, "key": "bcd348af418b5fc54dc8a6deb845e8ba"}, {"line": 23103, "relation": "increases", "evidence": "These results thus suggested that CIIA prevents the SOD1(G93A)-induced stimulation of ASK1 and p38 MAPK. It is noteworthy that CIIA did not affect ROS generation induced by SOD1(G93A)", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3720, "target": 3668, "key": "f59f55f0a82b17fd3ab246fd9406ae81"}, {"line": 23131, "relation": "increases", "evidence": "Activation of p38 and its upstream kinase apoptosis signal–regulated kinase 1 () was apparent in the lumbar spinal cord of 8-wk-old SOD1(G93A) mice, and this activation was abolished in SOD1(G93A)/MST1−/− mice. Collectively, these results suggested that SOD1(G93A) induces MST1 activation, which in turn mediates activation of the p38 signaling pathway as well as that of caspase-9 and -3 in the lumbar spinal cord of ALS mice. This suggesting that the p38 pathway mediates SOD1(G93A)-induced caspase activation.", "citation": {"db": "PubMed", "db_id": "23818595"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3720, "target": 3668, "key": "10bcfc922dd523259fff90fa1f62cb90"}, {"line": 23096, "relation": "association", "evidence": "Moreover, the scavengers of reactive oxygen species (ROS) including Trolox and N-acetylcysteine (NAC) abrogated the SOD1(G93A)-induced increase in ASK1 activity (Supplementary Figure 2), suggesting that SOD1(G93A) induces ASK1 activation in a ROS-dependent manner.", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 3720, "target": 170, "key": "037281987278619043628256bc761465"}, {"line": 23106, "relation": "increases", "evidence": "These results thus suggested that CIIA prevents the SOD1(G93A)-induced stimulation of ASK1 and p38 MAPK. It is noteworthy that CIIA did not affect ROS generation induced by SOD1(G93A)", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 3720, "target": 170, "key": "515f211eec67f8276c42a0393dea40c9"}, {"line": 23104, "relation": "increases", "evidence": "These results thus suggested that CIIA prevents the SOD1(G93A)-induced stimulation of ASK1 and p38 MAPK. It is noteworthy that CIIA did not affect ROS generation induced by SOD1(G93A)", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3720, "target": 2222, "key": "e9298540de1f5fc4dc3caf02cb4e1e14"}, {"line": 23132, "relation": "increases", "evidence": "Activation of p38 and its upstream kinase apoptosis signal–regulated kinase 1 () was apparent in the lumbar spinal cord of 8-wk-old SOD1(G93A) mice, and this activation was abolished in SOD1(G93A)/MST1−/− mice. Collectively, these results suggested that SOD1(G93A) induces MST1 activation, which in turn mediates activation of the p38 signaling pathway as well as that of caspase-9 and -3 in the lumbar spinal cord of ALS mice. This suggesting that the p38 pathway mediates SOD1(G93A)-induced caspase activation.", "citation": {"db": "PubMed", "db_id": "23818595"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3720, "target": 2222, "key": "f2a0567c902f6b4fe01c5035e4f778c0"}, {"line": 23113, "relation": "increases", "evidence": "We also examined a possible effect of CIIA on TRAF2-ASK1 interaction, because the recruitment of TRAF2 to ASK1 is an integral part of the mechanism for ROS-induced ASK1 activation. SOD1(G93A) induced the binding of TRAF2 to ASK1 in NSC34 cells and this binding was potentiated in those expressing a CIIA siRNA (Figure 2C, upper panel), suggesting that CIIA suppresses the SOD1(G93A)-induced TRAF2-ASK1 interaction.", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 3720, "target": 1647, "key": "dea8e63c4bb7500640f809fca05a52d5"}, {"line": 23133, "relation": "increases", "evidence": "Activation of p38 and its upstream kinase apoptosis signal–regulated kinase 1 () was apparent in the lumbar spinal cord of 8-wk-old SOD1(G93A) mice, and this activation was abolished in SOD1(G93A)/MST1−/− mice. Collectively, these results suggested that SOD1(G93A) induces MST1 activation, which in turn mediates activation of the p38 signaling pathway as well as that of caspase-9 and -3 in the lumbar spinal cord of ALS mice. This suggesting that the p38 pathway mediates SOD1(G93A)-induced caspase activation.", "citation": {"db": "PubMed", "db_id": "23818595"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3720, "target": 3726, "key": "c137fca7a94468fd49ca7655ae9d8a42"}, {"line": 23200, "relation": "decreases", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3720, "target": 3597, "key": "80ab42c73eaf5f6869bd71f7bf421278"}, {"line": 23201, "relation": "decreases", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3720, "target": 3598, "key": "cdac92984889afe88d1fb19321b1632e"}, {"line": 23202, "relation": "increases", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3720, "target": 3596, "key": "ff502dcd87c113512f2288f2712b38f2"}, {"line": 23203, "relation": "increases", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3720, "target": 3595, "key": "dc628df00e62fc74ce67dd6405aa4a4f"}, {"line": 23291, "relation": "positiveCorrelation", "evidence": "Metabotropic glutamate receptors (mGluR) are also heterogeneous and classified into three groups, based on their sequence homology, signaling and pharmacology. Group I mGluRs, comprising mGluR1 and mGluR5, are excitatory because of positive coupling to phosphatidylinositol breakdown. Thus, hyper-activation of Glu receptors may lead to an excessive increase of intracellular calcium due to either its entry through ionotropic Glu receptors and/or to its release from intracellular stores, mediated by Group I mGluRs and contributing to excitotoxicity and cell death. Therefore, activation of mGluR1 and mGluR5 produces abnormal glutamate release in SOD1G93A mice, suggesting that these receptors are implicated in ALS.", "citation": {"db": "PubMed", "db_id": "24361555"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 3720, "target": 3638, "key": "cade4895c41071f0e445293924b3a854"}, {"line": 23293, "relation": "positiveCorrelation", "evidence": "Metabotropic glutamate receptors (mGluR) are also heterogeneous and classified into three groups, based on their sequence homology, signaling and pharmacology. Group I mGluRs, comprising mGluR1 and mGluR5, are excitatory because of positive coupling to phosphatidylinositol breakdown. Thus, hyper-activation of Glu receptors may lead to an excessive increase of intracellular calcium due to either its entry through ionotropic Glu receptors and/or to its release from intracellular stores, mediated by Group I mGluRs and contributing to excitotoxicity and cell death. Therefore, activation of mGluR1 and mGluR5 produces abnormal glutamate release in SOD1G93A mice, suggesting that these receptors are implicated in ALS.", "citation": {"db": "PubMed", "db_id": "24361555"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 3720, "target": 3639, "key": "49ca619850ede0f671ae686dbcb391fb"}, {"relation": "hasVariant", "source": 3719, "target": 3720, "key": "220e4828ed2282e67abe1bb72fa027e6"}, {"line": 23003, "relation": "increases", "evidence": "Distinct from WT SOD1, mutant SOD1 induces morphological change and cytochrome c release in cultured neurons that resulted in apoptotic process. Two transgenic studies further indicated the involvement of mitochondria-mediated apoptotic process in mutant SOD1-linked ALS.", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 105, "target": 478, "key": "b0a71a9522a68185128bc4599696697f"}, {"line": 23010, "relation": "decreases", "evidence": "Inoue et al. [37] demonstrated that suppressing caspase-9 by overexpressing XIAP in motor neurons effectively slowed the progression of ALS in G93A SOD1 Tg mice, while Reyes et al. documented that neuron-specific deletion of BCL-associated X protein (BAX) or BCL2-homologous antagonist/killer (BAK), which are both proapoptotic BCL-2 family proteins, delayed the onset and extended the longevity of disease in the same mice [38].", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "MeSHAnatomy": {"Motor Neurons": true}}, "source": 3753, "target": 3601, "key": "cab4efda4e08a3fe7ab92bfe07eaff32"}, {"line": 23012, "relation": "decreases", "evidence": "Inoue et al. [37] demonstrated that suppressing caspase-9 by overexpressing XIAP in motor neurons effectively slowed the progression of ALS in G93A SOD1 Tg mice, while Reyes et al. documented that neuron-specific deletion of BCL-associated X protein (BAX) or BCL2-homologous antagonist/killer (BAK), which are both proapoptotic BCL-2 family proteins, delayed the onset and extended the longevity of disease in the same mice [38].", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "MeSHAnatomy": {"Motor Neurons": true}}, "object": {"modifier": "Activity"}, "source": 3753, "target": 3601, "key": "c7416e8d85208d0094c20118c229dfe8"}, {"line": 23065, "relation": "decreases", "evidence": "Caspase-9 activation is mainly triggered by cytochrome c release from the mitochondria, which occurs at the asymptomatic stage prior to disease onset (Guebetagan et al., 2001). Cytochrome c, together with ATP/ADP, Apaf-1 and procaspase-9, forms a complex termed `apoptosome', in which caspase-9 is activated. Caspase-9 then cleaves procaspase-3 to generate active caspase-3. Both caspase-9 and caspase-3 cleave procaspase-9 to form an `amplication loop' of caspase-9 activation. XIAP does not inhibit `apoptosome' formation and its upstream events, but suppresses the downstream `amplibetacation loop' by inhibition of caspase-9 and -3. Therefore, the activation of caspase-9 preceded by cytochrome c release can occur in the presence of a potent caspase-9 inhibitor XIAP and be considered an indicator of disease onset, regardless of the level of caspase-9.", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 3753, "target": 3601, "key": "e4176fe5c3d2cfbd2729dd0b1473a147"}, {"line": 23013, "relation": "decreases", "evidence": "Inoue et al. [37] demonstrated that suppressing caspase-9 by overexpressing XIAP in motor neurons effectively slowed the progression of ALS in G93A SOD1 Tg mice, while Reyes et al. documented that neuron-specific deletion of BCL-associated X protein (BAX) or BCL2-homologous antagonist/killer (BAK), which are both proapoptotic BCL-2 family proteins, delayed the onset and extended the longevity of disease in the same mice [38].", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "MeSHAnatomy": {"Motor Neurons": true}}, "source": 3753, "target": 3825, "key": "9391cd675aa3c331b853ae615b34ee84"}, {"line": 23041, "relation": "negativeCorrelation", "evidence": "Hence, as XIAP levels decrease in spinal motor neurons of mutant SOD1 mice during disease progression (Ishigaki et al., 2002), caspase-9-initiated apoptosis may be promoted.", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"XIAP subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}}, "source": 3753, "target": 3825, "key": "2f3ce1dbf56cbb0051cb49ac768dea37"}, {"line": 23048, "relation": "decreases", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 3753, "target": 3825, "key": "01dd2a307fe62aa37afd795d24a84480"}, {"line": 23064, "relation": "causesNoChange", "evidence": "Caspase-9 activation is mainly triggered by cytochrome c release from the mitochondria, which occurs at the asymptomatic stage prior to disease onset (Guebetagan et al., 2001). Cytochrome c, together with ATP/ADP, Apaf-1 and procaspase-9, forms a complex termed `apoptosome', in which caspase-9 is activated. Caspase-9 then cleaves procaspase-3 to generate active caspase-3. Both caspase-9 and caspase-3 cleave procaspase-9 to form an `amplication loop' of caspase-9 activation. XIAP does not inhibit `apoptosome' formation and its upstream events, but suppresses the downstream `amplibetacation loop' by inhibition of caspase-9 and -3. Therefore, the activation of caspase-9 preceded by cytochrome c release can occur in the presence of a potent caspase-9 inhibitor XIAP and be considered an indicator of disease onset, regardless of the level of caspase-9.", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 3753, "target": 973, "key": "2376a3a991dda4d5798aa7f6b89284a5"}, {"line": 23066, "relation": "decreases", "evidence": "Caspase-9 activation is mainly triggered by cytochrome c release from the mitochondria, which occurs at the asymptomatic stage prior to disease onset (Guebetagan et al., 2001). Cytochrome c, together with ATP/ADP, Apaf-1 and procaspase-9, forms a complex termed `apoptosome', in which caspase-9 is activated. Caspase-9 then cleaves procaspase-3 to generate active caspase-3. Both caspase-9 and caspase-3 cleave procaspase-9 to form an `amplication loop' of caspase-9 activation. XIAP does not inhibit `apoptosome' formation and its upstream events, but suppresses the downstream `amplibetacation loop' by inhibition of caspase-9 and -3. Therefore, the activation of caspase-9 preceded by cytochrome c release can occur in the presence of a potent caspase-9 inhibitor XIAP and be considered an indicator of disease onset, regardless of the level of caspase-9.", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 3753, "target": 3600, "key": "affd2b125198dd5bf1f9b8bd34706cd8"}, {"line": 23011, "relation": "association", "evidence": "Inoue et al. [37] demonstrated that suppressing caspase-9 by overexpressing XIAP in motor neurons effectively slowed the progression of ALS in G93A SOD1 Tg mice, while Reyes et al. documented that neuron-specific deletion of BCL-associated X protein (BAX) or BCL2-homologous antagonist/killer (BAK), which are both proapoptotic BCL-2 family proteins, delayed the onset and extended the longevity of disease in the same mice [38].", "citation": {"db": "PubMed", "db_id": "22072931"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "MeSHAnatomy": {"Motor Neurons": true}}, "source": 3601, "target": 3825, "key": "7b788ba8d6b71d2c0f088fcc2df83a63"}, {"line": 23054, "relation": "increases", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "object": {"modifier": "Activity"}, "source": 3601, "target": 3600, "key": "d366325aea8dc4af8a5f713dda09f8b0"}, {"line": 23056, "relation": "increases", "evidence": "XIAP expression in motor neurons significantly slows disease progression, whereas p35, a broad caspase inhibitory protein, does not. However, p35, which potently inhibits capase-3, does not slow disease progression in mouse ALS. There are two explanations for this apparent discrepancy. Since caspase-9 remains active in the presence of p35, procaspase-3 may be continuously processed into active caspase-3, finally overriding inhibition by p35. Alternatively, caspase-9 may utilize a substrate other than caspase-3, leading to caspase-3-independent cell death", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "source": 3601, "target": 505, "key": "971c9b84984de5d0a0e4750e5ab237f6"}, {"line": 23063, "relation": "increases", "evidence": "Caspase-9 activation is mainly triggered by cytochrome c release from the mitochondria, which occurs at the asymptomatic stage prior to disease onset (Guebetagan et al., 2001). Cytochrome c, together with ATP/ADP, Apaf-1 and procaspase-9, forms a complex termed `apoptosome', in which caspase-9 is activated. Caspase-9 then cleaves procaspase-3 to generate active caspase-3. Both caspase-9 and caspase-3 cleave procaspase-9 to form an `amplication loop' of caspase-9 activation. XIAP does not inhibit `apoptosome' formation and its upstream events, but suppresses the downstream `amplibetacation loop' by inhibition of caspase-9 and -3. Therefore, the activation of caspase-9 preceded by cytochrome c release can occur in the presence of a potent caspase-9 inhibitor XIAP and be considered an indicator of disease onset, regardless of the level of caspase-9.", "citation": {"db": "PubMed", "db_id": "14657037"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "XIAP subgraph": true}, "Species": {"10090": true}}, "object": {"modifier": "Activity"}, "source": 973, "target": 3601, "key": "90ab57ea969d2c99e7b159fade72e479"}, {"relation": "partOf", "source": 3583, "target": 973, "key": "ec1e4a19a09f12cae6b1acc73a8ee17c"}, {"line": 23087, "relation": "increases", "evidence": "ASK1, a member of the MAP3K family, has been shown to mediate SOD1(G93A)-induced neurotoxicity in an ALS mouse model", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"MAPK-ERK subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3668, "target": 648, "key": "477d5c84b6011a7447084393d96e6db6"}, {"line": 23097, "relation": "association", "evidence": "Moreover, the scavengers of reactive oxygen species (ROS) including Trolox and N-acetylcysteine (NAC) abrogated the SOD1(G93A)-induced increase in ASK1 activity (Supplementary Figure 2), suggesting that SOD1(G93A) induces ASK1 activation in a ROS-dependent manner.", "citation": {"db": "PubMed", "db_id": "25018698"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3668, "target": 170, "key": "6067c1bbd420a7fcbeb963b6836aef63"}, {"relation": "partOf", "source": 3668, "target": 1647, "key": "3db9a5b8b82c95caf68066403540f0c9"}, {"line": 23136, "relation": "increases", "evidence": "Activation of p38 and its upstream kinase apoptosis signal–regulated kinase 1 () was apparent in the lumbar spinal cord of 8-wk-old SOD1(G93A) mice, and this activation was abolished in SOD1(G93A)/MST1−/− mice. Collectively, these results suggested that SOD1(G93A) induces MST1 activation, which in turn mediates activation of the p38 signaling pathway as well as that of caspase-9 and -3 in the lumbar spinal cord of ALS mice. This suggesting that the p38 pathway mediates SOD1(G93A)-induced caspase activation.", "citation": {"db": "PubMed", "db_id": "23818595"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3668, "target": 2222, "key": "8066bcc6f8268257d6bf8d36beb9cfd1"}, {"relation": "isA", "source": 3669, "target": 2222, "key": "0e0bcb40f19682f900d7269872b872e3"}, {"relation": "isA", "source": 3670, "target": 2222, "key": "2bc28dec7163f99e1f533db2dab79437"}, {"relation": "isA", "source": 3671, "target": 2222, "key": "ff1c53e146047afc6298d4469a01c7fc"}, {"relation": "isA", "source": 3672, "target": 2222, "key": "2c9c6fd43b8970d73912ef44e7bacf58"}, {"relation": "partOf", "source": 3744, "target": 1647, "key": "cfcdcfe67465a32827b3e40ed5d9e565"}, {"line": 23135, "relation": "increases", "evidence": "Activation of p38 and its upstream kinase apoptosis signal–regulated kinase 1 () was apparent in the lumbar spinal cord of 8-wk-old SOD1(G93A) mice, and this activation was abolished in SOD1(G93A)/MST1−/− mice. Collectively, these results suggested that SOD1(G93A) induces MST1 activation, which in turn mediates activation of the p38 signaling pathway as well as that of caspase-9 and -3 in the lumbar spinal cord of ALS mice. This suggesting that the p38 pathway mediates SOD1(G93A)-induced caspase activation.", "citation": {"db": "PubMed", "db_id": "23818595"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3726, "target": 3668, "key": "a5c7e96e3a0fa99baf355dc2a64fcb2b"}, {"line": 23164, "relation": "increases", "evidence": "We showed that the localization of mutant SOD1 in the mitochondria triggered the release of mitochondrial cytochrome c followed by the activation of caspase cascade and induced neuronal cell death without cytoplasmic mutant SOD1 aggregate formation.", "citation": {"db": "PubMed", "db_id": "12393885"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 3393, "target": 105, "key": "cd6aef26d4477bc727cf986763ee27d6"}, {"line": 23165, "relation": "increases", "evidence": "We showed that the localization of mutant SOD1 in the mitochondria triggered the release of mitochondrial cytochrome c followed by the activation of caspase cascade and induced neuronal cell death without cytoplasmic mutant SOD1 aggregate formation.", "citation": {"db": "PubMed", "db_id": "12393885"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "source": 3393, "target": 2608, "key": "a39f81e8b2aee7032c0ab3ad60b9883b"}, {"line": 23311, "relation": "decreases", "evidence": "EAAT2 might be linked to ALS by other mechanisms. In one sporadic ALS case, a germline mutation in EAAT2 affected N-linked glycosylation and glutamate clearance143, 144. Moreover, in SOD1-linked ALS, oxidative damage inactivated EAAT2 ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3393, "target": 3369, "key": "a8634997edfe9de5219464efc76c5f17"}, {"line": 23313, "relation": "increases", "evidence": "EAAT2 might be linked to ALS by other mechanisms. In one sporadic ALS case, a germline mutation in EAAT2 affected N-linked glycosylation and glutamate clearance143, 144. Moreover, in SOD1-linked ALS, oxidative damage inactivated EAAT2 ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 3393, "target": 57, "key": "a7caebe38803d602c65187f61b2857a3"}, {"line": 23180, "relation": "increases", "evidence": "Once released from the mitochondria, cytochrome c interacts in the cytosol with Apaf-1, forming an ATP-dependent complex that activates caspase-9 (Liu et al., 1996; Li et al., 1997; Zou et al., 1997; Hu et al., 1999; Saleh et al., 1999), which is instrumental in the mitochondrial-dependent activation of downstream effector caspases such as caspase-3 and caspase-7 (Slee et al., 1999)", "citation": {"db": "PubMed", "db_id": "11517246"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Caspase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1074, "target": 2449, "key": "3417cf72c5a1a7fed35db6a33c3e97ab"}, {"line": 23198, "relation": "positiveCorrelation", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3595, "target": 3825, "key": "91472ca197f33db2b50df075775ddb7e"}, {"line": 23208, "relation": "increases", "evidence": "In contrast, in symptomatic mice, expression of Bcl-2 and Bcl-XL, which inhibit apoptotic process, is reduced, whereas expression of Bad and Bax, which stimulate apoptotic process, is increased. These alterations are specific to affected brain regions and are caused by the mutant and not by the normal SOD1 enzyme.", "citation": {"db": "PubMed", "db_id": "10582606"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true}, "Disease": {"amyotrophic lateral sclerosis": true}, "Subgraph": {"Electron transport chain": true, "Caspase subgraph": true, "Bcl-2 subgraph": true}}, "source": 3595, "target": 478, "key": "b61011019ec96750c21437d4fe139ab5"}, {"line": 41732, "relation": "positiveCorrelation", "evidence": "The apoptotic death in the glioma cell lines treated with PPARgamma agonists was correlated with the transient up-regulation of Bax and Bad protein levels.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Bcl-2 subgraph": true}, "MeSHDisease": {"Glioma": true}, "MeSHAnatomy": {"Cell Line": true}, "Confidence": {"High": true}}, "source": 3595, "target": 3699, "key": "a6962cf88609877bbd054d67ff53064f"}, {"line": 23256, "relation": "association", "evidence": "Reactive oxygen species (ROS), produced within mitochondria, inhibit the function of EAAT2, the main glial glutamate transporter protein, responsible for most of the reuptake of synaptically released glutamate. Glutamate excess increases intracellular calcium, which enhances oxidative stress and mitochondrial damage.", "citation": {"db": "PubMed", "db_id": "17015226"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 3369, "target": 567, "key": "833520ade10233f2fff5d3f013f7884b"}, {"line": 23312, "relation": "decreases", "evidence": "EAAT2 might be linked to ALS by other mechanisms. In one sporadic ALS case, a germline mutation in EAAT2 affected N-linked glycosylation and glutamate clearance143, 144. Moreover, in SOD1-linked ALS, oxidative damage inactivated EAAT2 ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3369, "target": 57, "key": "9ec0e0890e422f0e3078b3707ddaceb2"}, {"line": 46210, "relation": "negativeCorrelation", "evidence": "The YY1 pathway contributes to negative regulation of EAAT2", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 3369, "target": 3543, "key": "92c621723d32b5f9c347814c186651bc"}, {"line": 23256, "relation": "association", "evidence": "Reactive oxygen species (ROS), produced within mitochondria, inhibit the function of EAAT2, the main glial glutamate transporter protein, responsible for most of the reuptake of synaptically released glutamate. Glutamate excess increases intracellular calcium, which enhances oxidative stress and mitochondrial damage.", "citation": {"db": "PubMed", "db_id": "17015226"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 567, "target": 3369, "key": "e36950632b89b1ae3c9d28552d91d9ae"}, {"line": 23260, "relation": "increases", "evidence": "Reactive oxygen species (ROS), produced within mitochondria, inhibit the function of EAAT2, the main glial glutamate transporter protein, responsible for most of the reuptake of synaptically released glutamate. Glutamate excess increases intracellular calcium, which enhances oxidative stress and mitochondrial damage.", "citation": {"db": "PubMed", "db_id": "17015226"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 123, "target": 94, "key": "38d304322868e3900d7798a80ae7375d"}, {"line": 23514, "relation": "increases", "evidence": "In our study, following CLP plasma TNF-α levels were significantly increased, while these increases were partially depressed by riluzole treatment. Recently, 2 studies have shown that glutamate induces TNF-α release from neuronal cells in pathological conditions, and this process may be involved in apoptotic neuronal cell death. Moreover, they confirmed that NMDA antagonists do not affect TNF-α level under basal conditions [38].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 123, "target": 3808, "key": "c3e8d2689d353a3ebb6479b61b2c193d"}, {"line": 37689, "relation": "increases", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 123, "target": 649, "key": "3292a6cee074fa62f3d9d24981bfb9d8"}, {"line": 23291, "relation": "positiveCorrelation", "evidence": "Metabotropic glutamate receptors (mGluR) are also heterogeneous and classified into three groups, based on their sequence homology, signaling and pharmacology. Group I mGluRs, comprising mGluR1 and mGluR5, are excitatory because of positive coupling to phosphatidylinositol breakdown. Thus, hyper-activation of Glu receptors may lead to an excessive increase of intracellular calcium due to either its entry through ionotropic Glu receptors and/or to its release from intracellular stores, mediated by Group I mGluRs and contributing to excitotoxicity and cell death. Therefore, activation of mGluR1 and mGluR5 produces abnormal glutamate release in SOD1G93A mice, suggesting that these receptors are implicated in ALS.", "citation": {"db": "PubMed", "db_id": "24361555"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 3638, "target": 3720, "key": "9ae72d16281fac2517e44ff9d6724302"}, {"line": 23295, "relation": "increases", "evidence": "Metabotropic glutamate receptors (mGluR) are also heterogeneous and classified into three groups, based on their sequence homology, signaling and pharmacology. Group I mGluRs, comprising mGluR1 and mGluR5, are excitatory because of positive coupling to phosphatidylinositol breakdown. Thus, hyper-activation of Glu receptors may lead to an excessive increase of intracellular calcium due to either its entry through ionotropic Glu receptors and/or to its release from intracellular stores, mediated by Group I mGluRs and contributing to excitotoxicity and cell death. Therefore, activation of mGluR1 and mGluR5 produces abnormal glutamate release in SOD1G93A mice, suggesting that these receptors are implicated in ALS.", "citation": {"db": "PubMed", "db_id": "24361555"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 3638, "target": 94, "key": "f9a6d344c607290e06004553285e71e3"}, {"line": 23293, "relation": "positiveCorrelation", "evidence": "Metabotropic glutamate receptors (mGluR) are also heterogeneous and classified into three groups, based on their sequence homology, signaling and pharmacology. Group I mGluRs, comprising mGluR1 and mGluR5, are excitatory because of positive coupling to phosphatidylinositol breakdown. Thus, hyper-activation of Glu receptors may lead to an excessive increase of intracellular calcium due to either its entry through ionotropic Glu receptors and/or to its release from intracellular stores, mediated by Group I mGluRs and contributing to excitotoxicity and cell death. Therefore, activation of mGluR1 and mGluR5 produces abnormal glutamate release in SOD1G93A mice, suggesting that these receptors are implicated in ALS.", "citation": {"db": "PubMed", "db_id": "24361555"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 3639, "target": 3720, "key": "da556bc105b3f3bc36e38ff6b656beed"}, {"line": 23296, "relation": "increases", "evidence": "Metabotropic glutamate receptors (mGluR) are also heterogeneous and classified into three groups, based on their sequence homology, signaling and pharmacology. Group I mGluRs, comprising mGluR1 and mGluR5, are excitatory because of positive coupling to phosphatidylinositol breakdown. Thus, hyper-activation of Glu receptors may lead to an excessive increase of intracellular calcium due to either its entry through ionotropic Glu receptors and/or to its release from intracellular stores, mediated by Group I mGluRs and contributing to excitotoxicity and cell death. Therefore, activation of mGluR1 and mGluR5 produces abnormal glutamate release in SOD1G93A mice, suggesting that these receptors are implicated in ALS.", "citation": {"db": "PubMed", "db_id": "24361555"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 3639, "target": 94, "key": "cd6025cab8b2fdeea9ffb33c51c49f54"}, {"line": 23320, "relation": "increases", "evidence": "Another component of neuronal degeneration in many neurodegenerative disorders is excessive glutamate-induced stimulation of postsynaptic glutamate receptors. This activates massive calcium influxes that are potentially detrimental through calcium-activated processes and molecules (for example, proteases, nucleases and lipases). There is considerable evidence in support of this view, such as the observed threefold increase in glutamate levels in the cerebrospinal fluid of patients with ALS134, 135, 136 and the benefits in ALS of the anti-glutamate drug riluzole. EAATs are present at most synapses in the CNS, and transport glutamate from the synaptic space into astrocytes after glutamate release during neurotransmission137, 138, 139. ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 2788, "target": 94, "key": "9ea3e329b2bcfc9f8df1ec235b3e5fcf"}, {"line": 23321, "relation": "increases", "evidence": "Another component of neuronal degeneration in many neurodegenerative disorders is excessive glutamate-induced stimulation of postsynaptic glutamate receptors. This activates massive calcium influxes that are potentially detrimental through calcium-activated processes and molecules (for example, proteases, nucleases and lipases). There is considerable evidence in support of this view, such as the observed threefold increase in glutamate levels in the cerebrospinal fluid of patients with ALS134, 135, 136 and the benefits in ALS of the anti-glutamate drug riluzole. EAATs are present at most synapses in the CNS, and transport glutamate from the synaptic space into astrocytes after glutamate release during neurotransmission137, 138, 139. ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 2789, "target": 94, "key": "4449dc4f7426798e8d659ae8f88d21ca"}, {"line": 23323, "relation": "decreases", "evidence": "Another component of neuronal degeneration in many neurodegenerative disorders is excessive glutamate-induced stimulation of postsynaptic glutamate receptors. This activates massive calcium influxes that are potentially detrimental through calcium-activated processes and molecules (for example, proteases, nucleases and lipases). There is considerable evidence in support of this view, such as the observed threefold increase in glutamate levels in the cerebrospinal fluid of patients with ALS134, 135, 136 and the benefits in ALS of the anti-glutamate drug riluzole. EAATs are present at most synapses in the CNS, and transport glutamate from the synaptic space into astrocytes after glutamate release during neurotransmission137, 138, 139. ", "citation": {"db": "PubMed", "db_id": "16924260"}, "annotations": {"MeSHAnatomy": {"Motor Neurons": true}, "Subgraph": {"Glutamatergic subgraph": true}}, "source": 200, "target": 57, "key": "ae8c7915c698970a874b6f5210e35019"}, {"line": 23408, "relation": "decreases", "evidence": "Furthermore, the results demonstrated that the administration of riluzole can attenuate the morphine-induced elevation of glutamate in the lumbar spinal cord. In conclusion, i.c.v. administration of riluzole attenuated morphine-induced tolerance to analgesia and apoptosis in addition to preventing the morphine-induced increase of glutamate in the lumbar spinal cord of rats. In addition to being an anti-glutamatergic agent, riluzole also has antioxidant effects and can protect dopaminergic neurons against oxidative stress by reducing lipid peroxidation and ATP consumption [19, 27, 35, 36]. On the other hand, our results show that 80 µg/10 µl of riluzole increases the level of Bcl-2 in the lumbar spinal cord when administered with morphine", "citation": {"db": "PubMed", "db_id": "20885006"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 123, "key": "427f0a0398d345b04e2697871a703534"}, {"line": 23555, "relation": "decreases", "evidence": "Riluzole is a benzothiazole that has neuroprotective actions and has been used to treat patients with amyotrophic lateral sclerosis (ALS) (5, 6). This compound has been reported to inhibit the glutamate release from nerve terminals in the central nervous system (7, 8), the binding of excitatory amino acids to glutamate receptors (9) and the activity of the voltage-gated Na+ channels. However, the mechanism of drug action of riluzole is still unclear, partially because the number of ALS patients is very few.", "citation": {"db": "PubMed", "db_id": "19528481"}, "source": 200, "target": 123, "key": "0f6632871c99b505499ed2f3a2b06c1b"}, {"line": 23416, "relation": "decreases", "evidence": "Furthermore, the results demonstrated that the administration of riluzole can attenuate the morphine-induced elevation of glutamate in the lumbar spinal cord. In conclusion, i.c.v. administration of riluzole attenuated morphine-induced tolerance to analgesia and apoptosis in addition to preventing the morphine-induced increase of glutamate in the lumbar spinal cord of rats. In addition to being an anti-glutamatergic agent, riluzole also has antioxidant effects and can protect dopaminergic neurons against oxidative stress by reducing lipid peroxidation and ATP consumption [19, 27, 35, 36]. On the other hand, our results show that 80 µg/10 µl of riluzole increases the level of Bcl-2 in the lumbar spinal cord when administered with morphine", "citation": {"db": "PubMed", "db_id": "20885006"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 478, "key": "33713cd43709de748c210fb8219eb027"}, {"line": 23596, "relation": "decreases", "evidence": "Riluzole also prevented or attenuated ischemia-induced retinal cell death (necrosis and apoptotic process) and reduced the activation of p-JNK, c-jun phosphorylation, and the increase of cytoskeletal proteins induced by ischemic injury. The present study provides the first demonstration that retinal ischemia evokes a lasting activation of p-JNK and phosphorylation of c-jun at serine 73- Riluzole totally blocked the ischemia-induced increase in p-JNK expression during postischemia reperfusion.", "citation": {"db": "PubMed", "db_id": "10067977"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 478, "key": "91f613c4e96a25b185d612edea47194a"}, {"line": 23656, "relation": "decreases", "evidence": "The levels of the anti-apoptotic proteins Bcl-2 and HSP70 were higher in the riluzole groups than in the control. Furthermore, co-administration of riluzole with morphine significantly decreased caspase-3 protein levels and glutamate content of the cerebral cortex compared with the control. In conclusion, we found that icv administration of riluzole attenuates morphine-induced apoptosis in the cerebral cortex after the development of morphine tolerance.", "citation": {"db": "PubMed", "db_id": "21857080"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 478, "key": "515c51f260707316117a6aa4f31e45ac"}, {"line": 23424, "relation": "decreases", "evidence": "Furthermore, the results demonstrated that the administration of riluzole can attenuate the morphine-induced elevation of glutamate in the lumbar spinal cord. In conclusion, i.c.v. administration of riluzole attenuated morphine-induced tolerance to analgesia and apoptosis in addition to preventing the morphine-induced increase of glutamate in the lumbar spinal cord of rats. In addition to being an anti-glutamatergic agent, riluzole also has antioxidant effects and can protect dopaminergic neurons against oxidative stress by reducing lipid peroxidation and ATP consumption [19, 27, 35, 36]. On the other hand, our results show that 80 µg/10 µl of riluzole increases the level of Bcl-2 in the lumbar spinal cord when administered with morphine", "citation": {"db": "PubMed", "db_id": "20885006"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 593, "key": "5513a363a6845a57b60dcd4305c7ab43"}, {"line": 23492, "relation": "decreases", "evidence": "Riluzole decreases lipid peroxidation caused by iron-III and L-DOPA in mesencephalic cultures. The mechanism by which riluzole exerts its neuroprotective effect on DA neurons in culture appears to include multiple and independent ways of action: (a) Riluzole normalizes cellular energy supply altered by the complex I inhibitor MPP+, and (b) riluzole decreases lipid peroxidation caused by iron-III and L-DOPA in mesencephalic cultures. Riluzole decreases the intracellular ATP depletion caused by MPP+ in dopaminergic neuroblastoma SH-SY5Y cells. It is most likely that this normalization of cellular energy supply is responsible for the protective effect because ATP depletion in this cell system is the main cause of cell death induced by MPP+ (Storch et al., 2000b,c).", "citation": {"db": "PubMed", "db_id": "11080177"}, "annotations": {"Subgraph": {"Lipid peroxidation subgraph": true}}, "source": 200, "target": 593, "key": "4072e725d59a2bf42ab9041023cfa43a"}, {"line": 23525, "relation": "decreases", "evidence": "These findings show that riluzole maintains altered oxidant-antioxidant balance. Consistently, previous studies have shown the antioxidant effect of riluzole [19, 20 and 21]. In the study of Koh et al. [ 19], riluzole, besides preventing the excitotoxic neuronal damage, was also effective against FeCl3 induced nonexcitotoxic injury in cortical neuron cultures. In another study, riluzole was shown to protect the dopaminergic neurons against oxidative stress by reducing lipid peroxidation and adenosine triphosphate consumption [ 21]. It has been suggested that the mechanism involved in the protective effects in nonexcitotoxic oxidant damage was inhibition of PLA2, thereby reducing arachidonic acid and its metabolites, and further inhibition of protein kinase C [ 43].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}}, "source": 200, "target": 593, "key": "20ed39b84641b358ca4362831707e0e5"}, {"line": 23433, "relation": "decreases", "evidence": "Furthermore, the results demonstrated that the administration of riluzole can attenuate the morphine-induced elevation of glutamate in the lumbar spinal cord. In conclusion, i.c.v. administration of riluzole attenuated morphine-induced tolerance to analgesia and apoptosis in addition to preventing the morphine-induced increase of glutamate in the lumbar spinal cord of rats. In addition to being an anti-glutamatergic agent, riluzole also has antioxidant effects and can protect dopaminergic neurons against oxidative stress by reducing lipid peroxidation and ATP consumption [19, 27, 35, 36]. On the other hand, our results show that 80 µg/10 µl of riluzole increases the level of Bcl-2 in the lumbar spinal cord when administered with morphine", "citation": {"db": "PubMed", "db_id": "20885006"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 842, "key": "6f7df3bc96183c91673059fc8877bfe1"}, {"line": 23441, "relation": "increases", "evidence": "Furthermore, the results demonstrated that the administration of riluzole can attenuate the morphine-induced elevation of glutamate in the lumbar spinal cord. In conclusion, i.c.v. administration of riluzole attenuated morphine-induced tolerance to analgesia and apoptosis in addition to preventing the morphine-induced increase of glutamate in the lumbar spinal cord of rats. In addition to being an anti-glutamatergic agent, riluzole also has antioxidant effects and can protect dopaminergic neurons against oxidative stress by reducing lipid peroxidation and ATP consumption [19, 27, 35, 36]. On the other hand, our results show that 80 µg/10 µl of riluzole increases the level of Bcl-2 in the lumbar spinal cord when administered with morphine", "citation": {"db": "PubMed", "db_id": "20885006"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 3754, "key": "317c0e5922baa704dcf7ecbd26292d70"}, {"line": 23462, "relation": "decreases", "evidence": "In the present study, we confirmed that riluzole at ≤30 μM protects against excitotoxic neuronal injury induced by NMDA or kainate in mouse cortical cultures. The protective concentration range of riluzole is comparable with those in the previous reports (Malgouris et al., 1994 ; Estevez et al., 1995 ; Mary et al., 1995). However, unlike other potent direct glutamate receptor antagonist such as MK-801 and CNQX, riluzole protected against excitotoxic death only at modest levels of injury.", "citation": {"db": "PubMed", "db_id": "9930745"}, "object": {"modifier": "Activity"}, "source": 200, "target": 62, "key": "034783accb0815f6205a97cea19378b5"}, {"line": 23464, "relation": "decreases", "evidence": "In the present study, we confirmed that riluzole at ≤30 μM protects against excitotoxic neuronal injury induced by NMDA or kainate in mouse cortical cultures. The protective concentration range of riluzole is comparable with those in the previous reports (Malgouris et al., 1994 ; Estevez et al., 1995 ; Mary et al., 1995). However, unlike other potent direct glutamate receptor antagonist such as MK-801 and CNQX, riluzole protected against excitotoxic death only at modest levels of injury.", "citation": {"db": "PubMed", "db_id": "9930745"}, "object": {"modifier": "Activity"}, "source": 200, "target": 139, "key": "848531c3bb6426733a3bdc6aeb3f3c2e"}, {"line": 23470, "relation": "decreases", "evidence": "As a step in searching for possible antioxidative mechanisms of riluzole, we tested in test-tube conditions its effects on the activity of PLA2, an enzyme that is linked to oxidative injury via the AA cascades (Janssen-Timmen et al., 1994 ; Katsuki and Okuda, 1995). Riluzole (10-100 μM), in a concentration-dependent manner, attenuated the activity of cPLA2, but not that of group I and group II PLA2", "citation": {"db": "PubMed", "db_id": "9930745"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}}, "source": 200, "target": 3198, "key": "c1f0ead527ae9be51845f91e0bd7e3bf"}, {"line": 23483, "relation": "decreases", "evidence": "In light of evidence that protein kinase C (PKC) mediates oxidative stress in cortical culture, we examined the possibility that riluzole's antioxidative neuroprotection involves PKC inhibition. Present results have demonstrated that riluzole directly inhibits PKC, which action may contribute to its antioxidative neuroprotective effects. In addition, it appears possible that PKC inhibition may be able to explain some of its well-known channel inhibitory and neuroprotective effects. Combined with findings that PKC activity is increased in ALS, the present results suggest that PKC may be a potential therapeutic target in ALS.", "citation": {"db": "PubMed", "db_id": "10964608"}, "annotations": {"Subgraph": {"Lipid peroxidation subgraph": true}}, "object": {"modifier": "Activity"}, "source": 200, "target": 2205, "key": "8e88a1346d81f4564b21126ec03f747d"}, {"line": 23537, "relation": "decreases", "evidence": "These findings show that riluzole maintains altered oxidant-antioxidant balance. Consistently, previous studies have shown the antioxidant effect of riluzole [19, 20 and 21]. In the study of Koh et al. [ 19], riluzole, besides preventing the excitotoxic neuronal damage, was also effective against FeCl3 induced nonexcitotoxic injury in cortical neuron cultures. In another study, riluzole was shown to protect the dopaminergic neurons against oxidative stress by reducing lipid peroxidation and adenosine triphosphate consumption [ 21]. It has been suggested that the mechanism involved in the protective effects in nonexcitotoxic oxidant damage was inhibition of PLA2, thereby reducing arachidonic acid and its metabolites, and further inhibition of protein kinase C [ 43].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 200, "target": 2205, "key": "e42ee4b83c08f88fd84816a555a0639f"}, {"line": 23506, "relation": "decreases", "evidence": "In our study, following CLP plasma TNF-α levels were significantly increased, while these increases were partially depressed by riluzole treatment. Recently, 2 studies have shown that glutamate induces TNF-α release from neuronal cells in pathological conditions, and this process may be involved in apoptotic neuronal cell death. Moreover, they confirmed that NMDA antagonists do not affect TNF-α level under basal conditions [38].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 3808, "key": "7f37ca1cd088ce61eeeb47ae27a3ced4"}, {"line": 23533, "relation": "decreases", "evidence": "These findings show that riluzole maintains altered oxidant-antioxidant balance. Consistently, previous studies have shown the antioxidant effect of riluzole [19, 20 and 21]. In the study of Koh et al. [ 19], riluzole, besides preventing the excitotoxic neuronal damage, was also effective against FeCl3 induced nonexcitotoxic injury in cortical neuron cultures. In another study, riluzole was shown to protect the dopaminergic neurons against oxidative stress by reducing lipid peroxidation and adenosine triphosphate consumption [ 21]. It has been suggested that the mechanism involved in the protective effects in nonexcitotoxic oxidant damage was inhibition of PLA2, thereby reducing arachidonic acid and its metabolites, and further inhibition of protein kinase C [ 43].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 85, "key": "1f719c55a072ca0d713c66b41ca3ba4c"}, {"line": 23554, "relation": "decreases", "evidence": "Riluzole is a benzothiazole that has neuroprotective actions and has been used to treat patients with amyotrophic lateral sclerosis (ALS) (5, 6). This compound has been reported to inhibit the glutamate release from nerve terminals in the central nervous system (7, 8), the binding of excitatory amino acids to glutamate receptors (9) and the activity of the voltage-gated Na+ channels. However, the mechanism of drug action of riluzole is still unclear, partially because the number of ALS patients is very few.", "citation": {"db": "PubMed", "db_id": "19528481"}, "object": {"modifier": "Activity"}, "source": 200, "target": 896, "key": "24771ab03cff9fcb755d129756b7e7ed"}, {"line": 23560, "relation": "decreases", "evidence": "Riluzole has been shown to inhibit vascular endothelial growth factor (VEGF)-stimulated protein kinase c (PKC), beta2 activation and cell proliferation in bovine retinal endothelial cell and human umbilical vein endothelial cell cultures (17). ", "citation": {"db": "PubMed", "db_id": "19528481"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 200, "target": 3519, "key": "7030b9e66bd25500f977f93970a9dbf6"}, {"line": 23582, "relation": "decreases", "evidence": "Of the signaling molecules downstream of VEGF receptors, PKC has been demonstrated to be critical in mediating endothelial cell proliferation.17 18 19 Although inhibition of PKC may seem a reasonable approach to curtail endothelial cell proliferation in proliferative retinopathies,20 21 PKC serves diverse essential normal roles in intracellular signaling, indicating broad-spectrum inhibition of all PKCs may have harmful side effects, including cell apoptotic process. Riluzole inhibited PKC activity in cortical cell cultures, and also inhibited the activity of purified PKC in vitro.", "citation": {"db": "PubMed", "db_id": "16303979"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 3519, "key": "f8d77eb9d5843582570e639aae3adbe0"}, {"line": 23574, "relation": "decreases", "evidence": "Of the signaling molecules downstream of VEGF receptors, PKC has been demonstrated to be critical in mediating endothelial cell proliferation.17 18 19 Although inhibition of PKC may seem a reasonable approach to curtail endothelial cell proliferation in proliferative retinopathies,20 21 PKC serves diverse essential normal roles in intracellular signaling, indicating broad-spectrum inhibition of all PKCs may have harmful side effects, including cell apoptotic process. Riluzole inhibited PKC activity in cortical cell cultures, and also inhibited the activity of purified PKC in vitro.", "citation": {"db": "PubMed", "db_id": "16303979"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 200, "target": 3236, "key": "a4d15b5995c7e8c7817e431eea2fdf93"}, {"line": 23604, "relation": "decreases", "evidence": "Riluzole also prevented or attenuated ischemia-induced retinal cell death (necrosis and apoptotic process) and reduced the activation of p-JNK, c-jun phosphorylation, and the increase of cytoskeletal proteins induced by ischemic injury. The present study provides the first demonstration that retinal ischemia evokes a lasting activation of p-JNK and phosphorylation of c-jun at serine 73- Riluzole totally blocked the ischemia-induced increase in p-JNK expression during postischemia reperfusion.", "citation": {"db": "PubMed", "db_id": "10067977"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 2937, "key": "12c8a21eb95639fb455f282e89ffda88"}, {"line": 23640, "relation": "increases", "evidence": "The levels of the anti-apoptotic proteins Bcl-2 and HSP70 were higher in the riluzole groups than in the control. Furthermore, co-administration of riluzole with morphine significantly decreased caspase-3 protein levels and glutamate content of the cerebral cortex compared with the control. In conclusion, we found that icv administration of riluzole attenuates morphine-induced apoptosis in the cerebral cortex after the development of morphine tolerance.", "citation": {"db": "PubMed", "db_id": "21857080"}, "annotations": {"Subgraph": {"Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 2393, "key": "49e5e962495849074921405d77d3a1b2"}, {"line": 23648, "relation": "decreases", "evidence": "The levels of the anti-apoptotic proteins Bcl-2 and HSP70 were higher in the riluzole groups than in the control. Furthermore, co-administration of riluzole with morphine significantly decreased caspase-3 protein levels and glutamate content of the cerebral cortex compared with the control. In conclusion, we found that icv administration of riluzole attenuates morphine-induced apoptosis in the cerebral cortex after the development of morphine tolerance.", "citation": {"db": "PubMed", "db_id": "21857080"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "source": 200, "target": 2444, "key": "9cc10706cea5bafe24fb21b7a9a00e88"}, {"line": 23363, "relation": "increases", "evidence": "Similarly, high glucose levels inhibit AMPK activity and increase ROS generation. This leads to the upregulation of Nox4 and the activation of p53-induced apoptosis in glomerular epithelial cells (podocytes), the loss of which may contribute to albuminuria and diabetic kidney disease. The reactivation of AMPK by AICAR in this context leads to a reduction in Nox4 levels, resulting in a suppression of p53 (Eid et al., 2010).", "citation": {"db": "PubMed", "db_id": "23954639"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "p53 stabilization subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3129, "target": 3482, "key": "0c80110838fc667c8eada6d6abd4c8db"}, {"line": 23381, "relation": "increases", "evidence": "Taken together, these results suggest that glucose deprivation leads to p53 activation through a pathway that involves elevated ROS levels and ATM activation in the absence of DNA double-strand breaks. In this paper, we report that glucose deprivation results in ROS production and in the activation of endogenous p53 through an ATM-dependent mechanism.", "citation": {"db": "PubMed", "db_id": "22055193"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true, "Response DNA damage": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2368, "target": 3482, "key": "c85ffce63da8e3d2da003b71520fa4df"}, {"line": 23463, "relation": "increases", "evidence": "In the present study, we confirmed that riluzole at ≤30 μM protects against excitotoxic neuronal injury induced by NMDA or kainate in mouse cortical cultures. The protective concentration range of riluzole is comparable with those in the previous reports (Malgouris et al., 1994 ; Estevez et al., 1995 ; Mary et al., 1995). However, unlike other potent direct glutamate receptor antagonist such as MK-801 and CNQX, riluzole protected against excitotoxic death only at modest levels of injury.", "citation": {"db": "PubMed", "db_id": "9930745"}, "subject": {"modifier": "Activity"}, "source": 62, "target": 648, "key": "698e1f90ad4f8975d6d5655f6517db73"}, {"line": 33876, "relation": "increases", "evidence": "Exposure of mixed fetal cortical neuron/glia co-cultures to the neurotoxin N-methyl-D-aspartate results in increased apoE expression and release in a time- and concentration-dependent manner.", "citation": {"db": "PubMed", "db_id": "11311545"}, "annotations": {"Subgraph": {"APOE subgraph": true}}, "source": 62, "target": 2312, "key": "8518b361c15d33d229ff12e959ab11da"}, {"line": 23515, "relation": "increases", "evidence": "In our study, following CLP plasma TNF-α levels were significantly increased, while these increases were partially depressed by riluzole treatment. Recently, 2 studies have shown that glutamate induces TNF-α release from neuronal cells in pathological conditions, and this process may be involved in apoptotic neuronal cell death. Moreover, they confirmed that NMDA antagonists do not affect TNF-α level under basal conditions [38].", "citation": {"db": "PubMed", "db_id": "18718604"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 3808, "target": 478, "key": "f623a5b7adf023c6028426da693e3035"}, {"line": 23605, "relation": "increases", "evidence": "Riluzole also prevented or attenuated ischemia-induced retinal cell death (necrosis and apoptotic process) and reduced the activation of p-JNK, c-jun phosphorylation, and the increase of cytoskeletal proteins induced by ischemic injury. The present study provides the first demonstration that retinal ischemia evokes a lasting activation of p-JNK and phosphorylation of c-jun at serine 73- Riluzole totally blocked the ischemia-induced increase in p-JNK expression during postischemia reperfusion.", "citation": {"db": "PubMed", "db_id": "10067977"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "source": 3831, "target": 2937, "key": "6f417703954065e6914c680b23fa951e"}, {"line": 43258, "relation": "increases", "evidence": "Increases in free fatty acids, eicosanoids, and products of lipid peroxidation are known to occur in various)/ conditions of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's diseasens/ of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Beta-Oxidation of Fatty Acids": true}, "Confidence": {"Medium": true}}, "source": 3831, "target": 114, "key": "8e1823872bf27a2facb0d1b6032871b6"}, {"line": 43266, "relation": "increases", "evidence": "Increases in free fatty acids, eicosanoids, and products of lipid peroxidation are known to occur in various)/ conditions of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's diseasens/ of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3831, "target": 280, "key": "53f6d716f34baf768de062d20d58ccd5"}, {"line": 43274, "relation": "increases", "evidence": "Increases in free fatty acids, eicosanoids, and products of lipid peroxidation are known to occur in various)/ conditions of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's diseasens/ of acute and chronic CNS injury, including cerebral ischemia, traumatic brain injury, and Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"Medium": true}}, "source": 3831, "target": 605, "key": "8cb89e7df1b02805752f50a4170d3ccf"}, {"line": 23618, "relation": "increases", "evidence": "Activation of PLA2 in cerebral ischemia has been shown while other studies have separately demonstrated increased lipid peroxidation. Dissecting the contribution of PLA2 to lipid peroxidation in cerebral ischemia is challenging due to multiple forms of PLA2, cardiolipin hydrolysis, diverse sources of ROS arising from arachidonic acid metabolism, catecholamine autoxidation, xanthine oxidase activity, mitochondrial dysfunction, activated neutrophils coupled with NADPH oxidase activity, and lack of specific inhibitors. Cardiolipin hydrolysis by mitochondrial sPLA2 disrupts the mitochondrial respiratory chain and increases production of reactive oxygen species (ROS). Oxidative metabolism of arachidonic acid also generates ROS.", "citation": {"db": "PubMed", "db_id": "16443152"}, "annotations": {"Subgraph": {"Lipid peroxidation subgraph": true}, "Confidence": {"High": true}}, "source": 3196, "target": 593, "key": "766e2eed1e4cf65601f916059a378f30"}, {"line": 23626, "relation": "increases", "evidence": "Activation of PLA2 in cerebral ischemia has been shown while other studies have separately demonstrated increased lipid peroxidation. Dissecting the contribution of PLA2 to lipid peroxidation in cerebral ischemia is challenging due to multiple forms of PLA2, cardiolipin hydrolysis, diverse sources of ROS arising from arachidonic acid metabolism, catecholamine autoxidation, xanthine oxidase activity, mitochondrial dysfunction, activated neutrophils coupled with NADPH oxidase activity, and lack of specific inhibitors. Cardiolipin hydrolysis by mitochondrial sPLA2 disrupts the mitochondrial respiratory chain and increases production of reactive oxygen species (ROS). Oxidative metabolism of arachidonic acid also generates ROS.", "citation": {"db": "PubMed", "db_id": "16443152"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 170, "key": "db350aab25af590a62c5cff09dd2ef4f"}, {"line": 40730, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 51, "key": "9d16042187cafa0e4d03584f9d4d5aaf"}, {"line": 40760, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 51, "key": "be85380c0adf2870d8d74e5e5e781c7d"}, {"line": 40737, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 52, "key": "59f396e7a97f22bae31db0d0c53a9b1c"}, {"line": 40767, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 52, "key": "e334babb798f11141cce33186d19cab0"}, {"line": 40743, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 54, "key": "4ffbcc86eefb88080e423f99ac5dcce7"}, {"line": 40773, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 54, "key": "2fe01fd627405332e26bba28bf778cd7"}, {"line": 40751, "relation": "association", "evidence": "This study involves the reductionist fragment-based approach to understand the structure adopted by N-terminal fragment of Alzheimer's Abeta peptide in its complex with PLA2.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 3472, "key": "b21db110f9ff0257acb784744fcac528"}, {"line": 40782, "relation": "association", "evidence": "We speculate that higher affinity between Abeta and PLA2 has the therapeutic potential of decreasing the Abeta-Abeta interaction, thereby reducing the amyloid aggregation and plaque formation in AD.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 3823, "key": "e8aff5252c97d9091008d025ec530df9"}, {"line": 40783, "relation": "association", "evidence": "We speculate that higher affinity between Abeta and PLA2 has the therapeutic potential of decreasing the Abeta-Abeta interaction, thereby reducing the amyloid aggregation and plaque formation in AD.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3196, "target": 377, "key": "91a8c6a88a972d24dd4732a3bef13c8a"}, {"line": 23750, "relation": "decreases", "evidence": "Furthermore, Meserine (7.5 mg/kg) injected intraperitoneally once daily for 3 weeks lowered APP level by 28% and Abeta42 level by 42% in APP/PS1 transgenic mouse cerebrum. And both APP and Abeta42 lowering action of Meserine maintained longer than that of rivastigmin", "citation": {"db": "PubMed", "db_id": "24279603"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 437, "target": 2328, "key": "5188cfd58b520db4ff669b80eb235181"}, {"line": 23751, "relation": "decreases", "evidence": "Furthermore, Meserine (7.5 mg/kg) injected intraperitoneally once daily for 3 weeks lowered APP level by 28% and Abeta42 level by 42% in APP/PS1 transgenic mouse cerebrum. And both APP and Abeta42 lowering action of Meserine maintained longer than that of rivastigmin", "citation": {"db": "PubMed", "db_id": "24279603"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Cerebrum": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 437, "target": 2315, "key": "7992df24bb213986a90ea3ba9296b5b0"}, {"line": 28792, "relation": "decreases", "evidence": "An increase of the Abeta binding protein transthyretin suggests that increased clearance of Abeta underlies the reduction in plaques.", "citation": {"db": "PubMed", "db_id": "19523444"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3502, "target": 2328, "key": "feddf97732536d57dd6e298191cebd9a"}, {"relation": "partOf", "source": 3502, "target": 948, "key": "b616b736a4f53a89ef8ad67cae7af06c"}, {"line": 30853, "relation": "increases", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 2893, "target": 2328, "key": "353058fbbf105eea666a80bb932ca575"}, {"line": 30858, "relation": "positiveCorrelation", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "subject": {"modifier": "Activity"}, "source": 2893, "target": 2867, "key": "d3b8bf7f51dc3edd274775191fe3214b"}, {"line": 30859, "relation": "positiveCorrelation", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "subject": {"modifier": "Activity"}, "source": 2893, "target": 3057, "key": "dcdf0c3c3fdda3bb63c9c3e6757383d0"}, {"line": 34692, "relation": "increases", "evidence": "VIP, a neuropeptide released by dentate gyrus interneurons, enhances the proliferative and pro-neurogenic effect of microglia via the VPAC1 receptor. This VIP-induced enhancement is mediated by IL-4 release", "citation": {"db": "PubMed", "db_id": "24801739"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2893, "target": 1640, "key": "5137676a34d6a84b0dc9bb04f2d0b7d5"}, {"line": 39620, "relation": "increases", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2893, "target": 2470, "key": "bbdd0d5d54b03d1a89ff84b2e5724e8c"}, {"line": 24213, "relation": "decreases", "evidence": "Cyclosporine results in decreased activity of ABCB1 protein. Similarly, the P-gp inhibitor cyclosporin A (CsA, a calcineurin inhibitor) also decreased the rate of AVM-induced [Ca2+]i elevation", "citation": {"db": "PubMed", "db_id": "23523950"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 104, "target": 2232, "key": "d12663eaf317e40db36f4bc36e182657"}, {"line": 24226, "relation": "decreases", "evidence": "Cyclosporine results in decreased activity of ABCC2 protein", "citation": {"db": "PubMed", "db_id": "16316932"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 104, "target": 2237, "key": "7bf55569d0c2766e1fa8f4b85b6d3df2"}, {"line": 24238, "relation": "decreases", "evidence": "Cyclosporine A inhibits acetylcholinesterase activity in selected parts of the rat brain.", "citation": {"db": "PubMed", "db_id": "12633900"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 104, "target": 2244, "key": "789cee7333d31a56ae3a6f84828d021e"}, {"line": 24252, "relation": "decreases", "evidence": "Compared with the control group, acidic buffer or plus cyclosporine A post-conditioning significantly improved myocardial performance, decreased cytochrome C release into the cytosol, increased Bcl-2 expression and decreased Bax expression, decreased sensitivity of mPTP-opening to [Ca2+] and the rate of apoptosis after reperfusion. These findings suggested that acidic buffer or plus cyclosporine A post- conditioning prevented apoptotic process-related mitochondrial permeabilization and provided the myocardial protection after cardioplegic arrest.", "citation": {"db": "PubMed", "db_id": "21490080"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 104, "target": 3622, "key": "2b252066366a5dc959736e453c34e776"}, {"line": 24253, "relation": "increases", "evidence": "Compared with the control group, acidic buffer or plus cyclosporine A post-conditioning significantly improved myocardial performance, decreased cytochrome C release into the cytosol, increased Bcl-2 expression and decreased Bax expression, decreased sensitivity of mPTP-opening to [Ca2+] and the rate of apoptosis after reperfusion. These findings suggested that acidic buffer or plus cyclosporine A post- conditioning prevented apoptotic process-related mitochondrial permeabilization and provided the myocardial protection after cardioplegic arrest.", "citation": {"db": "PubMed", "db_id": "21490080"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 104, "target": 3597, "key": "f260171d4a7e3b0ccd9b4c9f666edb13"}, {"line": 24254, "relation": "decreases", "evidence": "Compared with the control group, acidic buffer or plus cyclosporine A post-conditioning significantly improved myocardial performance, decreased cytochrome C release into the cytosol, increased Bcl-2 expression and decreased Bax expression, decreased sensitivity of mPTP-opening to [Ca2+] and the rate of apoptosis after reperfusion. These findings suggested that acidic buffer or plus cyclosporine A post- conditioning prevented apoptotic process-related mitochondrial permeabilization and provided the myocardial protection after cardioplegic arrest.", "citation": {"db": "PubMed", "db_id": "21490080"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 104, "target": 3596, "key": "28cc510b97bcf7ec3e43bc98cacdc9e3"}, {"line": 24255, "relation": "decreases", "evidence": "Compared with the control group, acidic buffer or plus cyclosporine A post-conditioning significantly improved myocardial performance, decreased cytochrome C release into the cytosol, increased Bcl-2 expression and decreased Bax expression, decreased sensitivity of mPTP-opening to [Ca2+] and the rate of apoptosis after reperfusion. These findings suggested that acidic buffer or plus cyclosporine A post- conditioning prevented apoptotic process-related mitochondrial permeabilization and provided the myocardial protection after cardioplegic arrest.", "citation": {"db": "PubMed", "db_id": "21490080"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "source": 104, "target": 478, "key": "70d8f4777ba6c7ce7e00d6e45e50cf72"}, {"line": 24270, "relation": "increases", "evidence": "Activation of PI3K after CsA treatment appeared to trigger opposite effects. First, CsA induced PI3K-dependent activation of Akt, which mediated cellular responses against cell injury. Akt activation led to transient phosphorylation and inhibition of the pro-apoptotic GSK3beta and Bad, thus preventing GSK3beta-mediated phosphorylation and activation of the pro-apoptotic Bax, and Bad-sequestering of Bcl-2.", "citation": {"db": "PubMed", "db_id": "16316932"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 104, "target": 2153, "key": "61f584cf50039b6152acd0638c05e5a2"}, {"line": 24286, "relation": "decreases", "evidence": "Pretreatment with cyclosporin A (500 nM, 30 h) significantly decreased caspase-3 activation during extended incubations with paraoxon, parathion, and TPPi (p < 0.05).", "citation": {"db": "PubMed", "db_id": "11032765"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 104, "target": 2444, "key": "71707a1a6eb0e508b14af2998eb5c9c0"}, {"line": 24297, "relation": "decreases", "evidence": "Recent evidence indicates that reperfusion of the heart after a period of ischemia leads to the opening of the mitochondrial permeability transition pore (MPTP). The aim of this study was to investigate cardioprotective effects of cyclosporine A (CsA), an inhibitor of the MPTP, in an in vivo model of myocardial ischemia and reperfusionCsA significantly reduced infarct size (48.8 +/- 5.8% of left ventricle in vehicle + I/R group and 30.3 +/- 2.7% of left ventricle in CsA + I/R, respectively) and decreased caspase-3 activity in the myocardium [(0.62 +/- 0.17)/microg of protein and (0.42 +/- 0.15)/microg of protein, respectively] and relieved the injury of mitochondria. The cardioprotective effects of CsA might be associated with the protection of mitochondria and the inhibition of caspase-3 activity. It also suggests that the MPTP might play an important role in cardiomyocytes death after ischemia-reperfusion injury.", "citation": {"db": "PubMed", "db_id": "17578461"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 104, "target": 2444, "key": "95139c64595c9d7b6963b4c3f499dfd7"}, {"line": 24296, "relation": "decreases", "evidence": "Recent evidence indicates that reperfusion of the heart after a period of ischemia leads to the opening of the mitochondrial permeability transition pore (MPTP). The aim of this study was to investigate cardioprotective effects of cyclosporine A (CsA), an inhibitor of the MPTP, in an in vivo model of myocardial ischemia and reperfusionCsA significantly reduced infarct size (48.8 +/- 5.8% of left ventricle in vehicle + I/R group and 30.3 +/- 2.7% of left ventricle in CsA + I/R, respectively) and decreased caspase-3 activity in the myocardium [(0.62 +/- 0.17)/microg of protein and (0.42 +/- 0.15)/microg of protein, respectively] and relieved the injury of mitochondria. The cardioprotective effects of CsA might be associated with the protection of mitochondria and the inhibition of caspase-3 activity. It also suggests that the MPTP might play an important role in cardiomyocytes death after ischemia-reperfusion injury.", "citation": {"db": "PubMed", "db_id": "17578461"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 104, "target": 888, "key": "2d90e24bbc5d532358e6957090106f28"}, {"line": 24306, "relation": "decreases", "evidence": "In YAC46 MSNs, NMDA stimulated significantly higher activation of caspase-3 and caspase-9 but not caspase-8, and NMDA-induced caspase-3 and -9 activation was markedly attenuated by cyclosporin A", "citation": {"db": "PubMed", "db_id": "15033175"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Caspase subgraph": true}}, "source": 104, "target": 2449, "key": "af25ec4f5d78e45dae130c37de859354"}, {"line": 24315, "relation": "decreases", "evidence": "Cyclosporin A, an inhibitor of the mitochondrial permeability pore, partially decreased mitochondrial depolarization, caspase 3 activation, and caspase 9 activation, suggesting a role for mitochondrial dysfunction in these events.", "citation": {"db": "PubMed", "db_id": "12857937"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 104, "target": 2449, "key": "caaa17466bcdfcb33e57d8286bed63e3"}, {"line": 24324, "relation": "decreases", "evidence": "Crystal structure of human cyclophilin D in complex with its inhibitor, cyclosporin A at 0.96-A resolution.", "citation": {"db": "PubMed", "db_id": "18076075"}, "annotations": {"Subgraph": {"Mitochondrial translocation subgraph": true}}, "source": 104, "target": 3215, "key": "4872b82600f2a7738d3827f7212765ef"}, {"line": 24345, "relation": "decreases", "evidence": "Cyclosporine results in decreased expression of BCHE mRNA", "citation": {"db": "PubMed", "db_id": "20106945"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 104, "target": 3944, "key": "6b5399f7de830c46cfd0bbae06aa29f2"}, {"line": 24357, "relation": "decreases", "evidence": "Calcineurin triggers reactive/inflammatory processes in astrocytes and is upregulated in aging and Alzheimer's models. This effect was blocked by the calcineurin inhibitor cyclosporin A.", "citation": {"db": "PubMed", "db_id": "15872113"}, "source": 104, "target": 878, "key": "1b2fdba62b0195e3991ea9bc0b62172d"}, {"line": 24273, "relation": "increases", "evidence": "Activation of PI3K after CsA treatment appeared to trigger opposite effects. First, CsA induced PI3K-dependent activation of Akt, which mediated cellular responses against cell injury. Akt activation led to transient phosphorylation and inhibition of the pro-apoptotic GSK3beta and Bad, thus preventing GSK3beta-mediated phosphorylation and activation of the pro-apoptotic Bax, and Bad-sequestering of Bcl-2.", "citation": {"db": "PubMed", "db_id": "16316932"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2390, "target": 2389, "key": "4f6e6d0c3c12253a2e932348ff87f16f"}, {"line": 24355, "relation": "positiveCorrelation", "evidence": "Calcineurin triggers reactive/inflammatory processes in astrocytes and is upregulated in aging and Alzheimer's models. This effect was blocked by the calcineurin inhibitor cyclosporin A.", "citation": {"db": "PubMed", "db_id": "15872113"}, "source": 878, "target": 3823, "key": "c4f5d8fc2fe4f45ac14754ff336aa2d3"}, {"line": 24356, "relation": "increases", "evidence": "Calcineurin triggers reactive/inflammatory processes in astrocytes and is upregulated in aging and Alzheimer's models. This effect was blocked by the calcineurin inhibitor cyclosporin A.", "citation": {"db": "PubMed", "db_id": "15872113"}, "source": 878, "target": 3920, "key": "11ee0ad32976d531e1af81359be7bdbf"}, {"line": 35823, "relation": "decreases", "evidence": "The state of tau phosphorylation and proteolysis can be regulated by calcium-dependent mechanisms. CaMKII can phosphorylate tau [189]. Cyclin-dependent kinase 5 (cdk5), another kinase involved in tau phosphorylation [190], is indirectly activated by the calcium-activated protease calpain. Indeed, cdk5 has to be associated with its regulatory subunit, p35 to be activated. Conversion of p35 to p25 deregulates cdk5 activity, resulting in an increased cdk5 kinase activity [191]. Calpain cleaves p35 into p25, and thus controls cdk5 activation [192]. Furthermore, tau is dephosphorylated by the calcium/calmodulin-dependent phosphatase, calcineurin [193]. Calpain was also proposed to directly participate in tau proteolysis and degradation", "citation": {"db": "PubMed", "db_id": "19419557"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Calpastatin-calpain subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "source": 878, "target": 3015, "key": "d0d02a7968cbd9f6517e691e796c1bf0"}, {"relation": "partOf", "source": 2334, "target": 1251, "key": "7a18fc2e6b93ecb6e4bd4bc38e690244"}, {"line": 24579, "relation": "increases", "evidence": "Our results show that c-Abl pmodulates AICD dependent cellular responses, transcriptional induction as well as the apoptotic response, which could participate in the onset and progression of the neurodegenerative pathology, observed in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 1673, "target": 794, "key": "00cd9be85a52f9465975ad232cdfd8c7"}, {"line": 24580, "relation": "increases", "evidence": "Our results show that c-Abl pmodulates AICD dependent cellular responses, transcriptional induction as well as the apoptotic response, which could participate in the onset and progression of the neurodegenerative pathology, observed in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "19306298"}, "source": 1673, "target": 478, "key": "b0ee2085e6e5b87a078c22ff0bb900b5"}, {"line": 24687, "relation": "increases", "evidence": "The neurotoxicity induced by AChE-Abeta complexes indicated that they trigger more neurodegeneration than those of the Abeta peptide alone", "citation": {"db": "PubMed", "db_id": "15709485"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 910, "target": 648, "key": "3bafa4417fb329ed653ed55371e8c523"}, {"line": 24711, "relation": "increases", "evidence": "These results, together with binding assays, have suggested that AChE may contribute to the generation of amyloid deposits and/or physically affects fibril assembly. Moreover, it has also been shown that the neurotoxicity of Abeta peptide aggregates depends on the amount of AChE bound to the complexes, suggesting that AChE may play a key role in the neurodegeneration observed in an AD patients brain.", "citation": {"db": "PubMed", "db_id": "17681794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 910, "target": 648, "key": "1080259db8ec5c700f5211dac0758beb"}, {"relation": "partOf", "source": 3256, "target": 1031, "key": "7e0a3419213c69bab114dec51e9ec1e1"}, {"relation": "partOf", "source": 3257, "target": 1032, "key": "ef8e978cea4697883391002eae6e04d4"}, {"relation": "partOf", "source": 2534, "target": 1153, "key": "0ce1b4b104c9cd47ce7d179b1e58a159"}, {"relation": "partOf", "source": 2533, "target": 1152, "key": "b7e633e4d34b3070169f16a25e86a227"}, {"line": 38414, "relation": "association", "evidence": "To understand why sAPPß more readily drives differentiation of hESCs than sAPPa, the downstream targets of sAPP signaling will need to be identified. Toward that end, a recent study found that sAPPß can regulate the transcription of transthyretin and Klotho genes in the absence of full-length APP or APLP1 expression", "citation": {"db": "PubMed", "db_id": "21606494"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 2138, "target": 2004, "key": "35ad372b8edf4827b63d97adffcb6ce8"}, {"line": 38415, "relation": "association", "evidence": "To understand why sAPPß more readily drives differentiation of hESCs than sAPPa, the downstream targets of sAPP signaling will need to be identified. Toward that end, a recent study found that sAPPß can regulate the transcription of transthyretin and Klotho genes in the absence of full-length APP or APLP1 expression", "citation": {"db": "PubMed", "db_id": "21606494"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 2138, "target": 1855, "key": "bdf24abadeb8d48b8beab909c33e7f1f"}, {"line": 24938, "relation": "decreases", "evidence": "IL-1Ra, a physiological antagonist for the IL-1 receptor, reversed the effects of IL-1beta, suggesting that the IL-1beta-dependent up-regulation of alpha-cleavage is mediated by the IL-1 receptor. IL-1beta also induced this concomitant increase in alpha-cleavage and decrease in beta-cleavage in mouse primary cultured neurons. Taken together we conclude that IL-1beta is an anti-amyloidogenic factor, and that enhancement of its signaling or inhibition of IL-1Ra activity could represent potential therapeutic strategies against Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2886, "target": 2885, "key": "5f53e194392d827c7fd7e38ad13c3f61"}, {"relation": "partOf", "source": 2886, "target": 1479, "key": "16446db8533f8b4e4efdd5141745d453"}, {"line": 24958, "relation": "increases", "evidence": "The overexpression of ADAM19 in HEK293 cells resulted in an increase in sAPPalpha. Therefore, we suggest that ADAM19 has a constitutive alpha-secretase activity.", "citation": {"db": "PubMed", "db_id": "17112471"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2251, "target": 2137, "key": "5fcb655e107ae6e8aae62411fa358799"}, {"line": 24966, "relation": "association", "evidence": "ADAM19 is tightly associated with constitutive Alzheimer's disease APP alpha-secretase in A172 cells", "citation": {"db": "PubMed", "db_id": "17112471"}, "annotations": {"Confidence": {"High": true}}, "source": 2251, "target": 2249, "key": "cc3401ce138fe2dff5b57efce2fecb7f"}, {"line": 24972, "relation": "association", "evidence": "ADAM19 interacts with APP", "citation": {"db": "PubMed", "db_id": "17112471"}, "annotations": {"Confidence": {"High": true}}, "source": 2251, "target": 2315, "key": "f3277c02c5c1dc462d473923037e83bb"}, {"relation": "partOf", "source": 2251, "target": 1055, "key": "e563196ecf1c31d00e743fdec123cfaa"}, {"line": 25607, "relation": "association", "evidence": "ADAM19 is tightly associated with constitutive Alzheimer's disease APP alpha-secretase in A172 cells", "citation": {"db": "PubMed", "db_id": "17112471"}, "annotations": {"Subgraph": {"ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2251, "target": 3823, "key": "1694f83a0e4b13fcf58910a15db6cc6d"}, {"line": 24984, "relation": "increases", "evidence": "Interaction of mitochondrial Abeta with mitochondrial enzymes such as amyloid beta binding alcohol dehydrogenase (ABAD) exaggerates mitochondrial stress by inhibiting the enzyme activity, releasing reactive oxygen species (ROS), and affecting glycolytic, Krebs cycle and/or the respiratory chain pathways through the accumulation of deleterious intermediate metabolites.", "citation": {"db": "PubMed", "db_id": "17424907"}, "annotations": {"Subgraph": {"Alcohol dehydrogenase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 911, "target": 683, "key": "6cec476537fd4975ebd6e9befdc0cdc9"}, {"line": 24985, "relation": "increases", "evidence": "Interaction of mitochondrial Abeta with mitochondrial enzymes such as amyloid beta binding alcohol dehydrogenase (ABAD) exaggerates mitochondrial stress by inhibiting the enzyme activity, releasing reactive oxygen species (ROS), and affecting glycolytic, Krebs cycle and/or the respiratory chain pathways through the accumulation of deleterious intermediate metabolites.", "citation": {"db": "PubMed", "db_id": "17424907"}, "annotations": {"Subgraph": {"Alcohol dehydrogenase subgraph": true, "Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 911, "target": 700, "key": "e336e3d2f36395df4327b66ff51ba346"}, {"line": 25650, "relation": "decreases", "evidence": "Interaction of mitochondrial Abeta with mitochondrial enzymes such as amyloid beta binding alcohol dehydrogenase (ABAD) exaggerates mitochondrial stress by inhibiting the enzyme activity, releasing reactive oxygen species (ROS), and affecting glycolytic, Krebs cycle and/or the respiratory chain pathways through the accumulation of deleterious intermediate metabolites.", "citation": {"db": "PubMed", "db_id": "17424907"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true, "Alcohol dehydrogenase subgraph": true}, "Confidence": {"Medium": true}}, "source": 911, "target": 618, "key": "3049659aced6732fd1dd49316025aedc"}, {"line": 25654, "relation": "increases", "evidence": "Interaction of mitochondrial Abeta with mitochondrial enzymes such as amyloid beta binding alcohol dehydrogenase (ABAD) exaggerates mitochondrial stress by inhibiting the enzyme activity, releasing reactive oxygen species (ROS), and affecting glycolytic, Krebs cycle and/or the respiratory chain pathways through the accumulation of deleterious intermediate metabolites.", "citation": {"db": "PubMed", "db_id": "17424907"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Reactive oxygen species subgraph": true, "Alcohol dehydrogenase subgraph": true}, "Confidence": {"Medium": true}}, "source": 911, "target": 170, "key": "b89e7703daf94f5f1297a361c050a1f1"}, {"relation": "partOf", "source": 2258, "target": 911, "key": "f836e81ad362ba014f1f4f1d17f042ea"}, {"line": 24998, "relation": "increases", "evidence": "One candidate receptor is the receptor for advanced glycation endproducts (RAGE), which can bind Abeta and transduce signals leading to cellular activation. Data are presented showing a potential mechanism for Abeta activation of microglia that could be mediated by RAGE and macrophage colony-stimulating factor (M-CSF).", "citation": {"db": "PubMed", "db_id": "11520119"}, "annotations": {"Subgraph": {"Alcohol dehydrogenase subgraph": true}, "Confidence": {"Medium": true}}, "source": 912, "target": 675, "key": "76d66795f4f3897af1d65b0a946869ac"}, {"line": 25667, "relation": "increases", "evidence": "One candidate receptor is the receptor for advanced glycation endproducts (RAGE), which can bind Abeta and transduce signals leading to cellular activation.", "citation": {"db": "PubMed", "db_id": "11520119"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Immunoglobulin subgraph": true, "Alcohol dehydrogenase subgraph": true}, "Confidence": {"Medium": true}}, "source": 912, "target": 675, "key": "b2f96770d398f2bbf4e38f42ca641ad7"}, {"line": 33760, "relation": "increases", "evidence": "We demonstrate that binding of amyloid-beta peptide (Abeta) to neuronal Receptor for Advanced Glycation Endproduct (RAGE), a cell surface receptor for Abeta, induces macrophage-colony stimulating factor (M-CSF) by an oxidant sensitive, nuclear factor kappaB-dependent pathway. ", "citation": {"db": "PubMed", "db_id": "9144231"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Immunoglobulin subgraph": true}, "Confidence": {"Medium": true}}, "source": 912, "target": 2566, "key": "1b027b9e8825a92dd4597f0ea81b92de"}, {"relation": "partOf", "source": 2271, "target": 912, "key": "ba34baa4e05c8aecc1302866ab97fb8a"}, {"line": 25011, "relation": "increases", "evidence": "Previous experiments indicate that RAGE mediates A beta-induced oxidative stress and nuclear factor-kB activation (Yan et al., 1996) as well as neuronal expression of macrophage colony-stimulating factor (Du Yan et al., 1997), mitogen-activated protein (MAP) kinases signaling defects (Arancio et al., 2004), or cell death (Hadding et al., 2004).", "citation": {"db": "PubMed", "db_id": "18480271"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 2271, "target": 842, "key": "c146201331bd3fa2b0e275c2b5a9d2d8"}, {"line": 25019, "relation": "increases", "evidence": "Previous experiments indicate that RAGE mediates A beta-induced oxidative stress and nuclear factor-kB activation (Yan et al., 1996) as well as neuronal expression of macrophage colony-stimulating factor (Du Yan et al., 1997), mitogen-activated protein (MAP) kinases signaling defects (Arancio et al., 2004), or cell death (Hadding et al., 2004).", "citation": {"db": "PubMed", "db_id": "18480271"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2271, "target": 3112, "key": "6619437b2f57a2148d2543a38ab5e152"}, {"line": 25020, "relation": "increases", "evidence": "Previous experiments indicate that RAGE mediates A beta-induced oxidative stress and nuclear factor-kB activation (Yan et al., 1996) as well as neuronal expression of macrophage colony-stimulating factor (Du Yan et al., 1997), mitogen-activated protein (MAP) kinases signaling defects (Arancio et al., 2004), or cell death (Hadding et al., 2004).", "citation": {"db": "PubMed", "db_id": "18480271"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2271, "target": 3113, "key": "e7a2316d5469dccb998bf5218834d366"}, {"line": 25028, "relation": "increases", "evidence": "Previous experiments indicate that RAGE mediates A beta-induced oxidative stress and nuclear factor-kB activation (Yan et al., 1996) as well as neuronal expression of macrophage colony-stimulating factor (Du Yan et al., 1997), mitogen-activated protein (MAP) kinases signaling defects (Arancio et al., 2004), or cell death (Hadding et al., 2004).", "citation": {"db": "PubMed", "db_id": "18480271"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2271, "target": 2568, "key": "497eec1a376a5e8a270b2b54dbec1f52"}, {"line": 25036, "relation": "decreases", "evidence": "Previous experiments indicate that RAGE mediates A beta-induced oxidative stress and nuclear factor-kB activation (Yan et al., 1996) as well as neuronal expression of macrophage colony-stimulating factor (Du Yan et al., 1997), mitogen-activated protein (MAP) kinases signaling defects (Arancio et al., 2004), or cell death (Hadding et al., 2004).", "citation": {"db": "PubMed", "db_id": "18480271"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Immunoglobulin subgraph": true}, "Confidence": {"Medium": true}}, "source": 2271, "target": 707, "key": "2603f823775e30bb99b4c1b3224d117c"}, {"line": 25044, "relation": "increases", "evidence": "Previous experiments indicate that RAGE mediates A beta-induced oxidative stress and nuclear factor-kB activation (Yan et al., 1996) as well as neuronal expression of macrophage colony-stimulating factor (Du Yan et al., 1997), mitogen-activated protein (MAP) kinases signaling defects (Arancio et al., 2004), or cell death (Hadding et al., 2004).", "citation": {"db": "PubMed", "db_id": "18480271"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2271, "target": 505, "key": "dcfafc08328d6df3772bbfb6f577e8c7"}, {"line": 25076, "relation": "increases", "evidence": "RAGE stimulates functional BACE1 expression through NFAT1 activation, resulting in more Abeta production and deposition in the brain.", "citation": {"db": "PubMed", "db_id": "19332646"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Amyloidogenic subgraph": true, "T cells signaling": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2271, "target": 3106, "key": "f1b1e45585281c4da6688abf7dcee6b1"}, {"line": 25078, "relation": "increases", "evidence": "RAGE stimulates functional BACE1 expression through NFAT1 activation, resulting in more Abeta production and deposition in the brain.", "citation": {"db": "PubMed", "db_id": "19332646"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Amyloidogenic subgraph": true, "T cells signaling": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2271, "target": 2375, "key": "05574d860b7658703b2572eacdbc96bc"}, {"line": 28219, "relation": "association", "evidence": "Because beta-site APP-cleaving enzyme 1 (BACE1), an essential protease for Abeta production, is up-regulated in cells overexpressing RAGE and in RAGE-injected brains of Tg2576 mice, the molecular mechanisms underlying RAGE, BACE1 expression, and Abeta production were examined.", "citation": {"db": "PubMed", "db_id": "19332646"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2271, "target": 2375, "key": "3561e550c831026206803c21e94735f1"}, {"line": 25079, "relation": "increases", "evidence": "RAGE stimulates functional BACE1 expression through NFAT1 activation, resulting in more Abeta production and deposition in the brain.", "citation": {"db": "PubMed", "db_id": "19332646"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Amyloidogenic subgraph": true, "T cells signaling": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2271, "target": 80, "key": "1583c22d8c95910ee0c446bc80976238"}, {"relation": "partOf", "source": 2271, "target": 1066, "key": "6d3cd70d77509825e20da8ce380bafc0"}, {"line": 25704, "relation": "increases", "evidence": "Our data suggest that microvascular RAGE levels increase in conjunction with the onset of AD, and continue to increase linearly as a function of AD pathologic severity", "citation": {"db": "PubMed", "db_id": "18657529"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2271, "target": 3823, "key": "aaae4fcb4ba862b2d6ed1e9f7bf61629"}, {"line": 25721, "relation": "increases", "evidence": "Clinical studies have recently shown that higher plasma levels of sRAGE are associated with a reduced risk of coronary artery disease, hypertension, the metabolic syndrome, arthritis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16842191"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "source": 2271, "target": 3823, "key": "4dc5abaca7639045a950be63a2f7704e"}, {"line": 39069, "relation": "positiveCorrelation", "evidence": "Analysis of RAGE expression in non-demented and Alzheimer's disease (AD) brains indicated that increases in/ RAGE protein and percentage of RAGE-expressing microglia paralleled the severity of disease. Ligands for RAGE in AD / include amyloid beta peptide (Abeta), S100/calgranulins, advanced glycation endproduct-modified proteins, and amphoterin.", "citation": {"db": "PubMed", "db_id": "15975028"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2271, "target": 3823, "key": "340b5c96f43222ab0e1240197cdbc2b2"}, {"relation": "partOf", "source": 2271, "target": 1068, "key": "d574594aaa6def5ad2f22e7b4e5160cb"}, {"line": 25722, "relation": "increases", "evidence": "Clinical studies have recently shown that higher plasma levels of sRAGE are associated with a reduced risk of coronary artery disease, hypertension, the metabolic syndrome, arthritis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16842191"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "source": 2271, "target": 3916, "key": "8c648d909c398ce21255d2796dd83589"}, {"line": 25723, "relation": "increases", "evidence": "Clinical studies have recently shown that higher plasma levels of sRAGE are associated with a reduced risk of coronary artery disease, hypertension, the metabolic syndrome, arthritis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16842191"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "source": 2271, "target": 3840, "key": "9ec639a18c05e9b6cda983f39034ca89"}, {"line": 25724, "relation": "increases", "evidence": "Clinical studies have recently shown that higher plasma levels of sRAGE are associated with a reduced risk of coronary artery disease, hypertension, the metabolic syndrome, arthritis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "16842191"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "source": 2271, "target": 3891, "key": "cb090a0efd0bffdf8238add4e79b8f94"}, {"relation": "partOf", "source": 2271, "target": 1067, "key": "0e55cdc7752a764a040175191d320dad"}, {"relation": "partOf", "source": 2271, "target": 1069, "key": "969481cd56d3a29084e483d03babb9a2"}, {"relation": "partOf", "source": 2271, "target": 1065, "key": "553717d4fe9f73ddf7948ba02e04d62b"}, {"line": 39119, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2271, "target": 577, "key": "2d3322b8741f7881172ae50a0721d08f"}, {"line": 39147, "relation": "increases", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2271, "target": 3003, "key": "c77cd2be18ce4b0de6fa5d11b0a562ac"}, {"line": 39520, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "source": 2271, "target": 609, "key": "0ee2ebcac424a51675fcca6377fb6956"}, {"line": 39528, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "source": 2271, "target": 270, "key": "5fad96fa4f696983284ef385f9672bca"}, {"line": 39529, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "source": 2271, "target": 303, "key": "be84dfb9d2b27217b4d1694c12dbab65"}, {"line": 25064, "relation": "increases", "evidence": "The receptor for advanced glycation end products (RAGE) is a cell-bound receptor of the immunoglobulin superfamily which may be activated by a variety of proinflammatory ligands including advanced glycoxidation end products, S100/calgranulins, high mobility group box 1, and amyloid beta-peptide.", "citation": {"db": "PubMed", "db_id": "16842191"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2837, "target": 2271, "key": "22b33ec0f61d744517044118ce83ee4e"}, {"relation": "partOf", "source": 2837, "target": 1068, "key": "5ad39be2b673de5a9ac116f8b1e2b655"}, {"line": 25065, "relation": "increases", "evidence": "The receptor for advanced glycation end products (RAGE) is a cell-bound receptor of the immunoglobulin superfamily which may be activated by a variety of proinflammatory ligands including advanced glycoxidation end products, S100/calgranulins, high mobility group box 1, and amyloid beta-peptide.", "citation": {"db": "PubMed", "db_id": "16842191"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2215, "target": 2271, "key": "4c686af5ce22c4148dca34bbf60d9c65"}, {"line": 25077, "relation": "increases", "evidence": "RAGE stimulates functional BACE1 expression through NFAT1 activation, resulting in more Abeta production and deposition in the brain.", "citation": {"db": "PubMed", "db_id": "19332646"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Amyloidogenic subgraph": true, "T cells signaling": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3106, "target": 2375, "key": "4f70b49084e38ae77fde5d96a367e739"}, {"line": 25096, "relation": "increases", "evidence": "Recently, GRK2 and GRK5 have been demonstrated to phosphorylate alpha-synuclein (Ser129) and other synuclein isoforms.", "citation": {"db": "PubMed", "db_id": "17146290"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}, "Confidence": {"High": true}}, "source": 2786, "target": 3386, "key": "ffe04622358bf7a9e480546d518f47ea"}, {"line": 25757, "relation": "directlyIncreases", "evidence": "Recently, GRK2 and GRK5 have been demonstrated to phosphorylate alpha-synuclein (Ser129) and other synuclein isoforms.", "citation": {"db": "PubMed", "db_id": "17146290"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2786, "target": 3386, "key": "b4e62860bd27767d7d5df669934aadd2"}, {"line": 25106, "relation": "association", "evidence": "Our studies indicate that GRK2 is a novel component of neuronal and glial fibrillary tau deposits with no preference in tau isoform binding. GRK2 may play a role in hyperphosphorylation of tau in tauopathies.", "citation": {"db": "PubMed", "db_id": "17146290"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 2786, "target": 3015, "key": "e97d6219f2f58ced040980a2ea4ed1ba"}, {"line": 25764, "relation": "association", "evidence": "Our studies indicate that GRK2 is a novel component of neuronal and glial fibrillary tau deposits with no preference in tau isoform binding. GRK2 may play a role in hyperphosphorylation of tau in tauopathies", "citation": {"db": "PubMed", "db_id": "17146290"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2786, "target": 3015, "key": "8502cd8d3bdacc91422d3f8a4cc9afac"}, {"line": 25097, "relation": "increases", "evidence": "Recently, GRK2 and GRK5 have been demonstrated to phosphorylate alpha-synuclein (Ser129) and other synuclein isoforms.", "citation": {"db": "PubMed", "db_id": "17146290"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}, "Confidence": {"High": true}}, "source": 2787, "target": 3386, "key": "1a7f9d6d416ad2323bd9157554683fc8"}, {"line": 25758, "relation": "directlyIncreases", "evidence": "Recently, GRK2 and GRK5 have been demonstrated to phosphorylate alpha-synuclein (Ser129) and other synuclein isoforms.", "citation": {"db": "PubMed", "db_id": "17146290"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2787, "target": 3386, "key": "1d2f1cc5dd5e097ec8c5bc8929d88e3f"}, {"relation": "partOf", "source": 2787, "target": 1443, "key": "59c142ad769c0094307e3096ebcb1300"}, {"line": 25117, "relation": "increases", "evidence": "Agrin binds alpha-synuclein and pmodulates alpha-synuclein fibrillation.", "citation": {"db": "PubMed", "db_id": "16037493"}, "annotations": {"Subgraph": {"Synuclein subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1071, "target": 3384, "key": "d51e4768c60c44d5c2ee779a23434c44"}, {"line": 25126, "relation": "increases", "evidence": "Here, we investigated the effect of apoAII on the interaction between apoE and Abeta. Addition of apoAII to apoE monomers increased the binding of apoE2 and apoE3 to Abeta(1-42),", "citation": {"db": "PubMed", "db_id": "11070505"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 2309, "target": 1127, "key": "3881dd95aa08096fb4d928f2ae20c661"}, {"relation": "partOf", "source": 2309, "target": 1123, "key": "274efbd0390724641d7b2ff60de2b764"}, {"line": 25136, "relation": "increases", "evidence": "These data suggest that C-terminal residues of apoE bind to Abeta and that apoE may help aid in the progression of small Abeta deposits to larger deposits.", "citation": {"db": "PubMed", "db_id": "11305869"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 889, "key": "f0b7c1d7021aa0ebd26790b4e00a216e"}, {"line": 25153, "relation": "association", "evidence": "Role of apoe/Abeta interactions in the pathogenesis of Alzheimer's disease and cerebral amyloid angiopathy.", "citation": {"db": "PubMed", "db_id": "11816788"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3823, "key": "90e2f0b547383e33589c93885e2e2b58"}, {"line": 25186, "relation": "association", "evidence": "Different apoE isoforms may alter AD pathogenesis via their interactions with the amyloid beta-peptide (Abeta).", "citation": {"db": "PubMed", "db_id": "16207708"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3823, "key": "e22903c6295af8d43610879ff923ae01"}, {"line": 25251, "relation": "association", "evidence": "Apolipoprotein E binds avidly to beta amyloid (A beta) peptide, a major component of senile plaque of Alzheimer's disease, in an isoform-specific manner.", "citation": {"db": "PubMed", "db_id": "8040342"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3823, "key": "06b5f6259b163d5bb8fd27f1f48515c0"}, {"line": 25270, "relation": "association", "evidence": "It appears that the efficiency of binding between each of three main apoE isoforms and Abeta correlates inversely with the risk of developing late-onset familial AD and may indicate possible involvement of apoE in the binding and clearance of Abeta in vivo.", "citation": {"db": "PubMed", "db_id": "9265639"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3823, "key": "17369d0b1be6cc497b0899ece370a402"}, {"line": 25910, "relation": "association", "evidence": "Role of apoE/Abeta interactions in Alzheimer's disease: insights from transgenic mouse pmodels.", "citation": {"db": "PubMed", "db_id": "11840304"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3823, "key": "a53ae6da2e86d07f003e9027661148e2"}, {"line": 25975, "relation": "association", "evidence": "We will continue to investigate the effect of apoE isoform and Abeta conformation on the structural and functional interactions between these two proteins in relation to the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "15181252"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3823, "key": "dcaae6584b8773aee4836fd03203d7f0"}, {"line": 26260, "relation": "association", "evidence": "How apoE is involved in the pathogenesis of AD is unclear; however, evidence exists for a direct apoE/A beta interaction", "citation": {"db": "PubMed", "db_id": "8631862"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3823, "key": "aca104ff995343436d28d536dd4c4792"}, {"line": 26270, "relation": "association", "evidence": "To explore whether the genetic linkage between apolipoprotein E (ApoE) alleles and susceptibility to Alzheimer's disease might be attributable to a direct molecular interaction between ApoE and the amyloid peptide A beta, we have produced ApoE variants in Escherichia coli and studied their interactions with A beta under native conditions.", "citation": {"db": "PubMed", "db_id": "8679539"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3823, "key": "02f4eb2b8b4ae4fa9a11a39a103cdfaa"}, {"line": 25241, "relation": "association", "evidence": "In vitro, apoE, and in particular its apoE4 isoform, can bind to and promote fibrillogenesis of the amyloid A beta peptide, the main constituent of senile plaques.", "citation": {"db": "PubMed", "db_id": "7615568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 723, "key": "0cfe1ec49626d1b06a35f1db02eeaecf"}, {"line": 26110, "relation": "association", "evidence": "Early studies of the pathological roles of ApoE in neurodegenerative disease showed that ApoE binds in vitro to synthetic Abeta in an isoform-specific manner, potentially enhancing Abeta fibrillization and deposition23", "citation": {"db": "PubMed", "db_id": "18297066"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 914, "target": 474, "key": "04e6676c352b5999d6c38d673c38bbda"}, {"line": 26209, "relation": "association", "evidence": "Apo E and apo E4 in particular have been shown to pmodulate amyloid fibril formation by amyloid-beta peptides in vitro.", "citation": {"db": "PubMed", "db_id": "7639323"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 474, "key": "d5da1af98830a8defb02cc9146948c62"}, {"line": 26191, "relation": "increases", "evidence": "Blocking the apolipoprotein E/amyloid-beta interaction reduces fibrillar vascular amyloid deposition and cerebral microhemorrhages in TgSwDI mice.", "citation": {"db": "PubMed", "db_id": "21239853"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 863, "key": "c8e9d287c792c6f8f9c38023e68f8ba4"}, {"line": 26237, "relation": "association", "evidence": "These findings suggest that the interaction of ApoE with tau and amyloid-beta proteins in AD could play a important role in the formation of NFT and SP, respectively, contributing to the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 3881, "key": "b7022fc93e63b759584f929be476746f"}, {"line": 26294, "relation": "association", "evidence": "It appears that the efficiency of binding between each of three main apoE isoforms and Abeta correlates inversely with the risk of developing late-onset familial AD and may indicate possible involvement of apoE in the binding and clearance of Abeta in vivo.", "citation": {"db": "PubMed", "db_id": "9265639"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 914, "target": 80, "key": "a8957c5dcdd9ddf79c742b4ba33c8dbf"}, {"line": 25218, "relation": "association", "evidence": "APOE interacts with APP in human Alzheimer's brain", "citation": {"db": "PubMed", "db_id": "21297948"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 1125, "target": 3823, "key": "eac3b1e039707c3f49f38a369d101ca4"}, {"line": 25866, "relation": "association", "evidence": "Co-expression and pulse-chase experiments showed that a functional apoE:APP interaction occurs intracellularly which directly affects maturation and subsequently the secretion kinetics of APP.", "citation": {"db": "PubMed", "db_id": "11523796"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 1125, "target": 2315, "key": "0e62ae40bde048a584c8370d0a00a121"}, {"line": 25875, "relation": "association", "evidence": "The findings suggest that in cells that express both apoE and APP, such as astrocytes and microglia, a functional apoE:APP interaction may occur which pmodulates APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "11523796"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 1125, "target": 857, "key": "91bdc64f2fa2a9b02d3207a9db371bce"}, {"line": 25876, "relation": "association", "evidence": "The findings suggest that in cells that express both apoE and APP, such as astrocytes and microglia, a functional apoE:APP interaction may occur which pmodulates APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "11523796"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 1125, "target": 80, "key": "63811e152a4082464d0b463e9795dc0c"}, {"line": 25208, "relation": "decreases", "evidence": "We hypothesized LRP2 may be involved in efflux of apoJ out of the CNS, and Abeta binding to apoJ may enhance clearance of highly pathogenic Abeta42. Our data show that both RAP and LRP2-specific antibody block apoJ clearance, indicating LRP2 is required for apoJ efflux at the BBB. ", "citation": {"db": "PubMed", "db_id": "17077814"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1227, "target": 80, "key": "84d46b5d91f30c57950854d17366df42"}, {"line": 25229, "relation": "decreases", "evidence": "Abeta20-29 peptide blocking apoE/Abeta interaction reduces full-length Abeta42/40 fibril formation and cytotoxicity in vitro.", "citation": {"db": "PubMed", "db_id": "20363024"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Low": true}}, "source": 2321, "target": 1127, "key": "7c8ccb09794c124351e283b1f445a036"}, {"line": 25241, "relation": "association", "evidence": "In vitro, apoE, and in particular its apoE4 isoform, can bind to and promote fibrillogenesis of the amyloid A beta peptide, the main constituent of senile plaques.", "citation": {"db": "PubMed", "db_id": "7615568"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 723, "target": 914, "key": "646a9a72750b850e509ff82d46cba99b"}, {"relation": "partOf", "source": 2245, "target": 1050, "key": "740f80cabc782ae1c73f020759fad229"}, {"relation": "partOf", "source": 2245, "target": 1049, "key": "97de625031a1ae9228400919bd1d19f5"}, {"relation": "partOf", "source": 3968, "target": 1050, "key": "67f0b22fb1cc4923419feb3572e4a0e4"}, {"line": 25369, "relation": "increases", "evidence": "Our laboratory has previously shown that EGCG can increase non-amyloidogenic processing of APP through promotion of the beta-secretase ADAM10, which consequently reduced Abeta deposition and improved cognition in AD mice", "citation": {"db": "PubMed", "db_id": "20849853"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1, "target": 2249, "key": "a3c3d5d9eed174e20bebfae7e522fad8"}, {"line": 25372, "relation": "decreases", "evidence": "Our laboratory has previously shown that EGCG can increase non-amyloidogenic processing of APP through promotion of the beta-secretase ADAM10, which consequently reduced Abeta deposition and improved cognition in AD mice", "citation": {"db": "PubMed", "db_id": "20849853"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1, "target": 80, "key": "e2a1552177f26145639672b3e139e2e2"}, {"line": 25373, "relation": "increases", "evidence": "Our laboratory has previously shown that EGCG can increase non-amyloidogenic processing of APP through promotion of the beta-secretase ADAM10, which consequently reduced Abeta deposition and improved cognition in AD mice", "citation": {"db": "PubMed", "db_id": "20849853"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1, "target": 812, "key": "5d622275217af97b001088f568389bfb"}, {"line": 25384, "relation": "decreases", "evidence": "Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 2328, "key": "7c9dd33550d66de5f0424b990ed918f9"}, {"line": 25405, "relation": "decreases", "evidence": "Neuroprotectin D1 (NPD1) is a stereoselective mediator derived from the omega-3 essential fatty acid docosahexaenoic acid (DHA) with potent inflammatory resolving and neuroprotective bioactivity. NPD1 reduces Abeta42 peptide release from aging human brain cells and is severely depleted in Alzheimer's disease (AD) brain.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 166, "target": 2328, "key": "6163bd5f6ccfa3c05d99fa537464350c"}, {"line": 25464, "relation": "decreases", "evidence": "In human neural cells overexpressing beta-amyloid precursor protein (betaAPP), the lipid mediator suppressed Abeta42 shedding by downregulating beta-secretase (BACE1) while activating the alpha-secretase (ADAM10), thus shifting the alphaAPP cleavage from the noxious amyloidogenic pathway into a non-amyloidogenic, neurotrophic pathway.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 2328, "key": "27bd44cca1d936ae9b4840f283ff1dd7"}, {"line": 25386, "relation": "decreases", "evidence": "Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 166, "target": 2375, "key": "bd6a06a35117d4da79510795130ee908"}, {"line": 25436, "relation": "decreases", "evidence": "We also show that NPD1 downregulates Abeta42-triggered expression of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) and of B-94 (a TNF-alpha-inducible pro-inflammatory element) and apoptosis in HNG cells. Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 2375, "key": "2be17ca007edd44e2a6b3b7abe7cc850"}, {"line": 25465, "relation": "decreases", "evidence": "In human neural cells overexpressing beta-amyloid precursor protein (betaAPP), the lipid mediator suppressed Abeta42 shedding by downregulating beta-secretase (BACE1) while activating the alpha-secretase (ADAM10), thus shifting the alphaAPP cleavage from the noxious amyloidogenic pathway into a non-amyloidogenic, neurotrophic pathway.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 2375, "key": "e24fb538e4b4af12efaba288e04cc11a"}, {"line": 25390, "relation": "increases", "evidence": "Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 166, "target": 2249, "key": "cc49d1d3a9d951839d9c68d77ce709e8"}, {"line": 25444, "relation": "increases", "evidence": "We also show that NPD1 downregulates Abeta42-triggered expression of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) and of B-94 (a TNF-alpha-inducible pro-inflammatory element) and apoptosis in HNG cells. Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "ADAM Metallopeptidase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 166, "target": 2249, "key": "c8fd70859ab0df71c6a00ac4c238cd7c"}, {"line": 25473, "relation": "increases", "evidence": "In human neural cells overexpressing beta-amyloid precursor protein (betaAPP), the lipid mediator suppressed Abeta42 shedding by downregulating beta-secretase (BACE1) while activating the alpha-secretase (ADAM10), thus shifting the alphaAPP cleavage from the noxious amyloidogenic pathway into a non-amyloidogenic, neurotrophic pathway.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 166, "target": 2249, "key": "9fddba8ca96751b7a93424fa05ff8add"}, {"line": 25392, "relation": "increases", "evidence": "Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 2137, "key": "f99f0b77133792794c6834e0642603e5"}, {"line": 25452, "relation": "increases", "evidence": "We also show that NPD1 downregulates Abeta42-triggered expression of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) and of B-94 (a TNF-alpha-inducible pro-inflammatory element) and apoptosis in HNG cells. Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 2137, "key": "3c6fec246cdc6d6f6b7c4fb589b41698"}, {"line": 25474, "relation": "increases", "evidence": "In human neural cells overexpressing beta-amyloid precursor protein (betaAPP), the lipid mediator suppressed Abeta42 shedding by downregulating beta-secretase (BACE1) while activating the alpha-secretase (ADAM10), thus shifting the alphaAPP cleavage from the noxious amyloidogenic pathway into a non-amyloidogenic, neurotrophic pathway.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Beta secretase subgraph": true, "ADAM Metallopeptidase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 2137, "key": "831b2dc46fad234d4a606b6002be8169"}, {"line": 25402, "relation": "isA", "evidence": "Neuroprotectin D1 (NPD1) is a stereoselective mediator derived from the omega-3 essential fatty acid docosahexaenoic acid (DHA) with potent inflammatory resolving and neuroprotective bioactivity. NPD1 reduces Abeta42 peptide release from aging human brain cells and is severely depleted in Alzheimer's disease (AD) brain.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 108, "key": "a56e7f629a1169a19db1c64de919a92c"}, {"line": 25403, "relation": "increases", "evidence": "Neuroprotectin D1 (NPD1) is a stereoselective mediator derived from the omega-3 essential fatty acid docosahexaenoic acid (DHA) with potent inflammatory resolving and neuroprotective bioactivity. NPD1 reduces Abeta42 peptide release from aging human brain cells and is severely depleted in Alzheimer's disease (AD) brain.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 854, "key": "f4f97ecb88feb0ccf40247355a32fd69"}, {"line": 25404, "relation": "decreases", "evidence": "Neuroprotectin D1 (NPD1) is a stereoselective mediator derived from the omega-3 essential fatty acid docosahexaenoic acid (DHA) with potent inflammatory resolving and neuroprotective bioactivity. NPD1 reduces Abeta42 peptide release from aging human brain cells and is severely depleted in Alzheimer's disease (AD) brain.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 577, "key": "be6a34ad5cdfd9734c0b0e3ff231ddec"}, {"line": 25406, "relation": "negativeCorrelation", "evidence": "Neuroprotectin D1 (NPD1) is a stereoselective mediator derived from the omega-3 essential fatty acid docosahexaenoic acid (DHA) with potent inflammatory resolving and neuroprotective bioactivity. NPD1 reduces Abeta42 peptide release from aging human brain cells and is severely depleted in Alzheimer's disease (AD) brain.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Inflammatory response subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 3823, "key": "e59745b114f82845d83c861fd1b0a539"}, {"line": 25416, "relation": "decreases", "evidence": "We also show that NPD1 downregulates Abeta42-triggered expression of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) and of B-94 (a TNF-alpha-inducible pro-inflammatory element) and apoptosis in HNG cells. Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Prostaglandin subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 3278, "key": "254ff340b0f6482c68acc70a4f87bf68"}, {"line": 25422, "relation": "decreases", "evidence": "We also show that NPD1 downregulates Abeta42-triggered expression of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) and of B-94 (a TNF-alpha-inducible pro-inflammatory element) and apoptosis in HNG cells. Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Confidence": {"Medium": true}}, "source": 166, "target": 3473, "key": "3860cf29f1b5b3617aa9b533735bb38d"}, {"line": 25428, "relation": "decreases", "evidence": "We also show that NPD1 downregulates Abeta42-triggered expression of the pro-inflammatory enzyme cyclooxygenase-2 (COX-2) and of B-94 (a TNF-alpha-inducible pro-inflammatory element) and apoptosis in HNG cells. Moreover, NPD1 suppresses Abeta42 peptide shedding by down-regulating beta-secretase-1 (BACE1) while activating the beta-secretase ADAM10 and up-regulating sAPPalpha, thus shifting the cleavage of betaAPP holoenzyme from an amyloidogenic into the non-amyloidogenic pathway.", "citation": {"db": "PubMed", "db_id": "21246057"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 478, "key": "ee4edc44ffeaeaf699c9418ce4471cc0"}, {"relation": "partOf", "source": 166, "target": 1663, "key": "6e0ac77a5b8f4584dc842bd4365428f3"}, {"line": 25490, "relation": "increases", "evidence": "The mediator neuroprotectin D1 (NPD1) is an enzymatic derivative of the omega-3 essential fatty acid docosahexaenoic acid. NPD1 stereoselectively and specifically binds to human retinal pigment epithelium (RPE) cells and neutrophils. In turn, this lipid mediator induces dephosphorylation of Bcl-x(L) in a PP2A-dependent manner and induces PI3K/Akt and mTOR/p70S6K pathways leading to RPE cell survival during oxidative stress-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 667, "key": "f6377d9102292ee2ffd9a117eeae8ce5"}, {"line": 25491, "relation": "increases", "evidence": "The mediator neuroprotectin D1 (NPD1) is an enzymatic derivative of the omega-3 essential fatty acid docosahexaenoic acid. NPD1 stereoselectively and specifically binds to human retinal pigment epithelium (RPE) cells and neutrophils. In turn, this lipid mediator induces dephosphorylation of Bcl-x(L) in a PP2A-dependent manner and induces PI3K/Akt and mTOR/p70S6K pathways leading to RPE cell survival during oxidative stress-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 460, "key": "b87cb359812f7e4048025af3d1bdfd22"}, {"line": 25499, "relation": "decreases", "evidence": "The mediator neuroprotectin D1 (NPD1) is an enzymatic derivative of the omega-3 essential fatty acid docosahexaenoic acid. NPD1 stereoselectively and specifically binds to human retinal pigment epithelium (RPE) cells and neutrophils. In turn, this lipid mediator induces dephosphorylation of Bcl-x(L) in a PP2A-dependent manner and induces PI3K/Akt and mTOR/p70S6K pathways leading to RPE cell survival during oxidative stress-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Response to oxidative stress": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 166, "target": 504, "key": "4189f6b5dd8a146eed2b66aa1e16bf2b"}, {"line": 25489, "relation": "decreases", "evidence": "The mediator neuroprotectin D1 (NPD1) is an enzymatic derivative of the omega-3 essential fatty acid docosahexaenoic acid. NPD1 stereoselectively and specifically binds to human retinal pigment epithelium (RPE) cells and neutrophils. In turn, this lipid mediator induces dephosphorylation of Bcl-x(L) in a PP2A-dependent manner and induces PI3K/Akt and mTOR/p70S6K pathways leading to RPE cell survival during oxidative stress-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 1663, "target": 2395, "key": "311d9bf342fa3b6166236fd83132097e"}, {"line": 25500, "relation": "decreases", "evidence": "The mediator neuroprotectin D1 (NPD1) is an enzymatic derivative of the omega-3 essential fatty acid docosahexaenoic acid. NPD1 stereoselectively and specifically binds to human retinal pigment epithelium (RPE) cells and neutrophils. In turn, this lipid mediator induces dephosphorylation of Bcl-x(L) in a PP2A-dependent manner and induces PI3K/Akt and mTOR/p70S6K pathways leading to RPE cell survival during oxidative stress-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "21431475"}, "annotations": {"Subgraph": {"Neuroprotection subgraph": true, "Response to oxidative stress": true, "Bcl-2 subgraph": true}, "Confidence": {"Medium": true}}, "source": 667, "target": 504, "key": "a485f9d5bcf805c800220cec207821c6"}, {"line": 25574, "relation": "decreases", "evidence": "IL-1Ra, a physiological antagonist for the IL-1 receptor, reversed the effects of IL-1beta, suggesting that the IL-1beta-dependent up-regulation of alpha-cleavage is mediated by the IL-1 receptor. IL-1beta also induced this concomitant increase in alpha-cleavage and decrease in beta-cleavage in mouse primary cultured neurons. Taken together we conclude that IL-1beta is an anti-amyloidogenic factor, and that enhancement of its signaling or inhibition of IL-1Ra activity could represent potential therapeutic strategies against Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2887, "target": 1479, "key": "5cc9d5c57bb1e8410915a19d68ac69c0"}, {"line": 25575, "relation": "decreases", "evidence": "IL-1Ra, a physiological antagonist for the IL-1 receptor, reversed the effects of IL-1beta, suggesting that the IL-1beta-dependent up-regulation of alpha-cleavage is mediated by the IL-1 receptor. IL-1beta also induced this concomitant increase in alpha-cleavage and decrease in beta-cleavage in mouse primary cultured neurons. Taken together we conclude that IL-1beta is an anti-amyloidogenic factor, and that enhancement of its signaling or inhibition of IL-1Ra activity could represent potential therapeutic strategies against Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "18021299"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2887, "target": 2885, "key": "e5d3a6a6a77954cf721a78a67442a173"}, {"relation": "partOf", "source": 2253, "target": 1056, "key": "15d4dfca6e1f19218fae7086e2e1f584"}, {"relation": "partOf", "source": 2253, "target": 1058, "key": "eadb948b685342ce27f0cb5c61e2c621"}, {"relation": "partOf", "source": 2253, "target": 1057, "key": "bcd6d4304993d5bb031774eedaed79f2"}, {"relation": "partOf", "source": 2901, "target": 1056, "key": "de92d4e3c19757a206a9c1d0ebeaa122"}, {"relation": "partOf", "source": 3204, "target": 1058, "key": "9d7b31ca4847a88efbbdc38b698bf243"}, {"relation": "partOf", "source": 3093, "target": 1057, "key": "bcb8749cef695c7c50ff6ff379beb332"}, {"relation": "partOf", "source": 3093, "target": 1580, "key": "63123d5738bcbbfbd212a04a2fbec878"}, {"relation": "partOf", "source": 2841, "target": 1453, "key": "5994092569c98602bdf5c168bb716ea6"}, {"line": 25686, "relation": "increases", "evidence": "Finally,incubation of microglia with M-CSF and Abincreased expression of RAGE mRNA", "citation": {"db": "PubMed", "db_id": "15882940"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cytokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1659, "target": 3937, "key": "f334bc08ac405dced185446962632431"}, {"relation": "partOf", "source": 2290, "target": 1065, "key": "f6de2c670607f7d0e5f7908449c0a03e"}, {"line": 25746, "relation": "increases", "evidence": "Agrin binds to beta-amyloid (Abeta), accelerates abeta fibril formation, and is localized to Abeta deposits in Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "10673326"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 913, "target": 474, "key": "fa4899e9f7d38fe608cb623cb2350190"}, {"line": 26233, "relation": "association", "evidence": "These findings suggest that the interaction of ApoE with tau and amyloid-beta proteins in AD could play a important role in the formation of NFT and SP, respectively, contributing to the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "7695621"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 1136, "target": 889, "key": "4afb442cb9d2011dc6766caecc738630"}, {"line": 25875, "relation": "association", "evidence": "The findings suggest that in cells that express both apoE and APP, such as astrocytes and microglia, a functional apoE:APP interaction may occur which pmodulates APP processing and Abeta production", "citation": {"db": "PubMed", "db_id": "11523796"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "source": 857, "target": 1125, "key": "096c292f41a8920768d5a50544a9442f"}, {"line": 26042, "relation": "decreases", "evidence": "Medium", "citation": {"db": "PubMed", "db_id": "17077814"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 1126, "target": 2327, "key": "8756888df6af74f1ec429c4f5daf11c0"}, {"line": 25939, "relation": "increases", "evidence": "The upregulation of LRP would allow increased clearance of LRP ligands as well as clearance of Ab/ApoE complexes", "citation": {"db": "PubMed", "db_id": "12117549"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 2973, "target": 1127, "key": "a67720dc965964d1fb38c1f5689fd7cb"}, {"relation": "partOf", "source": 2311, "target": 1124, "key": "5b56b6823d0f4282a139ce6d6e2e0afa"}, {"line": 26172, "relation": "association", "evidence": "Recently, we have demonstrated that sulfatides are substantially and specifically depleted at the very early stage of AD", "citation": {"db": "PubMed", "db_id": "20052565"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 116, "target": 3823, "key": "4d8c1b92433c56cb513a98340e9ea6b9"}, {"line": 26179, "relation": "association", "evidence": "Moreover, our recent studies further demonstrated that (1) apoE mediates sulfatide depletion in amyloid-beta precursor protein transgenic mice; (2) sulfatides enhance amyloid beta (Abeta) peptides binding to apoE-associated particles; (3) Abeta42 content notably correlates with sulfatide content in CSF;(4) sulfatides markedly enhance the uptake of Abeta peptides; and (5) abnormal sulfatide-facilitated Abeta uptake results in the accumulation of Abeta in lysosomes.", "citation": {"db": "PubMed", "db_id": "20052565"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 116, "target": 2315, "key": "8eebed2748d942168610c42f9783fe1a"}, {"line": 26180, "relation": "increases", "evidence": "Moreover, our recent studies further demonstrated that (1) apoE mediates sulfatide depletion in amyloid-beta precursor protein transgenic mice; (2) sulfatides enhance amyloid beta (Abeta) peptides binding to apoE-associated particles; (3) Abeta42 content notably correlates with sulfatide content in CSF;(4) sulfatides markedly enhance the uptake of Abeta peptides; and (5) abnormal sulfatide-facilitated Abeta uptake results in the accumulation of Abeta in lysosomes.", "citation": {"db": "PubMed", "db_id": "20052565"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 116, "target": 914, "key": "0da5efb0227354dc3f134143112c5b7b"}, {"line": 26181, "relation": "association", "evidence": "Moreover, our recent studies further demonstrated that (1) apoE mediates sulfatide depletion in amyloid-beta precursor protein transgenic mice; (2) sulfatides enhance amyloid beta (Abeta) peptides binding to apoE-associated particles; (3) Abeta42 content notably correlates with sulfatide content in CSF;(4) sulfatides markedly enhance the uptake of Abeta peptides; and (5) abnormal sulfatide-facilitated Abeta uptake results in the accumulation of Abeta in lysosomes.", "citation": {"db": "PubMed", "db_id": "20052565"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "APOE subgraph": true}, "Confidence": {"Medium": true}}, "source": 116, "target": 2328, "key": "1e346270dac0b2fb15080074a5d85c33"}, {"line": 26440, "relation": "association", "evidence": "It has been shown that apoE binds and promotes the fibrillogenesis in vitro of Alzheimer's amyloid beta-peptide, suggesting an important role for apoE in the pmodulation of amyloidogenesis.", "citation": {"db": "PubMed", "db_id": "7672107"}, "source": 863, "target": 2312, "key": "72f96041491bd50d29a62c6a11f01e23"}, {"line": 26330, "relation": "increases", "evidence": "We further demonstrated that apolipoprotein E2 and E3 but not apolipoprotein E4 can decrease the fusogenic activity of Abeta(29-42) via a direct interaction.", "citation": {"db": "PubMed", "db_id": "10428074"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"APOE subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2314, "target": 2328, "key": "4141f89455b8b1fcb441e0a29ce3a3de"}, {"relation": "partOf", "source": 2352, "target": 1142, "key": "f1ab8a6d793379338bdc576d916718f1"}, {"relation": "partOf", "source": 2352, "target": 1116, "key": "a30f85d90f04f7ac617c1dfdcfba1e59"}, {"relation": "partOf", "source": 2352, "target": 1119, "key": "8b19dbdbc1f3304ced1f24c9964c9e3d"}, {"line": 26502, "relation": "increases", "evidence": "Finally, we show that PAT1a promotes APP/APLPs processing, resulting in increased secretion of beta-amyloid peptide", "citation": {"db": "PubMed", "db_id": "17050537"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2352, "target": 80, "key": "b486b1c9e8f55f1f0d567997f4ad8c06"}, {"line": 26503, "relation": "increases", "evidence": "Finally, we show that PAT1a promotes APP/APLPs processing, resulting in increased secretion of beta-amyloid peptide", "citation": {"db": "PubMed", "db_id": "17050537"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2352, "target": 2315, "key": "7149fddcf2358c9b354a596da9bc8765"}, {"line": 38474, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2352, "target": 3563, "key": "9ad8c1759eeeb27bcbb32dcb2b840be1"}, {"relation": "partOf", "source": 2352, "target": 1255, "key": "bb0cc99dedb154de0111f7b280acd1fa"}, {"relation": "partOf", "source": 2353, "target": 1143, "key": "980f30b8dfc97d7a6b701753551e5a68"}, {"line": 26515, "relation": "association", "evidence": "However, AQP1 expression is enhanced in reactive astrocytes, accumulating in brain lesions of Creutzfeldt-Jakob disease and multiple sclerosis, suggesting a role of AQP1-expressing astrocytes in brain water homeostasis under pathological conditions.", "citation": {"db": "PubMed", "db_id": "18509662"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2353, "target": 802, "key": "d2b67a671a9e21668a077f6092338034"}, {"line": 26515, "relation": "association", "evidence": "However, AQP1 expression is enhanced in reactive astrocytes, accumulating in brain lesions of Creutzfeldt-Jakob disease and multiple sclerosis, suggesting a role of AQP1-expressing astrocytes in brain water homeostasis under pathological conditions.", "citation": {"db": "PubMed", "db_id": "18509662"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 802, "target": 2353, "key": "6262137b4e89058a7d53201a7354fec1"}, {"line": 26552, "relation": "increases", "evidence": "Production of amyloid-beta protein (Abeta) is initiated by a beta-secretase that cleaves the Abeta precursor protein (APP) at the N terminus of Abeta (the beta site).", "citation": {"db": "PubMed", "db_id": "10931940"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1144, "target": 80, "key": "d904eefcf43b0f6c4dc7b375b32c2eca"}, {"line": 26569, "relation": "increases", "evidence": "Taken together, our results strongly suggest that furin, or a furin-like proprotein convertase, is responsible for cleaving the BACE propeptide domain to form the mature enzyme.", "citation": {"db": "PubMed", "db_id": "10956649"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2714, "target": 2375, "key": "1643dbb4bedfac02c029311c3d50406e"}, {"relation": "partOf", "source": 2714, "target": 1264, "key": "0276dd9bbd572feac47735fa5ed66333"}, {"line": 26670, "relation": "increases", "evidence": "These data indicate that AVs are a previously unrecognized and potentially highly active compartment for A beta generation and suggest that the abnormal accumulation of AVs in affected neurons of the AD brain contributes to beta-amyloid deposition.", "citation": {"db": "PubMed", "db_id": "15325590"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 387, "target": 80, "key": "4ea92b857944bcdc4d4290d5480fd80d"}, {"line": 26725, "relation": "association", "evidence": "These results suggest that PAR-4 may be directly involved in regulating the APP cleavage activity of BACE1.", "citation": {"db": "PubMed", "db_id": "15671026"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3165, "target": 2375, "key": "0ae16b1c2c28d1c066ce75f5ffce3748"}, {"line": 26740, "relation": "increases", "evidence": "PAR-4 (prostate apoptosis response-4) is a leucine zipper protein that was initially identified to be associated with neuronal degeneration and aberrant Abeta production in pmodels of AD.", "citation": {"db": "PubMed", "db_id": "15671026"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3165, "target": 80, "key": "b4cd47af5cc33bfe11434f39728de47a"}, {"line": 26802, "relation": "association", "evidence": "Our findings identify EP2 receptor signaling as a novel proinflammatory and proamyloidogenic pathway in this pmodel of AD, and suggest a rationale for development of therapeutics targeting the EP2 receptor in neuroinflammatory diseases such as AD.", "citation": {"db": "PubMed", "db_id": "16267225"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "Confidence": {"High": true}}, "source": 3275, "target": 3823, "key": "92f41b54756c4345fa669648d0e0bd64"}, {"line": 28861, "relation": "association", "evidence": "We found that BACE phosphorylation influences BACE-GGA interactions in cells using a new fluorescence-resonance-energy-transfer-based assay of protein proximity, fluorescence lifetime imaging.", "citation": {"db": "PubMed", "db_id": "15466887"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1265, "target": 2377, "key": "0dd1d59593238201c7d6fe7c35b11adc"}, {"relation": "partOf", "source": 2329, "target": 1249, "key": "aeba97f1b2a388da3e058d2c0c3da361"}, {"line": 26973, "relation": "decreases", "evidence": "Quetiapine also decreased brain Abeta peptides, beta-secretase activity and expression, and the level of C99 (an APP C-terminal fragment following cleavage by beta-secretase) in the transgenic mice. ", "citation": {"db": "PubMed", "db_id": "18079026"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 339, "target": 2329, "key": "90f3b8b0790475798c6349ff269737f9"}, {"line": 26975, "relation": "decreases", "evidence": "Quetiapine also decreased brain Abeta peptides, beta-secretase activity and expression, and the level of C99 (an APP C-terminal fragment following cleavage by beta-secretase) in the transgenic mice. ", "citation": {"db": "PubMed", "db_id": "18079026"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 339, "target": 2375, "key": "88f527f9497436c90d15a302906e7306"}, {"line": 26976, "relation": "decreases", "evidence": "Quetiapine also decreased brain Abeta peptides, beta-secretase activity and expression, and the level of C99 (an APP C-terminal fragment following cleavage by beta-secretase) in the transgenic mice. ", "citation": {"db": "PubMed", "db_id": "18079026"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "source": 339, "target": 2375, "key": "403b37330c6ce6931edcc7c1c308e53c"}, {"line": 27029, "relation": "increases", "evidence": "Here we show that glutaminyl cyclase (QC) catalyzes the formation of Abeta 3(pE)-40/42 after amyloidogenic processing of APP in two different cell lines, applying specific ELISAs and Western blotting based on urea-PAGE.", "citation": {"db": "PubMed", "db_id": "18570439"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3287, "target": 2328, "key": "6d2792a9e9114ec5713086fe06799d32"}, {"line": 27030, "relation": "increases", "evidence": "Here we show that glutaminyl cyclase (QC) catalyzes the formation of Abeta 3(pE)-40/42 after amyloidogenic processing of APP in two different cell lines, applying specific ELISAs and Western blotting based on urea-PAGE.", "citation": {"db": "PubMed", "db_id": "18570439"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3287, "target": 2327, "key": "efb4d96f111fabc6643d5f0f0a562639"}, {"relation": "partOf", "source": 3287, "target": 1204, "key": "7a454ff98f186aff7192585f8e911dad"}, {"line": 28472, "relation": "increases", "evidence": "Here we show that glutaminyl cyclase (QC) catalyzes the formation of Abeta 3(pE)-40/42 after amyloidogenic processing of APP in two different cell lines, applying specific ELISAs and Western blotting based on urea-PAGE. ", "citation": {"db": "PubMed", "db_id": "18570439"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3287, "target": 80, "key": "d77371313c8496147058925ae1bdebc9"}, {"line": 27055, "relation": "increases", "evidence": "urther, overexpression of the SUMO E2 enzyme ubc9 along with SUMO-1 results in decreased levels of Abeta aggregates in cells transfected with the familial Alzheimer's disease-associated V642F mutant APP, indicating the potential of up-regulating activity of the cellular sumoylation machinery as an approach against Alzheimer's disease. ", "citation": {"db": "PubMed", "db_id": "18675254"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3510, "target": 80, "key": "38d6fcf761980f3e51e5ee1438025a75"}, {"line": 27090, "relation": "increases", "evidence": "Overexpression of RanBP9 resulted in the enhancement of APP interactions with LRP and BACE1 and increased lipid raft association of APP.", "citation": {"db": "PubMed", "db_id": "19251705"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3295, "target": 1144, "key": "1270dece662d4450ab8841d46cb835a6"}, {"line": 48577, "relation": "increases", "evidence": "Accumulation of the amyloid beta (Abeta) peptide derived from the amyloid precursor protein (APP) plays a central role in the pathogenesis of Alzheimer's disease (AD). We previously reported that the scaffolding protein RanBP9 is markedly increased in AD brains and promotes Abeta generation by scaffolding APP/BACE1/LRP complexes together and accelerating APP endocytosis.", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3295, "target": 1144, "key": "14827bd0bf5146f05bf7dff305f15c91"}, {"line": 27091, "relation": "increases", "evidence": "Overexpression of RanBP9 resulted in the enhancement of APP interactions with LRP and BACE1 and increased lipid raft association of APP.", "citation": {"db": "PubMed", "db_id": "19251705"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3295, "target": 1184, "key": "831cb87c9fa60dc7542bc81d0a876d38"}, {"relation": "partOf", "source": 3295, "target": 1139, "key": "9eb02b32e5fd66349aff127fa067def3"}, {"line": 27102, "relation": "decreases", "evidence": "Importantly, knockdown of endogenous RanBP9 significantly reduced Abeta generation in Chinese hamster ovary cells and in primary neurons, demonstrating its physiological role in BACE1 cleavage of APP. ", "citation": {"db": "PubMed", "db_id": "19251705"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "Species": {"10029": true}}, "source": 3295, "target": 2328, "key": "a67af365972dd88bbc753516e416a52b"}, {"relation": "partOf", "source": 3295, "target": 1145, "key": "097f930efcfeaa7ab7973ce3155b266b"}, {"line": 48569, "relation": "decreases", "evidence": "Accelerated beta1-integrin and LRP endocytosis not only disrupted the binding of integrins to the extracellular matrix (cell attachment) but also the linkage to the cytoskeleton by talin and vinculin (cell spreading), since RanBP9 disrupted focal adhesion assembly, as seen by the reduction of talin and vinculin in focal adhesion complexes.", "citation": {"db": "PubMed", "db_id": "22223749"}, "source": 3295, "target": 557, "key": "687c7c1d32c2eb9cdad2f02c212b36a3"}, {"line": 48576, "relation": "increases", "evidence": "Accumulation of the amyloid beta (Abeta) peptide derived from the amyloid precursor protein (APP) plays a central role in the pathogenesis of Alzheimer's disease (AD). We previously reported that the scaffolding protein RanBP9 is markedly increased in AD brains and promotes Abeta generation by scaffolding APP/BACE1/LRP complexes together and accelerating APP endocytosis.", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3295, "target": 441, "key": "d0b1c37a46ebb819dc138ad155523145"}, {"line": 48581, "relation": "positiveCorrelation", "evidence": "Accumulation of the amyloid beta (Abeta) peptide derived from the amyloid precursor protein (APP) plays a central role in the pathogenesis of Alzheimer's disease (AD). We previously reported that the scaffolding protein RanBP9 is markedly increased in AD brains and promotes Abeta generation by scaffolding APP/BACE1/LRP complexes together and accelerating APP endocytosis.", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Brain": true}}, "source": 3295, "target": 3823, "key": "57cc159410c244590461c7bf59ef0b44"}, {"line": 28896, "relation": "decreases", "evidence": "Furthermore, we show that the negative pmodulation of BACE1 by RTN3 relies on the binding of RTN3 to BACE1.", "citation": {"db": "PubMed", "db_id": "16979658"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 1275, "target": 2375, "key": "7b6c41a13f75ec578af7b8fd04bd3427"}, {"relation": "partOf", "source": 3330, "target": 1275, "key": "9a7f5ab1a43c3621c0fed50e982239f2"}, {"line": 28905, "relation": "decreases", "evidence": "Within neuritic plaques, reticulon 3 (RTN3), a homolog of Nogo protein, appears to regulate the formation of both amyloid deposition via negative pmodulation of BACE1 activity and dystrophic neurites via the formation of RTN3 aggregates.", "citation": {"db": "PubMed", "db_id": "20144652"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Beta secretase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3330, "target": 2375, "key": "b7123da11ec8ead8a2efcb64f792c3b2"}, {"line": 28925, "relation": "decreases", "evidence": "Membrane bound reticulon (RTN) family proteins interact with BACE1 and negatively pmodulate BACE1 activity through preventing access of BACE1 to its cellular APP substrate.", "citation": {"db": "PubMed", "db_id": "16979658"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 1276, "target": 2375, "key": "ab27e11d105706666b7a059f51e5ed47"}, {"relation": "partOf", "source": 3331, "target": 1276, "key": "137653ba36bf58fea0fa6a9a9b497998"}, {"line": 38106, "relation": "negativeCorrelation", "evidence": "we found that the Nogo-66 receptor (NgR) interacts physically with both Abeta and the amyloid precursor protein (APP). The inverse correlation of Abeta levels with NgR levels within the brain may reflect regulation of Abeta production and/or Abeta clearance.", "citation": {"db": "PubMed", "db_id": "17182778"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3331, "target": 2328, "key": "b1837836bcf56288b69869731f1d9b1e"}, {"line": 27199, "relation": "increases", "evidence": "We previously reported that RanBP9 promotes Abeta generation by scaffolding APP/BACE1/LRP complexes together.", "citation": {"db": "PubMed", "db_id": "19729516"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1145, "target": 80, "key": "e5c83c5f1b1906c782b94d3d5d783e67"}, {"line": 27208, "relation": "decreases", "evidence": "We have recently demonstrated that bis(7)-Cognitin, a promising multifunctional anti-Alzheimer's dimer, can remarkably reduce the generation of amyloid beta peptide (Abeta) by inhibiting beta-secretase (BACE-1) and activating alpha-secretase activity.", "citation": {"db": "PubMed", "db_id": "19765582"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 92, "target": 2375, "key": "2a2096365593f3e216db1efa42d9948c"}, {"line": 27210, "relation": "decreases", "evidence": "We have recently demonstrated that bis(7)-Cognitin, a promising multifunctional anti-Alzheimer's dimer, can remarkably reduce the generation of amyloid beta peptide (Abeta) by inhibiting beta-secretase (BACE-1) and activating alpha-secretase activity.", "citation": {"db": "PubMed", "db_id": "19765582"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 92, "target": 80, "key": "b7596ecf42d4224e24b2313dccff525a"}, {"line": 27220, "relation": "increases", "evidence": "A beta is generated upon the sequential proteolytic cleavage of transmembrane amyloid precursor protein (APP) by two membrane-bound proteases, beta-secretase (BACE1) and the gamma-secretase complex comprising presenilin 1 (PS1), nicastrin, APH-1 and PEN-2.", "citation": {"db": "PubMed", "db_id": "19885829"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1113, "target": 80, "key": "aef98f2ddeac47a8edd3a839a1d1ceb9"}, {"line": 31455, "relation": "association", "evidence": "vitro studies and mouse pmodels of AD suggest that PrP may be involved in AD pathogenesis through a highly specific interaction with amyloidbeta (Abeta42) oligomers.", "citation": {"db": "PubMed", "db_id": "21393248"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 940, "target": 3823, "key": "a96373100ce08552b77d0b182551d6c0"}, {"line": 27248, "relation": "increases", "evidence": "We previously showed that the glycosaminoglycan (GAG) heparin can increase the enzyme activity of proBACE1. ", "citation": {"db": "PubMed", "db_id": "20067575"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 268, "target": 2375, "key": "22441c355404d8209f641a25d5c2fab5"}, {"line": 27273, "relation": "association", "evidence": "Interestingly, addition of the dominant-negative mutant of Rab5, a small G-protein Rab5 involved in the endocytic process, inhibits the aging-related APP-BACE1 interaction and Abeta production, suggesting that endocytosis contributes to AD progression.", "citation": {"db": "PubMed", "db_id": "20127045"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Low": true}}, "source": 3288, "target": 813, "key": "3d39bb37225bddb97643c978a0782402"}, {"relation": "partOf", "source": 3288, "target": 1578, "key": "3a18e08d233b1f01374576536cc7bba5"}, {"line": 27393, "relation": "association", "evidence": "Here we demonstrate a role of calpain in the neuropathology in amyloid precursor protein (APP) and presenilin 1 (PS1) double-transgenic mice, an established mouse pmodel of AD.", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2435, "target": 3819, "key": "bce0094c4c8a200c938f1633488ceeb8"}, {"line": 27461, "relation": "increases", "evidence": "Consistently, overexpression of calpain in heterologous APP expressing cells up-regulated the level of BACE1 and increased Abeta production. ", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2435, "target": 2375, "key": "dd5c694105ef101d867f8523be5ff93a"}, {"line": 27462, "relation": "increases", "evidence": "Consistently, overexpression of calpain in heterologous APP expressing cells up-regulated the level of BACE1 and increased Abeta production. ", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2435, "target": 80, "key": "fe7088b8d75fa6d74bacaeed7e150ed7"}, {"relation": "partOf", "source": 2435, "target": 1302, "key": "6a737ee79af1c8f8d618d9ebf2d3ed60"}, {"line": 27393, "relation": "association", "evidence": "Here we demonstrate a role of calpain in the neuropathology in amyloid precursor protein (APP) and presenilin 1 (PS1) double-transgenic mice, an established mouse pmodel of AD.", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3819, "target": 2435, "key": "5d79af391cda0d27b1c44714541ca413"}, {"line": 27399, "relation": "decreases", "evidence": "We found that overexpression of endogenous calpain inhibitor calpastatin (CAST) under the control of the calcium/calpmodulin-dependent protein kinase II promoter in APP/PS1 mice caused a remarkable decrease of amyloid plaque burdens and prevented Tau phosphorylation and the loss of synapses.", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Calpastatin-calpain subgraph": true}, "Confidence": {"Medium": true}}, "source": 2452, "target": 80, "key": "958a4639e359f2219dcefef705cc0b12"}, {"line": 27406, "relation": "decreases", "evidence": "We found that overexpression of endogenous calpain inhibitor calpastatin (CAST) under the control of the calcium/calpmodulin-dependent protein kinase II promoter in APP/PS1 mice caused a remarkable decrease of amyloid plaque burdens and prevented Tau phosphorylation and the loss of synapses.", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Calpastatin-calpain subgraph": true}}, "source": 2452, "target": 3015, "key": "4e4fcd10628c09dd6978ed518b5458e4"}, {"line": 27425, "relation": "increases", "evidence": "Furthermore, CAST overexpression prevented the decrease in the phosphorylation of the memory-related molecules CREB and ERK in the brain of APP/PS1 mice and improved spatial learning and memory.", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"CREB subgraph": true, "Calpastatin-calpain subgraph": true, "Cell cycle subgraph": true}, "Confidence": {"High": true}}, "source": 2452, "target": 2554, "key": "114bbcb8df83da8d4e0c5edee926f10d"}, {"line": 27431, "relation": "increases", "evidence": "Furthermore, CAST overexpression prevented the decrease in the phosphorylation of the memory-related molecules CREB and ERK in the brain of APP/PS1 mice and improved spatial learning and memory.", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Calpastatin-calpain subgraph": true}, "Confidence": {"High": true}}, "source": 2452, "target": 2990, "key": "fe47e9270756d8f202429a54f3feb545"}, {"line": 27452, "relation": "decreases", "evidence": "Interestingly, treatment of cultured primary neurons with amyloid-beta (Abeta) peptides caused an increase in the level of beta-site APP-cleaving enzyme 1 (BACE1), the key enzyme responsible for APP processing and Abeta production. This effect was inhibited by CAST overexpression. ", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Calpastatin-calpain subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2452, "target": 2328, "key": "302492f3f28a38ebfa325e7d9d73b142"}, {"line": 27455, "relation": "decreases", "evidence": "Interestingly, treatment of cultured primary neurons with amyloid-beta (Abeta) peptides caused an increase in the level of beta-site APP-cleaving enzyme 1 (BACE1), the key enzyme responsible for APP processing and Abeta production. This effect was inhibited by CAST overexpression. ", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Calpastatin-calpain subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2452, "target": 2375, "key": "ed0bba2b633761f7112a28de3758ad91"}, {"line": 27467, "relation": "decreases", "evidence": "Finally, CAST transgene prevented the increase of BACE1 in APP/PS1 mice.", "citation": {"db": "PubMed", "db_id": "20595388"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "Calpastatin-calpain subgraph": true}, "Confidence": {"Medium": true}}, "source": 2452, "target": 2375, "key": "738cf3663b0dd3a54ee9a69018744eab"}, {"line": 46053, "relation": "negativeCorrelation", "evidence": "The activity of calpains is regulated by the inhibitor calpastatin, and increased activity of calpains and decreased calpastastin are often found in AD", "citation": {"db": "PubMed", "db_id": "24200051"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 2452, "target": 2160, "key": "ebdc9e1a3a149c5512519e12d7fecfdb"}, {"line": 46078, "relation": "decreases", "evidence": "calpastatin was reported to inhibit both calpain 1 and calpain 2", "citation": {"db": "PubMed", "db_id": "24200051"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 2452, "target": 2428, "key": "1b1d3441d92f39d6bbf46bfe05014cec"}, {"line": 46079, "relation": "decreases", "evidence": "calpastatin was reported to inhibit both calpain 1 and calpain 2", "citation": {"db": "PubMed", "db_id": "24200051"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 2452, "target": 2435, "key": "a30c38c89f224d5b38d9d98f22545bd1"}, {"relation": "partOf", "source": 2554, "target": 1362, "key": "b1570a9f680b8ba4274dd8e084b5360a"}, {"line": 29983, "relation": "increases", "evidence": "The resulting data suggest that Abeta-induced learning and memory deficits are mediated by alterations in CREB function, based on the finding that restoring CREB activity by directly pmodulating CBP levels in the brains of adult mice is sufficient to ameliorate learning and memory.", "citation": {"db": "PubMed", "db_id": "21149712"}, "annotations": {"Subgraph": {"CREB subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2554, "target": 818, "key": "f020f08d159fae4c541c930993551f4d"}, {"line": 29984, "relation": "increases", "evidence": "The resulting data suggest that Abeta-induced learning and memory deficits are mediated by alterations in CREB function, based on the finding that restoring CREB activity by directly pmodulating CBP levels in the brains of adult mice is sufficient to ameliorate learning and memory.", "citation": {"db": "PubMed", "db_id": "21149712"}, "annotations": {"Subgraph": {"CREB subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 2554, "target": 820, "key": "a8b2669e889d9be073b020a7fa613cc1"}, {"relation": "partOf", "source": 2554, "target": 1363, "key": "1c11a6c7f14378a071d349aa11df9354"}, {"relation": "hasVariant", "source": 2554, "target": 2555, "key": "385badba878d1cb47984f47f49f1bf37"}, {"line": 27496, "relation": "increases", "evidence": "The Swedish mutation (K595N/M596L) of amyloid precursor protein (APP-swe) has been known to increase abnormal cleavage of cellular APP by Beta-secretase (BACE), which causes tau protein hyperphosphorylation and early-onset Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "21034535"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2346, "target": 2375, "key": "0045b162a8652053300fe0b83919e1f4"}, {"line": 27500, "relation": "increases", "evidence": "The Swedish mutation (K595N/M596L) of amyloid precursor protein (APP-swe) has been known to increase abnormal cleavage of cellular APP by Beta-secretase (BACE), which causes tau protein hyperphosphorylation and early-onset Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "21034535"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2346, "target": 80, "key": "6c30f44e01f219e91be59e5ae3619820"}, {"line": 27497, "relation": "increases", "evidence": "The Swedish mutation (K595N/M596L) of amyloid precursor protein (APP-swe) has been known to increase abnormal cleavage of cellular APP by Beta-secretase (BACE), which causes tau protein hyperphosphorylation and early-onset Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "21034535"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2349, "target": 2375, "key": "f40f86619fc65e33aeab85e1439c4274"}, {"line": 27548, "relation": "increases", "evidence": "It has also been shown in animal pmodels that under conditions of reduced M1/M3 muscarinic acetylcholine receptor stimulation the secretory pathway of APP processing is inhibited and that constitutive upregulation of M1/M3-associated PKC increases APP secretion.", "citation": {"db": "PubMed", "db_id": "9775403"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2513, "target": 857, "key": "f94cc8281b46d9790fee9d0964defdc9"}, {"line": 27606, "relation": "increases", "evidence": "IFNgamma in combination with TNFalpha or IL-1beta seems to trigger Abeta production by supporting beta-secretase cleavage of the immature APP molecule.", "citation": {"db": "PubMed", "db_id": "11114266"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true, "Interferon signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1706, "target": 80, "key": "79e07ff6d07c9e3cdd7b467b638fe57e"}, {"line": 27729, "relation": "association", "evidence": "Here we report evidence that heparan sulfate (HS) interacts with beta-site APP-cleaving enzyme (BACE) 1 and regulates its cleavage of APP.", "citation": {"db": "PubMed", "db_id": "14530380"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 960, "target": 80, "key": "d534390d60e720482b8b79ce17d0ef0d"}, {"relation": "partOf", "source": 128, "target": 960, "key": "6090941b6d67600ab99486bf16cf07b1"}, {"line": 27730, "relation": "association", "evidence": "Here we report evidence that heparan sulfate (HS) interacts with beta-site APP-cleaving enzyme (BACE) 1 and regulates its cleavage of APP.", "citation": {"db": "PubMed", "db_id": "14530380"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 128, "target": 2375, "key": "44bbe3b7f7de0aacb0d1c2a6f44f2815"}, {"relation": "partOf", "source": 3422, "target": 1278, "key": "83d1b3111b395ef5c241ffd4b4dc9353"}, {"line": 27837, "relation": "decreases", "evidence": "In contrast, BACE1 expression was suppressed by stimulation of M2-mediated pathways via selective M2-agonist binding or direct activation of adenylate cyclase with forskolin, an effect that was prevented by inhibiting protein kinase A.", "citation": {"db": "PubMed", "db_id": "15211591"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2254, "target": 2375, "key": "4bfd345e671f5cb0e04f5152a8144013"}, {"line": 27954, "relation": "increases", "evidence": "Heparin can promote beta-secretase cleavage of APP in neuroblastoma cells.", "citation": {"db": "PubMed", "db_id": "16716081"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 274, "target": 2375, "key": "25319fa061003e2b73be5745d95bffd2"}, {"line": 28439, "relation": "increases", "evidence": "Heparin promotes beta-secretase cleavage of the Alzheimer's amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "9152995"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 274, "target": 2375, "key": "01b691b84f5f83d2b8096cd6b0c34dd8"}, {"line": 28440, "relation": "increases", "evidence": "Heparin promotes beta-secretase cleavage of the Alzheimer's amyloid precursor protein.", "citation": {"db": "PubMed", "db_id": "9152995"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 274, "target": 80, "key": "9c0da69608ed535ab4d314ec08cbf61b"}, {"line": 28022, "relation": "increases", "evidence": "Using high-throughput siRNA screening technology, we assessed 15,200 genes for their role in Abeta42 secretion and identified leucine-rich repeat transmembrane 3 (LRRTM3) as a neuronal gene that promotes APP processing by BACE1. siRNAs targeting LRRTM3 inhibit the secretion of Abeta40, Abeta42, and sAPPbeta, the N-terminal APP fragment produced by BACE1 cleavage, from cultured cells and primary neurons by up to 60%, whereas overexpression increases Abeta secretion.", "citation": {"db": "PubMed", "db_id": "17098871"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Very High": true}}, "source": 1867, "target": 2328, "key": "16289a69214bac56724829f549db11e2"}, {"line": 28023, "relation": "increases", "evidence": "Using high-throughput siRNA screening technology, we assessed 15,200 genes for their role in Abeta42 secretion and identified leucine-rich repeat transmembrane 3 (LRRTM3) as a neuronal gene that promotes APP processing by BACE1. siRNAs targeting LRRTM3 inhibit the secretion of Abeta40, Abeta42, and sAPPbeta, the N-terminal APP fragment produced by BACE1 cleavage, from cultured cells and primary neurons by up to 60%, whereas overexpression increases Abeta secretion.", "citation": {"db": "PubMed", "db_id": "17098871"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Very High": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 1867, "target": 2375, "key": "ea4c9cde403879ee841ef5e7474180e3"}, {"relation": "partOf", "source": 254, "target": 977, "key": "238d0f9859d76abef8fa55d99fa9399b"}, {"relation": "partOf", "source": 2930, "target": 1267, "key": "17886ed98aa4062ec9edcfe950e842b5"}, {"line": 28297, "relation": "association", "evidence": "Ataxin 1 (ATXN1) is one of these four AD candidate genes and has been indicated to be the disease gene for spinocerebellar ataxia type 1, which is also a neurodegenerative disease.", "citation": {"db": "PubMed", "db_id": "20139999"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"Medium": true}}, "source": 3884, "target": 2371, "key": "4400da01cc0761e604fb99a981a88f0c"}, {"line": 43034, "relation": "association", "evidence": "TTBK2 is ubiquitously expressed in multiple tissues and genetically linked to spinocerebellar ataxia type 11.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHDisease": {"Spinocerebellar Ataxias": true}, "MeSHAnatomy": {"Tissues": true}, "Confidence": {"High": true}}, "source": 3884, "target": 3748, "key": "6e9a99a52f9dd92818d4b74082b3712c"}, {"line": 28366, "relation": "decreases", "evidence": "We discovered a nonpeptidic compound, TAK-070, that inhibited BACE1, a rate-limiting protease for the generation of Abeta peptides that are considered causative for Alzheimer's disease (AD), in a noncompetitive manner.", "citation": {"db": "PubMed", "db_id": "20720123"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 45, "target": 2375, "key": "a8f7a3d4b5af931088c887403d8c952f"}, {"line": 28462, "relation": "decreases", "evidence": "Additionally, coexpression with PS2 resulted in a decrease of APP secretion, suggesting a direct participation of presenilins in the intracellular sorting, trafficking and processing of APP molecule", "citation": {"db": "PubMed", "db_id": "9055862"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 1203, "target": 2315, "key": "1d1d46bc9c92a1b59d85b51183eb8ea6"}, {"relation": "partOf", "source": 29, "target": 900, "key": "be96f56003ab6ca9bd052874f06a55a1"}, {"line": 28481, "relation": "increases", "evidence": "Introduction of the APP KM595/596NL Swedish mutation causing overproduction of Abeta, however, surprisingly diminished the concentration of Abeta 3(pE)-40/42.", "citation": {"db": "PubMed", "db_id": "18570439"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2347, "target": 80, "key": "22a2103a4260f2b9d8a32ba96c528708"}, {"line": 28482, "relation": "increases", "evidence": "Introduction of the APP KM595/596NL Swedish mutation causing overproduction of Abeta, however, surprisingly diminished the concentration of Abeta 3(pE)-40/42.", "citation": {"db": "PubMed", "db_id": "18570439"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 2344, "target": 80, "key": "eb889bd2350e289e061963feeec52820"}, {"line": 28494, "relation": "decreases", "evidence": "Alzheimer precursor protein interaction with the Nogo-66 receptor reduces amyloid-beta plaque deposition.", "citation": {"db": "PubMed", "db_id": "16452662"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1206, "target": 2328, "key": "ed4445ebb3d5defb35a061ad74c13038"}, {"relation": "partOf", "source": 3332, "target": 1206, "key": "84089391d029f7eeb0ca594f08d4ce5c"}, {"line": 28492, "relation": "increases", "evidence": "Alzheimer precursor protein interaction with the Nogo-66 receptor reduces amyloid-beta plaque deposition.", "citation": {"db": "PubMed", "db_id": "16452662"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"Medium": true}}, "source": 3332, "target": 1206, "key": "514ba3ebf6b65c4320d4147961ae38b5"}, {"line": 28493, "relation": "association", "evidence": "Alzheimer precursor protein interaction with the Nogo-66 receptor reduces amyloid-beta plaque deposition.", "citation": {"db": "PubMed", "db_id": "16452662"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"Medium": true}}, "source": 3332, "target": 2315, "key": "b7a38874f3fdeaf8f6649a1c77bc08e7"}, {"line": 28502, "relation": "decreases", "evidence": "Overexpression of NgR decreases Abeta production in neuroblastoma culture, and targeted disruption of NgR expression increases transgenic mouse brain Abeta levels, Abeta plaque deposition, and dystrophic neurites. ", "citation": {"db": "PubMed", "db_id": "16452662"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3332, "target": 2328, "key": "290bc529aaf4e3401bf55626ddaa97b9"}, {"line": 28524, "relation": "decreases", "evidence": "Deletion of NgR expression increases Abeta plaque deposition in transgenic mice, while excess soluble NgR treatment reduces Abeta plaque deposition in mice.", "citation": {"db": "PubMed", "db_id": "18220524"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true}}, "source": 3332, "target": 2328, "key": "eb12c92b87aebf1e7110920d5f0fb638"}, {"relation": "partOf", "source": 3332, "target": 1246, "key": "28afa25afa19abae6ef4b292918d411e"}, {"relation": "partOf", "source": 3338, "target": 944, "key": "303cbf653311855bde1a2cdf06ba6d67"}, {"relation": "partOf", "source": 2926, "target": 920, "key": "9d7d379b0f50d8eef5ef44bf734fc9d5"}, {"line": 37279, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2926, "target": 3306, "key": "aa43b3545de77fed5a97f438d7a3d667"}, {"line": 37281, "relation": "increases", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2926, "target": 743, "key": "8369c158e1c4ed95c4c26f0e56d970ec"}, {"line": 48606, "relation": "decreases", "evidence": "Specifically, conditional loss of beta1-integrin prevented Abeta42O-induced Cofilin activation, and allosteric modulation or activation of beta1-integrin significantly reduced Abeta42O binding to neurons while blocking Abeta42O-induced reactive oxygen species (ROS) production, mitochondrial dysfunction, depletion of F-actin/focal Vinculin, and apoptotic process.", "citation": {"db": "PubMed", "db_id": "25698445"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2926, "target": 80, "key": "f325297abf8ce7e3fe20e1240743398b"}, {"line": 48614, "relation": "increases", "evidence": "Specifically, conditional loss of beta1-integrin prevented Abeta42O-induced Cofilin activation, and allosteric modulation or activation of beta1-integrin significantly reduced Abeta42O binding to neurons while blocking Abeta42O-induced reactive oxygen species (ROS) production, mitochondrial dysfunction, depletion of F-actin/focal Vinculin, and apoptotic process.", "citation": {"db": "PubMed", "db_id": "25698445"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2926, "target": 2507, "key": "89cbecf57f9c3786f08e31336033b85e"}, {"line": 28553, "relation": "increases", "evidence": "The binding of Abeta to membrane lipids facilitates Abeta fibrillation, which in turn disturbs the structure and function of the membranes, such as membrane fluidity or the formation of ion channels.", "citation": {"db": "PubMed", "db_id": "15160835"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 905, "target": 474, "key": "d623c21ad9ce81b7a80952b5a55e3da8"}, {"relation": "partOf", "source": 2135, "target": 906, "key": "06bd9aea870393e02daa0fbd7123fa99"}, {"relation": "partOf", "source": 2923, "target": 932, "key": "02b759dca74860d8ca73dc02e0afa1d1"}, {"relation": "partOf", "source": 2707, "target": 925, "key": "6f21b9b0d72c80c2c871fe990e735583"}, {"line": 39115, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2707, "target": 577, "key": "5fe9439d790506a7b1c46d0765db18d4"}, {"line": 28578, "relation": "association", "evidence": "Signal transduction through tyrosine-phosphorylated C-terminal fragments of amyloid precursor protein via an enhanced interaction with Shc/Grb2 adaptor proteins in reactive astrocytes of Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "12084708"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 780, "target": 1207, "key": "c1564c7d60ece0f8720d56aa0b44a9be"}, {"line": 28579, "relation": "association", "evidence": "Signal transduction through tyrosine-phosphorylated C-terminal fragments of amyloid precursor protein via an enhanced interaction with Shc/Grb2 adaptor proteins in reactive astrocytes of Alzheimer's disease brain.", "citation": {"db": "PubMed", "db_id": "12084708"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 780, "target": 1170, "key": "d4537b364a99942f1b49d97e68bd4d07"}, {"line": 28624, "relation": "association", "evidence": "The cytoplasmic tail of APP interacts with phosphotyrosine binding (PTB) domain containing proteins (Fe65, X11, mDab-1, and JIP-1) and may pmodulate gene expression and apoptotic process.", "citation": {"db": "PubMed", "db_id": "11877420"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1075, "target": 478, "key": "06ec1c186b1b29e49f7e603e327a36a8"}, {"line": 34001, "relation": "decreases", "evidence": "The phosphotyrosine binding domain of the neuronal protein X11alpha/mint-1 binds to the C-terminus of amyloid precursor protein (APP) and inhibits catabolism to beta-amyloid (Abeta), but the mechanism of this effect is unclear.", "citation": {"db": "PubMed", "db_id": "14756819"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1075, "target": 80, "key": "deddc7705cb16e1b2b9bc8406c3633fd"}, {"line": 34017, "relation": "decreases", "evidence": "The neuronal adaptor X11alpha interacts with the conserved -GYENPTY- sequence in the C-terminus of amyloid precursor protein (APP) or its Swedish mutation (APPswe) to inhibit Abeta40 and Abeta42 secretion.", "citation": {"db": "PubMed", "db_id": "12849748"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1075, "target": 2328, "key": "079c9159eeac9688f6e25a0fc463210d"}, {"line": 28625, "relation": "association", "evidence": "The cytoplasmic tail of APP interacts with phosphotyrosine binding (PTB) domain containing proteins (Fe65, X11, mDab-1, and JIP-1) and may pmodulate gene expression and apoptotic process.", "citation": {"db": "PubMed", "db_id": "11877420"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1187, "target": 478, "key": "f238f2a25a5c980d98848307e1ad49ca"}, {"relation": "partOf", "source": 3379, "target": 1211, "key": "79d3cee9f4cb64ec5f272e54a0f88c07"}, {"line": 28668, "relation": "association", "evidence": "SORCS1 is also genetically associated with types 1 and 2 diabetes mellitus (T1DM, T2DM).", "citation": {"db": "PubMed", "db_id": "20881129"}, "source": 1972, "target": 3849, "key": "8ed3c4b725b4af0aaa18ab833cc08129"}, {"line": 28669, "relation": "association", "evidence": "SORCS1 is also genetically associated with types 1 and 2 diabetes mellitus (T1DM, T2DM).", "citation": {"db": "PubMed", "db_id": "20881129"}, "source": 1972, "target": 3850, "key": "b7d43e12ca194d97bdb64a86c1f14d58"}, {"line": 36137, "relation": "increases", "evidence": "Sorting mechanisms that cause the amyloid precursor protein (APP) and the ß-secretases and gamma-secretases to colocalize in the same compartment play an important role in the regulation of ABeta¸ production in Alzheimer's disease (AD). We and others have reported that genetic variants in the Sortilin-related receptor (SORL1) increased the risk of AD, that SORL1 is involved in trafficking of APP, and that underexpression of SORL1 leads to overproduction of ABeta¸. Here we explored the role of one of its homologs, the sortilin-related VPS10 domain containing receptor 1 (SORCS1), in AD.SorCS1 influenced APP processing. While overexpression of SorCS1 reduced gamma-secretase activity and ABeta¸ levels, the suppression of SorCS1 increased gamma-secretase processing of APP and the levels of ABeta¸.", "citation": {"db": "PubMed", "db_id": "21280075"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation"}, "source": 1972, "target": 2315, "key": "cdebcbb198d44f0e9f9adafee0bd7f29"}, {"line": 38171, "relation": "increases", "evidence": "Using screening approaches in primary neurons, we identified brain-derived neurotrophic factor (BDNF) as a major inducer of Sorla that activates receptor gene transcription through the ERK (extracellular regulated kinase) pathway.These findings demonstrate that the beneficial effects ascribed to BDNF in APP metabolism act through induction of Sorla that encodes a negative regulator of neuronal APP processing", "citation": {"db": "PubMed", "db_id": "20007471"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1972, "target": 448, "key": "9ad8edac8f81a774a848f1626d8f6fbc"}, {"line": 38173, "relation": "increases", "evidence": "Using screening approaches in primary neurons, we identified brain-derived neurotrophic factor (BDNF) as a major inducer of Sorla that activates receptor gene transcription through the ERK (extracellular regulated kinase) pathway.These findings demonstrate that the beneficial effects ascribed to BDNF in APP metabolism act through induction of Sorla that encodes a negative regulator of neuronal APP processing", "citation": {"db": "PubMed", "db_id": "20007471"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1972, "target": 449, "key": "fe010ffb43a9dab07052a7f93b11f675"}, {"line": 38178, "relation": "decreases", "evidence": "Using screening approaches in primary neurons, we identified brain-derived neurotrophic factor (BDNF) as a major inducer of Sorla that activates receptor gene transcription through the ERK (extracellular regulated kinase) pathway.These findings demonstrate that the beneficial effects ascribed to BDNF in APP metabolism act through induction of Sorla that encodes a negative regulator of neuronal APP processing", "citation": {"db": "PubMed", "db_id": "20007471"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1972, "target": 2328, "key": "ce9422e2b2285e55db8e642b9d753bac"}, {"relation": "hasVariant", "source": 1972, "target": 1973, "key": "f7bb89280ac967708aa7bbbcdf2a6d5b"}, {"line": 28668, "relation": "association", "evidence": "SORCS1 is also genetically associated with types 1 and 2 diabetes mellitus (T1DM, T2DM).", "citation": {"db": "PubMed", "db_id": "20881129"}, "source": 3849, "target": 1972, "key": "e34c44576a1a2b1ee9d0665ee31a91d1"}, {"line": 28702, "relation": "association", "evidence": "Despite the wealth of in vivo and in vitro data that have accumulated regarding the connection of APP to kinesin transport, it is not yet clear if APP is coupled to its specific motor protein via an intracellular interaction partner, such as the c-Jun N-terminal kinase-interacting protein, or by yet another unknown molecular mechanism", "citation": {"db": "PubMed", "db_id": "17047360"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3403, "target": 2315, "key": "2dc6622d5f197352d78d6e30b8f636a3"}, {"relation": "partOf", "source": 3403, "target": 1336, "key": "176dbdcbebcee75eab9356e7f5ca723f"}, {"relation": "partOf", "source": 3403, "target": 1542, "key": "fb323814b34ed050b1ada3550664dbba"}, {"relation": "partOf", "source": 3408, "target": 1215, "key": "603e813e3af26d2ccf54af6fbded89c2"}, {"relation": "partOf", "source": 3408, "target": 1517, "key": "2968aa1e2b06a1908cadc684b8105cbf"}, {"line": 28733, "relation": "association", "evidence": "These results suggest that the breakdown of HRD1-mediated ERAD causes Abeta generation and ER stress, possibly linked to AD.", "citation": {"db": "PubMed", "db_id": "20237263"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 835, "target": 2328, "key": "68cf4558e686cc50f9bac296edc2f2d7"}, {"line": 28734, "relation": "association", "evidence": "These results suggest that the breakdown of HRD1-mediated ERAD causes Abeta generation and ER stress, possibly linked to AD.", "citation": {"db": "PubMed", "db_id": "20237263"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 835, "target": 771, "key": "a2157037214e04981c1a230b3c43f7cd"}, {"line": 28735, "relation": "association", "evidence": "These results suggest that the breakdown of HRD1-mediated ERAD causes Abeta generation and ER stress, possibly linked to AD.", "citation": {"db": "PubMed", "db_id": "20237263"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"High": true}}, "source": 835, "target": 3823, "key": "a1b4bec597167a380b8c36dc205ca4b9"}, {"relation": "partOf", "source": 3451, "target": 1218, "key": "68eee8ab21736272ca513497f8200649"}, {"relation": "partOf", "source": 3453, "target": 947, "key": "6a10a9036b9ac6b3dca3dcccb4177f37"}, {"relation": "partOf", "source": 3478, "target": 1220, "key": "93d86833c74fc0cf549c047d1cf043e4"}, {"relation": "partOf", "source": 3478, "target": 1618, "key": "723e58f536d053dd3963db8ea74d5267"}, {"relation": "partOf", "source": 3478, "target": 1247, "key": "230e34124f990dd0fd8b492af0e47848"}, {"line": 38042, "relation": "association", "evidence": "Two recent studies (Lauren et al., 2009; Nikolaev et al., 2009) now connect the physiological and pathological functions of APP processing products. Lauren et al. show that ABeta¸42 binds to the cellular prion protein (PrP), which itself can cause neuropathology when misfolded. In a separate study, Nikolaev et al. report that the N-terminal fragment of APP (N-APP) interacts with death receptor 6 (DR6), resulting in pruning of axons and neurons during development of the central nervous system (CNS).These studies suggest that APP processing constitutes a complex signaling center that serves multiple physiological functions that could trigger pathological events when deregulated during disease.", "citation": {"db": "PubMed", "db_id": "19524503"}, "source": 3478, "target": 2332, "key": "22655841d31dc3f4bb6c787d1a7e51cb"}, {"line": 38044, "relation": "increases", "evidence": "Two recent studies (Lauren et al., 2009; Nikolaev et al., 2009) now connect the physiological and pathological functions of APP processing products. Lauren et al. show that ABeta¸42 binds to the cellular prion protein (PrP), which itself can cause neuropathology when misfolded. In a separate study, Nikolaev et al. report that the N-terminal fragment of APP (N-APP) interacts with death receptor 6 (DR6), resulting in pruning of axons and neurons during development of the central nervous system (CNS).These studies suggest that APP processing constitutes a complex signaling center that serves multiple physiological functions that could trigger pathological events when deregulated during disease.", "citation": {"db": "PubMed", "db_id": "19524503"}, "source": 3478, "target": 654, "key": "f68da486dfdd7ff69dbac6c202571a99"}, {"relation": "partOf", "source": 3478, "target": 1250, "key": "2cc8e2c1c9452a49b676f0f8baf71365"}, {"line": 29967, "relation": "decreases", "evidence": "Twenty years ago, it was demonstrated that the expression of clusterin was clearly increased in Alzheimer's disease (AD). Later it was observed that clusterin can bind amyloid-beta peptides and prevent their fibrillization. ", "citation": {"db": "PubMed", "db_id": "19651157"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Endosomal lysosomal subgraph": true}, "Confidence": {"Medium": true}}, "source": 922, "target": 474, "key": "7014393ac7bd1bd3da8f512b0cb6d760"}, {"relation": "partOf", "source": 2685, "target": 1262, "key": "46d0b9ffa109bcba00a4ea1c5f119130"}, {"relation": "partOf", "source": 2692, "target": 1263, "key": "f6aa1c96127a5a8184be630ea29ef8ac"}, {"line": 28825, "relation": "increases", "evidence": "We report that the SCF(Fbx2) -E3 ligase is involved in the binding and ubiquitination of BACE1 via its Trp 280 residue of F-box-associated domain.", "citation": {"db": "PubMed", "db_id": "20854419"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true}}, "source": 2692, "target": 2380, "key": "43600e8221d6150919e862dba13ac402"}, {"line": 28861, "relation": "association", "evidence": "We found that BACE phosphorylation influences BACE-GGA interactions in cells using a new fluorescence-resonance-energy-transfer-based assay of protein proximity, fluorescence lifetime imaging.", "citation": {"db": "PubMed", "db_id": "15466887"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Beta secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2377, "target": 1265, "key": "ca1dfb488c3747530638468a2f6257c7"}, {"relation": "partOf", "source": 3276, "target": 1274, "key": "ecb231ed921dabdaac95007817ac0e89"}, {"relation": "partOf", "source": 3485, "target": 1266, "key": "5079325569f9b827ffed3f3b74597cb6"}, {"line": 28975, "relation": "regulates", "evidence": "Further investigation reveals that DOR forms a complex with BACE1 and gamma-secretase, and activation of DOR mediates the co-endocytic sorting of the ecretases/receptor complex for APP endoproteolysis.", "citation": {"db": "PubMed", "db_id": "20066010"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Chaperone subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3485, "target": 2315, "key": "eb3ab76b3dfe989e5e4c4e27f97ceb04"}, {"relation": "partOf", "source": 2384, "target": 1018, "key": "de77ebb9b0ef13f4cd20e1468b8952f9"}, {"relation": "partOf", "source": 2384, "target": 1281, "key": "5dcb4f58716ce32f0c0d91ec4f5bfb0b"}, {"line": 29008, "relation": "decreases", "evidence": "Overexpression of BAG-1 induced an increase in Tau levels, which is shown to be due to an inhibition of protein degradation. ", "citation": {"db": "PubMed", "db_id": "17954934"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2384, "target": 3010, "key": "84590eb27e7af8c3cada16d1f3f9cc3c"}, {"line": 29027, "relation": "decreases", "evidence": "We further show that BAG-1 can inhibit the degradation of Tau protein by the 20 S proteasome but does not affect the ubiquitination of Tau protein.", "citation": {"db": "PubMed", "db_id": "17954934"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Ubiquitin degradation subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2384, "target": 3010, "key": "ac661b551ecc544391c012dbd8b8a883"}, {"relation": "partOf", "source": 2384, "target": 1280, "key": "bacb892370375a5d0702b8958bee0c5c"}, {"line": 29017, "relation": "decreases", "evidence": "We have previously shown that the co-chaperone protein BAG-1 can inhibit the degradation of tau by forming a complex with Hsc-70 and tau.", "citation": {"db": "PubMed", "db_id": "19317853"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Ubiquitin degradation subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 1280, "target": 3010, "key": "a681fabff6fe00ca6331650c782a8c44"}, {"relation": "partOf", "source": 2850, "target": 1280, "key": "b095b8688fd843454d54ed96a7db38b9"}, {"line": 29036, "relation": "increases", "evidence": "The BAG2/Hsp70 complex is tethered to the microtubule and this complex can capture and deliver Tau to the proteasome for ubiquitin-independent degradation.", "citation": {"db": "PubMed", "db_id": "19228967"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Chaperone subgraph": true, "Tau protein subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 1019, "target": 3010, "key": "70ff205c13ef418402515691aac8f4fd"}, {"relation": "partOf", "source": 2385, "target": 1019, "key": "f34bacd57cf7def368ce6bb61f219b0e"}, {"line": 29065, "relation": "decreases", "evidence": "Clusterin is a stress-induced chaperone which is normally secreted but in conditions of cellular stress, it can be transported to cytoplasm where it can bind to Bax protein and inhibit neuronal apoptotic process.", "citation": {"db": "PubMed", "db_id": "16113678"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Bcl-2 subgraph": true}}, "source": 1288, "target": 478, "key": "840f7b945ee0996d46626630c1c70938"}, {"relation": "partOf", "source": 2842, "target": 1294, "key": "c0c3ae451a42eb2de599dc216c80ed6f"}, {"line": 29094, "relation": "increases", "evidence": "Autophagy is initiated by the formation of a complex containing Beclin 1 (BECN1) and its binding partner Phosphoinositide-3-kinase, class 3 (PIK3C3).", "citation": {"db": "PubMed", "db_id": "20559548"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Phosphatidylinositol 3 subgraph": true}}, "source": 1296, "target": 808, "key": "97d6864701b126e9f884c4cbc3282946"}, {"relation": "partOf", "source": 2398, "target": 1296, "key": "42f7613424800bbea4688ad9d8d9efd3"}, {"relation": "partOf", "source": 3185, "target": 1296, "key": "1d8f1e9115f1c4a62b27d75bcdde5a58"}, {"relation": "partOf", "source": 2399, "target": 1297, "key": "69d333bbc250d6ffb5950c0205e4ab3a"}, {"relation": "partOf", "source": 2902, "target": 1297, "key": "00820e4c9e97153c2683f1f7c86a295f"}, {"line": 29111, "relation": "association", "evidence": "Fetal Alz-50 clone 1 (FAC1) protein interacts with the Myc-associated zinc finger protein (ZF87/MAZ) and alters its transcriptional activity.", "citation": {"db": "PubMed", "db_id": "10727212"}, "source": 1298, "target": 3823, "key": "5d02d335c76d9c2f262073b0f1ec65a4"}, {"relation": "partOf", "source": 2403, "target": 1298, "key": "d3b0e3f0af21b8ba050ccb4a1c71ee3e"}, {"line": 29112, "relation": "association", "evidence": "Fetal Alz-50 clone 1 (FAC1) protein interacts with the Myc-associated zinc finger protein (ZF87/MAZ) and alters its transcriptional activity.", "citation": {"db": "PubMed", "db_id": "10727212"}, "source": 2403, "target": 3046, "key": "b93ee30d7b4e89eb338a085e233dbe1d"}, {"relation": "partOf", "source": 3046, "target": 1298, "key": "8783027a12194f21fb3c0b5acbb8cf43"}, {"line": 29112, "relation": "association", "evidence": "Fetal Alz-50 clone 1 (FAC1) protein interacts with the Myc-associated zinc finger protein (ZF87/MAZ) and alters its transcriptional activity.", "citation": {"db": "PubMed", "db_id": "10727212"}, "source": 3046, "target": 2403, "key": "6c4b0aff86397454d4051c8548e56add"}, {"relation": "partOf", "source": 2423, "target": 1299, "key": "c74456ac42307506e009631f33deba3c"}, {"relation": "partOf", "source": 2423, "target": 1300, "key": "775aeb0f7b0856a6136df76d906b9f08"}, {"relation": "partOf", "source": 2423, "target": 1301, "key": "f6dd5ce942716d7799217ef56ad8a58c"}, {"relation": "partOf", "source": 2740, "target": 1299, "key": "a8620e8e0187d0f928290cd589f0ab16"}, {"relation": "partOf", "source": 3442, "target": 1310, "key": "5d50014ac4e856b8216f0c60165a271f"}, {"relation": "partOf", "source": 2446, "target": 1312, "key": "de86ac171d602a2480ef0890fb25eccf"}, {"line": 29186, "relation": "increases", "evidence": "In addition, caspase-6 cleaved the N terminus of tau in vitro, preventing immunoreactivity with both Tau-12 and 5A6.", "citation": {"db": "PubMed", "db_id": "15356202"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Caspase subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2446, "target": 3012, "key": "902ba601532b8f12c5197932dd29c808"}, {"relation": "partOf", "source": 2446, "target": 1314, "key": "7f032ebbdc05aa6ba41bfd798e459c5a"}, {"relation": "partOf", "source": 2446, "target": 1051, "key": "fcad6ce94afc95955615333f8fac5912"}, {"relation": "partOf", "source": 2446, "target": 1313, "key": "22ec70299259b378f143b3de3575c885"}, {"relation": "partOf", "source": 2446, "target": 1311, "key": "fc16b5232a9addce58bd784250a5991c"}, {"line": 34210, "relation": "increases", "evidence": "In an attempt to reconcile these two hypotheses, we investigated APP processing during apoptosis and found that APP is processed by the cell death proteases caspase-6 and -8.", "citation": {"db": "PubMed", "db_id": "10409650"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2446, "target": 2315, "key": "ac088cd449e60f491fcb9e530aeebe1e"}, {"line": 34228, "relation": "increases", "evidence": "Caspase-6 directly cleaves APP at the C terminus and generates a C-terminal fragment of 3 kDa (Capp3) and an Abeta-containing 6.5-kDa fragment, Capp6.5, that increases in serum-deprived neurons.", "citation": {"db": "PubMed", "db_id": "10438520"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Caspase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 2446, "target": 2315, "key": "b084dde7b0ae5afda960da39bfbc5615"}, {"line": 38051, "relation": "increases", "evidence": "DR6 is required for the timely pruning of axons and the elimination of neurons during spinal cord or retinal development in vivo and in trophic factor-deprived neuronal cultures. DR6-dependent axonal pruning is mediated by caspase 6 and neuronal culling by caspase 3. Trophic factor deprivation induced cleavage of APP by ß-secretase, resulting in formation of sAPPß and subsequently N-APP. Surprisingly, N-APP acts as a necessary and sufficient ligand for DR6, inducing axonal and neuronal degeneration after trophic factor removal.", "citation": {"db": "PubMed", "db_id": "19524503"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 2446, "target": 1250, "key": "05edd95b0965c9003f13b86a3ea6e698"}, {"line": 38052, "relation": "increases", "evidence": "DR6 is required for the timely pruning of axons and the elimination of neurons during spinal cord or retinal development in vivo and in trophic factor-deprived neuronal cultures. DR6-dependent axonal pruning is mediated by caspase 6 and neuronal culling by caspase 3. Trophic factor deprivation induced cleavage of APP by ß-secretase, resulting in formation of sAPPß and subsequently N-APP. Surprisingly, N-APP acts as a necessary and sufficient ligand for DR6, inducing axonal and neuronal degeneration after trophic factor removal.", "citation": {"db": "PubMed", "db_id": "19524503"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 2446, "target": 654, "key": "b2366ec33621365c3bb3ebeb68f8876b"}, {"relation": "partOf", "source": 3503, "target": 1314, "key": "54b951500c883de1f96365a573390460"}, {"relation": "partOf", "source": 3503, "target": 1425, "key": "f4c86655854d8d63fa3948827b6c169c"}, {"relation": "partOf", "source": 2248, "target": 1051, "key": "81a0ca56bded628e163cf12953d6b5b5"}, {"relation": "partOf", "source": 3222, "target": 1313, "key": "1f0549d942285faf9232ce4e758a87db"}, {"relation": "partOf", "source": 2620, "target": 1311, "key": "a0ecc64d9372b7b9daf88e7a22f018dd"}, {"line": 45346, "relation": "negativeCorrelation", "evidence": "drebrin expression is decreased in AD brains ", "citation": {"db": "PubMed", "db_id": "25058791"}, "source": 2620, "target": 3823, "key": "6b39a8b1eb47f051f81da852b113b005"}, {"line": 29213, "relation": "association", "evidence": "Colocalization of cyclin C and its preferred binding partner, Cdk8, was only observed in astrocytes but not in neurons.", "citation": {"db": "PubMed", "db_id": "12600719"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 1321, "target": 3823, "key": "934ebca3d4a8598090cc79cdec3987c2"}, {"relation": "partOf", "source": 2462, "target": 1321, "key": "9dcd2d39460bc62bc25ddb3b6eb005b0"}, {"relation": "partOf", "source": 2491, "target": 1321, "key": "e2fa524ad652d38007bf33b0e33a4016"}, {"line": 29221, "relation": "decreases", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD.", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 1322, "target": 3920, "key": "7cab325c85393ba01bb1ca5f258ba965"}, {"relation": "partOf", "source": 2469, "target": 1322, "key": "c3b730fd00f00ad61b0a9928f364f171"}, {"line": 39616, "relation": "negativeCorrelation", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2469, "target": 3823, "key": "a4b86d180139a2ebdd245fd76c0e98de"}, {"line": 39622, "relation": "decreases", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2469, "target": 3815, "key": "b6c98c8c2ecb949ca2fa0b0211012f08"}, {"relation": "partOf", "source": 2470, "target": 1322, "key": "365a1ea58415cf5a792cfe50fe729c65"}, {"line": 39618, "relation": "negativeCorrelation", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2470, "target": 3823, "key": "2e11e302ededa88a02f32a8e4f525600"}, {"line": 39623, "relation": "decreases", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2470, "target": 3815, "key": "9443e57818e865da28fabb0721b94fe8"}, {"line": 29231, "relation": "increases", "evidence": "It has been reported that ligation of CD40 with CD40 ligand (CD40L) results in microglial activation as evidenced by p44/42 mitogen-activated protein kinase (MAPK) dependent tumor necrosis factor alpha (TNF-alpha) production.", "citation": {"db": "PubMed", "db_id": "10978311"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 1324, "target": 3472, "key": "a6cae4c538b4f798b3674cf56df1e0a2"}, {"line": 29240, "relation": "increases", "evidence": "The interaction between CD40 and its cognate ligand, CD40 ligand, is a primary regulator of the peripheral immune response, including pmodulation of T lymphocyte activation, B lymphocyte differentiation and antibody secretion, and innate immune cell activation, maturation, and survival.", "citation": {"db": "PubMed", "db_id": "11578772"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 1324, "target": 456, "key": "462b197b2e920f069ece27f9a9bc1e8d"}, {"line": 29241, "relation": "increases", "evidence": "The interaction between CD40 and its cognate ligand, CD40 ligand, is a primary regulator of the peripheral immune response, including pmodulation of T lymphocyte activation, B lymphocyte differentiation and antibody secretion, and innate immune cell activation, maturation, and survival.", "citation": {"db": "PubMed", "db_id": "11578772"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 1324, "target": 443, "key": "3b49791ee0326e1ca1c6ee8a6b07183b"}, {"line": 29249, "relation": "increases", "evidence": "We have shown that interaction of CD40 with CD40L enables microglial activation in response to amyloid-beta peptide (Abeta), which is associated with Alzheimer's disease (AD)-like neuronal tau hyperphosphorylation in vivo.", "citation": {"db": "PubMed", "db_id": "12402041"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 1324, "target": 609, "key": "4b6902d98d4bc6de6b2b8bb6be34af01"}, {"line": 29264, "relation": "association", "evidence": "We show that ligation of CD40 by CD40L pmodulates Abeta-induced innate immune responses in microglia, including decreased microglia phagocytosis of exogenous Abeta(1-42) and increased production of pro-inflammatory cytokines.", "citation": {"db": "PubMed", "db_id": "15688347"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 1324, "target": 575, "key": "00088fc5411bf04a1bc00540fec56fa2"}, {"line": 29265, "relation": "increases", "evidence": "We show that ligation of CD40 by CD40L pmodulates Abeta-induced innate immune responses in microglia, including decreased microglia phagocytosis of exogenous Abeta(1-42) and increased production of pro-inflammatory cytokines.", "citation": {"db": "PubMed", "db_id": "15688347"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 1324, "target": 537, "key": "e77e40cf5485163480cbc42ddc15ce9c"}, {"line": 29284, "relation": "association", "evidence": "In addition, we examine potential therapeutic strategies such as statins, flavonoids, and human umbilical cord blood transplantation, all of which have been shown to pmodulate CD40-CD40L interaction in mouse pmodels of AD.", "citation": {"db": "PubMed", "db_id": "20205645"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 1324, "target": 355, "key": "b7f6600c7bf534981d607bc6d0fbc69f"}, {"line": 29285, "relation": "association", "evidence": "In addition, we examine potential therapeutic strategies such as statins, flavonoids, and human umbilical cord blood transplantation, all of which have been shown to pmodulate CD40-CD40L interaction in mouse pmodels of AD.", "citation": {"db": "PubMed", "db_id": "20205645"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 1324, "target": 255, "key": "474c9f5c3e5060e4516e616976b2582c"}, {"line": 39003, "relation": "association", "evidence": "Astrocyte is the most abundant type of glial cells in the central nervous system (CNS) and appears to be/ involved in the induction of neuroinflammation. Under stress and injury, astrocytes become astrogliotic leading to an / upregulation of the expression of proinflammatory cytokines and chemokines, which are associated with the pathogenesis of AD. ", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 537, "target": 3912, "key": "073a59dbfed2cb7ce3ea7fe3c4fa687b"}, {"line": 39004, "relation": "positiveCorrelation", "evidence": "Astrocyte is the most abundant type of glial cells in the central nervous system (CNS) and appears to be/ involved in the induction of neuroinflammation. Under stress and injury, astrocytes become astrogliotic leading to an / upregulation of the expression of proinflammatory cytokines and chemokines, which are associated with the pathogenesis of AD. ", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 537, "target": 3823, "key": "42357f83c506736974a7e4ec03d62035"}, {"line": 43567, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 537, "target": 3823, "key": "2f37c93ec89baae209b6ce4f5f03833c"}, {"line": 43566, "relation": "association", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 537, "target": 3741, "key": "3834ee2d202fc17a1878c474ee3bbc2e"}, {"line": 29273, "relation": "association", "evidence": "CD40 ligation in the presence of Abeta(1-42) leads to adaptive activation of microglia, as evidenced by increased co-localization of MHC class II with Abeta. ", "citation": {"db": "PubMed", "db_id": "15688347"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 2224, "target": 2328, "key": "5985764f340b59dfd9562c37fc6e7b62"}, {"line": 29285, "relation": "association", "evidence": "In addition, we examine potential therapeutic strategies such as statins, flavonoids, and human umbilical cord blood transplantation, all of which have been shown to pmodulate CD40-CD40L interaction in mouse pmodels of AD.", "citation": {"db": "PubMed", "db_id": "20205645"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 255, "target": 1324, "key": "0cbcdef48e75555f49e570b82ead742b"}, {"relation": "partOf", "source": 2480, "target": 1325, "key": "67b7b63fd6dbd3b8ad566f8567722a83"}, {"line": 29304, "relation": "decreases", "evidence": "We found that suppression of Cdc37 destabilized tau, leading to its clearance, whereas Cdc37 overexpression preserved tau. ", "citation": {"db": "PubMed", "db_id": "21367866"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2480, "target": 3010, "key": "599902e2f4eb7538a7432dfa35c898a3"}, {"line": 29312, "relation": "increases", "evidence": "Cdc37 knockdown altered the phosphorylation profile of tau, an effect that was due in part to reduced tau kinase stability, specifically Cdk5 and Akt. ", "citation": {"db": "PubMed", "db_id": "21367866"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2480, "target": 2487, "key": "6bd74d4ecc19b0e5db0773d1445c77b0"}, {"line": 29318, "relation": "increases", "evidence": "Cdc37 knockdown altered the phosphorylation profile of tau, an effect that was due in part to reduced tau kinase stability, specifically Cdk5 and Akt. ", "citation": {"db": "PubMed", "db_id": "21367866"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Akt subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2480, "target": 2279, "key": "918afe72a87c74bae885d55960edba3f"}, {"line": 29324, "relation": "decreases", "evidence": "Cdc37 overexpression prevented whereas Cdc37 suppression potentiated tau clearance following Hsp90 inhibition.", "citation": {"db": "PubMed", "db_id": "21367866"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2480, "target": 2181, "key": "8fd90b7ec0822fb35298efc99a209bda"}, {"line": 29298, "relation": "association", "evidence": "The microtubule-associated protein tau, which becomes hyperphosphorylated and pathologically aggregates in a number of these diseases, is extremely sensitive to manipulations of chaperone signaling. ", "citation": {"db": "PubMed", "db_id": "21367866"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 865, "target": 3010, "key": "fc8c2defc36b330992fac744e04310bb"}, {"line": 29354, "relation": "decreases", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3260, "target": 1335, "key": "daa0b76640cd100ea738e30771dbb243"}, {"line": 30756, "relation": "decreases", "evidence": "We demonstrate that phosphorylation of serines 353 and 357 by glycogen synthase kinase-3beta (GSK3beta) induces a structural change of the hydrophilic loop of PS1 that can also be mimicked by substitution of the phosphorylation sites by negatively charged amino acids in vitro and in cultured cells. The structural change of PS1 reduces the interaction with beta-catenin leading to decreased phosphorylation and ubiquitination of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360711"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}}, "source": 3260, "target": 1373, "key": "9a2f9c40d2d95a941533f8d639e3909a"}, {"line": 32217, "relation": "decreases", "evidence": "Substitution of one or both of these residues greatly reduces the ability of PS1 to associate with beta-catenin.", "citation": {"db": "PubMed", "db_id": "11104755"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3260, "target": 1373, "key": "38fa02e803979c99d30b6e6c48db81ec"}, {"line": 30757, "relation": "decreases", "evidence": "We demonstrate that phosphorylation of serines 353 and 357 by glycogen synthase kinase-3beta (GSK3beta) induces a structural change of the hydrophilic loop of PS1 that can also be mimicked by substitution of the phosphorylation sites by negatively charged amino acids in vitro and in cultured cells. The structural change of PS1 reduces the interaction with beta-catenin leading to decreased phosphorylation and ubiquitination of beta-catenin.", "citation": {"db": "PubMed", "db_id": "17360711"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Gamma secretase subgraph": true}}, "source": 3261, "target": 1373, "key": "4242dc38934aa0fa685095ba650acdc8"}, {"line": 32218, "relation": "decreases", "evidence": "Substitution of one or both of these residues greatly reduces the ability of PS1 to associate with beta-catenin.", "citation": {"db": "PubMed", "db_id": "11104755"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3261, "target": 1373, "key": "607bc4873b659b1613e369ec1afcb5cb"}, {"line": 29413, "relation": "decreases", "evidence": "Remarkably, cleavage of N-cadherin by PS1 produces an intracellular fragment that downregulates CREB-mediated transcription, indicating a role of PS1 in gene expression.", "citation": {"db": "PubMed", "db_id": "16908988"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 1335, "target": 3258, "key": "ba7dd7e1e4f7751a6946b44608420a7f"}, {"line": 31469, "relation": "positiveCorrelation", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "cell surface"}}}, "source": 1335, "target": 3258, "key": "1aa32131e95c19e6b6f55fae6db8cd5a"}, {"line": 31468, "relation": "negativeCorrelation", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1335, "target": 3259, "key": "d940496a92909a17237ecf6224d13027"}, {"line": 29359, "relation": "decreases", "evidence": "Furthermore, phosphorylation of Presenilin 1 hinders epsilon-cleavage of N-cadherin, whereas epsilon-cleavage of APP remained unchanged.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3259, "target": 2483, "key": "8f0e4ab9cd86bbeebe5a556f2fdfbc90"}, {"line": 31468, "relation": "negativeCorrelation", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 3259, "target": 1335, "key": "19861f6c18c04d1d12e2bd63d4302c76"}, {"line": 32464, "relation": "decreases", "evidence": "Glycogen synthase kinase 3beta-mediated phosphorylation of Presenilin 1 reduces its binding to N-cadherin, thereby down-regulating its cell-surface expression.", "citation": {"db": "PubMed", "db_id": "17389597"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 3259, "target": 1335, "key": "2f6a85c7ac7b35a5acf54431231f5511"}, {"line": 29382, "relation": "negativeCorrelation", "evidence": "Synaptic loss, which strongly correlates with the decline of cognitive function, is one of the pathological hallmarks of Alzheimer disease.", "citation": {"db": "PubMed", "db_id": "21177868"}, "source": 432, "target": 3823, "key": "0dd49fdb4eb99334963a8f6e7db2301b"}, {"line": 33054, "relation": "negativeCorrelation", "evidence": "In the present review, we discuss our initial in vitro results and additional investigations showing that Abeta activates GSK-3beta through impairment of phosphatidylinositol-3 (PI3)/Akt signaling; that Abeta-activated GSK-3beta induces hyperphosphorylation of tau, NFT formation, neuronal death, and synaptic loss (all found in the AD brain); that GSK-3beta can induce memory deficits in vivo; and that inhibition of GSK-3alpha (an isoform of GSK-3beta) reduces Abeta production.", "citation": {"db": "PubMed", "db_id": "16914869"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 432, "target": 2794, "key": "23efdc099bbabce33ec97d10fb0d6789"}, {"line": 29395, "relation": "decreases", "evidence": "Moreover, expression levels of phosphorylated p38 MAPK were negatively correlated with that of N-cadherin in human brains.", "citation": {"db": "PubMed", "db_id": "21177868"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 2999, "target": 2483, "key": "f14024a7fb9deee7b36f8e94f87a9a58"}, {"relation": "partOf", "source": 2496, "target": 1337, "key": "a89a79ff1d8682895dc4615beae99250"}, {"relation": "partOf", "source": 2497, "target": 1338, "key": "e451937b63dd86c698584240d74393a1"}, {"relation": "partOf", "source": 2498, "target": 1339, "key": "10bf60f119fa2177e699d8cc24d45cee"}, {"relation": "partOf", "source": 2370, "target": 1261, "key": "faff4ccd3b95363c93f041371ff3ce57"}, {"relation": "partOf", "source": 2370, "target": 1260, "key": "5c108de92b3989017867634c7f4d08d6"}, {"relation": "partOf", "source": 2460, "target": 1260, "key": "62f6e484fdd269d453cf6a639597cd2b"}, {"line": 29454, "relation": "decreases", "evidence": "The phosphorylation of CRMP2 at Ser522 caused reduction of its affinity to tubulin.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 2643, "target": 1398, "key": "9ef37d5eb6e9a1f127bbaebb054c2dcb"}, {"line": 29466, "relation": "increases", "evidence": "Over-expression of CRMP2 mutant substituting either Ser522 or Thr509 to Ala attenuates Sema3A-induced growth cone collapse response.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 2643, "target": 402, "key": "d382e21e9e02c287e1f1cd2aa78f1526"}, {"line": 30269, "relation": "positiveCorrelation", "evidence": "Ser-522 prephosphorylated by Cdk5 is required for subsequent GSK-3alpha-mediated phosphorylation of CRMP-2 in vitro.", "citation": {"db": "PubMed", "db_id": "17902168"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2643, "target": 2792, "key": "8b9c123cdcd34254e1dc87736cd2a18d"}, {"line": 29467, "relation": "increases", "evidence": "Over-expression of CRMP2 mutant substituting either Ser522 or Thr509 to Ala attenuates Sema3A-induced growth cone collapse response.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 2644, "target": 402, "key": "7003f1ff25b0a9e317a98f76dc13f3c7"}, {"relation": "partOf", "source": 3504, "target": 1398, "key": "ce3199e9d1473cbf1ae31dd89a86c54c"}, {"line": 29460, "relation": "increases", "evidence": "In dorsal root ganglion neurones, Sema3A stimulation enhanced the levels of the phosphorylated form of CRMP2 detected by 3F4.", "citation": {"db": "PubMed", "db_id": "15676027"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"Medium": true}}, "source": 3349, "target": 2642, "key": "4d8285dea343d02f8597877263166939"}, {"line": 29478, "relation": "decreases", "evidence": "We demonstrate that Fyn-Cdk5 complex acts as a downstream mediator of Sema3A signaling cascades that induce growth cone collapse.", "citation": {"db": "PubMed", "db_id": "16866215"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 1344, "target": 402, "key": "9bf9c476b9b26aec9723b98639acd132"}, {"relation": "partOf", "source": 2715, "target": 1344, "key": "6c28aac113ec7586ee3f037bbbe18dd7"}, {"relation": "partOf", "source": 2715, "target": 1423, "key": "8848f8c245a88baf1ef6c937b48987da"}, {"relation": "partOf", "source": 2715, "target": 1424, "key": "758fcb167a71730beff395920a882799"}, {"relation": "partOf", "source": 2715, "target": 1425, "key": "c613e84b645200fbb4e41740ea366269"}, {"line": 30486, "relation": "increases", "evidence": "In this study we determined that human tau tyr18 was phosphorylated by the src family tyrosine kinase fyn.", "citation": {"db": "PubMed", "db_id": "14999081"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2715, "target": 3036, "key": "8d2acbb6bfb7997c642cc9921ed14292"}, {"relation": "partOf", "source": 2757, "target": 1345, "key": "db3015d592cb2b45f6072df23400926c"}, {"relation": "hasVariant", "source": 2757, "target": 2758, "key": "289561b8fa163675729388124080234b"}, {"relation": "partOf", "source": 2964, "target": 1347, "key": "35599661fe2ab2f5faff0f7219bf3772"}, {"relation": "hasVariant", "source": 2964, "target": 2965, "key": "deb4ba5867847247d838a469d5e52c24"}, {"relation": "hasVariant", "source": 2966, "target": 2967, "key": "bb5925b28c8ce45663fc95442c8b25f2"}, {"line": 29588, "relation": "increases", "evidence": "Deglycosylation by glycosidases depressed the subsequent phosphorylation of AD-tau (i) with cdk5 at Thr-181, Ser-199, Ser-202, Thr-205, and Ser-404, but not at Thr-212; and (ii) with GSK-3beta at Thr-181, Ser-202, Thr-205, Ser-217, and Ser-404, but not at Ser-199, Thr-212, Thr-231, or Ser-396. ", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 3013, "target": 3031, "key": "0e8037a3b3f94911d7b0f724357518e4"}, {"line": 29589, "relation": "increases", "evidence": "Deglycosylation by glycosidases depressed the subsequent phosphorylation of AD-tau (i) with cdk5 at Thr-181, Ser-199, Ser-202, Thr-205, and Ser-404, but not at Thr-212; and (ii) with GSK-3beta at Thr-181, Ser-202, Thr-205, Ser-217, and Ser-404, but not at Ser-199, Thr-212, Thr-231, or Ser-396. ", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 3013, "target": 3020, "key": "bd5c00df10dbb3995c591ff673198eb9"}, {"line": 29590, "relation": "increases", "evidence": "Deglycosylation by glycosidases depressed the subsequent phosphorylation of AD-tau (i) with cdk5 at Thr-181, Ser-199, Ser-202, Thr-205, and Ser-404, but not at Thr-212; and (ii) with GSK-3beta at Thr-181, Ser-202, Thr-205, Ser-217, and Ser-404, but not at Ser-199, Thr-212, Thr-231, or Ser-396. ", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 3013, "target": 3032, "key": "69f9ac80fddf791f8da9a9f76d1f6945"}, {"line": 29591, "relation": "increases", "evidence": "Deglycosylation by glycosidases depressed the subsequent phosphorylation of AD-tau (i) with cdk5 at Thr-181, Ser-199, Ser-202, Thr-205, and Ser-404, but not at Thr-212; and (ii) with GSK-3beta at Thr-181, Ser-202, Thr-205, Ser-217, and Ser-404, but not at Ser-199, Thr-212, Thr-231, or Ser-396. ", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 3013, "target": 3027, "key": "cf42addb1b262abea3df9858eef47332"}, {"line": 29592, "relation": "increases", "evidence": "Deglycosylation by glycosidases depressed the subsequent phosphorylation of AD-tau (i) with cdk5 at Thr-181, Ser-199, Ser-202, Thr-205, and Ser-404, but not at Thr-212; and (ii) with GSK-3beta at Thr-181, Ser-202, Thr-205, Ser-217, and Ser-404, but not at Ser-199, Thr-212, Thr-231, or Ser-396. ", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Low": true}}, "source": 3013, "target": 3034, "key": "99ee84794c2157b7a4ed6b90de3def95"}, {"line": 29598, "relation": "increases", "evidence": "These data suggest that aberrant glycosylation of tau in AD might be involved in neurofibrillary degeneration by promoting abnormal hyperphosphorylation by cdk5 and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3013, "target": 2487, "key": "e2f0dc7acc6f071b3a173bc18bf37c6d"}, {"line": 29600, "relation": "increases", "evidence": "These data suggest that aberrant glycosylation of tau in AD might be involved in neurofibrillary degeneration by promoting abnormal hyperphosphorylation by cdk5 and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3013, "target": 2794, "key": "7801d8cf0b8c66e0f2d684d88bf5cd7c"}, {"line": 29605, "relation": "association", "evidence": "These data suggest that aberrant glycosylation of tau in AD might be involved in neurofibrillary degeneration by promoting abnormal hyperphosphorylation by cdk5 and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "12387894"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}, "Confidence": {"High": true}}, "source": 3013, "target": 3814, "key": "cdde9f16b3ecf315a6cb33f2cb909dcc"}, {"line": 34487, "relation": "regulates", "evidence": "Aberrant glycosylation pmodulates phosphorylation of tau by protein kinase A and dephosphorylation of tau by protein phosphatase 2A and 5.", "citation": {"db": "PubMed", "db_id": "12435421"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3013, "target": 3015, "key": "60b3dbfe5cfc79e93c64a82bba305c47"}, {"line": 29618, "relation": "association", "evidence": "Pathological alterations in the microtubule-associated protein (MAP) tau are well-established in a number of neurodegenerative disorders, including Alzheimer's Disease (AD), frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), and others. ", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3855, "target": 3010, "key": "82a442649483398586a732cd6b5fd778"}, {"line": 29619, "relation": "association", "evidence": "Pathological alterations in the microtubule-associated protein (MAP) tau are well-established in a number of neurodegenerative disorders, including Alzheimer's Disease (AD), frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), and others. ", "citation": {"db": "PubMed", "db_id": "12428805"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3886, "target": 3010, "key": "490db6d897ec1a7559056cbafc840bcd"}, {"line": 29666, "relation": "increases", "evidence": "The basal kinase activities of protein kinase-A (PKA), CaM Kinase II and GSK-3 were stimulated more than two-fold by isoproterenol, bradykinin and wortmannin, respectively. ", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 285, "target": 3232, "key": "3aab4e5579eef59ab559bf519db2ff18"}, {"line": 29685, "relation": "increases", "evidence": "We found that cdk5 activity was co-stimulated with PKA by isoproterenol.", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 285, "target": 2487, "key": "e711976266b124597349b3db0e8e764b"}, {"line": 29672, "relation": "increases", "evidence": "The basal kinase activities of protein kinase-A (PKA), CaM Kinase II and GSK-3 were stimulated more than two-fold by isoproterenol, bradykinin and wortmannin, respectively. ", "citation": {"db": "PubMed", "db_id": "17120162"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 221, "target": 2425, "key": "4c44f85120640e651a3c08744c37a01f"}, {"line": 42173, "relation": "association", "evidence": "Although neuropeptides such as bradykinin (BK), somatostatin (Sst), and endothelin (ET) are known to be important mediators of inflammation in the periphery, evidence of a similar function in brain is scarce.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 221, "target": 3920, "key": "f7558496866e0e1d5ef86b1a40b0740e"}, {"line": 29805, "relation": "increases", "evidence": "Abnormal Alzheimer-like phosphorylation of tau-protein by cyclin-dependent kinases cdk2 and cdk5.", "citation": {"db": "PubMed", "db_id": "8282104"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2485, "target": 3015, "key": "f35b13fa5ddaff04e092b54ea7290ee1"}, {"line": 29842, "relation": "decreases", "evidence": "We here report that paullones also act as very potent inhibitors of glycogen synthase kinase-3beta (GSK-3beta) (IC50: 4-80 nM) and the neuronal CDK5/p25 (IC50: 20-200 nM).", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 323, "target": 2487, "key": "ef47ac63559fdd41ce5c72b3fed883c4"}, {"line": 29848, "relation": "decreases", "evidence": "We here report that paullones also act as very potent inhibitors of glycogen synthase kinase-3beta (GSK-3beta) (IC50: 4-80 nM) and the neuronal CDK5/p25 (IC50: 20-200 nM).", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 323, "target": 2794, "key": "f2ce299914ef1621feeb5733917d9f79"}, {"line": 29854, "relation": "decreases", "evidence": "Alsterpaullone inhibits the phosphorylation of tau in vivo at sites which are typically phosphorylated by GSK-3beta in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 207, "target": 3015, "key": "aa798665caefa8a4b4fa10c050c90818"}, {"line": 29868, "relation": "decreases", "evidence": "We show that alsterpaullone is able to inhibit the in vivo phosphorylation of tau at AD-specific sites by GSK-3beta and the in vivo phosphorylation of DARPP-32 in isolated striatum slices by CDK5. ", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 207, "target": 3015, "key": "55435b8854d1637538a8853c17d1fb9c"}, {"line": 29862, "relation": "decreases", "evidence": "Alsterpaullone also inhibits the CDK5/p25-dependent phosphorylation of DARPP-32 in mouse striatum slices in vitro.", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 207, "target": 3219, "key": "b1da00daeb9904f037caa680409c81a8"}, {"line": 29869, "relation": "negativeCorrelation", "evidence": "We show that alsterpaullone is able to inhibit the in vivo phosphorylation of tau at AD-specific sites by GSK-3beta and the in vivo phosphorylation of DARPP-32 in isolated striatum slices by CDK5. ", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 207, "target": 2794, "key": "79c5b5fc2c63ec4810ea33b4689deaed"}, {"line": 29870, "relation": "negativeCorrelation", "evidence": "We show that alsterpaullone is able to inhibit the in vivo phosphorylation of tau at AD-specific sites by GSK-3beta and the in vivo phosphorylation of DARPP-32 in isolated striatum slices by CDK5. ", "citation": {"db": "PubMed", "db_id": "10998059"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 207, "target": 2487, "key": "fca9a6362091770582c45f5bddb80d07"}, {"relation": "partOf", "source": 2508, "target": 1351, "key": "5d9f54fe836e57f8526b069d3bb5861b"}, {"line": 47674, "relation": "association", "evidence": "CRH-IR is significantly reduced in the cerebral cortex of individuals with AD, PD and PSP. Furthermore, we report that the decreases in CRH-IR in AD are accompanied by reciprocal increases in CRH receptors in affected cortical areas. The changes in pre- and postsynaptic markers for CRH are significantly correlated with decrements in ChAT activity. The demonstration of an up regulation of CRH receptors following a decrease in CRH-IR indicates a physiological relevance of the receptor site and is consistent with the concept that CRH acts as a neurotransmitter in normal cortical functions and that disease of this peptidergic systems may be important in certain clinical manifestations of dementia. While the clinical consequences of the changes in CRH in these various disorders are unclear, future therapies directed at increasing CRH levels in brain may prove useful for treatment.", "citation": {"db": "PubMed", "db_id": "3502064"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2508, "target": 2560, "key": "c043b8e9c6427f6aa1c8af24ee92e52c"}, {"line": 48861, "relation": "positiveCorrelation", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2508, "target": 2097, "key": "9045e55873ec7165b25f1322defd6133"}, {"line": 48881, "relation": "positiveCorrelation", "evidence": "Not only do these two molecules stimulate (miR-132 & EGR1) synaptic activity and plasticity, they are also involved in Alzheimer's disease pathology and might, in addition, affect cholinergic function. In addition, miR-132 and EGR1 showed a significant positive correlation with choline acetyltransferase expression.", "citation": {"db": "PubMed", "db_id": "26792551"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2508, "target": 2658, "key": "e830f7db553fbb9b487923495cffc134"}, {"line": 29905, "relation": "association", "evidence": "Functionally, calmyrin and PS2 increase cell death when cotransfected into HeLa cells. ", "citation": {"db": "PubMed", "db_id": "10366599"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 1353, "target": 505, "key": "56ffa33b76b972c1a6d11eef04106244"}, {"line": 29914, "relation": "association", "evidence": "The EF-hand calcium binding protein Calmyrin (also called CIB-1) was shown to interact with presenilin-2 (PS-2), suggesting that this interaction might play a role in the pathogenesis of Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "15885068"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 1353, "target": 3823, "key": "8a77a54b295b826571dc35c0a40e2992"}, {"line": 29934, "relation": "association", "evidence": "The interaction between the EF-hand Ca(2+)-binding protein calmyrin and presenilin 2 (PS2) has been suggested to play a role in Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "16257512"}, "source": 1353, "target": 3823, "key": "a3aead6db50771fd8b80daea86f70b6d"}, {"relation": "partOf", "source": 2529, "target": 1353, "key": "31ac7356860d0ebb11de2101265092c6"}, {"relation": "partOf", "source": 2529, "target": 1352, "key": "3992def4facfc2056766abc04b8f4e7d"}, {"line": 29957, "relation": "increases", "evidence": "ClC-7 forms a heterodimeric complex with another membrane protein, Ostm1, and formation of this complex is important for the folding, stability, and proper trafficking of ClC-7", "citation": {"db": "PubMed", "db_id": "21441306"}, "source": 1354, "target": 699, "key": "a91b47cc8320d3749c4a28d3361ddbd7"}, {"line": 29958, "relation": "increases", "evidence": "ClC-7 forms a heterodimeric complex with another membrane protein, Ostm1, and formation of this complex is important for the folding, stability, and proper trafficking of ClC-7", "citation": {"db": "PubMed", "db_id": "21441306"}, "object": {"modifier": "Translocation"}, "source": 1354, "target": 2531, "key": "df31571d0dbe9836cb007f730c4c8932"}, {"relation": "partOf", "source": 2531, "target": 1354, "key": "c737624f3498001963f35e4a13ba2643"}, {"line": 29959, "relation": "association", "evidence": "ClC-7 forms a heterodimeric complex with another membrane protein, Ostm1, and formation of this complex is important for the folding, stability, and proper trafficking of ClC-7", "citation": {"db": "PubMed", "db_id": "21441306"}, "subject": {"modifier": "Translocation"}, "source": 2531, "target": 699, "key": "5ce649ebf61f3fd7b92558853072b9ef"}, {"relation": "partOf", "source": 3158, "target": 1354, "key": "5aabee6a7c19b89fc8b14c286f6c17db"}, {"line": 29959, "relation": "association", "evidence": "ClC-7 forms a heterodimeric complex with another membrane protein, Ostm1, and formation of this complex is important for the folding, stability, and proper trafficking of ClC-7", "citation": {"db": "PubMed", "db_id": "21441306"}, "object": {"modifier": "Translocation"}, "source": 699, "target": 2531, "key": "7814bbbe20cf9eea9c01d6cf04711608"}, {"relation": "partOf", "source": 3383, "target": 1361, "key": "9c210bd9b56c184cfdd152860c900e90"}, {"relation": "partOf", "source": 2561, "target": 1364, "key": "bda1e2163cafac60737dfc05edfee8c2"}, {"relation": "hasVariant", "source": 2556, "target": 2557, "key": "668ce810c67c830e838c9780bc138fcd"}, {"relation": "hasVariant", "source": 3082, "target": 3083, "key": "9f25fd957aafc6b11f36267d49ec1689"}, {"relation": "partOf", "source": 3082, "target": 1371, "key": "a4c08e76957e6c6b696fa21b5e268902"}, {"relation": "hasVariant", "source": 3107, "target": 3108, "key": "61d396d347ad9b9a71f18cccb194d39b"}, {"relation": "hasVariant", "source": 2499, "target": 2500, "key": "f93cbabb282068e4f54bf275a26a507b"}, {"line": 30135, "relation": "decreases", "evidence": "Lithium, the primary therapeutic agent for bipolar mood disorder, is a selective inhibitor of GSK3beta.", "citation": {"db": "PubMed", "db_id": "11527574"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 147, "target": 2794, "key": "6e52123f95395ec2652d34ef4d9ded29"}, {"line": 30160, "relation": "increases", "evidence": "In addition, disease-causing mutant PS-1 (M146V and L286V) enhanced delta-catenin processing,", "citation": {"db": "PubMed", "db_id": "17097608"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}}, "source": 3267, "target": 862, "key": "184a42c39fa33f58fbb7b6f7c12b962c"}, {"line": 32282, "relation": "increases", "evidence": "The effects of PS-1 on endogenous delta-catenin processing were confirmed in hippocampal neurons overexpressing PS-1, as well as in the transgenic mice expressing the disease-causing mutant PS-1 (M146V). ", "citation": {"db": "PubMed", "db_id": "17097608"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}}, "source": 3267, "target": 1382, "key": "40562727f395dc518c4824ad8ae7bfff"}, {"line": 30161, "relation": "increases", "evidence": "In addition, disease-causing mutant PS-1 (M146V and L286V) enhanced delta-catenin processing,", "citation": {"db": "PubMed", "db_id": "17097608"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}}, "source": 3265, "target": 862, "key": "a5328df341e210de1ae605285ad11620"}, {"relation": "partOf", "source": 2589, "target": 1382, "key": "70931adf256613a98aa3fce0eb81839e"}, {"relation": "partOf", "source": 2589, "target": 1383, "key": "cf65dd1d3461c1231d4e60502ae2dea7"}, {"relation": "partOf", "source": 2589, "target": 1381, "key": "1d0c7289ade73be953b20d6f4ae929ff"}, {"relation": "partOf", "source": 2589, "target": 1380, "key": "79779a6bc0893eebd190c629618d3e06"}, {"relation": "partOf", "source": 2589, "target": 1327, "key": "c4accb4fd6d5eba5be5d1c4718d6b79e"}, {"relation": "partOf", "source": 2589, "target": 1376, "key": "b149b7f946a225b94572a3d35381988c"}, {"relation": "partOf", "source": 3410, "target": 1384, "key": "2e27730b0250e3dfac06ac82cfb7fbae"}, {"relation": "partOf", "source": 2607, "target": 1386, "key": "4e61dc0070c564707f9cfc14c4967114"}, {"relation": "partOf", "source": 2631, "target": 1392, "key": "3502a53885f81e625a7b7d6a15d5a7c6"}, {"line": 30301, "relation": "association", "evidence": "A synthetic peptide, S3 peptide, which acts as a specific competitor for ADF/cofilin phosphorylation by LIMK1, inhibited fAbeta-induced ADF/cofilin phosphorylation, preventing actin filament repmodeling and neuronal degeneration, indicating the involvement of LIMK1 in Abeta-induced neuronal degeneration in vitro.", "citation": {"db": "PubMed", "db_id": "16775141"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2963, "target": 2328, "key": "0da42f0ee0f7ccc0c200311180467e2a"}, {"line": 30307, "relation": "association", "evidence": "It's possible that dUTPase is one of the proteins interacting with GIF in Alzheimer's disease human brain extracts.", "citation": {"db": "PubMed", "db_id": "15468912"}, "source": 1399, "target": 3823, "key": "de39cdb73db28e32055f60c750bb8f43"}, {"relation": "partOf", "source": 2647, "target": 1399, "key": "f1df2059e20baa7fb9d2814ce76ee357"}, {"relation": "partOf", "source": 3070, "target": 1399, "key": "44d2012baaaf10d49ee56dc6b4367c71"}, {"relation": "partOf", "source": 3070, "target": 965, "key": "7c63ebbc2a0bf021367536f5118f6d7c"}, {"relation": "partOf", "source": 2648, "target": 1401, "key": "e6112a1b28ea044c8a66585c334d834e"}, {"line": 30316, "relation": "increases", "evidence": "Dyrk1 phosphorylates the human microtubule-associated protein tau at Thr212 in vitro, a residue that is phosphorylated in fetal tau and hyper-phosphorylated in Alzheimer disease (AD) and tauopathies, including Pick disease (PiD).", "citation": {"db": "PubMed", "db_id": "16242644"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2648, "target": 3033, "key": "05c3a5fcf820639ae7f545b71001a5ba"}, {"line": 30326, "relation": "increases", "evidence": "In vitro studies showing that DYRK1A phosphorylates tau protein suggest that this kinase is also involved in tau protein phosphorylation in the human brain and contributes to neurofibrillary degeneration, and that this contribution might be enhanced in patients with DS.", "citation": {"db": "PubMed", "db_id": "18696092"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2648, "target": 3015, "key": "a63f945d2d4ad0bd8d54215f0e45ceb1"}, {"line": 35694, "relation": "increases", "evidence": "Dual-specificity tyrosine (Y)-phosphorylation-regulated protein kinase 1A (Dyrk1A) is the mammalian homologue of Drosophila melanogaster minibrain and its human gene is mapped to the Down syndrome critical region of chromosome 21. Dyrk1A phosphorylates several transcription factors, including NFAT and CREB and a number of cytosolic proteins such as APP, tau, and a-synuclein. Although Dyrk1A is involved in the control of cell growth and postembryonic neurogenesis, its potential role during cell death and signaling pathway is not clearly understood. In the present study, we show that Dyrk1A is activated under the condition of apoptotic cell death. In addition, Dyrk1A is coupled to JNK1 activation, and directly interacts with apoptosis signal-regulating kinase 1 (ASK1). Moreover, Dyrk1A positively regulates ASK1-mediated JNK1-signaling, and appears to directly phosphorylate ASK1. These data indicate that Dyrk1A regulates cell death through facilitating ASK1-mediated signaling events.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 2648, "target": 3015, "key": "fe0aab43cec1ac6092b8f3fd1857ffe2"}, {"line": 30337, "relation": "increases", "evidence": "Results demonstrate that the beta-carboline compounds (1) potently reduce the expression of all three phosphorylated forms of tau protein, and (2) inhibit the DYRK1A catalyzed direct phosphorylation of tau protein on serine 396. ", "citation": {"db": "PubMed", "db_id": "21573099"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2648, "target": 3026, "key": "8d896425cfc09c4f7f50091aade005cc"}, {"relation": "partOf", "source": 2648, "target": 1402, "key": "3c708b05fd839b4d71a8eac95158c217"}, {"line": 30346, "relation": "increases", "evidence": "These results reveal a potential regulatory link between Dyrk1A and PS1 in the Abeta pathway of DS and AD brains, suggesting that up-regulated Dyrk1A may accelerate AD pathogenesis through PS1 phosphorylation.", "citation": {"db": "PubMed", "db_id": "20456003"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "DYRK1A subgraph": true}}, "source": 2648, "target": 3823, "key": "1fd3cf479817d2300f49e08c408cf75d"}, {"relation": "hasVariant", "source": 2648, "target": 2649, "key": "deb8c4a8434f2b3929f568b0a050d270"}, {"line": 34615, "relation": "increases", "evidence": "We confirm effects of three kinases from this screen, the eukaryotic translation initiation factor 2 alpha kinase 2 (EIF2AK2), the dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A), and the A-kinase anchor protein 13 (AKAP13) on tau phosphorylation at the 12E8 epitope (serine 262/serine 356).", "citation": {"db": "PubMed", "db_id": "20067632"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "source": 2648, "target": 3023, "key": "dae4b373c0619d4977088e98b1b32473"}, {"line": 34616, "relation": "increases", "evidence": "We confirm effects of three kinases from this screen, the eukaryotic translation initiation factor 2 alpha kinase 2 (EIF2AK2), the dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A), and the A-kinase anchor protein 13 (AKAP13) on tau phosphorylation at the 12E8 epitope (serine 262/serine 356).", "citation": {"db": "PubMed", "db_id": "20067632"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "source": 2648, "target": 3025, "key": "721ad332352e1a2f8460184002294992"}, {"line": 35691, "relation": "increases", "evidence": "Dual-specificity tyrosine (Y)-phosphorylation-regulated protein kinase 1A (Dyrk1A) is the mammalian homologue of Drosophila melanogaster minibrain and its human gene is mapped to the Down syndrome critical region of chromosome 21. Dyrk1A phosphorylates several transcription factors, including NFAT and CREB and a number of cytosolic proteins such as APP, tau, and a-synuclein. Although Dyrk1A is involved in the control of cell growth and postembryonic neurogenesis, its potential role during cell death and signaling pathway is not clearly understood. In the present study, we show that Dyrk1A is activated under the condition of apoptotic cell death. In addition, Dyrk1A is coupled to JNK1 activation, and directly interacts with apoptosis signal-regulating kinase 1 (ASK1). Moreover, Dyrk1A positively regulates ASK1-mediated JNK1-signaling, and appears to directly phosphorylate ASK1. These data indicate that Dyrk1A regulates cell death through facilitating ASK1-mediated signaling events.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 2648, "target": 2197, "key": "3509267c512723413b79825f49570733"}, {"line": 35692, "relation": "increases", "evidence": "Dual-specificity tyrosine (Y)-phosphorylation-regulated protein kinase 1A (Dyrk1A) is the mammalian homologue of Drosophila melanogaster minibrain and its human gene is mapped to the Down syndrome critical region of chromosome 21. Dyrk1A phosphorylates several transcription factors, including NFAT and CREB and a number of cytosolic proteins such as APP, tau, and a-synuclein. Although Dyrk1A is involved in the control of cell growth and postembryonic neurogenesis, its potential role during cell death and signaling pathway is not clearly understood. In the present study, we show that Dyrk1A is activated under the condition of apoptotic cell death. In addition, Dyrk1A is coupled to JNK1 activation, and directly interacts with apoptosis signal-regulating kinase 1 (ASK1). Moreover, Dyrk1A positively regulates ASK1-mediated JNK1-signaling, and appears to directly phosphorylate ASK1. These data indicate that Dyrk1A regulates cell death through facilitating ASK1-mediated signaling events.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 2648, "target": 2163, "key": "3d16ec272fd03009f749fdbc22177a01"}, {"line": 35693, "relation": "increases", "evidence": "Dual-specificity tyrosine (Y)-phosphorylation-regulated protein kinase 1A (Dyrk1A) is the mammalian homologue of Drosophila melanogaster minibrain and its human gene is mapped to the Down syndrome critical region of chromosome 21. Dyrk1A phosphorylates several transcription factors, including NFAT and CREB and a number of cytosolic proteins such as APP, tau, and a-synuclein. Although Dyrk1A is involved in the control of cell growth and postembryonic neurogenesis, its potential role during cell death and signaling pathway is not clearly understood. In the present study, we show that Dyrk1A is activated under the condition of apoptotic cell death. In addition, Dyrk1A is coupled to JNK1 activation, and directly interacts with apoptosis signal-regulating kinase 1 (ASK1). Moreover, Dyrk1A positively regulates ASK1-mediated JNK1-signaling, and appears to directly phosphorylate ASK1. These data indicate that Dyrk1A regulates cell death through facilitating ASK1-mediated signaling events.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 2648, "target": 2334, "key": "e6d5f78c5db7cf80776fff4d4a1b5bc8"}, {"line": 35696, "relation": "increases", "evidence": "Dual-specificity tyrosine (Y)-phosphorylation-regulated protein kinase 1A (Dyrk1A) is the mammalian homologue of Drosophila melanogaster minibrain and its human gene is mapped to the Down syndrome critical region of chromosome 21. Dyrk1A phosphorylates several transcription factors, including NFAT and CREB and a number of cytosolic proteins such as APP, tau, and a-synuclein. Although Dyrk1A is involved in the control of cell growth and postembryonic neurogenesis, its potential role during cell death and signaling pathway is not clearly understood. In the present study, we show that Dyrk1A is activated under the condition of apoptotic cell death. In addition, Dyrk1A is coupled to JNK1 activation, and directly interacts with apoptosis signal-regulating kinase 1 (ASK1). Moreover, Dyrk1A positively regulates ASK1-mediated JNK1-signaling, and appears to directly phosphorylate ASK1. These data indicate that Dyrk1A regulates cell death through facilitating ASK1-mediated signaling events.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 2648, "target": 3385, "key": "3078b36a2782ab73b076c972134558e4"}, {"relation": "partOf", "source": 2648, "target": 1400, "key": "909b725a91b022c98ea34bf5962bfc00"}, {"relation": "partOf", "source": 2648, "target": 1403, "key": "05802b6550d22654db0f14971b568568"}, {"line": 30335, "relation": "decreases", "evidence": "Results demonstrate that the beta-carboline compounds (1) potently reduce the expression of all three phosphorylated forms of tau protein, and (2) inhibit the DYRK1A catalyzed direct phosphorylation of tau protein on serine 396. ", "citation": {"db": "PubMed", "db_id": "21573099"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "source": 90, "target": 3026, "key": "a45666ec32052cbd48c8611c1f20e763"}, {"relation": "partOf", "source": 2666, "target": 1411, "key": "fea202b89a745966fc3a7e098848ac94"}, {"relation": "hasVariant", "source": 2666, "target": 2667, "key": "3abd2e2d68b4f34febbede9193457357"}, {"relation": "partOf", "source": 2670, "target": 1412, "key": "ad3fc399cc46d6e993e6a858be18ffd1"}, {"line": 30417, "relation": "association", "evidence": "In addition, a physical interaction between XPB and OGG1 may account for a potential mechanism involving these DNA repair responses.", "citation": {"db": "PubMed", "db_id": "19765189"}, "annotations": {"Subgraph": {"Response DNA damage": true}}, "source": 1413, "target": 517, "key": "b292fa426678e7a9f04f40312e4c1702"}, {"relation": "partOf", "source": 2677, "target": 1413, "key": "69427bd69ebae0a058db41578d51f167"}, {"relation": "partOf", "source": 3153, "target": 1413, "key": "43ec6a8f85f55dd63f0325eb0b984f50"}, {"line": 44651, "relation": "orthologous", "evidence": "the increase in Ogg1 activity tends to be greater in the Pb-exposed group as compared with the control.", "citation": {"db": "PubMed", "db_id": "16484331"}, "annotations": {"Species": {"10116": true}, "Developmental_Phase__of_patient": {"Old": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3153, "target": 3804, "key": "2ac6584474cf9fc13cb5a72da33ad515"}, {"line": 44678, "relation": "decreases", "evidence": "It also seems that a specific DNA repair enzyme 8-oxoguanine DNA glycosylase (OGG1) may contribute to downregulation of the inflammatory factor (TNF-alpha) level, especially in the early stages of dementia. ", "citation": {"db": "PubMed", "db_id": "21845541"}, "annotations": {"DiseaseState": {"Early-onset AD": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3153, "target": 3472, "key": "7c358584b6cfe002c635f3d1db49eeb6"}, {"line": 44690, "relation": "increases", "evidence": "In the stage of mild to moderate dementia along with the increase of oxidative DNA damage, there was an increase in OGG1 protein level", "citation": {"db": "PubMed", "db_id": "21845541"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3153, "target": 842, "key": "977235624d3a45c62c2befc384978e71"}, {"relation": "hasVariant", "source": 3153, "target": 3155, "key": "91456213899617815b30511f47bc0cfd"}, {"relation": "hasVariant", "source": 3153, "target": 3154, "key": "3c933d9534319b04716f006b16be3991"}, {"relation": "partOf", "source": 2696, "target": 1420, "key": "e3f32e18fe62439ca694ad2c89ee1054"}, {"relation": "partOf", "source": 2698, "target": 1421, "key": "12e952a151d08abe33fd7bf94191c0d9"}, {"relation": "partOf", "source": 2859, "target": 1423, "key": "d784596b841cfcfe979fee92fe0d845e"}, {"relation": "partOf", "source": 2741, "target": 1430, "key": "acfb46d78fc9d14a2917e97a860d1e28"}, {"relation": "partOf", "source": 2741, "target": 1167, "key": "0da79e31089f49dc6867414b2d16ff4d"}, {"relation": "partOf", "source": 2741, "target": 1431, "key": "d0e608e8543413d3c605aa6a581434bf"}, {"relation": "partOf", "source": 2861, "target": 1430, "key": "3119bb67630d2e69845e215b6804be4c"}, {"line": 30525, "relation": "decreases", "evidence": "Moreover, GLP-1 and exendin-4, a naturally occurring more stable analogue of GLP-1 that likewise binds at the GLP-1 receptor, were shown to reduce endogenous levels of amyloid-beta peptide (Abeta) in mouse brain and to reduce levels of beta-amyloid precursor protein (betaAPP) in neurons.", "citation": {"db": "PubMed", "db_id": "15270203"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1432, "target": 2328, "key": "931ff35a95f7dca8392048d7d13e1ef3"}, {"relation": "partOf", "source": 2753, "target": 1435, "key": "ae9c514092fb97f72edb67da5310f7a7"}, {"relation": "partOf", "source": 3506, "target": 1537, "key": "7d94f18c76a40d5f664ff7c555db06fb"}, {"line": 30628, "relation": "increases", "evidence": "These results, respectively, indicated that GSK-3beta is responsible for phosphorylating Ser-262 of tau through phosphorylation and activation of MARK2 and that the phosphorylation of tau at this particular site is predominantly mediated by a GSK-3beta-MARK2 pathway.", "citation": {"db": "PubMed", "db_id": "16257959"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3044, "target": 3043, "key": "7eacac3defd18d988f6cd25567c3744c"}, {"relation": "hasVariant", "source": 3043, "target": 3044, "key": "1a8bb3989922823a74c6eafa38f33177"}, {"line": 30629, "relation": "positiveCorrelation", "evidence": "These results, respectively, indicated that GSK-3beta is responsible for phosphorylating Ser-262 of tau through phosphorylation and activation of MARK2 and that the phosphorylation of tau at this particular site is predominantly mediated by a GSK-3beta-MARK2 pathway.", "citation": {"db": "PubMed", "db_id": "16257959"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "subject": {"modifier": "Activity"}, "source": 3043, "target": 3023, "key": "6858730023e84c13fdc9355a06e2ab52"}, {"line": 30710, "relation": "increases", "evidence": "In situ, FRAT-2 significantly increased GSK3 beta-mediated phosphorylation of tau at a primed epitope while not significantly affecting the phosphorylation of unprimed sites.", "citation": {"db": "PubMed", "db_id": "15522877"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "GSK3 subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2709, "target": 2794, "key": "8b7aa7750eba857712e25c2f8ef15cce"}, {"relation": "partOf", "source": 2818, "target": 1451, "key": "69183855bb51f70bd61bfa99a3da904f"}, {"line": 45258, "relation": "orthologous", "evidence": "We found a downregulation of histone deacetylase Hdac6 in the hippocampus of sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Species": {"10090": true}}, "source": 2818, "target": 3646, "key": "982902ab33f9b2f9266833a3e4197f65"}, {"line": 30786, "relation": "negativeCorrelation", "evidence": "The levels of hexokinase I (HXKI), which interacts with VDAC1 and affects its function, were decreased in mitochondrial samples from AD pmodels.", "citation": {"db": "PubMed", "db_id": "20930307"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 1452, "target": 430, "key": "3d860dfe6406898de6e6092b5be85498"}, {"relation": "partOf", "source": 2836, "target": 1452, "key": "c7049f6bdd57a8b13df8e297a1bb0113"}, {"relation": "partOf", "source": 3518, "target": 1452, "key": "8389fe88104232a3cd07a74b9abec546"}, {"relation": "partOf", "source": 2928, "target": 1462, "key": "6639ce51caec6df5592209e5e952c240"}, {"line": 30854, "relation": "decreases", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "source": 202, "target": 3057, "key": "031dac435edbc6fa9e6487daa7f768ec"}, {"line": 30856, "relation": "decreases", "evidence": "NEP and IDE also contributed to IL-4-induced degradation of Abeta(1-42), b ecause their inhibitors, thiorphan and insulin, respectively, significantly suppressed this activity.", "citation": {"db": "PubMed", "db_id": "18941241"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Non-amyloidogenic subgraph": true, "Insulin signal transduction": true}}, "object": {"modifier": "Activity"}, "source": 202, "target": 2893, "key": "f6ae180ed24b2836a767e05d7d8bdd68"}, {"relation": "partOf", "source": 3104, "target": 1465, "key": "e34e59eb942b93fe9b3f7102d563710a"}, {"line": 30881, "relation": "association", "evidence": "IDE is known to bind the cytoplasmic intermediate filament protein nestin with high affinity.", "citation": {"db": "PubMed", "db_id": "21185309"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3104, "target": 2867, "key": "b8bde2310e90c71d72eebc287a0671b9"}, {"relation": "partOf", "source": 2895, "target": 1482, "key": "8eabdac9149b9e5c2c464adebf65b5a5"}, {"relation": "partOf", "source": 3495, "target": 1497, "key": "ca5a186d9982e2b7d50f23061a9ac7ef"}, {"relation": "partOf", "source": 2944, "target": 1506, "key": "6eaf9c0c92b7eef00ff10422a6322205"}, {"relation": "partOf", "source": 2944, "target": 1507, "key": "d3b29f720af28f8524c8b27ea3c136a7"}, {"relation": "partOf", "source": 2944, "target": 1308, "key": "0cb666b6a3f1e07ec65d6a0a748e62b9"}, {"line": 31140, "relation": "increases", "evidence": "Thus , low-affinity LRP/Abeta interaction and/or Abeta-induced LRP loss at the BBB mediate brain accumulation of neurotoxic Abeta.", "citation": {"db": "PubMed", "db_id": "15294142"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 1234, "target": 3816, "key": "e102c4d7cb0c5d81b32c8b2cb9e7c56c"}, {"relation": "partOf", "source": 2969, "target": 1519, "key": "2034866f4af00890be75b60ff615e03f"}, {"relation": "partOf", "source": 2969, "target": 1132, "key": "4f3b9d9904dbbb5a5d4113c548a4b6ca"}, {"relation": "partOf", "source": 2985, "target": 1520, "key": "315826537ffb38e4f7b654e07121e8b3"}, {"relation": "partOf", "source": 2986, "target": 1534, "key": "cfaa2db31fb67dfa64de09e1eeaca2c6"}, {"relation": "partOf", "source": 2986, "target": 1533, "key": "48e949851059266ed0ff05d23306442b"}, {"relation": "partOf", "source": 3160, "target": 1239, "key": "5c6182893868d0ce2c3794c6886a6e27"}, {"line": 31260, "relation": "association", "evidence": "Peptidylarginine deiminase (PAD II) is an enzyme that uses arginine as a substrate and we now show that PAD II not only binds with the peptides Abeta(1-40), Abeta(22-35), Abeta(17-28), Abeta(25-35) and Abeta(32-35) but assists in the proteolytic degradation of these peptides with the concomitant formation of insoluble fibrils.", "citation": {"db": "PubMed", "db_id": "20224908"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3160, "target": 2328, "key": "e0faabe1f32d7f2bd5a5ce3cc0ab009f"}, {"relation": "partOf", "source": 3252, "target": 1244, "key": "edc58af66b40ed6aa2a4eaed70d33301"}, {"relation": "partOf", "source": 3164, "target": 1120, "key": "a85100bec1ac72c686e58f54a75a4587"}, {"relation": "partOf", "source": 3164, "target": 1118, "key": "0c2999291fa739d51990465cbe3b56be"}, {"relation": "partOf", "source": 3164, "target": 1200, "key": "25e62375d8f7aac511b04647c4f5206e"}, {"relation": "partOf", "source": 3171, "target": 1363, "key": "3e8419fd3c26d71bac06c29bad527dda"}, {"relation": "partOf", "source": 3540, "target": 1603, "key": "5d5c6d95d78f857249c1bb082b9fb55d"}, {"relation": "partOf", "source": 3016, "target": 1565, "key": "036ecb28c18e69d206d2db432b3a6447"}, {"line": 31341, "relation": "association", "evidence": "Pin1 regulates the conformation and function of certain phosphorylated proteins and plays an important role in cell cycle regulation , oncogenesis , and Alzheimer 's disease.", "citation": {"db": "PubMed", "db_id": "12388558"}, "source": 3898, "target": 3192, "key": "11adb3b70f64c0cb9506392fb6cb6950"}, {"relation": "partOf", "source": 2983, "target": 1185, "key": "507e9b74d227fdc9a0e55651ec8e95a5"}, {"relation": "partOf", "source": 2983, "target": 1270, "key": "87c6e2399ae7cf3d1ce6a9761f1875aa"}, {"line": 31394, "relation": "increases", "evidence": "OBJECTIVE: Urinary-type plasminogen activator (uPA) binding to uPA receptor (uPAR) promotes the activation of matrix metalloproteinase-9 (MMP-9), which degrades amyloid beta protein (Abeta) in vitro.", "citation": {"db": "PubMed", "db_id": "11327298"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 1609, 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by alpha(3)beta(1) and alpha(2)beta(1) integrin receptors.", "citation": {"db": "PubMed", "db_id": "17761425"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2922, "target": 3062, "key": "2d735dec6164125581c1739db69d5ecb"}, {"line": 31537, "relation": "increases", "evidence": "Up-regulation of MMP-9 expressed by SK-N-SH cells in the presence of Abeta(1-40) was mediated by alpha(3)beta(1) and alpha(2)beta(1) integrin receptors.", "citation": {"db": "PubMed", "db_id": "17761425"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2920, "target": 3062, "key": "aec24c8600b0314e56dda4b6d649d012"}, {"relation": "partOf", "source": 2308, "target": 1122, "key": "bd1093f430fc014552c0952676ca22eb"}, {"relation": "partOf", "source": 2308, "target": 1121, "key": "5a8968b43ff30da7dcf6e2ef2a01f034"}, {"line": 34097, "relation": "increases", "evidence": "Presenilin and nicastrin are essential components of the gamma-secretase complex that is required for the intramembrane proteolysis of an increasing number of membrane proteins including the amyloid-beta precursor protein (APP) and Notch.", "citation": {"db": "PubMed", "db_id": "12297508"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 1582, "target": 2315, "key": "211fb0c74be6a433ce15ec6a7b586956"}, {"line": 34098, "relation": "increases", "evidence": "Presenilin and nicastrin are essential components of the gamma-secretase complex that is required for the intramembrane proteolysis of an increasing number of membrane proteins including the amyloid-beta precursor protein (APP) and Notch.", "citation": {"db": "PubMed", "db_id": "12297508"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 1582, "target": 3126, "key": "20730e923aaceea9aef9f963532ad1c3"}, {"line": 31659, "relation": "association", "evidence": "APH-1 interacts with mature and immature forms of presenilins and nicastrin and may play a role in maturation of presenilin.nicastrin complexes.", "citation": {"db": "PubMed", "db_id": "12471034"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1595, "target": 2304, "key": "8852cd566218304ae5113efe68eeb356"}, {"line": 31717, "relation": "decreases", "evidence": "hXB51beta associates with X11L and inhibits its interaction with APP.", "citation": {"db": "PubMed", "db_id": "12780348"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1084, "target": 1081, "key": "803fab82f05cc993cba438fa5e459162"}, {"relation": "partOf", "source": 3097, "target": 1084, "key": "c1b4a84410e6d9d09f497779018a36d6"}, {"line": 31709, "relation": "association", "evidence": "XB51 protein is known to interact with the amino-terminal of the X11L protein and to be involved in Abeta40 generation, a hallmark of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19035353"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3097, "target": 2327, "key": "56d4ebaf69a87c8ecae509430e17e0be"}, {"relation": "partOf", "source": 3509, "target": 1584, "key": "b396787faee18e56f29679e2e4113774"}, {"relation": "partOf", "source": 3100, "target": 1543, "key": "010ce793bfd3d8ba803a3b6249b1130d"}, {"relation": "partOf", "source": 3103, "target": 1586, "key": "ff88de3961da4da95f323d183d187da6"}, {"line": 31757, "relation": "decreases", "evidence": "The kappaB motif-dependent production of Abeta42 was suppressed by binding of NF-kappaB/p65 to the PDZ domain of the X11-like protein (X11L), which a human homologue protein of LIN-10.", "citation": {"db": "PubMed", "db_id": "10777610"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1085, "target": 2328, "key": "251c818ec6433b2a252e2d7cc73f2456"}, {"line": 32097, "relation": "association", "evidence": "Thus, the interaction between PS1 and APP is central to the molecular mechanism of AD.", "citation": {"db": "PubMed", "db_id": "11489281"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1202, "target": 3823, "key": "ff227461c0b4bff9f87f750120fff73a"}, {"relation": "partOf", "source": 3130, "target": 1553, "key": "a17880216f21a27d949131b45219c5cf"}, {"line": 31878, "relation": "association", "evidence": "These results are consistent with observations that PSA pmodulates tau levels in vivo and suggest that this enzyme may be involved in tau degradation in human brain.", "citation": {"db": "PubMed", "db_id": "17154549"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 3130, "target": 3010, "key": "71d3992eb7b6b2c1476581bdb757a1c1"}, {"line": 32931, "relation": "increases", "evidence": "Puromycin-sensitive aminopeptidase (PSA/NPEPPS) is a novel pmodifier of TAU-induced neurodegeneration with neuroprotective effects via direct proteolysis of TAU protein.", "citation": {"db": "PubMed", "db_id": "21320871"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "object": {"modifier": "Degradation"}, "source": 3130, "target": 3010, "key": "243326072986986cf78ecacc3c3398d8"}, {"relation": "partOf", "source": 3141, "target": 1300, "key": "f265ac5621dcfb5fa927cafa650d496f"}, {"line": 46754, "relation": "positiveCorrelation", "evidence": "Nevertheless, the CSF level of neurogranin is selectively increased in AD dementia", "citation": {"db": "PubMed", "db_id": "26783546"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}, "Confidence": {"High": true}}, "source": 3141, "target": 3823, "key": "ffe64055f63e0ed4df57dab3f7fc24fb"}, {"line": 46762, "relation": "positiveCorrelation", "evidence": "Both neurogranin and YKL‐40 correlated with tau as well as with Abeta40 in all studied diagnostic groups", "citation": {"db": "PubMed", "db_id": "26783546"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3141, "target": 2509, "key": "a2edf8ff52494e79218a3c25bb67e270"}, {"line": 46764, "relation": "positiveCorrelation", "evidence": "Both neurogranin and YKL‐40 correlated with tau as well as with Abeta40 in all studied diagnostic groups", "citation": {"db": "PubMed", "db_id": "26783546"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3141, "target": 2327, "key": "567fade3532e2804005c18baab502e13"}, {"relation": "partOf", "source": 3168, "target": 1301, "key": "a1e0177c0c9c8caf8f10f42971d23ccf"}, {"relation": "isA", "source": 3148, "target": 2199, "key": "7cce8d9b0c9c9288a3697bf98b46ee93"}, {"relation": "partOf", "source": 3148, "target": 1598, "key": "df8852e3cea1cefc9d0816e49351d69f"}, {"relation": "partOf", "source": 3143, "target": 1598, "key": "b7a2eddf7c2f054a87fad9c544ffc136"}, {"relation": "partOf", "source": 3144, "target": 1599, "key": "40d5a1ec5331c994344a0c0734aff6a8"}, {"line": 31940, "relation": "decreases", "evidence": "More significantly, these and other studies may be interpreted to suggest that the abnormal phosphorylation of PHFtau may result from the failure of protein phosphatases (i.e., PP2A and 2B) to dephosphorylate PHFtau.", "citation": {"db": "PubMed", "db_id": "8529836"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3225, "target": 3015, "key": "7cc939bf59c7195b31d8ccae267bb978"}, {"line": 32022, "relation": "decreases", "evidence": "Confirming these results, SecPS2NT is able to inhibit PS1/APP interaction.", "citation": {"db": "PubMed", "db_id": "10078972"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}}, "source": 3269, "target": 1202, "key": "3dc4dbb37a6601eb7080553aea5ca27e"}, {"relation": "partOf", "source": 3195, "target": 1605, "key": "224b86c29b28d09de321aa87c00738ff"}, {"relation": "partOf", "source": 3195, "target": 1606, "key": "6ff58e8989eb6143d17f679f9adc7ad1"}, {"relation": "partOf", "source": 3195, "target": 1602, "key": "2df3a776652739e31079d44f49affa9f"}, {"line": 32250, "relation": "decreases", "evidence": "Presenilin-1 binds cytoplasmic epithelial cadherin, inhibits cadherin/p120 association, and regulates stability and function of the cadherin/catenin adhesion complex.", "citation": {"db": "PubMed", "db_id": "11226248"}, "annotations": {"Subgraph": {"Beta-Catenin subgraph": true, "Gamma secretase subgraph": true}}, "source": 1330, "target": 1329, "key": "10ad80d4246e4757a8d087983f2c6668"}, {"relation": "partOf", "source": 2482, "target": 1330, "key": "41c15c6b9218fe8f933c123abde73ed1"}, {"relation": "partOf", "source": 2482, "target": 1329, "key": "6d6cf4d0fe97883f9405c4a3f59c93e1"}, {"relation": "partOf", "source": 2588, "target": 1329, "key": "1bfb07dfe98240d122463ff4017e758f"}, {"relation": "partOf", "source": 2588, "target": 1379, "key": "5ce5b57277ef8cb78ba7ec398c32ae88"}, {"relation": "partOf", "source": 2481, "target": 1328, "key": "a1c44fc350e400119b16a6f4bc0cb4ab"}, {"relation": "partOf", "source": 2481, "target": 1326, "key": "a063ace1b972416bd89af7b0c8d73dc7"}, {"relation": "partOf", "source": 2481, "target": 1327, "key": "c6ba97837ae9cbafcd32361b0ad288e0"}, {"line": 34325, "relation": "regulates", "evidence": "Amyloid precursor protein and Presenilin1 interact with the adaptor GRB2 and modulate ERK 1,2 signaling.", "citation": {"db": "PubMed", "db_id": "17314098"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1440, "target": 448, "key": "f2e30d7d31ca0d45ac5948e6bf3c30cd"}, {"line": 35534, "relation": "increases", "evidence": "PS1 also modulates basal level of ERK1/2 activity through a ras-Raf-MEK-dependent pathway activated by a direct binding with the SH2 domain of Grb2. ERK family is one of the most ubiquitous cellular signaling mechanisms, whose activation links extracellular stimuli to cell proliferation, survival, and differentiation, but also cell death and apoptotic process. In this respect, it is worth of note to observe, as mentioned above, that ERK1/2 pathway is also modulated by AbetaPP", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1440, "target": 2193, "key": "ce0f29ba183df5d09f7c6299456ca803"}, {"line": 32478, "relation": "regulates", "evidence": "Presenilin 1 interacts with acetylcholinesterase and alters its enzymatic activity and glycosylation.", "citation": {"db": "PubMed", "db_id": "18299393"}, "annotations": {"Subgraph": {"Acetylcholine signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1048, "target": 816, "key": "faef06a72931d552cbed68fdc28063ee"}, {"line": 32498, "relation": "decreases", "evidence": "CONCLUSION: TXR could inhibit tau protein hyperphosphorylation, which might partially be due to the TXR caused binding of presenilin-1 with tau protein.", "citation": {"db": "PubMed", "db_id": "12575378"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Tau protein subgraph": true, "Gamma secretase subgraph": true}}, "source": 1557, "target": 3015, "key": "63954045460f87ebbacd8fe6d57e7af9"}, {"relation": "partOf", "source": 3424, "target": 1544, "key": "bd976c58010476af5805355fed06ee56"}, {"relation": "hasVariant", "source": 3424, "target": 3425, "key": "5c9afce19e986b3e666b5b712a068132"}, {"line": 32644, "relation": "increases", "evidence": "This processing is activated through a pathway involving the PDGF receptor, Src, and Rac1. ", "citation": {"db": "PubMed", "db_id": "14766758"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3173, "target": 2315, "key": "26b0fc1ca509a52739752ee859368e59"}, {"line": 32661, "relation": "negativeCorrelation", "evidence": "The pathological Tau/JIP1 interaction requires phosphorylation of Tau, and Tau competes with the physiological binding of JIP1 to kinesin light chain.", "citation": {"db": "PubMed", "db_id": "19491104"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "source": 1508, "target": 1548, "key": "29626148c27ed2d9e6f9e0614f3624b8"}, {"relation": "partOf", "source": 2946, "target": 1508, "key": "95b24f51808bd17914fbfc0cd9125138"}, {"line": 32661, "relation": "negativeCorrelation", "evidence": "The pathological Tau/JIP1 interaction requires phosphorylation of Tau, and Tau competes with the physiological binding of JIP1 to kinesin light chain.", "citation": {"db": "PubMed", "db_id": "19491104"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"MAPK-JNK subgraph": true, "Tau protein subgraph": true}}, "source": 1548, "target": 1508, "key": "2cdfb92d1b8d80fdaef2ca9285810fb3"}, {"relation": "partOf", "source": 2947, "target": 1509, "key": "918f498fa01038c36ded6f02ec2104a6"}, {"relation": "hasVariant", "source": 2363, "target": 2364, "key": "fc596a6665ea85e100e9f88126352653"}, {"relation": "partOf", "source": 2363, "target": 1257, "key": "fd5a2a30f737dfb9f79d7617e27a58e1"}, {"line": 32821, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3877, "target": 3015, "key": "748831be2ce8b39b578e85c592e54873"}, {"line": 32822, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3818, "target": 3015, "key": "d24773f3c888734ac302f75ba3c336d3"}, {"line": 32823, "relation": "association", "evidence": "Hyperphosphorylation and accumulation of tau in neurons (and glial cells) is one of the main pathologic hallmarks in Alzheimer's disease (AD) and other tauopathies, including Pick's disease (PiD), progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease and familial frontotemporal dementia and parkinsonism linked to chromosome 17 due to mutations in the tau gene (FTDP-17-tau).", "citation": {"db": "PubMed", "db_id": "15658002"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3811, "target": 3015, "key": "59c0b8f78abb31c5f51d18b45a8587b8"}, {"relation": "partOf", "source": 3009, "target": 1540, "key": "41e956a1887641ed2c3f2796fe7adc54"}, {"line": 32844, "relation": "increases", "evidence": "MAPK-activated protein kinase 2 (MK2) is one of several kinases that are regulated through direct phosphorylation by p38 MAPK, and MK2 has therefore been a candidate for an effector role in p38 action in the inflammatory response.æ¯�", "citation": {"db": "PubMed", "db_id": "16774924"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "MAPK-JNK subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3009, "target": 2999, "key": "178688524ed6030ac948e8cb10ae9966"}, {"line": 32873, "relation": "increases", "evidence": "Direct interaction of soluble human recombinant tau protein with Abeta 1-42 results in tau aggregation and hyperphosphorylation by tau protein kinase II.", "citation": {"db": "PubMed", "db_id": "11943163"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true}}, "source": 1235, "target": 3015, "key": "206565a18192fd5950e8d3144082db2f"}, {"relation": "partOf", "source": 3128, "target": 1499, "key": "76a8f9624c20e7c2315dc686d98c7bb0"}, {"relation": "partOf", "source": 3128, "target": 1195, "key": "59cd1af568a9f171127052afc496126c"}, {"line": 34731, "relation": "association", "evidence": "Recently, Notch receptors have been hypothesized to play a role in neurodegeneration and in particular in Alzheimer's disease (Notch1) and CADASIL (Notch3). Here we show that another family member (Notch2) is constitutively expressed in adult mouse hippocampus in DG and not in CA1 and CA3 neurons. Treatment with kainic acid resulted in marked Notch2 induction in pyramidal neurons of CA1 and in a subpopulation of CA3 neurons surviving the lesion and protein expression was still detectable 6 weeks after drug treatment. These results suggest Notch2 involvement in the response of postmitotic neurons to excitotoxic stimuli.", "citation": {"db": "PubMed", "db_id": "12802175"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3128, "target": 3823, "key": "42b339a05d4650310c345da5298bc5bc"}, {"line": 34770, "relation": "association", "evidence": "Furthermore, treatment with liquiritigenin inhibited astrocytosis in the hippocampus, and it may through its inhibitory activities on Notch-2, an important molecular regulating neural proliferation and differentiation. These findings provide evidence for beneficial activity of liquiritigenin in a mouse model of Alzheimer's disease and support the continued investigation of Notch signaling pathway as a target for treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21872584"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3128, "target": 3823, "key": "3ec3097451abaf6e9b96778927a6c79b"}, {"line": 34783, "relation": "association", "evidence": "Our results show that liquiritigenin treatment improves the behavioral performance of the model rats and attenuates neuronal loss in the brain. More importantly, liquiritigenin treatment decreases mRNA levels and protein expression of Notch-2, an effect that could promote the generation of new neurons. These findings provide evidence for the beneficial activity of liquiritigenin in a brain-injured rat model and support the continued investigation of SERMs such as liquiritigenin as an alternative to estrogen-based hormone therapy in reducing the risk of neurodegenerative diseases such as Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20117143"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3128, "target": 3823, "key": "c356f6e1234d5c5bf0e32332b0143cde"}, {"line": 34802, "relation": "association", "evidence": "The result showed that liquiritin significantly promotes the neurite outgrowth stimulated by NGF in PC12 cells in dose dependant manners whereas the liquiritin alone did not induce neurite outgrowth. Oligo microarray and RT-PCR analysis further clarified that the neurotrophic effect of liquiritin was related to the overexpression of neural related genes such as neurogenin 3, neurofibromatosis 1, notch gene homolog 2, neuromedin U receptor 2 and neurotrophin 5. Thus, liquiritin may be a good candidate for treatment of various neurodegenerative diseases such as Alzheimer's disease or Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "19789989"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3128, "target": 3823, "key": "70002f3eb9b5b48065ebbfe6b9849157"}, {"line": 34744, "relation": "association", "evidence": "To investigate potential homodimeric and heterodimeric interactions of APP and Notch2 (N2), we have visualized the subcellular localization of the APP/N2 complexes formed in living cells using bimolecular fluorescence complementation (BiFC) analysis. BiFC was accomplished by fusing the N-terminal fragment or the C-terminal fragment of yellow fluorescent protein (YFP) to APP, N2, and a C-terminally truncated form of N2.When expressed in COS-7 cells, these tagged proteins alone did not produce a fluorescent signal. The tagged APP homodimer produced a weak fluorescent signal, while neither full-length N2, nor a truncated N2 alone, produced a visible signal, suggesting that N2 receptors do not form homodimers. The strongest fluorescent signal was obtained with co-expression of the C-terminal fragment of YFP fused to APP and the N-terminal fragment of YFP fused to the truncated form of N2. This heterodimer localized to plasma membrane, endoplasmic reticulum (ER), Golgi and other compartments. The results were confirmed and quantified by flow cytometry. The BiFC method of specifically visualizing APP/Notch interactions can be applied to study APP and Notch signaling during development, aging and neurodegeneration.", "citation": {"db": "PubMed", "db_id": "16515557"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3128, "target": 3874, "key": "c5b2e067f4b3d775b63cb9a8f47e02b5"}, {"relation": "partOf", "source": 3063, "target": 1191, "key": "2016628df70345cc5cdb20d2f136a1fc"}, {"line": 32940, "relation": "increases", "evidence": "NQO1 binds STUB1 via the Hsc70-interacting domain (tetratricopeptide repeat domain) and undergoes ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "21220432"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "source": 1597, "target": 3132, "key": "a4e07593cbe30c626cb2e5e7fc9bc028"}, {"relation": "partOf", "source": 3131, "target": 1597, "key": "305dda6997f53c70ad815b153d2f47dc"}, {"relation": "hasVariant", "source": 3131, "target": 3132, "key": "6d1ddf2e3de7565f0e8bb578be0fa11a"}, {"line": 32942, "relation": "association", "evidence": "NQO1 binds STUB1 via the Hsc70-interacting domain (tetratricopeptide repeat domain) and undergoes ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "21220432"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "subject": {"modifier": "Degradation"}, "source": 3131, "target": 3823, "key": "aa5ee7c0884a84a5c9e6fb3d84fed933"}, {"relation": "partOf", "source": 3430, "target": 1597, "key": "e2f9ba38dd1d799c5e4d64434360843c"}, {"line": 32941, "relation": "increases", "evidence": "NQO1 binds STUB1 via the Hsc70-interacting domain (tetratricopeptide repeat domain) and undergoes ubiquitination and degradation.", "citation": {"db": "PubMed", "db_id": "21220432"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 3132, "target": 3131, "key": "04a3488a91f4770d0adda8fa0995b2dd"}, {"relation": "partOf", "source": 3140, "target": 1271, "key": "cfc048ba82cc88ebdeff77a8564996f8"}, {"relation": "partOf", "source": 3150, "target": 1198, "key": "fc61b28791601ec8405ff3b27b1d58e6"}, {"relation": "partOf", "source": 3179, "target": 1381, "key": "d2de1e2226d430e9990e3fa921338cda"}, {"relation": "partOf", "source": 3179, "target": 1602, "key": "452f658f824676e99f22eece70f3b2a1"}, {"relation": "partOf", "source": 3487, "target": 1560, "key": "5477af13fabbe21a5c77d1ab105cc6a5"}, {"relation": "partOf", "source": 3487, "target": 1601, "key": "81d93cfc9a7a353c497f98a48f9282ad"}, {"line": 33018, "relation": "increases", "evidence": "Tau protein kinase I phosphorylated not only tau protein but also pyruvate dehydrogenase, phosphorylation of which caused inactivation of this enzyme and finally led the cell to death.", "citation": {"db": "PubMed", "db_id": "20075608"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3487, "target": 3015, "key": "b7cbe841eb533b3b590261c8ed008442"}, {"line": 33029, "relation": "increases", "evidence": "When a primary culture of embryonic rat hippocampus was treated with 20 microM A beta, induction of TPKI, extensive phosphorylation of tau and then programmed cell death were observed, indicating that TPKI induced by A beta phosphorylates tau, followed by disruption of axonal transportation and finally cell death.", "citation": {"db": "PubMed", "db_id": "9089387"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Axonal transport subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3487, "target": 3015, "key": "50d588e880a5e9ae0846607821cef893"}, {"line": 33019, "relation": "increases", "evidence": "Tau protein kinase I phosphorylated not only tau protein but also pyruvate dehydrogenase, phosphorylation of which caused inactivation of this enzyme and finally led the cell to death.", "citation": {"db": "PubMed", "db_id": "20075608"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Disaccharide metabolism subgraph": true, "Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3487, "target": 3175, "key": "f4c211df912b1e422b816ac366e0d0ab"}, {"relation": "partOf", "source": 3174, "target": 1601, "key": "f0c0815e3312a6bf80f7eb902b33878e"}, {"relation": "hasVariant", "source": 3174, "target": 3175, "key": "1798bbde38c7901489fb9dde47a20a0c"}, {"line": 33157, "relation": "increases", "evidence": "Neurons overexpressing APP or APP(V642I) show increased APP-BP1 protein levels in lipid rafts. ", "citation": {"db": "PubMed", "db_id": "14557245"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2350, "target": 3087, "key": "4e3d5fc91047f084ff7dd8c7385d9955"}, {"relation": "partOf", "source": 2754, "target": 1168, "key": "fbd047aaf199ed9876c27120f01cdad9"}, {"relation": "partOf", "source": 3505, "target": 1222, "key": "7037177e86c857417b1a253bd70f92d2"}, {"relation": "partOf", "source": 3050, "target": 1188, "key": "d23fddfe543a368a38c3edd390839747"}, {"line": 33209, "relation": "association", "evidence": "Peripheral levels of Insulin Growth Factor-1 (IGF-I) are associated with glucose regulation and influence glucose disposal.", "citation": {"db": "PubMed", "db_id": "16444902"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 563, "target": 2871, "key": "17d5297ec21d7be4dbe66e98c89eceab"}, {"relation": "partOf", "source": 3209, "target": 1359, "key": "e08a2fad6341ed5d8abc0753c55678c7"}, {"line": 33395, "relation": "association", "evidence": "This interaction is regulated by the phosphorylation of Tau at selected sites, by glycogen synthase kinase-3beta (GSK3beta) and cyclin-dependent kinase 5 (Cdk5), and requires an intact microtubule network.", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 1103, "target": 3015, "key": "10c350d785a211c7c2cb9cba25ae84f3"}, {"line": 33398, "relation": "association", "evidence": "This interaction is regulated by the phosphorylation of Tau at selected sites, by glycogen synthase kinase-3beta (GSK3beta) and cyclin-dependent kinase 5 (Cdk5), and requires an intact microtubule network.", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1103, "target": 2794, "key": "274544b2cd7c7c18c3d37b53d1c410ad"}, {"line": 33401, "relation": "association", "evidence": "This interaction is regulated by the phosphorylation of Tau at selected sites, by glycogen synthase kinase-3beta (GSK3beta) and cyclin-dependent kinase 5 (Cdk5), and requires an intact microtubule network.", "citation": {"db": "PubMed", "db_id": "15686969"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 1103, "target": 2487, "key": "1b7b7e88136e4a16d54914f02c164d09"}, {"relation": "partOf", "source": 3463, "target": 1053, "key": "058ea7f7189988dce2685763bea132c0"}, {"line": 33436, "relation": "decreases", "evidence": "Tissue inhibitor of metalloproteinases 1 (TIMP-1) inhibits several proteinases including a disintegrin and metalloproteinase 10 (ADAM10), a major alpha-secretase that cleaves the beta-amyloid precursor protein within its amyloidogenic Abeta domain.", "citation": {"db": "PubMed", "db_id": "12218659"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "ADAM Metallopeptidase subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 3463, "target": 2249, "key": "602718e081fb1d29c220f5e020e983a2"}, {"line": 49102, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Matrix metalloproteinase subgraph": true, "Amyloidogenic subgraph": true}}, "source": 3463, "target": 2328, "key": "9e0f7a159216694c4dcd79c4bae84b8c"}, {"line": 49107, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3463, "target": 3010, "key": "c54a8e5fee1b5c3b65e56dea938528b9"}, {"line": 49108, "relation": "association", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3463, "target": 3015, "key": "f7f2e12d0f2dd4fecdf377f926c36403"}, {"line": 49112, "relation": "positiveCorrelation", "evidence": "Lower levels of TIMPs in AD patients with microbleeds suggest less MMP inhibition in patients with concurrent cerebral microbleeds, which may hypothetically lead to a more vulnerable blood-brain barrier in these patients.In addition, we assessed associations of MMPs and TIMPs with CSF amyloid-beta(1-42) (Abeta42), tau, and tau phosphorylated at threonine-181 (p-tau)", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3463, "target": 2194, "key": "6a40817d923fecc7420d708f027ccdcd"}, {"line": 49147, "relation": "positiveCorrelation", "evidence": "In this regard we find high levels of the tissue inhibitor of matrix metalloproteinases-1 (TIMP-1) in AD. Furthermore, we explore the ability of thrombin, previously shown to be present in AD microvessels, to affect TIMP expression in cultured brain endothelial cells and find that thrombin causes up regulation of TIMP-1", "citation": {"db": "PubMed", "db_id": "26402072"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "Confidence": {"High": true}}, "source": 3463, "target": 3823, "key": "f1723b1f10a60e2e95e12f30f8c2d330"}, {"relation": "partOf", "source": 3465, "target": 1054, "key": "fd5263f1c2bd36e502de74b63512e885"}, {"line": 33473, "relation": "association", "evidence": "Phospho-c-Jun (Ser73) was found to be strongly associated with neurofibrillary tangles and granulovacuolar degeneration (GVD) in addition to the nuclei in neurons in the hippocampal regions of the AD brain, but was virtually absent in most controls.", "citation": {"db": "PubMed", "db_id": "17455299"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}, "MeSHAnatomy": {"Neurofibrillary Tangles": true}}, "source": 2938, "target": 3872, "key": "86a1013bf7c116e93715eb0ec757e7d6"}, {"line": 33517, "relation": "association", "evidence": "The dysregulation of glycogen synthase kinase-3 (GSK3) has been implicated in Alzheimer disease (AD) pathogenesis and in Abeta-induced neurotoxicity, leading us to investigate it as a therapeutic target in an intracerebroventricular Abeta infusion pmodel. ", "citation": {"db": "PubMed", "db_id": "19038340"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 2316, "target": 3876, "key": "29ce2e8e9b684ca122d8e1be3a40e101"}, {"line": 33569, "relation": "increases", "evidence": "Indeed, we have found that a combination of three major pro-inflammatory cytokines, IL-1beta+IFN-gamma+TNF-alpha, causes normal adult human astrocytes (NAHA) to express nitric oxide synthase-2 (NOS-2) and make dangerously large amounts of NO via mitogen-activated protein kinases (MAPKs). ", "citation": {"db": "PubMed", "db_id": "17385278"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true, "Interferon signaling subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 1707, "target": 3123, "key": "a5b55ea24d6eb6eebf7a222dc0cbf3a8"}, {"line": 33725, "relation": "positiveCorrelation", "evidence": "In concordance, significant increases in the levels of phosphorylation of total Akt substrates, including: GSK3beta(Ser9), tau(Ser214), mTOR(Ser2448), and decreased levels of the Akt target, p27(kip1), were found in AD temporal cortex compared with controls.", "citation": {"db": "PubMed", "db_id": "15773910"}, "annotations": {"MeSHDisease": {"Alzheimer Disease": true}, "Subgraph": {"Akt subgraph": true, "GSK3 subgraph": true, "mTOR signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3078, "target": 3823, "key": "44eca73ed668f49aa32529ba68d22f01"}, {"line": 33734, "relation": "decreases", "evidence": "We found that betaE2 could attenuate tau hyperphosphorylation at multiple AD-related sites, including Ser396/404, Thr231, Thr205, and Ser199/202, induced by Wort/GFX or transient overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Estrogen subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 12, "target": 3035, "key": "a307c5f8dd067b93787e859ced00a369"}, {"line": 33735, "relation": "decreases", "evidence": "We found that betaE2 could attenuate tau hyperphosphorylation at multiple AD-related sites, including Ser396/404, Thr231, Thr205, and Ser199/202, induced by Wort/GFX or transient overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Estrogen subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 12, "target": 3032, "key": "39290bd376acfb61290bde8212772afc"}, {"line": 33736, "relation": "decreases", "evidence": "We found that betaE2 could attenuate tau hyperphosphorylation at multiple AD-related sites, including Ser396/404, Thr231, Thr205, and Ser199/202, induced by Wort/GFX or transient overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Estrogen subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 12, "target": 3026, "key": "27c358bbb216928fc974c78cf7520cef"}, {"line": 33737, "relation": "decreases", "evidence": "We found that betaE2 could attenuate tau hyperphosphorylation at multiple AD-related sites, including Ser396/404, Thr231, Thr205, and Ser199/202, induced by Wort/GFX or transient overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Estrogen subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 12, "target": 3027, "key": "247d4341a94e2bdc54343d3d46da7e96"}, {"line": 33738, "relation": "decreases", "evidence": "We found that betaE2 could attenuate tau hyperphosphorylation at multiple AD-related sites, including Ser396/404, Thr231, Thr205, and Ser199/202, induced by Wort/GFX or transient overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Estrogen subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 12, "target": 3019, "key": "dacab9db5b7a81c058f21c0ea4a4512e"}, {"line": 33739, "relation": "decreases", "evidence": "We found that betaE2 could attenuate tau hyperphosphorylation at multiple AD-related sites, including Ser396/404, Thr231, Thr205, and Ser199/202, induced by Wort/GFX or transient overexpression of GSK-3beta. ", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Estrogen subgraph": true, "Tau protein subgraph": true, "GSK3 subgraph": true}}, "source": 12, "target": 3020, "key": "81ed87dd3f99c14743323ca348612442"}, {"line": 33747, "relation": "increases", "evidence": "Simultaneously, it increased the level of Ser9-phosphorylated (inactive) GSK-3beta", "citation": {"db": "PubMed", "db_id": "18217188"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "GSK3 subgraph": true}, "Confidence": {"High": true}}, "source": 12, "target": 2796, "key": "2517dcfaf29be3ada6f6a0f627f08d8d"}, {"line": 33882, "relation": "decreases", "evidence": "Neuronal injury-induced glial apoE secretion is attenuated by the nuclear factor kappaB inhibitors, aspirin, Bay 11-7082 and MG-132, suggesting that this transcription factor is involved in both constitutive and induced glial apoE expression", "citation": {"db": "PubMed", "db_id": "11311545"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 63, "target": 2312, "key": "93b8593be3e36288eb82e274b8870268"}, {"line": 33886, "relation": "decreases", "evidence": "Neuronal injury-induced glial apoE secretion is attenuated by the nuclear factor kappaB inhibitors, aspirin, Bay 11-7082 and MG-132, suggesting that this transcription factor is involved in both constitutive and induced glial apoE expression", "citation": {"db": "PubMed", "db_id": "11311545"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 63, "target": 3112, "key": "53ec5cf5eabd34e606110cc77f07c487"}, {"line": 33883, "relation": "decreases", "evidence": "Neuronal injury-induced glial apoE secretion is attenuated by the nuclear factor kappaB inhibitors, aspirin, Bay 11-7082 and MG-132, suggesting that this transcription factor is involved in both constitutive and induced glial apoE expression", "citation": {"db": "PubMed", "db_id": "11311545"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 73, "target": 2312, "key": "7740964f15ce4e88469d4a110d886ad7"}, {"line": 42534, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 73, "target": 170, "key": "3d1cac5149aa00cff740cbf03c5d4bb5"}, {"line": 42542, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 73, "target": 224, "key": "0dc1200dc916fcf835ebc1bd9d01c4cf"}, {"line": 42543, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 73, "target": 325, "key": "8dcdb8b6a457822349918ca4ba667a3f"}, {"line": 33884, "relation": "decreases", "evidence": "Neuronal injury-induced glial apoE secretion is attenuated by the nuclear factor kappaB inhibitors, aspirin, Bay 11-7082 and MG-132, suggesting that this transcription factor is involved in both constitutive and induced glial apoE expression", "citation": {"db": "PubMed", "db_id": "11311545"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 4, "target": 2312, "key": "aa14523446526dc8beaafd6e10b6122a"}, {"line": 33885, "relation": "decreases", "evidence": "Neuronal injury-induced glial apoE secretion is attenuated by the nuclear factor kappaB inhibitors, aspirin, Bay 11-7082 and MG-132, suggesting that this transcription factor is involved in both constitutive and induced glial apoE expression", "citation": {"db": "PubMed", "db_id": "11311545"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nuclear factor Kappa beta subgraph": true}}, "source": 61, "target": 2312, "key": "37caea0c1b49668dfb52914829d43970"}, {"line": 37299, "relation": "association", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1626, "target": 743, "key": "26bcc7bd5f23c9428970b5c4c2868550"}, {"relation": "partOf", "source": 2987, "target": 1135, "key": "6aa24c2c3f5fcc26fb1d6c851fe01521"}, {"line": 33976, "relation": "increases", "evidence": "Herein, we present evidence that all ApoE isoforms bind to nitric oxide synthase 1 (NOS1) and that such protein-protein interaction results in S-nitrosylation of ApoE2 and ApoE3 but not ApoE4.", "citation": {"db": "PubMed", "db_id": "21443265"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 1138, "target": 664, "key": "304b2c1cb325129e9f437f0003bf2634"}, {"relation": "partOf", "source": 3180, "target": 1078, "key": "b70e7619878371c04cf8cdd9dc588da9"}, {"line": 34044, "relation": "increases", "evidence": "In vitro the large hydrophilic loop of PS-2 between transmembrane domains 6 and 7 can be phosphorylated by casein kinase-1 (CK-1) and CK-2, but not by PKA or PKC.", "citation": {"db": "PubMed", "db_id": "9558331"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2573, "target": 3270, "key": "52e28b460d8ec0ac740803de0f2b0a11"}, {"line": 34055, "relation": "increases", "evidence": "casein kinase (CK)-1 and CK-2 were shown to phosphorylate the N terminus of PS-2 in vitro.", "citation": {"db": "PubMed", "db_id": "8972483"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2573, "target": 3270, "key": "611742a629cced8780bb6e0fe972b767"}, {"relation": "partOf", "source": 2573, "target": 1367, "key": "1075b1b88ff11cd9c12ff7209b6c35e7"}, {"line": 34045, "relation": "increases", "evidence": "In vitro the large hydrophilic loop of PS-2 between transmembrane domains 6 and 7 can be phosphorylated by casein kinase-1 (CK-1) and CK-2, but not by PKA or PKC.", "citation": {"db": "PubMed", "db_id": "9558331"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2576, "target": 3270, "key": "bb33f0a7041f178844822f3ff92c692b"}, {"line": 34056, "relation": "increases", "evidence": "casein kinase (CK)-1 and CK-2 were shown to phosphorylate the N terminus of PS-2 in vitro.", "citation": {"db": "PubMed", "db_id": "8972483"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 2576, "target": 3270, "key": "4bb418fe9b62e646d7349d70922c2ad4"}, {"relation": "partOf", "source": 2576, "target": 1368, "key": "752fa0741e5901ba099017cefe84377a"}, {"relation": "partOf", "source": 2940, "target": 1505, "key": "7ea572b7489ac0f3ecc1e4b796df139f"}, {"relation": "partOf", "source": 3444, "target": 1621, "key": "86ae03dda7d8f2b3df717c2b078646d8"}, {"relation": "partOf", "source": 3071, "target": 1576, "key": "e0c721ef53cde15925ba7759cffb9013"}, {"relation": "partOf", "source": 3445, "target": 1622, "key": "938ba2478326d351e85ce39bcc416d59"}, {"relation": "partOf", "source": 3512, "target": 1623, "key": "40bfbfba47c1e732840aea209fd4c551"}, {"relation": "partOf", "source": 3512, "target": 1625, "key": "340bc1513de31b9779e60009b436d52f"}, {"relation": "partOf", "source": 3176, "target": 938, "key": "60962935fd68f75bff1c4955a391479f"}, {"relation": "partOf", "source": 3111, "target": 1439, "key": "11009d4f346deedaebf8e126911d65e7"}, {"relation": "partOf", "source": 2302, "target": 1110, "key": "3adb3fed4e27427d032f5eab5084b4db"}, {"relation": "partOf", "source": 2302, "target": 1109, "key": "f891194c6432a4c75be0dc26ba404d50"}, {"line": 37836, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2302, "target": 2136, "key": "0acc8467ff3611dd3c6a920ac19d4771"}, {"line": 34490, "relation": "decreases", "evidence": "Aberrant glycosylation pmodulates phosphorylation of tau by protein kinase A and dephosphorylation of tau by protein phosphatase 2A and 5.", "citation": {"db": "PubMed", "db_id": "12435421"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3227, "target": 3015, "key": "a3f5504e3a42bd498197b2c9d842930b"}, {"relation": "partOf", "source": 3542, "target": 1564, "key": "6bf0045bb3ce1ced70b8b6b5d16486d1"}, {"relation": "partOf", "source": 3542, "target": 1561, "key": "28779e740e844b63b08fc9a8fe60e099"}, {"line": 34538, "relation": "increases", "evidence": "In this study, we found that the 14-3-3zeta isoform is associated with tau in brain extract and profoundly stimulates cAMP-dependent protein kinase catalyzed in vitro phosphorylation on Ser(262)/Ser(356) located within the microtubule-binding region of tau. ", "citation": {"db": "PubMed", "db_id": "10840038"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3542, "target": 3235, "key": "22741e4ae08d127e632390ffcc221b07"}, {"line": 34505, "relation": "decreases", "evidence": "Mutation S156A slightly decreased interaction of phosphorylated tau3 with 14-3-3.", "citation": {"db": "PubMed", "db_id": "19647741"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3038, "target": 1561, "key": "354634cc95234402d9eef35238139a2d"}, {"line": 34511, "relation": "decreases", "evidence": "Double mutations S156A/S267A and especially S156A/S235A, strongly inhibited interaction of phosphorylated tau3 with 14-3-3. ", "citation": {"db": "PubMed", "db_id": "19647741"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3038, "target": 1561, "key": "c9e5b580c0e1e7fcb54c54328acc96e7"}, {"line": 34512, "relation": "decreases", "evidence": "Double mutations S156A/S267A and especially S156A/S235A, strongly inhibited interaction of phosphorylated tau3 with 14-3-3. ", "citation": {"db": "PubMed", "db_id": "19647741"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3040, "target": 1561, "key": "d0649d1dbe7ee26e7e9ba3a997257042"}, {"line": 34514, "relation": "decreases", "evidence": "Double mutations S156A/S267A and especially S156A/S235A, strongly inhibited interaction of phosphorylated tau3 with 14-3-3. ", "citation": {"db": "PubMed", "db_id": "19647741"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3039, "target": 1561, "key": "32941fc2099dc40ee94ecb3a2c380f0c"}, {"line": 34539, "relation": "increases", "evidence": "In this study, we found that the 14-3-3zeta isoform is associated with tau in brain extract and profoundly stimulates cAMP-dependent protein kinase catalyzed in vitro phosphorylation on Ser(262)/Ser(356) located within the microtubule-binding region of tau. ", "citation": {"db": "PubMed", "db_id": "10840038"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3235, "target": 3023, "key": "6935f9848bac2e22672481d9c4194604"}, {"line": 34540, "relation": "increases", "evidence": "In this study, we found that the 14-3-3zeta isoform is associated with tau in brain extract and profoundly stimulates cAMP-dependent protein kinase catalyzed in vitro phosphorylation on Ser(262)/Ser(356) located within the microtubule-binding region of tau. ", "citation": {"db": "PubMed", "db_id": "10840038"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3235, "target": 3025, "key": "be6ce080b48e13ae240239437b207dc8"}, {"line": 34574, "relation": "increases", "evidence": "Thus PKN serves as a regulator of microtubules by specific phosphorylation of tau, which leads to disruption of tubulin assembly.", "citation": {"db": "PubMed", "db_id": "11104762"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3194, "target": 3015, "key": "a2985cea39213d12a2cbef029d8928ea"}, {"line": 34580, "relation": "decreases", "evidence": "The immunoreactivity for phosphorylated tau at Ser-320 increased in the presence of a phosphatase inhibitor, FK506 treatment, which means that calcineurin (protein phosphatase 2B) may be involved in dephosphorylating tau at Ser-320 site.", "citation": {"db": "PubMed", "db_id": "11104762"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3226, "target": 3024, "key": "8e9476c041de66b2bbb34e4dd5430a14"}, {"line": 34599, "relation": "increases", "evidence": "The stable expression of 17A in SHSY5Y neuroblastoma cells induces the synthesis of an alternative splicing isoform that abolish GABA B2 intracellular signaling (i.e., inhibition of cAMP accumulation and activation of K(+) channels). Indeed, 17A is expressed in human brain, and we report that it is upregulated in cerebral tissues derived from Alzheimer disease patients. We demonstrate that 17A expression in neuroblastoma cells enhances the secretion of amyloid beta peptide (Abeta) and the Abeta x-42/Αbeta x-40 peptide ratio and that its synthesis is induced in response to inflammatory stimuli.", "citation": {"db": "PubMed", "db_id": "26147761"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}, "CellLine": {"SH-SY5Y": true}}, "source": 558, "target": 20, "key": "592fd4e505ba4e87674438137c7d50ce"}, {"line": 34600, "relation": "decreases", "evidence": "The stable expression of 17A in SHSY5Y neuroblastoma cells induces the synthesis of an alternative splicing isoform that abolish GABA B2 intracellular signaling (i.e., inhibition of cAMP accumulation and activation of K(+) channels). Indeed, 17A is expressed in human brain, and we report that it is upregulated in cerebral tissues derived from Alzheimer disease patients. We demonstrate that 17A expression in neuroblastoma cells enhances the secretion of amyloid beta peptide (Abeta) and the Abeta x-42/Αbeta x-40 peptide ratio and that its synthesis is induced in response to inflammatory stimuli.", "citation": {"db": "PubMed", "db_id": "26147761"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}, "CellLine": {"SH-SY5Y": true}}, "source": 558, "target": 692, "key": "83ff0a545b043624ff7b90545689db60"}, {"line": 34618, "relation": "increases", "evidence": "We confirm effects of three kinases from this screen, the eukaryotic translation initiation factor 2 alpha kinase 2 (EIF2AK2), the dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A), and the A-kinase anchor protein 13 (AKAP13) on tau phosphorylation at the 12E8 epitope (serine 262/serine 356).", "citation": {"db": "PubMed", "db_id": "20067632"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "source": 2278, "target": 3023, "key": "3c2823d47b5fd32a41bde1ec852e7da1"}, {"line": 34619, "relation": "increases", "evidence": "We confirm effects of three kinases from this screen, the eukaryotic translation initiation factor 2 alpha kinase 2 (EIF2AK2), the dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A), and the A-kinase anchor protein 13 (AKAP13) on tau phosphorylation at the 12E8 epitope (serine 262/serine 356).", "citation": {"db": "PubMed", "db_id": "20067632"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true, "DYRK1A subgraph": true}}, "source": 2278, "target": 3025, "key": "d943800821b77080766dc45f477c53e0"}, {"relation": "partOf", "source": 3629, "target": 1644, "key": "ea8840061c22be07e172a3aabbbd4970"}, {"line": 34630, "relation": "increases", "evidence": "In this study, we demonstrate that Dyrk1A interacts with and phosphorylates Munc18-1 at the Thr(479) residue. The phosphorylation of Munc18-1 at Thr(479) by Dyrk1A stimulated binding of Munc18-1 to Syntaxin 1 and X11α. Furthermore, the levels of phospho-Thr(479) -Munc18-1 were enhanced in the brains of transgenic mice over-expressing Dyrk1A protein, providing in vivo evidence of Munc18-1 phosphorylation by Dyrk1A. These results reveal a link between Munc18-1 and Dyrk1A in synaptic vesicle trafficking and amyloid precursor protein processing, suggesting that up-regulated Dyrk1A in Down's syndrome and Alzheimer's disease brains may contribute to some pathological features, including synaptic dysfunction and cognitive defect through abnormal phosphorylation of Munc18-1.", "citation": {"db": "PubMed", "db_id": "22765017"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 3629, "target": 1644, "key": "7aa0e4d2712a87d062637dce401025b8"}, {"line": 34631, "relation": "increases", "evidence": "In this study, we demonstrate that Dyrk1A interacts with and phosphorylates Munc18-1 at the Thr(479) residue. The phosphorylation of Munc18-1 at Thr(479) by Dyrk1A stimulated binding of Munc18-1 to Syntaxin 1 and X11α. Furthermore, the levels of phospho-Thr(479) -Munc18-1 were enhanced in the brains of transgenic mice over-expressing Dyrk1A protein, providing in vivo evidence of Munc18-1 phosphorylation by Dyrk1A. These results reveal a link between Munc18-1 and Dyrk1A in synaptic vesicle trafficking and amyloid precursor protein processing, suggesting that up-regulated Dyrk1A in Down's syndrome and Alzheimer's disease brains may contribute to some pathological features, including synaptic dysfunction and cognitive defect through abnormal phosphorylation of Munc18-1.", "citation": {"db": "PubMed", "db_id": "22765017"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 1644, "target": 3730, "key": "d0c9411c349963e3f1aab5ebafcf1752"}, {"relation": "partOf", "source": 3729, "target": 1644, "key": "e1537bd69883edf43bb0b753ed1fb90b"}, {"relation": "hasVariant", "source": 3729, "target": 3730, "key": "1d8fe06dc9ff2080ce480c30e3d35795"}, {"relation": "partOf", "source": 3729, "target": 1650, "key": "74ee86c225ae503b3b70db4da53865e7"}, {"line": 34653, "relation": "positiveCorrelation", "evidence": "We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3729, "target": 3823, "key": "608f12e28a0efe4177982f0f8f2b67db"}, {"relation": "partOf", "source": 3730, "target": 1651, "key": "67cb38c6d18362480ed465d592e37beb"}, {"line": 34632, "relation": "increases", "evidence": "In this study, we demonstrate that Dyrk1A interacts with and phosphorylates Munc18-1 at the Thr(479) residue. The phosphorylation of Munc18-1 at Thr(479) by Dyrk1A stimulated binding of Munc18-1 to Syntaxin 1 and X11α. Furthermore, the levels of phospho-Thr(479) -Munc18-1 were enhanced in the brains of transgenic mice over-expressing Dyrk1A protein, providing in vivo evidence of Munc18-1 phosphorylation by Dyrk1A. These results reveal a link between Munc18-1 and Dyrk1A in synaptic vesicle trafficking and amyloid precursor protein processing, suggesting that up-regulated Dyrk1A in Down's syndrome and Alzheimer's disease brains may contribute to some pathological features, including synaptic dysfunction and cognitive defect through abnormal phosphorylation of Munc18-1.", "citation": {"db": "PubMed", "db_id": "22765017"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 3730, "target": 1651, "key": "50b429189c8cc9a2f3ec6ec1e7ce8e43"}, {"line": 34636, "relation": "decreases", "evidence": "These results reveal a link between Munc18-1 and Dyrk1A in synaptic vesicle trafficking and amyloid precursor protein processing, suggesting that up-regulated Dyrk1A in Down's syndrome and Alzheimer's disease brains may contribute to some pathological features, including synaptic dysfunction and cognitive defect through abnormal phosphorylation of Munc18-1.", "citation": {"db": "PubMed", "db_id": "22765017"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 1651, "target": 597, "key": "0ccaa1f5f7a6a2ecae0123e67fe4a32a"}, {"line": 34637, "relation": "decreases", "evidence": "These results reveal a link between Munc18-1 and Dyrk1A in synaptic vesicle trafficking and amyloid precursor protein processing, suggesting that up-regulated Dyrk1A in Down's syndrome and Alzheimer's disease brains may contribute to some pathological features, including synaptic dysfunction and cognitive defect through abnormal phosphorylation of Munc18-1.", "citation": {"db": "PubMed", "db_id": "22765017"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 1651, "target": 812, "key": "dad52fc0d92737fd192d5ec4352d4ce7"}, {"relation": "partOf", "source": 3727, "target": 1651, "key": "5560d13490c9da91ef66330307ea9667"}, {"relation": "partOf", "source": 3727, "target": 1650, "key": "bf6e06c11c085779196fc4841857c8ba"}, {"line": 34654, "relation": "positiveCorrelation", "evidence": "We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 3727, "target": 3823, "key": "e734fb0747faf056f8c5e98304f204a6"}, {"line": 34647, "relation": "decreases", "evidence": "Cyclin-dependent kinase 5 and its activator p35 disrupt Munc18a-syntaxin 1 binding, thereby promoting synaptic vesicle fusion during exocytosis. We investigated protein levels of the signaling pathway: p35, cyclin-dependent kinase 5, Munc18a, syntaxin 1A and 1B, Munc18-interacting protein 1 and Munc18-interacting protein 2 in Alzheimer's disease cortex and found that this pathway was up-regulated in the Alzheimer's disease parietal and occipital cortex.", "citation": {"db": "PubMed", "db_id": "16413130"}, "annotations": {"Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 1650, "target": 791, "key": "4dd6c8c21005c1dba9d54d9e39526a8d"}, {"line": 34666, "relation": "increases", "evidence": "Finally, CaM kinase II is present in neurons but not in glial cells, thus suggesting no role of CaM kinase II in tau phosphorylation of glial cells. These observations, together with previous results of in vitro studies, support the idea that several MAPK/ERK, SAPK/JNK, p38 and CaM kinase II may participate in tau phosphorylation in tauopathies", "citation": {"db": "PubMed", "db_id": "11810404"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 2426, "target": 3015, "key": "9b00964b1bdc7b6ec8ae056e2d699d4c"}, {"line": 34673, "relation": "causesNoChange", "evidence": "Finally, CaM kinase II is present in neurons but not in glial cells, thus suggesting no role of CaM kinase II in tau phosphorylation of glial cells. These observations, together with previous results of in vitro studies, support the idea that several MAPK/ERK, SAPK/JNK, p38 and CaM kinase II may participate in tau phosphorylation in tauopathies", "citation": {"db": "PubMed", "db_id": "11810404"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Microglia": true}}, "source": 2426, "target": 3015, "key": "e741638d3777ccd42b3622e4edd7363c"}, {"relation": "partOf", "source": 2426, "target": 1696, "key": "d2e4ee664d2e66395efaef88dbcd381e"}, {"relation": "partOf", "source": 3523, "target": 1640, "key": "cb91a243decf7695a75fcb648dc7b2ba"}, {"relation": "partOf", "source": 3524, "target": 1640, "key": "e7f93965634b13c8a4c71d5852a1859e"}, {"relation": "partOf", "source": 3524, "target": 1060, "key": "a5c412e61241f36b58105b6d60dd0135"}, {"line": 34700, "relation": "increases", "evidence": "PACAP is widely distributed in the central and peripheral nervous systems and acts as a neurotransmitter, neuromodulator, and neurotrophic factor via three major receptors (PAC1, VPAC1, and VPAC2).", "citation": {"db": "PubMed", "db_id": "24856828"}, "source": 1060, "target": 523, "key": "1277a6624aca4e635aac7699f3c0710a"}, {"relation": "partOf", "source": 2255, "target": 1060, "key": "f06845a0a365e0ca524c58bc6971b9a1"}, {"relation": "partOf", "source": 2255, "target": 1059, "key": "4b0b1696418a06f6b269cf86d172d2e6"}, {"relation": "partOf", "source": 2255, "target": 1061, "key": "a9ba5c425e683c31b5ab80d5e3623f3f"}, {"line": 34701, "relation": "increases", "evidence": "PACAP is widely distributed in the central and peripheral nervous systems and acts as a neurotransmitter, neuromodulator, and neurotrophic factor via three major receptors (PAC1, VPAC1, and VPAC2).", "citation": {"db": "PubMed", "db_id": "24856828"}, "source": 1059, "target": 523, "key": "94656cee580911029bf94c49e59596d4"}, {"relation": "partOf", "source": 2256, "target": 1059, "key": "0779c845713f8bb2e1dc918ed4d89871"}, {"line": 34702, "relation": "increases", "evidence": "PACAP is widely distributed in the central and peripheral nervous systems and acts as a neurotransmitter, neuromodulator, and neurotrophic factor via three major receptors (PAC1, VPAC1, and VPAC2).", "citation": {"db": "PubMed", "db_id": "24856828"}, "source": 1061, "target": 523, "key": "b4e9d629b388fb805a954415612a5290"}, {"relation": "partOf", "source": 3525, "target": 1061, "key": "18be2404ff99a04891e0854dab9079d5"}, {"line": 34769, "relation": "decreases", "evidence": "Furthermore, treatment with liquiritigenin inhibited astrocytosis in the hippocampus, and it may through its inhibitory activities on Notch-2, an important molecular regulating neural proliferation and differentiation. These findings provide evidence for beneficial activity of liquiritigenin in a mouse model of Alzheimer's disease and support the continued investigation of Notch signaling pathway as a target for treatment of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21872584"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 292, "target": 3128, "key": "feacc91e8536913f9dbf486c0bfac87d"}, {"line": 34782, "relation": "decreases", "evidence": "Our results show that liquiritigenin treatment improves the behavioral performance of the model rats and attenuates neuronal loss in the brain. More importantly, liquiritigenin treatment decreases mRNA levels and protein expression of Notch-2, an effect that could promote the generation of new neurons. These findings provide evidence for the beneficial activity of liquiritigenin in a brain-injured rat model and support the continued investigation of SERMs such as liquiritigenin as an alternative to estrogen-based hormone therapy in reducing the risk of neurodegenerative diseases such as Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20117143"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 292, "target": 3128, "key": "9477ae9662debe066d1be37edc4213c2"}, {"line": 34784, "relation": "decreases", "evidence": "Our results show that liquiritigenin treatment improves the behavioral performance of the model rats and attenuates neuronal loss in the brain. More importantly, liquiritigenin treatment decreases mRNA levels and protein expression of Notch-2, an effect that could promote the generation of new neurons. These findings provide evidence for the beneficial activity of liquiritigenin in a brain-injured rat model and support the continued investigation of SERMs such as liquiritigenin as an alternative to estrogen-based hormone therapy in reducing the risk of neurodegenerative diseases such as Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20117143"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 292, "target": 3823, "key": "ea1e48128674bbe0fe38f8cd34823873"}, {"line": 34788, "relation": "increases", "evidence": "Our results show that liquiritigenin treatment improves the behavioral performance of the model rats and attenuates neuronal loss in the brain. More importantly, liquiritigenin treatment decreases mRNA levels and protein expression of Notch-2, an effect that could promote the generation of new neurons. These findings provide evidence for the beneficial activity of liquiritigenin in a brain-injured rat model and support the continued investigation of SERMs such as liquiritigenin as an alternative to estrogen-based hormone therapy in reducing the risk of neurodegenerative diseases such as Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20117143"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 292, "target": 431, "key": "a58213a510137a9afe96f0d447cdba29"}, {"line": 34801, "relation": "increases", "evidence": "The result showed that liquiritin significantly promotes the neurite outgrowth stimulated by NGF in PC12 cells in dose dependant manners whereas the liquiritin alone did not induce neurite outgrowth. Oligo microarray and RT-PCR analysis further clarified that the neurotrophic effect of liquiritin was related to the overexpression of neural related genes such as neurogenin 3, neurofibromatosis 1, notch gene homolog 2, neuromedin U receptor 2 and neurotrophin 5. Thus, liquiritin may be a good candidate for treatment of various neurodegenerative diseases such as Alzheimer's disease or Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "19789989"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 293, "target": 3128, "key": "6076c0fff634bde129a53d82e8bc22fd"}, {"line": 34803, "relation": "increases", "evidence": "The result showed that liquiritin significantly promotes the neurite outgrowth stimulated by NGF in PC12 cells in dose dependant manners whereas the liquiritin alone did not induce neurite outgrowth. Oligo microarray and RT-PCR analysis further clarified that the neurotrophic effect of liquiritin was related to the overexpression of neural related genes such as neurogenin 3, neurofibromatosis 1, notch gene homolog 2, neuromedin U receptor 2 and neurotrophin 5. Thus, liquiritin may be a good candidate for treatment of various neurodegenerative diseases such as Alzheimer's disease or Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "19789989"}, "annotations": {"Subgraph": {"Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 293, "target": 652, "key": "4dc41497440cb32c59ba5a592502acc1"}, {"line": 34829, "relation": "increases", "evidence": "Autopsy brain hippocampal tissues were obtained from controls and patients with AD and Western blots were performed using antibodies against mTOR signaling molecules and RagC, an upstream component of mTOR complex 1 (mTORC1) signaling. We found that expression of mTOR/p-mTOR and its downstream targets S6/p-S6 and Raptor/p-Raptor were expressed in the control and AD hippocampus. The expression levels of these signaling molecules were significantly increased in the hippocampus at the severe stages of AD, compared to controls and other stages of AD. Interestingly, Rictor expression level was unaltered. In addition, RagC was increased in the hippocampus at the early, moderate, and severe stages of AD. Our data indicate that mTORC1, but not mTORC2, was activated in the AD brains and that the level of mTOR signaling activation was correlated with cognitive severity of AD patients.", "citation": {"db": "PubMed", "db_id": "23979023"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "DiseaseState": {"Moderate AD": true, "Early-onset AD": true, "Late-onset AD": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3329, "target": 3076, "key": "5c66b1eaf40766a5a5c5737828adc224"}, {"line": 34830, "relation": "positiveCorrelation", "evidence": "Autopsy brain hippocampal tissues were obtained from controls and patients with AD and Western blots were performed using antibodies against mTOR signaling molecules and RagC, an upstream component of mTOR complex 1 (mTORC1) signaling. We found that expression of mTOR/p-mTOR and its downstream targets S6/p-S6 and Raptor/p-Raptor were expressed in the control and AD hippocampus. The expression levels of these signaling molecules were significantly increased in the hippocampus at the severe stages of AD, compared to controls and other stages of AD. Interestingly, Rictor expression level was unaltered. In addition, RagC was increased in the hippocampus at the early, moderate, and severe stages of AD. Our data indicate that mTORC1, but not mTORC2, was activated in the AD brains and that the level of mTOR signaling activation was correlated with cognitive severity of AD patients.", "citation": {"db": "PubMed", "db_id": "23979023"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "DiseaseState": {"Moderate AD": true, "Early-onset AD": true, "Late-onset AD": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3329, "target": 3823, "key": "c4dcf7a908ebfa1999599201963c9618"}, {"line": 34842, "relation": "association", "evidence": "Abnormal neuritic sprouting is a prominent feature of Alzheimer's disease (AD), and the Thy-1 glycoprotein has a role in neurite growth in culture. We therefore investigated the distribution of Thy-1 immunoreactivity in the hippocampus of normal elderly patients and of AD patients. Some Thy-1-immunoreactive dystrophic neurites entered senile plaques. The data confirm that there is extensive growth of abnormal neurites in AD and suggest that Thy-1 is involved in this process.", "citation": {"db": "PubMed", "db_id": "1347079"}, "annotations": {"Subgraph": {"Cell-cell communication subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 3461, "target": 652, "key": "95e1edff486210831a03427f87fa3c23"}, {"line": 34843, "relation": "association", "evidence": "Abnormal neuritic sprouting is a prominent feature of Alzheimer's disease (AD), and the Thy-1 glycoprotein has a role in neurite growth in culture. We therefore investigated the distribution of Thy-1 immunoreactivity in the hippocampus of normal elderly patients and of AD patients. Some Thy-1-immunoreactive dystrophic neurites entered senile plaques. The data confirm that there is extensive growth of abnormal neurites in AD and suggest that Thy-1 is involved in this process.", "citation": {"db": "PubMed", "db_id": "1347079"}, "annotations": {"Subgraph": {"Cell-cell communication subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 3461, "target": 2328, "key": "88cdaf8a9f2a72fe99b8f742249290b0"}, {"line": 34854, "relation": "increases", "evidence": "In this work, we describe a novel non-coding (nc) RNA (named 17A) RNA polymerase (pol) III-dependent embedded in the human G-protein-coupled receptor 51 gene (GPR51, GABA B2 receptor). The stable expression of 17A in SHSY5Y neuroblastoma cells induces the synthesis of an alternative splicing isoform that abolish GABA B2 intracellular signaling (i.e., inhibition of cAMP accumulation and activation of K(+) channels). Indeed, 17A is expressed in human brain, and we report that it is upregulated in cerebral tissues derived from Alzheimer disease patients. We demonstrate that 17A expression in neuroblastoma cells enhances the secretion of amyloid beta peptide (Abeta) and the Abeta x-42/Αbeta x-40 peptide ratio and that its synthesis is induced in response to inflammatory stimuli.", "citation": {"db": "PubMed", "db_id": "20888417"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2720, "target": 489, "key": "e041a1e1090e7ba366bb88fa90af630a"}, {"line": 34858, "relation": "increases", "evidence": "In this work, we describe a novel non-coding (nc) RNA (named 17A) RNA polymerase (pol) III-dependent embedded in the human G-protein-coupled receptor 51 gene (GPR51, GABA B2 receptor). The stable expression of 17A in SHSY5Y neuroblastoma cells induces the synthesis of an alternative splicing isoform that abolish GABA B2 intracellular signaling (i.e., inhibition of cAMP accumulation and activation of K(+) channels). Indeed, 17A is expressed in human brain, and we report that it is upregulated in cerebral tissues derived from Alzheimer disease patients. We demonstrate that 17A expression in neuroblastoma cells enhances the secretion of amyloid beta peptide (Abeta) and the Abeta x-42/Αbeta x-40 peptide ratio and that its synthesis is induced in response to inflammatory stimuli.", "citation": {"db": "PubMed", "db_id": "20888417"}, "annotations": {"Subgraph": {"GABA subgraph": true}, "Confidence": {"High": true}}, "source": 2720, "target": 892, "key": "26232014ca7e65acdc0ae714cb7ff3e5"}, {"line": 34871, "relation": "increases", "evidence": "Humanin (HN), a 24-amino acid peptide encoded by the mitochondrial 16S rRNA gene, was discovered by screening a cDNA library from the occipital cortex of a patient with Alzheimer's disease (AD) for a protection factor against AD-relevant insults. In the present work, we further confirmed interaction of HN with MPP8 in co-immunoprecipitation experiments and localized an MPP8-binding site in the region between 5 and 12 aa. of HN. We have also shown that an MPP8 fragment (residues 431-560) is sufficient to bind HN.", "citation": {"db": "PubMed", "db_id": "23532874"}, "annotations": {"MeSHAnatomy": {"Occipital Lobe": true}}, "source": 3068, "target": 1729, "key": "06b791adec6d89e9db5ef4d2bc74a01f"}, {"line": 34872, "relation": "negativeCorrelation", "evidence": "Humanin (HN), a 24-amino acid peptide encoded by the mitochondrial 16S rRNA gene, was discovered by screening a cDNA library from the occipital cortex of a patient with Alzheimer's disease (AD) for a protection factor against AD-relevant insults. In the present work, we further confirmed interaction of HN with MPP8 in co-immunoprecipitation experiments and localized an MPP8-binding site in the region between 5 and 12 aa. of HN. We have also shown that an MPP8 fragment (residues 431-560) is sufficient to bind HN.", "citation": {"db": "PubMed", "db_id": "23532874"}, "annotations": {"MeSHAnatomy": {"Occipital Lobe": true}}, "source": 1729, "target": 3823, "key": "38f7267b7c751f17d25ecd19e9176d0f"}, {"relation": "partOf", "source": 2226, "target": 1024, "key": "e9985b819e5045b2a39d9f73b7f7288b"}, {"relation": "partOf", "source": 3065, "target": 1024, "key": "e20ba6bab74c330aa585dd1daed60dbd"}, {"line": 34883, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3994, "target": 3823, "key": "c93fd76450e64d1210a0f159e06675f8"}, {"line": 34885, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}, "Subgraph": {"Gap junctions subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 3934, "target": 3823, "key": "5726e527fdbe1ebb606ee5df5f61a2e5"}, {"line": 34887, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3969, "target": 3823, "key": "9f8beb4e86626edf4b73d54816414b6d"}, {"line": 34888, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3970, "target": 3823, "key": "763ecaa20a37f15374a7f2dc2d477475"}, {"line": 34889, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 3974, "target": 3823, "key": "c83c578cec7176e0172ac0a60c1438e1"}, {"line": 34890, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 4001, "target": 3823, "key": "be81415d73b42ff910e3038aacf6128e"}, {"line": 34891, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 4004, "target": 3823, "key": "c74baee6a052519dd3094685f401f2b8"}, {"line": 34892, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 4029, "target": 3823, "key": "c4be6a8c0670a15e82356603452a860a"}, {"line": 34893, "relation": "positiveCorrelation", "evidence": "The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1, GUSB, M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n=20) using SYBR Green technology.", "citation": {"db": "PubMed", "db_id": "21672555"}, "annotations": {"MeSHAnatomy": {"Frontal Lobe": true}}, "source": 4031, "target": 3823, "key": "7b9cf7cbb9e4058f2f0d2032fc6355f4"}, {"line": 34912, "relation": "positiveCorrelation", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"DNA synthesis": true}, "Confidence": {"High": true}}, "source": 447, "target": 3823, "key": "51867bc31f7cd6f019fe11eb7889934f"}, {"line": 34921, "relation": "association", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 447, "target": 3162, "key": "db43d9eb4d323f3bdab72dc30e2fc0d2"}, {"line": 34920, "relation": "association", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 3162, "target": 478, "key": "66ad36b746004ea21d70f45f49a4a9cf"}, {"line": 34921, "relation": "association", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 3162, "target": 447, "key": "820f705d47ccef540cb475a6d99d4f5a"}, {"relation": "partOf", "source": 3162, "target": 1199, "key": "0c6774e3a2565a5df0eac14e3402335a"}, {"line": 38012, "relation": "association", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3162, "target": 2315, "key": "93b9839ed10b6616fd32269c37a0346b"}, {"line": 38013, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3162, "target": 445, "key": "c94b99ff6dbcf907e8c532ba0f071a88"}, {"line": 38019, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3162, "target": 645, "key": "4e1f5fe2a41c154ebd907456d0dd6a65"}, {"relation": "partOf", "source": 3162, "target": 1169, "key": "824c0cd8748380b74291ff01ba67805c"}, {"line": 34922, "relation": "association", "evidence": "Apoptosis pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12890786"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 1199, "target": 478, "key": "e7eb60b7ae2477eb312fc175f1754590"}, {"line": 34932, "relation": "increases", "evidence": "alpha-Ketoglutarate dehydrogenase (E1k), also designated oxoglutarate dehydrogenase (OGDH; EC 1.2.4.2), is a component of the enzyme complex that catalyzes the conversion of alpha-ketogluterate to succinyl coenzyme A, a critical step in the Krebs tricarboxylic acid cycle. Deficiencies in the activity of this enzyme complex have been observed in brain and peripheral cells of patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "8020988"}, "subject": {"modifier": "Activity"}, "source": 3152, "target": 4085, "key": "1b5df29f82c168f3649f58b25bd363ee"}, {"line": 34934, "relation": "negativeCorrelation", "evidence": "alpha-Ketoglutarate dehydrogenase (E1k), also designated oxoglutarate dehydrogenase (OGDH; EC 1.2.4.2), is a component of the enzyme complex that catalyzes the conversion of alpha-ketogluterate to succinyl coenzyme A, a critical step in the Krebs tricarboxylic acid cycle. Deficiencies in the activity of this enzyme complex have been observed in brain and peripheral cells of patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "8020988"}, "subject": {"modifier": "Activity"}, "source": 3152, "target": 3823, "key": "a9fe3d6349e7f543dfe88f4fb63abdcb"}, {"relation": "hasReactant", "source": 4085, "target": 18, "key": "2aead37360663d9451cbcc4069f4f9e0"}, {"relation": "hasProduct", "source": 4085, "target": 178, "key": "2512d14602d9058f0ba37ac3c4772fdf"}, {"line": 34933, "relation": "increases", "evidence": "alpha-Ketoglutarate dehydrogenase (E1k), also designated oxoglutarate dehydrogenase (OGDH; EC 1.2.4.2), is a component of the enzyme complex that catalyzes the conversion of alpha-ketogluterate to succinyl coenzyme A, a critical step in the Krebs tricarboxylic acid cycle. Deficiencies in the activity of this enzyme complex have been observed in brain and peripheral cells of patients with Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "8020988"}, "source": 4085, "target": 796, "key": "bac5c47e549f8aefb7707e9fdc39aa43"}, {"line": 34951, "relation": "increases", "evidence": "Complex I 24-kDa subunit was significantly reduced in occipital cortex and thalamus in patients with DS and temporal and occipital cortices in patients with AD. Complex I 75-kDa subunit was significantly reduced in brain regions from patients with DS (temporal, occipital and caudate nucleus) and AD (parietal cortex). Reductions of two subunits of complex I may lead to the impairment of energy metabolism and result in neuronal cell death (apoptotic process), a hallmark of both neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "11400916"}, "annotations": {"Subgraph": {"Electron transport chain": true}, "Confidence": {"High": true}}, "source": 3096, "target": 836, "key": "9644f186d0bc73e48fbb2f61fc814b9b"}, {"line": 34958, "relation": "decreases", "evidence": "Complex I 24-kDa subunit was significantly reduced in occipital cortex and thalamus in patients with DS and temporal and occipital cortices in patients with AD. Complex I 75-kDa subunit was significantly reduced in brain regions from patients with DS (temporal, occipital and caudate nucleus) and AD (parietal cortex). Reductions of two subunits of complex I may lead to the impairment of energy metabolism and result in neuronal cell death (apoptotic process), a hallmark of both neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "11400916"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3096, "target": 478, "key": "494aca89ef330b596d6b525848f613ba"}, {"line": 34952, "relation": "increases", "evidence": "Complex I 24-kDa subunit was significantly reduced in occipital cortex and thalamus in patients with DS and temporal and occipital cortices in patients with AD. Complex I 75-kDa subunit was significantly reduced in brain regions from patients with DS (temporal, occipital and caudate nucleus) and AD (parietal cortex). Reductions of two subunits of complex I may lead to the impairment of energy metabolism and result in neuronal cell death (apoptotic process), a hallmark of both neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "11400916"}, "annotations": {"Subgraph": {"Electron transport chain": true}, "Confidence": {"High": true}}, "source": 3095, "target": 836, "key": "13d73fda6d33a8d27ebb4cd17190a1df"}, {"line": 34957, "relation": "decreases", "evidence": "Complex I 24-kDa subunit was significantly reduced in occipital cortex and thalamus in patients with DS and temporal and occipital cortices in patients with AD. Complex I 75-kDa subunit was significantly reduced in brain regions from patients with DS (temporal, occipital and caudate nucleus) and AD (parietal cortex). Reductions of two subunits of complex I may lead to the impairment of energy metabolism and result in neuronal cell death (apoptotic process), a hallmark of both neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "11400916"}, "annotations": {"Subgraph": {"Electron transport chain": true, "Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3095, "target": 478, "key": "1526306f8394af46e1e5abf8a0995317"}, {"line": 34986, "relation": "increases", "evidence": "The neurotrophin, brain-derived neurotrophic factor (BDNF), is essential for synaptic function, plasticity and neuronal survival. At the axon terminal, when BDNF binds to its receptor, tropomyosin-related kinase B (TrkB), the signal is propagated along the axon to the cell body, via retrograde transport, regulating gene expression and neuronal function. Alzheimer disease (AD) is characterized by early impairments in synaptic function that may result in part from neurotrophin signaling deficits. Growing evidence suggests that soluble beta-amyloid (Abeta) assemblies cause synaptic dysfunction by disrupting both neurotransmitter and neurotrophin signaling. Furthermore, Abeta oligomers alone impair BDNF retrograde transport. Thus, Abeta reduces BDNF signaling by impairing axonal transport and this may underlie the synaptic dysfunction observed in AD.", "citation": {"db": "PubMed", "db_id": "19540623"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 1295, "target": 649, "key": "cc07864acc3f40abe42ed7a81ac0633d"}, {"line": 35032, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"High": true}}, "source": 1295, "target": 649, "key": "a2ee006bf35b29309fcf30c7e45a610c"}, {"line": 38626, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by ABeta¸ and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and ABeta¸ production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 1295, "target": 649, "key": "2c11273c5f37d0d99ee4b21b5f78861c"}, {"line": 34987, "relation": "increases", "evidence": "The neurotrophin, brain-derived neurotrophic factor (BDNF), is essential for synaptic function, plasticity and neuronal survival. At the axon terminal, when BDNF binds to its receptor, tropomyosin-related kinase B (TrkB), the signal is propagated along the axon to the cell body, via retrograde transport, regulating gene expression and neuronal function. Alzheimer disease (AD) is characterized by early impairments in synaptic function that may result in part from neurotrophin signaling deficits. Growing evidence suggests that soluble beta-amyloid (Abeta) assemblies cause synaptic dysfunction by disrupting both neurotransmitter and neurotrophin signaling. Furthermore, Abeta oligomers alone impair BDNF retrograde transport. Thus, Abeta reduces BDNF signaling by impairing axonal transport and this may underlie the synaptic dysfunction observed in AD.", "citation": {"db": "PubMed", "db_id": "19540623"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 1295, "target": 647, "key": "c83f52d838fdc5152a5f4fd532ce51f3"}, {"line": 35033, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"High": true}}, "source": 1295, "target": 647, "key": "1fe02fcaf2e24d11cf873c7552982c02"}, {"line": 38627, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by ABeta¸ and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and ABeta¸ production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 1295, "target": 647, "key": "5516b8be123dd05335ba4ed8534faa1a"}, {"line": 35123, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by Abeta and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and Abeta production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "CellStructure": {"Mitochondria": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1295, "target": 3147, "key": "62ba55342d12ce27e8b897fee821821e"}, {"line": 38628, "relation": "increases", "evidence": "The present review raises the hypothesis that the onset of AD pathology is closely related with mitochondrial dysfunction induced by ABeta¸ and brain-derived neurotrophic factor (BDNF) axonal transport deficits. It is well-known that axonal transport defect and attenuation of BDNF-neurotrophic tyrosine receptor kinase 2 (TrkB) signal are fatal to neuronal function and survival.We hypothesized that abnormal amyloid precursor protein (APP) processing and ABeta¸ production in mitochondria disturb the axonal transport by impairing mitochondrial function and attenuate BDNF-neurotrophic tyrosine receptor kinase 2 signal subsequently.", "citation": {"db": "PubMed", "db_id": "22212405"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1295, "target": 3147, "key": "2155556ebf11d5e50fa318d2076e1ec1"}, {"line": 36633, "relation": "association", "evidence": "Physiological levels of ABeta¸ also have trophic and neuroprotective actions in trophic deprived conditions [95]. Many ABeta¸ has a physiological role in normal synapse function. In organotypic hippocampal slices, BACE activity is increased by synaptic activity and the resulting ABeta¸ peptides depress excitatory transmission through a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) and N-methyl-D-aspartic acid (NMDA) receptors, suggesting a role for ABeta¸ in homeostatic plasticity [29]. ABeta¸ may have an important physiological role in synapse elimination during brain development", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 647, "target": 80, "key": "ac65a9eed5feef5a8d38d9eb3fb80c79"}, {"line": 35060, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 1254, "target": 3400, "key": "54744d6882505485249c59ce39ad1453"}, {"line": 35061, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 1254, "target": 3401, "key": "979bc3e23b684f32837cc1c619878b08"}, {"line": 35062, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "gtp", "namespace": "bel"}}, "source": 1254, "target": 2213, "key": "884272ffe0e5405188e933cf1978606d"}, {"line": 35063, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 1254, "target": 2212, "key": "5b500812624e221d4c6ade212c5a6f26"}, {"line": 35064, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 1254, "target": 2193, "key": "f7a70ada2fb7b8f96df0e6c6f05ba242"}, {"line": 35065, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 1252, "target": 3400, "key": "84e8b82e26d737d3a90eeb8bde2b71ba"}, {"line": 35066, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 1252, "target": 3401, "key": "d0333e0c8462061fce564701435bfee0"}, {"line": 35067, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "gtp", "namespace": "bel"}}, "source": 1252, "target": 2213, "key": "cb17ecf205051f14d9dfb2353db8fb76"}, {"line": 35068, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 1252, "target": 2212, "key": "0852d14c66eefb163b3678de4ca02154"}, {"line": 35069, "relation": "increases", "evidence": "The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 1252, "target": 2193, "key": "e177d9e5299508968883e864ffd42fd5"}, {"line": 35135, "relation": "negativeCorrelation", "evidence": "Utilizing a novel microfluidic culture chamber, we demonstrate that Abeta oligomers compromise BDNF-mediated retrograde transport by impairing endosomal vesicle velocities, resulting in impaired downstream signaling driven by BDNF/TrkB, including ERK5 activation, and CREB-dependent gene regulation. Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 4030, "target": 3823, "key": "0dce7504f670759ea5b77eea40cf88ae"}, {"line": 35138, "relation": "increases", "evidence": "Utilizing a novel microfluidic culture chamber, we demonstrate that Abeta oligomers compromise BDNF-mediated retrograde transport by impairing endosomal vesicle velocities, resulting in impaired downstream signaling driven by BDNF/TrkB, including ERK5 activation, and CREB-dependent gene regulation. Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 4030, "target": 2397, "key": "b06dc73c60cf3e2c7a3a7e9995e45d55"}, {"line": 35140, "relation": "increases", "evidence": "Utilizing a novel microfluidic culture chamber, we demonstrate that Abeta oligomers compromise BDNF-mediated retrograde transport by impairing endosomal vesicle velocities, resulting in impaired downstream signaling driven by BDNF/TrkB, including ERK5 activation, and CREB-dependent gene regulation. Our data suggest that a key mechanism mediating the deficit involves ubiquitin C-terminal hydrolase L1 (UCH-L1), a deubiquitinating enzyme that functions to regulate cellular ubiquitin. Abeta-induced deficits in BDNF trafficking and signaling are mimicked by LDN (an inhibitor of UCH-L1) and can be reversed by increasing cellular UCH-L1 levels, demonstrated here using a transducible TAT-UCH-L1 strategy. Finally, our data reveal that UCH-L1 mRNA levels are decreased in the hippocampi of AD brains. Taken together, our data implicate that UCH-L1 is important for regulating neurotrophin receptor sorting to signaling endosomes and supporting retrograde transport. Further, our results support the idea that in AD, Abeta may down-regulate UCH-L1 in the AD brain, which in turn impairs BDNF/TrkB-mediated retrograde signaling, compromising synaptic plasticity and neuronal survival.", "citation": {"db": "PubMed", "db_id": "23599427"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 4030, "target": 778, "key": "8e13b3feedb74b52aee223263e3c2c02"}, {"line": 35173, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 1238, "target": 648, "key": "f131e406d620e89784697d84b327a390"}, {"line": 35174, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 1238, "target": 1592, "key": "cf9800d3a007691cff27e3695e460feb"}, {"line": 35178, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 1238, "target": 3118, "key": "141934a79e9bff45e3e407bbc9f35041"}, {"line": 35176, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3118, "target": 648, "key": "bcd3dc10bd4ba399fc947b9b7184ebfd"}, {"line": 38661, "relation": "decreases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3118, "target": 648, "key": "3e76576311b03cc39b5f987ca7fe533b"}, {"line": 38662, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 3118, "target": 1473, "key": "39219760f1059eb3779012018b1666a4"}, {"line": 35185, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NF-κB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NF-κB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1473, "target": 3112, "key": "8460ad3658c8040835de40e55453e8a5"}, {"line": 38663, "relation": "increases", "evidence": "Amyloid beta (Abeta) aggregates are the primary component of senile plaques in Alzheimer disease (AD) patient's brain. Abeta is known to bind p75 neurotrophin receptor (p75(NTR)) and mediates Abeta-induced neuronal death. Recently, we showed that NGF leads to p75(NTR) polyubiquitination, which promotes neuronal cell survival. Here, we demonstrate that Abeta stimulation impaired the p75(NTR) polyubiquitination. TRAF6 and p62 are required for polyubiquitination of p75(NTR) on NGF stimulation. Interestingly, we found that overexpression of TRAF6/p62 restored p75(NTR) polyubiquitination upon Abeta/NGF treatment. Abeta significantly reduced NFkappaB activity by attenuating the interaction of p75(NTR) with IKKbeta. p75(NTR) increased NFkappaB activity by recruiting TRAF6/p62, which thereby mediated cell survival. These findings indicate that TRAF6/p62 abrogated the Abeta-mediated inhibition of p75(NTR) polyubiquitination and restored neuronal cell survival.", "citation": {"db": "PubMed", "db_id": "23017601"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1473, "target": 3112, "key": "80af06d3ce32c6fba32f7e2cdb7dab20"}, {"relation": "partOf", "source": 2877, "target": 1473, "key": "7250606a0a7c83af9684cdde9f3fbf9f"}, {"line": 35264, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1692, "target": 491, "key": "77e6ed643063992cc41bf5e2c77652f5"}, {"line": 35265, "relation": "decreases", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1692, "target": 523, "key": "f229ff3b529a6392ec6ce8ff7714b215"}, {"line": 35269, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 715, "target": 2412, "key": "05ac50e04963d0d4ea3371c83b616119"}, {"line": 35270, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 715, "target": 2145, "key": "a482e01e39580da27123fbf0c188eb77"}, {"line": 35271, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 715, "target": 2998, "key": "89ccab527eacbd33ac76fb4d32716787"}, {"line": 35272, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 715, "target": 2173, "key": "3079a19ac5756140eb6ff795d41f56c1"}, {"line": 35273, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 715, "target": 2187, "key": "d8a64cd520031883475af37feea4f796"}, {"line": 35274, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 715, "target": 2222, "key": "9c37df25294edc445b14d0efebf37529"}, {"line": 35269, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 2412, "target": 715, "key": "4a0648888db5d8e4253062bf52c9055b"}, {"relation": "partOf", "source": 2412, "target": 1669, "key": "eecbd5c0410db1db8948fb409cd012ba"}, {"line": 35275, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 744, "target": 1669, "key": "eaf1b8dbcfe96fd7f0c95b12a5613f29"}, {"line": 35275, "relation": "association", "evidence": "Both Abeta and presenilins seem to affect calcium homeostasis at very early stages of disease development affecting the synaptic transmission and function prior to neuritic plaque development. Altered calcium signaling differentially regulates genes such as calcineurin, calmodulin kinase II, MAP kinase etc and induces protein modifications and neurite degeneration. Since functional synapses and synaptic transmission are fundamental processes in memory formation, alterations in these processes can lead to neuronal dysfunction and memory deficit as seen in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "22453989"}, "annotations": {"Subgraph": {"Calcium-dependent signal transduction": true}, "Confidence": {"High": true}}, "source": 1669, "target": 744, "key": "0772fe12abd83e2c877b9e1095e06d78"}, {"line": 35324, "relation": "decreases", "evidence": "Genetically, AD is linked to mutations in few proteins amyloid precursor protein (APP) and presenilin 1 and 2 (PS1 and PS2). The molecular mechanisms underlying neurodegeneration in AD as well as the physiological function of APP are not yet known. A recent theory has proposed that APP and PS1 modulate intracellular signals to induce cell-cycle abnormalities responsible for neuronal death and possibly amyloid deposition.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1687, "target": 503, "key": "be63c4f9c98d83af5030339ae3322940"}, {"line": 35345, "relation": "association", "evidence": "Moreover, recent findings have also suggested that AbetaPP, through an NPTY motif located in its cytodomain, and PSs form functional complexes with different signaling protein, supporting the hypothesis that AbetaPP and PS1 are at the centre of a complex network of interactions, likely involved in multiple cell-signaling events which are still unknown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1687, "target": 515, "key": "7aef02beaa4e3a19dfd4f5b1cbd3f5f1"}, {"line": 35345, "relation": "association", "evidence": "Moreover, recent findings have also suggested that AbetaPP, through an NPTY motif located in its cytodomain, and PSs form functional complexes with different signaling protein, supporting the hypothesis that AbetaPP and PS1 are at the centre of a complex network of interactions, likely involved in multiple cell-signaling events which are still unknown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 515, "target": 1687, "key": "a779af82becc95179d17615b11619036"}, {"line": 35378, "relation": "increases", "evidence": "Schematic representation of AbetaPP processing, the adaptor proteins interacting with its intracellular domain and the pathway leading to ERK1/2 activation. In the left panels is reported the transmembrane protein APP, before and after ITS sequential beta secretase (BACE) and gamma secretase cleavage, with its final products, AICD, APP ectodomain, and beta amyloid peptide (1–40/1–42). In the right part of the figure are indicated the protein interacting with APP intracellular domain, upon or independently from tyrosine phosphorylation. The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1174, "target": 3400, "key": "1d7f044a15d55ed9ca1c0241f395b0f9"}, {"line": 35379, "relation": "increases", "evidence": "Schematic representation of AbetaPP processing, the adaptor proteins interacting with its intracellular domain and the pathway leading to ERK1/2 activation. In the left panels is reported the transmembrane protein APP, before and after ITS sequential beta secretase (BACE) and gamma secretase cleavage, with its final products, AICD, APP ectodomain, and beta amyloid peptide (1–40/1–42). In the right part of the figure are indicated the protein interacting with APP intracellular domain, upon or independently from tyrosine phosphorylation. The adaptor proteins Shc and Grb2 through their phosphotyrosine-binding domain (PTB) and src homology domain (SH2) are able to directly bind tyrosine-phosphorylated APP, resulting in the recruitment of the components of the MAP kinase cascade (SoS, ras, Raf, MEK) leading to ERK1/2 activation.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1174, "target": 3401, "key": "0ffc90e617f61ac5ee8c5d05db87fab8"}, {"line": 35394, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 1442, "target": 3298, "key": "223eb52acd523837e5a9ffd8835328d5"}, {"line": 35404, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 1442, "target": 3291, "key": "bdb620053854b9462962b3f14982a6eb"}, {"line": 35414, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "source": 1442, "target": 2959, "key": "cd7786b2a59aac3fa5102c2db1215ab5"}, {"line": 35418, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2959, "target": 2990, "key": "e2e5a1b153209b2c455a73f2c80621d4"}, {"line": 35419, "relation": "increases", "evidence": "Grb2 may participate in this pathway either by direct binding to APP or being recruited by Shc. Alteration in ERK1/2 activity induced in this way may contribute to neurodegeneration in AD. Transduction pathway adaptors (X11, disabled, Fe65, JIP1, and Numb) that bind APP in the absence of tyrosine phosphorylation depicted are also shown.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2959, "target": 3000, "key": "ef9fc5b399e2937cdbcedd52faf8478a"}, {"line": 35446, "relation": "increases", "evidence": "Schematic representation of the intracellular pathway by which AbetaPP and PS1 control the activation of the MAPK/ERK1/2 cascade and their final biological effects. In the figure is specified the interaction between APP intracellular domain and PS1 C-terminus, with the adaptor protein Grb2. Grb2 can bind simultaneously to APP and PS1 (as measured in FRET experiments) leading to the MAPK ERK1/2 cascade activation. In AD an aberrant activation of ERK1/2 induced by APP and/or PS1 can determine the tentative activation of the cell cycle that, in postmitotic neurons, may induce cells to undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "22496686"}, "object": {"modifier": "Activity"}, "source": 1172, "target": 2173, "key": "1ffc0c253014f49131de2770248ebc53"}, {"line": 35492, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1695, "target": 1701, "key": "68be6a0375a8897246e23a5394f3cb6f"}, {"relation": "partOf", "source": 2372, "target": 1695, "key": "3a355303a7ad93703ea781f71aa9ce17"}, {"line": 47919, "relation": "association", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2372, "target": 2584, "key": "0d99e3e2781fbc73df5e658b6a09e129"}, {"line": 47920, "relation": "association", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2372, "target": 2583, "key": "e994c810ae8f11e9b7d1723c63532733"}, {"line": 47921, "relation": "association", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2372, "target": 2582, "key": "7d66daf27cea65a807e569bc3a6ad263"}, {"relation": "partOf", "source": 2373, "target": 1695, "key": "6a76a6e5ba75645ea827390aa740ead0"}, {"line": 35493, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 1701, "target": 2587, "key": "8972e08a8e8ae9c65561d714e9026dd8"}, {"line": 35494, "relation": "increases", "evidence": "In the absence of Wnt ligand, axin recruits CK1 causing the initiation of the beta-catenin phosphorylation cascade by glycogen synthase kinase-3 beta (GSK-3beta). Phosphorylated beta-catenin is recognized by beta-transducin repeat-containing protein (beta-TrCP) and degraded by the proteosome, reducing the level of cytosolic beta-catenin.", "citation": {"db": "PubMed", "db_id": "22496686"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1701, "target": 2794, "key": "ed09fcf471165f76a99c8611ac18e1b0"}, {"relation": "partOf", "source": 2574, "target": 1701, "key": "7036a02f8dff909544123bcc8be4a2fd"}, {"relation": "partOf", "source": 2575, "target": 1701, "key": "9a6ebf7e6ad93327edc845ddca3757dd"}, {"line": 35577, "relation": "positiveCorrelation", "evidence": "Abeta-Amyloid (Abeta) plaques in Alzheimer (AD) brains are surrounded by severe dendritic and axonal changes, including local spine loss, axonal swellings and distorted neurite trajectories. Whether and how plaques induce these neuropil abnormalities remains unknown. We tested the hypothesis that oligomeric assemblies of Abeta, seen in the periphery of plaques, mediate the neurodegenerative phenotype of AD by triggering activation of the enzyme GSK-3beta, which in turn appears to inhibit a transcriptional program mediated by CREB. We detect increased activity of GSK-3beta after exposure to oligomeric Abeta in neurons in culture, in the brain of double transgenic APP/tau mice and in AD brains. Activation of GSK-3beta, even in the absence of Abeta, is sufficient to produce a phenocopy of Abeta-induced dendritic spine loss in neurons in culture, while pharmacological inhibition of GSK-3beta prevents spine loss and increases expression of CREB-target genes like BDNF. Of note, in transgenic mice GSK-3beta inhibition ameliorated plaque-related neuritic changes and increased CREB-mediated gene expression. Moreover, GSK-3beta inhibition robustly decreased the oligomeric Abeta load in the mouse brain. All these findings support the idea that GSK3beta is aberrantly activated by the presence of Abeta, and contributes, at least in part, to the neuronal anatomical derangement associated with Abeta plaques in AD brains and to Abeta pathology itself.", "citation": {"db": "PubMed", "db_id": "21945540"}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3946, "target": 2162, "key": "f48dd84c29dead52854350453bd19e5a"}, {"line": 46159, "relation": "negativeCorrelation", "evidence": "AD brains showed a significantly increased methylation state of the promoter region of the BDNF gene, There was a significant decrease in BDNF mRNA in the AD brain", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3946, "target": 1759, "key": "2355a6a1dde31f6658da9689e5b9533c"}, {"relation": "hasVariant", "source": 3627, "target": 3628, "key": "d59cf81a16aeb005473fe0c8240d02ed"}, {"line": 35678, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2223, "target": 2222, "key": "2cbcd3a72e1eb0e76999610abda0a35c"}, {"line": 35680, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2188, "target": 2187, "key": "89565159cf0cf7158610fbda4a031ef2"}, {"line": 35681, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3490, "target": 2989, "key": "43ade3a596f063bbb5a03d923d747beb"}, {"line": 35683, "relation": "increases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2619, "target": 2989, "key": "59d77f8bb0c462b7c8e42c28686779ea"}, {"line": 35684, "relation": "directlyDecreases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2220, "target": 2989, "key": "34db734558d881e8329e0c662f78a7ed"}, {"line": 35686, "relation": "directlyDecreases", "evidence": "Apoptosis signal-regulating kinase 1 (ASK1), a member of the mitogen-activated protein kinase kinase kinase family, is composed of an inhibitory N-terminal domain, a kinase domain, and a C-terminal regulatory domain (Ichijo et al., 1997). ASK1 can promote apoptosis in response to common pro-apoptotic stresses, such as oxidative stress (Song et al., 2002), death receptor ligands (Nishitoh et al., 1998), and endoplasmic reticulum stress (Nishitoh et al., 2002). ASK1 also phosphorylates and activates both p38 and JNK pathways (Ichijo et al., 1997). The mechanism of ASK1 activation is positively regulated by its binding proteins such as TNF receptor-ssociated factors 2/6 (Noguchi et al., 2005) and Daxx (Chang et al., 1998). On the other hand, several cellular proteins, including thioredoxin (Saitoh et al., 1998), Hsp90 (Zhang et al., 2005), and 14-3-3 (Zhang et al., 1999), have been reported to interact with ASK1 and inhibit ASK1 activity.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2151, "target": 2989, "key": "78847f8e899859399b2533230dc86d1f"}, {"relation": "hasVariant", "source": 2196, "target": 2197, "key": "30edd62aa98aed5c3a599a8f4fb57247"}, {"line": 35698, "relation": "increases", "evidence": "Dual-specificity tyrosine (Y)-phosphorylation-regulated protein kinase 1A (Dyrk1A) is the mammalian homologue of Drosophila melanogaster minibrain and its human gene is mapped to the Down syndrome critical region of chromosome 21. Dyrk1A phosphorylates several transcription factors, including NFAT and CREB and a number of cytosolic proteins such as APP, tau, and a-synuclein. Although Dyrk1A is involved in the control of cell growth and postembryonic neurogenesis, its potential role during cell death and signaling pathway is not clearly understood. In the present study, we show that Dyrk1A is activated under the condition of apoptotic cell death. In addition, Dyrk1A is coupled to JNK1 activation, and directly interacts with apoptosis signal-regulating kinase 1 (ASK1). Moreover, Dyrk1A positively regulates ASK1-mediated JNK1-signaling, and appears to directly phosphorylate ASK1. These data indicate that Dyrk1A regulates cell death through facilitating ASK1-mediated signaling events.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1400, "target": 2989, "key": "74e2d87db638c1fd5729457dcbb950bc"}, {"line": 35701, "relation": "increases", "evidence": "Dual-specificity tyrosine (Y)-phosphorylation-regulated protein kinase 1A (Dyrk1A) is the mammalian homologue of Drosophila melanogaster minibrain and its human gene is mapped to the Down syndrome critical region of chromosome 21. Dyrk1A phosphorylates several transcription factors, including NFAT and CREB and a number of cytosolic proteins such as APP, tau, and a-synuclein. Although Dyrk1A is involved in the control of cell growth and postembryonic neurogenesis, its potential role during cell death and signaling pathway is not clearly understood. In the present study, we show that Dyrk1A is activated under the condition of apoptotic cell death. In addition, Dyrk1A is coupled to JNK1 activation, and directly interacts with apoptosis signal-regulating kinase 1 (ASK1). Moreover, Dyrk1A positively regulates ASK1-mediated JNK1-signaling, and appears to directly phosphorylate ASK1. These data indicate that Dyrk1A regulates cell death through facilitating ASK1-mediated signaling events.", "citation": {"db": "PubMed", "db_id": "22110360"}, "annotations": {"Subgraph": {"DYRK1A subgraph": true}}, "source": 1400, "target": 684, "key": "2de79511a3b0626ea1e2b1c6e979b992"}, {"relation": "partOf", "source": 2935, "target": 1400, "key": "7a3c7f1c920ebceb7aa05f6d484ffed3"}, {"line": 35737, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1717, "target": 684, "key": "aeb8e6f4c141bb5d64ffaa413c7cf082"}, {"line": 35738, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1718, "target": 684, "key": "bfafe73a821959f04c591b3dba79a4a8"}, {"line": 35749, "relation": "increases", "evidence": "The death of cholinergic neurons in the cerebral cortex and certain subcortical regions is linked to irreversible dementia relevant to AD (Alzheimer's disease). Although multiple studies have shown that expression of a FAD (familial AD)-linked APP (amyloid Abeta precursor protein) or a PS (presenilin) mutant, but not that of wild-type APP or PS, induced neuronal death by activating intracellular death signals, it remains to be addressed how these signals are interrelated and what the key molecule involved in this process is. In the present study, we show that the PS1-mediated (or possibly the PS2-mediated) signal is essential for the APP-mediated death in a gamma-secretase-independent manner and vice versa. MOCA (modifier of cell adhesion), which was originally identified as being a PS- and Rac1-binding protein, is a common downstream constituent of these neuronal death signals. Detailed molecular analysis indicates that MOCA is a key molecule of the AD-relevant neuronal death signals that links the PS-mediated death signal with the APP-mediated death signal at a point between Rac1 [or Cdc42 (cell division cycle 42)] and ASK1 (apoptotic process signal-regulating kinase 1).", "citation": {"db": "PubMed", "db_id": "22115042"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 1702, "target": 684, "key": "2aba30e8084d03bdacf911eb10ff4b9a"}, {"line": 35816, "relation": "increases", "evidence": "The state of tau phosphorylation and proteolysis can be regulated by calcium-dependent mechanisms. CaMKII can phosphorylate tau [189]. Cyclin-dependent kinase 5 (cdk5), another kinase involved in tau phosphorylation [190], is indirectly activated by the calcium-activated protease calpain. Indeed, cdk5 has to be associated with its regulatory subunit, p35 to be activated. Conversion of p35 to p25 deregulates cdk5 activity, resulting in an increased cdk5 kinase activity [191]. Calpain cleaves p35 into p25, and thus controls cdk5 activation [192]. Furthermore, tau is dephosphorylated by the calcium/calmodulin-dependent phosphatase, calcineurin [193]. Calpain was also proposed to directly participate in tau proteolysis and degradation", "citation": {"db": "PubMed", "db_id": "19419557"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Calcium-dependent signal transduction": true, "Tau protein subgraph": true}}, "source": 1696, "target": 3015, "key": "6a9c1f1e770dd14e56f9b8e420b8ac63"}, {"line": 35982, "relation": "increases", "evidence": "Background and Objective: Could a normal - but persistent - stress response to impeded axonal transport lead to late-onset Alzheimer's disease (AD)? Our results offer an affirmative answer, suggesting a mechanism for the abnormal production of amyloid-beta (Abeta), triggered by the slowed axonal transport at old age. We hypothesize that Abeta precursor protein (APP) is a sensor at the endoplasmic reticulum (ER) that detects, and signals to the nucleus, abnormalities in axonal transport. When persistently activated, due to chronically slowed-down transport, this signaling pathway leads to accumulation of Abeta within the ER. Methods and Results: We tested this hypothesis with the neuronal cell line CAD. We show that, normally, a fraction of APP is transported into neurites by recruiting kinesin-1 via the adaptor protein, Fe65. Under conditions that block kinesin-1-dependent transport, APP, Fe65 and kinesin-1 accumulate in the soma, and form a complex at the ER.This complex recruits active c-Jun N-terminal kinase (JNK), which phosphorylates APP at Thr(668). This phosphorylation increases the cleavage of APP by the amyloidogenic pathway, which generates Abeta within the ER lumen, and releases Fe65 into the cytoplasm. Part of the released Fe65 translocates into the nucleus, likely to initiate a gene transcription response to arrested transport. Prolonged arrest of kinesin-1-dependent transport could thus lead to accumulation and oligomerization of Abeta in the ER. Conclusion: These results support a model where the APP:Fe65 complex is a sensor at the ER for detecting the increased level of kinesin-1 caused by halted transport, which signals to the nucleus, while concomitantly generating an oligomerization-prone pool of Abeta in the ER. Our hypothesis could thus explain a pathogenic mechanism in AD.", "citation": {"db": "PubMed", "db_id": "22156573"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true, "MAPK-JNK subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 1093, "target": 3002, "key": "2e99a54bf8b7dda9efc694c1455793df"}, {"line": 36184, "relation": "increases", "evidence": "Protein kinase C and rho activated coiled coil protein kinase 2 (ROCK2) modulate Alzheimer's APP metabolism and phosphorylation of the Vps10-domain protein, SorL1.Generation of the amyloid ß (ABeta¸) peptide of Alzheimer's disease (AD) is differentially regulated through the intracellular trafficking of the amyloid ß precursor protein (APP) within the secretory and endocytic pathways. Protein kinase C (PKC) and rho-activated coiled-coil kinases (ROCKs) are two third messenger signaling molecules that control the relative utilization of these two pathways.", "citation": {"db": "PubMed", "db_id": "21192821"}, "annotations": {"Subgraph": {"RhoA subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 3320, "target": 3398, "key": "2ccca09287cdf706d00dd29cc32e5ee7"}, {"relation": "partOf", "source": 3320, "target": 1627, "key": "b9b0a852c6039f535a2eb969377695dc"}, {"line": 36192, "relation": "increases", "evidence": "Several members of the Vps family of receptors (Vps35, SorL1, SorCS1) play important roles in post-trans-Golgi network (TGN) sorting and generation of APP derivatives, including ABeta¸ at the TGN, endosome and the plasma membrane. We now report that Vps10-domain proteins are candidate substrates for PKC and/or ROCK2 and act as phospho-state-sensitive physiological effectors for post-TGN sorting of APP and its derivatives. Activation of PKC resulted in increased shedding of the ectodomains of both APP and SorL1, and this was paralleled by an apparent increase in the level of the phosphorylated form of SorL1. ROCK2, the neuronal isoform of another protein kinase, was found to form complexes with SorL1, and both ROCK2 inhibition and ROCK2 knockdown enhanced generation of both soluble APP and ABeta¸.These results highlight the potential importance of SorL1 in elucidating phospho-state sensitive mechanisms in the regulation of metabolism of APP and ABeta¸ by PKC and ROCK2.", "citation": {"db": "PubMed", "db_id": "21192821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "RhoA subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3320, "target": 80, "key": "a42c047f469017504d136c9fe4841f1b"}, {"line": 36191, "relation": "increases", "evidence": "Several members of the Vps family of receptors (Vps35, SorL1, SorCS1) play important roles in post-trans-Golgi network (TGN) sorting and generation of APP derivatives, including ABeta¸ at the TGN, endosome and the plasma membrane. We now report that Vps10-domain proteins are candidate substrates for PKC and/or ROCK2 and act as phospho-state-sensitive physiological effectors for post-TGN sorting of APP and its derivatives. Activation of PKC resulted in increased shedding of the ectodomains of both APP and SorL1, and this was paralleled by an apparent increase in the level of the phosphorylated form of SorL1. ROCK2, the neuronal isoform of another protein kinase, was found to form complexes with SorL1, and both ROCK2 inhibition and ROCK2 knockdown enhanced generation of both soluble APP and ABeta¸.These results highlight the potential importance of SorL1 in elucidating phospho-state sensitive mechanisms in the regulation of metabolism of APP and ABeta¸ by PKC and ROCK2.", "citation": {"db": "PubMed", "db_id": "21192821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "RhoA subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 1627, "target": 2137, "key": "9bfd52ccff918aba8b5f12aa7668285d"}, {"line": 46616, "relation": "increases", "evidence": "The presence of misfolded proteins in the endoplasmic reticulum (ER) triggers a cellular stress response called the unfolded protein response (UPR) that may protect the cell against the toxic buildup of misfolded proteins. In this study we investigated the activation of the UPR in AD. Protein levels of BiP/GRP78, a molecular chaperone which is up-regulated during the UPR, was found to be increased in AD temporal cortex and hippocampus as determined by Western blot analysis.", "citation": {"db": "PubMed", "db_id": "15973543"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Chaperone subgraph": true, "Unfolded protein response subgraph": true}, "Confidence": {"Medium": true}}, "source": 543, "target": 550, "key": "e9b7a35e2f0f552cc48d0d3c258eed49"}, {"line": 36318, "relation": "increases", "evidence": "Cellular uptake and degradation by glial cells is one means by which ABeta¸ may be cleared from the brain. In the current study, we demonstrate that modulating levels of the low-density lipoprotein receptor (LDLR), a cell surface receptor that regulates the amount of apolipoprotein E (apoE) in the brain, altered both the uptake and degradation of ABeta¸ by astrocytes. Deletion of LDLR caused a decrease in ABeta¸ uptake, while increasing LDLR levels significantly enhanced both the uptake and clearance of ABeta¸. Increasing LDLR levels also enhanced the cellular degradation of ABeta¸ and facilitated the vesicular transport of ABeta¸ to lysosomes. Despite the fact that LDLR regulated the uptake of apoE by astrocytes, we found that the effect of LDLR on ABeta¸ uptake and clearance occurred in the absence of apoE. Finally, we provide evidence that ABeta¸ can directly bind to LDLR, suggesting an interaction between LDLR and ABeta¸ could be responsible for LDLR-mediated ABeta¸ uptake. Therefore, these results identify LDLR as a receptor that mediates ABeta¸ uptake and clearance by astrocytes, and provide evidence that increasing glial LDLR levels may promote ABeta¸ degradation within the brain", "citation": {"db": "PubMed", "db_id": "22383525"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 1233, "target": 2328, "key": "398660b01b3cebea5cbb9e4e1314958a"}, {"line": 36349, "relation": "decreases", "evidence": "Our previous work demonstrated that netrin-1 via its interaction with APP is a negative regulator of Abeta production in adult brain but the biological relevance of the APP/netrin-1 interaction in non pathological condition was unknown.", "citation": {"db": "PubMed", "db_id": "22782894"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 1196, "target": 2328, "key": "870057f42a9c065da4c74beb71ea5072"}, {"relation": "partOf", "source": 3145, "target": 1196, "key": "ec3c3cf30ee683bcbb61993ce6d36353"}, {"relation": "partOf", "source": 3145, "target": 1166, "key": "64d1704e1c2ca62712491e3697a8534e"}, {"line": 36357, "relation": "increases", "evidence": "We show here that during commissural axon navigation, APP, expressed at the growth cone, is part of the DCC receptor complex mediating netrin-1 dependent axon guidance. APP interacts with DCC in the presence of netrin-1 and enhances netrin-1 mediated-DCC intracellular signaling such as MAPK activation.Thus, APP functionally acts as a co-receptor for DCC to mediate axon guidance.", "citation": {"db": "PubMed", "db_id": "22782894"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1166, "target": 2173, "key": "6a71d2bdd8dd91b20dc2e87a9314f2bc"}, {"relation": "partOf", "source": 2621, "target": 1166, "key": "6a0533b608b20bab8b06212d0d34a099"}, {"relation": "partOf", "source": 2621, "target": 1165, "key": "442aa3952d2d0bbeca86d18b52131a46"}, {"line": 36358, "relation": "increases", "evidence": "We show here that during commissural axon navigation, APP, expressed at the growth cone, is part of the DCC receptor complex mediating netrin-1 dependent axon guidance. APP interacts with DCC in the presence of netrin-1 and enhances netrin-1 mediated-DCC intracellular signaling such as MAPK activation.Thus, APP functionally acts as a co-receptor for DCC to mediate axon guidance.", "citation": {"db": "PubMed", "db_id": "22782894"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 1165, "target": 481, "key": "e6c8e8daadfd8f79c660d04fdc74581c"}, {"line": 36391, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3177, "target": 2279, "key": "83025f9356379c94df9c9bb290d76d4c"}, {"line": 36396, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3177, "target": 2283, "key": "69550a67b8d6e94733c91281dabdbb47"}, {"line": 36409, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3177, "target": 2153, "key": "243c5068734bb74fb06a745a13ef2172"}, {"line": 36411, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "phos", "namespace": "bel"}}, "source": 3177, "target": 2280, "key": "3f1fc2b3fb052426642c0cea3be2f71d"}, {"line": 36434, "relation": "decreases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2450, "target": 2449, "key": "772fb7a15463ee41293f170636857327"}, {"line": 36438, "relation": "increases", "evidence": "Model of picomolar ABeta¸-induced insulin-PI3K-Akt-ERK signalling plus mitochondrial targets of intracellular ABeta¸: Extracellular ABeta¸ at picomolar concentration binds to the insulin receptor (IR) and activates PKB/Akt via PDK-1. PKB/Akt translocates into the nucleus and phosphorylates CREB. Activation of the lipid kinase PI3K is critical for the activation of PKB by PDK. PDK1 phosphorylates the activation loop of a number of protein serine/threonine kinases of the AGC kinase superfamily, including protein kinase B (PKB a; also called Akt1). Akt may also maintain the integrity of the mitochondria by a unknown mechanism or by a specific mechanism of Bad phosphorylation. Akt can also inhibit apoptosis by phosphorylation and inactivation of caspase-9.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Caspase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2450, "target": 478, "key": "27ad3db6f2f70d6f19cd4f9b1c8e7f92"}, {"line": 36454, "relation": "increases", "evidence": "ERK1/2 are activated by upstream MAPKK, such as MEK1/2, and MAPKKK, such as c-Raf. MEK1/2 induce ERK1/2 activation via dual phosphorylation on threonine 202 and tyrosine 204 residues.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2176, "target": 2173, "key": "924fca0af1dfabf4a73968ddc9774c06"}, {"line": 36482, "relation": "increases", "evidence": "For example, ERK activates pro-survival transcription factor CREB, by activating both p90RSK and MSK1/2.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "CREB subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 1719, "target": 2162, "key": "a1148963a0d9cda9df9b752ac2966dcd"}, {"relation": "partOf", "source": 3325, "target": 1719, "key": "5f01ded513c157a587de7b52df162d12"}, {"relation": "partOf", "source": 3326, "target": 1719, "key": "5a807a33997d74aac800eef1f6a00730"}, {"line": 37104, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157]. In contrast to CaMKs, ERKs cannot directly phosphorylate CREB. Two related RSKs and mitogen- and stress-activated protein kinases (MSKs) transmit the signal from activated ERKs to CREB [158]. CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis [159, 160]. Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity [161]. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP) [162]. There is no direct evidence indicating how lower levels of ABeta¸ might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3326, "target": 2555, "key": "5b8e6e4a237cce6f7c888138344b7075"}, {"line": 36489, "relation": "increases", "evidence": "Picomolar extracellular ABeta¸ also binds nAChR, glutamate receptors (NMDAR) and Ca2+ ion channels (e.g. VDCCs, TRPC) and causes Ca2+ influx at controlled rates into the cytoplasm and mitochondria.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Acetylcholine signaling subgraph": true, "Glutamatergic subgraph": true}, "Confidence": {"Medium": true}}, "source": 907, "target": 673, "key": "336c411c6cbab6d1ab9efa2ff4e636e9"}, {"relation": "partOf", "source": 2168, "target": 907, "key": "0a35d916f6b85ee854fe3b059dd26c95"}, {"line": 36497, "relation": "increases", "evidence": "Increased cytosolic calcium concentrations initiate the activation of several kinase-dependent signalling cascades including activation of PKC leading to CREB activation and phosphorylation at Ser133, a process critical for protein synthesis-dependent synaptic plasticity and LTP. PKC-a also activates ERK by interacting with Ras or Raf-1.Mitochondria are critical targets of intracellular ABeta¸. ABeta¸ interacts with CypD, a protein component of the membrane permeability transition pore (MPTP). The interaction of CypD with ABeta¸ causes functional modification of this protein leading to MPTP opening. ABeta¸ also binds with another mitochondrial protein, ABAD to distort the enzyme’s structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2131, "target": 3236, "key": "d64ef2a2e5f57cbc2ea27f42086edd25"}, {"line": 36517, "relation": "increases", "evidence": "Increased cytosolic calcium concentrations initiate the activation of several kinase-dependent signalling cascades including activation of PKC leading to CREB activation and phosphorylation at Ser133, a process critical for protein synthesis-dependent synaptic plasticity and LTP. PKC-a also activates ERK by interacting with Ras or Raf-1.Mitochondria are critical targets of intracellular ABeta¸. ABeta¸ interacts with CypD, a protein component of the membrane permeability transition pore (MPTP). The interaction of CypD with ABeta¸ causes functional modification of this protein leading to MPTP opening. ABeta¸ also binds with another mitochondrial protein, ABAD to distort the enzyme’s structure, rendering it inactive. This causes an increase in reactive oxygen species and oxidative stress leading to initiation of apoptotic process", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1616, "target": 2173, "key": "2fcea546b92fa35adf55969a5ebe18b9"}, {"line": 36944, "relation": "increases", "evidence": "Protein kinase C: PKC is part of a multigene family of serine-threonine kinases central to many signal transduction pathways [138] with a prominent role in memory [139]. It is likely that ABeta¸-induced increases in cytosolic Ca2+ signals are transmitted to PKC for PKC-mediated transcriptional activation. In addition, PKC activates ERK by interacting with Ras or Raf-1 [140] to initiate CREB phosphorylation. While PKC levels decline in AD [141], their activation restores K+ channel function in cells from AD patients [142]. In addition, activation of PKC directly or indirectly enhances the a-processing cleavage of APP [143].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1616, "target": 2173, "key": "4faef8c1ae293e50538df933ad3691c4"}, {"line": 36525, "relation": "increases", "evidence": "In animal models as well as in humans, the activation of muscarinic M1 acetylcholine receptors increases a-secretase cleavage of APP and consequently reduces ABeta¸ levels [63, 64] whereas activation of NMDARs decreases a-secretase cleavage, consequently increasing ABeta¸ levels [65]. Stimulation with muscarinic agonists or activators of protein kinase C (PKC), such as phorbol esters causes the up-regulation of the a-secretase cleavage of APP", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity", "effect": {"name": "pep", "namespace": "bel"}}, "source": 2512, "target": 2315, "key": "97af45524d922905788468b9ed343835"}, {"line": 37157, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 384, "target": 2776, "key": "61b4733745b16e68f799c2441598c5b0"}, {"line": 37159, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 384, "target": 2779, "key": "a9c94a5e686e416d8172da005506c2a9"}, {"line": 37161, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 384, "target": 2781, "key": "f77282ecb572c6cec02f6deb8d4dc0ee"}, {"line": 37163, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 384, "target": 2782, "key": "3e7dac5ed8fb38fdb61dbeaa85430479"}, {"line": 37165, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 384, "target": 2783, "key": "dca44e97e244e4598057c80b45968201"}, {"line": 37167, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 384, "target": 2784, "key": "d8948b48080f34692d47267d4e2e1a2f"}, {"line": 37169, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 384, "target": 2843, "key": "d5befb523df4ce4a0b9dcf11d7d7a612"}, {"line": 36648, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2910, "target": 420, "key": "939efb9478314e14263515400962009f"}, {"line": 36667, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2910, "target": 3191, "key": "ea07465e7225dc5269bc1849864f97ab"}, {"line": 36679, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2910, "target": 3178, "key": "9009dd34a5c8730e67a18fff167083f1"}, {"line": 36696, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2910, "target": 2769, "key": "c0a6aff8b3958df8dd92d22c0a3e6bdc"}, {"line": 36719, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2910, "target": 2792, "key": "91041bffa4839d9daaad660f77aa79f1"}, {"line": 36746, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2910, "target": 2187, "key": "d93eb81a5340fd608a560a2548dbe330"}, {"line": 36830, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2910, "target": 1670, "key": "2386972c75a84c03c03186e59b9a713a"}, {"line": 36650, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2915, "target": 420, "key": "d0c08bd32c093993b5e91bab87326923"}, {"line": 36678, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2915, "target": 3191, "key": "89f8236a282f77c81282d6860bbdfec1"}, {"line": 36680, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2915, "target": 3178, "key": "9a8cab97ecfac4e36c954343bd27ed92"}, {"line": 36698, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2915, "target": 2769, "key": "e684c78e2f4148f9d936497438ff9431"}, {"line": 36720, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2915, "target": 2794, "key": "dcd8fd63bf71652da410e3ab2a64a48f"}, {"line": 36731, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2915, "target": 2792, "key": "523616b716d15beb9723def791dde5a9"}, {"line": 36749, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 2915, "target": 2187, "key": "c9443e0970b86829afe9738eeb7a5507"}, {"line": 36832, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "source": 2915, "target": 1670, "key": "5e51071e4ba7cca7488992340cd96a17"}, {"line": 36652, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3355, "target": 420, "key": "2406049bf54fc483f45cb6ea96741fed"}, {"line": 36686, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3355, "target": 3191, "key": "2195ab72e5d797055580145d9ed71324"}, {"line": 36707, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3355, "target": 2769, "key": "a43a5d0cfce3ad8a528657928aaa55ef"}, {"line": 36758, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3355, "target": 2187, "key": "bd1ee74f5c85dfda378f8caddee312bc"}, {"line": 36654, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3357, "target": 420, "key": "4ba2b18d2eca3c9ca115a97af8efc5d0"}, {"line": 36688, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3357, "target": 3191, "key": "86fb38dcc4a9079e10b9c95498c0d126"}, {"line": 36708, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3357, "target": 2769, "key": "c41cbf4d5e8ac15922dcc63d09da2c32"}, {"line": 36656, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3359, "target": 420, "key": "d66e24de95bb756d7ea474c78eea1f15"}, {"line": 36689, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3359, "target": 3191, "key": "b56b72af471efd0e04e4ed379582e4d3"}, {"line": 36709, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3359, "target": 2769, "key": "f45b165f270f9e1a552a3596d2bbdcab"}, {"line": 36764, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3359, "target": 2187, "key": "0a462aa9353b3cd3e146b6ef01037f0b"}, {"line": 36658, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Insulin signal transduction": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3361, "target": 420, "key": "cf0ff3af044d1b4628e7e62210f2b11c"}, {"line": 36690, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3361, "target": 3191, "key": "80baffbf50bd86394e9436ef3a7da758"}, {"line": 36710, "relation": "association", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3361, "target": 2769, "key": "81d33be54635c38bd4a55aaea28832a8"}, {"line": 36767, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true, "Tumor necrosis factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3361, "target": 2187, "key": "5fcccec31ea9af367c2151983aee8886"}, {"relation": "hasVariant", "source": 3360, "target": 3361, "key": "91fbf8d65662922c9a5a26c736164e16"}, {"line": 38438, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3360, "target": 3563, "key": "4fa1669600adfee50dc7d2fa45d46607"}, {"relation": "partOf", "source": 3360, "target": 1632, "key": "f1cab4d803d9d0f7e96c343e67e573b9"}, {"line": 36788, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "G-protein-mediated signaling": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1444, "target": 566, "key": "9570f4ae8127acc297f21e58aec7753e"}, {"line": 36792, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "G-protein-mediated signaling": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1444, "target": 553, "key": "241e4bb68bf5c0ab06d57e5154dfd9a7"}, {"line": 36796, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "G-protein-mediated signaling": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1444, "target": 635, "key": "78cfc0c5d2295a78de6e158b9e3abf04"}, {"line": 36790, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "G-protein-mediated signaling": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1447, "target": 566, "key": "ce52cba904db194276b686720157592f"}, {"line": 36794, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "G-protein-mediated signaling": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1447, "target": 553, "key": "c3631650f20f5030746b3ce708f52c32"}, {"line": 36798, "relation": "increases", "evidence": "Insulin like growth factor-I (IGF-I) and insulin signaling: The underlying mechanism of neuronal survival with ABeta¸ is emerging. Neuronal synapses and astrocytes of memory-processing brain regions possess insulin receptors (IRs) [99] which when activated by insulin facilitate synaptic plasticity in normal brain [100]. IR and Insulin-like growth factor I (IGF-I) receptors consist of a-subunits and transmembrane ß-subunits. Binding of insulin or IGF-I to the a-subunit increases the intrinsic tyrosine kinase activity of the ß-subunit, and causes autophosphorylation of the ß-subunit, thus triggering tyrosine phosphorylation of insulin receptor substrate (IRS)-1 and IRS-2, as well as Shc [101] as an important pathway of cell survival. To protect against ABeta¸ toxicity, the tyrosine-phosphorylated sites create binding sites for various signal-transducing molecules containing Src homology-2 domain, such as phosphoinositide 3-kinase (PI3K) and growth factor receptor-bound protein 2 (Grb2), thus activating PI3K/phosphoinostide-dependent kinase 1 (PDK1)/Akt (protein kinase B)/glycogen synthase kinase (GSK)-3a/-3ß and Ras/Raf-1/mitogen-activated protein kinase/extracellular-signal regulated kinase (MEK/ERK) signaling pathways [101]. In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival [102], largely through PI3K/Akt/GSK-3ß signaling [103, 104].Consistent with positive effects of insulin on synaptic plasticity [105], acute insulin treatment improved memory in rats [106] and also in normaladults and AD patients [107] by strongly activating ERK and Akt and blocking c-Jun N-terminal kinase (JNK) activation in a PI3K-dependent manner [108]. The mechanism involves many steps beginning with ABeta¸ activation of IGF-1/insulin receptors by locally produced IGF-1 or, possibly, ABeta¸ monomers may bind to IGF-1/insulin receptors, as already shown for ABeta¸ oligomers ", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true, "G-protein-mediated signaling": true, "Insulin signal transduction": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1447, "target": 635, "key": "158f1cbf9dd6cf14893f090bb7039ef0"}, {"line": 36814, "relation": "association", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "source": 635, "target": 2149, "key": "c82f7234653a854a64c38964daaccf75"}, {"line": 36816, "relation": "association", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "source": 635, "target": 2153, "key": "7145c26991fd311a08391e0d5d995108"}, {"line": 36814, "relation": "association", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "source": 2149, "target": 635, "key": "31cac7fce538f6e0bd7bf426295160bb"}, {"line": 36818, "relation": "association", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "source": 2149, "target": 688, "key": "9ca62d6efee6c4cb6c218db6496618ed"}, {"relation": "partOf", "source": 2149, "target": 1670, "key": "54c6808a2998b1c5be4a66feb58660ee"}, {"line": 36838, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2149, "target": 2155, "key": "3ccfc330efcc48b8505673188569f385"}, {"line": 36840, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 2149, "target": 2156, "key": "9a690091c62e4fd060558604515c57bb"}, {"line": 36981, "relation": "decreases", "evidence": "Calcium signaling: Synaptic activity is required for neurons to survive[145] by entry of appropriate amounts of Ca2+ through synaptic NMDA receptors and other Ca2+ channels [146]. The process implicates key protein effectors, such as CaMKs, MAPK/ERKs, and CREB. Properly controlled homeostasis of calcium signaling not only supports normal brain physiology but also maintains neuronal integrity and long-term cell survival. Ca2+ signaling pathways can suppress apoptosis and promote survival through two mechanistically distinct processes. One process involves the PI3K/AKT signaling pathway which promotes survival [147]. The other pathway requires the generation of calcium transients in the cell nucleus which offers long-lasting neuroprotection [146, 148]. Malfunctioning of calcium signaling to the cell nucleus may lead to neurodegeneration and neuronal cell death .", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true, "Calcium-dependent signal transduction": true}, "Confidence": {"Medium": true}}, "source": 1670, "target": 742, "key": "d4ead48bc7d02dd4d9ff78cc5e941aec"}, {"line": 36841, "relation": "increases", "evidence": "The PI3K/Akt signaling: PI3K, a membrane-associated second messenger protein, and its downstream kinase, Akt, are associated with neuronal survival [111] and plasticity [112] via activation of transcription pathways and protein synthesis. PI3K pathway, which required the activation of IGF-1/insulin receptors, is the most convincing prosurvival effect of ABeta¸42 monomers. The PI3K signaling pathway is important in the transmission of survival signals in many cell types including neurons [113, 114]. The PI3K-Akt signaling cascade, initiated by IRS, is phosphorylated by stimulated insulin- andIGF-receptor tyrosine kinases [115]. One of the kinases known to lie downstream of PI3K is Akt, which can be directly activated by products of PI3K [116] by promoting its phosphorylation at Ser473 and Thr308 [117]. Activated Akt, in turn, phosphorylates a wide range of substrates activating anti-apoptotic (survival) factors and inactivating pro-apoptotic factors [114, 117]. Certain proapoptotic mediators, such as the transcription factor forkhead (FOXO), the tau kinase GSK-3ß, and the Bcl2 antagonist BAD proteins, are inactivated by Akt [118, 119]. Akt downregulates the activities of GSK-3a and GSK-3ß by phosphorylating the former at Ser21 and the latter at Ser9 [118]. Phosphorylation/inactivation of GSK-3ß, suppresses GSK-3ß-dependent phosphorylation of tau at residues overphosphorylated in AD and prevents apoptosis of confluent cells. Treatment of cortical neurons with ABeta¸42 monomers increased Ser9 phosphorylation (inhibition) of the Akt substrate, GSK- 3ß [97]. Inhibition of GSK-3ß promotes cell survival through a variety of mechanisms including a reduced degradation of ß-catenin, which then translocates into the nucleus and activates the transcription of protective genes [126].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true, "Akt subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2156, "target": 2153, "key": "b6e8051392d0b2caa380931d8101fcb1"}, {"line": 36891, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2175, "target": 2173, "key": "31b92fca8bc6e28309e6cf9b37c142d9"}, {"line": 37248, "relation": "increases", "evidence": "Nicotinic acetylcholine receptors (nAChRs). Activation of the neuronal pentameric nAChRs is involved in diverse brain functions including synaptic plasticity and memory [237, 238] and enhances transmitter release in several brain regions including the hippocampus [239], the spinal cord dorsal horn [240], the olfactory bulb, and the amygdala [241]. The increase of synaptic plasticity by ABeta¸ requires activation of nAChRs [242]. Because activation of nAChRs is necessary for the ABeta¸-induced increase of synaptic plasticity and memory under normal conditions, ABeta¸ may modify glutamate release with a mechanism dependent upon activation of nAChRs [83]. However, several reports of the effect of ABeta¸42 on nAChRs are conflicting. Some studies have reported that ABeta¸42 activates nAChRs [243, 244], while others indicate that ABeta¸42 inhibits nAChRs [245, 246]. Interestingly, picomolar concentrations of ABeta¸42 were effective in activating nAChRs while higher levels of ABeta¸ produced inhibitory action. The disparity may depend on the nanomolar ABeta¸1–42 inhibition of nicotine-induced Ca2+ responses while picomolar ABeta¸42 directly evokes sustained increases in presynaptic Ca2+ via nAChRs [244]. ABeta¸42 binds to the nAChR with picomolar affinity [247]. This binding can modulate presynaptic, glutamate-mediated synaptic transmission or glutamate release, suggesting that ABeta¸42-dependent cholinergic modulation activates signal transduction mechanisms that ultimately result in synaptic transmission and memory consolidation [248]. However, it remains to be determined whether these effects are mediated by a direct physical interaction of the ABeta¸ peptide with the nAChRs. Immunohistochemical studies on human sporadic AD brains show that ABeta¸42 and nAChR, are both present in neuritic plaques and co-localize in individual cortical neurons suggesting that ABeta¸ could be tightly associated with nAChR [247]. Alternatively, ABeta¸ might be responsible for regulation of nAChR function through strong binding with membrane lipids [249]. Picomolar or higher ABeta¸42 acting through nAChRs, can elicit ERK MAPK activation in hippocampal cultures [245, 250] possibly triggered by Ca2+ influx [243, 251]. ERK is known to regulate transcription factors such as CREB and Elk-1 by phosphorylation [140], which help initiate transcription of memory-associated genes that contain their respective regulatory elements [252]. Therefore, over-activation of nAChRs and excessive Ca2+ influx or dysregulation of Ca2+ homeostasis provide a molecular mechanism for the cholinergic dysfunction that is a hallmark of AD [253, 254].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2672, "target": 2671, "key": "6982640cf074c9ea31bb04c465eb0094"}, {"relation": "hasVariant", "source": 2671, "target": 2672, "key": "2a20a9486a85dafb3d6154a4d7b2cf1f"}, {"line": 36902, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3323, "target": 2555, "key": "35cd5ea7bc79782faad13b855131c124"}, {"line": 37106, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157]. In contrast to CaMKs, ERKs cannot directly phosphorylate CREB. Two related RSKs and mitogen- and stress-activated protein kinases (MSKs) transmit the signal from activated ERKs to CREB [158]. CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis [159, 160]. Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity [161]. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP) [162]. There is no direct evidence indicating how lower levels of ABeta¸ might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "source": 2555, "target": 669, "key": "c2f12e3c44efe4ec10ca8bd414ce3326"}, {"line": 36918, "relation": "increases", "evidence": "Extracellular-signal regulated kinase 1/2 signaling (ERK1 and ERK2): The mitogen-activated protein kinase (MAPK) family of protein kinases is traditionally viewed as important kinases in transmitting extracellular membrane signals intothe nucleus. The 44 kDa ERK1 and 42 kDa ERK2 are members of the MAPK superfamily that specifically respond to ABeta¸ in brain cells [127]. ERK1 and ERK2 are known to be activated through dual phosphorylation by the MAPK/ERK on threonine and tyrosine in the Thr-Glu-Tyr sequence of the activation loop [128, 129]. ERK signaling is critical for memory and tightly regulated by many proteins. ERKs are critical for human learning as revealed by human mental retardation syndromes [130]. They are also known to contribute to molecular information processing in dendrites, to stabilize structural changes in dendritic spines and to interact with scaffolding and structural proteins at the synapse [131]. ERK is an important neuronal marker for activity through activation by cytosolic calcium and depolarization of the membrane [132, 133]. On phosphorylation and activation, ERKs phosphorylate other cytoplasmic effectors and are translocated into the nucleus where they phosphorylate transcription factors such as Myc, Fos, Jun, and Elk1 [104, 134]. Direct substrates of the ERKs includetwo members of the RSK family of protein serine-threonine kinases, RSK1 and RSK2. The transcription factor CREB is phosphorylated on serine 133 in vivo by RSK2 in NGF-stimulated PC12 cells [135]. The dependence of CREB phosphorylation on activation of the ERK pathway is suggested by inhibition of ABeta¸-induced phosphorylation of CREB by piceatannol and the MEK inhibitor PD98059. Other kinases, such as protein kinase A (PKA) or Ca2+/calmodulin-dependent protein kinases [CAM kinases; 136] may also contribute to phosphorylation of cyclic AMP response element (CRE)-binding protein (CREB) in response to ABeta¸ but the complete inhibition of CREB phosphorylation by PD98059 suggests that the ERK pathway is the main signaling pathway elicited by ABeta¸ leading to transcriptional activation through CREB [137]. These data provide a mechanism by which ABeta¸ alters gene expression through the transcription factor CREB [137], possibly resulting in a ceiling of activation that limits further formation of new memories.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"High": true}}, "source": 2988, "target": 2163, "key": "2467a5ac812b62ba964917868f8334b7"}, {"line": 36941, "relation": "increases", "evidence": "Protein kinase C: PKC is part of a multigene family of serine-threonine kinases central to many signal transduction pathways [138] with a prominent role in memory [139]. It is likely that ABeta¸-induced increases in cytosolic Ca2+ signals are transmitted to PKC for PKC-mediated transcriptional activation. In addition, PKC activates ERK by interacting with Ras or Raf-1 [140] to initiate CREB phosphorylation. While PKC levels decline in AD [141], their activation restores K+ channel function in cells from AD patients [142]. In addition, activation of PKC directly or indirectly enhances the a-processing cleavage of APP [143].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 674, "target": 3236, "key": "84956aebf2d5b2984ad427b6a43bf5bf"}, {"relation": "partOf", "source": 2514, "target": 1226, "key": "d6d9e81d0bd7b2141618c287ef0475c7"}, {"line": 37570, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 758, "target": 2315, "key": "2af0ded6574a55ad5284aecded1d7576"}, {"line": 37571, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 758, "target": 2306, "key": "641ec231b7cea7983653eddb54ab2e67"}, {"line": 37572, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 758, "target": 2307, "key": "82b4b0e596a61cc978cce7414d44bc74"}, {"line": 37055, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2413, "target": 80, "key": "d3cc7d21c01fc5c3540813fc7d5e306c"}, {"line": 37056, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2413, "target": 688, "key": "236858192e487c39c93289932dc5f8b4"}, {"line": 37079, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2413, "target": 2328, "key": "87846d7be9997e4cb1b0fe6728c6fef3"}, {"line": 37080, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2413, "target": 94, "key": "4a4f6b015959d63f925df0c841e9b2c5"}, {"line": 37057, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2414, "target": 80, "key": "296f7ce248f52f905161c7a1745d2192"}, {"line": 37058, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2414, "target": 688, "key": "cd8eccfdf4f3e0a390daff393845b3cc"}, {"line": 37081, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2414, "target": 2328, "key": "e11e8ac597985bd81e03aa952bcc28e4"}, {"line": 37082, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2414, "target": 94, "key": "1677aef845177068dc1cf83a62bb047c"}, {"line": 37059, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2415, "target": 80, "key": "a946699145b5397d4836873569e21718"}, {"line": 37060, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2415, "target": 688, "key": "b382446dabd1b13fbbca2b7bb5b1bffd"}, {"line": 37083, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2415, "target": 2328, "key": "f1933f91ad5e5fba7af44406325591f0"}, {"line": 37084, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2415, "target": 94, "key": "caca81660115c4ef3682867fc9856529"}, {"line": 37061, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2416, "target": 80, "key": "e4d2abd44db0d478ca84414bafef48aa"}, {"line": 37062, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2416, "target": 688, "key": "5141a7107efdfe14873df79b5f589362"}, {"line": 37085, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2416, "target": 2328, "key": "ac6ee286082753773fda52c0a626a7d8"}, {"line": 37086, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2416, "target": 94, "key": "f8926109e92c6c4641182099f093da02"}, {"line": 37063, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2417, "target": 80, "key": "60c63898929724c87f4752985207c05b"}, {"line": 37064, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2417, "target": 688, "key": "ac8602f009b68a844e0bd2861189d637"}, {"line": 37087, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2417, "target": 2328, "key": "4f2ed7c4028aec469dd47976153dcf02"}, {"line": 37088, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2417, "target": 94, "key": "13c93ad8a9668bf88c04279f1fc14fdc"}, {"line": 37065, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2418, "target": 80, "key": "5077cec9db476e443507d7bf7e5597a2"}, {"line": 37066, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2418, "target": 688, "key": "822f967d3a5243d6d64d6bfd1f46c952"}, {"line": 37067, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2419, "target": 80, "key": "617ce2bc6f0d9f72e9ffd8efdb098c67"}, {"line": 37068, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2419, "target": 688, "key": "24c53f60ef37980da19ee316c7718423"}, {"line": 37089, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2419, "target": 2328, "key": "6df0c54d65340fe0daab1fa92245955f"}, {"line": 37090, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2419, "target": 94, "key": "50acb729963d21651c4b4eceb7786842"}, {"line": 37069, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2420, "target": 80, "key": "9f505198f33ca539c6bf3f4e54b30f97"}, {"line": 37070, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 2420, "target": 688, "key": "09805350f72bbad9a0dbe57536f4b1e3"}, {"line": 37091, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2420, "target": 2328, "key": "d4b11999d4ec606110346c880909f941"}, {"line": 37092, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "source": 2420, "target": 94, "key": "6d8f413b0005846c8764a6e0a6c3a7b9"}, {"line": 37075, "relation": "association", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3555, "target": 2328, "key": "2ca7a2b7498bf545ddeb0b2e88a0f9e0"}, {"line": 37076, "relation": "increases", "evidence": "ABeta¸ directly interacts with Ca2+ channels such as voltage-dependent calcium channels (VDCC) and TRP cation channels (TRPC) to produce a transient increase in Ca2+ necessary for synaptic plasticity and neuronal survival. ABeta¸ interacts directly with the recombinant L-type Ca2+ channel (a1C) subunit to increase Ca2+ channel protein at the cell membrane and hence increased Ca2+ conductance [80]. Within the TRPC subfamily, TRPC3 and 6 have been shown to protect cerebellar granule neurons against serum deprivation–induced cell death in cultures and promote neuronal survival in rat brain [155]. A neuronal survival mechanism of ABeta¸ may also involve altered expression of K+ channels [80]. In cerebellar granule neurons, 24-h pre-incubation with 1 µM unaggregated ABeta¸ protein resulted in a 60% increase in the ‘A’-type component of K+ current possibly reflecting Ca2+-mediated gene expression [156]. A full understanding of these signal transduction pathways of Ca2+ may lead to refined pharmacological strategies that minimize deadly effects of Ca2+ entry and optimize its growth- andsurvival-promoting properties.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3555, "target": 688, "key": "723793acef4b50e9859bb6f48227a8d4"}, {"line": 37100, "relation": "increases", "evidence": "Transcriptional activation: CREB is one of the best characterized stimulus-induced transcription factors that activate transcription of target genes in response to a diverse array of stimuli, including neuronal activity, a variety of protein kinases such as protein kinase A (PKA), MAPK/ERKs, pp90 ribosomal S6 kinase (RSK), and Ca2+/calmodulin-dependent protein kinases (CaMKs). These kinases all phosphorylate CREB at a particular residue, serine 133 (Ser133) which is required for CREB-mediated transcription [157]. In contrast to CaMKs, ERKs cannot directly phosphorylate CREB. Two related RSKs and mitogen- and stress-activated protein kinases (MSKs) transmit the signal from activated ERKs to CREB [158]. CREB has been shown to be involved in certain types of hippocampal LTP as well as long-term memory, neurogenesis and synaptogenesis [159, 160]. Transcriptional activation of CREB recruits a multiprotein assembly called a transcriptional co-activator complex. These often include proteins with intrinsic acetyltransferase activity [161]. Among the best characterized transcriptional co-activator proteins is CREB binding protein (CBP) [162]. There is no direct evidence indicating how lower levels of ABeta¸ might initiate CREB phosphorylation principally by Ca2+ signaling and/or through PKA/Atk/ERK pathways.", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"CREB subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3233, "target": 2555, "key": "b9cd08747d34b5fa0fb02e5cecd6a189"}, {"line": 37901, "relation": "regulates", "evidence": "Iron was further demonstrated to modulate expression of the Alzheimer's amyloid precursor holo-protein (APP) by a mechanism similar to that of regulation of ferritin-L and -H mRNA translation through an iron-responsive element (IRE) in their 5' untranslated regions (UTRs). Here, we discuss two aspects of the link between iron and AD, in relation to the recently discovered IRE in the 5'UTR of APP mRNA. The first is the physiological aspect: a compensatory neuroprotective response of amyloid-ß protein (ABeta¸) in reducing iron-induced neurotoxicity. Thus, given that ABeta¸ possesses iron chelation sites, it is hypothesized that OS-induced intracellular iron may stimulate APP holo-protein translation (via the APP 5'UTR) and subsequently the generation of its cleavage product, ABeta¸, as a compensatory response that eventually reduces OS.", "citation": {"db": "PubMed", "db_id": "19090990"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 138, "target": 3940, "key": "30aac2d0ead22cf043559ab5836f33a6"}, {"line": 37902, "relation": "increases", "evidence": "Iron was further demonstrated to modulate expression of the Alzheimer's amyloid precursor holo-protein (APP) by a mechanism similar to that of regulation of ferritin-L and -H mRNA translation through an iron-responsive element (IRE) in their 5' untranslated regions (UTRs). Here, we discuss two aspects of the link between iron and AD, in relation to the recently discovered IRE in the 5'UTR of APP mRNA. The first is the physiological aspect: a compensatory neuroprotective response of amyloid-ß protein (ABeta¸) in reducing iron-induced neurotoxicity. Thus, given that ABeta¸ possesses iron chelation sites, it is hypothesized that OS-induced intracellular iron may stimulate APP holo-protein translation (via the APP 5'UTR) and subsequently the generation of its cleavage product, ABeta¸, as a compensatory response that eventually reduces OS.", "citation": {"db": "PubMed", "db_id": "19090990"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 138, "target": 80, "key": "ee0774df913cea183e95f89e96609fc3"}, {"line": 37158, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 399, "target": 2776, "key": "0abc2696f7f1ff5600fb4ceba700564f"}, {"line": 37160, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 399, "target": 2779, "key": "42c89e04d8eb90a05a3df86b131b532f"}, {"line": 37162, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 399, "target": 2781, "key": "a6c115509dde65abebbd9cae0a7fdc38"}, {"line": 37164, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 399, "target": 2782, "key": "f0dc2a77729b78953842b08a70807891"}, {"line": 37166, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 399, "target": 2783, "key": "037dbc56ed833a6aaf00894904a184b3"}, {"line": 37168, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 399, "target": 2784, "key": "48cbaa8a9af6a15885e16ffdb9bfac08"}, {"line": 37170, "relation": "association", "evidence": "NMDA receptors: It is well known that excitatory synapses contain AMPA and NMDA ionotropic glutamate receptors as well as metabotropic type glutamate receptors (mGluRs) positioned on dendritic spines [210, 211]. ABeta¸-induced synaptic dysfunction has been attributed to the synaptic removal of AMPA receptors (AMPARs); however, it is unclear how ABeta¸ induces this loss [212]. Glutamatergic processes are strongly implicated in causing and mediating the symptoms of AD [213].", "citation": {"db": "PubMed", "db_id": "20847424"}, "annotations": {"Subgraph": {"NMDA receptor": true, "Glutamatergic subgraph": true}, "Confidence": {"High": true}}, "source": 399, "target": 2843, "key": "7387c491d99ac3cd7c5bdd44f17ac101"}, {"line": 37294, "relation": "association", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 743, "target": 2186, "key": "0ee4ef75499b6f28461a0717f1c37a65"}, {"line": 37298, "relation": "association", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 743, "target": 1532, "key": "2eaaf9fcb29ca28d2d9e6929648b153a"}, {"line": 37299, "relation": "association", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 743, "target": 1626, "key": "9e48fa8901b43efaa000bd05b738c195"}, {"line": 37551, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 743, "target": 2315, "key": "9a9c0e5ac20e7c950e13a687a5d825f8"}, {"line": 37552, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 743, "target": 2306, "key": "de3b1c789d66ef662e5971ec68711d22"}, {"line": 37553, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 743, "target": 2307, "key": "dd3ae3a957ad1630486d93c5e464c801"}, {"line": 37283, "relation": "association", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2927, "target": 2315, "key": "2aab4dafcf6b6c01b7be87b27070ad18"}, {"line": 37284, "relation": "increases", "evidence": "The extracellular domain of APP interacts with the extracellular matrix (ECM) protein F-spondin. F-spondin also interacts with members of the LDL receptor family.Reelin is another large, multi-domain, extracellular protein that interacts with members of the LDL receptor family.Reelin affects neurite outgrowth in vitro and regulates neuronal migration in development via phosphorylation of the cytoplasmic adaptor protein Disabled (Dab-1). Dab-1 interacts with the cytoplasmic domains of the proteins in the LDL receptor and APP families.Another important class of molecules involved in neurite outgrowth, cell adhesion, and cell migration is the family of integrins.Integrins are transmembrane proteins that form the link between the ECM and intracellular components. APP interacts and colocalizes with ß1 integrin, a molecule that is important for proper laminar organization and capable of enhancing neurite outgrowth. ß1 integrin interacts with Reelin, and this interaction is important for its effects on neuronal migration.we observed that interactions between Reelin, APP, and a3ß1 integrin promote neurite outgrowth in cultured neurons and that APP and Reelin affected dendritic processes in vivo.", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2927, "target": 652, "key": "ddb45a8f4a45c43ebec2c55d92fa3049"}, {"line": 37319, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 2927, "target": 467, "key": "7343b8f97bd3c7367f53b6fe8987afcc"}, {"line": 37320, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 2927, "target": 651, "key": "b05f3f7dcdcadfd92a46e2b325bc8e80"}, {"line": 37326, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 2927, "target": 763, "key": "dd5480f35745f933b59353f7ac2ba1e4"}, {"relation": "partOf", "source": 2927, "target": 1180, "key": "09b3568bcc27c9a9aa2a17d71e6ded4d"}, {"line": 37293, "relation": "association", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2186, "target": 652, "key": "32dd52f016dc1a189bf9e7efb2993a19"}, {"line": 37294, "relation": "association", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Low density lipoprotein subgraph": true, "Reelin signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2186, "target": 743, "key": "0ffb81d7f7baabd6af817a5776f19e7e"}, {"line": 37314, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 2778, "target": 738, "key": "d20ce2629dcb3f197680358c8ec647ad"}, {"line": 37321, "relation": "increases", "evidence": "integrins play important roles in neurite development, neuronal migration, and synapse functions. Several studies show that Reelin signaling has an important role in neuronal migration through its interaction with ApoE receptor 2 and the VLDL receptor, and the subsequent phosphorylation of Disabled-1. After removal of anyof these components (Reelin, receptors, or Disabled-1), cortical layer formation is severely disrupted, leading to the model that these interactions are important for laminar organization in development.In synapses, Reelin promotes tyrosine phosphorylation of NMDA receptor subunits (e.g., NR2A) and facilitates LTP. Reelin signaling has an important role in NMDA and AMPA receptor function, maturation of glutamatergic synapses.ß1 integrin facilitates reorganization of actin filaments and affects neuronal migration and neurite outgrowth. The Reelin-a3ß1 integrin interaction regulates neuronal migration, perhaps by reorganizing the actin cytoskeleton, or by stabilizing the cytoskeleton through n-cofilin phosphorylation. Integrins also regulate synaptic glutamate receptor function, synaptic plasticity, working memory, and LTP. The APP-ß1 integrin interaction is also important in neurite outgrowth. We suggest that the functions of Reelin and ß1 integrins on these processes may be modulated by APP (or other APP family members).", "citation": {"db": "PubMed", "db_id": "19515914"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Glutamatergic subgraph": true}, "MeSHAnatomy": {"Synapses": true}, "Confidence": {"High": true}}, "source": 467, "target": 651, "key": "9edd6621b7041aec4dd3233b719bb33c"}, {"line": 37343, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 3085, "target": 2960, "key": "3cd20374d6711db1deaf2c895b73bc77"}, {"line": 37359, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3085, "target": 3527, "key": "3839a7c223db75d563dd99d4d16ef4c0"}, {"line": 37361, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 3085, "target": 2982, "key": "68bcf54bf349fec54efcd89874e71602"}, {"line": 37348, "relation": "regulates", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3134, "target": 3085, "key": "9b7808fcf383041a0d099553715daf48"}, {"line": 37373, "relation": "decreases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3134, "target": 1626, "key": "64adfeb3bc0fb2efff7c424f36abef85"}, {"line": 37374, "relation": "decreases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3134, "target": 2617, "key": "f2ebde19102520763326ad8a7e850e89"}, {"line": 37349, "relation": "regulates", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3133, "target": 3085, "key": "7c828a77d5a9565ee3dea8b5daeb5501"}, {"line": 37375, "relation": "decreases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3133, "target": 1626, "key": "765b773556c960240d40ec85de36ec3e"}, {"line": 37376, "relation": "decreases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3133, "target": 2617, "key": "bbe3c7976bff8be6052714ab80d1f6f2"}, {"line": 37360, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 3527, "target": 2012, "key": "6243b88a9a49f8e54eeb42767ac74c82"}, {"line": 37366, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2012, "target": 521, "key": "4b25f1935fb60d086610078c336f2043"}, {"line": 37368, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 2012, "target": 592, "key": "eb9740fd934f77512c6d7bc148758f8c"}, {"line": 37362, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2982, "target": 2981, "key": "0424a951f35747a06795b5eec9e14b58"}, {"line": 37367, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 1866, "target": 521, "key": "db0adfa5785f30d07a22beb61d893991"}, {"line": 37369, "relation": "increases", "evidence": "We have previously identified the E3 ubiquitin ligase-inducible degrader of the low density lipoprotein receptor (LDLR) (Idol) as a post-translational modulator of LDLR levels. Idol is a direct target for regulation by liver X receptors (LXRs), and its expression is responsive to cellular sterol status independent of the sterol-response element-binding proteins. Here we demonstrate that Idol also targets two closely related LDLR family members, VLDLR and ApoE receptor 2 (ApoER2), proteins implicated in both neuronal development and lipid metabolism. Idol triggers ubiquitination of the VLDLR and ApoER2 on their cytoplasmic tails, leading to their degradation.We demonstrate that LXR activation results in decreased Reelin binding to VLDLR and reduced Dab1 phosphorylation. The identification of VLDLR and ApoER2 as Idol targets suggests potential roles for this LXR-inducible E3 ligase in the central nervous system in addition to lipid metabolism.", "citation": {"db": "PubMed", "db_id": "20427281"}, "annotations": {"Subgraph": {"Ubiquitin degradation subgraph": true, "Low density lipoprotein subgraph": true}, "Confidence": {"High": true}}, "source": 1866, "target": 592, "key": "1c4d3dc0de78df5452b778a9edafa6ad"}, {"line": 37389, "relation": "increases", "evidence": "The cleavage of APP by a-secretase results in the generation of APPs, which might have biological functions in growth regulation and neuroprotection, and, in the case of forms containing the Kunitz proteinase inhibitor domain, in blood coagulation. The C-terminal, 83- residue APP fragments (C83) remaining in the cell membrane have a relative long half-life and can be detected to different extents in metabolically labeled cells.", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 1213, "target": 486, "key": "ed6ad213127124e343798c8a382771cf"}, {"relation": "partOf", "source": 3406, "target": 1213, "key": "4dd60eb861ffa998177f2b346087cd37"}, {"line": 37391, "relation": "increases", "evidence": "The cleavage of APP by a-secretase results in the generation of APPs, which might have biological functions in growth regulation and neuroprotection, and, in the case of forms containing the Kunitz proteinase inhibitor domain, in blood coagulation. The C-terminal, 83- residue APP fragments (C83) remaining in the cell membrane have a relative long half-life and can be detected to different extents in metabolically labeled cells.", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 1214, "target": 486, "key": "e418dec9e90ba2bd7ab3190327c68679"}, {"relation": "partOf", "source": 3407, "target": 1214, "key": "b6756439eb2ee11b4857f99d542bf225"}, {"relation": "hasReactant", "source": 4089, "target": 101, "key": "c62d1eb90280e923f5f4656acf80f11b"}, {"relation": "hasProduct", "source": 4089, "target": 100, "key": "656599b96a1c7ba78bf5c02adc5f2d18"}, {"line": 37399, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2760, "target": 2315, "key": "fd1cf2882351152662659b0d1968ef83"}, {"line": 37400, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2761, "target": 2315, "key": "49bfe3e55b26010cc93b71220757d435"}, {"line": 37401, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2762, "target": 2315, "key": "a484ec14639ebdcc6992b758a2e95f46"}, {"line": 37402, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2763, "target": 2315, "key": "390bbe43457959e569c3951ff15a2c4a"}, {"line": 37403, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2764, "target": 2315, "key": "1b62a0577179a66e04e0ec129cb35feb"}, {"line": 37404, "relation": "association", "evidence": "Several functional subdomains (Fig. 1) have, however, been identified – for example, the RERMS sequence that appears to have growth-promoting properties (Ninomiya et al., 1993), and the two heparin-binding domains that are responsible for binding to the glycan moieties of proteoglycans, such as glypican (Williamson et al., 1996). The physiological role of these binding interactions remains to be elucidated. Best studied are the Cu(II)- and Zn(II)-binding activities of APP. The Zn(II) binding is assumed to play mainly a structural role (Bush et al., 1993), whereas APP is able to catalyse a reduction of Cu(II) to Cu(I)", "citation": {"db": "PubMed", "db_id": "10806097"}, "source": 2765, "target": 2315, "key": "9ac16a42b697d5e5d18a149fb5617448"}, {"line": 37466, "relation": "increases", "evidence": "APP/APLP expression is up-regulated during neuronal maturation and differentiation, undergoes rapid anterograde transport, and is targeted in vesicles distinct from synaptophysin transport vesicles to synaptic sites", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 477, "target": 2315, "key": "8180bb835e3d7771e65cbc5e0c8c1602"}, {"line": 37468, "relation": "increases", "evidence": "APP/APLP expression is up-regulated during neuronal maturation and differentiation, undergoes rapid anterograde transport, and is targeted in vesicles distinct from synaptophysin transport vesicles to synaptic sites", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "object": {"modifier": "Translocation"}, "source": 477, "target": 2306, "key": "6d76b0b93a14d7e3e72f6b79d8adf563"}, {"line": 37476, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 1158, "target": 516, "key": "75c40036213524127988f18d614f8cf9"}, {"relation": "partOf", "source": 2543, "target": 1158, "key": "582c819a4d2b77bcaa489ea6bc8bc226"}, {"line": 37478, "relation": "increases", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2543, "target": 516, "key": "831301e3feeeafbe47185a2c48dafeb5"}, {"line": 37476, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 516, "target": 1158, "key": "466dd4675d559ee59e9d53b3cc85d12d"}, {"line": 37477, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 516, "target": 2315, "key": "ee851fcae56593e4421db295721c6f75"}, {"line": 37480, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 516, "target": 1177, "key": "203d08052bfb6e6dfa71650dc9a6f279"}, {"line": 37484, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 516, "target": 1182, "key": "b1b7bef7cca01e9b1119a354854b78a4"}, {"line": 37479, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 514, "target": 2315, "key": "be229d5b51263b5cb6b81300edae6f49"}, {"line": 37480, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 1177, "target": 516, "key": "9c6e89e812436eb1b154b0c297ab9c96"}, {"relation": "partOf", "source": 2820, "target": 1177, "key": "a70a7bacc45b9b88ec0555da1266616d"}, {"line": 37481, "relation": "increases", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2820, "target": 516, "key": "b6af06f505a3879ae0054d2269b447c3"}, {"line": 37484, "relation": "association", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 1182, "target": 516, "key": "bb292f4179b5fdd7361921ef9151da0b"}, {"relation": "partOf", "source": 2956, "target": 1182, "key": "78adbcb3691ff1c552f4ab20aaef8ecc"}, {"line": 37485, "relation": "increases", "evidence": "Investigations of conserved domains support an adhesion property for all members of the APP family. The extracellular sequence of APP has been found to interact with various extracellular matrix components, such as heparin (Clarris et al. 1997; Mok et al. 1997), collagen type I (Beher et al. 1996), and laminin (Kibbey et al. 1993), indicating a role of APP in cell-matrix adhesion. Structural and functional studies also implicate a role of the APP extracellular domains in facilitating cell–cell adhesion through transcellular interactions.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2956, "target": 516, "key": "33cf058353d1cd93aeeef98e3619d805"}, {"line": 37494, "relation": "increases", "evidence": "Like NX/NL and SynCAM-mediated synaptic adhesion in which extracellular sequences engage transsynaptic interactions and the intracellular domains recruit pre- or postsynaptic complexes (reviewed in Dalva et al. 2007), both the extracellular and intracellular domains of APP are required to mediate the synaptogenic activity. Consistent with Soba et al. (2005), the E1 domain plays a more active role in synaptic adhesion. Interestingly, the highly conserved GYENPTY sequence of the APP intracellular domain could form a tripartite complex with Munc 18 interacting protein (Mint/X11) and calcium/calmodulin-dependent serine protein kinase (CASK) similar to that of neurexin and SynCAM (Hata et al. 1996; Biederer and Südhof 2000; Biederer et al. 2002), and the SynCAM carboxy-terminal sequence could functionally replace the corresponding APP domain in the coculture assay (Wang et al. 2009), suggesting that the Mint/CASK complexes may be the common mediators for the different classes of synaptic adhesion proteins. Thus, the precise role of APP-mediated synaptic adhesion in central synapses, whether it involves interaction with other SAMs, and the relationship between APP-mediated synaptogenesis and synaptic dysfunction occurring in AD are interesting questions that warrant further investigation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 1087, "target": 787, "key": "db0696fb35c9db6861266a6990e27016"}, {"line": 37496, "relation": "increases", "evidence": "Like NX/NL and SynCAM-mediated synaptic adhesion in which extracellular sequences engage transsynaptic interactions and the intracellular domains recruit pre- or postsynaptic complexes (reviewed in Dalva et al. 2007), both the extracellular and intracellular domains of APP are required to mediate the synaptogenic activity. Consistent with Soba et al. (2005), the E1 domain plays a more active role in synaptic adhesion. Interestingly, the highly conserved GYENPTY sequence of the APP intracellular domain could form a tripartite complex with Munc 18 interacting protein (Mint/X11) and calcium/calmodulin-dependent serine protein kinase (CASK) similar to that of neurexin and SynCAM (Hata et al. 1996; Biederer and Südhof 2000; Biederer et al. 2002), and the SynCAM carboxy-terminal sequence could functionally replace the corresponding APP domain in the coculture assay (Wang et al. 2009), suggesting that the Mint/CASK complexes may be the common mediators for the different classes of synaptic adhesion proteins. Thus, the precise role of APP-mediated synaptic adhesion in central synapses, whether it involves interaction with other SAMs, and the relationship between APP-mediated synaptogenesis and synaptic dysfunction occurring in AD are interesting questions that warrant further investigation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 1149, "target": 787, "key": "b2cbe50ae3286219824107110e5e7635"}, {"relation": "partOf", "source": 2441, "target": 1149, "key": "be36a380b941368a7e1ab121434dd7ee"}, {"relation": "partOf", "source": 2441, "target": 1090, "key": "a69655256615c31401c3be7407a8b9b5"}, {"line": 37497, "relation": "increases", "evidence": "Like NX/NL and SynCAM-mediated synaptic adhesion in which extracellular sequences engage transsynaptic interactions and the intracellular domains recruit pre- or postsynaptic complexes (reviewed in Dalva et al. 2007), both the extracellular and intracellular domains of APP are required to mediate the synaptogenic activity. Consistent with Soba et al. (2005), the E1 domain plays a more active role in synaptic adhesion. Interestingly, the highly conserved GYENPTY sequence of the APP intracellular domain could form a tripartite complex with Munc 18 interacting protein (Mint/X11) and calcium/calmodulin-dependent serine protein kinase (CASK) similar to that of neurexin and SynCAM (Hata et al. 1996; Biederer and Südhof 2000; Biederer et al. 2002), and the SynCAM carboxy-terminal sequence could functionally replace the corresponding APP domain in the coculture assay (Wang et al. 2009), suggesting that the Mint/CASK complexes may be the common mediators for the different classes of synaptic adhesion proteins. Thus, the precise role of APP-mediated synaptic adhesion in central synapses, whether it involves interaction with other SAMs, and the relationship between APP-mediated synaptogenesis and synaptic dysfunction occurring in AD are interesting questions that warrant further investigation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 1090, "target": 512, "key": "8c36c8890b36c3a02c4b60ad5fa138d8"}, {"line": 37506, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 512, "target": 2315, "key": "9a0d661757c3878268609c67325a13b1"}, {"line": 37507, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 512, "target": 3091, "key": "3d59a20e192b4abb0b2930bae2d4755d"}, {"line": 37509, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 512, "target": 3138, "key": "5b02b4d7abe36588ee6d3ca6f81aec77"}, {"line": 37511, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 512, "target": 2542, "key": "21a51b1d3a793cf69aa929464d931733"}, {"line": 37504, "relation": "increases", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Axons"}, "toLoc": {"namespace": "MESH", "name": "Synapses"}}}, "source": 1180, "target": 512, "key": "beb3bfd509a39c221ecccbef6c453df9"}, {"line": 37505, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 3091, "target": 2315, "key": "489038b9f5f267a294c1686646c024c9"}, {"line": 37507, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 3091, "target": 512, "key": "f67ed8d6547669e4637122ee90dac0f1"}, {"relation": "partOf", "source": 3091, "target": 1684, "key": "6dc1746a57aed2a6434019cccddf330f"}, {"line": 37508, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 3138, "target": 2315, "key": "82220ce6dffac8f0184d2c5d0edcf3d1"}, {"line": 37509, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 3138, "target": 512, "key": "1a7d32151805f8ef4ca7bf184d14f840"}, {"line": 37510, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2542, "target": 2315, "key": "0874dc0f374544b5f8339d3e580de9df"}, {"line": 37511, "relation": "association", "evidence": "Besides a direct role of APP/APP interaction in cell and synaptic adhesion, APP has been shown to colocalize with integrins on the surface of axons and at the sites of adhesion (Storey et al. 1996; Yamazaki et al. 1997; Young-Pearse et al. 2008). It has also been reported to interact with other cell adhesion molecules including NCAM (Ashley et al. 2005), NgCAM (Osterfield et al. 2008), and TAG 1 (Ma et al. 2008). As such, APP may play a modulatory role through interacting with these cell adhesion molecules.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 2542, "target": 512, "key": "d164e160f450a768cce687a630bbd443"}, {"line": 37534, "relation": "increases", "evidence": "Caille et al. provided evidence that APPsa and APLP2s act as cofactors for epidermal growth factor (EGF) to stimulate the proliferation of neurosphere cultures in vitro and neural stem cells in the subventricular zone of adult rodent brain in vivo (Caille et al. 2004). Gakhar-Koppole et al. (2008) and Rohe et al. (2008) also reported that APPs stimulated neurogenesis and neurite outgrowth, but suggested that it is mediated through enhanced ERK phosphorylation and may be dependent on membrane-bound APP. Han et al. (2005) offered yet a different mechanism that the growth promoting property is mediated by the ability of APPsa to down-regulate CDK5 and inhibit t hyperphosphorylation.", "citation": {"db": "PubMed", "db_id": "22355794"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "source": 2991, "target": 822, "key": "46129216b45618a6dbe266a6d71eb5f1"}, {"line": 37557, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 646, "target": 2315, "key": "5688c573bbe79ae3b3aaa15413e5fd42"}, {"line": 37558, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 646, "target": 2306, "key": "081f3e58fef2a0b1224a4c49ceb3b772"}, {"line": 37559, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 646, "target": 2307, "key": "5bc9f373251fdc2e8793fa30731009f3"}, {"line": 37567, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 737, "target": 2315, "key": "775f4205933937a0554f0c39de324a39"}, {"line": 37568, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 737, "target": 2306, "key": "6a40d857792adb058969fea0241bc049"}, {"line": 37569, "relation": "association", "evidence": "Taking this into account, it is not too surprising that within the nervous system, the APP/APLPs and their proteolytic fragments have been implicated in a bewildering variety of processes such as neurogenesis, neuronal migration and positioning, neurite outgrowth and neuronal diVerentiation, neuronal adhesion, synaptogenesis, synaptic function, control of excitation/inhibition balance, neuroprotection, synaptic long-term and short-term plasticity, as well as learning and memory. ", "citation": {"db": "PubMed", "db_id": "22349563"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 737, "target": 2307, "key": "57fa3286f6858fdac08732a12065bb2b"}, {"line": 37610, "relation": "increases", "evidence": "These findings suggest that interaction between Fas-II and APP is necessary for proper synaptic formation. Collectively, these findings demonstrate that APP and related proteins are important for synapse formation during development in diverse systems.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 1684, "target": 787, "key": "29e64e15bc0d672ed8e70c155a9ea5ab"}, {"line": 37615, "relation": "increases", "evidence": "These findings suggest that interaction between Fas-II and APP is necessary for proper synaptic formation. Collectively, these findings demonstrate that APP and related proteins are important for synapse formation during development in diverse systems.", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Synapse assembly subgraph": true}, "Confidence": {"High": true}}, "source": 1685, "target": 787, "key": "adc1e49b9e6a4c51529ac5f12503662b"}, {"relation": "partOf", "source": 3092, "target": 1685, "key": "dea2e90c47d88d38f8f38183ff14f507"}, {"line": 37650, "relation": "increases", "evidence": "Another study shows that APP, when phosphorylated at the Thr668 residue, is distributed in neuronal growth cones, and that the phosphorylated form of APP regulates neurite outgrowth in PC12 cells [52]. In addition, human APP and Drosophila APPL promoted postdevelopmental axonal arborization, depending on the interaction between the C-terminus of APP and Abelson (Abl) tyrosine kinase, suggesting a potential role for APP in axonal outgrowth following traumatic brain injury [4]. Furthermore, secreted sAPPa promoted axonal and dendritic growth and induced neurite outgrowth in neural stem cell-derived neurons through MAP kinase signaling", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 1672, "target": 603, "key": "74384516d7ec3cff3f8f4271530517ce"}, {"line": 37659, "relation": "increases", "evidence": "Recently, we found that full length APP increased dendritic neurite outgrowth, and that this effect was heightened by APP’s interaction with Reelin. Therefore, the interaction between Reelin and APP may act cooperatively to enhance neurite development", "citation": {"db": "PubMed", "db_id": "21199446"}, "annotations": {"Subgraph": {"Reelin signaling subgraph": true, "Axonal guidance subgraph": true}, "Confidence": {"High": true}}, "source": 1688, "target": 652, "key": "44e18e1ae9016558c53b47a9559aa896"}, {"line": 37679, "relation": "association", "evidence": "beta-Amyloid precursor protein is axonally transported and accumulates in presynaptic terminals and growth cones. A secreted form of beta-APP (sAPP alpha) is released from neurons in response to electrical activity and may function in modulation of neuronal excitability, synaptic plasticity, neurite outgrowth, synaptogenesis, and cell survival. A signaling pathway involving guanosine 3',5'-cyclic monophosphate is activated by sAPP alpha and modulates the activities of potassium channels, N-methyl-D-aspartate receptors, and the transcription factor NF kappa B. Additional functions of beta-APP may include modulation of cell adhesion and regulation of proliferation of nonneuronal cells.", "citation": {"db": "PubMed", "db_id": "9354812"}, "subject": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 3115, "target": 2137, "key": "186e1012e57b2d596ac0c2bcb785f1cb"}, {"line": 37695, "relation": "increases", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3170, "target": 2137, "key": "55e86e759f42e6a573653a35e49eaba9"}, {"line": 37698, "relation": "decreases", "evidence": "During development of the nervous system a common set of signal transduction pathways appear to regulate growth cone behaviors, synaptogenesis and natural cell death, three fundamental processes that comprise the neurodevelopmental triad. Among the intercellular signals that coordinate the developmental triad in the mammalian brain are glutamate (the major excitatory neurotransmitter) and beta-amyloid precursor protein (beta APP). Localization of ionotropic glutamate receptors to dendritic compartments allows for selective regulation of dendrite growth cones and spine formation by glutamate released from axonal growth cones and presynaptic terminals. Expression of particular subtypes of glutamate receptors peaks during a developmental time window within which synaptogenesis and natural neuronal death occur. Calcium is the preeminent second messenger mediating both acute (rapid remodelling of the microtubule and actin cytoskeletal systems) and delayed (transcriptional regulation of growth-related proteins; e.g., neurotrophins) actions of glutamate. The expression of beta APP in brain is developmentally regulated and it is expressed ubiquitously in differentiated neurons. beta APP is axonally transported and secreted forms of beta APP (sAPPs) are released from neurons in an activity-driven manner. Secreted APPs modulate neuronal excitability, counteract effects of glutamate on growth cone behaviors, and increase synaptic complexity. Acute actions of sAPPs appear to be transduced by cyclic GMP which promotes activation of K+ channels and reduces [Ca2+]i. Delayed actions of sAPPs may involve regulation of gene expression by the transcription factor NF kappa B. Finally, the striking effects of glutamate, neurotrophic factors, and sAPPs on synaptogenesis and neuronal survival in cell culture systems and in vivo suggest that each of these signals plays major roles in the process of natural cell death.", "citation": {"db": "PubMed", "db_id": "10533524"}, "annotations": {"Subgraph": {"Glutamatergic subgraph": true}}, "source": 3170, "target": 493, "key": "8d0927c1f9520dbea95e9a4ca85b8481"}, {"line": 37739, "relation": "increases", "evidence": "Recently, He et al. proposed that the Jak/STAT pathway is central to the gliogenic machinery and postulated a framework for understanding the control of gliogenesis during development (He et al., 2005). Treatment with sAPP increased phosphorylation of STAT3, which was suppressed when treated with L-685,458 (Fig 5C), indicating existence of crosstalk between the Notch and Jak/STAT pathway in APP-induced glial differentiation.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3427, "target": 561, "key": "d9d3ee585c45730dc99bff08c9927fb6"}, {"line": 37744, "relation": "increases", "evidence": "Recently, He et al. proposed that the Jak/STAT pathway is central to the gliogenic machinery and postulated a framework for understanding the control of gliogenesis during development (He et al., 2005). Treatment with sAPP increased phosphorylation of STAT3, which was suppressed when treated with L-685,458 (Fig 5C), indicating existence of crosstalk between the Notch and Jak/STAT pathway in APP-induced glial differentiation.", "citation": {"db": "PubMed", "db_id": "20883690"}, "annotations": {"Subgraph": {"JAK-STAT signaling subgraph": true, "Notch signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3427, "target": 2200, "key": "688d880f938de6f75d31d5012a4159cd"}, {"line": 37758, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity"}, "source": 988, "target": 2822, "key": "1099f959ab83265dbe1a429975d2b797"}, {"line": 37759, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity"}, "source": 988, "target": 2823, "key": "c388024138486666986649f858f4cc8e"}, {"line": 37760, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity"}, "source": 988, "target": 2824, "key": "ba032abbebcb6daac171c59b3026b066"}, {"line": 37761, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity"}, "source": 988, "target": 2825, "key": "0ea67a140aa1bfb86aedb46077834d87"}, {"line": 37762, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity"}, "source": 988, "target": 2826, "key": "8ed66fcfbe8be2adaaac52b769b7f257"}, {"line": 37763, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity"}, "source": 988, "target": 2827, "key": "c08c4819a726c4182b30fa7ed9142b6e"}, {"line": 37764, "relation": "increases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity"}, "source": 988, "target": 2828, "key": "7772ae68017b512abf7b1261c931ef30"}, {"relation": "partOf", "source": 373, "target": 988, "key": "230ed82bdbfbf908921cd4ef09f7a0a8"}, {"line": 37766, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2822, "target": 3105, "key": "3553c6d84ffc1ca4b03d25b68b88630b"}, {"line": 37774, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2822, "target": 2361, "key": "cb15fe70198e876224695ac29f4b4ff3"}, {"line": 37767, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2823, "target": 3105, "key": "16cd1eb7e6e6cd30f41f58ec507dc947"}, {"line": 37775, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2823, "target": 2361, "key": "37e5bf706d287fb99e87843b63a6ed4a"}, {"relation": "isA", "source": 2823, "target": 2180, "key": "e3097efa61f87b4655e3d7781d673fbf"}, {"line": 48521, "relation": "association", "evidence": "HES family proteins are implicated in the cell fate determination as effectors of the NOTCH signaling pathway", "citation": {"db": "PubMed", "db_id": "15254753"}, "source": 2823, "target": 507, "key": "009d11d8e2c4fbe8c5c085f29ef0f484"}, {"line": 37768, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2824, "target": 3105, "key": "ef063dba26d296950c80908c888f933f"}, {"line": 37776, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2824, "target": 2361, "key": "93bee00933a7fa54460a033212ecfa88"}, {"line": 37769, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2825, "target": 3105, "key": "0cf0802a2fb5f1b832acc44e6a6e94bf"}, {"line": 37777, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2825, "target": 2361, "key": "cc31920507f3407bfcfdadb8ffb215a5"}, {"relation": "isA", "source": 2825, "target": 2180, "key": "055647ef549424f122b0f18212384205"}, {"line": 37770, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2826, "target": 3105, "key": "d334bb5d22c295a4c66e57423b0fd8d9"}, {"line": 37778, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2826, "target": 2361, "key": "0a50ca97405afcc79886ca48bfdd39e4"}, {"relation": "isA", "source": 2826, "target": 2180, "key": "bc00d96030f8754751f5af1452a5fbbe"}, {"line": 37771, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2827, "target": 3105, "key": "f328defa0d0639a394b4ca35951c44dc"}, {"line": 37779, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2827, "target": 2361, "key": "ad90a39a1c06d16fbd03991c1cc4871e"}, {"line": 37772, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2828, "target": 3105, "key": "f88e6d2203249a77acb4e39af43984a5"}, {"line": 37780, "relation": "decreases", "evidence": "Notch signaling has been shown to control cell fate through local cell-to-cell interactions. During development, Notch suppresses neuronal differentiation in vivo and in vitro (Geling et al., 2004; Kabos et al., 2002). When ligands bind Notch, proteolytic cleavage of Notch receptors occurs by the gamma-secretase/nicastrin complex to release the signal-transducing Notch intracellular domain (NICD) (Yu et al., 2000). Cleaved NICDs translocate into the nucleus and interact with a nuclear protein named CBF1/Su(H)/Lag-1 (CSL) (Schroeter et al., 1998). The CSL and NICD complex activates expression of primary target genes of Notch, such as Hairy and enhancer of split (Hes) gene families (Jarriault et al., 1998). Following activation, Hes suppresses expression of transcription factors involved in neuronal differentiation, such as Mash1 and NeuroD (Pleasure et al., 2000). Notch activation is reported to strengthen glial differentiation by crosstalk to IL-6 signaling pathways, which is a known central regulator of gliogenesis. IL-6 cytokine signaling activation induces subsequent phosphorylation of gp130, Janus kinases (JAKs), and signal transducer and activator of transcription 3 (STAT3) (Kamakura et al., 2004). Upon Notch activation, increased Hes is known to facilitate complex formation between JAK2 and STAT3, promoting STAT3 phosphorylation. This facilitates accessibility of STAT3 to the DNA binding element of the GFAP promoter. In the present study, we demonstrate APP may induce glial differentiation of NPCs through activation of the Notch signaling pathway.", "citation": {"db": "PubMed", "db_id": "20883690"}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 2828, "target": 2361, "key": "0221f1e5cd3e2ffd6d7326064552ac14"}, {"relation": "hasVariant", "source": 2896, "target": 2897, "key": "07cbc7d538ee48c35ddf62621d90299a"}, {"relation": "hasReactant", "source": 4098, "target": 2315, "key": "29062ab9970f9e94aa7b18bceab707bf"}, {"relation": "hasProduct", "source": 4098, "target": 2136, "key": "d183744698fc012ce772245b847acf19"}, {"line": 37833, "relation": "decreases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2328, "key": "3134d8013007b66499620d280c1da374"}, {"line": 37834, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2299, "key": "e33a966deab2e5ba6267da86be60aa65"}, {"line": 37835, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2301, "key": "d9f29567d37c59200f143abeb24ebd0e"}, {"line": 37836, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2302, "key": "fb3334a691e14e7fc27b487612376086"}, {"line": 37837, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2294, "key": "b4172b6b31bf2ac28d8397bf693df8de"}, {"line": 37838, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2296, "key": "043808cbe2bcb93b7c2e491ed1c15572"}, {"line": 37839, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2298, "key": "fb79feaca447b5dda32eccb8534dd037"}, {"line": 37840, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2616, "key": "c944a3a187b052808e6fad88d6f9fcf8"}, {"line": 37848, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "MAPK-JNK subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 3002, "key": "ef32da22fe9e13f8fe942f705bf7b159"}, {"line": 37849, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "MAPK-JNK subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 3007, "key": "f46b1c5ba3a71025e3948457e2ade966"}, {"line": 37850, "relation": "association", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "MAPK-JNK subgraph": true, "Non-amyloidogenic subgraph": true, "Nerve growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 2136, "target": 2992, "key": "04ad157910b384241880a8539dd5f59f"}, {"line": 37857, "relation": "increases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 1095, "target": 2315, "key": "b847d00ed18bfad0908de4e9769f98c9"}, {"line": 37859, "relation": "increases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 1077, "target": 2315, "key": "4751715f032278d4e363e803890ac902"}, {"relation": "partOf", "source": 2358, "target": 1077, "key": "f9d62112a1bdbc802625b5c62517a7b6"}, {"relation": "partOf", "source": 2358, "target": 1083, "key": "6c02ea62b3c5b5a5b0331f0574bb3fb6"}, {"relation": "partOf", "source": 2358, "target": 1089, "key": "23b3a824f86eb0d44e7e88a271d4aec4"}, {"line": 37860, "relation": "increases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 1083, "target": 2315, "key": "48c154b0d0ee38010b6b3b4dadbb8adc"}, {"line": 37861, "relation": "increases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 1089, "target": 2315, "key": "9d5cd813705f1ba47dcd3c23c4790203"}, {"line": 37869, "relation": "decreases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1703, "target": 2328, "key": "455515897355fe1b634cd9d4f4e86ca8"}, {"relation": "partOf", "source": 3159, "target": 1703, "key": "f016ec65404efedd1a7c938a2350aa57"}, {"relation": "partOf", "source": 3159, "target": 1704, "key": "3c507c8b8cafc47c3f6c37fb493d2a3c"}, {"relation": "partOf", "source": 3159, "target": 1705, "key": "28fa639ad5406a556b0e9614da2cc723"}, {"line": 37870, "relation": "decreases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1704, "target": 2328, "key": "6b9e575a694d90e97583d293e66403c4"}, {"relation": "partOf", "source": 2748, "target": 1704, "key": "ce44208ef70d577022261f228b3a8f12"}, {"line": 37871, "relation": "decreases", "evidence": "Endocytic APP Sorting and ABeta¸ Productionâ€â€�Mutations within the YENPTY endocytosis motif selectively inhibit APP internalization and decrease ABeta¸ generation. This motif and the flanking region serve as the binding site for many cytosolic adaptors with phosphotyrosine-binding domains, including Fe65, Fe65L1, Fe65L2, Mint1 (also called X11a), Mint2, Mint3, Dab1, and JNK (c-Jun N-terminal kinase)-interacting protein family members. Interestingly, Fe65 acts as a functional linker between APP and LRP (another type I membrane protein containing two NPXY endocytosis motifs) in modulating endocytic APP trafficking and ABeta¸ production. A conformational change introduced by phosphorylation at Thr668 (14 amino acids proximal to the YENPTY motif) interferes with Fe65 binding to APP and facilitates BACE1 and gamma-secretase cleavage of APP. Moreover, Fe65 stabilizes the highly labile AICD, which may serve as a regulatory step in modulating the physiological function of AICD (see below). In addition to APP, Mint proteins can directly bind ADP-ribosylation factors; thus, Mint proteins can potentially regulate vesicular trafficking of APP by serving as coat proteins. Finally, the type I transmembrane protein SorLA/LR11 (a member of the VPS10p domain receptor family), which functionally interacts with cytosolic adaptors GGA and PACS-1, attenuates ABeta¸ production by acting as a Golgi/TGN retention factor. SorLA/LR11 is also genetically associated with AD, thus further implicating this sorting molecule in APP biology", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "Low density lipoprotein subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1705, "target": 2328, "key": "2fc8461506b0289b8f4f82db12ef1302"}, {"line": 37880, "relation": "association", "evidence": "APPs is constitutively released from cells following a-secretase cleavage, these findings indicated that APP has autocrine and paracrine functions in growth regulation.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Cytokine signaling subgraph": true}}, "source": 536, "target": 2315, "key": "c8e4434eaa447776238d86c72005894f"}, {"line": 37890, "relation": "increases", "evidence": "Cell Adhesion An RHDS motif near the extralumenal portion of APP or at the C terminus of APPs lying within the ABeta¸ region appears to promote cell adhesion. It is believed that this region acts in an integrin-like manner and can, accordingly, be blocked by RGDS peptide sequence derived from the fibronectin-binding domain. Similarly, APP colocalizes with integrins on the surface of axons and at sites of adhesion. Evidence of interaction with laminin and collagen provides further evidence of adhesion-promoting properties. Interestingly, because the RHDS sequence is contained within the N terminus of ABeta¸, similar cell adhesion-promoting properties have also been attributed to the ABeta¸ peptide itself. This latter property is, however, difficult to tease out in view of the cytotoxicity of ABeta¸ peptide when tested in a variety of cell systems in vitro. Furthermore, it is difficult to separate the cell adhesion-from the neurite outgrowth-promoting roles of APP. Clearly, these are probably somewhat inseparable, as neuronal migration, neurite outgrowth, and even synaptogenesis would involve substrate adhesion.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Inflammatory response subgraph": true, "Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 1683, "target": 497, "key": "dea22d9ab1f3022b52b6c7cf2a6cf5e2"}, {"relation": "partOf", "source": 2955, "target": 1683, "key": "eee07b1fcc0184105091ec5640499b92"}, {"line": 37891, "relation": "increases", "evidence": "Cell Adhesion An RHDS motif near the extralumenal portion of APP or at the C terminus of APPs lying within the ABeta¸ region appears to promote cell adhesion. It is believed that this region acts in an integrin-like manner and can, accordingly, be blocked by RGDS peptide sequence derived from the fibronectin-binding domain. Similarly, APP colocalizes with integrins on the surface of axons and at sites of adhesion. Evidence of interaction with laminin and collagen provides further evidence of adhesion-promoting properties. Interestingly, because the RHDS sequence is contained within the N terminus of ABeta¸, similar cell adhesion-promoting properties have also been attributed to the ABeta¸ peptide itself. This latter property is, however, difficult to tease out in view of the cytotoxicity of ABeta¸ peptide when tested in a variety of cell systems in vitro. Furthermore, it is difficult to separate the cell adhesion-from the neurite outgrowth-promoting roles of APP. Clearly, these are probably somewhat inseparable, as neuronal migration, neurite outgrowth, and even synaptogenesis would involve substrate adhesion.", "citation": {"db": "PubMed", "db_id": "18650430"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Inflammatory response subgraph": true, "Cell adhesion subgraph": true}, "Confidence": {"High": true}}, "source": 1681, "target": 497, "key": "36150634fa426cd73373ddf1bc33e07b"}, {"relation": "partOf", "source": 2544, "target": 1681, "key": "107319e4567ddda08dd3e88c93d6c0da"}, {"line": 37915, "relation": "association", "evidence": "We found that APP was present in the postsynaptic density of central excitatory synapses and coimmunoprecipitated with N-methyl-d-aspartate receptors (NMDARs). The presence of APP in the postsynaptic density was supported by the observation that NMDARs regulated trafficking and processing of APP; overexpression of the NR1 subunit increased surface levels of APP, whereas activation of NMDARs decreased surface APP and promoted production of ABeta¸. We transfected APP or APP RNA interference into primary neurons and used electrophysiological techniques to explore the effects of APP on postsynaptic function. Reduction of APP decreased (and overexpression of APP increased) NMDAR whole cell current density and peak amplitude of spontaneous miniature excitatory postsynaptic currents. The increase in NMDAR current by APP was due to specific recruitment of additional NR2B-containing receptors. Consistent with these findings, immunohistochemical experiments demonstrated that APP increased the surface levels and decreased internalization of NR2B subunits. These results demonstrate a novel physiological role of postsynaptic APP in enhancing NMDAR function.", "citation": {"db": "PubMed", "db_id": "19164281"}, "annotations": {"Subgraph": {"NMDA receptor": true}}, "source": 1682, "target": 795, "key": "8e126a14fce9658165dfd09fdeb0a4dc"}, {"line": 37944, "relation": "increases", "evidence": "As far as AICD fragments are concern, it was reported that, after binding Fe65 (Figure 1), an adaptor protein mediating assembly of multimolecular complexes through a variety of protein-interaction domains, and the histone acetyltransferase Tip60, AICD translocate into the nucleus where it acts as gene transcription regulators", "citation": {"db": "PubMed", "db_id": "22496686 "}, "source": 2941, "target": 766, "key": "ae3583749bdb113e4d23e841cd0dce64"}, {"line": 37950, "relation": "directlyDecreases", "evidence": "The increase in intracellular copper was correlated with a dramatic and rapid decrease in levels of extracellular ABeta¸ including ABeta¸1–40 and 1–42. It has been previously reported that CQ/copper complexes trigger the activation of PI3K and its downstream modulator Akt and the inhibition of glycogen synthase kinase 3 that in turn potentiated ERK1/2 phosphorylation.It is not clear if and how environmental factors take part to pathway discussed in this review, in which both ABeta¸PP and PS1 participate in the same signaling pathway leading, through Grb2 binding, to ERK1/2 activation and neurodegeneration. However, we may speculate that ERK1/2 activation by copper may contribute to the signal transduction system activated by ABeta¸PP, and PSs.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 98, "target": 2328, "key": "45bcc149e868b6a9eb81b505be8d823a"}, {"line": 37954, "relation": "increases", "evidence": "The increase in intracellular copper was correlated with a dramatic and rapid decrease in levels of extracellular ABeta¸ including ABeta¸1–40 and 1–42. It has been previously reported that CQ/copper complexes trigger the activation of PI3K and its downstream modulator Akt and the inhibition of glycogen synthase kinase 3 that in turn potentiated ERK1/2 phosphorylation.It is not clear if and how environmental factors take part to pathway discussed in this review, in which both ABeta¸PP and PS1 participate in the same signaling pathway leading, through Grb2 binding, to ERK1/2 activation and neurodegeneration. However, we may speculate that ERK1/2 activation by copper may contribute to the signal transduction system activated by ABeta¸PP, and PSs.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Phosphatidylinositol 3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 98, "target": 2149, "key": "232eb37d831955e2a6a87c079f76562e"}, {"line": 37958, "relation": "increases", "evidence": "The increase in intracellular copper was correlated with a dramatic and rapid decrease in levels of extracellular ABeta¸ including ABeta¸1–40 and 1–42. It has been previously reported that CQ/copper complexes trigger the activation of PI3K and its downstream modulator Akt and the inhibition of glycogen synthase kinase 3 that in turn potentiated ERK1/2 phosphorylation.It is not clear if and how environmental factors take part to pathway discussed in this review, in which both ABeta¸PP and PS1 participate in the same signaling pathway leading, through Grb2 binding, to ERK1/2 activation and neurodegeneration. However, we may speculate that ERK1/2 activation by copper may contribute to the signal transduction system activated by ABeta¸PP, and PSs.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Akt subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 98, "target": 2153, "key": "2aa9244b7bd8e6f014f2a96eb2250a73"}, {"line": 37964, "relation": "decreases", "evidence": "The increase in intracellular copper was correlated with a dramatic and rapid decrease in levels of extracellular ABeta¸ including ABeta¸1–40 and 1–42. It has been previously reported that CQ/copper complexes trigger the activation of PI3K and its downstream modulator Akt and the inhibition of glycogen synthase kinase 3 that in turn potentiated ERK1/2 phosphorylation.It is not clear if and how environmental factors take part to pathway discussed in this review, in which both ABeta¸PP and PS1 participate in the same signaling pathway leading, through Grb2 binding, to ERK1/2 activation and neurodegeneration. However, we may speculate that ERK1/2 activation by copper may contribute to the signal transduction system activated by ABeta¸PP, and PSs.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"GSK3 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 98, "target": 2794, "key": "926c8ff6750e151c3cce73b411237474"}, {"line": 37969, "relation": "increases", "evidence": "The increase in intracellular copper was correlated with a dramatic and rapid decrease in levels of extracellular ABeta¸ including ABeta¸1–40 and 1–42. It has been previously reported that CQ/copper complexes trigger the activation of PI3K and its downstream modulator Akt and the inhibition of glycogen synthase kinase 3 that in turn potentiated ERK1/2 phosphorylation.It is not clear if and how environmental factors take part to pathway discussed in this review, in which both ABeta¸PP and PS1 participate in the same signaling pathway leading, through Grb2 binding, to ERK1/2 activation and neurodegeneration. However, we may speculate that ERK1/2 activation by copper may contribute to the signal transduction system activated by ABeta¸PP, and PSs.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 98, "target": 2174, "key": "354c08c04d30c9dad61f642d2194ea5f"}, {"line": 37977, "relation": "association", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "MAPK-ERK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3005, "target": 2315, "key": "ef8afe98740cfb30299a12020e42bd04"}, {"line": 37978, "relation": "decreases", "evidence": "On the contrary, X11 stabilizes ABeta¸PP conformation in membrane, inhibiting ABeta¸ secretion in cultured cells [45], likely impairing ABeta¸PP trafficking to sites containing active gamma-secretase complexes [46]. JIP's are member of JNK-scaffolding family proteins kinases, implicated in different signal pathway, including neuronal apoptotic process. JNK-interacting proteins JIP1b and JIP2 bind to the cytoplasmic tail of ABeta¸PP. The expression of JIP1b stabilizes immature ABeta¸PP and decreases the ABeta¸PP ectodomain, ABeta¸40/ß42 and CTFs abundance [47].All these observations suggest that some of these protein-protein interactions may play a role in the modulation of the amyloidogenic pathway and thus might have a role in neurodegeneration.", "citation": {"db": "PubMed", "db_id": "22496686 "}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "MAPK-ERK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3005, "target": 2328, "key": "104ce51d4d4d1b3b2c764b90edd2a6e5"}, {"relation": "partOf", "source": 3005, "target": 1550, "key": "b2742db51c79b486c9cc889b27e4ebd1"}, {"line": 38006, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "source": 2755, "target": 649, "key": "89340f5f25e40a477d6528d9f540f129"}, {"relation": "partOf", "source": 2755, "target": 1169, "key": "e00ea310c6c35d79a7a2ac9f54e1b7eb"}, {"line": 38020, "relation": "increases", "evidence": "ABeta¸PP cytodomain also interacts with other proteins directly linked to signal transduction mechanisms. In particular, ABeta¸PP binds to the heterotrimeric GTP-binding protein Go [60–63] that comprises up to 1% of all membrane-associated proteins in the developing nervous system [55]. There is evidence that ABeta¸PP cytodomain binds proteins involved in cell-cycle regulation such as ABeta¸PP-binding protein 1 (APP-BP1) [64] and p-21-activated kinase 3 (PAK3) [65] which is a serine/threonine kinase involved in DNA synthesis and neuronal apoptotic process. These data are consistent with a model in which ABeta¸PP is a component of a Go multiprotein complex, including PAK3, to transduce extracellular signals to the cytoplasm. In this model, the FAD APP-mediated pathway, leading to tentative neuronal cell-cycle activation (see below), consists of the APP-Go-PAK3 formation, followed by the activation of the ABeta¸PP-BP1 through JNK", "citation": {"db": "PubMed", "db_id": "22496686 "}, "object": {"modifier": "Activity"}, "source": 1169, "target": 3087, "key": "a757ced7e412703927344bdbe2811345"}, {"relation": "hasReactant", "source": 4102, "target": 2315, "key": "4aece5829d400d5a67cebb0125a5c36e"}, {"relation": "hasProduct", "source": 4102, "target": 2332, "key": "706a5980fc7b4afc3b4a70875ecac3a4"}, {"line": 38053, "relation": "increases", "evidence": "DR6 is required for the timely pruning of axons and the elimination of neurons during spinal cord or retinal development in vivo and in trophic factor-deprived neuronal cultures. DR6-dependent axonal pruning is mediated by caspase 6 and neuronal culling by caspase 3. Trophic factor deprivation induced cleavage of APP by ß-secretase, resulting in formation of sAPPß and subsequently N-APP. Surprisingly, N-APP acts as a necessary and sufficient ligand for DR6, inducing axonal and neuronal degeneration after trophic factor removal.", "citation": {"db": "PubMed", "db_id": "19524503"}, "annotations": {"Subgraph": {"Caspase subgraph": true}}, "source": 1250, "target": 654, "key": "eeba32de62e812b909da7420c29bf2fb"}, {"line": 38112, "relation": "decreases", "evidence": "we found that the Nogo-66 receptor (NgR) interacts physically with both Abeta and the amyloid precursor protein (APP). The inverse correlation of Abeta levels with NgR levels within the brain may reflect regulation of Abeta production and/or Abeta clearance.", "citation": {"db": "PubMed", "db_id": "17182778"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1947, "target": 2328, "key": "b79d827fc7cb9760cac57a197527fa79"}, {"line": 38135, "relation": "increases", "evidence": "These studies indicate that ABCA7 has the capacity to stimulate cellular cholesterol efflux to apoE discs and regulate APP processing resulting in an inhibition of Abeta production", "citation": {"db": "PubMed", "db_id": "18429932"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 2231, "target": 526, "key": "d1726b939f546f5346662ee506218882"}, {"line": 38136, "relation": "decreases", "evidence": "These studies indicate that ABCA7 has the capacity to stimulate cellular cholesterol efflux to apoE discs and regulate APP processing resulting in an inhibition of Abeta production", "citation": {"db": "PubMed", "db_id": "18429932"}, "annotations": {"Subgraph": {"ATP binding cassette transport subgraph": true, "Non-amyloidogenic subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"Medium": true}}, "source": 2231, "target": 2328, "key": "8c99b2b41541454f0d44710f18f5a4c7"}, {"line": 38153, "relation": "increases", "evidence": "These data indicate that ABCA1 and ABCG1 play a significant role in the regulation of neuronal cholesterol efflux to apoE discs and in suppression of APP processing to generate Abeta peptides.", "citation": {"db": "PubMed", "db_id": "17121837"}, "annotations": {"MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2238, "target": 526, "key": "7ad69e52436ba976b4a56f47193748dd"}, {"line": 38154, "relation": "decreases", "evidence": "These data indicate that ABCA1 and ABCG1 play a significant role in the regulation of neuronal cholesterol efflux to apoE discs and in suppression of APP processing to generate Abeta peptides.", "citation": {"db": "PubMed", "db_id": "17121837"}, "annotations": {"MeSHAnatomy": {"Neurons": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 2238, "target": 2328, "key": "e350f6b119e81bc5020aa78381449e8c"}, {"line": 38167, "relation": "increases", "evidence": "Using screening approaches in primary neurons, we identified brain-derived neurotrophic factor (BDNF) as a major inducer of Sorla that activates receptor gene transcription through the ERK (extracellular regulated kinase) pathway.These findings demonstrate that the beneficial effects ascribed to BDNF in APP metabolism act through induction of Sorla that encodes a negative regulator of neuronal APP processing", "citation": {"db": "PubMed", "db_id": "20007471"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1758, "target": 1972, "key": "4e115727bd22b17ea0f435badd7b9e18"}, {"line": 38177, "relation": "decreases", "evidence": "Using screening approaches in primary neurons, we identified brain-derived neurotrophic factor (BDNF) as a major inducer of Sorla that activates receptor gene transcription through the ERK (extracellular regulated kinase) pathway.These findings demonstrate that the beneficial effects ascribed to BDNF in APP metabolism act through induction of Sorla that encodes a negative regulator of neuronal APP processing", "citation": {"db": "PubMed", "db_id": "20007471"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1758, "target": 2328, "key": "0b1e2f8c4e8211a35322a81f497131b3"}, {"line": 45218, "relation": "increases", "evidence": "In the hippocampus, Bdnf gene was underexpressed in sedentary mice and both Bdnf and its receptor TrkB were significantly upregulated in response to the exercise intervention", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}}, "source": 1758, "target": 2397, "key": "5433c6ac4f0e58f9fb43dcb9e8d5cbad"}, {"line": 45223, "relation": "association", "evidence": "after the exercise intervention Bdnf levels in SAMP8 mice were undistinguishable from those found in sedentary SAMR1 controls . Neuritin gene, a well characterized target of BDNF, was upregulated in both strains by exercise training ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 1758, "target": 1898, "key": "51bb06476177a6a1f0e6cda45bdbcd2d"}, {"relation": "hasVariant", "source": 1758, "target": 1759, "key": "674f5b27ca9d925f39e23273be0731fe"}, {"line": 46248, "relation": "association", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1758, "target": 2803, "key": "06ab9c6bb8ee7a563033ce06fe2e4e09"}, {"line": 38205, "relation": "directlyDecreases", "evidence": "We previously reported that genetic variants in SORCS1 increase the risk of AD, that over-expression of SorCS1 reduces gamma-secretase activity and ABeta¸ levels, and that SorCS1 suppression increases gamma-secretase processing of APP and ABeta¸ levels. We now explored the effect of variation in SORCS1 on memory.Variation in intron 1 in SORCS1 is associated with memory changes in AD", "citation": {"db": "PubMed", "db_id": "22046233"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1971, "target": 1925, "key": "e385b58f16503334ee0238a708aaa093"}, {"line": 38206, "relation": "directlyDecreases", "evidence": "We previously reported that genetic variants in SORCS1 increase the risk of AD, that over-expression of SorCS1 reduces gamma-secretase activity and ABeta¸ levels, and that SorCS1 suppression increases gamma-secretase processing of APP and ABeta¸ levels. We now explored the effect of variation in SORCS1 on memory.Variation in intron 1 in SORCS1 is associated with memory changes in AD", "citation": {"db": "PubMed", "db_id": "22046233"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1971, "target": 2328, "key": "5fbb42e13c62e5e4b20b8441b9bc74ba"}, {"line": 38218, "relation": "regulates", "evidence": "Alzheimer's disease (AD) is associated with a significant neuroinflammatory component. Mononuclear phagocytes including monocytes and microglia are the principal cells involved, and they accumulate at perivascular sites of beta-amyloid (Abeta) deposition and in senile plaques. Recent evidence suggests that mononuclear phagocyte accumulation in the AD brain is dependent on chemokines. CCL2, a major monocyte chemokine, is upregulated in the AD brain. Interaction of CCL2 with its receptor CCR2 regulates mononuclear phagocyte accumulation in a mouse model of AD. CCR2 deficiency leads to lower mononuclear phagocyte accumulation and is associated with higher brain Abeta levels, specifically around blood vessels, suggesting that monocytes accumulate at sites of Abeta deposition in an initial attempt to clear these deposits and stop or delay their neurotoxic effects. Indeed, enhancing mononuclear phagocyte accumulation delays progression of AD.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1008, "target": 608, "key": "75391fd01f8f2727c9bc03dff92d8e3a"}, {"line": 38222, "relation": "decreases", "evidence": "Alzheimer's disease (AD) is associated with a significant neuroinflammatory component. Mononuclear phagocytes including monocytes and microglia are the principal cells involved, and they accumulate at perivascular sites of beta-amyloid (Abeta) deposition and in senile plaques. Recent evidence suggests that mononuclear phagocyte accumulation in the AD brain is dependent on chemokines. CCL2, a major monocyte chemokine, is upregulated in the AD brain. Interaction of CCL2 with its receptor CCR2 regulates mononuclear phagocyte accumulation in a mouse model of AD. CCR2 deficiency leads to lower mononuclear phagocyte accumulation and is associated with higher brain Abeta levels, specifically around blood vessels, suggesting that monocytes accumulate at sites of Abeta deposition in an initial attempt to clear these deposits and stop or delay their neurotoxic effects. Indeed, enhancing mononuclear phagocyte accumulation delays progression of AD.", "citation": {"db": "PubMed", "db_id": "20205643"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1008, "target": 2328, "key": "ab82f55bebda1aff1c768b1db4d9b741"}, {"relation": "partOf", "source": 1765, "target": 1008, "key": "59dc2eb0d39d3bb5fd8d0ea1b240d8d4"}, {"relation": "partOf", "source": 1768, "target": 1008, "key": "9dd20a9da67ddb1803ff9cdf1943390d"}, {"relation": "partOf", "source": 3392, "target": 1457, "key": "55a78a851da60b3878fbd92b5fa56ea4"}, {"line": 38234, "relation": "increases", "evidence": "The pharmacological blockage of autophagy resulted in a dramatic increase of mutant SOD1 aggregates. Immunoprecipitation studies, performed during autophagic flux blockage, demonstrated that mutant SOD1 interacts with the HspB8/Bag3/Hsc70/CHIP multiheteromeric complex, known to selectively activate autophagic removal of misfolded proteins. ", "citation": {"db": "PubMed", "db_id": "20570967"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 1457, "target": 808, "key": "e4dc87be4c45674f548e92547c55dea1"}, {"line": 38239, "relation": "decreases", "evidence": "The pharmacological blockage of autophagy resulted in a dramatic increase of mutant SOD1 aggregates. Immunoprecipitation studies, performed during autophagic flux blockage, demonstrated that mutant SOD1 interacts with the HspB8/Bag3/Hsc70/CHIP multiheteromeric complex, known to selectively activate autophagic removal of misfolded proteins. ", "citation": {"db": "PubMed", "db_id": "20570967"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "source": 1284, "target": 2328, "key": "681d51474e6803dd3961e5d4d84091bc"}, {"relation": "partOf", "source": 2387, "target": 1284, "key": "5bae908bfe529922637cf83ea96aa363"}, {"line": 38248, "relation": "decreases", "evidence": "Gene-gene interaction between CARD8 and interleukin-6 reduces Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "19252766"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Caspase subgraph": true}}, "source": 1007, "target": 3823, "key": "7e56b3acfb6ce201bd9b0e67185eeeab"}, {"relation": "partOf", "source": 1763, "target": 1007, "key": "4b60d0b8406890822f1bf6ccc21a37eb"}, {"relation": "partOf", "source": 1848, "target": 1007, "key": "fc56e1991c5ce41fedbfdb3d7727d8cd"}, {"line": 38257, "relation": "directlyDecreases", "evidence": "Furthermore, overexpression of miR-195 in N2a/APP decreased the level of Abeta, while inhibition of miR-195 resulted in an increase of Abeta. Thus, we demonstrated that miR-195 could downregulate the level of Abeta by inhibiting the translation of BACE1. We conclude that miR-195 might provide a therapeutic strategy for AD", "citation": {"db": "PubMed", "db_id": "22721728"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2109, "target": 2328, "key": "9badba07a64bbd063d1594f112d444c0"}, {"line": 38258, "relation": "directlyDecreases", "evidence": "Furthermore, overexpression of miR-195 in N2a/APP decreased the level of Abeta, while inhibition of miR-195 resulted in an increase of Abeta. Thus, we demonstrated that miR-195 could downregulate the level of Abeta by inhibiting the translation of BACE1. We conclude that miR-195 might provide a therapeutic strategy for AD", "citation": {"db": "PubMed", "db_id": "22721728"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "miRNA subgraph": true}}, "source": 2109, "target": 3943, "key": "3d92ff2e882e2bd1dde08e7f79291fb6"}, {"line": 38363, "relation": "directlyDecreases", "evidence": "The study also revealed that the nuclear receptor peroxisome proliferators activated receptor gamma (PPARgamma) played an important role in the CLA-induced intracellular BACE1 decrease, as well as the extracellular sAPPalpha increase through knockdown of PPARgamma transcription using siRNA. We hypothesize that CLA acts as an agonist or ligand, which binds with PPARgamma and leads to the increase in APP cleavage via alpha-secretase-mediated pathway and the decrease in the deposition of Abeta.", "citation": {"db": "PubMed", "db_id": "21800078"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 144, "target": 2375, "key": "4aa6cf822970cead675d418fe83fab30"}, {"relation": "partOf", "source": 144, "target": 962, "key": "7fcbf25fef5ce15da6b3c454b518ea9c"}, {"line": 38365, "relation": "directlyDecreases", "evidence": "The study also revealed that the nuclear receptor peroxisome proliferators activated receptor gamma (PPARgamma) played an important role in the CLA-induced intracellular BACE1 decrease, as well as the extracellular sAPPalpha increase through knockdown of PPARgamma transcription using siRNA. We hypothesize that CLA acts as an agonist or ligand, which binds with PPARgamma and leads to the increase in APP cleavage via alpha-secretase-mediated pathway and the decrease in the deposition of Abeta.", "citation": {"db": "PubMed", "db_id": "21800078"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Beta secretase subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 962, "target": 2328, "key": "480c015af51cdd64f18ae3dde54a7b07"}, {"line": 38376, "relation": "increases", "evidence": "Here, we report a novel function of glutamate carboxypeptidase II (GCPII) in Abeta degradation in brain, which is a peptidase involved in N-acetylaspartylglutamate cleavage, folate metabolism, and prostate tumorigenesis.", "citation": {"db": "PubMed", "db_id": "20624932"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Degradation"}, "source": 2552, "target": 2328, "key": "e1c3df89b3ac8ba3000ff12c4c0efa40"}, {"line": 38377, "relation": "decreases", "evidence": "Here, we report a novel function of glutamate carboxypeptidase II (GCPII) in Abeta degradation in brain, which is a peptidase involved in N-acetylaspartylglutamate cleavage, folate metabolism, and prostate tumorigenesis.", "citation": {"db": "PubMed", "db_id": "20624932"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2552, "target": 2328, "key": "b17c0b7209e7175367770e3be41789ff"}, {"relation": "hasReactant", "source": 4100, "target": 2315, "key": "12d067e57c826ef2ca02c065aa6c2f8e"}, {"relation": "hasProduct", "source": 4100, "target": 2138, "key": "40a2734922ce62a8a4d200d7242f0e1d"}, {"line": 38414, "relation": "association", "evidence": "To understand why sAPPß more readily drives differentiation of hESCs than sAPPa, the downstream targets of sAPP signaling will need to be identified. Toward that end, a recent study found that sAPPß can regulate the transcription of transthyretin and Klotho genes in the absence of full-length APP or APLP1 expression", "citation": {"db": "PubMed", "db_id": "21606494"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 2004, "target": 2138, "key": "9e9909f2b4a08b519e0b53b7155b2102"}, {"line": 38415, "relation": "association", "evidence": "To understand why sAPPß more readily drives differentiation of hESCs than sAPPa, the downstream targets of sAPP signaling will need to be identified. Toward that end, a recent study found that sAPPß can regulate the transcription of transthyretin and Klotho genes in the absence of full-length APP or APLP1 expression", "citation": {"db": "PubMed", "db_id": "21606494"}, "annotations": {"Subgraph": {"Nerve growth factor subgraph": true}}, "source": 1855, "target": 2138, "key": "b2862f8b49c69fbd482761c3efc43aad"}, {"line": 38431, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2949, "target": 3563, "key": "9490573e61d1326c17cae0027bac6694"}, {"relation": "partOf", "source": 2949, "target": 1511, "key": "6c47cb896eb8f00d6900494ccdd8ddb3"}, {"line": 38432, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2950, "target": 3563, "key": "c9847b3b291084f59b676030e029bd7f"}, {"relation": "partOf", "source": 2950, "target": 1512, "key": "095df6304c1b9bf8c62797f5a2d1d7f9"}, {"line": 38433, "relation": "association", "evidence": "Many AICD-binding proteins have been identified. Some of the proteins, including kinesin light chain (KLC), Fe65, Shc, JNK-interacting protein (JIP), Numb, X11, Clathrin and mDab1, were found to share one or several common phosphotyrosine-binding domains that specifically interact with the Asn-Pro-X-Tyr amino acid sequence present in the YENPTY motif of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2951, "target": 3563, "key": "048eccee1f869b9037f15eb83245c18a"}, {"relation": "partOf", "source": 2951, "target": 1513, "key": "dcda6bfe45b0cb69cf684d68f9f2ceaf"}, {"line": 38581, "relation": "association", "evidence": "As an adaptor protein involved in protein sorting and trafficking, X11 has been suggested as affecting APP trafficking/metabolism by interacting with AICD, leading to reduced Ab production. X11 has also been found to suppress the transactivation of AICD, possibly by competing with AICD for the recruitment of Fe65, as they share the same binding motif", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation"}, "source": 1079, "target": 2315, "key": "2fff8954abad78637f998c0ee74c557f"}, {"line": 38582, "relation": "decreases", "evidence": "As an adaptor protein involved in protein sorting and trafficking, X11 has been suggested as affecting APP trafficking/metabolism by interacting with AICD, leading to reduced Ab production. X11 has also been found to suppress the transactivation of AICD, possibly by competing with AICD for the recruitment of Fe65, as they share the same binding motif", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1079, "target": 2328, "key": "1a089d3cbdc18a1fbf841e895ece5a25"}, {"line": 38475, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3435, "target": 3563, "key": "fa72a92a3384e0c1539762d1ba1efb02"}, {"relation": "partOf", "source": 3435, "target": 1634, "key": "60274ba395d17d14eff296e7bd9efc68"}, {"line": 38476, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3436, "target": 3563, "key": "7f455f818b734a56d8981e978e8cab0d"}, {"relation": "partOf", "source": 3436, "target": 1635, "key": "9bf4fc38fe537a6d6329dfd464bf5ba2"}, {"line": 38477, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2954, "target": 3563, "key": "d7ba1a4b655c1e056c1dc9ffc9669770"}, {"relation": "partOf", "source": 2954, "target": 1515, "key": "abdd857656540ef6d07106211bfd491b"}, {"line": 38478, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2681, "target": 3563, "key": "cf987455233424d444b45a5f8ba8e824"}, {"relation": "partOf", "source": 2681, "target": 1415, "key": "116540e18884eb489b1470459481d365"}, {"line": 38479, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2682, "target": 3563, "key": "0e7d288306e5d2047935bad7486a1a8d"}, {"relation": "partOf", "source": 2682, "target": 1416, "key": "72f92299c81e788ff19188293fcaeae0"}, {"line": 38480, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1820, "target": 3563, "key": "cbf6fc67b20454da2bd1c8c746ec6200"}, {"relation": "partOf", "source": 1820, "target": 1011, "key": "6519cd4dec6bbe2dacc785895d142508"}, {"line": 38481, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3056, "target": 3563, "key": "5fd9fe7d76855c1d65d6044c595586b3"}, {"relation": "partOf", "source": 3056, "target": 1575, "key": "8191126cc57843ffdcfabe82d311988f"}, {"line": 38482, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1873, "target": 3563, "key": "39f5f446fe3621c4d3fde8036890264f"}, {"relation": "partOf", "source": 1873, "target": 1012, "key": "561f62c7a47370a38f6d4e7cec688a49"}, {"line": 38483, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3052, "target": 3563, "key": "578a73becf5b5cab2a5180c45cc41954"}, {"relation": "partOf", "source": 3052, "target": 1571, "key": "fca57804bb63da2b6d7347d2dc97910e"}, {"line": 38484, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3055, "target": 3563, "key": "26afb4a5e5b9a53d74120fb0a156061d"}, {"relation": "partOf", "source": 3055, "target": 1574, "key": "5e37516b21d316302d9905c5e034edea"}, {"line": 38485, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2270, "target": 3563, "key": "01234942ad8d19cac20e960d7589c881"}, {"relation": "partOf", "source": 2270, "target": 1064, "key": "36ea99fa0573af0e50f3c8ad577341dc"}, {"line": 38486, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1874, "target": 3563, "key": "238ad8415bf1de42c48dd9e234689dac"}, {"relation": "partOf", "source": 1874, "target": 1013, "key": "4c90a37b11abb8a29d2d16395de800f0"}, {"line": 38487, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3054, "target": 3563, "key": "5c2ca10df885da7ca58d7b13f47d1c00"}, {"relation": "partOf", "source": 3054, "target": 1573, "key": "bf3a5b6c546d0624237e70b3da36b70e"}, {"line": 38488, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3053, "target": 3563, "key": "498451f1e8cfc87a899d435196a5f8fc"}, {"relation": "partOf", "source": 3053, "target": 1572, "key": "8106f40603fc78a2c45e2444e4d3a897"}, {"line": 38489, "relation": "association", "evidence": "Other proteins, such as protein interacting with APP tail 1 (PAT), suppressor of variegation, enhancer of zeste, and Trithorax (SET) and 14-3-3c, are believed to bind to the YTSI or VTPEER motif of AICD (Zheng et al. 1998; Madeira et al. 2005; Sumioka et al. 2005). AICD, therefore, may have different functions when interacting with its’ various binding partners", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2953, "target": 3563, "key": "56b7bf3459a31764014792973340522a"}, {"relation": "partOf", "source": 2953, "target": 1514, "key": "0671838f9d2d10499748932e81a30ecb"}, {"line": 38517, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 3251, "target": 2336, "key": "53e94f7589dcc4788498eb0980ed721d"}, {"line": 38519, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 3251, "target": 2338, "key": "c39e53b9d60486f7426c83899cd2a429"}, {"line": 38520, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 3251, "target": 2335, "key": "bdc151ef66b9046794b61a3e9fc4307b"}, {"line": 38518, "relation": "decreases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "object": {"modifier": "Activity"}, "source": 3564, "target": 3563, "key": "6b5202374622ab1f2343fc480b79a569"}, {"line": 38525, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3249, "target": 2336, "key": "b79390c9c1918ac2504de7805ea1e8e9"}, {"line": 38526, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3249, "target": 2338, "key": "912e6cc24e1aa39861f58b8d68e781db"}, {"line": 38527, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"High": true}}, "source": 3249, "target": 2335, "key": "beb476091e4bfc47fe8aeb98f47c1581"}, {"line": 38532, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 3250, "target": 2336, "key": "152ab0fc0831bc0416fbc12b35b78709"}, {"line": 38533, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 3250, "target": 2338, "key": "0786c90f074c98ab4517e44b1ec57a15"}, {"line": 38534, "relation": "increases", "evidence": "AICD also contains three phosphorylation sites, including two threonine residues at 654 and 668 and a serine residue at 665. AICD has been found to be phosphorylated by PKC, calcium-calmodulin dependent-kinase II, GSK3-b, Cdk5 and c-Jun N-terminal kinase (JNK) at the Ser/Thr sites mentioned above. Such phosphorylation may affect APP processing or the binding of AICD-interacting proteins, thus affecting the function of AICD", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 3250, "target": 2335, "key": "6566c1bee99c66a9a870bf3940a1d76a"}, {"line": 38542, "relation": "increases", "evidence": "The most widely accepted mechanism is that AICD, together with Fe65 and Tip60, forms a transcriptionally-active complex. Fe65 is one of the most well studied proteins that bind to the YENPTY motif of AICD.", "citation": {"db": "PubMed", "db_id": "22122372"}, "source": 1098, "target": 794, "key": "3a75631bd4d99c33632b5697c88781cd"}, {"line": 38547, "relation": "increases", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 1098, "target": 3563, "key": "2658023b0d96f02c5e69a31292af9f2d"}, {"line": 38548, "relation": "association", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 1771, "target": 3563, "key": "8b1b645befb6f3695f2be3139f200964"}, {"line": 38550, "relation": "association", "evidence": "Tip60, a histone acetyltransferase, is a component of a larger nuclear complex with DNA binding, ATPase and DNA helicase activities. Although Tip60 does not bind to AICD directly, an indirect interaction between AICD and Tip60 is mediated by Fe65. Upon forming this complex, AICD is stabilized and can be translocated into the nucleus to regulate expression of genes such as KAI1, Neprilysin, LRP1, p53, GSK-3b and EGF receptor", "citation": {"db": "PubMed", "db_id": "22122372"}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "MESH", "name": "Cytoplasm"}, "toLoc": {"namespace": "MESH", "name": "Cell Nucleus"}}}, "source": 1877, "target": 3563, "key": "31163aca98efcaa9ab52edbe199177b5"}, {"line": 38573, "relation": "association", "evidence": "Another transactivating complex consisting of AICD, Fe65 and Late SV40 Factor (LSF)/leader-binding protein-1 (LBP1)/transcription factor CP2 (TFCP2) has also been reported to induce the expression of GSK3-b (Kim et al. 2003).", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1104, "target": 1834, "key": "0e289bfa3b2bbd4293a83ceb75a6bbae"}, {"relation": "partOf", "source": 3452, "target": 1104, "key": "ebb31027c9ea8825d2b12e1c0435c045"}, {"line": 38573, "relation": "association", "evidence": "Another transactivating complex consisting of AICD, Fe65 and Late SV40 Factor (LSF)/leader-binding protein-1 (LBP1)/transcription factor CP2 (TFCP2) has also been reported to induce the expression of GSK3-b (Kim et al. 2003).", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"GSK3 subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 1834, "target": 1104, "key": "08797e119a90a961b86ec1b7e94745b6"}, {"relation": "hasVariant", "source": 1834, "target": 1835, "key": "9b3ddd74cba2a2c4d13d1ed130ac6422"}, {"line": 38589, "relation": "increases", "evidence": "These results indicate that the cytotoxicity of AICD may be mediated by its interacting proteins. For example, JIP is the scaffolding protein of the JNK pathway kinase and is involved in various cell events including neuronal apoptosis and axonal transporting. By binding to AICD, JIP mediates APP/AICD phosphorylation at Thr668, thus modulating APP trafficking, maturation and processing. Additionally, there is evidence suggesting that the cell death triggered by AICD is partially mediated by JIP", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1549, "target": 2338, "key": "7257870c8c16e0076feb661bc01e29a3"}, {"line": 38591, "relation": "increases", "evidence": "These results indicate that the cytotoxicity of AICD may be mediated by its interacting proteins. For example, JIP is the scaffolding protein of the JNK pathway kinase and is involved in various cell events including neuronal apoptosis and axonal transporting. By binding to AICD, JIP mediates APP/AICD phosphorylation at Thr668, thus modulating APP trafficking, maturation and processing. Additionally, there is evidence suggesting that the cell death triggered by AICD is partially mediated by JIP", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1550, "target": 2338, "key": "277f91e5cb1264a147205199b5240802"}, {"line": 38592, "relation": "increases", "evidence": "These results indicate that the cytotoxicity of AICD may be mediated by its interacting proteins. For example, JIP is the scaffolding protein of the JNK pathway kinase and is involved in various cell events including neuronal apoptosis and axonal transporting. By binding to AICD, JIP mediates APP/AICD phosphorylation at Thr668, thus modulating APP trafficking, maturation and processing. Additionally, there is evidence suggesting that the cell death triggered by AICD is partially mediated by JIP", "citation": {"db": "PubMed", "db_id": "22122372"}, "annotations": {"Subgraph": {"Axonal transport subgraph": true, "Amyloidogenic subgraph": true}, "Confidence": {"Very High": true}}, "source": 1551, "target": 2338, "key": "2fdc4e58d6cfa58c7d9bcfa5bab93d15"}, {"relation": "partOf", "source": 3006, "target": 1551, "key": "05cbc8f12504e70eb4b20a936f55e80b"}, {"line": 38603, "relation": "decreases", "evidence": "Here, to search for low-frequency variants in the amyloid-beta precursor protein (APP) gene with a significant effect on the risk of Alzheimer’s disease, we studied coding variants in APP in a set of whole-genome sequence data from 1,795 Icelanders. We found a coding mutation (A673T) in the APP gene that protects against Alzheimer’s disease and cognitive decline in the elderly without Alzheimer’s disease.", "citation": {"db": "PubMed", "db_id": "123"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1749, "target": 3823, "key": "69438d4118ae2d64f0336f2b4718b57f"}, {"line": 38612, "relation": "decreases", "evidence": "We found a coding mutation (A673T) in the APP gene that protects against Alzheimer's disease and cognitive decline in the elderly without Alzheimer's disease", "citation": {"db": "PubMed", "db_id": "22801501"}, "source": 1749, "target": 3823, "key": "fbdd5cfc9388c2576b114d8b596f70d2"}, {"line": 38857, "relation": "positiveCorrelation", "evidence": "CEBPD is upregulated in the astrocytes of AD patients. Therefore, we asked if activation of astrocytic/ CEBPD could contribute to AD pathogenesis. In this report, a novel role of CEBPD in attenuating macrophage-mediated/ phagocytosis of damaged neuron cells was found. By global gene expression profiling, we identified the inflammatory/ marker pentraxin-3 (PTX3, TNFAIP5, TSG-14) as a CEBPD target in astrocytes. Furthermore, we demonstrate that PTX3/ participates in the attenuation of macrophage-mediated phagocytosis of damaged neuron cells. This study provides the/ first demonstration of a role for astrocytic CEBPD and the CEBPD-regulated molecule PTX3 in the accumulation of damaged/ neurons, which is a hallmark of AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "21112127"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Innate immune system subgraph": true}}, "source": 2501, "target": 3823, "key": "92b5df087683e54608624a7bed7e233c"}, {"line": 38859, "relation": "decreases", "evidence": "CEBPD is upregulated in the astrocytes of AD patients. Therefore, we asked if activation of astrocytic/ CEBPD could contribute to AD pathogenesis. In this report, a novel role of CEBPD in attenuating macrophage-mediated/ phagocytosis of damaged neuron cells was found. By global gene expression profiling, we identified the inflammatory/ marker pentraxin-3 (PTX3, TNFAIP5, TSG-14) as a CEBPD target in astrocytes. Furthermore, we demonstrate that PTX3/ participates in the attenuation of macrophage-mediated phagocytosis of damaged neuron cells. This study provides the/ first demonstration of a role for astrocytic CEBPD and the CEBPD-regulated molecule PTX3 in the accumulation of damaged/ neurons, which is a hallmark of AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "21112127"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Innate immune system subgraph": true}}, "source": 2501, "target": 666, "key": "8508969225e52676869789cb74d2a397"}, {"line": 38866, "relation": "increases", "evidence": "CCAAT/enhancer binding protein delta (CEBPD) elevating PTX3 expression inhibits macrophage-mediated phagocytosis of dying neuron cells.", "citation": {"db": "PubMed", "db_id": "21112127"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Innate immune system subgraph": true}}, "source": 2501, "target": 4009, "key": "8e1dbfdd571e79c456021425239e125e"}, {"line": 38858, "relation": "decreases", "evidence": "CEBPD is upregulated in the astrocytes of AD patients. Therefore, we asked if activation of astrocytic/ CEBPD could contribute to AD pathogenesis. In this report, a novel role of CEBPD in attenuating macrophage-mediated/ phagocytosis of damaged neuron cells was found. By global gene expression profiling, we identified the inflammatory/ marker pentraxin-3 (PTX3, TNFAIP5, TSG-14) as a CEBPD target in astrocytes. Furthermore, we demonstrate that PTX3/ participates in the attenuation of macrophage-mediated phagocytosis of damaged neuron cells. This study provides the/ first demonstration of a role for astrocytic CEBPD and the CEBPD-regulated molecule PTX3 in the accumulation of damaged/ neurons, which is a hallmark of AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "21112127"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Innate immune system subgraph": true}}, "source": 666, "target": 431, "key": "3675a33e276d0a1d16a1e6d7a5a57902"}, {"line": 38862, "relation": "increases", "evidence": "CEBPD is upregulated in the astrocytes of AD patients. Therefore, we asked if activation of astrocytic/ CEBPD could contribute to AD pathogenesis. In this report, a novel role of CEBPD in attenuating macrophage-mediated/ phagocytosis of damaged neuron cells was found. By global gene expression profiling, we identified the inflammatory/ marker pentraxin-3 (PTX3, TNFAIP5, TSG-14) as a CEBPD target in astrocytes. Furthermore, we demonstrate that PTX3/ participates in the attenuation of macrophage-mediated phagocytosis of damaged neuron cells. This study provides the/ first demonstration of a role for astrocytic CEBPD and the CEBPD-regulated molecule PTX3 in the accumulation of damaged/ neurons, which is a hallmark of AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "21112127"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Innate immune system subgraph": true}}, "source": 666, "target": 3823, "key": "37c7b2d8049f97f638ab51a8d42890db"}, {"line": 38861, "relation": "decreases", "evidence": "CEBPD is upregulated in the astrocytes of AD patients. Therefore, we asked if activation of astrocytic/ CEBPD could contribute to AD pathogenesis. In this report, a novel role of CEBPD in attenuating macrophage-mediated/ phagocytosis of damaged neuron cells was found. By global gene expression profiling, we identified the inflammatory/ marker pentraxin-3 (PTX3, TNFAIP5, TSG-14) as a CEBPD target in astrocytes. Furthermore, we demonstrate that PTX3/ participates in the attenuation of macrophage-mediated phagocytosis of damaged neuron cells. This study provides the/ first demonstration of a role for astrocytic CEBPD and the CEBPD-regulated molecule PTX3 in the accumulation of damaged/ neurons, which is a hallmark of AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "21112127"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Innate immune system subgraph": true}}, "source": 3286, "target": 666, "key": "7f8698fd547f4ef1aa4319cfc7918a1d"}, {"line": 39128, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Binding and Uptake of Ligands by Scavenger Receptors": true, "Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3286, "target": 577, "key": "842f1ba685427d8d547ac1d39a698980"}, {"line": 39325, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3286, "target": 3815, "key": "9a62e9e4f97af0a3c62a06234b4a9a47"}, {"line": 38867, "relation": "decreases", "evidence": "CCAAT/enhancer binding protein delta (CEBPD) elevating PTX3 expression inhibits macrophage-mediated phagocytosis of dying neuron cells.", "citation": {"db": "PubMed", "db_id": "21112127"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Innate immune system subgraph": true}}, "source": 4009, "target": 666, "key": "66f3f25082852dfb33d94c1b523a4669"}, {"relation": "hasReactant", "source": 4093, "target": 272, "key": "552c5955c79e333220f883a1f8c8b9dc"}, {"relation": "hasProduct", "source": 4093, "target": 96, "key": "558b8ed00ba4dfb3148d93d11c93d53b"}, {"relation": "hasProduct", "source": 4093, "target": 135, "key": "a281ea91996008b7e5c04ad7b620af62"}, {"relation": "hasProduct", "source": 4093, "target": 218, "key": "ec146994d5fbe229a531e39e33335732"}, {"line": 38918, "relation": "increases", "evidence": "For example, when the brain is injured, microglia become activated by Abeta deposits and recruit astrocytes / by secreting acute-phase proteins such as complement factors and cytokines. Reactive microglia and astrocytes additionally / generate proinflammatory mediators, including cytokines, chemokines, prostaglandins, neurotoxic secretory products, / reactive oxygen species, and nitric oxide (Griffin et al., 1998; Tuppo and Arias, 2005). Cytokines and chemokines, / in turn, stimulate the synthesis of other enzymes, such as COXs and prostaglandin synthases. In AD, the expression / of COX-2, the inducible isoform, increases in response to inflammatory agents in neurons and glial cells (Pasinetti and / Aisen, 1998; Sairanen et al., 1998). Because COX is the rate-limiting enzyme in the production of prostaglandins (O'Banion,/ 1999; Smith et al., 1991), the increase in COX activity leads to an increase in prostaglandin production (Consilvio et al.,/ 2004). ", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 1665, "target": 2547, "key": "b45b39c84c78a4e197947f9c3c4f3e83"}, {"line": 38919, "relation": "increases", "evidence": "For example, when the brain is injured, microglia become activated by Abeta deposits and recruit astrocytes / by secreting acute-phase proteins such as complement factors and cytokines. Reactive microglia and astrocytes additionally / generate proinflammatory mediators, including cytokines, chemokines, prostaglandins, neurotoxic secretory products, / reactive oxygen species, and nitric oxide (Griffin et al., 1998; Tuppo and Arias, 2005). Cytokines and chemokines, / in turn, stimulate the synthesis of other enzymes, such as COXs and prostaglandin synthases. In AD, the expression / of COX-2, the inducible isoform, increases in response to inflammatory agents in neurons and glial cells (Pasinetti and / Aisen, 1998; Sairanen et al., 1998). Because COX is the rate-limiting enzyme in the production of prostaglandins (O'Banion,/ 1999; Smith et al., 1991), the increase in COX activity leads to an increase in prostaglandin production (Consilvio et al.,/ 2004). ", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 1665, "target": 2549, "key": "eb2ff88282ff11d0b3a5d5b2168ce6c0"}, {"line": 38920, "relation": "increases", "evidence": "For example, when the brain is injured, microglia become activated by Abeta deposits and recruit astrocytes / by secreting acute-phase proteins such as complement factors and cytokines. Reactive microglia and astrocytes additionally / generate proinflammatory mediators, including cytokines, chemokines, prostaglandins, neurotoxic secretory products, / reactive oxygen species, and nitric oxide (Griffin et al., 1998; Tuppo and Arias, 2005). Cytokines and chemokines, / in turn, stimulate the synthesis of other enzymes, such as COXs and prostaglandin synthases. In AD, the expression / of COX-2, the inducible isoform, increases in response to inflammatory agents in neurons and glial cells (Pasinetti and / Aisen, 1998; Sairanen et al., 1998). Because COX is the rate-limiting enzyme in the production of prostaglandins (O'Banion,/ 1999; Smith et al., 1991), the increase in COX activity leads to an increase in prostaglandin production (Consilvio et al.,/ 2004). ", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 1665, "target": 2225, "key": "4f05346ca63455b88af2a2c0d4312da0"}, {"relation": "partOf", "source": 419, "target": 1665, "key": "e1ca10623109ec613c1ef1c17149e943"}, {"line": 39265, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 419, "target": 577, "key": "df1fcc0be67cfa4b54446a25090d9714"}, {"line": 40311, "relation": "association", "evidence": "These results indicated that cultured human astrocytes express a distinct set of NF-kB-target cytokines and chemokines in resting and activated conditions, suggesting that the NF-kB signaling pathway differentially regulates gene expression of cytokines and chemokines in human astrocytes under physiological and inflammatory conditions.", "citation": {"db": "PubMed", "db_id": "24691121"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 419, "target": 3553, "key": "493cc4e8ff6e95ac47f40b6322fedc8b"}, {"line": 38921, "relation": "increases", "evidence": "For example, when the brain is injured, microglia become activated by Abeta deposits and recruit astrocytes / by secreting acute-phase proteins such as complement factors and cytokines. Reactive microglia and astrocytes additionally / generate proinflammatory mediators, including cytokines, chemokines, prostaglandins, neurotoxic secretory products, / reactive oxygen species, and nitric oxide (Griffin et al., 1998; Tuppo and Arias, 2005). Cytokines and chemokines, / in turn, stimulate the synthesis of other enzymes, such as COXs and prostaglandin synthases. In AD, the expression / of COX-2, the inducible isoform, increases in response to inflammatory agents in neurons and glial cells (Pasinetti and / Aisen, 1998; Sairanen et al., 1998). Because COX is the rate-limiting enzyme in the production of prostaglandins (O'Banion,/ 1999; Smith et al., 1991), the increase in COX activity leads to an increase in prostaglandin production (Consilvio et al.,/ 2004). ", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 2225, "target": 335, "key": "7ddd0255d8871ce945e04e07799e4d2b"}, {"line": 38940, "relation": "positiveCorrelation", "evidence": "In the central nervous system (CNS), prostaglandin (PG) and other bioactive lipids regulate vital aspects/ of neural membrane biology, including protein-lipid interactions, trans-membrane and trans-synaptic signaling. However, / a series of highly reactive PGs, free fatty acids, lysophospolipids, eicosanoids, platelet-activating factor, and reactive / oxygen species (ROS), all generated by enhanced phospholipase A2 (PLA2) activity and arachidonic acid (AA) release, / participate in cellular injury, particularly in neurodegeneration. PLA2 activation and PG production are among the earliest / initiating events in triggering brain-damage pathways, which can lead to long-term neurologic deficits. Altered / membrane-associated PLA2 activities have been correlated with several forms of acute and chronic brain injury, including / cerebral trauma, ischemic damage, induced seizures in the brain and epilepsy, schizophrenia, and in particular, Alzheimer's / disease (AD). Moreover, the expression of both COX-2 and PLA2 appears to be strongly activated / during Alzheimer's disease (AD), indicating the importance of inflammatory gene pathways as a response to brain injury./ This review addresses some current ideas concerning how brain PLA2 and brain PGs are early and key players in acute neural / trauma and in brain-cell damage associated with chronic neurodegenerative diseases such as AD.", "citation": {"db": "PubMed", "db_id": "12432919"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 2225, "target": 3823, "key": "1dd676a753a0815d456b061aae27fad2"}, {"line": 39271, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 2225, "target": 577, "key": "8be16161599306f4a72c6d0a5e90f0b5"}, {"line": 38927, "relation": "increases", "evidence": "As the levels of proinflammatory prostaglandins increase, PGT must also increase to maintain clearance of the/ prostaglandins and prevent additional inflammation. Because our data indicate lower than normal levels of PGT protein in AD/ brains, it is possible that the prostaglandins might not be cleared fast enough to limit the inflammatory cascade, which/ then could lead to neuronal cell death; however more work addressing this specific question is warranted.", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "source": 335, "target": 3380, "key": "89c5a1b9a4cefde127e48c6259286c5e"}, {"line": 38929, "relation": "decreases", "evidence": "As the levels of proinflammatory prostaglandins increase, PGT must also increase to maintain clearance of the/ prostaglandins and prevent additional inflammation. Because our data indicate lower than normal levels of PGT protein in AD/ brains, it is possible that the prostaglandins might not be cleared fast enough to limit the inflammatory cascade, which/ then could lead to neuronal cell death; however more work addressing this specific question is warranted.", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "subject": {"modifier": "Translocation"}, "source": 335, "target": 335, "key": "77a9f69d0df0621673c2dfd9b640035c"}, {"relation": "partOf", "source": 335, "target": 1662, "key": "22876aff30e17ca6d079188455098385"}, {"line": 38941, "relation": "positiveCorrelation", "evidence": "In the central nervous system (CNS), prostaglandin (PG) and other bioactive lipids regulate vital aspects/ of neural membrane biology, including protein-lipid interactions, trans-membrane and trans-synaptic signaling. However, / a series of highly reactive PGs, free fatty acids, lysophospolipids, eicosanoids, platelet-activating factor, and reactive / oxygen species (ROS), all generated by enhanced phospholipase A2 (PLA2) activity and arachidonic acid (AA) release, / participate in cellular injury, particularly in neurodegeneration. PLA2 activation and PG production are among the earliest / initiating events in triggering brain-damage pathways, which can lead to long-term neurologic deficits. Altered / membrane-associated PLA2 activities have been correlated with several forms of acute and chronic brain injury, including / cerebral trauma, ischemic damage, induced seizures in the brain and epilepsy, schizophrenia, and in particular, Alzheimer's / disease (AD). Moreover, the expression of both COX-2 and PLA2 appears to be strongly activated / during Alzheimer's disease (AD), indicating the importance of inflammatory gene pathways as a response to brain injury./ This review addresses some current ideas concerning how brain PLA2 and brain PGs are early and key players in acute neural / trauma and in brain-cell damage associated with chronic neurodegenerative diseases such as AD.", "citation": {"db": "PubMed", "db_id": "12432919"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 335, "target": 3823, "key": "aade32a48ce317e18a8942780540c584"}, {"line": 42605, "relation": "association", "evidence": "In the present study, we investigate the role of norepinephrine on cyclooxygenase- (COX-)2 expression/synthesis and prostaglandin (PG)E2 production in rat primary microglia.Interestingly, norepinephrine increased COX-2 mRNA, but not protein expression.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10116": true}}, "source": 335, "target": 317, "key": "8a7ae9a3338cc3fd9cdbf02cc9f56d23"}, {"line": 42606, "relation": "association", "evidence": "In the present study, we investigate the role of norepinephrine on cyclooxygenase- (COX-)2 expression/synthesis and prostaglandin (PG)E2 production in rat primary microglia.Interestingly, norepinephrine increased COX-2 mRNA, but not protein expression.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10116": true}}, "source": 335, "target": 150, "key": "fb1b72f14390819c95fd1b7cc54d4dbb"}, {"line": 38928, "relation": "increases", "evidence": "As the levels of proinflammatory prostaglandins increase, PGT must also increase to maintain clearance of the/ prostaglandins and prevent additional inflammation. Because our data indicate lower than normal levels of PGT protein in AD/ brains, it is possible that the prostaglandins might not be cleared fast enough to limit the inflammatory cascade, which/ then could lead to neuronal cell death; however more work addressing this specific question is warranted.", "citation": {"db": "PubMed", "db_id": "18353443"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "tport", "namespace": "bel"}}, "object": {"modifier": "Translocation"}, "source": 3380, "target": 335, "key": "a782f161cfff562ffdc813c246fbb979"}, {"relation": "partOf", "source": 3199, "target": 1662, "key": "148c5a845b66ba4adba17f1195c221e7"}, {"line": 38938, "relation": "increases", "evidence": "In the central nervous system (CNS), prostaglandin (PG) and other bioactive lipids regulate vital aspects/ of neural membrane biology, including protein-lipid interactions, trans-membrane and trans-synaptic signaling. However, / a series of highly reactive PGs, free fatty acids, lysophospolipids, eicosanoids, platelet-activating factor, and reactive / oxygen species (ROS), all generated by enhanced phospholipase A2 (PLA2) activity and arachidonic acid (AA) release, / participate in cellular injury, particularly in neurodegeneration. PLA2 activation and PG production are among the earliest / initiating events in triggering brain-damage pathways, which can lead to long-term neurologic deficits. Altered / membrane-associated PLA2 activities have been correlated with several forms of acute and chronic brain injury, including / cerebral trauma, ischemic damage, induced seizures in the brain and epilepsy, schizophrenia, and in particular, Alzheimer's / disease (AD). Moreover, the expression of both COX-2 and PLA2 appears to be strongly activated / during Alzheimer's disease (AD), indicating the importance of inflammatory gene pathways as a response to brain injury./ This review addresses some current ideas concerning how brain PLA2 and brain PGs are early and key players in acute neural / trauma and in brain-cell damage associated with chronic neurodegenerative diseases such as AD.", "citation": {"db": "PubMed", "db_id": "12432919"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3199, "target": 1662, "key": "4a0a9c568bc422eba5c7a9450f509630"}, {"line": 39269, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3199, "target": 577, "key": "7bf1030e4b8b48419484c1ae09293d94"}, {"relation": "partOf", "source": 114, "target": 1662, "key": "89a19762c6f9dcee919796628798e6e5"}, {"relation": "partOf", "source": 280, "target": 1662, "key": "b55884b7bf621872aa6cff62ac5b0b2e"}, {"relation": "partOf", "source": 406, "target": 1662, "key": "eb747badbe19559515b7c51170b2bcc4"}, {"line": 38966, "relation": "negativeCorrelation", "evidence": "Chronic neuroinflammation correlates with cognitive decline and brain atrophy in Alzheimer's disease (AD),/ and cytokines and chemokines mediate the inflammatory response. However, quantitation of cytokines and chemokines in AD/ brain tissue has only been carried out for a small number of mediators with variable results. We simultaneously quantified / 17 cytokines and chemokines in brain tissue extracts from controls (n = 10) and from patients with and without genetic / forms of AD (n = 12). Group comparisons accounting for multiple testing revealed that monocyte chemoattractant protein-1/ (MCP-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) were consistently upregulated in AD brain tissue. / Immunohistochemistry for MCP-1, IL-6 and IL-8 confirmed this increase and determined localization of these factors/ in neurons (MCP-1, IL-6, IL-8), astrocytes (MCP-1, IL-6) and plaque pathology (MCP-1, IL-8). Logistic linear regression/ modeling determined that MCP-1 was the most reliable predictor of disease. Our data support previous work on significant / increases in IL-6 and IL-8 in AD but indicate that MCP-1 may play a more dominant role in chronic inflammation in AD.", "citation": {"db": "PubMed", "db_id": "18637012"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3822, "target": 577, "key": "d1bf260dfcce78f6c5f07c48847a1535"}, {"relation": "partOf", "source": 3480, "target": 1636, "key": "50a1a58abc8b549fa873adb25f8ae077"}, {"line": 39017, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3480, "target": 1636, "key": "e12fae3f55b68ed7e5dcacb676610615"}, {"line": 39021, "relation": "increases", "evidence": "During early AD pathogenesis, amyloid beta (Abeta), S100B and IL-1beta could bring about a vicious cycle of Abeta/ generation between astrocytes and neurons leading to chronic, sustained and progressive neuroinflammation. In advanced/ stages of AD, TRAIL secreted from astrocytes have been shown to bind to death receptor 5 (DR5) on neurons to trigger / apoptotic process in a caspase-8-dependent manner. Furthermore, astrocytes could be reactivated by TGFbeta1 to generate more Abeta and / to undergo the aggravating astrogliosis. TGFbeta2 was also observed to cooperate with Abeta to cause neuronal demise by / destroying the stability of lysosomes in neurons.", "citation": {"db": "PubMed", "db_id": "21143158"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1636, "target": 2448, "key": "29be2e83651ce9521ef3eb78d2557e3c"}, {"relation": "partOf", "source": 3474, "target": 1636, "key": "820582e3c50a3af89e36848957262e3b"}, {"line": 39113, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 3120, "target": 577, "key": "691ff793d986d84130c96764e20a1f0a"}, {"line": 39117, "relation": "increases", "evidence": "increased production of amyloid-beta peptide species can activate the innate immunity system via pattern recognition receptors (PRRs) and evoke Alzheimer's pathology. We will focus on the role of innate immunity system of brain in the initiation and the propagation of inflammatory process in AD. We examine here in detail the significance of amyloid-beta oligomers and fibrils as danger-associated molecular patterns (DAMPs) in the activation of a wide array of PRRs in glial cells and neurons, such as Toll-like, NOD-like, formyl peptide, RAGE and scavenger receptors along with complement and pentraxin systems.", "citation": {"db": "PubMed", "db_id": "19388207"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "subject": {"modifier": "Activity"}, "source": 2708, "target": 577, "key": "e4917baf78b216033682101190cce68b"}, {"line": 39161, "relation": "positiveCorrelation", "evidence": "it has been demonstrated that micromolar S100B concentrations stimulate c-Jun N-terminal kinase (JNK) phosphorylation through the receptor for advanced glycation ending products, and subsequently activate nuclear AP-1/cJun transcription, in cultured human neural stem cells. In addition, as revealed by Western blot, small interfering RNA and immunofluorescence analysis, S100B-induced JNK activation increased expression of Dickopff-1 that, in turn, promoted glycogen synthase kinase 3beta phosphorylation and beta-catenin degradation, causing canonical Wnt signaling pathway disruption and tau protein hyperphosphorylation. These findings propose a previously unrecognized link between S100B and tau hyperphosphorylation, suggesting S100B can contribute to NFT formation in AD and in all other conditions in which neuroinflammation may have a crucial role.", "citation": {"db": "PubMed", "db_id": "18494933"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Calcium-dependent signal transduction": true, "GSK3 subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 2795, "key": "5ae1bac097bb7068d9a0ef48751d0708"}, {"line": 47723, "relation": "decreases", "evidence": "Although the mechanism of Ab action in the pathogenesis of Alzheimer’s disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1), whereas the silencing of Dkk1 blocks Ab neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}}, "source": 2629, "target": 462, "key": "32fd9ffe3c5e56a6d0037efe2e92a574"}, {"line": 47741, "relation": "decreases", "evidence": "Caricasole et al demonstrated that the Abeta peptide fragment, Abeta25-35, induces neuronal expression of the wnt antagonist Dkk1 and that silencing of DKK1 blocks Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 462, "key": "0e9fbad39c1c27736226bd24d0381fb6"}, {"line": 48044, "relation": "decreases", "evidence": "We identify possible links with Dickkopf-1, a negative regulator of the wnt pathway, and propose that the abnormal expression of Keratin 9 in AD blood and cerebrospinal fluid may be a result of blood brain barrier dysregulation and disruption of the ubiquitin proteasome system.", "citation": {"db": "PubMed", "db_id": "26973255"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 462, "key": "b9e6e15e13c6147729846ce65036ab17"}, {"line": 48174, "relation": "decreases", "evidence": "More importantly, persistent activation of Wnt signaling through Wnt ligands, or inhibition of negative regulators of Wnt signaling, such as Dickkopf-1 (DKK-1) and glycogen synthase kinase-3 beta (GSK-3 beta ) that are hyperactive in the disease state, is able to protect against Abeta toxicity and ameliorate cognitive performance in AD.", "citation": {"db": "PubMed", "db_id": "24883305"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 462, "key": "4b8d86d76ef1673f058ce2eab8bc8427"}, {"line": 48239, "relation": "decreases", "evidence": "Along these lines, one mechanism through which E2 protects the hippocampus from cerebral ischemia is by preventing the post-ischemic elevation of Dkk1, a neurodegenerative factor that serves as an antagonist of the canonical Wnt signaling pathway, and simultaneously inducing pro-survival Wnt/beta-Catenin signaling in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Wnt signaling subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 462, "key": "792ac15b3143fa99d48e3f1fd02e4204"}, {"line": 48650, "relation": "decreases", "evidence": "Although the mechanism of Abeta action in the pathogenesis of Alzheimer's disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1)", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Very High": true}}, "source": 2629, "target": 462, "key": "1bc2676bd1e12f92974bb937e60be3e3"}, {"line": 47725, "relation": "increases", "evidence": "Although the mechanism of Ab action in the pathogenesis of Alzheimer’s disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1), whereas the silencing of Dkk1 blocks Ab neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}}, "source": 2629, "target": 645, "key": "7b66130850bf590575f6d843b40dd6df"}, {"line": 47745, "relation": "increases", "evidence": "Caricasole et al demonstrated that the Abeta peptide fragment, Abeta25-35, induces neuronal expression of the wnt antagonist Dkk1 and that silencing of DKK1 blocks Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2629, "target": 648, "key": "31ba056c1f4f73d8a492fe0224d6d608"}, {"line": 47762, "relation": "positiveCorrelation", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 2538, "key": "06575f1392ce092850848338363fcea6"}, {"line": 47826, "relation": "increases", "evidence": "Dkk4 and Dkk1 induced EGR1 (Figure 6b) and FOS (data not shown) whereas Dkk2 and Dkk3 did not, mirroring the abilities of the Dkk1 family to antagonise canonical wnt.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "DKK1 subgraph": true}}, "source": 2629, "target": 2658, "key": "7c031b709b993190ecae814cfba53c89"}, {"line": 47830, "relation": "increases", "evidence": "Dkk4 and Dkk1 induced EGR1 (Figure 6b) and FOS (data not shown) whereas Dkk2 and Dkk3 did not, mirroring the abilities of the Dkk1 family to antagonise canonical wnt.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 2629, "target": 2699, "key": "92bba64388e0ddc452efca5b581b4909"}, {"line": 47888, "relation": "association", "evidence": "Therefore, we hypothesize that Dkk1 may play a role in both osteoporosis and AD.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 2629, "target": 3823, "key": "15ccd219025cf9da543d52e122cbb18f"}, {"line": 48178, "relation": "positiveCorrelation", "evidence": "More importantly, persistent activation of Wnt signaling through Wnt ligands, or inhibition of negative regulators of Wnt signaling, such as Dickkopf-1 (DKK-1) and glycogen synthase kinase-3 beta (GSK-3 beta ) that are hyperactive in the disease state, is able to protect against Abeta toxicity and ameliorate cognitive performance in AD.", "citation": {"db": "PubMed", "db_id": "24883305"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 3823, "key": "7d78f8ca4a8ca3e960e38c289ae7b2d3"}, {"line": 48262, "relation": "association", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 3823, "key": "6fb18863f661b49fb55a271032aba1b3"}, {"line": 47889, "relation": "association", "evidence": "Therefore, we hypothesize that Dkk1 may play a role in both osteoporosis and AD.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 2629, "target": 3926, "key": "b958cc30b07065dcb7ab8027d4833029"}, {"line": 48033, "relation": "association", "evidence": "Dickkopf-related protein 1 (Dkk1), a vital antagonist of the Wnt signaling, was reported to be closely associated with bone homeostasis and osteoporosis.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 3926, "key": "37648a43f62fe7ff2090c35108af462c"}, {"line": 47996, "relation": "increases", "evidence": "As a result, the Polymerization of and MAP-2 and NF-H induced by Abeta25-35 could be significantly inhibited by Wnt3a(40 ng/ml), however enhanced by Dkk1(100 ng/ml).", "citation": {"db": "PubMed", "db_id": "26809093"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"namespace": "GO", "name": "protein polymerization"}}, "source": 2629, "target": 2987, "key": "ae4c7223402526e3ef07e28095c62e4d"}, {"line": 48000, "relation": "increases", "evidence": "As a result, the Polymerization of and MAP-2 and NF-H induced by Abeta25-35 could be significantly inhibited by Wnt3a(40 ng/ml), however enhanced by Dkk1(100 ng/ml).", "citation": {"db": "PubMed", "db_id": "26809093"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"namespace": "GO", "name": "protein polymerization"}}, "source": 2629, "target": 3100, "key": "efb86cd08d6ba26ca621f0bd525e4831"}, {"line": 48020, "relation": "increases", "evidence": "Meanwhile, the protein abundance of phosphorylated tau in several sites is decreased by Wnt3a, but increased by Dkk1 significantly compared with the control group.", "citation": {"db": "PubMed", "db_id": "26809093"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 2629, "target": 3015, "key": "298fe21b936337f4f108c5203122d8ff"}, {"line": 48064, "relation": "association", "evidence": "In silico molecular target prediction docking studies suggest that ETH interacts with Akt, Dkk-1, and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "26420483"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 2629, "target": 6, "key": "447e92f8e089650b3197fc3069d1efe5"}, {"line": 48088, "relation": "increases", "evidence": "In this study, activation of the Wnt pathway by overexpression of the agonist Wnt3a or beta-catenin or by inhibition of glycogen kinase synthase-3 in N2a cells resulted in a reduction in Abeta levels and in the activity and expression of BACE1 (beta-APP cleaving enzyme). Conversely, inhibition of the pathway by transfection of the antagonists secreted frizzled receptor protein-1 or dickkopf-1 produced the opposite effects.", "citation": {"db": "PubMed", "db_id": "25384422"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2629, "target": 80, "key": "39d26e90577d47420e865449d04ad71e"}, {"line": 48093, "relation": "increases", "evidence": "In this study, activation of the Wnt pathway by overexpression of the agonist Wnt3a or beta-catenin or by inhibition of glycogen kinase synthase-3 in N2a cells resulted in a reduction in Abeta levels and in the activity and expression of BACE1 (beta-APP cleaving enzyme). Conversely, inhibition of the pathway by transfection of the antagonists secreted frizzled receptor protein-1 or dickkopf-1 produced the opposite effects.", "citation": {"db": "PubMed", "db_id": "25384422"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 2375, "key": "fb00cc999a62811377e8724518111c27"}, {"line": 48097, "relation": "increases", "evidence": "In this study, activation of the Wnt pathway by overexpression of the agonist Wnt3a or beta-catenin or by inhibition of glycogen kinase synthase-3 in N2a cells resulted in a reduction in Abeta levels and in the activity and expression of BACE1 (beta-APP cleaving enzyme). Conversely, inhibition of the pathway by transfection of the antagonists secreted frizzled receptor protein-1 or dickkopf-1 produced the opposite effects.", "citation": {"db": "PubMed", "db_id": "25384422"}, "annotations": {"Subgraph": {"Beta secretase subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 2629, "target": 2375, "key": "ff23d6446fe20b2a902b831398001864"}, {"line": 48109, "relation": "negativeCorrelation", "evidence": "Although the mechanism of Abeta action in the pathogenesis of Alzheimer's disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1), whereas the silencing of Dkk1 blocks Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "source": 2629, "target": 2328, "key": "7d211c1fca183f39759f2a1dc1285e28"}, {"line": 48184, "relation": "increases", "evidence": "More importantly, persistent activation of Wnt signaling through Wnt ligands, or inhibition of negative regulators of Wnt signaling, such as Dickkopf-1 (DKK-1) and glycogen synthase kinase-3 beta (GSK-3 beta ) that are hyperactive in the disease state, is able to protect against Abeta toxicity and ameliorate cognitive performance in AD.", "citation": {"db": "PubMed", "db_id": "24883305"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2629, "target": 2328, "key": "71eedb90c25156e8b11bebcffa0e4970"}, {"line": 48235, "relation": "increases", "evidence": "Along these lines, one mechanism through which E2 protects the hippocampus from cerebral ischemia is by preventing the post-ischemic elevation of Dkk1, a neurodegenerative factor that serves as an antagonist of the canonical Wnt signaling pathway, and simultaneously inducing pro-survival Wnt/beta-Catenin signaling in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Wnt signaling subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 3831, "key": "b3aba670f3f42d93de0725401055fb8c"}, {"line": 48250, "relation": "positiveCorrelation", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 649, "key": "b0e75c4b01f80d90512fae4144bd624c"}, {"line": 48254, "relation": "positiveCorrelation", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 3874, "key": "6fa097945a575c39d4a137cac076109d"}, {"line": 48258, "relation": "association", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 3930, "key": "3176a34ec8952668aaa86e8d975d5577"}, {"line": 48266, "relation": "association", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 3878, "key": "49d1a058dbe9ea280c08d1756e7f3f26"}, {"line": 48270, "relation": "association", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2629, "target": 3854, "key": "55d5b1042391479c06c2cb54e244d99a"}, {"line": 39214, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 398, "target": 2328, "key": "59303cb0167241996a6f37c5726f3a03"}, {"relation": "partOf", "source": 398, "target": 996, "key": "850c04bb46614c4a3b47f61214f87474"}, {"relation": "partOf", "source": 398, "target": 995, "key": "0d31788f573d15ab6f96917fda6c3e78"}, {"relation": "partOf", "source": 398, "target": 993, "key": "751de7bfb8bd7ed1e0f348641f71a329"}, {"relation": "partOf", "source": 398, "target": 994, "key": "6ccb4956b441a2c5dcad6cab0f9a7fc5"}, {"line": 39268, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 398, "target": 3199, "key": "e9a35c59c7ef8eb0ca32fce3c7a2a112"}, {"line": 39270, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 398, "target": 2225, "key": "59470ea79b61cc6337c6d52e69094a8e"}, {"line": 39218, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 996, "target": 419, "key": "7122f9da60e83595ddec0a7da62b483e"}, {"line": 39224, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 996, "target": 425, "key": "6c5187b284c7330bd9fd4760a3fb3e4b"}, {"relation": "partOf", "source": 3348, "target": 996, "key": "3284408564255961d89c4668bb7d3f36"}, {"line": 39820, "relation": "negativeCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "source": 3348, "target": 3823, "key": "7e87972c7ef6f52a4190898bcb560f49"}, {"line": 39266, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 425, "target": 577, "key": "5e78820234d3ba9aff66258459359ee1"}, {"line": 39230, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 995, "target": 419, "key": "ed0c488b2f7d4ed75d53b233e6e97592"}, {"line": 39236, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 995, "target": 425, "key": "be5fa916ceead11e0b331b4cc60306f7"}, {"line": 39242, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 993, "target": 419, "key": "5de2a6020e2af7ccb8c3f0b5d10e236d"}, {"line": 39248, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 993, "target": 425, "key": "c8edbb22dbdc60420218d71cb1447854"}, {"line": 39254, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 994, "target": 419, "key": "ead8857698ed26532b225b58d9655dfc"}, {"line": 39260, "relation": "increases", "evidence": "Platelets are an important source of amyloid-ss (Ass) in the circulatory system and play an important pro-inflammatory role. Upon activation, they adhere to leukocytes and endothelial cells by means of adhesive proteins like P-selectin, platelet endothelial cell adhesion molecule-1 (PECAM) and intercellular adhesion molecule-1 and -2 (ICAM-1 and -2) and secrete inflammatory mediators (chemokines, interleukins). In addition, platelets contain important enzymes involved in inflammatory intermediary synthesis like phospholipase A(2) (PLA(2)) and cyclooxygenase-2 (COX-2), and recent reports demonstrated significant changes in platelet levels and activities in Alzheimer's disease. Thus, as platelets represent an important link between Ass deposition and inflammatory reactions especially at endothelial level, they can be considered a valuable cellular model to evaluate potential peripheral inflammatory biomarkers in Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "20454929"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 994, "target": 425, "key": "84cd9afe023fe8a2ce79983ece4bc36b"}, {"relation": "partOf", "source": 2864, "target": 994, "key": "33e7409d05a04e52995d7753c52a23be"}, {"line": 39283, "relation": "increases", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 2889, "target": 577, "key": "2ea884a9f1665450745e143bfabb471f"}, {"line": 39294, "relation": "association", "evidence": "In the present study, Abeta(1-42) synergistically elevated the expression of IL-12 and IL-23 triggered by inflammatory activation of microglia, and the peroxisome proliferator-activated receptor (PPAR)-gamma agonist 15-deoxy-Delta(12,14)-PGJ(2) (15d-PGJ(2)) effectively blocked the elevation of these proinflammatory cytokines. Furthermore, 15d-PGJ(2) suppressed the Abeta-related synergistic induction of CD14, MyD88, and Toll-like receptor 2, molecules that play critical roles in neuroinflammatory conditions. Collectively, these studies suggest that PPAR-gamma agonists may be effective in modulating the development of AD.", "citation": {"db": "PubMed", "db_id": "18615183"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Very High": true}}, "source": 3084, "target": 577, "key": "01d9c0274e97a7d5f59a84f3c59d94ea"}, {"line": 39309, "relation": "positiveCorrelation", "evidence": "In addition, the presence of KP1, CR3/43 and GFAP decreases significantly with increasing age in AD.", "citation": {"db": "PubMed", "db_id": "22152162"}, "source": 2478, "target": 3823, "key": "43856e714410a018df4c7a19012d1c0a"}, {"line": 39311, "relation": "positiveCorrelation", "evidence": "In addition, the presence of KP1, CR3/43 and GFAP decreases significantly with increasing age in AD.", "citation": {"db": "PubMed", "db_id": "22152162"}, "annotations": {"Subgraph": {"Complement system subgraph": true}}, "source": 2925, "target": 3823, "key": "c5bf43b48ea7137bdb97c9cc12457f2e"}, {"line": 39324, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3815, "target": 418, "key": "d3a47e87c812067df17e4e3fd2b2f860"}, {"line": 39325, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Very High": true}}, "source": 3815, "target": 3286, "key": "10250c980d273af42b331bb741451786"}, {"line": 39335, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3815, "target": 2520, "key": "c45ca347d589fbcf4a20bae0072c7f54"}, {"line": 39339, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3815, "target": 2517, "key": "5c455b6b867c4c2898a67c44a92243bb"}, {"line": 39343, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3815, "target": 2522, "key": "cc8aa8ff65e7acd7d64113759c70a829"}, {"line": 39347, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3815, "target": 2518, "key": "637b0f0a532c47b4a28de575306efb74"}, {"line": 39351, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3815, "target": 2524, "key": "0cd1598b92e82a2685e6723b3e2714dd"}, {"line": 39355, "relation": "positiveCorrelation", "evidence": "Inflammatory components related to AD neuroinflammation include brain cells such as microglia and astrocytes, the classic and alternate pathways of the complement system, the pentraxin acute-phase proteins, neuronal-type nicotinic acetylcholine receptors (AChRs), peroxisomal proliferators-activated receptors (PPARs), as well as cytokines and chemokines. Both the microglia and astrocytes have been shown to generate beta-amyloid protein (Abeta), one of the main pathologic features of AD. Abeta itself has been shown to act as a pro-inflammatory agent causing the activation of many of the inflammatory components", "citation": {"db": "PubMed", "db_id": "15474976"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Acetylcholine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3815, "target": 3212, "key": "ef6d0b03f1ea6442b51c4472dcf95393"}, {"line": 39709, "relation": "increases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 3815, "target": 3823, "key": "5e8d03a1a91d2b278c8fed162d3cafe5"}, {"line": 40327, "relation": "association", "evidence": "Neuroinflammation plays a critical role in the pathogenesis of Alzheimer's disease (AD) and involves activation of the innate immune response via recognition of diverse stimuli by pattern recognition receptors (PRRs).", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3815, "target": 3823, "key": "fec15e58db561fa5035c222605b6c699"}, {"line": 41243, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3815, "target": 3823, "key": "eb16ed6d1bc1d89650e2801444b4a772"}, {"line": 39784, "relation": "positiveCorrelation", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "source": 3815, "target": 2884, "key": "cfc9bfe23135df298155a5d2ec263284"}, {"line": 39785, "relation": "positiveCorrelation", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "source": 3815, "target": 2894, "key": "80e6d31c1be88fcd5fe03a6d0b630f05"}, {"line": 39787, "relation": "positiveCorrelation", "evidence": "An important factor in the onset of inflammatory process is the overexpression of interleukin (IL)-1, which produces many reactions in a vicious circle that cause dysfunction and neuronal death. Other important cytokines in neuroinflammation are IL-6 and tumor necrosis factor (TNF)-α. By contrast, other cytokines such as IL-1 receptor antagonist (IL-1ra), IL-4, IL-10, and transforming growth factor (TGF)-beta can suppress both proinflammatory cytokine production and their action, subsequently protecting the brain. ", "citation": {"db": "PubMed", "db_id": "22566778"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}}, "source": 3815, "target": 3472, "key": "b9ea21b230a7ebd1bd4d5beff1c9e0b0"}, {"line": 40328, "relation": "increases", "evidence": "Neuroinflammation plays a critical role in the pathogenesis of Alzheimer's disease (AD) and involves activation of the innate immune response via recognition of diverse stimuli by pattern recognition receptors (PRRs).", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3815, "target": 578, "key": "527f29ea99c994e92d0daab67d61ce90"}, {"line": 40329, "relation": "increases", "evidence": "Neuroinflammation plays a critical role in the pathogenesis of Alzheimer's disease (AD) and involves activation of the innate immune response via recognition of diverse stimuli by pattern recognition receptors (PRRs).", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3815, "target": 3554, "key": "f85a09b2688b66cb34ce7c8f92fd8135"}, {"line": 40602, "relation": "association", "evidence": "The chemokine Interferon gamma-induced protein 10 (IP-10) and human leukocyte antigen (HLA) are widely used indicators of glial activation and neuroinflammation and are up-regulated in many brain disorders.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHDisease": {"Brain Diseases": true}, "MeSHAnatomy": {"Brain": true, "Leukocytes": true, "Neuroglia": true}, "Species": {"9606": true}}, "source": 3815, "target": 2603, "key": "ca721358632f1cfd60737525e347e409"}, {"line": 40797, "relation": "positiveCorrelation", "evidence": "Membrane-type 1 metalloproteinase is upregulated in microglia/brain macrophages in neurodegenerative and neuroinflammatory diseases.", "citation": {"db": "PubMed", "db_id": "24323769"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Membranes": true, "Macrophages": true}}, "source": 3815, "target": 3058, "key": "869b32b1e0f29af049f4a5e7090e4635"}, {"line": 41240, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3815, "target": 3930, "key": "b241f5dc99141fec1c940226b45cd33f"}, {"line": 41241, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3815, "target": 3869, "key": "0260ac57afbc4b522b46a017e4b325b6"}, {"line": 41242, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3815, "target": 3830, "key": "f7a860a40e807b2717cf6e1af828c2c0"}, {"line": 41244, "relation": "positiveCorrelation", "evidence": "Neuroinflammation contributes to the pathophysiology of diverse diseases including stroke, traumatic brain injury, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, resulting in neurodegeneration and loss of neurological function.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Parkinson Disease": true, "Brain Injuries": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3815, "target": 3878, "key": "88fd23a80efb42dfa18f49532922ca13"}, {"line": 43850, "relation": "association", "evidence": "An impairment of the PD-L1/PD1 pathway is present in AD and MCI. Such alteration results in reduced IL-10 production and diminished apoptosis of Abeta-specific CD4(+) T lymphocytes; these phenomena could play a role in the neuroinflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "21514692"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 3815, "target": 2471, "key": "be148bdbfa549f20e041a4e8d522bf38"}, {"line": 44256, "relation": "positiveCorrelation", "evidence": "Microglia manage immunosurveillance and mediate inflammation, both suggested to be important in Alzheimer's disease (AD). The aim of this study was to investigate if microglial markers could differentiate, firstly between AD and controls, and secondly between stable mild cognitive impairment (MCI) and those progressing to AD and vascular dementia (VaD). Furthermore, we investigated if these markers were sufficiently stable to be used in clinical trials. We quantified YKL-40 and sCD14 in cerebrospinal fluid (CSF) from 96 AD patients, 65 healthy controls, and 170 patients with MCI from baseline and over 5.7 years. For the stability analysis, two CSF samples were collected from 52 AD patients with a six-month interval in between. YKL-40, but not sCD14, was significantly elevated in AD compared with healthy controls (p = 0.003). Furthermore, YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and ABeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 x 10-25; r = 0.77, p = 2.0 x 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3815, "target": 2509, "key": "6e8073f416314e312c9190e1103731aa"}, {"line": 39402, "relation": "increases", "evidence": "The excitotoxin quinolinic acid (QUIN) is synthesized through the kynurenine pathway (KP) by activated monocyte lineage cells. QUIN is likely to play a role in the pathogenesis of several major neuroinflammatory diseases including Alzheimer's disease (AD). The presence of reactive astrocytes, astrogliosis, increased oxidative stress and inflammatory cytokines are important pathological hallmarks of AD. We found that QUIN induces IL-1beta expression, a key mediator in AD pathogenesis, in human astrocytes. At pathophysiological concentrations QUIN induced a switch between structural protein expressions in a dose dependent manner, increasing VIM and concomitantly decreasing GFAP expression. Glutamine synthetase (GS) activity was used as a functional metabolic test for astrocytes. We found a significant dose-dependent reduction in GS activity following QUIN treatment. All together, this study showed that QUIN is an important factor for astroglial activation, dysregulation and cell death with potential relevance to AD and other neuroinflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20003262"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 168, "target": 3815, "key": "3fe28b89d6083d2dcd3282d0db8122bc"}, {"line": 39403, "relation": "increases", "evidence": "The excitotoxin quinolinic acid (QUIN) is synthesized through the kynurenine pathway (KP) by activated monocyte lineage cells. QUIN is likely to play a role in the pathogenesis of several major neuroinflammatory diseases including Alzheimer's disease (AD). The presence of reactive astrocytes, astrogliosis, increased oxidative stress and inflammatory cytokines are important pathological hallmarks of AD. We found that QUIN induces IL-1beta expression, a key mediator in AD pathogenesis, in human astrocytes. At pathophysiological concentrations QUIN induced a switch between structural protein expressions in a dose dependent manner, increasing VIM and concomitantly decreasing GFAP expression. Glutamine synthetase (GS) activity was used as a functional metabolic test for astrocytes. We found a significant dose-dependent reduction in GS activity following QUIN treatment. All together, this study showed that QUIN is an important factor for astroglial activation, dysregulation and cell death with potential relevance to AD and other neuroinflammatory diseases.", "citation": {"db": "PubMed", "db_id": "20003262"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 168, "target": 2885, "key": "96ed17cea84f2fad8d7dbfe56f82315f"}, {"line": 39414, "relation": "increases", "evidence": "Acidic fibroblast growth factor (FGF) potentiates glial-mediated neurotoxicity by activating FGFR2 IIIb protein.", "citation": {"db": "PubMed", "db_id": "21990352"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2693, "target": 609, "key": "b6956a71603ffa348a91dcdc4ee3b5b9"}, {"line": 39415, "relation": "increases", "evidence": "Acidic fibroblast growth factor (FGF) potentiates glial-mediated neurotoxicity by activating FGFR2 IIIb protein.", "citation": {"db": "PubMed", "db_id": "21990352"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2693, "target": 2695, "key": "c0185785150b6de9179cd23de9102690"}, {"line": 39416, "relation": "increases", "evidence": "Acidic fibroblast growth factor (FGF) potentiates glial-mediated neurotoxicity by activating FGFR2 IIIb protein.", "citation": {"db": "PubMed", "db_id": "21990352"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2695, "target": 609, "key": "a580c3022acde3300aa3a8146d7cf44a"}, {"line": 39427, "relation": "increases", "evidence": "Secreted phospholipase A(2) group IIA (sPLA(2)IIA) has been implicated as an inflammatory mediator contributing to various peripheral inflammatory conditions; however, little is known about the role this enzyme plays in neuroinflammation. Agents inhibiting the non-enzymatic actions of sPLA(2)IIA could be used to slow down progression of neurodegenerative processes that are driven by inflammation.", "citation": {"db": "PubMed", "db_id": "22406427"}, "annotations": {"Confidence": {"Very High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 3197, "target": 3920, "key": "be9f9ccb148b12702ea6ffdd150a819f"}, {"line": 39509, "relation": "increases", "evidence": "In the cortex, cPLA2 immunoreactive astrocytes were detected in regions that contained numerous A beta deposits. The finding of elevated levels of cPLA2 immunoreactivity in AD brain supports the hypothesis that there is an active inflammatory process occurring in AD.", "citation": {"db": "PubMed", "db_id": "9173912"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}}, "source": 3197, "target": 480, "key": "0d5c4f8df1d3818114c43806ce6f8d24"}, {"line": 40754, "relation": "association", "evidence": "This study involves the reductionist fragment-based approach to understand the structure adopted by N-terminal fragment of Alzheimer's Abeta peptide in its complex with PLA2.", "citation": {"db": "PubMed", "db_id": "24619194"}, "source": 3197, "target": 2134, "key": "a2ceee0b878567a5ebaf87d19fed1920"}, {"line": 39440, "relation": "increases", "evidence": "misfolded tau could represent a trigger for microglial activation, suggesting the dual role of misfolded tau in the Alzheimer's disease inflammatory cascade.", "citation": {"db": "PubMed", "db_id": "22397366"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Tau protein subgraph": true}}, "source": 3037, "target": 609, "key": "46afc094eb5071dd6bc507a00cf6c7c1"}, {"line": 39466, "relation": "decreases", "evidence": "It has been shown that apoE increased the production of nitric oxide (NO) from human monocyte-derived macrophages (MDM); this effect could represent an important link between tissue redox balance and inflammation, since inflammation and oxidative stress are involved in chronic neurodegenerative disorders. Moreover, it has been evidenced that an overproduction of NO in the central nervous system (CNS) may play a key role in aging and that the glial cells (microglials cells and probably astrocytes) are able to form consistent amounts of NO through the induction of a nitric oxide synthase (iNOS) isoform so-called inducible or inflammatory.We observed a decreased NO production after incubation with both LDL and HDL and an increased peroxynitrite production. As it concerns NOS expression, densitometric analysis of bands indicated that iNOS protein levels were significantly higher in the cells incubated with both AD lipoproteins and offspring lipoproteins compared to cells incubated with control lipoproteins. These findings suggest the possibility to identify in NO pathway a precocious marker of AD.", "citation": {"db": "PubMed", "db_id": "16054114"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 887, "target": 156, "key": "67c22313b3f706dadd6288813d67120f"}, {"line": 39474, "relation": "decreases", "evidence": "It has been shown that apoE increased the production of nitric oxide (NO) from human monocyte-derived macrophages (MDM); this effect could represent an important link between tissue redox balance and inflammation, since inflammation and oxidative stress are involved in chronic neurodegenerative disorders. Moreover, it has been evidenced that an overproduction of NO in the central nervous system (CNS) may play a key role in aging and that the glial cells (microglials cells and probably astrocytes) are able to form consistent amounts of NO through the induction of a nitric oxide synthase (iNOS) isoform so-called inducible or inflammatory.We observed a decreased NO production after incubation with both LDL and HDL and an increased peroxynitrite production. As it concerns NOS expression, densitometric analysis of bands indicated that iNOS protein levels were significantly higher in the cells incubated with both AD lipoproteins and offspring lipoproteins compared to cells incubated with control lipoproteins. These findings suggest the possibility to identify in NO pathway a precocious marker of AD.", "citation": {"db": "PubMed", "db_id": "16054114"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 887, "target": 328, "key": "9f307af827ec3b2260761f9ce458b07b"}, {"line": 39470, "relation": "decreases", "evidence": "It has been shown that apoE increased the production of nitric oxide (NO) from human monocyte-derived macrophages (MDM); this effect could represent an important link between tissue redox balance and inflammation, since inflammation and oxidative stress are involved in chronic neurodegenerative disorders. Moreover, it has been evidenced that an overproduction of NO in the central nervous system (CNS) may play a key role in aging and that the glial cells (microglials cells and probably astrocytes) are able to form consistent amounts of NO through the induction of a nitric oxide synthase (iNOS) isoform so-called inducible or inflammatory.We observed a decreased NO production after incubation with both LDL and HDL and an increased peroxynitrite production. As it concerns NOS expression, densitometric analysis of bands indicated that iNOS protein levels were significantly higher in the cells incubated with both AD lipoproteins and offspring lipoproteins compared to cells incubated with control lipoproteins. These findings suggest the possibility to identify in NO pathway a precocious marker of AD.", "citation": {"db": "PubMed", "db_id": "16054114"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 883, "target": 156, "key": "348c0f214190bce1b670583d35a300c4"}, {"line": 39478, "relation": "decreases", "evidence": "It has been shown that apoE increased the production of nitric oxide (NO) from human monocyte-derived macrophages (MDM); this effect could represent an important link between tissue redox balance and inflammation, since inflammation and oxidative stress are involved in chronic neurodegenerative disorders. Moreover, it has been evidenced that an overproduction of NO in the central nervous system (CNS) may play a key role in aging and that the glial cells (microglials cells and probably astrocytes) are able to form consistent amounts of NO through the induction of a nitric oxide synthase (iNOS) isoform so-called inducible or inflammatory.We observed a decreased NO production after incubation with both LDL and HDL and an increased peroxynitrite production. As it concerns NOS expression, densitometric analysis of bands indicated that iNOS protein levels were significantly higher in the cells incubated with both AD lipoproteins and offspring lipoproteins compared to cells incubated with control lipoproteins. These findings suggest the possibility to identify in NO pathway a precocious marker of AD.", "citation": {"db": "PubMed", "db_id": "16054114"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 883, "target": 328, "key": "f2790d4aba5554735b9061e6ad5bab0c"}, {"line": 39498, "relation": "increases", "evidence": "Activation of innate immune mechanisms leading to pro-inflammatory cytokine up-regulation is involved in devastating and disabling human brain illnesses, as Alzheimer's disease (AD), a progressive neurodegenerative disease that causes dementia in the elderly. Emerging data indicates that the cytokine Interleukin (IL)-18, one of the key mediator of inflammation and immune response, has relevance in the physiopathological processes of the brain, by ultimately influencing the integrity of neurons and putatively contributing to AD.", "citation": {"db": "PubMed", "db_id": "21184660"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2883, "target": 693, "key": "f76b9ace1bcb168da1cc74296e73dca7"}, {"line": 39499, "relation": "positiveCorrelation", "evidence": "Activation of innate immune mechanisms leading to pro-inflammatory cytokine up-regulation is involved in devastating and disabling human brain illnesses, as Alzheimer's disease (AD), a progressive neurodegenerative disease that causes dementia in the elderly. Emerging data indicates that the cytokine Interleukin (IL)-18, one of the key mediator of inflammation and immune response, has relevance in the physiopathological processes of the brain, by ultimately influencing the integrity of neurons and putatively contributing to AD.", "citation": {"db": "PubMed", "db_id": "21184660"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}}, "source": 2883, "target": 3823, "key": "e02edc41f1474f818a1c3dd0c412884a"}, {"line": 39762, "relation": "increases", "evidence": "The innate immunity mediators in the brain, namely microglia and astrocytes, express certain Pattern Recognition Receptors (PRRs), which are always on 'high-alert' for pathogens or other inflammatory triggers and participate in the assembly and activation of the inflammasome. The inflammasome orchestrates the activation of the precursors of proinflammatory caspases, which in turn, cleave the precursor forms of interleukin-1beta, IL-18 and IL-33 into their active forms; the secretion of which leads to a potent inflammatory response, and/or influences the release of toxins from glial and endothelial cells. Altered expression of inflammasome mediators can either promote or inhibit neurodegenerative processes.", "citation": {"db": "PubMed", "db_id": "20127816"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2883, "target": 577, "key": "297769706fefca5bfeb41c05c5f512ed"}, {"line": 39549, "relation": "positiveCorrelation", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 270, "target": 3823, "key": "0a03eafe4e9a28322fbac8bbc8723d98"}, {"line": 39550, "relation": "positiveCorrelation", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 303, "target": 3823, "key": "9162958c20915df62e8301c839cb8c05"}, {"line": 39532, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 607, "target": 270, "key": "148dde78fe96113887c792144862e5e7"}, {"line": 39533, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Immunoglobulin subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Degradation"}, "source": 607, "target": 303, "key": "50c8121a3ab59ce454b2c47037d1fb47"}, {"line": 39537, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2751, "target": 270, "key": "232ace90988b069dcfcbb48df03095d4"}, {"line": 39538, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2751, "target": 303, "key": "b4512d6c0ecaa0fc5f3be9770820d63d"}, {"line": 39539, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "source": 2751, "target": 607, "key": "e9a23c9ef4d5baf86dbe90aebb2e8dd9"}, {"line": 39554, "relation": "positiveCorrelation", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 2751, "target": 3823, "key": "66e6fe516ce73df1d89cebcbd0773410"}, {"line": 39541, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2811, "target": 270, "key": "f48d2cb11b8788945df9c2dc1c66ec72"}, {"line": 39542, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2811, "target": 303, "key": "4ab6a8317e33747325116823f6c1c0f6"}, {"line": 39545, "relation": "increases", "evidence": "AGEs are also known to activate glia, resulting in inflammation and neuronal dysfunction. As reactive intermediates of AGE formation, neurotoxic reactive dicarbonyl compounds such as glyoxal and methylglyoxal have been identified. One of the most effective detoxification systems for methylglyoxal and glyoxal is the glutathione-dependent glyoxalase system, consisting of glyoxalase I and glyoxalase II. In this study, we have determined the methylglyoxal and glyoxal levels in the cerebrospinal fluid of AD patients compared to healthy controls. Methylglyoxal levels in AD patients were twofold higher than in controls, but this difference was not significant due to the large intergroup variations and the small sample size. However, the concentrations of both compounds were five to seven times higher in CSF than in plasma. We also investigated the glyoxalase I level in AD and healthy control brains. The number of glyoxalase I- positive neurons were increased in AD brains compared to controls. Our findings suggest that glyoxalase I is upregulated in AD in a compensatory manner to maintain physiological methylglyoxal and glyoxal levels.", "citation": {"db": "PubMed", "db_id": "16037241"}, "annotations": {"Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"Medium": true}}, "source": 2811, "target": 607, "key": "69d18e17fd89bd01a7cac893829f8b57"}, {"line": 39602, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 3556, "target": 2328, "key": "ae1cfbe9f3048f4d63f9f1b7fa09a831"}, {"line": 39603, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 3556, "target": 480, "key": "b1c0d7508b3420324db5260246f0ec3a"}, {"line": 39604, "relation": "association", "evidence": "In affected regions of AD brain, ACT and APOE colocalize with Abeta deposits and reactive microglia and astrocytes. We examined the regional distribution of ACT, APOE, and reactive glia in temporal cortex, where neuritic plaques are abundant, and cerebellum (in areas where diffuse plaques but not neuritic plaques accumulate) to examine the relationship of these markers to the deposition of Abeta. In temporal cortex, ACT and APOE staining was localized to plaque-like profiles, reactive astrocytes, and blood vessels; human leukocyte antigen-DR (HLA-DR) and glial fibrillary acidic protein (GFAP) staining revealed focal clusters of reactive microglia and astrocytes. In cerebellum, ACT and APOE immunoreactivity was never localized to plaque-like profiles but was weakly localized to unreactive astrocytes; weak HLA-DR and GFAP immunoreactivity was present on quiescent microglia throughout the cerebellum.", "citation": {"db": "PubMed", "db_id": " 9651008"}, "annotations": {"MeSHAnatomy": {"Temporal Lobe": true}, "Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 3556, "target": 609, "key": "31a35fb1ecbcb147a99fff6a417a9e03"}, {"line": 39617, "relation": "negativeCorrelation", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3951, "target": 3823, "key": "847fad7687e4d61aa986f388d58829e6"}, {"line": 39619, "relation": "negativeCorrelation", "evidence": "In this paper, the potential role of CD200 and CD200 receptor (CD200R), whose known functions are to activate anti-inflammatory pathways and induce immune tolerance through binding of CD200 to CD200 receptor (CD200R), was studied in AD. Quantitative studies showed a significant decrease in CD200 protein and mRNA in AD hippocampus and inferior temporal gyrus, but not cerebellum. Immunohistochemistry of brain tissue sections of hippocampus, superior frontal gyrus, inferior temporal gyrus and cerebellum from AD and non-demented cases demonstrated a predominant, though heterogeneous, neuronal localization for CD200. Decreased neuronal expression was apparent in brain regions affected by AD pathology. There was also a significant decrease in CD200R mRNA expression in AD hippocampus and inferior temporal gyrus, but not cerebellum. Low expression of CD200R by microglia was confirmed at the mRNA and protein level using cultured human microglia compared to blood-derived macrophages. Treatment of microglia and macrophages with interleukin-4 and interleukin-13 significantly increased expression of CD200R. Expression of these cytokines was not generally detectable in brain. These data indicate that the anti-inflammatory CD200/CD200R system may be deficient in AD brains", "citation": {"db": "PubMed", "db_id": "18938162"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3952, "target": 3823, "key": "11167ab76f8d5ba7d1d6ec86ee2e2a1b"}, {"line": 39658, "relation": "association", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 1456, "target": 3823, "key": "84771a53bd9a6cb619d93b1596e6b0fd"}, {"relation": "partOf", "source": 2853, "target": 1456, "key": "e8b07bb845f8761dfc22f8edcdc832e4"}, {"line": 39663, "relation": "increases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chaperone subgraph": true}}, "source": 2853, "target": 2606, "key": "82f006b1879f2b62b91f969dba22b3fe"}, {"line": 39669, "relation": "increases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2853, "target": 2465, "key": "aee55b547dbee10e79f743f280505a6f"}, {"line": 39675, "relation": "decreases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "TGF-Beta subgraph": true}}, "source": 2853, "target": 3457, "key": "dfd1e730610779400d55ea6a8a6687c3"}, {"line": 39659, "relation": "association", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 1454, "target": 3823, "key": "205782d1dd94e9c47c270b2fcb80055f"}, {"relation": "partOf", "source": 2851, "target": 1454, "key": "a00e3dbc87deb3cea62fc57691b460ce"}, {"line": 39664, "relation": "increases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chaperone subgraph": true}}, "source": 2851, "target": 2606, "key": "c1ee0f241f25d1c02f9ef806c1e80d5a"}, {"line": 39670, "relation": "increases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2851, "target": 2465, "key": "f1885704c116c6fdd1eda7a239bc0b3c"}, {"line": 39676, "relation": "decreases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "TGF-Beta subgraph": true}}, "source": 2851, "target": 3457, "key": "12fbfffd66b2433e47a0f54bc0104a30"}, {"line": 39660, "relation": "association", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 1455, "target": 3823, "key": "884107ee7c654254aef3dadfe432ec77"}, {"relation": "partOf", "source": 2852, "target": 1455, "key": "2d64ec8c76095de38b9ec373dd204de4"}, {"line": 39665, "relation": "increases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Chaperone subgraph": true}}, "source": 2852, "target": 2606, "key": "db9c39c69e22fbf192952df9dd063e25"}, {"line": 39671, "relation": "increases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "Chemokine signaling subgraph": true}}, "source": 2852, "target": 2465, "key": "4902a4ea8a22884f93087bdca52768df"}, {"line": 39677, "relation": "decreases", "evidence": "Abeta deposits in AD, parenchymal as well as (cap)CAA and dyshoric angiopathy, are associated with a local inflammatory reaction, including activation of microglial cells and astrocytes that, among others, produce cytokines and reactive oxygen species. This neuroinflammatory reaction may account for at least part of the cognitive decline. In previous studies we observed that small heat shock proteins (sHsps) are associated with Abeta deposits in AD. In this study the molecular chaperones Hsp20, HspB8 and HspB2B3 were found to colocalize with CAA and capCAA in AD brains. In addition, Hsp20, HspB8 and HspB2B3 colocalized with intercellular adhesion molecule 1 (ICAM-1) in capCAA-associated dyshoric angiopathy. Furthermore, we demonstrated that Hsp20, HspB8 and HspB2B3 induced production of interleukin 8, soluble ICAM-1 and monocyte chemoattractant protein 1 by human leptomeningeal smooth muscle cells and human brain astrocytes in vitro and that Hsp27 inhibited production of transforming growth factor beta 1 and CD40 ligand. Our results suggest a central role for sHsps in the neuroinflammatory reaction in AD and CAA and thus in contributing to cognitive decline.", "citation": {"db": "PubMed", "db_id": "21849559"}, "annotations": {"Subgraph": {"Chaperone subgraph": true, "TGF-Beta subgraph": true}}, "source": 2852, "target": 3457, "key": "2b69d95f66f0d8325d72aa72fd1fd0b7"}, {"line": 39690, "relation": "positiveCorrelation", "evidence": "YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and Abeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 × 10-25; r = 0.77, p = 2.0 × 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2509, "target": 3839, "key": "15ec2d305183842e4fb7f41f986fc5db"}, {"line": 44257, "relation": "positiveCorrelation", "evidence": "Microglia manage immunosurveillance and mediate inflammation, both suggested to be important in Alzheimer's disease (AD). The aim of this study was to investigate if microglial markers could differentiate, firstly between AD and controls, and secondly between stable mild cognitive impairment (MCI) and those progressing to AD and vascular dementia (VaD). Furthermore, we investigated if these markers were sufficiently stable to be used in clinical trials. We quantified YKL-40 and sCD14 in cerebrospinal fluid (CSF) from 96 AD patients, 65 healthy controls, and 170 patients with MCI from baseline and over 5.7 years. For the stability analysis, two CSF samples were collected from 52 AD patients with a six-month interval in between. YKL-40, but not sCD14, was significantly elevated in AD compared with healthy controls (p = 0.003). Furthermore, YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and ABeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 x 10-25; r = 0.77, p = 2.0 x 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2509, "target": 3839, "key": "83f5e968651bfd32cdebde36b1cf9ec9"}, {"line": 44255, "relation": "positiveCorrelation", "evidence": "Microglia manage immunosurveillance and mediate inflammation, both suggested to be important in Alzheimer's disease (AD). The aim of this study was to investigate if microglial markers could differentiate, firstly between AD and controls, and secondly between stable mild cognitive impairment (MCI) and those progressing to AD and vascular dementia (VaD). Furthermore, we investigated if these markers were sufficiently stable to be used in clinical trials. We quantified YKL-40 and sCD14 in cerebrospinal fluid (CSF) from 96 AD patients, 65 healthy controls, and 170 patients with MCI from baseline and over 5.7 years. For the stability analysis, two CSF samples were collected from 52 AD patients with a six-month interval in between. YKL-40, but not sCD14, was significantly elevated in AD compared with healthy controls (p = 0.003). Furthermore, YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and ABeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 x 10-25; r = 0.77, p = 2.0 x 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2509, "target": 3823, "key": "77888622853e3d471bd54515c6802d3f"}, {"line": 44307, "relation": "prognosticBiomarkerFor", "evidence": "Discovery and validation cohorts, showed higher mean CSF YKL-40 in very mild and mild AD-type dementia (Clinical Dementia Rating [CDR] 0.5 and 1) versus control subjects (CDR 0) and PSP subjects. Importantly, CSF YKL-40/Abeta42 ratio predicted risk of developing cognitive impairment (CDR 0 to CDR > 0 conversion), as well as the best CSF biomarkers identified to date, tau/Abeta42 and p-tau 181/Abeta42. Mean plasma YKL-40 was higher in CDR 0.5 and 1 versus CDR 0, and correlated with CSF levels. YKL-40 immunoreactivity labeled astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Abeta deposition. CONCLUSIONS: These data demonstrate that YKL-40, a putative indicator of neuroinflammation, is elevated in AD and, together with Abeta42, has potential prognostic utility as a biomarker for preclinical AD.", "citation": {"db": "PubMed", "db_id": "21035623"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2509, "target": 3823, "key": "28fd56a1ea6fce3fe4d8704f2bab13be"}, {"line": 44308, "relation": "biomarkerFor", "evidence": "Discovery and validation cohorts, showed higher mean CSF YKL-40 in very mild and mild AD-type dementia (Clinical Dementia Rating [CDR] 0.5 and 1) versus control subjects (CDR 0) and PSP subjects. Importantly, CSF YKL-40/Abeta42 ratio predicted risk of developing cognitive impairment (CDR 0 to CDR > 0 conversion), as well as the best CSF biomarkers identified to date, tau/Abeta42 and p-tau 181/Abeta42. Mean plasma YKL-40 was higher in CDR 0.5 and 1 versus CDR 0, and correlated with CSF levels. YKL-40 immunoreactivity labeled astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Abeta deposition. CONCLUSIONS: These data demonstrate that YKL-40, a putative indicator of neuroinflammation, is elevated in AD and, together with Abeta42, has potential prognostic utility as a biomarker for preclinical AD.", "citation": {"db": "PubMed", "db_id": "21035623"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2509, "target": 3823, "key": "36bb1d7fbe39013a38f0b6733f428bdf"}, {"line": 44256, "relation": "positiveCorrelation", "evidence": "Microglia manage immunosurveillance and mediate inflammation, both suggested to be important in Alzheimer's disease (AD). The aim of this study was to investigate if microglial markers could differentiate, firstly between AD and controls, and secondly between stable mild cognitive impairment (MCI) and those progressing to AD and vascular dementia (VaD). Furthermore, we investigated if these markers were sufficiently stable to be used in clinical trials. We quantified YKL-40 and sCD14 in cerebrospinal fluid (CSF) from 96 AD patients, 65 healthy controls, and 170 patients with MCI from baseline and over 5.7 years. For the stability analysis, two CSF samples were collected from 52 AD patients with a six-month interval in between. YKL-40, but not sCD14, was significantly elevated in AD compared with healthy controls (p = 0.003). Furthermore, YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and ABeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 x 10-25; r = 0.77, p = 2.0 x 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2509, "target": 3815, "key": "3458c9c4e0ef26f5b44292b4226e6bff"}, {"line": 44258, "relation": "positiveCorrelation", "evidence": "Microglia manage immunosurveillance and mediate inflammation, both suggested to be important in Alzheimer's disease (AD). The aim of this study was to investigate if microglial markers could differentiate, firstly between AD and controls, and secondly between stable mild cognitive impairment (MCI) and those progressing to AD and vascular dementia (VaD). Furthermore, we investigated if these markers were sufficiently stable to be used in clinical trials. We quantified YKL-40 and sCD14 in cerebrospinal fluid (CSF) from 96 AD patients, 65 healthy controls, and 170 patients with MCI from baseline and over 5.7 years. For the stability analysis, two CSF samples were collected from 52 AD patients with a six-month interval in between. YKL-40, but not sCD14, was significantly elevated in AD compared with healthy controls (p = 0.003). Furthermore, YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and ABeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 x 10-25; r = 0.77, p = 2.0 x 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2509, "target": 609, "key": "2a0e1da6166ddad090edbf49674fd41e"}, {"line": 44291, "relation": "positiveCorrelation", "evidence": "The secreted protein, YKL-40, has been proposed as a biomarker of a variety of human diseases characterized by ongoing inflammation, including chronic neurologic pathologies such as multiple sclerosis and Alzheimer's disease. However, inflammatory mediators and the molecular mechanism responsible for enhanced expression of YKL-40 remained elusive. Using several mouse models of inflammation, we now show that YKL-40 expression correlated with increased expression of both IL-1 and IL-6. Furthermore, IL-1 together with IL-6 or the IL-6 family cytokine, oncostatin M, synergistically upregulated YKL-40 expression in both primary human and mouse astrocytes in vitro. The robust cytokine-driven expression of YKL-40 in astrocytes required both STAT3 and NF-kB binding elements of the YKL-40 promoter. In addition, YKL-40 expression was enhanced by constitutively active STAT3 and inhibited by dominant-negative IkBalpha. Surprisingly, cytokine-driven expression of YKL-40 in astrocytes was independent of the p65 subunit of NF-kB and instead required subunits RelB and p50. Mechanistically, we show that IL-1-induced RelB/p50 complex formation was further promoted by oncostatin M and that these complexes directly bound to the YKL-40 promoter. Moreover, we found that expression of RelB was strongly upregulated during inflammation in vivo and by IL-1 in astrocytes in vitro. We propose that IL-1 and the IL-6 family of cytokines regulate YKL-40 expression during sterile inflammation via both STAT3 and RelB/p50 complexes. These results suggest that IL-1 may regulate the expression of specific anti-inflammatory genes in nonlymphoid tissues via the canonical activation of the RelB/p50 complexes.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2509, "target": 2885, "key": "e8d35e11b03f83f575a030088e0c4765"}, {"line": 46785, "relation": "positiveCorrelation", "evidence": "In addition, IL-6 and OSM moderately upregulate YKL-40 expression in human astrocytes [...] demonstrate that YKL-40 expression correlates with the expression of both IL-1beta and IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2509, "target": 2885, "key": "cb96dcddd53977c039b3a235f9705032"}, {"line": 44292, "relation": "positiveCorrelation", "evidence": "The secreted protein, YKL-40, has been proposed as a biomarker of a variety of human diseases characterized by ongoing inflammation, including chronic neurologic pathologies such as multiple sclerosis and Alzheimer's disease. However, inflammatory mediators and the molecular mechanism responsible for enhanced expression of YKL-40 remained elusive. Using several mouse models of inflammation, we now show that YKL-40 expression correlated with increased expression of both IL-1 and IL-6. Furthermore, IL-1 together with IL-6 or the IL-6 family cytokine, oncostatin M, synergistically upregulated YKL-40 expression in both primary human and mouse astrocytes in vitro. The robust cytokine-driven expression of YKL-40 in astrocytes required both STAT3 and NF-kB binding elements of the YKL-40 promoter. In addition, YKL-40 expression was enhanced by constitutively active STAT3 and inhibited by dominant-negative IkBalpha. Surprisingly, cytokine-driven expression of YKL-40 in astrocytes was independent of the p65 subunit of NF-kB and instead required subunits RelB and p50. Mechanistically, we show that IL-1-induced RelB/p50 complex formation was further promoted by oncostatin M and that these complexes directly bound to the YKL-40 promoter. Moreover, we found that expression of RelB was strongly upregulated during inflammation in vivo and by IL-1 in astrocytes in vitro. We propose that IL-1 and the IL-6 family of cytokines regulate YKL-40 expression during sterile inflammation via both STAT3 and RelB/p50 complexes. These results suggest that IL-1 may regulate the expression of specific anti-inflammatory genes in nonlymphoid tissues via the canonical activation of the RelB/p50 complexes.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2509, "target": 2894, "key": "905a4e7bab8043fd9fe0f1ca97331e3a"}, {"line": 46802, "relation": "decreases", "evidence": "However, in agreement with the proposed role of YKL-40 in limiting inflammation, it has recently been shown that YKL-40 also inhibits NF-κB activation and expression of IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2509, "target": 2894, "key": "c21ce460880c4cc04a072114a893fb0b"}, {"line": 44309, "relation": "positiveCorrelation", "evidence": "Discovery and validation cohorts, showed higher mean CSF YKL-40 in very mild and mild AD-type dementia (Clinical Dementia Rating [CDR] 0.5 and 1) versus control subjects (CDR 0) and PSP subjects. Importantly, CSF YKL-40/Abeta42 ratio predicted risk of developing cognitive impairment (CDR 0 to CDR > 0 conversion), as well as the best CSF biomarkers identified to date, tau/Abeta42 and p-tau 181/Abeta42. Mean plasma YKL-40 was higher in CDR 0.5 and 1 versus CDR 0, and correlated with CSF levels. YKL-40 immunoreactivity labeled astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Abeta deposition. CONCLUSIONS: These data demonstrate that YKL-40, a putative indicator of neuroinflammation, is elevated in AD and, together with Abeta42, has potential prognostic utility as a biomarker for preclinical AD.", "citation": {"db": "PubMed", "db_id": "21035623"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2509, "target": 3920, "key": "5f69b1ae9195c317577f1c4549852e70"}, {"line": 46762, "relation": "positiveCorrelation", "evidence": "Both neurogranin and YKL‐40 correlated with tau as well as with Abeta40 in all studied diagnostic groups", "citation": {"db": "PubMed", "db_id": "26783546"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2509, "target": 3141, "key": "c9055d83611f1d23d2be0a67d1bd262d"}, {"line": 46763, "relation": "positiveCorrelation", "evidence": "Both neurogranin and YKL‐40 correlated with tau as well as with Abeta40 in all studied diagnostic groups", "citation": {"db": "PubMed", "db_id": "26783546"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 2509, "target": 2327, "key": "1c6cc9edde5741a5438f1a3e20ded278"}, {"line": 46798, "relation": "decreases", "evidence": "However, in agreement with the proposed role of YKL-40 in limiting inflammation, it has recently been shown that YKL-40 also inhibits NF-κB activation and expression of IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2509, "target": 3112, "key": "01fe1b8a8a2b85a953c627b0d8b427f5"}, {"line": 46799, "relation": "decreases", "evidence": "However, in agreement with the proposed role of YKL-40 in limiting inflammation, it has recently been shown that YKL-40 also inhibits NF-κB activation and expression of IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2509, "target": 3113, "key": "86eeb32132e431bbcbabc17835fc6eee"}, {"line": 46825, "relation": "increases", "evidence": "In fact YKL-40 induces the interaction of αvbeta3 integrins with syndecan-1 in endothelial cells (27), it activates ERK, AKT, and Wnt/beta-catenin signaling in macrophages via IL-13 receptor alpha 2-dependent mechanism (55),", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2509, "target": 1496, "key": "783b7f7d5f399d7703190411c70d8287"}, {"line": 46826, "relation": "increases", "evidence": "In fact YKL-40 induces the interaction of αvbeta3 integrins with syndecan-1 in endothelial cells (27), it activates ERK, AKT, and Wnt/beta-catenin signaling in macrophages via IL-13 receptor alpha 2-dependent mechanism (55),", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Cell adhesion subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2509, "target": 1495, "key": "c9296e9a5832f8436ff9576675ff8e32"}, {"line": 39690, "relation": "positiveCorrelation", "evidence": "YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and Abeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 × 10-25; r = 0.77, p = 2.0 × 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3839, "target": 2509, "key": "7fe5cdcc21375aaada6e25a6f5962770"}, {"line": 44257, "relation": "positiveCorrelation", "evidence": "Microglia manage immunosurveillance and mediate inflammation, both suggested to be important in Alzheimer's disease (AD). The aim of this study was to investigate if microglial markers could differentiate, firstly between AD and controls, and secondly between stable mild cognitive impairment (MCI) and those progressing to AD and vascular dementia (VaD). Furthermore, we investigated if these markers were sufficiently stable to be used in clinical trials. We quantified YKL-40 and sCD14 in cerebrospinal fluid (CSF) from 96 AD patients, 65 healthy controls, and 170 patients with MCI from baseline and over 5.7 years. For the stability analysis, two CSF samples were collected from 52 AD patients with a six-month interval in between. YKL-40, but not sCD14, was significantly elevated in AD compared with healthy controls (p = 0.003). Furthermore, YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and ABeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 x 10-25; r = 0.77, p = 2.0 x 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3839, "target": 2509, "key": "1d100360694cd3397bc1d80a238d7d92"}, {"line": 39696, "relation": "positiveCorrelation", "evidence": "YKL-40 and sCD14 were increased in MCI patients who converted to VaD (p = 0.029 and p = 0.008), but not to AD according to NINCDS-ADRDA. However, when stratified according to CSF levels of tau and Abeta42, YKL-40 was elevated in those with an AD-indicative profile compared with stable MCI with a normal profile (p = 0.037). In addition, YKL-40 and sCD14 were very stable in AD patients with good correlation between time-points (r = 0.94, p = 3.4 × 10-25; r = 0.77, p = 2.0 × 10-11) and the cortical damage marker T-tau. Thus, microglial markers are stable and may be used as safety markers for monitoring CNS inflammation and microglia activation in clinical trials. Moreover, YKL-40 differentiates between AD and controls and between stable MCI to AD and those that convert to AD and VaD.", "citation": {"db": "PubMed", "db_id": "22890100"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3839, "target": 2468, "key": "ae9d1f3dadde9b84ac1da76104700b21"}, {"line": 39846, "relation": "positiveCorrelation", "evidence": "Some of them might increase steadily during disease progression or temporarily at the time of MCI to AD conversion.", "citation": {"db": "PubMed", "db_id": "24567119"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Cognitive Dysfunction": true, "Alzheimer Disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3839, "target": 420, "key": "0172f8da492d3d81f7b26e0b15fcad46"}, {"line": 40338, "relation": "association", "evidence": "We found that retinoic acid-inducible gene-I (RIG-1) is significantly elevated in the temporal cortex and plasma in patients with MCI.", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "MeSHDisease": {"Cognitive Dysfunction": true}, "MeSHAnatomy": {"Plasma": true}, "Confidence": {"Medium": true}}, "source": 3839, "target": 3297, "key": "f2499a81b2cf12cade24862023b638d5"}, {"line": 40348, "relation": "association", "evidence": "In addition, primary human astrocytes stimulated with the RIG-1 ligand 5'ppp RNA showed increased expression of amyloid precursor protein (APP) and amyloid-beta (Abeta), supporting the idea that RIG-1 is involved in the pathology of MCI associated with early progression to AD.These findings suggest that RIG-1 may play a critical role in incipient AD.", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "MeSHDisease": {"Cognitive Dysfunction": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}}, "source": 3839, "target": 418, "key": "bf8afd1f396bb73ec38aabac9c1dc63c"}, {"line": 46586, "relation": "increases", "evidence": "Accordingly, in the present study we measured the levels of HSPs in hippocampus, inferior parietal lobule (IPL) and cerebellum of subjects with aMCI. The results show a general induction of HSPs and decreased levels of Thioredoxin 1 in aMCI brain suggesting that alteration in the chaperone protein systems might contribute to the pathogenesis and progression of AD.", "citation": {"db": "PubMed", "db_id": "20362559"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebellum": true, "Parietal Lobe": true}, "Subgraph": {"Chaperone subgraph": true}, "Confidence": {"Medium": true}}, "source": 3839, "target": 2855, "key": "72fa439c2c983886bcc441f2bff02778"}, {"line": 46587, "relation": "increases", "evidence": "Accordingly, in the present study we measured the levels of HSPs in hippocampus, inferior parietal lobule (IPL) and cerebellum of subjects with aMCI. The results show a general induction of HSPs and decreased levels of Thioredoxin 1 in aMCI brain suggesting that alteration in the chaperone protein systems might contribute to the pathogenesis and progression of AD.", "citation": {"db": "PubMed", "db_id": "20362559"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebellum": true, "Parietal Lobe": true}, "Subgraph": {"Chaperone subgraph": true}, "Confidence": {"Medium": true}}, "source": 3839, "target": 2847, "key": "f0589adcd9a009f2cd4647c257ef7b94"}, {"line": 46588, "relation": "decreases", "evidence": "Accordingly, in the present study we measured the levels of HSPs in hippocampus, inferior parietal lobule (IPL) and cerebellum of subjects with aMCI. The results show a general induction of HSPs and decreased levels of Thioredoxin 1 in aMCI brain suggesting that alteration in the chaperone protein systems might contribute to the pathogenesis and progression of AD.", "citation": {"db": "PubMed", "db_id": "20362559"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true, "Cerebellum": true, "Parietal Lobe": true}, "Subgraph": {"Chaperone subgraph": true}, "Confidence": {"Medium": true}}, "source": 3839, "target": 3506, "key": "b5edb5f375f74cc6b7af80ec285b2795"}, {"line": 39711, "relation": "increases", "evidence": "Glial activation and increased inflammation characterize neuropathology in Alzheimer's disease (AD). The aim was to develop a model for studying phagocytosis of beta-amyloid (Abeta) peptide by human microglia and to test effects thereupon by immunomodulatory substances. Human CHME3 microglia showed intracellular Abeta(1-42) colocalized with lysosome-associated membrane protein-2, indicating phagocytosis. This was increased by interferon-gamma, and to a lesser degree with Protollin, a proteosome-based adjuvant. Secretion of brain-derived neurotrophic factor (BDNF) was decreased by Abeta(1-42) and by interferon-gamma and interleukin-1beta. These cytokines, but not Abeta(1-42), stimulated interleukin-6 release. Microglia which phagocytosed Abeta(1-42) exhibited a higher degree of expression of interleukin-1 receptor type I and inducible nitric oxide synthase. In conclusion, we show that human microglia are able to phagocytose Abeta(1-42) and that this is associated with expression of inflammatory markers. Abeta(1-42) and interferon-gamma decreased BDNF secretion suggesting a new neuropathological role for Abeta(1-42) and the inflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "20798889"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 1232, "target": 823, "key": "faf8b26830a77d1464041e8abec25523"}, {"relation": "partOf", "source": 2957, "target": 1232, "key": "7843f641d3248c611a81ef9adcf50e50"}, {"line": 39736, "relation": "positiveCorrelation", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 23, "target": 3823, "key": "2da02b9c2b897e142868a5566923ca2a"}, {"line": 39740, "relation": "association", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Response to oxidative stress": true}}, "source": 23, "target": 775, "key": "470814fef065aaea8fb93a005ab9e8fe"}, {"line": 39737, "relation": "positiveCorrelation", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 30, "target": 3823, "key": "bc47f87faa92d7605b48b7f0c327a48f"}, {"line": 44543, "relation": "biomarkerFor", "evidence": "8-oxo-dG is widely used as biomarker of oxidative DNA damage due to demethylation. 8-dihydro-2 �'-deoxyguanosine (8-oxo-dG), a general marker of oxidative stress", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 30, "target": 842, "key": "99fdbc41184ae4ea11ffce10f326829a"}, {"line": 44589, "relation": "decreases", "evidence": "8-oxo-dG inhibits adjacent cytosine methylation", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 30, "target": 446, "key": "66540e4cb87c0b6c454108a793c101ac"}, {"line": 44598, "relation": "decreases", "evidence": "we found that presence of either 5-methylctosine or 8-oxo-dG dramatically suppressed Sp1 DNA-binding; ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 30, "target": 3402, "key": "3f5d374ec17a140caa9c2c39b66b6bc4"}, {"line": 44662, "relation": "negativeCorrelation", "evidence": "It has been proposed that an accumulation of oxo8dG in the AD brain might be a result of a decrease in the activity of Ogg1,", "citation": {"db": "PubMed", "db_id": "16484331"}, "annotations": {"Species": {"10116": true}, "Developmental_Phase__of_patient": {"Old": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 30, "target": 3804, "key": "c9d1aad6089a1cf89ec39e33f5becd8f"}, {"line": 39743, "relation": "biomarkerFor", "evidence": "Glial fibrillary acidic protein and antibodies in CSF may be a marker for severe neurodegeneration. CSF concentrations of the oxidative stress markers 3-nitrotyrosine, 8-hydroxy-2'-deoxyguanosine and isoprostanes are increased in AD patients. Serum 24S-OH-cholesterol may be an early whereas glial fibrillary acidic protein autoantibody level may be a late marker for neurodegeneration. To date, serum alpha(1)-Antichymotripsin concentration is the most convincing marker for CNS inflammation. Increased serum homocysteine concentrations have also been consistently reported in AD", "citation": {"db": "PubMed", "db_id": "12009495"}, "annotations": {"Subgraph": {"Response to oxidative stress": true}, "Confidence": {"Medium": true}}, "source": 2, "target": 3872, "key": "1093d90e4675550fc3a8c4cc6a2350c9"}, {"line": 39757, "relation": "increases", "evidence": "The innate immunity mediators in the brain, namely microglia and astrocytes, express certain Pattern Recognition Receptors (PRRs), which are always on 'high-alert' for pathogens or other inflammatory triggers and participate in the assembly and activation of the inflammasome. The inflammasome orchestrates the activation of the precursors of proinflammatory caspases, which in turn, cleave the precursor forms of interleukin-1beta, IL-18 and IL-33 into their active forms; the secretion of which leads to a potent inflammatory response, and/or influences the release of toxins from glial and endothelial cells. Altered expression of inflammasome mediators can either promote or inhibit neurodegenerative processes.", "citation": {"db": "PubMed", "db_id": "20127816"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "object": {"modifier": "Activity"}, "source": 884, "target": 2442, "key": "ea473b6738618ed7dc525483e5e72a60"}, {"line": 39758, "relation": "increases", "evidence": "The innate immunity mediators in the brain, namely microglia and astrocytes, express certain Pattern Recognition Receptors (PRRs), which are always on 'high-alert' for pathogens or other inflammatory triggers and participate in the assembly and activation of the inflammasome. The inflammasome orchestrates the activation of the precursors of proinflammatory caspases, which in turn, cleave the precursor forms of interleukin-1beta, IL-18 and IL-33 into their active forms; the secretion of which leads to a potent inflammatory response, and/or influences the release of toxins from glial and endothelial cells. Altered expression of inflammasome mediators can either promote or inhibit neurodegenerative processes.", "citation": {"db": "PubMed", "db_id": "20127816"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "object": {"modifier": "Activity"}, "source": 884, "target": 2885, "key": "b5dcb4a5fbd3a0a229d41c1703a689bd"}, {"line": 46792, "relation": "increases", "evidence": "IL-1beta is processed from its inactive precursor in response to inflammasome activation", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 884, "target": 2885, "key": "29222a080cd8dd3300cc70110872c23b"}, {"line": 39759, "relation": "increases", "evidence": "The innate immunity mediators in the brain, namely microglia and astrocytes, express certain Pattern Recognition Receptors (PRRs), which are always on 'high-alert' for pathogens or other inflammatory triggers and participate in the assembly and activation of the inflammasome. The inflammasome orchestrates the activation of the precursors of proinflammatory caspases, which in turn, cleave the precursor forms of interleukin-1beta, IL-18 and IL-33 into their active forms; the secretion of which leads to a potent inflammatory response, and/or influences the release of toxins from glial and endothelial cells. Altered expression of inflammasome mediators can either promote or inhibit neurodegenerative processes.", "citation": {"db": "PubMed", "db_id": "20127816"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "object": {"modifier": "Activity"}, "source": 884, "target": 2883, "key": "dd1e9d375b1b26e8264c90a9b9bfad38"}, {"line": 39760, "relation": "increases", "evidence": "The innate immunity mediators in the brain, namely microglia and astrocytes, express certain Pattern Recognition Receptors (PRRs), which are always on 'high-alert' for pathogens or other inflammatory triggers and participate in the assembly and activation of the inflammasome. The inflammasome orchestrates the activation of the precursors of proinflammatory caspases, which in turn, cleave the precursor forms of interleukin-1beta, IL-18 and IL-33 into their active forms; the secretion of which leads to a potent inflammatory response, and/or influences the release of toxins from glial and endothelial cells. Altered expression of inflammasome mediators can either promote or inhibit neurodegenerative processes.", "citation": {"db": "PubMed", "db_id": "20127816"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "object": {"modifier": "Activity"}, "source": 884, "target": 2891, "key": "209ebea6d37a46c232d266349997b78e"}, {"line": 39763, "relation": "increases", "evidence": "The innate immunity mediators in the brain, namely microglia and astrocytes, express certain Pattern Recognition Receptors (PRRs), which are always on 'high-alert' for pathogens or other inflammatory triggers and participate in the assembly and activation of the inflammasome. The inflammasome orchestrates the activation of the precursors of proinflammatory caspases, which in turn, cleave the precursor forms of interleukin-1beta, IL-18 and IL-33 into their active forms; the secretion of which leads to a potent inflammatory response, and/or influences the release of toxins from glial and endothelial cells. Altered expression of inflammasome mediators can either promote or inhibit neurodegenerative processes.", "citation": {"db": "PubMed", "db_id": "20127816"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}}, "subject": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 2891, "target": 577, "key": "ebd9d39548ecde0e69f6aba09ea71a73"}, {"line": 39773, "relation": "increases", "evidence": "The C5a complement activation peptide increases IL-1beta and IL-6 release from amyloid-beta primed human monocytes: implications for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "10996210"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Complement system subgraph": true}}, "source": 2411, "target": 2885, "key": "31b75859aaa1cef9a97f632c6b9dcb36"}, {"line": 39774, "relation": "increases", "evidence": "The C5a complement activation peptide increases IL-1beta and IL-6 release from amyloid-beta primed human monocytes: implications for Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "10996210"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Complement system subgraph": true}}, "source": 2411, "target": 2894, "key": "bdbdb6d16e024a213589ccd6e3f835c8"}, {"line": 39817, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 3520, "target": 3823, "key": "8b1b66e3b5d7fba8823b599651597956"}, {"line": 39818, "relation": "positiveCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true}}, "source": 3521, "target": 3823, "key": "8c541d573892735b38bb3a691af57831"}, {"line": 39821, "relation": "negativeCorrelation", "evidence": "A complex picture emerged in this pilot study and IL-8, IFN-gamma, MCP-1 and VEGF levels were increased in AD. Levels of P-selectin and L-selectin were decreased in AD and lowest in AD patients with highest cognitive decline. ", "citation": {"db": "PubMed", "db_id": "21484243"}, "source": 3347, "target": 3823, "key": "5938b1a79a4abc14a48235455f984f57"}, {"line": 39865, "relation": "increases", "evidence": "The enzyme argininosuccinate synthetase (ASS) is the rate limiting enzyme in the metabolic pathway leading from L-citrulline to L-arginine, the physiological substrate of all isoforms of nitric oxide synthases (NOS).", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2362, "target": 4086, "key": "b3109e1afc76f3c899426b8cc328a958"}, {"line": 39917, "relation": "increases", "evidence": "Because an adequate supply of L-arginine is indispensable for prolonged NO generation, coinduction of ASS enables cells to sustain NO generation during AD by replenishing necessary supply of L-arginine.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 2362, "target": 156, "key": "dbe91d9e3074febd0b7db87e385d2297"}, {"relation": "hasReactant", "source": 4086, "target": 55, "key": "19c1f6cb42ea595d32138d49607b8257"}, {"relation": "hasProduct", "source": 4086, "target": 53, "key": "9dfef977f07faf5ae9bcd7e70ae24a55"}, {"relation": "partOf", "source": 53, "target": 901, "key": "2af47cacbf1799d86aa047a12e7123c4"}, {"line": 39876, "relation": "positiveCorrelation", "evidence": "In 3 areas examined (hippocampus, frontal, and entorhinal cortex), a marked increase in neuronal ASS and iNOS expression was observed in AD brains.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Entorhinal Cortex": true, "Brain": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"Medium": true}}, "source": 1753, "target": 3823, "key": "e452eb2c73bbb1e184378cff41311dc8"}, {"line": 39897, "relation": "association", "evidence": "Occasionally, both ASS-and iNOS expression was detectable in CD 68-positive activated microglia cells in close proximity to senile plaques.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 1753, "target": 609, "key": "e90b1ad3183caa5a20aa095169c2380a"}, {"line": 39908, "relation": "positiveCorrelation", "evidence": "These results suggest that neurons and astrocytes express ASS in human brain constitutively, whereas neuronal and glial ASS expression increases parallel to iNOS expression in AD.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 1753, "target": 1895, "key": "d4ae93314f4523a27b4523318175d422"}, {"line": 39877, "relation": "positiveCorrelation", "evidence": "In 3 areas examined (hippocampus, frontal, and entorhinal cortex), a marked increase in neuronal ASS and iNOS expression was observed in AD brains.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Entorhinal Cortex": true, "Brain": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"Medium": true}}, "source": 1895, "target": 3823, "key": "4acbd2a66db875399d3239a6ce40c605"}, {"line": 39891, "relation": "positiveCorrelation", "evidence": "GFAP-positive astrocytes expressing ASS were not increased in AD brains versus controls, whereas the number of iNOS expressing GFAP-positive astrocytes was significantly higher in AD brains.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 1895, "target": 3823, "key": "500383a13ec78b2049473929e9ba605b"}, {"line": 39898, "relation": "association", "evidence": "Occasionally, both ASS-and iNOS expression was detectable in CD 68-positive activated microglia cells in close proximity to senile plaques.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 1895, "target": 609, "key": "148e30ff510d3ac326ff841b592e4172"}, {"line": 39908, "relation": "positiveCorrelation", "evidence": "These results suggest that neurons and astrocytes express ASS in human brain constitutively, whereas neuronal and glial ASS expression increases parallel to iNOS expression in AD.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 1895, "target": 1753, "key": "8a3e8b4588752e75c88d84916e7f0703"}, {"line": 39887, "relation": "positiveCorrelation", "evidence": "GFAP-positive astrocytes expressing ASS were not increased in AD brains versus controls, whereas the number of iNOS expressing GFAP-positive astrocytes was significantly higher in AD brains.", "citation": {"db": "PubMed", "db_id": "11556547"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"Low": true}}, "source": 1831, "target": 3823, "key": "a099812aece1c6d5f75d59c98871919b"}, {"line": 39974, "relation": "decreases", "evidence": "We bring forward the hypothesis that inflammation via prolonged activation of key kinases (p38 and GSK-3beta) and activation of histone deacetylases gives rise to dysregulation of the NRF2 system in the brain, which contributes to oxidative stress and injury.", "citation": {"db": "PubMed", "db_id": "24262633"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Wounds and Injuries": true}, "Subgraph": {"Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 2814, "target": 3110, "key": "ce2547b8ae0df12a7af8812475de1b63"}, {"line": 40017, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 261, "target": 3468, "key": "dafbb641a9df0758c9c9a2497c8c90f0"}, {"line": 40068, "relation": "association", "evidence": "Gen also significantly reversed Abeta25-35-induced up-regulation of TLR4 and NF-κB expression and the DNA binding and transcriptional activities of NF-κB.These results indicated that Gen could alleviate the inflammation caused by Abeta25-35 treatment, which might be associated with the regulation of the TLR4/NF-κB signal pathway.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 261, "target": 3468, "key": "de4d0a47dd582993d9bc4adf8b6a1b20"}, {"line": 40021, "relation": "decreases", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 261, "target": 577, "key": "6470fbdf5dd170b70aa6eeaf3fc7c7b6"}, {"line": 40031, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 261, "target": 3112, "key": "27dd2854609aaaa61d3549e6e32f9ad4"}, {"line": 40036, "relation": "association", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"High": true}}, "source": 261, "target": 2315, "key": "f72fc252d1a749a74acf1a29f4486659"}, {"line": 40056, "relation": "association", "evidence": "The expression of inflammatory mediators, TLR4 and NF-κB and the activity of NF-κB were measured. The results showed that Gen could attenuate the cytotoxicity and inflammatory damage induced by Abeta25-35.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "source": 261, "target": 377, "key": "97716013affb328912c04af3f02b554d"}, {"line": 40065, "relation": "decreases", "evidence": "Gen also significantly reversed Abeta25-35-induced up-regulation of TLR4 and NF-κB expression and the DNA binding and transcriptional activities of NF-κB.These results indicated that Gen could alleviate the inflammation caused by Abeta25-35 treatment, which might be associated with the regulation of the TLR4/NF-κB signal pathway.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 261, "target": 2322, "key": "06056f7745b5a1345781ea30954c2f78"}, {"line": 40067, "relation": "increases", "evidence": "Gen also significantly reversed Abeta25-35-induced up-regulation of TLR4 and NF-κB expression and the DNA binding and transcriptional activities of NF-κB.These results indicated that Gen could alleviate the inflammation caused by Abeta25-35 treatment, which might be associated with the regulation of the TLR4/NF-κB signal pathway.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 261, "target": 3920, "key": "31cc9eb33541e5ea455d24d37e0184c7"}, {"line": 40027, "relation": "increases", "evidence": "Genistein antagonizes inflammatory damage induced by beta-amyloid peptide in microglia through TLR4 and NF-κB.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2130, "target": 3112, "key": "fd4c78b80e916342345f25cef85e7854"}, {"line": 40066, "relation": "increases", "evidence": "Gen also significantly reversed Abeta25-35-induced up-regulation of TLR4 and NF-κB expression and the DNA binding and transcriptional activities of NF-κB.These results indicated that Gen could alleviate the inflammation caused by Abeta25-35 treatment, which might be associated with the regulation of the TLR4/NF-κB signal pathway.", "citation": {"db": "PubMed", "db_id": "24290604"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "MeSHDisease": {"Inflammation": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2322, "target": 3468, "key": "294417e8247b90b4288550ca09516a30"}, {"line": 47737, "relation": "increases", "evidence": "Caricasole et al demonstrated that the Abeta peptide fragment, Abeta25-35, induces neuronal expression of the wnt antagonist Dkk1 and that silencing of DKK1 blocks Abeta neurotoxicity.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 2322, "target": 2629, "key": "0fc48852f67c24eeb4df7ce6b93eed73"}, {"line": 47989, "relation": "increases", "evidence": "As a result, the Polymerization of and MAP-2 and NF-H induced by Abeta25-35 could be significantly inhibited by Wnt3a(40 ng/ml), however enhanced by Dkk1(100 ng/ml).", "citation": {"db": "PubMed", "db_id": "26809093"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2322, "target": 3100, "key": "c249534ed8602dbec10988405ff9cef4"}, {"line": 47990, "relation": "increases", "evidence": "As a result, the Polymerization of and MAP-2 and NF-H induced by Abeta25-35 could be significantly inhibited by Wnt3a(40 ng/ml), however enhanced by Dkk1(100 ng/ml).", "citation": {"db": "PubMed", "db_id": "26809093"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2322, "target": 2987, "key": "638d82b7a448eab95beaf71de57d9782"}, {"line": 40086, "relation": "decreases", "evidence": "These findings suggest that PA may help to preserve hippocampal volume in individuals at increased genetic risk for AD.", "citation": {"db": "PubMed", "db_id": "24795624"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 849, "target": 3823, "key": "ec1a52ccddc71568178fdf0769824eda"}, {"line": 40094, "relation": "association", "evidence": "These data suggest that individuals at genetic risk for AD should be targeted for increased levels of PA as a means of reducing atrophy in a brain region critical for the formation of episodic memories.", "citation": {"db": "PubMed", "db_id": "24795624"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Atrophy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 849, "target": 3823, "key": "6f37045cb21a0018a8da871707027322"}, {"line": 40095, "relation": "decreases", "evidence": "These data suggest that individuals at genetic risk for AD should be targeted for increased levels of PA as a means of reducing atrophy in a brain region critical for the formation of episodic memories.", "citation": {"db": "PubMed", "db_id": "24795624"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHDisease": {"Atrophy": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}}, "source": 849, "target": 3896, "key": "1dc4eb54cb3a90894ccf52ef8604825d"}, {"line": 45207, "relation": "increases", "evidence": "Both exercised SAMR1 and SAMP8 mice showed significantly increased IGF1 plasma levels compared with their corresponding sedentary group ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Physical exercised group": true}}, "source": 849, "target": 3655, "key": "2245b04831afaa069e12415eb7a39246"}, {"line": 45216, "relation": "positiveCorrelation", "evidence": "In the hippocampus, Bdnf gene was underexpressed in sedentary mice and both Bdnf and its receptor TrkB were significantly upregulated in response to the exercise intervention", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}}, "source": 849, "target": 3599, "key": "d406062dd9c221ffa867ac4a4a8b9c5d"}, {"line": 45225, "relation": "positiveCorrelation", "evidence": "after the exercise intervention Bdnf levels in SAMP8 mice were undistinguishable from those found in sedentary SAMR1 controls . Neuritin gene, a well characterized target of BDNF, was upregulated in both strains by exercise training ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 849, "target": 3142, "key": "a591d6828c5e8e7d3787b19b90259cd3"}, {"line": 45231, "relation": "negativeCorrelation", "evidence": "Interestingly, miR28a-5p, miR-98-5p, and miR-148b-3p expression was significantly higher in sedentary SAMP8 compared with sedentary SAMR1 mice and this difference was further accentuated by exercise ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}, "Confidence": {"Medium": true}}, "source": 849, "target": 2113, "key": "492e9f5e9c2a88aa22f34249746746b3"}, {"line": 45232, "relation": "negativeCorrelation", "evidence": "Interestingly, miR28a-5p, miR-98-5p, and miR-148b-3p expression was significantly higher in sedentary SAMP8 compared with sedentary SAMR1 mice and this difference was further accentuated by exercise ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}, "Confidence": {"Medium": true}}, "source": 849, "target": 2125, "key": "06930f86543160749cb869a10b811964"}, {"line": 45233, "relation": "negativeCorrelation", "evidence": "Interestingly, miR28a-5p, miR-98-5p, and miR-148b-3p expression was significantly higher in sedentary SAMP8 compared with sedentary SAMR1 mice and this difference was further accentuated by exercise ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}, "Confidence": {"Medium": true}}, "source": 849, "target": 2103, "key": "217816e13bfc4d0488165a8df55409dc"}, {"line": 45240, "relation": "negativeCorrelation", "evidence": "Voluntary exercise led to a significant decrease in Hdac3 gene expression exclusively in SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 849, "target": 3644, "key": "8eaea3529f6438fe270e515b44a9b005"}, {"line": 45246, "relation": "negativeCorrelation", "evidence": ". ANOVA analysis showed a downregulation tendency for Hdac5 gene in exercised compared with sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 849, "target": 3645, "key": "8f095b833bc4d3e15591b7fb5e58a4bb"}, {"line": 45253, "relation": "negativeCorrelation", "evidence": "we found that the global acetylation levels of histone H3 (H3ac) were lower in sedentary SAMP8 than in SAMR1 mice and significantly increased upon exercise only in the senescent mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 849, "target": 2804, "key": "1a63ccb285bb9eb4594bcb24aa3a57f8"}, {"line": 45257, "relation": "positiveCorrelation", "evidence": "We found a downregulation of histone deacetylase Hdac6 in the hippocampus of sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Species": {"10090": true}}, "source": 849, "target": 3646, "key": "632b938afffca39aa36c09f0d5ac3bf1"}, {"line": 40134, "relation": "positiveCorrelation", "evidence": "Elevated levels of several proinflammatory factors including cytokines, peptides, pathogenic structures, and peroxidants in the central nervous system (CNS) have been detected in patients with neurodegenerative diseases such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 325, "target": 3823, "key": "1c64f1463ab5f7f9a4be3a3e380d3690"}, {"line": 40136, "relation": "positiveCorrelation", "evidence": "Elevated levels of several proinflammatory factors including cytokines, peptides, pathogenic structures, and peroxidants in the central nervous system (CNS) have been detected in patients with neurodegenerative diseases such as Alzheimer's disease (AD).", "citation": {"db": "PubMed", "db_id": "24455696"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Central Nervous System": true}, "Species": {"9606": true}, "Subgraph": {"Cytokine signaling subgraph": true}, "Confidence": {"High": true}}, "source": 325, "target": 3874, "key": "a0f5c6b78890e0adcde6258068d8e155"}, {"line": 40830, "relation": "association", "evidence": "To date, the most studied members of this family of peptides are hBD-1, -2, and -3.", "citation": {"db": "PubMed", "db_id": "24139179"}, "source": 325, "target": 2626, "key": "d994b9a31dc66401afbb74567266d2b6"}, {"line": 42543, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 325, "target": 73, "key": "f527f635658555b633d2008ab170cc59"}, {"line": 42547, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 325, "target": 351, "key": "1915e4a42da7defa62988a0ecd74fa2a"}, {"line": 42548, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 325, "target": 3697, "key": "9d21c5b74d9a3a9689639f7182b1d62b"}, {"line": 42549, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 325, "target": 3706, "key": "066dc66137ee6db7a3d37aaba10bfcad"}, {"line": 40338, "relation": "association", "evidence": "We found that retinoic acid-inducible gene-I (RIG-1) is significantly elevated in the temporal cortex and plasma in patients with MCI.", "citation": {"db": "PubMed", "db_id": "24694234"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Inflammatory response subgraph": true}, "MeSHDisease": {"Cognitive Dysfunction": true}, "MeSHAnatomy": {"Plasma": true}, "Confidence": {"Medium": true}}, "source": 3297, "target": 3839, "key": "26630353180c2fceb5c4e1b9028a37f3"}, {"line": 45033, "relation": "positiveCorrelation", "evidence": "Two genes were increased in LOAD (C10orf105 and RARRES3),while three genes were decreased in LOAD", "citation": {"db": "PubMed", "db_id": "25380588"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3297, "target": 3820, "key": "35a20a5587a2787d1c6ab8558e78c050"}, {"line": 40400, "relation": "association", "evidence": "ApoE regulates secretion of the potent neuroprotective signaling lipid Sphingosine 1-phosphate (S1P).", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Sphingolipid metabolic subgraph": true}}, "source": 177, "target": 2312, "key": "3e8e165ad4844358617228e55ef24e23"}, {"line": 40432, "relation": "association", "evidence": "S1P/sphingosine ratio was 2.5-fold higher in hippocampus of ApoE2 carriers compared to ApoE4 carriers, and multivariate regression showed a significant association between APOE genotype and hippocampal S1P/sphingosine (p = 0.0495), suggesting a new link between APOE genotype and pre-disposition to AD.This study demonstrates loss of S1P and sphingosine kinase activity early in AD pathogenesis, and prior to AD diagnosis.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 177, "target": 2312, "key": "6d413c00fa2208c90ce41f1599aa64d3"}, {"line": 40424, "relation": "association", "evidence": "The S1P/sphingosine ratio was 66% and 64% lower in Braak stage III/IV hippocampus (p = 0.010) and inferior temporal cortex (p = 0.014), respectively, compared to controls.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 177, "target": 354, "key": "159e75f408ed25488eef6c59cc42d2f8"}, {"line": 40407, "relation": "increases", "evidence": "S1P is derived by phosphorylation of sphingosine, catalysed by sphingosine kinases 1 and 2 (SphK1 and 2), and SphK1 positively regulates glutamate secretion and synaptic strength in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Neurons": true}}, "source": 354, "target": 177, "key": "59ea2064e092417a08beae25c9e6ef81"}, {"line": 40424, "relation": "association", "evidence": "The S1P/sphingosine ratio was 66% and 64% lower in Braak stage III/IV hippocampus (p = 0.010) and inferior temporal cortex (p = 0.014), respectively, compared to controls.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 354, "target": 177, "key": "2c585d20d0b764e902c0d12678279eaa"}, {"line": 40418, "relation": "decreases", "evidence": "S1P declined with increasing Braak stage, and this was most pronounced in brain regions most heavily affected by AD pathology.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Disease": {"Alzheimer's disease": true}}, "source": 354, "target": 3823, "key": "f052c12e608fc7f8819815b283d4c917"}, {"line": 40431, "relation": "association", "evidence": "S1P/sphingosine ratio was 2.5-fold higher in hippocampus of ApoE2 carriers compared to ApoE4 carriers, and multivariate regression showed a significant association between APOE genotype and hippocampal S1P/sphingosine (p = 0.0495), suggesting a new link between APOE genotype and pre-disposition to AD.This study demonstrates loss of S1P and sphingosine kinase activity early in AD pathogenesis, and prior to AD diagnosis.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 354, "target": 2312, "key": "2897b6f4b29f6c9c31b23c6c562a9c5f"}, {"line": 40408, "relation": "increases", "evidence": "S1P is derived by phosphorylation of sphingosine, catalysed by sphingosine kinases 1 and 2 (SphK1 and 2), and SphK1 positively regulates glutamate secretion and synaptic strength in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Neurons": true}}, "source": 3404, "target": 354, "key": "1f75bccddffa4ad8823d0bcafd67d57f"}, {"line": 40412, "relation": "increases", "evidence": "S1P is derived by phosphorylation of sphingosine, catalysed by sphingosine kinases 1 and 2 (SphK1 and 2), and SphK1 positively regulates glutamate secretion and synaptic strength in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Neurons": true}}, "source": 3404, "target": 123, "key": "ef05df7b3e77842524897d57726e01ac"}, {"line": 40410, "relation": "increases", "evidence": "S1P is derived by phosphorylation of sphingosine, catalysed by sphingosine kinases 1 and 2 (SphK1 and 2), and SphK1 positively regulates glutamate secretion and synaptic strength in hippocampal neurons.", "citation": {"db": "PubMed", "db_id": "24456642"}, "annotations": {"Subgraph": {"Sphingolipid metabolic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Neurons": true}}, "source": 3405, "target": 354, "key": "f1d56e0714008c9b3ffb13a6c949ebb7"}, {"line": 40446, "relation": "increases", "evidence": "Evodiamine Induces Transient Receptor Potential Vanilloid-1-Mediated Protective Autophagy in U87-MG Astrocytes.", "citation": {"db": "PubMed", "db_id": "24454492"}, "annotations": {"CellLine": {"obsolete: U87 MG cell": true}, "MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 192, "target": 3496, "key": "910141a9c0064a01b56022709911314d"}, {"line": 40464, "relation": "increases", "evidence": "A scavenger of extracellular calcium and an antagonist of transient receptor potential vanilloid-1 (TRPV-1) decreased the percentage of autophagy accompanied by an increase in apoptotic process, suggesting that Evo may induce calcium-mediated protective autophagy resulting from an influx of extracellular calcium.", "citation": {"db": "PubMed", "db_id": "24454492"}, "annotations": {"Subgraph": {"Autophagy signaling subgraph": true}, "Confidence": {"High": true}}, "source": 192, "target": 808, "key": "48c8fe12994de2b12844bc50acb7cbf2"}, {"line": 40478, "relation": "increases", "evidence": "Collectively, Evo induced an influx of extracellular calcium, which led to JNK-mediated protective autophagy, and this provides a new option for ischemic stroke treatment.", "citation": {"db": "PubMed", "db_id": "24454492"}, "annotations": {"MeSHDisease": {"Stroke": true}, "Confidence": {"High": true}}, "source": 192, "target": 94, "key": "0a9e7c667d092bc11fbca0d6c59430f2"}, {"line": 40447, "relation": "increases", "evidence": "Evodiamine Induces Transient Receptor Potential Vanilloid-1-Mediated Protective Autophagy in U87-MG Astrocytes.", "citation": {"db": "PubMed", "db_id": "24454492"}, "annotations": {"CellLine": {"obsolete: U87 MG cell": true}, "MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3496, "target": 808, "key": "968a5b495ae446c04acbc9c08636acdc"}, {"line": 40457, "relation": "decreases", "evidence": "A scavenger of extracellular calcium and an antagonist of transient receptor potential vanilloid-1 (TRPV-1) decreased the percentage of autophagy accompanied by an increase in apoptotic process, suggesting that Evo may induce calcium-mediated protective autophagy resulting from an influx of extracellular calcium.", "citation": {"db": "PubMed", "db_id": "24454492"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3496, "target": 478, "key": "29d55da0652604bf6b62086007cf22a8"}, {"line": 40523, "relation": "increases", "evidence": "The processing is induced by an increase in activity of caspase-1 and NOD-like receptor family, pyrin domain containing 3 (NLRP3) via mitochondrial reactive oxygen species (ROS) and partially via NADPH oxidase-induced ROS.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3119, "target": 2885, "key": "7f47550544376c9236c10eabeeb1e816"}, {"line": 40527, "relation": "increases", "evidence": "The processing is induced by an increase in activity of caspase-1 and NOD-like receptor family, pyrin domain containing 3 (NLRP3) via mitochondrial reactive oxygen species (ROS) and partially via NADPH oxidase-induced ROS.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Caspase subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 3119, "target": 170, "key": "e51deef262e50ac851b8e74afebabec9"}, {"line": 40536, "relation": "decreases", "evidence": "The caspase-1 inhibitor Z-YVAD-FMK inhibits the processing of IL-1beta, and attenuates microglial neurotoxicity.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 43, "target": 2885, "key": "45625207925371ea321ce462b2344b56"}, {"relation": "partOf", "source": 70, "target": 903, "key": "5c8e581208d19972573c619b1b99e9f0"}, {"line": 40546, "relation": "association", "evidence": "In the present study, we have found that plasmalogens (Pls), which are glycerophospholipids containing vinyl ether linkage at sn-1 position, can protect the neuronal cell death upon serum deprivation.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"High": true}}, "source": 70, "target": 65, "key": "366fdb13c5802824dab1e18c1b37d16c"}, {"line": 40548, "relation": "association", "evidence": "In the present study, we have found that plasmalogens (Pls), which are glycerophospholipids containing vinyl ether linkage at sn-1 position, can protect the neuronal cell death upon serum deprivation.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"High": true}}, "source": 70, "target": 266, "key": "de4300c136c04fe52d0ba229c6cad71d"}, {"line": 40546, "relation": "association", "evidence": "In the present study, we have found that plasmalogens (Pls), which are glycerophospholipids containing vinyl ether linkage at sn-1 position, can protect the neuronal cell death upon serum deprivation.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"High": true}}, "source": 65, "target": 70, "key": "20895953db34e73f38c85b1b9468f5fd"}, {"line": 40547, "relation": "association", "evidence": "In the present study, we have found that plasmalogens (Pls), which are glycerophospholipids containing vinyl ether linkage at sn-1 position, can protect the neuronal cell death upon serum deprivation.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"High": true}}, "source": 65, "target": 266, "key": "16b8fd338870a399a35c5001e5bbbb5c"}, {"line": 40551, "relation": "decreases", "evidence": "In the present study, we have found that plasmalogens (Pls), which are glycerophospholipids containing vinyl ether linkage at sn-1 position, can protect the neuronal cell death upon serum deprivation.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 65, "target": 648, "key": "7942ecfa4f556a9691941fa6802cc1a7"}, {"line": 40556, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 65, "target": 2190, "key": "02c71a82356ed1c6fcf11e569cfe81b5"}, {"line": 40559, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"High": true}}, "source": 65, "target": 3001, "key": "c62af22d8f38ac4a774b32efc2c307ec"}, {"line": 40562, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 65, "target": 3552, "key": "e58cbb785b125bb9368af3e646a5b29d"}, {"line": 40563, "relation": "increases", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 65, "target": 2154, "key": "4752d563b2f97db9eea76af3d0d71cdd"}, {"line": 40569, "relation": "association", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 65, "target": 2153, "key": "0abc2d760d4d16894eb827701e1253c9"}, {"line": 40570, "relation": "association", "evidence": "Furthermore, cellular signaling experiments showed that Pls enhanced phosphorylation of the phosphoinositide 3-kinase (PI3K)-dependent serine/threonine-specific protein kinase AKT and extracellular-signal-regulated kinases ERK1/2.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"Medium": true}}, "source": 65, "target": 890, "key": "78c5e2f80238850300feb8554c431f69"}, {"line": 40589, "relation": "association", "evidence": "Further studies on precise mechanisms of Pls-mediated protection against cell death may lead us to establish a novel therapeutic approach to cure neurodegenerative disorders.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "MeSHDisease": {"Neurodegenerative Diseases": true}, "Confidence": {"High": true}}, "source": 65, "target": 3874, "key": "72de1b1dd4b870690674db003cd11a74"}, {"line": 40547, "relation": "association", "evidence": "In the present study, we have found that plasmalogens (Pls), which are glycerophospholipids containing vinyl ether linkage at sn-1 position, can protect the neuronal cell death upon serum deprivation.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"High": true}}, "source": 266, "target": 65, "key": "fc2b9635f8f1b735c7c963ece4410a5b"}, {"line": 40548, "relation": "association", "evidence": "In the present study, we have found that plasmalogens (Pls), which are glycerophospholipids containing vinyl ether linkage at sn-1 position, can protect the neuronal cell death upon serum deprivation.", "citation": {"db": "PubMed", "db_id": "24357806"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Serum": true}, "Confidence": {"High": true}}, "source": 266, "target": 70, "key": "7f29e885f6c44a741f0af6452325284c"}, {"relation": "hasVariant", "source": 2189, "target": 2190, "key": "046ef9adeac2b63eb27d2bef32642ef3"}, {"line": 40608, "relation": "association", "evidence": "Interferongamma (IFNgamma) increased microglial HLA expression, but contrary to data in rodents, the anti-inflammatory cytokine transforming growth factor beta1 (TGFbeta1) did not inhibit this increase in HLA, nor did TGFbeta1 affect basal microglial HLA expression or IFNgamma-induced astrocytic HLA expression.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Leukocytes": true, "Neuroglia": true}, "Species": {"9606": true}}, "source": 2869, "target": 424, "key": "913c6de836889353db71394252814341"}, {"line": 40619, "relation": "association", "evidence": "In contrast, TGFbeta1 did not block the IFNgamma-induced increase in IP-10 in pericytes and meningeal fibroblasts.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Pericytes": true, "Meninges": true, "Fibroblasts": true}, "Species": {"9606": true}}, "source": 2869, "target": 2603, "key": "25f9e8cf06a8dbf24cb90901705b53d6"}, {"line": 40626, "relation": "association", "evidence": "These results show that IFNgamma, TGFbeta1 and M-CSF have species- and cell type-specific effects on human brain cells that may have implications for their roles in adult human brain inflammation.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "MeSHDisease": {"Encephalitis": true}}, "source": 2869, "target": 3, "key": "f5b422fbd9398c9d07d9bf985af3e9ab"}, {"line": 40608, "relation": "association", "evidence": "Interferongamma (IFNgamma) increased microglial HLA expression, but contrary to data in rodents, the anti-inflammatory cytokine transforming growth factor beta1 (TGFbeta1) did not inhibit this increase in HLA, nor did TGFbeta1 affect basal microglial HLA expression or IFNgamma-induced astrocytic HLA expression.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Leukocytes": true, "Neuroglia": true}, "Species": {"9606": true}}, "source": 424, "target": 2869, "key": "b42b838ee9c6ab8f19eb603e06190f3f"}, {"line": 40613, "relation": "decreases", "evidence": "In contrast, IFNgamma-induced and basal microglial HLA expression, but not IFNgamma-induced astrocytic HLA expression, were strongly inhibited by macrophage colony stimulating factor (M-CSF).", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Macrophages": true}, "Species": {"9606": true}}, "source": 3980, "target": 2566, "key": "d7f228cad999922416df0537d6848900"}, {"line": 40618, "relation": "association", "evidence": "In contrast, TGFbeta1 did not block the IFNgamma-induced increase in IP-10 in pericytes and meningeal fibroblasts.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Pericytes": true, "Meninges": true, "Fibroblasts": true}, "Species": {"9606": true}}, "source": 3, "target": 2603, "key": "5d770535740f8b4f997dbcff248cab78"}, {"line": 40626, "relation": "association", "evidence": "These results show that IFNgamma, TGFbeta1 and M-CSF have species- and cell type-specific effects on human brain cells that may have implications for their roles in adult human brain inflammation.", "citation": {"db": "PubMed", "db_id": "24339874"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "MeSHDisease": {"Encephalitis": true}}, "source": 3, "target": 2869, "key": "872c57fa9b174bfc23d2b1a385510b56"}, {"line": 40653, "relation": "association", "evidence": "We found that frontal CC regions were preserved with respect to the posterior ones in aMCI; in these individuals significant correlations were seen between DTI-derived metrics in frontal-parietal CC areas and Abeta42-stimulated BDNF-producing CD4+ T lymphocytes and PDL-1-expressing CD14+ cells.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"MeSHAnatomy": {"T-Lymphocytes": true}, "Confidence": {"High": true}}, "source": 181, "target": 2397, "key": "fbf68a6fd602a5d8dff4cfecefdea224"}, {"line": 40652, "relation": "association", "evidence": "We found that frontal CC regions were preserved with respect to the posterior ones in aMCI; in these individuals significant correlations were seen between DTI-derived metrics in frontal-parietal CC areas and Abeta42-stimulated BDNF-producing CD4+ T lymphocytes and PDL-1-expressing CD14+ cells.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"MeSHAnatomy": {"T-Lymphocytes": true}, "Confidence": {"High": true}}, "source": 2471, "target": 2397, "key": "58fc8eccec77e587da316c919c8a9984"}, {"line": 43844, "relation": "increases", "evidence": "The interaction between PD1 on T lymphocytes and PD-L1 on antigen presenting cells (APC) modulates the balance between inflammation and tolerance by inducing IL-10 production and apoptosis of antigen-specific cells.", "citation": {"db": "PubMed", "db_id": "21514692"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 2471, "target": 2878, "key": "3f0a5d5cb17a7873e27028c513527d71"}, {"line": 43845, "relation": "increases", "evidence": "The interaction between PD1 on T lymphocytes and PD-L1 on antigen presenting cells (APC) modulates the balance between inflammation and tolerance by inducing IL-10 production and apoptosis of antigen-specific cells.", "citation": {"db": "PubMed", "db_id": "21514692"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 2471, "target": 459, "key": "e83b1cd92694e2622b481e158ac09e75"}, {"line": 43860, "relation": "increases", "evidence": "T-cell activation is dependent on signals delivered through the antigen-specific T-cell receptor and accessory receptors on T-cells. Integration of signals through this family of costimulatory and inhibitory receptors and their ligands regulates the balance between T-cell activation, tolerance, and immunopathology. Programmed death 1 (PD-1) and its ligands, PD-L1 and PD-L2, deliver inhibitory signals and exert a vital and diverse range of immunoregulatory roles in T-cell activation, tolerance, and immune-mediated tissue damage.", "citation": {"db": "PubMed", "db_id": "22300137"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 2471, "target": 459, "key": "3a48f8364d0a16b081b7ad919acfc05a"}, {"line": 43846, "relation": "increases", "evidence": "The interaction between PD1 on T lymphocytes and PD-L1 on antigen presenting cells (APC) modulates the balance between inflammation and tolerance by inducing IL-10 production and apoptosis of antigen-specific cells.", "citation": {"db": "PubMed", "db_id": "21514692"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 2471, "target": 478, "key": "6a034fe622e8c8d6d62146019b7363df"}, {"line": 43850, "relation": "association", "evidence": "An impairment of the PD-L1/PD1 pathway is present in AD and MCI. Such alteration results in reduced IL-10 production and diminished apoptosis of Abeta-specific CD4(+) T lymphocytes; these phenomena could play a role in the neuroinflammation accompanying AD.", "citation": {"db": "PubMed", "db_id": "21514692"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 2471, "target": 3815, "key": "6ccbc9e6e4a144cb703c82468e5ebee0"}, {"line": 43859, "relation": "increases", "evidence": "T-cell activation is dependent on signals delivered through the antigen-specific T-cell receptor and accessory receptors on T-cells. Integration of signals through this family of costimulatory and inhibitory receptors and their ligands regulates the balance between T-cell activation, tolerance, and immunopathology. Programmed death 1 (PD-1) and its ligands, PD-L1 and PD-L2, deliver inhibitory signals and exert a vital and diverse range of immunoregulatory roles in T-cell activation, tolerance, and immune-mediated tissue damage.", "citation": {"db": "PubMed", "db_id": "22300137"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 2471, "target": 456, "key": "97ea5770bb6489e115bd6170160d0a21"}, {"line": 40666, "relation": "association", "evidence": "Ligands that target PPARs (peroxisome proliferator-activated receptors), a group of ligand-activated transcription factors, are promising therapeutics for neurologic disease and CNS injury because their activation affects many, if not all, of these interrelated pathologic mechanisms.", "citation": {"db": "PubMed", "db_id": "24324435"}, "annotations": {"MeSHAnatomy": {"Peroxisomes": true}, "MeSHDisease": {"Wounds and Injuries": true}, "Confidence": {"High": true}}, "source": 2207, "target": 3888, "key": "28d461ac9cb54d803587bc61b34d48e4"}, {"line": 40715, "relation": "decreases", "evidence": "Several findings indicate that the activation of both CB1 and CB2 receptors by natural or synthetic agonists, at non-psychoactive doses, have beneficial effects in Alzheimer experimental models by reducing the harmful beta-amyloid peptide action and tau phosphorylation, as well as by promoting the brain's intrinsic repair mechanisms.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2539, "target": 2130, "key": "28c1c3549af25e189a03b17a0e2aa38b"}, {"line": 40716, "relation": "decreases", "evidence": "Several findings indicate that the activation of both CB1 and CB2 receptors by natural or synthetic agonists, at non-psychoactive doses, have beneficial effects in Alzheimer experimental models by reducing the harmful beta-amyloid peptide action and tau phosphorylation, as well as by promoting the brain's intrinsic repair mechanisms.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2540, "target": 2130, "key": "7330e565027d6bec995ab3f97d9b53e9"}, {"line": 40730, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 51, "target": 3196, "key": "8de6d15f5774a9ae8d44e46e2e80bfdf"}, {"line": 40760, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 51, "target": 3196, "key": "8dd1ca398ea73eb01a4e0f13e96bdcfd"}, {"line": 40733, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 51, "target": 54, "key": "43efb98f6b567529a16495d60b862d9c"}, {"line": 40763, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 51, "target": 54, "key": "6f757c11f1e8087e8591e35806ce8e7f"}, {"line": 40734, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 51, "target": 52, "key": "909dcf3ab296a3582d74d7b122c127b9"}, {"line": 40764, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 51, "target": 52, "key": "10d1dc270283bffa8b350c62391a52c8"}, {"line": 40733, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 54, "target": 51, "key": "f2c2c88c37fcbd734beaa61ed9546f93"}, {"line": 40763, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 54, "target": 51, "key": "1a1f457262f700869624f2c8bf0395ad"}, {"line": 40740, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 54, "target": 52, "key": "1a5c40384c67b9d3a583e1ef3ab4c55a"}, {"line": 40770, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 54, "target": 52, "key": "4aa7c593ce6034591fd1e15d1d54dfb3"}, {"line": 40743, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 54, "target": 3196, "key": "09302979a442da9c2e07b10495c70eae"}, {"line": 40773, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 54, "target": 3196, "key": "b4b456ab933ba06495ff0f44eef70a75"}, {"line": 40734, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 52, "target": 51, "key": "2a99d4085a0b247f5def965a3f7e7b85"}, {"line": 40764, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 52, "target": 51, "key": "d789d5d26f6283e825fb4a734498dfc2"}, {"line": 40737, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 52, "target": 3196, "key": "ca5b123bcd0187f27f29fa94958f6d94"}, {"line": 40767, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 52, "target": 3196, "key": "dc13f9298284f4374d292999025ed87d"}, {"line": 40740, "relation": "association", "evidence": "Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Abeta-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution.", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 52, "target": 54, "key": "5d4b757b8f2d15d699e363ae396a2f89"}, {"line": 40770, "relation": "association", "evidence": "In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Abeta-peptide with a Group I PLA2 purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Ã… resolution (Protein Data Bank (PDB) Code: 3JQ5).", "citation": {"db": "PubMed", "db_id": "24619194"}, "annotations": {"MeSHAnatomy": {"Venoms": true}}, "source": 52, "target": 54, "key": "45aef98dcca9b219d6b566d5e91f19ec"}, {"line": 40754, "relation": "association", "evidence": "This study involves the reductionist fragment-based approach to understand the structure adopted by N-terminal fragment of Alzheimer's Abeta peptide in its complex with PLA2.", "citation": {"db": "PubMed", "db_id": "24619194"}, "source": 2134, "target": 3197, "key": "1651a470f222f617229dca5480a8a4fd"}, {"line": 40804, "relation": "positiveCorrelation", "evidence": "We found that microglial/macrophage MMP-14 expression was upregulated in Alzheimer's disease tissue, in active lesions of multiple sclerosis, and in tissue from stage II stroke as well as in the corresponding mouse models for the human diseases.", "citation": {"db": "PubMed", "db_id": "24323769"}, "annotations": {"MeSHAnatomy": {"Macrophages": true, "Tissues": true}, "MeSHDisease": {"Multiple Sclerosis": true, "Stroke": true, "Alzheimer Disease": true}}, "source": 3993, "target": 3823, "key": "dcb453974922f6bd6d8de27ef18351dc"}, {"line": 40816, "relation": "positiveCorrelation", "evidence": "Antimicrobial peptide beta-defensin-1 expression is upregulated in Alzheimer's brain.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 83, "target": 3823, "key": "8795fb4a0ee35358e435f6c42b97acc4"}, {"line": 40830, "relation": "association", "evidence": "To date, the most studied members of this family of peptides are hBD-1, -2, and -3.", "citation": {"db": "PubMed", "db_id": "24139179"}, "source": 2626, "target": 325, "key": "a7ef4581673a41af9793915699a568bc"}, {"line": 40853, "relation": "association", "evidence": "A higher level of hBD-1 was also seen in the choroid plexus of AD brain in comparison to age-matched control tissue.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Choroid Plexus": true, "Tissues": true}}, "source": 2626, "target": 3823, "key": "4ed1f1c1f00f85e70d9a8ecaecf7e4fb"}, {"line": 40858, "relation": "increases", "evidence": "Increased expression of hBD-1 mRNA was observed only in the choroid plexus of the AD brain when compared to expression level in age-matched control brain.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Choroid Plexus": true}}, "source": 3960, "target": 3823, "key": "4dee7de36044a24a62bbb624d69e1593"}, {"line": 40866, "relation": "increases", "evidence": "Redox-active iron was also elevated in the AD choroid plexus and in vitro addition of Feâ�ºÂ³Cl₃ to cultured epithelial cells induced hBD-1 mRNA expression.Our findings suggest interplay between hBD-1 and neuroimmunological responses in AD, marked by microglial and astrocytic activation, and increased expression of the peptide within the choroid plexus and accumulation within GVD.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Choroid Plexus": true, "Epithelial Cells": true}, "Disease": {"Alzheimer's disease": true}}, "source": 136, "target": 3823, "key": "120b128f20161803bbd00bab82fe26fe"}, {"line": 40874, "relation": "association", "evidence": "We also demonstrate that increased iron deposition in AD may contribute to the elevated expression of hBD-1 within the choroid plexus.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Choroid Plexus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 136, "target": 3823, "key": "2a83e257516e9e663ec41871e6a8a51b"}, {"line": 40873, "relation": "increases", "evidence": "We also demonstrate that increased iron deposition in AD may contribute to the elevated expression of hBD-1 within the choroid plexus.", "citation": {"db": "PubMed", "db_id": "24139179"}, "annotations": {"MeSHAnatomy": {"Choroid Plexus": true}, "Disease": {"Alzheimer's disease": true}}, "source": 136, "target": 3960, "key": "6cef8cf9aca27a95946c21157858184d"}, {"line": 40884, "relation": "association", "evidence": "Mutations in proteolipid protein (PLP), the most abundant myelin protein in the CNS, cause the X-linked dysmyelinating leukodystrophies, Pelizaeus-Merzbacher disease (PMD) and spastic paraplegia type 2 (SPG2).", "citation": {"db": "PubMed", "db_id": "24314267"}, "annotations": {"MeSHDisease": {"Paraplegia": true, "Pelizaeus-Merzbacher Disease": true}, "MeSHAnatomy": {"Myelin Sheath": true}}, "source": 3206, "target": 3928, "key": "f4bf391065f5bb700fed3791710a9b3e"}, {"line": 40890, "relation": "association", "evidence": "Deletion of an intronic splicing enhancer (ISEdel) within intron 3 of the PLP1 gene is associated with a mild form of PMD.", "citation": {"db": "PubMed", "db_id": "24314267"}, "annotations": {"MeSHDisease": {"Paraplegia": true, "Pelizaeus-Merzbacher Disease": true}}, "source": 3206, "target": 3880, "key": "a4f6acbb4d11057804963eb7aa1ec9fc"}, {"line": 40884, "relation": "association", "evidence": "Mutations in proteolipid protein (PLP), the most abundant myelin protein in the CNS, cause the X-linked dysmyelinating leukodystrophies, Pelizaeus-Merzbacher disease (PMD) and spastic paraplegia type 2 (SPG2).", "citation": {"db": "PubMed", "db_id": "24314267"}, "annotations": {"MeSHDisease": {"Paraplegia": true, "Pelizaeus-Merzbacher Disease": true}, "MeSHAnatomy": {"Myelin Sheath": true}}, "source": 3928, "target": 3206, "key": "138e5447a8989c12c5951ba3546c5ac6"}, {"line": 40890, "relation": "association", "evidence": "Deletion of an intronic splicing enhancer (ISEdel) within intron 3 of the PLP1 gene is associated with a mild form of PMD.", "citation": {"db": "PubMed", "db_id": "24314267"}, "annotations": {"MeSHDisease": {"Paraplegia": true, "Pelizaeus-Merzbacher Disease": true}}, "source": 3880, "target": 3206, "key": "a62b197f66b17947cf0cd1e40c7ee5f2"}, {"line": 40920, "relation": "association", "evidence": "Luteolin protects against high fat diet-induced cognitive deficits in obesity mice.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Obesity": true}, "Species": {"10090": true}}, "source": 297, "target": 3925, "key": "22a575d4ca4ac5c0737d515a2386d7f9"}, {"line": 40922, "relation": "increases", "evidence": "Luteolin protects against high fat diet-induced cognitive deficits in obesity mice.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHDisease": {"Obesity": true}, "Species": {"10090": true}}, "source": 297, "target": 812, "key": "0e2797699f805a3094d38bebc3820a20"}, {"line": 40946, "relation": "association", "evidence": "We showed that adding luteolin in high-fat diet can significantly reduce body weight gain, food intake and plasma cytokines as well as improving glucose metabolism of mice on HFD.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Species": {"10090": true}}, "source": 297, "target": 264, "key": "cee1c4754178b8a8da2e726197e868e0"}, {"line": 40947, "relation": "increases", "evidence": "We showed that adding luteolin in high-fat diet can significantly reduce body weight gain, food intake and plasma cytokines as well as improving glucose metabolism of mice on HFD.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Species": {"10090": true}}, "source": 297, "target": 566, "key": "c033a423be471ea2ec8e332043fc2514"}, {"line": 40957, "relation": "increases", "evidence": "Furthermore, luteolin increased the level of brain-derived neurotrophic factor (BDNF), the action of synapsin I (SYP) and postsynaptic density protein 95 (PSD-95) in the cortex and hippocampus as to that the behavioral performance in Morris water maze (MWM) and step-through task were significantly improved.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 297, "target": 3625, "key": "619cc46ddc0b7f090207dfa7e636e6cd"}, {"line": 40958, "relation": "increases", "evidence": "Furthermore, luteolin increased the level of brain-derived neurotrophic factor (BDNF), the action of synapsin I (SYP) and postsynaptic density protein 95 (PSD-95) in the cortex and hippocampus as to that the behavioral performance in Morris water maze (MWM) and step-through task were significantly improved.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 297, "target": 3599, "key": "a4d200631b37211bb75ce67a482af918"}, {"line": 40959, "relation": "increases", "evidence": "Furthermore, luteolin increased the level of brain-derived neurotrophic factor (BDNF), the action of synapsin I (SYP) and postsynaptic density protein 95 (PSD-95) in the cortex and hippocampus as to that the behavioral performance in Morris water maze (MWM) and step-through task were significantly improved.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 297, "target": 3735, "key": "f16d474b22a837be5f1ade56510d5250"}, {"line": 40960, "relation": "increases", "evidence": "Furthermore, luteolin increased the level of brain-derived neurotrophic factor (BDNF), the action of synapsin I (SYP) and postsynaptic density protein 95 (PSD-95) in the cortex and hippocampus as to that the behavioral performance in Morris water maze (MWM) and step-through task were significantly improved.", "citation": {"db": "PubMed", "db_id": "24667364"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 297, "target": 3733, "key": "787e379d3f1e56e99a713f23094a55e9"}, {"line": 45216, "relation": "positiveCorrelation", "evidence": "In the hippocampus, Bdnf gene was underexpressed in sedentary mice and both Bdnf and its receptor TrkB were significantly upregulated in response to the exercise intervention", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}}, "source": 3599, "target": 849, "key": "06e4cf0e469e4b7e690300cf77c0685b"}, {"line": 45217, "relation": "orthologous", "evidence": "In the hippocampus, Bdnf gene was underexpressed in sedentary mice and both Bdnf and its receptor TrkB were significantly upregulated in response to the exercise intervention", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}}, "source": 3599, "target": 2397, "key": "8643d509806dea185a5aa775367c46b3"}, {"line": 40983, "relation": "decreases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Neurons": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 253, "target": 3548, "key": "1385030c3f4c56b8e33b0317f3a567f1"}, {"line": 41020, "relation": "decreases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Interferon signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 253, "target": 3654, "key": "45302538f3f4cf91869c9c8fa2b891ba"}, {"line": 41027, "relation": "decreases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Interferon signaling subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"Low": true}}, "source": 253, "target": 3653, "key": "c651030d3aa9457561fbd8896bfeff77"}, {"line": 41037, "relation": "decreases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 253, "target": 291, "key": "c7c4608c544e16ae6261951d1a542c29"}, {"line": 41086, "relation": "increases", "evidence": "TLR4 mediates the impairment of ubiquitin-proteasome and autophagy-lysosome pathways induced by ethanol treatment in brain.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Lysosomes": true}, "Subgraph": {"Toll like receptor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 253, "target": 3739, "key": "d60b77c1be62a8d5b873e6522d25430d"}, {"line": 41094, "relation": "increases", "evidence": "We recently demonstrated that ethanol increases brain proinflammatory mediators and causes brain damage by activating Toll-like receptor 4 (TLR4) signaling in glia.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Neuroglia": true}, "Subgraph": {"Toll like receptor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 253, "target": 3739, "key": "be7bf8f9c2fe947ce3fe4c347b491c64"}, {"line": 41088, "relation": "increases", "evidence": "TLR4 mediates the impairment of ubiquitin-proteasome and autophagy-lysosome pathways induced by ethanol treatment in brain.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Lysosomes": true}, "Subgraph": {"Toll like receptor subgraph": true}}, "source": 253, "target": 3749, "key": "1b6ef9927edfc77a0b6032d009d89ea8"}, {"line": 41102, "relation": "increases", "evidence": "Using the cerebral cortex of WT and TLR4-knockout mice with and without chronic ethanol treatment, we demonstrate that ethanol induces poly-ubiquitinated proteins accumulation and promotes immunoproteasome activation by inducing the expression of beta2i, beta5i and PA28α, although it decreases the 20S constitutive proteasome subunits (α2, beta5).", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Species": {"10090": true}}, "source": 253, "target": 3705, "key": "aff0db325e4cd2db11bfed7628238dec"}, {"line": 41104, "relation": "association", "evidence": "Using the cerebral cortex of WT and TLR4-knockout mice with and without chronic ethanol treatment, we demonstrate that ethanol induces poly-ubiquitinated proteins accumulation and promotes immunoproteasome activation by inducing the expression of beta2i, beta5i and PA28α, although it decreases the 20S constitutive proteasome subunits (α2, beta5).", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Species": {"10090": true}}, "source": 253, "target": 893, "key": "4b652478b53c0f778fa7a4c8a7260a60"}, {"line": 41112, "relation": "increases", "evidence": "Ethanol also upregulates mTOR phosphorylation, leading to a downregulation of the autophagy-lysosome pathway (ATG12, ATG5, cathepsin B, p62, LC3) and alters the volume of autophagic vacuoles.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Lysosomes": true, "Vacuoles": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 253, "target": 3682, "key": "485ba0d124faef38a53df1457cc1c409"}, {"line": 40993, "relation": "increases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Interferon signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"Medium": true}}, "source": 3654, "target": 170, "key": "56f984adb0d1600acba27e39ae92aa13"}, {"line": 43410, "relation": "increases", "evidence": "Cytokines including TNF-α+IFN-gamma increase levels of endogenous BACE1, APP, and Abeta and stimulate amyloidogenic/ APP processing in astrocytes. Oligomeric and fibrillar Abeta42 also increase levels of astrocytic BACE1, APP, and beta-secretase/ processing. Together, our results suggest a cytokine- and Abeta42-driven feed-forward mechanism that promotes astrocytic Abeta/ production. Given that astrocytes greatly outnumber neurons, activated astrocytes may represent significant sources of Abeta/ during neuroinflammation in AD", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Interferon signaling subgraph": true}}, "source": 3654, "target": 3593, "key": "74e91862a864c7a26f13a7dfe2deaf78"}, {"line": 43411, "relation": "increases", "evidence": "Cytokines including TNF-α+IFN-gamma increase levels of endogenous BACE1, APP, and Abeta and stimulate amyloidogenic/ APP processing in astrocytes. Oligomeric and fibrillar Abeta42 also increase levels of astrocytic BACE1, APP, and beta-secretase/ processing. Together, our results suggest a cytokine- and Abeta42-driven feed-forward mechanism that promotes astrocytic Abeta/ production. Given that astrocytes greatly outnumber neurons, activated astrocytes may represent significant sources of Abeta/ during neuroinflammation in AD", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Interferon signaling subgraph": true}}, "source": 3654, "target": 3584, "key": "b2616dee03b0a316ebe0079b9fa5c156"}, {"line": 43412, "relation": "increases", "evidence": "Cytokines including TNF-α+IFN-gamma increase levels of endogenous BACE1, APP, and Abeta and stimulate amyloidogenic/ APP processing in astrocytes. Oligomeric and fibrillar Abeta42 also increase levels of astrocytic BACE1, APP, and beta-secretase/ processing. Together, our results suggest a cytokine- and Abeta42-driven feed-forward mechanism that promotes astrocytic Abeta/ production. Given that astrocytes greatly outnumber neurons, activated astrocytes may represent significant sources of Abeta/ during neuroinflammation in AD", "citation": {"db": "PubMed", "db_id": "22047170"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Interferon signaling subgraph": true}}, "source": 3654, "target": 80, "key": "2b85334778cefa5ed5de901ba3668859"}, {"line": 41000, "relation": "increases", "evidence": "Results of this study indicate that ethanol extracts of SF (SF-E) suppress NMDA-induced reactive oxygen species (ROS) production in neurons, and LPS- and IFNgamma-induced ROS and nitric oxide (NO) production in microglial cells.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Interferon signaling subgraph": true, "Nitric oxide subgraph": true}, "Confidence": {"High": true}}, "source": 3653, "target": 156, "key": "44a536b298547b7ec935a006eaec8815"}, {"line": 42790, "relation": "increases", "evidence": "Nicardipine also significantly inhibited LPS plus IFN-gamma-induced release of nitric oxide (NO), and the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2).", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3653, "target": 156, "key": "0a509f7b2723a8f192703b7096e13d86"}, {"line": 41061, "relation": "increases", "evidence": "SF-E's action on microglial cells appears to be mediated through inhibition of the IFNgamma-induced p-ERK1/2 signaling pathway which is central to regulating a number of intracellular metabolic processes including enhancing Stat1α phosphorylation and filopodia formation.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"High": true}}, "source": 3653, "target": 448, "key": "80a80671373a4193493b0daa9b50cfe9"}, {"line": 41066, "relation": "increases", "evidence": "SF-E's action on microglial cells appears to be mediated through inhibition of the IFNgamma-induced p-ERK1/2 signaling pathway which is central to regulating a number of intracellular metabolic processes including enhancing Stat1α phosphorylation and filopodia formation.", "citation": {"db": "PubMed", "db_id": "24587007"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"MAPK-ERK subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3653, "target": 3552, "key": "135712ea176f44381d7bd1e8110ce0e2"}, {"relation": "hasVariant", "source": 3723, "target": 3724, "key": "8a7f3b2ec7ba5186c95f387d69cccbe7"}, {"line": 41082, "relation": "decreases", "evidence": "TLR4 mediates the impairment of ubiquitin-proteasome and autophagy-lysosome pathways induced by ethanol treatment in brain.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Lysosomes": true}, "Subgraph": {"Toll like receptor subgraph": true}}, "source": 3739, "target": 893, "key": "8adb05466f8226c0219980b63f86a54e"}, {"line": 41084, "relation": "decreases", "evidence": "TLR4 mediates the impairment of ubiquitin-proteasome and autophagy-lysosome pathways induced by ethanol treatment in brain.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Lysosomes": true}, "Subgraph": {"Autophagy signaling subgraph": true, "Toll like receptor subgraph": true}}, "source": 3739, "target": 808, "key": "6777177981c900e588bf35a8ecc89b62"}, {"line": 41087, "relation": "association", "evidence": "TLR4 mediates the impairment of ubiquitin-proteasome and autophagy-lysosome pathways induced by ethanol treatment in brain.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Lysosomes": true}, "Subgraph": {"Toll like receptor subgraph": true}}, "source": 3739, "target": 3749, "key": "f04e938f7db25b53ea6622900ba82a36"}, {"line": 41087, "relation": "association", "evidence": "TLR4 mediates the impairment of ubiquitin-proteasome and autophagy-lysosome pathways induced by ethanol treatment in brain.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Brain": true, "Lysosomes": true}, "Subgraph": {"Toll like receptor subgraph": true}}, "source": 3749, "target": 3739, "key": "82d1eaaa81a642030da4ab2311d6040d"}, {"line": 41103, "relation": "increases", "evidence": "Using the cerebral cortex of WT and TLR4-knockout mice with and without chronic ethanol treatment, we demonstrate that ethanol induces poly-ubiquitinated proteins accumulation and promotes immunoproteasome activation by inducing the expression of beta2i, beta5i and PA28α, although it decreases the 20S constitutive proteasome subunits (α2, beta5).", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Species": {"10090": true}}, "source": 3705, "target": 893, "key": "315baf587a261a89ffcea06d6c13acc6"}, {"line": 41113, "relation": "decreases", "evidence": "Ethanol also upregulates mTOR phosphorylation, leading to a downregulation of the autophagy-lysosome pathway (ATG12, ATG5, cathepsin B, p62, LC3) and alters the volume of autophagic vacuoles.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Lysosomes": true, "Vacuoles": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3682, "target": 3667, "key": "154c6e1d53b1c69734948d6564c95535"}, {"line": 41114, "relation": "decreases", "evidence": "Ethanol also upregulates mTOR phosphorylation, leading to a downregulation of the autophagy-lysosome pathway (ATG12, ATG5, cathepsin B, p62, LC3) and alters the volume of autophagic vacuoles.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Lysosomes": true, "Vacuoles": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3682, "target": 3615, "key": "06535bb5069637f6d7a682c334488cf7"}, {"line": 41115, "relation": "decreases", "evidence": "Ethanol also upregulates mTOR phosphorylation, leading to a downregulation of the autophagy-lysosome pathway (ATG12, ATG5, cathepsin B, p62, LC3) and alters the volume of autophagic vacuoles.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Lysosomes": true, "Vacuoles": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3682, "target": 3592, "key": "faba6f5b3da224e55de0a2f24a0caa27"}, {"line": 41116, "relation": "decreases", "evidence": "Ethanol also upregulates mTOR phosphorylation, leading to a downregulation of the autophagy-lysosome pathway (ATG12, ATG5, cathepsin B, p62, LC3) and alters the volume of autophagic vacuoles.", "citation": {"db": "PubMed", "db_id": "24556681"}, "annotations": {"MeSHAnatomy": {"Lysosomes": true, "Vacuoles": true}, "Subgraph": {"mTOR signaling subgraph": true}}, "source": 3682, "target": 3591, "key": "607c881b48b910abe821924ace7e92b4"}, {"relation": "hasVariant", "source": 3681, "target": 3682, "key": "ecbb41da8e0f5ea2cdf27ff97d41203d"}, {"line": 41155, "relation": "increases", "evidence": "By coexpressing TLR1 or TLR6 in TLR2-transgenic HEK293 cells or silencing tlrs genes in RAW264.7 macrophages, we observed that TLR2-mediated Abeta42-triggered inflammatory activation was enhanced by TLR1 and suppressed by TLR6.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "MeSHAnatomy": {"Macrophages": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Confidence": {"Medium": true}}, "source": 1248, "target": 577, "key": "2e80f261bcd95c1e3d03081a3b9ac4e7"}, {"line": 41173, "relation": "increases", "evidence": "Our study demonstrated that TLR2 is a primary receptor for Abeta to trigger neuroinflammatory activation and suggested that inhibition of TLR2 in microglia could be beneficial in Alzheimer's disease pathogenesis.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 1248, "target": 577, "key": "1312d2273b042146235eac400ee56d76"}, {"relation": "partOf", "source": 3738, "target": 1248, "key": "660abbc1ed77531253abb9c12f539c9b"}, {"line": 41154, "relation": "increases", "evidence": "By coexpressing TLR1 or TLR6 in TLR2-transgenic HEK293 cells or silencing tlrs genes in RAW264.7 macrophages, we observed that TLR2-mediated Abeta42-triggered inflammatory activation was enhanced by TLR1 and suppressed by TLR6.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "MeSHAnatomy": {"Macrophages": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Confidence": {"Medium": true}}, "source": 3738, "target": 1248, "key": "7de4cdb05bd7aec87bceec9e5518da45"}, {"line": 41139, "relation": "increases", "evidence": "TLR2 deficiency reduced Abeta42-triggered inflammatory activation but enhanced Abeta phagocytosis in cultured microglia and macrophages.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "MeSHAnatomy": {"Microglia": true, "Macrophages": true}, "Confidence": {"High": true}}, "source": 3738, "target": 577, "key": "26532b9f73698700c9c7e6960278fa1b"}, {"line": 41144, "relation": "increases", "evidence": "TLR2 deficiency reduced Abeta42-triggered inflammatory activation but enhanced Abeta phagocytosis in cultured microglia and macrophages.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "MeSHAnatomy": {"Microglia": true, "Macrophages": true}, "Confidence": {"High": true}}, "source": 3738, "target": 823, "key": "3b63b4a4908343da46c4cb7342967370"}, {"line": 41164, "relation": "increases", "evidence": "By coexpressing TLR1 or TLR6 in TLR2-transgenic HEK293 cells or silencing tlrs genes in RAW264.7 macrophages, we observed that TLR2-mediated Abeta42-triggered inflammatory activation was enhanced by TLR1 and suppressed by TLR6.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "MeSHAnatomy": {"Macrophages": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3738, "target": 2328, "key": "b34ff785ea237049f78910ce3c9a01ab"}, {"line": 41156, "relation": "increases", "evidence": "By coexpressing TLR1 or TLR6 in TLR2-transgenic HEK293 cells or silencing tlrs genes in RAW264.7 macrophages, we observed that TLR2-mediated Abeta42-triggered inflammatory activation was enhanced by TLR1 and suppressed by TLR6.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "MeSHAnatomy": {"Macrophages": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Confidence": {"Medium": true}}, "source": 3737, "target": 577, "key": "17be7f6c3bcaf869f94f7264d223a434"}, {"line": 41162, "relation": "increases", "evidence": "By coexpressing TLR1 or TLR6 in TLR2-transgenic HEK293 cells or silencing tlrs genes in RAW264.7 macrophages, we observed that TLR2-mediated Abeta42-triggered inflammatory activation was enhanced by TLR1 and suppressed by TLR6.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "MeSHAnatomy": {"Macrophages": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3737, "target": 3738, "key": "bcb991a04c6264312aa7f06dd7af4e36"}, {"line": 41157, "relation": "decreases", "evidence": "By coexpressing TLR1 or TLR6 in TLR2-transgenic HEK293 cells or silencing tlrs genes in RAW264.7 macrophages, we observed that TLR2-mediated Abeta42-triggered inflammatory activation was enhanced by TLR1 and suppressed by TLR6.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Toll like receptor subgraph": true}, "MeSHAnatomy": {"Macrophages": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Confidence": {"Medium": true}}, "source": 3740, "target": 577, "key": "dcb2e9c1c5bb4dbca4cab4a8a36e05a4"}, {"line": 41163, "relation": "increases", "evidence": "By coexpressing TLR1 or TLR6 in TLR2-transgenic HEK293 cells or silencing tlrs genes in RAW264.7 macrophages, we observed that TLR2-mediated Abeta42-triggered inflammatory activation was enhanced by TLR1 and suppressed by TLR6.", "citation": {"db": "PubMed", "db_id": "22198949"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "MeSHAnatomy": {"Macrophages": true}, "CellLine": {"HEK293": true, "RAW264.7": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3740, "target": 3738, "key": "c438a6d4e9ed1f4141e0d027a263cc01"}, {"line": 41192, "relation": "association", "evidence": "Sirtuin modulators control reactive gliosis in an in vitro model of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24860504"}, "annotations": {"MeSHDisease": {"Gliosis": true, "Alzheimer Disease": true}}, "source": 3559, "target": 3912, "key": "86682dda2867bfd8763ead98afba2b74"}, {"line": 41229, "relation": "decreases", "evidence": "Neuregulin1-beta Decreases IL-1beta-Induced Neutrophil Adhesion to Human Brain Microvascular Endothelial Cells.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Neutrophils": true, "Brain": true, "Endothelial Cells": true}, "Species": {"9606": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3691, "target": 2183, "key": "48ff147b8016eb842b50dbea13b05d4c"}, {"line": 41259, "relation": "decreases", "evidence": "We hypothesized that NRG1 would decrease the endothelial response to inflammation and result in a decrease in neutrophil adhesion to endothelial cells.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Endothelium": true, "Endothelial Cells": true, "Neutrophils": true}, "Confidence": {"High": true}}, "source": 3691, "target": 3920, "key": "aa3b4648ce1dcbe1c69b4480ed37889d"}, {"line": 41260, "relation": "decreases", "evidence": "We hypothesized that NRG1 would decrease the endothelial response to inflammation and result in a decrease in neutrophil adhesion to endothelial cells.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Endothelium": true, "Endothelial Cells": true, "Neutrophils": true}, "Confidence": {"High": true}}, "source": 3691, "target": 659, "key": "6007758b15ba02b7ec74970cf674eea9"}, {"line": 41270, "relation": "decreases", "evidence": "Our data show that NRG1-beta decreased the levels of VCAM-1, E-selectin, and neutrophil adhesion to brain microvascular endothelial cells activated by IL1-beta.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHAnatomy": {"Neutrophils": true, "Brain": true, "Endothelial Cells": true}, "Confidence": {"High": true}}, "source": 3691, "target": 3750, "key": "763c25373bd4a751a06d4e01147f193b"}, {"line": 41271, "relation": "decreases", "evidence": "Our data show that NRG1-beta decreased the levels of VCAM-1, E-selectin, and neutrophil adhesion to brain microvascular endothelial cells activated by IL1-beta.", "citation": {"db": "PubMed", "db_id": "24863743"}, "annotations": {"MeSHAnatomy": {"Neutrophils": true, "Brain": true, "Endothelial Cells": true}, "Confidence": {"High": true}}, "source": 3691, "target": 3715, "key": "fd6fea36dd1105919cb2ec6bf1679951"}, {"line": 41288, "relation": "association", "evidence": "Norepinephrine increases I kappa B alpha expression in astrocytes.", "citation": {"db": "PubMed", "db_id": "12050158"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}}, "source": 3686, "target": 317, "key": "0fb04d7e0a2fd7db0a7a7865ed185769"}, {"line": 41332, "relation": "increases", "evidence": "CNS-targeted production of IL-17A induces glial activation, microvascular pathology and enhances the neuroinflammatory response to systemic endotoxemia.", "citation": {"db": "PubMed", "db_id": "23468966"}, "annotations": {"MeSHDisease": {"Endotoxemia": true}, "MeSHAnatomy": {"Neuroglia": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3659, "target": 609, "key": "16df459ab101140b5b9b14d98abb260a"}, {"line": 41335, "relation": "association", "evidence": "CNS-targeted production of IL-17A induces glial activation, microvascular pathology and enhances the neuroinflammatory response to systemic endotoxemia.", "citation": {"db": "PubMed", "db_id": "23468966"}, "annotations": {"MeSHDisease": {"Endotoxemia": true}, "MeSHAnatomy": {"Neuroglia": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3659, "target": 3906, "key": "47346486b82b1e42125b2a54909f05fc"}, {"line": 41344, "relation": "association", "evidence": "Interleukin-17A (IL-17A) is a key cytokine modulating the course of inflammatory diseases.", "citation": {"db": "PubMed", "db_id": "23468966"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3659, "target": 3920, "key": "d6ccaf5398fb9fbb5076ad8d33393168"}, {"line": 41335, "relation": "association", "evidence": "CNS-targeted production of IL-17A induces glial activation, microvascular pathology and enhances the neuroinflammatory response to systemic endotoxemia.", "citation": {"db": "PubMed", "db_id": "23468966"}, "annotations": {"MeSHDisease": {"Endotoxemia": true}, "MeSHAnatomy": {"Neuroglia": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3906, "target": 3659, "key": "96059ecba2fa86718f571000f86df7e9"}, {"line": 41359, "relation": "decreases", "evidence": "The inflammatory responses in many cell types are reduced by noradrenaline (NA) binding to beta-adrenergic receptors.", "citation": {"db": "PubMed", "db_id": "12675915"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 983, "target": 3920, "key": "0700778dcffd4002eeb8c40ab9ca501b"}, {"relation": "partOf", "source": 2267, "target": 983, "key": "7c10358d93e733481191a4101fa8bd56"}, {"line": 41360, "relation": "decreases", "evidence": "The inflammatory responses in many cell types are reduced by noradrenaline (NA) binding to beta-adrenergic receptors.", "citation": {"db": "PubMed", "db_id": "12675915"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 984, "target": 3920, "key": "4e4e305fc1309dd47c73979e6eb23a58"}, {"relation": "partOf", "source": 2268, "target": 984, "key": "f7e013b398416ce1e0ed6129abd425fa"}, {"line": 41361, "relation": "decreases", "evidence": "The inflammatory responses in many cell types are reduced by noradrenaline (NA) binding to beta-adrenergic receptors.", "citation": {"db": "PubMed", "db_id": "12675915"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 985, "target": 3920, "key": "69a159cfe9c86d0db8c53d72b4e2d350"}, {"line": 41383, "relation": "association", "evidence": "These findings suggest one mechanism by which PPARgamma agonists could provide benefit in neurological diseases having an inflammatory component.", "citation": {"db": "PubMed", "db_id": "12675915"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"High": true}}, "source": 3873, "target": 3699, "key": "0f841df4e061f6840c5cee9bf08e0f4c"}, {"line": 42513, "relation": "positiveCorrelation", "evidence": "The cellular generation of reactive oxygen species (ROS) has been implicated in contributing to the pathology of human neurological disorders including Alzheimer's disease (AD) and Parkinson's disease (PD).", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Disease": {"nervous system disease": true, "Parkinson's disease": true, "Alzheimer's disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3873, "target": 170, "key": "3d9d94678d669384a4b03f72eaee6cf5"}, {"line": 41383, "relation": "association", "evidence": "These findings suggest one mechanism by which PPARgamma agonists could provide benefit in neurological diseases having an inflammatory component.", "citation": {"db": "PubMed", "db_id": "12675915"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "MeSHDisease": {"Nervous System Diseases": true}, "Confidence": {"High": true}}, "source": 3699, "target": 3873, "key": "dc3b40147caa52fc46db5d0302732f06"}, {"line": 41675, "relation": "association", "evidence": "Induction of apoptosis in human and rat glioma by agonists of the nuclear receptor PPARgamma.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"MeSHDisease": {"Glioma": true}, "Species": {"9606": true, "10116": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3699, "target": 3911, "key": "6056f1ca1a45db8e1038ecd2f88fa5dd"}, {"line": 41699, "relation": "association", "evidence": "We report the effect of three structurally different PPARgamma agonists inducing apoptosis in human (U87MG and A172) and rat (C6) glioma cells.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Species": {"9606": true, "10116": true}, "CellLine": {"A172": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"High": true}}, "source": 3699, "target": 3911, "key": "540e9dcd69c1d1541def430182466749"}, {"line": 41763, "relation": "association", "evidence": "Taken together, treatment of glioma cells with PPARgamma agonists may hold therapeutic potential for the treatment of gliomas.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"MeSHDisease": {"Glioma": true}, "Confidence": {"Medium": true}}, "source": 3699, "target": 3911, "key": "b20ccde63a206e4f452e820dab697d9f"}, {"line": 41700, "relation": "association", "evidence": "We report the effect of three structurally different PPARgamma agonists inducing apoptosis in human (U87MG and A172) and rat (C6) glioma cells.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Species": {"9606": true, "10116": true}, "CellLine": {"A172": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"High": true}}, "source": 3699, "target": 194, "key": "839fa0330a7951bc8ec4cc65a2c08c49"}, {"line": 41717, "relation": "decreases", "evidence": "PPARgamma agonist-induced cell death was characterized by DNA fragmentation and nuclear condensation, as well as inhibited by the synthetic receptor-antagonist bisphenol A diglycidyl ether (BADGE).", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3699, "target": 505, "key": "6b9d2c8665e94030a81e9bee97bb9792"}, {"line": 41720, "relation": "decreases", "evidence": "PPARgamma agonist-induced cell death was characterized by DNA fragmentation and nuclear condensation, as well as inhibited by the synthetic receptor-antagonist bisphenol A diglycidyl ether (BADGE).", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3699, "target": 834, "key": "90fea18f85a3347f988d4b9b1e89dcf0"}, {"line": 41731, "relation": "positiveCorrelation", "evidence": "The apoptotic death in the glioma cell lines treated with PPARgamma agonists was correlated with the transient up-regulation of Bax and Bad protein levels.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Bcl-2 subgraph": true}, "MeSHDisease": {"Glioma": true}, "MeSHAnatomy": {"Cell Line": true}, "Confidence": {"High": true}}, "source": 3699, "target": 3596, "key": "3bbe9390cc3da38ac1ae2d7a19634b05"}, {"line": 41732, "relation": "positiveCorrelation", "evidence": "The apoptotic death in the glioma cell lines treated with PPARgamma agonists was correlated with the transient up-regulation of Bax and Bad protein levels.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Bcl-2 subgraph": true}, "MeSHDisease": {"Glioma": true}, "MeSHAnatomy": {"Cell Line": true}, "Confidence": {"High": true}}, "source": 3699, "target": 3595, "key": "29e08865fcfeed733c3d9e8578057dd9"}, {"line": 41753, "relation": "increases", "evidence": "However, PPARgamma agonists not only induced apoptosis but also caused redifferentiation as indicated by outgrowth of long processes and expression of the redifferentiation marker N-cadherin in response to PPARgamma agonists.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"High": true}}, "source": 3699, "target": 478, "key": "7bc4c1d0c56e1518ec9f42a9f085c8b5"}, {"line": 41754, "relation": "increases", "evidence": "However, PPARgamma agonists not only induced apoptosis but also caused redifferentiation as indicated by outgrowth of long processes and expression of the redifferentiation marker N-cadherin in response to PPARgamma agonists.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"High": true}}, "source": 3699, "target": 3608, "key": "da168ee083b39e58e94b06633b6ac29c"}, {"line": 42443, "relation": "positiveCorrelation", "evidence": "This PPARgamma-stimulated increase of Abeta phagocytosis was mediated by the upregulation of scavenger receptor CD36 expression.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3699, "target": 3605, "key": "42f41a34f090a15c469ebe5473602f77"}, {"line": 43390, "relation": "association", "evidence": "Experimental evidence suggests that cortical noradrenaline (NA) depletion due to degeneration of the locus/ ceruleus (LC) - a pathological hallmark of AD - plays a permissive role in the development of inflammation in AD. Our/ results indicate for the first time that PPARgamma expression can be modulated by the cAMP signalling pathway, and/ suggest that the anti-inflammatory effects of NA on brain cells may be partly mediated by increasing PPARgamma levels./ Conversely, decreased NA due to LC cell death in AD may reduce endogenous PPARgamma expression and therefore potentiate/ neuroinflammatory processes.", "citation": {"db": "PubMed", "db_id": "12887689"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 3699, "target": 490, "key": "8d4ac38cc6a62d876c2bbb734d5ac832"}, {"line": 41400, "relation": "regulates", "evidence": "Runx1t1 (Runt-related transcription factor 1; translocated to, 1) epigenetically regulates the proliferation and nitric oxide production of microglia.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3712, "target": 156, "key": "3565aca37a6b53e0f5adbd2896c77673"}, {"line": 41411, "relation": "association", "evidence": "Runx1t1 (Runt-related transcription factor 1, translocated to 1) has been implicated in recruiting histone deacetylases (HDACs) for transcriptional repression, thereby regulating cell proliferation.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3712, "target": 731, "key": "73a9c498604ca7c3055d0b9ff769d1b4"}, {"line": 41414, "relation": "regulates", "evidence": "Runx1t1 (Runt-related transcription factor 1, translocated to 1) has been implicated in recruiting histone deacetylases (HDACs) for transcriptional repression, thereby regulating cell proliferation.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 3712, "target": 829, "key": "3f4d4355e7ddb611b796942784de85af"}, {"relation": "partOf", "source": 3712, "target": 1649, "key": "c0439a5bc9e39486dd8a540b08211994"}, {"line": 41422, "relation": "decreases", "evidence": "knockdown of Runx1t1 significantly decreased the expression level of cell cycle-related gene, cyclin-dependent kinase 4 (Cdk4) and proliferation index in activated BV2 microglia.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 3713, "target": 3609, "key": "b9771c946a3988c64dc14815eaff637e"}, {"line": 41457, "relation": "regulates", "evidence": "However, the enhanced binding of Runx1t1 to the LAT2 promoter could not repress the LAT2 expression when the BV2 microglia cells were treated with HDACi, indicating that Runx1t1 requires HDACs to transcriptionally repress the expression of LAT2.In conclusion, it is suggested that Runx1t1 controls proliferation and the neurotoxic effect of microglia by epigenetically regulating Cdk4 and LAT2 via its interaction with HDACs.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Cyclin-CDK subgraph": true}, "Confidence": {"Medium": true}}, "source": 3713, "target": 3609, "key": "0bb0990b6163bb01a9eddbf29f8a41d6"}, {"line": 41428, "relation": "association", "evidence": "It was also shown that HDAC inhibitor (HDACi) treatment mimics the effects of Runx1t1 knockdown on microglial proliferation, confirming that microglial proliferation is associated with Runx1t1 expression and HDACs activity.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3713, "target": 610, "key": "890a0b8901e798a305c5a21e2b9a2e95"}, {"line": 41438, "relation": "decreases", "evidence": "Further, Runx1t1 and HDACs were shown to promote neurotoxic effect of microglia by repressing expression of LAT2, L-aminoacid transporter-2 (cationic amino acid transporter, y+ system), which normally inhibits NO production.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "source": 3713, "target": 4076, "key": "f5ff74ebe2faa748f94b5aa0806aeaad"}, {"relation": "partOf", "source": 3713, "target": 1649, "key": "1fbc2bec34d4db3e0f93fb0265996add"}, {"relation": "partOf", "source": 3713, "target": 1646, "key": "d7aa35c9074a34aef66e1bd4a6125551"}, {"line": 41428, "relation": "association", "evidence": "It was also shown that HDAC inhibitor (HDACi) treatment mimics the effects of Runx1t1 knockdown on microglial proliferation, confirming that microglial proliferation is associated with Runx1t1 expression and HDACs activity.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"Confidence": {"Medium": true}}, "source": 610, "target": 3713, "key": "002d97846ebebdf78c4274fbf9c31d7d"}, {"line": 41434, "relation": "decreases", "evidence": "Further, Runx1t1 and HDACs were shown to promote neurotoxic effect of microglia by repressing expression of LAT2, L-aminoacid transporter-2 (cationic amino acid transporter, y+ system), which normally inhibits NO production.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"Confidence": {"Medium": true}, "MeSHAnatomy": {"Microglia": true}}, "subject": {"modifier": "Activity"}, "source": 3549, "target": 4076, "key": "f3ba79307ba50c737b59fdd67ebb6352"}, {"line": 41441, "relation": "decreases", "evidence": "Further, Runx1t1 and HDACs were shown to promote neurotoxic effect of microglia by repressing expression of LAT2, L-aminoacid transporter-2 (cationic amino acid transporter, y+ system), which normally inhibits NO production.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Confidence": {"High": true}}, "source": 3718, "target": 156, "key": "998a8d72c5038fe3f291ac3c354eda69"}, {"line": 41447, "relation": "regulates", "evidence": "This was confirmed by chromatin immunoprecipitation (ChIP) assay, which revealed that Runx1t1 binds to the promoter region of LAT2 and this binding increased upon microglial activation.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Chromatin": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 429, "target": 1649, "key": "2c26e5b5491b4b36274d95d13000d91e"}, {"line": 41463, "relation": "causesNoChange", "evidence": "However, the enhanced binding of Runx1t1 to the LAT2 promoter could not repress the LAT2 expression when the BV2 microglia cells were treated with HDACi, indicating that Runx1t1 requires HDACs to transcriptionally repress the expression of LAT2.In conclusion, it is suggested that Runx1t1 controls proliferation and the neurotoxic effect of microglia by epigenetically regulating Cdk4 and LAT2 via its interaction with HDACs.", "citation": {"db": "PubMed", "db_id": "24586690"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "source": 1646, "target": 3665, "key": "310d52fc5a23b6db88f78d0350952886"}, {"relation": "partOf", "source": 3665, "target": 1646, "key": "63bc03df63dddcdc70bafb5ddc1369f4"}, {"line": 41483, "relation": "association", "evidence": "Therefore, we examined neurodegeneration in double knockout (DKO) mice of ganglioside GM2/GD2 synthase (B4GANLT1) and GD3 synthase (ST8SIA1) genes to clarify roles of gangliosides in the spinal cord.Motor neuron function was examined by gait analysis, and sensory function was analyzed by von Frey test.", "citation": {"db": "PubMed", "db_id": "24673754"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true, "Motor Neurons": true}}, "source": 4078, "target": 88, "key": "a5e12ae2af9da2c4c1d55210e03e830e"}, {"line": 41483, "relation": "association", "evidence": "Therefore, we examined neurodegeneration in double knockout (DKO) mice of ganglioside GM2/GD2 synthase (B4GANLT1) and GD3 synthase (ST8SIA1) genes to clarify roles of gangliosides in the spinal cord.Motor neuron function was examined by gait analysis, and sensory function was analyzed by von Frey test.", "citation": {"db": "PubMed", "db_id": "24673754"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Spinal Cord": true, "Motor Neurons": true}}, "source": 88, "target": 4078, "key": "712abcd0fd995a68c673a9c78bd0399d"}, {"line": 41523, "relation": "increases", "evidence": "S100A9 knockout decreases the memory impairment and neuropathology in crossbreed mice of Tg2576 and S100A9 knockout mice model.", "citation": {"db": "PubMed", "db_id": "24586443"}, "annotations": {"Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 3714, "target": 820, "key": "89fc13fb95c98e4fb9c7331f7c03852f"}, {"line": 43760, "relation": "decreases", "evidence": "These results clearly show that the upregulation of the S100a9 gene plays an important role in the neuropathology and memory impairment in AD, suggesting that the knockdown and knockout of this gene have a great therapeutic potential for AD.", "citation": {"db": "PubMed", "db_id": "22301734"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Toll like receptor subgraph": true}}, "source": 3714, "target": 820, "key": "93039fb69ae5842927a1c80f9a1fdadf"}, {"line": 41545, "relation": "increases", "evidence": "In addition, experiments have shown that knockdown of S100A9 expression improves cognition function in AD model mice (Tg2576), and these animals exhibit reduced amyloid plaque burden.", "citation": {"db": "PubMed", "db_id": "24586443"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3714, "target": 3881, "key": "f761a755fdf3d43450c5520026956439"}, {"line": 41556, "relation": "association", "evidence": "In this study, we established a new transgenic animal model of AD by crossbreeding the Tg2576 mouse with the S100A9 knockout (KO) mouse.", "citation": {"db": "PubMed", "db_id": "24586443"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Confidence": {"High": true}}, "source": 3714, "target": 3881, "key": "c6bf46dfa44956cd876e4ae6560e1151"}, {"line": 43773, "relation": "increases", "evidence": "These results suggest that S100a9 induced by Abeta or CT contributes to cause inflammation, which then affects the neuropathology including amyloid plaques burden and impairs cognitive function.", "citation": {"db": "PubMed", "db_id": "20098622"}, "annotations": {"Species": {"10090": true}, "Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Toll like receptor subgraph": true}}, "source": 3714, "target": 3815, "key": "a3b01f3e7bdc73cec0ae717a48cab62d"}, {"line": 41532, "relation": "positiveCorrelation", "evidence": "Our previous study presented evidence that the inflammation-related S100A9 gene is significantly upregulated in the brains of Alzheimer's disease (AD) animal models and human AD patients.", "citation": {"db": "PubMed", "db_id": "24586443"}, "annotations": {"Species": {"9606": true}, "MeSHDisease": {"Inflammation": true, "Plaque, Amyloid": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Medium": true}}, "source": 3334, "target": 3823, "key": "a35199cd86ee4eaf0d0c054df9c2f69b"}, {"line": 43715, "relation": "positiveCorrelation", "evidence": "Interestingly, S100A9/Mrp14 expression was also increased in the brains of AD mice and patients with AD (Chang et al., 2012) and contributes to cause inflammation, which then affects the neuropathology including amyloid plaques burden and impairs cognitive function (Ha et al., 2010).", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "source": 3334, "target": 3823, "key": "05d9b5d3671ca26ebd90f1d1f1968148"}, {"line": 43738, "relation": "increases", "evidence": "Evidence indicates that S100A9 contributes to Alzheimer's disease (AD) pathology, although the precise mechanisms are not clear.", "citation": {"db": "PubMed", "db_id": "23721320"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3334, "target": 3823, "key": "318a40f638fd34eb4d33ee1b3a4c4d87"}, {"line": 43704, "relation": "increases", "evidence": "An elevated level of S100A9/Mrp14 on Abeta amyloid fibril deposits induces further inflammation around amyloid fibril deposits and drives microglia into a proinflammatory state thereby compromising microglial phagocytosis (Kummer et al., 2012). ", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3334, "target": 3815, "key": "d9cd978c494916d6932ecc0fc83c94a4"}, {"line": 43716, "relation": "increases", "evidence": "Interestingly, S100A9/Mrp14 expression was also increased in the brains of AD mice and patients with AD (Chang et al., 2012) and contributes to cause inflammation, which then affects the neuropathology including amyloid plaques burden and impairs cognitive function (Ha et al., 2010).", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Toll like receptor subgraph": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "source": 3334, "target": 3815, "key": "9bb16dd2b79c1cc9c5fe9f830ee50dce"}, {"line": 43705, "relation": "increases", "evidence": "An elevated level of S100A9/Mrp14 on Abeta amyloid fibril deposits induces further inflammation around amyloid fibril deposits and drives microglia into a proinflammatory state thereby compromising microglial phagocytosis (Kummer et al., 2012). ", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3334, "target": 609, "key": "15f63d77128c9ba41ce52fa147ac9ec6"}, {"line": 43748, "relation": "increases", "evidence": "Pro-inflammatory S100A9 protein is increasingly recognized as an important contributor to inflammation-related neurodegeneration. Here, we provide insights into S100A9 specific mechanisms of action in Alzheimer's disease (AD). Due to its inherent amyloidogenicity S100A9 contributes to amyloid plaque formation together with Abeta.", "citation": {"db": "PubMed", "db_id": "24240735"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Toll like receptor subgraph": true}}, "source": 3334, "target": 80, "key": "2b0c2318aee97a81d6b47f8c5f63d571"}, {"line": 43784, "relation": "increases", "evidence": "We therefore conclude that Mrp14 promotes APP processing and Abeta accumulation under neuroinflammatory conditions.", "citation": {"db": "PubMed", "db_id": "23223301"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Toll like receptor subgraph": true}}, "source": 3334, "target": 80, "key": "5f4d6ecc4d6f546cad3551fb99e26d68"}, {"line": 41579, "relation": "association", "evidence": "Ccl2 affects beta-amyloidosis and progressive neurocognitive dysfunction in a mouse model of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true, "Alzheimer Disease": true}, "Species": {"10090": true}, "Confidence": {"High": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Chemokine signaling subgraph": true}}, "source": 3602, "target": 3889, "key": "3465460d4fe2380427dbe1cb28d6b4ec"}, {"relation": "partOf", "source": 3602, "target": 1641, "key": "d6b6762e657cf7aeb99e10cbf9fd0ace"}, {"line": 41620, "relation": "association", "evidence": "To this end, we now report that Ccl2 deficiency influences behavioral abnormalities and disease progression in Abeta precursor protein/presenilin-1 double-transgenic mice.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Activity"}, "source": 3602, "target": 3703, "key": "e80369e74b46ece8f5ded45f4eed8cdd"}, {"line": 41607, "relation": "association", "evidence": "One important control for such cell activation is through the CC-chemokine ligand 2 (Ccl2) and its receptor, the CC-chemokine receptor 2. Both affect microglia and peripheral macrophage immune responses and for the latter, cell ingress across the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Macrophages": true, "Blood-Brain Barrier": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1641, "target": 600, "key": "c66d54bfafe2bda819e0ac89fa0890ec"}, {"line": 41608, "relation": "association", "evidence": "One important control for such cell activation is through the CC-chemokine ligand 2 (Ccl2) and its receptor, the CC-chemokine receptor 2. Both affect microglia and peripheral macrophage immune responses and for the latter, cell ingress across the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Macrophages": true, "Blood-Brain Barrier": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 1641, "target": 609, "key": "8a2db99d425cadc909e08cc81e92debf"}, {"relation": "partOf", "source": 3604, "target": 1641, "key": "e4f84aa62c2a4d4e9702c565421697ea"}, {"line": 41607, "relation": "association", "evidence": "One important control for such cell activation is through the CC-chemokine ligand 2 (Ccl2) and its receptor, the CC-chemokine receptor 2. Both affect microglia and peripheral macrophage immune responses and for the latter, cell ingress across the blood-brain barrier.", "citation": {"db": "PubMed", "db_id": "23040664"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Macrophages": true, "Blood-Brain Barrier": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 600, "target": 1641, "key": "48d268f11235b207bbe1c1b02d099e37"}, {"line": 41645, "relation": "association", "evidence": "Finally, we discussed the possible impact of Cr1 on the pathogenesis of AD including amyloid-beta pathology, tauopathy, immune dysfunction and glial-mediated neuroinflammation.", "citation": {"db": "PubMed", "db_id": "24794147"}, "annotations": {"MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Neuroglia": true}, "Confidence": {"Medium": true}}, "source": 3614, "target": 3931, "key": "eb0ed26e44726fccf27846d390845bb2"}, {"line": 41656, "relation": "association", "evidence": "We hope that a more comprehensive understanding of the role that Cr1 played in AD may lead to the development of novel therapeutics for the prevention and treatment of AD.", "citation": {"db": "PubMed", "db_id": "24794147"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3614, "target": 3823, "key": "1efca5abdf7123f06b9d0b2f4297495b"}, {"line": 41675, "relation": "association", "evidence": "Induction of apoptosis in human and rat glioma by agonists of the nuclear receptor PPARgamma.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"MeSHDisease": {"Glioma": true}, "Species": {"9606": true, "10116": true}, "Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3911, "target": 3699, "key": "19047b676026db38d133224fb63b455b"}, {"line": 41699, "relation": "association", "evidence": "We report the effect of three structurally different PPARgamma agonists inducing apoptosis in human (U87MG and A172) and rat (C6) glioma cells.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Species": {"9606": true, "10116": true}, "CellLine": {"A172": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"High": true}}, "source": 3911, "target": 3699, "key": "2423c1632b4b0519aef0c75f54a358d3"}, {"line": 41763, "relation": "association", "evidence": "Taken together, treatment of glioma cells with PPARgamma agonists may hold therapeutic potential for the treatment of gliomas.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"MeSHDisease": {"Glioma": true}, "Confidence": {"Medium": true}}, "source": 3911, "target": 3699, "key": "483a75f3d176143a8d2cfef3546ff492"}, {"line": 41686, "relation": "association", "evidence": "Malignant astrocytomas are among the most common brain tumours and few therapeutic options exist.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "MeSHDisease": {"Astrocytoma": true, "Brain Neoplasms": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3893, "target": 3832, "key": "19d5a67c663d0bd302f201a73525f4dc"}, {"line": 41709, "relation": "decreases", "evidence": "The PPARgamma agonists ciglitazone, LY171 833 and prostaglandin-J2, but not the PPARalpha agonist WY14643, inhibited proliferation and induced cell death.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 164, "target": 3699, "key": "7f2b0be79ac05d3de144226b0a18936a"}, {"line": 41710, "relation": "decreases", "evidence": "The PPARgamma agonists ciglitazone, LY171 833 and prostaglandin-J2, but not the PPARalpha agonist WY14643, inhibited proliferation and induced cell death.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 232, "target": 3699, "key": "69613d679c3a088ba59e36ab313098d5"}, {"line": 41744, "relation": "association", "evidence": "Furthermore, inhibition of Bax expression by specific antisense oligonucleotides protected glioma cells against PPARgamma-mediated apoptotic process, indicating an essential role of Bax in PPARgamma-induced apoptotic process.", "citation": {"db": "PubMed", "db_id": "12065618"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true, "Bcl-2 subgraph": true}, "MeSHDisease": {"Glioma": true}, "Confidence": {"Medium": true}}, "source": 4036, "target": 478, "key": "ff633afabae63d6cd518ab69b3990f03"}, {"line": 41828, "relation": "positiveCorrelation", "evidence": "This increment of CysLT1R expression was accompanied by increases of inflammatory factors such as NF-κB p65, tumor necrosis factor-α (TNFα) and interleukin-1beta (IL-1beta) as well as pro-apoptotic protein Caspase-3 activation and anti-apoptotic process protein Bcl-2 reduction.", "citation": {"db": "PubMed", "db_id": "24879954"}, "annotations": {"Subgraph": {"G-protein-mediated signaling": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 3685, "target": 4044, "key": "ea47141e671498d8336ae46babcd25eb"}, {"line": 42708, "relation": "regulates", "evidence": "Nuclear factor-kappa B (NF-κB), a critical transcriptional factor regulating neuroinflammation, was much lower, but activation of signal transducer and activator of transcription 3 (STAT3), which plays a crucial role in cell survival and proliferation, was much higher in IL-32α-overexpressing mice brain compared to those of wild-type mice brain.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3685, "target": 3920, "key": "4aaf43e37554a0c06352236f99af83e1"}, {"line": 42745, "relation": "decreases", "evidence": "These results suggest that IL-32α can prevent cerebral ischemia damage via upregulation of anti-neuroinflammatory cytokine expression and STAT3 activation, but downregulation of neuroinflammatory cytokines and NF-κB activation.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Brain Ischemia": true}, "Confidence": {"High": true}}, "source": 3685, "target": 3831, "key": "4a46bb690a0e6c34b16f62a72df64ff6"}, {"line": 41913, "relation": "increases", "evidence": "Effects of Naproxen on Immune Responses in a Colchicine-Induced Rat Model of Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 234, "target": 3823, "key": "956326de5dab61e0a3112b83124c9144"}, {"line": 41916, "relation": "association", "evidence": "Effects of Naproxen on Immune Responses in a Colchicine-Induced Rat Model of Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 234, "target": 3823, "key": "1b655bd470704fb2b083ac19002ea818"}, {"line": 41915, "relation": "association", "evidence": "Effects of Naproxen on Immune Responses in a Colchicine-Induced Rat Model of Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 234, "target": 310, "key": "e612c25025d014446484b4f69273515f"}, {"line": 41963, "relation": "association", "evidence": "Alterations in immunological parameters [increased phagocytic activity of white blood cells and splenic polymorphonuclear cells (PMN), increased cytotoxicity and decreased leucocyte adhesive inhibition index (LAI) of splenic mononuclear cells (MNC)] were also observed in colchicine-injected rats, which showed a dose-dependent recovery after oral administration of naproxen in AD rats.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Leukocytes": true}}, "source": 234, "target": 310, "key": "a7e16845a7291d70f661b06921b88256"}, {"line": 41932, "relation": "increases", "evidence": "The participation of the immune system in the neurodegeneration in a rat model of colchicine-induced AD has not been explored.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Species": {"10116": true}}, "source": 234, "target": 3881, "key": "52bbf6f46dfca15989dedd8ddd1664ac"}, {"line": 41933, "relation": "association", "evidence": "The participation of the immune system in the neurodegeneration in a rat model of colchicine-induced AD has not been explored.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Species": {"10116": true}}, "source": 234, "target": 3881, "key": "879d3144b7291190cdefa28ba3fa92a4"}, {"line": 41951, "relation": "increases", "evidence": "Results: Chromatolysis and amyloid plaques were found along with higher ROS, nitrite and TNF-α levels in the hippocampus of colchicine-induced AD rats, and these changes were prevented by naproxen in a dose-dependent manner.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 234, "target": 3881, "key": "7853adb4266be189bad024ec176eb335"}, {"line": 41962, "relation": "association", "evidence": "Alterations in immunological parameters [increased phagocytic activity of white blood cells and splenic polymorphonuclear cells (PMN), increased cytotoxicity and decreased leucocyte adhesive inhibition index (LAI) of splenic mononuclear cells (MNC)] were also observed in colchicine-injected rats, which showed a dose-dependent recovery after oral administration of naproxen in AD rats.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Leukocytes": true}}, "source": 234, "target": 283, "key": "87fca22d709c2580006a66402ea6f0e2"}, {"line": 41914, "relation": "association", "evidence": "Effects of Naproxen on Immune Responses in a Colchicine-Induced Rat Model of Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 310, "target": 3823, "key": "8a72f6c2a5ad6ac83850e1a59bb7b9d7"}, {"line": 41915, "relation": "association", "evidence": "Effects of Naproxen on Immune Responses in a Colchicine-Induced Rat Model of Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 310, "target": 234, "key": "0d26abe0d444e136d5967cdecc424d6b"}, {"line": 41963, "relation": "association", "evidence": "Alterations in immunological parameters [increased phagocytic activity of white blood cells and splenic polymorphonuclear cells (PMN), increased cytotoxicity and decreased leucocyte adhesive inhibition index (LAI) of splenic mononuclear cells (MNC)] were also observed in colchicine-injected rats, which showed a dose-dependent recovery after oral administration of naproxen in AD rats.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Leukocytes": true}}, "source": 310, "target": 234, "key": "4faf3c8b06bf84c5221820feedf40799"}, {"line": 41942, "relation": "association", "evidence": "Methods: In the present study, hippocampal neurodegeneration along with reactive oxygen species (ROS), nitrite and TNF-α in the hippocampus and some systemic immune responses were measured after 15 and 21 days of intracerebroventricular colchicine injection in rats and again after oral administration of different doses of the anti-inflammatory drug naproxen in AD rats.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 310, "target": 3881, "key": "22c3d636e65124918b88b0b384fb4dcd"}, {"line": 41962, "relation": "association", "evidence": "Alterations in immunological parameters [increased phagocytic activity of white blood cells and splenic polymorphonuclear cells (PMN), increased cytotoxicity and decreased leucocyte adhesive inhibition index (LAI) of splenic mononuclear cells (MNC)] were also observed in colchicine-injected rats, which showed a dose-dependent recovery after oral administration of naproxen in AD rats.", "citation": {"db": "PubMed", "db_id": "24662962"}, "annotations": {"Species": {"10116": true}, "MeSHDisease": {"Plaque, Amyloid": true}, "MeSHAnatomy": {"Leukocytes": true}}, "source": 283, "target": 234, "key": "087d920223f634454812b96922c82c3a"}, {"line": 41978, "relation": "association", "evidence": "Cannabinoid receptor subtype 2 (CB2) has been shown to be up-regulated in activated microglia and therefore plays an important role in neuroinflammatory and neurodegenerative diseases such as multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neurodegenerative Diseases": true, "Amyotrophic Lateral Sclerosis": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3613, "target": 3874, "key": "762b104b6c6aaaed368c8c998671fa7f"}, {"line": 41979, "relation": "association", "evidence": "Cannabinoid receptor subtype 2 (CB2) has been shown to be up-regulated in activated microglia and therefore plays an important role in neuroinflammatory and neurodegenerative diseases such as multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neurodegenerative Diseases": true, "Amyotrophic Lateral Sclerosis": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3613, "target": 3823, "key": "8a143dc2b47534eef0cac92822b89719"}, {"line": 41980, "relation": "association", "evidence": "Cannabinoid receptor subtype 2 (CB2) has been shown to be up-regulated in activated microglia and therefore plays an important role in neuroinflammatory and neurodegenerative diseases such as multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neurodegenerative Diseases": true, "Amyotrophic Lateral Sclerosis": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3613, "target": 3869, "key": "1b653cf6b22e602843c2fdfbef79cdf0"}, {"line": 41981, "relation": "association", "evidence": "Cannabinoid receptor subtype 2 (CB2) has been shown to be up-regulated in activated microglia and therefore plays an important role in neuroinflammatory and neurodegenerative diseases such as multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHDisease": {"Multiple Sclerosis": true, "Neurodegenerative Diseases": true, "Amyotrophic Lateral Sclerosis": true, "Alzheimer Disease": true}, "MeSHAnatomy": {"Microglia": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3613, "target": 3825, "key": "90d2ea7dd87582ec930fd6a3cd82e997"}, {"relation": "partOf", "source": 3613, "target": 992, "key": "741078e42d5992a8b91ce88343ec3a90"}, {"line": 42959, "relation": "association", "evidence": "The endocannabinoid system is composed by a number of cannabinoid receptors, including the well-characterized CB1 and CB2 receptors, with their endogenous ligands and the enzymes related to the synthesis and degradation of these endocannabinoid compounds.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3613, "target": 249, "key": "0f542dd0a1c5ee1ff38e926346265bbd"}, {"line": 42968, "relation": "decreases", "evidence": "Several findings indicate that the activation of both CB1 and CB2 receptors by natural or synthetic agonists, at non-psychoactive doses, have beneficial effects in Alzheimer experimental models by reducing the harmful beta-amyloid peptide action and tau phosphorylation, as well as by promoting the brain's intrinsic repair mechanisms.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3613, "target": 2328, "key": "cc3cdb3f5e756471da9d534e7b45edc7"}, {"line": 42970, "relation": "decreases", "evidence": "Several findings indicate that the activation of both CB1 and CB2 receptors by natural or synthetic agonists, at non-psychoactive doses, have beneficial effects in Alzheimer experimental models by reducing the harmful beta-amyloid peptide action and tau phosphorylation, as well as by promoting the brain's intrinsic repair mechanisms.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3613, "target": 3676, "key": "447aed48adce6d4ed51255af18853698"}, {"relation": "partOf", "source": 396, "target": 992, "key": "ddd8e46b98c12e40b65ca498c8dd5a3a"}, {"line": 41992, "relation": "increases", "evidence": "In vitro autoradiography of rat and mouse spleen slices, as spleen expresses a high physiological expression of CB2 receptors, demonstrated that [11C]KP23 exhibits specific binding towards CB2.", "citation": {"db": "PubMed", "db_id": "24662272"}, "annotations": {"MeSHAnatomy": {"Spleen": true}, "Species": {"10090": true, "10116": true}, "Confidence": {"Medium": true}}, "source": 396, "target": 992, "key": "abd3e169c1b0ee9a19f6c091386175af"}, {"line": 42072, "relation": "decreases", "evidence": "Additionally, CDT also inhibited the increase of TNF-α and IL-6 level, and increased the expression of choline acetyltransferase (ChAT), receptor of activated protein kinase C1 (RACK1) and brain-derived neurotrophic factor (BDNF) in brain as compared to model mice.", "citation": {"db": "PubMed", "db_id": "24422705"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "MeSHDisease": {"Memory Disorders": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 413, "target": 3741, "key": "8e4c3b73b3b2b2c1b45f22d56da96ec8"}, {"line": 42073, "relation": "decreases", "evidence": "Additionally, CDT also inhibited the increase of TNF-α and IL-6 level, and increased the expression of choline acetyltransferase (ChAT), receptor of activated protein kinase C1 (RACK1) and brain-derived neurotrophic factor (BDNF) in brain as compared to model mice.", "citation": {"db": "PubMed", "db_id": "24422705"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "MeSHDisease": {"Memory Disorders": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 413, "target": 3662, "key": "9714007e9c3bcaf1ceacda5065159a53"}, {"line": 42074, "relation": "increases", "evidence": "Additionally, CDT also inhibited the increase of TNF-α and IL-6 level, and increased the expression of choline acetyltransferase (ChAT), receptor of activated protein kinase C1 (RACK1) and brain-derived neurotrophic factor (BDNF) in brain as compared to model mice.", "citation": {"db": "PubMed", "db_id": "24422705"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "MeSHDisease": {"Memory Disorders": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 413, "target": 3611, "key": "c94e914d62c163411d68de236f6482ef"}, {"line": 42075, "relation": "increases", "evidence": "Additionally, CDT also inhibited the increase of TNF-α and IL-6 level, and increased the expression of choline acetyltransferase (ChAT), receptor of activated protein kinase C1 (RACK1) and brain-derived neurotrophic factor (BDNF) in brain as compared to model mice.", "citation": {"db": "PubMed", "db_id": "24422705"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "MeSHDisease": {"Memory Disorders": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 413, "target": 3707, "key": "6e6bed1ffad5cd2c7c81990550d6f7e4"}, {"line": 42076, "relation": "increases", "evidence": "Additionally, CDT also inhibited the increase of TNF-α and IL-6 level, and increased the expression of choline acetyltransferase (ChAT), receptor of activated protein kinase C1 (RACK1) and brain-derived neurotrophic factor (BDNF) in brain as compared to model mice.", "citation": {"db": "PubMed", "db_id": "24422705"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "MeSHDisease": {"Memory Disorders": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 413, "target": 3599, "key": "c1a799a248c398b36faaf5f93be0db9e"}, {"line": 43646, "relation": "increases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3662, "target": 577, "key": "58e8d3d0b1a24a70cb35816ea995de0f"}, {"line": 42094, "relation": "decreases", "evidence": "Toll-like 4 receptor inhibitor TAK-242 decreases neuroinflammation in rat brain frontal cortex after stress.", "citation": {"db": "PubMed", "db_id": "24410883"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 415, "target": 3815, "key": "f38f6a39d91396c6617fdf4395adac63"}, {"line": 42100, "relation": "decreases", "evidence": "TAK-242 pre-stress administration prevents the accumulation of potentially deleterious inflammatory and oxidative/nitrosative mediators in the brain frontal cortex of rats.The use of TAK-242 or other TLR-4 signalling pathway inhibitory compounds could be considered as a potential therapeutic adjuvant strategy to constrain the inflammatory process taking place after stress exposure and in stress-related neuropsychiatric diseases.", "citation": {"db": "PubMed", "db_id": "24410883"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Toll like receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 415, "target": 3920, "key": "78c8b80377590c443fd0efe2e0e9ab41"}, {"line": 42138, "relation": "association", "evidence": "Four weeks after rAAV2-IL-1beta transduction, we found significant reductions in 6E10 and Congo red staining of amyloid plaques that was confirmed by decreased levels of insoluble Abeta1-42 and Abeta1-40 in the inflamed hippocampus.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 22, "target": 2183, "key": "a0370f28c1983d06daf4cd33f072b9b4"}, {"line": 42139, "relation": "association", "evidence": "Four weeks after rAAV2-IL-1beta transduction, we found significant reductions in 6E10 and Congo red staining of amyloid plaques that was confirmed by decreased levels of insoluble Abeta1-42 and Abeta1-40 in the inflamed hippocampus.", "citation": {"db": "PubMed", "db_id": "24874542"}, "annotations": {"MeSHDisease": {"Plaque, Amyloid": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Amyloidogenic subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Confidence": {"High": true}}, "source": 22, "target": 3881, "key": "f4635bf3d0726ac67436f65ce1b5c344"}, {"line": 42174, "relation": "association", "evidence": "Although neuropeptides such as bradykinin (BK), somatostatin (Sst), and endothelin (ET) are known to be important mediators of inflammation in the periphery, evidence of a similar function in brain is scarce.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 353, "target": 3920, "key": "dcf53a4b7470cf140b4db65e2cbda13f"}, {"line": 42175, "relation": "association", "evidence": "Although neuropeptides such as bradykinin (BK), somatostatin (Sst), and endothelin (ET) are known to be important mediators of inflammation in the periphery, evidence of a similar function in brain is scarce.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3722, "target": 3920, "key": "075896fa40bedf65a36c211e1fb59166"}, {"line": 42176, "relation": "association", "evidence": "Although neuropeptides such as bradykinin (BK), somatostatin (Sst), and endothelin (ET) are known to be important mediators of inflammation in the periphery, evidence of a similar function in brain is scarce.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"Confidence": {"High": true}, "MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 2171, "target": 3920, "key": "16828f3f96ff5dec885bf642f6a3f13d"}, {"line": 42193, "relation": "decreases", "evidence": "ET decreased the Abeta-induced expression of monocyte chemoattractant protein 1 and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"MeSHAnatomy": {"Monocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2171, "target": 4037, "key": "ede79d3644ecd917e35cbc3404b26cad"}, {"line": 42194, "relation": "decreases", "evidence": "ET decreased the Abeta-induced expression of monocyte chemoattractant protein 1 and interleukin-6.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"MeSHAnatomy": {"Monocytes": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 2171, "target": 4052, "key": "ab64d65b83e9d33bafcfe47674e376e9"}, {"line": 42185, "relation": "decreases", "evidence": "In addition, BK reduced Abeta-induced expression of proinflammatory genes including iNOS and COX-2.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3664, "target": 3689, "key": "d0d020323f1c2d41041fbf306aa8da4e"}, {"line": 42186, "relation": "decreases", "evidence": "In addition, BK reduced Abeta-induced expression of proinflammatory genes including iNOS and COX-2.", "citation": {"db": "PubMed", "db_id": "20937084"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3664, "target": 3706, "key": "ac0710f85d2fc2b58ebaef4dca489447"}, {"line": 42545, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 3706, "target": 351, "key": "f6f92a77817fe89d85d85dc858877279"}, {"line": 42549, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 3706, "target": 325, "key": "41231678adb3ac7d245770c7f6c83a70"}, {"line": 42579, "relation": "association", "evidence": "Norepinephrine enhances the LPS-induced expression of COX-2 and secretion of PGE2 in primary rat microglia.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Microglia": true, "Bodily Secretions": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 3706, "target": 317, "key": "cfa806ccd3947fec2c08c67562b8066a"}, {"line": 42604, "relation": "association", "evidence": "In the present study, we investigate the role of norepinephrine on cyclooxygenase- (COX-)2 expression/synthesis and prostaglandin (PG)E2 production in rat primary microglia.Interestingly, norepinephrine increased COX-2 mRNA, but not protein expression.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10116": true}}, "source": 3706, "target": 317, "key": "8cf9f0011a18c8a9ebb28330c38a916a"}, {"line": 42619, "relation": "association", "evidence": "Norepinephrine strongly enhanced COX-2 expression and PGE2 production induced by lipopolysaccharide (LPS).", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 3706, "target": 317, "key": "3219514445b1db764c74b9dd7186ad80"}, {"line": 42581, "relation": "association", "evidence": "Norepinephrine enhances the LPS-induced expression of COX-2 and secretion of PGE2 in primary rat microglia.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Microglia": true, "Bodily Secretions": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 3706, "target": 291, "key": "337378ddd0921ba996854be0d96eefbe"}, {"line": 42607, "relation": "association", "evidence": "In the present study, we investigate the role of norepinephrine on cyclooxygenase- (COX-)2 expression/synthesis and prostaglandin (PG)E2 production in rat primary microglia.Interestingly, norepinephrine increased COX-2 mRNA, but not protein expression.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10116": true}}, "source": 3706, "target": 150, "key": "438d18a38762ccd2538acb0186098280"}, {"line": 42698, "relation": "decreases", "evidence": "Reactive oxygen species generation and lipid peroxidation as well as expression of inducible nitric oxide and cyclooxygenase-2 were also reduced in the IL-32α mice brain.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Prostaglandin subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3706, "target": 2890, "key": "1066f699845bc04ad1471f68c62b7808"}, {"line": 42212, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 3650, "target": 342, "key": "20c000987f4f2c566d82073285f8c2b4"}, {"line": 42241, "relation": "association", "evidence": "GRP78 functions cytoprotectively in stressed cells, therefore, we hypothesized that GRP78 mediated rifampicin-induced neuroprotection.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3650, "target": 342, "key": "cf96b09811098165fa20831e765fca0b"}, {"line": 42245, "relation": "decreases", "evidence": "Using RNA interference, we found that GRP78 gene knockdown significantly attenuated the neuroprotective effects of rifampicin.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Chaperone subgraph": true}}, "source": 3650, "target": 342, "key": "ea347db350a08283a0978688ee8cac78"}, {"line": 42278, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3650, "target": 342, "key": "15640825e18569f90c9708ea914caa0f"}, {"line": 42216, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 3650, "target": 346, "key": "d0a478d546443a4807ee85048167b428"}, {"line": 42279, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3650, "target": 346, "key": "bfbe20d03d57ad3ddd75f8cd480a6389"}, {"line": 42280, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3650, "target": 3634, "key": "943e8bee4339d6bc31fec758ce7fccc5"}, {"line": 42210, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 881, "target": 342, "key": "fe36b63dbca39e5170d4067b9f1d6da4"}, {"line": 42276, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 881, "target": 342, "key": "ddebfac314adf16ca4893eda3c1cdbd7"}, {"line": 42214, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 881, "target": 346, "key": "37a12ce76cc1336d760b4de9fe1b5b2a"}, {"line": 42211, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 3634, "target": 342, "key": "ae7083f35f5b42cbd9733285595dfd49"}, {"line": 42253, "relation": "increases", "evidence": "Our results showed that PERK, eukaryotic initiation factor 2α (eIF2α), and activating transcription factor 4 (ATF4) were activated in rifampicin-treated PC12 cells.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}}, "source": 3634, "target": 342, "key": "1fcb13b9335b9ec3c9e3eeb12cb4c257"}, {"line": 42277, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3634, "target": 342, "key": "c4cdda8aa0abbefd5f0e6fdaf62ab728"}, {"line": 42215, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 3634, "target": 346, "key": "954ed51739ce518b0435b32597eb3ea9"}, {"line": 42280, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 3634, "target": 3650, "key": "b6a9ffdb940dda6341fd5dd5226b4d04"}, {"line": 42213, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 346, "target": 3590, "key": "c2b5d0195f0e5d147d7d68d5da7ad0ce"}, {"line": 42214, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 346, "target": 881, "key": "cbfd69b6040dfe29ba3610efc803720a"}, {"line": 42215, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 346, "target": 3634, "key": "4b9313f50105b2457cce40e798418d34"}, {"line": 42216, "relation": "association", "evidence": "Rifampicin protects PC12 cells from rotenone-induced cytotoxicity by activating GRP78 via PERK-eIF2α-ATF4 pathway.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"CellLine": {"PC12": true}, "MeSHAnatomy": {"PC12 Cells": true}, "Subgraph": {"Unfolded protein response subgraph": true}}, "source": 346, "target": 3650, "key": "b2e86ecc63b924adb1cf21a00471e780"}, {"line": 42279, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 346, "target": 3650, "key": "a61539bcf95313462a455b5112247b0c"}, {"line": 42274, "relation": "association", "evidence": "Taken together, our data suggested that rifampicin induced GRP78 via the PERK-eIF2α-ATF4 pathway to protect neurons against rotenone-induced cell damage.", "citation": {"db": "PubMed", "db_id": "24638036"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 346, "target": 342, "key": "a6863d197353bd67e62416a96ea338c2"}, {"relation": "partOf", "source": 150, "target": 963, "key": "139ed8c135321ec9cd6e25e61fcc5398"}, {"line": 42367, "relation": "association", "evidence": "Interestingly, this treatment resulted in upregulation of protein or mRNA of CysLT1R in both hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Bcl-2 subgraph": true}}, "source": 150, "target": 3623, "key": "6c6f9cc6719930d8bb67da8949fa30fe"}, {"line": 42603, "relation": "association", "evidence": "In the present study, we investigate the role of norepinephrine on cyclooxygenase- (COX-)2 expression/synthesis and prostaglandin (PG)E2 production in rat primary microglia.Interestingly, norepinephrine increased COX-2 mRNA, but not protein expression.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10116": true}}, "source": 150, "target": 317, "key": "ea9bc74a43e1dec05f1a17e80baee8fd"}, {"line": 42606, "relation": "association", "evidence": "In the present study, we investigate the role of norepinephrine on cyclooxygenase- (COX-)2 expression/synthesis and prostaglandin (PG)E2 production in rat primary microglia.Interestingly, norepinephrine increased COX-2 mRNA, but not protein expression.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10116": true}}, "source": 150, "target": 335, "key": "a6b0389cc932681c6cb9fd13b2cf9218"}, {"line": 42607, "relation": "association", "evidence": "In the present study, we investigate the role of norepinephrine on cyclooxygenase- (COX-)2 expression/synthesis and prostaglandin (PG)E2 production in rat primary microglia.Interestingly, norepinephrine increased COX-2 mRNA, but not protein expression.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10116": true}}, "source": 150, "target": 3706, "key": "94ffe0c1b676be684959621ce280b71d"}, {"line": 42306, "relation": "increases", "evidence": "In this study, we first showed that heptapeptide XD4 activates the class A scavenger receptor (SR-A) on the glia by increasing the binding of Abeta to SR-A, thereby promoting glial phagocytosis of Abeta oligomer in microglia and astrocytes and triggering intracellular mitogen-activated protein kinase (MAPK) signaling cascades.", "citation": {"db": "PubMed Central", "db_id": "PMC3981768"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"Microglia": true, "Astrocytes": true, "Neuroglia": true}, "Confidence": {"Medium": true}}, "source": 408, "target": 949, "key": "234c103f15e84ff4366b9778a6a122a7"}, {"line": 42315, "relation": "decreases", "evidence": "Furthermore, XD4 significantly inhibits Abeta oligomer-induced cytotoxicity to glial cells and decreases the production of proinflammatory cytokines, such as TNF-α and IL-1beta, in vitro and in vivo.", "citation": {"db": "PubMed Central", "db_id": "PMC3981768"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Neuroglia": true}, "Confidence": {"Medium": true}}, "source": 408, "target": 3661, "key": "7dc3e1076e9489af4edad573f4da7427"}, {"line": 42320, "relation": "decreases", "evidence": "Furthermore, XD4 significantly inhibits Abeta oligomer-induced cytotoxicity to glial cells and decreases the production of proinflammatory cytokines, such as TNF-α and IL-1beta, in vitro and in vivo.", "citation": {"db": "PubMed Central", "db_id": "PMC3981768"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "MeSHAnatomy": {"Neuroglia": true}, "Confidence": {"Medium": true}}, "source": 408, "target": 3741, "key": "7ab73879ca103b1d7d44ab95480638f9"}, {"line": 42307, "relation": "increases", "evidence": "In this study, we first showed that heptapeptide XD4 activates the class A scavenger receptor (SR-A) on the glia by increasing the binding of Abeta to SR-A, thereby promoting glial phagocytosis of Abeta oligomer in microglia and astrocytes and triggering intracellular mitogen-activated protein kinase (MAPK) signaling cascades.", "citation": {"db": "PubMed Central", "db_id": "PMC3981768"}, "annotations": {"Subgraph": {"MAPK-ERK subgraph": true}, "MeSHAnatomy": {"Microglia": true, "Astrocytes": true, "Neuroglia": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 949, "target": 3680, "key": "ae575ee55d356c7c518d243d6672ccc9"}, {"relation": "partOf", "source": 3680, "target": 949, "key": "785b819c40e14a6e3932d73a57812f37"}, {"line": 42329, "relation": "association", "evidence": "Our findings may provide a novel strategy for AD treatment by activating SR-A.", "citation": {"db": "PubMed Central", "db_id": "PMC3981768"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3680, "target": 3823, "key": "e210bc1741ca014da773c17b050060de"}, {"line": 42354, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Bcl-2 subgraph": true}}, "source": 369, "target": 3597, "key": "cefaee70e4c6b6e82625a7e78b1cc777"}, {"line": 42356, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Caspase subgraph": true}}, "source": 369, "target": 3600, "key": "f0580945e39aa86edfdeedb0817cb21d"}, {"line": 42358, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Interleukin signaling subgraph": true}}, "source": 369, "target": 2183, "key": "3ba27731ea6ef17a2599171e1aa12699"}, {"line": 42360, "relation": "association", "evidence": "The data demonstrated that intracerebroventrical infusions of aggregated Abeta1-42 (410 pmol/mouse) produced deficits in learning ability and memory, as evidenced by increase in escape latency during acquisition trials and decreases in exploratory activities in the probe trial in Morris water maze (MWM) task, and by decrease in the number of correct choices and increase in latency to enter the shock-free compartment in Y-maze test, and caused significant increases in pro-inflammatory cytokines such as NF-κB p65, TNF-α and IL-1beta as well as pro-apoptotic molecule caspase-3 activation and anti-apoptotic protein Bcl-2 downregulation in hippocampus and cortex.", "citation": {"db": "PubMed", "db_id": "24456746"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 369, "target": 3741, "key": "99f9c360dcb1b02dc212f8184c0bffca"}, {"line": 42396, "relation": "causesNoChange", "evidence": "Exogenous application of VEGF can increase the permeability of the BBB without causing brain edema.", "citation": {"db": "PubMed", "db_id": "24551038"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Brain Edema": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3751, "target": 3828, "key": "ad6a1714fade0b57e596be37f4e8a4f0"}, {"line": 42397, "relation": "association", "evidence": "Exogenous application of VEGF can increase the permeability of the BBB without causing brain edema.", "citation": {"db": "PubMed", "db_id": "24551038"}, "annotations": {"Species": {"10090": true}, "MeSHDisease": {"Brain Edema": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Vascular endothelial growth factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3751, "target": 601, "key": "bd45c783a99834e67beebe8684aa7b3f"}, {"line": 42433, "relation": "association", "evidence": "The PPARgamma agonist pioglitazone and a novel selective PPARα/gamma modulator, DSP-8658, currently in clinical development for the treatment of type 2 diabetes, enhanced the microglial uptake of Abeta in a PPARgamma-dependent manner.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"Medium": true}}, "source": 414, "target": 333, "key": "0c8577990a87c9fb8bb2179247c1fda7"}, {"line": 42434, "relation": "association", "evidence": "The PPARgamma agonist pioglitazone and a novel selective PPARα/gamma modulator, DSP-8658, currently in clinical development for the treatment of type 2 diabetes, enhanced the microglial uptake of Abeta in a PPARgamma-dependent manner.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Disease": {"type 2 diabetes mellitus": true}, "Confidence": {"Medium": true}}, "source": 414, "target": 3850, "key": "59449938f58967f03159166eb7326933"}, {"line": 42454, "relation": "association", "evidence": "Evaluation of DSP-8658 in the amyloid precursor protein/presenilin 1 mouse model confirmed an increased microglial Abeta phagocytosis in vivo, which subsequently resulted in a reduction of cortical and hippocampal Abeta levels.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "MeSHAnatomy": {"Hippocampus": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 414, "target": 3703, "key": "fe34af278c8074c914df89862181bbd9"}, {"line": 42443, "relation": "positiveCorrelation", "evidence": "This PPARgamma-stimulated increase of Abeta phagocytosis was mediated by the upregulation of scavenger receptor CD36 expression.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3605, "target": 3699, "key": "a0fbae0e6ae478d1b33bba28fc7509d5"}, {"line": 42444, "relation": "increases", "evidence": "This PPARgamma-stimulated increase of Abeta phagocytosis was mediated by the upregulation of scavenger receptor CD36 expression.", "citation": {"db": "PubMed", "db_id": "23197723"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}, "Confidence": {"High": true}}, "source": 3605, "target": 823, "key": "1c2bd8951c6ef7e687739002084f16f5"}, {"line": 42472, "relation": "decreases", "evidence": "In transgenic mice, both insulin and GLP1 analogs reduced inflammation, increased stem cell proliferation, reduced apoptotic process, and increased dendritic growth.", "citation": {"db": "PubMed", "db_id": "24529526"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Dendrites": true, "Stem Cells": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}}, "source": 3663, "target": 3920, "key": "9c26aa6e258adc8b5f41dc072edb4bce"}, {"line": 42476, "relation": "decreases", "evidence": "In transgenic mice, both insulin and GLP1 analogs reduced inflammation, increased stem cell proliferation, reduced apoptotic process, and increased dendritic growth.", "citation": {"db": "PubMed", "db_id": "24529526"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Dendrites": true, "Stem Cells": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}}, "source": 3663, "target": 478, "key": "b0a842aeb81a1bff0471c01322a5ce9e"}, {"line": 42473, "relation": "decreases", "evidence": "In transgenic mice, both insulin and GLP1 analogs reduced inflammation, increased stem cell proliferation, reduced apoptotic process, and increased dendritic growth.", "citation": {"db": "PubMed", "db_id": "24529526"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Dendrites": true, "Stem Cells": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}}, "source": 3635, "target": 3920, "key": "d4af17526082f4a0c8b62ed4f7c45f78"}, {"line": 42477, "relation": "decreases", "evidence": "In transgenic mice, both insulin and GLP1 analogs reduced inflammation, increased stem cell proliferation, reduced apoptotic process, and increased dendritic growth.", "citation": {"db": "PubMed", "db_id": "24529526"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Dendrites": true, "Stem Cells": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}, "Subgraph": {"Glucagon subgraph": true, "Insulin signal transduction": true}}, "source": 3635, "target": 478, "key": "4b2cba7a6b1b2b56d0ec6c89b823d46e"}, {"line": 42489, "relation": "association", "evidence": "In this review we discussed the role of PET and MRI in evaluating the effect of GLP1 analogs in disease progression in both Alzheimer's and Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "24529526"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3635, "target": 3878, "key": "ebc1514baed02d99ec8882c4896368a8"}, {"line": 42490, "relation": "association", "evidence": "In this review we discussed the role of PET and MRI in evaluating the effect of GLP1 analogs in disease progression in both Alzheimer's and Parkinson's disease.", "citation": {"db": "PubMed", "db_id": "24529526"}, "annotations": {"MeSHDisease": {"Parkinson Disease": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3635, "target": 3823, "key": "b7a05ae3fbb08116b9bf3b8971edaee5"}, {"relation": "partOf", "source": 14, "target": 898, "key": "2d28d71d924d8467159e76d9a463db5e"}, {"relation": "partOf", "source": 174, "target": 968, "key": "dd4f85d05a0fc065e0ee7b3453e1b9ae"}, {"relation": "partOf", "source": 276, "target": 969, "key": "003805019135df0834e10e950c5dd24e"}, {"line": 42542, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 224, "target": 73, "key": "0645ee9e8c6fff2719098cfa5b294066"}, {"line": 42544, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 3697, "target": 351, "key": "ce60fb72fb1d385de46ef208ce45d4b8"}, {"line": 42548, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 3697, "target": 325, "key": "50a7da42282724559a00406141df47c2"}, {"line": 43283, "relation": "increases", "evidence": "Although an inflammatory response can be induced by many different means, phospholipases, such as cytosolic/ phospholipase A(2) (cPLA(2)), may play an important role in the production of inflammatory mediators and in the production/ of other potential second messengers. cPLA(2) hydrolyzes membrane phospholipids and its activity liberates free fatty acids/ leading directly to the production of eicosanoids.", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3697, "target": 577, "key": "cf048439b89bf4040b54b95320961fa5"}, {"line": 43284, "relation": "increases", "evidence": "Although an inflammatory response can be induced by many different means, phospholipases, such as cytosolic/ phospholipase A(2) (cPLA(2)), may play an important role in the production of inflammatory mediators and in the production/ of other potential second messengers. cPLA(2) hydrolyzes membrane phospholipids and its activity liberates free fatty acids/ leading directly to the production of eicosanoids.", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3697, "target": 330, "key": "e4687ba3d864007fe1635226b5c92eb9"}, {"line": 43285, "relation": "increases", "evidence": "Although an inflammatory response can be induced by many different means, phospholipases, such as cytosolic/ phospholipase A(2) (cPLA(2)), may play an important role in the production of inflammatory mediators and in the production/ of other potential second messengers. cPLA(2) hydrolyzes membrane phospholipids and its activity liberates free fatty acids/ leading directly to the production of eicosanoids.", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Eicosanoids signaling subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3697, "target": 280, "key": "87127cdcbcf6b8f7268ca4f65b25b555"}, {"line": 43293, "relation": "association", "evidence": "We show that in every condition evaluated, cytosolic phospholipase A(2) is present in reactive glial cells/ within the precise region of neuron loss. In conditions where neurons did not degenerate or are protected from death,/ cytosolic phospholipase A(2) is not observed. Both astrocytes and microglial cells are immunoreactive for cytosolic/ phospholipase A(2) following injury, with astrocytes being the most consistent cell type expressing cytosolic/ phospholipase A(2).", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Eicosanoids signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3697, "target": 648, "key": "413f5ae47ea62814bd7a0ef4d917de0c"}, {"line": 43297, "relation": "increases", "evidence": "We show that in every condition evaluated, cytosolic phospholipase A(2) is present in reactive glial cells/ within the precise region of neuron loss. In conditions where neurons did not degenerate or are protected from death,/ cytosolic phospholipase A(2) is not observed. Both astrocytes and microglial cells are immunoreactive for cytosolic/ phospholipase A(2) following injury, with astrocytes being the most consistent cell type expressing cytosolic/ phospholipase A(2).", "citation": {"db": "PubMed", "db_id": "10417811"}, "annotations": {"Subgraph": {"Eicosanoids signaling subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3697, "target": 418, "key": "ffc8f999d9bbea0eaa99727f4bbe5fe5"}, {"line": 42544, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 351, "target": 3697, "key": "8a4a5d7c2a89e160c47de093481c67da"}, {"line": 42545, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 351, "target": 3706, "key": "a11ec46b3074a6cf273971957fd8c18a"}, {"line": 42546, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 351, "target": 170, "key": "0eb23b9cb427a98e703514e9adc8ef36"}, {"line": 42547, "relation": "association", "evidence": "The data indicate that apart from acetylsalicylic acid (aspirin) and simvastatin, several neurophysiologically-relevant concentrations of Abetapeptides and neurotoxic trace metals variably induced ROS induction, COX-2 and cPLA2 expression.", "citation": {"db": "PubMed", "db_id": "24619568"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 351, "target": 325, "key": "fb3f583048bac2b6c55ef6f2e172fc3b"}, {"line": 43819, "relation": "increases", "evidence": "Simvastatin results in increased expression of S100A9 mRNA", "citation": {"db": "PubMed", "db_id": "17428261"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Toll like receptor subgraph": true}}, "source": 351, "target": 3334, "key": "40a9d5a98720e4fdde07f018e541ab0c"}, {"line": 42587, "relation": "association", "evidence": "The monoamine norepinephrine reduces the production of cytokines by activated microglia in vitro.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Prostaglandin subgraph": true}}, "source": 306, "target": 317, "key": "d74de12c1dc90828b6d38d8e919c4760"}, {"line": 42593, "relation": "association", "evidence": "However, little is known about the effects of norepinephrine on prostanoid synthesis.", "citation": {"db": "PubMed", "db_id": "20064241"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}}, "source": 336, "target": 317, "key": "7f163cb94f67d421fd7fcd5b692dd77a"}, {"line": 42639, "relation": "association", "evidence": "Evidence of trem2 variant associated with triple risk of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24663666"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}}, "source": 3745, "target": 3823, "key": "c8c91c03626f9e28f3af5b5ee6295991"}, {"line": 42658, "relation": "decreases", "evidence": "Reducing Effect of IL-32α in the Development of Stroke Through Blocking of NF-κB, but Enhancement of STAT3 Pathways.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"MeSHDisease": {"Stroke": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 2890, "target": 3930, "key": "5fc7658cac5ea99e5f5a20ed8da97bed"}, {"line": 42674, "relation": "decreases", "evidence": "Middle cerebral artery occlusion (MCAO) induced development of ischemia, and ischemic neuronal cell death were reduced in IL-32α-overexpressing transgenic mice (IL-32α mice) brain through the decreased release of neuroinflammatory cytokines (IL-6, IL-1beta, TNF-α) and activation of astrocytes, but enhancement of anti- neuroinflammatory cytokines (IL-10).", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHDisease": {"Ischemia": true, "Infarction, Middle Cerebral Artery": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true, "Middle Cerebral Artery": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2890, "target": 3921, "key": "423d3f2dd03ae642802ee56fd19c669b"}, {"line": 42675, "relation": "decreases", "evidence": "Middle cerebral artery occlusion (MCAO) induced development of ischemia, and ischemic neuronal cell death were reduced in IL-32α-overexpressing transgenic mice (IL-32α mice) brain through the decreased release of neuroinflammatory cytokines (IL-6, IL-1beta, TNF-α) and activation of astrocytes, but enhancement of anti- neuroinflammatory cytokines (IL-10).", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHDisease": {"Ischemia": true, "Infarction, Middle Cerebral Artery": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true, "Middle Cerebral Artery": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2890, "target": 3662, "key": "e52fcc90ecbe7593c9b23e0dd28237a8"}, {"line": 42676, "relation": "decreases", "evidence": "Middle cerebral artery occlusion (MCAO) induced development of ischemia, and ischemic neuronal cell death were reduced in IL-32α-overexpressing transgenic mice (IL-32α mice) brain through the decreased release of neuroinflammatory cytokines (IL-6, IL-1beta, TNF-α) and activation of astrocytes, but enhancement of anti- neuroinflammatory cytokines (IL-10).", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHDisease": {"Ischemia": true, "Infarction, Middle Cerebral Artery": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true, "Middle Cerebral Artery": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2890, "target": 3741, "key": "4d58911c78c9f0a46d36080fae0505ac"}, {"line": 42677, "relation": "decreases", "evidence": "Middle cerebral artery occlusion (MCAO) induced development of ischemia, and ischemic neuronal cell death were reduced in IL-32α-overexpressing transgenic mice (IL-32α mice) brain through the decreased release of neuroinflammatory cytokines (IL-6, IL-1beta, TNF-α) and activation of astrocytes, but enhancement of anti- neuroinflammatory cytokines (IL-10).", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHDisease": {"Ischemia": true, "Infarction, Middle Cerebral Artery": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true, "Middle Cerebral Artery": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2890, "target": 3661, "key": "4a6c023bbea4f1f10948b5b7ef7f77f8"}, {"line": 42678, "relation": "increases", "evidence": "Middle cerebral artery occlusion (MCAO) induced development of ischemia, and ischemic neuronal cell death were reduced in IL-32α-overexpressing transgenic mice (IL-32α mice) brain through the decreased release of neuroinflammatory cytokines (IL-6, IL-1beta, TNF-α) and activation of astrocytes, but enhancement of anti- neuroinflammatory cytokines (IL-10).", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHDisease": {"Ischemia": true, "Infarction, Middle Cerebral Artery": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true, "Middle Cerebral Artery": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2890, "target": 3656, "key": "051f928d0d40c0433e754f6c814090a9"}, {"line": 42679, "relation": "increases", "evidence": "Middle cerebral artery occlusion (MCAO) induced development of ischemia, and ischemic neuronal cell death were reduced in IL-32α-overexpressing transgenic mice (IL-32α mice) brain through the decreased release of neuroinflammatory cytokines (IL-6, IL-1beta, TNF-α) and activation of astrocytes, but enhancement of anti- neuroinflammatory cytokines (IL-10).", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHDisease": {"Ischemia": true, "Infarction, Middle Cerebral Artery": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true, "Middle Cerebral Artery": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2890, "target": 480, "key": "aa90239623403e262f8b78599f2731a9"}, {"line": 42723, "relation": "positiveCorrelation", "evidence": "These results suggest that IL-32α can prevent cerebral ischemia damage via upregulation of anti-neuroinflammatory cytokine expression and STAT3 activation, but downregulation of neuroinflammatory cytokines and NF-κB activation.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Brain Ischemia": true}, "Confidence": {"High": true}}, "source": 2890, "target": 420, "key": "457c86b68341543f39e533654bbb4fc9"}, {"line": 42733, "relation": "increases", "evidence": "These results suggest that IL-32α can prevent cerebral ischemia damage via upregulation of anti-neuroinflammatory cytokine expression and STAT3 activation, but downregulation of neuroinflammatory cytokines and NF-κB activation.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Brain Ischemia": true}, "Confidence": {"Medium": true}}, "source": 2890, "target": 420, "key": "336d616dafd6a5cc52c95a37550a075e"}, {"line": 42728, "relation": "increases", "evidence": "These results suggest that IL-32α can prevent cerebral ischemia damage via upregulation of anti-neuroinflammatory cytokine expression and STAT3 activation, but downregulation of neuroinflammatory cytokines and NF-κB activation.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Brain Ischemia": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2890, "target": 3725, "key": "c5627fc418d09fb35c2248d299865446"}, {"line": 42741, "relation": "decreases", "evidence": "These results suggest that IL-32α can prevent cerebral ischemia damage via upregulation of anti-neuroinflammatory cytokine expression and STAT3 activation, but downregulation of neuroinflammatory cytokines and NF-κB activation.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Brain Ischemia": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 2890, "target": 3685, "key": "dad1111ff433a95c6231edfb0a53a22b"}, {"line": 42709, "relation": "regulates", "evidence": "Nuclear factor-kappa B (NF-κB), a critical transcriptional factor regulating neuroinflammation, was much lower, but activation of signal transducer and activator of transcription 3 (STAT3), which plays a crucial role in cell survival and proliferation, was much higher in IL-32α-overexpressing mice brain compared to those of wild-type mice brain.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"Medium": true}}, "source": 3725, "target": 830, "key": "b967691ec3b60f0be7fae917962178ac"}, {"line": 42732, "relation": "decreases", "evidence": "These results suggest that IL-32α can prevent cerebral ischemia damage via upregulation of anti-neuroinflammatory cytokine expression and STAT3 activation, but downregulation of neuroinflammatory cytokines and NF-κB activation.", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHAnatomy": {"Cerebrum": true}, "MeSHDisease": {"Brain Ischemia": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3725, "target": 3831, "key": "ea29ed542fe84b5fdd6a7dd991b94d29"}, {"line": 42673, "relation": "positiveCorrelation", "evidence": "Middle cerebral artery occlusion (MCAO) induced development of ischemia, and ischemic neuronal cell death were reduced in IL-32α-overexpressing transgenic mice (IL-32α mice) brain through the decreased release of neuroinflammatory cytokines (IL-6, IL-1beta, TNF-α) and activation of astrocytes, but enhancement of anti- neuroinflammatory cytokines (IL-10).", "citation": {"db": "PubMed", "db_id": "24854197"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "MeSHDisease": {"Ischemia": true, "Infarction, Middle Cerebral Artery": true}, "MeSHAnatomy": {"Brain": true, "Astrocytes": true, "Middle Cerebral Artery": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 3860, "target": 3921, "key": "56b456f15846a08b5b0080e072d7f33d"}, {"line": 43655, "relation": "decreases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 3656, "target": 577, "key": "cabb35c6dc44638893ae1eef26d0e6b1"}, {"line": 42776, "relation": "association", "evidence": "Nicardipine is a calcium channel blocker that has been widely used to control blood pressure in severe hypertension following events such as ischemic stroke, traumatic brain injury, and intracerebral hemorrhage.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Stroke": true, "Cerebral Hemorrhage": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Blood": true}, "Confidence": {"High": true}}, "source": 199, "target": 3836, "key": "ef739e0d2eaf1fe25a5546c29feeec9e"}, {"line": 42777, "relation": "association", "evidence": "Nicardipine is a calcium channel blocker that has been widely used to control blood pressure in severe hypertension following events such as ischemic stroke, traumatic brain injury, and intracerebral hemorrhage.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Stroke": true, "Cerebral Hemorrhage": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Blood": true}, "Confidence": {"High": true}}, "source": 199, "target": 3829, "key": "390c8a0f5fd41925fb9b3320b29411a0"}, {"line": 42778, "relation": "association", "evidence": "Nicardipine is a calcium channel blocker that has been widely used to control blood pressure in severe hypertension following events such as ischemic stroke, traumatic brain injury, and intracerebral hemorrhage.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Stroke": true, "Cerebral Hemorrhage": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Blood": true}, "Confidence": {"High": true}}, "source": 199, "target": 3930, "key": "7e563d098d83e73e5a21dd3157c8ac8f"}, {"line": 42779, "relation": "association", "evidence": "Nicardipine is a calcium channel blocker that has been widely used to control blood pressure in severe hypertension following events such as ischemic stroke, traumatic brain injury, and intracerebral hemorrhage.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Hypertension": true, "Stroke": true, "Cerebral Hemorrhage": true, "Brain Injuries": true}, "MeSHAnatomy": {"Brain": true, "Blood": true}, "Confidence": {"High": true}}, "source": 199, "target": 3916, "key": "01743e3c437dbd2576d2abc8a1570560"}, {"line": 42796, "relation": "decreases", "evidence": "Nicardipine also significantly inhibited LPS plus IFN-gamma-induced release of nitric oxide (NO), and the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2).", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"Subgraph": {"Prostaglandin subgraph": true}, "Confidence": {"High": true}}, "source": 199, "target": 4068, "key": "14ca318fb8f2c14f4e05443d075a3dc3"}, {"line": 42802, "relation": "decreases", "evidence": "Nicardipine also significantly inhibited LPS plus IFN-gamma-induced release of nitric oxide (NO), and the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2).", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"Subgraph": {"Interferon signaling subgraph": true}, "Confidence": {"High": true}}, "source": 199, "target": 3653, "key": "504856246620e3835c6ab94044535b9c"}, {"line": 42808, "relation": "decreases", "evidence": "Nicardipine also significantly inhibited LPS plus IFN-gamma-induced release of nitric oxide (NO), and the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2).", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 199, "target": 3689, "key": "2ad8e3e723d87e42e4cbe9a6183be1a0"}, {"line": 42812, "relation": "decreases", "evidence": "Nicardipine also significantly inhibited LPS plus IFN-gamma-induced release of nitric oxide (NO), and the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2).", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"Subgraph": {"Nitric oxide subgraph": true}, "Confidence": {"Medium": true}}, "source": 199, "target": 3706, "key": "57cce08e8ac7d3c1d3b259dfa4432a2c"}, {"line": 42837, "relation": "decreases", "evidence": "Notably, nicardipine also showed significant anti-neuroinflammatory effects on microglial activation in mice in vivo.The present study is the first to report a novel inhibitory role of nicardipine on neuroinflammation and provides a new candidate agent for the development of therapies for inflammation-related neurodegenerative diseases.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHDisease": {"Neurodegenerative Diseases": true, "Inflammation": true}, "Species": {"10090": true}, "Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 199, "target": 577, "key": "8e5a2c8320fe096eebcec6da66945108"}, {"line": 42828, "relation": "decreases", "evidence": "Furthermore, nicardipine also inhibited microglial activation by peptidoglycan, the major component of the Gram-positive bacterium cell wall.", "citation": {"db": "PubMed", "db_id": "24621589"}, "annotations": {"MeSHAnatomy": {"Cell Wall": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 326, "target": 429, "key": "338daf2e4246b5c92ad73034c0ad2686"}, {"line": 42859, "relation": "decreases", "evidence": "Rutin improves spatial memory in Alzheimer's disease transgenic mice by reducing Abeta oligomer level and attenuating oxidative stress and neuroinflammation.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 347, "target": 3823, "key": "44cb91fa7238dbb666076d27ea95a86a"}, {"line": 42860, "relation": "decreases", "evidence": "Rutin improves spatial memory in Alzheimer's disease transgenic mice by reducing Abeta oligomer level and attenuating oxidative stress and neuroinflammation.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 347, "target": 2328, "key": "5dde85f7370aabff67344e395b95ad61"}, {"line": 42904, "relation": "decreases", "evidence": "Results demonstrated that orally administered rutin significantly attenuated memory deficits in AD transgenic mice, decreased oligomeric Abeta level, increased super oxide dismutase (SOD) activity and glutathione (GSH)/glutathione disulfide (GSSG) ratio, reduced GSSG and malondialdehyde (MDA) levels, downregulated microgliosis and astrocytosis, and decreased interleukin (IL)-1beta and IL-6 levels in the brain.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Non-amyloidogenic subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "source": 347, "target": 2328, "key": "a87bb3000675040a2b8f4a1044fe0c28"}, {"line": 42866, "relation": "decreases", "evidence": "Rutin improves spatial memory in Alzheimer's disease transgenic mice by reducing Abeta oligomer level and attenuating oxidative stress and neuroinflammation.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Subgraph": {"Response to oxidative stress": true}, "Confidence": {"High": true}}, "source": 347, "target": 775, "key": "f74445c843e5b4c2f0551434d015c882"}, {"line": 42872, "relation": "decreases", "evidence": "Rutin improves spatial memory in Alzheimer's disease transgenic mice by reducing Abeta oligomer level and attenuating oxidative stress and neuroinflammation.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Species": {"10090": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 347, "target": 3920, "key": "883deafd1f8b134c3826d82376d1febf"}, {"line": 42894, "relation": "decreases", "evidence": "We have previously reported a polyphenol compound rutin that could inhibit Abeta aggregation and cytotoxicity, attenuate oxidative stress, and decrease the production of nitric oxide and proinflammatory cytokines in vitro.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Nitric oxide subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 347, "target": 156, "key": "3988306fcd8c0936c255c4089675b9ce"}, {"line": 42910, "relation": "increases", "evidence": "Results demonstrated that orally administered rutin significantly attenuated memory deficits in AD transgenic mice, decreased oligomeric Abeta level, increased super oxide dismutase (SOD) activity and glutathione (GSH)/glutathione disulfide (GSSG) ratio, reduced GSSG and malondialdehyde (MDA) levels, downregulated microgliosis and astrocytosis, and decreased interleukin (IL)-1beta and IL-6 levels in the brain.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Free radical formation subgraph": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 347, "target": 3719, "key": "35a2716c49ec73d41cc016bdb2abbf2e"}, {"line": 42919, "relation": "decreases", "evidence": "Results demonstrated that orally administered rutin significantly attenuated memory deficits in AD transgenic mice, decreased oligomeric Abeta level, increased super oxide dismutase (SOD) activity and glutathione (GSH)/glutathione disulfide (GSSG) ratio, reduced GSSG and malondialdehyde (MDA) levels, downregulated microgliosis and astrocytosis, and decreased interleukin (IL)-1beta and IL-6 levels in the brain.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 347, "target": 124, "key": "3917c56798465ac9bef1524a7fe6d4a1"}, {"line": 42920, "relation": "increases", "evidence": "Results demonstrated that orally administered rutin significantly attenuated memory deficits in AD transgenic mice, decreased oligomeric Abeta level, increased super oxide dismutase (SOD) activity and glutathione (GSH)/glutathione disulfide (GSSG) ratio, reduced GSSG and malondialdehyde (MDA) levels, downregulated microgliosis and astrocytosis, and decreased interleukin (IL)-1beta and IL-6 levels in the brain.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 347, "target": 3641, "key": "79e97a5a2c91a21055d095ff02087e12"}, {"line": 42921, "relation": "decreases", "evidence": "Results demonstrated that orally administered rutin significantly attenuated memory deficits in AD transgenic mice, decreased oligomeric Abeta level, increased super oxide dismutase (SOD) activity and glutathione (GSH)/glutathione disulfide (GSSG) ratio, reduced GSSG and malondialdehyde (MDA) levels, downregulated microgliosis and astrocytosis, and decreased interleukin (IL)-1beta and IL-6 levels in the brain.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"10090": true}, "Subgraph": {"Glutathione reductase subgraph": true}, "Confidence": {"High": true}}, "source": 347, "target": 298, "key": "7a885f5d008116d6c329c80621825153"}, {"line": 42928, "relation": "decreases", "evidence": "Results demonstrated that orally administered rutin significantly attenuated memory deficits in AD transgenic mice, decreased oligomeric Abeta level, increased super oxide dismutase (SOD) activity and glutathione (GSH)/glutathione disulfide (GSSG) ratio, reduced GSSG and malondialdehyde (MDA) levels, downregulated microgliosis and astrocytosis, and decreased interleukin (IL)-1beta and IL-6 levels in the brain.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "source": 347, "target": 3912, "key": "bc115829b79960a0fb766b64df639005"}, {"line": 42934, "relation": "decreases", "evidence": "Results demonstrated that orally administered rutin significantly attenuated memory deficits in AD transgenic mice, decreased oligomeric Abeta level, increased super oxide dismutase (SOD) activity and glutathione (GSH)/glutathione disulfide (GSSG) ratio, reduced GSSG and malondialdehyde (MDA) levels, downregulated microgliosis and astrocytosis, and decreased interleukin (IL)-1beta and IL-6 levels in the brain.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10090": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 347, "target": 3662, "key": "c21d90e8351f52b19e240f11b2892545"}, {"line": 42935, "relation": "decreases", "evidence": "Results demonstrated that orally administered rutin significantly attenuated memory deficits in AD transgenic mice, decreased oligomeric Abeta level, increased super oxide dismutase (SOD) activity and glutathione (GSH)/glutathione disulfide (GSSG) ratio, reduced GSSG and malondialdehyde (MDA) levels, downregulated microgliosis and astrocytosis, and decreased interleukin (IL)-1beta and IL-6 levels in the brain.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Microglia": true}, "Species": {"10090": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 347, "target": 3661, "key": "4869c81a42117bdf9577c34747220dfb"}, {"relation": "partOf", "source": 75, "target": 904, "key": "c0c0b5b6a4f0a54952611a11b0e2046b"}, {"line": 42945, "relation": "association", "evidence": "These results indicated that rutin is a promising agent for AD treatment because of its antioxidant, anti-inflammatory, and reducing Abeta oligomer activities.", "citation": {"db": "PubMed", "db_id": "24512768"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 75, "target": 3823, "key": "676002dd1c91ef374d03d9e1bb0b5b9a"}, {"line": 42959, "relation": "association", "evidence": "The endocannabinoid system is composed by a number of cannabinoid receptors, including the well-characterized CB1 and CB2 receptors, with their endogenous ligands and the enzymes related to the synthesis and degradation of these endocannabinoid compounds.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity"}, "source": 249, "target": 3613, "key": "b53e793da9835a41114eca8144b03e6d"}, {"line": 42960, "relation": "association", "evidence": "The endocannabinoid system is composed by a number of cannabinoid receptors, including the well-characterized CB1 and CB2 receptors, with their endogenous ligands and the enzymes related to the synthesis and degradation of these endocannabinoid compounds.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity"}, "source": 249, "target": 3612, "key": "cb6d5e923747ed0f943491ab1505cb8e"}, {"line": 42960, "relation": "association", "evidence": "The endocannabinoid system is composed by a number of cannabinoid receptors, including the well-characterized CB1 and CB2 receptors, with their endogenous ligands and the enzymes related to the synthesis and degradation of these endocannabinoid compounds.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "object": {"modifier": "Degradation"}, "source": 3612, "target": 249, "key": "36cddaff6025aa4706f9f307bac16dc9"}, {"line": 42969, "relation": "decreases", "evidence": "Several findings indicate that the activation of both CB1 and CB2 receptors by natural or synthetic agonists, at non-psychoactive doses, have beneficial effects in Alzheimer experimental models by reducing the harmful beta-amyloid peptide action and tau phosphorylation, as well as by promoting the brain's intrinsic repair mechanisms.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3612, "target": 2328, "key": "1017b99081447d5dfb6ac72c00206ef3"}, {"line": 42971, "relation": "decreases", "evidence": "Several findings indicate that the activation of both CB1 and CB2 receptors by natural or synthetic agonists, at non-psychoactive doses, have beneficial effects in Alzheimer experimental models by reducing the harmful beta-amyloid peptide action and tau phosphorylation, as well as by promoting the brain's intrinsic repair mechanisms.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 3612, "target": 3676, "key": "1cc0cf7dac77726837c3de7239378ba0"}, {"line": 42981, "relation": "regulates", "evidence": "Moreover, endocannabinoid signaling has been demonstrated to modulate numerous concomitant pathological processes, including neuroinflammation, excitotoxicity, mitochondrial dysfunction, and oxidative stress.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHDisease": {"Pathologic Processes": true}, "Confidence": {"Medium": true}}, "source": 548, "target": 3879, "key": "b21a18d1059561230f3d82cf3d05f965"}, {"line": 42989, "relation": "regulates", "evidence": "Moreover, endocannabinoid signaling has been demonstrated to modulate numerous concomitant pathological processes, including neuroinflammation, excitotoxicity, mitochondrial dysfunction, and oxidative stress.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHDisease": {"Pathologic Processes": true}, "Confidence": {"Medium": true}}, "source": 548, "target": 3812, "key": "4fa96815074e77f3892a71748d1e241d"}, {"line": 42990, "relation": "regulates", "evidence": "Moreover, endocannabinoid signaling has been demonstrated to modulate numerous concomitant pathological processes, including neuroinflammation, excitotoxicity, mitochondrial dysfunction, and oxidative stress.", "citation": {"db": "PubMed", "db_id": "24634659"}, "annotations": {"MeSHDisease": {"Pathologic Processes": true}, "Confidence": {"Medium": true}}, "source": 548, "target": 775, "key": "015f3c5194e61179da4a9a7fea933f9f"}, {"line": 43008, "relation": "increases", "evidence": "Tau-tubulin kinase (TTBK) belongs to casein kinase superfamily and phosphorylates microtubule-associated protein tau and tubulin.TTBK has two isoforms, TTBK1 and TTBK2, which contain highly homologous catalytic domains but their non-catalytic domains are distinctly different.TTBK1 is expressed specifically in the central nervous system and is involved in phosphorylation and aggregation of tau.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"MeSHAnatomy": {"Microtubules": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3748, "target": 3562, "key": "bdb5647fb5b21e0810c49b559cee36d9"}, {"line": 43012, "relation": "increases", "evidence": "Tau-tubulin kinase (TTBK) belongs to casein kinase superfamily and phosphorylates microtubule-associated protein tau and tubulin.TTBK has two isoforms, TTBK1 and TTBK2, which contain highly homologous catalytic domains but their non-catalytic domains are distinctly different.TTBK1 is expressed specifically in the central nervous system and is involved in phosphorylation and aggregation of tau.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"MeSHAnatomy": {"Microtubules": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3748, "target": 3676, "key": "95fa0448371a1dc4a17fd848c11a8866"}, {"line": 43044, "relation": "increases", "evidence": "TTBK1 directly phosphorylates tau protein, especially at Ser422, and also activates cycline-dependent kinase 5 in a unique mechanism.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3748, "target": 3676, "key": "199bfc31426864df14e1a78a3661e278"}, {"line": 43034, "relation": "association", "evidence": "TTBK2 is ubiquitously expressed in multiple tissues and genetically linked to spinocerebellar ataxia type 11.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHDisease": {"Spinocerebellar Ataxias": true}, "MeSHAnatomy": {"Tissues": true}, "Confidence": {"High": true}}, "source": 3748, "target": 3884, "key": "037975615aecf92ab53af30a7744c583"}, {"line": 43045, "relation": "increases", "evidence": "TTBK1 directly phosphorylates tau protein, especially at Ser422, and also activates cycline-dependent kinase 5 in a unique mechanism.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3748, "target": 3677, "key": "9574262bea8d7eaa07b3adc624f79520"}, {"line": 43047, "relation": "increases", "evidence": "TTBK1 directly phosphorylates tau protein, especially at Ser422, and also activates cycline-dependent kinase 5 in a unique mechanism.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true, "Cyclin-CDK subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3748, "target": 3610, "key": "47e360f7f68642af4c61cfb514253904"}, {"relation": "hasVariant", "source": 3561, "target": 3562, "key": "06bd5dcac280d0a72bc3ba1dca6ac0d3"}, {"line": 43018, "relation": "increases", "evidence": "Tau-tubulin kinase (TTBK) belongs to casein kinase superfamily and phosphorylates microtubule-associated protein tau and tubulin.TTBK has two isoforms, TTBK1 and TTBK2, which contain highly homologous catalytic domains but their non-catalytic domains are distinctly different.TTBK1 is expressed specifically in the central nervous system and is involved in phosphorylation and aggregation of tau.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"Medium": true}}, "source": 3747, "target": 3562, "key": "407c963dfc2c9dc9d77290756abde61a"}, {"line": 43022, "relation": "increases", "evidence": "Tau-tubulin kinase (TTBK) belongs to casein kinase superfamily and phosphorylates microtubule-associated protein tau and tubulin.TTBK has two isoforms, TTBK1 and TTBK2, which contain highly homologous catalytic domains but their non-catalytic domains are distinctly different.TTBK1 is expressed specifically in the central nervous system and is involved in phosphorylation and aggregation of tau.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"MeSHAnatomy": {"Central Nervous System": true}, "Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3747, "target": 3676, "key": "4ec0edfa217ab99ed88780954e8bdc9a"}, {"line": 43057, "relation": "positiveCorrelation", "evidence": "TTBK1 protein expression is significantly elevated in Alzheimer's disease (AD) brains, and genetic variations of the TTBK1 gene are associated with late-onset Alzheimer's disease in two cohorts of Chinese and Spanish populations.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Brain": true}, "Species": {"9606": true}, "Confidence": {"High": true}}, "source": 3747, "target": 3823, "key": "aed0b1c4401f12e655c72e547d172522"}, {"line": 43089, "relation": "positiveCorrelation", "evidence": "These studies suggest that TTBK1 is an important molecule for the inflammatory axonal degeneration, which may be relevant to the pathobiology of tauopathy including AD.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Axons": true}, "MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 3747, "target": 3823, "key": "57fff1d182ee2615a096f84e73c1a4c6"}, {"line": 43068, "relation": "association", "evidence": "Our recent study shows that there is a striking switch in mononuclear phagocyte and activation phenotypes in the anterior horn of the spinal cord from alternatively activated (M2-skewed) microglia in P301L tau mutant mice to pro-inflammatory (M1-skewed) infiltrating peripheral monocytes by crossing the tau mice with TTBK1 transgenic mice.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Monocytes": true, "Spinal Cord": true, "Phagocytes": true, "Microglia": true}, "Species": {"10090": true}, "Confidence": {"High": true}}, "source": 3747, "target": 3675, "key": "bcce8c4f58284bbf5863067992439a8b"}, {"line": 43077, "relation": "increases", "evidence": "TTBK1 is responsible for mediating M1-activated microglia-induced neurotoxicity, and its overexpression induces axonal degeneration in vitro.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Axons": true, "Microglia": true}, "Confidence": {"High": true}}, "source": 3747, "target": 648, "key": "e6187e09df769a68807671da15002405"}, {"line": 43078, "relation": "decreases", "evidence": "TTBK1 is responsible for mediating M1-activated microglia-induced neurotoxicity, and its overexpression induces axonal degeneration in vitro.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Axons": true, "Microglia": true}, "Confidence": {"High": true}}, "source": 3747, "target": 482, "key": "873da11f2848a11cdbde7fb09879254a"}, {"line": 43088, "relation": "decreases", "evidence": "These studies suggest that TTBK1 is an important molecule for the inflammatory axonal degeneration, which may be relevant to the pathobiology of tauopathy including AD.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Axons": true}, "MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 3747, "target": 482, "key": "796388af7ca61edca55a8f3d9d0ca92d"}, {"line": 43090, "relation": "positiveCorrelation", "evidence": "These studies suggest that TTBK1 is an important molecule for the inflammatory axonal degeneration, which may be relevant to the pathobiology of tauopathy including AD.", "citation": {"db": "PubMed", "db_id": "24808823"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "MeSHAnatomy": {"Axons": true}, "MeSHDisease": {"Tauopathies": true, "Alzheimer Disease": true}, "Confidence": {"High": true}}, "source": 3747, "target": 3931, "key": "fd543bb83e1235c865a238c6c9908ae9"}, {"line": 43131, "relation": "association", "evidence": "Cx3cl1/Cx3cr1 signalling is thought to maintain microglia in their resting state and disrupting this equilibrium may allow microglia to become activated.", "citation": {"db": "PubMed", "db_id": "24655482"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3882, "target": 3617, "key": "783df48657143a644b5ba6b05ff0ed18"}, {"line": 43126, "relation": "association", "evidence": "Cx3cl1/Cx3cr1 signalling is thought to maintain microglia in their resting state and disrupting this equilibrium may allow microglia to become activated.", "citation": {"db": "PubMed", "db_id": "24655482"}, "annotations": {"MeSHAnatomy": {"Microglia": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 1643, "target": 609, "key": "e34ffa0a4a7f8a09e4b60ad7dc9aa79d"}, {"relation": "partOf", "source": 3616, "target": 1643, "key": "3a098c9bcc7f823670cde8bbfb683992"}, {"relation": "partOf", "source": 3617, "target": 1643, "key": "9c5b11136a89279d67cf8cf3bdfc1700"}, {"line": 43131, "relation": "association", "evidence": "Cx3cl1/Cx3cr1 signalling is thought to maintain microglia in their resting state and disrupting this equilibrium may allow microglia to become activated.", "citation": {"db": "PubMed", "db_id": "24655482"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3617, "target": 3882, "key": "b28e492f8178e906b0bd90d78b2609a8"}, {"line": 43147, "relation": "association", "evidence": "Opposing roles for CXCR3 signaling in central nervous system versus ocular inflammation mediated by the astrocyte-targeted production of IL-12.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Central Nervous System": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3621, "target": 3920, "key": "ed02e7d1bb0901c7d5d32d852783732e"}, {"line": 43178, "relation": "decreases", "evidence": "Surprisingly, CXCR3-deficient GF-IL12 mice (GF-IL12/CXCR3KO) have drastically reduced ataxia but developed cataracts, severe ocular inflammation, and eye atrophy.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Atrophy": true}, "MeSHAnatomy": {"Astrocytes": true, "Neuroglia": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Species": {"10090": true}}, "source": 3621, "target": 3920, "key": "252199367776e002282762e7ffac5929"}, {"line": 43148, "relation": "increases", "evidence": "Opposing roles for CXCR3 signaling in central nervous system versus ocular inflammation mediated by the astrocyte-targeted production of IL-12.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Central Nervous System": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3621, "target": 3657, "key": "f3dd10f690cd5244f1c1ca67d81d3ea0"}, {"line": 43149, "relation": "increases", "evidence": "Opposing roles for CXCR3 signaling in central nervous system versus ocular inflammation mediated by the astrocyte-targeted production of IL-12.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Central Nervous System": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 3621, "target": 3658, "key": "84556a056d2963c795201a8f6dc1d8de"}, {"line": 43158, "relation": "association", "evidence": "CXCR3 and its ligands are important for the trafficking of activated CD4(+) T(H)1 T cells, CD8(+) T cells, and natural killer cells during inflammation.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Central Nervous System": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3621, "target": 457, "key": "4a2e2f1c43f79ae83b010171f199d83b"}, {"line": 43159, "relation": "association", "evidence": "CXCR3 and its ligands are important for the trafficking of activated CD4(+) T(H)1 T cells, CD8(+) T cells, and natural killer cells during inflammation.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Central Nervous System": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3621, "target": 624, "key": "402ecf4d75ad47e048cd9117592bce6f"}, {"line": 43169, "relation": "increases", "evidence": "We examined the impact of CXCR3 on a less complex interferon-gamma-dependent, type 1 cell-mediated immune response in the CNS, induced in mice by the transgenic production of glial fibrillary acidic protein IL-12 (GF-IL12) by astrocytes and retinal Müller cells.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Neuroglia": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}}, "source": 3621, "target": 1645, "key": "eb1cb92c07d7991aa19160a2c73a4f49"}, {"line": 43177, "relation": "decreases", "evidence": "Surprisingly, CXCR3-deficient GF-IL12 mice (GF-IL12/CXCR3KO) have drastically reduced ataxia but developed cataracts, severe ocular inflammation, and eye atrophy.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Atrophy": true}, "MeSHAnatomy": {"Astrocytes": true, "Neuroglia": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Species": {"10090": true}}, "source": 3621, "target": 3896, "key": "ee256605216ca8a91b55f6ab2d54eba2"}, {"line": 43179, "relation": "decreases", "evidence": "Surprisingly, CXCR3-deficient GF-IL12 mice (GF-IL12/CXCR3KO) have drastically reduced ataxia but developed cataracts, severe ocular inflammation, and eye atrophy.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Atrophy": true}, "MeSHAnatomy": {"Astrocytes": true, "Neuroglia": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Species": {"10090": true}}, "source": 3621, "target": 3899, "key": "86b1848da8be42ded7ca1e94a96914f4"}, {"line": 43180, "relation": "decreases", "evidence": "Surprisingly, CXCR3-deficient GF-IL12 mice (GF-IL12/CXCR3KO) have drastically reduced ataxia but developed cataracts, severe ocular inflammation, and eye atrophy.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true, "Atrophy": true}, "MeSHAnatomy": {"Astrocytes": true, "Neuroglia": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Species": {"10090": true}}, "source": 3621, "target": 3894, "key": "972c472e7841ee77a2e6dd860a6dccef"}, {"relation": "partOf", "source": 3658, "target": 1645, "key": "fa237d9a0ce6e28f0997b9d094919079"}, {"line": 43158, "relation": "association", "evidence": "CXCR3 and its ligands are important for the trafficking of activated CD4(+) T(H)1 T cells, CD8(+) T cells, and natural killer cells during inflammation.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHDisease": {"Inflammation": true}, "MeSHAnatomy": {"Astrocytes": true, "Central Nervous System": true}, "Subgraph": {"Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 457, "target": 3621, "key": "c083bbdf64e5e0ccebb10e930596d9c2"}, {"line": 43189, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 1645, "target": 3618, "key": "4ce5fc6f2802ac4d49f083bb8f8ae56b"}, {"line": 43190, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 1645, "target": 3620, "key": "71668eb3cea749c22f12fd31520dcbc6"}, {"line": 43192, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Tumor necrosis factor subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 1645, "target": 3741, "key": "79e4f423219bee182edc6c1993b207a4"}, {"line": 43194, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 1645, "target": 2183, "key": "6304ca6601daeb83574bfc60e33b0e72"}, {"line": 43195, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 1645, "target": 3603, "key": "e839d9cfbec88e8a4b2b558de0288376"}, {"relation": "partOf", "source": 3636, "target": 1645, "key": "72ffd9398865d70acc5c8c5a3738ae47"}, {"line": 43189, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 3618, "target": 1645, "key": "88ee2bad2760d2af76a1be3c4132658d"}, {"line": 43190, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 3620, "target": 1645, "key": "16476608d9b5bb145a09dddf34109bb7"}, {"line": 43195, "relation": "association", "evidence": "High levels of interferon-gamma, IL-1, tumor necrosis factor α, CXCL9, CXCL10, and CCL5 were found in GF-IL12 cerebelli and GF-IL12/CXCR3KO eyes.", "citation": {"db": "PubMed", "db_id": "21925471"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true, "Eye": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}, "Species": {"10090": true}, "MeSHDisease": {"Inflammation": true}}, "source": 3603, "target": 1645, "key": "072e22d46f879e85383d15e171ed335a"}, {"relation": "hasReactant", "source": 4107, "target": 3584, "key": "8f612c048210465bcabe456953d3f12c"}, {"relation": "hasProduct", "source": 4107, "target": 80, "key": "9e69323995585c6c570cdad0aa687544"}, {"line": 43342, "relation": "decreases", "evidence": "Curcumin has been shown to suppress activated astroglia in amyloid-beta protein precursor transgenic mice.", "citation": {"db": "PubMed", "db_id": "20413894"}, "annotations": {"MeSHAnatomy": {"Microglia": true, "Astrocytes": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 238, "target": 418, "key": "3971000006d070378f1714316eb44b6a"}, {"line": 44371, "relation": "increases", "evidence": "JAK-STAT signaling as an anti-inflammatory target. JAK-STAT signaling mediates the brain inflammation induced by LPS, IFN-gamma, ganglioside and thrombin. Curcumin activates SH2-containing phosphatase 2 (SHP2), while rosiglitazone and 15d-PGJ2 increase the expressions of SOCS1 and SOCS3. SHP2 and the SOCS proteins are typical negative feedback molecules of the JAK-STAT pathway.", "citation": {"db": "PubMed", "db_id": "26113788"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 238, "target": 3283, "key": "f5aefdadac50d6343e78cd7c0c363a2d"}, {"line": 45912, "relation": "decreases", "evidence": "Our studies showed that p300-HAT inhibitor curcumin abrogates H3 hyperacetylation of PS1 and BACE1, curcumin decreases PS1 activity", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 238, "target": 2673, "key": "b90640953344d51ab21da428c26a934f"}, {"line": 45913, "relation": "decreases", "evidence": "Our studies showed that p300-HAT inhibitor curcumin abrogates H3 hyperacetylation of PS1 and BACE1, curcumin decreases PS1 activity", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 238, "target": 2804, "key": "abc11a9f23292ea4c0d798529c98a8e9"}, {"line": 45916, "relation": "decreases", "evidence": "Our studies showed that p300-HAT inhibitor curcumin abrogates H3 hyperacetylation of PS1 and BACE1, curcumin decreases PS1 activity", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "object": {"modifier": "Activity"}, "source": 238, "target": 3258, "key": "ea552adab95b146090bfa3c958e017ac"}, {"line": 48362, "relation": "increases", "evidence": "Curcumin nanoparticles increase neuronal differentiation by activating the Wnt/beta-catenin pathway, involved in regulation of neurogenesis. These nanoparticles caused enhanced nuclear translocation of beta-catenin, decreased GSK-3beta levels, and increased promoter activity of the TCF/LEF and cyclin-D1.", "citation": {"db": "PubMed", "db_id": "24467380"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 238, "target": 462, "key": "cd899436c9806eaf7bc1e5bb67ffb0e1"}, {"line": 48365, "relation": "increases", "evidence": "Curcumin nanoparticles increase neuronal differentiation by activating the Wnt/beta-catenin pathway, involved in regulation of neurogenesis. These nanoparticles caused enhanced nuclear translocation of beta-catenin, decreased GSK-3beta levels, and increased promoter activity of the TCF/LEF and cyclin-D1.", "citation": {"db": "PubMed", "db_id": "24467380"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "Cell cycle subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "tscript", "namespace": "bel"}}, "source": 238, "target": 2463, "key": "87b264584a87c280aaac3667f1b1f547"}, {"line": 43426, "relation": "increases", "evidence": "Chronic neuroinflammatory processes including glial activation may play a role in the pathogenesis of/ Alzheimer's disease (AD). The immune and inflammatory mediator CD40 ligand (CD40L) can augment the activation of/ cultured microglia by amyloid beta-protein (Abeta) and promote neuron death. We investigated whether CD40L is/ increased in AD and in animal models of AD and neuroinflammation. These findings indicate that astrocytes are / the predominant source of CD40L in brain, and are consistent with the proposed role of CD40L-mediated neurotoxic/ inflammation in AD.", "citation": {"db": "PubMed", "db_id": "11755016"}, "annotations": {"Cell": {"microglial cell": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3607, "target": 2328, "key": "0c0e5af7b21e4222f573bf2be41011ec"}, {"line": 43437, "relation": "increases", "evidence": "Chronic neuroinflammatory processes including glial activation may play a role in the pathogenesis of/ Alzheimer's disease (AD). The immune and inflammatory mediator CD40 ligand (CD40L) can augment the activation of/ cultured microglia by amyloid beta-protein (Abeta) and promote neuron death. We investigated whether CD40L is/ increased in AD and in animal models of AD and neuroinflammation. These findings indicate that astrocytes are / the predominant source of CD40L in brain, and are consistent with the proposed role of CD40L-mediated neurotoxic/ inflammation in AD.", "citation": {"db": "PubMed", "db_id": "11755016"}, "annotations": {"Cell": {"microglial cell": true}, "Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3607, "target": 577, "key": "61a4bc3155e78c6e8f6d4cf03b3a5c80"}, {"relation": "partOf", "source": 3607, "target": 1642, "key": "f8e00fb1ddb7eec9c7b1f49fe130253e"}, {"line": 43452, "relation": "increases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}, "Confidence": {"Medium": true}}, "source": 129, "target": 522, "key": "c8b18592b5f4977c66c6a0148c4b2844"}, {"line": 43461, "relation": "decreases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 129, "target": 3717, "key": "29915b8a9937e86c8786ffed1eff0ae5"}, {"line": 43462, "relation": "decreases", "evidence": "We found that palmitic acid significantly increased de-novo synthesis of ceramide in astroglia, which/ in turn was involved in inducing both increased production of the Abeta protein and hyperphosphorylation of the tau/ protein. Increased amyloidogenesis and hyperphoshorylation of tau lead to formation of the two most important/ pathophysiological characteristics associated with AD, Abeta or senile plaques and neurofibrillary tangles, respectively./ In addition to these pathophysiological changes, AD is also characterized by certain metabolic changes; abnormal/ cerebral glucose metabolism is one of the distinct characteristics of AD. In this context, we found that palmitic/ acid significantly decreased the levels of astroglial glucose transporter (GLUT1) and down-regulated glucose uptake/ and lactate release by astroglia. Our present data establish an underlying mechanism by which saturated fatty acids/ induce AD-associated pathophysiological as well as metabolic changes, placing 'astroglial fatty acid metabolism' at/ the center of the pathogenic cascade in AD.", "citation": {"db": "PubMed", "db_id": "17908174"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Sphingolipid metabolic subgraph": true}}, "source": 129, "target": 418, "key": "f6b3df53e35a3120d2f458e5b2dccf0a"}, {"line": 43499, "relation": "increases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "object": {"modifier": "Degradation"}, "source": 3649, "target": 272, "key": "9d6a7cf449c9b466ba0442f42d8207f5"}, {"line": 43500, "relation": "directlyIncreases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "source": 3649, "target": 4093, "key": "33928eab5226bdca5f3de111e3c4de69"}, {"line": 43508, "relation": "increases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3649, "target": 135, "key": "34dbe73a8126b74ea23c4607364ffd0a"}, {"line": 43501, "relation": "increases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "source": 239, "target": 3649, "key": "9b357ae7a44fea9f02478fac9abe3b93"}, {"line": 43502, "relation": "increases", "evidence": "Heme oxygenase-1 (HO-1) is a 32kDa stress protein that degrades heme to biliverdin, free iron and carbon/ monoxide. Our laboratory has shown that cysteamine, dopamine, beta-amyloid, IL-1beta and TNF-alpha up-regulate HO-1/ followed by mitochondrial sequestration of non-transferrin-derived 55Fe in cultured rat astroglia. In these cells and / in rat astroglia transfected with the human HO-1 gene, mitochondrial iron trapping is abrogated by the HO-1 inhibitors, / tin-mesoporphyrin and dexamethasone.We determined that HO-1 immunoreactivity is enhanced greatly in neurons and astrocytes/ of the hippocampus and cerebral cortex of Alzheimer subjects and co-localizes to senile plaques and neurofibrillary/ tangles (NFT). HO-1 staining is also augmented in astrocytes and decorates neuronal Lewy bodies in the Parkinson nigra./ Collectively, our findings suggest that HO-1 over-expression contributes to the pathological iron deposition and/ mitochondrial damage documented in these aging-related neurodegenerative disorders. ", "citation": {"db": "PubMed", "db_id": "11053673"}, "source": 245, "target": 3649, "key": "d3ba194b400b582fe23074f6d78fb2d0"}, {"line": 43522, "relation": "decreases", "evidence": "PKR inhibition prevented Abeta42-induced activation of IκB and NF-κB, strongly decreased production and release/ of tumor necrosis factor (TNFα) and interleukin (IL)-1beta, and limited apoptotic process.", "citation": {"db": "PubMed", "db_id": "21699726"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3633, "target": 2328, "key": "f66d8b67dca9e698916779c7e4f46501"}, {"line": 43540, "relation": "increases", "evidence": "PKR inhibition prevented Abeta42-induced activation of IκB and NF-κB, strongly decreased production and release/ of tumor necrosis factor (TNFα) and interleukin (IL)-1beta, and limited apoptotic process.", "citation": {"db": "PubMed", "db_id": "21699726"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"Medium": true}}, "source": 3633, "target": 3741, "key": "eb566ddb9fefd30b5d909c921e20973a"}, {"line": 43546, "relation": "increases", "evidence": "PKR inhibition prevented Abeta42-induced activation of IκB and NF-κB, strongly decreased production and release/ of tumor necrosis factor (TNFα) and interleukin (IL)-1beta, and limited apoptotic process.", "citation": {"db": "PubMed", "db_id": "21699726"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3633, "target": 3661, "key": "20f05d185110722eb985b636d4e60cec"}, {"line": 43552, "relation": "increases", "evidence": "PKR inhibition prevented Abeta42-induced activation of IκB and NF-κB, strongly decreased production and release/ of tumor necrosis factor (TNFα) and interleukin (IL)-1beta, and limited apoptotic process.", "citation": {"db": "PubMed", "db_id": "21699726"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 3633, "target": 648, "key": "2a98707879541aafeef281735afbbbc7"}, {"line": 43569, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 1652, "target": 672, "key": "c880e32026b691cccc209672cdfbd743"}, {"line": 43571, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 1652, "target": 3666, "key": "c1f12eb885cf704a9905394449367b33"}, {"line": 43572, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 1652, "target": 3666, "key": "d4709342f5be82d16a413c46744a3ffd"}, {"relation": "partOf", "source": 3742, "target": 1652, "key": "4325f96b68628ff9901bb8aaac492a25"}, {"line": 43570, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 1653, "target": 854, "key": "a4cea640ac8cce8596fa5272574a02b1"}, {"relation": "partOf", "source": 3743, "target": 1653, "key": "7f80cd1c81fbb6d0474b41909adcc413"}, {"line": 43573, "relation": "positiveCorrelation", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3666, "target": 3823, "key": "b2816b1a410b6b6e40c2f8d523aad9ca"}, {"line": 43574, "relation": "increases", "evidence": "Alzheimer's disease (AD) is associated with an altered immune response, resulting in chronic increased inflammatory cytokine production with a prominent role of TNF-α. TNF-α signals are mediated by two receptors: TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2). Signaling through TNFR2 is associated with neuroprotection, whereas signaling through TNFR1 is generally proinflammatory and proapoptotic. Here, we have identified a TNF-α-induced proinflammatory agent, lipocalin 2 (Lcn2) via gene array in murine primary cortical neurons. Further investigation showed that Lcn2 protein production and secretion were activated solely upon TNFR1 stimulation when primary murine neurons, astrocytes, and microglia were treated with TNFR1 and TNFR2 agonistic antibodies. Lcn2 was found to be significantly decreased in CSF of human patients with mild cognitive impairment and AD and increased in brain regions associated with AD pathology in human postmortem brain tissue. Mechanistic studies in cultures of primary cortical neurons showed that Lcn2 sensitizes nerve cells to beta-amyloid toxicity. Moreover, Lcn2 silences a TNFR2-mediated protective neuronal signaling cascade in neurons, pivotal for TNF-α-mediated neuroprotection. The present study introduces Lcn2 as a molecular actor in neuroinflammation in early clinical stages of AD.", "citation": {"db": "PubMed", "db_id": "22441986"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true, "Tumor necrosis factor subgraph": true}}, "source": 3666, "target": 3815, "key": "8647f0e70b205ee349671907015dbaf6"}, {"line": 43599, "relation": "increases", "evidence": "This suggests that the CD40-CD40L system is a critical enhancer of microglial activation in an AD transgenic/ mouse model and that such activation is associated with an increase in a key indicator of neuronal stress. Conversely, / the finding that reduced CD40-CD40L interaction is associated with reduced chronic microgliosis and Tau / hyperphosphorylation supports the view that, in general, mechanisms that reduce microgliosis will be beneficial in AD.", "citation": {"db": "PubMed", "db_id": "12127879"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 1642, "target": 3912, "key": "8beaf64b7fb4746769d7ec95e2ead1bd"}, {"line": 43602, "relation": "increases", "evidence": "This suggests that the CD40-CD40L system is a critical enhancer of microglial activation in an AD transgenic/ mouse model and that such activation is associated with an increase in a key indicator of neuronal stress. Conversely, / the finding that reduced CD40-CD40L interaction is associated with reduced chronic microgliosis and Tau / hyperphosphorylation supports the view that, in general, mechanisms that reduce microgliosis will be beneficial in AD.", "citation": {"db": "PubMed", "db_id": "12127879"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 1642, "target": 3676, "key": "d65a250f4b7967d4f4d8b94e443956db"}, {"relation": "partOf", "source": 3606, "target": 1642, "key": "da6676c60955bba5b50f84fa535fc5ea"}, {"line": 43618, "relation": "decreases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2128, "target": 291, "key": "c4418221f647534bb76d34cef582af37"}, {"line": 43624, "relation": "decreases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2128, "target": 3741, "key": "539ebc84f4dcc3800e2925ecfb6c422f"}, {"line": 43630, "relation": "decreases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2128, "target": 3662, "key": "1f293ca1e51a1db644f96fd5f173461b"}, {"line": 43631, "relation": "decreases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2128, "target": 3661, "key": "eeb30ac3f16591bb000acf067272cfbe"}, {"line": 43635, "relation": "decreases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}, "Confidence": {"Medium": true}}, "source": 2128, "target": 3619, "key": "192c148f9fa1fcfe8e854a06c2059c61"}, {"line": 43639, "relation": "decreases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2128, "target": 3648, "key": "0480d19663f81e4e8da1a0516878f4ec"}, {"line": 43654, "relation": "increases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2128, "target": 3656, "key": "4c98bdf0c129efffe20b9f3d90097628"}, {"line": 43661, "relation": "association", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2128, "target": 4055, "key": "a33172110acd18f70e6d9d9b90e41fb1"}, {"line": 43662, "relation": "association", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 2128, "target": 4081, "key": "eed570c0719177f6cd028000223da025"}, {"line": 43648, "relation": "increases", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3648, "target": 577, "key": "ab47c41e4293fbdb2ab081d9d4e3d903"}, {"line": 43661, "relation": "association", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4055, "target": 2128, "key": "c63805a2e5fdcedd820dff2d3615d601"}, {"line": 43662, "relation": "association", "evidence": "Knockdown of miR-181 enhanced LPS-induced production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1beta, / IL-8) and HMGB1, while overexpression of miR-181 resulted in a significant increase in the expression of the anti-inflamma/ tory cytokine IL-10. To assess the effects of miR-181 on the astrocyte transcriptome, we performed gene array and pathway / analysis on astrocytes with reduced levels of miR-181b/c. To examine the pool of potential miR-181 targets, we employed / a biotin pull-down of miR-181c and gene array analysis. We validated the mRNAs encoding MeCP2 and X-linked inhibitor of / apoptotic process as targets of miR-181. These findings suggest that miR-181 plays important roles in the molecular responses of / astrocytes in inflammatory settings.", "citation": {"db": "PubMed", "db_id": "23650073"}, "annotations": {"Subgraph": {"miRNA subgraph": true}, "Confidence": {"High": true}}, "source": 4081, "target": 2128, "key": "91e720735289df02f83f0bdfe08518f1"}, {"line": 43672, "relation": "increases", "evidence": "Acat1-targeting AAV delivered to the brains of AD mice decreased the levels of brain amyloid-beta and full-length human amyloid precursor protein (hAPP), to levels similar to complete genetic ablation of Acat1. This study provides support for the potential therapeutic use of Acat1 knockdown gene therapy in AD.", "citation": {"db": "PubMed", "db_id": "23774792"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3580, "target": 2315, "key": "6ee79288bf7f85c3089fe500e90da41a"}, {"line": 43673, "relation": "increases", "evidence": "Acat1-targeting AAV delivered to the brains of AD mice decreased the levels of brain amyloid-beta and full-length human amyloid precursor protein (hAPP), to levels similar to complete genetic ablation of Acat1. This study provides support for the potential therapeutic use of Acat1 knockdown gene therapy in AD.", "citation": {"db": "PubMed", "db_id": "23774792"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Amyloidogenic subgraph": true}}, "source": 3580, "target": 80, "key": "a026a649eccfbb478e9bfa17a4eba45b"}, {"line": 43695, "relation": "decreases", "evidence": "We found that the quantity and the molecular pattern of Abeta and its derivatives clearly differed between the CAA case with and the one without corticosteroid treatment. Moreover, amyloid-associated proteins were significantly reduced in the CAA extracts with corticosteroid treatment. In particular, reduction of S100A9/Mrp14 was remarkable (Kametani and Ikeda, 2013).", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Metabolism of steroid hormones subgraph": true}, "Confidence": {"High": true}}, "source": 235, "target": 2328, "key": "458e2f20d98a414357d5a0cd3e36b7fb"}, {"line": 43727, "relation": "decreases", "evidence": "In CAA, and probably in AD, corticosteroid treatment suppresses the secondarily induced vascular inflammation (Chang et al., 2012, Kloppenborg et al., 2010 and Machida et al., 2012) and the activation of S100A9/Mrp14 (Gebhardt et al., 2002 and Kametani and Ikeda, 2013). Recently, it has been reported that anti-Abeta autoantibodies were progressively reduced in the cerebrospinal fluid of a patient with CAA-related inflammation, following intravenous steroid administration (Piazza et al., 2013). Reduction of S100A9/Mrp14 might induce suppression of amyloid fibril formation (Zhang et al., 2012) and increase phagocytosis of fibrillar Abeta in microglia cells (Kummer et al., 2012), resulting in reduced Abeta amyloid fibril deposition.", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Metabolism of steroid hormones subgraph": true}, "Confidence": {"Medium": true}}, "source": 235, "target": 2328, "key": "d641903950e6ce00b0317877b2f5eb66"}, {"line": 43696, "relation": "decreases", "evidence": "We found that the quantity and the molecular pattern of Abeta and its derivatives clearly differed between the CAA case with and the one without corticosteroid treatment. Moreover, amyloid-associated proteins were significantly reduced in the CAA extracts with corticosteroid treatment. In particular, reduction of S100A9/Mrp14 was remarkable (Kametani and Ikeda, 2013).", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Metabolism of steroid hormones subgraph": true}, "Confidence": {"High": true}}, "source": 235, "target": 3334, "key": "87649ba42323247c81fa82925a16d004"}, {"line": 43726, "relation": "decreases", "evidence": "In CAA, and probably in AD, corticosteroid treatment suppresses the secondarily induced vascular inflammation (Chang et al., 2012, Kloppenborg et al., 2010 and Machida et al., 2012) and the activation of S100A9/Mrp14 (Gebhardt et al., 2002 and Kametani and Ikeda, 2013). Recently, it has been reported that anti-Abeta autoantibodies were progressively reduced in the cerebrospinal fluid of a patient with CAA-related inflammation, following intravenous steroid administration (Piazza et al., 2013). Reduction of S100A9/Mrp14 might induce suppression of amyloid fibril formation (Zhang et al., 2012) and increase phagocytosis of fibrillar Abeta in microglia cells (Kummer et al., 2012), resulting in reduced Abeta amyloid fibril deposition.", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Metabolism of steroid hormones subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 235, "target": 3334, "key": "901dd331679f6f94b87002974cdcf245"}, {"line": 43725, "relation": "decreases", "evidence": "In CAA, and probably in AD, corticosteroid treatment suppresses the secondarily induced vascular inflammation (Chang et al., 2012, Kloppenborg et al., 2010 and Machida et al., 2012) and the activation of S100A9/Mrp14 (Gebhardt et al., 2002 and Kametani and Ikeda, 2013). Recently, it has been reported that anti-Abeta autoantibodies were progressively reduced in the cerebrospinal fluid of a patient with CAA-related inflammation, following intravenous steroid administration (Piazza et al., 2013). Reduction of S100A9/Mrp14 might induce suppression of amyloid fibril formation (Zhang et al., 2012) and increase phagocytosis of fibrillar Abeta in microglia cells (Kummer et al., 2012), resulting in reduced Abeta amyloid fibril deposition.", "citation": {"db": "PubMed", "db_id": "24262203"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Metabolism of steroid hormones subgraph": true}, "Confidence": {"Medium": true}}, "source": 235, "target": 3815, "key": "212e6d8a7bb98f6acf9583db913dcb74"}, {"line": 43800, "relation": "decreases", "evidence": "[Estradiol co-treated with Tetrachlorodibenzodioxin] results in decreased expression of S100A9 mRNA", "citation": {"db": "PubMed", "db_id": "19484750"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "Toll like receptor subgraph": true}}, "source": 250, "target": 3334, "key": "6f3d9cceb37bd1105aa2aa86f394a881"}, {"line": 44216, "relation": "increases", "evidence": "Estradiol results in increased expression of PPARG protein", "citation": {"db": "PubMed", "db_id": "15964169"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "Peroxisome proliferator activated receptor subgraph": true}}, "source": 250, "target": 3212, "key": "8e933674e2e750fd6f1b712b1a337ce4"}, {"line": 47603, "relation": "increases", "evidence": "Estradiol results in increased expression of CRHR1 mRNA alternative form", "citation": {"db": "PubMed", "db_id": "20702571"}, "annotations": {"Subgraph": {"CRH subgraph": true}}, "source": 250, "target": 2562, "key": "efc6de30e5aa9489366c76aa5afd64cc"}, {"line": 43803, "relation": "decreases", "evidence": "[Estradiol co-treated with Tetrachlorodibenzodioxin] results in decreased expression of S100A9 mRNA", "citation": {"db": "PubMed", "db_id": "19484750"}, "annotations": {"Subgraph": {"Estrogen subgraph": true, "Toll like receptor subgraph": true}, "Confidence": {"Medium": true}}, "source": 15, "target": 3334, "key": "e0ceec314d0652c5ec07836d24b60edc"}, {"line": 43812, "relation": "increases", "evidence": "Isotretinoin results in increased expression of S100A9 mRNA", "citation": {"db": "PubMed", "db_id": "15982314"}, "annotations": {"Subgraph": {"Vitamin subgraph": true, "Toll like receptor subgraph": true}}, "source": 287, "target": 3334, "key": "c5c81404c0afdf1e39200c7271a0aac4"}, {"line": 44321, "relation": "decreases", "evidence": "Isotretinoin results in decreased expression of CHI3L1 mRNA", "citation": {"db": "PubMed", "db_id": "20436886"}, "annotations": {"Species": {"9606": true}, "Confidence": {"Medium": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 287, "target": 3957, "key": "0a1531ab02869d086e9a935f9b7636ee"}, {"line": 43878, "relation": "increases", "evidence": "Inducible costimulator, expressed by T lymphocytes, and inducible costimulator ligand, expressed by macrophages within the peripheral nerve, might not only be relevant in inducing an acute immune response but might also be critically involved in perpetuating inflammation in chronically immune-mediated disorders of the peripheral nervous system.", "citation": {"db": "PubMed", "db_id": "17242332"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 1463, "target": 575, "key": "449f8c5f8ccc313125e800904929e575"}, {"line": 43879, "relation": "increases", "evidence": "Inducible costimulator, expressed by T lymphocytes, and inducible costimulator ligand, expressed by macrophages within the peripheral nerve, might not only be relevant in inducing an acute immune response but might also be critically involved in perpetuating inflammation in chronically immune-mediated disorders of the peripheral nervous system.", "citation": {"db": "PubMed", "db_id": "17242332"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 1463, "target": 3815, "key": "6dc88440f0ca81749211ff052109b5e3"}, {"relation": "partOf", "source": 2865, "target": 1463, "key": "4fc4a3c22476132cb31d7b285031fd4f"}, {"relation": "partOf", "source": 2866, "target": 1463, "key": "75517c3363d286d9ee8ea638906f91ea"}, {"line": 43890, "relation": "decreases", "evidence": "PD-1 deficiency in P0+/- mice leads to a stronger increase of CD8+ T-lymphocytes and a substantially aggravated histological phenotype in the PNS compared to P0+/- mice expressing PD-1.", "citation": {"db": "PubMed", "db_id": "18996482"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"T cells signaling": true}}, "source": 3694, "target": 444, "key": "dc8727423f0a2af5418cae48a7c79bd8"}, {"line": 43891, "relation": "association", "evidence": "PD-1 deficiency in P0+/- mice leads to a stronger increase of CD8+ T-lymphocytes and a substantially aggravated histological phenotype in the PNS compared to P0+/- mice expressing PD-1.", "citation": {"db": "PubMed", "db_id": "18996482"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"T cells signaling": true}}, "source": 3694, "target": 3169, "key": "29ff75fe05517d8ef88c3a9c4300f629"}, {"line": 43891, "relation": "association", "evidence": "PD-1 deficiency in P0+/- mice leads to a stronger increase of CD8+ T-lymphocytes and a substantially aggravated histological phenotype in the PNS compared to P0+/- mice expressing PD-1.", "citation": {"db": "PubMed", "db_id": "18996482"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"T cells signaling": true}}, "source": 3169, "target": 3694, "key": "0a30ff333635408e648b336cbd92fcc3"}, {"line": 43903, "relation": "increases", "evidence": "Dysregulation of type I programmed cell death (apoptotic process) leads to a variety of diseases, among which cancer, cardiovascular and neurodegenerative disorders are the most prominent and widespread. Effector caspases such as caspases-3 and -7 get activated during the apoptotic signaling cascade and hence represent a biological target for the diagnosis and therapy of apoptotic process-associated diseases.", "citation": {"db": "PubMed", "db_id": "23223301"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "T cells signaling": true}, "Confidence": {"High": true}}, "source": 3169, "target": 2444, "key": "8a023a76ba7a18caa8a9e19f4e3384b0"}, {"line": 43904, "relation": "increases", "evidence": "Dysregulation of type I programmed cell death (apoptotic process) leads to a variety of diseases, among which cancer, cardiovascular and neurodegenerative disorders are the most prominent and widespread. Effector caspases such as caspases-3 and -7 get activated during the apoptotic signaling cascade and hence represent a biological target for the diagnosis and therapy of apoptotic process-associated diseases.", "citation": {"db": "PubMed", "db_id": "23223301"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "T cells signaling": true}, "Confidence": {"High": true}}, "source": 3169, "target": 2447, "key": "8c7c4eb17470e78b3db897cf873c2bfe"}, {"line": 43905, "relation": "increases", "evidence": "Dysregulation of type I programmed cell death (apoptotic process) leads to a variety of diseases, among which cancer, cardiovascular and neurodegenerative disorders are the most prominent and widespread. Effector caspases such as caspases-3 and -7 get activated during the apoptotic signaling cascade and hence represent a biological target for the diagnosis and therapy of apoptotic process-associated diseases.", "citation": {"db": "PubMed", "db_id": "23223301"}, "annotations": {"Subgraph": {"Caspase subgraph": true, "T cells signaling": true}, "Confidence": {"High": true}}, "source": 3169, "target": 478, "key": "6a68dd035dff096e42740af9e90c7b5c"}, {"line": 43920, "relation": "increases", "evidence": "B7 is a costimulatory molecule which is expressed on antigen-presenting cells and which plays a pivotal role in T cell activation and proliferation.", "citation": {"db": "PubMed", "db_id": "7533208"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 2472, "target": 456, "key": "3f9dfb25554d44bcecb53d8306b29d1c"}, {"line": 43921, "relation": "increases", "evidence": "B7 is a costimulatory molecule which is expressed on antigen-presenting cells and which plays a pivotal role in T cell activation and proliferation.", "citation": {"db": "PubMed", "db_id": "7533208"}, "annotations": {"Subgraph": {"T cells signaling": true}}, "source": 2472, "target": 458, "key": "3b9a84f8c62b0c481d052844e01d3b46"}, {"line": 44208, "relation": "decreases", "evidence": "[celecoxib results in decreased activity of and results in decreased expression of PTGS2 protein] which results in increased expression of PPARG", "citation": {"db": "PubMed", "db_id": "19522023"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 227, "target": 3212, "key": "1b1a119c3d12d48d13a69bab553b36c3"}, {"line": 44224, "relation": "increases", "evidence": "Ibuprofen binds to and results in increased activity of PPARG protein", "citation": {"db": "PubMed", "db_id": "18946735"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 980, "target": 3212, "key": "bca5c882c9797a2fa2d20aaf80f1bcc3"}, {"relation": "partOf", "source": 279, "target": 980, "key": "d9d4ad2f564e7870ac76dbaca940e5ea"}, {"line": 44232, "relation": "decreases", "evidence": "2,2-bis(4-glycidyloxyphenyl)propane inhibits the reaction [Indomethacin results in increased activity of PPARG protein]", "citation": {"db": "PubMed", "db_id": "12065695"}, "annotations": {"Subgraph": {"Peroxisome proliferator activated receptor subgraph": true}}, "source": 282, "target": 3212, "key": "15a3913f300b29d2d98825907d68519e"}, {"line": 44270, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}}, "source": 3588, "target": 4034, "key": "9c8aa843a6455e161858438d3783228b"}, {"line": 44271, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}}, "source": 3588, "target": 4057, "key": "0ffb8d31fe29714386452239363b289b"}, {"line": 44272, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}}, "source": 3588, "target": 4038, "key": "db2957fbdef205d1a27c7bf601fb997f"}, {"line": 44273, "relation": "increases", "evidence": "Our findings confirmed that treatment of microglia with anti-inflammatory cytokines such as IL-4 and IL-13 induces a gene profile typical of alternative activation similar to that previously observed in peripheral macrophages. We then used this gene expression profile to examine two mouse models of AD, the APPsw (Tg-2576) and Tg-SwDI, models for amyloid deposition and for cerebral amyloid angiopathy (CAA) respectively. AGI, MRC1 and YM1 mRNA levels were significantly increased in the Tg-2576 mouse brains compared to age-matched controls while TNFalpha and NOS2 mRNA levels, genes commonly associated with classical activation, increased or did not change, respectively. Only TNFalpha mRNA increased in the Tg-SwDI mouse brain. Alternative activation genes were also identified in brain samples from individuals with AD and were compared to age-matched control individuals. In AD brain, mRNAs for TNFalpha, AGI, MRC1 and the chitinase-3 like 1 and 2 genes (CHI3L1; CHI3L2) were significantly increased while NOS2 and IL-1beta mRNAs were unchanged.", "citation": {"db": "PubMed", "db_id": "17005052"}, "annotations": {"Confidence": {"Medium": true}, "Subgraph": {"Tumor necrosis factor subgraph": true, "Inflammatory response subgraph": true}}, "source": 3587, "target": 4079, "key": "bbd8d014151aa0bbad07d1a08969490d"}, {"line": 44293, "relation": "increases", "evidence": "The secreted protein, YKL-40, has been proposed as a biomarker of a variety of human diseases characterized by ongoing inflammation, including chronic neurologic pathologies such as multiple sclerosis and Alzheimer's disease. However, inflammatory mediators and the molecular mechanism responsible for enhanced expression of YKL-40 remained elusive. Using several mouse models of inflammation, we now show that YKL-40 expression correlated with increased expression of both IL-1 and IL-6. Furthermore, IL-1 together with IL-6 or the IL-6 family cytokine, oncostatin M, synergistically upregulated YKL-40 expression in both primary human and mouse astrocytes in vitro. The robust cytokine-driven expression of YKL-40 in astrocytes required both STAT3 and NF-kB binding elements of the YKL-40 promoter. In addition, YKL-40 expression was enhanced by constitutively active STAT3 and inhibited by dominant-negative IkBalpha. Surprisingly, cytokine-driven expression of YKL-40 in astrocytes was independent of the p65 subunit of NF-kB and instead required subunits RelB and p50. Mechanistically, we show that IL-1-induced RelB/p50 complex formation was further promoted by oncostatin M and that these complexes directly bound to the YKL-40 promoter. Moreover, we found that expression of RelB was strongly upregulated during inflammation in vivo and by IL-1 in astrocytes in vitro. We propose that IL-1 and the IL-6 family of cytokines regulate YKL-40 expression during sterile inflammation via both STAT3 and RelB/p50 complexes. These results suggest that IL-1 may regulate the expression of specific anti-inflammatory genes in nonlymphoid tissues via the canonical activation of the RelB/p50 complexes.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 1710, "target": 2509, "key": "7ab78cc3867f353270f848173b8ae323"}, {"line": 44294, "relation": "increases", "evidence": "The secreted protein, YKL-40, has been proposed as a biomarker of a variety of human diseases characterized by ongoing inflammation, including chronic neurologic pathologies such as multiple sclerosis and Alzheimer's disease. However, inflammatory mediators and the molecular mechanism responsible for enhanced expression of YKL-40 remained elusive. Using several mouse models of inflammation, we now show that YKL-40 expression correlated with increased expression of both IL-1 and IL-6. Furthermore, IL-1 together with IL-6 or the IL-6 family cytokine, oncostatin M, synergistically upregulated YKL-40 expression in both primary human and mouse astrocytes in vitro. The robust cytokine-driven expression of YKL-40 in astrocytes required both STAT3 and NF-kB binding elements of the YKL-40 promoter. In addition, YKL-40 expression was enhanced by constitutively active STAT3 and inhibited by dominant-negative IkBalpha. Surprisingly, cytokine-driven expression of YKL-40 in astrocytes was independent of the p65 subunit of NF-kB and instead required subunits RelB and p50. Mechanistically, we show that IL-1-induced RelB/p50 complex formation was further promoted by oncostatin M and that these complexes directly bound to the YKL-40 promoter. Moreover, we found that expression of RelB was strongly upregulated during inflammation in vivo and by IL-1 in astrocytes in vitro. We propose that IL-1 and the IL-6 family of cytokines regulate YKL-40 expression during sterile inflammation via both STAT3 and RelB/p50 complexes. These results suggest that IL-1 may regulate the expression of specific anti-inflammatory genes in nonlymphoid tissues via the canonical activation of the RelB/p50 complexes.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 1711, "target": 2509, "key": "a161fe0dc7bb2c02dc8e78590629c6f6"}, {"relation": "partOf", "source": 3157, "target": 1711, "key": "43fc01ab19e0b83ad74f3520624068ec"}, {"line": 46784, "relation": "increases", "evidence": "In addition, IL-6 and OSM moderately upregulate YKL-40 expression in human astrocytes [...] demonstrate that YKL-40 expression correlates with the expression of both IL-1beta and IL-6", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3157, "target": 2509, "key": "acc3039d0efeb3ce952563e11560c219"}, {"line": 44330, "relation": "decreases", "evidence": "2-(2-amino-3-methoxyphenyl)-4H-1-benzopyran-4-one inhibits the reaction [resveratrol results in decreased expression of CHI3L1 protein]", "citation": {"db": "PubMed", "db_id": "21029458"}, "source": 341, "target": 2509, "key": "25e9d0b4da3c9444b732febf9239ab9d"}, {"line": 44365, "relation": "increases", "evidence": "JAK-STAT signaling as an anti-inflammatory target. JAK-STAT signaling mediates the brain inflammation induced by LPS, IFN-gamma, ganglioside and thrombin. Curcumin activates SH2-containing phosphatase 2 (SHP2), while rosiglitazone and 15d-PGJ2 increase the expressions of SOCS1 and SOCS3. SHP2 and the SOCS proteins are typical negative feedback molecules of the JAK-STAT pathway.", "citation": {"db": "PubMed", "db_id": "26113788"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 203, "target": 3815, "key": "3e914fb72d60331ebb218b8861a79f95"}, {"line": 44374, "relation": "increases", "evidence": "JAK-STAT signaling as an anti-inflammatory target. JAK-STAT signaling mediates the brain inflammation induced by LPS, IFN-gamma, ganglioside and thrombin. Curcumin activates SH2-containing phosphatase 2 (SHP2), while rosiglitazone and 15d-PGJ2 increase the expressions of SOCS1 and SOCS3. SHP2 and the SOCS proteins are typical negative feedback molecules of the JAK-STAT pathway.", "citation": {"db": "PubMed", "db_id": "26113788"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "JAK-STAT signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 11, "target": 3390, "key": "43abc6b9860cb2a8b14c36c7f0c464a2"}, {"line": 44415, "relation": "increases", "evidence": "Caffeine is a widely consumed psychoactive drug, which is emerging as a protective agent against AD progression and in aging associated deficits. This occurs mainly through the blockade of adenosine A2A receptors, whose expression and function become aberrant throughout aging and in age-related pathologies.", "citation": {"db": "PubMed", "db_id": "21427489"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 2260, "target": 709, "key": "c32aa5271a430307776beb1be146f52e"}, {"line": 44416, "relation": "association", "evidence": "Caffeine is a widely consumed psychoactive drug, which is emerging as a protective agent against AD progression and in aging associated deficits. This occurs mainly through the blockade of adenosine A2A receptors, whose expression and function become aberrant throughout aging and in age-related pathologies.", "citation": {"db": "PubMed", "db_id": "21427489"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 709, "target": 3823, "key": "cc6f7a4af9ad7c9d4de8970b31316d53"}, {"line": 44429, "relation": "increases", "evidence": "developmental exposure of rodents to the heavy metal lead (Pb) increases APP (amyloid precursor protein) and Abeta production later in the aging brain", "citation": {"db": "PubMed", "db_id": "18157652"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 142, "target": 3584, "key": "58ef2ea3795f0b07f5b1d090ab46d6dc"}, {"line": 44466, "relation": "causesNoChange", "evidence": "We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age.", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 142, "target": 3584, "key": "56a6ea61c1c67ab1c6e948dd1aa5e420"}, {"line": 44431, "relation": "increases", "evidence": "developmental exposure of rodents to the heavy metal lead (Pb) increases APP (amyloid precursor protein) and Abeta production later in the aging brain", "citation": {"db": "PubMed", "db_id": "18157652"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Brain": true}}, "source": 142, "target": 2328, "key": "4633aa8bd04c501f60d234c3d800acf2"}, {"line": 44472, "relation": "causesNoChange", "evidence": "We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age.", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 142, "target": 3721, "key": "9b42d391ab333139b59b31b5efa2ed74"}, {"line": 44579, "relation": "increases", "evidence": "we have found elevations in the oxidative DNA marker 8-oxo-dG in older rats that had been developmentally exposed to Pb", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Species": {"10116": true}, "Developmental_Phase__of_patient": {"Developmental stage": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 142, "target": 30, "key": "2bba8ae4ac0f9ee03e7d8f32f7a74dd5"}, {"line": 44626, "relation": "increases", "evidence": "Our study has shown that developmental Pb exposure increases Abeta levels as well as 8-oxo-dG production in old age", "citation": {"db": "PubMed", "db_id": "16484331"}, "annotations": {"Species": {"10116": true}, "Developmental_Phase__of_patient": {"Developmental stage": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 142, "target": 30, "key": "a366d757d817320f8bcf9497d158fc9d"}, {"line": 44627, "relation": "increases", "evidence": "Our study has shown that developmental Pb exposure increases Abeta levels as well as 8-oxo-dG production in old age", "citation": {"db": "PubMed", "db_id": "16484331"}, "annotations": {"Species": {"10116": true}, "Developmental_Phase__of_patient": {"Developmental stage": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 142, "target": 80, "key": "4f34b1e71d7f6a7e09cbdfb71a48723d"}, {"line": 44647, "relation": "increases", "evidence": "the increase in Ogg1 activity tends to be greater in the Pb-exposed group as compared with the control.", "citation": {"db": "PubMed", "db_id": "16484331"}, "annotations": {"Species": {"10116": true}, "Developmental_Phase__of_patient": {"Old": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 142, "target": 3804, "key": "8cc3a610a78e0e72f9db48cb6b333aa6"}, {"line": 46278, "relation": "increases", "evidence": "lead (Pb) exposure in early life may increase amyloid precursor protein (APP) expression and promote the pathogenesis of Alzheimer's disease in old age. ", "citation": {"db": "PubMed", "db_id": "22764079"}, "source": 142, "target": 2315, "key": "1c8267257eae18c09861ac0a26561ac6"}, {"line": 44445, "relation": "increases", "evidence": "in a large autosomal dominant Alzheimer disease (AD) family the APP A713T mutation is present in the homozygous and heterozygous state.", "citation": {"db": "PubMed", "db_id": "25948718"}, "annotations": {"Species": {"9606": true}, "Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 2342, "target": 3823, "key": "b50eef624c6347b35aa4a6cc1e5d4d4d"}, {"line": 44457, "relation": "positiveCorrelation", "evidence": "We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age.", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 4077, "target": 4033, "key": "ebe1990e2d5e11e8862d8026fa5cb1a2"}, {"line": 44462, "relation": "orthologous", "evidence": "We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age.", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3721, "target": 3402, "key": "950ec821a4427e9c856dae012853eb05"}, {"line": 44490, "relation": "increases", "evidence": "Sp1 is one of the transcription factors that can promote the production of APP mRNA", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3721, "target": 4033, "key": "51c26029776c8b66d119c8be88ae6fbe"}, {"line": 44462, "relation": "orthologous", "evidence": "We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age.", "citation": {"db": "PubMed", "db_id": "15673661"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 3402, "target": 3721, "key": "b448b22e07e54972e522a611b176c873"}, {"line": 44535, "relation": "increases", "evidence": "Down regulation of DNMT results in hypomethylation of BACE1 and APP which are involved in Abeta production and causes upregulation of their protein expression; in turn SP1 transcription factor increases which finally results in Abeta production.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 3402, "target": 80, "key": "843e73aaf9a0fabe785e226199440555"}, {"line": 44522, "relation": "orthologous", "evidence": "More recently, it was reported that subchronic exposure to Cd inhibited DNA-methyltransferase activity in cultured cells, while chronic exposure enhanced the activity of the DNA-methyltransferase.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}, "Duration_of_Chemical_Exposure": {"Chronic": true}}, "source": 3626, "target": 2636, "key": "aa5d7e64520070e714eaba9d4e704070"}, {"line": 44522, "relation": "orthologous", "evidence": "More recently, it was reported that subchronic exposure to Cd inhibited DNA-methyltransferase activity in cultured cells, while chronic exposure enhanced the activity of the DNA-methyltransferase.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}, "Duration_of_Chemical_Exposure": {"Chronic": true}}, "source": 2636, "target": 3626, "key": "d496ba3fb594b16f173b2c9955ef25b1"}, {"line": 44529, "relation": "increases", "evidence": "Down regulation of DNMT results in hypomethylation of BACE1 and APP which are involved in Abeta production and causes upregulation of their protein expression; in turn SP1 transcription factor increases which finally results in Abeta production.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 2636, "target": 1756, "key": "c376c2ef9c60d98e35dc10a7b71d75d5"}, {"line": 44530, "relation": "increases", "evidence": "Down regulation of DNMT results in hypomethylation of BACE1 and APP which are involved in Abeta production and causes upregulation of their protein expression; in turn SP1 transcription factor increases which finally results in Abeta production.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 2636, "target": 1747, "key": "26c178113790546df17983caee3a7e81"}, {"line": 44534, "relation": "increases", "evidence": "Down regulation of DNMT results in hypomethylation of BACE1 and APP which are involved in Abeta production and causes upregulation of their protein expression; in turn SP1 transcription factor increases which finally results in Abeta production.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 2636, "target": 3402, "key": "c7e5e17f60aaeb58745447e14b828fa8"}, {"line": 44863, "relation": "positiveCorrelation", "evidence": "Up-regulation of DNMTs secondary to DNA damage may lead to increased LINE-1 methylation.", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 2636, "target": 1859, "key": "1f9585cc9ea99d98f00b9888d961d193"}, {"line": 44531, "relation": "decreases", "evidence": "Down regulation of DNMT results in hypomethylation of BACE1 and APP which are involved in Abeta production and causes upregulation of their protein expression; in turn SP1 transcription factor increases which finally results in Abeta production.", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"Medium": true}}, "source": 1756, "target": 2375, "key": "02311aab199d773a2d36de9bf6f060d3"}, {"line": 45061, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Amyloidogenic subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 1756, "target": 3823, "key": "6219b5edb856ae2dd1bed8905f5363cc"}, {"line": 45972, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1756, "target": 3943, "key": "b4d5f3490d61fcf7717ab03913a2fd4d"}, {"line": 45973, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1756, "target": 2328, "key": "ce36d22f90943cb1d70294efc75af8b5"}, {"line": 44597, "relation": "decreases", "evidence": "we found that presence of either 5-methylctosine or 8-oxo-dG dramatically suppressed Sp1 DNA-binding; ", "citation": {"db": "PubMed", "db_id": "19245828"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 28, "target": 3402, "key": "6d36bcb79093bae9ca41d88158de9b73"}, {"line": 44838, "relation": "increases", "evidence": "Epigenetics is believed to play a role in Alzheimer's disease (AD). DNA methylation, the most investigated epigenetic hallmark, is a reversible mechanism that modifies genome function and chromosomal stability through the addition of methyl groups to cytosine located in CpG dinucleotides to form 5 methylcytosine (5mC)", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 28, "target": 3823, "key": "5fbbbb80bd268e10cd4a3bccac701dd6"}, {"line": 44651, "relation": "orthologous", "evidence": "the increase in Ogg1 activity tends to be greater in the Pb-exposed group as compared with the control.", "citation": {"db": "PubMed", "db_id": "16484331"}, "annotations": {"Species": {"10116": true}, "Developmental_Phase__of_patient": {"Old": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3804, "target": 3153, "key": "23cb0042e362c72d192b45c4c29eca1e"}, {"line": 44662, "relation": "negativeCorrelation", "evidence": "It has been proposed that an accumulation of oxo8dG in the AD brain might be a result of a decrease in the activity of Ogg1,", "citation": {"db": "PubMed", "db_id": "16484331"}, "annotations": {"Species": {"10116": true}, "Developmental_Phase__of_patient": {"Old": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Activity"}, "source": 3804, "target": 30, "key": "7a738c582486001112253326b3d31b73"}, {"line": 44712, "relation": "positiveCorrelation", "evidence": "Patients with AD have been shown to exhibit a much higher level of 8-oxoG DNA lesions in brain than age-matched normal control subjects The increased level of 8-oxoG lesions in AD is likely due to reduced DNA repair in these patients", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Response DNA damage": true}, "Confidence": {"Medium": true}}, "source": 31, "target": 833, "key": "77593b92d0219de6ba4323d5d6bcae2b"}, {"line": 44712, "relation": "positiveCorrelation", "evidence": "Patients with AD have been shown to exhibit a much higher level of 8-oxoG DNA lesions in brain than age-matched normal control subjects The increased level of 8-oxoG lesions in AD is likely due to reduced DNA repair in these patients", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true, "Response DNA damage": true}, "Confidence": {"Medium": true}}, "source": 833, "target": 31, "key": "b8d74f0fe75dc34dcfafe00b1311b837"}, {"line": 44722, "relation": "association", "evidence": "Two additional OGG1 mutations, A53T and A288V, were also identified in AD patients and both were found to reduce 8-oxoG glycosylase activity", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3155, "target": 3823, "key": "a338e848c29d81ef5326b76b99e2f8c7"}, {"line": 44723, "relation": "decreases", "evidence": "Two additional OGG1 mutations, A53T and A288V, were also identified in AD patients and both were found to reduce 8-oxoG glycosylase activity", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3155, "target": 3153, "key": "42ca8fd9e6f932bb3d4758262cabb3eb"}, {"line": 44727, "relation": "association", "evidence": "Two additional OGG1 mutations, A53T and A288V, were also identified in AD patients and both were found to reduce 8-oxoG glycosylase activity", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3154, "target": 3823, "key": "95544e53e946214bc8ba581384bae608"}, {"line": 44728, "relation": "decreases", "evidence": "Two additional OGG1 mutations, A53T and A288V, were also identified in AD patients and both were found to reduce 8-oxoG glycosylase activity", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 3154, "target": 3153, "key": "792bec6bcb2b8c4680306fa52acf311a"}, {"line": 44737, "relation": "decreases", "evidence": "The C796-deleted OGG1 gene encodes a proteins lacking 8-oxoG glycosylase activity", "citation": {"db": "PubMed", "db_id": "17426120"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Activity"}, "source": 1902, "target": 3153, "key": "8047a326899a5196328ae55e49f7c54f"}, {"relation": "hasVariant", "source": 1901, "target": 1902, "key": "854f855fe412ddc7df7644747be051c7"}, {"line": 44769, "relation": "increases", "evidence": "shorter variants of Abeta peptides such as Abeta(1-8), Abeta(9-16) and Abeta(16) have also been shown to be potential participants in AD pathology", "citation": {"db": "PubMed", "db_id": "18781964"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "source": 2320, "target": 3823, "key": "4520415595553dac8c88aefe4eb3f178"}, {"line": 44770, "relation": "increases", "evidence": "shorter variants of Abeta peptides such as Abeta(1-8), Abeta(9-16) and Abeta(16) have also been shown to be potential participants in AD pathology", "citation": {"db": "PubMed", "db_id": "18781964"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "source": 2331, "target": 3823, "key": "970accee73f295e985fd8052172695fb"}, {"line": 44771, "relation": "increases", "evidence": "shorter variants of Abeta peptides such as Abeta(1-8), Abeta(9-16) and Abeta(16) have also been shown to be potential participants in AD pathology", "citation": {"db": "PubMed", "db_id": "18781964"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"Low": true}}, "source": 2317, "target": 3823, "key": "b2f750c64194c0f8472437a0ad0d9526"}, {"line": 44844, "relation": "increases", "evidence": "methylation of LINE-1 was increased in AD patients compared with healthy volunteers ", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1859, "target": 3823, "key": "51a784f5fa3a3fd3b70d839610e7774d"}, {"line": 45022, "relation": "causesNoChange", "evidence": "We did not find differences in LINE-1 methylation levels between patients with Alzheimer's disease and control participants", "citation": {"db": "PubMed", "db_id": "24164934"}, "annotations": {"Race": {"Colombian": true}, "Cell": {"blood cell": true}, "DiseaseState": {"Late-onset AD": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1859, "target": 3823, "key": "fcdc77095d9f77264040a54b5b300a49"}, {"line": 44851, "relation": "negativeCorrelation", "evidence": "Alu methylation was significantly decreased in patients with AD compared with healthy volunteers, as well as a significant increase of LINE-1 methylation ", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1859, "target": 1978, "key": "2773c5d14bb9f4169912412a8a82efa4"}, {"line": 44863, "relation": "positiveCorrelation", "evidence": "Up-regulation of DNMTs secondary to DNA damage may lead to increased LINE-1 methylation.", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1859, "target": 2636, "key": "2baa7e9940a13bd1a00a452e5b295608"}, {"line": 44864, "relation": "positiveCorrelation", "evidence": "Up-regulation of DNMTs secondary to DNA damage may lead to increased LINE-1 methylation.", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1859, "target": 2637, "key": "a8151bd6f6ee55782b6272a713750460"}, {"line": 44865, "relation": "positiveCorrelation", "evidence": "Up-regulation of DNMTs secondary to DNA damage may lead to increased LINE-1 methylation.", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1859, "target": 2638, "key": "6ff11db6afeb630b30f9addd243ef66b"}, {"relation": "hasVariant", "source": 1858, "target": 1859, "key": "244da53e8491b2f5200ccbb0088bab6f"}, {"line": 44850, "relation": "increases", "evidence": "Alu methylation was significantly decreased in patients with AD compared with healthy volunteers, as well as a significant increase of LINE-1 methylation ", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1978, "target": 3823, "key": "4e7e90a829b206d80746a0c57e462e26"}, {"line": 44851, "relation": "negativeCorrelation", "evidence": "Alu methylation was significantly decreased in patients with AD compared with healthy volunteers, as well as a significant increase of LINE-1 methylation ", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1978, "target": 1859, "key": "f7b215cc6b270b011ca63043cdd20bdb"}, {"relation": "hasVariant", "source": 1977, "target": 1978, "key": "e8749a7ae5302b2b85a77b0cc55e2cd4"}, {"line": 44857, "relation": "negativeCorrelation", "evidence": "A trend towards a decreased SAT-alpha DNA methylation was observed in patients with AD as compared with healthy volunteers", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1950, "target": 3823, "key": "4a50e4acbdfcc82811148927101e2765"}, {"relation": "hasVariant", "source": 1949, "target": 1950, "key": "d95be3edc4f9754901eb0a09923077c8"}, {"line": 44864, "relation": "positiveCorrelation", "evidence": "Up-regulation of DNMTs secondary to DNA damage may lead to increased LINE-1 methylation.", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 2637, "target": 1859, "key": "c17604b8ab490900d5c459294ae7cf09"}, {"line": 44865, "relation": "positiveCorrelation", "evidence": "Up-regulation of DNMTs secondary to DNA damage may lead to increased LINE-1 methylation.", "citation": {"db": "PubMed", "db_id": "21296655"}, "annotations": {"Cell": {"blood cell": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 2638, "target": 1859, "key": "8391b3b73cbb33fa727857f32e70963c"}, {"line": 44877, "relation": "negativeCorrelation", "evidence": "AD individuals are characterized by decreased plasma folate values, as well as increased plasma homocysteine (Hcy) levels, and there is indication of impaired S-adenosylmethionine (SAM) levels in AD brains. ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 115, "target": 3823, "key": "716c3997824479924786e63a70072416"}, {"line": 44883, "relation": "increases", "evidence": "Folate metabolism, also known as one-carbon metabolism, is required for the production of S-adenosylmethionine (SAM), which is the major DNA methylating agent", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 115, "target": 68, "key": "9fe43601a9dad307479991a8217f1758"}, {"line": 44929, "relation": "decreases", "evidence": "It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects. the MTHFR 677C>T polymorphism as a candidate AD risk factor", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Serum": true}}, "source": 115, "target": 3075, "key": "a34a9c07c72342614b5f12f04981a522"}, {"line": 44939, "relation": "decreases", "evidence": "MTHFR 677TT homozygous AD subjects had higher plasma tHcy values and/or decreased folate values compared to carriers of the MTHFR 677CT or 677CC genotypes", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 115, "target": 1883, "key": "9ab4f23824c401cb9ecd333f8fee40a8"}, {"line": 45812, "relation": "decreases", "evidence": "folate deficiency can induce apoptosis by increasing DR4 expression with DNA promoter hypomethylation in AD, together with upregulating DNMTs expression, which may be associated with folate deficiency-induced DNA damage.", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 115, "target": 478, "key": "0032d2a5d55f962583763c0fe99a40a6"}, {"line": 45813, "relation": "increases", "evidence": "folate deficiency can induce apoptosis by increasing DR4 expression with DNA promoter hypomethylation in AD, together with upregulating DNMTs expression, which may be associated with folate deficiency-induced DNA damage.", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 115, "target": 4026, "key": "1ec15027e5aaf845a2bebeaffbd3fc6d"}, {"line": 45815, "relation": "increases", "evidence": "folate deficiency can induce apoptosis by increasing DR4 expression with DNA promoter hypomethylation in AD, together with upregulating DNMTs expression, which may be associated with folate deficiency-induced DNA damage.", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 115, "target": 3963, "key": "8a953e9699dbe660f8ae7dd330a8b7b4"}, {"line": 46090, "relation": "negativeCorrelation", "evidence": "folate deficiency has been shown to increase PS-1 expression.", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 115, "target": 3258, "key": "ff809aa09f001509c5e53e9f42add078"}, {"line": 49459, "relation": "decreases", "evidence": "Reduction of homocysteine levels can be readily achieved with high doses of folic acid, vitamin B12, and vitamin B6 in the absence of vitamin B deficiency in the general population.", "citation": {"db": "PubMed", "db_id": "18854539"}, "source": 115, "target": 275, "key": "b9a234d06c98e4a03d5f5ed8fa2b3b58"}, {"line": 44879, "relation": "negativeCorrelation", "evidence": "AD individuals are characterized by decreased plasma folate values, as well as increased plasma homocysteine (Hcy) levels, and there is indication of impaired S-adenosylmethionine (SAM) levels in AD brains. ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 68, "target": 3823, "key": "ad0992a0e636a67855d456550bb90703"}, {"line": 46096, "relation": "positiveCorrelation", "evidence": "impaired DNA methylation resulting from a deficiency in S-adenosylmethionine (SAM, which is rapidly depleted following folate deprivation) leads to PS-1 overexpression, and that direct supplementation with SAM attenuates PS-1 overexpression. We determined that apple juice concentrate (AJC)contained levels of SAM comparable to those capable of suppressing PS-1 overexpression, suggesting that the SAM content of AJC represents a potential mechanism for preventing PS-1 overexpression, and further highlighting the possibility that AJC provides neuroprotection by mechanisms in addition to its antioxidant potential.", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 68, "target": 1926, "key": "b28b4d7d64f207a5c1b74b66c8a0d3ef"}, {"line": 46100, "relation": "decreases", "evidence": "impaired DNA methylation resulting from a deficiency in S-adenosylmethionine (SAM, which is rapidly depleted following folate deprivation) leads to PS-1 overexpression, and that direct supplementation with SAM attenuates PS-1 overexpression. We determined that apple juice concentrate (AJC)contained levels of SAM comparable to those capable of suppressing PS-1 overexpression, suggesting that the SAM content of AJC represents a potential mechanism for preventing PS-1 overexpression, and further highlighting the possibility that AJC provides neuroprotection by mechanisms in addition to its antioxidant potential.", "citation": {"db": "PubMed", "db_id": "17183144"}, "annotations": {"Confidence": {"Medium": true}}, "source": 68, "target": 3258, "key": "613abe4d5f63c2a0055093759846ed96"}, {"line": 46236, "relation": "increases", "evidence": "we demonstrate that BACE (beta-secretase), as well as PS1, is regulated by methylation and that the reduction of folate and vitamin B12 in culture medium can cause a reduction of SAM levels with consequent increase in presenilin1 and BACE levels and with increase in Abeta production. ", "citation": {"db": "PubMed", "db_id": "15607954"}, "source": 68, "target": 2328, "key": "2e878a4f680bb837cee6daf902b1ccb8"}, {"line": 44892, "relation": "negativeCorrelation", "evidence": "some authors observed significantly decreased levels of vitamin B12 in plasma of AD subjects respect to controls", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"MeSHAnatomy": {"Plasma": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 233, "target": 3823, "key": "12228f67547735b5182453576896cc3e"}, {"line": 44994, "relation": "negativeCorrelation", "evidence": "we additionally found that the AD population had significantly lower levels of vitamin B12 ", "citation": {"db": "PubMed", "db_id": "12784029"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "Race": {"Swedish": true}}, "source": 233, "target": 3823, "key": "fd6e97579f9a50c25fea3a857a9e5388"}, {"line": 46235, "relation": "decreases", "evidence": "we demonstrate that BACE (beta-secretase), as well as PS1, is regulated by methylation and that the reduction of folate and vitamin B12 in culture medium can cause a reduction of SAM levels with consequent increase in presenilin1 and BACE levels and with increase in Abeta production. ", "citation": {"db": "PubMed", "db_id": "15607954"}, "source": 233, "target": 68, "key": "12d78337a14b0fed600862ef654ea18c"}, {"line": 49460, "relation": "decreases", "evidence": "Reduction of homocysteine levels can be readily achieved with high doses of folic acid, vitamin B12, and vitamin B6 in the absence of vitamin B deficiency in the general population.", "citation": {"db": "PubMed", "db_id": "18854539"}, "source": 233, "target": 275, "key": "9138dbfc5e73e2ab48c8df728bee84d4"}, {"line": 44908, "relation": "negativeCorrelation", "evidence": "mean SAM(S-Adenosylmethionine) and SAH(S-Adenosylhomocysteine) levels were significantly reduced in all the areas of AD brains examined ", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 67, "target": 3823, "key": "1cfd6cabf0335acacc92fffc9617f6ec"}, {"line": 45378, "relation": "negativeCorrelation", "evidence": "In all brain areas of AD patients (cerebral cortex subdivisions, hippocampus, and putamen) decreased levels of SAM and SAH(S-adenosylhomocysteine) were observed", "citation": {"db": "PubMed", "db_id": "16040194"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 67, "target": 3823, "key": "12e15f816f731a95736a53cf8c9b7613"}, {"line": 44911, "relation": "negativeCorrelation", "evidence": "a significant decrease in Hcy levels was paralleled by a significant increase in MAT activity", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3045, "target": 275, "key": "854913df42022f71c0a5cf68832909a2"}, {"line": 44915, "relation": "decreases", "evidence": "two common MTHFR polymorphisms, namely 677C>T (Ala222Val) and 1298A>C (Glu429Ala), are known to reduce MTHFR activity.", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1882, "target": 3072, "key": "cc1cf722232181ce7285dca6666ea78b"}, {"line": 44924, "relation": "increases", "evidence": "It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects. the MTHFR 677C>T polymorphism as a candidate AD risk factor", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 1882, "target": 3823, "key": "21a3d78d4cbc4f3a5b8d0952f1e773f7"}, {"line": 44930, "relation": "positiveCorrelation", "evidence": "It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects. the MTHFR 677C>T polymorphism as a candidate AD risk factor", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Serum": true}}, "source": 1882, "target": 3823, "key": "7a7deec346f967b5f095e91d2b84dc68"}, {"line": 44931, "relation": "increases", "evidence": "It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects. the MTHFR 677C>T polymorphism as a candidate AD risk factor", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Serum": true}}, "source": 1882, "target": 3075, "key": "c43bbe224708a72fc608721a24a74a43"}, {"line": 45004, "relation": "increases", "evidence": "Homocysteine metabolism is influenced by genetic polymorphisms of the methylenetetrahydrofolate reductase (MTHFR 677 C-->T and 1298 A-->C) and transcobalamin genes (TCN1 776 C-->G ). We evaluated the association of homocysteine with Alzheimer's disease (AD) and the influence of related polymorphisms and APOE,", "citation": {"db": "PubMed", "db_id": "15073531"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1882, "target": 275, "key": "306862d38bc56062312c2bcf503a38d1"}, {"relation": "hasVariant", "source": 1880, "target": 1882, "key": "1b84204fa175a475bf79be817c6b545d"}, {"relation": "hasVariant", "source": 1880, "target": 1883, "key": "fea5973117fca337e6e8359a10ecacba"}, {"relation": "hasVariant", "source": 1880, "target": 1881, "key": "cb19c17136b0895040bcb09487738460"}, {"line": 45006, "relation": "increases", "evidence": "Homocysteine metabolism is influenced by genetic polymorphisms of the methylenetetrahydrofolate reductase (MTHFR 677 C-->T and 1298 A-->C) and transcobalamin genes (TCN1 776 C-->G ). We evaluated the association of homocysteine with Alzheimer's disease (AD) and the influence of related polymorphisms and APOE,", "citation": {"db": "PubMed", "db_id": "15073531"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1880, "target": 3072, "key": "883fe62e5ffa587c627b88f916ce54f3"}, {"line": 45150, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1880, "target": 571, "key": "b4fc971d2ab0431ba6ebd4c82019b7e1"}, {"relation": "hasVariant", "source": 3072, "target": 3073, "key": "599b5b493e961c895e36f7946411b916"}, {"relation": "hasVariant", "source": 3072, "target": 3074, "key": "f71fbe6cb54556e877be6fbc6c9b2d4c"}, {"relation": "hasVariant", "source": 3072, "target": 3075, "key": "1e915c93ee22139b2a2ca0cf5cc36d4c"}, {"line": 44916, "relation": "decreases", "evidence": "two common MTHFR polymorphisms, namely 677C>T (Ala222Val) and 1298A>C (Glu429Ala), are known to reduce MTHFR activity.", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 3073, "target": 3072, "key": "e40fa0230e59957cff889e87fe77151f"}, {"line": 44925, "relation": "increases", "evidence": "It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects. the MTHFR 677C>T polymorphism as a candidate AD risk factor", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}, "MeSHAnatomy": {"Plasma": true}}, "source": 3073, "target": 3823, "key": "51a2238dffac28dd3f4364bb627ba2c7"}, {"line": 44947, "relation": "increases", "evidence": "RFC1 80G>A and MTHFR 677C>T polymorphisms in a large cohort of AD patients", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1941, "target": 3823, "key": "cdb192193f8c9c92219cda097dc674b2"}, {"relation": "hasVariant", "source": 1939, "target": 1941, "key": "c781d572b842e8a777e299d035c506df"}, {"line": 44948, "relation": "increases", "evidence": "RFC1 80G>A and MTHFR 677C>T polymorphisms in a large cohort of AD patients", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1939, "target": 3308, "key": "f2bcad26076d39d25f44785456cba228"}, {"relation": "hasVariant", "source": 1939, "target": 1940, "key": "091df5bbeb9e09a32f3db6ee7ce7691f"}, {"line": 44954, "relation": "positiveCorrelation", "evidence": "observed association between the MTR 2756AA genotype and increased AD risk", "citation": {"db": "PubMed", "db_id": "21119889"}, "source": 1885, "target": 3823, "key": "4297abcee439149a87bec24d93f77b38"}, {"relation": "hasVariant", "source": 1884, "target": 1885, "key": "74360be568dd7bbfc59cddaca4d4b795"}, {"line": 44959, "relation": "increases", "evidence": "A common TC 776C>G polymorphism results in the replacement of proline with arginine (Pro259Arg) and negatively affects vitamin B12 metabolism, thus increasing plasma Hcy levels", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 1991, "target": 3447, "key": "7f770f79e1e0dd5c77251d9b4c14adbe"}, {"relation": "hasVariant", "source": 1990, "target": 1991, "key": "42a181d4e5d5d3afd4e12b7a2b3ac051"}, {"line": 44960, "relation": "decreases", "evidence": "A common TC 776C>G polymorphism results in the replacement of proline with arginine (Pro259Arg) and negatively affects vitamin B12 metabolism, thus increasing plasma Hcy levels", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 3447, "target": 233, "key": "99f5cc401d7b35cae0117039fbdcb3fd"}, {"line": 44961, "relation": "increases", "evidence": "A common TC 776C>G polymorphism results in the replacement of proline with arginine (Pro259Arg) and negatively affects vitamin B12 metabolism, thus increasing plasma Hcy levels", "citation": {"db": "PubMed", "db_id": "21119889"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 3447, "target": 275, "key": "cebf768b7877da49e938c0dbefe3181f"}, {"relation": "hasVariant", "source": 3446, "target": 3447, "key": "3375063ebfe8a2409ea430a687351593"}, {"line": 44978, "relation": "positiveCorrelation", "evidence": "Significant associations of reduced folate carrier gene(RFC1) A80G G allele and GG genotype with SAD(sporadic AD) were found", "citation": {"db": "PubMed", "db_id": "18258338"}, "annotations": {"Race": {"Chinese": true}, "Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1940, "target": 3823, "key": "a558d54c345706b43d3a77f2f627cf5b"}, {"line": 45005, "relation": "increases", "evidence": "Homocysteine metabolism is influenced by genetic polymorphisms of the methylenetetrahydrofolate reductase (MTHFR 677 C-->T and 1298 A-->C) and transcobalamin genes (TCN1 776 C-->G ). We evaluated the association of homocysteine with Alzheimer's disease (AD) and the influence of related polymorphisms and APOE,", "citation": {"db": "PubMed", "db_id": "15073531"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1881, "target": 275, "key": "33e42c2f4b1a80abbf570ddc0bf53db2"}, {"line": 45032, "relation": "positiveCorrelation", "evidence": "Two genes were increased in LOAD (C10orf105 and RARRES3),while three genes were decreased in LOAD", "citation": {"db": "PubMed", "db_id": "25380588"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 2407, "target": 3820, "key": "e8d004da99dac869b9f48a4662898899"}, {"line": 45039, "relation": "negativeCorrelation", "evidence": "Two networks involved in myelination and innate immune response specifically correlated to LOAD. FRMD4B and ST18, hub genes within the myelination network, were previously implicated in LOAD.Overall hypomethylation was observed across the genome in LOAD when compared to both controls", "citation": {"db": "PubMed", "db_id": "25380588"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1828, "target": 3820, "key": "59161d0ea1f018d7a8a8c070cb2bdec2"}, {"relation": "hasVariant", "source": 1827, "target": 1828, "key": "0f88b43a5f47661ce78299d43bb4a2a3"}, {"line": 45040, "relation": "negativeCorrelation", "evidence": "Two networks involved in myelination and innate immune response specifically correlated to LOAD. FRMD4B and ST18, hub genes within the myelination network, were previously implicated in LOAD.Overall hypomethylation was observed across the genome in LOAD when compared to both controls", "citation": {"db": "PubMed", "db_id": "25380588"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true}}, "source": 1984, "target": 3820, "key": "ece2f93f508e728b977ad3bbebd34104"}, {"relation": "hasVariant", "source": 1983, "target": 1984, "key": "3d493cb114450dbd93c2bb3e436d7e22"}, {"line": 45056, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Gamma secretase subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 1889, "target": 3823, "key": "61a509b0eec66b04e446c7470f7382bf"}, {"relation": "hasVariant", "source": 1888, "target": 1889, "key": "64301072a1cf3b29591c35071a01dfae"}, {"line": 45951, "relation": "orthologous", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1888, "target": 2051, "key": "cb12946eb31630b7130771a24dc5823f"}, {"line": 45066, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 1959, "target": 3823, "key": "29952c4729d954d8c0fc641e2b9d3fbd"}, {"relation": "hasVariant", "source": 1958, "target": 1959, "key": "8a86fb2a530eff8bbf376f801883ae5d"}, {"line": 45074, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Gamma secretase subgraph": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 1742, "target": 3823, "key": "8f610ad4e5a83f66badc149dc00b3bce"}, {"relation": "hasVariant", "source": 1741, "target": 1742, "key": "1a25b4d9d7e2ee793c30069823032277"}, {"line": 45080, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Response DNA damage": true, "Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 1854, "target": 3823, "key": "29b9430d3d31c114844b876401721687"}, {"relation": "hasVariant", "source": 1853, "target": 1854, "key": "cd3c96e33c6703c953301885a162f5d3"}, {"line": 45086, "relation": "positiveCorrelation", "evidence": "Most genes such as APP, NCSTN, BACE, SIN3A, APH1B, HTATIP or DNMT1 revealed the hypermethylation patterns in the majority of brain tissues and in the lymphocytes in AD", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Cell": {"lymphocyte": true}, "MeSHAnatomy": {"Brain": true}, "Subgraph": {"Epigenetic modification subgraph": true}, "Confidence": {"High": true}}, "source": 1808, "target": 3823, "key": "c5d90a41d612aaa09e1584f8065489bd"}, {"relation": "hasVariant", "source": 1807, "target": 1808, "key": "84e35be5c50153cce0702398f06428e9"}, {"line": 45151, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 1807, "target": 571, "key": "271f9c880839e892dca3a52ea2b692b7"}, {"line": 45120, "relation": "association", "evidence": "APOE ε4 mRNA level is increased in AD compared to controls.The APOE gene was found to be of bimodal structure, with a hypomethylated CpG-poor promoter and a fully methylated 3′-CpG-island", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Epigenetic modification subgraph": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"Medium": true}}, "source": 1745, "target": 3823, "key": "aaeecd4ad5b0991de079e1e2536b1348"}, {"line": 45129, "relation": "positiveCorrelation", "evidence": "APOE ε4 mRNA level is increased in AD compared to controls.The APOE gene was found to be of bimodal structure, with a hypomethylated CpG-poor promoter and a fully methylated 3′-CpG-island", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"APOE subgraph": true, "Epigenetic modification subgraph": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "Confidence": {"Medium": true}}, "source": 3933, "target": 3823, "key": "715c5bd4702733e57b80126ded4d87cb"}, {"line": 45138, "relation": "association", "evidence": "LOAD patients were usually correlated with a further demethylation of the PSEN1 and TFAM promoters.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1926, "target": 3820, "key": "0a281a77dc3b1656233a4535b0bb785b"}, {"line": 45367, "relation": "negativeCorrelation", "evidence": "hypomethylation of the promoter of the presenilin 1 (PS1) gene, which will lead to overexpression of presenilin 1 and, consequently, to increased Abeta(1-42) (Abeta42) formation ", "citation": {"db": "PubMed", "db_id": "16040194"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1926, "target": 3258, "key": "b03a8a660d3ec2470a5ac46596c20892"}, {"line": 45514, "relation": "negativeCorrelation", "evidence": "A notable exception was PSEN1, which was modestly hypomethylated in LOAD cases LOAD cases had reduced DNA methylation that was associated with increased PSEN1 gene expression, suggesting the DNA methylation change may be functional at this site", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 1926, "target": 3258, "key": "3ba3f71528d9025d258de9b292a10f67"}, {"line": 46097, "relation": "negativeCorrelation", "evidence": "impaired DNA methylation resulting from a deficiency in S-adenosylmethionine (SAM, which is rapidly depleted following folate deprivation) leads to PS-1 overexpression, and that direct supplementation with SAM attenuates PS-1 overexpression. We determined that apple juice concentrate (AJC)contained levels of SAM comparable to those capable of suppressing PS-1 overexpression, suggesting that the SAM content of AJC represents a potential mechanism for preventing PS-1 overexpression, and further highlighting the possibility that AJC provides neuroprotection by mechanisms in addition to its antioxidant potential.", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 1926, "target": 3258, "key": "58017a7a681175717638d524db8bbb83"}, {"line": 45512, "relation": "negativeCorrelation", "evidence": "A notable exception was PSEN1, which was modestly hypomethylated in LOAD cases LOAD cases had reduced DNA methylation that was associated with increased PSEN1 gene expression, suggesting the DNA methylation change may be functional at this site", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 1926, "target": 3823, "key": "8386f5a92aa30d355284270ead952c32"}, {"line": 45513, "relation": "negativeCorrelation", "evidence": "A notable exception was PSEN1, which was modestly hypomethylated in LOAD cases LOAD cases had reduced DNA methylation that was associated with increased PSEN1 gene expression, suggesting the DNA methylation change may be functional at this site", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 1926, "target": 4006, "key": "64d733933cd1bf0ec746ec151f0358db"}, {"line": 45975, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1926, "target": 4006, "key": "729da5ac1a10c32a47c59457636ca0e6"}, {"line": 45976, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1926, "target": 2328, "key": "b21f4f72f61fd84b03037e71b89e98e6"}, {"line": 46096, "relation": "positiveCorrelation", "evidence": "impaired DNA methylation resulting from a deficiency in S-adenosylmethionine (SAM, which is rapidly depleted following folate deprivation) leads to PS-1 overexpression, and that direct supplementation with SAM attenuates PS-1 overexpression. We determined that apple juice concentrate (AJC)contained levels of SAM comparable to those capable of suppressing PS-1 overexpression, suggesting that the SAM content of AJC represents a potential mechanism for preventing PS-1 overexpression, and further highlighting the possibility that AJC provides neuroprotection by mechanisms in addition to its antioxidant potential.", "citation": {"db": "PubMed", "db_id": "17183144"}, "source": 1926, "target": 68, "key": "44e872dec424a9e2ce29bc3f64b85fe3"}, {"line": 45150, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 571, "target": 1880, "key": "03dce5ce8c3c4bf021c1e83e45c57f03"}, {"line": 45151, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 571, "target": 1807, "key": "12d6baf0e14b81df9f359d5087f51177"}, {"line": 45159, "relation": "association", "evidence": "we found that some genes that participate in amyloid-beta processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition.", "citation": {"db": "PubMed", "db_id": "18628954"}, "annotations": {"Subgraph": {"Epigenetic modification subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 571, "target": 3820, "key": "398aa928854c2a6f345ecdf7d205d658"}, {"line": 45170, "relation": "increases", "evidence": "5-Aza increased both BACE1 mRNA and protein levels in a dose-dependent manner in two CpG sites at positions +298 and +351 in the 5'-UTR in BV-2 microglial cells.", "citation": {"db": "PubMed", "db_id": "22166205"}, "annotations": {"Species": {"10090": true}, "Transcriptionally_active_region": {"5 prime UTR": true}, "Cell": {"microglial cell": true}}, "source": 26, "target": 3593, "key": "c66cc6d124d21b8914ef45808568ba99"}, {"line": 45171, "relation": "increases", "evidence": "5-Aza increased both BACE1 mRNA and protein levels in a dose-dependent manner in two CpG sites at positions +298 and +351 in the 5'-UTR in BV-2 microglial cells.", "citation": {"db": "PubMed", "db_id": "22166205"}, "annotations": {"Species": {"10090": true}, "Transcriptionally_active_region": {"5 prime UTR": true}, "Cell": {"microglial cell": true}}, "source": 26, "target": 4035, "key": "9833db742a4ae4c931251168cee5b4e7"}, {"line": 45189, "relation": "decreases", "evidence": "Two CpG sites(+298 and +351) in the 5′-UTR of the BACE1 gene are specifically demethylated by 5-Aza treatment", "citation": {"db": "PubMed", "db_id": "22166205"}, "annotations": {"Transcriptionally_active_region": {"5 prime UTR": true}}, "source": 26, "target": 1756, "key": "34f6527b1630889e18e25bf6352c06bb"}, {"line": 45755, "relation": "increases", "evidence": "Treatment with the DNA methyltransferase inhibitor 5-aza-2′-deoxycytidine restored suppressed IGFBP3 expression", "citation": {"db": "PubMed", "db_id": "24964199"}, "annotations": {"Race": {"Swedish": true}, "Species": {"10090": true}, "UserdefinedCellLine": {"App transgenic": true}}, "source": 26, "target": 2876, "key": "c6f427a5f1593cda5e0725b90efcb96a"}, {"line": 45943, "relation": "positiveCorrelation", "evidence": "BACE1 mRNA levels were increased in aged 3xTg-AD mice as well as in AD PBMCs along with an increase in promoter accessibility and histone H3 acetylation, while the BACE1 promoter region was less accessible in PBMCs from MCI individuals", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 4035, "target": 3643, "key": "5e11f0715a2cbea1a5d3e27640790889"}, {"line": 45178, "relation": "increases", "evidence": "BACE1 and BACE2, are involved in the development of Alzheimer's disease by producing Abeta", "citation": {"db": "PubMed", "db_id": "22166205"}, "annotations": {"Species": {"10090": true}}, "source": 3594, "target": 2328, "key": "270810e4ca70ebdd7169b4c0cdbe708b"}, {"line": 45179, "relation": "orthologous", "evidence": "BACE1 and BACE2, are involved in the development of Alzheimer's disease by producing Abeta", "citation": {"db": "PubMed", "db_id": "22166205"}, "annotations": {"Species": {"10090": true}}, "source": 3594, "target": 2381, "key": "a45204e9b458a9943f6017e6e1eac033"}, {"line": 45180, "relation": "increases", "evidence": "BACE1 and BACE2, are involved in the development of Alzheimer's disease by producing Abeta", "citation": {"db": "PubMed", "db_id": "22166205"}, "annotations": {"Species": {"10090": true}}, "source": 1757, "target": 2381, "key": "cde105b281b14ed4e40f0b3dbeec12f4"}, {"line": 45208, "relation": "orthologous", "evidence": "Both exercised SAMR1 and SAMP8 mice showed significantly increased IGF1 plasma levels compared with their corresponding sedentary group ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Physical exercised group": true}}, "source": 3655, "target": 2871, "key": "ad8d1e44f2e7717d66650007ecfab898"}, {"line": 45209, "relation": "association", "evidence": "Both exercised SAMR1 and SAMP8 mice showed significantly increased IGF1 plasma levels compared with their corresponding sedentary group ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Physical exercised group": true}}, "source": 3655, "target": 3823, "key": "ae031c7dad405e20d7eca0070eff2ac4"}, {"line": 45210, "relation": "increases", "evidence": "Both exercised SAMR1 and SAMP8 mice showed significantly increased IGF1 plasma levels compared with their corresponding sedentary group ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Physical exercised group": true}}, "source": 1845, "target": 2871, "key": "4622d96b24681d9fd23e8013c6b2e141"}, {"line": 45223, "relation": "association", "evidence": "after the exercise intervention Bdnf levels in SAMP8 mice were undistinguishable from those found in sedentary SAMR1 controls . Neuritin gene, a well characterized target of BDNF, was upregulated in both strains by exercise training ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 1898, "target": 1758, "key": "3787dd376834fdf3b7cda805a70023d3"}, {"line": 45224, "relation": "increases", "evidence": "after the exercise intervention Bdnf levels in SAMP8 mice were undistinguishable from those found in sedentary SAMR1 controls . Neuritin gene, a well characterized target of BDNF, was upregulated in both strains by exercise training ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 1898, "target": 3142, "key": "3bf52dcbb81b854ac69ad0156c6a6d09"}, {"line": 45225, "relation": "positiveCorrelation", "evidence": "after the exercise intervention Bdnf levels in SAMP8 mice were undistinguishable from those found in sedentary SAMR1 controls . Neuritin gene, a well characterized target of BDNF, was upregulated in both strains by exercise training ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 3142, "target": 849, "key": "d58ba1829cea9d23fa6e86b3a038cc1c"}, {"line": 45231, "relation": "negativeCorrelation", "evidence": "Interestingly, miR28a-5p, miR-98-5p, and miR-148b-3p expression was significantly higher in sedentary SAMP8 compared with sedentary SAMR1 mice and this difference was further accentuated by exercise ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}, "Confidence": {"Medium": true}}, "source": 2113, "target": 849, "key": "19fdb0cbdf54942319d34879a4ee867d"}, {"line": 45232, "relation": "negativeCorrelation", "evidence": "Interestingly, miR28a-5p, miR-98-5p, and miR-148b-3p expression was significantly higher in sedentary SAMP8 compared with sedentary SAMR1 mice and this difference was further accentuated by exercise ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}, "Confidence": {"Medium": true}}, "source": 2125, "target": 849, "key": "c54c1bde2d6e23229aca10cba4108744"}, {"line": 45233, "relation": "negativeCorrelation", "evidence": "Interestingly, miR28a-5p, miR-98-5p, and miR-148b-3p expression was significantly higher in sedentary SAMP8 compared with sedentary SAMR1 mice and this difference was further accentuated by exercise ", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Experimental_Group": {"Sedentary group": true}, "Confidence": {"Medium": true}}, "source": 2103, "target": 849, "key": "6691b339433c6505f507e1b0626329ff"}, {"line": 45240, "relation": "negativeCorrelation", "evidence": "Voluntary exercise led to a significant decrease in Hdac3 gene expression exclusively in SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 3644, "target": 849, "key": "5b3c32a26afe48e967a40400a159659b"}, {"line": 45241, "relation": "orthologous", "evidence": "Voluntary exercise led to a significant decrease in Hdac3 gene expression exclusively in SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 3644, "target": 2816, "key": "787c91e0ca837b7482645f7123df8730"}, {"line": 45241, "relation": "orthologous", "evidence": "Voluntary exercise led to a significant decrease in Hdac3 gene expression exclusively in SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 2816, "target": 3644, "key": "f7ff40731ce086fc0c5fec70fc0ec3c1"}, {"line": 45242, "relation": "increases", "evidence": "Voluntary exercise led to a significant decrease in Hdac3 gene expression exclusively in SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 1836, "target": 2816, "key": "62090333db2f13218c240903c989a24a"}, {"line": 45246, "relation": "negativeCorrelation", "evidence": ". ANOVA analysis showed a downregulation tendency for Hdac5 gene in exercised compared with sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 3645, "target": 849, "key": "b53917748219741d1c53ad4a40567ba5"}, {"line": 45247, "relation": "orthologous", "evidence": ". ANOVA analysis showed a downregulation tendency for Hdac5 gene in exercised compared with sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 3645, "target": 2817, "key": "05b2eec2425c38a6c7e9f5642ef18ff1"}, {"line": 45247, "relation": "orthologous", "evidence": ". ANOVA analysis showed a downregulation tendency for Hdac5 gene in exercised compared with sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 2817, "target": 3645, "key": "e1d38977cf2b4b47d034448cbc56e317"}, {"line": 45248, "relation": "increases", "evidence": ". ANOVA analysis showed a downregulation tendency for Hdac5 gene in exercised compared with sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"Species": {"10090": true}, "MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 1837, "target": 2817, "key": "e6e4ee4dce755fa125c0a3dc09f8e28b"}, {"line": 45253, "relation": "negativeCorrelation", "evidence": "we found that the global acetylation levels of histone H3 (H3ac) were lower in sedentary SAMP8 than in SAMR1 mice and significantly increased upon exercise only in the senescent mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}}, "source": 2804, "target": 849, "key": "1dfd73f598a7209c1320d09ec5eedb3c"}, {"line": 45897, "relation": "positiveCorrelation", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2804, "target": 3823, "key": "68b797a2f06630e5f02df529e922521c"}, {"line": 45909, "relation": "increases", "evidence": "We also found that the increasing trend of cellular histone H3 acetylation is consistent with the increased expression of Abeta1-42 and Abeta1-40 peptides in N2a/APPswe and N2a/APPwt cells. This data suggested that the cellular histone hyperacetylation may regulate the genes that produce the Abeta peptides", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2804, "target": 2328, "key": "b8c9cc40909bb1c703d17e840a73bf1d"}, {"line": 46229, "relation": "increases", "evidence": "sodium butyrate (a well-known HDAC inhibitor) increased DHCR24 expression in SH-SY5Y cells by recruiting acetylated core histones H3 and H4 to the enhancer region, as demonstrated by transient transfection and chromatin immunoprecipitation assays.", "citation": {"db": "PubMed", "db_id": "20568014"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}}, "source": 2804, "target": 2627, "key": "b2db23b183d4b277f8ab074980655d5b"}, {"line": 46249, "relation": "positiveCorrelation", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2804, "target": 2397, "key": "37b028693cefd6285bfb8176da73385f"}, {"line": 46270, "relation": "increases", "evidence": "show that T3 treatment decreases both histone H3 acetylation and histone H3 lysine 4 methylation at the APP promoter and that chemical inhibitors of histone deacetylases and histone lysine demethylase abrogate T3-dependent APP silencing.", "citation": {"db": "PubMed", "db_id": "21458529"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2804, "target": 2315, "key": "1a42cf6124bde02722ee095ea1dcf1ef"}, {"line": 46319, "relation": "increases", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2804, "target": 2601, "key": "9fb39529e9c63cf661544e5076fdd61a"}, {"line": 46320, "relation": "increases", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2804, "target": 3959, "key": "014e193646f970c08184640d79c5f870"}, {"relation": "hasVariant", "source": 2803, "target": 2804, "key": "fe6652029b47ddb3629e7f834daee2c1"}, {"line": 45321, "relation": "association", "evidence": "HDAC inhibitor trichostatin A (TSA) resulted in prolonged p65 acetylation ", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 2803, "target": 1937, "key": "c4b6fff14772f5f2586d2220a079703d"}, {"line": 45864, "relation": "orthologous", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2803, "target": 3783, "key": "f6a12695c069ca0c415de6404d2deded"}, {"line": 45898, "relation": "association", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2803, "target": 1925, "key": "1e4bc55ba5dd0ec6fc8fdab9d2d3756f"}, {"line": 45901, "relation": "association", "evidence": "Our results revealed that histone H3 acetylation in PS1 and BACE1 promoters is markedly increased in N2a/APPswe cells", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2803, "target": 1755, "key": "770a4c983d215c1db8306f4d749ff6cd"}, {"line": 45914, "relation": "association", "evidence": "Our studies showed that p300-HAT inhibitor curcumin abrogates H3 hyperacetylation of PS1 and BACE1, curcumin decreases PS1 activity", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2803, "target": 3258, "key": "1751cf08c5ca1ff702df040756365498"}, {"line": 45915, "relation": "association", "evidence": "Our studies showed that p300-HAT inhibitor curcumin abrogates H3 hyperacetylation of PS1 and BACE1, curcumin decreases PS1 activity", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2803, "target": 2375, "key": "3078413cdb487e46c268349d729cdfed"}, {"line": 45950, "relation": "orthologous", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2803, "target": 3642, "key": "94665de37ad370e990f40c7e9cb07706"}, {"relation": "hasVariant", "source": 2803, "target": 2808, "key": "4861faee84c676c46e4ae5f47705811c"}, {"line": 46027, "relation": "association", "evidence": "the observed DNA methylation-associated inactivation of SPTBN4 could relate to both tau hyperphosphorylation and an increase in the amyloidic processing of amyloid precursor protein", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 2803, "target": 1870, "key": "27e6310abc20a668be232685d1607e98"}, {"line": 46125, "relation": "association", "evidence": "The TAU protein could be an important target for DUSP22-mediated dephosphorylation in AD. TAU Thr231 phosphorylation is one of the first phosphorylation events in AD and has a major role in TAU regulation", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2803, "target": 1870, "key": "fa1aef64355af07b3429e1275bde6c71"}, {"line": 46066, "relation": "association", "evidence": "Trichostatin A increases calpastatin levels epigenetically via histone acetylations(H4K5-Ac(acetylation at lysine 5 of histone H4), H3K9-Ac and H3K14-Ac).", "citation": {"db": "PubMed", "db_id": "24200051"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 2803, "target": 1764, "key": "d87baba68a90fa2fb8ea504b54698321"}, {"relation": "hasVariant", "source": 2803, "target": 2806, "key": "fe2f1504b1fd94cb9c09970f943fdff5"}, {"relation": "hasVariant", "source": 2803, "target": 2805, "key": "98e53e45a1da91d12ee8d71e99843213"}, {"relation": "hasVariant", "source": 2803, "target": 2809, "key": "d4a1b428a1f8a7b94a64a9d154e95dd9"}, {"line": 46129, "relation": "association", "evidence": "studies reporting a decrease of CREB phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2803, "target": 1783, "key": "c98f5d179daa4ea3cdce57c47e897502"}, {"line": 46131, "relation": "association", "evidence": "studies reporting a decrease of CREB phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2803, "target": 1785, "key": "4dcfa6b44d4eae0c41f2646fb31eb401"}, {"line": 46214, "relation": "association", "evidence": "Ubiquitination of the C-terminal tail of EAAT2 and EAAT1 has also been reported to be associated with AD", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 2803, "target": 1963, "key": "04f3434efcfa16b128cd5ac32cc4fb27"}, {"line": 46215, "relation": "association", "evidence": "Ubiquitination of the C-terminal tail of EAAT2 and EAAT1 has also been reported to be associated with AD", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 2803, "target": 1962, "key": "cdb8744d777544b5d707953388a29aab"}, {"relation": "hasVariant", "source": 2803, "target": 2810, "key": "2bc14e165cb29196ce27335ccfcc4831"}, {"line": 46248, "relation": "association", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2803, "target": 1758, "key": "820ac3788864f3ca59ad98bde57fb7df"}, {"relation": "hasVariant", "source": 2803, "target": 2807, "key": "45b192da55dc3b7af6512543d08ed3ca"}, {"line": 46268, "relation": "association", "evidence": "show that T3 treatment decreases both histone H3 acetylation and histone H3 lysine 4 methylation at the APP promoter and that chemical inhibitors of histone deacetylases and histone lysine demethylase abrogate T3-dependent APP silencing.", "citation": {"db": "PubMed", "db_id": "21458529"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2803, "target": 1746, "key": "0c36458ecc5f48adea7bd7b21e4deed4"}, {"line": 46321, "relation": "association", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2803, "target": 1793, "key": "7de9a8634fc576ac84ffe801dcddf7b8"}, {"line": 45257, "relation": "positiveCorrelation", "evidence": "We found a downregulation of histone deacetylase Hdac6 in the hippocampus of sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Species": {"10090": true}}, "source": 3646, "target": 849, "key": "d87066df338182a6d0a8747112489d3a"}, {"line": 45258, "relation": "orthologous", "evidence": "We found a downregulation of histone deacetylase Hdac6 in the hippocampus of sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Species": {"10090": true}}, "source": 3646, "target": 2818, "key": "9d8618c335f969caac2a510fe4d93dcd"}, {"line": 45259, "relation": "increases", "evidence": "We found a downregulation of histone deacetylase Hdac6 in the hippocampus of sedentary SAMP8 mice", "citation": {"db": "PubMed", "db_id": "24688469"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Gender": {"Female": true}, "Species": {"10090": true}}, "source": 1838, "target": 2818, "key": "d96011158d654cd65bc7fb97b7f040d7"}, {"line": 45276, "relation": "association", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2832, "target": 1746, "key": "78bfee30b58c2456a4c41ee7858d305a"}, {"line": 45285, "relation": "positiveCorrelation", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2832, "target": 2658, "key": "cb9144ed05b2b4420185dd8f443b4596"}, {"line": 45286, "relation": "positiveCorrelation", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2832, "target": 2699, "key": "342725271cbc67ae22c33152e1861a1f"}, {"line": 45287, "relation": "positiveCorrelation", "evidence": "we observed that APP down regulates expression of four immediate early genes, Egr1, c-Fos, Bdnf and Arc. Down regulation of Egr1, c-Fos and Bdnf transcription resulted from a decreased enrichment of acetylated histone H4 on the corresponding gene promoter in APP-/- mice.", "citation": {"db": "PubMed", "db_id": "24919190"}, "annotations": {"KnockoutMice": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"High": true}}, "source": 2832, "target": 2397, "key": "35ddc827d3d0f009c028a1b1c1d60963"}, {"relation": "hasVariant", "source": 2831, "target": 2832, "key": "9ee2e5a5ff003452e8d3861fbbccaff5"}, {"relation": "hasVariant", "source": 2831, "target": 2834, "key": "0bfc27e76946860adfedafda8740b2ef"}, {"relation": "hasVariant", "source": 2831, "target": 2833, "key": "6a4db68f9d7549eab1026ae6788a1363"}, {"line": 45320, "relation": "association", "evidence": "HDAC inhibitor trichostatin A (TSA) resulted in prolonged p65 acetylation ", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 2831, "target": 1937, "key": "0a3d14593846c0c8b225781c5d77f5b9"}, 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2815, "target": 820, "key": "dc9ac4133b42bdd070409bfbbb20367a"}, {"line": 45314, "relation": "decreases", "evidence": "HDAC2 as a negative regulator of memory formation and synaptic plasticity.", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 2815, "target": 745, "key": "c07c2465796ae7f0d653a00394b08225"}, {"line": 46245, "relation": "decreases", "evidence": "Inhibition of HDAC2 activity by trichostatin A substantially recovered the histone H3 acetylation in the promoter region of Bdnf exon VI and BDNF expression, thus mitigating the synaptic dysfunction and memory deficiency induced by amyloid fibrils.", "citation": {"db": "PubMed", "db_id": "25242807"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2815, "target": 2804, "key": "5a222eacf03152cd8841596bd5271d6c"}, {"line": 45317, "relation": "isA", "evidence": "HDAC inhibitor trichostatin A (TSA) resulted in prolonged p65 acetylation ", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 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"relation": "increases", "evidence": "Trichostatin A increases calpastatin levels epigenetically via histone acetylations(H4K5-Ac(acetylation at lysine 5 of histone H4), H3K9-Ac and H3K14-Ac).", "citation": {"db": "PubMed", "db_id": "24200051"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 180, "target": 2834, "key": "4cffbc4dc0e21ac04bf4b8e330e30be0"}, {"line": 46070, "relation": "increases", "evidence": "Trichostatin A increases calpastatin levels epigenetically via histone acetylations(H4K5-Ac(acetylation at lysine 5 of histone H4), H3K9-Ac and H3K14-Ac).", "citation": {"db": "PubMed", "db_id": "24200051"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 180, "target": 2806, "key": "20b92ececac37992a5c90a444da373fd"}, {"line": 46071, "relation": "increases", "evidence": "Trichostatin A increases calpastatin levels epigenetically via histone acetylations(H4K5-Ac(acetylation at lysine 5 of histone H4), H3K9-Ac and H3K14-Ac).", "citation": 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"relation": "association", "evidence": "HDAC inhibitor trichostatin A (TSA) resulted in prolonged p65 acetylation ", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 1937, "target": 2831, "key": "01aea2823f64773075595763b686ccea"}, {"line": 45321, "relation": "association", "evidence": "HDAC inhibitor trichostatin A (TSA) resulted in prolonged p65 acetylation ", "citation": {"db": "PubMed", "db_id": "21419233"}, "source": 1937, "target": 2803, "key": "31874886b842ce169f39fa523059dbd4"}, {"line": 45336, "relation": "negativeCorrelation", "evidence": "The hypermethylation observed at the NPE promoter was confirmed with a decreased NPE mRNA expression in the cell cultures", "citation": {"db": "PubMed", "db_id": "21419233"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1876, "target": 3992, "key": "19b98ea80fd70c357e22a41889b79b93"}, {"relation": "hasVariant", "source": 1875, "target": 1876, "key": "80a31bcce993cdbcd93bfa1290229a39"}, {"line": 45332, "relation": "increases", "evidence": "external application of beta-amyloid cerebral endothelial cell cultures results in extensive methylation at the neprilysin (NPE) gene promoter", "citation": {"db": "PubMed", "db_id": "21419233"}, "annotations": {"MeSHAnatomy": {"Endothelium": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1875, "target": 3057, "key": "4506710b0756057796f91111d8e43dc1"}, {"line": 45336, "relation": "negativeCorrelation", "evidence": "The hypermethylation observed at the NPE promoter was confirmed with a decreased NPE mRNA expression in the cell cultures", "citation": {"db": "PubMed", "db_id": "21419233"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 3992, "target": 1876, "key": "ce7aa0d526bbb1d69bdbc6f6e8cd2827"}, {"line": 45349, "relation": "positiveCorrelation", "evidence": "histone deacetylase (HDAC) activity is elevated in the AD model", "citation": {"db": "PubMed", "db_id": "25058791"}, "subject": {"modifier": "Activity"}, "source": 2819, "target": 3823, "key": "59d4337867291af609908a04aa4b6d19"}, {"line": 45353, "relation": "increases", "evidence": "HDAC inhibitor, suberoylanilide hydroxamic acid (SAHA; vorinostat) were shown to improve memory and cognitive function in a mouse model of Alzheimer's disease (AD)", "citation": {"db": "PubMed", "db_id": "25058791"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 368, "target": 820, "key": "f7546e1f8bbfde980a8758bf55b139ea"}, {"line": 45354, "relation": "increases", "evidence": "HDAC inhibitor, suberoylanilide hydroxamic acid (SAHA; vorinostat) were shown to improve memory and cognitive function in a mouse model of Alzheimer's disease (AD)", "citation": {"db": "PubMed", "db_id": "25058791"}, "annotations": {"Disease": {"Alzheimer's disease": true}}, "source": 368, "target": 812, "key": "ba6800ee6161c2ba261fc1b1747b73da"}, {"line": 45372, "relation": "decreases", "evidence": "administration of SAM(S-Adenosylmethionine) to neuroblastoma cell cultures downregulates both PS1 gene expression and Abeta40 production", "citation": {"db": "PubMed", "db_id": "16040194"}, "source": 8, "target": 3258, "key": "deacc5ecdc600a2a7bc5d6151381dbbf"}, {"line": 46036, "relation": "decreases", "evidence": "Homocysteine accumulation, frequently observed in AD patients, may be a sign of a metabolic alteration in the S-adenosylmethionine (SAM) cycle, which generates the overexpression of genes controlled by methylation of their promoters, when the cytosine in CpG moieties becomes unmethylated. The methylation of a gene involved in the processing of amyloid precursor protein may prevent Ab formation by silencing the gene. Here we report that SAM administration, in human neuroblastoma SK-N-SH cell cultures, downregulates PS1 gene expression and Ab production.", "citation": {"db": "PubMed", "db_id": "12706835"}, "annotations": {"Cell": {"neuroblast": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 8, "target": 3258, "key": "a7627a1d177539d3c919c1eb39537672"}, {"line": 45373, "relation": "decreases", "evidence": "administration of SAM(S-Adenosylmethionine) to neuroblastoma cell cultures downregulates both PS1 gene expression and Abeta40 production", "citation": {"db": "PubMed", "db_id": "16040194"}, "source": 8, "target": 2327, "key": "ec91547ff704e5633f7899efcf01a545"}, {"line": 45377, "relation": "negativeCorrelation", "evidence": "In all brain areas of AD patients (cerebral cortex subdivisions, hippocampus, and putamen) decreased levels of SAM and SAH(S-adenosylhomocysteine) were observed", "citation": {"db": "PubMed", "db_id": "16040194"}, "annotations": {"MeSHAnatomy": {"Brain": true}}, "source": 8, "target": 3823, "key": "72439398b6e8f58292d4903c0b54fa75"}, {"line": 46037, "relation": "decreases", "evidence": "Homocysteine accumulation, frequently observed in AD patients, may be a sign of a metabolic alteration in the S-adenosylmethionine (SAM) cycle, which generates the overexpression of genes controlled by methylation of their promoters, when the cytosine in CpG moieties becomes unmethylated. The methylation of a gene involved in the processing of amyloid precursor protein may prevent Ab formation by silencing the gene. Here we report that SAM administration, in human neuroblastoma SK-N-SH cell cultures, downregulates PS1 gene expression and Ab production.", "citation": {"db": "PubMed", "db_id": "12706835"}, "annotations": {"Cell": {"neuroblast": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 8, "target": 2328, "key": "778345b0e27fff4a29e4f0c931cc24c2"}, {"line": 46040, "relation": "increases", "evidence": "we showed that SAM downregulates PS1 expression, remethylating at least one CpG site", "citation": {"db": "PubMed", "db_id": "12706835"}, "annotations": {"Cell": {"neuroblast": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 8, "target": 1926, "key": "792d90ff4de801408a3c2052e7a39bfd"}, {"line": 45387, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 1943, "target": 3823, "key": "7f361377e37a5c127964c593687020ac"}, {"line": 45823, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "source": 1943, "target": 3823, "key": "3e28ea67bb0e5a60e03a31d37c416eb1"}, {"relation": "hasVariant", "source": 1942, "target": 1943, "key": "61e9e7d7f895d9c6eab8e6c79b0cf488"}, {"line": 45403, "relation": "association", "evidence": "RHDBF2 was found to be part of the same network of interacting proteins as the Alzheimer's risk gene PTK2B, suggesting a potential role in microglia and macrophage activity.", "citation": {"db": "PubMed", "db_id": "25157507"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 1942, "target": 1933, "key": "032ac0ce4506864a7fbdfdf7691a4323"}, {"line": 45389, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 1945, "target": 3823, "key": "b9140f8727aba247db51002f90fe9fcb"}, {"line": 45824, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "source": 1945, "target": 3823, "key": "80c599555db6f62b11cd3bb2df343783"}, {"relation": "hasVariant", "source": 1944, "target": 1945, "key": "237c44a44c4a22e6d3da6f80a5735c56"}, {"line": 45390, "relation": "increases", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 1944, "target": 3321, "key": "6cd3d574c8b805de77856f5292889edf"}, {"line": 45410, "relation": "association", "evidence": "Networks analyses suggest that RPL13 interacts with PTK2B and APP", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 3321, "target": 3280, "key": "97189caa90528953991d6143989567b4"}, {"line": 45411, "relation": "association", "evidence": "Networks analyses suggest that RPL13 interacts with PTK2B and APP", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 3321, "target": 2315, "key": "93fd95b0722d14e295472ec3ab2939dc"}, {"line": 45392, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 2014, "target": 3823, "key": "d24fdeec7c4a9e4671556c73f0107c07"}, {"relation": "hasVariant", "source": 2013, "target": 2014, "key": "4cfb8a6efec4b835946150b9e5d482d1"}, {"line": 45394, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 1773, "target": 3823, "key": "9dcaec3aa3bc66ea04b7639e3f3935f5"}, {"relation": "hasVariant", "source": 1772, "target": 1773, "key": "72de62baa61298e658f44e626438db60"}, {"line": 45396, "relation": "association", "evidence": "our new loci with differential methylation, RHBDF2, RPL13, C10orf54–CDH23 and ANK1, were independently identified in both studies, suggesting that these signals represent a real association with Alzheimer's disease risk", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 1740, "target": 3823, "key": "1c502766975f5990d2f204e01ab08050"}, {"line": 45821, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "source": 1740, "target": 3823, "key": "43fec7b2b70856413c142f51cd3d4c9d"}, {"relation": "hasVariant", "source": 1739, "target": 1740, "key": "838e5645c094fde5369951085b4f5196"}, {"line": 45403, "relation": "association", "evidence": "RHDBF2 was found to be part of the same network of interacting proteins as the Alzheimer's risk gene PTK2B, suggesting a potential role in microglia and macrophage activity.", "citation": {"db": "PubMed", "db_id": "25157507"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 1933, "target": 1942, "key": "f9f8228559753a1286287671f65099ff"}, {"line": 45404, "relation": "association", "evidence": "RHDBF2 was found to be part of the same network of interacting proteins as the Alzheimer's risk gene PTK2B, suggesting a potential role in microglia and macrophage activity.", "citation": {"db": "PubMed", "db_id": "25157507"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 1933, "target": 3823, "key": "1ec663dcbadc12a5b2998f16db7cf560"}, {"line": 45405, "relation": "increases", "evidence": "RHDBF2 was found to be part of the same network of interacting proteins as the Alzheimer's risk gene PTK2B, suggesting a potential role in microglia and macrophage activity.", "citation": {"db": "PubMed", "db_id": "25157507"}, "annotations": {"Subgraph": {"Vascular endothelial growth factor subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 1933, "target": 3280, "key": "bd0100f6f810041f223caafd05b83060"}, {"line": 45410, "relation": "association", "evidence": "Networks analyses suggest that RPL13 interacts with PTK2B and APP", "citation": {"db": "PubMed", "db_id": "25157507"}, "source": 3280, "target": 3321, "key": "4b00cddc4160305d0f9870cfa550ea95"}, {"line": 45428, "relation": "positiveCorrelation", "evidence": "we confirmed the mRNA OTC over-expression in AD", "citation": {"db": "PubMed", "db_id": "17893704"}, "annotations": {"Species": {"9606": true}, "Race": {"Caucasian": true}, "Gender": {"Male": true}}, "source": 3999, "target": 3823, "key": "be2f3ec5b8b3d58e7fec5a0fb5f3e749"}, {"line": 45432, "relation": "positiveCorrelation", "evidence": "BRCA1 gene which have been recently described to be over-expressed in AD", "citation": {"db": "PubMed", "db_id": "17893704"}, "annotations": {"Species": {"9606": true}, "Race": {"Caucasian": true}, "Gender": {"Male": true}}, "source": 2405, "target": 3823, "key": "f86c3a40fbd57fccb1beef556085ef8e"}, {"line": 45441, "relation": "negativeCorrelation", "evidence": "OTC promoter conversely to the rare -389 G/A and -241 A/G haplotype, increasing the risk of developing AD and potentially associated with a lower level of methylation.", "citation": {"db": "PubMed", "db_id": "17893704"}, "annotations": {"Species": {"9606": true}, "Race": {"Caucasian": true}, "Gender": {"Male": true}}, "source": 1904, "target": 3823, "key": "379ac670300ec7525b4a8b21aec0ad88"}, {"relation": "hasVariant", "source": 1903, "target": 1904, "key": "f7dd9c21105bb5e61b854fdebd1b4363"}, {"line": 45455, "relation": "negativeCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 1871, "target": 3823, "key": "7394cecc56c94141095c8f98e6b17df8"}, {"line": 45456, "relation": "negativeCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 1871, "target": 3010, "key": "750171f9bc038760e24f1a2c278ca9f3"}, {"line": 45459, "relation": "association", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 1871, "target": 2097, "key": "1c1362eca3c7d3745f85959b83645790"}, {"relation": "hasVariant", "source": 1870, "target": 1871, "key": "0604be3b9807cce3bfb27426421b9c47"}, {"line": 46027, "relation": "association", "evidence": "the observed DNA methylation-associated inactivation of SPTBN4 could relate to both tau hyperphosphorylation and an increase in the amyloidic processing of amyloid precursor protein", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 1870, "target": 2803, "key": "2ae7ddee16a3764656d5e1f896b0f715"}, {"line": 46125, "relation": "association", "evidence": "The TAU protein could be an important target for DUSP22-mediated dephosphorylation in AD. TAU Thr231 phosphorylation is one of the first phosphorylation events in AD and has a major role in TAU regulation", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1870, "target": 2803, "key": "e8d43b4aba89dbc999178b7374956d3d"}, {"line": 46123, "relation": "association", "evidence": "The TAU protein could be an important target for DUSP22-mediated dephosphorylation in AD. TAU Thr231 phosphorylation is one of the first phosphorylation events in AD and has a major role in TAU regulation", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1870, "target": 1810, "key": "8495e1f7fceff316f19d2e8c6ae21d44"}, {"line": 45462, "relation": "negativeCorrelation", "evidence": "analysis of post-mortem brains revealed aberrant CpG methylation in APP, MAPT and GSK3B genes sporadic cases of the AD brain, which in turn highlighted an enhanced expression of APP and MAPT. increased APP CpG 60–63 methylation was associated with APP expression enhancement, whereas increased MAPT 58–62 methylation was associated with MAPT expression suppression, thus leading to the conclusion that epigenetic changes in AD brains, as observed in our study, are associated with an increased expression of both APP and MAPT. ", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 1835, "target": 3823, "key": "22acbaea02005945e3d886a6fd767159"}, {"line": 45465, "relation": "negativeCorrelation", "evidence": "considering the position of GSK3B 78–82, we speculate that hypermethylation may act as a gene expression suppressor", "citation": {"db": "PubMed", "db_id": "24101602"}, "source": 1835, "target": 2794, "key": "3bb3b0c057f72b9834de607d7f84298c"}, {"line": 45475, "relation": "positiveCorrelation", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 1920, "target": 3823, "key": "8066fd85481ef587b79ce50ff60bc8e5"}, {"relation": "hasVariant", "source": 1919, "target": 1920, "key": "13da4198367b4831658a88fe4af1929f"}, {"line": 45477, "relation": "positiveCorrelation", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 1842, "target": 3823, "key": "d257f526baa51a68a241ada4d47a2053"}, {"relation": "hasVariant", "source": 1841, "target": 1842, "key": "4b41e7f771d025c035f22f7985bc7042"}, {"line": 45479, "relation": "increases", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 2122, "target": 3823, "key": "d6460361758aa4e6823308f70cca5059"}, {"line": 45485, "relation": "increases", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 2087, "target": 3823, "key": "03aca0275eced9e8d973f93acf93b297"}, {"line": 45486, "relation": "increases", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 2099, "target": 3823, "key": "1761b7b91f850e8ca9c62b3cd2a45918"}, {"line": 45487, "relation": "increases", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 2117, "target": 3823, "key": "4df06ba420f1e6e3063bb703429fa1c2"}, {"line": 45489, "relation": "negativeCorrelation", "evidence": "Several promoter and 3′ UTR regulatory binding motifs were enriched in the disease associated gene list. Hypermethylation in LOAD cases was observed in genes containing binding site motifs for transcription factors POU3F2 (p < 0.001) and HOXA4 (p = 0.004), and microRNAs MIR-9 (p = 0.002), MIR-518C (p < 0.001), MIR-1 (p = 0.025), and MIR-326 (p = 0.019). Genes containing MIR-140 (p = 0.04) and NFE2 (p = 0.019) motifs were hypomethylated in LOAD cases. ", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 1891, "target": 3823, "key": "66ea0569c0a935894950aaa106989362"}, {"relation": "hasVariant", "source": 1890, "target": 1891, "key": "2a27d5c66c61d0326a477daf838d29e3"}, {"line": 45493, "relation": "positiveCorrelation", "evidence": "One of the two sites corresponding to EPHA1 was associated with hypermethylation with age (p = 0.029; cg02376703). One of the two sites associated with PSEN2 was associated with hypomethylation with age (p = 0.030; cg25514304) in LOAD case", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 1816, "target": 3823, "key": "6f473280539494aed9f6e83556cd86f3"}, {"line": 45494, "relation": "negativeCorrelation", "evidence": "One of the two sites corresponding to EPHA1 was associated with hypermethylation with age (p = 0.029; cg02376703). One of the two sites associated with PSEN2 was associated with hypomethylation with age (p = 0.030; cg25514304) in LOAD case", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"DiseaseState": {"Late-onset AD": true}, "Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 1929, "target": 3823, "key": "dd17199c6535cb82392a3b80f45140ad"}, {"line": 45499, "relation": "positiveCorrelation", "evidence": "One of the sites in the Differentially Methylated Regions (DMRs) for DIRAS3 was more highly methylated in AD cases (43.4%) than controls (38.5%) (p = 0.024; cg21808053). One of the sites in the DMR for GNAS was hypomethylated with age among controls (p = 0.012; cg21625881) and the site for KCNQ1 was hypermethylated with age (p = 0.023; cg27119222).", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 1801, "target": 3823, "key": "8759dd838039b9647518eb96a55edf9d"}, {"relation": "hasVariant", "source": 1800, "target": 1801, "key": "f835160b98b27ab8a8454ad575ec545d"}, {"line": 45500, "relation": "negativeCorrelation", "evidence": "One of the sites in the Differentially Methylated Regions (DMRs) for DIRAS3 was more highly methylated in AD cases (43.4%) than controls (38.5%) (p = 0.024; cg21808053). One of the sites in the DMR for GNAS was hypomethylated with age among controls (p = 0.012; cg21625881) and the site for KCNQ1 was hypermethylated with age (p = 0.023; cg27119222).", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}}, "source": 1833, "target": 3823, "key": "0bdd3bd330068365df035cfa231b67bc"}, {"relation": "hasVariant", "source": 1832, "target": 1833, "key": "8cf94b2952f17d8d6d7021f1e4323631"}, {"line": 45505, "relation": "negativeCorrelation", "evidence": "AD cases had 7.3% lower methylation at TMEM59 than controls.DNA methylation and RNA expression were negatively correlated at TMEM59 LOAD cases had lower methylation and higher expression of TMEM59 than control samples", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 1996, "target": 3823, "key": "4102a3e9c2d78a6f0e347a84c3b7c629"}, {"line": 45507, "relation": "negativeCorrelation", "evidence": "AD cases had 7.3% lower methylation at TMEM59 than controls.DNA methylation and RNA expression were negatively correlated at TMEM59 LOAD cases had lower methylation and higher expression of TMEM59 than control samples", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 1996, "target": 4024, "key": "4a3fda4c57d7b18d76a534e305e62426"}, {"line": 45508, "relation": "negativeCorrelation", "evidence": "AD cases had 7.3% lower methylation at TMEM59 than controls.DNA methylation and RNA expression were negatively correlated at TMEM59 LOAD cases had lower methylation and higher expression of TMEM59 than control samples", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 1996, "target": 3470, "key": "9ed1b23cae490b0aa4cf30c6a2b283e4"}, {"relation": "hasVariant", "source": 1995, "target": 1996, "key": "ecfe0c38d93bfb47c22aa44f9e8a684e"}, {"line": 45507, "relation": "negativeCorrelation", "evidence": "AD cases had 7.3% lower methylation at TMEM59 than controls.DNA methylation and RNA expression were negatively correlated at TMEM59 LOAD cases had lower methylation and higher expression of TMEM59 than control samples", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 4024, "target": 1996, "key": "511b20d0ada047abba3234014e1a3f35"}, {"line": 45508, "relation": "negativeCorrelation", "evidence": "AD cases had 7.3% lower methylation at TMEM59 than controls.DNA methylation and RNA expression were negatively correlated at TMEM59 LOAD cases had lower methylation and higher expression of TMEM59 than control samples", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 3470, "target": 1996, "key": "73d4fb4ebe0c202d48597b79b78dbb02"}, {"line": 45513, "relation": "negativeCorrelation", "evidence": "A notable exception was PSEN1, which was modestly hypomethylated in LOAD cases LOAD cases had reduced DNA methylation that was associated with increased PSEN1 gene expression, suggesting the DNA methylation change may be functional at this site", "citation": {"db": "PubMed", "db_id": "22451312"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Transcriptionally_active_region": {"3 prime UTR": true}, "DiseaseState": {"Late-onset AD": true}}, "source": 4006, "target": 1926, "key": "7a1b0f9e2e2c368b8b5bdb0cb5e988bf"}, {"line": 45975, "relation": "negativeCorrelation", "evidence": "The expression of amyloid-beta precursor protein (APP), beta-site APP-cleaving enzyme 1 (BACE1), and presenilin 1 (PS1) was upregulated by demethylation in three gene promoters associated with the reduction of methyltransferases (DNMTs) leading to Abeta overproduction", "citation": {"db": "PubMed", "db_id": "21843603"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 4006, "target": 1926, "key": "1ed543bbf76cc9da53aefd72e3dca641"}, {"line": 45526, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2042, "target": 3823, "key": "3943aaf56271bc62f0188f7fe44117e4"}, {"line": 45527, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2042, "target": 4050, "key": "78f39a54e3e2521a8ea368e15f809e9b"}, {"relation": "hasVariant", "source": 2041, "target": 2042, "key": "b3b340e070b9e7a7fbb19df7dfb1d7b9"}, {"line": 45528, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2041, "target": 1843, "key": "5d843a6ead411d0e9d99b9673cba2634"}, {"line": 45527, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4050, "target": 2042, "key": "7b04778232c2256dc53df28d5913359a"}, {"line": 45528, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1843, "target": 2041, "key": "0afbcb3474262ab85a8825311a07e4b1"}, {"line": 45530, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2040, "target": 3823, "key": "8fdbd5d90df9947dc04a4e4b79c28fa6"}, {"line": 45531, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2040, "target": 4049, "key": "28c15edcc2ca544fa5521ef34f2217ad"}, {"relation": "hasVariant", "source": 2039, "target": 2040, "key": "6e7fee2766aa3796472fd8c6fc9c846e"}, {"line": 45533, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2039, "target": 1767, "key": "efd5f544585beccb4eb5d3a85eca2605"}, {"line": 45531, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4049, "target": 2040, "key": "f5bb728972aa9527cad65c8dd3841e3a"}, {"line": 45533, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 1767, "target": 2039, "key": "f8a0b98671259fef5595d142e687b760"}, {"line": 45536, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2072, "target": 3823, "key": "65d2c1fb4945d5971dfb85e45723eb5a"}, {"line": 45537, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2072, "target": 4072, "key": "35ebf11c8c3a85c0932492cae3b01fa0"}, {"relation": "hasVariant", "source": 2071, "target": 2072, "key": "15e166bc4b3601d10feea5ff3f5aa95f"}, {"line": 45538, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2071, "target": 1952, "key": "655c4be37a7812d35b8ab29f5755f1f9"}, {"line": 45537, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4072, "target": 2072, "key": "71b1b660cc9c2ffd27b6917352e57c84"}, {"line": 45538, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1952, "target": 2071, "key": "00a809187f57d470707a063a699334bb"}, {"line": 45540, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2028, "target": 3823, "key": "f6a7b8f778901aea41ad5763ce15daa3"}, {"line": 45541, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2028, "target": 4042, "key": "6230bef4f9b55c1d57b963d5016303dd"}, {"relation": "hasVariant", "source": 2027, "target": 2028, "key": "2464096d42e97e9d16129f40888ca35c"}, {"line": 45542, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2027, "target": 1790, "key": "b99d7f34cba95f5f600fc65669bfc5e7"}, {"line": 45541, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4042, "target": 2028, "key": "b6813771946aa94df7487830aa29f90f"}, {"line": 45542, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1790, "target": 2027, "key": "0867afa0a322e4d4fb009330db29701a"}, {"line": 45544, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2057, "target": 3823, "key": "0a972d2dfefd444d912bf674aeec4f0a"}, {"line": 45545, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2057, "target": 4064, "key": "fba1b0bbdba8ec2747db9748cb4f1f7c"}, {"relation": "hasVariant", "source": 2056, "target": 2057, "key": "9e855fa713f733e4b6ab744232c957bb"}, {"line": 45546, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2056, "target": 1907, "key": "3d620fd18ec5d227cdb9af6a0e1d79e8"}, {"line": 45545, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4064, "target": 2057, "key": "19f8455a2936f1c47c22a132f3e74be1"}, {"line": 45546, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1907, "target": 2056, "key": "54bb3e16547a2dd7b46827a8de3419fe"}, {"line": 45548, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2034, "target": 3823, "key": "6cc8823fced94b9da788673c35cf0e24"}, {"line": 45549, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2034, "target": 4046, "key": "faf63deba29bd99f1f5489347a25b667"}, {"relation": "hasVariant", "source": 2033, "target": 2034, "key": "57228dd1526077f5e88bc8eb930c99e8"}, {"line": 45550, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2033, "target": 1803, "key": "bdc21e2e6dd3d7d4eaa4f26b6d8921fb"}, {"line": 45549, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4046, "target": 2034, "key": "9fd62dcd21a0e0128c1a510b116eec8a"}, {"line": 45550, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1803, "target": 2033, "key": "9d2c6a9a3eb74fc549c66071ffc0e26f"}, {"line": 45552, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2050, "target": 3823, "key": "05f83a34897bb97717ac15c536f55b32"}, {"line": 45553, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2050, "target": 4058, "key": "2242f7ade654f1a9c3781c5182bd2414"}, {"relation": "hasVariant", "source": 2049, "target": 2050, "key": "ec98a56f72005d330ff29c7941f7ac06"}, {"line": 45554, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2049, "target": 1878, "key": "4ebff97d7a5397169b095698824200a0"}, {"line": 45553, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4058, "target": 2050, "key": "c0a0278765a491c1b18b86dd8ba6ab53"}, {"line": 45554, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1878, "target": 2049, "key": "9a59e425326b30818c78fc8ce9e8fd6e"}, {"line": 45556, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2059, "target": 3823, "key": "f7e748878d43051dc8e7401ec7c50e93"}, {"line": 45557, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2059, "target": 4065, "key": "04c14567a7c9d52d94c8afc05c045fdc"}, {"relation": "hasVariant", "source": 2058, "target": 2059, "key": "626c836893b898a8466effc4a5accade"}, {"line": 45558, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2058, "target": 1909, "key": "30a83b900a677ccc2669be9ed16ac1de"}, {"line": 45557, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4065, "target": 2059, "key": "14aecb3b66fa462d961a8e43265b9201"}, {"line": 45558, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1909, "target": 2058, "key": "e03b5d9b0bb949c33e4fc3ca74ecd402"}, {"line": 45561, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2046, "target": 3823, "key": "abedb687ddca1971195f7a3ce28bd3b7"}, {"line": 45562, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2046, "target": 4054, "key": "116f30a2cde74563cabaaeb953d07c99"}, {"relation": "hasVariant", "source": 2045, "target": 2046, "key": "bf21956aee068139be4646275716f0b2"}, {"line": 45563, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2045, "target": 1862, "key": "f893ee398cf4dc1cd382ffc1fa72114a"}, {"line": 45562, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4054, "target": 2046, "key": "0b99a3d2f6ca3c60df1b21580aafecfa"}, {"line": 45563, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1862, "target": 2045, "key": "f34e1346b438725c43e2ffd2d425cc76"}, {"line": 45565, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2026, "target": 3823, "key": "c7879e9023771a223485d63985eb6799"}, {"line": 45566, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2026, "target": 4041, "key": "b940c0c4f3be6e46d6823c44bc5fd9a8"}, {"relation": "hasVariant", "source": 2025, "target": 2026, "key": "078f891bcdfd781fb2e295501c21f73b"}, {"line": 45567, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2025, "target": 1787, "key": "bfd46dbd90ff0676ed4a61882da39581"}, {"line": 45566, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4041, "target": 2026, "key": "ca6b076dc5e24ebd2b5e5bd7179b8ee2"}, {"line": 45567, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1787, "target": 2025, "key": "9010a94ed6c488c8fffd399c02eb8f1e"}, {"line": 45569, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2063, "target": 3823, "key": "93266d9344386bf8d31b34544822bbf1"}, {"line": 45570, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2063, "target": 4067, "key": "9f3ea830aba5955bc945498544a065bf"}, {"relation": "hasVariant", "source": 2062, "target": 2063, "key": "ea032aa6f909b51a31cc659134835519"}, {"line": 45571, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2062, "target": 1924, "key": "7c87cb7ff6df1605769d4ae762936eb3"}, {"line": 45570, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4067, "target": 2063, "key": "af4ca9791c39fe7f18cfff0dbd1c4282"}, {"line": 45571, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1924, "target": 2062, "key": "e8d38243fcf2d29acee0731af375f948"}, {"line": 45574, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2044, "target": 3823, "key": "37d165f9fdea0e93b27252d7d60295a5"}, {"line": 45575, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2044, "target": 4053, "key": "33741aa71350bb393afc4d805d68b793"}, {"relation": "hasVariant", "source": 2043, "target": 2044, "key": "d549ac2a45400c2692421dff3cb741ba"}, {"line": 45576, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2043, "target": 1860, "key": "12a71ae8881e708cc01f8c842ee8c4a1"}, {"line": 45575, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4053, "target": 2044, "key": "7901ebd71592808eea53110006956ceb"}, {"line": 45576, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1860, "target": 2043, "key": "d9ca89e8a9ba36a2fa2e21c2dc39b873"}, {"line": 45578, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2075, "target": 3823, "key": "ebcf937dc678c0f7bdc1973494e8c710"}, {"line": 45579, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2075, "target": 4074, "key": "0eb463c4fc4f45568db273abc5b84da9"}, {"relation": "hasVariant", "source": 2074, "target": 2075, "key": "690354e04e65120d0884829901ca0401"}, {"line": 45580, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2074, "target": 1967, "key": "29f0491e4b9cfdb8adce187c9c83a8e7"}, {"line": 45579, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4074, "target": 2075, "key": "df73f02a1b6eb4aa1a84028ec73eab0b"}, {"line": 45580, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1967, "target": 2074, "key": "6b5edc98d576bfb05396e403b678ed8e"}, {"line": 45583, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2055, "target": 3823, "key": "218eeb89a3bbc2d5fd7e1d3be9e28a38"}, {"line": 45584, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2055, "target": 4063, "key": "f7aec265979deaab40cec20c9577fb47"}, {"relation": "hasVariant", "source": 2054, "target": 2055, "key": "0c59704af616b18fb939d4fcfca68f77"}, {"line": 45585, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2054, "target": 1900, "key": "ec5c9abdf5f0604bda5ad32bb522370d"}, {"line": 45584, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4063, "target": 2055, "key": "221435e30021eea7ae8fc9d221b2774a"}, {"line": 45585, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1900, "target": 2054, "key": "00ac6860c16cfab6cf2fece956d9d829"}, {"line": 45587, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2038, "target": 3823, "key": "bac9f6b750031afc293b310be180d243"}, {"line": 45588, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2038, "target": 4048, "key": "d3770ade28bf23b67ff4d383a1547ff9"}, {"relation": "hasVariant", "source": 2037, "target": 2038, "key": "7fae70e077cf24c88aa06a70d75819a6"}, {"line": 45589, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2037, "target": 1805, "key": "2b171a9dada8e586adb8001a41955b45"}, {"line": 45588, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4048, "target": 2038, "key": "c90fa7a794fe1571d9b079cecd304404"}, {"line": 45589, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1805, "target": 2037, "key": "b3739e34e5e2ad95ecd8918aaa4f8bf6"}, {"line": 45591, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2079, "target": 3823, "key": "0245f784b0fcb1c0f87f201bd87ff53e"}, {"line": 45592, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2079, "target": 4080, "key": "e8de5a01dfcc6bbfac641d862c4c6297"}, {"relation": "hasVariant", "source": 2078, "target": 2079, "key": "1011240e4a9fdc7588ec8659409915d6"}, {"line": 45593, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2078, "target": 2015, "key": "44b0e864438bccd8a68bf9947335228d"}, {"line": 45592, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4080, "target": 2079, "key": "78d00691a491812e27f5a535ad68c652"}, {"line": 45593, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2015, "target": 2078, "key": "74bbc9da79f34ba2adbd354fb341b9a9"}, {"line": 45595, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2077, "target": 3823, "key": "2aec6a05d3175762a499388410e2834e"}, {"line": 45596, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2077, "target": 4075, "key": "5a891e7835e0d30e4abfe987b99ca65c"}, {"relation": "hasVariant", "source": 2076, "target": 2077, "key": "f7bc8093dbfaf2d98e14b85eb25f8768"}, {"line": 45597, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2076, "target": 1968, "key": "80a88caa9867234fe9b6f5bd960a164c"}, {"line": 45596, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4075, "target": 2077, "key": "e23c38258ba1b307368417b6e2303743"}, {"line": 45597, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1968, "target": 2076, "key": "1e99a4cf57873b8ef5aece4e133828f7"}, {"line": 45600, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2070, "target": 3823, "key": "c8ef2ea52953f86fa0220cfbc0e8ac48"}, {"line": 45601, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2070, "target": 4071, "key": "a960ee6cf901ee0c425bab850eca1007"}, {"relation": "hasVariant", "source": 2069, "target": 2070, "key": "44cd6e3bc6d1d1ea4dc00cd9e942f66f"}, {"line": 45602, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2069, "target": 1938, "key": "7702260a01b3468b2bbf477c866b559d"}, {"line": 45601, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4071, "target": 2070, "key": "14076997094e4fb5b65b4897970d561f"}, {"line": 45602, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1938, "target": 2069, "key": "ff745201fcb39eeb67459c32c72eb5e3"}, {"line": 45604, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2068, "target": 3823, "key": "9e5661b0628f27004002b1d689ca9a2d"}, {"line": 45605, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2068, "target": 4070, "key": "aa78e56a7d05ddfadfd4e43b388d5a10"}, {"relation": "hasVariant", "source": 2067, "target": 2068, "key": "a43b2b7a03afcb3a7550ad278d5ab10d"}, {"line": 45606, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2067, "target": 1936, "key": "bbcb4add06c2463e70c0a014031162a8"}, {"line": 45605, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4070, "target": 2068, "key": "610dd0b82152a6c3440f6e8b5d3cd7af"}, {"line": 45606, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1936, "target": 2067, "key": "db5d576f4afb47d8cc9ac56452f5a5dd"}, {"line": 45609, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2048, "target": 3823, "key": "c94358754485145470b954d46773e6be"}, {"line": 45610, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2048, "target": 4056, "key": "0ec972f83fc99427f9ea6fa0c8496f12"}, {"relation": "hasVariant", "source": 2047, "target": 2048, "key": "b864f364d49d373b19a2a174310b29f6"}, {"line": 45611, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 2047, "target": 1872, "key": "ff00458c6724af5196d3ec372f1361fb"}, {"line": 45610, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 4056, "target": 2048, "key": "81b683b1635a8fb6c982acebc6542f74"}, {"line": 45611, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Confidence": {"Medium": true}}, "source": 1872, "target": 2047, "key": "5cf3d0b3d95abaf82de94bdca159d445"}, {"line": 45614, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2030, "target": 3823, "key": "7c2539cbf4512249fca5bb4e549acabb"}, {"line": 45615, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2030, "target": 4043, "key": "8e569f2eac7ae0321a0e075a76a71e53"}, {"relation": "hasVariant", "source": 2029, "target": 2030, "key": "600421b7d7ac1357478800c72c1da5a0"}, {"line": 45616, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2029, "target": 1795, "key": "9f3808efef385b0f6ae810bc50eefe19"}, {"line": 45615, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4043, "target": 2030, "key": "9426959924b952c559169377e3228829"}, {"line": 45616, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1795, "target": 2029, "key": "ee175ac40f62859c77f7a776bdf9ea28"}, {"line": 45618, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2022, "target": 3823, "key": "5f60cce87a96cb180218155135d3eea7"}, {"line": 45619, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2022, "target": 4039, "key": "d678e5e2f28304c541aa1b46b8467383"}, {"relation": "hasVariant", "source": 2021, "target": 2022, "key": "1e107072714f3176989753901c78f183"}, {"line": 45620, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2021, "target": 1781, "key": "d0897ff8629b9ca626eb69930d50ac52"}, {"line": 45619, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4039, "target": 2022, "key": "d6f2fb296bb5f62ac11e14d3de7f1d96"}, {"line": 45620, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1781, "target": 2021, "key": "1b0e8ece1553a9295c50f1ec1699b9df"}, {"line": 45622, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2053, "target": 3823, "key": "c74dd812b68b2be42374723969e6ea32"}, {"line": 45623, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2053, "target": 4062, "key": "8f7a5696499bc91cc67a5b623c6309dd"}, {"relation": "hasVariant", "source": 2052, "target": 2053, "key": "67b7dc83f8f51d23ca841ccc8e49a7db"}, {"line": 45624, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2052, "target": 1899, "key": "7023cb1245544f54a4bb1ed1c02d1fc8"}, {"line": 45623, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4062, "target": 2053, "key": "5aa323a451e9f3949a60a2cd103ca89d"}, {"line": 45624, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1899, "target": 2052, "key": "2e9b9c51357a03844ce24e380d8f5bcf"}, {"line": 45628, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2032, "target": 3823, "key": "121dca6d9960869506032b4f1674a166"}, {"line": 45629, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2032, "target": 4045, "key": "647e98b6d7ed69be614100ef4b9698a5"}, {"relation": "hasVariant", "source": 2031, "target": 2032, "key": "484cf1f01e48df25e61b0390953144b0"}, {"line": 45630, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2031, "target": 1796, "key": "54ee1ff832483644b8cfe1abceb103b7"}, {"line": 45629, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4045, "target": 2032, "key": "3b7495c2d04fa98d92a800c686930ec0"}, {"line": 45630, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1796, "target": 2031, "key": "c474f7ebd63fe75fa5dccf4fffc2e028"}, {"line": 45633, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Subgraph": {"Axonal guidance subgraph": true}}, "source": 2024, "target": 3823, "key": "6eef0fbfbb4f84231e25f9fd422e22b4"}, {"line": 45634, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Subgraph": {"Axonal guidance subgraph": true}}, "source": 2024, "target": 4040, "key": "38ba375c1145e24a88cf732630c26185"}, {"relation": "hasVariant", "source": 2023, "target": 2024, "key": "dc74c3ee98b27afb4d794dc151e7af8a"}, {"line": 45635, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Subgraph": {"Axonal guidance subgraph": true}}, "source": 2023, "target": 1786, "key": "0c55be13a77db9e6a4bc601c016bd2e8"}, {"line": 45634, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Subgraph": {"Axonal guidance subgraph": true}}, "source": 4040, "target": 2024, "key": "630615bf7325205aaba157c08a756dbd"}, {"line": 45635, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}, "Subgraph": {"Axonal guidance subgraph": true}}, "source": 1786, "target": 2023, "key": "b65001ef081682b5c2ed15007e86902b"}, {"line": 45638, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2019, "target": 3823, "key": "1261f800296204180b21fbfa83605bd9"}, {"line": 45639, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2019, "target": 4032, "key": "69235e6bfac5fee64e4e114aca678542"}, {"relation": "hasVariant", "source": 2018, "target": 2019, "key": "baf269c58398d107a7bf4295c043be24"}, {"line": 45640, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2018, "target": 1735, "key": "23c767f3d665d0f4e0caa70911e825ba"}, {"line": 45639, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4032, "target": 2019, "key": "35244f2ae9e53a9f8f1f371717a657ad"}, {"line": 45640, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1735, "target": 2018, "key": "2fcbe39038c214d62f79530d27d27b0f"}, {"line": 45642, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2066, "target": 3823, "key": "703676a5b672115184b3f1ffc0a92982"}, {"line": 45643, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2066, "target": 4069, "key": "fb87c1a3352e35c068495fe7c6f769cd"}, {"relation": "hasVariant", "source": 2065, "target": 2066, "key": "da6b753866faf827aed9ecb6a6e21009"}, {"line": 45644, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2065, "target": 1934, "key": "37ec91589cdb9336d30270cf22761ccf"}, {"line": 45643, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4069, "target": 2066, "key": "1956809b01d0303c236161812ced6dfc"}, {"line": 45644, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1934, "target": 2065, "key": "9843f21543fc0f0b2768dffbb4f0d26f"}, {"line": 45646, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2061, "target": 3823, "key": "058e6736a67e31fcba1c5cb99cf58f94"}, {"line": 45647, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2061, "target": 4066, "key": "c1a50bbbe8335d5a11bc1b86f0721117"}, {"relation": "hasVariant", "source": 2060, "target": 2061, "key": "adc7d87c731f292cbbc0f338e42dcfb3"}, {"line": 45648, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2060, "target": 1916, "key": "0c55b2012ac0c8ef9ba1e0809c98071a"}, {"line": 45647, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4066, "target": 2061, "key": "e93874f93336fe7843766158669f7efb"}, {"line": 45648, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1916, "target": 2060, "key": "01656466c138bf16eaf3bc399001f776"}, {"line": 45650, "relation": "positiveCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2036, "target": 3823, "key": "49658aed80f767cc1e78363a1bc59863"}, {"line": 45651, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2036, "target": 4047, "key": "5381bf97fdec525ce619d4a33eb19b29"}, {"relation": "hasVariant", "source": 2035, "target": 2036, "key": "aee36de4eb668ae64fa0b86d7d0d9320"}, {"line": 45652, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 2035, "target": 1804, "key": "a7b35d070e742eea0b5a0015b8362e68"}, {"line": 45651, "relation": "negativeCorrelation", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 4047, "target": 2036, "key": "7cbca75726120b83d52c99ab201ee2c0"}, {"line": 45652, "relation": "orthologous", "evidence": "In AD cases Hypomethylated/up-regulated genes in H4-sw cells are- HOXD4,FAM58A,SCMH1,CTIF,PCDHB15,DKK2,MRTO4,PDPN,LHPP,CRYAB,PPP4C,L3MBTL,SLC38A3,OGDHL, DNAJC7,WT1,SLC6A11,RERG,RBM47,TMEM180,CYP26A1,COLEC10,NXT2. Hypermethylated/down-regulated genes DDR2,CRMP1,ACAD11,QDPR,PLAC8,DLK2 in H4-sw cells.", "citation": {"db": "PubMed", "db_id": "22001921"}, "annotations": {"Race": {"Swedish": true}, "Cell": {"glioblast": true}, "Species": {"10090": true}}, "source": 1804, "target": 2035, "key": "2a190d19901681e2afc387c8cb81f5e7"}, {"line": 45660, "relation": "positiveCorrelation", "evidence": "we observed a higher HTERT methylation frequency in AD compared with elderly controls. ", "citation": {"db": "PubMed", "db_id": "18376059"}, "source": 1993, "target": 3823, "key": "c7ad86e52a01ac3db8cc47ea4ccad446"}, {"relation": "hasVariant", "source": 1992, "target": 1993, "key": "ebdaf98c47a90ca3a73671eda30ab39b"}, {"line": 45668, "relation": "negativeCorrelation", "evidence": "The electropherograms showed a very low degree of methylation at all CpG sites within the SST and absence of methylation in the SSTR4 CGI in AD patients", "citation": {"db": "PubMed", "db_id": "24602981"}, "annotations": {"Cell": {"blood cell": true}}, "source": 1980, "target": 3823, "key": "a74766a8e0b6832fa16e4d518666a679"}, {"relation": "hasVariant", "source": 1979, "target": 1980, "key": "5c069a95094a1b763a4b026fc22f9e6b"}, {"line": 45670, "relation": "negativeCorrelation", "evidence": "The electropherograms showed a very low degree of methylation at all CpG sites within the SST and absence of methylation in the SSTR4 CGI in AD patients", "citation": {"db": "PubMed", "db_id": "24602981"}, "annotations": {"Cell": {"blood cell": true}}, "source": 1982, "target": 3823, "key": "90a2dba638ecdf27cd47e61864584ae2"}, {"relation": "hasVariant", "source": 1981, "target": 1982, "key": "c36ab6db8b7238d14b65d6316e9cea53"}, {"line": 45679, "relation": "negativeCorrelation", "evidence": "we present further evidence in a limited number of cortex samples that epigenetic alterations in the brain are associated with AD. CpG No. 10 of TNF-alpha protein was significantly hypomethylated and further CpGs were modestly hypomethylated in AD patients in comparison to controls.", "citation": {"db": "PubMed", "db_id": "24556805"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1998, "target": 3823, "key": "e85a4c895c5381441fb862f93d728f54"}, {"line": 45686, "relation": "negativeCorrelation", "evidence": "In blood monocytes from our AD patients, no aberrant methylation of the TNF-alpha promoter was detectable, suggesting that the upregulation of TNF-alpha protein levels in the blood", "citation": {"db": "PubMed", "db_id": "24556805"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Cell": {"blood cell": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1998, "target": 4025, "key": "fa2968be9175d75ff3dace7464b1c730"}, {"line": 45697, "relation": "association", "evidence": "Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 was associated with pathological AD", "citation": {"db": "PubMed", "db_id": "25365775"}, "source": 1973, "target": 3823, "key": "74e097cca9a7f9d82281068f77228a62"}, {"line": 45699, "relation": "association", "evidence": "Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 was associated with pathological AD", "citation": {"db": "PubMed", "db_id": "25365775"}, "source": 1732, "target": 3823, "key": "97cca3ea29de60cdc2f4e7f0c55f7377"}, {"line": 45701, "relation": "association", "evidence": "Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 was associated with pathological AD", "citation": {"db": "PubMed", "db_id": "25365775"}, "source": 1965, "target": 3823, "key": "145fbadd8123e2f885dc3d917d8f4c1e"}, {"relation": "hasVariant", "source": 1964, "target": 1965, "key": "beeace2b53564fd87247d03de91af7e9"}, {"line": 45703, "relation": "association", "evidence": "Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 was associated with pathological AD", "citation": {"db": "PubMed", "db_id": "25365775"}, "source": 1761, "target": 3823, "key": "a30c1a3e01f2d72c646cb60c61599d6d"}, {"line": 45711, "relation": "negativeCorrelation", "evidence": "In LOAD subjects, there was a statistically significant reduction in Ser(16) phosphorylation (-30%; p = 0.041) and promoter methylation (-8%; p = 0.001), whereas Pin1 expression was significantly increased", "citation": {"db": "PubMed", "db_id": "22261503"}, "annotations": {"Cell": {"blood cell": true}}, "source": 3193, "target": 3820, "key": "eda4ff0489e2151eff76323f75c1e0d9"}, {"line": 45714, "relation": "negativeCorrelation", "evidence": "In LOAD subjects, there was a statistically significant reduction in Ser(16) phosphorylation (-30%; p = 0.041) and promoter methylation (-8%; p = 0.001), whereas Pin1 expression was significantly increased", "citation": {"db": "PubMed", "db_id": "22261503"}, "annotations": {"Cell": {"blood cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1914, "target": 3820, "key": "9d575921b17436baa491ccfd24eba688"}, {"line": 45715, "relation": "negativeCorrelation", "evidence": "In LOAD subjects, there was a statistically significant reduction in Ser(16) phosphorylation (-30%; p = 0.041) and promoter methylation (-8%; p = 0.001), whereas Pin1 expression was significantly increased", "citation": {"db": "PubMed", "db_id": "22261503"}, "annotations": {"Cell": {"blood cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1914, "target": 4002, "key": "d0423b5d36387f27040b1c40bf50af3f"}, {"relation": "hasVariant", "source": 1913, "target": 1914, "key": "50ba4135cd79824fb785d9a71fdde88d"}, {"line": 45715, "relation": "negativeCorrelation", "evidence": "In LOAD subjects, there was a statistically significant reduction in Ser(16) phosphorylation (-30%; p = 0.041) and promoter methylation (-8%; p = 0.001), whereas Pin1 expression was significantly increased", "citation": {"db": "PubMed", "db_id": "22261503"}, "annotations": {"Cell": {"blood cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 4002, "target": 1914, "key": "46b97f3f9d41fcdc4aec3135052bb53a"}, {"line": 45721, "relation": "positiveCorrelation", "evidence": "the transforming growth factor-beta1 (TGF-beta1) signaling pathway has been demonstrated to be hypermethylated in the AD brain ", "citation": {"db": "PubMed", "db_id": "24347181"}, "source": 1869, "target": 3823, "key": "eaebb28227df512d04077a7391cc6a4d"}, {"relation": "hasVariant", "source": 1868, "target": 1869, "key": "7ed9e1f2e8762ef1ad5976a1ccaef5f1"}, {"line": 45727, "relation": "positiveCorrelation", "evidence": "Results showed significant hypermethylation of mammalian orthologue of Sir2 (SIRT1) gene in AD patients ", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 1961, "target": 3823, "key": "d3b0e1e04c9d97267617d5abef7a78cf"}, {"line": 45735, "relation": "negativeCorrelation", "evidence": "significant decrease in expression of SIRT1 gene and increase in expression of APP gene were also found in AD patients", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 1961, "target": 4010, "key": "18e9b2fb177f9fae5068a017b19caa2c"}, {"relation": "hasVariant", "source": 1960, "target": 1961, "key": "ba90a46ce1436fce0c3265e8ccf6a872"}, {"line": 45728, "relation": "increases", "evidence": "Results showed significant hypermethylation of mammalian orthologue of Sir2 (SIRT1) gene in AD patients ", "citation": {"db": "PubMed", "db_id": "25287307"}, "source": 1960, "target": 4010, "key": "b875a76f2b671502fbc4a4aeeaaeb884"}, {"line": 45956, "relation": "orthologous", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1960, "target": 2073, "key": "c2318f0b5e2b28c8d342af876e9df0ec"}, {"line": 45751, "relation": "positiveCorrelation", "evidence": "Our results show that IGFBP3 promoter CpGs (25 out of 32) within the CpG island were hypermethylated in H4-sw cells", "citation": {"db": "PubMed", "db_id": "24964199"}, "annotations": {"Race": {"Swedish": true}, "Species": {"10090": true}, "UserdefinedCellLine": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1847, "target": 3823, "key": "a0628dd71b45c10a4cac35b1d70220df"}, {"relation": "hasVariant", "source": 1846, "target": 1847, "key": "04568a3cfbd89aa2eccf5cd48d84c039"}, {"line": 45752, "relation": "increases", "evidence": "Our results show that IGFBP3 promoter CpGs (25 out of 32) within the CpG island were hypermethylated in H4-sw cells", "citation": {"db": "PubMed", "db_id": "24964199"}, "annotations": {"Race": {"Swedish": true}, "Species": {"10090": true}, "UserdefinedCellLine": {"App transgenic": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1846, "target": 2876, "key": "8710828fa6f4d5435819c0531e1442ae"}, {"line": 45771, "relation": "positiveCorrelation", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product. In addition, a consistent reduction in DNA methylation at 5-LOX gene promoter was documented in AD versus healthy subjects.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Cell": {"blood cell": true}, "Confidence": {"High": true}}, "source": 2984, "target": 3823, "key": "b8f9fb60c4be47507740d5a02bee934f"}, {"line": 45772, "relation": "negativeCorrelation", "evidence": "We found a significant increase in 5-LOX gene expression in AD subjects compared to healthy controls, paralleled by increased 5-LOX protein and leukotriene B4, the 5-LOX product. In addition, a consistent reduction in DNA methylation at 5-LOX gene promoter was documented in AD versus healthy subjects.", "citation": {"db": "PubMed", "db_id": "23727898"}, "annotations": {"Cell": {"blood cell": true}, "Confidence": {"High": true}}, "source": 1738, "target": 3823, "key": "32c9f92121c1be02ee8ca0551122e7dd"}, {"relation": "hasVariant", "source": 1737, "target": 1738, "key": "3c5fce633cf38ead3aa58b9382b4f402"}, {"line": 45782, "relation": "negativeCorrelation", "evidence": "we also observed in LOAD subjects an increase in FAAH protein levels and activity , as well as a reduction in DNA methylation at faah gene promoter", "citation": {"db": "PubMed", "db_id": "22720070"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1824, "target": 3820, "key": "4d20e7fb85d8001687b78ede3c60f8b1"}, {"line": 45783, "relation": "negativeCorrelation", "evidence": "we also observed in LOAD subjects an increase in FAAH protein levels and activity , as well as a reduction in DNA methylation at faah gene promoter", "citation": {"db": "PubMed", "db_id": "22720070"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1824, "target": 3967, "key": "ee40918ff555ec4f7c2a82d99a7de7c6"}, {"line": 45784, "relation": "negativeCorrelation", "evidence": "we also observed in LOAD subjects an increase in FAAH protein levels and activity , as well as a reduction in DNA methylation at faah gene promoter", "citation": {"db": "PubMed", "db_id": "22720070"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "object": {"modifier": "Activity"}, "source": 1824, "target": 2686, "key": "8d8ebecedeab413b48e37d10fa875a84"}, {"relation": "hasVariant", "source": 1823, "target": 1824, "key": "0f8dc8feb55e980e57ec066330434a75"}, {"line": 45783, "relation": "negativeCorrelation", "evidence": "we also observed in LOAD subjects an increase in FAAH protein levels and activity , as well as a reduction in DNA methylation at faah gene promoter", "citation": {"db": "PubMed", "db_id": "22720070"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3967, "target": 1824, "key": "fd526d749f271a896cad7c06718357f9"}, {"line": 45784, "relation": "negativeCorrelation", "evidence": "we also observed in LOAD subjects an increase in FAAH protein levels and activity , as well as a reduction in DNA methylation at faah gene promoter", "citation": {"db": "PubMed", "db_id": "22720070"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "subject": {"modifier": "Activity"}, "source": 2686, "target": 1824, "key": "98eadd520f3da81281e4231cd3fd01df"}, {"line": 45793, "relation": "positiveCorrelation", "evidence": "Our results showed that BDNF methylation was significantly higher in AD cases than in the controls", "citation": {"db": "PubMed", "db_id": "25364831"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}}, "source": 1759, "target": 3823, "key": "83a3c1c8edb896c5160c52169164c0d5"}, {"line": 46158, "relation": "positiveCorrelation", "evidence": "AD brains showed a significantly increased methylation state of the promoter region of the BDNF gene, There was a significant decrease in BDNF mRNA in the AD brain", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1759, "target": 3823, "key": "a70cf5066cc9eaa5afa99a9e716fe7fc"}, {"line": 46159, "relation": "negativeCorrelation", "evidence": "AD brains showed a significantly increased methylation state of the promoter region of the BDNF gene, There was a significant decrease in BDNF mRNA in the AD brain", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1759, "target": 3946, "key": "16777e75699882ff32a513938e3b7892"}, {"line": 45802, "relation": "negativeCorrelation", "evidence": "The results showed that, the promoter of DR4 was hypomethylated in AD patients ", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2001, "target": 3823, "key": "4534b8eaf18278cdcbd7cd3d74bfba37"}, {"line": 45805, "relation": "negativeCorrelation", "evidence": "Accordance with the hypomethylation, increased expression level of DR4 was observed", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2001, "target": 4026, "key": "3a9a3bcc2a05455984551da9cf7bf062"}, {"relation": "hasVariant", "source": 2000, "target": 2001, "key": "149c1efc37fdc0aa6972a7f9f64402b9"}, {"line": 45805, "relation": "negativeCorrelation", "evidence": "Accordance with the hypomethylation, increased expression level of DR4 was observed", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 4026, "target": 2001, "key": "1a38fa419558ab4deb419d15a70a9d02"}, {"line": 45814, "relation": "increases", "evidence": "folate deficiency can induce apoptosis by increasing DR4 expression with DNA promoter hypomethylation in AD, together with upregulating DNMTs expression, which may be associated with folate deficiency-induced DNA damage.", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 4026, "target": 478, "key": "140a213578c3828fe715ee96d367ce27"}, {"line": 45808, "relation": "positiveCorrelation", "evidence": "DNMT1 and DNMT3a mRNA level were elevated (P < 0.05) in AD patients and folate deficient medium cultured cells compared with controls (P < 0.05), together with lower folate concentration in AD", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3963, "target": 3823, "key": "8e7428d1af4dce1cca4b5e5c31115571"}, {"line": 45809, "relation": "positiveCorrelation", "evidence": "DNMT1 and DNMT3a mRNA level were elevated (P < 0.05) in AD patients and folate deficient medium cultured cells compared with controls (P < 0.05), together with lower folate concentration in AD", "citation": {"db": "PubMed", "db_id": "25232375"}, "annotations": {"Cell": {"blood cell": true, "mononuclear cell": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3964, "target": 3823, "key": "ef0a67c25a0e5491082110810353d22e"}, {"line": 45822, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "source": 1799, "target": 3823, "key": "b8d278b10971485aebf025a60ffe9396"}, {"relation": "hasVariant", "source": 1798, "target": 1799, "key": "f33c76d71e19cd9bd224d085d4b83b12"}, {"line": 45827, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}}, "source": 1955, "target": 3823, "key": "f296d4291d18f587be62e668d07ada85"}, {"relation": "hasVariant", "source": 1954, "target": 1955, "key": "48f4e3abb51a269337ce37cadec7eddb"}, {"line": 45828, "relation": "association", "evidence": "several of the differentially expressed genes found in the differentially methylated regions - ANK1, DIP2A, RHBDF2, RPL13, SERPINF1 and SERPINF2 - connect to the AD susceptibility network derived from genetic studies.", "citation": {"db": "PubMed", "db_id": "25129075"}, "annotations": {"Subgraph": {"Plasminogen activator subgraph": true}}, "source": 1957, "target": 3823, "key": "cdc8bb6779015c66929e48aa842b8ffc"}, {"relation": "hasVariant", "source": 1956, "target": 1957, "key": "047a477a05674680c82df74cff9561b4"}, {"line": 45860, "relation": "positiveCorrelation", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3784, "target": 3823, "key": "a3bea28f239c6382bebd553b937d140a"}, {"line": 45861, "relation": "positiveCorrelation", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3784, "target": 4084, "key": "9a87f6c752ad63483a7d711bb9bd6dab"}, {"relation": "hasVariant", "source": 3783, "target": 3784, "key": "6efef78996f21d4fc4fcc7425065af1d"}, {"line": 45862, "relation": "association", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3783, "target": 2080, "key": "2e23864f49f6b64978727557c8458400"}, {"line": 45864, "relation": "orthologous", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 3783, "target": 2803, "key": "7cd35e7c854b2566d92f487b757b743f"}, {"line": 45861, "relation": "positiveCorrelation", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 4084, "target": 3784, "key": "eb0266574b13de5aa789f240b56d365c"}, {"line": 45862, "relation": "association", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2080, "target": 3783, "key": "30ee669cead5750e43a8f035286dc5c0"}, {"line": 45863, "relation": "increases", "evidence": "Significantly increased mRNA and expression of Cdk5 were observed in the hippocampal CA1 in the rats injected with amyloid fibrils. Increased acetylation of histone H3 was detected in the Cdk5 promoter region in hippocampal CA1 in these rats.", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2080, "target": 4084, "key": "95cf163b896efaf254f5ec7aa8556ad1"}, {"line": 45871, "relation": "increases", "evidence": "Further chromatin immunoprecipitation and bisulfite sequencing studies illustrated the decreased cytosine methylation in the Cdk5 promoter region in hippocampal CA1 in the rodent model of AD", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2080, "target": 3757, "key": "5098b63d47c23c26892a6da375cbe657"}, {"line": 45872, "relation": "orthologous", "evidence": "Further chromatin immunoprecipitation and bisulfite sequencing studies illustrated the decreased cytosine methylation in the Cdk5 promoter region in hippocampal CA1 in the rodent model of AD", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 2080, "target": 1774, "key": "36f9a0988f395a7199c828d22dc02409"}, {"line": 45870, "relation": "negativeCorrelation", "evidence": "Further chromatin immunoprecipitation and bisulfite sequencing studies illustrated the decreased cytosine methylation in the Cdk5 promoter region in hippocampal CA1 in the rodent model of AD", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 1775, "target": 3823, "key": "e837bc3bc6c168b23db6fef36c03ccf8"}, {"relation": "hasVariant", "source": 1774, "target": 1775, "key": "57708092f642f1cc48a1371e5921f01f"}, {"line": 45872, "relation": "orthologous", "evidence": "Further chromatin immunoprecipitation and bisulfite sequencing studies illustrated the decreased cytosine methylation in the Cdk5 promoter region in hippocampal CA1 in the rodent model of AD", "citation": {"db": "PubMed", "db_id": "25234403"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Subgraph": {"Cyclin-CDK subgraph": true}}, "source": 1774, "target": 2080, "key": "343a89cafab9f6e911f078d4a6f14e08"}, {"line": 45884, "relation": "negativeCorrelation", "evidence": "methylation status of androgen receptor promoter,may show us any deviation from the 50 : 50% X inactivation status in peripheral blood lymphocytes of women with AD", "citation": {"db": "PubMed", "db_id": "25159673"}, "annotations": {"Gender": {"Female": true}, "Cell": {"lymphocyte": true, "blood cell": true}, "MeSHAnatomy": {"Neurons": true}}, "source": 1752, "target": 3823, "key": "73c0cb2f19740bb35be50694f31a85a3"}, {"relation": "hasVariant", "source": 1751, "target": 1752, "key": "66066ab829b445a130e10672a0c6cc04"}, {"line": 45890, "relation": "positiveCorrelation", "evidence": "These findings suggest that four mechanisms may participate in the regulation of the PAD gene: the stress-related heat shock; the AP-1/Fos binding; the GC-rich element, and the possible methylation of the CpG region. PAD gene regulation could be of relevance for the progression of amyloid deposition in Alzheimer's Disease.", "citation": {"db": "PubMed", "db_id": "2690103"}, "source": 1906, "target": 3823, "key": "63bf751552d58a5c8f502fa92d20eac0"}, {"relation": "hasVariant", "source": 1905, "target": 1906, "key": "b18ad52f549f545ed4d61d0cc32a2f91"}, {"line": 45919, "relation": "increases", "evidence": "HAT activity of p300 stimulates the PS1 and BACE1 promoter histone hyperacetylation", "citation": {"db": "PubMed", "db_id": "25051175"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2673, "target": 2804, "key": "58a67a3340afe9e854a191c9ea7fb695"}, {"line": 45943, "relation": "positiveCorrelation", "evidence": "BACE1 mRNA levels were increased in aged 3xTg-AD mice as well as in AD PBMCs along with an increase in promoter accessibility and histone H3 acetylation, while the BACE1 promoter region was less accessible in PBMCs from MCI individuals", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3643, "target": 4035, "key": "a11019cbed458e211faced577bc1de2d"}, {"line": 45948, "relation": "negativeCorrelation", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3643, "target": 4059, "key": "8890526ee89b5e83d7688e73052c6e0f"}, {"line": 45954, "relation": "negativeCorrelation", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3643, "target": 4073, "key": "f7e96bf886183259ae239041f6d4c118"}, {"relation": "hasVariant", "source": 3642, "target": 3643, "key": "26e62fee4686d490d74135087a5dda4c"}, {"line": 45949, "relation": "association", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3642, "target": 2051, "key": "08e0ae51242b6750a7fe9f8d91b5f251"}, {"line": 45950, "relation": "orthologous", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3642, "target": 2803, "key": "7ee882bd42a85ae058dc0297e06323ce"}, {"line": 45955, "relation": "association", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3642, "target": 2073, "key": "a4742e96919858906c943ae759ee0bdd"}, {"line": 45944, "relation": "orthologous", "evidence": "BACE1 mRNA levels were increased in aged 3xTg-AD mice as well as in AD PBMCs along with an increase in promoter accessibility and histone H3 acetylation, while the BACE1 promoter region was less accessible in PBMCs from MCI individuals", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2020, "target": 1755, "key": "a29b590a0c46c6b5557ce69967bbf73f"}, {"line": 45945, "relation": "increases", "evidence": "BACE1 mRNA levels were increased in aged 3xTg-AD mice as well as in AD PBMCs along with an increase in promoter accessibility and histone H3 acetylation, while the BACE1 promoter region was less accessible in PBMCs from MCI individuals", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2020, "target": 3593, "key": "d1eff6436cefa4c553f2515e3ea00506"}, {"line": 45948, "relation": "negativeCorrelation", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 4059, "target": 3643, "key": "4c692c8bb94bcc8cc0d3dbb783fb7a15"}, {"line": 45949, "relation": "association", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2051, "target": 3642, "key": "eb02197ed91683a34bcabc00809b6c09"}, {"line": 45951, "relation": "orthologous", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2051, "target": 1888, "key": "a1393a694e6ed1a19b335d8263db4959"}, {"line": 45952, "relation": "increases", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2051, "target": 3683, "key": "d61d1a6074ae7a39b9faba39ec507905"}, {"line": 45954, "relation": "negativeCorrelation", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 4073, "target": 3643, "key": "39a55f5eb53d064ebc54a13501a648fc"}, {"line": 45955, "relation": "association", "evidence": "Ncstn was downregulated in aged 3xTg-AD brains with a condensation of chromatin and Sirt1 mRNA levels were decreased in these animals despite alterations in histone H3 acetylation", "citation": {"db": "PubMed", "db_id": "22728099"}, "annotations": {"UserdefinedCellLine": {"App transgenic": true}, "Cell": {"blood cell": true}, "Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 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"citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 3395, "target": 1970, "key": "6fa510461d8a4f99e8b170e174ec1162"}, {"line": 46016, "relation": "negativeCorrelation", "evidence": "We found an association between the gain in hypermethylation of TBXA2R, SORBS3 and SPTBN4 in the frontal cortex of the patients with Alzheimer's disease with a reduction of the corresponding RNA transcripts (Fig. 3B) and proteins", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 3411, "target": 1976, "key": "19ed495b823711a16821068736c37058"}, {"line": 46019, "relation": "positiveCorrelation", "evidence": "we found a similar trend for F2RL2 DNA methylation and RNA expression although the great variability of expression among samples precluded a definitive conclusion for this gene. 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", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 1822, "target": 2684, "key": "e3ae0ede8d1821a4564343653484834c"}, {"relation": "hasVariant", "source": 1821, "target": 1822, "key": "136718ed425e12ba280dd3b4ca7e31f1"}, {"line": 46020, "relation": "negativeCorrelation", "evidence": "we found a similar trend for F2RL2 DNA methylation and RNA expression although the great variability of expression among samples precluded a definitive conclusion for this gene. ", "citation": {"db": "PubMed", "db_id": "24030951"}, "annotations": {"MeSHAnatomy": {"Prefrontal Cortex": true}}, "source": 3966, "target": 1822, "key": "2d8337c7839e1f2bd031b5d49eddc0d0"}, {"line": 46021, "relation": "negativeCorrelation", "evidence": "we found a similar trend for F2RL2 DNA methylation and RNA expression although the great variability of expression among samples precluded a definitive conclusion for this gene. 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46123, "relation": "association", "evidence": "The TAU protein could be an important target for DUSP22-mediated dephosphorylation in AD. TAU Thr231 phosphorylation is one of the first phosphorylation events in AD and has a major role in TAU regulation", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1810, "target": 1870, "key": "982b565ad774d18266cf8b0753a5d2e4"}, {"relation": "hasVariant", "source": 1809, "target": 1810, "key": "5a13142b86279ec85e3ee9dcfdf16705"}, {"line": 46115, "relation": "decreases", "evidence": "DUSP22 is a likely candidate gene for involvement in the pathogenesis of the disorder since, as we demonstrate here, it inhibits PKA activity and thereby determines TAU phosphorylation status and CREB signaling.", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1809, "target": 2204, "key": "746029e99fe0f94ebc755b97314f732b"}, {"line": 46120, "relation": "negativeCorrelation", "evidence": "we had identified the presence of DUSP22 promoter hypermethylation and downregulation in the hippocampus of AD patients", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2646, "target": 1810, "key": "2f72be8ee42d33d02b4998e319710f5d"}, {"line": 46124, "relation": "increases", "evidence": "The TAU protein could be an important target for DUSP22-mediated dephosphorylation in AD. TAU Thr231 phosphorylation is one of the first phosphorylation events in AD and has a major role in TAU regulation", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2809, "target": 3823, "key": "f011a2399af910991793b17cbc089dc8"}, {"line": 46129, "relation": "association", "evidence": "studies reporting a decrease of CREB phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1783, "target": 2803, "key": "45f39cd8de1090de7d3791be95e3af0f"}, {"relation": "hasVariant", "source": 1783, "target": 1784, "key": "557d3dbfd3aa5822dc226d3641954ada"}, {"line": 46131, "relation": "association", "evidence": "studies reporting a decrease of CREB phosphorylation in AD", "citation": {"db": "PubMed", "db_id": "24436131"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}, "Disease": {"Alzheimer's disease": true}}, "source": 1785, "target": 2803, "key": "454f9d1f0059f439f3a899ca50376e2c"}, {"line": 46150, "relation": "positiveCorrelation", "evidence": "Only the AD brain showed hyper- and hypomethylated CpG islands in promoter regions for cAMP response element-binding protein and nuclear transcription factor kappa B genes, respectively", "citation": {"db": "PubMed", "db_id": "22760556"}, "source": 1784, "target": 3823, "key": "32475f4280bb5c6f287f1d6a2965649e"}, {"line": 46151, "relation": "negativeCorrelation", "evidence": "Only the AD brain showed hyper- and hypomethylated CpG islands in promoter regions for cAMP response element-binding protein and nuclear transcription factor kappa B genes, respectively", "citation": {"db": "PubMed", "db_id": "22760556"}, "source": 1893, "target": 3823, "key": "d13876f30f20c15110a4821c0b97ad62"}, {"line": 46184, "relation": "negativeCorrelation", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1893, "target": 3112, "key": "95544b738ad6910fc6fbf5dcca50f465"}, {"relation": "hasVariant", "source": 1892, "target": 1893, "key": "eef188e709f455e1577390e9470210ec"}, {"line": 46204, "relation": "increases", "evidence": "We have shown that Mn activates YY1 via activation of NF-kB", "citation": {"db": "PubMed", "db_id": "25064045"}, "source": 1892, "target": 3543, "key": "c4d5008e29ba64bc892cb625a60bb6e0"}, {"line": 46154, "relation": "negativeCorrelation", "evidence": "the COX-2 promoter CpG region showed decreased methylation in AD", "citation": {"db": "PubMed", "db_id": "22760556"}, "source": 1932, "target": 3823, "key": "fd4e2ad2458c489ebe983386e444ae8e"}, {"relation": "hasVariant", "source": 1931, "target": 1932, "key": "9ffa3765d138bed4210ddd505a0a810b"}, {"line": 46163, "relation": "positiveCorrelation", "evidence": "There was a significant increase in DNA methylation at the promoter region of synaptophysin in the AD", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1987, "target": 3823, "key": "285281f7fe73709fd98b507b73cae0cd"}, {"line": 46164, "relation": "negativeCorrelation", "evidence": "There was a significant increase in DNA methylation at the promoter region of synaptophysin in the AD", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1987, "target": 3438, "key": "000635226e61982d003d3c5f0d8e5226"}, {"line": 46165, "relation": "negativeCorrelation", "evidence": "There was a significant increase in DNA methylation at the promoter region of synaptophysin in the AD", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 1987, "target": 4015, "key": "df6f2f7b683a10eea489601aa7aa4d7e"}, {"relation": "hasVariant", "source": 1986, "target": 1987, "key": "228837b6d406df7045b674e2cc419fdf"}, {"line": 46165, "relation": "negativeCorrelation", "evidence": "There was a significant increase in DNA methylation at the promoter region of synaptophysin in the AD", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 4015, "target": 1987, "key": "6c85524273f127ced9b6ca089a7316d5"}, {"line": 46189, "relation": "negativeCorrelation", "evidence": "AD frontal cortex showed disease-specific hypermethylation in the promoter region of CREB, which may exacerbate reduced BDNF. Hypomethylation of NF-κB in the AD cortex may explain reported increased neuroinflammation due to upregulated NF-κB activity associated with its reduced methylation state. Furthermore, altered synaptic plasticity in AD is associated with reduced protein and mRNA levels of synaptophysin, which may be due to the hypermethylated state of its promoter region in AD brain samples. ", "citation": {"db": "PubMed", "db_id": "22760556"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 4015, "target": 761, "key": "35f3a810e7447c51a43f30263eb09158"}, {"line": 46199, "relation": "increases", "evidence": "Mn neurotoxicity is also known to contribute to the development of multiple neurodegenerative disorders including AD,", "citation": {"db": "PubMed", "db_id": "25064045"}, "source": 149, "target": 648, "key": "c582ec2649d2b93bbd1afdc0df5cd1d9"}, {"line": 46203, "relation": "increases", "evidence": "We have shown that Mn activates YY1 via activation of NF-kB", "citation": {"db": "PubMed", "db_id": "25064045"}, "source": 149, "target": 1892, "key": "bc2ee90377c53551231067e6cf8334d2"}, {"line": 46210, "relation": "negativeCorrelation", "evidence": "The YY1 pathway contributes to negative regulation of EAAT2", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 3543, "target": 3369, "key": "5c6d94aeaba17665d711b8dfbcd35047"}, {"line": 46211, "relation": "negativeCorrelation", "evidence": "The YY1 pathway contributes to negative regulation of EAAT2", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 3543, "target": 3368, "key": "a69a4ac4067a6f356ed51c39e69efe3e"}, {"line": 46211, "relation": "negativeCorrelation", "evidence": "The YY1 pathway contributes to negative regulation of EAAT2", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 3368, "target": 3543, "key": "76ba575720b5d6b2eb884d854bebd92b"}, {"line": 46214, "relation": "association", "evidence": "Ubiquitination of the C-terminal tail of EAAT2 and EAAT1 has also been reported to be associated with AD", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 1963, "target": 2803, "key": "03c2c8df6fadf8a5ea4cf245544cfcaa"}, {"line": 46215, "relation": "association", "evidence": "Ubiquitination of the C-terminal tail of EAAT2 and EAAT1 has also been reported to be associated with AD", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 1962, "target": 2803, "key": "9c098e529b66c81965691393267cd42b"}, {"line": 46216, "relation": "increases", "evidence": "Ubiquitination of the C-terminal tail of EAAT2 and EAAT1 has also been reported to be associated with AD", "citation": {"db": "PubMed", "db_id": "25064045"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}}, "source": 2810, "target": 3823, "key": "a0bfbe78128cfd36ad79d91db573c4a9"}, {"line": 46228, "relation": "increases", "evidence": "sodium butyrate (a well-known HDAC inhibitor) increased DHCR24 expression in SH-SY5Y cells by recruiting acetylated core histones H3 and H4 to the enhancer region, as demonstrated by transient transfection and chromatin immunoprecipitation assays.", "citation": {"db": "PubMed", "db_id": "20568014"}, "annotations": {"MeSHAnatomy": {"Cerebral Cortex": true}}, "source": 175, "target": 2804, "key": "68ddc324f6a5b4072152311158f261f5"}, {"line": 46262, "relation": "decreases", "evidence": "Thyroid hormone (T3) suppresses cerebral gene expression of the beta-amyloid precursor protein (APP), an integral membrane protein that plays a key role in the onset and progression of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "21458529"}, "source": 179, "target": 2315, "key": "befd2b7244f8c65aeca92886e9995dfb"}, {"line": 46266, "relation": "decreases", "evidence": "show that T3 treatment decreases both histone H3 acetylation and histone H3 lysine 4 methylation at the APP promoter and that chemical inhibitors of histone deacetylases and histone lysine demethylase abrogate T3-dependent APP silencing.", "citation": {"db": "PubMed", "db_id": "21458529"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 179, "target": 2804, "key": "b790eb204a5bb904f963c4ae43f5eab7"}, {"line": 46267, "relation": "decreases", "evidence": "show that T3 treatment decreases both histone H3 acetylation and histone H3 lysine 4 methylation at the APP promoter and that chemical inhibitors of histone deacetylases and histone lysine demethylase abrogate T3-dependent APP silencing.", "citation": {"db": "PubMed", "db_id": "21458529"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 179, "target": 2807, "key": "44ddc51e134ec6856eb82dcae48c4786"}, {"line": 46271, "relation": "increases", "evidence": "show that T3 treatment decreases both histone H3 acetylation and histone H3 lysine 4 methylation at the APP promoter and that chemical inhibitors of histone deacetylases and histone lysine demethylase abrogate T3-dependent APP silencing.", "citation": {"db": "PubMed", "db_id": "21458529"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}}, "source": 2807, "target": 2315, "key": "ad11b783e262b656e70e590d902b8ebc"}, {"line": 46284, "relation": "increases", "evidence": "The results showed that the exposure of Pb acetate could make the APP promoter hypomethylated.", "citation": {"db": "PubMed", "db_id": "22764079"}, "annotations": {"Encode_Feature_Types": {"Promoter": true}, "Confidence": {"Medium": true}}, "source": 141, "target": 1747, "key": "d063b6a524e771a9401ff602cbbe3f64"}, {"line": 46299, "relation": "decreases", "evidence": "late-onset AD is the association of the disease with hyperhomocysteinemia, low B vitamins and impaired methylation", "citation": {"db": "PubMed", "db_id": "20573497"}, "source": 33, "target": 275, "key": "f9cf436c07bc471db0a7f55c157a98c3"}, {"line": 49465, "relation": "causesNoChange", "evidence": "High-dose B vitamin supplementation in individuals with normal levels of B vitamins was effective in reducing homocysteine levels. This regimen of high-dose B vitamin supplements does not slow cognitive decline in individuals with mild to moderate AD.", "citation": {"db": "PubMed", "db_id": "18854539"}, "source": 33, "target": 3823, "key": "ede3c8eba0335ddabaa015e95fbc5ce5"}, {"line": 46310, "relation": "orthologous", "evidence": "the demethylation of Presenilin1 gene promoter in nutritionally-induced hyperhomocysteinemia in a transgenic mouse model clearly demonstrated that Presenilin1 is regulated by DNA methylation.", "citation": {"db": "PubMed", "db_id": "22272624"}, "annotations": {"Species": {"10090": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 2064, "target": 1925, "key": "ad6179033b6423023b33520cc7e3e99b"}, {"line": 46321, "relation": "association", "evidence": "We found that bilateral microinjection of amyloid beta (Abeta)1-40 fibrils into the hippocampal CA1 area of resulted in significant upregulation of CX3CR1 messenger RNA (mRNA) and protein expression (via increasing histone H3 acetylation in the Cx3cr1 promoter region), synaptic dysfunction, and cognitive impairment,", "citation": {"db": "PubMed", "db_id": "23855980"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Encode_Feature_Types": {"Promoter": true}}, "source": 1793, "target": 2803, "key": "ff14537861a146c0f0749d658a7592e7"}, {"line": 46332, "relation": "decreases", "evidence": "PEBP plays a pivotal modulatory role in several signal transduction pathways. PEBP inhibits the MAPK pathway through interacting with Raf-1, so it's also known as Raf-1 kinase inhibitor protein (RKIP). PEBP is involved in the regulation of PKC, G-protein-coupled receptor and NF-kappaB signaling pathway as well. In clinical researches, it was found that as the precursor of hippocampal cholinergic neurostimulating peptide (HCNP), PEBP has an important effect on the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19803424"}, "source": 3181, "target": 451, "key": "0763836685ce4102f4c7313b190a4b75"}, {"line": 46334, "relation": "increases", "evidence": "PEBP plays a pivotal modulatory role in several signal transduction pathways. PEBP inhibits the MAPK pathway through interacting with Raf-1, so it's also known as Raf-1 kinase inhibitor protein (RKIP). PEBP is involved in the regulation of PKC, G-protein-coupled receptor and NF-kappaB signaling pathway as well. In clinical researches, it was found that as the precursor of hippocampal cholinergic neurostimulating peptide (HCNP), PEBP has an important effect on the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19803424"}, "source": 3181, "target": 3255, "key": "a218f91f3afab85989f98934646f21fa"}, {"line": 46335, "relation": "association", "evidence": "PEBP plays a pivotal modulatory role in several signal transduction pathways. PEBP inhibits the MAPK pathway through interacting with Raf-1, so it's also known as Raf-1 kinase inhibitor protein (RKIP). PEBP is involved in the regulation of PKC, G-protein-coupled receptor and NF-kappaB signaling pathway as well. In clinical researches, it was found that as the precursor of hippocampal cholinergic neurostimulating peptide (HCNP), PEBP has an important effect on the development of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "19803424"}, "source": 3181, "target": 3823, "key": "c64677fa1555ab6488b8e1e2224fedc6"}, {"line": 46365, "relation": "decreases", "evidence": "Recently, a novel variant in the gene encoding the triggering receptor expressed on myeloid cells 2 (TREM2) has been identified that has refocused the spotlight back onto inflammation as a major contributing factor in AD. Variants in TREM2 triple one's risk of developing late-onset AD. TREM2 is expressed on microglial cells, the resident macrophages in the CNS, and functions to stimulate phagocytosis on one hand and to suppress cytokine production and inflammation on the other hand. ", "citation": {"db": "PubMed", "db_id": "23150934"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3493, "target": 577, "key": "f21835ae6e71b88f4bccf608f9947e92"}, {"line": 46864, "relation": "decreases", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Innate immune system subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3493, "target": 577, "key": "d474e1e0bea81295bf2de660f57f6f6e"}, {"line": 46366, "relation": "decreases", "evidence": "Recently, a novel variant in the gene encoding the triggering receptor expressed on myeloid cells 2 (TREM2) has been identified that has refocused the spotlight back onto inflammation as a major contributing factor in AD. Variants in TREM2 triple one's risk of developing late-onset AD. TREM2 is expressed on microglial cells, the resident macrophages in the CNS, and functions to stimulate phagocytosis on one hand and to suppress cytokine production and inflammation on the other hand. ", "citation": {"db": "PubMed", "db_id": "23150934"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3493, "target": 538, "key": "9ba1e61500d66226d3333bf6293e085d"}, {"line": 46948, "relation": "decreases", "evidence": "TREM2 could suppress inflammatory response by repression of microglia-mediated cytokine production and secretion, which may prevent inflammation-induced bystander damage of neurons. TREM2 also participates in the regulation of phagocytic pathways that are responsible for the removal of neuronal debris.", "citation": {"db": "PubMed", "db_id": "23407992"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3493, "target": 538, "key": "95af0ff2a247d2b8dd87bb84aae41ef4"}, {"line": 46367, "relation": "increases", "evidence": "Recently, a novel variant in the gene encoding the triggering receptor expressed on myeloid cells 2 (TREM2) has been identified that has refocused the spotlight back onto inflammation as a major contributing factor in AD. Variants in TREM2 triple one's risk of developing late-onset AD. TREM2 is expressed on microglial cells, the resident macrophages in the CNS, and functions to stimulate phagocytosis on one hand and to suppress cytokine production and inflammation on the other hand. ", "citation": {"db": "PubMed", "db_id": "23150934"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3493, "target": 823, "key": "ea51ab828ac6033cff0a0d935d26a358"}, {"line": 46867, "relation": "increases", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3493, "target": 823, "key": "aa08b5403341e3cd6bbec76f5238967b"}, {"line": 46862, "relation": "increases", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Chemokine signaling subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3493, "target": 2467, "key": "2628a69eb366abfc917dde000d4386c2"}, {"line": 46981, "relation": "increases", "evidence": "TREM-2 regulates dendritic cell function by inducing CCR7 expression on peripheral dendritic cells and directing them from the periphery to the draining lymph node.", "citation": {"db": "PubMed", "db_id": "19149696"}, "annotations": {"Cell": {"dendritic cell": true}, "Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3493, "target": 2467, "key": "fb8d4865b74dfe2932f876981fe16e5f"}, {"line": 46866, "relation": "association", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3493, "target": 609, "key": "1a6acf848690d71c5ab89471d23ca5ec"}, {"line": 46869, "relation": "decreases", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3493, "target": 3472, "key": "f0938afbba36604865369c63b5e54f96"}, {"line": 46870, "relation": "decreases", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3493, "target": 2894, "key": "2c8661addaa8ffd22ce85eaadb98dc54"}, {"line": 46871, "relation": "decreases", "evidence": "In normal state TREM2 regulates microglial activity and induces phagocytosis that removes the neuron debris like Abeta 42 peptides from brain. TREM2 regulates also inflammation by inhibiting proinflammatory cytokines such as TNF, IL6 and IFNG. In addition, it also maintains dendritic cell function by inducing CCR7 activities.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3493, "target": 2870, "key": "30bc137ffda15fee3706ea3b58b1a712"}, {"relation": "hasVariant", "source": 3493, "target": 3494, "key": "2773f282b8c480c034dda3e1e0487269"}, {"relation": "partOf", "source": 3493, "target": 1638, "key": "32ca58c08d52dc1fd738dce5412eff26"}, {"line": 46915, "relation": "association", "evidence": "Late-onset Alzheimer's disease (AD) is a sporadic disorder with increasing prevalence in aging. The ɛ4 allele of Apolipoprotein E(ApoEɛ4) was the only known major risk factor for late onset AD. Recently, two groups of investigators independently identified variants of the TREM2 gene, encoding triggering receptor expressed on myeloid cells 2 as causing increased susceptibility to late onset AD with an odds ratio similar to that of ApoEɛ4. TREM2 is a receptor expressed on innate immune cells.", "citation": {"db": "PubMed", "db_id": "24355566"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3493, "target": 3823, "key": "086d895e3e8175815858ad577aad6f67"}, {"line": 46922, "relation": "increases", "evidence": "Microglia can be protective and promote phagocytosis, degradation and ultimately clearance of Abeta, the pathogenic protein deposited in the brains of Alzheimer's patients. However, with disease progression, microglia become dysfunctional, release neurotoxins, lose their ability to clear Abeta and produce pro-inflammatory cytokines that promote Abeta production and accumulation. TREM2 has been shown to regulate the phagocytic ability of myeloid cells and their inflammatory response. Here we propose that the mechanism(s) by which TREM2 variants cause Alzheimer's disease are via down regulation of the Abeta phagocytic ability of microglia and by dysregulation of the pro-inflammatory response of these cells. Based on our discussion we propose that TREM2 is a potential therapeutic target for stopping ordelaying progression of AD.", "citation": {"db": "PubMed", "db_id": "24355566"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3493, "target": 732, "key": "9aa1afd33a51e912fb032587d51e5a2e"}, {"line": 46923, "relation": "increases", "evidence": "Microglia can be protective and promote phagocytosis, degradation and ultimately clearance of Abeta, the pathogenic protein deposited in the brains of Alzheimer's patients. However, with disease progression, microglia become dysfunctional, release neurotoxins, lose their ability to clear Abeta and produce pro-inflammatory cytokines that promote Abeta production and accumulation. TREM2 has been shown to regulate the phagocytic ability of myeloid cells and their inflammatory response. Here we propose that the mechanism(s) by which TREM2 variants cause Alzheimer's disease are via down regulation of the Abeta phagocytic ability of microglia and by dysregulation of the pro-inflammatory response of these cells. Based on our discussion we propose that TREM2 is a potential therapeutic target for stopping ordelaying progression of AD.", "citation": {"db": "PubMed", "db_id": "24355566"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3493, "target": 749, "key": "f3e3a28344efbc3f975149ed11c14da7"}, {"line": 46949, "relation": "decreases", "evidence": "TREM2 could suppress inflammatory response by repression of microglia-mediated cytokine production and secretion, which may prevent inflammation-induced bystander damage of neurons. TREM2 also participates in the regulation of phagocytic pathways that are responsible for the removal of neuronal debris.", "citation": {"db": "PubMed", "db_id": "23407992"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3493, "target": 539, "key": "23b652f2b65fd529decb53d7c1ad1764"}, {"line": 46984, "relation": "increases", "evidence": "TREM-2 regulates dendritic cell function by inducing CCR7 expression on peripheral dendritic cells and directing them from the periphery to the draining lymph node.", "citation": {"db": "PubMed", "db_id": "19149696"}, "annotations": {"Cell": {"dendritic cell": true}, "Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3493, "target": 725, "key": "551873d5d23f4663262bbfcddd735947"}, {"relation": "partOf", "source": 3493, "target": 1458, "key": "5a77e1a81465dd2be30bc41eada6fd1b"}, {"line": 46946, "relation": "association", "evidence": "TREM2 could suppress inflammatory response by repression of microglia-mediated cytokine production and secretion, which may prevent inflammation-induced bystander damage of neurons. TREM2 also participates in the regulation of phagocytic pathways that are responsible for the removal of neuronal debris.", "citation": {"db": "PubMed", "db_id": "23407992"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 538, "target": 577, "key": "30d33d7e86bfbba1d79412674915eb21"}, {"line": 46375, "relation": "decreases", "evidence": "These results suggest that aluminum disrupts iron homeostasis in the brain by several mechanisms including the transferrin receptor, a nontransferrin iron transporter, and ferritin.", "citation": {"db": "PubMed", "db_id": "17376497"}, "source": 3449, "target": 585, "key": "6525651700a0a74f8eac3c72c4e660cb"}, {"line": 46394, "relation": "association", "evidence": "Alzheimer's disease (AD) is characterized by progressive cognitive decline. Recent studies have shown that synaptic loss in the cortex is the major correlate of cognitive decline in AD. In the present study we assessed synaptic proteins such as synaptobrevin, synaptophysin, synaptotagmin, synaptosomal-associated protein 25 (SNAP-25), and syntaxin1/HPC-1 in control and AD brains to determine whether synaptic proteins are equally or differentially affected in AD. Western analysis showed that in AD levels of synaptobrevin and synaptophysin were decreased by some 30% from amounts in controls, while those of synaptotagmin, SNAP-25, and syntaxin 1/HPC-1 were decreased by only about 10%. As synaptobrevin and synaptophysin are localized mainly in transmitter-containing synaptic vesicles while SNAP-25 and syntaxin 1/HPC-1 are found in presynaptic plasma membranes, these results suggest differential involvement of synaptic components in AD.", "citation": {"db": "PubMed", "db_id": "9240416"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "Synapse assembly subgraph": true}}, "source": 3431, "target": 655, "key": "6cbfaea180b4f36f1c79ad36b5e9e599"}, {"relation": "partOf", "source": 3431, "target": 1620, "key": "fe1ecc733fc7c83a29add6c034e8e82e"}, {"relation": "partOf", "source": 3431, "target": 1633, "key": "51110cd68a8865ad975170071d38145a"}, {"line": 46447, "relation": "negativeCorrelation", "evidence": "The p53-family member TAp73 is a transcription factor that plays a key role in many biological processes. Here, we show that p73 drives the expression of microRNA (miR)-34a, but not miR-34b and -c, by acting on specific binding sites on the miR-34a promoter. Expression of miR-34a is modulated in parallel with that of TAp73 during in vitro differentiation of neuroblastoma cells and cortical neurons. Retinoid-driven neuroblastoma differentiation is inhibited by knockdown of either p73 or miR-34a. Transcript expression of miR-34a is significantly reduced in vivo both in the cortex and hippocampus of p73(-/-) mice; miR-34a and TAp73 expression also increase during postnatal development of the brain and cerebellum when synaptogenesis occurs. Accordingly, overexpression or silencing of miR-34a inversely modulates expression of synaptic targets, including synaptotagmin-1 and syntaxin-1A. Notably, the axis TAp73/miR-34a/synaptotagmin-1 is conserved in brains from Alzheimer's patients. These data reinforce a role for TAp73 in neuronal development.", "citation": {"db": "PubMed", "db_id": "22160687"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3431, "target": 2119, "key": "17d3d20c21191bf9234c6e4dedc33bd7"}, {"relation": "partOf", "source": 3433, "target": 1633, "key": "64672fff7b51863f279f304511508859"}, {"relation": "partOf", "source": 3433, "target": 1403, "key": "00c40c44fd77eed006bc288a0a38f324"}, {"relation": "hasVariant", "source": 3433, "target": 3434, "key": "d1aa1396f5b6842e86fa31decb444c91"}, {"line": 46437, "relation": "increases", "evidence": "Dual-specificity tyrosine(Y)-phosphorylation-regulated kinase 1A (Dyrk1A) is a protein kinase that might be responsible for mental retardation and early onset of Alzheimer's disease in Down's syndrome patients. Dyrk1A plays a role in many cellular pathways through phosphorylation of diverse substrate proteins; however, its role in synaptic vesicle exocytosis is poorly understood. Munc18-1, a central regulator of neurotransmitter release, interacts with Syntaxin 1 and X11-alpha. Syntaxin 1 is a key soluble N-ethylmaleimide-sensitive factor attachment protein receptor protein involved in synaptic vesicle docking/fusion events, and X11-alpha modulates amyloid precursor protein processing and beta amyloid generation. In this study, we demonstrate that Dyrk1A interacts with and phosphorylates Munc18-1 at the Thr(479) residue. The phosphorylation of Munc18-1 at Thr(479) by Dyrk1A stimulated binding of Munc18-1 to Syntaxin 1 and X11-alpha. ", "citation": {"db": "PubMed", "db_id": "22765017"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true, "DYRK1A subgraph": true}}, "source": 1403, "target": 3434, "key": "6ab647fd2b68eec31498b3faae031ed1"}, {"line": 46446, "relation": "increases", "evidence": "The p53-family member TAp73 is a transcription factor that plays a key role in many biological processes. Here, we show that p73 drives the expression of microRNA (miR)-34a, but not miR-34b and -c, by acting on specific binding sites on the miR-34a promoter. Expression of miR-34a is modulated in parallel with that of TAp73 during in vitro differentiation of neuroblastoma cells and cortical neurons. Retinoid-driven neuroblastoma differentiation is inhibited by knockdown of either p73 or miR-34a. Transcript expression of miR-34a is significantly reduced in vivo both in the cortex and hippocampus of p73(-/-) mice; miR-34a and TAp73 expression also increase during postnatal development of the brain and cerebellum when synaptogenesis occurs. Accordingly, overexpression or silencing of miR-34a inversely modulates expression of synaptic targets, including synaptotagmin-1 and syntaxin-1A. Notably, the axis TAp73/miR-34a/synaptotagmin-1 is conserved in brains from Alzheimer's patients. These data reinforce a role for TAp73 in neuronal development.", "citation": {"db": "PubMed", "db_id": "22160687"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3486, "target": 2119, "key": "7653e3510b45f28fed09b40530541fdd"}, {"line": 46448, "relation": "association", "evidence": "The p53-family member TAp73 is a transcription factor that plays a key role in many biological processes. Here, we show that p73 drives the expression of microRNA (miR)-34a, but not miR-34b and -c, by acting on specific binding sites on the miR-34a promoter. Expression of miR-34a is modulated in parallel with that of TAp73 during in vitro differentiation of neuroblastoma cells and cortical neurons. Retinoid-driven neuroblastoma differentiation is inhibited by knockdown of either p73 or miR-34a. Transcript expression of miR-34a is significantly reduced in vivo both in the cortex and hippocampus of p73(-/-) mice; miR-34a and TAp73 expression also increase during postnatal development of the brain and cerebellum when synaptogenesis occurs. Accordingly, overexpression or silencing of miR-34a inversely modulates expression of synaptic targets, including synaptotagmin-1 and syntaxin-1A. Notably, the axis TAp73/miR-34a/synaptotagmin-1 is conserved in brains from Alzheimer's patients. These data reinforce a role for TAp73 in neuronal development.", "citation": {"db": "PubMed", "db_id": "22160687"}, "annotations": {"Subgraph": {"miRNA subgraph": true}}, "source": 3486, "target": 3823, "key": "8bcca884523933f039443bf1fa27e1be"}, {"line": 46471, "relation": "decreases", "evidence": "We have examined this by using oxidative stress to induce apoptosis in a mouse hippocampal neuronal cell line (HT-22). Oxidatively modified proteins were measured by high-resolution two-dimensional gel electrophoresis coupled with oxidation-specific immunostains.Under these conditions the oxidatively stressed cells undergo apoptotic process, and specific proteins are oxidized. The three proteins that appeared to be most susceptible to oxidation were identified by mass spectrometry. Those oxidized proteins are heat shock protein 60 and vimentin, both believed to function as antiapoptotic proteins, and a third protein with sequence homology to hemoglobin alpha-chain. When the cells were pretreated with vitamin E, these proteins were not oxidized and the cells did not undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "12548636"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3651, "target": 478, "key": "f9771f0fdfcda0f16f2103f773fe54c2"}, {"line": 46475, "relation": "decreases", "evidence": "We have examined this by using oxidative stress to induce apoptosis in a mouse hippocampal neuronal cell line (HT-22). Oxidatively modified proteins were measured by high-resolution two-dimensional gel electrophoresis coupled with oxidation-specific immunostains.Under these conditions the oxidatively stressed cells undergo apoptotic process, and specific proteins are oxidized. The three proteins that appeared to be most susceptible to oxidation were identified by mass spectrometry. Those oxidized proteins are heat shock protein 60 and vimentin, both believed to function as antiapoptotic proteins, and a third protein with sequence homology to hemoglobin alpha-chain. When the cells were pretreated with vitamin E, these proteins were not oxidized and the cells did not undergo apoptotic process.", "citation": {"db": "PubMed", "db_id": "12548636"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true, "Reactive oxygen species subgraph": true}, "Confidence": {"High": true}}, "source": 3752, "target": 478, "key": "02924abf71d8aff983a3f813e7724a6f"}, {"relation": "partOf", "source": 2855, "target": 1179, "key": "fd4e3faa3f74bd4e854d8de5fea43b83"}, {"line": 46536, "relation": "increases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Low": true}, "CellStructure": {"Mitochondria": true}, "Subgraph": {"Chaperone subgraph": true, "Non-amyloidogenic subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2855, "target": 894, "key": "4f3b690f9713af8981fcb3eaa5b2a08c"}, {"line": 46539, "relation": "increases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Low": true}, "CellStructure": {"Mitochondria": true}, "Subgraph": {"Chaperone subgraph": true, "Non-amyloidogenic subgraph": true}}, "source": 2855, "target": 662, "key": "40219c65ad32cbae1b81297fc855fca6"}, {"line": 46551, "relation": "increases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Chaperone subgraph": true}}, "source": 2855, "target": 442, "key": "da61ad457118f5da54a641551b9e942a"}, {"line": 46557, "relation": "decreases", "evidence": "Here we demonstrate that Hsp60, Hsp70, and Hsp90 both alone and in combination provide differential protection against intracellular beta-amyloid stress through the maintenance of mitochondrial oxidative phosphorylation and functionality of tricarboxylic acid cycle enzymes. Notably, beta-amyloid was found to selectively inhibit complex IV activity, an effect selectively neutralized by Hsp60. The combined effect of HSPs was to reduce the free radical burden, preserve ATP generation, decrease cytochrome c release, and prevent caspase-9 activation, all important mediators of beta-amyloid-induced neuronal dysfunction and death.", "citation": {"db": "PubMed", "db_id": "16887805"}, "annotations": {"Confidence": {"Low": true}, "Subgraph": {"Chaperone subgraph": true, "Caspase subgraph": true}}, "object": {"modifier": "Activity"}, "source": 2855, "target": 2449, "key": "d56357bacfe2dadb7bde1c5bc88728f2"}, {"relation": "partOf", "source": 2855, "target": 1458, "key": "f3912c8d1babae58a52a4fe96dd2ea66"}, {"line": 46599, "relation": "negativeCorrelation", "evidence": "In this study, we found that expression levels of HSP60, -70, and -90 were downregulated in the cerebella of rats with AD. ", "citation": {"db": "PubMed", "db_id": "23665061"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true}}, "source": 3787, "target": 3823, "key": "0fb045b7275410609cb8a51d1d046ae2"}, {"line": 46600, "relation": "negativeCorrelation", "evidence": "In this study, we found that expression levels of HSP60, -70, and -90 were downregulated in the cerebella of rats with AD. ", "citation": {"db": "PubMed", "db_id": "23665061"}, "annotations": {"MeSHAnatomy": {"Cerebellum": true}}, "source": 3785, "target": 3823, "key": "ec9aa9e8d9e55bf3eab447f8d2f4c9ba"}, {"line": 46619, "relation": "positiveCorrelation", "evidence": "The presence of misfolded proteins in the endoplasmic reticulum (ER) triggers a cellular stress response called the unfolded protein response (UPR) that may protect the cell against the toxic buildup of misfolded proteins. In this study we investigated the activation of the UPR in AD. Protein levels of BiP/GRP78, a molecular chaperone which is up-regulated during the UPR, was found to be increased in AD temporal cortex and hippocampus as determined by Western blot analysis.", "citation": {"db": "PubMed", "db_id": "15973543"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Chaperone subgraph": true, "Unfolded protein response subgraph": true}}, "source": 550, "target": 3823, "key": "e0e389a3180ec76e0db689c62dc48a6b"}, {"line": 46623, "relation": "positiveCorrelation", "evidence": "The presence of misfolded proteins in the endoplasmic reticulum (ER) triggers a cellular stress response called the unfolded protein response (UPR) that may protect the cell against the toxic buildup of misfolded proteins. In this study we investigated the activation of the UPR in AD. Protein levels of BiP/GRP78, a molecular chaperone which is up-regulated during the UPR, was found to be increased in AD temporal cortex and hippocampus as determined by Western blot analysis.", "citation": {"db": "PubMed", "db_id": "15973543"}, "annotations": {"CellStructure": {"Endoplasmic Reticulum": true}, "Subgraph": {"Chaperone subgraph": true, "Unfolded protein response subgraph": true}, "MeSHAnatomy": {"Hippocampus": true, "Temporal Lobe": true}}, "source": 550, "target": 2849, "key": "58dabf0b4cd46ca4d963bd0498be58db"}, {"line": 46659, "relation": "increases", "evidence": "Furthermore, Hcy induced ER stress responses in the hippocampus, as indicated by the upregulation of GRP78, CHOP, and cleaved caspase-12. ", "citation": {"db": "PubMed", "db_id": "24747165 "}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Chaperone subgraph": true, "Unfolded protein response subgraph": true}}, "source": 550, "target": 3786, "key": "090349b77770dea8cc70bc5fedad09ee"}, {"line": 46673, "relation": "increases", "evidence": "Striatal medium spiny neurons (MSN) are critically involved in motor control, and their degeneration is a principal component of Huntington's disease. We find that the transcription factor Ctip2 (also known as Bcl11b) is central to MSN differentiation and striatal development. Within the striatum, it is expressed by all MSN, although it is excluded from essentially all striatal interneurons. In the absence of Ctip2, MSN do not fully differentiate, as demonstrated by dramatically reduced expression of a large number of MSN markers, including DARPP-32, FOXP1, Chrm4, Reelin, MOR1 (mu-opioid receptor 1), glutamate receptor 1, and Plexin-D1.", "citation": {"db": "PubMed", "db_id": " 18199763"}, "annotations": {"Cell": {"medium spiny neuron": true}, "MeSHAnatomy": {"Corpus Striatum": true}, "Subgraph": {"Cell-cell communication subgraph": true}}, "source": 3208, "target": 650, "key": "b1232628fb88658d6662f5ed2ed7adac"}, {"line": 46685, "relation": "association", "evidence": "Taken together, these studies indicate that SULT4A1 stability is regulated by post-translational modification that involves the ERK pathway and PP2A. The phosphorylation of SULT4A1 allows interaction with Pin1, which then promotes degradation of the sulfotransferase.", "citation": {"db": "PubMed", "db_id": "20920535"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "source": 3731, "target": 451, "key": "5d29fa7c66ea7ebf6f34efa1aeba2eee"}, {"relation": "hasVariant", "source": 3731, "target": 3732, "key": "65045c51ee8969360ed721ebc951093f"}, {"line": 46686, "relation": "increases", "evidence": "Taken together, these studies indicate that SULT4A1 stability is regulated by post-translational modification that involves the ERK pathway and PP2A. The phosphorylation of SULT4A1 allows interaction with Pin1, which then promotes degradation of the sulfotransferase.", "citation": {"db": "PubMed", "db_id": "20920535"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "source": 3696, "target": 3732, "key": "6817e65c38ee7da6f8fa3a2dea538c55"}, {"relation": "partOf", "source": 3696, "target": 1648, "key": "5f9aa88ba1a29c534d40f42293485302"}, {"line": 46688, "relation": "association", "evidence": "Taken together, these studies indicate that SULT4A1 stability is regulated by post-translational modification that involves the ERK pathway and PP2A. The phosphorylation of SULT4A1 allows interaction with Pin1, which then promotes degradation of the sulfotransferase.", "citation": {"db": "PubMed", "db_id": "20920535"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "source": 3696, "target": 3192, "key": "ffae16ffb85ec6ffc88a8ba6508967c0"}, {"relation": "partOf", "source": 3732, "target": 1648, "key": "57a9ff4cfcbc66c8c35116b273db86d0"}, {"line": 46687, "relation": "increases", "evidence": "Taken together, these studies indicate that SULT4A1 stability is regulated by post-translational modification that involves the ERK pathway and PP2A. The phosphorylation of SULT4A1 allows interaction with Pin1, which then promotes degradation of the sulfotransferase.", "citation": {"db": "PubMed", "db_id": "20920535"}, "annotations": {"Subgraph": {"MAPK-JNK subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 1648, "target": 2528, "key": "28db485bd1706938623618fee645fd69"}, {"line": 46777, "relation": "increases", "evidence": "We propose that IL-1 and the IL-6 family of cytokines regulate YKL-40 expression during sterile inflammation via both STAT3 and RelB/p50 complexes", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true, "Nuclear factor Kappa beta subgraph": true}, "Confidence": {"Medium": true}}, "source": 1590, "target": 2509, "key": "bd88931413475521db27472242ee4cff"}, {"relation": "partOf", "source": 3305, "target": 1590, "key": "6ab00209b8f5913a94ca43f10e87a836"}, {"relation": "partOf", "source": 3305, "target": 1591, "key": "ec9956e88f55ddf4939b868c962c2b61"}, {"line": 46819, "relation": "increases", "evidence": "It is also accepted that RelB is activated in lymphoid cells, such as dendritic cells, by a noncanonical NF-κB pathway and generation of RelB/p52 complexes that are important for proper dendritic cell functions ", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Nuclear factor Kappa beta subgraph": true}, "Confidence": {"High": true}}, "source": 3305, "target": 1591, "key": "2805917d0805d96d4a45bfd1c0303155"}, {"relation": "partOf", "source": 2929, "target": 1496, "key": "e1592072c61dc8ad7f56ca558b58c6fb"}, {"relation": "partOf", "source": 3342, "target": 1496, "key": "87cf8b94a8d1e95e45b6035e4c66634f"}, {"relation": "partOf", "source": 3342, "target": 1495, "key": "3adc9e48d2bab840ae3c2c5eb4b1f0f3"}, {"line": 46832, "relation": "increases", "evidence": "In fact YKL-40 induces the interaction of αvbeta3 integrins with syndecan-1 in endothelial cells (27), it activates ERK, AKT, and Wnt/beta-catenin signaling in macrophages via IL-13 receptor alpha 2-dependent mechanism (55),", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Interleukin signaling subgraph": true}, "Confidence": {"High": true}}, "source": 3342, "target": 2880, "key": "eb46a327918a04ec31f79e7436af14d4"}, {"relation": "isA", "source": 3342, "target": 127, "key": "22c5f8c044fb2c4bd93f5bba341eb51a"}, {"relation": "partOf", "source": 3342, "target": 959, "key": "292a938fe32e106bbee2c6ea3d10b52e"}, {"relation": "hasVariant", "source": 3342, "target": 3343, "key": "e6f76bdb03356112b3b1c61fb3f381be"}, {"line": 47415, "relation": "increases", "evidence": "SDC1 but Not SDC2 Rapidly Internalizes Polyplexesâ€â€�To address this striking difference between SDC1 and SDC2, we examined the fate of polyplexes by confocal microscopy using RITC-labeled PEI (Fig. 2). Coexpression of SDC2 with SDC1 resulted in an 60% reduction in transfection efficiency compared with expression of SDC1 alone, indicating that SDC2 has a negative effect on SDC1-mediated PEI transfection (Fig. 5a). This result dramatically contrasts with the profile obtained when SDC1 was expressed alone (Fig. 2a), suggesting that SDC2 has a dominant-negative effect on SDC1-mediated polyplex endocytosis and gene transfer to the nucleus.", "citation": {"db": "PubMed", "db_id": "18216019"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 3342, "target": 954, "key": "0e471f95a441025c6f2abd5163b3c7a3"}, {"relation": "partOf", "source": 3342, "target": 1628, "key": "9895bab0869ae74d0f3ef2cc33d72699"}, {"relation": "partOf", "source": 2924, "target": 1495, "key": "1834c236f02ff2162f188853e79e8df9"}, {"line": 46982, "relation": "association", "evidence": "TREM-2 regulates dendritic cell function by inducing CCR7 expression on peripheral dendritic cells and directing them from the periphery to the draining lymph node.", "citation": {"db": "PubMed", "db_id": "19149696"}, "annotations": {"Cell": {"dendritic cell": true}, "Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 2467, "target": 725, "key": "457c6bc43db368d21eda3b412af236af"}, {"line": 46905, "relation": "positiveCorrelation", "evidence": "The rare variant discovered is a missense mutation in the loop region of exon 2 of Trem2 (rs75932628-T, Arg47His). Evidence of trem2 variant associated with triple risk of Alzheimer's disease.", "citation": {"db": "PubMed", "db_id": "24663666"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3494, "target": 3823, "key": "a9a4ef656b0f50aba28a64dac837cacc"}, {"line": 46924, "relation": "increases", "evidence": "Microglia can be protective and promote phagocytosis, degradation and ultimately clearance of Abeta, the pathogenic protein deposited in the brains of Alzheimer's patients. However, with disease progression, microglia become dysfunctional, release neurotoxins, lose their ability to clear Abeta and produce pro-inflammatory cytokines that promote Abeta production and accumulation. TREM2 has been shown to regulate the phagocytic ability of myeloid cells and their inflammatory response. Here we propose that the mechanism(s) by which TREM2 variants cause Alzheimer's disease are via down regulation of the Abeta phagocytic ability of microglia and by dysregulation of the pro-inflammatory response of these cells. Based on our discussion we propose that TREM2 is a potential therapeutic target for stopping ordelaying progression of AD.", "citation": {"db": "PubMed", "db_id": "24355566"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3494, "target": 637, "key": "15abb115a2938b5f0b7af8af80530d8f"}, {"line": 46925, "relation": "decreases", "evidence": "Microglia can be protective and promote phagocytosis, degradation and ultimately clearance of Abeta, the pathogenic protein deposited in the brains of Alzheimer's patients. However, with disease progression, microglia become dysfunctional, release neurotoxins, lose their ability to clear Abeta and produce pro-inflammatory cytokines that promote Abeta production and accumulation. TREM2 has been shown to regulate the phagocytic ability of myeloid cells and their inflammatory response. Here we propose that the mechanism(s) by which TREM2 variants cause Alzheimer's disease are via down regulation of the Abeta phagocytic ability of microglia and by dysregulation of the pro-inflammatory response of these cells. Based on our discussion we propose that TREM2 is a potential therapeutic target for stopping ordelaying progression of AD.", "citation": {"db": "PubMed", "db_id": "24355566"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 3494, "target": 732, "key": "e204f603407c44ee17a66df6b8c48a5d"}, {"line": 46892, "relation": "increases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 1638, "target": 599, "key": "090d2ba35057df36179bb5d835902652"}, {"line": 46893, "relation": "increases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 1638, "target": 623, "key": "5892d77553d159a20cc6d95c2ca10fc3"}, {"line": 46960, "relation": "increases", "evidence": "TREM2 forms a receptor signaling complex with TYROBP and triggers activation of the immune responses in macrophages and dendritic cells.", "citation": {"db": "PubMed", "db_id": "12080485"}, "annotations": {"Cell": {"dendritic cell": true, "macrophage": true}, "Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 1638, "target": 575, "key": "b5095d2d657da663393c1728d4a7d23e"}, {"relation": "partOf", "source": 3507, "target": 1638, "key": "26cf6430c928951a4a342f003976245c"}, {"line": 46895, "relation": "increases", "evidence": "In disease state, LPS (lipoploysacharide) induces TLR4, which increases NFKB1 activities. NFKB1 increases MIR34A which targets TREM2, decreasing normal TREM2 and increases the mutant variant. Recent GWAS studies associated SNP rs75932628 with TREM2 in LOAD patients. Also there are studies suggesting the link of this TREM2 variant with certain clinical and neuroimaging AD features such as frontobasal gray atrophy. Moreover, in disease brain TREM2 forms complex with TYROBP which triggers immune responses through activating macrophages and dendritic cells which leads to chronic neuroinflammation.", "citation": {"db": "PubMed", "db_id": "25681350"}, "annotations": {"Subgraph": {"Inflammatory response subgraph": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 623, "target": 577, "key": "4fe349361cfb4b0a9ab7a6f430291ed0"}, {"line": 46947, "relation": "association", "evidence": "TREM2 could suppress inflammatory response by repression of microglia-mediated cytokine production and secretion, which may prevent inflammation-induced bystander damage of neurons. TREM2 also participates in the regulation of phagocytic pathways that are responsible for the removal of neuronal debris.", "citation": {"db": "PubMed", "db_id": "23407992"}, "annotations": {"Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"High": true}}, "source": 539, "target": 577, "key": "f32f37be8c97bd93e000144741f84213"}, {"line": 46973, "relation": "increases", "evidence": "TREM-1 is an activating receptor on neutrophils and monocytes that plays an important role in the amplification of inflammation", "citation": {"db": "PubMed", "db_id": "19149696"}, "annotations": {"Cell": {"neutrophil": true, "monocyte": true}, "Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "source": 3492, "target": 577, "key": "23a27f1ba3d31fa11049eb174d3fc080"}, {"line": 46982, "relation": "association", "evidence": "TREM-2 regulates dendritic cell function by inducing CCR7 expression on peripheral dendritic cells and directing them from the periphery to the draining lymph node.", "citation": {"db": "PubMed", "db_id": "19149696"}, "annotations": {"Cell": {"dendritic cell": true}, "Subgraph": {"Cytokine signaling subgraph": true, "Inflammatory response subgraph": true}, "Confidence": {"Medium": true}}, "source": 725, "target": 2467, "key": "4a6d65020670eeb332dc6f9b703ae8cd"}, {"line": 46994, "relation": "increases", "evidence": "The surface-exposed chaperone, Hsp60, is an agonist of the microglial TREM2 receptor.", "citation": {"db": "PubMed", "db_id": "19457124"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"Inflammatory response subgraph": true}}, "object": {"modifier": "Activity"}, "source": 1458, "target": 3493, "key": "095c08d73f3f7279ef928fe1a71e74c8"}, {"line": 47059, "relation": "association", "evidence": "Soluble oligomers of the Amyloid beta peptide accumulate in Alzheimer's disease (AD) brain and have been implicated in mechanisms of pathogenesis. The neurotoxicity of Amyloid beta appears to be, at least in part, due to dysregulation of glutamate signaling. Here, we show that Amyloid beta promote extracellular accumulation of glutamate and d-serine, a co-agonist at glutamate receptors of the N-methyl-d-aspartate subtype (NMDARs), in hippocampal neuronal cultures. Activation of inhibitory GABA(A) receptors by taurine blocked the increase in extracellular glutamate levels, suggesting that selective pharmacological inhibition of neuronal activity can counteract the impact of Amyloid beta on glutamate dyshomeostasis. Results reveal a novel mechanism by which Ab oligomers promote abnormal release of glutamate in hippocampal neurons, which may contribute to dysregulation of excitatory signaling in the brain", "citation": {"db": "PubMed", "db_id": "21244351"}, "annotations": {"Confidence": {"Medium": true}}, "source": 568, "target": 80, "key": "7ff4cf6d2834f59ae412057af16c1751"}, {"line": 47105, "relation": "increases", "evidence": "Cell transfection requires cationic DNA complexes and heparan sulfate proteoglycans (HSPGs) at the cell surface. As efï¬betacient transfection also requires the condensed DNA particles to bear a cationic surface [4], it was inferred that anionic proteoglycans were potential receptors [2].", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellStructure": {"Cell Membrane": true}, "Confidence": {"Medium": true}}, "source": 956, "target": 856, "key": "24ce8b0394ef54b776033872b014196d"}, {"relation": "partOf", "source": 106, "target": 956, "key": "ab722fe419d35960a8a4e96a115707ca"}, {"relation": "partOf", "source": 106, "target": 955, "key": "67f75f3815965315d411494349cfde3d"}, {"relation": "partOf", "source": 106, "target": 954, "key": "2828b888c4c04a1fd3c05c85aad77883"}, {"relation": "partOf", "source": 403, "target": 956, "key": "06f2a33d0f712392fe1fdc035c7fa6f6"}, {"line": 47273, "relation": "increases", "evidence": "Among the SDC mutants, the highest luciferase activity could be detected on those expressing the HS chains (HSA.pSi4 and HAS.pSi1), while the lowest on the ones devoid of HS (CBD.pSi4 and .pSi4), thus showing the major role of polyanionic HS chains in attaching cationic lipoplexes like DMRIE-C (Fig 2B). ", "citation": {"db": "PubMed", "db_id": "23732629"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 403, "target": 1000, "key": "6db07b1f85b9883d4b222abf1841f5fc"}, {"line": 47113, "relation": "increases", "evidence": "HSPGs (presumably syndecans) represent anchors for the interaction of many pathogens with their host cell surface [8]. They have been shown to be involved in cellular binding/entry of viruses, such as HSV [9], AAV [10] and CMV [11], protozoan parasites [12] and pathogenic bacteria [13,14]. ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 3344, "target": 800, "key": "e7661a8b89bb5878a50c0f7edb34df90"}, {"line": 47114, "relation": "increases", "evidence": "HSPGs (presumably syndecans) represent anchors for the interaction of many pathogens with their host cell surface [8]. They have been shown to be involved in cellular binding/entry of viruses, such as HSV [9], AAV [10] and CMV [11], protozoan parasites [12] and pathogenic bacteria [13,14]. ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 3344, "target": 691, "key": "85e93212ab0970f10393780138f618f2"}, {"line": 47115, "relation": "increases", "evidence": "HSPGs (presumably syndecans) represent anchors for the interaction of many pathogens with their host cell surface [8]. They have been shown to be involved in cellular binding/entry of viruses, such as HSV [9], AAV [10] and CMV [11], protozoan parasites [12] and pathogenic bacteria [13,14]. ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 3344, "target": 831, "key": "d9801b7df362bffcdf624c2e34517480"}, {"line": 47118, "relation": "increases", "evidence": "HSPGs (presumably syndecans) represent anchors for the interaction of many pathogens with their host cell surface [8]. They have been shown to be involved in cellular binding/entry of viruses, such as HSV [9], AAV [10] and CMV [11], protozoan parasites [12] and pathogenic bacteria [13,14]. ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 3344, "target": 554, "key": "e798c8c6698befc5342fb5be9950c0e9"}, {"line": 47124, "relation": "increases", "evidence": "Following initial binding to HSPGs, most of these pathogens are internalized. The mechanism of internalization appears to depend solely, at least in some systems (e.g., epithelial cells), on clustering (ligation) of HSPGs [15,16].", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 3344, "target": 847, "key": "fccdb0b4b3e7a7437965e0f42c21760b"}, {"relation": "partOf", "source": 3344, "target": 1004, "key": "9c0be3c9760555412b6758f348d9354a"}, {"relation": "partOf", "source": 3344, "target": 1005, "key": "c2d92b62c06f62792ec03c7f6f3e214a"}, {"relation": "partOf", "source": 3344, "target": 1023, "key": "d0881a7f74d323456bf4de294cd75259"}, {"line": 47158, "relation": "increases", "evidence": "Protein kinase C is also recruited into the large multiprotein complex forming at the adhesion point where it regulates the associations. Previous studies have underlined the involvement of PKC in HSPG-dependent phagocytosis in HeLa cells ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellLine": {"HeLa cell": true}}, "source": 3344, "target": 823, "key": "fb38750d323a272f3edf7964b1883307"}, {"relation": "partOf", "source": 3344, "target": 955, "key": "d828e55154c5ec6b954a8039cd7906a3"}, {"relation": "partOf", "source": 3344, "target": 990, "key": "4f7693639afd0f422dbfd07ab3f98b87"}, {"relation": "isA", "source": 3344, "target": 127, "key": "94d970545c5041901cf20d3712a64314"}, {"relation": "partOf", "source": 3344, "target": 1628, "key": "64d3467a11fb511437950f49062d7284"}, {"line": 47425, "relation": "decreases", "evidence": "In conclusion, we have shown here for the first time that SDC1 and SDC2 are directly involved in polyplex binding and that SDC2 strongly delays polyplex endocytosis and inhibits PEI-mediated gene expression", "citation": {"db": "PubMed", "db_id": "18216019"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 3344, "target": 954, "key": "c87622332cb11e9d71314abecc99c5f8"}, {"line": 47132, "relation": "increases", "evidence": "Syndecans have been shown to serve as receptors for HSPG-dependent bacterial invasion of HeLa cells ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellLine": {"HeLa cell": true}, "Confidence": {"Medium": true}}, "source": 3560, "target": 554, "key": "b9b9725a3f2ad636e97f2ac7c7d267b9"}, {"relation": "partOf", "source": 3560, "target": 1000, "key": "eff316a604ca344eee8613598e501b40"}, {"line": 47141, "relation": "increases", "evidence": "The primary cellular function of the syndecan family (four members) is to act as adhesion molecules; they bind extracellular matrix proteins and contribute to coordinating the cellular processes of migration, adhesion and cytoskeleton organization. They have been shown to be involved in focal adhesion and stress ï¬betaber formation together with members of the integrin family ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 1004, "target": 613, "key": "f32059a78a8495414625fbcf5c797e03"}, {"line": 47142, "relation": "increases", "evidence": "The primary cellular function of the syndecan family (four members) is to act as adhesion molecules; they bind extracellular matrix proteins and contribute to coordinating the cellular processes of migration, adhesion and cytoskeleton organization. They have been shown to be involved in focal adhesion and stress ï¬betaber formation together with members of the integrin family ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 1004, "target": 497, "key": "866973bb08174340fb75242ed0d1c4c2"}, {"line": 47143, "relation": "increases", "evidence": "The primary cellular function of the syndecan family (four members) is to act as adhesion molecules; they bind extracellular matrix proteins and contribute to coordinating the cellular processes of migration, adhesion and cytoskeleton organization. They have been shown to be involved in focal adhesion and stress ï¬betaber formation together with members of the integrin family ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 1004, "target": 540, "key": "07bf4393817a93f2950526129452ae00"}, {"relation": "partOf", "source": 882, "target": 1004, "key": "e01267a1d76bbe853187849c98d63dea"}, {"line": 47146, "relation": "increases", "evidence": "The primary cellular function of the syndecan family (four members) is to act as adhesion molecules; they bind extracellular matrix proteins and contribute to coordinating the cellular processes of migration, adhesion and cytoskeleton organization. They have been shown to be involved in focal adhesion and stress ï¬betaber formation together with members of the integrin family ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 1005, "target": 557, "key": "953d0c81956e435f7841ed19f3d55785"}, {"line": 47149, "relation": "increases", "evidence": "The primary cellular function of the syndecan family (four members) is to act as adhesion molecules; they bind extracellular matrix proteins and contribute to coordinating the cellular processes of migration, adhesion and cytoskeleton organization. They have been shown to be involved in focal adhesion and stress ï¬betaber formation together with members of the integrin family ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 1005, "target": 784, "key": "dda4ef86647bdc90dfbed7151511336f"}, {"line": 47150, "relation": "increases", "evidence": "The primary cellular function of the syndecan family (four members) is to act as adhesion molecules; they bind extracellular matrix proteins and contribute to coordinating the cellular processes of migration, adhesion and cytoskeleton organization. They have been shown to be involved in focal adhesion and stress ï¬betaber formation together with members of the integrin family ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 1005, "target": 687, "key": "089309c0af297df0feea8f8315b6c494"}, {"relation": "partOf", "source": 885, "target": 1005, "key": "c3be170ef49945d165a4d30f0b776ec7"}, {"line": 48556, "relation": "regulates", "evidence": "The ability of integrins to bind the extracellular matrix and provide linkage to the actin cytoskeleton via talin and vinculin is critical for fundamental biological processes, including cell adhesion, cell survival and death, cell migration, and neurite outgrowth", "citation": {"db": "PubMed", "db_id": "22223749"}, "source": 557, "target": 652, "key": "464b42d58e09fffa618766da04d1262c"}, {"line": 48557, "relation": "regulates", "evidence": "The ability of integrins to bind the extracellular matrix and provide linkage to the actin cytoskeleton via talin and vinculin is critical for fundamental biological processes, including cell adhesion, cell survival and death, cell migration, and neurite outgrowth", "citation": {"db": "PubMed", "db_id": "22223749"}, "source": 557, "target": 509, "key": "c8b38c00d15bf77262c68957c89d1bdc"}, {"line": 48562, "relation": "regulates", "evidence": "The ability of integrins to bind the extracellular matrix and provide linkage to the actin cytoskeleton via talin and vinculin is critical for fundamental biological processes, including cell adhesion, cell survival and death, cell migration, and neurite outgrowth", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Apoptosis signaling subgraph": true}, "Confidence": {"High": true}}, "source": 557, "target": 505, "key": "243c4975723402ac4edbf69f83bfbe9e"}, {"line": 47156, "relation": "increases", "evidence": "Protein kinase C is also recruited into the large multiprotein complex forming at the adhesion point where it regulates the associations. Previous studies have underlined the involvement of PKC in HSPG-dependent phagocytosis in HeLa cells ", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellLine": {"HeLa cell": true}}, "source": 1023, "target": 823, "key": "ffbed300d633dfcb45bce35ac7d4985c"}, {"line": 47167, "relation": "increases", "evidence": "In the present study, we examined the possibility that the cellular receptors of PEI/DNA complexes might also be subjected to PKC regulation. To test if PKC might have a role in internalization of PEI/DNA complexes, we treated HeLa cells with staurosporine and tested the effects of this PKC inhibitor on transfection efï¬betaciency using a luciferase assay. As shown in Figure 1, staurosporine inhibited transfection in a concentration-dependent manner. The effects were observed at concentrations similar to those used to interfere with bacterial invasion [17] and uptake of HSPG-ligating beads [15] into HeLa cells.", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellLine": {"HeLa cell": true}, "Confidence": {"Medium": true}}, "source": 955, "target": 856, "key": "fc3455f40bb2f9ef33c37aa210aa4802"}, {"relation": "partOf", "source": 162, "target": 955, "key": "baa634ac1cc514919ef8c31af5641122"}, {"line": 47409, "relation": "increases", "evidence": "Polyethyleneimines (PEIs) are efficient non-viral vectors for gene transfer.", "citation": {"db": "PubMed", "db_id": "18216019"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 162, "target": 837, "key": "861ee191b0ad32953758869e9e58a667"}, {"relation": "partOf", "source": 162, "target": 954, "key": "048e912ae3530ba8143c28010a50eaab"}, {"line": 47170, "relation": "decreases", "evidence": "In the present study, we examined the possibility that the cellular receptors of PEI/DNA complexes might also be subjected to PKC regulation. To test if PKC might have a role in internalization of PEI/DNA complexes, we treated HeLa cells with staurosporine and tested the effects of this PKC inhibitor on transfection efï¬betaciency using a luciferase assay. As shown in Figure 1, staurosporine inhibited transfection in a concentration-dependent manner. The effects were observed at concentrations similar to those used to interfere with bacterial invasion [17] and uptake of HSPG-ligating beads [15] into HeLa cells.", "citation": {"db": "PubMed", "db_id": "15241784"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "CellLine": {"HeLa cell": true}}, "object": {"modifier": "Activity"}, "source": 356, "target": 2205, "key": "43d2ab5804733baffb4b6e0c35752f46"}, {"line": 47190, "relation": "increases", "evidence": "Liposomes labeled with AG73 showed high efficient transfection efficiency in syndecan-2 overexpressing cells, and found that AG73 could be a superior molecule in the development of non-viral vector using liposomes for the gene delivery to syndecan-2 overexpressing cancer cells.", "citation": {"db": "PubMed", "db_id": "20615691"}, "annotations": {"CellLine": {"cancer cell line": true}, "Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 990, "target": 856, "key": "ece6ea0a876089689baa4b863a7609a2"}, {"relation": "partOf", "source": 383, "target": 990, "key": "91e6616e06953ca36ff7670339eec3b8"}, {"relation": "partOf", "source": 427, "target": 990, "key": "3d429f0f7934132dc0038c7b0e4aedd4"}, {"line": 47204, "relation": "increases", "evidence": "Here we report that syndecan-4, the universally expressed isoform of the syndecan family of transmembrane proteoglycans, binds and mediates transport of the three most frequently utilized cationic CPPs (penetratin, octaarginine and TAT) into the cells.", "citation": {"db": "PubMed", "db_id": "20138023"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 1002, "target": 439, "key": "664bed4562fd0c9e36536547eecc93dd"}, {"relation": "partOf", "source": 439, "target": 1002, "key": "f886e8597539fbc8858da5ca9a9584b8"}, {"relation": "partOf", "source": 3346, "target": 1002, "key": "2a1ac03b147201705a6a01e7e7f7b612"}, {"relation": "partOf", "source": 3346, "target": 1001, "key": "f7e9c9debb96e8cd57885e2ec91659d3"}, {"relation": "partOf", "source": 3346, "target": 998, "key": "fcfacc62d3ed778f418c69f16569a196"}, {"line": 47212, "relation": "increases", "evidence": "SDC4 is the main, Ca2+ independent activator of the protein kinase C alpha (PKCalpha) [29–32]. It participates in focal adhesions, and via its cytoplasmic domain attaches to the cytoskeleton. SDC4 is targeted to lipid rafts, discrete regions of the plasma membrane enriched in cholesterol and sphingolipids.", "citation": {"db": "PubMed", "db_id": "20138023"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3346, "target": 3236, "key": "fbd4b5d33256e5c8b0fe1e93774acda6"}, {"line": 47215, "relation": "increases", "evidence": "SDC4 is the main, Ca2+ independent activator of the protein kinase C alpha (PKCalpha) [29–32]. It participates in focal adhesions, and via its cytoplasmic domain attaches to the cytoskeleton. SDC4 is targeted to lipid rafts, discrete regions of the plasma membrane enriched in cholesterol and sphingolipids.", "citation": {"db": "PubMed", "db_id": "20138023"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 3346, "target": 557, "key": "01684ad7f981d4b723a0934982b5d3d3"}, {"relation": "partOf", "source": 3346, "target": 1617, "key": "269eae874552500a55448401e4917319"}, {"relation": "partOf", "source": 3346, "target": 999, "key": "8df1b438b5bfa165d435198da75ae910"}, {"relation": "isA", "source": 3346, "target": 127, "key": "4c22a89f9917d7672a47debaa0c256e3"}, {"line": 47205, "relation": "increases", "evidence": "Here we report that syndecan-4, the universally expressed isoform of the syndecan family of transmembrane proteoglycans, binds and mediates transport of the three most frequently utilized cationic CPPs (penetratin, octaarginine and TAT) into the cells.", "citation": {"db": "PubMed", "db_id": "20138023"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 1001, "target": 438, "key": "c696573fef0344b93b6a40678c80b370"}, {"line": 47254, "relation": "increases", "evidence": "R8 enhances the binding of PKCa to Syn-4 in the cytosol. These results strongly suggest that the induced clustering of Syn-4 after treatment with R8 leads an enhanced intracellular interaction between PKCa and Syn-4.", "citation": {"db": "PubMed", "db_id": "24632200"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 1001, "target": 1617, "key": "4259719cecbd6067d2021ae62f2f1b85"}, {"relation": "partOf", "source": 438, "target": 1001, "key": "a73d002f732912e9c5ed2df78d0e1ce5"}, {"line": 47228, "relation": "increases", "evidence": "By treatment of cells with octaarginine (R8), enhanced clustering of syndecan-4 on plasma membranes and binding of protein kinase Calpha (PKCalpha) to the cytoplasmic domain of syndecan-4 were observed; these events potentially lead to the macropinocytic uptake of R8. ", "citation": {"db": "PubMed", "db_id": "24632200"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 438, "target": 3346, "key": "4642a40760a19df75dec10b08412f8ee"}, {"line": 47248, "relation": "increases", "evidence": "This observation suggests that R8 enhances Syn-4 clustering on plasma membranes during this short period of 30 min. ", "citation": {"db": "PubMed", "db_id": "24632200"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 438, "target": 3346, "key": "256ca9d4b041f1e94671cb829597ea4a"}, {"line": 47229, "relation": "increases", "evidence": "By treatment of cells with octaarginine (R8), enhanced clustering of syndecan-4 on plasma membranes and binding of protein kinase Calpha (PKCalpha) to the cytoplasmic domain of syndecan-4 were observed; these events potentially lead to the macropinocytic uptake of R8. ", "citation": {"db": "PubMed", "db_id": "24632200"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 438, "target": 1617, "key": "95e1992bc5dffd49e5f0890382b56c30"}, {"line": 47206, "relation": "increases", "evidence": "Here we report that syndecan-4, the universally expressed isoform of the syndecan family of transmembrane proteoglycans, binds and mediates transport of the three most frequently utilized cationic CPPs (penetratin, octaarginine and TAT) into the cells.", "citation": {"db": "PubMed", "db_id": "20138023"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 998, "target": 416, "key": "2da2eced9de837648577e380b0740182"}, {"relation": "partOf", "source": 416, "target": 998, "key": "b008233a13247313baef8386c4b930f1"}, {"line": 47230, "relation": "increases", "evidence": "By treatment of cells with octaarginine (R8), enhanced clustering of syndecan-4 on plasma membranes and binding of protein kinase Calpha (PKCalpha) to the cytoplasmic domain of syndecan-4 were observed; these events potentially lead to the macropinocytic uptake of R8. ", "citation": {"db": "PubMed", "db_id": "24632200"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 1617, "target": 819, "key": "6a65b0d89203d277918b8a3d08809823"}, {"line": 47232, "relation": "increases", "evidence": "By treatment of cells with octaarginine (R8), enhanced clustering of syndecan-4 on plasma membranes and binding of protein kinase Calpha (PKCalpha) to the cytoplasmic domain of syndecan-4 were observed; these events potentially lead to the macropinocytic uptake of R8. ", "citation": {"db": "PubMed", "db_id": "24632200"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 1617, "target": 438, "key": "811b00ae130de390cd572a7a166c321f"}, {"line": 47240, "relation": "increases", "evidence": "The Syn-4 core protein is composed of an extracellular domain, a transmembrane region, and a conserved short C-terminal cytoplasmic domain (CD) [10]. The CD harbors structural features that contribute to signal transduction across cell membranes [10], which act by binding to and activating protein kinase C-alpha (PKCa).", "citation": {"db": "PubMed", "db_id": "24632200"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 1617, "target": 3236, "key": "3ab4086d5307be88379a92aa42003e26"}, {"line": 47231, "relation": "increases", "evidence": "By treatment of cells with octaarginine (R8), enhanced clustering of syndecan-4 on plasma membranes and binding of protein kinase Calpha (PKCalpha) to the cytoplasmic domain of syndecan-4 were observed; these events potentially lead to the macropinocytic uptake of R8. ", "citation": {"db": "PubMed", "db_id": "24632200"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 819, "target": 438, "key": "2875946518a910663326d39ca3619da3"}, {"line": 47291, "relation": "directlyIncreases", "evidence": "After 1 h of fibril treatment, we observed tau RD fibrils adherent to the plasma membrane, and, in many instances, we observed engulfment of fibrils by lamellipodia-like membrane protrusions. Further, internalized fibrils were contained within large membrane-bound vacuoles that often exceeded 5 µM in diameter, which are significantly larger than other endocytic vesicles, and consistent with macropinosomes.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 819, "target": 375, "key": "e5556ada3f8d3d95befd6dbef0f6b87b"}, {"line": 47356, "relation": "increases", "evidence": "Aggregate Uptake by Macropinocytosis. Based on pharmacologic studies and colocalization with fluid phase markers, macropinocytosis was previously suggested as the mechanism for cell uptake of SOD1 (36). Likewise, macropinocytosis and HSPGs have been previously implicated in prion protein uptake (14, 37).", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 819, "target": 3254, "key": "f8057b142137526a0ee6a53bd05456bc"}, {"line": 47357, "relation": "increases", "evidence": "Aggregate Uptake by Macropinocytosis. Based on pharmacologic studies and colocalization with fluid phase markers, macropinocytosis was previously suggested as the mechanism for cell uptake of SOD1 (36). Likewise, macropinocytosis and HSPGs have been previously implicated in prion protein uptake (14, 37).", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 819, "target": 3391, "key": "2beeb1a7ffb6a9c580112c753a803402"}, {"line": 47265, "relation": "increases", "evidence": "In these studies, the highest luciferase activity was detected on SDC4 transfectants, showing that among SDC isoforms, SDC4 facilitates lipoplex-mediated gene delivery the most (Fig. 1A).", "citation": {"db": "PubMed", "db_id": "23732629"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 999, "target": 560, "key": "f084d37d342c1869c0d37758fbd58668"}, {"relation": "partOf", "source": 421, "target": 999, "key": "c63a0b58b3b6b0b9fe627897d8abdc8c"}, {"relation": "partOf", "source": 421, "target": 1000, "key": "d7d3df8b26aa1a36a9c56bce53851761"}, {"relation": "partOf", "source": 426, "target": 999, "key": "e7112a6ef02a263d4b6a62c83519f4be"}, {"relation": "partOf", "source": 426, "target": 1000, "key": "9b7fab483807005a5561f44e70f004e6"}, {"line": 47285, "relation": "increases", "evidence": "Prior studies by our laboratory and others have demonstrated that internalized tau aggregates can trigger fibrillization of native tau protein (6–11). We have previously observed that tau aggregates propagate the misfolded state among cells in culture via release of fibrils into the extracellular space. These aggregates trigger further fibrillization by direct protein–protein contact with native tau in the recipient cells (12). Thus, fibrillar tau appears to spread pathologic processes by mechanisms fundamentally similar to prion pathogenesis.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 375, "target": 644, "key": "7318c9ad330746b279666ff0344efb3c"}, {"line": 47297, "relation": "directlyIncreases", "evidence": "Thus, extracellular tau aggregates directly stimulate macropinocytosis to trigger their own uptake.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"location": {"namespace": "GO", "name": "extracellular region"}}, "source": 375, "target": 819, "key": "2a46e8b5082c9abd09130d5c49f6a00f"}, {"relation": "partOf", "source": 375, "target": 957, "key": "f4ad899206dfae351a2f71b76366f954"}, {"line": 47332, "relation": "increases", "evidence": "In addition to tau, alpha-synuclein and huntingtin accumulate in fibrillar aggregates and cause progressive neurodegeneration.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "source": 375, "target": 3872, "key": "44897e11a86f624dc6106e821151641b"}, {"line": 47306, "relation": "increases", "evidence": "HSPGs Mediate Tau Fibril Binding and Uptake. Thus, tau RD ï¬betabril binding to HSPGs is critical for uptake by macropinocytosis in C17.2 cells.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "object": {"location": {"namespace": "GO", "name": "cytoplasm"}}, "source": 127, "target": 375, "key": "c6fd3f7d2bc1a984cd2f3d4b3a7c8df1"}, {"relation": "partOf", "source": 127, "target": 957, "key": "b0712ba043e0d62e69fbe73958f4bae7"}, {"line": 47339, "relation": "increases", "evidence": "Taken together, these data indicate that tau and alpha-synuclein use a similar mechanism for uptake based on binding HSPGs, whereas Htt exon 1 fibril uptake is distinct.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 127, "target": 376, "key": "89cc7588877574ffdd1563a8a981bcc8"}, {"line": 47340, "relation": "causesNoChange", "evidence": "Taken together, these data indicate that tau and alpha-synuclein use a similar mechanism for uptake based on binding HSPGs, whereas Htt exon 1 fibril uptake is distinct.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "source": 127, "target": 382, "key": "76e686f8354bb453c6c1b74d32035fa4"}, {"line": 47355, "relation": "increases", "evidence": "Aggregate Uptake by Macropinocytosis. Based on pharmacologic studies and colocalization with fluid phase markers, macropinocytosis was previously suggested as the mechanism for cell uptake of SOD1 (36). Likewise, macropinocytosis and HSPGs have been previously implicated in prion protein uptake (14, 37).", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 127, "target": 3254, "key": "fd6d642354598b865306859b58a262fc"}, {"relation": "partOf", "source": 127, "target": 959, "key": "5c6000e3a77b9b672f144c39a13e459d"}, {"relation": "partOf", "source": 127, "target": 958, "key": "61bcf0763181813ef68010b92084f1b2"}, {"relation": "isA", "source": 3345, "target": 127, "key": "db7d6c9b89d761224091038afea865f7"}, {"line": 47307, "relation": "increases", "evidence": "HSPGs Mediate Tau Fibril Binding and Uptake. Thus, tau RD ï¬betabril binding to HSPGs is critical for uptake by macropinocytosis in C17.2 cells.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 957, "target": 819, "key": "9b27d5ba86c11100e06f7983ff49450d"}, {"line": 47316, "relation": "increases", "evidence": "HSPGs Are Required for FL Tau Fibril Entry in Vivo. Thus, FL tau ï¬betabril uptake into neurons in vivo also requires binding to HSPGs.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Cell": {"neuron": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "extracellular region"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 957, "target": 375, "key": "78437b21925c042443abb5e2feb71f1f"}, {"line": 47326, "relation": "increases", "evidence": "In addition to tau, alpha-synuclein and huntingtin accumulate in fibrillar aggregates and cause progressive neurodegeneration.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}, "Confidence": {"Medium": true}}, "source": 376, "target": 3872, "key": "bedf85d0c080777c13a542927989b683"}, {"line": 47333, "relation": "increases", "evidence": "In addition to tau, alpha-synuclein and huntingtin accumulate in fibrillar aggregates and cause progressive neurodegeneration.", "citation": {"db": "PubMed", "db_id": "23898162"}, "annotations": {"Confidence": {"Medium": true}}, "source": 382, "target": 3872, "key": "2c92fc967714863024620bf84518245f"}, {"relation": "partOf", "source": 440, "target": 959, "key": "b7968a34cc0998d0fc27a21bdff029be"}, {"line": 47367, "relation": "increases", "evidence": "In this work we strove to assess the role of heparan sulfate (HS) chains in HPV16 binding to the ECM and determine how HPV16 release from the ECM is regulated. We also assessed the extent to which capsids released from the ECM are infectious. We show that a large fraction of HPV16 particles binds to the ECM via HS chains, and that syndecan-1 (snd-1) molecules present in the ECM are involved in virus binding.", "citation": {"db": "PubMed", "db_id": "26289843"}, "annotations": {"Confidence": {"Medium": true}}, "source": 440, "target": 959, "key": "0b77fb00b340891785e5eb5e8bfa5ec5"}, {"line": 47377, "relation": "increases", "evidence": "The entry of HPV particles into human keratinocyte (HK) host cells is a multistep process initiated by binding to primary attachment factors, most commonly the heparan sulfate (HS) chains of proteoglycans (HSPGs) (Joyce et al., 1999; Combita et al., 2001; Giroglou et al., 2001). Syndecan-1 (snd-1), the most abundant HSPG in keratinocytes, serves as an HPV attachment receptor (Selinka et al., 2002; Shafti-Keramat et al., 2003).", "citation": {"db": "PubMed", "db_id": "26289843"}, "annotations": {"Cell": {"keratinocyte": true}, "Confidence": {"Medium": true}}, "source": 440, "target": 959, "key": "b283319cef24e106df574128f0e86ccc"}, {"relation": "partOf", "source": 440, "target": 958, "key": "f1b429f485f6e2dc5070717da539985b"}, {"line": 47376, "relation": "increases", "evidence": "The entry of HPV particles into human keratinocyte (HK) host cells is a multistep process initiated by binding to primary attachment factors, most commonly the heparan sulfate (HS) chains of proteoglycans (HSPGs) (Joyce et al., 1999; Combita et al., 2001; Giroglou et al., 2001). Syndecan-1 (snd-1), the most abundant HSPG in keratinocytes, serves as an HPV attachment receptor (Selinka et al., 2002; Shafti-Keramat et al., 2003).", "citation": {"db": "PubMed", "db_id": "26289843"}, "annotations": {"Cell": {"keratinocyte": true}, "Confidence": {"Medium": true}}, "source": 440, "target": 958, "key": "e8c12e4cb726fb939a52cd34f999ed0b"}, {"line": 47391, "relation": "increases", "evidence": "The ectodomains of all HS-chain-enriched snd-1 molecules are constitutively released (i.e. shed) from cell membranes as part of normal physiology (Bishop et al.,2007). Proteases, including matrix metalloproteinases (MMPs) and ADAM sheddases, mediate the ectodomain shedding of membrane-bound proteins (such as snd-1). Previously, we showed snd-1 shedding plays an active role in HPV16 infection in cultured HKs (Surviladze et al., 2012).", "citation": {"db": "PubMed", "db_id": "26289843"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 440, "target": 3843, "key": "97482d3c5c3387fe1d9369e98ede1084"}, {"line": 47378, "relation": "increases", "evidence": "The entry of HPV particles into human keratinocyte (HK) host cells is a multistep process initiated by binding to primary attachment factors, most commonly the heparan sulfate (HS) chains of proteoglycans (HSPGs) (Joyce et al., 1999; Combita et al., 2001; Giroglou et al., 2001). Syndecan-1 (snd-1), the most abundant HSPG in keratinocytes, serves as an HPV attachment receptor (Selinka et al., 2002; Shafti-Keramat et al., 2003).", "citation": {"db": "PubMed", "db_id": "26289843"}, "annotations": {"Cell": {"keratinocyte": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "GO", "name": "external side of plasma membrane"}, "toLoc": {"namespace": "GO", "name": "cytoplasm"}}}, "source": 959, "target": 440, "key": "4afcbd37c8068fc58e8479f4b3aaeb33"}, {"line": 47390, "relation": "increases", "evidence": "The ectodomains of all HS-chain-enriched snd-1 molecules are constitutively released (i.e. shed) from cell membranes as part of normal physiology (Bishop et al.,2007). Proteases, including matrix metalloproteinases (MMPs) and ADAM sheddases, mediate the ectodomain shedding of membrane-bound proteins (such as snd-1). Previously, we showed snd-1 shedding plays an active role in HPV16 infection in cultured HKs (Surviladze et al., 2012).", "citation": {"db": "PubMed", "db_id": "26289843"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 3343, "target": 440, "key": "dfa158560dfe544a783a3a118aaa58d6"}, {"line": 47388, "relation": "increases", "evidence": "The ectodomains of all HS-chain-enriched snd-1 molecules are constitutively released (i.e. shed) from cell membranes as part of normal physiology (Bishop et al.,2007). Proteases, including matrix metalloproteinases (MMPs) and ADAM sheddases, mediate the ectodomain shedding of membrane-bound proteins (such as snd-1). Previously, we showed snd-1 shedding plays an active role in HPV16 infection in cultured HKs (Surviladze et al., 2012).", "citation": {"db": "PubMed", "db_id": "26289843"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 3544, "target": 3343, "key": "d9c52cb786388bef405ef7d80e05b65c"}, {"line": 47410, "relation": "increases", "evidence": "Polyethyleneimines (PEIs) are efficient non-viral vectors for gene transfer.", "citation": {"db": "PubMed", "db_id": "18216019"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 954, "target": 856, "key": "e0429780ae74ce2008c607eddd904a18"}, {"line": 47426, "relation": "increases", "evidence": "In conclusion, we have shown here for the first time that SDC1 and SDC2 are directly involved in polyplex binding and that SDC2 strongly delays polyplex endocytosis and inhibits PEI-mediated gene expression", "citation": {"db": "PubMed", "db_id": "18216019"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 954, "target": 856, "key": "4f897ffc616926e397f9d1438658ea1b"}, {"line": 47416, "relation": "increases", "evidence": "SDC1 but Not SDC2 Rapidly Internalizes Polyplexesâ€â€�To address this striking difference between SDC1 and SDC2, we examined the fate of polyplexes by confocal microscopy using RITC-labeled PEI (Fig. 2). Coexpression of SDC2 with SDC1 resulted in an 60% reduction in transfection efficiency compared with expression of SDC1 alone, indicating that SDC2 has a negative effect on SDC1-mediated PEI transfection (Fig. 5a). This result dramatically contrasts with the profile obtained when SDC1 was expressed alone (Fig. 2a), suggesting that SDC2 has a dominant-negative effect on SDC1-mediated polyplex endocytosis and gene transfer to the nucleus.", "citation": {"db": "PubMed", "db_id": "18216019"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 954, "target": 813, "key": "48849706026b02714f8698b130ab6c4f"}, {"line": 47418, "relation": "decreases", "evidence": "SDC1 but Not SDC2 Rapidly Internalizes Polyplexesâ€â€�To address this striking difference between SDC1 and SDC2, we examined the fate of polyplexes by confocal microscopy using RITC-labeled PEI (Fig. 2). Coexpression of SDC2 with SDC1 resulted in an 60% reduction in transfection efficiency compared with expression of SDC1 alone, indicating that SDC2 has a negative effect on SDC1-mediated PEI transfection (Fig. 5a). This result dramatically contrasts with the profile obtained when SDC1 was expressed alone (Fig. 2a), suggesting that SDC2 has a dominant-negative effect on SDC1-mediated polyplex endocytosis and gene transfer to the nucleus.", "citation": {"db": "PubMed", "db_id": "18216019"}, "annotations": {"Subgraph": {"Syndecan subgraph": true}}, "source": 1628, "target": 954, "key": "f3192539c75b601bdcc308900c020ca8"}, {"line": 47461, "relation": "increases", "evidence": "The CRF-like peptide urocortin greatly attenuates loss of extracellular striatal dopamine in rat models of Parkinson's disease by activating CRF(1) receptors.", "citation": {"db": "PubMed", "db_id": "19026631"}, "annotations": {"Species": {"10116": true}, "MeSHDisease": {"Parkinson Disease": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Activity"}, "source": 3773, "target": 545, "key": "99ac54e2195270ffd90c20df5caed68c"}, {"line": 47477, "relation": "increases", "evidence": "overexpression of CRF or exposure to chronic stress in rodents can induce phosphorylation and solubility changes in the microtubule-associated protein, tau, a process that is reliant on CRFR1. Furthermore, exposing rodents to chronic emotional stress results in increased phosphorylation and decreased solubility of the tau protein; changes that are also strictly dependent on CRFR1 signaling. In addition to work on tau, several reports demonstrate that CRF or stress exposure can impact Abeta production and accumulation in AD models and that stress-induced Abeta plaque formation in adult AD mice can be reduced by CRFR1 antagonism . In particular, our recently published work demonstrates that genetic ablation of CRFR1 greatly reduces the production of APP CTFs and accumulation of Abeta in the brains of AD mice. ", "citation": {"db": "PubMed", "db_id": "26555315"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3798, "key": "3ca9db9a49dc64a33f8160dcfedd71ed"}, {"line": 47493, "relation": "increases", "evidence": "Stress exposure or increased levels of corticotropin-releasing factor (CRF) induce hippocampal tau phosphorylation (tau-P) in rodent models, a process that is dependent on the type-1 CRF receptor (CRFR1).", "citation": {"db": "PubMed", "db_id": "26790099"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3798, "key": "968632bbf4924e94f8c2fb4b7b30432d"}, {"line": 47523, "relation": "increases", "evidence": "Clinical and basic science research suggests that stress and/or changes in central stress signaling intermediates may be involved in Alzheimer's disease (AD) pathogenesis. Although the links between stress and AD remain unsettled, data from our group and others have established that stress exposure in rodents may confer susceptibility to AD pathology by inducing hippocampal tau phosphorylation (tau-P). Work in our laboratory has shown that stress-induced tau-P requires activation of the type-1 corticotropin-releasing factor receptor (CRFR1). ", "citation": {"db": "PubMed", "db_id": "25125464"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3798, "key": "f700b89f75e1d1283d80f4421e866489"}, {"line": 47552, "relation": "increases", "evidence": "When tested in hippocampal extracts from wt and knock-out mice, each of the stress-responsive kinase forms or regulators also exhibited modulation as a function of CRFR status that mirrored some or all of the effects of genotype on restraint-induced tau-P. The activated (pY216) form of GSK-3beta, implicated in phosphorylating tau at S199, S212, T231, and PHF-1 sites, was most similar in that the stress-induced increment seen in wt mice was not evident in CRFR1−/− animals and was exaggerated in CRFR2−/− mice. Phosphorylation responses of both JNK isoforms were also significantly greater in CRFR2 knock-outs than in wt controls (p < 0.05). These kinases also exhibited pronounced elevations in basal phosphorylation in CRFR1-deficient mice, whose magnitude rivaled or exceeded stress-induced levels in wt mice. This may relate to the elevated tau-P levels seen under this condition at the AT8 and PHF-1 sites, although less-marked elevations of phosphorylated GSK-3beta and ERK2, and of p35 levels, in unstressed CRFR1−/− mice may also contribute in this regard. Overall, these results identify several tau kinases as potential effectors of CRFR-dependent effects of acute emotional stress on tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3798, "key": "5dcce5e91014a39114fe2bb776966973"}, {"line": 47479, "relation": "association", "evidence": "overexpression of CRF or exposure to chronic stress in rodents can induce phosphorylation and solubility changes in the microtubule-associated protein, tau, a process that is reliant on CRFR1. Furthermore, exposing rodents to chronic emotional stress results in increased phosphorylation and decreased solubility of the tau protein; changes that are also strictly dependent on CRFR1 signaling. In addition to work on tau, several reports demonstrate that CRF or stress exposure can impact Abeta production and accumulation in AD models and that stress-induced Abeta plaque formation in adult AD mice can be reduced by CRFR1 antagonism . In particular, our recently published work demonstrates that genetic ablation of CRFR1 greatly reduces the production of APP CTFs and accumulation of Abeta in the brains of AD mice. ", "citation": {"db": "PubMed", "db_id": "26555315"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 2328, "key": "d0f1353f5206329d9a3036b193f52430"}, {"line": 47537, "relation": "increases", "evidence": "We found that both PSAPP-R1(+/-) and PSAPP-R1(-/-) had significantly reduced Abeta burden in the hippocampus, insular, rhinal, and retrosplenial cortices. Accordingly, we observed dramatic reductions in Abeta peptides and AbetaPP-CTFs, providing support for a direct relationship between CRFR1 and Abeta production pathways. In summary, our results suggest that interference of CRFR1 in an AD model is tolerable and is efficacious in impacting Abeta neuropathology.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 2328, "key": "15e321acce5e97b419b2c18487921761"}, {"line": 47509, "relation": "association", "evidence": "In cells, CRF treatment increases Abeta production and triggers CRF receptor 1 (CRFR1) and gamma-secretase internalization. Co-immunoprecipitation studies establish that gamma-secretase associates with CRFR1; this is mediated by beta-arrestin binding motifs. ", "citation": {"db": "PubMed", "db_id": "25964433"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 868, "key": "87fee343628cdd952bda4423b9ac2398"}, {"line": 47538, "relation": "association", "evidence": "We found that both PSAPP-R1(+/-) and PSAPP-R1(-/-) had significantly reduced Abeta burden in the hippocampus, insular, rhinal, and retrosplenial cortices. Accordingly, we observed dramatic reductions in Abeta peptides and AbetaPP-CTFs, providing support for a direct relationship between CRFR1 and Abeta production pathways. In summary, our results suggest that interference of CRFR1 in an AD model is tolerable and is efficacious in impacting Abeta neuropathology.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3823, "key": "78f47ee6ee5ab718d76050b6726ea959"}, {"line": 47553, "relation": "increases", "evidence": "When tested in hippocampal extracts from wt and knock-out mice, each of the stress-responsive kinase forms or regulators also exhibited modulation as a function of CRFR status that mirrored some or all of the effects of genotype on restraint-induced tau-P. The activated (pY216) form of GSK-3beta, implicated in phosphorylating tau at S199, S212, T231, and PHF-1 sites, was most similar in that the stress-induced increment seen in wt mice was not evident in CRFR1−/− animals and was exaggerated in CRFR2−/− mice. Phosphorylation responses of both JNK isoforms were also significantly greater in CRFR2 knock-outs than in wt controls (p < 0.05). These kinases also exhibited pronounced elevations in basal phosphorylation in CRFR1-deficient mice, whose magnitude rivaled or exceeded stress-induced levels in wt mice. This may relate to the elevated tau-P levels seen under this condition at the AT8 and PHF-1 sites, although less-marked elevations of phosphorylated GSK-3beta and ERK2, and of p35 levels, in unstressed CRFR1−/− mice may also contribute in this regard. Overall, these results identify several tau kinases as potential effectors of CRFR-dependent effects of acute emotional stress on tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3782, "key": "1800363e296e9b28926c2d8a8980ded2"}, {"line": 47554, "relation": "increases", "evidence": "When tested in hippocampal extracts from wt and knock-out mice, each of the stress-responsive kinase forms or regulators also exhibited modulation as a function of CRFR status that mirrored some or all of the effects of genotype on restraint-induced tau-P. The activated (pY216) form of GSK-3beta, implicated in phosphorylating tau at S199, S212, T231, and PHF-1 sites, was most similar in that the stress-induced increment seen in wt mice was not evident in CRFR1−/− animals and was exaggerated in CRFR2−/− mice. Phosphorylation responses of both JNK isoforms were also significantly greater in CRFR2 knock-outs than in wt controls (p < 0.05). These kinases also exhibited pronounced elevations in basal phosphorylation in CRFR1-deficient mice, whose magnitude rivaled or exceeded stress-induced levels in wt mice. This may relate to the elevated tau-P levels seen under this condition at the AT8 and PHF-1 sites, although less-marked elevations of phosphorylated GSK-3beta and ERK2, and of p35 levels, in unstressed CRFR1−/− mice may also contribute in this regard. Overall, these results identify several tau kinases as potential effectors of CRFR-dependent effects of acute emotional stress on tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3794, "key": "1c3ff9b50fbb6bc110db1134ed65f2c1"}, {"line": 47555, "relation": "increases", "evidence": "When tested in hippocampal extracts from wt and knock-out mice, each of the stress-responsive kinase forms or regulators also exhibited modulation as a function of CRFR status that mirrored some or all of the effects of genotype on restraint-induced tau-P. The activated (pY216) form of GSK-3beta, implicated in phosphorylating tau at S199, S212, T231, and PHF-1 sites, was most similar in that the stress-induced increment seen in wt mice was not evident in CRFR1−/− animals and was exaggerated in CRFR2−/− mice. Phosphorylation responses of both JNK isoforms were also significantly greater in CRFR2 knock-outs than in wt controls (p < 0.05). These kinases also exhibited pronounced elevations in basal phosphorylation in CRFR1-deficient mice, whose magnitude rivaled or exceeded stress-induced levels in wt mice. This may relate to the elevated tau-P levels seen under this condition at the AT8 and PHF-1 sites, although less-marked elevations of phosphorylated GSK-3beta and ERK2, and of p35 levels, in unstressed CRFR1−/− mice may also contribute in this regard. Overall, these results identify several tau kinases as potential effectors of CRFR-dependent effects of acute emotional stress on tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3759, "key": "ace01763cb2e33a2d498dc5b52a96c1b"}, {"line": 47556, "relation": "increases", "evidence": "When tested in hippocampal extracts from wt and knock-out mice, each of the stress-responsive kinase forms or regulators also exhibited modulation as a function of CRFR status that mirrored some or all of the effects of genotype on restraint-induced tau-P. The activated (pY216) form of GSK-3beta, implicated in phosphorylating tau at S199, S212, T231, and PHF-1 sites, was most similar in that the stress-induced increment seen in wt mice was not evident in CRFR1−/− animals and was exaggerated in CRFR2−/− mice. Phosphorylation responses of both JNK isoforms were also significantly greater in CRFR2 knock-outs than in wt controls (p < 0.05). These kinases also exhibited pronounced elevations in basal phosphorylation in CRFR1-deficient mice, whose magnitude rivaled or exceeded stress-induced levels in wt mice. This may relate to the elevated tau-P levels seen under this condition at the AT8 and PHF-1 sites, although less-marked elevations of phosphorylated GSK-3beta and ERK2, and of p35 levels, in unstressed CRFR1−/− mice may also contribute in this regard. Overall, these results identify several tau kinases as potential effectors of CRFR-dependent effects of acute emotional stress on tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 3796, "key": "411f791437bb377b666c632d3a325492"}, {"relation": "partOf", "source": 3773, "target": 1654, "key": "82a7f8ebc41ba9bae32c46070c3bcadd"}, {"line": 47591, "relation": "increases", "evidence": "The CRF family of peptides exert their biological effects via two G protein-coupled receptors (CRFRs) that are positively coupled to adenylate cyclase. CRF binds CRFR1 with high affinity, and in the pituitary gland this interaction mediates the neuroendocrine stress response [35]. CRFR1 is widely expressed in the brain, including AD-relevant areas such as the neocortex and hippocampus [36]. CRFR2 is a structurally related receptor but displays very limited CNS distribution [37].", "citation": {"db": "PubMed", "db_id": "26790099"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3773, "target": 1654, "key": "97c53b26e9c1a63dd99c75df5705ae23"}, {"line": 47460, "relation": "increases", "evidence": "The CRF-like peptide urocortin greatly attenuates loss of extracellular striatal dopamine in rat models of Parkinson's disease by activating CRF(1) receptors.", "citation": {"db": "PubMed", "db_id": "19026631"}, "annotations": {"Species": {"10116": true}, "MeSHDisease": {"Parkinson Disease": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity"}, "source": 3809, "target": 3773, "key": "2693ca28ed5811ef3fd8a35ae5ee80ed"}, {"line": 47478, "relation": "association", "evidence": "overexpression of CRF or exposure to chronic stress in rodents can induce phosphorylation and solubility changes in the microtubule-associated protein, tau, a process that is reliant on CRFR1. Furthermore, exposing rodents to chronic emotional stress results in increased phosphorylation and decreased solubility of the tau protein; changes that are also strictly dependent on CRFR1 signaling. In addition to work on tau, several reports demonstrate that CRF or stress exposure can impact Abeta production and accumulation in AD models and that stress-induced Abeta plaque formation in adult AD mice can be reduced by CRFR1 antagonism . In particular, our recently published work demonstrates that genetic ablation of CRFR1 greatly reduces the production of APP CTFs and accumulation of Abeta in the brains of AD mice. ", "citation": {"db": "PubMed", "db_id": "26555315"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3798, "target": 3823, "key": "dcea553a4f5aa69f4c9243b6065b025a"}, {"line": 47494, "relation": "association", "evidence": "Stress exposure or increased levels of corticotropin-releasing factor (CRF) induce hippocampal tau phosphorylation (tau-P) in rodent models, a process that is dependent on the type-1 CRF receptor (CRFR1).", "citation": {"db": "PubMed", "db_id": "26790099"}, "annotations": {"Species": {"10116": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3798, "target": 3823, "key": "31d25167198798ee4bc4e70a3e233940"}, {"line": 47524, "relation": "increases", "evidence": "Clinical and basic science research suggests that stress and/or changes in central stress signaling intermediates may be involved in Alzheimer's disease (AD) pathogenesis. Although the links between stress and AD remain unsettled, data from our group and others have established that stress exposure in rodents may confer susceptibility to AD pathology by inducing hippocampal tau phosphorylation (tau-P). Work in our laboratory has shown that stress-induced tau-P requires activation of the type-1 corticotropin-releasing factor receptor (CRFR1). ", "citation": {"db": "PubMed", "db_id": "25125464"}, "annotations": {"Species": {"10116": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3798, "target": 3823, "key": "a63994adc5ead94b53eef5aec2c2c71c"}, {"relation": "hasVariant", "source": 3797, "target": 3798, "key": "7ecb40d2bf8ef5f29ce55487591bab78"}, {"relation": "hasVariant", "source": 3797, "target": 3799, "key": "a513e08ffce50be473c21d444f665eaf"}, {"relation": "hasVariant", "source": 3797, "target": 3800, "key": "c03d40f95173695968c081a382d02dea"}, {"relation": "hasVariant", "source": 3797, "target": 3801, "key": "d66ce7c5e548d5ccaa80125e77c4a6d1"}, {"relation": "hasVariant", "source": 3797, "target": 3802, "key": "742347c0a7e55d368df79d293b1426ed"}, {"relation": "hasVariant", "source": 3797, "target": 3803, "key": "998734f98032bfece2ce0434671da426"}, {"line": 47507, "relation": "increases", "evidence": "In cells, CRF treatment increases Abeta production and triggers CRF receptor 1 (CRFR1) and gamma-secretase internalization. Co-immunoprecipitation studies establish that gamma-secretase associates with CRFR1; this is mediated by beta-arrestin binding motifs. ", "citation": {"db": "PubMed", "db_id": "25964433"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3772, "target": 2328, "key": "34e1ec64b7ff7cead568a788dcfa3afe"}, {"relation": "partOf", "source": 3772, "target": 1654, "key": "faa1cc78823aaf43355848b109a3109b"}, {"line": 47590, "relation": "increases", "evidence": "The CRF family of peptides exert their biological effects via two G protein-coupled receptors (CRFRs) that are positively coupled to adenylate cyclase. CRF binds CRFR1 with high affinity, and in the pituitary gland this interaction mediates the neuroendocrine stress response [35]. CRFR1 is widely expressed in the brain, including AD-relevant areas such as the neocortex and hippocampus [36]. CRFR2 is a structurally related receptor but displays very limited CNS distribution [37].", "citation": {"db": "PubMed", "db_id": "26790099"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3772, "target": 1654, "key": "13bfaba9a07c302d4ba6851d962ae1dd"}, {"relation": "hasVariant", "source": 3781, "target": 3782, "key": "e8a763c1fb4c22acbadd9c68293843f3"}, {"relation": "hasVariant", "source": 3793, "target": 3794, "key": "b7a2c734ebc64261426fbcf315329c29"}, {"relation": "hasVariant", "source": 3758, "target": 3759, "key": "9f4b4aa8e0621b71b04c4e31ef8f5c8d"}, {"relation": "hasVariant", "source": 3795, "target": 3796, "key": "b6e444978d204c9281cd3887e40fe388"}, {"line": 47557, "relation": "decreases", "evidence": "When tested in hippocampal extracts from wt and knock-out mice, each of the stress-responsive kinase forms or regulators also exhibited modulation as a function of CRFR status that mirrored some or all of the effects of genotype on restraint-induced tau-P. The activated (pY216) form of GSK-3beta, implicated in phosphorylating tau at S199, S212, T231, and PHF-1 sites, was most similar in that the stress-induced increment seen in wt mice was not evident in CRFR1−/− animals and was exaggerated in CRFR2−/− mice. Phosphorylation responses of both JNK isoforms were also significantly greater in CRFR2 knock-outs than in wt controls (p < 0.05). These kinases also exhibited pronounced elevations in basal phosphorylation in CRFR1-deficient mice, whose magnitude rivaled or exceeded stress-induced levels in wt mice. This may relate to the elevated tau-P levels seen under this condition at the AT8 and PHF-1 sites, although less-marked elevations of phosphorylated GSK-3beta and ERK2, and of p35 levels, in unstressed CRFR1−/− mice may also contribute in this regard. Overall, these results identify several tau kinases as potential effectors of CRFR-dependent effects of acute emotional stress on tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 3774, "target": 3796, "key": "3d1232c9ffc0065b64e2e7522c596501"}, {"line": 47572, "relation": "decreases", "evidence": "We therefore assessed the effect of antalarmin, a small-molecule, selective CRFR1 antagonist (Webster et al., 1996), on basal and stress-induced tau-P. Neither antalarmin nor the administration vehicle significantly altered basal levels of tau-P at the AT8 or PHF-1 sites, relative to untreated controls (Fig. 5). However, antalarmin treatment prevented stress-induced increments in phosphorylation at both sites (lanes 5–6; each p > 0.10 vs untreated controls). Phosphorylation responses in stressed, vehicle-treated animals were comparable with those of stressed, untreated controls and significantly elevated over vehicle control levels (p < 0.01). These findings support a specific involvement of CRFR1 signaling in stress-induced tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 211, "target": 3773, "key": "ae2f5bde4920ef30135a1e2e0ebc0ea2"}, {"line": 47573, "relation": "decreases", "evidence": "We therefore assessed the effect of antalarmin, a small-molecule, selective CRFR1 antagonist (Webster et al., 1996), on basal and stress-induced tau-P. Neither antalarmin nor the administration vehicle significantly altered basal levels of tau-P at the AT8 or PHF-1 sites, relative to untreated controls (Fig. 5). However, antalarmin treatment prevented stress-induced increments in phosphorylation at both sites (lanes 5–6; each p > 0.10 vs untreated controls). Phosphorylation responses in stressed, vehicle-treated animals were comparable with those of stressed, untreated controls and significantly elevated over vehicle control levels (p < 0.01). These findings support a specific involvement of CRFR1 signaling in stress-induced tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 211, "target": 3798, "key": "2666479128c2f8e1c68008ed95c49743"}, {"line": 47574, "relation": "decreases", "evidence": "We therefore assessed the effect of antalarmin, a small-molecule, selective CRFR1 antagonist (Webster et al., 1996), on basal and stress-induced tau-P. Neither antalarmin nor the administration vehicle significantly altered basal levels of tau-P at the AT8 or PHF-1 sites, relative to untreated controls (Fig. 5). However, antalarmin treatment prevented stress-induced increments in phosphorylation at both sites (lanes 5–6; each p > 0.10 vs untreated controls). Phosphorylation responses in stressed, vehicle-treated animals were comparable with those of stressed, untreated controls and significantly elevated over vehicle control levels (p < 0.01). These findings support a specific involvement of CRFR1 signaling in stress-induced tau-P.", "citation": {"db": "PubMed", "db_id": "25697705"}, "annotations": {"Species": {"10090": true}, "Disease": {"Alzheimer's disease": true}, "Confidence": {"Medium": true}}, "source": 211, "target": 1654, "key": "e0995b56471b830858f4a91422b7cf0b"}, {"line": 47644, "relation": "association", "evidence": "In cells, CRF treatment increases Abeta production and triggers CRF receptor 1 (CRFR1) and gamma-secretase internalization. Co-immunoprecipitation studies establish that gamma-secretase associates with CRFR1; this is mediated by beta-arrestin binding motifs. ", "citation": {"db": "PubMed", "db_id": "25964433"}, "annotations": {"Subgraph": {"CRH subgraph": true}, "Disease": {"Alzheimer's disease": true}}, "source": 2562, "target": 868, "key": "4390e3961d160e0b8239561efcd49ee0"}, {"line": 47786, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 866, "target": 2629, "key": "4289d5883940ae5424425e8ab9fd8ac0"}, {"line": 48146, "relation": "increases", "evidence": "Knockdown of clusterin in primary neurons reduced Abeta toxicity and DKK1 upregulation and, conversely, Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Endosomal lysosomal subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 866, "target": 2629, "key": "3fcc596fa3fb003f3036a3ad89823cd3"}, {"line": 47799, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 463, "target": 2658, "key": "eb9c0f6acbf45588bd7b05b47bb20510"}, {"line": 48686, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 2658, "key": "72670094924f404a4a08697c8da3c66b"}, {"line": 48951, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 2658, "key": "7a4e5493d7f0d631503eb762b9541fd2"}, {"line": 47800, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 463, "target": 3086, "key": "e87df03fdd8102aa568fe791256e3c77"}, {"line": 48682, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 3086, "key": "cb1fa2b1c7e7803856945acc24c0a834"}, {"line": 48959, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 3086, "key": "870d0f3c6f985898971cf5c4628a63c6"}, {"line": 47801, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 463, "target": 2952, "key": "08c72ed9ebeff206c8a3a040daebcab0"}, {"line": 48690, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 2952, "key": "4e095cf176b4725180482a013ea154f6"}, {"line": 48964, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"MeSHAnatomy": {"Neurons": true}, "Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 2952, "key": "b1aaef151fdf71cebf02ee2624ae49aa"}, {"line": 47803, "relation": "isA", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 463, "target": 661, "key": "ab603507fad23c0c75959adc87ced5a0"}, {"line": 48163, "relation": "positiveCorrelation", "evidence": "Thus, we have identified a pathway whereby Abeta induces a clusterin/p53/Dkk1/wnt-PCP-JNK pathway, which drives the upregulation of several genes that mediate the development of AD-like neuropathologies, thereby providing new mechanistic insights into the action of Abeta in neurodegenerative diseases.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 3931, "key": "b043536338537b1a6db281f8b33a99e8"}, {"line": 49054, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 1812, "key": "8b86a519cb97c6595af4d9c033bfda87"}, {"line": 49060, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 1886, "key": "e1bf179a5df50c740609aebe2c1ad933"}, {"line": 49064, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 463, "target": 1857, "key": "fd9887bcdefa8ea7966dfaf989e802ac"}, {"line": 47807, "relation": "positiveCorrelation", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 3086, "target": 2328, "key": "7383b17cab34ce9d12aed8d91b331813"}, {"line": 47812, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 3086, "target": 3015, "key": "a699862b24ba0bd91db74ef896edd2a6"}, {"line": 49084, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 3086, "target": 3015, "key": "47579d434f9cb9d2b6aed759a9742693"}, {"line": 48694, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 3086, "target": 648, "key": "286114eb14767f4d1aed2987c771ae63"}, {"line": 47808, "relation": "positiveCorrelation", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 2952, "target": 2328, "key": "1ccfdea435823945a0d4ae80a9e33138"}, {"line": 47813, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 2952, "target": 3015, "key": "9a064e19dd831e84112d34ed8f171c8b"}, {"line": 49085, "relation": "increases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Tau protein subgraph": true}, "Confidence": {"High": true}}, "source": 2952, "target": 3015, "key": "b5558bff15744130bc1a89133da741ce"}, {"line": 48702, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 2952, "target": 648, "key": "536040c1515c41a6f405c57fce5aa6bf"}, {"line": 49239, "relation": "association", "evidence": "Overall, KLF10 has been implicated in cell differentiation, as a target gene for a variety of signaling pathways", "citation": {"db": "PubMed", "db_id": "20087894"}, "source": 2952, "target": 506, "key": "c203bbaed70a6d02bbc6652bec0943d2"}, {"line": 49249, "relation": "association", "evidence": "KLF10 has been shown to play a major role in the TGFbeta inhibition of cell proliferation and inflammation and induction of apoptosis ", "citation": {"db": "PubMed", "db_id": "20087894"}, "annotations": {"Subgraph": {"TGF-Beta subgraph": true}}, "source": 2952, "target": 3454, "key": "d9342f87fab265106994cc0aefa77382"}, {"line": 47804, "relation": "isA", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation. To summarise, we show that Ab-induced neurotoxicity, including tau phosphorylation at specific epitopes, is via the CLU-dependent induction of Dkk1, with Dkk1 then driving wnt–PCP signalling to increase expression of genes that we have identified and shown to be necessary mediators of these pathological processes.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 661, "target": 462, "key": "e4898a88ebfd15b143ef926561a9d4f5"}, {"line": 47827, "relation": "increases", "evidence": "Dkk4 and Dkk1 induced EGR1 (Figure 6b) and FOS (data not shown) whereas Dkk2 and Dkk3 did not, mirroring the abilities of the Dkk1 family to antagonise canonical wnt.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"Gamma secretase subgraph": true, "DKK1 subgraph": true}}, "source": 2630, "target": 2658, "key": "044c46c00226163f87e3fe9de33169f6"}, {"line": 47831, "relation": "increases", "evidence": "Dkk4 and Dkk1 induced EGR1 (Figure 6b) and FOS (data not shown) whereas Dkk2 and Dkk3 did not, mirroring the abilities of the Dkk1 family to antagonise canonical wnt.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 2630, "target": 2699, "key": "b093dff9bc58d32e34f6fa5d99c749e5"}, {"line": 47876, "relation": "decreases", "evidence": "Several secreted protein families antagonize or modulate Wnt/beta-catenin signaling. sFRPs (secreted Frizzled related proteins), and WIF (Wnt inhibitory protein) bind to Wnt, and in the case of sFRPs, also to Fz,and thereby function as Wnt antagonists for both beta-catenin and non-canonical signaling", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2710, "target": 462, "key": "d5b3fc7a14d3911b183e19ad4519bb3e"}, {"line": 47877, "relation": "decreases", "evidence": "Several secreted protein families antagonize or modulate Wnt/beta-catenin signaling. sFRPs (secreted Frizzled related proteins), and WIF (Wnt inhibitory protein) bind to Wnt, and in the case of sFRPs, also to Fz,and thereby function as Wnt antagonists for both beta-catenin and non-canonical signaling", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 3534, "target": 462, "key": "58956c9f234dc3689136113269f8bc0a"}, {"line": 47899, "relation": "association", "evidence": "The Wnt signaling pathway plays a crucial role in the proper development and maintenance of brain and bone structure and function.", "citation": {"db": "PubMed", "db_id": "26880631"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"High": true}}, "source": 713, "target": 462, "key": "b85f40f38c8889a260f7b65212e30399"}, {"line": 47919, "relation": "association", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2584, "target": 2372, "key": "83c65a58cc9b8e32c3ad3407d1a17819"}, {"line": 47927, "relation": "increases", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2584, "target": 2580, "key": "babc094e00dfcf4b877e53c182cf432a"}, {"line": 47920, "relation": "association", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2583, "target": 2372, "key": "dff9d2b792e55dfb938b2b562417dc14"}, {"line": 47929, "relation": "increases", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2583, "target": 2580, "key": "83c0774d255ab4b8e88ec4701d3e875e"}, {"line": 47921, "relation": "association", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2582, "target": 2372, "key": "7b68e42384967bd28c38c302667c1bc1"}, {"line": 47928, "relation": "increases", "evidence": "The scaffolding protein Axin uses separate domains to interact with GSK3, CK1α, and beta-catenin and coordinates sequential phosphorylation of beta-catenin at serine 45 by CK1α and then at threonine 41, serine 37 and serine 33 by GSK3 (Kimelman and Xu, 2006). beta-catenin phosphorylation at serine 33 and 37 creates a binding site for the E3 ubiquitin ligase beta-Trcp, leading to beta-catenin ubiquitination and degradation", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "object": {"modifier": "Degradation"}, "source": 2582, "target": 2580, "key": "275abce2a8337ad4571d1fa9146989b8"}, {"line": 47940, "relation": "increases", "evidence": "Wnt signaling requires both Fz and LRP6 (or LRP5), likely through a Wnt-induced Fz-LRP6 complex (Figure 1). Wnt-induced LRP6 phosphorylation is a key event in receptor activation (Tamai et al., 2004). LRP6, LRP5 and Arrow each have five reiterated PPPSPxS motifs (P, proline; S, serine or threonine, x, a variable residue), which are essential for LRP6 function and are each transferrable to a heterologous receptor to result in constitutive beta-catenin signaling (MacDonald et al., 2008;Tamai et al., 2004;Zeng et al., 2005).", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 2980, "target": 461, "key": "1a4ca9e0e26d4f2b8a9c6aa7407fd3f9"}, {"line": 47945, "relation": "increases", "evidence": "Wnt signaling requires both Fz and LRP6 (or LRP5), likely through a Wnt-induced Fz-LRP6 complex (Figure 1). Wnt-induced LRP6 phosphorylation is a key event in receptor activation (Tamai et al., 2004). LRP6, LRP5 and Arrow each have five reiterated PPPSPxS motifs (P, proline; S, serine or threonine, x, a variable residue), which are essential for LRP6 function and are each transferrable to a heterologous receptor to result in constitutive beta-catenin signaling (MacDonald et al., 2008;Tamai et al., 2004;Zeng et al., 2005).", "citation": {"db": "PubMed", "db_id": "19619488"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 464, "target": 462, "key": "ea794b92dc49b52e73418cc18e274404"}, {"line": 47964, "relation": "decreases", "evidence": "A positive role of K63-linked ubiquitylation was recently uncovered by the identification of the DUB enzyme and tumor suppressor CYLD as a negative regulator of upstream Wnt/beta-catenin signaling.", "citation": {"db": "PubMed", "db_id": "20930545"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 1794, "target": 462, "key": "38cc2e33a104b72a3e94db8a96916b0f"}, {"line": 47975, "relation": "increases", "evidence": "This kinase acts as a positive regaulator of Wnt signaling in Drosophila and in other systems; and can phosphorylate Dsh directly.", "citation": {"db": "PubMed", "db_id": "15935773"}, "annotations": {"Subgraph": {"Wnt signaling subgraph": true}}, "source": 2127, "target": 2170, "key": "6e444c49e3bb92b0386e589e69dcce05"}, {"line": 48004, "relation": "decreases", "evidence": "As a result, the Polymerization of and MAP-2 and NF-H induced by Abeta25-35 could be significantly inhibited by Wnt3a(40 ng/ml), however enhanced by Dkk1(100 ng/ml).", "citation": {"db": "PubMed", "db_id": "26809093"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"namespace": "GO", "name": "protein polymerization"}}, "source": 3535, "target": 3100, "key": "f31bbb5fc7862083e48a0f80929dc804"}, {"line": 48008, "relation": "decreases", "evidence": "As a result, the Polymerization of and MAP-2 and NF-H induced by Abeta25-35 could be significantly inhibited by Wnt3a(40 ng/ml), however enhanced by Dkk1(100 ng/ml).", "citation": {"db": "PubMed", "db_id": "26809093"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "object": {"modifier": "Activity", "effect": {"namespace": "GO", "name": "protein polymerization"}}, "source": 3535, "target": 2987, "key": "2f45c503b8f42d64fdb3511dad6a4b83"}, {"line": 48021, "relation": "decreases", "evidence": "Meanwhile, the protein abundance of phosphorylated tau in several sites is decreased by Wnt3a, but increased by Dkk1 significantly compared with the control group.", "citation": {"db": "PubMed", "db_id": "26809093"}, "annotations": {"Confidence": {"High": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Tau protein subgraph": true, "DKK1 subgraph": true}}, "source": 3535, "target": 3015, "key": "a02f9b45f95b52f53ee86f20e4502760"}, {"line": 48064, "relation": "association", "evidence": "In silico molecular target prediction docking studies suggest that ETH interacts with Akt, Dkk-1, and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "26420483"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 6, "target": 2629, "key": "cd6fc8ef06e09cb1c9874b22060e36d6"}, {"line": 48065, "relation": "association", "evidence": "In silico molecular target prediction docking studies suggest that ETH interacts with Akt, Dkk-1, and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "26420483"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 6, "target": 2279, "key": "377af48f7c09a137975b9010376c9f3d"}, {"line": 48066, "relation": "association", "evidence": "In silico molecular target prediction docking studies suggest that ETH interacts with Akt, Dkk-1, and GSK-3beta.", "citation": {"db": "PubMed", "db_id": "26420483"}, "annotations": {"Confidence": {"High": true}, "Subgraph": {"DKK1 subgraph": true}}, "source": 6, "target": 2794, "key": "888db1c2b0f82e56f1e733a0fc8c3887"}, {"line": 48197, "relation": "negativeCorrelation", "evidence": "Here, we report that the Wnt antagonist Dkk-1 selectively increases tau phosphorylation in the hippocampus of aged rats at Ser199/202, Ser396/404, and Ser214 sites", "citation": {"db": "PubMed", "db_id": "24270208"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3776, "target": 462, "key": "b6a5ec5c28fbf6df01b4b02121f291f1"}, {"line": 48203, "relation": "increases", "evidence": "Here, we report that the Wnt antagonist Dkk-1 selectively increases tau phosphorylation in the hippocampus of aged rats at Ser199/202, Ser396/404, and Ser214 sites", "citation": {"db": "PubMed", "db_id": "24270208"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tau protein subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3776, "target": 3799, "key": "fe0636666822dda836f7b4393105be2c"}, {"line": 48204, "relation": "increases", "evidence": "Here, we report that the Wnt antagonist Dkk-1 selectively increases tau phosphorylation in the hippocampus of aged rats at Ser199/202, Ser396/404, and Ser214 sites", "citation": {"db": "PubMed", "db_id": "24270208"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tau protein subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3776, "target": 3800, "key": "99862b7501d6461f60d1d8d29f68144c"}, {"line": 48205, "relation": "increases", "evidence": "Here, we report that the Wnt antagonist Dkk-1 selectively increases tau phosphorylation in the hippocampus of aged rats at Ser199/202, Ser396/404, and Ser214 sites", "citation": {"db": "PubMed", "db_id": "24270208"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tau protein subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3776, "target": 3801, "key": "62cea65e559bc24a85bbcfec6fb08d73"}, {"line": 48206, "relation": "increases", "evidence": "Here, we report that the Wnt antagonist Dkk-1 selectively increases tau phosphorylation in the hippocampus of aged rats at Ser199/202, Ser396/404, and Ser214 sites", "citation": {"db": "PubMed", "db_id": "24270208"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tau protein subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3776, "target": 3802, "key": "2f9bcd2249fa1246009744381ddebde5"}, {"line": 48207, "relation": "increases", "evidence": "Here, we report that the Wnt antagonist Dkk-1 selectively increases tau phosphorylation in the hippocampus of aged rats at Ser199/202, Ser396/404, and Ser214 sites", "citation": {"db": "PubMed", "db_id": "24270208"}, "annotations": {"Species": {"10116": true}, "MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"Tau protein subgraph": true, "DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3776, "target": 3803, "key": "1055da24ee6c583e7a6b656dcb475581"}, {"line": 48270, "relation": "association", "evidence": "Intriguingly, while expression of Dkk1 is required for proper neural development, overexpression of Dkk1 is characteristic of many neurodegenerative diseases, such as stroke, Alzheimer's disease, Parkinson's disease, and temporal lobe epilepsy.", "citation": {"db": "PubMed", "db_id": "23261660"}, "annotations": {"MeSHAnatomy": {"Hippocampus": true}, "Subgraph": {"DKK1 subgraph": true}, "Confidence": {"High": true}}, "source": 3854, "target": 2629, "key": "e4b6c9b96bec0e3923d41ca0b8ac5537"}, {"line": 48289, "relation": "association", "evidence": ". IL-6, AP3B1, TC10, ONECUT2, IGF2BP1, MYO1D, and ANXA2 were confirmed to be miR-9 targets in HCC.", "citation": {"db": "PubMed", "db_id": "26547929"}, "source": 3317, "target": 2086, "key": "29d68a25f846e7b20354bec44076707f"}, {"relation": "isA", "source": 3317, "target": 2214, "key": "02056b3674be65ad223351fecb731ec5"}, {"line": 48323, "relation": "association", "evidence": "Furthermore, following literature-curated searches and recent mass spectrometric analysis of IQGAP1-binding partners, we report that IQGAP1 recruits other small GTPases, including RhoC, Rac2, M-Ras, RhoQ, Rab10, and Rab5, small GTPase regulators, including Tiam1, RacGAP1, srGAP2 and HERC1, and small GTPase effectors, including PAK6, N-WASP, several sub-units of the Arp2/3 complex and the formin mDia1.", "citation": {"db": "PubMed", "db_id": "24355937"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 3317, "target": 2903, "key": "7b0c50b0cb5ec1e721eec643871fb696"}, {"line": 48298, "relation": "increases", "evidence": "Arhgef5 can strongly activate RhoA and RhoB and weakly RhoC and RhoG, but not Rac1, RhoQ, RhoD, or RhoV, in transfected human embryonic kidney 293 cells.", "citation": {"db": "PubMed", "db_id": "19713215"}, "annotations": {"Subgraph": {"RhoA subgraph": true}, "CellLine": {"HEK293": true}}, "source": 2357, "target": 3315, "key": "e215c34f34e151a450c6e2cd399647c8"}, {"line": 48299, "relation": "increases", "evidence": "Arhgef5 can strongly activate RhoA and RhoB and weakly RhoC and RhoG, but not Rac1, RhoQ, RhoD, or RhoV, in transfected human embryonic kidney 293 cells.", "citation": {"db": "PubMed", "db_id": "19713215"}, "annotations": {"Subgraph": {"RhoA subgraph": true}, "CellLine": {"HEK293": true}}, "source": 2357, "target": 3310, "key": "24047bd150ead4f7fae74c1a8cc51702"}, {"line": 48300, "relation": "increases", "evidence": "Arhgef5 can strongly activate RhoA and RhoB and weakly RhoC and RhoG, but not Rac1, RhoQ, RhoD, or RhoV, in transfected human embryonic kidney 293 cells.", "citation": {"db": "PubMed", "db_id": "19713215"}, "annotations": {"Subgraph": {"RhoA subgraph": true}, "CellLine": {"HEK293": true}}, "source": 2357, "target": 3311, "key": "b021c5406689f19a0536353c6d242d1b"}, {"line": 48301, "relation": "increases", "evidence": "Arhgef5 can strongly activate RhoA and RhoB and weakly RhoC and RhoG, but not Rac1, RhoQ, RhoD, or RhoV, in transfected human embryonic kidney 293 cells.", "citation": {"db": "PubMed", "db_id": "19713215"}, "annotations": {"Subgraph": {"RhoA subgraph": true}, "CellLine": {"HEK293": true}}, "source": 2357, "target": 3313, "key": "2d833e94461e2f9cc0e4cca4cda3d781"}, {"line": 48303, "relation": "causesNoChange", "evidence": "Arhgef5 can strongly activate RhoA and RhoB and weakly RhoC and RhoG, but not Rac1, RhoQ, RhoD, or RhoV, in transfected human embryonic kidney 293 cells.", "citation": {"db": "PubMed", "db_id": "19713215"}, "annotations": {"Subgraph": {"RhoA subgraph": true}, "CellLine": {"HEK293": true}}, "source": 2357, "target": 3290, "key": "ad30ecece01d271741f0404654e8a2f0"}, {"line": 48304, "relation": "causesNoChange", "evidence": "Arhgef5 can strongly activate RhoA and RhoB and weakly RhoC and RhoG, but not Rac1, RhoQ, RhoD, or RhoV, in transfected human embryonic kidney 293 cells.", "citation": {"db": "PubMed", "db_id": "19713215"}, "annotations": {"Subgraph": {"RhoA subgraph": true}, "CellLine": {"HEK293": true}}, "source": 2357, "target": 3314, "key": "42f6a8627ead32449103a2fb683a84f9"}, {"line": 48305, "relation": "causesNoChange", "evidence": "Arhgef5 can strongly activate RhoA and RhoB and weakly RhoC and RhoG, but not Rac1, RhoQ, RhoD, or RhoV, in transfected human embryonic kidney 293 cells.", "citation": {"db": "PubMed", "db_id": "19713215"}, "annotations": {"Subgraph": {"RhoA subgraph": true}, "CellLine": {"HEK293": true}}, "source": 2357, "target": 3317, "key": "8d759dcdb44553978c60e445ac8e5735"}, {"line": 48306, "relation": "causesNoChange", "evidence": "Arhgef5 can strongly activate RhoA and RhoB and weakly RhoC and RhoG, but not Rac1, RhoQ, RhoD, or RhoV, in transfected human embryonic kidney 293 cells.", "citation": {"db": "PubMed", "db_id": "19713215"}, "annotations": {"Subgraph": {"RhoA subgraph": true}, "CellLine": {"HEK293": true}}, "source": 2357, "target": 3318, "key": "21de5c92d7369e126fe8fb99ec2b8f75"}, {"relation": "isA", "source": 3312, "target": 2214, "key": "b20d1cecf7438ad0db8751be5cd9d566"}, {"relation": "isA", "source": 3316, "target": 2214, "key": "284c97c3e9685bc35eceb777d3caa7f7"}, {"line": 48323, "relation": "association", "evidence": "Furthermore, following literature-curated searches and recent mass spectrometric analysis of IQGAP1-binding partners, we report that IQGAP1 recruits other small GTPases, including RhoC, Rac2, M-Ras, RhoQ, Rab10, and Rab5, small GTPase regulators, including Tiam1, RacGAP1, srGAP2 and HERC1, and small GTPase effectors, including PAK6, N-WASP, several sub-units of the Arp2/3 complex and the formin mDia1.", "citation": {"db": "PubMed", "db_id": "24355937"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2903, "target": 3317, "key": "2937b2c96699d9b329bc2007bd8048a7"}, {"line": 48436, "relation": "increases", "evidence": "p115 catalyzes the construction of a cognate GOS-28–syntaxin-5 (v-/t-SNARE) complex by first linking the SNAREs to promote their direct interaction. ", "citation": {"db": "PubMed", "db_id": "11927603"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "subject": {"modifier": "Activity", "effect": {"name": "cat", "namespace": "bel"}}, "source": 2356, "target": 455, "key": "4f10e9dc785d13215be4ea89a85736dc"}, {"line": 48444, "relation": "association", "evidence": "p115 catalyzes the construction of a cognate GOS-28–syntaxin-5 (v-/t-SNARE) complex by first linking the SNAREs to promote their direct interaction. ", "citation": {"db": "PubMed", "db_id": "11927603"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 2356, "target": 2759, "key": "1a2e11eb9bce7376a32451285386df24"}, {"line": 48444, "relation": "association", "evidence": "p115 catalyzes the construction of a cognate GOS-28–syntaxin-5 (v-/t-SNARE) complex by first linking the SNAREs to promote their direct interaction. ", "citation": {"db": "PubMed", "db_id": "11927603"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 2759, "target": 2356, "key": "fec10f7319977fe2bf00394efa85df8c"}, {"line": 48452, "relation": "association", "evidence": "p115 catalyzes the construction of a cognate GOS-28–syntaxin-5 (v-/t-SNARE) complex by first linking the SNAREs to promote their direct interaction. ", "citation": {"db": "PubMed", "db_id": "11927603"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 2759, "target": 3432, "key": "e74b3b8a408ca56d00fd9619e8e903a5"}, {"line": 48459, "relation": "association", "evidence": "Coimmunoprecipitation experiments suggest that GS15 exists in a distinct SNARE complex that contains SNAREs (syntaxin5, GS28, and Ykt6) that are implicated in both ER-to-Golgi and intra-Golgi transport but not with SNAREs involved exclusively in ER-to-Golgi traffic.", "citation": {"db": "PubMed", "db_id": "12388752"}, "annotations": {"Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 2759, "target": 3541, "key": "81c4870343ba938dab17e2ec147a8bfe"}, {"line": 48452, "relation": "association", "evidence": "p115 catalyzes the construction of a cognate GOS-28–syntaxin-5 (v-/t-SNARE) complex by first linking the SNAREs to promote their direct interaction. ", "citation": {"db": "PubMed", "db_id": "11927603"}, "annotations": {"Subgraph": {"Synaptic vesicle endocytosis subgraph": true}}, "source": 3432, "target": 2759, "key": "6e88d658f5d9b550d7860812d462d6d4"}, {"line": 48459, "relation": "association", "evidence": "Coimmunoprecipitation experiments suggest that GS15 exists in a distinct SNARE complex that contains SNAREs (syntaxin5, GS28, and Ykt6) that are implicated in both ER-to-Golgi and intra-Golgi transport but not with SNAREs involved exclusively in ER-to-Golgi traffic.", "citation": {"db": "PubMed", "db_id": "12388752"}, "annotations": {"Subgraph": {"Endoplasmic reticulum-Golgi protein export": true}}, "source": 3541, "target": 2759, "key": "c00aa462d8d45d0a2edb7929b62071fc"}, {"line": 48470, "relation": "association", "evidence": "However, the importance of genes within chromosomal 8p region for neuropsychiatric disorders and cancer is well established.... Molecular genetics and developmental studies have identified 21 genes in this region (ADRA1A, ARHGEF10, CHRNA2, CHRNA6, CHRNB3, DKK4, DPYSL2, EGR3, FGF17, FGF20, FGFR1, FZD3, LDL, NAT2, NEF3, NRG1, PCM1, PLAT, PPP3CC, SFRP1 and VMAT1/SLC18A1) that are most likely to contribute to neuropsychiatric disorders (schizophrenia, autism, bipolar disorder and depression), neurodegenerative disorders (Parkinson's and Alzheimer's disease) and cancer. ", "citation": {"db": "PubMed", "db_id": "19204725"}, "source": 2659, "target": 3823, "key": "708cc95206ab47742ea08cd12305d727"}, {"line": 48481, "relation": "association", "evidence": "Using Egr3-specific antibodies, we establish that Egr3 co-localizes with the spindle and cytosolic microtubule organizing centers (MTOCs) in oocytes during meiotic maturation.", "citation": {"db": "PubMed", "db_id": "24722338"}, "source": 2659, "target": 782, "key": "bf9ece374eb93afb042068127de395b0"}, {"line": 48476, "relation": "increases", "evidence": "To identify the genes involved in mAChR signalling, we used a differential display approach and found 11 genes that were readily activated by mAChR with 1 hour of activation. These included the transcription factors Egr-1, Egr-2, Egr-3, c-Jun, Jun-D and Gos-3", "citation": {"db": "PubMed", "db_id": "11447829"}, "source": 2161, "target": 2659, "key": "f4ef03084b490e153a0c038b1af99b12"}, {"line": 48481, "relation": "association", "evidence": "Using Egr3-specific antibodies, we establish that Egr3 co-localizes with the spindle and cytosolic microtubule organizing centers (MTOCs) in oocytes during meiotic maturation.", "citation": {"db": "PubMed", "db_id": "24722338"}, "source": 782, "target": 2659, "key": "1a3e61d3ef3a69a23fdc0cf9deb6b1ff"}, {"line": 48496, "relation": "decreases", "evidence": "In additional neuroblastoma tumor cell lines, expression of HES family members HES2/4/5 each individually inhibited neuroblastoma growth", "citation": {"db": "PubMed", "db_id": "21744479"}, "source": 2180, "target": 3924, "key": "ba50e265565782437fbdbd14dd7d624f"}, {"line": 48500, "relation": "decreases", "evidence": "Most neuroblastoma lines have moderate to heavy methylation of these HES2 and HES5 CpG islands, suggesting that expression of these genes is silenced in neuroblastoma tumors", "citation": {"db": "PubMed", "db_id": "21744479"}, "source": 3924, "target": 3975, "key": "0970dd029d10dc1eea04daf0a93bdde9"}, {"line": 48501, "relation": "decreases", "evidence": "Most neuroblastoma lines have moderate to heavy methylation of these HES2 and HES5 CpG islands, suggesting that expression of these genes is silenced in neuroblastoma tumors", "citation": {"db": "PubMed", "db_id": "21744479"}, "source": 3924, "target": 3976, "key": "f25c1a6419803597f5cc690622c11b8c"}, {"line": 48521, "relation": "association", "evidence": "HES family proteins are implicated in the cell fate determination as effectors of the NOTCH signaling pathway", "citation": {"db": "PubMed", "db_id": "15254753"}, "source": 507, "target": 2823, "key": "74a15a98dc6c5c156d875fc83439aac6"}, {"line": 48531, "relation": "association", "evidence": "Within focal adhesions, structural proteins, such as vinculin and talin, anchor beta-integrins to the actin cytoskeleton, while signaling proteins, such as focal adhesion kinase (FAK), Pyk2, paxillin, and Src, mediate downstream signaling events in a transient and controlled manner", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 2008, "target": 386, "key": "f32bb35dcf3db176a04d864d7d700d51"}, {"line": 48531, "relation": "association", "evidence": "Within focal adhesions, structural proteins, such as vinculin and talin, anchor beta-integrins to the actin cytoskeleton, while signaling proteins, such as focal adhesion kinase (FAK), Pyk2, paxillin, and Src, mediate downstream signaling events in a transient and controlled manner", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 386, "target": 2008, "key": "92d83236c506151da876c1f691eee69f"}, {"line": 48537, "relation": "association", "evidence": "Within focal adhesions, structural proteins, such as vinculin and talin, anchor beta-integrins to the actin cytoskeleton, while signaling proteins, such as focal adhesion kinase (FAK), Pyk2, paxillin, and Src, mediate downstream signaling events in a transient and controlled manner", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"Medium": true}}, "source": 386, "target": 3466, "key": "3a76f6afae0177f12f4721770f816754"}, {"line": 48537, "relation": "association", "evidence": "Within focal adhesions, structural proteins, such as vinculin and talin, anchor beta-integrins to the actin cytoskeleton, while signaling proteins, such as focal adhesion kinase (FAK), Pyk2, paxillin, and Src, mediate downstream signaling events in a transient and controlled manner", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3466, "target": 386, "key": "3eebac2af76489f6bc4eccb76835fe10"}, {"line": 48551, "relation": "increases", "evidence": "The ability of integrins to bind the extracellular matrix and provide linkage to the actin cytoskeleton via talin and vinculin is critical for fundamental biological processes, including cell adhesion, cell survival and death, cell migration, and neurite outgrowth", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Unfolded protein response subgraph": true}, "Confidence": {"Medium": true}}, "source": 3466, "target": 497, "key": "a37aff63fcefff90acdcb2d8805eea46"}, {"line": 48545, "relation": "increases", "evidence": "The ability of integrins to bind the extracellular matrix and provide linkage to the actin cytoskeleton via talin and vinculin is critical for fundamental biological processes, including cell adhesion, cell survival and death, cell migration, and neurite outgrowth", "citation": {"db": "PubMed", "db_id": "22223749"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}, "Confidence": {"Medium": true}}, "source": 3517, "target": 497, "key": "5aaf7ce274ca56e9ccf8c0150eb3ac8e"}, {"line": 48591, "relation": "association", "evidence": "Recent studies have implicated the filamentous actin (F-actin) severing protein, Cofilin, in synaptic remodeling, mitochondrial dysfunction, and AD pathogenesis", "citation": {"db": "PubMed", "db_id": "25698445"}, "source": 2507, "target": 3823, "key": "07fb65810cccb74d4f0656afec2f07e6"}, {"line": 48638, "relation": "orthologous", "evidence": "The HIVEP family of zinc finger proteins regulates a diverse array of developmental and biological processes through direct DNA binding, as well as interaction with other transcription factors and components of signal transduction pathways [1,2]. Representative members include three human genes: HIVEP1 (also called ZAS1/Shn1/MBP1/PRDII-BF1) [3–6], HIVEP2 (ZAS2/Shn2/Mbp2) [7,8] and HIVEP3 (ZAS3/Shn3) [7,9], as well as the corresponding mouse homologues αACRYBP1[10,11], MIBP1[12] and KRC[13]. Schnurri (Shn), a distantly related ortholog from Drosophila, which is most closely related to HIVEP1, has also been isolated and characterized ", "citation": {"db": "PubMed", "db_id": "15009192"}, "source": 2835, "target": 3647, "key": "1863c3e27560a4677acc955ad34cbc02"}, {"line": 48638, "relation": "orthologous", "evidence": "The HIVEP family of zinc finger proteins regulates a diverse array of developmental and biological processes through direct DNA binding, as well as interaction with other transcription factors and components of signal transduction pathways [1,2]. Representative members include three human genes: HIVEP1 (also called ZAS1/Shn1/MBP1/PRDII-BF1) [3–6], HIVEP2 (ZAS2/Shn2/Mbp2) [7,8] and HIVEP3 (ZAS3/Shn3) [7,9], as well as the corresponding mouse homologues αACRYBP1[10,11], MIBP1[12] and KRC[13]. Schnurri (Shn), a distantly related ortholog from Drosophila, which is most closely related to HIVEP1, has also been isolated and characterized ", "citation": {"db": "PubMed", "db_id": "15009192"}, "source": 3647, "target": 2835, "key": "1c762dd5c0eb14cb118976281e9501c8"}, {"line": 48678, "relation": "increases", "evidence": "Abeta increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt–planar cell polarity (PCP)–c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"DKK1 subgraph": true}, "Confidence": {"Medium": true}}, "source": 1802, "target": 463, "key": "c684c14e8dbd6ddddbf060849137c48c"}, {"line": 49075, "relation": "decreases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity"}, "source": 1886, "target": 2328, "key": "eeb39f9feca2562eddf63c59d7659475"}, {"line": 49070, "relation": "decreases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "subject": {"modifier": "Degradation"}, "source": 1812, "target": 2328, "key": "21d6831b26ffb2040def55ba2e23dd3f"}, {"line": 48734, "relation": "decreases", "evidence": "We report here, that indirect activation of canonical Wnt/beta-catenin signaling using Bromoindirubin-30-Oxime (6-BIO), an inhibitor of glycogen synthase kinase-3beta, protects hippocampal neurons from amyloid-beta (Abeta) oligomers with the concomitant blockade of neuronal apoptotic process. More importantly, activation with Wnt-5a, a non-canonical Wnt ligand, results in the modulation of mitochondrial dynamics, preventing the changes induced by Abeta oligomers (Abetao) in mitochondrial fission-fusion dynamics and modulates Bcl-2 increases induced by oligomers", "citation": {"db": "PubMed", "db_id": "3691552"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Wnt signaling subgraph": true}, "Confidence": {"Medium": true}}, "source": 496, "target": 80, "key": "4b5a21836d8f41fee652ddc4c73e55cd"}, {"line": 48751, "relation": "negativeCorrelation", "evidence": "This work provides evidence that chemotaxis and phagocytosis, two crucial innate immune functions, are impaired in AD and MCI patients. Correlations with miRNA levels suggest an epigenetic contribution to systemic immune dysfunction in AD.", "citation": {"db": "PubMed", "db_id": "4879648"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 811, "target": 3823, "key": "6232c40197306d81ec8270f03f351547"}, {"line": 48768, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Confidence": {"High": true}}, "source": 3109, "target": 3823, "key": "6f18b9cb2367e82d8dfc3566a595f866"}, {"line": 48774, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 3109, "target": 1797, "key": "138e191e5e15f0aae0455011d6d23da3"}, {"line": 48779, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3109, "target": 1743, "key": "557067f214b851a74ba58931d86499f6"}, {"line": 48784, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Confidence": {"High": true}}, "source": 3109, "target": 1994, "key": "56a7fe7167c858bbea99fe40b8f986cb"}, {"line": 48788, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Confidence": {"High": true}}, "source": 3109, "target": 2009, "key": "d50e189257058cc6c8f07b4f71e51108"}, {"line": 48792, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Confidence": {"High": true}}, "source": 3109, "target": 1985, "key": "9d1e0f82498576f60c56ca6afefaf5d0"}, {"line": 48774, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 1797, "target": 3109, "key": "844075508530c852b657662936dd92fa"}, {"line": 48804, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1797, "target": 2658, "key": "6e42efe98e14ca1a2168d3dff37dc0f1"}, {"line": 48832, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 1797, "target": 3514, "key": "7de40c446582e09757b3e70797ad9bf5"}, {"line": 48779, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1743, "target": 3109, "key": "de568375cdd7822b942e1cf73d69592b"}, {"line": 48809, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1743, "target": 2658, "key": "1ea87bf2bc20953922549d9259055cf9"}, {"line": 48837, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 1743, "target": 3514, "key": "000b50b9c59869bfa891b6de6ed3cd7f"}, {"line": 48784, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Confidence": {"High": true}}, "source": 1994, "target": 3109, "key": "dd4b150abd41583e693e5f88df9cc277"}, {"line": 48814, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1994, "target": 2658, "key": "bfc6b4d6c6ddacb7ee5832a59bc031a2"}, {"line": 48841, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "source": 1994, "target": 3514, "key": "d5d2e2015b80d7cb0d14f8a8bfbea775"}, {"line": 48788, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Confidence": {"High": true}}, "source": 2009, "target": 3109, "key": "9742926f5b9d0a6f7cdf564711d8a759"}, {"line": 48818, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 2009, "target": 2658, "key": "b39e5cdeb69c492d8f8c441425e0feb5"}, {"line": 48842, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "source": 2009, "target": 3514, "key": "fdeacf9d2be74fed2fa53e0d98167434"}, {"line": 48792, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Confidence": {"High": true}}, "source": 1985, "target": 3109, "key": "4c500937d9d4419f9ed33e65fc122ac1"}, {"line": 48822, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"High": true}}, "source": 1985, "target": 2658, "key": "ab39309bd6858c3a45faba13d0ece504"}, {"line": 48843, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "source": 1985, "target": 3514, "key": "aea5b66588348a390c353bb88df00adb"}, {"line": 48827, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "source": 3514, "target": 3823, "key": "d08e6368c0a608baba242b523d01de9e"}, {"line": 48832, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 3514, "target": 1797, "key": "13be11c52b51056ed18e3b869bc6dde1"}, {"line": 48837, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "annotations": {"Subgraph": {"Amyloidogenic subgraph": true}, "Confidence": {"High": true}}, "source": 3514, "target": 1743, "key": "835ecaadbdcf5b20a26df2a14ea5de5c"}, {"line": 48841, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "source": 3514, "target": 1994, "key": "fbad4e80fe07dd5bf8dbea89f2e1d74a"}, {"line": 48842, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "source": 3514, "target": 2009, "key": "5f9a82a50ba6852dd8b24788713b1dec"}, {"line": 48843, "relation": "association", "evidence": "Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2).", "citation": {"db": "PubMed", "db_id": "27050105"}, "source": 3514, "target": 1985, "key": "7111fd230f681f9d6d2e67fc78b91dcf"}, {"line": 48893, "relation": "increases", "evidence": "By contrast, junD and ΔfosB mRNA were both upregulated significantly above control levels after an acute injection of l-DOPA.", "citation": {"db": "PubMed", "db_id": "17055656"}, "annotations": {"Anatomy": {"corpus striatum": true}}, "source": 56, "target": 2939, "key": "28aa9cbe028f9354e68e26186a0ab2c9"}, {"line": 48903, "relation": "decreases", "evidence": "Besides, we observed that oA42i (oligomeric amyloid beta1-42 plus ibotenic acid) intoxication substantially down-regulated the expression of genes involved in the regulation of survival and memory functions including sirtuin-1, cyclic AMP response element-binding protein (CREB), CREB-target genes (BDNF, c-Fos, Nurr1, and Egr1) and a disintegrin and metalloprotease 10.", "citation": {"db": "PubMed", "db_id": "25274193"}, "source": 1656, "target": 2658, "key": "498e158cd542319610ab93b7924d28ff"}, {"line": 48904, "relation": "decreases", "evidence": "Besides, we observed that oA42i (oligomeric amyloid beta1-42 plus ibotenic acid) intoxication substantially down-regulated the expression of genes involved in the regulation of survival and memory functions including sirtuin-1, cyclic AMP response element-binding protein (CREB), CREB-target genes (BDNF, c-Fos, Nurr1, and Egr1) and a disintegrin and metalloprotease 10.", "citation": {"db": "PubMed", "db_id": "25274193"}, "source": 1656, "target": 3364, "key": "e997d216f5f68b80b68aa3e74142681e"}, {"line": 48905, "relation": "decreases", "evidence": "Besides, we observed that oA42i (oligomeric amyloid beta1-42 plus ibotenic acid) intoxication substantially down-regulated the expression of genes involved in the regulation of survival and memory functions including sirtuin-1, cyclic AMP response element-binding protein (CREB), CREB-target genes (BDNF, c-Fos, Nurr1, and Egr1) and a disintegrin and metalloprotease 10.", "citation": {"db": "PubMed", "db_id": "25274193"}, "source": 1656, "target": 2162, "key": "e3225ad2d225ae5d11b201ed398a8f54"}, {"line": 48906, "relation": "decreases", "evidence": "Besides, we observed that oA42i (oligomeric amyloid beta1-42 plus ibotenic acid) intoxication substantially down-regulated the expression of genes involved in the regulation of survival and memory functions including sirtuin-1, cyclic AMP response element-binding protein (CREB), CREB-target genes (BDNF, c-Fos, Nurr1, and Egr1) and a disintegrin and metalloprotease 10.", "citation": {"db": "PubMed", "db_id": "25274193"}, "source": 1656, "target": 2699, "key": "29c191ae24539315580f5d16dcf3d99c"}, {"line": 48907, "relation": "decreases", "evidence": "Besides, we observed that oA42i (oligomeric amyloid beta1-42 plus ibotenic acid) intoxication substantially down-regulated the expression of genes involved in the regulation of survival and memory functions including sirtuin-1, cyclic AMP response element-binding protein (CREB), CREB-target genes (BDNF, c-Fos, Nurr1, and Egr1) and a disintegrin and metalloprotease 10.", "citation": {"db": "PubMed", "db_id": "25274193"}, "source": 1656, "target": 3136, "key": "6a959be7af66e6cc6159a2d1a1a87b6b"}, {"line": 48908, "relation": "decreases", "evidence": "Besides, we observed that oA42i (oligomeric amyloid beta1-42 plus ibotenic acid) intoxication substantially down-regulated the expression of genes involved in the regulation of survival and memory functions including sirtuin-1, cyclic AMP response element-binding protein (CREB), CREB-target genes (BDNF, c-Fos, Nurr1, and Egr1) and a disintegrin and metalloprotease 10.", "citation": {"db": "PubMed", "db_id": "25274193"}, "source": 1656, "target": 2249, "key": "e826b8956dda8e8641b04cfd77151ac1"}, {"relation": "partOf", "source": 50, "target": 1656, "key": "bef257fbbde0913e0869c53b69773d51"}, {"line": 48937, "relation": "association", "evidence": "Early growth response gene 1 (Egr1) is a member of the immediate early gene (IEG) family of transcription factors and plays a role in memory formation. The results of this study suggest that EGR1 regulates the expression of genes involved in CME, vesicular transport and synaptic transmission that may be critical for AD pathogenesis.Functional annotation of genes associated with EGR1 binding revealed a set of related networks including synaptic vesicle transport, clathrin-dependent endocytosis (CME), intracellular membrane fusion and transmission of signals elicited by Ca(2+) influx.", "citation": {"db": "PubMed", "db_id": "24269917"}, "annotations": {"Subgraph": {"Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "source": 799, "target": 2658, "key": "59bc327ea29396604db70d5ccb625dac"}, {"line": 49000, "relation": "association", "evidence": "Previous studies have suggested that TNFRSF12A may serve a role in tumor growth and metastasis.", "citation": {"db": "PubMed", "db_id": "28138696"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3475, "target": 3871, "key": "f09d5f8a2c361d15d999d6a6b81b32c2"}, {"line": 49009, "relation": "association", "evidence": "Remarkably, TWEAK and its receptors, fibroblast growth factor inducible 14 (Fn14), are also present in intervertebral disc (IVD) tissue, where they play a role in the pathogenesis of IVD degeneration.The interaction of TWEAK with Fn14 is involved in physiological and pathological activities of IVD degeneration patients, which includes apoptosis of endplate chondrocytes, extracellular matrix degradation, reduction in proteoglycan synthesis and so on.", "citation": {"db": "PubMed", "db_id": "26907852"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3475, "target": 3862, "key": "7609483fe44d40ecc4659b2f9df3018f"}, {"relation": "partOf", "source": 3475, "target": 1722, "key": "78520724817ad07be39624d740d3f39d"}, {"relation": "partOf", "source": 3475, "target": 1637, "key": "b7c58912d36c7123c61d7b4401a954c8"}, {"line": 49009, "relation": "association", "evidence": "Remarkably, TWEAK and its receptors, fibroblast growth factor inducible 14 (Fn14), are also present in intervertebral disc (IVD) tissue, where they play a role in the pathogenesis of IVD degeneration.The interaction of TWEAK with Fn14 is involved in physiological and pathological activities of IVD degeneration patients, which includes apoptosis of endplate chondrocytes, extracellular matrix degradation, reduction in proteoglycan synthesis and so on.", "citation": {"db": "PubMed", "db_id": "26907852"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3862, "target": 3475, "key": "54dbdf4ba1663ee19bbfde72530c04c0"}, {"line": 49010, "relation": "association", "evidence": "Remarkably, TWEAK and its receptors, fibroblast growth factor inducible 14 (Fn14), are also present in intervertebral disc (IVD) tissue, where they play a role in the pathogenesis of IVD degeneration.The interaction of TWEAK with Fn14 is involved in physiological and pathological activities of IVD degeneration patients, which includes apoptosis of endplate chondrocytes, extracellular matrix degradation, reduction in proteoglycan synthesis and so on.", "citation": {"db": "PubMed", "db_id": "26907852"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 3862, "target": 1722, "key": "4061b103a1db0a2f5ddf4397ea6146ff"}, {"line": 49010, "relation": "association", "evidence": "Remarkably, TWEAK and its receptors, fibroblast growth factor inducible 14 (Fn14), are also present in intervertebral disc (IVD) tissue, where they play a role in the pathogenesis of IVD degeneration.The interaction of TWEAK with Fn14 is involved in physiological and pathological activities of IVD degeneration patients, which includes apoptosis of endplate chondrocytes, extracellular matrix degradation, reduction in proteoglycan synthesis and so on.", "citation": {"db": "PubMed", "db_id": "26907852"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "source": 1722, "target": 3862, "key": "432f48316dd9325e1817343615820b3a"}, {"relation": "partOf", "source": 3479, "target": 1722, "key": "d7b080d8a7f1da72c3ce9c031f159238"}, {"line": 49018, "relation": "increases", "evidence": "Tumor necrosis factor-like weak inducer of apoptosis (TWEAK) is a tumor necrosis factor superfamily cytokine that activates the fibroblast growth factor-inducible-14 (Fn14) receptor.", "citation": {"db": "PubMed", "db_id": "27339384"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true}}, "object": {"modifier": "Activity"}, "source": 3479, "target": 3475, "key": "7369ddffa8d14b4c5cc0f94b8fe167b5"}, {"relation": "partOf", "source": 3479, "target": 1637, "key": "4e2e2dbafed6962a602822daa224d723"}, {"line": 49030, "relation": "increases", "evidence": "The tumor necrosis factor like weak inducer of apoptosis (TWEAK) and its receptor, fibroblast growth factor-inducible 14 (Fn14), mediate inflammation and neuronal apoptosis in cerebral edema, ischemic stroke and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "26808775"}, "annotations": {"Disease": {"multiple sclerosis": true}, "Subgraph": {"Tumor necrosis factor subgraph": true}, "Confidence": {"High": true}}, "source": 1637, "target": 3920, "key": "fe7d2cefe566ded0b029b3e9442ee4a8"}, {"line": 49038, "relation": "increases", "evidence": "The tumor necrosis factor like weak inducer of apoptosis (TWEAK) and its receptor, fibroblast growth factor-inducible 14 (Fn14), mediate inflammation and neuronal apoptosis in cerebral edema, ischemic stroke and multiple sclerosis.", "citation": {"db": "PubMed", "db_id": "26808775"}, "annotations": {"Disease": {"multiple sclerosis": true}, "Subgraph": {"Apoptosis signaling subgraph": true, "Tumor necrosis factor subgraph": true}, "MeSHAnatomy": {"Neurons": true}, "Confidence": {"High": true}}, "source": 1637, "target": 478, "key": "fe5acc55a6f05640cf76158a98e58fce"}, {"line": 49076, "relation": "decreases", "evidence": "To further elucidate how the clusterin-dependent induction of Dkk1 by Abeta mediates neurotoxicity, we measured the effects of Abeta and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Abeta neurotoxicity and/or tau phosphorylation.", "citation": {"db": "PubMed", "db_id": "23164821"}, "annotations": {"Subgraph": {"Non-amyloidogenic subgraph": true, "Gamma secretase subgraph": true}, "Confidence": {"Medium": true}}, "subject": {"modifier": "Degradation"}, "object": {"modifier": "Activity"}, "source": 1856, "target": 2328, "key": "8ad42b0518a0cf6f02fd80fa970e15d0"}, {"line": 49169, "relation": "association", "evidence": "Moreover, IL-1beta increased astrocytic production of pro-inflammatory chemokines such as CCL2, CCL20, and CXCL2, which induce immune cell migration and exacerbate BBB disruption and neuroinflammation. Our findings suggest that astrocytic SHH is a potential therapeutic target that could be used to restore disrupted BBB in patients with neurologic diseases.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 3362, "target": 555, "key": "df920037e2121687481a3e7b392668af"}, {"line": 49169, "relation": "association", "evidence": "Moreover, IL-1beta increased astrocytic production of pro-inflammatory chemokines such as CCL2, CCL20, and CXCL2, which induce immune cell migration and exacerbate BBB disruption and neuroinflammation. Our findings suggest that astrocytic SHH is a potential therapeutic target that could be used to restore disrupted BBB in patients with neurologic diseases.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Interleukin signaling subgraph": true, "Chemokine signaling subgraph": true}}, "source": 555, "target": 3362, "key": "d4cd9b51ee00ec8249edd29aad02128f"}, {"line": 49177, "relation": "increases", "evidence": "Moreover, IL-1beta increased astrocytic production of pro-inflammatory chemokines such as CCL2, CCL20, and CXCL2, which induce immune cell migration and exacerbate BBB disruption and neuroinflammation. Our findings suggest that astrocytic SHH is a potential therapeutic target that could be used to restore disrupted BBB in patients with neurologic diseases.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2456, "target": 509, "key": "708f5e227ae9fb65a7d43cb95aa9d7cc"}, {"line": 49178, "relation": "increases", "evidence": "Moreover, IL-1beta increased astrocytic production of pro-inflammatory chemokines such as CCL2, CCL20, and CXCL2, which induce immune cell migration and exacerbate BBB disruption and neuroinflammation. Our findings suggest that astrocytic SHH is a potential therapeutic target that could be used to restore disrupted BBB in patients with neurologic diseases.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"Disease": {"Alzheimer's disease": true}, "MeSHAnatomy": {"Astrocytes": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 2605, "target": 509, "key": "5c54fdfb0b5aa7f953df2479fc286cb1"}, {"line": 49188, "relation": "increases", "evidence": "We demonstrate that desferrioxamine (DFX), an iron chelator used in clinics for the treatment of iron overload, neoplasias, and Alzheimer disease, stimulates the expression and secretion of CCL20, a chemoattractant for immature dendritic cells, activated/memory T lymphocytes, and naive B cells,in primary human monocytes and monocyte-derived macrophages.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "source": 107, "target": 2456, "key": "b8208ddd97ae38a0d0f3bf1e695cfc62"}, {"line": 49189, "relation": "increases", "evidence": "We demonstrate that desferrioxamine (DFX), an iron chelator used in clinics for the treatment of iron overload, neoplasias, and Alzheimer disease, stimulates the expression and secretion of CCL20, a chemoattractant for immature dendritic cells, activated/memory T lymphocytes, and naive B cells,in primary human monocytes and monocyte-derived macrophages.", "citation": {"db": "PubMed", "db_id": "25313834"}, "annotations": {"MeSHAnatomy": {"Astrocytes": true}, "Disease": {"Alzheimer's disease": true}, "Subgraph": {"Chemokine signaling subgraph": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 107, "target": 2456, "key": "a106f1ad20e2f6bb1856dead75391be2"}, {"line": 49200, "relation": "increases", "evidence": "Iron chelation was part of the mechanism by which DFX induced CCL20, because addition of iron sulfate counteracted its stimulatory effects", "citation": {"db": "PubMed", "db_id": "19939449"}, "annotations": {"Subgraph": {"Chemokine signaling subgraph": true}}, "source": 107, "target": 2456, "key": "7d52034f56434d608faae59d24a29031"}, {"line": 49208, "relation": "regulates", "evidence": "Functional studies of the CCL20 promoter, using a series of 5'-deleted and mutated reporter constructs, demonstrated that CCL20 mRNA induction was dependent on gene transcription activation and mediated by the NF-kappaB pathway.", "citation": {"db": "PubMed", "db_id": "19939449"}, "source": 858, "target": 3949, "key": "1dc71a649cd791e78fac15ed60485114"}, {"line": 49232, "relation": "increases", "evidence": "KLF10 has been shown to be rapidly induced by TGFbeta1, 2, 3, E2, epidermal growth factor, and bone morphogenetic protein-2.", "citation": {"db": "PubMed", "db_id": "20087894"}, "source": 2402, "target": 1857, "key": "a73bbe404f61af0dd732cd3af567ba1f"}, {"line": 49266, "relation": "positiveCorrelation", "evidence": "Reductions in Arc and alpha-actinin-2 correlated tightly with reductions in Fos and calbindin", "citation": {"db": "PubMed", "db_id": "16237173"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "source": 2421, "target": 2355, "key": "222b51f787ca22a21d26b96de7228b1b"}, {"line": 49269, "relation": "positiveCorrelation", "evidence": "Reductions in Arc and alpha-actinin-2 correlated tightly with reductions in Fos and calbindin", "citation": {"db": "PubMed", "db_id": "16237173"}, "annotations": {"Subgraph": {"Gap junctions subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 2421, "target": 2247, "key": "d1bc5aa9bd095d303057843a16b31477"}, {"line": 49268, "relation": "positiveCorrelation", "evidence": "Reductions in Arc and alpha-actinin-2 correlated tightly with reductions in Fos and calbindin", "citation": {"db": "PubMed", "db_id": "16237173"}, "annotations": {"Subgraph": {"Gap junctions subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 2247, "target": 2699, "key": "5e8d5fc141858d954c4450c821564f50"}, {"line": 49269, "relation": "positiveCorrelation", "evidence": "Reductions in Arc and alpha-actinin-2 correlated tightly with reductions in Fos and calbindin", "citation": {"db": "PubMed", "db_id": "16237173"}, "annotations": {"Subgraph": {"Gap junctions subgraph": true, "Regulation of actin cytoskeleton subgraph": true}}, "source": 2247, "target": 2421, "key": "d5b1d40b4fcd02aa256adad29e292fd1"}, {"line": 49278, "relation": "positiveCorrelation", "evidence": "Levels of TIMP-1 were significantly elevated in CSF samples from all disease groups. TIMP-2 was significantly increased in CSF of AD and HD patients. MMP-2 levels did not differ significantly between groups. These findings show that TIMPs are elevated in the CSF of patients with neurodegenerative diseases suggesting a potential role of these endogenous inhibitors of matrix metalloproteinases in neurodegenerative diseases.", "citation": {"db": "PubMed", "db_id": " 12614934"}, "annotations": {"Subgraph": {"Matrix metalloproteinase subgraph": true}, "MeSHAnatomy": {"Cerebrospinal Fluid": true}}, "source": 3464, "target": 3823, "key": "ac5f470f4209841741e0ba4ce993e310"}, {"line": 49297, "relation": "directlyDecreases", "evidence": "https://www.ebi.ac.uk/chembl/compound/inspect/CHEMBL255863", "citation": {"db": "PubMed", "db_id": "24214965"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true}}, "object": {"modifier": "Activity", "effect": {"name": "kin", "namespace": "bel"}}, "source": 313, "target": 2240, "key": "25c1af210e081ee211bcbe3149c0f0a3"}, {"line": 49298, "relation": "directlyDecreases", "evidence": "https://www.ebi.ac.uk/chembl/compound/inspect/CHEMBL255863", "citation": {"db": "PubMed", "db_id": "24214965"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true}}, "object": {"modifier": "Activity"}, "source": 313, "target": 2623, "key": "4e9d7ac567a4210f26ba7a021bcd2022"}, {"line": 49299, "relation": "directlyDecreases", "evidence": "https://www.ebi.ac.uk/chembl/compound/inspect/CHEMBL255863", "citation": {"db": "PubMed", "db_id": "24214965"}, "annotations": {"Subgraph": {"Cell cycle subgraph": true}}, "object": {"modifier": "Activity"}, "source": 313, "target": 2624, "key": "b00d56552b1011176267b242795199dc"}, {"line": 49302, "relation": "directlyDecreases", "evidence": "https://www.ebi.ac.uk/chembl/compound/inspect/CHEMBL255863", "citation": {"db": "PubMed", "db_id": "24214965"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "object": {"modifier": "Activity"}, "source": 313, "target": 3172, "key": "e1ab7e00b1b7f47c276ef83e9a594918"}, {"line": 49303, "relation": "directlyDecreases", "evidence": "https://www.ebi.ac.uk/chembl/compound/inspect/CHEMBL255863", "citation": {"db": "PubMed", "db_id": "24214965"}, "annotations": {"Subgraph": {"Regulation of actin cytoskeleton subgraph": true}}, "object": {"modifier": "Activity"}, "source": 313, "target": 2404, "key": "274f326785ebff7b3080ef5bbe18f1dc"}, {"line": 49318, "relation": "decreases", "evidence": "More specifically, carriers of BamI and TaqI polymorphisms presented with worse cognitive functioning unlike carrier of the ApaI variant. Examination of ApaI and TaqI gene polymorphisms in 255 AD cases and 260 cognitively screened elderly controls revealed that the presence of each of these haplotypes was associated with the risk of AD.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 1725, "target": 812, "key": "b3c080f73a19f33eb1f808237506431d"}, {"line": 49319, "relation": "decreases", "evidence": "More specifically, carriers of BamI and TaqI polymorphisms presented with worse cognitive functioning unlike carrier of the ApaI variant. Examination of ApaI and TaqI gene polymorphisms in 255 AD cases and 260 cognitively screened elderly controls revealed that the presence of each of these haplotypes was associated with the risk of AD.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 1726, "target": 812, "key": "22f41902afa92ef4e649b13d318ce975"}, {"line": 49320, "relation": "causesNoChange", "evidence": "More specifically, carriers of BamI and TaqI polymorphisms presented with worse cognitive functioning unlike carrier of the ApaI variant. Examination of ApaI and TaqI gene polymorphisms in 255 AD cases and 260 cognitively screened elderly controls revealed that the presence of each of these haplotypes was associated with the risk of AD.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 1728, "target": 812, "key": "f89ad1dc4a6a8949f5e6936119af3cc1"}, {"line": 49326, "relation": "negativeCorrelation", "evidence": "However, the frequency for allele A of ApaI was higher in the control group, which was later associated with a 30% lower risk of AD in Polish and British populations study.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 1728, "target": 3823, "key": "e38e32b939754d1d4cc399845c094e67"}, {"line": 49335, "relation": "increases", "evidence": "Finally, sex-specific gene variations in the VDR and megalin genes have been shown to modify age-related cognitive decline in a cohort of US adults aged 50 years and older.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 2011, "target": 812, "key": "3802dff4013c8c9fdc65802b12f1533b"}, {"relation": "hasVariant", "source": 2010, "target": 2011, "key": "6d518c2b8631a78eda0ae95734e586f7"}, {"line": 49338, "relation": "increases", "evidence": "Finally, sex-specific gene variations in the VDR and megalin genes have been shown to modify age-related cognitive decline in a cohort of US adults aged 50 years and older.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Low density lipoprotein subgraph": true, "Cholesterol metabolism subgraph": true}, "Confidence": {"High": true}}, "source": 1865, "target": 812, "key": "e50f13bcc181110ccf600aa039f752e2"}, {"line": 49346, "relation": "association", "evidence": "On top of the observation that vitamin D supplementation leads to improved cognitive function, all the studies in an AD-like context have also shown that vitamin D treatment, regardless of the model tested, the dosage, the molecule chosen, and the time of treatment decreases the amyloid burden, suggesting a link between vitamin D function and amyloidogenesis.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 187, "target": 2328, "key": "c7a67daa62337a98f66295ac7788d605"}, {"line": 49355, "relation": "decreases", "evidence": "AD-like context have also shown that vitamin D treatment, regardless of the model tested, the dosage, the molecule chosen, and the time of treatment decreases the amyloid burden, suggesting a link between vitamin D function and amyloidogenesis. The behavioral changes reported in the study by Yu and colleagues were accompanied by a diminished Abeta load, along with an increase in astrocytic reactivity, NGF levels, and decreased TNFalpha in the brain of treated mice.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true, "Non-amyloidogenic subgraph": true}, "Confidence": {"High": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}}, "source": 187, "target": 2328, "key": "dd60bd0e007acf36f9f40960965efef2"}, {"line": 49360, "relation": "increases", "evidence": "AD-like context have also shown that vitamin D treatment, regardless of the model tested, the dosage, the molecule chosen, and the time of treatment decreases the amyloid burden, suggesting a link between vitamin D function and amyloidogenesis. The behavioral changes reported in the study by Yu and colleagues were accompanied by a diminished Abeta load, along with an increase in astrocytic reactivity, NGF levels, and decreased TNFalpha in the brain of treated mice.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"Medium": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}}, "source": 187, "target": 480, "key": "70f8488e59b6a5cda8ece0627474d65b"}, {"line": 49365, "relation": "increases", "evidence": "AD-like context have also shown that vitamin D treatment, regardless of the model tested, the dosage, the molecule chosen, and the time of treatment decreases the amyloid burden, suggesting a link between vitamin D function and amyloidogenesis. The behavioral changes reported in the study by Yu and colleagues were accompanied by a diminished Abeta load, along with an increase in astrocytic reactivity, NGF levels, and decreased TNFalpha in the brain of treated mice.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true, "Nerve growth factor subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 187, "target": 3116, "key": "6ddcab8930046fd4440c9bb766197db7"}, {"line": 49410, "relation": "increases", "evidence": "Second, vitamin D stimulates the synthesis of NGF within the hippocampus, leading to an enhanced neurite outgrowth and a reduced cellular proliferation.", "citation": {"db": "PubMed", "db_id": "29318446"}, "annotations": {"Confidence": {"High": true}, "MeSHAnatomy": {"Hippocampus": true}}, "source": 187, "target": 3116, "key": "364dc2055dfabb5d75f740e1e2d56df8"}, {"line": 49371, "relation": "decreases", "evidence": "AD-like context have also shown that vitamin D treatment, regardless of the model tested, the dosage, the molecule chosen, and the time of treatment decreases the amyloid burden, suggesting a link between vitamin D function and amyloidogenesis. The behavioral changes reported in the study by Yu and colleagues were accompanied by a diminished Abeta load, along with an increase in astrocytic reactivity, NGF levels, and decreased TNFalpha in the brain of treated mice.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Tumor necrosis factor subgraph": true, "Vitamin subgraph": true}, "Species": {"10090": true}, "MeSHAnatomy": {"Brain": true}, "Confidence": {"High": true}}, "source": 187, "target": 3472, "key": "610c1eccdc19c97053cb1dd0dc7c6a99"}, {"line": 49383, "relation": "decreases", "evidence": "Vitamin D can i) upregulate expression of several neurotrophins, ii) increase secretion of the anti-inflammatory cytokine IL-4, iii) reduce secretion of pro-inflammatory cytokines TNF-beta and interleukin-1 beta (IL-1), and iv) inhibit differentiation of dendritic cells.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 187, "target": 3472, "key": "6bae3af2ffc7b26b107e93b78e3542d7"}, {"line": 49381, "relation": "increases", "evidence": "Vitamin D can i) upregulate expression of several neurotrophins, ii) increase secretion of the anti-inflammatory cytokine IL-4, iii) reduce secretion of pro-inflammatory cytokines TNF-beta and interleukin-1 beta (IL-1), and iv) inhibit differentiation of dendritic cells.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 187, "target": 3578, "key": "21d3aa4ee62f6eccc6573639d7489c82"}, {"line": 49382, "relation": "increases", "evidence": "Vitamin D can i) upregulate expression of several neurotrophins, ii) increase secretion of the anti-inflammatory cytokine IL-4, iii) reduce secretion of pro-inflammatory cytokines TNF-beta and interleukin-1 beta (IL-1), and iv) inhibit differentiation of dendritic cells.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "object": {"modifier": "Translocation", "effect": {"fromLoc": {"namespace": "bel", "name": "intracellular"}, "toLoc": {"namespace": "bel", "name": "extracellular space"}}}, "source": 187, "target": 2893, "key": "26d0daf9f27bd0b8087f7267e42e15b5"}, {"line": 49384, "relation": "decreases", "evidence": "Vitamin D can i) upregulate expression of several neurotrophins, ii) increase secretion of the anti-inflammatory cytokine IL-4, iii) reduce secretion of pro-inflammatory cytokines TNF-beta and interleukin-1 beta (IL-1), and iv) inhibit differentiation of dendritic cells.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 187, "target": 542, "key": "f136cb3d4c108c268f30639a86ff4c1e"}, {"line": 49390, "relation": "association", "evidence": "Moreover, we have shown that vitamin D likely interacts with the estrogen receptor, Esr1, to regulate molecular pathways relevant to AD pathogenesis.", "citation": {"db": "PubMed", "db_id": "27176073"}, "annotations": {"Subgraph": {"Vitamin subgraph": true}, "Confidence": {"High": true}}, "source": 187, "target": 2680, "key": "b48e23b01eccaf0b25070aaabcd61143"}, {"line": 49399, "relation": "increases", "evidence": "First, a prenatal vitamin D deficiency disrupts brain development and alters the expression of growth factors and neurotrophin receptors in the adult dentate gyrus.", "citation": {"db": "PubMed", "db_id": "29318446"}, "annotations": {"MeSHAnatomy": {"Dentate Gyrus": true}}, "source": 187, "target": 3575, "key": "cfcae6823c0d6ea1f3a56b8b104d690f"}, {"line": 49401, "relation": "increases", "evidence": "First, a prenatal vitamin D deficiency disrupts brain development and alters the expression of growth factors and neurotrophin receptors in the adult dentate gyrus.", "citation": {"db": "PubMed", "db_id": "29318446"}, "annotations": {"MeSHAnatomy": {"Dentate Gyrus": true}, "Confidence": {"Medium": true}}, "source": 187, "target": 3576, "key": "3ca28778512655152b9cd02159f6626e"}, {"line": 49424, "relation": "increases", "evidence": "Fourth, an adult hypovitaminosis D increases the proliferation of neuroblasts in the sub-granular zone of the hippocampus and alters their neuronal differentiation.", "citation": {"db": "PubMed", "db_id": "29318446"}, "annotations": {"Confidence": {"High": true}, "Anatomy": {"dentate gyrus subgranular zone": true}}, "source": 187, "target": 643, "key": "03f9749327194c4c4bb2bfc3dcb04379"}, {"line": 49425, "relation": "association", "evidence": "Fourth, an adult hypovitaminosis D increases the proliferation of neuroblasts in the sub-granular zone of the hippocampus and alters their neuronal differentiation.", "citation": {"db": "PubMed", "db_id": "29318446"}, "annotations": {"Confidence": {"High": true}, "Anatomy": {"dentate gyrus subgranular zone": true}}, "source": 187, "target": 642, "key": "9f96237c35de372c90d30cf0fa44a966"}, {"line": 49425, "relation": "association", "evidence": "Fourth, an adult hypovitaminosis D increases the proliferation of neuroblasts in the sub-granular zone of the hippocampus and alters their neuronal differentiation.", "citation": {"db": "PubMed", "db_id": "29318446"}, "annotations": {"Confidence": {"High": true}, "Anatomy": {"dentate gyrus subgranular zone": true}}, "source": 642, "target": 187, "key": "cc459e85767f03454a370d3434fc5ab3"}, {"line": 49439, "relation": "decreases", "evidence": "Antioxidants scavenge free radicals and other reactive oxygen species that damage cellular membranes, organelles, and macromolecules. Accumulation of reactive oxygen species may overwhelm the protective reserves of antioxidants in cells (oxidative stress). In neurons, which are especially vulnerable to free radical–mediated damage, these processes may be important in aging of the brain and the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "source": 417, "target": 400, "key": "525b02f84cfc6c88df2338088bcdbbe2"}, {"line": 49440, "relation": "decreases", "evidence": "Antioxidants scavenge free radicals and other reactive oxygen species that damage cellular membranes, organelles, and macromolecules. Accumulation of reactive oxygen species may overwhelm the protective reserves of antioxidants in cells (oxidative stress). In neurons, which are especially vulnerable to free radical–mediated damage, these processes may be important in aging of the brain and the pathogenesis of AD.", "citation": {"db": "PubMed", "db_id": "14732624"}, "source": 417, "target": 170, "key": "8fd4b797f04229684f93159a40147a3e"}, {"line": 49461, "relation": "decreases", "evidence": "Reduction of homocysteine levels can be readily achieved with high doses of folic acid, vitamin B12, and vitamin B6 in the absence of vitamin B deficiency in the general population.", "citation": {"db": "PubMed", "db_id": "18854539"}, "source": 185, "target": 275, "key": "2074766c072ca688251d8369655e877d"}, {"line": 49473, "relation": "association", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3224, "target": 3015, "key": "bd889e43c4fd7bb99add23f3a865156e"}, {"line": 49474, "relation": "association", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3224, "target": 441, "key": "3852fa9fac5fced6404fb02927cffd13"}, {"line": 49475, "relation": "association", "evidence": "PP2A dysfunction has been linked to tau hyperphosphorylation, amyloidogenesis and synaptic deficits that are pathological hallmarks of this neurodegenerative disorder.", "citation": {"db": "PubMed", "db_id": "24653673"}, "annotations": {"Confidence": {"Medium": true}}, "source": 3224, "target": 788, "key": "5141ac3e82e60345570a37ce9a13b192"}]}pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/000077500000000000000000000000001426625374700232365ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/ADAM Metallopeptidase subgraph.att000066400000000000000000000037721426625374700315340ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-ADAM Metallopeptidase subgraph-1 2 XBP1 INS 103 124 white rectangle gene,gene 0.5 black 46 17 12801,/,6081 N-ADAM Metallopeptidase subgraph-10 CLSTN2 29 128 white rectangle gene 0.5 black 46 17 17448 N-ADAM Metallopeptidase subgraph-11 CLSTN3 36 149 white rectangle gene 0.5 black 46 17 18371 N-ADAM Metallopeptidase subgraph-12 ADAM10 52 109 white rectangle gene 0.5 black 46 17 188 N-ADAM Metallopeptidase subgraph-13 ADAM17 58 129 white rectangle gene 0.5 black 46 17 195 N-ADAM Metallopeptidase subgraph-14 ADAM19 67 0 white rectangle gene 0.5 black 46 17 197 N-ADAM Metallopeptidase subgraph-15 ADAM9 75 195 white rectangle gene 0.5 black 46 17 216 N-ADAM Metallopeptidase subgraph-16 IGF1 0 113 white rectangle gene 0.5 black 46 17 5464 N-ADAM Metallopeptidase subgraph-17 IL1B 62 69 white rectangle gene 0.5 black 46 17 5992 N-ADAM Metallopeptidase subgraph-18 APP 73 158 white rectangle gene 0.5 black 46 17 620 N-ADAM Metallopeptidase subgraph-19 APP 84 26 white rectangle gene 0.5 black 46 17 620 N-ADAM Metallopeptidase subgraph-20 APP 91 67 white rectangle gene 0.5 black 46 17 620 N-ADAM Metallopeptidase subgraph-21 JUN 21 76 white rectangle gene 0.5 black 46 17 6204 N-ADAM Metallopeptidase subgraph-22 BACE1 134 45 white rectangle gene 0.5 black 46 17 933 N-ADAM Metallopeptidase subgraph-3 sAPP-beta 36 27 white rectangle gene 0.5 black 46 17 CONSO00042 N-ADAM Metallopeptidase subgraph-4 sAPP-alpha 66 50 white rectangle gene 0.5 black 46 17 CONSO00067 N-ADAM Metallopeptidase subgraph-5 Glutamate ionotropic receptor NMDA type subunits 57 162 white rectangle gene 0.5 black 46 17 1201 N-ADAM Metallopeptidase subgraph-6 TIMP1 5 139 white rectangle gene 0.5 black 46 17 11820 N-ADAM Metallopeptidase subgraph-7 XBP1 4 91 white rectangle gene 0.5 black 46 17 12801 N-ADAM Metallopeptidase subgraph-8 SIRT1 95 100 white rectangle gene 0.5 black 46 17 14929 N-ADAM Metallopeptidase subgraph-9 CLSTN1 81 133 white rectangle gene 0.5 black 46 17 17447 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/ADAM Metallopeptidase subgraph.sif000066400000000000000000000065001426625374700315150ustar00rootroot000000000000000 1 2 N-ADAM Metallopeptidase subgraph-22 activation N-ADAM Metallopeptidase subgraph-20 N-ADAM Metallopeptidase subgraph-22 activation N-ADAM Metallopeptidase subgraph-20 N-ADAM Metallopeptidase subgraph-17 activation N-ADAM Metallopeptidase subgraph-12 N-ADAM Metallopeptidase subgraph-17 activation N-ADAM Metallopeptidase subgraph-4 N-ADAM Metallopeptidase subgraph-17 inhibition N-ADAM Metallopeptidase subgraph-3 N-ADAM Metallopeptidase subgraph-17 inhibition N-ADAM Metallopeptidase subgraph-19 N-ADAM Metallopeptidase subgraph-17 inhibition N-ADAM Metallopeptidase subgraph-20 N-ADAM Metallopeptidase subgraph-17 activation N-ADAM Metallopeptidase subgraph-13 N-ADAM Metallopeptidase subgraph-17 activation N-ADAM Metallopeptidase subgraph-13 N-ADAM Metallopeptidase subgraph-14 activation N-ADAM Metallopeptidase subgraph-4 N-ADAM Metallopeptidase subgraph-21 inhibition N-ADAM Metallopeptidase subgraph-12 N-ADAM Metallopeptidase subgraph-20 inhibition N-ADAM Metallopeptidase subgraph-12 N-ADAM Metallopeptidase subgraph-20 inhibition N-ADAM Metallopeptidase subgraph-4 N-ADAM Metallopeptidase subgraph-1 2 activation N-ADAM Metallopeptidase subgraph-12 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-18 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-18 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-18 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-4 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-4 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-4 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-4 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-4 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-11 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-11 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-10 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-10 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-9 N-ADAM Metallopeptidase subgraph-12 activation N-ADAM Metallopeptidase subgraph-9 N-ADAM Metallopeptidase subgraph-7 activation N-ADAM Metallopeptidase subgraph-12 N-ADAM Metallopeptidase subgraph-15 activation N-ADAM Metallopeptidase subgraph-18 N-ADAM Metallopeptidase subgraph-6 inhibition N-ADAM Metallopeptidase subgraph-12 N-ADAM Metallopeptidase subgraph-8 activation N-ADAM Metallopeptidase subgraph-12 N-ADAM Metallopeptidase subgraph-5 inhibition N-ADAM Metallopeptidase subgraph-15 N-ADAM Metallopeptidase subgraph-5 inhibition N-ADAM Metallopeptidase subgraph-12 N-ADAM Metallopeptidase subgraph-5 inhibition N-ADAM Metallopeptidase subgraph-13 N-ADAM Metallopeptidase subgraph-13 activation N-ADAM Metallopeptidase subgraph-18 N-ADAM Metallopeptidase subgraph-13 activation N-ADAM Metallopeptidase subgraph-11 N-ADAM Metallopeptidase subgraph-13 activation N-ADAM Metallopeptidase subgraph-11 N-ADAM Metallopeptidase subgraph-13 activation N-ADAM Metallopeptidase subgraph-10 N-ADAM Metallopeptidase subgraph-13 activation N-ADAM Metallopeptidase subgraph-9 N-ADAM Metallopeptidase subgraph-16 activation N-ADAM Metallopeptidase subgraph-12 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/APOE subgraph.att000066400000000000000000000043451426625374700262760ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-APOE subgraph-1 2 3 APBA1 APP LRP8 129 0 white rectangle gene,gene,gene 0.5 black 46 17 578,/,620,/,6700 N-APOE subgraph-10 11 APOE APP 0 88 white rectangle gene,gene 0.5 black 46 17 613,/,620 N-APOE subgraph-12 13 APOE APP 75 42 white rectangle gene,gene 0.5 black 46 17 613,/,620 N-APOE subgraph-14 15 APOE LDLR 49 22 white rectangle gene,gene 0.5 black 46 17 613,/,6547 N-APOE subgraph-16 17 APOE LRP1 100 133 white rectangle gene,gene 0.5 black 46 17 613,/,6692 N-APOE subgraph-18 19 APOE LRP8 124 11 white rectangle gene,gene 0.5 black 46 17 613,/,6700 N-APOE subgraph-22 ERK 95 33 white rectangle gene 0.5 black 46 17 ERK N-APOE subgraph-23 JNK 89 25 white rectangle gene 0.5 black 46 17 JNK N-APOE subgraph-25 CASR 89 46 white rectangle gene 0.5 black 46 17 1514 N-APOE subgraph-26 DAB1 81 18 white rectangle gene 0.5 black 46 17 2661 N-APOE subgraph-30 IL1B 95 41 white rectangle gene 0.5 black 46 17 5992 N-APOE subgraph-31 APOA2 73 55 white rectangle gene 0.5 black 46 17 601 N-APOE subgraph-33 APOE 85 34 white rectangle gene 0.5 black 46 17 613 N-APOE subgraph-34 APOE 68 30 white rectangle gene 0.5 black 46 17 613 N-APOE subgraph-35 APOE 70 19 white rectangle gene 0.5 black 46 17 613 N-APOE subgraph-36 APP 102 18 white rectangle gene 0.5 black 46 17 620 N-APOE subgraph-37 APP 66 51 white rectangle gene 0.5 black 46 17 620 N-APOE subgraph-38 APP 0 94 white rectangle gene 0.5 black 46 17 620 N-APOE subgraph-39 APP 78 27 white rectangle gene 0.5 black 46 17 620 N-APOE subgraph-4 5 6 APBA2 APP LRP8 136 16 white rectangle gene,gene,gene 0.5 black 46 17 579,/,620,/,6700 N-APOE subgraph-40 LDLR 61 24 white rectangle gene 0.5 black 46 17 6547 N-APOE subgraph-41 LRP1 95 137 white rectangle gene 0.5 black 46 17 6692 N-APOE subgraph-42 LRP1B 81 44 white rectangle gene 0.5 black 46 17 6693 N-APOE subgraph-43 LRP8 136 9 white rectangle gene 0.5 black 46 17 6700 N-APOE subgraph-44 BACE1 130 19 white rectangle gene 0.5 black 46 17 933 N-APOE subgraph-45 BACE2 134 3 white rectangle gene 0.5 black 46 17 934 N-APOE subgraph-46 RELN 77 6 white rectangle gene 0.5 black 46 17 9957 N-APOE subgraph-7 8 9 APBA3 APP LRP8 122 0 white rectangle gene,gene,gene 0.5 black 46 17 580,/,620,/,6700 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/APOE subgraph.sif000066400000000000000000000027141426625374700262650ustar00rootroot000000000000000 1 2 N-APOE subgraph-18 19 activation N-APOE subgraph-36 N-APOE subgraph-18 19 activation N-APOE subgraph-44 N-APOE subgraph-18 19 activation N-APOE subgraph-45 N-APOE subgraph-18 19 activation N-APOE subgraph-43 N-APOE subgraph-39 activation N-APOE subgraph-12 13 N-APOE subgraph-39 inhibition N-APOE subgraph-40 N-APOE subgraph-40 activation N-APOE subgraph-14 15 N-APOE subgraph-31 activation N-APOE subgraph-12 13 N-APOE subgraph-42 activation N-APOE subgraph-12 13 N-APOE subgraph-34 activation N-APOE subgraph-39 N-APOE subgraph-37 inhibition N-APOE subgraph-12 13 N-APOE subgraph-7 8 9 activation N-APOE subgraph-18 19 N-APOE subgraph-35 activation N-APOE subgraph-39 N-APOE subgraph-4 5 6 activation N-APOE subgraph-18 19 N-APOE subgraph-46 activation N-APOE subgraph-26 N-APOE subgraph-33 activation N-APOE subgraph-12 13 N-APOE subgraph-33 activation N-APOE subgraph-26 N-APOE subgraph-33 activation N-APOE subgraph-22 N-APOE subgraph-33 inhibition N-APOE subgraph-23 N-APOE subgraph-33 activation N-APOE subgraph-39 N-APOE subgraph-33 inhibition N-APOE subgraph-39 N-APOE subgraph-33 activation N-APOE subgraph-42 N-APOE subgraph-33 activation N-APOE subgraph-25 N-APOE subgraph-30 activation N-APOE subgraph-33 N-APOE subgraph-41 activation N-APOE subgraph-16 17 N-APOE subgraph-12 13 activation N-APOE subgraph-39 N-APOE subgraph-36 activation N-APOE subgraph-39 N-APOE subgraph-10 11 inhibition N-APOE subgraph-38 N-APOE subgraph-1 2 3 activation N-APOE subgraph-18 19 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/ATP binding cassette transport subgraph.att000066400000000000000000000013611426625374700333750ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-ATP binding cassette transport subgraph-1 ABCA1 71 29 white rectangle gene 0.5 black 46 17 29 N-ATP binding cassette transport subgraph-11 BACE1 0 147 white rectangle gene 0.5 black 46 17 933 N-ATP binding cassette transport subgraph-12 Abcc1 32 16 white rectangle gene 0.5 black 46 17 102676 N-ATP binding cassette transport subgraph-2 ABCA2 14 159 white rectangle gene 0.5 black 46 17 32 N-ATP binding cassette transport subgraph-3 ABCA7 60 0 white rectangle gene 0.5 black 46 17 37 N-ATP binding cassette transport subgraph-8 APP 21 178 white rectangle gene 0.5 black 46 17 620 N-ATP binding cassette transport subgraph-9 APP 53 18 white rectangle gene 0.5 black 46 17 620 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/ATP binding cassette transport subgraph.sif000066400000000000000000000007671426625374700333770ustar00rootroot000000000000000 1 2 N-ATP binding cassette transport subgraph-12 inhibition N-ATP binding cassette transport subgraph-9 N-ATP binding cassette transport subgraph-3 inhibition N-ATP binding cassette transport subgraph-9 N-ATP binding cassette transport subgraph-1 inhibition N-ATP binding cassette transport subgraph-9 N-ATP binding cassette transport subgraph-2 activation N-ATP binding cassette transport subgraph-8 N-ATP binding cassette transport subgraph-2 activation N-ATP binding cassette transport subgraph-11 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Acetylcholine signaling subgraph.att000066400000000000000000000044221426625374700322650ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Acetylcholine signaling subgraph-1 2 ACHE APP 137 164 white rectangle gene,gene 0.5 black 46 17 108,/,620 N-Acetylcholine signaling subgraph-11 CHRNA1 125 142 white rectangle gene 0.5 black 46 17 1955 N-Acetylcholine signaling subgraph-23 GSS 158 182 white rectangle gene 0.5 black 46 17 4624 N-Acetylcholine signaling subgraph-25 APP 10 151 white rectangle gene 0.5 black 46 17 620 N-Acetylcholine signaling subgraph-26 APP 131 157 white rectangle gene 0.5 black 46 17 620 N-Acetylcholine signaling subgraph-27 NGF 148 186 white rectangle gene 0.5 black 46 17 7808 N-Acetylcholine signaling subgraph-28 PRNP 137 181 white rectangle gene 0.5 black 46 17 9449 N-Acetylcholine signaling subgraph-3 AKT 162 172 white rectangle gene 0.5 black 46 17 AKT N-Acetylcholine signaling subgraph-30 Chat 117 156 white rectangle gene 0.5 black 46 17 88392 N-Acetylcholine signaling subgraph-31 Chrna3 73 31 white rectangle gene 0.5 black 46 17 2345 N-Acetylcholine signaling subgraph-32 Chrna4 97 20 white rectangle gene 0.5 black 46 17 2346 N-Acetylcholine signaling subgraph-33 Chrna5 92 28 white rectangle gene 0.5 black 46 17 2347 N-Acetylcholine signaling subgraph-34 Chrna7 66 24 white rectangle gene 0.5 black 46 17 2348 N-Acetylcholine signaling subgraph-35 Chrnb2 82 31 white rectangle gene 0.5 black 46 17 2350 N-Acetylcholine signaling subgraph-36 Chrnb4 73 1 white rectangle gene 0.5 black 46 17 2351 N-Acetylcholine signaling subgraph-37 Ins2 80 17 white rectangle gene 0.5 black 46 17 2916 N-Acetylcholine signaling subgraph-38 Chrna10 66 7 white rectangle gene 0.5 black 46 17 620142 N-Acetylcholine signaling subgraph-39 Chrna2 96 10 white rectangle gene 0.5 black 46 17 621533 N-Acetylcholine signaling subgraph-4 Caspase 158 161 white rectangle gene 0.5 black 46 17 Caspase N-Acetylcholine signaling subgraph-40 Chrna9 63 15 white rectangle gene 0.5 black 46 17 621534 N-Acetylcholine signaling subgraph-41 Chrnb3 82 0 white rectangle gene 0.5 black 46 17 621544 N-Acetylcholine signaling subgraph-42 Chrna6 90 3 white rectangle gene 0.5 black 46 17 69281 N-Acetylcholine signaling subgraph-5 ACHE 146 170 white rectangle gene 0.5 black 46 17 108 N-Acetylcholine signaling subgraph-7 SLC5A7 0 157 white rectangle gene 0.5 black 46 17 14025 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Acetylcholine signaling subgraph.sif000066400000000000000000000035701426625374700322610ustar00rootroot000000000000000 1 2 N-Acetylcholine signaling subgraph-23 inhibition N-Acetylcholine signaling subgraph-5 N-Acetylcholine signaling subgraph-3 activation N-Acetylcholine signaling subgraph-5 N-Acetylcholine signaling subgraph-4 activation N-Acetylcholine signaling subgraph-5 N-Acetylcholine signaling subgraph-26 activation N-Acetylcholine signaling subgraph-11 N-Acetylcholine signaling subgraph-26 inhibition N-Acetylcholine signaling subgraph-30 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-39 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-31 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-32 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-33 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-42 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-34 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-40 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-38 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-35 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-41 N-Acetylcholine signaling subgraph-37 activation N-Acetylcholine signaling subgraph-36 N-Acetylcholine signaling subgraph-27 inhibition N-Acetylcholine signaling subgraph-5 N-Acetylcholine signaling subgraph-1 2 activation N-Acetylcholine signaling subgraph-26 N-Acetylcholine signaling subgraph-25 activation N-Acetylcholine signaling subgraph-7 N-Acetylcholine signaling subgraph-5 activation N-Acetylcholine signaling subgraph-1 2 N-Acetylcholine signaling subgraph-5 activation N-Acetylcholine signaling subgraph-26 N-Acetylcholine signaling subgraph-5 activation N-Acetylcholine signaling subgraph-28 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Akt subgraph.att000066400000000000000000000057251426625374700262740ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Akt subgraph-1 2 INSR APP 0 2 white rectangle gene,gene 0.5 black 46 17 6091,/,620 N-Akt subgraph-10 FOXO 100 82 white rectangle gene 0.5 black 46 17 FOXO N-Akt subgraph-11 PRKAC 141 36 white rectangle gene 0.5 black 46 17 PRKAC N-Akt subgraph-12 PRKAC 144 60 white rectangle gene 0.5 black 46 17 PRKAC N-Akt subgraph-13 ATXN1 144 53 white rectangle gene 0.5 black 46 17 10548 N-Akt subgraph-14 ACHE 112 62 white rectangle gene 0.5 black 46 17 108 N-Akt subgraph-15 STK11 148 64 white rectangle gene 0.5 black 46 17 11389 N-Akt subgraph-16 TSC2 60 59 white rectangle gene 0.5 black 46 17 12363 N-Akt subgraph-17 CAPN1 37 75 white rectangle gene 0.5 black 46 17 1476 N-Akt subgraph-18 CASP9 115 72 white rectangle gene 0.5 black 46 17 1511 N-Akt subgraph-19 CCND1 75 35 white rectangle gene 0.5 black 46 17 1582 N-Akt subgraph-20 CDC37 61 74 white rectangle gene 0.5 black 46 17 1735 N-Akt subgraph-21 PIK3R5 97 62 white rectangle gene 0.5 black 46 17 30035 N-Akt subgraph-22 FOXO3 58 65 white rectangle gene 0.5 black 46 17 3821 N-Akt subgraph-23 AKT1 69 66 white rectangle gene 0.5 black 46 17 391 N-Akt subgraph-24 AKT1 80 45 white rectangle gene 0.5 black 46 17 391 N-Akt subgraph-25 AKT2 5 0 white rectangle gene 0.5 black 46 17 392 N-Akt subgraph-26 AKT3 79 53 white rectangle gene 0.5 black 46 17 393 N-Akt subgraph-27 GSK3A 99 76 white rectangle gene 0.5 black 46 17 4616 N-Akt subgraph-28 GSK3A 103 77 white rectangle gene 0.5 black 46 17 4616 N-Akt subgraph-29 GSK3B 95 69 white rectangle gene 0.5 black 46 17 4617 N-Akt subgraph-3 PI3K_p110 98 106 white rectangle gene 0.5 black 46 17 PI3K_p110 N-Akt subgraph-30 GSK3B 87 69 white rectangle gene 0.5 black 46 17 4617 N-Akt subgraph-31 APP 134 56 white rectangle gene 0.5 black 46 17 620 N-Akt subgraph-32 MAPT 51 71 white rectangle gene 0.5 black 46 17 6893 N-Akt subgraph-33 MAPT 135 42 white rectangle gene 0.5 black 46 17 6893 N-Akt subgraph-34 MAPT 139 44 white rectangle gene 0.5 black 46 17 6893 N-Akt subgraph-35 MAPT 151 51 white rectangle gene 0.5 black 46 17 6893 N-Akt subgraph-36 MAPT 143 44 white rectangle gene 0.5 black 46 17 6893 N-Akt subgraph-37 NGF 127 69 white rectangle gene 0.5 black 46 17 7808 N-Akt subgraph-38 NTRK1 118 66 white rectangle gene 0.5 black 46 17 8031 N-Akt subgraph-39 PDK1 86 60 white rectangle gene 0.5 black 46 17 8809 N-Akt subgraph-4 AKT 106 71 white rectangle gene 0.5 black 46 17 AKT N-Akt subgraph-40 BAD 117 78 white rectangle gene 0.5 black 46 17 936 N-Akt subgraph-41 BAD 109 83 white rectangle gene 0.5 black 46 17 936 N-Akt subgraph-42 PRKAA1 163 47 white rectangle gene 0.5 black 46 17 9376 N-Akt subgraph-5 AKT 27 78 white rectangle gene 0.5 black 46 17 AKT N-Akt subgraph-6 AKT 95 117 white rectangle gene 0.5 black 46 17 AKT N-Akt subgraph-7 AKT 102 91 white rectangle gene 0.5 black 46 17 AKT N-Akt subgraph-8 CAMK 156 72 white rectangle gene 0.5 black 46 17 CAMK N-Akt subgraph-9 CREB 113 81 white rectangle gene 0.5 black 46 17 CREB pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Akt subgraph.sif000066400000000000000000000045121426625374700262560ustar00rootroot000000000000000 1 2 N-Akt subgraph-30 inhibition N-Akt subgraph-29 N-Akt subgraph-30 inhibition N-Akt subgraph-29 N-Akt subgraph-4 activation N-Akt subgraph-14 N-Akt subgraph-4 inhibition N-Akt subgraph-14 N-Akt subgraph-4 activation N-Akt subgraph-30 N-Akt subgraph-4 activation N-Akt subgraph-28 N-Akt subgraph-4 inhibition N-Akt subgraph-40 N-Akt subgraph-4 inhibition N-Akt subgraph-29 N-Akt subgraph-4 activation N-Akt subgraph-9 N-Akt subgraph-4 activation N-Akt subgraph-41 N-Akt subgraph-4 activation N-Akt subgraph-18 N-Akt subgraph-4 activation N-Akt subgraph-4 N-Akt subgraph-4 inhibition N-Akt subgraph-10 N-Akt subgraph-4 inhibition N-Akt subgraph-27 N-Akt subgraph-32 activation N-Akt subgraph-17 N-Akt subgraph-8 activation N-Akt subgraph-15 N-Akt subgraph-36 activation N-Akt subgraph-31 N-Akt subgraph-31 activation N-Akt subgraph-4 N-Akt subgraph-34 activation N-Akt subgraph-31 N-Akt subgraph-21 activation N-Akt subgraph-39 N-Akt subgraph-21 activation N-Akt subgraph-4 N-Akt subgraph-23 activation N-Akt subgraph-22 N-Akt subgraph-23 activation N-Akt subgraph-16 N-Akt subgraph-23 activation N-Akt subgraph-30 N-Akt subgraph-23 activation N-Akt subgraph-32 N-Akt subgraph-23 activation N-Akt subgraph-32 N-Akt subgraph-24 inhibition N-Akt subgraph-19 N-Akt subgraph-17 inhibition N-Akt subgraph-5 N-Akt subgraph-12 activation N-Akt subgraph-31 N-Akt subgraph-3 activation N-Akt subgraph-6 N-Akt subgraph-3 activation N-Akt subgraph-7 N-Akt subgraph-15 activation N-Akt subgraph-12 N-Akt subgraph-15 activation N-Akt subgraph-31 N-Akt subgraph-20 activation N-Akt subgraph-23 N-Akt subgraph-1 2 activation N-Akt subgraph-25 N-Akt subgraph-33 activation N-Akt subgraph-31 N-Akt subgraph-13 inhibition N-Akt subgraph-31 N-Akt subgraph-38 activation N-Akt subgraph-4 N-Akt subgraph-38 inhibition N-Akt subgraph-14 N-Akt subgraph-35 activation N-Akt subgraph-31 N-Akt subgraph-28 inhibition N-Akt subgraph-27 N-Akt subgraph-37 activation N-Akt subgraph-38 N-Akt subgraph-39 activation N-Akt subgraph-23 N-Akt subgraph-39 activation N-Akt subgraph-26 N-Akt subgraph-39 activation N-Akt subgraph-4 N-Akt subgraph-39 activation N-Akt subgraph-24 N-Akt subgraph-7 activation N-Akt subgraph-4 N-Akt subgraph-11 activation N-Akt subgraph-33 N-Akt subgraph-11 activation N-Akt subgraph-36 N-Akt subgraph-11 activation N-Akt subgraph-34 N-Akt subgraph-42 activation N-Akt subgraph-35 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Albumin subgraph.att000066400000000000000000000007241426625374700271360ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Albumin subgraph-1 2 copper(2+) APP 0 90 white rectangle gene,gene 0.5 black 46 17 29036,/,620 N-Albumin subgraph-3 TNF 147 0 white rectangle gene 0.5 black 46 17 11892 N-Albumin subgraph-4 TTR 172 88 white rectangle gene 0.5 black 46 17 12405 N-Albumin subgraph-5 ALB 56 73 white rectangle gene 0.5 black 46 17 399 N-Albumin subgraph-7 IGF1 122 54 white rectangle gene 0.5 black 46 17 5464 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Albumin subgraph.sif000066400000000000000000000003341426625374700271240ustar00rootroot000000000000000 1 2 N-Albumin subgraph-5 inhibition N-Albumin subgraph-1 2 N-Albumin subgraph-3 inhibition N-Albumin subgraph-7 N-Albumin subgraph-7 activation N-Albumin subgraph-5 N-Albumin subgraph-7 activation N-Albumin subgraph-4 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Alpha 2 macroglobulin subgraph.att000066400000000000000000000005151426625374700315320ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Alpha 2 macroglobulin subgraph-1 APP 199 187 white rectangle gene 0.5 black 46 17 620 N-Alpha 2 macroglobulin subgraph-2 LRP1 0 0 white rectangle gene 0.5 black 46 17 6692 N-Alpha 2 macroglobulin subgraph-3 A2M 99 93 white rectangle gene 0.5 black 46 17 7 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Alpha 2 macroglobulin subgraph.sif000066400000000000000000000002501426625374700315170ustar00rootroot000000000000000 1 2 N-Alpha 2 macroglobulin subgraph-3 inhibition N-Alpha 2 macroglobulin subgraph-1 N-Alpha 2 macroglobulin subgraph-2 activation N-Alpha 2 macroglobulin subgraph-3 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Amylin subgraph.att000066400000000000000000000007651426625374700270050ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Amylin subgraph-1 2 IAPP RAMP1 35 0 white rectangle gene,gene 0.5 black 46 17 5329,/,9843 N-Amylin subgraph-3 4 IAPP RAMP2 0 21 white rectangle gene,gene 0.5 black 46 17 5329,/,9844 N-Amylin subgraph-5 6 IAPP RAMP3 179 85 white rectangle gene,gene 0.5 black 46 17 5329,/,9845 N-Amylin subgraph-7 IAPP 20 14 white rectangle gene 0.5 black 46 17 5329 N-Amylin subgraph-8 RAMP3 162 77 white rectangle gene 0.5 black 46 17 9845 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Amylin subgraph.sif000066400000000000000000000002451426625374700267670ustar00rootroot000000000000000 1 2 N-Amylin subgraph-5 6 activation N-Amylin subgraph-8 N-Amylin subgraph-3 4 activation N-Amylin subgraph-7 N-Amylin subgraph-1 2 activation N-Amylin subgraph-7 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Amyloidogenic subgraph.att000066400000000000000000000410101426625374700303230ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Amyloidogenic subgraph-1 2 3 copper(2+) zinc(2+) APP 15 28 white rectangle gene,gene,gene 0.5 black 46 17 29036,/,29105,/,620 N-Amyloidogenic subgraph-10 11 SHC1 APP 97 47 white rectangle gene,gene 0.5 black 46 17 10840,/,620 N-Amyloidogenic subgraph-101 102 APP RELN 78 98 white rectangle gene,gene 0.5 black 46 17 620,/,9957 N-Amyloidogenic subgraph-103 104 APP LRP1 139 38 white rectangle gene,gene 0.5 black 46 17 620,/,6692 N-Amyloidogenic subgraph-105 106 APP MAPT 109 92 white rectangle gene,gene 0.5 black 46 17 620,/,6893 N-Amyloidogenic subgraph-111 112 MAPK8IP1 Amyloidogenic glycoprotein, intracellular domain, conserved site 71 107 white rectangle gene,gene 0.5 black 46 17 6882,/,IPR019745 N-Amyloidogenic subgraph-113 114 MAPK8IP2 Amyloidogenic glycoprotein, intracellular domain, conserved site 71 105 white rectangle gene,gene 0.5 black 46 17 6883,/,IPR019745 N-Amyloidogenic subgraph-115 116 MAPK8IP3 Amyloidogenic glycoprotein, intracellular domain, conserved site 72 109 white rectangle gene,gene 0.5 black 46 17 6884,/,IPR019745 N-Amyloidogenic subgraph-117 118 PLAU PLAUR 70 119 white rectangle gene,gene 0.5 black 46 17 9052,/,9053 N-Amyloidogenic subgraph-119 sAPP-beta 92 98 white rectangle gene 0.5 black 46 17 CONSO00042 N-Amyloidogenic subgraph-12 13 SRC APP 95 100 white rectangle gene,gene 0.5 black 46 17 11283,/,620 N-Amyloidogenic subgraph-120 sAPP-alpha 85 109 white rectangle gene 0.5 black 46 17 CONSO00067 N-Amyloidogenic subgraph-122 AKT 92 118 white rectangle gene 0.5 black 46 17 AKT N-Amyloidogenic subgraph-123 CREB 75 90 white rectangle gene 0.5 black 46 17 CREB N-Amyloidogenic subgraph-124 ERK 91 44 white rectangle gene 0.5 black 46 17 ERK N-Amyloidogenic subgraph-125 GSK3 111 90 white rectangle gene 0.5 black 46 17 GSK3 N-Amyloidogenic subgraph-130 BDNF 84 87 white rectangle gene 0.5 black 46 17 1033 N-Amyloidogenic subgraph-133 BID 57 84 white rectangle gene 0.5 black 46 17 1050 N-Amyloidogenic subgraph-135 ACHE 97 115 white rectangle gene 0.5 black 46 17 108 N-Amyloidogenic subgraph-139 SP1 188 112 white rectangle gene 0.5 black 46 17 11205 N-Amyloidogenic subgraph-140 SRC 77 178 white rectangle gene 0.5 black 46 17 11283 N-Amyloidogenic subgraph-141 STAT3 84 115 white rectangle gene 0.5 black 46 17 11364 N-Amyloidogenic subgraph-142 TAGLN 105 65 white rectangle gene 0.5 black 46 17 11553 N-Amyloidogenic subgraph-143 TNF 107 102 white rectangle gene 0.5 black 46 17 11892 N-Amyloidogenic subgraph-144 FAS 60 85 white rectangle gene 0.5 black 46 17 11920 N-Amyloidogenic subgraph-145 TP53 59 87 white rectangle gene 0.5 black 46 17 11998 N-Amyloidogenic subgraph-147 ACTA2 103 65 white rectangle gene 0.5 black 46 17 130 N-Amyloidogenic subgraph-148 ECE2 94 118 white rectangle gene 0.5 black 46 17 13275 N-Amyloidogenic subgraph-149 FBXL2 66 82 white rectangle gene 0.5 black 46 17 13598 N-Amyloidogenic subgraph-153 RANBP9 42 155 white rectangle gene 0.5 black 46 17 13727 N-Amyloidogenic subgraph-154 SLC5A7 80 89 white rectangle gene 0.5 black 46 17 14025 N-Amyloidogenic subgraph-160 CAPN2 88 99 white rectangle gene 0.5 black 46 17 1479 N-Amyloidogenic subgraph-162 CASP3 77 96 white rectangle gene 0.5 black 46 17 1504 N-Amyloidogenic subgraph-163 CASP6 79 94 white rectangle gene 0.5 black 46 17 1507 N-Amyloidogenic subgraph-164 CASP8 80 87 white rectangle gene 0.5 black 46 17 1509 N-Amyloidogenic subgraph-166 CASR 97 98 white rectangle gene 0.5 black 46 17 1514 N-Amyloidogenic subgraph-170 CCR2 8 77 white rectangle gene 0.5 black 46 17 1603 N-Amyloidogenic subgraph-171 CCR5 13 78 white rectangle gene 0.5 black 46 17 1606 N-Amyloidogenic subgraph-172 COX4I2 170 81 white rectangle gene 0.5 black 46 17 16232 N-Amyloidogenic subgraph-176 NCSTN 74 91 white rectangle gene 0.5 black 46 17 17091 N-Amyloidogenic subgraph-178 CYSLTR1 88 119 white rectangle gene 0.5 black 46 17 17451 N-Amyloidogenic subgraph-179 CDH2 87 120 white rectangle gene 0.5 black 46 17 1759 N-Amyloidogenic subgraph-18 19 VLDLR APP 129 106 white rectangle gene,gene 0.5 black 46 17 12698,/,620 N-Amyloidogenic subgraph-180 CDK5 107 91 white rectangle gene 0.5 black 46 17 1774 N-Amyloidogenic subgraph-181 NAT8 102 104 white rectangle gene 0.5 black 46 17 18069 N-Amyloidogenic subgraph-182 ADAM10 86 104 white rectangle gene 0.5 black 46 17 188 N-Amyloidogenic subgraph-183 ADAM17 77 89 white rectangle gene 0.5 black 46 17 195 N-Amyloidogenic subgraph-185 CHRNA1 95 120 white rectangle gene 0.5 black 46 17 1955 N-Amyloidogenic subgraph-187 ADAM9 82 86 white rectangle gene 0.5 black 46 17 216 N-Amyloidogenic subgraph-189 COX4I1 165 85 white rectangle gene 0.5 black 46 17 2265 N-Amyloidogenic subgraph-190 COX5B 173 84 white rectangle gene 0.5 black 46 17 2269 N-Amyloidogenic subgraph-193 CSF1 0 114 white rectangle gene 0.5 black 46 17 2432 N-Amyloidogenic subgraph-194 CTNNB1 10 159 white rectangle gene 0.5 black 46 17 2514 N-Amyloidogenic subgraph-195 CTSB 50 82 white rectangle gene 0.5 black 46 17 2527 N-Amyloidogenic subgraph-196 CTSD 67 88 white rectangle gene 0.5 black 46 17 2529 N-Amyloidogenic subgraph-197 CTSE 77 93 white rectangle gene 0.5 black 46 17 2530 N-Amyloidogenic subgraph-199 DAB1 36 166 white rectangle gene 0.5 black 46 17 2661 N-Amyloidogenic subgraph-200 DAB1 104 170 white rectangle gene 0.5 black 46 17 2661 N-Amyloidogenic subgraph-204 DKK1 85 125 white rectangle gene 0.5 black 46 17 2891 N-Amyloidogenic subgraph-205 APH1A 81 83 white rectangle gene 0.5 black 46 17 29509 N-Amyloidogenic subgraph-206 DNMT1 183 111 white rectangle gene 0.5 black 46 17 2976 N-Amyloidogenic subgraph-207 NAT8B 101 108 white rectangle gene 0.5 black 46 17 30235 N-Amyloidogenic subgraph-208 EDN1 94 116 white rectangle gene 0.5 black 46 17 3176 N-Amyloidogenic subgraph-209 ABCA2 74 96 white rectangle gene 0.5 black 46 17 32 N-Amyloidogenic subgraph-210 AGER 99 110 white rectangle gene 0.5 black 46 17 320 N-Amyloidogenic subgraph-211 EGR1 79 90 white rectangle gene 0.5 black 46 17 3238 N-Amyloidogenic subgraph-212 FADD 90 117 white rectangle gene 0.5 black 46 17 3573 N-Amyloidogenic subgraph-213 FOS 85 103 white rectangle gene 0.5 black 46 17 3796 N-Amyloidogenic subgraph-214 ALB 106 13 white rectangle gene 0.5 black 46 17 399 N-Amyloidogenic subgraph-220 GCG 110 94 white rectangle gene 0.5 black 46 17 4191 N-Amyloidogenic subgraph-222 GPR3 157 50 white rectangle gene 0.5 black 46 17 4484 N-Amyloidogenic subgraph-228 GRIN2A 106 167 white rectangle gene 0.5 black 46 17 4585 N-Amyloidogenic subgraph-230 GRIN2B 108 179 white rectangle gene 0.5 black 46 17 4586 N-Amyloidogenic subgraph-234 GSK3B 102 84 white rectangle gene 0.5 black 46 17 4617 N-Amyloidogenic subgraph-235 GSK3B 92 120 white rectangle gene 0.5 black 46 17 4617 N-Amyloidogenic subgraph-236 GSK3B 85 86 white rectangle gene 0.5 black 46 17 4617 N-Amyloidogenic subgraph-238 HIF1A 100 106 white rectangle gene 0.5 black 46 17 4910 N-Amyloidogenic subgraph-239 IAPP 151 58 white rectangle gene 0.5 black 46 17 5329 N-Amyloidogenic subgraph-240 IGF1 91 121 white rectangle gene 0.5 black 46 17 5464 N-Amyloidogenic subgraph-242 APBA2 76 183 white rectangle gene 0.5 black 46 17 579 N-Amyloidogenic subgraph-243 APBB1 122 105 white rectangle gene 0.5 black 46 17 581 N-Amyloidogenic subgraph-244 APBB1 121 84 white rectangle gene 0.5 black 46 17 581 N-Amyloidogenic subgraph-245 IL1A 80 97 white rectangle gene 0.5 black 46 17 5991 N-Amyloidogenic subgraph-246 IL1B 90 102 white rectangle gene 0.5 black 46 17 5992 N-Amyloidogenic subgraph-247 APOA2 93 133 white rectangle gene 0.5 black 46 17 601 N-Amyloidogenic subgraph-248 INS 84 117 white rectangle gene 0.5 black 46 17 6081 N-Amyloidogenic subgraph-249 APOE 94 98 white rectangle gene 0.5 black 46 17 613 N-Amyloidogenic subgraph-250 APOE 97 119 white rectangle gene 0.5 black 46 17 613 N-Amyloidogenic subgraph-252 APP 83 94 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-253 APP 117 85 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-256 APP 96 133 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-257 APP 80 135 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-258 APP 169 83 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-259 APP 169 85 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-26 27 NECAB3 APBA2 146 150 white rectangle gene,gene 0.5 black 46 17 15851,/,579 N-Amyloidogenic subgraph-260 APP 100 100 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-261 APP 91 112 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-262 APP 99 107 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-265 APP 104 89 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-266 APP 79 104 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-267 APP 102 52 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-268 APP 101 44 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-269 APP 73 87 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-271 APP 86 118 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-273 APP 19 31 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-274 APP 103 108 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-277 APP 100 103 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-278 APP 70 78 white rectangle gene 0.5 black 46 17 620 N-Amyloidogenic subgraph-279 NAE1 75 84 white rectangle gene 0.5 black 46 17 621 N-Amyloidogenic subgraph-280 APPBP2 76 91 white rectangle gene 0.5 black 46 17 622 N-Amyloidogenic subgraph-281 KLC1 160 134 white rectangle gene 0.5 black 46 17 6387 N-Amyloidogenic subgraph-282 ARC 74 93 white rectangle gene 0.5 black 46 17 648 N-Amyloidogenic subgraph-285 LRP1 142 32 white rectangle gene 0.5 black 46 17 6692 N-Amyloidogenic subgraph-286 LRP8 93 82 white rectangle gene 0.5 black 46 17 6700 N-Amyloidogenic subgraph-287 MAP2 76 140 white rectangle gene 0.5 black 46 17 6839 N-Amyloidogenic subgraph-288 MAPK1 73 106 white rectangle gene 0.5 black 46 17 6871 N-Amyloidogenic subgraph-289 MAPK14 85 119 white rectangle gene 0.5 black 46 17 6876 N-Amyloidogenic subgraph-290 MAPK3 101 110 white rectangle gene 0.5 black 46 17 6877 N-Amyloidogenic subgraph-291 MAPK8 99 93 white rectangle gene 0.5 black 46 17 6881 N-Amyloidogenic subgraph-292 MAPK8IP1 109 84 white rectangle gene 0.5 black 46 17 6882 N-Amyloidogenic subgraph-293 MAPT 165 136 white rectangle gene 0.5 black 46 17 6893 N-Amyloidogenic subgraph-294 MAPT 103 96 white rectangle gene 0.5 black 46 17 6893 N-Amyloidogenic subgraph-296 ARRB2 160 51 white rectangle gene 0.5 black 46 17 712 N-Amyloidogenic subgraph-297 MMP2 97 107 white rectangle gene 0.5 black 46 17 7166 N-Amyloidogenic subgraph-298 MMP9 77 115 white rectangle gene 0.5 black 46 17 7176 N-Amyloidogenic subgraph-299 ABL1 114 86 white rectangle gene 0.5 black 46 17 76 N-Amyloidogenic subgraph-300 NEFH 78 142 white rectangle gene 0.5 black 46 17 7737 N-Amyloidogenic subgraph-301 NFATC1 98 109 white rectangle gene 0.5 black 46 17 7775 N-Amyloidogenic subgraph-302 NFKB1 107 99 white rectangle gene 0.5 black 46 17 7794 N-Amyloidogenic subgraph-303 NGFR 112 103 white rectangle gene 0.5 black 46 17 7809 N-Amyloidogenic subgraph-304 ATF4 79 73 white rectangle gene 0.5 black 46 17 786 N-Amyloidogenic subgraph-305 NOS2 109 103 white rectangle gene 0.5 black 46 17 7873 N-Amyloidogenic subgraph-306 NOTCH1 78 78 white rectangle gene 0.5 black 46 17 7881 N-Amyloidogenic subgraph-307 FURIN 102 106 white rectangle gene 0.5 black 46 17 8568 N-Amyloidogenic subgraph-309 SERPINI1 115 3 white rectangle gene 0.5 black 46 17 8943 N-Amyloidogenic subgraph-310 PIN1 92 87 white rectangle gene 0.5 black 46 17 8988 N-Amyloidogenic subgraph-311 PLD1 81 72 white rectangle gene 0.5 black 46 17 9067 N-Amyloidogenic subgraph-312 PLG 115 0 white rectangle gene 0.5 black 46 17 9071 N-Amyloidogenic subgraph-314 BACE1 94 104 white rectangle gene 0.5 black 46 17 933 N-Amyloidogenic subgraph-315 BACE2 88 90 white rectangle gene 0.5 black 46 17 934 N-Amyloidogenic subgraph-317 EIF2AK2 86 114 white rectangle gene 0.5 black 46 17 9437 N-Amyloidogenic subgraph-318 PRNP 97 111 white rectangle gene 0.5 black 46 17 9449 N-Amyloidogenic subgraph-319 PSEN1 81 80 white rectangle gene 0.5 black 46 17 9508 N-Amyloidogenic subgraph-32 33 NCSTN PSEN1 80 84 white rectangle gene,gene 0.5 black 46 17 17091,/,9508 N-Amyloidogenic subgraph-320 PSEN1 88 116 white rectangle gene 0.5 black 46 17 9508 N-Amyloidogenic subgraph-321 PSEN2 82 90 white rectangle gene 0.5 black 46 17 9509 N-Amyloidogenic subgraph-322 PSEN2 89 121 white rectangle gene 0.5 black 46 17 9509 N-Amyloidogenic subgraph-324 QPCT 96 106 white rectangle gene 0.5 black 46 17 9753 N-Amyloidogenic subgraph-325 MOK 3 113 white rectangle gene 0.5 black 46 17 9833 N-Amyloidogenic subgraph-326 RAMP3 127 193 white rectangle gene 0.5 black 46 17 9845 N-Amyloidogenic subgraph-327 BCL2L1 108 98 white rectangle gene 0.5 black 46 17 992 N-Amyloidogenic subgraph-329 RELN 106 173 white rectangle gene 0.5 black 46 17 9957 N-Amyloidogenic subgraph-330 cAMP response element binding (CREB) protein 106 78 white rectangle gene 0.5 black 46 17 IPR001630 N-Amyloidogenic subgraph-331 Amyloidogenic glycoprotein, intracellular domain, conserved site 103 72 white rectangle gene 0.5 black 46 17 IPR019745 N-Amyloidogenic subgraph-332 Tnf 29 154 white rectangle gene 0.5 black 46 17 104798 N-Amyloidogenic subgraph-333 Ifng 28 152 white rectangle gene 0.5 black 46 17 107656 N-Amyloidogenic subgraph-335 Bace1 33 150 white rectangle gene 0.5 black 46 17 1346542 N-Amyloidogenic subgraph-336 Cysltr1 93 121 white rectangle gene 0.5 black 46 17 1926218 N-Amyloidogenic subgraph-337 Acat1 78 87 white rectangle gene 0.5 black 46 17 87870 N-Amyloidogenic subgraph-338 App 25 155 white rectangle gene 0.5 black 46 17 88059 N-Amyloidogenic subgraph-36 37 SHC3 APP 87 40 white rectangle gene,gene 0.5 black 46 17 18181,/,620 N-Amyloidogenic subgraph-4 5 copper(2+) APP 106 19 white rectangle gene,gene 0.5 black 46 17 29036,/,620 N-Amyloidogenic subgraph-44 45 DAB2 APP 109 100 white rectangle gene,gene 0.5 black 46 17 2662,/,620 N-Amyloidogenic subgraph-46 47 SHC2 APP 105 58 white rectangle gene,gene 0.5 black 46 17 29869,/,620 N-Amyloidogenic subgraph-48 49 GRB2 APP 104 40 white rectangle gene,gene 0.5 black 46 17 4566,/,620 N-Amyloidogenic subgraph-50 51 52 GRB2 APP PSEN1 89 39 white rectangle gene,gene,gene 0.5 black 46 17 4566,/,620,/,9508 N-Amyloidogenic subgraph-53 54 IAPP RAMP1 156 56 white rectangle gene,gene 0.5 black 46 17 5329,/,9843 N-Amyloidogenic subgraph-55 56 IAPP RAMP2 146 62 white rectangle gene,gene 0.5 black 46 17 5329,/,9844 N-Amyloidogenic subgraph-57 58 IAPP RAMP3 129 191 white rectangle gene,gene 0.5 black 46 17 5329,/,9845 N-Amyloidogenic subgraph-6 7 ROCK2 SORL1 80 113 white rectangle gene,gene 0.5 black 46 17 10252,/,11185 N-Amyloidogenic subgraph-61 62 63 APBA1 APP LRP8 96 82 white rectangle gene,gene,gene 0.5 black 46 17 578,/,620,/,6700 N-Amyloidogenic subgraph-66 67 APBA2 APP 149 153 white rectangle gene,gene 0.5 black 46 17 579,/,620 N-Amyloidogenic subgraph-68 69 70 APBA2 APP LRP8 94 83 white rectangle gene,gene,gene 0.5 black 46 17 579,/,620,/,6700 N-Amyloidogenic subgraph-71 72 73 APBA3 APP LRP8 97 84 white rectangle gene,gene,gene 0.5 black 46 17 580,/,620,/,6700 N-Amyloidogenic subgraph-74 75 APBB1 APP 73 109 white rectangle gene,gene 0.5 black 46 17 581,/,620 N-Amyloidogenic subgraph-76 77 78 APBB1 APP KLC1 91 92 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6387 N-Amyloidogenic subgraph-79 80 81 APBB1 APP LRP1 76 97 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6692 N-Amyloidogenic subgraph-8 9 ACHE APP 97 117 white rectangle gene,gene 0.5 black 46 17 108,/,620 N-Amyloidogenic subgraph-82 83 84 APBB1 APP LRP8 146 28 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6700 N-Amyloidogenic subgraph-85 86 APBB1 KLC1 76 94 white rectangle gene,gene 0.5 black 46 17 581,/,6387 N-Amyloidogenic subgraph-87 88 APBB2 Amyloidogenic glycoprotein, intracellular domain, conserved site 14 156 white rectangle gene,gene 0.5 black 46 17 582,/,IPR019745 N-Amyloidogenic subgraph-89 90 APOE APP 106 97 white rectangle gene,gene 0.5 black 46 17 613,/,620 N-Amyloidogenic subgraph-91 92 APOE APP 93 126 white rectangle gene,gene 0.5 black 46 17 613,/,620 N-Amyloidogenic subgraph-93 94 APOE LRP8 93 90 white rectangle gene,gene 0.5 black 46 17 613,/,6700 N-Amyloidogenic subgraph-95 96 APP LRP1 39 162 white rectangle gene,gene 0.5 black 46 17 620,/,6692 N-Amyloidogenic subgraph-97 98 APP BACE1 46 149 white rectangle gene,gene 0.5 black 46 17 620,/,933 N-Amyloidogenic subgraph-99 100 APP PSEN2 85 89 white rectangle gene,gene 0.5 black 46 17 620,/,9509 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Amyloidogenic subgraph.sif000066400000000000000000000425201426625374700303230ustar00rootroot000000000000000 1 2 N-Amyloidogenic subgraph-294 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-294 activation N-Amyloidogenic subgraph-260 N-Amyloidogenic subgraph-93 94 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-93 94 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-93 94 activation N-Amyloidogenic subgraph-315 N-Amyloidogenic subgraph-93 94 activation N-Amyloidogenic subgraph-286 N-Amyloidogenic subgraph-247 activation N-Amyloidogenic subgraph-91 92 N-Amyloidogenic subgraph-282 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-281 activation N-Amyloidogenic subgraph-293 N-Amyloidogenic subgraph-246 activation N-Amyloidogenic subgraph-182 N-Amyloidogenic subgraph-246 activation N-Amyloidogenic subgraph-120 N-Amyloidogenic subgraph-246 inhibition N-Amyloidogenic subgraph-119 N-Amyloidogenic subgraph-246 inhibition N-Amyloidogenic subgraph-260 N-Amyloidogenic subgraph-246 inhibition N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-246 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-274 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-180 activation N-Amyloidogenic subgraph-294 N-Amyloidogenic subgraph-180 activation N-Amyloidogenic subgraph-265 N-Amyloidogenic subgraph-307 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-207 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-6 7 activation N-Amyloidogenic subgraph-120 N-Amyloidogenic subgraph-160 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-32 33 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-32 33 activation N-Amyloidogenic subgraph-306 N-Amyloidogenic subgraph-87 88 inhibition N-Amyloidogenic subgraph-194 N-Amyloidogenic subgraph-68 69 70 activation N-Amyloidogenic subgraph-93 94 N-Amyloidogenic subgraph-214 inhibition N-Amyloidogenic subgraph-4 5 N-Amyloidogenic subgraph-164 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-164 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-164 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-257 activation N-Amyloidogenic subgraph-204 N-Amyloidogenic subgraph-257 activation N-Amyloidogenic subgraph-300 N-Amyloidogenic subgraph-257 activation N-Amyloidogenic subgraph-287 N-Amyloidogenic subgraph-206 activation N-Amyloidogenic subgraph-139 N-Amyloidogenic subgraph-271 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-258 inhibition N-Amyloidogenic subgraph-189 N-Amyloidogenic subgraph-258 inhibition N-Amyloidogenic subgraph-172 N-Amyloidogenic subgraph-258 inhibition N-Amyloidogenic subgraph-190 N-Amyloidogenic subgraph-113 114 activation N-Amyloidogenic subgraph-266 N-Amyloidogenic subgraph-324 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-324 activation N-Amyloidogenic subgraph-260 N-Amyloidogenic subgraph-183 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-260 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-120 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-262 N-Amyloidogenic subgraph-314 activation N-Amyloidogenic subgraph-262 N-Amyloidogenic subgraph-117 118 activation N-Amyloidogenic subgraph-298 N-Amyloidogenic subgraph-111 112 activation N-Amyloidogenic subgraph-266 N-Amyloidogenic subgraph-320 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-260 activation N-Amyloidogenic subgraph-327 N-Amyloidogenic subgraph-260 activation N-Amyloidogenic subgraph-302 N-Amyloidogenic subgraph-260 activation N-Amyloidogenic subgraph-291 N-Amyloidogenic subgraph-260 activation N-Amyloidogenic subgraph-305 N-Amyloidogenic subgraph-260 activation N-Amyloidogenic subgraph-143 N-Amyloidogenic subgraph-260 activation N-Amyloidogenic subgraph-303 N-Amyloidogenic subgraph-280 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-36 37 activation N-Amyloidogenic subgraph-124 N-Amyloidogenic subgraph-53 54 activation N-Amyloidogenic subgraph-239 N-Amyloidogenic subgraph-240 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-268 activation N-Amyloidogenic subgraph-10 11 N-Amyloidogenic subgraph-268 activation N-Amyloidogenic subgraph-48 49 N-Amyloidogenic subgraph-331 activation N-Amyloidogenic subgraph-142 N-Amyloidogenic subgraph-331 activation N-Amyloidogenic subgraph-147 N-Amyloidogenic subgraph-331 activation N-Amyloidogenic subgraph-234 N-Amyloidogenic subgraph-140 activation N-Amyloidogenic subgraph-242 N-Amyloidogenic subgraph-322 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-182 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-182 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-182 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-182 activation N-Amyloidogenic subgraph-120 N-Amyloidogenic subgraph-99 100 inhibition N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-220 inhibition N-Amyloidogenic subgraph-294 N-Amyloidogenic subgraph-135 activation N-Amyloidogenic subgraph-8 9 N-Amyloidogenic subgraph-135 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-135 activation N-Amyloidogenic subgraph-318 N-Amyloidogenic subgraph-133 activation N-Amyloidogenic subgraph-133 N-Amyloidogenic subgraph-261 inhibition N-Amyloidogenic subgraph-182 N-Amyloidogenic subgraph-261 inhibition N-Amyloidogenic subgraph-120 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-122 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-213 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-178 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-208 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-148 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-336 N-Amyloidogenic subgraph-261 inhibition N-Amyloidogenic subgraph-179 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-289 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-294 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-317 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-212 N-Amyloidogenic subgraph-261 inhibition N-Amyloidogenic subgraph-141 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-235 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-185 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-204 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-204 N-Amyloidogenic subgraph-261 activation N-Amyloidogenic subgraph-204 N-Amyloidogenic subgraph-101 102 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-325 activation N-Amyloidogenic subgraph-193 N-Amyloidogenic subgraph-71 72 73 activation N-Amyloidogenic subgraph-93 94 N-Amyloidogenic subgraph-199 inhibition N-Amyloidogenic subgraph-95 96 N-Amyloidogenic subgraph-278 activation N-Amyloidogenic subgraph-279 N-Amyloidogenic subgraph-171 inhibition N-Amyloidogenic subgraph-170 N-Amyloidogenic subgraph-105 106 activation N-Amyloidogenic subgraph-294 N-Amyloidogenic subgraph-285 activation N-Amyloidogenic subgraph-103 104 N-Amyloidogenic subgraph-285 inhibition N-Amyloidogenic subgraph-82 83 84 N-Amyloidogenic subgraph-303 inhibition N-Amyloidogenic subgraph-243 N-Amyloidogenic subgraph-303 activation N-Amyloidogenic subgraph-305 N-Amyloidogenic subgraph-298 activation N-Amyloidogenic subgraph-120 N-Amyloidogenic subgraph-196 activation N-Amyloidogenic subgraph-133 N-Amyloidogenic subgraph-196 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-196 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-196 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-196 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-196 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-196 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-318 inhibition N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-318 inhibition N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-299 activation N-Amyloidogenic subgraph-265 N-Amyloidogenic subgraph-299 activation N-Amyloidogenic subgraph-244 N-Amyloidogenic subgraph-333 activation N-Amyloidogenic subgraph-335 N-Amyloidogenic subgraph-333 activation N-Amyloidogenic subgraph-338 N-Amyloidogenic subgraph-291 activation N-Amyloidogenic subgraph-265 N-Amyloidogenic subgraph-61 62 63 activation N-Amyloidogenic subgraph-93 94 N-Amyloidogenic subgraph-85 86 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-149 activation N-Amyloidogenic subgraph-269 N-Amyloidogenic subgraph-288 activation N-Amyloidogenic subgraph-266 N-Amyloidogenic subgraph-187 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-57 58 activation N-Amyloidogenic subgraph-326 N-Amyloidogenic subgraph-337 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-243 activation N-Amyloidogenic subgraph-18 19 N-Amyloidogenic subgraph-329 activation N-Amyloidogenic subgraph-228 N-Amyloidogenic subgraph-329 activation N-Amyloidogenic subgraph-230 N-Amyloidogenic subgraph-329 activation N-Amyloidogenic subgraph-200 N-Amyloidogenic subgraph-125 activation N-Amyloidogenic subgraph-294 N-Amyloidogenic subgraph-125 activation N-Amyloidogenic subgraph-253 N-Amyloidogenic subgraph-26 27 inhibition N-Amyloidogenic subgraph-66 67 N-Amyloidogenic subgraph-89 90 inhibition N-Amyloidogenic subgraph-260 N-Amyloidogenic subgraph-50 51 52 activation N-Amyloidogenic subgraph-124 N-Amyloidogenic subgraph-259 inhibition N-Amyloidogenic subgraph-189 N-Amyloidogenic subgraph-259 inhibition N-Amyloidogenic subgraph-172 N-Amyloidogenic subgraph-259 inhibition N-Amyloidogenic subgraph-190 N-Amyloidogenic subgraph-311 activation N-Amyloidogenic subgraph-319 N-Amyloidogenic subgraph-245 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-256 inhibition N-Amyloidogenic subgraph-91 92 N-Amyloidogenic subgraph-296 activation N-Amyloidogenic subgraph-222 N-Amyloidogenic subgraph-249 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-249 inhibition N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-249 inhibition N-Amyloidogenic subgraph-265 N-Amyloidogenic subgraph-249 inhibition N-Amyloidogenic subgraph-12 13 N-Amyloidogenic subgraph-249 activation N-Amyloidogenic subgraph-166 N-Amyloidogenic subgraph-195 activation N-Amyloidogenic subgraph-133 N-Amyloidogenic subgraph-195 activation N-Amyloidogenic subgraph-133 N-Amyloidogenic subgraph-176 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-76 77 78 activation N-Amyloidogenic subgraph-291 N-Amyloidogenic subgraph-205 activation N-Amyloidogenic subgraph-319 N-Amyloidogenic subgraph-205 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-205 activation N-Amyloidogenic subgraph-306 N-Amyloidogenic subgraph-301 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-309 inhibition N-Amyloidogenic subgraph-312 N-Amyloidogenic subgraph-162 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-162 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-310 inhibition N-Amyloidogenic subgraph-234 N-Amyloidogenic subgraph-310 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-163 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-163 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-181 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-10 11 activation N-Amyloidogenic subgraph-124 N-Amyloidogenic subgraph-250 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-79 80 81 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-145 activation N-Amyloidogenic subgraph-196 N-Amyloidogenic subgraph-144 activation N-Amyloidogenic subgraph-196 N-Amyloidogenic subgraph-304 activation N-Amyloidogenic subgraph-319 N-Amyloidogenic subgraph-55 56 activation N-Amyloidogenic subgraph-239 N-Amyloidogenic subgraph-269 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-292 activation N-Amyloidogenic subgraph-265 N-Amyloidogenic subgraph-332 activation N-Amyloidogenic subgraph-335 N-Amyloidogenic subgraph-332 activation N-Amyloidogenic subgraph-338 N-Amyloidogenic subgraph-197 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-234 activation N-Amyloidogenic subgraph-294 N-Amyloidogenic subgraph-234 activation N-Amyloidogenic subgraph-294 N-Amyloidogenic subgraph-234 activation N-Amyloidogenic subgraph-294 N-Amyloidogenic subgraph-234 inhibition N-Amyloidogenic subgraph-330 N-Amyloidogenic subgraph-234 activation N-Amyloidogenic subgraph-265 N-Amyloidogenic subgraph-248 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-321 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-321 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-321 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-236 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-315 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-153 activation N-Amyloidogenic subgraph-97 98 N-Amyloidogenic subgraph-153 activation N-Amyloidogenic subgraph-97 98 N-Amyloidogenic subgraph-153 activation N-Amyloidogenic subgraph-95 96 N-Amyloidogenic subgraph-267 activation N-Amyloidogenic subgraph-46 47 N-Amyloidogenic subgraph-267 activation N-Amyloidogenic subgraph-10 11 N-Amyloidogenic subgraph-252 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-252 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-252 activation N-Amyloidogenic subgraph-260 N-Amyloidogenic subgraph-252 inhibition N-Amyloidogenic subgraph-76 77 78 N-Amyloidogenic subgraph-252 inhibition N-Amyloidogenic subgraph-213 N-Amyloidogenic subgraph-252 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-252 activation N-Amyloidogenic subgraph-154 N-Amyloidogenic subgraph-252 activation N-Amyloidogenic subgraph-162 N-Amyloidogenic subgraph-252 activation N-Amyloidogenic subgraph-160 N-Amyloidogenic subgraph-252 activation N-Amyloidogenic subgraph-279 N-Amyloidogenic subgraph-252 inhibition N-Amyloidogenic subgraph-211 N-Amyloidogenic subgraph-252 inhibition N-Amyloidogenic subgraph-130 N-Amyloidogenic subgraph-252 inhibition N-Amyloidogenic subgraph-282 N-Amyloidogenic subgraph-252 inhibition N-Amyloidogenic subgraph-123 N-Amyloidogenic subgraph-290 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-266 inhibition N-Amyloidogenic subgraph-74 75 N-Amyloidogenic subgraph-266 inhibition N-Amyloidogenic subgraph-74 75 N-Amyloidogenic subgraph-266 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-266 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-266 inhibition N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-209 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-273 activation N-Amyloidogenic subgraph-1 2 3 N-Amyloidogenic subgraph-277 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-319 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-319 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-319 activation N-Amyloidogenic subgraph-252 N-Amyloidogenic subgraph-210 activation N-Amyloidogenic subgraph-301 N-Amyloidogenic subgraph-210 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-148 activation N-Amyloidogenic subgraph-208 N-Amyloidogenic subgraph-115 116 activation N-Amyloidogenic subgraph-266 N-Amyloidogenic subgraph-297 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-297 activation N-Amyloidogenic subgraph-260 N-Amyloidogenic subgraph-238 activation N-Amyloidogenic subgraph-314 N-Amyloidogenic subgraph-8 9 activation N-Amyloidogenic subgraph-261 N-Amyloidogenic subgraph-44 45 activation N-Amyloidogenic subgraph-260 N-Amyloidogenic subgraph-91 92 activation N-Amyloidogenic subgraph-261 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Apoptosis signaling subgraph.att000066400000000000000000000044341426625374700314660ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Apoptosis signaling subgraph-10 11 12 CASP8 FADD PTPN13 171 72 white rectangle gene,gene,gene 0.5 black 46 17 1509,/,3573,/,9646 N-Apoptosis signaling subgraph-15 16 APBB2 Amyloidogenic glycoprotein, intracellular domain, conserved site 66 162 white rectangle gene,gene 0.5 black 46 17 582,/,IPR019745 N-Apoptosis signaling subgraph-18 CREB 33 43 white rectangle gene 0.5 black 46 17 CREB N-Apoptosis signaling subgraph-21 Notch 152 126 white rectangle gene 0.5 black 46 17 Notch N-Apoptosis signaling subgraph-22 TNF 64 6 white rectangle gene 0.5 black 46 17 11892 N-Apoptosis signaling subgraph-23 TP53 106 85 white rectangle gene 0.5 black 46 17 11998 N-Apoptosis signaling subgraph-25 WWOX 30 37 white rectangle gene 0.5 black 46 17 12799 N-Apoptosis signaling subgraph-26 RBPJL 148 131 white rectangle gene 0.5 black 46 17 13761 N-Apoptosis signaling subgraph-28 CASP8 180 71 white rectangle gene 0.5 black 46 17 1509 N-Apoptosis signaling subgraph-32 CTNNB1 61 157 white rectangle gene 0.5 black 46 17 2514 N-Apoptosis signaling subgraph-34 CTSD 102 92 white rectangle gene 0.5 black 46 17 2529 N-Apoptosis signaling subgraph-41 GDNF 8 73 white rectangle gene 0.5 black 46 17 4232 N-Apoptosis signaling subgraph-43 KAT5 2 126 white rectangle gene 0.5 black 46 17 5275 N-Apoptosis signaling subgraph-44 APP 4 119 white rectangle gene 0.5 black 46 17 620 N-Apoptosis signaling subgraph-47 MAPT 120 3 white rectangle gene 0.5 black 46 17 6893 N-Apoptosis signaling subgraph-48 ABL1 125 0 white rectangle gene 0.5 black 46 17 76 N-Apoptosis signaling subgraph-51 PAFAH2 66 14 white rectangle gene 0.5 black 46 17 8579 N-Apoptosis signaling subgraph-58 BCL2 0 71 white rectangle gene 0.5 black 46 17 990 N-Apoptosis signaling subgraph-59 BCL2L1 17 76 white rectangle gene 0.5 black 46 17 992 N-Apoptosis signaling subgraph-60 Amyloidogenic glycoprotein, intracellular domain, conserved site 110 78 white rectangle gene 0.5 black 46 17 IPR019745 N-Apoptosis signaling subgraph-62 Cdh2 125 170 white rectangle gene 0.5 black 46 17 88355 N-Apoptosis signaling subgraph-64 Pparg 132 171 white rectangle gene 0.5 black 46 17 97747 N-Apoptosis signaling subgraph-7 8 9 TRADD CASP8 FADD 187 75 white rectangle gene,gene,gene 0.5 black 46 17 12030,/,1509,/,3573 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Apoptosis signaling subgraph.sif000066400000000000000000000020251426625374700314510ustar00rootroot000000000000000 1 2 N-Apoptosis signaling subgraph-23 activation N-Apoptosis signaling subgraph-34 N-Apoptosis signaling subgraph-21 activation N-Apoptosis signaling subgraph-26 N-Apoptosis signaling subgraph-7 8 9 activation N-Apoptosis signaling subgraph-28 N-Apoptosis signaling subgraph-41 activation N-Apoptosis signaling subgraph-58 N-Apoptosis signaling subgraph-41 activation N-Apoptosis signaling subgraph-59 N-Apoptosis signaling subgraph-60 activation N-Apoptosis signaling subgraph-23 N-Apoptosis signaling subgraph-10 11 12 activation N-Apoptosis signaling subgraph-28 N-Apoptosis signaling subgraph-25 inhibition N-Apoptosis signaling subgraph-18 N-Apoptosis signaling subgraph-51 inhibition N-Apoptosis signaling subgraph-22 N-Apoptosis signaling subgraph-64 activation N-Apoptosis signaling subgraph-62 N-Apoptosis signaling subgraph-44 activation N-Apoptosis signaling subgraph-43 N-Apoptosis signaling subgraph-15 16 inhibition N-Apoptosis signaling subgraph-32 N-Apoptosis signaling subgraph-48 activation N-Apoptosis signaling subgraph-47 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Autophagy signaling subgraph.att000066400000000000000000000014321426625374700314410ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Autophagy signaling subgraph-10 HSPB8 4 113 white rectangle gene 0.5 black 46 17 30171 N-Autophagy signaling subgraph-11 APP 66 132 white rectangle gene 0.5 black 46 17 620 N-Autophagy signaling subgraph-12 APP 19 129 white rectangle gene 0.5 black 46 17 620 N-Autophagy signaling subgraph-13 MAPK8IP2 0 141 white rectangle gene 0.5 black 46 17 6883 N-Autophagy signaling subgraph-14 BACE1 69 8 white rectangle gene 0.5 black 46 17 933 N-Autophagy signaling subgraph-3 4 RTN4R APP 44 131 white rectangle gene,gene 0.5 black 46 17 18601,/,620 N-Autophagy signaling subgraph-8 RTN3 55 0 white rectangle gene 0.5 black 46 17 10469 N-Autophagy signaling subgraph-9 RTN4R 32 142 white rectangle gene 0.5 black 46 17 18601 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Autophagy signaling subgraph.sif000066400000000000000000000011751426625374700314360ustar00rootroot000000000000000 1 2 N-Autophagy signaling subgraph-8 inhibition N-Autophagy signaling subgraph-14 N-Autophagy signaling subgraph-10 activation N-Autophagy signaling subgraph-12 N-Autophagy signaling subgraph-13 inhibition N-Autophagy signaling subgraph-12 N-Autophagy signaling subgraph-3 4 inhibition N-Autophagy signaling subgraph-12 N-Autophagy signaling subgraph-9 activation N-Autophagy signaling subgraph-3 4 N-Autophagy signaling subgraph-9 inhibition N-Autophagy signaling subgraph-12 N-Autophagy signaling subgraph-9 inhibition N-Autophagy signaling subgraph-12 N-Autophagy signaling subgraph-11 activation N-Autophagy signaling subgraph-3 4 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Axonal guidance subgraph.att000066400000000000000000000033231426625374700305270ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Axonal guidance subgraph-1 2 TUBA4A DPYSL2 0 80 white rectangle gene,gene 0.5 black 46 17 12407,/,3014 N-Axonal guidance subgraph-10 11 APP NTN1 134 25 white rectangle gene,gene 0.5 black 46 17 620,/,8029 N-Axonal guidance subgraph-12 ERK 181 19 white rectangle gene 0.5 black 46 17 ERK N-Axonal guidance subgraph-13 SEMA3A 33 127 white rectangle gene 0.5 black 46 17 10723 N-Axonal guidance subgraph-14 CDK5 27 94 white rectangle gene 0.5 black 46 17 1774 N-Axonal guidance subgraph-15 RTN4R 110 0 white rectangle gene 0.5 black 46 17 18601 N-Axonal guidance subgraph-16 DPYSL2 36 113 white rectangle gene 0.5 black 46 17 3014 N-Axonal guidance subgraph-17 DPYSL2 12 86 white rectangle gene 0.5 black 46 17 3014 N-Axonal guidance subgraph-18 DPYSL2 29 82 white rectangle gene 0.5 black 46 17 3014 N-Axonal guidance subgraph-19 GSK3A 45 123 white rectangle gene 0.5 black 46 17 4616 N-Axonal guidance subgraph-20 GSK3B 48 111 white rectangle gene 0.5 black 46 17 4617 N-Axonal guidance subgraph-21 APP 101 19 white rectangle gene 0.5 black 46 17 620 N-Axonal guidance subgraph-22 APP 123 20 white rectangle gene 0.5 black 46 17 620 N-Axonal guidance subgraph-23 APP 133 162 white rectangle gene 0.5 black 46 17 620 N-Axonal guidance subgraph-24 MAPK1 138 154 white rectangle gene 0.5 black 46 17 6871 N-Axonal guidance subgraph-25 Amyloidogenic glycoprotein, intracellular domain, conserved site 23 119 white rectangle gene 0.5 black 46 17 IPR019745 N-Axonal guidance subgraph-3 4 RTN4R APP 112 13 white rectangle gene,gene 0.5 black 46 17 18601,/,620 N-Axonal guidance subgraph-7 8 9 DCC APP NTN1 175 12 white rectangle gene,gene,gene 0.5 black 46 17 2701,/,620,/,8029 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Axonal guidance subgraph.sif000066400000000000000000000026341426625374700305240ustar00rootroot000000000000000 1 2 N-Axonal guidance subgraph-25 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-13 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-14 activation N-Axonal guidance subgraph-17 N-Axonal guidance subgraph-14 activation N-Axonal guidance subgraph-17 N-Axonal guidance subgraph-14 activation N-Axonal guidance subgraph-18 N-Axonal guidance subgraph-14 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-14 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-14 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-17 inhibition N-Axonal guidance subgraph-1 2 N-Axonal guidance subgraph-7 8 9 activation N-Axonal guidance subgraph-12 N-Axonal guidance subgraph-20 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-20 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-20 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-20 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-3 4 inhibition N-Axonal guidance subgraph-22 N-Axonal guidance subgraph-10 11 inhibition N-Axonal guidance subgraph-22 N-Axonal guidance subgraph-19 activation N-Axonal guidance subgraph-16 N-Axonal guidance subgraph-23 activation N-Axonal guidance subgraph-24 N-Axonal guidance subgraph-15 activation N-Axonal guidance subgraph-3 4 N-Axonal guidance subgraph-21 activation N-Axonal guidance subgraph-3 4 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Axonal transport subgraph.att000066400000000000000000000045341426625374700310110ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Axonal transport subgraph-1 2 BDNF NTRK2 182 114 white rectangle gene,gene 0.5 black 46 17 1033,/,8032 N-Axonal transport subgraph-10 11 APBB1 KLC1 122 114 white rectangle gene,gene 0.5 black 46 17 581,/,6387 N-Axonal transport subgraph-12 13 MAPK8IP1 Amyloidogenic glycoprotein, intracellular domain, conserved site 39 0 white rectangle gene,gene 0.5 black 46 17 6882,/,IPR019745 N-Axonal transport subgraph-14 15 MAPK8IP2 Amyloidogenic glycoprotein, intracellular domain, conserved site 50 21 white rectangle gene,gene 0.5 black 46 17 6883,/,IPR019745 N-Axonal transport subgraph-16 17 MAPK8IP3 Amyloidogenic glycoprotein, intracellular domain, conserved site 34 10 white rectangle gene,gene 0.5 black 46 17 6884,/,IPR019745 N-Axonal transport subgraph-18 BDNF 110 124 white rectangle gene 0.5 black 46 17 1033 N-Axonal transport subgraph-19 TPK1 0 103 white rectangle gene 0.5 black 46 17 17358 N-Axonal transport subgraph-20 CLSTN1 50 163 white rectangle gene 0.5 black 46 17 17447 N-Axonal transport subgraph-21 CDK5 19 105 white rectangle gene 0.5 black 46 17 1774 N-Axonal transport subgraph-22 ADAM10 59 163 white rectangle gene 0.5 black 46 17 188 N-Axonal transport subgraph-23 CPEB1 110 134 white rectangle gene 0.5 black 46 17 21744 N-Axonal transport subgraph-24 APP 112 111 white rectangle gene 0.5 black 46 17 620 N-Axonal transport subgraph-26 APP 44 10 white rectangle gene 0.5 black 46 17 620 N-Axonal transport subgraph-27 KLC1 154 20 white rectangle gene 0.5 black 46 17 6387 N-Axonal transport subgraph-28 MAPK1 50 1 white rectangle gene 0.5 black 46 17 6871 N-Axonal transport subgraph-29 MAPK8 117 89 white rectangle gene 0.5 black 46 17 6881 N-Axonal transport subgraph-3 4 CLSTN1 KLC1 103 110 white rectangle gene,gene 0.5 black 46 17 17447,/,6387 N-Axonal transport subgraph-30 MAPT 148 26 white rectangle gene 0.5 black 46 17 6893 N-Axonal transport subgraph-31 MAPT 9 104 white rectangle gene 0.5 black 46 17 6893 N-Axonal transport subgraph-32 NEFL 29 105 white rectangle gene 0.5 black 46 17 7739 N-Axonal transport subgraph-33 NTRK2 181 106 white rectangle gene 0.5 black 46 17 8032 N-Axonal transport subgraph-34 BACE1 43 168 white rectangle gene 0.5 black 46 17 933 N-Axonal transport subgraph-7 8 9 APBB1 APP KLC1 114 98 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6387 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Axonal transport subgraph.sif000066400000000000000000000023631426625374700310000ustar00rootroot000000000000000 1 2 N-Axonal transport subgraph-1 2 activation N-Axonal transport subgraph-33 N-Axonal transport subgraph-10 11 activation N-Axonal transport subgraph-24 N-Axonal transport subgraph-20 activation N-Axonal transport subgraph-22 N-Axonal transport subgraph-20 inhibition N-Axonal transport subgraph-34 N-Axonal transport subgraph-12 13 activation N-Axonal transport subgraph-26 N-Axonal transport subgraph-24 inhibition N-Axonal transport subgraph-7 8 9 N-Axonal transport subgraph-24 inhibition N-Axonal transport subgraph-18 N-Axonal transport subgraph-7 8 9 activation N-Axonal transport subgraph-29 N-Axonal transport subgraph-28 activation N-Axonal transport subgraph-26 N-Axonal transport subgraph-21 activation N-Axonal transport subgraph-31 N-Axonal transport subgraph-21 activation N-Axonal transport subgraph-32 N-Axonal transport subgraph-19 activation N-Axonal transport subgraph-31 N-Axonal transport subgraph-3 4 activation N-Axonal transport subgraph-24 N-Axonal transport subgraph-23 activation N-Axonal transport subgraph-18 N-Axonal transport subgraph-14 15 activation N-Axonal transport subgraph-26 N-Axonal transport subgraph-16 17 activation N-Axonal transport subgraph-26 N-Axonal transport subgraph-27 activation N-Axonal transport subgraph-30 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Bcl-2 subgraph.att000066400000000000000000000051751426625374700264130ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Bcl-2 subgraph-10 CTSB 69 0 white rectangle gene 0.5 black 46 17 2527 N-Bcl-2 subgraph-11 CTSD 55 0 white rectangle gene 0.5 black 46 17 2529 N-Bcl-2 subgraph-12 DDIT3 65 188 white rectangle gene 0.5 black 46 17 2726 N-Bcl-2 subgraph-13 PIK3R5 141 69 white rectangle gene 0.5 black 46 17 30035 N-Bcl-2 subgraph-14 GDNF 28 131 white rectangle gene 0.5 black 46 17 4232 N-Bcl-2 subgraph-15 GSK3B 17 155 white rectangle gene 0.5 black 46 17 4617 N-Bcl-2 subgraph-16 APP 22 113 white rectangle gene 0.5 black 46 17 620 N-Bcl-2 subgraph-18 MAPT 150 113 white rectangle gene 0.5 black 46 17 6893 N-Bcl-2 subgraph-19 PTPA 37 121 white rectangle gene 0.5 black 46 17 9308 N-Bcl-2 subgraph-20 BAD 0 158 white rectangle gene 0.5 black 46 17 936 N-Bcl-2 subgraph-21 BAD 3 162 white rectangle gene 0.5 black 46 17 936 N-Bcl-2 subgraph-22 PSEN1 50 134 white rectangle gene 0.5 black 46 17 9508 N-Bcl-2 subgraph-23 BAX 19 143 white rectangle gene 0.5 black 46 17 959 N-Bcl-2 subgraph-24 BAX 21 151 white rectangle gene 0.5 black 46 17 959 N-Bcl-2 subgraph-25 RAF1 133 71 white rectangle gene 0.5 black 46 17 9829 N-Bcl-2 subgraph-26 BCL2 42 128 white rectangle gene 0.5 black 46 17 990 N-Bcl-2 subgraph-27 BCL2L1 28 120 white rectangle gene 0.5 black 46 17 992 N-Bcl-2 subgraph-28 BCL2L11 69 196 white rectangle gene 0.5 black 46 17 994 N-Bcl-2 subgraph-29 Casp3 44 64 white rectangle gene 0.5 black 46 17 107739 N-Bcl-2 subgraph-3 4 PPP2CA BCL2 145 110 white rectangle gene,gene 0.5 black 46 17 9299,/,990 N-Bcl-2 subgraph-30 Bad 53 46 white rectangle gene 0.5 black 46 17 1096330 N-Bcl-2 subgraph-31 Retn 44 57 white rectangle gene 0.5 black 46 17 1888506 N-Bcl-2 subgraph-32 Afap1 23 54 white rectangle gene 0.5 black 46 17 1917542 N-Bcl-2 subgraph-33 Cysltr1 65 48 white rectangle gene 0.5 black 46 17 1926218 N-Bcl-2 subgraph-34 Bcl2 56 54 white rectangle gene 0.5 black 46 17 88138 N-Bcl-2 subgraph-35 Bcl2l1 62 59 white rectangle gene 0.5 black 46 17 88139 N-Bcl-2 subgraph-36 Cycs 33 56 white rectangle gene 0.5 black 46 17 88578 N-Bcl-2 subgraph-37 Pparg 32 66 white rectangle gene 0.5 black 46 17 97747 N-Bcl-2 subgraph-38 Sod1 52 56 white rectangle gene 0.5 black 46 17 98351 N-Bcl-2 subgraph-39 Bax 40 61 white rectangle gene 0.5 black 46 17 99702 N-Bcl-2 subgraph-5 AKT 9 154 white rectangle gene 0.5 black 46 17 AKT N-Bcl-2 subgraph-6 BID 62 3 white rectangle gene 0.5 black 46 17 1050 N-Bcl-2 subgraph-7 CASP3 20 131 white rectangle gene 0.5 black 46 17 1504 N-Bcl-2 subgraph-8 BBC3 60 184 white rectangle gene 0.5 black 46 17 17868 N-Bcl-2 subgraph-9 CYCS 52 127 white rectangle gene 0.5 black 46 17 19986 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Bcl-2 subgraph.sif000066400000000000000000000042121426625374700263730ustar00rootroot000000000000000 1 2 N-Bcl-2 subgraph-24 activation N-Bcl-2 subgraph-23 N-Bcl-2 subgraph-5 inhibition N-Bcl-2 subgraph-20 N-Bcl-2 subgraph-5 inhibition N-Bcl-2 subgraph-20 N-Bcl-2 subgraph-5 inhibition N-Bcl-2 subgraph-15 N-Bcl-2 subgraph-5 inhibition N-Bcl-2 subgraph-23 N-Bcl-2 subgraph-5 activation N-Bcl-2 subgraph-21 N-Bcl-2 subgraph-6 activation N-Bcl-2 subgraph-6 N-Bcl-2 subgraph-19 activation N-Bcl-2 subgraph-27 N-Bcl-2 subgraph-19 activation N-Bcl-2 subgraph-26 N-Bcl-2 subgraph-13 activation N-Bcl-2 subgraph-25 N-Bcl-2 subgraph-16 activation N-Bcl-2 subgraph-27 N-Bcl-2 subgraph-18 inhibition N-Bcl-2 subgraph-3 4 N-Bcl-2 subgraph-36 activation N-Bcl-2 subgraph-32 N-Bcl-2 subgraph-33 inhibition N-Bcl-2 subgraph-34 N-Bcl-2 subgraph-14 activation N-Bcl-2 subgraph-26 N-Bcl-2 subgraph-14 inhibition N-Bcl-2 subgraph-26 N-Bcl-2 subgraph-14 activation N-Bcl-2 subgraph-27 N-Bcl-2 subgraph-14 inhibition N-Bcl-2 subgraph-27 N-Bcl-2 subgraph-14 activation N-Bcl-2 subgraph-27 N-Bcl-2 subgraph-14 inhibition N-Bcl-2 subgraph-7 N-Bcl-2 subgraph-14 activation N-Bcl-2 subgraph-7 N-Bcl-2 subgraph-14 inhibition N-Bcl-2 subgraph-23 N-Bcl-2 subgraph-39 activation N-Bcl-2 subgraph-36 N-Bcl-2 subgraph-39 activation N-Bcl-2 subgraph-29 N-Bcl-2 subgraph-39 activation N-Bcl-2 subgraph-37 N-Bcl-2 subgraph-10 activation N-Bcl-2 subgraph-6 N-Bcl-2 subgraph-10 activation N-Bcl-2 subgraph-6 N-Bcl-2 subgraph-22 inhibition N-Bcl-2 subgraph-26 N-Bcl-2 subgraph-31 inhibition N-Bcl-2 subgraph-39 N-Bcl-2 subgraph-31 inhibition N-Bcl-2 subgraph-34 N-Bcl-2 subgraph-31 inhibition N-Bcl-2 subgraph-29 N-Bcl-2 subgraph-31 inhibition N-Bcl-2 subgraph-36 N-Bcl-2 subgraph-26 activation N-Bcl-2 subgraph-9 N-Bcl-2 subgraph-15 activation N-Bcl-2 subgraph-24 N-Bcl-2 subgraph-15 activation N-Bcl-2 subgraph-24 N-Bcl-2 subgraph-11 activation N-Bcl-2 subgraph-6 N-Bcl-2 subgraph-21 inhibition N-Bcl-2 subgraph-20 N-Bcl-2 subgraph-38 inhibition N-Bcl-2 subgraph-34 N-Bcl-2 subgraph-38 inhibition N-Bcl-2 subgraph-35 N-Bcl-2 subgraph-38 activation N-Bcl-2 subgraph-39 N-Bcl-2 subgraph-38 activation N-Bcl-2 subgraph-30 N-Bcl-2 subgraph-12 activation N-Bcl-2 subgraph-8 N-Bcl-2 subgraph-12 activation N-Bcl-2 subgraph-28 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Beta secretase subgraph.att000066400000000000000000000115031426625374700303560ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Beta secretase subgraph-1 2 RTN3 BACE1 31 24 white rectangle gene,gene 0.5 black 46 17 10469,/,933 N-Beta secretase subgraph-13 14 APBB1 APP 60 62 white rectangle gene,gene 0.5 black 46 17 581,/,620 N-Beta secretase subgraph-15 16 APOE LRP8 68 35 white rectangle gene,gene 0.5 black 46 17 613,/,6700 N-Beta secretase subgraph-17 18 APP LRP1 1 126 white rectangle gene,gene 0.5 black 46 17 620,/,6692 N-Beta secretase subgraph-19 20 APP BACE1 5 137 white rectangle gene,gene 0.5 black 46 17 620,/,933 N-Beta secretase subgraph-21 22 APP PSEN2 89 36 white rectangle gene,gene 0.5 black 46 17 620,/,9509 N-Beta secretase subgraph-23 24 NFKB1 NFKB2 55 22 white rectangle gene,gene 0.5 black 46 17 7794,/,7795 N-Beta secretase subgraph-25 sAPP-alpha 59 10 white rectangle gene 0.5 black 46 17 CONSO00067 N-Beta secretase subgraph-26 RTN3 36 23 white rectangle gene 0.5 black 46 17 10469 N-Beta secretase subgraph-27 ST6GAL1 28 21 white rectangle gene 0.5 black 46 17 10860 N-Beta secretase subgraph-28 FBXO2 100 134 white rectangle gene 0.5 black 46 17 13581 N-Beta secretase subgraph-29 RANBP9 0 134 white rectangle gene 0.5 black 46 17 13727 N-Beta secretase subgraph-31 CAPN2 28 27 white rectangle gene 0.5 black 46 17 1479 N-Beta secretase subgraph-32 CAST 50 40 white rectangle gene 0.5 black 46 17 1515 N-Beta secretase subgraph-33 GGA3 46 35 white rectangle gene 0.5 black 46 17 17079 N-Beta secretase subgraph-34 NCSTN 89 31 white rectangle gene 0.5 black 46 17 17091 N-Beta secretase subgraph-35 CLSTN1 56 8 white rectangle gene 0.5 black 46 17 17447 N-Beta secretase subgraph-36 PTGES2 37 41 white rectangle gene 0.5 black 46 17 17822 N-Beta secretase subgraph-37 GGA1 61 25 white rectangle gene 0.5 black 46 17 17842 N-Beta secretase subgraph-38 NAT8 44 8 white rectangle gene 0.5 black 46 17 18069 N-Beta secretase subgraph-39 ADAM10 63 0 white rectangle gene 0.5 black 46 17 188 N-Beta secretase subgraph-40 ADCY4 29 42 white rectangle gene 0.5 black 46 17 235 N-Beta secretase subgraph-41 DKK1 38 15 white rectangle gene 0.5 black 46 17 2891 N-Beta secretase subgraph-42 NAT8B 56 16 white rectangle gene 0.5 black 46 17 30235 N-Beta secretase subgraph-44 ABCA2 35 12 white rectangle gene 0.5 black 46 17 32 N-Beta secretase subgraph-45 AGER 50 17 white rectangle gene 0.5 black 46 17 320 N-Beta secretase subgraph-46 ERN1 102 23 white rectangle gene 0.5 black 46 17 3449 N-Beta secretase subgraph-47 HIF1A 49 9 white rectangle gene 0.5 black 46 17 4910 N-Beta secretase subgraph-49 APP 76 30 white rectangle gene 0.5 black 46 17 620 N-Beta secretase subgraph-50 APP 47 45 white rectangle gene 0.5 black 46 17 620 N-Beta secretase subgraph-51 APP 55 41 white rectangle gene 0.5 black 46 17 620 N-Beta secretase subgraph-52 APP 40 9 white rectangle gene 0.5 black 46 17 620 N-Beta secretase subgraph-53 APP 54 47 white rectangle gene 0.5 black 46 17 620 N-Beta secretase subgraph-54 APP 42 41 white rectangle gene 0.5 black 46 17 620 N-Beta secretase subgraph-55 APP 61 19 white rectangle gene 0.5 black 46 17 620 N-Beta secretase subgraph-56 ARC 41 19 white rectangle gene 0.5 black 46 17 648 N-Beta secretase subgraph-57 LEP 34 37 white rectangle gene 0.5 black 46 17 6553 N-Beta secretase subgraph-58 LRP8 79 46 white rectangle gene 0.5 black 46 17 6700 N-Beta secretase subgraph-59 MAPK3 32 19 white rectangle gene 0.5 black 46 17 6877 N-Beta secretase subgraph-60 NFATC1 49 14 white rectangle gene 0.5 black 46 17 7775 N-Beta secretase subgraph-61 NFKB1 44 13 white rectangle gene 0.5 black 46 17 7794 N-Beta secretase subgraph-62 NFKB2 33 16 white rectangle gene 0.5 black 46 17 7795 N-Beta secretase subgraph-63 FURIN 30 34 white rectangle gene 0.5 black 46 17 8568 N-Beta secretase subgraph-64 PPARG 56 36 white rectangle gene 0.5 black 46 17 9236 N-Beta secretase subgraph-65 BACE1 46 26 white rectangle gene 0.5 black 46 17 933 N-Beta secretase subgraph-66 BACE1 58 28 white rectangle gene 0.5 black 46 17 933 N-Beta secretase subgraph-67 BACE1 50 33 white rectangle gene 0.5 black 46 17 933 N-Beta secretase subgraph-68 BACE1 96 126 white rectangle gene 0.5 black 46 17 933 N-Beta secretase subgraph-69 BACE2 80 37 white rectangle gene 0.5 black 46 17 934 N-Beta secretase subgraph-7 8 RTN4 BACE1 31 30 white rectangle gene,gene 0.5 black 46 17 14085,/,933 N-Beta secretase subgraph-70 PRKACA 17 51 white rectangle gene 0.5 black 46 17 9380 N-Beta secretase subgraph-71 PRKCA 39 35 white rectangle gene 0.5 black 46 17 9393 N-Beta secretase subgraph-72 PRNP 62 34 white rectangle gene 0.5 black 46 17 9449 N-Beta secretase subgraph-73 PSEN1 93 27 white rectangle gene 0.5 black 46 17 9508 N-Beta secretase subgraph-74 PSEN2 91 23 white rectangle gene 0.5 black 46 17 9509 N-Beta secretase subgraph-75 QPCT 52 55 white rectangle gene 0.5 black 46 17 9753 N-Beta secretase subgraph-76 RELA 37 30 white rectangle gene 0.5 black 46 17 9955 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Beta secretase subgraph.sif000066400000000000000000000167141426625374700303600ustar00rootroot000000000000000 1 2 N-Beta secretase subgraph-62 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-38 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-15 16 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-15 16 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-15 16 activation N-Beta secretase subgraph-69 N-Beta secretase subgraph-15 16 activation N-Beta secretase subgraph-58 N-Beta secretase subgraph-67 activation N-Beta secretase subgraph-33 N-Beta secretase subgraph-67 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-28 activation N-Beta secretase subgraph-68 N-Beta secretase subgraph-41 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-41 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-56 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-33 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-33 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-70 inhibition N-Beta secretase subgraph-40 N-Beta secretase subgraph-54 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-71 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-39 activation N-Beta secretase subgraph-25 N-Beta secretase subgraph-39 activation N-Beta secretase subgraph-25 N-Beta secretase subgraph-21 22 inhibition N-Beta secretase subgraph-49 N-Beta secretase subgraph-66 activation N-Beta secretase subgraph-37 N-Beta secretase subgraph-66 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-23 24 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-23 24 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-7 8 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-32 inhibition N-Beta secretase subgraph-51 N-Beta secretase subgraph-32 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-32 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-74 activation N-Beta secretase subgraph-46 N-Beta secretase subgraph-74 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-63 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-37 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-37 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-69 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-42 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-29 activation N-Beta secretase subgraph-19 20 N-Beta secretase subgraph-29 activation N-Beta secretase subgraph-17 18 N-Beta secretase subgraph-49 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-76 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-76 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-57 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-59 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-31 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-1 2 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-26 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-44 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-53 inhibition N-Beta secretase subgraph-13 14 N-Beta secretase subgraph-53 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-53 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-55 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-73 activation N-Beta secretase subgraph-46 N-Beta secretase subgraph-73 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-35 activation N-Beta secretase subgraph-39 N-Beta secretase subgraph-35 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-34 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-40 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-45 activation N-Beta secretase subgraph-60 N-Beta secretase subgraph-45 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-72 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-72 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-72 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-72 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-72 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-72 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-72 inhibition N-Beta secretase subgraph-51 N-Beta secretase subgraph-60 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-75 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-75 activation N-Beta secretase subgraph-50 N-Beta secretase subgraph-64 inhibition N-Beta secretase subgraph-51 N-Beta secretase subgraph-64 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-64 inhibition N-Beta secretase subgraph-65 N-Beta secretase subgraph-47 activation N-Beta secretase subgraph-65 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-49 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 inhibition N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-51 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-50 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-25 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-52 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-52 N-Beta secretase subgraph-65 activation N-Beta secretase subgraph-36 N-Beta secretase subgraph-65 activation N-Beta secretase 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48 white rectangle gene,gene,gene 0.5 black 46 17 2514,/,9508,/,9509 N-Beta-Catenin subgraph-20 21 CTNND2 PSEN1 120 11 white rectangle gene,gene 0.5 black 46 17 2516,/,9508 N-Beta-Catenin subgraph-22 AXIN 78 72 white rectangle gene 0.5 black 46 17 AXIN N-Beta-Catenin subgraph-23 CTNNBIP1 95 34 white rectangle gene 0.5 black 46 17 16913 N-Beta-Catenin subgraph-24 CDK5 123 84 white rectangle gene 0.5 black 46 17 1774 N-Beta-Catenin subgraph-25 CSNK1A1 110 84 white rectangle gene 0.5 black 46 17 2451 N-Beta-Catenin subgraph-26 CTNNB1 115 49 white rectangle gene 0.5 black 46 17 2514 N-Beta-Catenin subgraph-27 CTNNB1 115 70 white rectangle gene 0.5 black 46 17 2514 N-Beta-Catenin subgraph-28 CTNNB1 74 63 white rectangle gene 0.5 black 46 17 2514 N-Beta-Catenin subgraph-29 GSK3B 109 65 white rectangle gene 0.5 black 46 17 4617 N-Beta-Catenin subgraph-30 PPARG 121 40 white rectangle gene 0.5 black 46 17 9236 N-Beta-Catenin subgraph-31 PSEN1 109 27 white rectangle gene 0.5 black 46 17 9508 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activation N-Beta-Catenin subgraph-12 13 14 N-Beta-Catenin subgraph-31 activation N-Beta-Catenin subgraph-20 21 N-Beta-Catenin subgraph-31 activation N-Beta-Catenin subgraph-6 7 N-Beta-Catenin subgraph-31 activation N-Beta-Catenin subgraph-26 N-Beta-Catenin subgraph-31 inhibition N-Beta-Catenin subgraph-26 N-Beta-Catenin subgraph-31 inhibition N-Beta-Catenin subgraph-23 N-Beta-Catenin subgraph-31 inhibition N-Beta-Catenin subgraph-23 N-Beta-Catenin subgraph-1 2 3 4 5 activation N-Beta-Catenin subgraph-28 N-Beta-Catenin subgraph-24 activation N-Beta-Catenin subgraph-27 N-Beta-Catenin subgraph-24 activation N-Beta-Catenin subgraph-15 16 N-Beta-Catenin subgraph-33 inhibition N-Beta-Catenin subgraph-15 16 N-Beta-Catenin subgraph-27 activation N-Beta-Catenin subgraph-26 N-Beta-Catenin subgraph-27 inhibition N-Beta-Catenin subgraph-26 N-Beta-Catenin subgraph-27 inhibition N-Beta-Catenin subgraph-26 N-Beta-Catenin subgraph-29 activation N-Beta-Catenin subgraph-1 2 3 4 5 N-Beta-Catenin 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Receptors.att000066400000000000000000000024371426625374700353550ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/outputID label X Y color shape type label.cex label.color width height genesList N-Binding and Uptake of Ligands by Scavenger Receptors-10 SCARF1 138 79 white rectangle gene 0.5 black 46 17 16820 N-Binding and Uptake of Ligands by Scavenger Receptors-12 SCARA5 146 98 white rectangle gene 0.5 black 46 17 28701 N-Binding and Uptake of Ligands by Scavenger Receptors-13 PSENEN 26 0 white rectangle gene 0.5 black 46 17 30100 N-Binding and Uptake of Ligands by Scavenger Receptors-14 FTL 45 40 white rectangle gene 0.5 black 46 17 3999 N-Binding and Uptake of Ligands by Scavenger Receptors-16 IFNG 157 174 white rectangle gene 0.5 black 46 17 5438 N-Binding and Uptake of Ligands by Scavenger Receptors-17 IL1B 130 132 white rectangle gene 0.5 black 46 17 5992 N-Binding and Uptake of Ligands by Scavenger Receptors-18 APP 106 176 white rectangle gene 0.5 black 46 17 620 N-Binding and Uptake of Ligands by Scavenger Receptors-19 APP 99 76 white rectangle gene 0.5 black 46 17 620 N-Binding and Uptake of Ligands by Scavenger Receptors-20 MSR1 116 85 white rectangle gene 0.5 black 46 17 7376 N-Binding and Uptake of Ligands by Scavenger Receptors-21 PSEN1 0 37 white rectangle gene 0.5 black 46 17 9508 N-Binding and Uptake of Ligands by Scavenger Receptors-9 SCARB1 132 114 white rectangle gene 0.5 black 46 17 1664 Binding and Uptake of Ligands by Scavenger Receptors.sif000066400000000000000000000031671426625374700353470ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output0 1 2 N-Binding and Uptake of Ligands by Scavenger Receptors-16 activation N-Binding and Uptake of Ligands by Scavenger Receptors-17 N-Binding and Uptake of Ligands by Scavenger Receptors-14 activation N-Binding and Uptake of Ligands by Scavenger Receptors-13 N-Binding and Uptake of Ligands by Scavenger Receptors-14 activation N-Binding and Uptake of Ligands by Scavenger Receptors-21 N-Binding and Uptake of Ligands by Scavenger Receptors-14 activation N-Binding and Uptake of Ligands by Scavenger Receptors-19 N-Binding and Uptake of Ligands by Scavenger Receptors-19 activation N-Binding and Uptake of Ligands by Scavenger Receptors-20 N-Binding and Uptake of Ligands by Scavenger Receptors-19 activation N-Binding and Uptake of Ligands by Scavenger Receptors-10 N-Binding and Uptake of Ligands by Scavenger Receptors-19 activation N-Binding and Uptake of Ligands by Scavenger Receptors-9 N-Binding and Uptake of Ligands by Scavenger Receptors-19 activation N-Binding and Uptake of Ligands by Scavenger Receptors-12 N-Binding and Uptake of Ligands by Scavenger Receptors-9 activation N-Binding and Uptake of Ligands by Scavenger Receptors-17 N-Binding and Uptake of Ligands by Scavenger Receptors-10 activation N-Binding and Uptake of Ligands by Scavenger Receptors-17 N-Binding and Uptake of Ligands by Scavenger Receptors-18 activation N-Binding and Uptake of Ligands by Scavenger Receptors-17 N-Binding and Uptake of Ligands by Scavenger Receptors-20 activation N-Binding and Uptake of Ligands by Scavenger Receptors-17 N-Binding and Uptake of Ligands by Scavenger Receptors-12 activation N-Binding and Uptake of Ligands by Scavenger Receptors-17 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/CREB subgraph.att000066400000000000000000000036471426625374700262710ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-CREB subgraph-1 2 SHC1 APP 121 41 white rectangle gene,gene 0.5 black 46 17 10840,/,620 N-CREB subgraph-10 CREB 103 48 white rectangle gene 0.5 black 46 17 CREB N-CREB subgraph-11 CREB 31 109 white rectangle gene 0.5 black 46 17 CREB N-CREB subgraph-12 CREB 103 28 white rectangle gene 0.5 black 46 17 CREB N-CREB subgraph-13 ERK 103 37 white rectangle gene 0.5 black 46 17 ERK N-CREB subgraph-14 RPS6KA1 96 16 white rectangle gene 0.5 black 46 17 10430 N-CREB subgraph-15 RPS6KA3 97 23 white rectangle gene 0.5 black 46 17 10432 N-CREB subgraph-16 RPS6KA5 78 17 white rectangle gene 0.5 black 46 17 10434 N-CREB subgraph-17 WWOX 110 58 white rectangle gene 0.5 black 46 17 12799 N-CREB subgraph-19 CAST 116 145 white rectangle gene 0.5 black 46 17 1515 N-CREB subgraph-21 CREB3L4 6 0 white rectangle gene 0.5 black 46 17 18854 N-CREB subgraph-22 CREB1 122 141 white rectangle gene 0.5 black 46 17 2345 N-CREB subgraph-23 CREB1 91 21 white rectangle gene 0.5 black 46 17 2345 N-CREB subgraph-25 ALOX5 103 61 white rectangle gene 0.5 black 46 17 435 N-CREB subgraph-26 GSK3B 0 3 white rectangle gene 0.5 black 46 17 4617 N-CREB subgraph-27 APP 95 58 white rectangle gene 0.5 black 46 17 620 N-CREB subgraph-28 APP 136 45 white rectangle gene 0.5 black 46 17 620 N-CREB subgraph-29 PRKACA 91 29 white rectangle gene 0.5 black 46 17 9380 N-CREB subgraph-3 4 SHC3 APP 93 43 white rectangle gene,gene 0.5 black 46 17 18181,/,620 N-CREB subgraph-30 PRKACB 83 10 white rectangle gene 0.5 black 46 17 9381 N-CREB subgraph-31 PRKCA 116 17 white rectangle gene 0.5 black 46 17 9393 N-CREB subgraph-5 6 GRB2 APP 147 47 white rectangle gene,gene 0.5 black 46 17 4566,/,620 N-CREB subgraph-7 calcium ion 125 8 white rectangle gene 0.5 black 46 17 39124 N-CREB subgraph-8 AKT 26 104 white rectangle gene 0.5 black 46 17 AKT N-CREB subgraph-9 CAMK 101 18 white rectangle gene 0.5 black 46 17 CAMK pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/CREB subgraph.sif000066400000000000000000000031261426625374700262520ustar00rootroot000000000000000 1 2 N-CREB subgraph-29 activation N-CREB subgraph-12 N-CREB subgraph-29 activation N-CREB subgraph-12 N-CREB subgraph-29 activation N-CREB subgraph-23 N-CREB subgraph-8 activation N-CREB subgraph-11 N-CREB subgraph-12 activation N-CREB subgraph-10 N-CREB subgraph-12 activation N-CREB subgraph-10 N-CREB subgraph-12 activation N-CREB subgraph-10 N-CREB subgraph-17 inhibition N-CREB subgraph-10 N-CREB subgraph-9 activation N-CREB subgraph-12 N-CREB subgraph-9 activation N-CREB subgraph-12 N-CREB subgraph-9 activation N-CREB subgraph-23 N-CREB subgraph-27 inhibition N-CREB subgraph-10 N-CREB subgraph-13 activation N-CREB subgraph-12 N-CREB subgraph-13 activation N-CREB subgraph-12 N-CREB subgraph-13 activation N-CREB subgraph-10 N-CREB subgraph-13 activation N-CREB subgraph-23 N-CREB subgraph-31 activation N-CREB subgraph-12 N-CREB subgraph-1 2 activation N-CREB subgraph-13 N-CREB subgraph-26 activation N-CREB subgraph-21 N-CREB subgraph-30 activation N-CREB subgraph-23 N-CREB subgraph-19 activation N-CREB subgraph-22 N-CREB subgraph-14 activation N-CREB subgraph-12 N-CREB subgraph-14 activation N-CREB subgraph-12 N-CREB subgraph-14 activation N-CREB subgraph-23 N-CREB subgraph-3 4 activation N-CREB subgraph-13 N-CREB subgraph-16 activation N-CREB subgraph-23 N-CREB subgraph-25 activation N-CREB subgraph-10 N-CREB subgraph-7 activation N-CREB subgraph-31 N-CREB subgraph-28 activation N-CREB subgraph-1 2 N-CREB subgraph-28 activation N-CREB subgraph-5 6 N-CREB subgraph-15 activation N-CREB subgraph-12 N-CREB subgraph-15 activation N-CREB subgraph-12 N-CREB subgraph-15 activation N-CREB subgraph-23 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/CRH subgraph.att000066400000000000000000000017051426625374700261630ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-CRH subgraph-1 2 Crhr1 Crh 106 67 white rectangle gene,gene 0.5 black 46 17 61276,/,620505 N-CRH subgraph-10 Mapt 116 163 white rectangle gene 0.5 black 46 17 69329 N-CRH subgraph-11 Mapk1 142 102 white rectangle gene 0.5 black 46 17 70500 N-CRH subgraph-12 Crhr2 0 185 white rectangle gene 0.5 black 46 17 70547 N-CRH subgraph-13 Gsk3b 141 138 white rectangle gene 0.5 black 46 17 70982 N-CRH subgraph-3 CRH 45 0 white rectangle gene 0.5 black 46 17 2355 N-CRH subgraph-4 CRHR1 13 31 white rectangle gene 0.5 black 46 17 2357 N-CRH subgraph-5 APP 60 49 white rectangle gene 0.5 black 46 17 620 N-CRH subgraph-6 Crhr1 93 116 white rectangle gene 0.5 black 46 17 61276 N-CRH subgraph-7 Crh 96 29 white rectangle gene 0.5 black 46 17 620505 N-CRH subgraph-8 Mapk8 38 154 white rectangle gene 0.5 black 46 17 621506 N-CRH subgraph-9 Cdk5r1 81 160 white rectangle gene 0.5 black 46 17 629472 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/CRH subgraph.sif000066400000000000000000000012061426625374700261500ustar00rootroot000000000000000 1 2 N-CRH subgraph-6 activation N-CRH subgraph-1 2 N-CRH subgraph-6 activation N-CRH subgraph-10 N-CRH subgraph-6 activation N-CRH subgraph-10 N-CRH subgraph-6 activation N-CRH subgraph-10 N-CRH subgraph-6 activation N-CRH subgraph-13 N-CRH subgraph-6 activation N-CRH subgraph-11 N-CRH subgraph-6 activation N-CRH subgraph-9 N-CRH subgraph-6 activation N-CRH subgraph-8 N-CRH subgraph-12 inhibition N-CRH subgraph-8 N-CRH subgraph-5 activation N-CRH subgraph-6 N-CRH subgraph-5 activation N-CRH subgraph-4 N-CRH subgraph-3 activation N-CRH subgraph-5 N-CRH subgraph-7 activation N-CRH subgraph-1 2 N-CRH subgraph-7 activation N-CRH subgraph-5 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Calcium-dependent signal transduction.att000066400000000000000000000027641426625374700332360ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Calcium-dependent signal transduction-10 CDH2 90 0 white rectangle gene 0.5 black 46 17 1759 N-Calcium-dependent signal transduction-13 CTNNB1 33 126 white rectangle gene 0.5 black 46 17 2514 N-Calcium-dependent signal transduction-14 DKK1 32 156 white rectangle gene 0.5 black 46 17 2891 N-Calcium-dependent signal transduction-15 AGER 44 152 white rectangle gene 0.5 black 46 17 320 N-Calcium-dependent signal transduction-16 GSK3B 45 130 white rectangle gene 0.5 black 46 17 4617 N-Calcium-dependent signal transduction-18 APOE 0 25 white rectangle gene 0.5 black 46 17 613 N-Calcium-dependent signal transduction-20 MAPK8 22 148 white rectangle gene 0.5 black 46 17 6881 N-Calcium-dependent signal transduction-21 MAPT 17 131 white rectangle gene 0.5 black 46 17 6893 N-Calcium-dependent signal transduction-23 PSEN1 98 5 white rectangle gene 0.5 black 46 17 9508 N-Calcium-dependent signal transduction-27 MOK 50 142 white rectangle gene 0.5 black 46 17 9833 N-Calcium-dependent signal transduction-4 RYR3 150 91 white rectangle gene 0.5 black 46 17 10485 N-Calcium-dependent signal transduction-5 S100B 34 140 white rectangle gene 0.5 black 46 17 10500 N-Calcium-dependent signal transduction-6 XBP1 138 92 white rectangle gene 0.5 black 46 17 12801 N-Calcium-dependent signal transduction-7 CAMK2A 3 127 white rectangle gene 0.5 black 46 17 1460 N-Calcium-dependent signal transduction-9 CASR 9 28 white rectangle gene 0.5 black 46 17 1514 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Calcium-dependent signal 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transduction-7 activation N-Calcium-dependent signal transduction-21 N-Calcium-dependent signal transduction-20 activation N-Calcium-dependent signal transduction-5 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Calpastatin-calpain subgraph.att000066400000000000000000000025451426625374700314220ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Calpastatin-calpain subgraph-1 CDK5R1 p25 63 74 white rectangle gene 0.5 black 46 17 CONSO00172 N-Calpastatin-calpain subgraph-10 APP 0 46 white rectangle gene 0.5 black 46 17 620 N-Calpastatin-calpain subgraph-11 MAPK1 9 56 white rectangle gene 0.5 black 46 17 6871 N-Calpastatin-calpain subgraph-12 MAPT 38 49 white rectangle gene 0.5 black 46 17 6893 N-Calpastatin-calpain subgraph-13 BACE1 2 34 white rectangle gene 0.5 black 46 17 933 N-Calpastatin-calpain subgraph-14 PSEN1 158 7 white rectangle gene 0.5 black 46 17 9508 N-Calpastatin-calpain subgraph-15 PSEN2 164 11 white rectangle gene 0.5 black 46 17 9509 N-Calpastatin-calpain subgraph-2 AKT 57 24 white rectangle gene 0.5 black 46 17 AKT N-Calpastatin-calpain subgraph-3 CAPN 51 81 white rectangle gene 0.5 black 46 17 CAPN N-Calpastatin-calpain subgraph-4 CAPN1 49 35 white rectangle gene 0.5 black 46 17 1476 N-Calpastatin-calpain subgraph-5 CAPN2 152 2 white rectangle gene 0.5 black 46 17 1479 N-Calpastatin-calpain subgraph-6 CAST 16 43 white rectangle gene 0.5 black 46 17 1515 N-Calpastatin-calpain subgraph-7 CDK5 51 66 white rectangle gene 0.5 black 46 17 1774 N-Calpastatin-calpain subgraph-8 CREB1 14 28 white rectangle gene 0.5 black 46 17 2345 N-Calpastatin-calpain subgraph-9 APP 139 0 white rectangle gene 0.5 black 46 17 620 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Calpastatin-calpain subgraph.sif000066400000000000000000000021051426625374700314030ustar00rootroot000000000000000 1 2 N-Calpastatin-calpain subgraph-12 activation N-Calpastatin-calpain subgraph-4 N-Calpastatin-calpain subgraph-14 inhibition N-Calpastatin-calpain subgraph-5 N-Calpastatin-calpain subgraph-9 activation N-Calpastatin-calpain subgraph-5 N-Calpastatin-calpain subgraph-7 activation N-Calpastatin-calpain subgraph-12 N-Calpastatin-calpain subgraph-3 activation N-Calpastatin-calpain subgraph-7 N-Calpastatin-calpain subgraph-15 inhibition N-Calpastatin-calpain subgraph-5 N-Calpastatin-calpain subgraph-6 inhibition N-Calpastatin-calpain subgraph-12 N-Calpastatin-calpain subgraph-6 activation N-Calpastatin-calpain subgraph-8 N-Calpastatin-calpain subgraph-6 activation N-Calpastatin-calpain subgraph-11 N-Calpastatin-calpain subgraph-6 inhibition N-Calpastatin-calpain subgraph-10 N-Calpastatin-calpain subgraph-6 inhibition N-Calpastatin-calpain subgraph-13 N-Calpastatin-calpain subgraph-6 inhibition N-Calpastatin-calpain subgraph-13 N-Calpastatin-calpain subgraph-4 inhibition N-Calpastatin-calpain subgraph-2 N-Calpastatin-calpain subgraph-1 activation N-Calpastatin-calpain subgraph-7 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pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Calsyntenin subgraph.sif000066400000000000000000000017331426625374700300300ustar00rootroot000000000000000 1 2 N-Calsyntenin subgraph-3 activation N-Calsyntenin subgraph-8 N-Calsyntenin subgraph-3 activation N-Calsyntenin subgraph-6 N-Calsyntenin subgraph-3 inhibition N-Calsyntenin subgraph-10 N-Calsyntenin subgraph-7 activation N-Calsyntenin subgraph-5 N-Calsyntenin subgraph-7 activation N-Calsyntenin subgraph-5 N-Calsyntenin subgraph-7 activation N-Calsyntenin subgraph-4 N-Calsyntenin subgraph-7 activation N-Calsyntenin subgraph-3 N-Calsyntenin subgraph-9 activation N-Calsyntenin subgraph-8 N-Calsyntenin subgraph-6 activation N-Calsyntenin subgraph-5 N-Calsyntenin subgraph-6 activation N-Calsyntenin subgraph-5 N-Calsyntenin subgraph-6 activation N-Calsyntenin subgraph-4 N-Calsyntenin subgraph-6 activation N-Calsyntenin subgraph-4 N-Calsyntenin subgraph-6 activation N-Calsyntenin subgraph-3 N-Calsyntenin subgraph-6 activation N-Calsyntenin subgraph-3 N-Calsyntenin subgraph-1 2 activation N-Calsyntenin subgraph-8 N-Calsyntenin subgraph-1 2 activation N-Calsyntenin subgraph-9 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Caspase subgraph.att000066400000000000000000000123501426625374700271240ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Caspase subgraph-1 2 TNFRSF10B TNFSF10 79 69 white rectangle gene,gene 0.5 black 46 17 11905,/,11925 N-Caspase subgraph-14 15 16 CASP8 FADD PTPN13 75 69 white rectangle gene,gene,gene 0.5 black 46 17 1509,/,3573,/,9646 N-Caspase subgraph-17 18 CYCS APAF1 50 115 white rectangle gene,gene 0.5 black 46 17 19986,/,576 N-Caspase subgraph-19 AKT 62 138 white rectangle gene 0.5 black 46 17 AKT N-Caspase subgraph-20 Caspase 86 179 white rectangle gene 0.5 black 46 17 Caspase N-Caspase subgraph-21 GSK3 93 0 white rectangle gene 0.5 black 46 17 GSK3 N-Caspase subgraph-22 GSK3 84 3 white rectangle gene 0.5 black 46 17 GSK3 N-Caspase subgraph-24 Caspases 150 8 white rectangle gene 0.5 black 46 17 468 N-Caspase subgraph-25 BID 99 92 white rectangle gene 0.5 black 46 17 1050 N-Caspase subgraph-26 SCAF11 96 98 white rectangle gene 0.5 black 46 17 10784 N-Caspase subgraph-27 ACHE 86 184 white rectangle gene 0.5 black 46 17 108 N-Caspase subgraph-28 SNCA 112 119 white rectangle gene 0.5 black 46 17 11138 N-Caspase subgraph-29 SOD1 42 112 white rectangle gene 0.5 black 46 17 11179 N-Caspase subgraph-30 TGFB1 86 82 white rectangle gene 0.5 black 46 17 11766 N-Caspase subgraph-32 HTRA2 175 114 white rectangle gene 0.5 black 46 17 14348 N-Caspase subgraph-33 SLC25A21 146 14 white rectangle gene 0.5 black 46 17 14411 N-Caspase subgraph-34 CASP1 101 103 white rectangle gene 0.5 black 46 17 1499 N-Caspase subgraph-36 CASP3 49 90 white rectangle gene 0.5 black 46 17 1504 N-Caspase subgraph-37 CASP4 41 100 white rectangle gene 0.5 black 46 17 1505 N-Caspase subgraph-38 CASP6 57 91 white rectangle gene 0.5 black 46 17 1507 N-Caspase subgraph-39 CASP7 27 86 white rectangle gene 0.5 black 46 17 1508 N-Caspase subgraph-40 CASP8 75 77 white rectangle gene 0.5 black 46 17 1509 N-Caspase subgraph-41 CASP9 52 121 white rectangle gene 0.5 black 46 17 1511 N-Caspase subgraph-42 CASP9 59 131 white rectangle gene 0.5 black 46 17 1511 N-Caspase subgraph-43 NLRP3 116 115 white rectangle gene 0.5 black 46 17 16400 N-Caspase subgraph-44 CDK5R1 148 56 white rectangle gene 0.5 black 46 17 1775 N-Caspase subgraph-45 CDKN1A 42 90 white rectangle gene 0.5 black 46 17 1784 N-Caspase subgraph-46 CYCS 48 106 white rectangle gene 0.5 black 46 17 19986 N-Caspase subgraph-47 CST3 84 86 white rectangle gene 0.5 black 46 17 2475 N-Caspase subgraph-48 CTSB 91 93 white rectangle gene 0.5 black 46 17 2527 N-Caspase subgraph-49 CTSL 48 81 white rectangle gene 0.5 black 46 17 2537 N-Caspase subgraph-5 6 7 TRADD CASP8 FADD 81 73 white rectangle gene,gene,gene 0.5 black 46 17 12030,/,1509,/,3573 N-Caspase subgraph-50 GDNF 40 85 white rectangle gene 0.5 black 46 17 4232 N-Caspase subgraph-51 HSPA1B 53 128 white rectangle gene 0.5 black 46 17 5233 N-Caspase subgraph-52 HSPD1 48 128 white rectangle gene 0.5 black 46 17 5261 N-Caspase subgraph-53 XIAP 169 110 white rectangle gene 0.5 black 46 17 592 N-Caspase subgraph-54 IL1B 109 112 white rectangle gene 0.5 black 46 17 5992 N-Caspase subgraph-55 APP 61 83 white rectangle gene 0.5 black 46 17 620 N-Caspase subgraph-56 APP 48 155 white rectangle gene 0.5 black 46 17 620 N-Caspase subgraph-57 APP 79 87 white rectangle gene 0.5 black 46 17 620 N-Caspase subgraph-58 APP 65 88 white rectangle gene 0.5 black 46 17 620 N-Caspase subgraph-59 APP 59 75 white rectangle gene 0.5 black 46 17 620 N-Caspase subgraph-60 MAPT 49 95 white rectangle gene 0.5 black 46 17 6893 N-Caspase subgraph-61 NFKB1 50 150 white rectangle gene 0.5 black 46 17 7794 N-Caspase subgraph-62 NGF 87 191 white rectangle gene 0.5 black 46 17 7808 N-Caspase subgraph-63 PDCD1 36 88 white rectangle gene 0.5 black 46 17 8760 N-Caspase subgraph-64 PTPA 45 85 white rectangle gene 0.5 black 46 17 9308 N-Caspase subgraph-65 PRKCD 89 1 white rectangle gene 0.5 black 46 17 9399 N-Caspase subgraph-66 EIF2AK2 72 71 white rectangle gene 0.5 black 46 17 9437 N-Caspase subgraph-67 BAX 32 79 white rectangle gene 0.5 black 46 17 959 N-Caspase subgraph-68 BCL2 43 96 white rectangle gene 0.5 black 46 17 990 N-Caspase subgraph-69 BCL2L1 36 76 white rectangle gene 0.5 black 46 17 992 N-Caspase subgraph-70 Xiap 157 60 white rectangle gene 0.5 black 46 17 107572 N-Caspase subgraph-71 Casp3 151 63 white rectangle gene 0.5 black 46 17 107739 N-Caspase subgraph-72 Bad 159 85 white rectangle gene 0.5 black 46 17 1096330 N-Caspase subgraph-73 Casp9 156 65 white rectangle gene 0.5 black 46 17 1277950 N-Caspase subgraph-74 Retn 151 70 white rectangle gene 0.5 black 46 17 1888506 N-Caspase subgraph-75 Afap1 165 73 white rectangle gene 0.5 black 46 17 1917542 N-Caspase subgraph-76 Cysltr1 153 55 white rectangle gene 0.5 black 46 17 1926218 N-Caspase subgraph-77 Bcl2 150 77 white rectangle gene 0.5 black 46 17 88138 N-Caspase subgraph-78 Bcl2l1 154 88 white rectangle gene 0.5 black 46 17 88139 N-Caspase subgraph-79 Cycs 156 73 white rectangle gene 0.5 black 46 17 88578 N-Caspase subgraph-8 9 TNFRSF21 APP 59 97 white rectangle gene,gene 0.5 black 46 17 13469,/,620 N-Caspase subgraph-80 Sod1 152 81 white rectangle gene 0.5 black 46 17 98351 N-Caspase subgraph-81 Bax 151 72 white rectangle gene 0.5 black 46 17 99702 N-Caspase subgraph-82 Casp3 3 145 white rectangle gene 0.5 black 46 17 2275 N-Caspase subgraph-83 Casp9 0 142 white rectangle gene 0.5 black 46 17 61867 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Caspase subgraph.sif000066400000000000000000000110431426625374700271130ustar00rootroot000000000000000 1 2 N-Caspase subgraph-19 activation N-Caspase subgraph-42 N-Caspase subgraph-38 activation N-Caspase subgraph-60 N-Caspase subgraph-38 activation N-Caspase subgraph-55 N-Caspase subgraph-38 activation N-Caspase subgraph-55 N-Caspase subgraph-38 activation N-Caspase subgraph-8 9 N-Caspase subgraph-64 inhibition N-Caspase subgraph-36 N-Caspase subgraph-34 activation N-Caspase subgraph-54 N-Caspase subgraph-56 activation N-Caspase subgraph-61 N-Caspase subgraph-66 activation N-Caspase subgraph-40 N-Caspase subgraph-79 activation N-Caspase subgraph-73 N-Caspase subgraph-79 activation N-Caspase subgraph-75 N-Caspase subgraph-5 6 7 activation N-Caspase subgraph-40 N-Caspase subgraph-50 inhibition N-Caspase subgraph-68 N-Caspase subgraph-50 inhibition N-Caspase subgraph-69 N-Caspase subgraph-50 activation N-Caspase subgraph-69 N-Caspase subgraph-50 inhibition N-Caspase subgraph-36 N-Caspase subgraph-50 activation N-Caspase subgraph-36 N-Caspase subgraph-50 inhibition N-Caspase subgraph-67 N-Caspase subgraph-32 inhibition N-Caspase subgraph-53 N-Caspase subgraph-81 activation N-Caspase subgraph-79 N-Caspase subgraph-81 activation N-Caspase subgraph-71 N-Caspase subgraph-47 inhibition N-Caspase subgraph-40 N-Caspase subgraph-47 inhibition N-Caspase subgraph-48 N-Caspase subgraph-47 inhibition N-Caspase subgraph-57 N-Caspase subgraph-52 inhibition N-Caspase subgraph-41 N-Caspase subgraph-74 inhibition N-Caspase subgraph-81 N-Caspase subgraph-74 inhibition N-Caspase subgraph-77 N-Caspase subgraph-74 inhibition N-Caspase subgraph-71 N-Caspase subgraph-74 inhibition N-Caspase subgraph-79 N-Caspase subgraph-63 activation N-Caspase subgraph-36 N-Caspase subgraph-63 activation N-Caspase subgraph-39 N-Caspase subgraph-14 15 16 activation N-Caspase subgraph-40 N-Caspase subgraph-29 activation N-Caspase subgraph-46 N-Caspase subgraph-37 activation N-Caspase subgraph-60 N-Caspase subgraph-62 inhibition N-Caspase subgraph-27 N-Caspase subgraph-46 activation N-Caspase subgraph-17 18 N-Caspase subgraph-46 activation N-Caspase subgraph-41 N-Caspase subgraph-46 activation N-Caspase subgraph-36 N-Caspase subgraph-17 18 activation N-Caspase subgraph-41 N-Caspase subgraph-55 activation N-Caspase subgraph-36 N-Caspase subgraph-49 activation N-Caspase subgraph-36 N-Caspase subgraph-80 activation N-Caspase subgraph-79 N-Caspase subgraph-80 inhibition N-Caspase subgraph-77 N-Caspase subgraph-80 inhibition N-Caspase subgraph-78 N-Caspase subgraph-80 activation N-Caspase subgraph-81 N-Caspase subgraph-80 activation N-Caspase subgraph-72 N-Caspase subgraph-83 activation N-Caspase subgraph-82 N-Caspase subgraph-20 activation N-Caspase subgraph-27 N-Caspase subgraph-44 inhibition N-Caspase subgraph-71 N-Caspase subgraph-44 inhibition N-Caspase subgraph-71 N-Caspase subgraph-73 activation N-Caspase subgraph-71 N-Caspase subgraph-51 inhibition N-Caspase subgraph-41 N-Caspase subgraph-65 activation N-Caspase subgraph-22 N-Caspase subgraph-65 inhibition N-Caspase subgraph-21 N-Caspase subgraph-45 inhibition N-Caspase subgraph-36 N-Caspase subgraph-58 activation N-Caspase subgraph-36 N-Caspase subgraph-58 activation N-Caspase subgraph-57 N-Caspase subgraph-1 2 activation N-Caspase subgraph-40 N-Caspase subgraph-76 activation N-Caspase subgraph-71 N-Caspase subgraph-59 inhibition N-Caspase subgraph-55 N-Caspase subgraph-28 activation N-Caspase subgraph-54 N-Caspase subgraph-43 activation N-Caspase subgraph-54 N-Caspase subgraph-40 activation N-Caspase subgraph-55 N-Caspase subgraph-40 activation N-Caspase subgraph-55 N-Caspase subgraph-40 activation N-Caspase subgraph-55 N-Caspase subgraph-40 inhibition N-Caspase subgraph-57 N-Caspase subgraph-33 activation N-Caspase subgraph-24 N-Caspase subgraph-48 activation N-Caspase subgraph-34 N-Caspase subgraph-48 activation N-Caspase subgraph-26 N-Caspase subgraph-48 activation N-Caspase subgraph-25 N-Caspase subgraph-48 inhibition N-Caspase subgraph-57 N-Caspase subgraph-68 activation N-Caspase subgraph-46 N-Caspase subgraph-30 activation N-Caspase subgraph-57 N-Caspase subgraph-42 inhibition N-Caspase subgraph-41 N-Caspase subgraph-36 activation N-Caspase subgraph-60 N-Caspase subgraph-36 activation N-Caspase subgraph-60 N-Caspase subgraph-36 activation N-Caspase subgraph-60 N-Caspase subgraph-36 activation N-Caspase subgraph-60 N-Caspase subgraph-36 activation N-Caspase subgraph-55 N-Caspase subgraph-36 activation N-Caspase subgraph-55 N-Caspase subgraph-70 inhibition N-Caspase subgraph-73 N-Caspase subgraph-70 inhibition N-Caspase subgraph-73 N-Caspase subgraph-70 inhibition N-Caspase subgraph-73 N-Caspase subgraph-70 inhibition N-Caspase subgraph-71 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cell adhesion subgraph.att000066400000000000000000000023551426625374700302030ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Cell adhesion subgraph-1 2 SDC1 ITGA5 91 10 white rectangle gene,gene 0.5 black 46 17 10658,/,6141 N-Cell adhesion subgraph-10 TNF 141 122 white rectangle gene 0.5 black 46 17 11892 N-Cell adhesion subgraph-11 SIRT1 9 50 white rectangle gene 0.5 black 46 17 14929 N-Cell adhesion subgraph-12 CD44 71 70 white rectangle gene 0.5 black 46 17 1681 N-Cell adhesion subgraph-13 CDH2 54 84 white rectangle gene 0.5 black 46 17 1759 N-Cell adhesion subgraph-14 ADAM10 62 181 white rectangle gene 0.5 black 46 17 188 N-Cell adhesion subgraph-15 CHI3L1 98 1 white rectangle gene 0.5 black 46 17 1932 N-Cell adhesion subgraph-17 APP 138 142 white rectangle gene 0.5 black 46 17 620 N-Cell adhesion subgraph-18 PSEN1 63 77 white rectangle gene 0.5 black 46 17 9508 N-Cell adhesion subgraph-19 RARB 0 51 white rectangle gene 0.5 black 46 17 9865 N-Cell adhesion subgraph-3 4 SDC1 ITGB3 109 0 white rectangle gene,gene 0.5 black 46 17 10658,/,6156 N-Cell adhesion subgraph-7 Notch 67 190 white rectangle gene 0.5 black 46 17 Notch N-Cell adhesion subgraph-8 SYP 148 144 white rectangle gene 0.5 black 46 17 11506 N-Cell adhesion subgraph-9 THBS1 143 133 white rectangle gene 0.5 black 46 17 11785 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cell adhesion subgraph.sif000066400000000000000000000013421426625374700301670ustar00rootroot000000000000000 1 2 N-Cell adhesion subgraph-18 activation N-Cell adhesion subgraph-13 N-Cell adhesion subgraph-18 activation N-Cell adhesion subgraph-12 N-Cell adhesion subgraph-17 inhibition N-Cell adhesion subgraph-9 N-Cell adhesion subgraph-17 inhibition N-Cell adhesion subgraph-9 N-Cell adhesion subgraph-17 inhibition N-Cell adhesion subgraph-8 N-Cell adhesion subgraph-9 activation N-Cell adhesion subgraph-8 N-Cell adhesion subgraph-14 activation N-Cell adhesion subgraph-7 N-Cell adhesion subgraph-11 activation N-Cell adhesion subgraph-19 N-Cell adhesion subgraph-15 activation N-Cell adhesion subgraph-3 4 N-Cell adhesion subgraph-15 activation N-Cell adhesion subgraph-1 2 N-Cell adhesion subgraph-10 activation N-Cell adhesion subgraph-9 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cell cycle subgraph.att000066400000000000000000000023701426625374700275050ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Cell cycle subgraph-1 2 EFNB2 EPHB2 179 110 white rectangle gene,gene 0.5 black 46 17 3227,/,3393 N-Cell cycle subgraph-10 CSK 41 185 white rectangle gene 0.5 black 46 17 2444 N-Cell cycle subgraph-11 EFNB2 42 175 white rectangle gene 0.5 black 46 17 3227 N-Cell cycle subgraph-12 EFNB2 172 103 white rectangle gene 0.5 black 46 17 3227 N-Cell cycle subgraph-13 AKT1 24 64 white rectangle gene 0.5 black 46 17 391 N-Cell cycle subgraph-14 MAPT 137 47 white rectangle gene 0.5 black 46 17 6893 N-Cell cycle subgraph-15 ABL1 129 54 white rectangle gene 0.5 black 46 17 76 N-Cell cycle subgraph-16 PSEN1 33 181 white rectangle gene 0.5 black 46 17 9508 N-Cell cycle subgraph-17 RB1 10 77 white rectangle gene 0.5 black 46 17 9884 N-Cell cycle subgraph-18 RB1 0 58 white rectangle gene 0.5 black 46 17 9884 N-Cell cycle subgraph-3 BDNF 76 0 white rectangle gene 0.5 black 46 17 1033 N-Cell cycle subgraph-4 CAST 122 175 white rectangle gene 0.5 black 46 17 1515 N-Cell cycle subgraph-5 CCND1 11 65 white rectangle gene 0.5 black 46 17 1582 N-Cell cycle subgraph-8 CPEB1 76 11 white rectangle gene 0.5 black 46 17 21744 N-Cell cycle subgraph-9 CREB1 132 172 white rectangle gene 0.5 black 46 17 2345 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cell cycle subgraph.sif000066400000000000000000000012371426625374700274770ustar00rootroot000000000000000 1 2 N-Cell cycle subgraph-17 activation N-Cell cycle subgraph-5 N-Cell cycle subgraph-16 activation N-Cell cycle subgraph-11 N-Cell cycle subgraph-16 activation N-Cell cycle subgraph-11 N-Cell cycle subgraph-16 inhibition N-Cell cycle subgraph-10 N-Cell cycle subgraph-1 2 activation N-Cell cycle subgraph-12 N-Cell cycle subgraph-13 inhibition N-Cell cycle subgraph-5 N-Cell cycle subgraph-4 activation N-Cell cycle subgraph-9 N-Cell cycle subgraph-15 activation N-Cell cycle subgraph-14 N-Cell cycle subgraph-11 inhibition N-Cell cycle subgraph-10 N-Cell cycle subgraph-8 activation N-Cell cycle subgraph-3 N-Cell cycle subgraph-18 inhibition N-Cell cycle subgraph-5 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Chaperone subgraph.att000066400000000000000000000045261426625374700274570ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Chaperone subgraph-1 2 HSPA BAG2 47 41 white rectangle gene,gene 0.5 black 46 17 HSPA,/,938 N-Chaperone subgraph-10 HSPA 49 30 white rectangle gene 0.5 black 46 17 HSPA N-Chaperone subgraph-11 BDNF 75 130 white rectangle gene 0.5 black 46 17 1033 N-Chaperone subgraph-12 SOD1 100 134 white rectangle gene 0.5 black 46 17 11179 N-Chaperone subgraph-13 TGFBR1 134 6 white rectangle gene 0.5 black 46 17 11772 N-Chaperone subgraph-14 CASP9 0 46 white rectangle gene 0.5 black 46 17 1511 N-Chaperone subgraph-15 CCR2 129 3 white rectangle gene 0.5 black 46 17 1603 N-Chaperone subgraph-16 CDC37 67 39 white rectangle gene 0.5 black 46 17 1735 N-Chaperone subgraph-17 CDK5 76 41 white rectangle gene 0.5 black 46 17 1774 N-Chaperone subgraph-19 HSPB6 139 2 white rectangle gene 0.5 black 46 17 26511 N-Chaperone subgraph-20 HSPB8 85 144 white rectangle gene 0.5 black 46 17 30171 N-Chaperone subgraph-21 AKT1 71 30 white rectangle gene 0.5 black 46 17 391 N-Chaperone subgraph-22 GSK3B 180 75 white rectangle gene 0.5 black 46 17 4617 N-Chaperone subgraph-23 HSF1 190 73 white rectangle gene 0.5 black 46 17 5224 N-Chaperone subgraph-24 HSPA1B 5 40 white rectangle gene 0.5 black 46 17 5233 N-Chaperone subgraph-25 HSPB2 134 0 white rectangle gene 0.5 black 46 17 5247 N-Chaperone subgraph-26 HSPB3 129 13 white rectangle gene 0.5 black 46 17 5248 N-Chaperone subgraph-27 HSPD1 3 53 white rectangle gene 0.5 black 46 17 5261 N-Chaperone subgraph-28 IFNG 62 132 white rectangle gene 0.5 black 46 17 5438 N-Chaperone subgraph-29 IL1B 68 125 white rectangle gene 0.5 black 46 17 5992 N-Chaperone subgraph-30 CXCL8 138 9 white rectangle gene 0.5 black 46 17 6025 N-Chaperone subgraph-31 APP 89 134 white rectangle gene 0.5 black 46 17 620 N-Chaperone subgraph-32 MAPT 56 38 white rectangle gene 0.5 black 46 17 6893 N-Chaperone subgraph-33 MYC 183 82 white rectangle gene 0.5 black 46 17 7553 N-Chaperone subgraph-34 Atf4 99 146 white rectangle gene 0.5 black 46 17 88096 N-Chaperone subgraph-35 Hspa5 96 141 white rectangle gene 0.5 black 46 17 95835 N-Chaperone subgraph-5 6 HSPB8 BAG3 94 126 white rectangle gene,gene 0.5 black 46 17 30171,/,939 N-Chaperone subgraph-7 8 APP LAMP2 53 136 white rectangle gene,gene 0.5 black 46 17 620,/,6501 N-Chaperone subgraph-9 HSP90 61 44 white rectangle gene 0.5 black 46 17 HSP90 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Chaperone subgraph.sif000066400000000000000000000032661426625374700274500ustar00rootroot000000000000000 1 2 N-Chaperone subgraph-28 activation N-Chaperone subgraph-7 8 N-Chaperone subgraph-28 inhibition N-Chaperone subgraph-11 N-Chaperone subgraph-1 2 activation N-Chaperone subgraph-32 N-Chaperone subgraph-10 activation N-Chaperone subgraph-32 N-Chaperone subgraph-31 inhibition N-Chaperone subgraph-11 N-Chaperone subgraph-31 activation N-Chaperone subgraph-35 N-Chaperone subgraph-26 activation N-Chaperone subgraph-30 N-Chaperone subgraph-26 activation N-Chaperone subgraph-15 N-Chaperone subgraph-26 inhibition N-Chaperone subgraph-13 N-Chaperone subgraph-24 inhibition N-Chaperone subgraph-14 N-Chaperone subgraph-34 inhibition N-Chaperone subgraph-35 N-Chaperone subgraph-9 activation N-Chaperone subgraph-32 N-Chaperone subgraph-5 6 inhibition N-Chaperone subgraph-31 N-Chaperone subgraph-20 activation N-Chaperone subgraph-31 N-Chaperone subgraph-12 activation N-Chaperone subgraph-31 N-Chaperone subgraph-29 inhibition N-Chaperone subgraph-11 N-Chaperone subgraph-27 inhibition N-Chaperone subgraph-14 N-Chaperone subgraph-16 inhibition N-Chaperone subgraph-32 N-Chaperone subgraph-16 activation N-Chaperone subgraph-17 N-Chaperone subgraph-16 activation N-Chaperone subgraph-21 N-Chaperone subgraph-16 inhibition N-Chaperone subgraph-9 N-Chaperone subgraph-25 activation N-Chaperone subgraph-30 N-Chaperone subgraph-25 activation N-Chaperone subgraph-15 N-Chaperone subgraph-25 inhibition N-Chaperone subgraph-13 N-Chaperone subgraph-22 activation N-Chaperone subgraph-23 N-Chaperone subgraph-22 activation N-Chaperone subgraph-33 N-Chaperone subgraph-19 activation N-Chaperone subgraph-30 N-Chaperone subgraph-19 activation N-Chaperone subgraph-15 N-Chaperone subgraph-19 inhibition N-Chaperone subgraph-13 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Chemokine signaling subgraph.att000066400000000000000000000046651426625374700314150ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Chemokine signaling subgraph-11 CXCL12 90 0 white rectangle gene 0.5 black 46 17 10672 N-Chemokine signaling subgraph-13 CCR2 12 103 white rectangle gene 0.5 black 46 17 1603 N-Chemokine signaling subgraph-14 CCR5 0 103 white rectangle gene 0.5 black 46 17 1606 N-Chemokine signaling subgraph-15 CCR7 164 49 white rectangle gene 0.5 black 46 17 1608 N-Chemokine signaling subgraph-16 TREM2 160 42 white rectangle gene 0.5 black 46 17 17761 N-Chemokine signaling subgraph-17 CSF1R 38 52 white rectangle gene 0.5 black 46 17 2433 N-Chemokine signaling subgraph-18 HSPB6 8 93 white rectangle gene 0.5 black 46 17 26511 N-Chemokine signaling subgraph-19 IL34 35 59 white rectangle gene 0.5 black 46 17 28529 N-Chemokine signaling subgraph-20 CXCL1 51 115 white rectangle gene 0.5 black 46 17 4602 N-Chemokine signaling subgraph-21 CXCL2 43 131 white rectangle gene 0.5 black 46 17 4603 N-Chemokine signaling subgraph-22 GSK3B 46 107 white rectangle gene 0.5 black 46 17 4617 N-Chemokine signaling subgraph-23 HSPB2 2 111 white rectangle gene 0.5 black 46 17 5247 N-Chemokine signaling subgraph-24 HSPB3 2 95 white rectangle gene 0.5 black 46 17 5248 N-Chemokine signaling subgraph-25 IL10 24 119 white rectangle gene 0.5 black 46 17 5962 N-Chemokine signaling subgraph-26 IL1B 46 119 white rectangle gene 0.5 black 46 17 5992 N-Chemokine signaling subgraph-27 APP 33 67 white rectangle gene 0.5 black 46 17 620 N-Chemokine signaling subgraph-28 NFKB1 65 96 white rectangle gene 0.5 black 46 17 7794 N-Chemokine signaling subgraph-29 NFKBIA 97 1 white rectangle gene 0.5 black 46 17 7797 N-Chemokine signaling subgraph-3 4 Gfap Il12b 122 101 white rectangle gene,gene 0.5 black 46 17 95697,/,96540 N-Chemokine signaling subgraph-30 Cxcr3 133 104 white rectangle gene 0.5 black 46 17 1277207 N-Chemokine signaling subgraph-31 Il12a 140 98 white rectangle gene 0.5 black 46 17 96539 N-Chemokine signaling subgraph-32 Il12b 142 110 white rectangle gene 0.5 black 46 17 96540 N-Chemokine signaling subgraph-5 CCL2 31 111 white rectangle gene 0.5 black 46 17 10618 N-Chemokine signaling subgraph-6 CCL20 52 130 white rectangle gene 0.5 black 46 17 10619 N-Chemokine signaling subgraph-7 CCL3 73 87 white rectangle gene 0.5 black 46 17 10627 N-Chemokine signaling subgraph-8 CCL5 56 99 white rectangle gene 0.5 black 46 17 10632 N-Chemokine signaling subgraph-9 CXCL10 56 108 white rectangle gene 0.5 black 46 17 10637 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Chemokine signaling subgraph.sif000066400000000000000000000036631426625374700314030ustar00rootroot000000000000000 1 2 N-Chemokine signaling subgraph-24 activation N-Chemokine signaling subgraph-13 N-Chemokine signaling subgraph-25 activation N-Chemokine signaling subgraph-5 N-Chemokine signaling subgraph-26 activation N-Chemokine signaling subgraph-9 N-Chemokine signaling subgraph-26 activation N-Chemokine signaling subgraph-20 N-Chemokine signaling subgraph-26 activation N-Chemokine signaling subgraph-5 N-Chemokine signaling subgraph-26 activation N-Chemokine signaling subgraph-6 N-Chemokine signaling subgraph-26 activation N-Chemokine signaling subgraph-21 N-Chemokine signaling subgraph-14 inhibition N-Chemokine signaling subgraph-13 N-Chemokine signaling subgraph-23 activation N-Chemokine signaling subgraph-13 N-Chemokine signaling subgraph-30 activation N-Chemokine signaling subgraph-31 N-Chemokine signaling subgraph-30 activation N-Chemokine signaling subgraph-32 N-Chemokine signaling subgraph-30 activation N-Chemokine signaling subgraph-3 4 N-Chemokine signaling subgraph-16 activation N-Chemokine signaling subgraph-15 N-Chemokine signaling subgraph-11 activation N-Chemokine signaling subgraph-29 N-Chemokine signaling subgraph-22 activation N-Chemokine signaling subgraph-9 N-Chemokine signaling subgraph-22 activation N-Chemokine signaling subgraph-5 N-Chemokine signaling subgraph-22 activation N-Chemokine signaling subgraph-8 N-Chemokine signaling subgraph-22 activation N-Chemokine signaling subgraph-20 N-Chemokine signaling subgraph-19 activation N-Chemokine signaling subgraph-17 N-Chemokine signaling subgraph-19 inhibition N-Chemokine signaling subgraph-27 N-Chemokine signaling subgraph-18 activation N-Chemokine signaling subgraph-13 N-Chemokine signaling subgraph-28 activation N-Chemokine signaling subgraph-9 N-Chemokine signaling subgraph-28 activation N-Chemokine signaling subgraph-7 N-Chemokine signaling subgraph-28 activation N-Chemokine signaling subgraph-8 N-Chemokine signaling subgraph-5 activation N-Chemokine signaling subgraph-13 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cholesterol metabolism subgraph.att000066400000000000000000000024771426625374700321560ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Cholesterol metabolism subgraph-13 DHCR24 108 85 white rectangle gene 0.5 black 46 17 2859 N-Cholesterol metabolism subgraph-14 ABCA7 111 111 white rectangle gene 0.5 black 46 17 37 N-Cholesterol metabolism subgraph-17 APP 89 128 white rectangle gene 0.5 black 46 17 620 N-Cholesterol metabolism subgraph-18 APP 67 98 white rectangle gene 0.5 black 46 17 620 N-Cholesterol metabolism subgraph-19 LDLR 94 64 white rectangle gene 0.5 black 46 17 6547 N-Cholesterol metabolism subgraph-20 MAPT 31 129 white rectangle gene 0.5 black 46 17 6893 N-Cholesterol metabolism subgraph-21 MAPT 26 110 white rectangle gene 0.5 black 46 17 6893 N-Cholesterol metabolism subgraph-22 MAPT 77 157 white rectangle gene 0.5 black 46 17 6893 N-Cholesterol metabolism subgraph-23 MAPT 19 91 white rectangle gene 0.5 black 46 17 6893 N-Cholesterol metabolism subgraph-25 PRKAA1 86 198 white rectangle gene 0.5 black 46 17 9376 N-Cholesterol metabolism subgraph-3 CAMK 47 0 white rectangle gene 0.5 black 46 17 CAMK N-Cholesterol metabolism subgraph-4 PRKAC 0 117 white rectangle gene 0.5 black 46 17 PRKAC N-Cholesterol metabolism subgraph-5 PRKAC 57 63 white rectangle gene 0.5 black 46 17 PRKAC N-Cholesterol metabolism subgraph-6 STK11 55 41 white rectangle gene 0.5 black 46 17 11389 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cholesterol metabolism subgraph.sif000066400000000000000000000025151426625374700321400ustar00rootroot000000000000000 1 2 N-Cholesterol metabolism subgraph-13 inhibition N-Cholesterol metabolism subgraph-18 N-Cholesterol metabolism subgraph-3 activation N-Cholesterol metabolism subgraph-6 N-Cholesterol metabolism subgraph-18 inhibition N-Cholesterol metabolism subgraph-19 N-Cholesterol metabolism subgraph-21 activation N-Cholesterol metabolism subgraph-18 N-Cholesterol metabolism subgraph-23 activation N-Cholesterol metabolism subgraph-18 N-Cholesterol metabolism subgraph-5 activation N-Cholesterol metabolism subgraph-18 N-Cholesterol metabolism subgraph-6 activation N-Cholesterol metabolism subgraph-5 N-Cholesterol metabolism subgraph-6 activation N-Cholesterol metabolism subgraph-18 N-Cholesterol metabolism subgraph-4 activation N-Cholesterol metabolism subgraph-20 N-Cholesterol metabolism subgraph-4 activation N-Cholesterol metabolism subgraph-23 N-Cholesterol metabolism subgraph-4 activation N-Cholesterol metabolism subgraph-21 N-Cholesterol metabolism subgraph-14 inhibition N-Cholesterol metabolism subgraph-18 N-Cholesterol metabolism subgraph-25 activation N-Cholesterol metabolism subgraph-22 N-Cholesterol metabolism subgraph-20 activation N-Cholesterol metabolism subgraph-18 N-Cholesterol metabolism subgraph-22 activation N-Cholesterol metabolism subgraph-18 N-Cholesterol metabolism subgraph-17 activation N-Cholesterol metabolism subgraph-18 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Complement system subgraph.att000066400000000000000000000015021426625374700311520ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Complement system subgraph-10 APP 89 150 white rectangle gene 0.5 black 46 17 620 N-Complement system subgraph-11 APP 75 164 white rectangle gene 0.5 black 46 17 620 N-Complement system subgraph-12 NFKB1 140 37 white rectangle gene 0.5 black 46 17 7794 N-Complement system subgraph-3 CCL2 15 0 white rectangle gene 0.5 black 46 17 10618 N-Complement system subgraph-4 C5 158 32 white rectangle gene 0.5 black 46 17 1331 N-Complement system subgraph-5 CASP3 58 167 white rectangle gene 0.5 black 46 17 1504 N-Complement system subgraph-6 CCR2 0 3 white rectangle gene 0.5 black 46 17 1603 N-Complement system subgraph-8 IL1B 176 42 white rectangle gene 0.5 black 46 17 5992 N-Complement system subgraph-9 IL6 161 12 white rectangle gene 0.5 black 46 17 6018 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Complement system subgraph.sif000066400000000000000000000007001426625374700311420ustar00rootroot000000000000000 1 2 N-Complement system subgraph-11 activation N-Complement system subgraph-5 N-Complement system subgraph-11 activation N-Complement system subgraph-10 N-Complement system subgraph-12 activation N-Complement system subgraph-4 N-Complement system subgraph-4 activation N-Complement system subgraph-8 N-Complement system subgraph-4 activation N-Complement system subgraph-9 N-Complement system subgraph-3 activation N-Complement system subgraph-6 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cyclin-CDK subgraph.att000066400000000000000000000140161426625374700273660ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Cyclin-CDK subgraph-1 2 microtubule MAPT 103 67 white rectangle gene,gene 0.5 black 46 17 0005874,/,6893 N-Cyclin-CDK subgraph-14 15 CTNNB1 PSEN1 103 115 white rectangle gene,gene 0.5 black 46 17 2514,/,9508 N-Cyclin-CDK subgraph-16 17 Stxbp1 Stx1a 27 146 white rectangle gene,gene 0.5 black 46 17 107363,/,109355 N-Cyclin-CDK subgraph-18 CDK5R1 p25 97 85 white rectangle gene 0.5 black 46 17 CONSO00172 N-Cyclin-CDK subgraph-19 Histone_H1 144 76 white rectangle gene 0.5 black 46 17 Histone_H1 N-Cyclin-CDK subgraph-20 CAPN 112 108 white rectangle gene 0.5 black 46 17 CAPN N-Cyclin-CDK subgraph-21 MEF2 74 60 white rectangle gene 0.5 black 46 17 MEF2 N-Cyclin-CDK subgraph-22 MEF2 80 69 white rectangle gene 0.5 black 46 17 MEF2 N-Cyclin-CDK subgraph-23 FAS 196 123 white rectangle gene 0.5 black 46 17 11920 N-Cyclin-CDK subgraph-24 TP53 80 76 white rectangle gene 0.5 black 46 17 11998 N-Cyclin-CDK subgraph-3 4 CDK5R1 p25 CDK5 100 66 white rectangle gene,gene 0.5 black 46 17 CONSO00172,/,1774 N-Cyclin-CDK subgraph-33 CASP3 191 130 white rectangle gene 0.5 black 46 17 1504 N-Cyclin-CDK subgraph-34 CDC37 114 111 white rectangle gene 0.5 black 46 17 1735 N-Cyclin-CDK subgraph-35 CDK2 96 66 white rectangle gene 0.5 black 46 17 1771 N-Cyclin-CDK subgraph-36 CDK5 103 100 white rectangle gene 0.5 black 46 17 1774 N-Cyclin-CDK subgraph-37 CDK5 0 125 white rectangle gene 0.5 black 46 17 1774 N-Cyclin-CDK subgraph-38 CDK5R1 88 81 white rectangle gene 0.5 black 46 17 1775 N-Cyclin-CDK subgraph-39 CDKN1A 197 127 white rectangle gene 0.5 black 46 17 1784 N-Cyclin-CDK subgraph-40 CDKN1B 61 66 white rectangle gene 0.5 black 46 17 1785 N-Cyclin-CDK subgraph-41 CTNNB1 105 93 white rectangle gene 0.5 black 46 17 2514 N-Cyclin-CDK subgraph-42 DPYSL2 110 112 white rectangle gene 0.5 black 46 17 3014 N-Cyclin-CDK subgraph-43 DPYSL2 97 113 white rectangle gene 0.5 black 46 17 3014 N-Cyclin-CDK subgraph-44 DPYSL2 96 92 white rectangle gene 0.5 black 46 17 3014 N-Cyclin-CDK subgraph-45 EGR1 92 97 white rectangle gene 0.5 black 46 17 3238 N-Cyclin-CDK subgraph-46 GOLGA2 100 92 white rectangle gene 0.5 black 46 17 4425 N-Cyclin-CDK subgraph-47 GSK3B 93 68 white rectangle gene 0.5 black 46 17 4617 N-Cyclin-CDK subgraph-48 IAPP 146 80 white rectangle gene 0.5 black 46 17 5329 N-Cyclin-CDK subgraph-49 APP 8 126 white rectangle gene 0.5 black 46 17 620 N-Cyclin-CDK subgraph-5 6 CDK5 CDK5R1 136 83 white rectangle gene,gene 0.5 black 46 17 1774,/,1775 N-Cyclin-CDK subgraph-51 APP 117 102 white rectangle gene 0.5 black 46 17 620 N-Cyclin-CDK subgraph-52 JUN 113 98 white rectangle gene 0.5 black 46 17 6204 N-Cyclin-CDK subgraph-53 JUN 100 74 white rectangle gene 0.5 black 46 17 6204 N-Cyclin-CDK 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46 17 6893 N-Cyclin-CDK subgraph-78 NEFL 116 106 white rectangle gene 0.5 black 46 17 7739 N-Cyclin-CDK subgraph-79 NOS1 110 91 white rectangle gene 0.5 black 46 17 7872 N-Cyclin-CDK subgraph-80 NOS1 93 94 white rectangle gene 0.5 black 46 17 7872 N-Cyclin-CDK subgraph-81 PIN1 110 102 white rectangle gene 0.5 black 46 17 8988 N-Cyclin-CDK subgraph-82 PPP1CA 78 101 white rectangle gene 0.5 black 46 17 9281 N-Cyclin-CDK subgraph-83 PPP1CA 87 99 white rectangle gene 0.5 black 46 17 9281 N-Cyclin-CDK subgraph-84 PPP1R1B 121 91 white rectangle gene 0.5 black 46 17 9287 N-Cyclin-CDK subgraph-85 PPP2CA 106 109 white rectangle gene 0.5 black 46 17 9299 N-Cyclin-CDK subgraph-86 PSEN1 106 113 white rectangle gene 0.5 black 46 17 9508 N-Cyclin-CDK subgraph-87 RB1 73 73 white rectangle gene 0.5 black 46 17 9884 N-Cyclin-CDK subgraph-88 RB1 53 61 white rectangle gene 0.5 black 46 17 9884 N-Cyclin-CDK subgraph-89 Cdk5 26 141 white rectangle gene 0.5 black 46 17 101765 N-Cyclin-CDK subgraph-90 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activation N-Cyclin-CDK subgraph-73 N-Cyclin-CDK subgraph-62 activation N-Cyclin-CDK subgraph-66 N-Cyclin-CDK subgraph-62 activation N-Cyclin-CDK subgraph-74 N-Cyclin-CDK subgraph-62 activation N-Cyclin-CDK subgraph-71 N-Cyclin-CDK subgraph-62 activation N-Cyclin-CDK subgraph-76 N-Cyclin-CDK subgraph-62 activation N-Cyclin-CDK subgraph-36 N-Cyclin-CDK subgraph-3 4 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-3 4 inhibition N-Cyclin-CDK subgraph-1 2 N-Cyclin-CDK subgraph-40 activation N-Cyclin-CDK subgraph-87 N-Cyclin-CDK subgraph-92 activation N-Cyclin-CDK subgraph-89 N-Cyclin-CDK subgraph-45 activation N-Cyclin-CDK subgraph-36 N-Cyclin-CDK subgraph-18 activation N-Cyclin-CDK subgraph-36 N-Cyclin-CDK subgraph-18 activation N-Cyclin-CDK subgraph-53 N-Cyclin-CDK subgraph-5 6 activation N-Cyclin-CDK subgraph-19 N-Cyclin-CDK subgraph-5 6 activation N-Cyclin-CDK subgraph-84 N-Cyclin-CDK subgraph-5 6 activation N-Cyclin-CDK subgraph-48 N-Cyclin-CDK subgraph-5 6 activation N-Cyclin-CDK subgraph-48 N-Cyclin-CDK subgraph-7 8 9 activation N-Cyclin-CDK subgraph-36 N-Cyclin-CDK subgraph-60 inhibition N-Cyclin-CDK subgraph-58 N-Cyclin-CDK subgraph-60 inhibition N-Cyclin-CDK subgraph-61 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-41 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-41 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-78 N-Cyclin-CDK subgraph-36 inhibition N-Cyclin-CDK subgraph-59 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-64 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-72 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-83 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-69 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-38 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-80 N-Cyclin-CDK subgraph-36 inhibition N-Cyclin-CDK subgraph-79 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-43 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-43 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-44 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-42 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-42 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-46 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-52 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-54 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-55 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-65 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-66 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-67 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-68 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-70 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-70 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-71 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-71 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-73 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-73 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-74 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-74 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-75 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-75 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-76 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-76 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-77 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-84 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-86 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK subgraph-14 15 N-Cyclin-CDK subgraph-36 activation N-Cyclin-CDK 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subgraph-89 inhibition N-Cyclin-CDK subgraph-16 17 N-Cyclin-CDK subgraph-35 activation N-Cyclin-CDK subgraph-63 N-Cyclin-CDK subgraph-39 inhibition N-Cyclin-CDK subgraph-56 N-Cyclin-CDK subgraph-39 inhibition N-Cyclin-CDK subgraph-33 N-Cyclin-CDK subgraph-39 inhibition N-Cyclin-CDK subgraph-23 N-Cyclin-CDK subgraph-20 activation N-Cyclin-CDK subgraph-36 N-Cyclin-CDK subgraph-22 inhibition N-Cyclin-CDK subgraph-21 N-Cyclin-CDK subgraph-85 inhibition N-Cyclin-CDK subgraph-74 N-Cyclin-CDK subgraph-85 inhibition N-Cyclin-CDK subgraph-66 N-Cyclin-CDK subgraph-85 inhibition N-Cyclin-CDK subgraph-68 N-Cyclin-CDK subgraph-85 inhibition N-Cyclin-CDK subgraph-71 N-Cyclin-CDK subgraph-82 inhibition N-Cyclin-CDK subgraph-69 N-Cyclin-CDK subgraph-34 activation N-Cyclin-CDK subgraph-36 N-Cyclin-CDK subgraph-88 activation N-Cyclin-CDK subgraph-40 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cytokine signaling subgraph.att000066400000000000000000000014001426625374700312600ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Cytokine signaling subgraph-10 MAPKAPK2 62 187 white rectangle gene 0.5 black 46 17 6887 N-Cytokine signaling subgraph-11 MOK 116 14 white rectangle gene 0.5 black 46 17 9833 N-Cytokine signaling subgraph-4 CCR7 191 98 white rectangle gene 0.5 black 46 17 1608 N-Cytokine signaling subgraph-5 TREM2 184 111 white rectangle gene 0.5 black 46 17 17761 N-Cytokine signaling subgraph-6 CSF1 118 0 white rectangle gene 0.5 black 46 17 2432 N-Cytokine signaling subgraph-7 CSF2 0 59 white rectangle gene 0.5 black 46 17 2434 N-Cytokine signaling subgraph-8 AGER 12 52 white rectangle gene 0.5 black 46 17 320 N-Cytokine signaling subgraph-9 MAPK14 49 181 white rectangle gene 0.5 black 46 17 6876 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Cytokine signaling 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subgraph-14 MAPT 15 25 white rectangle gene 0.5 black 46 17 6893 N-DKK1 subgraph-15 NAB2 2 17 white rectangle gene 0.5 black 46 17 7627 N-DKK1 subgraph-16 NEFH 31 41 white rectangle gene 0.5 black 46 17 7737 N-DKK1 subgraph-17 BACE1 51 35 white rectangle gene 0.5 black 46 17 933 N-DKK1 subgraph-18 Dkk1 150 11 white rectangle gene 0.5 black 46 17 1307313 N-DKK1 subgraph-19 Mapt 154 0 white rectangle gene 0.5 black 46 17 69329 N-DKK1 subgraph-2 KLF10 0 28 white rectangle gene 0.5 black 46 17 11810 N-DKK1 subgraph-20 Mapt 144 3 white rectangle gene 0.5 black 46 17 69329 N-DKK1 subgraph-21 Mapt 162 18 white rectangle gene 0.5 black 46 17 69329 N-DKK1 subgraph-22 Mapt 164 7 white rectangle gene 0.5 black 46 17 69329 N-DKK1 subgraph-23 Mapt 152 23 white rectangle gene 0.5 black 46 17 69329 N-DKK1 subgraph-4 WNT3A 19 37 white rectangle gene 0.5 black 46 17 15983 N-DKK1 subgraph-5 CLU 50 24 white rectangle gene 0.5 black 46 17 2095 N-DKK1 subgraph-6 DKK1 36 29 white rectangle gene 0.5 black 46 17 2891 N-DKK1 subgraph-7 DKK4 27 3 white rectangle gene 0.5 black 46 17 2894 N-DKK1 subgraph-8 EGR1 25 17 white rectangle gene 0.5 black 46 17 3238 N-DKK1 subgraph-9 FOS 35 12 white rectangle gene 0.5 black 46 17 3796 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/DKK1 subgraph.sif000066400000000000000000000032301426625374700262250ustar00rootroot000000000000000 1 2 N-DKK1 subgraph-18 activation N-DKK1 subgraph-19 N-DKK1 subgraph-18 activation N-DKK1 subgraph-20 N-DKK1 subgraph-18 activation N-DKK1 subgraph-21 N-DKK1 subgraph-18 activation N-DKK1 subgraph-22 N-DKK1 subgraph-18 activation N-DKK1 subgraph-23 N-DKK1 subgraph-11 activation N-DKK1 subgraph-6 N-DKK1 subgraph-11 activation N-DKK1 subgraph-16 N-DKK1 subgraph-11 activation N-DKK1 subgraph-13 N-DKK1 subgraph-5 inhibition N-DKK1 subgraph-6 N-DKK1 subgraph-7 activation N-DKK1 subgraph-8 N-DKK1 subgraph-7 activation N-DKK1 subgraph-9 N-DKK1 subgraph-12 activation N-DKK1 subgraph-6 N-DKK1 subgraph-12 activation N-DKK1 subgraph-6 N-DKK1 subgraph-12 activation N-DKK1 subgraph-6 N-DKK1 subgraph-12 activation N-DKK1 subgraph-6 N-DKK1 subgraph-12 activation N-DKK1 subgraph-5 N-DKK1 subgraph-12 inhibition N-DKK1 subgraph-5 N-DKK1 subgraph-12 activation N-DKK1 subgraph-5 N-DKK1 subgraph-12 inhibition N-DKK1 subgraph-5 N-DKK1 subgraph-15 activation N-DKK1 subgraph-14 N-DKK1 subgraph-6 activation N-DKK1 subgraph-8 N-DKK1 subgraph-6 activation N-DKK1 subgraph-9 N-DKK1 subgraph-6 activation N-DKK1 subgraph-13 N-DKK1 subgraph-6 activation N-DKK1 subgraph-16 N-DKK1 subgraph-6 activation N-DKK1 subgraph-14 N-DKK1 subgraph-6 activation N-DKK1 subgraph-17 N-DKK1 subgraph-6 activation N-DKK1 subgraph-17 N-DKK1 subgraph-6 activation N-DKK1 subgraph-12 N-DKK1 subgraph-1 activation N-DKK1 subgraph-10 N-DKK1 subgraph-1 activation N-DKK1 subgraph-6 N-DKK1 subgraph-4 inhibition N-DKK1 subgraph-16 N-DKK1 subgraph-4 inhibition N-DKK1 subgraph-13 N-DKK1 subgraph-4 inhibition N-DKK1 subgraph-14 N-DKK1 subgraph-8 activation N-DKK1 subgraph-14 N-DKK1 subgraph-2 activation N-DKK1 subgraph-14 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/DNA synthesis.att000066400000000000000000000012631426625374700263660ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-DNA synthesis-1 2 3 DOCK3 MAP3K5 RAC1 143 176 white rectangle gene,gene,gene 0.5 black 46 17 2989,/,6857,/,9801 N-DNA synthesis-10 PSEN2 119 173 white rectangle gene 0.5 black 46 17 9509 N-DNA synthesis-4 JUN 26 41 white rectangle gene 0.5 black 46 17 6204 N-DNA synthesis-5 MAPK14 12 23 white rectangle gene 0.5 black 46 17 6876 N-DNA synthesis-6 MAPK9 26 17 white rectangle gene 0.5 black 46 17 6886 N-DNA synthesis-7 MAPT 27 0 white rectangle gene 0.5 black 46 17 6893 N-DNA synthesis-8 ATF2 0 9 white rectangle gene 0.5 black 46 17 784 N-DNA synthesis-9 PSEN1 165 166 white rectangle gene 0.5 black 46 17 9508 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/DNA synthesis.sif000066400000000000000000000006071426625374700263600ustar00rootroot000000000000000 1 2 N-DNA synthesis-6 activation N-DNA synthesis-7 N-DNA synthesis-6 activation N-DNA synthesis-4 N-DNA synthesis-6 activation N-DNA synthesis-8 N-DNA synthesis-5 activation N-DNA synthesis-7 N-DNA synthesis-5 activation N-DNA synthesis-4 N-DNA synthesis-5 activation N-DNA synthesis-8 N-DNA synthesis-1 2 3 activation N-DNA synthesis-9 N-DNA synthesis-1 2 3 activation N-DNA synthesis-10 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/DYRK1A subgraph.att000066400000000000000000000034641426625374700265060ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-DYRK1A subgraph-1 2 STXBP1 DYRK1A 5 125 white rectangle gene,gene 0.5 black 46 17 11444,/,3091 N-DYRK1A subgraph-10 NFAT 137 104 white rectangle gene 0.5 black 46 17 NFAT N-DYRK1A subgraph-11 SNCA 125 124 white rectangle gene 0.5 black 46 17 11138 N-DYRK1A subgraph-12 STXBP1 15 120 white rectangle gene 0.5 black 46 17 11444 N-DYRK1A subgraph-13 DYRK1A 123 110 white rectangle gene 0.5 black 46 17 3091 N-DYRK1A subgraph-14 DYRK1A 0 61 white rectangle gene 0.5 black 46 17 3091 N-DYRK1A subgraph-15 AKAP13 102 128 white rectangle gene 0.5 black 46 17 371 N-DYRK1A subgraph-16 APP 122 96 white rectangle gene 0.5 black 46 17 620 N-DYRK1A subgraph-17 MAP3K5 184 146 white rectangle gene 0.5 black 46 17 6857 N-DYRK1A subgraph-18 MAPT 113 101 white rectangle gene 0.5 black 46 17 6893 N-DYRK1A subgraph-19 MAPT 109 121 white rectangle gene 0.5 black 46 17 6893 N-DYRK1A subgraph-20 MAPT 106 116 white rectangle gene 0.5 black 46 17 6893 N-DYRK1A subgraph-21 MAPT 131 97 white rectangle gene 0.5 black 46 17 6893 N-DYRK1A subgraph-22 MAPT 134 121 white rectangle gene 0.5 black 46 17 6893 N-DYRK1A subgraph-23 EIF2AK2 97 120 white rectangle gene 0.5 black 46 17 9437 N-DYRK1A subgraph-24 PSEN1 6 56 white rectangle gene 0.5 black 46 17 9508 N-DYRK1A subgraph-25 Stxbp1 85 14 white rectangle gene 0.5 black 46 17 107363 N-DYRK1A subgraph-26 Dyrk1a 101 0 white rectangle gene 0.5 black 46 17 1330299 N-DYRK1A subgraph-3 4 JKAMP DYRK1A 185 155 white rectangle gene,gene 0.5 black 46 17 20184,/,3091 N-DYRK1A subgraph-5 6 Stxbp1 Dyrk1a 95 9 white rectangle gene,gene 0.5 black 46 17 107363,/,1330299 N-DYRK1A subgraph-7 8 Stxbp1 Stx1a 75 15 white rectangle gene,gene 0.5 black 46 17 107363,/,109355 N-DYRK1A subgraph-9 CREB 138 112 white rectangle gene 0.5 black 46 17 CREB pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/DYRK1A subgraph.sif000066400000000000000000000020561426625374700264730ustar00rootroot000000000000000 1 2 N-DYRK1A subgraph-26 activation N-DYRK1A subgraph-5 6 N-DYRK1A subgraph-24 activation N-DYRK1A subgraph-14 N-DYRK1A subgraph-1 2 activation N-DYRK1A subgraph-12 N-DYRK1A subgraph-23 activation N-DYRK1A subgraph-19 N-DYRK1A subgraph-23 activation N-DYRK1A subgraph-20 N-DYRK1A subgraph-3 4 activation N-DYRK1A subgraph-17 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-22 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-18 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-18 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-21 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-19 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-20 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-10 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-9 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-16 N-DYRK1A subgraph-13 activation N-DYRK1A subgraph-11 N-DYRK1A subgraph-25 activation N-DYRK1A subgraph-7 8 N-DYRK1A subgraph-15 activation N-DYRK1A subgraph-19 N-DYRK1A subgraph-15 activation N-DYRK1A subgraph-20 N-DYRK1A subgraph-5 6 activation N-DYRK1A subgraph-25 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Disaccharide metabolism subgraph.att000066400000000000000000000010171426625374700322230ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Disaccharide metabolism subgraph-1 TPK1 46 22 white rectangle gene 0.5 black 46 17 17358 N-Disaccharide metabolism subgraph-2 MAPT 153 74 white rectangle gene 0.5 black 46 17 6893 N-Disaccharide metabolism subgraph-3 MAPT 100 48 white rectangle gene 0.5 black 46 17 6893 N-Disaccharide metabolism subgraph-4 OGT 199 97 white rectangle gene 0.5 black 46 17 8127 N-Disaccharide metabolism subgraph-5 PDHA1 0 0 white rectangle gene 0.5 black 46 17 8806 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Disaccharide metabolism subgraph.sif000066400000000000000000000005321426625374700322150ustar00rootroot000000000000000 1 2 N-Disaccharide metabolism subgraph-1 activation N-Disaccharide metabolism subgraph-3 N-Disaccharide metabolism subgraph-1 activation N-Disaccharide metabolism subgraph-5 N-Disaccharide metabolism subgraph-4 activation N-Disaccharide metabolism subgraph-2 N-Disaccharide metabolism subgraph-2 inhibition N-Disaccharide metabolism subgraph-3 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Dopaminergic subgraph.att000066400000000000000000000004011426625374700301400ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Dopaminergic subgraph-1 2 CDK5 CDK5R1 0 0 white rectangle gene,gene 0.5 black 46 17 1774,/,1775 N-Dopaminergic subgraph-3 PPP1R1B 112 200 white rectangle gene 0.5 black 46 17 9287 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Dopaminergic subgraph.sif000066400000000000000000000001071426625374700301340ustar00rootroot000000000000000 1 2 N-Dopaminergic subgraph-1 2 activation N-Dopaminergic subgraph-3 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Eicosanoids signaling subgraph.att000066400000000000000000000003731426625374700317430ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Eicosanoids signaling subgraph-1 CREB 121 0 white rectangle gene 0.5 black 46 17 CREB N-Eicosanoids signaling subgraph-2 ALOX5 0 199 white rectangle gene 0.5 black 46 17 435 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Eicosanoids signaling subgraph.sif000066400000000000000000000001271426625374700317310ustar00rootroot000000000000000 1 2 N-Eicosanoids signaling subgraph-2 activation N-Eicosanoids signaling subgraph-1 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Electron transport chain.att000066400000000000000000000040101426625374700305760ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Electron transport chain-10 COX4I2 163 16 white rectangle gene 0.5 black 46 17 16232 N-Electron transport chain-11 CDK5R1 108 89 white rectangle gene 0.5 black 46 17 1775 N-Electron transport chain-12 CYCS 10 98 white rectangle gene 0.5 black 46 17 19986 N-Electron transport chain-13 COX4I1 157 14 white rectangle gene 0.5 black 46 17 2265 N-Electron transport chain-14 COX5B 158 0 white rectangle gene 0.5 black 46 17 2269 N-Electron transport chain-16 APP 164 7 white rectangle gene 0.5 black 46 17 620 N-Electron transport chain-17 APP 153 10 white rectangle gene 0.5 black 46 17 620 N-Electron transport chain-21 BCL2 18 93 white rectangle gene 0.5 black 46 17 990 N-Electron transport chain-22 Xiap 100 104 white rectangle gene 0.5 black 46 17 107572 N-Electron transport chain-23 Casp3 111 103 white rectangle gene 0.5 black 46 17 107739 N-Electron transport chain-24 Bad 145 128 white rectangle gene 0.5 black 46 17 1096330 N-Electron transport chain-25 Casp9 106 111 white rectangle gene 0.5 black 46 17 1277950 N-Electron transport chain-26 Retn 122 108 white rectangle gene 0.5 black 46 17 1888506 N-Electron transport chain-27 Afap1 111 133 white rectangle gene 0.5 black 46 17 1917542 N-Electron transport chain-28 Bcl2 134 112 white rectangle gene 0.5 black 46 17 88138 N-Electron transport chain-29 Bcl2l1 137 136 white rectangle gene 0.5 black 46 17 88139 N-Electron transport chain-30 Cycs 117 120 white rectangle gene 0.5 black 46 17 88578 N-Electron transport chain-31 Sod1 131 123 white rectangle gene 0.5 black 46 17 98351 N-Electron transport chain-32 Bax 121 114 white rectangle gene 0.5 black 46 17 99702 N-Electron transport chain-4 5 CYCS APAF1 0 103 white rectangle gene,gene 0.5 black 46 17 19986,/,576 N-Electron transport chain-7 SOD1 10 109 white rectangle gene 0.5 black 46 17 11179 N-Electron transport chain-8 CASP3 9 85 white rectangle gene 0.5 black 46 17 1504 N-Electron transport chain-9 CASP9 0 96 white rectangle gene 0.5 black 46 17 1511 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Electron transport chain.sif000066400000000000000000000043441426625374700306010ustar00rootroot000000000000000 1 2 N-Electron transport chain-11 inhibition N-Electron transport chain-23 N-Electron transport chain-11 inhibition N-Electron transport chain-23 N-Electron transport chain-17 inhibition N-Electron transport chain-13 N-Electron transport chain-17 inhibition N-Electron transport chain-10 N-Electron transport chain-17 inhibition N-Electron transport chain-14 N-Electron transport chain-25 activation N-Electron transport chain-23 N-Electron transport chain-30 activation N-Electron transport chain-25 N-Electron transport chain-30 activation N-Electron transport chain-27 N-Electron transport chain-22 inhibition N-Electron transport chain-25 N-Electron transport chain-22 inhibition N-Electron transport chain-25 N-Electron transport chain-22 inhibition N-Electron transport chain-25 N-Electron transport chain-22 inhibition N-Electron transport chain-23 N-Electron transport chain-32 activation N-Electron transport chain-30 N-Electron transport chain-32 activation N-Electron transport chain-23 N-Electron transport chain-26 inhibition N-Electron transport chain-32 N-Electron transport chain-26 inhibition N-Electron transport chain-28 N-Electron transport chain-26 inhibition N-Electron transport chain-23 N-Electron transport chain-26 inhibition N-Electron transport chain-30 N-Electron transport chain-21 activation N-Electron transport chain-12 N-Electron transport chain-7 activation N-Electron transport chain-12 N-Electron transport chain-12 activation N-Electron transport chain-4 5 N-Electron transport chain-12 activation N-Electron transport chain-9 N-Electron transport chain-12 activation N-Electron transport chain-8 N-Electron transport chain-16 inhibition N-Electron transport chain-13 N-Electron transport chain-16 inhibition N-Electron transport chain-10 N-Electron transport chain-16 inhibition N-Electron transport chain-14 N-Electron transport chain-4 5 activation N-Electron transport chain-9 N-Electron transport chain-31 activation N-Electron transport chain-30 N-Electron transport chain-31 inhibition N-Electron transport chain-28 N-Electron transport chain-31 inhibition N-Electron transport chain-29 N-Electron transport chain-31 activation N-Electron transport chain-32 N-Electron transport chain-31 activation N-Electron transport chain-24 Endoplasmic reticulum-Golgi protein export.att000066400000000000000000000015531426625374700341270ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/outputID label X Y color shape type label.cex label.color width height genesList N-Endoplasmic reticulum-Golgi protein export-1 VPS35 65 168 white rectangle gene 0.5 black 46 17 13487 N-Endoplasmic reticulum-Golgi protein export-2 CST3 131 46 white rectangle gene 0.5 black 46 17 2475 N-Endoplasmic reticulum-Golgi protein export-3 CST3 148 36 white rectangle gene 0.5 black 46 17 2475 N-Endoplasmic reticulum-Golgi protein export-4 ERN1 0 0 white rectangle gene 0.5 black 46 17 3449 N-Endoplasmic reticulum-Golgi protein export-5 APP 44 173 white rectangle gene 0.5 black 46 17 620 N-Endoplasmic reticulum-Golgi protein export-6 APP 49 194 white rectangle gene 0.5 black 46 17 620 N-Endoplasmic reticulum-Golgi protein export-7 APP 21 172 white rectangle gene 0.5 black 46 17 620 N-Endoplasmic reticulum-Golgi protein export-8 PSEN1 15 9 white rectangle gene 0.5 black 46 17 9508 Endoplasmic reticulum-Golgi protein export.sif000066400000000000000000000010231426625374700341100ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output0 1 2 N-Endoplasmic reticulum-Golgi protein export-8 activation N-Endoplasmic reticulum-Golgi protein export-4 N-Endoplasmic reticulum-Golgi protein export-5 activation N-Endoplasmic reticulum-Golgi protein export-7 N-Endoplasmic reticulum-Golgi protein export-5 activation N-Endoplasmic reticulum-Golgi protein export-6 N-Endoplasmic reticulum-Golgi protein export-3 inhibition N-Endoplasmic reticulum-Golgi protein export-2 N-Endoplasmic reticulum-Golgi protein export-1 activation N-Endoplasmic reticulum-Golgi protein export-5 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Endosomal lysosomal subgraph.att000066400000000000000000000040311426625374700314660ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Endosomal lysosomal subgraph-13 14 APOE LRP8 48 54 white rectangle gene,gene 0.5 black 46 17 613,/,6700 N-Endosomal lysosomal subgraph-15 16 APP LDLR 57 138 white rectangle gene,gene 0.5 black 46 17 620,/,6547 N-Endosomal lysosomal subgraph-17 YENPTY endocytosis motif (APP) 73 159 white rectangle gene 0.5 black 46 17 CONSO00041 N-Endosomal lysosomal subgraph-19 TGFB1 130 140 white rectangle gene 0.5 black 46 17 11766 N-Endosomal lysosomal subgraph-20 TP53 139 152 white rectangle gene 0.5 black 46 17 11998 N-Endosomal lysosomal subgraph-21 VPS35 37 98 white rectangle gene 0.5 black 46 17 13487 N-Endosomal lysosomal subgraph-24 GGA3 0 23 white rectangle gene 0.5 black 46 17 17079 N-Endosomal lysosomal subgraph-25 GGA1 15 0 white rectangle gene 0.5 black 46 17 17842 N-Endosomal lysosomal subgraph-26 CLU 114 156 white rectangle gene 0.5 black 46 17 2095 N-Endosomal lysosomal subgraph-27 CTSD 69 84 white rectangle gene 0.5 black 46 17 2529 N-Endosomal lysosomal subgraph-28 DKK1 97 148 white rectangle gene 0.5 black 46 17 2891 N-Endosomal lysosomal subgraph-29 IL1A 127 179 white rectangle gene 0.5 black 46 17 5991 N-Endosomal lysosomal subgraph-30 IL1B 112 181 white rectangle gene 0.5 black 46 17 5992 N-Endosomal lysosomal subgraph-31 APP 58 96 white rectangle gene 0.5 black 46 17 620 N-Endosomal lysosomal subgraph-32 APP 78 137 white rectangle gene 0.5 black 46 17 620 N-Endosomal lysosomal subgraph-33 LDLR 59 154 white rectangle gene 0.5 black 46 17 6547 N-Endosomal lysosomal subgraph-34 LRP2 137 167 white rectangle gene 0.5 black 46 17 6694 N-Endosomal lysosomal subgraph-35 LRP8 67 52 white rectangle gene 0.5 black 46 17 6700 N-Endosomal lysosomal subgraph-36 BACE1 21 22 white rectangle gene 0.5 black 46 17 933 N-Endosomal lysosomal subgraph-37 BACE1 26 0 white rectangle gene 0.5 black 46 17 933 N-Endosomal lysosomal subgraph-38 BACE1 0 12 white rectangle gene 0.5 black 46 17 933 N-Endosomal lysosomal subgraph-39 BACE2 65 38 white rectangle gene 0.5 black 46 17 934 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Endosomal lysosomal subgraph.sif000066400000000000000000000060361426625374700314660ustar00rootroot000000000000000 1 2 N-Endosomal lysosomal subgraph-26 inhibition N-Endosomal lysosomal subgraph-32 N-Endosomal lysosomal subgraph-26 inhibition N-Endosomal lysosomal subgraph-32 N-Endosomal lysosomal subgraph-26 inhibition N-Endosomal lysosomal subgraph-28 N-Endosomal lysosomal subgraph-26 activation N-Endosomal lysosomal subgraph-20 N-Endosomal lysosomal subgraph-13 14 activation N-Endosomal lysosomal subgraph-31 N-Endosomal lysosomal subgraph-13 14 activation N-Endosomal lysosomal subgraph-36 N-Endosomal lysosomal subgraph-13 14 activation N-Endosomal lysosomal subgraph-39 N-Endosomal lysosomal subgraph-13 14 activation N-Endosomal lysosomal subgraph-35 N-Endosomal lysosomal subgraph-38 activation N-Endosomal lysosomal subgraph-24 N-Endosomal lysosomal subgraph-38 activation N-Endosomal lysosomal subgraph-36 N-Endosomal lysosomal subgraph-32 activation N-Endosomal lysosomal subgraph-32 N-Endosomal lysosomal subgraph-32 activation N-Endosomal lysosomal subgraph-28 N-Endosomal lysosomal subgraph-32 activation N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-32 inhibition N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-32 activation N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-32 inhibition N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-33 activation N-Endosomal lysosomal subgraph-32 N-Endosomal lysosomal subgraph-29 activation N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-24 activation N-Endosomal lysosomal subgraph-36 N-Endosomal lysosomal subgraph-30 activation N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-30 activation N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-15 16 activation N-Endosomal lysosomal subgraph-32 N-Endosomal lysosomal subgraph-34 activation N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-19 activation N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-19 activation N-Endosomal lysosomal subgraph-26 N-Endosomal lysosomal subgraph-17 inhibition N-Endosomal lysosomal subgraph-32 N-Endosomal lysosomal subgraph-37 activation N-Endosomal lysosomal subgraph-25 N-Endosomal lysosomal subgraph-37 activation N-Endosomal lysosomal subgraph-36 N-Endosomal lysosomal subgraph-21 activation N-Endosomal lysosomal subgraph-31 N-Endosomal lysosomal subgraph-27 activation N-Endosomal lysosomal subgraph-31 N-Endosomal lysosomal subgraph-27 activation N-Endosomal lysosomal subgraph-31 N-Endosomal lysosomal subgraph-27 activation N-Endosomal lysosomal subgraph-31 N-Endosomal lysosomal subgraph-27 activation N-Endosomal lysosomal subgraph-31 N-Endosomal lysosomal subgraph-27 activation N-Endosomal lysosomal subgraph-31 N-Endosomal lysosomal subgraph-25 activation N-Endosomal lysosomal subgraph-36 N-Endosomal lysosomal subgraph-25 activation N-Endosomal lysosomal subgraph-36 N-Endosomal lysosomal subgraph-31 activation N-Endosomal lysosomal subgraph-32 N-Endosomal lysosomal subgraph-31 activation N-Endosomal lysosomal subgraph-32 N-Endosomal lysosomal subgraph-31 activation N-Endosomal lysosomal subgraph-32 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Endothelin subgraph.att000066400000000000000000000010301426625374700276270ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Endothelin subgraph-1 ECE2 112 130 white rectangle gene 0.5 black 46 17 13275 N-Endothelin subgraph-2 ECE1 22 69 white rectangle gene 0.5 black 46 17 3146 N-Endothelin subgraph-3 EDN1 45 132 white rectangle gene 0.5 black 46 17 3176 N-Endothelin subgraph-4 F2 0 199 white rectangle gene 0.5 black 46 17 3535 N-Endothelin subgraph-5 IDE 135 0 white rectangle gene 0.5 black 46 17 5381 N-Endothelin subgraph-6 APP 89 67 white rectangle gene 0.5 black 46 17 620 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Endothelin subgraph.sif000066400000000000000000000015001426625374700276220ustar00rootroot000000000000000 1 2 N-Endothelin subgraph-6 activation N-Endothelin subgraph-2 N-Endothelin subgraph-6 activation N-Endothelin subgraph-3 N-Endothelin subgraph-6 activation N-Endothelin subgraph-3 N-Endothelin subgraph-6 activation N-Endothelin subgraph-3 N-Endothelin subgraph-6 activation N-Endothelin subgraph-1 N-Endothelin subgraph-4 activation N-Endothelin subgraph-3 N-Endothelin subgraph-4 activation N-Endothelin subgraph-3 N-Endothelin subgraph-2 activation N-Endothelin subgraph-6 N-Endothelin subgraph-2 activation N-Endothelin subgraph-3 N-Endothelin subgraph-2 activation N-Endothelin subgraph-3 N-Endothelin subgraph-1 activation N-Endothelin subgraph-3 N-Endothelin subgraph-1 activation N-Endothelin subgraph-3 N-Endothelin subgraph-1 activation N-Endothelin subgraph-3 N-Endothelin subgraph-5 activation N-Endothelin subgraph-6 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Epigenetic modification subgraph.att000066400000000000000000000006671426625374700322570ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Epigenetic modification subgraph-1 SP1 0 21 white rectangle gene 0.5 black 46 17 11205 N-Epigenetic modification subgraph-3 DNMT1 14 0 white rectangle gene 0.5 black 46 17 2976 N-Epigenetic modification subgraph-5 MTHFR 170 98 white rectangle gene 0.5 black 46 17 7436 N-Epigenetic modification subgraph-6 MTHFR 195 112 white rectangle gene 0.5 black 46 17 7436 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Epigenetic modification subgraph.sif000066400000000000000000000002601426625374700322350ustar00rootroot000000000000000 1 2 N-Epigenetic modification subgraph-3 activation N-Epigenetic modification subgraph-1 N-Epigenetic modification subgraph-6 inhibition N-Epigenetic modification subgraph-5 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Estrogen subgraph.att000066400000000000000000000007071426625374700273360ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Estrogen subgraph-1 CCND1 0 139 white rectangle gene 0.5 black 46 17 1582 N-Estrogen subgraph-3 GSK3B 163 28 white rectangle gene 0.5 black 46 17 4617 N-Estrogen subgraph-4 GSK3B 155 0 white rectangle gene 0.5 black 46 17 4617 N-Estrogen subgraph-5 MAPT 166 58 white rectangle gene 0.5 black 46 17 6893 N-Estrogen subgraph-6 RB1 14 160 white rectangle gene 0.5 black 46 17 9884 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Estrogen subgraph.sif000066400000000000000000000002531426625374700273230ustar00rootroot000000000000000 1 2 N-Estrogen subgraph-4 inhibition N-Estrogen subgraph-3 N-Estrogen subgraph-6 activation N-Estrogen subgraph-1 N-Estrogen subgraph-3 activation N-Estrogen subgraph-5 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Free radical formation subgraph.att000066400000000000000000000003761426625374700317720ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Free radical formation subgraph-3 HSD17B10 199 75 white rectangle gene 0.5 black 46 17 4800 N-Free radical formation subgraph-5 APP 0 0 white rectangle gene 0.5 black 46 17 620 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Free radical formation subgraph.sif000066400000000000000000000001311426625374700317500ustar00rootroot000000000000000 1 2 N-Free radical formation subgraph-5 inhibition N-Free radical formation subgraph-3 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/G-protein-mediated signaling.att000066400000000000000000000024341426625374700313250ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-G-protein-mediated signaling-1 2 3 PIK3R5 GSK3A PDPK1 113 31 white rectangle gene,gene,gene 0.5 black 46 17 30035,/,4616,/,8816 N-G-protein-mediated signaling-10 RPS6KA1 151 124 white rectangle gene 0.5 black 46 17 10430 N-G-protein-mediated signaling-11 RPS6KA3 130 137 white rectangle gene 0.5 black 46 17 10432 N-G-protein-mediated signaling-12 CYSLTR1 46 6 white rectangle gene 0.5 black 46 17 17451 N-G-protein-mediated signaling-13 GPR3 0 68 white rectangle gene 0.5 black 46 17 4484 N-G-protein-mediated signaling-14 IGF1 120 20 white rectangle gene 0.5 black 46 17 5464 N-G-protein-mediated signaling-15 APP 52 0 white rectangle gene 0.5 black 46 17 620 N-G-protein-mediated signaling-16 ARRB2 8 64 white rectangle gene 0.5 black 46 17 712 N-G-protein-mediated signaling-17 PRKACA 145 138 white rectangle gene 0.5 black 46 17 9380 N-G-protein-mediated signaling-4 5 6 PIK3R5 GSK3B PDPK1 126 10 white rectangle gene,gene,gene 0.5 black 46 17 30035,/,4617,/,8816 N-G-protein-mediated signaling-7 CAMK 141 113 white rectangle gene 0.5 black 46 17 CAMK N-G-protein-mediated signaling-8 CREB 137 125 white rectangle gene 0.5 black 46 17 CREB N-G-protein-mediated signaling-9 ERK 125 123 white rectangle gene 0.5 black 46 17 ERK pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/G-protein-mediated signaling.sif000066400000000000000000000013141426625374700313120ustar00rootroot000000000000000 1 2 N-G-protein-mediated signaling-16 activation N-G-protein-mediated signaling-13 N-G-protein-mediated signaling-17 activation N-G-protein-mediated signaling-8 N-G-protein-mediated signaling-7 activation N-G-protein-mediated signaling-8 N-G-protein-mediated signaling-15 activation N-G-protein-mediated signaling-12 N-G-protein-mediated signaling-9 activation N-G-protein-mediated signaling-8 N-G-protein-mediated signaling-10 activation N-G-protein-mediated signaling-8 N-G-protein-mediated signaling-14 activation N-G-protein-mediated signaling-1 2 3 N-G-protein-mediated signaling-14 activation N-G-protein-mediated signaling-4 5 6 N-G-protein-mediated signaling-11 activation N-G-protein-mediated signaling-8 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/GABA subgraph.att000066400000000000000000000047021426625374700262410ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-GABA subgraph-15 EGR1 133 138 white rectangle gene 0.5 black 46 17 3238 N-GABA subgraph-16 GABBR1 14 74 white rectangle gene 0.5 black 46 17 4070 N-GABA subgraph-17 GABRA1 17 49 white rectangle gene 0.5 black 46 17 4075 N-GABA subgraph-18 GABRA2 8 46 white rectangle gene 0.5 black 46 17 4076 N-GABA subgraph-19 GABRA3 3 57 white rectangle gene 0.5 black 46 17 4077 N-GABA subgraph-2 TNF 19 62 white rectangle gene 0.5 black 46 17 11892 N-GABA subgraph-20 GABRA4 23 75 white rectangle gene 0.5 black 46 17 4078 N-GABA subgraph-21 GABRA5 30 70 white rectangle gene 0.5 black 46 17 4079 N-GABA subgraph-22 GABRA6 27 81 white rectangle gene 0.5 black 46 17 4080 N-GABA subgraph-23 GABRB1 10 54 white rectangle gene 0.5 black 46 17 4081 N-GABA subgraph-24 GABRB2 2 51 white rectangle gene 0.5 black 46 17 4082 N-GABA subgraph-25 GABRB3 23 43 white rectangle gene 0.5 black 46 17 4083 N-GABA subgraph-26 GABRD 8 66 white rectangle gene 0.5 black 46 17 4084 N-GABA subgraph-27 GABRE 7 78 white rectangle gene 0.5 black 46 17 4085 N-GABA subgraph-28 GABRG1 34 76 white rectangle gene 0.5 black 46 17 4086 N-GABA subgraph-29 GABRG2 24 51 white rectangle gene 0.5 black 46 17 4087 N-GABA subgraph-30 GABRG3 13 81 white rectangle gene 0.5 black 46 17 4088 N-GABA subgraph-31 GABRP 31 62 white rectangle gene 0.5 black 46 17 4089 N-GABA subgraph-34 GRIN2B 129 0 white rectangle gene 0.5 black 46 17 4586 N-GABA subgraph-35 APP 120 5 white rectangle gene 0.5 black 46 17 620 N-GABA subgraph-36 PSEN2 132 146 white rectangle gene 0.5 black 46 17 9509 N-GABA subgraph-37 metabotropic quisqualate receptor 15 42 white rectangle gene 0.5 black 46 17 C073111 N-GABA subgraph-38 NMDA receptor A1 0 68 white rectangle gene 0.5 black 46 17 C101911 N-GABA subgraph-39 NR2A NMDA receptor 4 73 white rectangle gene 0.5 black 46 17 C120997 N-GABA subgraph-40 NR2B NMDA receptor 31 46 white rectangle gene 0.5 black 46 17 C121001 N-GABA subgraph-41 NR1 NMDA receptor 0 61 white rectangle gene 0.5 black 46 17 C409466 N-GABA subgraph-42 NR2C NMDA receptor 20 82 white rectangle gene 0.5 black 46 17 C432823 N-GABA subgraph-43 NR2D NMDA receptor 38 69 white rectangle gene 0.5 black 46 17 C464436 N-GABA subgraph-44 NR3A NMDA receptor 38 55 white rectangle gene 0.5 black 46 17 C464437 N-GABA subgraph-45 NR3B NMDA receptor 38 62 white rectangle gene 0.5 black 46 17 C501155 N-GABA subgraph-5 GABRQ 32 53 white rectangle gene 0.5 black 46 17 14454 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/GABA subgraph.sif000066400000000000000000000025071426625374700262330ustar00rootroot000000000000000 1 2 N-GABA subgraph-15 activation N-GABA subgraph-36 N-GABA subgraph-35 activation N-GABA subgraph-34 N-GABA subgraph-2 activation N-GABA subgraph-38 N-GABA subgraph-2 activation N-GABA subgraph-41 N-GABA subgraph-2 activation N-GABA subgraph-39 N-GABA subgraph-2 activation N-GABA subgraph-40 N-GABA subgraph-2 activation N-GABA subgraph-42 N-GABA subgraph-2 activation N-GABA subgraph-43 N-GABA subgraph-2 activation N-GABA subgraph-44 N-GABA subgraph-2 activation N-GABA subgraph-45 N-GABA subgraph-2 activation N-GABA subgraph-37 N-GABA subgraph-2 inhibition N-GABA subgraph-17 N-GABA subgraph-2 inhibition N-GABA subgraph-18 N-GABA subgraph-2 inhibition N-GABA subgraph-19 N-GABA subgraph-2 inhibition N-GABA subgraph-20 N-GABA subgraph-2 inhibition N-GABA subgraph-21 N-GABA subgraph-2 inhibition N-GABA subgraph-22 N-GABA subgraph-2 inhibition N-GABA subgraph-23 N-GABA subgraph-2 inhibition N-GABA subgraph-24 N-GABA subgraph-2 inhibition N-GABA subgraph-25 N-GABA subgraph-2 inhibition N-GABA subgraph-28 N-GABA subgraph-2 inhibition N-GABA subgraph-29 N-GABA subgraph-2 inhibition N-GABA subgraph-30 N-GABA subgraph-2 inhibition N-GABA subgraph-26 N-GABA subgraph-2 inhibition N-GABA subgraph-27 N-GABA subgraph-2 inhibition N-GABA subgraph-31 N-GABA subgraph-2 inhibition N-GABA subgraph-5 N-GABA subgraph-2 inhibition N-GABA subgraph-16 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/GSK3 subgraph.att000066400000000000000000000156231426625374700262620ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-GSK3 subgraph-1 2 3 4 5 AXIN CSNK1A1 CTNNB1 GSK3B APC 91 97 white rectangle gene,gene,gene,gene,gene 0.5 black 46 17 AXIN,/,2451,/,2514,/,4617,/,583 N-GSK3 subgraph-100 cAMP response element binding (CREB) protein 99 109 white rectangle gene 0.5 black 46 17 IPR001630 N-GSK3 subgraph-101 Amyloidogenic glycoprotein, intracellular domain, conserved site 82 91 white rectangle gene 0.5 black 46 17 IPR019745 N-GSK3 subgraph-102 Cdk5 54 44 white rectangle gene 0.5 black 46 17 101765 N-GSK3 subgraph-103 Gsk3b 71 68 white rectangle gene 0.5 black 46 17 1861437 N-GSK3 subgraph-104 App 61 54 white rectangle gene 0.5 black 46 17 88059 N-GSK3 subgraph-105 Mapt 69 56 white rectangle gene 0.5 black 46 17 97180 N-GSK3 subgraph-12 13 CDH2 PSEN1 108 118 white rectangle gene,gene 0.5 black 46 17 1759,/,9508 N-GSK3 subgraph-14 15 CTNNB1 GSK3B 86 88 white rectangle gene,gene 0.5 black 46 17 2514,/,4617 N-GSK3 subgraph-16 17 CTNNB1 PSEN1 109 146 white rectangle gene,gene 0.5 black 46 17 2514,/,9508 N-GSK3 subgraph-18 19 GSK3B PPP2CA 8 0 white rectangle gene,gene 0.5 black 46 17 4617,/,9299 N-GSK3 subgraph-20 21 IGF1 INSR 78 136 white rectangle gene,gene 0.5 black 46 17 5464,/,6091 N-GSK3 subgraph-22 AKT 63 124 white rectangle gene 0.5 black 46 17 AKT N-GSK3 subgraph-23 AXIN 86 103 white rectangle gene 0.5 black 46 17 AXIN N-GSK3 subgraph-24 GSK3 92 79 white rectangle gene 0.5 black 46 17 GSK3 N-GSK3 subgraph-25 GSK3 120 59 white rectangle gene 0.5 black 46 17 GSK3 N-GSK3 subgraph-26 HSPA 65 102 white rectangle gene 0.5 black 46 17 HSPA N-GSK3 subgraph-27 S100B 44 110 white rectangle gene 0.5 black 46 17 10500 N-GSK3 subgraph-28 CCL2 93 107 white rectangle gene 0.5 black 46 17 10618 N-GSK3 subgraph-29 CCL5 100 124 white rectangle gene 0.5 black 46 17 10632 N-GSK3 subgraph-30 CXCL10 96 135 white rectangle gene 0.5 black 46 17 10637 N-GSK3 subgraph-31 SNCA 67 108 white rectangle gene 0.5 black 46 17 11138 N-GSK3 subgraph-32 BTRC 68 122 white rectangle gene 0.5 black 46 17 1144 N-GSK3 subgraph-33 FRAT2 105 125 white rectangle gene 0.5 black 46 17 16048 N-GSK3 subgraph-34 CDK5 56 104 white rectangle gene 0.5 black 46 17 1774 N-GSK3 subgraph-35 CREB3L4 103 106 white rectangle gene 0.5 black 46 17 18854 N-GSK3 subgraph-36 CSNK1A1 67 140 white rectangle gene 0.5 black 46 17 2451 N-GSK3 subgraph-37 CTNNB1 79 116 white rectangle gene 0.5 black 46 17 2514 N-GSK3 subgraph-38 CTNNB1 75 127 white rectangle gene 0.5 black 46 17 2514 N-GSK3 subgraph-39 CTNNB1 98 84 white rectangle gene 0.5 black 46 17 2514 N-GSK3 subgraph-40 DKK1 29 110 white rectangle gene 0.5 black 46 17 2891 N-GSK3 subgraph-41 DPYSL2 64 120 white rectangle gene 0.5 black 46 17 3014 N-GSK3 subgraph-42 MARK2 126 136 white rectangle gene 0.5 black 46 17 3332 N-GSK3 subgraph-43 MARK2 112 129 white rectangle gene 0.5 black 46 17 3332 N-GSK3 subgraph-44 AKT1 55 114 white rectangle gene 0.5 black 46 17 391 N-GSK3 subgraph-45 GRB2 0 42 white rectangle gene 0.5 black 46 17 4566 N-GSK3 subgraph-46 CXCL1 89 130 white rectangle gene 0.5 black 46 17 4602 N-GSK3 subgraph-47 GSK3A 47 134 white rectangle gene 0.5 black 46 17 4616 N-GSK3 subgraph-48 GSK3A 52 134 white rectangle gene 0.5 black 46 17 4616 N-GSK3 subgraph-49 GSK3B 90 118 white rectangle gene 0.5 black 46 17 4617 N-GSK3 subgraph-50 GSK3B 66 111 white rectangle gene 0.5 black 46 17 4617 N-GSK3 subgraph-51 GSK3B 71 115 white rectangle gene 0.5 black 46 17 4617 N-GSK3 subgraph-52 GSK3B 108 59 white rectangle gene 0.5 black 46 17 4617 N-GSK3 subgraph-53 GYS1 101 68 white rectangle gene 0.5 black 46 17 4706 N-GSK3 subgraph-54 HSF1 94 125 white rectangle gene 0.5 black 46 17 5224 N-GSK3 subgraph-55 IGF1 44 26 white rectangle gene 0.5 black 46 17 5464 N-GSK3 subgraph-56 IGF1R 33 28 white rectangle gene 0.5 black 46 17 5465 N-GSK3 subgraph-57 CXCL8 94 130 white rectangle gene 0.5 black 46 17 6025 N-GSK3 subgraph-58 INS 14 9 white rectangle gene 0.5 black 46 17 6081 N-GSK3 subgraph-59 INSR 21 22 white rectangle gene 0.5 black 46 17 6091 N-GSK3 subgraph-6 7 8 SNCA GSK3B MAPT 82 107 white rectangle gene,gene,gene 0.5 black 46 17 11138,/,4617,/,6893 N-GSK3 subgraph-60 IRS1 39 7 white rectangle gene 0.5 black 46 17 6125 N-GSK3 subgraph-61 IRS1 32 14 white rectangle gene 0.5 black 46 17 6125 N-GSK3 subgraph-62 IRS2 14 46 white rectangle gene 0.5 black 46 17 6126 N-GSK3 subgraph-63 IRS2 15 33 white rectangle gene 0.5 black 46 17 6126 N-GSK3 subgraph-64 IRS4 9 38 white rectangle gene 0.5 black 46 17 6128 N-GSK3 subgraph-65 IRS4 22 31 white rectangle gene 0.5 black 46 17 6128 N-GSK3 subgraph-66 APP 103 73 white rectangle gene 0.5 black 46 17 620 N-GSK3 subgraph-67 APP 79 121 white rectangle gene 0.5 black 46 17 620 N-GSK3 subgraph-68 APP 103 115 white rectangle gene 0.5 black 46 17 620 N-GSK3 subgraph-69 LEP 85 126 white rectangle gene 0.5 black 46 17 6553 N-GSK3 subgraph-70 MAPK10 59 107 white rectangle gene 0.5 black 46 17 6872 N-GSK3 subgraph-71 MAPK8 67 87 white rectangle gene 0.5 black 46 17 6881 N-GSK3 subgraph-73 MAPT 35 143 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-74 MAPT 99 130 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-75 MAPT 73 100 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-76 MAPT 107 114 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-77 MAPT 103 129 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-78 MAPT 38 146 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-79 MAPT 98 105 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-80 MAPT 108 122 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-81 MAPT 33 138 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-82 MAPT 91 136 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-83 MAPT 97 114 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-84 MAPT 102 120 white rectangle gene 0.5 black 46 17 6893 N-GSK3 subgraph-85 MYC 87 135 white rectangle gene 0.5 black 46 17 7553 N-GSK3 subgraph-86 NFATC2 84 131 white rectangle gene 0.5 black 46 17 7776 N-GSK3 subgraph-88 PIN1 99 93 white rectangle gene 0.5 black 46 17 8988 N-GSK3 subgraph-89 PPP1R1B 42 98 white rectangle gene 0.5 black 46 17 9287 N-GSK3 subgraph-9 10 11 CDH2 CTNNB1 PSEN1 129 108 white rectangle gene,gene,gene 0.5 black 46 17 1759,/,2514,/,9508 N-GSK3 subgraph-90 PPP2CA 68 147 white rectangle gene 0.5 black 46 17 9299 N-GSK3 subgraph-91 BAD 49 128 white rectangle gene 0.5 black 46 17 936 N-GSK3 subgraph-92 PRKACA 63 90 white rectangle gene 0.5 black 46 17 9380 N-GSK3 subgraph-93 PRKCD 109 67 white rectangle gene 0.5 black 46 17 9399 N-GSK3 subgraph-94 PSEN1 114 111 white rectangle gene 0.5 black 46 17 9508 N-GSK3 subgraph-95 PSEN1 105 110 white rectangle gene 0.5 black 46 17 9508 N-GSK3 subgraph-96 PSEN1 104 135 white rectangle gene 0.5 black 46 17 9508 N-GSK3 subgraph-97 PSEN1 101 137 white rectangle gene 0.5 black 46 17 9508 N-GSK3 subgraph-98 BAX 62 111 white rectangle gene 0.5 black 46 17 959 N-GSK3 subgraph-99 BAX 76 112 white rectangle gene 0.5 black 46 17 959 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/GSK3 subgraph.sif000066400000000000000000000211561426625374700262510ustar00rootroot000000000000000 1 2 N-GSK3 subgraph-61 activation N-GSK3 subgraph-60 N-GSK3 subgraph-99 activation N-GSK3 subgraph-98 N-GSK3 subgraph-51 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-51 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-51 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-51 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-51 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-51 inhibition N-GSK3 subgraph-75 N-GSK3 subgraph-22 activation N-GSK3 subgraph-51 N-GSK3 subgraph-22 activation N-GSK3 subgraph-51 N-GSK3 subgraph-22 activation N-GSK3 subgraph-48 N-GSK3 subgraph-22 activation N-GSK3 subgraph-48 N-GSK3 subgraph-22 inhibition N-GSK3 subgraph-91 N-GSK3 subgraph-22 inhibition N-GSK3 subgraph-91 N-GSK3 subgraph-22 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-22 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-22 inhibition N-GSK3 subgraph-98 N-GSK3 subgraph-22 inhibition N-GSK3 subgraph-47 N-GSK3 subgraph-88 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-88 activation N-GSK3 subgraph-66 N-GSK3 subgraph-26 inhibition N-GSK3 subgraph-75 N-GSK3 subgraph-26 inhibition N-GSK3 subgraph-31 N-GSK3 subgraph-74 activation N-GSK3 subgraph-49 N-GSK3 subgraph-43 activation N-GSK3 subgraph-42 N-GSK3 subgraph-44 activation N-GSK3 subgraph-51 N-GSK3 subgraph-33 activation N-GSK3 subgraph-49 N-GSK3 subgraph-55 activation N-GSK3 subgraph-56 N-GSK3 subgraph-23 activation N-GSK3 subgraph-1 2 3 4 5 N-GSK3 subgraph-23 inhibition N-GSK3 subgraph-37 N-GSK3 subgraph-23 activation N-GSK3 subgraph-14 15 N-GSK3 subgraph-23 activation N-GSK3 subgraph-49 N-GSK3 subgraph-92 activation N-GSK3 subgraph-75 N-GSK3 subgraph-92 activation N-GSK3 subgraph-75 N-GSK3 subgraph-101 activation N-GSK3 subgraph-49 N-GSK3 subgraph-101 activation N-GSK3 subgraph-49 N-GSK3 subgraph-101 activation N-GSK3 subgraph-1 2 3 4 5 N-GSK3 subgraph-101 activation N-GSK3 subgraph-103 N-GSK3 subgraph-96 inhibition N-GSK3 subgraph-16 17 N-GSK3 subgraph-70 activation N-GSK3 subgraph-75 N-GSK3 subgraph-1 2 3 4 5 activation N-GSK3 subgraph-49 N-GSK3 subgraph-1 2 3 4 5 activation N-GSK3 subgraph-49 N-GSK3 subgraph-1 2 3 4 5 activation N-GSK3 subgraph-39 N-GSK3 subgraph-34 activation N-GSK3 subgraph-75 N-GSK3 subgraph-34 activation N-GSK3 subgraph-75 N-GSK3 subgraph-34 activation N-GSK3 subgraph-75 N-GSK3 subgraph-34 activation N-GSK3 subgraph-75 N-GSK3 subgraph-34 activation N-GSK3 subgraph-41 N-GSK3 subgraph-34 activation N-GSK3 subgraph-89 N-GSK3 subgraph-38 activation N-GSK3 subgraph-32 N-GSK3 subgraph-38 activation N-GSK3 subgraph-32 N-GSK3 subgraph-38 inhibition N-GSK3 subgraph-37 N-GSK3 subgraph-38 inhibition N-GSK3 subgraph-37 N-GSK3 subgraph-49 activation N-GSK3 subgraph-1 2 3 4 5 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-75 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 activation N-GSK3 subgraph-38 N-GSK3 subgraph-49 inhibition N-GSK3 subgraph-100 N-GSK3 subgraph-49 activation N-GSK3 subgraph-30 N-GSK3 subgraph-49 activation N-GSK3 subgraph-28 N-GSK3 subgraph-49 activation N-GSK3 subgraph-57 N-GSK3 subgraph-49 activation N-GSK3 subgraph-29 N-GSK3 subgraph-49 activation N-GSK3 subgraph-46 N-GSK3 subgraph-49 activation N-GSK3 subgraph-20 21 N-GSK3 subgraph-49 activation N-GSK3 subgraph-76 N-GSK3 subgraph-49 activation N-GSK3 subgraph-83 N-GSK3 subgraph-49 activation N-GSK3 subgraph-99 N-GSK3 subgraph-49 activation N-GSK3 subgraph-99 N-GSK3 subgraph-49 activation N-GSK3 subgraph-96 N-GSK3 subgraph-49 activation N-GSK3 subgraph-96 N-GSK3 subgraph-49 activation N-GSK3 subgraph-96 N-GSK3 subgraph-49 activation N-GSK3 subgraph-96 N-GSK3 subgraph-49 activation N-GSK3 subgraph-97 N-GSK3 subgraph-49 activation N-GSK3 subgraph-97 N-GSK3 subgraph-49 activation N-GSK3 subgraph-97 N-GSK3 subgraph-49 activation N-GSK3 subgraph-97 N-GSK3 subgraph-49 activation N-GSK3 subgraph-41 N-GSK3 subgraph-49 activation N-GSK3 subgraph-41 N-GSK3 subgraph-49 activation N-GSK3 subgraph-41 N-GSK3 subgraph-49 activation N-GSK3 subgraph-41 N-GSK3 subgraph-49 activation N-GSK3 subgraph-77 N-GSK3 subgraph-49 activation N-GSK3 subgraph-84 N-GSK3 subgraph-49 activation N-GSK3 subgraph-80 N-GSK3 subgraph-49 activation N-GSK3 subgraph-82 N-GSK3 subgraph-49 activation N-GSK3 subgraph-35 N-GSK3 subgraph-49 activation N-GSK3 subgraph-54 N-GSK3 subgraph-49 activation N-GSK3 subgraph-85 N-GSK3 subgraph-49 activation N-GSK3 subgraph-86 N-GSK3 subgraph-49 inhibition N-GSK3 subgraph-37 N-GSK3 subgraph-49 activation N-GSK3 subgraph-43 N-GSK3 subgraph-49 activation N-GSK3 subgraph-43 N-GSK3 subgraph-49 activation N-GSK3 subgraph-79 N-GSK3 subgraph-49 activation N-GSK3 subgraph-79 N-GSK3 subgraph-49 activation N-GSK3 subgraph-95 N-GSK3 subgraph-49 activation N-GSK3 subgraph-95 N-GSK3 subgraph-49 inhibition N-GSK3 subgraph-12 13 N-GSK3 subgraph-49 inhibition N-GSK3 subgraph-94 N-GSK3 subgraph-49 activation N-GSK3 subgraph-68 N-GSK3 subgraph-58 activation N-GSK3 subgraph-59 N-GSK3 subgraph-58 activation N-GSK3 subgraph-18 19 N-GSK3 subgraph-24 activation N-GSK3 subgraph-75 N-GSK3 subgraph-24 activation N-GSK3 subgraph-75 N-GSK3 subgraph-24 activation N-GSK3 subgraph-75 N-GSK3 subgraph-24 activation N-GSK3 subgraph-53 N-GSK3 subgraph-52 activation N-GSK3 subgraph-66 N-GSK3 subgraph-59 activation N-GSK3 subgraph-61 N-GSK3 subgraph-59 activation N-GSK3 subgraph-63 N-GSK3 subgraph-59 activation N-GSK3 subgraph-65 N-GSK3 subgraph-47 activation N-GSK3 subgraph-41 N-GSK3 subgraph-47 activation N-GSK3 subgraph-41 N-GSK3 subgraph-47 activation N-GSK3 subgraph-73 N-GSK3 subgraph-47 activation N-GSK3 subgraph-78 N-GSK3 subgraph-47 activation N-GSK3 subgraph-81 N-GSK3 subgraph-103 activation N-GSK3 subgraph-104 N-GSK3 subgraph-103 activation N-GSK3 subgraph-105 N-GSK3 subgraph-69 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-69 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-102 activation N-GSK3 subgraph-104 N-GSK3 subgraph-67 activation N-GSK3 subgraph-49 N-GSK3 subgraph-67 activation N-GSK3 subgraph-51 N-GSK3 subgraph-50 inhibition N-GSK3 subgraph-49 N-GSK3 subgraph-50 activation N-GSK3 subgraph-75 N-GSK3 subgraph-93 activation N-GSK3 subgraph-25 N-GSK3 subgraph-93 inhibition N-GSK3 subgraph-24 N-GSK3 subgraph-97 inhibition N-GSK3 subgraph-16 17 N-GSK3 subgraph-27 activation N-GSK3 subgraph-50 N-GSK3 subgraph-27 activation N-GSK3 subgraph-40 N-GSK3 subgraph-90 activation N-GSK3 subgraph-20 21 N-GSK3 subgraph-63 activation N-GSK3 subgraph-62 N-GSK3 subgraph-64 activation N-GSK3 subgraph-45 N-GSK3 subgraph-36 activation N-GSK3 subgraph-38 N-GSK3 subgraph-31 activation N-GSK3 subgraph-75 N-GSK3 subgraph-32 inhibition N-GSK3 subgraph-37 N-GSK3 subgraph-32 inhibition N-GSK3 subgraph-37 N-GSK3 subgraph-56 activation N-GSK3 subgraph-65 N-GSK3 subgraph-94 activation N-GSK3 subgraph-9 10 11 N-GSK3 subgraph-94 activation N-GSK3 subgraph-49 N-GSK3 subgraph-6 7 8 activation N-GSK3 subgraph-75 N-GSK3 subgraph-6 7 8 activation N-GSK3 subgraph-49 N-GSK3 subgraph-48 inhibition N-GSK3 subgraph-47 N-GSK3 subgraph-65 activation N-GSK3 subgraph-64 N-GSK3 subgraph-71 activation N-GSK3 subgraph-75 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Gamma secretase subgraph.att000066400000000000000000000166711426625374700305400ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Gamma secretase subgraph-1 2 TCF_LEF CTNNBIP1 97 136 white rectangle gene,gene 0.5 black 46 17 TCF_LEF,/,16913 N-Gamma secretase subgraph-100 ATF4 100 105 white rectangle gene 0.5 black 46 17 786 N-Gamma secretase subgraph-101 NOTCH1 91 86 white rectangle gene 0.5 black 46 17 7881 N-Gamma secretase subgraph-102 ATF6 102 91 white rectangle gene 0.5 black 46 17 791 N-Gamma secretase subgraph-103 PLD1 96 93 white rectangle gene 0.5 black 46 17 9067 N-Gamma secretase subgraph-105 BACE1 114 75 white rectangle gene 0.5 black 46 17 933 N-Gamma secretase subgraph-107 PRKCA 86 0 white rectangle gene 0.5 black 46 17 9393 N-Gamma secretase subgraph-108 PSEN1 92 98 white rectangle gene 0.5 black 46 17 9508 N-Gamma secretase subgraph-109 PSEN1 72 103 white rectangle gene 0.5 black 46 17 9508 N-Gamma secretase subgraph-11 12 13 CDH2 CTNNB1 PSEN1 82 96 white rectangle gene,gene,gene 0.5 black 46 17 1759,/,2514,/,9508 N-Gamma secretase subgraph-110 PSEN1 69 109 white rectangle gene 0.5 black 46 17 9508 N-Gamma secretase subgraph-111 PSEN1 67 112 white rectangle gene 0.5 black 46 17 9508 N-Gamma secretase subgraph-112 PSEN1 84 43 white rectangle gene 0.5 black 46 17 9508 N-Gamma secretase subgraph-113 PSEN1 111 91 white rectangle gene 0.5 black 46 17 9508 N-Gamma secretase subgraph-117 PSEN1 105 120 white rectangle gene 0.5 black 46 17 9508 N-Gamma secretase subgraph-118 PSEN2 94 83 white rectangle gene 0.5 black 46 17 9509 N-Gamma secretase subgraph-119 PSEN2 113 188 white rectangle gene 0.5 black 46 17 9509 N-Gamma secretase subgraph-120 PSEN2 57 156 white rectangle gene 0.5 black 46 17 9509 N-Gamma secretase subgraph-121 PSEN2 112 88 white rectangle gene 0.5 black 46 17 9509 N-Gamma secretase subgraph-122 BCL2 85 105 white rectangle gene 0.5 black 46 17 990 N-Gamma secretase subgraph-14 15 CDH2 PSEN1 78 104 white rectangle gene,gene 0.5 black 46 17 1759,/,9508 N-Gamma secretase subgraph-16 17 CTNNB1 PSEN1 61 113 white rectangle gene,gene 0.5 black 46 17 2514,/,9508 N-Gamma secretase subgraph-18 19 20 CTNNB1 PSEN1 PSEN2 104 72 white rectangle gene,gene,gene 0.5 black 46 17 2514,/,9508,/,9509 N-Gamma secretase subgraph-21 22 CTNND2 PSEN1 100 111 white rectangle gene,gene 0.5 black 46 17 2516,/,9508 N-Gamma secretase subgraph-23 24 25 DOCK3 MAP3K5 RAC1 88 90 white rectangle gene,gene,gene 0.5 black 46 17 2989,/,6857,/,9801 N-Gamma secretase subgraph-3 4 TCF4 PSEN1 90 104 white rectangle gene,gene 0.5 black 46 17 11634,/,9508 N-Gamma secretase subgraph-32 33 PSENEN FTL 82 83 white rectangle gene,gene 0.5 black 46 17 30100,/,3999 N-Gamma secretase subgraph-34 35 EFNB2 EPHB2 134 2 white rectangle gene,gene 0.5 black 46 17 3227,/,3393 N-Gamma secretase subgraph-36 37 EGR1 PSEN2 100 82 white rectangle gene,gene 0.5 black 46 17 3238,/,9509 N-Gamma secretase subgraph-38 39 GRB2 PSEN1 0 60 white rectangle gene,gene 0.5 black 46 17 4566,/,9508 N-Gamma secretase subgraph-40 41 APBB1 APP 133 65 white rectangle gene,gene 0.5 black 46 17 581,/,620 N-Gamma secretase subgraph-42 43 APP PSEN1 111 181 white rectangle gene,gene 0.5 black 46 17 620,/,9508 N-Gamma secretase subgraph-46 47 MAPT PSEN1 66 48 white rectangle gene,gene 0.5 black 46 17 6893,/,9508 N-Gamma secretase subgraph-49 MEK 4 60 white rectangle gene 0.5 black 46 17 MEK N-Gamma secretase subgraph-5 6 NCSTN PSEN1 93 77 white rectangle gene,gene 0.5 black 46 17 17091,/,9508 N-Gamma secretase subgraph-50 Wnt 95 128 white rectangle gene 0.5 black 46 17 Wnt N-Gamma secretase subgraph-51 ROCK2 92 7 white rectangle gene 0.5 black 46 17 10252 N-Gamma secretase subgraph-52 RYR3 100 102 white rectangle gene 0.5 black 46 17 10485 N-Gamma secretase subgraph-53 SORL1 126 89 white rectangle gene 0.5 black 46 17 11185 N-Gamma secretase subgraph-54 SORL1 90 0 white rectangle gene 0.5 black 46 17 11185 N-Gamma secretase subgraph-55 KLF10 64 52 white rectangle gene 0.5 black 46 17 11810 N-Gamma secretase subgraph-56 CAPN2 87 88 white rectangle gene 0.5 black 46 17 1479 N-Gamma secretase subgraph-58 CD44 84 99 white rectangle gene 0.5 black 46 17 1681 N-Gamma secretase subgraph-59 CTNNBIP1 94 115 white rectangle gene 0.5 black 46 17 16913 N-Gamma secretase subgraph-61 CDH2 79 100 white rectangle gene 0.5 black 46 17 1759 N-Gamma secretase subgraph-62 CDK5 85 52 white rectangle gene 0.5 black 46 17 1774 N-Gamma secretase subgraph-63 CSK 97 107 white rectangle gene 0.5 black 46 17 2444 N-Gamma secretase subgraph-64 CSNK1E 59 160 white rectangle gene 0.5 black 46 17 2453 N-Gamma secretase subgraph-65 CSNK2A1 59 150 white rectangle gene 0.5 black 46 17 2457 N-Gamma secretase subgraph-66 CTNNB1 102 82 white rectangle gene 0.5 black 46 17 2514 N-Gamma secretase subgraph-67 CTNNB1 108 74 white rectangle gene 0.5 black 46 17 2514 N-Gamma secretase subgraph-68 DDIT3 87 108 white rectangle gene 0.5 black 46 17 2726 N-Gamma secretase subgraph-69 DKK1 87 57 white rectangle gene 0.5 black 46 17 2891 N-Gamma secretase subgraph-7 8 CDH13 CTNND1 133 11 white rectangle gene,gene 0.5 black 46 17 1753,/,2515 N-Gamma secretase subgraph-70 DKK4 80 60 white rectangle gene 0.5 black 46 17 2894 N-Gamma secretase subgraph-71 APH1A 95 88 white rectangle gene 0.5 black 46 17 29509 N-Gamma secretase subgraph-73 PSENEN 92 89 white rectangle gene 0.5 black 46 17 30100 N-Gamma secretase subgraph-74 DYRK1A 90 109 white rectangle gene 0.5 black 46 17 3091 N-Gamma secretase subgraph-75 EFNB2 96 104 white rectangle gene 0.5 black 46 17 3227 N-Gamma secretase subgraph-76 EFNB2 131 8 white rectangle gene 0.5 black 46 17 3227 N-Gamma secretase subgraph-77 EGR1 86 65 white rectangle gene 0.5 black 46 17 3238 N-Gamma secretase subgraph-78 EIF2A 112 81 white rectangle gene 0.5 black 46 17 3254 N-Gamma secretase subgraph-79 EIF2AK3 104 88 white rectangle gene 0.5 black 46 17 3255 N-Gamma secretase subgraph-80 ERN1 119 89 white rectangle gene 0.5 black 46 17 3449 N-Gamma secretase subgraph-81 ERN1 85 102 white rectangle gene 0.5 black 46 17 3449 N-Gamma secretase subgraph-82 FTL 102 94 white rectangle gene 0.5 black 46 17 3999 N-Gamma secretase subgraph-83 GPR3 42 181 white rectangle gene 0.5 black 46 17 4484 N-Gamma secretase subgraph-84 GSK3B 76 106 white rectangle gene 0.5 black 46 17 4617 N-Gamma secretase subgraph-85 APBA1 123 98 white rectangle gene 0.5 black 46 17 578 N-Gamma secretase subgraph-86 APBA2 126 93 white rectangle gene 0.5 black 46 17 579 N-Gamma secretase subgraph-87 APBA3 125 96 white rectangle gene 0.5 black 46 17 580 N-Gamma secretase subgraph-88 ITPR3 93 108 white rectangle gene 0.5 black 46 17 6182 N-Gamma secretase subgraph-89 APP 99 81 white rectangle gene 0.5 black 46 17 620 N-Gamma secretase subgraph-9 10 CDH13 PSEN1 128 8 white rectangle gene,gene 0.5 black 46 17 1753,/,9508 N-Gamma secretase subgraph-90 APP 115 91 white rectangle gene 0.5 black 46 17 620 N-Gamma secretase subgraph-91 APP 125 69 white rectangle gene 0.5 black 46 17 620 N-Gamma secretase subgraph-92 MAPT 73 55 white rectangle gene 0.5 black 46 17 6893 N-Gamma secretase subgraph-93 MAPT 83 58 white rectangle gene 0.5 black 46 17 6893 N-Gamma secretase subgraph-94 MAPT 78 63 white rectangle gene 0.5 black 46 17 6893 N-Gamma secretase subgraph-95 MAPT 90 57 white rectangle gene 0.5 black 46 17 6893 N-Gamma secretase subgraph-96 ARRB2 45 175 white rectangle gene 0.5 black 46 17 712 N-Gamma secretase subgraph-97 NAB2 69 46 white rectangle gene 0.5 black 46 17 7627 N-Gamma secretase subgraph-98 NFKB1 93 91 white rectangle gene 0.5 black 46 17 7794 N-Gamma secretase subgraph-99 NFKB2 90 91 white rectangle gene 0.5 black 46 17 7795 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Gamma secretase subgraph.sif000066400000000000000000000206641426625374700305260ustar00rootroot000000000000000 1 2 N-Gamma secretase subgraph-65 activation N-Gamma secretase subgraph-120 N-Gamma secretase subgraph-65 activation N-Gamma secretase subgraph-120 N-Gamma secretase subgraph-34 35 activation N-Gamma secretase subgraph-76 N-Gamma secretase subgraph-73 activation N-Gamma secretase subgraph-32 33 N-Gamma secretase subgraph-77 activation N-Gamma secretase subgraph-118 N-Gamma secretase subgraph-77 activation N-Gamma secretase subgraph-92 N-Gamma secretase subgraph-77 activation N-Gamma secretase subgraph-92 N-Gamma secretase subgraph-77 activation N-Gamma secretase subgraph-92 N-Gamma secretase subgraph-77 activation N-Gamma secretase subgraph-62 N-Gamma secretase subgraph-77 activation N-Gamma secretase subgraph-94 N-Gamma secretase subgraph-77 activation N-Gamma secretase subgraph-95 N-Gamma secretase subgraph-77 activation N-Gamma secretase subgraph-93 N-Gamma secretase subgraph-64 activation N-Gamma secretase subgraph-120 N-Gamma secretase subgraph-64 activation N-Gamma secretase subgraph-120 N-Gamma secretase subgraph-107 activation N-Gamma secretase subgraph-54 N-Gamma secretase subgraph-62 activation N-Gamma secretase subgraph-112 N-Gamma secretase subgraph-9 10 inhibition N-Gamma secretase subgraph-7 8 N-Gamma secretase subgraph-38 39 activation N-Gamma secretase subgraph-49 N-Gamma secretase subgraph-36 37 activation N-Gamma secretase subgraph-118 N-Gamma secretase subgraph-66 activation N-Gamma secretase subgraph-18 19 20 N-Gamma secretase subgraph-103 activation N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-86 inhibition N-Gamma secretase subgraph-90 N-Gamma secretase subgraph-5 6 activation N-Gamma secretase subgraph-89 N-Gamma secretase subgraph-5 6 activation N-Gamma secretase subgraph-101 N-Gamma secretase subgraph-111 inhibition N-Gamma secretase subgraph-16 17 N-Gamma secretase subgraph-111 inhibition N-Gamma secretase subgraph-16 17 N-Gamma secretase subgraph-96 activation N-Gamma secretase subgraph-83 N-Gamma secretase subgraph-78 activation N-Gamma secretase subgraph-105 N-Gamma secretase subgraph-117 activation N-Gamma secretase subgraph-21 22 N-Gamma secretase subgraph-97 activation N-Gamma secretase subgraph-92 N-Gamma secretase subgraph-71 activation N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-71 activation N-Gamma secretase subgraph-89 N-Gamma secretase subgraph-71 activation N-Gamma secretase subgraph-101 N-Gamma secretase subgraph-105 activation N-Gamma secretase subgraph-89 N-Gamma secretase subgraph-109 inhibition N-Gamma secretase subgraph-61 N-Gamma secretase subgraph-109 inhibition N-Gamma secretase subgraph-14 15 N-Gamma secretase subgraph-99 activation N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-99 activation N-Gamma secretase subgraph-118 N-Gamma secretase subgraph-113 activation N-Gamma secretase subgraph-90 N-Gamma secretase subgraph-113 inhibition N-Gamma secretase subgraph-80 N-Gamma secretase subgraph-113 inhibition N-Gamma secretase subgraph-79 N-Gamma secretase subgraph-113 inhibition N-Gamma secretase subgraph-102 N-Gamma secretase subgraph-53 inhibition N-Gamma secretase subgraph-90 N-Gamma secretase subgraph-70 activation N-Gamma secretase subgraph-77 N-Gamma secretase subgraph-51 activation N-Gamma secretase subgraph-54 N-Gamma secretase subgraph-69 activation N-Gamma secretase subgraph-77 N-Gamma secretase subgraph-100 activation N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-82 activation N-Gamma secretase subgraph-73 N-Gamma secretase subgraph-82 activation N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-82 activation N-Gamma secretase subgraph-90 N-Gamma secretase subgraph-121 activation N-Gamma secretase subgraph-90 N-Gamma secretase subgraph-121 inhibition N-Gamma secretase subgraph-80 N-Gamma secretase subgraph-121 inhibition N-Gamma secretase subgraph-79 N-Gamma secretase subgraph-121 inhibition N-Gamma secretase subgraph-102 N-Gamma secretase subgraph-46 47 inhibition N-Gamma secretase subgraph-92 N-Gamma secretase subgraph-1 2 activation N-Gamma secretase subgraph-50 N-Gamma secretase subgraph-67 inhibition N-Gamma secretase subgraph-66 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-110 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-110 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-110 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-110 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-111 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-111 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-111 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-111 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-109 N-Gamma secretase subgraph-84 activation N-Gamma secretase subgraph-109 N-Gamma secretase subgraph-84 inhibition N-Gamma secretase subgraph-14 15 N-Gamma secretase subgraph-84 inhibition N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-118 activation N-Gamma secretase subgraph-102 N-Gamma secretase subgraph-118 activation N-Gamma secretase subgraph-79 N-Gamma secretase subgraph-118 activation N-Gamma secretase subgraph-89 N-Gamma secretase subgraph-118 activation N-Gamma secretase subgraph-89 N-Gamma secretase subgraph-118 inhibition N-Gamma secretase subgraph-56 N-Gamma secretase subgraph-118 activation N-Gamma secretase subgraph-73 N-Gamma secretase subgraph-119 inhibition N-Gamma secretase subgraph-42 43 N-Gamma secretase subgraph-89 activation N-Gamma secretase subgraph-90 N-Gamma secretase subgraph-89 inhibition N-Gamma secretase subgraph-77 N-Gamma secretase subgraph-55 activation N-Gamma secretase subgraph-92 N-Gamma secretase subgraph-50 activation N-Gamma secretase subgraph-59 N-Gamma secretase subgraph-75 inhibition N-Gamma secretase subgraph-63 N-Gamma secretase subgraph-23 24 25 activation N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-23 24 25 activation N-Gamma secretase subgraph-118 N-Gamma secretase subgraph-91 inhibition N-Gamma secretase subgraph-40 41 N-Gamma secretase subgraph-91 activation N-Gamma secretase subgraph-105 N-Gamma secretase subgraph-79 activation N-Gamma secretase subgraph-78 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-11 12 13 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-21 22 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-3 4 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-88 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-52 N-Gamma secretase subgraph-108 inhibition N-Gamma secretase subgraph-66 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-84 N-Gamma secretase subgraph-108 inhibition N-Gamma secretase subgraph-122 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-61 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-58 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-102 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-79 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-89 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-89 N-Gamma secretase subgraph-108 inhibition N-Gamma secretase subgraph-101 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-75 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-75 N-Gamma secretase subgraph-108 inhibition N-Gamma secretase subgraph-63 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-68 N-Gamma secretase subgraph-108 inhibition N-Gamma secretase subgraph-56 N-Gamma secretase subgraph-108 inhibition N-Gamma secretase subgraph-59 N-Gamma secretase subgraph-108 inhibition N-Gamma secretase subgraph-59 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-74 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-81 N-Gamma secretase subgraph-108 activation N-Gamma secretase subgraph-73 N-Gamma secretase subgraph-14 15 inhibition N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-87 inhibition N-Gamma secretase subgraph-90 N-Gamma secretase subgraph-110 inhibition N-Gamma secretase subgraph-14 15 N-Gamma secretase subgraph-110 inhibition N-Gamma secretase subgraph-16 17 N-Gamma secretase subgraph-110 inhibition N-Gamma secretase subgraph-16 17 N-Gamma secretase subgraph-98 activation N-Gamma secretase subgraph-108 N-Gamma secretase subgraph-98 activation N-Gamma secretase subgraph-118 N-Gamma secretase subgraph-85 inhibition N-Gamma secretase subgraph-90 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Gap junctions subgraph.att000066400000000000000000000003721426625374700302520ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Gap junctions subgraph-1 2 DAB2 APP 0 0 white rectangle gene,gene 0.5 black 46 17 2662,/,620 N-Gap junctions subgraph-3 APP 200 113 white rectangle gene 0.5 black 46 17 620 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Gap junctions subgraph.sif000066400000000000000000000001111426625374700302320ustar00rootroot000000000000000 1 2 N-Gap junctions subgraph-1 2 activation N-Gap junctions subgraph-3 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Glucagon subgraph.att000066400000000000000000000004521426625374700273040ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Glucagon subgraph-1 GCG 56 99 white rectangle gene 0.5 black 46 17 4191 N-Glucagon subgraph-2 MAPT 112 0 white rectangle gene 0.5 black 46 17 6893 N-Glucagon subgraph-3 NGF 0 199 white rectangle gene 0.5 black 46 17 7808 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Glucagon subgraph.sif000066400000000000000000000001641426625374700272750ustar00rootroot000000000000000 1 2 N-Glucagon subgraph-1 inhibition N-Glucagon subgraph-3 N-Glucagon subgraph-1 inhibition N-Glucagon subgraph-2 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Glutamatergic subgraph.att000066400000000000000000000065331426625374700303430ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Glutamatergic subgraph-1 sAPP-alpha 17 135 white rectangle gene 0.5 black 46 17 CONSO00067 N-Glutamatergic subgraph-10 ADAM10 145 57 white rectangle gene 0.5 black 46 17 188 N-Glutamatergic subgraph-12 ADAM17 148 56 white rectangle gene 0.5 black 46 17 195 N-Glutamatergic subgraph-13 ADAM9 147 59 white rectangle gene 0.5 black 46 17 216 N-Glutamatergic subgraph-14 ITGB1BP1 81 92 white rectangle gene 0.5 black 46 17 23927 N-Glutamatergic subgraph-15 DNM1 93 48 white rectangle gene 0.5 black 46 17 2972 N-Glutamatergic subgraph-16 DNM2 103 51 white rectangle gene 0.5 black 46 17 2974 N-Glutamatergic subgraph-17 GRIA1 104 38 white rectangle gene 0.5 black 46 17 4571 N-Glutamatergic subgraph-18 GRIA2 73 86 white rectangle gene 0.5 black 46 17 4572 N-Glutamatergic subgraph-19 GRIA3 77 113 white rectangle gene 0.5 black 46 17 4573 N-Glutamatergic subgraph-2 Glutamate ionotropic receptor NMDA type subunits 142 65 white rectangle gene 0.5 black 46 17 1201 N-Glutamatergic subgraph-21 GRIN2A 155 62 white rectangle gene 0.5 black 46 17 4585 N-Glutamatergic subgraph-22 GRIN2A 86 114 white rectangle gene 0.5 black 46 17 4585 N-Glutamatergic subgraph-23 GRIN2B 135 43 white rectangle gene 0.5 black 46 17 4586 N-Glutamatergic subgraph-24 GRIN2C 153 51 white rectangle gene 0.5 black 46 17 4587 N-Glutamatergic subgraph-25 GRIN2D 147 67 white rectangle gene 0.5 black 46 17 4588 N-Glutamatergic subgraph-26 GRINA 19 149 white rectangle gene 0.5 black 46 17 4589 N-Glutamatergic subgraph-29 APP 85 83 white rectangle gene 0.5 black 46 17 620 N-Glutamatergic subgraph-3 SH3GLB2 97 50 white rectangle gene 0.5 black 46 17 10834 N-Glutamatergic subgraph-31 ARC 93 62 white rectangle gene 0.5 black 46 17 648 N-Glutamatergic subgraph-32 PDE2A 25 137 white rectangle gene 0.5 black 46 17 8777 N-Glutamatergic subgraph-33 BACE1 82 63 white rectangle gene 0.5 black 46 17 933 N-Glutamatergic subgraph-34 RELN 83 102 white rectangle gene 0.5 black 46 17 9957 N-Glutamatergic subgraph-35 metabotropic quisqualate receptor 129 16 white rectangle gene 0.5 black 46 17 C073111 N-Glutamatergic subgraph-36 NMDA receptor A1 103 17 white rectangle gene 0.5 black 46 17 C101911 N-Glutamatergic subgraph-37 NR2A NMDA receptor 109 17 white rectangle gene 0.5 black 46 17 C120997 N-Glutamatergic subgraph-38 NR2B NMDA receptor 108 5 white rectangle gene 0.5 black 46 17 C121001 N-Glutamatergic subgraph-39 NR1 NMDA receptor 122 3 white rectangle gene 0.5 black 46 17 C409466 N-Glutamatergic subgraph-4 SLC1A2 7 1 white rectangle gene 0.5 black 46 17 10940 N-Glutamatergic subgraph-40 NR2C NMDA receptor 114 2 white rectangle gene 0.5 black 46 17 C432823 N-Glutamatergic subgraph-41 NR2D NMDA receptor 128 9 white rectangle gene 0.5 black 46 17 C464436 N-Glutamatergic subgraph-42 NR3A NMDA receptor 117 8 white rectangle gene 0.5 black 46 17 C464437 N-Glutamatergic subgraph-43 NR3B NMDA receptor 104 10 white rectangle gene 0.5 black 46 17 C501155 N-Glutamatergic subgraph-5 SOD1 0 0 white rectangle gene 0.5 black 46 17 11179 N-Glutamatergic subgraph-6 TNF 117 19 white rectangle gene 0.5 black 46 17 11892 N-Glutamatergic subgraph-7 SKAP1 25 154 white rectangle gene 0.5 black 46 17 15605 N-Glutamatergic subgraph-8 GRIN3A 156 56 white rectangle gene 0.5 black 46 17 16767 N-Glutamatergic subgraph-9 GRIN3B 152 66 white rectangle gene 0.5 black 46 17 16768 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Glutamatergic subgraph.sif000066400000000000000000000061741426625374700303350ustar00rootroot000000000000000 1 2 N-Glutamatergic subgraph-16 activation N-Glutamatergic subgraph-17 N-Glutamatergic subgraph-21 inhibition N-Glutamatergic subgraph-13 N-Glutamatergic subgraph-21 inhibition N-Glutamatergic subgraph-10 N-Glutamatergic subgraph-21 inhibition N-Glutamatergic subgraph-12 N-Glutamatergic subgraph-2 inhibition N-Glutamatergic subgraph-13 N-Glutamatergic subgraph-2 inhibition N-Glutamatergic subgraph-10 N-Glutamatergic subgraph-2 inhibition N-Glutamatergic subgraph-12 N-Glutamatergic subgraph-31 activation N-Glutamatergic subgraph-3 N-Glutamatergic subgraph-31 activation N-Glutamatergic subgraph-15 N-Glutamatergic subgraph-31 activation N-Glutamatergic subgraph-16 N-Glutamatergic subgraph-31 activation N-Glutamatergic subgraph-33 N-Glutamatergic subgraph-31 activation N-Glutamatergic subgraph-29 N-Glutamatergic subgraph-9 inhibition N-Glutamatergic subgraph-13 N-Glutamatergic subgraph-9 inhibition N-Glutamatergic subgraph-10 N-Glutamatergic subgraph-9 inhibition N-Glutamatergic subgraph-12 N-Glutamatergic subgraph-3 activation N-Glutamatergic subgraph-17 N-Glutamatergic subgraph-23 inhibition N-Glutamatergic subgraph-13 N-Glutamatergic subgraph-23 inhibition N-Glutamatergic subgraph-10 N-Glutamatergic subgraph-23 inhibition N-Glutamatergic subgraph-12 N-Glutamatergic subgraph-34 activation N-Glutamatergic subgraph-22 N-Glutamatergic subgraph-34 activation N-Glutamatergic subgraph-19 N-Glutamatergic subgraph-25 inhibition N-Glutamatergic subgraph-13 N-Glutamatergic subgraph-25 inhibition N-Glutamatergic subgraph-10 N-Glutamatergic subgraph-25 inhibition N-Glutamatergic subgraph-12 N-Glutamatergic subgraph-15 activation N-Glutamatergic subgraph-17 N-Glutamatergic subgraph-24 inhibition N-Glutamatergic subgraph-13 N-Glutamatergic subgraph-24 inhibition N-Glutamatergic subgraph-10 N-Glutamatergic subgraph-24 inhibition N-Glutamatergic subgraph-12 N-Glutamatergic subgraph-5 inhibition N-Glutamatergic subgraph-4 N-Glutamatergic subgraph-29 activation N-Glutamatergic subgraph-34 N-Glutamatergic subgraph-29 activation N-Glutamatergic subgraph-14 N-Glutamatergic subgraph-29 activation N-Glutamatergic subgraph-18 N-Glutamatergic subgraph-32 activation N-Glutamatergic subgraph-1 N-Glutamatergic subgraph-7 activation N-Glutamatergic subgraph-26 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-36 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-39 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-37 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-38 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-40 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-41 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-42 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-43 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-35 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-17 N-Glutamatergic subgraph-6 activation N-Glutamatergic subgraph-23 N-Glutamatergic subgraph-8 inhibition N-Glutamatergic subgraph-13 N-Glutamatergic subgraph-8 inhibition N-Glutamatergic subgraph-10 N-Glutamatergic subgraph-8 inhibition N-Glutamatergic subgraph-12 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Glutathione reductase subgraph.att000066400000000000000000000003711426625374700317700ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Glutathione reductase subgraph-1 ACHE 0 199 white rectangle gene 0.5 black 46 17 108 N-Glutathione reductase subgraph-3 GSS 109 0 white rectangle gene 0.5 black 46 17 4624 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Glutathione reductase subgraph.sif000066400000000000000000000001271426625374700317600ustar00rootroot000000000000000 1 2 N-Glutathione reductase subgraph-3 inhibition N-Glutathione reductase subgraph-1 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Hypoxia response subgraph.att000066400000000000000000000005021426625374700310010ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Hypoxia response subgraph-1 HIF1A 0 0 white rectangle gene 0.5 black 46 17 4910 N-Hypoxia response subgraph-2 APP 199 32 white rectangle gene 0.5 black 46 17 620 N-Hypoxia response subgraph-3 BACE1 99 16 white rectangle gene 0.5 black 46 17 933 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Hypoxia response subgraph.sif000066400000000000000000000002241426625374700307730ustar00rootroot000000000000000 1 2 N-Hypoxia response subgraph-1 activation N-Hypoxia response subgraph-3 N-Hypoxia response subgraph-3 activation N-Hypoxia response subgraph-2 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Immunoglobulin subgraph.att000066400000000000000000000017221426625374700305460ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Immunoglobulin subgraph-10 NFATC1 0 116 white rectangle gene 0.5 black 46 17 7775 N-Immunoglobulin subgraph-11 NFKB1 13 137 white rectangle gene 0.5 black 46 17 7794 N-Immunoglobulin subgraph-12 NFKB2 17 104 white rectangle gene 0.5 black 46 17 7795 N-Immunoglobulin subgraph-14 BACE1 4 109 white rectangle gene 0.5 black 46 17 933 N-Immunoglobulin subgraph-3 S100A 32 124 white rectangle gene 0.5 black 46 17 S100A N-Immunoglobulin subgraph-4 TNF 34 8 white rectangle gene 0.5 black 46 17 11892 N-Immunoglobulin subgraph-5 CD47 40 0 white rectangle gene 0.5 black 46 17 1682 N-Immunoglobulin subgraph-6 CSF2 1 130 white rectangle gene 0.5 black 46 17 2434 N-Immunoglobulin subgraph-7 AGER 15 121 white rectangle gene 0.5 black 46 17 320 N-Immunoglobulin subgraph-8 HMGB1 29 111 white rectangle gene 0.5 black 46 17 4983 N-Immunoglobulin subgraph-9 APP 25 134 white rectangle gene 0.5 black 46 17 620 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Immunoglobulin subgraph.sif000066400000000000000000000012521426625374700305350ustar00rootroot000000000000000 1 2 N-Immunoglobulin subgraph-4 activation N-Immunoglobulin subgraph-5 N-Immunoglobulin subgraph-10 activation N-Immunoglobulin subgraph-14 N-Immunoglobulin subgraph-9 activation N-Immunoglobulin subgraph-7 N-Immunoglobulin subgraph-8 activation N-Immunoglobulin subgraph-7 N-Immunoglobulin subgraph-7 activation N-Immunoglobulin subgraph-11 N-Immunoglobulin subgraph-7 activation N-Immunoglobulin subgraph-12 N-Immunoglobulin subgraph-7 activation N-Immunoglobulin subgraph-6 N-Immunoglobulin subgraph-7 activation N-Immunoglobulin subgraph-10 N-Immunoglobulin subgraph-7 activation N-Immunoglobulin subgraph-14 N-Immunoglobulin subgraph-3 activation N-Immunoglobulin subgraph-7 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Inflammatory response subgraph.att000066400000000000000000000070051426625374700320270ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Inflammatory response subgraph-100 Lcn2 173 62 white rectangle gene 0.5 black 46 17 96757 N-Inflammatory response subgraph-101 Nos2 158 19 white rectangle gene 0.5 black 46 17 97361 N-Inflammatory response subgraph-102 Ptgs2 146 34 white rectangle gene 0.5 black 46 17 97798 N-Inflammatory response subgraph-14 15 TREM2 HSPD1 106 174 white rectangle gene,gene 0.5 black 46 17 17761,/,5261 N-Inflammatory response subgraph-18 19 APP Tlr2 0 139 white rectangle gene,gene 0.5 black 46 17 620,/,1346060 N-Inflammatory response subgraph-20 21 NFKB1 RELB 65 145 white rectangle gene,gene 0.5 black 46 17 7794,/,9956 N-Inflammatory response subgraph-24 25 Tnf Tnfrsf1a 177 66 white rectangle gene,gene 0.5 black 46 17 104798,/,1314884 N-Inflammatory response subgraph-29 S100B 4 85 white rectangle gene 0.5 black 46 17 10500 N-Inflammatory response subgraph-31 STAT3 84 141 white rectangle gene 0.5 black 46 17 11364 N-Inflammatory response subgraph-32 TLR2 36 29 white rectangle gene 0.5 black 46 17 11848 N-Inflammatory response subgraph-33 TLR4 191 85 white rectangle gene 0.5 black 46 17 11850 N-Inflammatory response subgraph-34 TNF 94 176 white rectangle gene 0.5 black 46 17 11892 N-Inflammatory response subgraph-35 TNFSF10 115 7 white rectangle gene 0.5 black 46 17 11925 N-Inflammatory response subgraph-38 IL23A 35 21 white rectangle gene 0.5 black 46 17 15488 N-Inflammatory response subgraph-4 5 SDC1 ITGA5 72 135 white rectangle gene,gene 0.5 black 46 17 10658,/,6141 N-Inflammatory response subgraph-40 CCR7 101 178 white rectangle gene 0.5 black 46 17 1608 N-Inflammatory response subgraph-41 CD14 50 19 white rectangle gene 0.5 black 46 17 1628 N-Inflammatory response subgraph-45 TREM2 98 168 white rectangle gene 0.5 black 46 17 17761 N-Inflammatory response subgraph-47 CHI3L1 76 144 white rectangle gene 0.5 black 46 17 1932 N-Inflammatory response subgraph-49 CD200R1 164 120 white rectangle gene 0.5 black 46 17 24235 N-Inflammatory response subgraph-6 7 SDC1 ITGB3 67 139 white rectangle gene,gene 0.5 black 46 17 10658,/,6156 N-Inflammatory response subgraph-60 IFNG 107 167 white rectangle gene 0.5 black 46 17 5438 N-Inflammatory response subgraph-61 IL12B 41 17 white rectangle gene 0.5 black 46 17 5970 N-Inflammatory response subgraph-62 IL13 170 124 white rectangle gene 0.5 black 46 17 5973 N-Inflammatory response subgraph-66 IL4 156 116 white rectangle gene 0.5 black 46 17 6014 N-Inflammatory response subgraph-67 IL6 88 155 white rectangle gene 0.5 black 46 17 6018 N-Inflammatory response subgraph-69 APP 192 90 white rectangle gene 0.5 black 46 17 620 N-Inflammatory response subgraph-70 APP 44 26 white rectangle gene 0.5 black 46 17 620 N-Inflammatory response subgraph-75 MYD88 52 26 white rectangle gene 0.5 black 46 17 7562 N-Inflammatory response subgraph-76 NFKB1 75 153 white rectangle gene 0.5 black 46 17 7794 N-Inflammatory response subgraph-77 NFKB2 68 151 white rectangle gene 0.5 black 46 17 7795 N-Inflammatory response subgraph-78 OSM 79 135 white rectangle gene 0.5 black 46 17 8506 N-Inflammatory response subgraph-8 9 TNFRSF10B TNFSF10 116 0 white rectangle gene,gene 0.5 black 46 17 11905,/,11925 N-Inflammatory response subgraph-86 MOK 0 81 white rectangle gene 0.5 black 46 17 9833 N-Inflammatory response subgraph-89 Kng1 152 26 white rectangle gene 0.5 black 46 17 1097705 N-Inflammatory response subgraph-93 Tlr2 7 136 white rectangle gene 0.5 black 46 17 1346060 N-Inflammatory response subgraph-96 Cd40lg 47 35 white rectangle gene 0.5 black 46 17 88337 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Inflammatory response subgraph.sif000066400000000000000000000050511426625374700320170ustar00rootroot000000000000000 1 2 N-Inflammatory response subgraph-93 activation N-Inflammatory response subgraph-18 19 N-Inflammatory response subgraph-14 15 activation N-Inflammatory response subgraph-45 N-Inflammatory response subgraph-89 inhibition N-Inflammatory response subgraph-101 N-Inflammatory response subgraph-89 inhibition N-Inflammatory response subgraph-102 N-Inflammatory response subgraph-45 activation N-Inflammatory response subgraph-40 N-Inflammatory response subgraph-45 activation N-Inflammatory response subgraph-40 N-Inflammatory response subgraph-45 inhibition N-Inflammatory response subgraph-34 N-Inflammatory response subgraph-45 inhibition N-Inflammatory response subgraph-67 N-Inflammatory response subgraph-45 inhibition N-Inflammatory response subgraph-60 N-Inflammatory response subgraph-67 activation N-Inflammatory response subgraph-47 N-Inflammatory response subgraph-31 activation N-Inflammatory response subgraph-47 N-Inflammatory response subgraph-70 activation N-Inflammatory response subgraph-61 N-Inflammatory response subgraph-70 activation N-Inflammatory response subgraph-38 N-Inflammatory response subgraph-70 activation N-Inflammatory response subgraph-41 N-Inflammatory response subgraph-70 activation N-Inflammatory response subgraph-75 N-Inflammatory response subgraph-70 activation N-Inflammatory response subgraph-32 N-Inflammatory response subgraph-66 activation N-Inflammatory response subgraph-49 N-Inflammatory response subgraph-35 activation N-Inflammatory response subgraph-8 9 N-Inflammatory response subgraph-29 activation N-Inflammatory response subgraph-86 N-Inflammatory response subgraph-96 activation N-Inflammatory response subgraph-70 N-Inflammatory response subgraph-47 inhibition N-Inflammatory response subgraph-67 N-Inflammatory response subgraph-47 inhibition N-Inflammatory response subgraph-76 N-Inflammatory response subgraph-47 inhibition N-Inflammatory response subgraph-77 N-Inflammatory response subgraph-47 activation N-Inflammatory response subgraph-6 7 N-Inflammatory response subgraph-47 activation N-Inflammatory response subgraph-4 5 N-Inflammatory response subgraph-69 activation N-Inflammatory response subgraph-33 N-Inflammatory response subgraph-20 21 activation N-Inflammatory response subgraph-47 N-Inflammatory response subgraph-78 activation N-Inflammatory response subgraph-47 N-Inflammatory response subgraph-24 25 activation N-Inflammatory response subgraph-100 N-Inflammatory response subgraph-24 25 activation N-Inflammatory response subgraph-100 N-Inflammatory response subgraph-62 activation N-Inflammatory response subgraph-49 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Innate immune system subgraph.att000066400000000000000000000007001426625374700315370ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Innate immune system subgraph-11 APP 91 0 white rectangle gene 0.5 black 46 17 620 N-Innate immune system subgraph-12 KLC1 88 100 white rectangle gene 0.5 black 46 17 6387 N-Innate immune system subgraph-13 MAPT 0 147 white rectangle gene 0.5 black 46 17 6893 N-Innate immune system 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transduction-100 IAPP 51 196 white rectangle gene 0.5 black 46 17 5329 N-Insulin signal transduction-101 IDE 125 90 white rectangle gene 0.5 black 46 17 5381 N-Insulin signal transduction-102 IDE 131 95 white rectangle gene 0.5 black 46 17 5381 N-Insulin signal transduction-103 IGF1 100 79 white rectangle gene 0.5 black 46 17 5464 N-Insulin signal transduction-104 IGF1R 107 71 white rectangle gene 0.5 black 46 17 5465 N-Insulin signal transduction-105 IL4 119 85 white rectangle gene 0.5 black 46 17 6014 N-Insulin signal transduction-106 INS 122 79 white rectangle gene 0.5 black 46 17 6081 N-Insulin signal transduction-107 INSR 124 62 white rectangle gene 0.5 black 46 17 6091 N-Insulin signal transduction-108 IRS1 128 34 white rectangle gene 0.5 black 46 17 6125 N-Insulin signal transduction-109 IRS1 126 47 white rectangle gene 0.5 black 46 17 6125 N-Insulin signal transduction-110 IRS1 129 26 white rectangle gene 0.5 black 46 17 6125 N-Insulin signal transduction-111 IRS1 126 27 white rectangle gene 0.5 black 46 17 6125 N-Insulin signal transduction-112 IRS1 101 91 white rectangle gene 0.5 black 46 17 6125 N-Insulin signal transduction-113 IRS1 85 151 white rectangle gene 0.5 black 46 17 6125 N-Insulin signal transduction-114 IRS2 133 46 white rectangle gene 0.5 black 46 17 6126 N-Insulin signal transduction-115 IRS2 129 53 white rectangle gene 0.5 black 46 17 6126 N-Insulin signal transduction-116 IRS2 100 93 white rectangle gene 0.5 black 46 17 6126 N-Insulin signal transduction-117 IRS2 71 156 white rectangle gene 0.5 black 46 17 6126 N-Insulin signal transduction-118 IRS4 105 80 white rectangle gene 0.5 black 46 17 6128 N-Insulin signal transduction-119 IRS4 113 70 white rectangle gene 0.5 black 46 17 6128 N-Insulin signal transduction-120 IRS4 65 135 white rectangle gene 0.5 black 46 17 6128 N-Insulin signal transduction-121 APP 130 78 white rectangle gene 0.5 black 46 17 620 N-Insulin signal transduction-122 APP 112 91 white rectangle gene 0.5 black 46 17 620 N-Insulin signal transduction-123 MAPK8 132 28 white rectangle gene 0.5 black 46 17 6881 N-Insulin signal transduction-124 MAPT 151 85 white rectangle gene 0.5 black 46 17 6893 N-Insulin signal transduction-125 MAPT 139 81 white rectangle gene 0.5 black 46 17 6893 N-Insulin signal transduction-127 OGT 159 86 white rectangle gene 0.5 black 46 17 8127 N-Insulin signal transduction-128 PDK1 139 51 white rectangle gene 0.5 black 46 17 8809 N-Insulin signal transduction-129 PDPK1 96 93 white rectangle gene 0.5 black 46 17 8816 N-Insulin signal transduction-13 14 15 PIK3R5 GSK3B PDPK1 100 69 white rectangle gene,gene,gene 0.5 black 46 17 30035,/,4617,/,8816 N-Insulin signal transduction-132 PPP2CA 103 108 white rectangle gene 0.5 black 46 17 9299 N-Insulin signal transduction-134 BAD 73 122 white rectangle gene 0.5 black 46 17 936 N-Insulin signal transduction-135 RAF1 89 80 white rectangle gene 0.5 black 46 17 9829 N-Insulin signal transduction-138 Chrna3 21 100 white rectangle gene 0.5 black 46 17 2345 N-Insulin signal transduction-139 Chrna4 2 99 white rectangle gene 0.5 black 46 17 2346 N-Insulin signal transduction-140 Chrna5 5 105 white rectangle gene 0.5 black 46 17 2347 N-Insulin signal transduction-141 Chrna7 13 102 white rectangle gene 0.5 black 46 17 2348 N-Insulin signal transduction-142 Chrnb2 0 98 white rectangle gene 0.5 black 46 17 2350 N-Insulin signal transduction-143 Chrnb4 13 96 white rectangle gene 0.5 black 46 17 2351 N-Insulin signal transduction-144 Ins2 8 99 white rectangle gene 0.5 black 46 17 2916 N-Insulin signal transduction-145 Chrna10 3 103 white rectangle gene 0.5 black 46 17 620142 N-Insulin signal transduction-146 Chrna2 0 102 white rectangle gene 0.5 black 46 17 621533 N-Insulin signal transduction-147 Chrna9 1 94 white rectangle gene 0.5 black 46 17 621534 N-Insulin signal transduction-148 Chrnb3 6 92 white rectangle gene 0.5 black 46 17 621544 N-Insulin signal transduction-149 Chrna6 4 94 white rectangle gene 0.5 black 46 17 69281 N-Insulin signal transduction-16 17 GAB1 GRB2 109 78 white rectangle gene,gene 0.5 black 46 17 4066,/,4566 N-Insulin signal transduction-18 19 GRB2 IRS1 91 152 white rectangle gene,gene 0.5 black 46 17 4566,/,6125 N-Insulin signal transduction-20 21 GRB2 IRS2 75 160 white rectangle gene,gene 0.5 black 46 17 4566,/,6126 N-Insulin signal transduction-22 23 GRB2 IRS4 60 128 white rectangle gene,gene 0.5 black 46 17 4566,/,6128 N-Insulin signal transduction-24 25 GSK3B PPP2CA 130 75 white rectangle gene,gene 0.5 black 46 17 4617,/,9299 N-Insulin signal transduction-26 27 28 IGF1 IGF1R INSR 115 191 white rectangle gene,gene,gene 0.5 black 46 17 5464,/,5465,/,6091 N-Insulin signal transduction-29 30 IGF1 INSR 103 100 white rectangle gene,gene 0.5 black 46 17 5464,/,6091 N-Insulin signal transduction-3 4 5 SOS1 GAB1 GRB2 98 79 white rectangle gene,gene,gene 0.5 black 46 17 11187,/,4066,/,4566 N-Insulin signal transduction-31 32 IGF1 LRP2 92 75 white rectangle gene,gene 0.5 black 46 17 5464,/,6694 N-Insulin signal transduction-33 34 35 IGF1R IGF2 INSR 116 181 white rectangle gene,gene,gene 0.5 black 46 17 5465,/,5466,/,6091 N-Insulin signal transduction-36 37 38 IGF1R INS INSR 120 194 white rectangle gene,gene,gene 0.5 black 46 17 5465,/,6081,/,6091 N-Insulin signal transduction-39 40 IGF1R INSR 117 187 white rectangle gene,gene 0.5 black 46 17 5465,/,6091 N-Insulin signal transduction-41 42 IGF1R INSR 74 145 white rectangle gene,gene 0.5 black 46 17 5465,/,6091 N-Insulin signal transduction-43 44 INS INSR 119 94 white rectangle gene,gene 0.5 black 46 17 6081,/,6091 N-Insulin signal transduction-45 46 INSR APP 132 56 white rectangle gene,gene 0.5 black 46 17 6091,/,620 N-Insulin signal transduction-47 48 IRS1 PIK3R1 84 157 white rectangle gene,gene 0.5 black 46 17 6125,/,8979 N-Insulin signal transduction-49 50 IRS1 PIK3R2 90 155 white rectangle gene,gene 0.5 black 46 17 6125,/,8980 N-Insulin signal transduction-51 52 IRS1 PTPN11 88 157 white rectangle gene,gene 0.5 black 46 17 6125,/,9644 N-Insulin signal transduction-53 54 IRS2 PIK3R1 72 162 white rectangle gene,gene 0.5 black 46 17 6126,/,8979 N-Insulin signal transduction-55 56 IRS2 PIK3R2 68 162 white rectangle gene,gene 0.5 black 46 17 6126,/,8980 N-Insulin signal transduction-57 58 IRS2 PTPN11 65 160 white rectangle gene,gene 0.5 black 46 17 6126,/,9644 N-Insulin signal transduction-59 60 IRS4 PIK3R1 62 130 white rectangle gene,gene 0.5 black 46 17 6128,/,8979 N-Insulin signal transduction-6 7 CDK5 CDK5R1 53 192 white rectangle gene,gene 0.5 black 46 17 1774,/,1775 N-Insulin signal transduction-61 62 IRS4 PIK3R2 58 136 white rectangle gene,gene 0.5 black 46 17 6128,/,8980 N-Insulin signal transduction-63 64 IRS4 PTPN11 58 131 white rectangle gene,gene 0.5 black 46 17 6128,/,9644 N-Insulin signal transduction-65 sAPP-alpha 86 61 white rectangle gene 0.5 black 46 17 CONSO00067 N-Insulin signal transduction-66 PI3K_p110 95 75 white rectangle gene 0.5 black 46 17 PI3K_p110 N-Insulin signal transduction-67 AKT 79 119 white rectangle gene 0.5 black 46 17 AKT N-Insulin signal transduction-68 ERK 50 3 white rectangle gene 0.5 black 46 17 ERK N-Insulin signal transduction-70 IRS 52 0 white rectangle gene 0.5 black 46 17 IRS N-Insulin signal transduction-71 JNK 99 97 white rectangle gene 0.5 black 46 17 JNK N-Insulin signal transduction-72 RAS 88 77 white rectangle gene 0.5 black 46 17 RAS N-Insulin signal transduction-73 Glutamate ionotropic receptor NMDA type subunits 125 72 white rectangle gene 0.5 black 46 17 1201 N-Insulin signal transduction-74 SHC1 110 98 white rectangle gene 0.5 black 46 17 10840 N-Insulin signal transduction-75 SLC2A4 129 81 white rectangle gene 0.5 black 46 17 11009 N-Insulin signal transduction-76 TNF 102 71 white rectangle gene 0.5 black 46 17 11892 N-Insulin signal transduction-77 TTR 96 70 white rectangle gene 0.5 black 46 17 12405 N-Insulin signal transduction-78 SLC2A8 128 73 white rectangle gene 0.5 black 46 17 13812 N-Insulin signal transduction-79 CAPN1 147 78 white rectangle gene 0.5 black 46 17 1476 N-Insulin signal transduction-8 9 ADD3 INS 153 185 white rectangle gene,gene 0.5 black 46 17 245,/,6081 N-Insulin signal transduction-80 SHC4 108 99 white rectangle gene 0.5 black 46 17 16743 N-Insulin signal transduction-81 SHC3 110 100 white rectangle gene 0.5 black 46 17 18181 N-Insulin signal transduction-82 ADAM10 91 68 white rectangle gene 0.5 black 46 17 188 N-Insulin signal transduction-83 ADD3 149 180 white rectangle gene 0.5 black 46 17 245 N-Insulin signal transduction-84 IL34 135 93 white rectangle gene 0.5 black 46 17 28529 N-Insulin signal transduction-85 SHC2 108 96 white rectangle gene 0.5 black 46 17 29869 N-Insulin signal transduction-86 PIK3R5 89 100 white rectangle gene 0.5 black 46 17 30035 N-Insulin signal transduction-87 ECE1 117 96 white rectangle gene 0.5 black 46 17 3146 N-Insulin signal transduction-89 FOXO3 98 72 white rectangle gene 0.5 black 46 17 3821 N-Insulin signal transduction-90 ALB 93 79 white rectangle gene 0.5 black 46 17 399 N-Insulin signal transduction-91 GCG 147 82 white rectangle gene 0.5 black 46 17 4191 N-Insulin signal transduction-92 GRB2 97 90 white rectangle gene 0.5 black 46 17 4566 N-Insulin signal transduction-93 GSK3A 94 95 white rectangle gene 0.5 black 46 17 4616 N-Insulin signal transduction-94 GSK3A 77 125 white rectangle gene 0.5 black 46 17 4616 N-Insulin signal transduction-95 GSK3B 94 101 white rectangle gene 0.5 black 46 17 4617 N-Insulin signal transduction-96 GSK3B 86 111 white rectangle gene 0.5 black 46 17 4617 N-Insulin signal transduction-97 HMOX1 142 94 white rectangle gene 0.5 black 46 17 5013 N-Insulin signal transduction-99 HSPA1B 117 99 white rectangle gene 0.5 black 46 17 5233 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Insulin signal transduction.sif000066400000000000000000000245001426625374700313170ustar00rootroot000000000000000 1 2 N-Insulin signal transduction-109 activation N-Insulin signal transduction-108 N-Insulin signal transduction-96 inhibition N-Insulin signal transduction-95 N-Insulin signal transduction-3 4 5 activation N-Insulin signal transduction-72 N-Insulin signal transduction-3 4 5 activation N-Insulin signal transduction-135 N-Insulin signal transduction-67 activation N-Insulin signal transduction-96 N-Insulin signal transduction-67 activation N-Insulin signal transduction-94 N-Insulin signal transduction-67 inhibition N-Insulin signal transduction-134 N-Insulin signal transduction-125 activation N-Insulin signal transduction-79 N-Insulin signal transduction-110 inhibition N-Insulin signal transduction-108 N-Insulin signal transduction-29 30 activation N-Insulin signal transduction-112 N-Insulin signal transduction-29 30 activation N-Insulin signal transduction-116 N-Insulin signal transduction-29 30 activation N-Insulin signal transduction-74 N-Insulin signal transduction-29 30 activation N-Insulin signal transduction-85 N-Insulin signal transduction-29 30 activation N-Insulin signal transduction-81 N-Insulin signal transduction-29 30 activation N-Insulin signal transduction-80 N-Insulin signal transduction-39 40 activation N-Insulin signal transduction-26 27 28 N-Insulin signal transduction-39 40 activation N-Insulin signal transduction-33 34 35 N-Insulin signal transduction-39 40 activation N-Insulin signal transduction-36 37 38 N-Insulin signal transduction-113 activation N-Insulin signal transduction-18 19 N-Insulin signal transduction-113 activation N-Insulin signal transduction-47 48 N-Insulin signal transduction-113 activation N-Insulin signal transduction-49 50 N-Insulin signal transduction-113 activation N-Insulin signal transduction-51 52 N-Insulin signal transduction-103 activation N-Insulin signal transduction-31 32 N-Insulin signal transduction-103 activation N-Insulin signal transduction-104 N-Insulin signal transduction-103 activation N-Insulin signal transduction-1 2 N-Insulin signal transduction-103 activation N-Insulin signal transduction-16 17 N-Insulin signal transduction-103 activation N-Insulin signal transduction-82 N-Insulin signal transduction-103 activation N-Insulin signal transduction-122 N-Insulin signal transduction-103 activation N-Insulin signal transduction-90 N-Insulin signal transduction-103 activation N-Insulin signal transduction-77 N-Insulin signal transduction-103 activation N-Insulin signal transduction-10 11 12 N-Insulin signal transduction-103 activation N-Insulin signal transduction-13 14 15 N-Insulin signal transduction-103 activation N-Insulin signal transduction-66 N-Insulin signal transduction-103 activation N-Insulin signal transduction-112 N-Insulin signal transduction-103 activation N-Insulin signal transduction-116 N-Insulin signal transduction-6 7 activation N-Insulin signal transduction-100 N-Insulin signal transduction-6 7 activation N-Insulin signal transduction-100 N-Insulin signal transduction-111 inhibition N-Insulin signal transduction-108 N-Insulin signal transduction-82 activation N-Insulin signal transduction-65 N-Insulin signal transduction-84 activation N-Insulin signal transduction-101 N-Insulin signal transduction-84 activation N-Insulin signal transduction-97 N-Insulin signal transduction-106 activation N-Insulin signal transduction-125 N-Insulin signal transduction-106 activation N-Insulin signal transduction-125 N-Insulin signal transduction-106 activation N-Insulin signal transduction-125 N-Insulin signal transduction-106 activation N-Insulin signal transduction-107 N-Insulin signal transduction-106 activation N-Insulin signal transduction-107 N-Insulin signal transduction-106 activation N-Insulin signal transduction-122 N-Insulin signal transduction-106 activation N-Insulin signal transduction-122 N-Insulin signal transduction-106 activation N-Insulin signal transduction-1 2 N-Insulin signal transduction-106 activation N-Insulin signal transduction-16 17 N-Insulin signal transduction-106 activation N-Insulin signal transduction-24 25 N-Insulin signal transduction-106 inhibition N-Insulin signal transduction-101 N-Insulin signal transduction-106 inhibition N-Insulin signal transduction-101 N-Insulin signal transduction-106 activation N-Insulin signal transduction-75 N-Insulin signal transduction-106 activation N-Insulin signal transduction-78 N-Insulin signal transduction-106 activation N-Insulin signal transduction-73 N-Insulin signal transduction-106 inhibition N-Insulin signal transduction-121 N-Insulin signal transduction-106 activation N-Insulin signal transduction-121 N-Insulin signal transduction-106 inhibition N-Insulin signal transduction-105 N-Insulin signal transduction-95 activation N-Insulin signal transduction-29 30 N-Insulin signal transduction-107 activation N-Insulin signal transduction-109 N-Insulin signal transduction-107 activation N-Insulin signal transduction-115 N-Insulin signal transduction-107 activation N-Insulin signal transduction-119 N-Insulin signal transduction-87 activation N-Insulin signal transduction-122 N-Insulin signal transduction-91 inhibition N-Insulin signal transduction-125 N-Insulin signal transduction-116 activation N-Insulin signal transduction-129 N-Insulin signal transduction-116 activation N-Insulin signal transduction-95 N-Insulin signal transduction-116 activation N-Insulin signal transduction-93 N-Insulin signal transduction-116 activation N-Insulin signal transduction-71 N-Insulin signal transduction-102 inhibition N-Insulin signal transduction-101 N-Insulin signal transduction-92 activation N-Insulin signal transduction-129 N-Insulin signal transduction-92 activation N-Insulin signal transduction-95 N-Insulin signal transduction-92 activation N-Insulin signal transduction-93 N-Insulin signal transduction-120 activation N-Insulin signal transduction-22 23 N-Insulin signal transduction-120 activation N-Insulin signal transduction-59 60 N-Insulin signal transduction-120 activation N-Insulin signal transduction-61 62 N-Insulin signal transduction-120 activation N-Insulin signal transduction-63 64 N-Insulin signal transduction-101 activation N-Insulin signal transduction-122 N-Insulin signal transduction-101 activation N-Insulin signal transduction-122 N-Insulin signal transduction-101 activation N-Insulin signal transduction-122 N-Insulin signal transduction-101 activation N-Insulin signal transduction-106 N-Insulin signal transduction-101 activation N-Insulin signal transduction-106 N-Insulin signal transduction-41 42 activation N-Insulin signal transduction-113 N-Insulin signal transduction-41 42 activation N-Insulin signal transduction-117 N-Insulin signal transduction-41 42 activation N-Insulin signal transduction-120 N-Insulin signal transduction-122 inhibition N-Insulin signal transduction-43 44 N-Insulin signal transduction-122 activation N-Insulin signal transduction-103 N-Insulin signal transduction-122 activation N-Insulin signal transduction-112 N-Insulin signal transduction-122 activation N-Insulin signal transduction-116 N-Insulin signal transduction-122 activation N-Insulin signal transduction-74 N-Insulin signal transduction-122 activation N-Insulin signal transduction-85 N-Insulin signal transduction-122 activation N-Insulin signal transduction-81 N-Insulin signal transduction-122 activation N-Insulin signal transduction-80 N-Insulin signal transduction-99 activation N-Insulin signal transduction-122 N-Insulin signal transduction-86 activation N-Insulin signal transduction-93 N-Insulin signal transduction-86 activation N-Insulin signal transduction-95 N-Insulin signal transduction-105 activation N-Insulin signal transduction-122 N-Insulin signal transduction-132 activation N-Insulin signal transduction-29 30 N-Insulin signal transduction-115 activation N-Insulin signal transduction-114 N-Insulin signal transduction-118 activation N-Insulin signal transduction-92 N-Insulin signal transduction-8 9 inhibition N-Insulin signal transduction-83 N-Insulin signal transduction-1 2 activation N-Insulin signal transduction-3 4 5 N-Insulin signal transduction-16 17 activation N-Insulin signal transduction-3 4 5 N-Insulin signal transduction-104 activation N-Insulin signal transduction-119 N-Insulin signal transduction-117 activation N-Insulin signal transduction-20 21 N-Insulin signal transduction-117 activation N-Insulin signal transduction-53 54 N-Insulin signal transduction-117 activation N-Insulin signal transduction-55 56 N-Insulin signal transduction-117 activation N-Insulin signal transduction-57 58 N-Insulin signal transduction-127 activation N-Insulin signal transduction-124 N-Insulin signal transduction-45 46 inhibition N-Insulin signal transduction-107 N-Insulin signal transduction-45 46 activation N-Insulin signal transduction-128 N-Insulin signal transduction-144 activation N-Insulin signal transduction-146 N-Insulin signal transduction-144 activation N-Insulin signal transduction-138 N-Insulin signal transduction-144 activation N-Insulin signal transduction-139 N-Insulin signal transduction-144 activation N-Insulin signal transduction-140 N-Insulin signal transduction-144 activation N-Insulin signal transduction-149 N-Insulin signal transduction-144 activation N-Insulin signal transduction-141 N-Insulin signal transduction-144 activation N-Insulin signal transduction-147 N-Insulin signal transduction-144 activation N-Insulin signal transduction-145 N-Insulin signal transduction-144 activation N-Insulin signal transduction-142 N-Insulin signal transduction-144 activation N-Insulin signal transduction-148 N-Insulin signal transduction-144 activation N-Insulin signal transduction-143 N-Insulin signal transduction-112 activation N-Insulin signal transduction-129 N-Insulin signal transduction-112 activation N-Insulin signal transduction-93 N-Insulin signal transduction-112 activation N-Insulin signal transduction-71 N-Insulin signal transduction-70 activation N-Insulin signal transduction-68 N-Insulin signal transduction-119 activation N-Insulin signal transduction-118 N-Insulin signal transduction-89 inhibition N-Insulin signal transduction-103 N-Insulin signal transduction-123 activation N-Insulin signal transduction-110 N-Insulin signal transduction-123 activation N-Insulin signal transduction-108 N-Insulin signal transduction-76 inhibition N-Insulin signal transduction-103 N-Insulin signal transduction-76 inhibition N-Insulin signal transduction-103 N-Insulin signal transduction-124 inhibition N-Insulin signal transduction-125 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Interferon signaling subgraph.att000066400000000000000000000035001426625374700316110ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Interferon signaling subgraph-1 JNK 52 133 white rectangle gene 0.5 black 46 17 JNK N-Interferon signaling subgraph-10 GRB2 46 123 white rectangle gene 0.5 black 46 17 4566 N-Interferon signaling subgraph-11 IFNG 131 44 white rectangle gene 0.5 black 46 17 5438 N-Interferon signaling subgraph-12 IRS1 56 143 white rectangle gene 0.5 black 46 17 6125 N-Interferon signaling subgraph-13 IRS2 62 128 white rectangle gene 0.5 black 46 17 6126 N-Interferon signaling subgraph-14 APP 6 52 white rectangle gene 0.5 black 46 17 620 N-Interferon signaling subgraph-16 NRAS 32 153 white rectangle gene 0.5 black 46 17 7989 N-Interferon signaling subgraph-17 PRKCD 52 0 white rectangle gene 0.5 black 46 17 9399 N-Interferon signaling subgraph-18 PRKCD 46 2 white rectangle gene 0.5 black 46 17 9399 N-Interferon signaling subgraph-19 RAC1 0 48 white rectangle gene 0.5 black 46 17 9801 N-Interferon signaling subgraph-2 SHC1 41 132 white rectangle gene 0.5 black 46 17 10840 N-Interferon signaling subgraph-20 RAF1 40 159 white rectangle gene 0.5 black 46 17 9829 N-Interferon signaling subgraph-21 Ifng 154 128 white rectangle gene 0.5 black 46 17 107656 N-Interferon signaling subgraph-22 Bace1 145 129 white rectangle gene 0.5 black 46 17 1346542 N-Interferon signaling subgraph-24 App 159 122 white rectangle gene 0.5 black 46 17 88059 N-Interferon signaling subgraph-3 TNF 137 37 white rectangle gene 0.5 black 46 17 11892 N-Interferon signaling subgraph-5 SHC4 55 122 white rectangle gene 0.5 black 46 17 16743 N-Interferon signaling subgraph-6 SHC3 63 137 white rectangle gene 0.5 black 46 17 18181 N-Interferon signaling subgraph-7 CYP27B1 124 51 white rectangle gene 0.5 black 46 17 2606 N-Interferon signaling subgraph-9 PIK3R5 42 147 white rectangle gene 0.5 black 46 17 30035 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Interferon signaling subgraph.sif000066400000000000000000000025301426625374700316040ustar00rootroot000000000000000 1 2 N-Interferon signaling subgraph-11 activation N-Interferon signaling subgraph-7 N-Interferon signaling subgraph-11 activation N-Interferon signaling subgraph-3 N-Interferon signaling subgraph-9 activation N-Interferon signaling subgraph-1 N-Interferon signaling subgraph-9 activation N-Interferon signaling subgraph-16 N-Interferon signaling subgraph-9 activation N-Interferon signaling subgraph-20 N-Interferon signaling subgraph-6 activation N-Interferon signaling subgraph-1 N-Interferon signaling subgraph-19 activation N-Interferon signaling subgraph-14 N-Interferon signaling subgraph-5 activation N-Interferon signaling subgraph-1 N-Interferon signaling subgraph-17 activation N-Interferon signaling subgraph-17 N-Interferon signaling subgraph-17 activation N-Interferon signaling subgraph-17 N-Interferon signaling subgraph-2 activation N-Interferon signaling subgraph-1 N-Interferon signaling subgraph-12 activation N-Interferon signaling subgraph-1 N-Interferon signaling subgraph-18 activation N-Interferon signaling subgraph-17 N-Interferon signaling subgraph-21 activation N-Interferon signaling subgraph-22 N-Interferon signaling subgraph-21 activation N-Interferon signaling subgraph-24 N-Interferon signaling subgraph-13 activation N-Interferon signaling subgraph-1 N-Interferon signaling subgraph-10 activation N-Interferon signaling subgraph-1 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Interleukin signaling subgraph.att000066400000000000000000000155471426625374700320050ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Interleukin signaling subgraph-1 2 VIP VIPR1 104 50 white rectangle gene,gene 0.5 black 46 17 12693,/,12694 N-Interleukin signaling subgraph-10 IL1 6 76 white rectangle gene 0.5 black 46 17 IL1 N-Interleukin signaling subgraph-11 Notch 77 43 white rectangle gene 0.5 black 46 17 Notch N-Interleukin signaling subgraph-12 TGFB 122 66 white rectangle gene 0.5 black 46 17 TGFB N-Interleukin signaling subgraph-13 CCL2 84 62 white rectangle gene 0.5 black 46 17 10618 N-Interleukin signaling subgraph-14 CCL20 90 81 white rectangle gene 0.5 black 46 17 10619 N-Interleukin signaling subgraph-15 CXCL10 95 64 white rectangle gene 0.5 black 46 17 10637 N-Interleukin signaling subgraph-17 SDC1 172 11 white rectangle gene 0.5 black 46 17 10658 N-Interleukin signaling subgraph-18 SNCA 79 69 white rectangle gene 0.5 black 46 17 11138 N-Interleukin signaling subgraph-19 STAT3 83 40 white rectangle gene 0.5 black 46 17 11364 N-Interleukin signaling subgraph-20 TGFB1 138 72 white rectangle gene 0.5 black 46 17 11766 N-Interleukin signaling subgraph-21 TGFB2 138 66 white rectangle gene 0.5 black 46 17 11768 N-Interleukin signaling subgraph-22 TGFB3 139 70 white rectangle gene 0.5 black 46 17 11769 N-Interleukin signaling subgraph-23 TNF 71 54 white rectangle gene 0.5 black 46 17 11892 N-Interleukin signaling subgraph-24 C5 86 57 white rectangle gene 0.5 black 46 17 1331 N-Interleukin signaling subgraph-26 SCARB1 95 76 white rectangle gene 0.5 black 46 17 1664 N-Interleukin signaling subgraph-27 SCARF1 91 64 white rectangle gene 0.5 black 46 17 16820 N-Interleukin signaling subgraph-28 IL32 128 21 white rectangle gene 0.5 black 46 17 16830 N-Interleukin signaling subgraph-29 ADAM10 98 76 white rectangle gene 0.5 black 46 17 188 N-Interleukin signaling subgraph-3 4 IL1B IL1R1 67 79 white rectangle gene,gene 0.5 black 46 17 5992,/,5993 N-Interleukin signaling subgraph-30 CHI3L1 77 36 white rectangle gene 0.5 black 46 17 1932 N-Interleukin signaling subgraph-31 ADAM17 97 68 white rectangle gene 0.5 black 46 17 195 N-Interleukin signaling subgraph-32 CLU 82 66 white rectangle gene 0.5 black 46 17 2095 N-Interleukin signaling subgraph-33 CSF1R 136 75 white rectangle gene 0.5 black 46 17 2433 N-Interleukin signaling subgraph-34 HSPB6 84 132 white rectangle gene 0.5 black 46 17 26511 N-Interleukin signaling subgraph-35 IL34 129 68 white rectangle gene 0.5 black 46 17 28529 N-Interleukin signaling subgraph-36 SCARA5 87 81 white rectangle gene 0.5 black 46 17 28701 N-Interleukin signaling subgraph-37 GNRH1 89 57 white rectangle gene 0.5 black 46 17 4419 N-Interleukin signaling subgraph-38 CXCL1 96 72 white rectangle gene 0.5 black 46 17 4602 N-Interleukin signaling subgraph-39 CXCL2 81 75 white rectangle gene 0.5 black 46 17 4603 N-Interleukin signaling subgraph-40 GSK3B 77 149 white rectangle gene 0.5 black 46 17 4617 N-Interleukin signaling subgraph-41 HMOX1 133 75 white rectangle gene 0.5 black 46 17 5013 N-Interleukin signaling subgraph-42 HSPA1B 119 60 white rectangle gene 0.5 black 46 17 5233 N-Interleukin signaling subgraph-43 HSPB2 82 150 white rectangle gene 0.5 black 46 17 5247 N-Interleukin signaling subgraph-44 HSPB3 77 140 white rectangle gene 0.5 black 46 17 5248 N-Interleukin signaling subgraph-45 IDE 123 62 white rectangle gene 0.5 black 46 17 5381 N-Interleukin signaling subgraph-46 IFNG 91 76 white rectangle gene 0.5 black 46 17 5438 N-Interleukin signaling subgraph-47 IL10 81 58 white rectangle gene 0.5 black 46 17 5962 N-Interleukin signaling subgraph-48 IL13 166 14 white rectangle gene 0.5 black 46 17 5973 N-Interleukin signaling subgraph-49 IL16 92 79 white rectangle gene 0.5 black 46 17 5980 N-Interleukin signaling subgraph-5 6 Gfap Il12b 127 149 white rectangle gene,gene 0.5 black 46 17 95697,/,96540 N-Interleukin signaling subgraph-51 IL1A 76 64 white rectangle gene 0.5 black 46 17 5991 N-Interleukin signaling subgraph-52 IL1B 90 69 white rectangle gene 0.5 black 46 17 5992 N-Interleukin signaling subgraph-53 IL1R1 88 77 white rectangle gene 0.5 black 46 17 5993 N-Interleukin signaling subgraph-54 IL1RN 76 75 white rectangle gene 0.5 black 46 17 6000 N-Interleukin signaling subgraph-55 IL4 100 57 white rectangle gene 0.5 black 46 17 6014 N-Interleukin signaling subgraph-56 IL6 85 47 white rectangle gene 0.5 black 46 17 6018 N-Interleukin signaling subgraph-57 IL6ST 84 37 white rectangle gene 0.5 black 46 17 6021 N-Interleukin signaling subgraph-58 CXCL8 81 142 white rectangle gene 0.5 black 46 17 6025 N-Interleukin signaling subgraph-59 INS 113 58 white rectangle gene 0.5 black 46 17 6081 N-Interleukin signaling subgraph-60 APOE 99 71 white rectangle gene 0.5 black 46 17 613 N-Interleukin signaling subgraph-61 JAK3 88 39 white rectangle gene 0.5 black 46 17 6193 N-Interleukin signaling subgraph-62 APP 81 69 white rectangle gene 0.5 black 46 17 620 N-Interleukin signaling subgraph-63 APP 82 78 white rectangle gene 0.5 black 46 17 620 N-Interleukin signaling subgraph-64 APP 113 60 white rectangle gene 0.5 black 46 17 620 N-Interleukin signaling subgraph-65 MSR1 95 79 white rectangle gene 0.5 black 46 17 7376 N-Interleukin signaling subgraph-66 NFKB1 78 71 white rectangle gene 0.5 black 46 17 7794 N-Interleukin signaling subgraph-67 NFKB2 80 65 white rectangle gene 0.5 black 46 17 7795 N-Interleukin signaling subgraph-68 OSM 72 28 white rectangle gene 0.5 black 46 17 8506 N-Interleukin signaling subgraph-69 PPARG 79 40 white rectangle gene 0.5 black 46 17 9236 N-Interleukin signaling subgraph-7 sAPP-beta 85 79 white rectangle gene 0.5 black 46 17 CONSO00042 N-Interleukin signaling subgraph-70 Stat3 133 17 white rectangle gene 0.5 black 46 17 103038 N-Interleukin signaling subgraph-71 Tnf 130 13 white rectangle gene 0.5 black 46 17 104798 N-Interleukin signaling subgraph-73 Cxcr3 124 143 white rectangle gene 0.5 black 46 17 1277207 N-Interleukin signaling subgraph-74 Eif2ak2 117 2 white rectangle gene 0.5 black 46 17 1353449 N-Interleukin signaling subgraph-75 Cysltr1 120 0 white rectangle gene 0.5 black 46 17 1926218 N-Interleukin signaling subgraph-76 Nrg1 0 76 white rectangle gene 0.5 black 46 17 96083 N-Interleukin signaling subgraph-78 Il10 126 15 white rectangle gene 0.5 black 46 17 96537 N-Interleukin signaling subgraph-79 Il1a 120 56 white rectangle gene 0.5 black 46 17 96542 N-Interleukin signaling subgraph-8 sAPP-alpha 85 74 white rectangle gene 0.5 black 46 17 CONSO00067 N-Interleukin signaling subgraph-80 Il1b 123 7 white rectangle gene 0.5 black 46 17 96543 N-Interleukin signaling subgraph-81 Il6 121 40 white rectangle gene 0.5 black 46 17 96559 N-Interleukin signaling subgraph-82 Mmp2 125 0 white rectangle gene 0.5 black 46 17 97009 N-Interleukin signaling subgraph-83 Nfkb1 136 18 white rectangle gene 0.5 black 46 17 97312 N-Interleukin signaling subgraph-84 Nos2 135 22 white rectangle gene 0.5 black 46 17 97361 N-Interleukin signaling subgraph-85 Ptgs2 134 14 white rectangle gene 0.5 black 46 17 97798 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Interleukin signaling subgraph.sif000066400000000000000000000164651426625374700317760ustar00rootroot000000000000000 1 2 N-Interleukin signaling subgraph-67 activation N-Interleukin signaling subgraph-51 N-Interleukin signaling subgraph-67 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-46 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-46 activation N-Interleukin signaling subgraph-49 N-Interleukin signaling subgraph-84 inhibition N-Interleukin signaling subgraph-28 N-Interleukin signaling subgraph-44 activation N-Interleukin signaling subgraph-58 N-Interleukin signaling subgraph-11 activation N-Interleukin signaling subgraph-56 N-Interleukin signaling subgraph-26 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-74 activation N-Interleukin signaling subgraph-80 N-Interleukin signaling subgraph-65 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-15 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-38 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-60 N-Interleukin signaling subgraph-52 inhibition N-Interleukin signaling subgraph-37 N-Interleukin signaling subgraph-52 inhibition N-Interleukin signaling subgraph-55 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-29 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-8 N-Interleukin signaling subgraph-52 inhibition N-Interleukin signaling subgraph-7 N-Interleukin signaling subgraph-52 inhibition N-Interleukin signaling subgraph-63 N-Interleukin signaling subgraph-52 inhibition N-Interleukin signaling subgraph-64 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-31 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-31 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-32 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-32 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-62 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-49 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-13 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-14 N-Interleukin signaling subgraph-52 activation N-Interleukin signaling subgraph-39 N-Interleukin signaling subgraph-43 activation N-Interleukin signaling subgraph-58 N-Interleukin signaling subgraph-73 activation N-Interleukin signaling subgraph-5 6 N-Interleukin signaling subgraph-35 activation N-Interleukin signaling subgraph-33 N-Interleukin signaling subgraph-35 inhibition N-Interleukin signaling subgraph-64 N-Interleukin signaling subgraph-35 activation N-Interleukin signaling subgraph-45 N-Interleukin signaling subgraph-35 activation N-Interleukin signaling subgraph-41 N-Interleukin signaling subgraph-35 activation N-Interleukin signaling subgraph-20 N-Interleukin signaling subgraph-35 activation N-Interleukin signaling subgraph-21 N-Interleukin signaling subgraph-35 activation N-Interleukin signaling subgraph-22 N-Interleukin signaling subgraph-35 activation N-Interleukin signaling subgraph-12 N-Interleukin signaling subgraph-40 activation N-Interleukin signaling subgraph-58 N-Interleukin signaling subgraph-59 inhibition N-Interleukin signaling subgraph-45 N-Interleukin signaling subgraph-59 inhibition N-Interleukin signaling subgraph-55 N-Interleukin signaling subgraph-56 inhibition N-Interleukin signaling subgraph-37 N-Interleukin signaling subgraph-56 inhibition N-Interleukin signaling subgraph-55 N-Interleukin signaling subgraph-56 activation N-Interleukin signaling subgraph-57 N-Interleukin signaling subgraph-56 activation N-Interleukin signaling subgraph-61 N-Interleukin signaling subgraph-56 activation N-Interleukin signaling subgraph-19 N-Interleukin signaling subgraph-56 activation N-Interleukin signaling subgraph-30 N-Interleukin signaling subgraph-17 activation N-Interleukin signaling subgraph-48 N-Interleukin signaling subgraph-62 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-36 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-64 activation N-Interleukin signaling subgraph-79 N-Interleukin signaling subgraph-64 activation N-Interleukin signaling subgraph-81 N-Interleukin signaling subgraph-51 activation N-Interleukin signaling subgraph-32 N-Interleukin signaling subgraph-51 activation N-Interleukin signaling subgraph-62 N-Interleukin signaling subgraph-51 activation N-Interleukin signaling subgraph-66 N-Interleukin signaling subgraph-51 activation N-Interleukin signaling subgraph-67 N-Interleukin signaling subgraph-42 activation N-Interleukin signaling subgraph-64 N-Interleukin signaling subgraph-55 activation N-Interleukin signaling subgraph-64 N-Interleukin signaling subgraph-55 activation N-Interleukin signaling subgraph-1 2 N-Interleukin signaling subgraph-85 inhibition N-Interleukin signaling subgraph-28 N-Interleukin signaling subgraph-53 inhibition N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-47 activation N-Interleukin signaling subgraph-51 N-Interleukin signaling subgraph-47 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-47 activation N-Interleukin signaling subgraph-56 N-Interleukin signaling subgraph-47 activation N-Interleukin signaling subgraph-23 N-Interleukin signaling subgraph-47 activation N-Interleukin signaling subgraph-13 N-Interleukin signaling subgraph-75 activation N-Interleukin signaling subgraph-80 N-Interleukin signaling subgraph-28 inhibition N-Interleukin signaling subgraph-81 N-Interleukin signaling subgraph-28 inhibition N-Interleukin signaling subgraph-71 N-Interleukin signaling subgraph-28 inhibition N-Interleukin signaling subgraph-80 N-Interleukin signaling subgraph-28 activation N-Interleukin signaling subgraph-78 N-Interleukin signaling subgraph-28 activation N-Interleukin signaling subgraph-70 N-Interleukin signaling subgraph-28 inhibition N-Interleukin signaling subgraph-83 N-Interleukin signaling subgraph-24 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-24 activation N-Interleukin signaling subgraph-56 N-Interleukin signaling subgraph-12 inhibition N-Interleukin signaling subgraph-64 N-Interleukin signaling subgraph-18 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-82 activation N-Interleukin signaling subgraph-80 N-Interleukin signaling subgraph-30 inhibition N-Interleukin signaling subgraph-56 N-Interleukin signaling subgraph-76 inhibition N-Interleukin signaling subgraph-10 N-Interleukin signaling subgraph-68 activation N-Interleukin signaling subgraph-30 N-Interleukin signaling subgraph-54 inhibition N-Interleukin signaling subgraph-3 4 N-Interleukin signaling subgraph-54 inhibition N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-34 activation N-Interleukin signaling subgraph-58 N-Interleukin signaling subgraph-69 activation N-Interleukin signaling subgraph-56 N-Interleukin signaling subgraph-69 inhibition N-Interleukin signaling subgraph-56 N-Interleukin signaling subgraph-27 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-66 activation N-Interleukin signaling subgraph-52 N-Interleukin signaling subgraph-66 activation N-Interleukin signaling subgraph-54 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/JAK-STAT signaling subgraph.att000066400000000000000000000031541426625374700306610ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-JAK-STAT signaling subgraph-1 sAPP-alpha 76 184 white rectangle gene 0.5 black 46 17 CONSO00067 N-JAK-STAT signaling subgraph-10 FOS 167 97 white rectangle gene 0.5 black 46 17 3796 N-JAK-STAT signaling subgraph-11 HSPA1A 53 0 white rectangle gene 0.5 black 46 17 5232 N-JAK-STAT signaling subgraph-13 JAK2 158 90 white rectangle gene 0.5 black 46 17 6192 N-JAK-STAT signaling subgraph-14 APP 158 102 white rectangle gene 0.5 black 46 17 620 N-JAK-STAT signaling subgraph-15 PIK3R1 68 84 white rectangle gene 0.5 black 46 17 8979 N-JAK-STAT signaling subgraph-16 BACE1 0 49 white rectangle gene 0.5 black 46 17 933 N-JAK-STAT signaling subgraph-17 PRKCA 70 103 white rectangle gene 0.5 black 46 17 9393 N-JAK-STAT signaling subgraph-18 PRKCD 61 4 white rectangle gene 0.5 black 46 17 9399 N-JAK-STAT signaling subgraph-19 RB1 0 109 white rectangle gene 0.5 black 46 17 9884 N-JAK-STAT signaling subgraph-2 ERK 0 41 white rectangle gene 0.5 black 46 17 ERK N-JAK-STAT signaling subgraph-3 Notch 78 194 white rectangle gene 0.5 black 46 17 Notch N-JAK-STAT signaling subgraph-4 p38 1 118 white rectangle gene 0.5 black 46 17 p38 N-JAK-STAT signaling subgraph-5 STAT1 74 93 white rectangle gene 0.5 black 46 17 11362 N-JAK-STAT signaling subgraph-6 STAT3 158 115 white rectangle gene 0.5 black 46 17 11364 N-JAK-STAT signaling subgraph-7 STAT3 70 192 white rectangle gene 0.5 black 46 17 11364 N-JAK-STAT signaling subgraph-8 CAMK2A 85 90 white rectangle gene 0.5 black 46 17 1460 N-JAK-STAT signaling subgraph-9 CHI3L1 161 126 white rectangle gene 0.5 black 46 17 1932 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/JAK-STAT signaling subgraph.sif000066400000000000000000000020551426625374700306510ustar00rootroot000000000000000 1 2 N-JAK-STAT signaling subgraph-4 activation N-JAK-STAT signaling subgraph-19 N-JAK-STAT signaling subgraph-6 activation N-JAK-STAT signaling subgraph-9 N-JAK-STAT signaling subgraph-2 activation N-JAK-STAT signaling subgraph-16 N-JAK-STAT signaling subgraph-14 inhibition N-JAK-STAT signaling subgraph-13 N-JAK-STAT signaling subgraph-14 activation N-JAK-STAT signaling subgraph-10 N-JAK-STAT signaling subgraph-14 inhibition N-JAK-STAT signaling subgraph-6 N-JAK-STAT signaling subgraph-15 activation N-JAK-STAT signaling subgraph-5 N-JAK-STAT signaling subgraph-7 activation N-JAK-STAT signaling subgraph-3 N-JAK-STAT signaling subgraph-1 activation N-JAK-STAT signaling subgraph-7 N-JAK-STAT signaling subgraph-1 activation N-JAK-STAT signaling subgraph-3 N-JAK-STAT signaling subgraph-18 activation N-JAK-STAT signaling subgraph-11 N-JAK-STAT signaling subgraph-18 activation N-JAK-STAT signaling subgraph-11 N-JAK-STAT signaling subgraph-17 activation N-JAK-STAT signaling subgraph-5 N-JAK-STAT signaling subgraph-8 activation N-JAK-STAT signaling subgraph-5 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Leptin subgraph.att000066400000000000000000000004471426625374700270040ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Leptin subgraph-1 GSK3B 172 126 white rectangle gene 0.5 black 46 17 4617 N-Leptin subgraph-2 LEP 0 173 white rectangle gene 0.5 black 46 17 6553 N-Leptin subgraph-3 MAPT 45 0 white rectangle gene 0.5 black 46 17 6893 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Leptin subgraph.sif000066400000000000000000000002371426625374700267720ustar00rootroot000000000000000 1 2 N-Leptin subgraph-2 inhibition N-Leptin subgraph-3 N-Leptin subgraph-2 inhibition N-Leptin subgraph-1 N-Leptin subgraph-1 activation N-Leptin subgraph-3 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Lipid peroxidation subgraph.att000066400000000000000000000003661426625374700313000ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Lipid peroxidation subgraph-2 TNF 0 199 white rectangle gene 0.5 black 46 17 11892 N-Lipid peroxidation subgraph-3 PAFAH2 72 0 white rectangle gene 0.5 black 46 17 8579 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Lipid peroxidation subgraph.sif000066400000000000000000000001211426625374700312560ustar00rootroot000000000000000 1 2 N-Lipid peroxidation subgraph-3 inhibition N-Lipid peroxidation subgraph-2 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Low density lipoprotein subgraph.att000066400000000000000000000123541426625374700322770ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Low density lipoprotein subgraph-1 2 ROCK2 SORL1 44 14 white rectangle gene,gene 0.5 black 46 17 10252,/,11185 N-Low density lipoprotein subgraph-11 12 13 APBA1 APP LRP8 135 4 white rectangle gene,gene,gene 0.5 black 46 17 578,/,620,/,6700 N-Low density lipoprotein subgraph-14 15 APBA1 ARL3 103 154 white rectangle gene,gene 0.5 black 46 17 578,/,694 N-Low density lipoprotein subgraph-16 17 18 APBA2 APP LRP8 127 18 white rectangle gene,gene,gene 0.5 black 46 17 579,/,620,/,6700 N-Low density lipoprotein subgraph-19 20 APBA2 ARL3 106 139 white rectangle gene,gene 0.5 black 46 17 579,/,694 N-Low density lipoprotein subgraph-21 22 23 APBA3 APP LRP8 138 15 white rectangle gene,gene,gene 0.5 black 46 17 580,/,620,/,6700 N-Low density lipoprotein subgraph-24 25 APBA3 ARL3 94 149 white rectangle gene,gene 0.5 black 46 17 580,/,694 N-Low density lipoprotein subgraph-26 27 28 APBB1 APP LRP1 98 153 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6692 N-Low density lipoprotein subgraph-29 30 31 APBB1 APP LRP2 107 144 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6694 N-Low density lipoprotein subgraph-3 4 VLDLR RELN 47 137 white rectangle gene,gene 0.5 black 46 17 12698,/,9957 N-Low density lipoprotein subgraph-32 33 APOE APP 40 39 white rectangle gene,gene 0.5 black 46 17 613,/,620 N-Low density lipoprotein subgraph-34 35 APOE LDLR 95 108 white rectangle gene,gene 0.5 black 46 17 613,/,6547 N-Low density lipoprotein subgraph-36 37 APOE LRP1 128 147 white rectangle gene,gene 0.5 black 46 17 613,/,6692 N-Low density lipoprotein subgraph-38 39 APOE LRP8 132 11 white rectangle gene,gene 0.5 black 46 17 613,/,6700 N-Low density lipoprotein subgraph-40 41 APP LRP1 134 152 white rectangle gene,gene 0.5 black 46 17 620,/,6692 N-Low density lipoprotein subgraph-42 43 APP LDLR 109 120 white rectangle gene,gene 0.5 black 46 17 620,/,6547 N-Low density lipoprotein subgraph-44 45 APP LRP1 132 134 white rectangle gene,gene 0.5 black 46 17 620,/,6692 N-Low density lipoprotein subgraph-46 47 LRP8 RELN 25 56 white rectangle gene,gene 0.5 black 46 17 6700,/,9957 N-Low density lipoprotein subgraph-48 YENPTY endocytosis motif (APP) 100 123 white rectangle gene 0.5 black 46 17 CONSO00041 N-Low density lipoprotein subgraph-49 sAPP-alpha 47 21 white rectangle gene 0.5 black 46 17 CONSO00067 N-Low density lipoprotein subgraph-5 6 CDK5 CDK5R1 0 141 white rectangle gene,gene 0.5 black 46 17 1774,/,1775 N-Low density lipoprotein subgraph-50 NCOA 70 94 white rectangle gene 0.5 black 46 17 NCOA N-Low density lipoprotein subgraph-51 SRC 17 58 white rectangle gene 0.5 black 46 17 SRC N-Low density lipoprotein subgraph-52 Glutamate ionotropic receptor NMDA type subunits 9 56 white rectangle gene 0.5 black 46 17 1201 N-Low density lipoprotein subgraph-54 SORL1 102 136 white rectangle gene 0.5 black 46 17 11185 N-Low density lipoprotein subgraph-55 SORL1 93 4 white rectangle gene 0.5 black 46 17 11185 N-Low density lipoprotein subgraph-56 VLDLR 95 93 white rectangle gene 0.5 black 46 17 12698 N-Low density lipoprotein subgraph-57 SORCS1 100 129 white rectangle gene 0.5 black 46 17 16697 N-Low density lipoprotein subgraph-58 MYLIP 92 102 white rectangle gene 0.5 black 46 17 21155 N-Low density lipoprotein subgraph-60 DAB1 143 158 white rectangle gene 0.5 black 46 17 2661 N-Low density lipoprotein subgraph-61 DAB1 55 124 white rectangle gene 0.5 black 46 17 2661 N-Low density lipoprotein subgraph-62 IAPP 6 139 white rectangle gene 0.5 black 46 17 5329 N-Low density lipoprotein subgraph-63 IGF1 186 96 white rectangle gene 0.5 black 46 17 5464 N-Low density lipoprotein subgraph-64 APBB1 137 161 white rectangle gene 0.5 black 46 17 581 N-Low density lipoprotein subgraph-65 APOE 32 51 white rectangle gene 0.5 black 46 17 613 N-Low density lipoprotein subgraph-67 APP 102 144 white rectangle gene 0.5 black 46 17 620 N-Low density lipoprotein subgraph-68 APP 107 129 white rectangle gene 0.5 black 46 17 620 N-Low density lipoprotein subgraph-69 LDLR 100 114 white rectangle gene 0.5 black 46 17 6547 N-Low density lipoprotein subgraph-7 8 AGRN LRP4 168 57 white rectangle gene,gene 0.5 black 46 17 329,/,6696 N-Low density lipoprotein subgraph-70 LRP1 125 140 white rectangle gene 0.5 black 46 17 6692 N-Low density lipoprotein subgraph-71 LRP1B 37 45 white rectangle gene 0.5 black 46 17 6693 N-Low density lipoprotein subgraph-73 LRP8 69 103 white rectangle gene 0.5 black 46 17 6700 N-Low density lipoprotein subgraph-74 LRP8 81 100 white rectangle gene 0.5 black 46 17 6700 N-Low density lipoprotein subgraph-75 A2M 135 138 white rectangle gene 0.5 black 46 17 7 N-Low density lipoprotein subgraph-76 MUSK 174 54 white rectangle gene 0.5 black 46 17 7525 N-Low density lipoprotein subgraph-77 NR1H2 52 132 white rectangle gene 0.5 black 46 17 7965 N-Low density lipoprotein subgraph-78 NR1H3 48 130 white rectangle gene 0.5 black 46 17 7966 N-Low density lipoprotein subgraph-79 SERPINE1 134 144 white rectangle gene 0.5 black 46 17 8583 N-Low density lipoprotein subgraph-82 PRKCA 95 0 white rectangle gene 0.5 black 46 17 9393 N-Low density lipoprotein subgraph-83 RELN 62 113 white rectangle gene 0.5 black 46 17 9957 N-Low density lipoprotein subgraph-9 10 IGF1 LRP2 193 95 white rectangle gene,gene 0.5 black 46 17 5464,/,6694 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Low density lipoprotein subgraph.sif000066400000000000000000000110211426625374700322560ustar00rootroot000000000000000 1 2 N-Low density lipoprotein subgraph-57 inhibition N-Low density lipoprotein subgraph-68 N-Low density lipoprotein subgraph-73 inhibition N-Low density lipoprotein subgraph-83 N-Low density lipoprotein subgraph-54 inhibition N-Low density lipoprotein subgraph-67 N-Low density lipoprotein subgraph-54 inhibition N-Low density lipoprotein subgraph-67 N-Low density lipoprotein subgraph-54 inhibition N-Low density lipoprotein subgraph-68 N-Low density lipoprotein subgraph-69 activation N-Low density lipoprotein subgraph-34 35 N-Low density lipoprotein subgraph-69 activation N-Low density lipoprotein subgraph-68 N-Low density lipoprotein subgraph-24 25 activation N-Low density lipoprotein subgraph-67 N-Low density lipoprotein subgraph-71 activation N-Low density lipoprotein subgraph-32 33 N-Low density lipoprotein subgraph-7 8 activation N-Low density lipoprotein subgraph-76 N-Low density lipoprotein subgraph-26 27 28 activation N-Low density lipoprotein subgraph-67 N-Low density lipoprotein subgraph-26 27 28 activation N-Low density lipoprotein subgraph-67 N-Low density lipoprotein subgraph-50 activation N-Low density lipoprotein subgraph-73 N-Low density lipoprotein subgraph-74 activation N-Low density lipoprotein subgraph-73 N-Low density lipoprotein subgraph-64 activation N-Low density lipoprotein subgraph-40 41 N-Low density lipoprotein subgraph-63 activation N-Low density lipoprotein subgraph-9 10 N-Low density lipoprotein subgraph-5 6 activation N-Low density lipoprotein subgraph-62 N-Low density lipoprotein subgraph-5 6 activation N-Low density lipoprotein subgraph-62 N-Low density lipoprotein subgraph-83 activation N-Low density lipoprotein subgraph-61 N-Low density lipoprotein subgraph-83 activation N-Low density lipoprotein subgraph-61 N-Low density lipoprotein subgraph-14 15 activation N-Low density lipoprotein subgraph-67 N-Low density lipoprotein subgraph-51 activation N-Low density lipoprotein subgraph-52 N-Low density lipoprotein subgraph-82 activation N-Low density lipoprotein subgraph-55 N-Low density lipoprotein subgraph-67 activation N-Low density lipoprotein subgraph-68 N-Low density lipoprotein subgraph-1 2 activation N-Low density lipoprotein subgraph-49 N-Low density lipoprotein subgraph-46 47 activation N-Low density lipoprotein subgraph-51 N-Low density lipoprotein subgraph-68 inhibition N-Low density lipoprotein subgraph-69 N-Low density lipoprotein subgraph-68 activation N-Low density lipoprotein subgraph-68 N-Low density lipoprotein subgraph-77 inhibition N-Low density lipoprotein subgraph-3 4 N-Low density lipoprotein subgraph-77 inhibition N-Low density lipoprotein subgraph-61 N-Low density lipoprotein subgraph-58 activation N-Low density lipoprotein subgraph-69 N-Low density lipoprotein subgraph-58 activation N-Low density lipoprotein subgraph-56 N-Low density lipoprotein subgraph-58 activation N-Low density lipoprotein subgraph-74 N-Low density lipoprotein subgraph-21 22 23 activation N-Low density lipoprotein subgraph-38 39 N-Low density lipoprotein subgraph-78 inhibition N-Low density lipoprotein subgraph-3 4 N-Low density lipoprotein subgraph-78 inhibition N-Low density lipoprotein subgraph-61 N-Low density lipoprotein subgraph-60 inhibition N-Low density lipoprotein subgraph-40 41 N-Low density lipoprotein subgraph-16 17 18 activation N-Low density lipoprotein subgraph-38 39 N-Low density lipoprotein subgraph-65 inhibition N-Low density lipoprotein subgraph-46 47 N-Low density lipoprotein subgraph-65 activation N-Low density lipoprotein subgraph-71 N-Low density lipoprotein subgraph-42 43 activation N-Low density lipoprotein subgraph-68 N-Low density lipoprotein subgraph-70 activation N-Low density lipoprotein subgraph-36 37 N-Low density lipoprotein subgraph-70 activation N-Low density lipoprotein subgraph-40 41 N-Low density lipoprotein subgraph-70 activation N-Low density lipoprotein subgraph-44 45 N-Low density lipoprotein subgraph-70 activation N-Low density lipoprotein subgraph-44 45 N-Low density lipoprotein subgraph-70 activation N-Low density lipoprotein subgraph-75 N-Low density lipoprotein subgraph-70 inhibition N-Low density lipoprotein subgraph-68 N-Low density lipoprotein subgraph-70 activation N-Low density lipoprotein subgraph-79 N-Low density lipoprotein subgraph-48 inhibition N-Low density lipoprotein subgraph-68 N-Low density lipoprotein subgraph-29 30 31 activation N-Low density lipoprotein subgraph-67 N-Low density lipoprotein subgraph-19 20 activation N-Low density lipoprotein subgraph-67 N-Low density lipoprotein subgraph-11 12 13 activation N-Low density lipoprotein subgraph-38 39 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/MAPK-ERK subgraph.att000066400000000000000000000160401426625374700267140ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-MAPK-ERK subgraph-1 2 IRS GRB2 86 14 white rectangle gene,gene 0.5 black 46 17 IRS,/,4566 N-MAPK-ERK subgraph-10 11 12 SOS1 GAB1 GRB2 90 26 white rectangle gene,gene,gene 0.5 black 46 17 11187,/,4066,/,4566 N-MAPK-ERK subgraph-100 BAD 185 32 white rectangle gene 0.5 black 46 17 936 N-MAPK-ERK subgraph-101 PRKACA 67 42 white rectangle gene 0.5 black 46 17 9380 N-MAPK-ERK subgraph-102 PRKCA 132 129 white rectangle gene 0.5 black 46 17 9393 N-MAPK-ERK subgraph-103 PSEN1 127 13 white rectangle gene 0.5 black 46 17 9508 N-MAPK-ERK subgraph-104 RAF1 98 34 white rectangle gene 0.5 black 46 17 9829 N-MAPK-ERK subgraph-105 RASGRF1 108 29 white rectangle gene 0.5 black 46 17 9875 N-MAPK-ERK subgraph-106 Casp3 8 73 white rectangle gene 0.5 black 46 17 107739 N-MAPK-ERK subgraph-107 Casp9 12 68 white rectangle gene 0.5 black 46 17 1277950 N-MAPK-ERK subgraph-108 Map3k5 18 78 white rectangle gene 0.5 black 46 17 1346876 N-MAPK-ERK subgraph-109 Stk4 13 83 white rectangle gene 0.5 black 46 17 1929004 N-MAPK-ERK subgraph-110 Ifna13 0 35 white rectangle gene 0.5 black 46 17 2667155 N-MAPK-ERK subgraph-111 Sod1 14 78 white rectangle gene 0.5 black 46 17 98351 N-MAPK-ERK subgraph-13 14 SPAG9 MAPK14 61 5 white rectangle gene,gene 0.5 black 46 17 14524,/,6876 N-MAPK-ERK subgraph-15 16 SHC3 APP 84 47 white rectangle gene,gene 0.5 black 46 17 18181,/,620 N-MAPK-ERK subgraph-17 18 SHC2 GRB2 105 27 white rectangle gene,gene 0.5 black 46 17 29869,/,4566 N-MAPK-ERK subgraph-19 20 21 SHC2 GRB2 APP 108 44 white rectangle gene,gene,gene 0.5 black 46 17 29869,/,4566,/,620 N-MAPK-ERK subgraph-22 23 24 SHC2 GRB2 APP 98 46 white rectangle gene,gene,gene 0.5 black 46 17 29869,/,4566,/,620 N-MAPK-ERK subgraph-25 26 GAB1 GRB2 87 14 white rectangle gene,gene 0.5 black 46 17 4066,/,4566 N-MAPK-ERK subgraph-27 28 GRB2 APP 109 34 white rectangle gene,gene 0.5 black 46 17 4566,/,620 N-MAPK-ERK subgraph-29 30 31 GRB2 APP PSEN1 91 54 white rectangle gene,gene,gene 0.5 black 46 17 4566,/,620,/,9508 N-MAPK-ERK subgraph-3 4 MEK GRB2 107 65 white rectangle gene,gene 0.5 black 46 17 MEK,/,4566 N-MAPK-ERK subgraph-32 33 GRB2 PSEN1 109 62 white rectangle gene,gene 0.5 black 46 17 4566,/,9508 N-MAPK-ERK subgraph-34 35 JUN PIN1 173 104 white rectangle gene,gene 0.5 black 46 17 6204,/,8988 N-MAPK-ERK subgraph-36 37 PRKCA RAF1 90 58 white rectangle gene,gene 0.5 black 46 17 9393,/,9829 N-MAPK-ERK subgraph-38 CAMK2_family 91 60 white rectangle gene 0.5 black 46 17 CAMK2_family N-MAPK-ERK subgraph-39 AKT 179 30 white rectangle gene 0.5 black 46 17 AKT N-MAPK-ERK subgraph-40 CAMK 63 40 white rectangle gene 0.5 black 46 17 CAMK N-MAPK-ERK subgraph-41 CREB 79 46 white rectangle gene 0.5 black 46 17 CREB N-MAPK-ERK subgraph-42 CREB 74 43 white rectangle gene 0.5 black 46 17 CREB N-MAPK-ERK subgraph-43 CREB 72 44 white rectangle gene 0.5 black 46 17 CREB N-MAPK-ERK subgraph-44 ERK 89 49 white rectangle gene 0.5 black 46 17 ERK N-MAPK-ERK subgraph-46 ERK 87 59 white rectangle gene 0.5 black 46 17 ERK N-MAPK-ERK subgraph-47 ERK 97 55 white rectangle gene 0.5 black 46 17 ERK N-MAPK-ERK subgraph-48 IRS 84 57 white rectangle gene 0.5 black 46 17 IRS N-MAPK-ERK subgraph-49 JNK 77 54 white rectangle gene 0.5 black 46 17 JNK N-MAPK-ERK subgraph-5 6 7 SHC1 GRB2 APP 80 51 white rectangle gene,gene,gene 0.5 black 46 17 10840,/,4566,/,620 N-MAPK-ERK subgraph-50 MEK 101 56 white rectangle gene 0.5 black 46 17 MEK N-MAPK-ERK subgraph-51 RAF 95 48 white rectangle gene 0.5 black 46 17 RAF N-MAPK-ERK subgraph-52 RAS 90 41 white rectangle gene 0.5 black 46 17 RAS N-MAPK-ERK subgraph-53 p38 15 73 white rectangle gene 0.5 black 46 17 p38 N-MAPK-ERK subgraph-54 Mitogen-activated protein kinases 0 39 white rectangle gene 0.5 black 46 17 651 N-MAPK-ERK subgraph-55 RPS6KA1 79 44 white rectangle gene 0.5 black 46 17 10430 N-MAPK-ERK subgraph-56 RPS6KA2 77 58 white rectangle gene 0.5 black 46 17 10431 N-MAPK-ERK subgraph-57 RPS6KA3 62 44 white rectangle gene 0.5 black 46 17 10432 N-MAPK-ERK subgraph-58 SOS1 100 41 white rectangle gene 0.5 black 46 17 11187 N-MAPK-ERK subgraph-59 SOS2 101 42 white rectangle gene 0.5 black 46 17 11188 N-MAPK-ERK subgraph-60 CAMK2A 65 47 white rectangle gene 0.5 black 46 17 1460 N-MAPK-ERK subgraph-61 CAST 116 29 white rectangle gene 0.5 black 46 17 1515 N-MAPK-ERK subgraph-62 LAMTOR3 110 30 white rectangle gene 0.5 black 46 17 15606 N-MAPK-ERK subgraph-63 CDH2 64 16 white rectangle gene 0.5 black 46 17 1759 N-MAPK-ERK subgraph-64 ADAM10 128 124 white rectangle gene 0.5 black 46 17 188 N-MAPK-ERK subgraph-65 CREB1 69 65 white rectangle gene 0.5 black 46 17 2345 N-MAPK-ERK subgraph-67 ELK1 85 72 white rectangle gene 0.5 black 46 17 3321 N-MAPK-ERK subgraph-68 ELK1 84 61 white rectangle gene 0.5 black 46 17 3321 N-MAPK-ERK subgraph-69 FOS 94 58 white rectangle gene 0.5 black 46 17 3796 N-MAPK-ERK subgraph-70 GRB2 118 12 white rectangle gene 0.5 black 46 17 4566 N-MAPK-ERK subgraph-71 GSK3A 178 25 white rectangle gene 0.5 black 46 17 4616 N-MAPK-ERK subgraph-72 GSK3B 189 23 white rectangle gene 0.5 black 46 17 4617 N-MAPK-ERK subgraph-73 GSK3B 184 26 white rectangle gene 0.5 black 46 17 4617 N-MAPK-ERK subgraph-74 IGF1 87 6 white rectangle gene 0.5 black 46 17 5464 N-MAPK-ERK subgraph-75 IGF1R 89 0 white rectangle gene 0.5 black 46 17 5465 N-MAPK-ERK subgraph-76 INS 81 9 white rectangle gene 0.5 black 46 17 6081 N-MAPK-ERK subgraph-77 INSR 73 8 white rectangle gene 0.5 black 46 17 6091 N-MAPK-ERK subgraph-78 IRS1 51 16 white rectangle gene 0.5 black 46 17 6125 N-MAPK-ERK subgraph-79 IRS1 60 13 white rectangle gene 0.5 black 46 17 6125 N-MAPK-ERK subgraph-8 9 SHC1 APP 104 47 white rectangle gene,gene 0.5 black 46 17 10840,/,620 N-MAPK-ERK subgraph-80 IRS2 54 0 white rectangle gene 0.5 black 46 17 6126 N-MAPK-ERK subgraph-81 IRS2 62 5 white rectangle gene 0.5 black 46 17 6126 N-MAPK-ERK subgraph-82 IRS4 104 9 white rectangle gene 0.5 black 46 17 6128 N-MAPK-ERK subgraph-83 IRS4 89 7 white rectangle gene 0.5 black 46 17 6128 N-MAPK-ERK subgraph-84 APOE 82 52 white rectangle gene 0.5 black 46 17 613 N-MAPK-ERK subgraph-86 APP 69 29 white rectangle gene 0.5 black 46 17 620 N-MAPK-ERK subgraph-87 APP 113 48 white rectangle gene 0.5 black 46 17 620 N-MAPK-ERK subgraph-88 APP 112 41 white rectangle gene 0.5 black 46 17 620 N-MAPK-ERK subgraph-89 JUN 124 125 white rectangle gene 0.5 black 46 17 6204 N-MAPK-ERK subgraph-90 JUN 84 53 white rectangle gene 0.5 black 46 17 6204 N-MAPK-ERK subgraph-91 MAPK1 107 34 white rectangle gene 0.5 black 46 17 6871 N-MAPK-ERK subgraph-92 MAPK14 62 21 white rectangle gene 0.5 black 46 17 6876 N-MAPK-ERK subgraph-93 MAPK3 103 35 white rectangle gene 0.5 black 46 17 6877 N-MAPK-ERK subgraph-94 MAPK8 78 63 white rectangle gene 0.5 black 46 17 6881 N-MAPK-ERK subgraph-95 MAPK8IP2 65 23 white rectangle gene 0.5 black 46 17 6883 N-MAPK-ERK subgraph-96 MAPT 78 37 white rectangle gene 0.5 black 46 17 6893 N-MAPK-ERK subgraph-97 MYC 95 53 white rectangle gene 0.5 black 46 17 7553 N-MAPK-ERK subgraph-98 PIN1 169 100 white rectangle gene 0.5 black 46 17 8988 N-MAPK-ERK subgraph-99 BACE1 95 40 white rectangle gene 0.5 black 46 17 933 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/MAPK-ERK subgraph.sif000066400000000000000000000157301426625374700267120ustar00rootroot000000000000000 1 2 N-MAPK-ERK subgraph-10 11 12 activation N-MAPK-ERK subgraph-52 N-MAPK-ERK subgraph-10 11 12 activation N-MAPK-ERK subgraph-104 N-MAPK-ERK subgraph-79 activation N-MAPK-ERK subgraph-78 N-MAPK-ERK subgraph-73 inhibition N-MAPK-ERK subgraph-72 N-MAPK-ERK subgraph-109 activation N-MAPK-ERK subgraph-108 N-MAPK-ERK subgraph-17 18 activation N-MAPK-ERK subgraph-105 N-MAPK-ERK subgraph-17 18 activation N-MAPK-ERK subgraph-104 N-MAPK-ERK subgraph-17 18 activation N-MAPK-ERK subgraph-62 N-MAPK-ERK subgraph-39 activation N-MAPK-ERK subgraph-73 N-MAPK-ERK subgraph-39 activation N-MAPK-ERK subgraph-71 N-MAPK-ERK subgraph-39 inhibition N-MAPK-ERK subgraph-100 N-MAPK-ERK subgraph-98 activation N-MAPK-ERK subgraph-34 35 N-MAPK-ERK subgraph-95 inhibition N-MAPK-ERK subgraph-86 N-MAPK-ERK subgraph-49 activation N-MAPK-ERK subgraph-68 N-MAPK-ERK subgraph-49 activation N-MAPK-ERK subgraph-42 N-MAPK-ERK subgraph-40 activation N-MAPK-ERK subgraph-43 N-MAPK-ERK subgraph-42 activation N-MAPK-ERK subgraph-41 N-MAPK-ERK subgraph-5 6 7 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-5 6 7 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-8 9 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-8 9 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-15 16 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-52 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-52 activation N-MAPK-ERK subgraph-90 N-MAPK-ERK subgraph-50 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-50 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-50 activation N-MAPK-ERK subgraph-47 N-MAPK-ERK subgraph-74 activation N-MAPK-ERK subgraph-75 N-MAPK-ERK subgraph-74 activation N-MAPK-ERK subgraph-1 2 N-MAPK-ERK subgraph-74 activation N-MAPK-ERK subgraph-25 26 N-MAPK-ERK subgraph-88 activation N-MAPK-ERK subgraph-8 9 N-MAPK-ERK subgraph-88 activation N-MAPK-ERK subgraph-27 28 N-MAPK-ERK subgraph-19 20 21 activation N-MAPK-ERK subgraph-58 N-MAPK-ERK subgraph-19 20 21 activation N-MAPK-ERK subgraph-59 N-MAPK-ERK subgraph-63 inhibition N-MAPK-ERK subgraph-13 14 N-MAPK-ERK subgraph-101 activation N-MAPK-ERK subgraph-43 N-MAPK-ERK subgraph-101 activation N-MAPK-ERK subgraph-42 N-MAPK-ERK subgraph-104 activation N-MAPK-ERK subgraph-91 N-MAPK-ERK subgraph-104 activation N-MAPK-ERK subgraph-93 N-MAPK-ERK subgraph-104 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-36 37 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-36 37 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-102 activation N-MAPK-ERK subgraph-64 N-MAPK-ERK subgraph-68 activation N-MAPK-ERK subgraph-67 N-MAPK-ERK subgraph-56 activation N-MAPK-ERK subgraph-65 N-MAPK-ERK subgraph-76 activation N-MAPK-ERK subgraph-77 N-MAPK-ERK subgraph-76 activation N-MAPK-ERK subgraph-1 2 N-MAPK-ERK subgraph-76 activation N-MAPK-ERK subgraph-25 26 N-MAPK-ERK subgraph-61 activation N-MAPK-ERK subgraph-91 N-MAPK-ERK subgraph-60 activation N-MAPK-ERK subgraph-42 N-MAPK-ERK subgraph-77 activation N-MAPK-ERK subgraph-79 N-MAPK-ERK subgraph-77 activation N-MAPK-ERK subgraph-81 N-MAPK-ERK subgraph-77 activation N-MAPK-ERK subgraph-83 N-MAPK-ERK subgraph-87 activation N-MAPK-ERK subgraph-8 9 N-MAPK-ERK subgraph-87 activation N-MAPK-ERK subgraph-19 20 21 N-MAPK-ERK subgraph-108 activation N-MAPK-ERK subgraph-53 N-MAPK-ERK subgraph-111 activation N-MAPK-ERK subgraph-108 N-MAPK-ERK subgraph-111 activation N-MAPK-ERK subgraph-108 N-MAPK-ERK subgraph-111 activation N-MAPK-ERK subgraph-53 N-MAPK-ERK subgraph-111 activation N-MAPK-ERK subgraph-109 N-MAPK-ERK subgraph-32 33 activation N-MAPK-ERK subgraph-50 N-MAPK-ERK subgraph-29 30 31 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-53 activation N-MAPK-ERK subgraph-107 N-MAPK-ERK subgraph-53 activation N-MAPK-ERK subgraph-106 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-99 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-96 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-96 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-96 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-96 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-43 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-41 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-97 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-69 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-90 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-68 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-55 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-56 N-MAPK-ERK subgraph-44 activation N-MAPK-ERK subgraph-42 N-MAPK-ERK subgraph-86 inhibition N-MAPK-ERK subgraph-63 N-MAPK-ERK subgraph-86 activation N-MAPK-ERK subgraph-92 N-MAPK-ERK subgraph-86 activation N-MAPK-ERK subgraph-96 N-MAPK-ERK subgraph-86 activation N-MAPK-ERK subgraph-42 N-MAPK-ERK subgraph-93 activation N-MAPK-ERK subgraph-99 N-MAPK-ERK subgraph-58 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-58 activation N-MAPK-ERK subgraph-91 N-MAPK-ERK subgraph-58 activation N-MAPK-ERK subgraph-93 N-MAPK-ERK subgraph-105 activation N-MAPK-ERK subgraph-91 N-MAPK-ERK subgraph-105 activation N-MAPK-ERK subgraph-93 N-MAPK-ERK subgraph-81 activation N-MAPK-ERK subgraph-80 N-MAPK-ERK subgraph-82 activation N-MAPK-ERK subgraph-70 N-MAPK-ERK subgraph-46 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-51 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-1 2 activation N-MAPK-ERK subgraph-10 11 12 N-MAPK-ERK subgraph-25 26 activation N-MAPK-ERK subgraph-10 11 12 N-MAPK-ERK subgraph-47 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-84 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-84 inhibition N-MAPK-ERK subgraph-49 N-MAPK-ERK subgraph-75 activation N-MAPK-ERK subgraph-83 N-MAPK-ERK subgraph-103 activation N-MAPK-ERK subgraph-70 N-MAPK-ERK subgraph-89 inhibition N-MAPK-ERK subgraph-64 N-MAPK-ERK subgraph-110 activation N-MAPK-ERK subgraph-54 N-MAPK-ERK subgraph-3 4 activation N-MAPK-ERK subgraph-50 N-MAPK-ERK subgraph-48 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-83 activation N-MAPK-ERK subgraph-82 N-MAPK-ERK subgraph-38 inhibition N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-55 activation N-MAPK-ERK subgraph-43 N-MAPK-ERK subgraph-62 activation N-MAPK-ERK subgraph-91 N-MAPK-ERK subgraph-62 activation N-MAPK-ERK subgraph-93 N-MAPK-ERK subgraph-92 inhibition N-MAPK-ERK subgraph-63 N-MAPK-ERK subgraph-94 activation N-MAPK-ERK subgraph-90 N-MAPK-ERK subgraph-59 activation N-MAPK-ERK subgraph-44 N-MAPK-ERK subgraph-59 activation N-MAPK-ERK subgraph-91 N-MAPK-ERK subgraph-59 activation N-MAPK-ERK subgraph-93 N-MAPK-ERK subgraph-57 activation N-MAPK-ERK subgraph-43 N-MAPK-ERK subgraph-22 23 24 activation N-MAPK-ERK subgraph-58 N-MAPK-ERK subgraph-22 23 24 activation N-MAPK-ERK subgraph-59 N-MAPK-ERK subgraph-22 23 24 activation N-MAPK-ERK subgraph-52 N-MAPK-ERK subgraph-22 23 24 activation N-MAPK-ERK subgraph-51 N-MAPK-ERK subgraph-22 23 24 activation N-MAPK-ERK subgraph-50 N-MAPK-ERK subgraph-27 28 activation N-MAPK-ERK subgraph-105 N-MAPK-ERK subgraph-27 28 activation N-MAPK-ERK subgraph-104 N-MAPK-ERK subgraph-27 28 activation N-MAPK-ERK subgraph-62 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/MAPK-JNK subgraph.att000066400000000000000000000126741426625374700267260ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-MAPK-JNK subgraph-1 2 STAT3 GFAP 11 26 white rectangle gene,gene 0.5 black 46 17 11364,/,4235 N-MAPK-JNK subgraph-11 12 JUN PIN1 47 164 white rectangle gene,gene 0.5 black 46 17 6204,/,8988 N-MAPK-JNK subgraph-13 14 Pin1 Sult4a1 28 148 white rectangle gene,gene 0.5 black 46 17 1346036,/,1888971 N-MAPK-JNK subgraph-15 CDK5R1 p25 86 88 white rectangle gene 0.5 black 46 17 CONSO00172 N-MAPK-JNK subgraph-16 p14_3_3 144 49 white rectangle gene 0.5 black 46 17 p14_3_3 N-MAPK-JNK subgraph-17 GSK3 118 2 white rectangle gene 0.5 black 46 17 GSK3 N-MAPK-JNK subgraph-18 HSP90 144 44 white rectangle gene 0.5 black 46 17 HSP90 N-MAPK-JNK subgraph-19 JNK 113 65 white rectangle gene 0.5 black 46 17 JNK N-MAPK-JNK subgraph-20 JNK 125 57 white rectangle gene 0.5 black 46 17 JNK N-MAPK-JNK subgraph-21 RAS 82 95 white rectangle gene 0.5 black 46 17 RAS N-MAPK-JNK subgraph-22 TXN 140 42 white rectangle gene 0.5 black 46 17 TXN N-MAPK-JNK subgraph-23 p38 156 48 white rectangle gene 0.5 black 46 17 p38 N-MAPK-JNK subgraph-24 p38 149 49 white rectangle gene 0.5 black 46 17 p38 N-MAPK-JNK subgraph-25 S100B 69 87 white rectangle gene 0.5 black 46 17 10500 N-MAPK-JNK subgraph-26 SRC 82 59 white rectangle gene 0.5 black 46 17 11283 N-MAPK-JNK subgraph-27 STAT1 42 11 white rectangle gene 0.5 black 46 17 11362 N-MAPK-JNK subgraph-28 STAT3 5 24 white rectangle gene 0.5 black 46 17 11364 N-MAPK-JNK subgraph-29 FASLG 69 91 white rectangle gene 0.5 black 46 17 11936 N-MAPK-JNK subgraph-3 4 5 CDK5 CDK5R1 CDK5R2 139 3 white rectangle gene,gene,gene 0.5 black 46 17 1774,/,1775,/,1776 N-MAPK-JNK subgraph-30 TRAF2 136 43 white rectangle gene 0.5 black 46 17 12032 N-MAPK-JNK subgraph-31 TRAF6 143 54 white rectangle gene 0.5 black 46 17 12036 N-MAPK-JNK subgraph-32 CAMK2A 47 12 white rectangle gene 0.5 black 46 17 1460 N-MAPK-JNK subgraph-33 CASP8 0 60 white rectangle gene 0.5 black 46 17 1509 N-MAPK-JNK subgraph-34 CDK5 140 8 white rectangle gene 0.5 black 46 17 1774 N-MAPK-JNK subgraph-35 CHST1 23 152 white rectangle gene 0.5 black 46 17 1969 N-MAPK-JNK subgraph-36 DAXX 134 47 white rectangle gene 0.5 black 46 17 2681 N-MAPK-JNK subgraph-37 AGER 56 94 white rectangle gene 0.5 black 46 17 320 N-MAPK-JNK subgraph-39 GSK3B 88 108 white rectangle gene 0.5 black 46 17 4617 N-MAPK-JNK subgraph-40 IL6 9 21 white rectangle gene 0.5 black 46 17 6018 N-MAPK-JNK subgraph-41 IRS1 73 99 white rectangle gene 0.5 black 46 17 6125 N-MAPK-JNK subgraph-42 IRS1 73 86 white rectangle gene 0.5 black 46 17 6125 N-MAPK-JNK subgraph-43 JAK3 8 16 white rectangle gene 0.5 black 46 17 6193 N-MAPK-JNK subgraph-44 APP 87 65 white rectangle gene 0.5 black 46 17 620 N-MAPK-JNK subgraph-45 APP 116 0 white rectangle gene 0.5 black 46 17 620 N-MAPK-JNK subgraph-46 APP 70 95 white rectangle gene 0.5 black 46 17 620 N-MAPK-JNK subgraph-47 APP 114 71 white rectangle gene 0.5 black 46 17 620 N-MAPK-JNK subgraph-48 APP 68 101 white rectangle gene 0.5 black 46 17 620 N-MAPK-JNK subgraph-49 APP 111 77 white rectangle gene 0.5 black 46 17 620 N-MAPK-JNK subgraph-50 JUN 145 3 white rectangle gene 0.5 black 46 17 6204 N-MAPK-JNK subgraph-51 JUN 87 94 white rectangle gene 0.5 black 46 17 6204 N-MAPK-JNK subgraph-52 MAP3K5 138 49 white rectangle gene 0.5 black 46 17 6857 N-MAPK-JNK subgraph-53 MAPK1 107 91 white rectangle gene 0.5 black 46 17 6871 N-MAPK-JNK subgraph-54 MAPK10 102 103 white rectangle gene 0.5 black 46 17 6872 N-MAPK-JNK subgraph-55 MAPK10 136 14 white rectangle gene 0.5 black 46 17 6872 N-MAPK-JNK subgraph-56 MAPK10 107 113 white rectangle gene 0.5 black 46 17 6872 N-MAPK-JNK subgraph-57 MAPK14 98 98 white rectangle gene 0.5 black 46 17 6876 N-MAPK-JNK subgraph-58 MAPK14 2 105 white rectangle gene 0.5 black 46 17 6876 N-MAPK-JNK subgraph-59 MAPK8 76 92 white rectangle gene 0.5 black 46 17 6881 N-MAPK-JNK subgraph-6 7 8 APBB1 APP KLC1 81 78 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6387 N-MAPK-JNK subgraph-60 MAPK8 64 93 white rectangle gene 0.5 black 46 17 6881 N-MAPK-JNK subgraph-61 MAPK8IP1 63 107 white rectangle gene 0.5 black 46 17 6882 N-MAPK-JNK subgraph-62 MAPK9 93 97 white rectangle gene 0.5 black 46 17 6886 N-MAPK-JNK subgraph-63 MAPK9 111 120 white rectangle gene 0.5 black 46 17 6886 N-MAPK-JNK subgraph-64 MAPKAPK2 8 103 white rectangle gene 0.5 black 46 17 6887 N-MAPK-JNK subgraph-65 MAPT 90 100 white rectangle gene 0.5 black 46 17 6893 N-MAPK-JNK subgraph-66 MAPT 105 100 white rectangle gene 0.5 black 46 17 6893 N-MAPK-JNK subgraph-67 MAPT 107 99 white rectangle gene 0.5 black 46 17 6893 N-MAPK-JNK subgraph-68 MAPT 101 97 white rectangle gene 0.5 black 46 17 6893 N-MAPK-JNK subgraph-69 MAPT 104 97 white rectangle gene 0.5 black 46 17 6893 N-MAPK-JNK subgraph-70 MAPT 106 97 white rectangle gene 0.5 black 46 17 6893 N-MAPK-JNK subgraph-71 ATF2 96 94 white rectangle gene 0.5 black 46 17 784 N-MAPK-JNK subgraph-72 PDGFRL 86 58 white rectangle gene 0.5 black 46 17 8805 N-MAPK-JNK subgraph-73 PIK3R1 39 5 white rectangle gene 0.5 black 46 17 8979 N-MAPK-JNK subgraph-74 PIN1 50 162 white rectangle gene 0.5 black 46 17 8988 N-MAPK-JNK subgraph-75 PRKCA 41 16 white rectangle gene 0.5 black 46 17 9393 N-MAPK-JNK subgraph-76 EIF2AK2 6 60 white rectangle gene 0.5 black 46 17 9437 N-MAPK-JNK subgraph-77 Pin1 72 173 white rectangle gene 0.5 black 46 17 1346036 N-MAPK-JNK subgraph-78 Sult4a1 76 174 white rectangle gene 0.5 black 46 17 1888971 N-MAPK-JNK subgraph-9 10 APBB1 KLC1 100 64 white rectangle gene,gene 0.5 black 46 17 581,/,6387 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/MAPK-JNK subgraph.sif000066400000000000000000000112261426625374700267070ustar00rootroot000000000000000 1 2 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-69 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-70 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-66 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-67 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-68 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-51 N-MAPK-JNK subgraph-57 activation N-MAPK-JNK subgraph-71 N-MAPK-JNK subgraph-9 10 activation N-MAPK-JNK subgraph-44 N-MAPK-JNK subgraph-74 activation N-MAPK-JNK subgraph-11 12 N-MAPK-JNK subgraph-19 activation N-MAPK-JNK subgraph-49 N-MAPK-JNK subgraph-19 inhibition N-MAPK-JNK subgraph-9 10 N-MAPK-JNK subgraph-19 activation N-MAPK-JNK subgraph-47 N-MAPK-JNK subgraph-53 activation N-MAPK-JNK subgraph-69 N-MAPK-JNK subgraph-53 activation N-MAPK-JNK subgraph-70 N-MAPK-JNK subgraph-53 activation N-MAPK-JNK subgraph-66 N-MAPK-JNK subgraph-53 activation N-MAPK-JNK subgraph-67 N-MAPK-JNK subgraph-53 activation N-MAPK-JNK subgraph-68 N-MAPK-JNK subgraph-53 activation N-MAPK-JNK subgraph-49 N-MAPK-JNK subgraph-46 activation N-MAPK-JNK subgraph-59 N-MAPK-JNK subgraph-31 activation N-MAPK-JNK subgraph-52 N-MAPK-JNK subgraph-76 activation N-MAPK-JNK subgraph-33 N-MAPK-JNK subgraph-16 inhibition N-MAPK-JNK subgraph-52 N-MAPK-JNK subgraph-62 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-62 activation N-MAPK-JNK subgraph-51 N-MAPK-JNK subgraph-62 activation N-MAPK-JNK subgraph-71 N-MAPK-JNK subgraph-62 activation N-MAPK-JNK subgraph-68 N-MAPK-JNK subgraph-60 activation N-MAPK-JNK subgraph-59 N-MAPK-JNK subgraph-21 activation N-MAPK-JNK subgraph-51 N-MAPK-JNK subgraph-36 activation N-MAPK-JNK subgraph-52 N-MAPK-JNK subgraph-15 activation N-MAPK-JNK subgraph-51 N-MAPK-JNK subgraph-61 activation N-MAPK-JNK subgraph-48 N-MAPK-JNK subgraph-3 4 5 activation N-MAPK-JNK subgraph-34 N-MAPK-JNK subgraph-24 activation N-MAPK-JNK subgraph-23 N-MAPK-JNK subgraph-56 inhibition N-MAPK-JNK subgraph-54 N-MAPK-JNK subgraph-56 inhibition N-MAPK-JNK subgraph-63 N-MAPK-JNK subgraph-26 activation N-MAPK-JNK subgraph-44 N-MAPK-JNK subgraph-54 activation N-MAPK-JNK subgraph-68 N-MAPK-JNK subgraph-54 activation N-MAPK-JNK subgraph-68 N-MAPK-JNK subgraph-54 activation N-MAPK-JNK subgraph-69 N-MAPK-JNK subgraph-54 activation N-MAPK-JNK subgraph-70 N-MAPK-JNK subgraph-54 activation N-MAPK-JNK subgraph-66 N-MAPK-JNK subgraph-54 activation N-MAPK-JNK subgraph-67 N-MAPK-JNK subgraph-54 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-75 activation N-MAPK-JNK subgraph-27 N-MAPK-JNK subgraph-34 inhibition N-MAPK-JNK subgraph-55 N-MAPK-JNK subgraph-34 activation N-MAPK-JNK subgraph-50 N-MAPK-JNK subgraph-39 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-39 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-32 activation N-MAPK-JNK subgraph-27 N-MAPK-JNK subgraph-17 activation N-MAPK-JNK subgraph-45 N-MAPK-JNK subgraph-40 activation N-MAPK-JNK subgraph-43 N-MAPK-JNK subgraph-40 activation N-MAPK-JNK subgraph-28 N-MAPK-JNK subgraph-40 activation N-MAPK-JNK subgraph-1 2 N-MAPK-JNK subgraph-22 inhibition N-MAPK-JNK subgraph-52 N-MAPK-JNK subgraph-44 inhibition N-MAPK-JNK subgraph-6 7 8 N-MAPK-JNK subgraph-13 14 activation N-MAPK-JNK subgraph-35 N-MAPK-JNK subgraph-64 activation N-MAPK-JNK subgraph-58 N-MAPK-JNK subgraph-20 activation N-MAPK-JNK subgraph-19 N-MAPK-JNK subgraph-28 activation N-MAPK-JNK subgraph-1 2 N-MAPK-JNK subgraph-77 activation N-MAPK-JNK subgraph-78 N-MAPK-JNK subgraph-52 activation N-MAPK-JNK subgraph-24 N-MAPK-JNK subgraph-52 activation N-MAPK-JNK subgraph-20 N-MAPK-JNK subgraph-73 activation N-MAPK-JNK subgraph-27 N-MAPK-JNK subgraph-49 activation N-MAPK-JNK subgraph-47 N-MAPK-JNK subgraph-18 inhibition N-MAPK-JNK subgraph-52 N-MAPK-JNK subgraph-30 activation N-MAPK-JNK subgraph-52 N-MAPK-JNK subgraph-6 7 8 activation N-MAPK-JNK subgraph-59 N-MAPK-JNK subgraph-37 activation N-MAPK-JNK subgraph-60 N-MAPK-JNK subgraph-72 activation N-MAPK-JNK subgraph-44 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-65 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-51 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-29 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-48 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-42 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-41 N-MAPK-JNK subgraph-59 activation N-MAPK-JNK subgraph-25 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Matrix metalloproteinase subgraph.att000066400000000000000000000064411426625374700325250ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Matrix metalloproteinase subgraph-1 2 PLAU PLAUR 91 26 white rectangle gene,gene 0.5 black 46 17 9052,/,9053 N-Matrix metalloproteinase subgraph-10 GRIN3B 76 135 white rectangle gene 0.5 black 46 17 16768 N-Matrix metalloproteinase subgraph-11 CLSTN1 93 116 white rectangle gene 0.5 black 46 17 17447 N-Matrix metalloproteinase subgraph-12 ADAM10 81 127 white rectangle gene 0.5 black 46 17 188 N-Matrix metalloproteinase subgraph-13 ADAM17 72 128 white rectangle gene 0.5 black 46 17 195 N-Matrix metalloproteinase subgraph-14 CHRM1 0 36 white rectangle gene 0.5 black 46 17 1950 N-Matrix metalloproteinase subgraph-15 ADAM9 74 126 white rectangle gene 0.5 black 46 17 216 N-Matrix metalloproteinase subgraph-16 F2 94 154 white rectangle gene 0.5 black 46 17 3535 N-Matrix metalloproteinase subgraph-17 GRIN2A 68 130 white rectangle gene 0.5 black 46 17 4585 N-Matrix metalloproteinase subgraph-18 GRIN2B 80 132 white rectangle gene 0.5 black 46 17 4586 N-Matrix metalloproteinase subgraph-19 GRIN2C 79 120 white rectangle gene 0.5 black 46 17 4587 N-Matrix metalloproteinase subgraph-20 GRIN2D 74 119 white rectangle gene 0.5 black 46 17 4588 N-Matrix metalloproteinase subgraph-21 ITGA2 90 44 white rectangle gene 0.5 black 46 17 6137 N-Matrix metalloproteinase subgraph-22 ITGA3 100 28 white rectangle gene 0.5 black 46 17 6139 N-Matrix metalloproteinase subgraph-23 APP 1 29 white rectangle gene 0.5 black 46 17 620 N-Matrix metalloproteinase subgraph-24 APP 131 63 white rectangle gene 0.5 black 46 17 620 N-Matrix metalloproteinase subgraph-25 APP 111 45 white rectangle gene 0.5 black 46 17 620 N-Matrix metalloproteinase subgraph-26 MMP14 135 53 white rectangle gene 0.5 black 46 17 7160 N-Matrix metalloproteinase subgraph-27 MMP2 124 54 white rectangle gene 0.5 black 46 17 7166 N-Matrix metalloproteinase subgraph-28 MMP3 102 9 white rectangle gene 0.5 black 46 17 7173 N-Matrix metalloproteinase subgraph-29 MMP7 119 39 white rectangle gene 0.5 black 46 17 7174 N-Matrix metalloproteinase subgraph-3 sAPP-alpha 85 31 white rectangle gene 0.5 black 46 17 CONSO00067 N-Matrix metalloproteinase subgraph-30 MMP9 95 37 white rectangle gene 0.5 black 46 17 7176 N-Matrix metalloproteinase subgraph-31 NGF 83 39 white rectangle gene 0.5 black 46 17 7808 N-Matrix metalloproteinase subgraph-32 BACE1 101 107 white rectangle gene 0.5 black 46 17 933 N-Matrix metalloproteinase subgraph-33 PRKCH 71 135 white rectangle gene 0.5 black 46 17 9403 N-Matrix metalloproteinase subgraph-34 RARB 184 130 white rectangle gene 0.5 black 46 17 9865 N-Matrix metalloproteinase subgraph-35 Il1b 2 91 white rectangle gene 0.5 black 46 17 96543 N-Matrix metalloproteinase subgraph-36 Mmp2 0 84 white rectangle gene 0.5 black 46 17 97009 N-Matrix metalloproteinase subgraph-4 MMP 104 0 white rectangle gene 0.5 black 46 17 MMP N-Matrix metalloproteinase subgraph-5 Notch 93 130 white rectangle gene 0.5 black 46 17 Notch N-Matrix metalloproteinase subgraph-6 TGFB 0 76 white rectangle gene 0.5 black 46 17 TGFB N-Matrix metalloproteinase subgraph-7 TIMP1 89 143 white rectangle gene 0.5 black 46 17 11820 N-Matrix metalloproteinase subgraph-8 SIRT1 175 126 white rectangle gene 0.5 black 46 17 14929 N-Matrix metalloproteinase subgraph-9 GRIN3A 69 122 white rectangle gene 0.5 black 46 17 16767 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Matrix metalloproteinase subgraph.sif000066400000000000000000000073601426625374700325170ustar00rootroot000000000000000 1 2 N-Matrix metalloproteinase subgraph-25 activation N-Matrix metalloproteinase subgraph-27 N-Matrix metalloproteinase subgraph-21 activation N-Matrix metalloproteinase subgraph-30 N-Matrix metalloproteinase subgraph-26 activation N-Matrix metalloproteinase subgraph-27 N-Matrix metalloproteinase subgraph-26 activation N-Matrix metalloproteinase subgraph-27 N-Matrix metalloproteinase subgraph-28 activation N-Matrix metalloproteinase subgraph-4 N-Matrix metalloproteinase subgraph-16 activation N-Matrix metalloproteinase subgraph-7 N-Matrix metalloproteinase subgraph-17 inhibition N-Matrix metalloproteinase subgraph-15 N-Matrix metalloproteinase subgraph-17 inhibition N-Matrix metalloproteinase subgraph-12 N-Matrix metalloproteinase subgraph-17 inhibition N-Matrix metalloproteinase subgraph-13 N-Matrix metalloproteinase subgraph-8 activation N-Matrix metalloproteinase subgraph-34 N-Matrix metalloproteinase subgraph-29 inhibition N-Matrix metalloproteinase subgraph-25 N-Matrix metalloproteinase subgraph-10 inhibition N-Matrix metalloproteinase subgraph-15 N-Matrix metalloproteinase subgraph-10 inhibition N-Matrix metalloproteinase subgraph-12 N-Matrix metalloproteinase subgraph-10 inhibition N-Matrix metalloproteinase subgraph-13 N-Matrix metalloproteinase subgraph-36 activation N-Matrix metalloproteinase subgraph-35 N-Matrix metalloproteinase subgraph-36 activation N-Matrix metalloproteinase subgraph-6 N-Matrix metalloproteinase subgraph-18 inhibition N-Matrix metalloproteinase subgraph-15 N-Matrix metalloproteinase subgraph-18 inhibition N-Matrix metalloproteinase subgraph-12 N-Matrix metalloproteinase subgraph-18 inhibition N-Matrix metalloproteinase subgraph-13 N-Matrix metalloproteinase subgraph-20 inhibition N-Matrix metalloproteinase subgraph-15 N-Matrix metalloproteinase subgraph-20 inhibition N-Matrix metalloproteinase subgraph-12 N-Matrix metalloproteinase subgraph-20 inhibition N-Matrix metalloproteinase subgraph-13 N-Matrix metalloproteinase subgraph-19 inhibition N-Matrix metalloproteinase subgraph-15 N-Matrix metalloproteinase subgraph-19 inhibition N-Matrix metalloproteinase subgraph-12 N-Matrix metalloproteinase subgraph-19 inhibition N-Matrix metalloproteinase subgraph-13 N-Matrix metalloproteinase subgraph-11 activation N-Matrix metalloproteinase subgraph-12 N-Matrix metalloproteinase subgraph-11 inhibition N-Matrix metalloproteinase subgraph-32 N-Matrix metalloproteinase subgraph-33 activation N-Matrix metalloproteinase subgraph-15 N-Matrix metalloproteinase subgraph-33 activation N-Matrix metalloproteinase subgraph-12 N-Matrix metalloproteinase subgraph-33 activation N-Matrix metalloproteinase subgraph-13 N-Matrix metalloproteinase subgraph-30 activation N-Matrix metalloproteinase subgraph-31 N-Matrix metalloproteinase subgraph-30 activation N-Matrix metalloproteinase subgraph-25 N-Matrix metalloproteinase subgraph-30 activation N-Matrix metalloproteinase subgraph-3 N-Matrix metalloproteinase subgraph-12 activation N-Matrix metalloproteinase subgraph-5 N-Matrix metalloproteinase subgraph-1 2 activation 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subgraph-7 N-Neuroprotection subgraph-8 activation N-Neuroprotection subgraph-7 N-Neuroprotection subgraph-4 inhibition N-Neuroprotection subgraph-7 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Nitric oxide subgraph.att000066400000000000000000000023621426625374700300700ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Nitric oxide subgraph-10 IDE 31 29 white rectangle gene 0.5 black 46 17 5381 N-Nitric oxide subgraph-12 APP 69 140 white rectangle gene 0.5 black 46 17 620 N-Nitric oxide subgraph-13 APP 0 0 white rectangle gene 0.5 black 46 17 620 N-Nitric oxide subgraph-14 APP 40 30 white rectangle gene 0.5 black 46 17 620 N-Nitric oxide subgraph-15 APP 53 35 white rectangle gene 0.5 black 46 17 620 N-Nitric oxide subgraph-18 NGF 62 147 white rectangle gene 0.5 black 46 17 7808 N-Nitric oxide subgraph-19 NGFR 10 8 white rectangle gene 0.5 black 46 17 7809 N-Nitric oxide subgraph-20 NOS1 123 68 white rectangle gene 0.5 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black 46 17 7 N-Non-amyloidogenic subgraph-123 MME 106 72 white rectangle gene 0.5 black 46 17 7154 N-Non-amyloidogenic subgraph-125 MMP7 105 49 white rectangle gene 0.5 black 46 17 7174 N-Non-amyloidogenic subgraph-127 NFKB1 90 66 white rectangle gene 0.5 black 46 17 7794 N-Non-amyloidogenic subgraph-128 NFKB2 74 81 white rectangle gene 0.5 black 46 17 7795 N-Non-amyloidogenic subgraph-129 NGF 10 43 white rectangle gene 0.5 black 46 17 7808 N-Non-amyloidogenic subgraph-130 NOS3 109 68 white rectangle gene 0.5 black 46 17 7876 N-Non-amyloidogenic subgraph-131 PDGFRL 71 79 white rectangle gene 0.5 black 46 17 8805 N-Non-amyloidogenic subgraph-132 PIN1 108 71 white rectangle gene 0.5 black 46 17 8988 N-Non-amyloidogenic subgraph-133 PLAT 94 55 white rectangle gene 0.5 black 46 17 9051 N-Non-amyloidogenic subgraph-134 PLG 95 54 white rectangle gene 0.5 black 46 17 9071 N-Non-amyloidogenic subgraph-135 PPARG 113 53 white rectangle gene 0.5 black 46 17 9236 N-Non-amyloidogenic subgraph-136 BACE1 112 57 white rectangle gene 0.5 black 46 17 933 N-Non-amyloidogenic subgraph-137 PRKCA 68 69 white rectangle gene 0.5 black 46 17 9393 N-Non-amyloidogenic subgraph-138 PRNP 112 61 white rectangle gene 0.5 black 46 17 9449 N-Non-amyloidogenic subgraph-139 RAC1 79 80 white rectangle gene 0.5 black 46 17 9801 N-Non-amyloidogenic subgraph-140 Cdk5 77 164 white rectangle gene 0.5 black 46 17 101765 N-Non-amyloidogenic subgraph-141 Abcc1 116 64 white rectangle gene 0.5 black 46 17 102676 N-Non-amyloidogenic subgraph-142 Casp3 115 55 white rectangle gene 0.5 black 46 17 107739 N-Non-amyloidogenic subgraph-143 Gsk3b 70 169 white rectangle gene 0.5 black 46 17 1861437 N-Non-amyloidogenic subgraph-144 Cysltr1 110 86 white rectangle gene 0.5 black 46 17 1926218 N-Non-amyloidogenic subgraph-145 App 73 165 white rectangle gene 0.5 black 46 17 88059 N-Non-amyloidogenic subgraph-146 Bcl2 107 76 white rectangle gene 0.5 black 46 17 88138 N-Non-amyloidogenic subgraph-16 17 HSPB8 BAG3 99 59 white rectangle gene,gene 0.5 black 46 17 30171,/,939 N-Non-amyloidogenic subgraph-18 19 GCG GLP1R 107 49 white rectangle gene,gene 0.5 black 46 17 4191,/,4324 N-Non-amyloidogenic subgraph-20 21 APBA1 APP 113 70 white rectangle gene,gene 0.5 black 46 17 578,/,620 N-Non-amyloidogenic subgraph-22 23 APBA1 ARL3 69 77 white rectangle gene,gene 0.5 black 46 17 578,/,694 N-Non-amyloidogenic subgraph-24 25 APBA1 Amyloidogenic glycoprotein, intracellular domain, conserved site 103 52 white rectangle gene,gene 0.5 black 46 17 578,/,IPR019745 N-Non-amyloidogenic subgraph-26 27 APBA2 ARL3 70 72 white rectangle gene,gene 0.5 black 46 17 579,/,694 N-Non-amyloidogenic subgraph-28 29 APBA2 NFKB1 103 56 white rectangle gene,gene 0.5 black 46 17 579,/,7794 N-Non-amyloidogenic subgraph-3 4 CLSTN1 KLC1 73 65 white rectangle gene,gene 0.5 black 46 17 17447,/,6387 N-Non-amyloidogenic subgraph-30 31 APBA3 ARL3 71 75 white rectangle gene,gene 0.5 black 46 17 580,/,694 N-Non-amyloidogenic subgraph-32 33 34 APBB1 APP LRP1 74 78 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6692 N-Non-amyloidogenic subgraph-35 36 37 APBB1 APP LRP2 71 68 white rectangle gene,gene,gene 0.5 black 46 17 581,/,620,/,6694 N-Non-amyloidogenic subgraph-38 39 APBB1 KLC1 71 64 white rectangle gene,gene 0.5 black 46 17 581,/,6387 N-Non-amyloidogenic subgraph-40 41 IL1B IL1R1 166 70 white rectangle gene,gene 0.5 black 46 17 5992,/,5993 N-Non-amyloidogenic subgraph-42 43 INS INSR 115 61 white rectangle gene,gene 0.5 black 46 17 6081,/,6091 N-Non-amyloidogenic subgraph-44 45 APOE APP 98 67 white rectangle gene,gene 0.5 black 46 17 613,/,620 N-Non-amyloidogenic subgraph-46 47 APP LRP1 106 154 white rectangle gene,gene 0.5 black 46 17 620,/,6692 N-Non-amyloidogenic subgraph-48 49 APP NTN1 114 68 white rectangle gene,gene 0.5 black 46 17 620,/,8029 N-Non-amyloidogenic subgraph-5 6 RTN4R APP 88 63 white rectangle gene,gene 0.5 black 46 17 18601,/,620 N-Non-amyloidogenic subgraph-50 51 APP NTRK1 7 44 white rectangle gene,gene 0.5 black 46 17 620,/,8031 N-Non-amyloidogenic subgraph-52 53 APP LRP1 102 35 white rectangle gene,gene 0.5 black 46 17 620,/,6692 N-Non-amyloidogenic subgraph-54 amyloid-beta 0 63 white rectangle gene 0.5 black 46 17 64645 N-Non-amyloidogenic subgraph-55 YENPTY endocytosis motif (APP) 102 49 white rectangle gene 0.5 black 46 17 CONSO00041 N-Non-amyloidogenic subgraph-56 sAPP-alpha 164 135 white rectangle gene 0.5 black 46 17 CONSO00067 N-Non-amyloidogenic subgraph-57 ERK 51 0 white rectangle gene 0.5 black 46 17 ERK N-Non-amyloidogenic subgraph-59 ATXN1 109 65 white rectangle gene 0.5 black 46 17 10548 N-Non-amyloidogenic subgraph-60 SOD1 101 66 white rectangle gene 0.5 black 46 17 11179 N-Non-amyloidogenic subgraph-61 SORL1 91 68 white rectangle gene 0.5 black 46 17 11185 N-Non-amyloidogenic subgraph-62 SRC 67 71 white rectangle gene 0.5 black 46 17 11283 N-Non-amyloidogenic subgraph-63 TTR 105 70 white rectangle gene 0.5 black 46 17 12405 N-Non-amyloidogenic subgraph-64 RANBP9 116 66 white rectangle gene 0.5 black 46 17 13727 N-Non-amyloidogenic subgraph-65 CASP8 100 55 white rectangle gene 0.5 black 46 17 1509 N-Non-amyloidogenic subgraph-66 CAST 110 59 white rectangle gene 0.5 black 46 17 1515 N-Non-amyloidogenic subgraph-67 SORCS1 110 72 white rectangle gene 0.5 black 46 17 16697 N-Non-amyloidogenic subgraph-68 CPQ 113 64 white rectangle gene 0.5 black 46 17 16910 N-Non-amyloidogenic subgraph-69 CLSTN1 69 66 white rectangle gene 0.5 black 46 17 17447 N-Non-amyloidogenic subgraph-70 CDK5 169 140 white rectangle gene 0.5 black 46 17 1774 N-Non-amyloidogenic subgraph-71 RTN4R 80 58 white rectangle gene 0.5 black 46 17 18601 N-Non-amyloidogenic subgraph-72 ADAM10 162 138 white rectangle gene 0.5 black 46 17 188 N-Non-amyloidogenic subgraph-73 ADAM17 188 69 white rectangle gene 0.5 black 46 17 195 N-Non-amyloidogenic subgraph-74 CHRM1 77 77 white rectangle gene 0.5 black 46 17 1950 N-Non-amyloidogenic subgraph-75 ADAM19 159 129 white rectangle gene 0.5 black 46 17 197 N-Non-amyloidogenic subgraph-76 SYVN1 120 40 white rectangle gene 0.5 black 46 17 20738 N-Non-amyloidogenic subgraph-77 CLU 118 52 white rectangle gene 0.5 black 46 17 2095 N-Non-amyloidogenic subgraph-78 CNR1 2 60 white rectangle gene 0.5 black 46 17 2159 N-Non-amyloidogenic subgraph-79 CNR2 1 67 white rectangle gene 0.5 black 46 17 2160 N-Non-amyloidogenic subgraph-80 CST3 100 53 white rectangle gene 0.5 black 46 17 2475 N-Non-amyloidogenic subgraph-81 CTSB 99 50 white rectangle gene 0.5 black 46 17 2527 N-Non-amyloidogenic subgraph-82 CTSD 68 74 white rectangle gene 0.5 black 46 17 2529 N-Non-amyloidogenic subgraph-83 CTSE 73 73 white rectangle gene 0.5 black 46 17 2530 N-Non-amyloidogenic subgraph-84 ACE 95 57 white rectangle gene 0.5 black 46 17 2707 N-Non-amyloidogenic subgraph-85 DHCR24 105 67 white rectangle gene 0.5 black 46 17 2859 N-Non-amyloidogenic subgraph-86 ABCA1 107 52 white rectangle gene 0.5 black 46 17 29 N-Non-amyloidogenic subgraph-87 DPP4 94 58 white rectangle gene 0.5 black 46 17 3009 N-Non-amyloidogenic subgraph-88 ECE1 112 67 white rectangle gene 0.5 black 46 17 3146 N-Non-amyloidogenic subgraph-89 ABCA7 98 62 white rectangle gene 0.5 black 46 17 37 N-Non-amyloidogenic subgraph-90 MTRNR2L2 111 52 white rectangle gene 0.5 black 46 17 37156 N-Non-amyloidogenic subgraph-94 GCG 76 81 white rectangle gene 0.5 black 46 17 4191 N-Non-amyloidogenic subgraph-98 HSPA1B 97 52 white rectangle gene 0.5 black 46 17 5233 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Non-amyloidogenic subgraph.sif000066400000000000000000000236611426625374700311200ustar00rootroot000000000000000 1 2 N-Non-amyloidogenic subgraph-141 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-85 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-38 39 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-77 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-77 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-104 activation N-Non-amyloidogenic subgraph-46 47 N-Non-amyloidogenic subgraph-16 17 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-105 activation N-Non-amyloidogenic subgraph-73 N-Non-amyloidogenic subgraph-75 activation N-Non-amyloidogenic subgraph-56 N-Non-amyloidogenic subgraph-80 inhibition N-Non-amyloidogenic subgraph-65 N-Non-amyloidogenic subgraph-80 inhibition N-Non-amyloidogenic subgraph-81 N-Non-amyloidogenic subgraph-80 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-119 activation N-Non-amyloidogenic subgraph-77 N-Non-amyloidogenic subgraph-18 19 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-137 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-130 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-130 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-59 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-129 activation N-Non-amyloidogenic subgraph-114 N-Non-amyloidogenic subgraph-129 activation N-Non-amyloidogenic subgraph-50 51 N-Non-amyloidogenic subgraph-88 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-74 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-143 activation N-Non-amyloidogenic subgraph-145 N-Non-amyloidogenic subgraph-63 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-79 inhibition N-Non-amyloidogenic subgraph-54 N-Non-amyloidogenic subgraph-84 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-84 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-84 activation N-Non-amyloidogenic subgraph-112 N-Non-amyloidogenic subgraph-100 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-100 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-100 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-100 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-140 activation N-Non-amyloidogenic subgraph-145 N-Non-amyloidogenic subgraph-102 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-121 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-107 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-87 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-87 activation N-Non-amyloidogenic subgraph-112 N-Non-amyloidogenic subgraph-125 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-139 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-48 49 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-71 activation N-Non-amyloidogenic subgraph-5 6 N-Non-amyloidogenic subgraph-90 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-60 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-109 activation N-Non-amyloidogenic subgraph-44 45 N-Non-amyloidogenic subgraph-109 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-65 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-81 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-55 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-11 12 13 activation N-Non-amyloidogenic subgraph-57 N-Non-amyloidogenic subgraph-78 inhibition N-Non-amyloidogenic subgraph-54 N-Non-amyloidogenic subgraph-131 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-135 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-135 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-135 activation N-Non-amyloidogenic subgraph-115 N-Non-amyloidogenic subgraph-135 activation N-Non-amyloidogenic subgraph-115 N-Non-amyloidogenic subgraph-135 inhibition N-Non-amyloidogenic subgraph-136 N-Non-amyloidogenic subgraph-86 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-136 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-136 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-136 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-128 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-132 activation N-Non-amyloidogenic subgraph-130 N-Non-amyloidogenic subgraph-132 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-120 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-67 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-61 inhibition N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-61 inhibition N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-61 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-61 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-30 31 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-32 33 34 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-115 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-115 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-20 21 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-110 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-114 activation N-Non-amyloidogenic subgraph-50 51 N-Non-amyloidogenic subgraph-22 23 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-62 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-134 activation N-Non-amyloidogenic subgraph-112 N-Non-amyloidogenic subgraph-134 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-72 activation N-Non-amyloidogenic subgraph-56 N-Non-amyloidogenic subgraph-72 activation N-Non-amyloidogenic subgraph-56 N-Non-amyloidogenic subgraph-72 activation N-Non-amyloidogenic subgraph-56 N-Non-amyloidogenic subgraph-83 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-108 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-108 inhibition N-Non-amyloidogenic subgraph-100 N-Non-amyloidogenic subgraph-108 inhibition N-Non-amyloidogenic subgraph-100 N-Non-amyloidogenic subgraph-108 inhibition N-Non-amyloidogenic subgraph-107 N-Non-amyloidogenic subgraph-66 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-66 inhibition N-Non-amyloidogenic subgraph-136 N-Non-amyloidogenic subgraph-5 6 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-94 inhibition N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-64 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-111 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-111 activation N-Non-amyloidogenic subgraph-5 6 N-Non-amyloidogenic subgraph-68 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-68 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-113 activation N-Non-amyloidogenic subgraph-44 45 N-Non-amyloidogenic subgraph-113 inhibition N-Non-amyloidogenic subgraph-42 43 N-Non-amyloidogenic subgraph-113 inhibition N-Non-amyloidogenic subgraph-130 N-Non-amyloidogenic subgraph-113 inhibition N-Non-amyloidogenic subgraph-146 N-Non-amyloidogenic subgraph-113 activation N-Non-amyloidogenic subgraph-142 N-Non-amyloidogenic subgraph-123 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-98 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-76 activation N-Non-amyloidogenic subgraph-115 N-Non-amyloidogenic subgraph-76 activation N-Non-amyloidogenic subgraph-115 N-Non-amyloidogenic subgraph-133 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-133 activation N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-133 activation N-Non-amyloidogenic subgraph-134 N-Non-amyloidogenic subgraph-133 activation N-Non-amyloidogenic subgraph-112 N-Non-amyloidogenic subgraph-144 inhibition N-Non-amyloidogenic subgraph-146 N-Non-amyloidogenic subgraph-56 inhibition N-Non-amyloidogenic subgraph-70 N-Non-amyloidogenic subgraph-28 29 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-89 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-69 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-118 activation N-Non-amyloidogenic subgraph-52 53 N-Non-amyloidogenic subgraph-118 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-35 36 37 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-82 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-106 inhibition N-Non-amyloidogenic subgraph-40 41 N-Non-amyloidogenic subgraph-106 inhibition N-Non-amyloidogenic subgraph-105 N-Non-amyloidogenic subgraph-138 inhibition N-Non-amyloidogenic subgraph-136 N-Non-amyloidogenic subgraph-138 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-138 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-103 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-24 25 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-3 4 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-26 27 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-127 inhibition N-Non-amyloidogenic subgraph-113 N-Non-amyloidogenic subgraph-127 activation N-Non-amyloidogenic subgraph-111 N-Non-amyloidogenic subgraph-101 inhibition N-Non-amyloidogenic subgraph-113 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Notch signaling subgraph.att000066400000000000000000000026561426625374700305640ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Notch signaling subgraph-1 2 NCSTN PSEN1 162 46 white rectangle gene,gene 0.5 black 46 17 17091,/,9508 N-Notch signaling subgraph-10 CTNNB1 164 96 white rectangle gene 0.5 black 46 17 2514 N-Notch signaling subgraph-11 CTNNB1 175 108 white rectangle gene 0.5 black 46 17 2514 N-Notch signaling subgraph-12 APH1A 147 62 white rectangle gene 0.5 black 46 17 29509 N-Notch signaling subgraph-13 IL6 28 177 white rectangle gene 0.5 black 46 17 6018 N-Notch signaling subgraph-14 APP 149 45 white rectangle gene 0.5 black 46 17 620 N-Notch signaling subgraph-15 NOTCH1 158 62 white rectangle gene 0.5 black 46 17 7881 N-Notch signaling subgraph-16 PSEN1 152 79 white rectangle gene 0.5 black 46 17 9508 N-Notch signaling subgraph-20 RARB 40 0 white rectangle gene 0.5 black 46 17 9865 N-Notch signaling subgraph-3 sAPP-alpha 0 164 white rectangle gene 0.5 black 46 17 CONSO00067 N-Notch signaling subgraph-4 Notch 16 166 white rectangle gene 0.5 black 46 17 Notch N-Notch signaling subgraph-5 STAT3 4 155 white rectangle gene 0.5 black 46 17 11364 N-Notch signaling subgraph-6 RBPJL 27 156 white rectangle gene 0.5 black 46 17 13761 N-Notch signaling subgraph-7 SIRT1 30 4 white rectangle gene 0.5 black 46 17 14929 N-Notch signaling subgraph-8 CDH2 142 90 white rectangle gene 0.5 black 46 17 1759 N-Notch signaling subgraph-9 ADAM10 8 182 white rectangle gene 0.5 black 46 17 188 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Notch signaling subgraph.sif000066400000000000000000000023651426625374700305520ustar00rootroot000000000000000 1 2 N-Notch signaling subgraph-16 activation N-Notch signaling subgraph-10 N-Notch signaling subgraph-16 activation N-Notch signaling subgraph-8 N-Notch signaling subgraph-16 inhibition N-Notch signaling subgraph-15 N-Notch signaling subgraph-9 activation N-Notch signaling subgraph-4 N-Notch signaling subgraph-9 activation N-Notch signaling subgraph-4 N-Notch signaling subgraph-1 2 activation N-Notch signaling subgraph-14 N-Notch signaling subgraph-1 2 activation N-Notch signaling subgraph-15 N-Notch signaling subgraph-11 activation N-Notch signaling subgraph-10 N-Notch signaling subgraph-5 activation N-Notch signaling subgraph-4 N-Notch signaling subgraph-7 activation N-Notch signaling subgraph-20 N-Notch signaling subgraph-12 activation N-Notch signaling subgraph-16 N-Notch signaling subgraph-12 activation N-Notch signaling subgraph-14 N-Notch signaling subgraph-12 activation N-Notch signaling subgraph-15 N-Notch signaling subgraph-4 activation N-Notch signaling subgraph-6 N-Notch signaling subgraph-4 activation N-Notch signaling subgraph-3 N-Notch signaling subgraph-4 activation N-Notch signaling subgraph-13 N-Notch signaling subgraph-3 activation N-Notch signaling subgraph-5 N-Notch signaling subgraph-3 activation N-Notch signaling subgraph-4 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Nuclear factor Kappa beta subgraph.att000066400000000000000000000064271426625374700323160ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Nuclear factor Kappa beta subgraph-1 2 APBA2 NFKB1 0 59 white rectangle gene,gene 0.5 black 46 17 579,/,7794 N-Nuclear factor Kappa beta subgraph-10 CCL3 35 83 white rectangle gene 0.5 black 46 17 10627 N-Nuclear factor Kappa beta subgraph-11 CCL5 24 73 white rectangle gene 0.5 black 46 17 10632 N-Nuclear factor Kappa beta subgraph-12 CXCL10 29 82 white rectangle gene 0.5 black 46 17 10637 N-Nuclear factor Kappa beta subgraph-13 CXCL12 136 9 white rectangle gene 0.5 black 46 17 10672 N-Nuclear factor Kappa beta subgraph-14 TLR4 26 78 white rectangle gene 0.5 black 46 17 11850 N-Nuclear factor Kappa beta subgraph-15 TNF 43 55 white rectangle gene 0.5 black 46 17 11892 N-Nuclear factor Kappa beta subgraph-17 C5 28 60 white rectangle gene 0.5 black 46 17 1331 N-Nuclear factor Kappa beta subgraph-18 CHI3L1 55 59 white rectangle gene 0.5 black 46 17 1932 N-Nuclear factor Kappa beta subgraph-19 AGER 45 69 white rectangle gene 0.5 black 46 17 320 N-Nuclear factor Kappa beta subgraph-21 IL1A 46 74 white rectangle gene 0.5 black 46 17 5991 N-Nuclear factor Kappa beta subgraph-22 IL1B 51 70 white rectangle gene 0.5 black 46 17 5992 N-Nuclear factor Kappa beta subgraph-23 IL1RN 25 67 white rectangle gene 0.5 black 46 17 6000 N-Nuclear factor Kappa beta subgraph-24 IL6 58 51 white rectangle gene 0.5 black 46 17 6018 N-Nuclear factor Kappa beta subgraph-25 APP 46 58 white rectangle gene 0.5 black 46 17 620 N-Nuclear factor Kappa beta subgraph-26 APP 13 56 white rectangle gene 0.5 black 46 17 620 N-Nuclear factor Kappa beta subgraph-27 NFKB1 38 69 white rectangle gene 0.5 black 46 17 7794 N-Nuclear factor Kappa beta subgraph-28 NFKB2 50 64 white rectangle gene 0.5 black 46 17 7795 N-Nuclear factor Kappa beta subgraph-29 NFKBIA 146 11 white rectangle gene 0.5 black 46 17 7797 N-Nuclear factor Kappa beta subgraph-3 4 NFKB1 NFKB2 65 90 white rectangle gene,gene 0.5 black 46 17 7794,/,7795 N-Nuclear factor Kappa beta subgraph-30 BACE1 54 80 white rectangle gene 0.5 black 46 17 933 N-Nuclear factor Kappa beta subgraph-31 PSEN1 41 63 white rectangle gene 0.5 black 46 17 9508 N-Nuclear factor Kappa beta subgraph-32 PSEN2 39 58 white rectangle gene 0.5 black 46 17 9509 N-Nuclear factor Kappa beta subgraph-33 PTGS2 153 14 white rectangle gene 0.5 black 46 17 9605 N-Nuclear factor Kappa beta subgraph-34 RELA 58 94 white rectangle gene 0.5 black 46 17 9955 N-Nuclear factor Kappa beta subgraph-35 RELB 57 70 white rectangle gene 0.5 black 46 17 9956 N-Nuclear factor Kappa beta subgraph-36 Rela 130 8 white rectangle gene 0.5 black 46 17 103290 N-Nuclear factor Kappa beta subgraph-37 Nfkbib 0 53 white rectangle gene 0.5 black 46 17 104752 N-Nuclear factor Kappa beta subgraph-38 Eif2ak2 3 47 white rectangle gene 0.5 black 46 17 1353449 N-Nuclear factor Kappa beta subgraph-39 Cysltr1 134 0 white rectangle gene 0.5 black 46 17 1926218 N-Nuclear factor Kappa beta subgraph-40 Nfkb1 8 43 white rectangle gene 0.5 black 46 17 97312 N-Nuclear factor Kappa beta subgraph-5 6 NFKB1 RELB 68 51 white rectangle gene,gene 0.5 black 46 17 7794,/,9956 N-Nuclear factor Kappa beta subgraph-7 8 NFKB2 RELB 72 73 white rectangle gene,gene 0.5 black 46 17 7795,/,9956 N-Nuclear factor Kappa beta subgraph-9 amyloid-beta 40 84 white rectangle gene 0.5 black 46 17 64645 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Nuclear factor Kappa beta subgraph.sif000066400000000000000000000107171426625374700323040ustar00rootroot000000000000000 1 2 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-21 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-22 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-15 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-24 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-30 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-25 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-31 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-32 N-Nuclear factor Kappa beta subgraph-28 activation N-Nuclear factor Kappa beta subgraph-35 N-Nuclear factor Kappa beta subgraph-34 activation N-Nuclear factor Kappa beta subgraph-30 N-Nuclear factor Kappa beta subgraph-34 activation N-Nuclear factor Kappa beta subgraph-30 N-Nuclear factor Kappa beta subgraph-26 activation N-Nuclear factor Kappa beta subgraph-40 N-Nuclear factor Kappa beta subgraph-26 activation N-Nuclear factor Kappa beta subgraph-40 N-Nuclear factor Kappa beta subgraph-26 activation N-Nuclear factor Kappa beta subgraph-37 N-Nuclear factor Kappa beta subgraph-21 activation N-Nuclear factor Kappa beta subgraph-27 N-Nuclear factor Kappa beta subgraph-21 activation N-Nuclear factor Kappa beta subgraph-27 N-Nuclear factor Kappa beta subgraph-21 activation N-Nuclear factor Kappa beta subgraph-28 N-Nuclear factor Kappa beta subgraph-21 activation N-Nuclear factor Kappa beta subgraph-28 N-Nuclear factor Kappa beta subgraph-29 activation N-Nuclear factor Kappa beta subgraph-33 N-Nuclear factor Kappa beta subgraph-14 activation N-Nuclear factor Kappa beta subgraph-27 N-Nuclear factor Kappa beta subgraph-39 activation N-Nuclear factor Kappa beta subgraph-36 N-Nuclear factor Kappa beta subgraph-35 activation N-Nuclear factor Kappa beta subgraph-7 8 N-Nuclear factor Kappa beta subgraph-38 inhibition N-Nuclear factor Kappa beta subgraph-26 N-Nuclear factor Kappa beta subgraph-1 2 inhibition N-Nuclear factor Kappa beta subgraph-26 N-Nuclear factor Kappa beta subgraph-18 inhibition N-Nuclear factor Kappa beta subgraph-27 N-Nuclear factor Kappa beta subgraph-18 inhibition N-Nuclear factor Kappa beta subgraph-28 N-Nuclear factor Kappa beta subgraph-22 activation N-Nuclear factor Kappa beta subgraph-28 N-Nuclear factor Kappa beta subgraph-22 activation N-Nuclear factor Kappa beta subgraph-27 N-Nuclear factor Kappa beta subgraph-5 6 activation N-Nuclear factor Kappa beta subgraph-18 N-Nuclear factor Kappa beta subgraph-19 activation N-Nuclear factor Kappa beta subgraph-27 N-Nuclear factor Kappa beta subgraph-19 activation N-Nuclear factor Kappa beta subgraph-28 N-Nuclear factor Kappa beta subgraph-3 4 activation N-Nuclear factor Kappa beta subgraph-30 N-Nuclear factor Kappa beta subgraph-13 activation N-Nuclear factor Kappa beta subgraph-29 N-Nuclear factor Kappa beta subgraph-9 activation N-Nuclear factor Kappa beta subgraph-27 N-Nuclear factor Kappa beta subgraph-15 activation N-Nuclear factor Kappa beta subgraph-27 N-Nuclear factor Kappa beta subgraph-15 activation N-Nuclear factor Kappa beta subgraph-28 N-Nuclear factor Kappa beta subgraph-27 inhibition N-Nuclear factor Kappa beta subgraph-30 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-30 N-Nuclear factor Kappa beta subgraph-27 inhibition N-Nuclear factor Kappa beta subgraph-26 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-25 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-31 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-32 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-22 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-23 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-15 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-12 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-10 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-11 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-17 N-Nuclear factor Kappa beta subgraph-27 activation N-Nuclear factor Kappa beta subgraph-35 Peroxisome proliferator activated receptor subgraph.att000066400000000000000000000040121426625374700360360ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/outputID label X Y color shape type label.cex label.color width height genesList N-Peroxisome proliferator activated receptor subgraph-10 APP 60 67 white rectangle gene 0.5 black 46 17 620 N-Peroxisome proliferator activated receptor subgraph-11 MYD88 38 10 white rectangle gene 0.5 black 46 17 7562 N-Peroxisome proliferator activated receptor subgraph-12 NOS2 0 46 white rectangle gene 0.5 black 46 17 7873 N-Peroxisome proliferator activated receptor subgraph-15 PPARG 37 39 white rectangle gene 0.5 black 46 17 9236 N-Peroxisome proliferator activated receptor subgraph-16 BACE1 23 0 white rectangle gene 0.5 black 46 17 933 N-Peroxisome proliferator activated receptor subgraph-18 Cdh2 159 114 white rectangle gene 0.5 black 46 17 88355 N-Peroxisome proliferator activated receptor subgraph-19 Gfap 70 121 white rectangle gene 0.5 black 46 17 95697 N-Peroxisome proliferator activated receptor subgraph-2 TLR2 27 70 white rectangle gene 0.5 black 46 17 11848 N-Peroxisome proliferator activated receptor subgraph-20 Pparg 126 112 white rectangle gene 0.5 black 46 17 97747 N-Peroxisome proliferator activated receptor subgraph-21 Ptgs2 94 114 white rectangle gene 0.5 black 46 17 97798 N-Peroxisome proliferator activated receptor subgraph-22 Bax 147 139 white rectangle gene 0.5 black 46 17 99702 N-Peroxisome proliferator activated receptor subgraph-3 TNF 0 28 white rectangle gene 0.5 black 46 17 11892 N-Peroxisome proliferator activated receptor subgraph-4 IL23A 53 0 white rectangle gene 0.5 black 46 17 15488 N-Peroxisome proliferator activated receptor subgraph-5 CD14 69 37 white rectangle gene 0.5 black 46 17 1628 N-Peroxisome proliferator activated receptor subgraph-6 CTNNB1 67 16 white rectangle gene 0.5 black 46 17 2514 N-Peroxisome proliferator activated receptor subgraph-7 IL12B 9 13 white rectangle gene 0.5 black 46 17 5970 N-Peroxisome proliferator activated receptor subgraph-8 IL6 8 62 white rectangle gene 0.5 black 46 17 6018 N-Peroxisome proliferator activated receptor subgraph-9 APP 78 87 white rectangle gene 0.5 black 46 17 620 Peroxisome proliferator activated receptor subgraph.sif000066400000000000000000000056561426625374700360460ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output0 1 2 N-Peroxisome proliferator activated receptor subgraph-9 activation N-Peroxisome proliferator activated receptor subgraph-21 N-Peroxisome proliferator activated receptor subgraph-9 activation N-Peroxisome proliferator activated receptor subgraph-19 N-Peroxisome proliferator activated receptor subgraph-9 inhibition N-Peroxisome proliferator activated receptor subgraph-20 N-Peroxisome proliferator activated receptor subgraph-10 inhibition N-Peroxisome proliferator activated receptor subgraph-9 N-Peroxisome proliferator activated receptor subgraph-20 activation N-Peroxisome proliferator activated receptor subgraph-18 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-9 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-9 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-9 N-Peroxisome proliferator activated receptor subgraph-15 activation N-Peroxisome proliferator activated receptor subgraph-6 N-Peroxisome proliferator activated receptor subgraph-15 activation N-Peroxisome proliferator activated receptor subgraph-10 N-Peroxisome proliferator activated receptor subgraph-15 activation N-Peroxisome proliferator activated receptor subgraph-10 N-Peroxisome proliferator activated receptor subgraph-15 activation N-Peroxisome proliferator activated receptor subgraph-3 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-3 N-Peroxisome proliferator activated receptor subgraph-15 activation N-Peroxisome proliferator activated receptor subgraph-8 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-8 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-12 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-16 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-16 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-7 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-4 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-5 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-11 N-Peroxisome proliferator activated receptor subgraph-15 inhibition N-Peroxisome proliferator activated receptor subgraph-2 N-Peroxisome proliferator activated receptor subgraph-22 activation N-Peroxisome proliferator activated receptor subgraph-20 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Phosphatidylinositol 3 subgraph.att000066400000000000000000000116201426625374700321200ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Phosphatidylinositol 3 subgraph-11 12 IRS1 PIK3R2 100 47 white rectangle gene,gene 0.5 black 46 17 6125,/,8980 N-Phosphatidylinositol 3 subgraph-13 14 IRS2 PIK3R1 101 180 white rectangle gene,gene 0.5 black 46 17 6126,/,8979 N-Phosphatidylinositol 3 subgraph-15 16 IRS2 PIK3R2 97 179 white rectangle gene,gene 0.5 black 46 17 6126,/,8980 N-Phosphatidylinositol 3 subgraph-17 18 IRS4 PIK3R1 37 38 white rectangle gene,gene 0.5 black 46 17 6128,/,8979 N-Phosphatidylinositol 3 subgraph-19 20 IRS4 PIK3R2 38 28 white rectangle gene,gene 0.5 black 46 17 6128,/,8980 N-Phosphatidylinositol 3 subgraph-21 PI3K_p110 122 115 white rectangle gene 0.5 black 46 17 PI3K_p110 N-Phosphatidylinositol 3 subgraph-22 AKT 94 111 white rectangle gene 0.5 black 46 17 AKT N-Phosphatidylinositol 3 subgraph-23 AKT 122 122 white rectangle gene 0.5 black 46 17 AKT N-Phosphatidylinositol 3 subgraph-24 AKT 108 113 white rectangle gene 0.5 black 46 17 AKT N-Phosphatidylinositol 3 subgraph-25 GSK3 73 0 white rectangle gene 0.5 black 46 17 GSK3 N-Phosphatidylinositol 3 subgraph-26 GSK3 74 11 white rectangle gene 0.5 black 46 17 GSK3 N-Phosphatidylinositol 3 subgraph-28 PKC 149 85 white rectangle gene 0.5 black 46 17 PKC N-Phosphatidylinositol 3 subgraph-29 RYR3 191 92 white rectangle gene 0.5 black 46 17 10485 N-Phosphatidylinositol 3 subgraph-3 4 GAB2 PTK2 123 3 white rectangle gene,gene 0.5 black 46 17 14458,/,9611 N-Phosphatidylinositol 3 subgraph-30 GAB2 120 11 white rectangle gene 0.5 black 46 17 14458 N-Phosphatidylinositol 3 subgraph-31 GAB2 38 142 white rectangle gene 0.5 black 46 17 14458 N-Phosphatidylinositol 3 subgraph-33 SIT1 38 150 white rectangle gene 0.5 black 46 17 17710 N-Phosphatidylinositol 3 subgraph-34 ADAM10 168 27 white rectangle gene 0.5 black 46 17 188 N-Phosphatidylinositol 3 subgraph-35 CRK 33 147 white rectangle gene 0.5 black 46 17 2362 N-Phosphatidylinositol 3 subgraph-36 PIK3R5 88 111 white rectangle gene 0.5 black 46 17 30035 N-Phosphatidylinositol 3 subgraph-37 AKT1 82 101 white rectangle gene 0.5 black 46 17 391 N-Phosphatidylinositol 3 subgraph-38 AKT1 77 114 white rectangle gene 0.5 black 46 17 391 N-Phosphatidylinositol 3 subgraph-39 AKT2 62 105 white rectangle gene 0.5 black 46 17 392 N-Phosphatidylinositol 3 subgraph-40 AKT3 76 103 white rectangle gene 0.5 black 46 17 393 N-Phosphatidylinositol 3 subgraph-42 HSPA1A 166 106 white rectangle gene 0.5 black 46 17 5232 N-Phosphatidylinositol 3 subgraph-43 IGF1 136 113 white rectangle gene 0.5 black 46 17 5464 N-Phosphatidylinositol 3 subgraph-44 IRS1 142 118 white rectangle gene 0.5 black 46 17 6125 N-Phosphatidylinositol 3 subgraph-45 IRS1 102 53 white rectangle gene 0.5 black 46 17 6125 N-Phosphatidylinositol 3 subgraph-46 IRS2 137 120 white rectangle gene 0.5 black 46 17 6126 N-Phosphatidylinositol 3 subgraph-47 IRS2 104 178 white rectangle gene 0.5 black 46 17 6126 N-Phosphatidylinositol 3 subgraph-48 IRS4 34 31 white rectangle gene 0.5 black 46 17 6128 N-Phosphatidylinositol 3 subgraph-49 ITPR3 194 82 white rectangle gene 0.5 black 46 17 6182 N-Phosphatidylinositol 3 subgraph-5 6 GAB2 PTK7 116 13 white rectangle gene,gene 0.5 black 46 17 14458,/,9618 N-Phosphatidylinositol 3 subgraph-50 APP 81 193 white rectangle gene 0.5 black 46 17 620 N-Phosphatidylinositol 3 subgraph-51 APP 147 103 white rectangle gene 0.5 black 46 17 620 N-Phosphatidylinositol 3 subgraph-52 JUN 163 32 white rectangle gene 0.5 black 46 17 6204 N-Phosphatidylinositol 3 subgraph-53 MARCKS 152 98 white rectangle gene 0.5 black 46 17 6759 N-Phosphatidylinositol 3 subgraph-54 MARCKS 147 93 white rectangle gene 0.5 black 46 17 6759 N-Phosphatidylinositol 3 subgraph-56 NOS2 76 194 white rectangle gene 0.5 black 46 17 7873 N-Phosphatidylinositol 3 subgraph-57 PDK1 82 108 white rectangle gene 0.5 black 46 17 8809 N-Phosphatidylinositol 3 subgraph-58 PIK3CA 3 75 white rectangle gene 0.5 black 46 17 8975 N-Phosphatidylinositol 3 subgraph-59 PIK3CB 0 86 white rectangle gene 0.5 black 46 17 8976 N-Phosphatidylinositol 3 subgraph-60 PIK3R1 2 81 white rectangle gene 0.5 black 46 17 8979 N-Phosphatidylinositol 3 subgraph-62 PIK3R2 31 140 white rectangle gene 0.5 black 46 17 8980 N-Phosphatidylinositol 3 subgraph-65 PRKCD 157 105 white rectangle gene 0.5 black 46 17 9399 N-Phosphatidylinositol 3 subgraph-66 PRKCD 74 6 white rectangle gene 0.5 black 46 17 9399 N-Phosphatidylinositol 3 subgraph-67 PRKCD 153 107 white rectangle gene 0.5 black 46 17 9399 N-Phosphatidylinositol 3 subgraph-68 PSEN1 196 87 white rectangle gene 0.5 black 46 17 9508 N-Phosphatidylinositol 3 subgraph-7 8 INSR APP 71 107 white rectangle gene,gene 0.5 black 46 17 6091,/,620 N-Phosphatidylinositol 3 subgraph-71 PTK2 44 143 white rectangle gene 0.5 black 46 17 9611 N-Phosphatidylinositol 3 subgraph-72 PTK7 34 136 white rectangle gene 0.5 black 46 17 9618 N-Phosphatidylinositol 3 subgraph-9 10 IRS1 PIK3R1 107 56 white rectangle gene,gene 0.5 black 46 17 6125,/,8979 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Phosphatidylinositol 3 subgraph.sif000066400000000000000000000075411426625374700321200ustar00rootroot000000000000000 1 2 N-Phosphatidylinositol 3 subgraph-22 activation N-Phosphatidylinositol 3 subgraph-22 N-Phosphatidylinositol 3 subgraph-50 activation N-Phosphatidylinositol 3 subgraph-56 N-Phosphatidylinositol 3 subgraph-31 activation N-Phosphatidylinositol 3 subgraph-62 N-Phosphatidylinositol 3 subgraph-31 activation N-Phosphatidylinositol 3 subgraph-33 N-Phosphatidylinositol 3 subgraph-31 activation N-Phosphatidylinositol 3 subgraph-35 N-Phosphatidylinositol 3 subgraph-71 activation N-Phosphatidylinositol 3 subgraph-31 N-Phosphatidylinositol 3 subgraph-45 activation N-Phosphatidylinositol 3 subgraph-9 10 N-Phosphatidylinositol 3 subgraph-45 activation N-Phosphatidylinositol 3 subgraph-11 12 N-Phosphatidylinositol 3 subgraph-43 activation N-Phosphatidylinositol 3 subgraph-21 N-Phosphatidylinositol 3 subgraph-43 activation N-Phosphatidylinositol 3 subgraph-44 N-Phosphatidylinositol 3 subgraph-43 activation N-Phosphatidylinositol 3 subgraph-46 N-Phosphatidylinositol 3 subgraph-53 activation N-Phosphatidylinositol 3 subgraph-51 N-Phosphatidylinositol 3 subgraph-5 6 activation N-Phosphatidylinositol 3 subgraph-30 N-Phosphatidylinositol 3 subgraph-67 activation N-Phosphatidylinositol 3 subgraph-65 N-Phosphatidylinositol 3 subgraph-57 activation N-Phosphatidylinositol 3 subgraph-37 N-Phosphatidylinositol 3 subgraph-57 activation N-Phosphatidylinositol 3 subgraph-40 N-Phosphatidylinositol 3 subgraph-57 activation N-Phosphatidylinositol 3 subgraph-22 N-Phosphatidylinositol 3 subgraph-57 activation N-Phosphatidylinositol 3 subgraph-38 N-Phosphatidylinositol 3 subgraph-48 activation N-Phosphatidylinositol 3 subgraph-17 18 N-Phosphatidylinositol 3 subgraph-48 activation N-Phosphatidylinositol 3 subgraph-19 20 N-Phosphatidylinositol 3 subgraph-28 activation N-Phosphatidylinositol 3 subgraph-54 N-Phosphatidylinositol 3 subgraph-3 4 activation N-Phosphatidylinositol 3 subgraph-30 N-Phosphatidylinositol 3 subgraph-51 activation N-Phosphatidylinositol 3 subgraph-65 N-Phosphatidylinositol 3 subgraph-51 activation N-Phosphatidylinositol 3 subgraph-67 N-Phosphatidylinositol 3 subgraph-51 activation N-Phosphatidylinositol 3 subgraph-53 N-Phosphatidylinositol 3 subgraph-51 activation N-Phosphatidylinositol 3 subgraph-54 N-Phosphatidylinositol 3 subgraph-51 activation N-Phosphatidylinositol 3 subgraph-43 N-Phosphatidylinositol 3 subgraph-36 activation N-Phosphatidylinositol 3 subgraph-57 N-Phosphatidylinositol 3 subgraph-36 activation N-Phosphatidylinositol 3 subgraph-22 N-Phosphatidylinositol 3 subgraph-66 activation N-Phosphatidylinositol 3 subgraph-26 N-Phosphatidylinositol 3 subgraph-66 inhibition N-Phosphatidylinositol 3 subgraph-25 N-Phosphatidylinositol 3 subgraph-60 activation N-Phosphatidylinositol 3 subgraph-58 N-Phosphatidylinositol 3 subgraph-60 activation N-Phosphatidylinositol 3 subgraph-59 N-Phosphatidylinositol 3 subgraph-21 activation N-Phosphatidylinositol 3 subgraph-23 N-Phosphatidylinositol 3 subgraph-21 activation N-Phosphatidylinositol 3 subgraph-24 N-Phosphatidylinositol 3 subgraph-68 activation N-Phosphatidylinositol 3 subgraph-49 N-Phosphatidylinositol 3 subgraph-68 activation N-Phosphatidylinositol 3 subgraph-29 N-Phosphatidylinositol 3 subgraph-52 inhibition N-Phosphatidylinositol 3 subgraph-34 N-Phosphatidylinositol 3 subgraph-47 activation N-Phosphatidylinositol 3 subgraph-13 14 N-Phosphatidylinositol 3 subgraph-47 activation N-Phosphatidylinositol 3 subgraph-15 16 N-Phosphatidylinositol 3 subgraph-65 activation N-Phosphatidylinositol 3 subgraph-42 N-Phosphatidylinositol 3 subgraph-65 activation N-Phosphatidylinositol 3 subgraph-42 N-Phosphatidylinositol 3 subgraph-7 8 activation N-Phosphatidylinositol 3 subgraph-57 N-Phosphatidylinositol 3 subgraph-7 8 activation N-Phosphatidylinositol 3 subgraph-39 N-Phosphatidylinositol 3 subgraph-72 activation N-Phosphatidylinositol 3 subgraph-31 N-Phosphatidylinositol 3 subgraph-24 activation N-Phosphatidylinositol 3 subgraph-22 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Plasminogen activator subgraph.att000066400000000000000000000020321426625374700317720ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Plasminogen activator subgraph-1 TIMP1 105 32 white rectangle gene 0.5 black 46 17 11820 N-Plasminogen activator subgraph-10 PLAT 12 171 white rectangle gene 0.5 black 46 17 9051 N-Plasminogen activator subgraph-12 PLAUR 48 182 white rectangle gene 0.5 black 46 17 9053 N-Plasminogen activator subgraph-13 PLG 14 162 white rectangle gene 0.5 black 46 17 9071 N-Plasminogen activator subgraph-3 EDN1 88 0 white rectangle gene 0.5 black 46 17 3176 N-Plasminogen activator subgraph-4 F2 92 19 white rectangle gene 0.5 black 46 17 3535 N-Plasminogen activator subgraph-5 APP 3 155 white rectangle gene 0.5 black 46 17 620 N-Plasminogen activator subgraph-6 APP 31 172 white rectangle gene 0.5 black 46 17 620 N-Plasminogen activator subgraph-7 MAPT 109 8 white rectangle gene 0.5 black 46 17 6893 N-Plasminogen activator subgraph-8 MAPT 74 20 white rectangle gene 0.5 black 46 17 6893 N-Plasminogen activator subgraph-9 SERPINI1 0 175 white rectangle gene 0.5 black 46 17 8943 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Plasminogen activator subgraph.sif000066400000000000000000000023201426625374700317630ustar00rootroot000000000000000 1 2 N-Plasminogen activator subgraph-13 activation N-Plasminogen activator subgraph-5 N-Plasminogen activator subgraph-13 activation N-Plasminogen activator subgraph-6 N-Plasminogen activator subgraph-8 inhibition N-Plasminogen activator subgraph-4 N-Plasminogen activator subgraph-6 activation N-Plasminogen activator subgraph-10 N-Plasminogen activator subgraph-6 activation N-Plasminogen activator subgraph-12 N-Plasminogen activator subgraph-4 activation N-Plasminogen activator subgraph-3 N-Plasminogen activator subgraph-4 activation N-Plasminogen activator subgraph-3 N-Plasminogen activator subgraph-4 activation N-Plasminogen activator subgraph-7 N-Plasminogen activator subgraph-4 activation N-Plasminogen activator subgraph-1 N-Plasminogen activator subgraph-9 inhibition N-Plasminogen activator subgraph-10 N-Plasminogen activator subgraph-9 inhibition N-Plasminogen activator subgraph-13 N-Plasminogen activator subgraph-10 activation N-Plasminogen activator subgraph-6 N-Plasminogen activator subgraph-10 activation N-Plasminogen activator subgraph-6 N-Plasminogen activator subgraph-10 activation N-Plasminogen activator subgraph-13 N-Plasminogen activator subgraph-10 activation N-Plasminogen activator subgraph-5 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Prostaglandin subgraph.att000066400000000000000000000014611426625374700303530ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Prostaglandin subgraph-10 PTGS2 0 84 white rectangle gene 0.5 black 46 17 9605 N-Prostaglandin subgraph-11 Gfap 134 154 white rectangle gene 0.5 black 46 17 95697 N-Prostaglandin subgraph-12 Pparg 140 130 white rectangle gene 0.5 black 46 17 97747 N-Prostaglandin subgraph-13 Ptgs2 108 135 white rectangle gene 0.5 black 46 17 97798 N-Prostaglandin subgraph-2 IL32 92 134 white rectangle gene 0.5 black 46 17 16830 N-Prostaglandin subgraph-3 PTGES2 150 0 white rectangle gene 0.5 black 46 17 17822 N-Prostaglandin subgraph-4 APP 126 139 white rectangle gene 0.5 black 46 17 620 N-Prostaglandin subgraph-6 NFKBIA 2 96 white rectangle gene 0.5 black 46 17 7797 N-Prostaglandin subgraph-8 BACE1 144 12 white rectangle gene 0.5 black 46 17 933 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Prostaglandin subgraph.sif000066400000000000000000000007231426625374700303440ustar00rootroot000000000000000 1 2 N-Prostaglandin subgraph-4 activation N-Prostaglandin subgraph-13 N-Prostaglandin subgraph-4 activation N-Prostaglandin subgraph-13 N-Prostaglandin subgraph-4 activation N-Prostaglandin subgraph-11 N-Prostaglandin subgraph-4 inhibition N-Prostaglandin subgraph-12 N-Prostaglandin subgraph-13 inhibition N-Prostaglandin subgraph-2 N-Prostaglandin subgraph-6 activation N-Prostaglandin subgraph-10 N-Prostaglandin subgraph-8 activation N-Prostaglandin subgraph-3 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Reactive oxygen species subgraph.att000066400000000000000000000035761426625374700322270ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Reactive oxygen species subgraph-10 CASP1 0 137 white rectangle gene 0.5 black 46 17 1499 N-Reactive oxygen species subgraph-11 COX4I2 91 94 white rectangle gene 0.5 black 46 17 16232 N-Reactive oxygen species subgraph-12 NLRP3 20 126 white rectangle gene 0.5 black 46 17 16400 N-Reactive oxygen species subgraph-13 COX4I1 82 80 white rectangle gene 0.5 black 46 17 2265 N-Reactive oxygen species subgraph-14 COX5B 75 87 white rectangle gene 0.5 black 46 17 2269 N-Reactive oxygen species subgraph-17 IL1B 8 129 white rectangle gene 0.5 black 46 17 5992 N-Reactive oxygen species subgraph-18 APP 86 86 white rectangle gene 0.5 black 46 17 620 N-Reactive oxygen species subgraph-19 APP 82 91 white rectangle gene 0.5 black 46 17 620 N-Reactive oxygen species subgraph-20 APP 0 123 white rectangle gene 0.5 black 46 17 620 N-Reactive oxygen species subgraph-21 NOX4 149 156 white rectangle gene 0.5 black 46 17 7891 N-Reactive oxygen species subgraph-22 ATM 131 157 white rectangle gene 0.5 black 46 17 795 N-Reactive oxygen species subgraph-23 OGG1 155 46 white rectangle gene 0.5 black 46 17 8125 N-Reactive oxygen species subgraph-24 OGG1 145 43 white rectangle gene 0.5 black 46 17 8125 N-Reactive oxygen species subgraph-25 OGG1 150 56 white rectangle gene 0.5 black 46 17 8125 N-Reactive oxygen species subgraph-29 Map3k5 61 0 white rectangle gene 0.5 black 46 17 1346876 N-Reactive oxygen species subgraph-31 Sod1 67 5 white rectangle gene 0.5 black 46 17 98351 N-Reactive oxygen species subgraph-5 Caspases 93 184 white rectangle gene 0.5 black 46 17 468 N-Reactive oxygen species subgraph-7 TNF 164 40 white rectangle gene 0.5 black 46 17 11892 N-Reactive oxygen species subgraph-8 TP53 140 157 white rectangle gene 0.5 black 46 17 11998 N-Reactive oxygen species subgraph-9 SLC25A21 95 193 white rectangle gene 0.5 black 46 17 14411 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Reactive oxygen species subgraph.sif000066400000000000000000000025611426625374700322110ustar00rootroot000000000000000 1 2 N-Reactive oxygen species subgraph-19 inhibition N-Reactive oxygen species subgraph-13 N-Reactive oxygen species subgraph-19 inhibition N-Reactive oxygen species subgraph-11 N-Reactive oxygen species subgraph-19 inhibition N-Reactive oxygen species subgraph-14 N-Reactive oxygen species subgraph-20 activation N-Reactive oxygen species subgraph-17 N-Reactive oxygen species subgraph-10 activation N-Reactive oxygen species subgraph-17 N-Reactive oxygen species subgraph-25 inhibition N-Reactive oxygen species subgraph-23 N-Reactive oxygen species subgraph-23 inhibition N-Reactive oxygen species subgraph-7 N-Reactive oxygen species subgraph-12 activation N-Reactive oxygen species subgraph-17 N-Reactive oxygen species subgraph-9 activation N-Reactive oxygen species subgraph-5 N-Reactive oxygen species subgraph-21 activation N-Reactive oxygen species subgraph-8 N-Reactive oxygen species subgraph-18 inhibition N-Reactive oxygen species subgraph-13 N-Reactive oxygen species subgraph-18 inhibition N-Reactive oxygen species subgraph-11 N-Reactive oxygen species subgraph-18 inhibition N-Reactive oxygen species subgraph-14 N-Reactive oxygen species subgraph-22 activation N-Reactive oxygen species subgraph-8 N-Reactive oxygen species subgraph-31 activation N-Reactive oxygen species subgraph-29 N-Reactive oxygen species subgraph-24 inhibition N-Reactive oxygen species subgraph-23 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Reelin signaling subgraph.att000066400000000000000000000034031426625374700307160ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Reelin signaling subgraph-1 2 DAB2 APP 72 195 white rectangle gene,gene 0.5 black 46 17 2662,/,620 N-Reelin signaling subgraph-10 SRC 11 112 white rectangle gene 0.5 black 46 17 SRC N-Reelin signaling subgraph-11 Glutamate ionotropic receptor NMDA type subunits 0 116 white rectangle gene 0.5 black 46 17 1201 N-Reelin signaling subgraph-12 ITGB1BP1 110 72 white rectangle gene 0.5 black 46 17 23927 N-Reelin signaling subgraph-13 DAB1 87 0 white rectangle gene 0.5 black 46 17 2661 N-Reelin signaling subgraph-14 DAB1 61 97 white rectangle gene 0.5 black 46 17 2661 N-Reelin signaling subgraph-15 GRIA3 87 81 white rectangle gene 0.5 black 46 17 4573 N-Reelin signaling subgraph-16 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subgraph-82 N-Regulation of actin cytoskeleton subgraph-70 activation N-Regulation of actin cytoskeleton subgraph-43 N-Regulation of actin cytoskeleton subgraph-70 activation N-Regulation of actin cytoskeleton subgraph-96 N-Regulation of actin cytoskeleton subgraph-70 activation N-Regulation of actin cytoskeleton subgraph-107 N-Regulation of actin cytoskeleton subgraph-100 activation N-Regulation of actin cytoskeleton subgraph-98 N-Regulation of actin cytoskeleton subgraph-100 activation N-Regulation of actin cytoskeleton subgraph-99 N-Regulation of actin cytoskeleton subgraph-76 activation N-Regulation of actin cytoskeleton subgraph-74 N-Regulation of actin cytoskeleton subgraph-76 activation N-Regulation of actin cytoskeleton subgraph-74 N-Regulation of actin cytoskeleton subgraph-76 activation N-Regulation of actin cytoskeleton subgraph-94 N-Regulation of actin cytoskeleton subgraph-76 activation N-Regulation of actin cytoskeleton subgraph-53 N-Regulation of actin cytoskeleton 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RB1 16 117 white rectangle gene 0.5 black 46 17 9884 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Retinoblastoma subgraph.sif000066400000000000000000000007341426625374700305240ustar00rootroot000000000000000 1 2 N-Retinoblastoma subgraph-4 activation N-Retinoblastoma subgraph-9 N-Retinoblastoma subgraph-8 activation N-Retinoblastoma subgraph-2 N-Retinoblastoma subgraph-7 inhibition N-Retinoblastoma subgraph-6 N-Retinoblastoma subgraph-1 activation N-Retinoblastoma subgraph-9 N-Retinoblastoma subgraph-3 activation N-Retinoblastoma subgraph-9 N-Retinoblastoma subgraph-5 activation N-Retinoblastoma subgraph-10 N-Retinoblastoma subgraph-9 inhibition N-Retinoblastoma subgraph-2 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/RhoA subgraph.att000066400000000000000000000013731426625374700264010ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-RhoA subgraph-1 2 ROCK2 SORL1 169 127 white rectangle gene,gene 0.5 black 46 17 10252,/,11185 N-RhoA subgraph-10 RHOA 30 121 white rectangle gene 0.5 black 46 17 667 N-RhoA subgraph-11 RHOB 20 98 white rectangle gene 0.5 black 46 17 668 N-RhoA subgraph-12 RHOC 9 129 white rectangle gene 0.5 black 46 17 669 N-RhoA subgraph-13 RHOG 0 109 white rectangle gene 0.5 black 46 17 672 N-RhoA subgraph-6 sAPP-alpha 158 132 white rectangle gene 0.5 black 46 17 CONSO00067 N-RhoA subgraph-7 ROCK2 114 9 white rectangle gene 0.5 black 46 17 10252 N-RhoA subgraph-8 SORL1 106 0 white rectangle gene 0.5 black 46 17 11185 N-RhoA subgraph-9 ARHGEF5 16 113 white rectangle gene 0.5 black 46 17 13209 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/RhoA subgraph.sif000066400000000000000000000004461426625374700263720ustar00rootroot000000000000000 1 2 N-RhoA subgraph-9 activation N-RhoA subgraph-13 N-RhoA subgraph-9 activation N-RhoA subgraph-10 N-RhoA subgraph-9 activation N-RhoA subgraph-11 N-RhoA subgraph-9 activation N-RhoA subgraph-12 N-RhoA subgraph-1 2 activation N-RhoA subgraph-6 N-RhoA subgraph-7 activation N-RhoA subgraph-8 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Serotonergic subgraph.att000066400000000000000000000003501426625374700302050ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Serotonergic subgraph-1 STAT3 0 0 white rectangle gene 0.5 black 46 17 11364 N-Serotonergic subgraph-3 APP 200 26 white rectangle gene 0.5 black 46 17 620 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Serotonergic subgraph.sif000066400000000000000000000001051426625374700301740ustar00rootroot000000000000000 1 2 N-Serotonergic subgraph-3 inhibition N-Serotonergic subgraph-1 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Smad subgraph.att000066400000000000000000000003331426625374700264270ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Smad subgraph-1 TGFB1 21 0 white rectangle gene 0.5 black 46 17 11766 N-Smad subgraph-2 SMAD2 0 200 white rectangle gene 0.5 black 46 17 6768 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Smad subgraph.sif000066400000000000000000000000651426625374700264220ustar00rootroot000000000000000 1 2 N-Smad subgraph-1 activation N-Smad subgraph-2 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Sphingolipid metabolic subgraph.att000066400000000000000000000026201426625374700321150ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Sphingolipid metabolic subgraph-1 2 N-acetyl-alpha-neuraminyl-(2->3)-beta-D-galactosyl-(1->4)-beta-D-glucose APP 30 117 white rectangle gene,gene 0.5 black 46 17 59226,/,620 N-Sphingolipid metabolic subgraph-12 STK11 66 142 white rectangle gene 0.5 black 46 17 11389 N-Sphingolipid metabolic subgraph-15 APP 58 86 white rectangle gene 0.5 black 46 17 620 N-Sphingolipid metabolic subgraph-16 MAPT 25 65 white rectangle gene 0.5 black 46 17 6893 N-Sphingolipid metabolic subgraph-17 MAPT 33 45 white rectangle gene 0.5 black 46 17 6893 N-Sphingolipid metabolic subgraph-18 MAPT 114 89 white rectangle gene 0.5 black 46 17 6893 N-Sphingolipid metabolic subgraph-19 MAPT 10 80 white rectangle gene 0.5 black 46 17 6893 N-Sphingolipid metabolic subgraph-20 PRKAA1 156 93 white rectangle gene 0.5 black 46 17 9376 N-Sphingolipid metabolic subgraph-3 4 APBB1 Amyloidogenic glycoprotein, intracellular domain, conserved site 106 0 white rectangle gene,gene 0.5 black 46 17 581,/,IPR019745 N-Sphingolipid metabolic subgraph-5 CAMK 68 185 white rectangle gene 0.5 black 46 17 CAMK N-Sphingolipid metabolic subgraph-6 PRKAC 0 49 white rectangle gene 0.5 black 46 17 PRKAC N-Sphingolipid metabolic subgraph-7 PRKAC 76 119 white rectangle gene 0.5 black 46 17 PRKAC N-Sphingolipid metabolic subgraph-8 ST8SIA1 87 37 white rectangle gene 0.5 black 46 17 10869 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Sphingolipid metabolic subgraph.sif000066400000000000000000000023731426625374700321130ustar00rootroot000000000000000 1 2 N-Sphingolipid metabolic subgraph-20 activation N-Sphingolipid metabolic subgraph-18 N-Sphingolipid metabolic subgraph-3 4 inhibition N-Sphingolipid metabolic subgraph-8 N-Sphingolipid metabolic subgraph-5 activation N-Sphingolipid metabolic subgraph-12 N-Sphingolipid metabolic subgraph-19 activation N-Sphingolipid metabolic subgraph-15 N-Sphingolipid metabolic subgraph-15 activation N-Sphingolipid metabolic subgraph-1 2 N-Sphingolipid metabolic subgraph-15 inhibition N-Sphingolipid metabolic subgraph-8 N-Sphingolipid metabolic subgraph-17 activation N-Sphingolipid metabolic subgraph-15 N-Sphingolipid metabolic subgraph-16 activation N-Sphingolipid metabolic subgraph-15 N-Sphingolipid metabolic subgraph-18 activation N-Sphingolipid metabolic subgraph-15 N-Sphingolipid metabolic subgraph-7 activation N-Sphingolipid metabolic subgraph-15 N-Sphingolipid metabolic subgraph-12 activation N-Sphingolipid metabolic subgraph-7 N-Sphingolipid metabolic subgraph-12 activation N-Sphingolipid metabolic subgraph-15 N-Sphingolipid metabolic subgraph-6 activation N-Sphingolipid metabolic subgraph-16 N-Sphingolipid metabolic subgraph-6 activation N-Sphingolipid metabolic subgraph-19 N-Sphingolipid metabolic subgraph-6 activation N-Sphingolipid metabolic subgraph-17 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Synapse assembly subgraph.att000066400000000000000000000003621426625374700307670ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Synapse assembly subgraph-10 APP 0 200 white rectangle gene 0.5 black 46 17 620 N-Synapse assembly subgraph-9 GRIN2B 104 0 white rectangle gene 0.5 black 46 17 4586 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Synapse assembly subgraph.sif000066400000000000000000000001161426625374700307550ustar00rootroot000000000000000 1 2 N-Synapse assembly subgraph-10 activation N-Synapse assembly subgraph-9 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Synaptic vesicle endocytosis subgraph.att000066400000000000000000000012441426625374700332760ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Synaptic vesicle endocytosis subgraph-1 2 STXBP1 DYRK1A 12 161 white rectangle gene,gene 0.5 black 46 17 11444,/,3091 N-Synaptic vesicle endocytosis subgraph-11 STXBP1 0 150 white rectangle gene 0.5 black 46 17 11444 N-Synaptic vesicle endocytosis subgraph-12 DNM1 67 16 white rectangle gene 0.5 black 46 17 2972 N-Synaptic vesicle endocytosis subgraph-13 DNM2 91 11 white rectangle gene 0.5 black 46 17 2974 N-Synaptic vesicle endocytosis subgraph-14 GRIA1 76 0 white rectangle gene 0.5 black 46 17 4571 N-Synaptic vesicle endocytosis subgraph-16 ARC 83 27 white rectangle gene 0.5 black 46 17 648 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Synaptic vesicle endocytosis subgraph.sif000066400000000000000000000007541426625374700332740ustar00rootroot000000000000000 1 2 N-Synaptic vesicle endocytosis subgraph-12 activation N-Synaptic vesicle endocytosis subgraph-14 N-Synaptic vesicle endocytosis subgraph-13 activation N-Synaptic vesicle endocytosis subgraph-14 N-Synaptic vesicle endocytosis subgraph-1 2 activation N-Synaptic vesicle endocytosis subgraph-11 N-Synaptic vesicle endocytosis subgraph-16 activation N-Synaptic vesicle endocytosis subgraph-12 N-Synaptic vesicle endocytosis subgraph-16 activation N-Synaptic vesicle endocytosis subgraph-13 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Syndecan subgraph.att000066400000000000000000000022571426625374700273160ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Syndecan subgraph-1 2 deoxyribonucleic acid poly(ethylene imine) 142 122 white rectangle gene,gene 0.5 black 46 17 16991,/,53231 N-Syndecan subgraph-10 11 SDC4 PRKCA 19 167 white rectangle gene,gene 0.5 black 46 17 10661,/,9393 N-Syndecan subgraph-12 MMP 36 22 white rectangle gene 0.5 black 46 17 MMP N-Syndecan subgraph-13 PKC 174 102 white rectangle gene 0.5 black 46 17 PKC N-Syndecan subgraph-14 ADAM metallopeptidase domain containing 50 0 white rectangle gene 0.5 black 46 17 47 N-Syndecan subgraph-16 SDC1 128 117 white rectangle gene 0.5 black 46 17 10658 N-Syndecan subgraph-17 SDC1 40 8 white rectangle gene 0.5 black 46 17 10658 N-Syndecan subgraph-18 SDC2 159 112 white rectangle gene 0.5 black 46 17 10659 N-Syndecan subgraph-19 SDC4 0 149 white rectangle gene 0.5 black 46 17 10661 N-Syndecan subgraph-20 PRKCA 6 161 white rectangle gene 0.5 black 46 17 9393 N-Syndecan subgraph-3 4 5 deoxyribonucleic acid poly(ethylene imine) SDC2 188 96 white rectangle gene,gene,gene 0.5 black 46 17 16991,/,53231,/,10659 N-Syndecan subgraph-8 9 SDC1 SDC2 135 137 white rectangle gene,gene 0.5 black 46 17 10658,/,10659 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Syndecan subgraph.sif000066400000000000000000000010211426625374700272730ustar00rootroot000000000000000 1 2 N-Syndecan subgraph-19 activation N-Syndecan subgraph-20 N-Syndecan subgraph-13 activation N-Syndecan subgraph-18 N-Syndecan subgraph-13 activation N-Syndecan subgraph-3 4 5 N-Syndecan subgraph-18 inhibition N-Syndecan subgraph-1 2 N-Syndecan subgraph-8 9 inhibition N-Syndecan subgraph-1 2 N-Syndecan subgraph-12 activation N-Syndecan subgraph-17 N-Syndecan subgraph-16 activation N-Syndecan subgraph-1 2 N-Syndecan subgraph-10 11 activation N-Syndecan subgraph-20 N-Syndecan subgraph-14 activation N-Syndecan subgraph-17 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Synuclein subgraph.att000066400000000000000000000017301426625374700275160ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Synuclein subgraph-1 2 SNCA AGRN 95 94 white rectangle gene,gene 0.5 black 46 17 11138,/,329 N-Synuclein subgraph-10 AGRN 62 130 white rectangle gene 0.5 black 46 17 329 N-Synuclein subgraph-11 GRK5 0 0 white rectangle gene 0.5 black 46 17 4544 N-Synuclein subgraph-12 GSK3B 114 164 white rectangle gene 0.5 black 46 17 4617 N-Synuclein subgraph-13 IL1B 66 103 white rectangle gene 0.5 black 46 17 5992 N-Synuclein subgraph-15 MAPT 101 143 white rectangle gene 0.5 black 46 17 6893 N-Synuclein subgraph-3 4 5 SNCA GSK3B MAPT 98 162 white rectangle gene,gene,gene 0.5 black 46 17 11138,/,4617,/,6893 N-Synuclein subgraph-6 HSPA 105 124 white rectangle gene 0.5 black 46 17 HSPA N-Synuclein subgraph-7 SNCA 85 118 white rectangle gene 0.5 black 46 17 11138 N-Synuclein subgraph-8 SNCA 12 18 white rectangle gene 0.5 black 46 17 11138 N-Synuclein subgraph-9 GRK2 28 40 white rectangle gene 0.5 black 46 17 289 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Synuclein subgraph.sif000066400000000000000000000013771426625374700275160ustar00rootroot000000000000000 1 2 N-Synuclein subgraph-6 inhibition N-Synuclein subgraph-15 N-Synuclein subgraph-6 inhibition N-Synuclein subgraph-7 N-Synuclein subgraph-10 activation N-Synuclein subgraph-7 N-Synuclein subgraph-3 4 5 activation N-Synuclein subgraph-15 N-Synuclein subgraph-3 4 5 activation N-Synuclein subgraph-12 N-Synuclein subgraph-7 activation N-Synuclein subgraph-13 N-Synuclein subgraph-7 activation N-Synuclein subgraph-15 N-Synuclein subgraph-1 2 activation N-Synuclein subgraph-7 N-Synuclein subgraph-9 activation N-Synuclein subgraph-8 N-Synuclein subgraph-9 activation N-Synuclein subgraph-8 N-Synuclein subgraph-11 activation N-Synuclein subgraph-8 N-Synuclein subgraph-11 activation N-Synuclein subgraph-8 N-Synuclein subgraph-12 activation N-Synuclein subgraph-15 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/T cells signaling.att000066400000000000000000000017431426625374700271770ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-T cells signaling-10 IL10 130 5 white rectangle gene 0.5 black 46 17 5962 N-T cells signaling-11 APP 86 26 white rectangle gene 0.5 black 46 17 620 N-T cells signaling-12 NFATC1 194 110 white rectangle gene 0.5 black 46 17 7775 N-T cells signaling-13 NFATC2 0 35 white rectangle gene 0.5 black 46 17 7776 N-T cells signaling-14 PDCD1 41 173 white rectangle gene 0.5 black 46 17 8760 N-T cells signaling-15 BACE1 192 100 white rectangle gene 0.5 black 46 17 933 N-T cells signaling-3 BDNF 97 10 white rectangle gene 0.5 black 46 17 1033 N-T cells signaling-4 CASP3 25 168 white rectangle gene 0.5 black 46 17 1504 N-T cells signaling-5 CASP7 57 164 white rectangle gene 0.5 black 46 17 1508 N-T cells signaling-6 CD274 113 0 white rectangle gene 0.5 black 46 17 17635 N-T cells signaling-8 AGER 187 118 white rectangle gene 0.5 black 46 17 320 N-T cells signaling-9 GSK3B 8 23 white rectangle gene 0.5 black 46 17 4617 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/T cells signaling.sif000066400000000000000000000007761426625374700271750ustar00rootroot000000000000000 1 2 N-T cells signaling-6 activation N-T cells signaling-10 N-T cells signaling-12 activation N-T cells signaling-15 N-T cells signaling-14 activation N-T cells signaling-4 N-T cells signaling-14 activation N-T cells signaling-5 N-T cells signaling-11 activation N-T cells signaling-3 N-T cells signaling-3 activation N-T cells signaling-6 N-T cells signaling-8 activation N-T cells signaling-12 N-T cells signaling-8 activation N-T cells signaling-15 N-T cells signaling-9 activation N-T cells signaling-13 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/TGF-Beta subgraph.att000066400000000000000000000020521426625374700270340ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-TGF-Beta subgraph-1 TGFB 15 67 white rectangle gene 0.5 black 46 17 TGFB N-TGF-Beta subgraph-10 HSPB3 130 28 white rectangle gene 0.5 black 46 17 5248 N-TGF-Beta subgraph-11 APP 28 77 white rectangle gene 0.5 black 46 17 620 N-TGF-Beta subgraph-12 SMAD2 50 6 white rectangle gene 0.5 black 46 17 6768 N-TGF-Beta subgraph-13 Mmp2 0 81 white rectangle gene 0.5 black 46 17 97009 N-TGF-Beta subgraph-2 TGFB1 35 18 white rectangle gene 0.5 black 46 17 11766 N-TGF-Beta subgraph-3 TGFB2 34 62 white rectangle gene 0.5 black 46 17 11768 N-TGF-Beta subgraph-4 TGFB3 8 37 white rectangle gene 0.5 black 46 17 11769 N-TGF-Beta subgraph-5 TGFBR1 149 23 white rectangle gene 0.5 black 46 17 11772 N-TGF-Beta subgraph-6 CLU 30 0 white rectangle gene 0.5 black 46 17 2095 N-TGF-Beta subgraph-7 HSPB6 152 7 white rectangle gene 0.5 black 46 17 26511 N-TGF-Beta subgraph-8 IL34 25 44 white rectangle gene 0.5 black 46 17 28529 N-TGF-Beta subgraph-9 HSPB2 163 32 white rectangle gene 0.5 black 46 17 5247 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/TGF-Beta subgraph.sif000066400000000000000000000012371426625374700270310ustar00rootroot000000000000000 1 2 N-TGF-Beta subgraph-9 inhibition N-TGF-Beta subgraph-5 N-TGF-Beta subgraph-2 activation N-TGF-Beta subgraph-12 N-TGF-Beta subgraph-2 activation N-TGF-Beta subgraph-6 N-TGF-Beta subgraph-10 inhibition N-TGF-Beta subgraph-5 N-TGF-Beta subgraph-8 activation N-TGF-Beta subgraph-2 N-TGF-Beta subgraph-8 activation N-TGF-Beta subgraph-3 N-TGF-Beta subgraph-8 activation N-TGF-Beta subgraph-4 N-TGF-Beta subgraph-8 activation N-TGF-Beta subgraph-1 N-TGF-Beta subgraph-3 activation N-TGF-Beta subgraph-11 N-TGF-Beta subgraph-7 inhibition N-TGF-Beta subgraph-5 N-TGF-Beta subgraph-1 inhibition N-TGF-Beta subgraph-11 N-TGF-Beta subgraph-13 activation N-TGF-Beta subgraph-1 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Tau protein subgraph.att000066400000000000000000000254741426625374700277520ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Tau protein subgraph-1 2 microtubule MAPT 55 111 white rectangle gene,gene 0.5 black 46 17 0005874,/,6893 N-Tau protein subgraph-100 MAPT 69 91 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-101 MAPT 62 82 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-102 MAPT 77 78 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-103 MAPT 73 93 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-104 MAPT 68 79 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-105 MAPT 71 88 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-106 MAPT 67 87 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-107 MAPT 70 96 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-108 MAPT 74 80 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-109 MAPT 71 79 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-110 MAPT 0 180 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-112 MAPT 152 108 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-113 MAPT 159 103 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-114 MAPT 166 107 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-115 ABL1 57 109 white rectangle gene 0.5 black 46 17 76 N-Tau protein subgraph-116 NAB2 49 102 white rectangle gene 0.5 black 46 17 7627 N-Tau protein subgraph-117 NEFL 61 75 white rectangle gene 0.5 black 46 17 7739 N-Tau protein subgraph-118 ATF2 76 97 white rectangle gene 0.5 black 46 17 784 N-Tau protein subgraph-119 NPEPPS 49 145 white rectangle gene 0.5 black 46 17 7900 N-Tau protein subgraph-12 13 14 SNCA GSK3B MAPT 61 94 white rectangle gene,gene,gene 0.5 black 46 17 11138,/,4617,/,6893 N-Tau protein subgraph-120 OGT 29 108 white rectangle gene 0.5 black 46 17 8127 N-Tau protein subgraph-121 PDHA1 28 99 white rectangle gene 0.5 black 46 17 8806 N-Tau protein subgraph-122 PIN1 64 80 white rectangle gene 0.5 black 46 17 8988 N-Tau protein subgraph-123 PPP1CA 83 95 white rectangle gene 0.5 black 46 17 9281 N-Tau protein subgraph-124 PPP1CA 78 89 white rectangle gene 0.5 black 46 17 9281 N-Tau protein subgraph-125 PPP1R1B 65 74 white rectangle gene 0.5 black 46 17 9287 N-Tau protein subgraph-126 PPP2CA 61 87 white rectangle gene 0.5 black 46 17 9299 N-Tau protein subgraph-127 PPP2CB 59 110 white rectangle gene 0.5 black 46 17 9300 N-Tau protein subgraph-128 PTPA 48 92 white rectangle gene 0.5 black 46 17 9308 N-Tau protein subgraph-129 PPP3CA 47 0 white rectangle gene 0.5 black 46 17 9314 N-Tau protein subgraph-130 PPP5C 51 105 white rectangle gene 0.5 black 46 17 9322 N-Tau protein subgraph-131 BAG1 54 144 white rectangle gene 0.5 black 46 17 937 N-Tau protein subgraph-132 PRKACA 50 108 white rectangle gene 0.5 black 46 17 9380 N-Tau protein subgraph-133 PRKAR1A 82 109 white rectangle gene 0.5 black 46 17 9388 N-Tau protein subgraph-134 PKN1 43 102 white rectangle gene 0.5 black 46 17 9405 N-Tau protein subgraph-135 EIF2AK2 82 103 white rectangle gene 0.5 black 46 17 9437 N-Tau protein subgraph-136 PSEN1 75 74 white rectangle gene 0.5 black 46 17 9508 N-Tau protein subgraph-137 PSEN1 73 73 white rectangle gene 0.5 black 46 17 9508 N-Tau protein subgraph-138 Cdk5 140 145 white rectangle gene 0.5 black 46 17 101765 N-Tau protein subgraph-139 Gsk3b 150 163 white rectangle gene 0.5 black 46 17 1861437 N-Tau protein subgraph-140 Ttbk1 142 160 white rectangle gene 0.5 black 46 17 2147036 N-Tau protein subgraph-141 Ttbk2 143 151 white rectangle gene 0.5 black 46 17 2155779 N-Tau protein subgraph-142 Mapt 146 158 white rectangle gene 0.5 black 46 17 97180 N-Tau protein subgraph-143 Mapt 147 148 white rectangle gene 0.5 black 46 17 97180 N-Tau protein subgraph-144 Dkk1 117 29 white rectangle gene 0.5 black 46 17 1307313 N-Tau protein subgraph-145 Mapt 110 39 white rectangle gene 0.5 black 46 17 69329 N-Tau protein subgraph-146 Mapt 123 25 white rectangle gene 0.5 black 46 17 69329 N-Tau protein subgraph-147 Mapt 122 29 white rectangle gene 0.5 black 46 17 69329 N-Tau protein subgraph-148 Mapt 117 22 white rectangle gene 0.5 black 46 17 69329 N-Tau protein subgraph-149 Mapt 120 23 white rectangle gene 0.5 black 46 17 69329 N-Tau protein subgraph-15 16 YWHAZ MAPT 160 107 white rectangle gene,gene 0.5 black 46 17 12855,/,6893 N-Tau protein subgraph-17 18 CTNNB1 PSEN1 76 67 white rectangle gene,gene 0.5 black 46 17 2514,/,9508 N-Tau protein subgraph-19 20 GSK3B PPP2CA 33 87 white rectangle gene,gene 0.5 black 46 17 4617,/,9299 N-Tau protein subgraph-21 22 23 HSPA8 MAPT BAG1 50 143 white rectangle gene,gene,gene 0.5 black 46 17 5241,/,6893,/,937 N-Tau protein subgraph-24 25 APP MAPT 48 110 white rectangle gene,gene 0.5 black 46 17 620,/,6893 N-Tau protein subgraph-26 27 MAPT PSEN1 46 97 white rectangle gene,gene 0.5 black 46 17 6893,/,9508 N-Tau protein subgraph-28 29 PPP2CA BCL2 52 145 white rectangle gene,gene 0.5 black 46 17 9299,/,990 N-Tau protein subgraph-3 4 CDK5R1 p25 CDK5 54 110 white rectangle gene,gene 0.5 black 46 17 CONSO00172,/,1774 N-Tau protein subgraph-30 CDK5R1 p25 59 78 white rectangle gene 0.5 black 46 17 CONSO00172 N-Tau protein subgraph-31 AKT 78 110 white rectangle gene 0.5 black 46 17 AKT N-Tau protein subgraph-32 CAMK 51 110 white rectangle gene 0.5 black 46 17 CAMK N-Tau protein subgraph-33 CAPN 68 74 white rectangle gene 0.5 black 46 17 CAPN N-Tau protein subgraph-34 ERK 47 88 white rectangle gene 0.5 black 46 17 ERK N-Tau protein subgraph-35 GSK3 48 99 white rectangle gene 0.5 black 46 17 GSK3 N-Tau protein subgraph-36 HSP90 47 144 white rectangle gene 0.5 black 46 17 HSP90 N-Tau protein subgraph-37 HSPA 52 119 white rectangle gene 0.5 black 46 17 HSPA N-Tau protein subgraph-38 PRKAC 43 99 white rectangle gene 0.5 black 46 17 PRKAC N-Tau protein subgraph-39 Tubulins 142 156 white rectangle gene 0.5 black 46 17 778 N-Tau protein subgraph-40 RPS6KB1 65 99 white rectangle gene 0.5 black 46 17 10436 N-Tau protein subgraph-41 S100B 60 108 white rectangle gene 0.5 black 46 17 10500 N-Tau protein subgraph-42 SNCA 52 113 white rectangle gene 0.5 black 46 17 11138 N-Tau protein subgraph-43 KLF10 46 94 white rectangle gene 0.5 black 46 17 11810 N-Tau protein subgraph-44 YWHAZ 87 117 white rectangle gene 0.5 black 46 17 12855 N-Tau protein subgraph-45 CAMK2A 59 105 white rectangle gene 0.5 black 46 17 1460 N-Tau protein subgraph-46 CAMK2B 44 95 white rectangle gene 0.5 black 46 17 1461 N-Tau protein subgraph-47 CAPN1 67 107 white rectangle gene 0.5 black 46 17 1476 N-Tau protein subgraph-48 CASP3 154 151 white rectangle gene 0.5 black 46 17 1504 N-Tau protein subgraph-49 CASP4 157 147 white rectangle gene 0.5 black 46 17 1505 N-Tau protein subgraph-5 6 HSPA BAG2 55 142 white rectangle gene,gene 0.5 black 46 17 HSPA,/,938 N-Tau protein subgraph-50 CASP6 145 145 white rectangle gene 0.5 black 46 17 1507 N-Tau protein subgraph-51 CAST 49 96 white rectangle gene 0.5 black 46 17 1515 N-Tau protein subgraph-52 WNT3A 51 93 white rectangle gene 0.5 black 46 17 15983 N-Tau protein subgraph-53 FRAT2 78 82 white rectangle gene 0.5 black 46 17 16048 N-Tau protein subgraph-54 CDC37 47 142 white rectangle gene 0.5 black 46 17 1735 N-Tau protein subgraph-55 TPK1 38 99 white rectangle gene 0.5 black 46 17 17358 N-Tau protein subgraph-56 CDK2 56 106 white rectangle gene 0.5 black 46 17 1771 N-Tau protein subgraph-57 CDK5 66 85 white rectangle gene 0.5 black 46 17 1774 N-Tau protein subgraph-58 CDK5R1 57 91 white rectangle gene 0.5 black 46 17 1775 N-Tau protein subgraph-59 CTNNB1 60 93 white rectangle gene 0.5 black 46 17 2514 N-Tau protein subgraph-60 DKK1 46 109 white rectangle gene 0.5 black 46 17 2891 N-Tau protein subgraph-61 DYRK1A 69 101 white rectangle gene 0.5 black 46 17 3091 N-Tau protein subgraph-62 EGR1 64 93 white rectangle gene 0.5 black 46 17 3238 N-Tau protein subgraph-63 MARK2 89 72 white rectangle gene 0.5 black 46 17 3332 N-Tau protein subgraph-64 MARK2 81 78 white rectangle gene 0.5 black 46 17 3332 N-Tau protein subgraph-65 F2 53 120 white rectangle gene 0.5 black 46 17 3535 N-Tau protein subgraph-66 AKAP13 81 104 white rectangle gene 0.5 black 46 17 371 N-Tau protein subgraph-67 AKT1 53 107 white rectangle gene 0.5 black 46 17 391 N-Tau protein subgraph-68 FYN 3 175 white rectangle gene 0.5 black 46 17 4037 N-Tau protein subgraph-69 GCG 43 104 white rectangle gene 0.5 black 46 17 4191 N-Tau protein subgraph-7 8 S100B MAPT 48 107 white rectangle gene,gene 0.5 black 46 17 10500,/,6893 N-Tau protein subgraph-70 GSK3A 59 70 white rectangle gene 0.5 black 46 17 4616 N-Tau protein subgraph-71 GSK3B 68 86 white rectangle gene 0.5 black 46 17 4617 N-Tau protein subgraph-72 GSK3B 45 101 white rectangle gene 0.5 black 46 17 4617 N-Tau protein subgraph-73 GSK3B 60 91 white rectangle gene 0.5 black 46 17 4617 N-Tau protein subgraph-74 INS 41 92 white rectangle gene 0.5 black 46 17 6081 N-Tau protein subgraph-75 APP 44 107 white rectangle gene 0.5 black 46 17 620 N-Tau protein subgraph-76 APP 45 105 white rectangle gene 0.5 black 46 17 620 N-Tau protein subgraph-77 JUN 76 95 white rectangle gene 0.5 black 46 17 6204 N-Tau protein subgraph-78 KLC1 44 141 white rectangle gene 0.5 black 46 17 6387 N-Tau protein subgraph-79 LEP 59 90 white rectangle gene 0.5 black 46 17 6553 N-Tau protein subgraph-80 MAPK1 74 88 white rectangle gene 0.5 black 46 17 6871 N-Tau protein subgraph-81 MAPK10 65 92 white rectangle gene 0.5 black 46 17 6872 N-Tau protein subgraph-82 MAPK14 68 94 white rectangle gene 0.5 black 46 17 6876 N-Tau protein subgraph-83 MAPK3 47 103 white rectangle gene 0.5 black 46 17 6877 N-Tau protein subgraph-84 MAPK8 43 97 white rectangle gene 0.5 black 46 17 6881 N-Tau protein subgraph-85 MAPK9 68 98 white rectangle gene 0.5 black 46 17 6886 N-Tau protein subgraph-86 MAPT 51 136 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-87 MAPT 57 61 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-88 MAPT 151 147 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-89 MAPT 69 83 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-9 10 11 SHC1 GRB2 APP 44 78 white rectangle gene,gene,gene 0.5 black 46 17 10840,/,4566,/,620 N-Tau protein subgraph-90 MAPT 39 105 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-91 MAPT 54 100 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-92 MAPT 66 79 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-93 MAPT 73 82 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-94 MAPT 68 88 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-95 MAPT 67 93 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-96 MAPT 60 78 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-97 MAPT 74 97 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-98 MAPT 48 4 white rectangle gene 0.5 black 46 17 6893 N-Tau protein subgraph-99 MAPT 78 106 white rectangle gene 0.5 black 46 17 6893 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Tau protein subgraph.sif000066400000000000000000000433321426625374700277340ustar00rootroot000000000000000 1 2 N-Tau protein subgraph-82 activation N-Tau protein subgraph-91 N-Tau protein subgraph-82 activation N-Tau protein subgraph-91 N-Tau protein subgraph-82 activation N-Tau protein subgraph-91 N-Tau protein subgraph-82 activation N-Tau protein subgraph-91 N-Tau protein subgraph-82 activation N-Tau protein subgraph-91 N-Tau protein subgraph-82 activation N-Tau protein subgraph-105 N-Tau protein subgraph-82 activation N-Tau protein subgraph-106 N-Tau protein subgraph-82 activation N-Tau protein subgraph-94 N-Tau protein subgraph-82 activation N-Tau protein subgraph-100 N-Tau protein subgraph-82 activation N-Tau protein subgraph-103 N-Tau protein subgraph-82 activation N-Tau protein subgraph-77 N-Tau protein subgraph-82 activation N-Tau protein subgraph-118 N-Tau protein subgraph-5 6 activation N-Tau protein subgraph-86 N-Tau protein subgraph-91 inhibition N-Tau protein subgraph-1 2 N-Tau protein subgraph-91 activation N-Tau protein subgraph-76 N-Tau protein subgraph-91 activation N-Tau protein subgraph-75 N-Tau protein subgraph-91 activation N-Tau protein subgraph-47 N-Tau protein subgraph-91 activation N-Tau protein subgraph-71 N-Tau protein subgraph-91 inhibition N-Tau protein subgraph-59 N-Tau protein subgraph-91 inhibition N-Tau protein subgraph-65 N-Tau protein subgraph-91 inhibition N-Tau protein subgraph-7 8 N-Tau protein subgraph-89 activation N-Tau protein subgraph-105 N-Tau protein subgraph-89 activation N-Tau protein subgraph-94 N-Tau protein subgraph-89 activation N-Tau protein subgraph-106 N-Tau protein subgraph-89 activation N-Tau protein subgraph-101 N-Tau protein subgraph-89 activation N-Tau protein subgraph-108 N-Tau protein subgraph-89 activation N-Tau protein subgraph-57 N-Tau protein subgraph-89 activation N-Tau protein subgraph-71 N-Tau protein subgraph-128 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-128 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-80 activation N-Tau protein subgraph-105 N-Tau protein subgraph-80 activation N-Tau protein subgraph-106 N-Tau protein subgraph-80 activation N-Tau protein subgraph-94 N-Tau protein subgraph-80 activation N-Tau protein subgraph-100 N-Tau protein subgraph-80 activation N-Tau protein subgraph-103 N-Tau protein subgraph-67 activation N-Tau protein subgraph-91 N-Tau protein subgraph-67 activation N-Tau protein subgraph-91 N-Tau protein subgraph-135 activation N-Tau protein subgraph-97 N-Tau protein subgraph-135 activation N-Tau protein subgraph-99 N-Tau protein subgraph-53 activation N-Tau protein subgraph-71 N-Tau protein subgraph-86 inhibition N-Tau protein subgraph-28 29 N-Tau protein subgraph-134 activation N-Tau protein subgraph-91 N-Tau protein subgraph-78 activation N-Tau protein subgraph-86 N-Tau protein subgraph-78 activation N-Tau protein subgraph-86 N-Tau protein subgraph-62 activation N-Tau protein subgraph-91 N-Tau protein subgraph-62 activation N-Tau protein subgraph-91 N-Tau protein subgraph-62 activation N-Tau protein subgraph-91 N-Tau protein subgraph-62 activation N-Tau protein subgraph-100 N-Tau protein subgraph-62 activation N-Tau protein subgraph-101 N-Tau protein subgraph-62 activation N-Tau protein subgraph-97 N-Tau protein subgraph-30 activation N-Tau protein subgraph-57 N-Tau protein subgraph-40 activation N-Tau protein subgraph-97 N-Tau protein subgraph-40 activation N-Tau protein subgraph-95 N-Tau protein subgraph-40 activation N-Tau protein subgraph-107 N-Tau protein subgraph-40 activation N-Tau protein subgraph-91 N-Tau protein subgraph-114 inhibition N-Tau protein subgraph-15 16 N-Tau protein subgraph-21 22 23 inhibition N-Tau protein subgraph-86 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-91 N-Tau protein subgraph-57 activation N-Tau protein subgraph-117 N-Tau protein subgraph-57 activation N-Tau protein subgraph-92 N-Tau protein subgraph-57 activation N-Tau protein subgraph-104 N-Tau protein subgraph-57 activation N-Tau protein subgraph-124 N-Tau protein subgraph-57 activation N-Tau protein subgraph-97 N-Tau protein subgraph-57 activation N-Tau protein subgraph-93 N-Tau protein subgraph-57 activation N-Tau protein subgraph-94 N-Tau protein subgraph-57 activation N-Tau protein subgraph-95 N-Tau protein subgraph-57 activation N-Tau protein subgraph-96 N-Tau protein subgraph-57 activation N-Tau protein subgraph-100 N-Tau protein subgraph-57 activation N-Tau protein subgraph-100 N-Tau protein subgraph-57 activation N-Tau protein subgraph-101 N-Tau protein subgraph-57 activation N-Tau protein subgraph-101 N-Tau protein subgraph-57 activation N-Tau protein subgraph-105 N-Tau protein subgraph-57 activation N-Tau protein subgraph-105 N-Tau protein subgraph-57 activation N-Tau protein subgraph-106 N-Tau protein subgraph-57 activation N-Tau protein subgraph-106 N-Tau protein subgraph-57 activation N-Tau protein subgraph-107 N-Tau protein subgraph-57 activation N-Tau protein subgraph-107 N-Tau protein subgraph-57 activation N-Tau protein subgraph-108 N-Tau protein subgraph-57 activation N-Tau protein subgraph-108 N-Tau protein subgraph-57 activation N-Tau protein subgraph-109 N-Tau protein subgraph-57 activation N-Tau protein subgraph-125 N-Tau protein subgraph-129 inhibition N-Tau protein subgraph-98 N-Tau protein subgraph-45 activation N-Tau protein subgraph-91 N-Tau protein subgraph-35 activation N-Tau protein subgraph-91 N-Tau protein subgraph-35 activation N-Tau protein subgraph-91 N-Tau protein subgraph-35 activation N-Tau protein subgraph-91 N-Tau protein subgraph-113 inhibition N-Tau protein subgraph-15 16 N-Tau protein subgraph-70 activation N-Tau protein subgraph-87 N-Tau protein subgraph-70 activation N-Tau protein subgraph-96 N-Tau protein subgraph-70 activation N-Tau protein subgraph-101 N-Tau protein subgraph-139 activation N-Tau protein subgraph-142 N-Tau protein subgraph-58 activation N-Tau protein subgraph-57 N-Tau protein subgraph-58 activation N-Tau protein subgraph-91 N-Tau protein subgraph-34 activation N-Tau protein subgraph-91 N-Tau protein subgraph-34 activation N-Tau protein subgraph-91 N-Tau protein subgraph-34 activation N-Tau protein subgraph-91 N-Tau protein subgraph-34 activation N-Tau protein subgraph-91 N-Tau protein subgraph-34 activation N-Tau protein subgraph-91 N-Tau protein subgraph-55 activation N-Tau protein subgraph-91 N-Tau protein subgraph-55 activation N-Tau protein subgraph-91 N-Tau protein subgraph-55 activation N-Tau protein subgraph-121 N-Tau protein subgraph-137 inhibition N-Tau protein subgraph-17 18 N-Tau protein subgraph-126 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-126 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-126 inhibition N-Tau protein subgraph-106 N-Tau protein subgraph-126 inhibition N-Tau protein subgraph-94 N-Tau protein subgraph-126 inhibition N-Tau protein subgraph-96 N-Tau protein subgraph-126 inhibition N-Tau protein subgraph-101 N-Tau protein subgraph-41 activation N-Tau protein subgraph-91 N-Tau protein subgraph-68 activation N-Tau protein subgraph-110 N-Tau protein subgraph-46 activation N-Tau protein subgraph-91 N-Tau protein subgraph-66 activation N-Tau protein subgraph-97 N-Tau protein subgraph-66 activation N-Tau protein subgraph-99 N-Tau protein subgraph-54 inhibition N-Tau protein subgraph-86 N-Tau protein subgraph-54 inhibition N-Tau protein subgraph-36 N-Tau protein subgraph-120 activation N-Tau protein subgraph-90 N-Tau protein subgraph-140 activation N-Tau protein subgraph-39 N-Tau protein subgraph-140 activation N-Tau protein subgraph-142 N-Tau protein subgraph-12 13 14 activation N-Tau protein subgraph-91 N-Tau protein subgraph-12 13 14 activation N-Tau protein subgraph-71 N-Tau protein subgraph-116 activation N-Tau protein subgraph-91 N-Tau protein subgraph-116 activation N-Tau protein subgraph-91 N-Tau protein subgraph-61 activation N-Tau protein subgraph-107 N-Tau protein subgraph-61 activation N-Tau protein subgraph-91 N-Tau protein subgraph-61 activation N-Tau protein subgraph-100 N-Tau protein subgraph-61 activation N-Tau protein subgraph-97 N-Tau protein subgraph-61 activation N-Tau protein subgraph-99 N-Tau protein subgraph-48 activation N-Tau protein subgraph-88 N-Tau protein subgraph-48 activation N-Tau protein subgraph-88 N-Tau protein subgraph-48 activation N-Tau protein subgraph-88 N-Tau protein subgraph-48 activation N-Tau protein subgraph-88 N-Tau protein subgraph-38 activation N-Tau protein subgraph-91 N-Tau protein subgraph-38 activation N-Tau protein subgraph-91 N-Tau protein subgraph-73 inhibition N-Tau protein subgraph-71 N-Tau protein subgraph-73 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-127 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-122 inhibition N-Tau protein subgraph-92 N-Tau protein subgraph-122 inhibition N-Tau protein subgraph-57 N-Tau protein subgraph-122 inhibition N-Tau protein subgraph-104 N-Tau protein subgraph-122 activation N-Tau protein subgraph-126 N-Tau protein subgraph-37 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-37 inhibition N-Tau protein subgraph-42 N-Tau protein subgraph-37 activation N-Tau protein subgraph-86 N-Tau protein subgraph-37 activation N-Tau protein subgraph-86 N-Tau protein subgraph-50 activation N-Tau protein subgraph-88 N-Tau protein subgraph-144 activation N-Tau protein subgraph-145 N-Tau protein subgraph-144 activation N-Tau protein subgraph-146 N-Tau protein subgraph-144 activation N-Tau protein subgraph-147 N-Tau protein subgraph-144 activation N-Tau protein subgraph-148 N-Tau protein subgraph-144 activation N-Tau protein subgraph-149 N-Tau protein subgraph-32 activation N-Tau protein subgraph-91 N-Tau protein subgraph-3 4 activation N-Tau protein subgraph-91 N-Tau protein subgraph-3 4 inhibition N-Tau protein subgraph-1 2 N-Tau protein subgraph-112 inhibition N-Tau protein subgraph-15 16 N-Tau protein subgraph-112 inhibition N-Tau protein subgraph-15 16 N-Tau protein subgraph-64 activation N-Tau protein subgraph-63 N-Tau protein subgraph-9 10 11 activation N-Tau protein subgraph-34 N-Tau protein subgraph-85 activation N-Tau protein subgraph-91 N-Tau protein subgraph-85 activation N-Tau protein subgraph-77 N-Tau protein subgraph-85 activation N-Tau protein subgraph-118 N-Tau protein subgraph-85 activation N-Tau protein subgraph-103 N-Tau protein subgraph-47 inhibition N-Tau protein subgraph-31 N-Tau protein subgraph-141 activation N-Tau protein subgraph-39 N-Tau protein subgraph-141 activation N-Tau protein subgraph-142 N-Tau protein subgraph-141 activation N-Tau protein subgraph-142 N-Tau protein subgraph-141 activation N-Tau protein subgraph-143 N-Tau protein subgraph-141 activation N-Tau protein subgraph-138 N-Tau protein subgraph-60 activation N-Tau protein subgraph-91 N-Tau protein subgraph-52 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-133 activation N-Tau protein subgraph-97 N-Tau protein subgraph-133 activation N-Tau protein subgraph-99 N-Tau protein subgraph-132 activation N-Tau protein subgraph-91 N-Tau protein subgraph-132 activation N-Tau protein subgraph-91 N-Tau protein subgraph-132 activation N-Tau protein subgraph-91 N-Tau protein subgraph-136 inhibition N-Tau protein subgraph-17 18 N-Tau protein subgraph-26 27 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-81 activation N-Tau protein subgraph-103 N-Tau protein subgraph-81 activation N-Tau protein subgraph-105 N-Tau protein subgraph-81 activation N-Tau protein subgraph-106 N-Tau protein subgraph-81 activation N-Tau protein subgraph-94 N-Tau protein subgraph-81 activation N-Tau protein subgraph-100 N-Tau protein subgraph-81 activation N-Tau protein subgraph-91 N-Tau protein subgraph-49 activation N-Tau protein subgraph-88 N-Tau protein subgraph-124 inhibition N-Tau protein subgraph-123 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-91 N-Tau protein subgraph-71 activation N-Tau protein subgraph-59 N-Tau protein subgraph-71 activation N-Tau protein subgraph-92 N-Tau protein subgraph-71 activation N-Tau protein subgraph-104 N-Tau protein subgraph-71 activation N-Tau protein subgraph-136 N-Tau protein subgraph-71 activation N-Tau protein subgraph-137 N-Tau protein subgraph-71 activation N-Tau protein subgraph-93 N-Tau protein subgraph-71 activation N-Tau protein subgraph-109 N-Tau protein subgraph-71 activation N-Tau protein subgraph-100 N-Tau protein subgraph-71 activation N-Tau protein subgraph-102 N-Tau protein subgraph-71 activation N-Tau protein subgraph-64 N-Tau protein subgraph-71 activation N-Tau protein subgraph-64 N-Tau protein subgraph-71 activation N-Tau protein subgraph-97 N-Tau protein subgraph-71 activation N-Tau protein subgraph-97 N-Tau protein subgraph-74 activation N-Tau protein subgraph-91 N-Tau protein subgraph-74 activation N-Tau protein subgraph-91 N-Tau protein subgraph-74 activation N-Tau protein subgraph-91 N-Tau protein subgraph-74 activation N-Tau protein subgraph-19 20 N-Tau protein subgraph-51 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-69 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-43 activation N-Tau protein subgraph-91 N-Tau protein subgraph-43 activation N-Tau protein subgraph-91 N-Tau protein subgraph-79 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-79 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-79 inhibition N-Tau protein subgraph-71 N-Tau protein subgraph-72 activation N-Tau protein subgraph-91 N-Tau protein subgraph-83 activation N-Tau protein subgraph-91 N-Tau protein subgraph-56 activation N-Tau protein subgraph-91 N-Tau protein subgraph-65 activation N-Tau protein subgraph-86 N-Tau protein subgraph-33 activation N-Tau protein subgraph-57 N-Tau protein subgraph-36 activation N-Tau protein subgraph-86 N-Tau protein subgraph-123 inhibition N-Tau protein subgraph-97 N-Tau protein subgraph-42 activation N-Tau protein subgraph-91 N-Tau protein subgraph-119 activation N-Tau protein subgraph-86 N-Tau protein subgraph-24 25 activation N-Tau protein subgraph-91 N-Tau protein subgraph-44 activation N-Tau protein subgraph-133 N-Tau protein subgraph-131 inhibition N-Tau protein subgraph-86 N-Tau protein subgraph-131 inhibition N-Tau protein subgraph-86 N-Tau protein subgraph-115 activation N-Tau protein subgraph-91 N-Tau protein subgraph-84 activation N-Tau protein subgraph-91 N-Tau protein subgraph-84 activation N-Tau protein subgraph-91 N-Tau protein subgraph-84 activation N-Tau protein subgraph-91 N-Tau protein subgraph-84 activation N-Tau protein subgraph-91 N-Tau protein subgraph-84 activation N-Tau protein subgraph-91 N-Tau protein subgraph-130 inhibition N-Tau protein subgraph-91 N-Tau protein subgraph-90 inhibition N-Tau protein subgraph-91 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Toll like receptor subgraph.att000066400000000000000000000030401426625374700311640ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Toll like receptor subgraph-1 2 3 TRADD CASP8 FADD 192 24 white rectangle gene,gene,gene 0.5 black 46 17 12030,/,1509,/,3573 N-Toll like receptor subgraph-10 TLR2 81 11 white rectangle gene 0.5 black 46 17 11848 N-Toll like receptor subgraph-11 TLR4 0 46 white rectangle gene 0.5 black 46 17 11850 N-Toll like receptor subgraph-12 CASP8 183 32 white rectangle gene 0.5 black 46 17 1509 N-Toll like receptor subgraph-13 CD14 89 10 white rectangle gene 0.5 black 46 17 1628 N-Toll like receptor subgraph-15 FADD 108 24 white rectangle gene 0.5 black 46 17 3573 N-Toll like receptor subgraph-16 APP 93 25 white rectangle gene 0.5 black 46 17 620 N-Toll like receptor subgraph-17 MYD88 98 7 white rectangle gene 0.5 black 46 17 7562 N-Toll like receptor subgraph-18 NFKB1 4 37 white rectangle gene 0.5 black 46 17 7794 N-Toll like receptor subgraph-19 PPARG 87 0 white rectangle gene 0.5 black 46 17 9236 N-Toll like receptor subgraph-20 EIF2AK2 79 30 white rectangle gene 0.5 black 46 17 9437 N-Toll like receptor subgraph-23 Tlr1 100 66 white rectangle gene 0.5 black 46 17 1341295 N-Toll like receptor subgraph-24 Tlr6 87 62 white rectangle gene 0.5 black 46 17 1341296 N-Toll like receptor subgraph-25 Tlr2 97 50 white rectangle gene 0.5 black 46 17 1346060 N-Toll like receptor subgraph-4 5 6 CASP8 FADD PTPN13 185 44 white rectangle gene,gene,gene 0.5 black 46 17 1509,/,3573,/,9646 N-Toll like receptor subgraph-7 8 APP Tlr2 110 58 white rectangle gene,gene 0.5 black 46 17 620,/,1346060 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Toll like receptor subgraph.sif000066400000000000000000000022201426625374700311540ustar00rootroot000000000000000 1 2 N-Toll like receptor subgraph-25 activation N-Toll like receptor subgraph-7 8 N-Toll like receptor subgraph-25 activation N-Toll like receptor subgraph-16 N-Toll like receptor subgraph-4 5 6 activation N-Toll like receptor subgraph-12 N-Toll like receptor subgraph-16 activation N-Toll like receptor subgraph-20 N-Toll like receptor subgraph-16 activation N-Toll like receptor subgraph-15 N-Toll like receptor subgraph-16 activation N-Toll like receptor subgraph-13 N-Toll like receptor subgraph-16 activation N-Toll like receptor subgraph-17 N-Toll like receptor subgraph-16 activation N-Toll like receptor subgraph-10 N-Toll like receptor subgraph-23 activation N-Toll like receptor subgraph-25 N-Toll like receptor subgraph-11 activation N-Toll like receptor subgraph-18 N-Toll like receptor subgraph-19 inhibition N-Toll like receptor subgraph-13 N-Toll like receptor subgraph-19 inhibition N-Toll like receptor subgraph-17 N-Toll like receptor subgraph-19 inhibition N-Toll like receptor subgraph-10 N-Toll like receptor subgraph-1 2 3 activation N-Toll like receptor subgraph-12 N-Toll like receptor subgraph-24 activation N-Toll like receptor subgraph-25 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Tumor necrosis factor subgraph.att000066400000000000000000000161571426625374700317310ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Tumor necrosis factor subgraph-1 2 TNFRSF10B TNFSF10 11 77 white rectangle gene,gene 0.5 black 46 17 11905,/,11925 N-Tumor necrosis factor subgraph-12 13 14 CASP8 FADD PTPN13 3 69 white rectangle gene,gene,gene 0.5 black 46 17 1509,/,3573,/,9646 N-Tumor necrosis factor subgraph-15 16 NFKB1 NFKB2 145 39 white rectangle gene,gene 0.5 black 46 17 7794,/,7795 N-Tumor necrosis factor subgraph-19 20 Tnf Tnfrsf1a 71 0 white rectangle gene,gene 0.5 black 46 17 104798,/,1314884 N-Tumor necrosis factor subgraph-21 JNK 34 20 white rectangle gene 0.5 black 46 17 JNK N-Tumor necrosis factor subgraph-22 M10 matrix metallopeptidases 170 59 white rectangle gene 0.5 black 46 17 891 N-Tumor necrosis factor subgraph-23 SCAF11 164 89 white rectangle gene 0.5 black 46 17 10784 N-Tumor necrosis factor subgraph-24 SHC1 39 16 white rectangle gene 0.5 black 46 17 10840 N-Tumor necrosis factor subgraph-25 TGFB1 59 44 white rectangle gene 0.5 black 46 17 11766 N-Tumor necrosis factor subgraph-26 THBS1 60 135 white rectangle gene 0.5 black 46 17 11785 N-Tumor necrosis factor subgraph-27 TNF 66 137 white rectangle gene 0.5 black 46 17 11892 N-Tumor necrosis factor subgraph-28 TNFRSF25 52 188 white rectangle gene 0.5 black 46 17 11910 N-Tumor necrosis factor subgraph-30 FAS 169 133 white rectangle gene 0.5 black 46 17 11920 N-Tumor necrosis factor subgraph-31 FASLG 169 64 white rectangle 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71 143 white rectangle gene 0.5 black 46 17 4076 N-Tumor necrosis factor subgraph-54 GABRA3 71 132 white rectangle gene 0.5 black 46 17 4077 N-Tumor necrosis factor subgraph-55 GABRA4 72 128 white rectangle gene 0.5 black 46 17 4078 N-Tumor necrosis factor subgraph-56 GABRA5 76 140 white rectangle gene 0.5 black 46 17 4079 N-Tumor necrosis factor subgraph-57 GABRA6 67 150 white rectangle gene 0.5 black 46 17 4080 N-Tumor necrosis factor subgraph-58 GABRB1 66 130 white rectangle gene 0.5 black 46 17 4081 N-Tumor necrosis factor subgraph-59 GABRB2 57 130 white rectangle gene 0.5 black 46 17 4082 N-Tumor necrosis factor subgraph-60 GABRB3 75 131 white rectangle gene 0.5 black 46 17 4083 N-Tumor necrosis factor subgraph-61 GABRD 57 146 white rectangle gene 0.5 black 46 17 4084 N-Tumor necrosis factor subgraph-62 GABRE 62 127 white rectangle gene 0.5 black 46 17 4085 N-Tumor necrosis factor subgraph-63 GABRG1 65 144 white rectangle gene 0.5 black 46 17 4086 N-Tumor necrosis factor 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N-Tumor necrosis factor subgraph-67 N-Tumor necrosis factor subgraph-27 activation N-Tumor necrosis factor subgraph-80 N-Tumor necrosis factor subgraph-27 activation N-Tumor necrosis factor subgraph-81 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Ubiquitin degradation subgraph.att000066400000000000000000000055261426625374700317670ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Ubiquitin degradation subgraph-1 2 STUB1 NQO1 130 17 white rectangle gene,gene 0.5 black 46 17 11427,/,2874 N-Ubiquitin degradation subgraph-12 13 APP NAE1 0 31 white rectangle gene,gene 0.5 black 46 17 620,/,621 N-Ubiquitin degradation subgraph-14 CUL 0 50 white rectangle gene 0.5 black 46 17 CUL N-Ubiquitin degradation subgraph-15 HSPA 107 157 white rectangle gene 0.5 black 46 17 HSPA N-Ubiquitin degradation subgraph-16 TP53 9 34 white rectangle gene 0.5 black 46 17 11998 N-Ubiquitin degradation subgraph-17 VLDLR 182 122 white rectangle gene 0.5 black 46 17 12698 N-Ubiquitin degradation subgraph-18 FBXL2 83 88 white rectangle gene 0.5 black 46 17 13598 N-Ubiquitin degradation subgraph-19 CDKN1B 150 19 white rectangle gene 0.5 black 46 17 1785 N-Ubiquitin degradation subgraph-20 MYLIP 177 115 white rectangle gene 0.5 black 46 17 21155 N-Ubiquitin degradation subgraph-21 DAB1 30 117 white rectangle gene 0.5 black 46 17 2661 N-Ubiquitin degradation subgraph-22 NQO1 130 0 white rectangle gene 0.5 black 46 17 2874 N-Ubiquitin degradation subgraph-23 NQO1 130 8 white rectangle gene 0.5 black 46 17 2874 N-Ubiquitin degradation subgraph-24 APP 88 76 white rectangle gene 0.5 black 46 17 620 N-Ubiquitin degradation subgraph-25 APP 95 93 white rectangle gene 0.5 black 46 17 620 N-Ubiquitin degradation subgraph-26 APP 91 85 white rectangle gene 0.5 black 46 17 620 N-Ubiquitin degradation subgraph-28 LDLR 186 111 white rectangle gene 0.5 black 46 17 6547 N-Ubiquitin degradation subgraph-29 LRP8 159 117 white rectangle gene 0.5 black 46 17 6700 N-Ubiquitin degradation subgraph-3 4 UBA3 NAE1 0 38 white rectangle gene,gene 0.5 black 46 17 12470,/,621 N-Ubiquitin degradation subgraph-30 LRP8 169 117 white rectangle gene 0.5 black 46 17 6700 N-Ubiquitin degradation subgraph-31 MAPT 100 162 white rectangle gene 0.5 black 46 17 6893 N-Ubiquitin degradation subgraph-32 MDM2 16 41 white rectangle gene 0.5 black 46 17 6973 N-Ubiquitin degradation subgraph-33 NEDD8 8 44 white rectangle gene 0.5 black 46 17 7732 N-Ubiquitin degradation subgraph-34 NR1H2 24 123 white rectangle gene 0.5 black 46 17 7965 N-Ubiquitin degradation subgraph-35 NR1H3 24 112 white rectangle gene 0.5 black 46 17 7966 N-Ubiquitin degradation subgraph-36 PPARG 99 81 white rectangle gene 0.5 black 46 17 9236 N-Ubiquitin degradation subgraph-37 BAG1 96 152 white rectangle gene 0.5 black 46 17 937 N-Ubiquitin degradation subgraph-38 RB1 158 14 white rectangle gene 0.5 black 46 17 9884 N-Ubiquitin degradation subgraph-5 6 UBE2M NEDD8 10 53 white rectangle gene,gene 0.5 black 46 17 12491,/,7732 N-Ubiquitin degradation subgraph-7 8 VLDLR RELN 19 118 white rectangle gene,gene 0.5 black 46 17 12698,/,9957 N-Ubiquitin degradation subgraph-9 10 11 HSPA8 MAPT BAG1 100 172 white rectangle gene,gene,gene 0.5 black 46 17 5241,/,6893,/,937 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Ubiquitin degradation subgraph.sif000066400000000000000000000042021426625374700317460ustar00rootroot000000000000000 1 2 N-Ubiquitin degradation subgraph-15 activation N-Ubiquitin degradation subgraph-31 N-Ubiquitin degradation subgraph-18 activation N-Ubiquitin degradation subgraph-26 N-Ubiquitin degradation subgraph-3 4 activation N-Ubiquitin degradation subgraph-33 N-Ubiquitin degradation subgraph-34 inhibition N-Ubiquitin degradation subgraph-7 8 N-Ubiquitin degradation subgraph-34 inhibition N-Ubiquitin degradation subgraph-21 N-Ubiquitin degradation subgraph-12 13 activation N-Ubiquitin degradation subgraph-3 4 N-Ubiquitin degradation subgraph-20 activation N-Ubiquitin degradation subgraph-28 N-Ubiquitin degradation subgraph-20 activation N-Ubiquitin degradation subgraph-17 N-Ubiquitin degradation subgraph-20 activation N-Ubiquitin degradation subgraph-30 N-Ubiquitin degradation subgraph-30 activation N-Ubiquitin degradation subgraph-29 N-Ubiquitin degradation subgraph-35 inhibition N-Ubiquitin degradation subgraph-7 8 N-Ubiquitin degradation subgraph-35 inhibition N-Ubiquitin degradation subgraph-21 N-Ubiquitin degradation subgraph-26 activation N-Ubiquitin degradation subgraph-24 N-Ubiquitin degradation subgraph-26 inhibition N-Ubiquitin degradation subgraph-25 N-Ubiquitin degradation subgraph-9 10 11 inhibition N-Ubiquitin degradation subgraph-31 N-Ubiquitin degradation subgraph-1 2 activation N-Ubiquitin degradation subgraph-23 N-Ubiquitin degradation subgraph-37 inhibition N-Ubiquitin degradation subgraph-31 N-Ubiquitin degradation subgraph-37 inhibition N-Ubiquitin degradation subgraph-31 N-Ubiquitin degradation subgraph-36 activation N-Ubiquitin degradation subgraph-26 N-Ubiquitin degradation subgraph-36 activation N-Ubiquitin degradation subgraph-26 N-Ubiquitin degradation subgraph-33 activation N-Ubiquitin degradation subgraph-5 6 N-Ubiquitin degradation subgraph-33 activation N-Ubiquitin degradation subgraph-32 N-Ubiquitin degradation subgraph-33 activation N-Ubiquitin degradation subgraph-16 N-Ubiquitin degradation subgraph-33 activation N-Ubiquitin degradation subgraph-14 N-Ubiquitin degradation subgraph-23 activation N-Ubiquitin degradation subgraph-22 N-Ubiquitin degradation subgraph-38 activation N-Ubiquitin degradation subgraph-19 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Unfolded protein response subgraph.att000066400000000000000000000036641426625374700325750ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Unfolded protein response subgraph-1 2 XBP1 INS 162 7 white rectangle gene,gene 0.5 black 46 17 12801,/,6081 N-Unfolded protein response subgraph-10 SYVN1 194 21 white rectangle gene 0.5 black 46 17 20738 N-Unfolded protein response subgraph-12 EIF2A 14 26 white rectangle gene 0.5 black 46 17 3254 N-Unfolded protein response subgraph-13 EIF2AK3 33 27 white rectangle gene 0.5 black 46 17 3255 N-Unfolded protein response subgraph-14 EIF2S1 119 131 white rectangle gene 0.5 black 46 17 3265 N-Unfolded protein response subgraph-15 ERN1 44 28 white rectangle gene 0.5 black 46 17 3449 N-Unfolded protein response subgraph-16 APP 184 36 white rectangle gene 0.5 black 46 17 620 N-Unfolded protein response subgraph-17 APP 198 32 white rectangle gene 0.5 black 46 17 620 N-Unfolded protein response subgraph-18 ATF4 41 0 white rectangle gene 0.5 black 46 17 786 N-Unfolded protein response subgraph-19 ATF6 50 30 white rectangle gene 0.5 black 46 17 791 N-Unfolded protein response subgraph-20 BACE1 0 26 white rectangle gene 0.5 black 46 17 933 N-Unfolded protein response subgraph-21 EIF2AK2 119 119 white rectangle gene 0.5 black 46 17 9437 N-Unfolded protein response subgraph-22 PSEN1 42 16 white rectangle gene 0.5 black 46 17 9508 N-Unfolded protein response subgraph-23 PSEN1 49 22 white rectangle gene 0.5 black 46 17 9508 N-Unfolded protein response subgraph-24 PSEN2 38 36 white rectangle gene 0.5 black 46 17 9509 N-Unfolded protein response subgraph-25 PSEN2 45 38 white rectangle gene 0.5 black 46 17 9509 N-Unfolded protein response subgraph-3 RYR3 169 49 white rectangle gene 0.5 black 46 17 10485 N-Unfolded protein response subgraph-5 TP53 120 107 white rectangle gene 0.5 black 46 17 11998 N-Unfolded protein response subgraph-6 XBP1 169 35 white rectangle gene 0.5 black 46 17 12801 N-Unfolded protein response subgraph-9 ADAM10 163 20 white rectangle gene 0.5 black 46 17 188 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Unfolded protein response subgraph.sif000066400000000000000000000042101426625374700325520ustar00rootroot000000000000000 1 2 N-Unfolded protein response subgraph-23 inhibition N-Unfolded protein response subgraph-15 N-Unfolded protein response subgraph-23 inhibition N-Unfolded protein response subgraph-13 N-Unfolded protein response subgraph-23 inhibition N-Unfolded protein response subgraph-19 N-Unfolded protein response subgraph-16 activation N-Unfolded protein response subgraph-6 N-Unfolded protein response subgraph-21 activation N-Unfolded protein response subgraph-5 N-Unfolded protein response subgraph-21 activation N-Unfolded protein response subgraph-14 N-Unfolded protein response subgraph-10 activation N-Unfolded protein response subgraph-17 N-Unfolded protein response subgraph-10 activation N-Unfolded protein response subgraph-17 N-Unfolded protein response subgraph-18 activation N-Unfolded protein response subgraph-22 N-Unfolded protein response subgraph-17 inhibition N-Unfolded protein response subgraph-16 N-Unfolded protein response subgraph-13 activation N-Unfolded protein response subgraph-12 N-Unfolded protein response subgraph-22 activation N-Unfolded protein response subgraph-15 N-Unfolded protein response subgraph-22 activation N-Unfolded protein response subgraph-19 N-Unfolded protein response subgraph-22 activation N-Unfolded protein response subgraph-13 N-Unfolded protein response subgraph-25 inhibition N-Unfolded protein response subgraph-15 N-Unfolded protein response subgraph-25 inhibition N-Unfolded protein response subgraph-13 N-Unfolded protein response subgraph-25 inhibition N-Unfolded protein response subgraph-19 N-Unfolded protein response subgraph-12 activation N-Unfolded protein response subgraph-20 N-Unfolded protein response subgraph-1 2 activation N-Unfolded protein response subgraph-9 N-Unfolded protein response subgraph-6 activation N-Unfolded protein response subgraph-9 N-Unfolded protein response subgraph-6 inhibition N-Unfolded protein response subgraph-3 N-Unfolded protein response subgraph-24 activation N-Unfolded protein response subgraph-15 N-Unfolded protein response subgraph-24 activation N-Unfolded protein response subgraph-19 N-Unfolded protein response subgraph-24 activation N-Unfolded protein response subgraph-13 Vascular endothelial growth factor subgraph.att000066400000000000000000000050211426625374700342460ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/outputID label X Y color shape type label.cex label.color width height genesList N-Vascular endothelial growth factor subgraph-1 2 GAB2 PTK2 0 55 white rectangle gene,gene 0.5 black 46 17 14458,/,9611 N-Vascular endothelial growth factor subgraph-10 CASR 6 158 white rectangle gene 0.5 black 46 17 1514 N-Vascular endothelial growth factor subgraph-11 SIT1 149 153 white rectangle gene 0.5 black 46 17 17710 N-Vascular endothelial growth factor subgraph-12 ADAM10 140 70 white rectangle gene 0.5 black 46 17 188 N-Vascular endothelial growth factor subgraph-13 ADAM17 132 57 white rectangle gene 0.5 black 46 17 195 N-Vascular endothelial growth factor subgraph-14 ADAM9 149 55 white rectangle gene 0.5 black 46 17 216 N-Vascular endothelial growth factor subgraph-15 APBA2 99 186 white rectangle gene 0.5 black 46 17 579 N-Vascular endothelial growth factor subgraph-16 APP 12 151 white rectangle gene 0.5 black 46 17 620 N-Vascular endothelial growth factor subgraph-17 APP 36 80 white rectangle gene 0.5 black 46 17 620 N-Vascular endothelial growth factor subgraph-18 APP 32 65 white rectangle gene 0.5 black 46 17 620 N-Vascular endothelial growth factor subgraph-19 APP 46 66 white rectangle gene 0.5 black 46 17 620 N-Vascular endothelial growth factor subgraph-20 PIK3R2 152 141 white rectangle gene 0.5 black 46 17 8980 N-Vascular endothelial growth factor subgraph-21 PRKCB 23 164 white rectangle gene 0.5 black 46 17 9395 N-Vascular endothelial growth factor subgraph-22 PRKCD 90 9 white rectangle gene 0.5 black 46 17 9399 N-Vascular endothelial growth factor subgraph-23 PRKCD 91 0 white rectangle gene 0.5 black 46 17 9399 N-Vascular endothelial growth factor subgraph-24 PRKCH 141 61 white rectangle gene 0.5 black 46 17 9403 N-Vascular endothelial growth factor subgraph-25 PRKD1 39 71 white rectangle gene 0.5 black 46 17 9407 N-Vascular endothelial growth factor subgraph-26 PTK2 136 135 white rectangle gene 0.5 black 46 17 9611 N-Vascular endothelial growth factor subgraph-27 PTK7 132 147 white rectangle gene 0.5 black 46 17 9618 N-Vascular endothelial growth factor subgraph-3 4 GAB2 PTK7 1 38 white rectangle gene,gene 0.5 black 46 17 14458,/,9618 N-Vascular endothelial growth factor subgraph-5 SRC 99 176 white rectangle gene 0.5 black 46 17 11283 N-Vascular endothelial growth factor subgraph-6 VEGFA 15 159 white rectangle gene 0.5 black 46 17 12680 N-Vascular endothelial growth factor subgraph-7 GAB2 2 46 white rectangle gene 0.5 black 46 17 14458 N-Vascular endothelial growth factor subgraph-8 GAB2 142 144 white rectangle gene 0.5 black 46 17 14458 Vascular endothelial growth factor subgraph.sif000066400000000000000000000043561426625374700342510ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output0 1 2 N-Vascular endothelial growth factor subgraph-5 activation N-Vascular endothelial growth factor subgraph-15 N-Vascular endothelial growth factor subgraph-3 4 activation N-Vascular endothelial growth factor subgraph-7 N-Vascular endothelial growth factor subgraph-1 2 activation N-Vascular endothelial growth factor subgraph-7 N-Vascular endothelial growth factor subgraph-24 activation N-Vascular endothelial growth factor subgraph-14 N-Vascular endothelial growth factor subgraph-24 activation N-Vascular endothelial growth factor subgraph-12 N-Vascular endothelial growth factor subgraph-24 activation N-Vascular endothelial growth factor subgraph-13 N-Vascular endothelial growth factor subgraph-22 activation N-Vascular endothelial growth factor subgraph-22 N-Vascular endothelial growth factor subgraph-22 activation N-Vascular endothelial growth factor subgraph-22 N-Vascular endothelial growth factor subgraph-16 activation N-Vascular endothelial growth factor subgraph-10 N-Vascular endothelial growth factor subgraph-16 activation N-Vascular endothelial growth factor subgraph-6 N-Vascular endothelial growth factor subgraph-27 activation N-Vascular endothelial growth factor subgraph-8 N-Vascular endothelial growth factor subgraph-8 activation N-Vascular endothelial growth factor subgraph-20 N-Vascular endothelial growth factor subgraph-8 activation N-Vascular endothelial growth factor subgraph-11 N-Vascular endothelial growth factor subgraph-23 activation N-Vascular endothelial growth factor subgraph-22 N-Vascular endothelial growth factor subgraph-25 activation N-Vascular endothelial growth factor subgraph-18 N-Vascular endothelial growth factor subgraph-25 activation N-Vascular endothelial growth factor subgraph-19 N-Vascular endothelial growth factor subgraph-25 activation N-Vascular endothelial growth factor subgraph-17 N-Vascular endothelial growth factor subgraph-10 activation N-Vascular endothelial growth factor subgraph-6 N-Vascular endothelial growth factor subgraph-26 activation N-Vascular endothelial growth factor subgraph-8 N-Vascular endothelial growth factor subgraph-6 activation N-Vascular endothelial growth factor subgraph-21 N-Vascular endothelial growth factor subgraph-6 activation N-Vascular endothelial growth factor subgraph-21 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Vitamin subgraph.att000066400000000000000000000004541426625374700271560ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Vitamin subgraph-2 TNF 0 199 white rectangle gene 0.5 black 46 17 11892 N-Vitamin subgraph-3 CYP27B1 62 99 white rectangle gene 0.5 black 46 17 2606 N-Vitamin subgraph-4 IFNG 125 0 white rectangle gene 0.5 black 46 17 5438 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Vitamin subgraph.sif000066400000000000000000000001601426625374700271410ustar00rootroot000000000000000 1 2 N-Vitamin subgraph-4 activation N-Vitamin subgraph-3 N-Vitamin subgraph-2 activation N-Vitamin subgraph-3 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Wnt signaling subgraph.att000066400000000000000000000045301426625374700302520ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Wnt signaling subgraph-1 2 TCF_LEF CTNNBIP1 119 2 white rectangle gene,gene 0.5 black 46 17 TCF_LEF,/,16913 N-Wnt signaling subgraph-10 S100B 40 16 white rectangle gene 0.5 black 46 17 10500 N-Wnt signaling subgraph-11 BTRC 80 35 white rectangle gene 0.5 black 46 17 1144 N-Wnt signaling subgraph-13 CTNNBIP1 95 23 white rectangle gene 0.5 black 46 17 16913 N-Wnt signaling subgraph-14 CTNNBIP1 86 87 white rectangle gene 0.5 black 46 17 16913 N-Wnt signaling subgraph-15 CDK5 95 61 white rectangle gene 0.5 black 46 17 1774 N-Wnt signaling subgraph-17 CSNK1A1 79 65 white rectangle gene 0.5 black 46 17 2451 N-Wnt signaling subgraph-18 CSNK1E 184 41 white rectangle gene 0.5 black 46 17 2453 N-Wnt signaling subgraph-19 CSNK2A1 160 40 white rectangle gene 0.5 black 46 17 2457 N-Wnt signaling subgraph-20 CTNNB1 60 24 white rectangle gene 0.5 black 46 17 2514 N-Wnt signaling subgraph-21 CTNNB1 86 57 white rectangle gene 0.5 black 46 17 2514 N-Wnt signaling subgraph-22 CTNNB1 50 26 white rectangle gene 0.5 black 46 17 2514 N-Wnt signaling subgraph-23 CTNNB1 66 16 white rectangle gene 0.5 black 46 17 2514 N-Wnt signaling subgraph-24 CTNNB1 55 33 white rectangle gene 0.5 black 46 17 2514 N-Wnt signaling subgraph-25 CTNNB1 57 13 white rectangle gene 0.5 black 46 17 2514 N-Wnt signaling subgraph-26 DKK1 35 6 white rectangle gene 0.5 black 46 17 2891 N-Wnt signaling subgraph-28 GSK3B 90 77 white rectangle gene 0.5 black 46 17 4617 N-Wnt signaling subgraph-29 GSK3B 24 14 white rectangle gene 0.5 black 46 17 4617 N-Wnt signaling subgraph-3 4 5 6 7 AXIN CSNK1A1 CTNNB1 GSK3B APC 98 90 white rectangle gene,gene,gene,gene,gene 0.5 black 46 17 AXIN,/,2451,/,2514,/,4617,/,583 N-Wnt signaling subgraph-30 LRP6 121 11 white rectangle gene 0.5 black 46 17 6698 N-Wnt signaling subgraph-31 MAPT 10 13 white rectangle gene 0.5 black 46 17 6893 N-Wnt signaling subgraph-32 PPP2CA 0 14 white rectangle gene 0.5 black 46 17 9299 N-Wnt signaling subgraph-35 PSEN2 173 41 white rectangle gene 0.5 black 46 17 9509 N-Wnt signaling subgraph-36 Amyloidogenic glycoprotein, intracellular domain, conserved site 110 0 white rectangle gene 0.5 black 46 17 IPR019745 N-Wnt signaling subgraph-8 AXIN 103 101 white rectangle gene 0.5 black 46 17 AXIN N-Wnt signaling subgraph-9 Wnt 110 11 white rectangle gene 0.5 black 46 17 Wnt pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/Wnt signaling subgraph.sif000066400000000000000000000044031426625374700302420ustar00rootroot000000000000000 1 2 N-Wnt signaling subgraph-22 activation N-Wnt signaling subgraph-20 N-Wnt signaling subgraph-19 activation N-Wnt signaling subgraph-35 N-Wnt signaling subgraph-19 activation N-Wnt signaling subgraph-35 N-Wnt signaling subgraph-25 activation N-Wnt signaling subgraph-20 N-Wnt signaling subgraph-29 activation N-Wnt signaling subgraph-31 N-Wnt signaling subgraph-9 activation N-Wnt signaling subgraph-13 N-Wnt signaling subgraph-9 activation N-Wnt signaling subgraph-30 N-Wnt signaling subgraph-10 activation N-Wnt signaling subgraph-29 N-Wnt signaling subgraph-10 activation N-Wnt signaling subgraph-26 N-Wnt signaling subgraph-10 activation N-Wnt signaling subgraph-20 N-Wnt signaling subgraph-32 inhibition N-Wnt signaling subgraph-31 N-Wnt signaling subgraph-17 activation N-Wnt signaling subgraph-21 N-Wnt signaling subgraph-17 activation N-Wnt signaling subgraph-21 N-Wnt signaling subgraph-13 activation N-Wnt signaling subgraph-13 N-Wnt signaling subgraph-11 inhibition N-Wnt signaling subgraph-20 N-Wnt signaling subgraph-11 inhibition N-Wnt signaling subgraph-20 N-Wnt signaling subgraph-11 activation N-Wnt signaling subgraph-13 N-Wnt signaling subgraph-11 inhibition N-Wnt signaling subgraph-13 N-Wnt signaling subgraph-8 activation N-Wnt signaling subgraph-3 4 5 6 7 N-Wnt signaling subgraph-8 activation N-Wnt signaling subgraph-3 4 5 6 7 N-Wnt signaling subgraph-24 activation N-Wnt signaling subgraph-20 N-Wnt signaling subgraph-18 activation N-Wnt signaling subgraph-35 N-Wnt signaling subgraph-18 activation N-Wnt signaling subgraph-35 N-Wnt signaling subgraph-36 inhibition N-Wnt signaling subgraph-9 N-Wnt signaling subgraph-1 2 activation N-Wnt signaling subgraph-9 N-Wnt signaling subgraph-3 4 5 6 7 activation N-Wnt signaling subgraph-28 N-Wnt signaling subgraph-3 4 5 6 7 activation N-Wnt signaling subgraph-28 N-Wnt signaling subgraph-15 activation N-Wnt signaling subgraph-21 N-Wnt signaling subgraph-21 activation N-Wnt signaling subgraph-11 N-Wnt signaling subgraph-21 activation N-Wnt signaling subgraph-11 N-Wnt signaling subgraph-28 activation N-Wnt signaling subgraph-21 N-Wnt signaling subgraph-28 activation N-Wnt signaling subgraph-21 N-Wnt signaling subgraph-28 activation N-Wnt signaling subgraph-14 N-Wnt signaling subgraph-23 activation N-Wnt signaling subgraph-20 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/XIAP subgraph.att000066400000000000000000000014551426625374700263120ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-XIAP subgraph-10 Xiap 109 147 white rectangle gene 0.5 black 46 17 107572 N-XIAP subgraph-11 Casp3 101 133 white rectangle gene 0.5 black 46 17 107739 N-XIAP subgraph-12 Casp9 117 133 white rectangle gene 0.5 black 46 17 1277950 N-XIAP subgraph-13 Cycs 133 128 white rectangle gene 0.5 black 46 17 88578 N-XIAP subgraph-14 Sod1 109 163 white rectangle gene 0.5 black 46 17 98351 N-XIAP subgraph-5 BID 112 11 white rectangle gene 0.5 black 46 17 1050 N-XIAP subgraph-6 HTRA2 1 72 white rectangle gene 0.5 black 46 17 14348 N-XIAP subgraph-7 CDK5R1 87 126 white rectangle gene 0.5 black 46 17 1775 N-XIAP subgraph-8 CTSB 110 0 white rectangle gene 0.5 black 46 17 2527 N-XIAP subgraph-9 XIAP 0 83 white rectangle gene 0.5 black 46 17 592 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/XIAP subgraph.sif000066400000000000000000000010331426625374700262730ustar00rootroot000000000000000 1 2 N-XIAP subgraph-13 activation N-XIAP subgraph-12 N-XIAP subgraph-8 activation N-XIAP subgraph-5 N-XIAP subgraph-7 inhibition N-XIAP subgraph-11 N-XIAP subgraph-7 inhibition N-XIAP subgraph-11 N-XIAP subgraph-12 activation N-XIAP subgraph-11 N-XIAP subgraph-10 inhibition N-XIAP subgraph-12 N-XIAP subgraph-10 inhibition N-XIAP subgraph-12 N-XIAP subgraph-10 inhibition N-XIAP subgraph-12 N-XIAP subgraph-10 inhibition N-XIAP subgraph-11 N-XIAP subgraph-6 inhibition N-XIAP subgraph-9 N-XIAP subgraph-14 inhibition N-XIAP subgraph-10 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/mTOR signaling subgraph.att000066400000000000000000000021011426625374700303130ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-mTOR signaling subgraph-10 MAPT 166 123 white rectangle gene 0.5 black 46 17 6893 N-mTOR signaling subgraph-11 MAPT 176 91 white rectangle gene 0.5 black 46 17 6893 N-mTOR signaling subgraph-12 MAPT 156 100 white rectangle gene 0.5 black 46 17 6893 N-mTOR signaling subgraph-13 Atg5 0 141 white rectangle gene 0.5 black 46 17 1277186 N-mTOR signaling subgraph-14 Atg12 20 146 white rectangle gene 0.5 black 46 17 1914776 N-mTOR signaling subgraph-15 Map1lc3a 2 120 white rectangle gene 0.5 black 46 17 1915661 N-mTOR signaling subgraph-16 Mtor 14 131 white rectangle gene 0.5 black 46 17 1928394 N-mTOR signaling subgraph-17 Ctsb 29 121 white rectangle gene 0.5 black 46 17 88561 N-mTOR signaling subgraph-5 RPS6KB1 169 107 white rectangle gene 0.5 black 46 17 10436 N-mTOR signaling subgraph-6 RRAGC 58 0 white rectangle gene 0.5 black 46 17 19902 N-mTOR signaling subgraph-7 MTOR 46 1 white rectangle gene 0.5 black 46 17 3942 N-mTOR signaling subgraph-9 MAPT 184 113 white rectangle gene 0.5 black 46 17 6893 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/mTOR signaling subgraph.sif000066400000000000000000000011541426625374700303130ustar00rootroot000000000000000 1 2 N-mTOR signaling subgraph-6 activation N-mTOR signaling subgraph-7 N-mTOR signaling subgraph-16 inhibition N-mTOR signaling subgraph-15 N-mTOR signaling subgraph-16 inhibition N-mTOR signaling subgraph-17 N-mTOR signaling subgraph-16 inhibition N-mTOR signaling subgraph-13 N-mTOR signaling subgraph-16 inhibition N-mTOR signaling subgraph-14 N-mTOR signaling subgraph-5 activation N-mTOR signaling subgraph-11 N-mTOR signaling subgraph-5 activation N-mTOR signaling subgraph-10 N-mTOR signaling subgraph-5 activation N-mTOR signaling subgraph-12 N-mTOR signaling subgraph-5 activation N-mTOR signaling subgraph-9 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/miRNA subgraph.att000066400000000000000000000012631426625374700265140ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-miRNA subgraph-1 2 Cd40 Cd40lg 105 114 white rectangle gene,gene 0.5 black 46 17 88336,/,88337 N-miRNA subgraph-10 MAPT 13 81 white rectangle gene 0.5 black 46 17 6893 N-miRNA subgraph-13 Mapt 114 106 white rectangle gene 0.5 black 46 17 97180 N-miRNA subgraph-3 TGFB1 130 6 white rectangle gene 0.5 black 46 17 11766 N-miRNA subgraph-5 SIRT1 163 183 white rectangle gene 0.5 black 46 17 14929 N-miRNA subgraph-6 ADAM10 152 179 white rectangle gene 0.5 black 46 17 188 N-miRNA subgraph-8 SMAD2 119 0 white rectangle gene 0.5 black 46 17 6768 N-miRNA subgraph-9 MAPK3 0 80 white rectangle gene 0.5 black 46 17 6877 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/miRNA subgraph.sif000066400000000000000000000003161426625374700265030ustar00rootroot000000000000000 1 2 N-miRNA subgraph-9 activation N-miRNA subgraph-10 N-miRNA subgraph-5 activation N-miRNA subgraph-6 N-miRNA subgraph-3 activation N-miRNA subgraph-8 N-miRNA subgraph-1 2 activation N-miRNA subgraph-13 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/p53 stabilization subgraph.att000066400000000000000000000014671426625374700310200ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-p53 stabilization subgraph-10 EIF2AK2 0 4 white rectangle gene 0.5 black 46 17 9437 N-p53 stabilization subgraph-11 Amyloidogenic glycoprotein, intracellular domain, conserved site 116 150 white rectangle gene 0.5 black 46 17 IPR019745 N-p53 stabilization subgraph-3 TP53 93 156 white rectangle gene 0.5 black 46 17 11998 N-p53 stabilization subgraph-4 TP53 19 12 white rectangle gene 0.5 black 46 17 11998 N-p53 stabilization subgraph-5 CDK5R1 35 0 white rectangle gene 0.5 black 46 17 1775 N-p53 stabilization subgraph-6 CLU 99 180 white rectangle gene 0.5 black 46 17 2095 N-p53 stabilization subgraph-7 CTSD 91 134 white rectangle gene 0.5 black 46 17 2529 N-p53 stabilization subgraph-8 NOX4 70 160 white rectangle gene 0.5 black 46 17 7891 pybel-0.15.5/notebooks/hipathia_demo/alzheimers/output/p53 stabilization subgraph.sif000066400000000000000000000011221426625374700307750ustar00rootroot000000000000000 1 2 N-p53 stabilization subgraph-3 activation N-p53 stabilization subgraph-7 N-p53 stabilization subgraph-11 activation N-p53 stabilization subgraph-3 N-p53 stabilization subgraph-11 activation N-p53 stabilization subgraph-3 N-p53 stabilization subgraph-11 activation N-p53 stabilization subgraph-3 N-p53 stabilization subgraph-6 activation N-p53 stabilization subgraph-3 N-p53 stabilization subgraph-5 activation N-p53 stabilization subgraph-4 N-p53 stabilization subgraph-8 activation N-p53 stabilization subgraph-3 N-p53 stabilization subgraph-10 activation N-p53 stabilization subgraph-4 pybel-0.15.5/notebooks/hipathia_demo/cbn/000077500000000000000000000000001426625374700202755ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/cbn/_convert_cbn.py000066400000000000000000000022731426625374700233140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Convert the `CausalBionet (CBN) `_ for Hipathia.""" import json import os import zipfile from urllib.request import urlretrieve import click from pyobo.cli_utils import verbose_option from tqdm import tqdm import pybel import pybel.grounding HERE = os.path.dirname(__file__) OUTPUT = os.path.join(HERE, 'output') SOURCE = os.path.join(HERE, 'source') os.makedirs(OUTPUT, exist_ok=True) # Get and unzip this in this directory URL = 'http://causalbionet.com/Content/jgf_bulk_files/Human-2.0.zip' PATH = os.path.join(HERE, 'Human-2.0.zip') @click.command() @verbose_option def main(): """Convert all CBN graphs to Hipathia.""" if not os.path.exists(PATH): urlretrieve(URL, PATH) if not os.path.exists(SOURCE): with zipfile.ZipFile(PATH) as file: file.extractall(SOURCE) for filename in tqdm(os.listdir(SOURCE)): path = os.path.join(SOURCE, filename) with open(path) as f: cbn_jgif_dict = json.load(f) graph = pybel.from_cbn_jgif(cbn_jgif_dict) graph = pybel.grounding.ground(graph) pybel.to_hipathia(graph, OUTPUT) if __name__ == '__main__': main() pybel-0.15.5/notebooks/hipathia_demo/cbn/output/000077500000000000000000000000001426625374700216355ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Angiogenesis-2.0-Hs.att000066400000000000000000000177151426625374700256420ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Angiogenesis-2.0-Hs-1 2 KLHL20 ECT2 56 21 white rectangle gene,gene 0.5 black 46 17 25056,/,3155 N-Angiogenesis-2.0-Hs-10 PKC 80 120 white rectangle gene 0.5 black 46 17 PKC N-Angiogenesis-2.0-Hs-100 PPARA 100 107 white rectangle gene 0.5 black 46 17 9232 N-Angiogenesis-2.0-Hs-102 PTK2 75 111 white rectangle gene 0.5 black 46 17 9611 N-Angiogenesis-2.0-Hs-104 PXN 89 113 white rectangle gene 0.5 black 46 17 9718 N-Angiogenesis-2.0-Hs-105 RAF1 83 5 white rectangle gene 0.5 black 46 17 9829 N-Angiogenesis-2.0-Hs-106 Bcl6b 114 46 white rectangle gene 0.5 black 46 17 1278332 N-Angiogenesis-2.0-Hs-107 Robo4 163 45 white rectangle gene 0.5 black 46 17 1921394 N-Angiogenesis-2.0-Hs-108 Plxnd1 131 70 white rectangle gene 0.5 black 46 17 2154244 N-Angiogenesis-2.0-Hs-109 Rbpj 114 52 white rectangle gene 0.5 black 46 17 96522 N-Angiogenesis-2.0-Hs-11 TGFB 33 88 white rectangle gene 0.5 black 46 17 TGFB N-Angiogenesis-2.0-Hs-12 p38 61 106 white rectangle gene 0.5 black 46 17 p38 N-Angiogenesis-2.0-Hs-13 ROCK1 54 11 white rectangle gene 0.5 black 46 17 10251 N-Angiogenesis-2.0-Hs-14 ROCK2 83 0 white rectangle gene 0.5 black 46 17 10252 N-Angiogenesis-2.0-Hs-15 BDKRB1 122 83 white rectangle gene 0.5 black 46 17 1029 N-Angiogenesis-2.0-Hs-16 BDKRB2 116 87 white rectangle gene 0.5 black 46 17 1030 N-Angiogenesis-2.0-Hs-17 CXCL12 30 14 white rectangle gene 0.5 black 46 17 10672 N-Angiogenesis-2.0-Hs-18 SEMA3E 136 74 white rectangle gene 0.5 black 46 17 10727 N-Angiogenesis-2.0-Hs-19 SHC1 86 121 white rectangle gene 0.5 black 46 17 10840 N-Angiogenesis-2.0-Hs-20 SHH 60 15 white rectangle gene 0.5 black 46 17 10848 N-Angiogenesis-2.0-Hs-21 SLIT2 158 47 white rectangle gene 0.5 black 46 17 11086 N-Angiogenesis-2.0-Hs-22 SRC 78 108 white rectangle gene 0.5 black 46 17 11283 N-Angiogenesis-2.0-Hs-23 TEK 79 58 white rectangle gene 0.5 black 46 17 11724 N-Angiogenesis-2.0-Hs-24 TGFA 77 95 white rectangle gene 0.5 black 46 17 11765 N-Angiogenesis-2.0-Hs-25 TGFBR1 51 96 white rectangle gene 0.5 black 46 17 11772 N-Angiogenesis-2.0-Hs-26 TGFBR2 40 88 white rectangle gene 0.5 black 46 17 11773 N-Angiogenesis-2.0-Hs-27 TIE1 74 62 white rectangle gene 0.5 black 46 17 11809 N-Angiogenesis-2.0-Hs-28 TIMP2 173 97 white rectangle gene 0.5 black 46 17 11821 N-Angiogenesis-2.0-Hs-29 TNXB 51 121 white rectangle gene 0.5 black 46 17 11976 N-Angiogenesis-2.0-Hs-3 4 ITGAV ITGB3 50 120 white rectangle gene,gene 0.5 black 46 17 6150,/,6156 N-Angiogenesis-2.0-Hs-30 EGLN1 62 83 white rectangle gene 0.5 black 46 17 1232 N-Angiogenesis-2.0-Hs-31 VEGFA 63 102 white rectangle gene 0.5 black 46 17 12680 N-Angiogenesis-2.0-Hs-32 VEGFB 48 116 white rectangle gene 0.5 black 46 17 12681 N-Angiogenesis-2.0-Hs-33 VTN 45 126 white rectangle gene 0.5 black 46 17 12724 N-Angiogenesis-2.0-Hs-34 EGLN2 68 79 white rectangle gene 0.5 black 46 17 14660 N-Angiogenesis-2.0-Hs-35 EGLN3 67 76 white rectangle gene 0.5 black 46 17 14661 N-Angiogenesis-2.0-Hs-38 KRIT1 141 67 white rectangle gene 0.5 black 46 17 1573 N-Angiogenesis-2.0-Hs-39 HIF1AN 62 80 white rectangle gene 0.5 black 46 17 17113 N-Angiogenesis-2.0-Hs-40 ACVRL1 35 80 white rectangle gene 0.5 black 46 17 175 N-Angiogenesis-2.0-Hs-41 CDH5 99 104 white rectangle gene 0.5 black 46 17 1764 N-Angiogenesis-2.0-Hs-42 EGFL7 133 59 white rectangle gene 0.5 black 46 17 20594 N-Angiogenesis-2.0-Hs-43 SESN2 93 117 white rectangle gene 0.5 black 46 17 20746 N-Angiogenesis-2.0-Hs-44 COL18A1 71 111 white rectangle gene 0.5 black 46 17 2195 N-Angiogenesis-2.0-Hs-45 CREBBP 71 76 white rectangle gene 0.5 black 46 17 2348 N-Angiogenesis-2.0-Hs-46 CXCR4 33 19 white rectangle gene 0.5 black 46 17 2561 N-Angiogenesis-2.0-Hs-47 DLL4 134 66 white rectangle gene 0.5 black 46 17 2910 N-Angiogenesis-2.0-Hs-48 EDN1 63 86 white rectangle gene 0.5 black 46 17 3176 N-Angiogenesis-2.0-Hs-49 EFNB2 96 173 white rectangle gene 0.5 black 46 17 3227 N-Angiogenesis-2.0-Hs-5 AKT 92 106 white rectangle gene 0.5 black 46 17 AKT N-Angiogenesis-2.0-Hs-50 EGFR 81 107 white rectangle gene 0.5 black 46 17 3236 N-Angiogenesis-2.0-Hs-51 EIF4E 64 78 white rectangle gene 0.5 black 46 17 3287 N-Angiogenesis-2.0-Hs-52 ENG 43 88 white rectangle gene 0.5 black 46 17 3349 N-Angiogenesis-2.0-Hs-53 AGTR1 5 37 white rectangle gene 0.5 black 46 17 336 N-Angiogenesis-2.0-Hs-54 EP300 76 79 white rectangle gene 0.5 black 46 17 3373 N-Angiogenesis-2.0-Hs-55 EPAS1 75 74 white rectangle gene 0.5 black 46 17 3374 N-Angiogenesis-2.0-Hs-56 EPHB4 96 176 white rectangle gene 0.5 black 46 17 3395 N-Angiogenesis-2.0-Hs-58 FGF2 65 81 white rectangle gene 0.5 black 46 17 3676 N-Angiogenesis-2.0-Hs-59 FLT1 54 112 white rectangle gene 0.5 black 46 17 3763 N-Angiogenesis-2.0-Hs-6 FGF 65 124 white rectangle gene 0.5 black 46 17 FGF N-Angiogenesis-2.0-Hs-60 FOXO1 83 52 white rectangle gene 0.5 black 46 17 3819 N-Angiogenesis-2.0-Hs-61 FOXO3 80 51 white rectangle gene 0.5 black 46 17 3821 N-Angiogenesis-2.0-Hs-62 GDF2 31 74 white rectangle gene 0.5 black 46 17 4217 N-Angiogenesis-2.0-Hs-63 ANGPT1 77 61 white rectangle gene 0.5 black 46 17 484 N-Angiogenesis-2.0-Hs-64 ANGPT2 74 68 white rectangle gene 0.5 black 46 17 485 N-Angiogenesis-2.0-Hs-65 HEY2 109 62 white rectangle gene 0.5 black 46 17 4881 N-Angiogenesis-2.0-Hs-66 HGF 93 130 white rectangle gene 0.5 black 46 17 4893 N-Angiogenesis-2.0-Hs-67 HIF1A 69 84 white rectangle gene 0.5 black 46 17 4910 N-Angiogenesis-2.0-Hs-68 ANXA2 99 110 white rectangle gene 0.5 black 46 17 537 N-Angiogenesis-2.0-Hs-69 IGF1 73 89 white rectangle gene 0.5 black 46 17 5464 N-Angiogenesis-2.0-Hs-7 FGFR 66 116 white rectangle gene 0.5 black 46 17 FGFR N-Angiogenesis-2.0-Hs-70 IGF1R 74 95 white rectangle gene 0.5 black 46 17 5465 N-Angiogenesis-2.0-Hs-71 RBPJ 116 60 white rectangle gene 0.5 black 46 17 5724 N-Angiogenesis-2.0-Hs-72 JAG1 133 62 white rectangle gene 0.5 black 46 17 6188 N-Angiogenesis-2.0-Hs-73 KDR 59 114 white rectangle gene 0.5 black 46 17 6307 N-Angiogenesis-2.0-Hs-74 KLF2 66 88 white rectangle gene 0.5 black 46 17 6347 N-Angiogenesis-2.0-Hs-75 KNG1 120 85 white rectangle gene 0.5 black 46 17 6383 N-Angiogenesis-2.0-Hs-76 LGALS3 53 119 white rectangle gene 0.5 black 46 17 6563 N-Angiogenesis-2.0-Hs-77 RHOA 56 16 white rectangle gene 0.5 black 46 17 667 N-Angiogenesis-2.0-Hs-78 SMAD1 29 76 white rectangle gene 0.5 black 46 17 6767 N-Angiogenesis-2.0-Hs-79 SMAD2 44 97 white rectangle gene 0.5 black 46 17 6768 N-Angiogenesis-2.0-Hs-8 Notch 127 64 white rectangle gene 0.5 black 46 17 Notch N-Angiogenesis-2.0-Hs-80 SMAD3 56 100 white rectangle gene 0.5 black 46 17 6769 N-Angiogenesis-2.0-Hs-82 SMAD5 27 79 white rectangle gene 0.5 black 46 17 6771 N-Angiogenesis-2.0-Hs-83 MAP2K1 88 119 white rectangle gene 0.5 black 46 17 6840 N-Angiogenesis-2.0-Hs-84 MAPK1 82 117 white rectangle gene 0.5 black 46 17 6871 N-Angiogenesis-2.0-Hs-85 MAPK3 83 116 white rectangle gene 0.5 black 46 17 6877 N-Angiogenesis-2.0-Hs-86 ARNT 73 78 white rectangle gene 0.5 black 46 17 700 N-Angiogenesis-2.0-Hs-87 MET 88 123 white rectangle gene 0.5 black 46 17 7029 N-Angiogenesis-2.0-Hs-88 MMP14 179 98 white rectangle gene 0.5 black 46 17 7160 N-Angiogenesis-2.0-Hs-89 MMP2 57 104 white rectangle gene 0.5 black 46 17 7166 N-Angiogenesis-2.0-Hs-9 PDGFR 78 122 white rectangle gene 0.5 black 46 17 PDGFR N-Angiogenesis-2.0-Hs-90 NOS3 78 104 white rectangle gene 0.5 black 46 17 7876 N-Angiogenesis-2.0-Hs-91 NOTCH4 112 58 white rectangle gene 0.5 black 46 17 7884 N-Angiogenesis-2.0-Hs-92 NR2F2 130 58 white rectangle gene 0.5 black 46 17 7976 N-Angiogenesis-2.0-Hs-93 NRP1 55 117 white rectangle gene 0.5 black 46 17 8004 N-Angiogenesis-2.0-Hs-94 OLR1 0 34 white rectangle gene 0.5 black 46 17 8133 N-Angiogenesis-2.0-Hs-95 PDGFB 73 82 white rectangle gene 0.5 black 46 17 8800 N-Angiogenesis-2.0-Hs-96 PDGFC 57 122 white rectangle gene 0.5 black 46 17 8801 N-Angiogenesis-2.0-Hs-97 PDGFRA 84 122 white rectangle gene 0.5 black 46 17 8803 N-Angiogenesis-2.0-Hs-98 PDGFRB 86 113 white rectangle gene 0.5 black 46 17 8804 N-Angiogenesis-2.0-Hs-99 PLCG1 75 117 white rectangle gene 0.5 black 46 17 9065 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Angiogenesis-2.0-Hs.sif000066400000000000000000000447351426625374700256350ustar00rootroot000000000000000 1 2 N-Angiogenesis-2.0-Hs-39 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-17 activation N-Angiogenesis-2.0-Hs-46 N-Angiogenesis-2.0-Hs-17 activation N-Angiogenesis-2.0-Hs-46 N-Angiogenesis-2.0-Hs-17 activation N-Angiogenesis-2.0-Hs-46 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-10 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-97 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-97 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-21 activation N-Angiogenesis-2.0-Hs-107 N-Angiogenesis-2.0-Hs-41 activation N-Angiogenesis-2.0-Hs-5 N-Angiogenesis-2.0-Hs-70 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-70 activation N-Angiogenesis-2.0-Hs-22 N-Angiogenesis-2.0-Hs-33 activation N-Angiogenesis-2.0-Hs-3 4 N-Angiogenesis-2.0-Hs-84 activation N-Angiogenesis-2.0-Hs-104 N-Angiogenesis-2.0-Hs-9 activation N-Angiogenesis-2.0-Hs-99 N-Angiogenesis-2.0-Hs-9 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-24 activation N-Angiogenesis-2.0-Hs-50 N-Angiogenesis-2.0-Hs-24 activation N-Angiogenesis-2.0-Hs-50 N-Angiogenesis-2.0-Hs-24 activation N-Angiogenesis-2.0-Hs-50 N-Angiogenesis-2.0-Hs-24 activation N-Angiogenesis-2.0-Hs-50 N-Angiogenesis-2.0-Hs-24 activation N-Angiogenesis-2.0-Hs-50 N-Angiogenesis-2.0-Hs-24 activation N-Angiogenesis-2.0-Hs-50 N-Angiogenesis-2.0-Hs-24 activation N-Angiogenesis-2.0-Hs-50 N-Angiogenesis-2.0-Hs-75 activation N-Angiogenesis-2.0-Hs-15 N-Angiogenesis-2.0-Hs-75 activation N-Angiogenesis-2.0-Hs-15 N-Angiogenesis-2.0-Hs-75 activation N-Angiogenesis-2.0-Hs-16 N-Angiogenesis-2.0-Hs-75 activation N-Angiogenesis-2.0-Hs-16 N-Angiogenesis-2.0-Hs-105 activation N-Angiogenesis-2.0-Hs-14 N-Angiogenesis-2.0-Hs-52 inhibition N-Angiogenesis-2.0-Hs-25 N-Angiogenesis-2.0-Hs-52 activation N-Angiogenesis-2.0-Hs-40 N-Angiogenesis-2.0-Hs-52 activation N-Angiogenesis-2.0-Hs-40 N-Angiogenesis-2.0-Hs-54 activation N-Angiogenesis-2.0-Hs-55 N-Angiogenesis-2.0-Hs-54 activation N-Angiogenesis-2.0-Hs-55 N-Angiogenesis-2.0-Hs-54 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-54 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-34 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-34 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-34 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-34 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-34 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-66 activation N-Angiogenesis-2.0-Hs-87 N-Angiogenesis-2.0-Hs-66 activation N-Angiogenesis-2.0-Hs-87 N-Angiogenesis-2.0-Hs-66 activation N-Angiogenesis-2.0-Hs-87 N-Angiogenesis-2.0-Hs-66 activation N-Angiogenesis-2.0-Hs-87 N-Angiogenesis-2.0-Hs-66 activation N-Angiogenesis-2.0-Hs-87 N-Angiogenesis-2.0-Hs-66 activation N-Angiogenesis-2.0-Hs-87 N-Angiogenesis-2.0-Hs-66 activation N-Angiogenesis-2.0-Hs-87 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-83 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-19 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-19 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-19 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-19 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-28 inhibition N-Angiogenesis-2.0-Hs-88 N-Angiogenesis-2.0-Hs-47 activation N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-47 activation N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-108 inhibition N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-80 activation N-Angiogenesis-2.0-Hs-31 N-Angiogenesis-2.0-Hs-6 activation N-Angiogenesis-2.0-Hs-7 N-Angiogenesis-2.0-Hs-6 activation N-Angiogenesis-2.0-Hs-7 N-Angiogenesis-2.0-Hs-64 inhibition N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-64 inhibition N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-64 inhibition N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-64 inhibition N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-64 inhibition N-Angiogenesis-2.0-Hs-63 N-Angiogenesis-2.0-Hs-64 inhibition N-Angiogenesis-2.0-Hs-63 N-Angiogenesis-2.0-Hs-64 inhibition N-Angiogenesis-2.0-Hs-27 N-Angiogenesis-2.0-Hs-35 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-35 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-35 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-35 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-93 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-93 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-93 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-5 activation N-Angiogenesis-2.0-Hs-104 N-Angiogenesis-2.0-Hs-5 activation N-Angiogenesis-2.0-Hs-90 N-Angiogenesis-2.0-Hs-5 activation N-Angiogenesis-2.0-Hs-90 N-Angiogenesis-2.0-Hs-5 activation N-Angiogenesis-2.0-Hs-90 N-Angiogenesis-2.0-Hs-5 activation N-Angiogenesis-2.0-Hs-90 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-70 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-70 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-70 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-70 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-70 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-70 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-70 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-69 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-87 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-87 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-87 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-87 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-87 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-87 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-87 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-29 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-72 activation N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-72 activation N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-72 activation N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-72 activation N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-106 activation N-Angiogenesis-2.0-Hs-109 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-12 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-102 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-102 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-31 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-44 inhibition N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-7 activation N-Angiogenesis-2.0-Hs-99 N-Angiogenesis-2.0-Hs-7 activation N-Angiogenesis-2.0-Hs-99 N-Angiogenesis-2.0-Hs-7 activation N-Angiogenesis-2.0-Hs-99 N-Angiogenesis-2.0-Hs-7 activation N-Angiogenesis-2.0-Hs-12 N-Angiogenesis-2.0-Hs-30 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-30 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-30 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-30 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-30 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-51 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-51 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-50 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-92 inhibition N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-86 activation N-Angiogenesis-2.0-Hs-55 N-Angiogenesis-2.0-Hs-86 activation N-Angiogenesis-2.0-Hs-55 N-Angiogenesis-2.0-Hs-86 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-86 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-86 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-3 4 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-3 4 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-3 4 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-45 activation N-Angiogenesis-2.0-Hs-55 N-Angiogenesis-2.0-Hs-45 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-45 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-89 activation N-Angiogenesis-2.0-Hs-31 N-Angiogenesis-2.0-Hs-43 inhibition N-Angiogenesis-2.0-Hs-98 N-Angiogenesis-2.0-Hs-74 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-71 activation N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-71 activation N-Angiogenesis-2.0-Hs-65 N-Angiogenesis-2.0-Hs-1 2 activation N-Angiogenesis-2.0-Hs-77 N-Angiogenesis-2.0-Hs-48 activation N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-109 inhibition N-Angiogenesis-2.0-Hs-71 N-Angiogenesis-2.0-Hs-18 activation N-Angiogenesis-2.0-Hs-108 N-Angiogenesis-2.0-Hs-49 activation N-Angiogenesis-2.0-Hs-56 N-Angiogenesis-2.0-Hs-49 activation N-Angiogenesis-2.0-Hs-56 N-Angiogenesis-2.0-Hs-77 activation N-Angiogenesis-2.0-Hs-13 N-Angiogenesis-2.0-Hs-77 activation N-Angiogenesis-2.0-Hs-13 N-Angiogenesis-2.0-Hs-77 activation N-Angiogenesis-2.0-Hs-13 N-Angiogenesis-2.0-Hs-25 activation N-Angiogenesis-2.0-Hs-80 N-Angiogenesis-2.0-Hs-25 activation N-Angiogenesis-2.0-Hs-12 N-Angiogenesis-2.0-Hs-25 activation N-Angiogenesis-2.0-Hs-79 N-Angiogenesis-2.0-Hs-25 activation N-Angiogenesis-2.0-Hs-31 N-Angiogenesis-2.0-Hs-42 inhibition N-Angiogenesis-2.0-Hs-8 N-Angiogenesis-2.0-Hs-67 activation N-Angiogenesis-2.0-Hs-64 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 inhibition N-Angiogenesis-2.0-Hs-67 N-Angiogenesis-2.0-Hs-67 activation N-Angiogenesis-2.0-Hs-31 N-Angiogenesis-2.0-Hs-67 activation N-Angiogenesis-2.0-Hs-58 N-Angiogenesis-2.0-Hs-67 activation N-Angiogenesis-2.0-Hs-95 N-Angiogenesis-2.0-Hs-67 activation N-Angiogenesis-2.0-Hs-24 N-Angiogenesis-2.0-Hs-63 activation N-Angiogenesis-2.0-Hs-27 N-Angiogenesis-2.0-Hs-63 activation N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-63 activation N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-63 activation N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-63 activation N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-63 activation N-Angiogenesis-2.0-Hs-23 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-12 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-90 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-31 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-96 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-96 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-11 activation N-Angiogenesis-2.0-Hs-26 N-Angiogenesis-2.0-Hs-76 activation N-Angiogenesis-2.0-Hs-73 N-Angiogenesis-2.0-Hs-76 activation N-Angiogenesis-2.0-Hs-3 4 N-Angiogenesis-2.0-Hs-23 inhibition N-Angiogenesis-2.0-Hs-61 N-Angiogenesis-2.0-Hs-23 inhibition N-Angiogenesis-2.0-Hs-60 N-Angiogenesis-2.0-Hs-100 inhibition N-Angiogenesis-2.0-Hs-5 N-Angiogenesis-2.0-Hs-53 activation N-Angiogenesis-2.0-Hs-94 N-Angiogenesis-2.0-Hs-53 activation N-Angiogenesis-2.0-Hs-94 N-Angiogenesis-2.0-Hs-53 activation N-Angiogenesis-2.0-Hs-94 N-Angiogenesis-2.0-Hs-85 activation N-Angiogenesis-2.0-Hs-104 N-Angiogenesis-2.0-Hs-22 activation N-Angiogenesis-2.0-Hs-99 N-Angiogenesis-2.0-Hs-22 activation N-Angiogenesis-2.0-Hs-99 N-Angiogenesis-2.0-Hs-22 activation N-Angiogenesis-2.0-Hs-99 N-Angiogenesis-2.0-Hs-22 activation N-Angiogenesis-2.0-Hs-99 N-Angiogenesis-2.0-Hs-22 activation N-Angiogenesis-2.0-Hs-102 N-Angiogenesis-2.0-Hs-22 activation N-Angiogenesis-2.0-Hs-102 N-Angiogenesis-2.0-Hs-26 activation N-Angiogenesis-2.0-Hs-40 N-Angiogenesis-2.0-Hs-26 activation N-Angiogenesis-2.0-Hs-25 N-Angiogenesis-2.0-Hs-68 activation N-Angiogenesis-2.0-Hs-5 N-Angiogenesis-2.0-Hs-20 activation N-Angiogenesis-2.0-Hs-77 N-Angiogenesis-2.0-Hs-20 activation N-Angiogenesis-2.0-Hs-77 N-Angiogenesis-2.0-Hs-99 activation N-Angiogenesis-2.0-Hs-10 N-Angiogenesis-2.0-Hs-99 activation N-Angiogenesis-2.0-Hs-10 N-Angiogenesis-2.0-Hs-99 activation N-Angiogenesis-2.0-Hs-10 N-Angiogenesis-2.0-Hs-99 activation N-Angiogenesis-2.0-Hs-10 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-85 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-84 N-Angiogenesis-2.0-Hs-98 activation N-Angiogenesis-2.0-Hs-22 N-Angiogenesis-2.0-Hs-62 activation N-Angiogenesis-2.0-Hs-40 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-82 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-82 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-82 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-82 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-78 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-78 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-78 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-78 N-Angiogenesis-2.0-Hs-40 activation N-Angiogenesis-2.0-Hs-78 N-Angiogenesis-2.0-Hs-91 activation N-Angiogenesis-2.0-Hs-71 N-Angiogenesis-2.0-Hs-32 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-32 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-32 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-32 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-32 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-32 activation N-Angiogenesis-2.0-Hs-59 N-Angiogenesis-2.0-Hs-38 activation N-Angiogenesis-2.0-Hs-47 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Apoptosis-2.0-Hs.att000066400000000000000000000241241426625374700252000ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Apoptosis-2.0-Hs-1 2 RIPK1 TNFRSF10A 120 98 white rectangle gene,gene 0.5 black 46 17 10019,/,11904 N-Apoptosis-2.0-Hs-10 AKT 52 114 white rectangle gene 0.5 black 46 17 AKT N-Apoptosis-2.0-Hs-100 MAP3K14 104 107 white rectangle gene 0.5 black 46 17 6853 N-Apoptosis-2.0-Hs-101 MAP3K5 77 109 white rectangle gene 0.5 black 46 17 6857 N-Apoptosis-2.0-Hs-102 MAP3K5 65 113 white rectangle gene 0.5 black 46 17 6857 N-Apoptosis-2.0-Hs-103 MAP3K7 100 106 white rectangle gene 0.5 black 46 17 6859 N-Apoptosis-2.0-Hs-104 MAPK1 48 83 white rectangle gene 0.5 black 46 17 6871 N-Apoptosis-2.0-Hs-105 MAPK10 66 128 white rectangle gene 0.5 black 46 17 6872 N-Apoptosis-2.0-Hs-106 MAPK3 49 83 white rectangle gene 0.5 black 46 17 6877 N-Apoptosis-2.0-Hs-107 MAPK8 69 127 white rectangle gene 0.5 black 46 17 6881 N-Apoptosis-2.0-Hs-108 MCL1 61 87 white rectangle gene 0.5 black 46 17 6943 N-Apoptosis-2.0-Hs-109 NFKBIA 128 165 white rectangle gene 0.5 black 46 17 7797 N-Apoptosis-2.0-Hs-11 FOXO 50 121 white rectangle gene 0.5 black 46 17 FOXO N-Apoptosis-2.0-Hs-110 NFKBIA 127 160 white rectangle gene 0.5 black 46 17 7797 N-Apoptosis-2.0-Hs-111 NFKBIA 123 163 white rectangle gene 0.5 black 46 17 7797 N-Apoptosis-2.0-Hs-112 NFKBIE 102 0 white rectangle gene 0.5 black 46 17 7799 N-Apoptosis-2.0-Hs-113 ATF4 146 50 white rectangle gene 0.5 black 46 17 786 N-Apoptosis-2.0-Hs-114 PAK2 54 54 white rectangle gene 0.5 black 46 17 8591 N-Apoptosis-2.0-Hs-117 PDGFA 21 6 white rectangle gene 0.5 black 46 17 8799 N-Apoptosis-2.0-Hs-118 PDPK1 35 111 white rectangle gene 0.5 black 46 17 8816 N-Apoptosis-2.0-Hs-119 PDPK1 28 113 white rectangle gene 0.5 black 46 17 8816 N-Apoptosis-2.0-Hs-12 JNK 64 106 white rectangle gene 0.5 black 46 17 JNK N-Apoptosis-2.0-Hs-121 PMAIP1 61 89 white rectangle gene 0.5 black 46 17 9108 N-Apoptosis-2.0-Hs-122 BAD 53 93 white rectangle gene 0.5 black 46 17 936 N-Apoptosis-2.0-Hs-123 BAD 53 103 white rectangle gene 0.5 black 46 17 936 N-Apoptosis-2.0-Hs-124 BAD 50 104 white rectangle gene 0.5 black 46 17 936 N-Apoptosis-2.0-Hs-125 PRKCA 40 117 white rectangle gene 0.5 black 46 17 9393 N-Apoptosis-2.0-Hs-126 PRKCD 46 105 white rectangle gene 0.5 black 46 17 9399 N-Apoptosis-2.0-Hs-127 PRKCD 39 105 white rectangle gene 0.5 black 46 17 9399 N-Apoptosis-2.0-Hs-128 PRKCE 4 128 white rectangle gene 0.5 black 46 17 9401 N-Apoptosis-2.0-Hs-129 PRKD1 61 112 white rectangle gene 0.5 black 46 17 9407 N-Apoptosis-2.0-Hs-13 PKC 24 111 white rectangle gene 0.5 black 46 17 PKC N-Apoptosis-2.0-Hs-130 PRKD1 49 112 white rectangle gene 0.5 black 46 17 9407 N-Apoptosis-2.0-Hs-131 PRKD1 49 113 white rectangle gene 0.5 black 46 17 9407 N-Apoptosis-2.0-Hs-132 PRKCZ 41 114 white rectangle gene 0.5 black 46 17 9412 N-Apoptosis-2.0-Hs-133 PRKCZ 34 115 white rectangle gene 0.5 black 46 17 9412 N-Apoptosis-2.0-Hs-134 BAK1 58 85 white rectangle gene 0.5 black 46 17 949 N-Apoptosis-2.0-Hs-135 PTEN 122 28 white rectangle gene 0.5 black 46 17 9588 N-Apoptosis-2.0-Hs-136 PTEN 121 32 white rectangle gene 0.5 black 46 17 9588 N-Apoptosis-2.0-Hs-137 PTEN 120 24 white rectangle gene 0.5 black 46 17 9588 N-Apoptosis-2.0-Hs-138 PTEN 127 24 white rectangle gene 0.5 black 46 17 9588 N-Apoptosis-2.0-Hs-139 BAX 58 91 white rectangle gene 0.5 black 46 17 959 N-Apoptosis-2.0-Hs-14 PRKAC 61 92 white rectangle gene 0.5 black 46 17 PRKAC N-Apoptosis-2.0-Hs-140 BAX 55 103 white rectangle gene 0.5 black 46 17 959 N-Apoptosis-2.0-Hs-141 RAC1 65 120 white rectangle gene 0.5 black 46 17 9801 N-Apoptosis-2.0-Hs-142 RAC1 58 120 white rectangle gene 0.5 black 46 17 9801 N-Apoptosis-2.0-Hs-143 RAF1 32 122 white rectangle gene 0.5 black 46 17 9829 N-Apoptosis-2.0-Hs-144 RAF1 41 120 white rectangle gene 0.5 black 46 17 9829 N-Apoptosis-2.0-Hs-145 BCL2 62 78 white rectangle gene 0.5 black 46 17 990 N-Apoptosis-2.0-Hs-147 BCL2L1 57 78 white rectangle gene 0.5 black 46 17 992 N-Apoptosis-2.0-Hs-148 BCL2L11 55 92 white rectangle gene 0.5 black 46 17 994 N-Apoptosis-2.0-Hs-149 BCL2L11 48 88 white rectangle gene 0.5 black 46 17 994 N-Apoptosis-2.0-Hs-15 RIPK1 34 185 white rectangle gene 0.5 black 46 17 10019 N-Apoptosis-2.0-Hs-150 BCL2L11 60 100 white rectangle gene 0.5 black 46 17 994 N-Apoptosis-2.0-Hs-151 RELA 157 103 white rectangle gene 0.5 black 46 17 9955 N-Apoptosis-2.0-Hs-16 ROCK1 47 59 white rectangle gene 0.5 black 46 17 10251 N-Apoptosis-2.0-Hs-17 RPS6KA1 57 98 white rectangle gene 0.5 black 46 17 10430 N-Apoptosis-2.0-Hs-18 BID 64 81 white rectangle gene 0.5 black 46 17 1050 N-Apoptosis-2.0-Hs-19 BMX 58 54 white rectangle gene 0.5 black 46 17 1079 N-Apoptosis-2.0-Hs-20 STAT3 57 69 white rectangle gene 0.5 black 46 17 11364 N-Apoptosis-2.0-Hs-21 TLR4 53 56 white rectangle gene 0.5 black 46 17 11850 N-Apoptosis-2.0-Hs-22 TNF 103 93 white rectangle gene 0.5 black 46 17 11892 N-Apoptosis-2.0-Hs-23 TNFRSF10A 105 98 white rectangle gene 0.5 black 46 17 11904 N-Apoptosis-2.0-Hs-24 TNFRSF10B 105 99 white rectangle gene 0.5 black 46 17 11905 N-Apoptosis-2.0-Hs-25 TNFRSF10C 110 91 white rectangle gene 0.5 black 46 17 11906 N-Apoptosis-2.0-Hs-26 TNFRSF1A 93 89 white rectangle gene 0.5 black 46 17 11916 N-Apoptosis-2.0-Hs-27 TNFRSF1B 101 98 white rectangle gene 0.5 black 46 17 11917 N-Apoptosis-2.0-Hs-28 FAS 74 64 white rectangle gene 0.5 black 46 17 11920 N-Apoptosis-2.0-Hs-29 TNFSF10 112 98 white rectangle gene 0.5 black 46 17 11925 N-Apoptosis-2.0-Hs-3 4 5 CASP9 CYCS APAF1 53 49 white rectangle gene,gene,gene 0.5 black 46 17 1511,/,19986,/,576 N-Apoptosis-2.0-Hs-30 FASLG 77 68 white rectangle gene 0.5 black 46 17 11936 N-Apoptosis-2.0-Hs-31 TP53 71 74 white rectangle gene 0.5 black 46 17 11998 N-Apoptosis-2.0-Hs-32 TRADD 82 77 white rectangle gene 0.5 black 46 17 12030 N-Apoptosis-2.0-Hs-33 TRAF2 95 102 white rectangle gene 0.5 black 46 17 12032 N-Apoptosis-2.0-Hs-35 XBP1 0 162 white rectangle gene 0.5 black 46 17 12801 N-Apoptosis-2.0-Hs-37 BACH2 69 112 white rectangle gene 0.5 black 46 17 14078 N-Apoptosis-2.0-Hs-38 HTRA2 64 47 white rectangle gene 0.5 black 46 17 14348 N-Apoptosis-2.0-Hs-41 SIRT1 163 104 white rectangle gene 0.5 black 46 17 14929 N-Apoptosis-2.0-Hs-42 CASP10 67 65 white rectangle gene 0.5 black 46 17 1500 N-Apoptosis-2.0-Hs-43 CASP2 47 62 white rectangle gene 0.5 black 46 17 1503 N-Apoptosis-2.0-Hs-44 CASP3 56 63 white rectangle gene 0.5 black 46 17 1504 N-Apoptosis-2.0-Hs-46 CASP6 51 50 white rectangle gene 0.5 black 46 17 1507 N-Apoptosis-2.0-Hs-47 CASP7 58 61 white rectangle gene 0.5 black 46 17 1508 N-Apoptosis-2.0-Hs-48 CASP8 70 69 white rectangle gene 0.5 black 46 17 1509 N-Apoptosis-2.0-Hs-49 CASP9 54 59 white rectangle gene 0.5 black 46 17 1511 N-Apoptosis-2.0-Hs-50 CASP9 49 73 white rectangle gene 0.5 black 46 17 1511 N-Apoptosis-2.0-Hs-51 ACIN1 49 58 white rectangle gene 0.5 black 46 17 17066 N-Apoptosis-2.0-Hs-52 BBC3 54 87 white rectangle gene 0.5 black 46 17 17868 N-Apoptosis-2.0-Hs-53 CFLAR 77 64 white rectangle gene 0.5 black 46 17 1876 N-Apoptosis-2.0-Hs-54 CHUK 105 0 white rectangle gene 0.5 black 46 17 1974 N-Apoptosis-2.0-Hs-55 CHUK 111 111 white rectangle gene 0.5 black 46 17 1974 N-Apoptosis-2.0-Hs-57 DIABLO 67 53 white rectangle gene 0.5 black 46 17 21528 N-Apoptosis-2.0-Hs-58 CREB1 67 88 white rectangle gene 0.5 black 46 17 2345 N-Apoptosis-2.0-Hs-59 CREB1 65 96 white rectangle gene 0.5 black 46 17 2345 N-Apoptosis-2.0-Hs-6 7 EIF2AK3 HSPA5 148 38 white rectangle gene,gene 0.5 black 46 17 3255,/,5238 N-Apoptosis-2.0-Hs-60 PARP1 58 57 white rectangle gene 0.5 black 46 17 270 N-Apoptosis-2.0-Hs-61 DDIT3 54 90 white rectangle gene 0.5 black 46 17 2726 N-Apoptosis-2.0-Hs-62 DFFA 47 53 white rectangle gene 0.5 black 46 17 2772 N-Apoptosis-2.0-Hs-63 DFFB 41 46 white rectangle gene 0.5 black 46 17 2773 N-Apoptosis-2.0-Hs-64 EIF2AK3 148 41 white rectangle gene 0.5 black 46 17 3255 N-Apoptosis-2.0-Hs-65 EIF2S1 145 46 white rectangle gene 0.5 black 46 17 3265 N-Apoptosis-2.0-Hs-66 ELK1 20 124 white rectangle gene 0.5 black 46 17 3321 N-Apoptosis-2.0-Hs-67 ELK1 11 126 white rectangle gene 0.5 black 46 17 3321 N-Apoptosis-2.0-Hs-69 EP300 75 90 white rectangle gene 0.5 black 46 17 3373 N-Apoptosis-2.0-Hs-70 ERN1 3 162 white rectangle gene 0.5 black 46 17 3449 N-Apoptosis-2.0-Hs-71 FADD 75 69 white rectangle gene 0.5 black 46 17 3573 N-Apoptosis-2.0-Hs-72 FLOT2 66 61 white rectangle gene 0.5 black 46 17 3758 N-Apoptosis-2.0-Hs-73 FOXO1 48 133 white rectangle gene 0.5 black 46 17 3819 N-Apoptosis-2.0-Hs-74 FOXO1 50 125 white rectangle gene 0.5 black 46 17 3819 N-Apoptosis-2.0-Hs-75 FOXO3 23 15 white rectangle gene 0.5 black 46 17 3821 N-Apoptosis-2.0-Hs-76 FOXO3 20 9 white rectangle gene 0.5 black 46 17 3821 N-Apoptosis-2.0-Hs-77 AKT1 88 188 white rectangle gene 0.5 black 46 17 391 N-Apoptosis-2.0-Hs-78 AKT1 87 182 white rectangle gene 0.5 black 46 17 391 N-Apoptosis-2.0-Hs-79 AKT1 89 192 white rectangle gene 0.5 black 46 17 391 N-Apoptosis-2.0-Hs-8 9 ERN1 HSPA5 7 160 white rectangle gene,gene 0.5 black 46 17 3449,/,5238 N-Apoptosis-2.0-Hs-80 AKT2 83 115 white rectangle gene 0.5 black 46 17 392 N-Apoptosis-2.0-Hs-81 XRCC6 64 94 white rectangle gene 0.5 black 46 17 4055 N-Apoptosis-2.0-Hs-82 GAS2 48 64 white rectangle gene 0.5 black 46 17 4167 N-Apoptosis-2.0-Hs-83 HSPA5 51 63 white rectangle gene 0.5 black 46 17 5238 N-Apoptosis-2.0-Hs-86 XIAP 61 56 white rectangle gene 0.5 black 46 17 592 N-Apoptosis-2.0-Hs-87 BIRC5 63 59 white rectangle gene 0.5 black 46 17 593 N-Apoptosis-2.0-Hs-88 IKBKB 28 181 white rectangle gene 0.5 black 46 17 5960 N-Apoptosis-2.0-Hs-89 IL6 57 80 white rectangle gene 0.5 black 46 17 6018 N-Apoptosis-2.0-Hs-91 LMNA 55 53 white rectangle gene 0.5 black 46 17 6636 N-Apoptosis-2.0-Hs-92 LMNB1 50 56 white rectangle gene 0.5 black 46 17 6637 N-Apoptosis-2.0-Hs-93 LMNB2 51 60 white rectangle gene 0.5 black 46 17 6638 N-Apoptosis-2.0-Hs-94 MARCKS 36 124 white rectangle gene 0.5 black 46 17 6759 N-Apoptosis-2.0-Hs-95 MARCKS 16 111 white rectangle gene 0.5 black 46 17 6759 N-Apoptosis-2.0-Hs-96 MAP2K7 72 110 white rectangle gene 0.5 black 46 17 6847 N-Apoptosis-2.0-Hs-97 MAP3K1 32 177 white rectangle gene 0.5 black 46 17 6848 N-Apoptosis-2.0-Hs-98 MAP3K1 34 182 white rectangle gene 0.5 black 46 17 6848 N-Apoptosis-2.0-Hs-99 MAP3K1 31 183 white rectangle gene 0.5 black 46 17 6848 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Apoptosis-2.0-Hs.sif000066400000000000000000000617521426625374700252010ustar00rootroot000000000000000 1 2 N-Apoptosis-2.0-Hs-87 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-142 inhibition N-Apoptosis-2.0-Hs-141 N-Apoptosis-2.0-Hs-31 inhibition N-Apoptosis-2.0-Hs-145 N-Apoptosis-2.0-Hs-31 inhibition N-Apoptosis-2.0-Hs-145 N-Apoptosis-2.0-Hs-31 activation N-Apoptosis-2.0-Hs-30 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-78 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-124 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-54 inhibition N-Apoptosis-2.0-Hs-112 N-Apoptosis-2.0-Hs-13 activation N-Apoptosis-2.0-Hs-95 N-Apoptosis-2.0-Hs-13 activation N-Apoptosis-2.0-Hs-95 N-Apoptosis-2.0-Hs-13 activation N-Apoptosis-2.0-Hs-95 N-Apoptosis-2.0-Hs-17 activation N-Apoptosis-2.0-Hs-59 N-Apoptosis-2.0-Hs-17 activation N-Apoptosis-2.0-Hs-59 N-Apoptosis-2.0-Hs-17 activation N-Apoptosis-2.0-Hs-59 N-Apoptosis-2.0-Hs-17 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-17 activation N-Apoptosis-2.0-Hs-123 N-Apoptosis-2.0-Hs-133 activation N-Apoptosis-2.0-Hs-132 N-Apoptosis-2.0-Hs-133 activation N-Apoptosis-2.0-Hs-132 N-Apoptosis-2.0-Hs-18 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-18 activation N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-46 activation N-Apoptosis-2.0-Hs-91 N-Apoptosis-2.0-Hs-122 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-122 activation N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-20 inhibition N-Apoptosis-2.0-Hs-47 N-Apoptosis-2.0-Hs-20 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-70 activation N-Apoptosis-2.0-Hs-35 N-Apoptosis-2.0-Hs-44 inhibition N-Apoptosis-2.0-Hs-145 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-51 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-16 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-16 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-93 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-47 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-47 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-82 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-91 N-Apoptosis-2.0-Hs-44 inhibition N-Apoptosis-2.0-Hs-62 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-19 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-43 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-92 N-Apoptosis-2.0-Hs-44 inhibition N-Apoptosis-2.0-Hs-147 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-114 N-Apoptosis-2.0-Hs-44 activation N-Apoptosis-2.0-Hs-114 N-Apoptosis-2.0-Hs-44 inhibition N-Apoptosis-2.0-Hs-60 N-Apoptosis-2.0-Hs-127 activation N-Apoptosis-2.0-Hs-126 N-Apoptosis-2.0-Hs-149 inhibition N-Apoptosis-2.0-Hs-148 N-Apoptosis-2.0-Hs-121 activation N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-121 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-131 activation N-Apoptosis-2.0-Hs-129 N-Apoptosis-2.0-Hs-81 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-104 activation N-Apoptosis-2.0-Hs-149 N-Apoptosis-2.0-Hs-104 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-104 activation N-Apoptosis-2.0-Hs-50 N-Apoptosis-2.0-Hs-42 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-42 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-128 activation N-Apoptosis-2.0-Hs-67 N-Apoptosis-2.0-Hs-143 activation N-Apoptosis-2.0-Hs-66 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-49 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-49 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-49 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-47 N-Apoptosis-2.0-Hs-86 inhibition N-Apoptosis-2.0-Hs-47 N-Apoptosis-2.0-Hs-72 inhibition N-Apoptosis-2.0-Hs-28 N-Apoptosis-2.0-Hs-72 activation N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-72 activation N-Apoptosis-2.0-Hs-87 N-Apoptosis-2.0-Hs-72 inhibition N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-72 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-72 inhibition N-Apoptosis-2.0-Hs-57 N-Apoptosis-2.0-Hs-27 activation N-Apoptosis-2.0-Hs-33 N-Apoptosis-2.0-Hs-27 activation N-Apoptosis-2.0-Hs-33 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-57 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-102 inhibition N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-102 inhibition N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-102 inhibition N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-102 inhibition N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-102 inhibition N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-69 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-69 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-21 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-21 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-21 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-59 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-74 inhibition N-Apoptosis-2.0-Hs-73 N-Apoptosis-2.0-Hs-74 inhibition N-Apoptosis-2.0-Hs-73 N-Apoptosis-2.0-Hs-74 inhibition N-Apoptosis-2.0-Hs-73 N-Apoptosis-2.0-Hs-15 activation N-Apoptosis-2.0-Hs-98 N-Apoptosis-2.0-Hs-15 activation N-Apoptosis-2.0-Hs-99 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-79 activation N-Apoptosis-2.0-Hs-77 N-Apoptosis-2.0-Hs-47 inhibition N-Apoptosis-2.0-Hs-60 N-Apoptosis-2.0-Hs-92 inhibition N-Apoptosis-2.0-Hs-92 N-Apoptosis-2.0-Hs-92 inhibition N-Apoptosis-2.0-Hs-92 N-Apoptosis-2.0-Hs-92 inhibition N-Apoptosis-2.0-Hs-92 N-Apoptosis-2.0-Hs-92 inhibition N-Apoptosis-2.0-Hs-92 N-Apoptosis-2.0-Hs-92 inhibition N-Apoptosis-2.0-Hs-92 N-Apoptosis-2.0-Hs-92 inhibition N-Apoptosis-2.0-Hs-92 N-Apoptosis-2.0-Hs-67 activation N-Apoptosis-2.0-Hs-66 N-Apoptosis-2.0-Hs-67 activation N-Apoptosis-2.0-Hs-66 N-Apoptosis-2.0-Hs-67 activation N-Apoptosis-2.0-Hs-66 N-Apoptosis-2.0-Hs-67 activation N-Apoptosis-2.0-Hs-66 N-Apoptosis-2.0-Hs-67 activation N-Apoptosis-2.0-Hs-66 N-Apoptosis-2.0-Hs-38 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-38 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-38 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-38 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-38 inhibition N-Apoptosis-2.0-Hs-86 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-26 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-23 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-23 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-24 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-24 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-27 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-27 N-Apoptosis-2.0-Hs-22 activation N-Apoptosis-2.0-Hs-25 N-Apoptosis-2.0-Hs-32 activation N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-32 activation N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-32 activation N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-32 activation N-Apoptosis-2.0-Hs-71 N-Apoptosis-2.0-Hs-150 activation N-Apoptosis-2.0-Hs-148 N-Apoptosis-2.0-Hs-30 activation N-Apoptosis-2.0-Hs-28 N-Apoptosis-2.0-Hs-30 activation N-Apoptosis-2.0-Hs-28 N-Apoptosis-2.0-Hs-30 activation N-Apoptosis-2.0-Hs-28 N-Apoptosis-2.0-Hs-30 activation N-Apoptosis-2.0-Hs-28 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-140 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-140 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-140 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-144 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-124 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-124 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-124 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-142 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-102 N-Apoptosis-2.0-Hs-10 inhibition N-Apoptosis-2.0-Hs-11 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-74 N-Apoptosis-2.0-Hs-10 activation N-Apoptosis-2.0-Hs-123 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-18 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-48 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-93 inhibition N-Apoptosis-2.0-Hs-93 N-Apoptosis-2.0-Hs-93 inhibition N-Apoptosis-2.0-Hs-93 N-Apoptosis-2.0-Hs-37 activation N-Apoptosis-2.0-Hs-12 N-Apoptosis-2.0-Hs-123 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-123 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-99 activation N-Apoptosis-2.0-Hs-97 N-Apoptosis-2.0-Hs-80 inhibition N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-140 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-140 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-98 activation N-Apoptosis-2.0-Hs-97 N-Apoptosis-2.0-Hs-137 inhibition N-Apoptosis-2.0-Hs-135 N-Apoptosis-2.0-Hs-137 inhibition N-Apoptosis-2.0-Hs-135 N-Apoptosis-2.0-Hs-12 activation N-Apoptosis-2.0-Hs-150 N-Apoptosis-2.0-Hs-12 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-119 activation N-Apoptosis-2.0-Hs-118 N-Apoptosis-2.0-Hs-14 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-14 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-14 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-14 activation N-Apoptosis-2.0-Hs-58 N-Apoptosis-2.0-Hs-83 inhibition N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-62 inhibition N-Apoptosis-2.0-Hs-63 N-Apoptosis-2.0-Hs-91 inhibition N-Apoptosis-2.0-Hs-91 N-Apoptosis-2.0-Hs-91 inhibition N-Apoptosis-2.0-Hs-91 N-Apoptosis-2.0-Hs-91 inhibition N-Apoptosis-2.0-Hs-91 N-Apoptosis-2.0-Hs-91 inhibition N-Apoptosis-2.0-Hs-91 N-Apoptosis-2.0-Hs-6 7 inhibition N-Apoptosis-2.0-Hs-64 N-Apoptosis-2.0-Hs-76 inhibition N-Apoptosis-2.0-Hs-75 N-Apoptosis-2.0-Hs-130 activation N-Apoptosis-2.0-Hs-129 N-Apoptosis-2.0-Hs-130 activation N-Apoptosis-2.0-Hs-129 N-Apoptosis-2.0-Hs-28 activation N-Apoptosis-2.0-Hs-71 N-Apoptosis-2.0-Hs-65 activation N-Apoptosis-2.0-Hs-113 N-Apoptosis-2.0-Hs-65 activation N-Apoptosis-2.0-Hs-113 N-Apoptosis-2.0-Hs-65 activation N-Apoptosis-2.0-Hs-113 N-Apoptosis-2.0-Hs-117 activation N-Apoptosis-2.0-Hs-76 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-133 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-133 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-133 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-133 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-125 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-125 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-13 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-13 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-126 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-132 N-Apoptosis-2.0-Hs-118 activation N-Apoptosis-2.0-Hs-132 N-Apoptosis-2.0-Hs-89 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-89 activation N-Apoptosis-2.0-Hs-20 N-Apoptosis-2.0-Hs-111 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-111 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-111 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-111 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-111 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-125 activation N-Apoptosis-2.0-Hs-131 N-Apoptosis-2.0-Hs-125 activation N-Apoptosis-2.0-Hs-130 N-Apoptosis-2.0-Hs-125 activation N-Apoptosis-2.0-Hs-94 N-Apoptosis-2.0-Hs-125 activation N-Apoptosis-2.0-Hs-143 N-Apoptosis-2.0-Hs-64 activation N-Apoptosis-2.0-Hs-65 N-Apoptosis-2.0-Hs-64 activation N-Apoptosis-2.0-Hs-65 N-Apoptosis-2.0-Hs-100 activation N-Apoptosis-2.0-Hs-55 N-Apoptosis-2.0-Hs-100 activation N-Apoptosis-2.0-Hs-55 N-Apoptosis-2.0-Hs-33 activation N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-33 activation N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-33 activation N-Apoptosis-2.0-Hs-103 N-Apoptosis-2.0-Hs-33 activation N-Apoptosis-2.0-Hs-100 N-Apoptosis-2.0-Hs-33 activation N-Apoptosis-2.0-Hs-100 N-Apoptosis-2.0-Hs-8 9 inhibition N-Apoptosis-2.0-Hs-70 N-Apoptosis-2.0-Hs-141 activation N-Apoptosis-2.0-Hs-12 N-Apoptosis-2.0-Hs-141 activation N-Apoptosis-2.0-Hs-12 N-Apoptosis-2.0-Hs-141 activation N-Apoptosis-2.0-Hs-12 N-Apoptosis-2.0-Hs-141 activation N-Apoptosis-2.0-Hs-12 N-Apoptosis-2.0-Hs-141 activation N-Apoptosis-2.0-Hs-12 N-Apoptosis-2.0-Hs-141 activation N-Apoptosis-2.0-Hs-12 N-Apoptosis-2.0-Hs-141 activation N-Apoptosis-2.0-Hs-105 N-Apoptosis-2.0-Hs-141 activation N-Apoptosis-2.0-Hs-107 N-Apoptosis-2.0-Hs-53 inhibition N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-53 inhibition N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-53 inhibition N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-53 inhibition N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-26 activation N-Apoptosis-2.0-Hs-32 N-Apoptosis-2.0-Hs-26 activation N-Apoptosis-2.0-Hs-32 N-Apoptosis-2.0-Hs-26 activation N-Apoptosis-2.0-Hs-33 N-Apoptosis-2.0-Hs-108 inhibition N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-108 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-23 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-23 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-23 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-23 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-23 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-24 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-24 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-24 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-24 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-24 N-Apoptosis-2.0-Hs-29 activation N-Apoptosis-2.0-Hs-1 2 N-Apoptosis-2.0-Hs-148 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-148 activation N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-71 activation N-Apoptosis-2.0-Hs-42 N-Apoptosis-2.0-Hs-71 activation N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-71 activation N-Apoptosis-2.0-Hs-48 N-Apoptosis-2.0-Hs-136 inhibition N-Apoptosis-2.0-Hs-135 N-Apoptosis-2.0-Hs-136 inhibition N-Apoptosis-2.0-Hs-135 N-Apoptosis-2.0-Hs-50 inhibition N-Apoptosis-2.0-Hs-49 N-Apoptosis-2.0-Hs-50 inhibition N-Apoptosis-2.0-Hs-49 N-Apoptosis-2.0-Hs-126 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-126 activation N-Apoptosis-2.0-Hs-131 N-Apoptosis-2.0-Hs-126 activation N-Apoptosis-2.0-Hs-131 N-Apoptosis-2.0-Hs-126 activation N-Apoptosis-2.0-Hs-130 N-Apoptosis-2.0-Hs-126 activation N-Apoptosis-2.0-Hs-130 N-Apoptosis-2.0-Hs-144 inhibition N-Apoptosis-2.0-Hs-143 N-Apoptosis-2.0-Hs-144 inhibition N-Apoptosis-2.0-Hs-143 N-Apoptosis-2.0-Hs-144 inhibition N-Apoptosis-2.0-Hs-143 N-Apoptosis-2.0-Hs-144 inhibition N-Apoptosis-2.0-Hs-143 N-Apoptosis-2.0-Hs-144 inhibition N-Apoptosis-2.0-Hs-143 N-Apoptosis-2.0-Hs-144 inhibition N-Apoptosis-2.0-Hs-143 N-Apoptosis-2.0-Hs-106 inhibition N-Apoptosis-2.0-Hs-122 N-Apoptosis-2.0-Hs-106 activation N-Apoptosis-2.0-Hs-149 N-Apoptosis-2.0-Hs-106 activation N-Apoptosis-2.0-Hs-50 N-Apoptosis-2.0-Hs-96 activation N-Apoptosis-2.0-Hs-12 N-Apoptosis-2.0-Hs-58 activation N-Apoptosis-2.0-Hs-145 N-Apoptosis-2.0-Hs-24 activation N-Apoptosis-2.0-Hs-33 N-Apoptosis-2.0-Hs-138 inhibition N-Apoptosis-2.0-Hs-135 N-Apoptosis-2.0-Hs-138 inhibition N-Apoptosis-2.0-Hs-135 N-Apoptosis-2.0-Hs-147 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-147 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-147 inhibition N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-97 activation N-Apoptosis-2.0-Hs-88 N-Apoptosis-2.0-Hs-97 activation N-Apoptosis-2.0-Hs-88 N-Apoptosis-2.0-Hs-97 activation N-Apoptosis-2.0-Hs-88 N-Apoptosis-2.0-Hs-97 activation N-Apoptosis-2.0-Hs-88 N-Apoptosis-2.0-Hs-49 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-49 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-49 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-49 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-49 activation N-Apoptosis-2.0-Hs-44 N-Apoptosis-2.0-Hs-49 activation N-Apoptosis-2.0-Hs-47 N-Apoptosis-2.0-Hs-49 activation N-Apoptosis-2.0-Hs-47 N-Apoptosis-2.0-Hs-49 activation N-Apoptosis-2.0-Hs-46 N-Apoptosis-2.0-Hs-145 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-145 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-145 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-145 inhibition N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-145 inhibition N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-132 inhibition N-Apoptosis-2.0-Hs-10 N-Apoptosis-2.0-Hs-110 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-110 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-110 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-110 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-110 activation N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-61 activation N-Apoptosis-2.0-Hs-52 N-Apoptosis-2.0-Hs-61 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-61 activation N-Apoptosis-2.0-Hs-148 N-Apoptosis-2.0-Hs-3 4 5 activation N-Apoptosis-2.0-Hs-49 N-Apoptosis-2.0-Hs-3 4 5 activation N-Apoptosis-2.0-Hs-49 N-Apoptosis-2.0-Hs-52 activation N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-52 activation N-Apoptosis-2.0-Hs-134 N-Apoptosis-2.0-Hs-52 activation N-Apoptosis-2.0-Hs-139 N-Apoptosis-2.0-Hs-101 activation N-Apoptosis-2.0-Hs-96 N-Apoptosis-2.0-Hs-101 activation N-Apoptosis-2.0-Hs-96 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-109 inhibition N-Apoptosis-2.0-Hs-109 N-Apoptosis-2.0-Hs-23 activation N-Apoptosis-2.0-Hs-33 N-Apoptosis-2.0-Hs-129 activation N-Apoptosis-2.0-Hs-101 N-Apoptosis-2.0-Hs-41 activation N-Apoptosis-2.0-Hs-151 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Autophagy-2.0-Hs.att000066400000000000000000000064211426625374700251600ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Autophagy-2.0-Hs-1 2 BECN1 PIK3C3 163 116 white rectangle gene,gene 0.5 black 46 17 1034,/,8974 N-Autophagy-2.0-Hs-10 HDAC 129 88 white rectangle gene 0.5 black 46 17 HDAC N-Autophagy-2.0-Hs-11 RHEB 69 64 white rectangle gene 0.5 black 46 17 10011 N-Autophagy-2.0-Hs-13 RPS6KB1 53 90 white rectangle gene 0.5 black 46 17 10436 N-Autophagy-2.0-Hs-14 RPS6KB1 45 85 white rectangle gene 0.5 black 46 17 10436 N-Autophagy-2.0-Hs-15 RPS6KB1 49 99 white rectangle gene 0.5 black 46 17 10436 N-Autophagy-2.0-Hs-16 RPS6KB1 61 77 white rectangle gene 0.5 black 46 17 10436 N-Autophagy-2.0-Hs-17 RPS6KB1 56 97 white rectangle gene 0.5 black 46 17 10436 N-Autophagy-2.0-Hs-18 RPS6KB1 44 93 white rectangle gene 0.5 black 46 17 10436 N-Autophagy-2.0-Hs-19 SH3GLB1 127 0 white rectangle gene 0.5 black 46 17 10833 N-Autophagy-2.0-Hs-22 SQSTM1 107 81 white rectangle gene 0.5 black 46 17 11280 N-Autophagy-2.0-Hs-24 TLR4 108 88 white rectangle gene 0.5 black 46 17 11850 N-Autophagy-2.0-Hs-26 TSC2 77 51 white rectangle gene 0.5 black 46 17 12363 N-Autophagy-2.0-Hs-27 TSC2 89 44 white rectangle gene 0.5 black 46 17 12363 N-Autophagy-2.0-Hs-28 TSC2 108 34 white rectangle gene 0.5 black 46 17 12363 N-Autophagy-2.0-Hs-3 4 BECN1 BCL2 162 108 white rectangle gene,gene 0.5 black 46 17 1034,/,990 N-Autophagy-2.0-Hs-30 UVRAG 171 119 white rectangle gene 0.5 black 46 17 12640 N-Autophagy-2.0-Hs-31 MAP1LC3B 112 97 white rectangle gene 0.5 black 46 17 13352 N-Autophagy-2.0-Hs-33 SIRT1 69 164 white rectangle gene 0.5 black 46 17 14929 N-Autophagy-2.0-Hs-34 CAV1 112 106 white rectangle gene 0.5 black 46 17 1527 N-Autophagy-2.0-Hs-38 EIF2AK4 0 106 white rectangle gene 0.5 black 46 17 19687 N-Autophagy-2.0-Hs-40 EIF2AK1 1 113 white rectangle gene 0.5 black 46 17 24921 N-Autophagy-2.0-Hs-42 PARP1 73 169 white rectangle gene 0.5 black 46 17 270 N-Autophagy-2.0-Hs-43 E2F4 119 103 white rectangle gene 0.5 black 46 17 3118 N-Autophagy-2.0-Hs-44 EGR1 122 92 white rectangle gene 0.5 black 46 17 3238 N-Autophagy-2.0-Hs-45 EIF2S1 9 107 white rectangle gene 0.5 black 46 17 3265 N-Autophagy-2.0-Hs-46 EIF4EBP1 111 184 white rectangle gene 0.5 black 46 17 3288 N-Autophagy-2.0-Hs-47 EIF4EBP1 151 179 white rectangle gene 0.5 black 46 17 3288 N-Autophagy-2.0-Hs-48 EIF4G1 111 192 white rectangle gene 0.5 black 46 17 3296 N-Autophagy-2.0-Hs-49 FOXO3 106 102 white rectangle gene 0.5 black 46 17 3821 N-Autophagy-2.0-Hs-5 6 SH3GLB1 UVRAG 156 118 white rectangle gene,gene 0.5 black 46 17 10833,/,12640 N-Autophagy-2.0-Hs-50 AKT1 100 39 white rectangle gene 0.5 black 46 17 391 N-Autophagy-2.0-Hs-56 PDPK1 69 32 white rectangle gene 0.5 black 46 17 8816 N-Autophagy-2.0-Hs-57 PIK3C3 124 4 white rectangle gene 0.5 black 46 17 8974 N-Autophagy-2.0-Hs-58 PRKAA1 184 72 white rectangle gene 0.5 black 46 17 9376 N-Autophagy-2.0-Hs-59 PRKAA1 181 68 white rectangle gene 0.5 black 46 17 9376 N-Autophagy-2.0-Hs-60 PRKAA2 10 30 white rectangle gene 0.5 black 46 17 9377 N-Autophagy-2.0-Hs-61 PRKAA2 7 35 white rectangle gene 0.5 black 46 17 9377 N-Autophagy-2.0-Hs-62 EIF2AK2 22 103 white rectangle gene 0.5 black 46 17 9437 N-Autophagy-2.0-Hs-7 8 EIF4E EIF4EBP1 156 183 white rectangle gene,gene 0.5 black 46 17 3287,/,3288 N-Autophagy-2.0-Hs-9 AKT 73 40 white rectangle gene 0.5 black 46 17 AKT pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Autophagy-2.0-Hs.sif000066400000000000000000000061101426625374700251440ustar00rootroot000000000000000 1 2 N-Autophagy-2.0-Hs-17 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-17 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-17 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-17 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-27 inhibition N-Autophagy-2.0-Hs-26 N-Autophagy-2.0-Hs-27 inhibition N-Autophagy-2.0-Hs-26 N-Autophagy-2.0-Hs-18 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-18 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-50 activation N-Autophagy-2.0-Hs-28 N-Autophagy-2.0-Hs-50 activation N-Autophagy-2.0-Hs-28 N-Autophagy-2.0-Hs-50 activation N-Autophagy-2.0-Hs-27 N-Autophagy-2.0-Hs-50 activation N-Autophagy-2.0-Hs-27 N-Autophagy-2.0-Hs-50 activation N-Autophagy-2.0-Hs-27 N-Autophagy-2.0-Hs-40 activation N-Autophagy-2.0-Hs-45 N-Autophagy-2.0-Hs-46 inhibition N-Autophagy-2.0-Hs-48 N-Autophagy-2.0-Hs-49 activation N-Autophagy-2.0-Hs-31 N-Autophagy-2.0-Hs-5 6 activation N-Autophagy-2.0-Hs-1 2 N-Autophagy-2.0-Hs-42 inhibition N-Autophagy-2.0-Hs-33 N-Autophagy-2.0-Hs-15 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-26 inhibition N-Autophagy-2.0-Hs-11 N-Autophagy-2.0-Hs-59 activation N-Autophagy-2.0-Hs-58 N-Autophagy-2.0-Hs-59 activation N-Autophagy-2.0-Hs-58 N-Autophagy-2.0-Hs-59 activation N-Autophagy-2.0-Hs-58 N-Autophagy-2.0-Hs-44 activation N-Autophagy-2.0-Hs-31 N-Autophagy-2.0-Hs-44 activation N-Autophagy-2.0-Hs-31 N-Autophagy-2.0-Hs-56 activation N-Autophagy-2.0-Hs-9 N-Autophagy-2.0-Hs-56 activation N-Autophagy-2.0-Hs-9 N-Autophagy-2.0-Hs-56 activation N-Autophagy-2.0-Hs-9 N-Autophagy-2.0-Hs-56 activation N-Autophagy-2.0-Hs-9 N-Autophagy-2.0-Hs-10 activation N-Autophagy-2.0-Hs-44 N-Autophagy-2.0-Hs-16 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-16 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-16 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-16 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-16 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-14 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-14 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-14 activation N-Autophagy-2.0-Hs-13 N-Autophagy-2.0-Hs-24 inhibition N-Autophagy-2.0-Hs-31 N-Autophagy-2.0-Hs-24 inhibition N-Autophagy-2.0-Hs-31 N-Autophagy-2.0-Hs-24 activation N-Autophagy-2.0-Hs-22 N-Autophagy-2.0-Hs-47 inhibition N-Autophagy-2.0-Hs-7 8 N-Autophagy-2.0-Hs-47 inhibition N-Autophagy-2.0-Hs-7 8 N-Autophagy-2.0-Hs-47 inhibition N-Autophagy-2.0-Hs-7 8 N-Autophagy-2.0-Hs-3 4 inhibition N-Autophagy-2.0-Hs-1 2 N-Autophagy-2.0-Hs-61 activation N-Autophagy-2.0-Hs-60 N-Autophagy-2.0-Hs-61 activation N-Autophagy-2.0-Hs-60 N-Autophagy-2.0-Hs-11 activation N-Autophagy-2.0-Hs-16 N-Autophagy-2.0-Hs-62 activation N-Autophagy-2.0-Hs-45 N-Autophagy-2.0-Hs-62 activation N-Autophagy-2.0-Hs-45 N-Autophagy-2.0-Hs-30 activation N-Autophagy-2.0-Hs-1 2 N-Autophagy-2.0-Hs-9 inhibition N-Autophagy-2.0-Hs-26 N-Autophagy-2.0-Hs-34 inhibition N-Autophagy-2.0-Hs-31 N-Autophagy-2.0-Hs-34 inhibition N-Autophagy-2.0-Hs-31 N-Autophagy-2.0-Hs-43 activation N-Autophagy-2.0-Hs-31 N-Autophagy-2.0-Hs-19 activation N-Autophagy-2.0-Hs-57 N-Autophagy-2.0-Hs-38 activation N-Autophagy-2.0-Hs-45 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/B-cell Signaling-2.0-Hs.att000066400000000000000000000052621426625374700262130ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-B-cell Signaling-2.0-Hs-1 SRC 101 11 white rectangle gene 0.5 black 46 17 11283 N-B-cell Signaling-2.0-Hs-10 IKZF3 145 75 white rectangle gene 0.5 black 46 17 13178 N-B-cell Signaling-2.0-Hs-11 AICDA 43 127 white rectangle gene 0.5 black 46 17 13203 N-B-cell Signaling-2.0-Hs-12 BLNK 19 91 white rectangle gene 0.5 black 46 17 14211 N-B-cell Signaling-2.0-Hs-13 CASP3 27 142 white rectangle gene 0.5 black 46 17 1504 N-B-cell Signaling-2.0-Hs-14 CASP6 31 155 white 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0.5 black 46 17 9884 N-Cell Cycle-2.0-Hs-95 RBL1 96 76 white rectangle gene 0.5 black 46 17 9893 N-Cell Cycle-2.0-Hs-96 RBL1 59 61 white rectangle gene 0.5 black 46 17 9893 N-Cell Cycle-2.0-Hs-97 RBL2 96 73 white rectangle gene 0.5 black 46 17 9894 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Cell Cycle-2.0-Hs.sif000066400000000000000000000321411426625374700251050ustar00rootroot000000000000000 1 2 N-Cell Cycle-2.0-Hs-20 inhibition N-Cell Cycle-2.0-Hs-42 N-Cell Cycle-2.0-Hs-20 inhibition N-Cell Cycle-2.0-Hs-42 N-Cell Cycle-2.0-Hs-20 inhibition N-Cell Cycle-2.0-Hs-42 N-Cell Cycle-2.0-Hs-20 inhibition N-Cell Cycle-2.0-Hs-15 N-Cell Cycle-2.0-Hs-35 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-35 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-35 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-35 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-35 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-35 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-35 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-35 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-49 activation N-Cell Cycle-2.0-Hs-96 N-Cell Cycle-2.0-Hs-49 activation N-Cell Cycle-2.0-Hs-89 N-Cell Cycle-2.0-Hs-49 activation N-Cell Cycle-2.0-Hs-91 N-Cell Cycle-2.0-Hs-49 activation N-Cell Cycle-2.0-Hs-88 N-Cell Cycle-2.0-Hs-49 activation N-Cell Cycle-2.0-Hs-88 N-Cell Cycle-2.0-Hs-49 activation N-Cell Cycle-2.0-Hs-92 N-Cell Cycle-2.0-Hs-51 activation N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-51 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-51 activation N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-48 activation N-Cell Cycle-2.0-Hs-91 N-Cell Cycle-2.0-Hs-48 activation N-Cell Cycle-2.0-Hs-89 N-Cell Cycle-2.0-Hs-48 activation N-Cell Cycle-2.0-Hs-64 N-Cell Cycle-2.0-Hs-48 activation N-Cell Cycle-2.0-Hs-88 N-Cell Cycle-2.0-Hs-33 activation N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-33 activation N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-84 activation N-Cell Cycle-2.0-Hs-43 N-Cell Cycle-2.0-Hs-84 activation N-Cell Cycle-2.0-Hs-29 N-Cell Cycle-2.0-Hs-84 activation N-Cell Cycle-2.0-Hs-62 N-Cell Cycle-2.0-Hs-84 activation N-Cell Cycle-2.0-Hs-41 N-Cell Cycle-2.0-Hs-84 activation N-Cell Cycle-2.0-Hs-47 N-Cell Cycle-2.0-Hs-38 activation N-Cell Cycle-2.0-Hs-22 N-Cell Cycle-2.0-Hs-38 activation N-Cell Cycle-2.0-Hs-22 N-Cell Cycle-2.0-Hs-53 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-53 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-53 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-87 activation N-Cell Cycle-2.0-Hs-68 N-Cell Cycle-2.0-Hs-87 inhibition N-Cell Cycle-2.0-Hs-65 N-Cell Cycle-2.0-Hs-87 inhibition N-Cell Cycle-2.0-Hs-65 N-Cell Cycle-2.0-Hs-87 inhibition N-Cell Cycle-2.0-Hs-67 N-Cell Cycle-2.0-Hs-87 inhibition N-Cell Cycle-2.0-Hs-66 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-85 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-52 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-75 inhibition N-Cell Cycle-2.0-Hs-72 N-Cell Cycle-2.0-Hs-81 activation N-Cell Cycle-2.0-Hs-80 N-Cell Cycle-2.0-Hs-46 inhibition N-Cell Cycle-2.0-Hs-74 N-Cell Cycle-2.0-Hs-78 activation N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-78 activation N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-78 activation N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-30 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-30 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-93 inhibition N-Cell Cycle-2.0-Hs-87 N-Cell Cycle-2.0-Hs-18 activation N-Cell Cycle-2.0-Hs-65 N-Cell Cycle-2.0-Hs-18 activation N-Cell Cycle-2.0-Hs-66 N-Cell Cycle-2.0-Hs-18 activation N-Cell Cycle-2.0-Hs-67 N-Cell Cycle-2.0-Hs-79 activation N-Cell Cycle-2.0-Hs-57 N-Cell Cycle-2.0-Hs-34 activation N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-34 activation N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-3 4 activation N-Cell Cycle-2.0-Hs-25 N-Cell Cycle-2.0-Hs-77 activation N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-77 activation N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-54 activation N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-54 inhibition N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-36 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-5 6 inhibition N-Cell Cycle-2.0-Hs-27 N-Cell Cycle-2.0-Hs-5 6 activation N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-22 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-22 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-31 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-31 activation N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-10 11 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-63 activation N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-63 activation N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-23 inhibition N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-39 activation N-Cell Cycle-2.0-Hs-38 N-Cell Cycle-2.0-Hs-71 activation N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-61 activation N-Cell Cycle-2.0-Hs-22 N-Cell Cycle-2.0-Hs-59 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-59 inhibition N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-24 inhibition N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-26 activation N-Cell Cycle-2.0-Hs-3 4 N-Cell Cycle-2.0-Hs-45 activation N-Cell Cycle-2.0-Hs-44 N-Cell Cycle-2.0-Hs-21 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-21 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-21 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-21 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-21 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-21 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-21 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-50 activation N-Cell Cycle-2.0-Hs-88 N-Cell Cycle-2.0-Hs-50 activation N-Cell Cycle-2.0-Hs-88 N-Cell Cycle-2.0-Hs-50 activation N-Cell Cycle-2.0-Hs-96 N-Cell Cycle-2.0-Hs-56 inhibition N-Cell Cycle-2.0-Hs-48 N-Cell Cycle-2.0-Hs-97 inhibition N-Cell Cycle-2.0-Hs-65 N-Cell Cycle-2.0-Hs-97 inhibition N-Cell Cycle-2.0-Hs-68 N-Cell Cycle-2.0-Hs-94 inhibition N-Cell Cycle-2.0-Hs-87 N-Cell Cycle-2.0-Hs-94 inhibition N-Cell Cycle-2.0-Hs-87 N-Cell Cycle-2.0-Hs-94 inhibition N-Cell Cycle-2.0-Hs-87 N-Cell Cycle-2.0-Hs-37 activation N-Cell Cycle-2.0-Hs-51 N-Cell Cycle-2.0-Hs-13 activation N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-13 activation N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-13 activation N-Cell Cycle-2.0-Hs-54 N-Cell Cycle-2.0-Hs-13 activation N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-58 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-58 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-86 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-86 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-86 inhibition N-Cell Cycle-2.0-Hs-40 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-76 activation N-Cell Cycle-2.0-Hs-30 N-Cell Cycle-2.0-Hs-76 activation N-Cell Cycle-2.0-Hs-15 N-Cell Cycle-2.0-Hs-55 inhibition N-Cell Cycle-2.0-Hs-73 N-Cell Cycle-2.0-Hs-19 inhibition N-Cell Cycle-2.0-Hs-81 N-Cell Cycle-2.0-Hs-19 inhibition N-Cell Cycle-2.0-Hs-81 N-Cell Cycle-2.0-Hs-19 inhibition N-Cell Cycle-2.0-Hs-81 N-Cell Cycle-2.0-Hs-42 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-42 inhibition N-Cell Cycle-2.0-Hs-52 N-Cell Cycle-2.0-Hs-57 inhibition N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-57 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-57 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-57 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-57 inhibition N-Cell Cycle-2.0-Hs-18 N-Cell Cycle-2.0-Hs-12 activation N-Cell Cycle-2.0-Hs-26 N-Cell Cycle-2.0-Hs-12 activation N-Cell Cycle-2.0-Hs-26 N-Cell Cycle-2.0-Hs-12 activation N-Cell Cycle-2.0-Hs-55 N-Cell Cycle-2.0-Hs-88 inhibition N-Cell Cycle-2.0-Hs-87 N-Cell Cycle-2.0-Hs-95 inhibition N-Cell Cycle-2.0-Hs-68 N-Cell Cycle-2.0-Hs-95 inhibition N-Cell Cycle-2.0-Hs-68 N-Cell Cycle-2.0-Hs-95 inhibition N-Cell Cycle-2.0-Hs-68 N-Cell Cycle-2.0-Hs-95 inhibition N-Cell Cycle-2.0-Hs-65 N-Cell Cycle-2.0-Hs-95 inhibition N-Cell Cycle-2.0-Hs-65 N-Cell Cycle-2.0-Hs-90 inhibition N-Cell Cycle-2.0-Hs-87 N-Cell Cycle-2.0-Hs-90 inhibition N-Cell Cycle-2.0-Hs-87 N-Cell Cycle-2.0-Hs-32 activation N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-32 activation N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-32 activation N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-32 activation N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-32 activation N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-69 inhibition N-Cell Cycle-2.0-Hs-16 N-Cell Cycle-2.0-Hs-14 activation N-Cell Cycle-2.0-Hs-87 N-Cell Cycle-2.0-Hs-14 activation N-Cell Cycle-2.0-Hs-15 N-Cell Cycle-2.0-Hs-14 activation N-Cell Cycle-2.0-Hs-83 N-Cell Cycle-2.0-Hs-14 inhibition N-Cell Cycle-2.0-Hs-65 N-Cell Cycle-2.0-Hs-1 2 inhibition N-Cell Cycle-2.0-Hs-76 N-Cell Cycle-2.0-Hs-60 inhibition N-Cell Cycle-2.0-Hs-49 N-Cell Cycle-2.0-Hs-60 inhibition N-Cell Cycle-2.0-Hs-50 N-Cell Cycle-2.0-Hs-82 activation N-Cell Cycle-2.0-Hs-21 N-Cell Cycle-2.0-Hs-82 activation N-Cell Cycle-2.0-Hs-21 N-Cell Cycle-2.0-Hs-82 activation N-Cell Cycle-2.0-Hs-21 N-Cell Cycle-2.0-Hs-82 activation N-Cell Cycle-2.0-Hs-21 N-Cell Cycle-2.0-Hs-82 activation N-Cell Cycle-2.0-Hs-21 N-Cell Cycle-2.0-Hs-82 activation N-Cell Cycle-2.0-Hs-21 N-Cell Cycle-2.0-Hs-82 activation N-Cell Cycle-2.0-Hs-21 N-Cell Cycle-2.0-Hs-82 activation N-Cell Cycle-2.0-Hs-21 N-Cell Cycle-2.0-Hs-28 inhibition N-Cell Cycle-2.0-Hs-24 N-Cell Cycle-2.0-Hs-70 activation N-Cell Cycle-2.0-Hs-78 N-Cell Cycle-2.0-Hs-70 activation N-Cell Cycle-2.0-Hs-78 N-Cell Cycle-2.0-Hs-70 activation N-Cell Cycle-2.0-Hs-20 N-Cell Cycle-2.0-Hs-70 inhibition N-Cell Cycle-2.0-Hs-32 N-Cell Cycle-2.0-Hs-16 activation N-Cell Cycle-2.0-Hs-45 N-Cell Cycle-2.0-Hs-16 activation N-Cell Cycle-2.0-Hs-23 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Cell Interaction-2.0-Hs.att000066400000000000000000000107471426625374700263440ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Cell Interaction-2.0-Hs-1 2 CDH1 CTNNB1 166 15 white rectangle gene,gene 0.5 black 46 17 1748,/,2514 N-Cell Interaction-2.0-Hs-11 12 ITGA2 ITGB1 89 71 white rectangle gene,gene 0.5 black 46 17 6137,/,6153 N-Cell Interaction-2.0-Hs-13 14 ITGA3 ITGB1 133 142 white rectangle gene,gene 0.5 black 46 17 6139,/,6153 N-Cell Interaction-2.0-Hs-15 16 ITGA5 ITGB1 74 117 white rectangle gene,gene 0.5 black 46 17 6141,/,6153 N-Cell Interaction-2.0-Hs-17 18 ITGA6 ITGB1 145 160 white rectangle gene,gene 0.5 black 46 17 6142,/,6153 N-Cell Interaction-2.0-Hs-19 20 ITGA6 ITGB4 151 156 white rectangle gene,gene 0.5 black 46 17 6142,/,6158 N-Cell Interaction-2.0-Hs-21 22 ITGAV ITGB3 63 104 white rectangle gene,gene 0.5 black 46 17 6150,/,6156 N-Cell Interaction-2.0-Hs-23 24 ITGAV ITGB6 80 132 white rectangle gene,gene 0.5 black 46 17 6150,/,6161 N-Cell Interaction-2.0-Hs-25 26 ITGAV ITGB8 99 146 white rectangle gene,gene 0.5 black 46 17 6150,/,6163 N-Cell Interaction-2.0-Hs-27 AKT 163 83 white rectangle gene 0.5 black 46 17 AKT N-Cell Interaction-2.0-Hs-28 AKT 158 87 white rectangle gene 0.5 black 46 17 AKT N-Cell Interaction-2.0-Hs-29 COL1 82 76 white rectangle gene 0.5 black 46 17 COL1 N-Cell Interaction-2.0-Hs-3 4 CDH1 CTNND1 49 121 white rectangle gene,gene 0.5 black 46 17 1748,/,2515 N-Cell Interaction-2.0-Hs-30 COL4 85 78 white rectangle gene 0.5 black 46 17 COL4 N-Cell Interaction-2.0-Hs-31 GSK3 128 83 white rectangle gene 0.5 black 46 17 GSK3 N-Cell Interaction-2.0-Hs-32 GSK3 139 83 white rectangle gene 0.5 black 46 17 GSK3 N-Cell Interaction-2.0-Hs-33 RAC 55 146 white rectangle gene 0.5 black 46 17 RAC N-Cell Interaction-2.0-Hs-34 RAS 50 115 white rectangle gene 0.5 black 46 17 RAS N-Cell Interaction-2.0-Hs-35 SRC 60 119 white rectangle gene 0.5 black 46 17 SRC N-Cell Interaction-2.0-Hs-36 RPS6KB1 173 83 white rectangle gene 0.5 black 46 17 10436 N-Cell Interaction-2.0-Hs-37 SHC1 67 94 white rectangle gene 0.5 black 46 17 10840 N-Cell Interaction-2.0-Hs-38 TGFB1 84 128 white rectangle gene 0.5 black 46 17 11766 N-Cell Interaction-2.0-Hs-39 YES1 61 96 white rectangle gene 0.5 black 46 17 12841 N-Cell Interaction-2.0-Hs-40 CAV1 69 103 white rectangle gene 0.5 black 46 17 1527 N-Cell Interaction-2.0-Hs-41 CDH1 158 160 white rectangle gene 0.5 black 46 17 1748 N-Cell Interaction-2.0-Hs-42 CDKN2C 116 114 white rectangle gene 0.5 black 46 17 1789 N-Cell Interaction-2.0-Hs-43 CTNNB1 161 22 white rectangle gene 0.5 black 46 17 2514 N-Cell Interaction-2.0-Hs-44 DSG1 7 57 white rectangle gene 0.5 black 46 17 3048 N-Cell Interaction-2.0-Hs-45 DSG2 109 7 white rectangle gene 0.5 black 46 17 3049 N-Cell Interaction-2.0-Hs-46 DSG3 0 64 white rectangle gene 0.5 black 46 17 3050 N-Cell Interaction-2.0-Hs-47 E2F1 108 119 white rectangle gene 0.5 black 46 17 3113 N-Cell Interaction-2.0-Hs-48 AHR 97 124 white rectangle gene 0.5 black 46 17 348 N-Cell Interaction-2.0-Hs-49 FN1 78 124 white rectangle gene 0.5 black 46 17 3778 N-Cell Interaction-2.0-Hs-5 6 CDH1 EGFR 67 28 white rectangle gene,gene 0.5 black 46 17 1748,/,3236 N-Cell Interaction-2.0-Hs-50 ILK 152 83 white rectangle gene 0.5 black 46 17 6040 N-Cell Interaction-2.0-Hs-51 ITGB3 86 137 white rectangle gene 0.5 black 46 17 6156 N-Cell Interaction-2.0-Hs-52 LAMB3 142 152 white rectangle gene 0.5 black 46 17 6490 N-Cell Interaction-2.0-Hs-53 LGALS3 110 0 white rectangle gene 0.5 black 46 17 6563 N-Cell Interaction-2.0-Hs-54 MAPK1 64 19 white rectangle gene 0.5 black 46 17 6871 N-Cell Interaction-2.0-Hs-55 MAPK14 0 57 white rectangle gene 0.5 black 46 17 6876 N-Cell Interaction-2.0-Hs-56 MAPK3 70 37 white rectangle gene 0.5 black 46 17 6877 N-Cell Interaction-2.0-Hs-57 MMP14 93 138 white rectangle gene 0.5 black 46 17 7160 N-Cell Interaction-2.0-Hs-58 PLAUR 68 110 white rectangle gene 0.5 black 46 17 9053 N-Cell Interaction-2.0-Hs-59 PTK2 67 127 white rectangle gene 0.5 black 46 17 9611 N-Cell Interaction-2.0-Hs-60 PTK2 53 133 white rectangle gene 0.5 black 46 17 9611 N-Cell Interaction-2.0-Hs-61 PTPN1 50 143 white rectangle gene 0.5 black 46 17 9642 N-Cell Interaction-2.0-Hs-62 PTPN12 60 146 white rectangle gene 0.5 black 46 17 9645 N-Cell Interaction-2.0-Hs-63 PTPRA 43 137 white rectangle gene 0.5 black 46 17 9664 N-Cell Interaction-2.0-Hs-64 BCAR1 59 136 white rectangle gene 0.5 black 46 17 971 N-Cell Interaction-2.0-Hs-7 8 ILK ITGB1 157 76 white rectangle gene,gene 0.5 black 46 17 6040,/,6153 N-Cell Interaction-2.0-Hs-9 10 ITGA1 ITGB1 78 87 white rectangle gene,gene 0.5 black 46 17 6134,/,6153 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Cell Interaction-2.0-Hs.sif000066400000000000000000000103041426625374700263220ustar00rootroot000000000000000 1 2 N-Cell Interaction-2.0-Hs-30 activation N-Cell Interaction-2.0-Hs-9 10 N-Cell Interaction-2.0-Hs-30 activation N-Cell Interaction-2.0-Hs-11 12 N-Cell Interaction-2.0-Hs-1 2 inhibition N-Cell Interaction-2.0-Hs-43 N-Cell Interaction-2.0-Hs-15 16 activation N-Cell Interaction-2.0-Hs-40 N-Cell Interaction-2.0-Hs-15 16 activation N-Cell Interaction-2.0-Hs-59 N-Cell Interaction-2.0-Hs-15 16 activation N-Cell Interaction-2.0-Hs-59 N-Cell Interaction-2.0-Hs-63 activation N-Cell Interaction-2.0-Hs-60 N-Cell Interaction-2.0-Hs-46 activation N-Cell Interaction-2.0-Hs-55 N-Cell Interaction-2.0-Hs-21 22 activation N-Cell Interaction-2.0-Hs-40 N-Cell Interaction-2.0-Hs-25 26 activation N-Cell Interaction-2.0-Hs-57 N-Cell Interaction-2.0-Hs-23 24 activation N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-23 24 activation N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-23 24 activation N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-23 24 activation N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-23 24 activation N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-59 activation N-Cell Interaction-2.0-Hs-64 N-Cell Interaction-2.0-Hs-57 activation N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-57 activation N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-57 activation N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-32 inhibition N-Cell Interaction-2.0-Hs-31 N-Cell Interaction-2.0-Hs-32 inhibition N-Cell Interaction-2.0-Hs-31 N-Cell Interaction-2.0-Hs-44 activation N-Cell Interaction-2.0-Hs-55 N-Cell Interaction-2.0-Hs-48 activation N-Cell Interaction-2.0-Hs-47 N-Cell Interaction-2.0-Hs-48 inhibition N-Cell Interaction-2.0-Hs-38 N-Cell Interaction-2.0-Hs-58 activation N-Cell Interaction-2.0-Hs-21 22 N-Cell Interaction-2.0-Hs-58 activation N-Cell Interaction-2.0-Hs-15 16 N-Cell Interaction-2.0-Hs-7 8 activation N-Cell Interaction-2.0-Hs-50 N-Cell Interaction-2.0-Hs-47 activation N-Cell Interaction-2.0-Hs-42 N-Cell Interaction-2.0-Hs-50 activation N-Cell Interaction-2.0-Hs-27 N-Cell Interaction-2.0-Hs-50 activation N-Cell Interaction-2.0-Hs-32 N-Cell Interaction-2.0-Hs-50 activation N-Cell Interaction-2.0-Hs-28 N-Cell Interaction-2.0-Hs-5 6 activation N-Cell Interaction-2.0-Hs-56 N-Cell Interaction-2.0-Hs-5 6 activation N-Cell Interaction-2.0-Hs-54 N-Cell Interaction-2.0-Hs-53 activation N-Cell Interaction-2.0-Hs-45 N-Cell Interaction-2.0-Hs-53 inhibition N-Cell Interaction-2.0-Hs-45 N-Cell Interaction-2.0-Hs-52 activation N-Cell Interaction-2.0-Hs-13 14 N-Cell Interaction-2.0-Hs-52 activation N-Cell Interaction-2.0-Hs-17 18 N-Cell Interaction-2.0-Hs-52 activation N-Cell Interaction-2.0-Hs-19 20 N-Cell Interaction-2.0-Hs-35 activation N-Cell Interaction-2.0-Hs-34 N-Cell Interaction-2.0-Hs-35 activation N-Cell Interaction-2.0-Hs-59 N-Cell Interaction-2.0-Hs-29 activation N-Cell Interaction-2.0-Hs-11 12 N-Cell Interaction-2.0-Hs-29 activation N-Cell Interaction-2.0-Hs-11 12 N-Cell Interaction-2.0-Hs-29 activation N-Cell Interaction-2.0-Hs-9 10 N-Cell Interaction-2.0-Hs-9 10 activation N-Cell Interaction-2.0-Hs-40 N-Cell Interaction-2.0-Hs-38 activation N-Cell Interaction-2.0-Hs-51 N-Cell Interaction-2.0-Hs-38 activation N-Cell Interaction-2.0-Hs-59 N-Cell Interaction-2.0-Hs-38 activation N-Cell Interaction-2.0-Hs-15 16 N-Cell Interaction-2.0-Hs-62 inhibition N-Cell Interaction-2.0-Hs-64 N-Cell Interaction-2.0-Hs-3 4 activation N-Cell Interaction-2.0-Hs-35 N-Cell Interaction-2.0-Hs-27 activation N-Cell Interaction-2.0-Hs-36 N-Cell Interaction-2.0-Hs-27 activation N-Cell Interaction-2.0-Hs-36 N-Cell Interaction-2.0-Hs-19 20 activation N-Cell Interaction-2.0-Hs-41 N-Cell Interaction-2.0-Hs-40 activation N-Cell Interaction-2.0-Hs-39 N-Cell Interaction-2.0-Hs-40 activation N-Cell Interaction-2.0-Hs-35 N-Cell Interaction-2.0-Hs-40 activation N-Cell Interaction-2.0-Hs-37 N-Cell Interaction-2.0-Hs-64 activation N-Cell Interaction-2.0-Hs-35 N-Cell Interaction-2.0-Hs-64 activation N-Cell Interaction-2.0-Hs-33 N-Cell Interaction-2.0-Hs-49 activation N-Cell Interaction-2.0-Hs-23 24 N-Cell Interaction-2.0-Hs-49 activation N-Cell Interaction-2.0-Hs-15 16 N-Cell Interaction-2.0-Hs-61 inhibition N-Cell Interaction-2.0-Hs-64 N-Cell Interaction-2.0-Hs-60 activation N-Cell Interaction-2.0-Hs-59 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Clock-2.0-Hs.att000066400000000000000000000045631426625374700242570ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Clock-2.0-Hs-1 2 CCNB1 CDK1 72 0 white rectangle gene,gene 0.5 black 46 17 1579,/,1722 N-Clock-2.0-Hs-11 12 CRY2 PER1 126 75 white rectangle gene,gene 0.5 black 46 17 2385,/,8845 N-Clock-2.0-Hs-13 14 CRY2 PER2 119 77 white rectangle gene,gene 0.5 black 46 17 2385,/,8846 N-Clock-2.0-Hs-15 16 CRY2 PER3 118 60 white rectangle gene,gene 0.5 black 46 17 2385,/,8847 N-Clock-2.0-Hs-17 18 ARNTL NPAS2 122 70 white rectangle gene,gene 0.5 black 46 17 701,/,7895 N-Clock-2.0-Hs-19 BHLHE40 105 64 white rectangle gene 0.5 black 46 17 1046 N-Clock-2.0-Hs-20 WEE1 76 5 white rectangle gene 0.5 black 46 17 12761 N-Clock-2.0-Hs-21 SIRT1 40 123 white rectangle gene 0.5 black 46 17 14929 N-Clock-2.0-Hs-22 CCNA2 57 157 white rectangle gene 0.5 black 46 17 1578 N-Clock-2.0-Hs-23 CCNB1 62 138 white rectangle gene 0.5 black 46 17 1579 N-Clock-2.0-Hs-24 BHLHE41 108 59 white rectangle gene 0.5 black 46 17 16617 N-Clock-2.0-Hs-25 CHEK1 169 53 white rectangle gene 0.5 black 46 17 1925 N-Clock-2.0-Hs-26 CHEK1 177 56 white rectangle gene 0.5 black 46 17 1925 N-Clock-2.0-Hs-28 CSNK1D 47 149 white rectangle gene 0.5 black 46 17 2452 N-Clock-2.0-Hs-29 CSNK1E 55 145 white rectangle gene 0.5 black 46 17 2453 N-Clock-2.0-Hs-3 4 CLOCK ARNTL 117 68 white rectangle gene,gene 0.5 black 46 17 2082,/,701 N-Clock-2.0-Hs-30 CTNNB1 8 108 white rectangle gene 0.5 black 46 17 2514 N-Clock-2.0-Hs-31 ARNTL 47 131 white rectangle gene 0.5 black 46 17 701 N-Clock-2.0-Hs-32 ARNTL 42 148 white rectangle gene 0.5 black 46 17 701 N-Clock-2.0-Hs-34 ATM 88 170 white rectangle gene 0.5 black 46 17 795 N-Clock-2.0-Hs-35 ATR 177 64 white rectangle gene 0.5 black 46 17 882 N-Clock-2.0-Hs-36 PER1 81 161 white rectangle gene 0.5 black 46 17 8845 N-Clock-2.0-Hs-37 PER1 56 149 white rectangle gene 0.5 black 46 17 8845 N-Clock-2.0-Hs-38 PER1 70 152 white rectangle gene 0.5 black 46 17 8845 N-Clock-2.0-Hs-39 PER2 0 107 white rectangle gene 0.5 black 46 17 8846 N-Clock-2.0-Hs-40 PER2 47 155 white rectangle gene 0.5 black 46 17 8846 N-Clock-2.0-Hs-41 PER3 46 141 white rectangle gene 0.5 black 46 17 8847 N-Clock-2.0-Hs-5 6 CRY1 PER1 124 61 white rectangle gene,gene 0.5 black 46 17 2384,/,8845 N-Clock-2.0-Hs-7 8 CRY1 PER2 128 67 white rectangle gene,gene 0.5 black 46 17 2384,/,8846 N-Clock-2.0-Hs-9 10 CRY1 PER3 113 74 white rectangle gene,gene 0.5 black 46 17 2384,/,8847 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Clock-2.0-Hs.sif000066400000000000000000000047671426625374700242560ustar00rootroot000000000000000 1 2 N-Clock-2.0-Hs-30 inhibition N-Clock-2.0-Hs-39 N-Clock-2.0-Hs-30 inhibition N-Clock-2.0-Hs-39 N-Clock-2.0-Hs-30 activation N-Clock-2.0-Hs-39 N-Clock-2.0-Hs-26 activation N-Clock-2.0-Hs-25 N-Clock-2.0-Hs-26 activation N-Clock-2.0-Hs-25 N-Clock-2.0-Hs-26 activation N-Clock-2.0-Hs-25 N-Clock-2.0-Hs-26 activation N-Clock-2.0-Hs-25 N-Clock-2.0-Hs-38 activation N-Clock-2.0-Hs-36 N-Clock-2.0-Hs-7 8 inhibition N-Clock-2.0-Hs-3 4 N-Clock-2.0-Hs-7 8 inhibition N-Clock-2.0-Hs-17 18 N-Clock-2.0-Hs-5 6 inhibition N-Clock-2.0-Hs-17 18 N-Clock-2.0-Hs-5 6 inhibition N-Clock-2.0-Hs-3 4 N-Clock-2.0-Hs-20 inhibition N-Clock-2.0-Hs-1 2 N-Clock-2.0-Hs-29 activation N-Clock-2.0-Hs-40 N-Clock-2.0-Hs-29 activation N-Clock-2.0-Hs-32 N-Clock-2.0-Hs-29 activation N-Clock-2.0-Hs-31 N-Clock-2.0-Hs-29 activation N-Clock-2.0-Hs-23 N-Clock-2.0-Hs-29 activation N-Clock-2.0-Hs-41 N-Clock-2.0-Hs-29 activation N-Clock-2.0-Hs-22 N-Clock-2.0-Hs-29 activation N-Clock-2.0-Hs-37 N-Clock-2.0-Hs-29 activation N-Clock-2.0-Hs-38 N-Clock-2.0-Hs-9 10 inhibition N-Clock-2.0-Hs-3 4 N-Clock-2.0-Hs-9 10 inhibition N-Clock-2.0-Hs-17 18 N-Clock-2.0-Hs-13 14 inhibition N-Clock-2.0-Hs-3 4 N-Clock-2.0-Hs-13 14 inhibition N-Clock-2.0-Hs-17 18 N-Clock-2.0-Hs-19 inhibition N-Clock-2.0-Hs-24 N-Clock-2.0-Hs-19 inhibition N-Clock-2.0-Hs-19 N-Clock-2.0-Hs-39 inhibition N-Clock-2.0-Hs-39 N-Clock-2.0-Hs-39 inhibition N-Clock-2.0-Hs-39 N-Clock-2.0-Hs-28 activation N-Clock-2.0-Hs-41 N-Clock-2.0-Hs-28 activation N-Clock-2.0-Hs-32 N-Clock-2.0-Hs-28 activation N-Clock-2.0-Hs-37 N-Clock-2.0-Hs-28 activation N-Clock-2.0-Hs-40 N-Clock-2.0-Hs-11 12 inhibition N-Clock-2.0-Hs-17 18 N-Clock-2.0-Hs-11 12 inhibition N-Clock-2.0-Hs-3 4 N-Clock-2.0-Hs-36 activation N-Clock-2.0-Hs-34 N-Clock-2.0-Hs-36 inhibition N-Clock-2.0-Hs-36 N-Clock-2.0-Hs-36 inhibition N-Clock-2.0-Hs-36 N-Clock-2.0-Hs-36 inhibition N-Clock-2.0-Hs-36 N-Clock-2.0-Hs-15 16 inhibition N-Clock-2.0-Hs-17 18 N-Clock-2.0-Hs-15 16 inhibition N-Clock-2.0-Hs-3 4 N-Clock-2.0-Hs-35 activation N-Clock-2.0-Hs-26 N-Clock-2.0-Hs-35 activation N-Clock-2.0-Hs-26 N-Clock-2.0-Hs-35 activation N-Clock-2.0-Hs-26 N-Clock-2.0-Hs-35 activation N-Clock-2.0-Hs-26 N-Clock-2.0-Hs-35 activation N-Clock-2.0-Hs-26 N-Clock-2.0-Hs-35 activation N-Clock-2.0-Hs-26 N-Clock-2.0-Hs-35 activation N-Clock-2.0-Hs-26 N-Clock-2.0-Hs-35 activation N-Clock-2.0-Hs-26 N-Clock-2.0-Hs-3 4 activation N-Clock-2.0-Hs-19 N-Clock-2.0-Hs-3 4 activation N-Clock-2.0-Hs-24 N-Clock-2.0-Hs-3 4 activation N-Clock-2.0-Hs-24 N-Clock-2.0-Hs-21 activation N-Clock-2.0-Hs-31 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Cytotoxic T-cell Signaling-2.0-Hs.att000066400000000000000000000026751426625374700302100ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Cytotoxic T-cell Signaling-2.0-Hs-1 CCL3 49 9 white rectangle gene 0.5 black 46 17 10627 N-Cytotoxic T-cell Signaling-2.0-Hs-10 IL15RA 141 20 white rectangle gene 0.5 black 46 17 5978 N-Cytotoxic T-cell Signaling-2.0-Hs-11 IL2RB 150 4 white rectangle gene 0.5 black 46 17 6009 N-Cytotoxic T-cell Signaling-2.0-Hs-13 LCK 175 103 white rectangle gene 0.5 black 46 17 6524 N-Cytotoxic T-cell Signaling-2.0-Hs-14 CXCL9 157 158 white rectangle gene 0.5 black 46 17 7098 N-Cytotoxic T-cell Signaling-2.0-Hs-15 PLCG1 0 88 white rectangle gene 0.5 black 46 17 9065 N-Cytotoxic T-cell Signaling-2.0-Hs-16 PLCG1 2 78 white rectangle gene 0.5 black 46 17 9065 N-Cytotoxic T-cell Signaling-2.0-Hs-17 Il2rg 135 0 white rectangle gene 0.5 black 46 17 96551 N-Cytotoxic T-cell Signaling-2.0-Hs-2 ZAP70 0 68 white rectangle gene 0.5 black 46 17 12858 N-Cytotoxic T-cell Signaling-2.0-Hs-3 CCR5 46 17 white rectangle gene 0.5 black 46 17 1606 N-Cytotoxic T-cell Signaling-2.0-Hs-4 CXCL16 61 173 white rectangle gene 0.5 black 46 17 16642 N-Cytotoxic T-cell Signaling-2.0-Hs-5 CXCR6 53 178 white rectangle gene 0.5 black 46 17 16647 N-Cytotoxic T-cell Signaling-2.0-Hs-7 FYN 182 97 white rectangle gene 0.5 black 46 17 4037 N-Cytotoxic T-cell Signaling-2.0-Hs-8 CXCR3 152 165 white rectangle gene 0.5 black 46 17 4540 N-Cytotoxic T-cell Signaling-2.0-Hs-9 IL15 140 10 white rectangle gene 0.5 black 46 17 5977 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Cytotoxic T-cell Signaling-2.0-Hs.sif000066400000000000000000000033441426625374700301730ustar00rootroot000000000000000 1 2 N-Cytotoxic T-cell Signaling-2.0-Hs-14 activation N-Cytotoxic T-cell Signaling-2.0-Hs-8 N-Cytotoxic T-cell Signaling-2.0-Hs-14 activation N-Cytotoxic T-cell Signaling-2.0-Hs-8 N-Cytotoxic T-cell Signaling-2.0-Hs-14 activation N-Cytotoxic T-cell Signaling-2.0-Hs-8 N-Cytotoxic T-cell Signaling-2.0-Hs-2 activation N-Cytotoxic T-cell Signaling-2.0-Hs-16 N-Cytotoxic T-cell Signaling-2.0-Hs-7 activation N-Cytotoxic T-cell Signaling-2.0-Hs-13 N-Cytotoxic T-cell Signaling-2.0-Hs-16 activation N-Cytotoxic T-cell Signaling-2.0-Hs-15 N-Cytotoxic T-cell Signaling-2.0-Hs-16 activation N-Cytotoxic T-cell Signaling-2.0-Hs-15 N-Cytotoxic T-cell Signaling-2.0-Hs-16 activation N-Cytotoxic T-cell Signaling-2.0-Hs-15 N-Cytotoxic T-cell Signaling-2.0-Hs-16 activation N-Cytotoxic T-cell Signaling-2.0-Hs-15 N-Cytotoxic T-cell Signaling-2.0-Hs-9 activation N-Cytotoxic T-cell Signaling-2.0-Hs-10 N-Cytotoxic T-cell Signaling-2.0-Hs-9 activation N-Cytotoxic T-cell Signaling-2.0-Hs-11 N-Cytotoxic T-cell Signaling-2.0-Hs-9 activation N-Cytotoxic T-cell Signaling-2.0-Hs-11 N-Cytotoxic T-cell Signaling-2.0-Hs-9 activation N-Cytotoxic T-cell Signaling-2.0-Hs-17 N-Cytotoxic T-cell Signaling-2.0-Hs-9 activation N-Cytotoxic T-cell Signaling-2.0-Hs-17 N-Cytotoxic T-cell Signaling-2.0-Hs-1 activation N-Cytotoxic T-cell Signaling-2.0-Hs-3 N-Cytotoxic T-cell Signaling-2.0-Hs-1 activation N-Cytotoxic T-cell Signaling-2.0-Hs-3 N-Cytotoxic T-cell Signaling-2.0-Hs-1 activation N-Cytotoxic T-cell Signaling-2.0-Hs-3 N-Cytotoxic T-cell Signaling-2.0-Hs-4 activation N-Cytotoxic T-cell Signaling-2.0-Hs-5 N-Cytotoxic T-cell Signaling-2.0-Hs-4 activation N-Cytotoxic T-cell Signaling-2.0-Hs-5 N-Cytotoxic T-cell Signaling-2.0-Hs-4 activation N-Cytotoxic T-cell Signaling-2.0-Hs-5 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Dendritic Cell Signaling-2.0-Hs.att000066400000000000000000000150211426625374700276540ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Dendritic Cell Signaling-2.0-Hs-10 CCL21 140 127 white rectangle gene 0.5 black 46 17 10620 N-Dendritic Cell Signaling-2.0-Hs-11 CCL3 56 147 white rectangle gene 0.5 black 46 17 10627 N-Dendritic Cell Signaling-2.0-Hs-12 CCL4 52 157 white rectangle gene 0.5 black 46 17 10630 N-Dendritic Cell Signaling-2.0-Hs-13 CCL5 60 154 white rectangle gene 0.5 black 46 17 10632 N-Dendritic Cell Signaling-2.0-Hs-14 CCL7 70 146 white rectangle gene 0.5 black 46 17 10634 N-Dendritic Cell Signaling-2.0-Hs-15 CCL8 67 148 white rectangle gene 0.5 black 46 17 10635 N-Dendritic Cell Signaling-2.0-Hs-16 CXCL10 24 35 white rectangle gene 0.5 black 46 17 10637 N-Dendritic Cell Signaling-2.0-Hs-17 CXCL11 14 35 white rectangle gene 0.5 black 46 17 10638 N-Dendritic Cell Signaling-2.0-Hs-18 CX3CL1 127 6 white rectangle gene 0.5 black 46 17 10647 N-Dendritic Cell Signaling-2.0-Hs-19 STAT3 57 91 white rectangle gene 0.5 black 46 17 11364 N-Dendritic Cell Signaling-2.0-Hs-20 TBK1 98 90 white rectangle gene 0.5 black 46 17 11584 N-Dendritic Cell Signaling-2.0-Hs-21 TGFBR1 34 104 white rectangle gene 0.5 black 46 17 11772 N-Dendritic Cell Signaling-2.0-Hs-22 TLR2 56 82 white rectangle gene 0.5 black 46 17 11848 N-Dendritic Cell Signaling-2.0-Hs-23 TLR3 113 107 white rectangle gene 0.5 black 46 17 11849 N-Dendritic Cell Signaling-2.0-Hs-24 TLR4 59 80 white rectangle gene 0.5 black 46 17 11850 N-Dendritic Cell Signaling-2.0-Hs-25 CD40 109 7 white rectangle gene 0.5 black 46 17 11919 N-Dendritic Cell Signaling-2.0-Hs-26 CD40LG 105 8 white rectangle gene 0.5 black 46 17 11935 N-Dendritic Cell Signaling-2.0-Hs-27 TRAF6 92 117 white rectangle gene 0.5 black 46 17 12036 N-Dendritic Cell Signaling-2.0-Hs-29 C3AR1 0 69 white rectangle gene 0.5 black 46 17 1319 N-Dendritic Cell Signaling-2.0-Hs-3 JNK 126 126 white rectangle gene 0.5 black 46 17 JNK N-Dendritic Cell Signaling-2.0-Hs-30 C5AR1 8 64 white rectangle gene 0.5 black 46 17 1338 N-Dendritic Cell Signaling-2.0-Hs-31 IKBKE 99 88 white rectangle gene 0.5 black 46 17 14552 N-Dendritic Cell Signaling-2.0-Hs-32 TLR9 57 88 white rectangle gene 0.5 black 46 17 15633 N-Dendritic Cell Signaling-2.0-Hs-33 CCR1 62 145 white rectangle gene 0.5 black 46 17 1602 N-Dendritic Cell Signaling-2.0-Hs-34 CCR2 75 145 white rectangle gene 0.5 black 46 17 1603 N-Dendritic Cell Signaling-2.0-Hs-35 CCR5 58 151 white rectangle gene 0.5 black 46 17 1606 N-Dendritic Cell Signaling-2.0-Hs-36 CCR6 22 55 white rectangle gene 0.5 black 46 17 1607 N-Dendritic Cell Signaling-2.0-Hs-37 CCR7 131 122 white rectangle gene 0.5 black 46 17 1608 N-Dendritic Cell Signaling-2.0-Hs-38 CXCL16 128 172 white rectangle gene 0.5 black 46 17 16642 N-Dendritic Cell Signaling-2.0-Hs-39 CXCR6 131 177 white rectangle gene 0.5 black 46 17 16647 N-Dendritic Cell Signaling-2.0-Hs-4 RHO 134 113 white rectangle gene 0.5 black 46 17 RHO N-Dendritic Cell Signaling-2.0-Hs-40 IRAK4 72 99 white rectangle gene 0.5 black 46 17 17967 N-Dendritic Cell Signaling-2.0-Hs-41 TICAM1 105 96 white rectangle gene 0.5 black 46 17 18348 N-Dendritic Cell Signaling-2.0-Hs-42 CFL1 140 99 white rectangle gene 0.5 black 46 17 1874 N-Dendritic Cell Signaling-2.0-Hs-43 SOCS3 51 96 white rectangle gene 0.5 black 46 17 19391 N-Dendritic Cell Signaling-2.0-Hs-44 CHUK 101 135 white rectangle gene 0.5 black 46 17 1974 N-Dendritic Cell Signaling-2.0-Hs-45 CX3CR1 131 0 white rectangle gene 0.5 black 46 17 2558 N-Dendritic Cell Signaling-2.0-Hs-46 AKT1 49 90 white rectangle gene 0.5 black 46 17 391 N-Dendritic Cell Signaling-2.0-Hs-47 CXCR3 20 40 white rectangle gene 0.5 black 46 17 4540 N-Dendritic Cell Signaling-2.0-Hs-48 HMGB1 54 76 white rectangle gene 0.5 black 46 17 4983 N-Dendritic Cell Signaling-2.0-Hs-49 IFNB1 94 78 white rectangle gene 0.5 black 46 17 5434 N-Dendritic Cell Signaling-2.0-Hs-5 TGFB 26 108 white rectangle gene 0.5 black 46 17 TGFB N-Dendritic Cell Signaling-2.0-Hs-50 IKBKB 144 55 white rectangle gene 0.5 black 46 17 5960 N-Dendritic Cell Signaling-2.0-Hs-51 IL10 73 82 white rectangle gene 0.5 black 46 17 5962 N-Dendritic Cell Signaling-2.0-Hs-52 IL12B 70 64 white rectangle gene 0.5 black 46 17 5970 N-Dendritic Cell Signaling-2.0-Hs-53 IL15 92 192 white rectangle gene 0.5 black 46 17 5977 N-Dendritic Cell Signaling-2.0-Hs-54 IL15RA 93 187 white rectangle gene 0.5 black 46 17 5978 N-Dendritic Cell Signaling-2.0-Hs-55 IL2RB 92 197 white rectangle gene 0.5 black 46 17 6009 N-Dendritic Cell Signaling-2.0-Hs-56 IL4 6 68 white rectangle gene 0.5 black 46 17 6014 N-Dendritic Cell Signaling-2.0-Hs-57 IL6 45 99 white rectangle gene 0.5 black 46 17 6018 N-Dendritic Cell Signaling-2.0-Hs-58 IRAK1 81 108 white rectangle gene 0.5 black 46 17 6112 N-Dendritic Cell Signaling-2.0-Hs-59 IRF3 89 84 white rectangle gene 0.5 black 46 17 6118 N-Dendritic Cell Signaling-2.0-Hs-6 p38 117 120 white rectangle gene 0.5 black 46 17 p38 N-Dendritic Cell Signaling-2.0-Hs-60 IRF7 91 122 white rectangle gene 0.5 black 46 17 6122 N-Dendritic Cell Signaling-2.0-Hs-61 RHOA 22 49 white rectangle gene 0.5 black 46 17 667 N-Dendritic Cell Signaling-2.0-Hs-62 MAP2K4 133 126 white rectangle gene 0.5 black 46 17 6844 N-Dendritic Cell Signaling-2.0-Hs-63 MAP3K7 104 124 white rectangle gene 0.5 black 46 17 6859 N-Dendritic Cell Signaling-2.0-Hs-64 MAPK1 132 129 white rectangle gene 0.5 black 46 17 6871 N-Dendritic Cell Signaling-2.0-Hs-65 MAPK3 129 129 white rectangle gene 0.5 black 46 17 6877 N-Dendritic Cell Signaling-2.0-Hs-66 CIITA 78 73 white rectangle gene 0.5 black 46 17 7067 N-Dendritic Cell Signaling-2.0-Hs-67 CXCL9 21 37 white rectangle gene 0.5 black 46 17 7098 N-Dendritic Cell Signaling-2.0-Hs-68 MYD88 63 89 white rectangle gene 0.5 black 46 17 7562 N-Dendritic Cell Signaling-2.0-Hs-7 CCL16 58 140 white rectangle gene 0.5 black 46 17 10614 N-Dendritic Cell Signaling-2.0-Hs-70 NFKBIA 150 57 white rectangle gene 0.5 black 46 17 7797 N-Dendritic Cell Signaling-2.0-Hs-71 NFKBIA 155 53 white rectangle gene 0.5 black 46 17 7797 N-Dendritic Cell Signaling-2.0-Hs-72 NFKBIA 152 62 white rectangle gene 0.5 black 46 17 7797 N-Dendritic Cell Signaling-2.0-Hs-73 NFKBIE 98 143 white rectangle gene 0.5 black 46 17 7799 N-Dendritic Cell Signaling-2.0-Hs-74 PPARA 72 72 white rectangle gene 0.5 black 46 17 9232 N-Dendritic Cell Signaling-2.0-Hs-75 PTK2B 137 105 white rectangle gene 0.5 black 46 17 9612 N-Dendritic Cell Signaling-2.0-Hs-76 RFX5 81 65 white rectangle gene 0.5 black 46 17 9986 N-Dendritic Cell Signaling-2.0-Hs-8 CCL19 140 122 white rectangle gene 0.5 black 46 17 10617 N-Dendritic Cell Signaling-2.0-Hs-9 CCL2 82 143 white rectangle gene 0.5 black 46 17 10618 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Dendritic Cell Signaling-2.0-Hs.sif000066400000000000000000000455161426625374700276610ustar00rootroot000000000000000 1 2 N-Dendritic Cell Signaling-2.0-Hs-44 inhibition N-Dendritic Cell Signaling-2.0-Hs-73 N-Dendritic Cell Signaling-2.0-Hs-15 activation N-Dendritic Cell Signaling-2.0-Hs-34 N-Dendritic Cell Signaling-2.0-Hs-15 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-15 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-9 activation N-Dendritic Cell Signaling-2.0-Hs-34 N-Dendritic Cell Signaling-2.0-Hs-9 activation N-Dendritic Cell Signaling-2.0-Hs-34 N-Dendritic Cell Signaling-2.0-Hs-9 activation N-Dendritic Cell Signaling-2.0-Hs-34 N-Dendritic Cell Signaling-2.0-Hs-9 activation N-Dendritic Cell Signaling-2.0-Hs-34 N-Dendritic Cell Signaling-2.0-Hs-17 activation N-Dendritic Cell Signaling-2.0-Hs-47 N-Dendritic Cell Signaling-2.0-Hs-17 activation N-Dendritic Cell Signaling-2.0-Hs-47 N-Dendritic Cell Signaling-2.0-Hs-75 inhibition N-Dendritic Cell Signaling-2.0-Hs-42 N-Dendritic Cell Signaling-2.0-Hs-19 activation N-Dendritic Cell Signaling-2.0-Hs-46 N-Dendritic Cell Signaling-2.0-Hs-18 activation N-Dendritic Cell Signaling-2.0-Hs-45 N-Dendritic Cell Signaling-2.0-Hs-18 activation N-Dendritic Cell Signaling-2.0-Hs-45 N-Dendritic Cell Signaling-2.0-Hs-18 activation N-Dendritic Cell Signaling-2.0-Hs-45 N-Dendritic Cell Signaling-2.0-Hs-43 inhibition N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-43 inhibition N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-43 inhibition N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-43 inhibition N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-43 inhibition N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-43 inhibition N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-43 inhibition N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-36 activation N-Dendritic Cell Signaling-2.0-Hs-61 N-Dendritic Cell Signaling-2.0-Hs-38 activation N-Dendritic Cell Signaling-2.0-Hs-39 N-Dendritic Cell Signaling-2.0-Hs-58 activation N-Dendritic Cell Signaling-2.0-Hs-27 N-Dendritic Cell Signaling-2.0-Hs-58 activation N-Dendritic Cell Signaling-2.0-Hs-27 N-Dendritic Cell Signaling-2.0-Hs-58 activation N-Dendritic Cell Signaling-2.0-Hs-27 N-Dendritic Cell Signaling-2.0-Hs-58 activation N-Dendritic Cell Signaling-2.0-Hs-27 N-Dendritic Cell Signaling-2.0-Hs-58 activation N-Dendritic Cell Signaling-2.0-Hs-27 N-Dendritic Cell Signaling-2.0-Hs-58 activation N-Dendritic Cell Signaling-2.0-Hs-27 N-Dendritic Cell Signaling-2.0-Hs-64 activation N-Dendritic Cell Signaling-2.0-Hs-3 N-Dendritic Cell Signaling-2.0-Hs-21 activation N-Dendritic Cell Signaling-2.0-Hs-57 N-Dendritic Cell Signaling-2.0-Hs-56 inhibition N-Dendritic Cell Signaling-2.0-Hs-29 N-Dendritic Cell Signaling-2.0-Hs-56 inhibition N-Dendritic Cell Signaling-2.0-Hs-29 N-Dendritic Cell Signaling-2.0-Hs-56 inhibition N-Dendritic Cell Signaling-2.0-Hs-30 N-Dendritic Cell Signaling-2.0-Hs-56 inhibition N-Dendritic Cell Signaling-2.0-Hs-30 N-Dendritic Cell Signaling-2.0-Hs-57 activation N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-57 activation N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-72 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-72 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-72 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-72 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-72 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-76 activation N-Dendritic Cell Signaling-2.0-Hs-66 N-Dendritic Cell Signaling-2.0-Hs-22 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-22 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-22 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-22 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-5 activation N-Dendritic Cell Signaling-2.0-Hs-21 N-Dendritic Cell Signaling-2.0-Hs-5 activation N-Dendritic Cell Signaling-2.0-Hs-21 N-Dendritic Cell Signaling-2.0-Hs-5 activation N-Dendritic Cell Signaling-2.0-Hs-21 N-Dendritic Cell Signaling-2.0-Hs-10 activation N-Dendritic Cell Signaling-2.0-Hs-37 N-Dendritic Cell Signaling-2.0-Hs-66 inhibition N-Dendritic Cell Signaling-2.0-Hs-51 N-Dendritic Cell Signaling-2.0-Hs-59 activation N-Dendritic Cell Signaling-2.0-Hs-51 N-Dendritic Cell Signaling-2.0-Hs-59 activation N-Dendritic Cell Signaling-2.0-Hs-49 N-Dendritic Cell Signaling-2.0-Hs-31 activation N-Dendritic Cell Signaling-2.0-Hs-59 N-Dendritic Cell Signaling-2.0-Hs-31 activation N-Dendritic Cell Signaling-2.0-Hs-59 N-Dendritic Cell Signaling-2.0-Hs-31 activation N-Dendritic Cell Signaling-2.0-Hs-59 N-Dendritic Cell Signaling-2.0-Hs-68 activation N-Dendritic Cell Signaling-2.0-Hs-40 N-Dendritic Cell Signaling-2.0-Hs-48 activation N-Dendritic Cell Signaling-2.0-Hs-22 N-Dendritic Cell Signaling-2.0-Hs-48 activation N-Dendritic Cell Signaling-2.0-Hs-22 N-Dendritic Cell Signaling-2.0-Hs-48 activation N-Dendritic Cell Signaling-2.0-Hs-22 N-Dendritic Cell Signaling-2.0-Hs-48 activation N-Dendritic Cell Signaling-2.0-Hs-22 N-Dendritic Cell Signaling-2.0-Hs-48 activation N-Dendritic Cell Signaling-2.0-Hs-24 N-Dendritic Cell Signaling-2.0-Hs-48 activation N-Dendritic Cell Signaling-2.0-Hs-24 N-Dendritic Cell Signaling-2.0-Hs-48 activation N-Dendritic Cell Signaling-2.0-Hs-24 N-Dendritic Cell Signaling-2.0-Hs-48 activation N-Dendritic Cell Signaling-2.0-Hs-24 N-Dendritic Cell Signaling-2.0-Hs-8 activation N-Dendritic Cell Signaling-2.0-Hs-37 N-Dendritic Cell Signaling-2.0-Hs-8 activation N-Dendritic Cell Signaling-2.0-Hs-37 N-Dendritic Cell Signaling-2.0-Hs-8 activation N-Dendritic Cell Signaling-2.0-Hs-37 N-Dendritic Cell Signaling-2.0-Hs-67 activation N-Dendritic Cell Signaling-2.0-Hs-47 N-Dendritic Cell Signaling-2.0-Hs-67 activation N-Dendritic Cell Signaling-2.0-Hs-47 N-Dendritic Cell Signaling-2.0-Hs-67 activation N-Dendritic Cell Signaling-2.0-Hs-47 N-Dendritic Cell Signaling-2.0-Hs-7 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-24 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-24 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-24 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-24 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-24 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-50 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-63 activation N-Dendritic Cell Signaling-2.0-Hs-44 N-Dendritic Cell Signaling-2.0-Hs-63 activation N-Dendritic Cell Signaling-2.0-Hs-6 N-Dendritic Cell Signaling-2.0-Hs-63 activation N-Dendritic Cell Signaling-2.0-Hs-6 N-Dendritic Cell Signaling-2.0-Hs-63 activation N-Dendritic Cell Signaling-2.0-Hs-6 N-Dendritic Cell Signaling-2.0-Hs-62 activation N-Dendritic Cell Signaling-2.0-Hs-3 N-Dendritic Cell Signaling-2.0-Hs-74 inhibition N-Dendritic Cell Signaling-2.0-Hs-52 N-Dendritic Cell Signaling-2.0-Hs-74 inhibition N-Dendritic Cell Signaling-2.0-Hs-51 N-Dendritic Cell Signaling-2.0-Hs-41 activation N-Dendritic Cell Signaling-2.0-Hs-20 N-Dendritic Cell Signaling-2.0-Hs-41 activation N-Dendritic Cell Signaling-2.0-Hs-31 N-Dendritic Cell Signaling-2.0-Hs-20 activation N-Dendritic Cell Signaling-2.0-Hs-59 N-Dendritic Cell Signaling-2.0-Hs-20 activation N-Dendritic Cell Signaling-2.0-Hs-59 N-Dendritic Cell Signaling-2.0-Hs-20 activation N-Dendritic Cell Signaling-2.0-Hs-59 N-Dendritic Cell Signaling-2.0-Hs-14 activation N-Dendritic Cell Signaling-2.0-Hs-34 N-Dendritic Cell Signaling-2.0-Hs-14 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-32 activation N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-32 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-32 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-32 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-32 activation N-Dendritic Cell Signaling-2.0-Hs-68 N-Dendritic Cell Signaling-2.0-Hs-16 activation N-Dendritic Cell Signaling-2.0-Hs-47 N-Dendritic Cell Signaling-2.0-Hs-16 activation N-Dendritic Cell Signaling-2.0-Hs-47 N-Dendritic Cell Signaling-2.0-Hs-16 activation N-Dendritic Cell Signaling-2.0-Hs-47 N-Dendritic Cell Signaling-2.0-Hs-47 activation N-Dendritic Cell Signaling-2.0-Hs-61 N-Dendritic Cell Signaling-2.0-Hs-65 activation N-Dendritic Cell Signaling-2.0-Hs-3 N-Dendritic Cell Signaling-2.0-Hs-53 activation N-Dendritic Cell Signaling-2.0-Hs-54 N-Dendritic Cell Signaling-2.0-Hs-53 activation N-Dendritic Cell Signaling-2.0-Hs-55 N-Dendritic Cell Signaling-2.0-Hs-53 activation N-Dendritic Cell Signaling-2.0-Hs-55 N-Dendritic Cell Signaling-2.0-Hs-11 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-11 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-11 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-11 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-13 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-13 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-13 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-13 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-13 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-13 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-13 activation N-Dendritic Cell Signaling-2.0-Hs-33 N-Dendritic Cell Signaling-2.0-Hs-37 activation N-Dendritic Cell Signaling-2.0-Hs-64 N-Dendritic Cell Signaling-2.0-Hs-37 activation N-Dendritic Cell Signaling-2.0-Hs-64 N-Dendritic Cell Signaling-2.0-Hs-37 activation N-Dendritic Cell Signaling-2.0-Hs-62 N-Dendritic Cell Signaling-2.0-Hs-37 activation N-Dendritic Cell Signaling-2.0-Hs-4 N-Dendritic Cell Signaling-2.0-Hs-37 activation N-Dendritic Cell Signaling-2.0-Hs-65 N-Dendritic Cell Signaling-2.0-Hs-37 activation N-Dendritic Cell Signaling-2.0-Hs-65 N-Dendritic Cell Signaling-2.0-Hs-37 activation N-Dendritic Cell Signaling-2.0-Hs-6 N-Dendritic Cell Signaling-2.0-Hs-23 activation N-Dendritic Cell Signaling-2.0-Hs-6 N-Dendritic Cell Signaling-2.0-Hs-23 activation N-Dendritic Cell Signaling-2.0-Hs-41 N-Dendritic Cell Signaling-2.0-Hs-23 activation N-Dendritic Cell Signaling-2.0-Hs-41 N-Dendritic Cell Signaling-2.0-Hs-23 activation N-Dendritic Cell Signaling-2.0-Hs-41 N-Dendritic Cell Signaling-2.0-Hs-12 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-12 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-12 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-12 activation N-Dendritic Cell Signaling-2.0-Hs-35 N-Dendritic Cell Signaling-2.0-Hs-51 activation N-Dendritic Cell Signaling-2.0-Hs-19 N-Dendritic Cell Signaling-2.0-Hs-40 activation N-Dendritic Cell Signaling-2.0-Hs-58 N-Dendritic Cell Signaling-2.0-Hs-40 activation N-Dendritic Cell Signaling-2.0-Hs-58 N-Dendritic Cell Signaling-2.0-Hs-40 activation N-Dendritic Cell Signaling-2.0-Hs-58 N-Dendritic Cell Signaling-2.0-Hs-4 activation N-Dendritic Cell Signaling-2.0-Hs-75 N-Dendritic Cell Signaling-2.0-Hs-71 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-71 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-71 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-71 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-71 activation N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-26 activation N-Dendritic Cell Signaling-2.0-Hs-25 N-Dendritic Cell Signaling-2.0-Hs-26 activation N-Dendritic Cell Signaling-2.0-Hs-25 N-Dendritic Cell Signaling-2.0-Hs-26 activation N-Dendritic Cell Signaling-2.0-Hs-25 N-Dendritic Cell Signaling-2.0-Hs-26 activation N-Dendritic Cell Signaling-2.0-Hs-25 N-Dendritic Cell Signaling-2.0-Hs-26 activation N-Dendritic Cell Signaling-2.0-Hs-25 N-Dendritic Cell Signaling-2.0-Hs-26 activation N-Dendritic Cell Signaling-2.0-Hs-25 N-Dendritic Cell Signaling-2.0-Hs-26 activation N-Dendritic Cell Signaling-2.0-Hs-25 N-Dendritic Cell Signaling-2.0-Hs-27 activation N-Dendritic Cell Signaling-2.0-Hs-63 N-Dendritic Cell Signaling-2.0-Hs-27 activation N-Dendritic Cell Signaling-2.0-Hs-63 N-Dendritic Cell Signaling-2.0-Hs-27 activation N-Dendritic Cell Signaling-2.0-Hs-60 N-Dendritic Cell Signaling-2.0-Hs-6 activation N-Dendritic Cell Signaling-2.0-Hs-3 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 N-Dendritic Cell Signaling-2.0-Hs-70 inhibition N-Dendritic Cell Signaling-2.0-Hs-70 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/ECM Degradation-2.0-Hs.att000066400000000000000000000047041426625374700260270ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-ECM Degradation-2.0-Hs-1 2 ITGA2 ITGB1 106 38 white rectangle gene,gene 0.5 black 46 17 6137,/,6153 N-ECM Degradation-2.0-Hs-10 TIMP3 164 63 white rectangle gene 0.5 black 46 17 11822 N-ECM Degradation-2.0-Hs-11 TIMP4 171 56 white rectangle gene 0.5 black 46 17 11823 N-ECM Degradation-2.0-Hs-12 TNF 80 56 white rectangle gene 0.5 black 46 17 11892 N-ECM Degradation-2.0-Hs-13 SIRT1 28 75 white rectangle gene 0.5 black 46 17 14929 N-ECM Degradation-2.0-Hs-14 COL1A1 60 13 white rectangle gene 0.5 black 46 17 2197 N-ECM Degradation-2.0-Hs-15 COL1A2 74 0 white rectangle gene 0.5 black 46 17 2198 N-ECM Degradation-2.0-Hs-16 COL2A1 56 21 white rectangle gene 0.5 black 46 17 2200 N-ECM Degradation-2.0-Hs-17 COL3A1 66 5 white rectangle gene 0.5 black 46 17 2201 N-ECM Degradation-2.0-Hs-18 COL4A1 173 45 white rectangle gene 0.5 black 46 17 2202 N-ECM Degradation-2.0-Hs-19 COL4A2 157 90 white rectangle gene 0.5 black 46 17 2203 N-ECM Degradation-2.0-Hs-21 ELANE 110 104 white rectangle gene 0.5 black 46 17 3309 N-ECM Degradation-2.0-Hs-22 ELN 107 73 white rectangle gene 0.5 black 46 17 3327 N-ECM Degradation-2.0-Hs-23 FN1 101 75 white rectangle gene 0.5 black 46 17 3778 N-ECM Degradation-2.0-Hs-24 IL1B 66 93 white rectangle gene 0.5 black 46 17 5992 N-ECM Degradation-2.0-Hs-25 MMP1 76 17 white rectangle gene 0.5 black 46 17 7155 N-ECM Degradation-2.0-Hs-26 MMP12 84 80 white rectangle gene 0.5 black 46 17 7158 N-ECM Degradation-2.0-Hs-27 MMP14 85 31 white rectangle gene 0.5 black 46 17 7160 N-ECM Degradation-2.0-Hs-28 MMP2 141 63 white rectangle gene 0.5 black 46 17 7166 N-ECM Degradation-2.0-Hs-29 MMP3 82 102 white rectangle gene 0.5 black 46 17 7173 N-ECM Degradation-2.0-Hs-3 4 ITGAV ITGB3 170 81 white rectangle gene,gene 0.5 black 46 17 6150,/,6156 N-ECM Degradation-2.0-Hs-30 MMP7 114 45 white rectangle gene 0.5 black 46 17 7174 N-ECM Degradation-2.0-Hs-31 MMP8 50 0 white rectangle gene 0.5 black 46 17 7175 N-ECM Degradation-2.0-Hs-32 MMP9 68 68 white rectangle gene 0.5 black 46 17 7176 N-ECM Degradation-2.0-Hs-33 SERPINA1 114 135 white rectangle gene 0.5 black 46 17 8941 N-ECM Degradation-2.0-Hs-5 MMP 158 42 white rectangle gene 0.5 black 46 17 MMP N-ECM Degradation-2.0-Hs-7 TIMP1 92 40 white rectangle gene 0.5 black 46 17 11820 N-ECM Degradation-2.0-Hs-8 TIMP1 0 79 white rectangle gene 0.5 black 46 17 11820 N-ECM Degradation-2.0-Hs-9 TIMP2 129 39 white rectangle gene 0.5 black 46 17 11821 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/ECM Degradation-2.0-Hs.sif000066400000000000000000000164101426625374700260150ustar00rootroot000000000000000 1 2 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-17 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-17 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-17 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-15 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-15 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-14 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-14 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-25 activation N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-26 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-26 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-26 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-26 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-26 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-29 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-29 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-24 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-7 inhibition N-ECM Degradation-2.0-Hs-30 N-ECM Degradation-2.0-Hs-7 inhibition N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-7 inhibition N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-7 inhibition N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-7 inhibition N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-21 activation N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-21 activation N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-30 activation N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-33 inhibition N-ECM Degradation-2.0-Hs-21 N-ECM Degradation-2.0-Hs-33 inhibition N-ECM Degradation-2.0-Hs-21 N-ECM Degradation-2.0-Hs-16 inhibition N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-16 inhibition N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-16 inhibition N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-16 inhibition N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-16 inhibition N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-9 inhibition N-ECM Degradation-2.0-Hs-5 N-ECM Degradation-2.0-Hs-9 inhibition N-ECM Degradation-2.0-Hs-5 N-ECM Degradation-2.0-Hs-9 inhibition N-ECM Degradation-2.0-Hs-5 N-ECM Degradation-2.0-Hs-9 inhibition N-ECM Degradation-2.0-Hs-27 N-ECM Degradation-2.0-Hs-9 inhibition N-ECM Degradation-2.0-Hs-28 N-ECM Degradation-2.0-Hs-11 inhibition N-ECM Degradation-2.0-Hs-5 N-ECM Degradation-2.0-Hs-11 inhibition N-ECM Degradation-2.0-Hs-28 N-ECM Degradation-2.0-Hs-3 4 activation N-ECM Degradation-2.0-Hs-28 N-ECM Degradation-2.0-Hs-28 activation N-ECM Degradation-2.0-Hs-19 N-ECM Degradation-2.0-Hs-28 activation N-ECM Degradation-2.0-Hs-18 N-ECM Degradation-2.0-Hs-28 activation N-ECM Degradation-2.0-Hs-18 N-ECM Degradation-2.0-Hs-28 activation N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-28 activation N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-10 inhibition N-ECM Degradation-2.0-Hs-28 N-ECM Degradation-2.0-Hs-10 inhibition N-ECM Degradation-2.0-Hs-5 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-27 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-27 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-26 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-26 N-ECM Degradation-2.0-Hs-12 activation N-ECM Degradation-2.0-Hs-26 N-ECM Degradation-2.0-Hs-15 inhibition N-ECM Degradation-2.0-Hs-15 N-ECM Degradation-2.0-Hs-15 inhibition N-ECM Degradation-2.0-Hs-15 N-ECM Degradation-2.0-Hs-22 inhibition N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-22 inhibition N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-22 inhibition N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-22 inhibition N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-22 inhibition N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-22 inhibition N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-17 inhibition N-ECM Degradation-2.0-Hs-17 N-ECM Degradation-2.0-Hs-17 inhibition N-ECM Degradation-2.0-Hs-17 N-ECM Degradation-2.0-Hs-17 inhibition N-ECM Degradation-2.0-Hs-17 N-ECM Degradation-2.0-Hs-32 activation N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-23 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-23 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-23 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-23 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-23 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-23 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-23 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-23 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-26 inhibition N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-26 activation N-ECM Degradation-2.0-Hs-22 N-ECM Degradation-2.0-Hs-29 activation N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-27 activation N-ECM Degradation-2.0-Hs-15 N-ECM Degradation-2.0-Hs-27 activation N-ECM Degradation-2.0-Hs-17 N-ECM Degradation-2.0-Hs-27 activation N-ECM Degradation-2.0-Hs-14 N-ECM Degradation-2.0-Hs-27 activation N-ECM Degradation-2.0-Hs-23 N-ECM Degradation-2.0-Hs-27 activation N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-1 2 activation N-ECM Degradation-2.0-Hs-27 N-ECM Degradation-2.0-Hs-1 2 activation N-ECM Degradation-2.0-Hs-28 N-ECM Degradation-2.0-Hs-1 2 activation N-ECM Degradation-2.0-Hs-28 N-ECM Degradation-2.0-Hs-1 2 activation N-ECM Degradation-2.0-Hs-25 N-ECM Degradation-2.0-Hs-18 inhibition N-ECM Degradation-2.0-Hs-18 N-ECM Degradation-2.0-Hs-18 inhibition N-ECM Degradation-2.0-Hs-18 N-ECM Degradation-2.0-Hs-14 inhibition N-ECM Degradation-2.0-Hs-14 N-ECM Degradation-2.0-Hs-14 inhibition N-ECM Degradation-2.0-Hs-14 N-ECM Degradation-2.0-Hs-19 inhibition N-ECM Degradation-2.0-Hs-19 N-ECM Degradation-2.0-Hs-13 inhibition N-ECM Degradation-2.0-Hs-8 N-ECM Degradation-2.0-Hs-13 inhibition N-ECM Degradation-2.0-Hs-32 N-ECM Degradation-2.0-Hs-31 activation N-ECM Degradation-2.0-Hs-15 N-ECM Degradation-2.0-Hs-31 activation N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-31 activation N-ECM Degradation-2.0-Hs-16 N-ECM Degradation-2.0-Hs-31 activation N-ECM Degradation-2.0-Hs-14 N-ECM Degradation-2.0-Hs-31 activation N-ECM Degradation-2.0-Hs-17 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Endoplasmic Reticulum Stress-2.0-Hs.att000066400000000000000000000055741426625374700306630ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Endoplasmic Reticulum Stress-2.0-Hs-1 JNK 107 107 white rectangle gene 0.5 black 46 17 JNK N-Endoplasmic Reticulum Stress-2.0-Hs-10 NQO1 64 28 white rectangle gene 0.5 black 46 17 2874 N-Endoplasmic Reticulum Stress-2.0-Hs-11 RCAN1 136 75 white rectangle gene 0.5 black 46 17 3040 N-Endoplasmic Reticulum Stress-2.0-Hs-12 EIF2AK3 88 112 white rectangle gene 0.5 black 46 17 3255 N-Endoplasmic Reticulum Stress-2.0-Hs-13 EIF2S1 80 109 white rectangle gene 0.5 black 46 17 3265 N-Endoplasmic Reticulum Stress-2.0-Hs-14 EIF4EBP1 55 12 white rectangle gene 0.5 black 46 17 3288 N-Endoplasmic Reticulum Stress-2.0-Hs-15 EIF4G1 60 20 white rectangle gene 0.5 black 46 17 3296 N-Endoplasmic Reticulum Stress-2.0-Hs-16 ERN1 121 117 white rectangle gene 0.5 black 46 17 3449 N-Endoplasmic Reticulum Stress-2.0-Hs-17 F2 113 82 white rectangle gene 0.5 black 46 17 3535 N-Endoplasmic Reticulum Stress-2.0-Hs-18 PDIA3 114 8 white rectangle gene 0.5 black 46 17 4606 N-Endoplasmic Reticulum Stress-2.0-Hs-2 p38 84 128 white rectangle gene 0.5 black 46 17 p38 N-Endoplasmic Reticulum Stress-2.0-Hs-20 HSPA5 129 69 white rectangle gene 0.5 black 46 17 5238 N-Endoplasmic Reticulum Stress-2.0-Hs-21 JUN 126 79 white rectangle gene 0.5 black 46 17 6204 N-Endoplasmic Reticulum Stress-2.0-Hs-22 MAP3K5 114 111 white rectangle gene 0.5 black 46 17 6857 N-Endoplasmic Reticulum Stress-2.0-Hs-23 MAPK8 73 151 white rectangle gene 0.5 black 46 17 6881 N-Endoplasmic Reticulum Stress-2.0-Hs-24 MAPK9 82 154 white rectangle gene 0.5 black 46 17 6886 N-Endoplasmic Reticulum Stress-2.0-Hs-25 MDM2 10 124 white rectangle gene 0.5 black 46 17 6973 N-Endoplasmic Reticulum Stress-2.0-Hs-27 ATF2 89 149 white rectangle gene 0.5 black 46 17 784 N-Endoplasmic Reticulum Stress-2.0-Hs-28 ATF2 81 144 white rectangle gene 0.5 black 46 17 784 N-Endoplasmic Reticulum Stress-2.0-Hs-29 ATF3 6 130 white rectangle gene 0.5 black 46 17 785 N-Endoplasmic Reticulum Stress-2.0-Hs-3 STAT3 117 0 white rectangle gene 0.5 black 46 17 11364 N-Endoplasmic Reticulum Stress-2.0-Hs-30 ATF4 0 135 white rectangle gene 0.5 black 46 17 786 N-Endoplasmic Reticulum Stress-2.0-Hs-31 ATF6 135 127 white rectangle gene 0.5 black 46 17 791 N-Endoplasmic Reticulum Stress-2.0-Hs-32 EIF2AK2 65 101 white rectangle gene 0.5 black 46 17 9437 N-Endoplasmic Reticulum Stress-2.0-Hs-33 DNAJC3 75 106 white rectangle gene 0.5 black 46 17 9439 N-Endoplasmic Reticulum Stress-2.0-Hs-5 WFS1 146 125 white rectangle gene 0.5 black 46 17 12762 N-Endoplasmic Reticulum Stress-2.0-Hs-6 XBP1 120 125 white rectangle gene 0.5 black 46 17 12801 N-Endoplasmic Reticulum Stress-2.0-Hs-7 MBTPS2 138 137 white rectangle gene 0.5 black 46 17 15455 N-Endoplasmic Reticulum Stress-2.0-Hs-8 MBTPS1 145 133 white rectangle gene 0.5 black 46 17 15456 N-Endoplasmic Reticulum Stress-2.0-Hs-9 DDIT3 115 90 white rectangle gene 0.5 black 46 17 2726 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Endoplasmic Reticulum Stress-2.0-Hs.sif000066400000000000000000000120771426625374700306500ustar00rootroot000000000000000 1 2 N-Endoplasmic Reticulum Stress-2.0-Hs-1 activation N-Endoplasmic Reticulum Stress-2.0-Hs-9 N-Endoplasmic Reticulum Stress-2.0-Hs-1 activation N-Endoplasmic Reticulum Stress-2.0-Hs-9 N-Endoplasmic Reticulum Stress-2.0-Hs-33 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-32 N-Endoplasmic Reticulum Stress-2.0-Hs-33 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-12 N-Endoplasmic Reticulum Stress-2.0-Hs-33 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-13 N-Endoplasmic Reticulum Stress-2.0-Hs-23 activation N-Endoplasmic Reticulum Stress-2.0-Hs-28 N-Endoplasmic Reticulum Stress-2.0-Hs-23 activation N-Endoplasmic Reticulum Stress-2.0-Hs-28 N-Endoplasmic Reticulum Stress-2.0-Hs-21 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-20 N-Endoplasmic Reticulum Stress-2.0-Hs-21 activation N-Endoplasmic Reticulum Stress-2.0-Hs-11 N-Endoplasmic Reticulum Stress-2.0-Hs-21 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-9 N-Endoplasmic Reticulum Stress-2.0-Hs-14 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-15 N-Endoplasmic Reticulum Stress-2.0-Hs-25 activation N-Endoplasmic Reticulum Stress-2.0-Hs-29 N-Endoplasmic Reticulum Stress-2.0-Hs-17 activation N-Endoplasmic Reticulum Stress-2.0-Hs-9 N-Endoplasmic Reticulum Stress-2.0-Hs-17 activation N-Endoplasmic Reticulum Stress-2.0-Hs-9 N-Endoplasmic Reticulum Stress-2.0-Hs-17 activation N-Endoplasmic Reticulum Stress-2.0-Hs-9 N-Endoplasmic Reticulum Stress-2.0-Hs-17 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-9 N-Endoplasmic Reticulum Stress-2.0-Hs-16 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-16 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-16 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-16 activation N-Endoplasmic Reticulum Stress-2.0-Hs-6 N-Endoplasmic Reticulum Stress-2.0-Hs-16 activation N-Endoplasmic Reticulum Stress-2.0-Hs-6 N-Endoplasmic Reticulum Stress-2.0-Hs-16 activation N-Endoplasmic Reticulum Stress-2.0-Hs-22 N-Endoplasmic Reticulum Stress-2.0-Hs-16 activation N-Endoplasmic Reticulum Stress-2.0-Hs-1 N-Endoplasmic Reticulum Stress-2.0-Hs-8 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-8 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-8 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-10 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-15 N-Endoplasmic Reticulum Stress-2.0-Hs-29 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-29 N-Endoplasmic Reticulum Stress-2.0-Hs-29 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-30 N-Endoplasmic Reticulum Stress-2.0-Hs-24 activation N-Endoplasmic Reticulum Stress-2.0-Hs-28 N-Endoplasmic Reticulum Stress-2.0-Hs-15 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-15 N-Endoplasmic Reticulum Stress-2.0-Hs-15 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-15 N-Endoplasmic Reticulum Stress-2.0-Hs-12 activation N-Endoplasmic Reticulum Stress-2.0-Hs-1 N-Endoplasmic Reticulum Stress-2.0-Hs-12 activation N-Endoplasmic Reticulum Stress-2.0-Hs-2 N-Endoplasmic Reticulum Stress-2.0-Hs-12 activation N-Endoplasmic Reticulum Stress-2.0-Hs-13 N-Endoplasmic Reticulum Stress-2.0-Hs-12 activation N-Endoplasmic Reticulum Stress-2.0-Hs-13 N-Endoplasmic Reticulum Stress-2.0-Hs-18 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-3 N-Endoplasmic Reticulum Stress-2.0-Hs-28 activation N-Endoplasmic Reticulum Stress-2.0-Hs-27 N-Endoplasmic Reticulum Stress-2.0-Hs-28 activation N-Endoplasmic Reticulum Stress-2.0-Hs-27 N-Endoplasmic Reticulum Stress-2.0-Hs-28 activation N-Endoplasmic Reticulum Stress-2.0-Hs-27 N-Endoplasmic Reticulum Stress-2.0-Hs-28 activation N-Endoplasmic Reticulum Stress-2.0-Hs-27 N-Endoplasmic Reticulum Stress-2.0-Hs-28 activation N-Endoplasmic Reticulum Stress-2.0-Hs-27 N-Endoplasmic Reticulum Stress-2.0-Hs-7 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-7 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-7 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-5 inhibition N-Endoplasmic Reticulum Stress-2.0-Hs-31 N-Endoplasmic Reticulum Stress-2.0-Hs-2 activation N-Endoplasmic Reticulum Stress-2.0-Hs-28 N-Endoplasmic Reticulum Stress-2.0-Hs-2 activation N-Endoplasmic Reticulum Stress-2.0-Hs-28 N-Endoplasmic Reticulum Stress-2.0-Hs-2 activation N-Endoplasmic Reticulum Stress-2.0-Hs-28 N-Endoplasmic Reticulum Stress-2.0-Hs-2 activation N-Endoplasmic Reticulum Stress-2.0-Hs-28 N-Endoplasmic Reticulum Stress-2.0-Hs-22 activation N-Endoplasmic Reticulum Stress-2.0-Hs-1 N-Endoplasmic Reticulum Stress-2.0-Hs-22 activation N-Endoplasmic Reticulum Stress-2.0-Hs-1 N-Endoplasmic Reticulum Stress-2.0-Hs-22 activation N-Endoplasmic Reticulum Stress-2.0-Hs-1 N-Endoplasmic Reticulum Stress-2.0-Hs-22 activation N-Endoplasmic Reticulum Stress-2.0-Hs-1 N-Endoplasmic Reticulum Stress-2.0-Hs-22 activation N-Endoplasmic Reticulum Stress-2.0-Hs-1 N-Endoplasmic Reticulum Stress-2.0-Hs-31 activation N-Endoplasmic Reticulum Stress-2.0-Hs-31 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Endothelial Innate Immune Activation-2.0-Hs.att000066400000000000000000000160531426625374700321450ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Endothelial Innate Immune Activation-2.0-Hs-10 p38 83 106 white rectangle gene 0.5 black 46 17 p38 N-Endothelial Innate Immune Activation-2.0-Hs-11 p38 123 120 white rectangle gene 0.5 black 46 17 p38 N-Endothelial Innate Immune Activation-2.0-Hs-12 ROCK1 100 95 white rectangle gene 0.5 black 46 17 10251 N-Endothelial Innate Immune Activation-2.0-Hs-13 CCL20 96 71 white rectangle gene 0.5 black 46 17 10619 N-Endothelial Innate 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gene 0.5 black 46 17 11919 N-Endothelial Innate Immune Activation-2.0-Hs-26 TNFSF11 156 81 white rectangle gene 0.5 black 46 17 11926 N-Endothelial Innate Immune Activation-2.0-Hs-27 TRAF6 15 111 white rectangle gene 0.5 black 46 17 12036 N-Endothelial Innate Immune Activation-2.0-Hs-28 VCAM1 94 92 white rectangle gene 0.5 black 46 17 12663 N-Endothelial Innate Immune Activation-2.0-Hs-29 VEGFA 84 120 white rectangle gene 0.5 black 46 17 12680 N-Endothelial Innate Immune Activation-2.0-Hs-30 SIRT1 27 174 white rectangle gene 0.5 black 46 17 14929 N-Endothelial Innate Immune Activation-2.0-Hs-31 TLR7 75 125 white rectangle gene 0.5 black 46 17 15631 N-Endothelial Innate Immune Activation-2.0-Hs-32 TLR9 73 122 white rectangle gene 0.5 black 46 17 15633 N-Endothelial Innate Immune Activation-2.0-Hs-33 IL33 0 111 white rectangle gene 0.5 black 46 17 16028 N-Endothelial Innate Immune Activation-2.0-Hs-34 TLR6 102 144 white rectangle gene 0.5 black 46 17 16711 N-Endothelial Innate Immune 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Activation-2.0-Hs-75 N-Endothelial Innate Immune Activation-2.0-Hs-7 activation N-Endothelial Innate Immune Activation-2.0-Hs-13 N-Endothelial Innate Immune Activation-2.0-Hs-45 activation N-Endothelial Innate Immune Activation-2.0-Hs-44 N-Endothelial Innate Immune Activation-2.0-Hs-45 activation N-Endothelial Innate Immune Activation-2.0-Hs-44 N-Endothelial Innate Immune Activation-2.0-Hs-60 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-60 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-77 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-61 N-Endothelial Innate Immune Activation-2.0-Hs-4 activation N-Endothelial Innate Immune Activation-2.0-Hs-36 N-Endothelial Innate Immune Activation-2.0-Hs-4 activation N-Endothelial Innate Immune Activation-2.0-Hs-73 N-Endothelial Innate Immune Activation-2.0-Hs-61 activation N-Endothelial Innate Immune Activation-2.0-Hs-68 N-Endothelial Innate Immune Activation-2.0-Hs-43 activation N-Endothelial Innate Immune Activation-2.0-Hs-62 N-Endothelial Innate Immune Activation-2.0-Hs-43 activation N-Endothelial Innate Immune Activation-2.0-Hs-62 N-Endothelial Innate Immune Activation-2.0-Hs-43 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-69 N-Endothelial Innate Immune Activation-2.0-Hs-43 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-73 N-Endothelial Innate Immune Activation-2.0-Hs-34 activation N-Endothelial Innate Immune Activation-2.0-Hs-38 N-Endothelial Innate Immune Activation-2.0-Hs-76 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-75 N-Endothelial Innate Immune Activation-2.0-Hs-25 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-62 activation N-Endothelial Innate Immune Activation-2.0-Hs-12 N-Endothelial Innate Immune Activation-2.0-Hs-55 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-63 N-Endothelial Innate Immune Activation-2.0-Hs-55 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-55 activation N-Endothelial Innate Immune Activation-2.0-Hs-57 N-Endothelial Innate Immune Activation-2.0-Hs-55 activation N-Endothelial Innate Immune Activation-2.0-Hs-57 N-Endothelial Innate Immune Activation-2.0-Hs-50 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-9 N-Endothelial Innate Immune Activation-2.0-Hs-56 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-46 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-44 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-44 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-44 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune Activation-2.0-Hs-29 activation N-Endothelial Innate Immune Activation-2.0-Hs-59 N-Endothelial Innate Immune 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Activation-2.0-Hs-20 activation N-Endothelial Innate Immune Activation-2.0-Hs-57 N-Endothelial Innate Immune Activation-2.0-Hs-20 activation N-Endothelial Innate Immune Activation-2.0-Hs-57 N-Endothelial Innate Immune Activation-2.0-Hs-20 activation N-Endothelial Innate Immune Activation-2.0-Hs-57 N-Endothelial Innate Immune Activation-2.0-Hs-73 activation N-Endothelial Innate Immune Activation-2.0-Hs-67 N-Endothelial Innate Immune Activation-2.0-Hs-74 activation N-Endothelial Innate Immune Activation-2.0-Hs-6 N-Endothelial Innate Immune Activation-2.0-Hs-65 activation N-Endothelial Innate Immune Activation-2.0-Hs-46 N-Endothelial Innate Immune Activation-2.0-Hs-65 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-65 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-65 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-48 activation N-Endothelial Innate Immune Activation-2.0-Hs-6 N-Endothelial Innate Immune Activation-2.0-Hs-48 activation N-Endothelial Innate Immune Activation-2.0-Hs-42 N-Endothelial Innate Immune Activation-2.0-Hs-9 activation N-Endothelial Innate Immune Activation-2.0-Hs-6 N-Endothelial Innate Immune Activation-2.0-Hs-9 activation N-Endothelial Innate Immune Activation-2.0-Hs-72 N-Endothelial Innate Immune Activation-2.0-Hs-9 activation N-Endothelial Innate Immune Activation-2.0-Hs-36 N-Endothelial Innate Immune Activation-2.0-Hs-9 activation N-Endothelial Innate Immune Activation-2.0-Hs-47 N-Endothelial Innate Immune Activation-2.0-Hs-9 activation N-Endothelial Innate Immune Activation-2.0-Hs-69 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-15 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-65 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-65 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-65 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-65 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-65 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-65 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-65 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-20 N-Endothelial Innate Immune Activation-2.0-Hs-21 activation N-Endothelial Innate Immune Activation-2.0-Hs-51 N-Endothelial Innate Immune Activation-2.0-Hs-26 activation N-Endothelial Innate Immune Activation-2.0-Hs-9 N-Endothelial Innate Immune Activation-2.0-Hs-16 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-73 N-Endothelial Innate Immune Activation-2.0-Hs-16 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-16 activation N-Endothelial Innate Immune Activation-2.0-Hs-11 N-Endothelial Innate Immune Activation-2.0-Hs-16 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-66 N-Endothelial Innate Immune Activation-2.0-Hs-41 activation N-Endothelial Innate Immune Activation-2.0-Hs-73 N-Endothelial Innate Immune Activation-2.0-Hs-41 activation N-Endothelial Innate Immune Activation-2.0-Hs-73 N-Endothelial Innate Immune Activation-2.0-Hs-12 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-12 activation N-Endothelial Innate Immune Activation-2.0-Hs-56 N-Endothelial Innate Immune Activation-2.0-Hs-54 activation N-Endothelial Innate Immune Activation-2.0-Hs-27 N-Endothelial Innate Immune Activation-2.0-Hs-39 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-32 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-14 activation N-Endothelial Innate Immune Activation-2.0-Hs-62 N-Endothelial Innate Immune Activation-2.0-Hs-52 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-52 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-24 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-24 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-28 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-62 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-4 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-10 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-51 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-15 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-15 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-15 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-15 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-15 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-15 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-22 activation N-Endothelial Innate Immune Activation-2.0-Hs-23 N-Endothelial Innate Immune Activation-2.0-Hs-49 activation N-Endothelial Innate Immune Activation-2.0-Hs-62 N-Endothelial Innate Immune Activation-2.0-Hs-53 activation N-Endothelial Innate Immune Activation-2.0-Hs-10 N-Endothelial Innate Immune Activation-2.0-Hs-5 activation N-Endothelial Innate Immune Activation-2.0-Hs-68 N-Endothelial Innate Immune Activation-2.0-Hs-5 activation N-Endothelial Innate Immune Activation-2.0-Hs-68 N-Endothelial Innate Immune Activation-2.0-Hs-5 activation N-Endothelial Innate Immune Activation-2.0-Hs-68 N-Endothelial Innate Immune Activation-2.0-Hs-5 activation N-Endothelial Innate Immune Activation-2.0-Hs-68 N-Endothelial Innate Immune Activation-2.0-Hs-8 activation N-Endothelial Innate Immune Activation-2.0-Hs-7 N-Endothelial Innate Immune Activation-2.0-Hs-64 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-64 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-64 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-64 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-59 activation N-Endothelial Innate Immune Activation-2.0-Hs-18 N-Endothelial Innate Immune Activation-2.0-Hs-40 activation N-Endothelial Innate Immune Activation-2.0-Hs-75 N-Endothelial Innate Immune Activation-2.0-Hs-10 activation N-Endothelial Innate Immune Activation-2.0-Hs-57 N-Endothelial Innate Immune Activation-2.0-Hs-31 activation N-Endothelial Innate Immune Activation-2.0-Hs-29 N-Endothelial Innate Immune Activation-2.0-Hs-30 inhibition N-Endothelial Innate Immune Activation-2.0-Hs-70 N-Endothelial Innate Immune Activation-2.0-Hs-33 activation N-Endothelial Innate Immune Activation-2.0-Hs-54 N-Endothelial Innate Immune Activation-2.0-Hs-33 activation N-Endothelial Innate Immune Activation-2.0-Hs-54 N-Endothelial Innate Immune Activation-2.0-Hs-33 activation N-Endothelial Innate Immune Activation-2.0-Hs-54 N-Endothelial Innate Immune Activation-2.0-Hs-33 activation N-Endothelial Innate Immune Activation-2.0-Hs-54 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Epigenetics-2.0-Hs.att000066400000000000000000000032651426625374700254610ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Epigenetics-2.0-Hs-1 HDAC 93 149 white rectangle gene 0.5 black 46 17 HDAC N-Epigenetics-2.0-Hs-10 CHFR 24 33 white rectangle gene 0.5 black 46 17 20455 N-Epigenetics-2.0-Hs-13 CTNNB1 78 182 white rectangle gene 0.5 black 46 17 2514 N-Epigenetics-2.0-Hs-14 DEFB1 107 10 white rectangle gene 0.5 black 46 17 2766 N-Epigenetics-2.0-Hs-15 DKK1 0 135 white rectangle gene 0.5 black 46 17 2891 N-Epigenetics-2.0-Hs-16 DNMT1 179 119 white rectangle gene 0.5 black 46 17 2976 N-Epigenetics-2.0-Hs-17 E2F4 84 160 white rectangle gene 0.5 black 46 17 3118 N-Epigenetics-2.0-Hs-18 EGR1 92 139 white rectangle gene 0.5 black 46 17 3238 N-Epigenetics-2.0-Hs-2 Wnt 2 143 white rectangle gene 0.5 black 46 17 Wnt N-Epigenetics-2.0-Hs-20 GDF11 161 33 white rectangle gene 0.5 black 46 17 4216 N-Epigenetics-2.0-Hs-21 GLI1 104 150 white rectangle gene 0.5 black 46 17 4317 N-Epigenetics-2.0-Hs-22 HDAC1 108 0 white rectangle gene 0.5 black 46 17 4852 N-Epigenetics-2.0-Hs-23 HDAC3 163 41 white rectangle gene 0.5 black 46 17 4854 N-Epigenetics-2.0-Hs-25 PLK1 20 25 white rectangle gene 0.5 black 46 17 9077 N-Epigenetics-2.0-Hs-26 RASSF1 65 176 white rectangle gene 0.5 black 46 17 9882 N-Epigenetics-2.0-Hs-27 RB1 171 125 white rectangle gene 0.5 black 46 17 9884 N-Epigenetics-2.0-Hs-4 VEGFA 184 110 white rectangle gene 0.5 black 46 17 12680 N-Epigenetics-2.0-Hs-5 CCNB1 15 39 white rectangle gene 0.5 black 46 17 1579 N-Epigenetics-2.0-Hs-6 CCND1 76 171 white rectangle gene 0.5 black 46 17 1582 N-Epigenetics-2.0-Hs-7 CDK1 32 27 white rectangle gene 0.5 black 46 17 1722 N-Epigenetics-2.0-Hs-8 CDKN2A 190 124 white rectangle gene 0.5 black 46 17 1787 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Epigenetics-2.0-Hs.sif000066400000000000000000000036071426625374700254520ustar00rootroot000000000000000 1 2 N-Epigenetics-2.0-Hs-13 activation N-Epigenetics-2.0-Hs-6 N-Epigenetics-2.0-Hs-13 activation N-Epigenetics-2.0-Hs-6 N-Epigenetics-2.0-Hs-13 activation N-Epigenetics-2.0-Hs-6 N-Epigenetics-2.0-Hs-15 inhibition N-Epigenetics-2.0-Hs-2 N-Epigenetics-2.0-Hs-15 inhibition N-Epigenetics-2.0-Hs-2 N-Epigenetics-2.0-Hs-15 inhibition N-Epigenetics-2.0-Hs-2 N-Epigenetics-2.0-Hs-15 inhibition N-Epigenetics-2.0-Hs-2 N-Epigenetics-2.0-Hs-15 inhibition N-Epigenetics-2.0-Hs-2 N-Epigenetics-2.0-Hs-15 inhibition N-Epigenetics-2.0-Hs-2 N-Epigenetics-2.0-Hs-15 inhibition N-Epigenetics-2.0-Hs-2 N-Epigenetics-2.0-Hs-10 inhibition N-Epigenetics-2.0-Hs-5 N-Epigenetics-2.0-Hs-10 inhibition N-Epigenetics-2.0-Hs-5 N-Epigenetics-2.0-Hs-10 inhibition N-Epigenetics-2.0-Hs-25 N-Epigenetics-2.0-Hs-10 inhibition N-Epigenetics-2.0-Hs-25 N-Epigenetics-2.0-Hs-10 inhibition N-Epigenetics-2.0-Hs-25 N-Epigenetics-2.0-Hs-10 inhibition N-Epigenetics-2.0-Hs-25 N-Epigenetics-2.0-Hs-10 inhibition N-Epigenetics-2.0-Hs-7 N-Epigenetics-2.0-Hs-10 inhibition N-Epigenetics-2.0-Hs-7 N-Epigenetics-2.0-Hs-16 activation N-Epigenetics-2.0-Hs-4 N-Epigenetics-2.0-Hs-16 activation N-Epigenetics-2.0-Hs-4 N-Epigenetics-2.0-Hs-16 inhibition N-Epigenetics-2.0-Hs-27 N-Epigenetics-2.0-Hs-16 inhibition N-Epigenetics-2.0-Hs-8 N-Epigenetics-2.0-Hs-16 inhibition N-Epigenetics-2.0-Hs-8 N-Epigenetics-2.0-Hs-17 activation N-Epigenetics-2.0-Hs-6 N-Epigenetics-2.0-Hs-26 inhibition N-Epigenetics-2.0-Hs-6 N-Epigenetics-2.0-Hs-26 inhibition N-Epigenetics-2.0-Hs-6 N-Epigenetics-2.0-Hs-26 inhibition N-Epigenetics-2.0-Hs-6 N-Epigenetics-2.0-Hs-26 inhibition N-Epigenetics-2.0-Hs-6 N-Epigenetics-2.0-Hs-1 inhibition N-Epigenetics-2.0-Hs-17 N-Epigenetics-2.0-Hs-1 activation N-Epigenetics-2.0-Hs-18 N-Epigenetics-2.0-Hs-1 inhibition N-Epigenetics-2.0-Hs-21 N-Epigenetics-2.0-Hs-22 inhibition N-Epigenetics-2.0-Hs-14 N-Epigenetics-2.0-Hs-23 inhibition N-Epigenetics-2.0-Hs-20 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Epithelial Innate Immune Activation-2.0-Hs.att000066400000000000000000000204221426625374700317700ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Epithelial Innate Immune Activation-2.0-Hs-1 ERK 27 88 white rectangle gene 0.5 black 46 17 ERK N-Epithelial Innate Immune Activation-2.0-Hs-10 CCL20 32 95 white rectangle gene 0.5 black 46 17 10619 N-Epithelial Innate Immune Activation-2.0-Hs-11 CCL26 31 54 white rectangle gene 0.5 black 46 17 10625 N-Epithelial Innate Immune Activation-2.0-Hs-12 CCL5 49 102 white rectangle gene 0.5 black 46 17 10632 N-Epithelial Innate Immune Activation-2.0-Hs-13 SLPI 95 44 white rectangle gene 0.5 black 46 17 11092 N-Epithelial Innate Immune Activation-2.0-Hs-14 SRC 61 117 white rectangle gene 0.5 black 46 17 11283 N-Epithelial Innate Immune Activation-2.0-Hs-15 STAT6 32 64 white rectangle gene 0.5 black 46 17 11368 N-Epithelial Innate Immune Activation-2.0-Hs-16 TGFB1 101 38 white rectangle gene 0.5 black 46 17 11766 N-Epithelial Innate Immune Activation-2.0-Hs-17 NKX2-1 10 64 white rectangle gene 0.5 black 46 17 11825 N-Epithelial Innate Immune Activation-2.0-Hs-18 TLR2 22 0 white rectangle gene 0.5 black 46 17 11848 N-Epithelial Innate Immune Activation-2.0-Hs-19 TLR4 23 115 white rectangle gene 0.5 black 46 17 11850 N-Epithelial Innate Immune Activation-2.0-Hs-2 JNK 39 168 white rectangle gene 0.5 black 46 17 JNK N-Epithelial Innate Immune Activation-2.0-Hs-20 TNF 37 110 white rectangle gene 0.5 black 46 17 11892 N-Epithelial Innate Immune Activation-2.0-Hs-21 TNFRSF1A 47 128 white rectangle gene 0.5 black 46 17 11916 N-Epithelial Innate Immune Activation-2.0-Hs-22 TNFRSF1B 51 114 white rectangle gene 0.5 black 46 17 11917 N-Epithelial Innate Immune Activation-2.0-Hs-23 TRADD 47 142 white rectangle gene 0.5 black 46 17 12030 N-Epithelial Innate Immune Activation-2.0-Hs-24 TRAF2 43 153 white rectangle gene 0.5 black 46 17 12032 N-Epithelial Innate Immune Activation-2.0-Hs-25 TRAF6 32 144 white rectangle gene 0.5 black 46 17 12036 N-Epithelial Innate Immune Activation-2.0-Hs-26 TTF1 50 91 white rectangle gene 0.5 black 46 17 12397 N-Epithelial Innate Immune Activation-2.0-Hs-27 SCGB1A1 36 115 white rectangle gene 0.5 black 46 17 12523 N-Epithelial Innate Immune Activation-2.0-Hs-28 VCAM1 44 116 white rectangle gene 0.5 black 46 17 12663 N-Epithelial Innate Immune Activation-2.0-Hs-29 WNT4 19 91 white rectangle gene 0.5 black 46 17 12783 N-Epithelial Innate Immune Activation-2.0-Hs-3 PKC 66 140 white rectangle gene 0.5 black 46 17 PKC N-Epithelial Innate Immune Activation-2.0-Hs-30 ADIPOQ 25 109 white rectangle gene 0.5 black 46 17 13633 N-Epithelial Innate Immune Activation-2.0-Hs-31 IL33 44 76 white rectangle gene 0.5 black 46 17 16028 N-Epithelial Innate Immune Activation-2.0-Hs-32 NOD1 0 97 white rectangle gene 0.5 black 46 17 16390 N-Epithelial Innate Immune Activation-2.0-Hs-33 CEBPA 16 93 white rectangle gene 0.5 black 46 17 1833 N-Epithelial Innate Immune Activation-2.0-Hs-34 CEBPB 11 97 white rectangle gene 0.5 black 46 17 1834 N-Epithelial Innate Immune Activation-2.0-Hs-35 LTB4R2 14 99 white rectangle gene 0.5 black 46 17 19260 N-Epithelial Innate Immune Activation-2.0-Hs-36 SOCS1 31 71 white rectangle gene 0.5 black 46 17 19383 N-Epithelial Innate Immune Activation-2.0-Hs-37 SOCS3 35 56 white rectangle gene 0.5 black 46 17 19391 N-Epithelial Innate Immune Activation-2.0-Hs-38 ADAM17 24 112 white rectangle gene 0.5 black 46 17 195 N-Epithelial Innate Immune Activation-2.0-Hs-39 CHUK 31 170 white rectangle gene 0.5 black 46 17 1974 N-Epithelial Innate Immune Activation-2.0-Hs-4 PLC 58 135 white rectangle gene 0.5 black 46 17 PLC N-Epithelial Innate Immune Activation-2.0-Hs-40 DEFA3 100 46 white rectangle gene 0.5 black 46 17 2762 N-Epithelial Innate Immune Activation-2.0-Hs-41 DEFB4A 29 119 white rectangle gene 0.5 black 46 17 2767 N-Epithelial Innate Immune Activation-2.0-Hs-42 AGER 40 97 white rectangle gene 0.5 black 46 17 320 N-Epithelial Innate Immune Activation-2.0-Hs-43 EGF 28 95 white rectangle gene 0.5 black 46 17 3229 N-Epithelial Innate Immune Activation-2.0-Hs-44 EGFR 30 87 white rectangle gene 0.5 black 46 17 3236 N-Epithelial Innate Immune Activation-2.0-Hs-45 EGR1 52 95 white rectangle gene 0.5 black 46 17 3238 N-Epithelial Innate Immune Activation-2.0-Hs-46 ELANE 87 50 white rectangle gene 0.5 black 46 17 3309 N-Epithelial Innate Immune Activation-2.0-Hs-47 ERBB2 6 128 white rectangle gene 0.5 black 46 17 3430 N-Epithelial Innate Immune Activation-2.0-Hs-48 FOXO3 25 78 white rectangle gene 0.5 black 46 17 3821 N-Epithelial Innate Immune Activation-2.0-Hs-49 CXCL1 14 107 white rectangle gene 0.5 black 46 17 4602 N-Epithelial Innate Immune Activation-2.0-Hs-5 p38 23 90 white rectangle gene 0.5 black 46 17 p38 N-Epithelial Innate Immune Activation-2.0-Hs-50 HMGB1 49 97 white rectangle gene 0.5 black 46 17 4983 N-Epithelial Innate Immune Activation-2.0-Hs-51 HRAS 52 99 white rectangle gene 0.5 black 46 17 5173 N-Epithelial Innate Immune Activation-2.0-Hs-52 ICAM1 24 104 white rectangle gene 0.5 black 46 17 5344 N-Epithelial Innate Immune Activation-2.0-Hs-53 IFNG 33 61 white rectangle gene 0.5 black 46 17 5438 N-Epithelial Innate Immune Activation-2.0-Hs-54 IKBKB 29 168 white rectangle gene 0.5 black 46 17 5960 N-Epithelial Innate Immune Activation-2.0-Hs-55 IL13 28 83 white rectangle gene 0.5 black 46 17 5973 N-Epithelial Innate Immune Activation-2.0-Hs-56 IL15 15 97 white rectangle gene 0.5 black 46 17 5977 N-Epithelial Innate Immune Activation-2.0-Hs-57 IL1B 32 110 white rectangle gene 0.5 black 46 17 5992 N-Epithelial Innate Immune Activation-2.0-Hs-58 IL1R1 32 128 white rectangle gene 0.5 black 46 17 5993 N-Epithelial Innate Immune Activation-2.0-Hs-59 IL1RL1 39 78 white rectangle gene 0.5 black 46 17 5998 N-Epithelial Innate Immune Activation-2.0-Hs-6 S100A12 39 104 white rectangle gene 0.5 black 46 17 10489 N-Epithelial Innate Immune Activation-2.0-Hs-60 IL4 31 80 white rectangle gene 0.5 black 46 17 6014 N-Epithelial Innate Immune Activation-2.0-Hs-61 IL6 19 74 white rectangle gene 0.5 black 46 17 6018 N-Epithelial Innate Immune Activation-2.0-Hs-62 CXCL8 19 100 white rectangle gene 0.5 black 46 17 6025 N-Epithelial Innate Immune Activation-2.0-Hs-63 IRAK1 25 140 white rectangle gene 0.5 black 46 17 6112 N-Epithelial Innate Immune Activation-2.0-Hs-64 MAP3K7 35 160 white rectangle gene 0.5 black 46 17 6859 N-Epithelial Innate Immune Activation-2.0-Hs-65 MAPK1 40 84 white rectangle gene 0.5 black 46 17 6871 N-Epithelial Innate Immune Activation-2.0-Hs-66 MAPK3 38 87 white rectangle gene 0.5 black 46 17 6877 N-Epithelial Innate Immune Activation-2.0-Hs-67 MAPK8 56 87 white rectangle gene 0.5 black 46 17 6881 N-Epithelial Innate Immune Activation-2.0-Hs-68 MMP14 31 97 white rectangle gene 0.5 black 46 17 7160 N-Epithelial Innate Immune Activation-2.0-Hs-69 MMP9 29 115 white rectangle gene 0.5 black 46 17 7176 N-Epithelial Innate Immune Activation-2.0-Hs-7 SAA1 8 105 white rectangle gene 0.5 black 46 17 10513 N-Epithelial Innate Immune Activation-2.0-Hs-70 MUC5AC 31 106 white rectangle gene 0.5 black 46 17 7515 N-Epithelial Innate Immune Activation-2.0-Hs-71 MYD88 22 129 white rectangle gene 0.5 black 46 17 7562 N-Epithelial Innate Immune Activation-2.0-Hs-72 NFE2L2 7 102 white rectangle gene 0.5 black 46 17 7782 N-Epithelial Innate Immune Activation-2.0-Hs-73 NFKBIA 25 174 white rectangle gene 0.5 black 46 17 7797 N-Epithelial Innate Immune Activation-2.0-Hs-74 NFKBIA 21 180 white rectangle gene 0.5 black 46 17 7797 N-Epithelial Innate Immune Activation-2.0-Hs-75 NFKBIA 18 176 white rectangle gene 0.5 black 46 17 7797 N-Epithelial Innate Immune Activation-2.0-Hs-76 NFKBIB 1 3 white rectangle gene 0.5 black 46 17 7798 N-Epithelial Innate Immune Activation-2.0-Hs-77 NRG1 14 122 white rectangle gene 0.5 black 46 17 7997 N-Epithelial Innate Immune Activation-2.0-Hs-78 PDE4A 22 112 white rectangle gene 0.5 black 46 17 8780 N-Epithelial Innate Immune Activation-2.0-Hs-79 PLA2G4A 27 0 white rectangle gene 0.5 black 46 17 9035 N-Epithelial Innate Immune Activation-2.0-Hs-8 CCL11 4 57 white rectangle gene 0.5 black 46 17 10610 N-Epithelial Innate Immune Activation-2.0-Hs-80 PRKCD 10 83 white rectangle gene 0.5 black 46 17 9399 N-Epithelial Innate Immune Activation-2.0-Hs-81 PRKCD 1 78 white rectangle gene 0.5 black 46 17 9399 N-Epithelial Innate Immune Activation-2.0-Hs-82 RAC1 44 86 white rectangle gene 0.5 black 46 17 9801 N-Epithelial Innate Immune Activation-2.0-Hs-83 RELA 47 92 white rectangle gene 0.5 black 46 17 9955 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Epithelial Innate Immune Activation-2.0-Hs.sif000066400000000000000000001011201426625374700317540ustar00rootroot000000000000000 1 2 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-82 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-82 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-82 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-82 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-68 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-44 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-48 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-1 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-44 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-34 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-34 activation N-Epithelial Innate Immune Activation-2.0-Hs-49 N-Epithelial Innate Immune Activation-2.0-Hs-57 activation N-Epithelial Innate Immune Activation-2.0-Hs-38 N-Epithelial Innate Immune Activation-2.0-Hs-57 activation N-Epithelial Innate Immune Activation-2.0-Hs-58 N-Epithelial Innate Immune Activation-2.0-Hs-57 activation N-Epithelial Innate Immune Activation-2.0-Hs-58 N-Epithelial Innate Immune Activation-2.0-Hs-57 activation N-Epithelial Innate Immune Activation-2.0-Hs-58 N-Epithelial Innate Immune Activation-2.0-Hs-57 activation N-Epithelial Innate Immune Activation-2.0-Hs-41 N-Epithelial Innate Immune Activation-2.0-Hs-57 activation N-Epithelial Innate Immune Activation-2.0-Hs-10 N-Epithelial Innate Immune Activation-2.0-Hs-57 activation N-Epithelial Innate Immune Activation-2.0-Hs-52 N-Epithelial Innate Immune Activation-2.0-Hs-39 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-6 activation N-Epithelial Innate Immune Activation-2.0-Hs-42 N-Epithelial Innate Immune Activation-2.0-Hs-6 activation N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-6 activation N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-35 activation N-Epithelial Innate Immune Activation-2.0-Hs-52 N-Epithelial Innate Immune Activation-2.0-Hs-40 activation N-Epithelial Innate Immune Activation-2.0-Hs-13 N-Epithelial Innate Immune Activation-2.0-Hs-55 activation N-Epithelial Innate Immune Activation-2.0-Hs-10 N-Epithelial Innate Immune Activation-2.0-Hs-55 activation N-Epithelial Innate Immune Activation-2.0-Hs-1 N-Epithelial Innate Immune Activation-2.0-Hs-55 activation N-Epithelial Innate Immune Activation-2.0-Hs-1 N-Epithelial Innate Immune Activation-2.0-Hs-55 activation N-Epithelial Innate Immune Activation-2.0-Hs-5 N-Epithelial Innate Immune Activation-2.0-Hs-55 activation N-Epithelial Innate Immune Activation-2.0-Hs-36 N-Epithelial Innate Immune Activation-2.0-Hs-4 activation N-Epithelial Innate Immune Activation-2.0-Hs-3 N-Epithelial Innate Immune Activation-2.0-Hs-72 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-29 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-29 activation N-Epithelial Innate Immune Activation-2.0-Hs-5 N-Epithelial Innate Immune Activation-2.0-Hs-29 activation N-Epithelial Innate Immune Activation-2.0-Hs-1 N-Epithelial Innate Immune Activation-2.0-Hs-32 activation N-Epithelial Innate Immune Activation-2.0-Hs-34 N-Epithelial Innate Immune Activation-2.0-Hs-27 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-37 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-78 activation N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-26 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-42 N-Epithelial Innate Immune Activation-2.0-Hs-63 activation N-Epithelial Innate Immune Activation-2.0-Hs-25 N-Epithelial Innate Immune Activation-2.0-Hs-63 activation N-Epithelial Innate Immune Activation-2.0-Hs-25 N-Epithelial Innate Immune Activation-2.0-Hs-63 activation N-Epithelial Innate Immune Activation-2.0-Hs-25 N-Epithelial Innate Immune Activation-2.0-Hs-63 activation N-Epithelial Innate Immune Activation-2.0-Hs-25 N-Epithelial Innate Immune Activation-2.0-Hs-63 activation N-Epithelial Innate Immune Activation-2.0-Hs-25 N-Epithelial Innate Immune Activation-2.0-Hs-63 activation N-Epithelial Innate Immune Activation-2.0-Hs-25 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-43 activation N-Epithelial Innate Immune Activation-2.0-Hs-1 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-51 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-12 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-5 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-20 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-57 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-57 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-83 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-83 N-Epithelial Innate Immune Activation-2.0-Hs-42 activation N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-59 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-10 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-5 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-1 N-Epithelial Innate Immune Activation-2.0-Hs-60 activation N-Epithelial Innate Immune Activation-2.0-Hs-36 N-Epithelial Innate Immune Activation-2.0-Hs-45 activation N-Epithelial Innate Immune Activation-2.0-Hs-42 N-Epithelial Innate Immune Activation-2.0-Hs-61 activation N-Epithelial Innate Immune Activation-2.0-Hs-44 N-Epithelial Innate Immune Activation-2.0-Hs-75 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-75 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-75 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-75 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-75 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-68 activation N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-18 activation N-Epithelial Innate Immune Activation-2.0-Hs-79 N-Epithelial Innate Immune Activation-2.0-Hs-24 activation N-Epithelial Innate Immune Activation-2.0-Hs-64 N-Epithelial Innate Immune Activation-2.0-Hs-38 activation N-Epithelial Innate Immune Activation-2.0-Hs-77 N-Epithelial Innate Immune Activation-2.0-Hs-38 activation N-Epithelial Innate Immune Activation-2.0-Hs-69 N-Epithelial Innate Immune Activation-2.0-Hs-38 activation N-Epithelial Innate Immune Activation-2.0-Hs-20 N-Epithelial Innate Immune Activation-2.0-Hs-38 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-82 activation N-Epithelial Innate Immune Activation-2.0-Hs-67 N-Epithelial Innate Immune Activation-2.0-Hs-82 activation N-Epithelial Innate Immune Activation-2.0-Hs-67 N-Epithelial Innate Immune Activation-2.0-Hs-82 activation N-Epithelial Innate Immune Activation-2.0-Hs-67 N-Epithelial Innate Immune Activation-2.0-Hs-22 activation N-Epithelial Innate Immune Activation-2.0-Hs-14 N-Epithelial Innate Immune Activation-2.0-Hs-71 activation N-Epithelial Innate Immune Activation-2.0-Hs-63 N-Epithelial Innate Immune Activation-2.0-Hs-71 activation N-Epithelial Innate Immune Activation-2.0-Hs-63 N-Epithelial Innate Immune Activation-2.0-Hs-50 activation N-Epithelial Innate Immune Activation-2.0-Hs-42 N-Epithelial Innate Immune Activation-2.0-Hs-1 activation N-Epithelial Innate Immune Activation-2.0-Hs-10 N-Epithelial Innate Immune Activation-2.0-Hs-30 activation N-Epithelial Innate Immune Activation-2.0-Hs-20 N-Epithelial Innate Immune Activation-2.0-Hs-30 activation N-Epithelial Innate Immune Activation-2.0-Hs-49 N-Epithelial Innate Immune Activation-2.0-Hs-21 activation N-Epithelial Innate Immune Activation-2.0-Hs-4 N-Epithelial Innate Immune Activation-2.0-Hs-21 activation N-Epithelial Innate Immune Activation-2.0-Hs-23 N-Epithelial Innate Immune Activation-2.0-Hs-21 activation N-Epithelial Innate Immune Activation-2.0-Hs-23 N-Epithelial Innate Immune Activation-2.0-Hs-21 activation N-Epithelial Innate Immune Activation-2.0-Hs-23 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-41 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-71 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-71 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-71 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-71 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-71 N-Epithelial Innate Immune Activation-2.0-Hs-19 activation N-Epithelial Innate Immune Activation-2.0-Hs-71 N-Epithelial Innate Immune Activation-2.0-Hs-54 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-64 activation N-Epithelial Innate Immune Activation-2.0-Hs-54 N-Epithelial Innate Immune Activation-2.0-Hs-64 activation N-Epithelial Innate Immune Activation-2.0-Hs-39 N-Epithelial Innate Immune Activation-2.0-Hs-64 activation N-Epithelial Innate Immune Activation-2.0-Hs-2 N-Epithelial Innate Immune Activation-2.0-Hs-64 activation N-Epithelial Innate Immune Activation-2.0-Hs-2 N-Epithelial Innate Immune Activation-2.0-Hs-64 activation N-Epithelial Innate Immune Activation-2.0-Hs-2 N-Epithelial Innate Immune Activation-2.0-Hs-64 activation N-Epithelial Innate Immune Activation-2.0-Hs-2 N-Epithelial Innate Immune Activation-2.0-Hs-46 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-13 N-Epithelial Innate Immune Activation-2.0-Hs-58 activation N-Epithelial Innate Immune Activation-2.0-Hs-25 N-Epithelial Innate Immune Activation-2.0-Hs-77 activation N-Epithelial Innate Immune Activation-2.0-Hs-47 N-Epithelial Innate Immune Activation-2.0-Hs-77 activation N-Epithelial Innate Immune Activation-2.0-Hs-47 N-Epithelial Innate Immune Activation-2.0-Hs-77 activation N-Epithelial Innate Immune Activation-2.0-Hs-47 N-Epithelial Innate Immune Activation-2.0-Hs-77 activation N-Epithelial Innate Immune Activation-2.0-Hs-47 N-Epithelial Innate Immune Activation-2.0-Hs-77 activation N-Epithelial Innate Immune Activation-2.0-Hs-47 N-Epithelial Innate Immune Activation-2.0-Hs-77 activation N-Epithelial Innate Immune Activation-2.0-Hs-47 N-Epithelial Innate Immune Activation-2.0-Hs-59 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-59 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-59 activation N-Epithelial Innate Immune Activation-2.0-Hs-66 N-Epithelial Innate Immune Activation-2.0-Hs-59 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-59 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-59 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 N-Epithelial Innate Immune Activation-2.0-Hs-7 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-81 activation N-Epithelial Innate Immune Activation-2.0-Hs-80 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-22 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-22 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-21 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-28 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-10 N-Epithelial Innate Immune Activation-2.0-Hs-20 activation N-Epithelial Innate Immune Activation-2.0-Hs-52 N-Epithelial Innate Immune Activation-2.0-Hs-80 activation N-Epithelial Innate Immune Activation-2.0-Hs-5 N-Epithelial Innate Immune Activation-2.0-Hs-80 activation N-Epithelial Innate Immune Activation-2.0-Hs-5 N-Epithelial Innate Immune Activation-2.0-Hs-16 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-13 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-76 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-76 N-Epithelial Innate Immune Activation-2.0-Hs-56 activation N-Epithelial Innate Immune Activation-2.0-Hs-52 N-Epithelial Innate Immune Activation-2.0-Hs-23 activation N-Epithelial Innate Immune Activation-2.0-Hs-24 N-Epithelial Innate Immune Activation-2.0-Hs-23 activation N-Epithelial Innate Immune Activation-2.0-Hs-24 N-Epithelial Innate Immune Activation-2.0-Hs-23 activation N-Epithelial Innate Immune Activation-2.0-Hs-24 N-Epithelial Innate Immune Activation-2.0-Hs-15 activation N-Epithelial Innate Immune Activation-2.0-Hs-36 N-Epithelial Innate Immune Activation-2.0-Hs-15 activation N-Epithelial Innate Immune Activation-2.0-Hs-11 N-Epithelial Innate Immune Activation-2.0-Hs-36 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-36 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-69 activation N-Epithelial Innate Immune Activation-2.0-Hs-70 N-Epithelial Innate Immune Activation-2.0-Hs-33 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-74 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-74 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-74 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-74 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-74 activation N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-53 activation N-Epithelial Innate Immune Activation-2.0-Hs-37 N-Epithelial Innate Immune Activation-2.0-Hs-53 activation N-Epithelial Innate Immune Activation-2.0-Hs-37 N-Epithelial Innate Immune Activation-2.0-Hs-53 activation N-Epithelial Innate Immune Activation-2.0-Hs-36 N-Epithelial Innate Immune Activation-2.0-Hs-53 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-53 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-15 N-Epithelial Innate Immune Activation-2.0-Hs-25 activation N-Epithelial Innate Immune Activation-2.0-Hs-64 N-Epithelial Innate Immune Activation-2.0-Hs-25 activation N-Epithelial Innate Immune Activation-2.0-Hs-64 N-Epithelial Innate Immune Activation-2.0-Hs-25 activation N-Epithelial Innate Immune Activation-2.0-Hs-64 N-Epithelial Innate Immune Activation-2.0-Hs-5 activation N-Epithelial Innate Immune Activation-2.0-Hs-33 N-Epithelial Innate Immune Activation-2.0-Hs-5 activation N-Epithelial Innate Immune Activation-2.0-Hs-34 N-Epithelial Innate Immune Activation-2.0-Hs-5 activation N-Epithelial Innate Immune Activation-2.0-Hs-34 N-Epithelial Innate Immune Activation-2.0-Hs-5 activation N-Epithelial Innate Immune Activation-2.0-Hs-34 N-Epithelial Innate Immune Activation-2.0-Hs-5 activation N-Epithelial Innate Immune Activation-2.0-Hs-62 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-73 inhibition N-Epithelial Innate Immune Activation-2.0-Hs-73 N-Epithelial Innate Immune Activation-2.0-Hs-17 activation N-Epithelial Innate Immune Activation-2.0-Hs-8 N-Epithelial Innate Immune Activation-2.0-Hs-17 activation N-Epithelial Innate Immune Activation-2.0-Hs-61 N-Epithelial Innate Immune Activation-2.0-Hs-31 activation N-Epithelial Innate Immune Activation-2.0-Hs-59 N-Epithelial Innate Immune Activation-2.0-Hs-31 activation N-Epithelial Innate Immune Activation-2.0-Hs-59 N-Epithelial Innate Immune Activation-2.0-Hs-31 activation N-Epithelial Innate Immune Activation-2.0-Hs-65 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Epithelial Mucus Hypersecretion-2.0-Hs.att000066400000000000000000000135401426625374700313400ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Epithelial Mucus Hypersecretion-2.0-Hs-1 ERK 88 56 white rectangle gene 0.5 black 46 17 ERK N-Epithelial Mucus Hypersecretion-2.0-Hs-10 SP1 79 77 white rectangle gene 0.5 black 46 17 11205 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 SRC 109 93 white rectangle gene 0.5 black 46 17 11283 N-Epithelial Mucus Hypersecretion-2.0-Hs-12 STAT6 92 113 white rectangle gene 0.5 black 46 17 11368 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 TGFA 81 93 white rectangle gene 0.5 black 46 17 11765 N-Epithelial Mucus Hypersecretion-2.0-Hs-14 TLR4 92 60 white rectangle gene 0.5 black 46 17 11850 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 TNF 76 98 white rectangle gene 0.5 black 46 17 11892 N-Epithelial Mucus Hypersecretion-2.0-Hs-16 TNFRSF1A 70 115 white rectangle gene 0.5 black 46 17 11916 N-Epithelial Mucus Hypersecretion-2.0-Hs-17 TRADD 66 131 white rectangle gene 0.5 black 46 17 12030 N-Epithelial Mucus Hypersecretion-2.0-Hs-18 TRAF2 62 144 white rectangle gene 0.5 black 46 17 12032 N-Epithelial Mucus Hypersecretion-2.0-Hs-19 EZR 62 87 white rectangle gene 0.5 black 46 17 12691 N-Epithelial Mucus Hypersecretion-2.0-Hs-20 EZR 63 80 white rectangle gene 0.5 black 46 17 12691 N-Epithelial Mucus Hypersecretion-2.0-Hs-21 CAMP 78 71 white rectangle gene 0.5 black 46 17 1472 N-Epithelial Mucus Hypersecretion-2.0-Hs-22 PLCB1 100 99 white rectangle gene 0.5 black 46 17 15917 N-Epithelial Mucus Hypersecretion-2.0-Hs-23 CCR2 113 107 white rectangle gene 0.5 black 46 17 1603 N-Epithelial Mucus Hypersecretion-2.0-Hs-24 RAPGEF3 91 73 white rectangle gene 0.5 black 46 17 16629 N-Epithelial Mucus Hypersecretion-2.0-Hs-25 CD44 103 88 white rectangle gene 0.5 black 46 17 1681 N-Epithelial Mucus Hypersecretion-2.0-Hs-26 SPDEF 4 132 white rectangle gene 0.5 black 46 17 17257 N-Epithelial Mucus Hypersecretion-2.0-Hs-27 ADAM17 86 88 white rectangle gene 0.5 black 46 17 195 N-Epithelial Mucus Hypersecretion-2.0-Hs-28 CHUK 50 163 white rectangle gene 0.5 black 46 17 1974 N-Epithelial Mucus Hypersecretion-2.0-Hs-29 CLCA1 115 90 white rectangle gene 0.5 black 46 17 2015 N-Epithelial Mucus Hypersecretion-2.0-Hs-3 PKC 85 91 white rectangle gene 0.5 black 46 17 PKC N-Epithelial Mucus Hypersecretion-2.0-Hs-30 EXOC1 61 83 white rectangle gene 0.5 black 46 17 30380 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 EGF 91 84 white rectangle gene 0.5 black 46 17 3229 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 EGFR 94 91 white rectangle gene 0.5 black 46 17 3236 N-Epithelial Mucus Hypersecretion-2.0-Hs-33 EGFR 93 82 white rectangle gene 0.5 black 46 17 3236 N-Epithelial Mucus Hypersecretion-2.0-Hs-34 EGFR 101 84 white rectangle gene 0.5 black 46 17 3236 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 ELANE 72 86 white rectangle gene 0.5 black 46 17 3309 N-Epithelial Mucus Hypersecretion-2.0-Hs-36 AHR 78 65 white rectangle gene 0.5 black 46 17 348 N-Epithelial Mucus Hypersecretion-2.0-Hs-37 HIF1A 83 70 white rectangle gene 0.5 black 46 17 4910 N-Epithelial Mucus Hypersecretion-2.0-Hs-38 FOXA2 0 132 white rectangle gene 0.5 black 46 17 5022 N-Epithelial Mucus Hypersecretion-2.0-Hs-39 IFNG 82 113 white rectangle gene 0.5 black 46 17 5438 N-Epithelial Mucus Hypersecretion-2.0-Hs-4 PRKAC 75 78 white rectangle gene 0.5 black 46 17 PRKAC N-Epithelial Mucus Hypersecretion-2.0-Hs-40 IKBKB 59 164 white rectangle gene 0.5 black 46 17 5960 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 IL13 100 92 white rectangle gene 0.5 black 46 17 5973 N-Epithelial Mucus Hypersecretion-2.0-Hs-42 IL13RA1 96 103 white rectangle gene 0.5 black 46 17 5974 N-Epithelial Mucus Hypersecretion-2.0-Hs-43 IL4R 99 105 white rectangle gene 0.5 black 46 17 6015 N-Epithelial Mucus Hypersecretion-2.0-Hs-44 CXCL8 73 55 white rectangle gene 0.5 black 46 17 6025 N-Epithelial Mucus Hypersecretion-2.0-Hs-45 AQP5 110 87 white rectangle gene 0.5 black 46 17 638 N-Epithelial Mucus Hypersecretion-2.0-Hs-48 MAP3K7 57 156 white rectangle gene 0.5 black 46 17 6859 N-Epithelial Mucus Hypersecretion-2.0-Hs-49 MAPK1 102 106 white rectangle gene 0.5 black 46 17 6871 N-Epithelial Mucus Hypersecretion-2.0-Hs-5 p38 95 121 white rectangle gene 0.5 black 46 17 p38 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 MAPK1 109 114 white rectangle gene 0.5 black 46 17 6871 N-Epithelial Mucus Hypersecretion-2.0-Hs-51 MAPK13 126 89 white rectangle gene 0.5 black 46 17 6875 N-Epithelial Mucus Hypersecretion-2.0-Hs-52 MAPK14 78 84 white rectangle gene 0.5 black 46 17 6876 N-Epithelial Mucus Hypersecretion-2.0-Hs-53 MAPK3 91 105 white rectangle gene 0.5 black 46 17 6877 N-Epithelial Mucus Hypersecretion-2.0-Hs-54 MAPK3 84 111 white rectangle gene 0.5 black 46 17 6877 N-Epithelial Mucus Hypersecretion-2.0-Hs-55 MAPK8 120 93 white rectangle gene 0.5 black 46 17 6881 N-Epithelial Mucus Hypersecretion-2.0-Hs-56 MUC5AC 85 79 white rectangle gene 0.5 black 46 17 7515 N-Epithelial Mucus Hypersecretion-2.0-Hs-57 MUC5B 97 114 white rectangle gene 0.5 black 46 17 7516 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 NFKBIA 55 165 white rectangle gene 0.5 black 46 17 7797 N-Epithelial Mucus Hypersecretion-2.0-Hs-59 NFKBIA 60 170 white rectangle gene 0.5 black 46 17 7797 N-Epithelial Mucus Hypersecretion-2.0-Hs-6 S100A12 87 64 white rectangle gene 0.5 black 46 17 10489 N-Epithelial Mucus Hypersecretion-2.0-Hs-60 NFKBIA 53 157 white rectangle gene 0.5 black 46 17 7797 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 NFKBIB 117 0 white rectangle gene 0.5 black 46 17 7798 N-Epithelial Mucus Hypersecretion-2.0-Hs-62 NFKBIE 43 165 white rectangle gene 0.5 black 46 17 7799 N-Epithelial Mucus Hypersecretion-2.0-Hs-64 PTGS2 77 104 white rectangle gene 0.5 black 46 17 9605 N-Epithelial Mucus Hypersecretion-2.0-Hs-7 S100A8 91 68 white rectangle gene 0.5 black 46 17 10498 N-Epithelial Mucus Hypersecretion-2.0-Hs-8 S100A9 89 65 white rectangle gene 0.5 black 46 17 10499 N-Epithelial Mucus Hypersecretion-2.0-Hs-9 CCL2 123 112 white rectangle gene 0.5 black 46 17 10618 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Epithelial Mucus Hypersecretion-2.0-Hs.sif000066400000000000000000000607051426625374700313360ustar00rootroot000000000000000 1 2 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-32 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-52 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-10 N-Epithelial Mucus Hypersecretion-2.0-Hs-3 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-52 N-Epithelial Mucus Hypersecretion-2.0-Hs-3 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-52 N-Epithelial Mucus Hypersecretion-2.0-Hs-28 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-62 N-Epithelial Mucus Hypersecretion-2.0-Hs-28 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-6 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-6 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-1 N-Epithelial Mucus Hypersecretion-2.0-Hs-6 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-14 N-Epithelial Mucus Hypersecretion-2.0-Hs-4 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-52 N-Epithelial Mucus Hypersecretion-2.0-Hs-4 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-9 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-23 N-Epithelial Mucus Hypersecretion-2.0-Hs-9 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-23 N-Epithelial Mucus Hypersecretion-2.0-Hs-9 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-23 N-Epithelial Mucus Hypersecretion-2.0-Hs-9 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-23 N-Epithelial Mucus Hypersecretion-2.0-Hs-54 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-54 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-54 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-54 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-54 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-54 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-53 N-Epithelial Mucus Hypersecretion-2.0-Hs-34 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-34 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-34 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-34 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-29 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-42 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-42 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-43 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-43 N-Epithelial Mucus Hypersecretion-2.0-Hs-41 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-45 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-50 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-49 N-Epithelial Mucus Hypersecretion-2.0-Hs-7 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-7 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-14 N-Epithelial Mucus Hypersecretion-2.0-Hs-43 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-12 N-Epithelial Mucus Hypersecretion-2.0-Hs-43 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-12 N-Epithelial Mucus Hypersecretion-2.0-Hs-43 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-12 N-Epithelial Mucus Hypersecretion-2.0-Hs-33 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-33 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-33 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-49 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-57 N-Epithelial Mucus Hypersecretion-2.0-Hs-23 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-22 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-31 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-64 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-13 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-37 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-60 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-60 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-60 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-60 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-60 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-26 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-38 N-Epithelial Mucus Hypersecretion-2.0-Hs-42 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-12 N-Epithelial Mucus Hypersecretion-2.0-Hs-18 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-48 N-Epithelial Mucus Hypersecretion-2.0-Hs-27 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-27 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-27 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-13 N-Epithelial Mucus Hypersecretion-2.0-Hs-27 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-13 N-Epithelial Mucus Hypersecretion-2.0-Hs-27 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-13 N-Epithelial Mucus Hypersecretion-2.0-Hs-27 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-13 N-Epithelial Mucus Hypersecretion-2.0-Hs-36 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-36 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-44 N-Epithelial Mucus Hypersecretion-2.0-Hs-25 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-16 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-17 N-Epithelial Mucus Hypersecretion-2.0-Hs-16 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-17 N-Epithelial Mucus Hypersecretion-2.0-Hs-40 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-48 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-40 N-Epithelial Mucus Hypersecretion-2.0-Hs-48 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-28 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-13 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-13 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-13 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-3 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-3 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-19 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-19 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-30 N-Epithelial Mucus Hypersecretion-2.0-Hs-35 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-20 N-Epithelial Mucus Hypersecretion-2.0-Hs-10 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-8 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-8 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-1 N-Epithelial Mucus Hypersecretion-2.0-Hs-8 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-14 N-Epithelial Mucus Hypersecretion-2.0-Hs-24 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-21 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-56 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-64 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-15 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-16 N-Epithelial Mucus Hypersecretion-2.0-Hs-53 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-57 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-32 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-55 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-55 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-55 N-Epithelial Mucus Hypersecretion-2.0-Hs-11 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-55 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-61 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-61 N-Epithelial Mucus Hypersecretion-2.0-Hs-17 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-18 N-Epithelial Mucus Hypersecretion-2.0-Hs-17 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-18 N-Epithelial Mucus Hypersecretion-2.0-Hs-12 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-5 N-Epithelial Mucus Hypersecretion-2.0-Hs-22 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-3 N-Epithelial Mucus Hypersecretion-2.0-Hs-22 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-3 N-Epithelial Mucus Hypersecretion-2.0-Hs-59 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-59 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-59 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-59 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-59 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-39 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-12 N-Epithelial Mucus Hypersecretion-2.0-Hs-39 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-12 N-Epithelial Mucus Hypersecretion-2.0-Hs-39 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-64 N-Epithelial Mucus Hypersecretion-2.0-Hs-5 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-57 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-58 inhibition N-Epithelial Mucus Hypersecretion-2.0-Hs-58 N-Epithelial Mucus Hypersecretion-2.0-Hs-29 activation N-Epithelial Mucus Hypersecretion-2.0-Hs-51 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Fibrosis-2.0-Hs.att000066400000000000000000000164671426625374700250120ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Fibrosis-2.0-Hs-1 2 ITGAV ITGB3 89 110 white rectangle gene,gene 0.5 black 46 17 6150,/,6156 N-Fibrosis-2.0-Hs-10 COL4 127 41 white rectangle gene 0.5 black 46 17 COL4 N-Fibrosis-2.0-Hs-100 ATF2 130 63 white rectangle gene 0.5 black 46 17 784 N-Fibrosis-2.0-Hs-101 NOX4 85 67 white rectangle gene 0.5 black 46 17 7891 N-Fibrosis-2.0-Hs-102 OLR1 106 70 white rectangle gene 0.5 black 46 17 8133 N-Fibrosis-2.0-Hs-103 PPARG 89 64 white rectangle gene 0.5 black 46 17 9236 N-Fibrosis-2.0-Hs-104 PTCH1 147 110 white rectangle gene 0.5 black 46 17 9585 N-Fibrosis-2.0-Hs-105 Idiopathic Pulmonary Fibrosis 98 124 white rectangle gene 0.5 black 46 17 D054990 N-Fibrosis-2.0-Hs-11 FZD 90 117 white rectangle gene 0.5 black 46 17 FZD N-Fibrosis-2.0-Hs-13 TGFB 116 73 white rectangle gene 0.5 black 46 17 TGFB N-Fibrosis-2.0-Hs-14 Wnt 95 109 white rectangle gene 0.5 black 46 17 15983 N-Fibrosis-2.0-Hs-15 p38 118 65 white rectangle gene 0.5 black 46 17 p38 N-Fibrosis-2.0-Hs-16 ROCK1 90 84 white rectangle gene 0.5 black 46 17 10251 N-Fibrosis-2.0-Hs-17 BGN 95 82 white rectangle gene 0.5 black 46 17 1044 N-Fibrosis-2.0-Hs-18 CCL18 92 55 white rectangle gene 0.5 black 46 17 10616 N-Fibrosis-2.0-Hs-19 BMP4 75 59 white rectangle gene 0.5 black 46 17 1071 N-Fibrosis-2.0-Hs-20 SHH 131 106 white rectangle gene 0.5 black 46 17 10848 N-Fibrosis-2.0-Hs-21 SMO 160 112 white rectangle gene 0.5 black 46 17 11119 N-Fibrosis-2.0-Hs-22 SP1 80 65 white rectangle gene 0.5 black 46 17 11205 N-Fibrosis-2.0-Hs-23 SRC 84 98 white rectangle gene 0.5 black 46 17 11283 N-Fibrosis-2.0-Hs-24 SRF 89 69 white rectangle gene 0.5 black 46 17 11291 N-Fibrosis-2.0-Hs-25 STAT6 79 56 white rectangle gene 0.5 black 46 17 11368 N-Fibrosis-2.0-Hs-26 TGFA 53 102 white rectangle gene 0.5 black 46 17 11765 N-Fibrosis-2.0-Hs-27 TGFB1 96 75 white rectangle gene 0.5 black 46 17 11766 N-Fibrosis-2.0-Hs-28 TGFBR1 103 65 white rectangle gene 0.5 black 46 17 11772 N-Fibrosis-2.0-Hs-29 TGFBR1 98 70 white rectangle gene 0.5 black 46 17 11772 N-Fibrosis-2.0-Hs-3 4 ITGAV ITGB8 83 77 white rectangle gene,gene 0.5 black 46 17 6150,/,6163 N-Fibrosis-2.0-Hs-30 TGFBR2 104 73 white rectangle gene 0.5 black 46 17 11773 N-Fibrosis-2.0-Hs-31 TGFBR2 96 88 white rectangle gene 0.5 black 46 17 11773 N-Fibrosis-2.0-Hs-32 TGFBR3 98 63 white rectangle gene 0.5 black 46 17 11774 N-Fibrosis-2.0-Hs-33 TGIF1 112 96 white rectangle gene 0.5 black 46 17 11776 N-Fibrosis-2.0-Hs-34 THBS1 81 88 white rectangle gene 0.5 black 46 17 11785 N-Fibrosis-2.0-Hs-36 TNF 107 37 white rectangle gene 0.5 black 46 17 11892 N-Fibrosis-2.0-Hs-37 VIM 85 81 white rectangle gene 0.5 black 46 17 12692 N-Fibrosis-2.0-Hs-38 WNT1 107 115 white rectangle gene 0.5 black 46 17 12774 N-Fibrosis-2.0-Hs-39 WNT10B 93 134 white rectangle gene 0.5 black 46 17 12775 N-Fibrosis-2.0-Hs-40 WNT7B 97 135 white rectangle gene 0.5 black 46 17 12787 N-Fibrosis-2.0-Hs-42 ACTA2 112 99 white rectangle gene 0.5 black 46 17 130 N-Fibrosis-2.0-Hs-43 WNT3A 101 87 white rectangle gene 0.5 black 46 17 15983 N-Fibrosis-2.0-Hs-44 WNT5B 4 18 white rectangle gene 0.5 black 46 17 16265 N-Fibrosis-2.0-Hs-45 CDH1 87 77 white rectangle gene 0.5 black 46 17 1748 N-Fibrosis-2.0-Hs-46 CEBPB 89 80 white rectangle gene 0.5 black 46 17 1834 N-Fibrosis-2.0-Hs-47 RETNLB 119 90 white rectangle gene 0.5 black 46 17 20388 N-Fibrosis-2.0-Hs-48 COL1A1 130 58 white rectangle gene 0.5 black 46 17 2197 N-Fibrosis-2.0-Hs-49 COL1A2 67 60 white rectangle gene 0.5 black 46 17 2198 N-Fibrosis-2.0-Hs-5 6 7 SMAD2 SMAD3 SMAD4 107 59 white rectangle gene,gene,gene 0.5 black 46 17 6768,/,6769,/,6770 N-Fibrosis-2.0-Hs-50 COL3A1 121 37 white rectangle gene 0.5 black 46 17 2201 N-Fibrosis-2.0-Hs-51 VCAN 93 64 white rectangle gene 0.5 black 46 17 2464 N-Fibrosis-2.0-Hs-52 CTNNB1 99 96 white rectangle gene 0.5 black 46 17 2514 N-Fibrosis-2.0-Hs-53 CTNNB1 91 90 white rectangle gene 0.5 black 46 17 2514 N-Fibrosis-2.0-Hs-54 DCN 92 67 white rectangle gene 0.5 black 46 17 2705 N-Fibrosis-2.0-Hs-55 DKK1 94 94 white rectangle gene 0.5 black 46 17 2891 N-Fibrosis-2.0-Hs-56 DVL3 92 108 white rectangle gene 0.5 black 46 17 3087 N-Fibrosis-2.0-Hs-57 S1PR2 122 106 white rectangle gene 0.5 black 46 17 3169 N-Fibrosis-2.0-Hs-58 EFEMP2 102 100 white rectangle gene 0.5 black 46 17 3219 N-Fibrosis-2.0-Hs-59 EGF 51 98 white rectangle gene 0.5 black 46 17 3229 N-Fibrosis-2.0-Hs-60 EGFR 62 94 white rectangle gene 0.5 black 46 17 3236 N-Fibrosis-2.0-Hs-61 EGR1 68 80 white rectangle gene 0.5 black 46 17 3238 N-Fibrosis-2.0-Hs-62 ELN 103 90 white rectangle gene 0.5 black 46 17 3327 N-Fibrosis-2.0-Hs-63 AGTR1 115 66 white rectangle gene 0.5 black 46 17 336 N-Fibrosis-2.0-Hs-64 AGTR2 124 72 white rectangle gene 0.5 black 46 17 338 N-Fibrosis-2.0-Hs-66 F2 64 72 white rectangle gene 0.5 black 46 17 3535 N-Fibrosis-2.0-Hs-67 F2R 77 74 white rectangle gene 0.5 black 46 17 3537 N-Fibrosis-2.0-Hs-68 FGF2 152 3 white rectangle gene 0.5 black 46 17 3676 N-Fibrosis-2.0-Hs-69 FGFR1 149 0 white rectangle gene 0.5 black 46 17 3688 N-Fibrosis-2.0-Hs-70 FN1 87 65 white rectangle gene 0.5 black 46 17 3778 N-Fibrosis-2.0-Hs-71 FOS 84 74 white rectangle gene 0.5 black 46 17 3796 N-Fibrosis-2.0-Hs-72 AKT1 115 77 white rectangle gene 0.5 black 46 17 391 N-Fibrosis-2.0-Hs-73 AKT1 131 81 white rectangle gene 0.5 black 46 17 391 N-Fibrosis-2.0-Hs-74 FZD6 0 16 white rectangle gene 0.5 black 46 17 4044 N-Fibrosis-2.0-Hs-75 GLI1 169 112 white rectangle gene 0.5 black 46 17 4317 N-Fibrosis-2.0-Hs-76 GSK3B 107 95 white rectangle gene 0.5 black 46 17 4617 N-Fibrosis-2.0-Hs-77 GSK3B 107 84 white rectangle gene 0.5 black 46 17 4617 N-Fibrosis-2.0-Hs-78 HSPG2 88 73 white rectangle gene 0.5 black 46 17 5273 N-Fibrosis-2.0-Hs-79 APCS 141 54 white rectangle gene 0.5 black 46 17 584 N-Fibrosis-2.0-Hs-8 AKT 109 90 white rectangle gene 0.5 black 46 17 AKT N-Fibrosis-2.0-Hs-80 IL10 88 43 white rectangle gene 0.5 black 46 17 5962 N-Fibrosis-2.0-Hs-81 IL13 81 67 white rectangle gene 0.5 black 46 17 5973 N-Fibrosis-2.0-Hs-82 IL4 89 59 white rectangle gene 0.5 black 46 17 6014 N-Fibrosis-2.0-Hs-83 ILK 142 83 white rectangle gene 0.5 black 46 17 6040 N-Fibrosis-2.0-Hs-84 ITGB3 98 65 white rectangle gene 0.5 black 46 17 6156 N-Fibrosis-2.0-Hs-85 JUN 102 79 white rectangle gene 0.5 black 46 17 6204 N-Fibrosis-2.0-Hs-86 JUNB 125 39 white rectangle gene 0.5 black 46 17 6205 N-Fibrosis-2.0-Hs-87 LGALS3 120 109 white rectangle gene 0.5 black 46 17 6563 N-Fibrosis-2.0-Hs-88 RHOA 117 110 white rectangle gene 0.5 black 46 17 667 N-Fibrosis-2.0-Hs-89 SMAD2 101 60 white rectangle gene 0.5 black 46 17 6768 N-Fibrosis-2.0-Hs-9 COL1 108 63 white rectangle gene 0.5 black 46 17 COL1 N-Fibrosis-2.0-Hs-90 SMAD2 104 54 white rectangle gene 0.5 black 46 17 6768 N-Fibrosis-2.0-Hs-91 SMAD3 104 63 white rectangle gene 0.5 black 46 17 6769 N-Fibrosis-2.0-Hs-92 SMAD3 110 66 white rectangle gene 0.5 black 46 17 6769 N-Fibrosis-2.0-Hs-93 SMAD4 117 48 white rectangle gene 0.5 black 46 17 6770 N-Fibrosis-2.0-Hs-94 SMAD7 101 67 white rectangle gene 0.5 black 46 17 6773 N-Fibrosis-2.0-Hs-95 SMAD7 105 49 white rectangle gene 0.5 black 46 17 6773 N-Fibrosis-2.0-Hs-96 MAPK1 72 95 white rectangle gene 0.5 black 46 17 6871 N-Fibrosis-2.0-Hs-97 MAPK14 94 51 white rectangle gene 0.5 black 46 17 6876 N-Fibrosis-2.0-Hs-98 MAPK3 73 97 white rectangle gene 0.5 black 46 17 6877 N-Fibrosis-2.0-Hs-99 MAPK8 100 108 white rectangle gene 0.5 black 46 17 6881 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Fibrosis-2.0-Hs.sif000066400000000000000000000467671426625374700250110ustar00rootroot000000000000000 1 2 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-60 activation N-Fibrosis-2.0-Hs-61 N-Fibrosis-2.0-Hs-46 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-46 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-19 inhibition N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-19 inhibition N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-97 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-73 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-79 inhibition N-Fibrosis-2.0-Hs-48 N-Fibrosis-2.0-Hs-55 inhibition N-Fibrosis-2.0-Hs-14 N-Fibrosis-2.0-Hs-5 6 7 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-5 6 7 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-5 6 7 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-5 6 7 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-5 6 7 activation N-Fibrosis-2.0-Hs-93 N-Fibrosis-2.0-Hs-1 2 activation N-Fibrosis-2.0-Hs-23 N-Fibrosis-2.0-Hs-1 2 activation N-Fibrosis-2.0-Hs-23 N-Fibrosis-2.0-Hs-72 activation N-Fibrosis-2.0-Hs-77 N-Fibrosis-2.0-Hs-72 activation N-Fibrosis-2.0-Hs-77 N-Fibrosis-2.0-Hs-72 activation N-Fibrosis-2.0-Hs-77 N-Fibrosis-2.0-Hs-72 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-8 N-Fibrosis-2.0-Hs-43 activation N-Fibrosis-2.0-Hs-8 N-Fibrosis-2.0-Hs-32 inhibition N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-32 inhibition N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-32 inhibition N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-94 inhibition N-Fibrosis-2.0-Hs-28 N-Fibrosis-2.0-Hs-94 inhibition N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-94 inhibition N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-94 inhibition N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-94 inhibition N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-94 inhibition N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-94 inhibition N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-94 inhibition N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-56 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-14 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-14 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-14 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-14 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-14 activation N-Fibrosis-2.0-Hs-11 N-Fibrosis-2.0-Hs-14 activation N-Fibrosis-2.0-Hs-11 N-Fibrosis-2.0-Hs-57 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-66 activation N-Fibrosis-2.0-Hs-67 N-Fibrosis-2.0-Hs-66 activation N-Fibrosis-2.0-Hs-67 N-Fibrosis-2.0-Hs-66 activation N-Fibrosis-2.0-Hs-67 N-Fibrosis-2.0-Hs-66 activation N-Fibrosis-2.0-Hs-67 N-Fibrosis-2.0-Hs-66 activation N-Fibrosis-2.0-Hs-67 N-Fibrosis-2.0-Hs-66 activation N-Fibrosis-2.0-Hs-67 N-Fibrosis-2.0-Hs-66 activation N-Fibrosis-2.0-Hs-67 N-Fibrosis-2.0-Hs-3 4 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-3 4 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-77 inhibition N-Fibrosis-2.0-Hs-76 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-18 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-18 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-18 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-18 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-18 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-61 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-81 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-68 activation N-Fibrosis-2.0-Hs-69 N-Fibrosis-2.0-Hs-68 activation N-Fibrosis-2.0-Hs-69 N-Fibrosis-2.0-Hs-88 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-34 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-34 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-34 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-34 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-34 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-105 activation N-Fibrosis-2.0-Hs-38 N-Fibrosis-2.0-Hs-105 activation N-Fibrosis-2.0-Hs-38 N-Fibrosis-2.0-Hs-105 activation N-Fibrosis-2.0-Hs-14 N-Fibrosis-2.0-Hs-105 activation N-Fibrosis-2.0-Hs-39 N-Fibrosis-2.0-Hs-105 activation N-Fibrosis-2.0-Hs-40 N-Fibrosis-2.0-Hs-28 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-28 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-28 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-28 activation N-Fibrosis-2.0-Hs-90 N-Fibrosis-2.0-Hs-28 activation N-Fibrosis-2.0-Hs-90 N-Fibrosis-2.0-Hs-28 activation N-Fibrosis-2.0-Hs-92 N-Fibrosis-2.0-Hs-28 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-28 activation N-Fibrosis-2.0-Hs-15 N-Fibrosis-2.0-Hs-53 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-59 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-64 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-64 activation N-Fibrosis-2.0-Hs-15 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-25 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-97 N-Fibrosis-2.0-Hs-82 activation N-Fibrosis-2.0-Hs-18 N-Fibrosis-2.0-Hs-26 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-26 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-26 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-26 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-26 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-26 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-26 activation N-Fibrosis-2.0-Hs-60 N-Fibrosis-2.0-Hs-18 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-13 activation N-Fibrosis-2.0-Hs-30 N-Fibrosis-2.0-Hs-47 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-47 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-47 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-47 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-83 activation N-Fibrosis-2.0-Hs-73 N-Fibrosis-2.0-Hs-83 activation N-Fibrosis-2.0-Hs-73 N-Fibrosis-2.0-Hs-102 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-33 inhibition N-Fibrosis-2.0-Hs-62 N-Fibrosis-2.0-Hs-87 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-52 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-52 activation N-Fibrosis-2.0-Hs-99 N-Fibrosis-2.0-Hs-67 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-22 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-22 activation N-Fibrosis-2.0-Hs-49 N-Fibrosis-2.0-Hs-22 activation N-Fibrosis-2.0-Hs-49 N-Fibrosis-2.0-Hs-22 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-63 activation N-Fibrosis-2.0-Hs-27 N-Fibrosis-2.0-Hs-31 activation N-Fibrosis-2.0-Hs-30 N-Fibrosis-2.0-Hs-36 activation N-Fibrosis-2.0-Hs-95 N-Fibrosis-2.0-Hs-93 activation N-Fibrosis-2.0-Hs-86 N-Fibrosis-2.0-Hs-93 activation N-Fibrosis-2.0-Hs-10 N-Fibrosis-2.0-Hs-93 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-93 activation N-Fibrosis-2.0-Hs-50 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-98 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-53 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-53 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-96 N-Fibrosis-2.0-Hs-23 activation N-Fibrosis-2.0-Hs-31 N-Fibrosis-2.0-Hs-29 activation N-Fibrosis-2.0-Hs-28 N-Fibrosis-2.0-Hs-29 activation N-Fibrosis-2.0-Hs-28 N-Fibrosis-2.0-Hs-29 activation N-Fibrosis-2.0-Hs-28 N-Fibrosis-2.0-Hs-30 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-30 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-30 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-30 activation N-Fibrosis-2.0-Hs-92 N-Fibrosis-2.0-Hs-30 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-38 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-85 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-85 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-103 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-103 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-103 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-28 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-28 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-28 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-28 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-32 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-70 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-45 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-45 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-45 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-77 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-5 6 7 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-29 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-29 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-54 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-54 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-24 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-24 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-16 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-17 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-17 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-37 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-37 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-37 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-37 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-78 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-78 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-71 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-30 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-30 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-30 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-30 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-30 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-84 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-72 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-53 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-62 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-102 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-51 N-Fibrosis-2.0-Hs-27 inhibition N-Fibrosis-2.0-Hs-55 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-101 N-Fibrosis-2.0-Hs-27 activation N-Fibrosis-2.0-Hs-101 N-Fibrosis-2.0-Hs-90 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-90 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-90 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-90 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-90 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-90 activation N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-20 activation N-Fibrosis-2.0-Hs-42 N-Fibrosis-2.0-Hs-20 inhibition N-Fibrosis-2.0-Hs-104 N-Fibrosis-2.0-Hs-8 activation N-Fibrosis-2.0-Hs-77 N-Fibrosis-2.0-Hs-8 activation N-Fibrosis-2.0-Hs-77 N-Fibrosis-2.0-Hs-8 activation N-Fibrosis-2.0-Hs-77 N-Fibrosis-2.0-Hs-80 activation N-Fibrosis-2.0-Hs-18 N-Fibrosis-2.0-Hs-21 activation N-Fibrosis-2.0-Hs-75 N-Fibrosis-2.0-Hs-21 activation N-Fibrosis-2.0-Hs-75 N-Fibrosis-2.0-Hs-44 activation N-Fibrosis-2.0-Hs-74 N-Fibrosis-2.0-Hs-92 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-92 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-92 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-92 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-92 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-92 activation N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-95 inhibition N-Fibrosis-2.0-Hs-91 N-Fibrosis-2.0-Hs-95 inhibition N-Fibrosis-2.0-Hs-89 N-Fibrosis-2.0-Hs-15 activation N-Fibrosis-2.0-Hs-100 N-Fibrosis-2.0-Hs-15 activation N-Fibrosis-2.0-Hs-100 N-Fibrosis-2.0-Hs-15 activation N-Fibrosis-2.0-Hs-100 N-Fibrosis-2.0-Hs-15 activation N-Fibrosis-2.0-Hs-100 N-Fibrosis-2.0-Hs-15 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-15 activation N-Fibrosis-2.0-Hs-9 N-Fibrosis-2.0-Hs-48 activation N-Fibrosis-2.0-Hs-63 N-Fibrosis-2.0-Hs-11 activation N-Fibrosis-2.0-Hs-56 N-Fibrosis-2.0-Hs-104 inhibition N-Fibrosis-2.0-Hs-21 N-Fibrosis-2.0-Hs-58 activation N-Fibrosis-2.0-Hs-62 N-Fibrosis-2.0-Hs-58 activation N-Fibrosis-2.0-Hs-62 N-Fibrosis-2.0-Hs-76 inhibition N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-76 inhibition N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-76 inhibition N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-76 inhibition N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-76 inhibition N-Fibrosis-2.0-Hs-52 N-Fibrosis-2.0-Hs-76 inhibition N-Fibrosis-2.0-Hs-52 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Growth Factor-2.0-Hs.att000066400000000000000000000301051426625374700256640ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Growth Factor-2.0-Hs-1 2 3 4 5 TFDP1 E2F4 SMAD3 SMAD4 RBL1 107 87 white rectangle gene,gene,gene,gene,gene 0.5 black 46 17 11749,/,3118,/,6769,/,6770,/,9893 N-Growth Factor-2.0-Hs-100 FGF2 24 37 white rectangle gene 0.5 black 46 17 3676 N-Growth Factor-2.0-Hs-101 FGF7 33 38 white rectangle gene 0.5 black 46 17 3685 N-Growth Factor-2.0-Hs-102 FGFR1 24 40 white rectangle gene 0.5 black 46 17 3688 N-Growth Factor-2.0-Hs-103 FGFR2 29 38 white rectangle gene 0.5 black 46 17 3689 N-Growth Factor-2.0-Hs-104 FLT4 122 112 white rectangle gene 0.5 black 46 17 3767 N-Growth Factor-2.0-Hs-106 FZD1 55 125 white rectangle gene 0.5 black 46 17 4038 N-Growth Factor-2.0-Hs-107 GAB1 61 135 white rectangle gene 0.5 black 46 17 4066 N-Growth Factor-2.0-Hs-108 GRB2 57 141 white rectangle gene 0.5 black 46 17 4566 N-Growth Factor-2.0-Hs-109 GRP 61 129 white rectangle gene 0.5 black 46 17 4605 N-Growth Factor-2.0-Hs-11 12 TGFB1 TGFBR2 135 49 white rectangle gene,gene 0.5 black 46 17 11766,/,11773 N-Growth Factor-2.0-Hs-110 GRPR 65 125 white rectangle gene 0.5 black 46 17 4609 N-Growth Factor-2.0-Hs-111 GSK3B 102 133 white rectangle gene 0.5 black 46 17 4617 N-Growth Factor-2.0-Hs-112 GSK3B 114 129 white rectangle gene 0.5 black 46 17 4617 N-Growth Factor-2.0-Hs-113 GSK3B 96 137 white rectangle gene 0.5 black 46 17 4617 N-Growth Factor-2.0-Hs-114 HGF 72 152 white rectangle gene 0.5 black 46 17 4893 N-Growth Factor-2.0-Hs-115 IGF1 168 67 white rectangle gene 0.5 black 46 17 5464 N-Growth Factor-2.0-Hs-116 IGF1R 161 69 white rectangle gene 0.5 black 46 17 5465 N-Growth Factor-2.0-Hs-117 IGF1R 156 73 white rectangle gene 0.5 black 46 17 5465 N-Growth Factor-2.0-Hs-118 IGF2 155 70 white rectangle gene 0.5 black 46 17 5466 N-Growth Factor-2.0-Hs-119 ILK 90 143 white rectangle gene 0.5 black 46 17 6040 N-Growth Factor-2.0-Hs-120 IRS2 165 70 white rectangle gene 0.5 black 46 17 6126 N-Growth Factor-2.0-Hs-121 JUN 100 58 white rectangle gene 0.5 black 46 17 6204 N-Growth Factor-2.0-Hs-122 JUN 103 65 white rectangle gene 0.5 black 46 17 6204 N-Growth Factor-2.0-Hs-123 KDR 111 105 white rectangle gene 0.5 black 46 17 6307 N-Growth Factor-2.0-Hs-124 KRAS 1 143 white rectangle gene 0.5 black 46 17 6407 N-Growth Factor-2.0-Hs-125 AREG 67 114 white rectangle gene 0.5 black 46 17 651 N-Growth Factor-2.0-Hs-126 SMAD1 108 84 white rectangle gene 0.5 black 46 17 6767 N-Growth Factor-2.0-Hs-127 SMAD2 109 77 white rectangle gene 0.5 black 46 17 6768 N-Growth Factor-2.0-Hs-128 SMAD2 121 71 white rectangle gene 0.5 black 46 17 6768 N-Growth Factor-2.0-Hs-129 SMAD3 118 59 white rectangle gene 0.5 black 46 17 6769 N-Growth Factor-2.0-Hs-13 14 15 TGFB1 TGFBR2 TGFBR3 142 49 white rectangle gene,gene,gene 0.5 black 46 17 11766,/,11773,/,11774 N-Growth Factor-2.0-Hs-130 SMAD3 122 72 white rectangle gene 0.5 black 46 17 6769 N-Growth Factor-2.0-Hs-131 SMAD4 119 62 white rectangle gene 0.5 black 46 17 6770 N-Growth Factor-2.0-Hs-132 SMAD5 105 84 white rectangle gene 0.5 black 46 17 6771 N-Growth Factor-2.0-Hs-133 SMAD7 125 68 white rectangle gene 0.5 black 46 17 6773 N-Growth Factor-2.0-Hs-134 MAP2K1 86 112 white rectangle gene 0.5 black 46 17 6840 N-Growth Factor-2.0-Hs-135 MAPK1 77 97 white rectangle gene 0.5 black 46 17 6871 N-Growth Factor-2.0-Hs-136 MAPK1 75 92 white rectangle gene 0.5 black 46 17 6871 N-Growth Factor-2.0-Hs-137 MAPK1 79 93 white rectangle gene 0.5 black 46 17 6871 N-Growth Factor-2.0-Hs-138 MAPK3 78 112 white rectangle gene 0.5 black 46 17 6877 N-Growth Factor-2.0-Hs-139 MAPK3 73 159 white rectangle gene 0.5 black 46 17 6877 N-Growth Factor-2.0-Hs-140 MAPK3 81 118 white rectangle gene 0.5 black 46 17 6877 N-Growth Factor-2.0-Hs-141 MAPK3 70 159 white rectangle gene 0.5 black 46 17 6877 N-Growth Factor-2.0-Hs-142 MAPK3 77 118 white rectangle gene 0.5 black 46 17 6877 N-Growth Factor-2.0-Hs-143 MET 73 140 white rectangle gene 0.5 black 46 17 7029 N-Growth Factor-2.0-Hs-144 MET 66 132 white rectangle gene 0.5 black 46 17 7029 N-Growth Factor-2.0-Hs-145 MET 72 147 white rectangle gene 0.5 black 46 17 7029 N-Growth Factor-2.0-Hs-146 MYCN 115 121 white rectangle gene 0.5 black 46 17 7559 N-Growth Factor-2.0-Hs-147 MYCN 110 127 white rectangle gene 0.5 black 46 17 7559 N-Growth Factor-2.0-Hs-148 NRG1 51 92 white rectangle gene 0.5 black 46 17 7997 N-Growth Factor-2.0-Hs-149 NRG2 50 94 white rectangle gene 0.5 black 46 17 7998 N-Growth Factor-2.0-Hs-150 NRG3 47 97 white rectangle gene 0.5 black 46 17 7999 N-Growth Factor-2.0-Hs-152 PDGFRA 82 107 white rectangle gene 0.5 black 46 17 8803 N-Growth Factor-2.0-Hs-153 PDGFRB 75 87 white rectangle gene 0.5 black 46 17 8804 N-Growth Factor-2.0-Hs-154 PDPK1 102 155 white rectangle gene 0.5 black 46 17 8816 N-Growth Factor-2.0-Hs-155 PDPK1 106 161 white rectangle gene 0.5 black 46 17 8816 N-Growth Factor-2.0-Hs-156 PIGF 0 47 white rectangle gene 0.5 black 46 17 8962 N-Growth Factor-2.0-Hs-157 PLAU 71 72 white rectangle gene 0.5 black 46 17 9052 N-Growth Factor-2.0-Hs-158 PRKCA 79 132 white rectangle gene 0.5 black 46 17 9393 N-Growth Factor-2.0-Hs-159 PTEN 92 151 white rectangle gene 0.5 black 46 17 9588 N-Growth Factor-2.0-Hs-16 17 TGFBR1 TGFBR2 138 53 white rectangle gene,gene 0.5 black 46 17 11772,/,11773 N-Growth Factor-2.0-Hs-160 PTEN 96 156 white rectangle gene 0.5 black 46 17 9588 N-Growth Factor-2.0-Hs-161 PTEN 92 158 white rectangle gene 0.5 black 46 17 9588 N-Growth Factor-2.0-Hs-162 PTEN 94 158 white rectangle gene 0.5 black 46 17 9588 N-Growth Factor-2.0-Hs-163 PTGS2 87 110 white rectangle gene 0.5 black 46 17 9605 N-Growth Factor-2.0-Hs-164 PTN 104 8 white rectangle gene 0.5 black 46 17 9630 N-Growth Factor-2.0-Hs-165 PTPN11 57 146 white rectangle gene 0.5 black 46 17 9644 N-Growth Factor-2.0-Hs-166 RAF1 5 139 white rectangle gene 0.5 black 46 17 9829 N-Growth Factor-2.0-Hs-167 RAF1 1 137 white rectangle gene 0.5 black 46 17 9829 N-Growth Factor-2.0-Hs-168 RAF1 2 138 white rectangle gene 0.5 black 46 17 9829 N-Growth Factor-2.0-Hs-169 RAF1 12 134 white rectangle gene 0.5 black 46 17 9829 N-Growth Factor-2.0-Hs-170 RB1 96 79 white rectangle gene 0.5 black 46 17 9884 N-Growth Factor-2.0-Hs-171 RB1 98 82 white rectangle gene 0.5 black 46 17 9884 N-Growth Factor-2.0-Hs-172 RB1 100 81 white rectangle gene 0.5 black 46 17 9884 N-Growth Factor-2.0-Hs-173 RB1 97 83 white rectangle gene 0.5 black 46 17 9884 N-Growth Factor-2.0-Hs-18 19 ADRB1 ARRB1 51 118 white rectangle gene,gene 0.5 black 46 17 285,/,711 N-Growth Factor-2.0-Hs-20 21 ADRB1 ARRB2 53 122 white rectangle gene,gene 0.5 black 46 17 285,/,712 N-Growth Factor-2.0-Hs-22 23 EGFR EGFR 62 119 white rectangle gene,gene 0.5 black 46 17 3236,/,3236 N-Growth Factor-2.0-Hs-24 25 EGFR ERBB2 60 113 white rectangle gene,gene 0.5 black 46 17 3236,/,3430 N-Growth Factor-2.0-Hs-26 27 EGFR ERBB3 55 114 white rectangle gene,gene 0.5 black 46 17 3236,/,3431 N-Growth Factor-2.0-Hs-28 29 EIF4E EIF4EBP1 144 146 white rectangle gene,gene 0.5 black 46 17 3287,/,3288 N-Growth Factor-2.0-Hs-30 31 ERBB2 ERBB3 54 95 white rectangle gene,gene 0.5 black 46 17 3430,/,3431 N-Growth Factor-2.0-Hs-32 33 ERBB2 ERBB4 52 100 white rectangle gene,gene 0.5 black 46 17 3430,/,3432 N-Growth Factor-2.0-Hs-34 35 ERBB3 PIK3R2 115 0 white rectangle gene,gene 0.5 black 46 17 3431,/,8980 N-Growth Factor-2.0-Hs-36 37 ERBB4 ERBB4 45 104 white rectangle gene,gene 0.5 black 46 17 3432,/,3432 N-Growth Factor-2.0-Hs-38 39 ITGA5 ITGB1 50 122 white rectangle gene,gene 0.5 black 46 17 6141,/,6153 N-Growth Factor-2.0-Hs-40 41 42 SMAD2 SMAD3 SMAD4 116 68 white rectangle gene,gene,gene 0.5 black 46 17 6768,/,6769,/,6770 N-Growth Factor-2.0-Hs-43 44 PDGFRB PLAUR 73 79 white rectangle gene,gene 0.5 black 46 17 8804,/,9053 N-Growth Factor-2.0-Hs-45 CAMK2_family 31 13 white rectangle gene 0.5 black 46 17 CAMK2_family N-Growth Factor-2.0-Hs-46 AKT 88 138 white rectangle gene 0.5 black 46 17 AKT N-Growth Factor-2.0-Hs-47 AKT 84 137 white rectangle gene 0.5 black 46 17 AKT N-Growth Factor-2.0-Hs-48 AKT 96 147 white rectangle gene 0.5 black 46 17 AKT N-Growth Factor-2.0-Hs-49 JNK 112 71 white rectangle gene 0.5 black 46 17 JNK N-Growth Factor-2.0-Hs-50 MMP 67 121 white rectangle gene 0.5 black 46 17 MMP N-Growth Factor-2.0-Hs-51 PPP2C 128 78 white rectangle gene 0.5 black 46 17 PPP2C N-Growth Factor-2.0-Hs-52 PRKAC 5 136 white rectangle gene 0.5 black 46 17 PRKAC N-Growth Factor-2.0-Hs-53 RAF 51 161 white rectangle gene 0.5 black 46 17 RAF N-Growth Factor-2.0-Hs-54 RAS 53 155 white rectangle gene 0.5 black 46 17 RAS N-Growth Factor-2.0-Hs-55 VEGF 90 100 white rectangle gene 0.5 black 46 17 VEGF N-Growth Factor-2.0-Hs-56 p38 127 107 white rectangle gene 0.5 black 46 17 p38 N-Growth Factor-2.0-Hs-57 p38 123 93 white rectangle gene 0.5 black 46 17 p38 N-Growth Factor-2.0-Hs-58 p38 131 113 white rectangle gene 0.5 black 46 17 p38 N-Growth Factor-2.0-Hs-59 p38 129 114 white rectangle gene 0.5 black 46 17 p38 N-Growth Factor-2.0-Hs-6 7 8 9 10 TFDP1 E2F5 SMAD3 SMAD4 RBL1 103 82 white rectangle gene,gene,gene,gene,gene 0.5 black 46 17 11749,/,3119,/,6769,/,6770,/,9893 N-Growth Factor-2.0-Hs-60 RPS6KA5 133 110 white rectangle gene 0.5 black 46 17 10434 N-Growth Factor-2.0-Hs-61 RPS6KB1 121 129 white rectangle gene 0.5 black 46 17 10436 N-Growth Factor-2.0-Hs-62 SGK1 121 127 white rectangle gene 0.5 black 46 17 10810 N-Growth Factor-2.0-Hs-63 BTC 51 107 white rectangle gene 0.5 black 46 17 1121 N-Growth Factor-2.0-Hs-64 SRC 74 126 white rectangle gene 0.5 black 46 17 11283 N-Growth Factor-2.0-Hs-65 STAT3 52 125 white rectangle gene 0.5 black 46 17 11364 N-Growth Factor-2.0-Hs-66 TGFA 66 116 white rectangle gene 0.5 black 46 17 11765 N-Growth Factor-2.0-Hs-67 TGFB1 102 88 white rectangle gene 0.5 black 46 17 11766 N-Growth Factor-2.0-Hs-68 TGFBR1 121 78 white rectangle gene 0.5 black 46 17 11772 N-Growth Factor-2.0-Hs-69 TGFBR1 129 70 white rectangle gene 0.5 black 46 17 11772 N-Growth Factor-2.0-Hs-70 TGFBR2 134 61 white rectangle gene 0.5 black 46 17 11773 N-Growth Factor-2.0-Hs-71 VEGFA 107 97 white rectangle gene 0.5 black 46 17 12680 N-Growth Factor-2.0-Hs-72 VEGFC 115 111 white rectangle gene 0.5 black 46 17 12682 N-Growth Factor-2.0-Hs-73 ADAM17 77 112 white rectangle gene 0.5 black 46 17 195 N-Growth Factor-2.0-Hs-74 CTNNB1 101 1 white rectangle gene 0.5 black 46 17 2514 N-Growth Factor-2.0-Hs-75 CTNNB1 104 3 white rectangle gene 0.5 black 46 17 2514 N-Growth Factor-2.0-Hs-76 ADRB1 52 120 white rectangle gene 0.5 black 46 17 285 N-Growth Factor-2.0-Hs-77 NRG4 42 96 white rectangle gene 0.5 black 46 17 29862 N-Growth Factor-2.0-Hs-78 HBEGF 55 108 white rectangle gene 0.5 black 46 17 3059 N-Growth Factor-2.0-Hs-79 DUOX1 82 112 white rectangle gene 0.5 black 46 17 3062 N-Growth Factor-2.0-Hs-80 EGF 61 121 white rectangle gene 0.5 black 46 17 3229 N-Growth Factor-2.0-Hs-81 EGFR 59 119 white rectangle gene 0.5 black 46 17 3236 N-Growth Factor-2.0-Hs-82 EGR1 72 99 white rectangle gene 0.5 black 46 17 3238 N-Growth Factor-2.0-Hs-83 EIF4E 147 149 white rectangle gene 0.5 black 46 17 3287 N-Growth Factor-2.0-Hs-84 EIF4EBP1 105 91 white rectangle gene 0.5 black 46 17 3288 N-Growth Factor-2.0-Hs-85 EIF4EBP1 108 89 white rectangle gene 0.5 black 46 17 3288 N-Growth Factor-2.0-Hs-86 ELANE 3 49 white rectangle gene 0.5 black 46 17 3309 N-Growth Factor-2.0-Hs-87 ELK1 75 104 white rectangle gene 0.5 black 46 17 3321 N-Growth Factor-2.0-Hs-88 ELK1 70 104 white rectangle gene 0.5 black 46 17 3321 N-Growth Factor-2.0-Hs-89 EP300 119 52 white rectangle gene 0.5 black 46 17 3373 N-Growth Factor-2.0-Hs-90 ERBB2 56 104 white rectangle gene 0.5 black 46 17 3430 N-Growth Factor-2.0-Hs-91 ERBB2 34 13 white rectangle gene 0.5 black 46 17 3430 N-Growth Factor-2.0-Hs-92 ERBB2 56 111 white rectangle gene 0.5 black 46 17 3430 N-Growth Factor-2.0-Hs-93 ERBB3 49 87 white rectangle gene 0.5 black 46 17 3431 N-Growth Factor-2.0-Hs-94 ERBB3 113 2 white rectangle gene 0.5 black 46 17 3431 N-Growth Factor-2.0-Hs-95 ERBB4 48 101 white rectangle gene 0.5 black 46 17 3432 N-Growth Factor-2.0-Hs-96 ERBB4 41 99 white rectangle gene 0.5 black 46 17 3432 N-Growth Factor-2.0-Hs-97 EREG 53 108 white rectangle gene 0.5 black 46 17 3443 N-Growth Factor-2.0-Hs-98 FGF1 29 43 white rectangle gene 0.5 black 46 17 3665 N-Growth Factor-2.0-Hs-99 FGF10 26 32 white rectangle gene 0.5 black 46 17 3666 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Growth Factor-2.0-Hs.sif000066400000000000000000000632261426625374700256670ustar00rootroot000000000000000 1 2 N-Growth Factor-2.0-Hs-57 activation N-Growth Factor-2.0-Hs-56 N-Growth Factor-2.0-Hs-149 activation N-Growth Factor-2.0-Hs-30 31 N-Growth Factor-2.0-Hs-149 activation N-Growth Factor-2.0-Hs-30 31 N-Growth Factor-2.0-Hs-149 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-149 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-149 activation N-Growth Factor-2.0-Hs-93 N-Growth Factor-2.0-Hs-149 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-38 39 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-58 activation N-Growth Factor-2.0-Hs-56 N-Growth Factor-2.0-Hs-20 21 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-152 activation N-Growth Factor-2.0-Hs-163 N-Growth Factor-2.0-Hs-152 activation N-Growth Factor-2.0-Hs-134 N-Growth Factor-2.0-Hs-152 activation N-Growth Factor-2.0-Hs-138 N-Growth Factor-2.0-Hs-152 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-133 inhibition N-Growth Factor-2.0-Hs-130 N-Growth Factor-2.0-Hs-133 inhibition N-Growth Factor-2.0-Hs-128 N-Growth Factor-2.0-Hs-97 activation N-Growth Factor-2.0-Hs-26 27 N-Growth Factor-2.0-Hs-97 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-97 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-97 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-97 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-97 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-97 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-164 activation N-Growth Factor-2.0-Hs-75 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-36 37 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-36 37 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-36 37 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-63 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-28 29 inhibition N-Growth Factor-2.0-Hs-83 N-Growth Factor-2.0-Hs-116 activation N-Growth Factor-2.0-Hs-117 N-Growth Factor-2.0-Hs-116 activation N-Growth Factor-2.0-Hs-120 N-Growth Factor-2.0-Hs-116 activation N-Growth Factor-2.0-Hs-120 N-Growth Factor-2.0-Hs-30 31 activation N-Growth Factor-2.0-Hs-90 N-Growth Factor-2.0-Hs-30 31 activation N-Growth Factor-2.0-Hs-90 N-Growth Factor-2.0-Hs-30 31 activation N-Growth Factor-2.0-Hs-90 N-Growth Factor-2.0-Hs-75 activation N-Growth Factor-2.0-Hs-74 N-Growth Factor-2.0-Hs-135 activation N-Growth Factor-2.0-Hs-87 N-Growth Factor-2.0-Hs-172 inhibition N-Growth Factor-2.0-Hs-170 N-Growth Factor-2.0-Hs-146 inhibition N-Growth Factor-2.0-Hs-146 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-22 23 N-Growth Factor-2.0-Hs-66 activation N-Growth Factor-2.0-Hs-22 23 N-Growth Factor-2.0-Hs-50 activation N-Growth Factor-2.0-Hs-66 N-Growth Factor-2.0-Hs-50 activation N-Growth Factor-2.0-Hs-66 N-Growth Factor-2.0-Hs-125 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-125 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-125 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-125 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-125 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-125 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-76 activation N-Growth Factor-2.0-Hs-18 19 N-Growth Factor-2.0-Hs-76 activation N-Growth Factor-2.0-Hs-20 21 N-Growth Factor-2.0-Hs-76 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-89 activation N-Growth Factor-2.0-Hs-129 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-141 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-114 activation N-Growth Factor-2.0-Hs-139 N-Growth Factor-2.0-Hs-86 activation N-Growth Factor-2.0-Hs-156 N-Growth Factor-2.0-Hs-62 activation N-Growth Factor-2.0-Hs-112 N-Growth Factor-2.0-Hs-88 activation N-Growth Factor-2.0-Hs-87 N-Growth Factor-2.0-Hs-88 activation N-Growth Factor-2.0-Hs-87 N-Growth Factor-2.0-Hs-88 activation N-Growth Factor-2.0-Hs-87 N-Growth Factor-2.0-Hs-88 activation N-Growth Factor-2.0-Hs-87 N-Growth Factor-2.0-Hs-88 activation N-Growth Factor-2.0-Hs-87 N-Growth Factor-2.0-Hs-173 inhibition N-Growth Factor-2.0-Hs-170 N-Growth Factor-2.0-Hs-148 activation N-Growth Factor-2.0-Hs-93 N-Growth Factor-2.0-Hs-148 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-148 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-148 activation N-Growth Factor-2.0-Hs-30 31 N-Growth Factor-2.0-Hs-148 activation N-Growth Factor-2.0-Hs-30 31 N-Growth Factor-2.0-Hs-148 activation N-Growth Factor-2.0-Hs-30 31 N-Growth Factor-2.0-Hs-79 activation N-Growth Factor-2.0-Hs-73 N-Growth Factor-2.0-Hs-147 activation N-Growth Factor-2.0-Hs-146 N-Growth Factor-2.0-Hs-150 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-150 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-72 activation N-Growth Factor-2.0-Hs-104 N-Growth Factor-2.0-Hs-72 activation N-Growth Factor-2.0-Hs-104 N-Growth Factor-2.0-Hs-72 activation N-Growth Factor-2.0-Hs-104 N-Growth Factor-2.0-Hs-72 activation N-Growth Factor-2.0-Hs-104 N-Growth Factor-2.0-Hs-72 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-72 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-72 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-72 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-48 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-48 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-48 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-48 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-11 12 activation N-Growth Factor-2.0-Hs-16 17 N-Growth Factor-2.0-Hs-46 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-46 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-46 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-46 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-46 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-46 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-46 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-46 activation N-Growth Factor-2.0-Hs-113 N-Growth Factor-2.0-Hs-46 activation N-Growth Factor-2.0-Hs-113 N-Growth Factor-2.0-Hs-46 activation N-Growth Factor-2.0-Hs-113 N-Growth Factor-2.0-Hs-115 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-115 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-115 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-115 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-115 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-115 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-115 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-143 activation N-Growth Factor-2.0-Hs-145 N-Growth Factor-2.0-Hs-143 activation N-Growth Factor-2.0-Hs-145 N-Growth Factor-2.0-Hs-143 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-143 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-143 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-143 activation N-Growth Factor-2.0-Hs-107 N-Growth Factor-2.0-Hs-171 inhibition N-Growth Factor-2.0-Hs-170 N-Growth Factor-2.0-Hs-171 inhibition N-Growth Factor-2.0-Hs-170 N-Growth Factor-2.0-Hs-136 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-136 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-136 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-130 activation N-Growth Factor-2.0-Hs-40 41 42 N-Growth Factor-2.0-Hs-56 activation N-Growth Factor-2.0-Hs-60 N-Growth Factor-2.0-Hs-112 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-112 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-112 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-112 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-112 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-112 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-112 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-122 activation N-Growth Factor-2.0-Hs-121 N-Growth Factor-2.0-Hs-96 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-96 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-161 inhibition N-Growth Factor-2.0-Hs-159 N-Growth Factor-2.0-Hs-161 inhibition N-Growth Factor-2.0-Hs-159 N-Growth Factor-2.0-Hs-137 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-137 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-137 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-92 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-65 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-65 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-65 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-65 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-65 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-65 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-65 N-Growth Factor-2.0-Hs-81 activation N-Growth Factor-2.0-Hs-107 N-Growth Factor-2.0-Hs-49 activation N-Growth Factor-2.0-Hs-122 N-Growth Factor-2.0-Hs-87 activation N-Growth Factor-2.0-Hs-82 N-Growth Factor-2.0-Hs-61 activation N-Growth Factor-2.0-Hs-112 N-Growth Factor-2.0-Hs-52 activation N-Growth Factor-2.0-Hs-167 N-Growth Factor-2.0-Hs-52 activation N-Growth Factor-2.0-Hs-167 N-Growth Factor-2.0-Hs-52 activation N-Growth Factor-2.0-Hs-167 N-Growth Factor-2.0-Hs-52 activation N-Growth Factor-2.0-Hs-167 N-Growth Factor-2.0-Hs-52 activation N-Growth Factor-2.0-Hs-169 N-Growth Factor-2.0-Hs-52 inhibition N-Growth Factor-2.0-Hs-168 N-Growth Factor-2.0-Hs-52 inhibition N-Growth Factor-2.0-Hs-168 N-Growth Factor-2.0-Hs-157 activation N-Growth Factor-2.0-Hs-43 44 N-Growth Factor-2.0-Hs-40 41 42 activation N-Growth Factor-2.0-Hs-129 N-Growth Factor-2.0-Hs-40 41 42 activation N-Growth Factor-2.0-Hs-129 N-Growth Factor-2.0-Hs-40 41 42 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-40 41 42 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-40 41 42 activation N-Growth Factor-2.0-Hs-131 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-159 inhibition N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-110 activation N-Growth Factor-2.0-Hs-64 N-Growth Factor-2.0-Hs-110 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-107 activation N-Growth Factor-2.0-Hs-108 N-Growth Factor-2.0-Hs-107 activation N-Growth Factor-2.0-Hs-108 N-Growth Factor-2.0-Hs-107 activation N-Growth Factor-2.0-Hs-165 N-Growth Factor-2.0-Hs-107 activation N-Growth Factor-2.0-Hs-165 N-Growth Factor-2.0-Hs-169 inhibition N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-101 activation N-Growth Factor-2.0-Hs-103 N-Growth Factor-2.0-Hs-101 activation N-Growth Factor-2.0-Hs-103 N-Growth Factor-2.0-Hs-101 activation N-Growth Factor-2.0-Hs-103 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-113 inhibition N-Growth Factor-2.0-Hs-111 N-Growth Factor-2.0-Hs-140 activation N-Growth Factor-2.0-Hs-138 N-Growth Factor-2.0-Hs-140 activation N-Growth Factor-2.0-Hs-138 N-Growth Factor-2.0-Hs-47 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-47 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-47 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-47 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-47 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-47 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-47 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-47 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-165 activation N-Growth Factor-2.0-Hs-54 N-Growth Factor-2.0-Hs-165 activation N-Growth Factor-2.0-Hs-54 N-Growth Factor-2.0-Hs-104 activation N-Growth Factor-2.0-Hs-56 N-Growth Factor-2.0-Hs-100 activation N-Growth Factor-2.0-Hs-102 N-Growth Factor-2.0-Hs-100 activation N-Growth Factor-2.0-Hs-102 N-Growth Factor-2.0-Hs-100 activation N-Growth Factor-2.0-Hs-103 N-Growth Factor-2.0-Hs-43 44 activation N-Growth Factor-2.0-Hs-153 N-Growth Factor-2.0-Hs-94 activation N-Growth Factor-2.0-Hs-34 35 N-Growth Factor-2.0-Hs-16 17 activation N-Growth Factor-2.0-Hs-70 N-Growth Factor-2.0-Hs-36 37 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-36 37 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-68 activation N-Growth Factor-2.0-Hs-128 N-Growth Factor-2.0-Hs-68 activation N-Growth Factor-2.0-Hs-128 N-Growth Factor-2.0-Hs-68 activation N-Growth Factor-2.0-Hs-49 N-Growth Factor-2.0-Hs-68 activation N-Growth Factor-2.0-Hs-130 N-Growth Factor-2.0-Hs-68 activation N-Growth Factor-2.0-Hs-57 N-Growth Factor-2.0-Hs-68 activation N-Growth Factor-2.0-Hs-51 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-144 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-22 23 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-22 23 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-26 27 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-80 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-154 activation N-Growth Factor-2.0-Hs-155 N-Growth Factor-2.0-Hs-154 activation N-Growth Factor-2.0-Hs-48 N-Growth Factor-2.0-Hs-22 23 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-22 23 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-22 23 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-158 activation N-Growth Factor-2.0-Hs-64 N-Growth Factor-2.0-Hs-158 activation N-Growth Factor-2.0-Hs-64 N-Growth Factor-2.0-Hs-158 activation N-Growth Factor-2.0-Hs-64 N-Growth Factor-2.0-Hs-158 activation N-Growth Factor-2.0-Hs-47 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-71 activation N-Growth Factor-2.0-Hs-123 N-Growth Factor-2.0-Hs-73 activation N-Growth Factor-2.0-Hs-55 N-Growth Factor-2.0-Hs-73 activation N-Growth Factor-2.0-Hs-55 N-Growth Factor-2.0-Hs-73 activation N-Growth Factor-2.0-Hs-66 N-Growth Factor-2.0-Hs-73 activation N-Growth Factor-2.0-Hs-66 N-Growth Factor-2.0-Hs-73 activation N-Growth Factor-2.0-Hs-66 N-Growth Factor-2.0-Hs-73 activation N-Growth Factor-2.0-Hs-125 N-Growth Factor-2.0-Hs-73 activation N-Growth Factor-2.0-Hs-125 N-Growth Factor-2.0-Hs-18 19 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-144 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-144 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-144 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-144 activation N-Growth Factor-2.0-Hs-143 N-Growth Factor-2.0-Hs-98 activation N-Growth Factor-2.0-Hs-102 N-Growth Factor-2.0-Hs-98 activation N-Growth Factor-2.0-Hs-103 N-Growth Factor-2.0-Hs-77 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-119 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-24 25 activation N-Growth Factor-2.0-Hs-90 N-Growth Factor-2.0-Hs-24 25 activation N-Growth Factor-2.0-Hs-90 N-Growth Factor-2.0-Hs-24 25 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-24 25 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-24 25 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-24 25 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-13 14 15 activation N-Growth Factor-2.0-Hs-16 17 N-Growth Factor-2.0-Hs-142 activation N-Growth Factor-2.0-Hs-138 N-Growth Factor-2.0-Hs-142 activation N-Growth Factor-2.0-Hs-138 N-Growth Factor-2.0-Hs-32 33 activation N-Growth Factor-2.0-Hs-90 N-Growth Factor-2.0-Hs-32 33 activation N-Growth Factor-2.0-Hs-90 N-Growth Factor-2.0-Hs-32 33 activation N-Growth Factor-2.0-Hs-90 N-Growth Factor-2.0-Hs-32 33 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-32 33 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-99 activation N-Growth Factor-2.0-Hs-103 N-Growth Factor-2.0-Hs-99 activation N-Growth Factor-2.0-Hs-103 N-Growth Factor-2.0-Hs-109 activation N-Growth Factor-2.0-Hs-110 N-Growth Factor-2.0-Hs-109 activation N-Growth Factor-2.0-Hs-110 N-Growth Factor-2.0-Hs-109 activation N-Growth Factor-2.0-Hs-110 N-Growth Factor-2.0-Hs-54 activation N-Growth Factor-2.0-Hs-53 N-Growth Factor-2.0-Hs-54 activation N-Growth Factor-2.0-Hs-53 N-Growth Factor-2.0-Hs-54 activation N-Growth Factor-2.0-Hs-53 N-Growth Factor-2.0-Hs-54 activation N-Growth Factor-2.0-Hs-53 N-Growth Factor-2.0-Hs-168 activation N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-168 activation N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-168 activation N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-168 activation N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-168 activation N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-168 activation N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-160 inhibition N-Growth Factor-2.0-Hs-159 N-Growth Factor-2.0-Hs-160 inhibition N-Growth Factor-2.0-Hs-159 N-Growth Factor-2.0-Hs-167 inhibition N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-167 inhibition N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-167 inhibition N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-167 inhibition N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-167 inhibition N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-167 inhibition N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-70 activation N-Growth Factor-2.0-Hs-69 N-Growth Factor-2.0-Hs-138 activation N-Growth Factor-2.0-Hs-87 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-46 N-Growth Factor-2.0-Hs-64 activation N-Growth Factor-2.0-Hs-73 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-1 2 3 4 5 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-71 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-71 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-71 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-71 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-71 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-132 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-127 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-84 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-172 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-85 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-173 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-55 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-6 7 8 9 10 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-126 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-126 N-Growth Factor-2.0-Hs-67 activation N-Growth Factor-2.0-Hs-171 N-Growth Factor-2.0-Hs-69 activation N-Growth Factor-2.0-Hs-68 N-Growth Factor-2.0-Hs-69 activation N-Growth Factor-2.0-Hs-68 N-Growth Factor-2.0-Hs-69 activation N-Growth Factor-2.0-Hs-68 N-Growth Factor-2.0-Hs-128 activation N-Growth Factor-2.0-Hs-40 41 42 N-Growth Factor-2.0-Hs-124 activation N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-124 activation N-Growth Factor-2.0-Hs-166 N-Growth Factor-2.0-Hs-162 inhibition N-Growth Factor-2.0-Hs-159 N-Growth Factor-2.0-Hs-162 inhibition N-Growth Factor-2.0-Hs-159 N-Growth Factor-2.0-Hs-153 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-153 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-153 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-153 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-153 activation N-Growth Factor-2.0-Hs-135 N-Growth Factor-2.0-Hs-90 activation N-Growth Factor-2.0-Hs-92 N-Growth Factor-2.0-Hs-90 activation N-Growth Factor-2.0-Hs-92 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-26 27 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-32 33 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-95 N-Growth Factor-2.0-Hs-78 activation N-Growth Factor-2.0-Hs-24 25 N-Growth Factor-2.0-Hs-118 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-118 activation N-Growth Factor-2.0-Hs-116 N-Growth Factor-2.0-Hs-45 activation N-Growth Factor-2.0-Hs-91 N-Growth Factor-2.0-Hs-59 activation N-Growth Factor-2.0-Hs-56 N-Growth Factor-2.0-Hs-106 activation N-Growth Factor-2.0-Hs-81 N-Growth Factor-2.0-Hs-111 activation N-Growth Factor-2.0-Hs-147 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Hedgehog-2.0-Hs.att000066400000000000000000000030521426625374700247260ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Hedgehog-2.0-Hs-1 AKT 22 185 white rectangle gene 0.5 black 46 17 AKT N-Hedgehog-2.0-Hs-10 CCND1 104 106 white rectangle gene 0.5 black 46 17 1582 N-Hedgehog-2.0-Hs-11 CCND2 70 63 white rectangle gene 0.5 black 46 17 1583 N-Hedgehog-2.0-Hs-12 CCNE1 29 70 white rectangle gene 0.5 black 46 17 1589 N-Hedgehog-2.0-Hs-13 SUFU 34 86 white rectangle gene 0.5 black 46 17 16466 N-Hedgehog-2.0-Hs-14 DISP1 158 64 white rectangle gene 0.5 black 46 17 19711 N-Hedgehog-2.0-Hs-15 GAS1 140 43 white rectangle gene 0.5 black 46 17 4165 N-Hedgehog-2.0-Hs-16 GLI1 41 129 white rectangle gene 0.5 black 46 17 4317 N-Hedgehog-2.0-Hs-17 GLI2 61 100 white rectangle gene 0.5 black 46 17 4318 N-Hedgehog-2.0-Hs-18 GLI3 50 119 white rectangle gene 0.5 black 46 17 4319 N-Hedgehog-2.0-Hs-19 PTCH1 117 157 white rectangle gene 0.5 black 46 17 9585 N-Hedgehog-2.0-Hs-2 GSK3 40 147 white rectangle gene 0.5 black 46 17 GSK3 N-Hedgehog-2.0-Hs-20 PTCH2 15 97 white rectangle gene 0.5 black 46 17 9586 N-Hedgehog-2.0-Hs-3 Hedgehog 149 146 white rectangle gene 0.5 black 46 17 Hedgehog N-Hedgehog-2.0-Hs-4 PRKAC 29 117 white rectangle gene 0.5 black 46 17 PRKAC N-Hedgehog-2.0-Hs-5 RAS 0 0 white rectangle gene 0.5 black 46 17 RAS N-Hedgehog-2.0-Hs-6 SHH 120 77 white rectangle gene 0.5 black 46 17 10848 N-Hedgehog-2.0-Hs-7 STIL 13 36 white rectangle gene 0.5 black 46 17 10879 N-Hedgehog-2.0-Hs-8 SMO 76 136 white rectangle gene 0.5 black 46 17 11119 N-Hedgehog-2.0-Hs-9 HHIP 138 111 white rectangle gene 0.5 black 46 17 14866 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Hedgehog-2.0-Hs.sif000066400000000000000000000033601426625374700247210ustar00rootroot000000000000000 1 2 N-Hedgehog-2.0-Hs-13 inhibition N-Hedgehog-2.0-Hs-17 N-Hedgehog-2.0-Hs-13 inhibition N-Hedgehog-2.0-Hs-18 N-Hedgehog-2.0-Hs-13 inhibition N-Hedgehog-2.0-Hs-16 N-Hedgehog-2.0-Hs-3 inhibition N-Hedgehog-2.0-Hs-19 N-Hedgehog-2.0-Hs-15 inhibition N-Hedgehog-2.0-Hs-6 N-Hedgehog-2.0-Hs-15 inhibition N-Hedgehog-2.0-Hs-6 N-Hedgehog-2.0-Hs-1 inhibition N-Hedgehog-2.0-Hs-2 N-Hedgehog-2.0-Hs-1 inhibition N-Hedgehog-2.0-Hs-2 N-Hedgehog-2.0-Hs-4 inhibition N-Hedgehog-2.0-Hs-17 N-Hedgehog-2.0-Hs-4 inhibition N-Hedgehog-2.0-Hs-18 N-Hedgehog-2.0-Hs-4 inhibition N-Hedgehog-2.0-Hs-16 N-Hedgehog-2.0-Hs-9 inhibition N-Hedgehog-2.0-Hs-6 N-Hedgehog-2.0-Hs-9 inhibition N-Hedgehog-2.0-Hs-6 N-Hedgehog-2.0-Hs-9 activation N-Hedgehog-2.0-Hs-3 N-Hedgehog-2.0-Hs-9 activation N-Hedgehog-2.0-Hs-10 N-Hedgehog-2.0-Hs-7 inhibition N-Hedgehog-2.0-Hs-13 N-Hedgehog-2.0-Hs-8 activation N-Hedgehog-2.0-Hs-17 N-Hedgehog-2.0-Hs-8 activation N-Hedgehog-2.0-Hs-18 N-Hedgehog-2.0-Hs-8 activation N-Hedgehog-2.0-Hs-16 N-Hedgehog-2.0-Hs-8 activation N-Hedgehog-2.0-Hs-16 N-Hedgehog-2.0-Hs-2 inhibition N-Hedgehog-2.0-Hs-18 N-Hedgehog-2.0-Hs-2 inhibition N-Hedgehog-2.0-Hs-17 N-Hedgehog-2.0-Hs-2 inhibition N-Hedgehog-2.0-Hs-16 N-Hedgehog-2.0-Hs-14 activation N-Hedgehog-2.0-Hs-6 N-Hedgehog-2.0-Hs-14 activation N-Hedgehog-2.0-Hs-6 N-Hedgehog-2.0-Hs-5 activation N-Hedgehog-2.0-Hs-7 N-Hedgehog-2.0-Hs-17 activation N-Hedgehog-2.0-Hs-10 N-Hedgehog-2.0-Hs-17 activation N-Hedgehog-2.0-Hs-11 N-Hedgehog-2.0-Hs-17 activation N-Hedgehog-2.0-Hs-18 N-Hedgehog-2.0-Hs-17 activation N-Hedgehog-2.0-Hs-8 N-Hedgehog-2.0-Hs-17 activation N-Hedgehog-2.0-Hs-6 N-Hedgehog-2.0-Hs-17 activation N-Hedgehog-2.0-Hs-20 N-Hedgehog-2.0-Hs-17 activation N-Hedgehog-2.0-Hs-12 N-Hedgehog-2.0-Hs-19 inhibition N-Hedgehog-2.0-Hs-8 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Hox-2.0-Hs.att000066400000000000000000000021711426625374700237530ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Hox-2.0-Hs-1 2 ITGAV ITGB3 123 173 white rectangle gene,gene 0.5 black 46 17 6150,/,6156 N-Hox-2.0-Hs-10 ITGB3 185 18 white rectangle gene 0.5 black 46 17 6156 N-Hox-2.0-Hs-11 MEOX2 36 123 white rectangle gene 0.5 black 46 17 7014 N-Hox-2.0-Hs-12 MMP14 113 172 white rectangle gene 0.5 black 46 17 7160 N-Hox-2.0-Hs-13 PDGFRB 156 0 white rectangle gene 0.5 black 46 17 8804 N-Hox-2.0-Hs-14 PLAU 163 10 white rectangle gene 0.5 black 46 17 9052 N-Hox-2.0-Hs-15 PLAUR 4 38 white rectangle gene 0.5 black 46 17 9053 N-Hox-2.0-Hs-16 PTGS2 106 77 white rectangle gene 0.5 black 46 17 9605 N-Hox-2.0-Hs-3 AKT 92 58 white rectangle gene 0.5 black 46 17 AKT N-Hox-2.0-Hs-4 CDKN1A 30 134 white rectangle gene 0.5 black 46 17 1784 N-Hox-2.0-Hs-5 HOXA3 0 29 white rectangle gene 0.5 black 46 17 5104 N-Hox-2.0-Hs-6 HOXA5 99 68 white rectangle gene 0.5 black 46 17 5106 N-Hox-2.0-Hs-7 HOXB4 32 144 white rectangle gene 0.5 black 46 17 5115 N-Hox-2.0-Hs-8 HOXD3 173 21 white rectangle gene 0.5 black 46 17 5137 N-Hox-2.0-Hs-9 ITGAV 180 33 white rectangle gene 0.5 black 46 17 6150 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Hox-2.0-Hs.sif000066400000000000000000000011741426625374700237460ustar00rootroot000000000000000 1 2 N-Hox-2.0-Hs-6 activation N-Hox-2.0-Hs-3 N-Hox-2.0-Hs-6 inhibition N-Hox-2.0-Hs-16 N-Hox-2.0-Hs-8 activation N-Hox-2.0-Hs-10 N-Hox-2.0-Hs-8 activation N-Hox-2.0-Hs-10 N-Hox-2.0-Hs-8 activation N-Hox-2.0-Hs-9 N-Hox-2.0-Hs-8 activation N-Hox-2.0-Hs-14 N-Hox-2.0-Hs-8 activation N-Hox-2.0-Hs-14 N-Hox-2.0-Hs-8 activation N-Hox-2.0-Hs-14 N-Hox-2.0-Hs-8 activation N-Hox-2.0-Hs-14 N-Hox-2.0-Hs-14 activation N-Hox-2.0-Hs-13 N-Hox-2.0-Hs-12 activation N-Hox-2.0-Hs-1 2 N-Hox-2.0-Hs-7 inhibition N-Hox-2.0-Hs-4 N-Hox-2.0-Hs-11 activation N-Hox-2.0-Hs-4 N-Hox-2.0-Hs-11 activation N-Hox-2.0-Hs-4 N-Hox-2.0-Hs-5 activation N-Hox-2.0-Hs-15 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Hypoxic Stress-2.0-Hs.att000066400000000000000000000057601426625374700261130ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Hypoxic Stress-2.0-Hs-1 2 TSC1 TSC2 23 66 white rectangle gene,gene 0.5 black 46 17 12362,/,12363 N-Hypoxic Stress-2.0-Hs-11 RHEB 18 70 white rectangle gene 0.5 black 46 17 10011 N-Hypoxic Stress-2.0-Hs-12 BNIP3 129 117 white rectangle gene 0.5 black 46 17 1084 N-Hypoxic Stress-2.0-Hs-15 TGFB1 8 114 white rectangle gene 0.5 black 46 17 11766 N-Hypoxic Stress-2.0-Hs-16 TGFBR1 8 121 white rectangle gene 0.5 black 46 17 11772 N-Hypoxic Stress-2.0-Hs-17 TLR2 127 122 white rectangle gene 0.5 black 46 17 11848 N-Hypoxic Stress-2.0-Hs-18 EGLN1 107 114 white rectangle gene 0.5 black 46 17 1232 N-Hypoxic Stress-2.0-Hs-19 VHL 123 102 white rectangle gene 0.5 black 46 17 12687 N-Hypoxic Stress-2.0-Hs-20 EGLN2 116 103 white rectangle gene 0.5 black 46 17 14660 N-Hypoxic Stress-2.0-Hs-21 EGLN3 108 106 white rectangle gene 0.5 black 46 17 14661 N-Hypoxic Stress-2.0-Hs-22 HIF3A 133 95 white rectangle gene 0.5 black 46 17 15825 N-Hypoxic Stress-2.0-Hs-23 TLR6 106 124 white rectangle gene 0.5 black 46 17 16711 N-Hypoxic Stress-2.0-Hs-24 TXNIP 117 129 white rectangle gene 0.5 black 46 17 16952 N-Hypoxic Stress-2.0-Hs-25 HIF1AN 103 96 white rectangle gene 0.5 black 46 17 17113 N-Hypoxic Stress-2.0-Hs-26 CFTR 60 0 white rectangle gene 0.5 black 46 17 1884 N-Hypoxic Stress-2.0-Hs-27 CFTR 63 7 white rectangle gene 0.5 black 46 17 1884 N-Hypoxic Stress-2.0-Hs-28 EIF2AK4 156 52 white rectangle gene 0.5 black 46 17 19687 N-Hypoxic Stress-2.0-Hs-29 CREBBP 104 111 white rectangle gene 0.5 black 46 17 2348 N-Hypoxic Stress-2.0-Hs-3 4 CREBBP HIF1A 96 86 white rectangle gene,gene 0.5 black 46 17 2348,/,4910 N-Hypoxic Stress-2.0-Hs-32 EIF2AK3 155 40 white rectangle gene 0.5 black 46 17 3255 N-Hypoxic Stress-2.0-Hs-33 EIF2S1 149 49 white rectangle gene 0.5 black 46 17 3265 N-Hypoxic Stress-2.0-Hs-35 EP300 120 108 white rectangle gene 0.5 black 46 17 3373 N-Hypoxic Stress-2.0-Hs-36 EPAS1 113 108 white rectangle gene 0.5 black 46 17 3374 N-Hypoxic Stress-2.0-Hs-37 HIF1A 116 114 white rectangle gene 0.5 black 46 17 4910 N-Hypoxic Stress-2.0-Hs-38 IFNA1 124 126 white rectangle gene 0.5 black 46 17 5417 N-Hypoxic Stress-2.0-Hs-39 IFNB1 111 127 white rectangle gene 0.5 black 46 17 5434 N-Hypoxic Stress-2.0-Hs-40 SMAD3 0 124 white rectangle gene 0.5 black 46 17 6769 N-Hypoxic Stress-2.0-Hs-41 MAP2K1 128 111 white rectangle gene 0.5 black 46 17 6840 N-Hypoxic Stress-2.0-Hs-42 ARNT 111 117 white rectangle gene 0.5 black 46 17 700 N-Hypoxic Stress-2.0-Hs-43 ATF4 141 49 white rectangle gene 0.5 black 46 17 786 N-Hypoxic Stress-2.0-Hs-46 PRKCZ 37 191 white rectangle gene 0.5 black 46 17 9412 N-Hypoxic Stress-2.0-Hs-47 PRKCZ 42 184 white rectangle gene 0.5 black 46 17 9412 N-Hypoxic Stress-2.0-Hs-5 6 EP300 EPAS1 92 91 white rectangle gene,gene 0.5 black 46 17 3373,/,3374 N-Hypoxic Stress-2.0-Hs-7 8 EP300 HIF1A 103 84 white rectangle gene,gene 0.5 black 46 17 3373,/,4910 N-Hypoxic Stress-2.0-Hs-9 10 HIF1A ARNT 120 124 white rectangle gene,gene 0.5 black 46 17 4910,/,700 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Hypoxic Stress-2.0-Hs.sif000066400000000000000000000160011426625374700260720ustar00rootroot000000000000000 1 2 N-Hypoxic Stress-2.0-Hs-25 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-25 inhibition N-Hypoxic Stress-2.0-Hs-5 6 N-Hypoxic Stress-2.0-Hs-25 inhibition N-Hypoxic Stress-2.0-Hs-3 4 N-Hypoxic Stress-2.0-Hs-25 inhibition N-Hypoxic Stress-2.0-Hs-7 8 N-Hypoxic Stress-2.0-Hs-42 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-42 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-42 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-42 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-42 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-47 activation N-Hypoxic Stress-2.0-Hs-46 N-Hypoxic Stress-2.0-Hs-47 activation N-Hypoxic Stress-2.0-Hs-46 N-Hypoxic Stress-2.0-Hs-29 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-29 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-29 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-24 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-16 activation N-Hypoxic Stress-2.0-Hs-40 N-Hypoxic Stress-2.0-Hs-33 activation N-Hypoxic Stress-2.0-Hs-43 N-Hypoxic Stress-2.0-Hs-33 activation N-Hypoxic Stress-2.0-Hs-43 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 inhibition N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-37 activation N-Hypoxic Stress-2.0-Hs-23 N-Hypoxic Stress-2.0-Hs-37 activation N-Hypoxic Stress-2.0-Hs-17 N-Hypoxic Stress-2.0-Hs-37 activation N-Hypoxic Stress-2.0-Hs-39 N-Hypoxic Stress-2.0-Hs-37 activation N-Hypoxic Stress-2.0-Hs-38 N-Hypoxic Stress-2.0-Hs-37 activation N-Hypoxic Stress-2.0-Hs-12 N-Hypoxic Stress-2.0-Hs-37 activation N-Hypoxic Stress-2.0-Hs-12 N-Hypoxic Stress-2.0-Hs-37 activation N-Hypoxic Stress-2.0-Hs-12 N-Hypoxic Stress-2.0-Hs-37 activation N-Hypoxic Stress-2.0-Hs-12 N-Hypoxic Stress-2.0-Hs-32 activation N-Hypoxic Stress-2.0-Hs-33 N-Hypoxic Stress-2.0-Hs-32 activation N-Hypoxic Stress-2.0-Hs-33 N-Hypoxic Stress-2.0-Hs-1 2 inhibition N-Hypoxic Stress-2.0-Hs-11 N-Hypoxic Stress-2.0-Hs-1 2 inhibition N-Hypoxic Stress-2.0-Hs-11 N-Hypoxic Stress-2.0-Hs-1 2 inhibition N-Hypoxic Stress-2.0-Hs-11 N-Hypoxic Stress-2.0-Hs-1 2 inhibition N-Hypoxic Stress-2.0-Hs-11 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-19 activation N-Hypoxic Stress-2.0-Hs-22 N-Hypoxic Stress-2.0-Hs-22 inhibition N-Hypoxic Stress-2.0-Hs-22 N-Hypoxic Stress-2.0-Hs-35 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-35 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-35 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-35 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-20 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-20 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-20 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-20 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-20 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-20 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-41 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-36 inhibition N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-36 inhibition N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-36 inhibition N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-36 inhibition N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-36 inhibition N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-27 inhibition N-Hypoxic Stress-2.0-Hs-26 N-Hypoxic Stress-2.0-Hs-9 10 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-9 10 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-9 10 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-15 activation N-Hypoxic Stress-2.0-Hs-16 N-Hypoxic Stress-2.0-Hs-15 activation N-Hypoxic Stress-2.0-Hs-16 N-Hypoxic Stress-2.0-Hs-15 activation N-Hypoxic Stress-2.0-Hs-16 N-Hypoxic Stress-2.0-Hs-15 activation N-Hypoxic Stress-2.0-Hs-16 N-Hypoxic Stress-2.0-Hs-21 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-21 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-21 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-21 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-21 activation N-Hypoxic Stress-2.0-Hs-36 N-Hypoxic Stress-2.0-Hs-28 activation N-Hypoxic Stress-2.0-Hs-33 N-Hypoxic Stress-2.0-Hs-18 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-18 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-18 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-18 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-18 activation N-Hypoxic Stress-2.0-Hs-37 N-Hypoxic Stress-2.0-Hs-18 activation N-Hypoxic Stress-2.0-Hs-36 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Immune Regulation of Tissue Repair-2.0-Hs.att000066400000000000000000000150611426625374700315700ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Immune Regulation of Tissue Repair-2.0-Hs-1 2 CD40 CD40LG 101 105 white rectangle gene,gene 0.5 black 46 17 11919,/,11935 N-Immune Regulation of Tissue Repair-2.0-Hs-10 STAT1 86 117 white rectangle gene 0.5 black 46 17 11362 N-Immune Regulation of Tissue Repair-2.0-Hs-11 STAT3 111 84 white rectangle gene 0.5 black 46 17 11364 N-Immune Regulation of Tissue Repair-2.0-Hs-12 STAT6 115 98 white rectangle gene 0.5 black 46 17 11368 N-Immune Regulation of Tissue Repair-2.0-Hs-13 TGFB1 96 101 white rectangle gene 0.5 black 46 17 11766 N-Immune Regulation of Tissue Repair-2.0-Hs-14 TLR2 59 64 white rectangle gene 0.5 black 46 17 11848 N-Immune Regulation of Tissue Repair-2.0-Hs-15 TLR4 80 84 white rectangle gene 0.5 black 46 17 11850 N-Immune Regulation of Tissue Repair-2.0-Hs-16 TNF 87 106 white rectangle gene 0.5 black 46 17 11892 N-Immune Regulation of Tissue Repair-2.0-Hs-17 TNFRSF1A 86 102 white rectangle gene 0.5 black 46 17 11916 N-Immune Regulation of Tissue Repair-2.0-Hs-18 CD40LG 63 120 white rectangle gene 0.5 black 46 17 11935 N-Immune Regulation of Tissue Repair-2.0-Hs-19 TRAF6 43 60 white rectangle gene 0.5 black 46 17 12036 N-Immune Regulation of Tissue Repair-2.0-Hs-20 SCGB1A1 45 198 white rectangle gene 0.5 black 46 17 12523 N-Immune Regulation of Tissue Repair-2.0-Hs-21 VEGFA 127 108 white rectangle gene 0.5 black 46 17 12680 N-Immune Regulation of Tissue Repair-2.0-Hs-23 CCR5 99 120 white rectangle gene 0.5 black 46 17 1606 N-Immune Regulation of Tissue Repair-2.0-Hs-24 TIRAP 107 104 white rectangle gene 0.5 black 46 17 17192 N-Immune Regulation of Tissue Repair-2.0-Hs-25 TIRAP 40 56 white rectangle gene 0.5 black 46 17 17192 N-Immune Regulation of Tissue Repair-2.0-Hs-26 SPDEF 181 143 white rectangle gene 0.5 black 46 17 17257 N-Immune Regulation of Tissue Repair-2.0-Hs-27 IRAK4 33 50 white rectangle gene 0.5 black 46 17 17967 N-Immune Regulation of Tissue Repair-2.0-Hs-28 SOCS3 121 80 white rectangle gene 0.5 black 46 17 19391 N-Immune Regulation of Tissue Repair-2.0-Hs-29 ADAM17 96 90 white rectangle gene 0.5 black 46 17 195 N-Immune Regulation of Tissue Repair-2.0-Hs-3 CCL11 105 130 white rectangle gene 0.5 black 46 17 10610 N-Immune Regulation of Tissue Repair-2.0-Hs-30 CHUK 0 108 white rectangle gene 0.5 black 46 17 1974 N-Immune Regulation of Tissue Repair-2.0-Hs-32 CSF2 95 134 white rectangle gene 0.5 black 46 17 2434 N-Immune Regulation of Tissue Repair-2.0-Hs-34 EGR1 93 98 white rectangle gene 0.5 black 46 17 3238 N-Immune Regulation of Tissue Repair-2.0-Hs-35 ERBB2 111 75 white rectangle gene 0.5 black 46 17 3430 N-Immune Regulation of Tissue Repair-2.0-Hs-36 F2 81 90 white rectangle gene 0.5 black 46 17 3535 N-Immune Regulation of Tissue Repair-2.0-Hs-37 F2R 88 85 white rectangle gene 0.5 black 46 17 3537 N-Immune Regulation of Tissue Repair-2.0-Hs-38 FOS 86 97 white rectangle gene 0.5 black 46 17 3796 N-Immune Regulation of Tissue Repair-2.0-Hs-4 CCL2 97 109 white rectangle gene 0.5 black 46 17 10618 N-Immune Regulation of Tissue Repair-2.0-Hs-40 CXCL2 71 107 white rectangle gene 0.5 black 46 17 4603 N-Immune Regulation of Tissue Repair-2.0-Hs-41 ICAM1 74 98 white rectangle gene 0.5 black 46 17 5344 N-Immune Regulation of Tissue Repair-2.0-Hs-42 IFNG 94 119 white rectangle gene 0.5 black 46 17 5438 N-Immune Regulation of Tissue Repair-2.0-Hs-43 APCS 88 127 white rectangle gene 0.5 black 46 17 584 N-Immune Regulation of Tissue Repair-2.0-Hs-45 IL13 102 98 white rectangle gene 0.5 black 46 17 5973 N-Immune Regulation of Tissue Repair-2.0-Hs-47 IL1B 83 102 white rectangle gene 0.5 black 46 17 5992 N-Immune Regulation of Tissue Repair-2.0-Hs-48 IL4 113 103 white rectangle gene 0.5 black 46 17 6014 N-Immune Regulation of Tissue Repair-2.0-Hs-49 IL5 104 104 white rectangle gene 0.5 black 46 17 6016 N-Immune Regulation of Tissue Repair-2.0-Hs-5 CCL3 96 111 white rectangle gene 0.5 black 46 17 10627 N-Immune Regulation of Tissue Repair-2.0-Hs-50 IL6 96 97 white rectangle gene 0.5 black 46 17 6018 N-Immune Regulation of Tissue Repair-2.0-Hs-51 IL6ST 106 89 white rectangle gene 0.5 black 46 17 6021 N-Immune Regulation of Tissue Repair-2.0-Hs-52 CXCL8 109 100 white rectangle gene 0.5 black 46 17 6025 N-Immune Regulation of Tissue Repair-2.0-Hs-53 IRAK1 50 63 white rectangle gene 0.5 black 46 17 6112 N-Immune Regulation of Tissue Repair-2.0-Hs-54 JUN 79 99 white rectangle gene 0.5 black 46 17 6204 N-Immune Regulation of Tissue Repair-2.0-Hs-55 MMP10 119 76 white rectangle gene 0.5 black 46 17 7156 N-Immune Regulation of Tissue Repair-2.0-Hs-56 MMP12 93 106 white rectangle gene 0.5 black 46 17 7158 N-Immune Regulation of Tissue Repair-2.0-Hs-57 MMP9 80 110 white rectangle gene 0.5 black 46 17 7176 N-Immune Regulation of Tissue Repair-2.0-Hs-58 MUC5AC 102 88 white rectangle gene 0.5 black 46 17 7515 N-Immune Regulation of Tissue Repair-2.0-Hs-59 MYD88 64 71 white rectangle gene 0.5 black 46 17 7562 N-Immune Regulation of Tissue Repair-2.0-Hs-6 CCL5 94 109 white rectangle gene 0.5 black 46 17 10632 N-Immune Regulation of Tissue Repair-2.0-Hs-61 NFKB1 48 191 white rectangle gene 0.5 black 46 17 7794 N-Immune Regulation of Tissue Repair-2.0-Hs-62 NFKBIA 111 5 white rectangle gene 0.5 black 46 17 7797 N-Immune Regulation of Tissue Repair-2.0-Hs-63 NFKBIA 114 0 white rectangle gene 0.5 black 46 17 7797 N-Immune Regulation of Tissue Repair-2.0-Hs-64 NFKBIA 106 5 white rectangle gene 0.5 black 46 17 7797 N-Immune Regulation of Tissue Repair-2.0-Hs-65 NFKBIE 8 107 white rectangle gene 0.5 black 46 17 7799 N-Immune Regulation of Tissue Repair-2.0-Hs-66 NGF 78 108 white rectangle gene 0.5 black 46 17 7808 N-Immune Regulation of Tissue Repair-2.0-Hs-67 NRG1 103 78 white rectangle gene 0.5 black 46 17 7997 N-Immune Regulation of Tissue Repair-2.0-Hs-68 PDGFB 136 111 white rectangle gene 0.5 black 46 17 8800 N-Immune Regulation of Tissue Repair-2.0-Hs-69 PPARA 77 103 white rectangle gene 0.5 black 46 17 9232 N-Immune Regulation of Tissue Repair-2.0-Hs-7 CXCL10 91 115 white rectangle gene 0.5 black 46 17 10637 N-Immune Regulation of Tissue Repair-2.0-Hs-70 PRKCA 86 74 white rectangle gene 0.5 black 46 17 9393 N-Immune Regulation of Tissue Repair-2.0-Hs-72 PTGER4 106 97 white rectangle gene 0.5 black 46 17 9596 N-Immune Regulation of Tissue Repair-2.0-Hs-74 PTGS2 86 92 white rectangle gene 0.5 black 46 17 9605 N-Immune Regulation of Tissue Repair-2.0-Hs-75 REL 73 115 white rectangle gene 0.5 black 46 17 9954 N-Immune Regulation of Tissue Repair-2.0-Hs-8 SOX2 182 148 white rectangle gene 0.5 black 46 17 11195 N-Immune Regulation of Tissue Repair-2.0-Hs-9 SPI1 96 144 white rectangle gene 0.5 black 46 17 11241 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Immune Regulation of Tissue Repair-2.0-Hs.sif000066400000000000000000001060471426625374700315660ustar00rootroot000000000000000 1 2 N-Immune Regulation of Tissue Repair-2.0-Hs-1 2 activation N-Immune Regulation of Tissue Repair-2.0-Hs-6 N-Immune Regulation of Tissue Repair-2.0-Hs-1 2 activation N-Immune Regulation of Tissue Repair-2.0-Hs-52 N-Immune Regulation of Tissue Repair-2.0-Hs-1 2 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-1 2 activation N-Immune Regulation of Tissue Repair-2.0-Hs-4 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-40 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-40 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-40 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-40 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-66 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-66 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-66 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-54 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-54 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-54 N-Immune Regulation of Tissue Repair-2.0-Hs-47 activation N-Immune Regulation of Tissue Repair-2.0-Hs-38 N-Immune Regulation of Tissue Repair-2.0-Hs-30 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-65 N-Immune Regulation of Tissue Repair-2.0-Hs-43 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-4 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-4 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-4 activation N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-4 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-4 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-4 activation N-Immune Regulation of Tissue Repair-2.0-Hs-42 N-Immune Regulation of Tissue Repair-2.0-Hs-4 activation N-Immune Regulation of Tissue Repair-2.0-Hs-42 N-Immune Regulation of Tissue Repair-2.0-Hs-36 activation N-Immune Regulation of Tissue Repair-2.0-Hs-38 N-Immune Regulation of Tissue Repair-2.0-Hs-36 activation N-Immune Regulation of Tissue Repair-2.0-Hs-38 N-Immune Regulation of Tissue Repair-2.0-Hs-36 activation N-Immune Regulation of Tissue Repair-2.0-Hs-54 N-Immune Regulation of Tissue Repair-2.0-Hs-36 activation N-Immune Regulation of Tissue Repair-2.0-Hs-37 N-Immune Regulation of Tissue Repair-2.0-Hs-36 activation N-Immune Regulation of Tissue Repair-2.0-Hs-37 N-Immune Regulation of Tissue Repair-2.0-Hs-32 activation N-Immune Regulation of Tissue Repair-2.0-Hs-42 N-Immune Regulation of Tissue Repair-2.0-Hs-32 activation N-Immune Regulation of Tissue Repair-2.0-Hs-9 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-52 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-52 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-52 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-13 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-13 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-58 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-58 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-58 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-34 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-34 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-45 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-10 activation N-Immune Regulation of Tissue Repair-2.0-Hs-42 N-Immune Regulation of Tissue Repair-2.0-Hs-10 activation N-Immune Regulation of Tissue Repair-2.0-Hs-42 N-Immune Regulation of Tissue Repair-2.0-Hs-10 activation N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-10 activation N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-11 activation N-Immune Regulation of Tissue Repair-2.0-Hs-55 N-Immune Regulation of Tissue Repair-2.0-Hs-11 activation N-Immune Regulation of Tissue Repair-2.0-Hs-28 N-Immune Regulation of Tissue Repair-2.0-Hs-20 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-61 N-Immune Regulation of Tissue Repair-2.0-Hs-28 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-28 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-28 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-28 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-28 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-28 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-51 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-51 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-51 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-51 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-51 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-51 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-75 activation N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-53 activation N-Immune Regulation of Tissue Repair-2.0-Hs-19 N-Immune Regulation of Tissue Repair-2.0-Hs-53 activation N-Immune Regulation of Tissue Repair-2.0-Hs-19 N-Immune Regulation of Tissue Repair-2.0-Hs-53 activation N-Immune Regulation of Tissue Repair-2.0-Hs-19 N-Immune Regulation of Tissue Repair-2.0-Hs-53 activation N-Immune Regulation of Tissue Repair-2.0-Hs-19 N-Immune Regulation of Tissue Repair-2.0-Hs-53 activation N-Immune Regulation of Tissue Repair-2.0-Hs-19 N-Immune Regulation of Tissue Repair-2.0-Hs-53 activation N-Immune Regulation of Tissue Repair-2.0-Hs-19 N-Immune Regulation of Tissue Repair-2.0-Hs-53 activation N-Immune Regulation of Tissue Repair-2.0-Hs-19 N-Immune Regulation of Tissue Repair-2.0-Hs-53 activation N-Immune Regulation of Tissue Repair-2.0-Hs-25 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-21 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-12 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-48 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-34 activation N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-5 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-5 N-Immune Regulation of Tissue Repair-2.0-Hs-50 activation N-Immune Regulation of Tissue Repair-2.0-Hs-51 N-Immune Regulation of Tissue Repair-2.0-Hs-64 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-64 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-64 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-64 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-64 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-14 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-14 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-14 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-14 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-29 activation N-Immune Regulation of Tissue Repair-2.0-Hs-67 N-Immune Regulation of Tissue Repair-2.0-Hs-29 activation N-Immune Regulation of Tissue Repair-2.0-Hs-58 N-Immune Regulation of Tissue Repair-2.0-Hs-29 activation N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-29 activation N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-72 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-59 activation N-Immune Regulation of Tissue Repair-2.0-Hs-53 N-Immune Regulation of Tissue Repair-2.0-Hs-24 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-17 activation N-Immune Regulation of Tissue Repair-2.0-Hs-6 N-Immune Regulation of Tissue Repair-2.0-Hs-17 activation N-Immune Regulation of Tissue Repair-2.0-Hs-41 N-Immune Regulation of Tissue Repair-2.0-Hs-17 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-59 N-Immune Regulation of Tissue Repair-2.0-Hs-15 activation N-Immune Regulation of Tissue Repair-2.0-Hs-74 N-Immune Regulation of Tissue Repair-2.0-Hs-37 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-37 activation N-Immune Regulation of Tissue Repair-2.0-Hs-70 N-Immune Regulation of Tissue Repair-2.0-Hs-37 activation N-Immune Regulation of Tissue Repair-2.0-Hs-70 N-Immune Regulation of Tissue Repair-2.0-Hs-69 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-69 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-16 N-Immune Regulation of Tissue Repair-2.0-Hs-69 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-54 N-Immune Regulation of Tissue Repair-2.0-Hs-69 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-54 N-Immune Regulation of Tissue Repair-2.0-Hs-69 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-47 N-Immune Regulation of Tissue Repair-2.0-Hs-67 activation N-Immune Regulation of Tissue Repair-2.0-Hs-35 N-Immune Regulation of Tissue Repair-2.0-Hs-67 activation N-Immune Regulation of Tissue Repair-2.0-Hs-35 N-Immune Regulation of Tissue Repair-2.0-Hs-67 activation N-Immune Regulation of Tissue Repair-2.0-Hs-35 N-Immune Regulation of Tissue Repair-2.0-Hs-67 activation N-Immune Regulation of Tissue Repair-2.0-Hs-35 N-Immune Regulation of Tissue Repair-2.0-Hs-67 activation N-Immune Regulation of Tissue Repair-2.0-Hs-35 N-Immune Regulation of Tissue Repair-2.0-Hs-67 activation N-Immune Regulation of Tissue Repair-2.0-Hs-35 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-66 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-17 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-5 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-5 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-38 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-6 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-6 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-6 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-6 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-54 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-54 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-16 activation N-Immune Regulation of Tissue Repair-2.0-Hs-57 N-Immune Regulation of Tissue Repair-2.0-Hs-7 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-7 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-13 activation N-Immune Regulation of Tissue Repair-2.0-Hs-38 N-Immune Regulation of Tissue Repair-2.0-Hs-13 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-49 activation N-Immune Regulation of Tissue Repair-2.0-Hs-13 N-Immune Regulation of Tissue Repair-2.0-Hs-49 activation N-Immune Regulation of Tissue Repair-2.0-Hs-13 N-Immune Regulation of Tissue Repair-2.0-Hs-49 activation N-Immune Regulation of Tissue Repair-2.0-Hs-48 N-Immune Regulation of Tissue Repair-2.0-Hs-49 activation N-Immune Regulation of Tissue Repair-2.0-Hs-48 N-Immune Regulation of Tissue Repair-2.0-Hs-49 activation N-Immune Regulation of Tissue Repair-2.0-Hs-4 N-Immune Regulation of Tissue Repair-2.0-Hs-49 activation N-Immune Regulation of Tissue Repair-2.0-Hs-45 N-Immune Regulation of Tissue Repair-2.0-Hs-5 activation N-Immune Regulation of Tissue Repair-2.0-Hs-23 N-Immune Regulation of Tissue Repair-2.0-Hs-6 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-6 activation N-Immune Regulation of Tissue Repair-2.0-Hs-23 N-Immune Regulation of Tissue Repair-2.0-Hs-6 activation N-Immune Regulation of Tissue Repair-2.0-Hs-23 N-Immune Regulation of Tissue Repair-2.0-Hs-6 activation N-Immune Regulation of Tissue Repair-2.0-Hs-23 N-Immune Regulation of Tissue Repair-2.0-Hs-3 activation N-Immune Regulation of Tissue Repair-2.0-Hs-23 N-Immune Regulation of Tissue Repair-2.0-Hs-56 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-35 activation N-Immune Regulation of Tissue Repair-2.0-Hs-11 N-Immune Regulation of Tissue Repair-2.0-Hs-27 activation N-Immune Regulation of Tissue Repair-2.0-Hs-25 N-Immune Regulation of Tissue Repair-2.0-Hs-68 activation N-Immune Regulation of Tissue Repair-2.0-Hs-21 N-Immune Regulation of Tissue Repair-2.0-Hs-74 activation N-Immune Regulation of Tissue Repair-2.0-Hs-50 N-Immune Regulation of Tissue Repair-2.0-Hs-74 activation N-Immune Regulation of Tissue Repair-2.0-Hs-47 N-Immune Regulation of Tissue Repair-2.0-Hs-63 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-63 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-63 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-63 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-63 activation N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-18 activation N-Immune Regulation of Tissue Repair-2.0-Hs-75 N-Immune Regulation of Tissue Repair-2.0-Hs-42 activation N-Immune Regulation of Tissue Repair-2.0-Hs-23 N-Immune Regulation of Tissue Repair-2.0-Hs-42 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-42 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-42 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-42 activation N-Immune Regulation of Tissue Repair-2.0-Hs-7 N-Immune Regulation of Tissue Repair-2.0-Hs-42 activation N-Immune Regulation of Tissue Repair-2.0-Hs-6 N-Immune Regulation of Tissue Repair-2.0-Hs-42 activation N-Immune Regulation of Tissue Repair-2.0-Hs-6 N-Immune Regulation of Tissue Repair-2.0-Hs-42 activation N-Immune Regulation of Tissue Repair-2.0-Hs-56 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-62 inhibition N-Immune Regulation of Tissue Repair-2.0-Hs-62 N-Immune Regulation of Tissue Repair-2.0-Hs-8 activation N-Immune Regulation of Tissue Repair-2.0-Hs-26 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Jak Stat-2.0-Hs.att000066400000000000000000000052271426625374700246230ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Jak Stat-2.0-Hs-1 JAK 25 93 white rectangle gene 0.5 black 46 17 JAK N-Jak Stat-2.0-Hs-10 STAT2 131 66 white rectangle gene 0.5 black 46 17 11363 N-Jak Stat-2.0-Hs-11 STAT3 104 54 white rectangle gene 0.5 black 46 17 11364 N-Jak Stat-2.0-Hs-12 STAT3 90 59 white rectangle gene 0.5 black 46 17 11364 N-Jak Stat-2.0-Hs-13 STAT3 95 63 white rectangle gene 0.5 black 46 17 11364 N-Jak Stat-2.0-Hs-14 STAT5A 120 13 white rectangle gene 0.5 black 46 17 11366 N-Jak Stat-2.0-Hs-15 STAT5A 119 0 white rectangle gene 0.5 black 46 17 11366 N-Jak Stat-2.0-Hs-16 STAT5B 127 20 white rectangle gene 0.5 black 46 17 11367 N-Jak Stat-2.0-Hs-17 STAT6 46 93 white rectangle gene 0.5 black 46 17 11368 N-Jak Stat-2.0-Hs-18 STAT6 35 94 white rectangle gene 0.5 black 46 17 11368 N-Jak Stat-2.0-Hs-19 TYK2 118 61 white rectangle gene 0.5 black 46 17 12440 N-Jak Stat-2.0-Hs-2 PDGF 83 80 white rectangle gene 0.5 black 46 17 PDGF N-Jak Stat-2.0-Hs-20 TYK2 127 72 white rectangle gene 0.5 black 46 17 12440 N-Jak Stat-2.0-Hs-21 TYK2 121 75 white rectangle gene 0.5 black 46 17 12440 N-Jak Stat-2.0-Hs-22 CCND3 108 65 white rectangle gene 0.5 black 46 17 1585 N-Jak Stat-2.0-Hs-23 CDKN1B 102 68 white rectangle gene 0.5 black 46 17 1785 N-Jak Stat-2.0-Hs-24 PIAS1 124 38 white rectangle gene 0.5 black 46 17 2752 N-Jak Stat-2.0-Hs-25 ERBB2 87 28 white rectangle gene 0.5 black 46 17 3430 N-Jak Stat-2.0-Hs-26 JAK1 148 34 white rectangle gene 0.5 black 46 17 6190 N-Jak Stat-2.0-Hs-27 JAK1 81 30 white rectangle gene 0.5 black 46 17 6190 N-Jak Stat-2.0-Hs-28 JAK2 122 31 white rectangle gene 0.5 black 46 17 6192 N-Jak Stat-2.0-Hs-29 JAK3 91 40 white rectangle gene 0.5 black 46 17 6193 N-Jak Stat-2.0-Hs-3 RAS 151 11 white rectangle gene 0.5 black 46 17 RAS N-Jak Stat-2.0-Hs-30 RHOA 0 34 white rectangle gene 0.5 black 46 17 667 N-Jak Stat-2.0-Hs-31 MAP2K1 158 22 white rectangle gene 0.5 black 46 17 6840 N-Jak Stat-2.0-Hs-32 MAPK3 157 16 white rectangle gene 0.5 black 46 17 6877 N-Jak Stat-2.0-Hs-33 NRG1 104 49 white rectangle gene 0.5 black 46 17 7997 N-Jak Stat-2.0-Hs-34 RAC1 91 67 white rectangle gene 0.5 black 46 17 9801 N-Jak Stat-2.0-Hs-35 RAF1 145 23 white rectangle gene 0.5 black 46 17 9829 N-Jak Stat-2.0-Hs-4 STAT 17 89 white rectangle gene 0.5 black 46 17 STAT N-Jak Stat-2.0-Hs-5 STAT5 78 37 white rectangle gene 0.5 black 46 17 STAT5 N-Jak Stat-2.0-Hs-6 ROCK1 7 34 white rectangle gene 0.5 black 46 17 10251 N-Jak Stat-2.0-Hs-7 STAT1 124 47 white rectangle gene 0.5 black 46 17 11362 N-Jak Stat-2.0-Hs-8 STAT1 109 43 white rectangle gene 0.5 black 46 17 11362 N-Jak Stat-2.0-Hs-9 STAT1 139 43 white rectangle gene 0.5 black 46 17 11362 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Jak Stat-2.0-Hs.sif000066400000000000000000000131501426625374700246060ustar00rootroot000000000000000 1 2 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-5 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-5 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-5 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-5 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-5 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-5 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-27 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-29 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-24 inhibition N-Jak Stat-2.0-Hs-28 N-Jak Stat-2.0-Hs-24 inhibition N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-21 activation N-Jak Stat-2.0-Hs-19 N-Jak Stat-2.0-Hs-21 activation N-Jak Stat-2.0-Hs-19 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-31 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-26 activation N-Jak Stat-2.0-Hs-9 N-Jak Stat-2.0-Hs-26 activation N-Jak Stat-2.0-Hs-35 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-13 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-9 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-9 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-18 activation N-Jak Stat-2.0-Hs-17 N-Jak Stat-2.0-Hs-15 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-15 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-3 activation N-Jak Stat-2.0-Hs-35 N-Jak Stat-2.0-Hs-3 activation N-Jak Stat-2.0-Hs-35 N-Jak Stat-2.0-Hs-3 activation N-Jak Stat-2.0-Hs-35 N-Jak Stat-2.0-Hs-3 activation N-Jak Stat-2.0-Hs-35 N-Jak Stat-2.0-Hs-33 activation N-Jak Stat-2.0-Hs-29 N-Jak Stat-2.0-Hs-33 activation N-Jak Stat-2.0-Hs-19 N-Jak Stat-2.0-Hs-1 activation N-Jak Stat-2.0-Hs-18 N-Jak Stat-2.0-Hs-1 activation N-Jak Stat-2.0-Hs-4 N-Jak Stat-2.0-Hs-1 activation N-Jak Stat-2.0-Hs-4 N-Jak Stat-2.0-Hs-1 activation N-Jak Stat-2.0-Hs-4 N-Jak Stat-2.0-Hs-1 activation N-Jak Stat-2.0-Hs-4 N-Jak Stat-2.0-Hs-1 activation N-Jak Stat-2.0-Hs-4 N-Jak Stat-2.0-Hs-1 activation N-Jak Stat-2.0-Hs-4 N-Jak Stat-2.0-Hs-11 activation N-Jak Stat-2.0-Hs-22 N-Jak Stat-2.0-Hs-11 activation N-Jak Stat-2.0-Hs-23 N-Jak Stat-2.0-Hs-11 activation N-Jak Stat-2.0-Hs-23 N-Jak Stat-2.0-Hs-8 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-8 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-8 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-8 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-8 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-8 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-12 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-12 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-12 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-12 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-12 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-12 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-12 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-20 activation N-Jak Stat-2.0-Hs-19 N-Jak Stat-2.0-Hs-20 activation N-Jak Stat-2.0-Hs-19 N-Jak Stat-2.0-Hs-19 activation N-Jak Stat-2.0-Hs-10 N-Jak Stat-2.0-Hs-19 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-19 activation N-Jak Stat-2.0-Hs-7 N-Jak Stat-2.0-Hs-19 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-19 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-30 activation N-Jak Stat-2.0-Hs-6 N-Jak Stat-2.0-Hs-30 activation N-Jak Stat-2.0-Hs-6 N-Jak Stat-2.0-Hs-30 activation N-Jak Stat-2.0-Hs-6 N-Jak Stat-2.0-Hs-25 activation N-Jak Stat-2.0-Hs-29 N-Jak Stat-2.0-Hs-34 activation N-Jak Stat-2.0-Hs-12 N-Jak Stat-2.0-Hs-34 activation N-Jak Stat-2.0-Hs-13 N-Jak Stat-2.0-Hs-34 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-34 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-31 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-31 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-31 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-31 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-31 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-31 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-35 activation N-Jak Stat-2.0-Hs-32 N-Jak Stat-2.0-Hs-2 activation N-Jak Stat-2.0-Hs-34 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-35 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-16 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-14 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-11 N-Jak Stat-2.0-Hs-28 activation N-Jak Stat-2.0-Hs-11 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Macrophage Signaling-2.0-Hs.att000066400000000000000000000175741426625374700271740ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Macrophage Signaling-2.0-Hs-1 JNK 95 66 white rectangle gene 0.5 black 46 17 JNK N-Macrophage Signaling-2.0-Hs-10 STAT1 117 112 white rectangle gene 0.5 black 46 17 11362 N-Macrophage Signaling-2.0-Hs-100 NFKBIA 43 89 white rectangle gene 0.5 black 46 17 7797 N-Macrophage Signaling-2.0-Hs-101 NFKBIA 46 96 white rectangle gene 0.5 black 46 17 7797 N-Macrophage Signaling-2.0-Hs-102 NFKBIE 53 86 white rectangle gene 0.5 black 46 17 7799 N-Macrophage Signaling-2.0-Hs-103 NOS2 127 45 white rectangle gene 0.5 black 46 17 7873 N-Macrophage Signaling-2.0-Hs-104 P2RX7 78 105 white rectangle gene 0.5 black 46 17 8537 N-Macrophage Signaling-2.0-Hs-105 PPARA 115 107 white rectangle gene 0.5 black 46 17 9232 N-Macrophage Signaling-2.0-Hs-108 PTK2 137 50 white rectangle gene 0.5 black 46 17 9611 N-Macrophage Signaling-2.0-Hs-109 RAC1 27 1 white rectangle gene 0.5 black 46 17 9801 N-Macrophage Signaling-2.0-Hs-11 STAT2 140 124 white rectangle gene 0.5 black 46 17 11363 N-Macrophage Signaling-2.0-Hs-12 STAT3 114 84 white rectangle gene 0.5 black 46 17 11364 N-Macrophage Signaling-2.0-Hs-14 TGM2 3 83 white rectangle gene 0.5 black 46 17 11778 N-Macrophage Signaling-2.0-Hs-15 TLR2 106 37 white rectangle gene 0.5 black 46 17 11848 N-Macrophage Signaling-2.0-Hs-16 TLR3 135 29 white rectangle gene 0.5 black 46 17 11849 N-Macrophage Signaling-2.0-Hs-17 TLR4 120 39 white rectangle gene 0.5 black 46 17 11850 N-Macrophage Signaling-2.0-Hs-18 TNF 108 69 white rectangle gene 0.5 black 46 17 11892 N-Macrophage Signaling-2.0-Hs-19 TNFRSF1A 98 65 white rectangle gene 0.5 black 46 17 11916 N-Macrophage Signaling-2.0-Hs-2 STAT5 162 157 white rectangle gene 0.5 black 46 17 STAT5 N-Macrophage Signaling-2.0-Hs-20 CD40LG 80 101 white rectangle gene 0.5 black 46 17 11935 N-Macrophage Signaling-2.0-Hs-21 TRADD 88 64 white rectangle gene 0.5 black 46 17 12030 N-Macrophage Signaling-2.0-Hs-22 TRAF2 82 70 white rectangle gene 0.5 black 46 17 12032 N-Macrophage Signaling-2.0-Hs-23 TRAF6 83 80 white rectangle gene 0.5 black 46 17 12036 N-Macrophage Signaling-2.0-Hs-24 VDR 55 2 white rectangle gene 0.5 black 46 17 12679 N-Macrophage Signaling-2.0-Hs-25 VIP 85 164 white rectangle gene 0.5 black 46 17 12693 N-Macrophage Signaling-2.0-Hs-26 VIPR1 87 158 white rectangle gene 0.5 black 46 17 12694 N-Macrophage Signaling-2.0-Hs-27 ZBTB16 60 2 white rectangle gene 0.5 black 46 17 12930 N-Macrophage Signaling-2.0-Hs-28 CTCF 114 68 white rectangle gene 0.5 black 46 17 13723 N-Macrophage Signaling-2.0-Hs-29 CAMP 87 121 white rectangle gene 0.5 black 46 17 1472 N-Macrophage Signaling-2.0-Hs-3 CCL2 29 183 white rectangle gene 0.5 black 46 17 10618 N-Macrophage Signaling-2.0-Hs-30 SIRT1 148 57 white rectangle gene 0.5 black 46 17 14929 N-Macrophage Signaling-2.0-Hs-31 CASP1 86 104 white rectangle gene 0.5 black 46 17 1499 N-Macrophage Signaling-2.0-Hs-32 CCND1 124 110 white rectangle gene 0.5 black 46 17 1582 N-Macrophage Signaling-2.0-Hs-33 CCR2 33 178 white rectangle gene 0.5 black 46 17 1603 N-Macrophage Signaling-2.0-Hs-34 CD14 64 3 white rectangle gene 0.5 black 46 17 1628 N-Macrophage Signaling-2.0-Hs-35 CD36 178 113 white rectangle gene 0.5 black 46 17 1663 N-Macrophage Signaling-2.0-Hs-36 CD44 97 112 white rectangle gene 0.5 black 46 17 1681 N-Macrophage Signaling-2.0-Hs-38 IRAK3 106 100 white rectangle gene 0.5 black 46 17 17020 N-Macrophage Signaling-2.0-Hs-39 CDK4 123 115 white rectangle gene 0.5 black 46 17 1773 N-Macrophage Signaling-2.0-Hs-40 CDKN2D 121 109 white rectangle gene 0.5 black 46 17 1790 N-Macrophage Signaling-2.0-Hs-41 IRAK4 97 57 white rectangle gene 0.5 black 46 17 17967 N-Macrophage Signaling-2.0-Hs-42 SOCS3 121 85 white rectangle gene 0.5 black 46 17 19391 N-Macrophage Signaling-2.0-Hs-43 CHUK 59 85 white rectangle gene 0.5 black 46 17 1974 N-Macrophage Signaling-2.0-Hs-44 CRK 31 0 white rectangle gene 0.5 black 46 17 2362 N-Macrophage Signaling-2.0-Hs-45 CSF2 152 142 white rectangle gene 0.5 black 46 17 2434 N-Macrophage Signaling-2.0-Hs-49 ABCA1 0 85 white rectangle gene 0.5 black 46 17 29 N-Macrophage Signaling-2.0-Hs-5 CXCL10 21 36 white rectangle gene 0.5 black 46 17 10637 N-Macrophage Signaling-2.0-Hs-50 IRGM 84 116 white rectangle gene 0.5 black 46 17 29597 N-Macrophage Signaling-2.0-Hs-51 DOCK1 26 6 white rectangle gene 0.5 black 46 17 2987 N-Macrophage Signaling-2.0-Hs-54 GAS6 14 122 white rectangle gene 0.5 black 46 17 4168 N-Macrophage Signaling-2.0-Hs-55 GATA1 128 117 white rectangle gene 0.5 black 46 17 4170 N-Macrophage Signaling-2.0-Hs-58 ALOX5 141 5 white rectangle gene 0.5 black 46 17 435 N-Macrophage Signaling-2.0-Hs-59 CXCR3 22 32 white rectangle gene 0.5 black 46 17 4540 N-Macrophage Signaling-2.0-Hs-6 SFTPA1 102 104 white rectangle gene 0.5 black 46 17 10798 N-Macrophage Signaling-2.0-Hs-62 HDAC3 155 136 white rectangle gene 0.5 black 46 17 4854 N-Macrophage Signaling-2.0-Hs-63 HMGB1 121 32 white rectangle gene 0.5 black 46 17 4983 N-Macrophage Signaling-2.0-Hs-65 NOD2 90 114 white rectangle gene 0.5 black 46 17 5331 N-Macrophage Signaling-2.0-Hs-66 IFNG 135 120 white rectangle gene 0.5 black 46 17 5438 N-Macrophage Signaling-2.0-Hs-67 IGF1 186 48 white rectangle gene 0.5 black 46 17 5464 N-Macrophage Signaling-2.0-Hs-68 IGF1R 190 49 white rectangle gene 0.5 black 46 17 5465 N-Macrophage Signaling-2.0-Hs-7 SKI 161 140 white rectangle gene 0.5 black 46 17 10896 N-Macrophage Signaling-2.0-Hs-70 IKBKB 59 82 white rectangle gene 0.5 black 46 17 5960 N-Macrophage Signaling-2.0-Hs-71 IL10 118 72 white rectangle gene 0.5 black 46 17 5962 N-Macrophage Signaling-2.0-Hs-72 IL10RA 118 78 white rectangle gene 0.5 black 46 17 5964 N-Macrophage Signaling-2.0-Hs-73 IL12A 152 115 white rectangle gene 0.5 black 46 17 5969 N-Macrophage Signaling-2.0-Hs-74 IL1B 98 106 white rectangle gene 0.5 black 46 17 5992 N-Macrophage Signaling-2.0-Hs-75 IL1R1 90 93 white rectangle gene 0.5 black 46 17 5993 N-Macrophage Signaling-2.0-Hs-77 IL6 118 100 white rectangle gene 0.5 black 46 17 6018 N-Macrophage Signaling-2.0-Hs-78 CXCL8 80 109 white rectangle gene 0.5 black 46 17 6025 N-Macrophage Signaling-2.0-Hs-79 IRAK1 89 68 white rectangle gene 0.5 black 46 17 6112 N-Macrophage Signaling-2.0-Hs-8 SPI1 146 131 white rectangle gene 0.5 black 46 17 11241 N-Macrophage Signaling-2.0-Hs-80 IRF1 144 117 white rectangle gene 0.5 black 46 17 6116 N-Macrophage Signaling-2.0-Hs-81 IRF3 129 33 white rectangle gene 0.5 black 46 17 6118 N-Macrophage Signaling-2.0-Hs-82 IRF5 114 104 white rectangle gene 0.5 black 46 17 6120 N-Macrophage Signaling-2.0-Hs-83 JAK2 158 151 white rectangle gene 0.5 black 46 17 6192 N-Macrophage Signaling-2.0-Hs-84 RHOA 179 117 white rectangle gene 0.5 black 46 17 667 N-Macrophage Signaling-2.0-Hs-85 LRP1 178 122 white rectangle gene 0.5 black 46 17 6692 N-Macrophage Signaling-2.0-Hs-86 MAP3K3 43 93 white rectangle gene 0.5 black 46 17 6855 N-Macrophage Signaling-2.0-Hs-87 MAP3K7 71 79 white rectangle gene 0.5 black 46 17 6859 N-Macrophage Signaling-2.0-Hs-88 MAP3K8 108 62 white rectangle gene 0.5 black 46 17 6860 N-Macrophage Signaling-2.0-Hs-89 MAPK1 137 8 white rectangle gene 0.5 black 46 17 6871 N-Macrophage Signaling-2.0-Hs-9 SRC 130 54 white rectangle gene 0.5 black 46 17 11283 N-Macrophage Signaling-2.0-Hs-90 MAPK3 145 8 white rectangle gene 0.5 black 46 17 6877 N-Macrophage Signaling-2.0-Hs-91 MERTK 14 126 white rectangle gene 0.5 black 46 17 7027 N-Macrophage Signaling-2.0-Hs-92 CXCL9 26 29 white rectangle gene 0.5 black 46 17 7098 N-Macrophage Signaling-2.0-Hs-93 MMP12 120 61 white rectangle gene 0.5 black 46 17 7158 N-Macrophage Signaling-2.0-Hs-94 MMP9 140 56 white rectangle gene 0.5 black 46 17 7176 N-Macrophage Signaling-2.0-Hs-96 MST1 99 177 white rectangle gene 0.5 black 46 17 7380 N-Macrophage Signaling-2.0-Hs-97 MST1R 99 171 white rectangle gene 0.5 black 46 17 7381 N-Macrophage Signaling-2.0-Hs-98 MYD88 107 45 white rectangle gene 0.5 black 46 17 7562 N-Macrophage Signaling-2.0-Hs-99 NFKBIA 51 89 white rectangle gene 0.5 black 46 17 7797 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Macrophage Signaling-2.0-Hs.sif000066400000000000000000000537071426625374700271630ustar00rootroot000000000000000 1 2 N-Macrophage Signaling-2.0-Hs-74 activation N-Macrophage Signaling-2.0-Hs-75 N-Macrophage Signaling-2.0-Hs-74 activation N-Macrophage Signaling-2.0-Hs-75 N-Macrophage Signaling-2.0-Hs-43 inhibition N-Macrophage Signaling-2.0-Hs-102 N-Macrophage Signaling-2.0-Hs-43 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-6 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-6 activation N-Macrophage Signaling-2.0-Hs-38 N-Macrophage Signaling-2.0-Hs-45 activation N-Macrophage Signaling-2.0-Hs-83 N-Macrophage Signaling-2.0-Hs-45 activation N-Macrophage Signaling-2.0-Hs-83 N-Macrophage Signaling-2.0-Hs-45 activation N-Macrophage Signaling-2.0-Hs-83 N-Macrophage Signaling-2.0-Hs-45 activation N-Macrophage Signaling-2.0-Hs-8 N-Macrophage Signaling-2.0-Hs-45 activation N-Macrophage Signaling-2.0-Hs-8 N-Macrophage Signaling-2.0-Hs-10 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-12 inhibition N-Macrophage Signaling-2.0-Hs-18 N-Macrophage Signaling-2.0-Hs-12 inhibition N-Macrophage Signaling-2.0-Hs-18 N-Macrophage Signaling-2.0-Hs-89 activation N-Macrophage Signaling-2.0-Hs-58 N-Macrophage Signaling-2.0-Hs-82 activation N-Macrophage Signaling-2.0-Hs-77 N-Macrophage Signaling-2.0-Hs-14 activation N-Macrophage Signaling-2.0-Hs-49 N-Macrophage Signaling-2.0-Hs-44 activation N-Macrophage Signaling-2.0-Hs-109 N-Macrophage Signaling-2.0-Hs-92 activation N-Macrophage Signaling-2.0-Hs-59 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-98 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-98 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-98 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-98 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-98 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-98 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-103 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-103 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-17 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-96 activation N-Macrophage Signaling-2.0-Hs-97 N-Macrophage Signaling-2.0-Hs-96 activation N-Macrophage Signaling-2.0-Hs-97 N-Macrophage Signaling-2.0-Hs-96 activation N-Macrophage Signaling-2.0-Hs-97 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-31 activation N-Macrophage Signaling-2.0-Hs-78 N-Macrophage Signaling-2.0-Hs-103 activation N-Macrophage Signaling-2.0-Hs-9 N-Macrophage Signaling-2.0-Hs-27 inhibition N-Macrophage Signaling-2.0-Hs-34 N-Macrophage Signaling-2.0-Hs-27 inhibition N-Macrophage Signaling-2.0-Hs-24 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-19 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-93 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-93 N-Macrophage Signaling-2.0-Hs-18 activation N-Macrophage Signaling-2.0-Hs-93 N-Macrophage Signaling-2.0-Hs-21 activation N-Macrophage Signaling-2.0-Hs-22 N-Macrophage Signaling-2.0-Hs-21 activation N-Macrophage Signaling-2.0-Hs-22 N-Macrophage Signaling-2.0-Hs-67 activation N-Macrophage Signaling-2.0-Hs-68 N-Macrophage Signaling-2.0-Hs-67 activation N-Macrophage Signaling-2.0-Hs-68 N-Macrophage Signaling-2.0-Hs-67 activation N-Macrophage Signaling-2.0-Hs-68 N-Macrophage Signaling-2.0-Hs-67 activation N-Macrophage Signaling-2.0-Hs-68 N-Macrophage Signaling-2.0-Hs-67 activation N-Macrophage Signaling-2.0-Hs-68 N-Macrophage Signaling-2.0-Hs-67 activation N-Macrophage Signaling-2.0-Hs-68 N-Macrophage Signaling-2.0-Hs-67 activation N-Macrophage Signaling-2.0-Hs-68 N-Macrophage Signaling-2.0-Hs-71 activation N-Macrophage Signaling-2.0-Hs-72 N-Macrophage Signaling-2.0-Hs-85 activation N-Macrophage Signaling-2.0-Hs-84 N-Macrophage Signaling-2.0-Hs-20 inhibition N-Macrophage Signaling-2.0-Hs-31 N-Macrophage Signaling-2.0-Hs-23 activation N-Macrophage Signaling-2.0-Hs-87 N-Macrophage Signaling-2.0-Hs-23 activation N-Macrophage Signaling-2.0-Hs-87 N-Macrophage Signaling-2.0-Hs-40 inhibition N-Macrophage Signaling-2.0-Hs-39 N-Macrophage Signaling-2.0-Hs-62 inhibition N-Macrophage Signaling-2.0-Hs-8 N-Macrophage Signaling-2.0-Hs-86 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-28 activation N-Macrophage Signaling-2.0-Hs-71 N-Macrophage Signaling-2.0-Hs-28 activation N-Macrophage Signaling-2.0-Hs-18 N-Macrophage Signaling-2.0-Hs-51 activation N-Macrophage Signaling-2.0-Hs-109 N-Macrophage Signaling-2.0-Hs-51 activation N-Macrophage Signaling-2.0-Hs-109 N-Macrophage Signaling-2.0-Hs-54 activation N-Macrophage Signaling-2.0-Hs-91 N-Macrophage Signaling-2.0-Hs-54 activation N-Macrophage Signaling-2.0-Hs-91 N-Macrophage Signaling-2.0-Hs-54 activation N-Macrophage Signaling-2.0-Hs-91 N-Macrophage Signaling-2.0-Hs-3 activation N-Macrophage Signaling-2.0-Hs-33 N-Macrophage Signaling-2.0-Hs-88 activation N-Macrophage Signaling-2.0-Hs-18 N-Macrophage Signaling-2.0-Hs-55 activation N-Macrophage Signaling-2.0-Hs-32 N-Macrophage Signaling-2.0-Hs-55 activation N-Macrophage Signaling-2.0-Hs-32 N-Macrophage Signaling-2.0-Hs-42 inhibition N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-42 inhibition N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-42 inhibition N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-42 inhibition N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-42 inhibition N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-42 inhibition N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-84 inhibition N-Macrophage Signaling-2.0-Hs-35 N-Macrophage Signaling-2.0-Hs-79 activation N-Macrophage Signaling-2.0-Hs-23 N-Macrophage Signaling-2.0-Hs-79 activation N-Macrophage Signaling-2.0-Hs-23 N-Macrophage Signaling-2.0-Hs-79 activation N-Macrophage Signaling-2.0-Hs-23 N-Macrophage Signaling-2.0-Hs-79 activation N-Macrophage Signaling-2.0-Hs-23 N-Macrophage Signaling-2.0-Hs-79 activation N-Macrophage Signaling-2.0-Hs-23 N-Macrophage Signaling-2.0-Hs-79 activation N-Macrophage Signaling-2.0-Hs-23 N-Macrophage Signaling-2.0-Hs-77 inhibition N-Macrophage Signaling-2.0-Hs-32 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-40 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-40 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-77 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-101 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-101 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-101 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-101 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-101 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-15 activation N-Macrophage Signaling-2.0-Hs-98 N-Macrophage Signaling-2.0-Hs-22 activation N-Macrophage Signaling-2.0-Hs-87 N-Macrophage Signaling-2.0-Hs-22 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-22 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-7 activation N-Macrophage Signaling-2.0-Hs-62 N-Macrophage Signaling-2.0-Hs-36 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-63 activation N-Macrophage Signaling-2.0-Hs-17 N-Macrophage Signaling-2.0-Hs-63 activation N-Macrophage Signaling-2.0-Hs-17 N-Macrophage Signaling-2.0-Hs-63 activation N-Macrophage Signaling-2.0-Hs-17 N-Macrophage Signaling-2.0-Hs-98 activation N-Macrophage Signaling-2.0-Hs-41 N-Macrophage Signaling-2.0-Hs-19 activation N-Macrophage Signaling-2.0-Hs-21 N-Macrophage Signaling-2.0-Hs-19 activation N-Macrophage Signaling-2.0-Hs-21 N-Macrophage Signaling-2.0-Hs-70 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-87 activation N-Macrophage Signaling-2.0-Hs-70 N-Macrophage Signaling-2.0-Hs-87 activation N-Macrophage Signaling-2.0-Hs-43 N-Macrophage Signaling-2.0-Hs-105 inhibition N-Macrophage Signaling-2.0-Hs-77 N-Macrophage Signaling-2.0-Hs-75 activation N-Macrophage Signaling-2.0-Hs-23 N-Macrophage Signaling-2.0-Hs-72 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-72 activation N-Macrophage Signaling-2.0-Hs-12 N-Macrophage Signaling-2.0-Hs-104 activation N-Macrophage Signaling-2.0-Hs-31 N-Macrophage Signaling-2.0-Hs-5 activation N-Macrophage Signaling-2.0-Hs-59 N-Macrophage Signaling-2.0-Hs-90 activation N-Macrophage Signaling-2.0-Hs-58 N-Macrophage Signaling-2.0-Hs-9 activation N-Macrophage Signaling-2.0-Hs-108 N-Macrophage Signaling-2.0-Hs-9 activation N-Macrophage Signaling-2.0-Hs-94 N-Macrophage Signaling-2.0-Hs-9 activation N-Macrophage Signaling-2.0-Hs-93 N-Macrophage Signaling-2.0-Hs-16 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-16 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-16 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-16 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-16 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-16 activation N-Macrophage Signaling-2.0-Hs-81 N-Macrophage Signaling-2.0-Hs-41 activation N-Macrophage Signaling-2.0-Hs-79 N-Macrophage Signaling-2.0-Hs-41 activation N-Macrophage Signaling-2.0-Hs-79 N-Macrophage Signaling-2.0-Hs-41 activation N-Macrophage Signaling-2.0-Hs-79 N-Macrophage Signaling-2.0-Hs-41 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-41 activation N-Macrophage Signaling-2.0-Hs-1 N-Macrophage Signaling-2.0-Hs-32 activation N-Macrophage Signaling-2.0-Hs-39 N-Macrophage Signaling-2.0-Hs-32 activation N-Macrophage Signaling-2.0-Hs-39 N-Macrophage Signaling-2.0-Hs-32 activation N-Macrophage Signaling-2.0-Hs-39 N-Macrophage Signaling-2.0-Hs-100 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-100 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-100 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-100 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-100 activation N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-66 activation N-Macrophage Signaling-2.0-Hs-80 N-Macrophage Signaling-2.0-Hs-66 activation N-Macrophage Signaling-2.0-Hs-10 N-Macrophage Signaling-2.0-Hs-66 activation N-Macrophage Signaling-2.0-Hs-11 N-Macrophage Signaling-2.0-Hs-66 activation N-Macrophage Signaling-2.0-Hs-8 N-Macrophage Signaling-2.0-Hs-80 activation N-Macrophage Signaling-2.0-Hs-73 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-99 inhibition N-Macrophage Signaling-2.0-Hs-99 N-Macrophage Signaling-2.0-Hs-65 activation N-Macrophage Signaling-2.0-Hs-50 N-Macrophage Signaling-2.0-Hs-65 activation N-Macrophage Signaling-2.0-Hs-29 N-Macrophage Signaling-2.0-Hs-65 activation N-Macrophage Signaling-2.0-Hs-74 N-Macrophage Signaling-2.0-Hs-25 activation N-Macrophage Signaling-2.0-Hs-26 N-Macrophage Signaling-2.0-Hs-25 activation N-Macrophage Signaling-2.0-Hs-26 N-Macrophage Signaling-2.0-Hs-25 activation N-Macrophage Signaling-2.0-Hs-26 N-Macrophage Signaling-2.0-Hs-25 activation N-Macrophage Signaling-2.0-Hs-26 N-Macrophage Signaling-2.0-Hs-30 inhibition N-Macrophage Signaling-2.0-Hs-94 N-Macrophage Signaling-2.0-Hs-83 activation N-Macrophage Signaling-2.0-Hs-2 N-Macrophage Signaling-2.0-Hs-83 activation N-Macrophage Signaling-2.0-Hs-2 N-Macrophage Signaling-2.0-Hs-83 activation N-Macrophage Signaling-2.0-Hs-2 N-Macrophage Signaling-2.0-Hs-83 activation N-Macrophage Signaling-2.0-Hs-2 N-Macrophage Signaling-2.0-Hs-83 activation N-Macrophage Signaling-2.0-Hs-2 N-Macrophage Signaling-2.0-Hs-83 activation N-Macrophage Signaling-2.0-Hs-2 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Mapk-2.0-Hs.att000066400000000000000000000031071426625374700241050ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Mapk-2.0-Hs-1 CEBP 0 147 white rectangle gene 0.5 black 46 17 CEBP N-Mapk-2.0-Hs-10 CCND1 120 175 white rectangle gene 0.5 black 46 17 1582 N-Mapk-2.0-Hs-11 EGFR 17 151 white rectangle gene 0.5 black 46 17 3236 N-Mapk-2.0-Hs-13 GGPS1 10 4 white rectangle gene 0.5 black 46 17 4249 N-Mapk-2.0-Hs-14 HRAS 34 51 white rectangle gene 0.5 black 46 17 5173 N-Mapk-2.0-Hs-15 HRAS 43 45 white rectangle gene 0.5 black 46 17 5173 N-Mapk-2.0-Hs-16 JUN 121 188 white rectangle gene 0.5 black 46 17 6204 N-Mapk-2.0-Hs-17 MAPK14 7 153 white rectangle gene 0.5 black 46 17 6876 N-Mapk-2.0-Hs-18 NF1 125 24 white rectangle gene 0.5 black 46 17 7765 N-Mapk-2.0-Hs-19 RAF1 21 57 white rectangle gene 0.5 black 46 17 9829 N-Mapk-2.0-Hs-2 ERK 17 0 white rectangle gene 0.5 black 46 17 ERK N-Mapk-2.0-Hs-20 RAF1 8 59 white rectangle gene 0.5 black 46 17 9829 N-Mapk-2.0-Hs-21 RAF1 20 68 white rectangle gene 0.5 black 46 17 9829 N-Mapk-2.0-Hs-22 RAF1 13 47 white rectangle gene 0.5 black 46 17 9829 N-Mapk-2.0-Hs-23 RASSF1 111 181 white rectangle gene 0.5 black 46 17 9882 N-Mapk-2.0-Hs-3 JNK 102 188 white rectangle gene 0.5 black 46 17 JNK N-Mapk-2.0-Hs-4 JNK 108 193 white rectangle gene 0.5 black 46 17 JNK N-Mapk-2.0-Hs-5 RAF 140 5 white rectangle gene 0.5 black 46 17 RAF N-Mapk-2.0-Hs-6 RAS 133 14 white rectangle gene 0.5 black 46 17 RAS N-Mapk-2.0-Hs-7 p38 156 98 white rectangle gene 0.5 black 46 17 p38 N-Mapk-2.0-Hs-8 p38 167 98 white rectangle gene 0.5 black 46 17 p38 N-Mapk-2.0-Hs-9 p38 144 98 white rectangle gene 0.5 black 46 17 p38 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Mapk-2.0-Hs.sif000066400000000000000000000041411426625374700240750ustar00rootroot000000000000000 1 2 N-Mapk-2.0-Hs-15 activation N-Mapk-2.0-Hs-14 N-Mapk-2.0-Hs-15 activation N-Mapk-2.0-Hs-14 N-Mapk-2.0-Hs-15 activation N-Mapk-2.0-Hs-14 N-Mapk-2.0-Hs-15 activation N-Mapk-2.0-Hs-14 N-Mapk-2.0-Hs-15 activation N-Mapk-2.0-Hs-14 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-14 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-17 inhibition N-Mapk-2.0-Hs-11 N-Mapk-2.0-Hs-17 activation N-Mapk-2.0-Hs-1 N-Mapk-2.0-Hs-4 activation N-Mapk-2.0-Hs-3 N-Mapk-2.0-Hs-13 activation N-Mapk-2.0-Hs-2 N-Mapk-2.0-Hs-18 inhibition N-Mapk-2.0-Hs-6 N-Mapk-2.0-Hs-18 inhibition N-Mapk-2.0-Hs-6 N-Mapk-2.0-Hs-18 inhibition N-Mapk-2.0-Hs-6 N-Mapk-2.0-Hs-8 activation N-Mapk-2.0-Hs-7 N-Mapk-2.0-Hs-23 inhibition N-Mapk-2.0-Hs-4 N-Mapk-2.0-Hs-23 inhibition N-Mapk-2.0-Hs-10 N-Mapk-2.0-Hs-23 inhibition N-Mapk-2.0-Hs-10 N-Mapk-2.0-Hs-23 inhibition N-Mapk-2.0-Hs-10 N-Mapk-2.0-Hs-23 inhibition N-Mapk-2.0-Hs-16 N-Mapk-2.0-Hs-23 inhibition N-Mapk-2.0-Hs-16 N-Mapk-2.0-Hs-23 inhibition N-Mapk-2.0-Hs-16 N-Mapk-2.0-Hs-23 inhibition N-Mapk-2.0-Hs-3 N-Mapk-2.0-Hs-6 activation N-Mapk-2.0-Hs-5 N-Mapk-2.0-Hs-6 activation N-Mapk-2.0-Hs-5 N-Mapk-2.0-Hs-6 activation N-Mapk-2.0-Hs-5 N-Mapk-2.0-Hs-6 activation N-Mapk-2.0-Hs-5 N-Mapk-2.0-Hs-9 activation N-Mapk-2.0-Hs-7 N-Mapk-2.0-Hs-22 inhibition N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-21 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-21 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-21 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-21 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-21 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-21 activation N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-20 inhibition N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-20 inhibition N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-20 inhibition N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-20 inhibition N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-20 inhibition N-Mapk-2.0-Hs-19 N-Mapk-2.0-Hs-20 inhibition N-Mapk-2.0-Hs-19 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Mast cell activation-2.0-Hs.att000066400000000000000000000030361426625374700271440ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Mast cell activation-2.0-Hs-10 INPP5D 94 156 white rectangle gene 0.5 black 46 17 6079 N-Mast cell activation-2.0-Hs-11 INPP5D 111 154 white rectangle gene 0.5 black 46 17 6079 N-Mast cell activation-2.0-Hs-12 KIT 123 110 white rectangle gene 0.5 black 46 17 6342 N-Mast cell activation-2.0-Hs-13 KITLG 137 125 white rectangle gene 0.5 black 46 17 6343 N-Mast cell activation-2.0-Hs-14 LYN 100 137 white rectangle gene 0.5 black 46 17 6735 N-Mast cell activation-2.0-Hs-15 MAPK1 70 143 white rectangle gene 0.5 black 46 17 6871 N-Mast cell activation-2.0-Hs-16 PIK3CD 140 101 white rectangle gene 0.5 black 46 17 8977 N-Mast cell activation-2.0-Hs-17 PLA2G4A 19 149 white rectangle gene 0.5 black 46 17 9035 N-Mast cell activation-2.0-Hs-18 PLA2G5 43 147 white rectangle gene 0.5 black 46 17 9038 N-Mast cell activation-2.0-Hs-2 BTK 144 111 white rectangle gene 0.5 black 46 17 1133 N-Mast cell activation-2.0-Hs-20 PTPN6 116 76 white rectangle gene 0.5 black 46 17 9658 N-Mast cell activation-2.0-Hs-22 BCL2L1 89 42 white rectangle gene 0.5 black 46 17 992 N-Mast cell activation-2.0-Hs-3 STAT6 108 45 white rectangle gene 0.5 black 46 17 11368 N-Mast cell activation-2.0-Hs-5 TLR2 0 151 white rectangle gene 0.5 black 46 17 11848 N-Mast cell activation-2.0-Hs-6 FCER1A 98 28 white rectangle gene 0.5 black 46 17 3609 N-Mast cell activation-2.0-Hs-8 IL4 119 0 white rectangle gene 0.5 black 46 17 6014 N-Mast cell activation-2.0-Hs-9 IL4R 116 19 white rectangle gene 0.5 black 46 17 6015 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Mast cell activation-2.0-Hs.sif000066400000000000000000000042511426625374700271350ustar00rootroot000000000000000 1 2 N-Mast cell activation-2.0-Hs-3 inhibition N-Mast cell activation-2.0-Hs-6 N-Mast cell activation-2.0-Hs-3 activation N-Mast cell activation-2.0-Hs-22 N-Mast cell activation-2.0-Hs-16 activation N-Mast cell activation-2.0-Hs-2 N-Mast cell activation-2.0-Hs-13 activation N-Mast cell activation-2.0-Hs-12 N-Mast cell activation-2.0-Hs-13 activation N-Mast cell activation-2.0-Hs-12 N-Mast cell activation-2.0-Hs-13 activation N-Mast cell activation-2.0-Hs-12 N-Mast cell activation-2.0-Hs-8 activation N-Mast cell activation-2.0-Hs-9 N-Mast cell activation-2.0-Hs-8 activation N-Mast cell activation-2.0-Hs-9 N-Mast cell activation-2.0-Hs-8 activation N-Mast cell activation-2.0-Hs-9 N-Mast cell activation-2.0-Hs-14 activation N-Mast cell activation-2.0-Hs-10 N-Mast cell activation-2.0-Hs-14 activation N-Mast cell activation-2.0-Hs-11 N-Mast cell activation-2.0-Hs-14 activation N-Mast cell activation-2.0-Hs-15 N-Mast cell activation-2.0-Hs-14 activation N-Mast cell activation-2.0-Hs-15 N-Mast cell activation-2.0-Hs-5 activation N-Mast cell activation-2.0-Hs-17 N-Mast cell activation-2.0-Hs-20 inhibition N-Mast cell activation-2.0-Hs-12 N-Mast cell activation-2.0-Hs-20 inhibition N-Mast cell activation-2.0-Hs-12 N-Mast cell activation-2.0-Hs-20 inhibition N-Mast cell activation-2.0-Hs-3 N-Mast cell activation-2.0-Hs-20 inhibition N-Mast cell activation-2.0-Hs-3 N-Mast cell activation-2.0-Hs-12 activation N-Mast cell activation-2.0-Hs-2 N-Mast cell activation-2.0-Hs-12 activation N-Mast cell activation-2.0-Hs-14 N-Mast cell activation-2.0-Hs-12 activation N-Mast cell activation-2.0-Hs-14 N-Mast cell activation-2.0-Hs-12 activation N-Mast cell activation-2.0-Hs-16 N-Mast cell activation-2.0-Hs-12 activation N-Mast cell activation-2.0-Hs-16 N-Mast cell activation-2.0-Hs-9 activation N-Mast cell activation-2.0-Hs-3 N-Mast cell activation-2.0-Hs-9 activation N-Mast cell activation-2.0-Hs-3 N-Mast cell activation-2.0-Hs-9 activation N-Mast cell activation-2.0-Hs-3 N-Mast cell activation-2.0-Hs-9 activation N-Mast cell activation-2.0-Hs-3 N-Mast cell activation-2.0-Hs-18 activation N-Mast cell activation-2.0-Hs-17 N-Mast cell activation-2.0-Hs-18 activation N-Mast cell activation-2.0-Hs-15 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Megakaryocyte Differentiation-2.0-Hs.att000066400000000000000000000166741426625374700311310ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Megakaryocyte Differentiation-2.0-Hs-1 ERK 77 91 white rectangle gene 0.5 black 46 17 ERK N-Megakaryocyte Differentiation-2.0-Hs-10 BMP4 106 98 white rectangle gene 0.5 black 46 17 1071 N-Megakaryocyte Differentiation-2.0-Hs-11 BMPR1A 118 109 white rectangle gene 0.5 black 46 17 1076 N-Megakaryocyte Differentiation-2.0-Hs-12 BMPR1B 116 112 white rectangle gene 0.5 black 46 17 1077 N-Megakaryocyte Differentiation-2.0-Hs-13 BMPR2 118 106 white rectangle gene 0.5 black 46 17 1078 N-Megakaryocyte Differentiation-2.0-Hs-14 BRAF 57 73 white rectangle gene 0.5 black 46 17 1097 N-Megakaryocyte Differentiation-2.0-Hs-15 STAT1 94 101 white rectangle gene 0.5 black 46 17 11362 N-Megakaryocyte Differentiation-2.0-Hs-16 STAT3 105 101 white rectangle gene 0.5 black 46 17 11364 N-Megakaryocyte Differentiation-2.0-Hs-17 TBXAS1 107 93 white rectangle gene 0.5 black 46 17 11609 N-Megakaryocyte Differentiation-2.0-Hs-18 TCF3 87 144 white rectangle gene 0.5 black 46 17 11633 N-Megakaryocyte Differentiation-2.0-Hs-19 THPO 108 106 white rectangle gene 0.5 black 46 17 11795 N-Megakaryocyte Differentiation-2.0-Hs-2 MEK 66 81 white rectangle gene 0.5 black 46 17 MEK N-Megakaryocyte Differentiation-2.0-Hs-20 TLR2 77 101 white rectangle gene 0.5 black 46 17 11848 N-Megakaryocyte Differentiation-2.0-Hs-21 TYK2 100 108 white rectangle gene 0.5 black 46 17 12440 N-Megakaryocyte Differentiation-2.0-Hs-22 VIP 109 10 white rectangle gene 0.5 black 46 17 12693 N-Megakaryocyte Differentiation-2.0-Hs-23 VIPR1 113 5 white rectangle gene 0.5 black 46 17 12694 N-Megakaryocyte Differentiation-2.0-Hs-24 VIPR2 109 2 white rectangle gene 0.5 black 46 17 12695 N-Megakaryocyte Differentiation-2.0-Hs-25 VWF 80 119 white rectangle gene 0.5 black 46 17 12726 N-Megakaryocyte Differentiation-2.0-Hs-27 CBFA2T3 98 116 white rectangle gene 0.5 black 46 17 1537 N-Megakaryocyte Differentiation-2.0-Hs-28 MYL9 83 93 white rectangle gene 0.5 black 46 17 15754 N-Megakaryocyte Differentiation-2.0-Hs-29 MYL9 173 121 white rectangle gene 0.5 black 46 17 15754 N-Megakaryocyte Differentiation-2.0-Hs-3 PRKAC 83 152 white rectangle gene 0.5 black 46 17 PRKAC N-Megakaryocyte Differentiation-2.0-Hs-30 TUBB1 103 105 white rectangle gene 0.5 black 46 17 16257 N-Megakaryocyte Differentiation-2.0-Hs-31 CDKN1A 92 133 white rectangle gene 0.5 black 46 17 1784 N-Megakaryocyte Differentiation-2.0-Hs-32 CDKN1B 96 153 white rectangle gene 0.5 black 46 17 1785 N-Megakaryocyte Differentiation-2.0-Hs-33 IRAK4 97 79 white rectangle gene 0.5 black 46 17 17967 N-Megakaryocyte Differentiation-2.0-Hs-34 SOCS3 115 99 white rectangle gene 0.5 black 46 17 19391 N-Megakaryocyte Differentiation-2.0-Hs-35 CHUK 139 66 white rectangle gene 0.5 black 46 17 1974 N-Megakaryocyte Differentiation-2.0-Hs-36 ZFPM1 90 111 white rectangle gene 0.5 black 46 17 19762 N-Megakaryocyte Differentiation-2.0-Hs-37 RASA3 46 59 white rectangle gene 0.5 black 46 17 20331 N-Megakaryocyte Differentiation-2.0-Hs-38 CREB1 83 85 white rectangle gene 0.5 black 46 17 2345 N-Megakaryocyte Differentiation-2.0-Hs-39 CREBBP 92 90 white rectangle gene 0.5 black 46 17 2348 N-Megakaryocyte Differentiation-2.0-Hs-4 RAS 83 88 white rectangle gene 0.5 black 46 17 RAS N-Megakaryocyte Differentiation-2.0-Hs-40 ADCYAP1 112 0 white rectangle gene 0.5 black 46 17 241 N-Megakaryocyte Differentiation-2.0-Hs-41 CXCR4 0 100 white rectangle gene 0.5 black 46 17 2561 N-Megakaryocyte Differentiation-2.0-Hs-42 TESC 81 102 white rectangle gene 0.5 black 46 17 26065 N-Megakaryocyte Differentiation-2.0-Hs-43 SH2B3 100 88 white rectangle gene 0.5 black 46 17 29605 N-Megakaryocyte Differentiation-2.0-Hs-45 AHR 145 34 white rectangle gene 0.5 black 46 17 348 N-Megakaryocyte Differentiation-2.0-Hs-46 ETS1 86 111 white rectangle gene 0.5 black 46 17 3488 N-Megakaryocyte Differentiation-2.0-Hs-47 FLI1 90 105 white rectangle gene 0.5 black 46 17 3749 N-Megakaryocyte Differentiation-2.0-Hs-48 FOXO3 96 144 white rectangle gene 0.5 black 46 17 3821 N-Megakaryocyte Differentiation-2.0-Hs-49 AKT1 102 139 white rectangle gene 0.5 black 46 17 391 N-Megakaryocyte Differentiation-2.0-Hs-5 RHO 104 119 white rectangle gene 0.5 black 46 17 RHO N-Megakaryocyte Differentiation-2.0-Hs-50 GATA1 96 105 white rectangle gene 0.5 black 46 17 4170 N-Megakaryocyte Differentiation-2.0-Hs-51 GP1BA 84 106 white rectangle gene 0.5 black 46 17 4439 N-Megakaryocyte Differentiation-2.0-Hs-52 GP9 97 107 white rectangle gene 0.5 black 46 17 4444 N-Megakaryocyte Differentiation-2.0-Hs-53 GSK3B 106 148 white rectangle gene 0.5 black 46 17 4617 N-Megakaryocyte Differentiation-2.0-Hs-54 HES1 150 28 white rectangle gene 0.5 black 46 17 5192 N-Megakaryocyte Differentiation-2.0-Hs-55 IKBKB 158 70 white rectangle gene 0.5 black 46 17 5960 N-Megakaryocyte Differentiation-2.0-Hs-56 IL11 91 69 white rectangle gene 0.5 black 46 17 5966 N-Megakaryocyte Differentiation-2.0-Hs-57 IL6 87 64 white rectangle gene 0.5 black 46 17 6018 N-Megakaryocyte Differentiation-2.0-Hs-58 IL6R 84 55 white rectangle gene 0.5 black 46 17 6019 N-Megakaryocyte Differentiation-2.0-Hs-59 IL6ST 91 78 white rectangle gene 0.5 black 46 17 6021 N-Megakaryocyte Differentiation-2.0-Hs-6 ROCK 174 115 white rectangle gene 0.5 black 46 17 ROCK N-Megakaryocyte Differentiation-2.0-Hs-60 IRAK1 112 72 white rectangle gene 0.5 black 46 17 6112 N-Megakaryocyte Differentiation-2.0-Hs-61 IRF1 96 100 white rectangle gene 0.5 black 46 17 6116 N-Megakaryocyte Differentiation-2.0-Hs-62 ITGA2B 88 104 white rectangle gene 0.5 black 46 17 6138 N-Megakaryocyte Differentiation-2.0-Hs-63 JAK2 96 96 white rectangle gene 0.5 black 46 17 6192 N-Megakaryocyte Differentiation-2.0-Hs-65 RHOA 175 110 white rectangle gene 0.5 black 46 17 667 N-Megakaryocyte Differentiation-2.0-Hs-66 MAP3K7 126 68 white rectangle gene 0.5 black 46 17 6859 N-Megakaryocyte Differentiation-2.0-Hs-67 MAPK1 67 103 white rectangle gene 0.5 black 46 17 6871 N-Megakaryocyte Differentiation-2.0-Hs-68 MAPK3 68 106 white rectangle gene 0.5 black 46 17 6877 N-Megakaryocyte Differentiation-2.0-Hs-69 MMP9 12 100 white rectangle gene 0.5 black 46 17 7176 N-Megakaryocyte Differentiation-2.0-Hs-7 STAT5 95 115 white rectangle gene 0.5 black 46 17 STAT5 N-Megakaryocyte Differentiation-2.0-Hs-70 MPL 98 111 white rectangle gene 0.5 black 46 17 7217 N-Megakaryocyte Differentiation-2.0-Hs-71 MYD88 85 89 white rectangle gene 0.5 black 46 17 7562 N-Megakaryocyte Differentiation-2.0-Hs-72 MYLK 170 126 white rectangle gene 0.5 black 46 17 7590 N-Megakaryocyte Differentiation-2.0-Hs-73 NFE2 99 100 white rectangle gene 0.5 black 46 17 7780 N-Megakaryocyte Differentiation-2.0-Hs-74 NFKBIA 152 67 white rectangle gene 0.5 black 46 17 7797 N-Megakaryocyte Differentiation-2.0-Hs-75 NFKBIA 159 66 white rectangle gene 0.5 black 46 17 7797 N-Megakaryocyte Differentiation-2.0-Hs-76 NFKBIA 158 62 white rectangle gene 0.5 black 46 17 7797 N-Megakaryocyte Differentiation-2.0-Hs-77 NFKBIE 144 60 white rectangle gene 0.5 black 46 17 7799 N-Megakaryocyte Differentiation-2.0-Hs-79 PF4 92 107 white rectangle gene 0.5 black 46 17 8861 N-Megakaryocyte Differentiation-2.0-Hs-8 RUNX1 86 99 white rectangle gene 0.5 black 46 17 10471 N-Megakaryocyte Differentiation-2.0-Hs-80 PIK3CA 101 127 white rectangle gene 0.5 black 46 17 8975 N-Megakaryocyte Differentiation-2.0-Hs-82 RAF1 73 82 white rectangle gene 0.5 black 46 17 9829 N-Megakaryocyte Differentiation-2.0-Hs-83 RAP1A 49 65 white rectangle gene 0.5 black 46 17 9855 N-Megakaryocyte Differentiation-2.0-Hs-9 CXCL12 5 100 white rectangle gene 0.5 black 46 17 10672 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Megakaryocyte Differentiation-2.0-Hs.sif000066400000000000000000000520351426625374700311110ustar00rootroot000000000000000 1 2 N-Megakaryocyte Differentiation-2.0-Hs-40 activation N-Megakaryocyte Differentiation-2.0-Hs-24 N-Megakaryocyte Differentiation-2.0-Hs-40 activation N-Megakaryocyte Differentiation-2.0-Hs-23 N-Megakaryocyte Differentiation-2.0-Hs-40 activation N-Megakaryocyte Differentiation-2.0-Hs-23 N-Megakaryocyte Differentiation-2.0-Hs-10 activation N-Megakaryocyte Differentiation-2.0-Hs-63 N-Megakaryocyte Differentiation-2.0-Hs-9 activation N-Megakaryocyte Differentiation-2.0-Hs-41 N-Megakaryocyte Differentiation-2.0-Hs-9 activation N-Megakaryocyte Differentiation-2.0-Hs-41 N-Megakaryocyte Differentiation-2.0-Hs-9 activation N-Megakaryocyte Differentiation-2.0-Hs-41 N-Megakaryocyte Differentiation-2.0-Hs-9 activation N-Megakaryocyte Differentiation-2.0-Hs-41 N-Megakaryocyte Differentiation-2.0-Hs-9 activation N-Megakaryocyte Differentiation-2.0-Hs-69 N-Megakaryocyte Differentiation-2.0-Hs-35 inhibition N-Megakaryocyte Differentiation-2.0-Hs-77 N-Megakaryocyte Differentiation-2.0-Hs-35 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-42 activation N-Megakaryocyte Differentiation-2.0-Hs-47 N-Megakaryocyte Differentiation-2.0-Hs-42 activation N-Megakaryocyte Differentiation-2.0-Hs-46 N-Megakaryocyte Differentiation-2.0-Hs-3 inhibition N-Megakaryocyte Differentiation-2.0-Hs-18 N-Megakaryocyte Differentiation-2.0-Hs-80 activation N-Megakaryocyte Differentiation-2.0-Hs-49 N-Megakaryocyte Differentiation-2.0-Hs-49 inhibition N-Megakaryocyte Differentiation-2.0-Hs-48 N-Megakaryocyte Differentiation-2.0-Hs-49 inhibition N-Megakaryocyte Differentiation-2.0-Hs-53 N-Megakaryocyte Differentiation-2.0-Hs-39 activation N-Megakaryocyte Differentiation-2.0-Hs-73 N-Megakaryocyte Differentiation-2.0-Hs-39 activation N-Megakaryocyte Differentiation-2.0-Hs-38 N-Megakaryocyte Differentiation-2.0-Hs-48 activation N-Megakaryocyte Differentiation-2.0-Hs-31 N-Megakaryocyte Differentiation-2.0-Hs-48 activation N-Megakaryocyte Differentiation-2.0-Hs-32 N-Megakaryocyte Differentiation-2.0-Hs-47 activation N-Megakaryocyte Differentiation-2.0-Hs-8 N-Megakaryocyte Differentiation-2.0-Hs-47 activation N-Megakaryocyte Differentiation-2.0-Hs-50 N-Megakaryocyte Differentiation-2.0-Hs-47 activation N-Megakaryocyte Differentiation-2.0-Hs-50 N-Megakaryocyte Differentiation-2.0-Hs-47 activation N-Megakaryocyte Differentiation-2.0-Hs-70 N-Megakaryocyte Differentiation-2.0-Hs-47 activation N-Megakaryocyte Differentiation-2.0-Hs-62 N-Megakaryocyte Differentiation-2.0-Hs-47 activation N-Megakaryocyte Differentiation-2.0-Hs-62 N-Megakaryocyte Differentiation-2.0-Hs-47 activation N-Megakaryocyte Differentiation-2.0-Hs-52 N-Megakaryocyte Differentiation-2.0-Hs-37 inhibition N-Megakaryocyte Differentiation-2.0-Hs-83 N-Megakaryocyte Differentiation-2.0-Hs-15 activation N-Megakaryocyte Differentiation-2.0-Hs-61 N-Megakaryocyte Differentiation-2.0-Hs-15 activation N-Megakaryocyte Differentiation-2.0-Hs-61 N-Megakaryocyte Differentiation-2.0-Hs-15 activation N-Megakaryocyte Differentiation-2.0-Hs-61 N-Megakaryocyte Differentiation-2.0-Hs-15 activation N-Megakaryocyte Differentiation-2.0-Hs-8 N-Megakaryocyte Differentiation-2.0-Hs-16 activation N-Megakaryocyte Differentiation-2.0-Hs-34 N-Megakaryocyte Differentiation-2.0-Hs-7 activation N-Megakaryocyte Differentiation-2.0-Hs-31 N-Megakaryocyte Differentiation-2.0-Hs-50 inhibition N-Megakaryocyte Differentiation-2.0-Hs-16 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-62 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-79 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-70 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-70 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-51 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-30 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-73 N-Megakaryocyte Differentiation-2.0-Hs-50 activation N-Megakaryocyte Differentiation-2.0-Hs-52 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-73 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-73 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-50 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-50 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-70 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-70 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-70 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-70 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-10 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-11 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-12 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-13 N-Megakaryocyte Differentiation-2.0-Hs-19 activation N-Megakaryocyte Differentiation-2.0-Hs-16 N-Megakaryocyte Differentiation-2.0-Hs-72 activation N-Megakaryocyte Differentiation-2.0-Hs-29 N-Megakaryocyte Differentiation-2.0-Hs-18 activation N-Megakaryocyte Differentiation-2.0-Hs-31 N-Megakaryocyte Differentiation-2.0-Hs-46 activation N-Megakaryocyte Differentiation-2.0-Hs-79 N-Megakaryocyte Differentiation-2.0-Hs-46 activation N-Megakaryocyte Differentiation-2.0-Hs-25 N-Megakaryocyte Differentiation-2.0-Hs-46 activation N-Megakaryocyte Differentiation-2.0-Hs-62 N-Megakaryocyte Differentiation-2.0-Hs-46 activation N-Megakaryocyte Differentiation-2.0-Hs-70 N-Megakaryocyte Differentiation-2.0-Hs-59 activation N-Megakaryocyte Differentiation-2.0-Hs-63 N-Megakaryocyte Differentiation-2.0-Hs-36 activation N-Megakaryocyte Differentiation-2.0-Hs-62 N-Megakaryocyte Differentiation-2.0-Hs-36 activation N-Megakaryocyte Differentiation-2.0-Hs-50 N-Megakaryocyte Differentiation-2.0-Hs-36 activation N-Megakaryocyte Differentiation-2.0-Hs-50 N-Megakaryocyte Differentiation-2.0-Hs-6 activation N-Megakaryocyte Differentiation-2.0-Hs-29 N-Megakaryocyte Differentiation-2.0-Hs-65 activation N-Megakaryocyte Differentiation-2.0-Hs-6 N-Megakaryocyte Differentiation-2.0-Hs-65 activation N-Megakaryocyte Differentiation-2.0-Hs-6 N-Megakaryocyte Differentiation-2.0-Hs-65 activation N-Megakaryocyte Differentiation-2.0-Hs-6 N-Megakaryocyte Differentiation-2.0-Hs-60 activation N-Megakaryocyte Differentiation-2.0-Hs-66 N-Megakaryocyte Differentiation-2.0-Hs-57 activation N-Megakaryocyte Differentiation-2.0-Hs-59 N-Megakaryocyte Differentiation-2.0-Hs-57 activation N-Megakaryocyte Differentiation-2.0-Hs-58 N-Megakaryocyte Differentiation-2.0-Hs-76 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-76 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-76 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-76 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-76 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-62 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-51 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-67 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-67 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-68 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-71 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-71 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-71 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-71 N-Megakaryocyte Differentiation-2.0-Hs-20 activation N-Megakaryocyte Differentiation-2.0-Hs-71 N-Megakaryocyte Differentiation-2.0-Hs-14 activation N-Megakaryocyte Differentiation-2.0-Hs-2 N-Megakaryocyte Differentiation-2.0-Hs-45 activation N-Megakaryocyte Differentiation-2.0-Hs-54 N-Megakaryocyte Differentiation-2.0-Hs-82 activation N-Megakaryocyte Differentiation-2.0-Hs-2 N-Megakaryocyte Differentiation-2.0-Hs-8 activation N-Megakaryocyte Differentiation-2.0-Hs-28 N-Megakaryocyte Differentiation-2.0-Hs-8 activation N-Megakaryocyte Differentiation-2.0-Hs-28 N-Megakaryocyte Differentiation-2.0-Hs-8 activation N-Megakaryocyte Differentiation-2.0-Hs-79 N-Megakaryocyte Differentiation-2.0-Hs-8 activation N-Megakaryocyte Differentiation-2.0-Hs-62 N-Megakaryocyte Differentiation-2.0-Hs-71 activation N-Megakaryocyte Differentiation-2.0-Hs-33 N-Megakaryocyte Differentiation-2.0-Hs-1 activation N-Megakaryocyte Differentiation-2.0-Hs-42 N-Megakaryocyte Differentiation-2.0-Hs-1 activation N-Megakaryocyte Differentiation-2.0-Hs-8 N-Megakaryocyte Differentiation-2.0-Hs-1 activation N-Megakaryocyte Differentiation-2.0-Hs-38 N-Megakaryocyte Differentiation-2.0-Hs-55 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-66 activation N-Megakaryocyte Differentiation-2.0-Hs-35 N-Megakaryocyte Differentiation-2.0-Hs-27 inhibition N-Megakaryocyte Differentiation-2.0-Hs-79 N-Megakaryocyte Differentiation-2.0-Hs-4 activation N-Megakaryocyte Differentiation-2.0-Hs-1 N-Megakaryocyte Differentiation-2.0-Hs-4 activation N-Megakaryocyte Differentiation-2.0-Hs-82 N-Megakaryocyte Differentiation-2.0-Hs-70 activation N-Megakaryocyte Differentiation-2.0-Hs-5 N-Megakaryocyte Differentiation-2.0-Hs-70 activation N-Megakaryocyte Differentiation-2.0-Hs-63 N-Megakaryocyte Differentiation-2.0-Hs-70 activation N-Megakaryocyte Differentiation-2.0-Hs-63 N-Megakaryocyte Differentiation-2.0-Hs-70 activation N-Megakaryocyte Differentiation-2.0-Hs-21 N-Megakaryocyte Differentiation-2.0-Hs-70 activation N-Megakaryocyte Differentiation-2.0-Hs-21 N-Megakaryocyte Differentiation-2.0-Hs-70 activation N-Megakaryocyte Differentiation-2.0-Hs-80 N-Megakaryocyte Differentiation-2.0-Hs-56 activation N-Megakaryocyte Differentiation-2.0-Hs-59 N-Megakaryocyte Differentiation-2.0-Hs-56 activation N-Megakaryocyte Differentiation-2.0-Hs-59 N-Megakaryocyte Differentiation-2.0-Hs-2 activation N-Megakaryocyte Differentiation-2.0-Hs-1 N-Megakaryocyte Differentiation-2.0-Hs-73 activation N-Megakaryocyte Differentiation-2.0-Hs-52 N-Megakaryocyte Differentiation-2.0-Hs-73 activation N-Megakaryocyte Differentiation-2.0-Hs-62 N-Megakaryocyte Differentiation-2.0-Hs-73 activation N-Megakaryocyte Differentiation-2.0-Hs-17 N-Megakaryocyte Differentiation-2.0-Hs-73 activation N-Megakaryocyte Differentiation-2.0-Hs-30 N-Megakaryocyte Differentiation-2.0-Hs-21 activation N-Megakaryocyte Differentiation-2.0-Hs-7 N-Megakaryocyte Differentiation-2.0-Hs-21 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-21 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-21 activation N-Megakaryocyte Differentiation-2.0-Hs-16 N-Megakaryocyte Differentiation-2.0-Hs-21 activation N-Megakaryocyte Differentiation-2.0-Hs-16 N-Megakaryocyte Differentiation-2.0-Hs-33 activation N-Megakaryocyte Differentiation-2.0-Hs-60 N-Megakaryocyte Differentiation-2.0-Hs-75 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-75 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-75 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-75 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-75 activation N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-61 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-61 activation N-Megakaryocyte Differentiation-2.0-Hs-79 N-Megakaryocyte Differentiation-2.0-Hs-61 activation N-Megakaryocyte Differentiation-2.0-Hs-73 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-74 inhibition N-Megakaryocyte Differentiation-2.0-Hs-74 N-Megakaryocyte Differentiation-2.0-Hs-22 activation N-Megakaryocyte Differentiation-2.0-Hs-24 N-Megakaryocyte Differentiation-2.0-Hs-22 activation N-Megakaryocyte Differentiation-2.0-Hs-24 N-Megakaryocyte Differentiation-2.0-Hs-22 activation N-Megakaryocyte Differentiation-2.0-Hs-23 N-Megakaryocyte Differentiation-2.0-Hs-22 activation N-Megakaryocyte Differentiation-2.0-Hs-23 N-Megakaryocyte Differentiation-2.0-Hs-83 activation N-Megakaryocyte Differentiation-2.0-Hs-14 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-4 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-16 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-16 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-15 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-7 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-7 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-7 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-7 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-7 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-7 N-Megakaryocyte Differentiation-2.0-Hs-63 activation N-Megakaryocyte Differentiation-2.0-Hs-7 N-Megakaryocyte Differentiation-2.0-Hs-43 inhibition N-Megakaryocyte Differentiation-2.0-Hs-63 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/NFE2L2 Signaling-2.0-Hs.att000066400000000000000000000073641426625374700260120ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-NFE2L2 Signaling-2.0-Hs-1 2 SQSTM1 KEAP1 82 137 white rectangle gene,gene 0.5 black 46 17 11280,/,23177 N-NFE2L2 Signaling-2.0-Hs-11 12 Bach1 Maff 62 16 white rectangle gene,gene 0.5 black 46 17 894680,/,96910 N-NFE2L2 Signaling-2.0-Hs-13 PI3K_p110 115 10 white rectangle gene 0.5 black 46 17 PI3K_p110 N-NFE2L2 Signaling-2.0-Hs-14 PKC 69 125 white rectangle gene 0.5 black 46 17 PKC N-NFE2L2 Signaling-2.0-Hs-15 p38 76 135 white rectangle gene 0.5 black 46 17 p38 N-NFE2L2 Signaling-2.0-Hs-16 SOD1 82 113 white rectangle gene 0.5 black 46 17 11179 N-NFE2L2 Signaling-2.0-Hs-17 TXNRD1 96 176 white rectangle gene 0.5 black 46 17 12437 N-NFE2L2 Signaling-2.0-Hs-18 SRXN1 85 107 white rectangle gene 0.5 black 46 17 16132 N-NFE2L2 Signaling-2.0-Hs-19 PARK7 85 121 white rectangle gene 0.5 black 46 17 16369 N-NFE2L2 Signaling-2.0-Hs-20 KEAP1 76 109 white rectangle gene 0.5 black 46 17 23177 N-NFE2L2 Signaling-2.0-Hs-21 CSNK2A1 99 119 white rectangle gene 0.5 black 46 17 2457 N-NFE2L2 Signaling-2.0-Hs-22 NQO1 74 0 white rectangle gene 0.5 black 46 17 2874 N-NFE2L2 Signaling-2.0-Hs-23 GADD45GIP1 97 115 white rectangle gene 0.5 black 46 17 29996 N-NFE2L2 Signaling-2.0-Hs-24 EIF2AK3 173 80 white rectangle gene 0.5 black 46 17 3255 N-NFE2L2 Signaling-2.0-Hs-25 AHR 94 106 white rectangle gene 0.5 black 46 17 348 N-NFE2L2 Signaling-2.0-Hs-26 FYN 115 92 white rectangle gene 0.5 black 46 17 4037 N-NFE2L2 Signaling-2.0-Hs-27 FYN 125 83 white rectangle gene 0.5 black 46 17 4037 N-NFE2L2 Signaling-2.0-Hs-28 GCLM 80 8 white rectangle gene 0.5 black 46 17 4312 N-NFE2L2 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MAPK14 5 29 white rectangle gene 0.5 black 46 17 6876 N-NFE2L2 Signaling-2.0-Hs-40 MAPK3 91 123 white rectangle gene 0.5 black 46 17 6877 N-NFE2L2 Signaling-2.0-Hs-41 NFE2L1 73 8 white rectangle gene 0.5 black 46 17 7781 N-NFE2L2 Signaling-2.0-Hs-42 NFE2L2 89 113 white rectangle gene 0.5 black 46 17 7782 N-NFE2L2 Signaling-2.0-Hs-43 NFE2L2 172 85 white rectangle gene 0.5 black 46 17 7782 N-NFE2L2 Signaling-2.0-Hs-44 NFE2L2 8 36 white rectangle gene 0.5 black 46 17 7782 N-NFE2L2 Signaling-2.0-Hs-45 NFE2L2 78 120 white rectangle gene 0.5 black 46 17 7782 N-NFE2L2 Signaling-2.0-Hs-46 NFE2L2 103 101 white rectangle gene 0.5 black 46 17 7782 N-NFE2L2 Signaling-2.0-Hs-47 NFE2L2 87 100 white rectangle gene 0.5 black 46 17 7782 N-NFE2L2 Signaling-2.0-Hs-48 BACH1 57 19 white rectangle gene 0.5 black 46 17 935 N-NFE2L2 Signaling-2.0-Hs-49 PRKCI 95 121 white rectangle gene 0.5 black 46 17 9404 N-NFE2L2 Signaling-2.0-Hs-50 PTGS2 116 16 white rectangle gene 0.5 black 46 17 9605 N-NFE2L2 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N-NFE2L2 Signaling-2.0-Hs-39 activation N-NFE2L2 Signaling-2.0-Hs-44 N-NFE2L2 Signaling-2.0-Hs-39 activation N-NFE2L2 Signaling-2.0-Hs-44 N-NFE2L2 Signaling-2.0-Hs-14 activation N-NFE2L2 Signaling-2.0-Hs-45 N-NFE2L2 Signaling-2.0-Hs-45 inhibition N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-45 inhibition N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-45 inhibition N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-45 inhibition N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-45 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-45 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-41 activation N-NFE2L2 Signaling-2.0-Hs-32 N-NFE2L2 Signaling-2.0-Hs-41 activation N-NFE2L2 Signaling-2.0-Hs-28 N-NFE2L2 Signaling-2.0-Hs-41 activation N-NFE2L2 Signaling-2.0-Hs-22 N-NFE2L2 Signaling-2.0-Hs-38 activation N-NFE2L2 Signaling-2.0-Hs-44 N-NFE2L2 Signaling-2.0-Hs-38 activation N-NFE2L2 Signaling-2.0-Hs-44 N-NFE2L2 Signaling-2.0-Hs-38 activation N-NFE2L2 Signaling-2.0-Hs-44 N-NFE2L2 Signaling-2.0-Hs-26 activation N-NFE2L2 Signaling-2.0-Hs-46 N-NFE2L2 Signaling-2.0-Hs-30 activation N-NFE2L2 Signaling-2.0-Hs-29 N-NFE2L2 Signaling-2.0-Hs-30 activation N-NFE2L2 Signaling-2.0-Hs-29 N-NFE2L2 Signaling-2.0-Hs-30 activation N-NFE2L2 Signaling-2.0-Hs-29 N-NFE2L2 Signaling-2.0-Hs-30 activation N-NFE2L2 Signaling-2.0-Hs-29 N-NFE2L2 Signaling-2.0-Hs-46 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-47 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-42 activation N-NFE2L2 Signaling-2.0-Hs-18 N-NFE2L2 Signaling-2.0-Hs-42 activation N-NFE2L2 Signaling-2.0-Hs-35 N-NFE2L2 Signaling-2.0-Hs-42 activation N-NFE2L2 Signaling-2.0-Hs-16 N-NFE2L2 Signaling-2.0-Hs-1 2 inhibition N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-1 2 inhibition N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-1 2 inhibition N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-48 inhibition N-NFE2L2 Signaling-2.0-Hs-32 N-NFE2L2 Signaling-2.0-Hs-48 inhibition N-NFE2L2 Signaling-2.0-Hs-32 N-NFE2L2 Signaling-2.0-Hs-48 inhibition N-NFE2L2 Signaling-2.0-Hs-32 N-NFE2L2 Signaling-2.0-Hs-48 inhibition N-NFE2L2 Signaling-2.0-Hs-32 N-NFE2L2 Signaling-2.0-Hs-40 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-23 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-31 inhibition N-NFE2L2 Signaling-2.0-Hs-48 N-NFE2L2 Signaling-2.0-Hs-34 activation N-NFE2L2 Signaling-2.0-Hs-50 N-NFE2L2 Signaling-2.0-Hs-37 activation N-NFE2L2 Signaling-2.0-Hs-44 N-NFE2L2 Signaling-2.0-Hs-37 activation N-NFE2L2 Signaling-2.0-Hs-44 N-NFE2L2 Signaling-2.0-Hs-37 activation N-NFE2L2 Signaling-2.0-Hs-44 N-NFE2L2 Signaling-2.0-Hs-47 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-49 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-9 10 activation N-NFE2L2 Signaling-2.0-Hs-32 N-NFE2L2 Signaling-2.0-Hs-50 inhibition N-NFE2L2 Signaling-2.0-Hs-13 N-NFE2L2 Signaling-2.0-Hs-24 activation N-NFE2L2 Signaling-2.0-Hs-43 N-NFE2L2 Signaling-2.0-Hs-25 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-15 activation N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-7 8 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-21 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-21 activation N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-29 activation N-NFE2L2 Signaling-2.0-Hs-27 N-NFE2L2 Signaling-2.0-Hs-17 activation N-NFE2L2 Signaling-2.0-Hs-17 N-NFE2L2 Signaling-2.0-Hs-20 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-20 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-20 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-20 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-20 inhibition N-NFE2L2 Signaling-2.0-Hs-33 N-NFE2L2 Signaling-2.0-Hs-20 inhibition N-NFE2L2 Signaling-2.0-Hs-33 N-NFE2L2 Signaling-2.0-Hs-19 inhibition N-NFE2L2 Signaling-2.0-Hs-7 8 N-NFE2L2 Signaling-2.0-Hs-19 inhibition N-NFE2L2 Signaling-2.0-Hs-42 N-NFE2L2 Signaling-2.0-Hs-19 inhibition N-NFE2L2 Signaling-2.0-Hs-42 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/NK Signaling-2.0-Hs.att000066400000000000000000000044671426625374700254330ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-NK Signaling-2.0-Hs-1 JNK 99 45 white rectangle gene 0.5 black 46 17 JNK N-NK Signaling-2.0-Hs-11 GAS6 142 13 white rectangle gene 0.5 black 46 17 4168 N-NK Signaling-2.0-Hs-12 ICAM1 71 140 white rectangle gene 0.5 black 46 17 5344 N-NK Signaling-2.0-Hs-13 IFNA1 123 1 white rectangle gene 0.5 black 46 17 5417 N-NK Signaling-2.0-Hs-14 IFNB1 129 38 white rectangle gene 0.5 black 46 17 5434 N-NK Signaling-2.0-Hs-15 IFNG 114 75 white rectangle gene 0.5 black 46 17 5438 N-NK Signaling-2.0-Hs-17 BIRC3 0 49 white rectangle gene 0.5 black 46 17 591 N-NK Signaling-2.0-Hs-18 IL15 104 20 white rectangle gene 0.5 black 46 17 5977 N-NK Signaling-2.0-Hs-19 IL18 101 60 white rectangle gene 0.5 black 46 17 5986 N-NK Signaling-2.0-Hs-2 STAT5 117 10 white rectangle gene 0.5 black 46 17 STAT5 N-NK Signaling-2.0-Hs-20 IL2 110 48 white rectangle gene 0.5 black 46 17 6001 N-NK Signaling-2.0-Hs-21 IL7 115 0 white rectangle gene 0.5 black 46 17 6023 N-NK Signaling-2.0-Hs-22 IRF1 120 42 white rectangle gene 0.5 black 46 17 6116 N-NK Signaling-2.0-Hs-23 ITGB2 62 137 white rectangle gene 0.5 black 46 17 6155 N-NK Signaling-2.0-Hs-24 JAK2 98 93 white rectangle gene 0.5 black 46 17 6192 N-NK Signaling-2.0-Hs-25 MAP2K1 109 60 white rectangle gene 0.5 black 46 17 6840 N-NK Signaling-2.0-Hs-26 MAPK1 109 66 white rectangle gene 0.5 black 46 17 6871 N-NK Signaling-2.0-Hs-27 MAPK3 105 71 white rectangle gene 0.5 black 46 17 6877 N-NK Signaling-2.0-Hs-28 MICA 81 37 white rectangle gene 0.5 black 46 17 7090 N-NK Signaling-2.0-Hs-29 MICB 80 30 white rectangle gene 0.5 black 46 17 7091 N-NK Signaling-2.0-Hs-30 PAK1 158 147 white rectangle gene 0.5 black 46 17 8590 N-NK Signaling-2.0-Hs-31 AXL 131 13 white rectangle gene 0.5 black 46 17 905 N-NK Signaling-2.0-Hs-32 RAC1 153 141 white rectangle gene 0.5 black 46 17 9801 N-NK Signaling-2.0-Hs-4 STAT1 123 81 white rectangle gene 0.5 black 46 17 11362 N-NK Signaling-2.0-Hs-5 STAT3 101 83 white rectangle gene 0.5 black 46 17 11364 N-NK Signaling-2.0-Hs-6 VAV1 56 133 white rectangle gene 0.5 black 46 17 12657 N-NK Signaling-2.0-Hs-7 C3 79 138 white rectangle gene 0.5 black 46 17 1318 N-NK Signaling-2.0-Hs-8 CASP3 5 53 white rectangle gene 0.5 black 46 17 1504 N-NK Signaling-2.0-Hs-9 KLRK1 92 32 white rectangle gene 0.5 black 46 17 18788 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/NK Signaling-2.0-Hs.sif000066400000000000000000000067761426625374700254310ustar00rootroot000000000000000 1 2 N-NK Signaling-2.0-Hs-20 activation N-NK Signaling-2.0-Hs-1 N-NK Signaling-2.0-Hs-20 activation N-NK Signaling-2.0-Hs-25 N-NK Signaling-2.0-Hs-20 activation N-NK Signaling-2.0-Hs-22 N-NK Signaling-2.0-Hs-20 activation N-NK Signaling-2.0-Hs-22 N-NK Signaling-2.0-Hs-20 activation N-NK Signaling-2.0-Hs-22 N-NK Signaling-2.0-Hs-1 activation N-NK Signaling-2.0-Hs-9 N-NK Signaling-2.0-Hs-31 activation N-NK Signaling-2.0-Hs-2 N-NK Signaling-2.0-Hs-17 inhibition N-NK Signaling-2.0-Hs-8 N-NK Signaling-2.0-Hs-11 activation N-NK Signaling-2.0-Hs-31 N-NK Signaling-2.0-Hs-11 activation N-NK Signaling-2.0-Hs-31 N-NK Signaling-2.0-Hs-11 activation N-NK Signaling-2.0-Hs-31 N-NK Signaling-2.0-Hs-23 activation N-NK Signaling-2.0-Hs-6 N-NK Signaling-2.0-Hs-23 activation N-NK Signaling-2.0-Hs-6 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-25 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-7 activation N-NK Signaling-2.0-Hs-12 N-NK Signaling-2.0-Hs-13 activation N-NK Signaling-2.0-Hs-2 N-NK Signaling-2.0-Hs-13 activation N-NK Signaling-2.0-Hs-2 N-NK Signaling-2.0-Hs-12 activation N-NK Signaling-2.0-Hs-23 N-NK Signaling-2.0-Hs-12 activation N-NK Signaling-2.0-Hs-23 N-NK Signaling-2.0-Hs-4 activation N-NK Signaling-2.0-Hs-15 N-NK Signaling-2.0-Hs-21 activation N-NK Signaling-2.0-Hs-2 N-NK Signaling-2.0-Hs-21 activation N-NK Signaling-2.0-Hs-2 N-NK Signaling-2.0-Hs-27 activation N-NK Signaling-2.0-Hs-5 N-NK Signaling-2.0-Hs-27 activation N-NK Signaling-2.0-Hs-15 N-NK Signaling-2.0-Hs-27 activation N-NK Signaling-2.0-Hs-15 N-NK Signaling-2.0-Hs-18 activation N-NK Signaling-2.0-Hs-9 N-NK Signaling-2.0-Hs-18 activation N-NK Signaling-2.0-Hs-2 N-NK Signaling-2.0-Hs-18 activation N-NK Signaling-2.0-Hs-2 N-NK Signaling-2.0-Hs-26 activation N-NK Signaling-2.0-Hs-15 N-NK Signaling-2.0-Hs-14 activation N-NK Signaling-2.0-Hs-22 N-NK Signaling-2.0-Hs-14 activation N-NK Signaling-2.0-Hs-22 N-NK Signaling-2.0-Hs-14 activation N-NK Signaling-2.0-Hs-22 N-NK Signaling-2.0-Hs-19 activation N-NK Signaling-2.0-Hs-1 N-NK Signaling-2.0-Hs-19 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-19 activation N-NK Signaling-2.0-Hs-26 N-NK Signaling-2.0-Hs-19 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-19 activation N-NK Signaling-2.0-Hs-27 N-NK Signaling-2.0-Hs-32 activation N-NK Signaling-2.0-Hs-30 N-NK Signaling-2.0-Hs-28 activation N-NK Signaling-2.0-Hs-9 N-NK Signaling-2.0-Hs-29 activation N-NK Signaling-2.0-Hs-9 N-NK Signaling-2.0-Hs-29 activation N-NK Signaling-2.0-Hs-9 N-NK Signaling-2.0-Hs-24 activation N-NK Signaling-2.0-Hs-5 N-NK Signaling-2.0-Hs-24 activation N-NK Signaling-2.0-Hs-5 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Necroptosis-2.0-Hs.att000066400000000000000000000045711426625374700255330ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Necroptosis-2.0-Hs-10 TNF 18 82 white rectangle gene 0.5 black 46 17 11892 N-Necroptosis-2.0-Hs-11 TNFAIP3 22 171 white rectangle gene 0.5 black 46 17 11896 N-Necroptosis-2.0-Hs-12 TNFRSF1A 50 76 white rectangle gene 0.5 black 46 17 11916 N-Necroptosis-2.0-Hs-13 FAS 80 78 white rectangle gene 0.5 black 46 17 11920 N-Necroptosis-2.0-Hs-14 FASLG 94 67 white rectangle gene 0.5 black 46 17 11936 N-Necroptosis-2.0-Hs-15 TRADD 59 97 white rectangle gene 0.5 black 46 17 12030 N-Necroptosis-2.0-Hs-16 TRAF2 75 88 white rectangle gene 0.5 black 46 17 12032 N-Necroptosis-2.0-Hs-18 PINK1 56 0 white rectangle gene 0.5 black 46 17 14581 N-Necroptosis-2.0-Hs-19 CASP8 56 117 white rectangle gene 0.5 black 46 17 1509 N-Necroptosis-2.0-Hs-20 CFLAR 65 111 white rectangle gene 0.5 black 46 17 1876 N-Necroptosis-2.0-Hs-21 CYLD 47 145 white rectangle gene 0.5 black 46 17 2584 N-Necroptosis-2.0-Hs-22 MLKL 50 16 white rectangle gene 0.5 black 46 17 26617 N-Necroptosis-2.0-Hs-24 PGAM5 30 17 white rectangle gene 0.5 black 46 17 28763 N-Necroptosis-2.0-Hs-25 DNM1L 21 2 white rectangle gene 0.5 black 46 17 2973 N-Necroptosis-2.0-Hs-26 FADD 64 94 white rectangle gene 0.5 black 46 17 3573 N-Necroptosis-2.0-Hs-27 GLUD1 55 30 white rectangle gene 0.5 black 46 17 4335 N-Necroptosis-2.0-Hs-28 GLUL 28 35 white rectangle gene 0.5 black 46 17 4341 N-Necroptosis-2.0-Hs-29 BIRC2 29 183 white rectangle gene 0.5 black 46 17 590 N-Necroptosis-2.0-Hs-3 PLA2 6 72 white rectangle gene 0.5 black 46 17 PLA2 N-Necroptosis-2.0-Hs-30 BIRC3 42 184 white rectangle gene 0.5 black 46 17 591 N-Necroptosis-2.0-Hs-31 MAP3K14 118 101 white rectangle gene 0.5 black 46 17 6853 N-Necroptosis-2.0-Hs-32 MAP3K7 100 97 white rectangle gene 0.5 black 46 17 6859 N-Necroptosis-2.0-Hs-33 MAP3K7 92 88 white rectangle gene 0.5 black 46 17 6859 N-Necroptosis-2.0-Hs-34 NOX1 1 90 white rectangle gene 0.5 black 46 17 7889 N-Necroptosis-2.0-Hs-35 PLA2G4A 0 80 white rectangle gene 0.5 black 46 17 9035 N-Necroptosis-2.0-Hs-36 PYGL 38 27 white rectangle gene 0.5 black 46 17 9725 N-Necroptosis-2.0-Hs-4 RIPK1 63 82 white rectangle gene 0.5 black 46 17 10019 N-Necroptosis-2.0-Hs-5 RIPK1 37 169 white rectangle gene 0.5 black 46 17 10019 N-Necroptosis-2.0-Hs-6 RIPK3 43 39 white rectangle gene 0.5 black 46 17 10021 N-Necroptosis-2.0-Hs-7 BNIP3 9 96 white rectangle gene 0.5 black 46 17 1084 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activation N-Necroptosis-2.0-Hs-16 N-Necroptosis-2.0-Hs-4 activation N-Necroptosis-2.0-Hs-16 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-26 activation N-Necroptosis-2.0-Hs-4 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-35 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-35 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-34 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-34 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-12 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-3 N-Necroptosis-2.0-Hs-10 activation N-Necroptosis-2.0-Hs-7 N-Necroptosis-2.0-Hs-29 activation N-Necroptosis-2.0-Hs-5 N-Necroptosis-2.0-Hs-11 inhibition N-Necroptosis-2.0-Hs-5 N-Necroptosis-2.0-Hs-11 inhibition N-Necroptosis-2.0-Hs-5 N-Necroptosis-2.0-Hs-13 activation N-Necroptosis-2.0-Hs-26 N-Necroptosis-2.0-Hs-13 activation N-Necroptosis-2.0-Hs-26 N-Necroptosis-2.0-Hs-15 activation N-Necroptosis-2.0-Hs-16 N-Necroptosis-2.0-Hs-15 activation N-Necroptosis-2.0-Hs-26 N-Necroptosis-2.0-Hs-15 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-15 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-15 activation N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-14 activation N-Necroptosis-2.0-Hs-13 N-Necroptosis-2.0-Hs-14 activation N-Necroptosis-2.0-Hs-13 N-Necroptosis-2.0-Hs-14 activation N-Necroptosis-2.0-Hs-13 N-Necroptosis-2.0-Hs-14 activation N-Necroptosis-2.0-Hs-13 N-Necroptosis-2.0-Hs-14 activation N-Necroptosis-2.0-Hs-13 N-Necroptosis-2.0-Hs-19 inhibition N-Necroptosis-2.0-Hs-21 N-Necroptosis-2.0-Hs-19 inhibition N-Necroptosis-2.0-Hs-21 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-22 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-27 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-27 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-28 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-28 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-28 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-24 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-24 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-36 N-Necroptosis-2.0-Hs-6 activation N-Necroptosis-2.0-Hs-36 N-Necroptosis-2.0-Hs-16 activation N-Necroptosis-2.0-Hs-33 N-Necroptosis-2.0-Hs-16 activation N-Necroptosis-2.0-Hs-32 N-Necroptosis-2.0-Hs-21 inhibition N-Necroptosis-2.0-Hs-5 N-Necroptosis-2.0-Hs-24 activation N-Necroptosis-2.0-Hs-25 N-Necroptosis-2.0-Hs-20 inhibition N-Necroptosis-2.0-Hs-19 N-Necroptosis-2.0-Hs-20 inhibition N-Necroptosis-2.0-Hs-26 N-Necroptosis-2.0-Hs-20 inhibition N-Necroptosis-2.0-Hs-26 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Neutrophil Signaling-2.0-Hs.att000066400000000000000000000212421426625374700272420ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Neutrophil Signaling-2.0-Hs-1 2 CXCL8 SERPINA1 80 109 white rectangle gene,gene 0.5 black 46 17 6025,/,8941 N-Neutrophil Signaling-2.0-Hs-10 p38 92 72 white rectangle gene 0.5 black 46 17 p38 N-Neutrophil Signaling-2.0-Hs-100 MAP2K5 190 117 white rectangle gene 0.5 black 46 17 6845 N-Neutrophil Signaling-2.0-Hs-101 MAP3K7 98 80 white rectangle gene 0.5 black 46 17 6859 N-Neutrophil Signaling-2.0-Hs-102 MAPK1 77 74 white rectangle gene 0.5 black 46 17 6871 N-Neutrophil Signaling-2.0-Hs-103 MAPK1 109 54 white rectangle gene 0.5 black 46 17 6871 N-Neutrophil Signaling-2.0-Hs-104 MAPK3 76 71 white rectangle gene 0.5 black 46 17 6877 N-Neutrophil Signaling-2.0-Hs-105 MAPK3 102 52 white rectangle gene 0.5 black 46 17 6877 N-Neutrophil Signaling-2.0-Hs-106 MAPK7 189 114 white rectangle gene 0.5 black 46 17 6880 N-Neutrophil Signaling-2.0-Hs-107 MMP12 89 102 white rectangle gene 0.5 black 46 17 7158 N-Neutrophil Signaling-2.0-Hs-108 MPO 118 5 white rectangle gene 0.5 black 46 17 7218 N-Neutrophil Signaling-2.0-Hs-109 MYD88 80 53 white rectangle gene 0.5 black 46 17 7562 N-Neutrophil Signaling-2.0-Hs-11 S100A8 58 65 white rectangle gene 0.5 black 46 17 10498 N-Neutrophil Signaling-2.0-Hs-110 NFKBIA 106 83 white rectangle gene 0.5 black 46 17 7797 N-Neutrophil Signaling-2.0-Hs-111 NFKBIA 114 88 white rectangle gene 0.5 black 46 17 7797 N-Neutrophil Signaling-2.0-Hs-112 NFKBIA 115 84 white rectangle gene 0.5 black 46 17 7797 N-Neutrophil Signaling-2.0-Hs-116 SERPINA1 123 161 white rectangle gene 0.5 black 46 17 8941 N-Neutrophil Signaling-2.0-Hs-117 PIK3CG 28 142 white rectangle gene 0.5 black 46 17 8978 N-Neutrophil Signaling-2.0-Hs-118 PRTN3 168 114 white rectangle gene 0.5 black 46 17 9495 N-Neutrophil Signaling-2.0-Hs-119 RAC2 27 146 white rectangle gene 0.5 black 46 17 9802 N-Neutrophil Signaling-2.0-Hs-12 S100A9 59 62 white rectangle gene 0.5 black 46 17 10499 N-Neutrophil Signaling-2.0-Hs-120 RAF1 107 53 white rectangle gene 0.5 black 46 17 9829 N-Neutrophil Signaling-2.0-Hs-121 RELA 108 83 white rectangle gene 0.5 black 46 17 9955 N-Neutrophil Signaling-2.0-Hs-13 SAA1 86 73 white rectangle gene 0.5 black 46 17 10513 N-Neutrophil Signaling-2.0-Hs-14 CCL19 104 75 white rectangle gene 0.5 black 46 17 10617 N-Neutrophil Signaling-2.0-Hs-15 CCL2 189 84 white rectangle gene 0.5 black 46 17 10618 N-Neutrophil Signaling-2.0-Hs-16 CCL20 104 70 white rectangle gene 0.5 black 46 17 10619 N-Neutrophil Signaling-2.0-Hs-17 CCL3 98 74 white rectangle gene 0.5 black 46 17 10627 N-Neutrophil Signaling-2.0-Hs-18 CCL4 95 78 white rectangle gene 0.5 black 46 17 10630 N-Neutrophil Signaling-2.0-Hs-20 CXCL10 58 86 white rectangle gene 0.5 black 46 17 10637 N-Neutrophil Signaling-2.0-Hs-21 CXCL5 86 106 white rectangle gene 0.5 black 46 17 10642 N-Neutrophil Signaling-2.0-Hs-22 CXCL12 84 98 white rectangle gene 0.5 black 46 17 10672 N-Neutrophil Signaling-2.0-Hs-23 SELL 48 89 white rectangle gene 0.5 black 46 17 10720 N-Neutrophil Signaling-2.0-Hs-26 SYK 67 50 white rectangle gene 0.5 black 46 17 11491 N-Neutrophil Signaling-2.0-Hs-27 TLR2 77 107 white rectangle gene 0.5 black 46 17 11848 N-Neutrophil Signaling-2.0-Hs-28 TLR4 71 56 white rectangle gene 0.5 black 46 17 11850 N-Neutrophil Signaling-2.0-Hs-29 TNF 96 75 white rectangle gene 0.5 black 46 17 11892 N-Neutrophil Signaling-2.0-Hs-3 4 ITGA4 ITGB1 52 87 white rectangle gene,gene 0.5 black 46 17 6140,/,6153 N-Neutrophil Signaling-2.0-Hs-30 TNFRSF1A 85 74 white rectangle gene 0.5 black 46 17 11916 N-Neutrophil Signaling-2.0-Hs-31 TNFRSF1B 84 75 white rectangle gene 0.5 black 46 17 11917 N-Neutrophil Signaling-2.0-Hs-32 TRAF6 76 47 white rectangle gene 0.5 black 46 17 12036 N-Neutrophil Signaling-2.0-Hs-33 TXN 105 52 white rectangle gene 0.5 black 46 17 12435 N-Neutrophil Signaling-2.0-Hs-35 VEGFA 104 115 white rectangle gene 0.5 black 46 17 12680 N-Neutrophil Signaling-2.0-Hs-37 WAS 2 133 white rectangle gene 0.5 black 46 17 12731 N-Neutrophil Signaling-2.0-Hs-38 C5 61 60 white rectangle gene 0.5 black 46 17 1331 N-Neutrophil Signaling-2.0-Hs-40 CCR1 64 92 white rectangle gene 0.5 black 46 17 1602 N-Neutrophil Signaling-2.0-Hs-41 CCR3 62 89 white rectangle gene 0.5 black 46 17 1604 N-Neutrophil Signaling-2.0-Hs-42 CCR5 96 69 white rectangle gene 0.5 black 46 17 1606 N-Neutrophil Signaling-2.0-Hs-43 IL26 115 14 white rectangle gene 0.5 black 46 17 17119 N-Neutrophil Signaling-2.0-Hs-44 CDC42 0 131 white rectangle gene 0.5 black 46 17 1736 N-Neutrophil Signaling-2.0-Hs-45 IRAK4 88 61 white rectangle gene 0.5 black 46 17 17967 N-Neutrophil Signaling-2.0-Hs-46 CEBPB 105 73 white rectangle gene 0.5 black 46 17 1834 N-Neutrophil Signaling-2.0-Hs-47 CEBPE 184 84 white rectangle gene 0.5 black 46 17 1836 N-Neutrophil Signaling-2.0-Hs-49 SOCS3 89 167 white rectangle gene 0.5 black 46 17 19391 N-Neutrophil Signaling-2.0-Hs-5 AKT 88 92 white rectangle gene 0.5 black 46 17 AKT N-Neutrophil Signaling-2.0-Hs-50 CHUK 106 80 white rectangle gene 0.5 black 46 17 1974 N-Neutrophil Signaling-2.0-Hs-51 CREB1 82 71 white rectangle gene 0.5 black 46 17 2345 N-Neutrophil Signaling-2.0-Hs-52 CSF2 103 60 white rectangle gene 0.5 black 46 17 2434 N-Neutrophil Signaling-2.0-Hs-53 CSF3 72 74 white rectangle gene 0.5 black 46 17 2438 N-Neutrophil Signaling-2.0-Hs-54 CTSG 85 118 white rectangle gene 0.5 black 46 17 2532 N-Neutrophil Signaling-2.0-Hs-55 CXCR4 39 90 white rectangle gene 0.5 black 46 17 2561 N-Neutrophil Signaling-2.0-Hs-56 EDN1 70 78 white rectangle gene 0.5 black 46 17 3176 N-Neutrophil Signaling-2.0-Hs-57 EDNRB 121 0 white rectangle gene 0.5 black 46 17 3180 N-Neutrophil Signaling-2.0-Hs-58 AGER 73 68 white rectangle gene 0.5 black 46 17 320 N-Neutrophil Signaling-2.0-Hs-59 ELANE 126 164 white rectangle gene 0.5 black 46 17 3309 N-Neutrophil Signaling-2.0-Hs-6 AKT 32 137 white rectangle gene 0.5 black 46 17 AKT N-Neutrophil Signaling-2.0-Hs-60 F2 168 105 white rectangle gene 0.5 black 46 17 3535 N-Neutrophil Signaling-2.0-Hs-61 F2R 169 110 white rectangle gene 0.5 black 46 17 3537 N-Neutrophil Signaling-2.0-Hs-62 F2RL1 61 87 white rectangle gene 0.5 black 46 17 3538 N-Neutrophil Signaling-2.0-Hs-63 FCGR1A 60 91 white rectangle gene 0.5 black 46 17 3613 N-Neutrophil Signaling-2.0-Hs-64 FGR 101 120 white rectangle gene 0.5 black 46 17 3697 N-Neutrophil Signaling-2.0-Hs-65 FPR1 71 70 white rectangle gene 0.5 black 46 17 3826 N-Neutrophil Signaling-2.0-Hs-66 FPR2 85 61 white rectangle gene 0.5 black 46 17 3827 N-Neutrophil Signaling-2.0-Hs-67 AKT1 92 64 white rectangle gene 0.5 black 46 17 391 N-Neutrophil Signaling-2.0-Hs-7 ERK 100 72 white rectangle gene 0.5 black 46 17 ERK N-Neutrophil Signaling-2.0-Hs-71 CXCL1 99 112 white rectangle gene 0.5 black 46 17 4602 N-Neutrophil Signaling-2.0-Hs-72 CXCL2 85 109 white rectangle gene 0.5 black 46 17 4603 N-Neutrophil Signaling-2.0-Hs-73 CXCL3 92 109 white rectangle gene 0.5 black 46 17 4604 N-Neutrophil Signaling-2.0-Hs-74 HCK 106 116 white rectangle gene 0.5 black 46 17 4840 N-Neutrophil Signaling-2.0-Hs-75 HMGB1 93 81 white rectangle gene 0.5 black 46 17 4983 N-Neutrophil Signaling-2.0-Hs-76 HRAS 71 73 white rectangle gene 0.5 black 46 17 5173 N-Neutrophil Signaling-2.0-Hs-77 ANXA1 87 52 white rectangle gene 0.5 black 46 17 533 N-Neutrophil Signaling-2.0-Hs-78 ICAM1 163 101 white rectangle gene 0.5 black 46 17 5344 N-Neutrophil Signaling-2.0-Hs-79 IFNG 66 84 white rectangle gene 0.5 black 46 17 5438 N-Neutrophil Signaling-2.0-Hs-8 JNK 69 54 white rectangle gene 0.5 black 46 17 JNK N-Neutrophil Signaling-2.0-Hs-81 IKBKB 104 83 white rectangle gene 0.5 black 46 17 5960 N-Neutrophil Signaling-2.0-Hs-82 IL10 89 174 white rectangle gene 0.5 black 46 17 5962 N-Neutrophil Signaling-2.0-Hs-83 IL10RA 110 179 white rectangle gene 0.5 black 46 17 5964 N-Neutrophil Signaling-2.0-Hs-84 IL15 78 91 white rectangle gene 0.5 black 46 17 5977 N-Neutrophil Signaling-2.0-Hs-86 IL1B 53 91 white rectangle gene 0.5 black 46 17 5992 N-Neutrophil Signaling-2.0-Hs-87 IL1RN 102 66 white rectangle gene 0.5 black 46 17 6000 N-Neutrophil Signaling-2.0-Hs-88 IL4 109 182 white rectangle gene 0.5 black 46 17 6014 N-Neutrophil Signaling-2.0-Hs-89 IL6 52 84 white rectangle gene 0.5 black 46 17 6018 N-Neutrophil Signaling-2.0-Hs-9 STAT5 111 57 white rectangle gene 0.5 black 46 17 STAT5 N-Neutrophil Signaling-2.0-Hs-90 CXCL8 81 85 white rectangle gene 0.5 black 46 17 6025 N-Neutrophil Signaling-2.0-Hs-91 CXCR2 82 100 white rectangle gene 0.5 black 46 17 6027 N-Neutrophil Signaling-2.0-Hs-92 IRAK1 82 46 white rectangle gene 0.5 black 46 17 6112 N-Neutrophil Signaling-2.0-Hs-94 ITGAM 67 67 white rectangle gene 0.5 black 46 17 6149 N-Neutrophil Signaling-2.0-Hs-95 ITGB2 58 89 white rectangle gene 0.5 black 46 17 6155 N-Neutrophil Signaling-2.0-Hs-99 MAP2K1 75 67 white rectangle gene 0.5 black 46 17 6840 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Neutrophil Signaling-2.0-Hs.sif000066400000000000000000000521251426625374700272370ustar00rootroot000000000000000 1 2 N-Neutrophil Signaling-2.0-Hs-22 activation N-Neutrophil Signaling-2.0-Hs-5 N-Neutrophil Signaling-2.0-Hs-22 activation N-Neutrophil Signaling-2.0-Hs-5 N-Neutrophil Signaling-2.0-Hs-60 activation N-Neutrophil Signaling-2.0-Hs-61 N-Neutrophil Signaling-2.0-Hs-60 activation N-Neutrophil Signaling-2.0-Hs-78 N-Neutrophil Signaling-2.0-Hs-52 activation N-Neutrophil Signaling-2.0-Hs-103 N-Neutrophil Signaling-2.0-Hs-52 activation N-Neutrophil Signaling-2.0-Hs-105 N-Neutrophil Signaling-2.0-Hs-52 activation N-Neutrophil Signaling-2.0-Hs-120 N-Neutrophil Signaling-2.0-Hs-52 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-52 activation N-Neutrophil Signaling-2.0-Hs-7 N-Neutrophil Signaling-2.0-Hs-52 activation N-Neutrophil Signaling-2.0-Hs-87 N-Neutrophil Signaling-2.0-Hs-52 activation N-Neutrophil Signaling-2.0-Hs-9 N-Neutrophil Signaling-2.0-Hs-91 activation N-Neutrophil Signaling-2.0-Hs-5 N-Neutrophil Signaling-2.0-Hs-102 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-102 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-102 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-102 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-102 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-102 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-102 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-102 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-33 inhibition N-Neutrophil Signaling-2.0-Hs-52 N-Neutrophil Signaling-2.0-Hs-100 activation N-Neutrophil Signaling-2.0-Hs-106 N-Neutrophil Signaling-2.0-Hs-100 activation N-Neutrophil Signaling-2.0-Hs-106 N-Neutrophil Signaling-2.0-Hs-100 activation N-Neutrophil Signaling-2.0-Hs-106 N-Neutrophil Signaling-2.0-Hs-100 activation N-Neutrophil Signaling-2.0-Hs-106 N-Neutrophil Signaling-2.0-Hs-100 activation N-Neutrophil Signaling-2.0-Hs-106 N-Neutrophil Signaling-2.0-Hs-90 activation N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-31 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-31 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-47 activation N-Neutrophil Signaling-2.0-Hs-15 N-Neutrophil Signaling-2.0-Hs-76 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-76 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-76 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-76 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-76 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-76 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-76 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-28 activation N-Neutrophil Signaling-2.0-Hs-109 N-Neutrophil Signaling-2.0-Hs-28 activation N-Neutrophil Signaling-2.0-Hs-109 N-Neutrophil Signaling-2.0-Hs-28 activation N-Neutrophil Signaling-2.0-Hs-109 N-Neutrophil Signaling-2.0-Hs-28 activation N-Neutrophil Signaling-2.0-Hs-109 N-Neutrophil Signaling-2.0-Hs-28 activation N-Neutrophil Signaling-2.0-Hs-109 N-Neutrophil Signaling-2.0-Hs-28 activation N-Neutrophil Signaling-2.0-Hs-26 N-Neutrophil Signaling-2.0-Hs-28 activation N-Neutrophil Signaling-2.0-Hs-26 N-Neutrophil Signaling-2.0-Hs-28 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-99 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-62 activation N-Neutrophil Signaling-2.0-Hs-86 N-Neutrophil Signaling-2.0-Hs-62 activation N-Neutrophil Signaling-2.0-Hs-89 N-Neutrophil Signaling-2.0-Hs-62 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-62 activation N-Neutrophil Signaling-2.0-Hs-23 N-Neutrophil Signaling-2.0-Hs-62 activation N-Neutrophil Signaling-2.0-Hs-3 4 N-Neutrophil Signaling-2.0-Hs-38 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-13 activation N-Neutrophil Signaling-2.0-Hs-66 N-Neutrophil Signaling-2.0-Hs-13 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-13 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-13 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-13 activation N-Neutrophil Signaling-2.0-Hs-29 N-Neutrophil Signaling-2.0-Hs-21 activation N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-21 activation N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-21 activation N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-21 activation N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-30 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-31 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-31 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-101 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-14 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-16 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-46 N-Neutrophil Signaling-2.0-Hs-29 activation N-Neutrophil Signaling-2.0-Hs-87 N-Neutrophil Signaling-2.0-Hs-77 activation N-Neutrophil Signaling-2.0-Hs-66 N-Neutrophil Signaling-2.0-Hs-84 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-17 activation N-Neutrophil Signaling-2.0-Hs-42 N-Neutrophil Signaling-2.0-Hs-72 activation N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-72 activation N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-72 activation N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-82 activation N-Neutrophil Signaling-2.0-Hs-49 N-Neutrophil Signaling-2.0-Hs-32 activation N-Neutrophil Signaling-2.0-Hs-8 N-Neutrophil Signaling-2.0-Hs-32 activation N-Neutrophil Signaling-2.0-Hs-8 N-Neutrophil Signaling-2.0-Hs-65 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-65 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-10 activation N-Neutrophil Signaling-2.0-Hs-17 N-Neutrophil Signaling-2.0-Hs-10 activation N-Neutrophil Signaling-2.0-Hs-18 N-Neutrophil Signaling-2.0-Hs-10 activation N-Neutrophil Signaling-2.0-Hs-29 N-Neutrophil Signaling-2.0-Hs-10 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-10 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-10 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-10 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-8 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-117 activation N-Neutrophil Signaling-2.0-Hs-119 N-Neutrophil Signaling-2.0-Hs-117 activation N-Neutrophil Signaling-2.0-Hs-6 N-Neutrophil Signaling-2.0-Hs-26 activation N-Neutrophil Signaling-2.0-Hs-8 N-Neutrophil Signaling-2.0-Hs-67 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-11 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-108 activation N-Neutrophil Signaling-2.0-Hs-57 N-Neutrophil Signaling-2.0-Hs-56 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-56 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-92 activation N-Neutrophil Signaling-2.0-Hs-32 N-Neutrophil Signaling-2.0-Hs-92 activation N-Neutrophil Signaling-2.0-Hs-32 N-Neutrophil Signaling-2.0-Hs-92 activation N-Neutrophil Signaling-2.0-Hs-32 N-Neutrophil Signaling-2.0-Hs-92 activation N-Neutrophil Signaling-2.0-Hs-32 N-Neutrophil Signaling-2.0-Hs-92 activation N-Neutrophil Signaling-2.0-Hs-32 N-Neutrophil Signaling-2.0-Hs-92 activation N-Neutrophil Signaling-2.0-Hs-32 N-Neutrophil Signaling-2.0-Hs-54 activation N-Neutrophil Signaling-2.0-Hs-72 N-Neutrophil Signaling-2.0-Hs-43 inhibition N-Neutrophil Signaling-2.0-Hs-108 N-Neutrophil Signaling-2.0-Hs-118 activation N-Neutrophil Signaling-2.0-Hs-61 N-Neutrophil Signaling-2.0-Hs-58 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-58 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-88 activation N-Neutrophil Signaling-2.0-Hs-83 N-Neutrophil Signaling-2.0-Hs-112 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-112 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-112 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-112 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-112 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-27 inhibition N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-53 activation N-Neutrophil Signaling-2.0-Hs-76 N-Neutrophil Signaling-2.0-Hs-53 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-53 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-53 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-53 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-75 activation N-Neutrophil Signaling-2.0-Hs-29 N-Neutrophil Signaling-2.0-Hs-75 activation N-Neutrophil Signaling-2.0-Hs-5 N-Neutrophil Signaling-2.0-Hs-75 activation N-Neutrophil Signaling-2.0-Hs-7 N-Neutrophil Signaling-2.0-Hs-75 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-109 activation N-Neutrophil Signaling-2.0-Hs-92 N-Neutrophil Signaling-2.0-Hs-109 activation N-Neutrophil Signaling-2.0-Hs-45 N-Neutrophil Signaling-2.0-Hs-1 2 inhibition N-Neutrophil Signaling-2.0-Hs-91 N-Neutrophil Signaling-2.0-Hs-71 activation N-Neutrophil Signaling-2.0-Hs-64 N-Neutrophil Signaling-2.0-Hs-71 activation N-Neutrophil Signaling-2.0-Hs-74 N-Neutrophil Signaling-2.0-Hs-71 activation N-Neutrophil Signaling-2.0-Hs-35 N-Neutrophil Signaling-2.0-Hs-30 activation N-Neutrophil Signaling-2.0-Hs-102 N-Neutrophil Signaling-2.0-Hs-30 activation N-Neutrophil Signaling-2.0-Hs-104 N-Neutrophil Signaling-2.0-Hs-30 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-30 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-42 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-29 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-7 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-17 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-18 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-50 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-81 N-Neutrophil Signaling-2.0-Hs-101 activation N-Neutrophil Signaling-2.0-Hs-121 N-Neutrophil Signaling-2.0-Hs-116 inhibition N-Neutrophil Signaling-2.0-Hs-59 N-Neutrophil Signaling-2.0-Hs-12 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-104 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-104 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-104 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-104 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-104 activation N-Neutrophil Signaling-2.0-Hs-51 N-Neutrophil Signaling-2.0-Hs-23 activation N-Neutrophil Signaling-2.0-Hs-55 N-Neutrophil Signaling-2.0-Hs-107 inhibition N-Neutrophil Signaling-2.0-Hs-73 N-Neutrophil Signaling-2.0-Hs-107 inhibition N-Neutrophil Signaling-2.0-Hs-71 N-Neutrophil Signaling-2.0-Hs-107 inhibition N-Neutrophil Signaling-2.0-Hs-72 N-Neutrophil Signaling-2.0-Hs-107 inhibition N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-107 inhibition N-Neutrophil Signaling-2.0-Hs-21 N-Neutrophil Signaling-2.0-Hs-45 activation N-Neutrophil Signaling-2.0-Hs-67 N-Neutrophil Signaling-2.0-Hs-45 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-45 activation N-Neutrophil Signaling-2.0-Hs-10 N-Neutrophil Signaling-2.0-Hs-111 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-111 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-111 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-111 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-111 activation N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-79 inhibition N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-79 inhibition N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-90 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-20 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-40 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-41 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-63 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-94 N-Neutrophil Signaling-2.0-Hs-79 activation N-Neutrophil Signaling-2.0-Hs-95 N-Neutrophil Signaling-2.0-Hs-74 activation N-Neutrophil Signaling-2.0-Hs-35 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-110 inhibition N-Neutrophil Signaling-2.0-Hs-110 N-Neutrophil Signaling-2.0-Hs-44 activation N-Neutrophil Signaling-2.0-Hs-37 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Notch-2.0-Hs.att000066400000000000000000000034241426625374700242720ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Notch-2.0-Hs-1 Notch 138 98 white rectangle gene 0.5 black 46 17 Notch N-Notch-2.0-Hs-10 DLK1 136 109 white rectangle gene 0.5 black 46 17 2907 N-Notch-2.0-Hs-11 E2F1 115 175 white rectangle gene 0.5 black 46 17 3113 N-Notch-2.0-Hs-12 GSK3B 70 21 white rectangle gene 0.5 black 46 17 4617 N-Notch-2.0-Hs-13 IL4 64 1 white rectangle gene 0.5 black 46 17 6014 N-Notch-2.0-Hs-14 JAG1 147 97 white rectangle gene 0.5 black 46 17 6188 N-Notch-2.0-Hs-15 JAG2 136 89 white rectangle gene 0.5 black 46 17 6189 N-Notch-2.0-Hs-16 SMAD3 73 0 white rectangle gene 0.5 black 46 17 6769 N-Notch-2.0-Hs-17 MYC 60 11 white rectangle gene 0.5 black 46 17 7553 N-Notch-2.0-Hs-18 NOTCH1 71 11 white rectangle gene 0.5 black 46 17 7881 N-Notch-2.0-Hs-19 NOTCH2 15 105 white rectangle gene 0.5 black 46 17 7882 N-Notch-2.0-Hs-2 STAT3 173 85 white rectangle gene 0.5 black 46 17 11364 N-Notch-2.0-Hs-20 NOTCH3 4 81 white rectangle gene 0.5 black 46 17 7883 N-Notch-2.0-Hs-21 NOTCH4 23 124 white rectangle gene 0.5 black 46 17 7884 N-Notch-2.0-Hs-22 NR2F2 120 170 white rectangle gene 0.5 black 46 17 7976 N-Notch-2.0-Hs-23 Cdkn1c 16 117 white rectangle gene 0.5 black 46 17 104564 N-Notch-2.0-Hs-24 Cdkn1b 23 114 white rectangle gene 0.5 black 46 17 104565 N-Notch-2.0-Hs-3 STAT3 180 89 white rectangle gene 0.5 black 46 17 11364 N-Notch-2.0-Hs-4 TGFB1 27 134 white rectangle gene 0.5 black 46 17 11766 N-Notch-2.0-Hs-5 CD163 93 4 white rectangle gene 0.5 black 46 17 1631 N-Notch-2.0-Hs-6 ADAM10 9 93 white rectangle gene 0.5 black 46 17 188 N-Notch-2.0-Hs-7 ADAM17 84 9 white rectangle gene 0.5 black 46 17 195 N-Notch-2.0-Hs-8 HES5 0 72 white rectangle gene 0.5 black 46 17 19764 N-Notch-2.0-Hs-9 CTNNB1 138 119 white rectangle gene 0.5 black 46 17 2514 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Notch-2.0-Hs.sif000066400000000000000000000027641426625374700242710ustar00rootroot000000000000000 1 2 N-Notch-2.0-Hs-9 inhibition N-Notch-2.0-Hs-10 N-Notch-2.0-Hs-18 activation N-Notch-2.0-Hs-13 N-Notch-2.0-Hs-18 activation N-Notch-2.0-Hs-17 N-Notch-2.0-Hs-18 activation N-Notch-2.0-Hs-17 N-Notch-2.0-Hs-18 activation N-Notch-2.0-Hs-16 N-Notch-2.0-Hs-22 activation N-Notch-2.0-Hs-11 N-Notch-2.0-Hs-19 inhibition N-Notch-2.0-Hs-23 N-Notch-2.0-Hs-19 inhibition N-Notch-2.0-Hs-24 N-Notch-2.0-Hs-6 activation N-Notch-2.0-Hs-20 N-Notch-2.0-Hs-6 activation N-Notch-2.0-Hs-19 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-3 activation N-Notch-2.0-Hs-2 N-Notch-2.0-Hs-15 activation N-Notch-2.0-Hs-1 N-Notch-2.0-Hs-15 activation N-Notch-2.0-Hs-1 N-Notch-2.0-Hs-10 activation N-Notch-2.0-Hs-1 N-Notch-2.0-Hs-20 activation N-Notch-2.0-Hs-8 N-Notch-2.0-Hs-14 activation N-Notch-2.0-Hs-1 N-Notch-2.0-Hs-14 activation N-Notch-2.0-Hs-1 N-Notch-2.0-Hs-14 activation N-Notch-2.0-Hs-1 N-Notch-2.0-Hs-14 activation N-Notch-2.0-Hs-1 N-Notch-2.0-Hs-7 activation N-Notch-2.0-Hs-18 N-Notch-2.0-Hs-7 activation N-Notch-2.0-Hs-5 N-Notch-2.0-Hs-21 inhibition N-Notch-2.0-Hs-24 N-Notch-2.0-Hs-21 inhibition N-Notch-2.0-Hs-23 N-Notch-2.0-Hs-21 inhibition N-Notch-2.0-Hs-4 N-Notch-2.0-Hs-12 activation N-Notch-2.0-Hs-18 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Nuclear Receptors-2.0-Hs.att000066400000000000000000000033561426625374700265430ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Nuclear Receptors-2.0-Hs-1 2 CEBPA NR3C1 66 166 white rectangle gene,gene 0.5 black 46 17 1833,/,7978 N-Nuclear Receptors-2.0-Hs-10 CDKN1C 48 130 white rectangle gene 0.5 black 46 17 1786 N-Nuclear Receptors-2.0-Hs-11 CEBPA 17 149 white rectangle gene 0.5 black 46 17 1833 N-Nuclear Receptors-2.0-Hs-12 E2F1 70 0 white rectangle gene 0.5 black 46 17 3113 N-Nuclear Receptors-2.0-Hs-13 AHR 85 1 white rectangle gene 0.5 black 46 17 348 N-Nuclear Receptors-2.0-Hs-14 LPL 110 152 white rectangle gene 0.5 black 46 17 6677 N-Nuclear Receptors-2.0-Hs-15 MAP3K1 111 70 white rectangle gene 0.5 black 46 17 6848 N-Nuclear Receptors-2.0-Hs-16 MAPK8 96 76 white rectangle gene 0.5 black 46 17 6881 N-Nuclear Receptors-2.0-Hs-17 MMP14 106 23 white rectangle gene 0.5 black 46 17 7160 N-Nuclear Receptors-2.0-Hs-18 NR3C1 60 124 white rectangle gene 0.5 black 46 17 7978 N-Nuclear Receptors-2.0-Hs-19 NR4A1 59 98 white rectangle gene 0.5 black 46 17 7980 N-Nuclear Receptors-2.0-Hs-20 NR4A1 78 86 white rectangle gene 0.5 black 46 17 7980 N-Nuclear Receptors-2.0-Hs-21 PPARG 92 148 white rectangle gene 0.5 black 46 17 9236 N-Nuclear Receptors-2.0-Hs-3 TGFB1 99 9 white rectangle gene 0.5 black 46 17 11766 N-Nuclear Receptors-2.0-Hs-4 CCNA1 52 83 white rectangle gene 0.5 black 46 17 1577 N-Nuclear Receptors-2.0-Hs-5 CCND1 108 138 white rectangle gene 0.5 black 46 17 1582 N-Nuclear Receptors-2.0-Hs-6 CDK2 0 146 white rectangle gene 0.5 black 46 17 1771 N-Nuclear Receptors-2.0-Hs-7 CDK4 40 151 white rectangle gene 0.5 black 46 17 1773 N-Nuclear Receptors-2.0-Hs-8 CDKN1A 65 147 white rectangle gene 0.5 black 46 17 1784 N-Nuclear Receptors-2.0-Hs-9 CDKN1B 42 94 white rectangle gene 0.5 black 46 17 1785 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Nuclear Receptors-2.0-Hs.sif000066400000000000000000000054071426625374700265330ustar00rootroot000000000000000 1 2 N-Nuclear Receptors-2.0-Hs-19 inhibition N-Nuclear Receptors-2.0-Hs-18 N-Nuclear Receptors-2.0-Hs-19 inhibition N-Nuclear Receptors-2.0-Hs-18 N-Nuclear Receptors-2.0-Hs-19 inhibition N-Nuclear Receptors-2.0-Hs-4 N-Nuclear Receptors-2.0-Hs-19 activation N-Nuclear Receptors-2.0-Hs-9 N-Nuclear Receptors-2.0-Hs-19 activation N-Nuclear Receptors-2.0-Hs-9 N-Nuclear Receptors-2.0-Hs-20 inhibition N-Nuclear Receptors-2.0-Hs-19 N-Nuclear Receptors-2.0-Hs-20 inhibition N-Nuclear Receptors-2.0-Hs-19 N-Nuclear Receptors-2.0-Hs-15 activation N-Nuclear Receptors-2.0-Hs-16 N-Nuclear Receptors-2.0-Hs-15 activation N-Nuclear Receptors-2.0-Hs-16 N-Nuclear Receptors-2.0-Hs-15 activation N-Nuclear Receptors-2.0-Hs-16 N-Nuclear Receptors-2.0-Hs-15 activation N-Nuclear Receptors-2.0-Hs-16 N-Nuclear Receptors-2.0-Hs-15 activation N-Nuclear Receptors-2.0-Hs-16 N-Nuclear Receptors-2.0-Hs-11 inhibition N-Nuclear Receptors-2.0-Hs-6 N-Nuclear Receptors-2.0-Hs-11 inhibition N-Nuclear Receptors-2.0-Hs-7 N-Nuclear Receptors-2.0-Hs-18 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-18 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-18 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-18 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-18 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-18 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-18 activation N-Nuclear Receptors-2.0-Hs-10 N-Nuclear Receptors-2.0-Hs-18 activation N-Nuclear Receptors-2.0-Hs-10 N-Nuclear Receptors-2.0-Hs-17 activation N-Nuclear Receptors-2.0-Hs-3 N-Nuclear Receptors-2.0-Hs-1 2 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-1 2 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-1 2 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-1 2 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-1 2 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-13 activation N-Nuclear Receptors-2.0-Hs-12 N-Nuclear Receptors-2.0-Hs-13 inhibition N-Nuclear Receptors-2.0-Hs-3 N-Nuclear Receptors-2.0-Hs-13 inhibition N-Nuclear Receptors-2.0-Hs-3 N-Nuclear Receptors-2.0-Hs-13 inhibition N-Nuclear Receptors-2.0-Hs-3 N-Nuclear Receptors-2.0-Hs-8 inhibition N-Nuclear Receptors-2.0-Hs-7 N-Nuclear Receptors-2.0-Hs-8 inhibition N-Nuclear Receptors-2.0-Hs-7 N-Nuclear Receptors-2.0-Hs-8 inhibition N-Nuclear Receptors-2.0-Hs-7 N-Nuclear Receptors-2.0-Hs-16 activation N-Nuclear Receptors-2.0-Hs-20 N-Nuclear Receptors-2.0-Hs-16 activation N-Nuclear Receptors-2.0-Hs-20 N-Nuclear Receptors-2.0-Hs-21 activation N-Nuclear Receptors-2.0-Hs-8 N-Nuclear Receptors-2.0-Hs-21 inhibition N-Nuclear Receptors-2.0-Hs-5 N-Nuclear Receptors-2.0-Hs-21 activation N-Nuclear Receptors-2.0-Hs-14 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Osmotic Stress-2.0-Hs.att000066400000000000000000000025271426625374700261030ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Osmotic Stress-2.0-Hs-1 PRKAC 109 152 white rectangle gene 0.5 black 46 17 PRKAC N-Osmotic Stress-2.0-Hs-10 AQP5 0 64 white rectangle gene 0.5 black 46 17 638 N-Osmotic Stress-2.0-Hs-11 MAPK14 143 143 white rectangle gene 0.5 black 46 17 6876 N-Osmotic Stress-2.0-Hs-12 NFAT5 129 151 white rectangle gene 0.5 black 46 17 7774 N-Osmotic Stress-2.0-Hs-13 NFKBIA 82 173 white rectangle gene 0.5 black 46 17 7797 N-Osmotic Stress-2.0-Hs-14 ATM 140 163 white rectangle gene 0.5 black 46 17 795 N-Osmotic Stress-2.0-Hs-15 NRG1 187 31 white rectangle gene 0.5 black 46 17 7997 N-Osmotic Stress-2.0-Hs-16 PLCG1 70 1 white rectangle gene 0.5 black 46 17 9065 N-Osmotic Stress-2.0-Hs-2 SLC6A12 136 174 white rectangle gene 0.5 black 46 17 11045 N-Osmotic Stress-2.0-Hs-3 CFTR 89 159 white rectangle gene 0.5 black 46 17 1884 N-Osmotic Stress-2.0-Hs-4 CFTR 74 157 white rectangle gene 0.5 black 46 17 1884 N-Osmotic Stress-2.0-Hs-5 EGF 81 13 white rectangle gene 0.5 black 46 17 3229 N-Osmotic Stress-2.0-Hs-6 EGFR 88 0 white rectangle gene 0.5 black 46 17 3236 N-Osmotic Stress-2.0-Hs-7 EGFR 84 29 white rectangle gene 0.5 black 46 17 3236 N-Osmotic Stress-2.0-Hs-8 ERBB3 175 38 white rectangle gene 0.5 black 46 17 3431 N-Osmotic Stress-2.0-Hs-9 HIF1A 5 74 white rectangle gene 0.5 black 46 17 4910 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Osmotic Stress-2.0-Hs.sif000066400000000000000000000035661426625374700261000ustar00rootroot000000000000000 1 2 N-Osmotic Stress-2.0-Hs-11 activation N-Osmotic Stress-2.0-Hs-12 N-Osmotic Stress-2.0-Hs-1 activation N-Osmotic Stress-2.0-Hs-12 N-Osmotic Stress-2.0-Hs-1 activation N-Osmotic Stress-2.0-Hs-3 N-Osmotic Stress-2.0-Hs-15 activation N-Osmotic Stress-2.0-Hs-8 N-Osmotic Stress-2.0-Hs-15 activation N-Osmotic Stress-2.0-Hs-8 N-Osmotic Stress-2.0-Hs-4 inhibition N-Osmotic Stress-2.0-Hs-3 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-6 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-7 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-7 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-7 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-7 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-7 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-7 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-7 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-7 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-16 N-Osmotic Stress-2.0-Hs-5 activation N-Osmotic Stress-2.0-Hs-16 N-Osmotic Stress-2.0-Hs-9 activation N-Osmotic Stress-2.0-Hs-10 N-Osmotic Stress-2.0-Hs-3 inhibition N-Osmotic Stress-2.0-Hs-13 N-Osmotic Stress-2.0-Hs-14 activation N-Osmotic Stress-2.0-Hs-12 N-Osmotic Stress-2.0-Hs-14 activation N-Osmotic Stress-2.0-Hs-12 N-Osmotic Stress-2.0-Hs-14 activation N-Osmotic Stress-2.0-Hs-2 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Oxidative Stress-2.0-Hs.att000066400000000000000000000210211426625374700264100ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Oxidative Stress-2.0-Hs-1 2 TXN MAP3K5 75 94 white rectangle gene,gene 0.5 black 46 17 12435,/,6857 N-Oxidative Stress-2.0-Hs-100 MAPK1 93 7 white rectangle gene 0.5 black 46 17 6871 N-Oxidative Stress-2.0-Hs-101 MAPK11 71 103 white rectangle gene 0.5 black 46 17 6873 N-Oxidative Stress-2.0-Hs-102 MAPK12 72 84 white rectangle gene 0.5 black 46 17 6874 N-Oxidative Stress-2.0-Hs-103 MAPK14 70 100 white rectangle gene 0.5 black 46 17 6876 N-Oxidative Stress-2.0-Hs-104 MAPK14 73 94 white rectangle gene 0.5 black 46 17 6876 N-Oxidative Stress-2.0-Hs-105 MAPK14 71 94 white rectangle gene 0.5 black 46 17 6876 N-Oxidative Stress-2.0-Hs-106 MAPK3 98 23 white rectangle gene 0.5 black 46 17 6877 N-Oxidative Stress-2.0-Hs-107 MAPK7 92 44 white rectangle gene 0.5 black 46 17 6880 N-Oxidative Stress-2.0-Hs-108 MAPK8 89 82 white rectangle gene 0.5 black 46 17 6881 N-Oxidative Stress-2.0-Hs-109 MAPK8 90 88 white rectangle gene 0.5 black 46 17 6881 N-Oxidative Stress-2.0-Hs-110 MAPK8 92 81 white rectangle gene 0.5 black 46 17 6881 N-Oxidative Stress-2.0-Hs-111 MAPK9 92 72 white rectangle gene 0.5 black 46 17 6886 N-Oxidative Stress-2.0-Hs-112 MAPK9 90 80 white rectangle gene 0.5 black 46 17 6886 N-Oxidative Stress-2.0-Hs-113 MAPK9 89 75 white rectangle gene 0.5 black 46 17 6886 N-Oxidative Stress-2.0-Hs-114 MAPKAPK2 63 105 white rectangle gene 0.5 black 46 17 6887 N-Oxidative Stress-2.0-Hs-115 MDM2 11 44 white rectangle gene 0.5 black 46 17 6973 N-Oxidative Stress-2.0-Hs-117 MUC5AC 110 78 white rectangle gene 0.5 black 46 17 7515 N-Oxidative Stress-2.0-Hs-118 MUC5B 126 80 white rectangle gene 0.5 black 46 17 7516 N-Oxidative Stress-2.0-Hs-119 NCF1 149 145 white rectangle gene 0.5 black 46 17 7660 N-Oxidative Stress-2.0-Hs-12 13 MAFG NFE2L1 69 173 white rectangle gene,gene 0.5 black 46 17 6781,/,7781 N-Oxidative Stress-2.0-Hs-120 NFE2L1 72 171 white rectangle gene 0.5 black 46 17 7781 N-Oxidative Stress-2.0-Hs-121 NFE2L2 102 23 white rectangle gene 0.5 black 46 17 7782 N-Oxidative Stress-2.0-Hs-122 NFKB1 127 98 white rectangle gene 0.5 black 46 17 7794 N-Oxidative Stress-2.0-Hs-123 NFKBIA 31 164 white rectangle gene 0.5 black 46 17 7797 N-Oxidative Stress-2.0-Hs-124 NFKBIA 35 166 white rectangle gene 0.5 black 46 17 7797 N-Oxidative Stress-2.0-Hs-125 NFKBIA 30 170 white rectangle gene 0.5 black 46 17 7797 N-Oxidative Stress-2.0-Hs-126 NFKBIA 26 169 white rectangle gene 0.5 black 46 17 7797 N-Oxidative Stress-2.0-Hs-127 NFKBIE 23 157 white rectangle gene 0.5 black 46 17 7799 N-Oxidative Stress-2.0-Hs-130 NOS3 4 15 white rectangle gene 0.5 black 46 17 7876 N-Oxidative Stress-2.0-Hs-131 NOX1 116 64 white rectangle gene 0.5 black 46 17 7889 N-Oxidative Stress-2.0-Hs-133 NOX4 100 16 white rectangle gene 0.5 black 46 17 7891 N-Oxidative Stress-2.0-Hs-134 PDPK1 60 91 white rectangle gene 0.5 black 46 17 8816 N-Oxidative Stress-2.0-Hs-135 PDPK1 53 88 white rectangle gene 0.5 black 46 17 8816 N-Oxidative Stress-2.0-Hs-136 PLA2G4A 161 118 white rectangle gene 0.5 black 46 17 9035 N-Oxidative Stress-2.0-Hs-137 PLA2G4A 167 120 white rectangle gene 0.5 black 46 17 9035 N-Oxidative Stress-2.0-Hs-138 PRDX1 72 183 white rectangle gene 0.5 black 46 17 9352 N-Oxidative Stress-2.0-Hs-139 PRDX2 78 173 white rectangle gene 0.5 black 46 17 9353 N-Oxidative Stress-2.0-Hs-140 PRKCA 125 165 white rectangle gene 0.5 black 46 17 9393 N-Oxidative Stress-2.0-Hs-141 PRKCA 128 166 white rectangle gene 0.5 black 46 17 9393 N-Oxidative Stress-2.0-Hs-142 PTGS2 6 109 white rectangle gene 0.5 black 46 17 9605 N-Oxidative Stress-2.0-Hs-15 ERK 111 187 white rectangle gene 0.5 black 46 17 ERK N-Oxidative Stress-2.0-Hs-16 JNK 92 96 white rectangle gene 0.5 black 46 17 JNK N-Oxidative Stress-2.0-Hs-17 PKC 117 60 white rectangle gene 0.5 black 46 17 PKC N-Oxidative Stress-2.0-Hs-18 SRC 92 30 white rectangle gene 0.5 black 46 17 SRC N-Oxidative Stress-2.0-Hs-19 p38 110 91 white rectangle gene 0.5 black 46 17 p38 N-Oxidative Stress-2.0-Hs-20 RPS6KB1 52 92 white rectangle gene 0.5 black 46 17 10436 N-Oxidative Stress-2.0-Hs-21 RPS6KB2 55 98 white rectangle gene 0.5 black 46 17 10437 N-Oxidative Stress-2.0-Hs-23 SOD1 108 21 white rectangle gene 0.5 black 46 17 11179 N-Oxidative Stress-2.0-Hs-24 SOD2 17 21 white rectangle gene 0.5 black 46 17 11180 N-Oxidative Stress-2.0-Hs-26 SP1 79 94 white rectangle gene 0.5 black 46 17 11205 N-Oxidative Stress-2.0-Hs-27 SRC 101 75 white rectangle gene 0.5 black 46 17 11283 N-Oxidative Stress-2.0-Hs-28 STAT3 68 106 white rectangle gene 0.5 black 46 17 11364 N-Oxidative Stress-2.0-Hs-29 SUV39H1 10 50 white rectangle gene 0.5 black 46 17 11479 N-Oxidative Stress-2.0-Hs-3 4 CD44 EGFR 133 78 white rectangle gene,gene 0.5 black 46 17 1681,/,3236 N-Oxidative Stress-2.0-Hs-30 SUV39H1 3 52 white rectangle gene 0.5 black 46 17 11479 N-Oxidative Stress-2.0-Hs-31 SUV39H1 18 48 white rectangle gene 0.5 black 46 17 11479 N-Oxidative Stress-2.0-Hs-32 SUV39H1 7 46 white rectangle gene 0.5 black 46 17 11479 N-Oxidative Stress-2.0-Hs-34 TNF 14 38 white rectangle gene 0.5 black 46 17 11892 N-Oxidative Stress-2.0-Hs-35 TP53 18 42 white rectangle gene 0.5 black 46 17 11998 N-Oxidative Stress-2.0-Hs-36 TXN 73 176 white rectangle gene 0.5 black 46 17 12435 N-Oxidative Stress-2.0-Hs-37 TXNRD1 77 180 white rectangle gene 0.5 black 46 17 12437 N-Oxidative Stress-2.0-Hs-41 ACE2 121 94 white rectangle gene 0.5 black 46 17 13557 N-Oxidative Stress-2.0-Hs-42 FOSL1 103 28 white rectangle gene 0.5 black 46 17 13718 N-Oxidative Stress-2.0-Hs-44 SIRT1 26 44 white rectangle gene 0.5 black 46 17 14929 N-Oxidative Stress-2.0-Hs-45 CAT 19 18 white rectangle gene 0.5 black 46 17 1516 N-Oxidative Stress-2.0-Hs-46 CAV1 7 13 white rectangle gene 0.5 black 46 17 1527 N-Oxidative Stress-2.0-Hs-47 PARK7 153 144 white rectangle gene 0.5 black 46 17 16369 N-Oxidative Stress-2.0-Hs-48 CD44 105 181 white rectangle gene 0.5 black 46 17 1681 N-Oxidative Stress-2.0-Hs-49 TXNIP 68 172 white rectangle gene 0.5 black 46 17 16952 N-Oxidative Stress-2.0-Hs-50 CFTR 123 56 white rectangle gene 0.5 black 46 17 1884 N-Oxidative Stress-2.0-Hs-52 CHUK 26 161 white rectangle gene 0.5 black 46 17 1974 N-Oxidative Stress-2.0-Hs-53 KEAP1 93 22 white rectangle gene 0.5 black 46 17 23177 N-Oxidative Stress-2.0-Hs-54 CYBA 128 94 white rectangle gene 0.5 black 46 17 2577 N-Oxidative Stress-2.0-Hs-55 CYBB 35 48 white rectangle gene 0.5 black 46 17 2578 N-Oxidative Stress-2.0-Hs-57 NAMPT 117 83 white rectangle gene 0.5 black 46 17 30092 N-Oxidative Stress-2.0-Hs-58 DUOX1 121 160 white rectangle gene 0.5 black 46 17 3062 N-Oxidative Stress-2.0-Hs-60 EGFR 108 186 white rectangle gene 0.5 black 46 17 3236 N-Oxidative Stress-2.0-Hs-61 ELANE 110 67 white rectangle gene 0.5 black 46 17 3309 N-Oxidative Stress-2.0-Hs-62 ELK1 67 78 white rectangle gene 0.5 black 46 17 3321 N-Oxidative Stress-2.0-Hs-64 ETS2 96 103 white rectangle gene 0.5 black 46 17 3489 N-Oxidative Stress-2.0-Hs-66 FOXO1 115 95 white rectangle gene 0.5 black 46 17 3819 N-Oxidative Stress-2.0-Hs-67 FOXO3 21 25 white rectangle gene 0.5 black 46 17 3821 N-Oxidative Stress-2.0-Hs-68 FOXO3 23 34 white rectangle gene 0.5 black 46 17 3821 N-Oxidative Stress-2.0-Hs-8 9 CTTN NCF1 88 27 white rectangle gene,gene 0.5 black 46 17 3338,/,7660 N-Oxidative Stress-2.0-Hs-81 GSTP1 94 91 white rectangle gene 0.5 black 46 17 4638 N-Oxidative Stress-2.0-Hs-82 HMOX1 106 17 white rectangle gene 0.5 black 46 17 5013 N-Oxidative Stress-2.0-Hs-85 HSPA1A 68 84 white rectangle gene 0.5 black 46 17 5232 N-Oxidative Stress-2.0-Hs-86 HSPB1 58 112 white rectangle gene 0.5 black 46 17 5246 N-Oxidative Stress-2.0-Hs-87 HSPB1 55 104 white rectangle gene 0.5 black 46 17 5246 N-Oxidative Stress-2.0-Hs-88 IKBKB 88 18 white rectangle gene 0.5 black 46 17 5960 N-Oxidative Stress-2.0-Hs-89 JUN 91 60 white rectangle gene 0.5 black 46 17 6204 N-Oxidative Stress-2.0-Hs-90 JUN 90 70 white rectangle gene 0.5 black 46 17 6204 N-Oxidative Stress-2.0-Hs-91 KRAS 0 111 white rectangle gene 0.5 black 46 17 6407 N-Oxidative Stress-2.0-Hs-92 MAP2K1 94 0 white rectangle gene 0.5 black 46 17 6840 N-Oxidative Stress-2.0-Hs-93 MAP2K3 79 90 white rectangle gene 0.5 black 46 17 6843 N-Oxidative Stress-2.0-Hs-94 MAP2K4 85 80 white rectangle gene 0.5 black 46 17 6844 N-Oxidative Stress-2.0-Hs-95 MAP2K6 71 89 white rectangle gene 0.5 black 46 17 6846 N-Oxidative Stress-2.0-Hs-96 MAP2K7 87 89 white rectangle gene 0.5 black 46 17 6847 N-Oxidative Stress-2.0-Hs-97 MAP3K5 75 87 white rectangle gene 0.5 black 46 17 6857 N-Oxidative Stress-2.0-Hs-98 MAPK1 92 17 white rectangle gene 0.5 black 46 17 6871 N-Oxidative Stress-2.0-Hs-99 MAPK1 90 10 white rectangle gene 0.5 black 46 17 6871 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Oxidative Stress-2.0-Hs.sif000066400000000000000000000422621426625374700264130ustar00rootroot000000000000000 1 2 N-Oxidative Stress-2.0-Hs-35 activation N-Oxidative Stress-2.0-Hs-115 N-Oxidative Stress-2.0-Hs-103 activation N-Oxidative Stress-2.0-Hs-114 N-Oxidative Stress-2.0-Hs-103 activation N-Oxidative Stress-2.0-Hs-114 N-Oxidative Stress-2.0-Hs-103 activation N-Oxidative Stress-2.0-Hs-26 N-Oxidative Stress-2.0-Hs-103 activation N-Oxidative Stress-2.0-Hs-28 N-Oxidative Stress-2.0-Hs-103 activation N-Oxidative Stress-2.0-Hs-28 N-Oxidative Stress-2.0-Hs-103 activation N-Oxidative Stress-2.0-Hs-28 N-Oxidative Stress-2.0-Hs-17 inhibition N-Oxidative Stress-2.0-Hs-50 N-Oxidative Stress-2.0-Hs-52 inhibition N-Oxidative Stress-2.0-Hs-127 N-Oxidative Stress-2.0-Hs-52 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-52 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-67 activation N-Oxidative Stress-2.0-Hs-45 N-Oxidative Stress-2.0-Hs-67 activation N-Oxidative Stress-2.0-Hs-24 N-Oxidative Stress-2.0-Hs-67 activation N-Oxidative Stress-2.0-Hs-24 N-Oxidative Stress-2.0-Hs-57 activation N-Oxidative Stress-2.0-Hs-118 N-Oxidative Stress-2.0-Hs-57 activation N-Oxidative Stress-2.0-Hs-117 N-Oxidative Stress-2.0-Hs-57 activation N-Oxidative Stress-2.0-Hs-19 N-Oxidative Stress-2.0-Hs-113 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-113 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-113 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-111 activation N-Oxidative Stress-2.0-Hs-89 N-Oxidative Stress-2.0-Hs-111 activation N-Oxidative Stress-2.0-Hs-89 N-Oxidative Stress-2.0-Hs-1 2 inhibition N-Oxidative Stress-2.0-Hs-97 N-Oxidative Stress-2.0-Hs-1 2 inhibition N-Oxidative Stress-2.0-Hs-97 N-Oxidative Stress-2.0-Hs-1 2 inhibition N-Oxidative Stress-2.0-Hs-97 N-Oxidative Stress-2.0-Hs-1 2 inhibition N-Oxidative Stress-2.0-Hs-97 N-Oxidative Stress-2.0-Hs-36 activation N-Oxidative Stress-2.0-Hs-138 N-Oxidative Stress-2.0-Hs-36 activation N-Oxidative Stress-2.0-Hs-139 N-Oxidative Stress-2.0-Hs-110 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-110 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-105 activation N-Oxidative Stress-2.0-Hs-103 N-Oxidative Stress-2.0-Hs-105 activation N-Oxidative Stress-2.0-Hs-103 N-Oxidative Stress-2.0-Hs-105 activation N-Oxidative Stress-2.0-Hs-103 N-Oxidative Stress-2.0-Hs-107 activation N-Oxidative Stress-2.0-Hs-89 N-Oxidative Stress-2.0-Hs-114 activation N-Oxidative Stress-2.0-Hs-86 N-Oxidative Stress-2.0-Hs-114 activation N-Oxidative Stress-2.0-Hs-87 N-Oxidative Stress-2.0-Hs-114 activation N-Oxidative Stress-2.0-Hs-87 N-Oxidative Stress-2.0-Hs-41 inhibition N-Oxidative Stress-2.0-Hs-19 N-Oxidative Stress-2.0-Hs-41 inhibition N-Oxidative Stress-2.0-Hs-122 N-Oxidative Stress-2.0-Hs-41 inhibition N-Oxidative Stress-2.0-Hs-54 N-Oxidative Stress-2.0-Hs-21 activation N-Oxidative Stress-2.0-Hs-87 N-Oxidative Stress-2.0-Hs-18 activation N-Oxidative Stress-2.0-Hs-106 N-Oxidative Stress-2.0-Hs-18 activation N-Oxidative Stress-2.0-Hs-107 N-Oxidative Stress-2.0-Hs-18 activation N-Oxidative Stress-2.0-Hs-8 9 N-Oxidative Stress-2.0-Hs-18 activation N-Oxidative Stress-2.0-Hs-98 N-Oxidative Stress-2.0-Hs-18 activation N-Oxidative Stress-2.0-Hs-98 N-Oxidative Stress-2.0-Hs-92 activation N-Oxidative Stress-2.0-Hs-100 N-Oxidative Stress-2.0-Hs-92 activation N-Oxidative Stress-2.0-Hs-100 N-Oxidative Stress-2.0-Hs-61 activation N-Oxidative Stress-2.0-Hs-131 N-Oxidative Stress-2.0-Hs-61 activation N-Oxidative Stress-2.0-Hs-27 N-Oxidative Stress-2.0-Hs-61 activation N-Oxidative Stress-2.0-Hs-17 N-Oxidative Stress-2.0-Hs-42 inhibition N-Oxidative Stress-2.0-Hs-121 N-Oxidative Stress-2.0-Hs-34 activation N-Oxidative Stress-2.0-Hs-35 N-Oxidative Stress-2.0-Hs-34 activation N-Oxidative Stress-2.0-Hs-35 N-Oxidative Stress-2.0-Hs-34 activation N-Oxidative Stress-2.0-Hs-35 N-Oxidative Stress-2.0-Hs-19 activation N-Oxidative Stress-2.0-Hs-66 N-Oxidative Stress-2.0-Hs-90 activation N-Oxidative Stress-2.0-Hs-89 N-Oxidative Stress-2.0-Hs-100 activation N-Oxidative Stress-2.0-Hs-98 N-Oxidative Stress-2.0-Hs-100 activation N-Oxidative Stress-2.0-Hs-98 N-Oxidative Stress-2.0-Hs-100 activation N-Oxidative Stress-2.0-Hs-98 N-Oxidative Stress-2.0-Hs-60 activation N-Oxidative Stress-2.0-Hs-15 N-Oxidative Stress-2.0-Hs-60 activation N-Oxidative Stress-2.0-Hs-15 N-Oxidative Stress-2.0-Hs-101 activation N-Oxidative Stress-2.0-Hs-26 N-Oxidative Stress-2.0-Hs-101 activation N-Oxidative Stress-2.0-Hs-114 N-Oxidative Stress-2.0-Hs-101 activation N-Oxidative Stress-2.0-Hs-28 N-Oxidative Stress-2.0-Hs-16 activation N-Oxidative Stress-2.0-Hs-64 N-Oxidative Stress-2.0-Hs-104 activation N-Oxidative Stress-2.0-Hs-103 N-Oxidative Stress-2.0-Hs-104 activation N-Oxidative Stress-2.0-Hs-103 N-Oxidative Stress-2.0-Hs-104 activation N-Oxidative Stress-2.0-Hs-103 N-Oxidative Stress-2.0-Hs-137 activation N-Oxidative Stress-2.0-Hs-136 N-Oxidative Stress-2.0-Hs-112 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-112 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-115 activation N-Oxidative Stress-2.0-Hs-32 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-124 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-121 activation N-Oxidative Stress-2.0-Hs-82 N-Oxidative Stress-2.0-Hs-121 activation N-Oxidative Stress-2.0-Hs-23 N-Oxidative Stress-2.0-Hs-49 inhibition N-Oxidative Stress-2.0-Hs-36 N-Oxidative Stress-2.0-Hs-3 4 activation N-Oxidative Stress-2.0-Hs-118 N-Oxidative Stress-2.0-Hs-141 activation N-Oxidative Stress-2.0-Hs-140 N-Oxidative Stress-2.0-Hs-102 activation N-Oxidative Stress-2.0-Hs-62 N-Oxidative Stress-2.0-Hs-81 activation N-Oxidative Stress-2.0-Hs-16 N-Oxidative Stress-2.0-Hs-81 activation N-Oxidative Stress-2.0-Hs-96 N-Oxidative Stress-2.0-Hs-81 activation N-Oxidative Stress-2.0-Hs-19 N-Oxidative Stress-2.0-Hs-81 activation N-Oxidative Stress-2.0-Hs-93 N-Oxidative Stress-2.0-Hs-85 inhibition N-Oxidative Stress-2.0-Hs-97 N-Oxidative Stress-2.0-Hs-85 inhibition N-Oxidative Stress-2.0-Hs-97 N-Oxidative Stress-2.0-Hs-68 activation N-Oxidative Stress-2.0-Hs-67 N-Oxidative Stress-2.0-Hs-134 activation N-Oxidative Stress-2.0-Hs-135 N-Oxidative Stress-2.0-Hs-134 activation N-Oxidative Stress-2.0-Hs-20 N-Oxidative Stress-2.0-Hs-134 activation N-Oxidative Stress-2.0-Hs-20 N-Oxidative Stress-2.0-Hs-134 activation N-Oxidative Stress-2.0-Hs-20 N-Oxidative Stress-2.0-Hs-134 activation N-Oxidative Stress-2.0-Hs-20 N-Oxidative Stress-2.0-Hs-134 activation N-Oxidative Stress-2.0-Hs-21 N-Oxidative Stress-2.0-Hs-134 activation N-Oxidative Stress-2.0-Hs-21 N-Oxidative Stress-2.0-Hs-109 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-109 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-140 activation N-Oxidative Stress-2.0-Hs-58 N-Oxidative Stress-2.0-Hs-126 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-126 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-126 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-126 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-126 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-48 activation N-Oxidative Stress-2.0-Hs-60 N-Oxidative Stress-2.0-Hs-30 inhibition N-Oxidative Stress-2.0-Hs-29 N-Oxidative Stress-2.0-Hs-93 activation N-Oxidative Stress-2.0-Hs-102 N-Oxidative Stress-2.0-Hs-93 activation N-Oxidative Stress-2.0-Hs-105 N-Oxidative Stress-2.0-Hs-93 activation N-Oxidative Stress-2.0-Hs-104 N-Oxidative Stress-2.0-Hs-55 inhibition N-Oxidative Stress-2.0-Hs-44 N-Oxidative Stress-2.0-Hs-53 inhibition N-Oxidative Stress-2.0-Hs-121 N-Oxidative Stress-2.0-Hs-53 inhibition N-Oxidative Stress-2.0-Hs-88 N-Oxidative Stress-2.0-Hs-53 inhibition N-Oxidative Stress-2.0-Hs-88 N-Oxidative Stress-2.0-Hs-94 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-94 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-94 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-94 activation N-Oxidative Stress-2.0-Hs-113 N-Oxidative Stress-2.0-Hs-94 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-94 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-94 activation N-Oxidative Stress-2.0-Hs-110 N-Oxidative Stress-2.0-Hs-94 activation N-Oxidative Stress-2.0-Hs-110 N-Oxidative Stress-2.0-Hs-106 activation N-Oxidative Stress-2.0-Hs-121 N-Oxidative Stress-2.0-Hs-106 activation N-Oxidative Stress-2.0-Hs-133 N-Oxidative Stress-2.0-Hs-27 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-27 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-27 activation N-Oxidative Stress-2.0-Hs-111 N-Oxidative Stress-2.0-Hs-27 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-27 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-27 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-27 activation N-Oxidative Stress-2.0-Hs-108 N-Oxidative Stress-2.0-Hs-27 activation N-Oxidative Stress-2.0-Hs-117 N-Oxidative Stress-2.0-Hs-96 activation N-Oxidative Stress-2.0-Hs-112 N-Oxidative Stress-2.0-Hs-96 activation N-Oxidative Stress-2.0-Hs-16 N-Oxidative Stress-2.0-Hs-96 activation N-Oxidative Stress-2.0-Hs-109 N-Oxidative Stress-2.0-Hs-96 activation N-Oxidative Stress-2.0-Hs-109 N-Oxidative Stress-2.0-Hs-99 activation N-Oxidative Stress-2.0-Hs-98 N-Oxidative Stress-2.0-Hs-31 inhibition N-Oxidative Stress-2.0-Hs-29 N-Oxidative Stress-2.0-Hs-91 activation N-Oxidative Stress-2.0-Hs-142 N-Oxidative Stress-2.0-Hs-46 inhibition N-Oxidative Stress-2.0-Hs-130 N-Oxidative Stress-2.0-Hs-46 inhibition N-Oxidative Stress-2.0-Hs-130 N-Oxidative Stress-2.0-Hs-32 inhibition N-Oxidative Stress-2.0-Hs-29 N-Oxidative Stress-2.0-Hs-95 activation N-Oxidative Stress-2.0-Hs-102 N-Oxidative Stress-2.0-Hs-95 activation N-Oxidative Stress-2.0-Hs-105 N-Oxidative Stress-2.0-Hs-95 activation N-Oxidative Stress-2.0-Hs-104 N-Oxidative Stress-2.0-Hs-125 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-125 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-125 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-125 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-125 activation N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-97 activation N-Oxidative Stress-2.0-Hs-93 N-Oxidative Stress-2.0-Hs-97 inhibition N-Oxidative Stress-2.0-Hs-134 N-Oxidative Stress-2.0-Hs-97 activation N-Oxidative Stress-2.0-Hs-94 N-Oxidative Stress-2.0-Hs-97 activation N-Oxidative Stress-2.0-Hs-94 N-Oxidative Stress-2.0-Hs-97 activation N-Oxidative Stress-2.0-Hs-95 N-Oxidative Stress-2.0-Hs-97 activation N-Oxidative Stress-2.0-Hs-96 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-123 inhibition N-Oxidative Stress-2.0-Hs-123 N-Oxidative Stress-2.0-Hs-12 13 activation N-Oxidative Stress-2.0-Hs-120 N-Oxidative Stress-2.0-Hs-12 13 activation N-Oxidative Stress-2.0-Hs-120 N-Oxidative Stress-2.0-Hs-37 activation N-Oxidative Stress-2.0-Hs-36 N-Oxidative Stress-2.0-Hs-108 activation N-Oxidative Stress-2.0-Hs-90 N-Oxidative Stress-2.0-Hs-108 activation N-Oxidative Stress-2.0-Hs-90 N-Oxidative Stress-2.0-Hs-108 activation N-Oxidative Stress-2.0-Hs-90 N-Oxidative Stress-2.0-Hs-108 activation N-Oxidative Stress-2.0-Hs-90 N-Oxidative Stress-2.0-Hs-108 inhibition N-Oxidative Stress-2.0-Hs-26 N-Oxidative Stress-2.0-Hs-44 inhibition N-Oxidative Stress-2.0-Hs-68 N-Oxidative Stress-2.0-Hs-44 inhibition N-Oxidative Stress-2.0-Hs-31 N-Oxidative Stress-2.0-Hs-44 inhibition N-Oxidative Stress-2.0-Hs-35 N-Oxidative Stress-2.0-Hs-47 activation N-Oxidative Stress-2.0-Hs-119 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/PGE2-2.0-Hs.att000066400000000000000000000022671426625374700237200ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-PGE2-2.0-Hs-1 MEK 50 145 white rectangle gene 0.5 black 46 17 MEK N-PGE2-2.0-Hs-10 CREB1 161 25 white rectangle gene 0.5 black 46 17 2345 N-PGE2-2.0-Hs-11 CREB1 175 29 white rectangle gene 0.5 black 46 17 2345 N-PGE2-2.0-Hs-12 HRAS 121 148 white rectangle gene 0.5 black 46 17 5173 N-PGE2-2.0-Hs-13 NPPA 131 29 white rectangle gene 0.5 black 46 17 7939 N-PGE2-2.0-Hs-15 PTGS2 127 159 white rectangle gene 0.5 black 46 17 9605 N-PGE2-2.0-Hs-16 RAC1 128 7 white rectangle gene 0.5 black 46 17 9801 N-PGE2-2.0-Hs-17 RAP1A 111 13 white rectangle gene 0.5 black 46 17 9855 N-PGE2-2.0-Hs-2 PRKAC 143 20 white rectangle gene 0.5 black 46 17 PRKAC N-PGE2-2.0-Hs-3 RAF 50 131 white rectangle gene 0.5 black 46 17 RAF N-PGE2-2.0-Hs-4 RAS 37 133 white rectangle gene 0.5 black 46 17 RAS N-PGE2-2.0-Hs-5 TIAM1 119 0 white rectangle gene 0.5 black 46 17 11805 N-PGE2-2.0-Hs-6 VAV2 112 3 white rectangle gene 0.5 black 46 17 12658 N-PGE2-2.0-Hs-7 VIP 11 44 white rectangle gene 0.5 black 46 17 12693 N-PGE2-2.0-Hs-8 VIPR1 0 41 white rectangle gene 0.5 black 46 17 12694 N-PGE2-2.0-Hs-9 RAPGEF3 117 27 white rectangle gene 0.5 black 46 17 16629 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/PGE2-2.0-Hs.sif000066400000000000000000000031551426625374700237060ustar00rootroot000000000000000 1 2 N-PGE2-2.0-Hs-13 activation N-PGE2-2.0-Hs-9 N-PGE2-2.0-Hs-13 activation N-PGE2-2.0-Hs-2 N-PGE2-2.0-Hs-13 activation N-PGE2-2.0-Hs-2 N-PGE2-2.0-Hs-12 activation N-PGE2-2.0-Hs-15 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-11 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-2 activation N-PGE2-2.0-Hs-10 N-PGE2-2.0-Hs-2 activation N-PGE2-2.0-Hs-16 N-PGE2-2.0-Hs-4 activation N-PGE2-2.0-Hs-3 N-PGE2-2.0-Hs-4 activation N-PGE2-2.0-Hs-3 N-PGE2-2.0-Hs-4 activation N-PGE2-2.0-Hs-3 N-PGE2-2.0-Hs-4 activation N-PGE2-2.0-Hs-3 N-PGE2-2.0-Hs-9 activation N-PGE2-2.0-Hs-17 N-PGE2-2.0-Hs-6 activation N-PGE2-2.0-Hs-16 N-PGE2-2.0-Hs-6 activation N-PGE2-2.0-Hs-16 N-PGE2-2.0-Hs-6 activation N-PGE2-2.0-Hs-16 N-PGE2-2.0-Hs-3 activation N-PGE2-2.0-Hs-1 N-PGE2-2.0-Hs-5 activation N-PGE2-2.0-Hs-16 N-PGE2-2.0-Hs-7 activation N-PGE2-2.0-Hs-8 N-PGE2-2.0-Hs-17 activation N-PGE2-2.0-Hs-5 N-PGE2-2.0-Hs-17 activation N-PGE2-2.0-Hs-5 N-PGE2-2.0-Hs-17 activation N-PGE2-2.0-Hs-6 N-PGE2-2.0-Hs-17 activation N-PGE2-2.0-Hs-6 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Response to DNA Damage-2.0-Hs.att000066400000000000000000000212011426625374700271330ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Response to DNA Damage-2.0-Hs-1 2 RNF8 MDC1 111 52 white rectangle gene,gene 0.5 black 46 17 10071,/,21163 N-Response to DNA Damage-2.0-Hs-101 RHOA 136 62 white rectangle gene 0.5 black 46 17 667 N-Response to DNA Damage-2.0-Hs-102 MDM2 119 28 white rectangle gene 0.5 black 46 17 6973 N-Response to DNA Damage-2.0-Hs-103 MDM4 116 35 white rectangle gene 0.5 black 46 17 6974 N-Response to DNA Damage-2.0-Hs-104 MYC 86 7 white rectangle gene 0.5 black 46 17 7553 N-Response to DNA Damage-2.0-Hs-105 ABL1 60 6 white rectangle gene 0.5 black 46 17 76 N-Response to DNA Damage-2.0-Hs-106 ABL1 90 10 white rectangle gene 0.5 black 46 17 76 N-Response to DNA Damage-2.0-Hs-107 ABL1 75 12 white rectangle gene 0.5 black 46 17 76 N-Response to DNA Damage-2.0-Hs-108 NBN 84 0 white rectangle gene 0.5 black 46 17 7652 N-Response to DNA Damage-2.0-Hs-109 NBN 0 20 white rectangle gene 0.5 black 46 17 7652 N-Response to DNA Damage-2.0-Hs-110 ATF2 113 31 white rectangle gene 0.5 black 46 17 784 N-Response to DNA Damage-2.0-Hs-111 ATF2 93 8 white rectangle gene 0.5 black 46 17 784 N-Response to DNA Damage-2.0-Hs-112 ATF2 84 12 white rectangle gene 0.5 black 46 17 784 N-Response to DNA Damage-2.0-Hs-113 ATF2 123 41 white rectangle gene 0.5 black 46 17 784 N-Response to DNA Damage-2.0-Hs-114 ATF2 122 43 white rectangle gene 0.5 black 46 17 784 N-Response to DNA Damage-2.0-Hs-115 ATM 93 19 white rectangle gene 0.5 black 46 17 795 N-Response to DNA Damage-2.0-Hs-116 ATM 93 11 white rectangle gene 0.5 black 46 17 795 N-Response to DNA Damage-2.0-Hs-118 ATR 107 12 white rectangle gene 0.5 black 46 17 882 N-Response to DNA Damage-2.0-Hs-119 PLK1 89 79 white rectangle gene 0.5 black 46 17 9077 N-Response to DNA Damage-2.0-Hs-126 PRKDC 81 40 white rectangle gene 0.5 black 46 17 9413 N-Response to DNA Damage-2.0-Hs-127 PRKDC 71 39 white rectangle gene 0.5 black 46 17 9413 N-Response to DNA Damage-2.0-Hs-128 PRKDC 71 42 white rectangle gene 0.5 black 46 17 9413 N-Response to DNA Damage-2.0-Hs-129 PRKDC 71 44 white rectangle gene 0.5 black 46 17 9413 N-Response to DNA Damage-2.0-Hs-13 14 15 HUS1 RAD1 RAD9A 110 1 white rectangle gene,gene,gene 0.5 black 46 17 5309,/,9806,/,9827 N-Response to DNA Damage-2.0-Hs-130 PRKDC 74 45 white rectangle gene 0.5 black 46 17 9413 N-Response to DNA Damage-2.0-Hs-131 PRKDC 77 48 white rectangle gene 0.5 black 46 17 9413 N-Response to DNA Damage-2.0-Hs-134 RAD51 52 3 white rectangle gene 0.5 black 46 17 9817 N-Response to DNA Damage-2.0-Hs-135 RAD51 51 6 white rectangle gene 0.5 black 46 17 9817 N-Response to DNA Damage-2.0-Hs-137 RAD52 54 0 white rectangle gene 0.5 black 46 17 9824 N-Response to DNA Damage-2.0-Hs-138 RASSF1 124 25 white rectangle gene 0.5 black 46 17 9882 N-Response to DNA Damage-2.0-Hs-139 RASSF1 94 40 white rectangle gene 0.5 black 46 17 9882 N-Response to DNA Damage-2.0-Hs-16 AKT 181 9 white rectangle gene 0.5 black 46 17 AKT N-Response to DNA Damage-2.0-Hs-17 PPP1R 91 34 white rectangle gene 0.5 black 46 17 PPP1R N-Response to DNA Damage-2.0-Hs-18 p38 129 51 white rectangle gene 0.5 black 46 17 p38 N-Response to DNA Damage-2.0-Hs-19 SFN 117 30 white rectangle gene 0.5 black 46 17 10773 N-Response to DNA Damage-2.0-Hs-20 BRCA1 108 41 white rectangle gene 0.5 black 46 17 1100 N-Response to DNA Damage-2.0-Hs-21 BRCA1 83 15 white rectangle gene 0.5 black 46 17 1100 N-Response to DNA Damage-2.0-Hs-22 BRCA1 87 25 white rectangle gene 0.5 black 46 17 1100 N-Response to DNA Damage-2.0-Hs-23 BRCA1 100 13 white rectangle gene 0.5 black 46 17 1100 N-Response to DNA Damage-2.0-Hs-24 BRCA1 99 11 white rectangle gene 0.5 black 46 17 1100 N-Response to DNA Damage-2.0-Hs-25 BRCA1 82 21 white rectangle gene 0.5 black 46 17 1100 N-Response to DNA Damage-2.0-Hs-26 BRCA1 100 10 white rectangle gene 0.5 black 46 17 1100 N-Response to DNA Damage-2.0-Hs-27 BRCA2 103 36 white rectangle gene 0.5 black 46 17 1101 N-Response to DNA Damage-2.0-Hs-28 SP1 87 11 white rectangle gene 0.5 black 46 17 11205 N-Response to DNA Damage-2.0-Hs-29 SRC 111 77 white rectangle gene 0.5 black 46 17 11283 N-Response to DNA Damage-2.0-Hs-31 TP53 108 28 white rectangle gene 0.5 black 46 17 11998 N-Response to DNA Damage-2.0-Hs-32 TP53 96 27 white rectangle gene 0.5 black 46 17 11998 N-Response to DNA Damage-2.0-Hs-33 TP53 108 24 white rectangle gene 0.5 black 46 17 11998 N-Response to DNA Damage-2.0-Hs-34 TP53 94 35 white rectangle gene 0.5 black 46 17 11998 N-Response to DNA Damage-2.0-Hs-35 TP53 123 31 white rectangle gene 0.5 black 46 17 11998 N-Response to DNA Damage-2.0-Hs-36 TP53 111 25 white rectangle gene 0.5 black 46 17 11998 N-Response to DNA Damage-2.0-Hs-37 TP53BP1 114 57 white rectangle gene 0.5 black 46 17 11999 N-Response to DNA Damage-2.0-Hs-38 TP53BP1 89 13 white rectangle gene 0.5 black 46 17 11999 N-Response to DNA Damage-2.0-Hs-39 TP53BP2 94 67 white rectangle gene 0.5 black 46 17 12000 N-Response to DNA Damage-2.0-Hs-40 TP73 97 60 white rectangle gene 0.5 black 46 17 12003 N-Response to DNA Damage-2.0-Hs-41 TP73 102 45 white rectangle gene 0.5 black 46 17 12003 N-Response to DNA Damage-2.0-Hs-42 TP73 92 71 white rectangle gene 0.5 black 46 17 12003 N-Response to DNA Damage-2.0-Hs-43 TP73 105 72 white rectangle gene 0.5 black 46 17 12003 N-Response to DNA Damage-2.0-Hs-47 WRN 86 19 white rectangle gene 0.5 black 46 17 12791 N-Response to DNA Damage-2.0-Hs-48 WRN 86 15 white rectangle gene 0.5 black 46 17 12791 N-Response to DNA Damage-2.0-Hs-49 WWOX 92 65 white rectangle gene 0.5 black 46 17 12799 N-Response to DNA Damage-2.0-Hs-52 YY1 118 32 white rectangle gene 0.5 black 46 17 12856 N-Response to DNA Damage-2.0-Hs-53 HIPK2 133 32 white rectangle gene 0.5 black 46 17 14402 N-Response to DNA Damage-2.0-Hs-54 NET1 142 71 white rectangle gene 0.5 black 46 17 14592 N-Response to DNA Damage-2.0-Hs-55 NET1 147 77 white rectangle gene 0.5 black 46 17 14592 N-Response to DNA Damage-2.0-Hs-56 SIRT1 6 20 white rectangle gene 0.5 black 46 17 14929 N-Response to DNA Damage-2.0-Hs-57 PPP1R13B 97 69 white rectangle gene 0.5 black 46 17 14950 N-Response to DNA Damage-2.0-Hs-59 CCND1 129 18 white rectangle gene 0.5 black 46 17 1582 N-Response to DNA Damage-2.0-Hs-61 YAP1 100 67 white rectangle gene 0.5 black 46 17 16262 N-Response to DNA Damage-2.0-Hs-62 TRIM28 75 30 white rectangle gene 0.5 black 46 17 16384 N-Response to DNA Damage-2.0-Hs-63 TRIM28 82 25 white rectangle gene 0.5 black 46 17 16384 N-Response to DNA Damage-2.0-Hs-64 CHEK2 102 24 white rectangle gene 0.5 black 46 17 16627 N-Response to DNA Damage-2.0-Hs-65 CHEK2 98 21 white rectangle gene 0.5 black 46 17 16627 N-Response to DNA Damage-2.0-Hs-66 CHEK2 98 18 white rectangle gene 0.5 black 46 17 16627 N-Response to DNA Damage-2.0-Hs-67 CHEK2 96 24 white rectangle gene 0.5 black 46 17 16627 N-Response to DNA Damage-2.0-Hs-68 CHEK2 96 22 white rectangle gene 0.5 black 46 17 16627 N-Response to DNA Damage-2.0-Hs-69 TOPBP1 110 3 white rectangle gene 0.5 black 46 17 17008 N-Response to DNA Damage-2.0-Hs-70 CDC25A 115 23 white rectangle gene 0.5 black 46 17 1725 N-Response to DNA Damage-2.0-Hs-71 CDKN1A 182 13 white rectangle gene 0.5 black 46 17 1784 N-Response to DNA Damage-2.0-Hs-72 CDKN1A 175 12 white rectangle gene 0.5 black 46 17 1784 N-Response to DNA Damage-2.0-Hs-73 CHEK1 106 29 white rectangle gene 0.5 black 46 17 1925 N-Response to DNA Damage-2.0-Hs-74 CHEK1 105 18 white rectangle gene 0.5 black 46 17 1925 N-Response to DNA Damage-2.0-Hs-75 CHEK1 103 19 white rectangle gene 0.5 black 46 17 1925 N-Response to DNA Damage-2.0-Hs-76 RIF1 113 51 white rectangle gene 0.5 black 46 17 23207 N-Response to DNA Damage-2.0-Hs-77 FANCI 116 6 white rectangle gene 0.5 black 46 17 25568 N-Response to DNA Damage-2.0-Hs-78 PARP1 100 32 white rectangle gene 0.5 black 46 17 270 N-Response to DNA Damage-2.0-Hs-81 DNMT3A 112 36 white rectangle gene 0.5 black 46 17 2978 N-Response to DNA Damage-2.0-Hs-82 ATRIP 115 4 white rectangle gene 0.5 black 46 17 33499 N-Response to DNA Damage-2.0-Hs-83 EP300 118 25 white rectangle gene 0.5 black 46 17 3373 N-Response to DNA Damage-2.0-Hs-84 EP300 88 6 white rectangle gene 0.5 black 46 17 3373 N-Response to DNA Damage-2.0-Hs-9 10 XRCC5 XRCC6 74 48 white rectangle gene,gene 0.5 black 46 17 12833,/,4055 N-Response to DNA Damage-2.0-Hs-91 FANCD2 85 22 white rectangle gene 0.5 black 46 17 3585 N-Response to DNA Damage-2.0-Hs-92 FANCD2 82 18 white rectangle gene 0.5 black 46 17 3585 N-Response to DNA Damage-2.0-Hs-93 FANCD2 113 2 white rectangle gene 0.5 black 46 17 3585 N-Response to DNA Damage-2.0-Hs-94 FANCD2 107 1 white rectangle gene 0.5 black 46 17 3585 N-Response to DNA Damage-2.0-Hs-96 FHIT 111 12 white rectangle gene 0.5 black 46 17 3701 N-Response to DNA Damage-2.0-Hs-97 HCK 107 81 white rectangle gene 0.5 black 46 17 4840 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Response to DNA Damage-2.0-Hs.sif000066400000000000000000000603661426625374700271430ustar00rootroot000000000000000 1 2 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-31 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-84 activation N-Response to DNA Damage-2.0-Hs-108 N-Response to DNA Damage-2.0-Hs-13 14 15 activation N-Response to DNA Damage-2.0-Hs-118 N-Response to DNA Damage-2.0-Hs-96 inhibition N-Response to DNA Damage-2.0-Hs-118 N-Response to DNA Damage-2.0-Hs-96 inhibition N-Response to DNA Damage-2.0-Hs-118 N-Response to DNA Damage-2.0-Hs-96 activation N-Response to DNA Damage-2.0-Hs-74 N-Response to DNA Damage-2.0-Hs-81 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-17 inhibition N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-17 inhibition N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-17 inhibition N-Response to DNA Damage-2.0-Hs-34 N-Response to DNA Damage-2.0-Hs-116 activation N-Response to DNA Damage-2.0-Hs-115 N-Response to DNA Damage-2.0-Hs-116 activation N-Response to DNA Damage-2.0-Hs-115 N-Response to DNA Damage-2.0-Hs-52 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-82 activation N-Response to DNA Damage-2.0-Hs-118 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-94 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-24 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-23 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-23 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-93 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-26 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-77 N-Response to DNA Damage-2.0-Hs-118 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-36 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-36 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-33 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-33 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-73 activation N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-73 activation N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-73 activation N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-73 activation N-Response to DNA Damage-2.0-Hs-41 N-Response to DNA Damage-2.0-Hs-73 activation N-Response to DNA Damage-2.0-Hs-33 N-Response to DNA Damage-2.0-Hs-73 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-19 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-19 inhibition N-Response to DNA Damage-2.0-Hs-102 N-Response to DNA Damage-2.0-Hs-83 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-83 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-75 activation N-Response to DNA Damage-2.0-Hs-73 N-Response to DNA Damage-2.0-Hs-75 activation N-Response to DNA Damage-2.0-Hs-73 N-Response to DNA Damage-2.0-Hs-75 activation N-Response to DNA Damage-2.0-Hs-73 N-Response to DNA Damage-2.0-Hs-75 activation N-Response to DNA Damage-2.0-Hs-73 N-Response to DNA Damage-2.0-Hs-43 inhibition N-Response to DNA Damage-2.0-Hs-40 N-Response to DNA Damage-2.0-Hs-103 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-103 inhibition N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-37 activation N-Response to DNA Damage-2.0-Hs-76 N-Response to DNA Damage-2.0-Hs-66 activation N-Response to DNA Damage-2.0-Hs-64 N-Response to DNA Damage-2.0-Hs-16 activation N-Response to DNA Damage-2.0-Hs-72 N-Response to DNA Damage-2.0-Hs-65 activation N-Response to DNA Damage-2.0-Hs-64 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-70 inhibition N-Response to DNA Damage-2.0-Hs-70 N-Response to DNA Damage-2.0-Hs-41 activation N-Response to DNA Damage-2.0-Hs-40 N-Response to DNA Damage-2.0-Hs-138 inhibition N-Response to DNA Damage-2.0-Hs-59 N-Response to DNA Damage-2.0-Hs-138 inhibition N-Response to DNA Damage-2.0-Hs-59 N-Response to DNA Damage-2.0-Hs-138 inhibition N-Response to DNA Damage-2.0-Hs-59 N-Response to DNA Damage-2.0-Hs-138 inhibition N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-138 inhibition N-Response to DNA Damage-2.0-Hs-102 N-Response to DNA Damage-2.0-Hs-113 activation N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-113 activation N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-113 activation N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-139 activation N-Response to DNA Damage-2.0-Hs-40 N-Response to DNA Damage-2.0-Hs-18 activation N-Response to DNA Damage-2.0-Hs-114 N-Response to DNA Damage-2.0-Hs-18 activation N-Response to DNA Damage-2.0-Hs-114 N-Response to DNA Damage-2.0-Hs-18 activation N-Response to DNA Damage-2.0-Hs-114 N-Response to DNA Damage-2.0-Hs-18 activation N-Response to DNA Damage-2.0-Hs-114 N-Response to DNA Damage-2.0-Hs-18 activation N-Response to DNA Damage-2.0-Hs-113 N-Response to DNA Damage-2.0-Hs-18 activation N-Response to DNA Damage-2.0-Hs-113 N-Response to DNA Damage-2.0-Hs-18 activation N-Response to DNA Damage-2.0-Hs-113 N-Response to DNA Damage-2.0-Hs-18 activation N-Response to DNA Damage-2.0-Hs-113 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-38 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-28 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-106 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-68 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-68 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-68 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-68 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-68 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-68 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-75 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-112 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-112 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-111 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-111 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-92 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-92 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-22 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-22 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-74 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-74 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-74 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-25 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-48 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-139 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-107 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-107 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-26 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-66 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-66 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-63 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-23 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-23 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-91 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-47 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-24 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-24 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-21 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-67 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-67 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-84 N-Response to DNA Damage-2.0-Hs-115 activation N-Response to DNA Damage-2.0-Hs-65 N-Response to DNA Damage-2.0-Hs-42 inhibition N-Response to DNA Damage-2.0-Hs-40 N-Response to DNA Damage-2.0-Hs-49 inhibition N-Response to DNA Damage-2.0-Hs-40 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-127 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-128 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-128 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-129 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-129 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-130 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-130 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-131 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-131 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-34 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-34 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-126 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-119 activation N-Response to DNA Damage-2.0-Hs-42 N-Response to DNA Damage-2.0-Hs-119 activation N-Response to DNA Damage-2.0-Hs-42 N-Response to DNA Damage-2.0-Hs-107 activation N-Response to DNA Damage-2.0-Hs-105 N-Response to DNA Damage-2.0-Hs-107 activation N-Response to DNA Damage-2.0-Hs-105 N-Response to DNA Damage-2.0-Hs-64 activation N-Response to DNA Damage-2.0-Hs-68 N-Response to DNA Damage-2.0-Hs-64 activation N-Response to DNA Damage-2.0-Hs-33 N-Response to DNA Damage-2.0-Hs-64 activation N-Response to DNA Damage-2.0-Hs-33 N-Response to DNA Damage-2.0-Hs-64 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-64 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-64 activation N-Response to DNA Damage-2.0-Hs-36 N-Response to DNA Damage-2.0-Hs-64 activation N-Response to DNA Damage-2.0-Hs-32 N-Response to DNA Damage-2.0-Hs-102 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-102 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-102 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-72 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-72 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-72 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-1 2 activation N-Response to DNA Damage-2.0-Hs-20 N-Response to DNA Damage-2.0-Hs-1 2 activation N-Response to DNA Damage-2.0-Hs-20 N-Response to DNA Damage-2.0-Hs-1 2 activation N-Response to DNA Damage-2.0-Hs-37 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-71 inhibition N-Response to DNA Damage-2.0-Hs-71 N-Response to DNA Damage-2.0-Hs-63 inhibition N-Response to DNA Damage-2.0-Hs-62 N-Response to DNA Damage-2.0-Hs-78 inhibition N-Response to DNA Damage-2.0-Hs-115 N-Response to DNA Damage-2.0-Hs-54 activation N-Response to DNA Damage-2.0-Hs-101 N-Response to DNA Damage-2.0-Hs-54 activation N-Response to DNA Damage-2.0-Hs-101 N-Response to DNA Damage-2.0-Hs-54 activation N-Response to DNA Damage-2.0-Hs-101 N-Response to DNA Damage-2.0-Hs-101 activation N-Response to DNA Damage-2.0-Hs-18 N-Response to DNA Damage-2.0-Hs-35 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-35 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-35 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-35 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-35 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-35 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-35 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-105 activation N-Response to DNA Damage-2.0-Hs-137 N-Response to DNA Damage-2.0-Hs-105 activation N-Response to DNA Damage-2.0-Hs-134 N-Response to DNA Damage-2.0-Hs-105 activation N-Response to DNA Damage-2.0-Hs-134 N-Response to DNA Damage-2.0-Hs-105 activation N-Response to DNA Damage-2.0-Hs-135 N-Response to DNA Damage-2.0-Hs-57 activation N-Response to DNA Damage-2.0-Hs-40 N-Response to DNA Damage-2.0-Hs-61 activation N-Response to DNA Damage-2.0-Hs-40 N-Response to DNA Damage-2.0-Hs-114 activation N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-114 activation N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-114 activation N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-114 activation N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-114 activation N-Response to DNA Damage-2.0-Hs-110 N-Response to DNA Damage-2.0-Hs-39 activation N-Response to DNA Damage-2.0-Hs-40 N-Response to DNA Damage-2.0-Hs-53 activation N-Response to DNA Damage-2.0-Hs-35 N-Response to DNA Damage-2.0-Hs-53 activation N-Response to DNA Damage-2.0-Hs-35 N-Response to DNA Damage-2.0-Hs-53 activation N-Response to DNA Damage-2.0-Hs-35 N-Response to DNA Damage-2.0-Hs-55 inhibition N-Response to DNA Damage-2.0-Hs-54 N-Response to DNA Damage-2.0-Hs-32 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-32 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-32 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-32 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-32 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-32 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-32 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-34 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-34 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-110 activation N-Response to DNA Damage-2.0-Hs-115 N-Response to DNA Damage-2.0-Hs-104 activation N-Response to DNA Damage-2.0-Hs-115 N-Response to DNA Damage-2.0-Hs-104 activation N-Response to DNA Damage-2.0-Hs-108 N-Response to DNA Damage-2.0-Hs-29 activation N-Response to DNA Damage-2.0-Hs-43 N-Response to DNA Damage-2.0-Hs-67 activation N-Response to DNA Damage-2.0-Hs-64 N-Response to DNA Damage-2.0-Hs-20 activation N-Response to DNA Damage-2.0-Hs-78 N-Response to DNA Damage-2.0-Hs-20 activation N-Response to DNA Damage-2.0-Hs-78 N-Response to DNA Damage-2.0-Hs-20 activation N-Response to DNA Damage-2.0-Hs-31 N-Response to DNA Damage-2.0-Hs-74 activation N-Response to DNA Damage-2.0-Hs-73 N-Response to DNA Damage-2.0-Hs-74 activation N-Response to DNA Damage-2.0-Hs-73 N-Response to DNA Damage-2.0-Hs-27 activation N-Response to DNA Damage-2.0-Hs-78 N-Response to DNA Damage-2.0-Hs-97 activation N-Response to DNA Damage-2.0-Hs-43 N-Response to DNA Damage-2.0-Hs-69 activation N-Response to DNA Damage-2.0-Hs-118 N-Response to DNA Damage-2.0-Hs-69 activation N-Response to DNA Damage-2.0-Hs-118 N-Response to DNA Damage-2.0-Hs-9 10 activation N-Response to DNA Damage-2.0-Hs-126 N-Response to DNA Damage-2.0-Hs-56 inhibition N-Response to DNA Damage-2.0-Hs-109 N-Response to DNA Damage-2.0-Hs-76 inhibition N-Response to DNA Damage-2.0-Hs-20 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Senescence-2.0-Hs.att000066400000000000000000000143061426625374700252730ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Senescence-2.0-Hs-1 E2F 73 60 white rectangle gene 0.5 black 46 17 E2F N-Senescence-2.0-Hs-10 BRCA1 51 111 white rectangle gene 0.5 black 46 17 1100 N-Senescence-2.0-Hs-11 SP1 107 40 white rectangle gene 0.5 black 46 17 11205 N-Senescence-2.0-Hs-12 SP1 108 34 white rectangle gene 0.5 black 46 17 11205 N-Senescence-2.0-Hs-13 SRC 89 171 white rectangle gene 0.5 black 46 17 11283 N-Senescence-2.0-Hs-14 STAT1 88 179 white rectangle gene 0.5 black 46 17 11362 N-Senescence-2.0-Hs-16 TERT 93 175 white rectangle gene 0.5 black 46 17 11730 N-Senescence-2.0-Hs-17 TFDP1 70 55 white rectangle gene 0.5 black 46 17 11749 N-Senescence-2.0-Hs-18 TP53 60 111 white rectangle gene 0.5 black 46 17 11998 N-Senescence-2.0-Hs-19 TP53 73 148 white rectangle gene 0.5 black 46 17 11998 N-Senescence-2.0-Hs-2 ERK 164 57 white rectangle gene 0.5 black 46 17 ERK N-Senescence-2.0-Hs-21 USP11 147 31 white rectangle gene 0.5 black 46 17 12609 N-Senescence-2.0-Hs-22 USP7 143 30 white rectangle gene 0.5 black 46 17 12630 N-Senescence-2.0-Hs-24 WRN 10 93 white rectangle gene 0.5 black 46 17 12791 N-Senescence-2.0-Hs-25 YY1 181 86 white rectangle gene 0.5 black 46 17 12856 N-Senescence-2.0-Hs-26 PCGF2 142 35 white rectangle gene 0.5 black 46 17 12929 N-Senescence-2.0-Hs-27 DNAJC2 39 28 white rectangle gene 0.5 black 46 17 13192 N-Senescence-2.0-Hs-28 SIRT1 11 85 white rectangle gene 0.5 black 46 17 14929 N-Senescence-2.0-Hs-3 RAS 0 150 white rectangle gene 0.5 black 46 17 RAS N-Senescence-2.0-Hs-30 CBX7 148 150 white rectangle gene 0.5 black 46 17 1557 N-Senescence-2.0-Hs-31 CCND1 82 73 white rectangle gene 0.5 black 46 17 1582 N-Senescence-2.0-Hs-32 CHEK2 54 105 white rectangle gene 0.5 black 46 17 16627 N-Senescence-2.0-Hs-33 JDP2 54 117 white rectangle gene 0.5 black 46 17 17546 N-Senescence-2.0-Hs-34 CDKN1A 45 41 white rectangle gene 0.5 black 46 17 1784 N-Senescence-2.0-Hs-35 CDKN2A 44 36 white rectangle gene 0.5 black 46 17 1787 N-Senescence-2.0-Hs-36 CDKN2B 35 23 white rectangle gene 0.5 black 46 17 1788 N-Senescence-2.0-Hs-37 CEBPB 92 117 white rectangle gene 0.5 black 46 17 1834 N-Senescence-2.0-Hs-38 CHEK1 59 103 white rectangle gene 0.5 black 46 17 1925 N-Senescence-2.0-Hs-39 HBP1 93 135 white rectangle gene 0.5 black 46 17 23200 N-Senescence-2.0-Hs-4 p38 86 133 white rectangle gene 0.5 black 46 17 p38 N-Senescence-2.0-Hs-40 CREBBP 7 98 white rectangle gene 0.5 black 46 17 2348 N-Senescence-2.0-Hs-41 CTNNB1 89 86 white rectangle gene 0.5 black 46 17 2514 N-Senescence-2.0-Hs-42 PARP1 6 80 white rectangle gene 0.5 black 46 17 270 N-Senescence-2.0-Hs-43 DUSP4 170 54 white rectangle gene 0.5 black 46 17 3070 N-Senescence-2.0-Hs-44 ENO1 74 115 white rectangle gene 0.5 black 46 17 3350 N-Senescence-2.0-Hs-45 EP300 10 89 white rectangle gene 0.5 black 46 17 3373 N-Senescence-2.0-Hs-47 FOXO1 102 0 white rectangle gene 0.5 black 46 17 3819 N-Senescence-2.0-Hs-48 FOXO3 14 82 white rectangle gene 0.5 black 46 17 3821 N-Senescence-2.0-Hs-49 FOXO3 15 80 white rectangle gene 0.5 black 46 17 3821 N-Senescence-2.0-Hs-5 RPS6KA6 165 110 white rectangle gene 0.5 black 46 17 10435 N-Senescence-2.0-Hs-51 CXCL1 111 97 white rectangle gene 0.5 black 46 17 4602 N-Senescence-2.0-Hs-52 HDAC1 52 44 white rectangle gene 0.5 black 46 17 4852 N-Senescence-2.0-Hs-53 HDAC2 53 109 white rectangle gene 0.5 black 46 17 4853 N-Senescence-2.0-Hs-54 HDAC3 184 89 white rectangle gene 0.5 black 46 17 4854 N-Senescence-2.0-Hs-55 HRAS 86 139 white rectangle gene 0.5 black 46 17 5173 N-Senescence-2.0-Hs-57 IGFBP7 171 119 white rectangle gene 0.5 black 46 17 5476 N-Senescence-2.0-Hs-58 IL1A 100 106 white rectangle gene 0.5 black 46 17 5991 N-Senescence-2.0-Hs-59 IL6 99 113 white rectangle gene 0.5 black 46 17 6018 N-Senescence-2.0-Hs-6 BMI1 148 26 white rectangle gene 0.5 black 46 17 1066 N-Senescence-2.0-Hs-60 CXCL8 95 101 white rectangle gene 0.5 black 46 17 6025 N-Senescence-2.0-Hs-61 CXCR2 104 98 white rectangle gene 0.5 black 46 17 6027 N-Senescence-2.0-Hs-62 ING1 65 109 white rectangle gene 0.5 black 46 17 6062 N-Senescence-2.0-Hs-63 IRF3 58 118 white rectangle gene 0.5 black 46 17 6118 N-Senescence-2.0-Hs-65 MAP2K1 166 113 white rectangle gene 0.5 black 46 17 6840 N-Senescence-2.0-Hs-66 MAP2K3 82 137 white rectangle gene 0.5 black 46 17 6843 N-Senescence-2.0-Hs-67 MAP2K6 86 144 white rectangle gene 0.5 black 46 17 6846 N-Senescence-2.0-Hs-68 MAP3K5 87 152 white rectangle gene 0.5 black 46 17 6857 N-Senescence-2.0-Hs-69 MAPK1 112 28 white rectangle gene 0.5 black 46 17 6871 N-Senescence-2.0-Hs-7 SKP2 101 6 white rectangle gene 0.5 black 46 17 10901 N-Senescence-2.0-Hs-71 MAPK3 107 28 white rectangle gene 0.5 black 46 17 6877 N-Senescence-2.0-Hs-72 MAPKAPK5 78 142 white rectangle gene 0.5 black 46 17 6889 N-Senescence-2.0-Hs-73 MDM2 52 114 white rectangle gene 0.5 black 46 17 6973 N-Senescence-2.0-Hs-74 MMP9 3 84 white rectangle gene 0.5 black 46 17 7176 N-Senescence-2.0-Hs-75 ATF2 76 128 white rectangle gene 0.5 black 46 17 784 N-Senescence-2.0-Hs-76 ATM 56 104 white rectangle gene 0.5 black 46 17 795 N-Senescence-2.0-Hs-77 NR2E1 143 145 white rectangle gene 0.5 black 46 17 7973 N-Senescence-2.0-Hs-78 ATR 62 103 white rectangle gene 0.5 black 46 17 882 N-Senescence-2.0-Hs-79 PML 68 113 white rectangle gene 0.5 black 46 17 9113 N-Senescence-2.0-Hs-8 BRAF 162 115 white rectangle gene 0.5 black 46 17 1097 N-Senescence-2.0-Hs-80 PPM1D 91 138 white rectangle gene 0.5 black 46 17 9277 N-Senescence-2.0-Hs-81 PPP1CA 3 149 white rectangle gene 0.5 black 46 17 9281 N-Senescence-2.0-Hs-82 BACH1 65 105 white rectangle gene 0.5 black 46 17 935 N-Senescence-2.0-Hs-83 PRKCA 110 25 white rectangle gene 0.5 black 46 17 9393 N-Senescence-2.0-Hs-84 PTEN 63 115 white rectangle gene 0.5 black 46 17 9588 N-Senescence-2.0-Hs-85 PTGS2 68 121 white rectangle gene 0.5 black 46 17 9605 N-Senescence-2.0-Hs-86 PTPN11 89 186 white rectangle gene 0.5 black 46 17 9644 N-Senescence-2.0-Hs-87 RAF1 40 37 white rectangle gene 0.5 black 46 17 9829 N-Senescence-2.0-Hs-88 RB1 62 51 white rectangle gene 0.5 black 46 17 9884 N-Senescence-2.0-Hs-89 RBL1 74 53 white rectangle gene 0.5 black 46 17 9893 N-Senescence-2.0-Hs-9 BRAF 165 116 white rectangle gene 0.5 black 46 17 1097 N-Senescence-2.0-Hs-90 RBL2 78 56 white rectangle gene 0.5 black 46 17 9894 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Senescence-2.0-Hs.sif000066400000000000000000000161711426625374700252660ustar00rootroot000000000000000 1 2 N-Senescence-2.0-Hs-18 activation N-Senescence-2.0-Hs-85 N-Senescence-2.0-Hs-18 inhibition N-Senescence-2.0-Hs-33 N-Senescence-2.0-Hs-12 activation N-Senescence-2.0-Hs-11 N-Senescence-2.0-Hs-27 activation N-Senescence-2.0-Hs-35 N-Senescence-2.0-Hs-27 activation N-Senescence-2.0-Hs-36 N-Senescence-2.0-Hs-33 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-82 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-25 activation N-Senescence-2.0-Hs-54 N-Senescence-2.0-Hs-69 activation N-Senescence-2.0-Hs-12 N-Senescence-2.0-Hs-78 activation N-Senescence-2.0-Hs-38 N-Senescence-2.0-Hs-78 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-8 activation N-Senescence-2.0-Hs-65 N-Senescence-2.0-Hs-8 activation N-Senescence-2.0-Hs-65 N-Senescence-2.0-Hs-8 activation N-Senescence-2.0-Hs-65 N-Senescence-2.0-Hs-87 activation N-Senescence-2.0-Hs-35 N-Senescence-2.0-Hs-87 activation N-Senescence-2.0-Hs-34 N-Senescence-2.0-Hs-60 activation N-Senescence-2.0-Hs-61 N-Senescence-2.0-Hs-63 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-38 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-45 activation N-Senescence-2.0-Hs-24 N-Senescence-2.0-Hs-7 inhibition N-Senescence-2.0-Hs-47 N-Senescence-2.0-Hs-7 inhibition N-Senescence-2.0-Hs-47 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-5 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-8 N-Senescence-2.0-Hs-9 activation N-Senescence-2.0-Hs-57 N-Senescence-2.0-Hs-58 activation N-Senescence-2.0-Hs-60 N-Senescence-2.0-Hs-58 activation N-Senescence-2.0-Hs-59 N-Senescence-2.0-Hs-58 activation N-Senescence-2.0-Hs-59 N-Senescence-2.0-Hs-58 activation N-Senescence-2.0-Hs-59 N-Senescence-2.0-Hs-58 activation N-Senescence-2.0-Hs-59 N-Senescence-2.0-Hs-89 inhibition N-Senescence-2.0-Hs-1 N-Senescence-2.0-Hs-52 inhibition N-Senescence-2.0-Hs-34 N-Senescence-2.0-Hs-52 inhibition N-Senescence-2.0-Hs-35 N-Senescence-2.0-Hs-52 inhibition N-Senescence-2.0-Hs-88 N-Senescence-2.0-Hs-77 activation N-Senescence-2.0-Hs-30 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-72 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-39 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-75 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-75 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-75 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-75 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-37 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-37 N-Senescence-2.0-Hs-4 activation N-Senescence-2.0-Hs-37 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-38 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-32 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-32 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-32 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-32 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-32 N-Senescence-2.0-Hs-76 activation N-Senescence-2.0-Hs-32 N-Senescence-2.0-Hs-37 activation N-Senescence-2.0-Hs-59 N-Senescence-2.0-Hs-37 activation N-Senescence-2.0-Hs-60 N-Senescence-2.0-Hs-84 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-84 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-21 activation N-Senescence-2.0-Hs-6 N-Senescence-2.0-Hs-21 activation N-Senescence-2.0-Hs-26 N-Senescence-2.0-Hs-40 inhibition N-Senescence-2.0-Hs-24 N-Senescence-2.0-Hs-32 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-32 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-73 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-73 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-73 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-73 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-88 inhibition N-Senescence-2.0-Hs-1 N-Senescence-2.0-Hs-86 inhibition N-Senescence-2.0-Hs-14 N-Senescence-2.0-Hs-72 activation N-Senescence-2.0-Hs-19 N-Senescence-2.0-Hs-42 inhibition N-Senescence-2.0-Hs-28 N-Senescence-2.0-Hs-17 activation N-Senescence-2.0-Hs-1 N-Senescence-2.0-Hs-22 activation N-Senescence-2.0-Hs-6 N-Senescence-2.0-Hs-22 activation N-Senescence-2.0-Hs-26 N-Senescence-2.0-Hs-49 inhibition N-Senescence-2.0-Hs-48 N-Senescence-2.0-Hs-83 activation N-Senescence-2.0-Hs-71 N-Senescence-2.0-Hs-83 activation N-Senescence-2.0-Hs-69 N-Senescence-2.0-Hs-80 inhibition N-Senescence-2.0-Hs-4 N-Senescence-2.0-Hs-66 activation N-Senescence-2.0-Hs-4 N-Senescence-2.0-Hs-66 activation N-Senescence-2.0-Hs-4 N-Senescence-2.0-Hs-55 activation N-Senescence-2.0-Hs-4 N-Senescence-2.0-Hs-51 activation N-Senescence-2.0-Hs-61 N-Senescence-2.0-Hs-79 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-79 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-41 activation N-Senescence-2.0-Hs-31 N-Senescence-2.0-Hs-41 activation N-Senescence-2.0-Hs-60 N-Senescence-2.0-Hs-62 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-62 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-90 inhibition N-Senescence-2.0-Hs-1 N-Senescence-2.0-Hs-3 activation N-Senescence-2.0-Hs-81 N-Senescence-2.0-Hs-44 inhibition N-Senescence-2.0-Hs-79 N-Senescence-2.0-Hs-53 inhibition N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-75 activation N-Senescence-2.0-Hs-85 N-Senescence-2.0-Hs-71 activation N-Senescence-2.0-Hs-12 N-Senescence-2.0-Hs-13 activation N-Senescence-2.0-Hs-14 N-Senescence-2.0-Hs-13 activation N-Senescence-2.0-Hs-16 N-Senescence-2.0-Hs-43 inhibition N-Senescence-2.0-Hs-2 N-Senescence-2.0-Hs-10 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-10 activation N-Senescence-2.0-Hs-18 N-Senescence-2.0-Hs-67 activation N-Senescence-2.0-Hs-4 N-Senescence-2.0-Hs-67 activation N-Senescence-2.0-Hs-4 N-Senescence-2.0-Hs-31 activation N-Senescence-2.0-Hs-1 N-Senescence-2.0-Hs-68 activation N-Senescence-2.0-Hs-67 N-Senescence-2.0-Hs-28 activation N-Senescence-2.0-Hs-24 N-Senescence-2.0-Hs-28 inhibition N-Senescence-2.0-Hs-24 N-Senescence-2.0-Hs-28 inhibition N-Senescence-2.0-Hs-45 N-Senescence-2.0-Hs-28 inhibition N-Senescence-2.0-Hs-48 N-Senescence-2.0-Hs-28 inhibition N-Senescence-2.0-Hs-49 N-Senescence-2.0-Hs-28 inhibition N-Senescence-2.0-Hs-74 N-Senescence-2.0-Hs-28 inhibition N-Senescence-2.0-Hs-74 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Th1-Th2 Signaling-2.0-Hs.att000066400000000000000000000122061426625374700262000ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Th1-Th2 Signaling-2.0-Hs-1 2 IL12A IL12B 46 108 white rectangle gene,gene 0.5 black 46 17 5969,/,5970 N-Th1-Th2 Signaling-2.0-Hs-10 STAT3 43 92 white rectangle gene 0.5 black 46 17 11364 N-Th1-Th2 Signaling-2.0-Hs-11 STAT4 47 97 white rectangle gene 0.5 black 46 17 11365 N-Th1-Th2 Signaling-2.0-Hs-12 STAT6 75 106 white rectangle gene 0.5 black 46 17 11368 N-Th1-Th2 Signaling-2.0-Hs-13 TBX21 59 103 white rectangle gene 0.5 black 46 17 11599 N-Th1-Th2 Signaling-2.0-Hs-15 TYK2 48 111 white rectangle gene 0.5 black 46 17 12440 N-Th1-Th2 Signaling-2.0-Hs-16 VIP 120 47 white rectangle gene 0.5 black 46 17 12693 N-Th1-Th2 Signaling-2.0-Hs-17 VIPR2 113 50 white rectangle gene 0.5 black 46 17 12695 N-Th1-Th2 Signaling-2.0-Hs-18 IKZF1 70 108 white rectangle gene 0.5 black 46 17 13176 N-Th1-Th2 Signaling-2.0-Hs-19 C3 104 114 white rectangle gene 0.5 black 46 17 1318 N-Th1-Th2 Signaling-2.0-Hs-21 IL25 99 68 white rectangle gene 0.5 black 46 17 13765 N-Th1-Th2 Signaling-2.0-Hs-22 CBLB 141 123 white rectangle gene 0.5 black 46 17 1542 N-Th1-Th2 Signaling-2.0-Hs-23 CCR5 36 71 white rectangle gene 0.5 black 46 17 1606 N-Th1-Th2 Signaling-2.0-Hs-24 CXCL16 161 167 white rectangle gene 0.5 black 46 17 16642 N-Th1-Th2 Signaling-2.0-Hs-25 CXCR6 167 165 white rectangle gene 0.5 black 46 17 16647 N-Th1-Th2 Signaling-2.0-Hs-26 IRAK4 121 158 white rectangle gene 0.5 black 46 17 17967 N-Th1-Th2 Signaling-2.0-Hs-27 SOCS1 84 151 white rectangle gene 0.5 black 46 17 19383 N-Th1-Th2 Signaling-2.0-Hs-29 DEF6 128 70 white rectangle gene 0.5 black 46 17 2760 N-Th1-Th2 Signaling-2.0-Hs-3 Notch 83 65 white rectangle gene 0.5 black 46 17 Notch N-Th1-Th2 Signaling-2.0-Hs-30 DLL1 86 54 white rectangle gene 0.5 black 46 17 2908 N-Th1-Th2 Signaling-2.0-Hs-31 DLL4 77 56 white rectangle gene 0.5 black 46 17 2910 N-Th1-Th2 Signaling-2.0-Hs-32 DNMT3A 90 129 white rectangle gene 0.5 black 46 17 2978 N-Th1-Th2 Signaling-2.0-Hs-33 DPP4 72 128 white rectangle gene 0.5 black 46 17 3009 N-Th1-Th2 Signaling-2.0-Hs-34 DUSP4 149 110 white rectangle gene 0.5 black 46 17 3070 N-Th1-Th2 Signaling-2.0-Hs-35 EGR2 134 119 white rectangle gene 0.5 black 46 17 3239 N-Th1-Th2 Signaling-2.0-Hs-36 GATA3 88 85 white rectangle gene 0.5 black 46 17 4172 N-Th1-Th2 Signaling-2.0-Hs-37 CXCR3 154 5 white rectangle gene 0.5 black 46 17 4540 N-Th1-Th2 Signaling-2.0-Hs-38 IFNG 99 141 white rectangle gene 0.5 black 46 17 5438 N-Th1-Th2 Signaling-2.0-Hs-39 IFNGR1 98 153 white rectangle gene 0.5 black 46 17 5439 N-Th1-Th2 Signaling-2.0-Hs-41 IL12RB1 44 118 white rectangle gene 0.5 black 46 17 5971 N-Th1-Th2 Signaling-2.0-Hs-42 IL12RB2 45 98 white rectangle gene 0.5 black 46 17 5972 N-Th1-Th2 Signaling-2.0-Hs-43 IL13 71 134 white rectangle gene 0.5 black 46 17 5973 N-Th1-Th2 Signaling-2.0-Hs-44 IL15 3 62 white rectangle gene 0.5 black 46 17 5977 N-Th1-Th2 Signaling-2.0-Hs-45 IL15RA 0 53 white rectangle gene 0.5 black 46 17 5978 N-Th1-Th2 Signaling-2.0-Hs-46 IL18 112 150 white rectangle gene 0.5 black 46 17 5986 N-Th1-Th2 Signaling-2.0-Hs-47 IL18R1 107 147 white rectangle gene 0.5 black 46 17 5988 N-Th1-Th2 Signaling-2.0-Hs-48 IL2 124 115 white rectangle gene 0.5 black 46 17 6001 N-Th1-Th2 Signaling-2.0-Hs-49 IL2RA 138 111 white rectangle gene 0.5 black 46 17 6008 N-Th1-Th2 Signaling-2.0-Hs-5 RUNX1 90 78 white rectangle gene 0.5 black 46 17 10471 N-Th1-Th2 Signaling-2.0-Hs-50 IL2RB 12 72 white rectangle gene 0.5 black 46 17 6009 N-Th1-Th2 Signaling-2.0-Hs-51 IL4 82 114 white rectangle gene 0.5 black 46 17 6014 N-Th1-Th2 Signaling-2.0-Hs-52 IL4R 64 102 white rectangle gene 0.5 black 46 17 6015 N-Th1-Th2 Signaling-2.0-Hs-53 IL5 63 138 white rectangle gene 0.5 black 46 17 6016 N-Th1-Th2 Signaling-2.0-Hs-54 IL6 82 163 white rectangle gene 0.5 black 46 17 6018 N-Th1-Th2 Signaling-2.0-Hs-55 IRF1 93 127 white rectangle gene 0.5 black 46 17 6116 N-Th1-Th2 Signaling-2.0-Hs-56 IRF2 87 135 white rectangle gene 0.5 black 46 17 6117 N-Th1-Th2 Signaling-2.0-Hs-57 JAG1 81 52 white rectangle gene 0.5 black 46 17 6188 N-Th1-Th2 Signaling-2.0-Hs-58 JAK2 42 86 white rectangle gene 0.5 black 46 17 6192 N-Th1-Th2 Signaling-2.0-Hs-59 JUNB 105 56 white rectangle gene 0.5 black 46 17 6205 N-Th1-Th2 Signaling-2.0-Hs-6 RUNX3 73 92 white rectangle gene 0.5 black 46 17 10473 N-Th1-Th2 Signaling-2.0-Hs-62 LYN 26 82 white rectangle gene 0.5 black 46 17 6735 N-Th1-Th2 Signaling-2.0-Hs-63 NBR1 104 79 white rectangle gene 0.5 black 46 17 6746 N-Th1-Th2 Signaling-2.0-Hs-64 MAF 108 58 white rectangle gene 0.5 black 46 17 6776 N-Th1-Th2 Signaling-2.0-Hs-66 CXCL9 158 0 white rectangle gene 0.5 black 46 17 7098 N-Th1-Th2 Signaling-2.0-Hs-67 NFATC1 117 74 white rectangle gene 0.5 black 46 17 7775 N-Th1-Th2 Signaling-2.0-Hs-68 NOTCH1 69 112 white rectangle gene 0.5 black 46 17 7881 N-Th1-Th2 Signaling-2.0-Hs-69 ONECUT2 50 104 white rectangle gene 0.5 black 46 17 8139 N-Th1-Th2 Signaling-2.0-Hs-7 CCL3 28 64 white rectangle gene 0.5 black 46 17 10627 N-Th1-Th2 Signaling-2.0-Hs-70 RAF1 104 152 white rectangle gene 0.5 black 46 17 9829 N-Th1-Th2 Signaling-2.0-Hs-8 CCL5 34 61 white rectangle gene 0.5 black 46 17 10632 N-Th1-Th2 Signaling-2.0-Hs-9 SFTPD 77 126 white rectangle gene 0.5 black 46 17 10803 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Th1-Th2 Signaling-2.0-Hs.sif000066400000000000000000000202571426625374700261760ustar00rootroot000000000000000 1 2 N-Th1-Th2 Signaling-2.0-Hs-68 activation N-Th1-Th2 Signaling-2.0-Hs-13 N-Th1-Th2 Signaling-2.0-Hs-68 activation N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-19 activation N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-32 inhibition N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-32 inhibition N-Th1-Th2 Signaling-2.0-Hs-38 N-Th1-Th2 Signaling-2.0-Hs-56 inhibition N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-56 inhibition N-Th1-Th2 Signaling-2.0-Hs-27 N-Th1-Th2 Signaling-2.0-Hs-58 activation N-Th1-Th2 Signaling-2.0-Hs-11 N-Th1-Th2 Signaling-2.0-Hs-17 activation N-Th1-Th2 Signaling-2.0-Hs-59 N-Th1-Th2 Signaling-2.0-Hs-17 activation N-Th1-Th2 Signaling-2.0-Hs-64 N-Th1-Th2 Signaling-2.0-Hs-42 activation N-Th1-Th2 Signaling-2.0-Hs-58 N-Th1-Th2 Signaling-2.0-Hs-43 activation N-Th1-Th2 Signaling-2.0-Hs-9 N-Th1-Th2 Signaling-2.0-Hs-52 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-52 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-52 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-52 activation N-Th1-Th2 Signaling-2.0-Hs-10 N-Th1-Th2 Signaling-2.0-Hs-24 activation N-Th1-Th2 Signaling-2.0-Hs-25 N-Th1-Th2 Signaling-2.0-Hs-35 activation N-Th1-Th2 Signaling-2.0-Hs-22 N-Th1-Th2 Signaling-2.0-Hs-35 inhibition N-Th1-Th2 Signaling-2.0-Hs-48 N-Th1-Th2 Signaling-2.0-Hs-69 activation N-Th1-Th2 Signaling-2.0-Hs-13 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-9 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-52 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-52 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-52 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-51 activation N-Th1-Th2 Signaling-2.0-Hs-12 N-Th1-Th2 Signaling-2.0-Hs-62 activation N-Th1-Th2 Signaling-2.0-Hs-10 N-Th1-Th2 Signaling-2.0-Hs-62 activation N-Th1-Th2 Signaling-2.0-Hs-10 N-Th1-Th2 Signaling-2.0-Hs-62 activation N-Th1-Th2 Signaling-2.0-Hs-10 N-Th1-Th2 Signaling-2.0-Hs-54 activation N-Th1-Th2 Signaling-2.0-Hs-27 N-Th1-Th2 Signaling-2.0-Hs-54 activation N-Th1-Th2 Signaling-2.0-Hs-27 N-Th1-Th2 Signaling-2.0-Hs-46 activation N-Th1-Th2 Signaling-2.0-Hs-26 N-Th1-Th2 Signaling-2.0-Hs-46 activation N-Th1-Th2 Signaling-2.0-Hs-38 N-Th1-Th2 Signaling-2.0-Hs-46 activation N-Th1-Th2 Signaling-2.0-Hs-38 N-Th1-Th2 Signaling-2.0-Hs-13 activation N-Th1-Th2 Signaling-2.0-Hs-6 N-Th1-Th2 Signaling-2.0-Hs-13 activation N-Th1-Th2 Signaling-2.0-Hs-42 N-Th1-Th2 Signaling-2.0-Hs-29 activation N-Th1-Th2 Signaling-2.0-Hs-67 N-Th1-Th2 Signaling-2.0-Hs-70 activation N-Th1-Th2 Signaling-2.0-Hs-38 N-Th1-Th2 Signaling-2.0-Hs-5 inhibition N-Th1-Th2 Signaling-2.0-Hs-36 N-Th1-Th2 Signaling-2.0-Hs-48 activation N-Th1-Th2 Signaling-2.0-Hs-49 N-Th1-Th2 Signaling-2.0-Hs-48 activation N-Th1-Th2 Signaling-2.0-Hs-19 N-Th1-Th2 Signaling-2.0-Hs-48 activation N-Th1-Th2 Signaling-2.0-Hs-19 N-Th1-Th2 Signaling-2.0-Hs-66 activation N-Th1-Th2 Signaling-2.0-Hs-37 N-Th1-Th2 Signaling-2.0-Hs-66 activation N-Th1-Th2 Signaling-2.0-Hs-37 N-Th1-Th2 Signaling-2.0-Hs-66 activation N-Th1-Th2 Signaling-2.0-Hs-37 N-Th1-Th2 Signaling-2.0-Hs-6 inhibition N-Th1-Th2 Signaling-2.0-Hs-36 N-Th1-Th2 Signaling-2.0-Hs-23 activation N-Th1-Th2 Signaling-2.0-Hs-58 N-Th1-Th2 Signaling-2.0-Hs-64 activation N-Th1-Th2 Signaling-2.0-Hs-59 N-Th1-Th2 Signaling-2.0-Hs-41 activation N-Th1-Th2 Signaling-2.0-Hs-15 N-Th1-Th2 Signaling-2.0-Hs-1 2 activation N-Th1-Th2 Signaling-2.0-Hs-42 N-Th1-Th2 Signaling-2.0-Hs-1 2 activation N-Th1-Th2 Signaling-2.0-Hs-41 N-Th1-Th2 Signaling-2.0-Hs-1 2 activation N-Th1-Th2 Signaling-2.0-Hs-13 N-Th1-Th2 Signaling-2.0-Hs-11 activation N-Th1-Th2 Signaling-2.0-Hs-42 N-Th1-Th2 Signaling-2.0-Hs-47 activation N-Th1-Th2 Signaling-2.0-Hs-38 N-Th1-Th2 Signaling-2.0-Hs-30 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-30 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-30 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-30 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-31 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-36 activation N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-36 activation N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-36 activation N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-36 inhibition N-Th1-Th2 Signaling-2.0-Hs-6 N-Th1-Th2 Signaling-2.0-Hs-34 inhibition N-Th1-Th2 Signaling-2.0-Hs-49 N-Th1-Th2 Signaling-2.0-Hs-21 activation N-Th1-Th2 Signaling-2.0-Hs-59 N-Th1-Th2 Signaling-2.0-Hs-21 activation N-Th1-Th2 Signaling-2.0-Hs-64 N-Th1-Th2 Signaling-2.0-Hs-21 activation N-Th1-Th2 Signaling-2.0-Hs-36 N-Th1-Th2 Signaling-2.0-Hs-44 activation N-Th1-Th2 Signaling-2.0-Hs-45 N-Th1-Th2 Signaling-2.0-Hs-44 activation N-Th1-Th2 Signaling-2.0-Hs-50 N-Th1-Th2 Signaling-2.0-Hs-44 activation N-Th1-Th2 Signaling-2.0-Hs-50 N-Th1-Th2 Signaling-2.0-Hs-7 activation N-Th1-Th2 Signaling-2.0-Hs-23 N-Th1-Th2 Signaling-2.0-Hs-8 activation N-Th1-Th2 Signaling-2.0-Hs-23 N-Th1-Th2 Signaling-2.0-Hs-8 activation N-Th1-Th2 Signaling-2.0-Hs-23 N-Th1-Th2 Signaling-2.0-Hs-8 activation N-Th1-Th2 Signaling-2.0-Hs-23 N-Th1-Th2 Signaling-2.0-Hs-12 activation N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-15 activation N-Th1-Th2 Signaling-2.0-Hs-11 N-Th1-Th2 Signaling-2.0-Hs-18 activation N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-18 inhibition N-Th1-Th2 Signaling-2.0-Hs-13 N-Th1-Th2 Signaling-2.0-Hs-57 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-57 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-57 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-57 activation N-Th1-Th2 Signaling-2.0-Hs-3 N-Th1-Th2 Signaling-2.0-Hs-3 activation N-Th1-Th2 Signaling-2.0-Hs-36 N-Th1-Th2 Signaling-2.0-Hs-63 activation N-Th1-Th2 Signaling-2.0-Hs-67 N-Th1-Th2 Signaling-2.0-Hs-63 activation N-Th1-Th2 Signaling-2.0-Hs-36 N-Th1-Th2 Signaling-2.0-Hs-38 activation N-Th1-Th2 Signaling-2.0-Hs-39 N-Th1-Th2 Signaling-2.0-Hs-38 activation N-Th1-Th2 Signaling-2.0-Hs-56 N-Th1-Th2 Signaling-2.0-Hs-38 activation N-Th1-Th2 Signaling-2.0-Hs-55 N-Th1-Th2 Signaling-2.0-Hs-50 activation N-Th1-Th2 Signaling-2.0-Hs-62 N-Th1-Th2 Signaling-2.0-Hs-50 activation N-Th1-Th2 Signaling-2.0-Hs-62 N-Th1-Th2 Signaling-2.0-Hs-55 inhibition N-Th1-Th2 Signaling-2.0-Hs-51 N-Th1-Th2 Signaling-2.0-Hs-16 activation N-Th1-Th2 Signaling-2.0-Hs-17 N-Th1-Th2 Signaling-2.0-Hs-16 activation N-Th1-Th2 Signaling-2.0-Hs-17 N-Th1-Th2 Signaling-2.0-Hs-16 activation N-Th1-Th2 Signaling-2.0-Hs-17 N-Th1-Th2 Signaling-2.0-Hs-33 inhibition N-Th1-Th2 Signaling-2.0-Hs-43 N-Th1-Th2 Signaling-2.0-Hs-33 inhibition N-Th1-Th2 Signaling-2.0-Hs-43 N-Th1-Th2 Signaling-2.0-Hs-33 inhibition N-Th1-Th2 Signaling-2.0-Hs-53 N-Th1-Th2 Signaling-2.0-Hs-33 inhibition N-Th1-Th2 Signaling-2.0-Hs-53 N-Th1-Th2 Signaling-2.0-Hs-33 inhibition N-Th1-Th2 Signaling-2.0-Hs-51 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Th17 Signaling-2.0-Hs.att000066400000000000000000000064631426625374700256440ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Th17 Signaling-2.0-Hs-1 2 IL23A IL12B 120 109 white rectangle gene,gene 0.5 black 46 17 15488,/,5970 N-Th17 Signaling-2.0-Hs-10 CCL3 33 9 white rectangle gene 0.5 black 46 17 10627 N-Th17 Signaling-2.0-Hs-11 STAT1 149 23 white rectangle gene 0.5 black 46 17 11362 N-Th17 Signaling-2.0-Hs-12 STAT3 93 101 white rectangle gene 0.5 black 46 17 11364 N-Th17 Signaling-2.0-Hs-13 STAT5A 7 91 white rectangle gene 0.5 black 46 17 11366 N-Th17 Signaling-2.0-Hs-14 TGFB1 25 132 white rectangle gene 0.5 black 46 17 11766 N-Th17 Signaling-2.0-Hs-15 TGFBR1 30 119 white rectangle gene 0.5 black 46 17 11772 N-Th17 Signaling-2.0-Hs-16 TGFBR2 28 125 white rectangle gene 0.5 black 46 17 11773 N-Th17 Signaling-2.0-Hs-17 TYK2 103 111 white rectangle gene 0.5 black 46 17 12440 N-Th17 Signaling-2.0-Hs-18 C3 79 185 white rectangle gene 0.5 black 46 17 1318 N-Th17 Signaling-2.0-Hs-19 IL22 104 120 white rectangle gene 0.5 black 46 17 14900 N-Th17 Signaling-2.0-Hs-20 IL23A 105 6 white rectangle gene 0.5 black 46 17 15488 N-Th17 Signaling-2.0-Hs-21 CCR5 37 14 white rectangle gene 0.5 black 46 17 1606 N-Th17 Signaling-2.0-Hs-22 CXCL16 132 144 white rectangle gene 0.5 black 46 17 16642 N-Th17 Signaling-2.0-Hs-23 CXCR6 137 151 white rectangle gene 0.5 black 46 17 16647 N-Th17 Signaling-2.0-Hs-24 IL23R 115 103 white rectangle gene 0.5 black 46 17 19100 N-Th17 Signaling-2.0-Hs-25 IL27 149 29 white rectangle gene 0.5 black 46 17 19157 N-Th17 Signaling-2.0-Hs-26 SOCS3 82 107 white rectangle gene 0.5 black 46 17 19391 N-Th17 Signaling-2.0-Hs-27 DLL4 4 38 white rectangle gene 0.5 black 46 17 2910 N-Th17 Signaling-2.0-Hs-28 ETS1 152 17 white rectangle gene 0.5 black 46 17 3488 N-Th17 Signaling-2.0-Hs-29 CXCR3 66 7 white rectangle gene 0.5 black 46 17 4540 N-Th17 Signaling-2.0-Hs-3 4 IL12A IL12B 172 67 white rectangle gene,gene 0.5 black 46 17 5969,/,5970 N-Th17 Signaling-2.0-Hs-30 HIF1A 65 116 white rectangle gene 0.5 black 46 17 4910 N-Th17 Signaling-2.0-Hs-32 IL12RB1 113 112 white rectangle gene 0.5 black 46 17 5971 N-Th17 Signaling-2.0-Hs-33 IL15 109 79 white rectangle gene 0.5 black 46 17 5977 N-Th17 Signaling-2.0-Hs-34 IL15RA 104 85 white rectangle gene 0.5 black 46 17 5978 N-Th17 Signaling-2.0-Hs-35 IL17A 73 114 white rectangle gene 0.5 black 46 17 5981 N-Th17 Signaling-2.0-Hs-36 IL2 0 91 white rectangle gene 0.5 black 46 17 6001 N-Th17 Signaling-2.0-Hs-37 IL21 92 94 white rectangle gene 0.5 black 46 17 6005 N-Th17 Signaling-2.0-Hs-38 IL2RB 113 72 white rectangle gene 0.5 black 46 17 6009 N-Th17 Signaling-2.0-Hs-39 IL4 85 184 white rectangle gene 0.5 black 46 17 6014 N-Th17 Signaling-2.0-Hs-40 IL6 81 82 white rectangle gene 0.5 black 46 17 6018 N-Th17 Signaling-2.0-Hs-41 IL6R 86 91 white rectangle gene 0.5 black 46 17 6019 N-Th17 Signaling-2.0-Hs-42 JAK2 105 101 white rectangle gene 0.5 black 46 17 6192 N-Th17 Signaling-2.0-Hs-43 SMAD2 25 119 white rectangle gene 0.5 black 46 17 6768 N-Th17 Signaling-2.0-Hs-44 CXCL9 64 0 white rectangle gene 0.5 black 46 17 7098 N-Th17 Signaling-2.0-Hs-5 Notch 9 40 white rectangle gene 0.5 black 46 17 Notch N-Th17 Signaling-2.0-Hs-6 ROR 101 2 white rectangle gene 0.5 black 46 17 ROR N-Th17 Signaling-2.0-Hs-8 RUNX1 170 72 white rectangle gene 0.5 black 46 17 10471 N-Th17 Signaling-2.0-Hs-9 SAA1 70 122 white rectangle gene 0.5 black 46 17 10513 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Th17 Signaling-2.0-Hs.sif000066400000000000000000000071741426625374700256350ustar00rootroot000000000000000 1 2 N-Th17 Signaling-2.0-Hs-36 activation N-Th17 Signaling-2.0-Hs-13 N-Th17 Signaling-2.0-Hs-36 activation N-Th17 Signaling-2.0-Hs-13 N-Th17 Signaling-2.0-Hs-36 activation N-Th17 Signaling-2.0-Hs-13 N-Th17 Signaling-2.0-Hs-44 activation N-Th17 Signaling-2.0-Hs-29 N-Th17 Signaling-2.0-Hs-44 activation N-Th17 Signaling-2.0-Hs-29 N-Th17 Signaling-2.0-Hs-44 activation N-Th17 Signaling-2.0-Hs-29 N-Th17 Signaling-2.0-Hs-32 activation N-Th17 Signaling-2.0-Hs-17 N-Th17 Signaling-2.0-Hs-18 activation N-Th17 Signaling-2.0-Hs-39 N-Th17 Signaling-2.0-Hs-3 4 activation N-Th17 Signaling-2.0-Hs-8 N-Th17 Signaling-2.0-Hs-9 activation N-Th17 Signaling-2.0-Hs-35 N-Th17 Signaling-2.0-Hs-1 2 activation N-Th17 Signaling-2.0-Hs-32 N-Th17 Signaling-2.0-Hs-1 2 activation N-Th17 Signaling-2.0-Hs-24 N-Th17 Signaling-2.0-Hs-27 activation N-Th17 Signaling-2.0-Hs-5 N-Th17 Signaling-2.0-Hs-27 activation N-Th17 Signaling-2.0-Hs-5 N-Th17 Signaling-2.0-Hs-27 activation N-Th17 Signaling-2.0-Hs-5 N-Th17 Signaling-2.0-Hs-25 activation N-Th17 Signaling-2.0-Hs-11 N-Th17 Signaling-2.0-Hs-25 activation N-Th17 Signaling-2.0-Hs-11 N-Th17 Signaling-2.0-Hs-16 activation N-Th17 Signaling-2.0-Hs-43 N-Th17 Signaling-2.0-Hs-16 activation N-Th17 Signaling-2.0-Hs-43 N-Th17 Signaling-2.0-Hs-16 activation N-Th17 Signaling-2.0-Hs-43 N-Th17 Signaling-2.0-Hs-16 activation N-Th17 Signaling-2.0-Hs-15 N-Th17 Signaling-2.0-Hs-14 activation N-Th17 Signaling-2.0-Hs-16 N-Th17 Signaling-2.0-Hs-14 activation N-Th17 Signaling-2.0-Hs-16 N-Th17 Signaling-2.0-Hs-14 activation N-Th17 Signaling-2.0-Hs-16 N-Th17 Signaling-2.0-Hs-14 activation N-Th17 Signaling-2.0-Hs-16 N-Th17 Signaling-2.0-Hs-14 activation N-Th17 Signaling-2.0-Hs-16 N-Th17 Signaling-2.0-Hs-26 inhibition N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-26 inhibition N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-26 inhibition N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-26 inhibition N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-26 inhibition N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-26 inhibition N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-26 inhibition N-Th17 Signaling-2.0-Hs-35 N-Th17 Signaling-2.0-Hs-33 activation N-Th17 Signaling-2.0-Hs-34 N-Th17 Signaling-2.0-Hs-33 activation N-Th17 Signaling-2.0-Hs-38 N-Th17 Signaling-2.0-Hs-33 activation N-Th17 Signaling-2.0-Hs-38 N-Th17 Signaling-2.0-Hs-10 activation N-Th17 Signaling-2.0-Hs-21 N-Th17 Signaling-2.0-Hs-28 activation N-Th17 Signaling-2.0-Hs-11 N-Th17 Signaling-2.0-Hs-22 activation N-Th17 Signaling-2.0-Hs-23 N-Th17 Signaling-2.0-Hs-37 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-37 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-41 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-41 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-41 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-41 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-17 activation N-Th17 Signaling-2.0-Hs-19 N-Th17 Signaling-2.0-Hs-17 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-17 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-6 inhibition N-Th17 Signaling-2.0-Hs-20 N-Th17 Signaling-2.0-Hs-24 activation N-Th17 Signaling-2.0-Hs-42 N-Th17 Signaling-2.0-Hs-24 activation N-Th17 Signaling-2.0-Hs-42 N-Th17 Signaling-2.0-Hs-15 activation N-Th17 Signaling-2.0-Hs-43 N-Th17 Signaling-2.0-Hs-30 activation N-Th17 Signaling-2.0-Hs-35 N-Th17 Signaling-2.0-Hs-40 activation N-Th17 Signaling-2.0-Hs-41 N-Th17 Signaling-2.0-Hs-40 activation N-Th17 Signaling-2.0-Hs-41 N-Th17 Signaling-2.0-Hs-42 activation N-Th17 Signaling-2.0-Hs-12 N-Th17 Signaling-2.0-Hs-42 activation N-Th17 Signaling-2.0-Hs-12 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Tissue Damage-2.0-Hs.att000066400000000000000000000044471426625374700256400ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Tissue Damage-2.0-Hs-1 2 TLR4 LY96 54 15 white rectangle gene,gene 0.5 black 46 17 11850,/,17156 N-Tissue Damage-2.0-Hs-10 TLR4 59 26 white rectangle gene 0.5 black 46 17 11850 N-Tissue Damage-2.0-Hs-11 TLR5 106 13 white rectangle gene 0.5 black 46 17 11851 N-Tissue Damage-2.0-Hs-12 TRAF6 99 91 white rectangle gene 0.5 black 46 17 12036 N-Tissue Damage-2.0-Hs-13 IKBKE 3 0 white rectangle gene 0.5 black 46 17 14552 N-Tissue Damage-2.0-Hs-14 TLR9 110 22 white rectangle gene 0.5 black 46 17 15633 N-Tissue Damage-2.0-Hs-15 IRAK4 97 49 white rectangle gene 0.5 black 46 17 17967 N-Tissue Damage-2.0-Hs-16 TICAM1 31 16 white rectangle gene 0.5 black 46 17 18348 N-Tissue Damage-2.0-Hs-17 CHUK 80 124 white rectangle gene 0.5 black 46 17 1974 N-Tissue Damage-2.0-Hs-18 VCAN 115 40 white rectangle gene 0.5 black 46 17 2464 N-Tissue Damage-2.0-Hs-19 HMGB1 77 33 white rectangle gene 0.5 black 46 17 4983 N-Tissue Damage-2.0-Hs-20 IKBKB 85 129 white rectangle gene 0.5 black 46 17 5960 N-Tissue Damage-2.0-Hs-21 IRAK1 103 67 white rectangle gene 0.5 black 46 17 6112 N-Tissue Damage-2.0-Hs-22 IRF3 11 11 white rectangle gene 0.5 black 46 17 6118 N-Tissue Damage-2.0-Hs-23 IRF5 98 9 white rectangle gene 0.5 black 46 17 6120 N-Tissue Damage-2.0-Hs-24 IRF7 104 46 white rectangle gene 0.5 black 46 17 6122 N-Tissue Damage-2.0-Hs-25 MAP3K7 91 113 white rectangle gene 0.5 black 46 17 6859 N-Tissue Damage-2.0-Hs-26 MYD88 92 26 white rectangle gene 0.5 black 46 17 7562 N-Tissue Damage-2.0-Hs-27 NFKBIA 71 136 white rectangle gene 0.5 black 46 17 7797 N-Tissue Damage-2.0-Hs-28 NFKBIA 55 134 white rectangle gene 0.5 black 46 17 7797 N-Tissue Damage-2.0-Hs-29 NFKBIA 67 151 white rectangle gene 0.5 black 46 17 7797 N-Tissue Damage-2.0-Hs-3 S100A12 45 28 white rectangle gene 0.5 black 46 17 10489 N-Tissue Damage-2.0-Hs-4 S100A8 56 40 white rectangle gene 0.5 black 46 17 10498 N-Tissue Damage-2.0-Hs-5 S100A9 47 38 white rectangle gene 0.5 black 46 17 10499 N-Tissue Damage-2.0-Hs-6 TBK1 0 9 white rectangle gene 0.5 black 46 17 11584 N-Tissue Damage-2.0-Hs-7 TLR1 89 10 white rectangle gene 0.5 black 46 17 11847 N-Tissue Damage-2.0-Hs-8 TLR2 97 35 white rectangle gene 0.5 black 46 17 11848 N-Tissue Damage-2.0-Hs-9 TLR3 13 6 white rectangle gene 0.5 black 46 17 11849 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Tissue Damage-2.0-Hs.sif000066400000000000000000000224311426625374700256220ustar00rootroot000000000000000 1 2 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-16 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-16 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-16 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-16 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-16 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-10 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-20 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-17 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-25 activation N-Tissue Damage-2.0-Hs-17 N-Tissue Damage-2.0-Hs-25 activation N-Tissue Damage-2.0-Hs-20 N-Tissue Damage-2.0-Hs-3 activation N-Tissue Damage-2.0-Hs-10 N-Tissue Damage-2.0-Hs-5 activation N-Tissue Damage-2.0-Hs-10 N-Tissue Damage-2.0-Hs-5 activation N-Tissue Damage-2.0-Hs-10 N-Tissue Damage-2.0-Hs-6 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-6 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-6 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-16 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-16 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-16 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-1 2 activation N-Tissue Damage-2.0-Hs-10 N-Tissue Damage-2.0-Hs-11 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-11 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-14 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-14 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-14 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-14 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-4 activation N-Tissue Damage-2.0-Hs-10 N-Tissue Damage-2.0-Hs-19 activation N-Tissue Damage-2.0-Hs-8 N-Tissue Damage-2.0-Hs-19 activation N-Tissue Damage-2.0-Hs-8 N-Tissue Damage-2.0-Hs-19 activation N-Tissue Damage-2.0-Hs-8 N-Tissue Damage-2.0-Hs-19 activation N-Tissue Damage-2.0-Hs-8 N-Tissue Damage-2.0-Hs-19 activation N-Tissue Damage-2.0-Hs-10 N-Tissue Damage-2.0-Hs-19 activation N-Tissue Damage-2.0-Hs-10 N-Tissue Damage-2.0-Hs-19 activation N-Tissue Damage-2.0-Hs-10 N-Tissue Damage-2.0-Hs-24 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-9 activation N-Tissue Damage-2.0-Hs-13 N-Tissue Damage-2.0-Hs-9 activation N-Tissue Damage-2.0-Hs-16 N-Tissue Damage-2.0-Hs-9 activation N-Tissue Damage-2.0-Hs-16 N-Tissue Damage-2.0-Hs-9 activation N-Tissue Damage-2.0-Hs-16 N-Tissue Damage-2.0-Hs-9 activation N-Tissue Damage-2.0-Hs-6 N-Tissue Damage-2.0-Hs-21 activation N-Tissue Damage-2.0-Hs-24 N-Tissue Damage-2.0-Hs-21 activation N-Tissue Damage-2.0-Hs-12 N-Tissue Damage-2.0-Hs-21 activation N-Tissue Damage-2.0-Hs-12 N-Tissue Damage-2.0-Hs-21 activation N-Tissue Damage-2.0-Hs-12 N-Tissue Damage-2.0-Hs-21 activation N-Tissue Damage-2.0-Hs-12 N-Tissue Damage-2.0-Hs-21 activation N-Tissue Damage-2.0-Hs-12 N-Tissue Damage-2.0-Hs-21 activation N-Tissue Damage-2.0-Hs-12 N-Tissue Damage-2.0-Hs-18 activation N-Tissue Damage-2.0-Hs-8 N-Tissue Damage-2.0-Hs-23 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-15 activation N-Tissue Damage-2.0-Hs-21 N-Tissue Damage-2.0-Hs-15 activation N-Tissue Damage-2.0-Hs-21 N-Tissue Damage-2.0-Hs-15 activation N-Tissue Damage-2.0-Hs-21 N-Tissue Damage-2.0-Hs-29 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-29 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-29 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-29 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-29 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-8 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-8 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-8 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-8 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-28 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-28 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-28 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-28 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-28 activation N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-12 activation N-Tissue Damage-2.0-Hs-25 N-Tissue Damage-2.0-Hs-12 activation N-Tissue Damage-2.0-Hs-25 N-Tissue Damage-2.0-Hs-7 activation N-Tissue Damage-2.0-Hs-26 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-27 inhibition N-Tissue Damage-2.0-Hs-27 N-Tissue Damage-2.0-Hs-13 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-13 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-13 activation N-Tissue Damage-2.0-Hs-22 N-Tissue Damage-2.0-Hs-26 activation N-Tissue Damage-2.0-Hs-23 N-Tissue Damage-2.0-Hs-26 activation N-Tissue Damage-2.0-Hs-15 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Treg Signaling-2.0-Hs.att000066400000000000000000000063331426625374700260160ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Treg Signaling-2.0-Hs-1 2 IL12A IL12B 62 20 white rectangle gene,gene 0.5 black 46 17 5969,/,5970 N-Treg Signaling-2.0-Hs-10 KLF10 123 93 white rectangle gene 0.5 black 46 17 11810 N-Treg Signaling-2.0-Hs-11 TNFRSF4 139 103 white rectangle gene 0.5 black 46 17 11918 N-Treg Signaling-2.0-Hs-12 UBE2N 9 85 white rectangle gene 0.5 black 46 17 12492 N-Treg Signaling-2.0-Hs-13 USP7 140 98 white rectangle gene 0.5 black 46 17 12630 N-Treg Signaling-2.0-Hs-14 ITCH 125 108 white rectangle gene 0.5 black 46 17 13890 N-Treg Signaling-2.0-Hs-15 BACH2 130 111 white rectangle gene 0.5 black 46 17 14078 N-Treg Signaling-2.0-Hs-16 CRLF2 106 166 white rectangle gene 0.5 black 46 17 14281 N-Treg Signaling-2.0-Hs-17 CCR5 59 171 white rectangle gene 0.5 black 46 17 1606 N-Treg Signaling-2.0-Hs-18 CXCL16 80 1 white rectangle gene 0.5 black 46 17 16642 N-Treg Signaling-2.0-Hs-19 CXCR6 75 0 white rectangle gene 0.5 black 46 17 16647 N-Treg Signaling-2.0-Hs-20 CD80 132 60 white rectangle gene 0.5 black 46 17 1700 N-Treg Signaling-2.0-Hs-21 SOCS1 3 90 white rectangle gene 0.5 black 46 17 19383 N-Treg Signaling-2.0-Hs-22 CREBBP 135 94 white rectangle gene 0.5 black 46 17 2348 N-Treg Signaling-2.0-Hs-23 CTLA4 142 62 white rectangle gene 0.5 black 46 17 2505 N-Treg Signaling-2.0-Hs-24 TSLP 110 174 white rectangle gene 0.5 black 46 17 30743 N-Treg Signaling-2.0-Hs-25 FOXO1 133 84 white rectangle gene 0.5 black 46 17 3819 N-Treg Signaling-2.0-Hs-26 FOXO3 143 68 white rectangle gene 0.5 black 46 17 3821 N-Treg Signaling-2.0-Hs-27 GATA3 140 111 white rectangle gene 0.5 black 46 17 4172 N-Treg Signaling-2.0-Hs-28 CXCR3 45 64 white rectangle gene 0.5 black 46 17 4540 N-Treg Signaling-2.0-Hs-29 HIF1A 121 103 white rectangle gene 0.5 black 46 17 4910 N-Treg Signaling-2.0-Hs-3 IFNA 148 118 white rectangle gene 0.5 black 46 17 IFNA N-Treg Signaling-2.0-Hs-30 IFNG 109 115 white rectangle gene 0.5 black 46 17 5438 N-Treg Signaling-2.0-Hs-31 IL10 3 79 white rectangle gene 0.5 black 46 17 5962 N-Treg Signaling-2.0-Hs-33 IL7 124 174 white rectangle gene 0.5 black 46 17 6023 N-Treg Signaling-2.0-Hs-34 IL7R 117 178 white rectangle gene 0.5 black 46 17 6024 N-Treg Signaling-2.0-Hs-35 FOXP3 129 100 white rectangle gene 0.5 black 46 17 6106 N-Treg Signaling-2.0-Hs-36 SMAD2 137 60 white rectangle gene 0.5 black 46 17 6768 N-Treg Signaling-2.0-Hs-37 SMAD3 129 84 white rectangle gene 0.5 black 46 17 6769 N-Treg Signaling-2.0-Hs-38 SMAD7 119 97 white rectangle gene 0.5 black 46 17 6773 N-Treg Signaling-2.0-Hs-39 CXCL9 49 60 white rectangle gene 0.5 black 46 17 7098 N-Treg Signaling-2.0-Hs-4 CCL3 58 165 white rectangle gene 0.5 black 46 17 10627 N-Treg Signaling-2.0-Hs-40 NR4A2 117 109 white rectangle gene 0.5 black 46 17 7981 N-Treg Signaling-2.0-Hs-41 FURIN 127 63 white rectangle gene 0.5 black 46 17 8568 N-Treg Signaling-2.0-Hs-5 STAT3 0 73 white rectangle gene 0.5 black 46 17 11364 N-Treg Signaling-2.0-Hs-6 STAT4 57 15 white rectangle gene 0.5 black 46 17 11365 N-Treg Signaling-2.0-Hs-7 STUB1 134 107 white rectangle gene 0.5 black 46 17 11427 N-Treg Signaling-2.0-Hs-8 TBX21 39 67 white rectangle gene 0.5 black 46 17 11599 N-Treg Signaling-2.0-Hs-9 TGFB1 134 70 white rectangle gene 0.5 black 46 17 11766 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Treg Signaling-2.0-Hs.sif000066400000000000000000000120441426625374700260030ustar00rootroot000000000000000 1 2 N-Treg Signaling-2.0-Hs-40 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-40 inhibition N-Treg Signaling-2.0-Hs-30 N-Treg Signaling-2.0-Hs-39 activation N-Treg Signaling-2.0-Hs-28 N-Treg Signaling-2.0-Hs-39 activation N-Treg Signaling-2.0-Hs-28 N-Treg Signaling-2.0-Hs-39 activation N-Treg Signaling-2.0-Hs-28 N-Treg Signaling-2.0-Hs-1 2 activation N-Treg Signaling-2.0-Hs-6 N-Treg Signaling-2.0-Hs-22 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-38 inhibition N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-38 inhibition N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-38 inhibition N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-24 activation N-Treg Signaling-2.0-Hs-34 N-Treg Signaling-2.0-Hs-24 activation N-Treg Signaling-2.0-Hs-16 N-Treg Signaling-2.0-Hs-24 activation N-Treg Signaling-2.0-Hs-16 N-Treg Signaling-2.0-Hs-14 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-12 activation N-Treg Signaling-2.0-Hs-21 N-Treg Signaling-2.0-Hs-12 activation N-Treg Signaling-2.0-Hs-31 N-Treg Signaling-2.0-Hs-12 activation N-Treg Signaling-2.0-Hs-31 N-Treg Signaling-2.0-Hs-33 activation N-Treg Signaling-2.0-Hs-34 N-Treg Signaling-2.0-Hs-33 activation N-Treg Signaling-2.0-Hs-34 N-Treg Signaling-2.0-Hs-37 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-11 inhibition N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-7 inhibition N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-27 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-27 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-26 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-36 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-25 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-23 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-23 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-20 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-9 activation N-Treg Signaling-2.0-Hs-37 N-Treg Signaling-2.0-Hs-4 activation N-Treg Signaling-2.0-Hs-17 N-Treg Signaling-2.0-Hs-4 activation N-Treg Signaling-2.0-Hs-17 N-Treg Signaling-2.0-Hs-18 activation N-Treg Signaling-2.0-Hs-19 N-Treg Signaling-2.0-Hs-18 activation N-Treg Signaling-2.0-Hs-19 N-Treg Signaling-2.0-Hs-25 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-3 inhibition N-Treg Signaling-2.0-Hs-27 N-Treg Signaling-2.0-Hs-41 activation N-Treg Signaling-2.0-Hs-9 N-Treg Signaling-2.0-Hs-31 activation N-Treg Signaling-2.0-Hs-5 N-Treg Signaling-2.0-Hs-31 activation N-Treg Signaling-2.0-Hs-5 N-Treg Signaling-2.0-Hs-13 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-29 inhibition N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-15 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-10 activation N-Treg Signaling-2.0-Hs-35 N-Treg Signaling-2.0-Hs-8 activation N-Treg Signaling-2.0-Hs-28 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Wnt-2.0-Hs.att000066400000000000000000000062701426625374700237710ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Wnt-2.0-Hs-1 DVL 77 74 white rectangle gene 0.5 black 46 17 DVL N-Wnt-2.0-Hs-10 CCL2 122 159 white rectangle gene 0.5 black 46 17 10618 N-Wnt-2.0-Hs-11 CCL5 136 167 white rectangle gene 0.5 black 46 17 10632 N-Wnt-2.0-Hs-12 TGFB1 73 170 white rectangle gene 0.5 black 46 17 11766 N-Wnt-2.0-Hs-13 VEGFA 132 160 white rectangle gene 0.5 black 46 17 12680 N-Wnt-2.0-Hs-14 WNT1 147 86 white rectangle gene 0.5 black 46 17 12774 N-Wnt-2.0-Hs-15 WNT4 129 166 white rectangle gene 0.5 black 46 17 12783 N-Wnt-2.0-Hs-16 WNT7B 141 78 white rectangle gene 0.5 black 46 17 12787 N-Wnt-2.0-Hs-17 WNT3A 140 94 white rectangle gene 0.5 black 46 17 15983 N-Wnt-2.0-Hs-18 FRAT2 64 22 white rectangle gene 0.5 black 46 17 16048 N-Wnt-2.0-Hs-19 DKKL1 66 95 white rectangle gene 0.5 black 46 17 16528 N-Wnt-2.0-Hs-2 FZD 98 79 white rectangle gene 0.5 black 46 17 FZD N-Wnt-2.0-Hs-20 NKD1 80 69 white rectangle gene 0.5 black 46 17 17045 N-Wnt-2.0-Hs-21 NKD2 70 71 white rectangle gene 0.5 black 46 17 17046 N-Wnt-2.0-Hs-22 WIF1 119 91 white rectangle gene 0.5 black 46 17 18081 N-Wnt-2.0-Hs-23 CTNNB1 83 18 white rectangle gene 0.5 black 46 17 2514 N-Wnt-2.0-Hs-24 CTNNB1 73 20 white rectangle gene 0.5 black 46 17 2514 N-Wnt-2.0-Hs-25 CTNNB1 77 22 white rectangle gene 0.5 black 46 17 2514 N-Wnt-2.0-Hs-26 CTNNB1 81 27 white rectangle gene 0.5 black 46 17 2514 N-Wnt-2.0-Hs-27 CTNNB1 85 23 white rectangle gene 0.5 black 46 17 2514 N-Wnt-2.0-Hs-28 CTNNB1 79 25 white rectangle gene 0.5 black 46 17 2514 N-Wnt-2.0-Hs-29 CTNNB1 97 0 white rectangle gene 0.5 black 46 17 2514 N-Wnt-2.0-Hs-3 GSK3 70 32 white rectangle gene 0.5 black 46 17 GSK3 N-Wnt-2.0-Hs-30 DKK1 60 89 white rectangle gene 0.5 black 46 17 2891 N-Wnt-2.0-Hs-31 DKK2 60 84 white rectangle gene 0.5 black 46 17 2892 N-Wnt-2.0-Hs-32 DKK3 71 93 white rectangle gene 0.5 black 46 17 2893 N-Wnt-2.0-Hs-33 DKK4 62 93 white rectangle gene 0.5 black 46 17 2894 N-Wnt-2.0-Hs-34 DLK1 184 19 white rectangle gene 0.5 black 46 17 2907 N-Wnt-2.0-Hs-35 FGF10 0 135 white rectangle gene 0.5 black 46 17 3666 N-Wnt-2.0-Hs-36 FRAT1 60 31 white rectangle gene 0.5 black 46 17 3944 N-Wnt-2.0-Hs-37 FZD2 145 91 white rectangle gene 0.5 black 46 17 4040 N-Wnt-2.0-Hs-38 FZD3 146 81 white rectangle gene 0.5 black 46 17 4041 N-Wnt-2.0-Hs-39 GATA6 122 78 white rectangle gene 0.5 black 46 17 4174 N-Wnt-2.0-Hs-4 GSK3 73 53 white rectangle gene 0.5 black 46 17 GSK3 N-Wnt-2.0-Hs-40 CXCL8 130 175 white rectangle gene 0.5 black 46 17 6025 N-Wnt-2.0-Hs-41 LRP5 69 87 white rectangle gene 0.5 black 46 17 6697 N-Wnt-2.0-Hs-42 LRP6 67 86 white rectangle gene 0.5 black 46 17 6698 N-Wnt-2.0-Hs-43 MAP3K7 70 178 white rectangle gene 0.5 black 46 17 6859 N-Wnt-2.0-Hs-44 MYC 3 131 white rectangle gene 0.5 black 46 17 7553 N-Wnt-2.0-Hs-45 PTN 92 8 white rectangle gene 0.5 black 46 17 9630 N-Wnt-2.0-Hs-46 Idiopathic Pulmonary Fibrosis 137 85 white rectangle gene 0.5 black 46 17 D054990 N-Wnt-2.0-Hs-5 GSK3 62 35 white rectangle gene 0.5 black 46 17 GSK3 N-Wnt-2.0-Hs-6 GSK3 60 26 white rectangle gene 0.5 black 46 17 GSK3 N-Wnt-2.0-Hs-8 Notch 191 15 white rectangle gene 0.5 black 46 17 Notch N-Wnt-2.0-Hs-9 Wnt 118 83 white rectangle gene 0.5 black 46 17 15983 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Wnt-2.0-Hs.sif000066400000000000000000000112601426625374700237550ustar00rootroot000000000000000 1 2 N-Wnt-2.0-Hs-1 activation N-Wnt-2.0-Hs-4 N-Wnt-2.0-Hs-31 inhibition N-Wnt-2.0-Hs-41 N-Wnt-2.0-Hs-31 inhibition N-Wnt-2.0-Hs-42 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-23 inhibition N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-43 activation N-Wnt-2.0-Hs-12 N-Wnt-2.0-Hs-32 inhibition N-Wnt-2.0-Hs-42 N-Wnt-2.0-Hs-32 inhibition N-Wnt-2.0-Hs-41 N-Wnt-2.0-Hs-30 inhibition N-Wnt-2.0-Hs-42 N-Wnt-2.0-Hs-30 inhibition N-Wnt-2.0-Hs-42 N-Wnt-2.0-Hs-30 inhibition N-Wnt-2.0-Hs-41 N-Wnt-2.0-Hs-30 inhibition N-Wnt-2.0-Hs-41 N-Wnt-2.0-Hs-42 activation N-Wnt-2.0-Hs-1 N-Wnt-2.0-Hs-18 inhibition N-Wnt-2.0-Hs-3 N-Wnt-2.0-Hs-9 activation N-Wnt-2.0-Hs-2 N-Wnt-2.0-Hs-9 activation N-Wnt-2.0-Hs-2 N-Wnt-2.0-Hs-9 activation N-Wnt-2.0-Hs-2 N-Wnt-2.0-Hs-9 activation N-Wnt-2.0-Hs-2 N-Wnt-2.0-Hs-45 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-45 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-45 activation N-Wnt-2.0-Hs-29 N-Wnt-2.0-Hs-34 activation N-Wnt-2.0-Hs-8 N-Wnt-2.0-Hs-24 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-24 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-36 activation N-Wnt-2.0-Hs-5 N-Wnt-2.0-Hs-36 inhibition N-Wnt-2.0-Hs-3 N-Wnt-2.0-Hs-36 activation N-Wnt-2.0-Hs-6 N-Wnt-2.0-Hs-44 activation N-Wnt-2.0-Hs-35 N-Wnt-2.0-Hs-20 inhibition N-Wnt-2.0-Hs-1 N-Wnt-2.0-Hs-15 activation N-Wnt-2.0-Hs-13 N-Wnt-2.0-Hs-15 activation N-Wnt-2.0-Hs-10 N-Wnt-2.0-Hs-15 activation N-Wnt-2.0-Hs-11 N-Wnt-2.0-Hs-15 activation N-Wnt-2.0-Hs-40 N-Wnt-2.0-Hs-15 activation N-Wnt-2.0-Hs-40 N-Wnt-2.0-Hs-5 inhibition N-Wnt-2.0-Hs-3 N-Wnt-2.0-Hs-33 inhibition N-Wnt-2.0-Hs-42 N-Wnt-2.0-Hs-33 inhibition N-Wnt-2.0-Hs-41 N-Wnt-2.0-Hs-41 activation N-Wnt-2.0-Hs-1 N-Wnt-2.0-Hs-26 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-26 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-26 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-21 inhibition N-Wnt-2.0-Hs-1 N-Wnt-2.0-Hs-46 activation N-Wnt-2.0-Hs-37 N-Wnt-2.0-Hs-46 activation N-Wnt-2.0-Hs-14 N-Wnt-2.0-Hs-46 activation N-Wnt-2.0-Hs-17 N-Wnt-2.0-Hs-46 activation N-Wnt-2.0-Hs-9 N-Wnt-2.0-Hs-46 activation N-Wnt-2.0-Hs-16 N-Wnt-2.0-Hs-46 activation N-Wnt-2.0-Hs-38 N-Wnt-2.0-Hs-25 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-25 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-25 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-3 activation N-Wnt-2.0-Hs-24 N-Wnt-2.0-Hs-3 activation N-Wnt-2.0-Hs-25 N-Wnt-2.0-Hs-3 activation N-Wnt-2.0-Hs-28 N-Wnt-2.0-Hs-3 activation N-Wnt-2.0-Hs-26 N-Wnt-2.0-Hs-22 inhibition N-Wnt-2.0-Hs-9 N-Wnt-2.0-Hs-6 inhibition N-Wnt-2.0-Hs-3 N-Wnt-2.0-Hs-6 inhibition N-Wnt-2.0-Hs-3 N-Wnt-2.0-Hs-39 inhibition N-Wnt-2.0-Hs-9 N-Wnt-2.0-Hs-2 activation N-Wnt-2.0-Hs-1 N-Wnt-2.0-Hs-2 activation N-Wnt-2.0-Hs-1 N-Wnt-2.0-Hs-28 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-28 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-19 inhibition N-Wnt-2.0-Hs-42 N-Wnt-2.0-Hs-19 inhibition N-Wnt-2.0-Hs-41 N-Wnt-2.0-Hs-4 inhibition N-Wnt-2.0-Hs-3 N-Wnt-2.0-Hs-27 activation N-Wnt-2.0-Hs-28 N-Wnt-2.0-Hs-27 activation N-Wnt-2.0-Hs-26 N-Wnt-2.0-Hs-27 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-27 activation N-Wnt-2.0-Hs-23 N-Wnt-2.0-Hs-27 activation N-Wnt-2.0-Hs-25 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Wound Healing-2.0-Hs.att000066400000000000000000000132521426625374700256430ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Wound Healing-2.0-Hs-100 Mylpf 0 88 white rectangle gene 0.5 black 46 17 97273 N-Wound Healing-2.0-Hs-23 24 Itgav Itgb3 89 104 white rectangle gene,gene 0.5 black 46 17 1310613,/,628868 N-Wound Healing-2.0-Hs-25 AKT 64 66 white rectangle gene 0.5 black 46 17 AKT N-Wound Healing-2.0-Hs-26 COL1 90 66 white rectangle gene 0.5 black 46 17 COL1 N-Wound Healing-2.0-Hs-27 COL4 108 56 white rectangle gene 0.5 black 46 17 COL4 N-Wound Healing-2.0-Hs-28 JNK 54 101 white rectangle gene 0.5 black 46 17 JNK N-Wound Healing-2.0-Hs-29 P2RY 93 83 white rectangle gene 0.5 black 46 17 P2RY N-Wound Healing-2.0-Hs-30 PDGF 72 83 white rectangle gene 0.5 black 46 17 PDGF N-Wound Healing-2.0-Hs-31 PRKAC 111 7 white rectangle gene 0.5 black 46 17 PRKAC N-Wound Healing-2.0-Hs-32 ROCK 47 103 white rectangle gene 0.5 black 46 17 ROCK N-Wound Healing-2.0-Hs-33 SRC 36 97 white rectangle gene 0.5 black 46 17 SRC N-Wound Healing-2.0-Hs-34 p38 73 77 white rectangle gene 0.5 black 46 17 p38 N-Wound Healing-2.0-Hs-35 ROCK1 79 105 white rectangle gene 0.5 black 46 17 10251 N-Wound Healing-2.0-Hs-37 SRC 80 101 white rectangle gene 0.5 black 46 17 11283 N-Wound Healing-2.0-Hs-38 STAT3 57 104 white rectangle gene 0.5 black 46 17 11364 N-Wound Healing-2.0-Hs-39 STAT3 52 113 white rectangle gene 0.5 black 46 17 11364 N-Wound Healing-2.0-Hs-40 TGFA 86 84 white rectangle gene 0.5 black 46 17 11765 N-Wound Healing-2.0-Hs-42 TIAM1 70 104 white rectangle gene 0.5 black 46 17 11805 N-Wound Healing-2.0-Hs-43 TIMP1 80 61 white rectangle gene 0.5 black 46 17 11820 N-Wound Healing-2.0-Hs-44 TIMP2 90 62 white rectangle gene 0.5 black 46 17 11821 N-Wound Healing-2.0-Hs-46 TIMP4 90 48 white rectangle gene 0.5 black 46 17 11823 N-Wound Healing-2.0-Hs-47 VASP 117 14 white rectangle gene 0.5 black 46 17 12652 N-Wound Healing-2.0-Hs-48 VASP 117 8 white rectangle gene 0.5 black 46 17 12652 N-Wound Healing-2.0-Hs-51 CCND1 169 71 white rectangle gene 0.5 black 46 17 1582 N-Wound Healing-2.0-Hs-52 CDC42 13 93 white rectangle gene 0.5 black 46 17 1736 N-Wound Healing-2.0-Hs-53 SOX17 173 70 white rectangle gene 0.5 black 46 17 18122 N-Wound Healing-2.0-Hs-54 ADAM10 98 96 white rectangle gene 0.5 black 46 17 188 N-Wound Healing-2.0-Hs-55 ADAM17 85 81 white rectangle gene 0.5 black 46 17 195 N-Wound Healing-2.0-Hs-56 COL2A1 165 45 white rectangle gene 0.5 black 46 17 2200 N-Wound Healing-2.0-Hs-57 CTNNB1 93 176 white rectangle gene 0.5 black 46 17 2514 N-Wound Healing-2.0-Hs-58 ADRB2 113 0 white rectangle gene 0.5 black 46 17 286 N-Wound Healing-2.0-Hs-59 PDPN 88 176 white rectangle gene 0.5 black 46 17 29602 N-Wound Healing-2.0-Hs-60 HBEGF 90 89 white rectangle gene 0.5 black 46 17 3059 N-Wound Healing-2.0-Hs-61 DUOX1 86 76 white rectangle gene 0.5 black 46 17 3062 N-Wound Healing-2.0-Hs-62 AGER 94 170 white rectangle gene 0.5 black 46 17 320 N-Wound Healing-2.0-Hs-63 EGFR 77 85 white rectangle gene 0.5 black 46 17 3236 N-Wound Healing-2.0-Hs-64 ELN 73 58 white rectangle gene 0.5 black 46 17 3327 N-Wound Healing-2.0-Hs-65 F2 86 46 white rectangle gene 0.5 black 46 17 3535 N-Wound Healing-2.0-Hs-66 FGF2 69 88 white rectangle gene 0.5 black 46 17 3676 N-Wound Healing-2.0-Hs-67 FGF7 57 78 white rectangle gene 0.5 black 46 17 3685 N-Wound Healing-2.0-Hs-68 FN1 49 73 white rectangle gene 0.5 black 46 17 3778 N-Wound Healing-2.0-Hs-69 AKT1 18 102 white rectangle gene 0.5 black 46 17 391 N-Wound Healing-2.0-Hs-7 8 ITGA1 ITGB1 100 60 white rectangle gene,gene 0.5 black 46 17 6134,/,6153 N-Wound Healing-2.0-Hs-70 AKT1 15 99 white rectangle gene 0.5 black 46 17 391 N-Wound Healing-2.0-Hs-71 HGF 64 93 white rectangle gene 0.5 black 46 17 4893 N-Wound Healing-2.0-Hs-72 CXCL8 59 82 white rectangle gene 0.5 black 46 17 6025 N-Wound Healing-2.0-Hs-73 ILK 20 94 white rectangle gene 0.5 black 46 17 6040 N-Wound Healing-2.0-Hs-74 JUN 44 105 white rectangle gene 0.5 black 46 17 6204 N-Wound Healing-2.0-Hs-75 KLF5 8 91 white rectangle gene 0.5 black 46 17 6349 N-Wound Healing-2.0-Hs-77 RHOA 73 97 white rectangle gene 0.5 black 46 17 667 N-Wound Healing-2.0-Hs-78 MAP3K1 62 105 white rectangle gene 0.5 black 46 17 6848 N-Wound Healing-2.0-Hs-79 MAPK1 66 83 white rectangle gene 0.5 black 46 17 6871 N-Wound Healing-2.0-Hs-80 MAPK3 64 83 white rectangle gene 0.5 black 46 17 6877 N-Wound Healing-2.0-Hs-81 MAPK8 37 107 white rectangle gene 0.5 black 46 17 6881 N-Wound Healing-2.0-Hs-82 MAPK8IP3 61 102 white rectangle gene 0.5 black 46 17 6884 N-Wound Healing-2.0-Hs-83 ARRB1 58 87 white rectangle gene 0.5 black 46 17 711 N-Wound Healing-2.0-Hs-84 ARRB2 61 78 white rectangle gene 0.5 black 46 17 712 N-Wound Healing-2.0-Hs-85 MMP1 53 93 white rectangle gene 0.5 black 46 17 7155 N-Wound Healing-2.0-Hs-86 MMP12 62 59 white rectangle gene 0.5 black 46 17 7158 N-Wound Healing-2.0-Hs-87 MMP14 88 71 white rectangle gene 0.5 black 46 17 7160 N-Wound Healing-2.0-Hs-88 MMP2 85 55 white rectangle gene 0.5 black 46 17 7166 N-Wound Healing-2.0-Hs-89 MMP3 77 59 white rectangle gene 0.5 black 46 17 7173 N-Wound Healing-2.0-Hs-9 10 ITGA2 ITGB1 82 81 white rectangle gene,gene 0.5 black 46 17 6137,/,6153 N-Wound Healing-2.0-Hs-90 MMP7 64 59 white rectangle gene 0.5 black 46 17 7174 N-Wound Healing-2.0-Hs-91 MMP9 76 68 white rectangle gene 0.5 black 46 17 7176 N-Wound Healing-2.0-Hs-93 PTEN 68 74 white rectangle gene 0.5 black 46 17 9588 N-Wound Healing-2.0-Hs-94 PTK2 70 99 white rectangle gene 0.5 black 46 17 9611 N-Wound Healing-2.0-Hs-95 PTK2 70 109 white rectangle gene 0.5 black 46 17 9611 N-Wound Healing-2.0-Hs-96 PXN 74 108 white rectangle gene 0.5 black 46 17 9718 N-Wound Healing-2.0-Hs-97 RAC1 64 98 white rectangle gene 0.5 black 46 17 9801 N-Wound Healing-2.0-Hs-99 Mylpf 0 93 white rectangle gene 0.5 black 46 17 97273 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Wound Healing-2.0-Hs.sif000066400000000000000000000317021426625374700256340ustar00rootroot000000000000000 1 2 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-55 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-63 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-27 activation N-Wound Healing-2.0-Hs-7 8 N-Wound Healing-2.0-Hs-28 activation N-Wound Healing-2.0-Hs-74 N-Wound Healing-2.0-Hs-28 activation N-Wound Healing-2.0-Hs-74 N-Wound Healing-2.0-Hs-28 activation N-Wound Healing-2.0-Hs-74 N-Wound Healing-2.0-Hs-28 activation N-Wound Healing-2.0-Hs-74 N-Wound Healing-2.0-Hs-29 activation N-Wound Healing-2.0-Hs-60 N-Wound Healing-2.0-Hs-29 activation N-Wound Healing-2.0-Hs-61 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-70 activation N-Wound Healing-2.0-Hs-69 N-Wound Healing-2.0-Hs-81 activation N-Wound Healing-2.0-Hs-74 N-Wound Healing-2.0-Hs-31 activation N-Wound Healing-2.0-Hs-48 N-Wound Healing-2.0-Hs-90 activation N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-91 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-93 inhibition N-Wound Healing-2.0-Hs-25 N-Wound Healing-2.0-Hs-88 activation N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-65 activation N-Wound Healing-2.0-Hs-88 N-Wound Healing-2.0-Hs-67 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-67 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-67 activation N-Wound Healing-2.0-Hs-68 N-Wound Healing-2.0-Hs-67 activation N-Wound Healing-2.0-Hs-68 N-Wound Healing-2.0-Hs-67 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-67 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-83 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-83 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-83 activation N-Wound Healing-2.0-Hs-85 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-77 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-97 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-97 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-34 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-34 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-34 N-Wound Healing-2.0-Hs-66 activation N-Wound Healing-2.0-Hs-34 N-Wound Healing-2.0-Hs-58 activation N-Wound Healing-2.0-Hs-31 N-Wound Healing-2.0-Hs-58 activation N-Wound Healing-2.0-Hs-31 N-Wound Healing-2.0-Hs-58 activation N-Wound Healing-2.0-Hs-31 N-Wound Healing-2.0-Hs-32 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-77 activation N-Wound Healing-2.0-Hs-37 N-Wound Healing-2.0-Hs-77 activation N-Wound Healing-2.0-Hs-35 N-Wound Healing-2.0-Hs-77 activation N-Wound Healing-2.0-Hs-35 N-Wound Healing-2.0-Hs-77 activation N-Wound Healing-2.0-Hs-35 N-Wound Healing-2.0-Hs-94 activation N-Wound Healing-2.0-Hs-96 N-Wound Healing-2.0-Hs-94 activation N-Wound Healing-2.0-Hs-78 N-Wound Healing-2.0-Hs-94 activation N-Wound Healing-2.0-Hs-77 N-Wound Healing-2.0-Hs-94 activation N-Wound Healing-2.0-Hs-77 N-Wound Healing-2.0-Hs-94 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-79 activation N-Wound Healing-2.0-Hs-72 N-Wound Healing-2.0-Hs-79 activation N-Wound Healing-2.0-Hs-72 N-Wound Healing-2.0-Hs-40 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-40 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-40 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-40 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-40 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-40 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-40 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-89 activation N-Wound Healing-2.0-Hs-91 N-Wound Healing-2.0-Hs-97 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-97 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-97 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-97 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-97 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-97 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-30 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-30 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-73 activation N-Wound Healing-2.0-Hs-70 N-Wound Healing-2.0-Hs-73 activation N-Wound Healing-2.0-Hs-52 N-Wound Healing-2.0-Hs-33 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-33 activation N-Wound Healing-2.0-Hs-73 N-Wound Healing-2.0-Hs-57 activation N-Wound Healing-2.0-Hs-62 N-Wound Healing-2.0-Hs-57 activation N-Wound Healing-2.0-Hs-59 N-Wound Healing-2.0-Hs-43 inhibition N-Wound Healing-2.0-Hs-91 N-Wound Healing-2.0-Hs-43 inhibition N-Wound Healing-2.0-Hs-91 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-38 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-77 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-71 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-26 inhibition N-Wound Healing-2.0-Hs-44 N-Wound Healing-2.0-Hs-26 activation N-Wound Healing-2.0-Hs-88 N-Wound Healing-2.0-Hs-26 activation N-Wound Healing-2.0-Hs-9 10 N-Wound Healing-2.0-Hs-26 activation N-Wound Healing-2.0-Hs-9 10 N-Wound Healing-2.0-Hs-26 activation N-Wound Healing-2.0-Hs-7 8 N-Wound Healing-2.0-Hs-95 activation N-Wound Healing-2.0-Hs-94 N-Wound Healing-2.0-Hs-56 inhibition N-Wound Healing-2.0-Hs-56 N-Wound Healing-2.0-Hs-56 inhibition N-Wound Healing-2.0-Hs-56 N-Wound Healing-2.0-Hs-56 inhibition N-Wound Healing-2.0-Hs-56 N-Wound Healing-2.0-Hs-56 inhibition N-Wound Healing-2.0-Hs-56 N-Wound Healing-2.0-Hs-56 inhibition N-Wound Healing-2.0-Hs-56 N-Wound Healing-2.0-Hs-44 inhibition N-Wound Healing-2.0-Hs-88 N-Wound Healing-2.0-Hs-44 inhibition N-Wound Healing-2.0-Hs-88 N-Wound Healing-2.0-Hs-44 inhibition N-Wound Healing-2.0-Hs-87 N-Wound Healing-2.0-Hs-53 activation N-Wound Healing-2.0-Hs-51 N-Wound Healing-2.0-Hs-46 inhibition N-Wound Healing-2.0-Hs-88 N-Wound Healing-2.0-Hs-82 activation N-Wound Healing-2.0-Hs-94 N-Wound Healing-2.0-Hs-82 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-54 activation N-Wound Healing-2.0-Hs-60 N-Wound Healing-2.0-Hs-54 activation N-Wound Healing-2.0-Hs-60 N-Wound Healing-2.0-Hs-54 activation N-Wound Healing-2.0-Hs-60 N-Wound Healing-2.0-Hs-84 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-84 activation N-Wound Healing-2.0-Hs-80 N-Wound Healing-2.0-Hs-84 activation N-Wound Healing-2.0-Hs-79 N-Wound Healing-2.0-Hs-61 activation N-Wound Healing-2.0-Hs-55 N-Wound Healing-2.0-Hs-61 activation N-Wound Healing-2.0-Hs-55 N-Wound Healing-2.0-Hs-61 activation N-Wound Healing-2.0-Hs-91 N-Wound Healing-2.0-Hs-61 activation N-Wound Healing-2.0-Hs-40 N-Wound Healing-2.0-Hs-23 24 activation N-Wound Healing-2.0-Hs-37 N-Wound Healing-2.0-Hs-80 activation N-Wound Healing-2.0-Hs-72 N-Wound Healing-2.0-Hs-80 activation N-Wound Healing-2.0-Hs-72 N-Wound Healing-2.0-Hs-37 activation N-Wound Healing-2.0-Hs-94 N-Wound Healing-2.0-Hs-64 inhibition N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-64 inhibition N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-64 inhibition N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-64 inhibition N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-64 inhibition N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-64 inhibition N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-39 activation N-Wound Healing-2.0-Hs-38 N-Wound Healing-2.0-Hs-39 activation N-Wound Healing-2.0-Hs-38 N-Wound Healing-2.0-Hs-39 activation N-Wound Healing-2.0-Hs-38 N-Wound Healing-2.0-Hs-39 activation N-Wound Healing-2.0-Hs-38 N-Wound Healing-2.0-Hs-39 activation N-Wound Healing-2.0-Hs-38 N-Wound Healing-2.0-Hs-39 activation N-Wound Healing-2.0-Hs-38 N-Wound Healing-2.0-Hs-39 activation N-Wound Healing-2.0-Hs-38 N-Wound Healing-2.0-Hs-78 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-78 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-78 activation N-Wound Healing-2.0-Hs-28 N-Wound Healing-2.0-Hs-91 activation N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-48 inhibition N-Wound Healing-2.0-Hs-47 N-Wound Healing-2.0-Hs-86 activation N-Wound Healing-2.0-Hs-64 N-Wound Healing-2.0-Hs-60 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-60 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-60 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-60 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-60 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-60 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-60 activation N-Wound Healing-2.0-Hs-63 N-Wound Healing-2.0-Hs-9 10 activation N-Wound Healing-2.0-Hs-87 N-Wound Healing-2.0-Hs-9 10 activation N-Wound Healing-2.0-Hs-94 N-Wound Healing-2.0-Hs-34 activation N-Wound Healing-2.0-Hs-91 N-Wound Healing-2.0-Hs-34 activation N-Wound Healing-2.0-Hs-91 N-Wound Healing-2.0-Hs-42 activation N-Wound Healing-2.0-Hs-97 N-Wound Healing-2.0-Hs-42 activation N-Wound Healing-2.0-Hs-97 N-Wound Healing-2.0-Hs-75 activation N-Wound Healing-2.0-Hs-73 N-Wound Healing-2.0-Hs-75 activation N-Wound Healing-2.0-Hs-99 N-Wound Healing-2.0-Hs-75 activation N-Wound Healing-2.0-Hs-100 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Xenobiotic Metabolism Response-2.0-Hs.att000066400000000000000000000037631426625374700311640ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-Xenobiotic Metabolism Response-2.0-Hs-1 2 AHR ARNT 20 120 white rectangle gene,gene 0.5 black 46 17 348,/,700 N-Xenobiotic Metabolism Response-2.0-Hs-12 CYP1A1 4 125 white rectangle gene 0.5 black 46 17 2595 N-Xenobiotic Metabolism Response-2.0-Hs-13 CYP1A2 15 109 white rectangle gene 0.5 black 46 17 2596 N-Xenobiotic Metabolism Response-2.0-Hs-14 CYP1B1 29 120 white rectangle gene 0.5 black 46 17 2597 N-Xenobiotic Metabolism Response-2.0-Hs-16 CYP3A5 22 7 white rectangle gene 0.5 black 46 17 2638 N-Xenobiotic Metabolism Response-2.0-Hs-17 NQO1 75 103 white rectangle gene 0.5 black 46 17 2874 N-Xenobiotic Metabolism Response-2.0-Hs-18 AHRR 46 121 white rectangle gene 0.5 black 46 17 346 N-Xenobiotic Metabolism Response-2.0-Hs-19 ESR1 60 121 white rectangle gene 0.5 black 46 17 3467 N-Xenobiotic Metabolism Response-2.0-Hs-20 AHR 37 107 white rectangle gene 0.5 black 46 17 348 N-Xenobiotic Metabolism Response-2.0-Hs-21 ALDH3A1 49 104 white rectangle gene 0.5 black 46 17 405 N-Xenobiotic Metabolism Response-2.0-Hs-22 GSTA2 30 92 white rectangle gene 0.5 black 46 17 4627 N-Xenobiotic Metabolism Response-2.0-Hs-23 GSTP1 91 97 white rectangle gene 0.5 black 46 17 4638 N-Xenobiotic Metabolism Response-2.0-Hs-24 ABCC1 73 126 white rectangle gene 0.5 black 46 17 51 N-Xenobiotic Metabolism Response-2.0-Hs-25 ARNT 59 107 white rectangle gene 0.5 black 46 17 700 N-Xenobiotic Metabolism Response-2.0-Hs-26 NFE2L2 60 112 white rectangle gene 0.5 black 46 17 7782 N-Xenobiotic Metabolism Response-2.0-Hs-27 NR1I2 0 115 white rectangle gene 0.5 black 46 17 7968 N-Xenobiotic Metabolism Response-2.0-Hs-28 NR3C1 27 0 white rectangle gene 0.5 black 46 17 7978 N-Xenobiotic Metabolism Response-2.0-Hs-4 BRCA1 69 115 white rectangle gene 0.5 black 46 17 1100 N-Xenobiotic Metabolism Response-2.0-Hs-6 UGT1A1 24 99 white rectangle gene 0.5 black 46 17 12530 N-Xenobiotic Metabolism Response-2.0-Hs-9 UGT1A6 40 91 white rectangle gene 0.5 black 46 17 12538 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/Xenobiotic Metabolism Response-2.0-Hs.sif000066400000000000000000000065071426625374700311540ustar00rootroot000000000000000 1 2 N-Xenobiotic Metabolism Response-2.0-Hs-1 2 activation N-Xenobiotic Metabolism Response-2.0-Hs-12 N-Xenobiotic Metabolism Response-2.0-Hs-1 2 activation N-Xenobiotic Metabolism Response-2.0-Hs-12 N-Xenobiotic Metabolism Response-2.0-Hs-1 2 activation N-Xenobiotic Metabolism Response-2.0-Hs-14 N-Xenobiotic Metabolism Response-2.0-Hs-1 2 activation N-Xenobiotic Metabolism Response-2.0-Hs-14 N-Xenobiotic Metabolism Response-2.0-Hs-1 2 activation N-Xenobiotic Metabolism Response-2.0-Hs-20 N-Xenobiotic Metabolism Response-2.0-Hs-25 activation N-Xenobiotic Metabolism Response-2.0-Hs-17 N-Xenobiotic Metabolism Response-2.0-Hs-25 activation N-Xenobiotic Metabolism Response-2.0-Hs-19 N-Xenobiotic Metabolism Response-2.0-Hs-25 activation N-Xenobiotic Metabolism Response-2.0-Hs-20 N-Xenobiotic Metabolism Response-2.0-Hs-28 activation N-Xenobiotic Metabolism Response-2.0-Hs-16 N-Xenobiotic Metabolism Response-2.0-Hs-28 activation N-Xenobiotic Metabolism Response-2.0-Hs-16 N-Xenobiotic Metabolism Response-2.0-Hs-18 inhibition N-Xenobiotic Metabolism Response-2.0-Hs-19 N-Xenobiotic Metabolism Response-2.0-Hs-18 inhibition N-Xenobiotic Metabolism Response-2.0-Hs-20 N-Xenobiotic Metabolism Response-2.0-Hs-18 inhibition N-Xenobiotic Metabolism Response-2.0-Hs-20 N-Xenobiotic Metabolism Response-2.0-Hs-26 activation N-Xenobiotic Metabolism Response-2.0-Hs-24 N-Xenobiotic Metabolism Response-2.0-Hs-26 activation N-Xenobiotic Metabolism Response-2.0-Hs-17 N-Xenobiotic Metabolism Response-2.0-Hs-26 activation N-Xenobiotic Metabolism Response-2.0-Hs-21 N-Xenobiotic Metabolism Response-2.0-Hs-17 activation N-Xenobiotic Metabolism Response-2.0-Hs-23 N-Xenobiotic Metabolism Response-2.0-Hs-27 activation N-Xenobiotic Metabolism Response-2.0-Hs-12 N-Xenobiotic Metabolism Response-2.0-Hs-27 activation N-Xenobiotic Metabolism Response-2.0-Hs-13 N-Xenobiotic Metabolism Response-2.0-Hs-4 activation N-Xenobiotic Metabolism Response-2.0-Hs-25 N-Xenobiotic Metabolism Response-2.0-Hs-4 activation N-Xenobiotic Metabolism Response-2.0-Hs-26 N-Xenobiotic Metabolism Response-2.0-Hs-4 inhibition N-Xenobiotic Metabolism Response-2.0-Hs-19 N-Xenobiotic Metabolism Response-2.0-Hs-4 inhibition N-Xenobiotic Metabolism Response-2.0-Hs-19 N-Xenobiotic Metabolism Response-2.0-Hs-4 inhibition N-Xenobiotic Metabolism Response-2.0-Hs-19 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-21 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-21 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-25 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-6 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-9 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-22 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-13 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-13 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-26 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-18 N-Xenobiotic Metabolism Response-2.0-Hs-20 activation N-Xenobiotic Metabolism Response-2.0-Hs-14 pybel-0.15.5/notebooks/hipathia_demo/cbn/output/mTor-2.0-Hs.att000066400000000000000000000034051426625374700241370ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-mTor-2.0-Hs-1 2 TSC1 TSC2 95 133 white rectangle gene,gene 0.5 black 46 17 12362,/,12363 N-mTor-2.0-Hs-10 STAT 37 2 white rectangle gene 0.5 black 46 17 STAT N-mTor-2.0-Hs-11 RHEB 88 139 white rectangle gene 0.5 black 46 17 10011 N-mTor-2.0-Hs-12 RPS6KA1 29 94 white rectangle gene 0.5 black 46 17 10430 N-mTor-2.0-Hs-13 RPS6KA1 34 85 white rectangle gene 0.5 black 46 17 10430 N-mTor-2.0-Hs-14 RPS6KB1 11 114 white rectangle gene 0.5 black 46 17 10436 N-mTor-2.0-Hs-15 RPS6KB1 3 108 white rectangle gene 0.5 black 46 17 10436 N-mTor-2.0-Hs-16 RPS6KB1 15 124 white rectangle gene 0.5 black 46 17 10436 N-mTor-2.0-Hs-17 RPS6KB1 0 117 white rectangle gene 0.5 black 46 17 10436 N-mTor-2.0-Hs-18 TSC2 84 173 white rectangle gene 0.5 black 46 17 12363 N-mTor-2.0-Hs-19 TSC2 86 167 white rectangle gene 0.5 black 46 17 12363 N-mTor-2.0-Hs-20 TSC2 103 130 white rectangle gene 0.5 black 46 17 12363 N-mTor-2.0-Hs-21 EIF4E 16 31 white rectangle gene 0.5 black 46 17 3287 N-mTor-2.0-Hs-22 EIF4E 22 105 white rectangle gene 0.5 black 46 17 3287 N-mTor-2.0-Hs-23 EIF4EBP1 147 119 white rectangle gene 0.5 black 46 17 3288 N-mTor-2.0-Hs-24 EIF4EBP1 32 24 white rectangle gene 0.5 black 46 17 3288 N-mTor-2.0-Hs-25 EIF4G1 7 32 white rectangle gene 0.5 black 46 17 3296 N-mTor-2.0-Hs-26 SMAD4 150 125 white rectangle gene 0.5 black 46 17 6770 N-mTor-2.0-Hs-3 4 EIF3A EIF4E 32 107 white rectangle gene,gene 0.5 black 46 17 3271,/,3287 N-mTor-2.0-Hs-5 6 EIF4E EIF4EBP1 25 29 white rectangle gene,gene 0.5 black 46 17 3287,/,3288 N-mTor-2.0-Hs-7 AKT 112 128 white rectangle gene 0.5 black 46 17 AKT N-mTor-2.0-Hs-8 JAK 30 0 white rectangle gene 0.5 black 46 17 JAK N-mTor-2.0-Hs-9 PPP2C 5 124 white rectangle gene 0.5 black 46 17 PPP2C pybel-0.15.5/notebooks/hipathia_demo/cbn/output/mTor-2.0-Hs.sif000066400000000000000000000021601426625374700241250ustar00rootroot000000000000000 1 2 N-mTor-2.0-Hs-15 inhibition N-mTor-2.0-Hs-14 N-mTor-2.0-Hs-13 activation N-mTor-2.0-Hs-12 N-mTor-2.0-Hs-19 activation N-mTor-2.0-Hs-18 N-mTor-2.0-Hs-7 activation N-mTor-2.0-Hs-20 N-mTor-2.0-Hs-7 activation N-mTor-2.0-Hs-20 N-mTor-2.0-Hs-7 activation N-mTor-2.0-Hs-20 N-mTor-2.0-Hs-24 inhibition N-mTor-2.0-Hs-5 6 N-mTor-2.0-Hs-24 inhibition N-mTor-2.0-Hs-5 6 N-mTor-2.0-Hs-24 inhibition N-mTor-2.0-Hs-5 6 N-mTor-2.0-Hs-14 activation N-mTor-2.0-Hs-16 N-mTor-2.0-Hs-14 activation N-mTor-2.0-Hs-22 N-mTor-2.0-Hs-12 activation N-mTor-2.0-Hs-22 N-mTor-2.0-Hs-17 activation N-mTor-2.0-Hs-14 N-mTor-2.0-Hs-17 activation N-mTor-2.0-Hs-14 N-mTor-2.0-Hs-25 activation N-mTor-2.0-Hs-21 N-mTor-2.0-Hs-9 inhibition N-mTor-2.0-Hs-14 N-mTor-2.0-Hs-8 activation N-mTor-2.0-Hs-10 N-mTor-2.0-Hs-1 2 inhibition N-mTor-2.0-Hs-11 N-mTor-2.0-Hs-1 2 inhibition N-mTor-2.0-Hs-11 N-mTor-2.0-Hs-1 2 inhibition N-mTor-2.0-Hs-11 N-mTor-2.0-Hs-1 2 inhibition N-mTor-2.0-Hs-11 N-mTor-2.0-Hs-5 6 inhibition N-mTor-2.0-Hs-21 N-mTor-2.0-Hs-22 activation N-mTor-2.0-Hs-3 4 N-mTor-2.0-Hs-26 activation N-mTor-2.0-Hs-23 N-mTor-2.0-Hs-20 inhibition N-mTor-2.0-Hs-1 2 pybel-0.15.5/notebooks/hipathia_demo/covid19/000077500000000000000000000000001426625374700210115ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/covid19/COVID-19 Knowledge Graph.att000066400000000000000000000402211426625374700255370ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-COVID-19 Knowledge Graph-1 2 lopinavir 3.4.22.69 129 23 white rectangle gene,gene 0.5 black 46 17 31781,/,3.4.22.69 N-COVID-19 Knowledge Graph-101 G3BP1 7 52 white rectangle gene 0.5 black 46 17 30292 N-COVID-19 Knowledge Graph-102 DUSP1 35 136 white rectangle gene 0.5 black 46 17 3064 N-COVID-19 Knowledge Graph-103 EIF2A 68 83 white rectangle gene 0.5 black 46 17 3254 N-COVID-19 Knowledge Graph-104 EIF2AK3 78 82 white rectangle gene 0.5 black 46 17 3255 N-COVID-19 Knowledge Graph-105 EIF2S1 159 58 white rectangle gene 0.5 black 46 17 3265 N-COVID-19 Knowledge Graph-106 EIF2S1 81 80 white rectangle gene 0.5 black 46 17 3265 N-COVID-19 Knowledge Graph-107 EIF4A1 3 50 white rectangle gene 0.5 black 46 17 3282 N-COVID-19 Knowledge Graph-109 ERN1 78 65 white rectangle gene 0.5 black 46 17 3449 N-COVID-19 Knowledge Graph-110 ERN1 75 60 white rectangle gene 0.5 black 46 17 3449 N-COVID-19 Knowledge Graph-111 FGA 95 68 white rectangle gene 0.5 black 46 17 3661 N-COVID-19 Knowledge Graph-112 FGB 98 74 white rectangle gene 0.5 black 46 17 3662 N-COVID-19 Knowledge Graph-113 FGG 93 70 white rectangle gene 0.5 black 46 17 3694 N-COVID-19 Knowledge Graph-114 AKT1 77 59 white rectangle gene 0.5 black 46 17 391 N-COVID-19 Knowledge Graph-115 MTOR 154 33 white rectangle gene 0.5 black 46 17 3942 N-COVID-19 Knowledge Graph-116 GFAP 95 158 white rectangle gene 0.5 black 46 17 4235 N-COVID-19 Knowledge Graph-117 GPT 102 75 white rectangle gene 0.5 black 46 17 4552 N-COVID-19 Knowledge Graph-118 HAS2 83 68 white rectangle gene 0.5 black 46 17 4819 N-COVID-19 Knowledge Graph-119 HDAC2 185 93 white rectangle gene 0.5 black 46 17 4853 N-COVID-19 Knowledge Graph-121 HSPA5 95 71 white rectangle gene 0.5 black 46 17 5238 N-COVID-19 Knowledge Graph-122 HSPB1 91 156 white rectangle gene 0.5 black 46 17 5246 N-COVID-19 Knowledge Graph-123 HYAL1 81 62 white rectangle gene 0.5 black 46 17 5320 N-COVID-19 Knowledge Graph-124 IFNA1 117 102 white rectangle gene 0.5 black 46 17 5417 N-COVID-19 Knowledge Graph-125 IFNAR1 99 78 white rectangle gene 0.5 black 46 17 5432 N-COVID-19 Knowledge Graph-126 IFNAR1 96 79 white rectangle gene 0.5 black 46 17 5432 N-COVID-19 Knowledge Graph-127 IFNAR1 98 76 white rectangle gene 0.5 black 46 17 5432 N-COVID-19 Knowledge Graph-128 IFNB1 93 114 white rectangle gene 0.5 black 46 17 5434 N-COVID-19 Knowledge Graph-129 IFNB1 107 112 white rectangle gene 0.5 black 46 17 5434 N-COVID-19 Knowledge Graph-130 IFNG 117 104 white rectangle gene 0.5 black 46 17 5438 N-COVID-19 Knowledge Graph-131 ATG5 106 119 white rectangle gene 0.5 black 46 17 589 N-COVID-19 Knowledge Graph-132 IL1B 87 68 white rectangle gene 0.5 black 46 17 5992 N-COVID-19 Knowledge Graph-133 IL6 85 73 white rectangle gene 0.5 black 46 17 6018 N-COVID-19 Knowledge Graph-137 IRF3 83 109 white rectangle gene 0.5 black 46 17 6118 N-COVID-19 Knowledge Graph-138 IRF3 99 112 white rectangle gene 0.5 black 46 17 6118 N-COVID-19 Knowledge Graph-139 IRF7 88 117 white rectangle gene 0.5 black 46 17 6122 N-COVID-19 Knowledge Graph-140 NAE1 90 4 white rectangle gene 0.5 black 46 17 621 N-COVID-19 Knowledge Graph-141 LY6E 43 1 white rectangle gene 0.5 black 46 17 6727 N-COVID-19 Knowledge Graph-142 MAP1LC3A 105 121 white rectangle gene 0.5 black 46 17 6838 N-COVID-19 Knowledge Graph-143 MAP3K7 82 73 white rectangle gene 0.5 black 46 17 6859 N-COVID-19 Knowledge Graph-144 MAPK1 78 86 white rectangle gene 0.5 black 46 17 6871 N-COVID-19 Knowledge Graph-145 MAPK1 32 134 white rectangle gene 0.5 black 46 17 6871 N-COVID-19 Knowledge Graph-146 MAPK14 31 131 white rectangle gene 0.5 black 46 17 6876 N-COVID-19 Knowledge Graph-148 MAPK3 96 159 white rectangle gene 0.5 black 46 17 6877 N-COVID-19 Knowledge Graph-149 MAPK8 58 59 white rectangle gene 0.5 black 46 17 6881 N-COVID-19 Knowledge Graph-150 MAPK8 74 62 white rectangle gene 0.5 black 46 17 6881 N-COVID-19 Knowledge Graph-151 MAPK8 33 133 white rectangle gene 0.5 black 46 17 6881 N-COVID-19 Knowledge Graph-153 MCL1 74 93 white rectangle gene 0.5 black 46 17 6943 N-COVID-19 Knowledge Graph-154 MYD88 94 99 white rectangle gene 0.5 black 46 17 7562 N-COVID-19 Knowledge Graph-155 NFKBIA 158 61 white rectangle gene 0.5 black 46 17 7797 N-COVID-19 Knowledge Graph-156 NFKBIA 162 61 white rectangle gene 0.5 black 46 17 7797 N-COVID-19 Knowledge Graph-157 ATF3 35 139 white rectangle gene 0.5 black 46 17 785 N-COVID-19 Knowledge Graph-159 ATF4 88 80 white rectangle gene 0.5 black 46 17 786 N-COVID-19 Knowledge Graph-160 ATF6 77 88 white rectangle gene 0.5 black 46 17 791 N-COVID-19 Knowledge Graph-161 ATF6 151 126 white rectangle gene 0.5 black 46 17 791 N-COVID-19 Knowledge Graph-163 PRKAA1 86 82 white rectangle gene 0.5 black 46 17 9376 N-COVID-19 Knowledge Graph-164 EIF2AK2 74 83 white rectangle gene 0.5 black 46 17 9437 N-COVID-19 Knowledge Graph-165 DNAJC3 114 13 white rectangle gene 0.5 black 46 17 9439 N-COVID-19 Knowledge Graph-166 BCL2 80 87 white rectangle gene 0.5 black 46 17 990 N-COVID-19 Knowledge Graph-167 BCL2L11 81 88 white rectangle gene 0.5 black 46 17 994 N-COVID-19 Knowledge Graph-168 tryptase Clara 124 158 white rectangle gene 0.5 black 46 17 C075431 N-COVID-19 Knowledge Graph-170 Interferon Type I 92 102 white rectangle gene 0.5 black 46 17 D007370 N-COVID-19 Knowledge Graph-171 Proteins 85 5 white rectangle gene 0.5 black 46 17 D011506 N-COVID-19 Knowledge Graph-172 Viral Envelope Proteins 172 138 white rectangle gene 0.5 black 46 17 D014759 N-COVID-19 Knowledge Graph-173 Viral Matrix Proteins 143 13 white rectangle gene 0.5 black 46 17 D014763 N-COVID-19 Knowledge Graph-175 Spike Glycoprotein, Coronavirus 60 21 white rectangle gene 0.5 black 46 17 D064370 N-COVID-19 Knowledge Graph-176 Spike Glycoprotein, Coronavirus 0 128 white rectangle gene 0.5 black 46 17 D064370 N-COVID-19 Knowledge Graph-177 Tnf 64 182 white rectangle gene 0.5 black 46 17 104798 N-COVID-19 Knowledge Graph-178 Ace2 106 144 white rectangle gene 0.5 black 46 17 1917258 N-COVID-19 Knowledge Graph-179 Arg1 66 172 white rectangle gene 0.5 black 46 17 88070 N-COVID-19 Knowledge Graph-180 Dpp4 65 176 white rectangle gene 0.5 black 46 17 94919 N-COVID-19 Knowledge Graph-181 Il6 62 178 white rectangle gene 0.5 black 46 17 96559 N-COVID-19 Knowledge Graph-182 Actin 103 121 white rectangle gene 0.5 black 46 17 PF00022 N-COVID-19 Knowledge Graph-183 Trypsin 122 159 white rectangle gene 0.5 black 46 17 PF00089 N-COVID-19 Knowledge Graph-185 NKAP 96 101 white rectangle gene 0.5 black 46 17 PF15692 N-COVID-19 Knowledge Graph-186 A0A0D3MU52_9BETC 36 105 white rectangle gene 0.5 black 46 17 A0A0D3MU52 N-COVID-19 Knowledge Graph-187 A0A0D9R1K1_CHLSB 18 48 white rectangle gene 0.5 black 46 17 A0A0D9R1K1 N-COVID-19 Knowledge Graph-19 20 CLEC4M Spike Glycoprotein, Coronavirus 58 17 white rectangle gene,gene 0.5 black 46 17 13523,/,D064370 N-COVID-19 Knowledge Graph-190 A0A0D9RBG0_CHLSB 15 51 white rectangle gene 0.5 black 46 17 A0A0D9RBG0 N-COVID-19 Knowledge Graph-191 A0A0D9RCP6_CHLSB 15 55 white rectangle gene 0.5 black 46 17 A0A0D9RCP6 N-COVID-19 Knowledge Graph-192 A0A0D9RCS6_CHLSB 26 56 white rectangle gene 0.5 black 46 17 A0A0D9RCS6 N-COVID-19 Knowledge Graph-193 A0A0D9REG4_CHLSB 101 68 white rectangle gene 0.5 black 46 17 A0A0D9REG4 N-COVID-19 Knowledge Graph-195 A0A0D9RP74_CHLSB 97 70 white rectangle gene 0.5 black 46 17 A0A0D9RP74 N-COVID-19 Knowledge Graph-197 A0A0D9RUU7_CHLSB 115 154 white rectangle gene 0.5 black 46 17 A0A0D9RUU7 N-COVID-19 Knowledge Graph-198 A0A0D9S017_CHLSB 16 49 white rectangle gene 0.5 black 46 17 A0A0D9S017 N-COVID-19 Knowledge Graph-199 A0A0D9S1P0_CHLSB 98 79 white rectangle gene 0.5 black 46 17 A0A0D9S1P0 N-COVID-19 Knowledge Graph-200 A0A0D9S8I4_CHLSB 102 68 white rectangle gene 0.5 black 46 17 A0A0D9S8I4 N-COVID-19 Knowledge Graph-202 A0A0D9SAF4_CHLSB 11 56 white rectangle gene 0.5 black 46 17 A0A0D9SAF4 N-COVID-19 Knowledge Graph-204 G4XXN2_CHLAE 100 75 white rectangle gene 0.5 black 46 17 G4XXN2 N-COVID-19 Knowledge Graph-206 ORF4B_CVEMC 84 114 white rectangle gene 0.5 black 46 17 K9N643 N-COVID-19 Knowledge Graph-207 M2_I34A1 88 61 white rectangle gene 0.5 black 46 17 P06821 N-COVID-19 Knowledge Graph-21 22 ACE2 Spike Glycoprotein, Coronavirus 63 24 white rectangle gene,gene 0.5 black 46 17 13557,/,D064370 N-COVID-19 Knowledge Graph-210 R1AB_CVHSA 37 112 white rectangle gene 0.5 black 46 17 P0C6X7 N-COVID-19 Knowledge Graph-211 R1AB_CVHSA 89 162 white rectangle gene 0.5 black 46 17 P0C6X7 N-COVID-19 Knowledge Graph-212 R1AB_CVHSA 44 162 white rectangle gene 0.5 black 46 17 P0C6X7 N-COVID-19 Knowledge Graph-214 R1AB_CVHSA 92 159 white rectangle gene 0.5 black 46 17 P0C6X7 N-COVID-19 Knowledge Graph-215 R1AB_CVHSA 41 167 white rectangle gene 0.5 black 46 17 P0C6X7 N-COVID-19 Knowledge Graph-216 R1AB_CVHSA 42 27 white rectangle gene 0.5 black 46 17 P0C6X7 N-COVID-19 Knowledge Graph-218 SPIKE_CVH22 45 0 white rectangle gene 0.5 black 46 17 P15423 N-COVID-19 Knowledge Graph-219 SPIKE_CVHSA 113 150 white rectangle gene 0.5 black 46 17 P59594 N-COVID-19 Knowledge Graph-220 SPIKE_CVHSA 120 154 white rectangle gene 0.5 black 46 17 P59594 N-COVID-19 Knowledge Graph-221 NCAP_CVHSA 19 52 white rectangle gene 0.5 black 46 17 P59595 N-COVID-19 Knowledge Graph-222 AP3A_CVHSA 93 75 white rectangle gene 0.5 black 46 17 P59632 N-COVID-19 Knowledge Graph-224 AP3A_CVHSA 98 71 white rectangle gene 0.5 black 46 17 P59632 N-COVID-19 Knowledge Graph-227 AP3A_CVHSA 106 66 white rectangle gene 0.5 black 46 17 P59632 N-COVID-19 Knowledge Graph-228 AP3A_CVHSA 104 64 white rectangle gene 0.5 black 46 17 P59632 N-COVID-19 Knowledge Graph-229 AP3A_CVHSA 171 116 white rectangle gene 0.5 black 46 17 P59632 N-COVID-19 Knowledge Graph-231 AP3A_CVHSA 105 65 white rectangle gene 0.5 black 46 17 P59632 N-COVID-19 Knowledge Graph-232 ORF9B_CVHSA 102 116 white rectangle gene 0.5 black 46 17 P59636 N-COVID-19 Knowledge Graph-233 VEMP_CVHSA 90 63 white rectangle gene 0.5 black 46 17 P59637 N-COVID-19 Knowledge Graph-237 Q19QW2_CVHSA 79 111 white rectangle gene 0.5 black 46 17 Q19QW2 N-COVID-19 Knowledge Graph-238 Q19QW5_CVHSA 164 98 white rectangle gene 0.5 black 46 17 Q19QW5 N-COVID-19 Knowledge Graph-240 POLG_EMCVR 85 63 white rectangle gene 0.5 black 46 17 Q66765 N-COVID-19 Knowledge Graph-241 Q6S8E2_CVHSA 81 113 white rectangle gene 0.5 black 46 17 Q6S8E2 N-COVID-19 Knowledge Graph-242 R9UNW8_9BETC 48 108 white rectangle gene 0.5 black 46 17 R9UNW8 N-COVID-19 Knowledge Graph-25 26 CANX Spike Glycoprotein, Coronavirus 62 18 white rectangle gene,gene 0.5 black 46 17 1473,/,D064370 N-COVID-19 Knowledge Graph-27 28 CD209 Spike Glycoprotein, Coronavirus 59 25 white rectangle gene,gene 0.5 black 46 17 1641,/,D064370 N-COVID-19 Knowledge Graph-3 4 lopinavir 3.4.22.69 0 80 white rectangle gene,gene 0.5 black 46 17 31781,/,3.4.22.69 N-COVID-19 Knowledge Graph-31 32 EIF2AK2 RNA, Double-Stranded 68 85 white rectangle gene,gene 0.5 black 46 17 9437,/,D012330 N-COVID-19 Knowledge Graph-33 34 Immunoglobulin G SPIKE_CVHSA 113 155 white rectangle gene,gene 0.5 black 46 17 D007074,/,P59594 N-COVID-19 Knowledge Graph-35 36 Ace2 SPIKE_CVHSA 101 141 white rectangle gene,gene 0.5 black 46 17 1917258,/,P59594 N-COVID-19 Knowledge Graph-37 38 AP3A_CVHSA AP3A_CVHSA 173 115 white rectangle gene,gene 0.5 black 46 17 P59632,/,P59632 N-COVID-19 Knowledge Graph-39 3.2.1.20 62 13 white rectangle gene 0.5 black 46 17 3.2.1.20 N-COVID-19 Knowledge Graph-40 3.4.22.69 131 20 white rectangle gene 0.5 black 46 17 3.4.22.69 N-COVID-19 Knowledge Graph-41 3.4.22.69 0 83 white rectangle gene 0.5 black 46 17 3.4.22.69 N-COVID-19 Knowledge Graph-42 NF-kappa B complex subunits 46 111 white rectangle gene 0.5 black 46 17 1254 N-COVID-19 Knowledge Graph-45 Interferons 41 106 white rectangle gene 0.5 black 46 17 598 N-COVID-19 Knowledge Graph-46 Protein phosphatase 1 regulatory subunits 78 77 white rectangle gene 0.5 black 46 17 694 N-COVID-19 Knowledge Graph-48 BECN1 27 153 white rectangle gene 0.5 black 46 17 1034 N-COVID-19 Knowledge Graph-49 BECN1 25 152 white rectangle gene 0.5 black 46 17 1034 N-COVID-19 Knowledge Graph-5 6 ritonavir 3.4.22.69 133 17 white rectangle gene,gene 0.5 black 46 17 45409,/,3.4.22.69 N-COVID-19 Knowledge Graph-50 CCL2 91 80 white rectangle gene 0.5 black 46 17 10618 N-COVID-19 Knowledge Graph-51 CCL3 31 113 white rectangle gene 0.5 black 46 17 10627 N-COVID-19 Knowledge Graph-52 CCL5 32 111 white rectangle gene 0.5 black 46 17 10632 N-COVID-19 Knowledge Graph-53 CXCL10 33 115 white rectangle gene 0.5 black 46 17 10637 N-COVID-19 Knowledge Graph-54 SKP2 29 149 white rectangle gene 0.5 black 46 17 10901 N-COVID-19 Knowledge Graph-55 STAT1 159 98 white rectangle gene 0.5 black 46 17 11362 N-COVID-19 Knowledge Graph-56 STAT1 72 92 white rectangle gene 0.5 black 46 17 11362 N-COVID-19 Knowledge Graph-57 TBK1 56 110 white rectangle gene 0.5 black 46 17 11584 N-COVID-19 Knowledge Graph-58 TBK1 92 119 white rectangle gene 0.5 black 46 17 11584 N-COVID-19 Knowledge Graph-59 TGFB1 94 165 white rectangle gene 0.5 black 46 17 11766 N-COVID-19 Knowledge Graph-60 TLR3 111 103 white rectangle gene 0.5 black 46 17 11849 N-COVID-19 Knowledge Graph-61 TMPRSS2 127 158 white rectangle gene 0.5 black 46 17 11876 N-COVID-19 Knowledge Graph-62 TNF 85 75 white rectangle gene 0.5 black 46 17 11892 N-COVID-19 Knowledge Graph-63 HSP90B1 100 73 white rectangle gene 0.5 black 46 17 12028 N-COVID-19 Knowledge Graph-64 TRAF2 70 63 white rectangle gene 0.5 black 46 17 12032 N-COVID-19 Knowledge Graph-66 VIM 92 164 white rectangle gene 0.5 black 46 17 12692 N-COVID-19 Knowledge Graph-67 XBP1 117 12 white rectangle gene 0.5 black 46 17 12801 N-COVID-19 Knowledge Graph-68 ERO1A 77 84 white rectangle gene 0.5 black 46 17 13280 N-COVID-19 Knowledge Graph-69 ACE2 94 75 white rectangle gene 0.5 black 46 17 13557 N-COVID-19 Knowledge Graph-7 8 ritonavir 3.4.22.69 0 85 white rectangle gene,gene 0.5 black 46 17 45409,/,3.4.22.69 N-COVID-19 Knowledge Graph-70 ACE2 131 160 white rectangle gene 0.5 black 46 17 13557 N-COVID-19 Knowledge Graph-71 ACE2 130 163 white rectangle gene 0.5 black 46 17 13557 N-COVID-19 Knowledge Graph-72 PPP1R15A 82 77 white rectangle gene 0.5 black 46 17 14375 N-COVID-19 Knowledge Graph-73 CASP1 88 63 white rectangle gene 0.5 black 46 17 1499 N-COVID-19 Knowledge Graph-74 MBTPS2 154 128 white rectangle gene 0.5 black 46 17 15455 N-COVID-19 Knowledge Graph-75 MBTPS1 148 127 white rectangle gene 0.5 black 46 17 15456 N-COVID-19 Knowledge Graph-76 IL1F10 86 61 white rectangle gene 0.5 black 46 17 15552 N-COVID-19 Knowledge Graph-77 IL37 89 82 white rectangle gene 0.5 black 46 17 15563 N-COVID-19 Knowledge Graph-78 TLR7 106 106 white rectangle gene 0.5 black 46 17 15631 N-COVID-19 Knowledge Graph-79 TLR8 108 105 white rectangle gene 0.5 black 46 17 15632 N-COVID-19 Knowledge Graph-80 TLR9 107 103 white rectangle gene 0.5 black 46 17 15633 N-COVID-19 Knowledge Graph-81 TRIB3 84 87 white rectangle gene 0.5 black 46 17 16228 N-COVID-19 Knowledge Graph-82 TICAM1 104 103 white rectangle gene 0.5 black 46 17 18348 N-COVID-19 Knowledge Graph-83 RNF41 69 109 white rectangle gene 0.5 black 46 17 18401 N-COVID-19 Knowledge Graph-84 IFIH1 107 107 white rectangle gene 0.5 black 46 17 18873 N-COVID-19 Knowledge Graph-85 EDEM1 119 10 white rectangle gene 0.5 black 46 17 18967 N-COVID-19 Knowledge Graph-86 CASP12 63 61 white rectangle gene 0.5 black 46 17 19004 N-COVID-19 Knowledge Graph-87 DDX58 105 108 white rectangle gene 0.5 black 46 17 19102 N-COVID-19 Knowledge Graph-89 CGAS 106 103 white rectangle gene 0.5 black 46 17 21367 N-COVID-19 Knowledge Graph-9 10 viral replication complex R1AB_CVHSA 45 31 white rectangle gene,gene 0.5 black 46 17 0019034,/,P0C6X7 N-COVID-19 Knowledge Graph-90 TMPRSS11D 122 150 white rectangle gene 0.5 black 46 17 24059 N-COVID-19 Knowledge Graph-91 EIF2AK1 75 79 white rectangle gene 0.5 black 46 17 24921 N-COVID-19 Knowledge Graph-92 CTSL 3 127 white rectangle gene 0.5 black 46 17 2537 N-COVID-19 Knowledge Graph-93 CUL2 88 6 white rectangle gene 0.5 black 46 17 2552 N-COVID-19 Knowledge Graph-95 DDIT3 82 83 white rectangle gene 0.5 black 46 17 2726 N-COVID-19 Knowledge Graph-96 MAVS 103 106 white rectangle gene 0.5 black 46 17 29233 N-COVID-19 Knowledge Graph-97 LARP1 150 35 white rectangle gene 0.5 black 46 17 29531 N-COVID-19 Knowledge Graph-98 DNM1L 107 116 white rectangle gene 0.5 black 46 17 2973 N-COVID-19 Knowledge Graph-99 DNM1L 101 121 white rectangle gene 0.5 black 46 17 2973 pybel-0.15.5/notebooks/hipathia_demo/covid19/COVID-19 Knowledge 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&sϜGvښ;w$}$mV[.^ZRv?7G}W_}Uz*nW>}5kϯ+lUڴiqiԩ*,,ݻ[n馛{iРAzwrk!(Pُ":r|Kҭ7@NS͓$͛7O͚5S˖mƌG}[_~_hѢ O#@BYY}Jh4O?UϞ=5f8u"IFˑ}O6mՠA͝;W[nՎ;\}~!-ZaÆ_~2eJN>}w;wnT1 r\%ڗ.]*Ijܸq cǎzꩧÒjCStoq~>~7/X%3vARTM͛5j( s=RϪU4h uIs)L3F>LW)z81brrrԷo_5iDZn>3EEE9V,Y8= T||Fh6uKrd6TϢ7W)Un(&?UR3Z?yR$$וnbb֐!C4vX-ZH;vTTTE?obbe0Կm%u]'Icx;6܁mY|fupe9:K=0M ﾧȼ͜?:G[dll`܁m[da?[tu˟ӂ[ֈG^}FƖ_yѬ\u5[wf;GO]zsd8ht8h590tf͙zon}qrY{^v` ̺g/N?8҅m^5'm5㕾<7+WktKh\o ::TùU l>0IENDB`pybel-0.15.5/notebooks/hipathia_demo/covid19/COVID-19 Knowledge Graph.sif000066400000000000000000000352351426625374700255410ustar00rootroot000000000000000 1 2 N-COVID-19 Knowledge Graph-31 32 activation N-COVID-19 Knowledge Graph-164 N-COVID-19 Knowledge Graph-33 34 inhibition N-COVID-19 Knowledge Graph-219 N-COVID-19 Knowledge Graph-35 36 inhibition N-COVID-19 Knowledge Graph-178 N-COVID-19 Knowledge Graph-39 inhibition N-COVID-19 Knowledge Graph-25 26 N-COVID-19 Knowledge Graph-40 activation N-COVID-19 Knowledge Graph-1 2 N-COVID-19 Knowledge Graph-40 activation N-COVID-19 Knowledge Graph-5 6 N-COVID-19 Knowledge Graph-41 activation N-COVID-19 Knowledge Graph-3 4 N-COVID-19 Knowledge Graph-41 activation N-COVID-19 Knowledge Graph-7 8 N-COVID-19 Knowledge Graph-46 inhibition N-COVID-19 Knowledge Graph-106 N-COVID-19 Knowledge Graph-54 inhibition N-COVID-19 Knowledge Graph-48 N-COVID-19 Knowledge Graph-54 activation N-COVID-19 Knowledge Graph-49 N-COVID-19 Knowledge Graph-57 activation N-COVID-19 Knowledge Graph-42 N-COVID-19 Knowledge Graph-58 activation N-COVID-19 Knowledge Graph-128 N-COVID-19 Knowledge Graph-60 activation N-COVID-19 Knowledge Graph-82 N-COVID-19 Knowledge Graph-60 activation N-COVID-19 Knowledge Graph-96 N-COVID-19 Knowledge Graph-60 activation N-COVID-19 Knowledge Graph-124 N-COVID-19 Knowledge Graph-60 activation N-COVID-19 Knowledge Graph-130 N-COVID-19 Knowledge Graph-61 activation N-COVID-19 Knowledge Graph-70 N-COVID-19 Knowledge Graph-61 activation N-COVID-19 Knowledge Graph-71 N-COVID-19 Knowledge Graph-61 activation N-COVID-19 Knowledge Graph-220 N-COVID-19 Knowledge Graph-62 activation N-COVID-19 Knowledge Graph-118 N-COVID-19 Knowledge Graph-64 activation N-COVID-19 Knowledge Graph-86 N-COVID-19 Knowledge Graph-67 activation N-COVID-19 Knowledge Graph-67 N-COVID-19 Knowledge Graph-67 activation N-COVID-19 Knowledge Graph-85 N-COVID-19 Knowledge Graph-67 activation N-COVID-19 Knowledge Graph-165 N-COVID-19 Knowledge Graph-69 activation N-COVID-19 Knowledge Graph-133 N-COVID-19 Knowledge Graph-72 activation N-COVID-19 Knowledge Graph-46 N-COVID-19 Knowledge Graph-72 activation N-COVID-19 Knowledge Graph-62 N-COVID-19 Knowledge Graph-72 inhibition N-COVID-19 Knowledge Graph-106 N-COVID-19 Knowledge Graph-72 activation N-COVID-19 Knowledge Graph-133 N-COVID-19 Knowledge Graph-72 inhibition N-COVID-19 Knowledge Graph-143 N-COVID-19 Knowledge Graph-73 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-74 activation N-COVID-19 Knowledge Graph-161 N-COVID-19 Knowledge Graph-75 activation N-COVID-19 Knowledge Graph-161 N-COVID-19 Knowledge Graph-76 inhibition N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-77 inhibition N-COVID-19 Knowledge Graph-50 N-COVID-19 Knowledge Graph-77 inhibition N-COVID-19 Knowledge Graph-62 N-COVID-19 Knowledge Graph-77 inhibition N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-77 inhibition N-COVID-19 Knowledge Graph-133 N-COVID-19 Knowledge Graph-77 inhibition N-COVID-19 Knowledge Graph-154 N-COVID-19 Knowledge Graph-77 activation N-COVID-19 Knowledge Graph-163 N-COVID-19 Knowledge Graph-78 activation N-COVID-19 Knowledge Graph-82 N-COVID-19 Knowledge Graph-78 activation N-COVID-19 Knowledge Graph-96 N-COVID-19 Knowledge Graph-79 activation N-COVID-19 Knowledge Graph-82 N-COVID-19 Knowledge Graph-79 activation N-COVID-19 Knowledge Graph-96 N-COVID-19 Knowledge Graph-80 activation N-COVID-19 Knowledge Graph-82 N-COVID-19 Knowledge Graph-80 activation N-COVID-19 Knowledge Graph-96 N-COVID-19 Knowledge Graph-82 activation N-COVID-19 Knowledge Graph-154 N-COVID-19 Knowledge Graph-83 activation N-COVID-19 Knowledge Graph-57 N-COVID-19 Knowledge Graph-83 activation N-COVID-19 Knowledge Graph-137 N-COVID-19 Knowledge Graph-84 activation N-COVID-19 Knowledge Graph-82 N-COVID-19 Knowledge Graph-84 activation N-COVID-19 Knowledge Graph-96 N-COVID-19 Knowledge Graph-86 activation N-COVID-19 Knowledge Graph-149 N-COVID-19 Knowledge Graph-87 activation N-COVID-19 Knowledge Graph-82 N-COVID-19 Knowledge Graph-87 activation N-COVID-19 Knowledge Graph-96 N-COVID-19 Knowledge Graph-89 activation N-COVID-19 Knowledge Graph-82 N-COVID-19 Knowledge Graph-89 activation N-COVID-19 Knowledge Graph-96 N-COVID-19 Knowledge Graph-90 activation N-COVID-19 Knowledge Graph-220 N-COVID-19 Knowledge Graph-91 activation N-COVID-19 Knowledge Graph-106 N-COVID-19 Knowledge Graph-92 activation N-COVID-19 Knowledge Graph-176 N-COVID-19 Knowledge Graph-93 activation N-COVID-19 Knowledge Graph-171 N-COVID-19 Knowledge Graph-95 activation N-COVID-19 Knowledge Graph-68 N-COVID-19 Knowledge Graph-95 activation N-COVID-19 Knowledge Graph-72 N-COVID-19 Knowledge Graph-95 activation N-COVID-19 Knowledge Graph-81 N-COVID-19 Knowledge Graph-95 inhibition N-COVID-19 Knowledge Graph-144 N-COVID-19 Knowledge Graph-95 inhibition N-COVID-19 Knowledge Graph-166 N-COVID-19 Knowledge Graph-95 activation N-COVID-19 Knowledge Graph-167 N-COVID-19 Knowledge Graph-96 activation N-COVID-19 Knowledge Graph-129 N-COVID-19 Knowledge Graph-96 activation N-COVID-19 Knowledge Graph-138 N-COVID-19 Knowledge Graph-96 activation N-COVID-19 Knowledge Graph-154 N-COVID-19 Knowledge Graph-97 inhibition N-COVID-19 Knowledge Graph-115 N-COVID-19 Knowledge Graph-98 activation N-COVID-19 Knowledge Graph-129 N-COVID-19 Knowledge Graph-102 inhibition N-COVID-19 Knowledge Graph-145 N-COVID-19 Knowledge Graph-102 inhibition N-COVID-19 Knowledge Graph-151 N-COVID-19 Knowledge Graph-104 activation N-COVID-19 Knowledge Graph-95 N-COVID-19 Knowledge Graph-104 activation N-COVID-19 Knowledge Graph-106 N-COVID-19 Knowledge Graph-105 inhibition N-COVID-19 Knowledge Graph-155 N-COVID-19 Knowledge Graph-106 inhibition N-COVID-19 Knowledge Graph-160 N-COVID-19 Knowledge Graph-106 inhibition N-COVID-19 Knowledge Graph-160 N-COVID-19 Knowledge Graph-107 inhibition N-COVID-19 Knowledge Graph-101 N-COVID-19 Knowledge Graph-109 activation N-COVID-19 Knowledge Graph-64 N-COVID-19 Knowledge Graph-109 activation N-COVID-19 Knowledge Graph-110 N-COVID-19 Knowledge Graph-109 inhibition N-COVID-19 Knowledge Graph-114 N-COVID-19 Knowledge Graph-109 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-109 activation N-COVID-19 Knowledge Graph-133 N-COVID-19 Knowledge Graph-109 activation N-COVID-19 Knowledge Graph-150 N-COVID-19 Knowledge Graph-117 activation N-COVID-19 Knowledge Graph-69 N-COVID-19 Knowledge Graph-119 activation N-COVID-19 Knowledge Graph-119 N-COVID-19 Knowledge Graph-123 inhibition N-COVID-19 Knowledge Graph-118 N-COVID-19 Knowledge Graph-131 activation N-COVID-19 Knowledge Graph-232 N-COVID-19 Knowledge Graph-132 activation N-COVID-19 Knowledge Graph-118 N-COVID-19 Knowledge Graph-137 activation N-COVID-19 Knowledge Graph-128 N-COVID-19 Knowledge Graph-138 activation N-COVID-19 Knowledge Graph-128 N-COVID-19 Knowledge Graph-139 activation N-COVID-19 Knowledge Graph-128 N-COVID-19 Knowledge Graph-140 activation N-COVID-19 Knowledge Graph-93 N-COVID-19 Knowledge Graph-141 inhibition N-COVID-19 Knowledge Graph-218 N-COVID-19 Knowledge Graph-141 inhibition N-COVID-19 Knowledge Graph-218 N-COVID-19 Knowledge Graph-143 inhibition N-COVID-19 Knowledge Graph-62 N-COVID-19 Knowledge Graph-143 inhibition N-COVID-19 Knowledge Graph-133 N-COVID-19 Knowledge Graph-146 inhibition N-COVID-19 Knowledge Graph-145 N-COVID-19 Knowledge Graph-146 inhibition N-COVID-19 Knowledge Graph-151 N-COVID-19 Knowledge Graph-154 activation N-COVID-19 Knowledge Graph-137 N-COVID-19 Knowledge Graph-154 activation N-COVID-19 Knowledge Graph-170 N-COVID-19 Knowledge Graph-154 activation N-COVID-19 Knowledge Graph-185 N-COVID-19 Knowledge Graph-156 activation N-COVID-19 Knowledge Graph-155 N-COVID-19 Knowledge Graph-157 activation N-COVID-19 Knowledge Graph-102 N-COVID-19 Knowledge Graph-159 activation N-COVID-19 Knowledge Graph-95 N-COVID-19 Knowledge Graph-160 inhibition N-COVID-19 Knowledge Graph-56 N-COVID-19 Knowledge Graph-160 activation N-COVID-19 Knowledge Graph-95 N-COVID-19 Knowledge Graph-160 inhibition N-COVID-19 Knowledge Graph-153 N-COVID-19 Knowledge Graph-164 activation N-COVID-19 Knowledge Graph-95 N-COVID-19 Knowledge Graph-164 activation N-COVID-19 Knowledge Graph-103 N-COVID-19 Knowledge Graph-164 activation N-COVID-19 Knowledge Graph-106 N-COVID-19 Knowledge Graph-168 activation N-COVID-19 Knowledge Graph-220 N-COVID-19 Knowledge Graph-172 activation N-COVID-19 Knowledge Graph-172 N-COVID-19 Knowledge Graph-173 activation N-COVID-19 Knowledge Graph-173 N-COVID-19 Knowledge Graph-175 activation N-COVID-19 Knowledge Graph-19 20 N-COVID-19 Knowledge Graph-175 activation N-COVID-19 Knowledge Graph-21 22 N-COVID-19 Knowledge Graph-175 activation N-COVID-19 Knowledge Graph-25 26 N-COVID-19 Knowledge Graph-175 activation N-COVID-19 Knowledge Graph-27 28 N-COVID-19 Knowledge Graph-175 activation N-COVID-19 Knowledge Graph-175 N-COVID-19 Knowledge Graph-180 activation N-COVID-19 Knowledge Graph-177 N-COVID-19 Knowledge Graph-180 activation N-COVID-19 Knowledge Graph-179 N-COVID-19 Knowledge Graph-180 activation N-COVID-19 Knowledge Graph-181 N-COVID-19 Knowledge Graph-183 activation N-COVID-19 Knowledge Graph-220 N-COVID-19 Knowledge Graph-186 inhibition N-COVID-19 Knowledge Graph-45 N-COVID-19 Knowledge Graph-191 activation N-COVID-19 Knowledge Graph-202 N-COVID-19 Knowledge Graph-197 inhibition N-COVID-19 Knowledge Graph-219 N-COVID-19 Knowledge Graph-197 activation N-COVID-19 Knowledge Graph-219 N-COVID-19 Knowledge Graph-206 inhibition N-COVID-19 Knowledge Graph-137 N-COVID-19 Knowledge Graph-206 inhibition N-COVID-19 Knowledge Graph-139 N-COVID-19 Knowledge Graph-207 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-210 activation N-COVID-19 Knowledge Graph-42 N-COVID-19 Knowledge Graph-210 activation N-COVID-19 Knowledge Graph-51 N-COVID-19 Knowledge Graph-210 activation N-COVID-19 Knowledge Graph-52 N-COVID-19 Knowledge Graph-210 activation N-COVID-19 Knowledge Graph-53 N-COVID-19 Knowledge Graph-212 activation N-COVID-19 Knowledge Graph-215 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-59 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-59 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-66 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-66 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-66 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-66 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-116 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-122 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-122 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-122 N-COVID-19 Knowledge Graph-214 inhibition N-COVID-19 Knowledge Graph-148 N-COVID-19 Knowledge Graph-214 activation N-COVID-19 Knowledge Graph-211 N-COVID-19 Knowledge Graph-216 inhibition N-COVID-19 Knowledge Graph-9 10 N-COVID-19 Knowledge Graph-219 inhibition N-COVID-19 Knowledge Graph-178 N-COVID-19 Knowledge Graph-220 activation N-COVID-19 Knowledge Graph-219 N-COVID-19 Knowledge Graph-221 inhibition N-COVID-19 Knowledge Graph-187 N-COVID-19 Knowledge Graph-221 inhibition N-COVID-19 Knowledge Graph-190 N-COVID-19 Knowledge Graph-221 activation N-COVID-19 Knowledge Graph-191 N-COVID-19 Knowledge Graph-221 activation N-COVID-19 Knowledge Graph-192 N-COVID-19 Knowledge Graph-221 activation N-COVID-19 Knowledge Graph-192 N-COVID-19 Knowledge Graph-221 inhibition N-COVID-19 Knowledge Graph-198 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-63 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-95 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-106 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-111 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-112 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-113 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-121 N-COVID-19 Knowledge Graph-222 inhibition N-COVID-19 Knowledge Graph-125 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-125 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-126 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-127 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-159 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-193 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-195 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-199 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-200 N-COVID-19 Knowledge Graph-222 activation N-COVID-19 Knowledge Graph-204 N-COVID-19 Knowledge Graph-224 activation N-COVID-19 Knowledge Graph-222 N-COVID-19 Knowledge Graph-227 inhibition N-COVID-19 Knowledge Graph-193 N-COVID-19 Knowledge Graph-227 inhibition N-COVID-19 Knowledge Graph-200 N-COVID-19 Knowledge Graph-228 inhibition N-COVID-19 Knowledge Graph-193 N-COVID-19 Knowledge Graph-228 inhibition N-COVID-19 Knowledge Graph-200 N-COVID-19 Knowledge Graph-229 inhibition N-COVID-19 Knowledge Graph-37 38 N-COVID-19 Knowledge Graph-231 inhibition N-COVID-19 Knowledge Graph-193 N-COVID-19 Knowledge Graph-231 inhibition N-COVID-19 Knowledge Graph-200 N-COVID-19 Knowledge Graph-232 activation N-COVID-19 Knowledge Graph-96 N-COVID-19 Knowledge Graph-232 inhibition N-COVID-19 Knowledge Graph-98 N-COVID-19 Knowledge Graph-232 inhibition N-COVID-19 Knowledge Graph-98 N-COVID-19 Knowledge Graph-232 inhibition N-COVID-19 Knowledge Graph-98 N-COVID-19 Knowledge Graph-232 activation N-COVID-19 Knowledge Graph-99 N-COVID-19 Knowledge Graph-232 inhibition N-COVID-19 Knowledge Graph-128 N-COVID-19 Knowledge Graph-232 inhibition N-COVID-19 Knowledge Graph-128 N-COVID-19 Knowledge Graph-232 inhibition N-COVID-19 Knowledge Graph-138 N-COVID-19 Knowledge Graph-232 inhibition N-COVID-19 Knowledge Graph-142 N-COVID-19 Knowledge Graph-232 inhibition N-COVID-19 Knowledge Graph-182 N-COVID-19 Knowledge Graph-233 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-233 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-233 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-237 inhibition N-COVID-19 Knowledge Graph-137 N-COVID-19 Knowledge Graph-238 inhibition N-COVID-19 Knowledge Graph-55 N-COVID-19 Knowledge Graph-238 inhibition N-COVID-19 Knowledge Graph-55 N-COVID-19 Knowledge Graph-240 activation N-COVID-19 Knowledge Graph-132 N-COVID-19 Knowledge Graph-241 inhibition N-COVID-19 Knowledge Graph-137 N-COVID-19 Knowledge Graph-242 inhibition N-COVID-19 Knowledge Graph-42 N-COVID-19 Knowledge Graph-242 inhibition N-COVID-19 Knowledge Graph-45 N-COVID-19 Knowledge Graph-242 inhibition N-COVID-19 Knowledge Graph-57 pybel-0.15.5/notebooks/hipathia_demo/covid19/_convert_covid19kg.py000066400000000000000000000016431426625374700250660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Convert the COVID-19 graph for Hipathia.""" import os from urllib.request import urlretrieve import click from pyobo.cli_utils import verbose_option import pybel import pybel.grounding HERE = os.path.dirname(__file__) URL = 'https://github.com/covid19kg/covid19kg/raw/master/covid19kg/_cache.bel.nodelink.json' PATH = os.path.join(HERE, 'covid19.bel.nodelink.json') GROUNDED_PATH = os.path.join(HERE, 'covid19-grounded.bel.nodelink.json') @click.command() @verbose_option def main(): """Convert the COVID-19 graph to Hipathia.""" if not os.path.exists(PATH): urlretrieve(URL, PATH) if not os.path.exists(GROUNDED_PATH): graph = pybel.load(PATH) graph = pybel.grounding.ground(graph) pybel.dump(graph, GROUNDED_PATH, indent=2) else: graph = pybel.load(GROUNDED_PATH) pybel.to_hipathia(graph, HERE) if __name__ == '__main__': main() pybel-0.15.5/notebooks/hipathia_demo/hemekg/000077500000000000000000000000001426625374700207735ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/hemekg/HemeKG.att000066400000000000000000000127601426625374700226130ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-HemeKG-1 2 3 hydrogen peroxide nitrite heme 6 55 white rectangle gene,gene,gene 0.5 black 46 17 16240,/,16301,/,30413 N-HemeKG-100 Il1b 107 180 white rectangle gene 0.5 black 46 17 96543 N-HemeKG-101 Casp1 111 178 white rectangle gene 0.5 black 46 17 96544 N-HemeKG-102 Tlr4 127 99 white rectangle gene 0.5 black 46 17 96824 N-HemeKG-103 Mmp12 119 88 white rectangle gene 0.5 black 46 17 97005 N-HemeKG-104 Mmp9 132 86 white rectangle gene 0.5 black 46 17 97011 N-HemeKG-105 Nfkb1 84 126 white rectangle gene 0.5 black 46 17 97312 N-HemeKG-106 Pln 135 85 white rectangle gene 0.5 black 46 17 97622 N-HemeKG-107 Ccl2 121 80 white rectangle gene 0.5 black 46 17 98259 N-HemeKG-108 Sele 129 106 white rectangle gene 0.5 black 46 17 98278 N-HemeKG-109 Syp 119 84 white rectangle gene 0.5 black 46 17 98467 N-HemeKG-110 Tgfb1 128 92 white rectangle gene 0.5 black 46 17 98725 N-HemeKG-111 Tfrc 133 82 white rectangle gene 0.5 black 46 17 98822 N-HemeKG-112 Vcam1 121 102 white rectangle gene 0.5 black 46 17 98926 N-HemeKG-113 Ryr2 135 88 white rectangle gene 0.5 black 46 17 99685 N-HemeKG-114 ubiquitin 107 112 white rectangle gene 0.5 black 46 17 PF00240 N-HemeKG-115 PDGF 133 90 white rectangle gene 0.5 black 46 17 PF00341 N-HemeKG-116 Pkinase_C 57 115 white rectangle gene 0.5 black 46 17 PF00433 N-HemeKG-117 Leukocidin 84 121 white rectangle gene 0.5 black 46 17 PF07968 N-HemeKG-119 Tnf 94 127 white rectangle gene 0.5 black 46 17 3876 N-HemeKG-16 17 18 MAPK1 MAPK14 MAPK8 56 71 white rectangle gene,gene,gene 0.5 black 46 17 6871,/,6876,/,6881 N-HemeKG-19 20 Slc40a1 Hamp 127 66 white rectangle gene,gene 0.5 black 46 17 1315204,/,1933533 N-HemeKG-21 22 23 Txnrd2 Txnrd1 Txnrd3 131 80 white rectangle gene,gene,gene 0.5 black 46 17 1347023,/,1354175,/,2386711 N-HemeKG-24 RIPK1 63 65 white rectangle gene 0.5 black 46 17 10019 N-HemeKG-25 RIPK3 55 64 white rectangle gene 0.5 black 46 17 10021 N-HemeKG-27 SELP 83 140 white rectangle gene 0.5 black 46 17 10721 N-HemeKG-28 SOD1 9 149 white rectangle gene 0.5 black 46 17 11179 N-HemeKG-29 SOD2 13 139 white rectangle gene 0.5 black 46 17 11180 N-HemeKG-33 TLR4 55 76 white rectangle gene 0.5 black 46 17 11850 N-HemeKG-34 TNF 59 70 white rectangle gene 0.5 black 46 17 11892 N-HemeKG-36 VIM 57 111 white rectangle gene 0.5 black 46 17 12692 N-HemeKG-37 VWF 58 189 white rectangle gene 0.5 black 46 17 12726 N-HemeKG-38 ADAMTS13 90 126 white rectangle gene 0.5 black 46 17 1366 N-HemeKG-4 5 heme HPX 58 2 white rectangle gene,gene 0.5 black 46 17 30413,/,5171 N-HemeKG-40 CD14 63 51 white rectangle gene 0.5 black 46 17 1628 N-HemeKG-41 CD163 69 125 white rectangle gene 0.5 black 46 17 1631 N-HemeKG-46 CD59 86 132 white rectangle gene 0.5 black 46 17 1689 N-HemeKG-47 CD86 58 51 white rectangle gene 0.5 black 46 17 1705 N-HemeKG-48 TICAM1 49 80 white rectangle gene 0.5 black 46 17 18348 N-HemeKG-50 ELANE 76 115 white rectangle gene 0.5 black 46 17 3309 N-HemeKG-51 F10 76 18 white rectangle gene 0.5 black 46 17 3528 N-HemeKG-53 F8 57 194 white rectangle gene 0.5 black 46 17 3546 N-HemeKG-54 FLT1 22 144 white rectangle gene 0.5 black 46 17 3763 N-HemeKG-55 FTH1 68 68 white rectangle gene 0.5 black 46 17 3976 N-HemeKG-56 ALB 0 54 white rectangle gene 0.5 black 46 17 399 N-HemeKG-57 ALB 4 50 white rectangle gene 0.5 black 46 17 399 N-HemeKG-59 AMBP 15 144 white rectangle gene 0.5 black 46 17 453 N-HemeKG-60 HBB 89 120 white rectangle gene 0.5 black 46 17 4827 N-HemeKG-61 HMOX1 75 119 white rectangle gene 0.5 black 46 17 5013 N-HemeKG-62 HP 89 128 white rectangle gene 0.5 black 46 17 5141 N-HemeKG-63 HPX 61 58 white rectangle gene 0.5 black 46 17 5171 N-HemeKG-65 HSPA5 94 122 white rectangle gene 0.5 black 46 17 5238 N-HemeKG-66 IFNA1 37 80 white rectangle gene 0.5 black 46 17 5417 N-HemeKG-69 IL6 18 141 white rectangle gene 0.5 black 46 17 6018 N-HemeKG-70 CXCL8 59 97 white rectangle gene 0.5 black 46 17 6025 N-HemeKG-71 IRF3 45 81 white rectangle gene 0.5 black 46 17 6118 N-HemeKG-72 LRP1 55 0 white rectangle gene 0.5 black 46 17 6692 N-HemeKG-74 MAPK8 75 69 white rectangle gene 0.5 black 46 17 6881 N-HemeKG-75 MYD88 54 75 white rectangle gene 0.5 black 46 17 7562 N-HemeKG-76 NFKB1 54 82 white rectangle gene 0.5 black 46 17 7794 N-HemeKG-78 SERPINA1 64 111 white rectangle gene 0.5 black 46 17 8941 N-HemeKG-79 PRTN3 60 117 white rectangle gene 0.5 black 46 17 9495 N-HemeKG-8 9 F3 F7 76 11 white rectangle gene,gene 0.5 black 46 17 3541,/,3544 N-HemeKG-81 Tnf 128 83 white rectangle gene 0.5 black 46 17 104798 N-HemeKG-82 Hpx 125 88 white rectangle gene 0.5 black 46 17 105112 N-HemeKG-83 Sqstm1 112 110 white rectangle gene 0.5 black 46 17 107931 N-HemeKG-84 Cxcl1 168 132 white rectangle gene 0.5 black 46 17 108068 N-HemeKG-86 Slc40a1 125 74 white rectangle gene 0.5 black 46 17 1315204 N-HemeKG-87 Mmp13 132 93 white rectangle gene 0.5 black 46 17 1340026 N-HemeKG-88 Spic 146 176 white rectangle gene 0.5 black 46 17 1341168 N-HemeKG-89 Slc11a2 127 79 white rectangle gene 0.5 black 46 17 1345279 N-HemeKG-91 Acta2 124 81 white rectangle gene 0.5 black 46 17 87909 N-HemeKG-92 Serpina1a 166 135 white rectangle gene 0.5 black 46 17 891971 N-HemeKG-93 Bach1 150 182 white rectangle gene 0.5 black 46 17 894680 N-HemeKG-94 Fth1 118 122 white rectangle gene 0.5 black 46 17 95588 N-HemeKG-96 Hmox1 108 106 white rectangle gene 0.5 black 46 17 96163 N-HemeKG-97 Hp 114 115 white rectangle gene 0.5 black 46 17 96211 N-HemeKG-98 Hspa2 104 103 white rectangle gene 0.5 black 46 17 96243 N-HemeKG-99 Icam1 133 103 white rectangle gene 0.5 black 46 17 96392 pybel-0.15.5/notebooks/hipathia_demo/hemekg/HemeKG.sif000066400000000000000000000067041426625374700226050ustar00rootroot000000000000000 1 2 N-HemeKG-92 inhibition N-HemeKG-84 N-HemeKG-71 activation N-HemeKG-66 N-HemeKG-71 activation N-HemeKG-76 N-HemeKG-46 activation N-HemeKG-27 N-HemeKG-46 activation N-HemeKG-60 N-HemeKG-55 inhibition N-HemeKG-34 N-HemeKG-55 inhibition N-HemeKG-74 N-HemeKG-33 activation N-HemeKG-75 N-HemeKG-33 activation N-HemeKG-75 N-HemeKG-33 activation N-HemeKG-75 N-HemeKG-33 activation N-HemeKG-48 N-HemeKG-33 activation N-HemeKG-48 N-HemeKG-33 activation N-HemeKG-16 17 18 N-HemeKG-33 activation N-HemeKG-76 N-HemeKG-33 activation N-HemeKG-34 N-HemeKG-57 inhibition N-HemeKG-1 2 3 N-HemeKG-37 inhibition N-HemeKG-53 N-HemeKG-19 20 activation N-HemeKG-86 N-HemeKG-48 activation N-HemeKG-71 N-HemeKG-48 activation N-HemeKG-76 N-HemeKG-60 activation N-HemeKG-61 N-HemeKG-60 activation N-HemeKG-65 N-HemeKG-60 activation N-HemeKG-105 N-HemeKG-60 activation N-HemeKG-96 N-HemeKG-60 inhibition N-HemeKG-38 N-HemeKG-60 activation N-HemeKG-119 N-HemeKG-93 inhibition N-HemeKG-88 N-HemeKG-93 inhibition N-HemeKG-88 N-HemeKG-93 activation N-HemeKG-88 N-HemeKG-76 activation N-HemeKG-34 N-HemeKG-76 activation N-HemeKG-34 N-HemeKG-76 activation N-HemeKG-70 N-HemeKG-72 activation N-HemeKG-4 5 N-HemeKG-72 inhibition N-HemeKG-4 5 N-HemeKG-117 activation N-HemeKG-60 N-HemeKG-59 activation N-HemeKG-69 N-HemeKG-59 inhibition N-HemeKG-28 N-HemeKG-59 activation N-HemeKG-29 N-HemeKG-59 inhibition N-HemeKG-54 N-HemeKG-16 17 18 activation N-HemeKG-34 N-HemeKG-56 inhibition N-HemeKG-1 2 3 N-HemeKG-41 activation N-HemeKG-61 N-HemeKG-96 inhibition N-HemeKG-83 N-HemeKG-96 inhibition N-HemeKG-114 N-HemeKG-96 inhibition N-HemeKG-98 N-HemeKG-8 9 activation N-HemeKG-51 N-HemeKG-101 activation N-HemeKG-100 N-HemeKG-82 inhibition N-HemeKG-81 N-HemeKG-82 inhibition N-HemeKG-107 N-HemeKG-82 inhibition N-HemeKG-110 N-HemeKG-82 inhibition N-HemeKG-115 N-HemeKG-82 activation N-HemeKG-104 N-HemeKG-82 activation N-HemeKG-103 N-HemeKG-82 activation N-HemeKG-87 N-HemeKG-82 inhibition N-HemeKG-91 N-HemeKG-82 activation N-HemeKG-109 N-HemeKG-82 inhibition N-HemeKG-102 N-HemeKG-82 inhibition N-HemeKG-96 N-HemeKG-82 inhibition N-HemeKG-96 N-HemeKG-82 inhibition N-HemeKG-86 N-HemeKG-82 activation N-HemeKG-111 N-HemeKG-82 activation N-HemeKG-111 N-HemeKG-82 inhibition N-HemeKG-21 22 23 N-HemeKG-82 activation N-HemeKG-89 N-HemeKG-82 inhibition N-HemeKG-113 N-HemeKG-82 inhibition N-HemeKG-106 N-HemeKG-102 activation N-HemeKG-112 N-HemeKG-102 activation N-HemeKG-99 N-HemeKG-102 activation N-HemeKG-108 N-HemeKG-34 activation N-HemeKG-24 N-HemeKG-34 activation N-HemeKG-25 N-HemeKG-75 activation N-HemeKG-76 N-HemeKG-75 activation N-HemeKG-76 N-HemeKG-75 activation N-HemeKG-34 N-HemeKG-97 inhibition N-HemeKG-96 N-HemeKG-97 inhibition N-HemeKG-96 N-HemeKG-97 inhibition N-HemeKG-94 N-HemeKG-78 inhibition N-HemeKG-36 N-HemeKG-78 inhibition N-HemeKG-70 N-HemeKG-78 inhibition N-HemeKG-61 N-HemeKG-78 inhibition N-HemeKG-50 N-HemeKG-78 inhibition N-HemeKG-79 N-HemeKG-78 inhibition N-HemeKG-116 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-62 inhibition N-HemeKG-60 N-HemeKG-50 activation N-HemeKG-60 N-HemeKG-63 inhibition N-HemeKG-47 N-HemeKG-63 inhibition N-HemeKG-40 N-HemeKG-63 inhibition N-HemeKG-34 pybel-0.15.5/notebooks/hipathia_demo/hemekg/_convert_hemekg.py000066400000000000000000000016041426625374700245050ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Convert the HemeKG for Hipathia.""" import os from urllib.request import urlretrieve import click from pyobo.cli_utils import verbose_option import pybel import pybel.grounding HERE = os.path.dirname(__file__) URL = 'https://github.com/hemekg/hemekg/raw/master/hemekg/_cache.bel.nodelink.json' PATH = os.path.join(HERE, 'hemekg.bel.nodelink.json') GROUNDED_PATH = os.path.join(HERE, 'hemekg-grounded.bel.nodelink.json') @click.command() @verbose_option def main(): """Convert the HemeKG graph to Hipathia.""" if not os.path.exists(PATH): urlretrieve(URL, PATH) if not os.path.exists(GROUNDED_PATH): graph = pybel.load(PATH) graph = pybel.grounding.ground(graph) pybel.dump(graph, GROUNDED_PATH) else: graph = pybel.load(GROUNDED_PATH) pybel.to_hipathia(graph, HERE) if __name__ == '__main__': main() pybel-0.15.5/notebooks/hipathia_demo/selventa/000077500000000000000000000000001426625374700213545ustar00rootroot00000000000000pybel-0.15.5/notebooks/hipathia_demo/selventa/BEL Framework Small Corpus Document.att000066400000000000000000001317521426625374700304430ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-BEL Framework Small Corpus Document-1 2 HDAC_II p14_3_3 9 73 white rectangle gene,gene 0.5 black 46 17 HDAC_II,/,p14_3_3 N-BEL Framework Small Corpus Document-101 102 CREBBP CTNNB1 103 67 white rectangle gene,gene 0.5 black 46 17 2348,/,2514 N-BEL Framework Small Corpus Document-103 104 CREBBP AKT1 96 106 white rectangle gene,gene 0.5 black 46 17 2348,/,391 N-BEL Framework Small Corpus Document-105 106 CREBBP HIF1A 101 123 white rectangle gene,gene 0.5 black 46 17 2348,/,4910 N-BEL Framework Small Corpus Document-107 108 CTNNB1 LEF1 104 67 white rectangle gene,gene 0.5 black 46 17 2514,/,6551 N-BEL Framework Small Corpus Document-11 12 p14_3_3 PAWR 90 112 white rectangle gene,gene 0.5 black 46 17 p14_3_3,/,8614 N-BEL Framework Small Corpus Document-111 112 EP300 AKT1 95 105 white rectangle gene,gene 0.5 black 46 17 3373,/,391 N-BEL Framework Small Corpus Document-113 114 EP300 HIF1A 100 123 white rectangle gene,gene 0.5 black 46 17 3373,/,4910 N-BEL Framework Small Corpus Document-115 116 F3 F7 97 58 white rectangle gene,gene 0.5 black 46 17 3541,/,3544 N-BEL Framework Small Corpus Document-120 121 FN1 ITGB1 68 93 white rectangle gene,gene 0.5 black 46 17 3778,/,6153 N-BEL Framework Small Corpus Document-122 123 FNTA FNTB 114 88 white rectangle gene,gene 0.5 black 46 17 3782,/,3785 N-BEL Framework Small Corpus Document-13 14 p14_3_3 RAF1 91 77 white rectangle gene,gene 0.5 black 46 17 p14_3_3,/,9829 N-BEL Framework Small Corpus Document-130 131 APC IQGAP1 127 88 white rectangle gene,gene 0.5 black 46 17 583,/,6110 N-BEL Framework Small Corpus Document-132 133 IRAK1 MYD88 92 97 white rectangle gene,gene 0.5 black 46 17 6112,/,7562 N-BEL Framework Small Corpus Document-134 135 IRAK1 PELI2 79 96 white rectangle gene,gene 0.5 black 46 17 6112,/,8828 N-BEL Framework Small Corpus Document-136 137 ITGA2 ITGB1 124 79 white rectangle gene,gene 0.5 black 46 17 6137,/,6153 N-BEL Framework Small Corpus Document-138 139 ITGA2B ITGB3 53 179 white rectangle gene,gene 0.5 black 46 17 6138,/,6156 N-BEL Framework Small Corpus Document-140 141 ITGA5 ITGB1 55 179 white rectangle gene,gene 0.5 black 46 17 6141,/,6153 N-BEL Framework Small Corpus Document-142 143 ITGA6 ITGB1 141 58 white rectangle gene,gene 0.5 black 46 17 6142,/,6153 N-BEL Framework Small Corpus Document-144 145 ITGA6 ITGB4 111 94 white rectangle gene,gene 0.5 black 46 17 6142,/,6158 N-BEL Framework Small Corpus Document-146 147 ITGAM ITGB2 185 88 white rectangle gene,gene 0.5 black 46 17 6149,/,6155 N-BEL Framework Small Corpus Document-148 149 ITGAV ITGB3 103 103 white rectangle gene,gene 0.5 black 46 17 6150,/,6156 N-BEL Framework Small Corpus Document-15 16 17 ARRB PDE4 ADRB2 179 37 white rectangle gene,gene,gene 0.5 black 46 17 ARRB,/,PDE4,/,286 N-BEL Framework Small Corpus Document-150 151 152 ITGAV ITGB3 KDR 126 96 white rectangle gene,gene,gene 0.5 black 46 17 6150,/,6156,/,6307 N-BEL Framework Small Corpus Document-153 154 155 ITGAV ITGB3 PDGFRB 118 102 white rectangle gene,gene,gene 0.5 black 46 17 6150,/,6156,/,8804 N-BEL Framework Small Corpus Document-156 157 158 ITGB1 PRKCA PTK2B 60 101 white rectangle gene,gene,gene 0.5 black 46 17 6153,/,9393,/,9612 N-BEL Framework Small Corpus Document-159 160 KDR PTPN11 126 93 white rectangle gene,gene 0.5 black 46 17 6307,/,9644 N-BEL Framework Small Corpus Document-161 162 KDR PTPN6 125 94 white rectangle gene,gene 0.5 black 46 17 6307,/,9658 N-BEL Framework Small Corpus Document-163 164 ARF6 RAC1 125 97 white rectangle gene,gene 0.5 black 46 17 659,/,9801 N-BEL Framework Small Corpus Document-165 166 Vegfa Flt1 128 11 white rectangle gene,gene 0.5 black 46 17 103178,/,95558 N-BEL Framework Small Corpus Document-171 172 173 Itgb4 Shc1 Itga6 176 162 white rectangle gene,gene,gene 0.5 black 46 17 2928,/,620446,/,621633 N-BEL Framework Small Corpus Document-174 CAMK2_family 14 76 white rectangle gene 0.5 black 46 17 CAMK2_family N-BEL Framework Small Corpus Document-175 G_i 125 122 white rectangle gene 0.5 black 46 17 G_i N-BEL Framework Small Corpus Document-176 HDAC_II 12 72 white rectangle gene 0.5 black 46 17 HDAC_II N-BEL Framework Small Corpus Document-177 HDAC_II 10 75 white rectangle gene 0.5 black 46 17 HDAC_II N-BEL Framework Small Corpus Document-178 PI3K_p110 52 99 white rectangle gene 0.5 black 46 17 PI3K_p110 N-BEL Framework Small Corpus Document-179 PIK3R_I 58 99 white rectangle gene 0.5 black 46 17 PIK3R_I N-BEL Framework Small Corpus Document-18 19 AXIN APC 101 79 white rectangle gene,gene 0.5 black 46 17 AXIN,/,583 N-BEL Framework Small Corpus Document-181 p14_3_3 100 96 white rectangle gene 0.5 black 46 17 p14_3_3 N-BEL Framework Small Corpus Document-184 AKT 100 93 white rectangle gene 0.5 black 46 17 AKT N-BEL Framework Small Corpus Document-185 AKT 95 96 white rectangle gene 0.5 black 46 17 AKT N-BEL Framework Small Corpus Document-186 AKT 91 102 white rectangle gene 0.5 black 46 17 AKT N-BEL Framework Small Corpus Document-187 AKT 103 90 white rectangle gene 0.5 black 46 17 AKT N-BEL Framework Small Corpus Document-189 CAMK 90 100 white rectangle gene 0.5 black 46 17 CAMK N-BEL Framework Small Corpus Document-190 CDC25 100 81 white rectangle gene 0.5 black 46 17 CDC25 N-BEL Framework Small Corpus Document-191 COL1 127 73 white rectangle gene 0.5 black 46 17 COL1 N-BEL Framework Small Corpus Document-192 COL4 138 66 white rectangle gene 0.5 black 46 17 COL4 N-BEL Framework Small Corpus Document-194 DNM 88 84 white rectangle gene 0.5 black 46 17 DNM N-BEL Framework Small Corpus Document-195 DVL 94 63 white rectangle gene 0.5 black 46 17 DVL N-BEL Framework Small Corpus Document-196 E2F 30 14 white rectangle gene 0.5 black 46 17 E2F N-BEL Framework Small Corpus Document-197 ERK 105 88 white rectangle gene 0.5 black 46 17 ERK N-BEL Framework Small Corpus Document-198 ERK 93 77 white rectangle gene 0.5 black 46 17 ERK N-BEL Framework Small Corpus Document-199 FOXO 105 103 white rectangle gene 0.5 black 46 17 FOXO N-BEL Framework Small Corpus Document-20 21 DVL DVL 92 57 white rectangle gene,gene 0.5 black 46 17 DVL,/,DVL N-BEL Framework Small Corpus Document-200 FOXO 101 103 white rectangle gene 0.5 black 46 17 FOXO N-BEL Framework Small Corpus Document-201 FOXO 110 106 white rectangle gene 0.5 black 46 17 FOXO N-BEL Framework Small Corpus Document-202 GSK3 119 75 white rectangle gene 0.5 black 46 17 GSK3 N-BEL Framework Small Corpus Document-204 IL1 84 99 white rectangle gene 0.5 black 46 17 IL1 N-BEL Framework Small Corpus Document-205 IRS 6 140 white rectangle gene 0.5 black 46 17 IRS N-BEL Framework Small Corpus Document-206 JAK 79 99 white rectangle gene 0.5 black 46 17 JAK N-BEL Framework Small Corpus Document-207 JNK 101 105 white rectangle gene 0.5 black 46 17 JNK N-BEL Framework Small Corpus Document-208 JNK 102 111 white rectangle gene 0.5 black 46 17 JNK N-BEL Framework Small Corpus Document-209 MEF2 82 111 white rectangle gene 0.5 black 46 17 MEF2 N-BEL Framework Small Corpus Document-210 NFAT 72 124 white rectangle gene 0.5 black 46 17 NFAT N-BEL Framework Small Corpus Document-212 P70S6K 95 84 white rectangle gene 0.5 black 46 17 P70S6K N-BEL Framework Small Corpus Document-213 P70S6K 96 82 white rectangle gene 0.5 black 46 17 P70S6K N-BEL Framework Small Corpus Document-214 P90RSK 108 98 white rectangle gene 0.5 black 46 17 P90RSK N-BEL Framework Small Corpus Document-215 P90RSK 108 83 white rectangle gene 0.5 black 46 17 P90RSK N-BEL Framework Small Corpus Document-216 PAK 108 113 white rectangle gene 0.5 black 46 17 PAK N-BEL Framework Small Corpus Document-217 PDE4 93 189 white rectangle gene 0.5 black 46 17 PDE4 N-BEL Framework Small Corpus Document-218 PDGF 113 100 white rectangle gene 0.5 black 46 17 PDGF N-BEL Framework Small Corpus Document-219 PKC 75 102 white rectangle gene 0.5 black 46 17 PKC N-BEL Framework Small Corpus Document-22 23 DVL FZD 93 57 white rectangle gene,gene 0.5 black 46 17 DVL,/,FZD N-BEL Framework Small Corpus Document-220 PLA2 110 85 white rectangle gene 0.5 black 46 17 PLA2 N-BEL Framework Small Corpus Document-221 PLC 116 140 white rectangle gene 0.5 black 46 17 PLC N-BEL Framework Small Corpus Document-222 PRKAC 99 88 white rectangle gene 0.5 black 46 17 PRKAC N-BEL Framework Small Corpus Document-223 RAC 106 105 white rectangle gene 0.5 black 46 17 RAC N-BEL Framework Small Corpus Document-224 RAF 89 90 white rectangle gene 0.5 black 46 17 RAF N-BEL Framework Small Corpus Document-225 RAS 101 95 white rectangle gene 0.5 black 46 17 RAS N-BEL Framework Small Corpus Document-226 RAS 109 91 white rectangle gene 0.5 black 46 17 RAS N-BEL Framework Small Corpus Document-227 RHO 107 84 white rectangle gene 0.5 black 46 17 RHO N-BEL Framework Small Corpus Document-228 SRC 93 105 white rectangle gene 0.5 black 46 17 SRC N-BEL Framework Small Corpus Document-229 STAT5 73 75 white rectangle gene 0.5 black 46 17 STAT5 N-BEL Framework Small Corpus Document-230 STAT5 80 75 white rectangle gene 0.5 black 46 17 STAT5 N-BEL Framework Small Corpus Document-231 VEGF 119 94 white rectangle gene 0.5 black 46 17 VEGF N-BEL Framework Small Corpus Document-232 Wnt 98 77 white rectangle gene 0.5 black 46 17 Wnt N-BEL Framework Small Corpus Document-233 p38 119 96 white rectangle gene 0.5 black 46 17 p38 N-BEL Framework Small Corpus Document-234 ROCK1 111 78 white rectangle gene 0.5 black 46 17 10251 N-BEL Framework Small Corpus Document-235 RPS6 77 0 white rectangle gene 0.5 black 46 17 10429 N-BEL Framework Small Corpus Document-236 RPS6KB2 77 4 white rectangle gene 0.5 black 46 17 10437 N-BEL Framework Small Corpus Document-237 SAA1 58 71 white rectangle gene 0.5 black 46 17 10513 N-BEL Framework Small Corpus Document-238 CXCL12 86 137 white rectangle gene 0.5 black 46 17 10672 N-BEL Framework Small Corpus Document-239 SELE 90 123 white rectangle gene 0.5 black 46 17 10718 N-BEL Framework Small Corpus Document-24 25 E2F EP300 93 52 white rectangle gene,gene 0.5 black 46 17 E2F,/,3373 N-BEL Framework Small Corpus Document-240 SELP 108 57 white rectangle gene 0.5 black 46 17 10721 N-BEL Framework Small Corpus Document-241 SGK1 91 80 white rectangle gene 0.5 black 46 17 10810 N-BEL Framework Small Corpus Document-242 SHC1 116 91 white rectangle gene 0.5 black 46 17 10840 N-BEL Framework Small Corpus Document-243 SHC1 118 86 white rectangle gene 0.5 black 46 17 10840 N-BEL Framework Small Corpus Document-244 SKP2 101 107 white rectangle gene 0.5 black 46 17 10901 N-BEL Framework Small Corpus Document-245 BRAF 95 83 white rectangle gene 0.5 black 46 17 1097 N-BEL Framework Small Corpus Document-246 BRAF 92 77 white rectangle gene 0.5 black 46 17 1097 N-BEL Framework Small Corpus Document-247 BRAF 91 78 white rectangle gene 0.5 black 46 17 1097 N-BEL Framework Small Corpus Document-248 SLC2A4 89 101 white rectangle gene 0.5 black 46 17 11009 N-BEL Framework Small Corpus Document-249 SMARCA4 58 49 white rectangle gene 0.5 black 46 17 11100 N-BEL Framework Small Corpus Document-251 SRC 101 92 white rectangle gene 0.5 black 46 17 11283 N-BEL Framework Small Corpus Document-253 BTK 115 177 white rectangle gene 0.5 black 46 17 1133 N-BEL Framework Small Corpus Document-254 STAT1 59 73 white rectangle gene 0.5 black 46 17 11362 N-BEL Framework Small Corpus Document-255 STAT3 56 73 white rectangle gene 0.5 black 46 17 11364 N-BEL Framework Small Corpus Document-258 SYK 137 61 white rectangle gene 0.5 black 46 17 11491 N-BEL Framework Small Corpus Document-259 TEC 115 184 white rectangle gene 0.5 black 46 17 11719 N-BEL Framework Small Corpus Document-26 27 E2F RB1 95 61 white rectangle gene,gene 0.5 black 46 17 E2F,/,9884 N-BEL Framework Small Corpus Document-260 TEK 113 113 white rectangle gene 0.5 black 46 17 11724 N-BEL Framework Small Corpus Document-261 TGFA 98 101 white rectangle gene 0.5 black 46 17 11765 N-BEL Framework Small Corpus Document-262 THBD 113 56 white rectangle gene 0.5 black 46 17 11784 N-BEL Framework Small Corpus Document-264 TIMP2 91 121 white rectangle gene 0.5 black 46 17 11821 N-BEL Framework Small Corpus Document-266 TNF 94 115 white rectangle gene 0.5 black 46 17 11892 N-BEL Framework Small Corpus Document-267 TNFRSF1A 90 105 white rectangle gene 0.5 black 46 17 11916 N-BEL Framework Small Corpus Document-269 TP53 106 113 white rectangle gene 0.5 black 46 17 11998 N-BEL Framework Small Corpus Document-270 TP53 97 81 white rectangle gene 0.5 black 46 17 11998 N-BEL Framework Small Corpus Document-271 TP73 133 167 white rectangle gene 0.5 black 46 17 12003 N-BEL Framework Small Corpus Document-273 ACP1 117 93 white rectangle gene 0.5 black 46 17 122 N-BEL Framework Small Corpus Document-274 EGLN1 107 124 white rectangle gene 0.5 black 46 17 1232 N-BEL Framework Small Corpus Document-275 TYK2 65 77 white rectangle gene 0.5 black 46 17 12440 N-BEL Framework Small Corpus Document-276 UCP1 90 120 white rectangle gene 0.5 black 46 17 12517 N-BEL Framework Small Corpus Document-278 VEGFA 65 99 white rectangle gene 0.5 black 46 17 12680 N-BEL Framework Small Corpus Document-279 VHL 93 124 white rectangle gene 0.5 black 46 17 12687 N-BEL Framework Small Corpus Document-28 29 E2F RBL1 92 46 white rectangle gene,gene 0.5 black 46 17 E2F,/,9893 N-BEL Framework Small Corpus Document-280 VTN 102 106 white rectangle gene 0.5 black 46 17 12724 N-BEL Framework Small Corpus Document-281 VWF 111 53 white rectangle gene 0.5 black 46 17 12726 N-BEL Framework Small Corpus Document-282 WNT1 57 96 white rectangle gene 0.5 black 46 17 12774 N-BEL Framework Small Corpus Document-284 WNT5A 119 118 white rectangle gene 0.5 black 46 17 12784 N-BEL Framework Small Corpus Document-285 XPO1 102 104 white rectangle gene 0.5 black 46 17 12825 N-BEL Framework Small Corpus Document-286 ADAMTS13 112 48 white rectangle gene 0.5 black 46 17 1366 N-BEL Framework Small Corpus Document-287 HDAC4 53 19 white rectangle gene 0.5 black 46 17 14063 N-BEL Framework Small Corpus Document-288 HDAC4 55 22 white rectangle gene 0.5 black 46 17 14063 N-BEL Framework Small Corpus Document-289 GP6 133 66 white rectangle gene 0.5 black 46 17 14388 N-BEL Framework Small Corpus Document-290 CALM1 121 71 white rectangle gene 0.5 black 46 17 1442 N-BEL Framework Small Corpus Document-291 CAMK1 51 21 white rectangle gene 0.5 black 46 17 1459 N-BEL Framework Small Corpus Document-292 EGLN2 105 124 white rectangle gene 0.5 black 46 17 14660 N-BEL Framework Small Corpus Document-293 EGLN3 106 125 white rectangle gene 0.5 black 46 17 14661 N-BEL Framework Small Corpus Document-295 CASP9 13 167 white rectangle gene 0.5 black 46 17 1511 N-BEL Framework Small Corpus Document-297 CAV1 118 95 white rectangle gene 0.5 black 46 17 1527 N-BEL Framework Small Corpus Document-298 CAV1 132 98 white rectangle gene 0.5 black 46 17 1527 N-BEL Framework Small Corpus Document-299 ANAPC5 164 38 white rectangle gene 0.5 black 46 17 15713 N-BEL Framework Small Corpus Document-3 4 PIK3R_I IRS 2 142 white rectangle gene,gene 0.5 black 46 17 PIK3R_I,/,IRS N-BEL Framework Small Corpus Document-30 31 E2F RBL2 91 46 white rectangle gene,gene 0.5 black 46 17 E2F,/,9894 N-BEL Framework Small Corpus Document-300 CCND1 109 68 white rectangle gene 0.5 black 46 17 1582 N-BEL Framework Small Corpus Document-301 HIF3A 93 130 white rectangle gene 0.5 black 46 17 15825 N-BEL Framework Small Corpus Document-302 CCND2 108 67 white rectangle gene 0.5 black 46 17 1583 N-BEL Framework Small Corpus Document-303 PRPF6 56 49 white rectangle gene 0.5 black 46 17 15860 N-BEL Framework Small Corpus Document-304 TP63 134 171 white rectangle gene 0.5 black 46 17 15979 N-BEL Framework Small Corpus Document-306 CD24 51 96 white rectangle gene 0.5 black 46 17 1645 N-BEL Framework Small Corpus Document-308 RAPGEF3 91 73 white rectangle gene 0.5 black 46 17 16629 N-BEL Framework Small Corpus Document-309 CD36 132 70 white rectangle gene 0.5 black 46 17 1663 N-BEL Framework Small Corpus Document-310 TAB2 83 110 white rectangle gene 0.5 black 46 17 17075 N-BEL Framework Small Corpus Document-311 TAB2 54 188 white rectangle gene 0.5 black 46 17 17075 N-BEL Framework Small Corpus Document-312 HIF1AN 102 134 white rectangle gene 0.5 black 46 17 17113 N-BEL Framework Small Corpus Document-313 CDC16 158 41 white rectangle gene 0.5 black 46 17 1720 N-BEL Framework Small Corpus Document-314 CDK1 161 40 white rectangle gene 0.5 black 46 17 1722 N-BEL Framework Small Corpus Document-315 CDC23 163 40 white rectangle gene 0.5 black 46 17 1724 N-BEL Framework Small Corpus Document-316 CDC27 161 41 white rectangle gene 0.5 black 46 17 1728 N-BEL Framework Small Corpus Document-317 PRPF4B 59 52 white rectangle gene 0.5 black 46 17 17346 N-BEL Framework Small Corpus Document-318 PRPF4B 60 56 white rectangle gene 0.5 black 46 17 17346 N-BEL Framework Small Corpus Document-319 CDC42 109 120 white rectangle gene 0.5 black 46 17 1736 N-BEL Framework Small Corpus Document-32 33 FOXO SKP2 98 107 white rectangle gene,gene 0.5 black 46 17 FOXO,/,10901 N-BEL Framework Small Corpus Document-320 LRIG1 86 84 white rectangle gene 0.5 black 46 17 17360 N-BEL Framework Small Corpus Document-321 CDH5 106 90 white rectangle gene 0.5 black 46 17 1764 N-BEL Framework Small Corpus Document-322 CDK2 98 105 white rectangle gene 0.5 black 46 17 1771 N-BEL Framework Small Corpus Document-323 CDK4 105 73 white rectangle gene 0.5 black 46 17 1773 N-BEL Framework Small Corpus Document-324 CDKN1A 103 96 white rectangle gene 0.5 black 46 17 1784 N-BEL Framework Small Corpus Document-325 CDKN1A 112 96 white rectangle gene 0.5 black 46 17 1784 N-BEL Framework Small Corpus Document-326 CDKN1B 102 106 white rectangle gene 0.5 black 46 17 1785 N-BEL Framework Small Corpus Document-327 CDKN1B 98 112 white rectangle gene 0.5 black 46 17 1785 N-BEL Framework Small Corpus Document-328 CDKN1B 103 111 white rectangle gene 0.5 black 46 17 1785 N-BEL Framework Small Corpus Document-329 BBC3 134 170 white rectangle gene 0.5 black 46 17 17868 N-BEL Framework Small Corpus Document-330 CDKN2A 99 57 white rectangle gene 0.5 black 46 17 1787 N-BEL Framework Small Corpus Document-331 IRAK4 78 93 white rectangle gene 0.5 black 46 17 17967 N-BEL Framework Small Corpus Document-332 TAB1 84 112 white rectangle gene 0.5 black 46 17 18157 N-BEL Framework Small Corpus Document-334 NAA10 101 120 white rectangle gene 0.5 black 46 17 18704 N-BEL Framework Small Corpus Document-335 RIN1 94 84 white rectangle gene 0.5 black 46 17 18749 N-BEL Framework Small Corpus Document-336 MAPKAP1 85 104 white rectangle gene 0.5 black 46 17 18752 N-BEL Framework Small Corpus Document-337 SOCS1 73 102 white rectangle gene 0.5 black 46 17 19383 N-BEL Framework Small Corpus Document-338 SOCS3 73 101 white rectangle gene 0.5 black 46 17 19391 N-BEL Framework Small Corpus Document-34 35 RAS RAF1 88 72 white rectangle gene,gene 0.5 black 46 17 RAS,/,9829 N-BEL Framework Small Corpus Document-340 CYCS 105 109 white rectangle gene 0.5 black 46 17 19986 N-BEL Framework Small Corpus Document-341 ANAPC1 163 37 white rectangle gene 0.5 black 46 17 19988 N-BEL Framework Small Corpus Document-344 CNTF 67 79 white rectangle gene 0.5 black 46 17 2169 N-BEL Framework Small Corpus Document-345 COPS5 99 120 white rectangle gene 0.5 black 46 17 2240 N-BEL Framework Small Corpus Document-346 HBP1 129 95 white rectangle gene 0.5 black 46 17 23200 N-BEL Framework Small Corpus Document-347 HBP1 127 96 white rectangle gene 0.5 black 46 17 23200 N-BEL Framework Small Corpus Document-348 CREB1 105 93 white rectangle gene 0.5 black 46 17 2345 N-BEL Framework Small Corpus Document-349 CRP 59 71 white rectangle gene 0.5 black 46 17 2367 N-BEL Framework Small Corpus Document-350 CSNK1A1 101 69 white rectangle gene 0.5 black 46 17 2451 N-BEL Framework Small Corpus Document-351 CSNK1G2 98 73 white rectangle gene 0.5 black 46 17 2455 N-BEL Framework Small Corpus Document-353 CTNNB1 102 73 white rectangle gene 0.5 black 46 17 2514 N-BEL Framework Small Corpus Document-354 CTNNB1 101 75 white rectangle gene 0.5 black 46 17 2514 N-BEL Framework Small Corpus Document-355 CXCR4 89 132 white rectangle gene 0.5 black 46 17 2561 N-BEL Framework Small Corpus Document-356 CYBB 120 111 white rectangle gene 0.5 black 46 17 2578 N-BEL Framework Small Corpus Document-358 DCN 87 86 white rectangle gene 0.5 black 46 17 2705 N-BEL Framework Small Corpus Document-359 DDR1 130 125 white rectangle gene 0.5 black 46 17 2730 N-BEL Framework Small Corpus Document-36 37 BCL9 CTNNB1 104 68 white rectangle gene,gene 0.5 black 46 17 1008,/,2514 N-BEL Framework Small Corpus Document-360 DDR1 133 69 white rectangle gene 0.5 black 46 17 2730 N-BEL Framework Small Corpus Document-361 ADRB2 182 35 white rectangle gene 0.5 black 46 17 286 N-BEL Framework Small Corpus Document-362 DKK1 111 112 white rectangle gene 0.5 black 46 17 2891 N-BEL Framework Small Corpus Document-363 EDN1 119 125 white rectangle gene 0.5 black 46 17 3176 N-BEL Framework Small Corpus Document-364 EDN2 118 138 white rectangle gene 0.5 black 46 17 3177 N-BEL Framework Small Corpus Document-365 EDN3 120 140 white rectangle gene 0.5 black 46 17 3178 N-BEL Framework Small Corpus Document-366 EDNRA 117 134 white rectangle gene 0.5 black 46 17 3179 N-BEL Framework Small Corpus Document-367 EDNRB 119 134 white rectangle gene 0.5 black 46 17 3180 N-BEL Framework Small Corpus Document-369 EGF 85 80 white rectangle gene 0.5 black 46 17 3229 N-BEL Framework Small Corpus Document-370 EGFR 93 89 white rectangle gene 0.5 black 46 17 3236 N-BEL Framework Small Corpus Document-371 EGFR 88 84 white rectangle gene 0.5 black 46 17 3236 N-BEL Framework Small Corpus Document-372 EPAS1 90 129 white rectangle gene 0.5 black 46 17 3374 N-BEL Framework Small Corpus Document-373 EPO 86 92 white rectangle gene 0.5 black 46 17 3415 N-BEL Framework Small Corpus Document-374 ERBB2 83 83 white rectangle gene 0.5 black 46 17 3430 N-BEL Framework Small Corpus Document-375 ERBB3 93 88 white rectangle gene 0.5 black 46 17 3431 N-BEL Framework Small Corpus Document-376 ERBB4 82 80 white rectangle gene 0.5 black 46 17 3432 N-BEL Framework Small Corpus Document-377 ERBB4 82 74 white rectangle gene 0.5 black 46 17 3432 N-BEL Framework Small Corpus Document-38 39 CLIP1 APC 129 89 white rectangle gene,gene 0.5 black 46 17 10461,/,583 N-BEL Framework Small Corpus Document-381 F10 101 60 white rectangle gene 0.5 black 46 17 3528 N-BEL Framework Small Corpus Document-382 F2 108 62 white rectangle gene 0.5 black 46 17 3535 N-BEL Framework Small Corpus Document-383 F2R 109 57 white rectangle gene 0.5 black 46 17 3537 N-BEL Framework Small Corpus Document-384 F3 105 58 white rectangle gene 0.5 black 46 17 3541 N-BEL Framework Small Corpus Document-385 F5 119 49 white rectangle gene 0.5 black 46 17 3542 N-BEL Framework Small Corpus Document-386 F8 114 50 white rectangle gene 0.5 black 46 17 3546 N-BEL Framework Small Corpus Document-387 F9 102 59 white rectangle gene 0.5 black 46 17 3551 N-BEL Framework Small Corpus Document-388 FES 114 175 white rectangle gene 0.5 black 46 17 3657 N-BEL Framework Small Corpus Document-390 FLT1 61 103 white rectangle gene 0.5 black 46 17 3763 N-BEL Framework Small Corpus Document-391 FLT1 60 99 white rectangle gene 0.5 black 46 17 3763 N-BEL Framework Small Corpus Document-392 FN1 56 175 white rectangle gene 0.5 black 46 17 3778 N-BEL Framework Small Corpus Document-393 FOXO3 96 107 white rectangle gene 0.5 black 46 17 3821 N-BEL Framework Small Corpus Document-394 FOXO3 109 103 white rectangle gene 0.5 black 46 17 3821 N-BEL Framework Small Corpus Document-395 AKT1 98 101 white rectangle gene 0.5 black 46 17 391 N-BEL Framework Small Corpus Document-396 AKT1 91 101 white rectangle gene 0.5 black 46 17 391 N-BEL Framework Small Corpus Document-397 AKT1 94 99 white rectangle gene 0.5 black 46 17 391 N-BEL Framework Small Corpus Document-398 AKT2 93 117 white rectangle gene 0.5 black 46 17 392 N-BEL Framework Small Corpus Document-399 MTOR 78 9 white rectangle gene 0.5 black 46 17 3942 N-BEL Framework Small Corpus Document-40 41 CLIP1 IQGAP1 128 88 white rectangle gene,gene 0.5 black 46 17 10461,/,6110 N-BEL Framework Small Corpus Document-400 MTOR 94 104 white rectangle gene 0.5 black 46 17 3942 N-BEL Framework Small Corpus Document-401 ALB 58 74 white rectangle gene 0.5 black 46 17 399 N-BEL Framework Small Corpus Document-405 GSK3A 99 90 white rectangle gene 0.5 black 46 17 4616 N-BEL Framework Small Corpus Document-406 GSK3A 105 86 white rectangle gene 0.5 black 46 17 4616 N-BEL Framework Small Corpus Document-407 GSK3B 99 84 white rectangle gene 0.5 black 46 17 4617 N-BEL Framework Small Corpus Document-408 GSK3B 105 85 white rectangle gene 0.5 black 46 17 4617 N-BEL Framework Small Corpus Document-409 ANGPT1 118 116 white rectangle gene 0.5 black 46 17 484 N-BEL Framework Small Corpus Document-410 HCK 117 184 white rectangle gene 0.5 black 46 17 4840 N-BEL Framework Small Corpus Document-411 ANGPT2 122 116 white rectangle gene 0.5 black 46 17 485 N-BEL Framework Small Corpus Document-412 HIF1A 99 114 white rectangle gene 0.5 black 46 17 4910 N-BEL Framework Small Corpus Document-413 HIF1A 100 120 white rectangle gene 0.5 black 46 17 4910 N-BEL Framework Small Corpus Document-414 HIF1A 101 129 white rectangle gene 0.5 black 46 17 4910 N-BEL Framework Small Corpus Document-415 HIF1A 102 119 white rectangle gene 0.5 black 46 17 4910 N-BEL Framework Small Corpus Document-416 HIF1A 104 121 white rectangle gene 0.5 black 46 17 4910 N-BEL Framework Small Corpus Document-417 HIF1A 104 122 white rectangle gene 0.5 black 46 17 4910 N-BEL Framework Small Corpus Document-418 HIF1A 104 122 white rectangle gene 0.5 black 46 17 4910 N-BEL Framework Small Corpus Document-419 HRAS 101 85 white rectangle gene 0.5 black 46 17 5173 N-BEL Framework Small Corpus Document-42 43 SHC1 GRB2 107 94 white rectangle gene,gene 0.5 black 46 17 10840,/,4566 N-BEL Framework Small Corpus Document-420 HRAS 108 68 white rectangle gene 0.5 black 46 17 5173 N-BEL Framework Small Corpus Document-421 HSPB1 86 128 white rectangle gene 0.5 black 46 17 5246 N-BEL Framework Small Corpus Document-422 ICAM1 92 120 white rectangle gene 0.5 black 46 17 5344 N-BEL Framework Small Corpus Document-423 IFNB1 83 88 white rectangle gene 0.5 black 46 17 5434 N-BEL Framework Small Corpus Document-424 IGF1 92 95 white rectangle gene 0.5 black 46 17 5464 N-BEL Framework Small Corpus Document-425 IGF1R 83 101 white rectangle gene 0.5 black 46 17 5465 N-BEL Framework Small Corpus Document-426 IKBKB 103 102 white rectangle gene 0.5 black 46 17 5960 N-BEL Framework Small Corpus Document-427 IKBKB 69 105 white rectangle gene 0.5 black 46 17 5960 N-BEL Framework Small Corpus Document-429 IL1B 95 101 white rectangle gene 0.5 black 46 17 5992 N-BEL Framework Small Corpus Document-430 IL2 60 70 white rectangle gene 0.5 black 46 17 6001 N-BEL Framework Small Corpus Document-431 IL3 91 190 white rectangle gene 0.5 black 46 17 6011 N-BEL Framework Small Corpus Document-432 IL4 92 193 white rectangle gene 0.5 black 46 17 6014 N-BEL Framework Small Corpus Document-433 IL6 63 75 white rectangle gene 0.5 black 46 17 6018 N-BEL Framework Small Corpus Document-434 IL6R 115 180 white rectangle gene 0.5 black 46 17 6019 N-BEL Framework Small Corpus Document-435 INS 92 105 white rectangle gene 0.5 black 46 17 6081 N-BEL Framework Small Corpus Document-436 INSR 83 105 white rectangle gene 0.5 black 46 17 6091 N-BEL Framework Small Corpus Document-437 INSR 88 109 white rectangle gene 0.5 black 46 17 6091 N-BEL Framework Small Corpus Document-439 IRAK1 89 96 white rectangle gene 0.5 black 46 17 6112 N-BEL Framework Small Corpus Document-44 45 SKP2 CDKN1B 97 119 white rectangle gene,gene 0.5 black 46 17 10901,/,1785 N-BEL Framework Small Corpus Document-440 IRAK1 70 91 white rectangle gene 0.5 black 46 17 6112 N-BEL Framework Small Corpus Document-441 IRAK1 93 96 white rectangle gene 0.5 black 46 17 6112 N-BEL Framework Small Corpus Document-443 IRS1 89 110 white rectangle gene 0.5 black 46 17 6125 N-BEL Framework Small Corpus Document-444 IRS1 79 104 white rectangle gene 0.5 black 46 17 6125 N-BEL Framework Small Corpus Document-445 IRS2 87 107 white rectangle gene 0.5 black 46 17 6126 N-BEL Framework Small Corpus Document-446 IRS2 87 108 white rectangle gene 0.5 black 46 17 6126 N-BEL Framework Small Corpus Document-447 IRS2 80 105 white rectangle gene 0.5 black 46 17 6126 N-BEL Framework Small Corpus Document-448 IRS4 79 105 white rectangle gene 0.5 black 46 17 6128 N-BEL Framework Small Corpus Document-449 ITGB1 65 96 white rectangle gene 0.5 black 46 17 6153 N-BEL Framework Small Corpus Document-450 ITGB2 181 88 white rectangle gene 0.5 black 46 17 6155 N-BEL Framework Small Corpus Document-451 JAK1 74 81 white rectangle gene 0.5 black 46 17 6190 N-BEL Framework Small Corpus Document-452 JAK2 65 76 white rectangle gene 0.5 black 46 17 6192 N-BEL Framework Small Corpus Document-453 JAK2 79 76 white rectangle gene 0.5 black 46 17 6192 N-BEL Framework Small Corpus Document-455 JUN 104 110 white rectangle gene 0.5 black 46 17 6204 N-BEL Framework Small Corpus Document-456 KDR 112 96 white rectangle gene 0.5 black 46 17 6307 N-BEL Framework Small Corpus Document-457 KDR 126 94 white rectangle gene 0.5 black 46 17 6307 N-BEL Framework Small Corpus Document-458 KDR 124 97 white rectangle gene 0.5 black 46 17 6307 N-BEL Framework Small Corpus Document-459 KDR 117 97 white rectangle gene 0.5 black 46 17 6307 N-BEL Framework Small Corpus Document-46 47 BRAF RAF1 106 96 white rectangle gene,gene 0.5 black 46 17 1097,/,9829 N-BEL Framework Small Corpus Document-460 KDR 117 95 white rectangle gene 0.5 black 46 17 6307 N-BEL Framework Small Corpus Document-461 KRAS 94 93 white rectangle gene 0.5 black 46 17 6407 N-BEL Framework Small Corpus Document-462 ARAF 93 72 white rectangle gene 0.5 black 46 17 646 N-BEL Framework Small Corpus Document-463 LEF1 135 94 white rectangle gene 0.5 black 46 17 6551 N-BEL Framework Small Corpus Document-464 ARF6 124 92 white rectangle gene 0.5 black 46 17 659 N-BEL Framework Small Corpus Document-465 LIF 68 77 white rectangle gene 0.5 black 46 17 6596 N-BEL Framework Small Corpus Document-466 RHOA 116 92 white rectangle gene 0.5 black 46 17 667 N-BEL Framework Small Corpus Document-467 LRP5 99 77 white rectangle gene 0.5 black 46 17 6697 N-BEL Framework Small Corpus Document-468 LRP6 96 73 white rectangle gene 0.5 black 46 17 6698 N-BEL Framework Small Corpus Document-469 MAP2K1 95 79 white rectangle gene 0.5 black 46 17 6840 N-BEL Framework Small Corpus Document-470 MAP2K2 96 81 white rectangle gene 0.5 black 46 17 6842 N-BEL Framework Small Corpus Document-471 MAP2K3 111 95 white rectangle gene 0.5 black 46 17 6843 N-BEL Framework Small Corpus Document-472 MAP2K4 102 100 white rectangle gene 0.5 black 46 17 6844 N-BEL Framework Small Corpus Document-473 MAP2K6 111 96 white rectangle gene 0.5 black 46 17 6846 N-BEL Framework Small Corpus Document-474 MAP2K7 98 97 white rectangle gene 0.5 black 46 17 6847 N-BEL Framework Small Corpus Document-475 MAP3K11 105 110 white rectangle gene 0.5 black 46 17 6850 N-BEL Framework Small Corpus Document-476 MAP3K11 97 112 white rectangle gene 0.5 black 46 17 6850 N-BEL Framework Small Corpus Document-478 MAP3K5 99 80 white rectangle gene 0.5 black 46 17 6857 N-BEL Framework Small Corpus Document-479 MAP3K7 89 108 white rectangle gene 0.5 black 46 17 6859 N-BEL Framework Small Corpus Document-48 49 SMARCA4 PRPF4B 58 56 white rectangle gene,gene 0.5 black 46 17 11100,/,17346 N-BEL Framework Small Corpus Document-480 MAP3K7 58 182 white rectangle gene 0.5 black 46 17 6859 N-BEL Framework Small Corpus Document-481 MAPK1 88 92 white rectangle gene 0.5 black 46 17 6871 N-BEL Framework Small Corpus Document-482 MAPK11 92 125 white rectangle gene 0.5 black 46 17 6873 N-BEL Framework Small Corpus Document-483 MAPK3 89 91 white rectangle gene 0.5 black 46 17 6877 N-BEL Framework Small Corpus Document-484 MAPKAPK2 92 132 white rectangle gene 0.5 black 46 17 6887 N-BEL Framework Small Corpus Document-485 MAPKAPK3 93 131 white rectangle gene 0.5 black 46 17 6888 N-BEL Framework Small Corpus Document-486 MDM2 103 108 white rectangle gene 0.5 black 46 17 6973 N-BEL Framework Small Corpus Document-487 MMP14 92 117 white rectangle gene 0.5 black 46 17 7160 N-BEL Framework Small Corpus Document-488 MMP2 93 112 white rectangle gene 0.5 black 46 17 7166 N-BEL Framework Small Corpus Document-489 MMP9 89 126 white rectangle gene 0.5 black 46 17 7176 N-BEL Framework Small Corpus Document-491 MYD88 72 92 white rectangle gene 0.5 black 46 17 7562 N-BEL Framework Small Corpus Document-493 NFKBIA 96 103 white rectangle gene 0.5 black 46 17 7797 N-BEL Framework Small Corpus Document-494 NFKBIA 97 80 white rectangle gene 0.5 black 46 17 7797 N-BEL Framework Small Corpus Document-495 NOS1 125 67 white rectangle gene 0.5 black 46 17 7872 N-BEL Framework Small Corpus Document-496 NOS3 116 77 white rectangle gene 0.5 black 46 17 7876 N-BEL Framework Small Corpus Document-5 6 PIK3R_I DDR1 131 69 white rectangle gene,gene 0.5 black 46 17 PIK3R_I,/,2730 N-BEL Framework Small Corpus Document-50 51 BTRC CTNNB1 103 70 white rectangle gene,gene 0.5 black 46 17 1144,/,2514 N-BEL Framework Small Corpus Document-500 NRAS 101 82 white rectangle gene 0.5 black 46 17 7989 N-BEL Framework Small Corpus Document-501 NRG1 81 81 white rectangle gene 0.5 black 46 17 7997 N-BEL Framework Small Corpus Document-502 OSM 68 78 white rectangle gene 0.5 black 46 17 8506 N-BEL Framework Small Corpus Document-503 SERPINE1 3 116 white rectangle gene 0.5 black 46 17 8583 N-BEL Framework Small Corpus Document-504 PAK1 109 86 white rectangle gene 0.5 black 46 17 8590 N-BEL Framework Small Corpus Document-505 PAK3 110 87 white rectangle gene 0.5 black 46 17 8592 N-BEL Framework Small Corpus Document-506 PAWR 92 109 white rectangle gene 0.5 black 46 17 8614 N-BEL Framework Small Corpus Document-507 PAWR 93 108 white rectangle gene 0.5 black 46 17 8614 N-BEL Framework Small Corpus Document-508 PAWR 88 114 white rectangle gene 0.5 black 46 17 8614 N-BEL Framework Small Corpus Document-510 PDE4B 109 84 white rectangle gene 0.5 black 46 17 8781 N-BEL Framework Small Corpus Document-511 PDE4C 109 83 white rectangle gene 0.5 black 46 17 8782 N-BEL Framework Small Corpus Document-512 PDE4D 111 84 white rectangle gene 0.5 black 46 17 8783 N-BEL Framework Small Corpus Document-513 PDGFRB 127 92 white rectangle gene 0.5 black 46 17 8804 N-BEL Framework Small Corpus Document-514 PDPK1 93 102 white rectangle gene 0.5 black 46 17 8816 N-BEL Framework Small Corpus Document-515 PELI1 78 97 white rectangle gene 0.5 black 46 17 8827 N-BEL Framework Small Corpus Document-516 PELI2 78 99 white rectangle gene 0.5 black 46 17 8828 N-BEL Framework Small Corpus Document-517 PGR 69 71 white rectangle gene 0.5 black 46 17 8910 N-BEL Framework Small Corpus Document-518 SERPINA1 58 73 white rectangle gene 0.5 black 46 17 8941 N-BEL Framework Small Corpus Document-519 PLAT 0 117 white rectangle gene 0.5 black 46 17 9051 N-BEL Framework Small Corpus Document-52 53 THBD F2 106 57 white rectangle gene,gene 0.5 black 46 17 11784,/,3535 N-BEL Framework Small Corpus Document-520 PLK1 161 39 white rectangle gene 0.5 black 46 17 9077 N-BEL Framework Small Corpus Document-521 PMAIP1 136 169 white rectangle gene 0.5 black 46 17 9108 N-BEL Framework Small Corpus Document-522 BAD 102 98 white rectangle gene 0.5 black 46 17 936 N-BEL Framework Small Corpus Document-523 BAD 113 102 white rectangle gene 0.5 black 46 17 936 N-BEL Framework Small Corpus Document-524 BAD 103 92 white rectangle gene 0.5 black 46 17 936 N-BEL Framework Small Corpus Document-525 BAG1 96 80 white rectangle gene 0.5 black 46 17 937 N-BEL Framework Small Corpus Document-526 BAG1 99 81 white rectangle gene 0.5 black 46 17 937 N-BEL Framework Small Corpus Document-527 PRKACA 115 79 white rectangle gene 0.5 black 46 17 9380 N-BEL Framework Small Corpus Document-528 PRKCA 74 94 white rectangle gene 0.5 black 46 17 9393 N-BEL Framework Small Corpus Document-529 PRKCD 90 130 white rectangle gene 0.5 black 46 17 9399 N-BEL Framework Small Corpus Document-530 PRKCD 120 76 white rectangle gene 0.5 black 46 17 9399 N-BEL Framework Small Corpus Document-531 PRKCI 92 130 white rectangle gene 0.5 black 46 17 9404 N-BEL Framework Small Corpus Document-532 PKN1 112 79 white rectangle gene 0.5 black 46 17 9405 N-BEL Framework Small Corpus Document-533 PKN2 110 79 white rectangle gene 0.5 black 46 17 9406 N-BEL Framework Small Corpus Document-534 PRKCZ 87 105 white rectangle gene 0.5 black 46 17 9412 N-BEL Framework Small Corpus Document-535 PROC 116 52 white rectangle gene 0.5 black 46 17 9451 N-BEL Framework Small Corpus Document-536 PROS1 120 50 white rectangle gene 0.5 black 46 17 9456 N-BEL Framework Small Corpus Document-537 PTEN 94 107 white rectangle gene 0.5 black 46 17 9588 N-BEL Framework Small Corpus Document-539 PTK2B 60 100 white rectangle gene 0.5 black 46 17 9612 N-BEL Framework Small Corpus Document-540 PTK2B 67 101 white rectangle gene 0.5 black 46 17 9612 N-BEL Framework Small Corpus Document-541 PTPN11 106 108 white rectangle gene 0.5 black 46 17 9644 N-BEL Framework Small Corpus Document-542 PTPRJ 117 94 white rectangle gene 0.5 black 46 17 9673 N-BEL Framework Small Corpus Document-543 RAB5A 94 85 white rectangle gene 0.5 black 46 17 9783 N-BEL Framework Small Corpus Document-544 RAC1 122 91 white rectangle gene 0.5 black 46 17 9801 N-BEL Framework Small Corpus Document-545 RAF1 97 86 white rectangle gene 0.5 black 46 17 9829 N-BEL Framework Small Corpus Document-546 RAF1 95 84 white rectangle gene 0.5 black 46 17 9829 N-BEL Framework Small Corpus Document-547 RAF1 104 88 white rectangle gene 0.5 black 46 17 9829 N-BEL Framework Small Corpus Document-548 RAF1 93 81 white rectangle gene 0.5 black 46 17 9829 N-BEL Framework Small Corpus Document-549 RAF1 89 95 white rectangle gene 0.5 black 46 17 9829 N-BEL Framework Small Corpus Document-550 RALA 106 109 white rectangle gene 0.5 black 46 17 9839 N-BEL Framework Small Corpus Document-551 RANBP2 57 24 white rectangle gene 0.5 black 46 17 9848 N-BEL Framework Small Corpus Document-552 RAP1A 94 79 white rectangle gene 0.5 black 46 17 9855 N-BEL Framework Small Corpus Document-553 RB1 27 11 white rectangle gene 0.5 black 46 17 9884 N-BEL Framework Small Corpus Document-554 RB1 98 71 white rectangle gene 0.5 black 46 17 9884 N-BEL Framework Small Corpus Document-555 BCL2 92 86 white rectangle gene 0.5 black 46 17 990 N-BEL Framework Small Corpus Document-556 BCL2 99 82 white rectangle gene 0.5 black 46 17 990 N-BEL Framework Small Corpus Document-557 RELA 101 111 white rectangle gene 0.5 black 46 17 9955 N-BEL Framework Small Corpus Document-558 Tsc2 122 110 white rectangle gene 0.5 black 46 17 102548 N-BEL Framework Small Corpus Document-559 Tsc2 122 109 white rectangle gene 0.5 black 46 17 102548 N-BEL Framework Small Corpus Document-560 Stat3 142 9 white rectangle gene 0.5 black 46 17 103038 N-BEL Framework Small Corpus Document-561 Stat1 187 117 white rectangle gene 0.5 black 46 17 103063 N-BEL Framework Small Corpus Document-562 Vegfa 127 5 white rectangle gene 0.5 black 46 17 103178 N-BEL Framework Small Corpus Document-563 Eif4ebp1 157 12 white rectangle gene 0.5 black 46 17 103267 N-BEL Framework Small Corpus Document-564 Eif4ebp1 156 11 white rectangle gene 0.5 black 46 17 103267 N-BEL Framework Small Corpus Document-565 Ccl11 32 147 white rectangle gene 0.5 black 46 17 103576 N-BEL Framework Small Corpus Document-566 Cdkn1b 121 102 white rectangle gene 0.5 black 46 17 104565 N-BEL Framework Small Corpus Document-567 Tnf 36 141 white rectangle gene 0.5 black 46 17 104798 N-BEL Framework Small Corpus Document-568 Pgf 128 7 white rectangle gene 0.5 black 46 17 105095 N-BEL Framework Small Corpus Document-571 Irak1 77 98 white rectangle gene 0.5 black 46 17 107420 N-BEL Framework Small Corpus Document-572 Lrig1 136 105 white rectangle gene 0.5 black 46 17 107935 N-BEL Framework Small Corpus Document-573 Angpt1 39 9 white rectangle gene 0.5 black 46 17 108448 N-BEL Framework Small Corpus Document-575 Itch 186 107 white rectangle gene 0.5 black 46 17 1202301 N-BEL Framework Small Corpus Document-577 Trp63 191 108 white rectangle gene 0.5 black 46 17 1330810 N-BEL Framework Small Corpus Document-578 Ppargc1a 78 116 white rectangle gene 0.5 black 46 17 1342774 N-BEL Framework Small Corpus Document-579 Mapk1 183 136 white rectangle gene 0.5 black 46 17 1346858 N-BEL Framework Small Corpus Document-580 Mapk3 184 134 white rectangle gene 0.5 black 46 17 1346859 N-BEL Framework Small Corpus Document-581 Foxo1 123 106 white rectangle gene 0.5 black 46 17 1890077 N-BEL Framework Small Corpus Document-582 Foxo3 123 107 white rectangle gene 0.5 black 46 17 1890081 N-BEL Framework Small Corpus Document-583 Pias1 187 116 white rectangle gene 0.5 black 46 17 1913125 N-BEL Framework Small Corpus Document-584 Pias3 51 72 white rectangle gene 0.5 black 46 17 1913126 N-BEL Framework Small Corpus Document-587 Mtor 154 15 white rectangle gene 0.5 black 46 17 1928394 N-BEL Framework Small Corpus Document-59 60 61 TRAF6 UBE2N UBE2V1 84 111 white rectangle gene,gene,gene 0.5 black 46 17 12036,/,12492,/,12494 N-BEL Framework Small Corpus Document-590 Mapkap1 126 107 white rectangle gene 0.5 black 46 17 2444554 N-BEL Framework Small Corpus Document-592 Hdac4 85 102 white rectangle gene 0.5 black 46 17 3036234 N-BEL Framework Small Corpus Document-593 Akt1 117 105 white rectangle gene 0.5 black 46 17 87986 N-BEL Framework Small Corpus Document-594 Akt1 32 47 white rectangle gene 0.5 black 46 17 87986 N-BEL Framework Small Corpus Document-595 Akt1 122 104 white rectangle gene 0.5 black 46 17 87986 N-BEL Framework Small Corpus Document-598 Camk4 76 119 white rectangle gene 0.5 black 46 17 88258 N-BEL Framework Small Corpus Document-599 Ctnnb1 180 134 white rectangle gene 0.5 black 46 17 88276 N-BEL Framework Small Corpus Document-600 Creb1 73 124 white rectangle gene 0.5 black 46 17 88494 N-BEL Framework Small Corpus Document-601 Egf 130 105 white rectangle gene 0.5 black 46 17 95290 N-BEL Framework Small Corpus Document-602 Flt1 130 3 white rectangle gene 0.5 black 46 17 95558 N-BEL Framework Small Corpus Document-603 Hck 27 147 white rectangle gene 0.5 black 46 17 96052 N-BEL Framework Small Corpus Document-604 Hck 31 145 white rectangle gene 0.5 black 46 17 96052 N-BEL Framework Small Corpus Document-605 Hras 127 104 white rectangle gene 0.5 black 46 17 96224 N-BEL Framework Small Corpus Document-606 Il5 28 148 white rectangle gene 0.5 black 46 17 96557 N-BEL Framework Small Corpus Document-607 Il6st 142 12 white rectangle gene 0.5 black 46 17 96560 N-BEL Framework Small Corpus Document-609 Kdr 125 4 white rectangle gene 0.5 black 46 17 96683 N-BEL Framework Small Corpus Document-615 Tek 42 13 white rectangle gene 0.5 black 46 17 98664 N-BEL Framework Small Corpus Document-617 Irs1 33 46 white rectangle gene 0.5 black 46 17 99454 N-BEL Framework Small Corpus Document-619 Ptpn11 144 11 white rectangle gene 0.5 black 46 17 99511 N-BEL Framework Small Corpus Document-62 63 64 65 66 67 TRAF6 TAB2 IRAK4 TAB1 IRAK1 MAP3K7 56 186 white rectangle gene,gene,gene,gene,gene,gene 0.5 black 46 17 12036,/,17075,/,17967,/,18157,/,6112,/,6859 N-BEL Framework Small Corpus Document-621 Chuk 68 5 white rectangle gene 0.5 black 46 17 1306661 N-BEL Framework Small Corpus Document-622 Chuk 70 10 white rectangle gene 0.5 black 46 17 1306661 N-BEL Framework Small Corpus Document-623 Chuk 67 1 white rectangle gene 0.5 black 46 17 1306661 N-BEL Framework Small Corpus Document-624 Igf1 99 87 white rectangle gene 0.5 black 46 17 2868 N-BEL Framework Small Corpus Document-625 Itgb4 178 165 white rectangle gene 0.5 black 46 17 2928 N-BEL Framework Small Corpus Document-626 Jun 102 110 white rectangle gene 0.5 black 46 17 2943 N-BEL Framework Small Corpus Document-627 Ikbkb 153 143 white rectangle gene 0.5 black 46 17 621375 N-BEL Framework Small Corpus Document-628 Ikbkb 152 145 white rectangle gene 0.5 black 46 17 621375 N-BEL Framework Small Corpus Document-629 Ikbkb 155 142 white rectangle gene 0.5 black 46 17 621375 N-BEL Framework Small Corpus Document-68 69 TRAF6 IRAK1 65 90 white rectangle gene,gene 0.5 black 46 17 12036,/,6112 N-BEL Framework Small Corpus Document-7 8 PIK3R_I ERBB3 81 75 white rectangle gene,gene 0.5 black 46 17 PIK3R_I,/,3431 N-BEL Framework Small Corpus Document-70 71 ACP1 CAV1 139 98 white rectangle gene,gene 0.5 black 46 17 122,/,1527 N-BEL Framework Small Corpus Document-72 73 ACP1 KDR 125 95 white rectangle gene,gene 0.5 black 46 17 122,/,6307 N-BEL Framework Small Corpus Document-74 75 VHL HIF1A 105 125 white rectangle gene,gene 0.5 black 46 17 12687,/,4910 N-BEL Framework Small Corpus Document-76 77 78 VTN ITGAV ITGB3 121 93 white rectangle gene,gene,gene 0.5 black 46 17 12724,/,6150,/,6156 N-BEL Framework Small Corpus Document-82 83 CAV1 CSK 139 100 white rectangle gene,gene 0.5 black 46 17 1527,/,2444 N-BEL Framework Small Corpus Document-84 85 CAV1 GRB7 138 101 white rectangle gene,gene 0.5 black 46 17 1527,/,4567 N-BEL Framework Small Corpus Document-86 87 CAV1 KDR 138 99 white rectangle gene,gene 0.5 black 46 17 1527,/,6307 N-BEL Framework Small Corpus Document-88 89 CCND1 CDK4 99 63 white rectangle gene,gene 0.5 black 46 17 1582,/,1773 N-BEL Framework Small Corpus Document-9 10 p14_3_3 FOXO 97 106 white rectangle gene,gene 0.5 black 46 17 p14_3_3,/,FOXO N-BEL Framework Small Corpus Document-90 91 PRPF6 PRPF4B 60 59 white rectangle gene,gene 0.5 black 46 17 15860,/,17346 N-BEL Framework Small Corpus Document-92 93 SH3RF1 MAP3K11 103 110 white rectangle gene,gene 0.5 black 46 17 17650,/,6850 N-BEL Framework Small Corpus Document-94 95 CDK2 CDKN1A 97 96 white rectangle gene,gene 0.5 black 46 17 1771,/,1784 N-BEL Framework Small Corpus Document-96 97 98 IRAK4 IRAK1 MYD88 72 91 white rectangle gene,gene,gene 0.5 black 46 17 17967,/,6112,/,7562 N-BEL Framework Small Corpus Document-99 100 CYCS APAF1 13 165 white rectangle gene,gene 0.5 black 46 17 19986,/,576 pybel-0.15.5/notebooks/hipathia_demo/selventa/BEL Framework Small Corpus Document.sif000066400000000000000000001735721426625374700304420ustar00rootroot000000000000000 1 2 N-BEL Framework Small Corpus Document-528 activation N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-528 inhibition N-BEL Framework Small Corpus Document-449 N-BEL Framework Small Corpus Document-528 activation N-BEL Framework Small Corpus Document-449 N-BEL Framework Small Corpus Document-528 activation N-BEL Framework Small Corpus Document-278 N-BEL Framework Small Corpus Document-623 activation N-BEL Framework Small Corpus Document-621 N-BEL Framework Small Corpus Document-194 activation N-BEL Framework Small Corpus Document-369 N-BEL Framework Small Corpus Document-194 activation N-BEL Framework Small Corpus Document-369 N-BEL Framework Small Corpus Document-194 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-194 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-445 activation N-BEL Framework Small Corpus Document-435 N-BEL Framework Small Corpus Document-445 activation N-BEL Framework Small Corpus Document-534 N-BEL Framework Small Corpus Document-573 activation N-BEL Framework Small Corpus Document-615 N-BEL Framework Small Corpus Document-426 activation N-BEL Framework Small Corpus Document-394 N-BEL Framework Small Corpus Document-226 activation N-BEL Framework Small Corpus Document-225 N-BEL Framework Small Corpus Document-527 inhibition N-BEL Framework Small Corpus Document-191 N-BEL Framework Small Corpus Document-527 inhibition N-BEL Framework Small Corpus Document-191 N-BEL Framework Small Corpus Document-527 inhibition N-BEL Framework Small Corpus Document-530 N-BEL Framework Small Corpus Document-527 activation N-BEL Framework Small Corpus Document-406 N-BEL Framework Small Corpus Document-527 activation N-BEL Framework Small Corpus Document-406 N-BEL Framework Small Corpus Document-527 activation N-BEL Framework Small Corpus Document-408 N-BEL Framework Small Corpus Document-527 activation N-BEL Framework Small Corpus Document-408 N-BEL Framework Small Corpus Document-527 inhibition N-BEL Framework Small Corpus Document-202 N-BEL Framework Small Corpus Document-527 inhibition N-BEL Framework Small Corpus Document-202 N-BEL Framework Small Corpus Document-486 inhibition N-BEL Framework Small Corpus Document-269 N-BEL Framework Small Corpus Document-544 activation N-BEL Framework Small Corpus Document-144 145 N-BEL Framework Small Corpus Document-544 activation N-BEL Framework Small Corpus Document-40 41 N-BEL Framework Small Corpus Document-544 activation N-BEL Framework Small Corpus Document-130 131 N-BEL Framework Small Corpus Document-544 activation N-BEL Framework Small Corpus Document-38 39 N-BEL Framework Small Corpus Document-396 activation N-BEL Framework Small Corpus Document-395 N-BEL Framework Small Corpus Document-319 activation N-BEL Framework Small Corpus Document-216 N-BEL Framework Small Corpus Document-242 activation N-BEL Framework Small Corpus Document-144 145 N-BEL Framework Small Corpus Document-406 inhibition N-BEL Framework Small Corpus Document-405 N-BEL Framework Small Corpus Document-397 activation N-BEL Framework Small Corpus Document-395 N-BEL Framework Small Corpus Document-548 inhibition N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-548 activation N-BEL Framework Small Corpus Document-13 14 N-BEL Framework Small Corpus Document-471 activation N-BEL Framework Small Corpus Document-233 N-BEL Framework Small Corpus Document-471 activation N-BEL Framework Small Corpus Document-233 N-BEL Framework Small Corpus Document-274 activation N-BEL Framework Small Corpus Document-416 N-BEL Framework Small Corpus Document-274 activation N-BEL Framework Small Corpus Document-417 N-BEL Framework Small Corpus Document-274 activation N-BEL Framework Small Corpus Document-418 N-BEL Framework Small Corpus Document-52 53 inhibition N-BEL Framework Small Corpus Document-382 N-BEL Framework Small Corpus Document-244 activation N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-244 activation N-BEL Framework Small Corpus Document-328 N-BEL Framework Small Corpus Document-244 activation N-BEL Framework Small Corpus Document-326 N-BEL Framework Small Corpus Document-290 activation N-BEL Framework Small Corpus Document-495 N-BEL Framework Small Corpus Document-290 activation N-BEL Framework Small Corpus Document-496 N-BEL Framework Small Corpus Document-629 activation N-BEL Framework Small Corpus Document-627 N-BEL Framework Small Corpus Document-408 inhibition N-BEL Framework Small Corpus Document-407 N-BEL Framework Small Corpus Document-487 activation N-BEL Framework Small Corpus Document-488 N-BEL Framework Small Corpus Document-541 inhibition N-BEL Framework Small Corpus Document-260 N-BEL Framework Small Corpus Document-541 inhibition N-BEL Framework Small Corpus Document-456 N-BEL Framework Small Corpus Document-331 activation N-BEL Framework Small Corpus Document-440 N-BEL Framework Small Corpus Document-331 activation N-BEL Framework Small Corpus Document-439 N-BEL Framework Small Corpus Document-292 activation N-BEL Framework Small Corpus Document-416 N-BEL Framework Small Corpus Document-292 activation N-BEL Framework Small Corpus Document-417 N-BEL Framework Small Corpus Document-292 activation N-BEL Framework Small Corpus Document-418 N-BEL Framework Small Corpus Document-353 activation N-BEL Framework Small Corpus Document-36 37 N-BEL Framework Small Corpus Document-353 activation N-BEL Framework Small Corpus Document-101 102 N-BEL Framework Small Corpus Document-353 activation N-BEL Framework Small Corpus Document-107 108 N-BEL Framework Small Corpus Document-214 activation N-BEL Framework Small Corpus Document-523 N-BEL Framework Small Corpus Document-214 inhibition N-BEL Framework Small Corpus Document-522 N-BEL Framework Small Corpus Document-214 activation N-BEL Framework Small Corpus Document-348 N-BEL Framework Small Corpus Document-415 inhibition N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-273 inhibition N-BEL Framework Small Corpus Document-456 N-BEL Framework Small Corpus Document-337 inhibition N-BEL Framework Small Corpus Document-206 N-BEL Framework Small Corpus Document-476 inhibition N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-536 activation N-BEL Framework Small Corpus Document-535 N-BEL Framework Small Corpus Document-354 activation N-BEL Framework Small Corpus Document-353 N-BEL Framework Small Corpus Document-354 activation N-BEL Framework Small Corpus Document-353 N-BEL Framework Small Corpus Document-354 activation N-BEL Framework Small Corpus Document-50 51 N-BEL Framework Small Corpus Document-76 77 78 activation N-BEL Framework Small Corpus Document-513 N-BEL Framework Small Corpus Document-76 77 78 activation N-BEL Framework Small Corpus Document-456 N-BEL Framework Small Corpus Document-450 activation N-BEL Framework Small Corpus Document-146 147 N-BEL Framework Small Corpus Document-392 activation N-BEL Framework Small Corpus Document-140 141 N-BEL Framework Small Corpus Document-392 activation N-BEL Framework Small Corpus Document-138 139 N-BEL Framework Small Corpus Document-590 activation N-BEL Framework Small Corpus Document-595 N-BEL Framework Small Corpus Document-590 activation N-BEL Framework Small Corpus Document-595 N-BEL Framework Small Corpus Document-590 activation N-BEL Framework Small Corpus Document-581 N-BEL Framework Small Corpus Document-590 activation N-BEL Framework Small Corpus Document-582 N-BEL Framework Small Corpus Document-387 activation N-BEL Framework Small Corpus Document-382 N-BEL Framework Small Corpus Document-617 activation N-BEL Framework Small Corpus Document-594 N-BEL Framework Small Corpus Document-115 116 activation N-BEL Framework Small Corpus Document-387 N-BEL Framework Small Corpus Document-115 116 activation N-BEL Framework Small Corpus Document-381 N-BEL Framework Small Corpus Document-115 116 activation N-BEL Framework Small Corpus Document-381 N-BEL Framework Small Corpus Document-297 inhibition N-BEL Framework Small Corpus Document-456 N-BEL Framework Small Corpus Document-308 activation N-BEL Framework Small Corpus Document-552 N-BEL Framework Small Corpus Document-201 inhibition N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-322 activation N-BEL Framework Small Corpus Document-327 N-BEL Framework Small Corpus Document-628 activation N-BEL Framework Small Corpus Document-627 N-BEL Framework Small Corpus Document-94 95 inhibition N-BEL Framework Small Corpus Document-322 N-BEL Framework Small Corpus Document-474 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-474 inhibition N-BEL Framework Small Corpus Document-419 N-BEL Framework Small Corpus Document-148 149 activation N-BEL Framework Small Corpus Document-218 N-BEL Framework Small Corpus Document-148 149 activation N-BEL Framework Small Corpus Document-435 N-BEL Framework Small Corpus Document-364 activation N-BEL Framework Small Corpus Document-366 N-BEL Framework Small Corpus Document-364 activation N-BEL Framework Small Corpus Document-367 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-184 activation N-BEL Framework Small Corpus Document-546 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-429 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-429 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-429 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-439 N-BEL Framework Small Corpus Document-184 activation N-BEL Framework Small Corpus Document-441 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-132 133 N-BEL Framework Small Corpus Document-184 activation N-BEL Framework Small Corpus Document-406 N-BEL Framework Small Corpus Document-184 activation N-BEL Framework Small Corpus Document-408 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-405 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-407 N-BEL Framework Small Corpus Document-184 activation N-BEL Framework Small Corpus Document-524 N-BEL Framework Small Corpus Document-184 inhibition N-BEL Framework Small Corpus Document-522 N-BEL Framework Small Corpus Document-262 activation N-BEL Framework Small Corpus Document-535 N-BEL Framework Small Corpus Document-191 activation N-BEL Framework Small Corpus Document-136 137 N-BEL Framework Small Corpus Document-191 activation N-BEL Framework Small Corpus Document-289 N-BEL Framework Small Corpus Document-191 activation N-BEL Framework Small Corpus Document-309 N-BEL Framework Small Corpus Document-191 activation N-BEL Framework Small Corpus Document-360 N-BEL Framework Small Corpus Document-191 activation N-BEL Framework Small Corpus Document-360 N-BEL Framework Small Corpus Document-191 activation N-BEL Framework Small Corpus Document-360 N-BEL Framework Small Corpus Document-191 activation N-BEL Framework Small Corpus Document-360 N-BEL Framework Small Corpus Document-191 activation N-BEL Framework Small Corpus Document-5 6 N-BEL Framework Small Corpus Document-320 activation N-BEL Framework Small Corpus Document-371 N-BEL Framework Small Corpus Document-320 activation N-BEL Framework Small Corpus Document-371 N-BEL Framework Small Corpus Document-320 activation N-BEL Framework Small Corpus Document-371 N-BEL Framework Small Corpus Document-320 activation N-BEL Framework Small Corpus Document-371 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-369 N-BEL Framework Small Corpus Document-320 activation N-BEL Framework Small Corpus Document-369 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-369 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-501 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-501 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-374 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-375 N-BEL Framework Small Corpus Document-320 inhibition N-BEL Framework Small Corpus Document-376 N-BEL Framework Small Corpus Document-619 inhibition N-BEL Framework Small Corpus Document-607 N-BEL Framework Small Corpus Document-28 29 inhibition N-BEL Framework Small Corpus Document-24 25 N-BEL Framework Small Corpus Document-11 12 inhibition N-BEL Framework Small Corpus Document-506 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-103 104 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-111 112 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-400 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-200 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-285 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-181 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-32 33 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-244 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-9 10 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-324 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-324 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-94 95 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-507 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-507 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-506 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-506 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-506 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-506 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-326 N-BEL Framework Small Corpus Document-395 activation N-BEL Framework Small Corpus Document-486 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-522 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-407 N-BEL Framework Small Corpus Document-395 inhibition N-BEL Framework Small Corpus Document-393 N-BEL Framework Small Corpus Document-192 activation N-BEL Framework Small Corpus Document-360 N-BEL Framework Small Corpus Document-223 activation N-BEL Framework Small Corpus Document-216 N-BEL Framework Small Corpus Document-204 activation N-BEL Framework Small Corpus Document-439 N-BEL Framework Small Corpus Document-204 activation N-BEL Framework Small Corpus Document-132 133 N-BEL Framework Small Corpus Document-204 activation N-BEL Framework Small Corpus Document-134 135 N-BEL Framework Small Corpus Document-122 123 activation N-BEL Framework Small Corpus Document-226 N-BEL Framework Small Corpus Document-187 inhibition N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-461 activation N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-461 activation N-BEL Framework Small Corpus Document-481 N-BEL Framework Small Corpus Document-461 activation N-BEL Framework Small Corpus Document-483 N-BEL Framework Small Corpus Document-461 activation N-BEL Framework Small Corpus Document-395 N-BEL Framework Small Corpus Document-451 activation N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-607 activation N-BEL Framework Small Corpus Document-560 N-BEL Framework Small Corpus Document-568 activation N-BEL Framework Small Corpus Document-602 N-BEL Framework Small Corpus Document-568 activation N-BEL Framework Small Corpus Document-562 N-BEL Framework Small Corpus Document-568 inhibition N-BEL Framework Small Corpus Document-165 166 N-BEL Framework Small Corpus Document-371 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-365 activation N-BEL Framework Small Corpus Document-367 N-BEL Framework Small Corpus Document-302 activation N-BEL Framework Small Corpus Document-323 N-BEL Framework Small Corpus Document-185 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-179 activation N-BEL Framework Small Corpus Document-178 N-BEL Framework Small Corpus Document-62 63 64 65 66 67 activation N-BEL Framework Small Corpus Document-480 N-BEL Framework Small Corpus Document-62 63 64 65 66 67 activation N-BEL Framework Small Corpus Document-311 N-BEL Framework Small Corpus Document-423 activation N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-144 145 activation N-BEL Framework Small Corpus Document-243 N-BEL Framework Small Corpus Document-144 145 activation N-BEL Framework Small Corpus Document-42 43 N-BEL Framework Small Corpus Document-144 145 activation N-BEL Framework Small Corpus Document-225 N-BEL Framework Small Corpus Document-144 145 activation N-BEL Framework Small Corpus Document-197 N-BEL Framework Small Corpus Document-144 145 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-59 60 61 activation N-BEL Framework Small Corpus Document-479 N-BEL Framework Small Corpus Document-413 inhibition N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-413 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-503 inhibition N-BEL Framework Small Corpus Document-519 N-BEL Framework Small Corpus Document-443 activation N-BEL Framework Small Corpus Document-435 N-BEL Framework Small Corpus Document-547 activation N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-464 activation N-BEL Framework Small Corpus Document-544 N-BEL Framework Small Corpus Document-583 inhibition N-BEL Framework Small Corpus Document-561 N-BEL Framework Small Corpus Document-550 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-599 activation N-BEL Framework Small Corpus Document-579 N-BEL Framework Small Corpus Document-599 activation N-BEL Framework Small Corpus Document-580 N-BEL Framework Small Corpus Document-107 108 activation N-BEL Framework Small Corpus Document-353 N-BEL Framework Small Corpus Document-412 activation N-BEL Framework Small Corpus Document-261 N-BEL Framework Small Corpus Document-411 activation N-BEL Framework Small Corpus Document-356 N-BEL Framework Small Corpus Document-411 activation N-BEL Framework Small Corpus Document-356 N-BEL Framework Small Corpus Document-411 inhibition N-BEL Framework Small Corpus Document-409 N-BEL Framework Small Corpus Document-425 activation N-BEL Framework Small Corpus Document-444 N-BEL Framework Small Corpus Document-425 activation N-BEL Framework Small Corpus Document-447 N-BEL Framework Small Corpus Document-425 activation N-BEL Framework Small Corpus Document-448 N-BEL Framework Small Corpus Document-241 activation N-BEL Framework Small Corpus Document-546 N-BEL Framework Small Corpus Document-390 activation N-BEL Framework Small Corpus Document-540 N-BEL Framework Small Corpus Document-466 activation N-BEL Framework Small Corpus Document-144 145 N-BEL Framework Small Corpus Document-105 106 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-409 activation N-BEL Framework Small Corpus Document-260 N-BEL Framework Small Corpus Document-507 activation N-BEL Framework Small Corpus Document-11 12 N-BEL Framework Small Corpus Document-175 activation N-BEL Framework Small Corpus Document-359 N-BEL Framework Small Corpus Document-449 activation N-BEL Framework Small Corpus Document-528 N-BEL Framework Small Corpus Document-449 activation N-BEL Framework Small Corpus Document-278 N-BEL Framework Small Corpus Document-449 activation N-BEL Framework Small Corpus Document-278 N-BEL Framework Small Corpus Document-449 activation N-BEL Framework Small Corpus Document-540 N-BEL Framework Small Corpus Document-351 activation N-BEL Framework Small Corpus Document-467 N-BEL Framework Small Corpus Document-351 activation N-BEL Framework Small Corpus Document-468 N-BEL Framework Small Corpus Document-332 activation N-BEL Framework Small Corpus Document-479 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-496 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-496 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-496 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-150 151 152 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-456 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-456 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-356 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-356 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-457 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-458 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-161 162 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-159 160 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-72 73 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-544 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-298 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-464 N-BEL Framework Small Corpus Document-231 activation N-BEL Framework Small Corpus Document-163 164 N-BEL Framework Small Corpus Document-472 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-600 activation N-BEL Framework Small Corpus Document-598 N-BEL Framework Small Corpus Document-543 activation N-BEL Framework Small Corpus Document-197 N-BEL Framework Small Corpus Document-543 activation N-BEL Framework Small Corpus Document-369 N-BEL Framework Small Corpus Document-543 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-622 activation N-BEL Framework Small Corpus Document-621 N-BEL Framework Small Corpus Document-239 activation N-BEL Framework Small Corpus Document-482 N-BEL Framework Small Corpus Document-239 activation N-BEL Framework Small Corpus Document-482 N-BEL Framework Small Corpus Document-239 activation N-BEL Framework Small Corpus Document-421 N-BEL Framework Small Corpus Document-572 inhibition N-BEL Framework Small Corpus Document-601 N-BEL Framework Small Corpus Document-572 inhibition N-BEL Framework Small Corpus Document-601 N-BEL Framework Small Corpus Document-345 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-304 inhibition N-BEL Framework Small Corpus Document-271 N-BEL Framework Small Corpus Document-304 inhibition N-BEL Framework Small Corpus Document-271 N-BEL Framework Small Corpus Document-304 inhibition N-BEL Framework Small Corpus Document-329 N-BEL Framework Small Corpus Document-304 inhibition N-BEL Framework Small Corpus Document-521 N-BEL Framework Small Corpus Document-435 activation N-BEL Framework Small Corpus Document-436 N-BEL Framework Small Corpus Document-435 activation N-BEL Framework Small Corpus Document-248 N-BEL Framework Small Corpus Document-435 activation N-BEL Framework Small Corpus Document-248 N-BEL Framework Small Corpus Document-435 activation N-BEL Framework Small Corpus Document-248 N-BEL Framework Small Corpus Document-435 activation N-BEL Framework Small Corpus Document-248 N-BEL Framework Small Corpus Document-435 activation N-BEL Framework Small Corpus Document-534 N-BEL Framework Small Corpus Document-435 activation N-BEL Framework Small Corpus Document-437 N-BEL Framework Small Corpus Document-435 activation N-BEL Framework Small Corpus Document-446 N-BEL Framework Small Corpus Document-557 inhibition N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-271 activation N-BEL Framework Small Corpus Document-329 N-BEL Framework Small Corpus Document-271 activation N-BEL Framework Small Corpus Document-521 N-BEL Framework Small Corpus Document-431 activation N-BEL Framework Small Corpus Document-217 N-BEL Framework Small Corpus Document-604 activation N-BEL Framework Small Corpus Document-603 N-BEL Framework Small Corpus Document-604 activation N-BEL Framework Small Corpus Document-606 N-BEL Framework Small Corpus Document-604 activation N-BEL Framework Small Corpus Document-565 N-BEL Framework Small Corpus Document-604 activation N-BEL Framework Small Corpus Document-567 N-BEL Framework Small Corpus Document-361 activation N-BEL Framework Small Corpus Document-15 16 17 N-BEL Framework Small Corpus Document-361 activation N-BEL Framework Small Corpus Document-15 16 17 N-BEL Framework Small Corpus Document-287 activation N-BEL Framework Small Corpus Document-288 N-BEL Framework Small Corpus Document-491 activation N-BEL Framework Small Corpus Document-96 97 98 N-BEL Framework Small Corpus Document-491 activation N-BEL Framework Small Corpus Document-331 N-BEL Framework Small Corpus Document-605 inhibition N-BEL Framework Small Corpus Document-566 N-BEL Framework Small Corpus Document-456 activation N-BEL Framework Small Corpus Document-459 N-BEL Framework Small Corpus Document-456 activation N-BEL Framework Small Corpus Document-460 N-BEL Framework Small Corpus Document-456 activation N-BEL Framework Small Corpus Document-251 N-BEL Framework Small Corpus Document-456 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-200 activation N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-200 inhibition N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-99 100 activation N-BEL Framework Small Corpus Document-295 N-BEL Framework Small Corpus Document-416 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-416 activation N-BEL Framework Small Corpus Document-74 75 N-BEL Framework Small Corpus Document-42 43 activation N-BEL Framework Small Corpus Document-225 N-BEL Framework Small Corpus Document-362 inhibition N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-362 inhibition N-BEL Framework Small Corpus Document-284 N-BEL Framework Small Corpus Document-598 activation N-BEL Framework Small Corpus Document-600 N-BEL Framework Small Corpus Document-598 activation N-BEL Framework Small Corpus Document-209 N-BEL Framework Small Corpus Document-598 activation N-BEL Framework Small Corpus Document-210 N-BEL Framework Small Corpus Document-382 activation N-BEL Framework Small Corpus Document-262 N-BEL Framework Small Corpus Document-382 activation N-BEL Framework Small Corpus Document-281 N-BEL Framework Small Corpus Document-382 activation N-BEL Framework Small Corpus Document-384 N-BEL Framework Small Corpus Document-382 activation N-BEL Framework Small Corpus Document-240 N-BEL Framework Small Corpus Document-382 activation N-BEL Framework Small Corpus Document-240 N-BEL Framework Small Corpus Document-382 activation N-BEL Framework Small Corpus Document-383 N-BEL Framework Small Corpus Document-382 activation N-BEL Framework Small Corpus Document-496 N-BEL Framework Small Corpus Document-369 activation N-BEL Framework Small Corpus Document-230 N-BEL Framework Small Corpus Document-369 activation N-BEL Framework Small Corpus Document-229 N-BEL Framework Small Corpus Document-369 activation N-BEL Framework Small Corpus Document-453 N-BEL Framework Small Corpus Document-369 activation N-BEL Framework Small Corpus Document-7 8 N-BEL Framework Small Corpus Document-369 activation N-BEL Framework Small Corpus Document-371 N-BEL Framework Small Corpus Document-369 activation N-BEL Framework Small Corpus Document-377 N-BEL Framework Small Corpus Document-542 inhibition N-BEL Framework Small Corpus Document-456 N-BEL Framework Small Corpus Document-174 activation N-BEL Framework Small Corpus Document-177 N-BEL Framework Small Corpus Document-1 2 activation N-BEL Framework Small Corpus Document-176 N-BEL Framework Small Corpus Document-552 activation N-BEL Framework Small Corpus Document-245 N-BEL Framework Small Corpus Document-552 activation N-BEL Framework Small Corpus Document-245 N-BEL Framework Small Corpus Document-552 inhibition N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-552 inhibition N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-515 activation N-BEL Framework Small Corpus Document-204 N-BEL Framework Small Corpus Document-293 activation N-BEL Framework Small Corpus Document-416 N-BEL Framework Small Corpus Document-293 activation N-BEL Framework Small Corpus Document-417 N-BEL Framework Small Corpus Document-293 activation N-BEL Framework Small Corpus Document-418 N-BEL Framework Small Corpus Document-517 activation N-BEL Framework Small Corpus Document-229 N-BEL Framework Small Corpus Document-358 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-358 inhibition N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-358 activation N-BEL Framework Small Corpus Document-374 N-BEL Framework Small Corpus Document-358 inhibition N-BEL Framework Small Corpus Document-374 N-BEL Framework Small Corpus Document-330 inhibition N-BEL Framework Small Corpus Document-88 89 N-BEL Framework Small Corpus Document-462 activation N-BEL Framework Small Corpus Document-469 N-BEL Framework Small Corpus Document-462 activation N-BEL Framework Small Corpus Document-469 N-BEL Framework Small Corpus Document-436 activation N-BEL Framework Small Corpus Document-444 N-BEL Framework Small Corpus Document-436 activation N-BEL Framework Small Corpus Document-447 N-BEL Framework Small Corpus Document-436 activation N-BEL Framework Small Corpus Document-448 N-BEL Framework Small Corpus Document-546 activation N-BEL Framework Small Corpus Document-13 14 N-BEL Framework Small Corpus Document-546 inhibition N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-289 activation N-BEL Framework Small Corpus Document-258 N-BEL Framework Small Corpus Document-465 activation N-BEL Framework Small Corpus Document-451 N-BEL Framework Small Corpus Document-465 activation N-BEL Framework Small Corpus Document-452 N-BEL Framework Small Corpus Document-465 activation N-BEL Framework Small Corpus Document-275 N-BEL Framework Small Corpus Document-285 activation N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-398 activation N-BEL Framework Small Corpus Document-476 N-BEL Framework Small Corpus Document-325 inhibition N-BEL Framework Small Corpus Document-324 N-BEL Framework Small Corpus Document-233 activation N-BEL Framework Small Corpus Document-325 N-BEL Framework Small Corpus Document-233 activation N-BEL Framework Small Corpus Document-347 N-BEL Framework Small Corpus Document-233 inhibition N-BEL Framework Small Corpus Document-346 N-BEL Framework Small Corpus Document-562 activation N-BEL Framework Small Corpus Document-609 N-BEL Framework Small Corpus Document-562 activation N-BEL Framework Small Corpus Document-609 N-BEL Framework Small Corpus Document-18 19 activation N-BEL Framework Small Corpus Document-407 N-BEL Framework Small Corpus Document-291 activation N-BEL Framework Small Corpus Document-287 N-BEL Framework Small Corpus Document-578 activation N-BEL Framework Small Corpus Document-209 N-BEL Framework Small Corpus Document-414 inhibition N-BEL Framework Small Corpus Document-105 106 N-BEL Framework Small Corpus Document-414 inhibition N-BEL Framework Small Corpus Document-113 114 N-BEL Framework Small Corpus Document-414 inhibition N-BEL Framework Small Corpus Document-113 114 N-BEL Framework Small Corpus Document-281 inhibition N-BEL Framework Small Corpus Document-386 N-BEL Framework Small Corpus Document-555 activation N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-247 activation N-BEL Framework Small Corpus Document-245 N-BEL Framework Small Corpus Document-284 activation N-BEL Framework Small Corpus Document-175 N-BEL Framework Small Corpus Document-181 inhibition N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-181 activation N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-469 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-469 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-470 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-470 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-494 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-556 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-526 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-555 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-525 N-BEL Framework Small Corpus Document-545 inhibition N-BEL Framework Small Corpus Document-478 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-554 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-270 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-190 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-324 N-BEL Framework Small Corpus Document-545 activation N-BEL Framework Small Corpus Document-94 95 N-BEL Framework Small Corpus Document-26 27 inhibition N-BEL Framework Small Corpus Document-24 25 N-BEL Framework Small Corpus Document-468 activation N-BEL Framework Small Corpus Document-195 N-BEL Framework Small Corpus Document-482 activation N-BEL Framework Small Corpus Document-484 N-BEL Framework Small Corpus Document-482 activation N-BEL Framework Small Corpus Document-484 N-BEL Framework Small Corpus Document-482 activation N-BEL Framework Small Corpus Document-485 N-BEL Framework Small Corpus Document-584 inhibition N-BEL Framework Small Corpus Document-255 N-BEL Framework Small Corpus Document-535 inhibition N-BEL Framework Small Corpus Document-386 N-BEL Framework Small Corpus Document-535 inhibition N-BEL Framework Small Corpus Document-385 N-BEL Framework Small Corpus Document-113 114 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-506 activation N-BEL Framework Small Corpus Document-537 N-BEL Framework Small Corpus Document-245 activation N-BEL Framework Small Corpus Document-198 N-BEL Framework Small Corpus Document-245 activation N-BEL Framework Small Corpus Document-469 N-BEL Framework Small Corpus Document-245 activation N-BEL Framework Small Corpus Document-470 N-BEL Framework Small Corpus Document-553 inhibition N-BEL Framework Small Corpus Document-196 N-BEL Framework Small Corpus Document-205 activation N-BEL Framework Small Corpus Document-3 4 N-BEL Framework Small Corpus Document-50 51 activation N-BEL Framework Small Corpus Document-353 N-BEL Framework Small Corpus Document-504 activation N-BEL Framework Small Corpus Document-547 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-267 N-BEL Framework Small Corpus Document-266 inhibition N-BEL Framework Small Corpus Document-435 N-BEL Framework Small Corpus Document-266 inhibition N-BEL Framework Small Corpus Document-276 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-239 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-239 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-482 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-482 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-422 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-488 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-487 N-BEL Framework Small Corpus Document-266 activation N-BEL Framework Small Corpus Document-541 N-BEL Framework Small Corpus Document-218 activation N-BEL Framework Small Corpus Document-595 N-BEL Framework Small Corpus Document-218 activation N-BEL Framework Small Corpus Document-153 154 155 N-BEL Framework Small Corpus Document-218 activation N-BEL Framework Small Corpus Document-593 N-BEL Framework Small Corpus Document-218 activation N-BEL Framework Small Corpus Document-197 N-BEL Framework Small Corpus Document-218 inhibition N-BEL Framework Small Corpus Document-566 N-BEL Framework Small Corpus Document-593 activation N-BEL Framework Small Corpus Document-558 N-BEL Framework Small Corpus Document-593 activation N-BEL Framework Small Corpus Document-559 N-BEL Framework Small Corpus Document-593 activation N-BEL Framework Small Corpus Document-581 N-BEL Framework Small Corpus Document-593 activation N-BEL Framework Small Corpus Document-581 N-BEL Framework Small Corpus Document-593 activation N-BEL Framework Small Corpus Document-582 N-BEL Framework Small Corpus Document-593 activation N-BEL Framework Small Corpus Document-582 N-BEL Framework Small Corpus Document-593 inhibition N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-534 activation N-BEL Framework Small Corpus Document-435 N-BEL Framework Small Corpus Document-534 activation N-BEL Framework Small Corpus Document-248 N-BEL Framework Small Corpus Document-195 activation N-BEL Framework Small Corpus Document-20 21 N-BEL Framework Small Corpus Document-195 activation N-BEL Framework Small Corpus Document-22 23 N-BEL Framework Small Corpus Document-323 inhibition N-BEL Framework Small Corpus Document-420 N-BEL Framework Small Corpus Document-323 activation N-BEL Framework Small Corpus Document-554 N-BEL Framework Small Corpus Document-298 inhibition N-BEL Framework Small Corpus Document-86 87 N-BEL Framework Small Corpus Document-298 activation N-BEL Framework Small Corpus Document-84 85 N-BEL Framework Small Corpus Document-298 activation N-BEL Framework Small Corpus Document-70 71 N-BEL Framework Small Corpus Document-298 activation N-BEL Framework Small Corpus Document-82 83 N-BEL Framework Small Corpus Document-206 activation N-BEL Framework Small Corpus Document-549 N-BEL Framework Small Corpus Document-88 89 activation N-BEL Framework Small Corpus Document-554 N-BEL Framework Small Corpus Document-375 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-375 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-197 activation N-BEL Framework Small Corpus Document-510 N-BEL Framework Small Corpus Document-197 activation N-BEL Framework Small Corpus Document-511 N-BEL Framework Small Corpus Document-197 activation N-BEL Framework Small Corpus Document-512 N-BEL Framework Small Corpus Document-197 activation N-BEL Framework Small Corpus Document-220 N-BEL Framework Small Corpus Document-197 activation N-BEL Framework Small Corpus Document-215 N-BEL Framework Small Corpus Document-286 activation N-BEL Framework Small Corpus Document-281 N-BEL Framework Small Corpus Document-120 121 activation N-BEL Framework Small Corpus Document-528 N-BEL Framework Small Corpus Document-399 activation N-BEL Framework Small Corpus Document-236 N-BEL Framework Small Corpus Document-142 143 activation N-BEL Framework Small Corpus Document-258 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-430 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-349 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-518 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-237 N-BEL Framework Small Corpus Document-433 inhibition N-BEL Framework Small Corpus Document-401 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-451 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-452 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-275 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-255 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-254 N-BEL Framework Small Corpus Document-433 activation N-BEL Framework Small Corpus Document-229 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-426 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-426 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-493 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-186 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-185 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-439 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-439 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-132 133 N-BEL Framework Small Corpus Document-429 activation N-BEL Framework Small Corpus Document-488 N-BEL Framework Small Corpus Document-366 activation N-BEL Framework Small Corpus Document-221 N-BEL Framework Small Corpus Document-625 activation N-BEL Framework Small Corpus Document-171 172 173 N-BEL Framework Small Corpus Document-278 activation N-BEL Framework Small Corpus Document-449 N-BEL Framework Small Corpus Document-278 activation N-BEL Framework Small Corpus Document-540 N-BEL Framework Small Corpus Document-278 activation N-BEL Framework Small Corpus Document-539 N-BEL Framework Small Corpus Document-278 activation N-BEL Framework Small Corpus Document-156 157 158 N-BEL Framework Small Corpus Document-278 activation N-BEL Framework Small Corpus Document-391 N-BEL Framework Small Corpus Document-278 activation N-BEL Framework Small Corpus Document-179 N-BEL Framework Small Corpus Document-587 activation N-BEL Framework Small Corpus Document-563 N-BEL Framework Small Corpus Document-587 activation N-BEL Framework Small Corpus Document-564 N-BEL Framework Small Corpus Document-336 activation N-BEL Framework Small Corpus Document-396 N-BEL Framework Small Corpus Document-575 activation N-BEL Framework Small Corpus Document-577 N-BEL Framework Small Corpus Document-514 activation N-BEL Framework Small Corpus Document-186 N-BEL Framework Small Corpus Document-514 activation N-BEL Framework Small Corpus Document-395 N-BEL Framework Small Corpus Document-508 activation N-BEL Framework Small Corpus Document-506 N-BEL Framework Small Corpus Document-219 activation N-BEL Framework Small Corpus Document-427 N-BEL Framework Small Corpus Document-219 activation N-BEL Framework Small Corpus Document-540 N-BEL Framework Small Corpus Document-219 activation N-BEL Framework Small Corpus Document-278 N-BEL Framework Small Corpus Document-417 activation N-BEL Framework Small Corpus Document-74 75 N-BEL Framework Small Corpus Document-417 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-34 35 inhibition N-BEL Framework Small Corpus Document-13 14 N-BEL Framework Small Corpus Document-346 inhibition N-BEL Framework Small Corpus Document-463 N-BEL Framework Small Corpus Document-317 activation N-BEL Framework Small Corpus Document-318 N-BEL Framework Small Corpus Document-317 activation N-BEL Framework Small Corpus Document-303 N-BEL Framework Small Corpus Document-317 activation N-BEL Framework Small Corpus Document-249 N-BEL Framework Small Corpus Document-327 activation N-BEL Framework Small Corpus Document-44 45 N-BEL Framework Small Corpus Document-327 activation N-BEL Framework Small Corpus Document-244 N-BEL Framework Small Corpus Document-363 activation N-BEL Framework Small Corpus Document-356 N-BEL Framework Small Corpus Document-363 activation N-BEL Framework Small Corpus Document-366 N-BEL Framework Small Corpus Document-363 activation N-BEL Framework Small Corpus Document-367 N-BEL Framework Small Corpus Document-228 activation N-BEL Framework Small Corpus Document-549 N-BEL Framework Small Corpus Document-228 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-432 activation N-BEL Framework Small Corpus Document-217 N-BEL Framework Small Corpus Document-279 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-279 inhibition N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-279 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-279 activation N-BEL Framework Small Corpus Document-372 N-BEL Framework Small Corpus Document-279 activation N-BEL Framework Small Corpus Document-301 N-BEL Framework Small Corpus Document-279 activation N-BEL Framework Small Corpus Document-266 N-BEL Framework Small Corpus Document-279 activation N-BEL Framework Small Corpus Document-531 N-BEL Framework Small Corpus Document-279 activation N-BEL Framework Small Corpus Document-531 N-BEL Framework Small Corpus Document-279 inhibition N-BEL Framework Small Corpus Document-529 N-BEL Framework Small Corpus Document-279 activation N-BEL Framework Small Corpus Document-264 N-BEL Framework Small Corpus Document-279 inhibition N-BEL Framework Small Corpus Document-355 N-BEL Framework Small Corpus Document-136 137 activation N-BEL Framework Small Corpus Document-243 N-BEL Framework Small Corpus Document-595 activation N-BEL Framework Small Corpus Document-581 N-BEL Framework Small Corpus Document-595 activation N-BEL Framework Small Corpus Document-582 N-BEL Framework Small Corpus Document-595 activation N-BEL Framework Small Corpus Document-593 N-BEL Framework Small Corpus Document-232 activation N-BEL Framework Small Corpus Document-351 N-BEL Framework Small Corpus Document-232 activation N-BEL Framework Small Corpus Document-407 N-BEL Framework Small Corpus Document-282 activation N-BEL Framework Small Corpus Document-449 N-BEL Framework Small Corpus Document-282 activation N-BEL Framework Small Corpus Document-306 N-BEL Framework Small Corpus Document-207 activation N-BEL Framework Small Corpus Document-340 N-BEL Framework Small Corpus Document-207 activation N-BEL Framework Small Corpus Document-626 N-BEL Framework Small Corpus Document-207 activation N-BEL Framework Small Corpus Document-200 N-BEL Framework Small Corpus Document-207 activation N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-207 activation N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-207 activation N-BEL Framework Small Corpus Document-455 N-BEL Framework Small Corpus Document-334 activation N-BEL Framework Small Corpus Document-413 N-BEL Framework Small Corpus Document-334 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-551 activation N-BEL Framework Small Corpus Document-288 N-BEL Framework Small Corpus Document-373 activation N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-373 activation N-BEL Framework Small Corpus Document-549 N-BEL Framework Small Corpus Document-516 activation N-BEL Framework Small Corpus Document-204 N-BEL Framework Small Corpus Document-418 activation N-BEL Framework Small Corpus Document-74 75 N-BEL Framework Small Corpus Document-418 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-505 activation N-BEL Framework Small Corpus Document-547 N-BEL Framework Small Corpus Document-554 inhibition N-BEL Framework Small Corpus Document-26 27 N-BEL Framework Small Corpus Document-554 inhibition N-BEL Framework Small Corpus Document-26 27 N-BEL Framework Small Corpus Document-601 activation N-BEL Framework Small Corpus Document-595 N-BEL Framework Small Corpus Document-189 activation N-BEL Framework Small Corpus Document-592 N-BEL Framework Small Corpus Document-189 activation N-BEL Framework Small Corpus Document-209 N-BEL Framework Small Corpus Document-189 inhibition N-BEL Framework Small Corpus Document-429 N-BEL Framework Small Corpus Document-189 inhibition N-BEL Framework Small Corpus Document-429 N-BEL Framework Small Corpus Document-189 activation N-BEL Framework Small Corpus Document-186 N-BEL Framework Small Corpus Document-189 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-189 inhibition N-BEL Framework Small Corpus Document-439 N-BEL Framework Small Corpus Document-189 activation N-BEL Framework Small Corpus Document-441 N-BEL Framework Small Corpus Document-189 inhibition N-BEL Framework Small Corpus Document-132 133 N-BEL Framework Small Corpus Document-624 activation N-BEL Framework Small Corpus Document-212 N-BEL Framework Small Corpus Document-624 activation N-BEL Framework Small Corpus Document-212 N-BEL Framework Small Corpus Document-624 activation N-BEL Framework Small Corpus Document-225 N-BEL Framework Small Corpus Document-624 activation N-BEL Framework Small Corpus Document-213 N-BEL Framework Small Corpus Document-624 activation N-BEL Framework Small Corpus Document-197 N-BEL Framework Small Corpus Document-441 inhibition N-BEL Framework Small Corpus Document-439 N-BEL Framework Small Corpus Document-335 inhibition N-BEL Framework Small Corpus Document-543 N-BEL Framework Small Corpus Document-335 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-335 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-335 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-335 activation N-BEL Framework Small Corpus Document-197 N-BEL Framework Small Corpus Document-335 activation N-BEL Framework Small Corpus Document-369 N-BEL Framework Small Corpus Document-549 activation N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-227 activation N-BEL Framework Small Corpus Document-225 N-BEL Framework Small Corpus Document-227 activation N-BEL Framework Small Corpus Document-532 N-BEL Framework Small Corpus Document-227 activation N-BEL Framework Small Corpus Document-533 N-BEL Framework Small Corpus Document-227 activation N-BEL Framework Small Corpus Document-234 N-BEL Framework Small Corpus Document-350 activation N-BEL Framework Small Corpus Document-354 N-BEL Framework Small Corpus Document-328 activation N-BEL Framework Small Corpus Document-326 N-BEL Framework Small Corpus Document-381 activation N-BEL Framework Small Corpus Document-382 N-BEL Framework Small Corpus Document-381 activation N-BEL Framework Small Corpus Document-382 N-BEL Framework Small Corpus Document-424 activation N-BEL Framework Small Corpus Document-185 N-BEL Framework Small Corpus Document-424 activation N-BEL Framework Small Corpus Document-425 N-BEL Framework Small Corpus Document-424 activation N-BEL Framework Small Corpus Document-248 N-BEL Framework Small Corpus Document-424 activation N-BEL Framework Small Corpus Document-405 N-BEL Framework Small Corpus Document-424 activation N-BEL Framework Small Corpus Document-407 N-BEL Framework Small Corpus Document-424 activation N-BEL Framework Small Corpus Document-396 N-BEL Framework Small Corpus Document-424 activation N-BEL Framework Small Corpus Document-397 N-BEL Framework Small Corpus Document-370 activation N-BEL Framework Small Corpus Document-225 N-BEL Framework Small Corpus Document-370 activation N-BEL Framework Small Corpus Document-184 N-BEL Framework Small Corpus Document-246 activation N-BEL Framework Small Corpus Document-245 N-BEL Framework Small Corpus Document-96 97 98 activation N-BEL Framework Small Corpus Document-331 N-BEL Framework Small Corpus Document-312 activation N-BEL Framework Small Corpus Document-414 N-BEL Framework Small Corpus Document-312 activation N-BEL Framework Small Corpus Document-414 N-BEL Framework Small Corpus Document-434 activation N-BEL Framework Small Corpus Document-253 N-BEL Framework Small Corpus Document-434 activation N-BEL Framework Small Corpus Document-259 N-BEL Framework Small Corpus Document-434 activation N-BEL Framework Small Corpus Document-388 N-BEL Framework Small Corpus Document-434 activation N-BEL Framework Small Corpus Document-410 N-BEL Framework Small Corpus Document-318 inhibition N-BEL Framework Small Corpus Document-48 49 N-BEL Framework Small Corpus Document-318 inhibition N-BEL Framework Small Corpus Document-90 91 N-BEL Framework Small Corpus Document-92 93 activation N-BEL Framework Small Corpus Document-455 N-BEL Framework Small Corpus Document-92 93 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-261 activation N-BEL Framework Small Corpus Document-370 N-BEL Framework Small Corpus Document-261 activation N-BEL Framework Small Corpus Document-225 N-BEL Framework Small Corpus Document-300 activation N-BEL Framework Small Corpus Document-323 N-BEL Framework Small Corpus Document-344 activation N-BEL Framework Small Corpus Document-451 N-BEL Framework Small Corpus Document-344 activation N-BEL Framework Small Corpus Document-452 N-BEL Framework Small Corpus Document-344 activation N-BEL Framework Small Corpus Document-275 N-BEL Framework Small Corpus Document-475 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-475 activation N-BEL Framework Small Corpus Document-455 N-BEL Framework Small Corpus Document-419 activation N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-419 inhibition N-BEL Framework Small Corpus Document-323 N-BEL Framework Small Corpus Document-571 activation N-BEL Framework Small Corpus Document-204 N-BEL Framework Small Corpus Document-280 activation N-BEL Framework Small Corpus Document-148 149 N-BEL Framework Small Corpus Document-440 activation N-BEL Framework Small Corpus Document-68 69 N-BEL Framework Small Corpus Document-479 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-479 activation N-BEL Framework Small Corpus Document-204 N-BEL Framework Small Corpus Document-479 activation N-BEL Framework Small Corpus Document-266 N-BEL Framework Small Corpus Document-288 activation N-BEL Framework Small Corpus Document-287 N-BEL Framework Small Corpus Document-288 activation N-BEL Framework Small Corpus Document-287 N-BEL Framework Small Corpus Document-177 activation N-BEL Framework Small Corpus Document-1 2 N-BEL Framework Small Corpus Document-310 activation N-BEL Framework Small Corpus Document-479 N-BEL Framework Small Corpus Document-199 activation N-BEL Framework Small Corpus Document-324 N-BEL Framework Small Corpus Document-199 activation N-BEL Framework Small Corpus Document-326 N-BEL Framework Small Corpus Document-199 activation N-BEL Framework Small Corpus Document-199 N-BEL Framework Small Corpus Document-251 activation N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-251 activation N-BEL Framework Small Corpus Document-321 N-BEL Framework Small Corpus Document-407 activation N-BEL Framework Small Corpus Document-467 N-BEL Framework Small Corpus Document-407 activation N-BEL Framework Small Corpus Document-468 N-BEL Framework Small Corpus Document-407 activation N-BEL Framework Small Corpus Document-354 N-BEL Framework Small Corpus Document-407 activation N-BEL Framework Small Corpus Document-353 N-BEL Framework Small Corpus Document-338 inhibition N-BEL Framework Small Corpus Document-206 N-BEL Framework Small Corpus Document-222 inhibition N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-222 activation N-BEL Framework Small Corpus Document-546 N-BEL Framework Small Corpus Document-222 activation N-BEL Framework Small Corpus Document-406 N-BEL Framework Small Corpus Document-222 activation N-BEL Framework Small Corpus Document-408 N-BEL Framework Small Corpus Document-222 inhibition N-BEL Framework Small Corpus Document-405 N-BEL Framework Small Corpus Document-222 inhibition N-BEL Framework Small Corpus Document-407 N-BEL Framework Small Corpus Document-222 activation N-BEL Framework Small Corpus Document-348 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-224 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-223 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-227 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-471 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-473 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-472 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-474 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-245 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-245 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-46 47 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-547 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-197 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-144 145 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-412 N-BEL Framework Small Corpus Document-225 activation N-BEL Framework Small Corpus Document-435 N-BEL Framework Small Corpus Document-30 31 inhibition N-BEL Framework Small Corpus Document-24 25 N-BEL Framework Small Corpus Document-473 activation N-BEL Framework Small Corpus Document-233 N-BEL Framework Small Corpus Document-473 activation N-BEL Framework Small Corpus Document-233 N-BEL Framework Small Corpus Document-502 activation N-BEL Framework Small Corpus Document-451 N-BEL Framework Small Corpus Document-502 activation N-BEL Framework Small Corpus Document-452 N-BEL Framework Small Corpus Document-502 activation N-BEL Framework Small Corpus Document-275 N-BEL Framework Small Corpus Document-500 activation N-BEL Framework Small Corpus Document-545 N-BEL Framework Small Corpus Document-238 activation N-BEL Framework Small Corpus Document-355 N-BEL Framework Small Corpus Document-314 activation N-BEL Framework Small Corpus Document-341 N-BEL Framework Small Corpus Document-314 activation N-BEL Framework Small Corpus Document-316 N-BEL Framework Small Corpus Document-314 activation N-BEL Framework Small Corpus Document-299 N-BEL Framework Small Corpus Document-314 activation N-BEL Framework Small Corpus Document-313 N-BEL Framework Small Corpus Document-314 activation N-BEL Framework Small Corpus Document-315 N-BEL Framework Small Corpus Document-520 activation N-BEL Framework Small Corpus Document-341 N-BEL Framework Small Corpus Document-520 activation N-BEL Framework Small Corpus Document-316 N-BEL Framework Small Corpus Document-520 activation N-BEL Framework Small Corpus Document-299 N-BEL Framework Small Corpus Document-520 activation N-BEL Framework Small Corpus Document-313 N-BEL Framework Small Corpus Document-520 activation N-BEL Framework Small Corpus Document-315 N-BEL Framework Small Corpus Document-264 inhibition N-BEL Framework Small Corpus Document-488 N-BEL Framework Small Corpus Document-264 inhibition N-BEL Framework Small Corpus Document-489 N-BEL Framework Small Corpus Document-208 activation N-BEL Framework Small Corpus Document-207 N-BEL Framework Small Corpus Document-236 activation N-BEL Framework Small Corpus Document-235 N-BEL Framework Small Corpus Document-537 inhibition N-BEL Framework Small Corpus Document-395 N-BEL Framework Small Corpus Document-267 activation N-BEL Framework Small Corpus Document-219 N-BEL Framework Small Corpus Document-267 activation N-BEL Framework Small Corpus Document-474 N-BEL Framework Small Corpus Document-267 activation N-BEL Framework Small Corpus Document-207 pybel-0.15.5/notebooks/hipathia_demo/selventa/_convert_selventa.py000066400000000000000000000017011426625374700254450ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Convert the Selventa graph for Hipathia.""" import os from urllib.request import urlretrieve import click from pyobo.cli_utils import verbose_option import pybel import pybel.grounding HERE = os.path.dirname(__file__) URL = 'https://github.com/cthoyt/selventa-knowledge/raw/master/selventa_knowledge/small_corpus.bel.nodelink.json.gz' PATH = os.path.join(HERE, 'small_corpus.bel.nodelink.json.gz') GROUNDED_PATH = os.path.join(HERE, 'small_corpus-grounded.bel.nodelink.json.gz') @click.command() @verbose_option def main(): """Convert the Selventa graph to Hipathia.""" if not os.path.exists(PATH): urlretrieve(URL, PATH) if not os.path.exists(GROUNDED_PATH): graph = pybel.load(PATH) graph = pybel.grounding.ground(graph) pybel.dump(graph, GROUNDED_PATH) else: graph = pybel.load(GROUNDED_PATH) pybel.to_hipathia(graph, HERE) if __name__ == '__main__': main() pybel-0.15.5/notebooks/hipathia_demo/selventa/small_corpus.bel.nodelink.json.gz000066400000000000000000011131231426625374700277360ustar00rootroot00000000000000OF]small_corpus.bel.nodelink.jsonks\Gr-WbM\aޏtAR|%9O8^d4nxݨnB i2EYVUV}Wj{gwGg+:tg׿N.R=_t<Ɵ|'(wKeϏ3*tFQW{OWߗtBi^-Nή.^)!':.{|?*{gt{m<ڳ~yz]YH[%NJ~:7{?` )ǽh޻zZW2O)KLJ\OWTX|t<>;8;[-9U^OrŦw9ȃާ'ox]?.W۟Py:^}:;;9ǏO&`ǼUz\#?۝qElٛ;GCw:8~!Oj]'ס,N+Gdg]GGK[gԫe~}*w:U ?)_#z(QهCYO \7RVum*%cs-)UVWv]a>d/O9ݪj|J֒H#6.qN}_#DORXI @[Isfp_"֤z{8^׽T(AIBք@QVƒ*;)'oXq.T=r1E\R:6>95xy0RR-!Jc>IToU" aRW[IwJ:x(CőAj JK(MEh:9@|yqS?H4g]$B _m4'g=բ cD/FK8G0tjTڬz}Z,Nct)B,ds.oN]asʮ5-epDW]S]%oͩ]ü8?c)#w'jp4Bb[k/h0IaT_pVa^"MT[iȢ԰W\C)^M766آu JS"T>=)\HR*!9ڔXإSjyl$ZdA(BF ٗlMUZΜ1zCX, )fLPd<75˟ݸp 2(iab~,kVaO,N4` Q`E_Ǒyu);S`YȚsN !or,mN=O>-Wiy' ?X-БAJXܩ&)\BQ?F̖|3.% 87+ɜddkMOFTb>"Ih|֟x$tWZ.[0!eY9jLas֤gM0=g Pj}JI0\Tw.Ĉy$RuyDW`]_N[m @UU<(\m~M$SͪR P #mJΩ/ts7#0$<2R14'Y\x1B 2 }q n7xQ7P4.#o$S]Tx&y+̨dSӚtFޒ߁EcHIbNy=6N&JA&ӿ5msRt. `:aw#gVݖV˵Zd`E Lb8> )uo̹ `ut@VMKYQы#A3jwNjva2z:r/""k)3[_j[DAuI(o̸YW(=VEcdd|鵔*W':.c` 4oocNDͨ)|1PA)5C,6p-e>agɦyMa9*Wy%{-eFi5"ED@L.煒kh|y%dg.9B R&5UNYM݈O|vHNhLD]Dn#Ask7K-d ȣefj(i1^Țq̀o@Jش554ioL93K2 lͻF{_F̌'eɗa4E8)j XV`ޢ /oE=94v~  &ilFJXI`zk`1F˓)Vkx$͠ck7ZUԑ+jN[7aUi gjJ`1jX&[65#rcMYf@g% |sFOד%$+UG%6 VЬ@ 'v2/JML6ï*w+jֽ1Q"Z k yRFԛ~cI3w:eʤ A ;2hCÜQzZʜI)2R)ꔥ(@qodq-e>NϖS2,8 8ה2rWLv3f ,"?x#TQ9Z6Dn1䍘5Clx&,R=EVEYr @]zF̌wJ/x1VN8CJomʙO7.ڔbhT3VE?*Ujڍ5D'C)X `YU'^KQ?DcsOM+K8T}B\-ӱ;"jLP}:5@ x"ɻc "fh:;iX#>1#cN v+œvǎm6 ɓ/ɟ!j{ѻ$oRZ5dc" dN ގw Sw!"`QR* nVY uz$u2}$d_+2VJFll%JoS-4ji7h!sjeEpf|UKlF>2H1(}2V/qߡ/"pkJȘ l̫VX+OoIA19ؠ̪6QLjyX7@V"kBf*&rE]r,iQ 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.RR8*.\UB9?}tٴ¡QY--0af|7kipNxӦl}_g5ͫhft,d\BFQ*猷%Y$֓X9rا[hIa9׳u7mxmtoqI˓Piڽ^m|A|PSGm$F6&)S5r*14*@i]hH$SZ0W y[ Wcޭi>bhQ4R/)9rٿrȯNwB*uhO#QRg"{Vnd\vJ9*HӨ&21+8,HAaz6rDӡr=|DR9 ̛qD$._[ &S4YD)$PeDj 5 &I,*+B-!(ޠYBk0s760g/ Kh/'=ݪW|f|6=Hr. dܫu+7W,'䫣'ߺHR2da9É): x\$uP${D2yZ41_vv&pybel-0.15.5/pyproject.toml000066400000000000000000000005461426625374700156560ustar00rootroot00000000000000# See https://setuptools.readthedocs.io/en/latest/build_meta.html [build-system] requires = ["setuptools", "wheel"] build-backend = "setuptools.build_meta:__legacy__" [tool.black] line-length = 120 target-version = ["py37", "py38", "py39", "py310"] [tool.isort] profile = "black" multi_line_output = 3 include_trailing_comma = true reverse_relative = true pybel-0.15.5/setup.cfg000066400000000000000000000141371426625374700145640ustar00rootroot00000000000000########################## # Setup.py Configuration # ########################## [metadata] name = pybel version = 0.15.5 description = Parsing, validation, compilation, and data exchange of Biological Expression Language (BEL) long_description = file: README.rst # URLs associated with the project url = https://github.com/pybel/pybel download_url = https://github.com/pybel/pybel/releases project_urls = Bug Tracker = https://github.com/pybel/pybel/issues Source Code = https://github.com/pybel/pybel Documentation = https://pybel.readthedocs.io # Author information author = Charles Tapley Hoyt author_email = cthoyt@gmail.com maintainer = Charles Tapley Hoyt maintainer_email = cthoyt@gmail.com # License Information license = MIT license_file = LICENSE # Search tags classifiers = Development Status :: 5 - Production/Stable Environment :: Console Intended Audience :: Developers Intended Audience :: Science/Research License :: OSI Approved :: MIT License Operating System :: OS Independent Programming Language :: Python Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.6 Programming Language :: Python :: 3 :: Only Topic :: Scientific/Engineering :: Bio-Informatics Topic :: Scientific/Engineering :: Chemistry keywords = Biological Expression Language BEL Domain Specific Language DSL Systems Biology Networks Biology [options] install_requires = dataclasses; python_version < "3.7" pickle5; python_version < "3.8" networkx>=2.4 sqlalchemy click click-plugins bel_resources>=0.0.3 more_itertools requests requests_file pyparsing tqdm humanize tabulate pandas jsonschema bioregistry ratelimit pystow>=0.1.2 psycopg2-binary # Random options zip_safe = false include_package_data = True python_requires = >=3.6 # Where is my code packages = find: package_dir = = src [options.packages.find] where = src [options.extras_require] indra = indra jupyter = jinja2 ipython neo4j = py2neo grounding = pyobo protmapper docs = sphinx sphinx-rtd-theme sphinx-click sphinx-autodoc-typehints [options.entry_points] console_scripts = pybel = pybel.cli:main pybel.importer = bel = pybel.io.lines:from_bel_script bel.gz = pybel.io.lines:from_bel_script_gz bel.nodelink.json = pybel.io.nodelink:from_nodelink_file bel.nodelink.json.gz = pybel.io.nodelink:from_nodelink_gz bel.jsonl = pybel.io.sbel:from_sbel_file bel.jsonl.gz = pybel.io.sbel:from_sbel_gz bel.cx.json = pybel.io.cx:from_cx_file bel.cx.json.gz = pybel.io.cx:from_cx_gz bel.graphdati.json = pybel.io.graphdati:from_graphdati_file bel.graphdati.json.gz = pybel.io.graphdati:from_graphdati_gz bel.jgif.json = pybel.io.jgif:from_jgif_file bel.jgif.json.gz = pybel.io.jgif:from_jgif_gz bel.pickle = pybel.io.gpickle:from_pickle bel.gpickle = pybel.io.gpickle:from_pickle bel.pkl = pybel.io.gpickle:from_pickle bel.pickle.gz = pybel.io.gpickle:from_pickle_gz bel.gpickle.gz = pybel.io.gpickle:from_pickle_gz bel.pkl.gz = pybel.io.gpickle:from_pickle_gz indra.json = pybel.io.indra:from_indra_statements_json_file pybel.exporter = bel = pybel.canonicalize:to_bel_script bel.gz = pybel.canonicalize:to_bel_script_gz bel.nodelink.json = pybel.io.nodelink:to_nodelink_file bel.nodelink.json.gz = pybel.io.nodelink:to_nodelink_gz bel.jsonl = pybel.io.sbel:to_sbel_file bel.jsonl.gz = pybel.io.sbel:to_sbel_gz bel.cx.json = pybel.io.cx:to_cx_file bel.cx.json.gz = pybel.io.cx:to_cx_gz bel.graphdati.json = pybel.io.graphdati:to_graphdati_file bel.graphdati.json.gz = pybel.io.graphdati:to_graphdati_gz bel.jgif.json = pybel.io.jgif:to_jgif_file bel.jgif.json.gz = pybel.io.jgif:to_jgif_gz bel.pickle = pybel.io.gpickle:to_pickle bel.gpickle = pybel.io.gpickle:to_pickle bel.pkl = pybel.io.gpickle:to_pickle bel.pickle.gz = pybel.io.gpickle:to_pickle_gz bel.gpickle.gz = pybel.io.gpickle:to_pickle_gz bel.pkl.gz = pybel.io.gpickle:to_pickle_gz indra.json = pybel.io.indra:to_indra_statements_json_file # No re-import bel.unodelink.json = pybel.io.umbrella_nodelink:to_umbrella_nodelink_file bel.unodelink.json.gz = pybel.io.umbrella_nodelink:to_umbrella_nodelink_gz tsv = pybel.io.triples.api:to_triples_file gsea = pybel.io.extras:to_gsea # Importers for PyKEEN pykeen.triples.extension_importer = # Import BEL script to PyKEEN bel = pybel.io.pykeen:get_triples_from_bel # Import BEL Graph pickle to PyKEEN bel.gpickle = pybel.io.pykeen:get_triples_from_bel_pickle bel.pickle = pybel.io.pykeen:get_triples_from_bel_pickle bel.pkl = pybel.io.pykeen:get_triples_from_bel_pickle # Import BEL NodeLink JSON to PyKEEN bel.nodelink.json = pybel.io.pykeen:get_triples_from_bel_nodelink pykeen.triples.prefix_importer = # Import BEL from BEL Commons to PyKEEN bel-commons = pybel.io.pykeen:get_triples_from_bel_commons ###################### # Doc8 Configuration # # (doc8.ini) # ###################### [doc8] max-line-length = 120 ########################## # Coverage Configuration # # (.coveragerc) # ########################## [coverage:run] branch = True source = pybel omit = src/pybel/__main__.py src/pybel/cli.py src/pybel/io/indra.py src/pybel/io/web.py tests/* docs/* scripts/* [coverage:paths] source = src/pybel .tox/*/lib/python*/site-packages/pybel [coverage:report] show_missing = True exclude_lines = def __str__ def __repr__ pybel-0.15.5/src/000077500000000000000000000000001426625374700135245ustar00rootroot00000000000000pybel-0.15.5/src/pybel/000077500000000000000000000000001426625374700146375ustar00rootroot00000000000000pybel-0.15.5/src/pybel/__init__.py000066400000000000000000000055301426625374700167530ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Parsing, validation, compilation, and data exchange of Biological Expression Language (BEL).""" from .canonicalize import ( edge_to_bel, to_bel_script, to_bel_script_gz, to_bel_script_lines, ) from .dsl import BaseAbundance, BaseEntity from .io.api import dump, load from .io.aws import from_s3, to_s3 from .io.bel_commons_client import from_bel_commons, to_bel_commons from .io.biodati_client import from_biodati, to_biodati from .io.cx import ( from_cx, from_cx_file, from_cx_gz, from_cx_jsons, to_cx, to_cx_file, to_cx_gz, to_cx_jsons, ) from .io.emmaa import from_emmaa from .io.extras import to_csv, to_gsea, to_sif from .io.fraunhofer_orientdb import from_fraunhofer_orientdb from .io.gpickle import ( from_bytes, from_bytes_gz, from_pickle, from_pickle_gz, to_bytes, to_bytes_gz, to_pickle, to_pickle_gz, ) from .io.graphdati import ( from_graphdati, from_graphdati_file, from_graphdati_gz, from_graphdati_jsons, to_graphdati, to_graphdati_file, to_graphdati_gz, to_graphdati_jsonl, to_graphdati_jsonl_gz, to_graphdati_jsons, ) from .io.graphml import to_graphml from .io.hetionet import ( from_hetionet_file, from_hetionet_gz, from_hetionet_json, get_hetionet, ) from .io.hipathia import ( from_hipathia_dfs, from_hipathia_paths, to_hipathia, to_hipathia_dfs, ) from .io.indra import ( from_biopax, from_indra_pickle, from_indra_statements, from_indra_statements_json, from_indra_statements_json_file, to_indra_statements, to_indra_statements_json, to_indra_statements_json_file, ) from .io.jgif import ( from_cbn_jgif, from_cbn_jgif_file, from_jgif, from_jgif_file, from_jgif_gz, from_jgif_jsons, post_jgif, to_jgif, to_jgif_file, to_jgif_gz, to_jgif_jsons, ) from .io.jupyter import to_jupyter, to_jupyter_str from .io.lines import from_bel_script, from_bel_script_url from .io.neo4j import to_neo4j from .io.nodelink import ( from_nodelink, from_nodelink_file, from_nodelink_gz, from_nodelink_jsons, to_nodelink, to_nodelink_file, to_nodelink_gz, to_nodelink_jsons, ) from .io.pynpa import to_npa_dfs, to_npa_directory from .io.sbel import ( from_sbel, from_sbel_file, from_sbel_gz, to_sbel, to_sbel_file, to_sbel_gz, ) from .io.spia import to_spia_dfs, to_spia_excel, to_spia_tsvs from .io.triples import to_edgelist, to_triples, to_triples_file from .io.umbrella_nodelink import ( to_umbrella_nodelink, to_umbrella_nodelink_file, to_umbrella_nodelink_gz, ) from .manager import Manager, from_database, to_database from .parser.parse_bel import parse from .struct import BELGraph, Pipeline, Query from .struct.operations import union from .version import get_version pybel-0.15.5/src/pybel/__main__.py000066400000000000000000000005001426625374700167240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Entrypoint module, in case you use `python -m pybel`. Why does this file exist, and why __main__? For more info, read: - https://www.python.org/dev/peps/pep-0338/ - https://docs.python.org/3/using/cmdline.html#cmdoption-m """ from .cli import main if __name__ == "__main__": main() pybel-0.15.5/src/pybel/apps/000077500000000000000000000000001426625374700156025ustar00rootroot00000000000000pybel-0.15.5/src/pybel/apps/parser.py000066400000000000000000000023171426625374700174530ustar00rootroot00000000000000# -*- coding: utf-8 -*- """A simple web-based BEL parser implemented with Flask. Run from the command line with ``python -m pybel.apps.parser``. """ import flask import pyparsing import pybel import pybel.exceptions app = flask.Flask(__name__) @app.route("/parse", methods=["POST"]) def parse(): """Parse the BEL in the `text` JSON field. Example usage: >>> import requests >>> requests.post('http://localhost:5000/parse', json={'text': 'p(HGNC:123) increases p(HGNC:456)'}).json() {'input': 'p(HGNC:123) increases p(HGNC:456)', 'output': {'object': {'concept': {'name': '456', 'namespace': 'HGNC'}, 'function': 'Protein'}, 'relation': 'increases', 'subject': {'concept': {'name': '123', 'namespace': 'HGNC'}, 'function': 'Protein'}}, 'success': True} """ text = flask.request.json.get("text") if text is None: return flask.jsonify(success=False, message="missing `text`") try: rv = pybel.parse(text) except (pyparsing.ParseException, pybel.exceptions.PyBELWarning) as e: return flask.jsonify(success=False, input=text, exception=str(e)) else: return flask.jsonify(success=True, input=text, output=rv) if __name__ == "__main__": app.run() pybel-0.15.5/src/pybel/canonicalize.py000066400000000000000000000305741426625374700176610ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains output functions to BEL scripts.""" import gzip import itertools as itt import logging import time import warnings from typing import Iterable, List, Mapping, Optional, TextIO, Tuple, Union import bel_resources.constants from bel_resources import make_knowledge_header from networkx.utils import open_file from .constants import ( ACTIVITY, ANNOTATIONS, CITATION, CITATION_TYPE_PUBMED, DEGRADATION, EFFECT, EVIDENCE, FROM_LOC, LOCATION, MODIFIER, NAME, NAMESPACE, PYBEL_AUTOEVIDENCE, PYBEL_PUBMED, RELATION, SET_CITATION_FMT, SOURCE_MODIFIER, TARGET_MODIFIER, TO_LOC, TRANSLOCATION, UNQUALIFIED_EDGES, VARIANTS, ) from .dsl import BaseAbundance, BaseEntity, FusionBase, ListAbundance, Reaction from .language import Entity from .typing import EdgeData from .utils import ensure_quotes from .version import VERSION __all__ = [ "to_bel_script", "to_bel_script_gz", "to_bel_script_lines", "edge_to_bel", "edge_to_tuple", "calculate_canonical_name", ] logger = logging.getLogger(__name__) EdgeTuple = Tuple[BaseEntity, BaseEntity, str, EdgeData] @open_file(1, mode="w") def to_bel_script(graph, path: Union[str, TextIO], use_identifiers: bool = True) -> None: """Write the BELGraph as a canonical BEL script. :param BELGraph graph: the BEL Graph to output as a BEL Script :param path: A path or file-like. :param use_identifiers: Enables extended `BEP-0008 `_ syntax """ for line in to_bel_script_lines(graph, use_identifiers=use_identifiers): print(line, file=path) def to_bel_script_gz(graph, path, **kwargs) -> None: """Write the graph as a BEL Script a gzip file.""" with gzip.open(path, "wt") as file: to_bel_script(graph, file, **kwargs) def to_bel_script_lines(graph, use_identifiers: bool = True) -> Iterable[str]: """Iterate over the lines of the BEL graph as a canonical BEL script. :param pybel.BELGraph graph: A BEL Graph :param use_identifiers: Enables extended `BEP-0008 `_ syntax """ return itt.chain( _to_bel_lines_header(graph), _to_bel_lines_body(graph, use_identifiers=use_identifiers), _to_bel_lines_footer(graph, use_identifiers=use_identifiers), ) def postpend_location(bel_string: str, location_model) -> str: """Rip off the closing parentheses and adds canonicalized modification. I did this because writing a whole new parsing model for the data would be sad and difficult :param bel_string: BEL string representing node :param dict location_model: A dictionary containing keys :code:`pybel.constants.TO_LOC` and :code:`pybel.constants.FROM_LOC` :return: A part of a BEL string representing the location """ if not all(k in location_model for k in {NAMESPACE, NAME}): raise ValueError("Location model missing namespace and/or name keys: {}".format(location_model)) return "{}, loc({}:{}))".format( bel_string[:-1], location_model[NAMESPACE], ensure_quotes(location_model[NAME]), ) def _decanonicalize_edge_node( node: BaseEntity, edge_data: EdgeData, node_position: str, *, use_identifiers: bool = True, ) -> str: """Canonicalize a node with its modifiers stored in the given edge to a BEL string. :param node: A PyBEL node data dictionary :param edge_data: A PyBEL edge data dictionary :param node_position: Either :data:`pybel.constants.SUBJECT` or :data:`pybel.constants.OBJECT` :param use_identifiers: Enables extended `BEP-0008 `_ syntax """ node_str = node.as_bel(use_identifiers=use_identifiers) if node_position not in edge_data: return node_str node_edge_data = edge_data[node_position] if LOCATION in node_edge_data: node_str = postpend_location(node_str, node_edge_data[LOCATION]) modifier = node_edge_data.get(MODIFIER) if modifier is None: return node_str if DEGRADATION == modifier: warnings.warn("degradation is deprecated", DeprecationWarning) return f"deg({node_str})" effect = node_edge_data.get(EFFECT) if ACTIVITY == modifier: if effect is None: return f"act({node_str})" return f"act({node_str}, ma({effect}))" if TRANSLOCATION == modifier: if effect is None: return f"tloc({node_str})" to_loc_data: Entity = effect[TO_LOC] from_loc_data: Entity = effect[FROM_LOC] return f"tloc({node_str}, fromLoc({from_loc_data}), toLoc({to_loc_data}))" raise ValueError("invalid modifier: {}".format(modifier)) def edge_to_tuple( source: BaseEntity, target: BaseEntity, data: EdgeData, use_identifiers: bool = True, ) -> Tuple[str, str, str]: """Take two nodes and gives back a BEL string representing the statement. :param source: The edge's source's PyBEL node data dictionary :param target: The edge's target's PyBEL node data dictionary :param data: The edge's data dictionary :param use_identifiers: Enables extended `BEP-0008 `_ syntax """ u_str = _decanonicalize_edge_node(source, data, node_position=SOURCE_MODIFIER, use_identifiers=use_identifiers) v_str = _decanonicalize_edge_node(target, data, node_position=TARGET_MODIFIER, use_identifiers=use_identifiers) return u_str, data[RELATION], v_str def edge_to_bel( source: BaseEntity, target: BaseEntity, data: EdgeData, sep: Optional[str] = None, use_identifiers: bool = True, ) -> str: """Take two nodes and gives back a BEL string representing the statement. :param source: The edge's source's PyBEL node data dictionary :param target: The edge's target's PyBEL node data dictionary :param data: The edge's data dictionary :param sep: The separator between the source, relation, and target. Defaults to ' ' :param use_identifiers: Enables extended `BEP-0008 `_ syntax """ sep = sep or " " return sep.join(edge_to_tuple(source=source, target=target, data=data, use_identifiers=use_identifiers)) def _sort_qualified_edges_helper(t: EdgeTuple) -> Tuple[str, str, str]: return ( t[3][CITATION].namespace, t[3][CITATION].identifier, t[3][EVIDENCE], ) def sort_qualified_edges(graph) -> Iterable[EdgeTuple]: """Return the qualified edges, sorted first by citation, then by evidence, then by annotations. :param BELGraph graph: A BEL graph """ qualified_edges = ( (u, v, k, d) for u, v, k, d in graph.edges(keys=True, data=True) if graph.has_edge_citation(u, v, k) and graph.has_edge_evidence(u, v, k) ) return sorted(qualified_edges, key=_sort_qualified_edges_helper) def _citation_sort_key(t: EdgeTuple) -> Tuple[str, str]: """Make a confusing 4 tuple sortable by citation.""" return t[3][CITATION].namespace, t[3][CITATION].identifier def _evidence_sort_key(t: EdgeTuple) -> str: """Make a confusing 4 tuple sortable by citation.""" return t[3][EVIDENCE] def _set_annotation_to_str(annotation_data: Mapping[str, List[Entity]], key: str, use_curie: bool = False) -> str: """Return a set annotation string.""" value = annotation_data[key] if len(value) == 1: value = list(value)[0] return f'SET {key} = "{value if use_curie else value.identifier}"' value_strings = ", ".join( f'"{v if use_curie else v.identifier}"' for v in sorted(value, key=lambda e: (e.namespace, e.identifier, e.name)) ) return f"SET {key} = {{{value_strings}}}" def _unset_annotation_to_str(keys: List[str]) -> str: """Return an unset annotation string.""" if len(keys) == 1: return "UNSET {}".format(list(keys)[0]) return "UNSET {{{}}}".format(", ".join("{}".format(key) for key in keys)) def _to_bel_lines_header(graph) -> Iterable[str]: """Iterate the lines of a BEL graph's corresponding BEL script's header. :param pybel.BELGraph graph: A BEL graph """ yield "# This document was created by PyBEL v{} and bel-resources v{} on {}\n".format( VERSION, bel_resources.constants.VERSION, time.asctime(), ) yield from make_knowledge_header( namespace_url=graph.namespace_url, namespace_patterns=graph.namespace_pattern, annotation_url=graph.annotation_url, annotation_patterns=graph.annotation_pattern, annotation_list=graph.annotation_list, **graph.document, ) def group_citation_edges( edges: Iterable[EdgeTuple], ) -> Iterable[Tuple[Tuple[str, str], Iterable[EdgeTuple]]]: """Return an iterator over pairs of citation values and their corresponding edge iterators.""" return itt.groupby(edges, key=_citation_sort_key) def group_evidence_edges( edges: Iterable[EdgeTuple], ) -> Iterable[Tuple[str, Iterable[EdgeTuple]]]: """Return an iterator over pairs of evidence values and their corresponding edge iterators.""" return itt.groupby(edges, key=_evidence_sort_key) def _to_bel_lines_body(graph, use_identifiers: bool = False) -> Iterable[str]: """Iterate the lines of a BEL graph's corresponding BEL script's body. :param pybel.BELGraph graph: A BEL graph :param use_identifiers: Enables extended `BEP-0008 `_ syntax """ qualified_edges = sort_qualified_edges(graph) for (citation_db, citation_id), citation_edges in group_citation_edges(qualified_edges): yield SET_CITATION_FMT.format(citation_db, citation_id) + "\n" for evidence, evidence_edges in group_evidence_edges(citation_edges): yield 'SET SupportingText = "{}"'.format(evidence) for u, v, _, data in evidence_edges: annotations_data = data.get(ANNOTATIONS) keys = sorted(annotations_data) if annotations_data is not None else tuple() for key in keys: yield _set_annotation_to_str(annotations_data, key, use_curie=key in graph.annotation_curie) yield graph.edge_to_bel(u, v, data, use_identifiers=use_identifiers) if keys: yield _unset_annotation_to_str(keys) yield "UNSET SupportingText" yield "UNSET Citation\n" yield "#" * 80 def _to_bel_lines_footer(graph, use_identifiers: bool = False) -> Iterable[str]: """Iterate the lines of a BEL graph's corresponding BEL script's footer. :param pybel.BELGraph graph: A BEL graph :param use_identifiers: Enables extended `BEP-0008 `_ syntax """ unqualified_edges_to_serialize = [ (u, v, d) for u, v, d in graph.edges(data=True) if d[RELATION] in UNQUALIFIED_EDGES and EVIDENCE not in d ] isolated_nodes_to_serialize = [node for node in graph if not graph.pred[node] and not graph.succ[node]] if unqualified_edges_to_serialize or isolated_nodes_to_serialize: yield "###############################################\n" yield SET_CITATION_FMT.format(CITATION_TYPE_PUBMED, PYBEL_PUBMED) yield 'SET SupportingText = "{}"'.format(PYBEL_AUTOEVIDENCE) for u, v, data in unqualified_edges_to_serialize: yield "{} {} {}".format( u.as_bel(use_identifiers=use_identifiers), data[RELATION], v.as_bel(use_identifiers=use_identifiers), ) for node in isolated_nodes_to_serialize: yield node.as_bel(use_identifiers=use_identifiers) yield "UNSET SupportingText" yield "UNSET Citation" def calculate_canonical_name(node: BaseEntity, use_identifiers: bool = True) -> str: """Calculate the canonical name for a given node. If it is a simple node, uses the already given name. Otherwise, it uses the BEL string. """ if isinstance(node, (Reaction, ListAbundance, FusionBase)): return node.as_bel(use_identifiers=True) elif isinstance(node, BaseAbundance): if VARIANTS in node: return node.as_bel(use_identifiers=True) elif use_identifiers and node.entity.identifier and node.entity.name: return node.obo else: return node.curie else: raise TypeError("Unhandled node: {}".format(node)) pybel-0.15.5/src/pybel/cli.py000066400000000000000000000422731426625374700157700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Command line interface for PyBEL. Why does this file exist, and why not put this in ``__main__``? You might be tempted to import things from ``__main__`` later, but that will cause problems--the code will get executed twice: - When you run ``python3 -m pybel`` python will execute``__main__.py`` as a script. That means there won't be any ``pybel.__main__`` in ``sys.modules``. - When you import __main__ it will get executed again (as a module) because there's no ``pybel.__main__`` in ``sys.modules``. .. seealso:: http://click.pocoo.org/5/setuptools/#setuptools-integration """ import json import logging import os import sys import time from typing import List, Optional import click from click_plugins import with_plugins from pkg_resources import iter_entry_points from tqdm.autonotebook import tqdm from .canonicalize import to_bel_script from .constants import get_cache_connection from .examples import ( braf_graph, egf_graph, homology_graph, sialic_acid_graph, statin_graph, ) from .exceptions import BELParserWarning from .io import ( from_bel_script, from_pickle, load, to_bel_commons, to_edgelist, to_graphml, to_gsea, to_neo4j, to_nodelink_file, to_pickle, to_sif, to_triples_file, ) from .io.bel_commons_client import _get_host, _get_password, _get_user from .manager import Manager from .manager.database_io import to_database from .manager.models import Edge, Namespace, Node from .struct import ( get_unused_annotations, get_unused_list_annotation_values, get_unused_namespaces, ) from .struct.graph import BELGraph, WarningTuple from .utils import get_corresponding_pickle_path logger = logging.getLogger(__name__) def _page(it): click.echo_via_pager("\n".join(map(str, it))) connection_option = click.option( "-c", "--connection", default=get_cache_connection(), show_default=True, help="Database connection string.", ) host_option = click.option( "--host", default=_get_host(), show_default=True, help="URL of BEL Commons.", ) user_option = click.option( "--user", default=_get_user, show_default=True, prompt=True, help="User for BEL Commons", ) password_option = click.option( "--password", default=_get_password, show_default=True, prompt=True, hide_input=True, help="Password for BEL Commons", ) def _from_pickle_callback(ctx, param, file): path = file.name if not path.endswith(".bel"): return from_pickle(file) cache_path = get_corresponding_pickle_path(path) if not os.path.exists(cache_path): click.echo( "The BEL script {path} has not yet been compiled. First, try running the following command:\n\n " "pybel compile {path}\n".format(path=path), ) sys.exit(1) return from_pickle(cache_path) graph_pickle_argument = click.argument( "graph", metavar="path", type=click.File("rb"), callback=_from_pickle_callback, ) graph_argument = click.argument( "graph", metavar="path", callback=lambda _, __, path: load(path), ) LOG_FMT = "%(asctime)s %(levelname)-8s %(message)s" LOG_DATEFMT = "%Y-%m-%d %H:%M:%S" def _debug_callback(_ctx, _param, value): if not value: logging.basicConfig(level=logging.WARNING, format=LOG_FMT, datefmt=LOG_DATEFMT) elif value == 1: logging.basicConfig(level=logging.INFO, format=LOG_FMT, datefmt=LOG_DATEFMT) else: logging.basicConfig(level=logging.DEBUG, format=LOG_FMT, datefmt=LOG_DATEFMT) verbose_option = click.option( "-v", "--verbose", count=True, callback=_debug_callback, expose_value=False, ) @with_plugins(iter_entry_points("pybel.cli_plugins")) @click.group(help="PyBEL CLI on {}".format(sys.executable)) @click.version_option() @connection_option @click.pass_context def main(ctx, connection): """Command line interface for PyBEL.""" ctx.obj = Manager(connection=connection) ctx.obj.bind() # add the engine to the metadata and query property to the session @main.command() @click.argument("text") @click.option("--pprint", is_flag=True) def parse(text: str, pprint: bool): """Parse a single BEL statement and pring JSON output.""" from .parser.parse_bel import parse as _parse click.echo(json.dumps(_parse(text), indent=2 if pprint else None)) @main.command() @click.argument("path") @click.option("--allow-naked-names", is_flag=True, help="Enable lenient parsing for naked names") @click.option( "--disallow-nested", is_flag=True, help="Disable lenient parsing for nested statements", ) @click.option( "--disallow-unqualified-translocations", is_flag=True, help="Disallow unqualified translocations", ) @click.option("--no-identifier-validation", is_flag=True, help="Turn off identifier validation") @click.option("--no-citation-clearing", is_flag=True, help="Turn off citation clearing") @click.option( "-r", "--required-annotations", multiple=True, help="Specify multiple required annotations", ) @click.option("--upgrade-urls", is_flag=True) @click.option("--skip-tqdm", is_flag=True) @verbose_option @click.pass_obj def compile( manager, path, allow_naked_names, disallow_nested, disallow_unqualified_translocations, no_identifier_validation, no_citation_clearing, required_annotations, upgrade_urls, skip_tqdm, ): """Compile a BEL script to a graph.""" logger.debug("using connection: %s", manager.engine.url) click.secho("Compilation", fg="red", bold=True) if skip_tqdm: click.echo("```") graph = from_bel_script( path, manager=manager, use_tqdm=(not skip_tqdm), disallow_nested=disallow_nested, allow_naked_names=allow_naked_names, disallow_unqualified_translocations=disallow_unqualified_translocations, citation_clearing=(not no_citation_clearing), required_annotations=required_annotations, no_identifier_validation=no_identifier_validation, allow_definition_failures=True, upgrade_urls=upgrade_urls, ) if skip_tqdm: click.echo("```") to_pickle(graph, get_corresponding_pickle_path(path)) click.echo("") _print_summary(graph, ticks=skip_tqdm) sys.exit(0 if 0 == graph.number_of_warnings() else 1) @main.command() @graph_pickle_argument def summarize(graph: BELGraph): """Summarize a graph.""" _print_summary(graph) def _print_summary(graph: BELGraph, ticks: bool = False): if not ticks: click.secho("Summary", fg="red", bold=True) graph.summarize() unused_namespaces = get_unused_namespaces(graph) if unused_namespaces: click.secho( "\nUnused Namespaces ({}/{})".format(len(unused_namespaces), len(graph.defined_namespace_keywords)), fg="red", bold=True, ) if ticks: click.echo("```") for namespace in sorted(unused_namespaces): click.echo(namespace) if ticks: click.echo("```") unused_annotations = get_unused_annotations(graph) if unused_annotations: click.secho( "\nUnused Annotations ({}/{})".format(len(unused_annotations), len(graph.defined_annotation_keywords)), fg="red", bold=True, ) if ticks: click.echo("```") for annotation in sorted(unused_annotations): click.echo(annotation) if ticks: click.echo("```") unused_annotation_list_values = get_unused_list_annotation_values(graph) if unused_annotation_list_values: click.secho("\nUnused List Annotation Values", fg="red", bold=True) if ticks: click.echo("```") for annotation, values in sorted(unused_annotation_list_values.items()): click.echo("{} ({}/{})".format(annotation, len(values), len(graph.annotation_list[annotation]))) for value in sorted(values): click.echo(" {}".format(value)) if ticks: click.echo("```") @main.command() @graph_pickle_argument def warnings(graph: BELGraph): """List warnings from a graph.""" echo_warnings_via_pager(graph.warnings) @main.command() @graph_pickle_argument @click.pass_obj def insert(manager, graph: BELGraph): """Insert a graph to the database.""" to_database(graph, manager=manager, use_tqdm=True) @main.command() @graph_argument @host_option @user_option @password_option def upload(graph: BELGraph, host: str, user: str, password: str): """Upload a graph to BEL Commons.""" resp = to_bel_commons(graph, host=host, user=user, password=password) resp.raise_for_status() click.echo(json.dumps(resp.json())) @main.command() @graph_pickle_argument @click.option("--tsv", type=click.File("w"), help="Path to output a TSV file.") @click.option("--edgelist", type=click.File("w"), help="Path to output a edgelist file.") @click.option("--sif", type=click.File("w"), help="Path to output an SIF file.") @click.option( "--gsea", type=click.File("w"), help="Path to output a GRP file for gene set enrichment analysis.", ) @click.option("--graphml", help="Path to output a GraphML file. Use .graphml for Cytoscape.") @click.option("--nodelink", type=click.File("w"), help="Path to output a node-link JSON file.") @click.option("--bel", type=click.File("w"), help="Output canonical BEL.") def serialize(graph: BELGraph, tsv, edgelist, sif, gsea, graphml, nodelink, bel): """Serialize a graph to various formats.""" if tsv: logger.info("Outputting TSV to %s", tsv) to_triples_file(graph, tsv) if edgelist: logger.info("Outputting edgelist to %s", edgelist) to_edgelist(graph, edgelist) if sif: logger.info("Outputting SIF to %s", sif) to_sif(graph, sif) if graphml: logger.info("Outputting GraphML to %s", graphml) to_graphml(graph, graphml) if gsea: logger.info("Outputting GRP to %s", gsea) to_gsea(graph, gsea) if nodelink: logger.info("Outputting Nodelink JSON to %s", nodelink) to_nodelink_file(graph, nodelink) if bel: logger.info("Outputting BEL script to %s", bel) to_bel_script(graph, bel) @main.command() @graph_pickle_argument @click.option( "--connection", default="http://localhost:7474/db/data/", help="Connection string for neo4j upload.", ) @click.password_option() def neo(graph: BELGraph, connection: str, password: str): """Upload to neo4j.""" import py2neo neo_graph = py2neo.Graph(connection, password=password) to_neo4j(graph, neo_graph) @main.command() @click.pass_obj @click.argument("agents", nargs=-1) @click.option("--local", is_flag=True, help="Upload to local database.") @host_option def machine(manager: Manager, agents: List[str], local: bool, host: str): """Get content from the INDRA machine and upload to BEL Commons.""" from indra.sources import indra_db_rest from pybel import from_indra_statements statements = indra_db_rest.get_statements(agents=agents) click.echo("got {} statements from INDRA".format(len(statements))) graph = from_indra_statements( statements, name="INDRA Machine for {}".format(", ".join(sorted(agents))), version=time.strftime("%Y%m%d"), ) click.echo("built BEL graph with {} nodes and {} edges".format(graph.number_of_nodes(), graph.number_of_edges())) if 0 == len(graph): click.echo("not uploading empty graph") sys.exit(-1) if local: to_database(graph, manager=manager) else: resp = to_bel_commons(graph, host=host) resp.raise_for_status() @main.group() def manage(): """Manage the database.""" @manage.command() @click.confirmation_option() @click.pass_obj def drop(manager: Manager): """Drop the database.""" manager.drop_all() @manage.command() @click.option("-v", "--debug", is_flag=True) @click.pass_obj def examples(manager: Manager, debug: bool): """Load examples to the database.""" level = logging.DEBUG if debug else logging.INFO logging.basicConfig(level=level) logging.getLogger("pybel").setLevel(level) for graph in ( sialic_acid_graph, statin_graph, homology_graph, braf_graph, egf_graph, ): if manager.has_name_version(graph.name, graph.version): click.echo("already inserted {}".format(graph)) continue click.echo("inserting {}".format(graph)) manager.insert_graph(graph, use_tqdm=True) @manage.group() def namespaces(): """Manage namespaces.""" @namespaces.command() # noqa:F811 @click.argument("url") @click.pass_obj def insert(manager: Manager, url: str): """Add a namespace by URL.""" manager.get_or_create_namespace(url) def _ls(manager: Manager, model_cls, model_id: int): if model_id: n = manager.session.query(model_cls).get(model_id) _page(n.entries) else: for n in manager.session.query(model_cls).order_by(model_cls.uploaded.desc()): click.echo("\t".join(map(str, (n.id, n.keyword, n.version, n.url)))) @namespaces.command() @click.option("-u", "--url", help="Specific resource URL to list") @click.option("-i", "--namespace-id", type=int, help="Specific resource URL to list") @click.pass_obj def ls(manager: Manager, url: Optional[str], namespace_id: Optional[int]): """List cached namespaces.""" if url: n = manager.get_or_create_namespace(url) _page(n.entries) elif namespace_id is not None: _ls(manager, Namespace, namespace_id) else: click.echo_via_pager( "\n".join("{}\t{}\t{}".format(n.id, n.name, n.url) for n in manager.session.query(Namespace)), ) @namespaces.command() # noqa:F811 @click.argument("url") @click.pass_obj def drop(manager: Manager, url: str): """Drop a namespace by URL.""" manager.drop_namespace_by_url(url) @manage.group() def networks(): """Manage networks.""" @networks.command() # noqa:F811 @click.pass_obj def ls(manager: Manager): """List network names, versions, and optionally, descriptions.""" for n in manager.list_networks(): click.echo("{}\t{}\t{}".format(n.id, n.name, n.version)) @networks.command() # noqa:F811 @click.option("-n", "--network-id", type=int, help="Identifier of network to drop") @click.option( "-y", "--yes", is_flag=True, help="Drop all networks without confirmation if no identifier is given", ) @click.pass_obj def drop(manager: Manager, network_id: Optional[int], yes): """Drop a network by its identifier or drop all networks.""" if network_id: manager.drop_network_by_id(network_id) elif yes or click.confirm("Drop all networks?"): manager.drop_networks() @manage.group() def edges(): """Manage edges.""" @edges.command() # noqa:F811 @click.option("--offset", type=int) @click.option("--limit", type=int, default=10) @click.pass_obj def ls(manager: Manager, offset: Optional[int], limit: Optional[int]): """List edges.""" q = manager.session.query(Edge) if offset: q = q.offset(offset) if limit > 0: q = q.limit(limit) for e in q: click.echo(e.bel) @manage.group() def nodes(): """Manage nodes.""" @nodes.command() @click.pass_obj def prune(manager: Manager): """Prune nodes not belonging to any edges.""" nodes_to_delete = [ node for node in tqdm(manager.session.query(Node), total=manager.count_nodes()) if not node.networks ] manager.session.delete(nodes_to_delete) manager.session.commit() @manage.command() # noqa:F811 @click.pass_obj def summarize(manager: Manager): """Summarize the contents of the database.""" click.echo("Networks: {}".format(manager.count_networks())) click.echo("Edges: {}".format(manager.count_edges())) click.echo("Nodes: {}".format(manager.count_nodes())) click.echo("Namespaces: {}".format(manager.count_namespaces())) click.echo("Namespaces entries: {}".format(manager.count_namespace_entries())) click.echo("Annotations: {}".format(manager.count_annotations())) click.echo("Annotation entries: {}".format(manager.count_annotation_entries())) def echo_warnings_via_pager(warnings: List[WarningTuple], sep: str = "\t") -> None: """Output the warnings from a BEL graph with Click and the system's pager.""" # Exit if no warnings if not warnings: click.echo("Congratulations! No warnings.") sys.exit(0) max_line_width = max(len(str(exc.line_number)) for _, exc, _ in warnings) max_warning_width = max(len(exc.__class__.__name__) for _, exc, _ in warnings) s1 = "{:>" + str(max_line_width) + "}" + sep s2 = "{:>" + str(max_warning_width) + "}" + sep def _make_line(path: str, exc: BELParserWarning): s = click.style(path, fg="cyan") + sep s += click.style(s1.format(exc.line_number), fg="blue", bold=True) s += click.style( s2.format(exc.__class__.__name__), fg=("red" if exc.__class__.__name__.endswith("Error") else "yellow"), ) s += click.style(exc.line, bold=True) + sep s += click.style(str(exc)) return s click.echo_via_pager( "\n".join(_make_line(path, exc) for path, exc, _ in warnings), ) if __name__ == "__main__": main() pybel-0.15.5/src/pybel/config.py000066400000000000000000000013511426625374700164560ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Connection configuration for PyBEL.""" import logging import pystow __all__ = [ "connection", "PYBEL_MINIMUM_IMPORT_VERSION", "PYBEL_HOME", ] logger = logging.getLogger(__name__) #: The last PyBEL version where the graph data definition changed PYBEL_MINIMUM_IMPORT_VERSION = 0, 14, 0 PYBEL_HOME = pystow.join("pybel") DEFAULT_CACHE_NAME = "pybel_{}.{}.{}_cache.db".format(*PYBEL_MINIMUM_IMPORT_VERSION) DEFAULT_CACHE_PATH = pystow.join("pybel", name=DEFAULT_CACHE_NAME) #: The default cache connection string uses sqlite. DEFAULT_CACHE_CONNECTION = "sqlite:///" + DEFAULT_CACHE_PATH.as_posix() connection = pystow.get_config( "pybel", "connection", default=DEFAULT_CACHE_CONNECTION, ) pybel-0.15.5/src/pybel/constants.py000066400000000000000000000441711426625374700172340ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Constants for PyBEL. This module maintains the strings used throughout the PyBEL codebase to promote consistency. """ from .config import connection def get_cache_connection() -> str: """Get the preferred RFC-1738 database connection string. 1. Check the environment variable ``PYBEL_CONNECTION`` 2. Check the ``PYBEL_CONNECTION`` key in the config file ``~/.config/pybel/config.json``. Optionally, this config file might be in a different place if the environment variable ``PYBEL_CONFIG_DIRECTORY`` has been set. 3. Return a default connection string using a SQLite database in the ``~/.pybel``. Optionally, this directory might be in a different place if the environment variable ``PYBEL_RESOURCE_DIRECTORY`` has been set. """ return connection PYBEL_CONTEXT_TAG = "pybel_context" PYBEL_AUTOEVIDENCE = "Automatically added by PyBEL" CITATION_TYPE_BOOK = "book" CITATION_TYPE_PUBMED = "pubmed" CITATION_TYPE_PMC = "pmc" CITATION_TYPE_URL = "url" CITATION_TYPE_DOI = "doi" CITATION_TYPE_OTHER = "other" CITATION_TYPES = { CITATION_TYPE_BOOK, CITATION_TYPE_PUBMED, CITATION_TYPE_PMC, CITATION_TYPE_URL, CITATION_TYPE_DOI, CITATION_TYPE_OTHER, } CITATION_NORMALIZER = { "pubmed central": "pmc", "pmid": "pubmed", "online resource": "url", } NAMESPACE_DOMAIN_BIOPROCESS = "BiologicalProcess" NAMESPACE_DOMAIN_CHEMICAL = "Chemical" NAMESPACE_DOMAIN_GENE = "Gene and Gene Products" NAMESPACE_DOMAIN_OTHER = "Other" #: The valid namespace types #: .. seealso:: https://wiki.openbel.org/display/BELNA/Custom+Namespaces NAMESPACE_DOMAIN_TYPES = { NAMESPACE_DOMAIN_BIOPROCESS, NAMESPACE_DOMAIN_CHEMICAL, NAMESPACE_DOMAIN_GENE, NAMESPACE_DOMAIN_OTHER, } #: Represents the key for the citation date in a citation dictionary CITATION_DATE = "date" #: Represents the key for the citation authors in a citation dictionary CITATION_AUTHORS = "authors" #: Represents the key for the citation comment in a citation dictionary CITATION_JOURNAL = "journal" #: Represents the key for the optional PyBEL citation volume entry in a citation dictionary CITATION_VOLUME = "volume" #: Represents the key for the optional PyBEL citation issue entry in a citation dictionary CITATION_ISSUE = "issue" #: Represents the key for the optional PyBEL citation pages entry in a citation dictionary CITATION_PAGES = "pages" #: Represents the key for the optional PyBEL citation first author entry in a citation dictionary CITATION_FIRST_AUTHOR = "first" #: Represents the key for the optional PyBEL citation last author entry in a citation dictionary CITATION_LAST_AUTHOR = "last" #: Represents the type of article (Journal Article, Review, etc.) CITATION_ARTICLE_TYPE = "article_type" # Used during BEL parsing MODIFIER = "modifier" EFFECT = "effect" FROM_LOC = "fromLoc" TO_LOC = "toLoc" LOCATION = "location" ACTIVITY = "Activity" DEGRADATION = "Degradation" TRANSLOCATION = "Translocation" CELL_SECRETION = "CellSecretion" CELL_SURFACE_EXPRESSION = "CellSurfaceExpression" INTRACELLULAR = "intracellular" EXTRACELLULAR = "extracellular space" CELL_SURFACE = "cell surface" # Internal node data format keys #: The node data key specifying the node's function (e.g. :data:`GENE`, :data:`MIRNA`, :data:`BIOPROCESS`, etc.) FUNCTION = "function" #: The key specifying a concept CONCEPT = "concept" #: The key specifying an identifier dictionary's namespace. Used for nodes, activities, and transformations. NAMESPACE = "namespace" #: The key specifying an identifier dictionary's name. Used for nodes, activities, and transformations. NAME = "name" #: The key specifying an identifier dictionary IDENTIFIER = "identifier" #: The key specifying an optional label for the node LABEL = "label" #: The key specifying an optional description for the node DESCRIPTION = "description" #: The key specifying xrefs XREFS = "xref" #: They key representing the nodes that are a member of a composite or complex MEMBERS = "members" #: The key representing the nodes appearing in the reactant side of a biochemical reaction REACTANTS = "reactants" #: The key representing the nodes appearing in the product side of a biochemical reaction PRODUCTS = "products" #: The node data key specifying a fusion dictionary, containing :data:`PARTNER_3P`, :data:`PARTNER_5P`, # :data:`RANGE_3P`, and :data:`RANGE_5P` FUSION = "fusion" #: The key specifying the identifier dictionary of the fusion's 3-Prime partner PARTNER_3P = "partner_3p" #: The key specifying the identifier dictionary of the fusion's 5-Prime partner PARTNER_5P = "partner_5p" #: The key specifying the range dictionary of the fusion's 3-Prime partner RANGE_3P = "range_3p" #: The key specifying the range dictionary of the fusion's 5-Prime partner RANGE_5P = "range_5p" FUSION_REFERENCE = "reference" FUSION_START = "left" FUSION_STOP = "right" FUSION_MISSING = "missing" #: The key specifying the node has a list of associated variants VARIANTS = "variants" #: The key representing what kind of variation is being represented KIND = "kind" #: The value for :data:`KIND` for an HGVS variant HGVS = "hgvs" #: The value for :data:`KIND` for a protein modification PMOD = "pmod" #: The value for :data:`KIND` for a gene modification GMOD = "gmod" #: The value for :data:`KIND` for a fragment FRAGMENT = "frag" #: The allowed values for :data:`KIND` PYBEL_VARIANT_KINDS = { HGVS, PMOD, GMOD, FRAGMENT, } #: The group of all BEL-provided keys for node data dictionaries, used for hashing. PYBEL_NODE_DATA_KEYS = { FUNCTION, NAMESPACE, NAME, IDENTIFIER, VARIANTS, FUSION, MEMBERS, REACTANTS, PRODUCTS, } #: Used as a namespace when none is given when lenient parsing mode is turned on. Not recommended! DIRTY = "dirty" #: Represents the BEL abundance, abundance() ABUNDANCE = "Abundance" #: Represents the BEL abundance, geneAbundance() #: .. seealso:: http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#Xabundancea GENE = "Gene" #: Represents the BEL abundance, rnaAbundance() RNA = "RNA" #: Represents the BEL abundance, microRNAAbundance() MIRNA = "miRNA" #: Represents the BEL abundance, proteinAbundance() PROTEIN = "Protein" #: Represents the BEL function, biologicalProcess() BIOPROCESS = "BiologicalProcess" #: Represents the BEL function, pathology() PATHOLOGY = "Pathology" #: Represents the BEL function, populationAbundance() POPULATION = "Population" #: Represents the BEL abundance, compositeAbundance() COMPOSITE = "Composite" #: Represents the BEL abundance, complexAbundance() COMPLEX = "Complex" #: Represents the BEL transformation, reaction() REACTION = "Reaction" #: A set of all of the valid PyBEL node functions PYBEL_NODE_FUNCTIONS = { ABUNDANCE, GENE, RNA, MIRNA, PROTEIN, BIOPROCESS, PATHOLOGY, COMPOSITE, COMPLEX, REACTION, POPULATION, } #: The mapping from PyBEL node functions to BEL strings rev_abundance_labels = { ABUNDANCE: "a", GENE: "g", MIRNA: "m", PROTEIN: "p", RNA: "r", BIOPROCESS: "bp", PATHOLOGY: "path", COMPLEX: "complex", COMPOSITE: "composite", POPULATION: "pop", } # Internal edge data keys #: The key for an internal edge data dictionary for the relation string RELATION = "relation" #: The key for an internal edge data dictionary for the citation dictionary CITATION = "citation" CITATION_DB = NAMESPACE # for backwards compatibility CITATION_IDENTIFIER = IDENTIFIER # for backwards compatibility #: The key for an internal edge data dictionary for the evidence string EVIDENCE = "evidence" #: The key for an internal edge data dictionary for the annotations dictionary ANNOTATIONS = "annotations" SOURCE = "source" SUBJECT = SOURCE # for backwards compatibility TARGET = "target" OBJECT = TARGET # for backwards compatibility #: The key for an internal edge data dictionary for the source modifier dictionary SOURCE_MODIFIER = "source_modifier" #: The key for an internal edge data dictionary for the target modifier dictionary TARGET_MODIFIER = "target_modifier" #: The key or an internal edge data dictionary for the line number LINE = "line" #: The key representing the hash of the other HASH = "hash" #: The group of all BEL-provided keys for edge data dictionaries, used for hashing. PYBEL_EDGE_DATA_KEYS = { RELATION, CITATION, EVIDENCE, ANNOTATIONS, SOURCE_MODIFIER, TARGET_MODIFIER, } #: The group of all PyBEL-specific keys for edge data dictionaries, not used for hashing. PYBEL_EDGE_METADATA_KEYS = { LINE, HASH, } #: The group of all PyBEL annotated keys for edge data dictionaries PYBEL_EDGE_ALL_KEYS = PYBEL_EDGE_DATA_KEYS | PYBEL_EDGE_METADATA_KEYS #: A BEL relationship HAS_REACTANT = "hasReactant" #: A BEL relationship HAS_PRODUCT = "hasProduct" #: A BEL relationship HAS_VARIANT = "hasVariant" #: A BEL relationship #: :data:`GENE` to :data:`RNA` is called transcription TRANSCRIBED_TO = "transcribedTo" #: A BEL relationship #: :data:`RNA` to :data:`PROTEIN` is called translation TRANSLATED_TO = "translatedTo" #: A BEL relationship INCREASES = "increases" #: A BEL relationship DIRECTLY_INCREASES = "directlyIncreases" #: A BEL relationship DECREASES = "decreases" #: A BEL relationship DIRECTLY_DECREASES = "directlyDecreases" #: A BEL relationship CAUSES_NO_CHANGE = "causesNoChange" #: A BEL relationship REGULATES = "regulates" #: A BEL relationship DIRECTLY_REGULATES = "directlyRegulates" #: A BEL relationship BINDS = "binds" #: A BEL relationship CORRELATION = "correlation" #: A BEL relationship NO_CORRELATION = "noCorrelation" #: A BEL relationship NEGATIVE_CORRELATION = "negativeCorrelation" #: A BEL relationship POSITIVE_CORRELATION = "positiveCorrelation" #: A BEL relationship ASSOCIATION = "association" #: A BEL relationship ORTHOLOGOUS = "orthologous" #: A BEL relationship ANALOGOUS_TO = "analogousTo" #: A BEL relationship IS_A = "isA" #: A BEL relationship RATE_LIMITING_STEP_OF = "rateLimitingStepOf" #: A BEL relationship SUBPROCESS_OF = "subProcessOf" #: A BEL relationship BIOMARKER_FOR = "biomarkerFor" #: A BEL relationship PROGONSTIC_BIOMARKER_FOR = "prognosticBiomarkerFor" #: A BEL relationship, added by PyBEL EQUIVALENT_TO = "equivalentTo" #: A BEL relationship, added by PyBEL PART_OF = "partOf" #: A set of all causal relationships that have an increasing effect CAUSAL_INCREASE_RELATIONS = {INCREASES, DIRECTLY_INCREASES} #: A set of all causal relationships that have a decreasing effect CAUSAL_DECREASE_RELATIONS = {DECREASES, DIRECTLY_DECREASES} #: A set of all causal relationships that have an inderminate polarity CAUSAL_APOLAR_RELATIONS = {REGULATES, DIRECTLY_REGULATES} #: A set of direct causal relations DIRECT_CAUSAL_RELATIONS = {DIRECTLY_DECREASES, DIRECTLY_INCREASES, DIRECTLY_REGULATES} #: A set of direct causal relations INDIRECT_CAUSAL_RELATIONS = {DECREASES, INCREASES, REGULATES} #: A set of causal relationships that are polar CAUSAL_POLAR_RELATIONS = CAUSAL_INCREASE_RELATIONS | CAUSAL_DECREASE_RELATIONS #: A set of all causal relationships CAUSAL_RELATIONS = CAUSAL_INCREASE_RELATIONS | CAUSAL_DECREASE_RELATIONS | CAUSAL_APOLAR_RELATIONS APOLAR_CORRELATIVE_RELATIONS = { CORRELATION, NO_CORRELATION, } POLAR_CORRELATIVE_RELATIONS = { POSITIVE_CORRELATION, NEGATIVE_CORRELATION, } #: A set of all correlative relationships CORRELATIVE_RELATIONS = APOLAR_CORRELATIVE_RELATIONS | POLAR_CORRELATIVE_RELATIONS #: A set of polar relations POLAR_RELATIONS = CAUSAL_POLAR_RELATIONS | POLAR_CORRELATIVE_RELATIONS #: A set of all relationships that are inherently directionless, and are therefore added to the graph twice TWO_WAY_RELATIONS = CORRELATIVE_RELATIONS | { ASSOCIATION, ORTHOLOGOUS, ANALOGOUS_TO, EQUIVALENT_TO, BINDS, } #: A list of relationship types that don't require annotations or evidence UNQUALIFIED_EDGES = { HAS_REACTANT, HAS_PRODUCT, HAS_VARIANT, TRANSCRIBED_TO, TRANSLATED_TO, IS_A, EQUIVALENT_TO, PART_OF, ORTHOLOGOUS, } # BEL Keywords BEL_KEYWORD_SET = "SET" BEL_KEYWORD_DOCUMENT = "DOCUMENT" BEL_KEYWORD_DEFINE = "DEFINE" BEL_KEYWORD_NAMESPACE = "NAMESPACE" BEL_KEYWORD_ANNOTATION = "ANNOTATION" BEL_KEYWORD_AS = "AS" BEL_KEYWORD_URL = "URL" BEL_KEYWORD_LIST = "LIST" BEL_KEYWORD_OWL = "OWL" BEL_KEYWORD_PATTERN = "PATTERN" BEL_KEYWORD_UNSET = "UNSET" BEL_KEYWORD_STATEMENT_GROUP = "STATEMENT_GROUP" BEL_KEYWORD_CITATION = "Citation" BEL_KEYWORD_EVIDENCE = "Evidence" BEL_KEYWORD_SUPPORT = "SupportingText" BEL_KEYWORD_ALL = "ALL" BEL_KEYWORD_METADATA_NAME = "Name" BEL_KEYWORD_METADATA_VERSION = "Version" BEL_KEYWORD_METADATA_DESCRIPTION = "Description" BEL_KEYWORD_METADATA_AUTHORS = "Authors" BEL_KEYWORD_METADATA_CONTACT = "ContactInfo" BEL_KEYWORD_METADATA_LICENSES = "Licenses" BEL_KEYWORD_METADATA_COPYRIGHT = "Copyright" BEL_KEYWORD_METADATA_DISCLAIMER = "Disclaimer" BEL_KEYWORD_METADATA_PROJECT = "Project" # Internal metadata representation. See BELGraph documentation, since these are shielded from the user by properties. #: The key for the document metadata dictionary. Can be accessed by :code:`graph.graph[GRAPH_METADATA]`, or by using #: the property built in to the :class:`pybel.BELGraph`, :func:`pybel.BELGraph.document` GRAPH_METADATA = "document_metadata" GRAPH_NAMESPACE_URL = "namespace_url" GRAPH_NAMESPACE_PATTERN = "namespace_pattern" GRAPH_ANNOTATION_URL = "annotation_url" GRAPH_ANNOTATION_MIRIAM = "annotation_miriam" GRAPH_ANNOTATION_CURIE = "annotation_curie" GRAPH_ANNOTATION_PATTERN = "annotation_pattern" GRAPH_ANNOTATION_LIST = "annotation_list" GRAPH_WARNINGS = "warnings" GRAPH_PYBEL_VERSION = "pybel_version" GRAPH_PATH = "path" #: The key for the document name. Can be accessed by :code:`graph.document[METADATA_NAME]` or by using the property #: built into the :class:`pybel.BELGraph` class, :func:`pybel.BELGraph.name` METADATA_NAME = "name" #: The key for the document version. Can be accessed by :code:`graph.document[METADATA_VERSION]` METADATA_VERSION = "version" #: The key for the document description. Can be accessed by :code:`graph.document[METADATA_DESCRIPTION]` METADATA_DESCRIPTION = "description" #: The key for the document authors. Can be accessed by :code:`graph.document[METADATA_NAME]` METADATA_AUTHORS = "authors" #: The key for the document contact email. Can be accessed by :code:`graph.document[METADATA_CONTACT]` METADATA_CONTACT = "contact" #: The key for the document licenses. Can be accessed by :code:`graph.document[METADATA_LICENSES]` METADATA_LICENSES = "licenses" #: The key for the document copyright information. Can be accessed by :code:`graph.document[METADATA_COPYRIGHT]` METADATA_COPYRIGHT = "copyright" #: The key for the document disclaimer. Can be accessed by :code:`graph.document[METADATA_DISCLAIMER]` METADATA_DISCLAIMER = "disclaimer" #: The key for the document project. Can be accessed by :code:`graph.document[METADATA_PROJECT]` METADATA_PROJECT = "project" #: Provides a mapping from BEL language keywords to internal PyBEL strings DOCUMENT_KEYS = { BEL_KEYWORD_METADATA_AUTHORS: METADATA_AUTHORS, BEL_KEYWORD_METADATA_CONTACT: METADATA_CONTACT, BEL_KEYWORD_METADATA_COPYRIGHT: METADATA_COPYRIGHT, BEL_KEYWORD_METADATA_DESCRIPTION: METADATA_DESCRIPTION, BEL_KEYWORD_METADATA_DISCLAIMER: METADATA_DISCLAIMER, BEL_KEYWORD_METADATA_LICENSES: METADATA_LICENSES, BEL_KEYWORD_METADATA_NAME: METADATA_NAME, BEL_KEYWORD_METADATA_VERSION: METADATA_VERSION, BEL_KEYWORD_METADATA_PROJECT: METADATA_PROJECT, } #: The keys to use when inserting a graph to the cache METADATA_INSERT_KEYS = { METADATA_NAME, METADATA_VERSION, METADATA_DESCRIPTION, METADATA_AUTHORS, METADATA_CONTACT, METADATA_LICENSES, METADATA_COPYRIGHT, METADATA_DISCLAIMER, } #: Provides a mapping from internal PyBEL strings to BEL language keywords. Is the inverse of :data:`DOCUMENT_KEYS` INVERSE_DOCUMENT_KEYS = {v: k for k, v in DOCUMENT_KEYS.items()} #: A set representing the required metadata during BEL document parsing REQUIRED_METADATA = { METADATA_NAME, METADATA_VERSION, METADATA_DESCRIPTION, METADATA_AUTHORS, METADATA_CONTACT, } # Modifier parser constants #: The key for the starting position of a fragment range FRAGMENT_START = "start" #: The key for the stopping position of a fragment range FRAGMENT_STOP = "stop" #: The key signifying that there is neither a start nor stop position defined FRAGMENT_MISSING = "missing" #: The key for any additional descriptive data about a fragment FRAGMENT_DESCRIPTION = "description" #: The order for serializing gene modification data GMOD_ORDER = [KIND, IDENTIFIER] #: The key for the reference nucleotide in a gene substitution. #: Only used during parsing since this is converted to HGVS. GSUB_REFERENCE = "reference" #: The key for the position of a gene substitution. #: Only used during parsing since this is converted to HGVS GSUB_POSITION = "position" #: The key for the effect of a gene substitution. #: Only used during parsing since this is converted to HGVS GSUB_VARIANT = "variant" #: The key for the protein modification code. PMOD_CODE = "code" #: The key for the protein modification position. PMOD_POSITION = "pos" #: The order for serializing information about a protein modification PMOD_ORDER = [KIND, IDENTIFIER, PMOD_CODE, PMOD_POSITION] #: The key for the reference amino acid in a protein substitution. #: Only used during parsing since this is concerted to HGVS PSUB_REFERENCE = "reference" #: The key for the position of a protein substitution. Only used during parsing since this is converted to HGVS. PSUB_POSITION = "position" #: The key for the variant of a protein substitution.Only used during parsing since this is converted to HGVS. PSUB_VARIANT = "variant" #: The key for the position at which a protein is truncated TRUNCATION_POSITION = "position" #: The mapping from BEL namespace codes to PyBEL internal abundance constants #: ..seealso:: https://wiki.openbel.org/display/BELNA/Assignment+of+Encoding+%28Allowed+Functions%29+for+BEL+Namespaces belns_encodings = { "G": {GENE}, "R": {RNA, MIRNA}, "P": {PROTEIN}, "M": {MIRNA}, "A": {ABUNDANCE, RNA, MIRNA, PROTEIN, GENE, COMPLEX}, "B": {PATHOLOGY, BIOPROCESS}, "O": {PATHOLOGY}, "C": {COMPLEX}, } BELNS_ENCODING_STR = "".join(sorted(belns_encodings)) PYBEL_PUBMED = "29048466" SET_CITATION_FMT = 'SET Citation = {{"{}", "{}"}}' pybel-0.15.5/src/pybel/dsl/000077500000000000000000000000001426625374700154215ustar00rootroot00000000000000pybel-0.15.5/src/pybel/dsl/__init__.py000066400000000000000000000043331426625374700175350ustar00rootroot00000000000000# -*- coding: utf-8 -*- """PyBEL implements an internal domain-specific language (DSL). This enables you to write BEL using Python scripts. Even better, you can programatically generate BEL using Python. See the Bio2BEL `paper `_ and `repository `_ for many examples. Internally, the BEL parser converts BEL script into the BEL DSL then adds it to a BEL graph object. When you iterate through the :class:`pybel.BELGraph`, the nodes are instances of subclasses of :class:`pybel.dsl.BaseEntity`. """ from .constants import FUNC_TO_DSL, FUNC_TO_FUSION_DSL, FUNC_TO_LIST_DSL from .edges import ( activity, cell_surface_expression, degradation, location, secretion, translocation, ) from .exc import ( InferCentralDogmaException, ListAbundanceEmptyException, PyBELDSLException, ReactionEmptyException, ) from .namespaces import chebi, hgnc, mirbase from .node_classes import ( Abundance, BaseAbundance, BaseConcept, BaseEntity, BiologicalProcess, CentralDogma, ComplexAbundance, CompositeAbundance, Entity, EntityVariant, EnumeratedFusionRange, Fragment, FusionBase, FusionRangeBase, Gene, GeneFusion, GeneModification, Hgvs, HgvsReference, HgvsUnspecified, ListAbundance, MicroRna, MissingFusionRange, NamedComplexAbundance, Pathology, Population, Protein, ProteinFusion, ProteinModification, ProteinSubstitution, Reaction, Rna, RnaFusion, Transcribable, Variant, ) entity = Entity abundance = Abundance bioprocess = BiologicalProcess pathology = Pathology pmod = ProteinModification gmod = GeneModification hgvs = Hgvs hgvs_unspecified = HgvsUnspecified hgvs_reference = HgvsReference protein_substitution = ProteinSubstitution fragment = Fragment gene = Gene rna = Rna mirna = MicroRna protein = Protein reaction = Reaction complex_abundance = ComplexAbundance named_complex_abundance = NamedComplexAbundance composite_abundance = CompositeAbundance missing_fusion_range = MissingFusionRange fusion_range = EnumeratedFusionRange protein_fusion = ProteinFusion rna_fusion = RnaFusion gene_fusion = GeneFusion pybel-0.15.5/src/pybel/dsl/constants.py000066400000000000000000000020141426625374700200040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Convenient dictionaries for mapping constants to DSL classes.""" from .node_classes import ( Abundance, BiologicalProcess, ComplexAbundance, CompositeAbundance, Gene, GeneFusion, MicroRna, NamedComplexAbundance, Pathology, Population, Protein, ProteinFusion, Rna, RnaFusion, ) from ..constants import ( ABUNDANCE, BIOPROCESS, COMPLEX, COMPOSITE, GENE, MIRNA, PATHOLOGY, POPULATION, PROTEIN, RNA, ) __all__ = [ "FUNC_TO_DSL", "FUNC_TO_FUSION_DSL", "FUNC_TO_LIST_DSL", ] FUNC_TO_DSL = { PROTEIN: Protein, RNA: Rna, MIRNA: MicroRna, GENE: Gene, PATHOLOGY: Pathology, BIOPROCESS: BiologicalProcess, COMPLEX: NamedComplexAbundance, ABUNDANCE: Abundance, POPULATION: Population, } FUNC_TO_FUSION_DSL = { GENE: GeneFusion, RNA: RnaFusion, PROTEIN: ProteinFusion, } FUNC_TO_LIST_DSL = { COMPLEX: ComplexAbundance, COMPOSITE: CompositeAbundance, } pybel-0.15.5/src/pybel/dsl/edges.py000066400000000000000000000115011426625374700170600ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Internal DSL functions for edges.""" import warnings from typing import Dict, Optional, Union from ..constants import ( ACTIVITY, CELL_SURFACE, DEGRADATION, EFFECT, EXTRACELLULAR, FROM_LOC, INTRACELLULAR, LOCATION, MODIFIER, NAME, NAMESPACE, TO_LOC, TRANSLOCATION, ) from ..language import Entity, activity_mapping, compartment_mapping __all__ = [ "activity", "degradation", "translocation", "secretion", "cell_surface_expression", "location", ] ModifierDict = Dict LocationDict = Dict def _modifier_helper( modifier: str, location: Optional[LocationDict] = None, ) -> ModifierDict: """Make a modifier dictionary. :param modifier: The name of the modifier :param location: An entity from :func:`pybel.dsl.entity` """ rv = { MODIFIER: modifier, } if location: rv[LOCATION] = location return rv def activity( name: Optional[str] = None, namespace: Optional[str] = None, identifier: Optional[str] = None, location: Optional[LocationDict] = None, ) -> ModifierDict: """Make a subject/object modifier dictionary. :param name: The name of the activity. If no namespace given, uses BEL default namespace :param namespace: The namespace of the activity :param identifier: The identifier of the name in the database :param location: An entity from :func:`pybel.dsl.entity` representing the location of the node """ rv = _modifier_helper(ACTIVITY, location=location) if name and not namespace: rv[EFFECT] = activity_mapping[name] elif not name and not namespace and not identifier: rv[EFFECT] = activity_mapping["act"] else: rv[EFFECT] = Entity( namespace=namespace, name=name, identifier=identifier, ) return rv def degradation(location: Optional[LocationDict] = None) -> ModifierDict: """Make a degradation dictionary. :param location: An entity from :func:`pybel.dsl.entity` representing the location of the node """ return _modifier_helper(DEGRADATION, location=location) def translocation( from_loc: Union[str, Entity], to_loc: Union[str, Entity], ) -> ModifierDict: """Make a translocation dictionary. :param dict from_loc: An entity dictionary from :func:`pybel.dsl.entity` :param dict to_loc: An entity dictionary from :func:`pybel.dsl.entity` :rtype: dict """ rv = _modifier_helper(TRANSLOCATION) if isinstance(from_loc, str): from_loc = compartment_mapping[from_loc] if not isinstance(from_loc, Entity): raise TypeError if isinstance(to_loc, str): to_loc = compartment_mapping[to_loc] if not isinstance(to_loc, Entity): raise TypeError rv[EFFECT] = { FROM_LOC: from_loc, TO_LOC: to_loc, } return rv def secretion() -> ModifierDict: """Make a secretion translocation dictionary. This is a convenient wrapper representing the :func:`translocation` from the intracellular location to the extracellular space. """ return translocation(INTRACELLULAR, EXTRACELLULAR) def cell_surface_expression() -> ModifierDict: """Make a cellular surface expression translocation dictionary. This is a convenient wrapper representing the :func:`translocation` from the intracellular location to the cell surface. """ return translocation(INTRACELLULAR, CELL_SURFACE) def location(identifier: Entity) -> LocationDict: """Make a location object modifier dictionary. :param identifier: A namespace/name/identifier pair Usage: X increases the abundance of Y in the cytoplasm .. code-block:: python from pybel import BELGraph from pybel.dsl import protein, location graph = BELGraph() source = protein('HGNC', 'IRAK1') target = protein('HGNC', 'IRF7, variants=[ pmod('Ph', 'Ser', 477), pmod('Ph', 'Ser', 479), ]) graph.add_increases( source, target, citation=..., evidence=..., target_modifier=location(entity(namespace='GO', name='cytosol', identifier='GO:0005829')), ) X increases the kinase activity of Y in the cytoplasm. In this case, the :func:`activity` function takes a location as an optional argument. .. code-block:: python from pybel import BELGraph from pybel.dsl import protein, location graph = BELGraph() source = ... target = ... graph.add_increases( source, target, citation=..., evidence=..., target_modifier=activity('kin', location=entity(namespace='GO', name='cytosol', identifier='GO:0005829')), ) """ return { LOCATION: identifier, } pybel-0.15.5/src/pybel/dsl/exc.py000066400000000000000000000012471426625374700165560ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Exceptions for the internal DSL.""" from ..exceptions import PyBELWarning __all__ = [ "PyBELDSLException", "InferCentralDogmaException", "ListAbundanceEmptyException", "ReactionEmptyException", ] class PyBELDSLException(PyBELWarning, ValueError): """Raised when problems with the DSL.""" class InferCentralDogmaException(PyBELDSLException): """Raised when unable to infer central dogma.""" class ListAbundanceEmptyException(PyBELDSLException): """Raised when a list abundance has no members.""" class ReactionEmptyException(PyBELDSLException): """Raised when a reaction has neither reactants nor products.""" pybel-0.15.5/src/pybel/dsl/namespaces.py000066400000000000000000000015451426625374700201170ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains simple wrappers around node DSL functions for common namespaces.""" from typing import Optional from .node_classes import Abundance, MicroRna, Protein __all__ = [ "chebi", "hgnc", "mirbase", ] def chebi(*, name: Optional[str] = None, identifier: Optional[str] = None) -> Abundance: """Build a ChEBI abundance node.""" return Abundance(namespace="CHEBI", name=name, identifier=identifier) def hgnc(*, name: Optional[str] = None, identifier: Optional[str] = None) -> Protein: """Build an HGNC protein node.""" return Protein(namespace="HGNC", name=name, identifier=identifier) def mirbase(*, name: Optional[str] = None, identifier: Optional[str] = None) -> MicroRna: """Build an miRBase micro-rna node.""" return MicroRna(namespace="MIRBASE", name=name, identifier=identifier) pybel-0.15.5/src/pybel/dsl/node_classes.py000066400000000000000000001035741426625374700204470ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Classes for DSL nodes.""" import hashlib from abc import ABCMeta, abstractmethod from operator import methodcaller from typing import Iterable, List, Optional, Set, Union from .exc import ( InferCentralDogmaException, ListAbundanceEmptyException, ReactionEmptyException, ) from ..constants import ( ABUNDANCE, BIOPROCESS, COMPLEX, COMPOSITE, CONCEPT, FRAGMENT, FRAGMENT_DESCRIPTION, FRAGMENT_MISSING, FRAGMENT_START, FRAGMENT_STOP, FUNCTION, FUSION, FUSION_MISSING, FUSION_REFERENCE, FUSION_START, FUSION_STOP, GENE, GMOD, HGVS, KIND, MEMBERS, MIRNA, PARTNER_3P, PARTNER_5P, PATHOLOGY, PMOD, PMOD_CODE, PMOD_ORDER, PMOD_POSITION, POPULATION, PRODUCTS, PROTEIN, RANGE_3P, RANGE_5P, REACTANTS, REACTION, RNA, VARIANTS, XREFS, rev_abundance_labels, ) from ..language import Entity, gmod_mappings, pmod_mappings __all__ = [ # Base Classes "Entity", "BaseEntity", "BaseAbundance", "ListAbundance", # Named entities "Abundance", "BiologicalProcess", "Pathology", "Population", "NamedComplexAbundance", # Central Dogma Stuff "CentralDogma", "Gene", "Transcribable", "Rna", "MicroRna", "Protein", # Fusions "FusionBase", "ProteinFusion", "RnaFusion", "GeneFusion", # Fusion Ranges "FusionRangeBase", "EnumeratedFusionRange", "MissingFusionRange", # Variants "Variant", "EntityVariant", "ProteinModification", "GeneModification", "Hgvs", "HgvsReference", "HgvsUnspecified", "ProteinSubstitution", "Fragment", # List Entities "ComplexAbundance", "CompositeAbundance", "Reaction", ] # A methodcaller for the key argument of sorted() _as_bel = methodcaller("as_bel") class BaseEntity(dict, metaclass=ABCMeta): """This is the superclass for all BEL terms. A BEL term has three properties: 1. It has a type. Subclasses of this function should set the class variable ``function``. 2. It can be converted to BEL. Note, this is an abstract class, so all sub-classes must implement this functionality in ``as_bel()``. 3. It can be hashed, based on the BEL conversion """ function = ... def __init__(self) -> None: super().__init__(**{FUNCTION: self.function}) self._md5 = None @property def _bel_function(self) -> str: return rev_abundance_labels[self.function] @abstractmethod def as_bel(self, use_identifiers: bool = True) -> str: """Return this entity as a BEL string.""" @property def md5(self) -> str: """Get the MD5 hash of this node.""" if self._md5 is None: self._md5 = hashlib.md5(self.as_bel().encode("utf8")).hexdigest() # noqa: S303 return self._md5 def __hash__(self): # noqa: D105 return hash(self.as_bel()) def __eq__(self, other): return isinstance(other, BaseEntity) and self.as_bel() == other.as_bel() def __repr__(self): return "".format(bel=self.as_bel(use_identifiers=True)) def __str__(self): # noqa: D105 return self.as_bel() @property def safe_label(self) -> str: """Get the safe label for the node (name or BEL).""" if isinstance(self, CentralDogma) and self.variants: return self.as_bel() if isinstance(self, BaseConcept): return self.curie return self.as_bel() class BaseConcept(dict): """A dictionary containing a concept entry.""" @property def entity(self) -> Entity: # noqa:D401 """This node's concept.""" return self[CONCEPT] @property def xrefs(self) -> List[Entity]: # noqa:D401 """Alternative identifiers for the node's concept.""" return self.get(XREFS, []) @property def namespace(self) -> str: # noqa:D401 """The namespace of this abundance.""" return self.entity.namespace @property def name(self) -> Optional[str]: # noqa:D401 """The name of this abundance.""" return self.entity.name @property def identifier(self) -> Optional[str]: # noqa:D401 """The identifier of this abundance.""" return self.entity.identifier @property def curie(self): # noqa: D401 """The CURIE-style identifier for this node.""" return self.entity.curie @property def obo(self) -> str: # noqa: D401 """The OBO-style identifier for this node.""" return self.entity.obo class BaseAbundance(BaseEntity, BaseConcept): """The superclass for all named BEL terms. A named BEL term has: 1. A type (taken care of by being a subclass of :class:`BaseEntity`) 2. A named :class:`Entity`. Though this doesn't directly inherit from :class:`Entity`, it creates one internally using the namespace, identifier, and name. Ideally, both the identifier and name are given. If one is missing, it can be looked up with :func:`pybel.grounding.ground` 3. An optional list of xrefs, corresponding to the whole entity, not just the namespace/name. For example, the BEL term ``p(HGNC:APP, frag(672_713)`` could xref CHEBI:64647. """ def __init__( self, namespace: str, name: Optional[str] = None, identifier: Optional[str] = None, xrefs: Optional[List[Entity]] = None, ) -> None: """Build an abundance from a function, namespace, and a name and/or identifier. :param namespace: The name of the namespace :param name: The name of this abundance :param identifier: The database identifier for this abundance :param xrefs: Alternate identifiers for the entity """ super().__init__() self[CONCEPT] = Entity( namespace=namespace, name=name, identifier=identifier, ) if xrefs: self[XREFS] = xrefs def as_bel(self, use_identifiers: bool = True) -> str: """Return this node as a BEL string.""" return "{}({})".format( self._bel_function, self.obo if use_identifiers and self.entity.identifier and self.entity.name else self.curie, ) class Abundance(BaseAbundance): """Builds an abundance node. >>> from pybel.dsl import Abundance >>> Abundance(namespace='CHEBI', name='water') """ function = ABUNDANCE class BiologicalProcess(BaseAbundance): """Builds a biological process node. >>> from pybel.dsl import BiologicalProcess >>> BiologicalProcess(namespace='GO', name='apoptosis') """ function = BIOPROCESS class Pathology(BaseAbundance): """Build a pathology node. >>> from pybel.dsl import Pathology >>> Pathology(namespace='DO', name='Alzheimer Disease') """ function = PATHOLOGY class Population(BaseAbundance): """Builds a population node. >>> from pybel.dsl import Population >>> Population(namespace='uberon', name='blood') """ function = POPULATION class Variant(dict, metaclass=ABCMeta): """The superclass for variant dictionaries.""" def __init__(self, kind: str) -> None: """Build the variant data dictionary. :param kind: The kind of variant """ super().__init__({KIND: kind}) @abstractmethod def as_bel(self, use_identifiers: bool = True) -> str: """Return this variant as a BEL string.""" def __str__(self): # noqa: D105 return self.as_bel() class CentralDogma(BaseAbundance): """The base class for "central dogma" abundances (i.e., genes, miRNAs, RNAs, and proteins).""" def __init__( self, namespace: str, name: Optional[str] = None, identifier: Optional[str] = None, xrefs: Optional[List[Entity]] = None, variants: Union[None, Variant, Iterable[Variant]] = None, ) -> None: """Build a node for a gene, RNA, miRNA, or protein. :param namespace: The name of the database used to identify this entity :param name: The database's preferred name or label for this entity :param identifier: The database's identifier for this entity :param xrefs: Alternative database cross references :param variants: An optional variant or list of variants """ super().__init__(namespace=namespace, name=name, identifier=identifier, xrefs=xrefs) if isinstance(variants, Variant): self[VARIANTS] = [variants] elif isinstance(variants, (list, tuple, set)): self[VARIANTS] = sorted(variants, key=_as_bel) @property def variants(self) -> Optional[List[Variant]]: """Return this entity's variants, if they exist.""" return self.get(VARIANTS) def as_bel(self, use_identifiers: bool = True) -> str: """Return this node as a BEL string.""" if not self.variants: return super().as_bel(use_identifiers=use_identifiers) variants_canon = sorted([variant.as_bel(use_identifiers=use_identifiers) for variant in self.variants]) return "{}({}, {})".format( self._bel_function, self.obo if use_identifiers and self.entity.identifier and self.entity.name else self.curie, ", ".join(variants_canon), ) def get_parent(self) -> Optional["CentralDogma"]: """Get the parent, or none if it's already a reference node. >>> from pybel.dsl import Protein, Fragment >>> ab42 = Protein(name='APP', namespace='HGNC', variants=[Fragment(start=672, stop=713)]) >>> app = ab42.get_parent() >>> assert 'p(HGNC:APP)' == app.as_bel() """ if VARIANTS not in self: return None return self.__class__( namespace=self.namespace, name=self.name, identifier=self.identifier, xrefs=self.xrefs, ) def with_variants(self, variants: Union[Variant, List[Variant]]) -> "CentralDogma": """Create a new entity with the given variants. :param variants: An optional variant or list of variants >>> from pybel.dsl import Protein, Fragment >>> app = Protein(name='APP', namespace='HGNC') >>> ab42 = app.with_variants([Fragment(start=672, stop=713)]) >>> assert 'p(HGNC:APP, frag(672_713))' == ab42.as_bel() """ return self.__class__( namespace=self.namespace, name=self.name, identifier=self.identifier, xrefs=self.xrefs, variants=variants, ) class EntityVariant(Variant, BaseConcept): """A variant that contains a reference.""" function = ... def __init__( self, name: str, namespace: Optional[str] = None, identifier: Optional[str] = None, xrefs: Optional[List[Entity]] = None, ) -> None: """Build a variant that has a reference. :param name: The name of the modification :param namespace: The namespace to which the name of this modification belongs :param identifier: The identifier of the name of the modification :param xrefs: Alternative database xrefs Either the name or the identifier must be used. If the namespace is omitted, it is assumed that a name is specified from the BEL default namespace. """ super().__init__(kind=self.function) self[CONCEPT] = Entity( namespace=namespace, name=name, identifier=identifier, ) if xrefs: self["xref"] = xrefs class ProteinModification(EntityVariant): """Build a protein modification variant dictionary.""" function = PMOD def __init__( self, name: str, code: Optional[str] = None, position: Optional[int] = None, namespace: Optional[str] = None, identifier: Optional[str] = None, xrefs: Optional[List[Entity]] = None, ) -> None: """Build a protein modification variant data dictionary. :param name: The name of the modification :param code: The three letter amino acid code for the affected residue. Capital first letter. :param position: The position of the affected residue :param namespace: The namespace to which the name of this modification belongs :param identifier: The identifier of the name of the modification :param xrefs: Alternative database xrefs Either the name or the identifier must be used. If the namespace is omitted, it is assumed that a name is specified from the BEL default namespace. Example from BEL default namespace: >>> from pybel.dsl import ProteinModification >>> ProteinModification('Ph', code='Thr', position=308) Example from custom namespace: >>> from pybel.dsl import ProteinModification >>> ProteinModification(name='protein phosphorylation', namespace='GO', code='Thr', position=308) Example from custom namespace additionally qualified with identifier: >>> from pybel.dsl import ProteinModification >>> ProteinModification(name='protein phosphorylation', namespace='GO', >>> identifier='0006468', code='Thr', position=308) """ if name and not namespace and not identifier: x = pmod_mappings[name]["xrefs"][0] namespace, identifier, name = x.namespace, x.identifier, x.name super().__init__( name=name, namespace=namespace, identifier=identifier, xrefs=xrefs, ) if code: self[PMOD_CODE] = code if position: self[PMOD_POSITION] = position def as_bel(self, use_identifiers: bool = True) -> str: """Return this protein modification variant as a BEL string.""" if use_identifiers and self.entity.identifier and self.entity.name: x = self.entity.obo else: x = self.entity.curie return "pmod({}{})".format( x, "".join(", {}".format(self[x]) for x in PMOD_ORDER[2:] if x in self), ) class GeneModification(EntityVariant): """Build a gene modification variant dictionary.""" function = GMOD def __init__( self, name: str, namespace: Optional[str] = None, identifier: Optional[str] = None, xrefs: Optional[List[Entity]] = None, ) -> None: """Build a protein modification variant data dictionary. :param name: The name of the modification :param namespace: The namespace to which the name of this modification belongs :param identifier: The identifier of the name of the modification :param xrefs: Alternative database xrefs Either the name or the identifier must be used. If the namespace is omitted, it is assumed that a name is specified from the BEL default namespace. Example from BEL default namespace: >>> from pybel.dsl import GeneModification >>> GeneModification(name='Me') Example from custom namespace: >>> from pybel.dsl import GeneModification >>> GeneModification(name='DNA methylation', namespace='GO', identifier='0006306') """ if name and not namespace and not identifier: x = gmod_mappings[name]["xrefs"][0] namespace, identifier, name = x.namespace, x.identifier, x.name super().__init__( name=name, namespace=namespace, identifier=identifier, xrefs=xrefs, ) def as_bel(self, use_identifiers: bool = True) -> str: """Return this gene modification variant as a BEL string.""" if use_identifiers and self.entity.identifier and self.entity.name: x = self.entity.obo else: x = self.entity.curie return "gmod({})".format(x) class Hgvs(Variant): """Builds a HGVS variant dictionary.""" def __init__(self, variant: str) -> None: """Build an HGVS variant data dictionary. :param variant: The HGVS variant string >>> from pybel.dsl import Protein, Hgvs >>> Protein(namespace='HGNC', name='AKT1', variants=[Hgvs('p.Ala127Tyr')]) """ super().__init__(kind=HGVS) self[HGVS] = variant @property def variant(self) -> str: # noqa: D401 """The HGVS variant string.""" return self[HGVS] def as_bel(self, use_identifiers: bool = True) -> str: """Return this HGVS variant as a BEL string.""" return 'var("{}")'.format(self.variant) class HgvsReference(Hgvs): """Represents the "reference" variant in HGVS.""" def __init__(self) -> None: super().__init__(variant="=") class HgvsUnspecified(Hgvs): """Represents an unspecified variant in HGVS.""" def __init__(self) -> None: super().__init__(variant="?") class ProteinSubstitution(Hgvs): """A protein substitution variant.""" def __init__(self, from_aa: str, position: int, to_aa: str) -> None: """Build an HGVS variant data dictionary for the given protein substitution. :param from_aa: The 3-letter amino acid code of the original residue :param position: The position of the residue :param to_aa: The 3-letter amino acid code of the new residue >>> from pybel.dsl import Protein, ProteinSubstitution >>> Protein(namespace='HGNC', name='AKT1', variants=[ProteinSubstitution('Ala', 127, 'Tyr')]) """ super().__init__("p.{}{}{}".format(from_aa, position, to_aa)) class Fragment(Variant): """Represent the information about a protein fragment.""" def __init__( self, start: Union[None, int, str] = None, stop: Union[None, int, str] = None, description: Optional[str] = None, ) -> None: """Build a protein fragment data dictionary. :param start: The starting position :param stop: The stopping position :param description: An optional description Example of specified fragment: >>> from pybel.dsl import Protein, Fragment >>> Protein(name='APP', namespace='HGNC', variants=[Fragment(start=672, stop=713)]) Example of unspecified fragment: >>> from pybel.dsl import Protein, Fragment >>> Protein(name='APP', namespace='HGNC', variants=[Fragment()]) """ super().__init__(kind=FRAGMENT) if start and stop: self[FRAGMENT_START] = start self[FRAGMENT_STOP] = stop else: self[FRAGMENT_MISSING] = "?" if description: self[FRAGMENT_DESCRIPTION] = description @property def range(self) -> str: """Get the range of this fragment.""" if FRAGMENT_MISSING in self: return "?" return "{}_{}".format(self[FRAGMENT_START], self[FRAGMENT_STOP]) def as_bel(self, use_identifiers=False) -> str: """Return this fragment variant as a BEL string.""" res = '"{}"'.format(self.range) if FRAGMENT_DESCRIPTION in self: res += ', "{}"'.format(self[FRAGMENT_DESCRIPTION]) return "frag({})".format(res) class Gene(CentralDogma): """Builds a gene node.""" function = GENE def get_rna(self) -> "Rna": """Get the corresponding RNA.""" if self.variants: raise InferCentralDogmaException("can not get gene for variant") return Rna( namespace=self.namespace, name=self.name, identifier=self.identifier, xrefs=self.xrefs, ) class Transcribable(CentralDogma): """A base class for RNA and micro-RNA to share getting of their corresponding genes.""" def get_gene(self) -> Gene: """Get the corresponding gene or raise an exception if it's not the reference node. :raises: InferCentralDogmaException """ if self.variants: raise InferCentralDogmaException("can not get gene for variant") return Gene( namespace=self.namespace, name=self.name, identifier=self.identifier, xrefs=self.xrefs, ) class Rna(Transcribable): """Builds an RNA node. Example: AKT1 protein coding gene's RNA: >>> from pybel.dsl import Rna >>> Rna(namespace='HGNC', name='AKT1', identifier='391') Non-coding RNAs can also be encoded such as `U85 `_: >>> from pybel.dsl import Rna >>> Rna(namespace='SNORNABASE', identifier='SR0000073') """ function = RNA class MicroRna(Transcribable): """Represents an micro-RNA. Human miRNA's are listed on HUGO's `MicroRNAs (MIR) `_ gene family. MIR1-1 from `HGNC `_: >>> from pybel.dsl import MicroRna >>> MicroRna(namespace='HGNC', name='MIR1-1', identifier='31499') MIR1-1 from `miRBase `_: >>> from pybel.dsl import MicroRna >>> MicroRna(namespace='MIRBASE', identifier='MI0000651') MIR1-1 from `Entrez Gene `_ >>> from pybel.dsl import MicroRna >>> MicroRna(namespace='ENTREZ', identifier='406904') """ function = MIRNA class Protein(CentralDogma): """Builds a protein node. Example: AKT >>> from pybel.dsl import Protein >>> Protein(namespace='HGNC', name='AKT1') Example: AKT with optionally included HGNC database identifier >>> from pybel.dsl import Protein >>> Protein(namespace='HGNC', name='AKT1', identifier='391') Example: AKT with phosphorylation >>> from pybel.dsl import Protein, ProteinModification >>> Protein(namespace='HGNC', name='AKT', variants=[ProteinModification('Ph', code='Thr', position=308)]) """ function = PROTEIN def get_rna(self) -> Rna: """Get the corresponding RNA or raise an exception if it's not the reference node. :raises: InferCentralDogmaException """ if self.variants: raise InferCentralDogmaException("can not get rna for variant") return Rna( namespace=self.namespace, name=self.name, identifier=self.identifier, xrefs=self.xrefs, ) def _entity_list_as_bel(entities: Iterable[BaseEntity], use_identifiers: bool = True) -> str: """Stringify a list of BEL entities.""" return ", ".join(e.as_bel(use_identifiers=use_identifiers) for e in entities) def _help_named(self, namespace, identifier, name, xrefs): if namespace: self[CONCEPT] = Entity( namespace=namespace, name=name, identifier=identifier, ) if xrefs: self[XREFS] = xrefs class Reaction(BaseEntity): """Build a reaction node.""" function = REACTION def __init__( self, reactants: Union[BaseAbundance, Iterable[BaseAbundance]], products: Union[BaseAbundance, Iterable[BaseAbundance]], namespace: Optional[str] = None, name: Optional[str] = None, identifier: Optional[str] = None, xrefs: Optional[List[Entity]] = None, ) -> None: """Build a reaction node. :param reactants: A list of PyBEL node data dictionaries representing the reactants :param products: A list of PyBEL node data dictionaries representing the products :param namespace: The namespace from which the name originates :param name: The name of the complex :param identifier: The identifier in the namespace in which the name originates :param xrefs: Alternate identifiers for the entity if it is named >>> from pybel.dsl import Reaction, Protein, Abundance >>> Reaction([Protein(namespace='HGNC', name='KNG1')], [Abundance(namespace='CHEBI', name='bradykinin')]) """ super().__init__() _help_named(self, namespace=namespace, identifier=identifier, name=name, xrefs=xrefs) if isinstance(reactants, BaseEntity): reactants = [reactants] else: reactants = sorted(reactants, key=_as_bel) if isinstance(products, BaseEntity): products = [products] else: products = sorted(products, key=_as_bel) if not reactants and not products and not namespace: raise ReactionEmptyException("Reaction can not be instantiated with an empty members list.") self.update( { REACTANTS: reactants, PRODUCTS: products, } ) @property def reactants(self) -> List[BaseAbundance]: """Return the list of reactants in this reaction.""" return self[REACTANTS] @property def products(self) -> List[BaseAbundance]: """Return the list of products in this reaction.""" return self[PRODUCTS] def get_catalysts(self) -> Set[BaseAbundance]: """Get entities appearing in both the reactants and products.""" return set(self.reactants).intersection(self.products) def as_bel(self, use_identifiers: bool = True) -> str: """Return this reaction as a BEL string.""" return "rxn(reactants({}), products({}))".format( _entity_list_as_bel(self.reactants, use_identifiers=use_identifiers), _entity_list_as_bel(self.products, use_identifiers=use_identifiers), ) class ListAbundance(BaseEntity): """The superclass for all BEL terms defined by lists, as opposed to by names like in :class:`BaseAbundance`.""" def __init__(self, members: Union[BaseAbundance, Iterable[BaseAbundance]]) -> None: """Build a list abundance node. :param members: A list of PyBEL node data dictionaries """ super().__init__() if isinstance(members, BaseEntity): self[MEMBERS] = [members] else: self[MEMBERS] = sorted(members, key=_as_bel) if not self[MEMBERS]: raise ListAbundanceEmptyException("List abundance can not be instantiated with an empty members list.") @property def members(self) -> List[BaseAbundance]: """Return the list of members in this list abundance.""" return self[MEMBERS] def as_bel(self, use_identifiers: bool = True) -> str: """Return this list abundance as a BEL string.""" return "{}({})".format( self._bel_function, _entity_list_as_bel(self.members, use_identifiers=use_identifiers), ) class ComplexAbundance(ListAbundance): """Build a complex abundance node with the optional ability to specify a name.""" function = COMPLEX def __init__( self, members: Iterable[BaseAbundance], namespace: Optional[str] = None, name: Optional[str] = None, identifier: Optional[str] = None, xrefs: Optional[List[Entity]] = None, ) -> None: """Build a complex list node. :param members: A list of PyBEL node data dictionaries :param namespace: The namespace from which the name originates :param name: The name of the complex :param identifier: The identifier in the namespace in which the name originates :param xrefs: Alternate identifiers for the entity if it is named """ super().__init__(members=members) _help_named(self, namespace=namespace, identifier=identifier, name=name, xrefs=xrefs) @property def entity(self) -> Optional[Entity]: # noqa:D401 """The concept represented by this complex if it has been named.""" return self.get(CONCEPT) @property def xrefs(self) -> List[Entity]: # noqa:D401 """Alternative identifiers for the concept if it has been named.""" return self.get(XREFS, []) class NamedComplexAbundance(BaseAbundance): """Build a named complex abundance node. >>> from pybel.dsl import NamedComplexAbundance >>> NamedComplexAbundance(namespace='FPLX', name='Calcineurin Complex') """ function = COMPLEX class CompositeAbundance(ListAbundance): """Build a composite abundance node. This node is effectively the "AND" inside BEL, which can help represent when two things need to be true at the same time. For example, in COVID 19, if both the NF-KB and IL6-STAT complex are present, then acute respiratory distress syndrome happens. >>> from pybel.dsl import CompositeAbundance, ComplexAbundance, Protein, NamedComplexAbundance >>> CompositeAbundance([ ... NamedComplexAbundance('fplx', 'nfkb'), ... ComplexAbundance([ ... Protein('hgnc', identifier='6018', name='IL6'), ... Protein('hgnc', identifier='11364', name='STAT3'), ... ]), ... ]) """ function = COMPOSITE class FusionRangeBase(dict, metaclass=ABCMeta): """The superclass for fusion range data dictionaries.""" @abstractmethod def as_bel(self) -> str: """Return this fusion range as BEL.""" def __str__(self): # noqa: D105 return self.as_bel() class MissingFusionRange(FusionRangeBase): """Represents a fusion range with no defined start or end.""" def __init__(self): """Build a missing fusion range.""" super(MissingFusionRange, self).__init__( { FUSION_MISSING: "?", } ) def as_bel(self) -> str: """Return this missing fusion range as BEL.""" return "?" class EnumeratedFusionRange(FusionRangeBase): """Represents an enumerated fusion range.""" def __init__(self, reference: str, start, stop): """Build an enumerated fusion range. :param reference: The reference code :param int or str start: The start position, either specified by its integer position, or '?' :param int or str stop: The stop position, either specified by its integer position, '?', or '* Example fully specified RNA fusion range: >>> EnumeratedFusionRange('r', 1, 79) """ super().__init__( { FUSION_REFERENCE: reference, FUSION_START: start, FUSION_STOP: stop, } ) def as_bel(self) -> str: """Return this fusion range as a BEL string.""" return "{reference}.{start}_{stop}".format( reference=self[FUSION_REFERENCE], start=self[FUSION_START], stop=self[FUSION_STOP], ) class FusionBase(BaseEntity): """The superclass for building fusion node data dictionaries.""" def __init__( self, partner_5p: CentralDogma, partner_3p: CentralDogma, range_5p: Optional[FusionRangeBase] = None, range_3p: Optional[FusionRangeBase] = None, ) -> None: """Build a fusion node. :param partner_5p: A PyBEL node for the 5-prime partner :param partner_3p: A PyBEL node for the 3-prime partner :param range_5p: A fusion range for the 5-prime partner :param range_3p: A fusion range for the 3-prime partner """ super().__init__() self[FUSION] = { PARTNER_5P: partner_5p, PARTNER_3P: partner_3p, RANGE_5P: range_5p or MissingFusionRange(), RANGE_3P: range_3p or MissingFusionRange(), } @property def partner_5p(self) -> CentralDogma: """Get the 5' partner.""" return self[FUSION][PARTNER_5P] @property def partner_3p(self) -> CentralDogma: """Get the 3' partner.""" return self[FUSION][PARTNER_3P] @property def range_5p(self) -> FusionRangeBase: """Get the 5' partner's range.""" return self[FUSION][RANGE_5P] @property def range_3p(self) -> FusionRangeBase: """Get the 3' partner's range.""" return self[FUSION][RANGE_3P] def as_bel(self, use_identifiers: bool = True) -> str: """Return this fusion as a BEL string.""" if use_identifiers and self.partner_3p.entity.identifier and self.partner_3p.entity.name: p3p = self.partner_3p.obo else: p3p = self.partner_3p.curie if use_identifiers and self.partner_5p.entity.identifier and self.partner_5p.entity.name: p5p = self.partner_5p.obo else: p5p = self.partner_5p.curie return '{}(fus({}, "{}", {}, "{}"))'.format( self._bel_function, p5p, self.range_5p.as_bel(), p3p, self.range_3p.as_bel(), ) class ProteinFusion(FusionBase): """Builds a protein fusion node.""" function = PROTEIN class RnaFusion(FusionBase): """Builds an RNA fusion node. Example, with fusion ranges using the 'r' qualifier: >>> from pybel.dsl import RnaFusion, Rna >>> RnaFusion( >>> ... partner_5p=Rna(namespace='HGNC', name='TMPRSS2'), >>> ... range_5p=EnumeratedFusionRange('r', 1, 79), >>> ... partner_3p=Rna(namespace='HGNC', name='ERG'), >>> ... range_3p=EnumeratedFusionRange('r', 312, 5034) >>> ) Example with missing fusion ranges: >>> from pybel.dsl import RnaFusion, Rna >>> RnaFusion( >>> ... partner_5p=Rna(namespace='HGNC', name='TMPRSS2'), >>> ... partner_3p=Rna(namespace='HGNC', name='ERG'), >>> ) """ function = RNA class GeneFusion(FusionBase): """Builds a gene fusion node. Example, using fusion ranges with the 'c' qualifier >>> from pybel.dsl import GeneFusion, Gene >>> GeneFusion( >>> ... partner_5p=Gene(namespace='HGNC', name='TMPRSS2'), >>> ... range_5p=EnumeratedFusionRange('c', 1, 79), >>> ... partner_3p=Gene(namespace='HGNC', name='ERG'), >>> ... range_3p=EnumeratedFusionRange('c', 312, 5034) >>> ) Example with missing fusion ranges: >>> from pybel.dsl import GeneFusion, Gene >>> GeneFusion( >>> ... partner_5p=Gene(namespace='HGNC', name='TMPRSS2'), >>> ... partner_3p=Gene(namespace='HGNC', name='ERG'), >>> ) """ function = GENE pybel-0.15.5/src/pybel/examples/000077500000000000000000000000001426625374700164555ustar00rootroot00000000000000pybel-0.15.5/src/pybel/examples/__init__.py000066400000000000000000000012001426625374700205570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This directory contains example networks, precompiled as BEL graphs that are appropriate to use in examples.""" from .ampk_example import ampk_graph from .braf_example import braf_graph from .egf_example import egf_graph from .homology_example import homology_graph from .sialic_acid_example import sialic_acid_graph from .statin_example import statin_graph from .tloc_example import ras_tloc_graph from .vegf_example import vegf_graph __all__ = [ "egf_graph", "sialic_acid_graph", "statin_graph", "braf_graph", "homology_graph", "ras_tloc_graph", "ampk_graph", "vegf_graph", ] pybel-0.15.5/src/pybel/examples/ampk_example.py000066400000000000000000000045411426625374700214760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """An example graph in which a famplex (complex of families) activates something.""" from ..dsl import ComplexAbundance, NamedComplexAbundance, Protein, ProteinModification from ..struct import BELGraph ampk_graph = BELGraph() ampk = NamedComplexAbundance(namespace="fplx", name="AMPK") # Alpha subunits of AMPK ampk_alpha = Protein(namespace="fplx", name="AMPK_alpha") ampk_graph.add_part_of(ampk_alpha, ampk) prkaa1 = Protein(namespace="hgnc", identifier="9376", name="PRKAA1") prkaa2 = Protein(namespace="hgnc", identifier="9377", name="PRKAA2") ampk_graph.add_is_a(prkaa1, ampk_alpha) ampk_graph.add_is_a(prkaa2, ampk_alpha) # Beta subunits of AMPK ampk_beta = Protein(namespace="fplx", name="AMPK_beta") ampk_graph.add_part_of(ampk_beta, ampk) prkab1 = Protein(namespace="hgnc", identifier="9378", name="PRKAB1") prkab2 = Protein(namespace="hgnc", identifier="9379", name="PRKAB2") ampk_graph.add_is_a(prkab1, ampk_beta) ampk_graph.add_is_a(prkab2, ampk_beta) # Gamma subunits of AMPK ampk_gamma = Protein(namespace="fplx", name="AMPK_gamma") ampk_graph.add_part_of(ampk_gamma, ampk) prkag1 = Protein(namespace="hgnc", identifier="9385", name="PRKAG1") prkag2 = Protein(namespace="hgnc", identifier="9386", name="PRKAG2") prkag3 = Protein(namespace="hgnc", identifier="9387", name="PRKAG3") ampk_graph.add_is_a(prkag1, ampk_gamma) ampk_graph.add_is_a(prkag2, ampk_gamma) ampk_graph.add_is_a(prkag3, ampk_gamma) mtorc1 = NamedComplexAbundance(namespace="fplx", name="mTORC1") mtor = Protein(namespace="hgnc", identifier="3942", name="MTOR") rptor = Protein(namespace="hgnc", identifier="30287", name="RPTOR") ampk_graph.add_part_of(mtor, mtorc1) ampk_graph.add_part_of(rptor, mtorc1) # FamPlex says this is a family but I thought it was a complex p14_3_3 = Protein(namespace="fplx", name="p14_3_3") ev = ( "We report here that AMPK directly phosphorylates the" " mTOR binding partner raptor on two well conserved serine" " residues, and this phosphorylation induces 14-3-3" " binding to raptor." ) ampk_graph.add_directly_phosphorylates( ampk, rptor, "Ser", # on Ser722 and Ser792 evidence=ev, citation="18439900", ) ampk_graph.add_binds( ComplexAbundance( [ rptor.with_variants(ProteinModification("Ph")), mtor, ] ), p14_3_3, evidence=ev, citation="18439900", ) pybel-0.15.5/src/pybel/examples/braf_example.py000066400000000000000000000044221426625374700214560ustar00rootroot00000000000000# -*- coding: utf-8 -*- """An example describing a single evidence about BRAF. .. code-block:: none SET Citation = {"PubMed", "11283246"} SET Evidence = "Expression of both dominant negative forms, RasN17 and Rap1N17, in UT7-Mpl cells decreased thrombopoietin-mediated Elk1-dependent transcription. This suggests that both Ras and Rap1 contribute to thrombopoietin-induced ELK1 transcription." SET Species = 9606 p(HGNC:THPO) increases kin(p(HGNC:BRAF)) p(HGNC:THPO) increases kin(p(HGNC:RAF1)) kin(p(HGNC:BRAF)) increases tscript(p(HGNC:ELK1)) UNSET ALL """ from ..dsl import Entity, Protein, activity from ..resources import HGNC_URL, SPECIES_PATTERN from ..struct.graph import BELGraph __all__ = [ "braf_graph", ] braf_graph = BELGraph( name="BRAF Subgraph", version="1.0.0", description="Some relations surrounding BRAF", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) braf_graph.namespace_url.update( { "HGNC": HGNC_URL, } ) braf_graph.annotation_pattern.update( { "Species": SPECIES_PATTERN, } ) thpo = Protein( namespace="HGNC", name="THPO", identifier="11795", xrefs=[ Entity(namespace="uniprot", identifier="P40225"), ], ) braf = Protein(namespace="HGNC", name="BRAF", identifier="1097") raf1 = Protein(namespace="HGNC", name="RAF1", identifier="9829") elk1 = Protein(namespace="HGNC", name="ELK1", identifier="3321") evidence = ( "Expression of both dominant negative forms, RasN17 and Rap1N17, in UT7-Mpl cells decreased " "thrombopoietin-mediated Elk1-dependent transcription. This suggests that both Ras and Rap1 contribute to " "thrombopoietin-induced ELK1 transcription." ) braf_graph.add_increases( thpo, braf, evidence=evidence, citation="11283246", target_modifier=activity(name="kin"), annotations={"Species": "9606"}, ) braf_graph.add_increases( thpo, raf1, evidence=evidence, citation="11283246", target_modifier=activity(name="kin"), annotations={"Species": "9606"}, ) braf_graph.add_increases( braf, elk1, evidence=evidence, citation="11283246", source_modifier=activity(name="kin"), target_modifier=activity(name="tscript"), annotations={"Species": "9606"}, ) pybel-0.15.5/src/pybel/examples/egf_example.py000066400000000000000000000121431426625374700213040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """An example describing EGF's effect on cellular processes. .. code-block:: none SET Citation = {"PubMed","Clin Cancer Res 2003 Jul 9(7) 2416-25","12855613"} SET Evidence = "This induction was not seen either when LNCaP cells were treated with flutamide or conditioned medium were pretreated with antibody to the epidermal growth factor (EGF)" SET Species = 9606 tscript(p(HGNC:AR)) increases p(HGNC:EGF) UNSET ALL SET Citation = {"PubMed","Int J Cancer 1998 Jul 3 77(1) 138-45","9639405"} SET Evidence = "DU-145 cells treated with 5000 U/ml of IFNgamma and IFN alpha, both reduced EGF production with IFN gamma reduction more significant." SET Species = 9606 p(HGNC:IFNA1) decreases p(HGNC:EGF) p(HGNC:IFNG) decreases p(HGNC:EGF) UNSET ALL SET Citation = {"PubMed","DNA Cell Biol 2000 May 19(5) 253-63","10855792"} SET Evidence = "Although found predominantly in the cytoplasm and, less abundantly, in the nucleus, VCP can be translocated from the nucleus after stimulation with epidermal growth factor." SET Species = 9606 p(HGNC:EGF) increases tloc(p(HGNC:VCP), GO:nucleus, GO:cytoplasm) UNSET ALL SET Citation = {"PubMed","J Clin Oncol 2003 Feb 1 21(3) 447-52","12560433"} SET Evidence = "Valosin-containing protein (VCP; also known as p97) has been shown to be associated with antiapoptotic function and metastasis via activation of the nuclear factor-kappaB signaling pathway." SET Species = 9606 cat(p(HGNC:VCP)) increases tscript(complex(p(HGNC:NFKB1), p(HGNC:NFKB2), p(HGNC:REL), p(HGNC:RELA), p(HGNC:RELB))) tscript(complex(p(HGNC:NFKB1), p(HGNC:NFKB2), p(HGNC:REL), p(HGNC:RELA), p(HGNC:RELB))) decreases bp(MESHPP:Apoptosis) UNSET ALL """ from ..dsl import BiologicalProcess, ComplexAbundance, Protein, activity, translocation from ..language import cytoplasm, nucleus from ..resources import CHEBI_URL, CONFIDENCE_URL, GO_URL, HGNC_URL, SPECIES_PATTERN from ..struct.graph import BELGraph __all__ = [ "egf_graph", ] egf_graph = BELGraph( name="EGF Pathway", version="1.0.0", description="The downstream effects of EGF", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) egf_graph.namespace_url.update( { "hgnc": HGNC_URL, "chebi": CHEBI_URL, "go": GO_URL, } ) egf_graph.annotation_url.update( { "Confidence": CONFIDENCE_URL, } ) egf_graph.annotation_pattern.update( { "Species": SPECIES_PATTERN, } ) ar = Protein(name="AR", namespace="hgnc") egf = Protein(name="EGF", namespace="hgnc") ifna1 = Protein(name="IFNA1", namespace="hgnc") ifng = Protein(name="IFNG", namespace="hgnc") vcp = Protein(name="VCP", namespace="hgnc") nfkb1 = Protein(name="NFKB1", namespace="hgnc") nfkb2 = Protein(name="NFKB2", namespace="hgnc") rel = Protein(name="REL", namespace="hgnc") rela = Protein(name="RELA", namespace="hgnc") relb = Protein(name="RELB", namespace="hgnc") nfkb_complex = ComplexAbundance([nfkb1, nfkb2, rel, rela, relb]) apoptosis = BiologicalProcess(namespace="go", name="apoptotic process", identifier="0006915") egf_graph.add_increases( ar, egf, citation="12855613", evidence="This induction was not seen either when LNCaP cells were treated with flutamide or conditioned medium " "were pretreated with antibody to the epidermal growth factor (EGF)", annotations={"Species": "9606"}, source_modifier=activity("tscript"), ) egf_graph.add_decreases( ifna1, egf, citation="9639405", evidence="DU-145 cells treated with 5000 U/ml of IFNgamma and IFN alpha, both reduced EGF production with IFN " "gamma reduction more significant.", annotations={"Species": "9606"}, ) egf_graph.add_decreases( ifng, egf, citation="9639405", evidence="DU-145 cells treated with 5000 U/ml of IFNgamma and IFN alpha, both reduced EGF production with IFN " "gamma reduction more significant.", annotations={"Species": "9606"}, ) egf_graph.add_increases( egf, vcp, citation="10855792", evidence="Although found predominantly in the cytoplasm and, less abundantly, in the nucleus, VCP can be " "translocated from the nucleus after stimulation with epidermal growth factor.", annotations={"Species": "9606"}, target_modifier=translocation( from_loc=nucleus, to_loc=cytoplasm, ), ) egf_graph.add_increases( vcp, nfkb_complex, citation="12560433", evidence="Valosin-containing protein (VCP; also known as p97) has been shown to be associated with antiapoptotic" " function and metastasis via activation of the nuclear factor-kappaB signaling pathway.", annotations={"Species": "9606"}, source_modifier=activity("cat"), target_modifier=activity("tscript"), ) egf_graph.add_decreases( nfkb_complex, apoptosis, citation="12560433", evidence="Valosin-containing protein (VCP; also known as p97) has been shown to be associated with antiapoptotic " "function and metastasis via activation of the nuclear factor-kappaB signaling pathway.", annotations={"Species": "9606"}, source_modifier=activity("tscript"), ) pybel-0.15.5/src/pybel/examples/homology_example.py000066400000000000000000000106601426625374700224020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """An example with orthology statements. The following is an example of orthology annotations from `HomoloGene:37670 `_ .. code-block: none SET Citation = {"PubMed","J Immunol 1999 Sep 1 163(5) 2452-62","10452980","","",""} SET Evidence = "M-CSF triggers the activation of extracellular signal-regulated protein kinases (ERK)-1/2." SET Species = 10090 p(MGI:Csf1) increases kin(p(MGI:Mapk1)) """ from ..dsl import Gene, Protein, Rna, activity from ..resources import ( FB_URL, HGNC_URL, MGI_URL, NCBIGENE_URL, RGD_URL, SPECIES_PATTERN, ) from ..struct.graph import BELGraph __all__ = [ "homology_graph", ] # TODO make SGD resource homology_graph = BELGraph( name="Homology and Equivalence Example Graph", version="1.0.1", description="Adds several equivalence and orthology relationships related to the mitogen-activated protein kinase " "(MAPK1)", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) homology_graph.namespace_url.update( { "HGNC": HGNC_URL, "MGI": MGI_URL, "RGD": RGD_URL, "FB": FB_URL, "NCBIGENE": NCBIGENE_URL, # 'SGD': '?', } ) homology_graph.annotation_pattern.update( { "Species": SPECIES_PATTERN, } ) human_mapk1_gene = Gene(namespace="HGNC", name="MAPK1", identifier="HGNC:6871") human_mapk1_gene_entrez = Gene(namespace="NCBIGENE", name="5594") human_mapk1_rna = Rna(namespace="HGNC", name="MAPK1", identifier="HGNC:6871") human_mapk1_protein = Protein(namespace="HGNC", name="MAPK1", identifier="HGNC:6871") mouse_mapk1_gene = Gene(namespace="MGI", name="Mapk1", identifier="MGI:1346858") mouse_mapk1_gene_entrez = Gene(namespace="NCBIGENE", name="26413") mouse_mapk1_rna = Rna(namespace="MGI", name="Mapk1", identifier="MGI:1346858") mouse_mapk1_protein = Protein(namespace="MGI", name="Mapk1", identifier="MGI:1346858") rat_mapk1 = Gene(namespace="RGD", name="Mapk1", identifier="70500") rat_mapk1_entrez = Gene(namespace="NCBIGENE", name="116590") fly_mapk1 = Gene(namespace="FB", name="rl", identifier="FBgn0003256") fly_mapk1_entrez = Gene(namespace="NCBIGENE", name="3354888") human_csf1_gene = Gene(namespace="HGNC", name="CSF1", identifier="HGNC:2432") human_csf1_rna = Rna(namespace="HGNC", name="CSF1", identifier="HGNC:2432") human_csf1_protein = Protein(namespace="HGNC", name="CSF1", identifier="HGNC:2432") mouse_csf1_gene = Gene(namespace="MGI", name="Csf1", identifier="MGI:1339753") mouse_csf1_rna = Rna(namespace="MGI", name="Csf1", identifier="MGI:1339753") mouse_csf1_protein = Protein(namespace="MGI", name="Csf1", identifier="MGI:1339753") # yeast_mapk1 = Gene(namespace='SGD', name='KSS1', identifier='SGD:S000003272') # yeast_mapk1_entrez = Gene(namespace='NCBIGENE', name='KSS1', identifier='852931') # TODO make homologene resource and add is_a relationships for this # mapk1_homologene = Gene(namespace='HOMOLOGENE', identifier='37670') homology_graph.add_equivalence(human_mapk1_gene, human_mapk1_gene_entrez) homology_graph.add_equivalence(mouse_mapk1_gene, mouse_mapk1_gene_entrez) homology_graph.add_equivalence(rat_mapk1, rat_mapk1_entrez) homology_graph.add_equivalence(fly_mapk1, fly_mapk1_entrez) # graph.add_equivalence(yeast_mapk1, yeast_mapk1_entrez) homology_graph.add_orthology(human_csf1_gene, mouse_csf1_gene) homology_graph.add_orthology(human_mapk1_gene, mouse_mapk1_gene) homology_graph.add_orthology(human_mapk1_gene, rat_mapk1) homology_graph.add_orthology(human_mapk1_gene, fly_mapk1) # graph.add_orthology(human_mapk1, yeast_mapk1) homology_graph.add_increases( source=mouse_csf1_protein, target=mouse_mapk1_protein, citation="10452980", evidence="M-CSF triggers the activation of extracellular signal-regulated protein kinases (ERK)-1/2.", target_modifier=activity("kin"), annotations={"Species": "10090"}, ) homology_graph.add_transcription(mouse_mapk1_gene, mouse_mapk1_rna) homology_graph.add_translation(mouse_mapk1_rna, mouse_mapk1_protein) homology_graph.add_transcription(human_mapk1_gene, human_mapk1_rna) homology_graph.add_translation(human_mapk1_rna, human_mapk1_protein) homology_graph.add_transcription(human_csf1_gene, human_csf1_rna) homology_graph.add_translation(human_csf1_rna, human_csf1_protein) homology_graph.add_transcription(mouse_csf1_gene, mouse_csf1_rna) homology_graph.add_translation(mouse_csf1_rna, mouse_csf1_protein) pybel-0.15.5/src/pybel/examples/sialic_acid_example.py000066400000000000000000000124031426625374700227660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Curation of the article "Genetics ignite focus on microglial inflammation in Alzheimer's disease". .. code-block:: none SET Citation = {"PubMed", "26438529"} SET Evidence = "Sialic acid binding activates CD33, resulting in phosphorylation of the CD33 immunoreceptor tyrosine-based inhibitory motif (ITIM) domains and activation of the SHP-1 and SHP-2 tyrosine phosphatases [66, 67]." complex(p(HGNC:CD33),a(CHEBI:"sialic acid")) -> p(HGNC:CD33, pmod(P)) act(p(HGNC:CD33, pmod(P))) => act(p(HGNC:PTPN6), ma(phos)) act(p(HGNC:CD33, pmod(P))) => act(p(HGNC:PTPN11), ma(phos)) UNSET {Evidence, Species} SET Evidence = "These phosphatases act on multiple substrates, including Syk, to inhibit immune activation [68, 69]. Hence, CD33 activation leads to increased SHP-1 and SHP-2 activity that antagonizes Syk, inhibiting ITAM-signaling proteins, possibly including TREM2/DAP12 (Fig. 1, [70, 71])." SET Species = 9606 act(p(HGNC:PTPN6)) =| act(p(HGNC:SYK)) act(p(HGNC:PTPN11)) =| act(p(HGNC:SYK)) act(p(HGNC:SYK)) -> act(p(HGNC:TREM2)) act(p(HGNC:SYK)) -> act(p(HGNC:TYROBP)) UNSET ALL """ from ..dsl import ( Abundance, BiologicalProcess, ComplexAbundance, Entity, Protein, ProteinModification, activity, ) from ..resources import CHEBI_URL, CONFIDENCE_URL, GO_URL, HGNC_URL, SPECIES_PATTERN from ..struct.graph import BELGraph __all__ = [ "sialic_acid_graph", ] citation = "26438529" evidence_1 = """ Sialic acid binding activates CD33, resulting in phosphorylation of the CD33 immunoreceptor tyrosine-based inhibitory motif (ITIM) domains and activation of the SHP-1 and SHP-2 tyrosine phosphatases [66, 67]. """.replace( "\n", " " ).strip() evidence_2 = """These phosphatases act on multiple substrates, including Syk, to inhibit immune activation [68, 69]. Hence, CD33 activation leads to increased SHP-1 and SHP-2 activity that antagonizes Syk, inhibiting ITAM-signaling proteins, possibly including TREM2/DAP12 (Fig. 1, [70, 71]). """.replace( "\n", " " ).strip() sialic_acid_graph = BELGraph( name="Sialic Acid Graph", version="1.0.0", description="The downstream effects of sialic acid in immune signaling", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) sialic_acid_graph.namespace_url.update( { "hgnc": HGNC_URL, "chebi": CHEBI_URL, "go": GO_URL, } ) sialic_acid_graph.annotation_url.update( { "Confidence": CONFIDENCE_URL, } ) sialic_acid_graph.annotation_pattern.update( { "Species": SPECIES_PATTERN, } ) sialic_acid = Abundance(name="sialic acid", namespace="chebi", identifier="26667") cd33 = Protein( name="CD33", namespace="hgnc", identifier="1659", xrefs=[ Entity(namespace="uniprot", identifier="P20138"), ], ) sialic_acid_cd33_complex = ComplexAbundance([sialic_acid, cd33]) shp1 = Protein(namespace="hgnc", name="PTPN6", identifier="9658") shp2 = Protein(namespace="hgnc", name="PTPN11", identifier="9644") syk = Protein(namespace="hgnc", name="SYK", identifier="11491") dap12 = Protein(namespace="hgnc", name="TYROBP", identifier="12449") trem2 = Protein(namespace="hgnc", name="TREM2", identifier="17761") cd33_phosphorylated = Protein(name="CD33", namespace="hgnc", identifier="1659", variants=ProteinModification("Ph")) immune_response = BiologicalProcess(name="immune response", namespace="go", identifier="0006955") sialic_acid_graph.add_increases( sialic_acid_cd33_complex, cd33, citation=citation, annotations={"Species": "9606", "Confidence": "High"}, evidence=evidence_1, target_modifier=activity(), ) sialic_acid_graph.add_increases( cd33, cd33_phosphorylated, citation=citation, annotations={"Species": "9606", "Confidence": "High"}, evidence=evidence_1, source_modifier=activity(), ) sialic_acid_graph.add_directly_increases( cd33_phosphorylated, shp1, citation=citation, evidence=evidence_1, annotations={"Species": "9606", "Confidence": "High"}, source_modifier=activity(), target_modifier=activity("phos"), ) sialic_acid_graph.add_directly_increases( cd33_phosphorylated, shp2, citation=citation, evidence=evidence_1, annotations={"Species": "9606", "Confidence": "High"}, source_modifier=activity(), target_modifier=activity("phos"), ) sialic_acid_graph.add_directly_decreases( shp1, syk, citation=citation, evidence=evidence_2, annotations={"Species": "9606", "Confidence": "High"}, source_modifier=activity(), target_modifier=activity(), ) sialic_acid_graph.add_directly_decreases( shp2, syk, citation=citation, evidence=evidence_2, annotations={"Species": "9606", "Confidence": "High"}, source_modifier=activity(), target_modifier=activity(), ) sialic_acid_graph.add_increases( syk, trem2, citation=citation, evidence=evidence_2, annotations={"Species": "9606", "Confidence": "Low"}, source_modifier=activity(), target_modifier=activity(), ) sialic_acid_graph.add_increases( syk, dap12, citation=citation, evidence=evidence_2, annotations={"Species": "9606", "Confidence": "Low"}, source_modifier=activity(), target_modifier=activity(), ) pybel-0.15.5/src/pybel/examples/statin_example.py000066400000000000000000000040201426625374700220400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """An example describing statins.""" from ..dsl import Abundance, Protein from ..resources import CHEBI_URL, CONFIDENCE_URL, EC_URL, HGNC_URL from ..struct.graph import BELGraph __all__ = [ "statin_graph", ] statin_graph = BELGraph( name="Statin Graph", version="1.0.1", description="The effects of statins from ChEBI", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) statin_graph.namespace_url.update( { "HGNC": HGNC_URL, "CHEBI": CHEBI_URL, "EC": EC_URL, } ) statin_graph.annotation_url.update( { "Confidence": CONFIDENCE_URL, } ) fluvastatin = Abundance(namespace="CHEBI", name="fluvastatin", identifier="38561") avorastatin = Abundance(namespace="CHEBI", name="atorvastatin", identifier="39548") synthetic_statin = Abundance(namespace="CHEBI", name="statin (synthetic)", identifier="87635") statin = Abundance(namespace="CHEBI", name="statin", identifier="87631") mevinolinic_acid = Abundance(namespace="CHEBI", name="mevinolinic acid", identifier="82985") hmgcr_inhibitor = Abundance( namespace="CHEBI", identifier="35664", name="EC 1.1.1.34/EC 1.1.1.88 (hydroxymethylglutaryl-CoA reductase) inhibitor", ) ec_11134 = Protein(namespace="EC", name="1.1.1.34") ec_11188 = Protein(namespace="EC", name="1.1.1.88") hmgcr = Protein(namespace="HGNC", name="HMGCR", identifier="5006") statin_graph.add_is_a(avorastatin, synthetic_statin) statin_graph.add_is_a(fluvastatin, synthetic_statin) statin_graph.add_is_a(synthetic_statin, statin) statin_graph.add_is_a(statin, hmgcr_inhibitor) statin_graph.add_is_a(mevinolinic_acid, hmgcr_inhibitor) statin_graph.add_is_a(hmgcr, ec_11134) statin_graph.add_inhibits( hmgcr_inhibitor, ec_11134, evidence="From ChEBI", citation="23180789", annotations={ "Confidence": "Axiomatic", }, ) statin_graph.add_inhibits( hmgcr_inhibitor, ec_11188, evidence="From ChEBI", citation="23180789", annotations={ "Confidence": "Axiomatic", }, ) pybel-0.15.5/src/pybel/examples/tloc_example.py000066400000000000000000000047301426625374700215070ustar00rootroot00000000000000# -*- coding: utf-8 -*- """An example describing a translocation. .. code-block:: none SET Citation = {"PubMed", "16170185"} SET Evidence = "These modifications render Ras functional and capable of localizing to the lipid-rich inner surface of the cell membrane. The first and most critical modification, farnesylation, which is principally catalyzed by protein FTase, adds a 15-carbon hydrobobic farnesyl isoprenyl tail to the carboxyl terminus of Ras." SET TextLocation = Review cat(complex(p(HGNC:FNTA),p(HGNC:FNTB))) directlyIncreases p(SFAM:"RAS Family",pmod(F)) p(SFAM:"RAS Family",pmod(F)) directlyIncreases tloc(p(SFAM:"RAS Family"),MESHCS:"Intracellular Space",MESHCS:"Cell Membrane") """ from ..dsl import ( ComplexAbundance, Protein, ProteinModification, activity, translocation, ) from ..language import Entity from ..resources import FPLX_URL, GO_URL, HGNC_URL from ..struct.graph import BELGraph __all__ = [ "ras_tloc_graph", ] ras_tloc_graph = BELGraph( name="RAS Transocation Graph", version="1.0.1", description="The farnesylation of RAS causes its translocation to the cell membrane.", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) ras_tloc_graph.namespace_url.update( { "HGNC": HGNC_URL, "GO": GO_URL, "FPLX": FPLX_URL, } ) evidence = "These modifications render Ras functional and capable of localizing to the lipid-rich inner surface of the cell membrane. The first and most critical modification, farnesylation, which is principally catalyzed by protein FTase, adds a 15-carbon hydrobobic farnesyl isoprenyl tail to the carboxyl terminus of Ras." pmid = "16170185" fnta = Protein(namespace="HGNC", name="FNTA", identifier="3782") fntb = Protein(namespace="HGNC", name="FNTA", identifier="3785") fnt = ComplexAbundance(namespace="FPLX", name="FNT", identifier="RAS", members=[fnta, fntb]) ras = Protein(namespace="FPLX", name="RAS", identifier="RAS") ras_farn = ras.with_variants(ProteinModification("Farn")) ras_tloc_graph.add_directly_increases( fnt, ras_farn, evidence=evidence, citation=pmid, source_modifier=activity("cat"), ) ras_tloc_graph.add_directly_increases( ras_farn, ras, evidence=evidence, citation=pmid, target_modifier=translocation( from_loc=Entity(namespace="GO", name="intracellular", identifier="GO:0005622"), to_loc=Entity(namespace="GO", name="plasma membrane", identifier="GO:0005886"), ), ) pybel-0.15.5/src/pybel/examples/various_example.py000066400000000000000000000044021426625374700222320ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Small graphs with grouped nodes".""" from ..dsl import Abundance, ComplexAbundance, CompositeAbundance, Protein, Reaction from ..resources import CHEBI_URL, GO_URL, HGNC_URL from ..struct.graph import BELGraph __all__ = [ "single_reaction_graph", "single_composite_graph", "single_complex_graph", ] citation = "None" evidence = """None""".replace("\n", " ").strip() single_reaction_graph = BELGraph( name="Single Reaction graph", version="1.0.0", description="Example graph", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) single_reaction_graph.namespace_url.update( { "HGNC": HGNC_URL, "CHEBI": CHEBI_URL, "GO": GO_URL, } ) hk1 = Protein(name="HK1", namespace="HGNC", identifier="4922") atp = Abundance(name="ATP", namespace="CHEBI", identifier="15422") adp = Abundance(name="ADP", namespace="CHEBI", identifier="16761") phosphate = Abundance(name="phosphoric acid", namespace="CHEBI", identifier="26078") glucose = Abundance(name="glucose", namespace="CHEBI", identifier="17234") glucose_6_phosphate = Abundance(name="D-glucopyranose 6-phosphate", namespace="CHEBI", identifier="4170") glycolisis_step_1 = Reaction(reactants=[glucose, hk1, atp, phosphate], products=[glucose_6_phosphate, adp, hk1]) composite_example = CompositeAbundance(members=[glucose_6_phosphate, adp, hk1]) complex_example = ComplexAbundance(members=[glucose_6_phosphate, adp, hk1]) single_reaction_graph.add_node_from_data(glycolisis_step_1) single_complex_graph = BELGraph( name="Single Complex graph", version="1.0.0", description="Example graph", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) single_complex_graph.namespace_url.update( { "HGNC": HGNC_URL, "CHEBI": CHEBI_URL, "GO": GO_URL, } ) single_complex_graph.add_node_from_data(complex_example) single_composite_graph = BELGraph( name="Single Composite graph", version="1.0.0", description="Example graph", authors="Charles Tapley Hoyt", contact="cthoyt@gmail.com", ) single_composite_graph.namespace_url.update( { "HGNC": HGNC_URL, "CHEBI": CHEBI_URL, "GO": GO_URL, } ) single_composite_graph.add_node_from_data(composite_example) pybel-0.15.5/src/pybel/examples/vegf_example.py000066400000000000000000000007331426625374700214740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """An example graph in which a family activates another family.""" from ..dsl import Protein from ..struct import BELGraph vegf_graph = BELGraph() vegf = Protein(namespace="fplx", name="VEGF") vegfr = Protein(namespace="fplx", name="VEGFR") ev = "VEGF activates the endothelial VEGF receptors" " (VEGFR) 1 and 2, and VEGF-C activates VEGFR-3 and VEGFR-2." vegf_graph.add_activates( vegf, vegfr, evidence=ev, citation="9506953", ) pybel-0.15.5/src/pybel/exceptions.py000066400000000000000000000350201426625374700173720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains base exceptions that are shared through the package. A message for "General Parser Failure" is displayed when a problem was caused due to an unforeseen error. The line number and original statement are printed for the user to debug. """ from .utils import ensure_quotes class PyBELWarning(Exception): """The base class for warnings during compilation from which PyBEL can recover.""" class BELParserWarning(PyBELWarning): """The base PyBEL parser exception, which holds the line and position where a parsing problem occurred.""" def __init__(self, line_number: int, line: str, position: int, *args): """Initialize the BEL parser warning. :param line_number: The line number on which this warning occurred :param line: The content of the line :param position: The position within the line where the warning occurred """ super().__init__(line_number, line, position, *args) self.line_number = line_number self.line = line self.position = position def __str__(self): return "General Parser Failure on line {} at pos {}: {}".format(self.line_number, self.position, self.line) class BELSyntaxError(BELParserWarning, SyntaxError): """For general syntax errors.""" class InconsistentDefinitionError(BELParserWarning): """Base PyBEL error for redefinition.""" def __init__(self, line_number: int, line: str, position: int, definition: str): super(InconsistentDefinitionError, self).__init__(line_number, line, position, definition) self.definition = definition def __str__(self): return "Tried to redefine {} with: {}".format(self.definition, self.line) class RedefinedNamespaceError(InconsistentDefinitionError): """Raised when a namespace is redefined.""" class RedefinedAnnotationError(InconsistentDefinitionError): """Raised when an annotation is redefined.""" # Naming Warnings class NameWarning(BELParserWarning): """The base class for errors related to nomenclature.""" def __init__(self, line_number: int, line: str, position: int, name: str, *args): """Build a warning wrapping a given name.""" super().__init__(line_number, line, position, name, *args) self.name = name class NakedNameWarning(NameWarning): """Raised when there is an identifier without a namespace. Enable lenient mode to suppress.""" def __str__(self): return '"{}" should be qualified with a valid namespace'.format(self.name) class MissingDefaultNameWarning(NameWarning): """Raised if reference to value not in default namespace.""" def __str__(self): return '"{}" is not in the default namespace'.format(self.name) class NamespaceIdentifierWarning(NameWarning): """The base class for warnings related to namespace:name identifiers.""" def __init__(self, line_number: int, line: str, position: int, namespace: str, name: str): """Initialize the namespace identifier warning. :param line_number: The line number of the line that caused the exception :param line: The line that caused the exception :param position: The line's position of the exception :param namespace: The namespace of the identifier :param name: The name of the identifier """ super(NamespaceIdentifierWarning, self).__init__(line_number, line, position, name, namespace) self.namespace = namespace class UndefinedNamespaceWarning(NamespaceIdentifierWarning): """Raised if reference made to undefined namespace.""" def __str__(self): return '"{}" is not a defined namespace'.format(self.namespace) class MissingNamespaceNameWarning(NamespaceIdentifierWarning): """Raised if reference to value not in namespace.""" def __str__(self): return '"{}" is not in the {} namespace'.format(self.name, self.namespace) class MissingNamespaceRegexWarning(NamespaceIdentifierWarning): """Raised if reference not matching regex.""" def __str__(self): return """"{}" doesn't match the regex for {} namespace""".format(self.name, self.namespace) class AnnotationWarning(BELParserWarning): """Base exception for annotation warnings.""" def __init__(self, line_number, line, position, annotation, *args): """Build an AnnotationWarning. :param int line_number: The line number on which the warning occurred :param str line: The line on which the warning occurred :param int position: The position in the line that caused the warning :param str annotation: The annotation name that caused the warning """ super(AnnotationWarning, self).__init__(line_number, line, position, annotation, *args) self.annotation = annotation class UndefinedAnnotationWarning(AnnotationWarning): """Raised when an undefined annotation is used.""" def __str__(self): return """"{}" is not defined""".format(self.annotation) class MissingAnnotationKeyWarning(AnnotationWarning): """Raised when trying to unset an annotation that is not set.""" def __str__(self): return """"{}" is not set, so it can't be unset""".format(self.annotation) class AnnotationIdentifierWarning(AnnotationWarning): """Base exception for annotation:value pairs.""" def __init__(self, line_number, line, position, annotation, value): super(AnnotationIdentifierWarning, self).__init__(line_number, line, position, annotation, value) self.value = value class IllegalAnnotationValueWarning(AnnotationIdentifierWarning): """Raised when an annotation has a value that does not belong to the original set of valid annotation values.""" def __str__(self): return '"{}" is not defined in the {} annotation'.format(self.value, self.annotation) class MissingAnnotationRegexWarning(AnnotationIdentifierWarning): """Raised if annotation doesn't match regex.""" def __str__(self): return """"{}" doesn't match the regex for {} annotation""".format(self.value, self.annotation) # Provenance Warnings class VersionFormatWarning(BELParserWarning): """Raised if the version string doesn't adhere to semantic versioning or ``YYYYMMDD`` format.""" def __init__(self, line_number, line, position, version_string): super(VersionFormatWarning, self).__init__(line_number, line, position, version_string) self.version_string = version_string def __str__(self): return ( 'Version string "{}" neither is a date like YYYYMMDD nor adheres to semantic versioning.' " See http://semver.org/".format(self.version_string) ) class MetadataException(BELParserWarning): """Base exception for issues with document metadata.""" def __str__(self): return 'Invalid metadata - "{}"'.format(self.line) class MalformedMetadataException(MetadataException): """Raised when an invalid metadata line is encountered.""" class InvalidMetadataException(BELParserWarning): """Raised when an incorrect document metadata key is used. .. hint:: Valid document metadata keys are: - ``Authors`` - ``ContactInfo`` - ``Copyright`` - ``Description`` - ``Disclaimer`` - ``Licenses`` - ``Name`` - ``Version`` .. seealso:: BEL specification on the `properties section `_ """ def __init__(self, line_number, line, position, key, value): super(InvalidMetadataException, self).__init__(line_number, line, position, key, value) self.key = key self.value = value def __str__(self): return "Invalid document metadata key: {}".format(self.key) class MissingMetadataException(BELParserWarning): """Raised when a BEL Script is missing critical metadata.""" def __init__(self, line_number, line, position, key): super(MissingMetadataException, self).__init__(line_number, line, position, key) self.key = key def __str__(self): return "Missing required document metadata: {}".format(self.key) @staticmethod def make(key: str): """Build an instance of this class with auto-filled dummy values. Unlike normal classes, polymorphism on __init__ can't be used for exceptions when pickling/unpickling. """ return MissingMetadataException(0, "", 0, key) class InvalidCitationLengthException(BELParserWarning): """Base exception raised when the format for a citation is wrong.""" class CitationTooShortException(InvalidCitationLengthException): """Raised when a citation does not have the minimum of {type, name, reference}.""" def __str__(self): return "Citation is missing required fields: {}".format(self.line) class CitationTooLongException(InvalidCitationLengthException): """Raised when a citation has more than the allowed entries, {type, name, reference, date, authors, comments}.""" def __str__(self): return "Citation contains too many entries: {}".format(self.line) class MissingCitationException(BELParserWarning): """Raised when trying to parse a BEL statement, but no citation is currently set. This might be due to a previous error in the formatting of a citation. Though it's not a best practice, some BEL curators set other annotations before the citation. If this is the case in your BEL document, and you're *absolutely* sure that all ``UNSET`` statements are correctly written, you can use ``citation_clearing=True`` as a keyword argument in any of the IO functions in :func:`pybel.from_lines`, :func:`pybel.from_url`, or :func:`pybel.from_path`. """ def __str__(self): return "Missing citation; can't add: {}".format(self.line) class MissingSupportWarning(BELParserWarning): """Raised when trying to parse a BEL statement, but no evidence is currently set. All BEL statements must be qualified with evidence. If your data is serialized from a database and provenance information is not readily accessible, consider referencing the publication for the database, or a url pointing to the data from either a programmatically or human-readable endpoint. """ def __str__(self): return "Missing evidence; can't add: {}".format(self.line) class MissingAnnotationWarning(BELParserWarning): """Raised when trying to parse a BEL statement and a required annotation is not present.""" def __init__(self, line_number, line, position, required_annotations): super(MissingAnnotationWarning, self).__init__(line_number, line, position, required_annotations) self.required_annotations = required_annotations def __str__(self): return "Missing annotations: {}".format(", ".join(sorted(self.required_annotations))) class InvalidCitationType(BELParserWarning): """Raised when a citation is set with an incorrect type. .. hint:: Valid citation types include: - ``Book`` - ``PubMed`` - ``Journal`` - ``Online Resource`` - ``URL`` - ``DOI`` - ``Other`` .. seealso:: OpenBEL wiki on `citations `_ """ def __init__(self, line_number, line, position, citation_type): super(InvalidCitationType, self).__init__(line_number, line, position, citation_type) self.citation_type = citation_type def __str__(self): return '"{}" is not a valid citation type'.format(self.citation_type) class InvalidPubMedIdentifierWarning(BELParserWarning): """Raised when a citation is set whose type is ``PubMed`` but whose database identifier is not a valid integer.""" def __init__(self, line_number, line, position, reference): super(InvalidPubMedIdentifierWarning, self).__init__(line_number, line, position, reference) self.reference = reference def __str__(self): return '"{}" is not a valid PubMed identifier'.format(self.reference) # BEL Syntax Warnings class MalformedTranslocationWarning(BELParserWarning): """Raised when there is a translocation statement without location information.""" def __init__(self, line_number, line, position, tokens): super(MalformedTranslocationWarning, self).__init__(line_number, line, position, tokens) self.tokens = tokens def __str__(self): return "Unqualified translocation: {} {}".format(self.line, self.tokens) class PlaceholderAminoAcidWarning(BELParserWarning): """Raised when an invalid amino acid code is given. One example might be the usage of X, which is a colloquial signifier for a truncation in a given position. Text mining efforts for knowledge extraction make this mistake often. X might also signify a placeholder amino acid. """ def __init__(self, line_number, line, position, code): super(PlaceholderAminoAcidWarning, self).__init__(line_number, line, position, code) self.code = code def __str__(self): return "Placeholder amino acid found: {}".format(self.code) class NestedRelationWarning(BELParserWarning): """Raised when encountering a nested statement. See our the docs for an explanation of why we explicitly do not support nested statements. """ def __str__(self): return "Nesting is not supported. Split this statement: {}".format(self.line) # Semantic Warnings class InvalidEntity(BELParserWarning): """Raised when using a non-entity name for a name.""" def __init__(self, line_number, line, position, namespace, name): super().__init__(line_number, line, position, namespace, name) self.namespace = namespace self.name = name def __str__(self): return "{}:{} should not be coded as an entity".format(self.namespace, ensure_quotes(self.name)) class InvalidFunctionSemantic(BELParserWarning): """Raised when an invalid function is used for a given node. For example, an HGNC symbol for a protein-coding gene YFG cannot be referenced as an miRNA with ``m(HGNC:YFG)`` """ def __init__(self, line_number, line, position, func, namespace, name, allowed_functions): super().__init__(line_number, line, position, func, namespace, name, allowed_functions) self.func = func self.namespace = namespace self.name = name self.allowed_functions = allowed_functions def __str__(self): return "{} {}:{} should be encoded as one of: {}".format( self.func, self.namespace, ensure_quotes(self.name), ", ".join(self.allowed_functions), ) pybel-0.15.5/src/pybel/grounding.py000066400000000000000000000421611426625374700172110ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Grounding for PyBEL. Why does this module exist, even though BEL relies on definitions of external vocabularies? BEL namespaces only have names, and there's no standard for mapping back to identifiers. PyBEL has a internal rule that for any given namespace, it will try and look up another "identifier" space in the same directory. However, this is an implementation detail and does This module uses PyOBO to look the identifiers for nodes in a BEL graph when possible or replace the label given with BEP-0008 (OBO-style) syntax. It also normalizes namespace names to their standards, as defined by Identifiers.org/OBOFoundry/PyOBO internal standard. Finally, it has a tool that allows for the definition of remapping of namespace/name pairs. Right now, it's only set up to use the FamPlex mappings, but it will be made more extensible to help support the clean-up of curation efforts that created their own low-quality terminologies (publicly accessible examples include the Selventa Large Corpus, Selventa Small Corpus, HemeKG, covid19kg, and the Causal Biological Networks database). After installation with ``pip install git+https://github.com/hemekg/hemekg.git``, it can be run with: .. code-block:: python import hemekg heme_graph = hemekg.get_graph() import pybel.grounding pybel.grounding.ground(heme_graph) After installation with ``pip install git+https://github.com/covid19kg/covid19kg.git``, it can be run with: .. code-block:: python import covid19kg covid19_graph = covid19kg.get_graph() import pybel.grounding pybel.grounding.ground(covid19_graph) After installation with ``pip install git+https://github.com/cthoyt/selventa-knowledge.git``, it can be run with: .. code-block:: python import selventa_knowledge selventa_graph = selventa_knowledge.get_graph() import pybel.grounding pybel.grounding.ground(selventa_graph) """ import logging from typing import Any, Collection, Mapping, Optional, Tuple, Union import pyobo from protmapper.uniprot_client import get_id_from_mnemonic, get_mnemonic from pyobo.getters import NoBuild from pyobo.identifier_utils import normalize_prefix from pyobo.xrefdb.sources.famplex import get_remapping from tqdm.autonotebook import tqdm from pybel.constants import ( ACTIVITY, ANNOTATIONS, CONCEPT, EFFECT, FROM_LOC, FUSION, GMOD, IDENTIFIER, KIND, LOCATION, MEMBERS, MODIFIER, NAME, NAMESPACE, PARTNER_3P, PARTNER_5P, PMOD, PRODUCTS, REACTANTS, SOURCE_MODIFIER, TARGET_MODIFIER, TO_LOC, TRANSLOCATION, VARIANTS, ) from pybel.dsl import BaseConcept from pybel.io import from_nodelink, to_nodelink from pybel.language import ( Entity, activity_mapping, compartment_mapping, gmod_mappings, pmod_mappings, text_location_labels, ) from pybel.struct import BELGraph, get_annotations, get_namespaces, get_ungrounded_nodes __all__ = [ "ground", "ground_nodelink", ] logger = logging.getLogger(__name__) SKIP = {"ncbigene", "pubchem.compound", "chembl.compound"} NO_NAMES = {"fplx", "eccode", "dbsnp", "smiles", "inchi", "inchikey"} # TODO will get updated #: A mapping of (prefix, name) pairs to (prefix, identifier, name) triples _NAME_REMAPPING = get_remapping() #: A mapping of (prefix, identifier) pairs to (prefix, identifier, name) triples _ID_REMAPPING = {} def _get_name_remapping(prefix: str, name: str) -> Union[Tuple[str, str, str], Tuple[None, None, None]]: if prefix.lower() in {"sfam", "scomp"} and ("bel", name) in _NAME_REMAPPING: return _NAME_REMAPPING["bel", name] return _NAME_REMAPPING.get((prefix, name), (None, None, None)) def _get_id_remapping(prefix: str, identifier: str) -> Union[Tuple[str, str, str], Tuple[None, None, None]]: return _ID_REMAPPING.get((prefix, identifier), (None, None, None)) def ground( graph: BELGraph, remove_ungrounded: bool = True, skip_namespaces: Optional[Collection[str]] = None, ) -> BELGraph: """Ground all entities in a BEL graph.""" j = to_nodelink(graph) ground_nodelink(j, skip_namespaces=skip_namespaces) graph = from_nodelink(j) remove_unused_annotation_metadata(graph) if remove_ungrounded: ungrounded_nodes = { node for node in get_ungrounded_nodes(graph) if not isinstance(node, BaseConcept) or node.namespace not in NO_NAMES } graph.remove_nodes_from(ungrounded_nodes) graph.namespace_url.clear() graph.namespace_pattern.clear() graph.namespace_pattern.update({namespace: ".*" for namespace in get_namespaces(graph)}) graph.annotation_url.clear() graph.annotation_pattern.clear() graph.annotation_list.clear() graph.annotation_pattern.update({annotation: ".*" for annotation in get_annotations(graph)}) return graph def remove_unused_annotation_metadata(graph) -> None: used_annotations = get_annotations(graph) unused_patterns = set(graph.annotation_pattern) - used_annotations for annotation in unused_patterns: logger.warning("deleting unused annotation pattern: %s", annotation) del graph.annotation_pattern[annotation] unused_urls = set(graph.annotation_pattern) - used_annotations for annotation in unused_urls: logger.warning("deleting unused annotation URL: %s", annotation) del graph.annotation_url[annotation] unused_lists = set(graph.annotation_list) - used_annotations for annotation in unused_lists: logger.warning("deleting unused annotation list: %s", annotation) del graph.annotation_list[annotation] def ground_nodelink(graph_nodelink_dict, skip_namespaces: Optional[Collection[str]] = None) -> None: """Ground entities in a nodelink data structure.""" name = graph_nodelink_dict.get("graph", {}).get("name", "graph") for data in tqdm(graph_nodelink_dict["links"], desc="grounding edges in {}".format(name)): _process_edge_side(data.get(SOURCE_MODIFIER), skip_namespaces=skip_namespaces) _process_edge_side(data.get(TARGET_MODIFIER), skip_namespaces=skip_namespaces) if ANNOTATIONS in data: _process_annotations(data, skip_namespaces=skip_namespaces) for node in tqdm(graph_nodelink_dict["nodes"], desc="grounding nodes in {}".format(name)): _process_node(node, skip_namespaces=skip_namespaces) _BEL_ANNOTATION_PREFIX_MAP = { "MeSHDisease": "mesh", "MeSHAnatomy": "mesh", "CellStructure": "mesh", "Species": "ncbitaxon", "Disease": "doid", "Cell": "cl", "Anatomy": "uberon", } _BEL_ANNOTATION_PREFIX_CATEGORY_MAP = { "MeSHDisease": "Disease", "MeSHAnatomy": "Anatomy", } _UNHANDLED_ANNOTATION = set() CATEGORY_BLACKLIST = { "TextLocation", } def _process_annotations( data, remove_ungrounded: bool = False, skip_namespaces: Optional[Collection[str]] = None, ) -> None: """Process the annotations in a PyBEL edge data dictionary.""" cell_line_entities = data[ANNOTATIONS].get("CellLine") if cell_line_entities: ne = [] for entity in cell_line_entities: if entity[NAMESPACE] == "CellLine": _namespaces = [ "efo", # 'clo', # FIXME implement CLO in PyOBO then uncomment ] g_prefix, g_identifier, g_name = pyobo.ground(_namespaces, entity[IDENTIFIER]) if g_prefix and g_identifier: ne.append(Entity(namespace=g_prefix, identifier=g_identifier, name=g_name)) elif not remove_ungrounded: logger.warning('could not ground CellLine: "%s"', entity[IDENTIFIER]) ne.append(entity) data[ANNOTATIONS]["CellLine"] = ne # fix text locations text_location = data[ANNOTATIONS].get("TextLocation") if text_location: data[ANNOTATIONS]["TextLocation"] = [ text_location_labels.get(entity.identifier, entity) for entity in text_location ] # remap category names data[ANNOTATIONS] = { _BEL_ANNOTATION_PREFIX_CATEGORY_MAP.get(category, category): entities for category, entities in data[ANNOTATIONS].items() } # fix namespaces that were categories before for category, entities in data[ANNOTATIONS].items(): if category in CATEGORY_BLACKLIST: continue ne = [] for entity in entities: if not isinstance(entity, dict): raise TypeError(f"entity should be a dict. got: {entity}") nn = _BEL_ANNOTATION_PREFIX_MAP.get(entity[NAMESPACE]) if nn is not None: entity[NAMESPACE] = nn _process_concept(concept=entity, skip_namespaces=skip_namespaces) ne.append(entity) data[ANNOTATIONS][category] = ne def _process_edge_side(side_data, skip_namespaces: Optional[Collection[str]] = None) -> bool: """Process an edge JSON object, in place.""" if side_data is None: return True modifier = side_data.get(MODIFIER) effect = side_data.get(EFFECT) if modifier == ACTIVITY and effect is not None: _process_concept(concept=effect, skip_namespaces=skip_namespaces) elif modifier == TRANSLOCATION and effect is not None: _process_concept(concept=effect[FROM_LOC], skip_namespaces=skip_namespaces) _process_concept(concept=effect[TO_LOC], skip_namespaces=skip_namespaces) location = side_data.get(LOCATION) if location is not None: _process_concept(concept=location, skip_namespaces=skip_namespaces) _UNHANDLED_NAMESPACES = set() def _process_node(node: Mapping[str, Any], skip_namespaces: Optional[Collection[str]] = None) -> bool: """Process a node JSON object, in place. :return: If all parts of the node were successfully grounded """ success = True if CONCEPT in node: success = success and _process_concept(concept=node[CONCEPT], node=node, skip_namespaces=skip_namespaces) if VARIANTS in node: success = success and _process_list(node[VARIANTS], skip_namespaces=skip_namespaces) if MEMBERS in node: success = success and _process_list(node[MEMBERS], skip_namespaces=skip_namespaces) if REACTANTS in node: success = success and _process_list(node[REACTANTS], skip_namespaces=skip_namespaces) if PRODUCTS in node: success = success and _process_list(node[PRODUCTS], skip_namespaces=skip_namespaces) if FUSION in node: success = success and _process_fusion(node[FUSION], skip_namespaces=skip_namespaces) return success def _process_concept(*, concept, node=None, skip_namespaces: Optional[Collection[str]] = None) -> bool: """Process a node JSON object.""" namespace = concept[NAMESPACE] if namespace.lower() in {"text", "fixme"}: return False if skip_namespaces and namespace in skip_namespaces: return True prefix = normalize_prefix(namespace) if prefix is None: logger.warning('could not normalize namespace "%s" in concept "%s"', namespace, concept) return False concept[NAMESPACE] = prefix identifier = concept.get(IDENTIFIER) name = concept.get(NAME) if identifier: # don't trust whatever was put for the name, even if it's available map_success = _handle_identifier_not_name( concept=concept, prefix=prefix, identifier=identifier, skip_namespaces=skip_namespaces, ) if not map_success: # just in case the name gets put in the identifier map_success = _handle_name_and_not_identifier( concept=concept, prefix=prefix, name=identifier, node=node, skip_namespaces=skip_namespaces, ) else: map_success = _handle_name_and_not_identifier( concept=concept, prefix=prefix, name=name, node=node, skip_namespaces=skip_namespaces, ) if not map_success: return False _remap_by_identifier(concept) return True def _remap_by_identifier(concept) -> bool: identifier = concept.get(IDENTIFIER) if identifier is None: return False namespace = concept[NAMESPACE] logger.debug("attempting to remap %s:%s", namespace, identifier) remapped_prefix, remapped_identifier, remapped_name = _get_id_remapping(namespace, identifier) logger.debug( "remapping result %s:%s ! %s", remapped_prefix, remapped_identifier, remapped_name, ) if remapped_prefix: concept[NAMESPACE] = remapped_prefix concept[IDENTIFIER] = remapped_identifier concept[NAME] = remapped_name return True return False def _handle_identifier_not_name( *, concept, prefix, identifier, skip_namespaces: Optional[Collection[str]] = None, ) -> bool: # Some namespaces are just too much of a problem at the moment to look up if prefix in SKIP: return False if skip_namespaces and prefix in skip_namespaces: return True if prefix in NO_NAMES: concept[NAME] = concept[IDENTIFIER] return True if prefix == "uniprot": concept[NAME] = get_mnemonic(identifier) return True try: id_name_mapping = pyobo.api.names.get_id_name_mapping(prefix) except NoBuild: return False if id_name_mapping is None: logger.warning('could not get names for prefix "%s"', prefix) return False name = id_name_mapping.get(identifier) if name is None: logger.warning("could not get name for curie %s:%s", prefix, identifier) return False concept[NAME] = name return True def _handle_name_and_not_identifier( *, concept, prefix, name, node=None, skip_namespaces: Optional[Collection[str]] = None, ) -> bool: remapped_prefix, remapped_identifier, remapped_name = _get_name_remapping(prefix, name) if remapped_prefix: concept[NAMESPACE] = remapped_prefix concept[IDENTIFIER] = remapped_identifier concept[NAME] = remapped_name return True # Some namespaces are just too much of a problem at the moment to look up if prefix in SKIP: return False if skip_namespaces and prefix in skip_namespaces: return True concept[NAMESPACE] = prefix if prefix in NO_NAMES: concept[IDENTIFIER] = name return True if prefix == "bel" and node is not None and KIND in node: kind = node[KIND] if kind == PMOD and name in pmod_mappings: # the 0th position xref is the preferred one (usually GO) _mapped = pmod_mappings[name]["xrefs"][0] elif kind == GMOD and name in gmod_mappings: _mapped = gmod_mappings[name]["xrefs"][0] else: raise ValueError(f"invalid kind: {kind}") concept[NAMESPACE] = _mapped[NAMESPACE] concept[IDENTIFIER] = _mapped[IDENTIFIER] concept[NAME] = _mapped[NAME] return True elif prefix == "bel" and name in activity_mapping: _mapped = activity_mapping[name] concept[NAMESPACE] = _mapped[NAMESPACE] concept[IDENTIFIER] = _mapped[IDENTIFIER] concept[NAME] = _mapped[NAME] return True elif prefix == "bel" and name in compartment_mapping: _mapped = compartment_mapping[name] concept[NAMESPACE] = _mapped[NAMESPACE] concept[IDENTIFIER] = _mapped[IDENTIFIER] concept[NAME] = _mapped[NAME] return True elif prefix == "bel": logger.warning('could not figure out how to map bel ! "%s"', name) return False if prefix == "uniprot": # assume identifier given as name identifier = get_id_from_mnemonic(name) if identifier is not None: concept[IDENTIFIER] = identifier return True mnemomic = get_mnemonic(name, web_fallback=False) if mnemomic is not None: concept[IDENTIFIER] = name concept[NAME] = mnemomic return True logger.warning('could not interpret uniprot name: "%s"', name) return False try: id_name_mapping = pyobo.api.names.get_name_id_mapping(prefix) except NoBuild as e: logger.warning("could not get namespace %s - %s", prefix, e) return False if id_name_mapping is None: logger.warning("unhandled namespace in %s ! %s", prefix, name) return False identifier = id_name_mapping.get(name) if identifier is None: logger.warning('could not find name "%s" in namespace "%s"', name, prefix) return False concept[IDENTIFIER] = identifier concept[NAME] = name return True def _process_fusion(fusion, skip_namespaces: Optional[Collection[str]] = None) -> bool: success_3p = _process_node(fusion[PARTNER_3P], skip_namespaces=skip_namespaces) success_5p = _process_node(fusion[PARTNER_5P], skip_namespaces=skip_namespaces) return success_3p and success_5p def _process_list(members, skip_namespaces: Optional[Collection[str]] = None) -> bool: success = True for member in members: success = success and _process_node(member, skip_namespaces=skip_namespaces) return success pybel-0.15.5/src/pybel/io/000077500000000000000000000000001426625374700152465ustar00rootroot00000000000000pybel-0.15.5/src/pybel/io/__init__.py000066400000000000000000000052701426625374700173630ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Input and output functions for BEL graphs. PyBEL provides multiple lossless interchange options for BEL. Lossy output formats are also included for convenient export to other programs. Notably, a *de facto* interchange using Resource Description Framework (RDF) to match the ability of other existing software is excluded due the immaturity of the BEL to RDF mapping. """ from .api import dump, load from .aws import from_s3, to_s3 from .bel_commons_client import from_bel_commons, to_bel_commons from .biodati_client import from_biodati, to_biodati from .cx import ( from_cx, from_cx_file, from_cx_gz, from_cx_jsons, to_cx, to_cx_file, to_cx_gz, to_cx_jsons, ) from .emmaa import from_emmaa from .extras import to_csv, to_gsea, to_sif from .fraunhofer_orientdb import from_fraunhofer_orientdb from .gpickle import ( from_bytes, from_bytes_gz, from_pickle, from_pickle_gz, to_bytes, to_bytes_gz, to_pickle, to_pickle_gz, ) from .graphdati import ( from_graphdati, from_graphdati_file, from_graphdati_gz, from_graphdati_jsons, to_graphdati, to_graphdati_file, to_graphdati_gz, to_graphdati_jsonl, to_graphdati_jsonl_gz, to_graphdati_jsons, ) from .graphml import to_graphml from .hetionet import ( from_hetionet_file, from_hetionet_gz, from_hetionet_json, get_hetionet, ) from .hipathia import ( from_hipathia_dfs, from_hipathia_paths, to_hipathia, to_hipathia_dfs, ) from .indra import ( from_biopax, from_indra_pickle, from_indra_statements, from_indra_statements_json, from_indra_statements_json_file, to_indra_statements, to_indra_statements_json, to_indra_statements_json_file, ) from .jgif import ( from_cbn_jgif, from_cbn_jgif_file, from_jgif, from_jgif_file, from_jgif_gz, from_jgif_jsons, post_jgif, to_jgif, to_jgif_file, to_jgif_gz, to_jgif_jsons, ) from .jupyter import to_jupyter, to_jupyter_str from .lines import from_bel_script, from_bel_script_url from .neo4j import to_neo4j from .nodelink import ( from_nodelink, from_nodelink_file, from_nodelink_gz, from_nodelink_jsons, to_nodelink, to_nodelink_file, to_nodelink_gz, to_nodelink_jsons, ) from .pynpa import to_npa_dfs, to_npa_directory from .sbel import ( from_sbel, from_sbel_file, from_sbel_gz, to_sbel, to_sbel_file, to_sbel_gz, ) from .spia import to_spia_dfs, to_spia_excel, to_spia_tsvs from .triples import to_edgelist, to_triples, to_triples_file from .umbrella_nodelink import ( to_umbrella_nodelink, to_umbrella_nodelink_file, to_umbrella_nodelink_gz, ) pybel-0.15.5/src/pybel/io/api.py000066400000000000000000000050571426625374700164000ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Contains the main data structure for PyBEL.""" import os from typing import TextIO, Union from networkx.utils import open_file from pkg_resources import iter_entry_points from ..struct import BELGraph __all__ = [ "load", "dump", "InvalidExtensionError", ] #: Mapping from extension to importer function IMPORTERS = {entry.name: entry.load() for entry in iter_entry_points(group="pybel.importer")} #: Mapping from extension to exporter function EXPORTERS = {entry.name: entry.load() for entry in iter_entry_points(group="pybel.exporter")} class InvalidExtensionError(ValueError): """Raised when an invalid extension is used.""" def __init__(self, path): fname = os.path.basename(path) super().__init__("Invalid extension for file: {}".format(fname)) def load(path: str, **kwargs) -> BELGraph: """Read a BEL graph. :param path: The path to a BEL graph in any of the formats with extensions described below :param kwargs: The keyword arguments are passed to the importer function :return: A BEL graph. This is the universal loader, which means any file path can be given and PyBEL will look up the appropriate load function. Allowed extensions are: - bel - bel.nodelink.json - bel.cx.json - bel.jgif.json The previous extensions also support gzipping. Other allowed extensions that don't support gzip are: - bel.pickle / bel.gpickle / bel.pkl - indra.json """ for extension, importer in IMPORTERS.items(): if path.endswith(extension): return importer(path, **kwargs) raise InvalidExtensionError(path=path) def dump(graph: BELGraph, path: str, **kwargs) -> None: """Write a BEL graph. :param graph: A BEL graph :param path: The path to which the BEL graph is written. :param kwargs: The keyword arguments are passed to the exporter function This is the universal loader, which means any file path can be given and PyBEL will look up the appropriate writer function. Allowed extensions are: - bel - bel.nodelink.json - bel.unodelink.json - bel.cx.json - bel.jgif.json - bel.graphdati.json The previous extensions also support gzipping. Other allowed extensions that don't support gzip are: - bel.pickle / bel.gpickle / bel.pkl - indra.json - tsv - gsea """ for extension, exporter in EXPORTERS.items(): if path.endswith(extension): return exporter(graph, path, **kwargs) raise InvalidExtensionError(path=path) pybel-0.15.5/src/pybel/io/aws.py000066400000000000000000000057521426625374700164230ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Transport functions for Amazon Web Services (AWS). AWS has a cloud-based file storage service called S3 that can be programatically accessed using the :mod:`boto3` package. This module provides functions for quickly wrapping upload/download of BEL graphs using the gzipped Node-Link schema. """ import logging from io import BytesIO from typing import Any, Optional from .nodelink import from_nodelink_gz_io, to_nodelink_gz_io from ..struct import BELGraph __all__ = [ "to_s3", "from_s3", ] logger = logging.getLogger(__name__) S3Client = Any def to_s3(graph: BELGraph, *, bucket: str, key: str, client: Optional[S3Client] = None) -> None: """Save BEL to S3 as gzipped node-link JSON. If you don't specify an instantiated client, PyBEL will do its best to load a default one using :func:`boto3.client` like in the following example: .. code-block:: python import pybel from pybel.examples import sialic_acid_graph graph = pybel.to_s3( sialic_acid_graph, bucket='your bucket', key='your file name.bel.nodelink.json.gz', ) However, if you would like to configure your own, you can do it with something like this: .. code-block:: python import boto3 s3_client = boto3.client('s3') import pybel from pybel.examples import sialic_acid_graph graph = pybel.to_s3( sialic_acid_graph, client=s3_client, bucket='your bucket', key='your file name.bel.nodelink.json.gz', ) .. warning:: This assumes you already have credentials set up on your machine If you don't already have a bucket, you can create one using ``boto3`` by following this tutorial: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-example-creating-buckets.html """ if client is None: import boto3 client = boto3.client("s3") io = to_nodelink_gz_io(graph) client.upload_fileobj(io, bucket, key) def from_s3(*, bucket: str, key: str, client: Optional[S3Client] = None) -> BELGraph: """Get BEL from gzipped node-link JSON from Amazon S3. If you don't specify an instantiated client, PyBEL will do its best to load a default one using :func:`boto3.client` like in the following example: .. code-block:: python graph = pybel.from_s3(bucket='your bucket', key='your file name.bel.nodelink.json.gz') However, if you would like to configure your own, you can do it with something like this: .. code-block:: python import boto3 s3_client = boto3.client('s3') import pybel graph = pybel.from_s3( client=s3_client, bucket='your bucket', key='your file name.bel.nodelink.json.gz', ) """ if client is None: import boto3 client = boto3.client("s3") io = BytesIO() client.download_fileobj(bucket, key, io) io.seek(0) return from_nodelink_gz_io(io) pybel-0.15.5/src/pybel/io/bel_commons_client.py000066400000000000000000000076361426625374700214670ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Transport functions for `BEL Commons `_. BEL Commons is a free, open-source platform for hosting BEL content. Because it was originally developed and published in an academic capacity at Fraunhofer SCAI, a public instance can be found at https://bel-commons-dev.scai.fraunhofer.de. However, this instance is only supported out of posterity and will not be updated. If you would like to host your own instance of BEL Commons, there are instructions on its GitHub page. """ import logging from typing import Optional import pystow import requests from .nodelink import from_nodelink, to_nodelink from ..struct.graph import BELGraph from ..version import get_version __all__ = [ "to_bel_commons", "from_bel_commons", ] logger = logging.getLogger(__name__) RECIEVE_ENDPOINT = "/api/receive/" GET_ENDPOINT = "/api/network/{}/export/nodelink" def _get_host() -> Optional[str]: """Find the host with :func:`pystow.get_config`. Has two possibilities: 1. The PyBEL config entry ``PYBEL_REMOTE_HOST``, loaded in :mod:`pybel.constants` 2. The environment variable ``PYBEL_REMOTE_HOST`` """ return pystow.get_config("pybel", "remote_host") def _get_user() -> Optional[str]: return pystow.get_config("pybel", "remote_user") def _get_password() -> Optional[str]: return pystow.get_config("pybel", "remote_password") def to_bel_commons( graph: BELGraph, host: Optional[str] = None, user: Optional[str] = None, password: Optional[str] = None, public: bool = True, ) -> requests.Response: """Send a graph to the receiver service and returns the :mod:`requests` response object. :param graph: A BEL graph :param host: The location of the BEL Commons server. Alternatively, looks up in PyBEL config with ``PYBEL_REMOTE_HOST`` or the environment as ``PYBEL_REMOTE_HOST``. :param user: Username for BEL Commons. Alternatively, looks up in PyBEL config with ``PYBEL_REMOTE_USER`` or the environment as ``PYBEL_REMOTE_USER`` :param password: Password for BEL Commons. Alternatively, looks up in PyBEL config with ``PYBEL_REMOTE_PASSWORD`` or the environment as ``PYBEL_REMOTE_PASSWORD`` :param public: Should the network be made public? :return: The response object from :mod:`requests` """ if host is None: host = _get_host() logger.debug("using host: %s", host) if user is None: user = _get_user() if user is None: raise ValueError("no user found") if password is None: password = _get_password() if password is None: raise ValueError("no password found") url = host.rstrip("/") + RECIEVE_ENDPOINT response = requests.post( url, json=to_nodelink(graph), headers={ "content-type": "application/json", "User-Agent": "PyBEL v{}".format(get_version()), "bel-commons-public": "true" if public else "false", }, auth=(user, password), ) logger.debug("received response: %s", response) return response def from_bel_commons(network_id: int, host: Optional[str] = None) -> BELGraph: """Retrieve a public network from BEL Commons. In the future, this function may be extended to support authentication. :param network_id: The BEL Commons network identifier :param host: The location of the BEL Commons server. Alternatively, looks up in PyBEL config with ``PYBEL_REMOTE_HOST`` or the environment as ``PYBEL_REMOTE_HOST``. :raises: ValueError if host configuration can not be found """ if host is None: host = _get_host() if host is None: raise ValueError("host not specified in arguments, PyBEL configuration, or environment.") url = host + GET_ENDPOINT.format(network_id) res = requests.get(url) graph_json = res.json() graph = from_nodelink(graph_json) return graph pybel-0.15.5/src/pybel/io/biodati_client.py000066400000000000000000000264541426625374700206040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Transport functions for `BioDati `_. BioDati is a paid, closed-source platform for hosting BEL content. However, they do have a demo instance running at https://studio.demo.biodati.com with which the examples in this module will be described. As noted in the transport functions for BioDati, you should change the URLs to point to your own instance of BioDati. If you're looking for an open source storage system for hosting your own BEL content, you may consider `BEL Commons `_, with the caveat that it is currently maintained in an academic capacity. Disclosure: BEL Commons is developed by the developers of PyBEL. """ import json import logging from io import BytesIO from typing import Any, Iterable, List, Mapping, Optional, Union import requests from more_itertools import chunked from .graphdati import _iter_graphdati, from_graphdati, to_graphdati from ..struct import BELGraph __all__ = [ "to_biodati", "from_biodati", ] logger = logging.getLogger(__name__) def to_biodati( # noqa: S107 graph: BELGraph, *, username: str = "demo@biodati.com", password: str = "demo", base_url: str = "https://nanopubstore.demo.biodati.com", chunksize: Optional[int] = None, use_tqdm: bool = True, collections: Optional[Iterable[str]] = None, overwrite: bool = False, validate: bool = True, email: Union[bool, str] = False, ) -> requests.Response: """Post this graph to a BioDati server. :param graph: A BEL graph :param username: The email address to log in to BioDati. Defaults to "demo@biodati.com" for the demo server :param password: The password to log in to BioDati. Defaults to "demo" for the demo server :param base_url: The BioDati nanopub store base url. Defaults to "https://nanopubstore.demo.biodati.com" for the demo server's nanopub store :param chunksize: The number of nanopubs to post at a time. By default, does all. :param use_tqdm: Should tqdm be used when iterating? :param collections: Tags to add to the nanopubs for lookup on BioDati :param overwrite: Set the BioDati upload "overwrite" setting :param validate: Set the BioDati upload "validate" setting :param email: Who should get emailed with results about the upload? If true, emails to user used for login. If string, emails to that user. If false, no email. :return: The response from the BioDati server (last response if using chunking) .. warning:: BioDati does not support large uploads (yet?). .. warning:: The default public BioDati server has been put here. You should switch it to yours. It will look like ``https://nanopubstore..biodati.com``. """ biodati_client = BiodatiClient(username, password, base_url) if chunksize: return biodati_client.post_graph_chunked( graph, chunksize, use_tqdm=use_tqdm, collections=collections, overwrite=overwrite, validate=validate, email=email, ) else: return biodati_client.post_graph( graph, use_tqdm=use_tqdm, collections=collections, overwrite=overwrite, validate=validate, email=email, ) def from_biodati( # noqa: S107 network_id: str, username: str = "demo@biodati.com", password: str = "demo", base_url: str = "https://networkstore.demo.biodati.com", ) -> BELGraph: """Get a graph from a BioDati network store based on its network identifier. :param network_id: The internal identifier of the network you want to download. :param username: The email address to log in to BioDati. Defaults to "demo@biodati.com" for the demo server :param password: The password to log in to BioDati. Defaults to "demo" for the demo server :param base_url: The BioDati network store base url. Defaults to "https://networkstore.demo.biodati.com" for the demo server's network store Example usage: .. code-block:: python from pybel import from_biodati network_id = '01E46GDFQAGK5W8EFS9S9WMH12' # COVID-19 graph example from Wendy Zimmermann graph = from_biodati( network_id=network_id, username='demo@biodati.com', password='demo', base_url='https://networkstore.demo.biodati.com', ) graph.summarize() .. warning:: The default public BioDati server has been put here. You should switch it to yours. It will look like ``https://networkstore..biodati.com``. """ biodati_client = BiodatiClient(username, password, base_url) return biodati_client.get_graph(network_id) class BiodatiClient: """A client for the BioDati nanopub store and network store's APIs.""" def __init__(self, username: str, password: str, base_url: str): self.base_url = base_url.rstrip("/") self.username = username res = requests.post( "{}/token".format(base_url), data=dict(username=username, password=password), ) token_dict = res.json() self.token_type = token_dict["token_type"] self.id_token = token_dict["id_token"] self.access_token = token_dict["access_token"] def post(self, endpoint: str, **kwargs): """Send a post request to BioDati.""" return self._help_request(requests.post, endpoint, **kwargs) def get(self, endpoint: str, **kwargs): """Send a get request to BioDati.""" return self._help_request(requests.get, endpoint, **kwargs) def _help_request(self, requester, endpoint: str, **kwargs): """Send a request to BioDati.""" url = "{}/{}".format(self.base_url, endpoint) logger.info("requesting %s with params %s", url, kwargs.get("params", {})) headers = {"Authorization": "{} {}".format(self.token_type, self.id_token)} return requester(url, headers=headers, **kwargs) def get_graph(self, network_id: str) -> BELGraph: """Get a graph from BioDati.""" return from_graphdati(self.get_graph_json(network_id)) def get_graph_json(self, network_id: str, network_format: str = "normal"): """Get the graph JSON.""" res = self.get( "networks/{network_id}".format(network_id=network_id), params={"format": network_format}, ) # FIXME network_format='full' causes internal server error currently res_json = res.json() return res_json def post_graph( self, graph: BELGraph, *, use_tqdm: bool = True, collections: Optional[List[str]] = None, overwrite: bool = False, validate: bool = True, email: Union[bool, str] = False, ) -> requests.Response: """Post the graph to BioDati. :param graph: A BEL graph :param use_tqdm: Should tqdm be used when iterating? :param collections: Tags to add to the nanopubs for lookup on BioDati :param overwrite: Set the BioDati upload "overwrite" setting :param validate: Set the BioDati upload "validate" setting :param email: Who should get emailed with results about the upload? If true, emails to user used for login. If string, emails to that user. If false, no email. :return: Last response from upload """ metadata_extras = dict() if collections is not None: metadata_extras.update(collections=list(collections)) j = to_graphdati(graph, use_tqdm=use_tqdm, metadata_extras=metadata_extras) return self.post_graph_json( j, overwrite=overwrite, validate=validate, email=email, ) def post_graph_chunked( self, graph: BELGraph, chunksize: int, *, use_tqdm: bool = True, collections: Optional[Iterable[str]] = None, overwrite: bool = False, validate: bool = True, email: Union[bool, str] = False, ) -> requests.Response: """Post the graph to BioDati in chunks, when the graph is too big for a normal upload. :param graph: A BEL graph :param chunksize: The size of the chunks of nanopubs to upload :param use_tqdm: Should tqdm be used when iterating? :param collections: Tags to add to the nanopubs for lookup on BioDati :param overwrite: Set the BioDati upload "overwrite" setting :param validate: Set the BioDati upload "validate" setting :param email: Who should get emailed with results about the upload? If true, emails to user used for login. If string, emails to that user. If false, no email. :return: Last response from upload """ metadata_extras = dict() if collections is not None: metadata_extras.update(collections=list(collections)) iterable = _iter_graphdati(graph, use_tqdm=use_tqdm, metadata_extras=metadata_extras) res = None for chunk in chunked(iterable, chunksize): res = self.post_graph_json( chunk, overwrite=overwrite, validate=validate, email=email, ) return res def post_graph_json( self, graph_json: List[Mapping[str, Any]], overwrite: bool = False, validate: bool = True, email: Union[bool, str] = False, ) -> requests.Response: """Post the GraphDati object to BioDati. :param graph_json: The JSON object (in GraphDati schema) to upload to BioDati :param overwrite: Set the BioDati upload "overwrite" setting :param validate: Set the BioDati upload "validate" setting :param email: Who should get emailed with results about the upload? If true, emails to user used for login. If string, emails to that user. If false, no email. """ file = BytesIO() file.write(json.dumps(graph_json).encode("utf-8")) file.seek(0) return self.post_graph_file( file, overwrite=overwrite, validate=validate, email=email, ) def post_graph_file( self, file: BytesIO, overwrite: bool = False, validate: bool = True, email: Union[bool, str] = False, ) -> requests.Response: """Post a graph to BioDati. :param file: A file in bytes mode or BytesIO object :param overwrite: Set the BioDati upload "overwrite" setting :param validate: Set the BioDati upload "validate" setting :param email: Who should get emailed with results about the upload? If true, emails to user used for login. If string, emails to that user. If false, no email. """ params = dict(overwrite=overwrite, validate=validate) if isinstance(email, str): params["email"] = email elif email: params["email"] = self.username return self.post( "nanopubs/import/file", files=dict(file=file), params=params, ) def _main(): """Run with python -m pybel.io.graphdati.""" network_id = "01E46GDFQAGK5W8EFS9S9WMH12" graph = from_biodati(network_id=network_id) graph.summarize() if __name__ == "__main__": logging.basicConfig(level=logging.INFO) _main() pybel-0.15.5/src/pybel/io/cx.py000066400000000000000000000465171426625374700162470ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module wraps conversion between :class:`pybel.BELGraph` and the Cyberinfrastructure Exchange (CX) JSON. CX is an aspect-oriented network interchange format encoded in JSON with a format inspired by the JSON-LD encoding of Resource Description Framework (RDF). It is primarily used by the Network Data Exchange (NDEx) and more recent versions of Cytoscape. .. seealso:: - The NDEx Data Model `Specification `_ - `Cytoscape.js `_ - CX Support for Cytoscape.js on the Cytoscape `App Store `_ """ import gzip import json import logging import time from collections import defaultdict from operator import methodcaller from typing import Dict, List, Mapping, Optional, TextIO, Union from networkx.utils import open_file from ..canonicalize import calculate_canonical_name from ..constants import ( ANNOTATIONS, CITATION, EVIDENCE, FUSION, GRAPH_ANNOTATION_LIST, GRAPH_ANNOTATION_PATTERN, GRAPH_ANNOTATION_URL, GRAPH_METADATA, GRAPH_NAMESPACE_PATTERN, GRAPH_NAMESPACE_URL, MEMBERS, NAME, PARTNER_3P, PARTNER_5P, PRODUCTS, RANGE_3P, RANGE_5P, REACTANTS, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, UNQUALIFIED_EDGES, VARIANTS, ) from ..dsl import BaseAbundance, BaseEntity from ..language import Entity from ..struct import BELGraph from ..tokens import parse_result_to_dsl from ..utils import expand_dict, flatten_dict __all__ = [ "to_cx", "to_cx_file", "to_cx_gz", "to_cx_jsons", "from_cx", "from_cx_file", "from_cx_gz", "from_cx_jsons", "NDEX_SOURCE_FORMAT", ] log = logging.getLogger(__name__) CX_NODE_NAME = "label" NDEX_SOURCE_FORMAT = "ndex:sourceFormat" NDEX_SOURCE_MODIFIER = "sourceModifier" NDEX_TARGET_MODIFIER = "targetModifier" def _cx_to_dict(list_of_dicts: List[Dict], key_tag: str = "k", value_tag: str = "v") -> Dict: """Convert a CX list of dictionaries to a flat dictionary.""" return {d[key_tag]: d[value_tag] for d in list_of_dicts} def _cleanse_fusion_dict(d: Dict) -> Dict: """Fix the fusion partner names.""" return {k.replace("_", ""): v for k, v in d.items()} _p_dict = { "partner5p": PARTNER_5P, "partner3p": PARTNER_3P, "range5p": RANGE_5P, "range3p": RANGE_3P, } def _restore_fusion_dict(d: Dict) -> Dict: return {_p_dict[k]: v for k, v in d.items()} def build_node_mapping(graph: BELGraph) -> Mapping[BaseEntity, int]: """Build a mapping from a graph's nodes to their canonical sort order.""" return {node: node_index for node_index, node in enumerate(sorted(graph, key=methodcaller("as_bel")))} def to_cx(graph: BELGraph) -> List[Dict]: # noqa: C901 """Convert a BEL Graph to a CX JSON object for use with `NDEx `_. .. seealso:: - `NDEx Python Client `_ """ node_mapping = build_node_mapping(graph) node_index_data = {} nodes_entry = [] node_attributes_entry = [] for node, node_index in node_mapping.items(): node_index_data[node_index] = node node_entry_dict = { "@id": node_index, "n": calculate_canonical_name(node), } if isinstance(node, BaseAbundance): node_entry_dict["r"] = node.curie nodes_entry.append(node_entry_dict) aliases = [] if isinstance(node, BaseAbundance): aliases.extend(xref.curie for xref in node.xrefs) if aliases: node_attributes_entry.append( { "po": node_index, "n": "alias", "v": aliases, "d": "list_of_str", } ) for k, v in node.items(): if k == VARIANTS: for i, el in enumerate(v): for a, b in flatten_dict(el).items(): node_attributes_entry.append( { "po": node_index, "n": "{}_{}_{}".format(k, i, a), "v": b, } ) elif k == FUSION: v = _cleanse_fusion_dict(v) for a, b in flatten_dict(v).items(): node_attributes_entry.append( { "po": node_index, "n": "{}_{}".format(k, a), "v": b, } ) elif k == NAME: node_attributes_entry.append( { "po": node_index, "n": CX_NODE_NAME, "v": v, } ) elif k in {PRODUCTS, REACTANTS, MEMBERS}: node_attributes_entry.append( { "po": node_index, "n": k, "v": json.dumps(v), } ) else: node_attributes_entry.append( { "po": node_index, "n": k, "v": v, } ) edges_entry = [] edge_attributes_entry = [] for edge_index, (source, target, d) in enumerate(graph.edges(data=True)): uid = node_mapping[source] vid = node_mapping[target] edges_entry.append( { "@id": edge_index, "s": uid, "t": vid, "i": d[RELATION], } ) if EVIDENCE in d: edge_attributes_entry.append( { "po": edge_index, "n": EVIDENCE, "v": d[EVIDENCE], } ) for k, v in d[CITATION].items(): edge_attributes_entry.append( { "po": edge_index, "n": "{}_{}".format(CITATION, k), "v": v, } ) if ANNOTATIONS in d: for annotation, values in d[ANNOTATIONS].items(): edge_attributes_entry.append( { "po": edge_index, "n": annotation, "v": sorted(values, key=lambda e: (e.namespace, e.identifier, e.name)), "d": "list_of_string", } ) if SOURCE_MODIFIER in d: for k, v in flatten_dict(d[SOURCE_MODIFIER]).items(): edge_attributes_entry.append( { "po": edge_index, "n": "{}_{}".format(NDEX_SOURCE_MODIFIER, k), "v": v, } ) if TARGET_MODIFIER in d: for k, v in flatten_dict(d[TARGET_MODIFIER]).items(): edge_attributes_entry.append( { "po": edge_index, "n": "{}_{}".format(NDEX_TARGET_MODIFIER, k), "v": v, } ) context_legend = {} for key in graph.namespace_url: context_legend[key] = GRAPH_NAMESPACE_URL for key in graph.namespace_pattern: context_legend[key] = GRAPH_NAMESPACE_PATTERN for key in graph.annotation_url: context_legend[key] = GRAPH_ANNOTATION_URL for key in graph.annotation_pattern: context_legend[key] = GRAPH_ANNOTATION_PATTERN for key in graph.annotation_list: context_legend[key] = GRAPH_ANNOTATION_LIST context_legend_entry = [] for keyword, resource_type in context_legend.items(): context_legend_entry.append( { "k": keyword, "v": resource_type, } ) annotation_list_keys_lookup = {keyword: i for i, keyword in enumerate(sorted(graph.annotation_list))} annotation_lists_entry = [] for keyword, values in graph.annotation_list.items(): for v in values: annotation_lists_entry.append( { "k": annotation_list_keys_lookup[keyword], "v": v, } ) context_entry_dict = {} context_entry_dict.update(graph.namespace_url) context_entry_dict.update(graph.namespace_pattern) context_entry_dict.update(graph.annotation_url) context_entry_dict.update(graph.annotation_pattern) context_entry_dict.update(annotation_list_keys_lookup) context_entry_dict.update(graph.namespace_url) context_entry = [context_entry_dict] network_attributes_entry = [ { "n": NDEX_SOURCE_FORMAT, "v": "PyBEL", } ] for k, v in graph.document.items(): network_attributes_entry.append( { "n": k, "v": v, } ) # Coalesce to cx # cx = create_aspect.number_verification() cx = [{"numberVerification": [{"longNumber": 281474976710655}]}] cx_pairs = [ ("@context", context_entry), ("context_legend", context_legend_entry), ("annotation_lists", annotation_lists_entry), ("networkAttributes", network_attributes_entry), ("nodes", nodes_entry), ("nodeAttributes", node_attributes_entry), ("edges", edges_entry), ("edgeAttributes", edge_attributes_entry), ] cx_metadata = [] for key, aspect in cx_pairs: aspect_dict = { "name": key, "elementCount": len(aspect), "lastUpdate": time.time(), "consistencyGroup": 1, "properties": [], "version": "1.0", } if key in {"citations", "supports", "nodes", "edges"}: aspect_dict["idCounter"] = len(aspect) cx_metadata.append(aspect_dict) cx.append( { "metaData": cx_metadata, } ) for key, aspect in cx_pairs: cx.append( { key: aspect, } ) cx.append({"status": [{"error": "", "success": True}]}) return cx @open_file(1, mode="w") def to_cx_file(graph: BELGraph, path: Union[str, TextIO], indent: Optional[int] = 2, **kwargs) -> None: """Write a BEL graph to a JSON file in CX format. :param graph: A BEL graph :param path: A writable file or file-like :param indent: How many spaces to use to pretty print. Change to None for no pretty printing The example below shows how to output a BEL graph as CX to an open file. .. code-block:: python from pybel.examples import sialic_acid_graph from pybel import to_cx_file with open('graph.bel.cx.json', 'w') as file: to_cx_file(sialic_acid_graph, file) The example below shows how to output a BEL graph as CX to a file at a given path. .. code-block:: python from pybel.examples import sialic_acid_graph from pybel import to_cx_file to_cx_file(sialic_acid_graph, 'graph.bel.cx.json') If you have a big graph, you might consider storing it as a gzipped JGIF file by using :func:`to_cx_gz`. """ graph_cx_json_dict = to_cx(graph) json.dump(graph_cx_json_dict, path, ensure_ascii=False, indent=indent, **kwargs) def to_cx_gz(graph, path: str, **kwargs) -> None: """Write a graph as CX JSON to a gzip file.""" with gzip.open(path, "wt") as file: json.dump(to_cx(graph), file, ensure_ascii=False, **kwargs) def to_cx_jsons(graph: BELGraph, **kwargs) -> str: """Dump this graph as a CX JSON object to a string.""" return json.dumps(to_cx(graph), ensure_ascii=False, **kwargs) def _iterate_list_of_dicts(list_of_dicts: List[Dict]): """Iterate over a list of dictionaries. :type list_of_dicts: list[dict[A,B]] :rtype: iter[tuple[A,B]] """ for dictionary in list_of_dicts: for key, value in dictionary.items(): yield key, value def from_cx(cx: List[Dict]) -> BELGraph: # noqa: C901 """Rebuild a BELGraph from CX JSON output from PyBEL. :param cx: The CX JSON object for this graph """ graph = BELGraph() context_legend_aspect = [] annotation_lists_aspect = [] context_entry = {} network_attributes_aspect = [] nodes_aspect = [] node_attributes_aspect = [] edge_annotations_aspect = [] edges_aspect = [] meta_entries = defaultdict(list) for key, value in _iterate_list_of_dicts(cx): if key == "context_legend": context_legend_aspect.extend(value) elif key == "annotation_lists": annotation_lists_aspect.extend(value) elif key == "@context": for element in value: context_entry.update(element) elif key == "networkAttributes": network_attributes_aspect.extend(value) elif key == "nodes": nodes_aspect.extend(value) elif key == "nodeAttributes": node_attributes_aspect.extend(value) elif key == "edges": edges_aspect.extend(value) elif key == "edgeAttributes": edge_annotations_aspect.extend(value) else: meta_entries[key].extend(value) context_legend = _cx_to_dict(context_legend_aspect) annotation_lists = defaultdict(set) for data in annotation_lists_aspect: annotation_lists[data["k"]].add(data["v"]) for keyword, entry in context_entry.items(): if context_legend[keyword] == GRAPH_NAMESPACE_URL: graph.namespace_url[keyword] = entry elif context_legend[keyword] == GRAPH_NAMESPACE_PATTERN: graph.namespace_pattern[keyword] = entry elif context_legend[keyword] == GRAPH_ANNOTATION_URL: graph.annotation_url[keyword] = entry elif context_legend[keyword] == GRAPH_ANNOTATION_PATTERN: graph.annotation_pattern[keyword] = entry elif context_legend[keyword] == GRAPH_ANNOTATION_LIST: graph.annotation_list[keyword] = annotation_lists[entry] for data in network_attributes_aspect: if data["n"] == NDEX_SOURCE_FORMAT: continue graph.graph[GRAPH_METADATA][data["n"]] = data["v"] node_name = {} for data in nodes_aspect: node_name[data["@id"]] = data["n"] node_data = defaultdict(dict) for data in node_attributes_aspect: node_data[data["po"]][data["n"]] = data["v"] # put all normal data here node_data_pp = defaultdict(dict) # Group all fusion-related data here node_data_fusion = defaultdict(dict) # Group all variant-related data node_data_variants = defaultdict(lambda: defaultdict(dict)) for nid, data in node_data.items(): for key, value in data.items(): if key.startswith(FUSION): node_data_fusion[nid][key] = value elif key.startswith(VARIANTS): _, i, vls = key.split("_", 2) node_data_variants[nid][i][vls] = value elif key in {PRODUCTS, REACTANTS, MEMBERS}: node_data_pp[nid][key] = json.loads(value) else: node_data_pp[nid][key] = value for nid, data in node_data_fusion.items(): data = expand_dict(data) data[FUSION] = _restore_fusion_dict(data[FUSION]) node_data_pp[nid].update(data) for nid, data in node_data_variants.items(): node_data_pp[nid][VARIANTS] = [expand_dict(value) for _, value in sorted(data.items())] nid_node_tuple = {} for nid, data in node_data_pp.items(): if CX_NODE_NAME in data: data[NAME] = data.pop(CX_NODE_NAME) nid_node_tuple[nid] = _node = parse_result_to_dsl(data) graph.add_node_from_data(_node) edge_relation = {} eid_source_nid = {} eid_target_nid = {} for data in edges_aspect: eid = data["@id"] edge_relation[eid] = data["i"] eid_source_nid[eid] = data["s"] eid_target_nid[eid] = data["t"] edge_data = defaultdict(dict) # type: Dict[str, Dict[str, str]] for data in edge_annotations_aspect: edge_data[data["po"]][data["n"]] = data["v"] edge_citation = defaultdict(dict) # type: Dict[str, Dict[str, str]] edge_subject = defaultdict(dict) edge_object = defaultdict(dict) edge_annotations = defaultdict(lambda: defaultdict(dict)) edge_data_pp = defaultdict(dict) for eid, data in edge_data.items(): for key, value in data.items(): if key.startswith(CITATION): vl = _after_underscore(key) edge_citation[eid][vl] = value elif key.startswith(NDEX_SOURCE_MODIFIER): vl = _after_underscore(key) edge_subject[eid][vl] = value elif key.startswith(NDEX_TARGET_MODIFIER): vl = _after_underscore(key) edge_object[eid][vl] = value elif key == EVIDENCE: edge_data_pp[eid][EVIDENCE] = value else: edge_annotations[eid][key] = value for eid, data in edge_citation.items(): edge_data_pp[eid][CITATION] = data for eid, data in edge_subject.items(): edge_data_pp[eid][SOURCE_MODIFIER] = expand_dict(data) for eid, data in edge_object.items(): edge_data_pp[eid][TARGET_MODIFIER] = expand_dict(data) for eid in edge_relation: if eid in edge_annotations: # FIXME stick this in edge_data.items() iteration edge_data_pp[eid][ANNOTATIONS] = { key: [Entity(**v) for v in values] for key, values in edge_annotations[eid].items() } if eid in edge_citation: graph.add_qualified_edge( nid_node_tuple[eid_source_nid[eid]], nid_node_tuple[eid_target_nid[eid]], relation=edge_relation[eid], citation=edge_data_pp[eid][CITATION], evidence=edge_data_pp[eid][EVIDENCE], source_modifier=edge_data_pp[eid].get(SOURCE_MODIFIER), target_modifier=edge_data_pp[eid].get(TARGET_MODIFIER), annotations=edge_data_pp[eid].get(ANNOTATIONS), ) elif edge_relation[eid] in UNQUALIFIED_EDGES: graph.add_unqualified_edge( nid_node_tuple[eid_source_nid[eid]], nid_node_tuple[eid_target_nid[eid]], edge_relation[eid], ) else: raise ValueError("problem adding edge: {}".format(eid)) return graph def _after_underscore(key): _, vl = key.split("_", 1) return vl @open_file(0, mode="r") def from_cx_file(path: Union[str, TextIO]) -> BELGraph: """Read a file containing CX JSON and converts to a BEL graph. :param path: A readable file or file-like containing the CX JSON for this graph :return: A BEL Graph representing the CX graph contained in the file """ return from_cx(json.load(path)) def from_cx_gz(path: str) -> BELGraph: """Read a graph as CX JSON from a gzip file.""" with gzip.open(path, "rt") as file: return from_cx(json.load(file)) def from_cx_jsons(graph_json_str: str) -> BELGraph: """Read a BEL graph from a CX JSON string.""" return from_cx(json.loads(graph_json_str)) pybel-0.15.5/src/pybel/io/emmaa.py000066400000000000000000000074421426625374700167070ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Ecosystem of Machine-maintained Models with Automated Analysis (EMMAA). `EMMAA `_ is a project built on top of INDRA by the Sorger Lab at Harvard Medical School. It automatically builds knowledge graphs around pathways/indications periodically (almost daily) using the INDRA Database, which in turn is updated periodically (almost daily) with the most recent literature from MEDLINE, PubMed Central, several major publishers, and other bespoke text corpora such as CORD-19. """ import json import logging from typing import Iterable, Optional from xml.etree import ElementTree # noqa:S405 import click import requests from more_click import verbose_option from .indra import from_indra_statements from ..struct import BELGraph __all__ = [ "from_emmaa", ] logger = logging.getLogger(__name__) def from_emmaa( model: str, *, date: Optional[str] = None, extension: Optional[str] = None, suppress_warnings: bool = False, ) -> BELGraph: """Get an EMMAA model as a BEL graph. Get the most recent COVID-19 model from EMMAA with the following: .. code-block:: python import pybel covid19_emmaa_graph = pybel.from_emmaa('covid19', extension='jsonl') covid19_emmaa_graph.summarize() PyBEL does its best to look up the most recent model, but if that doesn't work, you can specify it explicitly with the ``date`` keyword argument in the form of ``%Y-%m-%d-%H-%M-%S`` like in the following: .. code-block:: python import pybel covid19_emmaa_graph = pybel.from_emmaa('covid19', '2020-04-23-17-44-57', extension='jsonl') covid19_emmaa_graph.summarize() """ statements = get_statements_from_emmaa( model=model, date=date, extension=extension, suppress_warnings=suppress_warnings, ) return from_indra_statements(statements, name=model, version=date) def get_statements_from_emmaa( model: str, *, date: Optional[str] = None, extension: Optional[str] = None, suppress_warnings: bool = False, ): """Get INDRA statements from EMMAA. :rtype: List[indra.statements.Statement] """ from indra.statements import stmts_from_json if suppress_warnings: logging.getLogger("indra.assemblers.pybel.assembler").setLevel(logging.ERROR) logging.getLogger("indra.sources.bel.processor").setLevel(logging.ERROR) if extension is None: extension = "json" if date is None: url = f"https://emmaa.s3.amazonaws.com/assembled/{model}/latest_statements_{model}.{extension}" else: url = f"https://emmaa.s3.amazonaws.com/assembled/{model}/statements_{date}.{extension}" res = requests.get(url) if extension == "jsonl": res_json = [json.loads(line) for line in res.text.splitlines()] return stmts_from_json(res_json) elif extension == "json": res_json = res.json() return stmts_from_json(res_json) elif extension == "gz": raise NotImplementedError else: raise ValueError(f"unhandled extension: {extension}") def _get_latest_date(model: str) -> str: res = requests.get("https://emmaa.s3.amazonaws.com/") tree = ElementTree.fromstring(res.text) # noqa:S314 return max(_iter_dates(tree, model)) def _iter_dates(tree: ElementTree, model: str) -> Iterable[str]: aws = "{http://s3.amazonaws.com/doc/2006-03-01/}" for x in tree.findall(f"{aws}Contents/{aws}Key"): prefix = f"assembled/{model}/statements_" if x.text.startswith(prefix): yield x.text @click.command() @verbose_option @click.option("--extension", default="jsonl") def main(extension: str): """Run the EMMAA converter.""" from_emmaa("covid19", extension=extension).summarize() if __name__ == "__main__": main() pybel-0.15.5/src/pybel/io/exc.py000066400000000000000000000016251426625374700164030ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Exceptions for input/output.""" from typing import Tuple from ..exceptions import PyBELWarning import_version_message_fmt = "Tried importing from PyBEL v{}. Need at least v{}" class ImportVersionWarning(PyBELWarning, ValueError): """Raised when trying to import data from an old version of PyBEL.""" def __init__( self, actual_version_tuple: Tuple[int, int, int], minimum_version_tuple: Tuple[int, int, int], ) -> None: """Build an import version warning.""" super().__init__(actual_version_tuple, minimum_version_tuple) self.actual_tuple = actual_version_tuple self.minimum_tuple = minimum_version_tuple def __str__(self): actual_s = ".".join(map(str, self.actual_tuple)) minimum_s = ".".join(map(str, self.minimum_tuple)) return import_version_message_fmt.format(actual_s, minimum_s) pybel-0.15.5/src/pybel/io/extras.py000066400000000000000000000047171426625374700171370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains IO functions for outputting BEL graphs to lossy formats, such as GraphML and CSV.""" import json from typing import Optional, TextIO, Union from networkx.utils import open_file from ..dsl import CentralDogma from ..struct import BELGraph __all__ = [ "to_csv", "to_sif", "to_gsea", ] @open_file(1, mode="w") def to_csv(graph: BELGraph, path: Union[str, TextIO], sep: Optional[str] = None) -> None: """Write the graph as a tab-separated edge list. The resulting file will contain the following columns: 1. Source BEL term 2. Relation 3. Target BEL term 4. Edge data dictionary See the Data Models section of the documentation for which data are stored in the edge data dictionary, such as queryable information about transforms on the subject and object and their associated metadata. """ if sep is None: sep = "\t" for u, v, data in graph.edges(data=True): print( graph.edge_to_bel(u, v, edge_data=data, sep=sep), json.dumps(data), sep=sep, file=path, ) @open_file(1, mode="w") def to_sif(graph: BELGraph, path: Union[str, TextIO], sep: Optional[str] = None) -> None: """Write the graph as a tab-separated SIF file. The resulting file will contain the following columns: 1. Source BEL term 2. Relation 3. Target BEL term This format is simple and can be used readily with many applications, but is lossy in that it does not include relation metadata. """ if sep is None: sep = "\t" for u, v, data in graph.edges(data=True): print( graph.edge_to_bel(u, v, edge_data=data, sep=sep), file=path, ) @open_file(1, mode="w") def to_gsea(graph: BELGraph, path: Union[str, TextIO]) -> None: """Write the genes/gene products to a GRP file for use with GSEA gene set enrichment analysis. .. seealso:: - GRP `format specification `_ - GSEA `publication `_ """ print("# {}".format(graph.name), file=path) hgnc_gene_symbols = { node.name for node in graph if isinstance(node, CentralDogma) and node.namespace.lower() == "hgnc" } for hgnc_gene_symbol in sorted(hgnc_gene_symbols): print(hgnc_gene_symbol, file=path) pybel-0.15.5/src/pybel/io/fraunhofer_orientdb.py000066400000000000000000000156471426625374700216620ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Transport functions for `Fraunhofer's OrientDB `_. `Fraunhofer `_ hosts an instance of `OrientDB `_ that contains BEL in a schema similar to :mod:`pybel.io.umbrella_nodelink`. However, they include custom relations that do not come from a controlled vocabulary, and have not made the schema, ETL scripts, or documentation available. Unlike BioDati and BEL Commons, the Fraunhofer OrientDB does not allow for uploads, so only a single function :func:`pybel.from_fraunhofer_orientdb` is provided by PyBEL. """ import logging from typing import Any, Iterable, Mapping, Optional from urllib.parse import quote_plus import requests from pyparsing import ParseException from .. import constants as pc from ..parser import BELParser from ..struct import BELGraph __all__ = [ "from_fraunhofer_orientdb", ] logger = logging.getLogger(__name__) def from_fraunhofer_orientdb( # noqa:S107 database: str = "covid", user: str = "covid_user", password: str = "covid", query: Optional[str] = None, ) -> BELGraph: """Get a BEL graph from the Fraunhofer OrientDB. :param database: The OrientDB database to connect to :param user: The user to connect to OrientDB :param password: The password to connect to OrientDB :param query: The query to run. Defaults to the URL encoded version of ``select from E``, where ``E`` is all edges in the OrientDB edge database. Likely does not need to be changed, except in the case of selecting specific subsets of edges. Make sure you URL encode it properly, because OrientDB's RESTful API puts it in the URL's path. By default, this function connects to the ``covid`` database, that corresponds to the COVID-19 Knowledge Graph [0]_. If other databases in the Fraunhofer OrientDB are published and demo username/password combinations are given, the following table will be updated. +----------+------------+----------+ | Database | Username | Password | +==========+============+==========+ | covid | covid_user | covid | +----------+------------+----------+ The ``covid`` database can be downloaded and converted to a BEL graph like this: .. code-block:: python import pybel graph = pybel.from_fraunhofer_orientdb( database='covid', user='covid_user', password='covid', ) graph.summarize() However, because the source BEL scripts for the COVID-19 Knowledge Graph are available on `GitHub `_ and the authors pre-enabled it for PyBEL, it can be downloaded with ``pip install git+https://github.com/covid19kg/covid19kg.git`` and used with the following python code: .. code-block:: python import covid19kg graph = covid19kg.get_graph() graph.summarize() .. warning:: It was initially planned to handle some of the non-standard relationships listed in the Fraunhofer OrientDB's `schema `_ in their OrientDB Studio instance, but none of them actually appear in the only network that is accessible. If this changes, please leave an issue at https://github.com/pybel/pybel/issues so it can be addressed. .. [0] Domingo-Fernández, D., *et al.* (2020). `COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology `_. *bioRxiv* 2020.04.14.040667. """ graph = BELGraph(name="Fraunhofer OrientDB: {}".format(database)) parser = BELParser(graph, skip_validation=True) results = _request_graphstore(database, user, password, select_query_template=query) for result in results: _parse_result(parser, result) return graph def _parse_result(parser: BELParser, result: Mapping[str, Any]) -> None: citation_db, citation_id = pc.CITATION_TYPE_PUBMED, result.get("pmid") if citation_id is None: citation_db, citation_id = pc.CITATION_TYPE_PMC, result.get("pmc") if citation_id is None: if "citation" in result: logger.warning( "incorrect citation information for %s: %s", result["@rid"], result["citation"], ) else: logger.debug("no citation information for %s", result["@rid"]) return parser.control_parser.clear() parser.control_parser.citation_db = citation_db parser.control_parser.citation_db_id = citation_id parser.control_parser.evidence = result["evidence"] parser.control_parser.annotations.update(result["annotation"]) source = result["in"]["bel"] relation = result["@class"] relation = RELATION_MAP.get(relation, relation) target = result["out"]["bel"] statement = " ".join([source, relation, target]) try: parser.parseString(statement) except ParseException: logger.warning("could not parse %s", statement) RELATION_MAP = { "causes_no_change": pc.CAUSES_NO_CHANGE, "positive_correlation": pc.POSITIVE_CORRELATION, "negative_correlation": pc.NEGATIVE_CORRELATION, "is_a": pc.IS_A, "has_member": "hasMember", "has_members": "hasMembers", "has_component": "hasComponent", "has_components": "hasComponents", } def _request_graphstore( database: str, user: str, password: str, count_query: Optional[str] = None, select_query_template: Optional[str] = None, page_size: int = 500, base: str = "http://graphstore.scai.fraunhofer.de/query", ) -> Iterable[Mapping[str, Any]]: """Make an API call to the OrientDB.""" if count_query is None: count_query = "select count(@rid) from E" count_query = quote_plus(count_query) count_url = "{base}/{database}/sql/{count_query}".format(base=base, database=database, count_query=count_query) count_res = requests.get(count_url, auth=(user, password)) count = count_res.json()["result"][0]["count"] logging.debug("fraunhofer orientdb has %d edges", count) if select_query_template is None: select_query_template = "select from E order by @rid limit {limit} offset {offset}" offsets = count // page_size for offset in range(offsets + 1): select_query = select_query_template.format(limit=page_size, offset=offset * page_size) logger.debug("query: %s", select_query) select_query = quote_plus(select_query) select_url = "{base}/{database}/sql/{select_query}/{page_size}/*:1".format( base=base, database=database, select_query=select_query, page_size=page_size, ) res = requests.get(select_url, auth=(user, password)) res_json = res.json() result = res_json["result"] yield from result pybel-0.15.5/src/pybel/io/gpickle.py000066400000000000000000000072531426625374700172450ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Conversion functions for BEL graphs with bytes and Python pickles.""" import gzip from io import BytesIO from typing import BinaryIO, Union from networkx.utils import open_file from .utils import raise_for_not_bel, raise_for_old_graph from ..struct.graph import BELGraph try: import pickle5 as pickle except ImportError: import pickle __all__ = [ "to_bytes", "from_bytes", "to_bytes_gz", "from_bytes_gz", "to_pickle", "to_pickle_gz", "from_pickle", "from_pickle_gz", ] def to_bytes(graph: BELGraph, protocol: int = pickle.HIGHEST_PROTOCOL) -> bytes: """Convert a graph to bytes with pickle. Note that the pickle module has some incompatibilities between Python 2 and 3. To export a universally importable pickle, choose 0, 1, or 2. :param graph: A BEL graph :param protocol: Pickling protocol to use. Defaults to ``HIGHEST_PROTOCOL``. .. seealso:: https://docs.python.org/3.6/library/pickle.html#data-stream-format """ raise_for_not_bel(graph) return pickle.dumps(graph, protocol=protocol) def from_bytes(bytes_graph: bytes, check_version: bool = True) -> BELGraph: """Read a graph from bytes (the result of pickling the graph). :param bytes_graph: File or filename to write :param check_version: Checks if the graph was produced by this version of PyBEL """ graph = pickle.loads(bytes_graph) raise_for_not_bel(graph) if check_version: raise_for_old_graph(graph) return graph def to_bytes_gz(graph: BELGraph, protocol: int = pickle.HIGHEST_PROTOCOL) -> bytes: """Convert a graph to gzipped bytes with pickle. :param graph: A BEL graph :param protocol: Pickling protocol to use. Defaults to ``HIGHEST_PROTOCOL``. """ io = BytesIO() with gzip.open(io, mode="wb") as file: pickle.dump(graph, file, protocol=protocol) return io.getvalue() def from_bytes_gz(bytes_graph: bytes) -> BELGraph: """Read a graph from gzipped bytes (the result of pickling the graph). :param bytes_graph: File or filename to write """ with gzip.GzipFile(fileobj=BytesIO(bytes_graph), mode="rb") as file: return pickle.load(file) @open_file(1, mode="wb") def to_pickle(graph: BELGraph, path: Union[str, BinaryIO], protocol: int = pickle.HIGHEST_PROTOCOL) -> None: """Write this graph to a pickle file. Note that the pickle module has some incompatibilities between Python 2 and 3. To export a universally importable pickle, choose 0, 1, or 2. :param graph: A BEL graph :param path: A path or file-like :param protocol: Pickling protocol to use. Defaults to ``HIGHEST_PROTOCOL``. .. seealso:: https://docs.python.org/3.6/library/pickle.html#data-stream-format """ raise_for_not_bel(graph) pickle.dump(graph, path, protocol) def to_pickle_gz(graph: BELGraph, path: str, protocol: int = pickle.HIGHEST_PROTOCOL) -> None: """Write this graph to a gzipped pickle file.""" with gzip.open(path, "wb") as file: to_pickle(graph, file, protocol=protocol) @open_file(0, mode="rb") def from_pickle(path: Union[str, BinaryIO], check_version: bool = True) -> BELGraph: """Read a graph from a pickle file. :param path: File or filename to read. Filenames ending in .gz or .bz2 will be uncompressed. :param bool check_version: Checks if the graph was produced by this version of PyBEL """ graph = pickle.load(path) raise_for_not_bel(graph) if check_version: raise_for_old_graph(graph) return graph def from_pickle_gz(path: str) -> BELGraph: """Read a graph from a gzipped pickle file.""" with gzip.open(path, "rb") as file: return from_pickle(file) pybel-0.15.5/src/pybel/io/graphdati.py000066400000000000000000000241331426625374700175660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Conversion functions for BEL graphs with GraphDati. Note that these are not exact I/O - you can't currently use them as a round trip because the input functions expect the GraphDati format that's output by BioDati. """ import gzip import json import logging from collections import defaultdict from typing import Any, Iterable, List, Mapping, Optional, TextIO, Tuple, Union import pyparsing from networkx.utils import open_file from tqdm.autonotebook import tqdm from .jgif import NAMESPACE_TO_PATTERN from ..canonicalize import edge_to_tuple from ..constants import ( ANNOTATIONS, CITATION, CITATION_TYPE_PUBMED, CITATION_TYPE_URL, EVIDENCE, IDENTIFIER, NAMESPACE, RELATION, UNQUALIFIED_EDGES, ) from ..parser import BELParser from ..struct import BELGraph from ..typing import EdgeData __all__ = [ "to_graphdati", "from_graphdati", "to_graphdati_file", "from_graphdati_file", "to_graphdati_gz", "from_graphdati_gz", "to_graphdati_jsons", "from_graphdati_jsons", "to_graphdati_jsonl", "to_graphdati_jsonl_gz", ] logger = logging.getLogger(__name__) NanopubMapping = Mapping[str, Mapping[str, Any]] SCHEMA_URI = "https://github.com/belbio/schemas/blob/master/schemas/nanopub_bel-1.0.0.yaml" GRAPHDATI_PUBLICATION_TYPES = { "PMID": CITATION_TYPE_PUBMED, "http": CITATION_TYPE_URL, "https": CITATION_TYPE_URL, } @open_file(1, mode="w") def to_graphdati_file(graph: BELGraph, path: Union[str, TextIO], use_identifiers: bool = True, **kwargs) -> None: """Write this graph as GraphDati JSON to a file. :param graph: A BEL graph :param path: A path or file-like """ json.dump(to_graphdati(graph, use_identifiers=use_identifiers), path, ensure_ascii=False, **kwargs) def from_graphdati_file(path: Union[str, TextIO]) -> BELGraph: """Load a file containing GraphDati JSON. :param path: A path or file-like """ return from_graphdati(json.load(path)) def to_graphdati_gz(graph: BELGraph, path: str, **kwargs) -> None: """Write a graph as GraphDati JSON to a gzip file.""" with gzip.open(path, "wt") as file: to_graphdati_file(graph, file, **kwargs) def from_graphdati_gz(path: str) -> BELGraph: """Read a graph as GraphDati JSON from a gzip file.""" with gzip.open(path, "rt") as file: return from_graphdati(json.load(file)) def to_graphdati_jsons(graph: BELGraph, **kwargs) -> str: """Dump this graph as a GraphDati JSON object to a string. :param graph: A BEL graph """ return json.dumps(to_graphdati(graph), ensure_ascii=False, **kwargs) def from_graphdati_jsons(s: str) -> BELGraph: """Load a graph from a GraphDati JSON string. :param graph: A BEL graph """ return from_graphdati(json.loads(s)) @open_file(1, mode="w") def to_graphdati_jsonl(graph, file, use_identifiers: bool = True, use_tqdm: bool = True): """Write this graph as a GraphDati JSON lines file. :param graph: A BEL graph """ for nanopub in _iter_graphdati(graph, use_identifiers=use_identifiers, use_tqdm=use_tqdm): print(json.dumps(nanopub), file=file) def to_graphdati_jsonl_gz(graph: BELGraph, path: str, **kwargs) -> None: """Write a graph as GraphDati JSONL to a gzip file. :param graph: A BEL graph """ with gzip.open(path, "wt") as file: to_graphdati_jsonl(graph, file, **kwargs) def to_graphdati( graph, *, use_identifiers: bool = True, skip_unqualified: bool = True, use_tqdm: bool = False, metadata_extras: Optional[Mapping[str, Any]] = None, ) -> List[NanopubMapping]: """Export a GraphDati list using the nanopub. :param graph: A BEL graph :param use_identifiers: use OBO-style identifiers :param use_tqdm: Show a progress bar while generating nanopubs :param skip_unqualified: Should unqualified edges be output as nanopubs? Defaults to false. :param metadata_extras: Extra information to pass into the metadata part of nanopubs """ return list( _iter_graphdati( graph, use_identifiers=use_identifiers, skip_unqualified=skip_unqualified, metadata_extras=metadata_extras, use_tqdm=use_tqdm, ) ) def _iter_graphdati( graph, *, skip_unqualified: bool = True, use_identifiers: bool = True, use_tqdm: bool = False, metadata_extras: Optional[Mapping[str, Any]] = None, ) -> Iterable[NanopubMapping]: it = graph.edges(keys=True, data=True) if use_tqdm: it = tqdm(it, total=graph.number_of_edges(), desc="iterating as nanopubs") for u, v, k, d in it: if skip_unqualified and d[RELATION] in UNQUALIFIED_EDGES: continue yield _make_nanopub(graph, u, v, k, d, use_identifiers, metadata_extras=metadata_extras) def _make_nanopub(graph: BELGraph, u, v, k, d, use_identifiers, metadata_extras=None) -> NanopubMapping: return dict( nanopub=dict( schema_uri=SCHEMA_URI, type=dict(name="BEL", version="2.1.0"), annotations=_get_annotations(d), citation=_get_citation(d), assertions=_get_assertions(u, v, d, use_identifiers), evidence=_get_evidence(d), metadata=_get_metadata(graph, d, extras=metadata_extras), id="pybel_{}".format(k), ), ) def _get_assertions(u, v, d, use_identifiers): return [ dict( zip( ("subject", "relation", "object"), edge_to_tuple(u, v, d, use_identifiers=use_identifiers), ) ), ] def _get_evidence(d): return d.get(EVIDENCE, "Not Available") def _get_citation(d): citation = d.get(CITATION) rv = {} if citation is None: rv["reference"] = "Not Available" else: rv["database"] = dict(name=citation[NAMESPACE], id=citation[IDENTIFIER]) return rv def _get_metadata(graph: BELGraph, _, extras=None): rv = dict( gd_creator=graph.authors, version=graph.version, ) # TODO later if extras is not None: rv.update(extras) return rv def _get_annotations(d: EdgeData) -> List[Mapping[str, str]]: rv = [] for key, values in d.get(ANNOTATIONS, {}).items(): if isinstance(values, dict): for value in values: rv.append( { "type": "Evidence", "label": key, "id": str(value), } ) else: rv.append( { "type": "Evidence", "label": key, "id": str(values), } ) return rv def from_graphdati(j, use_tqdm: bool = True) -> BELGraph: """Convert data from the "normal" network format. .. warning:: BioDati crashes when requesting the ``full`` network format, so this isn't yet explicitly supported """ root = j["graph"] graph = BELGraph( name=root.get("label"), version=root["metadata"].get("gd_rev"), authors=root["metadata"].get("gd_creator"), description=root.get("gd_description"), ) # Just in case you want to find it again graph.graph["biodati_network_id"] = root["metadata"]["id"] parser = BELParser( graph=graph, namespace_to_pattern=NAMESPACE_TO_PATTERN, # To be updated manually depending on what William is up to ) it = root["edges"] if use_tqdm: it = tqdm(it, desc="iterating edges") for i, edge in enumerate(it): relation = edge.get("relation") if relation is None: logger.warning("no relation for edge: %s", edge) if relation in {"actsIn", "translocates"}: continue # don't need legacy BEL format bel_statement = edge.get("label") # this is actually the BEL statement if bel_statement is None: logger.debug("No BEL statement for edge %s", edge) continue # Fill up that sweet, sweet metadata metadata_entries = edge["metadata"]["nanopub_data"] for metadata in metadata_entries: parser.control_parser.clear() citation = metadata["citation_id"] # as CURIE citation_db, citation_id = _parse_biodati_citation(citation) if citation_db is None: continue parser.control_parser.citation_db = citation_db parser.control_parser.citation_db_id = citation_id # FIXME where is the evidence/support/summary text? parser.control_parser.evidence = "No evidence available from BioDai" nanopub_id = metadata["nanopub_id"] parser.control_parser.annotations["biodati_nanopub_id"] = [nanopub_id] annotations = metadata["annotations"] parser.control_parser.annotations.update(_parse_biodati_annotations(annotations)) # Finally, parse the BEL statement (once to go with each set of metadata) # TODO change parser to give back pre-compiled info so this doesn't need to be repeated try: parser.parseString(bel_statement, line_number=i) except pyparsing.ParseException as e: logger.warning("parse error for %s: %s", bel_statement, e) return graph def _parse_biodati_citation(citation: str) -> Union[Tuple[None, None], Tuple[str, str]]: try: citation_db, citation_id = citation.split(":") except ValueError: logger.warning("structured citation not available for %s", citation) return None, None try: citation_db = GRAPHDATI_PUBLICATION_TYPES[citation_db] except KeyError: logger.warning("invalid citation structure: %s", citation) return None, None return citation_db, citation_id def _parse_biodati_annotations(annotations: List[Mapping[str, str]]) -> Mapping[str, Mapping[str, bool]]: rv = defaultdict(set) for annotation in annotations: annotation_curie = annotation["id"] annotation_prefix, annotation_id = annotation_curie.split(":", 1) rv[annotation_prefix].add(annotation_id) return dict(rv) pybel-0.15.5/src/pybel/io/graphml.py000066400000000000000000000044011426625374700172510ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Conversion functions for BEL graphs with `GraphML `_.""" from typing import BinaryIO, Optional, Union import networkx as nx from ..canonicalize import edge_to_tuple from ..constants import RELATION from ..struct import BELGraph __all__ = [ "to_graphml", ] def to_graphml(graph: BELGraph, path: Union[str, BinaryIO], schema: Optional[str] = None) -> None: """Write a graph to a GraphML XML file using :func:`networkx.write_graphml`. :param graph: BEL Graph :param path: Path to the new exported file :param schema: Type of export. Currently supported: "simple" and "umbrella". The .graphml file extension is suggested so Cytoscape can recognize it. By default, this function exports using the PyBEL schema of including modifier information into the edges. As an alternative, this function can also distinguish between """ if schema is None or schema == "simple": rv = _to_graphml_simple(graph) elif schema == "umbrella": rv = _to_graphml_umbrella(graph) else: raise ValueError("Unhandled schema: {}".format(schema)) nx.write_graphml(rv, path) def _to_graphml_simple(graph: BELGraph) -> nx.MultiDiGraph: """Convert a BEL graph to a simple graph. :param graph: A BEL graph """ rv = nx.MultiDiGraph() for node in graph: rv.add_node(node.as_bel(), function=node.function) for u, v, key, edge_data in graph.edges(data=True, keys=True): u_key, v_key = u.as_bel(), v.as_bel() rv.add_edge( u_key, v_key, key=key, interaction=edge_data[RELATION], bel=graph.edge_to_bel(u, v, edge_data), ) return rv def _to_graphml_umbrella(graph: BELGraph) -> nx.MultiDiGraph: """Convert a BEL graph to a new graph the nodes as original BEL terms strings. :param graph: A BEL graph """ rv = nx.MultiDiGraph() for u, v, key, edge_data in graph.edges(data=True, keys=True): u_key, _, v_key = edge_to_tuple(u, v, edge_data) rv.add_edge( u_key, v_key, key=key, relation=edge_data[RELATION], bel=graph.edge_to_bel(u, v, edge_data), ) return rv pybel-0.15.5/src/pybel/io/hetionet/000077500000000000000000000000001426625374700170655ustar00rootroot00000000000000pybel-0.15.5/src/pybel/io/hetionet/__init__.py000066400000000000000000000002561426625374700212010ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Importer for Hetionet JSON.""" from .hetionet import ( from_hetionet_file, from_hetionet_gz, from_hetionet_json, get_hetionet, ) pybel-0.15.5/src/pybel/io/hetionet/__main__.py000066400000000000000000000001641426625374700211600ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Make hetionet exports.""" from .cli import main if __name__ == "__main__": main() pybel-0.15.5/src/pybel/io/hetionet/cli.py000066400000000000000000000066471426625374700202230ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Make hetionet exports.""" import os from random import choice import click import networkx as nx import pandas as pd from tqdm.autonotebook import tqdm from pybel import ( from_nodelink_gz, get_hetionet, to_bel_script, to_bel_script_gz, to_graphdati_file, to_graphdati_gz, to_graphdati_jsonl_gz, to_nodelink_gz, ) from pybel.canonicalize import edge_to_bel from pybel.struct.summary.edge_summary import get_metaedge_to_key @click.command() @click.option( "--directory", default=os.getcwd(), required=True, show_default=True, type=click.Path(dir_okay=True), ) def main(directory: str): """Make hetionet exports.""" path = os.path.join(directory, "hetionet.bel.nodelink.json.gz") if not os.path.exists(path): graph = get_hetionet() to_nodelink_gz(graph, path) else: click.echo("loading pickle from {}".format(path)) graph = from_nodelink_gz(path) output_bel_gz_path = os.path.join(directory, "hetionet.bel.gz") if not os.path.exists(output_bel_gz_path): click.echo("outputting whole hetionet as BEL GZ to {}".format(output_bel_gz_path)) to_bel_script_gz(graph, output_bel_gz_path, use_identifiers=True) output_graphdati_jsonl_gz_path = os.path.join(directory, "hetionet.bel.graphdati.jsonl.gz") if not os.path.exists(output_graphdati_jsonl_gz_path): click.echo("outputting whole hetionet as BEL GraphDati JSONL GZ to {}".format(output_graphdati_jsonl_gz_path)) to_graphdati_jsonl_gz(graph, output_graphdati_jsonl_gz_path, use_identifiers=True) output_graphdati_gz_path = os.path.join(directory, "hetionet.bel.graphdati.json.gz") if not os.path.exists(output_graphdati_gz_path): click.echo("outputting whole hetionet as BEL GraphDati JSON GZ to {}".format(output_graphdati_gz_path)) to_graphdati_gz(graph, output_graphdati_gz_path, use_identifiers=True) summary_tsv_path = os.path.join(directory, "hetionet_summary.tsv") if not os.path.exists(summary_tsv_path): click.echo("getting metaedges") rows = [] keep_keys = set() for value in get_metaedge_to_key(graph).values(): u, v, key = choice(list(value)) keep_keys.add(key) d = graph[u][v][key] bel = edge_to_bel(u, v, d, use_identifiers=True) rows.append((key[:8], bel)) df = pd.DataFrame(rows, columns=["key", "bel"]) df.to_csv(summary_tsv_path, sep="\t", index=False) non_sample_edges = [ (u, v, k, d) for u, v, k, d in tqdm( graph.edges(keys=True, data=True), desc="Getting non-sample edges to remove", ) if k not in keep_keys ] click.echo("Removing non-sample edges") graph.remove_edges_from(non_sample_edges) graph.remove_nodes_from(list(nx.isolates(graph))) sample_bel_path = os.path.join(directory, "hetionet_sample.bel") click.echo("outputting sample hetionet in BEL to {}".format(sample_bel_path)) to_bel_script(graph, sample_bel_path, use_identifiers=True) sample_graphdati_path = os.path.join(directory, "hetionet_sample.bel.graphdati.json") click.echo("outputting sample hetionet in BEL to {}".format(sample_bel_path)) to_graphdati_file(graph, sample_graphdati_path, use_identifiers=True, indent=2) if __name__ == "__main__": main() pybel-0.15.5/src/pybel/io/hetionet/constants.py000066400000000000000000000111271426625374700214550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Constants for Hetionet.""" from ...dsl import Abundance, BiologicalProcess, Pathology, Population, Protein, Rna from ...struct import BELGraph HETIONET_PUBMED = "28936969" ################## # Hetionet types # ################## ANATOMY = "Anatomy" GENE = "Gene" PATHWAY = "Pathway" BIOPROCESS = "Biological Process" COMPOUND = "Compound" SIDE_EFFECT = "Side Effect" DISEASE = "Disease" PHARMACOLOGICAL_CLASS = "Pharmacologic Class" SYMPTOM = "Symptom" DSL_MAP = { ANATOMY: "uberon", BIOPROCESS: "go", COMPOUND: "drugbank", DISEASE: "doid", GENE: "ncbigene", PATHWAY: "reactome", PHARMACOLOGICAL_CLASS: "drugcentral", SIDE_EFFECT: "umls", SYMPTOM: "mesh", } TYPE_BLACKLIST = {"Molecular Function", "Cellular Component"} QUALIFIED_MAPPING = { (ANATOMY, Population, "upregulates", GENE, Rna, BELGraph.add_positive_correlation), ( ANATOMY, Population, "downregulates", GENE, Rna, BELGraph.add_negative_correlation, ), (ANATOMY, Population, "expresses", GENE, Rna, BELGraph.add_correlation), (COMPOUND, Abundance, "resembles", COMPOUND, Abundance, BELGraph.add_association), (COMPOUND, Abundance, "upregulates", GENE, Protein, BELGraph.add_increases), (COMPOUND, Abundance, "downregulates", GENE, Protein, BELGraph.add_decreases), (COMPOUND, Abundance, "treats", DISEASE, Pathology, BELGraph.add_decreases), (COMPOUND, Abundance, "palliates", DISEASE, Pathology, BELGraph.add_decreases), (COMPOUND, Abundance, "causes", SIDE_EFFECT, Pathology, BELGraph.add_increases), ( GENE, Protein, "interacts", GENE, Protein, BELGraph.add_binds, ), # FIXME look into this (GENE, Protein, "regulates", GENE, Protein, BELGraph.add_regulates), (GENE, Rna, "covaries", GENE, Rna, BELGraph.add_correlation), (DISEASE, Pathology, "localizes", ANATOMY, Population, BELGraph.add_association), (DISEASE, Pathology, "associates", GENE, Protein, BELGraph.add_association), (DISEASE, Pathology, "upregulates", GENE, Rna, BELGraph.add_positive_correlation), (DISEASE, Pathology, "downregulates", GENE, Rna, BELGraph.add_negative_correlation), (DISEASE, Pathology, "presents", SYMPTOM, Pathology, BELGraph.add_association), (DISEASE, Pathology, "resembles", DISEASE, Pathology, BELGraph.add_association), } UNQUALIFIED_MAPPING = { (GENE, Protein, "participates", PATHWAY, BiologicalProcess, BELGraph.add_part_of), ( GENE, Protein, "participates", BIOPROCESS, BiologicalProcess, BELGraph.add_part_of, ), } #################### # Drug action tags # #################### ACTIVATES_ACTIONS = { "agonist", "potentiator", "inducer", "positive modulator", "partial agonist", "positive allosteric modulator", "activator", "stimulator", } INHIBITS_ACTIONS = { "inhibitor", "antagonist", "blocker", "partial antagonist", "inhibitor, competitive", "negative modulator", "negative allosteric modulator", "allosteric antagonist", "suppressor", "inhibitory allosteric modulator", "conversion inhibitor", } REGULATES_ACTIONS = { "modulator", "allosteric modulator", } BINDS_ACTIONS = { "substrate", "binder", "other/unknown", "ligand", "cofactor", "product of", "opener", "desensitize the target", "other", "unknown", "antibody", "binding", "adduct", "multitarget", "releasing agent", } TBH_ACTIONS = {} ACTIVATES_ACTION_PAIRS = { ("activator", "substrate"), ("agonist", "binder"), ("agonist", "partial agonist"), ("inducer", "substrate"), ("agonist", "positive allosteric modulator"), ("positive allosteric modulator", "potentiator"), } INHIBITS_ACTION_PAIR = { ("agonist", "positive modulator"), ("allosteric antagonist", "antagonist"), ("antagonist", "blocker"), ("antagonist", "inhibitor"), ("antagonist", "multitarget"), ("antagonist", "substrate"), ("blocker", "inhibitor"), ("blocker", "modulator"), ("inhibitor", "modulator"), ("inhibitor", "multitarget"), ("inhibitor", "negative modulator"), ("inhibitor", "other"), ("inhibitor", "substrate"), ("negative modulator", "releasing agent"), } CONFLICTING_ACTION_PAIR = { ("inducer", "inhibitor", "substrate"), ("inducer", "inhibitor"), ("agonist", "antagonist"), ("antagonist", "partial agonist"), ("adduct", "inhibitor"), ("agonist", "antagonist", "modulator"), } UNINTERPRETABLE_ACTION_PAIR = { ("binder", "opener"), } pybel-0.15.5/src/pybel/io/hetionet/hetionet.py000066400000000000000000000176701426625374700212710ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Importer for Hetionet JSON.""" import bz2 import json import logging from typing import Any, Mapping, Set, Tuple, Union import pystow from tqdm.autonotebook import tqdm from .constants import ( ACTIVATES_ACTIONS, BINDS_ACTIONS, COMPOUND, DSL_MAP, GENE, HETIONET_PUBMED, INHIBITS_ACTIONS, PHARMACOLOGICAL_CLASS, QUALIFIED_MAPPING, REGULATES_ACTIONS, UNQUALIFIED_MAPPING, ) from ...dsl import Abundance, Protein from ...struct import BELGraph __all__ = [ "get_hetionet", "from_hetionet_json", "from_hetionet_gz", "from_hetionet_file", ] logger = logging.getLogger(__name__) JSON_BZ2_URL = "https://github.com/hetio/hetionet/raw/master/hetnet/json/hetionet-v1.0.json.bz2" def get_hetionet() -> BELGraph: """Get Hetionet from GitHub, cache, and convert to BEL.""" path = pystow.ensure("bio2bel", "hetionet", url=JSON_BZ2_URL) return from_hetionet_gz(path.as_posix()) def from_hetionet_gz(path: str) -> BELGraph: """Get Hetionet from its JSON GZ file.""" logger.info("opening %s", path) with bz2.open(path) as file: return from_hetionet_file(file) def from_hetionet_file(file) -> BELGraph: """Get Hetionet from a JSON file.""" logger.info("parsing json from %s", file) j = json.load(file) logger.info("converting hetionet dict to BEL") return from_hetionet_json(j) def from_hetionet_json( hetionet_dict: Mapping[str, Any], use_tqdm: bool = True, ) -> BELGraph: """Convert a Hetionet dictionary to a BEL graph.""" graph = BELGraph( # FIXME what metadata is appropriate? name="Hetionet", version="1.0", authors="Daniel Himmelstein", ) # FIXME add namespaces # graph.namespace_pattern.update({}) kind_identifier_to_name = {(x["kind"], x["identifier"]): x["name"] for x in hetionet_dict["nodes"]} edges = hetionet_dict["edges"] if use_tqdm: edges = tqdm(edges, desc="Converting Hetionet", unit_scale=True) it_logger = edges.write else: it_logger = logger.info for edge in edges: _add_edge(graph, edge, kind_identifier_to_name, it_logger) return graph def _get_node(edge, key, kind_identifier_to_name) -> Union[Tuple[None, None, None, None], Tuple[str, str, str, str]]: node_type, node_identifier = edge[key] namespace = DSL_MAP.get(node_type) if namespace is None: return None, None, None, None node_name = kind_identifier_to_name[node_type, node_identifier] node_identifier = str(node_identifier) if node_identifier.lower().startswith(namespace): node_identifier = node_identifier[1 + len(namespace) :] # remove redundant prefix return node_type, namespace, node_identifier, node_name def _add_edge( # noqa: C901 graph, edge, kind_identifier_to_name, it_logger, ) -> Union[None, str, Set[str]]: source_type, source_ns, source_identifier, source_name = _get_node(edge, "source_id", kind_identifier_to_name) target_type, target_ns, target_identifier, target_name = _get_node(edge, "target_id", kind_identifier_to_name) if source_type is None or target_type is None: return kind = edge["kind"] # direction = e['direction'] data = edge["data"] if "unbiased" in data: del data["unbiased"] annotations = {} if "source" in data: source = data.pop("source") annotations["source"] = {source: True} elif "sources" in data: annotations["source"] = {source: True for source in data.pop("sources")} else: pass # it_logger(f'Missing source for {source_identifier}-{kind}-{target_identifier}\n{e}') if "pubmed_ids" in data: citations = list(data.pop("pubmed_ids")) else: citations = [HETIONET_PUBMED] for k, v in data.items(): if k in {"actions", "urls", "subtypes"}: continue # handled explicitly later if not isinstance(v, (str, int, bool, float)): it_logger( "Unhandled: {source_identifier}-{kind}-{target_identifier} {k}: {v}".format( source_identifier=source_identifier, kind=kind, target_identifier=target_identifier, k=k, v=v, ) ) continue annotations[k] = {v: True} for _h_type, h_dsl, _r, _t_type, t_dsl, f in QUALIFIED_MAPPING: if source_type != _h_type or kind != _r or target_type != _t_type: continue rv = set() for citation in citations: key = f( graph, h_dsl(namespace=source_ns, identifier=source_identifier, name=source_name), t_dsl(namespace=target_ns, identifier=target_identifier, name=target_name), citation=citation, evidence="", annotations=annotations, ) rv.add(key) return rv for _h_type, h_dsl, _r, _t_type, t_dsl, f in UNQUALIFIED_MAPPING: if source_type == _h_type and kind == _r and target_type == _t_type: return f( graph, h_dsl(namespace=source_ns, identifier=source_identifier, name=source_name), t_dsl(namespace=target_ns, identifier=target_identifier, name=target_name), ) def _check(_source_type: str, _kind: str, _target_type: str) -> bool: """Check the metaedge.""" return kind == _kind and source_type == _source_type and target_type == _target_type if _check(COMPOUND, "binds", GENE): drug = Abundance(namespace="drugbank", name=source_name, identifier=source_identifier) protein = Protein(namespace="ncbigene", name=target_name, identifier=target_identifier) rv = set() for action in data.get("actions", []): action = action.lower() if action in ACTIVATES_ACTIONS: key = graph.add_directly_activates( drug, protein, citation=HETIONET_PUBMED, evidence="", annotations=annotations, ) elif action in INHIBITS_ACTIONS: key = graph.add_directly_inhibits( drug, protein, citation=HETIONET_PUBMED, evidence="", annotations=annotations, ) elif action in REGULATES_ACTIONS: key = graph.add_regulates( drug, protein, citation=HETIONET_PUBMED, evidence="", annotations=annotations, ) elif action in BINDS_ACTIONS: key = graph.add_binds( drug, protein, citation=HETIONET_PUBMED, evidence="", annotations=annotations, ) else: key = graph.add_binds( drug, protein, citation=HETIONET_PUBMED, evidence="", annotations=annotations, ) it_logger( "Unhandled action for {source_identifier}-{kind}-{target_identifier}: {action}".format( source_identifier=source_identifier, kind=kind, target_identifier=target_identifier, action=action, ) ) rv.add(key) return rv if _check(PHARMACOLOGICAL_CLASS, "includes", COMPOUND): return graph.add_is_a( Abundance(namespace="drugbank", name=target_name, identifier=target_identifier), Abundance(namespace="drugcentral", name=source_name, identifier=source_identifier), ) it_logger("missed: {edge}".format(edge=edge)) pybel-0.15.5/src/pybel/io/hipathia.py000066400000000000000000000424021426625374700174110ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Convert a BEL graph to HiPathia inputs. Input ----- SIF File ~~~~~~~~ - Text file with three columns separated by tabs. - Each row represents an interaction in the pathway. First column is the source node, third column the target node, and the second is the type of relation between them. - Only activation and inhibition interactions are allowed. - The name of the nodes in this file will be stored as the IDs of the nodes. - The nodes IDs should have the following structure: N (dash) pathway ID (dash) node ID. - HiPathia distinguish between two types of nodes: simple and complex. Simple nodes: - Simple nodes may include many genes, but only one is needed to perform the function of the node. This could correspond to a protein family of enzymes that all have the same function - only one of them needs to be present for the action to take place. Simple nodes are defined within - Node IDs from simple nodes do not include any space, i.e. N-hsa04370-11. Complex nodes: - Complex nodes include different simple nodes and represent protein complexes. Each simple node within the complex represents one protein in the complex. This node requires the presence of all their simple nodes to perform its function. - Node IDs from complex nodes are the juxtaposition of the included simple node IDs, separated by spaces, i.e. N-hsa04370-10 26. ATT File ~~~~~~~~ Text file with twelve (12) columns separated by tabulars. Each row represents a node (either simple or complex). The columns included are: 1. ``ID``: Node ID as explained above. 2. ``label``: Name to be shown in the picture of the pathway en HGNC. Generally, the gene name of the first included EntrezID gene is used as label. For complex nodes, we juxtapose the gene names of the first genes of each simple node included (see genesList column below). 3. ``X``: The X-coordinate of the position of the node in the pathway. 4. ``Y``: The Y-coordinate of the position of the node in the pathway. 5. ``color``: The default color of the node. 6. ``shape``: The shape of the node. "rectangle" should be used for genes and "circle" for metabolites. 7. ``type``: The type of the node, either "gene" for genes or "compound" for metabolites. For complex nodes, the type of each of their included simple nodes is juxtaposed separated by commas, i.e. gene,gene. 8. ``label.cex``: Amount by which plotting label should be scaled relative to the default. 9. ``label.color``: Default color of the node. 10. ``width``: Default width of the node. 11. ``height``: Default height of the node. 12. ``genesList``: List of genes included in each node, with EntrezID: - Simple nodes: EntrezIDs of the genes included, separated by commas (",") and no spaces, i.e. 56848,8877 for node N-hsa04370-11. - Complex nodes: GenesList of the simple nodes included, separated by a slash ("/") and no spaces, and in the same order as in the node ID. For example, node N-hsa04370-10 26 includes two simple nodes: 10 and 26. Its genesList column is 5335,5336,/,9047, meaning that the genes included in node 10 are 5335 and 5336, and the gene included in node 26 is 9047. """ import logging import os from collections import defaultdict from itertools import groupby from operator import itemgetter from typing import List, Optional, Set, Tuple, Union import networkx as nx import pandas as pd from ..constants import ( CAUSAL_INCREASE_RELATIONS, CAUSAL_POLAR_RELATIONS, CITATION_TYPE_OTHER, IS_A, RELATION, ) from ..dsl import ComplexAbundance, Protein, hgnc from ..struct import BELGraph __all__ = [ "from_hipathia_paths", "from_hipathia_dfs", "to_hipathia", "to_hipathia_dfs", ] logger = logging.getLogger(__name__) ATT_COLS = ["ID", "label", "genesList"] def from_hipathia_paths(name: str, att_path: str, sif_path: str) -> BELGraph: """Get a BEL graph from HiPathia files.""" att_df = pd.read_csv(att_path, sep="\t") sif_df = pd.read_csv(sif_path, sep="\t", header=None, names=["source", "relation", "target"]) return from_hipathia_dfs(name=name, att_df=att_df, sif_df=sif_df) def group_delimited_list(entries: List[str], sep: str = "/") -> List[List[str]]: """Group delimited things in a list.""" return [list(b) for a, b in groupby(entries, lambda z: z == sep) if not a] def _p(identifier: str): return Protein( namespace="ncbigene", identifier=identifier, # name=name, ) def _f(identifier: str): return Protein( namespace="hipathia.family", identifier=identifier, # name=name, ) def from_hipathia_dfs(name: str, att_df: pd.DataFrame, sif_df: pd.DataFrame) -> BELGraph: """Get a BEL graph from HiPathia dataframes.""" def _clean_name(s): prefix = "N-{name}-".format(name=name) if prefix not in s: raise ValueError("wrong name for pathway") return tuple(sorted(s[len(prefix) :].split(" "))) att_df["ID"] = att_df["ID"].map(_clean_name) att_df["label"] = att_df["label"].str.split(" ") att_df["genesList"] = att_df["genesList"].str.split(",").map(group_delimited_list) simple_node_to_dsl = {} family_node_to_dsl = {} complex_node_to_dsl = {} graph = BELGraph(name=name) for components, component_label_lists, component_gene_lists in att_df[["ID", "label", "genesList"]].values: if not components: print(att_df[["ID", "label", "genesList"]]) raise ValueError("missing components in row") if len(components) == 1: # This is a simple node, representing a protein or protein family component, label, entrez_ids = ( components[0], component_label_lists[0], component_gene_lists[0], ) if len(entrez_ids) == 1: # just a protein simple_node_to_dsl[component] = _p(identifier=entrez_ids[0]) else: # a protein family family_dsl = _f(identifier=label) for entrez_id in entrez_ids: child_dsl = _p(entrez_id) graph.add_is_a(child_dsl, family_dsl) family_node_to_dsl[component] = family_dsl else: # This is a complex node, representing a protein complex of simple nodes component_dsls = [] components = tuple(sorted(components)) for component, label, entrez_ids in zip(components, component_label_lists, component_gene_lists): if len(entrez_ids) == 1: simple_dsl = _p(identifier=entrez_ids[0]) simple_node_to_dsl[component] = simple_dsl component_dsls.append(simple_dsl) else: family_dsl = _f(identifier=label) for entrez_id in entrez_ids: child_dsl = _p(identifier=entrez_id) graph.add_is_a(child_dsl, family_dsl) family_node_to_dsl[component] = family_dsl component_dsls.append(family_dsl) component_dsl = ComplexAbundance(component_dsls) graph.add_node_from_data(component_dsl) complex_node_to_dsl[components] = component_dsl # Remap all of the dictionaries x = {} x.update(complex_node_to_dsl) for k, v in simple_node_to_dsl.items(): x[(k,)] = v for k, v in family_node_to_dsl.items(): x[(k,)] = v sif_df["source"] = sif_df["source"].map(_clean_name).map(x.get) sif_df["target"] = sif_df["target"].map(_clean_name).map(x.get) for source, relation, target in sif_df.values: if relation == "activation": graph.add_increases(source, target, citation=(CITATION_TYPE_OTHER, "HiPathia"), evidence="") elif relation == "inhibition": graph.add_decreases(source, target, citation=(CITATION_TYPE_OTHER, "HiPathia"), evidence="") else: raise ValueError("unknown relation: {relation}".format(relation=relation)) return graph def to_hipathia( graph: BELGraph, directory: str, draw: bool = True, ) -> None: """Export HiPathia artifacts for the graph.""" att_df, sif_df = to_hipathia_dfs(graph, draw_directory=directory if draw else None) if att_df is None and sif_df is None: logger.warning("can not convert graph %s", graph.name) return att_df.to_csv(os.path.join(directory, "{}.att".format(graph.name)), sep="\t", index=False) sif_df.to_csv(os.path.join(directory, "{}.sif".format(graph.name)), sep="\t", index=False) def _is_node_family(graph: BELGraph, node: Protein) -> Optional[Set[Protein]]: """Get the children of the protein node, if some exist.""" children = set() for child, _, data in graph.in_edges(node, data=True): if data[RELATION] == IS_A: children.add(child) if children and not all(isinstance(child, Protein) for child in children): logger.warning("not all children of {} are proteins: {}".format(node, children)) return return children def to_hipathia_dfs( graph: BELGraph, draw_directory: Optional[str] = None, ) -> Union[Tuple[None, None], Tuple[pd.DataFrame, pd.DataFrame]]: """Get the ATT and SIF dataframes. :param graph: A BEL graph :param draw_directory: The directory in which a drawing should be output 1. Identify nodes: 1. Identify all proteins 2. Identify all protein families 3. Identify all complexes with just a protein or a protein family in them 2. Identify interactions between any of those things that are causal 3. Profit! """ proteins = set() families = defaultdict(set) complexes = set() for node in sorted(graph, key=str): if isinstance(node, Protein): children = _is_node_family(graph, node) if children: families[node] = children else: proteins.add(node) elif isinstance(node, ComplexAbundance) and all(isinstance(m, Protein) for m in node.members): complexes.add(node) families = {node: sorted(values, key=str) for node, values in sorted(families.items(), key=itemgetter(0))} nodes = sorted(proteins.union(families).union(complexes), key=str) new_nodes = set() edges = [] for u, v, _, d in sorted( graph.out_edges(nodes, keys=True, data=True), key=lambda t: (str(t[0]), str(t[1]), t[2]), ): relation = d[RELATION] if relation not in CAUSAL_POLAR_RELATIONS: continue new_nodes.add(u) new_nodes.add(v) edges.append( ( u, "activation" if relation in CAUSAL_INCREASE_RELATIONS else "inhibition", v, ) ) att = {} dsl_to_k = {} i = 0 for node in sorted(new_nodes, key=str): if node in families: i += 1 k = (i,) children = families[node] child_identifiers = [child.identifier for child in children] if not all(child_identifiers): logger.warning("not all children were grounded: %s", child_identifiers) continue labels, genes_lists = [node.name], [child_identifiers] elif isinstance(node, Protein): if not node.identifier or not node.name: logger.warning("node was not grounded: %s", node) continue i += 1 k = (i,) labels, genes_lists = [node.name], [[node.identifier]] elif isinstance(node, ComplexAbundance): k, labels, genes_lists = [], [], [] for member in node.members: i += 1 k.append(i) labels.append(member.name) if member in families: children = families[member] child_identifiers = [child.identifier for child in children] if not all(child_identifiers): logger.warning("not all children were grounded: %s", child_identifiers) continue genes_lists.append(child_identifiers) else: if not member.identifier: logger.warning("member was not grounded: %s", member) continue genes_lists.append([member.identifier]) k = tuple(k) else: logger.debug("skipping node {}".format(node)) continue k = "N-{}-{}".format(graph.name, " ".join(map(str, k))) att[k] = labels, genes_lists dsl_to_k[node] = k edges = [ (dsl_to_k[source], relation, dsl_to_k[target]) for source, relation, target in edges if source in dsl_to_k and target in dsl_to_k ] sif_df = pd.DataFrame(edges) # DONE composite_graph = nx.Graph([(k_source, k_target) for k_source, _, k_target in edges]) try: from networkx.drawing.nx_agraph import pygraphviz_layout pos = pygraphviz_layout(composite_graph, prog="neato", args="-Gstart=5") except ImportError: logger.warning("could not import pygraphviz. Falling back to force directed") pos = nx.fruchterman_reingold_layout(composite_graph, seed=5) if not pos: return None, None nx_labels = {} # from k to label min_x = min(x for x, y in pos.values()) min_y = min(y for x, y in pos.values()) att_rows = [] for k, (labels, genes_lists) in sorted(att.items()): if k not in pos: logger.warning("node not in graph: %s", k) continue nx_labels[k] = label = " ".join(labels) types = ",".join(["gene"] * len(labels)) gene_list = ",/,".join(",".join(gene_list) for gene_list in genes_lists) x, y = pos[k] att_rows.append( ( k, # 1. ID label, # 2. label int(100 * (x - min_x)), # 3. X int(100 * (y - min_y)), # 4. Y "white", # 5. color "rectangle", # 6. shape types, # 7. 0.5, # 8. label.cex "black", # 9. label.color 46, # 10. width 17, # 11. height gene_list, # 12. gene list ) ) att_df = pd.DataFrame( att_rows, columns=[ "ID", "label", "X", "Y", "color", "shape", "type", "label.cex", "label.color", "width", "height", "genesList", ], ) if draw_directory is not None: try: import matplotlib.pyplot as plt except ImportError: logger.warning("could not draw graph because matplotlib is not installed") else: plt.figure(figsize=(20, 20)) nx.draw_networkx(composite_graph, pos, labels=nx_labels) plt.axis("off") plt.savefig(os.path.join(draw_directory, "{}.png".format(graph.name))) return att_df, sif_df def make_hsa047370() -> BELGraph: """Make an example BEL graph corresponding to the example data from Marina.""" graph = BELGraph(name="hsa04370") node_1 = hgnc(name="CDC42") node_9 = hgnc(name="KDR") node_11 = hgnc(name="SPHK2") node_17 = hgnc(name="MAPKAPK3") node_18 = hgnc(name="PPP3CA") node_19 = hgnc(name="AKT3") node_20 = hgnc(name="PIK3R5") node_21 = hgnc(name="NFATC2") node_22 = hgnc(name="PRKCA") node_24 = hgnc(name="MAPK14") node_27 = hgnc(name="SRC") node_29 = hgnc(name="VEGFA") node_32 = hgnc(name="MAPK1") node_33 = hgnc(name="MAP2K1") node_34 = hgnc(name="RAF1") node_35 = hgnc(name="HRAS") node_10 = ComplexAbundance([hgnc(name="PLCG1"), hgnc(name="SH2D2A")]) node_28 = hgnc(name="SHC2") node_23 = hgnc(name="PTK2") node_25 = hgnc(name="PXN") node_16 = hgnc(name="HSPB1") node_36 = hgnc(name="NOS3") node_37 = hgnc(name="CASP9") node_38 = hgnc(name="BAD") node_39 = hgnc(name="RAC1") node_14 = hgnc(name="PTGS2") node_15 = hgnc(name="PLA2G4B") def _add_increases(a, b): graph.add_directly_increases(a, b, citation="", evidence="") def _add_decreases(a, b): graph.add_directly_decreases(a, b, citation="", evidence="") _add_increases(node_1, node_24) _add_increases(node_9, node_28) _add_increases(node_9, node_23) _add_increases(node_9, node_25) _add_increases(node_9, node_20) _add_increases(node_9, node_27) _add_increases(node_9, node_10) _add_increases(node_11, node_35) _add_increases(node_17, node_16) _add_increases(node_18, node_21) _add_increases(node_19, node_36) _add_decreases(node_19, node_37) _add_decreases(node_19, node_38) _add_increases(node_20, node_39) _add_increases(node_20, node_19) _add_increases(node_21, node_14) _add_increases(node_22, node_34) _add_increases(node_22, node_11) _add_increases(node_24, node_17) _add_increases(node_27, node_20) _add_increases(node_29, node_9) _add_increases(node_32, node_15) _add_increases(node_33, node_32) _add_increases(node_34, node_33) _add_increases(node_35, node_34) _add_increases(node_10, node_18) _add_increases(node_10, node_22) _add_increases(node_10, node_15) _add_increases(node_10, node_36) return graph pybel-0.15.5/src/pybel/io/indra.py000066400000000000000000000125301426625374700167160ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Conversion functions for BEL graphs with INDRA. After assembling a model with `INDRA `_, a list of :class:`indra.statements.Statement` can be converted to a :class:`pybel.BELGraph` with :class:`indra.assemblers.pybel.PybelAssembler`. .. code-block:: python from indra.assemblers.pybel import PybelAssembler import pybel stmts = [ # A list of INDRA statements ] pba = PybelAssembler( stmts, name='Graph Name', version='0.0.1', description='Graph Description' ) graph = pba.make_model() # Write to BEL file pybel.to_bel_path(belgraph, 'simple_pybel.bel') .. warning:: These functions are hard to unit test because they rely on a whole set of java dependencies and will likely not be for a while. """ import json from typing import Any, List, Mapping, Optional, TextIO, Union from networkx.utils import open_file try: from pickle5 import load except ImportError: from pickle import load __all__ = [ "from_indra_statements", "from_indra_statements_json", "from_indra_statements_json_file", "from_indra_pickle", "to_indra_statements", "to_indra_statements_json", "to_indra_statements_json_file", "from_biopax", ] def from_indra_statements( stmts, name: Optional[str] = None, version: Optional[str] = None, description: Optional[str] = None, authors: Optional[str] = None, contact: Optional[str] = None, license: Optional[str] = None, copyright: Optional[str] = None, disclaimer: Optional[str] = None, ): """Import a model from :mod:`indra`. :param List[indra.statements.Statement] stmts: A list of statements :param name: The graph's name :param version: The graph's version. Recommended to use `semantic versioning `_ or ``YYYYMMDD`` format. :param description: The description of the graph :param authors: The authors of this graph :param contact: The contact email for this graph :param license: The license for this graph :param copyright: The copyright for this graph :param disclaimer: The disclaimer for this graph :rtype: pybel.BELGraph """ from indra.assemblers.pybel import PybelAssembler if authors is None: authors = "INDRA" pba = PybelAssembler( stmts=stmts, name=name, version=version, description=description, authors=authors, contact=contact, license=license, copyright=copyright, disclaimer=disclaimer, ) graph = pba.make_model() return graph def from_indra_statements_json(stmts_json: List[Mapping[str, Any]], **kwargs): """Get a BEL graph from INDRA statements JSON. :rtype: BELGraph Other kwargs are passed to :func:`from_indra_statements`. """ from indra.statements import stmts_from_json statements = stmts_from_json(stmts_json) return from_indra_statements(statements, **kwargs) @open_file(0, mode="r") def from_indra_statements_json_file(file, **kwargs): """Get a BEL graph from INDRA statements JSON file. :rtype: BELGraph Other kwargs are passed to :func:`from_indra_statements`. """ return from_indra_statements_json(json.load(file), **kwargs) def from_indra_pickle(path: str, **kwargs): """Import a model from :mod:`indra`. :param path: Path to pickled list of :class:`indra.statements.Statement` :rtype: pybel.BELGraph Other kwargs are passed to :func:`from_indra_statements`. """ with open(path, "rb") as f: statements = load(f) return from_indra_statements(stmts=statements, **kwargs) def to_indra_statements(graph): """Export this graph as a list of INDRA statements using the :py:class:`indra.sources.pybel.PybelProcessor`. :param pybel.BELGraph graph: A BEL graph :rtype: list[indra.statements.Statement] """ from indra.sources.bel import process_pybel_graph pbp = process_pybel_graph(graph) return pbp.statements def to_indra_statements_json(graph) -> List[Mapping[str, Any]]: """Export this graph as INDRA JSON list. :param pybel.BELGraph graph: A BEL graph """ return [statement.to_json() for statement in to_indra_statements(graph)] @open_file(1, mode="w") def to_indra_statements_json_file(graph, path: Union[str, TextIO], indent: Optional[int] = 2, **kwargs): """Export this graph as INDRA statement JSON. :param pybel.BELGraph graph: A BEL graph :param path: A writable file or file-like Other kwargs are passed to :func:`json.dump`. """ json.dump(to_indra_statements_json(graph), path, indent=indent, **kwargs) def from_biopax(path: str, encoding: Optional[str] = None, **kwargs): """Import a model encoded in Pathway Commons `BioPAX `_ via :mod:`indra`. :param path: Path to a BioPAX OWL file :param encoding: The encoding passed to :func:`indra.sources.biopax.process_owl`. See https://github.com/sorgerlab/indra/pull/1199. :rtype: pybel.BELGraph Other kwargs are passed to :func:`from_indra_statements`. .. warning:: Not compatible with all BioPAX! See INDRA documentation. """ from indra.sources.biopax import process_owl model = process_owl(path, encoding=encoding) return from_indra_statements(stmts=model.statements, **kwargs) pybel-0.15.5/src/pybel/io/jgif.py000066400000000000000000000424231426625374700165440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Conversion functions for BEL graphs with JGIF JSON. The JSON Graph Interchange Format (JGIF) is `specified `_ similarly to the Node-Link JSON. Interchange with this format provides compatibilty with other software and repositories, such as the `Causal Biological Network Database `_. """ import gzip import json import logging import re from collections import defaultdict from operator import methodcaller from typing import Any, Mapping, Optional, TextIO, Union import requests from networkx.utils import open_file from pyparsing import ParseException from .. import constants as pc from ..constants import ( ANNOTATIONS, CITATION, EVIDENCE, GRAPH_ANNOTATION_URL, GRAPH_NAMESPACE_URL, METADATA_AUTHORS, METADATA_CONTACT, METADATA_INSERT_KEYS, METADATA_LICENSES, RELATION, UNQUALIFIED_EDGES, ) from ..exceptions import NakedNameWarning, UndefinedNamespaceWarning from ..parser import BELParser from ..struct import BELGraph from ..version import get_version __all__ = [ "from_cbn_jgif", "from_cbn_jgif_file", "from_jgif", "from_jgif_file", "from_jgif_gz", "from_jgif_jsons", "to_jgif", "to_jgif_file", "to_jgif_gz", "to_jgif_jsons", "post_jgif", ] logger = logging.getLogger(__name__) annotation_map = { "tissue": "Tissue", "disease": "Disease", "species_common_name": "Species", "cell": "Cell", } species_map = { "human": "9606", "rat": "10116", "mouse": "10090", } placeholder_evidence = ( "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting." ) EXPERIMENT_CONTEXT = "experiment_context" def map_cbn(d): """Pre-processes the JSON from the CBN. - removes statements without evidence, or with placeholder evidence :param dict d: Raw JGIF from the CBN :return: Preprocessed JGIF :rtype: dict """ for i, edge in enumerate(d["graph"]["edges"]): if "metadata" not in edge: continue if "evidences" not in edge["metadata"]: continue for j, evidence in enumerate(edge["metadata"]["evidences"]): if EXPERIMENT_CONTEXT not in evidence: continue # ctx = {k.strip().lower(): v.strip() for k, v in evidence[EXPERIMENT_CONTEXT].items() if v.strip()} new_context = {} for key, value in evidence[EXPERIMENT_CONTEXT].items(): if not value: logger.debug("key %s without value", key) continue value = value.strip() if not value: logger.debug("key %s without value", key) continue key = key.strip().lower() if key == "species_common_name": new_context["Species"] = species_map[value.lower()] elif key in annotation_map: new_context[annotation_map[key]] = value else: new_context[key] = value """ for k, v in annotation_map.items(): if k not in ctx: continue d['graph']['edges'][i]['metadata']['evidences'][j][EXPERIMENT_CONTEXT][v] = ctx[k] del d['graph']['edges'][i]['metadata']['evidences'][j][EXPERIMENT_CONTEXT][k] if 'species_common_name' in ctx: species_name = ctx['species_common_name'].strip().lower() d['graph']['edges'][i]['metadata']['evidences'][j][EXPERIMENT_CONTEXT]['Species'] = species_map[ species_name] del d['graph']['edges'][i]['metadata']['evidences'][j][EXPERIMENT_CONTEXT][ 'species_common_name'] """ # TODO can this be replaced with edge as well? d["graph"]["edges"][i]["metadata"]["evidences"][j][EXPERIMENT_CONTEXT] = new_context return d NAMESPACE_URLS = { "TAX": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/ncbi-taxonomy/ncbi-taxonomy-20200322.belns", "HGNC": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/hgnc-human-genes/hgnc-human-genes-20150601.belns", "GOBP": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/go-biological-process/go-biological-process-20150601.belns", "SFAM": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/selventa-protein-families/selventa-protein-families-20150601.belns", "GOCC": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/go-cellular-component/go-cellular-component-20170511.belns", "MESHPP": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/mesh-processes/mesh-processes-20150601.belns", "MGI": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/mgi-mouse-genes/mgi-mouse-genes-20150601.belns", "RGD": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/rgd-rat-genes/rgd-rat-genes-20150601.belns", "CHEBI": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/chebi/chebi-20150601.belns", "SCHEM": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/selventa-legacy-chemicals/selventa-legacy-chemicals-20150601.belns", "EGID": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/entrez-gene-ids/entrez-gene-ids-20150601.belns", "MESHD": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/mesh-diseases/mesh-diseases-20150601.belns", "SDIS": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/selventa-legacy-diseases/selventa-legacy-diseases-20150601.belns", "SCOMP": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/selventa-named-complexes/selventa-named-complexes-20150601.belns", "MESHC": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/mesh-chemicals/mesh-chemicals-20170511.belns", "GOBPID": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/go-biological-process-ids/go-biological-process-ids-20150601.belns", "GOCCID": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/go-cellular-component-ids/go-cellular-component-ids-20150601.belns", "MESHCS": "https://arty.scai.fraunhofer.de/artifactory/bel/namespace/mesh-cell-structures/mesh-cell-structures-20150601.belns", } ANNOTATION_URLS = { "Cell": "https://arty.scai.fraunhofer.de/artifactory/bel/annotation/cell-line/cell-line-20150601.belanno", "Disease": "https://arty.scai.fraunhofer.de/artifactory/bel/annotation/disease/disease-20150601.belanno", "Species": "https://arty.scai.fraunhofer.de/artifactory/bel/annotation/species-taxonomy-id/species-taxonomy-id-20170511.belanno", "Tissue": "https://arty.scai.fraunhofer.de/artifactory/bel/annotation/mesh-anatomy/mesh-anatomy-20150601.belanno", } NAMESPACE_TO_PATTERN = { namespace: re.compile(r".*") for namespace in (set(NAMESPACE_URLS) | {"GO", "MESH"}) # don't validate anything } @open_file(0, mode="r") def from_cbn_jgif_file(path: Union[str, TextIO]) -> BELGraph: """Build a graph from a file containing the CBN variant of JGIF. :param path: A path or file-like """ return from_cbn_jgif(json.load(path)) def from_cbn_jgif(graph_jgif_dict): """Build a BEL graph from CBN JGIF. Map the JGIF used by the Causal Biological Network Database to standard namespace and annotations, then builds a BEL graph using :func:`pybel.from_jgif`. :param dict graph_jgif_dict: The JSON object representing the graph in JGIF format :rtype: BELGraph Example: .. code-block:: python import requests from pybel import from_cbn_jgif apoptosis_url = 'http://causalbionet.com/Networks/GetJSONGraphFile?networkId=810385422' graph_jgif_dict = requests.get(apoptosis_url).json() graph = from_cbn_jgif(graph_jgif_dict) .. warning:: Handling the annotations is not yet supported, since the CBN documents do not refer to the resources used to create them. This may be added in the future, but the annotations must be stripped from the graph before uploading to the network store using :func:`pybel.struct.mutation.strip_annotations`. """ graph_jgif_dict = map_cbn(graph_jgif_dict) graph_jgif_dict["graph"][GRAPH_NAMESPACE_URL] = NAMESPACE_URLS graph_jgif_dict["graph"][GRAPH_ANNOTATION_URL] = ANNOTATION_URLS graph_jgif_dict["graph"]["metadata"].update( { METADATA_AUTHORS: "Causal Biological Networks Database", METADATA_LICENSES: """ Please cite: - www.causalbionet.com - https://bionet.sbvimprover.com as well as any relevant publications. The sbv IMPROVER project, the website and the Symposia are part of a collaborative project designed to enable scientists to learn about and contribute to the development of a new crowd sourcing method for verification of scientific data and results. The current challenges, website and biological network models were developed and are maintained as part of a collaboration among Selventa, OrangeBus and ADS. The project is led and funded by Philip Morris International. For more information on the focus of Philip Morris International’s research, please visit www.pmi.com. """.replace( "\n", "\t" ), METADATA_CONTACT: "CausalBiologicalNetworks.RD@pmi.com", } ) graph = from_jgif(graph_jgif_dict) return graph def from_jgif(graph_jgif_dict, parser_kwargs: Optional[Mapping[str, Any]] = None): # noqa:C901 """Build a BEL graph from a JGIF JSON object. :param dict graph_jgif_dict: The JSON object representing the graph in JGIF format :rtype: BELGraph """ graph = BELGraph() root = graph_jgif_dict["graph"] if "label" in root: graph.name = root["label"] if "metadata" in root: metadata = root["metadata"] for key in METADATA_INSERT_KEYS: if key in metadata: graph.document[key] = metadata[key] for k in (GRAPH_ANNOTATION_URL, GRAPH_NAMESPACE_URL): if k in root: graph.graph[k] = root[k] parser = BELParser(graph, namespace_to_pattern=NAMESPACE_TO_PATTERN) parser.bel_term.addParseAction(parser.handle_term) for node in root["nodes"]: node_label = node.get("label") if node_label is None: logger.warning("node missing label: %s", node) continue try: parser.bel_term.parseString(node_label) except NakedNameWarning as e: logger.info("Naked name: %s", e) except UndefinedNamespaceWarning as e: logger.info("Undefined namespace: %s", e) except ParseException: logger.info("Parse exception for %s", node_label) for i, edge in enumerate(root["edges"]): relation = edge.get("relation") if relation is None: logger.warning("no relation for edge: %s", edge) if relation in {"actsIn", "translocates"}: continue # don't need legacy BEL format edge_metadata = edge.get("metadata") if edge_metadata is None: logger.warning("no metadata for edge: %s", edge) continue bel_statement = edge.get("label") if bel_statement is None: logger.debug("No BEL statement for edge %s", edge) evidences = edge_metadata.get("evidences") if relation in UNQUALIFIED_EDGES: pass # FIXME? else: if not evidences: # is none or is empty list logger.debug("No evidence for edge %s", edge) continue for evidence in evidences: citation = evidence.get("citation") if not citation: continue if "type" not in citation or "id" not in citation: continue summary_text = evidence["summary_text"].strip() if not summary_text or summary_text == placeholder_evidence: continue parser.control_parser.clear() citation_namespace = citation["type"].lower().strip() citation_namespace = pc.CITATION_NORMALIZER.get(citation_namespace, citation_namespace) parser.control_parser.citation_db = citation_namespace parser.control_parser.citation_db_id = citation["id"].strip() parser.control_parser.evidence = summary_text annotations = parser.graph._clean_annotations(evidence[EXPERIMENT_CONTEXT]) parser.control_parser.annotations.update(annotations) try: parser.parseString(bel_statement, line_number=i) except Exception as e: logger.warning("JGIF relation parse error: %s for %s", e, bel_statement) return graph @open_file(0, mode="r") def from_jgif_file(path: Union[str, TextIO]) -> BELGraph: """Build a graph from the JGIF JSON contained in the given file. :param path: A path or file-like """ return from_jgif(json.load(path)) def from_jgif_gz(path: str) -> BELGraph: """Read a graph as JGIF JSON from a gzip file.""" with gzip.open(path, "rt") as file: return from_jgif(json.load(file)) def from_jgif_jsons(graph_json_str: str) -> BELGraph: """Read a BEL graph from a JGIF JSON string.""" return from_jgif(json.loads(graph_json_str)) def to_jgif(graph): """Build a JGIF dictionary from a BEL graph. :param pybel.BELGraph graph: A BEL graph :return: A JGIF dictionary :rtype: dict .. warning:: Untested! This format is not general purpose and is therefore time is not heavily invested. If you want to use Cytoscape.js, we suggest using :func:`pybel.to_cx` instead. The example below shows how to output a BEL graph as a JGIF dictionary. .. code-block:: python import os from pybel.examples import sialic_acid_graph graph_jgif_json = pybel.to_jgif(sialic_acid_graph) If you want to write the graph directly to a file as JGIF, see func:`to_jgif_file`. """ u_v_r_bel = {} nodes_entry = [] edges_entry = [] for node in sorted(graph, key=methodcaller("as_bel")): nodes_entry.append( { "id": node.md5, "label": node.as_bel(), "bel_function_type": node.function, } ) for u, v in graph.edges(): relation_evidences = defaultdict(list) for key, data in graph[u][v].items(): if (u, v, data[RELATION]) not in u_v_r_bel: u_v_r_bel[u, v, data[RELATION]] = graph.edge_to_bel(u, v, edge_data=data) bel = u_v_r_bel[u, v, data[RELATION]] evidence_dict = { "bel_statement": bel, "key": key, } if ANNOTATIONS in data: evidence_dict["experiment_context"] = data[ANNOTATIONS] if EVIDENCE in data: evidence_dict["summary_text"] = data[EVIDENCE] if CITATION in data: evidence_dict["citation"] = data[CITATION] relation_evidences[data[RELATION]].append(evidence_dict) for relation, evidences in relation_evidences.items(): edges_entry.append( { "source": u.md5, "target": v.md5, "relation": relation, "label": u_v_r_bel[u, v, relation], "metadata": { "evidences": evidences, }, } ) return { "graph": { "metadata": dict( origin=dict(name="pybel", version=get_version()), **graph.document, ), "nodes": nodes_entry, "edges": edges_entry, }, } @open_file(1, mode="w") def to_jgif_file(graph: BELGraph, file: Union[str, TextIO], **kwargs) -> None: """Write JGIF to a file. :param graph: A BEL graph :param file: A writable file or file-like The example below shows how to output a BEL graph as JGIF to an open file. .. code-block:: python from pybel.examples import sialic_acid_graph from pybel import to_jgif_file with open('graph.bel.jgif.json', 'w') as file: to_jgif_file(sialic_acid_graph, file) The example below shows how to output a BEL graph as JGIF to a file at a given path. .. code-block:: python from pybel.examples import sialic_acid_graph from pybel import to_jgif_file to_jgif_file(sialic_acid_graph, 'graph.bel.jgif.json') If you have a big graph, you might consider storing it as a gzipped JGIF file by using :func:`to_jgif_gz`. """ json.dump(to_jgif(graph), file, ensure_ascii=False, **kwargs) def to_jgif_gz(graph, path: str, **kwargs) -> None: """Write a graph as JGIF JSON to a gzip file.""" with gzip.open(path, "wt") as file: json.dump(to_jgif(graph), file, ensure_ascii=False, **kwargs) def to_jgif_jsons(graph: BELGraph, **kwargs) -> str: """Dump this graph as a JGIF JSON object to a string.""" return json.dumps(to_jgif(graph), ensure_ascii=False, **kwargs) def post_jgif(graph: BELGraph, url: str, **kwargs) -> requests.Response: """Post the JGIF to a given URL.""" return requests.post(url, json=to_jgif(graph), **kwargs) pybel-0.15.5/src/pybel/io/jinja_utils.py000066400000000000000000000030641426625374700201360ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for Jinja2 templating.""" import os __all__ = [ "build_template_environment", "build_template_renderer", ] def build_template_environment(here: str): """Build a custom templating environment so Flask apps can get data from lots of different places. :param here: Give this the result of :code:`os.path.dirname(os.path.abspath(__file__))` :rtype: jinja2.Environment """ from jinja2 import Environment, FileSystemLoader loader = FileSystemLoader(os.path.join(here, "templates")) environment = Environment( autoescape=True, loader=loader, trim_blocks=False, ) environment.globals["STATIC_PREFIX"] = here + "/static/" return environment def build_template_renderer(path: str): """Build a render template function. :param path: The location of the current file. Pass it :code:`__file__` like in the example below. >>> render_template = build_template_renderer(__file__) """ here = os.path.dirname(os.path.abspath(path)) template_environment = build_template_environment(here) def render_template_enclosure(template_filename: str, **context) -> str: """Render a template as a unicode string. :param template_filename: The name of the file to render in the template directory :param dict context: The variables to template """ return template_environment.get_template(template_filename).render(context) render_template_enclosure.environment = template_environment return render_template_enclosure pybel-0.15.5/src/pybel/io/jupyter/000077500000000000000000000000001426625374700167505ustar00rootroot00000000000000pybel-0.15.5/src/pybel/io/jupyter/__init__.py000066400000000000000000000004221426625374700210570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Support for displaying BEL graphs in Jupyter notebooks.""" from .inline import to_jupyter, to_jupyter_str from .visualization import to_html, to_html_file __all__ = [ "to_html", "to_html_file", "to_jupyter", "to_jupyter_str", ] pybel-0.15.5/src/pybel/io/jupyter/constants.py000066400000000000000000000010421426625374700213330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Constants for PyBEL-Jupyter.""" from ...constants import ( ABUNDANCE, BIOPROCESS, COMPLEX, COMPOSITE, GENE, MIRNA, PATHOLOGY, PROTEIN, REACTION, RNA, ) #: The color map defining the node colors in visualization DEFAULT_COLOR_MAP = { PROTEIN: "#1F77B4", PATHOLOGY: "#FF7F0E", BIOPROCESS: "#2CA02C", MIRNA: "#D62728", COMPLEX: "#98DF8A", COMPOSITE: "#9467BD", REACTION: "#000000", GENE: "#FFBB78", ABUNDANCE: "#AEC7E8", RNA: "#FF9896", } pybel-0.15.5/src/pybel/io/jupyter/inline.py000066400000000000000000000050211426625374700205760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for displaying graphs with inline HTML in Jupyter Notebooks.""" from random import sample from typing import Mapping, Optional from .constants import DEFAULT_COLOR_MAP from ..jinja_utils import build_template_renderer from ..nodelink import to_nodelink_jsons from ...struct import BELGraph __all__ = ["to_jupyter", "to_jupyter_str"] DEFAULT_WIDTH = 1000 DEFAULT_HEIGHT = 650 def _generate_id() -> str: """Generate a random string of letters.""" return "".join(sample("abcdefghjkmopqrstuvqxyz", 16)) def to_jupyter( graph: BELGraph, width: int = DEFAULT_WIDTH, height: int = DEFAULT_HEIGHT, color_map: Optional[Mapping[str, str]] = None, ): """Display a BEL graph inline in a Jupyter notebook. To use successfully, make run as the last statement in a cell inside a Jupyter notebook. :param graph: A BEL graph :param width: The width of the visualization window to render :param height: The height of the visualization window to render :param color_map: A dictionary from PyBEL internal node functions to CSS color strings like #FFEE00. Defaults to :data:`default_color_map` :return: An IPython notebook Javascript object :rtype: :class:`IPython.display.Javascript` """ from IPython.display import Javascript return Javascript( to_jupyter_str( graph, width=width, height=height, color_map=color_map, ) ) def to_jupyter_str( graph: BELGraph, width: int = DEFAULT_WIDTH, height: int = DEFAULT_HEIGHT, color_map: Optional[Mapping[str, str]] = None, ) -> str: """Return the string to be javascript-ified by the Jupyter notebook function :class:`IPython.display.Javascript`. :param graph: A BEL graph :param width: The width of the visualization window to render :param height: The height of the visualization window to render :param color_map: A dictionary from PyBEL internal node functions to CSS color strings like #FFEE00. Defaults to :data:`default_color_map` :return: The javascript string to turn into magic """ gjson = to_nodelink_jsons(graph) chart_id = _generate_id() #: Renders templates from pybel.io.jupyter.templates folder render_template = build_template_renderer(__file__) return render_template( "pybel_jupyter.js", graph=gjson, chart=chart_id, width=width, height=height, color_map=(color_map or DEFAULT_COLOR_MAP), ) pybel-0.15.5/src/pybel/io/jupyter/templates/000077500000000000000000000000001426625374700207465ustar00rootroot00000000000000pybel-0.15.5/src/pybel/io/jupyter/templates/graph_template.html000066400000000000000000000015601426625374700246320ustar00rootroot00000000000000

pybel-0.15.5/src/pybel/io/jupyter/templates/pybel_jupyter.js000066400000000000000000000005661426625374700242100ustar00rootroot00000000000000{% include "pybel_vis.js" %} require.config({ paths: { d3: '//cdnjs.cloudflare.com/ajax/libs/d3/4.5.0/d3.min' } }); var elementInnerHTML = "
"; element.append(elementInnerHTML); require(['d3'], function (d3) { return init_d3_force(d3, {{ graph|safe }}, "#{{ chart }}", {{ width }}, {{ height }}, {{ color_map|safe }}); }); pybel-0.15.5/src/pybel/io/jupyter/templates/pybel_vis.js000066400000000000000000000216131426625374700233030ustar00rootroot00000000000000function init_d3_force(d3, graph, chart, width, height, function_colors) { var focus_node = null; var highlight_node = null; // Highlight color variables // Highlight color of the node boundering const highlight_node_boundering = "#4EB2D4"; // Highlight color of the edge const highlighted_link_color = "#4EB2D4"; // Text highlight color const highlight_text = "#4EB2D4"; // Size when zooming scale var size = d3.scalePow().exponent(1) .domain([1, 100]) .range([8, 24]); // Simulation parameters const linkDistance = 100; const fCharge = -1000; const linkStrength = 0.7; const collideStrength = 1; // Simulation defined with variables var simulation = d3.forceSimulation() .force("link", d3.forceLink() .distance(linkDistance) .strength(linkStrength) ) .force("collide", d3.forceCollide() .radius(function (d) { return d.r + 10 }) .strength(collideStrength) ) .force("charge", d3.forceManyBody() .strength(fCharge) ) .force("center", d3.forceCenter(width / 2, height / 2)) .force("y", d3.forceY(0)) .force("x", d3.forceX(0)); // Pin down functionality var node_drag = d3.drag() .on("start", dragstarted) .on("drag", dragged) .on("end", dragended); function dragstarted(d) { if (!d3.event.active) simulation.alphaTarget(0.3).restart(); d.fx = d.x; d.fy = d.y; } function dragged(d) { d.fx = d3.event.x; d.fy = d3.event.y; } function dragended(d) { if (!d3.event.active) simulation.alphaTarget(0); } function releasenode(d) { d.fx = null; d.fy = null; } //END Pin down functionality /** * Gets the best name for a node object * @param {object} d object * @returns {str} canonical name of the node */ function getCanonicalName(d) { if (d.concept && d.concept.name && !(d.variants || d.reactants || d.products || d.members)) { return d.concept.name } else if (d.bel) { return d.bel } else { console.log('Undefined node: ' + d); return 'UNDEFINED' } } const color_circunferencia = "black"; const default_link_color = "#AAAAAA"; const nominal_base_node_size = 8; // Normal and highlighted stroke of the links (double the width of the link when highlighted) const nominal_stroke = 1.5; // Zoom variables const min_zoom = 0.1; const max_zoom = 10; var svg = d3.select(chart).append("svg") .attr("width", width) .attr("height", height); // // Create definition for arrowhead. svg.append("defs").append("marker") .attr("id", "arrowhead") .attr("viewBox", "0 -5 10 10") .attr("refX", 20) .attr("refY", 0) .attr("markerUnits", "strokeWidth") .attr("markerWidth", 6) .attr("markerHeight", 6) .attr("orient", "auto") .append("path") .attr("d", "M0,-5L10,0L0,5"); // // Create definition for stub. svg.append("defs").append("marker") .attr("id", "stub") .attr("viewBox", "-1 -5 2 10") .attr("refX", 15) .attr("refY", 0) .attr("markerUnits", "strokeWidth") .attr("markerWidth", 6) .attr("markerHeight", 6) .attr("orient", "auto") .append("path") .attr("d", "M 0,0 m -1,-5 L 1,-5 L 1,5 L -1,5 Z"); // Background svg.append("rect") .attr("width", "100%") .attr("height", "100%") .attr("fill", "#ffffff") .style("pointer-events", "all") // Zoom + panning functionality .call(d3.zoom() .scaleExtent([min_zoom, max_zoom]) .on("zoom", zoomed)) .on("dblclick.zoom", null); function zoomed() { g.attr("transform", d3.event.transform); } // g = svg object where the graph will be appended var g = svg.append("g"); var linkedByIndex = {}; graph.links.forEach(function (d) { linkedByIndex[d.source + "," + d.target] = true; }); function isConnected(a, b) { return linkedByIndex[a.index + "," + b.index] || linkedByIndex[b.index + "," + a.index] || a.index == b.index; } function ticked() { link.attr("x1", function (d) { return d.source.x; }) .attr("y1", function (d) { return d.source.y; }) .attr("x2", function (d) { return d.target.x; }) .attr("y2", function (d) { return d.target.y; }); node .attr("transform", function (d) { return "translate(" + d.x + ", " + d.y + ")"; }); } simulation .nodes(graph.nodes) .on("tick", ticked); simulation.force("link") .links(graph.links); // Definition of links nodes text... var link = g.selectAll(".link") .data(graph.links) .enter().append("line") .style("stroke-width", nominal_stroke) .style("stroke", default_link_color) .style("stroke-dasharray", function (d) { if (['decreases', 'directlyDecreases', 'increases', 'directlyIncreases', 'negativeCorrelation', 'positiveCorrelation'].indexOf(d.relation) >= 0) { return "none" } else { return "4, 4" } }) .attr("marker-start", function (d) { if ('positiveCorrelation' == d.relation) { return "url(#arrowhead)" } else if ('negativeCorrelation' == d.relation) { return "url(#stub)" } else { return "" } }) .attr("marker-end", function (d) { if (['increases', 'directlyIncreases', 'positiveCorrelation', 'isA', 'partOf'].indexOf(d.relation) >= 0) { return "url(#arrowhead)" } else if (['decreases', 'directlyDecreases', 'negativeCorrelation'].indexOf(d.relation) >= 0) { return "url(#stub)" } else { return "" } }); var node = g.selectAll(".nodes") .data(graph.nodes) .enter().append("g") .attr("class", "node") // Next two lines -> Pin down functionality .on('dblclick', releasenode) .call(node_drag); var circle = node.append("path") .attr("d", d3.symbol() .size(function (d) { return Math.PI * Math.pow(size(d.size) || nominal_base_node_size, 2); }) ) .attr("class", function (d) { return d.function }) .style('fill', function (d) { return function_colors[d.function] }) .style("stroke-width", nominal_stroke) .style("stroke", color_circunferencia); var text = node.append("text") .attr("class", "node-name") // .attr("id", nodehashes[d]) .attr("fill", "black") .attr("dx", 12) .attr("dy", ".35em") .text(function (d) { return getCanonicalName(d) }); // Highlight on mouseenter and back to normal on mouseout node.on("mouseenter", function (d) { set_highlight(d); }) .on("mousedown", function () { d3.event.stopPropagation(); }).on("mouseout", function () { exit_highlight(); }); function exit_highlight() { highlight_node = null; if (focus_node === null) { if (highlight_node_boundering != color_circunferencia) { circle.style("stroke", color_circunferencia); text.style("fill", "black"); link.style("stroke", default_link_color); } } } function set_highlight(d) { if (focus_node !== null) d = focus_node; highlight_node = d; if (highlight_node_boundering != color_circunferencia) { circle.style("stroke", function (o) { return isConnected(d, o) ? highlight_node_boundering : color_circunferencia; }); text.style("fill", function (o) { return isConnected(d, o) ? highlight_text : "black"; }); link.style("stroke", function (o) { return o.source.index == d.index || o.target.index == d.index ? highlighted_link_color : default_link_color; }); } } // Freeze the graph when space is pressed function freezeGraph() { // Space button Triggers STOP if (d3.event.keyCode == 32) { simulation.stop(); } } // Call freezeGraph when a key is pressed, freezeGraph checks whether this key is "Space" that triggers the freeze d3.select(window).on("keydown", freezeGraph); } pybel-0.15.5/src/pybel/io/jupyter/visualization.py000066400000000000000000000037151426625374700222310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Convert BEL graphs to HTML. This module provides functions for making HTML visualizations of BEL Graphs. Because the :class:`pybel.BELGraph` inherits from :class:`networkx.MultiDiGraph`, it can also be visualized using :mod:`networkx` `library `_. """ import json from typing import Mapping, Optional, TextIO, Union from networkx.utils import open_file from .constants import DEFAULT_COLOR_MAP from ..jinja_utils import build_template_renderer from ..nodelink import to_nodelink_jsons from ...struct import BELGraph __all__ = [ "to_html", "to_html_file", ] def to_html(graph: BELGraph, color_map: Optional[Mapping[str, str]] = None) -> str: """Create an HTML visualization for the given JSON representation of a BEL graph. :param graph: A BEL graph :param color_map: A dictionary from PyBEL internal node functions to CSS color strings like #FFEE00. Defaults to :data:`default_color_map` :return: HTML string representing the graph """ color_map = DEFAULT_COLOR_MAP if color_map is None else color_map render_template = build_template_renderer(__file__) return render_template( "graph_template.html", graph=to_nodelink_jsons(graph), color_map=json.dumps(color_map), number_nodes=graph.number_of_nodes(), number_edges=graph.number_of_edges(), ) @open_file(1, mode="w") def to_html_file( graph: BELGraph, file: Union[str, TextIO], color_map: Optional[Mapping[str, str]] = None, ) -> None: """Write the HTML visualization to a file or file-like. :param graph: A BEL graph :param color_map: A dictionary from PyBEL internal node functions to CSS color strings like #FFEE00. Defaults to :data:`default_color_map` :param file file: A writable file or file-like or file path """ print(to_html(graph, color_map=color_map), file=file) pybel-0.15.5/src/pybel/io/line_utils.py000066400000000000000000000255221426625374700177750ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains helper functions for reading BEL scripts.""" import logging import os import re import time from typing import Any, Iterable, List, Mapping, Optional, Tuple from bel_resources import ResourceError, split_file_to_annotations_and_definitions from pyparsing import ParseException from sqlalchemy.exc import OperationalError from tqdm.autonotebook import tqdm from ..constants import INVERSE_DOCUMENT_KEYS, REQUIRED_METADATA from ..exceptions import ( BELParserWarning, BELSyntaxError, InconsistentDefinitionError, MalformedMetadataException, MissingMetadataException, PlaceholderAminoAcidWarning, VersionFormatWarning, ) from ..manager import Manager from ..parser import BELParser, MetadataParser from ..struct.graph import BELGraph __all__ = [ "parse_lines", ] logger = logging.getLogger(__name__) parser_logger = logging.getLogger("pybel.parser") METADATA_LINE_RE = re.compile(r"(SET\s+DOCUMENT|DEFINE\s+NAMESPACE|DEFINE\s+ANNOTATION)") LOG_FMT = "%d:%d %s %s" LOG_FMT_PATH = "%s:%d:%d %s %s" def parse_lines( graph: BELGraph, lines: Iterable[str], manager: Optional[Manager] = None, disallow_nested: bool = False, citation_clearing: bool = True, use_tqdm: bool = False, tqdm_kwargs: Optional[Mapping[str, Any]] = None, no_identifier_validation: bool = False, disallow_unqualified_translocations: bool = False, allow_redefinition: bool = False, allow_definition_failures: bool = False, allow_naked_names: bool = False, required_annotations: Optional[List[str]] = None, upgrade_urls: bool = False, ) -> None: """Parse an iterable of lines into this graph. Delegates to :func:`parse_document`, :func:`parse_definitions`, and :func:`parse_statements`. :param graph: A BEL graph :param lines: An iterable over lines of BEL script :param manager: A PyBEL database manager :param disallow_nested: If true, turns on nested statement failures :param citation_clearing: Should :code:`SET Citation` statements clear evidence and all annotations? Delegated to :class:`pybel.parser.ControlParser` :param use_tqdm: Use :mod:`tqdm` to show a progress bar? :param tqdm_kwargs: Keywords to pass to ``tqdm`` :param disallow_unqualified_translocations: If true, allow translocations without TO and FROM clauses. :param required_annotations: Annotations that are required for all statements :param upgrade_urls: Automatically upgrade old namespace URLs. Defaults to false. .. warning:: These options allow concessions for parsing BEL that is either **WRONG** or **UNSCIENTIFIC**. Use them at risk to reproducibility and validity of your results. :param no_identifier_validation: If true, turns off namespace validation :param allow_naked_names: If true, turns off naked namespace failures :param allow_redefinition: If true, doesn't fail on second definition of same name or annotation :param allow_definition_failures: If true, allows parsing to continue if a terminology file download/parse fails """ docs, definitions, statements = split_file_to_annotations_and_definitions(lines) if manager is None: manager = Manager() metadata_parser = MetadataParser( manager, allow_redefinition=allow_redefinition, skip_validation=no_identifier_validation, upgrade_urls=upgrade_urls, ) parse_document( graph, docs, metadata_parser, ) parse_definitions( graph, definitions, metadata_parser, allow_failures=allow_definition_failures, use_tqdm=use_tqdm, tqdm_kwargs=tqdm_kwargs, ) bel_parser = BELParser( graph=graph, # terminologies namespace_to_term_to_encoding=metadata_parser.namespace_to_term_to_encoding, namespace_to_pattern=metadata_parser.namespace_to_pattern, annotation_to_term=metadata_parser.annotation_to_term, annotation_to_pattern=metadata_parser.annotation_to_pattern, annotation_to_local=metadata_parser.annotation_to_local, # language settings disallow_nested=disallow_nested, citation_clearing=citation_clearing, skip_validation=no_identifier_validation, allow_naked_names=allow_naked_names, disallow_unqualified_translocations=disallow_unqualified_translocations, required_annotations=required_annotations, ) parse_statements( graph, statements, bel_parser, use_tqdm=use_tqdm, tqdm_kwargs=tqdm_kwargs, ) logger.info( "Network has %d nodes and %d edges", graph.number_of_nodes(), graph.number_of_edges(), ) def parse_document( graph: BELGraph, enumerated_lines: Iterable[Tuple[int, str]], metadata_parser: MetadataParser, ) -> None: """Parse the lines in the document section of a BEL script.""" parse_document_start_time = time.time() for line_number, line in enumerated_lines: try: metadata_parser.parseString(line, line_number=line_number) except VersionFormatWarning as exc: _log_parse_exception(graph, exc) graph.add_warning(exc) except Exception as e: exc = MalformedMetadataException(line_number, line, 0) _log_parse_exception(graph, exc) raise exc from e for required in REQUIRED_METADATA: required_metadatum = metadata_parser.document_metadata.get(required) if required_metadatum is not None: continue required_metadatum_key = INVERSE_DOCUMENT_KEYS[required] # This has to be insert since it needs to go on the front! exc = MissingMetadataException.make(required_metadatum_key) graph.warnings.insert(0, (None, exc, {})) _log_parse_exception(graph, exc) graph.document.update(metadata_parser.document_metadata) logger.info( "Finished parsing document section in %.02f seconds", time.time() - parse_document_start_time, ) def parse_definitions( graph: BELGraph, enumerated_lines: Iterable[Tuple[int, str]], metadata_parser: MetadataParser, allow_failures: bool = False, use_tqdm: bool = False, tqdm_kwargs: Optional[Mapping[str, Any]] = None, ) -> None: """Parse the lines in the definitions section of a BEL script. :param graph: A BEL graph :param enumerated_lines: An enumerated iterable over the lines in the definitions section of a BEL script :param metadata_parser: A metadata parser :param allow_failures: If true, allows parser to continue past strange failures :param use_tqdm: Use :mod:`tqdm` to show a progress bar? :param tqdm_kwargs: Keywords to pass to ``tqdm`` :raises: pybel.parser.parse_exceptions.InconsistentDefinitionError :raises: pybel.resources.exc.ResourceError :raises: sqlalchemy.exc.OperationalError """ parse_definitions_start_time = time.time() if use_tqdm: _tqdm_kwargs = dict(desc="Definitions", leave=False) if tqdm_kwargs: _tqdm_kwargs.update(tqdm_kwargs) enumerated_lines = tqdm(list(enumerated_lines), **_tqdm_kwargs) for line_number, line in enumerated_lines: try: metadata_parser.parseString(line, line_number=line_number) except (InconsistentDefinitionError, ResourceError) as e: parser_logger.exception(LOG_FMT, line_number, 0, e.__class__.__name__, line) raise e except OperationalError as e: parser_logger.warning( "Need to upgrade database. See http://pybel.readthedocs.io/en/latest/installation.html#upgrading", ) raise e except Exception as e: if not allow_failures: exc = MalformedMetadataException(line_number, line, 0) _log_parse_exception(graph, exc) raise exc from e graph.namespace_url.update(metadata_parser.namespace_url_dict) graph.namespace_pattern.update( {keyword: pattern.pattern for keyword, pattern in metadata_parser.namespace_to_pattern.items()} ) graph.annotation_url.update(metadata_parser.annotation_url_dict) graph.annotation_pattern.update( {keyword: pattern.pattern for keyword, pattern in metadata_parser.annotation_to_pattern.items()} ) graph.annotation_list.update(metadata_parser.annotation_to_local) logger.info( "Finished parsing definitions section in %.02f seconds", time.time() - parse_definitions_start_time, ) metadata_parser.ensure_resources() logger.info("Finished ensuring namespaces in cache") def parse_statements( graph: BELGraph, enumerated_lines: Iterable[Tuple[int, str]], bel_parser: BELParser, use_tqdm: bool = True, tqdm_kwargs: Optional[Mapping[str, Any]] = None, ) -> None: """Parse a list of statements from a BEL Script. :param graph: A BEL graph :param enumerated_lines: An enumerated iterable over the lines in the statements section of a BEL script :param bel_parser: A BEL parser :param use_tqdm: Use :mod:`tqdm` to show a progress bar? Requires reading whole file to memory. :param tqdm_kwargs: Keywords to pass to ``tqdm`` """ parse_statements_start_time = time.time() if use_tqdm: tqdm_kwargs = {} if tqdm_kwargs is None else dict(tqdm_kwargs) tqdm_kwargs.setdefault("desc", "Statements") tqdm_kwargs.setdefault("leave", False) enumerated_lines = tqdm(list(enumerated_lines), **tqdm_kwargs) for line_number, line in enumerated_lines: try: bel_parser.parseString(line, line_number=line_number) except ParseException as e: exc = BELSyntaxError(line_number, line, e.loc) _log_parse_exception(graph, exc) graph.add_warning(exc, bel_parser.get_annotations()) except PlaceholderAminoAcidWarning as exc: exc.line_number = line_number _log_parse_exception(graph, exc) graph.add_warning(exc, bel_parser.get_annotations()) except BELParserWarning as exc: _log_parse_exception(graph, exc) graph.add_warning(exc, bel_parser.get_annotations()) except Exception: parser_logger.exception(LOG_FMT, line_number, 0, "General Failure", line) raise logger.info( "Parsed statements section in %.02f seconds with %d warnings", time.time() - parse_statements_start_time, len(graph.warnings), ) def _log_parse_exception(graph: BELGraph, exc: BELParserWarning): if graph.path: s = LOG_FMT_PATH % ( os.path.basename(graph.path), exc.line_number, exc.position, exc.__class__.__name__, exc, ) else: s = LOG_FMT % (exc.line_number, exc.position, exc.__class__.__name__, exc) tqdm.write(s) pybel-0.15.5/src/pybel/io/lines.py000066400000000000000000000031571426625374700167400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains IO functions for BEL scripts.""" import gzip import logging from typing import TextIO, Union from bel_resources.utils import download from networkx.utils import open_file from .line_utils import parse_lines from ..struct import BELGraph __all__ = [ "from_bel_script", "from_bel_script_gz", "from_bel_script_url", ] logger = logging.getLogger(__name__) @open_file(0, mode="r") def from_bel_script(path: Union[str, TextIO], **kwargs) -> BELGraph: """Load a BEL graph from a file resource. This function is a thin wrapper around :func:`from_lines`. :param path: A path or file-like The remaining keyword arguments are passed to :func:`pybel.io.line_utils.parse_lines`, which populates a :class:`BELGraph`. """ logger.info("Reading BEL script at %s", path.name) graph = BELGraph(path=path.name) parse_lines(graph=graph, lines=path, **kwargs) return graph def from_bel_script_gz(path, **kwargs) -> BELGraph: """Parse a BEL graph from a gzipped BEL Script.""" with gzip.open(path, "rt") as file: return from_bel_script(file, **kwargs) def from_bel_script_url(url: str, **kwargs) -> BELGraph: """Load a BEL graph from a URL resource. :param url: A valid URL pointing to a BEL document The remaining keyword arguments are passed to :func:`pybel.io.line_utils.parse_lines`. """ logger.info("Loading from url: %s", url) res = download(url) lines = (line.decode("utf-8") for line in res.iter_lines()) graph = BELGraph(path=url) parse_lines(graph=graph, lines=lines, **kwargs) return graph pybel-0.15.5/src/pybel/io/neo4j.py000066400000000000000000000054711426625374700166460ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Output functions for BEL graphs to Neo4j.""" from tqdm.autonotebook import tqdm from ..constants import ( ANNOTATIONS, CITATION, EVIDENCE, FUSION, MEMBERS, NAMESPACE, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, VARIANTS, ) from ..utils import flatten_dict __all__ = [ "to_neo4j", ] def to_neo4j(graph, neo_connection, use_tqdm: bool = False): """Upload a BEL graph to a Neo4j graph database using :mod:`py2neo`. :param pybel.BELGraph graph: A BEL Graph :param neo_connection: A :mod:`py2neo` connection object. Refer to the `py2neo documentation `_ for how to build this object. :type neo_connection: str or py2neo.Graph Example Usage: >>> import py2neo >>> import pybel >>> from pybel.examples import sialic_acid_graph >>> neo_graph = py2neo.Graph("http://localhost:7474/db/data/") # use your own connection settings >>> pybel.to_neo4j(sialic_acid_graph, neo_graph) """ import py2neo if isinstance(neo_connection, str): neo_connection = py2neo.Graph(neo_connection) tx = neo_connection.begin() node_map = {} nodes = list(graph) if use_tqdm: nodes = tqdm(nodes, desc="nodes") for node in nodes: if NAMESPACE not in node or VARIANTS in node or MEMBERS in node or FUSION in node: attrs = {"name": node.as_bel()} else: attrs = {"namespace": node.namespace} if node.name and node.identifier: attrs["name"] = node.name attrs["identifier"] = node.identifier elif node.identifier and not node.name: attrs["name"] = node.identifier elif node.name and not node.identifier: attrs["name"] = node.name node_map[node] = py2neo.Node(node.function, **attrs) tx.create(node_map[node]) edges = graph.edges(keys=True, data=True) if use_tqdm: edges = tqdm(edges, desc="edges") for u, v, key, node in edges: rel_type = node[RELATION] d = node.copy() del d[RELATION] attrs = {} annotations = d.pop(ANNOTATIONS, None) if annotations: for annotation, values in annotations.items(): attrs[annotation] = list(values) citation = d.pop(CITATION, None) if citation: attrs[CITATION] = citation.curie if EVIDENCE in d: attrs[EVIDENCE] = d[EVIDENCE] for side in (SOURCE_MODIFIER, TARGET_MODIFIER): side_data = d.get(side) if side_data: attrs.update(flatten_dict(side_data, parent_key=side)) rel = py2neo.Relationship(node_map[u], rel_type, node_map[v], key=key, **attrs) tx.create(rel) tx.commit() pybel-0.15.5/src/pybel/io/nodelink.py000066400000000000000000000160161426625374700174270ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Conversion functions for BEL graphs with node-link JSON.""" import gzip import json from io import BytesIO from itertools import chain, count from operator import methodcaller from typing import Any, Mapping, TextIO, Union from networkx.utils import open_file from .utils import ensure_version from ..constants import ( ANNOTATIONS, CITATION, FUSION, GRAPH_ANNOTATION_CURIE, GRAPH_ANNOTATION_LIST, GRAPH_ANNOTATION_MIRIAM, MEMBERS, PARTNER_3P, PARTNER_5P, PRODUCTS, REACTANTS, SOURCE_MODIFIER, TARGET_MODIFIER, ) from ..dsl import BaseEntity from ..language import citation_dict from ..struct import BELGraph from ..struct.graph import _handle_modifier from ..tokens import parse_result_to_dsl from ..utils import hash_edge, tokenize_version __all__ = [ "to_nodelink", "to_nodelink_file", "to_nodelink_gz", "to_nodelink_jsons", "from_nodelink", "from_nodelink_file", "from_nodelink_gz", "from_nodelink_jsons", "to_nodelink_gz_io", "from_nodelink_gz_io", ] def to_nodelink(graph: BELGraph) -> Mapping[str, Any]: """Convert this graph to a node-link JSON object. :param graph: BEL Graph """ graph_json_dict = _to_nodelink_json_helper(graph) _prepare_graph_dict(graph_json_dict["graph"]) return graph_json_dict def _prepare_graph_dict(g): # Convert annotation list definitions (which are sets) to canonicalized/sorted lists g[GRAPH_ANNOTATION_LIST] = { keyword: list(sorted(values)) for keyword, values in g.get(GRAPH_ANNOTATION_LIST, {}).items() } g[GRAPH_ANNOTATION_CURIE] = list(sorted(g[GRAPH_ANNOTATION_CURIE])) g[GRAPH_ANNOTATION_MIRIAM] = list(sorted(g[GRAPH_ANNOTATION_MIRIAM])) @open_file(1, mode="w") def to_nodelink_file(graph: BELGraph, path: Union[str, TextIO], **kwargs) -> None: """Write this graph as node-link JSON to a file. :param graph: A BEL graph :param path: A path or file-like """ graph_json_dict = to_nodelink(graph) json.dump(graph_json_dict, path, ensure_ascii=False, **kwargs) def to_nodelink_gz(graph, path: str, **kwargs) -> None: """Write a graph as node-link JSON to a gzip file.""" with gzip.open(path, "wt") as file: json.dump(to_nodelink(graph), file, ensure_ascii=False, **kwargs) def to_nodelink_jsons(graph: BELGraph, **kwargs) -> str: """Dump this graph as a node-link JSON object to a string.""" return json.dumps(to_nodelink(graph), ensure_ascii=False, **kwargs) def from_nodelink(graph_json_dict: Mapping[str, Any], check_version: bool = True) -> BELGraph: """Build a graph from node-link JSON Object.""" pybel_version = tokenize_version(graph_json_dict["graph"]["pybel_version"]) if pybel_version[1] < 14: # if minor version is less than 14 raise ValueError("Invalid NodeLink JSON from old version of PyBEL (v{}.{}.{})".format(*pybel_version)) graph = _from_nodelink_json_helper(graph_json_dict) return ensure_version(graph, check_version=check_version) @open_file(0, mode="r") def from_nodelink_file(path: Union[str, TextIO], check_version: bool = True) -> BELGraph: """Build a graph from the node-link JSON contained in the given file. :param path: A path or file-like """ return from_nodelink(json.load(path), check_version=check_version) def from_nodelink_gz(path: str) -> BELGraph: """Read a graph as node-link JSON from a gzip file.""" with gzip.open(path, "rt") as file: return from_nodelink(json.load(file)) def from_nodelink_jsons(graph_json_str: str, check_version: bool = True) -> BELGraph: """Read a BEL graph from a node-link JSON string.""" return from_nodelink(json.loads(graph_json_str), check_version=check_version) def _to_nodelink_json_helper(graph: BELGraph) -> Mapping[str, Any]: """Convert a BEL graph to a node-link format. :param graph: BEL Graph Adapted from :func:`networkx.readwrite.json_graph.node_link_data` """ nodes = sorted(graph, key=methodcaller("as_bel")) mapping = dict(zip(nodes, count())) return { "directed": True, "multigraph": True, "graph": graph.graph.copy(), "nodes": [_augment_node(node) for node in nodes], "links": [ dict( chain( data.copy().items(), [("source", mapping[u]), ("target", mapping[v]), ("key", key)], ), ) for u, v, key, data in graph.edges(keys=True, data=True) ], } def _augment_node(node: BaseEntity) -> BaseEntity: """Add the SHA-512 identifier to a node's dictionary.""" rv = node.copy() rv["id"] = node.md5 rv["bel"] = node.as_bel() for m in chain(node.get(MEMBERS, []), node.get(REACTANTS, []), node.get(PRODUCTS, [])): m.update(_augment_node(m)) if FUSION in node: node[FUSION][PARTNER_3P].update(_augment_node(node[FUSION][PARTNER_3P])) node[FUSION][PARTNER_5P].update(_augment_node(node[FUSION][PARTNER_5P])) return rv def _recover_graph_dict(graph: BELGraph): graph.graph[GRAPH_ANNOTATION_LIST] = { keyword: set(values) for keyword, values in graph.graph.get(GRAPH_ANNOTATION_LIST, {}).items() } graph.graph[GRAPH_ANNOTATION_CURIE] = set(graph.graph.get(GRAPH_ANNOTATION_CURIE, [])) graph.graph[GRAPH_ANNOTATION_MIRIAM] = set(graph.graph.get(GRAPH_ANNOTATION_MIRIAM, [])) def _from_nodelink_json_helper(data: Mapping[str, Any]) -> BELGraph: """Return graph from node-link data format. Adapted from :func:`networkx.readwrite.json_graph.node_link_graph` """ graph = BELGraph() graph.graph = data.get("graph", {}) _recover_graph_dict(graph) mapping = [] for node_data in data["nodes"]: node = parse_result_to_dsl(node_data) graph.add_node_from_data(node) mapping.append(node) for data in data["links"]: u = mapping[data["source"]] v = mapping[data["target"]] edge_data = {k: v for k, v in data.items() if k not in {"source", "target", "key"}} for side in (SOURCE_MODIFIER, TARGET_MODIFIER): side_data = edge_data.get(side) if side_data: _handle_modifier(side_data) if CITATION in edge_data: edge_data[CITATION] = citation_dict(**edge_data[CITATION]) if ANNOTATIONS in edge_data: edge_data[ANNOTATIONS] = graph._clean_annotations(edge_data[ANNOTATIONS]) graph.add_edge(u, v, key=hash_edge(u, v, edge_data), **edge_data) return graph def to_nodelink_gz_io(graph: BELGraph) -> BytesIO: """Get a BEL graph as a compressed BytesIO.""" bytes_io = BytesIO() with gzip.GzipFile(fileobj=bytes_io, mode="w") as file: s = to_nodelink_jsons(graph) file.write(s.encode("utf-8")) bytes_io.seek(0) return bytes_io def from_nodelink_gz_io(bytes_io: BytesIO) -> BELGraph: """Get BEL from gzipped nodelink JSON.""" with gzip.GzipFile(fileobj=bytes_io, mode="r") as file: s = file.read() j = s.decode("utf-8") return from_nodelink_jsons(j) pybel-0.15.5/src/pybel/io/pykeen.py000066400000000000000000000101371426625374700171150ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Entry points for PyKEEN. PyKEEN is a machine learning library for knowledge graph embeddings that supports node clustering, link prediction, entity disambiguation, question/answering, and other tasks with knowledge graphs. It provides an interface for registering plugins using Python's entrypoints under the ``pykeen.triples.extension_importer`` and ``pykeen.triples.prefix_importer`` groups. More specific information about how the PyBEL plugins are loaded into PyKEEN can be found in PyBEL's `setup.cfg `_ under the ``[options.entry_points]`` header. The following example shows how you can parse/load the triples from a BEL document with the `*.bel` extension. .. code-block:: python from urllib.request import urlretrieve url = 'https://raw.githubusercontent.com/cthoyt/selventa-knowledge/master/selventa_knowledge/small_corpus.bel' urlretrieve(url, 'small_corpus.bel') # Example 1A: Make triples factory from pykeen.triples import TriplesFactory tf = TriplesFactory(path='small_corpus.bel') # Example 1B: Use directly in the pipeline, which automatically invokes training/testing set stratification from pykeen.pipeline import pipeline results = pipeline( dataset='small_corpus.bel', model='TransE', ) The same is true for precompiled BEL documents in the node-link format with the `*.bel.nodelink.json` extension and the pickle format with the `*.bel.pickle` extension. The following example shows how you can load/parse the triples from a BEL document stored in BEL Commons using the ``bel-commons`` prefix in combination with the network's identifier. .. code-block:: python # Example 2A: Make a triples factory from pykeen.triples import TriplesFactory # the network's identifier is 528 tf = TriplesFactory(path='bel-commons:528') # Example 1B: Use directly in the pipeline, which automatically invokes training/testing set stratification from pykeen.pipeline import pipeline results = pipeline( dataset='bel-commons:528', model='TransR', ) Currently, this relies on the default BEL Commons service provider at https://bel-commons-dev.scai.fraunhofer.de, whose location might change in the future. """ import numpy as np from .bel_commons_client import from_bel_commons from .gpickle import from_pickle from .nodelink import from_nodelink_file from .triples import to_triples __all__ = [ "get_triples_from_bel", "get_triples_from_bel_nodelink", "get_triples_from_bel_pickle", "get_triples_from_bel_commons", ] def get_triples_from_bel(path: str) -> np.ndarray: """Get triples from a BEL file by wrapping :func:`pybel.io.tsv.api.get_triples`. :param path: the file path to a BEL Script :return: A three column array with head, relation, and tail in each row """ from pybel import from_bel_script return _from_bel(path, from_bel_script) def get_triples_from_bel_nodelink(path: str) -> np.ndarray: """Get triples from a BEL Node-link JSON file by wrapping :func:`pybel.io.tsv.api.get_triples`. :param path: the file path to a BEL Node-link JSON file :return: A three column array with head, relation, and tail in each row """ return _from_bel(path, from_nodelink_file) def get_triples_from_bel_pickle(path: str) -> np.ndarray: """Get triples from a BEL pickle file by wrapping :func:`pybel.io.tsv.api.get_triples`. :param path: the file path to a BEL pickle file :return: A three column array with head, relation, and tail in each row """ return _from_bel(path, from_pickle) def get_triples_from_bel_commons(network_id: str) -> np.ndarray: """Load a BEL document from BEL Commons by wrapping :func:`pybel.io.tsv.api.get_triples`. :param network_id: The network identifier for a graph in BEL Commons :return: A three column array with head, relation, and tail in each row """ return _from_bel(str(network_id), from_bel_commons) def _from_bel(path, bel_importer) -> np.ndarray: graph = bel_importer(path) triples = to_triples(graph) return np.array(triples) pybel-0.15.5/src/pybel/io/pynpa.py000066400000000000000000000127121426625374700167520ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Exporter for PyNPA. .. seealso:: https://github.com/pynpa """ import logging import os from typing import List, Mapping, Optional, Tuple import pandas as pd from ..constants import CAUSAL_DECREASE_RELATIONS, CAUSAL_INCREASE_RELATIONS, RELATION from ..dsl import Gene, MicroRna, Protein, Rna from ..struct import BELGraph from ..struct.getters import get_tf_pairs from ..struct.node_utils import ( list_abundance_cartesian_expansion, reaction_cartesian_expansion, ) __all__ = [ "to_npa_directory", "to_npa_dfs", "to_npa_layers", ] logger = logging.getLogger(__name__) Layer = Mapping[Tuple[Gene, Gene], int] #: Code to distinguish between between iNodes when nodes have been debelized DEBELIZED_CODE_FOR_INODES = "*" def to_npa_directory(graph: BELGraph, directory: str, **kwargs) -> None: """Write the BEL file to two files in the directory for :mod:`pynpa`.""" ppi_df, transcription_df = to_npa_dfs(graph, **kwargs) ppi_df.to_csv(os.path.join(directory, "ppi_layer.tsv"), sep="\t", index=False) transcription_df.to_csv(os.path.join(directory, "transcriptional_layer.tsv"), sep="\t", index=False) def to_npa_dfs( graph: BELGraph, cartesian_expansion: bool = False, nomenclature_method_first_layer: Optional[str] = None, nomenclature_method_second_layer: Optional[str] = None, direct_tf_only: bool = False, ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Export the BEL graph as two lists of triples for the :mod:`pynpa`. :param graph: A BEL graph :param cartesian_expansion: If true, applies cartesian expansion on both reactions (reactants x products) as well as list abundances using :func:`list_abundance_cartesian_expansion` and :func:`reaction_cartesian_expansion` :param nomenclature_method_first_layer: Either "curie", "name" or "inodes. Defaults to "curie". :param nomenclature_method_second_layer: Either "curie", "name" or "inodes. Defaults to "curie". 1. Pick out all transcription factor relationships. Protein X is a transcription factor for gene Y IFF ``complex(p(X), g(Y)) -> r(Y)`` 2. Get all other interactions between any gene/rna/protein that are directed causal for the PPI layer """ ppi_layer, transcription_layer = to_npa_layers( graph, cartesian_expansion=cartesian_expansion, direct_tf_only=direct_tf_only, ) return ( _get_df(ppi_layer, method=nomenclature_method_first_layer), _get_df(transcription_layer, method=nomenclature_method_second_layer), ) def _get_df(layer: Layer, method: Optional[str] = None) -> pd.DataFrame: rows = _normalize_layer(layer, method=method) return pd.DataFrame(rows, columns=["source", "target", "relation"]).sort_values(["source", "target"]) def _normalize_layer(layer: Layer, method: Optional[str] = None) -> List[Tuple[str, str, int]]: if method == "curie" or method is None: return [(source.curie, target.curie, direction) for (source, target), direction in layer.items()] elif method == "name": return [(source.name, target.name, direction) for (source, target), direction in layer.items()] elif method == "inodes": return [ ( "{}{}".format(DEBELIZED_CODE_FOR_INODES, source.name), "{}{}".format(DEBELIZED_CODE_FOR_INODES, target.name), direction, ) for (source, target), direction in layer.items() ] else: raise ValueError("Invalid export method: {method}".format(method=method)) def to_npa_layers( graph: BELGraph, cartesian_expansion: bool = False, direct_tf_only: bool = False, ) -> Tuple[Layer, Layer]: """Get the two layers for the network. :param graph: A BEL graph :param cartesian_expansion: If true, applies cartesian expansion on both reactions (reactants x products) as well as list abundances using :func:`list_abundance_cartesian_expansion` and :func:`reaction_cartesian_expansion` :param direct_tf_only: If true, only uses directlyIncreases and directlyDecreases relations for TF relations ``complex(p(X), g(Y)) =>/=| r(Y)``. If false, also allows indirect relations ``complex(p(X), g(Y)) ->/-| r(Y)``. """ if cartesian_expansion: list_abundance_cartesian_expansion(graph) reaction_cartesian_expansion(graph) transcription_layer = { (u.get_rna().get_gene(), v.get_gene()): r for u, v, r in get_tf_pairs(graph, direct_only=direct_tf_only) } logger.info("extracted %d pairs for the transcription layer", len(transcription_layer)) ppi_layer = {} for u, v, d in graph.edges(data=True): u, v = _normalize(u), _normalize(v) if u is None or v is None: continue if (u, v) in transcription_layer: continue relation = d[RELATION] if relation in CAUSAL_INCREASE_RELATIONS: ppi_layer[u, v] = +1 elif relation in CAUSAL_DECREASE_RELATIONS: ppi_layer[u, v] = -1 # TODO what about contradictions logger.info("extracted %d pairs for the ppi layer", len(ppi_layer)) return ppi_layer, transcription_layer def _normalize(n): if isinstance(n, Protein): if n.variants: n = n.get_parent() n = n.get_rna() if isinstance(n, (Rna, MicroRna)): if n.variants: n = n.get_parent() n = n.get_gene() if isinstance(n, Gene): if n.variants: n = n.get_parent() return n pybel-0.15.5/src/pybel/io/sbel.py000066400000000000000000000074751426625374700165620ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Streamable BEL as JSON.""" import gzip import json from typing import Any, Iterable, List, TextIO, Union from networkx.utils import open_file from .nodelink import _augment_node, _prepare_graph_dict, _recover_graph_dict from ..constants import CITATION, SOURCE_MODIFIER, TARGET_MODIFIER from ..language import CitationDict from ..struct.graph import BELGraph, _handle_modifier from ..tokens import parse_result_to_dsl from ..utils import hash_edge __all__ = [ "to_sbel_file", "to_sbel", "to_sbel_gz", "from_sbel", "from_sbel_gz", "from_sbel_file", ] SBEL = Any @open_file(1, mode="w") def to_sbel_file(graph: BELGraph, path: Union[str, TextIO], separators=(",", ":"), **kwargs) -> None: """Write this graph as BEL JSONL to a file. :param graph: A BEL graph :param separators: The separators used in :func:`json.dumps` :param path: A path or file-like """ for i in iterate_sbel(graph): print( json.dumps(i, ensure_ascii=False, separators=separators, **kwargs), file=path, ) def to_sbel_gz(graph: BELGraph, path: str, separators=(",", ":"), **kwargs) -> None: """Write a graph as BEL JSONL to a gzip file. :param graph: A BEL graph :param separators: The separators used in :func:`json.dumps` :param path: A path for a gzip file """ with gzip.open(path, "wt") as file: to_sbel_file(graph, file, separators=separators, **kwargs) def to_sbel(graph: BELGraph) -> List[SBEL]: """Create a list of JSON dictionaries corresponding to lines in BEL JSONL.""" return list(iterate_sbel(graph)) def iterate_sbel(graph: BELGraph) -> Iterable[SBEL]: """Iterate over JSON dictionaries corresponding to lines in BEL JSONL.""" g = graph.graph.copy() _prepare_graph_dict(g) yield g for u, v, k, d in graph.edges(data=True, keys=True): yield { "source": _augment_node(u), "target": _augment_node(v), "key": k, **d, } def from_sbel(it: Iterable[SBEL], includes_metadata: bool = True) -> BELGraph: """Load a BEL graph from an iterable of dictionaries corresponding to lines in BEL JSONL. :param it: An iterable of dictionaries. :param includes_metadata: By default, interprets the first element of the iterable as the graph's metadata. Switch to ``False`` to disable. :return: A BEL graph """ it = iter(it) rv = BELGraph() if includes_metadata: rv.graph.update(next(it)) _recover_graph_dict(rv) add_sbel(rv, it) return rv def add_sbel(graph: BELGraph, it: Iterable[SBEL]) -> None: """Add dictionaries to a BEL graph. :param graph: A BEL graph :param it: An iterable of dictionaries. """ for data in it: add_sbel_row(graph, data) def add_sbel_row(graph: BELGraph, data: SBEL) -> str: """Add a single SBEL data dictionary to a graph.""" u = parse_result_to_dsl(data["source"]) v = parse_result_to_dsl(data["target"]) edge_data = {k: v for k, v in data.items() if k not in {"source", "target", "key"}} for side in (SOURCE_MODIFIER, TARGET_MODIFIER): side_data = edge_data.get(side) if side_data: _handle_modifier(side_data) if CITATION in edge_data: edge_data[CITATION] = CitationDict(**edge_data[CITATION]) return graph.add_edge(u, v, key=hash_edge(u, v, edge_data), **edge_data) @open_file(0, mode="r") def from_sbel_file(path: Union[str, TextIO]) -> BELGraph: """Build a graph from the BEL JSONL contained in the given file. :param path: A path or file-like """ return from_sbel((json.loads(line) for line in path)) def from_sbel_gz(path: str) -> BELGraph: """Read a graph as BEL JSONL from a gzip file.""" with gzip.open(path, "rt") as file: return from_sbel_file(file) pybel-0.15.5/src/pybel/io/spia.py000066400000000000000000000176731426625374700165720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """An exporter for signaling pathway impact analysis (SPIA) described by [Tarca2009]_. .. [Tarca2009] Tarca, A. L., *et al* (2009). `A novel signaling pathway impact analysis `_. Bioinformatics, 25(1), 75–82. .. seealso:: https://bioconductor.org/packages/release/bioc/html/SPIA.html """ import itertools as itt import os from collections import OrderedDict from typing import Dict, Mapping, Set import pandas as pd from ..constants import ( ASSOCIATION, CAUSAL_DECREASE_RELATIONS, CAUSAL_INCREASE_RELATIONS, RELATION, ) from ..dsl import CentralDogma, Gene, ListAbundance, ProteinModification, Rna from ..language import pmod_mappings from ..struct import BELGraph from ..typing import EdgeData __all__ = [ "to_spia_dfs", "to_spia_excel", "to_spia_tsvs", ] SPIADataFrames = Mapping[str, pd.DataFrame] KEGG_RELATIONS = [ "activation", "compound", "binding/association", "expression", "inhibition", "activation_phosphorylation", "phosphorylation", "inhibition_phosphorylation", "inhibition_dephosphorylation", "dissociation", "dephosphorylation", "activation_dephosphorylation", "state change", "activation_indirect effect", "inhibition_ubiquination", "ubiquination", "expression_indirect effect", "inhibition_indirect effect", "repression", "dissociation_phosphorylation", "indirect effect_phosphorylation", "activation_binding/association", "indirect effect", "activation_compound", "activation_ubiquination", ] def to_spia_excel(graph: BELGraph, path: str) -> None: """Write the BEL graph as an SPIA-formatted excel sheet at the given path.""" x = to_spia_dfs(graph) spia_matrices_to_excel(x, path) def to_spia_tsvs(graph: BELGraph, directory: str) -> None: """Write the BEL graph as a set of SPIA-formatted TSV files in a given directory.""" x = to_spia_dfs(graph) spia_matrices_to_tsvs(x, directory) def to_spia_dfs(graph: BELGraph) -> SPIADataFrames: """Create an excel sheet ready to be used in SPIA software. :param graph: BELGraph :return: dictionary with matrices """ index_nodes = get_matrix_index(graph) spia_matrices = build_spia_matrices(index_nodes) for u, v, edge_data in graph.edges(data=True): # Both nodes are CentralDogma abundances if isinstance(u, CentralDogma) and isinstance(v, CentralDogma): # Update matrix dict update_spia_matrices(spia_matrices, u, v, edge_data) # Subject is CentralDogmaAbundance and node is ListAbundance elif isinstance(u, CentralDogma) and isinstance(v, ListAbundance): # Add a relationship from subject to each of the members in the object for node in v.members: # Skip if the member is not in CentralDogma if not isinstance(node, CentralDogma): continue update_spia_matrices(spia_matrices, u, node, edge_data) # Subject is ListAbundance and node is CentralDogmaAbundance elif isinstance(u, ListAbundance) and isinstance(v, CentralDogma): # Add a relationship from each of the members of the subject to the object for node in u.members: # Skip if the member is not in CentralDogma if not isinstance(node, CentralDogma): continue update_spia_matrices(spia_matrices, node, v, edge_data) # Both nodes are ListAbundance elif isinstance(u, ListAbundance) and isinstance(v, ListAbundance): for sub_member, obj_member in itt.product(u.members, v.members): # Update matrix if both are CentralDogma if isinstance(sub_member, CentralDogma) and isinstance(obj_member, CentralDogma): update_spia_matrices(spia_matrices, sub_member, obj_member, edge_data) # else Not valid edge return spia_matrices def get_matrix_index(graph: BELGraph) -> Set[str]: """Return set of HGNC names from Proteins/Rnas/Genes/miRNA, nodes that can be used by SPIA.""" # TODO: Using HGNC Symbols for now return {node.name for node in graph if isinstance(node, CentralDogma) and node.namespace.upper() == "HGNC"} def build_spia_matrices(nodes: Set[str]) -> Dict[str, pd.DataFrame]: """Build an adjacency matrix for each KEGG relationship and return in a dictionary. :param nodes: A set of HGNC gene symbols :return: Dictionary of adjacency matrix for each relationship """ nodes = list(sorted(nodes)) # Create sheets of the excel in the given order matrices = OrderedDict() for relation in KEGG_RELATIONS: matrices[relation] = pd.DataFrame(0, index=nodes, columns=nodes) return matrices UB_NAMES = {"Ub"} | {e.name for e in pmod_mappings["Ub"]["xrefs"]} PH_NAMES = {"Ph"} | {e.name for e in pmod_mappings["Ph"]["xrefs"]} def update_spia_matrices( spia_matrices: Dict[str, pd.DataFrame], u: CentralDogma, v: CentralDogma, edge_data: EdgeData, ) -> None: """Populate the adjacency matrix.""" if u.namespace.lower() != "hgnc" or v.namespace.lower() != "hgnc": return u_name = u.name v_name = v.name relation = edge_data[RELATION] if relation in CAUSAL_INCREASE_RELATIONS: # If it has pmod check which one and add it to the corresponding matrix if v.variants and any(isinstance(variant, ProteinModification) for variant in v.variants): for variant in v.variants: if not isinstance(variant, ProteinModification): continue elif variant.entity.name in UB_NAMES: spia_matrices["activation_ubiquination"][u_name][v_name] = 1 elif variant.entity.name in PH_NAMES: spia_matrices["activation_phosphorylation"][u_name][v_name] = 1 elif isinstance(v, (Gene, Rna)): # Normal increase, add activation spia_matrices["expression"][u_name][v_name] = 1 else: spia_matrices["activation"][u_name][v_name] = 1 elif relation in CAUSAL_DECREASE_RELATIONS: # If it has pmod check which one and add it to the corresponding matrix if v.variants and any(isinstance(variant, ProteinModification) for variant in v.variants): for variant in v.variants: if not isinstance(variant, ProteinModification): continue elif variant.entity.name in UB_NAMES: spia_matrices["inhibition_ubiquination"][u_name][v_name] = 1 elif variant.entity.name in PH_NAMES: spia_matrices["inhibition_phosphorylation"][u_name][v_name] = 1 elif isinstance(v, (Gene, Rna)): # Normal decrease, check which matrix spia_matrices["repression"][u_name][v_name] = 1 else: spia_matrices["inhibition"][u_name][v_name] = 1 elif relation == ASSOCIATION: spia_matrices["binding_association"][u_name][v_name] = 1 def spia_matrices_to_excel(spia_matrices: SPIADataFrames, path: str) -> None: """Export a SPIA data dictionary into an Excel sheet at the given path. .. note:: # The R import should add the values: # ["nodes"] from the columns # ["title"] from the name of the file # ["NumberOfReactions"] set to "0" """ writer = pd.ExcelWriter(path, engine="xlsxwriter") for relation, df in spia_matrices.items(): df.to_excel(writer, sheet_name=relation, index=False) # Save excel writer.save() def spia_matrices_to_tsvs(spia_matrices: SPIADataFrames, directory: str) -> None: """Export a SPIA data dictionary into a directory as several TSV documents.""" os.makedirs(directory, exist_ok=True) for relation, df in spia_matrices.items(): df.to_csv( os.path.join(directory, "{relation}.tsv".format(relation=relation)), index=True, ) pybel-0.15.5/src/pybel/io/triples/000077500000000000000000000000001426625374700167305ustar00rootroot00000000000000pybel-0.15.5/src/pybel/io/triples/__init__.py000066400000000000000000000013061426625374700210410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Export functions for Machine Learning. While BEL is a fantastic medium for storing metadata and high granularity information on edges, machine learning algorithms can not consume BEL graphs directly. This module provides functions that make inferences and interpretations of BEL graphs in order to interface with machine learning platforms. One example where we've done this is `BioKEEN `_, which uses this module to convert BEL graphs into a format for knowledge graph embeddings. """ from .api import to_edgelist, to_triples, to_triples_file # noqa: F401 __all__ = [ "to_triples_file", "to_triples", "to_edgelist", ] pybel-0.15.5/src/pybel/io/triples/api.py000066400000000000000000000117211426625374700200550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """TSV conversion.""" import json import logging from typing import List, Optional, TextIO, Tuple, Union from networkx.utils import open_file from tqdm.autonotebook import tqdm from . import converters from ...dsl import BaseEntity from ...struct import BELGraph __all__ = [ "to_triples_file", "to_edgelist", "to_triples", "to_triple", ] logger = logging.getLogger(__name__) class NoTriplesValueError(ValueError): """Raised when no triples could be converted.""" @open_file(1, mode="w") def to_triples_file( graph: BELGraph, path: Union[str, TextIO], *, use_tqdm: bool = False, sep="\t", raise_on_none: bool = False ) -> None: """Write the graph as a TSV. :param graph: A BEL graph :param path: A path or file-like :param use_tqdm: Should a progress bar be shown? :param sep: The separator to use :param raise_on_none: Should an exception be raised if no triples are returned? :raises: NoTriplesValueError """ for h, r, t in to_triples(graph, use_tqdm=use_tqdm, raise_on_none=raise_on_none): print(h, r, t, sep=sep, file=path) @open_file(1, mode="w") def to_edgelist( graph: BELGraph, path: Union[str, TextIO], *, use_tqdm: bool = False, sep="\t", raise_on_none: bool = False ) -> None: """Write the graph as an edgelist. :param graph: A BEL graph :param path: A path or file-like :param use_tqdm: Should a progress bar be shown? :param sep: The separator to use :param raise_on_none: Should an exception be raised if no triples are returned? :raises: NoTriplesValueError """ for h, r, t in to_triples(graph, use_tqdm=use_tqdm, raise_on_none=raise_on_none): print(h, t, json.dumps(dict(relation=r)), sep=sep, file=path) def to_triples(graph: BELGraph, use_tqdm: bool = False, raise_on_none: bool = False) -> List[Tuple[str, str, str]]: """Get a non-redundant list of triples representing the graph. :param graph: A BEL graph :param use_tqdm: Should a progress bar be shown? :param raise_on_none: Should an exception be raised if no triples are returned? :raises: NoTriplesValueError """ it = graph.edges(keys=True) if use_tqdm: it = tqdm( it, total=graph.number_of_edges(), desc="Preparing TSV for {}".format(graph), unit_scale=True, unit="edge", ) triples = (to_triple(graph, u, v, key) for u, v, key in it) # clean duplicates and Nones rv = list( sorted({triple for triple in triples if triple is not None}), ) if raise_on_none and not rv: raise NoTriplesValueError("Could not convert any triples") return rv def to_triple( graph: BELGraph, u: BaseEntity, v: BaseEntity, key: str, ) -> Optional[Tuple[str, str, str]]: # noqa: C901 """Get the triples' strings that should be written to the file.""" data = graph[u][v][key] # order is important _converters = [ converters.ListComplexHasComponentConverter, converters.PartOfNamedComplexConverter, converters.SubprocessPartOfBiologicalProcessConverter, converters.ProteinPartOfBiologicalProcessConverter, converters.AbundancePartOfPopulationConverter, converters.PopulationPartOfAbundanceConverter, converters.RegulatesActivityConverter, converters.MiRNADecreasesExpressionConverter, converters.MiRNADirectlyDecreasesExpressionConverter, converters.AbundanceDirectlyDecreasesProteinActivityConverter, converters.AbundanceDirectlyIncreasesProteinActivityConverter, converters.IsAConverter, converters.EquivalenceConverter, converters.CorrelationConverter, converters.AssociationConverter, converters.DrugIndicationConverter, converters.DrugSideEffectConverter, converters.RegulatesAmountConverter, converters.ProcessCausalConverter, converters.IncreasesAmountConverter, converters.DecreasesAmountConverter, converters.NoChangeAmountConverter, converters.IncreasesActivityConverter, converters.DecreasesActivityConverter, converters.NoChangeActivityConverter, converters.ReactionHasProductConverter, converters.ReactionHasReactantConverter, converters.ReactionHasCatalystConverter, converters.HasVariantConverter, converters.IncreasesDegradationConverter, converters.DecreasesDegradationConverter, converters.RegulatesDegradationConverter, converters.NoChangeDegradationConverter, converters.TranscriptionFactorForConverter, converters.HomomultimerConverter, converters.BindsGeneConverter, converters.BindsProteinConverter, converters.ProteinRegulatesComplex, ] for converter in _converters: if converter.predicate(u, v, key, data): return converter.convert(u, v, key, data) logger.warning("unhandled: {}".format(graph.edge_to_bel(u, v, data))) pybel-0.15.5/src/pybel/io/triples/converters.py000066400000000000000000000444071426625374700215050ustar00rootroot00000000000000# -*- coding: utf-8 -*- """TSV converter classes.""" from abc import ABC, abstractmethod from typing import Dict, Tuple from ...constants import ( ACTIVITY, ASSOCIATION, CAUSAL_DECREASE_RELATIONS, CAUSAL_INCREASE_RELATIONS, CAUSAL_RELATIONS, CAUSES_NO_CHANGE, CORRELATIVE_RELATIONS, DECREASES, DEGRADATION, DIRECTLY_DECREASES, DIRECTLY_INCREASES, EQUIVALENT_TO, HAS_PRODUCT, HAS_REACTANT, HAS_VARIANT, INCREASES, IS_A, MODIFIER, PART_OF, REGULATES, RELATION, TARGET_MODIFIER, ) from ...dsl import ( Abundance, BaseAbundance, BaseEntity, BiologicalProcess, CentralDogma, ComplexAbundance, Gene, MicroRna, NamedComplexAbundance, Pathology, Population, Protein, Reaction, Rna, ) from ...typing import EdgeData def _safe_label(base_entity: BaseEntity): return base_entity.safe_label class Converter(ABC): """A condition and converter for a BEL edge.""" @staticmethod @abstractmethod def predicate(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" @staticmethod @abstractmethod def convert(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a BEL edge.""" class SimpleConverter(Converter): """A class for converting the source and target that have simple names.""" @classmethod def convert(cls, u: BaseAbundance, v: BaseAbundance, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a BEL edge.""" return u.safe_label, edge_data[RELATION], v.safe_label class TypedConverter(Converter): """A class for converting the source and target but replaces the relation.""" target_relation = None @classmethod def convert(cls, u: BaseAbundance, v: BaseAbundance, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a BEL edge.""" return u.safe_label, cls.target_relation, v.safe_label class SimplePredicate(Converter): """Converts BEL statements based on a given relation.""" relation = ... @classmethod def predicate(cls, u, v, key, edge_data) -> bool: """Test a BEL edge has a given relation.""" return edge_data[RELATION] == cls.relation class SimpleTypedPredicate(SimplePredicate): """Finds BEL statements like ``A(X) B C(Y)`` where relation B and types A and C are defined in the class.""" subject_type = ... object_type = ... @classmethod def predicate(cls, u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return ( super().predicate(u, v, key, edge_data) and isinstance(u, cls.subject_type) and isinstance(v, cls.object_type) ) class HasVariantConverter(SimpleConverter, SimpleTypedPredicate): """Converts BEL statements like ``p(X) hasVariant p(X, ...)``.""" subject_type = CentralDogma relation = HAS_VARIANT object_type = CentralDogma class _PartOfConverter(SimpleTypedPredicate, TypedConverter): relation = PART_OF target_relation = "partOf" class PartOfNamedComplexConverter(_PartOfConverter): """Converts BEL statements like ``p(X) partOf complex(Y)``.""" subject_type = Protein object_type = NamedComplexAbundance class ProcessCausalConverter(SimpleConverter, SimpleTypedPredicate): """Converts BEL statements like ``bp(X) increases/decreases bp(Y)``.""" subject_type = BiologicalProcess relations = CAUSAL_RELATIONS object_type = BiologicalProcess @classmethod def predicate(cls, u, v, key, edge_data) -> bool: """Test a BEL edge has a given relation.""" return ( isinstance(u, cls.subject_type) and edge_data[RELATION] in cls.relations and isinstance(v, cls.object_type) ) class SubprocessPartOfBiologicalProcessConverter(_PartOfConverter): """Converts BEL statements like ``bp(X) partOf bp(Y)``.""" subject_type = BiologicalProcess object_type = BiologicalProcess class ProteinPartOfBiologicalProcessConverter(_PartOfConverter): """Converts BEL statements like ``p(X) partOf bp(Y)``.""" subject_type = Protein object_type = BiologicalProcess class AbundancePartOfPopulationConverter(_PartOfConverter): """Converts BEL statements like ``a(X) partOf pop(Y)``.""" subject_type = Abundance object_type = Population class PopulationPartOfAbundanceConverter(_PartOfConverter): """Converts BEL statements like ``pop(X) partOf a(Y)``.""" subject_type = Population object_type = Abundance class _ReactionTypedPredicate(SimpleTypedPredicate): subject_type = Reaction object_type = BaseAbundance class _ReactionHasMemberConverter(_ReactionTypedPredicate): """Converts BEL statements like ``complex(X) hasComponent p(Y)``.""" target_relation = ... @classmethod def predicate(cls, u: Reaction, v: BaseAbundance, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return super().predicate(u, v, key, edge_data) and v not in u.get_catalysts() @classmethod def convert(cls, u: Reaction, v: BaseAbundance, key: str, data: Dict) -> Tuple[str, str, str]: """Convert a BEL edge.""" return u.as_bel(), cls.target_relation, v.curie class ReactionHasReactantConverter(_ReactionHasMemberConverter): """Converts BEL statements like ``rxn(X) hasReactant a(Y)``.""" relation = HAS_REACTANT target_relation = "hasReactant" class ReactionHasProductConverter(_ReactionHasMemberConverter): """Converts BEL statements like ``rxn(X) hasProduct a(Y)``.""" relation = HAS_PRODUCT target_relation = "hasProduct" class ReactionHasCatalystConverter(_ReactionTypedPredicate): """Converts BEL statements that simultaneously ``rxn(X) hasProduct a(Y)`` and ``rxn(X) hasReactant a(Y)``.""" target_relation = "hasCatalyst" @classmethod def predicate(cls, u: Reaction, v: BaseAbundance, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return super().predicate(u, v, key, edge_data) and v in u.get_catalysts() @classmethod def convert(cls, u: Reaction, v: BaseAbundance, key: str, data: Dict) -> Tuple[str, str, str]: """Convert a BEL edge.""" return u.as_bel(), cls.target_relation, v.curie class ListComplexHasComponentConverter(SimpleTypedPredicate): """Converts BEL statements like ``complex(p(X), p(Y), ...) hasComponent p(X)``.""" subject_type = BaseAbundance relation = PART_OF object_type = ComplexAbundance target_relation = "partOf" @classmethod def convert(cls, u: ComplexAbundance, v: BaseAbundance, key: str, data: Dict) -> Tuple[str, str, str]: """Convert a BEL edge.""" return u.curie, cls.target_relation, v.as_bel() class IsAConverter(SimplePredicate, SimpleConverter): """Converts BEL statements like ``X isA Y``.""" relation = IS_A target_relation = "isA" class EquivalenceConverter(SimplePredicate, SimpleConverter): """Converts BEL statements like ``X eq Y``.""" relation = EQUIVALENT_TO target_relation = "equivalentTo" class CorrelationConverter(SimpleConverter): """Converts BEL statements like ``A(B) pos|neg|noCorrelation C(D)``.""" @staticmethod def predicate(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return edge_data[RELATION] in CORRELATIVE_RELATIONS class AssociationConverter(Converter): """Converts BEL statements like ``a(X) -- path(Y)``.""" @staticmethod def predicate(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return edge_data[RELATION] == ASSOCIATION @staticmethod def convert(u: BaseAbundance, v: BaseAbundance, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a BEL edge.""" relation = edge_data.get("association_type", ASSOCIATION) # allow more specific association to be defined return u.safe_label, relation, v.safe_label class DrugEffectConverter(SimpleConverter, SimpleTypedPredicate): """Converts BEL statements like ``a(X) ? path(Y)``.""" subject_type = Abundance relation = ... object_type = Pathology class DrugIndicationConverter(DrugEffectConverter): """Converts BEL statements like ``a(X) -| path(Y)``.""" relation = DECREASES class DrugSideEffectConverter(DrugEffectConverter): """Converts BEL statements like ``a(X) -> path(Y)``.""" relation = INCREASES class RegulatesAmountConverter(TypedConverter): """Converts BEL statements like ``A(B) reg C(D)``.""" relation = REGULATES target_relation = "regulatesAmountOf" @classmethod def predicate(cls, u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" target_modifier = edge_data.get(TARGET_MODIFIER) return edge_data[RELATION] == cls.relation and (not target_modifier or not target_modifier.get(MODIFIER)) class IncreasesAmountConverter(RegulatesAmountConverter): """Converts BEL statements like ``A(B) -> C(D)``.""" relation = INCREASES target_relation = "increasesAmountOf" class DecreasesAmountConverter(RegulatesAmountConverter): """Converts BEL statements like ``A(B) -| C(D)``.""" relation = DECREASES target_relation = "decreasesAmountOf" class NoChangeAmountConverter(RegulatesAmountConverter): """Converts BEL statements like ``A(B) cnc C(D)``.""" relation = CAUSES_NO_CHANGE target_relation = "notRegulatesAmountOf" class RegulatesDegradationConverter(TypedConverter): """Converts BEL statements like ``A(B) reg deg(C(D))``.""" relation = REGULATES target_relation = "regulatesAmountOf" @classmethod def predicate(cls, u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" target_modifier = edge_data.get(TARGET_MODIFIER) return edge_data[RELATION] == cls.relation and target_modifier and target_modifier.get(MODIFIER) == DEGRADATION class IncreasesDegradationConverter(RegulatesDegradationConverter): """Converts BEL statements like ``A(B) -> deg(C(D))``.""" relation = INCREASES target_relation = "decreasesAmountOf" class DecreasesDegradationConverter(RegulatesDegradationConverter): """Converts BEL statements like ``A(B) -| deg(C(D))``.""" relation = DECREASES target_relation = "increasesAmountOf" class NoChangeDegradationConverter(RegulatesDegradationConverter): """Converts BEL statements like ``A(B) cnc deg(C(D))``.""" relation = CAUSES_NO_CHANGE target_relation = "notRegulatesAmountOf" class RegulatesActivityConverter(TypedConverter): """Converts BEL statements like ``A(B) reg act(C(D) [, ma(E)])``.""" relation = REGULATES target_relation = "activityDirectlyRegulatesActivityOf" @classmethod def predicate(cls, u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" target_modifier = edge_data.get(TARGET_MODIFIER) return edge_data[RELATION] == cls.relation and target_modifier and target_modifier.get(MODIFIER) == ACTIVITY class IncreasesActivityConverter(RegulatesActivityConverter): """Converts BEL statements like ``A(B) -> act(C(D) [, ma(E)])``.""" relation = INCREASES target_relation = "activityPositivelyRegulatesActivityOf" class DirectlyIncreasesActivityConverter(RegulatesActivityConverter): """Converts BEL statements like ``A(B) => act(C(D) [, ma(E)])``.""" relation = DIRECTLY_INCREASES target_relation = "activityDirectlyPositivelyRegulatesActivityOf" class DecreasesActivityConverter(RegulatesActivityConverter): """Converts BEL statements like ``A(B) -| act(C(D) [, ma(E)])``.""" relation = DECREASES target_relation = "activityNegativelyRegulatesActivityOf" class DirectlyDecreasesActivityConverter(RegulatesActivityConverter): """Converts BEL statements like ``A(B) =| act(C(D) [, ma(E)])``.""" relation = DIRECTLY_DECREASES target_relation = "activityDirectlyNegativelyRegulatesActivityOf" class NoChangeActivityConverter(RegulatesActivityConverter): """Converts BEL statements like ``A(B) cnc act(C(D) [, ma(E)])``.""" relation = CAUSES_NO_CHANGE target_relation = "notActivityDirectlyRegulatesActivityOf" class AbundanceDirectlyDecreasesProteinActivityConverter(DirectlyDecreasesActivityConverter): """Converts BEL statements like ``a(X) =| act(p(Y))``.""" subject_type = Abundance object_type = Protein class AbundanceDirectlyIncreasesProteinActivityConverter(DirectlyIncreasesActivityConverter): """Converts BEL statements like ``a(X) => act(p(Y))``.""" subject_type = Abundance object_type = Protein class MiRNARegulatesExpressionConverter(TypedConverter, SimpleTypedPredicate): """Converts BEL statements like ``m(X) reg r(Y)``.""" subject_type = MicroRna relation = REGULATES object_type = Rna target_relation = "regulatesExpressionOf" class MiRNAIncreasesExpressionConverter(MiRNARegulatesExpressionConverter): """Converts BEL statements like ``m(X) -> r(Y)``.""" relation = INCREASES target_relation = "increasesExpressionOf" class MiRNADirectlyIncreasesExpressionConverter(MiRNARegulatesExpressionConverter): """Converts BEL statements like ``m(X) => r(Y)``.""" relation = DIRECTLY_INCREASES target_relation = "increasesExpressionOf" class MiRNADecreasesExpressionConverter(MiRNARegulatesExpressionConverter): """Converts BEL statements like ``m(X) -| r(Y)``.""" relation = DECREASES target_relation = "repressesExpressionOf" class MiRNADirectlyDecreasesExpressionConverter(MiRNARegulatesExpressionConverter): """Converts BEL statements like ``m(X) =| r(Y)``.""" relation = DIRECTLY_DECREASES target_relation = "repressesExpressionOf" class TranscriptionFactorForConverter(Converter): """Converts ``complex(g(A), p(B)) directlyIncreases r(A)```.""" @classmethod def convert(cls, u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a transcription factor for edge.""" gene = v.get_gene() if gene == u.members[0]: return u.members[1].safe_label, edge_data[RELATION], v.safe_label else: return u.members[0].safe_label, edge_data[RELATION], v.safe_label @classmethod def predicate(cls, u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" if not isinstance(u, ComplexAbundance) or len(u.members) != 2: return False if isinstance(u.members[0], Gene) and isinstance(u.members[1], Protein): gene = u.members[0] elif isinstance(u.members[1], Gene) and isinstance(u.members[0], Protein): gene = u.members[1] else: return False if not isinstance(v, Rna): return False return gene == v.get_gene() class BindsProteinConverter(Converter): """Converts ``x(B) => complex(p(A), x(B))```.""" @staticmethod def predicate(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return ( edge_data[RELATION] == DIRECTLY_INCREASES and isinstance(v, ComplexAbundance) and len(v.members) == 2 and u in v.members and isinstance([m for m in v.members if m != u][0], Protein) ) @staticmethod def convert(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a binds protein factor for edge.""" v = [m for m in v.members if m != u][0] return u.safe_label, "bindsToProtein", v.safe_label class HomomultimerConverter(Converter): """Converts ``p(A) directlyIncreases complex(p(A), p(A))```.""" @staticmethod def predicate(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return ( isinstance(u, Protein) and edge_data[RELATION] == DIRECTLY_INCREASES and isinstance(v, ComplexAbundance) and all(member == u for member in v.members) ) @staticmethod def convert(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a homomultimer formation.""" return u.safe_label, "bindsToProtein", u.safe_label class BindsGeneConverter(Converter): """Converts ``p(B) directlyIncreases complex(g(A), p(B))```.""" @staticmethod def predicate(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return ( isinstance(u, Protein) and edge_data[RELATION] == DIRECTLY_INCREASES and isinstance(v, ComplexAbundance) and len(v.members) == 2 and u in v.members and isinstance([m for m in v.members if m != u][0], Gene) ) @staticmethod def convert(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a transcription factor for edge.""" v = [m for m in v.members if m != u][0] return u.safe_label, "bindsToGene", v.safe_label class ProteinRegulatesComplex(Converter): """Converts ``p(B) directlyIncreases complex(x(X), y(Y))```.""" @staticmethod def predicate(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> bool: """Test a BEL edge.""" return ( isinstance(u, Protein) and isinstance(v, ComplexAbundance) and u not in v.members and edge_data[RELATION] in CAUSAL_RELATIONS and edge_data[RELATION] != CAUSES_NO_CHANGE ) @staticmethod def convert(u: BaseEntity, v: BaseEntity, key: str, edge_data: EdgeData) -> Tuple[str, str, str]: """Convert a transcription factor for edge.""" relation = edge_data[RELATION] if relation in CAUSAL_INCREASE_RELATIONS: relation = "increasesAmountOf" elif relation in CAUSAL_DECREASE_RELATIONS: relation = "decreasesAmountOf" elif relation == REGULATES: relation = "regulatesAmountOf" else: raise ValueError("invalid relation type") return u.safe_label, relation, v.safe_label pybel-0.15.5/src/pybel/io/tsv/000077500000000000000000000000001426625374700160625ustar00rootroot00000000000000pybel-0.15.5/src/pybel/io/tsv/__init__.py000066400000000000000000000006471426625374700202020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Warnings for old TSV conversion module.""" import warnings from ..triples import to_edgelist from ..triples import to_triples_file as to_tsv __all__ = [ "to_tsv", "to_edgelist", ] warnings.warn( """Use pybel.io.triples module instead. Changes in PyBEL v0.15.0: - pybel.to_tsv renamed to pybel.to_triples_file Will be removed in PyBEL v0.16.* """, DeprecationWarning, ) pybel-0.15.5/src/pybel/io/tsv/api.py000066400000000000000000000010201426625374700171760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Warnings for old TSV conversion module.""" import warnings from ..triples.api import to_triple as get_triple from ..triples.api import to_triples as get_triples __all__ = [ "get_triple", "get_triples", ] warnings.warn( """Use pybel.io.triples module instead. Changes in PyBEL v0.15.0: - pybel.io.tsv.api.get_triples renamed to pybel.to_triples - pybel.io.tsv.api.get_triple renamed to pybel.io.triples.to_triple Will be removed in PyBEL v0.16.* """, DeprecationWarning, ) pybel-0.15.5/src/pybel/io/tsv/converters.py000066400000000000000000000006351426625374700206320ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Warnings for old TSV conversion module.""" import warnings from ..triples.converters import _safe_label __all__ = [ "_safe_label", ] warnings.warn( """Use pybel.io.triples module instead. Changes in PyBEL v0.15.0: - pybel.io.tsv.converters._safe_label renamed to pybel.io.triples.converters._safe_label Will be removed in PyBEL v0.16.* """, DeprecationWarning, ) pybel-0.15.5/src/pybel/io/umbrella_nodelink.py000066400000000000000000000071461426625374700213160ustar00rootroot00000000000000# -*- coding: utf-8 -*- """The Umbrella Node-Link JSON format is similar to node-link but uses full BEL terms as nodes. Given a BEL statement describing that ``X`` phosphorylates ``Y`` like ``act(p(X)) -> p(Y, pmod(Ph))``, PyBEL usually stores the ``act()`` information about ``X`` as part of the relationship. In Umbrella mode, this stays as part of the node. Note that this generates additional nodes in the network for each of the "modified" versions of the node. For example, ``act(p(X))`` will be represented as individual node instead of ``p(X)``, as in the standard node-link JSON exporter. A user might want to use this exporter in the following scenarios: - Represent transitivity in activities like in ``p(X, pmod(Ph)) -> act(p(X)) -> p(Y, pmod(Ph)) -> act(p(Y))`` with four nodes that are more ammenable to simulatons (e.g., boolean networks, petri nets). - Visualizing networks that in similar way to the legacy BEL `Cytoscape plugin `_ from the BEL Framework (warning: now defunct) using tools like Cytoscape. """ import gzip import json from itertools import chain, count from typing import Any, Mapping, TextIO, Union from networkx.utils import open_file from ..canonicalize import _decanonicalize_edge_node, edge_to_tuple from ..constants import GRAPH_ANNOTATION_LIST, SOURCE_MODIFIER, TARGET_MODIFIER from ..struct import BELGraph __all__ = [ "to_umbrella_nodelink", "to_umbrella_nodelink_file", "to_umbrella_nodelink_gz", ] def to_umbrella_nodelink(graph: BELGraph) -> Mapping[str, Any]: """Convert this graph to an umbrella node-link JSON object. :param graph: A BEL graph """ nodes = set() for u, v, data in graph.edges(data=True): u_key, _, v_key = edge_to_tuple(u, v, data) nodes.add(u_key) nodes.add(v_key) nodes = sorted(list(nodes)) mapping = dict(zip(nodes, count())) graph_json_dict = { "directed": True, "multigraph": True, "graph": graph.graph.copy(), "nodes": nodes, "links": [ dict( chain( data.copy().items(), [ ( "source", mapping[_decanonicalize_edge_node(u, data, node_position=SOURCE_MODIFIER)], ), ( "target", mapping[_decanonicalize_edge_node(v, data, node_position=TARGET_MODIFIER)], ), ("key", key), ], ), ) for u, v, key, data in graph.edges(keys=True, data=True) ], } # Convert annotation list definitions (which are sets) to canonicalized/sorted lists graph_json_dict["graph"][GRAPH_ANNOTATION_LIST] = { keyword: list(sorted(values)) for keyword, values in graph_json_dict["graph"].get(GRAPH_ANNOTATION_LIST, {}).items() } return graph_json_dict @open_file(1, mode="w") def to_umbrella_nodelink_file(graph: BELGraph, path: Union[str, TextIO], **kwargs) -> None: """Write this graph as an umbrella node-link JSON to a file. :param graph: A BEL graph :param path: A path or file-like """ graph_json_dict = to_umbrella_nodelink(graph) json.dump(graph_json_dict, path, ensure_ascii=False, **kwargs) def to_umbrella_nodelink_gz(graph, path: str, **kwargs) -> None: """Write this graph as an umbrella node-link JSON to a gzipped file.""" with gzip.open(path, "wt") as file: to_umbrella_nodelink_file(graph, file, **kwargs) pybel-0.15.5/src/pybel/io/utils.py000066400000000000000000000030611426625374700167600ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains helper functions for other IO functions.""" from .exc import ImportVersionWarning from ..config import PYBEL_MINIMUM_IMPORT_VERSION from ..struct import BELGraph from ..utils import tokenize_version def raise_for_old_graph(graph): """Raise an ImportVersionWarning if the BEL graph was produced by a legacy version of PyBEL. :param graph: A BEL graph :raises ImportVersionWarning: If the BEL graph was produced by a legacy version of PyBEL """ graph_version = tokenize_version(graph.pybel_version) if graph_version < PYBEL_MINIMUM_IMPORT_VERSION: raise ImportVersionWarning(graph_version, PYBEL_MINIMUM_IMPORT_VERSION) def raise_for_not_bel(graph): """Raise a TypeError if the argument is not a BEL graph. :param graph: A BEL graph :raises TypeError: If the argument is not a BEL graph """ if not isinstance(graph, BELGraph): raise TypeError("Not a BELGraph: {}".format(graph)) def ensure_version(graph: BELGraph, check_version: bool = True) -> BELGraph: """Ensure that the graph was produced by a minimum of PyBEL v:data:`PYBEL_MINIMUM_IMPORT_VERSION`. This variable is defined by last release with a change in the graph data definition. :param graph: A BEL Graph :param check_version: Should the version be checked, or should the graph just be returned without inspection :raises ImportVersionWarning: If the BEL graph was produced by a legacy version of PyBEL """ if check_version: raise_for_old_graph(graph) return graph pybel-0.15.5/src/pybel/language.py000066400000000000000000000513521426625374700170020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Language constants for BEL. This module contains mappings between PyBEL's internal constants and BEL language keywords. """ import warnings from typing import Optional from .constants import ( ABUNDANCE, BIOPROCESS, CELL_SURFACE, COMPLEX, COMPOSITE, EXTRACELLULAR, GENE, IDENTIFIER, INTRACELLULAR, MIRNA, NAME, NAMESPACE, PATHOLOGY, PROTEIN, RNA, TRANSCRIBED_TO, TRANSLATED_TO, ) from .utils import ensure_quotes class Entity(dict): """Represents a named entity with a namespace and name/identifier.""" def __init__( self, *, namespace: str, name: Optional[str] = None, identifier: Optional[str] = None, ) -> None: """Create a dictionary representing a reference to an entity. :param namespace: The namespace to which the entity belongs :param name: The name of the entity :param identifier: The identifier of the entity in the namespace """ if name is None and identifier is None: raise ValueError("cannot create an entity with neither a name nor identifier") if not isinstance(namespace, str): raise TypeError("namespace should be a string: {}".format(namespace)) if not namespace: raise ValueError("namespace should be non-empty") super().__init__( { NAMESPACE: namespace, } ) if name is not None: if not isinstance(name, str): raise TypeError("name should be a string: {}".format(name)) if not name: raise ValueError("name should be non-empty") self[NAME] = name if identifier is not None: if not isinstance(identifier, str): raise TypeError(f"identifier should be a string. Got {type(identifier)} {identifier}") if not identifier: raise ValueError("identifier should be non-empty") self[IDENTIFIER] = identifier @property def namespace(self) -> str: # noqa: D401 """The entity's namespace.""" return self[NAMESPACE] @property def name(self) -> str: # noqa: D401 """The entity's name or label.""" return self.get(NAME) @property def identifier(self) -> str: # noqa: D401 """The entity's identifier.""" return self.get(IDENTIFIER) @property def curie(self) -> str: """Return this entity as a CURIE.""" return "{}:{}".format( self.namespace, ensure_quotes(self.identifier if self.identifier else self.name), ) @property def obo(self) -> str: """Return this entity as an OBO-style CURIE.""" return "{}:{} ! {}".format( self.namespace, ensure_quotes(self.identifier), ensure_quotes(self.name), ) def __str__(self): # noqa: D105 return self.obo if self.identifier and self.name else self.curie def __hash__(self) -> int: return hash((self.namespace, self.identifier, self.name)) text_location_labels = { "Abstract": Entity(namespace="iao", identifier="0000315", name="abstract"), "Review": Entity(namespace="iao", identifier="0000311", name="publication"), # sue me "Results": Entity(namespace="iao", identifier="0000318", name="results section"), "Legend": Entity(namespace="sio", identifier="000468 ", name="legend"), } #: A dictionary of activity labels used in the ma() function in activity(p(X), ma(Y)) activity_labels = { "catalyticActivity": "cat", "cat": "cat", "chaperoneActivity": "chap", "chap": "chap", "gtpBoundActivity": "gtp", "gtp": "gtp", "kinaseActivity": "kin", "kin": "kin", "peptidaseActivity": "pep", "pep": "pep", "phosphataseActivity": "phos", "phos": "phos", "ribosylationActivity": "ribo", "ribo": "ribo", "transcriptionalActivity": "tscript", "tscript": "tscript", "transportActivity": "tport", "tport": "tport", "molecularActivity": "molecularActivity", # Added by PyBEL "guanineNucleotideExchangeFactorActivity": "gef", "gef": "gef", "gtpaseActivatingProteinActivity": "gap", "gap": "gap", } #: Maps the default BEL molecular activities to Gene Ontology Molecular Functions activity_mapping = { "act": Entity(namespace="go", name="molecular function", identifier="0003674"), "cat": Entity(namespace="go", name="catalytic activity", identifier="0003824"), "chap": Entity( namespace="go", name="protein binding involved in protein folding", identifier="0044183", ), "gtp": Entity(namespace="go", name="GTP binding", identifier="0005525"), "kin": Entity(namespace="go", name="kinase activity", identifier="0016301"), "pep": Entity(namespace="go", name="peptidase activity", identifier="0008233"), "phos": Entity(namespace="go", name="phosphatase activity", identifier="0016791"), "ribo": Entity( namespace="go", name="NAD(P)+-protein-arginine ADP-ribosyltransferase activity", identifier="0003956", ), "tscript": Entity( namespace="go", name="nucleic acid binding transcription factor activity", identifier="0001071", ), "tport": Entity(namespace="go", name="transporter activity", identifier="0005215"), "molecularActivity": Entity(namespace="go", name="molecular_function", identifier="0003674"), "gef": Entity( namespace="go", name="guanyl-nucleotide exchange factor activity", identifier="0005085", ), "gap": Entity(namespace="go", name="GTPase activating protein binding", identifier="0032794"), } activities = list(activity_labels.keys()) cytoplasm = Entity(name="cytoplasm", namespace="go", identifier="0005737") nucleus = Entity(name="nucleus", namespace="go", identifier="0005634") intracellular = Entity(name="intracellular", namespace="go", identifier="0005622") extracellular = Entity(name="extracellular space", namespace="go", identifier="0005615") cell_surface = Entity(name="cell surface", namespace="go", identifier="0009986") #: Maps the default BEL cellular components to Gene Ontology Cellular Components compartment_mapping = { INTRACELLULAR: intracellular, EXTRACELLULAR: extracellular, CELL_SURFACE: cell_surface, "cytoplasm": cytoplasm, "nucleus": nucleus, } compartments = list(compartment_mapping) #: Provides a mapping from BEL terms to PyBEL internal constants abundance_labels = { "abundance": ABUNDANCE, "a": ABUNDANCE, "geneAbundance": GENE, "g": GENE, "microRNAAbundance": MIRNA, "m": MIRNA, "proteinAbundance": PROTEIN, "p": PROTEIN, "rnaAbundance": RNA, "r": RNA, "biologicalProcess": BIOPROCESS, "bp": BIOPROCESS, "pathology": PATHOLOGY, "path": PATHOLOGY, "composite": COMPOSITE, "compositeAbundance": COMPOSITE, "complex": COMPLEX, "complexAbundance": COMPLEX, } #: Maps the BEL abundance types to the Systems Biology Ontology abundance_sbo_mapping = { MIRNA: Entity(namespace="sbo", name="microRNA", identifier="0000316"), BIOPROCESS: Entity(namespace="sbo", name="process", identifier="0000375"), GENE: Entity(namespace="sbo", name="gene", identifier="0000243"), RNA: Entity(namespace="sbo", name="messenger RNA", identifier="0000278"), COMPLEX: Entity(namespace="sbo", name="protein complex", identifier="0000297"), PATHOLOGY: Entity(namespace="sbo", name="phenotype", identifier="0000358"), } relation_sbo_mapping = { TRANSLATED_TO: Entity(namespace="sbo", name="translation", identifier="0000184"), TRANSCRIBED_TO: Entity(namespace="sbo", name="transcription", identifier="0000183"), } amino_acid_dict = { "A": "Ala", "R": "Arg", "N": "Asn", "D": "Asp", "C": "Cys", "E": "Glu", "Q": "Gln", "G": "Gly", "H": "His", "I": "Ile", "L": "Leu", "K": "Lys", "M": "Met", "F": "Phe", "P": "Pro", "S": "Ser", "T": "Thr", "W": "Trp", "Y": "Tyr", "V": "Val", } dna_nucleotide_labels = { "A": "Adenine", "T": "Thymine", "C": "Cytosine", "G": "Guanine", } rna_nucleotide_labels = { "a": "adenine", "u": "uracil", "c": "cytosine", "g": "guanine", } #: A dictionary of default protein modifications to their preferred value pmod_namespace = { "Ac": "Ac", "acetylation": "Ac", "ADPRib": "ADPRib", "ADP-ribosylation": "ADPRib", "adenosine diphosphoribosyl": "ADPRib", "Farn": "Farn", "farnesylation": "Farn", "Gerger": "Gerger", "geranylgeranylation": "Gerger", "Glyco": "Glyco", "glycosylation": "Glyco", "Hy": "Hy", "hydroxylation": "Hy", "ISG": "ISG", "ISGylation": "ISG", "ISG15-protein conjugation": "ISG", "Me": "Me", "methylation": "Me", "Me1": "Me1", "monomethylation": "Me1", "mono-methylation": "Me1", "Me2": "Me2", "dimethylation": "Me2", "di-methylation": "Me2", "Me3": "Me3", "trimethylation": "Me3", "tri-methylation": "Me3", "Myr": "Myr", "myristoylation": "Myr", "Nedd": "Nedd", "neddylation": "Nedd", "NGlyco": "NGlyco", "N-linked glycosylation": "NGlyco", "NO": "NO", "Nitrosylation": "NO", "OGlyco": "OGlyco", "O-linked glycosylation": "OGlyco", "Palm": "Palm", "palmitoylation": "Palm", "Ph": "Ph", "phosphorylation": "Ph", "Sulf": "Sulf", "sulfation": "Sulf", "sulphation": "Sulf", "sulfur addition": "Sulf", "sulphur addition": "Sulf", "sulfonation": "sulfonation", "sulphonation": "sulfonation", "Sumo": "Sumo", "SUMOylation": "Sumo", "Ub": "Ub", "ubiquitination": "Ub", "ubiquitinylation": "Ub", "ubiquitylation": "Ub", "UbK48": "UbK48", "Lysine 48-linked polyubiquitination": "UbK48", "UbK63": "UbK63", "Lysine 63-linked polyubiquitination": "UbK63", "UbMono": "UbMono", "monoubiquitination": "UbMono", "UbPoly": "UbPoly", "polyubiquitination": "UbPoly", # PyBEL Variants "Ox": "Ox", "oxidation": "Ox", } #: Use Gene Ontology children of go_0006464: "cellular protein modification process" pmod_mappings = { "Ac": { "synonyms": ["Ac", "acetylation"], "xrefs": [ Entity(namespace="go", identifier="0006473", name="protein acetylation"), Entity(namespace="mod", identifier="00394", name="acetylated residue"), Entity(namespace="mop", identifier="0000030", name="acetylation"), Entity(namespace="sbo", identifier="0000215", name="acetylation"), ], }, "ADPRib": { "synonyms": [ "ADPRib", "ADP-ribosylation", "ADPRib", "ADP-rybosylation", "adenosine diphosphoribosyl", ], "xrefs": [ Entity(namespace="go", identifier="0006471", name="protein ADP-ribosylation"), Entity( namespace="mod", identifier="00752", name="adenosine diphosphoribosyl (ADP-ribosyl) modified residue", ), Entity( namespace="mop", identifier="0000220", name="adenosinediphosphoribosylation", ), ], }, "Farn": { "synonyms": ["Farn", "farnesylation"], "xrefs": [ Entity(namespace="go", identifier="0018343", name="protein farnesylation"), Entity(namespace="mod", identifier="00437", name="farnesylated residue"), Entity(namespace="mop", identifier="0000429", name="farnesylation"), ], }, "Gerger": { "synonyms": ["Gerger", "geranylgeranylation"], "xrefs": [ Entity(namespace="go", identifier="0018344", name="protein geranylgeranylation"), Entity(namespace="mod", identifier="00441", name="geranylgeranylated residue "), Entity(namespace="mop", identifier="0000431", name="geranylgeranylation"), ], }, "Glyco": { "synonyms": ["Glyco", "glycosylation"], "xrefs": [ Entity(namespace="go", identifier="0006486", name="protein glycosylation"), Entity(namespace="mod", identifier="00693", name="glycosylated residue"), Entity(namespace="mop", identifier="0000162", name="glycosylation"), ], }, "Hy": { "synonyms": ["Hy" "hydroxylation"], "xrefs": [ Entity(namespace="go", identifier="0018126", name="protein hydroxylation"), Entity(namespace="mod", identifier="00677", name="hydroxylated residue"), Entity(namespace="mop", identifier="0000673", name="hydroxylation"), ], }, "ISG": { "synonyms": ["ISG", "ISGylation", "ISG15-protein conjugation"], "xrefs": [ Entity(namespace="go", identifier="0032020", name="ISG15-protein conjugation"), ], "activities": [ Entity(namespace="go", identifier="0042296", name="ISG15 transferase activity"), ], }, "Me": { "synonyms": ["Me", "methylation"], "xrefs": [ Entity(namespace="go", identifier="0006479", name="protein methylation"), Entity(namespace="mod", identifier="00427", name="methylated residue"), ], }, "Me1": { "synonyms": ["Me1", "monomethylation", "mono-methylation"], "xrefs": [ Entity(namespace="mod", identifier="00599", name="monomethylated residue"), ], "is_a": ["Me"], }, "Me2": { "synonyms": ["Me2", "dimethylation", "di-methylation"], "xrefs": [ Entity(namespace="mod", identifier="00429", name="dimethylated residue"), ], "is_a": ["Me"], }, "Me3": { "synonyms": ["Me3", "trimethylation", "tri-methylation"], "xrefs": [ Entity(namespace="mod", identifier="00430", name="trimethylated residue"), ], "is_a": ["Me"], }, "Myr": { "synonyms": ["Myr", "myristoylation"], "xrefs": [ Entity(namespace="go", identifier="0018377", name="protein myristoylation"), Entity(namespace="mod", identifier="00438", name="myristoylated residue"), ], }, "Nedd": { "synonyms": ["Nedd", "neddylation", "RUB1-protein conjugation"], "xrefs": [ Entity(namespace="go", identifier="0045116", name="protein neddylation"), Entity(namespace="mod", identifier="01150", name="neddylated lysine"), ], }, "NGlyco": { "synonyms": ["NGlyco", "N-linked glycosylation"], "xrefs": [ Entity( namespace="go", identifier="0006487", name="protein N-linked glycosylation", ), Entity(namespace="mod", identifier="00006", name="N-glycosylated residue"), Entity(namespace="mop", identifier="0002162", name="N-glycosylation"), ], "is_a": ["Glyco"], }, "NO": { "synonyms": ["NO", "Nitrosylation"], "xrefs": [ Entity(namespace="go", identifier="0017014", name="protein nitrosylation"), ], }, "Ox": { "synonyms": ["Ox", "oxidation"], "xrefs": [ Entity(namespace="go", identifier="0018158", name="protein oxidation"), ], }, "OGlyco": { "synonyms": ["OGlyco", "O-linked glycosylation"], "xrefs": [ Entity( namespace="go", identifier="0006493", name="protein O-linked glycosylation", ), Entity(namespace="mod", identifier="00396", name="O-glycosylated residue"), Entity(namespace="mop", identifier="0003162", name="O-glycosylation"), ], "is_a": ["Glyco"], }, "Palm": { "synonyms": ["Palm", "palmitoylation"], "xrefs": [ Entity(namespace="go", identifier="0018345", name="protein palmitoylation"), Entity(namespace="mod", identifier="00440", name="palmitoylated residue"), ], }, "Ph": { "synonyms": ["Ph", "phosphorylation"], "xrefs": [ Entity(namespace="go", identifier="0006468", name="protein phosphorylation"), Entity(namespace="mod", identifier="00696"), ], }, "Sulf": { "synonyms": [ "Sulf", "sulfation", "sulphation", "sulfur addition", "sulphur addition", "sulfonation", "sulphonation", ], "xrefs": [ Entity(namespace="go", identifier="0006477", name="protein sulfation"), Entity(namespace="mod", identifier="00695", name="sulfated residue"), Entity(namespace="mop", identifier="0000559", name="sulfonation"), ], "target": [ Entity(namespace="chebi", identifier="29922", name="sulfo group"), ], }, "Sumo": { "synonyms": ["Sumo", "SUMOylation", "Sumoylation"], "xrefs": [ Entity(namespace="go", identifier="0016925", name="protein sumoylation"), Entity(namespace="mod", identifier="01149", name="sumoylated lysine"), ], "activities": [ Entity(namespace="go", identifier="0019789", name="SUMO transferase activity"), ], }, "Ub": { "synonyms": ["Ub", "ubiquitination", "ubiquitinylation", "ubiquitylation"], "xrefs": [ Entity(namespace="go", identifier="0016567", name="protein ubiquitination"), Entity(namespace="mod", identifier="01148", name="ubiquitinylated lysine"), Entity(namespace="sbo", identifier="0000224", name="ubiquitination"), ], }, "UbK48": { "synonyms": ["UbK48", "Lysine 48-linked polyubiquitination"], "xrefs": [ Entity( namespace="go", identifier="0070936", name="protein K48-linked ubiquitination", ), ], }, "UbK63": { "synonyms": ["UbK63", "Lysine 63-linked polyubiquitination"], "xrefs": [ Entity( namespace="go", identifier="0070534", name="protein K63-linked ubiquitination", ), ], }, "UbMono": { "synonyms": ["UbMono", "monoubiquitination"], "xrefs": [ Entity(namespace="go", identifier="0006513", name="protein monoubiquitination"), ], }, "UbPoly": { "synonyms": ["UbPoly", "polyubiquitination"], "xrefs": [ Entity(namespace="go", identifier="0000209", name="protein polyubiquitination"), ], }, } #: A dictionary of legacy (BEL 1.0) default namespace protein modifications to their BEL 2.0 preferred value pmod_legacy_labels = { "P": "Ph", "A": "Ac", "F": "Farn", "G": "Glyco", "H": "Hy", "M": "Me", "R": "ADPRib", "S": "Sumo", "U": "Ub", "O": "Ox", } #: A dictionary of default gene modifications. This is a PyBEL variant to the BEL specification. gmod_namespace = { "methylation": "Me", "Me": "Me", "M": "Me", "ADPRib": "ADPRib", } #: Use Gene Ontology children of go:0006304 ! "DNA modification" gmod_mappings = { "Me": { "synonyms": ["Me", "M", "methylation"], "xrefs": [ Entity(namespace="go", identifier="0006306", name="DNA methylation"), ], }, "ADPRib": { "synonyms": ["ADPRib"], "xrefs": [ Entity(namespace="go", identifier="0030592", name="DNA ADP-ribosylation"), ], }, } class CitationDict(Entity): """A dictionary describing a citation.""" def __init__(self, namespace: str, identifier: str, *, name: Optional[str] = None, **kwargs): super().__init__(namespace=namespace, identifier=identifier, name=name) self.update(kwargs) def citation_dict( *, namespace: Optional[str] = None, db: Optional[str] = None, identifier: Optional[str] = None, db_id: Optional[str] = None, name: Optional[str] = None, **kwargs, ) -> CitationDict: """Make a citation dictionary.""" if namespace and db: raise ValueError("can not specify both namespace and db") if identifier and db_id: raise ValueError("can not specify both identifier and db_id") if db: warnings.warn( "usage of keyword argument `db` in citation_dict() should be replaced with `namespace`. " "Will be removed in PyBEL 16.", DeprecationWarning, ) namespace = db if db_id: warnings.warn( "usage of keyword argument `db_id` in citation_dict() should be replaced with `identifier`. " "Will be removed in PyBEL 16.", DeprecationWarning, ) identifier = db_id return CitationDict(namespace=namespace, identifier=identifier, name=name, **kwargs) pybel-0.15.5/src/pybel/manager/000077500000000000000000000000001426625374700162515ustar00rootroot00000000000000pybel-0.15.5/src/pybel/manager/__init__.py000066400000000000000000000026511426625374700203660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Managers and subclasses for PyBEL. The :mod:`pybel.manager` module serves as an interface between the BEL graph data structure and underlying relational databases. Its inclusion allows for the caching of namespaces and annotations for much faster lookup than downloading and parsing upon each compilation. """ from . import ( base_manager, cache_manager, citation_utils, database_io, make_json_serializable, models, query_manager, ) from .base_manager import BaseManager, build_engine_session from .cache_manager import Manager, NetworkManager from .citation_utils import enrich_pmc_citations, enrich_pubmed_citations from .database_io import from_database, to_database from .models import ( Author, Base, Citation, Edge, Evidence, Namespace, NamespaceEntry, Network, Node, edge_annotation, network_edge, network_node, ) from .query_manager import QueryManager, graph_from_edges __all__ = [ "BaseManager", "build_engine_session", "Manager", "NetworkManager", "QueryManager", "graph_from_edges", "enrich_pubmed_citations", "enrich_pmc_citations", # I/O "from_database", "to_database", # Models "Base", "Namespace", "NamespaceEntry", "Network", "Node", "Author", "Citation", "Evidence", "Edge", "edge_annotation", "network_edge", "network_node", ] pybel-0.15.5/src/pybel/manager/base_manager.py000066400000000000000000000101041426625374700212230ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains the base class for connection managers in SQLAlchemy.""" import logging from typing import List, Optional, Tuple, Type, TypeVar import pystow from sqlalchemy import create_engine from sqlalchemy.orm import scoped_session, sessionmaker from .models import Base __all__ = [ "BaseManager", "build_engine_session", ] logger = logging.getLogger(__name__) X = TypeVar("X") def build_engine_session( connection: str, echo: bool = False, autoflush: Optional[bool] = None, autocommit: Optional[bool] = None, expire_on_commit: Optional[bool] = None, scopefunc=None, ) -> Tuple: """Build an engine and a session. :param connection: An RFC-1738 database connection string :param echo: Turn on echoing SQL :param autoflush: Defaults to True if not specified in kwargs or configuration. :param autocommit: Defaults to False if not specified in kwargs or configuration. :param expire_on_commit: Defaults to False if not specified in kwargs or configuration. :param scopefunc: Scoped function to pass to :func:`sqlalchemy.orm.scoped_session` :rtype: tuple[Engine,Session] From the Flask-SQLAlchemy documentation: An extra key ``'scopefunc'`` can be set on the ``options`` dict to specify a custom scope function. If it's not provided, Flask's app context stack identity is used. This will ensure that sessions are created and removed with the request/response cycle, and should be fine in most cases. """ if connection is None: raise ValueError("can not build engine when connection is None") engine = create_engine(connection, echo=echo) autoflush = pystow.get_config("pybel", "manager_autoflush", passthrough=autoflush, dtype=bool, default=True) autocommit = pystow.get_config("pybel", "manager_autocommit", passthrough=autocommit, dtype=bool, default=False) expire_on_commit = pystow.get_config( "pybel", "manager_autoexpire", passthrough=expire_on_commit, dtype=bool, default=True, ) logger.debug( "auto flush: %s, auto commit: %s, expire on commmit: %s", autoflush, autocommit, expire_on_commit, ) #: A SQLAlchemy session maker session_maker = sessionmaker( bind=engine, autoflush=autoflush, autocommit=autocommit, expire_on_commit=expire_on_commit, ) #: A SQLAlchemy session object session = scoped_session( session_maker, scopefunc=scopefunc, ) return engine, session class BaseManager(object): """A wrapper around a SQLAlchemy engine and session.""" #: The declarative base for this manager base = Base def __init__(self, engine, session) -> None: """Instantiate a manager from an engine and session.""" self.engine = engine self.session = session def create_all(self, checkfirst: bool = True) -> None: """Create the PyBEL cache's database and tables. :param checkfirst: Check if the database exists before trying to re-make it """ self.base.metadata.create_all(bind=self.engine, checkfirst=checkfirst) def drop_all(self, checkfirst: bool = True) -> None: """Drop all data, tables, and databases for the PyBEL cache. :param checkfirst: Check if the database exists before trying to drop it """ self.session.close() self.base.metadata.drop_all(bind=self.engine, checkfirst=checkfirst) def bind(self) -> None: """Bind the metadata to the engine and session.""" self.base.metadata.bind = self.engine self.base.query = self.session.query_property() def _list_model(self, model_cls: Type[X]) -> List[X]: """List the models in this class.""" return self.session.query(model_cls).all() def _count_model(self, model_cls) -> int: """Count the number of models in the database.""" return self.session.query(model_cls).count() def __repr__(self): return "<{} connection={}>".format(self.__class__.__name__, self.engine.url) pybel-0.15.5/src/pybel/manager/cache_manager.py000066400000000000000000001173271426625374700213730ustar00rootroot00000000000000# -*- coding: utf-8 -*- """The database manager for PyBEL. Under the hood, PyBEL caches namespace and annotation files for quick recall on later use. The user doesn't need to enable this option, but can specify a database location if they choose. """ import logging import time from typing import Iterable, List, Mapping, Optional, Set, Tuple import pandas as pd import requests import sqlalchemy from bel_resources import get_bel_resource from sqlalchemy import and_, exists, func from sqlalchemy.orm import aliased from tqdm.autonotebook import tqdm from .base_manager import BaseManager, build_engine_session from .exc import EdgeAddError from .lookup_manager import LookupManager from .models import ( Author, Citation, Edge, Evidence, Namespace, NamespaceEntry, Network, Node, edge_annotation, network_edge, network_node, ) from .query_manager import QueryManager from .utils import ( extract_shared_optional, extract_shared_required, update_insert_values, ) from ..constants import ( ANNOTATIONS, CITATION, CITATION_TYPE_PUBMED, EVIDENCE, IDENTIFIER, METADATA_INSERT_KEYS, NAMESPACE, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, UNQUALIFIED_EDGES, belns_encodings, get_cache_connection, ) from ..dsl import BaseConcept, BaseEntity from ..language import Entity from ..struct.graph import AnnotationsDict, BELGraph from ..struct.operations import union from ..typing import EdgeData __all__ = [ "Manager", "NetworkManager", ] logger = logging.getLogger(__name__) DEFAULT_BELNS_ENCODING = "".join(sorted(belns_encodings)) _optional_namespace_entries_mapping = { "species": ("Namespace", "SpeciesString"), "query_url": ("Namespace", "QueryValueURL"), "domain": ("Namespace", "DomainString"), } def _get_namespace_insert_values(bel_resource): namespace_insert_values = { "name": bel_resource["Namespace"]["NameString"], } namespace_insert_values.update(extract_shared_required(bel_resource, "Namespace")) namespace_insert_values.update(extract_shared_optional(bel_resource, "Namespace")) update_insert_values( bel_resource=bel_resource, mapping=_optional_namespace_entries_mapping, values=namespace_insert_values, ) return namespace_insert_values _annotation_mapping = { "name": ("Citation", "NameString"), } def _get_annotation_insert_values(bel_resource): annotation_insert_values = extract_shared_required(bel_resource, "AnnotationDefinition") annotation_insert_values.update(extract_shared_optional(bel_resource, "AnnotationDefinition")) update_insert_values( bel_resource=bel_resource, mapping=_annotation_mapping, values=annotation_insert_values, ) return annotation_insert_values def not_resource_cachable(bel_resource): """Check if the BEL resource is cacheable. :param dict bel_resource: A dictionary returned by :func:`get_bel_resource`. """ return bel_resource["Processing"].get("CacheableFlag") not in { "yes", "Yes", "True", "true", } def _clean_bel_namespace_values(bel_resource): bel_resource["Values"] = { name: (encoding if encoding else DEFAULT_BELNS_ENCODING) for name, encoding in bel_resource["Values"].items() if name } class NamespaceManager(BaseManager): """Manages BEL namespaces.""" def list_namespaces(self) -> List[Namespace]: """List all namespaces.""" return self._list_model(Namespace) def count_namespaces(self) -> int: """Count the number of namespaces in the database.""" return self._count_model(Namespace) def count_namespace_entries(self) -> int: """Count the number of namespace entries in the database.""" return self._count_model(NamespaceEntry) def drop_namespaces(self): """Drop all namespaces.""" self.session.query(NamespaceEntry).delete() self.session.query(Namespace).delete() self.session.commit() def drop_namespace_by_url(self, url: str) -> None: """Drop the namespace at the given URL. Won't work if the edge store is in use. :param url: The URL of the namespace to drop """ namespace = self.get_namespace_by_url(url) self.session.query(NamespaceEntry).filter(NamespaceEntry.namespace == namespace).delete() self.session.delete(namespace) self.session.commit() def get_namespace_by_url(self, url: str) -> Optional[Namespace]: """Look up a namespace by url.""" return self.session.query(Namespace).filter(Namespace.url == url).one_or_none() def get_namespace_by_keyword_version(self, keyword: str, version: str) -> Optional[Namespace]: """Get a namespace with a given keyword and version.""" filt = and_(Namespace.keyword == keyword, Namespace.version == version) return self.session.query(Namespace).filter(filt).one_or_none() def _ensure_namespace_urls( self, urls: Iterable[str], use_tqdm: bool = True, is_annotation: bool = False, ) -> List[Namespace]: ext = "belanno" if is_annotation else "belns" rv = [] url_to_namespace = {} url_to_values = {} url_to_name_to_id = {} tag = "annotations" if is_annotation else "namespaces" if use_tqdm: urls = tqdm(urls, desc=f"downloading {tag}") for url in urls: result = self.get_namespace_by_url(url) if result: rv.append(result) continue bel_resource = get_bel_resource(url) _clean_bel_namespace_values(bel_resource) url_to_values[url] = bel_resource["Values"] if is_annotation: namespace_kwargs = _get_annotation_insert_values(bel_resource) else: namespace_kwargs = _get_namespace_insert_values(bel_resource) result = url_to_namespace[url] = Namespace(url=url, **namespace_kwargs) rv.append(result) if url.endswith(f"-names.{ext}"): mapping_url = url[: -len(f"-names.{ext}")] + f".{ext}.mapping" try: res = requests.get(mapping_url) res.raise_for_status() except requests.exceptions.HTTPError: logger.warning("No mappings found for %s", url) else: mappings = res.json() logger.debug("got %d mappings", len(mappings)) url_to_name_to_id[url] = {v: k for k, v in res.json().items()} self.session.add_all(url_to_namespace.values()) self.session.commit() url_to_id = {url: namespace.id for url, namespace in url_to_namespace.items()} if not url_to_values: return rv rows = [] it = url_to_values.items() if use_tqdm: it = tqdm(it, desc=f"making {tag} entry table") if is_annotation: for url, values in it: for name, identifier in values.items(): if not name: continue rows.append((url_to_id[url], name, None, identifier)) # TODO is this a fair assumption? else: for url, values in it: name_to_id = url_to_name_to_id.get(url, {}) for name, encoding in values.items(): if not name: continue rows.append((url_to_id[url], name, encoding, name_to_id.get(name))) df = pd.DataFrame(rows, columns=["namespace_id", "name", "encoding", "identifier"]) logger.info("preparing sql objects for %s", tag) df.to_sql( NamespaceEntry.__tablename__, con=self.engine, if_exists="append", index=False, ) logger.info("committing %s", tag) start_commit_time = time.time() self.session.commit() logger.info( "done committing %s after %.2f seconds", tag, time.time() - start_commit_time, ) return rv def get_or_create_namespace(self, url: str) -> Namespace: """Insert the namespace file at the given location to the cache. If not cachable, returns the dict of the values of this namespace. :raises: pybel.resources.exc.ResourceError """ return self._ensure_namespace_urls([url])[0] def get_namespace_by_keyword_pattern(self, keyword: str, pattern: str) -> Optional[Namespace]: """Get a namespace with a given keyword and pattern.""" filt = and_(Namespace.keyword == keyword, Namespace.pattern == pattern) return self.session.query(Namespace).filter(filt).one_or_none() def ensure_regex_namespace(self, keyword: str, pattern: str) -> Namespace: """Get or create a regular expression namespace. :param keyword: The keyword of a regular expression namespace :param pattern: The pattern for a regular expression namespace """ if pattern is None: raise ValueError("cannot have null pattern") namespace = self.get_namespace_by_keyword_pattern(keyword, pattern) if namespace is None: logger.info("creating regex namespace: %s:%s", keyword, pattern) namespace = Namespace( keyword=keyword, pattern=pattern, ) self.session.add(namespace) self.session.commit() return namespace def get_namespace_entry(self, url: str, name: str) -> Optional[NamespaceEntry]: """Get a given NamespaceEntry object. :param url: The url of the namespace source :param name: The value of the namespace from the given url's document """ entry_filter = and_(Namespace.url == url, NamespaceEntry.name == name) result = self.session.query(NamespaceEntry).join(Namespace).filter(entry_filter).all() if 0 == len(result): logger.debug("could not find namespace entry for %s in url=%s", name, url) return if 1 < len(result): logger.warning( "result for get_namespace_entry is too long. Returning first of %s", [str(r) for r in result], ) return result[0] def get_entity_by_identifier(self, url: str, identifier: str) -> Optional[NamespaceEntry]: """Get a given entity by its url/identifier combination.""" entry_filter = and_(Namespace.url == url, NamespaceEntry.identifier == identifier) return self.session.query(NamespaceEntry).join(Namespace).filter(entry_filter).one_or_none() def get_or_create_regex_namespace_entry(self, *, pattern: str, concept: Entity) -> NamespaceEntry: """Get a namespace entry from a regular expression. Need to commit after! :param pattern: The regular expression pattern for the namespace :param concept: The prefix/identifier/name triple """ namespace = self.ensure_regex_namespace(concept.namespace, pattern) n_filter = and_(Namespace.pattern == pattern, NamespaceEntry.name == concept.name) namespace_entry = self.session.query(NamespaceEntry).join(Namespace).filter(n_filter).one_or_none() if namespace_entry is None: namespace_entry = NamespaceEntry( namespace=namespace, name=concept.name, identifier=concept.identifier, ) self.session.add(namespace_entry) return namespace_entry def list_annotations(self) -> List[Namespace]: """List all annotations.""" return self.session.query(Namespace).filter(Namespace.is_annotation).all() def count_annotations(self) -> int: """Count the number of annotations in the database.""" return self.session.query(Namespace).filter(Namespace.is_annotation).count() def count_annotation_entries(self) -> int: """Count the number of annotation entries in the database.""" return self.session.query(NamespaceEntry).filter(NamespaceEntry.is_annotation).count() def get_or_create_annotation(self, url: str) -> Namespace: """Insert the namespace file at the given location to the cache. :raises: pybel.resources.exc.ResourceError """ return self._ensure_namespace_urls([url], is_annotation=True)[0] def get_annotation_entries_by_names(self, url: str, entities: Iterable[Entity]) -> List[NamespaceEntry]: """Get annotation entries by URL and names. :param url: The url of the annotation source :param entities: The names of the annotation entries from the given url's document """ names = [e.identifier if isinstance(e, Entity) else e for e in entities] annotation_filter = and_(Namespace.url == url, NamespaceEntry.name.in_(names)) return self.session.query(NamespaceEntry).join(Namespace).filter(annotation_filter).all() class NetworkManager(NamespaceManager): """Groups functions for inserting and querying networks in the database's network store.""" def count_networks(self) -> int: """Count the networks in the database.""" return self._count_model(Network) def list_networks(self) -> List[Network]: """List all networks in the database.""" return self._list_model(Network) def list_recent_networks(self) -> List[Network]: """List the most recently created version of each network (by name).""" most_recent_times = self.session.query( Network.name.label("network_name"), func.max(Network.created).label("max_created"), ) most_recent_times = most_recent_times.group_by(Network.name).subquery("most_recent_times") and_condition = and_( most_recent_times.c.network_name == Network.name, most_recent_times.c.max_created == Network.created, ) most_recent_networks = self.session.query(Network).join(most_recent_times, and_condition) return most_recent_networks.all() def has_name_version(self, name: str, version: str) -> bool: """Check if there exists a network with the name/version combination in the database.""" return self.session.query(exists().where(and_(Network.name == name, Network.version == version))).scalar() def drop_networks(self) -> None: """Drop all networks.""" for network in self.session.query(Network).all(): self.drop_network(network) def drop_network_by_id(self, network_id: int) -> None: """Drop a network by its database identifier.""" network = self.session.query(Network).get(network_id) self.drop_network(network) def drop_network(self, network: Network) -> None: """Drop a network, while also cleaning up any edges that are no longer part of any network.""" # get the IDs of the edges that will be orphaned by deleting this network # FIXME: this list could be a problem if it becomes very large; possible optimization is a temporary table in DB edge_ids = [result.edge_id for result in self.query_singleton_edges_from_network(network)] # delete the network-to-node mappings for this network self.session.query(network_node).filter(network_node.c.network_id == network.id).delete( synchronize_session=False, ) # delete the edge-to-annotation mappings for the to-be-orphaned edges self.session.query(edge_annotation).filter(edge_annotation.c.edge_id.in_(edge_ids)).delete( synchronize_session=False, ) # delete the edge-to-network mappings for this network self.session.query(network_edge).filter(network_edge.c.network_id == network.id).delete( synchronize_session=False, ) # delete the now-orphaned edges self.session.query(Edge).filter(Edge.id.in_(edge_ids)).delete(synchronize_session=False) # delete the network self.session.query(Network).filter(Network.id == network.id).delete(synchronize_session=False) # commit it! self.session.commit() def query_singleton_edges_from_network(self, network: Network) -> sqlalchemy.orm.query.Query: """Return a query selecting all edge ids that only belong to the given network.""" ne1 = aliased(network_edge, name="ne1") ne2 = aliased(network_edge, name="ne2") singleton_edge_ids_for_network = ( self.session.query(ne1.c.edge_id) .outerjoin( ne2, and_( ne1.c.edge_id == ne2.c.edge_id, ne1.c.network_id != ne2.c.network_id, ), ) .filter( # noqa: E131 and_( ne1.c.network_id == network.id, ne2.c.edge_id == None, # noqa: E711 ), ) ) return singleton_edge_ids_for_network def get_network_versions(self, name: str) -> Set[str]: """Return all of the versions of a network with the given name.""" return {version for version, in self.session.query(Network.version).filter(Network.name == name).all()} def get_network_by_name_version(self, name: str, version: str) -> Optional[Network]: """Load the network with the given name and version if it exists.""" name_version_filter = and_(Network.name == name, Network.version == version) network = self.session.query(Network).filter(name_version_filter).one_or_none() return network def get_graph_by_name_version(self, name: str, version: str) -> Optional[BELGraph]: """Load the BEL graph with the given name, or allows for specification of version.""" network = self.get_network_by_name_version(name, version) if network is None: return return network.as_bel() def get_networks_by_name(self, name: str) -> List[Network]: """Get all networks with the given name. Useful for getting all versions of a given network.""" return self.session.query(Network).filter(Network.name.like(name)).all() def get_most_recent_network_by_name(self, name: str) -> Optional[Network]: """Get the most recently created network with the given name.""" return self.session.query(Network).filter(Network.name == name).order_by(Network.created.desc()).first() def get_graph_by_most_recent(self, name: str) -> Optional[BELGraph]: """Get the most recently created network with the given name as a :class:`pybel.BELGraph`.""" network = self.get_most_recent_network_by_name(name) if network is None: return return network.as_bel() def get_network_by_id(self, network_id: int) -> Network: """Get a network from the database by its identifier.""" return self.session.query(Network).get(network_id) def get_graph_by_id(self, network_id: int) -> BELGraph: """Get a network from the database by its identifier and converts it to a BEL graph.""" network = self.get_network_by_id(network_id) logger.debug("converting network [id=%d] %s to bel graph", network_id, network) return network.as_bel() def get_networks_by_ids(self, network_ids: Iterable[int]) -> List[Network]: """Get a list of networks with the given identifiers. Note: order is not necessarily preserved. """ logger.debug("getting networks by identifiers: %s", network_ids) return self.session.query(Network).filter(Network.id_in(network_ids)).all() def get_graphs_by_ids(self, network_ids: Iterable[int]) -> List[BELGraph]: """Get a list of networks with the given identifiers and converts to BEL graphs.""" rv = [self.get_graph_by_id(network_id) for network_id in network_ids] logger.debug("returning graphs for network identifiers: %s", network_ids) return rv def get_graph_by_ids(self, network_ids: List[int]) -> BELGraph: """Get a combine BEL Graph from a list of network identifiers.""" if len(network_ids) == 1: return self.get_graph_by_id(network_ids[0]) logger.debug("getting graph by identifiers: %s", network_ids) graphs = self.get_graphs_by_ids(network_ids) logger.debug("getting union of graphs: %s", network_ids) rv = union(graphs) return rv class InsertManager(NamespaceManager, LookupManager): """Manages inserting data into the edge store.""" def __init__(self, *args, **kwargs): super(InsertManager, self).__init__(*args, **kwargs) # A set of dictionaries that contains objects of the type described by the key self.object_cache_modification = {} self.object_cache_property = {} self.object_cache_node = {} self.object_cache_edge = {} self.object_cache_evidence = {} self.curie_to_citation = {} self.object_cache_author = {} def insert_graph( self, graph: BELGraph, use_tqdm: bool = True, ) -> Network: """Insert a graph in the database and returns the corresponding Network model. :raises: pybel.resources.exc.ResourceError """ if not graph.name: raise ValueError("Can not upload a graph without a name") if not graph.version: raise ValueError("Can not upload a graph without a version") logger.debug("inserting %s v%s", graph.name, graph.version) t = time.time() namespace_urls = graph.namespace_url.values() self._ensure_namespace_urls(namespace_urls, use_tqdm=use_tqdm) for keyword, pattern in graph.namespace_pattern.items(): self.ensure_regex_namespace(keyword, pattern) annotation_urls = graph.annotation_url.values() self._ensure_namespace_urls(annotation_urls, use_tqdm=use_tqdm, is_annotation=True) network = Network(**{key: value for key, value in graph.document.items() if key in METADATA_INSERT_KEYS}) network.store_bel(graph) network.nodes, network.edges = self._store_graph_parts(graph, use_tqdm=use_tqdm) self.session.add(network) self.session.commit() logger.info( "inserted %s v%s in %.2f seconds", graph.name, graph.version, time.time() - t, ) return network def _store_graph_parts(self, graph: BELGraph, use_tqdm: bool = False) -> Tuple[List[Node], List[Edge]]: """Store the given graph into the edge store. :raises: pybel.resources.exc.ResourceError :raises: EdgeAddError """ logger.debug("inserting %s into edge store", graph) logger.debug("building node models") node_model_build_start = time.time() nodes = list(graph) if use_tqdm: nodes = tqdm(nodes, total=graph.number_of_nodes(), desc="nodes") node_model = {} for node in nodes: node_object = self.get_or_create_node(graph, node) if node_object is None: logger.warning("can not add node %s", node) continue node_model[node] = node_object node_models = list(node_model.values()) logger.debug( "built %d node models in %.2f seconds", len(node_models), time.time() - node_model_build_start, ) node_model_commit_start = time.time() self.session.add_all(node_models) self.session.commit() logger.debug( "stored %d node models in %.2f seconds", len(node_models), time.time() - node_model_commit_start, ) logger.debug("building edge models") edge_model_build_start = time.time() edges = graph.edges(keys=True, data=True) if use_tqdm: edges = tqdm(edges, total=graph.number_of_edges(), desc="edges") edge_models = list(self._get_edge_models(graph, node_model, edges)) logger.debug( "built %d edge models in %.2f seconds", len(edge_models), time.time() - edge_model_build_start, ) edge_model_commit_start = time.time() self.session.add_all(edge_models) self.session.commit() logger.debug( "stored %d edge models in %.2f seconds", len(edge_models), time.time() - edge_model_commit_start, ) return node_models, edge_models def _get_edge_models( self, graph: BELGraph, tuple_model: Mapping[BaseEntity, Node], edges, ) -> Iterable[Edge]: for u, v, key, data in edges: source = tuple_model.get(u) if source is None or source.md5 not in self.object_cache_node: logger.warning("skipping uncached source node: %s", u) continue target = tuple_model.get(v) if target is None or target.md5 not in self.object_cache_node: logger.warning("skipping uncached target node: %s", v) continue relation = data[RELATION] if relation in UNQUALIFIED_EDGES: try: edge = self._add_unqualified_edge( source=source, target=target, bel=graph.edge_to_bel(u, v, data), key=key, data=data, ) if edge is None: continue except Exception as e: self.session.rollback() logger.exception("error storing edge in database. edge data: %s", data) raise EdgeAddError(e, u, v, key, data) from e else: yield edge elif EVIDENCE not in data or CITATION not in data: continue elif NAMESPACE not in data[CITATION] or IDENTIFIER not in data[CITATION]: continue else: try: bel = graph.edge_to_bel(u, v, data) edge = self._add_qualified_edge( graph=graph, source=source, target=target, key=key, bel=bel, data=data, ) if edge is None: continue except Exception as e: self.session.rollback() logger.exception("error storing edge in database. edge data: %s", data) raise EdgeAddError(e, u, v, key, data) else: yield edge @staticmethod def _iter_from_annotations_dict( graph: BELGraph, annotations_dict: AnnotationsDict, ) -> Iterable[Tuple[str, Set[Entity]]]: """Iterate over the key/value pairs in this edge data dictionary normalized to their source URLs.""" for key, entities in annotations_dict.items(): if key in graph.annotation_url: url = graph.annotation_url[key] elif key in graph.annotation_list: continue # skip those elif key in graph.annotation_pattern: logger.debug("pattern annotation in database not implemented yet not implemented") # FIXME continue else: raise ValueError("Graph resources does not contain keyword: {}".format(key)) yield url, set(entities) def _get_annotation_entries_from_data(self, graph: BELGraph, data: EdgeData) -> Optional[List[NamespaceEntry]]: """Get the annotation entries from an edge data dictionary.""" annotations_dict = data.get(ANNOTATIONS) if annotations_dict is None: return rv = [] for url, entities in self._iter_from_annotations_dict(graph, annotations_dict=annotations_dict): for entry in self.get_annotation_entries_by_names(url, entities): rv.append(entry) return rv def _add_qualified_edge( self, graph: BELGraph, source: Node, target: Node, key: str, bel: str, data: EdgeData, ) -> Optional[Edge]: """Add a qualified edge to the network.""" citation_dict = data[CITATION] citation = self.get_or_create_citation( namespace=citation_dict[NAMESPACE], identifier=citation_dict[IDENTIFIER], ) evidence = self.get_or_create_evidence(citation, data[EVIDENCE]) annotations = self._get_annotation_entries_from_data(graph, data) return self.get_or_create_edge( source=source, target=target, relation=data[RELATION], bel=bel, md5=key, data=data, evidence=evidence, annotations=annotations, ) def _add_unqualified_edge(self, source: Node, target: Node, key: str, bel: str, data: EdgeData) -> Edge: """Add an unqualified edge to the network.""" return self.get_or_create_edge( source=source, target=target, relation=data[RELATION], bel=bel, md5=key, data=data, ) def get_or_create_evidence(self, citation: Citation, text: str) -> Evidence: """Create an entry and object for given evidence if it does not exist.""" evidence_tuple = citation.db, citation.db_id, text if evidence_tuple in self.object_cache_evidence: evidence = self.object_cache_evidence[evidence_tuple] self.session.add(evidence) return evidence evidence = self.get_evidence_by_citation_text(citation, text) if evidence is not None: self.object_cache_evidence[evidence_tuple] = evidence return evidence self.object_cache_evidence[evidence_tuple] = evidence = Evidence( citation=citation, text=text, ) self.session.add(evidence) return evidence def get_or_create_node(self, graph: BELGraph, node: BaseEntity) -> Optional[Node]: """Create an entry and object for given node if it does not exist.""" node_md5 = node.md5 if node_md5 in self.object_cache_node: return self.object_cache_node[node_md5] node_model = self.get_node_by_hash(node_md5) if node_model is not None: self.object_cache_node[node_md5] = node_model return node_model node_model = Node._start_from_base_entity(node) if not isinstance(node, BaseConcept): self.session.add(node_model) self.object_cache_node[node_md5] = node_model return node_model if node.namespace in graph.namespace_url: url = graph.namespace_url[node.namespace] name = node.name entry = self.get_namespace_entry(url, name) if entry is None: logger.debug( "skipping node with entity %s:%s from url=%s", node.namespace, name, url, ) return self.session.add(entry) node_model.namespace_entry = entry elif node.namespace in graph.namespace_pattern: entry = self.get_or_create_regex_namespace_entry( concept=node.entity, pattern=graph.namespace_pattern[node.namespace], ) self.session.add(entry) node_model.namespace_entry = entry else: logger.warning("No reference in BELGraph for namespace: {}".format(node.namespace)) return self.session.add(node_model) self.object_cache_node[node_md5] = node_model return node_model def drop_nodes(self) -> None: """Drop all nodes in the database.""" t = time.time() self.session.query(Node).delete() self.session.commit() logger.info("dropped all nodes in %.2f seconds", time.time() - t) def drop_edges(self) -> None: """Drop all edges in the database.""" t = time.time() self.session.query(Edge).delete() self.session.commit() logger.info("dropped all edges in %.2f seconds", time.time() - t) def get_or_create_edge( self, source: Node, target: Node, relation: str, bel: str, md5: str, data: EdgeData, evidence: Optional[Evidence] = None, annotations: Optional[List[NamespaceEntry]] = None, ) -> Edge: """Create an edge if it does not exist, or return it if it does. :param source: Source node of the relation :param target: Target node of the relation :param relation: Type of the relation between source and target node :param bel: BEL statement that describes the relation :param md5: The MD5 hash of the edge as a string :param data: The PyBEL data dictionary :param evidence: Evidence object that proves the given relation :param annotations: List of all annotations that belong to the edge """ if md5 in self.object_cache_edge: edge = self.object_cache_edge[md5] self.session.add(edge) return edge edge = self.get_edge_by_hash(md5) if edge is not None: self.object_cache_edge[md5] = edge return edge edge = Edge( source=source, source_modifier=data.get(SOURCE_MODIFIER), target=target, target_modifier=data.get(TARGET_MODIFIER), relation=relation, bel=bel, md5=md5, data=data, ) if evidence is not None: edge.evidence = evidence if annotations is not None: edge.annotations = annotations self.session.add(edge) self.object_cache_edge[md5] = edge return edge def get_or_create_citation( self, *, identifier: str, namespace: Optional[str] = None, ) -> Citation: """Create a citation if it does not exist, or return it if it does. :param identifier: Identifier of the given citation (e.g. PubMed id) :param namespace: Citation type (defaults to PubMed) """ if namespace is None: namespace = CITATION_TYPE_PUBMED citation_curie = f"{namespace}:{identifier}" if citation_curie in self.curie_to_citation: citation = self.curie_to_citation[citation_curie] self.session.add(citation) return citation citation = self.get_citation_by_reference(namespace, identifier) if citation is not None: self.curie_to_citation[citation_curie] = citation return citation self.curie_to_citation[citation_curie] = citation = Citation(db=namespace, db_id=identifier) self.session.add(citation) return citation def get_or_create_author(self, name: str) -> Author: """Get an author by name, or creates one if it does not exist.""" author = self.object_cache_author.get(name) if author is not None: self.session.add(author) return author author = self.get_author_by_name(name) if author is not None: self.object_cache_author[name] = author return author author = self.object_cache_author[name] = Author(name=name) self.session.add(author) return author class _Manager(QueryManager, InsertManager, NetworkManager): """A wrapper around PyBEL managers that can be directly instantiated with an engine and session.""" def count_citations(self) -> int: """Count the number of citations stored in the database.""" return self._count_model(Citation) def list_citations(self) -> List[Citation]: """List the citations in the database.""" return self._list_model(Citation) class Manager(_Manager): """A manager for the PyBEL database.""" def __init__(self, connection: Optional[str] = None, engine=None, session=None, **kwargs) -> None: """Create a connection to database and a persistent session using SQLAlchemy. A custom default can be set as an environment variable with the name :data:`pybel.constants.PYBEL_CONNECTION`, using an `RFC-1738 `_ string. For example, a MySQL string can be given with the following form: :code:`mysql+pymysql://:@/?charset=utf8[&]` A SQLite connection string can be given in the form: ``sqlite:///~/Desktop/cache.db`` Further options and examples can be found on the SQLAlchemy documentation on `engine configuration `_. :param connection: An RFC-1738 database connection string. If ``None``, tries to load from the environment variable ``PYBEL_CONNECTION`` then from the config file ``~/.config/pybel/config.json`` whose value for ``PYBEL_CONNECTION`` defaults to :data:`pybel.constants.DEFAULT_CACHE_CONNECTION`. :param engine: Optional engine to use. Must be specified with a session and no connection. :param session: Optional session to use. Must be specified with an engine and no connection. :param bool echo: Turn on echoing sql :param Optional[bool] autoflush: Defaults to True if not specified in kwargs or configuration. :param Optional[bool] autocommit: Defaults to False if not specified in kwargs or configuration. :param Optional[bool] expire_on_commit: Defaults to False if not specified in kwargs or configuration. :param scopefunc: Scoped function to pass to :func:`sqlalchemy.orm.scoped_session` From the Flask-SQLAlchemy documentation: An extra key ``'scopefunc'`` can be set on the ``options`` dict to specify a custom scope function. If it's not provided, Flask's app context stack identity is used. This will ensure that sessions are created and removed with the request/response cycle, and should be fine in most cases. Allowed Usages: Instantiation with connection string as positional argument >>> my_connection = 'sqlite:///~/Desktop/cache.db' >>> manager = Manager(my_connection) Instantiation with connection string as positional argument with keyword arguments >>> my_connection = 'sqlite:///~/Desktop/cache.db' >>> manager = Manager(my_connection, echo=True) Instantiation with connection string as keyword argument >>> my_connection = 'sqlite:///~/Desktop/cache.db' >>> manager = Manager(connection=my_connection) Instantiation with connection string as keyword argument with keyword arguments >>> my_connection = 'sqlite:///~/Desktop/cache.db' >>> manager = Manager(connection=my_connection, echo=True) Instantiation with user-supplied engine and session objects as keyword arguments >>> my_engine, my_session = ... # magical creation! See SQLAlchemy documentation >>> manager = Manager(engine=my_engine, session=my_session) """ if connection and (engine or session): raise ValueError("can not specify connection with engine/session") if engine is None and session is None: if connection is None: connection = get_cache_connection() engine, session = build_engine_session(connection=connection, **kwargs) elif engine is None or session is None: raise ValueError("need both engine and session to be specified") elif kwargs: raise ValueError("keyword arguments should not be used with engine/session") super().__init__(engine=engine, session=session) self.create_all() pybel-0.15.5/src/pybel/manager/citation_utils.py000066400000000000000000000352521426625374700216640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Citation utilities for the database manager.""" import logging import re from datetime import date, datetime from functools import lru_cache from typing import Any, Dict, Iterable, List, Mapping, Optional, Set, Tuple, Union import ratelimit import requests from more_itertools import chunked from sqlalchemy import and_ from tqdm.autonotebook import tqdm from . import models from .cache_manager import Manager from ..constants import CITATION from ..struct.filters import filter_edges from ..struct.filters.edge_predicates import CITATION_PREDICATES from ..struct.graph import BELGraph from ..struct.summary.provenance import get_citation_identifiers __all__ = [ "enrich_pubmed_citations", "enrich_pmc_citations", ] logger = logging.getLogger(__name__) EUTILS_URL_FMT = "http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id={}" re1 = re.compile(r"^[12][0-9]{3} [a-zA-Z]{3} \d{1,2}$") re2 = re.compile(r"^[12][0-9]{3} [a-zA-Z]{3}$") re3 = re.compile(r"^[12][0-9]{3}$") re4 = re.compile(r"^[12][0-9]{3} [a-zA-Z]{3}-[a-zA-Z]{3}$") re5 = re.compile(r"^([12][0-9]{3}) (Spring|Fall|Winter|Summer)$") re6 = re.compile(r"^[12][0-9]{3} [a-zA-Z]{3} \d{1,2}-(\d{1,2})$") re7 = re.compile(r"^[12][0-9]{3} [a-zA-Z]{3} \d{1,2}-([a-zA-Z]{3} \d{1,2})$") # TODO "Winter 2016" probably with re.compile(r'^(Spring|Fall|Winter|Summer) ([12][0-9]{3})$') # TODO "YYYY Oct - Dec" update re4 to allow spaces before and after the dash season_map = {"Spring": "03", "Summer": "06", "Fall": "09", "Winter": "12"} def sanitize_date(publication_date: str) -> str: """Sanitize lots of different date strings into ISO-8601.""" if re1.search(publication_date): return datetime.strptime(publication_date, "%Y %b %d").strftime("%Y-%m-%d") if re2.search(publication_date): return datetime.strptime(publication_date, "%Y %b").strftime("%Y-%m-01") if re3.search(publication_date): return publication_date + "-01-01" if re4.search(publication_date): return datetime.strptime(publication_date[:-4], "%Y %b").strftime("%Y-%m-01") s = re5.search(publication_date) if s: year, season = s.groups() return "{}-{}-01".format(year, season_map[season]) s = re6.search(publication_date) if s: return datetime.strptime(publication_date, "%Y %b %d-{}".format(s.groups()[0])).strftime("%Y-%m-%d") s = re7.search(publication_date) if s: return datetime.strptime(publication_date, "%Y %b %d-{}".format(s.groups()[0])).strftime("%Y-%m-%d") def clean_pubmed_identifiers(identifiers: Iterable[str]) -> List[str]: """Clean a list of identifiers with string strips, deduplicates, and sorting.""" _identifiers = (str(identifier).strip() for identifier in identifiers if identifier) return sorted({i for i in _identifiers if i}) @ratelimit.limits(calls=3, period=1) def get_pubmed_citation_response(pubmed_identifiers: Iterable[str]): """Get the response from PubMed E-Utils for a given list of PubMed identifiers. Rate limit of 3 requests per second is from: https://ncbiinsights.ncbi.nlm.nih.gov/2018/08/14/release-plan-for-e-utility-api-keys/ :param pubmed_identifiers: :rtype: dict """ pubmed_identifiers = list(pubmed_identifiers) url = EUTILS_URL_FMT.format( ",".join(pubmed_identifier for pubmed_identifier in pubmed_identifiers if pubmed_identifier), ) response = requests.get(url) return response.json() def enrich_citation_model(manager: Manager, citation: models.Citation, p: Mapping[str, Any]) -> bool: """Enrich a citation model with the information from PubMed. :param manager: A database manager :param citation: A citation model :param p: The dictionary from PubMed E-Utils corresponding to d["result"][pmid] """ if "error" in p: logger.warning("Error downloading PubMed") return False citation.title = p["title"] citation.journal = p["fulljournalname"] citation.volume = p["volume"] citation.issue = p["issue"] citation.pages = p["pages"] citation.first = manager.get_or_create_author(p["sortfirstauthor"]) citation.last = manager.get_or_create_author(p["lastauthor"]) pubtypes = p["pubtype"] if pubtypes: citation.article_type = pubtypes[0] if "authors" in p: for author in p["authors"]: author_model = manager.get_or_create_author(author["name"]) if author_model not in citation.authors: citation.authors.append(author_model) publication_date = p["pubdate"] try: sanitized_publication_date = sanitize_date(publication_date) except ValueError: logger.warning( "could not parse publication date %s for pubmed:%s", publication_date, citation.db_id, ) sanitized_publication_date = None if sanitized_publication_date: citation.date = datetime.strptime(sanitized_publication_date, "%Y-%m-%d") else: logger.info("result had date with strange format: %s", publication_date) return True def get_citations_by_pmids( manager: Manager, pmids: Iterable[Union[str, int]], *, group_size: Optional[int] = None, offline: bool = False, ) -> Tuple[Dict[str, Dict], Set[str]]: return _get_citations_by_identifiers( manager=manager, identifiers=pmids, group_size=group_size, offline=offline, prefix="pubmed", ) def _get_citations_by_identifiers( manager: Manager, identifiers: Iterable[Union[str, int]], *, group_size: Optional[int] = None, offline: bool = False, prefix: Optional[str] = None, ) -> Tuple[Dict[str, Dict], Set[str]]: """Get citation information for the given list of PubMed identifiers using the NCBI's eUtils service. :type manager: pybel.Manager :param identifiers: an iterable of PubMed identifiers :param group_size: The number of PubMed identifiers to query at a time. Defaults to 200 identifiers. :return: A dictionary of {identifier: data dictionary} or a pair of this dictionary and a set ot erroneous identifiers. """ if prefix is None: prefix = "pubmed" helper = _HELPERS.get(prefix) if helper is None: raise ValueError(f"can not work on prefix: {prefix}") group_size = group_size if group_size is not None else 200 identifiers = clean_pubmed_identifiers(identifiers) logger.info("ensuring %d %s identifiers", len(identifiers), prefix) enriched_models = {} unenriched_models = {} id_to_model = { citation_model.db_id: citation_model for citation_model in _get_citation_models(identifiers, prefix=prefix, manager=manager) } logger.info( "%d of %d %s identifiers are already cached", len(id_to_model), len(identifiers), prefix, ) for identifier in tqdm(identifiers, desc=f"creating {prefix} models"): model = id_to_model.get(identifier) if model is None: model = id_to_model[identifier] = manager.get_or_create_citation(identifier=identifier, namespace=prefix) if model.is_enriched: enriched_models[identifier] = model.to_json() else: unenriched_models[identifier] = model logger.info( "%d of %d %s are identifiers already enriched", len(enriched_models), len(identifiers), prefix, ) manager.session.commit() errors = set() if not unenriched_models or offline: return enriched_models, errors it = tqdm(unenriched_models, desc=f"getting {prefix} data in chunks of {group_size}") for identifier_chunk in chunked(it, n=group_size): helper( identifier_chunk, manager=manager, enriched_models=enriched_models, unenriched_models=unenriched_models, errors=errors, ) return enriched_models, errors def _help_enrich_pmids(identifiers: Iterable[str], *, manager, unenriched_models, enriched_models, errors): response = get_pubmed_citation_response(identifiers) response_pmids = response["result"]["uids"] for pmid in response_pmids: p = response["result"][pmid] citation = unenriched_models.get(pmid) if citation is None: tqdm.write(f"problem looking up pubmed:{pmid}") continue successful_enrichment = enrich_citation_model(manager, citation, p) if not successful_enrichment: tqdm.write(f"Error downloading pubmed:{pmid}") errors.add(pmid) continue enriched_models[pmid] = citation.to_json() manager.session.add(citation) manager.session.commit() # commit in groups def _help_enrich_pmc_identifiers( identifiers: Iterable[str], *, manager: Manager, unenriched_models, enriched_models, errors, ): for pmcid in identifiers: try: csl = get_pmc_csl_item(pmcid) except Exception: tqdm.write(f"Error downloading pmc:{pmcid}") errors.add(pmcid) continue model = unenriched_models[pmcid] enrich_citation_model_from_pmc(manager=manager, citation=model, csl=csl) manager.session.add(model) enriched_models[pmcid] = model.to_json() manager.session.commit() # commit in groups _HELPERS = { "pubmed": _help_enrich_pmids, "pmc": _help_enrich_pmc_identifiers, } def _get_citation_models( identifiers: Iterable[str], *, prefix: str, manager: Manager, chunksize: int = 200, ) -> Iterable[models.Citation]: for identifiers_chunk in chunked(identifiers, chunksize): citation_filter = and_( models.Citation.db == prefix, models.Citation.db_id.in_(identifiers_chunk), ) yield from manager.session.query(models.Citation).filter(citation_filter).all() def enrich_pubmed_citations( graph: BELGraph, *, manager: Optional[Manager] = None, group_size: Optional[int] = None, offline: bool = False, ) -> Set[str]: """Overwrite all PubMed citations with values from NCBI's eUtils lookup service. :param graph: A BEL graph :param manager: A PyBEL database manager :param group_size: The number of PubMed identifiers to query at a time. Defaults to 200 identifiers. :param offline: An override for when you don't want to hit the eUtils :return: A set of PMIDs for which the eUtils service crashed """ return _enrich_citations( manager=manager, graph=graph, group_size=group_size, offline=offline, prefix="pubmed", ) def enrich_pmc_citations( graph: BELGraph, *, manager: Optional[Manager] = None, group_size: Optional[int] = None, offline: bool = False, ) -> Set[str]: """Overwrite all PubMed citations with values from NCBI's eUtils lookup service. :param graph: A BEL graph :param manager: A PyBEL database manager :param group_size: The number of PubMed identifiers to query at a time. Defaults to 200 identifiers. :param offline: An override for when you don't want to hit the eUtils :return: A set of PMIDs for which the eUtils service crashed """ return _enrich_citations( manager=manager, graph=graph, group_size=group_size, offline=offline, prefix="pmc", ) def _enrich_citations( graph: BELGraph, manager: Optional[Manager], group_size: Optional[int] = None, offline: bool = False, prefix: Optional[str] = None, ) -> Set[str]: """Overwrite all citations of the given prefix using the predefined lookup functions. :param graph: A BEL Graph :param group_size: The number of identifiers to query at a time. Defaults to 200 identifiers. :return: A set of identifiers for which lookup was not possible """ if manager is None: manager = Manager() if prefix is None: prefix = "pubmed" identifiers = {identifier for identifier in get_citation_identifiers(graph, prefix) if identifier} identifier_map, errors = _get_citations_by_identifiers( manager, identifiers=identifiers, group_size=group_size, offline=offline, prefix=prefix, ) for u, v, k in filter_edges(graph, CITATION_PREDICATES[prefix]): identifier = graph[u][v][k][CITATION].identifier identifier_data = identifier_map.get(identifier) if identifier_data is None: logger.warning("Missing data for %s:%s", prefix, identifier) errors.add(identifier) continue graph[u][v][k][CITATION].update(identifier_data) return errors @lru_cache() def get_pmc_csl_item(pmcid: str) -> Mapping[str, Any]: """Get the CSL Item for a PubMed Central record by its PMID, PMCID, or DOI, using the NCBI Citation Exporter API.""" if not pmcid.startswith("PMC"): raise ValueError(f"not a valid pmd id: {pmcid}") from manubot.cite.pubmed import get_pmc_csl_item csl_item = get_pmc_csl_item(pmcid) if "URL" not in csl_item: csl_item["URL"] = f"https://www.ncbi.nlm.nih.gov/pmc/articles/{csl_item.get('PMCID', pmcid)}/" return csl_item def enrich_citation_model_from_pmc(manager: Manager, citation: models.Citation, csl: Mapping[str, Any]) -> bool: """Enrich a citation model with the information from PubMed Central. :param manager: A database manager :param citation: A citation model :param dict csl: The dictionary from PMC """ citation.title = csl.get("title") citation.journal = csl.get("container-title") citation.volume = csl.get("volume") # citation.issue = csl['issue'] citation.pages = csl.get("page") citation.article_type = csl.get("type") for author in csl.get("author", []): try: author_name = f'{author["given"]} {author["family"]}' except KeyError: print(f"problem with author in pmc:{citation.db_id}", author) continue author_model = manager.get_or_create_author(author_name) if author_model not in citation.authors: citation.authors.append(author_model) if citation.authors: citation.first = citation.authors[0] citation.last = citation.authors[-1] issued = csl.get("issued") if issued is not None: date_parts = issued["date-parts"][0] if len(date_parts) == 3: citation.date = date(year=date_parts[0], month=date_parts[1], day=date_parts[2]) elif len(date_parts) == 2: citation.date = date(year=date_parts[0], month=date_parts[1], day=1) elif len(date_parts) == 1: citation.date = date(year=date_parts[0], month=1, day=1) else: logger.warning("not sure about date parts: %s", date_parts) return True pybel-0.15.5/src/pybel/manager/database_io.py000066400000000000000000000033451426625374700210630ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Conversion functions for BEL graphs with a SQL database.""" import logging from typing import Optional from sqlalchemy.exc import IntegrityError, OperationalError from .cache_manager import Manager __all__ = [ "to_database", "from_database", ] logger = logging.getLogger(__name__) def to_database( graph, manager: Optional[Manager] = None, use_tqdm: bool = True, ): """Store a graph in a database. :param BELGraph graph: A BEL graph :return: If successful, returns the network object from the database. :rtype: Optional[Network] """ if manager is None: manager = Manager() try: return manager.insert_graph(graph, use_tqdm=use_tqdm) except (IntegrityError, OperationalError): manager.session.rollback() logger.exception("Error storing graph") except Exception as e: manager.session.rollback() raise e def from_database( name: str, version: Optional[str] = None, manager: Optional[Manager] = None, ): """Load a BEL graph from a database. If name and version are given, finds it exactly with :meth:`pybel.manager.Manager.get_network_by_name_version`. If just the name is given, finds most recent with :meth:`pybel.manager.Manager.get_network_by_name_version` :param name: The name of the graph :param version: The version string of the graph. If not specified, loads most recent graph added with this name :return: A BEL graph loaded from the database :rtype: Optional[BELGraph] """ if manager is None: manager = Manager() if version is None: return manager.get_graph_by_most_recent(name) return manager.get_graph_by_name_version(name, version) pybel-0.15.5/src/pybel/manager/exc.py000066400000000000000000000021771426625374700174110ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Exceptions for the manager.""" from ..constants import LINE from ..exceptions import PyBELWarning MSG = ( "Error adding edge {line_s} to database. Check this line in the file and make sure the citation, " "evidence, and annotations all use valid UTF-8 characters: {source} {target} {key} {data} with " "original error:\n {error}" ) class EdgeAddError(PyBELWarning): """When there's a problem inserting an edge.""" def __init__(self, e, u, v, key, data): # noqa: D107 super().__init__(e, u, v, key, data) self.error = e self.source = u self.target = v self.key = key self.data = data def __str__(self): line_s = "from line {} ".format(self.line) if LINE in self.data else "" return MSG.format( line_s=line_s, source=self.source, target=self.target, key=self.key, data=self.data, error=self.error, ) @property def line(self) -> str: """Return the BEL script's line on which this error occurred.""" return self.data.get(LINE) pybel-0.15.5/src/pybel/manager/lookup_manager.py000066400000000000000000000067611426625374700216400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """A manager for looking up nodes.""" from typing import List, Optional from sqlalchemy import and_ from .base_manager import BaseManager from .models import Author, Citation, Edge, Evidence, Node from ..constants import CITATION_TYPE_PUBMED from ..dsl import BaseEntity class LookupManager(BaseManager): """Groups functions for looking up entries by hashes.""" def get_dsl_by_hash(self, node_hash: str) -> Optional[BaseEntity]: """Look up a node by the hash and returns the corresponding PyBEL node tuple.""" node = self.get_node_by_hash(node_hash) if node is not None: return node.as_bel() def get_node_by_hash(self, node_hash: str) -> Optional[Node]: """Look up a node by its hash.""" return self.session.query(Node).filter(Node.md5 == node_hash).one_or_none() def get_nodes_by_hashes(self, node_hashes: List[str]) -> List[Node]: """Look up several nodes by their hashes.""" return self.session.query(Node).filter(Node.md5.in_(node_hashes)).all() def get_node_by_dsl(self, node: BaseEntity) -> Optional[Node]: """Look up a node by its data dictionary by hashing it then using :func:`get_node_by_hash`.""" return self.get_node_by_hash(node.md5) def get_edge_by_hash(self, edge_hash: str) -> Optional[Edge]: """Look up an edge by the hash of a PyBEL edge data dictionary.""" return self.session.query(Edge).filter(Edge.md5 == edge_hash).one_or_none() def get_edges_by_hashes(self, edge_hashes: List[str]) -> List[Edge]: """Look up several edges by hashes of their PyBEL edge data dictionaries.""" return self.session.query(Edge).filter(Edge.md5.in_(edge_hashes)).all() def get_citation_by_pmid(self, pubmed_identifier: str) -> Optional[Citation]: """Get a citation object by its PubMed identifier.""" return self.get_citation_by_reference(db_id=pubmed_identifier, db=CITATION_TYPE_PUBMED) def get_citation_by_reference(self, db: str, db_id: str) -> Optional[Citation]: """Get a citation object by its database and reference.""" return self.session.query(Citation).filter(Citation.db == db, Citation.db_id == db_id).one_or_none() def get_citation_by_curie(self, curie: str) -> Optional[Citation]: """Get a citation object by its hash.""" db, db_id = curie.split(":") return self.get_citation_by_reference(db=db, db_id=db_id) def get_author_by_name(self, name: str) -> Optional[Author]: """Get an author by name, if it exists in the database.""" return self.session.query(Author).filter(Author.name == name).one_or_none() def get_evidence_by_hash(self, evidence_hash: str) -> Optional[Evidence]: """Look up an evidence by its hash.""" return self.session.query(Evidence).filter(Evidence.md5 == evidence_hash).one_or_none() def get_evidence_by_reference_text(self, db: str, db_id: str, text: str) -> Optional[Evidence]: """Look up an evidence by its citation's database/identifier and text.""" citation = self.get_citation_by_reference(db=db, db_id=db_id) if citation is not None: return self.get_evidence_by_citation_text(citation, text) def get_evidence_by_citation_text(self, citation: Citation, text: str) -> Optional[Evidence]: """Look up an evidence by its citation and text.""" f = and_(Evidence.citation == citation, Evidence.text == text) return self.session.query(Evidence).filter(f).one_or_none() pybel-0.15.5/src/pybel/manager/make_json_serializable.py000066400000000000000000000011561426625374700233220ustar00rootroot00000000000000# -*- coding: utf-8 -*- """A module for monkey-patching the JSON encoder. When it's imported. JSONEncoder.default() automatically checks for a special "to_json()" method and uses it to encode the object if found. Provided by user martineau at: http://stackoverflow.com/questions/18478287/making-object-json-serializable-with-regular-encoder/18561055#18561055 """ from json import JSONEncoder __all__ = [] def _default(self, obj): return getattr(obj.__class__, "to_json", _default.default)(obj) _default.default = JSONEncoder().default # Save unmodified default. JSONEncoder.default = _default # replacement pybel-0.15.5/src/pybel/manager/models.py000066400000000000000000000555451426625374700201240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains the SQLAlchemy database models that support the definition cache and graph cache.""" import datetime from collections import defaultdict from typing import Any, Iterable, Mapping, Optional, Tuple from sqlalchemy import ( JSON, Boolean, Column, Date, DateTime, ForeignKey, Integer, LargeBinary, String, Table, Text, UniqueConstraint, ) from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import backref, relationship from .. import constants as pc from ..constants import ( CITATION, CITATION_AUTHORS, CITATION_DATE, CITATION_FIRST_AUTHOR, CITATION_JOURNAL, CITATION_LAST_AUTHOR, CITATION_PAGES, CITATION_VOLUME, EVIDENCE, IDENTIFIER, METADATA_AUTHORS, METADATA_CONTACT, METADATA_COPYRIGHT, METADATA_DESCRIPTION, METADATA_DISCLAIMER, METADATA_LICENSES, METADATA_NAME, METADATA_VERSION, NAME, NAMESPACE, ) from ..io.gpickle import from_bytes_gz, to_bytes_gz from ..language import CitationDict, Entity from ..struct.graph import BELGraph from ..tokens import parse_result_to_dsl __all__ = [ "Base", "Namespace", "NamespaceEntry", "Network", "Node", "Author", "Citation", "Evidence", "Edge", "edge_annotation", "network_edge", "network_node", ] NAME_TABLE_NAME = "pybel_name" NAMESPACE_TABLE_NAME = "pybel_namespace" NODE_TABLE_NAME = "pybel_node" EDGE_TABLE_NAME = "pybel_edge" EDGE_ANNOTATION_TABLE_NAME = "pybel_edge_name" AUTHOR_TABLE_NAME = "pybel_author" CITATION_TABLE_NAME = "pybel_citation" AUTHOR_CITATION_TABLE_NAME = "pybel_author_citation" EVIDENCE_TABLE_NAME = "pybel_evidence" NETWORK_TABLE_NAME = "pybel_network" NETWORK_NODE_TABLE_NAME = "pybel_network_node" NETWORK_EDGE_TABLE_NAME = "pybel_network_edge" NETWORK_NAMESPACE_TABLE_NAME = "pybel_network_namespace" NETWORK_ANNOTATION_TABLE_NAME = "pybel_network_annotation" LONGBLOB = 4294967295 Base = declarative_base() class Namespace(Base): """Represents a BEL Namespace.""" __tablename__ = NAMESPACE_TABLE_NAME id = Column(Integer, primary_key=True) uploaded = Column( DateTime, nullable=False, default=datetime.datetime.utcnow, doc="The date of upload", ) # logically the "namespace" keyword = Column( String(255), nullable=True, index=True, doc="Keyword that is used in a BEL file to identify a specific namespace", ) # A namespace either needs a URL or a pattern pattern = Column( String(255), nullable=True, index=True, doc="Contains regex pattern for value identification.", ) miriam_id = Column( String(16), nullable=True, doc=r"MIRIAM resource identifier matching the regular expression ``^MIR:001\d{5}$``", ) miriam_name = Column(String(255), nullable=True) miriam_namespace = Column(String(255), nullable=True) miriam_uri = Column(String(255), nullable=True) miriam_description = Column(Text, nullable=True) version = Column(String(255), nullable=True, doc="Version of the namespace") url = Column( String(255), nullable=True, unique=True, index=True, doc="BELNS Resource location as URL", ) name = Column(String(255), nullable=True, doc="Name of the given namespace") domain = Column(String(255), nullable=True, doc="Domain for which this namespace is valid") species = Column( String(255), nullable=True, doc="Taxonomy identifiers for which this namespace is valid", ) description = Column(Text, nullable=True, doc="Optional short description of the namespace") created = Column( DateTime, nullable=True, doc="DateTime of the creation of the namespace definition file", ) query_url = Column( Text, nullable=True, doc="URL that can be used to query the namespace (externally from PyBEL)", ) author = Column(String(255), nullable=True, doc="The author of the namespace") license = Column(String(255), nullable=True, doc="License information") contact = Column(String(255), nullable=True, doc="Contact information") citation = Column(String(255), nullable=True) citation_description = Column(Text, nullable=True) citation_version = Column(String(255), nullable=True) citation_published = Column(Date, nullable=True) citation_url = Column(String(255), nullable=True) is_annotation = Column(Boolean) def __str__(self): return f"[id={self.id}] {self.keyword}" def get_term_to_encodings(self) -> Mapping[Tuple[Optional[str], str], str]: """Return the term (db, id, name) to encodings from this namespace.""" return {(entry.identifier, entry.name): entry.encoding for entry in self.entries} def to_json(self, include_id: bool = False) -> Mapping[str, str]: """Return the most useful entries as a dictionary. :param include_id: If true, includes the model identifier """ result = { "keyword": self.keyword, "name": self.name, "version": self.version, } if self.url: result["url"] = self.url else: result["pattern"] = self.pattern if include_id: result["id"] = self.id return result class NamespaceEntry(Base): """Represents a name within a BEL namespace.""" __tablename__ = NAME_TABLE_NAME id = Column(Integer, primary_key=True) name = Column( String(1023), index=True, nullable=True, doc="Name that is defined in the corresponding namespace definition file", ) identifier = Column(String(255), index=True, nullable=True, doc="The database accession number") encoding = Column( String(8), nullable=True, doc="The biological entity types for which this name is valid", ) namespace_id = Column( Integer, ForeignKey("{}.id".format(NAMESPACE_TABLE_NAME)), nullable=False, index=True, ) namespace = relationship(Namespace, backref=backref("entries", lazy="dynamic")) is_name = Column(Boolean) is_annotation = Column(Boolean) def to_json(self, include_id: bool = False) -> Mapping[str, str]: """Describe the namespaceEntry as dictionary of Namespace-Keyword and Name. :param include_id: If true, includes the model identifier """ result = { NAMESPACE: self.namespace.keyword, } if self.name: result[NAME] = self.name if self.identifier: result[IDENTIFIER] = self.identifier if include_id: result["id"] = self.id return result @classmethod def name_contains(cls, name_query: str): """Make a filter if the name contains a certain substring.""" return cls.name.contains(name_query) def __str__(self): return "[id={namespace_id}] {namespace_name}:{identifier} ! {name}".format( namespace_id=self.namespace.id, namespace_name=self.namespace.keyword, identifier=self.identifier, name=self.name, ) network_edge = Table( NETWORK_EDGE_TABLE_NAME, Base.metadata, Column( "network_id", Integer, ForeignKey("{}.id".format(NETWORK_TABLE_NAME)), primary_key=True, ), Column( "edge_id", Integer, ForeignKey("{}.id".format(EDGE_TABLE_NAME)), primary_key=True, ), ) network_node = Table( NETWORK_NODE_TABLE_NAME, Base.metadata, Column( "network_id", Integer, ForeignKey("{}.id".format(NETWORK_TABLE_NAME)), primary_key=True, ), Column( "node_id", Integer, ForeignKey("{}.id".format(NODE_TABLE_NAME)), primary_key=True, ), ) class Network(Base): """Represents a collection of edges, specified by a BEL Script.""" __tablename__ = NETWORK_TABLE_NAME id = Column(Integer, primary_key=True) name = Column( String(255), nullable=False, index=True, doc="Name of the given Network (from the BEL file)", ) version = Column( String(255), nullable=False, doc="Release version of the given Network (from the BEL file)", ) authors = Column(Text, nullable=True, doc="Authors of the underlying BEL file") contact = Column(String(255), nullable=True, doc="Contact email from the underlying BEL file") description = Column(Text, nullable=True, doc="Descriptive text from the underlying BEL file") copyright = Column(Text, nullable=True, doc="Copyright information") disclaimer = Column(Text, nullable=True, doc="Disclaimer information") licenses = Column(Text, nullable=True, doc="License information") created = Column(DateTime, nullable=False, default=datetime.datetime.utcnow) blob = Column(LargeBinary(LONGBLOB), doc="A pickled version of this network") nodes = relationship( "Node", secondary=network_node, lazy="dynamic", backref=backref("networks", lazy="dynamic"), ) edges = relationship( "Edge", secondary=network_edge, lazy="dynamic", backref=backref("networks", lazy="dynamic"), ) def to_json(self, include_id: bool = False) -> Mapping[str, Any]: """Return this network as JSON. :param include_id: If true, includes the model identifier """ result = { METADATA_NAME: self.name, METADATA_VERSION: self.version, } if self.created: result["created"] = str(self.created) if include_id: result["id"] = self.id if self.authors: result[METADATA_AUTHORS] = self.authors if self.contact: result[METADATA_CONTACT] = self.contact if self.description: result[METADATA_DESCRIPTION] = self.description if self.copyright: result[METADATA_COPYRIGHT] = self.copyright if self.disclaimer: result[METADATA_DISCLAIMER] = self.disclaimer if self.licenses: result[METADATA_LICENSES] = self.licenses return result @classmethod def name_contains(cls, name_query: str): """Build a filter for networks whose names contain the query.""" return cls.name.contains(name_query) @classmethod def description_contains(cls, description_query: str): """Build a filter for networks whose descriptions contain the query.""" return cls.description.contains(description_query) @classmethod def id_in(cls, network_ids: Iterable[int]): """Build a filter for networks whose identifiers appear in the given sequence.""" return cls.id.in_(network_ids) def __repr__(self): return "{} v{}".format(self.name, self.version) def __str__(self): return repr(self) def as_bel(self) -> BELGraph: """Get this network and loads it into a :class:`BELGraph`.""" return from_bytes_gz(self.blob) def store_bel(self, graph: BELGraph): """Insert a BEL graph.""" self.blob = to_bytes_gz(graph) class Node(Base): """Represents a BEL Term.""" __tablename__ = NODE_TABLE_NAME id = Column(Integer, primary_key=True) type = Column( String(32), nullable=False, doc="The type of the represented biological entity e.g. Protein or Gene", ) bel = Column( String(1023), nullable=False, doc="Canonical BEL term that represents the given node", ) md5 = Column(String(255), nullable=False, unique=True, index=True) namespace_entry_id = Column(Integer, ForeignKey("{}.id".format(NAME_TABLE_NAME)), nullable=True) namespace_entry = relationship( NamespaceEntry, foreign_keys=[namespace_entry_id], backref=backref("nodes", lazy="dynamic"), ) data = Column(JSON, nullable=False, doc="PyBEL BaseEntity as JSON") @staticmethod def _start_from_base_entity(base_entity) -> "Node": """Convert a base entity to a node model. :type base_entity: pybel.dsl.BaseEntity """ return Node( type=base_entity.function, bel=base_entity.as_bel(), md5=base_entity.md5, data=base_entity, ) @classmethod def bel_contains(cls, bel_query: str): """Build a filter for nodes whose BEL contain the query.""" return cls.bel.contains(bel_query) def __str__(self): return self.bel def __repr__(self): return "".format(self.md5[:10], self.bel) def _get_list_by_relation(self, relation): return [edge.target.to_json() for edge in self.out_edges.filter(Edge.relation == relation)] def as_bel(self): """Serialize this node as a PyBEL DSL object. :rtype: pybel.dsl.BaseEntity """ return parse_result_to_dsl(self.data) def to_json(self): """Serialize this node as a JSON object using as_bel().""" return self.as_bel() author_citation = Table( AUTHOR_CITATION_TABLE_NAME, Base.metadata, Column( "author_id", Integer, ForeignKey("{}.id".format(AUTHOR_TABLE_NAME)), primary_key=True, ), Column( "citation_id", Integer, ForeignKey("{}.id".format(CITATION_TABLE_NAME)), primary_key=True, ), ) class Author(Base): """Contains all author names.""" __tablename__ = AUTHOR_TABLE_NAME id = Column(Integer, primary_key=True) name = Column(String(255), nullable=False, unique=True, index=True) @classmethod def name_contains(cls, name_query: str): """Build a filter for authors whose names contain the given query.""" return cls.name.contains(name_query) @classmethod def has_name_in(cls, names: Iterable[str]): """Build a filter if the author has any of the given names.""" return cls.name.in_(names) def __str__(self): return self.name def __repr__(self): return f'Author(name="{self.name}")' class Citation(Base): """The information about the citations that are used to prove a specific relation are stored in this table.""" __tablename__ = CITATION_TABLE_NAME id = Column(Integer, primary_key=True) db = Column(String(16), nullable=False, doc="Type of the stored publication e.g. PubMed") db_id = Column( String(255), nullable=False, doc="Reference identifier of the publication e.g. PubMed_ID", ) article_type = Column(Text, nullable=True, doc="Type of the publication") title = Column(Text, nullable=True, doc="Title of the publication") journal = Column(Text, nullable=True, doc="Journal name") volume = Column(Text, nullable=True, doc="Volume of the journal") issue = Column(Text, nullable=True, doc="Issue within the volume") pages = Column(Text, nullable=True, doc="Pages of the publication") date = Column(Date, nullable=True, doc="Publication date") first_id = Column( Integer, ForeignKey("{}.id".format(AUTHOR_TABLE_NAME)), nullable=True, doc="First author", ) first = relationship(Author, foreign_keys=[first_id]) last_id = Column( Integer, ForeignKey("{}.id".format(AUTHOR_TABLE_NAME)), nullable=True, doc="Last author", ) last = relationship(Author, foreign_keys=[last_id]) authors = relationship(Author, secondary=author_citation, backref="citations") __table_args__ = (UniqueConstraint(db, db_id),) def __str__(self): return "{}:{}".format(self.db, self.db_id) @property def is_pubmed(self) -> bool: """Return if this is a PubMed citation.""" return self.db == "pubmed" @property def is_enriched(self) -> bool: """Return if this citation has been enriched for name, title, and other metadata.""" return all(f is not None for f in (self.title, self.journal)) def to_json(self, include_id: bool = False) -> Mapping[str, Any]: """Create a citation dictionary that is used to recreate the edge data dictionary of a :class:`BELGraph`. :param bool include_id: If true, includes the model identifier :return: Citation dictionary for the recreation of a :class:`BELGraph`. """ result = CitationDict( namespace=self.db, identifier=self.db_id, name=self.title, ) if include_id: result["id"] = self.id if self.title: result[NAME] = self.title if self.journal: result[CITATION_JOURNAL] = self.journal if self.volume: result[CITATION_VOLUME] = self.volume if self.pages: result[CITATION_PAGES] = self.pages if self.date: result[CITATION_DATE] = self.date.strftime("%Y-%m-%d") if self.first: result[CITATION_FIRST_AUTHOR] = self.first.name if self.last: result[CITATION_LAST_AUTHOR] = self.last.name if self.article_type: result[pc.CITATION_ARTICLE_TYPE] = self.article_type if self.authors: result[CITATION_AUTHORS] = sorted(author.name for author in self.authors) return result class Evidence(Base): """This table contains the evidence text that proves a specific relationship and refers the source that is cited.""" __tablename__ = EVIDENCE_TABLE_NAME id = Column(Integer, primary_key=True) text = Column(Text, nullable=False, doc="Supporting text from a given publication") citation_id = Column(Integer, ForeignKey("{}.id".format(CITATION_TABLE_NAME)), nullable=False) citation = relationship(Citation, backref=backref("evidences")) __table_args__ = (UniqueConstraint(citation_id, text),) def __str__(self): return "{}:{}:{}".format(self.citation.db, self.citation.db_id, self.text) def to_json(self, include_id: bool = False): """Create a dictionary that is used to recreate the edge data dictionary for a :class:`BELGraph`. :param include_id: If true, includes the model identifier :return: Dictionary containing citation and evidence for a :class:`BELGraph` edge. :rtype: dict """ result = { CITATION: self.citation.to_json(include_id=include_id), EVIDENCE: self.text, } if include_id: result["id"] = self.id return result edge_annotation = Table( EDGE_ANNOTATION_TABLE_NAME, Base.metadata, Column( "edge_id", Integer, ForeignKey("{}.id".format(EDGE_TABLE_NAME)), primary_key=True, ), Column( "name_id", Integer, ForeignKey("{}.id".format(NAME_TABLE_NAME)), primary_key=True, ), ) class Edge(Base): """Relationships between BEL nodes and their properties, annotations, and provenance.""" __tablename__ = EDGE_TABLE_NAME id = Column(Integer, primary_key=True) bel = Column(Text, nullable=False, doc="Valid BEL statement that represents the given edge") relation = Column(String(32), nullable=False) source_id = Column(Integer, ForeignKey("{}.id".format(NODE_TABLE_NAME)), nullable=False) source = relationship( Node, foreign_keys=[source_id], backref=backref("out_edges", lazy="dynamic", cascade="all, delete-orphan"), ) target_id = Column(Integer, ForeignKey("{}.id".format(NODE_TABLE_NAME)), nullable=False) target = relationship( Node, foreign_keys=[target_id], backref=backref("in_edges", lazy="dynamic", cascade="all, delete-orphan"), ) evidence_id = Column(Integer, ForeignKey("{}.id".format(EVIDENCE_TABLE_NAME)), nullable=True) evidence = relationship(Evidence, backref=backref("edges", lazy="dynamic")) annotations = relationship( NamespaceEntry, secondary=edge_annotation, lazy="dynamic", backref=backref("edges", lazy="dynamic"), ) # free_annotations = Column(JSON, nullable=True, doc='Ungrounded extra annotations') source_modifier = Column(JSON, nullable=True, doc="Modifiers for the source of the edge") target_modifier = Column(JSON, nullable=True, doc="Modifiers for the target of the edge") md5 = Column( String(255), index=True, unique=True, doc="The hash of the source, target, and associated metadata", ) data = Column(JSON, nullable=False, doc="The stringified JSON representing this edge") def __str__(self): return self.bel def __repr__(self): return "".format(self.md5, self.bel) def get_annotations_json(self): """Format the annotations properly. :rtype: Optional[dict[str,dict[str,bool]] """ annotations = defaultdict(dict) for entry in self.annotations: annotations[entry.namespace.keyword][entry.name] = True return dict(annotations) or None def to_json(self, include_id: bool = False) -> Mapping[str, Any]: """Create a dictionary of one BEL Edge that can be used to create an edge in a :class:`BELGraph`. :param bool include_id: Include the database identifier? :return: Dictionary that contains information about an edge of a :class:`BELGraph`. Including participants and edge data information. """ source_dict = self.source.to_json() source_dict["md5"] = source_dict.md5 target_dict = self.target.to_json() target_dict["md5"] = target_dict.md5 result = { "source": source_dict, "target": target_dict, "key": self.md5, "data": self.data, } if include_id: result["id"] = self.id return result def insert_into_graph(self, graph: BELGraph) -> str: """Insert this edge into a BEL graph.""" u = self.source.as_bel() v = self.target.as_bel() if self.evidence: return graph.add_qualified_edge(u, v, **self.data) else: return graph.add_unqualified_edge(u, v, self.relation) pybel-0.15.5/src/pybel/manager/query_manager.py000066400000000000000000000236341426625374700214720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """The query manager for the database.""" import datetime from typing import Iterable, List, Optional, Union from sqlalchemy import and_, or_ from sqlalchemy.orm import aliased from .lookup_manager import LookupManager from .models import Author, Citation, Edge, Evidence, Namespace, NamespaceEntry, Node from ..constants import CITATION_TYPE_PUBMED from ..struct import BELGraph from ..utils import parse_datetime __all__ = [ "QueryManager", "graph_from_edges", ] def graph_from_edges(edges: Iterable[Edge], **kwargs) -> BELGraph: """Build a BEL graph from edges.""" graph = BELGraph(**kwargs) graph.raise_on_missing_annotations = False for edge in edges: edge.insert_into_graph(graph) graph.raise_on_missing_annotations = True return graph class QueryManager(LookupManager): """An extension to the Manager to make queries over the database.""" def count_nodes(self) -> int: """Count the number of nodes in the database.""" return self._count_model(Node) def query_nodes( self, bel: Optional[str] = None, type: Optional[str] = None, namespace: Optional[str] = None, name: Optional[str] = None, ) -> List[Node]: """Query nodes in the database. :param bel: BEL term that describes the biological entity. e.g. ``p(HGNC:APP)`` :param type: Type of the biological entity. e.g. Protein :param namespace: Namespace keyword that is used in BEL. e.g. HGNC :param name: Name of the biological entity. e.g. APP """ q = self.session.query(Node) if bel: q = q.filter(Node.bel.ilike(f"%{bel}%")) if type: q = q.filter(Node.type == type) if namespace or name: q = q.join(NamespaceEntry) if namespace: q = q.join(Namespace).filter(Namespace.keyword.ilike(namespace)) if name: q = q.filter(NamespaceEntry.name.ilike(f"%{name}%")) return q def count_edges(self) -> int: """Count the number of edges in the database.""" return self._count_model(Edge) def get_edges_with_citation(self, citation: Citation) -> List[Edge]: """Get the edges with the given citation.""" return self.session.query(Edge).join(Evidence).filter(Evidence.citation == citation) def get_edges_with_citations(self, citations: Iterable[Citation]) -> List[Edge]: """Get edges with one of the given citations.""" return self.session.query(Edge).join(Evidence).filter(Evidence.citation.in_(citations)).all() def search_edges_with_evidence(self, evidence: str) -> List[Edge]: """Search edges with the given evidence. :param evidence: A string to search evidences. Can use wildcard percent symbol (%). """ return self.session.query(Edge).join(Evidence).filter(Evidence.text.like(evidence)).all() def search_edges_with_bel(self, bel: str) -> List[Edge]: """Search edges with given BEL. :param bel: A BEL string to use as a search """ return self.session.query(Edge).filter(Edge.bel.like(bel)) def get_edges_with_annotation(self, annotation: str, value: str) -> List[Edge]: """Search edges with the given annotation/value pair.""" query = self.session.query(Edge).join(NamespaceEntry, Edge.annotations).join(Namespace) query = query.filter(Namespace.keyword == annotation).filter(NamespaceEntry.name == value) return query.all() @staticmethod def _add_edge_function_filter(query, edge_node_id, node_type): """See usage in self.query_edges.""" return query.join(Node, edge_node_id == Node.id).filter(Node.type == node_type) def query_edges( self, bel: Optional[str] = None, source_function: Optional[str] = None, source: Union[None, str, Node] = None, target_function: Optional[str] = None, target: Union[None, str, Node] = None, relation: Optional[str] = None, ): """Return a query over the edges in the database. Usually this means that you should call ``list()`` or ``.all()`` on this result. :param bel: BEL statement that represents the desired edge. :param source_function: Filter source nodes with the given BEL function :param source: BEL term of source node e.g. ``p(HGNC:APP)`` or :class:`Node` object. :param target_function: Filter target nodes with the given BEL function :param target: BEL term of target node e.g. ``p(HGNC:APP)`` or :class:`Node` object. :param relation: The relation that should be present between source and target node. """ if bel: return self.search_edges_with_bel(bel) query = self.session.query(Edge) if relation: query = query.filter(Edge.relation.like(relation)) if source_function: source_node_table = aliased(Node) query = query.join(source_node_table, Edge.source_id == source_node_table.id).filter( source_node_table.type == source_function ) if target_function: target_node_table = aliased(Node) query = query.join(target_node_table, Edge.target_id == target_node_table.id).filter( target_node_table.type == target_function ) if source: if isinstance(source, str): source = self.query_nodes(bel=source) if source.count() == 0: return [] source = source.first() # FIXME what if this matches multiple? query = query.filter(Edge.source == source) elif isinstance(source, Node): query = query.filter(Edge.source == source) else: raise TypeError("Invalid type of {}: {}".format(source, source.__class__.__name__)) if target: if isinstance(target, str): targets = self.query_nodes(bel=target).all() target = targets[0] # FIXME what if this matches multiple? query = query.filter(Edge.target == target) elif isinstance(target, Node): query = query.filter(Edge.target == target) else: raise TypeError("Invalid type of {}: {}".format(target, target.__class__.__name__)) return query def query_citations( self, db: Optional[str] = None, db_id: Optional[str] = None, name: Optional[str] = None, author: Union[None, str, List[str]] = None, date: Union[None, str, datetime.date] = None, evidence_text: Optional[str] = None, ) -> List[Citation]: """Query citations in the database. :param db: Type of the citation. e.g. PubMed :param db_id: The identifier used for the citation. e.g. PubMed_ID :param name: Title of the citation. :param author: The name or a list of names of authors participated in the citation. :param date: Publishing date of the citation. :param evidence_text: """ query = self.session.query(Citation) if author is not None: query = query.join(Author, Citation.authors) if isinstance(author, str): query = query.filter(Author.name.like(author)) elif isinstance(author, Iterable): query = query.filter(Author.has_name_in(set(author))) else: raise TypeError if db and not db_id: query = query.filter(Citation.db.like(db)) elif db_id and db: query = query.filter(Citation.db_id == db_id) elif db_id and not db: raise ValueError("reference specified without type") if name: query = query.filter(Citation.name.like(name)) if date: if isinstance(date, datetime.date): query = query.filter(Citation.date == date) elif isinstance(date, str): query = query.filter(Citation.date == parse_datetime(date)) if evidence_text: query = query.join(Evidence).filter(Evidence.text.like(evidence_text)) return query.all() def query_edges_by_pubmed_identifiers(self, pubmed_identifiers: List[str]) -> List[Edge]: """Get all edges annotated to the documents identified by the given PubMed identifiers.""" fi = and_(Citation.db == CITATION_TYPE_PUBMED, Citation.db_id.in_(pubmed_identifiers)) return self.session.query(Edge).join(Evidence).join(Citation).filter(fi).all() @staticmethod def _edge_both_nodes(nodes: List[Node]): """Get edges where both the source and target are in the list of nodes.""" node_ids = [node.id for node in nodes] return and_( Edge.source_id.in_(node_ids), Edge.target_id.in_(node_ids), ) def query_induction(self, nodes: List[Node]) -> List[Edge]: """Get all edges between any of the given nodes (minimum length of 2).""" if len(nodes) < 2: raise ValueError("not enough nodes given to induce over") return self.session.query(Edge).filter(self._edge_both_nodes(nodes)).all() @staticmethod def _edge_one_node(nodes: List[Node]): """Get edges where either the source or target are in the list of nodes. Note: doing this with the nodes directly is not yet supported by SQLAlchemy .. code-block:: python return or_( Edge.source.in_(nodes), Edge.target.in_(nodes), ) """ node_ids = [node.id for node in nodes] return or_( Edge.source_id.in_(node_ids), Edge.target_id.in_(node_ids), ) def query_neighbors(self, nodes: List[Node]) -> List[Edge]: """Get all edges incident to any of the given nodes.""" return self.session.query(Edge).filter(self._edge_one_node(nodes)).all() pybel-0.15.5/src/pybel/manager/utils.py000066400000000000000000000046741426625374700177760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for the PyBEL database manager.""" from typing import Dict, Mapping, Optional, Tuple, Union from ..utils import parse_datetime def extract_shared_required(config, definition_header: str = "Namespace"): """Get the required annotations shared by BEL namespace and annotation resource documents. :param dict config: The configuration dictionary representing a BEL resource :param definition_header: ``Namespace`` or ``AnnotationDefinition`` :rtype: dict """ return { "keyword": config[definition_header]["Keyword"], "created": parse_datetime(config[definition_header]["CreatedDateTime"]), } def extract_shared_optional(bel_resource, definition_header: str = "Namespace"): """Get the optional annotations shared by BEL namespace and annotation resource documents. :param dict bel_resource: A configuration dictionary representing a BEL resource :param definition_header: ``Namespace`` or ``AnnotationDefinition`` :rtype: dict """ shared_mapping = { "description": (definition_header, "DescriptionString"), "version": (definition_header, "VersionString"), "author": ("Author", "NameString"), "license": ("Author", "CopyrightString"), "contact": ("Author", "ContactInfoString"), "citation": ("Citation", "NameString"), "citation_description": ("Citation", "DescriptionString"), "citation_version": ("Citation", "PublishedVersionString"), "citation_url": ("Citation", "ReferenceURL"), } result = {} update_insert_values(bel_resource, shared_mapping, result) if "PublishedDate" in bel_resource.get("Citation", {}): result["citation_published"] = parse_datetime(bel_resource["Citation"]["PublishedDate"]) return result def update_insert_values( bel_resource: Mapping, mapping: Mapping[str, Tuple[str, str]], values: Dict[str, str], ) -> None: """Update the value dictionary with a BEL resource dictionary.""" for database_column, (section, key) in mapping.items(): if section in bel_resource and key in bel_resource[section]: values[database_column] = bel_resource[section][key] def int_or_str(v: Optional[str]) -> Union[None, int, str]: """Safe converts an string represent an integer to an integer or passes through ``None``.""" if v is None: return try: return int(v) except ValueError: return v pybel-0.15.5/src/pybel/parser/000077500000000000000000000000001426625374700161335ustar00rootroot00000000000000pybel-0.15.5/src/pybel/parser/__init__.py000066400000000000000000000004671426625374700202530ustar00rootroot00000000000000# -*- coding: utf-8 -*- """The :mod:`pybel.parser` module contains utilities for parsing BEL documents and BEL statements.""" from .modifiers import * from .parse_bel import BELParser from .parse_concept import ConceptParser from .parse_control import ControlParser from .parse_metadata import MetadataParser pybel-0.15.5/src/pybel/parser/baseparser.py000066400000000000000000000037031426625374700206370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """The base parser class shared by several BEL parsers.""" import logging import time from typing import Iterable, List from pyparsing import ParserElement, ParseResults __all__ = ["BaseParser"] logger = logging.getLogger(__name__) class BaseParser(object): """This abstract class represents a language backed by a PyParsing statement. Multiple parsers can be easily chained together when they are all inheriting from this base class. """ def __init__(self, language: ParserElement, streamline: bool = False) -> None: """Build a parser wrapper using a PyParsing language. :param language: The PyParsing language to use :param streamline: Should the language be streamlined on instantiation? """ self.language = language #: The parser holds an internal state of the current line self._line_number = 0 if streamline: self.streamline() def parse_lines(self, lines: Iterable[str]) -> List[ParseResults]: """Parse multiple lines in succession.""" return [self.parseString(line, line_number) for line_number, line in enumerate(lines)] def parseString(self, line: str, line_number: int = 0) -> ParseResults: # noqa: N802 """Parse a string with the language represented by this parser. :param line: A string representing an instance of this parser's language :param line_number: The current line number of the parser """ self._line_number = line_number return self.language.parseString(line) def get_line_number(self) -> int: """Get the current line number.""" return self._line_number def streamline(self) -> None: """Streamline the language represented by this parser to make queries run faster.""" t = time.time() self.language.streamline() logger.info("streamlined %s in %.02f seconds", self.__class__.__name__, time.time() - t) pybel-0.15.5/src/pybel/parser/constants.py000066400000000000000000000005151426625374700205220ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Type hints for the parsers.""" from typing import Mapping, Optional, Tuple __all__ = [ "Term", "TermEncodingMapping", "NamespaceTermEncodingMapping", ] Term = Tuple[Optional[str], str] TermEncodingMapping = Mapping[Term, str] NamespaceTermEncodingMapping = Mapping[str, Mapping[Term, str]] pybel-0.15.5/src/pybel/parser/modifiers/000077500000000000000000000000001426625374700201145ustar00rootroot00000000000000pybel-0.15.5/src/pybel/parser/modifiers/__init__.py000066400000000000000000000011001426625374700222150ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Parsers for modifications to abundances.""" from .fragment import get_fragment_language from .fusion import get_fusion_language, get_legacy_fusion_langauge from .gene_modification import get_gene_modification_language from .gene_substitution import get_gene_substitution_language from .location import get_location_language from .protein_modification import get_protein_modification_language from .protein_substitution import get_protein_substitution_language from .truncation import get_truncation_language from .variant import get_hgvs_language pybel-0.15.5/src/pybel/parser/modifiers/constants.py000066400000000000000000000016431426625374700225060ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Constants for modifier parsers.""" from pyparsing import Keyword, MatchFirst, oneOf from ... import language from ...exceptions import PlaceholderAminoAcidWarning aa_single = oneOf(list(language.amino_acid_dict.keys())) aa_single.setParseAction(lambda s, l, t: [language.amino_acid_dict[t[0]]]) aa_triple = oneOf(list(language.amino_acid_dict.values())) #: In biological literature, the X is used to denote a truncation. Text mining efforts often encode X as an amino #: acid, for which we will throw an error using :func:`handle_aa_placeholder` aa_placeholder = Keyword("X") def handle_aa_placeholder(line, position, tokens): """Raise an exception when encountering a placeholder amino acid, ``X``.""" raise PlaceholderAminoAcidWarning(-1, line, position, tokens[0]) aa_placeholder.setParseAction(handle_aa_placeholder) amino_acid = MatchFirst([aa_triple, aa_single, aa_placeholder]) pybel-0.15.5/src/pybel/parser/modifiers/fragment.py000066400000000000000000000055331426625374700222770ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Fragments. The addition of a fragment results in an entry called :data:`pybel.constants.VARIANTS` in the data dictionary associated with a given node. This entry is a list with dictionaries describing each of the variants. All variants have the entry :data:`pybel.constants.KIND` to identify whether it is a PTM, gene modification, fragment, or HGVS variant. The :data:`pybel.constants.KIND` value for a fragment is :data:`pybel.constants.FRAGMENT`. Each fragment contains an identifier, which is a dictionary with the namespace and name, and can optionally include the position ('pos') and/or amino acid code ('code'). For example, the node :code:`p(HGNC:GSK3B, frag(45_129))` is represented with the following: .. code-block:: python from pybel.constants import * { FUNCTION: PROTEIN, NAMESPACE: 'HGNC', NAME: 'GSK3B', VARIANTS: [ { KIND: FRAGMENT, FRAGMENT_START: 45, FRAGMENT_STOP: 129, }, ], } Additionally, nodes can have an asterick (*) or question mark (?) representing unbound or unknown fragments, respectively. A fragment may also be unknown, such as in the node :code:`p(HGNC:GSK3B, frag(?))`. This is represented with the key :data:`pybel.constants.FRAGMENT_MISSING` and the value of '?' like: .. code-block:: python from pybel.constants import * { FUNCTION: PROTEIN, NAMESPACE: 'HGNC', NAME: 'GSK3B', VARIANTS: [ { KIND: FRAGMENT, FRAGMENT_MISSING: '?', }, ], } .. seealso:: - BEL 2.0 specification on `proteolytic fragments (2.2.3) `_ - PyBEL module :py:class:`pybel.parser.modifiers.get_fragment_language` """ from pyparsing import And, Keyword, Optional, ParserElement, Suppress from pyparsing import pyparsing_common as ppc from ..utils import WCW, nest, one_of_tags, quote from ...constants import ( FRAGMENT, FRAGMENT_DESCRIPTION, FRAGMENT_MISSING, FRAGMENT_START, FRAGMENT_STOP, KIND, ) __all__ = [ "get_fragment_language", ] fragment_tag = one_of_tags(tags=["frag", "fragment"], canonical_tag=FRAGMENT, name=KIND) fragment_range = (ppc.integer | "?")(FRAGMENT_START) + "_" + (ppc.integer | "?" | "*")(FRAGMENT_STOP) missing_fragment = Keyword("?")(FRAGMENT_MISSING) def get_fragment_language() -> ParserElement: """Build a protein fragment parser.""" _fragment_value_inner = fragment_range | missing_fragment(FRAGMENT_MISSING) _fragment_value = _fragment_value_inner | And([Suppress('"'), _fragment_value_inner, Suppress('"')]) parser_element = fragment_tag + nest(_fragment_value + Optional(WCW + quote(FRAGMENT_DESCRIPTION))) return parser_element pybel-0.15.5/src/pybel/parser/modifiers/fusion.py000066400000000000000000000077311426625374700220010ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Fusions. Gene, RNA, miRNA, and protein fusions are all represented with the same underlying data structure. Below it is shown with uppercase letters referring to constants from :code:`pybel.constants` and. For example, :code:`g(HGNC:BCR, fus(HGNC:JAK2, 1875, 2626))` is represented as: .. code-block:: python from pybel.constants import * { FUNCTION: GENE, FUSION: { PARTNER_5P: {NAMESPACE: 'HGNC', NAME: 'BCR'}, PARTNER_3P: {NAMESPACE: 'HGNC', NAME: 'JAK2'}, RANGE_5P: { FUSION_REFERENCE: 'c', FUSION_START: '?', FUSION_STOP: 1875, }, RANGE_3P: { FUSION_REFERENCE: 'c', FUSION_START: 2626, FUSION_STOP: '?', }, }, } .. seealso:: - BEL 2.0 specification on `fusions (2.6.1) `_ - PyBEL module :py:class:`pybel.parser.modifiers.get_fusion_language` - PyBEL module :py:class:`pybel.parser.modifiers.get_legacy_fusion_language` """ from pyparsing import Group, Keyword, Optional, ParserElement, Suppress, oneOf from pyparsing import pyparsing_common from pyparsing import pyparsing_common as ppc from pyparsing import replaceWith from ..utils import WCW, nest from ...constants import ( CONCEPT, FUSION, FUSION_MISSING, FUSION_REFERENCE, FUSION_START, FUSION_STOP, PARTNER_3P, PARTNER_5P, RANGE_3P, RANGE_5P, ) __all__ = [ "fusion_tags", "get_fusion_language", "get_legacy_fusion_langauge", ] fusion_tags = oneOf(["fus", "fusion"]).setParseAction(replaceWith(FUSION)) reference_seq = oneOf(["r", "p", "c"]) coordinate = pyparsing_common.integer | "?" missing = Keyword("?") range_coordinate_unquoted = missing(FUSION_MISSING) | ( reference_seq(FUSION_REFERENCE) + Suppress(".") + coordinate(FUSION_START) + Suppress("_") + coordinate(FUSION_STOP) ) def get_fusion_language(concept: ParserElement, permissive: bool = True) -> ParserElement: """Build a fusion parser.""" range_coordinate = Suppress('"') + range_coordinate_unquoted + Suppress('"') if permissive: # permissive to wrong quoting range_coordinate = range_coordinate | range_coordinate_unquoted return fusion_tags + nest( Group(Group(concept)(CONCEPT))(PARTNER_5P), Group(range_coordinate)(RANGE_5P), Group(Group(concept)(CONCEPT))(PARTNER_3P), Group(range_coordinate)(RANGE_3P), ) def get_legacy_fusion_langauge(concept: ParserElement, reference: str) -> ParserElement: """Build a legacy fusion parser.""" break_start = (ppc.integer | "?").setParseAction(_fusion_break_handler_wrapper(reference, start=True)) break_end = (ppc.integer | "?").setParseAction(_fusion_break_handler_wrapper(reference, start=False)) res = ( Group(concept(CONCEPT))(PARTNER_5P) + WCW + fusion_tags + nest( Group(concept(CONCEPT))(PARTNER_3P) + Optional(WCW + Group(break_start)(RANGE_5P) + WCW + Group(break_end)(RANGE_3P)), ) ) res.setParseAction(_fusion_legacy_handler) return res def _fusion_legacy_handler(_, __, tokens): """Handle a legacy fusion.""" if RANGE_5P not in tokens: tokens[RANGE_5P] = {FUSION_MISSING: "?"} if RANGE_3P not in tokens: tokens[RANGE_3P] = {FUSION_MISSING: "?"} return tokens def _fusion_break_handler_wrapper(reference: str, start: bool): def fusion_break_handler(_, __, tokens): if tokens[0] == "?": tokens[FUSION_MISSING] = "?" return tokens else: # The break point is specified as an integer tokens[FUSION_REFERENCE] = reference tokens[FUSION_START if start else FUSION_STOP] = "?" tokens[FUSION_STOP if start else FUSION_START] = int(tokens[0]) return tokens return fusion_break_handler pybel-0.15.5/src/pybel/parser/modifiers/gene_modification.py000066400000000000000000000042001426625374700241250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Gene Modifications. PyBEL introduces the gene modification tag, gmod(), to allow for the encoding of epigenetic modifications. Its syntax follows the same style s the pmod() tags for proteins, and can include the following values: - M - Me - methylation - A - Ac - acetylation For example, the node :code:`g(HGNC:GSK3B, gmod(M))` is represented with the following: .. code-block:: python from pybel.constants import * { FUNCTION: GENE, NAMESPACE: 'HGNC', NAME: 'GSK3B', VARIANTS: [ { KIND: GMOD, IDENTIFIER: { NAMESPACE: BEL_DEFAULT_NAMESPACE, NAME: 'Me', }, }, ], } The addition of this function does not preclude the use of all other standard functions in BEL; however, other compilers probably won't support these standards. If you agree that this is useful, please contribute to discussion in the OpenBEL community. .. seealso:: - PyBEL module :py:func:`pybel.parser.modifiers.get_gene_modification_language` """ from pyparsing import Group, MatchFirst, ParserElement, oneOf from ..utils import nest, one_of_tags from ...constants import CONCEPT, GMOD, IDENTIFIER, KIND, NAME, NAMESPACE from ...language import gmod_mappings, gmod_namespace __all__ = [ "get_gene_modification_language", ] def _handle_gmod_default(_, __, tokens): e = gmod_mappings[gmod_namespace[tokens[0]]]["xrefs"][0] tokens[NAMESPACE] = e.namespace tokens[IDENTIFIER] = e.identifier tokens[NAME] = e.name return tokens gmod_tag = one_of_tags(tags=["gmod", "geneModification"], canonical_tag=GMOD, name=KIND) gmod_default_ns = oneOf(list(gmod_namespace)).setParseAction(_handle_gmod_default) def get_gene_modification_language( concept_fqualified: ParserElement, concept_qualified: ParserElement, ) -> ParserElement: """Build a gene modification parser.""" concept = MatchFirst( [ concept_fqualified, concept_qualified, gmod_default_ns, ] ) return gmod_tag + nest(Group(concept)(CONCEPT)) pybel-0.15.5/src/pybel/parser/modifiers/gene_substitution.py000066400000000000000000000042521426625374700242430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Gene Substitutions. Gene substitutions are legacy statements defined in BEL 1.0. BEL 2.0 recommends using HGVS strings. Luckily, the information contained in a BEL 1.0 encoding, such as :code:`g(HGNC:APP,sub(G,275341,C))` can be automatically translated to the appropriate HGVS :code:`g(HGNC:APP, var(c.275341G>C))`, assuming that all substitutions are using the reference coding gene sequence for numbering and not the genomic reference. The previous statements both produce the underlying data: .. code-block:: python from pybel.constants import * { FUNCTION: GENE, NAMESPACE: 'HGNC', NAME: 'APP', VARIANTS: [ { KIND: HGVS, IDENTIFIER: 'c.275341G>C', }, ], } .. seealso:: - BEL 2.0 specification on `gene substitutions `_ - PyBEL module :py:class:`pybel.parser.modifiers.get_gene_substitution_language` """ import logging from pyparsing import ParserElement, oneOf from pyparsing import pyparsing_common as ppc from ..utils import nest, one_of_tags from ... import language from ...constants import GSUB_POSITION, GSUB_REFERENCE, GSUB_VARIANT, HGVS, KIND __all__ = [ "get_gene_substitution_language", ] logger = logging.getLogger(__name__) dna_nucleotide = oneOf(list(language.dna_nucleotide_labels.keys())) gsub_tag = one_of_tags(tags=["sub", "substitution"], canonical_tag=HGVS, name=KIND) def get_gene_substitution_language() -> ParserElement: """Build a gene substitution parser.""" parser_element = gsub_tag + nest( dna_nucleotide(GSUB_REFERENCE), ppc.integer(GSUB_POSITION), dna_nucleotide(GSUB_VARIANT), ) parser_element.setParseAction(_handle_gsub) return parser_element def _handle_gsub(line, _, tokens): upgraded = "c.{}{}>{}".format(tokens[GSUB_POSITION], tokens[GSUB_REFERENCE], tokens[GSUB_VARIANT]) logger.debug("legacy sub() %s upgraded to %s", line, upgraded) tokens[HGVS] = upgraded del tokens[GSUB_POSITION] del tokens[GSUB_REFERENCE] del tokens[GSUB_VARIANT] return tokens pybel-0.15.5/src/pybel/parser/modifiers/location.py000066400000000000000000000037561426625374700223110ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Locations. Location data also is added into the information in the edge for the node (subject or object) for which it was annotated. :code:`p(HGNC:GSK3B, pmod(P, S, 9), loc(GO:lysozome)) pos act(p(HGNC:GSK3B), ma(kin))` becomes: .. code-block:: python from pybel.constants import * { SUBJECT: { LOCATION: { NAMESPACE: 'GO', NAME: 'lysozome', } }, RELATION: POSITIVE_CORRELATION, OBJECT: { MODIFIER: ACTIVITY, EFFECT: { NAMESPACE: BEL_DEFAULT_NAMESPACE NAME: 'kin', } }, EVIDENCE: ..., CITATION: { ... }, } The addition of the :code:`location()` element in BEL 2.0 allows for the unambiguous expression of the differences between the process of hypothetical :code:`HGNC:A` moving from one place to another and the existence of hypothetical :code:`HGNC:A` in a specific location having different effects. In BEL 1.0, this action had its own node, but this introduced unnecessary complexity to the network and made querying more difficult. This calls for thoughtful consideration of the following two statements: - :code:`tloc(p(HGNC:A), fromLoc(GO:intracellular), toLoc(GO:"cell membrane")) -> p(HGNC:B)` - :code:`p(HGNC:A, location(GO:"cell membrane")) -> p(HGNC:B)` .. seealso:: - BEL 2.0 specification on `cellular location (2.2.4) `_ - PyBEL module :py:class:`pybel.parser.modifiers.get_location_language` """ from pyparsing import Group, ParserElement, Suppress, oneOf from ..utils import nest from ...constants import LOCATION __all__ = [ "get_location_language", ] location_tag = Suppress(oneOf(["loc", "location"])) def get_location_language(identifier: ParserElement) -> ParserElement: """Build a location parser.""" return Group(location_tag + nest(identifier))(LOCATION) pybel-0.15.5/src/pybel/parser/modifiers/protein_modification.py000066400000000000000000000100211426625374700246650ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Protein Modifications. The addition of a post-translational modification (PTM) tag results in an entry called 'variants' in the data dictionary associated with a given node. This entry is a list with dictionaries describing each of the variants. All variants have the entry 'kind' to identify whether it is a PTM, gene modification, fragment, or HGVS variant. The 'kind' value for PTM is 'pmod'. Each PMOD contains an identifier, which is a dictionary with the namespace and name, and can optionally include the position ('pos') and/or amino acid code ('code'). For example, the node :code:`p(HGNC:GSK3B, pmod(P, S, 9))` is represented with the following: .. code-block:: python from pybel.constants import * { FUNCTION: PROTEIN, NAMESPACE: 'HGNC', NAME: 'GSK3B', VARIANTS: [ { KIND: PMOD, IDENTIFIER: { NAMESPACE: BEL_DEFAULT_NAMESPACE NAME: 'Ph', }, PMOD_CODE: 'Ser', PMOD_POSITION: 9, }, ], } As an additional example, in :code:`p(HGNC:MAPK1, pmod(Ph, Thr, 202), pmod(Ph, Tyr, 204))`, MAPK is phosphorylated twice to become active. This results in the following: .. code:: { FUNCTION: PROTEIN, NAMESPACE: 'HGNC', NAME: 'MAPK1', VARIANTS: [ { KIND: PMOD, IDENTIFIER: { NAMESPACE: BEL_DEFAULT_NAMESPACE NAME: 'Ph', }, PMOD_CODE: 'Thr', PMOD_POSITION: 202 }, { KIND: PMOD, IDENTIFIER: { NAMESPACE: BEL_DEFAULT_NAMESPACE NAME: 'Ph', }, PMOD_CODE: 'Tyr', PMOD_POSITION: 204 } ] } .. seealso:: - BEL 2.0 specification on `protein modifications `_ - PyBEL module :py:class:`pybel.parser.modifiers.get_protein_modification_language` """ import logging from pyparsing import Group, MatchFirst, Optional, ParserElement, ParseResults, oneOf from pyparsing import pyparsing_common as ppc from .constants import amino_acid from ..utils import WCW, nest, one_of_tags from ...constants import ( CONCEPT, IDENTIFIER, KIND, NAME, NAMESPACE, PMOD, PMOD_CODE, PMOD_POSITION, ) from ...language import pmod_legacy_labels, pmod_mappings, pmod_namespace __all__ = [ "get_protein_modification_language", ] logger = logging.getLogger(__name__) def _handle_pmod_default_ns(_, __, tokens: ParseResults) -> ParseResults: upgraded = pmod_namespace[tokens[0]] return _r(upgraded, tokens) def _handle_pmod_legacy_ns(line, _, tokens: ParseResults) -> ParseResults: upgraded = pmod_legacy_labels[tokens[0]] logger.log(5, "legacy pmod() value %s upgraded to %s", line, upgraded) return _r(upgraded, tokens) def _r(upgraded, tokens): e = pmod_mappings[upgraded]["xrefs"][0] tokens[NAMESPACE] = e.namespace tokens[IDENTIFIER] = e.identifier tokens[NAME] = e.name return tokens pmod_tag = one_of_tags(tags=["pmod", "proteinModification"], canonical_tag=PMOD, name=KIND) pmod_default_ns = oneOf(list(pmod_namespace)).setParseAction(_handle_pmod_default_ns) pmod_legacy_ns = oneOf(list(pmod_legacy_labels)).setParseAction(_handle_pmod_legacy_ns) def get_protein_modification_language( concept_fqualified: ParserElement, concept_qualified: ParserElement, ) -> ParserElement: """Build a protein modification parser.""" pmod_concept = MatchFirst( [ concept_fqualified, concept_qualified, pmod_default_ns, pmod_legacy_ns, ] ) return pmod_tag + nest( Group(pmod_concept)(CONCEPT) + Optional( WCW + amino_acid(PMOD_CODE) + Optional(WCW + ppc.integer(PMOD_POSITION)), ), ) pybel-0.15.5/src/pybel/parser/modifiers/protein_substitution.py000066400000000000000000000042041426625374700250020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Protein Substitutions. Protein substitutions are legacy statements defined in BEL 1.0. BEL 2.0 recommends using HGVS strings. Luckily, the information contained in a BEL 1.0 encoding, such as :code:`p(HGNC:APP,sub(R,275,H))` can be automatically translated to the appropriate HGVS :code:`p(HGNC:APP, var(p.Arg275His))`, assuming that all substitutions are using the reference protein sequence for numbering and not the genomic reference. The previous statements both produce the underlying data: .. code-block:: python from pybel.constants import * { FUNCTION: GENE, NAMESPACE: 'HGNC', NAME: 'APP', VARIANTS: [ { KIND: HGVS, IDENTIFIER: 'p.Arg275His', }, ], } .. seealso:: - BEL 2.0 specification on `protein substitutions `_ - PyBEL module :py:class:`pybel.parser.modifiers.get_protein_substitution_language` """ import logging from pyparsing import ParserElement from pyparsing import pyparsing_common as ppc from .constants import amino_acid from ..utils import nest, one_of_tags from ...constants import HGVS, KIND, PSUB_POSITION, PSUB_REFERENCE, PSUB_VARIANT __all__ = [ "get_protein_substitution_language", ] logger = logging.getLogger(__name__) psub_tag = one_of_tags(tags=["sub", "substitution"], canonical_tag=HGVS, name=KIND) def get_protein_substitution_language() -> ParserElement: """Build a protein substitution parser.""" parser_element = psub_tag + nest( amino_acid(PSUB_REFERENCE), ppc.integer(PSUB_POSITION), amino_acid(PSUB_VARIANT), ) parser_element.setParseAction(_handle_psub) return parser_element def _handle_psub(line, _, tokens): upgraded = "p.{}{}{}".format(tokens[PSUB_REFERENCE], tokens[PSUB_POSITION], tokens[PSUB_VARIANT]) logger.log(5, "sub() in p() is deprecated: %s. Upgraded to %s", line, upgraded) tokens[HGVS] = upgraded del tokens[PSUB_REFERENCE] del tokens[PSUB_POSITION] del tokens[PSUB_VARIANT] return tokens pybel-0.15.5/src/pybel/parser/modifiers/truncation.py000066400000000000000000000053731426625374700226640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Truncations. Truncations in the legacy BEL 1.0 specification are automatically translated to BEL 2.0 with HGVS nomenclature. :code:`p(HGNC:AKT1, trunc(40))` becomes :code:`p(HGNC:AKT1, var(p.40*))` and is represented with the following dictionary: .. code-block:: python from pybel.constants import * { FUNCTION: PROTEIN, NAMESPACE: 'HGNC', NAME: 'AKT1', VARIANTS: [ { KIND: HGVS, IDENTIFIER: 'p.40*', }, ], } Unfortunately, the HGVS nomenclature requires the encoding of the terminal amino acid which is exchanged for a stop codon, and this information is not required by BEL 1.0. For this example, the proper encoding of the truncation at position also includes the information that the 40th amino acid in the AKT1 is Cys. Its BEL encoding should be :code:`p(HGNC:AKT1, var(p.Cys40*))`. Temporary support has been added to compile these statements, but it's recommended they are upgraded by reexamining the supporting text, or looking up the amino acid sequence. .. seealso:: - BEL 2.0 specification on `truncations `_ - PyBEL module :py:class:`pybel.parser.modifiers.get_truncation_language` """ import logging from pyparsing import ParserElement from pyparsing import pyparsing_common as ppc from .constants import amino_acid from ..utils import nest, one_of_tags from ...constants import HGVS, KIND, TRUNCATION_POSITION __all__ = [ "get_truncation_language", ] logger = logging.getLogger(__name__) truncation_tag = one_of_tags(tags=["trunc", "truncation"], canonical_tag=HGVS, name=KIND) AMINO_ACID = "aminoacid" def get_truncation_language() -> ParserElement: """Build a parser for protein truncations.""" l1 = truncation_tag + nest(amino_acid(AMINO_ACID) + ppc.integer(TRUNCATION_POSITION)) l1.setParseAction(_handle_trunc) l2 = truncation_tag + nest(ppc.integer(TRUNCATION_POSITION)) l2.setParseAction(_handle_trunc_legacy) return l1 | l2 def _handle_trunc_legacy(line, _, tokens): # FIXME this isn't correct HGVS nomenclature, but truncation isn't forward compatible without more information upgraded = "p.{}*".format(tokens[TRUNCATION_POSITION]) logger.warning( "trunc() is deprecated. Re-encode with reference terminal amino acid in HGVS: %s", line, ) tokens[HGVS] = upgraded del tokens[TRUNCATION_POSITION] return tokens def _handle_trunc(_, __, tokens): aa, position = tokens[AMINO_ACID], tokens[TRUNCATION_POSITION] tokens[HGVS] = "p.{aa}{position}*".format(aa=aa, position=position) del tokens[AMINO_ACID] del tokens[TRUNCATION_POSITION] return tokens pybel-0.15.5/src/pybel/parser/modifiers/variant.py000066400000000000000000000034501426625374700221340ustar00rootroot00000000000000# -*- coding: utf-8 -*- """HGVS Variants. For example, the BEL term :code:`p(HGNC:GSK3B, var(p.Gly123Arg))` is translated to the following internal DSL: .. code-block:: python from pybel.dsl import Protein, Hgvs gsk3b_variant = Protien(namespace='HGNC', name='GSK3B', variants=Hgvs('p.Gly123Arg')) Further, the shorthand for protein substitutions, :class:`pybel.dsl.ProteinSubstitution`, can be used to produce the same result, as it inherits from :class:`pybel.dsl.Hgvs`: .. code-block:: python from pybel.dsl import Protein, ProteinSubstitution gsk3b_variant = Protien(namespace='HGNC', name='GSK3B', variants=ProteinSubstitution('Gly', 123, 'Arg')) Either way, the resulting object can be used like a dict that looks like: .. code-block:: python from pybel.constants import * { FUNCTION: PROTEIN, NAMESPACE: 'HGNC', NAME: 'GSK3B', VARIANTS: [ { KIND: HGVS, IDENTIFIER: 'p.Gly123Arg', }, ], } .. seealso:: - BEL 2.0 specification on `variants `_ - HGVS `conventions `_ - PyBEL module :py:class:`pybel.parser.modifiers.get_hgvs_language` """ from pyparsing import ParserElement, Word, alphanums from ..utils import nest, one_of_tags, quote from ...constants import HGVS, KIND __all__ = [ "get_hgvs_language", ] variant_tags = one_of_tags(tags=["var", "variant"], canonical_tag=HGVS, name=KIND) variant_characters = Word(alphanums + "._*=?>") def get_hgvs_language() -> ParserElement: """Build a HGVS :class:`pyparsing.ParseElement`.""" hgvs = (variant_characters | quote)(HGVS) language = variant_tags + nest(hgvs) return language pybel-0.15.5/src/pybel/parser/parse_bel.py000066400000000000000000001224421426625374700204460ustar00rootroot00000000000000# -*- coding: utf-8 -*- """A parser for BEL. This module handles parsing BEL relations and validation of semantics. """ import itertools as itt import logging from functools import lru_cache from typing import Any, Dict, List, Mapping, Optional, Pattern, Set, Union import pyparsing from pyparsing import ( Group, Keyword, MatchFirst, ParseResults, StringEnd, Suppress, delimitedList, oneOf, replaceWith, ) from .baseparser import BaseParser from .constants import NamespaceTermEncodingMapping from .modifiers import ( get_fragment_language, get_fusion_language, get_gene_modification_language, get_gene_substitution_language, get_hgvs_language, get_legacy_fusion_langauge, get_location_language, get_protein_modification_language, get_protein_substitution_language, get_truncation_language, ) from .parse_concept import ConceptParser from .parse_control import ControlParser from .utils import WCW, nest, one_of_tags, triple from .. import language from ..constants import ( ABUNDANCE, ACTIVITY, ASSOCIATION, BINDS, BIOPROCESS, CAUSES_NO_CHANGE, CELL_SECRETION, CELL_SURFACE_EXPRESSION, COMPLEX, COMPOSITE, CONCEPT, CORRELATION, DECREASES, DEGRADATION, DIRECTLY_DECREASES, DIRECTLY_INCREASES, DIRTY, EFFECT, EQUIVALENT_TO, FROM_LOC, FUNCTION, FUSION, GENE, IDENTIFIER, INCREASES, IS_A, LINE, LOCATION, MEMBERS, MIRNA, MODIFIER, NAME, NAMESPACE, NEGATIVE_CORRELATION, NO_CORRELATION, PART_OF, PATHOLOGY, POPULATION, POSITIVE_CORRELATION, PRODUCTS, PROTEIN, REACTANTS, REACTION, REGULATES, RELATION, RNA, SOURCE, TARGET, TO_LOC, TRANSCRIBED_TO, TRANSLATED_TO, TRANSLOCATION, TWO_WAY_RELATIONS, VARIANTS, belns_encodings, ) from ..dsl import BaseEntity from ..exceptions import ( InvalidEntity, InvalidFunctionSemantic, MalformedTranslocationWarning, MissingAnnotationWarning, MissingCitationException, MissingSupportWarning, NestedRelationWarning, ) from ..struct.graph import BELGraph from ..tokens import parse_result_to_dsl __all__ = [ "BELParser", "modifier_po_to_dict", "parse", ] logger = logging.getLogger("pybel.parser") ########################### # 2.1 Abundance Functions # ########################### #: 2.1.1 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#Xabundancea> general_abundance_tags = one_of_tags(["a", "abundance"], ABUNDANCE, FUNCTION) #: 2.1.2 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#XcomplexA complex_tag = one_of_tags(["complex", "complexAbundance"], COMPLEX, FUNCTION) #: 2.1.3 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#XcompositeA composite_abundance_tag = one_of_tags(["composite", "compositeAbundance"], COMPOSITE, FUNCTION) #: 2.1.4 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#XgeneA gene_tag = one_of_tags(["g", "geneAbundance"], GENE, FUNCTION) #: 2.1.5 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#XmicroRNAA mirna_tag = one_of_tags(["m", "microRNAAbundance"], MIRNA, FUNCTION) #: 2.1.6 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#XproteinA protein_tag = one_of_tags(["p", "proteinAbundance"], PROTEIN, FUNCTION) #: 2.1.7 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#XrnaA rna_tag = one_of_tags(["r", "rnaAbundance"], RNA, FUNCTION) ###################### # Modifier Functions # ###################### # `2.2.1 `_ # See below (needs identifier) #: `2.2.2 `_ variant = get_hgvs_language() #: `2.2.3 `_ fragment = get_fragment_language() # `2.2.4 `_ # See below (needs identifier) #: DEPRECATED #: psub = get_protein_substitution_language() #: DEPRECATED #: http://openbel.org/language/version_1.0/bel_specification_version_1.0.html#_sequence_variations> gsub = get_gene_substitution_language() #: DEPRECATED #: http://openbel.org/language/version_1.0/bel_specification_version_1.0.html#_truncated_proteins> trunc = get_truncation_language() ############################### # 2.3 & 2.4 Process Functions # ############################### #: 2.3.1 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#_biologicalprocess_bp biological_process_tag = one_of_tags(["bp", "biologicalProcess"], BIOPROCESS, FUNCTION) #: 2.3.2 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#_pathology_path pathology_tag = one_of_tags(["o", "path", "pathology"], PATHOLOGY, FUNCTION) population_tag = one_of_tags(["pop", "populationAbundance"], POPULATION, FUNCTION) #: 2.3.3 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#Xactivity activity_tag = one_of_tags(["act", "activity"], ACTIVITY, MODIFIER) #: 2.4.1 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#XmolecularA molecular_activity_tags = Suppress(oneOf(["ma", "molecularActivity"])) ################################ # 2.5 Transformation Functions # ################################ #: 2.5.1a http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#_translocation_tloc translocation_tag = one_of_tags(["translocation", "tloc"], TRANSLOCATION, MODIFIER) #: 2.5.1b http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#_cellsecretion_sec cell_secretion_tag = one_of_tags(["sec", "cellSecretion"], CELL_SECRETION, MODIFIER) #: 2.5.1c http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#_cellsurfaceexpression_surf cell_surface_expression_tag = one_of_tags(["surf", "cellSurfaceExpression"], CELL_SURFACE_EXPRESSION, MODIFIER) #: 2.5.2 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#_degradation_deg degradation_tags = one_of_tags(["deg", "degradation"], DEGRADATION, MODIFIER) #: 2.5.3 http://openbel.org/language/version_2.0/bel_specification_version_2.0.html#_reaction_rxn reaction_tags = one_of_tags(["reaction", "rxn"], REACTION, FUNCTION) ##################### # BEL Relationships # ##################### #: `3.1.1 `_ increases_tag = oneOf(["->", "→", "increases"]).setParseAction(replaceWith(INCREASES)) #: `3.1.2 `_ directly_increases_tag = one_of_tags(["=>", "⇒", "directlyIncreases"], DIRECTLY_INCREASES) #: `3.1.3 `_ decreases_tag = one_of_tags(["-|", "decreases"], DECREASES) #: `3.1.4 `_ directly_decreases_tag = one_of_tags(["=|", "directlyDecreases"], DIRECTLY_DECREASES) #: `3.1.5 `_ rate_limit_tag = Keyword("rateLimitingStepOf") #: `3.1.6 `_ causes_no_change_tag = one_of_tags(["cnc", "causesNoChange"], CAUSES_NO_CHANGE) #: `3.1.7 `_ regulates_tag = one_of_tags(["reg", "regulates"], REGULATES) #: Binds relation binds_tag = Keyword(BINDS) #: Correlation relation correlation_tag = one_of_tags(["cor", "correlation"], CORRELATION) #: No Correlation relation no_correlation_tag = one_of_tags(["noCor", "noCorrelation"], NO_CORRELATION) #: `3.2.1 `_ negative_correlation_tag = one_of_tags(["neg", "negativeCorrelation"], NEGATIVE_CORRELATION) #: `3.2.2 `_ positive_correlation_tag = one_of_tags(["pos", "positiveCorrelation"], POSITIVE_CORRELATION) #: `3.2.3 `_ association_tag = one_of_tags(["--", "association"], ASSOCIATION) #: `3.3.1 `_ orthologous_tag = Keyword("orthologous") #: `3.3.2 `_ transcribed_tag = oneOf([":>", "transcribedTo"]).setParseAction(replaceWith(TRANSCRIBED_TO)) #: `3.3.3 `_ translated_tag = oneOf([">>", "translatedTo"]).setParseAction(replaceWith(TRANSLATED_TO)) #: `3.4.1 `_ has_member_tag = Keyword("hasMember") #: `3.4.2 `_ has_members_tag = Keyword("hasMembers") #: `3.4.3 `_ has_component_tag = Keyword("hasComponent") #: `3.4.4 `_ has_components_tag = Keyword("hasComponents") #: `3.4.5 `_ is_a_tag = Keyword(IS_A) #: `3.4.6 `_ subprocess_of_tag = Keyword("subProcessOf") #: `3.5.1 `_ analogous_tag = Keyword("analogousTo") #: `3.5.2 `_ biomarker_tag = Keyword("biomarkerFor") #: `3.5.3 `_ prognostic_biomarker_tag = Keyword("prognosticBiomarkerFor") biomarker_tags = biomarker_tag | prognostic_biomarker_tag # Computed edges has_variant_tags = Keyword("hasVariant") has_reactant_tags = Keyword("hasReactant") has_product_tags = Keyword("hasProduct") part_of_reaction_tags = has_reactant_tags | has_product_tags #: The ``equivalentTp`` relationship has been proposed for BEL 2.0.0+ equivalent_tag = one_of_tags(["eq", EQUIVALENT_TO], EQUIVALENT_TO) #: The ``partOf`` relationship has been proposed for BEL 2.0.0+ partof_tag = Keyword(PART_OF) class BELParser(BaseParser): """Build a parser backed by a given dictionary of namespaces.""" def __init__( self, graph: Optional[BELGraph] = None, namespace_to_term_to_encoding: Optional[NamespaceTermEncodingMapping] = None, namespace_to_pattern: Optional[Mapping[str, Pattern]] = None, annotation_to_term: Optional[Mapping[str, Set[str]]] = None, annotation_to_pattern: Optional[Mapping[str, Pattern]] = None, annotation_to_local: Optional[Mapping[str, Set[str]]] = None, allow_naked_names: bool = False, disallow_nested: bool = False, disallow_unqualified_translocations: bool = False, citation_clearing: bool = True, skip_validation: bool = False, autostreamline: bool = True, required_annotations: Optional[List[str]] = None, ) -> None: """Build a BEL parser. :param graph: The BEL graph to use to store the network :param namespace_to_term_to_encoding: A dictionary of {namespace: {name: encoding}}. Delegated to :class:`pybel.parser.parse_identifier.IdentifierParser` :param namespace_to_pattern: A dictionary of {namespace: compiled regular expression}. Delegated to :class:`pybel.parser.parse_identifier.IdentifierParser` :param annotation_to_term: A dictionary of {annotation: set of values}. Delegated to :class:`pybel.parser.ControlParser` :param annotation_to_pattern: A dictionary of {annotation: regular expression strings}. Delegated to :class:`pybel.parser.ControlParser` :param annotation_to_local: A dictionary of {annotation: set of values}. Delegated to :class:`pybel.parser.ControlParser` :param allow_naked_names: If true, turn off naked namespace failures. Delegated to :class:`pybel.parser.parse_identifier.IdentifierParser` :param disallow_nested: If true, turn on nested statement failures. Delegated to :class:`pybel.parser.parse_identifier.IdentifierParser` :param disallow_unqualified_translocations: If true, allow translocations without TO and FROM clauses. :param citation_clearing: Should :code:`SET Citation` statements clear evidence and all annotations? Delegated to :class:`pybel.parser.ControlParser` :param autostreamline: Should the parser be streamlined on instantiation? :param required_annotations: Optional list of required annotations """ self.graph = graph self.disallow_nested = disallow_nested self.disallow_unqualified_translocations = disallow_unqualified_translocations if skip_validation: self.control_parser = ControlParser( citation_clearing=citation_clearing, required_annotations=required_annotations, ) self.concept_parser = ConceptParser( allow_naked_names=allow_naked_names, skip_validation=skip_validation, ) else: self.control_parser = ControlParser( annotation_to_term=annotation_to_term, annotation_to_pattern=annotation_to_pattern, annotation_to_local=annotation_to_local, # citation_clearing=citation_clearing, required_annotations=required_annotations, ) self.concept_parser = ConceptParser( namespace_to_term_to_encoding=namespace_to_term_to_encoding, namespace_to_pattern=namespace_to_pattern, # allow_naked_names=allow_naked_names, skip_validation=skip_validation, ) self.control_parser.get_line_number = self.get_line_number self.concept_parser.get_line_number = self.get_line_number concept = Group(self.concept_parser.language)(CONCEPT) # 2.2 Abundance Modifier Functions #: `2.2.1 `_ self.pmod = get_protein_modification_language( concept_fqualified=self.concept_parser.identifier_fqualified, concept_qualified=self.concept_parser.identifier_qualified, ) #: `2.2.4 `_ self.location = get_location_language(self.concept_parser.language) opt_location = pyparsing.Optional(WCW + self.location) #: PyBEL BEL Specification variant self.gmod = get_gene_modification_language( concept_fqualified=self.concept_parser.identifier_fqualified, concept_qualified=self.concept_parser.identifier_qualified, ) # 2.6 Other Functions #: `2.6.1 `_ self.fusion = get_fusion_language(self.concept_parser.language) # 2.1 Abundance Functions #: `2.1.1 `_ self.general_abundance = general_abundance_tags + nest(concept + opt_location) self.gene_modified = concept + pyparsing.Optional( WCW + delimitedList(Group(variant | gsub | self.gmod))(VARIANTS), ) self.gene_fusion = Group(self.fusion)(FUSION) self.gene_fusion_legacy = Group(get_legacy_fusion_langauge(concept, "c"))(FUSION) #: `2.1.4 `_ self.gene = gene_tag + nest( MatchFirst( [ self.gene_fusion, self.gene_fusion_legacy, self.gene_modified, ] ) + opt_location, ) self.mirna_modified = ( concept + pyparsing.Optional( WCW + delimitedList(Group(variant))(VARIANTS), ) + opt_location ) #: `2.1.5 `_ self.mirna = mirna_tag + nest(self.mirna_modified) self.protein_modified = concept + pyparsing.Optional( WCW + delimitedList(Group(MatchFirst([self.pmod, variant, fragment, psub, trunc])))( VARIANTS, ), ) self.protein_fusion = Group(self.fusion)(FUSION) self.protein_fusion_legacy = Group(get_legacy_fusion_langauge(concept, "p"))(FUSION) #: `2.1.6 `_ self.protein = protein_tag + nest( MatchFirst( [ self.protein_fusion, self.protein_fusion_legacy, self.protein_modified, ] ) + opt_location, ) self.rna_modified = concept + pyparsing.Optional(WCW + delimitedList(Group(variant))(VARIANTS)) self.rna_fusion = Group(self.fusion)(FUSION) self.rna_fusion_legacy = Group(get_legacy_fusion_langauge(concept, "r"))(FUSION) #: `2.1.7 `_ self.rna = rna_tag + nest( MatchFirst( [ self.rna_fusion, self.rna_fusion_legacy, self.rna_modified, ] ) + opt_location, ) self.population = population_tag + nest(concept) self.single_abundance = MatchFirst( [ self.general_abundance, self.gene, self.mirna, self.protein, self.rna, self.population, ] ) #: `2.1.2 `_ self.complex_singleton = complex_tag + nest(concept + opt_location) self.complex_list = complex_tag + nest( delimitedList(Group(self.single_abundance | self.complex_singleton))(MEMBERS) + opt_location, ) self.complex_abundances = self.complex_list | self.complex_singleton # Definition of all simple abundances that can be used in a composite abundance self.simple_abundance = self.complex_abundances | self.single_abundance self.simple_abundance.setParseAction(self.check_function_semantics) #: `2.1.3 `_ self.composite_abundance = composite_abundance_tag + nest( delimitedList(Group(self.simple_abundance))(MEMBERS) + opt_location, ) self.abundance = self.simple_abundance | self.composite_abundance # 2.4 Process Modifier Function # backwards compatibility with BEL v1.0 molecular_activity_default = oneOf(list(language.activity_labels)).setParseAction( handle_molecular_activity_default, ) #: `2.4.1 `_ self.molecular_activity = molecular_activity_tags + nest( molecular_activity_default | self.concept_parser.language, ) # 2.3 Process Functions #: `2.3.1 `_ self.biological_process = biological_process_tag + nest(concept) #: `2.3.2 `_ self.pathology = pathology_tag + nest(concept) self.bp_path = self.biological_process | self.pathology self.bp_path.setParseAction(self.check_function_semantics) self.activity_standard = activity_tag + nest( self.simple_abundance + pyparsing.Optional(WCW + Group(self.molecular_activity)(EFFECT)), ) activity_legacy_tags = oneOf(language.activities)(MODIFIER) self.activity_legacy = activity_legacy_tags + nest(self.simple_abundance) self.activity_legacy.setParseAction(handle_activity_legacy) #: `2.3.3 `_ self.activity = self.activity_standard | self.activity_legacy self.process = self.bp_path | self.activity # 2.5 Transformation Functions from_loc = Suppress(FROM_LOC) + nest(concept(FROM_LOC)) to_loc = Suppress(TO_LOC) + nest(concept(TO_LOC)) self.cell_secretion = cell_secretion_tag + nest(self.simple_abundance) self.cell_secretion.addParseAction(handle_secretion) self.cell_surface_expression = cell_surface_expression_tag + nest(self.simple_abundance) self.cell_surface_expression.addParseAction(handle_surface_expression) self.translocation_standard = nest( self.simple_abundance + WCW + Group(from_loc + WCW + to_loc)(EFFECT), ) self.translocation_legacy = nest( self.simple_abundance + WCW + Group(concept(FROM_LOC) + WCW + concept(TO_LOC))(EFFECT), ) self.translocation_legacy.addParseAction(handle_legacy_tloc) self.translocation_unqualified = nest(self.simple_abundance) if self.disallow_unqualified_translocations: self.translocation_unqualified.setParseAction(self.handle_translocation_illegal) #: `2.5.1 `_ self.translocation = translocation_tag + MatchFirst( [ self.translocation_unqualified, self.translocation_standard, self.translocation_legacy, ] ) #: `2.5.2 `_ self.degradation = degradation_tags + nest(self.simple_abundance) #: `2.5.3 `_ self.reactants = Suppress(REACTANTS) + nest(delimitedList(Group(self.simple_abundance))) self.products = Suppress(PRODUCTS) + nest(delimitedList(Group(self.simple_abundance))) self.reaction = reaction_tags + nest(Group(self.reactants)(REACTANTS), Group(self.products)(PRODUCTS)) self.transformation = MatchFirst( [ self.cell_secretion, self.cell_surface_expression, self.translocation, self.degradation, self.reaction, ] ) # 3 BEL Relationships self.bel_term = MatchFirst([self.transformation, self.process, self.abundance]).streamline() self.bel_to_bel_relations = [ association_tag, increases_tag, decreases_tag, positive_correlation_tag, negative_correlation_tag, correlation_tag, no_correlation_tag, binds_tag, causes_no_change_tag, orthologous_tag, is_a_tag, equivalent_tag, partof_tag, directly_increases_tag, directly_decreases_tag, analogous_tag, regulates_tag, ] self.bel_to_bel = triple(self.bel_term, MatchFirst(self.bel_to_bel_relations), self.bel_term) # Mixed Relationships #: `3.1.5 `_ self.rate_limit = triple( MatchFirst([self.biological_process, self.activity, self.transformation]), rate_limit_tag, self.biological_process, ) #: `3.4.6 `_ self.subprocess_of = triple( MatchFirst([self.process, self.activity, self.transformation]), subprocess_of_tag, self.process, ) #: `3.3.2 `_ self.transcribed = triple(self.gene, transcribed_tag, self.rna) #: `3.3.3 `_ self.translated = triple(self.rna, translated_tag, self.protein) #: `3.4.1 `_ self.has_member = triple(self.abundance, has_member_tag, self.abundance) #: `3.4.2 `_ self.abundance_list = Suppress("list") + nest(delimitedList(Group(self.abundance))) self.has_members = triple(self.abundance, has_members_tag, self.abundance_list) self.has_members.setParseAction(self.handle_has_members) self.has_components = triple(self.abundance, has_components_tag, self.abundance_list) self.has_components.setParseAction(self.handle_has_components) self.has_list = self.has_members | self.has_components # `3.4.3 `_ self.has_component = triple( self.abundance, has_component_tag, self.abundance, ) self.biomarker = triple(self.bel_term, biomarker_tags, self.process) self.has_variant_relation = triple(self.abundance, has_variant_tags, self.abundance) self.part_of_reaction = triple(self.reaction, part_of_reaction_tags, self.abundance) self.relation = MatchFirst( [ self.bel_to_bel, # self.has_member, # self.has_component, self.subprocess_of, self.rate_limit, self.biomarker, self.transcribed, self.translated, # self.has_variant_relation, # self.part_of_reaction, ] ) if self.graph is not None: self.relation.setParseAction(self._handle_relation_harness) self.inverted_unqualified_relation = MatchFirst( [ self.has_member, self.has_component, ] ) if self.graph is not None: self.inverted_unqualified_relation.setParseAction(self.handle_inverse_unqualified_relation) self.normal_unqualified_relation = MatchFirst( [ self.has_member, self.has_component, self.has_variant_relation, self.part_of_reaction, ] ) if self.graph is not None: self.normal_unqualified_relation.setParseAction(self.handle_unqualified_relation) #: 3.1 Causal Relationships - nested. causal_relation_tags = MatchFirst( [ increases_tag, decreases_tag, directly_decreases_tag, directly_increases_tag, ] ) self.nested_causal_relationship = triple( self.bel_term, causal_relation_tags, nest(triple(self.bel_term, causal_relation_tags, self.bel_term)), ) if self.graph is not None: self.nested_causal_relationship.setParseAction(self.handle_nested_relation) # has_members is handled differently from all other relations becuase it gets distrinbuted self.relation = MatchFirst( [ self.has_list, self.nested_causal_relationship, self.relation, self.inverted_unqualified_relation, self.normal_unqualified_relation, ] ) self.singleton_term = self.bel_term + StringEnd() if self.graph is not None: self.singleton_term.setParseAction(self.handle_term) self.statement = self.relation | self.singleton_term self.language = self.control_parser.language | self.statement self.language.setName("BEL") super(BELParser, self).__init__(self.language, streamline=autostreamline) def parse(self, s: str) -> Mapping[str, Any]: """Parse the string.""" return self.parseString(s).asDict() @property def _namespace_dict(self) -> Mapping[str, Mapping[str, str]]: """Get the dictionary of {namespace: {name: encoding}} stored in the internal identifier parser.""" return self.concept_parser.namespace_to_name_to_encoding @property def _allow_naked_names(self) -> bool: """Return if naked names should be parsed (``True``), or if errors should be thrown (``False``).""" return self.concept_parser.allow_naked_names def get_annotations(self) -> Dict: """Get the current annotations in this parser.""" return self.control_parser.get_annotations() def clear(self): """Clear the graph and all control parser data (current citation, annotations, and statement group).""" if self.graph is not None: self.graph.clear() self.control_parser.clear() def handle_nested_relation(self, line: str, position: int, tokens: ParseResults): """Handle nested statements. If :code:`self.disallow_nested` is True, raises a ``NestedRelationWarning``. :raises: NestedRelationWarning """ if self.disallow_nested: raise NestedRelationWarning(self.get_line_number(), line, position) subject_hash = self._handle_relation_checked( line, position, { SOURCE: tokens[SOURCE], RELATION: tokens[RELATION], TARGET: tokens[TARGET][SOURCE], }, ) object_hash = self._handle_relation_checked( line, position, { SOURCE: tokens[TARGET][SOURCE], RELATION: tokens[TARGET][RELATION], TARGET: tokens[TARGET][TARGET], }, ) self.graph.add_transitivity(subject_hash, object_hash) return tokens def check_function_semantics(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Raise an exception if the function used on the tokens is wrong. :raises: InvalidFunctionSemantic """ concept = tokens.get(CONCEPT) if not self._namespace_dict or concept is None: return tokens namespace, name = concept[NAMESPACE], concept[NAME] if namespace in self.concept_parser.namespace_to_pattern: return tokens if self._allow_naked_names and namespace == DIRTY: # Don't check dirty names in lenient mode return tokens valid_functions = set( itt.chain.from_iterable( belns_encodings.get(encoding, set()) for encoding in self._namespace_dict[namespace][name] ), ) if not valid_functions: raise InvalidEntity(self.get_line_number(), line, position, namespace, name) if tokens[FUNCTION] not in valid_functions: raise InvalidFunctionSemantic( line_number=self.get_line_number(), line=line, position=position, func=tokens[FUNCTION], namespace=namespace, name=name, allowed_functions=valid_functions, ) return tokens def handle_term(self, _, __, tokens: ParseResults) -> ParseResults: """Handle BEL terms (the subject and object of BEL relations).""" self.ensure_node(tokens) return tokens def _handle_list_helper(self, tokens: ParseResults, relation: str) -> ParseResults: """Provide the functionality for :meth:`handle_has_members` and :meth:`handle_has_components`.""" parent_node_dsl = self.ensure_node(tokens[0]) for child_tokens in tokens[2]: child_node_dsl = self.ensure_node(child_tokens) # Note that the polarity is switched since this is just for hasMembers # and hasComponents, which are both deprecated as of BEL v2.2 self.graph.add_unqualified_edge(child_node_dsl, parent_node_dsl, relation) return tokens def handle_has_members(self, _, __, tokens: ParseResults) -> ParseResults: """Handle list relations like ``p(X) hasMembers list(p(Y), p(Z), ...)``.""" return self._handle_list_helper(tokens, IS_A) def handle_has_components(self, _, __, tokens: ParseResults) -> ParseResults: """Handle list relations like ``p(X) hasComponents list(p(Y), p(Z), ...)``.""" return self._handle_list_helper(tokens, PART_OF) def _add_qualified_edge_helper( self, *, source, source_modifier, relation, target, target_modifier, annotations, ) -> str: """Add a qualified edge from the internal aspects of the parser.""" m = { BINDS: self.graph.add_binds, } adder = m.get(relation) d = dict( evidence=self.control_parser.evidence, citation=self.control_parser.get_citation(), annotations=annotations, source_modifier=source_modifier, target_modifier=target_modifier, **{LINE: self.get_line_number()}, ) if adder is not None: return adder(source=source, target=target, **d) else: return self.graph.add_qualified_edge(source=source, target=target, relation=relation, **d) def _add_qualified_edge(self, *, source, source_modifier, relation, target, target_modifier) -> str: """Add an edge, then adds the opposite direction edge if it should.""" d = dict( relation=relation, annotations=self.control_parser.annotations, ) if relation in TWO_WAY_RELATIONS: self._add_qualified_edge_helper( source=target, source_modifier=target_modifier, target=source, target_modifier=source_modifier, **d, ) return self._add_qualified_edge_helper( source=source, source_modifier=source_modifier, target=target, target_modifier=target_modifier, **d, ) def _handle_relation(self, tokens: ParseResults) -> str: """Handle a relation.""" source = self.ensure_node(tokens[SOURCE]) source_modifier = modifier_po_to_dict(tokens[SOURCE]) relation = tokens[RELATION] target = self.ensure_node(tokens[TARGET]) target_modifier = modifier_po_to_dict(tokens[TARGET]) return self._add_qualified_edge( source=source, source_modifier=source_modifier, relation=relation, target=target, target_modifier=target_modifier, ) def _handle_relation_harness(self, line: str, position: int, tokens: Union[ParseResults, Dict]) -> ParseResults: """Handle BEL relations based on the policy specified on instantiation. Note: this can't be changed after instantiation! """ self._handle_relation_checked(line, position, tokens) return tokens def _handle_relation_checked(self, line, position, tokens): if not self.control_parser.citation_is_set: raise MissingCitationException(self.get_line_number(), line, position) if not self.control_parser.evidence: raise MissingSupportWarning(self.get_line_number(), line, position) missing_required_annotations = self.control_parser.get_missing_required_annotations() if missing_required_annotations: raise MissingAnnotationWarning(self.get_line_number(), line, position, missing_required_annotations) return self._handle_relation(tokens) def handle_unqualified_relation(self, _, __, tokens: ParseResults) -> ParseResults: """Handle unqualified relations.""" subject_node_dsl = self.ensure_node(tokens[SOURCE]) object_node_dsl = self.ensure_node(tokens[TARGET]) relation = tokens[RELATION] self.graph.add_unqualified_edge(subject_node_dsl, object_node_dsl, relation) return tokens def handle_inverse_unqualified_relation(self, _, __, tokens: ParseResults) -> ParseResults: """Handle unqualified relations that should go reverse.""" source = self.ensure_node(tokens[SOURCE]) target = self.ensure_node(tokens[TARGET]) relation = tokens[RELATION] self.graph.add_unqualified_edge(source=target, target=source, relation=relation) return tokens def ensure_node(self, tokens: ParseResults) -> BaseEntity: """Turn parsed tokens into canonical node name and makes sure its in the graph.""" node = parse_result_to_dsl(tokens) self.graph.add_node_from_data(node) return node def handle_translocation_illegal(self, line: str, position: int, tokens: ParseResults) -> None: """Handle a malformed translocation.""" raise MalformedTranslocationWarning(self.get_line_number(), line, position, tokens) # HANDLERS def handle_molecular_activity_default(_: str, __: int, tokens: ParseResults) -> ParseResults: """Handle a BEL 2.0 style molecular activity with BEL default names.""" upgraded_cls = language.activity_labels[tokens[0]] upgraded_concept = language.activity_mapping[upgraded_cls] tokens[NAMESPACE] = upgraded_concept.namespace tokens[NAME] = upgraded_concept.name tokens[IDENTIFIER] = upgraded_concept.identifier return tokens def handle_activity_legacy(_: str, __: int, tokens: ParseResults) -> ParseResults: """Handle BEL 1.0 activities.""" legacy_cls = language.activity_labels[tokens[MODIFIER]] tokens[MODIFIER] = ACTIVITY tokens[EFFECT] = language.activity_mapping[legacy_cls] logger.log(5, "upgraded legacy activity to %s", legacy_cls) return tokens def handle_legacy_tloc(line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle translocations that lack the ``fromLoc`` and ``toLoc`` entries.""" logger.log(5, "legacy translocation statement: %s [%d]", line, position) return tokens def handle_secretion(_, __, tokens: ParseResults) -> ParseResults: tokens[MODIFIER] = TRANSLOCATION tokens[EFFECT] = { FROM_LOC: language.intracellular, TO_LOC: language.extracellular, } return tokens def handle_surface_expression(_, __, tokens: ParseResults) -> ParseResults: tokens[MODIFIER] = TRANSLOCATION tokens[EFFECT] = { FROM_LOC: language.intracellular, TO_LOC: language.cell_surface, } return tokens def modifier_po_to_dict(tokens): """Get the location, activity, and/or transformation information as a dictionary. :return: a dictionary describing the modifier :rtype: dict """ attrs = {} if LOCATION in tokens: attrs[LOCATION] = dict(tokens[LOCATION]) if MODIFIER not in tokens: return attrs if tokens[MODIFIER] == DEGRADATION: attrs[MODIFIER] = tokens[MODIFIER] elif tokens[MODIFIER] == ACTIVITY: attrs[MODIFIER] = tokens[MODIFIER] if EFFECT in tokens: attrs[EFFECT] = dict(tokens[EFFECT]) elif tokens[MODIFIER] == TRANSLOCATION: attrs[MODIFIER] = tokens[MODIFIER] if EFFECT in tokens: try: attrs[EFFECT] = tokens[EFFECT].asDict() except AttributeError: # for when it was auto-upgraded attrs[EFFECT] = dict(tokens[EFFECT]) elif tokens[MODIFIER] == CELL_SECRETION: attrs[MODIFIER] = TRANSLOCATION attrs[EFFECT] = { FROM_LOC: language.intracellular, TO_LOC: language.extracellular, } elif tokens[MODIFIER] == CELL_SURFACE_EXPRESSION: attrs[MODIFIER] = TRANSLOCATION attrs[EFFECT] = { FROM_LOC: language.intracellular, TO_LOC: language.cell_surface, } else: raise ValueError("Invalid value for tokens[MODIFIER]: {}".format(tokens[MODIFIER])) return attrs @lru_cache() def _default_parser(): return BELParser(skip_validation=True, citation_clearing=False) @lru_cache() def parse(s: str, pprint=False): """Parse a BEL statement (without validation).""" rv = _default_parser().parse(s) if pprint: import json print(json.dumps(rv, indent=2)) else: return rv pybel-0.15.5/src/pybel/parser/parse_concept.py000066400000000000000000000161101426625374700213310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """A module holding the :class:`IdentifierParser`.""" import logging import re from collections import defaultdict from typing import Mapping, Optional, Pattern, Set from pyparsing import ParseResults, Suppress from .baseparser import BaseParser from .constants import NamespaceTermEncodingMapping from .utils import ns, quote from ..constants import DIRTY, IDENTIFIER, NAME, NAMESPACE from ..exceptions import ( MissingDefaultNameWarning, MissingNamespaceNameWarning, MissingNamespaceRegexWarning, NakedNameWarning, UndefinedNamespaceWarning, ) __all__ = [ "ConceptParser", ] logger = logging.getLogger(__name__) class ConceptParser(BaseParser): """A parser for concepts in the form of ``namespace:name`` or ``namespace:identifier!name``. Can be made more lenient when given a default namespace or enabling the use of naked names. """ def __init__( self, namespace_to_term_to_encoding: Optional[NamespaceTermEncodingMapping] = None, namespace_to_pattern: Optional[Mapping[str, Pattern]] = None, default_namespace: Optional[Set[str]] = None, allow_naked_names: bool = False, skip_validation: bool = False, ensure_go: bool = True, ) -> None: """Initialize the concept parser. :param namespace_to_term_to_encoding: A dictionary of {namespace: {(identifier, name): encoding}} :param namespace_to_pattern: A dictionary of {namespace: regular expression string} to compile :param default_namespace: A set of strings that can be used without a namespace :param allow_naked_names: If true, turn off naked namespace failures """ self.identifier_fqualified = ( ns(NAMESPACE) + Suppress(":") + (ns | quote)(IDENTIFIER) + Suppress("!") + (ns | quote)(NAME) ) self.identifier_qualified = ns(NAMESPACE) + Suppress(":") + (ns | quote)(NAME) if namespace_to_term_to_encoding is not None: self.namespace_to_name_to_encoding = defaultdict(dict) self.namespace_to_identifier_to_encoding = defaultdict(dict) for namespace, term_mapping in namespace_to_term_to_encoding.items(): for (identifier, name), encoding in term_mapping.items(): self.namespace_to_name_to_encoding[namespace][name] = encoding self.namespace_to_identifier_to_encoding[namespace][identifier] = encoding self.namespace_to_name_to_encoding = dict(self.namespace_to_name_to_encoding) self.namespace_to_identifier_to_encoding = dict(self.namespace_to_identifier_to_encoding) else: self.namespace_to_name_to_encoding = {} self.namespace_to_identifier_to_encoding = {} if not skip_validation: self.identifier_fqualified.setParseAction(self.handle_identifier_fqualified) self.identifier_qualified.setParseAction(self.handle_identifier_qualified) self.namespace_to_pattern = namespace_to_pattern or {} if ensure_go and "go" not in self.namespace_to_name_to_encoding: self.namespace_to_pattern["go"] = re.compile(r"^\d+$") self.default_namespace = set(default_namespace) if default_namespace is not None else None self.allow_naked_names = allow_naked_names self.identifier_bare = (ns | quote)(NAME) self.identifier_bare.setParseAction( self.handle_namespace_default if self.default_namespace else self.handle_namespace_lenient if self.allow_naked_names else self.handle_namespace_invalid, ) super().__init__( self.identifier_fqualified | self.identifier_qualified | self.identifier_bare, ) def has_enumerated_namespace(self, namespace: str) -> bool: """Check that the namespace has been defined by an enumeration.""" return namespace in self.namespace_to_name_to_encoding def has_regex_namespace(self, namespace: str) -> bool: """Check that the namespace has been defined by a regular expression.""" return namespace in self.namespace_to_pattern def raise_for_missing_namespace(self, line: str, position: int, namespace: str, name: str) -> None: """Raise an exception if the namespace is not defined.""" if not self.has_enumerated_namespace(namespace) and not self.has_regex_namespace(namespace): raise UndefinedNamespaceWarning(self.get_line_number(), line, position, namespace, name) def raise_for_missing_name(self, line: str, position: int, namespace: str, name: str) -> None: """Raise an exception if the namespace is not defined or if it does not validate the given name.""" self.raise_for_missing_namespace(line, position, namespace, name) if self.has_enumerated_namespace(namespace) and name not in self.namespace_to_name_to_encoding[namespace]: raise MissingNamespaceNameWarning(self.get_line_number(), line, position, namespace, name) if self.has_regex_namespace(namespace) and not self.namespace_to_pattern[namespace].match(name): raise MissingNamespaceRegexWarning(self.get_line_number(), line, position, namespace, name) def handle_identifier_fqualified(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle parsing a qualified OBO-style identifier.""" return self._handle_identifier(line, position, tokens, key=IDENTIFIER) def handle_identifier_qualified(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle parsing a qualified identifier.""" return self._handle_identifier(line, position, tokens, key=NAME) def _handle_identifier(self, line: str, position: int, tokens: ParseResults, key) -> ParseResults: """Handle parsing a qualified identifier.""" namespace, name = tokens[NAMESPACE], tokens[key] self.raise_for_missing_namespace(line, position, namespace, name) self.raise_for_missing_name(line, position, namespace, name) return tokens def handle_namespace_default(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle parsing an identifier for the default namespace.""" name = tokens[NAME] if not self.default_namespace: raise ValueError("Default namespace is not set") if name not in self.default_namespace: raise MissingDefaultNameWarning(self.get_line_number(), line, position, name) return tokens @staticmethod def handle_namespace_lenient(line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle parsing an identifier for names missing a namespace that are outside the default namespace.""" tokens[NAMESPACE] = DIRTY logger.debug("Naked namespace: [%d] %s", position, line) return tokens def handle_namespace_invalid(self, line: str, position: int, tokens: ParseResults) -> None: """Raise an exception when parsing a name missing a namespace.""" name = tokens[NAME] raise NakedNameWarning(self.get_line_number(), line, position, name) pybel-0.15.5/src/pybel/parser/parse_control.py000066400000000000000000000372221426625374700213650ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Control parser. This module handles parsing control statement, which add annotations and namespaces to the document. .. see also:: https://wiki.openbel.org/display/BLD/Control+Records """ import logging from typing import Any, Dict, List, Mapping, Optional, Pattern, Set from pyparsing import And, Keyword, MatchFirst, ParseResults, Suppress, oneOf from pyparsing import pyparsing_common as ppc from .baseparser import BaseParser from .utils import delimited_quoted_list, delimited_unquoted_list, is_int, qid, quote from .. import constants as pc from ..constants import ( ANNOTATIONS, BEL_KEYWORD_ALL, BEL_KEYWORD_CITATION, BEL_KEYWORD_EVIDENCE, BEL_KEYWORD_SET, BEL_KEYWORD_STATEMENT_GROUP, BEL_KEYWORD_SUPPORT, BEL_KEYWORD_UNSET, CITATION, CITATION_TYPES, EVIDENCE, ) from ..exceptions import ( CitationTooLongException, CitationTooShortException, IllegalAnnotationValueWarning, InvalidCitationType, InvalidPubMedIdentifierWarning, MissingAnnotationKeyWarning, MissingAnnotationRegexWarning, MissingCitationException, UndefinedAnnotationWarning, ) from ..language import CitationDict, Entity __all__ = ["ControlParser"] logger = logging.getLogger(__name__) set_tag = Keyword(BEL_KEYWORD_SET) unset_tag = Keyword(BEL_KEYWORD_UNSET) unset_all = Suppress(BEL_KEYWORD_ALL) supporting_text_tags = oneOf([BEL_KEYWORD_EVIDENCE, BEL_KEYWORD_SUPPORT]) set_statement_group_stub = And([Suppress(BEL_KEYWORD_STATEMENT_GROUP), Suppress("="), qid("group")]) set_citation_stub = And([Suppress(BEL_KEYWORD_CITATION), Suppress("="), delimited_quoted_list("values")]) set_evidence_stub = And([Suppress(supporting_text_tags), Suppress("="), quote("value")]) class ControlParser(BaseParser): """A parser for BEL control statements. .. seealso:: BEL 1.0 specification on `control records `_ """ def __init__( self, annotation_to_term: Optional[Mapping[str, Set[str]]] = None, annotation_to_pattern: Optional[Mapping[str, Pattern]] = None, annotation_to_local: Optional[Mapping[str, Set[str]]] = None, citation_clearing: bool = True, required_annotations: Optional[List[str]] = None, ) -> None: """Initialize the control statement parser. :param annotation_to_term: A dictionary of {annotation: set of valid values} defined with URL for parsing :param annotation_to_pattern: A dictionary of {annotation: regular expression string} :param annotation_to_local: A dictionary of {annotation: set of valid values} for parsing defined with LIST :param citation_clearing: Should :code:`SET Citation` statements clear evidence and all annotations? :param required_annotations: Annotations that are required """ self.citation_clearing = citation_clearing self.annotation_to_term = annotation_to_term or {} self.annotation_to_pattern = annotation_to_pattern or {} self.annotation_to_local = annotation_to_local or {} self.statement_group = None self.citation_db = None self.citation_db_id = None self.evidence = None self.annotations = {} self.required_annotations = required_annotations or [] annotation_key = ppc.identifier("key").setParseAction(self.handle_annotation_key) self.set_statement_group = set_statement_group_stub().setParseAction(self.handle_set_statement_group) self.set_citation = set_citation_stub.setParseAction(self.handle_set_citation) self.set_evidence = set_evidence_stub.setParseAction(self.handle_set_evidence) set_command_prefix = And([annotation_key("key"), Suppress("=")]) self.set_command = set_command_prefix + qid("value") self.set_command.setParseAction(self.handle_set_command) self.set_command_list = set_command_prefix + delimited_quoted_list("values") self.set_command_list.setParseAction(self.handle_set_command_list) self.unset_command = annotation_key("key") self.unset_command.addParseAction(self.handle_unset_command) self.unset_evidence = supporting_text_tags(EVIDENCE) self.unset_evidence.setParseAction(self.handle_unset_evidence) self.unset_citation = Suppress(BEL_KEYWORD_CITATION) self.unset_citation.setParseAction(self.handle_unset_citation) self.unset_statement_group = Suppress(BEL_KEYWORD_STATEMENT_GROUP) self.unset_statement_group.setParseAction(self.handle_unset_statement_group) self.unset_list = delimited_unquoted_list("values") self.unset_list.setParseAction(self.handle_unset_list) self.unset_all = unset_all.setParseAction(self.handle_unset_all) self.set_statements = set_tag("action") + MatchFirst( [ self.set_statement_group, self.set_citation, self.set_evidence, self.set_command, self.set_command_list, ] ) self.unset_statements = unset_tag("action") + MatchFirst( [ self.unset_all, self.unset_citation, self.unset_evidence, self.unset_statement_group, self.unset_command, self.unset_list, ] ) self.language = self.set_statements | self.unset_statements super(ControlParser, self).__init__(self.language) @property def _in_debug_mode(self) -> bool: return not self.annotation_to_term and not self.annotation_to_pattern @property def citation_is_set(self) -> bool: """Check if the citation is set.""" return self.citation_db is not None and self.citation_db_id is not None def has_enumerated_annotation(self, annotation: str) -> bool: """Check if the annotation is defined as an enumeration.""" return annotation in self.annotation_to_term def has_regex_annotation(self, annotation: str) -> bool: """Check if the annotation is defined as a regular expression.""" return annotation in self.annotation_to_pattern def has_local_annotation(self, annotation: str) -> bool: """Check if the annotation is defined locally.""" return annotation in self.annotation_to_local def has_annotation(self, annotation: str) -> bool: """Check if the annotation is defined.""" return ( self.has_enumerated_annotation(annotation) or self.has_regex_annotation(annotation) or self.has_local_annotation(annotation) ) def raise_for_undefined_annotation(self, line: str, position: int, annotation: str) -> None: """Raise an exception if the annotation is not defined. :raises: UndefinedAnnotationWarning """ if self._in_debug_mode: return if not self.has_annotation(annotation): raise UndefinedAnnotationWarning(self.get_line_number(), line, position, annotation) def raise_for_invalid_annotation_value(self, line: str, position: int, key: str, value: str) -> None: """Raise an exception if the annotation is not defined. :raises: IllegalAnnotationValueWarning or MissingAnnotationRegexWarning """ if self._in_debug_mode: return if self.has_enumerated_annotation(key) and value not in self.annotation_to_term[key]: raise IllegalAnnotationValueWarning(self.get_line_number(), line, position, key, value) elif self.has_regex_annotation(key) and not self.annotation_to_pattern[key].match(value): raise MissingAnnotationRegexWarning(self.get_line_number(), line, position, key, value) elif self.has_local_annotation(key) and value not in self.annotation_to_local[key]: # TODO condense raise IllegalAnnotationValueWarning(self.get_line_number(), line, position, key, value) def raise_for_missing_citation(self, line: str, position: int) -> None: """Raise an exception if there is no citation present in the parser. :raises: MissingCitationException """ if self.citation_clearing and not self.citation_is_set: raise MissingCitationException(self.get_line_number(), line, position) def handle_annotation_key(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle an annotation key before parsing to validate that it's either enumerated or as a regex. :raise: MissingCitationException or UndefinedAnnotationWarning """ key = tokens["key"] self.raise_for_missing_citation(line, position) self.raise_for_undefined_annotation(line, position, key) return tokens def handle_set_statement_group(self, _, __, tokens: ParseResults) -> ParseResults: """Handle a ``SET STATEMENT_GROUP = "X"`` statement.""" self.statement_group = tokens["group"] return tokens def handle_set_citation(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle a ``SET Citation = {"X", "Y", "Z", ...}`` statement.""" self.clear_citation() values = tokens["values"] if len(values) < 2: raise CitationTooShortException(self.get_line_number(), line, position) citation_namespace = values[0].lower() citation_namespace = pc.CITATION_NORMALIZER.get(citation_namespace, citation_namespace) if citation_namespace not in CITATION_TYPES: raise InvalidCitationType(self.get_line_number(), line, position, citation_namespace) if 2 == len(values): citation_db_id = values[1] elif 6 < len(values): raise CitationTooLongException(self.get_line_number(), line, position) else: if 3 == len(values): logger.debug("Throwing away JOURNAL entry in position 2") else: logger.debug("Throwing away JOURNAL entry in position 2 and everything after position 3") citation_db_id = values[2] if citation_namespace == "pubmed" and not is_int(citation_db_id): raise InvalidPubMedIdentifierWarning(self.get_line_number(), line, position, citation_db_id) self.citation_db = citation_namespace self.citation_db_id = citation_db_id return tokens def handle_set_evidence(self, _, __, tokens: ParseResults) -> ParseResults: """Handle a ``SET Evidence = ""`` statement.""" self.evidence = tokens["value"] return tokens def handle_set_command(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle a ``SET X = "Y"`` statement.""" key, value = tokens["key"], tokens["value"] self.raise_for_invalid_annotation_value(line, position, key, value) self.annotations[key] = [Entity(namespace=key, identifier=value)] return tokens def handle_set_command_list(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle a ``SET X = {"Y", "Z", ...}`` statement.""" key, values = tokens["key"], tokens["values"] for value in values: self.raise_for_invalid_annotation_value(line, position, key, value) self.annotations[key] = [Entity(namespace=key, identifier=value) for value in values] return tokens def handle_unset_statement_group(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Unset the statement group, or raises an exception if it is not set. :raises: MissingAnnotationKeyWarning """ if self.statement_group is None: raise MissingAnnotationKeyWarning(self.get_line_number(), line, position, BEL_KEYWORD_STATEMENT_GROUP) self.statement_group = None return tokens def handle_unset_citation(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Unset the citation, or raise an exception if it is not set. :raises: MissingAnnotationKeyWarning """ if not self.citation_is_set: raise MissingAnnotationKeyWarning(self.get_line_number(), line, position, BEL_KEYWORD_CITATION) self.clear_citation() return tokens def handle_unset_evidence(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Unset the evidence, or throws an exception if it is not already set. The value for ``tokens[EVIDENCE]`` corresponds to which alternate of SupportingText or Evidence was used in the BEL script. :raises: MissingAnnotationKeyWarning """ if self.evidence is None: raise MissingAnnotationKeyWarning(self.get_line_number(), line, position, tokens[EVIDENCE]) self.evidence = None return tokens def validate_unset_command(self, line: str, position: int, annotation: str) -> None: """Raise an exception when trying to ``UNSET X`` if ``X`` is not already set. :raises: MissingAnnotationKeyWarning """ if annotation not in self.annotations: raise MissingAnnotationKeyWarning(self.get_line_number(), line, position, annotation) def handle_unset_command(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle an ``UNSET X`` statement or raises an exception if it is not already set. :raises: MissingAnnotationKeyWarning """ key = tokens["key"] self.validate_unset_command(line, position, key) del self.annotations[key] return tokens def handle_unset_list(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle ``UNSET {A, B, ...}`` or raises an exception of any of them are not present. Consider that all unsets are in peril if just one of them is wrong! :raises: MissingAnnotationKeyWarning """ for key in tokens["values"]: if key in {BEL_KEYWORD_EVIDENCE, BEL_KEYWORD_SUPPORT}: self.evidence = None else: self.validate_unset_command(line, position, key) del self.annotations[key] return tokens def handle_unset_all(self, _, __, tokens) -> ParseResults: """Handle an ``UNSET_ALL`` statement.""" self.clear() return tokens def get_annotations(self) -> Dict[str, Any]: """Get the current annotations.""" return { EVIDENCE: self.evidence, CITATION: self.get_citation(), ANNOTATIONS: self.annotations.copy(), } def get_citation(self) -> Optional[CitationDict]: """Get the citation dictionary.""" return ( CitationDict(namespace=self.citation_db, identifier=self.citation_db_id) if self.citation_db and self.citation_db_id else None ) def get_missing_required_annotations(self) -> List[str]: """Return missing required annotations.""" return [ required_annotation for required_annotation in self.required_annotations if required_annotation not in self.annotations ] def clear_citation(self) -> None: """Clear the citation and if citation clearing is enabled, clear the evidence and annotations.""" self.citation_db = None self.citation_db_id = None if self.citation_clearing: self.evidence = None self.annotations.clear() def clear(self) -> None: """Clear the statement_group, citation, evidence, and annotations.""" self.statement_group = None self.citation_db = None self.citation_db_id = None self.evidence = None self.annotations.clear() pybel-0.15.5/src/pybel/parser/parse_metadata.py000066400000000000000000000321641426625374700214650ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module supports the relation parser by handling statements.""" import logging import re from typing import Mapping, Optional, Pattern, Set from pyparsing import And, MatchFirst, ParseResults, Suppress, Word from .baseparser import BaseParser from .constants import NamespaceTermEncodingMapping from .utils import delimited_quoted_list, ns, qid, quote, word from ..constants import ( BEL_KEYWORD_ANNOTATION, BEL_KEYWORD_AS, BEL_KEYWORD_DEFINE, BEL_KEYWORD_DOCUMENT, BEL_KEYWORD_LIST, BEL_KEYWORD_NAMESPACE, BEL_KEYWORD_PATTERN, BEL_KEYWORD_SET, BEL_KEYWORD_URL, DOCUMENT_KEYS, METADATA_VERSION, belns_encodings, ) from ..exceptions import ( InvalidMetadataException, RedefinedAnnotationError, RedefinedNamespaceError, VersionFormatWarning, ) from ..resources.resources import keyword_to_url from ..utils import valid_date_version __all__ = [ "MetadataParser", ] logger = logging.getLogger(__name__) as_tag = Suppress(BEL_KEYWORD_AS) url_tag = Suppress(BEL_KEYWORD_URL) list_tag = Suppress(BEL_KEYWORD_LIST) set_tag = Suppress(BEL_KEYWORD_SET) define_tag = Suppress(BEL_KEYWORD_DEFINE) function_tags = Word("".join(belns_encodings)) SEMANTIC_VERSION_STRING_RE = re.compile( r"(?P\d+)\.(?P\d+)\.(?P\d+)(?:-(?P[0-9A-Za-z-]+(?:\.[0-9A-Za-z-]+)*))?(?:\+(?P[0-9A-Za-z-]+(?:\.[0-9A-Za-z-]+)*))?", ) MALFORMED_VERSION_STRING_RE = re.compile(r"(?P\d+)(\.(?P\d+)(\.(?P\d+))?)?") NAMESPACE_BLACKLIST = {} # TODO: {'SCOMP', 'SFAM'} class MetadataParser(BaseParser): """A parser for the document and definitions section of a BEL document. .. seealso:: BEL 1.0 Specification for the `DEFINE `_ keyword """ def __init__( self, manager, namespace_to_term_to_encoding: Optional[NamespaceTermEncodingMapping] = None, namespace_to_pattern: Optional[Mapping[str, Pattern]] = None, annotation_to_term: Optional[Mapping[str, Set[str]]] = None, annotation_to_pattern: Optional[Mapping[str, Pattern]] = None, annotation_to_local: Optional[Mapping[str, Set[str]]] = None, default_namespace: Optional[Set[str]] = None, allow_redefinition: bool = False, skip_validation: bool = False, upgrade_urls: bool = False, ) -> None: """Build a metadata parser. :param manager: A cache manager :param namespace_to_term_to_encoding: An enumerated namespace mapping from {namespace keyword: {(identifier, name): encoding}} :param namespace_to_pattern: A regular expression namespace mapping from {namespace keyword: regex string} :param annotation_to_term: Enumerated annotation mapping from {annotation keyword: set of valid values} :param annotation_to_pattern: Regular expression annotation mapping from {annotation keyword: regex string} :param default_namespace: A set of strings that can be used without a namespace :param skip_validation: If true, don't download and cache namespaces/annotations """ #: This metadata parser's internal definition cache manager self.manager = manager self.disallow_redefinition = not allow_redefinition self.skip_validation = skip_validation self.upgrade_urls = upgrade_urls #: A dictionary of cached {namespace keyword: {(identifier, name): encoding}} self.namespace_to_term_to_encoding = namespace_to_term_to_encoding or {} #: A set of namespaces's URLs that can't be cached self.uncachable_namespaces = set() #: A dictionary of {namespace keyword: regular expression string} self.namespace_to_pattern = namespace_to_pattern or {} #: A set of names that can be used without a namespace self.default_namespace = set(default_namespace) if default_namespace is not None else None #: A dictionary of cached {annotation keyword: set of values} self.annotation_to_term = annotation_to_term or {} #: A dictionary of {annotation keyword: regular expression string} self.annotation_to_pattern = annotation_to_pattern or {} #: A dictionary of cached {annotation keyword: set of values} self.annotation_to_local = annotation_to_local or {} #: A dictionary containing the document metadata self.document_metadata = {} #: A dictionary from {namespace keyword: BEL namespace URL} self.namespace_url_dict = {} #: A dictionary from {annotation keyword: BEL annotation URL} self.annotation_url_dict = {} self.document = And( [ set_tag, Suppress(BEL_KEYWORD_DOCUMENT), word("key"), Suppress("="), qid("value"), ] ) namespace_tag = And([define_tag, Suppress(BEL_KEYWORD_NAMESPACE), ns("name"), as_tag]) self.namespace_url = And([namespace_tag, url_tag, quote("url")]) self.namespace_pattern = And([namespace_tag, Suppress(BEL_KEYWORD_PATTERN), quote("value")]) annotation_tag = And([define_tag, Suppress(BEL_KEYWORD_ANNOTATION), ns("name"), as_tag]) self.annotation_url = And([annotation_tag, url_tag, quote("url")]) self.annotation_list = And([annotation_tag, list_tag, delimited_quoted_list("values")]) self.annotation_pattern = And([annotation_tag, Suppress(BEL_KEYWORD_PATTERN), quote("value")]) self.document.setParseAction(self.handle_document) self.namespace_url.setParseAction(self.handle_namespace_url) self.namespace_pattern.setParseAction(self.handle_namespace_pattern) self.annotation_url.setParseAction(self.handle_annotations_url) self.annotation_list.setParseAction(self.handle_annotation_list) self.annotation_pattern.setParseAction(self.handle_annotation_pattern) self.language = MatchFirst( [ self.document, self.namespace_url, self.annotation_url, self.annotation_list, self.annotation_pattern, self.namespace_pattern, ] ).setName("BEL Metadata") super().__init__(self.language) def handle_document(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle statements like ``SET DOCUMENT X = "Y"``. :raises: InvalidMetadataException :raises: VersionFormatWarning """ key = tokens["key"] value = tokens["value"] if key not in DOCUMENT_KEYS: raise InvalidMetadataException(self.get_line_number(), line, position, key, value) norm_key = DOCUMENT_KEYS[key] if norm_key in self.document_metadata: logger.warning("Tried to overwrite metadata: %s", key) return tokens self.document_metadata[norm_key] = value if norm_key == METADATA_VERSION: self.raise_for_version(line, position, value) return tokens def raise_for_redefined_namespace(self, line: str, position: int, namespace: str) -> None: """Raise an exception if a namespace is already defined. :raises: RedefinedNamespaceError """ if self.disallow_redefinition and self.has_namespace(namespace): raise RedefinedNamespaceError(self.get_line_number(), line, position, namespace) def handle_namespace_url(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle statements like ``DEFINE NAMESPACE X AS URL "Y"``. :raises: RedefinedNamespaceError :raises: pybel.resources.exc.ResourceError """ namespace_keyword = tokens["name"] if namespace_keyword in NAMESPACE_BLACKLIST: raise ValueError("Upgrade usage to FamPlex") self.raise_for_redefined_namespace(line, position, namespace_keyword) url = tokens["url"] if self.upgrade_urls and namespace_keyword.lower() in keyword_to_url: url = keyword_to_url[namespace_keyword.lower()] self.namespace_url_dict[namespace_keyword] = url return tokens def ensure_resources(self): """Load all namespaces/annotations that have been encountered so far during parsing.""" if self.skip_validation: return if self.namespace_url_dict: keywords, urls = zip(*self.namespace_url_dict.items()) namespaces = self.manager._ensure_namespace_urls(urls) for keyword, namespace in zip(keywords, namespaces): self.namespace_to_term_to_encoding[keyword] = namespace.get_term_to_encodings() if self.annotation_url_dict: keywords, urls = zip(*self.annotation_url_dict.items()) namespaces = self.manager._ensure_namespace_urls(urls, is_annotation=True) for keyword, namespace in zip(keywords, namespaces): self.annotation_to_term[keyword] = {entry.name for entry in namespace.entries} def handle_namespace_pattern(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle statements like ``DEFINE NAMESPACE X AS PATTERN "Y"``. :raises: RedefinedNamespaceError """ namespace = tokens["name"] self.raise_for_redefined_namespace(line, position, namespace) self.namespace_to_pattern[namespace] = re.compile(tokens["value"]) return tokens def raise_for_redefined_annotation(self, line: str, position: int, annotation: str) -> None: """Raise an exception if the given annotation is already defined. :raises: RedefinedAnnotationError """ if self.disallow_redefinition and self.has_annotation(annotation): raise RedefinedAnnotationError(self.get_line_number(), line, position, annotation) def handle_annotations_url(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle statements like ``DEFINE ANNOTATION X AS URL "Y"``. :raises: RedefinedAnnotationError """ keyword = tokens["name"] self.raise_for_redefined_annotation(line, position, keyword) self.annotation_url_dict[keyword] = tokens["url"] return tokens def handle_annotation_list(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle statements like ``DEFINE ANNOTATION X AS LIST {"Y","Z", ...}``. :raises: RedefinedAnnotationError """ annotation = tokens["name"] self.raise_for_redefined_annotation(line, position, annotation) self.annotation_to_local[annotation] = set(tokens["values"]) return tokens def handle_annotation_pattern(self, line: str, position: int, tokens: ParseResults) -> ParseResults: """Handle statements like ``DEFINE ANNOTATION X AS PATTERN "Y"``. :raises: RedefinedAnnotationError """ annotation = tokens["name"] self.raise_for_redefined_annotation(line, position, annotation) self.annotation_to_pattern[annotation] = re.compile(tokens["value"]) return tokens def has_enumerated_annotation(self, annotation: str) -> bool: """Check if this annotation is defined by an enumeration.""" return annotation in self.annotation_to_term def has_regex_annotation(self, annotation: str) -> bool: """Check if this annotation is defined by a regular expression.""" return annotation in self.annotation_to_pattern def has_local_annotation(self, annotation: str) -> bool: """Check if this annotation is defined by an locally.""" return annotation in self.annotation_to_local def has_annotation(self, annotation: str) -> bool: """Check if this annotation is defined.""" return ( self.has_enumerated_annotation(annotation) or self.has_regex_annotation(annotation) or self.has_local_annotation(annotation) ) def has_enumerated_namespace(self, namespace: str) -> bool: """Check if this namespace is defined by an enumeration.""" return namespace in self.namespace_to_term_to_encoding def has_regex_namespace(self, namespace: str) -> bool: """Check if this namespace is defined by a regular expression.""" return namespace in self.namespace_to_pattern def has_namespace(self, namespace: str) -> bool: """Check if this namespace is defined.""" return self.has_enumerated_namespace(namespace) or self.has_regex_namespace(namespace) def raise_for_version(self, line: str, position: int, version: str) -> None: """Check that a version string is valid for BEL documents. This means it's either in the YYYYMMDD or semantic version format. :param line: The line being parsed :param position: The position in the line being parsed :param str version: A version string :raises: VersionFormatWarning """ if valid_date_version(version): return if not SEMANTIC_VERSION_STRING_RE.match(version): raise VersionFormatWarning(self.get_line_number(), line, position, version) pybel-0.15.5/src/pybel/parser/utils.py000066400000000000000000000051771426625374700176570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for the parsers.""" import itertools as itt import logging from typing import Any, List, Optional from pyparsing import ( And, Group, ParserElement, Suppress, White, Word, ZeroOrMore, alphanums, dblQuotedString, delimitedList, oneOf, removeQuotes, replaceWith, ) from ..constants import RELATION, SOURCE, TARGET logger = logging.getLogger("pybel") def is_int(s: Any) -> bool: """Determine if an object can be cast to an int. :param s: any object :return: true if argument can be cast to an int: """ try: int(s) return True except ValueError: return False W = Suppress(ZeroOrMore(White())) C = Suppress(",") WCW = W + C + W LPF, RPF = map(Suppress, "()") LP = Suppress("(") + W RP = W + Suppress(")") word = Word(alphanums) ns = Word(alphanums + "_-.") identifier = Word(alphanums + "_") quote = dblQuotedString().setParseAction(removeQuotes) qid = quote | identifier delimited_quoted_list = And([Suppress("{"), delimitedList(quote), Suppress("}")]) delimited_unquoted_list = And([Suppress("{"), delimitedList(identifier), Suppress("}")]) def nest(*content): """Define a delimited list by enumerating each element of the list.""" if len(content) == 0: raise ValueError("no arguments supplied") return And([LPF, content[0]] + list(itt.chain.from_iterable(zip(itt.repeat(C), content[1:]))) + [RPF]) def one_of_tags( tags: List[str], canonical_tag: str, name: Optional[str] = None, ) -> ParserElement: """Define the tags usable in the :class:`BelParser`. For example, statements like ``g(HGNC:SNCA)`` can be expressed also as ``geneAbundance(HGNC:SNCA)``. The language must define multiple different tags that get normalized to the same thing. :param tags: a list of strings that are the tags for a function. For example, ['g', 'geneAbundance'] for the abundance of a gene :param canonical_tag: the preferred tag name. Does not have to be one of the tags. For example, 'GeneAbundance' (note capitalization) is used for the abundance of a gene :param name: this is the key under which the value for this tag is put in the PyParsing framework. """ element = oneOf(tags).setParseAction(replaceWith(canonical_tag)) if name is None: return element return element.setResultsName(name) def triple(subject, relation, obj) -> ParserElement: """Build a simple triple in PyParsing that has a ``subject relation object`` format.""" return And([Group(subject)(SOURCE), relation(RELATION), Group(obj)(TARGET)]) pybel-0.15.5/src/pybel/repository.py000066400000000000000000000447311426625374700174410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for BEL repositories.""" import json import logging import os import sys import time from dataclasses import dataclass, field from itertools import chain from typing import Any, Iterable, Mapping, Optional, Set, TextIO, Tuple, Union import click import pandas as pd from tqdm.autonotebook import tqdm from .cli import ( connection_option, host_option, password_option, user_option, verbose_option, ) from .constants import CITATION from .io import from_bel_script, to_bel_commons, to_indra_statements from .io.api import dump, load from .manager import Manager from .manager.citation_utils import enrich_pubmed_citations from .struct import BELGraph from .struct.operations import union from .version import get_version __all__ = [ "BELMetadata", "BELRepository", "append_click_group", ] logger = logging.getLogger(__name__) private_option = click.option("--private", is_flag=True) OUTPUT_KWARGS = { "nodelink.json": dict(indent=2, sort_keys=True), "cx.json": dict(indent=2, sort_keys=True), "jgif.json": dict(indent=2, sort_keys=True), } @dataclass class BELMetadata: """A container for BEL document metadata.""" name: Optional[str] = None version: Optional[str] = None description: Optional[str] = None authors: Optional[str] = None contact: Optional[str] = None license: Optional[str] = None copyright: Optional[str] = None disclaimer: Optional[str] = None def new(self) -> BELGraph: """Generate a new BEL graph with the given metadata.""" graph = BELGraph() self.update(graph) return graph def update(self, graph: BELGraph) -> None: """Update the BEL graph's metadata.""" if self.name: graph.name = self.name if self.version: graph.version = self.version if self.authors: graph.authors = self.authors if self.description: graph.description = self.description if self.contact: graph.contact = self.contact if self.license: graph.licenses = self.license if self.copyright: graph.copyright = self.copyright if self.disclaimer: graph.disclaimer = self.disclaimer @dataclass class BELRepository: """A container for a BEL repository.""" directory: str output_directory: Optional[str] = None bel_cache_name: str = "_cache.bel" metadata: Optional[BELMetadata] = None formats: Tuple[str, ...] = ("pickle", "nodelink.json") #: Must include {file_name} and {extension} cache_fmt: str = "{file_name}.{extension}" global_summary_ext: str = "summary.tsv" warnings_ext: str = "warnings.tsv" #: Arguments passed to :func:`pybel.from_path` during compilation from_path_kwargs: Mapping[str, Any] = field(default_factory=dict) #: The location where the summary DataFrame will be output as a TSV. bel_summary_path: str = field(init=False) def __post_init__(self) -> None: # noqa: D105 if self.output_directory is None: self.output_directory = self.directory self.bel_summary_path = self._build_cache_ext_path( root=self.output_directory, file_name=self.bel_cache_name, extension=self.global_summary_ext.lstrip("."), ) def _get_global_cache_path_by_extension(self, extension: str) -> str: return self._build_cache_ext_path(self.output_directory, self.bel_cache_name, extension) def _build_warnings_path(self, root: str, file_name: str) -> str: return self._build_cache_ext_path(root, file_name, self.warnings_ext.lstrip(".")) def _build_summary_path(self, root: str, file_name: str) -> str: return self._build_cache_ext_path(root, file_name, "summary.json") def _build_cache_ext_path(self, root: str, file_name: str, extension: str) -> str: return os.path.join( root, self.cache_fmt.format(file_name=file_name, extension=extension.lstrip(".")), ) def walk(self) -> Iterable[Tuple[str, Iterable[str], Iterable[str]]]: """Recursively walk this directory.""" return os.walk(self.directory) def iterate_bel(self) -> Iterable[Tuple[str, str]]: """Yield all paths to BEL documents.""" for root, _dirs, file_names in self.walk(): for file_name in sorted(file_names): if not file_name.startswith("_") and file_name.endswith(".bel"): yield root, file_name def clear_global_cache(self) -> None: """Clear the global cache.""" self._remove_root_file_name(self.output_directory, self.bel_cache_name) def clear_local_caches(self) -> None: """Clear all caches of BEL documents in the repository.""" for root, file_name in self.iterate_bel(): self._remove_root_file_name(root, file_name) def clear_local_warned(self) -> None: """Clear caches for BEL documents with errors.""" for root, file_name in self.iterate_bel(): if self._has_warnings(root, file_name): self._remove_root_file_name(root, file_name) def _has_warnings(self, root: str, file_name: str) -> bool: return os.path.exists(self._build_warnings_path(root, file_name)) def _remove_root_file_name(self, root: str, file_name: str) -> None: for _, path in self._iterate_extension_path(root, file_name): if os.path.exists(path): os.remove(path) def _iterate_extension_path(self, root: str, file_name: str) -> Iterable[Tuple[str, str]]: for extension in self.formats: yield extension, self._build_cache_ext_path(root, file_name, extension) def _import_local(self, root: str, file_name: str) -> Optional[BELGraph]: for _, path in self._iterate_extension_path(root, file_name): if os.path.exists(path): return load(path) return None def _import_global(self) -> Optional[BELGraph]: return self._import_local(self.output_directory, self.bel_cache_name) def _export_local(self, graph: BELGraph, root: str, file_name: str) -> None: for extension, path in self._iterate_extension_path(root, file_name): kwargs = OUTPUT_KWARGS.get(extension, {}) dump(graph, path, **kwargs) with open(self._build_summary_path(root, file_name), "w") as file: json.dump(graph.summarize.dict(), file, indent=2) if graph.warnings: logger.info(f" - {graph.number_of_warnings()} warnings") warnings_path = self._build_warnings_path(root, file_name) warnings_df = pd.DataFrame( [ ( exc.line_number, exc.position, exc.line, exc.__class__.__name__, str(exc), ) for _, exc, _ in graph.warnings ], columns=["Line Number", "Position", "Line", "Error", "Message"], ) warnings_df.to_csv(warnings_path, sep="\t", index=False) def _export_global(self, graph: BELGraph) -> None: self._export_local(graph, self.output_directory, self.bel_cache_name) def get_graph( self, manager: Optional[Manager] = None, use_cached: bool = True, use_tqdm: bool = False, tqdm_kwargs: Optional[Mapping[str, Any]] = None, from_path_kwargs: Optional[Mapping[str, Any]] = None, ) -> BELGraph: """Get a combine graph.""" if use_cached: graph = self._import_global() if graph is not None: return graph graphs = self.get_graphs( manager=manager, use_tqdm=use_tqdm, tqdm_kwargs=tqdm_kwargs, from_path_kwargs=from_path_kwargs, ) graph = union(graphs.values()) if self.metadata is not None: self.metadata.update(graph) self._get_summary_df_from_graphs(graphs) self._export_global(graph) return graph def get_indra_statements(self, **kwargs): """Get INDRA statements for all graphs. :rtype: List[indra.statements.Statement] """ return list(chain.from_iterable(to_indra_statements(graph) for graph in self.get_graphs(**kwargs).values())) def get_graphs( self, manager: Optional[Manager] = None, use_cached: bool = True, use_tqdm: bool = False, tqdm_kwargs: Optional[Mapping[str, Any]] = None, from_path_kwargs: Optional[Mapping[str, Any]] = None, ) -> Mapping[str, BELGraph]: """Get a mapping of all graphs' paths to their compiled BEL graphs.""" if manager is None: manager = Manager() paths = self.iterate_bel() if use_tqdm: paths = tqdm(list(paths), **(tqdm_kwargs or {})) rv = {} for root, file_name in paths: path = os.path.join(root, file_name) if use_cached: graph = self._import_local(root, file_name) if graph is not None: rv[path] = graph continue _from_path_kwargs = from_path_kwargs or {} _from_path_kwargs.update(self.from_path_kwargs) try: graph = rv[path] = from_bel_script(path, manager=manager, **_from_path_kwargs) graph.path = os.path.relpath(os.path.join(root, file_name), self.directory) except Exception as exc: logger.warning(f"problem with {path}: {exc}") continue enrich_pubmed_citations(graph=graph, manager=manager) self._export_local(graph, root, file_name) return rv def get_summary_df( self, manager: Optional[Manager] = None, use_cached: bool = False, use_tqdm: bool = False, tqdm_kwargs: Optional[Mapping[str, Any]] = None, from_path_kwargs: Optional[Mapping[str, Any]] = None, save: Union[bool, str, TextIO] = True, ) -> pd.DataFrame: """Get a pandas DataFrame summarizing the contents of all graphs in the repository.""" graphs = self.get_graphs( manager=manager, use_cached=use_cached, use_tqdm=use_tqdm, tqdm_kwargs=tqdm_kwargs, from_path_kwargs=from_path_kwargs, ) return self._get_summary_df_from_graphs(graphs, save=save) def _get_summary_df_from_graphs(self, graphs, save: Union[str, bool, TextIO] = True): summary_dicts = { os.path.relpath(path, self.directory): graph.summarize.dict() for path, graph in graphs.items() } df = pd.DataFrame.from_dict(summary_dicts, orient="index") if isinstance(save, str): df.to_csv(save, sep="\t") elif save: df.to_csv(self.bel_summary_path, sep="\t") return df def build_cli(self): # noqa: D202 """Build a command line interface.""" @click.group(help=f"Tools for the BEL repository at {self.directory} using PyBEL v{get_version()}") @click.pass_context def main(ctx): """Group the commands.""" ctx.obj = self append_click_group(main) return main def get_extensions(self, root: str, file_name: str) -> Set[str]: """Get all compiled files for the given BEL.""" # TODO check that this is a valid BEL path! return {extension for extension, path in self._iterate_extension_path(root, file_name) if os.path.exists(path)} def _get_global_caches(self): return self.get_extensions(self.output_directory, self.bel_cache_name) def _iterate_citations(self, **kwargs) -> Iterable[Tuple[str, str]]: """List all citations in documents in this repository.""" for _, _, data in self.get_graph(**kwargs).edges(data=True): citation = data.get(CITATION) if citation is not None: yield citation.namespace, citation.identifier def _write_caches(bel_repository: BELRepository, root: str, file_name: str): extensions = ", ".join(sorted(bel_repository.get_extensions(root, file_name))) has_warnings = os.path.exists(bel_repository._build_warnings_path(root, file_name)) try: with open(bel_repository._build_summary_path(root, file_name)) as file: summary = json.load(file) except FileNotFoundError: summary = None if extensions and has_warnings: s = click.style("✘️ ", fg="red") elif extensions and not has_warnings: s = click.style("✔︎ ", fg="green") else: s = click.style("? ", fg="yellow", bold=True) path = os.path.join(root, file_name) s += os.path.relpath(path, bel_repository.directory) if extensions: s += click.style(f" ({extensions})", fg="green") if summary: s += click.style( f' ({summary["Number of Nodes"]} nodes, {summary["Number of Edges"]} edges)', fg="blue", ) click.echo(s) def append_click_group(group: click.Group) -> None: # noqa: D202, C901 """Append a :py:class:`click.Group`.""" @group.command() @click.pass_obj def ls(bel_repository: BELRepository): """List the contents of the repository.""" global_caches = bel_repository._get_global_caches() if global_caches: click.secho("Global Cache", fg="red", bold=True) _write_caches( bel_repository, bel_repository.output_directory, bel_repository.bel_cache_name, ) click.secho("Local Caches", fg="red", bold=True) for root, file_name in bel_repository.iterate_bel(): _write_caches(bel_repository, root, file_name) @group.command() @click.pass_obj def citations(repository: BELRepository): """List citations in the repository.""" for database, reference in sorted(set(repository._iterate_citations(use_tqdm=True)), key=lambda x: int(x[1])): click.echo(f"{database}\t{reference}") @group.command() @host_option @user_option @password_option @click.option("-s", "--sleep", type=int, default=3, help="Seconds to sleep between sending") @private_option @click.pass_obj def upload_separate( repository: BELRepository, host: str, user: str, password: str, sleep: int, private: bool, ): """Upload all to BEL Commons.""" it = tqdm(repository.get_graphs().items()) for name, graph in it: res = to_bel_commons(graph, host=host, user=user, password=password, public=not private) res_json = res.json() task_id = res_json.get("task_id") if task_id is not None: it.write(f"task:{task_id} - {name}") it.write(f'see: {host.rstrip("/")}/api/task/{task_id}') time.sleep(sleep) else: it.write(f"problem with {name}: {res_json}") @group.command() @host_option @user_option @password_option @private_option @click.pass_obj def upload_combine(repository: BELRepository, host: str, user: str, password: str, private: bool): """Upload the combine graph.""" graph = repository.get_graph() res = to_bel_commons(graph, host=host, user=user, password=password, public=not private) res_json = res.json() task_id = res_json.get("task_id") if task_id is not None: click.echo(f"task:{task_id} - {graph}") click.echo(f'see: {host.rstrip("/")}/api/task/{task_id}') else: click.echo(f"problem with {graph.name}: {res_json}") @group.command() @click.confirmation_option() @click.pass_obj def uncache(bel_repository: BELRepository): """Clear the cached data for the repository.""" bel_repository.clear_global_cache() bel_repository.clear_local_caches() @group.command() @click.confirmation_option() @click.pass_obj def uncache_global(bel_repository: BELRepository): """Clear the cached data for the repository.""" bel_repository.clear_global_cache() @group.command() @click.confirmation_option() @click.pass_obj def uncache_local(bel_repository: BELRepository): """Clear the cached data for the repository.""" bel_repository.clear_local_caches() @group.command() @click.confirmation_option() @click.pass_obj def uncache_warned(bel_repository: BELRepository): """Clear the cached data for the documents that have warnings.""" bel_repository.clear_local_warned() @group.command() @connection_option @click.option("-r", "--reload", is_flag=True) @click.option("--no-tqdm", is_flag=True) @verbose_option @click.pass_obj def compile(bel_repository: BELRepository, connection: str, reload: bool, no_tqdm: bool): """Summarize the repository.""" if reload: bel_repository.clear_global_cache() bel_repository.clear_local_caches() manager = Manager(connection=connection) graph = bel_repository.get_graph( manager=manager, use_cached=(not reload), use_tqdm=(not no_tqdm), tqdm_kwargs=dict( desc="Loading BEL", leave=False, ), from_path_kwargs=dict( use_tqdm=(not no_tqdm), tqdm_kwargs=dict( leave=False, ), ), ) click.echo(graph.summarize.str()) @group.command() @click.argument("file", type=click.File("w")) @click.pass_obj def html(bel_repository: BELRepository, file: TextIO): """Output an HTML summary.""" graph = bel_repository.get_graph() try: from pybel_tools.assembler.html import to_html_file except ImportError: click.secho("pybel_tools.assembler.html is not available", fg="red") sys.exit(1) else: to_html_file(graph, file) @click.group() @click.version_option() @click.option( "-d", "--directory", default=os.getcwd(), type=click.Path(file_okay=False, dir_okay=True, exists=True), help="Defaults to current working directory", ) @click.pass_context def main(ctx, directory: str): """Command line interface for bel-repository.""" ctx.obj = BELRepository(directory=directory) append_click_group(main) if __name__ == "__main__": main() pybel-0.15.5/src/pybel/resources/000077500000000000000000000000001426625374700166515ustar00rootroot00000000000000pybel-0.15.5/src/pybel/resources/__init__.py000066400000000000000000000001471426625374700207640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Resources for PyBEL.""" from .constants import * from .resources import * pybel-0.15.5/src/pybel/resources/constants.py000066400000000000000000000003361426625374700212410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Resources that don't change over time for PyBEL.""" SPECIES_PATTERN = r"^\d+$" CONFIDENCE_URL = "https://arty.scai.fraunhofer.de/artifactory/bel/annotation/confidence/confidence-1.0.0.belanno" pybel-0.15.5/src/pybel/resources/resources.py000066400000000000000000000031231426625374700212340ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Resources for PyBEL.""" CHEBI_URL = "https://raw.githubusercontent.com/pharmacome/conso/d67144bc27a21626a514837b3b4382413dd6866b/external/chebi-names.belns" EC_URL = ( "https://raw.githubusercontent.com/pharmacome/conso/d67144bc27a21626a514837b3b4382413dd6866b/external/ec-code.belns" ) FB_URL = "https://raw.githubusercontent.com/pharmacome/conso/80171ae62cf43aa1fc8a6c326b94537ab342458c/external/fb-names.belns" GO_URL = "https://raw.githubusercontent.com/pharmacome/conso/d9d270e11aac480542c412d4222983a5f042b8ae/external/go-names.belns" HGNC_URL = "https://raw.githubusercontent.com/pharmacome/conso/d67144bc27a21626a514837b3b4382413dd6866b/external/hgnc-names.belns" MESH_URL = "https://raw.githubusercontent.com/pharmacome/conso/f02c6ad4a4791a8ed45448513b9de8c8f1b00c87/external/mesh-names.belns" MGI_URL = "https://raw.githubusercontent.com/pharmacome/conso/efc856fb009a39e4d284269a6801f79ed3d3cf56/external/mgi-names.belns" NCBIGENE_URL = "https://raw.githubusercontent.com/pharmacome/conso/d67144bc27a21626a514837b3b4382413dd6866b/external/ncbigene-names.belns" RGD_URL = "https://raw.githubusercontent.com/pharmacome/conso/efc856fb009a39e4d284269a6801f79ed3d3cf56/external/rgd-names.belns" FPLX_URL = ( "https://raw.githubusercontent.com/sorgerlab/famplex/da9f2187b694e6b425e668604e24ac9fac0f2c31/export/famplex.belns" ) #: Default URL lookup for some keywords keyword_to_url = dict( chebi=CHEBI_URL, ec=EC_URL, fb=FB_URL, go=GO_URL, hgnc=HGNC_URL, mesh=MESH_URL, mgi=MGI_URL, ncbigene=NCBIGENE_URL, rgd=RGD_URL, fplx=FPLX_URL, ) pybel-0.15.5/src/pybel/resources/update_resources.py000066400000000000000000000032171426625374700226020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """URLs for default BEL resources. This script is susceptible to rate limits from the GitHub API, so don't run it over and over! """ import logging import os from bel_resources.github import get_famplex_url, get_github_url HERE = os.path.abspath(os.path.dirname(__file__)) logging.basicConfig(level=logging.DEBUG) logging.getLogger("pybel").setLevel(logging.DEBUG) def _get_conso_url(name): return get_github_url( owner="pharmacome", repo="conso", path="external/{}.belns".format(name), ) keyword_to_suffix = dict( chebi="chebi-names", ec="ec-code", fb="fb-names", go="go-names", hgnc="hgnc-names", mesh="mesh-names", mgi="mgi-names", ncbigene="ncbigene-names", rgd="rgd-names", ) def main(): """Update the resources links file.""" keyword_to_url = {keyword: _get_conso_url(suffix) for keyword, suffix in keyword_to_suffix.items()} with open(os.path.join(HERE, "resources.py"), "w") as file: print("# -*- coding: utf-8 -*-\n", file=file) print('"""Resources for PyBEL."""\n', file=file) for keyword, url in sorted(keyword_to_url.items()): print("{}_URL = '{}'".format(keyword.upper(), url), file=file) print("\nFPLX_URL = '{}'".format(get_famplex_url()), file=file) print("\n#: Default URL lookup for some keywords", file=file) print("keyword_to_url = dict(", file=file) for k in sorted(keyword_to_suffix): print(" {}={}_URL,".format(k, k.upper()), file=file) print(" fplx=FPLX_URL,", file=file) print(")", file=file) if __name__ == "__main__": main() pybel-0.15.5/src/pybel/schema/000077500000000000000000000000001426625374700160775ustar00rootroot00000000000000pybel-0.15.5/src/pybel/schema/__init__.py000066400000000000000000000046131426625374700202140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Validation for PyBEL data. The :mod:`pybel.schema` module houses functions to verify the format of a given node or edge. Its inclusion will help ensure that all PyBEL data is stored in a consistent and clearly defined manner across the repository. """ import json import logging import os import pathlib from typing import Any, Mapping, Optional, Tuple import jsonschema __all__ = ["is_valid_node", "is_valid_edge"] logger = logging.getLogger(__name__) HERE = os.path.abspath(os.path.dirname(__file__)) NODE_FILENAME = "base_node.schema.json" EDGE_FILENAME = "edge.schema.json" # To use schemas from other files, jsonschema needs to know where the references point to, so # create a resolver that directs any references (like "entity.schema.json") to the schema's dir schema_uri = pathlib.PurePath(__file__).as_uri() RESOLVER = jsonschema.RefResolver(base_uri=schema_uri, referrer=__file__) def _build_validator(filename: str) -> jsonschema.Draft7Validator: """ Return a validator that checks against a given schema. :param filename: The relative path to the schema, e.g. "base_node.schema.json" """ path = os.path.join(HERE, filename) with open(path) as json_schema: schema = json.load(json_schema) return jsonschema.Draft7Validator(schema, resolver=RESOLVER) def _validate(validator: jsonschema.Draft7Validator, entity: Mapping[str, Any]) -> bool: """ Determine whether a given entity is valid based on its JSON schema. :param input: A dict representing a PyBEL entity. :return: if the input is valid """ try: validator.validate(entity) return True except jsonschema.exceptions.ValidationError as err: logger.info(err) return False node_validator = _build_validator(NODE_FILENAME) edge_validator = _build_validator(EDGE_FILENAME) def is_valid_node(node: Mapping[str, Any]) -> bool: """ Determine whether a given node is valid based on the node's JSON schema. :param node: A dict representing a PyBEL node. :return: if the node is valid """ return _validate(node_validator, node) def is_valid_edge(edge: Mapping[str, Any]) -> bool: """ Determine whether a given edge is valid based on the edge's JSON schema. :param node: A dict representing an edge between two PyBEL nodes. :return: if the edge is valid """ return _validate(edge_validator, edge) pybel-0.15.5/src/pybel/schema/base_node.schema.json000066400000000000000000000024351426625374700221540ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Node", "description": "Top-level schema to validate JSON files containing any node.", "type": "object", "allOf": [ { "$comment": "Check if the key 'fusion' is present. If so, validate based on the fusion schema.", "if": { "required": ["fusion"] }, "then": { "$ref": "node_types/fusion.schema.json" } }, { "$comment": "Check whether the node is a Reaction. If so, validate accordingly.", "if": { "properties": {"function": {"const": "Reaction"}} }, "then": { "$ref": "node_types/reaction.schema.json" } }, { "$comment": "Check whether the node is a Complex or Composite. If so, validate as a ListAbundance; if not, validate as a BaseAbundance.", "if": { "properties": {"function": {"enum": ["Complex", "Composite"]}} }, "then": { "$ref": "node_types/list_abundance.schema.json" }, "else": { "$ref": "node_types/base_abundance.schema.json" } } ] }pybel-0.15.5/src/pybel/schema/edge.schema.json000066400000000000000000000013071426625374700211360ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Edge", "description": "Top-level schema to validate JSON files containing an edge.", "type": "object", "properties": { "source": {"$ref": "modifier.schema.json"}, "relation": {"type": "string"}, "target": {"$ref": "modifier.schema.json"}, "evidence": {"type": "string"}, "citation": { "type": "object", "properties": { "db": {"type": "string"}, "db_id": {"type": "string"} } }, "annotations": { "type": "object" } }, "required": ["source", "relation", "target"] }pybel-0.15.5/src/pybel/schema/entity.schema.json000066400000000000000000000012121426625374700215410ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Entity", "description": "Schema to validate a named entity with a namespace and a name/identifier", "type": "object", "properties": { "namespace": { "description": "Namespace of the node, e.g. ChemBL", "type": "string" }, "name": { "description": "Name of the node, e.g. ACE2", "type": "string" }, "identifier": {"type": "string"} }, "anyOf": [ {"required": ["namespace"]}, {"required": ["name"]}, {"required": ["identifier"]} ] }pybel-0.15.5/src/pybel/schema/modifier.schema.json000066400000000000000000000024231426625374700220300ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Subject/Object Modifier", "description": "Schema to validate JSON files containing a modifier for a subject/object node in an edge.", "type": "object", "allOf": [ {"$ref": "base_node.schema.json"}, { "properties": { "modifier": { "type": "string" }, "effect": { "type": "object", "oneOf": [ { "$comment": "Generally, the effect entry will be a pybel.dsl.Entity.", "$ref": "entity.schema.json" }, { "$comment": "However, translocations have a slightly different format.", "properties": { "fromLoc": {"$ref": "entity.schema.json"}, "toLoc": {"$ref": "entity.schema.json"} }, "required": ["fromLoc", "toLoc"] } ] }, "location": {"$ref": "entity.schema.json"} } } ] }pybel-0.15.5/src/pybel/schema/node_types/000077500000000000000000000000001426625374700202505ustar00rootroot00000000000000pybel-0.15.5/src/pybel/schema/node_types/base_abundance.schema.json000066400000000000000000000042431426625374700253170ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL BaseAbundance Node", "description": "Schema to validate JSON files containing a BaseAbundance (or subclass) node.", "type": "object", "properties": { "function": {"description": "The function of the node."}, "concept": {"$ref": "../entity.schema.json"}, "xrefs": { "description": "An optional list of extra identifiers for the node.", "type": "array", "items": {"$ref": "../entity.schema.json"} } }, "allOf": [ { "if": { "properties": {"function": {"const": "Protein"}} }, "then": { "properties": { "variants": { "type": "array", "items": { "anyOf": [ {"$ref": "variants/protein_mod.schema.json"}, {"$ref": "variants/hgvs/base_hgvs.schema.json"}, {"$ref": "variants/fragment.schema.json"} ] } } } } }, { "if": { "properties": {"function": {"const": "Gene"}} }, "then": { "properties": { "variants": { "type": "array", "items": { "$ref": "variants/gene_mod.schema.json" } } } } }, { "if": { "not": { "properties": {"function": {"enum": ["Protein", "Gene"]}} } }, "then": { "properties": { "variants": { "type": "array", "items": { "$ref": "variants/base_variant.schema.json" } } } } } ], "required": ["function"] } pybel-0.15.5/src/pybel/schema/node_types/fusion.schema.json000066400000000000000000000020611426625374700237040ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Fusion Node", "description": "Basic schema to validate JSON files describing a BEL fusion object", "type": "object", "properties": { "fusion": { "type": "object", "properties": { "partner_5p": { "description": "5-prime fusion partner", "$ref": "base_abundance.schema.json" }, "partner_3p": { "description": "3-prime fusion partner", "$ref": "base_abundance.schema.json" }, "range_5p": { "description": "Fusion range for the 5-prime partner", "$ref": "fusion_range.schema.json" }, "range_3p": { "description": "Fusion range for the 3-prime partner", "$ref": "fusion_range.schema.json" } } } }, "required": ["function", "fusion"] }pybel-0.15.5/src/pybel/schema/node_types/fusion_range.schema.json000066400000000000000000000013401426625374700250570ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL FusionRange Node", "description": "Schema to validate JSON files describing a BEL FusionRange object", "type": "object", "properties": { "reference": { "description": "The reference code", "type": "string" }, "left": { "description": "The start position", "type": ["string", "integer"] }, "right": { "description": "The stop position", "type": ["string", "integer"] }, "missing": {"const": "?"} }, "oneOf": [ {"required": ["reference", "left", "right"]}, {"required": ["missing"]} ] }pybel-0.15.5/src/pybel/schema/node_types/list_abundance.schema.json000066400000000000000000000020341426625374700253540ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL ListAbundance Node", "description": "Schema to validate JSON files containing a ListAbundance (ComplexAbundance or CompositeAbundance) node.", "type": "object", "properties": { "function": {"enum": ["Complex", "Composite"]}, "members": { "type": "array", "items": {"$ref": "base_abundance.schema.json"} }, "concept": {"$ref": "../entity.schema.json"}, "xrefs": { "description": "An optional list of extra identifiers for the node.", "type": "array", "items": {"$ref": "../entity.schema.json"} }, "variants": { "type": "array", "items": {"$ref": "variants/base_variant.schema.json"} } }, "if": { "properties": { "function": {"const": "Complex"} } }, "then": { "required": ["function", "concept"] }, "else": { "required": ["function"] } }pybel-0.15.5/src/pybel/schema/node_types/reaction.schema.json000066400000000000000000000010731426625374700242070ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Reaction Node", "description": "Schema to validate JSON files containing a Reaction node.", "type": "object", "properties": { "function": {"const": "Reaction"}, "reactants": { "type": "array", "items": {"$ref": "base_abundance.schema.json"} }, "products": { "type": "array", "items": {"$ref": "base_abundance.schema.json"} } }, "required": ["function", "reactants", "products"] }pybel-0.15.5/src/pybel/schema/node_types/variants/000077500000000000000000000000001426625374700220775ustar00rootroot00000000000000pybel-0.15.5/src/pybel/schema/node_types/variants/base_variant.schema.json000066400000000000000000000022651426625374700266740ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Variant Node", "description": "Basic schema to validate JSON files describing a BEL variant object", "type": "object", "properties": { "concept": { "description": "Entity description if the variant is a protein or gene modification", "$ref": "../../entity.schema.json" }, "code": { "description": "Amino acid code for the affected residue in a protein modification", "enum": [ "Ala", "Arg", "Asn", "Asp", "Cys", "Glu", "Gln", "Gly", "His", "Ile", "Leu", "Lys", "Met", "Phe", "Pro", "Ser", "Thr", "Trp", "Tyr", "Val" ] }, "pos": { "description": "Position of the affected residue in a protein modification", "type": "number" }, "hgvs": { "description": "HGVS variant string", "type": "string" }, "start": { "description": "Starting position of a protein fragment", "type": ["number", "string"] }, "stop": { "description": "Stopping position of a protein fragment", "type": ["number", "string"] } } } pybel-0.15.5/src/pybel/schema/node_types/variants/fragment.schema.json000066400000000000000000000006271426625374700260410ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Variant Node", "description": "Schema to validate JSON files describing a protein fragment variant.", "type": "object", "allOf": [ { "$ref": "base_variant.schema.json" }, { "properties": { "kind": {"const": "frag"} } } ] } pybel-0.15.5/src/pybel/schema/node_types/variants/gene_mod.schema.json000066400000000000000000000006301426625374700260050ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Variant Node", "description": "Schema to validate JSON files describing a gene modification variant.", "type": "object", "allOf": [ { "$ref": "base_variant.schema.json" }, { "properties": { "kind": {"const": "gmod"} } } ] } pybel-0.15.5/src/pybel/schema/node_types/variants/hgvs/000077500000000000000000000000001426625374700230465ustar00rootroot00000000000000pybel-0.15.5/src/pybel/schema/node_types/variants/hgvs/base_hgvs.schema.json000066400000000000000000000007031426625374700271410ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Variant Node", "description": "Schema to validate JSON files describing a HGVS variant.", "type": "object", "allOf": [ { "$ref": "../base_variant.schema.json" }, { "properties": { "kind": {"const": "hgvs"} } }, { "required": ["hgvs"] } ] }pybel-0.15.5/src/pybel/schema/node_types/variants/hgvs/gene_hgvs.schema.json000066400000000000000000000006651426625374700271540ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Variant Node", "description": "Schema to validate JSON files describing a gene HGVS variant.", "type": "object", "allOf": [ { "$ref": "base_hgvs.schema.json" }, { "properties": { "hgvs": { "pattern": "^[cg]" } } } ] }pybel-0.15.5/src/pybel/schema/node_types/variants/hgvs/protein_hgvs.schema.json000066400000000000000000000006651426625374700277160ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Variant Node", "description": "Schema to validate JSON files describing a protein HGVS variant.", "type": "object", "allOf": [ { "$ref": "base_hgvs.schema.json" }, { "properties": { "hgvs": { "pattern": "^p" } } } ] }pybel-0.15.5/src/pybel/schema/node_types/variants/hgvs/rna_hgvs.schema.json000066400000000000000000000006611426625374700270120ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Variant Node", "description": "Schema to validate JSON files describing a RNA HGVS variant.", "type": "object", "allOf": [ { "$ref": "base_hgvs.schema.json" }, { "properties": { "hgvs": { "pattern": "^r" } } } ] }pybel-0.15.5/src/pybel/schema/node_types/variants/protein_mod.schema.json000066400000000000000000000006331426625374700265520ustar00rootroot00000000000000{ "$schema": "https://json-schema.org/draft/2019-09/schema#", "title": "BEL Variant Node", "description": "Schema to validate JSON files describing a protein modification variant.", "type": "object", "allOf": [ { "$ref": "base_variant.schema.json" }, { "properties": { "kind": {"const": "pmod"} } } ] } pybel-0.15.5/src/pybel/struct/000077500000000000000000000000001426625374700161635ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/__init__.py000066400000000000000000000031601426625374700202740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """The :mod:`pybel.struct` module houses functions for handling the main data structure in PyBEL. Because BEL expresses how biological entities interact within many different contexts, with descriptive annotations, PyBEL represents data as a directed multi-graph by sub-classing the :class:`networkx.MultiDiGraph`. Each node is an instance of a subclass of the :class:`pybel.dsl.BaseEntity` and each edge has a stable key and associated data dictionary for storing relevant contextual information. The graph contains metadata for the PyBEL version, the BEL script metadata, the namespace definitions, the annotation definitions, and the warnings produced in analysis. Like any :mod:`networkx` graph, all attributes of a given object can be accessed through the :code:`graph` property, like in: :code:`my_graph.graph['my key']`. Convenient property definitions are given for these attributes that are outlined in the documentation for :class:`pybel.BELGraph`. This allows for much easier programmatic access to answer more complicated questions, which can be written with python code. Because the data structure is the same in Neo4J, the data can be directly exported with :func:`pybel.to_neo4j`. Neo4J supports the Cypher querying language so that the same queries can be written in an elegant and simple way. """ from . import filters, graph, grouping, mutation, node_utils, operations, summary from .filters import * from .graph import * from .grouping import * from .mutation import * from .node_utils import * from .operations import * from .pipeline import Pipeline from .query import Query from .summary import * pybel-0.15.5/src/pybel/struct/filters/000077500000000000000000000000001426625374700176335ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/filters/__init__.py000066400000000000000000000014311426625374700217430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains functions for filtering node and edge iterables. It relies heavily on the concepts of `functional programming `_ and the concept of `predicates `_. """ from . import ( edge_filters, edge_predicate_builders, edge_predicates, node_filters, node_predicate_builders, node_predicates, typing, utils, ) from .edge_filters import * from .edge_predicate_builders import * from .edge_predicates import * from .node_filters import * from .node_predicate_builders import * from .node_predicates import * from .typing import * from .utils import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/filters/edge_filters.py000066400000000000000000000052231426625374700226430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Filter functions for edges in BEL graphs. A edge predicate is a function that takes five arguments: a :class:`BELGraph`, a source node, a target node, a key, and a data dictionary. It returns a boolean representing whether the edge passed the given test. This module contains a set of default functions for filtering lists of edges and building edge predicate functions. A general use for an edge predicate is to use the built-in :func:`filter` in code like :code:`filter(your_edge_predicate, graph.edges(keys=True, data=True))` """ from typing import Iterable from .typing import EdgeIterator, EdgePredicate, EdgePredicates from ..graph import BELGraph from ...dsl import BaseEntity __all__ = [ "invert_edge_predicate", "and_edge_predicates", "filter_edges", "count_passed_edge_filter", ] def invert_edge_predicate(edge_predicate: EdgePredicate) -> EdgePredicate: # noqa: D202 """Build an edge predicate that is the inverse of the given edge predicate.""" def _inverse_filter(graph, u, v, k): return not edge_predicate(graph, u, v, k) return _inverse_filter def and_edge_predicates(edge_predicates: EdgePredicates) -> EdgePredicate: """Concatenate multiple edge predicates to a new predicate that requires all predicates to be met.""" # If something that isn't a list or tuple is given, assume it's a function and return it if not isinstance(edge_predicates, Iterable): return edge_predicates edge_predicates = tuple(edge_predicates) # If only one predicate is given, don't bother wrapping it if 1 == len(edge_predicates): return edge_predicates[0] def concatenated_edge_predicate(graph: BELGraph, u: BaseEntity, v: BaseEntity, k: str) -> bool: """Pass only for an edge that pass all enclosed predicates. :return: If the edge passes all enclosed predicates """ return all(edge_predicate(graph, u, v, k) for edge_predicate in edge_predicates) return concatenated_edge_predicate def filter_edges(graph: BELGraph, edge_predicates: EdgePredicates) -> EdgeIterator: """Apply a set of filters to the edges iterator of a BEL graph. :return: An iterable of edges that pass all predicates """ compound_edge_predicate = and_edge_predicates(edge_predicates=edge_predicates) for u, v, k in graph.edges(keys=True): if compound_edge_predicate(graph, u, v, k): yield u, v, k def count_passed_edge_filter(graph: BELGraph, edge_predicates: EdgePredicates) -> int: """Return the number of edges passing a given set of predicates.""" return sum(1 for _ in filter_edges(graph, edge_predicates=edge_predicates)) pybel-0.15.5/src/pybel/struct/filters/edge_predicate_builders.py000066400000000000000000000160411426625374700250240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for predicates for edge data from BEL graphs.""" from typing import Iterable, Mapping from .edge_filters import invert_edge_predicate from .edge_predicates import ( edge_predicate, has_authors, has_pubmed, true_edge_predicate, ) from .typing import EdgePredicate from ..graph import BELGraph from ...constants import ( ANNOTATIONS, CAUSAL_RELATIONS, CITATION, CITATION_AUTHORS, IDENTIFIER, RELATION, ) from ...dsl import BaseEntity from ...typing import EdgeData, Strings __all__ = [ "build_pmid_exclusion_filter", "build_annotation_dict_all_filter", "build_annotation_dict_any_filter", "build_upstream_edge_predicate", "build_downstream_edge_predicate", "build_relation_predicate", "build_pmid_inclusion_filter", "build_pmid_exclusion_filter", "build_author_inclusion_filter", ] def _annotation_dict_all_filter(edge_data: EdgeData, query: Mapping[str, Iterable[str]]) -> bool: """Match edges with the given dictionary as a sub-dictionary. :param query: The annotation query dict to match """ annotations = edge_data.get(ANNOTATIONS) if annotations is None: return False for key, values in query.items(): ak = annotations.get(key) if ak is None: return False for value in values: if value not in ak: return False return True def build_annotation_dict_all_filter(annotations: Mapping[str, Iterable[str]]) -> EdgePredicate: """Build an edge predicate for edges whose annotations are super-dictionaries of the given dictionary. If no annotations are given, will always evaluate to true. :param annotations: The annotation query dict to match """ if not annotations: return true_edge_predicate @edge_predicate def annotation_dict_all_filter(edge_data: EdgeData) -> bool: """Check if the all of the annotations in the enclosed query match.""" return _annotation_dict_all_filter(edge_data, query=annotations) return annotation_dict_all_filter def _annotation_dict_any_filter(edge_data: EdgeData, query: Mapping[str, Iterable[str]]) -> bool: """Match edges with the given dictionary as a sub-dictionary. :param query: The annotation query dict to match """ annotations = edge_data.get(ANNOTATIONS) if annotations is None: return False return any(key in annotations and value in annotations[key] for key, values in query.items() for value in values) def build_annotation_dict_any_filter(annotations: Mapping[str, Iterable[str]]) -> EdgePredicate: """Build an edge predicate that passes for edges whose data dictionaries match the given dictionary. If the given dictionary is empty, will always evaluate to true. :param annotations: The annotation query dict to match """ if not annotations: return true_edge_predicate @edge_predicate def annotation_dict_any_filter(edge_data: EdgeData) -> bool: """Check if the any of the annotations in the enclosed query match.""" return _annotation_dict_any_filter(edge_data, query=annotations) return annotation_dict_any_filter def build_upstream_edge_predicate(nodes: Iterable[BaseEntity]) -> EdgePredicate: """Build an edge predicate that pass for relations for which one of the given nodes is the object.""" nodes = set(nodes) def upstream_filter(graph: BELGraph, u: BaseEntity, v: BaseEntity, k: str) -> bool: """Pass for relations for which one of the given nodes is the object.""" return v in nodes and graph[u][v][k][RELATION] in CAUSAL_RELATIONS return upstream_filter def build_downstream_edge_predicate(nodes: Iterable[BaseEntity]) -> EdgePredicate: """Build an edge predicate that passes for edges for which one of the given nodes is the subject.""" nodes = set(nodes) def downstream_filter(graph: BELGraph, u: BaseEntity, v: BaseEntity, k: str) -> bool: """Pass for relations for which one of the given nodes is the subject.""" return u in nodes and graph[u][v][k][RELATION] in CAUSAL_RELATIONS return downstream_filter def build_relation_predicate(relations: Strings) -> EdgePredicate: """Build an edge predicate that passes for edges with the given relation.""" if isinstance(relations, str): @edge_predicate def relation_predicate(edge_data: EdgeData) -> bool: """Pass for relations matching the enclosed value.""" return edge_data[RELATION] == relations elif isinstance(relations, Iterable): relation_set = set(relations) @edge_predicate def relation_predicate(edge_data: EdgeData) -> bool: """Pass for relations matching the enclosed values.""" return edge_data[RELATION] in relation_set else: raise TypeError return relation_predicate def build_pmid_inclusion_filter(pmids: Strings) -> EdgePredicate: """Build an edge predicate that passes for edges with citations from the given PubMed identifier(s). :param pmids: A PubMed identifier or list of PubMed identifiers to filter for """ if isinstance(pmids, str): @edge_predicate def pmid_inclusion_filter(edge_data: EdgeData) -> bool: """Pass for edges with PubMed citations matching the contained PubMed identifier.""" return has_pubmed(edge_data) and edge_data[CITATION].identifier == pmids elif isinstance(pmids, Iterable): pmids = set(pmids) @edge_predicate def pmid_inclusion_filter(edge_data: EdgeData) -> bool: """Pass for edges with PubMed citations matching one of the contained PubMed identifiers.""" return has_pubmed(edge_data) and edge_data[CITATION].identifier in pmids else: raise TypeError return pmid_inclusion_filter def build_pmid_exclusion_filter(pmids: Strings) -> EdgePredicate: """Fail for edges with citations whose references are one of the given PubMed identifiers. :param pmids: A PubMed identifier or list of PubMed identifiers to filter against """ return invert_edge_predicate(build_pmid_inclusion_filter(pmids)) def build_author_inclusion_filter(authors: Strings) -> EdgePredicate: """Build an edge predicate that passes for edges with citations written by the given author(s).""" if isinstance(authors, str): @edge_predicate def author_filter(edge_data: EdgeData) -> bool: """Pass for edges with citations with an author that matches the contained author.""" return has_authors(edge_data) and authors in edge_data[CITATION][CITATION_AUTHORS] elif isinstance(authors, Iterable): authors = set(authors) @edge_predicate def author_filter(edge_data: EdgeData) -> bool: """Pass for edges with citations with an author that matches one or more of the contained authors.""" return has_authors(edge_data) and any(author in edge_data[CITATION][CITATION_AUTHORS] for author in authors) else: raise TypeError return author_filter pybel-0.15.5/src/pybel/struct/filters/edge_predicates.py000066400000000000000000000144631426625374700233240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Predicates for edge data from BEL graphs.""" from functools import wraps from typing import Any, Callable, Optional from .typing import EdgePredicate from .utils import part_has_modifier from ..graph import BELGraph from ...constants import ( ACTIVITY, ANNOTATIONS, ASSOCIATION, CAUSAL_RELATIONS, CITATION, CITATION_AUTHORS, DEGRADATION, DIRECT_CAUSAL_RELATIONS, EVIDENCE, NAMESPACE, POLAR_RELATIONS, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, TRANSLOCATION, ) from ...dsl import BaseEntity, BiologicalProcess, Pathology from ...typing import EdgeData __all__ = [ "edge_predicate", "true_edge_predicate", "false_edge_predicate", "has_provenance", "has_pubmed", "has_pmc", "has_authors", "is_causal_relation", "not_causal_relation", "is_direct_causal_relation", "is_associative_relation", "has_polarity", "edge_has_activity", "edge_has_degradation", "edge_has_translocation", "edge_has_annotation", "has_pathology_causal", ] DictEdgePredicate = Callable[[EdgeData], bool] def edge_predicate(func: DictEdgePredicate) -> EdgePredicate: # noqa: D202 """Decorate an edge predicate function that only takes a dictionary as its singular argument. Apply this as a decorator to a function that takes a single argument, a PyBEL node data dictionary, to make sure that it can also accept a pair of arguments, a BELGraph and a PyBEL node tuple as well. """ @wraps(func) def _wrapped(*args): x = args[0] if isinstance(x, BELGraph): u, v, k = args[1:4] return func(x[u][v][k]) return func(*args) return _wrapped def true_edge_predicate(graph: BELGraph, u: BaseEntity, v: BaseEntity, k: str) -> bool: """Return true for all edges.""" return True def false_edge_predicate(graph: BELGraph, u: BaseEntity, v: BaseEntity, k: str) -> bool: """Return false for all edges.""" return False @edge_predicate def has_provenance(edge_data: EdgeData) -> bool: """Check if the edge has provenance information (i.e. citation and evidence).""" return CITATION in edge_data and EVIDENCE in edge_data @edge_predicate def has_pubmed(edge_data: EdgeData) -> bool: """Check if the edge has a PubMed citation.""" return CITATION in edge_data and edge_data[CITATION][NAMESPACE].lower() in ( "pubmed", "pmid", ) @edge_predicate def has_pmc(edge_data: EdgeData) -> bool: """Check if the edge has a PMC citation.""" return CITATION in edge_data and edge_data[CITATION][NAMESPACE].lower() in ( "pmc", "pmcid", ) CITATION_PREDICATES = { "pubmed": has_pubmed, "pmc": has_pmc, } @edge_predicate def has_authors(edge_data: EdgeData) -> bool: """Check if the edge contains author information for its citation.""" return CITATION in edge_data and CITATION_AUTHORS in edge_data[CITATION] and edge_data[CITATION][CITATION_AUTHORS] @edge_predicate def is_causal_relation(edge_data: EdgeData) -> bool: """Check if the given relation is causal.""" return edge_data[RELATION] in CAUSAL_RELATIONS @edge_predicate def not_causal_relation(edge_data: EdgeData) -> bool: """Check if the given relation is not causal.""" return edge_data[RELATION] not in CAUSAL_RELATIONS @edge_predicate def is_direct_causal_relation(edge_data: EdgeData) -> bool: """Check if the edge is a direct causal relation.""" return edge_data[RELATION] in DIRECT_CAUSAL_RELATIONS @edge_predicate def is_associative_relation(edge_data: EdgeData) -> bool: """Check if the edge has an association relation.""" return edge_data[RELATION] == ASSOCIATION @edge_predicate def has_polarity(edge_data: EdgeData) -> bool: """Check if the edge has polarity.""" return edge_data[RELATION] in POLAR_RELATIONS def _has_modifier(edge_data: EdgeData, modifier: str) -> bool: """Check if the edge has the given modifier. :param edge_data: The edge data dictionary :param modifier: The modifier to check. One of :data:`pybel.constants.ACTIVITY`, :data:`pybel.constants.DEGRADATION`, or :data:`pybel.constants.TRANSLOCATION`. :return: Does either the subject or object have the given modifier """ return part_has_modifier(edge_data, SOURCE_MODIFIER, modifier) or part_has_modifier( edge_data, TARGET_MODIFIER, modifier ) @edge_predicate def edge_has_activity(edge_data: EdgeData) -> bool: """Check if the edge contains an activity in either the subject or object.""" return _has_modifier(edge_data, ACTIVITY) @edge_predicate def edge_has_translocation(edge_data: EdgeData) -> bool: """Check if the edge has a translocation in either the subject or object.""" return _has_modifier(edge_data, TRANSLOCATION) @edge_predicate def edge_has_degradation(edge_data: EdgeData) -> bool: """Check if the edge contains a degradation in either the subject or object.""" return _has_modifier(edge_data, DEGRADATION) def edge_has_annotation(edge_data: EdgeData, key: str) -> Optional[Any]: """Check if an edge has the given annotation. :param edge_data: The data dictionary from a BELGraph's edge :param key: An annotation key :return: If the annotation key is present in the current data dictionary For example, it might be useful to print all edges that are annotated with 'Subgraph': >>> from pybel.examples import sialic_acid_graph >>> from pybel.examples.sialic_acid_example import sialic_acid_cd33_complex, cd33 >>> edges = { ... (u, v) ... for u, v, data in sialic_acid_graph.edges(data=True) ... if edge_has_annotation(data, 'Species') ... } >>> assert (sialic_acid_cd33_complex, cd33) in edges """ annotations = edge_data.get(ANNOTATIONS) if annotations is None: return None return annotations.get(key) def has_pathology_causal(graph: BELGraph, u: BaseEntity, v: BaseEntity, k: str) -> bool: """Check if the subject is a pathology and has a causal relationship with a non bioprocess/pathology. :return: If the subject of this edge is a pathology and it participates in a causal reaction. """ return ( isinstance(u, Pathology) and is_causal_relation(graph, u, v, k) and not isinstance(v, (Pathology, BiologicalProcess)) ) pybel-0.15.5/src/pybel/struct/filters/node_filters.py000066400000000000000000000050401426625374700226610ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Filter functions for nodes in BEL graphs. A node predicate is a function that takes two arguments: a :class:`BELGraph` and a node. It returns a boolean representing whether the node passed the given test. This module contains a set of default functions for filtering lists of nodes and building node predicates. A general use for a node predicate is to use the built-in :func:`filter` in code like :code:`filter(your_node_predicate, graph)` """ from typing import Iterable, Set from .node_predicate_builders import ( function_inclusion_filter_builder, namespace_inclusion_builder, ) from .node_predicates import concatenate_node_predicates from .typing import NodePredicates from ..graph import BELGraph from ...dsl import BaseEntity from ...typing import Strings __all__ = [ "filter_nodes", "get_nodes", "count_passed_node_filter", "summarize_node_filter", "get_nodes_by_function", "get_nodes_by_namespace", ] def filter_nodes(graph: BELGraph, node_predicates: NodePredicates) -> Iterable[BaseEntity]: """Apply a set of predicates to the nodes iterator of a BEL graph.""" concatenated_predicate = concatenate_node_predicates(node_predicates=node_predicates) for node in graph: if concatenated_predicate(graph, node): yield node def get_nodes(graph: BELGraph, node_predicates: NodePredicates) -> Set[BaseEntity]: """Get the set of all nodes that pass the predicates.""" return set(filter_nodes(graph, node_predicates=node_predicates)) def count_passed_node_filter(graph: BELGraph, node_predicates: NodePredicates) -> int: """Count how many nodes pass a given set of node predicates.""" return sum(1 for _ in filter_nodes(graph, node_predicates=node_predicates)) def summarize_node_filter(graph: BELGraph, node_filters: NodePredicates) -> None: """Print a summary of the number of nodes passing a given set of filters. :param graph: A BEL graph :param node_filters: A node filter or list/tuple of node filters """ passed = count_passed_node_filter(graph, node_filters) print("{}/{} nodes passed".format(passed, graph.number_of_nodes())) def get_nodes_by_function(graph: BELGraph, func: Strings) -> Set[BaseEntity]: """Get all nodes with the given function(s).""" return get_nodes(graph, function_inclusion_filter_builder(func)) def get_nodes_by_namespace(graph, namespaces: Strings) -> Set[BaseEntity]: """Get all nodes identified by the given namespace(s).""" return get_nodes(graph, namespace_inclusion_builder(namespaces)) pybel-0.15.5/src/pybel/struct/filters/node_predicate_builders.py000066400000000000000000000145361426625374700250540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for building node predicates.""" from typing import Any, Callable, Iterable, List, Union from .node_predicates import concatenate_node_predicates, invert_node_predicate from .typing import NodePredicate from ..graph import BELGraph from ...constants import CONCEPT, NAME from ...dsl import BaseConcept, BaseEntity from ...typing import Strings __all__ = [ "function_inclusion_filter_builder", "function_exclusion_filter_builder", "data_missing_key_builder", "build_node_data_search", "build_node_graph_data_search", "build_node_key_search", "build_node_name_search", "namespace_inclusion_builder", ] def function_inclusion_filter_builder(func: Strings) -> NodePredicate: """Build a filter that only passes on nodes of the given function(s). :param func: A BEL Function or list/set/tuple of BEL functions """ if isinstance(func, str): return _single_function_inclusion_filter_builder(func) elif isinstance(func, Iterable): return _collection_function_inclusion_builder(func) raise TypeError("Invalid type for argument: {}".format(func)) def _single_function_inclusion_filter_builder(func: str) -> NodePredicate: # noqa: D202 """Build a function inclusion filter for a single function.""" def function_inclusion_filter(_: BELGraph, node: BaseEntity) -> bool: """Pass only for a node that has the enclosed function.""" return node.function == func return function_inclusion_filter def _collection_function_inclusion_builder(funcs: Iterable[str]) -> NodePredicate: """Build a function inclusion filter for a collection of functions.""" funcs = set(funcs) if not funcs: raise ValueError("can not build function inclusion filter with empty list of functions") def functions_inclusion_filter(_: BELGraph, node: BaseEntity) -> bool: """Pass only for a node that is one of the enclosed functions.""" return node.function in funcs return functions_inclusion_filter def function_exclusion_filter_builder(func: Strings) -> NodePredicate: """Build a filter that fails on nodes of the given function(s). :param func: A BEL Function or list/set/tuple of BEL functions """ return invert_node_predicate(function_inclusion_filter_builder(func)) def data_missing_key_builder(key: str) -> NodePredicate: # noqa: D202 """Build a filter that passes only on nodes that don't have the given key in their data dictionary. :param str key: A key for the node's data dictionary """ def data_does_not_contain_key(graph: BELGraph, node: BaseEntity) -> bool: """Pass only for a node that doesn't contain the enclosed key in its data dictionary.""" return key not in graph.nodes[node] return data_does_not_contain_key def build_node_data_search( # noqa: D202 key: Union[str, List[str]], data_predicate: Callable[[Any], bool], ) -> NodePredicate: """Build a filter for nodes whose associated data with the given key passes the given predicate. :param key: The node data dictionary key to check :param data_predicate: The filter to apply to the node data dictionary """ if isinstance(key, list): _getter = _get_multi_value else: _getter = _get_single_value def node_data_filter(_: BELGraph, node: BaseEntity) -> bool: """Pass if the given node has a given data annotated and passes the contained filter.""" value = _getter(node, key) return value is not None and data_predicate(value) return node_data_filter def build_node_graph_data_search( # noqa: D202 key: Union[str, List[str]], data_predicate: Callable[[Any], bool], ) -> NodePredicate: """Build a function for testing data associated with the node in the graph. :param key: The node data dictionary key to check :param data_predicate: The filter to apply to the node data dictionary """ if isinstance(key, tuple): _getter = _get_multi_value else: _getter = _get_single_value def node_data_filter(graph: BELGraph, node: BaseEntity) -> bool: """Pass if the given node has a given data annotated and passes the contained filter.""" value = _getter(graph.nodes[node], key) return value is not None and data_predicate(value) return node_data_filter def _get_multi_value(d, keys): value = d.get(keys[0]) for key in keys[1:]: if value is None: return value = value.get(key) return value def _get_single_value(d, key): return d.get(key) def build_node_key_search( query: Strings, key: Union[str, List[str]], ) -> NodePredicate: """Build a node filter for nodes whose values for the given key are superstrings of the query string(s). :param query: The query string or strings to check if they're in the node name :param key: The key for the node data dictionary. Should refer only to entries that have str values """ if isinstance(query, str): return build_node_data_search(key, lambda s: query.lower() in s.lower()) if isinstance(query, Iterable): return build_node_data_search(key, lambda s: any(q.lower() in s.lower() for q in query)) raise TypeError("query is wrong type: %s", query) def build_node_name_search(query: Strings) -> NodePredicate: """Search nodes' names. Is a thin wrapper around :func:`build_node_key_search` with :data:`pybel.constants.NAME` :param query: The query string or strings to check if they're in the node name """ return build_node_key_search(query=query, key=[CONCEPT, NAME]) def namespace_inclusion_builder(namespace: Strings) -> NodePredicate: """Build a predicate for namespace inclusion.""" if isinstance(namespace, str): def namespace_filter(_: BELGraph, node: BaseEntity) -> bool: """Pass only for a node that has the enclosed namespace.""" return isinstance(node, BaseConcept) and node.namespace == namespace elif isinstance(namespace, Iterable): namespaces = set(namespace) def namespace_filter(_: BELGraph, node: BaseEntity) -> bool: """Pass only for a node that has a namespace in the enclosed set.""" return isinstance(node, BaseConcept) and node.namespace in namespaces else: raise TypeError("Invalid type for argument: {}".format(namespace)) return namespace_filter pybel-0.15.5/src/pybel/struct/filters/node_predicates/000077500000000000000000000000001426625374700227635ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/filters/node_predicates/__init__.py000066400000000000000000000026051426625374700250770ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Predicate functions for filtering lists of nodes.""" from .activities import has_activity, has_edge_modifier, is_degraded, is_translocated from .misc import is_isolated_list_abundance, none_of, one_of from .modifications import ( has_fragment, has_gene_modification, has_hgvs, has_protein_modification, has_variant, ) from .relations import ( has_causal_edges, has_causal_in_edges, has_causal_out_edges, has_in_edges, has_out_edges, is_causal_central, is_causal_sink, is_causal_source, no_causal_edges, no_causal_in_edges, no_causal_out_edges, no_in_edges, no_out_edges, ) from .types import ( is_abundance, is_biological_process, is_central_dogma, is_complex, is_composite, is_gene, is_list, is_mirna, is_pathology, is_population, is_protein, is_reaction, is_rna, is_transcribable, not_abundance, not_biological_process, not_central_dogma, not_complex, not_composite, not_gene, not_list, not_mirna, not_pathology, not_population, not_protein, not_reaction, not_rna, not_transcribable, ) from .utils import ( concatenate_node_predicates, false_node_predicate, invert_node_predicate, node_predicate, true_node_predicate, ) __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/filters/node_predicates/activities.py000066400000000000000000000034101426625374700254770ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Pre-defined predicates for nodes.""" from ..utils import part_has_modifier from ...graph import BELGraph from ....constants import ( ACTIVITY, DEGRADATION, SOURCE_MODIFIER, TARGET_MODIFIER, TRANSLOCATION, ) from ....dsl import BaseEntity __all__ = [ "has_edge_modifier", "has_activity", "is_degraded", "is_translocated", ] def has_edge_modifier(graph: BELGraph, node: BaseEntity, modifier: str) -> bool: """Return true if over any of a nodes edges, it has a given modifier. Modifier can be one of: - :data:`pybel.constants.ACTIVITY`, - :data:`pybel.constants.DEGRADATION` - :data:`pybel.constants.TRANSLOCATION`. :param modifier: One of :data:`pybel.constants.ACTIVITY`, :data:`pybel.constants.DEGRADATION`, or :data:`pybel.constants.TRANSLOCATION` """ modifier_in_subject = any( part_has_modifier(d, SOURCE_MODIFIER, modifier) for _, _, d in graph.out_edges(node, data=True) ) modifier_in_object = any( part_has_modifier(d, TARGET_MODIFIER, modifier) for _, _, d in graph.in_edges(node, data=True) ) return modifier_in_subject or modifier_in_object def has_activity(graph: BELGraph, node: BaseEntity) -> bool: """Return true if over any of the node's edges, it has a molecular activity.""" return has_edge_modifier(graph, node, ACTIVITY) def is_degraded(graph: BELGraph, node: BaseEntity) -> bool: """Return true if over any of the node's edges, it is degraded.""" return has_edge_modifier(graph, node, DEGRADATION) def is_translocated(graph: BELGraph, node: BaseEntity) -> bool: """Return true if over any of the node's edges, it is translocated.""" return has_edge_modifier(graph, node, TRANSLOCATION) pybel-0.15.5/src/pybel/struct/filters/node_predicates/misc.py000066400000000000000000000026361426625374700242770ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Misc node predicates.""" from typing import Iterable, Type from .utils import node_predicate from ..typing import NodePredicate from ...graph import BELGraph from ....constants import PART_OF, RELATION from ....dsl import BaseEntity, ListAbundance __all__ = [ "none_of", "one_of", "is_isolated_list_abundance", ] def none_of(nodes: Iterable[BaseEntity]) -> NodePredicate: """Build a node predicate that returns false for the given nodes.""" nodes = set(nodes) @node_predicate def _predicate(node: BaseEntity) -> bool: """Return true if the node is not in the given set of nodes.""" return node not in nodes return _predicate def one_of(nodes: Iterable[BaseEntity]) -> NodePredicate: """Build a function that returns true for the given nodes.""" nodes = set(nodes) @node_predicate def _predicate(node: BaseEntity) -> bool: """Return true if the node is in the given set of nodes.""" return node in nodes return _predicate def is_isolated_list_abundance( graph: BELGraph, node: BaseEntity, cls: Type[ListAbundance] = ListAbundance, ) -> bool: """Return if the node is a list abundance but has no qualified edges.""" return ( isinstance(node, cls) and 0 == graph.out_degree(node) and all(data[RELATION] == PART_OF for _, __, data in graph.in_edges(node, data=True)) ) pybel-0.15.5/src/pybel/struct/filters/node_predicates/modifications.py000066400000000000000000000025761426625374700261770ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Predicates for checking nodes' variants.""" from functools import wraps from typing import Tuple, Type, Union from .utils import node_predicate from ..typing import NodePredicate from ....dsl import ( BaseEntity, CentralDogma, Fragment, GeneModification, Hgvs, ProteinModification, Variant, ) __all__ = [ "has_variant", "has_protein_modification", "has_gene_modification", "has_fragment", "has_hgvs", ] @node_predicate def has_variant(node: BaseEntity) -> bool: """Return true if the node has any variants.""" return isinstance(node, CentralDogma) and node.variants def _variant_checker(variant_cls: Union[Type[Variant], Tuple[Type[Variant], ...]]) -> NodePredicate: @node_predicate @wraps(node_has_variant) def _rv(node: BaseEntity): return node_has_variant(node, variant_cls) return _rv def node_has_variant(node: BaseEntity, variant_cls) -> bool: """Return true if the node has at least one of the given variant.""" return ( isinstance(node, CentralDogma) and node.variants and any(isinstance(variant, variant_cls) for variant in node.variants) ) has_protein_modification = _variant_checker(ProteinModification) has_gene_modification = _variant_checker(GeneModification) has_hgvs = _variant_checker(Hgvs) has_fragment = _variant_checker(Fragment) pybel-0.15.5/src/pybel/struct/filters/node_predicates/relations.py000066400000000000000000000106331426625374700253400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Predicate functions for nodes based on their incident edges' relations.""" from typing import Set from ...graph import BELGraph from ....constants import CAUSAL_RELATIONS, RELATION from ....dsl import BaseEntity __all__ = [ "has_in_edges", "has_causal_out_edges", "has_causal_in_edges", "has_causal_edges", "has_out_edges", "is_causal_central", "is_causal_sink", "is_causal_source", "no_causal_out_edges", "no_causal_in_edges", "no_out_edges", "no_in_edges", "no_causal_edges", ] def has_in_edges(graph: BELGraph, node: BaseEntity, edge_types: Set[str]) -> bool: """Check if the node has any in-edges in the given set. :param graph: A BEL graph :param node: A BEL term :param edge_types: A collection of edge types to check against """ return any(data[RELATION] in edge_types for _, _, data in graph.in_edges(node, data=True)) def no_in_edges(graph: BELGraph, node: BaseEntity, edge_types: Set[str]) -> bool: """Check if the node does not have any in-edges in the given set. :param graph: A BEL graph :param node: A BEL term :param edge_types: A collection of edge types to check against """ return all(data[RELATION] not in edge_types for _, _, data in graph.in_edges(node, data=True)) def has_out_edges(graph: BELGraph, node: BaseEntity, edge_types: Set[str]) -> bool: """Check if the node has any out-edges in the given set. :param graph: A BEL graph :param node: A BEL term :param edge_types: A collection of edge types to check against """ return any(data[RELATION] in edge_types for _, _, data in graph.out_edges(node, data=True)) def no_out_edges(graph: BELGraph, node: BaseEntity, edge_types: Set[str]) -> bool: """Check if the node does not have any out-edges in the given set. :param graph: A BEL graph :param node: A BEL term :param edge_types: A collection of edge types to check against """ return all(data[RELATION] not in edge_types for _, _, data in graph.out_edges(node, data=True)) def has_causal_in_edges(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node has any causal in-edges. :param graph: A BEL graph :param node: A BEL term """ return has_in_edges(graph, node, CAUSAL_RELATIONS) def no_causal_in_edges(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node does not have any causal in-edges. :param graph: A BEL graph :param node: A BEL term """ return no_in_edges(graph, node, CAUSAL_RELATIONS) def has_causal_out_edges(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node has any causal out-edges. :param graph: A BEL graph :param node: A BEL term """ return has_out_edges(graph, node, CAUSAL_RELATIONS) def no_causal_out_edges(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node does not have any causal out-edges. :param graph: A BEL graph :param node: A BEL term """ return no_out_edges(graph, node, CAUSAL_RELATIONS) def has_causal_edges(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node has any causal out-edges or in-edges. :param graph: A BEL graph :param node: A BEL term """ return has_causal_in_edges(graph, node) or has_causal_out_edges(graph, node) def no_causal_edges(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node does not have any causal out-edges or in-edges. :param graph: A BEL graph :param node: A BEL term """ return no_causal_in_edges(graph, node) and no_causal_out_edges(graph, node) def is_causal_source(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node has causal out-edges but no causal in-edges. :param graph: A BEL graph :param node: A BEL term """ return no_causal_in_edges(graph, node) and has_causal_out_edges(graph, node) def is_causal_sink(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node has causal in-edges but no causal out-edges. :param graph: A BEL graph :param node: A BEL term """ return has_causal_in_edges(graph, node) and no_causal_out_edges(graph, node) def is_causal_central(graph: BELGraph, node: BaseEntity) -> bool: """Check if the node has both causal in-edges and also causal out-edges. :param graph: A BEL graph :param node: A BEL term """ return has_causal_in_edges(graph, node) and has_causal_out_edges(graph, node) pybel-0.15.5/src/pybel/struct/filters/node_predicates/types.py000066400000000000000000000053211426625374700245020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Predicates for checking a node's type.""" from typing import Tuple, Type, Union from .utils import node_predicate from ..typing import NodePredicate from ....dsl import ( Abundance, BaseEntity, BiologicalProcess, CentralDogma, ComplexAbundance, CompositeAbundance, Gene, ListAbundance, MicroRna, Pathology, Population, Protein, Reaction, Rna, Transcribable, ) __all__ = [ "is_abundance", "is_biological_process", "is_pathology", "is_transcribable", "is_rna", "is_reaction", "is_protein", "is_population", "is_mirna", "is_list", "is_gene", "is_composite", "is_central_dogma", "is_complex", "not_abundance", "not_biological_process", "not_pathology", "not_rna", "not_reaction", "not_protein", "not_population", "not_mirna", "not_gene", "not_composite", "not_complex", "not_central_dogma", "not_list", "not_transcribable", ] def _type_checker(cls: Union[Type[BaseEntity], Tuple[Type[BaseEntity], ...]]) -> NodePredicate: @node_predicate def _is_type(node: BaseEntity) -> bool: return isinstance(node, cls) return _is_type def _not_type_checker(cls: Union[Type[BaseEntity], Tuple[Type[BaseEntity], ...]]) -> NodePredicate: @node_predicate def _not_type(node: BaseEntity) -> bool: return not isinstance(node, cls) return _not_type is_abundance = _type_checker(Abundance) not_abundance = _not_type_checker(Abundance) is_biological_process = _type_checker(BiologicalProcess) not_biological_process = _not_type_checker(BiologicalProcess) is_pathology = _type_checker(Pathology) not_pathology = _not_type_checker(Pathology) is_population = _type_checker(Population) not_population = _not_type_checker(Population) #: Return true if the node is a gene, RNA, miRNA, or Protein is_central_dogma = _type_checker(CentralDogma) not_central_dogma = _not_type_checker(CentralDogma) is_gene = _type_checker(Gene) not_gene = _not_type_checker(Gene) is_transcribable = _type_checker(Transcribable) not_transcribable = _not_type_checker(Transcribable) is_rna = _type_checker(Rna) not_rna = _not_type_checker(Rna) is_mirna = _type_checker(MicroRna) not_mirna = _not_type_checker(MicroRna) is_protein = _type_checker(Protein) not_protein = _not_type_checker(Protein) is_list = _type_checker(ListAbundance) not_list = _not_type_checker(ListAbundance) is_composite = _type_checker(CompositeAbundance) not_composite = _not_type_checker(CompositeAbundance) is_complex = _type_checker(ComplexAbundance) not_complex = _not_type_checker(ComplexAbundance) is_reaction = _type_checker(Reaction) not_reaction = _not_type_checker(Reaction) pybel-0.15.5/src/pybel/struct/filters/node_predicates/utils.py000066400000000000000000000065001426625374700244760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Pre-defined predicates for nodes.""" from functools import wraps from typing import Callable, Iterable from ..typing import NodePredicate, NodePredicates from ...graph import BELGraph from ....dsl import BaseEntity __all__ = [ "node_predicate", "invert_node_predicate", "concatenate_node_predicates", "true_node_predicate", "false_node_predicate", ] def node_predicate(f: Callable[[BaseEntity], bool]) -> NodePredicate: # noqa: D202 """Tag a node predicate that takes a dictionary to also accept a pair of (BELGraph, node). Apply this as a decorator to a function that takes a single argument, a PyBEL node, to make sure that it can also accept a pair of arguments, a BELGraph and a PyBEL node as well. """ @wraps(f) def wrapped(*args): x = args[0] if isinstance(x, BELGraph): return f(args[1], *args[2:]) elif isinstance(x, BaseEntity): return f(*args) else: raise TypeError return wrapped def invert_node_predicate(f: NodePredicate) -> NodePredicate: # noqa: D202 """Build a node predicate that is the inverse of the given node predicate.""" def inverse_predicate(graph: BELGraph, node: BaseEntity) -> bool: """Return the inverse of the enclosed node predicate applied to the graph and node.""" return not f(graph, node) return inverse_predicate def concatenate_node_predicates(node_predicates: NodePredicates) -> NodePredicate: """Concatenate multiple node predicates to a new predicate that requires all predicates to be met. Example usage: >>> from pybel import BELGraph >>> from pybel.dsl import Protein >>> from pybel.struct.filters import not_gene, not_rna >>> app_protein = Protein(name='APP', namespace='hgnc', identifier='620') >>> app_rna = app_protein.get_rna() >>> app_gene = app_rna.get_gene() >>> graph = BELGraph() >>> _ = graph.add_transcription(app_gene, app_rna) >>> _ = graph.add_translation(app_rna, app_protein) >>> node_predicate = concatenate_node_predicates([not_rna, not_gene]) >>> assert node_predicate(graph, app_protein) >>> assert not node_predicate(graph, app_rna) >>> assert not node_predicate(graph, app_gene) """ # If a predicate outside a list is given, just return it if not isinstance(node_predicates, Iterable): return node_predicates node_predicates = tuple(node_predicates) # If only one predicate is given, don't bother wrapping it if 1 == len(node_predicates): return node_predicates[0] def concatenated_node_predicate(graph: BELGraph, node: BaseEntity) -> bool: """Pass only for a nodes that pass all enclosed predicates.""" return all(node_predicate(graph, node) for node_predicate in node_predicates) return concatenated_node_predicate @node_predicate def true_node_predicate(_: BaseEntity) -> bool: """Return true for all nodes. Given BEL graph :code:`graph`, applying :func:`true_predicate` with a predicate on the nodes iterable as in :code:`filter(keep_node_permissive, graph)` will result in the same iterable as iterating directly over a :class:`BELGraph` """ return True @node_predicate def false_node_predicate(_: BaseEntity) -> bool: """Return false for all nodes.""" return False pybel-0.15.5/src/pybel/struct/filters/typing.py000066400000000000000000000011771426625374700215250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Types for filters.""" from typing import Callable, Iterable, Tuple, Union from ..graph import BELGraph from ...dsl import BaseEntity __all__ = [ "NodePredicate", "NodePredicates", "EdgeTuple", "EdgeIterator", "EdgePredicate", "EdgePredicates", ] NodePredicate = Callable[[BELGraph, BaseEntity], bool] NodePredicates = Union[NodePredicate, Iterable[NodePredicate]] EdgeTuple = Tuple[BaseEntity, BaseEntity, str] EdgeIterator = Iterable[EdgeTuple] EdgePredicate = Callable[[BELGraph, BaseEntity, BaseEntity, str], bool] EdgePredicates = Union[EdgePredicate, Iterable[EdgePredicate]] pybel-0.15.5/src/pybel/struct/filters/utils.py000066400000000000000000000012031426625374700213410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for node filters.""" from ...constants import MODIFIER from ...typing import EdgeData __all__ = [ "part_has_modifier", ] def part_has_modifier(edge_data: EdgeData, part: str, modifier: str) -> bool: """Return true if the modifier is in the given subject/object part. :param edge_data: PyBEL edge data dictionary :param part: either :data:`pybel.constants.SUBJECT` or :data:`pybel.constants.OBJECT` :param modifier: The modifier to look for """ part_data = edge_data.get(part) if part_data is None: return False return part_data.get(MODIFIER) == modifier pybel-0.15.5/src/pybel/struct/getters.py000066400000000000000000000036551426625374700202230ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Misc. getters.""" from typing import Iterable, Tuple from .graph import BELGraph from ..constants import ( CAUSAL_DECREASE_RELATIONS, CAUSAL_INCREASE_RELATIONS, DIRECTLY_DECREASES, DIRECTLY_INCREASES, RELATION, ) from ..dsl import ComplexAbundance, Gene, Protein, Rna __all__ = [ "get_tf_pairs", ] def get_tf_pairs(graph: BELGraph, direct_only: bool = False) -> Iterable[Tuple[Protein, Rna, int]]: """Iterate pairs of ``p(X)`` and ``r(Y)`` such that ``complex(p(X), g(Y)) -> r(Y)``. :param graph: A BEL graph :param direct_only: If true, only uses directlyIncreases and directlyDecreases relations. Otherwise, allows indirect relations. """ if direct_only: _inc, _dec = {DIRECTLY_INCREASES}, {DIRECTLY_DECREASES} else: _inc, _dec = CAUSAL_INCREASE_RELATIONS, CAUSAL_DECREASE_RELATIONS for tf in _iterate_proteins(graph): for tf_gene in graph[tf]: if not isinstance(tf_gene, ComplexAbundance): continue if tf not in tf_gene.members: continue other_members = [m for m in tf_gene.members if m != tf] if 1 != len(other_members): continue target_gene = other_members[0] if not isinstance(target_gene, Gene): continue if target_gene.variants: target_gene = target_gene.get_parent() target_rna = target_gene.get_rna() if target_rna not in graph: continue for edge in graph[tf_gene][target_rna].values(): relation = edge[RELATION] if relation in _inc: yield tf, target_rna, +1 elif relation in _dec: yield tf, target_rna, -1 def _iterate_proteins(graph: BELGraph) -> Iterable[Protein]: return (node for node in graph if isinstance(node, Protein)) pybel-0.15.5/src/pybel/struct/graph.py000066400000000000000000001452151426625374700176460ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Contains the main data structure for PyBEL.""" import logging import warnings from collections import Counter, defaultdict from copy import deepcopy from functools import partialmethod from itertools import chain from textwrap import dedent from typing import ( Any, Dict, Hashable, Iterable, List, Mapping, Optional, Set, TextIO, Tuple, Union, ) import networkx as nx from tabulate import tabulate from .operations import left_full_join, left_node_intersection_join, left_outer_join from .utils import update_metadata from ..canonicalize import edge_to_bel from ..constants import ( ACTIVITY, ANNOTATIONS, ASSOCIATION, CAUSES_NO_CHANGE, CITATION, CITATION_AUTHORS, CITATION_TYPE_PUBMED, CORRELATION, DECREASES, DEGRADATION, DIRECTLY_DECREASES, DIRECTLY_INCREASES, DIRECTLY_REGULATES, EFFECT, EQUIVALENT_TO, EVIDENCE, FROM_LOC, GRAPH_ANNOTATION_CURIE, GRAPH_ANNOTATION_LIST, GRAPH_ANNOTATION_MIRIAM, GRAPH_ANNOTATION_PATTERN, GRAPH_ANNOTATION_URL, GRAPH_METADATA, GRAPH_NAMESPACE_PATTERN, GRAPH_NAMESPACE_URL, GRAPH_PATH, GRAPH_PYBEL_VERSION, HAS_PRODUCT, HAS_REACTANT, HAS_VARIANT, IDENTIFIER, INCREASES, IS_A, LOCATION, METADATA_AUTHORS, METADATA_CONTACT, METADATA_COPYRIGHT, METADATA_DESCRIPTION, METADATA_DISCLAIMER, METADATA_LICENSES, METADATA_NAME, METADATA_VERSION, MODIFIER, NAMESPACE, NEGATIVE_CORRELATION, NO_CORRELATION, ORTHOLOGOUS, PART_OF, POSITIVE_CORRELATION, REGULATES, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, TO_LOC, TRANSCRIBED_TO, TRANSLATED_TO, TRANSLOCATION, ) from ..dsl import ( BaseAbundance, BaseConcept, BaseEntity, CentralDogma, ComplexAbundance, Gene, ListAbundance, MicroRna, Protein, ProteinModification, Reaction, Rna, activity, ) from ..exceptions import BELParserWarning from ..language import CitationDict, Entity, citation_dict from ..typing import EdgeData from ..utils import hash_edge from ..version import get_version __all__ = [ "BELGraph", ] logger = logging.getLogger(__name__) AnnotationsDict = Mapping[str, List[Entity]] AnnotationsHint = Union[Mapping[str, str], Mapping[str, Set[str]], AnnotationsDict] WarningTuple = Tuple[Optional[str], BELParserWarning, EdgeData] class BELGraph(nx.MultiDiGraph): """An extension to :class:`networkx.MultiDiGraph` to represent BEL.""" #: A set of pairs of hashes of edges over which there is transitivity. #: For example, for the nested statement (P(X) -> P(Y)) -> P(Z) will have #: a pair for (hash(P(X) -> P(Y)), hash(P(Y) -> P(Z))) transitivities: Set[Tuple[str, str]] def __init__( self, name: Optional[str] = None, version: Optional[str] = None, description: Optional[str] = None, authors: Optional[str] = None, contact: Optional[str] = None, license: Optional[str] = None, copyright: Optional[str] = None, disclaimer: Optional[str] = None, path: Optional[str] = None, ) -> None: """Initialize a BEL graph with its associated metadata. :param name: The graph's name :param version: The graph's version. Recommended to use `semantic versioning `_ or ``YYYYMMDD`` format. :param description: A description of the graph :param authors: The authors of this graph :param contact: The contact email for this graph :param license: The license for this graph :param copyright: The copyright for this graph :param disclaimer: The disclaimer for this graph """ super().__init__() self._warnings = [] self.graph.update( { GRAPH_PYBEL_VERSION: get_version(), GRAPH_METADATA: {}, GRAPH_NAMESPACE_URL: {}, GRAPH_NAMESPACE_PATTERN: {}, GRAPH_ANNOTATION_URL: {}, GRAPH_ANNOTATION_PATTERN: {}, GRAPH_ANNOTATION_LIST: defaultdict(set), GRAPH_ANNOTATION_CURIE: set(), GRAPH_ANNOTATION_MIRIAM: set(), } ) if name: self.name = name if version: self.version = version if description: self.description = description if authors: self.authors = authors if contact: self.contact = contact if license: self.license = license if copyright: self.copyright = copyright if disclaimer: self.disclaimer = disclaimer if path: self.path = path self.transitivities = set() #: A reference to the parent graph self.parent = None self._count = CountDispatch(self) self._expand = ExpandDispatch(self) self._induce = InduceDispatch(self) self._plot = PlotDispatch(self) self._summary = SummarizeDispatch(self) self.raise_on_missing_annotations = True def child(self) -> "BELGraph": """Create an empty graph with a "parent" reference back to this one.""" rv = BELGraph() rv.parent = self update_metadata(source=self, target=rv) return rv @property def count(self) -> "CountDispatch": # noqa: D401 """A dispatch to count functions. Can be used like this: >>> from pybel.examples import sialic_acid_graph >>> sialic_acid_graph.count.functions() Counter({'Protein': 7, 'Complex': 1, 'Abundance': 1}) """ return self._count @property def summarize(self) -> "SummarizeDispatch": # noqa: D401 """A dispatch to summarize the graph.""" return self._summary def _repr_html_(self): return self._summary._repr_html_() @property def expand(self) -> "ExpandDispatch": # noqa: D401 """A dispatch to expand the graph w.r.t. its parent.""" return self._expand @property def induce(self) -> "InduceDispatch": # noqa: D401 """A dispatch to mutate the graph.""" return self._induce @property def plot(self) -> "PlotDispatch": # noqa: D401 """A dispatch to plot the graph using :mod:`matplotlib` and :mod:`seaborn`.""" return self._plot @property def path(self) -> Optional[str]: # noqa: D401 """The graph's path, if it was derived from a BEL document.""" return self.graph.get(GRAPH_PATH) @path.setter def path(self, path: str) -> None: """Set the graph's path.""" self.graph[GRAPH_PATH] = path @property def document(self) -> Dict[str, Any]: # noqa: D401 """The dictionary holding the metadata from the ``SET DOCUMENT`` statements in the source BEL script. All keys are normalized according to :data:`pybel.constants.DOCUMENT_KEYS`. """ return self.graph[GRAPH_METADATA] @property def name(self, *attrs) -> Optional[str]: # noqa: D401 # Needs *attrs since it's an override """The graph's name. .. hint:: Can be set with the ``SET DOCUMENT Name = "..."`` entry in the source BEL script. """ return self.document.get(METADATA_NAME) @name.setter def name(self, *attrs, **kwargs): # Needs *attrs and **kwargs since it's an override """Set the graph's name.""" self.document[METADATA_NAME] = attrs[0] @property def version(self) -> Optional[str]: # noqa: D401 """The graph's version. .. hint:: Can be set with the ``SET DOCUMENT Version = "..."`` entry in the source BEL script. """ return self.document.get(METADATA_VERSION) @version.setter def version(self, version): """Set the graph's version.""" self.document[METADATA_VERSION] = version @property def description(self) -> Optional[str]: # noqa: D401 """The graph's description. .. hint:: Can be set with the ``SET DOCUMENT Description = "..."`` entry in the source BEL document. """ return self.document.get(METADATA_DESCRIPTION) @description.setter def description(self, description: str) -> None: """Set the graph's description.""" self.document[METADATA_DESCRIPTION] = description @property def authors(self) -> Optional[str]: # noqa: D401 """The graph's authors. .. hint:: Can be set with the ``SET DOCUMENT Authors = "..."`` entry in the source BEL document. """ return self.document.get(METADATA_AUTHORS) @authors.setter def authors(self, authors: str) -> None: """Set the graph's authors.""" self.document[METADATA_AUTHORS] = authors @property def contact(self) -> Optional[str]: # noqa: D401 """The graph's contact information. .. hint:: Can be set with the ``SET DOCUMENT ContactInfo = "..."`` entry in the source BEL document. """ return self.document.get(METADATA_CONTACT) @contact.setter def contact(self, contact: str) -> None: """Set the graph's contact.""" self.document[METADATA_CONTACT] = contact @property def license(self) -> Optional[str]: # noqa: D401 """The graph's license. .. hint:: Can be set with the ``SET DOCUMENT Licenses = "..."`` entry in the source BEL document """ return self.document.get(METADATA_LICENSES) @license.setter def license(self, license_str: str) -> None: """Set the graph's license.""" self.document[METADATA_LICENSES] = license_str @property def copyright(self) -> Optional[str]: # noqa: D401 """The graph's copyright. .. hint:: Can be set with the ``SET DOCUMENT Copyright = "..."`` entry in the source BEL document """ return self.document.get(METADATA_COPYRIGHT) @copyright.setter def copyright(self, copyright_str: str) -> None: """Set the graph's copyright.""" self.document[METADATA_COPYRIGHT] = copyright_str @property def disclaimer(self) -> Optional[str]: # noqa: D401 """The graph's disclaimer. .. hint:: Can be set with the ``SET DOCUMENT Disclaimer = "..."`` entry in the source BEL document. """ return self.document.get(METADATA_DISCLAIMER) @disclaimer.setter def disclaimer(self, disclaimer: str) -> None: """Set the graph's disclaimer.""" self.document[METADATA_DISCLAIMER] = disclaimer @property def namespace_url(self) -> Dict[str, str]: # noqa: D401 """The mapping from the keywords used in this graph to their respective BEL namespace URLs. .. hint:: Can be appended with the ``DEFINE NAMESPACE [key] AS URL "[value]"`` entries in the definitions section of the source BEL document. """ return self.graph[GRAPH_NAMESPACE_URL] @property def defined_namespace_keywords(self) -> Set[str]: # noqa: D401 """The set of all keywords defined as namespaces in this graph.""" return set(self.namespace_pattern) | set(self.namespace_url) @property def namespace_pattern(self) -> Dict[str, str]: # noqa: D401 """The mapping from the namespace keywords used to create this graph to their regex patterns. .. hint:: Can be appended with the ``DEFINE NAMESPACE [key] AS PATTERN "[value]"`` entries in the definitions section of the source BEL document. """ return self.graph[GRAPH_NAMESPACE_PATTERN] @property def annotation_url(self) -> Dict[str, str]: # noqa: D401 """The mapping from the annotation keywords used to create this graph to the URLs of the BELANNO files. .. hint:: Can be appended with the ``DEFINE ANNOTATION [key] AS URL "[value]"`` entries in the definitions section of the source BEL document. """ return self.graph[GRAPH_ANNOTATION_URL] @property def annotation_miriam(self) -> Set[str]: # noqa: D401 """The set of annotations defined by MIRIAM.""" if GRAPH_ANNOTATION_MIRIAM not in self.graph: self.graph[GRAPH_ANNOTATION_MIRIAM] = set() return self.graph[GRAPH_ANNOTATION_MIRIAM] @property def annotation_curie(self) -> Set[str]: # noqa: D401 """The set of annotations defined by CURIE.""" if GRAPH_ANNOTATION_CURIE not in self.graph: self.graph[GRAPH_ANNOTATION_CURIE] = set() return self.graph[GRAPH_ANNOTATION_CURIE] @property def annotation_pattern(self) -> Dict[str, str]: # noqa: D401 """The mapping from the annotation keywords used to create this graph to their regex patterns as strings. .. hint:: Can be appended with the ``DEFINE ANNOTATION [key] AS PATTERN "[value]"`` entries in the definitions section of the source BEL document. """ return self.graph[GRAPH_ANNOTATION_PATTERN] @property def annotation_list(self) -> Dict[str, Set[str]]: # noqa: D401 """The mapping from the keywords of locally defined annotations to their respective sets of values. .. hint:: Can be appended with the ``DEFINE ANNOTATION [key] AS LIST {"[value]", ...}`` entries in the definitions section of the source BEL document. """ return self.graph[GRAPH_ANNOTATION_LIST] @property def defined_annotation_keywords(self) -> Set[str]: """Get the set of all keywords defined as annotations in this graph.""" return set(self.annotation_pattern) | set(self.annotation_url) | set(self.annotation_list) @property def pybel_version(self) -> str: # noqa: D401 """The version of PyBEL with which this graph was produced as a string.""" return self.graph[GRAPH_PYBEL_VERSION] @property def warnings(self) -> List[WarningTuple]: # noqa: D401 """A list of warnings associated with this graph.""" return self._warnings def number_of_warnings(self) -> int: """Return the number of warnings.""" return len(self.warnings) def number_of_citations(self) -> int: """Return the number of citations contained within the graph.""" return self.count.citations() def number_of_authors(self) -> int: """Return the number of authors contained within the graph.""" return len(self.get_authors()) def get_authors(self) -> Set[str]: """Get the authors for the citations in the graph.""" return set(self.count.authors()) def __str__(self): return "{} v{}".format(self.name, self.version) def add_transitivity(self, k1: str, k2: str) -> None: """Add a pair of edge hashes over which there is transitivity. :param k1: The hash of the subject edge :param k2: The hash of the object edge """ self.transitivities.add((k1, k2)) def add_warning( self, exception: BELParserWarning, context: Optional[Mapping[str, Any]] = None, ) -> None: """Add a warning to the internal warning log in the graph, with optional context information. :param exception: The exception that occurred :param context: The context from the parser when the exception occurred """ self.warnings.append( ( self.path, exception, {} if context is None else context, ) ) def _help_add_edge(self, source: BaseEntity, target: BaseEntity, attr: Mapping) -> str: """Help add a pre-built edge.""" self.add_node_from_data(source) self.add_node_from_data(target) return self._help_add_edge_helper(source=source, target=target, attr=attr) def _help_add_edge_helper(self, source: BaseEntity, target: BaseEntity, attr: Mapping[str, Any]) -> str: key = hash_edge(source, target, attr) if not self.has_edge(source, target, key): self.add_edge(source, target, key=key, **attr) return key def add_unqualified_edge(self, source: BaseEntity, target: BaseEntity, relation: str) -> str: """Add a unique edge that has no annotations. :param source: The source node :param target: The target node :param relation: A relationship label from :mod:`pybel.constants` :return: The key for this edge (a unique hash) """ attr = {RELATION: relation} return self._help_add_edge(source=source, target=target, attr=attr) def add_transcription(self, gene: Gene, rna: Union[Rna, MicroRna]) -> str: """Add a transcription relation from a gene to an RNA or miRNA node. :param gene: A gene node :param rna: An RNA or microRNA node """ return self.add_unqualified_edge(gene, rna, TRANSCRIBED_TO) def add_translation(self, rna: Rna, protein: Protein) -> str: """Add a translation relation from a RNA to a protein. :param rna: An RNA node :param protein: A protein node """ return self.add_unqualified_edge(rna, protein, TRANSLATED_TO) def _add_two_way_qualified_edge(self, source: BaseEntity, target: BaseEntity, *args, **kwargs) -> str: """Add an qualified edge both ways.""" self.add_qualified_edge(source=target, target=source, *args, **kwargs) return self.add_qualified_edge(source=source, target=target, *args, **kwargs) def _add_two_way_unqualified_edge(self, source: BaseEntity, target: BaseEntity, *args, **kwargs) -> str: """Add an unqualified edge both ways.""" self.add_unqualified_edge(target, source, *args, **kwargs) return self.add_unqualified_edge(source, target, *args, **kwargs) add_equivalence = partialmethod(_add_two_way_unqualified_edge, relation=EQUIVALENT_TO) """Add two equivalence relations for the nodes.""" add_orthology = partialmethod(_add_two_way_unqualified_edge, relation=ORTHOLOGOUS) """Add two orthology relations for the nodes such that ``u orthologousTo v`` and ``v orthologousTo u``.""" add_is_a = partialmethod(add_unqualified_edge, relation=IS_A) """Add an ``isA`` relationship such that ``u isA v``.""" add_part_of = partialmethod(add_unqualified_edge, relation=PART_OF) """Add a ``partOf`` relationship such that ``u partOf v``.""" add_has_variant = partialmethod(add_unqualified_edge, relation=HAS_VARIANT) """Add a ``hasVariant`` relationship such that ``u hasVariant v``.""" add_has_reactant = partialmethod(add_unqualified_edge, relation=HAS_REACTANT) """Add a ``hasReactant`` relationship such that ``u hasReactant v``.""" add_has_product = partialmethod(add_unqualified_edge, relation=HAS_PRODUCT) """Add a ``hasProduct`` relationship such that ``u hasProduct v``.""" def add_qualified_edge( self, source: BaseEntity, target: BaseEntity, *, relation: str, evidence: str, citation: Union[str, Tuple[str, str], CitationDict], annotations: Optional[AnnotationsHint] = None, source_modifier: Optional[Mapping[str, Any]] = None, target_modifier: Optional[Mapping[str, Any]] = None, **attr, ) -> str: """Add a qualified edge. Qualified edges have a relation, evidence, citation, and optional annotations, subject modifications, and object modifications. :param source: The source node :param target: The target node :param relation: The type of relation this edge represents :param evidence: The evidence string from an article :param citation: The citation data dictionary for this evidence. If a string is given, assumes it's a PubMed identifier and auto-fills the citation type. :param annotations: The annotations data dictionary :param source_modifier: The modifiers (like activity) on the subject node. See data model documentation. :param target_modifier: The modifiers (like activity) on the object node. See data model documentation. :return: The hash of the edge """ if "subject_modifier" in attr: warnings.warn("subject_modifier has been renamed to source_modifier") source_modifier = attr.pop("subject_modifier") if "object_modifier" in attr: warnings.warn("object_modifier has been renamed to target_modifier") target_modifier = attr.pop("object_modifier") attr = self._build_attr( relation=relation, evidence=evidence, citation=citation, annotations=annotations, source_modifier=source_modifier, target_modifier=target_modifier, **attr, ) return self._help_add_edge(source=source, target=target, attr=attr) def _build_attr( self, relation: str, evidence: str, citation: Union[str, Tuple[str, str], CitationDict], annotations: Optional[AnnotationsHint] = None, source_modifier: Optional[Dict[str, Any]] = None, target_modifier: Optional[Dict[str, Any]] = None, **attr, ): attr.update( { RELATION: relation, EVIDENCE: evidence, CITATION: _handle_citation(citation), } ) if annotations: # clean up annotations attr[ANNOTATIONS] = self._clean_annotations(annotations) if source_modifier: attr[SOURCE_MODIFIER] = _handle_modifier(source_modifier) if target_modifier: attr[TARGET_MODIFIER] = _handle_modifier(target_modifier) return attr def add_binds( self, source: BaseAbundance, target: BaseAbundance, *, evidence: str, citation: Union[str, Tuple[str, str], CitationDict], annotations: Optional[AnnotationsHint] = None, **attr, ) -> str: """Add a "binding" relationship between the two entities such that ``u => complex(u, v)``.""" complex_abundance = ComplexAbundance([source, target]) return self.add_directly_increases( source=source, target=complex_abundance, citation=citation, evidence=evidence, annotations=annotations, **attr, ) add_increases = partialmethod(add_qualified_edge, relation=INCREASES) """Wrap :meth:`add_qualified_edge` for the :data:`pybel.constants.INCREASES` relation.""" add_directly_increases = partialmethod(add_qualified_edge, relation=DIRECTLY_INCREASES) """Add a :data:`pybel.constants.DIRECTLY_INCREASES` with :meth:`add_qualified_edge`.""" add_decreases = partialmethod(add_qualified_edge, relation=DECREASES) """Add a :data:`pybel.constants.DECREASES` relationship with :meth:`add_qualified_edge`.""" add_directly_decreases = partialmethod(add_qualified_edge, relation=DIRECTLY_DECREASES) """Add a :data:`pybel.constants.DIRECTLY_DECREASES` relationship with :meth:`add_qualified_edge`.""" add_association = partialmethod(_add_two_way_qualified_edge, relation=ASSOCIATION) """Add a :data:`pybel.constants.ASSOCIATION` relationship with :meth:`add_qualified_edge`.""" add_regulates = partialmethod(add_qualified_edge, relation=REGULATES) """Add a :data:`pybel.constants.REGULATES` relationship with :meth:`add_qualified_edge`.""" add_directly_regulates = partialmethod(add_qualified_edge, relation=DIRECTLY_REGULATES) """Add a :data:`pybel.constants.DIRECTLY_REGULATES` relationship with :meth:`add_qualified_edge`.""" add_correlation = partialmethod(_add_two_way_qualified_edge, relation=CORRELATION) """Add a :data:`pybel.constants.CORRELATION` relationship with :meth:`add_qualified_edge`.""" add_no_correlation = partialmethod(_add_two_way_qualified_edge, relation=NO_CORRELATION) """Add a :data:`pybel.constants.NO_CORRELATION` relationship with :meth:`add_qualified_edge`.""" add_positive_correlation = partialmethod(_add_two_way_qualified_edge, relation=POSITIVE_CORRELATION) """Add a :data:`pybel.constants.POSITIVE_CORRELATION` relationship with :meth:`add_qualified_edge`.""" add_negative_correlation = partialmethod(_add_two_way_qualified_edge, relation=NEGATIVE_CORRELATION) """Add a :data:`pybel.constants.NEGATIVE_CORRELATION` relationship with :meth:`add_qualified_edge`.""" add_causes_no_change = partialmethod(add_qualified_edge, relation=CAUSES_NO_CHANGE) """Add a :data:`pybel.constants.CAUSES_NO_CHANGE` relationship with :meth:`add_qualified_edge`.""" add_inhibits = partialmethod(add_decreases, target_modifier=activity()) """Add an "inhibits" relationship. A more specific version of :meth:`add_decreases` that automatically populates the object modifier with an activity.""" add_directly_inhibits = partialmethod(add_directly_decreases, target_modifier=activity()) add_activates = partialmethod(add_increases, target_modifier=activity()) """Add an "activates" relationship. A more specific version of :meth:`add_increases` that automatically populates the object modifier with an activity.""" add_directly_activates = partialmethod(add_directly_increases, target_modifier=activity()) def _modify( self, add_edge_fn: str, name: str, source: BaseEntity, target: CentralDogma, code: Optional[str] = None, position: Optional[int] = None, *, evidence: str, citation: Union[str, Mapping[str, str]], annotations: Optional[AnnotationsHint] = None, source_modifier: Optional[Mapping] = None, target_modifier: Optional[Mapping] = None, **attr, ): """Add a simple modification.""" adder = getattr(self, add_edge_fn) return adder( source=source, target=target.with_variants( ProteinModification( name=name, code=code, position=position, ) ), evidence=evidence, citation=citation, annotations=annotations, source_modifier=source_modifier, target_modifier=target_modifier, **attr, ) add_phosphorylates = partialmethod(_modify, "add_increases", "Ph") """Add an increase of modified object with phosphorylation.""" add_directly_phosphorylates = partialmethod(_modify, "add_directly_increases", "Ph") """Add a direct increase of modified object with phosphorylation.""" add_dephosphorylates = partialmethod(_modify, "add_decreases", "Ph") """Add a decrease of modified object with phosphorylation.""" add_directly_dephosphorylates = partialmethod(_modify, "add_directly_decreases", "Ph") """Add a direct decrease of modified object with phosphorylation.""" def add_node_from_data(self, node: BaseEntity) -> None: """Add an entity to the graph.""" assert isinstance(node, BaseEntity) if node in self: return self.add_node(node) if isinstance(node, CentralDogma) and node.variants: self.add_has_variant(node.get_parent(), node) elif isinstance(node, ListAbundance): for member in node.members: self.add_part_of(member, node) elif isinstance(node, Reaction): for reactant in node.reactants: self.add_has_reactant(node, reactant) for product in node.products: self.add_has_product(node, product) def add_reaction( self, reactants: Union[BaseAbundance, Iterable[BaseAbundance]], products: Union[BaseAbundance, Iterable[BaseAbundance]], ) -> None: """Add a reaction directly to the graph.""" return self.add_node_from_data(Reaction(reactants=reactants, products=products)) def _has_edge_attr(self, u: BaseEntity, v: BaseEntity, key: str, attr: Hashable) -> bool: assert isinstance(u, BaseEntity) assert isinstance(v, BaseEntity) return attr in self[u][v][key] def has_edge_citation(self, u: BaseEntity, v: BaseEntity, key: str) -> bool: """Check if the given edge has a citation.""" return self._has_edge_attr(u, v, key, CITATION) def has_edge_evidence(self, u: BaseEntity, v: BaseEntity, key: str) -> bool: """Check if the given edge has an evidence.""" return self._has_edge_attr(u, v, key, EVIDENCE) def _get_edge_attr(self, u: BaseEntity, v: BaseEntity, key: str, attr: str): return self[u][v][key].get(attr) def get_edge_citation(self, u: BaseEntity, v: BaseEntity, key: str) -> Optional[CitationDict]: """Get the citation for a given edge.""" return self._get_edge_attr(u, v, key, CITATION) def get_edge_evidence(self, u: BaseEntity, v: BaseEntity, key: str) -> Optional[str]: """Get the evidence for a given edge.""" return self._get_edge_attr(u, v, key, EVIDENCE) def get_edge_annotations(self, u, v, key: str) -> Optional[AnnotationsDict]: """Get the annotations for a given edge.""" return self._get_edge_attr(u, v, key, ANNOTATIONS) def __add__(self, other: "BELGraph") -> "BELGraph": """Copy this graph and join it with another graph with it using :func:`pybel.struct.left_full_join`. :param other: Another BEL graph Example usage: >>> from pybel.examples import ras_tloc_graph, braf_graph >>> k = ras_tloc_graph + braf_graph """ if not isinstance(other, BELGraph): raise TypeError("{} is not a {}".format(other, self.__class__.__name__)) result = deepcopy(self) left_full_join(result, other) return result def __iadd__(self, other: "BELGraph") -> "BELGraph": """Join another graph into this one, in-place, using :func:`pybel.struct.left_full_join`. :param other: Another BEL graph Example usage: >>> from pybel.examples import ras_tloc_graph, braf_graph >>> ras_tloc_graph += braf_graph """ if not isinstance(other, BELGraph): raise TypeError("{} is not a {}".format(other, self.__class__.__name__)) left_full_join(self, other) return self def __and__(self, other: "BELGraph") -> "BELGraph": """Create a deep copy of this graph and left outer joins another graph. Uses :func:`pybel.struct.left_outer_join`. :param other: Another BEL graph Example usage: >>> from pybel.examples import ras_tloc_graph, braf_graph >>> k = ras_tloc_graph & braf_graph """ if not isinstance(other, BELGraph): raise TypeError("{} is not a {}".format(other, self.__class__.__name__)) result = deepcopy(self) left_outer_join(result, other) return result def __iand__(self, other: "BELGraph") -> "BELGraph": """Join another graph into this one, in-place, using :func:`pybel.struct.left_outer_join`. :param other: Another BEL graph Example usage: >>> from pybel.examples import ras_tloc_graph, braf_graph >>> ras_tloc_graph &= braf_graph """ if not isinstance(other, BELGraph): raise TypeError("{} is not a {}".format(other, self.__class__.__name__)) left_outer_join(self, other) return self def __xor__(self, other: "BELGraph") -> "BELGraph": """Join this graph with another using :func:`pybel.struct.left_node_intersection_join`. :param other: Another BEL graph Example usage: >>> from pybel.examples import ras_tloc_graph, braf_graph >>> k = ras_tloc_graph ^ braf_graph """ if not isinstance(other, BELGraph): raise TypeError("{} is not a {}".format(other, self.__class__.__name__)) return left_node_intersection_join(self, other) @staticmethod def node_to_bel(n: BaseEntity) -> str: """Serialize a node as BEL.""" warnings.warn("use node.as_bel()", DeprecationWarning) return n.as_bel() @staticmethod def edge_to_bel( u: BaseEntity, v: BaseEntity, edge_data: EdgeData, sep: Optional[str] = None, use_identifiers: bool = True, ) -> str: """Serialize a pair of nodes and related edge data as a BEL relation.""" return edge_to_bel(u, v, data=edge_data, sep=sep, use_identifiers=use_identifiers) def _has_no_equivalent_edge(self, u: BaseEntity, v: BaseEntity) -> bool: return not any(EQUIVALENT_TO == data[RELATION] for data in self[u][v].values()) def _equivalent_node_iterator_helper(self, node: BaseEntity, visited: Set[BaseEntity]) -> BaseEntity: """Iterate over nodes and their data that are equal to the given node, starting with the original.""" for v in self[node]: if v in visited: continue if self._has_no_equivalent_edge(node, v): continue visited.add(v) yield v yield from self._equivalent_node_iterator_helper(v, visited) def iter_equivalent_nodes(self, node: BaseEntity) -> Iterable[BaseEntity]: """Iterate over nodes that are equivalent to the given node, including the original.""" yield node yield from self._equivalent_node_iterator_helper(node, {node}) def get_equivalent_nodes(self, node: BaseEntity) -> Set[BaseEntity]: """Get a set of equivalent nodes to this node, excluding the given node.""" if isinstance(node, BaseEntity): return set(self.iter_equivalent_nodes(node)) return set(self.iter_equivalent_nodes(node)) @staticmethod def _node_has_namespace_helper(node: BaseEntity, namespace: str) -> bool: """Check that the node has namespace information. Might have cross references in future. """ return isinstance(node, BaseConcept) and node.namespace.lower() == namespace.lower() def node_has_namespace(self, node: BaseEntity, namespace: str) -> bool: """Check if the node have the given namespace. This also should look in the equivalent nodes. """ return any(self._node_has_namespace_helper(n, namespace) for n in self.iter_equivalent_nodes(node)) def _describe_list(self) -> List[Tuple[str, float]]: """Return useful information about the graph as a list of tuples.""" warnings.warn("use graph.summary.list()", DeprecationWarning) return self.summarize.list() def summary_dict(self) -> Mapping[str, float]: """Return a dictionary that summarizes the graph.""" warnings.warn("use graph.summary.dict()", DeprecationWarning) return self.summarize.dict() def summary_str(self) -> str: """Return a string that summarizes the graph.""" warnings.warn("use graph.summary.str()", DeprecationWarning) return self.summarize.str() def ground(self, **kwargs) -> "BELGraph": """Ground this graph.""" try: from ..grounding import ground except ImportError: logger.warning("Must install pyobo and protmapper, use pip install pybel[grounding] extra.") raise return ground(self, **kwargs) def _clean_annotations(self, annotations_dict: AnnotationsHint) -> AnnotationsDict: """Fix the formatting of annotation dict. .. seealso:: https://github.com/vtoure/bep/blob/master/docs/published/BEP-0013.md Scenarios: 1. ``DEFINE ANNOTATION CellLine AS URL "..."`` ``SET CellLine = "NIH-3T3 cell"`` ``{'CellLine': dict(namespace='CellLine', identifier=None, name='NIH-3T3 cell')}`` 2. ``DEFINE ANNOTATION CellLine AS CURIE`` ``SET CellLine = "bto:0000944 ! NIH-3T3 cell"`` ``{'CellLine': dict(namespace='bto', identifier='0000944', name='NIH-3T3 cell')}`` 3. ``DEFINE ANNOTATION ECO AS MIRIAM`` ``SET ECO = "0007682 ! reporter gene assay evidence used in manual assertion"`` ``{'ECO': dict(namespace='ECO', identifier='0007682', name='reporter gene assay...')}`` """ return { key: sorted( self._clean_value(key, values), key=lambda e: (e.namespace, e.identifier, e.name), ) for key, values in annotations_dict.items() } def _clean_value( self, key, values: Union[str, Entity, List[str], List[Mapping[str, str]], List[Entity]], ) -> List[Entity]: if key in self.annotation_miriam: # this annotation was given by a lookup return self._clean_value_helper(key=key, namespace=key, values=values) if key in self.annotation_curie: if isinstance(values, Entity): return [values] if all(isinstance(v, dict) for v in values): return [Entity(**v) for v in values] if not all(isinstance(v, Entity) for v in values): raise ValueError("if annotation_curie, all must be given as Entity instances") return values if key in self.annotation_miriam: raise NotImplementedError("parsing of annotation as MIRIAM not yet implemented") if key in self.annotation_list or key in self.annotation_pattern: if isinstance(values, str): return [Entity(namespace=key, identifier=values)] if isinstance(values, Entity): return [values] if all(isinstance(v, Entity) for v in values): return values if all(isinstance(v, dict) for v in values): return [Entity(**v) for v in values] if all(isinstance(v, str) for v in values): return [Entity(namespace=key, identifier=v) for v in values] raise TypeError(f"Mixed values: {values}") if key in self.annotation_url: # this is a name given return self._clean_value_helper(key=key, namespace=key, values=values) if self.raise_on_missing_annotations: raise NotImplementedError(f"where is key {key}?") return self._clean_value_helper(key=key, namespace=key, values=values) @staticmethod def _clean_value_helper(key, namespace, values): if isinstance(values, str): return [ Entity(namespace=namespace, identifier=values), ] if isinstance(values, (list, set)): if all(isinstance(v, str) for v in values): return [Entity(namespace=namespace, identifier=identifier) for identifier in sorted(values)] elif all(isinstance(v, Entity) for v in values): return values elif all(isinstance(v, dict) for v in values): return [Entity(**v) for v in values] else: raise TypeError(f"list of wrong format for key {key}: {values}") if isinstance(values, dict): if all(isinstance(v, bool) for v in values.values()): return [Entity(namespace=namespace, identifier=identifier) for identifier in sorted(values)] raise TypeError(f"dictionary of wrong format for key {key}: {values}") raise TypeError(f"values of wrong data type for key {key}: {values}") def _handle_modifier(side_data: Dict[str, Any]) -> Mapping[str, Any]: modifier = side_data.get(MODIFIER) effect = side_data.get(EFFECT) if modifier == ACTIVITY: if effect is not None: side_data[EFFECT] = Entity(**effect) elif modifier == TRANSLOCATION: if effect is not None: effect[FROM_LOC] = Entity(**effect[FROM_LOC]) effect[TO_LOC] = Entity(**effect[TO_LOC]) elif modifier == DEGRADATION or modifier is None: pass else: raise ValueError("invalid modifier: {}".format(modifier)) if LOCATION in side_data: side_data[LOCATION] = Entity(**side_data[LOCATION]) return side_data def _handle_citation(citation: Union[str, Tuple[str, str], CitationDict]) -> CitationDict: if isinstance(citation, str): return citation_dict(namespace=CITATION_TYPE_PUBMED, identifier=citation) elif isinstance(citation, tuple): return citation_dict(namespace=citation[0], identifier=citation[1]) elif isinstance(citation, CitationDict): return citation elif isinstance(citation, dict): return CitationDict(**citation) elif citation is None: raise ValueError("citation was None") else: raise TypeError(f"citation is the wrong type: {citation}") class Dispatch: def __init__(self, graph: BELGraph): self.graph = graph class CountDispatch(Dispatch): """A dispatch for count functions that can be found at :data:`pybel.BELGraph.count`.""" def functions(self) -> Counter: """Count the functions in a graph. >>> from pybel.examples import sialic_acid_graph >>> sialic_acid_graph.count.functions() Counter({'Protein': 7, 'Complex': 1, 'Abundance': 1}) """ from .summary import count_functions return count_functions(self.graph) def namespaces(self) -> Counter: """Return a counter of namespaces' occurrences in nodes in the graph.""" from .summary import count_namespaces return count_namespaces(self.graph) def pathologies(self) -> Counter: """Return a counter of pathologies' occurrences in edges in the graph.""" from .summary import count_pathologies return count_pathologies(self.graph) def annotations(self) -> Counter: """Return a counter of annotations' occurrences in edges in the graph.""" from .summary import count_annotations return count_annotations(self.graph) def variants(self) -> Counter: """Return a counter of variants' occurrences in nodes in the graph.""" from .summary import count_variants return count_variants(self.graph) def relations(self) -> Counter: """Return a counter of relations' occurrences in edges in the graph.""" from .summary import count_relations return count_relations(self.graph) def error_types(self) -> Counter: """Return a counter of error types' occurrences in BEL script underlying the graph.""" from .summary import count_error_types return count_error_types(self.graph) def names_by_namespace(self, namespace: str) -> Counter: from .summary import count_names_by_namespace return count_names_by_namespace(self.graph, namespace=namespace) def modifications(self) -> Counter: """Return a counter of relation modifications' occurrences (activity, translocation, etc.) in the graph.""" from .summary.node_summary import count_modifications return count_modifications(self.graph) def authors(self) -> Counter: """Return a counter of the number of edges to which each author contributed in the graph.""" return Counter(_iterate_authors(self.graph)) def citations(self) -> int: """Return the number of citations.""" return len(set(_iterate_citations(self.graph))) def _iterate_citations(graph: BELGraph) -> Iterable[Tuple[str, str]]: for _, _, data in graph.edges(data=True): if CITATION in data: yield data[CITATION][NAMESPACE], data[CITATION][IDENTIFIER] def _iterate_authors(graph: BELGraph) -> Iterable[str]: return chain.from_iterable( data[CITATION][CITATION_AUTHORS] for _, _, data in graph.edges(data=True) if CITATION in data and CITATION_AUTHORS in data[CITATION] ) class SummarizeDispatch(Dispatch): """A dispatch for summary printing functions that can be found at :data:`pybel.BELGraph.summarize`.""" def __call__(self, file: Optional[TextIO] = None, examples: bool = True) -> None: self.statistics(file=file) print("", file=file) self.nodes(file=file, examples=examples) print("", file=file) self.namespaces(file=file, examples=examples) print("", file=file) self.edges(file=file, examples=examples) print("", file=file) def _repr_html_(self) -> str: from .summary import supersummary as ss return dedent( f"""\

Metadata

{tabulate(self._metadata_list(), tablefmt='html')}

Statistics

{tabulate(self._statistics_list(prose_prefix=False), tablefmt='html')}

Nodes

{ss.functions_str(self.graph, examples=True, add_count=False, tablefmt='html')}

Namespaces

{ss.namespaces_str(self.graph, examples=True, add_count=False, tablefmt='html')}

Edges

{ss.edges_str(self.graph, examples=True, add_count=False, tablefmt='html')} """ ) def statistics(self, file: Optional[TextIO] = None): """Print summary statistics on the graph.""" print(self.str(), file=file) def nodes(self, file: Optional[TextIO] = None, examples: bool = True): """Print a summary of the nodes' functions in the graph.""" from .summary.supersummary import functions_str print(functions_str(self.graph, examples=examples), file=file) def namespaces(self, file: Optional[TextIO] = None, examples: bool = True): """Print a summary of the nodes' namespaces in the graph.""" from .summary.supersummary import namespaces namespaces(self.graph, file=file, examples=examples) def edges(self, file: Optional[TextIO] = None, examples: bool = True): """Print a summary of the edges' types in the graph.""" from .summary.supersummary import edges edges(self.graph, file=file, examples=examples) def citations(self, n: Optional[int] = 15, file: Optional[TextIO] = None): """Print a summary of the top citations' frequencies in the graph.""" from .summary.supersummary import citations citations(self.graph, n=n, file=file) def dict(self) -> Mapping[str, float]: """Return a dictionary that summarizes the graph.""" return dict(self.list()) def str(self, **kwargs) -> str: """Return a string that summarizes the graph.""" return tabulate(self.list(), **kwargs) def _metadata_list(self) -> List[Tuple[str, Any]]: rv = [ ("Name", self.graph.name), ("Version", self.graph.version), ] if self.graph.authors: rv.append(("Authors", self.graph.authors)) return rv def _statistics_list(self, prose_prefix: bool = True) -> List[Tuple[str, Any]]: number_nodes = self.graph.number_of_nodes() rv = [ ("Nodes", number_nodes), ("Namespaces", len(self.graph.count.namespaces())), ("Edges", self.graph.number_of_edges()), ("Annotations", len(self.graph.count.annotations())), ("Citations", self.graph.number_of_citations()), ("Authors", self.graph.number_of_authors()), ("Components", nx.number_weakly_connected_components(self.graph)), ("Warnings", self.graph.number_of_warnings()), ] if prose_prefix: rv = [(f"Number of {x}", y) for x, y in rv] rv.append(("Network Density", "{:.2E}".format(nx.density(self.graph)))) return rv def list(self) -> List[Tuple[str, Any]]: """Return a list of tuples that summarize the graph.""" return [ *self._metadata_list(), *self._statistics_list(), ] class PlotDispatch(Dispatch): """A dispatch for count functions that can be found at :data:`pybel.BELGraph.plot`.""" def summary(self, save: Optional[str] = None, **kwargs): """Plot a summary of the graph's nodes and edges using :mod:`matplotlib`.""" from pybel_tools.summary.visualization import plot_summary fig, axes = plot_summary(self.graph, **kwargs) if save: fig.save(save) class ExpandDispatch(Dispatch): """A dispatch for count functions that can be found at :data:`pybel.BELGraph.expand`.""" @property def parent(self) -> BELGraph: """Get the parent BEL graph.""" if not self.graph.parent: raise RuntimeError("Can not use expand dispatch on graph without a parent") return self.graph.parent def neighborhood(self, node: BaseEntity) -> BELGraph: """Expand around the neighborhood of a given node. >>> from pybel.examples import braf_graph >>> from pybel.dsl import Protein >>> thpo = Protein(namespace='HGNC', name='THPO', identifier='11795') >>> braf = Protein(namespace='HGNC', name='BRAF', identifier='1097') >>> raf1 = Protein(namespace='HGNC', name='RAF1', identifier='9829') >>> elk1 = Protein(namespace='HGNC', name='ELK1', identifier='3321') >>> subgraph_1 = braf_graph.induce.paths([braf, elk1]) >>> assert thpo not in subgraph_1 and raf1 not in subgraph_1 >>> subgraph_2 = subgraph_1.expand.neighborhood(braf) >>> assert thpo in subgraph_2 and raf1 not in subgraph_2 """ from .mutation import expand_node_neighborhood cp = self.graph.copy() expand_node_neighborhood(universe=self.parent, graph=cp, node=node) return cp def periphery(self, **kwargs): """Expand around the periphery of the graph w.r.t. its parent graph.""" from pybel_tools.mutation.expansion import expand_periphery cp = self.graph.copy() expand_periphery(universe=self.parent, graph=cp, **kwargs) return cp def internal(self, **kwargs): """Expand missing edges between nodes in the graph w.r.t. its parent graph.""" from pybel_tools.mutation.expansion import expand_internal cp = self.graph.copy() expand_internal(universe=self.parent, graph=cp, **kwargs) return cp class InduceDispatch(Dispatch): """A dispatch for induction functions that can be found at :data:`pybel.BELGraph.induce`.""" def paths(self, nodes: Iterable[BaseEntity]) -> Optional[BELGraph]: """Induce a subgraph on shortest paths between the nodes.""" from .mutation import get_subgraph_by_all_shortest_paths return get_subgraph_by_all_shortest_paths(self.graph, nodes) def neighborhood(self, nodes: Iterable[BaseEntity]) -> Optional[BELGraph]: """Induce a subgraph around the neighborhood.""" from .mutation import get_subgraph_by_neighborhood return get_subgraph_by_neighborhood(self.graph, nodes) def random(self, **kwargs) -> Optional[BELGraph]: """Induce a random subgraph.""" from .mutation import get_random_subgraph return get_random_subgraph(self.graph, **kwargs) def annotation(self, prefix: str, identifier: str) -> Optional[BELGraph]: """Induce a subgraph on edges with the given annotation.""" from .mutation import get_subgraph_by_annotation_value return get_subgraph_by_annotation_value(self.graph, prefix, identifier) pybel-0.15.5/src/pybel/struct/grouping/000077500000000000000000000000001426625374700200155ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/grouping/__init__.py000066400000000000000000000003421426625374700221250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for grouping BEL graphs into sub-graphs.""" from . import annotations, provenance from .annotations import * from .provenance import * __all__ = annotations.__all__ + provenance.__all__ pybel-0.15.5/src/pybel/struct/grouping/annotations.py000066400000000000000000000042061426625374700227260ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for grouping sub-graphs.""" import logging from collections import defaultdict from typing import Mapping, Optional from ..graph import BELGraph from ...constants import ANNOTATIONS from ...language import Entity __all__ = [ "get_subgraphs_by_annotation", ] logger = logging.getLogger(__name__) def _get_subgraphs_by_annotation_disregard_undefined(graph: BELGraph, annotation: str) -> Mapping[Entity, BELGraph]: result = defaultdict(graph.child) for source, target, key, data in graph.edges(keys=True, data=True): annotation_dict = data.get(ANNOTATIONS) if annotation_dict is None: continue if annotation not in annotation_dict: continue for entity in annotation_dict[annotation]: result[entity].add_edge(source, target, key=key, **data) return dict(result) def _get_subgraphs_by_annotation_keep_undefined( graph: BELGraph, annotation: str, sentinel: Optional[str], ) -> Mapping[Entity, BELGraph]: result = defaultdict(graph.child) for source, target, key, data in graph.edges(keys=True, data=True): annotation_dict = data.get(ANNOTATIONS) if annotation_dict is None or annotation not in annotation_dict: result[sentinel].add_edge(source, target, key=key, **data) else: for entity in annotation_dict[annotation]: result[entity].add_edge(source, target, key=key, **data) return dict(result) def get_subgraphs_by_annotation( graph: BELGraph, annotation: str, sentinel: Optional[str] = None, ) -> Mapping[Entity, BELGraph]: """Stratify the given graph into sub-graphs based on the values for edges' annotations. :param graph: A BEL graph :param annotation: The annotation to group by :param sentinel: The value to stick unannotated edges into. If none, does not keep undefined. """ if sentinel is not None: subgraphs = _get_subgraphs_by_annotation_keep_undefined(graph, annotation, sentinel) else: subgraphs = _get_subgraphs_by_annotation_disregard_undefined(graph, annotation) return subgraphs pybel-0.15.5/src/pybel/struct/grouping/provenance.py000066400000000000000000000014701426625374700225310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utility functions for grouping sub-graphs by citation.""" from collections import defaultdict from typing import Mapping, Tuple from ..graph import BELGraph from ...constants import CITATION, IDENTIFIER, NAMESPACE __all__ = [ "get_subgraphs_by_citation", ] def get_subgraphs_by_citation(graph: BELGraph) -> Mapping[Tuple[str, str], BELGraph]: """Stratify the graph based on citations. :param graph: A BEL graph :return: A mapping of each citation db/id to the BEL graph from it. """ rv = defaultdict(graph.child) for u, v, key, data in graph.edges(keys=True, data=True): if CITATION not in data: continue dk = data[CITATION][NAMESPACE], data[CITATION][IDENTIFIER] rv[dk].add_edge(u, v, key=key, **data) return dict(rv) pybel-0.15.5/src/pybel/struct/mutation/000077500000000000000000000000001426625374700200235ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/mutation/__init__.py000066400000000000000000000011051426625374700221310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains functions that mutate or make transformations on a network.""" from . import ( collapse, deletion, expansion, induction, induction_expansion, inference, metadata, utils, ) from .collapse import * from .deletion import * from .expansion import * from .induction import * from .induction_expansion import * from .inference import * from .inference import transfer from .inference.transfer import * from .metadata import * from .utils import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/mutation/collapse/000077500000000000000000000000001426625374700216255ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/mutation/collapse/__init__.py000066400000000000000000000003411426625374700237340ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for collapsing nodes.""" from . import collapse, protein_rna_origins from .collapse import * from .protein_rna_origins import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/mutation/collapse/collapse.py000066400000000000000000000063551426625374700240120ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for functions for collapsing nodes.""" import itertools as itt from typing import Mapping, Set from ...filters.edge_filters import filter_edges from ...filters.edge_predicate_builders import build_relation_predicate from ...pipeline import in_place_transformation from ....constants import HAS_VARIANT from ....dsl import BaseEntity from ....utils import hash_edge __all__ = [ "collapse_pair", "collapse_nodes", "collapse_all_variants", "surviors_are_inconsistent", ] def _remove_self_edges(graph): self_edges = [(u, u, k) for u in graph if u in graph[u] for k in graph[u][u]] graph.remove_edges_from(self_edges) @in_place_transformation def collapse_pair(graph, survivor: BaseEntity, victim: BaseEntity) -> None: """Rewire all edges from the synonymous node to the survivor node, then deletes the synonymous node. Does not keep edges between the two nodes. :param pybel.BELGraph graph: A BEL graph :param survivor: The BEL node to collapse all edges on the synonym to :param victim: The BEL node to collapse into the surviving node """ graph.add_edges_from( (survivor, successor, hash_edge(survivor, successor, edge_data), edge_data) for _, successor, edge_data in graph.out_edges(victim, data=True) if successor != survivor ) graph.add_edges_from( (predecessor, survivor, hash_edge(predecessor, survivor, edge_data), edge_data) for predecessor, _, edge_data in graph.in_edges(victim, data=True) if predecessor != survivor ) if victim in graph: graph.remove_node(victim) # TODO what happens when collapsing is not consistent? Need to build intermediate mappings and test their consistency. @in_place_transformation def collapse_nodes(graph, survivor_mapping: Mapping[BaseEntity, Set[BaseEntity]]) -> None: """Collapse all nodes in values to the key nodes, in place. :param pybel.BELGraph graph: A BEL graph :param survivor_mapping: A dictionary with survivors as their keys, and iterables of the corresponding victims as values. """ inconsistencies = surviors_are_inconsistent(survivor_mapping) if inconsistencies: raise ValueError("survivor mapping is inconsistent: {}".format(inconsistencies)) for survivor, victims in survivor_mapping.items(): for victim in victims: collapse_pair(graph, survivor=survivor, victim=victim) _remove_self_edges(graph) def surviors_are_inconsistent(survivor_mapping: Mapping[BaseEntity, Set[BaseEntity]]) -> Set[BaseEntity]: """Check that there's no transitive shit going on.""" victim_mapping = set() for victim in itt.chain.from_iterable(survivor_mapping.values()): if victim in survivor_mapping: victim_mapping.add(victim) return victim_mapping @in_place_transformation def collapse_all_variants(graph) -> None: """Collapse all genes', RNAs', miRNAs', and proteins' variants to their parents. :param pybel.BELGraph graph: A BEL Graph """ has_variant_predicate = build_relation_predicate(HAS_VARIANT) edges = list(filter_edges(graph, has_variant_predicate)) for u, v, _ in edges: collapse_pair(graph, survivor=u, victim=v) _remove_self_edges(graph) pybel-0.15.5/src/pybel/struct/mutation/collapse/protein_rna_origins.py000066400000000000000000000031311426625374700262470ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for collapsing proteins, RNAs, microRNAs, and variants to their correspongind genes.""" from collections import defaultdict from typing import Dict, Set from .collapse import collapse_nodes from ..inference import enrich_protein_and_rna_origins from ...pipeline.decorators import in_place_transformation from ....constants import RELATION, TRANSCRIBED_TO, TRANSLATED_TO from ....dsl import BaseEntity __all__ = [ "collapse_to_genes", ] def _build_collapse_to_gene_dict(graph) -> Dict[BaseEntity, Set[BaseEntity]]: """Build a collapse dictionary. :param pybel.BELGraph graph: A BEL graph :return: A dictionary of {node: set of PyBEL node tuples} """ collapse_dict = defaultdict(set) r2g = {} for gene_node, rna_node, d in graph.edges(data=True): if d[RELATION] != TRANSCRIBED_TO: continue collapse_dict[gene_node].add(rna_node) r2g[rna_node] = gene_node for rna_node, protein_node, d in graph.edges(data=True): if d[RELATION] != TRANSLATED_TO: continue if rna_node not in r2g: raise ValueError("Should complete origin before running this function") collapse_dict[r2g[rna_node]].add(protein_node) return collapse_dict @in_place_transformation def collapse_to_genes(graph): """Collapse all protein, RNA, and miRNA nodes to their corresponding gene nodes. :param pybel.BELGraph graph: A BEL graph """ enrich_protein_and_rna_origins(graph) collapse_dict = _build_collapse_to_gene_dict(graph) collapse_nodes(graph, collapse_dict) pybel-0.15.5/src/pybel/struct/mutation/deletion/000077500000000000000000000000001426625374700216265ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/mutation/deletion/__init__.py000066400000000000000000000003701426625374700237370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Modules supporting deletion and degradation of graphs.""" from . import deletion, protein_rna_origins from .deletion import * from .protein_rna_origins import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/mutation/deletion/deletion.py000066400000000000000000000052021426625374700240020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for deleting nodes and edges in networks.""" from ...filters.edge_filters import filter_edges from ...filters.edge_predicates import is_associative_relation, not_causal_relation from ...filters.node_filters import filter_nodes from ...filters.node_predicates import ( is_biological_process, is_isolated_list_abundance, is_pathology, ) from ...pipeline import in_place_transformation __all__ = [ "remove_filtered_edges", "remove_filtered_nodes", "remove_associations", "remove_pathologies", "remove_biological_processes", "remove_isolated_list_abundances", "remove_non_causal_edges", ] @in_place_transformation def remove_filtered_edges(graph, edge_predicates=None): """Remove edges passing the given edge predicates. :param pybel.BELGraph graph: A BEL graph :param edge_predicates: A predicate or list of predicates :type edge_predicates: None or ((pybel.BELGraph, tuple, tuple, int) -> bool) or iter[(pybel.BELGraph, tuple, tuple, int) -> bool]] :return: """ edges = list(filter_edges(graph, edge_predicates=edge_predicates)) graph.remove_edges_from(edges) @in_place_transformation def remove_filtered_nodes(graph, node_predicates=None): """Remove nodes passing the given node predicates. :param pybel.BELGraph graph: A BEL graph :type node_predicates: None or ((pybel.BELGraph, tuple) -> bool) or iter[(pybel.BELGraph, tuple) -> bool)] """ nodes = list(filter_nodes(graph, node_predicates=node_predicates)) graph.remove_nodes_from(nodes) @in_place_transformation def remove_associations(graph): """Remove all associative relationships from the graph. :param pybel.BELGraph graph: A BEL graph """ remove_filtered_edges(graph, is_associative_relation) @in_place_transformation def remove_pathologies(graph): """Remove pathology nodes from the graph. :param pybel.BELGraph graph: A BEL graph """ remove_filtered_nodes(graph, node_predicates=is_pathology) @in_place_transformation def remove_biological_processes(graph): """Remove biological process nodes from the graph. :param pybel.BELGraph graph: A BEL graph """ remove_filtered_nodes(graph, node_predicates=is_biological_process) @in_place_transformation def remove_isolated_list_abundances(graph): """Remove isolated list abundances from the graph. :param pybel.BELGraph graph: A BEL graph """ remove_filtered_nodes(graph, is_isolated_list_abundance) @in_place_transformation def remove_non_causal_edges(graph): """Remove non-causal edges from the graph.""" remove_filtered_edges(graph, not_causal_relation) pybel-0.15.5/src/pybel/struct/mutation/deletion/protein_rna_origins.py000066400000000000000000000041611426625374700262540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for deleting proteins and genes that are leaves.""" from typing import Iterable from ...filters.node_filters import get_nodes_by_function from ...pipeline.decorators import in_place_transformation from ....constants import GENE, RELATION, RNA, TRANSCRIBED_TO, TRANSLATED_TO from ....dsl import BaseEntity __all__ = [ "prune_protein_rna_origins", ] def get_gene_leaves(graph) -> Iterable[BaseEntity]: """Iterate over all genes who have only one connection, that's a transcription to its RNA. :param pybel.BELGraph graph: A BEL graph """ yield from _iterate_leaves(graph, GENE, TRANSCRIBED_TO) def get_rna_leaves(graph) -> Iterable[BaseEntity]: """Iterate over all RNAs who have only one connection, that's a translation to its protein. :param pybel.BELGraph graph: A BEL graph """ yield from _iterate_leaves(graph, RNA, TRANSLATED_TO) def _iterate_leaves(graph, func, relation): for node in get_nodes_by_function(graph, func): if graph.in_degree(node) != 0: continue if graph.out_degree(node) != 1: continue _, _, d = list(graph.out_edges(node, data=True))[0] if d[RELATION] == relation: yield node @in_place_transformation def prune_rna_origins(graph): """Delete gene nodes that are only connected to one node, their correspond RNA, by a transcription edge. :param pybel.BELGraph graph: A BEL graph """ gene_leaves = list(get_gene_leaves(graph)) graph.remove_nodes_from(gene_leaves) @in_place_transformation def prune_protein_origins(graph): """Delete RNA nodes that are only connected to one node - their correspond protein - by a translation edge. :param pybel.BELGraph graph: A BEL graph """ rna_leaves = list(get_rna_leaves(graph)) graph.remove_nodes_from(rna_leaves) @in_place_transformation def prune_protein_rna_origins(graph): """Delete genes that are only connected to one node, their correspond RNA, by a translation edge. :param pybel.BELGraph graph: A BEL graph """ prune_rna_origins(graph) prune_protein_origins(graph) pybel-0.15.5/src/pybel/struct/mutation/expansion/000077500000000000000000000000001426625374700220275ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/mutation/expansion/__init__.py000066400000000000000000000003241426625374700241370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Mutations that expand the graph.""" from . import neighborhood, upstream from .neighborhood import * from .upstream import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/mutation/expansion/neighborhood.py000066400000000000000000000106271426625374700250560ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for expanding the neighborhoods of nodes.""" import itertools as itt from typing import Iterable from ...filters.node_predicates import is_pathology from ...filters.typing import EdgeIterator from ...graph import BELGraph from ...pipeline import uni_in_place_transformation from ...utils import update_metadata from ....dsl import BaseEntity __all__ = [ "expand_node_predecessors", "expand_node_successors", "expand_node_neighborhood", "expand_nodes_neighborhoods", "expand_all_node_neighborhoods", "expand_internal", ] @uni_in_place_transformation def expand_node_predecessors(universe: BELGraph, graph: BELGraph, node: BaseEntity): """Expand around the predecessors of the given node in the result graph. :param universe: The graph containing the stuff to add :param graph: The graph to add stuff to :param node: A BEL node """ skip_successors = set() for successor in universe.successors(node): if successor in graph: skip_successors.add(successor) continue graph.add_node_from_data(successor) graph.add_edges_from( (source, successor, key, data) for source, successor, key, data in universe.out_edges(node, data=True, keys=True) if successor not in skip_successors ) update_metadata(universe, graph) @uni_in_place_transformation def expand_node_successors(universe: BELGraph, graph: BELGraph, node: BaseEntity) -> None: """Expand around the successors of the given node in the result graph. :param universe: The graph containing the stuff to add :param graph: The graph to add stuff to :param node: A BEL node """ skip_predecessors = set() for predecessor in universe.predecessors(node): if predecessor in graph: skip_predecessors.add(predecessor) continue graph.add_node_from_data(predecessor) graph.add_edges_from( (predecessor, target, key, data) for predecessor, target, key, data in universe.in_edges(node, data=True, keys=True) if predecessor not in skip_predecessors ) update_metadata(universe, graph) @uni_in_place_transformation def expand_node_neighborhood(universe: BELGraph, graph: BELGraph, node: BaseEntity) -> None: """Expand around the neighborhoods of the given node in the result graph. Note: expands complexes' members :param universe: The graph containing the stuff to add :param graph: The graph to add stuff to :param node: A BEL node """ expand_node_predecessors(universe, graph, node) expand_node_successors(universe, graph, node) @uni_in_place_transformation def expand_nodes_neighborhoods(universe: BELGraph, graph: BELGraph, nodes: Iterable[BaseEntity]) -> None: """Expand around the neighborhoods of the given node in the result graph. :param universe: The graph containing the stuff to add :param graph: The graph to add stuff to :param nodes: Nodes from the query graph """ for node in nodes: expand_node_neighborhood(universe, graph, node) @uni_in_place_transformation def expand_all_node_neighborhoods(universe: BELGraph, graph: BELGraph, filter_pathologies: bool = False) -> None: """Expand the neighborhoods of all nodes in the given graph. :param pybel.BELGraph universe: The graph containing the stuff to add :param pybel.BELGraph graph: The graph to add stuff to :param filter_pathologies: Should expansion take place around pathologies? """ for node in list(graph): if filter_pathologies and is_pathology(node): continue expand_node_neighborhood(universe, graph, node) @uni_in_place_transformation def expand_internal( universe: BELGraph, graph: BELGraph, ) -> None: """Expand on edges between entities in the sub-graph that pass the given filters, in place. :param universe: The full graph :param graph: A sub-graph to find the upstream information """ for u, v, key in iterate_internal(universe, graph): graph.add_edge(u, v, key=key, **universe[u][v][key]) def iterate_internal(universe: BELGraph, graph: BELGraph) -> EdgeIterator: """Iterate over edges that are in the universe but not the target graph.""" for u, v in itt.product(graph, repeat=2): if graph.has_edge(u, v) or not universe.has_edge(u, v): continue for key in universe[u][v]: yield u, v, key pybel-0.15.5/src/pybel/struct/mutation/expansion/upstream.py000066400000000000000000000024001426625374700242350ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for expanding a graph based on the upstream/downstream edges.""" from ..utils import expand_by_edge_filter from ...filters.edge_predicate_builders import ( build_downstream_edge_predicate, build_upstream_edge_predicate, ) from ...pipeline import uni_in_place_transformation __all__ = [ "expand_upstream_causal", "expand_downstream_causal", ] @uni_in_place_transformation def expand_upstream_causal(universe, graph): """Add the upstream causal relations to the given sub-graph. :param pybel.BELGraph universe: A BEL graph representing the universe of all knowledge :param pybel.BELGraph graph: The target BEL graph to enrich with upstream causal controllers of contained nodes """ expand_by_edge_filter(universe, graph, build_upstream_edge_predicate(graph)) @uni_in_place_transformation def expand_downstream_causal(universe, graph): """Add the downstream causal relations to the given sub-graph. :param pybel.BELGraph universe: A BEL graph representing the universe of all knowledge :param pybel.BELGraph graph: The target BEL graph to enrich with upstream causal controllers of contained nodes """ expand_by_edge_filter(universe, graph, build_downstream_edge_predicate(graph)) pybel-0.15.5/src/pybel/struct/mutation/induction/000077500000000000000000000000001426625374700220175ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/mutation/induction/__init__.py000066400000000000000000000006511426625374700241320ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Mutations that induce a sub-graph.""" from . import ( annotations, citation, neighborhood, paths, random_subgraph, upstream, utils, ) from .annotations import * from .citation import * from .neighborhood import * from .paths import * from .random_subgraph import * from .upstream import * from .utils import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/mutation/induction/annotations.py000066400000000000000000000035161426625374700247330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for inducing graphs based on edge annotations.""" import logging from typing import Iterable, Optional, Union from .utils import get_subgraph_by_edge_filter from ...filters.edge_predicate_builders import ( build_annotation_dict_all_filter, build_annotation_dict_any_filter, ) from ...graph import AnnotationsHint, BELGraph from ...pipeline import transformation __all__ = [ "get_subgraph_by_annotation_value", "get_subgraph_by_annotations", ] logger = logging.getLogger(__name__) @transformation def get_subgraph_by_annotations( graph: BELGraph, annotations: AnnotationsHint, or_: Optional[bool] = None, ) -> BELGraph: """Induce a sub-graph given an annotations filter. :param graph: A BEL graph :param annotations: Annotation filters (match all with :func:`pybel.utils.subdict_matches`) :param or_: if True any annotation should be present, if False all annotations should be present in the edge. Defaults to True. :return: A subgraph of the original BEL graph """ edge_filter_builder = build_annotation_dict_any_filter if (or_ is None or or_) else build_annotation_dict_all_filter annotations = graph._clean_annotations(annotations) return get_subgraph_by_edge_filter(graph, edge_filter_builder(annotations)) @transformation def get_subgraph_by_annotation_value(graph: BELGraph, annotation: str, values: Union[str, Iterable[str]]) -> BELGraph: """Induce a sub-graph over all edges whose annotations match the given key and value. :param graph: A BEL graph :param annotation: The annotation to group by :param values: The value(s) for the annotation :return: A subgraph of the original BEL graph """ if isinstance(values, str): values = {values} return get_subgraph_by_annotations(graph, {annotation: values}) pybel-0.15.5/src/pybel/struct/mutation/induction/citation.py000066400000000000000000000023271426625374700242070ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Induction functions based on provenance information.""" import logging from .utils import get_subgraph_by_edge_filter from ...filters.edge_predicate_builders import ( build_author_inclusion_filter, build_pmid_inclusion_filter, ) from ...pipeline import transformation __all__ = [ "get_subgraph_by_pubmed", "get_subgraph_by_authors", ] logger = logging.getLogger(__name__) @transformation def get_subgraph_by_pubmed(graph, pubmed_identifiers): """Induce a sub-graph over the edges retrieved from the given PubMed identifier(s). :param pybel.BELGraph graph: A BEL graph :param str or list[str] pubmed_identifiers: A PubMed identifier or list of PubMed identifiers :rtype: pybel.BELGraph """ return get_subgraph_by_edge_filter(graph, build_pmid_inclusion_filter(pubmed_identifiers)) @transformation def get_subgraph_by_authors(graph, authors): """Induce a sub-graph over the edges retrieved publications by the given author(s). :param pybel.BELGraph graph: A BEL graph :param str or list[str] authors: An author or list of authors :rtype: pybel.BELGraph """ return get_subgraph_by_edge_filter(graph, build_author_inclusion_filter(authors)) pybel-0.15.5/src/pybel/struct/mutation/induction/neighborhood.py000066400000000000000000000020171426625374700250400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for selecting by the neighborhoods of nodes.""" import itertools as itt from typing import Iterable, Optional from ...graph import BELGraph from ...pipeline import transformation from ...utils import update_metadata from ....dsl import BaseEntity __all__ = [ "get_subgraph_by_neighborhood", ] @transformation def get_subgraph_by_neighborhood(graph: BELGraph, nodes: Iterable[BaseEntity]) -> Optional[BELGraph]: """Get a BEL graph around the neighborhoods of the given nodes. Returns none if no nodes are in the graph. :param graph: A BEL graph :param nodes: An iterable of BEL nodes :return: A BEL graph induced around the neighborhoods of the given nodes """ node_set = set(nodes) if not any(node in graph for node in node_set): return rv = graph.child() rv.add_edges_from( itt.chain( graph.in_edges(nodes, keys=True, data=True), graph.out_edges(nodes, keys=True, data=True), ), ) return rv pybel-0.15.5/src/pybel/struct/mutation/induction/paths.py000066400000000000000000000102661426625374700235150ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Induction methods for graphs over shortest paths.""" import itertools as itt import logging import random from typing import Iterable, List, Optional, Set, Tuple import networkx as nx from .utils import get_subgraph_by_induction from ...graph import BELGraph from ...pipeline import transformation from ....constants import FUNCTION, PATHOLOGY from ....dsl import BaseEntity __all__ = [ "get_nodes_in_all_shortest_paths", "get_subgraph_by_all_shortest_paths", "get_random_path", ] logger = logging.getLogger(__name__) def _remove_pathologies_oop(graph: BELGraph): """Remove pathology nodes from the graph.""" rv = graph.copy() victims = [node for node in rv if node[FUNCTION] == PATHOLOGY] rv.remove_nodes_from(victims) return rv def _iterate_nodes_in_shortest_paths( graph: BELGraph, nodes: Iterable[BaseEntity], weight: Optional[str] = None, ) -> Iterable[BaseEntity]: """Iterate over nodes in the shortest paths between all pairs of nodes in the given list.""" for source, target in itt.product(nodes, repeat=2): try: paths = nx.all_shortest_paths(graph, source, target, weight=weight) for path in paths: for node in path: yield node except nx.exception.NetworkXNoPath: continue def get_nodes_in_all_shortest_paths( graph: BELGraph, nodes: Iterable[BaseEntity], weight: Optional[str] = None, remove_pathologies: bool = False, ) -> Set[BaseEntity]: """Get a set of nodes in all shortest paths between the given nodes. Thinly wraps :func:`networkx.all_shortest_paths`. :param graph: A BEL graph :param nodes: The list of nodes to use to use to find all shortest paths :param weight: Edge data key corresponding to the edge weight. If none, uses unweighted search. :param remove_pathologies: Should pathology nodes be removed first? :return: A set of nodes appearing in the shortest paths between nodes in the BEL graph .. note:: This can be trivially parallelized using :func:`networkx.single_source_shortest_path` """ if remove_pathologies: graph = _remove_pathologies_oop(graph) return set(_iterate_nodes_in_shortest_paths(graph, nodes, weight=weight)) @transformation def get_subgraph_by_all_shortest_paths( graph, nodes: Iterable[BaseEntity], weight: Optional[str] = None, remove_pathologies: bool = False, ) -> Optional[BELGraph]: """Induce a subgraph over the nodes in the pairwise shortest paths between all of the nodes in the given list. :param pybel.BELGraph graph: A BEL graph :param nodes: A set of nodes over which to calculate shortest paths :param weight: Edge data key corresponding to the edge weight. If None, performs unweighted search :param remove_pathologies: Should the pathology nodes be deleted before getting shortest paths? :return: A BEL graph induced over the nodes appearing in the shortest paths between the given nodes :rtype: Optional[pybel.BELGraph] """ query_nodes = [] for node in nodes: if node not in graph: logger.debug("%s not in %s", node, graph) continue query_nodes.append(node) if not query_nodes: return induced_nodes = get_nodes_in_all_shortest_paths( graph, query_nodes, weight=weight, remove_pathologies=remove_pathologies, ) if not induced_nodes: return return get_subgraph_by_induction(graph, induced_nodes) def get_random_path(graph: BELGraph) -> List[BaseEntity]: """Get a random path from the graph as a list of nodes. :param graph: A BEL graph """ wg = graph.to_undirected() nodes = wg.nodes() def pick_random_pair() -> Tuple[BaseEntity, BaseEntity]: """Get a pair of random nodes.""" return random.sample(nodes, k=2) source, target = pick_random_pair() tries = 0 sentinel_tries = 5 while not nx.has_path(wg, source, target) and tries < sentinel_tries: tries += 1 source, target = pick_random_pair() if tries == sentinel_tries: return [source] return nx.shortest_path(wg, source=source, target=target) pybel-0.15.5/src/pybel/struct/mutation/induction/random_subgraph.py000066400000000000000000000162051426625374700255500ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for inducing random sub-graphs.""" import bisect import logging import random from operator import itemgetter from typing import Any, Iterable, Mapping, Optional, Set, Tuple from ..utils import remove_isolated_nodes from ...graph import BELGraph from ...pipeline import transformation from ...utils import update_metadata from ....dsl import BaseEntity __all__ = [ "get_graph_with_random_edges", "get_random_node", "get_random_subgraph", ] logger = logging.getLogger(__name__) def _random_edge_iterator(graph: BELGraph, n_edges: int) -> Iterable[Tuple[BaseEntity, BaseEntity, int, Mapping]]: """Get a random set of edges from the graph and randomly samples a key from each. :param graph: A BEL graph :param n_edges: Number of edges to randomly select from the given graph """ edges = list(graph.edges()) edge_sample = random.sample(edges, n_edges) for u, v in edge_sample: keys = list(graph[u][v]) k = random.choice(keys) yield u, v, k, graph[u][v][k] @transformation def get_graph_with_random_edges(graph: BELGraph, n_edges: int) -> BELGraph: """Build a new graph from a seeding of edges. :param graph: A BEL graph :param n_edges: Number of edges to randomly select from the given graph """ rv = graph.child() rv.add_edges_from(_random_edge_iterator(graph, n_edges)) return rv #: How many edges should be sampled from a graph that's still reasonable to display SAMPLE_RANDOM_EDGE_COUNT = 250 #: How many edges should be sampled as "seed" edges SAMPLE_RANDOM_EDGE_SEED_COUNT = 5 class WeightedRandomGenerator: """A weighted random number generator. Adapted from: https://eli.thegreenplace.net/2010/01/22/weighted-random-generation-in-python """ def __init__(self, values, weights): """Build a weighted random generator. :param Any values: A sequence corresponding to the weights :param weights: Weights for each. Should all be positive, but not necessarily normalized. """ self.values = values self.totals = [] weight_total = 0 for weight in weights: weight_total += weight self.totals.append(weight_total) @property def total(self): """Get the total weight stored.""" return self.totals[-1] def next_index(self) -> int: """Get a random index.""" return bisect.bisect_right(self.totals, random.random() * self.total) def next(self) -> Any: """Get a random value.""" return self.values[self.next_index()] def get_random_node( graph, node_blacklist: Set[BaseEntity], invert_degrees: Optional[bool] = None, ) -> Optional[BaseEntity]: """Choose a node from the graph with probabilities based on their degrees. :type graph: networkx.Graph :param node_blacklist: Nodes to filter out :param invert_degrees: Should the degrees be inverted? Defaults to true. """ try: nodes, degrees = zip( *( (node, degree) for node, degree in sorted(graph.degree(), key=itemgetter(1)) if node not in node_blacklist ), ) except ValueError: # something wrong with graph, probably no elements in graph.degree_iter return if invert_degrees is None or invert_degrees: # More likely to choose low degree nodes to explore, so don't make hubs degrees = [1 / degree for degree in degrees] wrg = WeightedRandomGenerator(nodes, degrees) return wrg.next() # noqa: B305 def _helper( result, graph, number_edges_remaining: int, node_blacklist: Set[BaseEntity], invert_degrees: Optional[bool] = None, ) -> None: """Help build a random graph. :type result: networkx.Graph :type graph: networkx.Graph """ original_node_count = graph.number_of_nodes() logger.debug("adding remaining %d edges", number_edges_remaining) for _ in range(number_edges_remaining): source, possible_step_nodes, c = None, set(), 0 while not source or not possible_step_nodes: source = get_random_node(result, node_blacklist, invert_degrees=invert_degrees) c += 1 if c >= original_node_count: logger.warning("infinite loop happening") logger.warning("source: %s", source) logger.warning("no grow: %s", node_blacklist) return # Happens when after exhausting the connected components. Try increasing the number seed edges if source is None: continue # maybe do something else? # Only keep targets in the original graph that aren't in the result graph possible_step_nodes = set(graph[source]) - set(result[source]) if not possible_step_nodes: node_blacklist.add( source, ) # there aren't any possible nodes to step to, so try growing from somewhere else step_node = random.choice(list(possible_step_nodes)) # it's not really a big deal which, but it might be possible to weight this by the utility of edges later key, attr_dict = random.choice(list(graph[source][step_node].items())) result.add_edge(source, step_node, key=key, **attr_dict) @transformation def get_random_subgraph( graph: BELGraph, number_edges: Optional[int] = None, number_seed_edges: Optional[int] = None, seed: Optional[int] = None, invert_degrees: Optional[bool] = None, ) -> BELGraph: """Generate a random subgraph based on weighted random walks from random seed edges. :type graph: pybel.BELGraph graph :param number_edges: Maximum number of edges. Defaults to :data:`pybel_tools.constants.SAMPLE_RANDOM_EDGE_COUNT` (250). :param number_seed_edges: Number of nodes to start with (which likely results in different components in large graphs). Defaults to :data:`SAMPLE_RANDOM_EDGE_SEED_COUNT` (5). :param seed: A seed for the random state :param invert_degrees: Should the degrees be inverted? Defaults to true. """ if number_edges is None: number_edges = SAMPLE_RANDOM_EDGE_COUNT if number_seed_edges is None: number_seed_edges = SAMPLE_RANDOM_EDGE_SEED_COUNT if seed is not None: random.seed(seed) # Check if graph will sample full graph, and just return it if it would if graph.number_of_edges() <= number_edges: logger.info("sampled full graph") return graph.copy() logger.debug( "getting random sub-graph with %d seed edges, %d final edges, and seed=%s", number_seed_edges, number_edges, seed, ) # Get initial graph with `number_seed_edges` edges result = get_graph_with_random_edges(graph, number_seed_edges) number_edges_remaining = number_edges - result.number_of_edges() _helper( result, graph, number_edges_remaining, node_blacklist=set(), # This is the set of nodes that should no longer be chosen to grow from invert_degrees=invert_degrees, ) logger.debug("removing isolated nodes") remove_isolated_nodes(result) return result pybel-0.15.5/src/pybel/struct/mutation/induction/upstream.py000066400000000000000000000023071426625374700242330ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for inducing up/downstream causal subgraphs.""" import logging from typing import Iterable, Union from .utils import get_subgraph_by_edge_filter from ...filters.edge_predicate_builders import ( build_downstream_edge_predicate, build_upstream_edge_predicate, ) from ...pipeline import transformation from ....dsl import BaseEntity __all__ = [ "get_upstream_causal_subgraph", "get_downstream_causal_subgraph", ] logger = logging.getLogger(__name__) @transformation def get_upstream_causal_subgraph(graph, nbunch: Union[BaseEntity, Iterable[BaseEntity]]): """Induce a sub-graph from all of the upstream causal entities of the nodes in the nbunch. :type graph: pybel.BELGraph :rtype: pybel.BELGraph """ return get_subgraph_by_edge_filter(graph, build_upstream_edge_predicate(nbunch)) @transformation def get_downstream_causal_subgraph(graph, nbunch: Union[BaseEntity, Iterable[BaseEntity]]): """Induce a sub-graph from all of the downstream causal entities of the nodes in the nbunch. :type graph: pybel.BELGraph :rtype: pybel.BELGraph """ return get_subgraph_by_edge_filter(graph, build_downstream_edge_predicate(nbunch)) pybel-0.15.5/src/pybel/struct/mutation/induction/utils.py000066400000000000000000000047211426625374700235350ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for inducing graphs by nodes and by edge filters.""" from typing import Iterable, Optional import networkx as nx from ..utils import expand_by_edge_filter from ...filters.edge_predicates import is_causal_relation from ...filters.node_filters import filter_nodes from ...filters.typing import EdgePredicates, NodePredicates from ...graph import BELGraph from ...operations import subgraph from ...pipeline import transformation from ....dsl import BaseEntity __all__ = [ "get_subgraph_by_edge_filter", "get_subgraph_by_induction", "get_subgraph_by_node_filter", "get_largest_component", "get_causal_subgraph", ] @transformation def get_subgraph_by_edge_filter(graph: BELGraph, edge_predicates: Optional[EdgePredicates] = None) -> BELGraph: """Induce a sub-graph on all edges that pass the given filters. :param graph: A BEL graph :param edge_predicates: An edge predicate or list of edge predicates :return: A BEL sub-graph induced over the edges passing the given filters """ rv = graph.child() expand_by_edge_filter(graph, rv, edge_predicates=edge_predicates) return rv @transformation def get_subgraph_by_induction(graph: BELGraph, nodes: Iterable[BaseEntity]) -> Optional[BELGraph]: """Induce a sub-graph over the given nodes or return None if none of the nodes are in the given graph. :param graph: A BEL graph :param nodes: A list of BEL nodes in the graph """ nodes = tuple(nodes) if all(node not in graph for node in nodes): return return subgraph(graph, nodes) @transformation def get_subgraph_by_node_filter(graph: BELGraph, node_predicates: NodePredicates) -> BELGraph: """Induce a sub-graph on the nodes that pass the given predicate(s). :param graph: A BEL graph :param node_predicates: A node predicate or list of node predicates """ return get_subgraph_by_induction(graph, filter_nodes(graph, node_predicates)) @transformation def get_largest_component(graph: BELGraph) -> BELGraph: """Get the giant component of a graph. :param graph: A BEL graph """ biggest_component_nodes = max(nx.weakly_connected_components(graph), key=len) return subgraph(graph, biggest_component_nodes) @transformation def get_causal_subgraph(graph: BELGraph) -> BELGraph: """Build a new sub-graph induced over the causal edges. :param graph: A BEL graph """ return get_subgraph_by_edge_filter(graph, is_causal_relation) pybel-0.15.5/src/pybel/struct/mutation/induction_expansion.py000066400000000000000000000046551426625374700244670ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for building graphs that use both expansion and induction procedures.""" import logging from typing import Iterable, Union from .expansion import expand_all_node_neighborhoods from .expansion.upstream import expand_downstream_causal, expand_upstream_causal from .induction.neighborhood import get_subgraph_by_neighborhood from .induction.upstream import ( get_downstream_causal_subgraph, get_upstream_causal_subgraph, ) from ..pipeline import transformation from ...dsl import BaseEntity __all__ = [ "get_multi_causal_upstream", "get_multi_causal_downstream", "get_subgraph_by_second_neighbors", ] logger = logging.getLogger(__name__) @transformation def get_multi_causal_upstream(graph, nbunch: Union[BaseEntity, Iterable[BaseEntity]]): """Get the union of all the 2-level deep causal upstream subgraphs from the nbunch. :param pybel.BELGraph graph: A BEL graph :param nbunch: A BEL node or list of BEL nodes :return: A subgraph of the original BEL graph :rtype: pybel.BELGraph """ result = get_upstream_causal_subgraph(graph, nbunch) expand_upstream_causal(graph, result) return result @transformation def get_multi_causal_downstream(graph, nbunch: Union[BaseEntity, Iterable[BaseEntity]]): """Get the union of all of the 2-level deep causal downstream subgraphs from the nbunch. :param pybel.BELGraph graph: A BEL graph :param nbunch: A BEL node or list of BEL nodes :return: A subgraph of the original BEL graph :rtype: pybel.BELGraph """ result = get_downstream_causal_subgraph(graph, nbunch) expand_downstream_causal(graph, result) return result @transformation def get_subgraph_by_second_neighbors(graph, nodes: Iterable[BaseEntity], filter_pathologies: bool = False): """Get a graph around the neighborhoods of the given nodes and expand to the neighborhood of those nodes. Returns none if none of the nodes are in the graph. :param pybel.BELGraph graph: A BEL graph :param nodes: An iterable of BEL nodes :param filter_pathologies: Should expansion take place around pathologies? :return: A BEL graph induced around the neighborhoods of the given nodes :rtype: Optional[pybel.BELGraph] """ result = get_subgraph_by_neighborhood(graph, nodes) if result is None: return expand_all_node_neighborhoods(graph, result, filter_pathologies=filter_pathologies) return result pybel-0.15.5/src/pybel/struct/mutation/inference/000077500000000000000000000000001426625374700217615ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/mutation/inference/__init__.py000066400000000000000000000003611426625374700240720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Mutations for inferring new edges in the graph.""" from . import protein_rna_origins, transfer from .protein_rna_origins import * from .transfer import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/mutation/inference/protein_rna_origins.py000066400000000000000000000031461426625374700264110ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions for enriching the origins of Proteins, RNAs, and miRNAs.""" from ...graph import BELGraph from ...pipeline import in_place_transformation from ....constants import FUNCTION, FUSION, MIRNA, RNA, VARIANTS from ....dsl import Protein __all__ = [ "enrich_rnas_with_genes", "enrich_proteins_with_rnas", "enrich_protein_and_rna_origins", ] @in_place_transformation def enrich_proteins_with_rnas(graph: BELGraph) -> None: """Add the corresponding RNA node for each protein node and connect them with a translation edge. :param graph: A BEL graph """ for protein_node in list(graph): if not isinstance(protein_node, Protein): continue if protein_node.variants: continue rna_node = protein_node.get_rna() graph.add_translation(rna_node, protein_node) @in_place_transformation def enrich_rnas_with_genes(graph: BELGraph) -> None: """Add the corresponding gene node for each RNA/miRNA node and connect them with a transcription edge. :param graph: A BEL graph """ for rna_node in list(graph): if rna_node[FUNCTION] not in {MIRNA, RNA} or FUSION in rna_node or VARIANTS in rna_node: continue gene_node = rna_node.get_gene() graph.add_transcription(gene_node, rna_node) @in_place_transformation def enrich_protein_and_rna_origins(graph: BELGraph) -> None: """Add the corresponding RNA for each protein then the corresponding gene for each RNA/miRNA. :param graph: A BEL graph """ enrich_proteins_with_rnas(graph) enrich_rnas_with_genes(graph) pybel-0.15.5/src/pybel/struct/mutation/inference/transfer.py000066400000000000000000000043601426625374700241620ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module facilitates the transfer of knowledge through ontological relationships.""" from typing import Iterable, List from ...graph import BELGraph from ....constants import ( ANNOTATIONS, CAUSAL_RELATIONS, CITATION, EVIDENCE, IS_A, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, ) from ....dsl import BaseEntity __all__ = [ "infer_child_relations", ] def iter_children(graph: BELGraph, node: BaseEntity) -> Iterable[BaseEntity]: """Iterate over children of the node.""" return (node for node, _, d in graph.in_edges(node, data=True) if d[RELATION] == IS_A) def transfer_causal_edges(graph: BELGraph, source: BaseEntity, target: BaseEntity) -> Iterable[str]: """Transfer causal edges that the source has to the target and yield the resulting hashes.""" for _, v, data in graph.out_edges(source, data=True): if data[RELATION] not in CAUSAL_RELATIONS: continue yield graph.add_qualified_edge( target, v, relation=data[RELATION], evidence=data[EVIDENCE], citation=data[CITATION], annotations=data.get(ANNOTATIONS), source_modifier=data.get(SOURCE_MODIFIER), target_modifier=data.get(TARGET_MODIFIER), ) for u, _, data in graph.in_edges(source, data=True): if data[RELATION] not in CAUSAL_RELATIONS: continue yield graph.add_qualified_edge( u, target, relation=data[RELATION], evidence=data[EVIDENCE], citation=data[CITATION], annotations=data.get(ANNOTATIONS), source_modifier=data.get(SOURCE_MODIFIER), target_modifier=data.get(TARGET_MODIFIER), ) def infer_child_relations(graph: BELGraph, node: BaseEntity) -> List[str]: """Propagate causal relations to children.""" return list(_infer_child_relations_iter(graph, node)) def _infer_child_relations_iter(graph: BELGraph, node: BaseEntity) -> Iterable[str]: """Propagate causal relations to children.""" for child in iter_children(graph, node): yield from transfer_causal_edges(graph, node, child) yield from infer_child_relations(graph, child) pybel-0.15.5/src/pybel/struct/mutation/metadata.py000066400000000000000000000053771426625374700221710ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Functions to modify the metadata of graphs, their edges, and their nodes.""" import logging from ..graph import BELGraph from ..pipeline import in_place_transformation from ...constants import ANNOTATIONS, CITATION, IDENTIFIER, NAMESPACE __all__ = [ "strip_annotations", "add_annotation_value", "remove_annotation_value", "remove_extra_citation_metadata", ] logger = logging.getLogger(__name__) @in_place_transformation def strip_annotations(graph: BELGraph) -> None: """Strip all the annotations from a BEL graph. :param graph: A BEL graph """ for u, v, k in graph.edges(keys=True): if ANNOTATIONS in graph[u][v][k]: del graph[u][v][k][ANNOTATIONS] @in_place_transformation def add_annotation_value(graph: BELGraph, annotation: str, value: str, strict: bool = True) -> None: """Add the given annotation/value pair to all qualified edges. :param graph: A BEL graph :param annotation: :param value: :param strict: Should the function ensure the annotation has already been defined? """ if strict and annotation not in graph.defined_annotation_keywords: raise ValueError("annotation not defined: {}".format(annotation)) for u, v, k in graph.edges(keys=True): if ANNOTATIONS not in graph[u][v][k]: continue if annotation not in graph[u][v][k][ANNOTATIONS]: graph[u][v][k][ANNOTATIONS][annotation] = {} graph[u][v][k][ANNOTATIONS][annotation][value] = True @in_place_transformation def remove_annotation_value(graph: BELGraph, annotation: str, value: str) -> None: """Remove the given annotation/value pair to all qualified edges. :param graph: A BEL graph :param annotation: :param value: """ if annotation not in graph.defined_annotation_keywords: logger.warning("annotation was not defined: %s", annotation) return for u, v, k in graph.edges(keys=True): if ANNOTATIONS not in graph[u][v][k]: continue if annotation not in graph[u][v][k][ANNOTATIONS]: continue if value not in graph[u][v][k][ANNOTATIONS][annotation]: continue del graph[u][v][k][ANNOTATIONS][annotation][value] _CITATION_KEEP_KEYS = {IDENTIFIER, NAMESPACE} @in_place_transformation def remove_extra_citation_metadata(graph) -> None: """Remove superfluous metadata associated with a citation (that isn't the db/id). Best practice is to add this information programmatically. """ for u, v, k in graph.edges(keys=True): if CITATION not in graph[u][v][k]: continue for key in list(graph[u][v][k][CITATION]): if key not in _CITATION_KEEP_KEYS: del graph[u][v][k][CITATION][key] pybel-0.15.5/src/pybel/struct/mutation/utils.py000066400000000000000000000031671426625374700215440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """General-use induction functions.""" import networkx as nx from ..filters.edge_filters import filter_edges from ..filters.typing import EdgePredicates from ..pipeline import ( in_place_transformation, transformation, uni_in_place_transformation, ) from ..utils import update_metadata __all__ = [ "remove_isolated_nodes", "remove_isolated_nodes_op", "expand_by_edge_filter", ] @in_place_transformation def remove_isolated_nodes(graph): """Remove isolated nodes from the network, in place. :param pybel.BELGraph graph: A BEL graph """ nodes = list(nx.isolates(graph)) graph.remove_nodes_from(nodes) @transformation def remove_isolated_nodes_op(graph): """Build a new graph excluding the isolated nodes. :param pybel.BELGraph graph: A BEL graph :rtype: pybel.BELGraph """ rv = graph.copy() nodes = list(nx.isolates(rv)) rv.remove_nodes_from(nodes) return rv @uni_in_place_transformation def expand_by_edge_filter(source, target, edge_predicates: EdgePredicates): """Expand a target graph by edges in the source matching the given predicates. :param pybel.BELGraph source: A BEL graph :param pybel.BELGraph target: A BEL graph :param edge_predicates: An edge predicate or list of edge predicates :return: A BEL sub-graph induced over the edges passing the given filters :rtype: pybel.BELGraph """ target.add_edges_from( (u, v, k, source[u][v][k]) for u, v, k in filter_edges(source, edge_predicates=edge_predicates) ) update_metadata(source, target) # TODO smarter ways of ensuring metadata pybel-0.15.5/src/pybel/struct/node_utils.py000066400000000000000000000222161426625374700207050ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for handling nodes.""" import itertools as itt import logging from itertools import chain from typing import Set, Tuple, Type, Union from networkx import relabel_nodes from ..constants import ANNOTATIONS, CITATION, EVIDENCE, INCREASES, RELATION from ..dsl import BaseAbundance, BaseEntity, ListAbundance, Reaction __all__ = [ "flatten_list_abundance", "list_abundance_cartesian_expansion", "reaction_cartesian_expansion", ] logger = logging.getLogger(__name__) def flatten_list_abundance(node: ListAbundance) -> ListAbundance: """Flattens the complex or composite abundance.""" return node.__class__( list( chain.from_iterable( (flatten_list_abundance(member).members if isinstance(member, ListAbundance) else [member]) for member in node.members ) ) ) def list_abundance_expansion(graph) -> None: """Flatten list abundances.""" mapping = {node: flatten_list_abundance(node) for node in graph if isinstance(node, ListAbundance)} relabel_nodes(graph, mapping, copy=False) def list_abundance_cartesian_expansion(graph) -> None: """Expand all list abundances to simple subject-predicate-object networks.""" for u, v, d in list(graph.edges(data=True)): if CITATION not in d: continue if isinstance(u, ListAbundance) and isinstance(v, ListAbundance): for u_member, v_member in itt.product(u.members, v.members): graph.add_qualified_edge( u_member, v_member, relation=d[RELATION], citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) elif isinstance(u, ListAbundance): for member in u.members: graph.add_qualified_edge( member, v, relation=d[RELATION], citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) elif isinstance(v, ListAbundance): for member in v.members: graph.add_qualified_edge( u, member, relation=d[RELATION], citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) _remove_list_abundance_nodes(graph) def _reaction_cartesian_expansion_unqualified_helper( graph, u: BaseEntity, v: BaseEntity, d: dict, ) -> None: """Help deal with cartesian expansion in unqualified edges.""" if isinstance(u, Reaction) and isinstance(v, Reaction): enzymes = _get_catalysts_in_reaction(u) | _get_catalysts_in_reaction(v) for reactant, product in chain(itt.product(u.reactants, u.products), itt.product(v.reactants, v.products)): if reactant in enzymes or product in enzymes: continue graph.add_unqualified_edge(reactant, product, INCREASES) for product, reactant in itt.product(u.products, u.reactants): if reactant in enzymes or product in enzymes: continue graph.add_unqualified_edge( product, reactant, d[RELATION], ) elif isinstance(u, Reaction): enzymes = _get_catalysts_in_reaction(u) for product in u.products: # Skip create increases edges between enzymes if product in enzymes: continue # Only add edge between v and reaction if the node is not part of the reaction # In practice skips hasReactant, hasProduct edges if v not in u.products and v not in u.reactants: graph.add_unqualified_edge(product, v, INCREASES) for reactant in u.reactants: graph.add_unqualified_edge(reactant, product, INCREASES) elif isinstance(v, Reaction): enzymes = _get_catalysts_in_reaction(v) for reactant in v.reactants: # Skip create increases edges between enzymes if reactant in enzymes: continue # Only add edge between v and reaction if the node is not part of the reaction # In practice skips hasReactant, hasProduct edges if u not in v.products and u not in v.reactants: graph.add_unqualified_edge(u, reactant, INCREASES) for product in v.products: graph.add_unqualified_edge(reactant, product, INCREASES) def _get_catalysts_in_reaction(reaction: Reaction) -> Set[BaseAbundance]: """Return nodes that are both in reactants and reactions in a reaction.""" # TODO replace with reaction.get_catalysts() return set(reaction.reactants).intersection(reaction.products) def reaction_cartesian_expansion(graph, accept_unqualified_edges: bool = True) -> None: """Expand all reactions to simple subject-predicate-object networks.""" for u, v, d in list(graph.edges(data=True)): # Deal with unqualified edges if CITATION not in d and accept_unqualified_edges: _reaction_cartesian_expansion_unqualified_helper(graph, u, v, d) continue if isinstance(u, Reaction) and isinstance(v, Reaction): catalysts = _get_catalysts_in_reaction(u) | _get_catalysts_in_reaction(v) for reactant, product in chain( itt.product(u.reactants, u.products), itt.product(v.reactants, v.products), ): if reactant in catalysts or product in catalysts: continue graph.add_increases( reactant, product, citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) for product, reactant in itt.product(u.products, u.reactants): if reactant in catalysts or product in catalysts: continue graph.add_qualified_edge( product, reactant, relation=d[RELATION], citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) elif isinstance(u, Reaction): catalysts = _get_catalysts_in_reaction(u) for product in u.products: # Skip create increases edges between enzymes if product in catalysts: continue # Only add edge between v and reaction if the node is not part of the reaction # In practice skips hasReactant, hasProduct edges if v not in u.products and v not in u.reactants: graph.add_increases( product, v, citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) for reactant in u.reactants: graph.add_increases( reactant, product, citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) elif isinstance(v, Reaction): catalysts = _get_catalysts_in_reaction(v) for reactant in v.reactants: # Skip create increases edges between enzymes if reactant in catalysts: continue # Only add edge between v and reaction if the node is not part of the reaction # In practice skips hasReactant, hasProduct edges if u not in v.products and u not in v.reactants: graph.add_increases( u, reactant, citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) for product in v.products: graph.add_increases( reactant, product, citation=d.get(CITATION), evidence=d.get(EVIDENCE), annotations=d.get(ANNOTATIONS), ) _remove_reaction_nodes(graph) def remove_reified_nodes(graph) -> None: """Remove complex nodes.""" _remove_list_abundance_nodes(graph) _remove_reaction_nodes(graph) def _remove_list_abundance_nodes(graph): _remove_typed_nodes(graph, ListAbundance) def _remove_reaction_nodes(graph): _remove_typed_nodes(graph, Reaction) def _remove_typed_nodes( graph, cls: Union[Type[BaseEntity], Tuple[Type[BaseEntity], ...]], ) -> None: graph.remove_nodes_from({node for node in graph if isinstance(node, cls)}) pybel-0.15.5/src/pybel/struct/operations.py000066400000000000000000000120751426625374700207250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Operations for BEL graphs.""" from typing import Iterable import networkx as nx from tqdm.autonotebook import tqdm from .utils import update_metadata from ..dsl import BaseEntity __all__ = [ "subgraph", "left_full_join", "left_outer_join", "union", "left_node_intersection_join", "node_intersection", ] def subgraph(graph, nodes: Iterable[BaseEntity]): """Induce a sub-graph over the given nodes. :rtype: BELGraph """ sg = graph.subgraph(nodes) # see implementation for .copy() rv = graph.child() rv.graph.update(sg.graph) for node, data in sg.nodes(data=True): rv.add_node(node, **data) rv.add_edges_from((u, v, key, datadict.copy()) for u, v, key, datadict in sg.edges(keys=True, data=True)) return rv def left_full_join(g, h) -> None: """Add all nodes and edges from ``h`` to ``g``, in-place for ``g``. :param pybel.BELGraph g: A BEL graph :param pybel.BELGraph h: A BEL graph Example usage: >>> import pybel >>> g = pybel.from_bel_script('...') >>> h = pybel.from_bel_script('...') >>> left_full_join(g, h) """ g.add_nodes_from((node, data) for node, data in h.nodes(data=True) if node not in g) g.add_edges_from( (u, v, key, data) for u, v, key, data in h.edges(keys=True, data=True) if u not in g or v not in g[u] or key not in g[u][v] ) update_metadata(h, g) g.warnings.extend(h.warnings) def left_outer_join(g, h) -> None: """Only add components from the ``h`` that are touching ``g``. Algorithm: 1. Identify all weakly connected components in ``h`` 2. Add those that have an intersection with the ``g`` :param BELGraph g: A BEL graph :param BELGraph h: A BEL graph Example usage: >>> import pybel >>> g = pybel.from_bel_script('...') >>> h = pybel.from_bel_script('...') >>> left_outer_join(g, h) """ g_nodes = set(g) for comp in nx.weakly_connected_components(h): if g_nodes.intersection(comp): h_subgraph = subgraph(h, comp) left_full_join(g, h_subgraph) def _left_outer_join_graphs(target, graphs): """Outer join a list of graphs to a target graph. Note: the order of graphs will have significant results! :param BELGraph target: A BEL graph :param iter[BELGraph] graphs: An iterator of BEL graphs :rtype: BELGraph """ for graph in graphs: left_outer_join(target, graph) return target def union(graphs, use_tqdm: bool = False): """Take the union over a collection of graphs into a new graph. Assumes iterator is longer than 2, but not infinite. :param iter[BELGraph] graphs: An iterator over BEL graphs. Can't be infinite. :param use_tqdm: Should a progress bar be displayed? :return: A merged graph :rtype: BELGraph Example usage: >>> import pybel >>> g = pybel.from_bel_script('...') >>> h = pybel.from_bel_script('...') >>> k = pybel.from_bel_script('...') >>> merged = union([g, h, k]) """ it = iter(graphs) if use_tqdm: it = tqdm(it, desc="taking union") try: target = next(it) except StopIteration as e: raise ValueError("no graphs given") from e try: graph = next(it) except StopIteration: return target else: target = target.copy() left_full_join(target, graph) for graph in it: left_full_join(target, graph) return target def left_node_intersection_join(g, h): """Take the intersection over two graphs. This intersection of two graphs is defined by the union of the sub-graphs induced over the intersection of their nodes :param BELGraph g: A BEL graph :param BELGraph h: A BEL graph :rtype: BELGraph Example usage: >>> import pybel >>> g = pybel.from_bel_script('...') >>> h = pybel.from_bel_script('...') >>> merged = left_node_intersection_join(g, h) """ intersecting = set(g).intersection(set(h)) g_inter = subgraph(g, intersecting) h_inter = subgraph(h, intersecting) left_full_join(g_inter, h_inter) return g_inter def node_intersection(graphs): """Take the node intersection over a collection of graphs into a new graph. This intersection is defined the same way as by :func:`left_node_intersection_join` :param iter[BELGraph] graphs: An iterable of graphs. Since it's iterated over twice, it gets converted to a tuple first, so this isn't a safe operation for infinite lists. :rtype: BELGraph Example usage: >>> import pybel >>> g = pybel.from_bel_script('...') >>> h = pybel.from_bel_script('...') >>> k = pybel.from_bel_script('...') >>> merged = node_intersection([g, h, k]) """ graphs = tuple(graphs) n_graphs = len(graphs) if n_graphs == 0: raise ValueError("no graphs given") if n_graphs == 1: return graphs[0] nodes = set(graphs[0].nodes()) for graph in graphs[1:]: nodes.intersection_update(graph) return union(subgraph(graph, nodes) for graph in graphs) pybel-0.15.5/src/pybel/struct/pipeline/000077500000000000000000000000001426625374700177705ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/pipeline/__init__.py000066400000000000000000000004071426625374700221020ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module assists in running complex workflows on BEL graphs.""" from . import decorators, exc, pipeline from .decorators import * from .exc import * from .pipeline import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/pipeline/decorators.py000066400000000000000000000067451426625374700225230ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains the functions for decorating transformation functions. A transformation function takes in a :class:`pybel.BELGraph` and either returns None (in-place) or a new :class:`pybel.BELGraph` (out-of-place). """ import logging from inspect import signature from .exc import MissingPipelineFunctionError, PipelineNameError __all__ = [ "in_place_transformation", "uni_in_place_transformation", "uni_transformation", "transformation", "get_transformation", "mapped", "has_arguments_map", "no_arguments_map", ] logger = logging.getLogger(__name__) mapped = {} universe_map = {} in_place_map = {} has_arguments_map = {} no_arguments_map = {} def _has_arguments(func, universe): sig = signature(func) return (universe and 3 <= len(sig.parameters)) or (not universe and 2 <= len(sig.parameters)) def _register_function(name: str, func, universe: bool, in_place: bool): """Register a transformation function under the given name. :param name: Name to register the function under :param func: A function :param universe: :param in_place: :return: The same function, with additional properties added """ if name in mapped: mapped_func = mapped[name] raise PipelineNameError( "{name} is already registered with {func_mod}.{func_name}".format( name=name, func_mod=mapped_func.__module__, func_name=mapped_func.__name__, ), ) mapped[name] = func if universe: universe_map[name] = func if in_place: in_place_map[name] = func if _has_arguments(func, universe): has_arguments_map[name] = func else: no_arguments_map[name] = func return func def _build_register_function(universe: bool, in_place: bool): # noqa: D202 """Build a decorator function to tag transformation functions. :param universe: Does the first positional argument of this function correspond to a universe graph? :param in_place: Does this function return a new graph, or just modify it in-place? """ def register(func): """Tag a transformation function. :param func: A function :return: The same function, with additional properties added """ return _register_function(func.__name__, func, universe, in_place) return register #: A decorator for functions that modify BEL graphs in-place in_place_transformation = _build_register_function(universe=False, in_place=True) #: A decorator for functions that require a "universe" graph and modify BEL graphs in-place uni_in_place_transformation = _build_register_function(universe=True, in_place=True) #: A decorator for functions that require a "universe" graph and create new BEL graphs from old BEL graphs uni_transformation = _build_register_function(universe=True, in_place=False) #: A decorator for functions that create new BEL graphs from old BEL graphs transformation = _build_register_function(universe=False, in_place=False) def get_transformation(name: str): """Get a transformation function and error if its name is not registered. :param name: The name of a function to look up :return: A transformation function :raises MissingPipelineFunctionError: If the given function name is not registered """ func = mapped.get(name) if func is None: raise MissingPipelineFunctionError("{} is not registered as a pipeline function".format(name)) return func pybel-0.15.5/src/pybel/struct/pipeline/exc.py000066400000000000000000000015301426625374700211200ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Exceptions for the :mod:`pybel.struct.pipeline` module.""" __all__ = [ "MissingPipelineFunctionError", "MetaValueError", "MissingUniverseError", "DeprecationMappingError", "PipelineNameError", ] class MissingPipelineFunctionError(KeyError): """Raised when trying to run the pipeline with a function that isn't registered.""" class MetaValueError(ValueError): """Raised when getting an invalid meta value.""" class MissingUniverseError(ValueError): """Raised when running a universe function without a universe being present.""" class DeprecationMappingError(ValueError): """Raised when applying the deprecation function annotation and the given name already is being used.""" class PipelineNameError(ValueError): """Raised when a second function tries to use the same name.""" pybel-0.15.5/src/pybel/struct/pipeline/pipeline.py000066400000000000000000000260241426625374700221530ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module holds the Pipeline class.""" import json import logging import types from functools import wraps from typing import Any, Dict, Iterable, List, Optional, TextIO, Tuple, Union from .decorators import get_transformation, in_place_map, mapped, universe_map from .exc import MetaValueError, MissingPipelineFunctionError, MissingUniverseError from ..operations import node_intersection, union __all__ = [ "Pipeline", ] logger = logging.getLogger(__name__) META_UNION = "union" META_INTERSECTION = "intersection" def _get_protocol_tuple(data: Dict[str, Any]) -> Tuple[str, List, Dict]: """Convert a dictionary to a tuple.""" return data["function"], data.get("args", []), data.get("kwargs", {}) class Pipeline: """Build and runs analytical pipelines on BEL graphs. Example usage: >>> from pybel import BELGraph >>> from pybel.struct.pipeline import Pipeline >>> from pybel.struct.mutation import enrich_protein_and_rna_origins, prune_protein_rna_origins >>> graph = BELGraph() >>> example = Pipeline() >>> example.append(enrich_protein_and_rna_origins) >>> example.append(prune_protein_rna_origins) >>> result = example.run(graph) """ def __init__(self, protocol: Optional[Iterable[Dict]] = None): """Initialize the pipeline with an optional pre-defined protocol. :param protocol: An iterable of dictionaries describing how to transform a network """ self.universe = None self.protocol = protocol or [] def __len__(self): return len(self.protocol) def __iter__(self): return iter(self.protocol) @staticmethod def from_functions(functions) -> "Pipeline": """Build a pipeline from a list of functions. :param functions: A list of functions or names of functions :type functions: iter[((pybel.BELGraph) -> pybel.BELGraph) or ((pybel.BELGraph) -> None) or str] Example with function: >>> from pybel.struct.pipeline import Pipeline >>> from pybel.struct.mutation import remove_associations >>> pipeline = Pipeline.from_functions([remove_associations]) Equivalent example with function names: >>> from pybel.struct.pipeline import Pipeline >>> pipeline = Pipeline.from_functions(['remove_associations']) Lookup by name is possible for built in functions, and those that have been registered correctly using one of the four decorators: 1. :func:`pybel.struct.pipeline.transformation`, 2. :func:`pybel.struct.pipeline.in_place_transformation`, 3. :func:`pybel.struct.pipeline.uni_transformation`, 4. :func:`pybel.struct.pipeline.uni_in_place_transformation`, """ result = Pipeline() for func in functions: result.append(func) return result def _get_function(self, name: str): """Wrap a function with the universe and in-place. :param name: The name of the function :rtype: types.FunctionType :raises MissingPipelineFunctionError: If the functions is not registered """ f = mapped.get(name) if f is None: raise MissingPipelineFunctionError("{} is not registered as a pipeline function".format(name)) if name in universe_map and name in in_place_map: return self._wrap_in_place(self._wrap_universe(f)) if name in universe_map: return self._wrap_universe(f) if name in in_place_map: return self._wrap_in_place(f) return f def append(self, name, *args, **kwargs) -> "Pipeline": """Add a function (either as a reference, or by name) and arguments to the pipeline. :param name: The name of the function :type name: str or (pybel.BELGraph -> pybel.BELGraph) :param args: The positional arguments to call in the function :param kwargs: The keyword arguments to call in the function :return: This pipeline for fluid query building :raises MissingPipelineFunctionError: If the function is not registered """ if isinstance(name, types.FunctionType): return self.append(name.__name__, *args, **kwargs) elif isinstance(name, str): get_transformation(name) else: raise TypeError("invalid function argument: {}".format(name)) av = { "function": name, } if args: av["args"] = args if kwargs: av["kwargs"] = kwargs self.protocol.append(av) return self def extend(self, protocol: Union[Iterable[Dict], "Pipeline"]) -> "Pipeline": """Add another pipeline to the end of the current pipeline. :param protocol: An iterable of dictionaries (or another Pipeline) :return: This pipeline for fluid query building Example: >>> p1 = Pipeline.from_functions(['enrich_protein_and_rna_origins']) >>> p2 = Pipeline.from_functions(['remove_pathologies']) >>> p1.extend(p2) """ for data in protocol: name, args, kwargs = _get_protocol_tuple(data) self.append(name, *args, **kwargs) return self def _run_helper(self, graph, protocol: Iterable[Dict]): """Help run the protocol. :param pybel.BELGraph graph: A BEL graph :param protocol: The protocol to run, as JSON :rtype: pybel.BELGraph """ result = graph for entry in protocol: meta_entry = entry.get("meta") if meta_entry is None: name, args, kwargs = _get_protocol_tuple(entry) func = self._get_function(name) result = func(result, *args, **kwargs) else: networks = (self._run_helper(graph, subprotocol) for subprotocol in entry["pipelines"]) if meta_entry == META_UNION: result = union(networks) elif meta_entry == META_INTERSECTION: result = node_intersection(networks) else: raise MetaValueError("invalid meta-command: {}".format(meta_entry)) return result def run(self, graph, universe=None): """Run the contained protocol on a seed graph. :param pybel.BELGraph graph: The seed BEL graph :param pybel.BELGraph universe: Allows just-in-time setting of the universe in case it wasn't set before. Defaults to the given network. :return: The new graph is returned if not applied in-place :rtype: pybel.BELGraph """ self.universe = universe or graph.copy() return self._run_helper(graph.copy(), self.protocol) def __call__(self, graph, universe=None): """Call :meth:`Pipeline.run`. :param pybel.BELGraph graph: The seed BEL graph :param pybel.BELGraph universe: Allows just-in-time setting of the universe in case it wasn't set before. Defaults to the given network. :param bool in_place: Should the graph be copied before applying the algorithm? :return: The new graph is returned if not applied in-place :rtype: pybel.BELGraph Using __call__ allows for methods to be chained together then applied >>> from pybel.struct.mutation import remove_associations, remove_pathologies >>> from pybel.struct.pipeline.pipeline import Pipeline >>> from pybel import BELGraph >>> pipe = Pipeline.from_functions([remove_associations, remove_pathologies]) >>> graph = BELGraph() ... >>> new_graph = pipe(graph) """ return self.run(graph=graph, universe=universe) def _wrap_universe(self, func): # noqa: D202 """Take a function that needs a universe graph as the first argument and returns a wrapped one.""" @wraps(func) def wrapper(graph, *args, **kwargs): """Apply the enclosed function with the universe given as the first argument.""" if self.universe is None: raise MissingUniverseError( "Can not run universe function [{}] - No universe is set".format(func.__name__), ) return func(self.universe, graph, *args, **kwargs) return wrapper @staticmethod def _wrap_in_place(func): # noqa: D202 """Take a function that doesn't return the graph and returns the graph.""" @wraps(func) def wrapper(graph, *args, **kwargs): """Apply the enclosed function and returns the graph.""" func(graph, *args, **kwargs) return graph return wrapper def to_json(self) -> List: """Return this pipeline as a JSON list.""" return self.protocol def dumps(self, **kwargs) -> str: """Dump this pipeline as a JSON string.""" return json.dumps(self.to_json(), **kwargs) def dump(self, file: TextIO, **kwargs) -> None: """Dump this protocol to a file in JSON.""" return json.dump(self.to_json(), file, **kwargs) @staticmethod def from_json(data: List) -> "Pipeline": """Build a pipeline from a JSON list.""" return Pipeline(data) @staticmethod def load(file: TextIO) -> "Pipeline": """Load a protocol from JSON contained in file. :return: The pipeline represented by the JSON in the file :raises MissingPipelineFunctionError: If any functions are not registered """ return Pipeline.from_json(json.load(file)) @staticmethod def loads(s: str) -> "Pipeline": """Load a protocol from a JSON string. :param s: A JSON string :return: The pipeline represented by the JSON in the file :raises MissingPipelineFunctionError: If any functions are not registered """ return Pipeline.from_json(json.loads(s)) def __str__(self): return json.dumps(self.protocol, indent=2) @staticmethod def _build_meta(meta: str, pipelines: Iterable["Pipeline"]) -> "Pipeline": """Build a pipeline with a given meta-argument. :param meta: either union or intersection :param pipelines: """ return Pipeline( protocol=[ { "meta": meta, "pipelines": [pipeline.protocol for pipeline in pipelines], }, ], ) @staticmethod def union(pipelines: Iterable["Pipeline"]) -> "Pipeline": """Take the union of multiple pipelines. :param pipelines: A list of pipelines :return: The union of the results from multiple pipelines """ return Pipeline._build_meta(META_UNION, pipelines) @staticmethod def intersection(pipelines: Iterable["Pipeline"]) -> "Pipeline": """Take the intersection of the results from multiple pipelines. :param pipelines: A list of pipelines :return: The intersection of results from multiple pipelines """ return Pipeline._build_meta(META_INTERSECTION, pipelines) pybel-0.15.5/src/pybel/struct/query/000077500000000000000000000000001426625374700173305ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/query/__init__.py000066400000000000000000000003701426625374700214410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Query builder for PyBEL.""" from .exc import * from .query import Query from .seeding import SEED_DATA, SEED_METHOD, Seeding from .selection import get_subgraph __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/query/constants.py000066400000000000000000000031601426625374700217160ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Constants for the query builder.""" #: Induce a subgraph over the given nodes SEED_TYPE_INDUCTION = "induction" #: Induce a subgraph over the given nodes and expand to their first neighbors SEED_TYPE_NEIGHBORS = "neighbors" #: Induce a subgraph over the given nodes and expand to their second neighbors SEED_TYPE_DOUBLE_NEIGHBORS = "dneighbors" #: Induce a subgraph over the nodes in all shortest paths between the given nodes SEED_TYPE_PATHS = "shortest_paths" #: Induce a subgraph over the edges provided by the given authors and their neighboring nodes SEED_TYPE_AUTHOR = "authors" #: Induce a subgraph over the edges provided by the given citations and their neighboring nodes SEED_TYPE_PUBMED = "pubmed" #: Generate an upstream candidate mechanism SEED_TYPE_UPSTREAM = "upstream" #: Generate a downstream candidate mechanism SEED_TYPE_DOWNSTREAM = "downstream" #: Induce a subgraph over the edges matching the given annotations SEED_TYPE_ANNOTATION = "annotation" #: Induce a subgraph over a random set of (hopefully) connected edges SEED_TYPE_SAMPLE = "sample" #: A set of the allowed seed type strings, as defined above SEED_TYPES = { SEED_TYPE_INDUCTION, SEED_TYPE_NEIGHBORS, SEED_TYPE_DOUBLE_NEIGHBORS, SEED_TYPE_PATHS, SEED_TYPE_UPSTREAM, SEED_TYPE_DOWNSTREAM, SEED_TYPE_PUBMED, SEED_TYPE_AUTHOR, SEED_TYPE_ANNOTATION, SEED_TYPE_SAMPLE, } #: Seed types that don't take node lists as their arguments NONNODE_SEED_TYPES = { SEED_TYPE_ANNOTATION, SEED_TYPE_AUTHOR, SEED_TYPE_PUBMED, SEED_TYPE_SAMPLE, } NODE_SEED_TYPES = SEED_TYPES - NONNODE_SEED_TYPES pybel-0.15.5/src/pybel/struct/query/exc.py000066400000000000000000000006021426625374700204570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Exceptions for the query builder.""" __all__ = [ "QueryMissingNetworksError", "NodeDegreeIterError", ] class QueryMissingNetworksError(KeyError): """Raised if a query is created from json but doesn't have a listing of network identifiers.""" class NodeDegreeIterError(ValueError): """Raised when failing to iterate over node degrees.""" pybel-0.15.5/src/pybel/struct/query/query.py000066400000000000000000000144361426625374700210570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Query builder.""" import json import logging from typing import Dict, Iterable, List, Mapping, Optional, Set, TextIO, Union from .exc import QueryMissingNetworksError from .seeding import Seeding from ..pipeline import Pipeline from ...dsl import BaseEntity __all__ = [ "Query", ] logger = logging.getLogger(__name__) class Query: """Represents a query over a network store.""" def __init__( self, network_ids: Union[None, int, Iterable[int]] = None, seeding: Optional[Seeding] = None, pipeline: Optional[Pipeline] = None, ) -> None: """Build a query. :param network_ids: Database network identifiers identifiers """ if not network_ids: self.network_ids = [] elif isinstance(network_ids, int): self.network_ids = [network_ids] elif isinstance(network_ids, Iterable): network_ids = list(network_ids) for network_id in network_ids: if not isinstance(network_id, int): raise TypeError(network_ids) self.network_ids = network_ids else: raise TypeError(network_ids) if seeding is not None and not isinstance(seeding, Seeding): raise TypeError("Not a Seeding: {}".format(seeding)) self.seeding = seeding or Seeding() if pipeline is not None and not isinstance(pipeline, Pipeline): raise TypeError("Not a pipeline: {}".format(pipeline)) self.pipeline = pipeline or Pipeline() def append_network(self, network_id: int) -> "Query": """Add a network to this query. :param network_id: The database identifier of the network :returns: self for fluid API """ self.network_ids.append(network_id) return self def append_seeding_induction(self, nodes: Union[BaseEntity, List[BaseEntity], List[Dict]]) -> Seeding: """Add a seed induction method. :returns: seeding container for fluid API """ return self.seeding.append_induction(nodes) def append_seeding_neighbors(self, nodes: Union[BaseEntity, List[BaseEntity], List[Dict]]) -> Seeding: """Add a seed by neighbors. :returns: seeding container for fluid API """ return self.seeding.append_neighbors(nodes) def append_seeding_annotation(self, annotation: str, values: Set[str]) -> Seeding: """Add a seed induction method for single annotation's values. :param annotation: The annotation to filter by :param values: The values of the annotation to keep """ return self.seeding.append_annotation(annotation, values) def append_seeding_sample(self, **kwargs) -> Seeding: """Add seed induction methods. Kwargs can have ``number_edges`` or ``number_seed_nodes``. """ return self.seeding.append_sample(**kwargs) def append_pipeline(self, name, *args, **kwargs) -> Pipeline: """Add an entry to the pipeline. Defers to :meth:`pybel_tools.pipeline.Pipeline.append`. :param name: The name of the function :type name: str or types.FunctionType :return: This pipeline for fluid query building """ return self.pipeline.append(name, *args, **kwargs) def __call__(self, manager): """Run this query and returns the resulting BEL graph with :meth:`Query.run`. :param pybel.manager.Manager manager: A cache manager :rtype: Optional[pybel.BELGraph] """ return self.run(manager) def run(self, manager): """Run this query and returns the resulting BEL graph. :param manager: A cache manager :rtype: Optional[pybel.BELGraph] """ universe = self._get_universe(manager) graph = self.seeding.run(universe) return self.pipeline.run(graph, universe=universe) def _get_universe(self, manager): if not self.network_ids: raise QueryMissingNetworksError("can not run query without network identifiers") logger.debug("query universe consists of networks: %s", self.network_ids) universe = manager.get_graph_by_ids(self.network_ids) logger.debug( "query universe has %d nodes/%d edges", universe.number_of_nodes(), universe.number_of_edges(), ) return universe def to_json(self) -> Dict: """Return this query as a JSON object.""" rv = { "network_ids": self.network_ids, } if self.seeding: rv["seeding"] = self.seeding.to_json() if self.pipeline: rv["pipeline"] = self.pipeline.to_json() return rv def dump(self, file: TextIO, **kwargs) -> None: """Dump this query to a file as JSON.""" json.dump(self.to_json(), file, **kwargs) def dumps(self, **kwargs) -> str: """Dump this query to a string as JSON.""" return json.dumps(self.to_json(), **kwargs) @staticmethod def from_json(data: Mapping) -> "Query": """Load a query from a JSON dictionary. :param data: A JSON dictionary :raises: QueryMissingNetworksError """ network_ids = data.get("network_ids") if network_ids is None: raise QueryMissingNetworksError('query JSON did not have key "network_ids"') seeding_data = data.get("seeding") seeding = Seeding.from_json(seeding_data) if seeding_data is not None else None pipeline_data = data.get("pipeline") pipeline = Pipeline.from_json(pipeline_data) if pipeline_data is not None else None return Query( network_ids=network_ids, seeding=seeding, pipeline=pipeline, ) @staticmethod def load(file: TextIO) -> "Query": """Load a query from a JSON file. :raises: QueryMissingNetworksError """ return Query.from_json(json.load(file)) @staticmethod def loads(s: str) -> "Query": """Load a query from a JSON string. :param s: A stringified JSON query :raises: QueryMissingNetworksError """ return Query.from_json(json.loads(s)) def __str__(self): return "Query(networks={}, seeding={}, pipeline={})".format(self.network_ids, self.seeding, self.pipeline) pybel-0.15.5/src/pybel/struct/query/seeding.py000066400000000000000000000115041426625374700213210ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Query builder.""" import json import logging import random from collections import UserList from typing import Any, Dict, List, Set, TextIO, Union from .constants import ( SEED_TYPE_ANNOTATION, SEED_TYPE_INDUCTION, SEED_TYPE_NEIGHBORS, SEED_TYPE_SAMPLE, ) from .selection import get_subgraph from ...dsl import BaseEntity from ...struct import union from ...tokens import parse_result_to_dsl logger = logging.getLogger(__name__) SEED_METHOD = "type" SEED_DATA = "data" MaybeNodeList = Union[BaseEntity, List[BaseEntity], List[Dict]] class Seeding(UserList): """Represents a container of seeding methods to apply to a network.""" def append_induction(self, nodes: MaybeNodeList) -> "Seeding": """Add a seed induction method. :param nodes: A node or list of nodes :returns: self for fluid API """ return self._append_seed_handle_nodes(SEED_TYPE_INDUCTION, nodes) def append_neighbors(self, nodes: MaybeNodeList) -> "Seeding": """Add a seed by neighbors. :param nodes: A node or list of nodes :returns: self for fluid API """ return self._append_seed_handle_nodes(SEED_TYPE_NEIGHBORS, nodes) def append_annotation(self, annotation: str, values: Set[str]) -> "Seeding": """Add a seed induction method for single annotation's values. :param annotation: The annotation to filter by :param values: The values of the annotation to keep :returns: self for fluid API """ return self._append_seed( SEED_TYPE_ANNOTATION, { "annotations": { annotation: values, }, }, ) def append_sample(self, **kwargs) -> "Seeding": """Add seed induction methods. Kwargs can have ``number_edges`` or ``number_seed_nodes``. :returns: self for fluid API """ data = { "seed": random.randint(0, 1000000), } data.update(kwargs) return self._append_seed(SEED_TYPE_SAMPLE, data) def _append_seed(self, seed_type: str, data: Any) -> "Seeding": """Add a seeding method and returns self. :returns: self for fluid API """ self.append( { SEED_METHOD: seed_type, SEED_DATA: data, } ) return self def _append_seed_handle_nodes(self, seed_type: str, nodes: MaybeNodeList) -> "Seeding": """Add a seeding method and returns self. :param seed_type: The seed type :param nodes: A node or list of nodes :returns: self for fluid API """ return self._append_seed(seed_type, _handle_nodes(nodes)) def run(self, graph): """Seed the graph or return none if not possible. :type graph: pybel.BELGraph :rtype: Optional[pybel.BELGraph] """ if not self: logger.debug("no seeding, returning graph: %s", graph) return graph subgraphs = [] for seed in self: seed_method, seed_data = seed[SEED_METHOD], seed[SEED_DATA] logger.debug("seeding with %s: %s", seed_method, seed_data) subgraph = get_subgraph(graph, seed_method=seed_method, seed_data=seed_data) if subgraph is None: logger.debug("seed returned empty graph: %s", seed) continue subgraphs.append(subgraph) if not subgraphs: logger.debug("no subgraphs returned") return return union(subgraphs) def to_json(self) -> List[Dict]: """Serialize this seeding container to a JSON object.""" return list(self) def dump(self, file, sort_keys: bool = True, **kwargs) -> None: """Dump this seeding container to a file as JSON.""" json.dump(self.to_json(), file, sort_keys=sort_keys, **kwargs) def dumps(self, sort_keys: bool = True, **kwargs) -> str: """Dump this query to a string as JSON.""" return json.dumps(self.to_json(), sort_keys=sort_keys, **kwargs) @staticmethod def from_json(data) -> "Seeding": """Build a seeding container from a JSON list.""" return Seeding(data) @staticmethod def load(file: TextIO) -> "Seeding": """Load a seeding container from a JSON file.""" return Seeding.from_json(json.load(file)) @staticmethod def loads(s: str) -> "Seeding": """Load a seeding container from a JSON string.""" return Seeding.from_json(json.loads(s)) def _handle_nodes(nodes: MaybeNodeList) -> List[BaseEntity]: """Handle node(s) that might be dictionaries.""" if isinstance(nodes, BaseEntity): return [nodes] return [(parse_result_to_dsl(node) if not isinstance(node, BaseEntity) else node) for node in nodes] pybel-0.15.5/src/pybel/struct/query/selection.py000066400000000000000000000104131426625374700216660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """A wrapper around selection methods.""" import logging from typing import Any, List, Optional from .constants import ( SEED_TYPE_ANNOTATION, SEED_TYPE_AUTHOR, SEED_TYPE_DOUBLE_NEIGHBORS, SEED_TYPE_DOWNSTREAM, SEED_TYPE_INDUCTION, SEED_TYPE_NEIGHBORS, SEED_TYPE_PATHS, SEED_TYPE_PUBMED, SEED_TYPE_SAMPLE, SEED_TYPE_UPSTREAM, ) from ..mutation import ( expand_nodes_neighborhoods, get_multi_causal_downstream, get_multi_causal_upstream, get_random_subgraph, get_subgraph_by_all_shortest_paths, get_subgraph_by_annotations, get_subgraph_by_authors, get_subgraph_by_induction, get_subgraph_by_neighborhood, get_subgraph_by_pubmed, get_subgraph_by_second_neighbors, ) from ...dsl import BaseEntity __all__ = [ "get_subgraph", ] logger = logging.getLogger(__name__) def get_subgraph( graph, seed_method: Optional[str] = None, seed_data: Optional[Any] = None, expand_nodes: Optional[List[BaseEntity]] = None, remove_nodes: Optional[List[BaseEntity]] = None, ): """Run a pipeline query on graph with multiple sub-graph filters and expanders. Order of Operations: 1. Seeding by given function name and data 2. Add nodes 3. Remove nodes :param pybel.BELGraph graph: A BEL graph :param seed_method: The name of the get_subgraph_by_* function to use :param seed_data: The argument to pass to the get_subgraph function :param expand_nodes: Add the neighborhoods around all of these nodes :param remove_nodes: Remove these nodes and all of their in/out edges :rtype: Optional[pybel.BELGraph] """ # Seed by the given function if seed_method == SEED_TYPE_INDUCTION: result = get_subgraph_by_induction(graph, seed_data) elif seed_method == SEED_TYPE_PATHS: result = get_subgraph_by_all_shortest_paths(graph, seed_data) elif seed_method == SEED_TYPE_NEIGHBORS: result = get_subgraph_by_neighborhood(graph, seed_data) elif seed_method == SEED_TYPE_DOUBLE_NEIGHBORS: result = get_subgraph_by_second_neighbors(graph, seed_data) elif seed_method == SEED_TYPE_UPSTREAM: result = get_multi_causal_upstream(graph, seed_data) elif seed_method == SEED_TYPE_DOWNSTREAM: result = get_multi_causal_downstream(graph, seed_data) elif seed_method == SEED_TYPE_PUBMED: result = get_subgraph_by_pubmed(graph, seed_data) elif seed_method == SEED_TYPE_AUTHOR: result = get_subgraph_by_authors(graph, seed_data) elif seed_method == SEED_TYPE_ANNOTATION: result = get_subgraph_by_annotations(graph, seed_data["annotations"], or_=seed_data.get("or")) elif seed_method == SEED_TYPE_SAMPLE: result = get_random_subgraph( graph, number_edges=seed_data.get("number_edges"), seed=seed_data.get("seed"), ) elif not seed_method: # Otherwise, don't seed a sub-graph result = graph.copy() logger.debug("no seed function - using full network: %s", result.name) else: raise ValueError("Invalid seed method: {}".format(seed_method)) if result is None: logger.debug("query returned no results") return logger.debug( "original graph has (%s nodes / %s edges)", result.number_of_nodes(), result.number_of_edges(), ) # Expand around the given nodes if expand_nodes: expand_nodes_neighborhoods(graph, result, expand_nodes) logger.debug( "graph expanded to (%s nodes / %s edges)", result.number_of_nodes(), result.number_of_edges(), ) # Delete the given nodes if remove_nodes: for node in remove_nodes: if node not in result: logger.debug("%s is not in graph %s", node, graph.name) continue result.remove_node(node) logger.debug( "graph contracted to (%s nodes / %s edges)", result.number_of_nodes(), result.number_of_edges(), ) logger.debug( "Subgraph coming from %s (seed type) %s (data) contains %d nodes and %d edges", seed_method, seed_data, result.number_of_nodes(), result.number_of_edges(), ) return result pybel-0.15.5/src/pybel/struct/summary/000077500000000000000000000000001426625374700176605ustar00rootroot00000000000000pybel-0.15.5/src/pybel/struct/summary/__init__.py000066400000000000000000000004411426625374700217700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Summary functions for BEL graphs.""" from . import edge_summary, errors, node_summary, provenance from .edge_summary import * from .errors import * from .node_summary import * from .provenance import * __all__ = [k for k in locals() if not k.startswith("_")] pybel-0.15.5/src/pybel/struct/summary/edge_summary.py000066400000000000000000000120341426625374700227130ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Summary functions for edges in BEL graphs.""" from collections import Counter, defaultdict from random import choice from typing import Iterable, Mapping, Set, Tuple from ..filters.edge_predicates import edge_has_annotation from ..graph import BELGraph from ...canonicalize import edge_to_bel from ...constants import ANNOTATIONS, RELATION from ...dsl import BaseEntity from ...language import Entity from ...utils import CanonicalEdge, canonicalize_edge __all__ = [ "iter_annotation_value_pairs", "iter_annotation_values", "get_annotation_values_by_annotation", "get_annotation_values", "count_relations", "get_annotations", "count_annotations", "get_unused_annotations", "get_unused_list_annotation_values", "get_metaedge_to_key", "iter_sample_metaedges", ] def iter_annotation_value_pairs(graph: BELGraph) -> Iterable[Tuple[str, Entity]]: """Iterate over the key/value pairs, with duplicates, for each annotation used in a BEL graph. :param graph: A BEL graph """ return ( (key, entity) for _, _, data in graph.edges(data=True) for key, entities in data.get(ANNOTATIONS, {}).items() for entity in entities ) def iter_annotation_values(graph: BELGraph, annotation: str) -> Iterable[Entity]: """Iterate over all of the values for an annotation used in the graph. :param graph: A BEL graph :param annotation: The annotation to grab """ return ( entity for _, _, data in graph.edges(data=True) if edge_has_annotation(data, annotation) for entity in data[ANNOTATIONS][annotation] ) def _group_dict_set(iterator): """Make a dict that accumulates the values for each key in an iterator of doubles. :param iter[tuple[A,B]] iterator: An iterator :rtype: dict[A,set[B]] """ d = defaultdict(set) for key, value in iterator: d[key].add(value) return dict(d) def get_annotation_values_by_annotation(graph: BELGraph) -> Mapping[str, Set[Entity]]: """Get the set of values for each annotation used in a BEL graph. :param graph: A BEL graph :return: A dictionary of {annotation key: set of annotation values} """ return _group_dict_set(iter_annotation_value_pairs(graph)) def get_annotation_values(graph: BELGraph, annotation: str) -> Set[Entity]: """Get all values for the given annotation. :param graph: A BEL graph :param annotation: The annotation to summarize :return: A set of all annotation values """ return set(iter_annotation_values(graph, annotation)) def count_relations(graph: BELGraph) -> Counter: """Return a histogram over all relationships in a graph. :param graph: A BEL graph :return: A Counter from {relation type: frequency} """ return Counter(data[RELATION] for _, _, data in graph.edges(data=True)) def get_unused_annotations(graph: BELGraph) -> Set[str]: """Get the set of all annotations that are defined in a graph, but are never used. :param graph: A BEL graph :return: A set of annotations """ return graph.defined_annotation_keywords - get_annotations(graph) def get_annotations(graph: BELGraph) -> Set[str]: """Get the set of annotations used in the graph. :param graph: A BEL graph :return: A set of annotation keys """ return set(_annotation_iter_helper(graph)) def count_annotations(graph: BELGraph) -> Counter: """Count how many times each annotation is used in the graph. :param graph: A BEL graph :return: A Counter from {annotation key: frequency} """ return Counter(_annotation_iter_helper(graph)) def _annotation_iter_helper(graph: BELGraph) -> Iterable[str]: """Iterate over the annotation keys. :param graph: A BEL graph """ return (key for _, _, data in graph.edges(data=True) if ANNOTATIONS in data for key in data[ANNOTATIONS]) def get_unused_list_annotation_values(graph: BELGraph) -> Mapping[str, Set[str]]: """Get all of the unused values for list annotations. :param graph: A BEL graph :return: A dictionary of {str annotation: set of str values that aren't used} """ result = {} for annotation, values in graph.annotation_list.items(): unused = values - {e.identifier for e in get_annotation_values(graph, annotation)} if unused: result[annotation] = unused return result def get_metaedge_to_key( graph: BELGraph, ) -> Mapping[CanonicalEdge, Set[Tuple[BaseEntity, BaseEntity, str]]]: """Get all edge types.""" rv = defaultdict(set) for u, v, k, d in graph.edges(keys=True, data=True): rel, u_mod, v_mod = canonicalize_edge(d) rv[u.__class__.__name__, u_mod, rel, v.__class__.__name__, v_mod].add((u, v, k)) return dict(rv) def iter_sample_metaedges(graph: BELGraph): """Iterate sampled metaedges.""" for k, value in get_metaedge_to_key(graph).items(): u, v, key = choice(list(value)) d = graph[u][v][key] bel = edge_to_bel(u, v, d, use_identifiers=True) yield (u, v, key, d, *k, bel) pybel-0.15.5/src/pybel/struct/summary/errors.py000066400000000000000000000056531426625374700215570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Summary functions for errors and warnings encountered during the compilation of BEL script.""" import typing from collections import Counter, defaultdict from typing import Iterable, List, Mapping, Set from ..filters.edge_predicates import edge_has_annotation from ..graph import BELGraph, WarningTuple from ...constants import ANNOTATIONS from ...exceptions import ( BELSyntaxError, MissingNamespaceNameWarning, MissingNamespaceRegexWarning, NakedNameWarning, ) __all__ = [ "get_syntax_errors", "count_error_types", "count_naked_names", "get_naked_names", "calculate_incorrect_name_dict", "calculate_error_by_annotation", ] def get_syntax_errors(graph: BELGraph) -> List[WarningTuple]: """List the syntax errors encountered during compilation of a BEL script.""" return [(path, exc, an) for path, exc, an in graph.warnings if isinstance(exc, BELSyntaxError)] def count_error_types(graph: BELGraph) -> typing.Counter[str]: """Count the occurrence of each type of error in a graph. :return: A Counter of {error type: frequency} """ return Counter(exc.__class__.__name__ for _, exc, _ in graph.warnings) def _naked_names_iter(graph: BELGraph) -> Iterable[str]: """Iterate over naked name warnings from a graph.""" for _, exc, _ in graph.warnings: if isinstance(exc, NakedNameWarning): yield exc.name def count_naked_names(graph: BELGraph) -> typing.Counter[str]: """Count the frequency of each naked name (names without namespaces). :return: A Counter from {name: frequency} """ return Counter(_naked_names_iter(graph)) def get_naked_names(graph: BELGraph) -> Set[str]: """Get the set of naked names in the graph.""" return set(_naked_names_iter(graph)) def _iterate_namespace_name(graph: BELGraph) -> Iterable[typing.Tuple[str, str]]: for _, exc, _ in graph.warnings: if not isinstance(exc, (MissingNamespaceNameWarning, MissingNamespaceRegexWarning)): continue yield exc.namespace, exc.name def calculate_incorrect_name_dict(graph: BELGraph) -> Mapping[str, List[str]]: """Get missing names grouped by namespace.""" missing = defaultdict(list) for namespace, name in _iterate_namespace_name(graph): missing[namespace].append(name) return dict(missing) def calculate_error_by_annotation(graph: BELGraph, annotation: str) -> Mapping[str, List[str]]: """Group error names by a given annotation.""" results = defaultdict(list) for _, exc, ctx in graph.warnings: if not ctx or not edge_has_annotation(ctx, annotation): continue values = ctx[ANNOTATIONS][annotation] if isinstance(values, str): results[values].append(exc.__class__.__name__) elif isinstance(values, Iterable): for value in values: results[value].append(exc.__class__.__name__) return dict(results) pybel-0.15.5/src/pybel/struct/summary/node_summary.py000066400000000000000000000307231426625374700227410ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Summary functions for nodes in BEL graphs.""" import itertools as itt import typing from collections import Counter, defaultdict from typing import Any, Iterable, List, Mapping, Optional, Set, Tuple from ..filters import get_nodes, has_activity, has_variant, is_degraded, is_translocated from ..graph import BELGraph from ...constants import ( ACTIVITY, CONCEPT, EFFECT, FROM_LOC, FUSION, KIND, LOCATION, MEMBERS, MODIFIER, NAME, NAMESPACE, PARTNER_3P, PARTNER_5P, SOURCE_MODIFIER, TARGET_MODIFIER, TO_LOC, TRANSLOCATION, VARIANTS, ) from ...dsl import ( BaseConcept, BaseEntity, CentralDogma, EntityVariant, FusionBase, ListAbundance, Pathology, Reaction, ) from ...language import Entity __all__ = [ "get_functions", "count_functions", "get_namespaces", "count_namespaces", "get_unused_namespaces", "count_names_by_namespace", "get_names", "get_names_by_namespace", "iterate_node_entities", "iterate_entities", "node_is_grounded", "get_ungrounded_nodes", "count_variants", "count_pathologies", "get_top_pathologies", "get_top_hubs", ] def _function_iterator(graph: BELGraph) -> Iterable[str]: """Iterate over the functions in a graph. :param graph: A BEL graph """ return (node.function for node in graph) def get_functions(graph: BELGraph) -> Set[str]: """Get the set of all functions used in this graph. :param graph: A BEL graph :return: A set of functions """ return set(_function_iterator(graph)) def count_functions(graph: BELGraph) -> typing.Counter[str]: """Count the frequency of each function present in a graph. :param graph: A BEL graph :return: A Counter from {function: frequency} """ return Counter(_function_iterator(graph)) def _iterate_namespaces(graph: BELGraph) -> Iterable[str]: """Iterate over all namespaces found in the graph. :param graph: A BEL graph """ for entity in itt.chain(iterate_entities(graph), _iterate_edge_entities(graph)): yield entity.namespace def _iterate_edge_entities(graph: BELGraph) -> Iterable[Entity]: for ((_, _, data), side) in itt.product(graph.edges(data=True), (SOURCE_MODIFIER, TARGET_MODIFIER)): side_data = data.get(side) if side_data is None: continue modifier = side_data.get(MODIFIER) effect = side_data.get(EFFECT) if modifier == ACTIVITY and effect is not None: assert isinstance(effect, Entity) yield effect elif modifier == TRANSLOCATION and effect is not None: from_loc = effect[FROM_LOC] assert isinstance(from_loc, Entity) yield from_loc to_loc = effect[TO_LOC] assert isinstance(to_loc, Entity) yield to_loc location = side_data.get(LOCATION) if location is not None: assert isinstance(location, Entity) yield location def count_namespaces(graph: BELGraph) -> typing.Counter[str]: """Count the frequency of each namespace across all nodes (that have namespaces). :param graph: A BEL graph :return: A Counter from {namespace: frequency} """ return Counter(_iterate_namespaces(graph)) def get_namespaces(graph: BELGraph) -> Set[str]: """Get the set of all namespaces used in this graph. :param graph: A BEL graph :return: A set of namespaces """ return set(_iterate_namespaces(graph)) def get_unused_namespaces(graph: BELGraph) -> Set[str]: """Get the set of all namespaces that are defined in a graph, but are never used. :param graph: A BEL graph :return: A set of namespaces that are included but not used """ return graph.defined_namespace_keywords - get_namespaces(graph) def get_names(graph: BELGraph) -> Mapping[str, Set[str]]: """Get all names for each namespace. :param graph: A BEL graph """ rv = defaultdict(set) for namespace, name in _identifier_filtered_iterator(graph): rv[namespace].add(name) return dict(rv) def iterate_entities(graph: BELGraph) -> Iterable[Entity]: """Iterate over all entities in the graph. :param graph: A BEL graph """ for node in graph: yield from iterate_node_entities(node) def iterate_node_entities(node: BaseEntity) -> Iterable[Entity]: """Iterate over all named entities that comprise a node. This includes the node's name, the members/reactants/products of the node, the fusion partners, the named variants, and all recursive ones too. :param node: A BEL node Entities in a simple protein: >>> from pybel.dsl import Protein >>> from pybel.language import Entity >>> from pybel.struct.summary import iterate_entities >>> protein = Protein(namespace='hgnc', identifier='1455', name='CALR') >>> protein_entities = list(iterate_node_entities(protein)) >>> assert [Entity(namespace='hgnc', identifier='1455', name='CALR')] == protein_entities Entities in a protein complex: >>> from pybel.dsl import Protein, ComplexAbundance >>> from pybel.language import Entity >>> from pybel.struct.summary import iterate_entities >>> protein_1 = Protein(namespace='hgnc', identifier='1') >>> protein_2 = Protein(namespace='hgnc', identifier='2') >>> complex_1 = ComplexAbundance([protein_1, protein_2]) >>> complex_entities = list(iterate_node_entities(complex_1)) >>> assert [Entity(namespace='hgnc', identifier='1'), Entity(namespace='hgnc', identifier='2')] == complex_entities """ if isinstance(node, BaseConcept): yield node.entity if isinstance(node, ListAbundance): for member in node.members: yield from iterate_node_entities(member) if isinstance(node, Reaction): for member in itt.chain(node.reactants, node.products): yield from iterate_node_entities(member) if isinstance(node, CentralDogma): for variant in node.variants or []: if isinstance(variant, EntityVariant): yield variant.entity if isinstance(node, FusionBase): yield from iterate_node_entities(node.partner_5p) yield from iterate_node_entities(node.partner_3p) def _identifier_filtered_iterator(graph) -> Iterable[Tuple[str, str]]: """Iterate over names in the given namespace.""" for data in graph: for pair in _get_node_names(data): yield pair for member in data.get(MEMBERS, []): for pair in _get_node_names(member): yield pair for ((_, _, data), side) in itt.product(graph.edges(data=True), (SOURCE_MODIFIER, TARGET_MODIFIER)): side_data = data.get(side) if side_data is None: continue modifier = side_data.get(MODIFIER) effect = side_data.get(EFFECT) if modifier == ACTIVITY and effect is not None and NAMESPACE in effect and NAME in effect: yield effect[NAMESPACE], effect[NAME] elif modifier == TRANSLOCATION and effect is not None: from_loc = effect.get(FROM_LOC) if NAMESPACE in from_loc and NAME in from_loc: yield from_loc[NAMESPACE], from_loc[NAME] to_loc = effect.get(TO_LOC) if NAMESPACE in to_loc and NAME in to_loc: yield to_loc[NAMESPACE], to_loc[NAME] location = side_data.get(LOCATION) if location is not None and NAMESPACE in location and NAME in location: yield location[NAMESPACE], location[NAME] def _get_node_names(data: Mapping[str, Any]) -> Iterable[Tuple[str, str]]: if CONCEPT in data: yield data[CONCEPT][NAMESPACE], data[CONCEPT][NAME] elif FUSION in data: partner_5p_concept = data[FUSION][PARTNER_5P][CONCEPT] partner_3p_concept = data[FUSION][PARTNER_3P][CONCEPT] yield partner_5p_concept[NAMESPACE], partner_5p_concept[NAME] yield partner_3p_concept[NAMESPACE], partner_3p_concept[NAME] if VARIANTS in data: for variant in data[VARIANTS]: concept = variant.get(CONCEPT) if concept is not None and NAMESPACE in concept and NAME in concept: yield concept[NAMESPACE], concept[NAME] def _namespace_filtered_iterator(graph: BELGraph, namespace: str) -> Iterable[str]: """Iterate over names in the given namespace. :param graph: A BEL graph :param namespace: A namespace prefix """ for it_namespace, name in _identifier_filtered_iterator(graph): if namespace == it_namespace: yield name def count_names_by_namespace(graph: BELGraph, namespace: str) -> typing.Counter[str]: """Get the set of all of the names in a given namespace that are in the graph. :param graph: A BEL graph :param namespace: A namespace prefix :return: A counter from {name: frequency} :raises IndexError: if the namespace is not defined in the graph. """ if namespace not in graph.defined_namespace_keywords: raise IndexError("{} is not defined in {}".format(namespace, graph)) return Counter(_namespace_filtered_iterator(graph, namespace)) def get_names_by_namespace(graph: BELGraph, namespace: str) -> Set[str]: """Get the set of all of the names in a given namespace that are in the graph. :param pybel.BELGraph graph: A BEL graph :param namespace: A namespace prefix :return: A set of names belonging to the given namespace that are in the given graph :raises IndexError: if the namespace is not defined in the graph. """ if namespace not in graph.defined_namespace_keywords: raise IndexError("{} is not defined in {}".format(namespace, graph)) return set(_namespace_filtered_iterator(graph, namespace)) def count_variants(graph: BELGraph) -> typing.Counter[str]: """Count how many of each type of variant a graph has. :param graph: A BEL graph """ return Counter(variant_data[KIND] for data in graph if has_variant(graph, data) for variant_data in data[VARIANTS]) def get_top_hubs(graph: BELGraph, *, n: Optional[int] = 15) -> List[Tuple[BaseEntity, int]]: """Get the top hubs in the graph by BEL. :param graph: A BEL graph :param n: The number of top hubs to return. If None, returns all nodes """ return Counter(dict(graph.degree())).most_common(n=n) def count_pathologies(graph: BELGraph) -> typing.Counter[BaseEntity]: """Count the number of edges in which each pathology is incident. :param graph: A BEL graph """ # Don't double count relationships edges = {tuple(sorted([u, v], key=lambda node: node.as_bel())) for u, v in graph.edges()} return Counter(node for node in itt.chain.from_iterable(edges) if isinstance(node, Pathology)) def get_top_pathologies(graph: BELGraph, n: Optional[int] = 15) -> List[Tuple[BaseEntity, int]]: """Get the top highest relationship-having edges in the graph by BEL. :param graph: A BEL graph :param n: The number of top connected pathologies to return. If None, returns all nodes """ return count_pathologies(graph).most_common(n) def get_ungrounded_nodes(graph: BELGraph) -> Set[BaseEntity]: """Get all ungrounded nodes in the graph. :param graph: A BEL graph """ return {node for node in graph if not node_is_grounded(node)} def node_is_grounded(node: BaseEntity) -> bool: """Check if a node is grounded. :param node: A BEL node """ return all(entity.identifier is not None and entity.name is not None for entity in iterate_node_entities(node)) def get_degradations(graph: BELGraph) -> Set[BaseEntity]: """Get all nodes that are degraded.""" return get_nodes(graph, is_degraded) def get_activities(graph: BELGraph) -> Set[BaseEntity]: """Get all nodes that have molecular activities.""" return get_nodes(graph, has_activity) def get_translocated(graph: BELGraph) -> Set[BaseEntity]: """Get all nodes that are translocated.""" return get_nodes(graph, is_translocated) def count_modifications(graph: BELGraph) -> Counter: """Get a modifications count dictionary.""" return Counter( remove_falsy_values( { "Translocations": len(get_translocated(graph)), "Degradations": len(get_degradations(graph)), "Molecular Activities": len(get_activities(graph)), } ) ) def remove_falsy_values(counter: Mapping[Any, int]) -> Mapping[Any, int]: """Remove all values that are zero.""" return {label: count for label, count in counter.items() if count} pybel-0.15.5/src/pybel/struct/summary/provenance.py000066400000000000000000000047401426625374700223770ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Summary functions for citation and provenance information in BEL graphs.""" from typing import Iterable, Set from ..filters.edge_predicates import CITATION_PREDICATES from ..graph import BELGraph from ...constants import CITATION, IDENTIFIER __all__ = [ "iterate_pubmed_identifiers", "iterate_pmc_identifiers", "get_pubmed_identifiers", "get_pmc_identifiers", ] def iterate_citation_identifiers(graph, prefix: str): """Iterate over all citation identifiers with the given prefix in a graph. :param graph: A BEL graph :param prefix: The citation prefix to keep :return: An iterator over the PubMed identifiers in the graph """ predicate = CITATION_PREDICATES.get(prefix) if predicate is None: raise ValueError(f"Invalid citation prefix: {prefix}") return (data[CITATION][IDENTIFIER].strip() for _, _, data in graph.edges(data=True) if predicate(data)) def iterate_pubmed_identifiers(graph: BELGraph) -> Iterable[str]: """Iterate over all PubMed identifiers in a graph. :param graph: A BEL graph :return: An iterator over the PubMed identifiers in the graph """ return iterate_citation_identifiers(graph, "pubmed") def iterate_pmc_identifiers(graph: BELGraph) -> Iterable[str]: """Iterate over all PMC identifiers in a graph. :param graph: A BEL graph :return: An iterator over the PMC identifiers in the graph """ return iterate_citation_identifiers(graph, "pmc") def get_citation_identifiers(graph: BELGraph, prefix: str) -> Set[str]: """Get the set of all identifiers with the give prefix cited in the construction of a graph. :param graph: A BEL graph :param prefix: The citation prefix to keep :return: A set of all PubMed identifiers cited in the construction of this graph """ return set(iterate_citation_identifiers(graph, prefix)) def get_pubmed_identifiers(graph: BELGraph) -> Set[str]: """Get the set of all PubMed identifiers cited in the construction of a graph. :param graph: A BEL graph :return: A set of all PubMed identifiers cited in the construction of this graph """ return get_citation_identifiers(graph, "pubmed") def get_pmc_identifiers(graph: BELGraph) -> Set[str]: """Get the set of all PMC identifiers cited in the construction of a graph. :param graph: A BEL graph :return: A set of all PMC identifiers cited in the construction of this graph """ return get_citation_identifiers(graph, "pmc") pybel-0.15.5/src/pybel/struct/summary/supersummary.py000066400000000000000000000141371426625374700230140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for BEL graphs.""" import logging import random from collections import Counter from typing import Optional, TextIO import bioregistry import pandas as pd from humanize import intword from tabulate import tabulate from .node_summary import count_namespaces from ..graph import BELGraph from ...constants import CITATION, IDENTIFIER, NAMESPACE, RELATION, TWO_WAY_RELATIONS from ...dsl import BaseConcept from ...utils import multidict logger = logging.getLogger(__name__) def function_table_df(graph: BELGraph, examples: bool = True) -> pd.DataFrame: """Create a dataframe describing the functions in the graph.""" function_mapping = multidict((node.function, node) for node in graph) function_c = Counter({function: len(nodes) for function, nodes in function_mapping.items()}) if not examples: return pd.DataFrame(function_c.most_common(), columns=["Type", "Count"]) return pd.DataFrame( [ (function, count, random.choice(function_mapping[function])) # noqa:S311 for function, count in function_c.most_common() ], columns=["Type", "Count", "Example"], ) def functions_str(graph, examples: bool = True, add_count: bool = True, **kwargs) -> str: """Make a summary string of the functions in the graph.""" df = function_table_df(graph, examples=examples) headers = list(df.columns) if add_count: headers[0] += " ({})".format(len(df.index)) return tabulate(df.values, headers=headers, **kwargs) def functions(graph, file: Optional[TextIO] = None, examples: bool = True, **kwargs) -> None: """Print a summary of the functions in the graph.""" print(functions_str(graph=graph, examples=examples, **kwargs), file=file) def namespaces_table_df(graph: BELGraph, examples: bool = True) -> pd.DataFrame: """Create a dataframe describing the namespaces in the graph.""" namespace_mapping = multidict((node.namespace, node) for node in graph if isinstance(node, BaseConcept)) namespace_c = count_namespaces(graph) if not examples: return pd.DataFrame(namespace_c.most_common(), columns=["Namespace", "Count"]) return pd.DataFrame( [ ( prefix, bioregistry.get_name(prefix), count, random.choice(namespace_mapping[prefix]) if prefix in namespace_mapping else "", # noqa:S311 ) for prefix, count in namespace_c.most_common() ], columns=["Prefix", "Name", "Count", "Example"], ) def namespaces_str(graph: BELGraph, examples: bool = True, add_count: bool = True, **kwargs) -> None: """Make a summary string of the namespaces in the graph.""" df = namespaces_table_df(graph, examples=examples) headers = list(df.columns) if add_count: headers[0] += " ({})".format(len(df.index)) return tabulate(df.values, headers=headers, **kwargs) def namespaces(graph: BELGraph, file: Optional[TextIO] = None, examples: bool = True, **kwargs) -> None: """Print a summary of the namespaces in the graph.""" print(namespaces_str(graph=graph, examples=examples, **kwargs), file=file) def edge_table_df(graph: BELGraph, *, examples: bool = True, minimum: Optional[int] = None) -> pd.DataFrame: """Create a dataframe describing the edges in the graph.""" edge_mapping = multidict( ( f"{u.function} {d[RELATION]} {v.function}", graph.edge_to_bel(u, v, d, use_identifiers=True), ) for u, v, d in graph.edges(data=True) if d[RELATION] not in TWO_WAY_RELATIONS or u.function > v.function ) edge_c = Counter({top_level_edge: len(edges) for top_level_edge, edges in edge_mapping.items()}) if examples: rows = [ ( top_level_edge, count, random.choice(edge_mapping[top_level_edge]), ) # noqa:S311 for top_level_edge, count in edge_c.most_common() if not minimum or count >= minimum ] columns = ["Edge Type", "Count", "Example"] else: rows = edge_c.most_common() if minimum: rows = [(k, count) for k, count in rows if count >= minimum] columns = ["Edge Type", "Count"] return pd.DataFrame(rows, columns=columns) def edges_str( graph: BELGraph, *, examples: bool = True, add_count: bool = True, minimum: Optional[int] = None, **kwargs, ) -> str: """Make a summary str of the edges in the graph.""" df = edge_table_df(graph, examples=examples, minimum=minimum) headers = list(df.columns) if add_count: headers[0] += " ({})".format(intword(len(df.index))) return tabulate(df.values, headers=headers, **kwargs) def edges( graph: BELGraph, *, examples: bool = True, minimum: Optional[int] = None, file: Optional[TextIO] = None, **kwargs, ) -> None: """Print a summary of the edges in the graph.""" print(edges_str(graph=graph, examples=examples, minimum=minimum, **kwargs), file=file) def citations(graph: BELGraph, n: Optional[int] = 15, file: Optional[TextIO] = None) -> None: """Print a summary of the citations in the graph.""" edge_mapping = multidict( ( (data[CITATION][NAMESPACE], data[CITATION][IDENTIFIER]), graph.edge_to_bel(u, v, data), ) for u, v, data in graph.edges(data=True) if CITATION in data ) edge_c = Counter({top_level_edge: len(edges) for top_level_edge, edges in edge_mapping.items()}) df = pd.DataFrame( [ ( ":".join(top_level_edge), count, random.choice(edge_mapping[top_level_edge]), ) # noqa:S311 for top_level_edge, count in edge_c.most_common(n=n) ], columns=["Citation", "Count", "Example"], ) if n is None or len(edge_mapping) < n: print("{} Citation Count: {}".format(graph, len(edge_mapping))) else: print("{} Citation Count: {} (Showing top {})".format(graph, len(edge_mapping), n)) print(tabulate(df.values, headers=df.columns), file=file) pybel-0.15.5/src/pybel/struct/utils.py000066400000000000000000000020241426625374700176730ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for :mod:`pybel.struct`.""" from ..constants import ( GRAPH_ANNOTATION_LIST, GRAPH_ANNOTATION_PATTERN, GRAPH_ANNOTATION_URL, GRAPH_NAMESPACE_PATTERN, GRAPH_NAMESPACE_URL, ) __all__ = [ "update_metadata", ] def update_metadata(source, target) -> None: """Update the namespace and annotation metadata in the target graph. :param pybel.BELGraph source: :param pybel.BELGraph target: """ target.namespace_url.update(source.graph.get(GRAPH_NAMESPACE_URL, {})) target.namespace_pattern.update(source.graph.get(GRAPH_NAMESPACE_PATTERN, {})) target.annotation_url.update(source.graph.get(GRAPH_ANNOTATION_URL, {})) target.annotation_pattern.update(source.graph.get(GRAPH_ANNOTATION_PATTERN, {})) for keyword, values in source.graph.get(GRAPH_ANNOTATION_LIST, {}).items(): if keyword not in target.annotation_list: target.annotation_list[keyword] = values else: target.annotation_list[keyword].update(values) pybel-0.15.5/src/pybel/testing/000077500000000000000000000000001426625374700163145ustar00rootroot00000000000000pybel-0.15.5/src/pybel/testing/__init__.py000066400000000000000000000000741426625374700204260ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Testing utilities for PyBEL.""" pybel-0.15.5/src/pybel/testing/cases.py000066400000000000000000000052431426625374700177700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test cases for PyBEL testing.""" import logging import os import tempfile import unittest import pystow from ..manager import Manager __all__ = [ "TEST_CONNECTION", "TemporaryCacheMixin", "TemporaryCacheClsMixin", "FleetingTemporaryCacheMixin", ] logger = logging.getLogger(__name__) TEST_CONNECTION = pystow.get_config("pybel", "test_connection") class TemporaryCacheMixin(unittest.TestCase): """A test case that has a connection and a manager that is created for each test function.""" def setUp(self): """Set up the test function with a connection and manager.""" if TEST_CONNECTION: self.connection = TEST_CONNECTION else: self.fd, self.path = tempfile.mkstemp() self.connection = "sqlite:///" + self.path logger.info("Test generated connection string %s", self.connection) self.manager = Manager(connection=self.connection, autoflush=True) self.manager.create_all() def tearDown(self): """Tear down the test function by closing the session and removing the database.""" self.manager.session.close() if not TEST_CONNECTION: os.close(self.fd) os.remove(self.path) else: self.manager.drop_all() class TemporaryCacheClsMixin(unittest.TestCase): """A test case that has a connection and a manager that is created for each test class.""" fd, path, manager = None, None, None @classmethod def setUpClass(cls): """Set up the test class with a connection and manager.""" if TEST_CONNECTION: cls.connection = TEST_CONNECTION else: cls.fd, cls.path = tempfile.mkstemp() cls.connection = "sqlite:///" + cls.path logger.info("Test generated connection string %s", cls.connection) cls.manager = Manager(connection=cls.connection, autoflush=True) cls.manager.create_all() @classmethod def tearDownClass(cls): """Tear down the test class by closing the session and removing the database.""" cls.manager.session.close() if not TEST_CONNECTION: os.close(cls.fd) os.remove(cls.path) else: cls.manager.drop_all() class FleetingTemporaryCacheMixin(TemporaryCacheClsMixin): """A test case that clears the database before each function.""" def setUp(self): """Set up the function by clearing the database.""" super(FleetingTemporaryCacheMixin, self).setUp() self.manager.drop_networks() self.manager.drop_edges() self.manager.drop_nodes() self.manager.drop_namespaces() pybel-0.15.5/src/pybel/testing/constants.py000066400000000000000000000027741426625374700207140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Testing resources for PyBEL.""" import os __all__ = [ # BELNS "test_ns_1", "test_ns_2", "test_ns_empty", # BELANNO "test_an_1", # BEL "test_bel_simple", "test_bel_slushy", "test_bel_thorough", "test_bel_isolated", "test_bel_misordered", "test_bel_no_identifier_valiation", # JSON "test_jgif_path", ] HERE = os.path.dirname(os.path.realpath(__file__)) resources_dir = os.path.join(HERE, "resources") # BELNS Files belns_dir_path = os.path.join(resources_dir, "belns") test_ns_1 = os.path.join(belns_dir_path, "test_ns_1.belns") test_ns_2 = os.path.join(belns_dir_path, "test_ns_1_updated.belns") test_ns_empty = os.path.join(belns_dir_path, "test_ns_empty.belns") # BELANNO Files belanno_dir_path = os.path.join(resources_dir, "belanno") test_an_1 = os.path.join(belanno_dir_path, "test_an_1.belanno") # BEL Files bel_dir_path = os.path.join(resources_dir, "bel") test_bel_simple = os.path.join(bel_dir_path, "test_bel.bel") test_bel_slushy = os.path.join(bel_dir_path, "slushy.bel") test_bel_thorough = os.path.join(bel_dir_path, "thorough.bel") test_bel_isolated = os.path.join(bel_dir_path, "isolated.bel") test_bel_misordered = os.path.join(bel_dir_path, "misordered.bel") test_bel_no_identifier_valiation = os.path.join(bel_dir_path, "no_identifier_validation_test.bel") test_bel_with_obo = os.path.join(bel_dir_path, "obo.bel") # JSON Files test_jgif_path = os.path.join(bel_dir_path, "Cytotoxic T-cell Signaling-2.0-Hs.json") pybel-0.15.5/src/pybel/testing/generate.py000066400000000000000000000015161426625374700204630ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for PyBEL testing.""" import itertools as itt import random from .utils import n from ..dsl import protein from ..struct import BELGraph __all__ = [ "generate_random_graph", ] def generate_random_graph(n_nodes, n_edges, namespace="NS"): """Generate a sub-graph with random nodes and edges. :param int n_nodes: Number of nodes to make :param int n_edges: Number of edges to make :param str namespace: The namespace of the nodes to use :rtype: pybel.BELGraph """ graph = BELGraph() nodes = [protein(namespace=namespace, name=str(i)) for i in range(1, n_nodes)] edges = list(itt.combinations(nodes, r=2)) edge_sample = random.sample(edges, n_edges) for u, v in edge_sample: graph.add_increases(u, v, citation=n(), evidence=n()) return graph pybel-0.15.5/src/pybel/testing/mock_manager.py000066400000000000000000000035241426625374700213150ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Mocks for PyBEL testing.""" from typing import Iterable from ..manager.models import Network from ..struct import union class MockQueryManager: """A mock manager.""" def __init__(self, graphs=None): """Build a mock manager appropriate for testing the pipeline and query builders. :param Optional[list[pybel.BELGraph]] graphs: A list of BEL graphs to index """ self.graphs = [] #: A lookup for nodes from the node hash (string) to the node tuple self.hash_to_node = {} #: A lookup from network identifier to graph self.id_graph = {} if graphs is not None: for graph in graphs: self.insert_graph(graph) def count_networks(self) -> int: """Count networks in the manager.""" return len(self.graphs) def insert_graph(self, graph) -> Network: """Insert a graph and ensure its nodes are cached. :param pybel.BELGraph graph: """ network_id = len(self.graphs) self.graphs.append(graph) self.id_graph[network_id] = graph for node in graph: self.hash_to_node[node.md5] = node return Network(id=network_id) def get_graph_by_ids(self, network_ids: Iterable[int]): """Get a graph from the union of multiple networks. :param network_ids: The identifiers of networks in the database :rtype: pybel.BELGraph """ network_ids = list(network_ids) if len(network_ids) == 1: return self.id_graph[network_ids[0]] graphs = [self.id_graph[graph_id] for graph_id in network_ids] return union(graphs) def get_dsl_by_hash(self, md5: str): """Get a DSL by its hash. :rtype: Optional[BaseEntity] """ return self.hash_to_node.get(md5) pybel-0.15.5/src/pybel/testing/mocks.py000066400000000000000000000047101426625374700200040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Mocks for PyBEL testing.""" import itertools as itt import os from unittest import mock from .constants import bel_dir_path, belanno_dir_path, belns_dir_path from .utils import get_uri_name __all__ = [ "MockResponse", "MockSession", "mock_bel_resources", ] _responses = [ ("go.belns", os.path.join(belns_dir_path, "go-names.belns")), ( "hgnc-human-genes-20170725.belns", os.path.join(belns_dir_path, "hgnc-names.belns"), ), ("chebi-20170725.belns", os.path.join(belns_dir_path, "chebi-names.belns")), ( "species-taxonomy-id-20170511.belanno", os.path.join(belanno_dir_path, "species-taxonomy-id.belanno"), ), ( "confidence-1.0.0.belanno", os.path.join(belanno_dir_path, "confidence-1.0.0.belanno"), ), ] class MockResponse: """See http://stackoverflow.com/questions/15753390/python-mock-requests-and-the-response.""" def __init__(self, url_to_mock: str): """Build a mock for the requests Response object.""" _r = [ (".belns", os.path.join(belns_dir_path, get_uri_name(url_to_mock))), (".belanno", os.path.join(belanno_dir_path, get_uri_name(url_to_mock))), (".bel", os.path.join(bel_dir_path, get_uri_name(url_to_mock))), ] self.path = None for suffix, path in itt.chain(_responses, _r): if url_to_mock.endswith(suffix): self.path = path break if self.path is None: raise ValueError("missing file") if not os.path.exists(self.path): raise ValueError("file doesn't exist: {}".format(self.path)) def iter_lines(self): """Iterate the lines of the mock file.""" with open(self.path, "rb") as file: yield from file def raise_for_status(self): """Mock raising an error, by not doing anything at all.""" class MockSession: """Patches the session object so requests can be redirected through the filesystem without rewriting BEL files.""" def mount(self, prefix, adapter): """Mock mounting an adapter by not doing anything.""" @staticmethod def get(url: str): """Mock getting a URL by returning a mock response.""" return MockResponse(url) def close(self): """Mock closing a connection by not doing anything.""" mock_bel_resources = mock.patch("bel_resources.utils.requests.Session", side_effect=MockSession) pybel-0.15.5/src/pybel/testing/resources/000077500000000000000000000000001426625374700203265ustar00rootroot00000000000000pybel-0.15.5/src/pybel/testing/resources/bel/000077500000000000000000000000001426625374700210705ustar00rootroot00000000000000pybel-0.15.5/src/pybel/testing/resources/bel/Cytotoxic T-cell Signaling-2.0-Hs.json000066400000000000000000015224601426625374700276240ustar00rootroot00000000000000{ "graph": { "directed": false, "type": "BEL-V1.0", "label": "Cytotoxic T-cell Signaling-2.0-Hs", "metadata": { "description": "The Cytotoxic T-cell Signaling network depicts the causal mechanisms that are activated in CD8+ cytotoxic T-cells following T-cell receptor (TCR) ligation. Expanding on these processes, the network highlights the chemokines secreted by macrophages and dendritic cells, as well as the cognate T-cell receptors, involved in mediating T-cell recruitment to compromised lung tissue during COPD development.", "species_common_name": "Human", "version": "2.0", "boundary_conditions": "The human model sets represent early COPD (GOLD stage I and II) processes. If supporting literature from early COPD studies were not available, stage-independent COPD studies were used. If COPD studies were not found, we expanded our inclusion criteria to studies from healthy context and incorporated mechanisms active in processes implicated in COPD into the disease models. Literature describing processes active in acute exacerbation in COPD patients was excluded from supporting edges of the network models. We prioritized data collected from studies of lung and COPD-relevant cell types, but excluded literature related to asthma or bronchitis. Human-specific connections were preferred, but rat and mouse were also included where human data was not available." }, "nodes": [ { "id": "p(HGNC:CXCR6)", "label": "p(HGNC:CXCR6)", "metadata": { "coordinate": [ 0.4865359758724688, 0.6775492383816824 ], "bel_function_type": "proteinAbundance", "createdBy": "selventa", "nodeId": "524b3517d3fbfd4c3405149d" } }, { "id": "p(HGNC:IL15RA)", "label": "p(HGNC:IL15RA)", "metadata": { "coordinate": [ 0.14885825075398534, 0.46895313864364024 ], "bel_function_type": "proteinAbundance", "createdBy": "selventa", "nodeId": "524b3517d3fbfd4c3405149e" } }, { "id": "cat(p(EGID:21577))", "label": "cat(p(EGID:21577))", "metadata": { "coordinate": [ 0.5867083153813012, 0.26035703890559814 ], "bel_function_type": "catalyticActivity", "createdBy": "selventa", "nodeId": "524b3517d3fbfd4c3405149f" } }, { "id": "bp(GO:\"lymphocyte chemotaxis\")", "label": "bp(GO:\"lymphocyte chemotaxis\")", "metadata": { "coordinate": [ 0.5333907798362775, 0.9545454545454545 ], 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"evidences": [ { "bel_statement": "kin(p(HGNC:FYN)) increases kin(p(HGNC:LCK))", "summary_text": "Fyn plays an essential role by positive regulation of Lck activity.", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2004 Oct 12 101(41) 14859-64", "id": "15465914" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead1" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "naive T-cell", "tissue": "" } } ] } }, { "source": "kin(p(HGNC:ZAP70))", "relation": "directlyIncreases", "target": "p(HGNC:PLCG1,pmod(P,Y))", "directed": false, "label": "kin(p(HGNC:ZAP70)) directlyIncreases p(HGNC:PLCG1,pmod(P,Y))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051499", "evidences": [ { "bel_statement": "kin(p(HGNC:ZAP70)) directlyIncreases p(HGNC:PLCG1,pmod(P,Y))", "summary_text": "dual phosphorylation by ZAP70 and Itk triggers the activation of PLCg1", "citation": { "type": "PubMed", "name": 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Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eafa" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "a(SCHEM:Calcium)", "relation": "directlyIncreases", "target": "phos(complex(SCOMP:\"Calcineurin Complex\"))", "directed": false, "label": "a(SCHEM:Calcium) directlyIncreases phos(complex(SCOMP:\"Calcineurin Complex\"))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405147b", "evidences": [ { "bel_statement": "a(SCHEM:Calcium) directlyIncreases phos(complex(SCOMP:\"Calcineurin Complex\"))", "summary_text": "NMDA-mediated influx of calcium led to activated of the calcium-dependent phosphatase calcineurin and the subsequent dephosphorylation and activation of the protein-tyrosine phosphatase STEP", "citation": { "type": "PubMed", "name": "Nat Neurosci 2003 Jan 6(1) 34-42", "id": "12483215" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb04" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "neuron", "tissue": "" } } ] } }, { "source": "cat(p(EGID:21577))", "relation": "increases", "target": "bp(GO:\"T cell activation\")", "directed": false, "label": "cat(p(EGID:21577)) increases bp(GO:\"T cell activation\")", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051473", "evidences": [ { "bel_statement": "cat(p(EGID:21577)) increases bp(GO:\"T cell activation\")", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb30" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:CCR3)", "relation": " isA", "target": "p(SFAM:\"Chemokine Receptor Family\")", "directed": false, "label": "p(HGNC:CCR3) isA p(SFAM:\"Chemokine Receptor Family\")", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051491", "evidences": [ ] } }, { "source": "cat(p(HGNC:IL2RB))", "relation": "decreases", "target": "bp(GO:\"T cell activation\")", "directed": false, "label": "cat(p(HGNC:IL2RB)) decreases bp(GO:\"T cell activation\")", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051485", "evidences": [ { "bel_statement": "cat(p(HGNC:IL2RB)) decreases bp(GO:\"T cell activation\")", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb39" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:CCR5)", "relation": " isA", "target": "p(SFAM:\"Chemokine Receptor Family\")", "directed": false, "label": "p(HGNC:CCR5) isA p(SFAM:\"Chemokine Receptor Family\")", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051492", "evidences": [ ] } }, { "source": "p(HGNC:BCL2)", "relation": "increases", "target": "path(SDIS:\"Cytotoxic T-cell activation\")", "directed": false, "label": "p(HGNC:BCL2) increases path(SDIS:\"Cytotoxic T-cell activation\")", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051498", "evidences": [ { "bel_statement": "p(HGNC:BCL2) increases path(SDIS:\"Cytotoxic T-cell activation\")", "summary_text": "While production of IL-2 is confined to periods of lymphocyte activation, the constitutive expression of IL-15 maintains the homeostatic proliferation of lymphocytes, most notably memory CD8 T cells, in the steady-state through the sustained expression of bcl-2 (10–13). ", "citation": { "type": "PubMed", "name": "J Immunol 2011 Jan 1 186(1) 174-82", "id": "21098221" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb60" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "CD8+ T-cell", "tissue": "" } } ] } }, { "source": "p(HGNC:IL15)", "relation": "increases", "target": "path(SDIS:\"T-cell migration\")", "directed": false, "label": "p(HGNC:IL15) increases path(SDIS:\"T-cell migration\")", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051487", "evidences": [ { "bel_statement": "p(HGNC:IL15) increases path(SDIS:\"T-cell migration\")", "summary_text": "In this paper, we show that localized increases in the homeostatic cytokine IL-15 induced by influenza infection is responsible for the migration of CD8 effector T cells to the site of infection", "citation": { "type": "PubMed", "name": "J Immunol 2011 Jan 1 186(1) 174-82", "id": "21098221" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb66" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "CD8+ T-cell", "tissue": "" } } ] } }, { "source": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B)))", "relation": "increases", "target": "cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "directed": false, "label": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B))) increases cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051481", "evidences": [ { "bel_statement": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B))) increases cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb6a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "path(SDIS:\"Cytotoxic T-cell activation\")", "relation": " isA", "target": "bp(GO:\"T cell activation\")", "directed": false, "label": "path(SDIS:\"Cytotoxic T-cell activation\") isA bp(GO:\"T cell activation\")", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405149b", "evidences": [ ] } }, { "source": "path(SDIS:\"Cytotoxic T-cell activation\")", "relation": "increases", "target": "p(HGNC:FASLG)", "directed": false, "label": "path(SDIS:\"Cytotoxic T-cell activation\") increases p(HGNC:FASLG)", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051497", "evidences": [ { "bel_statement": "path(SDIS:\"Cytotoxic T-cell activation\") increases p(HGNC:FASLG)", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb9b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "relation": "increases", "target": "kin(p(HGNC:ZAP70))", "directed": false, "label": "cat(complex(SCOMP:\"T Cell Receptor Complex\")) increases kin(p(HGNC:ZAP70))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405148b", "evidences": [ { "bel_statement": "cat(complex(SCOMP:\"T Cell Receptor Complex\")) increases kin(p(HGNC:ZAP70))", "summary_text": "We show that Crry increases early TCR-dependent activation signals, including p56lck-, zeta-associated protein-70 (ZAP-70), Vav-1, Akt, and extracellular signal-regulated kinase (ERK) phosphorylation but also costimulation-dependent mitogen-activated protein kinases (MAPK), such as the stress-activated c-Jun N-terminal kinase (JNK). It is intriguing that Crry costimulus enhanced p38 MAPK activation in T helper cell type 1 (Th1) but not in Th2 cells. ", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2005 Dec 78(6) 1386-96", "id": "16301324" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb3a" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "cat(complex(SCOMP:\"T Cell Receptor Complex\")) increases kin(p(HGNC:ZAP70))", "summary_text": "HIP-55 interacted with ZAP-70, a critical protein-tyrosine kinase in TCR signaling, and this interaction was induced by TCR signaling.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Dec 26 278(52) 52195-202", "id": "14557276" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba2" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "cat(complex(SCOMP:\"T Cell Receptor Complex\")) increases kin(p(HGNC:ZAP70))", "summary_text": "This redistribution brings VHR into the vicinity of the triggered TCRs, where VHR is phosphorylated at Tyr138 by ZAP-70. We found that this phosphorylation is required for the function of VHR as an inhibitor of the Erk2 and Jnk MAPKs", "citation": { "type": "PubMed", "name": "Nat Immunol 2003 Jan 4(1) 44-8", "id": "12447358" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb22" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } } ] } }, { "source": "p(HGNC:CXCR6)", "relation": " isA", "target": "p(SFAM:\"Chemokine Receptor Family\")", "directed": false, "label": "p(HGNC:CXCR6) isA p(SFAM:\"Chemokine Receptor Family\")", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051493", "evidences": [ ] } }, { "source": "kin(p(HGNC:LCK))", "relation": "directlyIncreases", "target": "p(HGNC:PLCG1,pmod(P,Y))", "directed": false, "label": "kin(p(HGNC:LCK)) directlyIncreases p(HGNC:PLCG1,pmod(P,Y))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405147e", "evidences": [ { "bel_statement": "kin(p(HGNC:LCK)) directlyIncreases p(HGNC:PLCG1,pmod(P,Y))", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebcb" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B)))", "relation": "increases", "target": "kin(p(HGNC:LCK))", "directed": false, "label": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B))) increases kin(p(HGNC:LCK))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051480", "evidences": [ { "bel_statement": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B))) increases kin(p(HGNC:LCK))", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe6" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:IL15)", "relation": "directlyIncreases", "target": "cat(p(HGNC:IL15RA))", "directed": false, "label": "p(HGNC:IL15) directlyIncreases cat(p(HGNC:IL15RA))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051488", "evidences": [ { "bel_statement": "p(HGNC:IL15) directlyIncreases cat(p(HGNC:IL15RA))", "summary_text": "IL-15 is a common gamma chain cytokine sharing overlapping signaling and biological properties with IL-2 as a result of their mutual usage of the IL-2/15b and common gamma chain (gc) receptor subunits (7,8).", "citation": { "type": "PubMed", "name": "J Immunol 2011 Jan 1 186(1) 174-82", "id": "21098221" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf8" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "CD8+ T-cell", "tissue": "" } } ] } }, { "source": "cat(p(HGNC:CD28))", "relation": "increases", "target": "cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "directed": false, "label": "cat(p(HGNC:CD28)) increases cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051476", "evidences": [ { "bel_statement": "cat(p(HGNC:CD28)) increases cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec0c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:CXCR3)", "relation": " isA", "target": "p(SFAM:\"Chemokine Receptor Family\")", "directed": false, "label": "p(HGNC:CXCR3) isA p(SFAM:\"Chemokine Receptor Family\")", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051494", "evidences": [ ] } }, { "source": "p(HGNC:CXCR3)", "relation": "actsIn", "target": "cat(p(HGNC:CXCR3))", "directed": false, "label": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051490", "evidences": [ { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "The chemokine receptor CXCR3 is critical for the function of activated T cells. We studied the molecular mechanisms of CXCR3 signalling. The addition of CXCR3 ligands to normal human T cells expressing CXCR3 led to the tyrosine phosphorylation of multiple proteins. Addition of the same ligands to Jurkat T cells engineered to express CXCR3 induced tyrosine phosphorylation of proteins with molecular weights similar to those in normal cells. Immunoblotting with phosphotyrosine-specific antibodies identified Zeta-associated protein of 70,000 molecular weight (ZAP-70), linker for the activation of T cells (LAT), and phospholipase-C-gamma1 (PLCgamma1) to be among the proteins that become phosphorylated upon CXCR3 activation. ZAP-70 was phosphorylated on tyrosine 319, LAT on tyrosines 171 and 191, and PLCgamma1 on tyrosine 783", "citation": { "type": "PubMed", "name": "Immunology 2007 Apr 120(4) 467-85", "id": "17250586" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec10" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "By using a CXCR3 ligand reporter mouse, we found that stromal cells predominately expressed the chemokine ligand CXCL9 whereas hematopoietic cells expressed CXCL10 in lymph nodes (LNs). Dendritic cell (DC)-derived CXCL10 facilitated T cell-DC interactions in LNs during T cell priming while both chemokines guided intranodal positioning of CD4(+) T cells to interfollicular and medullary zones. Thus, different chemokines acting on the same receptor can function locally to facilitate DC-T cell interactions and globally to influence intranodal positioning, and both functions contribute to Th1 cell differentiation.", "citation": { "type": "PubMed", "name": "Immunity 2012 Dec 14 37(6) 1091-103", "id": "23123063" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb9d" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "dendritic cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "CXCR3, CXCR4, CXCR5, and CCR6 were detected on human chondrocytes. CXCR3 and CXCR4 expression was increased in exponentially growing chondrocyte subcultures. Ligands of all receptors enhanced the release of MMPs 1, 3, and 13. Release of NAG and cathepsin B was significantly higher in chemokine-stimulated cultures than in unstimulated cultures.", "citation": { "type": "PubMed", "name": "Arthritis Rheum 2004 Jan 50(1) 112-22", "id": "14730607" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe5" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "chondrocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "Numerous studies have shown that immature human and mouse blood- and bone marrow-derived DC subsets express a panel of inflammatory chemokine receptors (CCR1-6,8,9, CXCR3,4, CX3CR1) [Table 1 and reviewed in (1-5)]. [Table 1 Chemokine receptors expressed by DC and the functional outcome of receptor ligation}]", "citation": { "type": "PubMed", "name": "Clin Lab Med 2008 Sep 28(3) 375-84, v", "id": "19028258" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc8" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "dendritic cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "Chemokine receptors signal through Galphai2 proteins to activate DOCK2 (dedicator of cytokinesis 2) and other guanine nucleotide exchange factors (GEFs), leading to the activation of RAC1 and RHOA. ", "citation": { "type": "PubMed", "name": "Nat Rev Immunol 2009 Sep 9(9) 630-44", "id": "19696767" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb9e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "b-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "the CXC chemokines Mig and interferon-? inducible protein 10 can be expressed and bound by endothelium stimulated with interferon-? and tumor necrosis factor (TNF) ?, and can induce the firm adhesion of T lymphocyte in shear flow via CXCR3.", "citation": { "type": "PubMed", "name": "J Mol Med 2003 Jan 81(1) 4-19", "id": "12545245" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec05" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Endothelial Cells", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "supernatants harvested from stimulated PMN induced migration and rapid integrin-dependent adhesion of CXCR3-expressing lymphocytes; these activities were significantly reduced by neutralizing anti-MIG and anti-IP-10 Abs,", "citation": { "type": "PubMed", "name": "J Immunol 1999 Apr 15 162(8) 4928-37", "id": "10202039" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebfc" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "the chemokine Mig, a ligand for CXCR3, activates the small GTPases RhoA and Rac1, induces a reorganization of the actin cytoskeleton, and triggers cell chemotaxis and modulation of integrin VLA-5- and VLA-4-dependent cell adhesion to fibronectin.", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Nov 30 276(48) 45098-105", "id": "11571298" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb42" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "neutralization of CXCR3 reduced MIG/CXCL9-induced T lymphocyte proliferation and the number of IFN-gamma-positive spots", "citation": { "type": "PubMed", "name": "J Immunol 2004 Jun 15 172(12) 7417-24", "id": "15187119" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb2f" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "The ELR-negative CXC chemokines CXCL9, CXCL10, and CXCL11) are potent chemoattractants for mononuclear cells and act through their shared receptor, CXCR3.", "citation": { "type": "PubMed", "name": "J Immunol 2003 Nov 1 171(9) 4844-52", "id": "14568964" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb1b" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "There is increased secretion of Cxcr3-activating chemokines in COPD airways", "citation": { "type": "PubMed", "name": "Pharmacol Rev 2004 Dec 56(4) 515-48", "id": "15602009" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb0d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "Table 1. Lymphoid chemokine receptors", "citation": { "type": "PubMed", "name": "Trends Immunol 2004 Feb 25(2) 67-74", "id": "15102365" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eadf" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR3) actsIn cat(p(HGNC:CXCR3))", "summary_text": "Activation of CXCR3 induces chemotactic responses to I-TAC and reorganization reorganization of the actin cytoskeleton in human airway epithelial cells (20).", "citation": { "type": "PubMed", "name": "Am J Physiol Cell Physiol 2006 Jul 291(1) C34-9", "id": "16467404" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead3" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Epithelial Cells", "tissue": "" } } ] } }, { "source": "p(HGNC:IL15)", "relation": "directlyIncreases", "target": "cat(p(HGNC:IL2RB))", "directed": false, "label": "p(HGNC:IL15) directlyIncreases cat(p(HGNC:IL2RB))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051489", "evidences": [ { "bel_statement": "p(HGNC:IL15) directlyIncreases cat(p(HGNC:IL2RB))", "summary_text": "Considering these points in the context of IL-2- and IL-15-dependent signaling under physiological conditions, the lack of a cytoplasmic domain for IL-2R alpha and trans-presentation of IL-15 by IL-15Ralpha to CD122 and gamma C results in a qualitative identical utilization of signaling pathways associated with CD122 and gamma C. High levels of IL-2Ralpha provide a mean for continual capture of IL-2 to sustain signaling whereas limiting IL-15Ralpha tempers engagement of CD122 and gamma C, limiting signal transduction. Thus, varied levels of IL-2Ralpha and IL-15Ralpha provide a simple, yet powerful, mechanism to quantify and tune signaling through common intermediates leading to distinctive biological responses.", "citation": { "type": "PubMed", "name": "J Immunol 2012 May 1 188(9) 4149-57", "id": "22447977" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb3" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:IL15) directlyIncreases cat(p(HGNC:IL2RB))", "summary_text": "Interleukin 15 (IL-15) is a 14-15 kDa polypeptide that belongs to the 4 alpha-helix-bundle family of cytokines and was originally discovered due to its T cell proliferative activity. It utilizes the signal-transducing beta/gamma polypeptides of the IL-2 receptor complex, thus sharing many biological activities with IL-2,", "citation": { "type": "PubMed", "name": "Arch Immunol Ther Exp (Warsz) 2000 48(6) 457-64", "id": "11197599" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec11" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } } ] } }, { "source": "cat(p(EGID:21577))", "relation": " isA", "target": "cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "directed": false, "label": "cat(p(EGID:21577)) isA cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405148c", "evidences": [ ] } }, { "source": "phos(complex(SCOMP:\"Calcineurin Complex\"))", "relation": "increases", "target": "bp(GO:\"T cell activation\")", "directed": false, "label": "phos(complex(SCOMP:\"Calcineurin Complex\")) increases bp(GO:\"T cell activation\")", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051474", "evidences": [ { "bel_statement": "phos(complex(SCOMP:\"Calcineurin Complex\")) increases bp(GO:\"T cell activation\")", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec1e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:IL2)", "relation": "increases", "target": "cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "directed": false, "label": "p(HGNC:IL2) increases cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405148d", "evidences": [ { "bel_statement": "p(HGNC:IL2) increases cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec2d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "cat(p(HGNC:PLCG1))", "relation": "increases", "target": "a(SCHEM:Calcium)", "directed": false, "label": "cat(p(HGNC:PLCG1)) increases a(SCHEM:Calcium)", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051483", "evidences": [ { "bel_statement": "cat(p(HGNC:PLCG1)) increases a(SCHEM:Calcium)", "summary_text": "In this study we demonstrate that acute addition of monomeric IgE elicits a wide spectrum of responses in the rat basophilic leukemia-2H3 mast cell line, including activation of phospholipases Cgamma and D, a rise in cytosol Ca(2+), NFAT translocation, degranulation, and membrane ruffling within minutes. Calcium transients persist for hours as long as IgE is present resulting in the maintained translocation of the transcription factor NFAT to the nucleus", "citation": { "type": "PubMed", "name": "J Immunol 2004 Apr 1 172(7) 4048-58", "id": "15034016" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae4" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "cat(p(HGNC:PLCG1)) increases a(SCHEM:Calcium)", "summary_text": "Interestingly, the depletion of Grb2 from HEK-293 cells by RNA interference significantly enhanced increased EGF-induced PLC-gamma1 enzymatic activity and mobilization of the intracellular Ca2+, while it did not affect EGF-induced tyrosine phosphorylation of PLC-gamma1.", "citation": { "type": "PubMed", "name": "Cell Signal 2005 Oct 17(10) 1289-99", "id": "16038803" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec36" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "cat(p(HGNC:PLCG1)) increases a(SCHEM:Calcium)", "summary_text": "Reconstitution of deficient mast cells with Vav1 restored normal tyrosine phosphorylation of PLCgamma1 and PLCgamma2 and calcium responses. Thus, Vav1 is essential to FcepsilonRI-mediated activation of PLCgamma and calcium mobilization in mast cells. ", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2001 Jun 21(11) 3763-74", "id": "11340169" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec2e" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "cat(p(HGNC:PLCG1)) increases a(SCHEM:Calcium)", "summary_text": "activation of c-src was reported to mediate VEGF signaling through PLC-gamma, leading to inositol 1,4,5-trisphosphate formation and calcium mobilization", "citation": { "type": "PubMed", "name": "Am J Physiol Cell Physiol 2001 Jun 280(6) C1375-86", "id": "11350732" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd4" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "cat(p(HGNC:PLCG1)) increases a(SCHEM:Calcium)", "summary_text": "Fig.1", "citation": { "type": "PubMed", "name": "Science 2004 Nov 26 306(5701) 1506-7", "id": "15567848" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb29" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "cat(p(HGNC:CXCR3))", "relation": "increases", "target": "bp(GO:\"lymphocyte chemotaxis\")", "directed": false, "label": "cat(p(HGNC:CXCR3)) increases bp(GO:\"lymphocyte chemotaxis\")", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405148e", "evidences": [ { "bel_statement": "cat(p(HGNC:CXCR3)) increases bp(GO:\"lymphocyte chemotaxis\")", "summary_text": "neutralization of CXCR3 reduced MIG/CXCL9-induced T lymphocyte proliferation and the number of IFN-gamma-positive spots", "citation": { "type": "PubMed", "name": "J Immunol 2004 Jun 15 172(12) 7417-24", "id": "15187119" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec39" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "cat(p(HGNC:CXCR3)) increases bp(GO:\"lymphocyte chemotaxis\")", "summary_text": "supernatants harvested from stimulated PMN induced migration and rapid integrin-dependent adhesion of CXCR3-expressing lymphocytes; these activities were significantly reduced by neutralizing anti-MIG and anti-IP-10 Abs,", "citation": { "type": "PubMed", "name": "J Immunol 1999 Apr 15 162(8) 4928-37", "id": "10202039" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb40" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } } ] } }, { "source": "p(HGNC:IL2RG)", "relation": "actsIn", "target": "cat(p(MGI:Il2rg))", "directed": false, "label": "p(HGNC:IL2RG) actsIn cat(p(MGI:Il2rg))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051479", "evidences": [ { "bel_statement": "p(HGNC:IL2RG) actsIn cat(p(MGI:Il2rg))", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec3f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:PLCG1)", "relation": "actsIn", "target": "cat(p(HGNC:PLCG1))", "directed": false, "label": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405147a", "evidences": [ { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "The gene whose expression correlated most strongly with lack of invasion was identified as a potential invasion suppressor and called prostin-1. Pharmacological inhibition of PLC gamma (U73122) confirmed that PLC gamma signaling suppressed prostin-1 in that U73122 treatment caused induction of prostin-1 in PLC gamma competent cells. The prostin-1 gene, conserved through phylogeny, is induced by androgen in LNCaP cells and encodes a 92 amino acid protein. The protein shares no extensive homologies with other known genes, yet was recently identified as a small stabilizer subunit of the dolichol-phosphate-mannose (DPM) synthase complex. ***Did not curate*** line below because COS cells are monkey That DPM3/prostin-1 might suppress tumor progression was supported by the finding that exogenous expression in COS cells leads to apoptosis. These findings support the use of model cell lines to identify putative tumor suppressors and promoters.", "citation": { "type": "PubMed", "name": "Oncogene 2001 May 17 20(22) 2781-90", "id": "11420690" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec4f" }, "experiment_context": { "species_common_name": "Human", "disease": "Prostatic Neoplasms", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Induction of germline C epsilon transcripts in DND39 cells by IL-4 required at least two distinct signaling cascades. One was mediated by enhancement of tyrosine phosphorylation of a 57 kd protein associated with phospholipase C-gamma 1 (PLC-gamma 1) that resulted in PLC-gamma 1 activation, inositol lipid hydrolysis, and protein kinase C delta translocation. The other was dependent on phosphatidylinositol 3-kinase, whose activation induced protein kinase C zeta translocation.", "citation": { "type": "PubMed", "name": "J Allergy Clin Immunol 1995 Dec 96(6 Pt 2) 1145-51", "id": "8543771" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec2c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "b-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "\tPLC gamma is phosphorylated by activated FGFR, resulting in PLC gamma activation, stimulation of phosphatidyl inositol hydrolysis and generation of two second messengers, diacylglycerol and inositol (1,4,5) P3. Tyrosine phosphorylation of PLCgamma by FGFR4 is weaker than that seen by other isoforms of FGFR.Three tyrosine residues in PLC gamma have been identified as sites of receptor tyrosine kinase phosphorylation. Mutagenesis indicates that the phosphorylation at Tyr 783 is essential for IP3 formation, phosphorylation of Tyr 771 is dispensable, and phosphorylation of Tyr 1254 is necessary to achieve maximal IP3 formation. ", "citation": { "type": "PubMed", "name": "Cytokine Growth Factor Rev 2005 Apr 16(2) 139-49", "id": "15863030" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec00" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Intracellular calcium signaling is regulated by PLC?, which generates inositol 1,4,5-trisphosphate and diacylglycerol from PIP2. Inositol 1,4,5-trisphosphate stimulates the release of intracellular calcium stores upon binding to its receptor in the ER. The depletion of ER calcium stores then triggers extracellular calcium influx. Diacylglycerol and intracellular calcium signals cooperate to activate PKCs, which then activate other pathways such as the NF-?B pathway, ultimately leading to mast cell degranulation and cytokine production.", "citation": { "type": "PubMed", "name": "J Biol Chem 2011 Sep 23 286(38) 32891-7", "id": "21799019" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf3" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "A phospholipase C inhibitor, U73122, abrogated Rap1 activation triggered by both the TCR and SDF-1 (CXCL12). PLC-gamma1-deficient Jurkat T cells showed a marked reduction of TCR-triggered Rap1 activation and adhesion to intercellular adhesion molecule-1 (ICAM-1) mediated by LFA-1. In contrast, SDF-1-triggered Rap1 activation and adhesion were not affected in these cells. Transfection of these cells with an expression plasmid encoding PLC-gamma1 restored Rap1 activation by the TCR and the ability to adhere to ICAM-1, accompanied by polarized LFA-1 surface clustering colocalized with regulator of adhesion and polarization enriched in lymphoid tissues (RAPL).", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Mar 19 279(12) 11875-81", "id": "14702343" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe8" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Despite extensive overlap in the molecules recruited to the active receptors, there is some preferential modulation of signalling pathways. Tumour cells that express EGFR with kinase-domain mutations preferentially activate the pro-survival PI3K?AKT and signal transducer and activator of transcription (STAT) pathways67. Although EGFR has no consensus sequence for the p85 adaptor subunit of PI3K, it couples to this pathway through GAB1, which binds growth-factorreceptor- bound protein 2 (GRB2).", "citation": { "type": "PubMed", "name": "Nat Rev Cancer 2005 May 5(5) 341-54", "id": "15864276" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe4" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "In this study we demonstrate that acute addition of monomeric IgE elicits a wide spectrum of responses in the rat basophilic leukemia-2H3 mast cell line, including activation of phospholipases Cgamma and D, a rise in cytosol Ca(2+), NFAT translocation, degranulation, and membrane ruffling within minutes. Calcium transients persist for hours as long as IgE is present resulting in the maintained translocation of the transcription factor NFAT to the nucleus", "citation": { "type": "PubMed", "name": "J Immunol 2004 Apr 1 172(7) 4048-58", "id": "15034016" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebca" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "E2-stimulated ERK required ER in breast cancer and endothelial cells and was substantially prevented by expression of a dominant negative EGFR or by tyrphostin AG1478, a specific inhibitor for EGFR tyrosine kinase activity. Transactivation/phosphorylation of EGFR by E2 was dependent on the rapid liberation of heparin-binding EGF (HB-EGF) from cultured MCF-7 cells and was blocked by antibodies to this ligand for EGFR. Expression of dominant negative mini-genes for Galpha(q) and Galpha(i) blocked E2-induced, EGFR-dependent ERK activation, and Gbetagamma also contributed. G protein activation led to activation of matrix metalloproteinases (MMP)-2 and -9. This resulted from Src-induced MMP activation, implicated using PP2 (Src family kinase inhibitor) or the expression of a dominant negative Src protein.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Jan 24 278(4) 2701-12", "id": "12421825" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb7" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "By substituting these tyrosine residues in LAT with phenylalanine and by utilizing phosphorylated peptides derived from these sites, we mapped the tyrosine residues in LAT required for the direct interaction and activation of Vav, p85/p110alpha and phospholipase Cgamma1 (PLCgamma1). Our results indicate that Tyr(226) and Tyr(191) are required for Vav binding, whereas Tyr(171) and Tyr(132) are necessary for association and activation of phosphoinositide 3-kinase activity and PLCgamma1 respectively. ", "citation": { "type": "PubMed", "name": "Biochem J 2001 Jun 1 356(Pt 2) 461-71", "id": "11368773" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb8b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "natural killer cell", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "# Ariadne: Ly-GDI is phosphorylated on tyrosine residues following T cell receptor stimulation, and it associates with the Src homology 2 region of an adapter protein, Shc. [Regulation] Ly-GDI is phosphorylated on tyrosine residues following T cell receptor stimulation, and it associates with the Src homology 2 region of an adapter protein, Shc. In addition, the interaction between Ly-GDI and Vav1 requires tyrosine phosphorylation. Overexpression of Ly-GDI alone is inhibitory to NFAT stimulation and calcium mobilization. However, when co-expressed with Vav1, Ly-GDI enhances Vav1 induction of NFAT activation, phospholipase Cgamma phosphorylation, and calcium mobilization.", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Dec 20 277(51) 50121-30", "id": "12386169" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb51" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Mutation of Lys650-->Glu in the activation loop of the FGFR3 kinase domain causes the lethal human skeletal disorder thanatophoric dysplasia type II (TDII) and is also found in patients with multiple myeloma, bladder and cervical carcinomas. This mutation leads to constitutive activation of FGFR3. We show that the kinase domains of FGFR1, FGFR3, and FGFR4 containing the activation loop mutation, when targeted to the plasma membrane by a myristylation signal, can transform NIH3T3 cells and induce neurite outgrowth in PC12 cells. Phosphorylation of Shp2, PLC-gamma, and MAPK was also stimulated by all three 'TDII-like' FGFR derivatives. Additionally, activation of Stat1 and Stat3 was observed in cells expressing the activated FGFR derivatives. Finally, we demonstrate that FGFR1, FGFR3, and FGFR4 derivatives can stimulate PI-3 kinase activity. ", "citation": { "type": "PubMed", "name": "Oncogene 2000 Jul 6 19(29) 3309-20", "id": "10918587" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb3d" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "We analyzed here the possible role of the tyrosine 769 in KGFR, corresponding to tyrosine 766 in FGFR1, in the regulation of KGFR signal transduction and MAPK activation as well as in the control of the endocytic process of KGFR. A mutant KGFR in which tyrosine 769 was substituted by phenylalanine was generated and transfected in NIH3T3 and HeLa cells. Our results indicate that tyrosine 769 is required for the binding to KGFR and tyrosine phosphorylation of PLCgamma as well as for the full activation of MAPKs and for cell proliferation through the regulation of FRS2 tyrosine phosphorylation", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 2005 Feb 11 327(2) 523-32", "id": "15629145" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb2b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Many angiogenic growth factors, including bFGF and VEGF, stimulate endothelial cell invasion (51) through transmembrane protein kinase receptor dimerization and phosphorylation (11, 53), leading to the activation of phospholipase Cg (PLCg) and Ras, both of which ultimately result in activation of cPLA2", "citation": { "type": "PubMed", "name": "FASEB J 2004 Mar 18(3) 568-70", "id": "14715700" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec3e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "From full text: in null cells the expression of FIC or JE, respectively, is severely compromised in null cells treated with EGF or PDGF. In contrast, each growth factor provokes a similar increase in mRNA levels when added to null+ cells. These data indicated that PLC-?1 is an essential signaling component for both EGFand PDGF-dependent expression of these mRNAs.", "citation": { "type": "PubMed", "name": "Exp Cell Res 2006 Apr 1 312(6) 807-16", "id": "16427622" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec25" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "This Syk-mediated signal amplification results in a direct or indirect activation of several proteins, including linker for activation of T cells (LAT), Vav, phospholipase C-?1 (PLC-?1), and PLC-?2.", "citation": { "type": "PubMed", "name": "J Lipids 2011 2011 752906", "id": "21490812" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebfa" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Further analysis showed that tyrosine 1006 is responsible for phospholipase Cgamma1 (PLCgamma1) activation and intracellular calcium release in endothelial cells. Activation of PLCgamma1 was selectively mediated by tyrosine 1006.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 May 2 278(18) 16347-55", "id": "12598525" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebec" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "In this study, we have identified the Rac-GAP beta2-chimaerin as an effector of the epidermal growth factor receptor (EGFR) via coupling to phospholipase Cgamma (PLCgamma) and generation of the lipid second messenger diacylglycerol (DAG).", "citation": { "type": "PubMed", "name": "EMBO J 2006 May 17 25(10) 2062-74", "id": "16628218" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc5" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "These results indicate that continuous stretch-induced IL-6 secretion in HUVECs depends on outside-in signaling via integrins followed by a PI3-K-PLC-gamma-PKC-IKK-NF-kappaB signaling cascade.", "citation": { "type": "PubMed", "name": "Am J Physiol Cell Physiol 2005 May 288(5) C1012-22", "id": "15613495" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebae" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "umbilical vein endothelial cell", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Table 1 Docking rules for adaptors on EGFR cytoplasmic tails, as established by coarse-grained molecular docking modeling simulations", "citation": { "type": "PubMed", "name": "BMC Syst Biol 2010 4 57", "id": "20459599" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba1" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "GIT1 interaction with PLCgamma is required for PLCgamma activation based on inhibition of tyrosine phosphorylation and calcium mobilization after GIT1 knockdown with antisense GIT1 oligonucleotides.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Dec 12 278(50) 49936-44", "id": "14523024" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba0" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "CD40 lacks intrinsic catalytic activity, but the cytoplasmic domain of CD40 has two binding sites for TRAF...TRAFs 1, 2, 3, 5, and 6 have been found in association with CD40, and these adaptors couple CD40 to the phosphoinositide 3-kinase (PI3K), phospholipase Cγ (PLC-γ), mitogen-activated protein kinase (MAPK-ERK, p38, and JNK), and nuclear factor κB (NF-κB) signaling pathways", "citation": { "type": "PubMed", "name": "Sci STKE 2004 Jun 15 2004(237) pe25", "id": "15199223" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb9f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "The phosphatidylinositol 4,5-bisphosphate (PIP(2)) hydrolyzing activity of PLC-gamma1 was substantially increased in the presence of purified tubulin in vitro, whereas the activity was not promoted by bovine serum albumin, suggesting that beta-tubulin activates PLC-gamma1.", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Feb 25 280(8) 6897-905", "id": "15579910" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb83" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "The P-1 domain mediates a constitutive interaction of SLP-76 with the SH3 domain of PLC-gamma1 and is required for TCR-mediated activation of Erk, PLC-gamma1, and NFAT (nuclear factor of activated T cells).", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2001 Jul 21(13) 4208-18", "id": "11390650" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb7b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Phospholipase Cgamma1 is expressed ubiquitously, especially in the brain, thymus and lungs. PLCgamma1 can be activated by receptor tyrosine kinases (i.e.: PDGFR, EGFR, FGFR, Trk), as well as non-receptor protein kinases (Src, Syk, Tec)", "citation": { "type": "PubMed", "name": "Postepy Hig Med Dosw (Online) 2011 65 470-7", "id": "21918248" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb78" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "The FGFR-1 mediates activation of protein kinase C (PKC) through direct binding and activation of phospholipase C-gamma (PLC-gamma)", "citation": { "type": "PubMed", "name": "Oncogene 1999 Jun 3 18(22) 3354-64", "id": "10362356" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb72" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Endothelial Cells", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Activation of FGF receptors can activate multiple signal transduction pathways including the phospholipase Cgamma, phosphatidyl inositol 3-kinase, mitogen-activated protein kinase and signal transducers and activators of transcription (STAT) pathways", "citation": { "type": "PubMed", "name": "Endocr Relat Cancer 2004 Dec 11(4) 709-24", "id": "15613447" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb68" }, "experiment_context": { "species_common_name": "Human", "disease": "Prostatic Neoplasms", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "PLC-gamma 1 contains three tyrosine phosphorylation sites, which have been identified as residues 771, 783 and 1254. Phosphorylation of tyrosine residues is sufficient to increase the catalytic activity of PLC-gamma 1,", "citation": { "type": "PubMed", "name": "Ciba Found Symp 1992 164 223-33; discussion 233-9", "id": "1395933" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb45" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "CD38 dimerization induced tyrosine phosphorylation of the protein kinase syk and increased syk kinase activity. CD38 dimerization also induced tyrosine phosphorylation of phospholipase C-gamma and of the p85 subunit of phosphatidylinositol 3-kinase (PI 3-K)", "citation": { "type": "PubMed", "name": "J Immunol 1996 Jan 1 156(1) 100-7", "id": "8598449" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb21" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Through its phosphorylated tyrosine residues, the activated VEGFR-2 associates with the adapter molecules Shc, Grb2 and Nck, to Ras GTPase activating protein, p59fyn, pp62yes and phospholipase Cg, and to the tyrosine phosphatases SHP-1 and SHP-2", "citation": { "type": "PubMed", "name": "EMBO J 1999 Feb 15 18(4) 882-92", "id": "10022831" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eafd" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 1997 Dec 19 272(51) 32411-8", "id": "9405450" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec52" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Collectively, these data suggest that the EGF receptor triggers activation of Rap2B via PLC-gamma1 activation and tyrosine phosphorylation of RasGRP3 by c-Src, finally resulting in stimulation of PLC-epsilon.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2004 Jun 24(11) 4664-76", "id": "15143162" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec4a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "dual phosphorylation by ZAP70 and Itk triggers the activation of PLCg1", "citation": { "type": "PubMed", "name": "Mol Immunol 2002 Jun 38(15) 1087-99", "id": "12044776" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec47" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "Cell 1989 Jun 30 57(7) 1101-7", "id": "2472218" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec2f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Phosphorylation of Tyr-783, which is essential for lipase activation, was observed in all stimulated cell types examined. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Jul 30 279(31) 32181-90", "id": "15161916" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec0d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "Oncogene 2003 Apr 17 22(15) 2248-59", "id": "12700661" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec0b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Based on these results, we propose that CD148 negatively regulates TCR signaling by interfering with the phosphorylation and function of PLCgamma1 and LAT.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2001 Apr 21(7) 2393-403", "id": "11259588" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec08" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Aug 1 278(31) 29208-15", "id": "12738795" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec07" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2001 Jun 21(11) 3763-74", "id": "11340169" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebfe" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 1999 Jul 16 274(29) 20421-4", "id": "10400667" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebfb" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Our results suggest that gelsolin modulates bradykinin-mediated PLD activation via suppression of PLC and PKC activities but did not affect S1P-mediated PLD activation.", "citation": { "type": "PubMed", "name": "J Biol Chem 1999 Sep 24 274(39) 27385-91", "id": "10488069" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf4" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Fig.1", "citation": { "type": "PubMed", "name": "Science 2004 Nov 26 306(5701) 1506-7", "id": "15567848" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf2" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Table 2 | The effects on mast-cell function of gene knockout or knockdown of key signalling molecules", "citation": { "type": "PubMed", "name": "Nat Rev Immunol 2006 Mar 6(3) 218-30", "id": "16470226" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf1" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Monitoring of the intracellular levels of inositol phosphates in c-Src-as1 SNF cells revealed a moderate (~30%), but statistically significant, decrease in PLC{gamma} activity when c-Src was inhibited", "citation": { "type": "PubMed", "name": "Mol Biol Cell 2005 Nov 16(11) 5418-32", "id": "16135530" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe1" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Phosphorylated Y1175 creates a binding site for phospholipase Cgamma1 (PLC-gamma1) and Shb. Activation of PLC-gamma1 and Shb regulates VEGF-A-dependent cell proliferation and cell migration, respectively.", "citation": { "type": "PubMed", "name": "J Atheroscler Thromb 2006 Jun 13(3) 130-5", "id": "16835467" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc3" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Exp Med 2004 Mar 15 199(6) 785-95", "id": "15007095" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc2" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Recently, we reported that phospholipase Cgamma1 (PLC-gamma1) binds to and regulates TRPC3 channels, components of agonist-induced Ca2+ entry into cells. ", "citation": { "type": "PubMed", "name": "Nature 2005 Mar 3 434(7029) 99-104", "id": "15744307" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc0" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Upon the stimulation of growth factors and hormones, PLC-gamma1 is rapidly phosphorylated at three known sites; Tyr771, Tyr783 and Tyr1254 and its enzymatic activity is up-regulated.", "citation": { "type": "PubMed", "name": "Cell Signal 2005 Oct 17(10) 1289-99", "id": "16038803" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb9a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "the activation of Erk and phospholipase C (PLC)-g observed following stimulation of HeLa cells with a low dose of EGF was accelerated and potentiated following co-treatment with a low dose of GA (Fig 2E).", "citation": { "type": "PubMed", "name": "EMBO Rep 2004 Dec 5(12) 1165-70", "id": "15568014" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb97" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Adhesion to fibronectin induced PLC-gamma1 tyrosine phosphorylation that was inhibited by a Src-kinase inhibitor", "citation": { "type": "PubMed", "name": "J Cell Sci 2005 Feb 1 118(Pt 3) 601-10", "id": "15657076" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb7d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Fibroblasts", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "In CHO cells expressing the wild-type B2 receptor, bradykinin-induced transient recruitment and activation of PLCgamma1.", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 2005 Jan 28 326(4) 894-900", "id": "15607753" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb71" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "NGF induces prolonged activation of the Shc/MAP kinase pathway and phospholipase Cgamma compared with PDGF-BB.", "citation": { "type": "PubMed", "name": "Arterioscler Thromb Vasc Biol 1999 Apr 19(4) 1041-50", "id": "10195934" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb6e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "Muscle, Smooth" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Phospholipase C gamma 1 (PLC gamma 1) and p21ras guanosine triphosphatase (GTPase) activating protein (GAP) bind to and are phosphorylated by activated growth factor receptors", "citation": { "type": "PubMed", "name": "Science 1990 Nov 16 250(4983) 979-82", "id": "2173144" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb4b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "some studies have reported activation of PLC-gamma in VEGFR1 expressing cells", "citation": { "type": "PubMed", "name": "Am J Physiol Cell Physiol 2001 Jun 280(6) C1375-86", "id": "11350732" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb2d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 1999 Nov 12 274(46) 33057-63", "id": "10551875" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb27" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "These findings suggest that protein kinase C phosphorylates PI-PLC, resulting in a decrease in PI-PLC activity", "citation": { "type": "PubMed", "name": "Biochim Biophys Acta 1994 Nov 10 1224(2) 302-10", "id": "7981246" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb24" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "Adipocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 1998 May 29 273(22) 13808-18", "id": "9593725" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb13" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Reconstitution of deficient mast cells with Vav1 restored normal tyrosine phosphorylation of PLCgamma1 and PLCgamma2 and calcium responses. Thus, Vav1 is essential to FcepsilonRI-mediated activation of PLCgamma and calcium mobilization in mast cells. ", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2001 Jun 21(11) 3763-74", "id": "11340169" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb11" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Figure 1 | The ErbB signalling network.", "citation": { "type": "PubMed", "name": "Nat Rev Mol Cell Biol 2001 Feb 2(2) 127-37", "id": "11252954" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae9" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "Phospholipase C-gamma1 (PLC-gamma1) is a lipase that hydrolyzes PIP2 to generate two second messengers, IP3 and DAG.", "citation": { "type": "PubMed", "name": "Mol Cells 1999 Dec 31 9(6) 631-7", "id": "10672930" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae8" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1) actsIn cat(p(HGNC:PLCG1))", "summary_text": "PLC? hydrolyzes phosphatidylinositol-4,5-biphosphate to produce inositol-1,4,5-triphosphate (IP3) and diacylglycerol (DAG). ", "citation": { "type": "PubMed", "name": "BMC Genomics 2009 10 233", "id": "19450280" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eadc" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "CD4+ T-cell", "tissue": "" } } ] } }, { "source": "p(HGNC:CXCR6)", "relation": "actsIn", "target": "cat(p(HGNC:CXCR6))", "directed": false, "label": "p(HGNC:CXCR6) actsIn cat(p(HGNC:CXCR6))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405148f", "evidences": [ { "bel_statement": "p(HGNC:CXCR6) actsIn cat(p(HGNC:CXCR6))", "summary_text": "Exposure of HASMC to CXCL16 increased NF-kappa B DNA binding activity, induced kappa B-driven luciferase activity, and up-regulated tumor necrosis factor-alpha expression in an NF-kappa B-dependent manner. However, treatment with pertussis toxin (G(i) inhibitor), wortmannin or LY294002 (phosphatidylinositol 3-kinase (PI3K inhibitors)), or Akt inhibitor or overexpression of dominant-negative (dn) PI3K gamma, dnPDK-1, kinase-dead (kd) Akt, kdIKK-beta, dnIKK-gamma, dnI kappa B-alpha, or dnI kappa B-beta significantly attenuated CXCL16-induced NF-kappa B activation.", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Jan 30 279(5) 3188-96", "id": "14625285" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec20" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR6) actsIn cat(p(HGNC:CXCR6))", "summary_text": "Chemokine receptors signal through Galphai2 proteins to activate DOCK2 (dedicator of cytokinesis 2) and other guanine nucleotide exchange factors (GEFs), leading to the activation of RAC1 and RHOA. ", "citation": { "type": "PubMed", "name": "Nat Rev Immunol 2009 Sep 9(9) 630-44", "id": "19696767" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec55" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "b-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCR6) actsIn cat(p(HGNC:CXCR6))", "summary_text": "Table 1. Lymphoid chemokine receptors", "citation": { "type": "PubMed", "name": "Trends Immunol 2004 Feb 25(2) 67-74", "id": "15102365" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead7" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "T-cell", "tissue": "" } } ] } }, { "source": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B)))", "relation": "increases", "target": "kin(p(HGNC:FYN))", "directed": false, "label": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B))) increases kin(p(HGNC:FYN))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051482", "evidences": [ { "bel_statement": "cat(complex(p(HGNC:CD8A),p(HGNC:CD8B))) increases kin(p(HGNC:FYN))", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec56" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:IL2RB)", "relation": "actsIn", "target": "cat(p(HGNC:IL2RB))", "directed": false, "label": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051471", "evidences": [ { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "Considering these points in the context of IL-2- and IL-15-dependent signaling under physiological conditions, the lack of a cytoplasmic domain for IL-2R alpha and trans-presentation of IL-15 by IL-15Ralpha to CD122 and gamma C results in a qualitative identical utilization of signaling pathways associated with CD122 and gamma C. High levels of IL-2Ralpha provide a mean for continual capture of IL-2 to sustain signaling whereas limiting IL-15Ralpha tempers engagement of CD122 and gamma C, limiting signal transduction. Thus, varied levels of IL-2Ralpha and IL-15Ralpha provide a simple, yet powerful, mechanism to quantify and tune signaling through common intermediates leading to distinctive biological responses.", "citation": { "type": "PubMed", "name": "J Immunol 2012 May 1 188(9) 4149-57", "id": "22447977" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebdc" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "Stat5 proteins activated by IL-2. The phosphorylated tyrosines on IL-2Rbeta can then serve as docking sites for signaling molecules that otherwise cannot associate with IL-2Rbeta, including the adaptor protein Shc, Stat5a, and Stat5b (Figure 2). For example, only phosphorylated (but not non-phosphorylated) peptides spanning either Tyr-392 or Tyr-510 of IL-2Rbeta can efficiently compete with IL-2-induced Stat5 DNA binding to a GAS motif IL-2-mediated hetero-dimerization of its receptor triggers a rapid increase in the recruitment of Jak3 and activation of both Jak1 and Jak3 (Johnston et al., 1994; Witthuhn et al., 1994). These kinases phosphorylate the receptor as well as each other, and activate other signaling molecules associated with the receptor. The phosphorylated tyrosines on IL-2Rbeta can then serve as docking sites for signaling molecules that otherwise cannot associate with IL-2Rbeta, including the adaptor protein Shc, Stat5a, and Stat5b", "citation": { "type": "PubMed", "name": "Oncogene 2000 May 15 19(21) 2566-76", "id": "10851055" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb54" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "We show that Gab2 was transiently phosphorylated by tyrosine in human mycosis fungoides (MF) tumor T cells upon IL-2 stimulation and that SHP2 as well as Stat5a associated inducibly with Gab2. IL-15, but not IL-4, also induced tyrosine phosphorylation of Gab2, suggesting that the IL-2 receptor beta-chain is important for IL-2-induced Gab2 phosphorylation. ", "citation": { "type": "PubMed", "name": "Exp Clin Immunogenet 2001 18(2) 86-95", "id": "11340297" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec57" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "the association of lyn with IL-2Rbeta was markedly elevated by IL-2 stimulation. Furthermore the activity of lyn kinase, evaluated by an in vitro kinase assay with enolase as a substrate, increased following IL-2 stimulation.", "citation": { "type": "PubMed", "name": "Immunobiology 2000 Nov 202(4) 363-82", "id": "11131153" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebcd" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "Moreover, the finding that at least a small proportion of the p53/56'yn kinase in F7 cells can be coimmunoprecipitated by using a mAb specific for the p75/IL-2Rj3 molecule preliminarily suggests that the lyn kinase may receive its activation signals directly from the IL-2R (Fig. 4).", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 1992 Apr 1 89(7) 2674-8", "id": "1557373" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb4" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "b-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "Stat5 proteins activated by IL-2. The phosphorylated tyrosines on IL-2Rbeta can then serve as docking sites for signaling molecules that otherwise cannot associate with IL-2Rbeta, including the adaptor protein Shc, Stat5a, and Stat5b (Figure 2). For example, only phosphorylated (but not non-phosphorylated) peptides spanning either Tyr-392 or Tyr-510 of IL-2Rbeta can efficiently compete with IL-2-induced Stat5 DNA binding to a GAS motif ", "citation": { "type": "PubMed", "name": "Oncogene 2000 May 15 19(21) 2566-76", "id": "10851055" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebac" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "Interleukin 15 (IL-15) is a 14-15 kDa polypeptide that belongs to the 4 alpha-helix-bundle family of cytokines and was originally discovered due to its T cell proliferative activity. It utilizes the signal-transducing beta/gamma polypeptides of the IL-2 receptor complex, thus sharing many biological activities with IL-2,", "citation": { "type": "PubMed", "name": "Arch Immunol Ther Exp (Warsz) 2000 48(6) 457-64", "id": "11197599" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb23" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": " TM-beta1 (anti-CD122) antibody blocks the interaction of trans-presented IL-15 by IL-15Ralpha with the CD122/CD132 signaling receptor complex on responsive NK, and CD8+T cell subsets. As shown in Fig. S2, the TM-beta1 antibody was very effective in inhibiting IL-15-induced proliferation of murine splenocytes", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2009 Sep 15 106(37) 15849-54", "id": "19805228" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec35" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "Lyn kinase phosphorylates both IL-5Ralpha and beta in vitro.", "citation": { "type": "PubMed", "name": "J Immunol 2002 Feb 15 168(4) 1978-83", "id": "11823534" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb6b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "In hematopoietic cell line BAF-B03 F7 cells, gene transfer mediated expression of the IL-2R beta c chain is sufficient to confer proliferation and cell survival responses to IL-2. In these IL-2R beta c-expressing cells, BAG-1 mRNA was dramatically induced by IL-2. The IL-2-mediated induction of BAG-1 expression required the activation of tyrosine kinase(s) and was sensitive to rapamycin as the induction of bcl-2 expression was.", "citation": { "type": "PubMed", "name": "Blood 1996 Dec 1 88(11) 4118-23", "id": "8943845" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf6" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IL2RB) actsIn cat(p(HGNC:IL2RB))", "summary_text": "These proteins have been implicated in immune regulation, apoptosis, activation-induced cell death, and control of autoimmunity.", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Mar 19 279(12) 11553-61", "id": "14701862" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae7" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:PLCG1,pmod(P,Y))", "relation": "directlyIncreases", "target": "cat(p(HGNC:PLCG1))", "directed": false, "label": "p(HGNC:PLCG1,pmod(P,Y)) directlyIncreases cat(p(HGNC:PLCG1))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405148a", "evidences": [ { "bel_statement": "p(HGNC:PLCG1,pmod(P,Y)) directlyIncreases cat(p(HGNC:PLCG1))", "summary_text": "CD38 dimerization induced tyrosine phosphorylation of the protein kinase syk and increased syk kinase activity. CD38 dimerization also induced tyrosine phosphorylation of phospholipase C-gamma and of the p85 subunit of phosphatidylinositol 3-kinase (PI 3-K)", "citation": { "type": "PubMed", "name": "J Immunol 1996 Jan 1 156(1) 100-7", "id": "8598449" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec5d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1,pmod(P,Y)) directlyIncreases cat(p(HGNC:PLCG1))", "summary_text": "SLP-76 is an adapter protein required for T-cell receptor (TCR) signaling. In particular, TCR-induced tyrosine phosphorylation and activation of phospholipase C-gamma1 (PLC-gamma1), and the resultant TCR-inducible gene expression, depend on SLP-76.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2001 Jul 21(13) 4208-18", "id": "11390650" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebdf" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1,pmod(P,Y)) directlyIncreases cat(p(HGNC:PLCG1))", "summary_text": "dual phosphorylation by ZAP70 and Itk triggers the activation of PLCg1", "citation": { "type": "PubMed", "name": "Mol Immunol 2002 Jun 38(15) 1087-99", "id": "12044776" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb58" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:PLCG1,pmod(P,Y)) directlyIncreases cat(p(HGNC:PLCG1))", "summary_text": "Based on these results, we propose that CD148 negatively regulates TCR signaling by interfering with the phosphorylation and function of PLCgamma1 and LAT.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2001 Apr 21(7) 2393-403", "id": "11259588" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb14" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:IL15RA)", "relation": "actsIn", "target": "cat(p(HGNC:IL15RA))", "directed": false, "label": "p(HGNC:IL15RA) actsIn cat(p(HGNC:IL15RA))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051484", "evidences": [ { "bel_statement": "p(HGNC:IL15RA) actsIn cat(p(HGNC:IL15RA))", "summary_text": "The re-establishment of the interaction of IL-15 with the IL-15Ralpha by incubating IL-15(-/-) DC with IL-15 completely restored the capacity to prime T cells for DTH induction in vivo. Moreover, IL-15 also enhanced secretion of pro-inflammatory cytokines by DC and triggered in vitro CD8(+) T cell proliferation and IL-2 release. Taken together, the data suggest that an autocrine IL-15/IL-15Ralpha signaling loop in DC is essential for inducing CD8(+)-dependent Th1 immune responses in mice", "citation": { "type": "PubMed", "name": "Eur J Immunol 2003 Dec 33(12) 3493-503", "id": "14635060" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec64" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "dendritic cell", "tissue": "" } }, { "bel_statement": "p(HGNC:IL15RA) actsIn cat(p(HGNC:IL15RA))", "summary_text": " Our current study provided 2 pieces of evidence that IL-15RA presented IL-15 in trans to memory CD8 T cells in vivo. First, we demonstrated that IL-15RA expression by opposing BM-derived cells was required for long-term basal proliferation of memory CD8 T cells. Second, proliferation of memory CD8 T cells in response to soluble IL-15 required IL-15RA expression by the host cells and IL-15RB expression by the responding CD8 T cells. ", "citation": { "type": "PubMed", "name": "Blood 2004 Feb 1 103(3) 988-94", "id": "14512307" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebdb" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:IL15RA) actsIn cat(p(HGNC:IL15RA))", "summary_text": "In addition, IL-15 was found to induce tyrosine phosphorylation of Syk that was largely inhibited by pretreating cells with piceatannol. Moreover, we found that Syk kinase is physically associated with IL-15Ralpha", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2004 Jul 76(1) 162-8", "id": "15123770" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec61" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IL15RA) actsIn cat(p(HGNC:IL15RA))", "summary_text": "Lyn kinase phosphorylates both IL-5Ralpha and beta in vitro.", "citation": { "type": "PubMed", "name": "J Immunol 2002 Feb 15 168(4) 1978-83", "id": "11823534" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec5f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IL15RA) actsIn cat(p(HGNC:IL15RA))", "summary_text": "IL-15 is a common gamma chain cytokine sharing overlapping signaling and biological properties with IL-2 as a result of their mutual usage of the IL-2/15b and common gamma chain (gc) receptor subunits (7,8).", "citation": { "type": "PubMed", "name": "J Immunol 2011 Jan 1 186(1) 174-82", "id": "21098221" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead8" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "CD8+ T-cell", "tissue": "" } } ] } }, { "source": "p(HGNC:CXCL9)", "relation": "directlyIncreases", "target": "cat(p(HGNC:CXCR3))", "directed": false, "label": "p(HGNC:CXCL9) directlyIncreases cat(p(HGNC:CXCR3))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051477", "evidences": [ { "bel_statement": "p(HGNC:CXCL9) directlyIncreases cat(p(HGNC:CXCR3))", "summary_text": "By using a CXCR3 ligand reporter mouse, we found that stromal cells predominately expressed the chemokine ligand CXCL9 whereas hematopoietic cells expressed CXCL10 in lymph nodes (LNs). Dendritic cell (DC)-derived CXCL10 facilitated T cell-DC interactions in LNs during T cell priming while both chemokines guided intranodal positioning of CD4(+) T cells to interfollicular and medullary zones. Thus, different chemokines acting on the same receptor can function locally to facilitate DC-T cell interactions and globally to influence intranodal positioning, and both functions contribute to Th1 cell differentiation.", "citation": { "type": "PubMed", "name": "Immunity 2012 Dec 14 37(6) 1091-103", "id": "23123063" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec66" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "dendritic cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCL9) directlyIncreases cat(p(HGNC:CXCR3))", "summary_text": " T cells in peripheral airways of COPD patients show increased expression of CXCR3, a receptor activated by interferon-{gamma} inducible protein of 10 kDa (IP-10; CXCL10), monokine induced by interferon-{gamma} (Mig; CXCL9), and interferon-inducible T cell-{alpha} chemoattractant (I-TAC; CXCL11). All three cytokines activate CXCR3, although CXCL11 has the highest affinity.", "citation": { "type": "PubMed", "name": "Pharmacol Rev 2004 Dec 56(4) 515-48", "id": "15602009" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec58" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCL9) directlyIncreases cat(p(HGNC:CXCR3))", "summary_text": "Numerous studies have shown that immature human and mouse blood- and bone marrow-derived DC subsets express a panel of inflammatory chemokine receptors (CCR1-6,8,9, CXCR3,4, CX3CR1) [Table 1 and reviewed in (1-5)]. [Table 1 Chemokine receptors expressed by DC and the functional outcome of receptor ligation}]", "citation": { "type": "PubMed", "name": "Clin Lab Med 2008 Sep 28(3) 375-84, v", "id": "19028258" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb6" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "dendritic cell", "tissue": "" } } ] } }, { "source": "p(HGNC:CCR5)", "relation": "actsIn", "target": "cat(p(HGNC:CCR5))", "directed": false, "label": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051496", "evidences": [ { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "HIV envelope binds to and signals through its primary cellular receptor, CD4, and through a coreceptor, either CC chemokine receptor 5 (CCR5) or CXC chemokine receptor 4 (CXCR4). Here, we evaluate the response of peripheral blood mononuclear cells to a panel of genetically diverse R5 and X4 envelope proteins. Modulation of gene expression was evaluated by using oligonucleotide microarrays. Activation of transcription factors was evaluated by using an array of oligonucleotides encoding transcription factor binding sites. Responses were strongly influenced by coreceptor specificity. Treatment of cells from CCR5delta32 homozygous donors with glycoprotein (gp)120 derived from an R5 virus demonstrated that the majority of responses elicited by R5 envelopes required engagement of CCR5. R5 envelopes, to a greater extent than X4 envelopes, induced the expression of genes belonging to mitogen-activated protein kinase signal transduction pathways and genes regulating the cell cycle. A number of genes induced by R5, but not X4, envelopes were also up-regulated in the resting CD4+ T cell population of HIV-infected individuals. These results suggest that R5 envelope facilitates replication of HIV in the pool of resting CD4+ T cells. Additionally, signaling by R5 gp120 may facilitate the transmission of R5 viruses by inducing a permissive environment for HIV replication.", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2006 Mar 7 103(10) 3746-51", "id": "16505369" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec65" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "peripheral blood mononuclear cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "RANTES treatment of PM1 T cells results in the rapid phosphorylation-activation of CCR5, Jak2, and Jak3. RANTES-inducible Jak phosphorylation is insensitive to pertussis toxin inhibition, indicating that RANTES-CCR5-mediated tyrosine phosphorylation events are not coupled directly to Galpha(i) protein-mediated events. In addition to Jaks, several other proteins are rapidly phosphorylated on tyrosine residues in a RANTES-dependent manner, including the Src kinase p56(lck), which associates with Jak3", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Apr 6 276(14) 11427-31", "id": "11278738" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec38" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "In vivo, different chemokines orchestrate the recruitment of DCs into the lung depending on the inflammatory stimulus present. In a rat model of inhaled heat-killed Moraxella catarrhalis, the phenomenon seemed dependent on the expression of CCR1 and CCR5, which are receptors for the chemokines CCL5 (regulated on activation, normal T-cell expressed and secreted [RANTES]) and/or CCL3 (macrophage inflammatory protein [MIP]-1{alpha}) produced at the airway level ", "citation": { "type": "PubMed", "name": "Am J Respir Crit Care Med 2005 Sep 1 172(5) 530-51", "id": "15879415" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb20" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "dendritic cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Anti-CCR1, anti-CCR5, or BX471 also inhibited the upregulation of beta1 integrin mRNA in myeloma cells induced by MIP-1alpha, as well as the adherence of myeloma cells to stromal cells and IL-6 production by stromal cells in response to myeloma cells. ", "citation": { "type": "PubMed", "name": "Exp Hematol 2005 Mar 33(3) 272-8", "id": "15730850" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec67" }, "experiment_context": { "species_common_name": "Human", "disease": "Multiple Myeloma", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "CCL3- and CCL4-triggered migration of GM-CSF-primed neutrophils was inhibited by the CCR5 antagonist TAK-779. Accordingly, freshly isolated neutrophils express CCR5. Extracellular signal-regulated kinases (ERK)-1/2 and p38 mitogen-activated protein kinase (MAPK) inhibitors blocked CCL3-induced migration of GM-CSF-primed neutrophils.", "citation": { "type": "PubMed", "name": "Cell Signal 2005 Mar 17(3) 355-63", "id": "15567066" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec51" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Neutrophils", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Numerous studies have shown that immature human and mouse blood- and bone marrow-derived DC subsets express a panel of inflammatory chemokine receptors (CCR1-6,8,9, CXCR3,4, CX3CR1) [Table 1 and reviewed in (1-5)]. [Table 1 Chemokine receptors expressed by DC and the functional outcome of receptor ligation}]", "citation": { "type": "PubMed", "name": "Clin Lab Med 2008 Sep 28(3) 375-84, v", "id": "19028258" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec50" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "dendritic cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Syk was also activated upon MIP1beta stimulation of CCR5 L1.2 transfectants or T-cells and associated with RAFTK. Overexpression of a dominant-negative Src-binding mutant of RAFTK (RAFTK(m402)) significantly attenuated Syk activation, whereas overexpression of wild-type RAFTK enhanced Syk activity, indicating that RAFTK acts upstream of CCR5-mediated Syk activation.", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Jun 9 275(23) 17263-8", "id": "10747947" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec48" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "The CCR5 chemokine receptor is a member of the G protein-coupled receptor (GPCR) family that is expressed by macrophages, memory T-lymphocytes and dendritic cells and is activated by chemotactic proteins (e.g. MIP-1alpha [CCL3], MIP-1beta [CCL4] and RANTES [CCL5])", "citation": { "type": "PubMed", "name": "Br J Pharmacol 2008 Apr 153(7) 1513-27", "id": "18223665" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb49" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Expression of beta-arrestin2 also augmented chemokine receptor CCR5-mediated but not epidermal growth factor receptor-mediated chemotaxis, indicating the specific effect of beta-arrestin2", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Dec 20 277(51) 49212-9", "id": "12370187" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec43" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "CCR1, CCR2 and CCR5 mediate recruitment of both infiltrating macrophages", "citation": { "type": "PubMed", "name": "J Neuroinflammation 2007 4 14", "id": "17484785" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec1d" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Eotaxin-3/CCL26 is an agonist for chemokine receptor 3 (CCR3) and a natural antagonist for CCR1, CCR2 and CCR5. ", "citation": { "type": "PubMed", "name": "Immunology 2010 May 130(1) 74-82", "id": "20059579" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec14" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Monocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "In addition, CXCL12 selectively activates STAT 5 whereas CCL5 activates STAT 1.", "citation": { "type": "PubMed", "name": "Stem Cells 2005 Oct 27", "id": "16253981" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec09" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Table 1. Lymphoid chemokine receptors", "citation": { "type": "PubMed", "name": "Trends Immunol 2004 Feb 25(2) 67-74", "id": "15102365" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc1" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Th1 cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "In addition, CXCL12 selectively activates STAT 5 whereas CCL5 activates STAT 1.", "citation": { "type": "PubMed", "name": "Stem Cells 2005 Oct 27", "id": "16253981" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb6c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "mesenchyme" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Additional studies demonstrate that isolated CCR5 activation by R5 Env leads to both de novo expression of FasL and induction of susceptibility to Fas-mediated apoptosis in resting primary CD4 T cells", "citation": { "type": "PubMed", "name": "AIDS 2002 Jul 26 16(11) 1467-78", "id": "12131184" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb4c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "these in vivo data demonstrate that Ccr1, Ccr2, and Ccr5 mediate the postischemic recruitment of neutrophils through effects on intravascular adherence and subsequent transmigration.", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2006 Jan 79(1) 114-22", "id": "16275892" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb43" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Taken together, these results showed that MIP- 1b released from ET-1–treated PBMs was biologically functional in mediating chemotaxis of THP-1 cells via the CCR5 receptor.", "citation": { "type": "PubMed", "name": "J Immunol 2010 Oct 15", "id": "20952681" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb26" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Monocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Chemokine receptors signal through Galphai2 proteins to activate DOCK2 (dedicator of cytokinesis 2) and other guanine nucleotide exchange factors (GEFs), leading to the activation of RAC1 and RHOA. ", "citation": { "type": "PubMed", "name": "Nat Rev Immunol 2009 Sep 9(9) 630-44", "id": "19696767" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb1a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "b-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Previous studies have shown that CCL16 is a low-affinity ligand for CCR1, CCR2, CCR5, and CCR8 and attracts monocytes and T cells. ", "citation": { "type": "PubMed", "name": "J Immunol 2004 Aug 1 173(3) 2078-83", "id": "15265943" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb09" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "This study establishes CCR5 as a critical receptor guiding NK cell trafficking in host defense", "citation": { "type": "PubMed", "name": "PLoS Pathog 2006 Jun 2(6) e49", "id": "16789839" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf3" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CCR5) actsIn cat(p(HGNC:CCR5))", "summary_text": "Although the mechanisms of DC precursor recruitment to the lung mucosa are incompletely defined, CCR1 and CCR5 have been implicated under both homeostatic and pathogen-induced conditions", "citation": { "type": "PubMed", "name": "Cell Res 2010 Aug 20(8) 872-85", "id": "20603644" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaed" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "dendritic cell", "tissue": "" } } ] } }, { "source": "p(HGNC:ZAP70)", "relation": "actsIn", "target": "kin(p(HGNC:ZAP70))", "directed": false, "label": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405149a", "evidences": [ { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "[From Introduction] Here we report that LMPTP, like CD45, plays a positive role in TCR signaling by preferentially dephosphorylating a negative regulatory site, namely Tyr-292 of ZAP-70. This leads to a severalfold increase in the tyrosine phosphorylation of the kinase at its positive regulatory sites and enhanced kinase activity. [From abstract] Expression of low levels of LMPTP resulted in increased ZAP-70 phosphorylation, presumably at the activating Tyr-493 and other sites, increased kinase activity, and augmented downstream signaling to the mitogen-activated protein kinase pathway.", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Jul 5 277(27) 24220-4", "id": "11976341" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebea" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "Simultaneous overexpression of selenophosphate synthetase and phospholipid-hydroperoxide GSH peroxidase (PHGPx) [250] blocks activation of NF-kB by IL-1. Overexpression of SOD [84] or GSH peroxidase [81, 211] abolished NF-kB activation by preventing degradation of IkB after stimulation with TNF-a. The precise mechanism(s) through which oxidants and reductants influence activation of NF-kB is presently unknown; however, there is evidence that antioxidant enzyme (AOE372), a redox-sensitive thioredoxin peroxidase, regulates IkB phosphorylation [246]. Phosphatases The phosphatases are an important component of most signal transduction pathways, because failure to reverse kinase actions can disrupt normal cellular functions. For example, transfection of human fibroblasts with constitutively active ras (hRasV12) inhibits cell growth and ultimately results in a senescentlike phenotype [441]. Similarly, constitutive ERK activation has an inhibitory effect on cell cycle progression [442,443]. Both the serine/threonine phosphatases and the PTPs are known to be redox-sensitive [82,144,153,156,271,281, 444-449]. The mechanism of redox effects on activity is probably best understood for the PTPs. Without exception, the PTPs contain a highly conserved region of 11 amino acid residues in their catalytic domain; specifi- cally, (Ile/Val)-His-Cys-X-Ala-Gly-X-X-Arg-(Ser/Thr)- Gly, where X is a nonconserved amino acid [17]. Either oxidation or mutation of the cysteine renders these molecules inactive [17,281]. H2O2 is a potent inhibitor of PTPs. As in the case of other oxidants, H2O2 probably oxidizes the thiolate anion at the catalytic site [280]. Because formation of a phosphorylcysteine intermediate seems to be critical to PTP activity [450-452], blocking it through oxidation of the cysteine inactivates the molecules. In many cases, treatment of cells with H2O2 stimulates increases in protein phosphorylation by inhibiting phosphatase-catalyzed removal of phosphate groups. Furthermore, mitogens that increase cellular ox- idant production may stimulate phosphorylation indirectly by decreasing phosphatase activity. Additional mechanisms are involved in stimulation of pathways activated by growth factors that increase oxidant production, however, because there are known instances in which the oxidants they produce have no effect on protein phosphorylation. For example, TGF-b1 stimulates phosphorylation of numerous proteins and has been shown to cause a large increase in H2O2 production; however, its effects on protein phosphorylation are not blocked by catalase [453]. Furthermore, H2O2 is effective in promoting phosphorylation of phospholipase D, the PDGF receptor, and PKC-a even after pretreatment of Swiss 3T3 fibroblasts with orthovanadate to inhibit phosphatases [454]. Thus, although diminished phosphatase activity may partially account for increased phosphorylation in some cases, it cannot totally account for oxidation effects on phosphorylation in every case. SPECIFICITY In general, there is good agreement between studies on redox effects on any given gene; albeit, not all oxidizing or reducing treatments exert equivalent effects. This is clearly demonstrated in studies of pag , which encodes a protein associated with cellular proliferation. Pag protein inhibits the tyrosine kinase activity of the Abelson (abl ) protein by binding to its SH3-binding domain [455]. BSO, menadione, sodium arsenate, and diethyl maleate all stimulate pag expression, but H2O2 does not [269]. Conversely, H2O2 stimulates c-fos expression (Table 1), although 4-hydroynonenal (a product of v-6-polyunsaturated fatty acid peroxidation) not only fails to induce c-fos expression but is actually inhibitory to c-fos induction by EGF and PDGF [185]. Similarly, some oxidants such as diamide decrease hypoxia-induced signals [201], although others such as H2O2 increase them [124]. As might be expected, the effects of any stimu...", "citation": { "type": "PubMed", "name": "Free Radic Biol Med 2000 Feb 1 28(3) 463-99", "id": "10699758" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb8d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "Simultaneous overexpression of selenophosphate synthetase and phospholipid-hydroperoxide GSH peroxidase (PHGPx) [250] blocks activation of NF-kB by IL-1. Overexpression of SOD [84] or GSH peroxidase [81, 211] abolished NF-kB activation by preventing degradation of IkB after stimulation with TNF-a. The precise mechanism(s) through which oxidants and reductants influence activation of NF-kB is presently unknown; however, there is evidence that antioxidant enzyme (AOE372), a redox-sensitive thioredoxin peroxidase, regulates IkB phosphorylation [246]. Phosphatases The phosphatases are an important component of most signal transduction pathways, because failure to reverse kinase actions can disrupt normal cellular functions. For example, transfection of human fibroblasts with constitutively active ras (hRasV12) inhibits cell growth and ultimately results in a senescentlike phenotype [441]. Similarly, constitutive ERK activation has an inhibitory effect on cell cycle progression [442,443]. Both the serine/threonine phosphatases and the PTPs are known to be redox-sensitive [82,144,153,156,271,281, 444-449]. The mechanism of redox effects on activity is probably best understood for the PTPs. Without exception, the PTPs contain a highly conserved region of 11 amino acid residues in their catalytic domain; specifi- cally, (Ile/Val)-His-Cys-X-Ala-Gly-X-X-Arg-(Ser/Thr)- Gly, where X is a nonconserved amino acid [17]. Either oxidation or mutation of the cysteine renders these molecules inactive [17,281]. H2O2 is a potent inhibitor of PTPs. As in the case of other oxidants, H2O2 probably oxidizes the thiolate anion at the catalytic site [280]. Because formation of a phosphorylcysteine intermediate seems to be critical to PTP activity [450-452], blocking it through oxidation of the cysteine inactivates the molecules. In many cases, treatment of cells with H2O2 stimulates increases in protein phosphorylation by inhibiting phosphatase-catalyzed removal of phosphate groups. Furthermore, mitogens that increase cellular ox- idant production may stimulate phosphorylation indirectly by decreasing phosphatase activity. Additional mechanisms are involved in stimulation of pathways activated by growth factors that increase oxidant production, however, because there are known instances in which the oxidants they produce have no effect on protein phosphorylation. For example, TGF-b1 stimulates phosphorylation of numerous proteins and has been shown to cause a large increase in H2O2 production; however, its effects on protein phosphorylation are not blocked by catalase [453]. Furthermore, H2O2 is effective in promoting phosphorylation of phospholipase D, the PDGF receptor, and PKC-a even after pretreatment of Swiss 3T3 fibroblasts with orthovanadate to inhibit phosphatases [454]. Thus, although diminished phosphatase activity may partially account for increased phosphorylation in some cases, it cannot totally account for oxidation effects on phosphorylation in every case. SPECIFICITY In general, there is good agreement between studies on redox effects on any given gene; albeit, not all oxidizing or reducing treatments exert equivalent effects. This is clearly demonstrated in studies of pag , which encodes a protein associated with cellular proliferation. Pag protein inhibits the tyrosine kinase activity of the Abelson (abl ) protein by binding to its SH3-binding domain [455]. BSO, menadione, sodium arsenate, and diethyl maleate all stimulate pag expression, but H2O2 does not [269]. Conversely, H2O2 stimulates c-fos expression (Table 1), although 4-hydroynonenal (a product of v-6-polyunsaturated fatty acid peroxidation) not only fails to induce c-fos expression but is actually inhibitory to c-fos induction by EGF and PDGF [185]. Similarly, some oxidants such as diamide decrease hypoxia-induced signals [201], although others such as H2O2 increase them [124]. As might be expected, the effects of any stimu...", "citation": { "type": "PubMed", "name": "Free Radic Biol Med 2000 Feb 1 28(3) 463-99", "id": "10699758" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead4" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "The proto-oncogene product Cbl has emerged as a negative regulator of a number of protein-tyrosine kinases, including the ZAP-70/Syk tyrosine kinases that are critical for signaling in hematopoietic cells.", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Jan 7 275(1) 414-22", "id": "10617633" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf5" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "We show that Crry increases early TCR-dependent activation signals, including p56lck-, zeta-associated protein-70 (ZAP-70), Vav-1, Akt, and extracellular signal-regulated kinase (ERK) phosphorylation but also costimulation-dependent mitogen-activated protein kinases (MAPK), such as the stress-activated c-Jun N-terminal kinase (JNK). It is intriguing that Crry costimulus enhanced p38 MAPK activation in T helper cell type 1 (Th1) but not in Th2 cells. ", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2005 Dec 78(6) 1386-96", "id": "16301324" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd8" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "Engagement of the T-cell receptor (TCR) triggers a series of signaling events that lead to the activation of T cells. HIP-55 (SH3P7 or mAbp1), an actin-binding adaptor protein, interacts with and is tyrosine phosphorylated by ZAP-70, which is a crucial proximal protein tyrosine kinase for TCR signaling.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2005 Aug 25(16) 6869-78", "id": "16055701" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebbc" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "SHP-1 was found to bind to the protein tyrosine kinase ZAP-70. This interaction resulted in an increase in SHP-1 phosphatase activity and a decrease in ZAP-70 kinase activity. ", "citation": { "type": "PubMed", "name": "Science 1996 May 24 272(5265) 1173-6", "id": "8638162" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebab" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "We show that Crry increases early TCR-dependent activation signals, including p56lck-, zeta-associated protein-70 (ZAP-70), Vav-1, Akt, and extracellular signal-regulated kinase (ERK) phosphorylation but also costimulation-dependent mitogen-activated protein kinases (MAPK), such as the stress-activated c-Jun N-terminal kinase (JNK). It is intriguing that Crry costimulus enhanced p38 MAPK activation in T helper cell type 1 (Th1) but not in Th2 cells. ", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2005 Dec 78(6) 1386-96", "id": "16301324" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba7" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "Here we show that a spontaneous activating point mutation of the gene encoding an SH2 domain of ZAP-70 (tryptophan-to-cysteine substitution at residue 163 (W163C) ), a key signal transduction molecule in T cells, causes chronic autoimmune arthritis in mice that resembles human rheumatoid arthritis in many aspects.", "citation": { "type": "PubMed", "name": "Nature 2003 Nov 27 426(6965) 454-60", "id": "14647385" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb63" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "Linker for activation of T cells (LAT) is an adaptor protein whose tyrosine phosphorylation is critical for transduction of the T cell receptor (TCR) signal. LAT phosphorylation is accomplished by the protein tyrosine kinase ZAP-70, but it is not at all clear how LAT (which is not associated with the TCR) encounters ZAP-70 (which is bound to the TCR).", "citation": { "type": "PubMed", "name": "J Exp Med 1999 Nov 15 190(10) 1517-26", "id": "10562325" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb34" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "8892604;9047237;15388330;12817019", "citation": { "type": "PubMed", "name": "BMC Bioinformatics 2004 Jun 22 5 79", "id": "15212693" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec6b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "We report here that VHR, a Vaccinia virus VH1-related dual-specific protein phosphatase that inactivates the mitogen-activated kinases Erk2 and Jnk, is phosphorylated at Y138 by ZAP-70.", "citation": { "type": "PubMed", "name": "Nat Immunol 2003 Jan 4(1) 44-8", "id": "12447358" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec60" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Nat Immunol 2005 Apr 6(4) 390-5", "id": "15735648" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec49" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "ZAP-70 phosphorylated HIP-55 at Tyr-334 and Tyr-344 in vitro and in vivo,", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Dec 26 278(52) 52195-202", "id": "14557276" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf9" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "Zap-70 efficiently phosphorylates LAT on tyrosine residues at positions 226, 191, 171, 132 and 127", "citation": { "type": "PubMed", "name": "Biochem J 2001 Jun 1 356(Pt 2) 461-71", "id": "11368773" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebcc" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "natural killer cell", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Nov 30 276(48) 45175-83", "id": "11572860" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb1" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Immunol 1999 Jul 15 163(2) 844-53", "id": "10395678" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb84" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "10811803;11368773", "citation": { "type": "PubMed", "name": "BMC Bioinformatics 2004 Jun 22 5 79", "id": "15212693" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb3b" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "In this report, we show that during TCR signaling, the tyrosines Y239, Y240 and Y317 of Shc are the primary sites of tyrosine phosphorylation.", "citation": { "type": "PubMed", "name": "Eur J Immunol 1998 Aug 28(8) 2265-75", "id": "9710204" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb1f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 1997 Jun 6 272(23) 14562-70", "id": "9169414" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb06" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "In heterogeneous COS-1 cells, Cbl-b was phosphorylated on tyrosine residues by both Syk- (Syk/Zap-70) and Src- (Fyn/Lck) family kinases, with Syk kinase inducing the most prominent effect.", "citation": { "type": "PubMed", "name": "Oncogene 1999 Feb 4 18(5) 1147-56", "id": "10022120" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaec" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Immunol 1996 Nov 1 157(9) 3769-73", "id": "8892604" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaeb" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Nat Immunol 2003 Jan 4(1) 44-8", "id": "12447358" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae2" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 1994 Nov 25 269(47) 29520-9", "id": "7961936" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae0" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:ZAP70) actsIn kin(p(HGNC:ZAP70))", "summary_text": "dual phosphorylation by ZAP70 and Itk triggers the activation of PLCg1", "citation": { "type": "PubMed", "name": "Mol Immunol 2002 Jun 38(15) 1087-99", "id": "12044776" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eadd" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:IDO1)", "relation": "increases", "target": "bp(GO:\"T cell activation\")", "directed": false, "label": "p(HGNC:IDO1) increases bp(GO:\"T cell activation\")", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405149c", "evidences": [ { "bel_statement": "p(HGNC:IDO1) increases bp(GO:\"T cell activation\")", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec72" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:CD28)", "relation": "actsIn", "target": "cat(p(HGNC:CD28))", "directed": false, "label": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051475", "evidences": [ { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "To analyze whether SARA and Hgs mRNA expression could be regulated by TCR-mediated signals, expression of resting or 1, 3, and 5 days with allergen or anti-CD3/CD28 mAbs-activated CD4+ T cells was analyzed. SARA and Hgs mRNA were detected in resting T cells, but their expression was reduced 1 day after the CD3/CD28 activation. The reduction was even more pronounced on days 3 and 5 of stimulation (Fig. 3 A, B). As reported (26) , we found a down-regulation of TGF-bRII transcripts after CD3/CD28 activation. TGF-bRI expression was unchanged under these conditions, whereas IL-10 expression used as a positive control was enhanced.", "citation": { "type": "PubMed", "name": "FASEB J 2003 Feb 17(2) 194-202", "id": "12554698" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec24" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "CD28 does not stimulate all signaling effectors that are activated by the TCR, but primarily provides a potent synergistic signal for transcription factors such as nuclear factor-?B (NF-?B), nuclear factor of activated T cells (NFAT), and activator protein-1 (AP1) [24-26]. ", "citation": { "type": "PubMed", "name": "Arthritis Res Ther 2008 10 Suppl 1 S3", "id": "19007423" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec73" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "naive T-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "Entrez Gene:Human: The B-lymphocyte activation antigen B7-1 (formerly referred to as B7) provides regulatory signals for T lymphocytes as a consequence of binding to the CD28 (MIM 186760) and CTLA4 (MIM 123890) ligands of T cells.[supplied by OMIM]", "citation": { "type": "PubMed", "name": "Chem Biol 2004 Dec 11(12) 1651-8", "id": "15610849" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec12" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "Kinetic studies reveal that an early phase (1 to 5 min) of IKK activation following TCR/CD28 cross-linking is PKCalpha dependent and that a later phase (5 to 25 min) of IKK activation is PKCtheta dependent", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2003 Oct 23(19) 7068-81", "id": "12972622" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb1c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "Jurkat transfectants overexpressing Chat-H show a marked increase in interleukin-2 production after costimulation of T cell receptor and CD28. The degree of JNK activation is enhanced substantially in the Chat-H transfectants upon costimulation. WE found that Chat-H forms a complex with Pyk2H and enhances its tyrosine 402 phosphorylation, an up-regulator of the JNK pathway. The Src homology-2 domain mutant of Chat-H loses this signal modulating activity.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Feb 21 278(8) 6012-7", "id": "12486027" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb07" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Immunol 2001 Jan 1 166(1) 197-206", "id": "11123293" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec6a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "EMBO J 2003 Sep 15 22(18) 4689-98", "id": "12970181" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec15" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "CTLA-4 delivers signals that inhibit selection, indicating that CTLA-4 and CD28 have opposing functions in thymic development.", "citation": { "type": "PubMed", "name": "J Immunol 2003 Jun 1 170(11) 5421-8", "id": "12759417" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb88" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Sep 12 278(37) 35812-8", "id": "12842899" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eafe" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CD28) actsIn cat(p(HGNC:CD28))", "summary_text": "CTLA-4-deficient mice develop a lethal autoimmune lymphoproliferative disorder that is strictly dependent on in vivo CD28 costimulation", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2007 Aug 21 104(34) 13756-61", "id": "17702861" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead5" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "T-cell", "tissue": "" } } ] } }, { "source": "p(HGNC:FYN)", "relation": "actsIn", "target": "kin(p(HGNC:FYN))", "directed": false, "label": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405147c", "evidences": [ { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "TNF-alpha activated multiple PTKs, including src family PTKs.To identify which src family kinase(s) was required for TNF-alpha-induced vascular permeability, small interfering RNA (siRNA) targeting each of the three src family PTKs expressed in human EC, c-src, fyn, and yes, were introduced into the barrier function assay. Only fyn siRNA protected against the TNF-alpha effect, whereas the c-src and yes siRNAs did not. ", "citation": { "type": "PubMed", "name": "Am J Physiol Lung Cell Mol Physiol 2006 Dec 291(6) L1232-45", "id": "16891393" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec68" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "Endothelium, Vascular" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Using Fyn as bait, p190 RhoGAP was isolated in the screen of an oligodendrocyte cDNA library. Coimmunoprecipitation and in vitro binding assays verified that p190 RhoGAP bound to the Fyn SH2 domain. These findings define a pathway in which Fyn activity regulates the phosphorylation of p190, leading to an increase in RhoGAP activity with a subsequent increase in RhoGDP, which in turn, regulates the morphological changes that accompany oligodendrocyte differentiation. ", "citation": { "type": "PubMed", "name": "J Neurobiol 2001 Oct 49(1) 62-78", "id": "11536198" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec3a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "oligodendrocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In this study, we provide evidence that the VEGF-dependent tyrosine phosphorylation of caveolin-1 induces interaction of the protein with the membrane-type 1 matrix metalloproteinase (MT1-MMP). This interaction requires the phosphorylation of caveolin-1 on tyrosine 14 by members of the Src family of protein kinases, such as Src and Fyn, because it is completely abolished by expression of a catalytically inactive Src mutant or by site-directed mutagenesis of tyrosine 14 of caveolin-1. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Dec 10 279(50) 52132-40", "id": "15466865" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec18" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Following the anti-PrPc antibody-mediated stimulation of live GN11 cells, we observed that PrPc clustered on plasma membrane domains rich in Cav-1 in which Fyn kinase converged to be activated. After these events, a signaling cascade through p42/44 MAP kinase (Erk 1/2) was triggered, suggesting that following translocations from rafts to caveolae or caveolae-like domains PrPc could interact with Cav-1 and induce signal transduction events.", "citation": { "type": "PubMed", "name": "J Biomed Biotechnol 2006 2006(5) 69469", "id": "17489019" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebff" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "neuron", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Caveolin-1 functions as a membrane adaptor to link the integrin alpha subunit to the tyrosine kinase Fyn. Upon integrin ligation, Fyn is activated and binds, via its SH3 domain, to Shc. Shc is subsequently phosphorylated at tyrosine 317 and recruits Grb2. This sequence of events is necessary to couple integrins to the Ras-ERK pathway and promote cell cycle progression. These findings reveal an unexpected function of caveolin-1 and Fyn in integrin signaling and anchorage-dependent cell growth.", "citation": { "type": "PubMed", "name": "Cell 1998 Sep 4 94(5) 625-34", "id": "9741627" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb96" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Here we have examined the role of p62(dok) in CD2-dependent signaling in Jurkat T cells. As previously reported, we find that ligation of the CD2 molecule by mitogenic pairs of anti-CD2 mAbs led to phosphorylation of p62(dok). While CD2-induced p62(dok) tyrosine phosphorylation was independent of both the p36/38 membrane adapter protein linker of activated T cells (LAT) and the ZAP70/Syk family of kinases, it was dependent upon the Src family of kinases including Lck and Fyn. We find further that CD2 engagement induced the association of tyrosine-phosphorylated p62(dok) to Crk-L. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Dec 7 276(49) 45654-61", "id": "11553620" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb94" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Significant tyrosine phosphorylation of cortactin, stable complex formation between activated Fyn and cortactin, and co-localization of cortactin with Fyn at cell membranes were all observed only in cells with high metastatic potential. Both integrin-mediated Fyn activation and hyperphosphorylation of cortactin were observed 2-5 h after stimulation in highly metastatic cells, and they required de novo protein synthesis. We demonstrate that cortactin is a specific substrate and cooperative effector of Fyn in integrin-mediated signaling processes regulating metastatic potential.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Nov 28 278(48) 48367-76", "id": "13129922" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb85" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We observed that overexpression of active Src and Fyn resulted in tyrosine phosphorylation of RSK2....We identified that RSK2 was tyrosinephosphorylated at a group of tyrosine sites (Table 1), including Tyr-529 (spectra presented in Fig. 4B), due to expression of the constitutively activated Src and Fyn....As shown in Fig. 7, wild-type RSK2 CTD domain was highly tyrosine-phosphorylated at Tyr-529 by rSrc or rFyn, whereas Tyr-529 phosphorylation was abolished in the RSK2 CTD Y529F mutant (in vitro)", "citation": { "type": "PubMed", "name": "J Biol Chem 2008 Feb 22 283(8) 4652-7", "id": "18156174" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb79" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We have studied the phosphorylation of the closely related lck, fyn, and c-src tyrosine protein kinases in leukemic murine T-cell lines that have lost the expression of CD45. The phosphorylation of the lck kinase at an inhibitory site of tyrosine phosphorylation, Tyr-505, was increased by two-, six-, and eightfold in three different cell lines. Phosphorylation of the fyn kinase at the homologous site, Tyr-531, was unaltered in one of these cell lines, but increased by 2.5-fold in the two others.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 1993 Mar 13(3) 1651-6", "id": "8441403" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb5e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Basic fibroblast growth factor (FGF)-2 induced a specific signaling response within the caveolae-like domain of LAN-1 cells, characterized by the tyrosine phosphorylation of a 75-80-kDa protein. This protein present in the caveolae-like domains has properties suggesting that it is a member of the SNT family of adapter proteins. The signaling event originating in the caveolae-like domains in response to FGF-2 appeared to require the activation of at least Fyn and Lyn, two members of the Src family of tyrosine kinases.", "citation": { "type": "PubMed", "name": "J Neurochem 2000 Feb 74(2) 676-83", "id": "10646519" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb46" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "A caveolin peptide derived from this region (residues 82-101) functionally suppressed the auto-activation of purified recombinant c-Src tyrosine kinase and Fyn, a related Src family tyrosine kinase. We further analyzed the effect of caveolin on c-Src activity in vivo by transiently co-expressing full-length caveolin and c-Src tyrosine kinase in 293T cells. Co-expression with caveolin dramatically suppressed the tyrosine kinase activity of c-Src as measured via an immune complex kinase assay. Thus, it appears that caveolin structurally and functionally interacts with wild-type c-Src via caveolin residues 82-101. ", "citation": { "type": "PubMed", "name": "J Biol Chem 1996 Nov 15 271(46) 29182-90", "id": "8910575" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb2e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In the present study, we identified a Rho GTPase-activating protein (GAP), TCGAP (Tc10/Cdc42 GTPase-activating protein), as a novel Fyn substrate. TCGAP interacted with Fyn and was phosphorylated by Fyn, with Tyr-406 in the GAP domain as a major Fyn-mediated phosphorylation site. Fyn suppressed the GAP activity of wild-type TCGAP but not the Y406F mutant of TCGAP in a phosphorylation-dependent manner, suggesting that Fyn-mediated Tyr-406 phosphorylation negatively regulated the TCGAP activity.", "citation": { "type": "PubMed", "name": "J Biol Chem 2006 Aug 18 281(33) 23611-9", "id": "16777849" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb12" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Given that the three ubiquitously expressed SFK members c-Src, Fyn, and Yes have been previously identified in 3T3-L1 cells (34) , we immunoprecipitated Src, Fyn, and Yes from IGF-I-stimulated proliferating cells and measured kinase activity with an in vitro assay using enolase as a substrate (35) . IGF-I stimulated c-Src kinase activity in proliferating cells at a peak stimulation time of 1 min (Fig. 5A)Citation . The kinetics of IGF-I-stimulated c-Src and MAPK are consistent with SFK activation occurring upstream of MAPK: both activities are back to baseline by 10 min, and our previous studies demonstrated peak MAPK activation was at 5 min (15) . IGF-I stimulation of c-Src and Fyn activity was comparable, ~3-fold more than baseline, and dependent on immunoprecipitation with specific antibodies (Fig. 5B)Citation . IGF-I activation of both c-Src and Fyn is not surprising,", "citation": { "type": "PubMed", "name": "Cell Growth Differ 2001 Jul 12(7) 379-86", "id": "11457735" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb0f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "To determine if Src family kinases (SFKs) are involved, we demonstrated that CSF-1 activated Fyn and Lyn in cells expressing wild-type (WT) or DeltaKI receptors. Moreover, CSF-1-induced Akt activity in cells expressing DeltaKI is SFK dependent since Akt activation was prevented by pharmacological or genetic inhibition of SFK activity. The docking protein Gab2 may link SFK to PI3-kinase. CSF-1 induced Gab2 tyrosyl phosphorylation and association with PI3-kinase in cells expressing WT or DeltaKI receptors. ", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2000 Sep 20(18) 6779-98", "id": "10958675" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb0e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "CD146 cross-linking induces the tyrosine phosphorylation of the protein tyrosine kinase p125(FAK) as well as p125(FAK) association with paxillin, both events being inhibited by cytochalasin D. No direct association of CD146 with p125(FAK) was observed. Consistent with these data, CD146 associates with p59(fyn), a Src family kinase known to phosphorylate p125(FAK).", "citation": { "type": "PubMed", "name": "J Biol Chem 1998 Oct 9 273(41) 26852-6", "id": "9756930" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec6d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We found that UVB only stimulated ERKs (Thr-202/Tyr-204) and Akt (serine 473) phosphorylation (Fig. 8, E and G) but not p38 MAP kinase or JNKs (Fig. 8, A and C) in WT-Fyn cells. ERKs and Akt were not affected by UVB in DNM-Fyn cells.", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Jan 28 280(4) 2446-54", "id": "15537652" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec69" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "To unravel the cellular functions of magicin, we used a yeast two-hybrid system and identified Fyn tyrosine kinase as a specific binding partner for magicin. Fyn phosphorylates magicin in vitro. In addition to Fyn, Src and Lck also interact with magicin. ", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 2006 Sep 29 348(3) 826-31", "id": "16899217" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec62" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "The CD45 tyrosine phosphatase has been reported to activate the src family tyrosine kinases Lck and Fyn by dephosphorylating regulatory COOH-terminal tyrosine residues 505 and 528, respectively. ", "citation": { "type": "PubMed", "name": "Mol Cell Biol 1996 Sep 16(9) 4996-5003", "id": "8756658" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec5b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Moreover, activation of the LHR in MA-10 cells results in the stimulation of the activity of Fyn and Yes, and overexpression of either of these two tyrosine kinases enhances the LHR-mediated phosphorylation of FAK-Tyr576. ", "citation": { "type": "PubMed", "name": "Mol Endocrinol 2006 Mar 20(3) 619-30", "id": "16293639" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec59" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "RESULTS: Immunoprecipitation experiments showed that Crk-associated substrate (Cas) was tyrosine-phosphorylated in response to ethanol administration. Fyn kinase was shown to be activated by ethanol administration and to phosphorylate Cas on tyrosine residue in vitro. CONCLUSIONS: Cas was tyrosine-phosphorylated in rat brain by ethanol administration, and Fyn kinase was most likely involved in the process.", "citation": { "type": "PubMed", "name": "Alcohol Clin Exp Res 2002 Aug 26(8 Suppl) 38S-43S", "id": "12198373" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec45" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "The phosphorylation generated a consensus sequence for the binding of the SH2 domain of Grb2 (pYSN). Pull-down assays with SH2-Grb2 from human fetal brain homogenates, and co-immunoprecipitation of Grb2 and MAP-2 confirmed the interaction in vivo, and demonstrated that MAP-2c is tyrosine-phosphorylated in human fetal brain. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Jan 21 280(3) 1962-70", "id": "15536091" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec3d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "The binding of beta-catenin to these partners is regulated by phosphorylation of at least three critical tyrosine residues. Each of these residues is targeted by one or more specific kinases: Y142 by Fyn, Fer and cMet; Y489 by Abl; and Y654 by Src and the epidermal growth factor receptor.", "citation": { "type": "PubMed", "name": "Curr Opin Cell Biol 2005 Oct 17(5) 459-65", "id": "16099633" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec32" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We show that the overactive FGFR2 S252W mutation induced decreased Src family kinase tyrosine phosphorylation and activity associated with decreased Lyn and Fyn protein expression in human osteoblasts. Thus, constitutive FGFR2 activation induces c-Cbl-dependent Lyn and Fyn proteasome degradation, resulting in reduced Lyn and Fyn kinase activity, increased ALP expression, and FGFR2 down-regulation.", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Aug 27 279(35) 36259-67", "id": "15190072" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec31" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In transfected COS cells, Dok-4 was a substrate for the cytosolic tyrosine kinases Src and Fyn as well as for Jak2. Dok-4 could also be phosphorylated by the receptor tyrosine kinase Ret ", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Apr 30 279(18) 19335-49", "id": "14963042" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec2b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Data presented here demonstrate that epidermal growth factor (EGF) receptor ligands promote the tyrosine phosphorylation of endogenous and adenovirally transduced Srcasm in keratinocytes, and that increased levels of Srcasm activate endogenous SFKs, with a preference for Fyn and Src. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Feb 18 280(7) 6036-46", "id": "15579470" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec29" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Keratinocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Recombinant PSD-93 was phosphorylated by Fyn in vitro, and Tyr-384 was identified as a major phosphorylation site", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Nov 28 278(48) 47610-21", "id": "13129934" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec28" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Increased PRK2 expression induces catenin tyrosine phosphorylation and Fyn activation Tyrosine phosphorylation of beta and gamma catenin and p120ctn was also induced by PRK2 overexpression", "citation": { "type": "PubMed", "name": "J Cell Biol 2002 Jan 7 156(1) 137-48", "id": "11777936" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec27" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "Keratinocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "and dominant-negative and constitutively active Fyn mutants rescue and recapitulate the Sca-1 antisense phenotype, respectively.", "citation": { "type": "PubMed", "name": "J Cell Sci 2004 Dec 1 117(Pt 25) 6185-95", "id": "15546912" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec1c" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "These observations collectively suggest that Fyn plays critical roles in promoting accelerated MBP expression during myelinogenesis in a MBP isoform-preferential manner, and QKI may act in the same pathway downstream of Fyn for MBP mRNA homeostasis.", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Jan 7 280(1) 389-95", "id": "15528192" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec1b" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "oligodendrocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "\\\"Ly-6A is required for T cell receptor expression and protein tyrosine kinase fyn activity.\\\"", "citation": { "type": "PubMed", "name": "EMBO J 1994 May 1 13(9) 2167-76", "id": "8187770" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec1a" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PTPalpha-null thymocytes develop normally, but unstimulated PTPalpha-/- cells exhibit increased tyrosine phosphorylation of specific proteins, increased Fyn activity, and hyperphosphorylation of Cbp/PAG that promotes its association with C-terminal Src kinase. Elevated Fyn activity in the absence of PTPalpha is due to enhanced phosphorylation of Fyn tyrosines 528 and 417. ", "citation": { "type": "PubMed", "name": "J Immunol 2005 Dec 15 175(12) 7947-56", "id": "16339530" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec19" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "TCR mediated activation of protein tyrosine kinases, such as lck (LCK), fyn (FYN), and ZAP-70 occurs, resulting in the phosphorylation of a number of substrates, including the transmembrane adaptor proteins LAT and TRIM, which can both bind, and therefore recruit PI3K", "citation": { "type": "PubMed", "name": "Mol Immunol 2002 Jun 38(15) 1087-99", "id": "12044776" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec0a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Depletion of Sam68 by RNA interference caused accumulation of antiapoptotic Bcl-x(L), whereas its up-regulation increased the levels of proapoptotic Bcl-x(s). Tyrosine phosphorylation of Sam68 by Fyn inverted this effect and favored the Bcl-x(L) splice site selection. ", "citation": { "type": "PubMed", "name": "J Cell Biol 2007 Mar 26 176(7) 929-39", "id": "17371836" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf6" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Transient expression of mSKAP55R in COS cells demonstrated that tyrosine 260 was the predominant site of phosphorylation by FYN kinase. Furthermore, this phosphotyrosine was essential for coimmunoprecipitation of FYN with mSKAP55R", "citation": { "type": "PubMed", "name": "Exp Hematol 2000 Nov 28(11) 1250-9", "id": "11063873" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebef" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PKCh is a direct upstream activator of Fyn; PKCh associates with, and activates Fyn, leading to keratinocyte growth arrest and differentiation", "citation": { "type": "PubMed", "name": "J Biochem (Tokyo) 2002 Dec 132(6) 853-7", "id": "12473186" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebeb" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Transient expression of actively mutated Fyn, having Phe-528 instead of Tyr-528, in Jurkat T cells stimulated the fos promoter and serum response element (SRE), suggesting that the Fyn kinase stimulates c-fos expression through SRE.", "citation": { "type": "PubMed", "name": "Princess Takamatsu Symp 1991 22 293-305", "id": "1668889" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe7" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Srcasm itself exerts a negative feedback, as it inhibits its activator, the Src kinase Fyn (Li et al., 2007).", "citation": { "type": "PubMed", "name": "J Invest Dermatol 2008 Mar 128(3) 501-16", "id": "18268536" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe3" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Keratinocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Reconstitution of complexes containing p62 and the src family kinase p59fyn in HeLa cells demonstrated that complex formation resulted in tyrosine phosphorylation of p62 and was mediated by both the SH3 and SH2 domains of p59fyn.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 1995 Jan 15(1) 186-97", "id": "7799925" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd3" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Furthermore, we show that NRG1 signaling, through activation of Fyn and Pyk2 kinases, stimulates phosphorylation of Y1472 on the NR2B subunit of the NMDA receptor (NMDAR), a key regulatory site that modulates channel properties. ", "citation": { "type": "PubMed", "name": "J Neurosci 2007 Apr 25 27(17) 4519-29", "id": "17460065" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebce" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "As shown in Fig. 2A, whereas hypertonic induction for 4 h greatly stimulated the ORE-SVLuc activity in mock-transfected cells, co-transfection with increasing amounts of pCMV-FynDN resulted in an incremental reduction in the hypertonicity-induced ORE-Luc activity.", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Nov 29 277(48) 46085-92", "id": "12359721" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebba" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Upon integrin binding to ECM, Fyn becomes activated, and its SH3 domain interacts with a proline-rich site in Shc. Shc is then phosphorylated by Fyn at Tyr317 and combines with the Grb2-mSOS complex (4) (Fig. 3B).", "citation": { "type": "PubMed", "name": "Science 1999 Aug 13 285(5430) 1028-32", "id": "10446041" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb8" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Pleiotrophin (PTN the protein, Ptn the gene) signals downstream targets through inactivation of its receptor, the transmembrane receptor protein tyrosine phosphatase (RPTP)beta zeta We further demonstrate that Fyn is a substrate of RPTPbeta zeta, and that tyrosine phosphorylation of Fyn is sharply increased in PTN-stimulated cells.", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 2005 Jul 8 332(3) 664-9", "id": "15925565" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba8" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "This hypothesis is supported by the findings that shear stress activation of ERK is inhibited by a polyclonal anti–Cav-158 and that shear stress can activate Fyn (S. Jalali, S. Chien, unpublished data, 1998).", "citation": { "type": "PubMed", "name": "Circ Res 2002 Nov 1 91(9) 769-75", "id": "12411390" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba5" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Consistent with an involvement of this kinase, fyn-deficient keratinocytes have strongly decreased tyrosine phosphorylation levels of beta- and gamma-catenins and p120-Cas, and structural and functional abnormalities in cell adhesion similar to those caused by tyrosine kinase inhibitors.", "citation": { "type": "PubMed", "name": "J Cell Biol 1998 Jun 15 141(6) 1449-65", "id": "9628900" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb91" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "Keratinocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Using chemical and genetic inhibitors, we show that Fyn activity is required for SAPK2/p38 but not for FAK activation in response to VEGF. In contrast, c-Src permits activation of FAK, but not that of SAPK2/p38. In addition, Fyn is required for stress fiber formation and endothelial cell migration. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2006 Nov 10 281(45) 34009-20", "id": "16966330" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb8a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Because Fyn is the kinase primarily responsible for the phosphorylation of PAG (the phosphoprotein associated with glycosphingolipid-enriched microdomains), which negatively regulates Src-kinase activity by recruiting Csk (the C-terminal Src kinase) to the membrane, we investigated whether anergy induction also affects PAG.", "citation": { "type": "PubMed", "name": "Blood 2007 Jul 15 110(2) 596-625", "id": "17389760" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb82" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We examined the effect of PEDF on kinase activity of Fyn and found that PEDF downregulated FGF-2-promoted Fyn activity by tyrosine phosphorylation at the C-terminus in a Fes-dependent manner. ", "citation": { "type": "PubMed", "name": "J Cell Sci 2005 Mar 1 118(Pt 5) 961-70", "id": "15713745" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb7a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Through its phosphorylated tyrosine residues, the activated VEGFR-2 associates with the adapter molecules Shc, Grb2 and Nck, to Ras GTPase activating protein, p59fyn, pp62yes and phospholipase Cg, and to the tyrosine phosphatases SHP-1 and SHP-2", "citation": { "type": "PubMed", "name": "EMBO J 1999 Feb 15 18(4) 882-92", "id": "10022831" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb6f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "The protein-tyrosine kinase Csk is one of the main down-regulators of the Src family of kinases. Csk may be involved in the down-regulation of T cell receptor (TCR) signaling by C-terminal tyrosine phosphorylation of Lck and Fyn; however, it is not known how Csk activity is regulated or how it targets these Src family members.", "citation": { "type": "PubMed", "name": "J Biol Chem 1996 Apr 19 271(16) 9698-703", "id": "8621646" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb6d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Immunoprecipitation experiments showed that the amount of coimmunoprecipitated Fyn kinase with an anti-Cbl antibody increased in extracts from ethanol-administered rats compared to those from saline-administered rats. Exogenous Fyn kinase was shown to phosphorylate on tyrosine residue(s) of Cbl from the cerebellum in vitro.", "citation": { "type": "PubMed", "name": "Brain Res 2002 Sep 20 950(1-2) 203-9", "id": "12231245" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb64" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "JAK2 tyrosine kinase and the Src family p59 Fyn tyrosine kinase are required for Ang II-induced STAT1 tyrosine phosphorylation in VSMCs.", "citation": { "type": "PubMed", "name": "J Biol Chem 1999 Jul 9 274(28) 19846-51", "id": "10391929" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb5c" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "", "tissue": "vascular smooth muscle" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We conclude that p59(fyn) and p56(lck) differently participate in regulating the phosphorylation state of Sam68 in T cells and that ZAP-70 may contribute to Sam68 tyrosine phosphorylation and to the specific recruitment of this molecule after CD3 stimulation.", "citation": { "type": "PubMed", "name": "Eur J Immunol 1997 Dec 27(12) 3360-7", "id": "9464824" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb5a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Yes tyrosine kinase also binds to p120 catenin but only upon activation, and stimulates Fer and Fyn tyrosine kinases. p120 catenin acts as a docking protein facilitating the activation of Fer/Fyn tyrosine kinases by Yes", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2003 Apr 23(7) 2287-97", "id": "12640114" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb57" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Employing fyn-deficient mouse embryonic fibroblast cells and tissues, we demonstrate that fyn is essential for phosphorylating PIKE-A and protects it from apoptotic cleavage. Active but not kinase-dead fyn interacts with PIKE-A and phosphorylates it on both Y682 and Y774 residues. ", "citation": { "type": "PubMed", "name": "Cell Death Differ 2007 Feb 14(2) 368-77", "id": "16841086" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb56" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We also provide evidence that upon CD43 cross-linking, Fyn is tyrosine-phosphorylated in a time-dependent manner. Our results suggest that CD43 cross-linking on the T cell surface induces the interaction between CD43 and Fyn, presumably through the Fyn SH3 domain and a putative SH3 binding site in CD43, leading to Fyn tyrosine phosphorylation and signal propagation.", "citation": { "type": "PubMed", "name": "J Biol Chem 1996 Nov 1 271(44) 27564-8", "id": "8910342" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb1d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Carlos Ibanez and collaborators demonstrated that neural cell adhesion molecule (NCAM) functions as an alternative signaling receptor for GFLs (fig. 2). In the presence of GFR-a, GDNF binds with high affinity to p140-NCAM and intracellularly activates the Src-like kinase c-Fyn and the focal adhesion kinase FAK [29].", "citation": { "type": "PubMed", "name": "Cell Mol Life Sci 2004 Dec 61(23) 2954-64", "id": "15583857" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead9" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "On the contrary, the Cas-associated kinase activity was remarkably decreased in Fyn-/- cells.", "citation": { "type": "PubMed", "name": "Oncogene 1997 Mar 27 14(12) 1419-26", "id": "9136985" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec74" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "Fibroblasts", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "ROS activate Fyn, which phosphorylates JAK2", "citation": { "type": "PubMed", "name": "Arterioscler Thromb Vasc Biol 2002 Dec 1 22(12) 1962-71", "id": "12482820" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec70" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "and dominant-negative and constitutively active Fyn mutants rescue and recapitulate the Sca-1 antisense phenotype, respectively.", "citation": { "type": "PubMed", "name": "J Cell Sci 2004 Dec 1 117(Pt 25) 6185-95", "id": "15546912" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec6f" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "Muscle, Skeletal" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Hypertonicity provoked Fyn-dependent tyrosine phosphorylation in beta-catenin, alpha-catenin, and p120(Cas) and caused the dissociation of beta-catenin from the contacts.", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Oct 13 275(41) 32289-98", "id": "10921917" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec6c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Caveolin-1 is a substrate for nonreceptor tyrosine kinases including Src, Fyn, and Abl.", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Mar 15 277(11) 8771-4", "id": "11805080" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec5e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Hck negatively regulates Lyn, and Lyn negatively regulates Fyn", "citation": { "type": "PubMed", "name": "Blood 2007 May 18", "id": "17513616" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec5c" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Further experiments with small interfering RNA revealed that Fyn phosphorylated Nrf 2Y568 leading to nuclear export and degradation of Nrf 2.", "citation": { "type": "PubMed", "name": "J Biol Chem 2006 Apr 28 281(17) 12132-42", "id": "16513647" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec5a" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Caveolin-1 is phosphorylated on Tyr(14) in response to both oxidative and hyperosmotic stress. In the present paper, we show that this phosphorylation requires activation of the Src family kinase Fyn. ", "citation": { "type": "PubMed", "name": "Biochem J 2003 Nov 15 376(Pt 1) 159-68", "id": "12921535" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec54" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "MAPK. Stimulation of ERK by endothelial-cell integrins is mediated by either FAK, or the Src-family kinases Fyn and Yes.", "citation": { "type": "PubMed", "name": "Biochim Biophys Acta 2004 Mar 4 1654(1) 51-67", "id": "14984767" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec53" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "exposure to PDGF resulted in a marked increase in c-Src, Fyn, and Yes kinase activities", "citation": { "type": "PubMed", "name": "Mol Biol Cell 2005 Nov 16(11) 5418-32", "id": "16135530" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec4e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Jan 5 276(1) 693-9", "id": "11024032" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec4d" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Importantly, tyrosine phosphorylation of Itch appears to reduce its interaction with its substrate JunB. The turnover of JunB is accelerated in Fyn-deficient T cells, which is further reconstituted by Itch Tyr371 mutation. ", "citation": { "type": "PubMed", "name": "Mol Cell 2006 Jan 6 21(1) 135-41", "id": "16387660" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec4b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "p90RSK activation by H(2)O(2) was significantly reduced in fibroblasts derived from transgenic mice deficient in Fyn, but not c-Src. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Jan 21 275(3) 1739-48", "id": "10636870" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec46" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PRAP was found to function as a substrate for Src family kinases, such as c-Src or Fyn, but not for Pyk2/RAFTK", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Oct 24 278(43) 42225-33", "id": "12893833" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec44" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In cells lacking RET, GDNF binds with high affinity to the NCAM and GFRalpha1 complex, which activates Fyn and FAK.", "citation": { "type": "PubMed", "name": "J Cell Sci 2003 Oct 1 116(Pt 19) 3855-62", "id": "12953054" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec42" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "The Csk tyrosine kinase negatively regulates the Src family kinases Lck and Fyn in T cells.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2005 Mar 25(6) 2227-41", "id": "15743820" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec41" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Biochemical and in vitro experiments implicate Src and Fyn in the Reelin-dependent tyrosine phosphorylation of Dab1, which controls the positioning of radially migrating neurons in many brain regions. ", "citation": { "type": "PubMed", "name": "J Neurosci 2005 Sep 14 25(37) 8578-86", "id": "16162939" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec40" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Immunol 1998 Apr 1 160(7) 3305-14", "id": "9531288" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec3c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "CD146 engagement initiates an outside-in signaling pathway involving the protein tyrosine kinases FYN and FAK as well as paxillin.", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Jan 12 276(2) 1564-9", "id": "11036077" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec34" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Virol 1991 Jan 65(1) 170-9", "id": "1985196" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec2a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Fyn, by phosphorylating a residue located in the regulatory domain of p120-catenin (Tyr112), inhibits the interaction of this protein with RhoA", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2007 Mar 27(5) 1745-57", "id": "17194753" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec22" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "FEBS Lett 2000 Jun 2 474(2-3) 179-83", "id": "10838081" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec16" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 1997 Dec 18 241(2) 355-62", "id": "9425276" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec06" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2004 Aug 24(16) 6980-92", "id": "15282299" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec04" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Mutational analysis revealed that tyrosine 271 in SKAP55 played a pivotal role for interaction with both Fyn kinase and adapter protein Grb-2, indicating that the Fyn-phosphorylated SKAP55 transiently associates with adapter Grb-2 to mediate mitogen-activated protein kinase activation. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Oct 25 277(43) 40420-7", "id": "12171928" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec03" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Moreover, expression of a constitutive active form of Fyn also promoted the recruitment of Tom1L1 to enlarged endosomes.", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Mar 11 280(10) 9258-64", "id": "15611048" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebfd" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We show herethat RPTP is a physiological activator of two additional Src family kinases, Yes and Fyn.", "citation": { "type": "PubMed", "name": "Exp Cell Res 2004 Mar 10 294(1) 236-43", "id": "14980517" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf7" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Src is associated with c-Cbl, and we have previously demonstrated that the Src-like kinase Fyn can phosphorylate c-Cbl at a preferred binding site for the p85 subunit of phosphatidylinositol 3'-kinase. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Jul 12 277(28) 24967-75", "id": "11994282" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebee" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "N.N.;12374739", "citation": { "type": "PubMed", "name": "BMC Bioinformatics 2004 Jun 22 5 79", "id": "15212693" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebed" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Blood 2002 Feb 1 99(3) 957-65", "id": "11806999" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe9" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We found previously that a Src type tyrosine kinase Fyn and cyclin-dependent kinase 5 (Cdk5) mediate Sema3A-signaling.", "citation": { "type": "PubMed", "name": "J Neurosci 2004 Jul 7 24(27) 6161-70", "id": "15240808" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe2" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Here we show that 3BP2 is tyrosine phosphorylated following BCR aggregation on B lymphoma cells, and that 3BP2 is a substrate for Syk and Fyn, but not Btk. ", "citation": { "type": "PubMed", "name": "Blood 2005 Feb 1 105(3) 1106-13", "id": "15345594" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd9" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PTP? is required for SCF-induced c-Kit and Fyn activation, and in this way regulates a Fyn-based c-Kit signaling axis (Fyn/Gab2/Shp2/Vav/PAK/Rac/JNK) that mediates mast cell migration.", "citation": { "type": "PubMed", "name": "J Immunol 2010 Nov 15 185(10) 5993-6002", "id": "20944008" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd7" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "However, Fyn-deficient mast cells showed a significant reduction in phosphorylation of Shp2 phosphatase and p38 mitogen-activated protein kinase. ", "citation": { "type": "PubMed", "name": "Cell Signal 2006 Sep 18(9) 1447-54", "id": "16442778" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd2" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2002 Apr 22(8) 2673-86", "id": "11909961" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd1" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "The functional significance of pp115 association with p59fyn is suggested by the ability of alpha 4 integrin stimulation to activate Fyn tyrosine kinase activity. ", "citation": { "type": "PubMed", "name": "J Immunol 1997 Nov 15 159(10) 4806-14", "id": "9366405" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebcf" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Nov 28 278(48) 47610-21", "id": "13129934" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc9" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Mol Pharmacol 2002 Sep 62(3) 672-9", "id": "12181444" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc6" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Cotransfection of tau and kinases showed that Tyr-18 was the major site for Fyn phosphorylation, but Tyr-394 was the main residue for Abl.", "citation": { "type": "PubMed", "name": "J Neurosci 2005 Jul 13 25(28) 6584-93", "id": "16014719" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc4" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "upon ligation of the integrin beta6 with fibronectin, beta6 complexed with Fyn and activated it.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Oct 24 278(43) 41646-53", "id": "12917446" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebbf" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "p250GAP is tyrosine phosphorylated by Fyn", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 2003 Jun 20 306(1) 151-5", "id": "12788081" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebbe" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "However, purified EGFR did not phosphorylate recombinant PKC delta in vitro, whereas members of the Src family (c-Src, c-Fyn) and membrane preparations from keratinocytes did. ", "citation": { "type": "PubMed", "name": "J Biol Chem 1996 Mar 8 271(10) 5325-31", "id": "8621384" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebbd" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "Keratinocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Feb 9 276(6) 3879-84", "id": "11078745" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba4" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Dec 6 277(49) 47373-9", "id": "12239221" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba3" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In Fyn-/- fibroblasts, activation of Ras by H(2)O(2) was significantly attenuated.", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Jan 21 275(3) 1739-48", "id": "10636870" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb9c" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "Fibroblasts", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Oncogene 2000 Jun 8 19(25) 2895-903", "id": "10871840" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb98" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "EGF-R causes disassembly of hemidesmosomes by activating Fyn, which in turn phosphorylates the beta4 cytoplasmic domain. Neoplastic cells expressing dominant negative Fyn display increased hemidesmosomes and migrate poorly in vitro in response to EGF.", "citation": { "type": "PubMed", "name": "J Cell Biol 2001 Oct 29 155(3) 447-58", "id": "11684709" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb93" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In heterogeneous COS-1 cells, Cbl-b was phosphorylated on tyrosine residues by both Syk- (Syk/Zap-70) and Src- (Fyn/Lck) family kinases", "citation": { "type": "PubMed", "name": "Oncogene 1999 Feb 4 18(5) 1147-56", "id": "10022120" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb92" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Cdk5 phosphorylates p80 Dab1 at multiple sites in its carboxyl-terminal region, and tyrosine phosphorylation of p80 Dab1 by Fyn tyrosine kinase is attenuated by this Cdk5-mediated phosphorylation in vitro. ", "citation": { "type": "PubMed", "name": "Brain Res 2007 Apr 6 1140 84-95", "id": "16529723" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb8f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "These data suggest that Fyn prefers Y731 to other tyrosines in the C-terminus of c-Cbl.", "citation": { "type": "PubMed", "name": "FEBS Lett 2004 Nov 19 577(3) 555-62", "id": "15556646" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb89" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Recently, it was shown that Fyn is one of the kinases responsible for the phosphorylation of caveolin", "citation": { "type": "PubMed", "name": "J Clin Invest 1999 Apr 103(7) 931-43", "id": "10194465" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb87" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 2001 Oct 19 288(1) 233-9", "id": "11594778" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb80" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "IL-6-treatment of INA-6 cells induced the kinase activities of Fyn, Lyn and Hck", "citation": { "type": "PubMed", "name": "Oncogene 2007 Jul 26 26(34) 4987-98", "id": "17310994" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb7c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Using dominant-negative mutants of c-Src and Fyn and Src-deficient SYF cells as well as by co-immunoprecipitation studies, we can demonstrate that the M2R-mediated transactivation of EGFR specifically involves Fyn but not c-Src or Yes. ", "citation": { "type": "PubMed", "name": "Cell Signal 2006 Aug 18(8) 1338-49", "id": "16337776" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb74" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Exogenous Fyn kinase was shown to phosphorylate on tyrosine residue(s) of Cbl from the cerebellum in vitro.", "citation": { "type": "PubMed", "name": "Brain Res 2002 Sep 20 950(1-2) 203-9", "id": "12231245" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb73" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "IL-6 induced the activation and tyrosine phosphorylation of p59Fyn, p56/59Hck, and p56Lyn.", "citation": { "type": "PubMed", "name": "Exp Hematol 1997 Dec 25(13) 1367-77", "id": "9406996" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb70" }, "experiment_context": { "species_common_name": "Human", "disease": "Multiple Myeloma", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Mol Cell Biol 1996 Sep 16(9) 4735-43", "id": "8756631" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb61" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Both SKAP55 and SKAP55R were found to bind FYB through their SH3 domains and to act as substrates for the FYN kinase in T cells", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 1998 Jul 21 95(15) 8779-84", "id": "9671755" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb5d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Lyn-deficient Mast Cells Show Increased Fyn Kinase Activity.", "citation": { "type": "PubMed", "name": "J Exp Med 2004 Jun 7 199(11) 1491-502", "id": "15173205" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb59" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In Fyn-/- fibroblasts, activation of Ras by H(2)O(2) was significantly attenuated.", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Jan 21 275(3) 1739-48", "id": "10636870" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb50" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Fyn plays an essential role by positive regulation of Lck activity.", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2004 Oct 12 101(41) 14859-64", "id": "15465914" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb4f" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "naive T-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "We identify Tyr-131 as the major phosphorylation site and Tyr-132 as a minor site and the Src family PTKs Lck and Fyn as enzymes capable of phosphorylating these sites in vivo and in vitro. ", "citation": { "type": "PubMed", "name": "J Biol Chem 1997 Feb 28 272(9) 5371-4", "id": "9038134" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb4d" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Collectively, our results suggest that BDNF enhances phosphorylation of NR2B tyrosine 1472 through activation of Fyn, leading to alteration of NMDA receptor activity and increased synaptic transmission.", "citation": { "type": "PubMed", "name": "Brain Res 2006 Nov 22 1121(1) 22-34", "id": "17045972" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb44" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "96108162;12522270;16094384;15659558", "citation": { "type": "PubMed", "name": "BMC Bioinformatics 2004 Jun 22 5 79", "id": "15212693" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb3f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Sema3A promotes Cdk5 activity through phosphorylation of Tyr15, a phosphorylation site with Fyn.", "citation": { "type": "PubMed", "name": "Neuron 2002 Aug 29 35(5) 907-20", "id": "12372285" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb3e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "neuron", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "alpha PTP phosphorylation occurred at Tyr789 and required SFKs (Src or Fyn/Yes), FAK, and an intact cytoskeleton.", "citation": { "type": "PubMed", "name": "J Biol Chem 2006 Apr 28 281(17) 11972-80", "id": "16507567" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb3c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Fyn but not JAK2 is the major kinase that phosphorylates Cbl", "citation": { "type": "PubMed", "name": "J Biol Chem 1999 Jan 22 274(4) 2097-106", "id": "9890970" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb38" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "This phosphorylation was mediated by Src family tyrosine kinases (STKs), with Fyn appearing to be the dominant kinase. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Nov 11 280(45) 37974-87", "id": "16144838" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb37" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "The activated GSK-3beta phosphorylates Fyn at threonine residue(s). Phosphorylated Fyn accumulates in the nucleus and phosphorylates Nrf2 at tyrosine 568. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2007 Jun 1 282(22) 16502-10", "id": "17403689" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb2a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PKC-eta activity is both necessary and sufficient for Fyn activation, PKC-eta and Fyn are found in association, and recombinant PKC-eta directly activates Fyn. ", "citation": { "type": "PubMed", "name": "Mol Cell 2000 Nov 6(5) 1121-9", "id": "11106751" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb25" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "Keratinocytes", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Apr 16 279(16) 16311-6", "id": "14761954" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb18" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "we found that PP2, a Fyn kinase inhibitor (33), and leflunomide, an Src kinase inhibitor (34), markedly inhibited the UVB-induced phosphorylation of histone H3 at serine 10 in a dose-dependent manner (Fig. 4, A and C, respectively) but did not change the total histone H3 protein level (Fig. 4, B and D).", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Jan 28 280(4) 2446-54", "id": "15537652" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb0b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Thus, in contrast to the activation pathway mediated by serine/threonine phosphorylation, tyrosine phosphorylation of Itch plays a negative role in modulating Itch-promoted ubiquitination.", "citation": { "type": "PubMed", "name": "Mol Cell 2006 Jan 6 21(1) 135-41", "id": "16387660" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb08" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "nterestingly, we also observed that APOBEC3G can be phosphorylated on tyrosine in the presence of Fyn or Hck, suggesting that both kinases may regulate APOBEC3G function. ", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 2005 Apr 15 329(3) 917-24", "id": "15752743" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb05" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "2) both Fyn and Lck are capable of phosphorylating DAP12; and 3) both kinases coimmunoprecipitate with the Ly-49D/DAP12 complex in NK cells.", "citation": { "type": "PubMed", "name": "J Immunol 2006 Jun 1 176(11) 6615-23", "id": "16709819" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb03" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "The LHR-mediated phosphorylation of the EGFR and Shc, the activation of Ras, and the phosphorylation of ERK1/2 are inhibited by expression of a dominant-negative mutant of Fyn, a member of the Src family kinases (SFKs) expressed in MA-10 cells and by PP2, a pharmacological inhibitor of the SFKs. ", "citation": { "type": "PubMed", "name": "Endocrinology 2006 Jul 147(7) 3419-27", "id": "16614081" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb02" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "SKAP-HOM is a cytosolic adaptor protein representing a specific substrate for the Src family protein tyrosine kinase Fyn", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2005 Sep 25(18) 8052-63", "id": "16135797" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb01" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Interaction with DAF also activates Fyn kinase, an event that is required for the phosphorylation of caveolin and transport of virus into the cell within caveolar vesicles.", "citation": { "type": "PubMed", "name": "Cell 2006 Jan 13 124(1) 119-31", "id": "16413486" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb00" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "SK is activated following FcepsilonRI aggregation at the surface of mast cells in a LYN- and FYN-dependent manner78, and this results in the phosphorylation of lipid-raft associated sphingosine to form S1P 79,80.", "citation": { "type": "PubMed", "name": "Nat Rev Immunol 2006 Mar 6(3) 218-30", "id": "16470226" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf8" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Kinase-active Lck complexes with and activates Fyn", "citation": { "type": "PubMed", "name": "J Biol Chem 2008 Sep 26 283(39) 26409-22", "id": "18660530" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf5" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "Fibroblasts", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Several lipid signaling pathways are activated downstream of Fc?RI via Fyn, including pathways mediated by PI3K, SphK, and PLD", "citation": { "type": "PubMed", "name": "Immunol Rev 2007 Jun 217 255-68", "id": "17498064" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf4" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Furthermore, dominant negative Fyn decreases the ability of squamous carcinoma cells to invade through Matrigel in vitro and to form lung metastases following intravenous injection in nude mice. ", "citation": { "type": "PubMed", "name": "J Cell Biol 2001 Oct 29 155(3) 447-58", "id": "11684709" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf2" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "squamous cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "SHP2 KO BMMCs displayed several phenotypes associated with reduced Fyn activity, including elevated phosphorylation of the inhibitory pY531 site in Fyn, impaired signaling to Grb2-associated binder 2, Akt/PKB, and IkappaB kinase, and decreased TNF-alpha release compared with control cells.", "citation": { "type": "PubMed", "name": "J Immunol 2009 Oct 15 183(8) 4940-7", "id": "19786542" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf0" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "mast cell", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Hence, we identified p59fyn and p53/56lyn to be stimulated by IL-7.", "citation": { "type": "PubMed", "name": "J Immunol 1994 Jul 1 153(1) 97-109", "id": "7515933" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaee" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In response to PDGF, Fyn associated with PKCdelta via tyrosine 187. Finally, overexpression of dominant negative Fyn abrogated the decrease in GS expression and reduced the tyrosine phosphorylation of PKCdelta induced by PDGF. We conclude that the tyrosine phosphorylation of PKCdelta and its association with tyrosine kinases may be an important point of divergence in PKC signaling.", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Nov 10 275(45) 35491-8", "id": "10945993" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae1" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "Activation of tyrosine kinases Fyn and Lyn, but not Lck, also occurred within 2 min after PAF stimulation in the cells", "citation": { "type": "PubMed", "name": "Prog Lipid Res 2000 Jan 39(1) 41-82", "id": "10729607" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eada" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:FYN) actsIn kin(p(HGNC:FYN))", "summary_text": "In a human embryonic kidney (HEK) 293 cell expression system, PTPalpha enhanced fyn-mediated NR2A and NR2B tyrosine phosphorylation by several-fold.", "citation": { "type": "PubMed", "name": "J Neurochem 2006 Sep 98(6) 1798-809", "id": "16899073" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead2" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:LCK)", "relation": "actsIn", "target": "kin(p(HGNC:LCK))", "directed": false, "label": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051470", "evidences": [ { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Src, Lyn and Lck tyrosine kinases phosphorylate DAPP1 at Tyr(139) in vitro at similar rates in the presence or absence of PtdIns(3,4,5)P(3), and overexpression of these kinases in HEK-293 cells induces the phosphorylation of Tyr(139). co-expression of DAPP1 with Src, Lyn or Lck induced a very high level of phosphorylation of DAPP1 at Tyr139, even in unstimulated cells, which was not increased further by agonist stimulation of cells. As Src-family kinases activate the PI 3-kinase pathway in many cells [1], it is possible that the overexpression of Src, Lyn or Lck in HEK-293 cells induces the activation of PI 3-kinase, thereby promoting DAPP1 phosphorylation in unstimulated cells. As Src-family tyrosine kinases are located at the plasma membrane by virtue of myristoylation and palmitoylation of their N-termini [26], it is likely that the role of PtdIns(3,4,5)P3 is to recruit DAPP1 to the cell membrane, where it can be phosphorylated with Src-family tyrosine kinases.", "citation": { "type": "PubMed", "name": "Biochem J 2000 Jul 15 349(Pt 2) 605-10", "id": "10880360" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec63" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "On stimulation of the cells through their T cell antigen receptor, the phosphotyrosine content of LMPTP-B declined rapidly. In co-transfected COS cells, Lck and Fyn caused phosphorylation of LMPTP, whereas Csk, Zap, and Jak2 did not. Most of the phosphate was located at Tyr-131, and some was also located at Tyr-132. Incubation of wild-type LMPTP with Lck and adenosine 5'-O-(thiotriphosphate) caused a 2-fold increase in the activity of LMPTP. Site-directed mutagenesis showed that Tyr-131 is important for the catalytic activity of LMPTP, and that thiophosphorylation of Tyr-131, and to a lesser degree Tyr-132, is responsible for the activation.", "citation": { "type": "PubMed", "name": "J Biol Chem 1997 Feb 28 272(9) 5371-4", "id": "9038134" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec0f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "We have studied the phosphorylation of the closely related lck, fyn, and c-src tyrosine protein kinases in leukemic murine T-cell lines that have lost the expression of CD45. The phosphorylation of the lck kinase at an inhibitory site of tyrosine phosphorylation, Tyr-505, was increased by two-, six-, and eightfold in three different cell lines. Phosphorylation of the fyn kinase at the homologous site, Tyr-531, was unaltered in one of these cell lines, but increased by 2.5-fold in the two others.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 1993 Mar 13(3) 1651-6", "id": "8441403" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec02" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "We show that Crry increases early TCR-dependent activation signals, including p56lck-, zeta-associated protein-70 (ZAP-70), Vav-1, Akt, and extracellular signal-regulated kinase (ERK) phosphorylation but also costimulation-dependent mitogen-activated protein kinases (MAPK), such as the stress-activated c-Jun N-terminal kinase (JNK). It is intriguing that Crry costimulus enhanced p38 MAPK activation in T helper cell type 1 (Th1) but not in Th2 cells. ", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2005 Dec 78(6) 1386-96", "id": "16301324" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebe0" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Here we have examined the role of p62(dok) in CD2-dependent signaling in Jurkat T cells. As previously reported, we find that ligation of the CD2 molecule by mitogenic pairs of anti-CD2 mAbs led to phosphorylation of p62(dok). While CD2-induced p62(dok) tyrosine phosphorylation was independent of both the p36/38 membrane adapter protein linker of activated T cells (LAT) and the ZAP70/Syk family of kinases, it was dependent upon the Src family of kinases including Lck and Fyn. We find further that CD2 engagement induced the association of tyrosine-phosphorylated p62(dok) to Crk-L. ", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Dec 7 276(49) 45654-61", "id": "11553620" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb86" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Simultaneous overexpression of selenophosphate synthetase and phospholipid-hydroperoxide GSH peroxidase (PHGPx) [250] blocks activation of NF-kB by IL-1. Overexpression of SOD [84] or GSH peroxidase [81, 211] abolished NF-kB activation by preventing degradation of IkB after stimulation with TNF-a. The precise mechanism(s) through which oxidants and reductants influence activation of NF-kB is presently unknown; however, there is evidence that antioxidant enzyme (AOE372), a redox-sensitive thioredoxin peroxidase, regulates IkB phosphorylation [246]. Phosphatases The phosphatases are an important component of most signal transduction pathways, because failure to reverse kinase actions can disrupt normal cellular functions. For example, transfection of human fibroblasts with constitutively active ras (hRasV12) inhibits cell growth and ultimately results in a senescentlike phenotype [441]. Similarly, constitutive ERK activation has an inhibitory effect on cell cycle progression [442,443]. Both the serine/threonine phosphatases and the PTPs are known to be redox-sensitive [82,144,153,156,271,281, 444-449]. The mechanism of redox effects on activity is probably best understood for the PTPs. Without exception, the PTPs contain a highly conserved region of 11 amino acid residues in their catalytic domain; specifi- cally, (Ile/Val)-His-Cys-X-Ala-Gly-X-X-Arg-(Ser/Thr)- Gly, where X is a nonconserved amino acid [17]. Either oxidation or mutation of the cysteine renders these molecules inactive [17,281]. H2O2 is a potent inhibitor of PTPs. As in the case of other oxidants, H2O2 probably oxidizes the thiolate anion at the catalytic site [280]. Because formation of a phosphorylcysteine intermediate seems to be critical to PTP activity [450-452], blocking it through oxidation of the cysteine inactivates the molecules. In many cases, treatment of cells with H2O2 stimulates increases in protein phosphorylation by inhibiting phosphatase-catalyzed removal of phosphate groups. Furthermore, mitogens that increase cellular ox- idant production may stimulate phosphorylation indirectly by decreasing phosphatase activity. Additional mechanisms are involved in stimulation of pathways activated by growth factors that increase oxidant production, however, because there are known instances in which the oxidants they produce have no effect on protein phosphorylation. For example, TGF-b1 stimulates phosphorylation of numerous proteins and has been shown to cause a large increase in H2O2 production; however, its effects on protein phosphorylation are not blocked by catalase [453]. Furthermore, H2O2 is effective in promoting phosphorylation of phospholipase D, the PDGF receptor, and PKC-a even after pretreatment of Swiss 3T3 fibroblasts with orthovanadate to inhibit phosphatases [454]. Thus, although diminished phosphatase activity may partially account for increased phosphorylation in some cases, it cannot totally account for oxidation effects on phosphorylation in every case. SPECIFICITY In general, there is good agreement between studies on redox effects on any given gene; albeit, not all oxidizing or reducing treatments exert equivalent effects. This is clearly demonstrated in studies of pag , which encodes a protein associated with cellular proliferation. Pag protein inhibits the tyrosine kinase activity of the Abelson (abl ) protein by binding to its SH3-binding domain [455]. BSO, menadione, sodium arsenate, and diethyl maleate all stimulate pag expression, but H2O2 does not [269]. Conversely, H2O2 stimulates c-fos expression (Table 1), although 4-hydroynonenal (a product of v-6-polyunsaturated fatty acid peroxidation) not only fails to induce c-fos expression but is actually inhibitory to c-fos induction by EGF and PDGF [185]. Similarly, some oxidants such as diamide decrease hypoxia-induced signals [201], although others such as H2O2 increase them [124]. As might be expected, the effects of any stimu...", "citation": { "type": "PubMed", "name": "Free Radic Biol Med 2000 Feb 1 28(3) 463-99", "id": "10699758" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb33" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Results demonstrated that autophosphorylation of Lck (at Tyr394) facilitates Csk-mediated phosphorylation of Lck at its regulatory site (Tyr505). Subsequent peptide binding studies revealed that Csk can bind to a peptide corresponding to the Lck-autophosphorylation site only when it is phosphorylated. These findings suggest that autophosphorylation of Lck at Tyr394 triggers an interaction with Csk and thereby facilitates subsequent phosphorylation and inactivation of Lck.", "citation": { "type": "PubMed", "name": "Farmaco 1998 Apr 53(4) 266-72", "id": "9658584" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec71" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "The CD45 tyrosine phosphatase has been reported to activate the src family tyrosine kinases Lck and Fyn by dephosphorylating regulatory COOH-terminal tyrosine residues 505 and 528, respectively. ", "citation": { "type": "PubMed", "name": "Mol Cell Biol 1996 Sep 16(9) 4996-5003", "id": "8756658" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec33" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "The Csk tyrosine kinase negatively regulates the Src family kinases Lck and Fyn in T cells.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2005 Mar 25(6) 2227-41", "id": "15743820" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec17" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Magicin phosphorylation is not observed in an Lck-deficient line, J.CaM1.6, indicating that Lck is the major Src family kinase for phosphorylating magicin in Jurkat cells. Employing site-directed mutagenesis along with in vitro kinase assays, we found that Y64 of magicin is phosphorylated by Lck creating a SH2-Grb2 binding motif. ", "citation": { "type": "PubMed", "name": "Biochem Biophys Res Commun 2006 Sep 29 348(3) 826-31", "id": "16899217" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebaf" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Moreover, SH-PTP1 is constitutively phosphorylated on tyrosine in the Lck-overexpressing lymphoma cell line LSTRA. SH-PTP1 is also a good substrate for recombinant Lck in vitro. Comparisons of the tryptic phosphopeptide maps of wild-type SH-PTP1 and deletion and point mutations establish that the two sites (Y-536 and Y-564) which are directly phosphorylated by Lck in vitro are also phosphorylated in vivo in LSTRA cells.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 1994 Mar 14(3) 1824-34", "id": "8114715" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba9" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "We show that Crry increases early TCR-dependent activation signals, including p56lck-, zeta-associated protein-70 (ZAP-70), Vav-1, Akt, and extracellular signal-regulated kinase (ERK) phosphorylation but also costimulation-dependent mitogen-activated protein kinases (MAPK), such as the stress-activated c-Jun N-terminal kinase (JNK). It is intriguing that Crry costimulus enhanced p38 MAPK activation in T helper cell type 1 (Th1) but not in Th2 cells. ", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2005 Dec 78(6) 1386-96", "id": "16301324" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb67" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Tyr174 of Vav is thought to be the site of phosphorylation by Lck that regulates Vav function (18). A Vav mutant protein with a Tyr (Y) to Phe (F) substitution at position 174 (Y174F) was not phosphorylated by Lck in vitro (Fig. 2A)", "citation": { "type": "PubMed", "name": "Science 1998 Jan 23 279(5350) 558-60", "id": "9438848" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb55" }, "experiment_context": { "species_common_name": "Rat", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Here we report a novel signaling pathway whereby RhoA can efficiently modulate Stat3 transcriptional activity by inducing its simultaneous tyrosine and serine phosphorylation. Tyrosine phosphorylation is exerted via a member of the Src family of kinases (SrcFK) and JAK2, whereas the JNK pathway mediates serine phosphorylation.", "citation": { "type": "PubMed", "name": "Mol Biol Cell 2001 Oct 12(10) 3282-94", "id": "11598209" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb4a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Evasion from apoptosis is a hallmark of cancer, and recent success using targeted therapeutics underscores the importance of identifying anti-apoptotic survival pathways. Here we utilize RNA interference (RNAi) to systematically screen the kinase and phosphatase component of the human genome.", "citation": { "type": "PubMed", "name": "Nat Cell Biol 2005 Jun 7(6) 591-600", "id": "15864305" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb1e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "The protein-tyrosine kinase Csk is one of the main down-regulators of the Src family of kinases. Csk may be involved in the down-regulation of T cell receptor (TCR) signaling by C-terminal tyrosine phosphorylation of Lck and Fyn; however, it is not known how Csk activity is regulated or how it targets these Src family members.", "citation": { "type": "PubMed", "name": "J Biol Chem 1996 Apr 19 271(16) 9698-703", "id": "8621646" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb16" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "The phosphatidylinositol 3-kinase (PI3K) and the tyrosine phosphatase SHP-1 are two Lck substrates that have been implicated in TCR signaling. By contrast, a truncated SHP-1 mutant lacking the Lck phosphorylation site (Tyr(564)) failed to bind p85.", "citation": { "type": "PubMed", "name": "J Biol Chem 1999 Sep 24 274(39) 27583-9", "id": "10488096" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf9" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Defects in TCR-mediated signals underlying these abnormalities have now been investigated using CD45-null T cells. No T cell proliferation was detected in response to a CD3 mAb. In thymocytes the p56(lck) and p59(fyn) tyrosine kinases were hyperphosphorylated, and p56(lck) was in its inactive conformation.", "citation": { "type": "PubMed", "name": "J Immunol 1997 Jun 15 158(12) 5773-82", "id": "9190928" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae5" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "In addition, CD38 ligation resulted in an elevated tyrosine kinase activity of the CD38-associated Lck and ultimate activation of interleukin-2 gene transcription. Furthermore, expression of a kinase-deficient Lck mutant suppressed interleukin-2 gene activation in a dose-dependent manner.", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Jan 21 275(3) 1685-90", "id": "10636863" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae3" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "In contrast to receptor tyrosine kinases, both CD2 and TCR lack cytoplasmic kinase domains and must activate Lck, a lipid-modified protein diffusing in the inner leaflet of the membrane. ", "citation": { "type": "PubMed", "name": "J Cell Biol 2009 May 4 185(3) 521-34", "id": "19398758" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec76" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "2) both Fyn and Lck are capable of phosphorylating DAP12; and 3) both kinases coimmunoprecipitate with the Ly-49D/DAP12 complex in NK cells.", "citation": { "type": "PubMed", "name": "J Immunol 2006 Jun 1 176(11) 6615-23", "id": "16709819" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec75" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Nov 24 275(47) 37224-31", "id": "10978311" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec37" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2002 May 22(10) 3527-36", "id": "11971983" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec30" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "The protein-tyrosine kinase p56lck catalyzed phosphorylation of GST-Erk1 at two autophosphorylations sites, including Tyr-204, and at a novel site.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 1993 Aug 13(8) 4679-90", "id": "7687743" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec23" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Yes and Lck are known to be enriched in rafts and may mediate the activation of Shc when Fyn is not expressed.", "citation": { "type": "PubMed", "name": "Science 1999 Aug 13 285(5430) 1028-32", "id": "10446041" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec21" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Eur J Biochem 2001 Dec 268(23) 6083-96", "id": "11733002" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec1f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Lck phosphorylated ezrin in vitro, and the major phosphotyrosine was identified as Y145", "citation": { "type": "PubMed", "name": "FEBS Lett 2003 Jan 30 535(1-3) 82-6", "id": "12560083" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebf0" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Interestingly, lck (null) CD8+ T cells expressed higher levels of CD48 when compared with wt T cells", "citation": { "type": "PubMed", "name": "J Immunol 2004 Jul 1 173(1) 174-80", "id": "15210772" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebde" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 1997 Jun 6 272(23) 14562-70", "id": "9169414" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebda" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "We have identified IL-7-induced activation of three cyoplasmic tyrosine kinases in T cells, Jak1, Jak3, and the src-like kinase p56lck.", "citation": { "type": "PubMed", "name": "Blood 1995 Sep 15 86(6) 2077-85", "id": "7662955" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd6" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "FEBS Lett 1999 Mar 26 447(2-3) 241-6", "id": "10214954" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd5" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Slit-2 can block the CXCL12-induced activation of the Src and Lck kinases", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2007 Sep 82(3) 465-76", "id": "17565045" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebc7" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Feb 14 278(7) 5163-71", "id": "12454019" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebbb" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Eur J Immunol 2001 Apr 31(4) 1191-8", "id": "11298344" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb9" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Oncogene 2002 Apr 4 21(15) 2357-64", "id": "11948419" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb5" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Coexpression in 293T cells demonstrated that Lck kinase activity and Cbl ubiquitin ligase activity were essential for Lck ubiquitination", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2002 Mar 19 99(6) 3794-9", "id": "11904433" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eba6" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Mol Pharmacol 2002 Sep 62(3) 672-9", "id": "12181444" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb90" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 2000 Feb 4 275(5) 3603-9", "id": "10652356" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb77" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "9525940;9102067;12522270;11997497;10829062", "citation": { "type": "PubMed", "name": "BMC Bioinformatics 2004 Jun 22 5 79", "id": "15212693" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb75" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "In heterogeneous COS-1 cells, Cbl-b was phosphorylated on tyrosine residues by both Syk- (Syk/Zap-70) and Src- (Fyn/Lck) family kinases", "citation": { "type": "PubMed", "name": "Oncogene 1999 Feb 4 18(5) 1147-56", "id": "10022120" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb69" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "A synthetic peptide modeled after the putative regulatory phosphorylation site in murine p42mapk (Tyr185) was phosphorylated by p56lck with a similar Vmax, but a fivefold lower Michaelis constant (Km) than a peptide containing the Tyr394 autophosphorylation site from p56lck. ", "citation": { "type": "PubMed", "name": "Science 1992 Feb 14 255(5046) 853-5", "id": "1311128" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb65" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 1998 Aug 7 273(32) 20487-93", "id": "9685404" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb62" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Mol Endocrinol 1995 Jan 9(1) 24-33", "id": "7539106" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb5f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Lck-mediated phosphorylation and activation of ZAP-70 was defective in Fyn-/- cells.", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2004 Oct 12 101(41) 14859-64", "id": "15465914" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb4e" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Biochemistry 2005 Nov 22 44(46) 15257-68", "id": "16285729" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb41" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Modified assertion", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 1994 Feb 1 91(3) 873-7", "id": "7508123" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb35" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "the src-family PTK, lck, phosphorylates PI3K but also SHP-1, an SH2 domain-containing non-receptor tyrosine phosphatase SHP-1 was proposed to regulate lck-induced PI3K phosphorylation and activity", "citation": { "type": "PubMed", "name": "Mol Immunol 2002 Jun 38(15) 1087-99", "id": "12044776" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb2c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Fyn plays an essential role by positive regulation of Lck activity.", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2004 Oct 12 101(41) 14859-64", "id": "15465914" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb28" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "naive T-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": " Overexpression of c-Cbl, a ligand of the Lck SH3 domain, depleted Lck from lipid rafts in Jurkat cells", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Feb 15 277(7) 5683-91", "id": "11741956" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb19" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "# Ariadne: Cross-linking CD44 on T cells activates the tyrosine kinase, p56 Lck (Lck), which associates with CD44 ( 14 ). [Regulation]", "citation": { "type": "PubMed", "name": "J Biol Chem 2001 Aug 3 276(31) 28767-73", "id": "11369760" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb17" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Kinase-active Lck complexes with and activates Fyn", "citation": { "type": "PubMed", "name": "J Biol Chem 2008 Sep 26 283(39) 26409-22", "id": "18660530" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb10" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "Fibroblasts", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "We first observed that in the absence of Jak3, both Lck and Syk had the capacity to phosphorylate Stat3 and Stat5a.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2000 Jun 20(12) 4371-80", "id": "10825200" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb0c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Blood 2002 Feb 1 99(3) 957-65", "id": "11806999" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb0a" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Aug 22 278(34) 31972-9", "id": "12783885" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaff" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "J Biol Chem 1998 Jun 19 273(25) 15765-72", "id": "9624175" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaf1" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "In T cells, CD4/Lck-dependent tyrosine phosphorylation on Shc was markedly diminished when Y317 was mutated, suggesting a preference of Lck for the Y317 site.", "citation": { "type": "PubMed", "name": "Eur J Immunol 1998 Aug 28(8) 2265-75", "id": "9710204" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaef" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "the association of APS with Vav3 in turn enhanced the Lck-mediated phosphorylation of Vav3.", "citation": { "type": "PubMed", "name": "Oncogene 2002 Oct 31 21(50) 7720-9", "id": "12400014" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eae6" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Luciferase reporter gene assay indicated that Lck induces NFkappaB-dependent urokinase type plasminogen activator (uPA) promoter activity in presence of H/R.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Dec 26 278(52) 52598-612", "id": "14534291" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eade" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "Lnk is tyrosine-phosphorylated by p56lck.", "citation": { "type": "PubMed", "name": "J Immunol 2000 May 15 164(10) 5199-206", "id": "10799879" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead6" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:LCK) actsIn kin(p(HGNC:LCK))", "summary_text": "PhosphoElm data from PMID 15212693", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2001 Jun 5 98(12) 6587-92", "id": "11381116" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eacf" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:FOXO3)", "relation": "decreases", "target": "bp(GO:\"CD8-positive, alpha-beta T cell proliferation\")", "directed": false, "label": "p(HGNC:FOXO3) decreases bp(GO:\"CD8-positive, alpha-beta T cell proliferation\")", "metadata": { "casual": false, "createdBy": "edwardsanders", "edgeId": "52d177f3bf21ca0758e0b198", "evidences": [ { "bel_statement": "p(HGNC:FOXO3) decreases bp(GO:\"CD8-positive, alpha-beta T cell proliferation\")", "summary_text": "\"These data suggested that FOXO3 downregulates the accumulation of CD8 T cells in tissue specific fashion during an acute LCMV [lymphocytic choriomeningitis virus] infection.\" (p. 3)", "citation": { "type": "Other", "name": "Sullivan JA, Kim EH, Plisch EH, \"FOXO3 regulates CD8 T cell memory by T cell-intrinsic mechanisms,\" PLoS Pathog, 2012, 8:1002533.", "id": "22359505" }, "metadata": { "created_by": "edwardsanders", "id": "52d177f3bf21ca0758e0b130" }, "experiment_context": { "species_common_name": "mouse", "disease": "Viral infection", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:IL15)", "relation": "directlyIncreases", "target": "cat(p(MGI:Il2rg))", "directed": false, "label": "p(HGNC:IL15) directlyIncreases cat(p(MGI:Il2rg))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051486", "evidences": [ { "bel_statement": "p(HGNC:IL15) directlyIncreases cat(p(MGI:Il2rg))", "summary_text": "IL-7 and IL-15 were identified as the cytokines responsible for CD8+ cytotoxic T cell lineage specification in vivo. Additionally, we found that small numbers of aberrant CD8+ T cells expressing Runx3d could arise without γc signaling, but these cells were developmentally arrested before expressing cytotoxic lineage genes. Thus, γc-transduced cytokine signals are required for cytotoxic lineage specification", "citation": { "type": "Other", "name": "", "id": "23109710" }, "metadata": { "created_by": "ilyayudkevichstudent", "id": "5305896f89e3620d90b7de09" }, "experiment_context": { "species_common_name": "mouse", "disease": "", "cell": "", "tissue": "cd8+ t cells" } }, { "bel_statement": "p(HGNC:IL15) directlyIncreases cat(p(MGI:Il2rg))", "summary_text": "IL-15 utilizes ... the common cytokine receptor γ-chain (CD132) for signal transduction in lymphocytes", "citation": { "type": "Other", "name": "", "id": "20335267" }, "metadata": { "created_by": "ilyayudkevichstudent", "id": "52fadfeb89e3620e1c996675" }, "experiment_context": { "species_common_name": "human", "disease": "", "cell": "", "tissue": "lung" } }, { "bel_statement": "p(HGNC:IL15) directlyIncreases cat(p(MGI:Il2rg))", "summary_text": "This Network edge has no supporting evidence. Please add real evidence to this edge prior to deleting.", "citation": { "type": "PubMed", "name": "", "id": "0" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb31" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:IDO2)", "relation": "negativeCorrelation", "target": "p(HGNC:IDO1)", "directed": false, "label": "p(HGNC:IDO2) negativeCorrelation p(HGNC:IDO1)", "metadata": { "casual": false, "createdBy": "csauco", "edgeId": "548b0b2d89e3620fe090276c", "evidences": [ { "bel_statement": "p(HGNC:IDO2) negativeCorrelation p(HGNC:IDO1)", "summary_text": "Heme-binding-mediated negative regulation of the tryptophan metabolic enzyme indoleamine 2,3-dioxygenase 1 (IDO1) by IDO2 .// hIDO2 plays a novel role as a negative regulator of hIDO1 by competing for heme-binding with hIDO1", "citation": { "type": "Other", "name": "Lee YK1, Lee HB1, Shin DM2, Kang MJ3, Yi EC3, Noh S4, Lee J4, Lee C4, Min CK5, Choi EY1", "id": "25394548" }, "metadata": { "created_by": "mirymg96", "id": "548b3a6989e3620fe091fff0" }, "experiment_context": { "species_common_name": "human", "disease": "cancer and immunological disorders", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:IDO2) negativeCorrelation p(HGNC:IDO1)", "summary_text": "These results demonstrate that hIDO2 plays a novel role as a negative regulator of hIDO1 by competing for heme-binding with hIDO1, and provide information useful for development of therapeutic strategies to control cancer and immunological disorders that target IDO molecules.", "citation": { "type": "Other", "name": "", "id": "25394548" }, "metadata": { "created_by": "csauco", "id": "548b0b2d89e3620fe09026ff" }, "experiment_context": { "species_common_name": "human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:CCR5)", "relation": "increases", "target": "path(SDIS:\"T-cell migration\")", "directed": false, "label": "p(HGNC:CCR5) increases path(SDIS:\"T-cell migration\")", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051495", "evidences": [ { "bel_statement": "p(HGNC:CCR5) increases path(SDIS:\"T-cell migration\")", "summary_text": "Most importantly, CCR5 deficiency resulted in decreased recruitment of memory T cells expressing key effector molecules and impaired control of virus replication during the initial stages of a secondary response. ", "citation": { "type": "PubMed", "name": "Immunity 2008 Jul 29(1) 101-13", "id": "18617426" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec4c" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "t-cell", "tissue": "" } } ] } }, { "source": "p(HGNC:CCL3)", "relation": "directlyIncreases", "target": "cat(p(HGNC:CCR5))", "directed": false, "label": "p(HGNC:CCL3) directlyIncreases cat(p(HGNC:CCR5))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051472", "evidences": [ { "bel_statement": "p(HGNC:CCL3) directlyIncreases cat(p(HGNC:CCR5))", "summary_text": "Numerous studies have shown that immature human and mouse blood- and bone marrow-derived DC subsets express a panel of inflammatory chemokine receptors (CCR1-6,8,9, CXCR3,4, CX3CR1) [Table 1 and reviewed in (1-5)]. [Table 1 Chemokine receptors expressed by DC and the functional outcome of receptor ligation}]", "citation": { "type": "PubMed", "name": "Clin Lab Med 2008 Sep 28(3) 375-84, v", "id": "19028258" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec01" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "dendritic cell", "tissue": "" } }, { "bel_statement": "p(HGNC:CCL3) directlyIncreases cat(p(HGNC:CCR5))", "summary_text": "CL3 (previously known as MIP-1α) is a ligand for the chemokine receptors CCR1 and CCR5, and we previously showed that expression of CCR5 by lung CD8+ T cells increases with spirometrically-defined COPD severity", "citation": { "type": "Other", "name": "", "id": "23374856" }, "metadata": { "created_by": "ilyayudkevichstudent", "id": "53057fa789e36207f81f6980" }, "experiment_context": { "species_common_name": "human", "disease": "copd", "cell": "", "tissue": "cd8+ t cells" } }, { "bel_statement": "p(HGNC:CCL3) directlyIncreases cat(p(HGNC:CCR5))", "summary_text": "The CCR5 ligands CCL3 and CCL4 are produced by Th cells and DCs after their antigen-specific interaction, thereby creating a chemokine micromilieu which recruits naïve CCR5-expressing CTLs.", "citation": { "type": "Other", "name": "", "id": "22566821" }, "metadata": { "created_by": "ilyayudkevichstudent", "id": "52f9947a89e3620ba81606ae" }, "experiment_context": { "species_common_name": "human", "disease": "", "cell": "cytotoxic t cells", "tissue": "" } } ] } }, { "source": "complex(SCOMP:\"T Cell Receptor Complex\")", "relation": "actsIn", "target": "cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "directed": false, "label": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "metadata": { "casual": false, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c3405146f", "evidences": [ { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "Jurkat transfectants overexpressing Chat-H show a marked increase in interleukin-2 production after costimulation of T cell receptor and CD28. The degree of JNK activation is enhanced substantially in the Chat-H transfectants upon costimulation. WE found that Chat-H forms a complex with Pyk2H and enhances its tyrosine 402 phosphorylation, an up-regulator of the JNK pathway. The Src homology-2 domain mutant of Chat-H loses this signal modulating activity.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Feb 21 278(8) 6012-7", "id": "12486027" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eafc" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "Indeed, our present findings that in vivo CD28 costimulation induced both TCR? down-regulation and CD69 up-regulation supports the importance of Lck binding to CD28 for costimulatory function, because both TCR? down-regulation and CD69 up-regulation require Lck activation (18, 19, 25), and we found that signaling of both functions required an intact Lck binding motif in the CD28 cytosolic tail. In CTLA-4-deficient mice, disease induction requires CD28 enhancement of TCR signaling by autoreactive TCR specificities with presumably high affinity for self-ligands, because disease induction is delayed by in vivo expression of transgenic TCRs with low affinity for self ligands (26, 27). Thus, we think that the importance of an intact Lck binding motif in the CD28 cytosolic tail for disease induction in CTLA-4-deficient mice reflects the fact that, by increasing the residency time of Lck in the immunological synapse, CD28 costimulation specifically increases the intensity and duration of in vivo TCR signaling by T cells with autoreactive TCR specificities. ", "citation": { "type": "PubMed", "name": "Proc Natl Acad Sci U S A 2007 Aug 21 104(34) 13756-61", "id": "17702861" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec6e" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "We show that Crry increases early TCR-dependent activation signals, including p56lck-, zeta-associated protein-70 (ZAP-70), Vav-1, Akt, and extracellular signal-regulated kinase (ERK) phosphorylation but also costimulation-dependent mitogen-activated protein kinases (MAPK), such as the stress-activated c-Jun N-terminal kinase (JNK). It is intriguing that Crry costimulus enhanced p38 MAPK activation in T helper cell type 1 (Th1) but not in Th2 cells. ", "citation": { "type": "PubMed", "name": "J Leukoc Biol 2005 Dec 78(6) 1386-96", "id": "16301324" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebdd" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "On stimulation of the cells through their T cell antigen receptor, the phosphotyrosine content of LMPTP-B declined rapidly. In co-transfected COS cells, Lck and Fyn caused phosphorylation of LMPTP, whereas Csk, Zap, and Jak2 did not. Most of the phosphate was located at Tyr-131, and some was also located at Tyr-132. Incubation of wild-type LMPTP with Lck and adenosine 5'-O-(thiotriphosphate) caused a 2-fold increase in the activity of LMPTP. Site-directed mutagenesis showed that Tyr-131 is important for the catalytic activity of LMPTP, and that thiophosphorylation of Tyr-131, and to a lesser degree Tyr-132, is responsible for the activation.", "citation": { "type": "PubMed", "name": "J Biol Chem 1997 Feb 28 272(9) 5371-4", "id": "9038134" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb0" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "Conclusion. The molecular basis of {gamma}{delta} TCR recognition has remained an enigma, as has the immunobiology of {gamma}{delta} T cells. Many characteristics of {gamma}{delta} T cells suggest that they participate early in the immune response, similar to other members of the innate immune system. These features include direct recognition of antigen and immediate effector outcomes such as cytokine release (30) and cytotoxicity (31).", "citation": { "type": "PubMed", "name": "Science 2005 Apr 8 308(5719) 227-31", "id": "15821084" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb76" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "It is well established that T lymphocytes undergo homeostatic proliferation in lymphopenic environment. The homeostatic proliferation requires recognition of the major histocompatibility complex on the host. Recent studies have demonstrated that costimulation-mediated CD28, 4-1BB, and CD40 is not required for T cell homeostatic proliferation. It has been suggested that homeostatic proliferation is costimulation independent. Here, we report that T cells from mice with a targeted mutation of CD24 have a remarkably reduced rate of proliferation when adoptively transferred into syngeneic lymphopenic hosts.", "citation": { "type": "PubMed", "name": "J Exp Med 2004 Oct 18 200(8) 1083-9", "id": "15477346" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb53" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "lymphocyte", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "It is well established that T lymphocytes undergo homeostatic proliferation in lymphopenic environment. The homeostatic proliferation requires recognition of the major histocompatibility complex on the host. Recent studies have demonstrated that costimulation-mediated CD28, 4-1BB, and CD40 is not required for T cell homeostatic proliferation. It has been suggested that homeostatic proliferation is costimulation independent. Here, we report that T cells from mice with a targeted mutation of CD24 have a remarkably reduced rate of proliferation when adoptively transferred into syngeneic lymphopenic hosts.", "citation": { "type": "PubMed", "name": "J Exp Med 2004 Oct 18 200(8) 1083-9", "id": "15477346" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb48" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "Overexpression of an SH2 domain-defective Shb causes diminished phosphorylation of SLP-76 and Vav and consequently decreased activation of c-Jun kinase upon T cell receptor (TCR) stimulation. -- [bcd] from FFT figure 5C: The precipitated proteins were resolved on SDS/PAGE and blotted for phosphotyrosine (4G10)", "citation": { "type": "PubMed", "name": "Eur J Biochem 2002 Jul 269(13) 3279-88", "id": "12084069" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec3b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "recognition of the MHC class II heterodimer-antigen complex by the T-cell receptor and the accessory protein CD4 of T lymphocytes leads to the generation of an immune response", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2001 Oct 21(19) 6495-506", "id": "11533238" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebd0" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "We have previously demonstrated that a signal via TSA-1/Sca-2 inhibits T cell receptor (TCR)-mediated T cell activation and apoptosis.", "citation": { "type": "PubMed", "name": "J Biol Chem 1998 May 15 273(20) 12301-6", "id": "9575182" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebb2" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "In contrast, DCs incubated with aluminum/OVA activated CD4(+) T cells to secrete IL-4 and IL-5 as well as IFN-gamma.", "citation": { "type": "PubMed", "name": "Vaccine 2007 Jun 6 25(23) 4575-85", "id": "17485153" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ebad" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "T-cell", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "Pleckstrin-2 expressed in Jurkat T cells bound to the cellular membrane and enhanced actin-dependent spreading only after stimulation of the T-cell antigen receptor or the integrin alpha4beta1.", "citation": { "type": "PubMed", "name": "Blood 2007 Feb 1 109(3) 1147-55", "id": "17008542" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb8c" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "K9/vIRF-1 and K10.5/K10.6/vIRF-3. In addition to the latently expressed vIRFs discussed above, KSHV encodes two other homologs of these proteins (Fig. 1). vIRF-1, encoded by K9, transforms cells in culture, is tumorigenic in nude mice, and inhibits apoptosis induced by Sendai virus infection, IFN-{alpha}, IFN-ß, TNF-{alpha}, TCR/CD3 cross-linking, and p53", "citation": { "type": "PubMed", "name": "Microbiol Mol Biol Rev 2003 Jun 67(2) 175-212, table of contents", "id": "12794189" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb7f" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "HIP-55 (SH3P7 or mAbp1), an actin-binding adaptor protein, interacts with and is tyrosine phosphorylated by ZAP-70, which is a crucial proximal protein tyrosine kinase for TCR signaling. HIP-55 knockout T cells displayed defective T-cell proliferation, decreased cytokine production, and decreased up-regulation of the activation markers induced by TCR stimulation.", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2005 Aug 25(16) 6869-78", "id": "16055701" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb7e" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "An increase in the surface density of activating ligand (immobilized anti-TcR mAb) enhanced both secretion of IFN and secretion of granules.", "citation": { "type": "PubMed", "name": "Cell Immunol 1989 Nov 124(1) 64-76", "id": "2478302" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb5b" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "To better understand the contribution of the P-I region to PLC-gamma1 activation, we mapped the PLC-gamma1-binding site within the region, and created a SLP-76 mutant that fails to bind SH3(PLC), but is fully functional, mediating TCR-induced phosphorylation of PLC-gamma1 at tyrosine 783, calcium flux, and nuclear factor of activated T cells activation.", "citation": { "type": "PubMed", "name": "J Biol Chem 2005 Mar 4 280(9) 8364-70", "id": "15623534" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb47" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "The interaction of anti-TSA with FcgammaRIIB resulted in an inhibition of the ability of the FcgammaRIIB to cross-link and/or aggregate soluble anti-CD3 or soluble anti-Cbeta T-cell receptor (TCR), leading to an inhibition of induction of expression of CD25 and CD69, interleukin (IL)-2 production and proliferation of naive T cells.", "citation": { "type": "PubMed", "name": "Immunology 2001 Sep 104(1) 28-36", "id": "11576217" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb15" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "This redistribution brings VHR into the vicinity of the triggered TCRs, where VHR is phosphorylated at Tyr138 by ZAP-70. We found that this phosphorylation is required for the function of VHR as an inhibitor of the Erk2 and Jnk MAPKs", "citation": { "type": "PubMed", "name": "Nat Immunol 2003 Jan 4(1) 44-8", "id": "12447358" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ead0" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "t-cell", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "HIP-55 interacted with ZAP-70, a critical protein-tyrosine kinase in TCR signaling, and this interaction was induced by TCR signaling.", "citation": { "type": "PubMed", "name": "J Biol Chem 2003 Dec 26 278(52) 52195-202", "id": "14557276" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb99" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "An increase in the surface density of activating ligand (immobilized anti-TcR mAb) enhanced both secretion of IFN and secretion of granules.", "citation": { "type": "PubMed", "name": "Cell Immunol 1989 Nov 124(1) 64-76", "id": "2478302" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb95" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "Hef1 is tyrosine-phosphorylated following beta-1-integrin and/or T cell receptor stimulation and is thus considered to be important for immunological reactions", "citation": { "type": "PubMed", "name": "J Biol Chem 2002 Apr 26 277(17) 14933-41", "id": "11827972" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb8e" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "T cell receptor signaling increased expression of the protein arginine methyltransferase PRMT1, which in turn methylated the nuclear factor of activated T cells (NFAT) cofactor protein, NIP45.", "citation": { "type": "PubMed", "name": "Mol Cell 2004 Aug 27 15(4) 559-71", "id": "15327772" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eb32" }, "experiment_context": { "species_common_name": "Mouse", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "We have previously demonstrated that a signal via TSA-1/Sca-2 inhibits T cell receptor (TCR)-mediated T cell activation and apoptosis.", "citation": { "type": "PubMed", "name": "J Biol Chem 1998 May 15 273(20) 12301-6", "id": "9575182" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eafb" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "Using RNA interference and overexpression experiments, the HIP-55-HPK1 complex was found to negatively regulate nuclear factor of activated T cell (NFAT) activation by the T cell antigen receptor.", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Apr 9 279(15) 15550-60", "id": "14729663" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eaea" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "complex(SCOMP:\"T Cell Receptor Complex\") actsIn cat(complex(SCOMP:\"T Cell Receptor Complex\"))", "summary_text": "In fact, GILZ overexpression inhibits TCR-activated NF-kappaB nuclear translocation, interleukin-2 production, FasL upregulation, and the consequent activation-induced apoptosis", "citation": { "type": "PubMed", "name": "Mol Cell Biol 2002 Nov 22(22) 7929-41", "id": "12391160" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405eadb" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "a(CHEBI:acrolein)", "relation": "increases", "target": "bp(GO:\"CD8-positive, alpha-beta T cell proliferation\")", "directed": false, "label": "a(CHEBI:acrolein) increases bp(GO:\"CD8-positive, alpha-beta T cell proliferation\")", "metadata": { "casual": false, "createdBy": "edwardsanders", "edgeId": "52e8181cbf21ca0b1807399f", "evidences": [ { "bel_statement": "a(CHEBI:acrolein) increases bp(GO:\"CD8-positive, alpha-beta T cell proliferation\")", "summary_text": "\"Acrolein exposure induces a time-dependent increase in the number of CD8+ cells in the lungs of wild-type mice.\"", "citation": { "type": "Other", "name": "Borchers MT, Wesselkamper SC, Harris NL, \"CD8+ T cells contribute to macrophage accumulation and airspace enlargement following repeated irritant exposures,\" Exp Mol Pathol, 2007, 83:301-10.", "id": "17950725" }, "metadata": { "created_by": "edwardsanders", "id": "52e8181cbf21ca0b1807392e" }, "experiment_context": { "species_common_name": "mouse", "disease": "", "cell": "", "tissue": "Lung" } } ] } }, { "source": "p(HGNC:IL2RG)", "relation": "increases", "target": "bp(GO:\"T cell activation\")", "directed": false, "label": "p(HGNC:IL2RG) increases bp(GO:\"T cell activation\")", "metadata": { "casual": false, "createdBy": "mberra", "edgeId": "5477834189e36203806fca55", "evidences": [ { "bel_statement": "p(HGNC:IL2RG) increases bp(GO:\"T cell activation\")", "summary_text": "Thus, sγc expression is a naturally occurring immunomodulator that regulates γc cytokine signaling and controls T cell activation and differentiation.", "citation": { "type": "Other", "name": "Immunity. 2014 Jun 19;40(6):910-23. doi: 10.1016/j.immuni.2014.04.020. Epub 2014 Jun 5.", "id": "24909888" }, "metadata": { "created_by": "mberra", "id": "5477834189e36203806fc9e5" }, "experiment_context": { "species_common_name": "mouse", "disease": "", "cell": "", "tissue": "" } } ] } }, { "source": "p(HGNC:CXCL16)", "relation": "directlyIncreases", "target": "cat(p(HGNC:CXCR6))", "directed": false, "label": "p(HGNC:CXCL16) directlyIncreases cat(p(HGNC:CXCR6))", "metadata": { "casual": true, "createdBy": "selventa", "edgeId": "524b3517d3fbfd4c34051478", "evidences": [ { "bel_statement": "p(HGNC:CXCL16) directlyIncreases cat(p(HGNC:CXCR6))", "summary_text": "Interleukin-22 (IL-22) plays a critical role in mucosal defense, although the molecular mechanisms that ensure IL-22 tissue distribution remain poorly understood. We show that the CXCL16-CXCR6 chemokine-chemokine receptor axis regulated group 3 innate lymphoid cell (ILC3) diversity and function. CXCL16 was constitutively expressed by CX3CR1(+) intestinal dendritic cells (DCs) and coexpressed with IL-23 after Citrobacter rodentium infection. Intestinal ILC3s expressed CXCR6 and its ablation generated a selective loss of the NKp46(+) ILC3 subset, a depletion of intestinal IL-22, and the inability to control C. rodentium infection. CD4(+) ILC3s were unaffected by CXCR6 deficiency and remained clustered within lymphoid follicles. In contrast, the lamina propria of Cxcr6(-/-) mice was devoid of ILC3s. The loss of ILC3-dependent IL-22 epithelial stimulation reduced antimicrobial peptide expression that explained the sensitivity of Cxcr6(-/-) mice to C. rodentium. Our results delineate a critical CXCL16-CXCR6 crosstalk that coordinates the intestinal topography of IL-22 secretion required for mucosal defense.", "citation": { "type": "Other", "name": "Immunity. 2014 Nov 20;41(5):776-88. doi: 10.1016/j.immuni.2014.10.007. Epub 2014 Nov 6", "id": "25456160" }, "metadata": { "created_by": "alejandroferreiromorales.cr", "id": "548c384c89e3620fe0a1ec79" }, "experiment_context": { "species_common_name": "human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCL16) directlyIncreases cat(p(HGNC:CXCR6))", "summary_text": "CXCR6, the receptor for CXCL16", "citation": { "type": "PubMed", "name": "J Biol Chem 2004 Jan 30 279(5) 3188-96", "id": "14625285" }, "metadata": { "created_by": "selventa", "id": "524b351fd3fbfd4c3405ec26" }, "experiment_context": { "species_common_name": "Human", "disease": "", "cell": "", "tissue": "" } }, { "bel_statement": "p(HGNC:CXCL16) directlyIncreases cat(p(HGNC:CXCR6))", "summary_text": "We have found that expression of CXCR6 is greatly increased on BAL T cells compared with blood T cells and that there are very high levels of CXCL16 in both the normal and inflamed lung which is predominantly located in AM.", "citation": { "type": "Other", "name": "", "id": "16393323" }, "metadata": { "created_by": "ilyayudkevichstudent", "id": "52fadeec89e3620e1c9964c6" }, "experiment_context": { "species_common_name": "human", "disease": "", "cell": "", "tissue": "lung" } } ] } }, { "source": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\")", "relation": "increases", "target": "p(HGNC:IFNG)", "directed": false, "label": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:IFNG)", "metadata": { "casual": false, "createdBy": "ganna.androsova", "edgeId": "5538c4a689e362097c008f8b", "evidences": [ { "bel_statement": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:IFNG)", "summary_text": "CD8/CD28(null) cells were increased in both current- and ex-smoker COPD groups; these cells expressed significantly more interferon (IFN)-γ, OX40, 4-1BB, CTLA4, granzyme and perforin when stimulated than CD8/CD28(+) T cells.", "citation": { "type": "Other", "name": "", "id": "21910726" }, "metadata": { "created_by": "ganna.androsova", "id": "5538c4a589e362097c008f0b" }, "experiment_context": { "species_common_name": "human", "disease": "COPD", "cell": "CD8/CD28(null) T cells", "tissue": "lung" } } ] } }, { "source": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\")", "relation": "increases", "target": "p(HGNC:TNFRSF4)", "directed": false, "label": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:TNFRSF4)", "metadata": { "casual": false, "createdBy": "ganna.androsova", "edgeId": "5538c59389e362097c010f2a", "evidences": [ { "bel_statement": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:TNFRSF4)", "summary_text": "CD8/CD28(null) cells were increased in both current- and ex-smoker COPD groups; these cells expressed significantly more interferon (IFN)-γ, OX40, 4-1BB, CTLA4, granzyme and perforin when stimulated than CD8/CD28(+) T cells.", "citation": { "type": "Other", "name": "", "id": "21910726" }, "metadata": { "created_by": "ganna.androsova", "id": "5538c59389e362097c010ea8" }, "experiment_context": { "species_common_name": "human", "disease": "COPD", "cell": "CD8/CD28(null) cells", "tissue": "lung" } } ] } }, { "source": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\")", "relation": "increases", "target": "p(HGNC:CTLA4)", "directed": false, "label": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:CTLA4)", "metadata": { "casual": false, "createdBy": "ganna.androsova", "edgeId": "5538dd3389e362097c020e86", "evidences": [ { "bel_statement": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:CTLA4)", "summary_text": "CD8/CD28(null) cells were increased in both current- and ex-smoker COPD groups; these cells expressed significantly more interferon (IFN)-γ, OX40, 4-1BB, CTLA4, granzyme and perforin when stimulated than CD8/CD28(+) T cells.", "citation": { "type": "Other", "name": "", "id": "21910726" }, "metadata": { "created_by": "ganna.androsova", "id": "5538dd3289e362097c020e02" }, "experiment_context": { "species_common_name": "human", "disease": "COPD", "cell": "CD8/CD28(null) cells", "tissue": "lung" } } ] } }, { "source": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\")", "relation": "increases", "target": "p(HGNC:GZMA)", "directed": false, "label": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:GZMA)", "metadata": { "casual": false, "createdBy": "ganna.androsova", "edgeId": "5538ddd889e362097c02512f", "evidences": [ { "bel_statement": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:GZMA)", "summary_text": "CD8/CD28(null) cells were increased in both current- and ex-smoker COPD groups; these cells expressed significantly more interferon (IFN)-γ, OX40, 4-1BB, CTLA4, granzyme and perforin when stimulated than CD8/CD28(+) T cells.", "citation": { "type": "Other", "name": "", "id": "21910726" }, "metadata": { "created_by": "ganna.androsova", "id": "5538ddd889e362097c0250a9" }, "experiment_context": { "species_common_name": "human", "disease": "COPD", "cell": "CD8/CD28(null) cells", "tissue": "lung" } } ] } }, { "source": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\")", "relation": "increases", "target": "p(HGNC:TNF)", "directed": false, "label": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:TNF)", "metadata": { "casual": false, "createdBy": "ganna.androsova", "edgeId": "5538e40e89e362097c02f11e", "evidences": [ { "bel_statement": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:TNF)", "summary_text": "There was an increase in intracellular CD8(+) T cell Th1 proinflammatory cytokines in some COPD groups in the peripheral blood and in CD8(+) T cell tumour necrosis factor (TNF)-alpha in some COPD groups and smoker controls in BAL and BB.", "citation": { "type": "Other", "name": "", "id": "17614970" }, "metadata": { "created_by": "ganna.androsova", "id": "5538e40e89e362097c02f092" }, "experiment_context": { "species_common_name": "human", "disease": "COPD", "cell": "CD8(+) T cell", "tissue": "lung" } } ] } }, { "source": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\")", "relation": "increases", "target": "p(HGNC:PRF1)", "directed": false, "label": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:PRF1)", "metadata": { "casual": false, "createdBy": "ganna.androsova", "edgeId": "5538decb89e362097c025643", "evidences": [ { "bel_statement": "path(MESHD:\"Pulmonary Disease, Chronic Obstructive\") increases p(HGNC:PRF1)", "summary_text": "CD8/CD28(null) cells were increased in both current- and ex-smoker COPD groups; these cells expressed significantly more interferon (IFN)-γ, OX40, 4-1BB, CTLA4, granzyme and perforin when stimulated than CD8/CD28(+) T cells.", "citation": { "type": "Other", "name": "", "id": "21910726" }, "metadata": { "created_by": "ganna.androsova", "id": "5538decb89e362097c0255bb" }, "experiment_context": { "species_common_name": "human", "disease": "COPD", "cell": "CD8/CD28(null) cells", "tissue": "lung" } } ] } } ] } } pybel-0.15.5/src/pybel/testing/resources/bel/isolated.bel000066400000000000000000000025431426625374700233640ustar00rootroot00000000000000################################################################################## # Document Properties Section SET DOCUMENT Name = "PyBEL Test Isolated Nodes" SET DOCUMENT Description = "Tests the effect of using isolated nodes in IO" SET DOCUMENT Version = "0.1.0" SET DOCUMENT Copyright = "Copyright (c) Charles Tapley Hoyt. All Rights Reserved." SET DOCUMENT Authors = "Charles Tapley Hoyt" SET DOCUMENT Licenses = "WTF License" SET DOCUMENT ContactInfo = "cthoyt@gmail.com" ################################################################################## # Definitions Section DEFINE NAMESPACE HGNC AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/hgnc-names.belns" DEFINE NAMESPACE MESHD AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/mesh-names.belns" ################################################################################## # Statements Section ################################################################################## SET Citation = {"PubMed","That one article from last week","123455"} SET Evidence = "These are mostly made up" #: Test that there's an isolated node that makes it path(MESHD:Achlorhydria) #: Test an isolated node that gets some extra stuff induced complex(p(HGNC:ADGRB1), p(HGNC:ADGRB2)) pybel-0.15.5/src/pybel/testing/resources/bel/misordered.bel000066400000000000000000000025741426625374700237210ustar00rootroot00000000000000################################################################################## # Document Properties Section SET DOCUMENT Name = "PyBEL Test Citation Clearing" SET DOCUMENT Description = "Made for testing PyBEL parsing without citation clearance" SET DOCUMENT Version = "1.0.0" SET DOCUMENT Copyright = "Copyright (c) Charles Tapley Hoyt. All Rights Reserved." SET DOCUMENT Authors = "Charles Tapley Hoyt" SET DOCUMENT Licenses = "WTF License" SET DOCUMENT ContactInfo = "cthoyt@gmail.com" ################################################################################## # Definitions Section DEFINE NAMESPACE HGNC AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/hgnc-names.belns" DEFINE ANNOTATION TESTAN1 AS LIST {"1","2","3"} ################################################################################## # Statements Section SET STATEMENT_GROUP = "Group 1" SET TESTAN1 = "1" SET Citation = {"PubMed","That one article from last week","123455"} SET Evidence = "Evidence 1" p(HGNC:AKT1) -> p(HGNC:EGFR) UNSET ALL SET Evidence = "Evidence 1" SET TESTAN1 = "1" SET Citation = {"PubMed","That one article from last week","123455"} p(HGNC:EGFR) -| p(HGNC:FADD) UNSET ALL SET TESTAN1 = "1" SET Evidence = "Evidence 1" SET Citation = {"PubMed","That one article from last week","123455"} p(HGNC:EGFR) =| p(HGNC:CASP8) pybel-0.15.5/src/pybel/testing/resources/bel/obo.bel000066400000000000000000000010561426625374700223350ustar00rootroot00000000000000SET DOCUMENT Name = "PyBEL Test Simple" SET DOCUMENT Description = "Made for testing PyBEL parsing" SET DOCUMENT Version = "1.6.0" SET DOCUMENT Copyright = "Copyright (c) Charles Tapley Hoyt. All Rights Reserved." SET DOCUMENT Authors = "Charles Tapley Hoyt" SET DOCUMENT Licenses = "WTF License" SET DOCUMENT ContactInfo = "cthoyt@gmail.com" SET DOCUMENT Project = "PyBEL Testing" # Definition DEFINE NAMESPACE hgnc AS PATTERN "\d+" # Statement SET Citation = {"PubMed", "123456"} SET Evidence = "Test Evidence" p(hgnc:391 ! AKT1) -> p(hgnc:3236 ! EGFR) pybel-0.15.5/src/pybel/testing/resources/bel/slushy.bel000066400000000000000000000075151426625374700231130ustar00rootroot00000000000000SET DOCUMENT Name = "Worst. BEL Document. Ever." SET DOCUMENT Description = "This document outlines all of the evil and awful work that is possible during BEL curation" SET DOCUMENT Version = "0.0" SET DOCUMENT Authors = "Charles Tapley Hoyt" SET DOCUMENT Licenses = "WTF License" # SET DOCUMENT ContactInfo = "cthoyt@gmail.com" # Missing Contact Info is required # SET DOCUMENT InvalidMetadata = "very invalid, indeed" DEFINE NAMESPACE CHEBI AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/chebi-names.belns" DEFINE NAMESPACE HGNC AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/hgnc-names.belns" DEFINE NAMESPACE MESHD AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/mesh-names.belns" DEFINE NAMESPACE GO AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/go-names.belns" DEFINE NAMESPACE dbSNP AS PATTERN "rs[0-9]+$" DEFINE ANNOTATION CellLine AS URL "https://owncloud.scai.fraunhofer.de/index.php/s/JsfpQvkdx3Y5EMx/download?path=cell-line.belanno" DEFINE ANNOTATION TextLocation AS LIST {"Abstract","Results","Legend","Review"} DEFINE ANNOTATION Disease AS URL "https://owncloud.scai.fraunhofer.de/index.php/s/JsfpQvkdx3Y5EMx/download?path=mesh-diseases.belanno" #: Name doesn't match annotation file Keyword (LexicographyWarning, not implemented yet) DEFINE ANNOTATION Specieses AS URL "https://owncloud.scai.fraunhofer.de/index.php/s/JsfpQvkdx3Y5EMx/download?path=species-taxonomy-id.belanno" DEFINE ANNOTATION PowerLevel AS PATTERN "[0-9]+$" # MissingAnnotationKeyWarning UNSET STATEMENT_GROUP # MissingAnnotationKeyWarning UNSET Citation SET STATEMENT_GROUP = "Group 1" # InvalidCitationException SET Citation = {"PubMed"} # InvalidCitationType SET Citation = {"Nope", "Incomplete", "1234"} # InvalidPubMedIdentifierWarning SET Citation = {"PubMed", "Fake Name", "Fake Reference"} # MissingCitationException p(HGNC:AKT1) -- p(HGNC:AKT2) SET Citation = {"PubMed","Trends in molecular medicine","12928037"} # MissingAnnotationKeyWarning UNSET Evidence # MissingAnnotationKeyWarning UNSET PowerLevel # MissingSupportWarning p(HGNC:AKT1) -- p(HGNC:AKT2) SET Evidence = "This is definitely real evidence" # Naked name (NakedNameWarning) biologicalProcess("response to oxidative stress") increases biologicalProcess(GO:necrosis) # UndefinedNamespaceWarning p(UNDEFINED:"YFG") -- p(HGNC:AKT1) # Missing name (MissingNamespaceNameWarning) biologicalProcess(GO:"maybe response to oxidative stress") increases biologicalProcess(GO:necrosis) # UndefinedAnnotationWarning SET UNDEFINED_ANNOTATION = "Nope." # MissingAnnotationKeyWarning UNSET TextLocation # IllegalAnnotationValueWarning SET TextLocation = "Nope" # MissingAnnotationRegexWarning SET PowerLevel = "Nine Thousand" # MissingNamespaceRegexWarning g(dbSNP:"rs123123-A") eq g(HGNC:TP53) # MalformedTranslocationWarning tloc(p(HGNC:AKT1)) -- p(HGNC:AKT2) # PlaceholderAminoAcidWarning p(HGNC:AKT1, sub(G, 1, X)) -- p(HGNC:AKT2) # NestedRelationWarning p(HGNC:AKT1) -> (p(HGNC:AKT2) -> biologicalProcess(GO:"response to oxidative stress")) # InvalidFunctionSemantic bp(HGNC:AKT1) -> p(HGNC:AKT2) # Forgot quotes (ParseException) # SET Disease = Atherosclerosis # Mixed up arguments (ParseException) p(HGNC:TP53,sub(Q,R,248)) directlyDecreases transcriptionalActivity(proteinAbundance(HGNC:TP53)) UNSET STATEMENT_GROUP ######## The following statements have no errors ############# SET STATEMENT_GROUP = "Group 2" SET Citation = {"PubMed","That one article from last week","123455"} SET Evidence = "Evidence 1" p(HGNC:AKT1) -> p(HGNC:EGFR) # As of PyBEL 15, the type is auto-lowercased, so this is valid now SET Citation = {"Pubmed", "Incomplete", "1234"} pybel-0.15.5/src/pybel/testing/resources/bel/test_bel.bel000066400000000000000000000034601426625374700233600ustar00rootroot00000000000000################################################################################## # Document Properties Section SET DOCUMENT Name = "PyBEL Test Simple" SET DOCUMENT Description = "Made for testing PyBEL parsing" SET DOCUMENT Version = "1.6.0" SET DOCUMENT Copyright = "Copyright (c) Charles Tapley Hoyt. All Rights Reserved." SET DOCUMENT Authors = "Charles Tapley Hoyt" SET DOCUMENT Licenses = "WTF License" SET DOCUMENT ContactInfo = "cthoyt@gmail.com" SET DOCUMENT Project = "PyBEL Testing" ################################################################################## # Definitions Section DEFINE NAMESPACE CHEBI AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/chebi-names.belns" DEFINE NAMESPACE HGNC AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/hgnc-names.belns" DEFINE ANNOTATION Species AS URL "https://owncloud.scai.fraunhofer.de/index.php/s/JsfpQvkdx3Y5EMx/download?path=species-taxonomy-id.belanno" DEFINE ANNOTATION CellLine AS URL "https://owncloud.scai.fraunhofer.de/index.php/s/JsfpQvkdx3Y5EMx/download?path=cell-line.belanno" ################################################################################## # Statements Section SET STATEMENT_GROUP = "Group 1" SET Citation = {"PubMed","That one article from last week","123455","2012-01-31","Example Author|Example Author2"} SET Species = "9606" SET Evidence = "Evidence 1 \ w extra notes" p(HGNC:AKT1) -> p(HGNC:EGFR) SET Evidence = "Evidence 2" SET CellLine = "10B9 cell" p(HGNC:EGFR) -| p(HGNC:FADD) p(HGNC:EGFR) =| p(HGNC:CASP8) SET Citation = {"PubMed","That other article from last week","123456"} SET Species = "10116" SET Evidence = "Evidence 3" p(HGNC:FADD) -> p(HGNC:CASP8) p(HGNC:AKT1) -- p(HGNC:CASP8) pybel-0.15.5/src/pybel/testing/resources/bel/thorough.bel000066400000000000000000000142101426625374700234110ustar00rootroot00000000000000################################################################################## # Document Properties Section SET DOCUMENT Name = "PyBEL Test Thorough" SET DOCUMENT Description = "Statements made up to contain many conceivable variants of nodes from BEL" SET DOCUMENT Version = "1.0.0" SET DOCUMENT Copyright = "Copyright (c) Charles Tapley Hoyt. All Rights Reserved." SET DOCUMENT Authors = "Charles Tapley Hoyt" SET DOCUMENT Licenses = "WTF License" SET DOCUMENT ContactInfo = "cthoyt@gmail.com" ################################################################################## # Definitions Section DEFINE NAMESPACE CHEBI AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/chebi-names.belns" DEFINE NAMESPACE HGNC AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/hgnc-names.belns" DEFINE NAMESPACE GO AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/go-names.belns" DEFINE NAMESPACE MESHD AS URL "https://raw.githubusercontent.com/pharmacome/terminology/73688d6dc24e309fca59a1340dc9ee971e9f3baa/external/mesh-names.belns" DEFINE NAMESPACE dbSNP AS PATTERN "rs[0-9]*" DEFINE NAMESPACE TESTNS2 AS URL "https://raw.githubusercontent.com/pybel/pybel/develop/tests/belns/test_ns_2.belns" DEFINE ANNOTATION TESTAN1 AS LIST {"1","2","3"} DEFINE ANNOTATION TESTAN2 AS LIST {"1","2","3"} DEFINE ANNOTATION TestRegex AS PATTERN "[0-9]+" ################################################################################## # Statements Section ################################################################################## SET Citation = {"PubMed","That one article from last week","123455"} SET Evidence = "These are mostly made up" SET TESTAN1 = {"1", "2"} SET TestRegex = "9000" a(CHEBI:"oxygen atom") -> geneAbundance(HGNC:AKT1,gmod(M)) UNSET {TESTAN1, TestRegex} g(HGNC:AKT1, loc(GO:intracellular)) -| abundance(CHEBI:"oxygen atom", loc(GO:intracellular)) g(HGNC:AKT1, var(p.Phe508del)) =| p(HGNC:AKT1) g(HGNC:AKT1,sub(G,308,A)) cnc g(fus(HGNC:TMPRSS2, c.1_79, HGNC:ERG, c.312_5034)) g(HGNC:AKT1,sub(G,308,A),loc(GO:intracellular)) -> g(HGNC:AKT1, var(p.Phe508del), sub(G,308,A), var(c.1521_1523delCTT)) m(HGNC:MIR21) => g(HGNC:BCR, fus(HGNC:JAK2, 1875, 2626)) g(HGNC:CFTR, var(c.1521_1523delCTT)) -> deg(p(HGNC:AKT1)) g(HGNC:CFTR, var(g.117199646_117199648delCTT)) -> g(HGNC:CFTR, var(c.1521_1523delCTT)) microRNAAbundance(HGNC:MIR21) -| p(HGNC:AKT1, pmod(TESTNS2:PhosRes, Ser, 473)) m(HGNC:MIR21,loc(GO:intracellular)) -| p(HGNC:AKT1, pmod(Ph, Ser, 473)) m(HGNC:MIR21,var(p.Phe508del)) -| p(HGNC:AKT1, pmod(Ph, S, 473)) m(HGNC:MIR21,var(p.Phe508del),loc(GO:intracellular)) -> p(HGNC:AKT1, var(p.C40*)) p(HGNC:AKT1, loc(GO:intracellular)) =| p(HGNC:AKT1,sub(A,127,Y),pmod(Ph, Ser),loc(GO:intracellular)) g(HGNC:CHCHD4, fusion(HGNC:AIFM1)) -> p(fus(HGNC:TMPRSS2, p.1_79, HGNC:ERG, p.312_5034)) p(HGNC:AKT1, var(p.Arg1851*)) -> p(HGNC:BCR, fus(HGNC:JAK2, 1875, 2626)) p(HGNC:AKT1, trunc(40)) -> p(HGNC:CHCHD4, fusion(HGNC:AIFM1)) p(HGNC:CFTR, var(=)) -> surf(p(HGNC:EGFR)) -> p(HGNC:MIA, frag(?_*)) p(HGNC:CFTR, var(?)) -> pathology(MESHD:Adenocarcinoma) p(HGNC:MIA, frag(5_20)) -> sec(complex(GO:"interleukin-23 complex")) p(HGNC:MIA, frag(1_?)) -> tloc(p(HGNC:EGFR), GO:"cell surface", GO:endosome) deg(p(HGNC:AKT1)) -> p(HGNC:MIA, frag(?)) p(HGNC:CFTR, var(p.Phe508del)) -- p(HGNC:MIA, frag(?, "55kD")) p(HGNC:AKT1) -> p(HGNC:CFTR, var(p.Gly576Ala)) r(HGNC:AKT1) -> tloc(p(HGNC:EGFR), fromLoc(GO:"cell surface"), toLoc(GO:endosome)) r(HGNC:AKT1, var(p.Phe508del), var(c.1521_1523delCTT)) => r(fus(HGNC:TMPRSS2, r.1_79, HGNC:ERG, r.312_5034)) r(fus(HGNC:TMPRSS2, ?, HGNC:ERG, ?)) -> complexAbundance(proteinAbundance(HGNC:HBP1),geneAbundance(HGNC:NCF1)) r(HGNC:BCR, fus(HGNC:JAK2, 1875, 2626)) -- p(HGNC:EGFR) r(HGNC:CHCHD4, fusion(HGNC:AIFM1)) -> complex(p(HGNC:FOS), p(HGNC:JUN)) act(p(HGNC:AKT1), ma(kin)) -> r(HGNC:CFTR, var(r.1521_1523delcuu)) act(p(HGNC:AKT1)) -> r(HGNC:CFTR, var(r.1653_1655delcuu)) complex(TESTNS2:"AP-1 Complex") -> p(HGNC:HRAS, pmod(Palm)) composite(p(HGNC:IL6), complex(GO:"interleukin-23 complex")) -| bp(GO:"cell cycle arrest") act(p(HGNC:AKT1), ma(catalyticActivity)) -> deg(p(HGNC:EGFR)) kin(p(HGNC:AKT1)) -> sec(p(HGNC:EGFR)) ################################################################################################################ SET Citation = {"PubMed","That one article from last week #2","123456"} SET Evidence = "These were all explicitly stated in the BEL 2.0 Specification" composite(p(HGNC:CASP8),p(HGNC:FADD),a(TESTNS2:"Abeta_42")) -> bp(GO:"neuron apoptotic process") pep(p(TESTNS2:"CAPN Family", location(GO:intracellular))) -| reaction(reactants(p(HGNC:CDK5R1)),products(p(HGNC:CDK5))) proteinAbundance(HGNC:CAT, location(GO:intracellular)) directlyDecreases abundance(CHEBI:"hydrogen peroxide") g(HGNC:CAT, location(GO:intracellular)) directlyDecreases abundance(CHEBI:"hydrogen peroxide") act(p(HGNC:HMGCR), ma(cat)) rateLimitingStepOf bp(GO:"cholesterol biosynthetic process") g(HGNC:APP,sub(G,275341,C)) cnc path(MESHD:"Alzheimer Disease") pep(complex(p(HGNC:F3),p(HGNC:F7))) regulates pep(p(HGNC:F9)) p(HGNC:CAT) -| (a(CHEBI:"hydrogen peroxide") -> bp(GO:"apoptotic process")) p(HGNC:CAT) -| (a(CHEBI:"hydrogen peroxide") -> bp(GO:"apoptotic process")) kin(p(TESTNS2:"GSK3 Family")) neg p(HGNC:MAPT,pmod(P)) p(HGNC:GSK3B, pmod(P, S, 9)) pos act(p(HGNC:GSK3B), ma(kin)) g(HGNC:AKT1) orthologous g(TESTNS2:"AKT1 ortholog") g(HGNC:AKT1) :> r(HGNC:AKT1) r(HGNC:AKT1) >> p(HGNC:AKT1) p(TESTNS2:PRKC) hasMembers list(p(HGNC:PRKCA), p(HGNC:PRKCB), p(HGNC:PRKCD), p(HGNC:PRKCE)) pathology(MESHD:Psoriasis) isA pathology(MESHD:"Skin Diseases") rxn(reactants(a(CHEBI:"(3S)-3-hydroxy-3-methylglutaryl-CoA"),a(CHEBI:NADPH), a(CHEBI:hydron)),products(a(CHEBI:mevalonate), a(CHEBI:"NADP(+)"))) subProcessOf bp(GO:"cholesterol biosynthetic process") a(CHEBI:"nitric oxide") increases surf(complex(p(HGNC:ITGAV),p(HGNC:ITGB3))) # Test that the equivalentTo relation works g(HGNC:ARRDC2) eq g(HGNC:ARRDC3) g(HGNC:CFTR, var(c.1521_1523delCTT)) -- g(dbSNP:rs123456) pybel-0.15.5/src/pybel/testing/resources/belanno/000077500000000000000000000000001426625374700217445ustar00rootroot00000000000000pybel-0.15.5/src/pybel/testing/resources/belanno/cell-line.belanno000066400000000000000000000025731426625374700251570ustar00rootroot00000000000000[AnnotationDefinition] Keyword=CellLine TypeString=list DescriptionString=Cell Line Ontology (CLO) and Experimental Factor Ontology (EFO) Cell Lines UsageString=Use this annotation to indicate the cell line context of a statement. Use to annotate a statement that was demonstrated in a given cell line. VersionString=20150611 CreatedDateTime=2015-06-11T19:57:45 [Author] NameString=OpenBEL CopyrightString=Copyright (c) 2015, OpenBEL Project. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=info@openbel.org [Citation] NameString=Cell Line Ontology (CLO) DescriptionString=The Cell Line Ontology (CLO) is a community-driven ontology that is developed to standardize and integrate cell line information and support computer-assisted reasoning. PublishedVersionString=2.1.63 PublishedDate=2015-04-25 ReferenceURL=http://www.clo-ontology.org/ NameString=Experimental Factor Ontology (EFO) DescriptionString=The Experimental Factor Ontology (EFO) provides a systematic description of many experimental variables available in EBI databases. PublishedVersionString=2.60 PublishedDate=2015-05-15 ReferenceURL=http://www.ebi.ac.uk/efo/ [Processing] CaseSensitiveFlag=yes DelimiterString=| CacheableFlag=yes [Values] 10B9 cell|CLO_0001031 mouse x rat hybridoma cell line cell|CLO_0000498 olfactory neurosphere cell line|EFO_0005705 1321N1 cell|CLO_0001072 pybel-0.15.5/src/pybel/testing/resources/belanno/confidence-1.0.0.belanno000066400000000000000000000012511426625374700260320ustar00rootroot00000000000000[AnnotationDefinition] Keyword=Confidence TypeString=list DescriptionString=Confidence annotations for statements UsageString=A curator can use this annotation to indicate the confidence in which they have for the correctness of their statements VersionString=20170430 CreatedDateTime=2017-01-22T12:00:00 [Author] NameString=Charles Tapley Hoyt CopyrightString=Charles Tapley Hoyt (c) 2017. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=cthoyt@gmail.com [Citation] NameString=Confidence [Processing] CaseSensitiveFlag=no DelimiterString=| CacheableFlag=yes [Values] Wrong| Very Low| Low| Medium| High| Very High| Axiomatic| pybel-0.15.5/src/pybel/testing/resources/belanno/mesh-diseases.belanno000066400000000000000000000017271426625374700260450ustar00rootroot00000000000000[AnnotationDefinition] Keyword=MeSHDisease TypeString=list DescriptionString=Disease terms from the [C] branch of Medical Subject Headings (MeSH). UsageString=Use to annotate a statement demonstrated in the context of a specific disease. VersionString=20150611 CreatedDateTime=2015-06-11T19:57:45 [Author] NameString=OpenBEL CopyrightString=Copyright (c) 2015, OpenBEL Project. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=info@openbel.org [Citation] NameString=MeSH DescriptionString=MeSH (Medical Subject Headings) is a controlled vocabulary thesaurus created, maintained, and provided by the U.S. National Library of Medicine. PublishedVersionString=2015 ReferenceURL=http://www.nlm.nih.gov/mesh/meshhome.html [Processing] CaseSensitiveFlag=yes DelimiterString=| CacheableFlag=yes [Values] 22q11 Deletion Syndrome|D058165 46, XX Disorders of Sex Development|D058489 46, XX Testicular Disorders of Sex Development|D058531 pybel-0.15.5/src/pybel/testing/resources/belanno/species-taxonomy-id.belanno000066400000000000000000000015241426625374700272070ustar00rootroot00000000000000[AnnotationDefinition] Keyword=Species TypeString=list DescriptionString=The annotation values for species provided by NCBI Taxonomy Identifiers. UsageString=Use this annotation to indicate the species context for a statement. VersionString=20120202 CreatedDateTime=2012-02-02T12:00:00 [Author] NameString=OpenBEL CopyrightString=OpenBEL (c) 2013, OpenBEL Project. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=belframework@selventa.com [Citation] NameString=NCBI Taxonomy Ids DescriptionString=NCBI maintains species taxonomies PublishedVersionString=20120202 PublishedDate=2011-02-02 ReferenceURL=http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/ [Processing] CaseSensitiveFlag=no DelimiterString=| CacheableFlag=yes [Values] 9606|Homo sapiens 10090|Mus musculus 10116|Rattus norvegicus pybel-0.15.5/src/pybel/testing/resources/belanno/test_an_1.belanno000066400000000000000000000012641426625374700251640ustar00rootroot00000000000000[AnnotationDefinition] Keyword=TESTAN1 TypeString=list NameString=Test Annotations 1 for PyBEL DomainString=TestAn1 SpeciesString=all DescriptionString=Test Annotations 1 for PyBEL to make a subset of useful BEL VersionString=1.0.0 CreatedDateTime=2016-09-17T20:50:00 [Author] NameString=Charles Tapley Hoyt CopyrightString=Copyright (c) Charles Tapley Hoyt. All Rights Reserved. ContactInfoString=cthoyt@gmail.com [Citation] NameString=Test Annotations 1 for PyBEL DescriptionString=Test Annotations 1 for PyBEL to make a subset of useful BEL [Processing] CaseSensitiveFlag=no DelimiterString=| CacheableFlag=yes [Values] TestAnnot1|O TestAnnot2|O TestAnnot3|O TestAnnot4|O TestAnnot5|O pybel-0.15.5/src/pybel/testing/resources/belns/000077500000000000000000000000001426625374700214315ustar00rootroot00000000000000pybel-0.15.5/src/pybel/testing/resources/belns/chebi-names.belns000066400000000000000000000021551426625374700246340ustar00rootroot00000000000000[Namespace] NameString=Chemicals of Biological Interest (Names) Keyword=CHEBI DomainString=Chemical SpeciesString=all DescriptionString=Chemical Entities of Biological Interest (ChEBI) unique names. The values used are the ChEBI ASCII Names. These names may be used to specify abundances. VersionString=20150611 CreatedDateTime=2015-06-11T19:51:16 QueryValueURL=http://www.ebi.ac.uk/chebi/searchFreeText.do?searchString=[VALUE] [Author] NameString=OpenBEL CopyrightString=Copyright (c) 2015, OpenBEL Project. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=info@openbel.org [Citation] NameString=ChEBI DescriptionString=Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on .small. chemical compounds. PublishedVersionString=127 PublishedDate=2015-06-01 ReferenceURL=http://www.ebi.ac.uk/chebi/ [Processing] CaseSensitiveFlag=yes DelimiterString=| CacheableFlag=yes [Values] nitric oxide|A (3S)-3-hydroxy-3-methylglutaryl-CoA|A NADPH|A hydrogen peroxide|A hydron|A mevalonate|A oxygen atom|A NADP(+)|A sialic acid|A pybel-0.15.5/src/pybel/testing/resources/belns/disease-ontology.belns000066400000000000000000000023051426625374700257430ustar00rootroot00000000000000[Namespace] NameString=Disease Ontology Names Keyword=DO DomainString=Biological Process SpeciesString=all DescriptionString=Disease names from the Disease Ontology (DO). These names may be used to specify pathologies. VersionString=20150611 CreatedDateTime=2015-06-11T19:51:16 [Author] NameString=OpenBEL CopyrightString=Copyright (c) 2015, OpenBEL Project. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=info@openbel.org [Citation] NameString=Disease Ontology (DO) DescriptionString=The Disease Ontology has been developed as a standardized ontology for human disease with the purpose of providing the biomedical community with consistent, reusable and sustainable descriptions of human disease terms, phenotype characteristics and related medical vocabulary disease concepts through collaborative efforts of researchers at Northwestern University, Center for Genetic Medicine and the University of Maryland School of Medicine, Institute for Genome Sciences. PublishedVersionString=2015-06-11 PublishedDate=2015-06-11 ReferenceURL=http://disease-ontology.org/ [Processing] CaseSensitiveFlag=yes DelimiterString=| CacheableFlag=yes [Values] Alzheimer's disease|O pybel-0.15.5/src/pybel/testing/resources/belns/go-names.belns000066400000000000000000000024041426625374700241640ustar00rootroot00000000000000[Namespace] NameString=Gene Ontology Keyword=GO DomainString=Biological Process SpeciesString=all DescriptionString=The Gene Ontology (GO) names for biological process. These names may be used to represent biological processes. VersionString=20150611 CreatedDateTime=2015-06-11T19:51:23 QueryValueURL=http://amigo.geneontology.org/cgi-bin/amigo/search.cgi?search_query=[VALUE]&search_constraint=term&exact_match=1&action=new-search [Author] NameString=OpenBEL CopyrightString=Copyright (c) 2015, OpenBEL Project. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=info@openbel.org [Citation] NameString=The Gene Ontology Consortium DescriptionString=The Gene Ontology project is a major bioinformatics initiative with the aim of standardizing the representation of gene and gene product attributes across species and databases. PublishedVersionString=2015-05-30 PublishedDate=2015-05-29 ReferenceURL=http://www.geneontology.org/ [Processing] CaseSensitiveFlag=yes DelimiterString=| CacheableFlag=yes [Values] cholesterol biosynthetic process|B cell cycle arrest|B neuron apoptotic process|B apoptotic process|B response to oxidative stress|B interleukin-23 complex|C intracellular|A endosome|A cell surface|A extracellular space|A pybel-0.15.5/src/pybel/testing/resources/belns/hgnc-names.belns000066400000000000000000000030641426625374700245010ustar00rootroot00000000000000[Namespace] NameString=HGNC Approved Gene Symbols Keyword=HGNC DomainString=Gene and Gene Products SpeciesString=9606 DescriptionString=HUGO Gene Nomenclature Committee (HGNC) approved gene symbols. These symbols may be used to specify human gene, RNA, microRNA and protein abundances. Single character encoding following each value specify which abundance types are valid. VersionString=20150611 CreatedDateTime=2015-06-11T19:51:19 QueryValueURL=http://www.genenames.org/data/hgnc_data.php?match=[VALUE] [Author] NameString=OpenBEL CopyrightString=Copyright (c) 2015, OpenBEL Project. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=info@openbel.org [Citation] NameString=HUGO Gene Nomenclature Committee at the European Bioinformatics Institute DescriptionString=The HGNC approved gene symbols for human. Each symbol is unique and each gene is only given one approved gene symbol. PublishedVersionString=Thu, 11 Jun 2015 06:00:09 PublishedDate=2015-06-11 ReferenceURL=http://www.genenames.org/ [Processing] CaseSensitiveFlag=yes DelimiterString=| CacheableFlag=yes [Values] ADGRB1|GPR ADGRB2|GPR AIFM1|GPR AKT1|GPR AKT2|GRP APP|GPR ARRDC2|GPR ARRDC3|GPR BCR|GPR CASP8|GPR CAT|GPR CDK5|GPR CDK5R1|GPR CFTR|GPR CHCHD4|GPR EGFR|GPR ERG|GPR F3|GPR F7|GPR F9|GPR FADD|GPR FOS|GPR GSK3B|GPR HBP1|GPR HMGCR|GPR HRAS|GPR IL6|GPR ITGAV|GPR ITGB3|GPR JAK2|GPR JUN|GPR MAPT|GPR MHS2|G MIA|GPR MIATNB|GR MIR21|GPR NCF1|GPR PRKCA|GPR PRKCB|GPR PRKCD|GPR PRKCE|GPR TMPRSS2|GPR TP53|GPR CD33|GPR PTPN6|GPR PTPN11|GPR SYK|GRP TYROBP|GPR TREM2|GPR pybel-0.15.5/src/pybel/testing/resources/belns/mesh-names.belns000066400000000000000000000020301426625374700245060ustar00rootroot00000000000000[Namespace] NameString=MeSH Diseases (Names) Keyword=MESHD DomainString=Biological Process SpeciesString=all DescriptionString=Medical Subject Headings (MeSH) from the Diseases [C] branch. These headings may be used to specify pathologies. VersionString=20150611 CreatedDateTime=2015-06-11T19:51:19 QueryValueURL=http://www.nlm.nih.gov/cgi/mesh/2013/MB_cgi?mode=&term=[VALUE]&field=entry [Author] NameString=OpenBEL CopyrightString=Copyright (c) 2015, OpenBEL Project. This work is licensed under a Creative Commons Attribution 3.0 Unported License. ContactInfoString=info@openbel.org [Citation] NameString=MeSH DescriptionString=MeSH (Medical Subject Headings) is a controlled vocabulary thesaurus created, maintained, and provided by the U.S. National Library of Medicine. PublishedVersionString=2015 PublishedDate=2015-06-11 ReferenceURL=http://www.nlm.nih.gov/mesh/meshhome.html [Processing] CaseSensitiveFlag=yes DelimiterString=| CacheableFlag=yes [Values] Achlorhydria|O Adenocarcinoma|O Skin Diseases|O Psoriasis|O Alzheimer Disease|O pybel-0.15.5/src/pybel/testing/resources/belns/test_ns_1.belns000066400000000000000000000012211426625374700243510ustar00rootroot00000000000000[Namespace] Keyword=TESTNS1 NameString=Test Namespace 1 for PyBEL DomainString=TestNs1 SpeciesString=all DescriptionString=Test Namespace 1 for PyBEL to make a subset of useful BEL VersionString=1.0.0 CreatedDateTime=2016-09-17T20:50:00 [Author] NameString=Charles Tapley Hoyt CopyrightString=Copyright (c) Charles Tapley Hoyt. All Rights Reserved. ContactInfoString=cthoyt@gmail.com [Citation] NameString=Test Namespace 1 for PyBEL DescriptionString=Test Namespace 1 for PyBEL to make a subset of useful BEL [Processing] CaseSensitiveFlag=no DelimiterString=| CacheableFlag=yes [Values] TestValue1|O TestValue2|O TestValue3|O TestValue4|O TestValue5|O pybel-0.15.5/src/pybel/testing/resources/belns/test_ns_1_updated.belns000066400000000000000000000012641426625374700260660ustar00rootroot00000000000000[Namespace] Keyword=TESTNS1 NameString=Test Namespace 1 for PyBEL DomainString=TestNs1 SpeciesString=all DescriptionString=Test Namespace 1 for PyBEL to make a subset of useful BEL VersionString=1.1.0 CreatedDateTime=2016-10-20T17:15:00 [Author] NameString=Charles Tapley Hoyt CopyrightString=Copyright (c) Charles Tapley Hoyt. All Rights Reserved. ContactInfoString=cthoyt@gmail.com [Citation] NameString=Test Namespace 1 for PyBEL DescriptionString=Test Namespace 1 for PyBEL to make a subset of useful BEL [Processing] CaseSensitiveFlag=no DelimiterString=| CacheableFlag=yes [Values] ImprovedTestValue1|O TestValue2|O TestValue3|O ImprovedTestValue4|O TestValue5|O AdditionalValue6|O pybel-0.15.5/src/pybel/testing/resources/belns/test_ns_2.belns000066400000000000000000000012511426625374700243550ustar00rootroot00000000000000[Namespace] Keyword=TESTNS2 NameString=Test Namespace 2 for PyBEL DomainString=TestNs1 SpeciesString=all DescriptionString=Test Namespace 1 for PyBEL to make a subset of useful BEL VersionString=1.0.0 CreatedDateTime=2016-09-17T20:50:00 [Author] NameString=Charles Tapley Hoyt CopyrightString=Copyright (c) Charles Tapley Hoyt. All Rights Reserved. ContactInfoString=cthoyt@gmail.com [Citation] NameString=Test Namespace 1 for PyBEL DescriptionString=Test Namespace 1 for PyBEL to make a subset of useful BEL [Processing] CaseSensitiveFlag=no DelimiterString=| CacheableFlag=yes [Values] PhosRes| AP-1 Complex|CA Abeta_42|A CAPN Family|P GSK3 Family|P AKT1 ortholog|GRP PRKC|P pybel-0.15.5/src/pybel/testing/resources/belns/test_ns_empty.belns000066400000000000000000000011301426625374700253460ustar00rootroot00000000000000[Namespace] Keyword=TESTNSEMPTY NameString=Test Empty Namespace for PyBEL DomainString=TestNs1 SpeciesString=all DescriptionString=Test Namespace 1 for PyBEL to make a subset of useful BEL VersionString=1.0.0 CreatedDateTime=2016-09-17T20:50:00 [Author] NameString=Charles Tapley Hoyt CopyrightString=Copyright (c) Charles Tapley Hoyt. All Rights Reserved. ContactInfoString=cthoyt@gmail.com [Citation] NameString=Test Namespace 1 for PyBEL DescriptionString=Test Namespace 1 for PyBEL to make a subset of useful BEL [Processing] CaseSensitiveFlag=no DelimiterString=| CacheableFlag=yes [Values] pybel-0.15.5/src/pybel/testing/utils.py000066400000000000000000000037011426625374700200270ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for PyBEL testing.""" from uuid import uuid4 from requests.compat import urlparse from ..manager import Manager from ..manager.models import Namespace, NamespaceEntry from ..struct import BELGraph from ..struct.summary import ( get_annotation_values_by_annotation, iter_annotation_value_pairs, ) from ..struct.summary.node_summary import get_names _FRAUNHOFER_RESOURCES = "https://owncloud.scai.fraunhofer.de/index.php/s/JsfpQvkdx3Y5EMx/download?path=" def get_uri_name(url: str) -> str: """Get the file name from the end of the URL.""" url_parsed = urlparse(url) if url.startswith(_FRAUNHOFER_RESOURCES): return url_parsed.query.split("=")[-1] else: url_parts = url_parsed.path.split("/") return url_parts[-1] def n() -> str: """Return a UUID string for testing.""" return str(uuid4())[:15] def make_dummy_namespaces(manager: Manager, graph: BELGraph) -> None: """Make dummy namespaces for the test.""" for keyword, names in get_names(graph).items(): graph.namespace_url[keyword] = url = n() namespace = Namespace(keyword=keyword, url=url) manager.session.add(namespace) for name in names: entry = NamespaceEntry(name=name, namespace=namespace) manager.session.add(entry) manager.session.commit() def make_dummy_annotations(manager: Manager, graph: BELGraph): """Make dummy annotations for the test.""" namespaces = {} for keyword, entity in iter_annotation_value_pairs(graph): namespace = namespaces.get(keyword) if namespace is None: graph.annotation_url[keyword] = url = n() namespace = Namespace(keyword=keyword, url=url, is_annotation=True) manager.session.add(namespace) entry = NamespaceEntry(name=entity.name, identifier=entity.identifier, namespace=namespace) manager.session.add(entry) manager.session.commit() pybel-0.15.5/src/pybel/tokens.py000066400000000000000000000153521426625374700165220ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module helps handle node data dictionaries.""" from typing import Any, List, Mapping, Union from pyparsing import ParseResults from .constants import ( CONCEPT, FRAGMENT, FRAGMENT_DESCRIPTION, FRAGMENT_START, FRAGMENT_STOP, FUNCTION, FUSION, FUSION_MISSING, FUSION_REFERENCE, FUSION_START, FUSION_STOP, GMOD, HGVS, IDENTIFIER, KIND, MEMBERS, NAME, NAMESPACE, PARTNER_3P, PARTNER_5P, PMOD, PMOD_CODE, PMOD_POSITION, PRODUCTS, RANGE_3P, RANGE_5P, REACTANTS, REACTION, VARIANTS, XREFS, ) from .dsl import ( FUNC_TO_DSL, FUNC_TO_FUSION_DSL, FUNC_TO_LIST_DSL, BaseAbundance, BaseEntity, CentralDogma, EnumeratedFusionRange, Fragment, FusionBase, FusionRangeBase, GeneModification, Hgvs, ListAbundance, MissingFusionRange, ProteinModification, Reaction, Variant, ) __all__ = [ "parse_result_to_dsl", ] def parse_result_to_dsl(tokens) -> BaseEntity: """Convert a ParseResult to a PyBEL DSL object. :type tokens: dict or pyparsing.ParseResults """ # if MODIFIER in tokens: # return parse_result_to_dsl(tokens[TARGET]) if REACTION == tokens[FUNCTION]: return _reaction_po_to_dict(tokens) elif VARIANTS in tokens: return _variant_po_to_dict(tokens) elif MEMBERS in tokens: if CONCEPT in tokens: return _list_po_with_concept_to_dict(tokens) return _list_po_to_dict(tokens) elif FUSION in tokens: return _fusion_to_dsl(tokens) return _simple_po_to_dict(tokens) def _fusion_to_dsl(tokens) -> FusionBase: """Convert a PyParsing data dictionary to a PyBEL fusion data dictionary. :param tokens: A PyParsing data dictionary representing a fusion :type tokens: ParseResult """ func = tokens[FUNCTION] fusion_dsl = FUNC_TO_FUSION_DSL[func] member_dsl = FUNC_TO_DSL[func] partner_5p = tokens[FUSION][PARTNER_5P] partner_5p_concept = partner_5p[CONCEPT] if CONCEPT in tokens[FUSION][PARTNER_5P] else partner_5p partner_5p_node = member_dsl( namespace=partner_5p_concept[NAMESPACE], name=partner_5p_concept[NAME], identifier=partner_5p_concept.get(IDENTIFIER), xrefs=partner_5p.get(XREFS), ) partner_3p = tokens[FUSION][PARTNER_3P] partner_3p_concept = partner_3p[CONCEPT] if CONCEPT in tokens[FUSION][PARTNER_3P] else partner_3p partner_3p_node = member_dsl( namespace=partner_3p_concept[NAMESPACE], name=partner_3p_concept[NAME], identifier=partner_3p_concept.get(IDENTIFIER), xrefs=partner_3p.get(XREFS), ) range_5p = _fusion_range_to_dsl(tokens[FUSION][RANGE_5P]) range_3p = _fusion_range_to_dsl(tokens[FUSION][RANGE_3P]) return fusion_dsl( partner_5p=partner_5p_node, partner_3p=partner_3p_node, range_5p=range_5p, range_3p=range_3p, ) def _fusion_range_to_dsl(tokens) -> FusionRangeBase: """Convert a PyParsing data dictionary into a PyBEL. :type tokens: ParseResult """ if FUSION_MISSING in tokens: return MissingFusionRange() return EnumeratedFusionRange( reference=tokens[FUSION_REFERENCE], start=tokens[FUSION_START], stop=tokens[FUSION_STOP], ) def _simple_po_to_dict(tokens) -> BaseAbundance: """Convert a simple named entity to a DSL object. :type tokens: ParseResult """ dsl = FUNC_TO_DSL.get(tokens[FUNCTION]) if dsl is None: raise ValueError("invalid tokens: {}".format(tokens)) concept = tokens[CONCEPT] return dsl( namespace=concept[NAMESPACE], name=concept.get(NAME), identifier=concept.get(IDENTIFIER), xrefs=tokens.get(XREFS), ) def _variant_po_to_dict(tokens) -> CentralDogma: """Convert a PyParsing data dictionary to a central dogma abundance (i.e., Protein, RNA, miRNA, Gene). :type tokens: ParseResult """ dsl = FUNC_TO_DSL.get(tokens[FUNCTION]) if dsl is None: raise ValueError("invalid tokens: {}".format(tokens)) concept = tokens[CONCEPT] return dsl( namespace=concept[NAMESPACE], name=concept[NAME], identifier=concept.get(IDENTIFIER), xrefs=tokens.get(XREFS), variants=[_variant_to_dsl_helper(variant_tokens) for variant_tokens in tokens[VARIANTS]], ) def _variant_to_dsl_helper(tokens) -> Variant: """Convert variant tokens to DSL objects. :type tokens: ParseResult """ kind = tokens[KIND] if kind == HGVS: return Hgvs(tokens[HGVS]) if kind == GMOD: concept = tokens[CONCEPT] return GeneModification( name=concept[NAME], namespace=concept[NAMESPACE], identifier=concept.get(IDENTIFIER), xrefs=tokens.get(XREFS), ) if kind == PMOD: concept = tokens[CONCEPT] return ProteinModification( name=concept[NAME], namespace=concept[NAMESPACE], identifier=concept.get(IDENTIFIER), xrefs=tokens.get(XREFS), code=tokens.get(PMOD_CODE), position=tokens.get(PMOD_POSITION), ) if kind == FRAGMENT: start, stop = tokens.get(FRAGMENT_START), tokens.get(FRAGMENT_STOP) return Fragment( start=start, stop=stop, description=tokens.get(FRAGMENT_DESCRIPTION), ) raise ValueError("invalid fragment kind: {}".format(kind)) def _reaction_po_to_dict(tokens) -> Reaction: """Convert a reaction parse object to a DSL. :type tokens: ParseResult """ return Reaction( reactants=_parse_tokens_list(tokens[REACTANTS]), products=_parse_tokens_list(tokens[PRODUCTS]), ) def _list_po_with_concept_to_dict(tokens: Union[ParseResults, Mapping[str, Any]]) -> ListAbundance: """Convert a list parse object to a node. :type tokens: ParseResult """ func = tokens[FUNCTION] dsl = FUNC_TO_LIST_DSL[func] members = _parse_tokens_list(tokens[MEMBERS]) concept = tokens[CONCEPT] return dsl( members=members, namespace=concept[NAMESPACE], name=concept.get(NAME), identifier=concept.get(IDENTIFIER), xrefs=tokens.get(XREFS), ) def _list_po_to_dict(tokens) -> ListAbundance: """Convert a list parse object to a node. :type tokens: ParseResult """ func = tokens[FUNCTION] dsl = FUNC_TO_LIST_DSL[func] members = _parse_tokens_list(tokens[MEMBERS]) return dsl(members) def _parse_tokens_list(tokens) -> List[BaseEntity]: """Convert a PyParsing result to a reaction. :type tokens: ParseResult """ return [parse_result_to_dsl(token) for token in tokens] pybel-0.15.5/src/pybel/typing.py000066400000000000000000000003041426625374700165200ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Types for PyBEL.""" from typing import Iterable, Mapping, Union __all__ = [ "Strings", "EdgeData", ] Strings = Union[str, Iterable[str]] EdgeData = Mapping pybel-0.15.5/src/pybel/utils.py000066400000000000000000000230061426625374700163520ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Utilities for PyBEL.""" import hashlib import json import logging import re import typing from collections import defaultdict from collections.abc import Iterable, MutableMapping from datetime import datetime from typing import Any, List, Mapping, Optional, Tuple, TypeVar from .constants import ( ACTIVITY, CITATION, DEGRADATION, EFFECT, EVIDENCE, FROM_LOC, IDENTIFIER, LOCATION, MODIFIER, NAME, NAMESPACE, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, TO_LOC, TRANSLOCATION, ) from .typing import EdgeData try: import pickle5 as pickle except ImportError: import pickle logger = logging.getLogger(__name__) CanonicalEdge = Tuple[str, Optional[Tuple], Optional[Tuple]] def expand_dict(flat_dict, sep: str = "_"): """Expand a flattened dictionary. :param dict flat_dict: a nested dictionary that has been flattened so the keys are composite :param sep: the separator between concatenated keys :rtype: dict """ res = {} rdict = defaultdict(list) for flat_key, value in flat_dict.items(): key = flat_key.split(sep, 1) if 1 == len(key): res[key[0]] = value else: rdict[key[0]].append((key[1:], value)) for k, v in rdict.items(): res[k] = expand_dict({ik: iv for (ik,), iv in v}) return res def flatten_dict( data: Mapping[str, Any], parent_key: str = "", sep: str = "_", ) -> Mapping[str, str]: """Flatten a nested dictionary. :param data: A nested dictionary :param parent_key: The parent's key. This is a value for tail recursion, so don't set it yourself. :param sep: The separator used between dictionary levels :rtype: dict .. seealso:: http://stackoverflow.com/a/6027615 """ items = {} for key, value in data.items(): # prepend the parent key key = parent_key + sep + key if parent_key else key if isinstance(value, (dict, MutableMapping)): items.update(flatten_dict(value, key, sep=sep)) elif isinstance(value, (set, list)): items[key] = ",".join(value) else: items[key] = value return items def tokenize_version(version_string: str) -> Tuple[int, int, int]: """Tokenize a version string to a tuple. Truncates qualifiers like ``-dev``. :param version_string: A version string :return: A tuple representing the version string >>> tokenize_version('0.1.2-dev') (0, 1, 2) """ before_dash = version_string.split("-")[0] major, minor, patch = before_dash.split(".")[:3] # take only the first 3 in case there's an extension like -dev.0 return int(major), int(minor), int(patch) _re = re.compile(r"^[a-zA-Z0-9-\.]*$") def ensure_quotes(s: str) -> str: """Quote a string that isn't solely alphanumeric.""" s.isalnum() return s if _re.match(s) else f'"{s}"' CREATION_DATE_FMT = "%Y-%m-%dT%H:%M:%S" PUBLISHED_DATE_FMT = "%Y-%m-%d" PUBLISHED_DATE_FMT_2 = "%d:%m:%Y %H:%M" DATE_VERSION_FMT = "%Y%m%d" def valid_date(s: str) -> bool: """Check that a string represents a valid date in ISO 8601 format YYYY-MM-DD.""" return _validate_date_fmt(s, PUBLISHED_DATE_FMT) def valid_date_version(s: str) -> bool: """Check that the string is a valid date versions string.""" return _validate_date_fmt(s, DATE_VERSION_FMT) def _validate_date_fmt(s: str, fmt: str) -> bool: try: datetime.strptime(s, fmt) except ValueError: return False else: return True def parse_datetime(s: str) -> datetime.date: """Try to parse a datetime object from a standard datetime format or date format.""" for fmt in (CREATION_DATE_FMT, PUBLISHED_DATE_FMT, PUBLISHED_DATE_FMT_2): try: dt = datetime.strptime(s, fmt) except ValueError: pass else: return dt raise ValueError("Incorrect datetime format for {}".format(s)) def _get_citation_str(data: Mapping) -> Optional[str]: citation = data.get(CITATION) if citation is not None: return citation.curie def hash_edge(source, target, edge_data: EdgeData) -> str: """Convert an edge tuple to a MD5 hash. :param BaseEntity source: The source BEL node :param BaseEntity target: The target BEL node :param edge_data: The edge's data dictionary :return: A hashed version of the edge tuple using MD5 hash of the binary pickle dump of u, v, and the json dump of d """ edge_tuple = _get_edge_tuple(source, target, edge_data) edge_tuple_bytes = pickle.dumps(edge_tuple) return hashlib.md5(edge_tuple_bytes).hexdigest() # noqa: S303 def _get_edge_tuple( source, target, edge_data: EdgeData, ) -> Tuple[str, str, Optional[str], Optional[str], CanonicalEdge]: """Convert an edge to a consistent tuple. :param BaseEntity source: The source BEL node :param BaseEntity target: The target BEL node :param edge_data: The edge's data dictionary :return: A tuple that can be hashed representing this edge. Makes no promises to its structure. """ return ( source.as_bel(), target.as_bel(), _get_citation_str(edge_data), edge_data.get(EVIDENCE), canonicalize_edge(edge_data), ) def subdict_matches(target: Mapping, query: Mapping, partial_match: bool = True) -> bool: """Check if all the keys in the query dict are in the target dict, and that their values match. 1. Checks that all keys in the query dict are in the target dict 2. Matches the values of the keys in the query dict a. If the value is a string, then must match exactly b. If the value is a set/list/tuple, then will match any of them c. If the value is a dict, then recursively check if that subdict matches :param target: The dictionary to search :param query: A query dict with keys to match :param partial_match: Should the query values be used as partial or exact matches? Defaults to :code:`True`. :return: if all keys in b are in target_dict and their values match """ for k, v in query.items(): if k not in target: return False elif not isinstance(v, (int, str, dict, Iterable)): raise ValueError("invalid value: {}".format(v)) elif isinstance(v, (int, str)) and target[k] != v: return False elif isinstance(v, dict): if partial_match: if not isinstance(target[k], dict): return False elif not subdict_matches(target[k], v, partial_match): return False elif not partial_match and target[k] != v: return False elif isinstance(v, Iterable) and target[k] not in v: return False return True def hash_dump(data) -> str: """Hash an arbitrary JSON dictionary by dumping it in sorted order, encoding it in UTF-8, then hashing the bytes. :param data: An arbitrary JSON-serializable object :type data: dict or list or tuple """ return hashlib.md5(json.dumps(data, sort_keys=True).encode("utf-8")).hexdigest() # noqa: S303 def canonicalize_edge(edge_data: EdgeData) -> CanonicalEdge: """Canonicalize the edge to a tuple based on the relation, subject modifications, and object modifications.""" return ( edge_data[RELATION], _canonicalize_edge_modifications(edge_data.get(SOURCE_MODIFIER)), _canonicalize_edge_modifications(edge_data.get(TARGET_MODIFIER)), ) def _canonicalize_edge_modifications(edge_data: EdgeData) -> Optional[Tuple]: """Return the SUBJECT or OBJECT entry of a PyBEL edge data dictionary as a canonical tuple.""" if edge_data is None: return modifier = edge_data.get(MODIFIER) location = edge_data.get(LOCATION) effect = edge_data.get(EFFECT) if modifier is None and location is None: return result = [] if modifier == ACTIVITY: if effect: t = ( ACTIVITY, effect[NAMESPACE], effect.get(IDENTIFIER), effect.get(NAME), ) else: t = (ACTIVITY,) result.append(t) elif modifier == DEGRADATION: t = (DEGRADATION,) result.append(t) elif modifier == TRANSLOCATION: if effect: from_loc_concept = effect[FROM_LOC] to_loc_concept = effect[TO_LOC] t = ( TRANSLOCATION, from_loc_concept[NAMESPACE], from_loc_concept.get(IDENTIFIER), from_loc_concept.get(NAME), to_loc_concept[NAMESPACE], to_loc_concept.get(IDENTIFIER), to_loc_concept.get(NAME), ) else: t = (TRANSLOCATION,) result.append(t) if location: t = ( LOCATION, location[NAMESPACE], location.get(IDENTIFIER), location.get(NAME), ) result.append(t) if not result: raise ValueError("Invalid data: {}".format(edge_data)) return tuple(result) def get_corresponding_pickle_path(path: str) -> str: """Get the same path with a pickle extension. :param path: A path to a BEL file. """ return "{path}.pickle".format(path=path) X = TypeVar("X") Y = TypeVar("Y") def multidict(pairs: typing.Iterable[Tuple[X, Y]]) -> Mapping[X, List[Y]]: """Accumulate a multidict from a list of pairs.""" rv = defaultdict(list) for key, value in pairs: rv[key].append(value) return dict(rv) pybel-0.15.5/src/pybel/version.py000066400000000000000000000016121426625374700166760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """The current version of PyBEL.""" import os import subprocess # noqa:S404 __all__ = [ "VERSION", "get_version", "get_git_hash", ] VERSION = "0.15.5" def get_git_hash() -> str: """Get the PyBEL git hash.""" with open(os.devnull, "w") as devnull: try: ret = subprocess.check_output( # noqa: S603,S607 ["git", "rev-parse", "HEAD"], cwd=os.path.dirname(__file__), stderr=devnull, ) except subprocess.CalledProcessError: return "UNHASHED" else: return ret.strip().decode("utf-8")[:8] def get_version(with_git_hash: bool = False): """Get the PyBEL version string, including a git hash.""" return f"{VERSION}-{get_git_hash()}" if with_git_hash else VERSION if __name__ == "__main__": print(get_version(with_git_hash=True)) pybel-0.15.5/tests/000077500000000000000000000000001426625374700140775ustar00rootroot00000000000000pybel-0.15.5/tests/__init__.py000066400000000000000000000000671426625374700162130ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for :mod:`pybel`.""" pybel-0.15.5/tests/constant_helper.py000066400000000000000000000723321426625374700176500ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Constants for PyBEL tests.""" import logging from pybel.constants import * from pybel.dsl import ( Abundance, BiologicalProcess, ComplexAbundance, CompositeAbundance, EnumeratedFusionRange, Fragment, Gene, GeneFusion, GeneModification, Hgvs, HgvsReference, HgvsUnspecified, MicroRna, NamedComplexAbundance, Pathology, Protein, ProteinFusion, ProteinModification, Reaction, Rna, RnaFusion, secretion, translocation, ) from pybel.dsl.namespaces import hgnc from pybel.language import activity_mapping, citation_dict, compartment_mapping logger = logging.getLogger(__name__) expected_test_simple_metadata = { METADATA_NAME: "PyBEL Test Simple", METADATA_DESCRIPTION: "Made for testing PyBEL parsing", METADATA_VERSION: "1.6.0", METADATA_COPYRIGHT: "Copyright (c) Charles Tapley Hoyt. All Rights Reserved.", METADATA_AUTHORS: "Charles Tapley Hoyt", METADATA_LICENSES: "WTF License", METADATA_CONTACT: "cthoyt@gmail.com", METADATA_PROJECT: "PyBEL Testing", } expected_test_thorough_metadata = { METADATA_NAME: "PyBEL Test Thorough", METADATA_DESCRIPTION: "Statements made up to contain many conceivable variants of nodes from BEL", METADATA_VERSION: "1.0.0", METADATA_COPYRIGHT: "Copyright (c) Charles Tapley Hoyt. All Rights Reserved.", METADATA_AUTHORS: "Charles Tapley Hoyt", METADATA_LICENSES: "WTF License", METADATA_CONTACT: "cthoyt@gmail.com", } citation_1 = citation_dict(namespace=CITATION_TYPE_PUBMED, identifier="123455") citation_2 = citation_dict(namespace=CITATION_TYPE_PUBMED, identifier="123456") evidence_1 = "Evidence 1" dummy_evidence = "These are mostly made up" akt1 = hgnc(name="AKT1") egfr = hgnc(name="EGFR") fadd = hgnc(name="FADD") casp8 = hgnc(name="CASP8") mia = hgnc(name="MIA") il6 = Protein("HGNC", "IL6") adgrb1 = Protein(namespace="HGNC", name="ADGRB1") adgrb2 = Protein(namespace="HGNC", name="ADGRB2") adgrb_complex = ComplexAbundance([adgrb1, adgrb2]) achlorhydria = Pathology(namespace="MESHD", name="Achlorhydria") akt1_rna = akt1.get_rna() akt1_gene = akt1_rna.get_gene() akt_methylated = akt1_gene.with_variants(GeneModification("Me")) akt1_phe_508_del = akt1_gene.with_variants(Hgvs("p.Phe508del")) cftr = hgnc(name="CFTR") cftr_protein_unspecified_variant = cftr.with_variants(HgvsUnspecified()) cftr_protein_phe_508_del = cftr.with_variants(Hgvs("p.Phe508del")) adenocarcinoma = Pathology("MESHD", "Adenocarcinoma") interleukin_23_complex = NamedComplexAbundance("GO", "interleukin-23 complex") oxygen_atom = Abundance(namespace="CHEBI", name="oxygen atom") hydrogen_peroxide = Abundance("CHEBI", "hydrogen peroxide") tmprss2_gene = Gene("HGNC", "TMPRSS2") tmprss2_erg_gene_fusion = GeneFusion( partner_5p=tmprss2_gene, range_5p=EnumeratedFusionRange("c", 1, 79), partner_3p=Gene("HGNC", "ERG"), range_3p=EnumeratedFusionRange("c", 312, 5034), ) bcr_jak2_gene_fusion = GeneFusion( partner_5p=Gene("HGNC", "BCR"), range_5p=EnumeratedFusionRange("c", "?", 1875), partner_3p=Gene("HGNC", "JAK2"), range_3p=EnumeratedFusionRange("c", 2626, "?"), ) chchd4_aifm1_gene_fusion = GeneFusion( partner_5p=Gene("HGNC", "CHCHD4"), partner_3p=Gene("HGNC", "AIFM1"), ) tmprss2_erg_protein_fusion = ProteinFusion( partner_5p=Protein("HGNC", "TMPRSS2"), range_5p=EnumeratedFusionRange("p", 1, 79), partner_3p=Protein("HGNC", "ERG"), range_3p=EnumeratedFusionRange("p", 312, 5034), ) bcr_jak2_protein_fusion = ProteinFusion( partner_5p=Protein("HGNC", "BCR"), range_5p=EnumeratedFusionRange("p", "?", 1875), partner_3p=Protein("HGNC", "JAK2"), range_3p=EnumeratedFusionRange("p", 2626, "?"), ) chchd4_aifm1_protein_fusion = ProteinFusion(Protein("HGNC", "CHCHD4"), Protein("HGNC", "AIFM1")) bcr_jak2_rna_fusion = RnaFusion( partner_5p=Rna("HGNC", "BCR"), range_5p=EnumeratedFusionRange("r", "?", 1875), partner_3p=Rna("HGNC", "JAK2"), range_3p=EnumeratedFusionRange("r", 2626, "?"), ) chchd4_aifm1_rna_fusion = RnaFusion(partner_5p=Rna("HGNC", "CHCHD4"), partner_3p=Rna("HGNC", "AIFM1")) tmprss2_erg_rna_fusion = RnaFusion( partner_5p=Rna("HGNC", "TMPRSS2"), range_5p=EnumeratedFusionRange("r", 1, 79), partner_3p=Rna("HGNC", "ERG"), range_3p=EnumeratedFusionRange("r", 312, 5034), ) tmprss2_erg_rna_fusion_unspecified = RnaFusion(partner_5p=Rna("HGNC", "TMPRSS2"), partner_3p=Rna("HGNC", "ERG")) BEL_THOROUGH_NODES = { oxygen_atom, tmprss2_erg_rna_fusion, tmprss2_erg_rna_fusion_unspecified, akt_methylated, bcr_jak2_rna_fusion, chchd4_aifm1_rna_fusion, akt1_gene, akt1_phe_508_del, akt1, Gene("HGNC", "AKT1", variants=Hgvs("c.308G>A")), tmprss2_erg_gene_fusion, Gene( "HGNC", "AKT1", variants=[Hgvs("c.1521_1523delCTT"), Hgvs("c.308G>A"), Hgvs("p.Phe508del")], ), MicroRna("HGNC", "MIR21"), bcr_jak2_gene_fusion, Gene("HGNC", "CFTR", variants=Hgvs("c.1521_1523delCTT")), Gene("HGNC", "CFTR"), Gene("HGNC", "CFTR", variants=Hgvs("g.117199646_117199648delCTT")), Gene("HGNC", "CFTR", variants=Hgvs("c.1521_1523delCTT")), Protein("HGNC", "AKT1", variants=ProteinModification("Ph", "Ser", 473)), MicroRna("HGNC", "MIR21", variants=Hgvs("p.Phe508del")), Protein("HGNC", "AKT1", variants=Hgvs("p.C40*")), Protein("HGNC", "AKT1", variants=[Hgvs("p.Ala127Tyr"), ProteinModification("Ph", "Ser")]), chchd4_aifm1_gene_fusion, tmprss2_erg_protein_fusion, Protein("HGNC", "AKT1", variants=Hgvs("p.Arg1851*")), bcr_jak2_protein_fusion, Protein("HGNC", "AKT1", variants=Hgvs("p.40*")), chchd4_aifm1_protein_fusion, Protein("HGNC", "CFTR", variants=HgvsReference()), cftr, egfr, cftr_protein_unspecified_variant, adenocarcinoma, cftr_protein_phe_508_del, Protein("HGNC", "MIA", variants=Fragment(5, 20)), mia, interleukin_23_complex, Protein("HGNC", "MIA", variants=Fragment(1, "?")), Protein("HGNC", "MIA", variants=Fragment()), Protein("HGNC", "MIA", variants=Fragment(description="55kD")), Protein("HGNC", "CFTR", variants=Hgvs("p.Gly576Ala")), akt1_rna, Rna("HGNC", "AKT1", variants=[Hgvs("c.1521_1523delCTT"), Hgvs("p.Phe508del")]), Gene("HGNC", "NCF1"), ComplexAbundance([Gene("HGNC", "NCF1"), Protein("HGNC", "HBP1")]), Protein("HGNC", "HBP1"), ComplexAbundance([Protein("HGNC", "FOS"), Protein("HGNC", "JUN")]), Protein("HGNC", "FOS"), Protein("HGNC", "JUN"), Rna("HGNC", "CFTR", variants=Hgvs("r.1521_1523delcuu")), Rna("HGNC", "CFTR"), Rna("HGNC", "CFTR", variants=Hgvs("r.1653_1655delcuu")), CompositeAbundance([interleukin_23_complex, il6]), il6, BiologicalProcess("GO", "cell cycle arrest"), hydrogen_peroxide, Protein("HGNC", "CAT"), Gene("HGNC", "CAT"), Protein("HGNC", "HMGCR"), BiologicalProcess("GO", "cholesterol biosynthetic process"), Gene("HGNC", "APP", variants=Hgvs("c.275341G>C")), Gene("HGNC", "APP"), Pathology("MESHD", "Alzheimer Disease"), ComplexAbundance([Protein("HGNC", "F3"), Protein("HGNC", "F7")]), Protein("HGNC", "F3"), Protein("HGNC", "F7"), Protein("HGNC", "F9"), Protein("HGNC", "GSK3B", variants=ProteinModification("Ph", "Ser", 9)), Protein("HGNC", "GSK3B"), Pathology("MESHD", "Psoriasis"), Pathology("MESHD", "Skin Diseases"), Reaction( reactants=[ Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), Abundance("CHEBI", "NADPH"), Abundance("CHEBI", "hydron"), ], products=[Abundance("CHEBI", "NADP(+)"), Abundance("CHEBI", "mevalonate")], ), Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), Abundance("CHEBI", "NADPH"), Abundance("CHEBI", "hydron"), Abundance("CHEBI", "mevalonate"), Abundance("CHEBI", "NADP(+)"), Abundance("CHEBI", "nitric oxide"), ComplexAbundance([Protein("HGNC", "ITGAV"), Protein("HGNC", "ITGB3")]), Protein("HGNC", "ITGAV"), Protein("HGNC", "ITGB3"), Protein("HGNC", "FADD"), Abundance("TESTNS2", "Abeta_42"), Protein("TESTNS2", "GSK3 Family"), Protein("HGNC", "PRKCA"), Protein("HGNC", "CDK5"), Protein("HGNC", "CASP8"), Protein( "HGNC", "AKT1", variants=ProteinModification(namespace="TESTNS2", name="PhosRes", code="Ser", position=473), ), Protein("HGNC", "HRAS", variants=ProteinModification("Palm")), BiologicalProcess("GO", "apoptotic process"), CompositeAbundance( [ Abundance("TESTNS2", "Abeta_42"), Protein("HGNC", "CASP8"), Protein("HGNC", "FADD"), ] ), Reaction( reactants=[Protein("HGNC", "CDK5R1")], products=[Protein("HGNC", "CDK5")], ), Protein("HGNC", "PRKCB"), NamedComplexAbundance("TESTNS2", "AP-1 Complex"), Protein("HGNC", "PRKCE"), Protein("HGNC", "PRKCD"), Protein("TESTNS2", "CAPN Family"), Gene("TESTNS2", "AKT1 ortholog"), Protein("HGNC", "HRAS"), Protein("HGNC", "CDK5R1"), Protein("TESTNS2", "PRKC"), BiologicalProcess("GO", "neuron apoptotic process"), Protein("HGNC", "MAPT", variants=ProteinModification("Ph")), Protein("HGNC", "MAPT"), Gene("HGNC", "ARRDC2"), Gene("HGNC", "ARRDC3"), Gene("dbSNP", "rs123456"), } BEL_THOROUGH_EDGES = [ ( Gene("HGNC", "AKT1", variants=Hgvs("p.Phe508del")), akt1, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: DIRECTLY_DECREASES, }, ), ( akt1, Protein("HGNC", "AKT1", variants=ProteinModification("Ph", "Ser", 473)), { RELATION: HAS_VARIANT, }, ), ( akt1, Protein("HGNC", "AKT1", variants=Hgvs("p.C40*")), { RELATION: HAS_VARIANT, }, ), ( akt1, Protein( "HGNC", "AKT1", variants=[Hgvs("p.Ala127Tyr"), ProteinModification("Ph", "Ser")], ), { RELATION: HAS_VARIANT, }, ), ( akt1, Protein( "HGNC", "AKT1", variants=[Hgvs("p.Ala127Tyr"), ProteinModification("Ph", "Ser")], ), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: DIRECTLY_DECREASES, SOURCE_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, TARGET_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, }, ), ( akt1, Protein("HGNC", "AKT1", variants=Hgvs("p.Arg1851*")), { RELATION: HAS_VARIANT, }, ), ( akt1, Protein("HGNC", "AKT1", variants=Hgvs("p.40*")), { RELATION: HAS_VARIANT, }, ), ( akt1, Protein("HGNC", "MIA", variants=Fragment()), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, SOURCE_MODIFIER: {MODIFIER: DEGRADATION}, }, ), ( akt1, Protein("HGNC", "CFTR", variants=Hgvs("p.Gly576Ala")), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, }, ), ( akt1, Rna("HGNC", "CFTR", variants=Hgvs("r.1521_1523delcuu")), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, SOURCE_MODIFIER: {MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"]}, }, ), ( akt1, Rna("HGNC", "CFTR", variants=Hgvs("r.1653_1655delcuu")), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, SOURCE_MODIFIER: {MODIFIER: ACTIVITY}, }, ), ( akt1, egfr, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, SOURCE_MODIFIER: { MODIFIER: ACTIVITY, EFFECT: activity_mapping["cat"], }, TARGET_MODIFIER: {MODIFIER: DEGRADATION}, }, ), ( akt1, egfr, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, SOURCE_MODIFIER: { MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"], }, TARGET_MODIFIER: secretion(), }, ), ( Gene("HGNC", "AKT1", variants=Hgvs("c.308G>A")), tmprss2_erg_gene_fusion, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: CAUSES_NO_CHANGE, }, ), ( Gene("HGNC", "AKT1", variants=Hgvs("c.308G>A")), Gene( "HGNC", "AKT1", variants=[Hgvs("c.1521_1523delCTT"), Hgvs("c.308G>A"), Hgvs("p.Phe508del")], ), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, SOURCE_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, }, ), ( MicroRna("HGNC", "MIR21"), bcr_jak2_gene_fusion, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: DIRECTLY_INCREASES, }, ), ( MicroRna("HGNC", "MIR21"), Protein("HGNC", "AKT1", variants=ProteinModification("Ph", "Ser", 473)), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: DECREASES, SOURCE_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, }, ), ( MicroRna("HGNC", "MIR21"), MicroRna("HGNC", "MIR21", variants=Hgvs("p.Phe508del")), { RELATION: HAS_VARIANT, }, ), ( Gene("HGNC", "CFTR", variants=Hgvs("c.1521_1523delCTT")), akt1, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, TARGET_MODIFIER: {MODIFIER: DEGRADATION}, }, ), ( Gene("HGNC", "CFTR"), Gene("HGNC", "CFTR", variants=Hgvs("c.1521_1523delCTT")), { RELATION: HAS_VARIANT, }, ), ( Gene("HGNC", "CFTR"), Gene("HGNC", "CFTR", variants=Hgvs("g.117199646_117199648delCTT")), { RELATION: HAS_VARIANT, }, ), ( Gene("HGNC", "CFTR"), Gene("HGNC", "CFTR", variants=Hgvs("c.1521_1523delCTT")), { RELATION: HAS_VARIANT, }, ), ( Gene("HGNC", "CFTR", variants=Hgvs("g.117199646_117199648delCTT")), Gene("HGNC", "CFTR", variants=Hgvs("c.1521_1523delCTT")), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, }, ), ( MicroRna("HGNC", "MIR21", variants=Hgvs("p.Phe508del")), Protein("HGNC", "AKT1", variants=Hgvs("p.C40*")), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, SOURCE_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, }, ), ( chchd4_aifm1_gene_fusion, tmprss2_erg_protein_fusion, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, }, ), ( Protein("HGNC", "AKT1", variants=Hgvs("p.Arg1851*")), bcr_jak2_protein_fusion, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, }, ), ( Protein("HGNC", "AKT1", variants=Hgvs("p.40*")), chchd4_aifm1_protein_fusion, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, }, ), ( Protein("HGNC", "CFTR", variants=HgvsReference()), egfr, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, TARGET_MODIFIER: translocation(INTRACELLULAR, CELL_SURFACE), }, ), ( cftr, Protein("HGNC", "CFTR", variants=Hgvs("=")), { RELATION: HAS_VARIANT, }, ), ( cftr, Protein("HGNC", "CFTR", variants=Hgvs("?")), { RELATION: HAS_VARIANT, }, ), ( cftr, Protein("HGNC", "CFTR", variants=Hgvs("p.Phe508del")), { RELATION: HAS_VARIANT, }, ), ( cftr, Protein("HGNC", "CFTR", variants=Hgvs("p.Gly576Ala")), { RELATION: HAS_VARIANT, }, ), ( mia, Protein("HGNC", "MIA", variants=Fragment(5, 20)), { RELATION: HAS_VARIANT, }, ), ( mia, Protein("HGNC", "MIA", variants=Fragment(1, "?")), { RELATION: HAS_VARIANT, }, ), ( mia, Protein("HGNC", "MIA", variants=Fragment()), { RELATION: HAS_VARIANT, }, ), ( mia, Protein("HGNC", "MIA", variants=Fragment(description="55kD")), { RELATION: HAS_VARIANT, }, ), ( akt1_rna, Rna("HGNC", "AKT1", variants=[Hgvs("c.1521_1523delCTT"), Hgvs("p.Phe508del")]), { RELATION: HAS_VARIANT, }, ), ( akt1_rna, akt1, { RELATION: TRANSLATED_TO, }, ), ( Gene("HGNC", "APP"), Gene("HGNC", "APP", variants=Hgvs("c.275341G>C")), { RELATION: HAS_VARIANT, }, ), ( Protein("HGNC", "F3"), ComplexAbundance([Protein("HGNC", "F3"), Protein("HGNC", "F7")]), { RELATION: PART_OF, }, ), ( Protein("HGNC", "F7"), ComplexAbundance([Protein("HGNC", "F3"), Protein("HGNC", "F7")]), { RELATION: PART_OF, }, ), ( Protein("HGNC", "GSK3B"), Protein("HGNC", "GSK3B", variants=ProteinModification("Ph", "Ser", 9)), { RELATION: HAS_VARIANT, }, ), ( Pathology("MESHD", "Psoriasis"), Pathology("MESHD", "Skin Diseases"), { RELATION: IS_A, }, ), ( Protein("HGNC", "HBP1"), ComplexAbundance([Gene("HGNC", "NCF1"), Protein("HGNC", "HBP1")]), { RELATION: PART_OF, }, ), ( Gene("HGNC", "NCF1"), ComplexAbundance([Gene("HGNC", "NCF1"), Protein("HGNC", "HBP1")]), { RELATION: PART_OF, }, ), ( Protein("HGNC", "FOS"), ComplexAbundance([Protein("HGNC", "FOS"), Protein("HGNC", "JUN")]), { RELATION: PART_OF, }, ), ( Protein("HGNC", "JUN"), ComplexAbundance([Protein("HGNC", "FOS"), Protein("HGNC", "JUN")]), { RELATION: PART_OF, }, ), ( Rna("HGNC", "CFTR"), Rna("HGNC", "CFTR", variants=Hgvs("r.1521_1523delcuu")), { RELATION: HAS_VARIANT, }, ), ( Rna("HGNC", "CFTR"), Rna("HGNC", "CFTR", variants=Hgvs("r.1653_1655delcuu")), { RELATION: HAS_VARIANT, }, ), ( il6, CompositeAbundance([interleukin_23_complex, il6]), { RELATION: PART_OF, }, ), ( interleukin_23_complex, CompositeAbundance([interleukin_23_complex, il6]), { RELATION: PART_OF, }, ), ( Protein("HGNC", "CFTR", variants=Hgvs("?")), Pathology("MESHD", "Adenocarcinoma"), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, }, ), ( Rna("HGNC", "AKT1", variants=[Hgvs("c.1521_1523delCTT"), Hgvs("p.Phe508del")]), tmprss2_erg_rna_fusion, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: DIRECTLY_INCREASES, }, ), ( RnaFusion(Rna("HGNC", "TMPRSS2"), Rna("HGNC", "ERG")), ComplexAbundance([Gene("HGNC", "NCF1"), Protein("HGNC", "HBP1")]), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, }, ), ( Protein("HGNC", "MIA", variants=Fragment(5, 20)), NamedComplexAbundance("GO", "interleukin-23 complex"), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, TARGET_MODIFIER: secretion(), }, ), ( Protein("HGNC", "MIA", variants=Fragment(1, "?")), egfr, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, TARGET_MODIFIER: { MODIFIER: TRANSLOCATION, EFFECT: { FROM_LOC: {NAMESPACE: "GO", NAME: "cell surface"}, TO_LOC: {NAMESPACE: "GO", NAME: "endosome"}, }, }, }, ), ( akt1_rna, egfr, { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, TARGET_MODIFIER: { MODIFIER: TRANSLOCATION, EFFECT: { FROM_LOC: {NAMESPACE: "GO", NAME: "cell surface"}, TO_LOC: {NAMESPACE: "GO", NAME: "endosome"}, }, }, }, ), ( RnaFusion( Rna("HGNC", "CHCHD4"), Rna("HGNC", "AIFM1"), ), ComplexAbundance([Protein("HGNC", "FOS"), Protein("HGNC", "JUN")]), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: INCREASES, }, ), ( CompositeAbundance([interleukin_23_complex, il6]), BiologicalProcess("GO", "cell cycle arrest"), { EVIDENCE: dummy_evidence, CITATION: citation_1, RELATION: DECREASES, }, ), ( Protein("HGNC", "CAT"), hydrogen_peroxide, { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: DIRECTLY_DECREASES, SOURCE_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, }, ), ( Gene("HGNC", "CAT"), hydrogen_peroxide, { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: DIRECTLY_DECREASES, SOURCE_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, }, ), ( Protein("HGNC", "HMGCR"), BiologicalProcess("GO", "cholesterol biosynthetic process"), { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: RATE_LIMITING_STEP_OF, SOURCE_MODIFIER: {MODIFIER: ACTIVITY, EFFECT: activity_mapping["cat"]}, }, ), ( Gene("HGNC", "APP", variants=Hgvs("c.275341G>C")), Pathology("MESHD", "Alzheimer Disease"), { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: CAUSES_NO_CHANGE, }, ), ( ComplexAbundance([Protein("HGNC", "F3"), Protein("HGNC", "F7")]), Protein("HGNC", "F9"), { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: REGULATES, SOURCE_MODIFIER: {MODIFIER: ACTIVITY, EFFECT: activity_mapping["pep"]}, TARGET_MODIFIER: {MODIFIER: ACTIVITY, EFFECT: activity_mapping["pep"]}, }, ), ( Protein("HGNC", "GSK3B", variants=ProteinModification("Ph", "Ser", 9)), Protein("HGNC", "GSK3B"), { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: POSITIVE_CORRELATION, TARGET_MODIFIER: {MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"]}, }, ), ( Protein("HGNC", "GSK3B"), Protein("HGNC", "GSK3B", variants=ProteinModification("Ph", "Ser", 9)), { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: POSITIVE_CORRELATION, SOURCE_MODIFIER: {MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"]}, }, ), ( Reaction( reactants=( Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), Abundance("CHEBI", "NADPH"), Abundance("CHEBI", "hydron"), ), products=(Abundance("CHEBI", "NADP(+)"), Abundance("CHEBI", "mevalonate")), ), Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), { RELATION: HAS_REACTANT, }, ), ( Reaction( reactants=( Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), Abundance("CHEBI", "NADPH"), Abundance("CHEBI", "hydron"), ), products=(Abundance("CHEBI", "NADP(+)"), Abundance("CHEBI", "mevalonate")), ), Abundance("CHEBI", "NADPH"), { RELATION: HAS_REACTANT, }, ), ( Reaction( reactants=( Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), Abundance("CHEBI", "NADPH"), Abundance("CHEBI", "hydron"), ), products=(Abundance("CHEBI", "NADP(+)"), Abundance("CHEBI", "mevalonate")), ), Abundance("CHEBI", "hydron"), { RELATION: HAS_REACTANT, }, ), ( Reaction( reactants=( Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), Abundance("CHEBI", "NADPH"), Abundance("CHEBI", "hydron"), ), products=(Abundance("CHEBI", "NADP(+)"), Abundance("CHEBI", "mevalonate")), ), Abundance("CHEBI", "mevalonate"), { RELATION: HAS_PRODUCT, }, ), ( Reaction( reactants=( Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), Abundance("CHEBI", "NADPH"), Abundance("CHEBI", "hydron"), ), products=(Abundance("CHEBI", "NADP(+)"), Abundance("CHEBI", "mevalonate")), ), Abundance("CHEBI", "NADP(+)"), { RELATION: HAS_PRODUCT, }, ), ( Reaction( reactants=( Abundance("CHEBI", "(3S)-3-hydroxy-3-methylglutaryl-CoA"), Abundance("CHEBI", "NADPH"), Abundance("CHEBI", "hydron"), ), products=(Abundance("CHEBI", "NADP(+)"), Abundance("CHEBI", "mevalonate")), ), BiologicalProcess("GO", "cholesterol biosynthetic process"), { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: SUBPROCESS_OF, }, ), ( Abundance("CHEBI", "nitric oxide"), ComplexAbundance([Protein("HGNC", "ITGAV"), Protein("HGNC", "ITGB3")]), { EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", CITATION: citation_2, RELATION: INCREASES, TARGET_MODIFIER: { MODIFIER: TRANSLOCATION, EFFECT: { FROM_LOC: compartment_mapping["intracellular"], TO_LOC: compartment_mapping["cell surface"], }, }, }, ), ( Protein("HGNC", "ITGAV"), ComplexAbundance([Protein("HGNC", "ITGAV"), Protein("HGNC", "ITGB3")]), { RELATION: PART_OF, }, ), ( Protein("HGNC", "ITGB3"), ComplexAbundance([Protein("HGNC", "ITGAV"), Protein("HGNC", "ITGB3")]), { RELATION: PART_OF, }, ), ( Gene("HGNC", "ARRDC2"), Gene("HGNC", "ARRDC3"), { RELATION: EQUIVALENT_TO, }, ), ( Gene("HGNC", "ARRDC3"), Gene("HGNC", "ARRDC2"), { RELATION: EQUIVALENT_TO, CITATION: citation_2, EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", }, ), ( Gene("dbSNP", "rs123456"), Gene("HGNC", "CFTR", variants=Hgvs("c.1521_1523delCTT")), { RELATION: ASSOCIATION, CITATION: citation_2, EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", }, ), ( Gene("HGNC", "CFTR", variants=Hgvs("c.1521_1523delCTT")), Gene("dbSNP", "rs123456"), { RELATION: ASSOCIATION, CITATION: citation_2, EVIDENCE: "These were all explicitly stated in the BEL 2.0 Specification", }, ), ] pybel-0.15.5/tests/constants.py000066400000000000000000000513251426625374700164730ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Constants for PyBEL tests.""" import logging import re import unittest from json import dumps from pybel import BELGraph from pybel.canonicalize import edge_to_bel from pybel.constants import ( ANNOTATIONS, ASSOCIATION, CITATION, CITATION_TYPE_PUBMED, DECREASES, DIRECTLY_DECREASES, EVIDENCE, IDENTIFIER, INCREASES, LINE, METADATA_AUTHORS, METADATA_DESCRIPTION, METADATA_LICENSES, METADATA_NAME, METADATA_VERSION, NAMESPACE, PART_OF, RELATION, ) from pybel.dsl import BaseEntity, ComplexAbundance, Pathology, Protein from pybel.dsl.namespaces import hgnc from pybel.exceptions import ( BELParserWarning, BELSyntaxError, CitationTooShortException, IllegalAnnotationValueWarning, InvalidCitationLengthException, InvalidCitationType, InvalidFunctionSemantic, InvalidPubMedIdentifierWarning, MalformedTranslocationWarning, MissingAnnotationKeyWarning, MissingAnnotationRegexWarning, MissingCitationException, MissingMetadataException, MissingNamespaceNameWarning, MissingNamespaceRegexWarning, MissingSupportWarning, NakedNameWarning, NestedRelationWarning, PlaceholderAminoAcidWarning, UndefinedAnnotationWarning, UndefinedNamespaceWarning, VersionFormatWarning, ) from pybel.language import Entity, citation_dict from pybel.parser.parse_bel import BELParser from pybel.parser.parse_control import ControlParser from pybel.testing.constants import test_bel_thorough from pybel.utils import subdict_matches from tests.constant_helper import ( BEL_THOROUGH_EDGES, BEL_THOROUGH_NODES, citation_1, evidence_1, expected_test_simple_metadata, expected_test_thorough_metadata, ) logger = logging.getLogger(__name__) OPENBEL_DOMAIN = "http://resources.openbel.org" OPENBEL_ANNOTATION_RESOURCES = OPENBEL_DOMAIN + "/belframework/20150611/annotation/" test_citation_dict = citation_dict(namespace=CITATION_TYPE_PUBMED, identifier="1235813") SET_CITATION_TEST = f'SET Citation = {{"{test_citation_dict.namespace}", "{test_citation_dict.identifier}"}}' test_evidence_text = "I read it on Twitter" test_set_evidence = f'SET Evidence = "{test_evidence_text}"' HGNC_KEYWORD = "HGNC" MESH_DISEASES_KEYWORD = "MeSHDisease" MESH_DISEASES_URL = OPENBEL_ANNOTATION_RESOURCES + "mesh-diseases.belanno" akt1 = hgnc(name="AKT1") egfr = hgnc(name="EGFR") fadd = hgnc(name="FADD") casp8 = hgnc(name="CASP8") def update_provenance(control_parser: ControlParser) -> None: """Put a default evidence and citation in a BEL parser.""" control_parser.citation_db = test_citation_dict.namespace control_parser.citation_db_id = test_citation_dict.identifier control_parser.evidence = test_evidence_text def assert_has_node(self: unittest.TestCase, node: BaseEntity, graph: BELGraph, **kwargs): """Check if a node with the given properties is contained within a graph.""" self.assertIsInstance(node, BaseEntity) self.assertIn( node, graph, msg="{} not found in graph. Other nodes:\n{}".format(node.as_bel(), "\n".join(n.as_bel() for n in graph)), ) if kwargs: missing = set(kwargs) - set(graph.nodes[node]) self.assertFalse(missing, msg="Missing {} in node data".format(", ".join(sorted(missing)))) self.assertTrue( all(kwarg in graph.nodes[node] for kwarg in kwargs), msg="Missing kwarg in node data", ) self.assertEqual( kwargs, {k: graph.nodes[node][k] for k in kwargs}, msg="Wrong values in node data", ) def any_dict_matches(dict_of_dicts, query_dict) -> bool: """ :param dict_of_dicts: :param query_dict: :return: """ return any(query_dict == sd for sd in dict_of_dicts.values()) def any_subdict_matches(dict_of_dicts, query_dict) -> bool: """Checks if dictionary target_dict matches one of the subdictionaries of a :param dict[any,dict] dict_of_dicts: dictionary of dictionaries :param dict query_dict: dictionary :return: if dictionary target_dict matches one of the subdictionaries of a """ return any(subdict_matches(sub_dict, query_dict) for sub_dict in dict_of_dicts.values()) def _remove_line(d): return {k: v for k, v in d.items() if k != LINE} def assert_has_edge( self: unittest.TestCase, u: BaseEntity, v: BaseEntity, graph: BELGraph, *, only: bool = False, permissive: bool = True, use_identifiers: bool = False, **expected_edge_data, ): """A helper function for checking if an edge with the given properties is contained within a graph.""" self.assertIsInstance(u, BaseEntity) self.assertIsInstance(v, BaseEntity) self.assertTrue( graph.has_edge(u, v), msg="Edge ({}, {}) not in graph. Other edges:\n{}".format( u, v, "\n".join(edge_to_bel(u, v, d, use_identifiers=use_identifiers) for u, v, d in graph.edges(data=True)), ), ) if not expected_edge_data: return if ANNOTATIONS in expected_edge_data: expected_edge_data[ANNOTATIONS] = graph._clean_annotations(expected_edge_data[ANNOTATIONS]) if only: _key, actual_edge_data = list(graph[u][v].items())[0] self.assertEqual( _remove_line(expected_edge_data), _remove_line(actual_edge_data), msg="Only entry not equal", ) else: actual_dicts = {k: _remove_line(v) for k, v in graph[u][v].items()} if permissive: matches = any_subdict_matches(actual_dicts, _remove_line(expected_edge_data)) else: matches = any_dict_matches(actual_dicts, _remove_line(expected_edge_data)) msg = "No edge ({}, {}) with correct properties. expected:\n {}\nbut got:\n{}".format( u, v, dumps(expected_edge_data, indent=2, sort_keys=True), dumps(actual_dicts, indent=2, sort_keys=True), ) self.assertTrue(matches, msg=msg) class TestGraphMixin(unittest.TestCase): """A test case with additional functions for testing graphs.""" def assert_has_node(self, g: BELGraph, n: BaseEntity, **kwargs): """Help assert node membership.""" assert_has_node(self, n, g, **kwargs) def assert_has_edge( self, g: BELGraph, u: BaseEntity, v: BaseEntity, only=False, permissive=True, **kwargs, ): """Help assert edge membership.""" assert_has_edge(self, u, v, g, only=only, permissive=permissive, **kwargs) class TestTokenParserBase(unittest.TestCase): """A test case that has a BEL parser available.""" @classmethod def setUpClass(cls): """Build a BEL graph and BEL parser that persist through the class.""" cls.parser = BELParser( BELGraph(), # gets overwritten in each test autostreamline=False, disallow_unqualified_translocations=True, namespace_to_pattern={ "HGNC": re.compile(r".*"), "SNP": re.compile(r".*"), "CHEBI": re.compile(r".*"), "REF": re.compile(r".*"), "MOD": re.compile(r".*"), "EFO": re.compile(r".*"), "GO": re.compile(r".*"), "UBERON": re.compile(r".*"), "FPLX": re.compile(r".*"), "MESH": re.compile(r".*"), "MGI": re.compile(r".*"), "dbSNP": re.compile(r".*"), }, ) @property def graph(self): return self.parser.graph def setUp(self): """Clear the parser at the beginning of each test.""" self.parser.clear() self.parser.graph = BELGraph() # because all the good stuff in the graph is cleared def assert_has_node(self, member: BaseEntity, **kwargs): """Assert that this test case's graph has the given node.""" assert_has_node(self, member, self.graph, **kwargs) def assert_has_edge(self, u: BaseEntity, v: BaseEntity, only: bool = False, **kwargs): """Assert that this test case's graph has the given edge.""" assert_has_edge(self, u, v, self.graph, only=only, **kwargs) def add_default_provenance(self): """Add a default citation and evidence to the parser.""" update_provenance(self.parser.control_parser) def help_check_hgnc(test_case: unittest.TestCase, namespace_dict) -> None: """Assert that the namespace dictionary is correct.""" test_case.assertIn(HGNC_KEYWORD, namespace_dict) mhs2 = "7071", "MHS2" test_case.assertIn(mhs2, namespace_dict[HGNC_KEYWORD]) test_case.assertEqual(set("G"), set(namespace_dict[HGNC_KEYWORD][mhs2])) miatnb = "50731", "MIATNB" test_case.assertIn(miatnb, namespace_dict[HGNC_KEYWORD]) test_case.assertEqual(set("GR"), set(namespace_dict[HGNC_KEYWORD][miatnb])) mia = "7076", "MIA" test_case.assertIn(mia, namespace_dict[HGNC_KEYWORD]) test_case.assertEqual(set("GRP"), set(namespace_dict[HGNC_KEYWORD][mia])) class BelReconstitutionMixin(TestGraphMixin): """A test case that has checks for properly loading several BEL Scripts.""" def bel_simple_reconstituted(self, graph: BELGraph, check_metadata: bool = True): """Check that test_bel.bel was loaded properly.""" self.assertIsNotNone(graph) self.assertIsInstance(graph, BELGraph) if check_metadata: self.assertIsNotNone(graph.document) self.assertEqual(expected_test_simple_metadata[METADATA_NAME], graph.name) self.assertEqual(expected_test_simple_metadata[METADATA_VERSION], graph.version) self.assertEqual(4, graph.number_of_nodes()) # FIXME this should work, but is getting 8 for the upgrade function # self.assertEqual(6, graph.number_of_edges(), # msg='Edges:\n{}'.format('\n'.join(map(str, graph.edges(keys=True, data=True))))) for node in graph: self.assertIsInstance(node, BaseEntity) self.assertIn(akt1, graph) self.assertIn(egfr, graph) self.assertIn(fadd, graph) self.assertIn(casp8, graph) bel_simple_citation_1 = citation_dict(namespace=CITATION_TYPE_PUBMED, identifier="123455") bel_simple_citation_2 = citation_dict(namespace=CITATION_TYPE_PUBMED, identifier="123456") evidence_1_extra = "Evidence 1 w extra notes" evidence_2 = "Evidence 2" evidence_3 = "Evidence 3" assert_has_edge( self, akt1, egfr, graph, only=True, **{ RELATION: INCREASES, CITATION: bel_simple_citation_1, EVIDENCE: evidence_1_extra, ANNOTATIONS: { "Species": [Entity(namespace="Species", identifier="9606")], }, }, ) assert_has_edge( self, egfr, fadd, graph, only=True, **{ RELATION: DECREASES, ANNOTATIONS: { "Species": [Entity(namespace="Species", identifier="9606")], "CellLine": [Entity(namespace="CellLine", identifier="10B9 cell")], }, CITATION: bel_simple_citation_1, EVIDENCE: evidence_2, }, ) assert_has_edge( self, egfr, casp8, graph, only=True, **{ RELATION: DIRECTLY_DECREASES, ANNOTATIONS: { "Species": [Entity(namespace="Species", identifier="9606")], "CellLine": [Entity(namespace="CellLine", identifier="10B9 cell")], }, CITATION: bel_simple_citation_1, EVIDENCE: evidence_2, }, ) assert_has_edge( self, fadd, casp8, graph, only=True, **{ RELATION: INCREASES, ANNOTATIONS: { "Species": [Entity(namespace="Species", identifier="10116")], }, CITATION: bel_simple_citation_2, EVIDENCE: evidence_3, }, ) assert_has_edge( self, akt1, casp8, graph, only=True, **{ RELATION: ASSOCIATION, ANNOTATIONS: { "Species": [Entity(namespace="Species", identifier="10116")], }, CITATION: bel_simple_citation_2, EVIDENCE: evidence_3, }, ) assert_has_edge( self, casp8, akt1, graph, only=True, **{ RELATION: ASSOCIATION, ANNOTATIONS: { "Species": [Entity(namespace="Species", identifier="10116")], }, CITATION: bel_simple_citation_2, EVIDENCE: evidence_3, }, ) def bel_thorough_reconstituted( self, graph: BELGraph, check_metadata: bool = True, check_warnings: bool = True, check_provenance: bool = True, check_citation_name: bool = True, check_path: bool = True, ): """Check that thorough.bel was loaded properly. :param graph: A BEL graph :param check_metadata: Check the graph's document section is correct :param check_warnings: Check the graph produced the expected warnings :param check_provenance: Check the graph's definition section is correct :param check_citation_name: Check that the names in the citations get reconstituted. This isn't strictly necessary since this data can be looked up :param check_path: Check the graph contains provenance for its original file """ self.assertIsNotNone(graph) self.assertIsInstance(graph, BELGraph) if check_warnings: self.assertEqual( 0, len(graph.warnings), msg="Document warnings:\n{}".format("\n".join(map(str, graph.warnings))), ) if check_metadata: self.assertLessEqual(set(expected_test_thorough_metadata), set(graph.document)) self.assertEqual(expected_test_thorough_metadata[METADATA_NAME], graph.name) self.assertEqual(expected_test_thorough_metadata[METADATA_VERSION], graph.version) self.assertEqual(expected_test_thorough_metadata[METADATA_DESCRIPTION], graph.description) if check_path: self.assertEqual(test_bel_thorough, graph.path) if check_provenance: self.assertEqual({"CHEBI", "HGNC", "GO", "MESHD", "TESTNS2"}, set(graph.namespace_url)) self.assertEqual({"dbSNP"}, set(graph.namespace_pattern)) self.assertIn("TESTAN1", graph.annotation_list) self.assertIn("TESTAN2", graph.annotation_list) self.assertEqual( 2, len(graph.annotation_list), msg="Wrong number of locally defined annotations", ) self.assertEqual({"TestRegex"}, set(graph.annotation_pattern)) for node in graph: self.assertIsInstance(node, BaseEntity) self.assertEqual( sorted(BEL_THOROUGH_NODES, key=str), sorted(graph, key=str), msg="Nodes not equal", ) # FIXME # self.assertEqual(set((u, v) for u, v, _ in e), set(g.edges())) self.assertLess(0, graph.number_of_edges()) for u, v, data in BEL_THOROUGH_EDGES: assert_has_edge(self, u, v, graph, permissive=True, **data) def bel_slushy_reconstituted(self, graph: BELGraph, check_metadata: bool = True, check_warnings: bool = True): """Check that slushy.bel was loaded properly.""" self.assertIsNotNone(graph) self.assertIsInstance(graph, BELGraph) if check_metadata: self.assertIsNotNone(graph.document) self.assertIsInstance(graph.document, dict) expected_test_slushy_metadata = { METADATA_NAME: "Worst. BEL Document. Ever.", METADATA_DESCRIPTION: "This document outlines all of the evil and awful work that is possible during BEL curation", METADATA_VERSION: "0.0", METADATA_AUTHORS: "Charles Tapley Hoyt", METADATA_LICENSES: "WTF License", } self.assertEqual(expected_test_slushy_metadata[METADATA_NAME], graph.name) self.assertEqual(expected_test_slushy_metadata[METADATA_VERSION], graph.version) self.assertEqual(expected_test_slushy_metadata[METADATA_DESCRIPTION], graph.description) if check_warnings: expected_warnings = [ (0, MissingMetadataException), (3, VersionFormatWarning), (26, MissingAnnotationKeyWarning), (29, MissingAnnotationKeyWarning), (34, CitationTooShortException), (37, InvalidCitationType), (40, InvalidPubMedIdentifierWarning), (43, MissingCitationException), (48, MissingAnnotationKeyWarning), (51, MissingAnnotationKeyWarning), (54, MissingSupportWarning), (59, NakedNameWarning), (62, UndefinedNamespaceWarning), (65, MissingNamespaceNameWarning), (68, UndefinedAnnotationWarning), (71, MissingAnnotationKeyWarning), (74, IllegalAnnotationValueWarning), (77, MissingAnnotationRegexWarning), (80, MissingNamespaceRegexWarning), (83, MalformedTranslocationWarning), (86, PlaceholderAminoAcidWarning), (89, NestedRelationWarning), (92, InvalidFunctionSemantic), # (95, Exception), (98, BELSyntaxError), ] for warning_tuple in graph.warnings: self.assertEqual( 3, len(warning_tuple), msg="Warning tuple is wrong size: {}".format(warning_tuple), ) _sliced_warnings = [(w.line_number, w.__class__) for _, w, _ in graph.warnings] self.assertEqual(expected_warnings, _sliced_warnings, msg="wrong warnings") for (el, ew), (_, exc, _) in zip(expected_warnings, graph.warnings): self.assertIsInstance(exc, BELParserWarning) self.assertIsInstance(exc.line, str) self.assertIsInstance(exc.line_number, int) self.assertIsInstance(exc.position, int) self.assertEqual( el, exc.line_number, msg="Expected {} on line {} but got {} on line {}: {}".format( ew, el, exc.__class__, exc.line_number, exc ), ) self.assertIsInstance(exc, ew, msg="Line: {}".format(el)) for node in graph: self.assertIsInstance(node, BaseEntity) self.assertIn(akt1, graph, msg="AKT1 not in graph") self.assertIn(egfr, graph, msg="EGFR not in graph") self.assertEqual( 2, graph.number_of_nodes(), msg="Wrong number of nodes retrieved from slushy.bel", ) self.assertEqual( 1, graph.number_of_edges(), msg="Wrong nunber of edges retrieved from slushy.bel", ) assert_has_edge( self, akt1, egfr, graph, only=True, **{ RELATION: INCREASES, CITATION: citation_1, EVIDENCE: evidence_1, }, ) def bel_isolated_reconstituted(self, graph: BELGraph): """Run the isolated node test.""" self.assertIsNotNone(graph) self.assertIsInstance(graph, BELGraph) adgrb1 = Protein(namespace="HGNC", name="ADGRB1") adgrb2 = Protein(namespace="HGNC", name="ADGRB2") adgrb_complex = ComplexAbundance([adgrb1, adgrb2]) achlorhydria = Pathology(namespace="MESHD", name="Achlorhydria") for node in graph: self.assertIsInstance(node, BaseEntity) self.assertIn(adgrb1, graph) self.assertIn(adgrb2, graph) self.assertIn(adgrb_complex, graph) self.assertIn(achlorhydria, graph) assert_has_edge(self, adgrb1, adgrb_complex, graph, only=True, relation=PART_OF) assert_has_edge(self, adgrb2, adgrb_complex, graph, only=True, relation=PART_OF) pybel-0.15.5/tests/example_edge.json000066400000000000000000000021121426625374700174050ustar00rootroot00000000000000{ "source": { "function": "Protein", "concept": { "namespace": "HGNC", "name": "123" }, "variants": [ { "kind": "pmod", "concept": { "namespace": "bel", "name": "Ph" } } ], "modifier": "Activity", "effect": { "namespace": "go", "name": "transporter activity", "identifier": "0005215" } }, "relation": "directlyDecreases", "target": { "function": "Protein", "concept": { "namespace": "HGNC", "name": "234" }, "modifier": "Translocation", "effect": { "fromLoc": { "namespace": "go", "name": "intracellular", "identifier": "0005622" }, "toLoc": { "namespace": "go", "name": "extracellular space", "identifier": "0005615" } } } } pybel-0.15.5/tests/test_bel_repository.py000066400000000000000000000037501426625374700205560ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the repository class.""" import os import tempfile from pybel import to_bel_script, to_nodelink_file, to_pickle from pybel.examples import egf_graph from pybel.repository import BELRepository from pybel.testing.cases import TemporaryCacheMixin class TestRepository(TemporaryCacheMixin): """Tests for the repository class.""" def test_repository(self): """Test the repository class.""" name = "egf.bel" with tempfile.TemporaryDirectory() as temporary_directory: bel_path = os.path.join(temporary_directory, name) json_path = os.path.join(temporary_directory, f"{name}.json") pickle_path = os.path.join(temporary_directory, f"{name}.pickle") to_bel_script(egf_graph, bel_path) to_nodelink_file(egf_graph, json_path) to_pickle(egf_graph, pickle_path) repository = BELRepository(temporary_directory) graphs = repository.get_graphs( manager=self.manager, use_cached=True, use_tqdm=False, ) self.assertNotEqual(0, len(graphs), msg="No graphs returned") self.assertEqual(1, len(graphs)) self.assertIn(bel_path, graphs) graph = graphs[bel_path] self.assertEqual(graph.document, egf_graph.document) self.assertEqual( set(graph.nodes()), set(egf_graph.nodes()), msg=f""" Original nodes: {set(egf_graph.nodes())} New nodes: {set(graph.nodes())} """, ) self.assertEqual( set(graph.edges()), set(egf_graph.edges()), msg=f""" Original edges: {set(egf_graph.edges())} New edges: {set(graph.edges())} """, ) self.assertTrue(os.path.exists(json_path)) self.assertTrue(os.path.exists(pickle_path)) pybel-0.15.5/tests/test_canonicalization.py000066400000000000000000000333011426625374700210350ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for canonicalization functions.""" import unittest from typing import Iterable from pybel import BELGraph from pybel.canonicalize import _to_bel_lines_body, postpend_location from pybel.constants import CITATION_TYPE_PUBMED, EXTRACELLULAR, INTRACELLULAR, MODIFIER from pybel.dsl import ( Abundance, BiologicalProcess, ComplexAbundance, CompositeAbundance, EnumeratedFusionRange, Fragment, Gene, GeneFusion, GeneModification, Hgvs, MicroRna, NamedComplexAbundance, Pathology, Protein, ProteinModification, ProteinSubstitution, Reaction, Rna, RnaFusion, activity, degradation, secretion, translocation, ) from pybel.language import Entity from pybel.testing.utils import n from pybel.utils import canonicalize_edge class TestCanonicalize(unittest.TestCase): def test_postpend_location_failure(self): with self.assertRaises(ValueError): postpend_location("", dict(name="failure")) def test_canonicalize_variant_dsl(self): """Use the __str__ functions in the DSL to create BEL instead of external pybel.canonicalize.""" self.assertEqual('var("p.Val600Glu")', str(Hgvs("p.Val600Glu"))) self.assertEqual('var("p.Val600Glu")', str(ProteinSubstitution("Val", 600, "Glu"))) self.assertEqual( 'pmod(go:0006468 ! "protein phosphorylation")', str(ProteinModification("Ph")), ) self.assertEqual("pmod(TEST:Ph)", str(ProteinModification("Ph", namespace="TEST"))) self.assertEqual( "pmod(TEST:Ph, Ser)", str(ProteinModification("Ph", namespace="TEST", code="Ser")), ) self.assertEqual( "pmod(TEST:Ph, Ser, 5)", str(ProteinModification("Ph", namespace="TEST", code="Ser", position=5)), ) self.assertEqual( 'pmod(GO:"protein phosphorylation", Thr, 308)', str( ProteinModification( name="protein phosphorylation", namespace="GO", code="Thr", position=308, ) ), ) self.assertEqual('frag("?")', str(Fragment())) self.assertEqual('frag("672_713")', str(Fragment(start=672, stop=713))) self.assertEqual('frag("?", "descr")', str(Fragment(description="descr"))) self.assertEqual( 'frag("672_713", "descr")', str(Fragment(start=672, stop=713, description="descr")), ) self.assertEqual('gmod(go:0006306 ! "DNA methylation")', str(GeneModification("Me"))) self.assertEqual("gmod(TEST:Me)", str(GeneModification("Me", namespace="TEST"))) self.assertEqual( 'gmod(GO:"DNA Methylation")', str(GeneModification("DNA Methylation", namespace="GO")), ) def test_canonicalize_fusion_range_dsl(self): """Test canonicalization of enumerated fusion ranges.""" self.assertEqual("p.1_15", str(EnumeratedFusionRange("p", 1, 15))) self.assertEqual("p.*_15", str(EnumeratedFusionRange("p", "*", 15))) def test_Abundance(self): """Test canonicalization of abundances.""" short = Abundance(namespace="CHEBI", name="water") self.assertEqual("a(CHEBI:water)", str(short)) long = Abundance(namespace="CHEBI", name="test name") self.assertEqual('a(CHEBI:"test name")', str(long)) def test_protein_reference(self): self.assertEqual("p(HGNC:AKT1)", str(Protein(namespace="HGNC", name="AKT1"))) def test_gene_reference(self): node = Gene(namespace="EGID", name="780") self.assertEqual("g(EGID:780)", str(node)) def test_protein_pmod(self): node = Protein( name="PLCG1", namespace="HGNC", variants=[ProteinModification(name="Ph", code="Tyr")], ) self.assertEqual( 'p(HGNC:PLCG1, pmod(go:0006468 ! "protein phosphorylation", Tyr))', str(node), ) def test_protein_fragment(self): node = Protein(name="APP", namespace="HGNC", variants=[Fragment(start=672, stop=713)]) self.assertEqual('p(HGNC:APP, frag("672_713"))', str(node)) def test_mirna_reference(self): self.assertEqual("m(HGNC:MIR1)", str(MicroRna(namespace="HGNC", name="MIR1"))) def test_rna_fusion_specified(self): node = RnaFusion( partner_5p=Rna(namespace="HGNC", name="TMPRSS2"), range_5p=EnumeratedFusionRange("r", 1, 79), partner_3p=Rna(namespace="HGNC", name="ERG"), range_3p=EnumeratedFusionRange("r", 312, 5034), ) self.assertEqual('r(fus(HGNC:TMPRSS2, "r.1_79", HGNC:ERG, "r.312_5034"))', str(node)) def test_rna_fusion_unspecified(self): node = RnaFusion( partner_5p=Rna(namespace="HGNC", name="TMPRSS2"), partner_3p=Rna(namespace="HGNC", name="ERG"), ) self.assertEqual('r(fus(HGNC:TMPRSS2, "?", HGNC:ERG, "?"))', str(node)) def test_gene_fusion_specified(self): node = GeneFusion( partner_5p=Gene(namespace="HGNC", name="TMPRSS2"), range_5p=EnumeratedFusionRange("c", 1, 79), partner_3p=Gene(namespace="HGNC", name="ERG"), range_3p=EnumeratedFusionRange("c", 312, 5034), ) self.assertEqual('g(fus(HGNC:TMPRSS2, "c.1_79", HGNC:ERG, "c.312_5034"))', str(node)) def test_pathology(self): node = Pathology(namespace="DO", name="Alzheimer disease") self.assertEqual('path(DO:"Alzheimer disease")', str(node)) def test_bioprocess(self): node = BiologicalProcess(namespace="GO", name="apoptosis") self.assertEqual("bp(GO:apoptosis)", str(node)) def test_named_complex_abundance(self): node = NamedComplexAbundance(namespace="SCOMP", name="Calcineurin Complex") self.assertEqual('complex(SCOMP:"Calcineurin Complex")', str(node)) def test_complex_abundance(self): node = ComplexAbundance( members=[ Protein(namespace="HGNC", name="FOS"), Protein(namespace="HGNC", name="JUN"), ] ) self.assertEqual("complex(p(HGNC:FOS), p(HGNC:JUN))", str(node)) def test_composite_abundance(self): node = CompositeAbundance( members=[ Protein(namespace="HGNC", name="FOS"), Protein(namespace="HGNC", name="JUN"), ] ) self.assertEqual("composite(p(HGNC:FOS), p(HGNC:JUN))", str(node)) def test_reaction(self): node = Reaction( reactants=[Abundance(namespace="CHEBI", name="A")], products=[Abundance(namespace="CHEBI", name="B")], ) self.assertEqual("rxn(reactants(a(CHEBI:A)), products(a(CHEBI:B)))", str(node)) class TestCanonicalizeEdge(unittest.TestCase): """This class houses all testing for the canonicalization of edges such that the relation/modifications can be used as a second level hash""" def setUp(self): self.g = BELGraph() self.g.annotation_pattern["Species"] = r"\d+" self.u = Protein(name="u", namespace="TEST") self.v = Protein(name="v", namespace="TEST") self.g.add_node_from_data(self.u) self.g.add_node_from_data(self.v) def get_data(self, k): return self.g[self.u][self.v][k] def add_edge(self, source_modifier=None, target_modifier=None, annotations=None): key = self.g.add_increases( self.u, self.v, evidence=n(), citation=n(), source_modifier=source_modifier, target_modifier=target_modifier, annotations=annotations, ) return canonicalize_edge(self.get_data(key)) def test_failure(self): with self.assertRaises(ValueError): self.add_edge(source_modifier={MODIFIER: "nope"}) def test_canonicalize_edge_info(self): c1 = self.add_edge(annotations={"Species": "9606"}) c2 = self.add_edge(annotations={"Species": "9606"}) c3 = self.add_edge( source_modifier=activity("tport"), ) c4 = self.add_edge( source_modifier=activity(namespace="go", name="transporter activity", identifier="0005215"), ) self.assertEqual(c1, c2) self.assertNotEqual(c1, c3) self.assertEqual(c3, c4) def test_subject_degradation_location(self): self.assertEqual( self.add_edge(source_modifier=degradation()), self.add_edge(source_modifier=degradation()), ) self.assertEqual( self.add_edge(source_modifier=degradation(location=Entity(name="somewhere", namespace="GO"))), self.add_edge(source_modifier=degradation(location=Entity(name="somewhere", namespace="GO"))), ) self.assertNotEqual( self.add_edge(source_modifier=degradation()), self.add_edge(source_modifier=degradation(location=Entity(name="somewhere", namespace="GO"))), ) def test_translocation(self): self.assertEqual( self.add_edge(source_modifier=secretion()), self.add_edge(source_modifier=secretion()), ) self.assertEqual( self.add_edge(source_modifier=secretion()), self.add_edge(source_modifier=translocation(INTRACELLULAR, EXTRACELLULAR)), ) class TestSerializeBEL(unittest.TestCase): def setUp(self): self.citation = n() self.evidence = n() self.url = n() self.graph = BELGraph() self.graph.namespace_url["HGNC"] = self.url def _help_check_lines(self, lines: Iterable[str]): """Check the given lines match the graph built during the tests.""" self.assertEqual(lines, list(_to_bel_lines_body(self.graph))) def test_simple(self): """Test a scenario with a qualified edge, but no annotations.""" self.graph.add_increases( Protein(namespace="HGNC", name="YFG1"), Protein(namespace="HGNC", name="YFG"), citation=self.citation, evidence=self.evidence, ) self.assertEqual(2, self.graph.number_of_nodes()) self.assertEqual(1, self.graph.number_of_edges()) expected_lines = [ f'SET Citation = {{"{CITATION_TYPE_PUBMED}", "{self.citation}"}}\n', 'SET SupportingText = "{}"'.format(self.evidence), "p(HGNC:YFG1) increases p(HGNC:YFG)", "UNSET SupportingText", "UNSET Citation\n", "#" * 80, ] self._help_check_lines(expected_lines) def test_different_key_and_namespace(self): key, namespace, value = map(lambda _: n(), range(3)) self.graph.annotation_curie.add(key) self.graph.add_increases( Protein(namespace="HGNC", name="YFG1"), Protein(namespace="HGNC", name="YFG"), citation=self.citation, evidence=self.evidence, annotations={ key: Entity(namespace=namespace, identifier=value), }, ) self.assertEqual(2, self.graph.number_of_nodes()) self.assertEqual(1, self.graph.number_of_edges()) expected_lines = [ f'SET Citation = {{"{CITATION_TYPE_PUBMED}", "{self.citation}"}}\n', f'SET SupportingText = "{self.evidence}"', f'SET {key} = "{namespace}:{value}"', "p(HGNC:YFG1) increases p(HGNC:YFG)", f"UNSET {key}", "UNSET SupportingText", "UNSET Citation\n", ("#" * 80), ] self._help_check_lines(expected_lines) def test_single_annotation(self): """Test a scenario with a qualified edge, but no annotations.""" a1, v1 = map(lambda _: n(), range(2)) self.graph.annotation_list[a1] = {v1} self.graph.add_increases( Protein(namespace="HGNC", name="YFG1"), Protein(namespace="HGNC", name="YFG"), citation=self.citation, evidence=self.evidence, annotations={ a1: {v1}, }, ) self.assertEqual(2, self.graph.number_of_nodes()) self.assertEqual(1, self.graph.number_of_edges()) # Means that only the identifier needs to be written out self.assertNotIn(a1, self.graph.annotation_curie) expected_lines = [ f'SET Citation = {{"{CITATION_TYPE_PUBMED}", "{self.citation}"}}\n', f'SET SupportingText = "{self.evidence}"', f'SET {a1} = "{v1}"', "p(HGNC:YFG1) increases p(HGNC:YFG)", f"UNSET {a1}", "UNSET SupportingText", "UNSET Citation\n", "#" * 80, ] self._help_check_lines(expected_lines) def test_multiple_annotations(self): a1, v1, v2 = map(lambda _: n(), range(3)) v1, v2 = sorted([v1, v2]) self.graph.annotation_list[a1] = {v1, v2} self.graph.add_increases( Protein(namespace="HGNC", name="YFG1"), Protein(namespace="HGNC", name="YFG"), citation=self.citation, evidence=self.evidence, annotations={ a1: {v1, v2}, }, ) self.assertEqual(2, self.graph.number_of_nodes()) self.assertEqual(1, self.graph.number_of_edges()) expected_lines = [ f'SET Citation = {{"{CITATION_TYPE_PUBMED}", "{self.citation}"}}\n', f'SET SupportingText = "{self.evidence}"', f'SET {a1} = {{"{v1}", "{v2}"}}', "p(HGNC:YFG1) increases p(HGNC:YFG)", f"UNSET {a1}", "UNSET SupportingText", "UNSET Citation\n", ("#" * 80), ] self._help_check_lines(expected_lines) pybel-0.15.5/tests/test_cli.py000066400000000000000000000105151426625374700162610ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the command line interface.""" import json import logging import os import traceback import unittest from click.testing import CliRunner from pybel import Manager, cli from pybel.constants import METADATA_NAME, PYBEL_CONTEXT_TAG from pybel.io import from_bel_script, from_nodelink, from_pickle from pybel.manager.database_io import from_database from pybel.testing.cases import FleetingTemporaryCacheMixin from pybel.testing.constants import test_bel_simple, test_bel_thorough from pybel.testing.mocks import mock_bel_resources from tests.constants import BelReconstitutionMixin, expected_test_thorough_metadata log = logging.getLogger(__name__) @unittest.skip class TestCli(FleetingTemporaryCacheMixin, BelReconstitutionMixin): def setUp(self): super(TestCli, self).setUp() self.runner = CliRunner() @mock_bel_resources def test_convert(self, mock_get): """Test conversion via the CLI.""" with self.runner.isolated_filesystem(): test_csv = os.path.abspath("test.csv") test_gpickle = os.path.abspath("test.gpickle") test_canon = os.path.abspath("test.bel") args = [ "convert", # Input "--path", test_bel_thorough, "--connection", self.connection, # Outputs "--csv", test_csv, "--pickle", test_gpickle, "--bel", test_canon, "--store", "--allow-nested", ] result = self.runner.invoke(cli.main, args) self.assertEqual( 0, result.exit_code, msg="{}\n{}\n{}".format( result.exc_info[0], result.exc_info[1], traceback.format_tb(result.exc_info[2]), ), ) self.assertTrue(os.path.exists(test_csv)) self.bel_thorough_reconstituted(from_pickle(test_gpickle)) self.bel_thorough_reconstituted(from_bel_script(test_canon)) manager = Manager(connection=self.connection) self.bel_thorough_reconstituted( from_database(expected_test_thorough_metadata[METADATA_NAME], manager=manager) ) @mock_bel_resources def test_convert_json(self, mock_get): with self.runner.isolated_filesystem(): test_json = os.path.abspath("test.json") args = [ "convert", "--path", test_bel_thorough, "--json", test_json, "--connection", self.connection, "--allow-nested", ] result = self.runner.invoke(cli.main, args) self.assertEqual(0, result.exit_code, msg=result.exc_info) with open(test_json) as f: self.bel_thorough_reconstituted(from_nodelink(json.load(f))) @unittest.skipUnless("NEO_PATH" in os.environ, "Need environmental variable $NEO_PATH") @mock_bel_resources def test_neo4j_remote(self, mock_get): from py2neo import Graph from py2neo.database.status import GraphError test_context = "PYBEL_TEST_CTX" neo_path = os.environ["NEO_PATH"] try: neo = Graph(neo_path) neo.data('match (n)-[r]->() where r.{}="{}" detach delete n'.format(PYBEL_CONTEXT_TAG, test_context)) except GraphError: self.skipTest("Can't query Neo4J ") except Exception: self.skipTest("Can't connect to Neo4J server") else: with self.runner.isolated_filesystem(): args = [ "convert", "--path", test_bel_simple, "--connection", self.connection, "--neo", neo_path, "--neo-context", test_context, ] self.runner.invoke(cli.main, args) q = 'match (n)-[r]->() where r.{}="{}" return count(n) as count'.format(PYBEL_CONTEXT_TAG, test_context) count = neo.data(q)[0]["count"] self.assertEqual(14, count) pybel-0.15.5/tests/test_dsl.py000066400000000000000000000236471426625374700163060ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the internal DSL.""" import unittest import pybel.constants as pc from pybel import BELGraph from pybel.constants import NAME from pybel.dsl import ( Abundance, ComplexAbundance, CompositeAbundance, EnumeratedFusionRange, Fragment, Gene, GeneFusion, ListAbundanceEmptyException, MissingFusionRange, Protein, Reaction, ReactionEmptyException, ) from pybel.language import Entity from pybel.testing.utils import n from pybel.tokens import parse_result_to_dsl from pybel.utils import ensure_quotes class TestDSL(unittest.TestCase): """Tests for the internal DSL.""" def test_add_robust_node(self): """Test adding a node with both a name and identifier.""" graph = BELGraph() namespace, name, identifier = n(), n(), n() node = Protein(namespace=namespace, name=name, identifier=identifier) graph.add_node_from_data(node) self.assertIn(node, graph) def test_add_identified_node(self): """Test what happens when a node with only an identifier is added to a graph.""" graph = BELGraph() namespace, identifier = n(), n() node = Protein(namespace=namespace, identifier=identifier) self.assertNotIn(NAME, node) graph.add_node_from_data(node) self.assertIn(node, graph) def test_add_named_node(self): """Test adding a named node to a BEL graph.""" graph = BELGraph() namespace, name = n(), n() node = Protein(namespace=namespace, name=name) graph.add_node_from_data(node) self.assertIn(node, graph) def test_missing_information(self): """Test that entity and abundance functions raise on missing name/identifier.""" with self.assertRaises(ValueError): Entity(namespace="test") with self.assertRaises(ValueError): Protein(namespace="test") with self.assertRaises(ValueError): Protein(namespace="") with self.assertRaises(TypeError): Protein(namespace="uniprot", name=1234) with self.assertRaises(TypeError): Protein(namespace="uniprot", identifier=1234) with self.assertRaises(ValueError): Protein(namespace="uniprot", name="") with self.assertRaises(ValueError): Protein(namespace="uniprot", identifier="") with self.assertRaises(ValueError): Protein(namespace="uniprot", identifier="12345", name="") with self.assertRaises(ValueError): Protein(namespace="uniprot", identifier="", name="123") def test_abundance_as_bel_dash_unquoted(self): """Test converting an abundance to BEL with a name that needs quotation.""" namespace, name = "HGNC", "YFG-1" node = Abundance(namespace=namespace, name=name) self.assertEqual("a(HGNC:YFG-1)", node.as_bel()) def test_abundance_as_no_quotes(self): """Test converting an abundance that doesn't need quotes, but looks crazy.""" namespace, name = "a-c", "d.e.f" node = Abundance(namespace=namespace, name=name) self.assertEqual("a(a-c:d.e.f)", node.as_bel()) def test_abundance_as_bel_quoted(self): """Test converting an abundance to BEL with a name that needs quotation.""" namespace, name = "HGNC", "YFG~1" node = Abundance(namespace=namespace, name=name) self.assertEqual('a(HGNC:"YFG~1")', node.as_bel()) def test_abundance_as_bel(self): """Test converting an abundance to BEL with a name that does not need quotation.""" namespace, name = "HGNC", "YFG" node = Abundance(namespace=namespace, name=name) self.assertEqual("a(HGNC:YFG)", node.as_bel()) def test_str_has_identifier(self): namespace, identifier = n(), n() node = Abundance(namespace=namespace, identifier=identifier) self.assertEqual( "a({namespace}:{identifier})".format(namespace=namespace, identifier=ensure_quotes(identifier)), node.as_bel(), ) def test_str_has_both(self): namespace, identifier = n(), n() node = Abundance(namespace=namespace, identifier=identifier) self.assertEqual( "a({namespace}:{identifier})".format(namespace=namespace, identifier=ensure_quotes(identifier)), node.as_bel(), ) def test_as_tuple(self): namespace, name = n(), n() node = Abundance(namespace=namespace, name=name) self.assertEqual(hash(node), hash(node.as_bel())) def test_empty_complex(self): """Test that an empty complex causes a failure.""" with self.assertRaises(ValueError): ComplexAbundance(members=[]) def test_empty_composite(self): """Test that an empty complex causes a failure.""" with self.assertRaises(ValueError): CompositeAbundance(members=[]) def test_complex_with_name(self): """Test what happens with a named complex. .. code-block:: complex(SCOMP:"9-1-1 Complex") hasComponent p(HGNC:HUS1) complex(SCOMP:"9-1-1 Complex") hasComponent p(HGNC:RAD1) complex(SCOMP:"9-1-1 Complex") hasComponent p(HGNC:RAD9A) """ hus1 = Protein(namespace="HGNC", name="HUS1") rad1 = Protein(namespace="HGNC", name="RAD1") rad9a = Protein(namespace="HGNC", name="RAD9A") members = [hus1, rad1, rad9a] nine_one_one = ComplexAbundance(members=members, namespace="SCOMP", name="9-1-1 Complex") graph = BELGraph() graph.add_node_from_data(nine_one_one) self.assertIn(nine_one_one, graph) self.assertIn(hus1, graph) self.assertIn(rad1, graph) self.assertIn(rad9a, graph) def test_GeneFusion(self): """Test serialization of a gene fusion to BEL with a explicit fusion ranges.""" dsl = GeneFusion( Gene("HGNC", "TMPRSS2"), Gene("HGNC", "ERG"), EnumeratedFusionRange("c", 1, 79), EnumeratedFusionRange("c", 312, 5034), ) self.assertEqual('g(fus(HGNC:TMPRSS2, "c.1_79", HGNC:ERG, "c.312_5034"))', dsl.as_bel()) def test_gene_fusion_missing_implicit(self): """Test serialization of a gene fusion to BEL with a implicit missing fusion ranges.""" dsl = GeneFusion( Gene("HGNC", "TMPRSS2"), Gene("HGNC", "ERG"), ) self.assertEqual('g(fus(HGNC:TMPRSS2, "?", HGNC:ERG, "?"))', dsl.as_bel()) def test_gene_fusion_missing_explicit(self): """Test serialization of a gene fusion to BEL with an explicit missing fusion ranges.""" dsl = GeneFusion( Gene("HGNC", "TMPRSS2"), Gene("HGNC", "ERG"), MissingFusionRange(), MissingFusionRange(), ) self.assertEqual('g(fus(HGNC:TMPRSS2, "?", HGNC:ERG, "?"))', dsl.as_bel()) class TestCentralDogma(unittest.TestCase): """Test functions specific for :class:`CentralDogmaAbundance`s.""" def test_get_parent(self): """Test the get_parent function in :class:`CentralDogmaAbundance`s.""" ab42 = Protein(name="APP", namespace="HGNC", variants=[Fragment(start=672, stop=713)]) app = ab42.get_parent() self.assertEqual("p(HGNC:APP)", app.as_bel()) self.assertEqual('p(HGNC:APP, frag("672_713"))', ab42.as_bel()) def test_with_variants(self): """Test the `with_variant` function in :class:`CentralDogmaAbundance`s.""" app = Protein(name="APP", namespace="HGNC") ab42 = app.with_variants(Fragment(start=672, stop=713)) self.assertEqual("p(HGNC:APP)", app.as_bel()) self.assertEqual('p(HGNC:APP, frag("672_713"))', ab42.as_bel()) def test_with_variants_list(self): """Test the `with_variant` function in :class:`CentralDogmaAbundance`s.""" app = Protein(name="APP", namespace="HGNC") ab42 = app.with_variants([Fragment(start=672, stop=713)]) self.assertEqual("p(HGNC:APP)", app.as_bel()) self.assertEqual('p(HGNC:APP, frag("672_713"))', ab42.as_bel()) def test_list_abundance_has_contents(self): """Test that the construction of list abundance doesn't have empty lists.""" with self.assertRaises(ListAbundanceEmptyException): ComplexAbundance([]) with self.assertRaises(ListAbundanceEmptyException): CompositeAbundance([]) def test_reaction(self): """Add identified reaction.""" graph = BELGraph() reaction = Reaction( namespace="rhea", identifier="44104", reactants=[ Abundance(namespace="chebi", identifier="17478"), Abundance(namespace="chebi", identifier="15377"), Abundance(namespace="chebi", identifier="57540"), ], products=[ Abundance(namespace="chebi", identifier="29067"), Abundance(namespace="chebi", identifier="15378"), Abundance(namespace="chebi", identifier="57945"), ], ) graph.add_node_from_data(reaction) self.assertEqual(7, graph.number_of_nodes()) self.assertEqual(6, graph.number_of_edges()) def test_reaction_has_contents(self): """Test that the construction of reaction doesn't have empty lists.""" with self.assertRaises(ReactionEmptyException): Reaction([], []) class TestParse(unittest.TestCase): """Test that :func:`parse_result_to_dsl` works correctly.""" def test_named_complex(self): x = ComplexAbundance( namespace="a", identifier="b", members=[ Protein(namespace="c", identifier="d"), Protein(namespace="c", identifier="e"), ], ) y = parse_result_to_dsl(dict(x)) self.assertIsInstance(y, ComplexAbundance) self.assertIn(pc.MEMBERS, y) self.assertIn(pc.CONCEPT, y) if __name__ == "__main__": unittest.main() pybel-0.15.5/tests/test_grounding.py000066400000000000000000000476141426625374700175200ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test grounding.""" import unittest from unittest import mock import bioregistry import pyobo from pyobo.mocks import get_mock_id_name_mapping from pybel.constants import ( ANNOTATIONS, CONCEPT, GMOD, IDENTIFIER, KIND, MEMBERS, NAME, NAMESPACE, PMOD, VARIANTS, ) from pybel.grounding import ( _NAME_REMAPPING, _process_annotations, _process_concept, _process_node, ) from pybel.language import Entity def _failer(*_, **__): """Fail for all calls to this function.""" raise ValueError("Called a PyOBO function that should be mocked") pyobo.getters.get = _failer pyobo.api.names.cached_mapping = _failer pyobo.api.names.cached_multidict = _failer mock_id_name_data = { "mesh": { "D009474": "Neurons", "D010300": "Parkinson Disease", "D013378": "Substantia Nigra", }, "doid": { "14330": "Parkinson's disease", }, "go": { "0006468": "protein phosphorylation", }, "complexportal": { "CPX-1829": "Checkpoint clamp complex", }, "ncbitaxon": { "9606": "Homo sapiens", }, "cl": { "0000030": "glioblast", }, "fplx": { "TAP": "TAP", "Gamma_secretase": "Gamma_secretase", }, } mock_id_name_mapping = get_mock_id_name_mapping(mock_id_name_data) _mock_mnemonic_data = { "O60921": "HUS1_HUMAN", "Q99638": "RAD9A_HUMAN", "O60671": "RAD1_HUMAN", } _mock_reverse_mnemonic_data = {v: k for k, v in _mock_mnemonic_data.items()} def _mock_get_mnemonic(identifier, *_, **__): return _mock_mnemonic_data[identifier] mock_get_mnemonic = mock.patch("pybel.grounding.get_mnemonic", side_effect=_mock_get_mnemonic) mock_get_id_from_mnemonic = mock.patch( "pybel.grounding.get_id_from_mnemonic", side_effect=_mock_reverse_mnemonic_data.get, ) @mock_id_name_mapping @mock_get_mnemonic @mock_get_id_from_mnemonic class TestProcessConcept(unittest.TestCase): """Test the :func:`_process_concept` function.""" def _help(self, expected, original, msg=None): expected = {CONCEPT: expected} d = {CONCEPT: original} self.assertIsNotNone( bioregistry.normalize_prefix(expected[CONCEPT][NAMESPACE]), msg="Unrecognized namespace", ) _process_concept(concept=d[CONCEPT], node=d) self.assertEqual(expected[CONCEPT], d[CONCEPT], msg=msg) def test_normalize_prefix_case(self, *_): """Test normalizing the prefix to the correct case.""" self._help( {NAMESPACE: "mesh", NAME: "Neurons", IDENTIFIER: "D009474"}, {NAMESPACE: "MESH", NAME: "Neurons", IDENTIFIER: "D009474"}, ) def test_normalize_prefix_synonym(self, *_): """Test normalizing the prefix based on the synonym dictionary.""" self._help( {NAMESPACE: "mesh", NAME: "Neurons", IDENTIFIER: "D009474"}, {NAMESPACE: "MESHA", NAME: "Neurons", IDENTIFIER: "D009474"}, ) def test_lookup_identifier(self, *_): """Test look up of the identifier when given the name.""" self._help( {NAMESPACE: "mesh", NAME: "Neurons", IDENTIFIER: "D009474"}, {NAMESPACE: "MESH", NAME: "Neurons"}, ) def test_lookup_name_as_identifier(self, *_): """Test look up of the name when given as the identifier.""" self._help( {NAMESPACE: "mesh", NAME: "Neurons", IDENTIFIER: "D009474"}, {NAMESPACE: "MESH", IDENTIFIER: "Neurons"}, ) def test_lookup_uniprot_identifier(self, *_): """Test looking up a uniprot identifier.""" self._help( {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921"}, {NAMESPACE: "UniProt", NAME: "HUS1_HUMAN"}, ) def test_fix_uniprot_identifier_as_name(self, *_): """Test lookup of the UniProt identifier when given a UniProt identifier as the name.""" self._help( {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921"}, {NAMESPACE: "UniProt", NAME: "O60921"}, ) def test_fix_wrong_name(self, *_): """Test overwriting a wrong name (not UniProt).""" self._help( {NAMESPACE: "mesh", NAME: "Neurons", IDENTIFIER: "D009474"}, {NAMESPACE: "MESH", NAME: "Nonsense name!", IDENTIFIER: "D009474"}, ) def test_fix_wrong_uniprot_name(self, *_): """Test overwriting a wrong name (UniProt).""" self._help( {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921"}, {NAMESPACE: "UniProt", NAME: "WRONG!!!!", IDENTIFIER: "O60921"}, ) def test_remap_sfam(self, *_): """Test remapping SFAM to FPLX.""" self.assertIn(("bel", "TAP Family"), _NAME_REMAPPING) self._help( {NAMESPACE: "fplx", NAME: "TAP", IDENTIFIER: "TAP"}, {NAMESPACE: "SFAM", NAME: "TAP Family"}, ) def test_remap_scomp(self, *_): """Test remapping SFAM to FPLX.""" self.assertIsNotNone(bioregistry.normalize_prefix("BEL")) self.assertIn( ("bel", "gamma Secretase Complex"), _NAME_REMAPPING, msg="name remapping is not populated properly", ) self._help( {NAMESPACE: "fplx", NAME: "Gamma_secretase", IDENTIFIER: "Gamma_secretase"}, {NAMESPACE: "SCOMP", NAME: "gamma Secretase Complex"}, ) @mock_id_name_mapping @mock_get_mnemonic @mock_get_id_from_mnemonic class TestGround(unittest.TestCase): """Test grounding.""" def _help(self, expected, result): _process_node(result) self.assertEqual(expected, result) def test_lookup_identifier_member(self, *_): """Test looking up the identifier of a member by name.""" self._help( { MEMBERS: [ { CONCEPT: { NAMESPACE: "mesh", NAME: "Neurons", IDENTIFIER: "D009474", } } ] }, {MEMBERS: [{CONCEPT: {NAMESPACE: "MESH", NAME: "Neurons"}}]}, ) def test_lookup_identifier_complex(self, *_): """Test looking up the identifier of a named complex and its members at the same time.""" self._help( { CONCEPT: { NAMESPACE: "complexportal", NAME: "Checkpoint clamp complex", IDENTIFIER: "CPX-1829", }, MEMBERS: [ { CONCEPT: { NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921", } }, { CONCEPT: { NAMESPACE: "uniprot", NAME: "RAD9A_HUMAN", IDENTIFIER: "Q99638", } }, { CONCEPT: { NAMESPACE: "uniprot", NAME: "RAD1_HUMAN", IDENTIFIER: "O60671", } }, ], }, { CONCEPT: {NAMESPACE: "complexportal", NAME: "Checkpoint clamp complex"}, MEMBERS: [ {CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN"}}, {CONCEPT: {NAMESPACE: "uniprot", NAME: "RAD9A_HUMAN"}}, {CONCEPT: {NAMESPACE: "uniprot", NAME: "RAD1_HUMAN"}}, ], }, ) def test_lookup_identifier_protein(self, *_): """Test looking up the identifier based on a protein's name.""" self._help( {CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921"}}, {CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN"}}, ) def test_lookup_name_protein(self, *_): """Test looking up the name based on a protein's identifier.""" self._help( {CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921"}}, {CONCEPT: {NAMESPACE: "uniprot", IDENTIFIER: "O60921"}}, ) def test_fix_name_protein(self, *_): """Test fixing a wrong name by overwriting by identifier-based lookup.""" self._help( {CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921"}}, {CONCEPT: {NAMESPACE: "uniprot", IDENTIFIER: "O60921", NAME: "wrong!!!"}}, ) def test_lookup_identifier_pmod(self, *_): """Test looking up a protein modification's identifier by name.""" self._help( { CONCEPT: { NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921", }, VARIANTS: [ { KIND: PMOD, CONCEPT: { NAMESPACE: "go", IDENTIFIER: "0006468", NAME: "protein phosphorylation", }, }, ], }, { CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN"}, VARIANTS: [ { KIND: PMOD, CONCEPT: {NAMESPACE: "GO", NAME: "protein phosphorylation"}, }, ], }, ) def test_lookup_name_pmod(self, *_): """Test looking up a protein modification's name by identifier.""" self._help( { CONCEPT: { NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921", }, VARIANTS: [ { KIND: PMOD, CONCEPT: { NAMESPACE: "go", IDENTIFIER: "0006468", NAME: "protein phosphorylation", }, }, ], }, { CONCEPT: {NAMESPACE: "uniprot", IDENTIFIER: "O60921"}, VARIANTS: [ { KIND: PMOD, CONCEPT: {NAMESPACE: "GO", IDENTIFIER: "0006468"}, }, ], }, ) def test_fix_pmod_name(self, *_): """Test fixing a wrong name in a pmod.""" self._help( { CONCEPT: { NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921", }, VARIANTS: [ { KIND: PMOD, CONCEPT: { NAMESPACE: "go", IDENTIFIER: "0006468", NAME: "protein phosphorylation", }, }, ], }, { CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN"}, VARIANTS: [ { KIND: PMOD, CONCEPT: { NAMESPACE: "GO", IDENTIFIER: "0006468", NAME: "WRONG!", }, }, ], }, ) def test_normalize_pmod_default(self, *_): """Test normalizing a pmod using the default bel namespace.""" self._help( { CONCEPT: { NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921", }, VARIANTS: [ { KIND: PMOD, CONCEPT: { NAMESPACE: "go", IDENTIFIER: "0006468", NAME: "protein phosphorylation", }, }, ], }, { CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN"}, VARIANTS: [ { KIND: PMOD, CONCEPT: {NAMESPACE: "bel", NAME: "Ph"}, }, ], }, ) def test_normalize_pmod_default_methylation(self, *_): """Test normalizing the default namespace's Me entry because of conflict with gmods.""" self._help( { CONCEPT: { NAMESPACE: "uniprot", NAME: "HUS1_HUMAN", IDENTIFIER: "O60921", }, VARIANTS: [ { KIND: PMOD, CONCEPT: { NAMESPACE: "go", IDENTIFIER: "0006479", NAME: "protein methylation", }, }, ], }, { CONCEPT: {NAMESPACE: "uniprot", NAME: "HUS1_HUMAN"}, VARIANTS: [ {KIND: PMOD, CONCEPT: {NAMESPACE: "bel", NAME: "Me"}}, ], }, ) def normalize_gmod_default_methylation(self, *_): """Test normalizing the default namespace's Me entry because of conflict with pmods.""" self._help( { CONCEPT: {NAMESPACE: "hgnc", NAME: "MAPT", IDENTIFIER: "6893"}, VARIANTS: [ { CONCEPT: { NAMESPACE: "go", IDENTIFIER: "0006306", NAME: "DNA methylation", }, KIND: GMOD, }, ], }, { CONCEPT: {NAMESPACE: "HGNC", NAME: "MAPT"}, VARIANTS: [ {CONCEPT: {NAMESPACE: "bel", NAME: "Me"}, KIND: GMOD}, ], }, ) @mock_id_name_mapping class TestAnnotations(unittest.TestCase): """Test processing annotations.""" def _help(self, expected_data, data): expected_data = {ANNOTATIONS: expected_data} data = {ANNOTATIONS: data} _process_annotations(data) self.assertEqual(expected_data, data) def test_lookup_by_identifier(self, *_): """Test lookup by identifier.""" self._help( {"Disease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")]}, {"Disease": [Entity(namespace="mesh", identifier="D010300")]}, ) def test_lookup_by_name(self, *_): """Test lookup by name.""" self._help( {"Disease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")]}, {"Disease": [Entity(namespace="mesh", name="Parkinson Disease")]}, ) def test_lookup_by_name_as_identifier(self, *_): """Test lookup by name if it's accidentally in the identifier slot.""" self._help( {"Disease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")]}, {"Disease": [Entity(namespace="mesh", identifier="Parkinson Disease")]}, ) def test_upgrade_category(self, *_): """Test upgrading the category.""" self._help( {"Disease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")]}, {"MeSHDisease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")]}, ) def test_upgrade_category_and_namespace(self, *_): """Test upgrading the category and the namespace simultaneously.""" self._help( {"Disease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")]}, { "MeSHDisease": [ Entity( namespace="MeSHDisease", identifier="D010300", name="Parkinson Disease", ) ] }, ) def test_upgrade_with_name_as_identifier(self, *_): """Test upgrading MeSH disease, MeSH anatomy, and Species tags and lookup by name, in the identifiers space.""" self._help( { # Expected "Disease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")], "Anatomy": [Entity(namespace="mesh", identifier="D013378", name="Substantia Nigra")], "Species": [Entity(namespace="ncbitaxon", identifier="9606", name="Homo sapiens")], }, { # Original "MeSHDisease": [Entity(namespace="MeSHDisease", identifier="Parkinson Disease")], "MeSHAnatomy": [Entity(namespace="MeSHAnatomy", identifier="Substantia Nigra")], "Species": [Entity(namespace="Species", identifier="Homo sapiens")], }, ) def test_upgrade_by_identifier(self, *_): """Test upgrading and lookup by identifier.""" self._help( { # Expected "Disease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")], "Anatomy": [Entity(namespace="mesh", identifier="D013378", name="Substantia Nigra")], "Species": [Entity(namespace="ncbitaxon", identifier="9606", name="Homo sapiens")], }, { # Original "MeSHDisease": [Entity(namespace="MeSHDisease", identifier="D010300")], "MeSHAnatomy": [Entity(namespace="MeSHAnatomy", identifier="D013378")], "Species": [Entity(namespace="Species", identifier="9606")], }, ) def test_upgrade_by_name(self, *_): """Test upgrading and lookup by name.""" self._help( { # Expected "Disease": [Entity(namespace="mesh", identifier="D010300", name="Parkinson Disease")], "Anatomy": [Entity(namespace="mesh", identifier="D013378", name="Substantia Nigra")], "Species": [Entity(namespace="ncbitaxon", identifier="9606", name="Homo sapiens")], }, { # Original "MeSHDisease": [Entity(namespace="MeSHDisease", name="Parkinson Disease")], "MeSHAnatomy": [Entity(namespace="MeSHAnatomy", name="Substantia Nigra")], "Species": [Entity(namespace="Species", name="Homo sapiens")], }, ) def test_unmappable_category(self, *_): """Test when the category can't be mapped.""" self._help( { # Expected "Custom Annotation": [Entity(namespace="Custom Annotation", identifier="Custom Value")], }, { "Custom Annotation": [Entity(namespace="Custom Annotation", identifier="Custom Value")], }, ) def test_unmappable_identifier(self, *_): """Test when the identifier can not be resolved.""" self._help( { # Expected "Disease": [Entity(namespace="doid", identifier="Failure")], }, { "Disease": [Entity(namespace="Disease", identifier="Failure")], }, ) def test_unmappable_name(self, *_): """Test when the identifier can not be looked up by name.""" self._help( { # Expected "Disease": [Entity(namespace="doid", name="Failure")], }, { "Disease": [Entity(namespace="Disease", name="Failure")], }, ) pybel-0.15.5/tests/test_io/000077500000000000000000000000001426625374700155455ustar00rootroot00000000000000pybel-0.15.5/tests/test_io/__init__.py000066400000000000000000000000721426625374700176550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for :mod:`pybel.io`.""" pybel-0.15.5/tests/test_io/test_cx/000077500000000000000000000000001426625374700172165ustar00rootroot00000000000000pybel-0.15.5/tests/test_io/test_cx/__init__.py000066400000000000000000000000661426625374700213310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test cases for CX I/O.""" pybel-0.15.5/tests/test_io/test_cx/cases.py000066400000000000000000000070151426625374700206710ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test cases for PyBEL-CX.""" import json import unittest from typing import Mapping, Tuple from pybel import BaseEntity, BELGraph from pybel.constants import ( ANNOTATIONS, CITATION, EVIDENCE, IDENTIFIER, NAMESPACE, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, ) from pybel.typing import EdgeData __all__ = [ "TestCase", ] def _edge_to_tuple(u: BaseEntity, v: BaseEntity, edge_data: EdgeData): """Convert an edge to tuple. :return: A tuple that can be hashed representing this edge. Makes no promises to its structure. """ citation = edge_data.get(CITATION) if citation is None: citation_hashable = None else: citation_hashable = (citation[NAMESPACE], citation[IDENTIFIER]) evidence_hashable = edge_data.get(EVIDENCE) annotations = edge_data.get(ANNOTATIONS) if annotations is None: annotations_hashable = None else: annotations_hashable = tuple((key, tuple(sorted(values))) for key, values in sorted(annotations.items())) source_modifier = edge_data.get(SOURCE_MODIFIER) if source_modifier is None: subject_hashable = None else: subject_hashable = json.dumps(source_modifier, ensure_ascii=True, sort_keys=True, indent=0) target_modifier = edge_data.get(TARGET_MODIFIER) if target_modifier is None: object_hashable = None else: object_hashable = json.dumps(target_modifier, ensure_ascii=True, sort_keys=True, indent=0) return ( u, v, edge_data[RELATION], citation_hashable, evidence_hashable, annotations_hashable, subject_hashable, object_hashable, ) def _hash_edge(u: BaseEntity, v: BaseEntity, data: EdgeData) -> int: """Convert an edge tuple to a hash. :return: A hashed version of the edge tuple using md5 hash of the binary pickle dump of u, v, and the json dump of d """ return hash(_edge_to_tuple(u, v, data)) def _get_edge_dict( graph: BELGraph, ) -> Mapping[int, Tuple[BaseEntity, BaseEntity, EdgeData]]: return {_hash_edge(u, v, data): (u, v, data) for u, v, k, data in graph.edges(keys=True, data=True)} class TestCase(unittest.TestCase): """Extension to base :class:`unittest.TestCase` with :class:`pybel.BELGraph` comparison.""" def assert_graph_equal(self, g1: BELGraph, g2: BELGraph) -> None: """Assert two BEL graphs are the same.""" self.assertEqual(g1.graph, g2.graph, msg="Metadata were not the same") # self.assertEqual(g1.number_of_nodes(), g2.number_of_nodes()) self.assertEqual(set(g1), set(g2), msg="Nodes were not the same") # self.assertEqual(g1.number_of_edges(), g2.number_of_edges()) self.assertEqual(set(g1.edges()), set(g2.edges())) g1_edge_hashes = _get_edge_dict(g1) g2_edge_hashes = _get_edge_dict(g2) g1k = g1_edge_hashes.keys() g2k = g2_edge_hashes.keys() g1ng2 = g1k - g2k g2ng1 = g2k - g1k if g1ng2 and not g2ng1: for k in g1ng2: print(k[:6], g1_edge_hashes[k]) self.fail() elif not g1ng2 and g2ng1: for k in g2ng1: print(k[:6], g2_edge_hashes[k]) self.fail() elif g1ng2 and g2ng1: print("in g1 but not g2:") for k in g1ng2: print(k[:6], g1_edge_hashes[k]) print("in g2 but not g1:") for k in g2ng1: print(k[:6], g2_edge_hashes[k]) self.fail() pybel-0.15.5/tests/test_io/test_cx/examples.py000066400000000000000000000272211426625374700214120ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Example BEL graphs for testing PyBEL-CX.""" from pybel import BELGraph from pybel.constants import CITATION_TYPE_OTHER, HAS_VARIANT from pybel.dsl import ( ComplexAbundance, Protein, abundance, activity, bioprocess, gene, gmod, named_complex_abundance, pathology, pmod, protein_fusion, protein_substitution, reaction, rna, ) from pybel.language import citation_dict example_graph = BELGraph() example_graph.annotation_list["Confidence"] = {"Low", "Medium", "High", "Very High"} example_graph.annotation_pattern["Number"] = r"\d+" example_graph.annotation_pattern["Species"] = r"\d+" ptk2 = Protein(namespace="hgnc", name="PTK2", variants=pmod("Ph", "Tyr", 925)) mapk1 = Protein(namespace="hgnc", name="MAPK1") mapk3 = Protein(namespace="hgnc", name="MAPK3") grb2 = Protein(namespace="hgnc", name="GRB2") sos1 = Protein(namespace="hgnc", name="SOS1") ptk2_rgb2_sos1 = ComplexAbundance([mapk1, grb2, sos1]) ras_family = Protein(namespace="fplx", name="RAS") pi3k_complex = named_complex_abundance(namespace="fplx", name="p85/p110 PI3Kinase Complex") kinase_activity = activity("kin") catalytic_activity = activity("cat") gtp_activity = activity("gtp") c1 = "10446041" e1 = ( "FAK also combines with, and may activate, phosphoinositide 3-OH kinase (PI 3-kinase), either directly or " "through the Src kinase (13). Finally, there is evidence that Src phosphorylates FAK at Tyr925, creating a" " binding site for the complex of the adapter Grb2 and Ras guanosine 5'-triphosphate exchange factor mSOS (10)." " These interactions link FAK to signaling pathways that modify the cytoskeleton and activate mitogen-activated" " protein kinase (MAPK) cascades (Fig. 3A)." ) e1 = str(hash(e1)) """ p(HGNC:PTK2,pmod(P,Y,925)) increases kin(p(HGNC:MAPK1)) complex(p(HGNC:PTK2),p(HGNC:GRB2),p(HGNC:SOS1)) increases gtp(p(SFAM:"RAS Family")) gtp(p(SFAM:"RAS Family")) increases kin(p(HGNC:MAPK1)) kin(p(HGNC:PTK2)) increases complex(p(HGNC:PTK2),p(HGNC:GRB2),p(HGNC:SOS1)) kin(p(HGNC:PTK2)) increases kin(p(HGNC:MAPK1)) kin(p(HGNC:PTK2)) increases kin(p(HGNC:MAPK3)) kin(p(HGNC:PTK2)) increases kin(complex(SCOMP:"p85/p110 PI3Kinase Complex")) """ example_graph.add_increases(ptk2, mapk1, evidence=e1, citation=c1, target_modifier=kinase_activity) example_graph.add_increases(ptk2_rgb2_sos1, ras_family, evidence=e1, citation=c1, target_modifier=gtp_activity) example_graph.add_increases( ras_family, mapk1, evidence=e1, citation=c1, source_modifier=gtp_activity, target_modifier=kinase_activity, ) example_graph.add_increases(ptk2, ptk2_rgb2_sos1, evidence=e1, citation=c1, source_modifier=kinase_activity) example_graph.add_increases( ptk2, mapk1, evidence=e1, citation=c1, source_modifier=kinase_activity, target_modifier=kinase_activity, ) example_graph.add_increases( ptk2, mapk3, evidence=e1, citation=c1, source_modifier=kinase_activity, target_modifier=kinase_activity, ) example_graph.add_increases( ptk2, pi3k_complex, evidence=e1, citation=c1, source_modifier=kinase_activity, target_modifier=kinase_activity, ) """ SET Evidence=" two common MTHFR polymorphisms, namely 677C>T (Ala222Val) and 1298A>C (Glu429Ala), are known to reduce MTHFR activity. \ It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects.\ the MTHFR 677C>T polymorphism as a candidate AD risk factor" SET Subgraph = "Epigenetic modification subgraph" g(HGNC:MTHFR,sub(C,677,T)) =| p(HGNC:MTHFR) g(HGNC:MTHFR,sub(A,1298,C)) =| p(HGNC:MTHFR) g(HGNC:MTHFR,sub(C,677,T)) neg a(CHEBI:"folic acid") g(HGNC:MTHFR,sub(C,677,T)) pos path(MESH:"Alzheimer Disease") """ c2 = "21119889" e2 = "Two common MTHFR polymorphisms, namely 677C>T (Ala222Val) and 1298A>C (Glu429Ala), are known to reduce MTHFR activity. \ It has been shown that the MTHFR 677T allele is associated with increased total plasma Hcy levels (tHcy) and decreased serum folate levels, mainly in 677TT homozygous subjects.\ the MTHFR 677C>T polymorphism as a candidate AD risk factor" e2 = str(hash(e2)) mthfr = Protein(namespace="hgnc", name="MTHFR") mthfr_c677t = Protein(namespace="hgnc", name="MTHFR", variants=[protein_substitution("Ala", 222, "Val")]) mthfr_a1298c = Protein(namespace="hgnc", name="MTHFR", variants=[protein_substitution("Glu", 429, "Ala")]) folic_acid = abundance("CHEBI", "folic acid") alzheimer_disease = pathology("MESH", "Alzheimer Disease") example_graph.add_decreases(mthfr_c677t, mthfr, citation=c2, evidence=e2, target_modifier=activity()) example_graph.add_decreases(mthfr_a1298c, mthfr, citation=c2, evidence=e2, target_modifier=activity()) example_graph.add_negative_correlation(mthfr_c677t, folic_acid, citation=c2, evidence=e2) example_graph.add_positive_correlation(mthfr_c677t, alzheimer_disease, citation=c2, evidence=e2) c3 = "17948130" e3 = ( "A polymorphism in the NDUFB6 promoter region that creates a possible DNA methylation site (rs629566, A/G) was " "associated with a decline in muscle NDUFB6 expression with age. Although young subjects with the rs629566 G/G " "genotype exhibited higher muscle NDUFB6 expression, this genotype was associated with reduced expression in" " elderly subjects. This was subsequently explained by the finding of increased DNA methylation in the promoter " "of elderly, but not young, subjects carrying the rs629566 G/G genotype. Furthermore, the degree of DNA" " methylation correlated negatively with muscle NDUFB6 expression, which in turn was associated with insulin " "sensitivity." ) e3 = str(hash(e3)) rs629566 = gene("DBSNP", "rs629566", variants=[gmod("Me")]) ndufb6_gene = gene("HGNC", "NDUFB6") ndufb6_rna = rna("HGNC", "NDUFB6") example_graph.add_unqualified_edge(ndufb6_gene, rs629566, HAS_VARIANT) example_graph.add_negative_correlation( rs629566, ndufb6_rna, citation=c3, evidence=e3, annotations={"Confidence": "Low", "Number": "50"}, ) """ SET Evidence = "% Entrez Gene summary: Rat: SUMMARY: precursor protein of kinin which is found in plasma; cysteine protease inhibitor and a major acute phase reactant [RGD] OMIM summary: (summary is not available from this source) kininogens; Endogenous peptides present in most body fluids. Certain enzymes convert them to active kinins which are involved in inflammation, blood clotting, complement reactions, etc. Kininogens belong to the cystatin superfamily. They are cysteine proteinase inhibitors. High-molecular-weight kininogen (hmwk) is split by plasma kallikrein to produce bradykinin. Low-molecular-weight kininogen (lmwk) is split by tissue kallikrein to produce kallidin. kinins; Inflammatory mediators that cause dilation of blood vessels and altered vascular permeability. Kinins are small peptides produced from kininogen by kallikrein and are broken down by kininases. Act on phospholipase and increase arachidonic acid release and thus prostaglandin (PGE2) production. bradykinin; Vasoactive nonapeptide (RPPGFSPFR) formed by action of proteases on kininogens. Very similar to kallidin (which has the same sequence but with an additional N terminal lysine). Bradykinin is a very potent vasodilator and increases permeability of post capillary venules, it acts on endothelial cells to activate phospholipase A2. It is also spasmogenic for some smooth muscle and will cause pain. kallidin; Decapeptide (lysyl bradykinin, amino acid sequence KRPPGFSPFR) produced in kidney. Like bradykinin, an inflammatory mediator (a kinin), causes dilation of renal blood vessels and increased water excretion." SET Species = 9606 SET Citation = {"Other","Genstruct Kininogen Overview","Genstruct Kininogen Overview","","",""} bp(GOBP:"inflammatory response") increases rxn(reactants(p(HGNC:KNG1)),products(a(SCHEM:Kallidin))) path(SDIS:"tissue damage") increases rxn(reactants(p(HGNC:KNG1)),products(a(SCHEM:Kallidin))) a(SCHEM:Kallidin) increases cat(p(HGNC:BDKRB1)) cat(p(HGNC:BDKRB1)) increases cat(p(SFAM:"PLA2 Family")) """ c4 = citation_dict(namespace=CITATION_TYPE_OTHER, identifier="Genstruct Reference") e4 = "% Entrez Gene summary: Rat: SUMMARY: precursor protein of kinin which is found in plasma; cysteine protease inhibitor and a major acute phase reactant [RGD] OMIM summary: (summary is not available from this source) kininogens; Endogenous peptides present in most body fluids. Certain enzymes convert them to active kinins which are involved in inflammation, blood clotting, complement reactions, etc. Kininogens belong to the cystatin superfamily. They are cysteine proteinase inhibitors. High-molecular-weight kininogen (hmwk) is split by plasma kallikrein to produce bradykinin. Low-molecular-weight kininogen (lmwk) is split by tissue kallikrein to produce kallidin. kinins; Inflammatory mediators that cause dilation of blood vessels and altered vascular permeability. Kinins are small peptides produced from kininogen by kallikrein and are broken down by kininases. Act on phospholipase and increase arachidonic acid release and thus prostaglandin (PGE2) production. bradykinin; Vasoactive nonapeptide (RPPGFSPFR) formed by action of proteases on kininogens. Very similar to kallidin (which has the same sequence but with an additional N terminal lysine). Bradykinin is a very potent vasodilator and increases permeability of post capillary venules, it acts on endothelial cells to activate phospholipase A2. It is also spasmogenic for some smooth muscle and will cause pain. kallidin; Decapeptide (lysyl bradykinin, amino acid sequence KRPPGFSPFR) produced in kidney. Like bradykinin, an inflammatory mediator (a kinin), causes dilation of renal blood vessels and increased water excretion." e4 = str(hash(e4)) bdkrb1 = Protein(namespace="hgnc", name="BDKRB1") inflammatory_process = bioprocess("GO", "inflammatory process") kng1 = Protein(namespace="hgnc", name="KNG1") kallidin = abundance("CHEBI", "Kallidin") pla2_family = Protein("SFAM", "PLA2 Family") kng1_to_kallidin = reaction(reactants=[kng1], products=[kallidin]) example_graph.add_increases(inflammatory_process, kng1_to_kallidin, citation=c4, evidence=e4) example_graph.add_increases(kallidin, bdkrb1, citation=c4, evidence=e4, target_modifier=catalytic_activity) example_graph.add_increases( bdkrb1, pla2_family, citation=c4, evidence=e4, source_modifier=catalytic_activity, target_modifier=catalytic_activity, ) c5 = "10866298" e5 = "We found that PD180970 inhibited in vivo tyrosine phosphorylation of p210Bcr-Abl (IC50 = 170 nM) and the p210BcrAbl substrates Gab2 and CrkL (IC50 = 80 nM) in human K562 chronic myelogenous leukemic cells. In vitro, PD180970 potently inhibited autophosphorylation of p210Bcr-Abl (IC50 = 5 nM) and the kinase activity of purified recombinant Abl tyrosine kinase (IC50 = 2.2 nM)." """ SET Species = 9606 SET Citation = {"PubMed","Cancer Res 2000 Jun 15 60(12) 3127-31","10866298","","",""} kin(p(HGNC:BCR,fus(HGNC:ABL1))) directlyIncreases p(HGNC:CRKL,pmod(P,Y)) kin(p(HGNC:BCR,fus(HGNC:ABL1))) directlyIncreases p(HGNC:GAB2,pmod(P,Y)) """ bcr_abl1_fus = protein_fusion( partner_5p=Protein(namespace="hgnc", name="BCR"), partner_3p=Protein(namespace="hgnc", name="ABL1"), ) crkl_ph = Protein(namespace="hgnc", name="CRKL", variants=[pmod("Ph", "Tyr")]) gab2_ph = Protein(namespace="hgnc", name="GAB2", variants=[pmod("Ph", "Tyr")]) example_graph.add_directly_increases( bcr_abl1_fus, crkl_ph, citation=c5, evidence=e5, annotations={"Species": "9606", "Confidence": "High"}, source_modifier=kinase_activity, ) example_graph.add_directly_increases( bcr_abl1_fus, gab2_ph, citation=c5, evidence=e5, annotations={"Species": "9606"}, source_modifier=kinase_activity, ) pybel-0.15.5/tests/test_io/test_cx/test_import.py000066400000000000000000000037631426625374700221520ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Testing for CX and NDEx import/export.""" import os import tempfile from pybel import BELGraph, from_cx, from_cx_file, to_cx, to_cx_file from pybel.examples import braf_graph, egf_graph, sialic_acid_graph, statin_graph from tests.test_io.test_cx.cases import TestCase from tests.test_io.test_cx.examples import example_graph class TestSchema1(TestCase): """Test mapping schema 1.""" def help_test_graph(self, graph: BELGraph) -> None: """Help test a graph round trip through a JSON object.""" graph_cx = to_cx(graph) reconstituted = from_cx(graph_cx) self.assert_graph_equal(graph, reconstituted) def help_test_file(self, graph: BELGraph) -> None: """Help test a graph round trip through a file.""" fd, path = tempfile.mkstemp() with open(path, "w") as file: to_cx_file(graph, file) with open(path) as file: reconstituted = from_cx_file(file) self.assert_graph_equal(graph, reconstituted) os.close(fd) os.remove(path) def test_sialic_acid_graph(self): """Test the round trip in the sialic acid graph.""" self.help_test_graph(sialic_acid_graph) def test_braf_graph(self): """Test the round trip in the BRAF graph.""" self.help_test_graph(braf_graph) def test_egf_graph(self): """Test the round trip in the EGF graph.""" self.help_test_graph(egf_graph) def test_statin_graph(self): """Test the round trip in the statin graph.""" self.help_test_graph(statin_graph) def test_example(self): """Test the round trip in an example graph.""" self.help_test_graph(example_graph) def test_example_jsons(self): """Test the round trip to a JSON string with the example graph.""" self.help_test_graph(example_graph) def test_example_file(self): """Test the round trip to a file with the example graph.""" self.help_test_file(example_graph) pybel-0.15.5/tests/test_io/test_hetionet.py000066400000000000000000000035361426625374700210040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Importer for Hetionet JSON.""" import unittest import pybel.io.hetionet.constants as hioc from pybel import dsl from pybel.io.hetionet import from_hetionet_json class TestHetionet(unittest.TestCase): def test_import(self): self._help( h_type=hioc.ANATOMY, h_dsl=dsl.Population, h_namespace="uberon", h_id="UBERON:1", h_name="anatomy1", kind="upregulates", t_type=hioc.GENE, t_dsl=dsl.Rna, t_namespace="ncbigene", t_id="1", t_name="gene1", ) def _help( self, h_type, h_dsl, h_namespace, h_id, h_name, kind, t_type, t_dsl, t_namespace, t_id, t_name, ): source = dict(kind=h_type, identifier=h_id, name=h_name) target = dict(kind=t_type, identifier=t_id, name=t_name) edge = dict( source_id=(h_type, h_id), kind=kind, target_id=(t_type, t_id), data={}, ) if h_id.lower().startswith("{}:".format(h_namespace.lower())): h_id = h_id[len(h_namespace) + 1 :] if t_id.lower().startswith("{}:".format(t_namespace.lower())): t_id = t_id[len(t_namespace) + 1 :] graph = from_hetionet_json(dict(nodes=[source, target], edges=[edge]), use_tqdm=False) source_node = h_dsl(namespace=h_namespace, identifier=h_id, name=h_name) self.assertIn(source_node, graph, msg="Nodes: {}".format(list(graph))) target_node = t_dsl(namespace=t_namespace, identifier=t_id, name=t_name) self.assertIn(target_node, graph, msg="Nodes: {}".format(list(graph))) self.assertEqual(2, graph.number_of_nodes()) self.assertIn(target_node, graph[source_node]) pybel-0.15.5/tests/test_io/test_hipathia/000077500000000000000000000000001426625374700203735ustar00rootroot00000000000000pybel-0.15.5/tests/test_io/test_hipathia/__init__.py000066400000000000000000000000631426625374700225030ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for Hipathia.""" pybel-0.15.5/tests/test_io/test_hipathia/hsa04370.att000066400000000000000000000041451426625374700222620ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-hsa04370-1 CDC42 301 201 white rectangle gene 0.5 black 46 17 998 N-hsa04370-9 KDR 143 240 white rectangle gene 0.5 black 46 17 3791 N-hsa04370-11 SPHK2 506 433 white rectangle gene 0.5 black 46 17 56848,8877 N-hsa04370-17 MAPKAPK3 519 184 white rectangle gene 0.5 black 46 17 7867,9261 N-hsa04370-18 PPP3CA 520 319 white rectangle gene 0.5 black 46 17 5530,5532,5533,5534,5535 N-hsa04370-19 AKT3 373 52 white rectangle gene 0.5 black 46 17 10000,207,208 N-hsa04370-20 PIK3R5 270 131 white rectangle gene 0.5 black 46 17 23533,5290,5291,5293,5294,5295,5296,8503 N-hsa04370-21 NFATC2 603 319 white rectangle gene 0.5 black 46 17 4773 N-hsa04370-22 PRKCA 456 394 white rectangle gene 0.5 black 46 17 5578,5579,5582 N-hsa04370-24 MAPK14 398 184 white rectangle gene 0.5 black 46 17 1432,5600,5603,6300 N-hsa04370-27 SRC 199 164 white rectangle gene 0.5 black 46 17 6714 N-hsa04370-29 VEGFA 72 240 white rectangle gene 0.5 black 46 17 7422 N-hsa04370-32 MAPK1 779 415 white rectangle gene 0.5 black 46 17 5594,5595 N-hsa04370-33 MAP2K1 703 415 white rectangle gene 0.5 black 46 17 5604,5605 N-hsa04370-34 RAF1 653 441 white rectangle gene 0.5 black 46 17 5894 N-hsa04370-35 HRAS 576 452 white rectangle gene 0.5 black 46 17 3265,3845,4893 N-hsa04370-10 26 PLCG1 SH2D2A 270 320 white rectangle gene,gene 0.5 black 46 17 5335,5336,/,9047 N-hsa04370-28 SHC2 270 286 white rectangle gene 0.5 black 46 17 25759 N-hsa04370-23 PTK2 518 251 white rectangle gene 0.5 black 46 17 5747 N-hsa04370-25 PXN 518 229 white rectangle gene 0.5 black 46 17 5829 N-hsa04370-16 HSPB1 628 184 white rectangle gene 0.5 black 46 17 3315 N-hsa04370-36 NOS3 491 78 white rectangle gene 0.5 black 46 17 4846 N-hsa04370-37 CASP9 491 39 white rectangle gene 0.5 black 46 17 842 N-hsa04370-38 BAD 491 0 white rectangle gene 0.5 black 46 17 572 N-hsa04370-39 RAC1 554 130 white rectangle gene 0.5 black 46 17 5879,5880,5881 N-hsa04370-14 PTGS2 749 319 white rectangle gene 0.5 black 46 17 5743 N-hsa04370-15 PLA2G4B 817 373 white rectangle gene 0.5 black 46 17 100137049,123745,255189,283748,5321,8605,8681 pybel-0.15.5/tests/test_io/test_hipathia/hsa04370.sif000066400000000000000000000022271426625374700222520ustar00rootroot00000000000000N-hsa04370-1 activation N-hsa04370-24 N-hsa04370-9 activation N-hsa04370-28 N-hsa04370-9 activation N-hsa04370-23 N-hsa04370-9 activation N-hsa04370-25 N-hsa04370-9 activation N-hsa04370-1 N-hsa04370-9 activation N-hsa04370-20 N-hsa04370-9 activation N-hsa04370-27 N-hsa04370-9 activation N-hsa04370-10 26 N-hsa04370-11 activation N-hsa04370-35 N-hsa04370-17 activation N-hsa04370-16 N-hsa04370-18 activation N-hsa04370-21 N-hsa04370-19 activation N-hsa04370-36 N-hsa04370-19 inhibition N-hsa04370-37 N-hsa04370-19 inhibition N-hsa04370-38 N-hsa04370-20 activation N-hsa04370-39 N-hsa04370-20 activation N-hsa04370-19 N-hsa04370-21 activation N-hsa04370-14 N-hsa04370-22 activation N-hsa04370-34 N-hsa04370-22 activation N-hsa04370-11 N-hsa04370-24 activation N-hsa04370-17 N-hsa04370-27 activation N-hsa04370-20 N-hsa04370-29 activation N-hsa04370-9 N-hsa04370-32 activation N-hsa04370-15 N-hsa04370-33 activation N-hsa04370-32 N-hsa04370-34 activation N-hsa04370-33 N-hsa04370-35 activation N-hsa04370-34 N-hsa04370-10 26 activation N-hsa04370-18 N-hsa04370-10 26 activation N-hsa04370-22 N-hsa04370-10 26 activation N-hsa04370-15 N-hsa04370-10 26 activation N-hsa04370-36 pybel-0.15.5/tests/test_io/test_hipathia/test_1.att000066400000000000000000000004331426625374700223040ustar00rootroot00000000000000ID label X Y color shape type label.cex label.color width height genesList N-test1-1 A 301 201 white rectangle gene 0.5 black 46 17 1 N-test1-2 B_Family 506 433 white rectangle gene 0.5 black 46 17 2,3 N-test1-3 4 C_Family D 270 320 white rectangle gene,gene 0.5 black 46 17 4,5,/,6 pybel-0.15.5/tests/test_io/test_hipathia/test_1.sif000066400000000000000000000001001426625374700222640ustar00rootroot00000000000000N-test1-3 4 activation N-test1-1 N-test1-1 activation N-test1-2 pybel-0.15.5/tests/test_io/test_hipathia/test_export.py000066400000000000000000000123431426625374700233300ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for Hipathia export.""" import unittest from typing import Tuple import pandas as pd from pybel import BELGraph from pybel.dsl import ComplexAbundance, Protein from pybel.io.hipathia import ATT_COLS, to_hipathia_dfs from pybel.testing.utils import n PROTEIN_NAMESPACE = "ncbigene" FAMILY_NAMESPACE = "hipathia.family" a = Protein(namespace="ncbigene", identifier="P001", name="A") b_family = Protein(namespace="hipathia.family", identifier="F001", name="B_Family") b1 = Protein(namespace="ncbigene", identifier="P002", name="B1") b2 = Protein(namespace="ncbigene", identifier="P003", name="B2") c_family = Protein(namespace="hipathia.family", identifier="F002", name="C_Family") c1 = Protein(namespace="ncbigene", identifier="P004", name="C1") c2 = Protein(namespace="ncbigene", identifier="P005", name="C2") d = Protein(namespace="ncbigene", identifier="P006", name="D") c_d = ComplexAbundance([c_family, d]) e = Protein(namespace="ncbigene", identifier="P007", name="E") f = Protein(namespace="ncbigene", identifier="P008", name="F") e_f = ComplexAbundance([e, f]) name = "test" class TestExportHipathia(unittest.TestCase): """Test Hipathia.""" def setUp(self) -> None: self.graph = BELGraph(name=name) def _get_dfs(self) -> Tuple[pd.DataFrame, pd.DataFrame]: return to_hipathia_dfs(self.graph) def test_protein_activates_protein(self): """Test conversion of ``p(A) -> p(D)``.""" self.graph.add_increases(a, d, citation=n(), evidence="") att_df, sif_df = self._get_dfs() att_df = att_df[ATT_COLS] self.assertEqual(2, len(att_df.index)) self.assertEqual( [ ["N-{name}-1".format(name=name), a.name, a.identifier], ["N-{name}-2".format(name=name), d.name, d.identifier], ], att_df.values.tolist(), ) self.assertEqual(1, len(sif_df)) self.assertEqual( [ [ "N-{name}-1".format(name=name), "activation", "N-{name}-2".format(name=name), ], ], sif_df.values.tolist(), ) def test_protein_activates_family(self): """Test conversion of ``p(A) -> p(B); p(B1) isA p(B); p(B2) isA p(B)``.""" self.graph.add_increases(a, b_family, citation=n(), evidence=n()) self.graph.add_is_a(b1, b_family) self.graph.add_is_a(b2, b_family) att_df, sif_df = self._get_dfs() att_df = att_df[ATT_COLS] self.assertEqual(2, len(att_df.index)) self.assertEqual( [ [ "N-{name}-1".format(name=name), b_family.name, ",".join((b1.identifier, b2.identifier)), ], ["N-{name}-2".format(name=name), a.name, a.identifier], ], att_df.values.tolist(), ) self.assertEqual(1, len(sif_df)) self.assertEqual( [ [ "N-{name}-2".format(name=name), "activation", "N-{name}-1".format(name=name), ], ], sif_df.values.tolist(), ) def test_protein_activates_complex_proteins(self): """Test conversion of ``p(A) -> complex(p(E), p(F))``.""" self.graph.add_increases(a, e_f, citation=n(), evidence="") att_df, sif_df = self._get_dfs() att_df = att_df[ATT_COLS] self.assertEqual(2, len(att_df.index)) self.assertEqual( [ [ "N-{name}-1 2".format(name=name), " ".join((e.name, f.name)), "{},/,{}".format(e.identifier, f.identifier), ], ["N-{name}-3".format(name=name), a.name, a.identifier], ], att_df.values.tolist(), ) self.assertEqual(1, len(sif_df)) self.assertEqual( [ [ "N-{name}-3".format(name=name), "activation", "N-{name}-1 2".format(name=name), ], ], sif_df.values.tolist(), ) def test_protein_activates_complex_mixed(self): """Test conversion of ``p(A) -> complex(p(E), p(B))`` when b is a family.""" self.graph.add_increases(a, e_f, citation=n(), evidence="") att_df, sif_df = self._get_dfs() att_df = att_df[ATT_COLS] self.assertEqual(2, len(att_df.index)) self.assertEqual( [ [ "N-{name}-1 2".format(name=name), " ".join((e.name, f.name)), "{},/,{}".format(e.identifier, f.identifier), ], ["N-{name}-3".format(name=name), a.name, a.identifier], ], att_df.values.tolist(), ) self.assertEqual(1, len(sif_df)) self.assertEqual( [ [ "N-{name}-3".format(name=name), "activation", "N-{name}-1 2".format(name=name), ], ], sif_df.values.tolist(), ) pybel-0.15.5/tests/test_io/test_hipathia/test_hipathia.py000066400000000000000000000032021426625374700235700ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for Hipathia.""" import os import unittest from pybel.dsl import ComplexAbundance, Protein from pybel.io.hipathia import from_hipathia_paths, group_delimited_list HERE = os.path.abspath(os.path.dirname(__file__)) TEST_1_ATT_PATH = os.path.join(HERE, "test_1.att") TEST_1_SIF_PATH = os.path.join(HERE, "test_1.sif") TEST_ATT_PATH = os.path.join(HERE, "hsa04370.att") TEST_SIF_PATH = os.path.join(HERE, "hsa04370.sif") class TestImportHipathia(unittest.TestCase): """Test Hipathia import.""" def test_import(self): """Test importing a hipathia network as a BEL graph.""" graph = from_hipathia_paths( name="test1", att_path=TEST_1_ATT_PATH, sif_path=TEST_1_SIF_PATH, ) a = Protein(namespace="ncbigene", identifier="1") b_family = Protein(namespace="hipathia.family", identifier="B_Family") b_2 = Protein(namespace="ncbigene", identifier="2") b_3 = Protein(namespace="ncbigene", identifier="3") c_family = Protein(namespace="hipathia.family", identifier="C_Family") c_4 = Protein(namespace="ncbigene", identifier="4") c_5 = Protein(namespace="ncbigene", identifier="5") d = Protein(namespace="ncbigene", identifier="6") c_d = ComplexAbundance([c_family, d]) self.assertEqual( sorted({a, b_family, b_2, b_3, c_family, c_4, c_5, d, c_d}, key=str), sorted(graph, key=str), ) class TestUtils(unittest.TestCase): def test_group_delimited(self): self.assertEqual([[5335, 5336], [9047]], group_delimited_list([5335, 5336, "/", 9047], "/")) pybel-0.15.5/tests/test_io/test_import.py000066400000000000000000000461251426625374700205000ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for input and output.""" import logging import os import re import tempfile import unittest from io import BytesIO, StringIO from pathlib import Path import pybel from pybel import ( BELGraph, from_bel_script, from_bel_script_url, from_bytes, from_nodelink, from_nodelink_file, from_pickle, to_bel_script_lines, to_bytes, to_csv, to_graphml, to_gsea, to_nodelink, to_nodelink_file, to_pickle, to_sif, ) from pybel.config import PYBEL_MINIMUM_IMPORT_VERSION from pybel.constants import ( ANNOTATIONS, CITATION, DECREASES, DIRECTLY_DECREASES, EVIDENCE, GRAPH_ANNOTATION_MIRIAM, GRAPH_PYBEL_VERSION, INCREASES, RELATION, ) from pybel.dsl import BaseEntity, Gene, Protein from pybel.examples.sialic_acid_example import sialic_acid_graph from pybel.exceptions import ( BELSyntaxError, InvalidFunctionSemantic, MissingCitationException, MissingNamespaceRegexWarning, ) from pybel.io.exc import ImportVersionWarning, import_version_message_fmt from pybel.io.line_utils import parse_lines from pybel.io.nodelink import from_nodelink_jsons, to_nodelink_jsons from pybel.language import Entity from pybel.parser import BELParser from pybel.struct.summary import get_syntax_errors from pybel.testing.cases import TemporaryCacheClsMixin, TemporaryCacheMixin from pybel.testing.constants import ( test_bel_isolated, test_bel_misordered, test_bel_simple, test_bel_slushy, test_bel_thorough, test_bel_with_obo, ) from pybel.testing.mocks import mock_bel_resources from tests.constants import ( BelReconstitutionMixin, TestTokenParserBase, akt1, casp8, citation_1, egfr, evidence_1, fadd, test_citation_dict, test_evidence_text, test_set_evidence, ) logging.getLogger("requests").setLevel(logging.WARNING) logger = logging.getLogger(__name__) testan1 = "1" class TestExampleInterchange(unittest.TestCase): """Test round-trip interchange of the sialic acid graph example.""" def _help_test_equal(self, graph: BELGraph): """Check that a graph is equal to the sialic acid graph example.""" for node in graph: self.assertIsInstance(node, BaseEntity) self.assertEqual(set(sialic_acid_graph), set(graph)) self.assertEqual(set(sialic_acid_graph.edges()), set(graph.edges())) for node in sialic_acid_graph: if not isinstance(node, Protein): continue if node.namespace == "HGNC" and node.name == "CD33" and not node.variants: self.assertIsNotNone(node.xrefs) self.assertEqual(1, len(node.xrefs)) def test_example_bytes(self): """Test the round-trip through bytes.""" graph_bytes = to_bytes(sialic_acid_graph) graph = from_bytes(graph_bytes) self._help_test_equal(graph) def test_thorough_bytes_gz(self): """Test the round-trip through gzipped bytes.""" graph_bytes = pybel.to_bytes_gz(sialic_acid_graph) graph = pybel.from_bytes_gz(graph_bytes) self._help_test_equal(graph) def test_example_pickle(self): """Test the round-trip through a pickle.""" bio = BytesIO() to_pickle(sialic_acid_graph, bio) bio.seek(0) graph = from_pickle(bio) self._help_test_equal(graph) def test_example_pickle_gz(self): """Test the round-trip through a gzipped pickle.""" with tempfile.TemporaryDirectory() as directory: path = os.path.join(directory, "test.gz") pybel.to_pickle_gz(sialic_acid_graph, path) graph = pybel.from_pickle_gz(path) self._help_test_equal(graph) def test_thorough_json(self): """Test the round-trip through node-link JSON.""" graph_json_dict = to_nodelink(sialic_acid_graph) graph = from_nodelink(graph_json_dict) self._help_test_equal(graph) def test_thorough_jsons(self): """Test the round-trip through a node-link JSON string.""" graph_json_str = to_nodelink_jsons(sialic_acid_graph) graph = from_nodelink_jsons(graph_json_str) self._help_test_equal(graph) def test_thorough_json_file(self): """Test the round-trip through a node-link JSON file.""" sio = StringIO() to_nodelink_file(sialic_acid_graph, sio) sio.seek(0) graph = from_nodelink_file(sio) self._help_test_equal(graph) def test_thorough_sbel(self): """Test the round-trip through SBEL.""" s = pybel.to_sbel(sialic_acid_graph) graph = pybel.from_sbel(s) self._help_test_equal(graph) def test_thorough_sbel_file(self): """Test the round-trip through a SBEL file.""" sio = StringIO() pybel.to_sbel_file(sialic_acid_graph, sio) sio.seek(0) graph = pybel.from_sbel_file(sio) self._help_test_equal(graph) def test_thorough_sbel_gzip_path(self): """Test round trip through a SBEL gzipped file.""" with tempfile.TemporaryDirectory() as directory: path = os.path.join(directory, "test.gzip") pybel.to_sbel_gz(sialic_acid_graph, path) graph = pybel.from_sbel_gz(path) self._help_test_equal(graph) class TestInterchange(TemporaryCacheClsMixin, BelReconstitutionMixin): @classmethod def setUpClass(cls): """Set up this class with several pre-loaded BEL graphs.""" super().setUpClass() with mock_bel_resources: cls.thorough_graph = from_bel_script(test_bel_thorough, manager=cls.manager, disallow_nested=False) cls.slushy_graph = from_bel_script( test_bel_slushy, manager=cls.manager, disallow_unqualified_translocations=True, disallow_nested=True, ) cls.simple_graph = from_bel_script_url(Path(test_bel_simple).as_uri(), manager=cls.manager) cls.isolated_graph = from_bel_script(test_bel_isolated, manager=cls.manager) cls.misordered_graph = from_bel_script(test_bel_misordered, manager=cls.manager, citation_clearing=False) def test_thorough_path(self): self.bel_thorough_reconstituted(self.thorough_graph) def test_thorough_bytes(self): graph_bytes = to_bytes(self.thorough_graph) graph = from_bytes(graph_bytes) self.bel_thorough_reconstituted(graph) def test_thorough_pickle(self): bio = BytesIO() to_pickle(self.thorough_graph, bio) bio.seek(0) graph = from_pickle(bio) self.bel_thorough_reconstituted(graph) def test_thorough_json(self): graph_json_dict = to_nodelink(self.thorough_graph) graph = from_nodelink(graph_json_dict) self.bel_thorough_reconstituted(graph) def test_thorough_jsons(self): graph_json_str = to_nodelink_jsons(self.thorough_graph) graph = from_nodelink_jsons(graph_json_str) self.bel_thorough_reconstituted(graph) def test_thorough_json_file(self): sio = StringIO() to_nodelink_file(self.thorough_graph, sio) sio.seek(0) graph = from_nodelink_file(sio) self.bel_thorough_reconstituted(graph) def test_thorough_graphml(self): handle, path = tempfile.mkstemp() with open(path, "wb") as f: to_graphml(self.thorough_graph, f) os.close(handle) os.remove(path) def test_thorough_csv(self): handle, path = tempfile.mkstemp() with open(path, "w") as f: to_csv(self.thorough_graph, f) os.close(handle) os.remove(path) def test_thorough_sif(self): handle, path = tempfile.mkstemp() with open(path, "w") as f: to_sif(self.thorough_graph, f) os.close(handle) os.remove(path) def test_thorough_gsea(self): handle, path = tempfile.mkstemp() with open(path, "w") as f: to_gsea(self.thorough_graph, f) os.close(handle) os.remove(path) def test_thorough_upgrade(self): lines = to_bel_script_lines(self.thorough_graph, use_identifiers=True) reconstituted = BELGraph() parse_lines(reconstituted, lines, manager=self.manager) self.bel_thorough_reconstituted(reconstituted, check_citation_name=False, check_path=False) def test_slushy(self): self.bel_slushy_reconstituted(self.slushy_graph) def test_slushy_bytes(self): graph_bytes = to_bytes(self.slushy_graph) graph = from_bytes(graph_bytes) self.bel_slushy_reconstituted(graph) def test_slushy_syntax_errors(self): syntax_errors = get_syntax_errors(self.slushy_graph) for _, exc, _ in syntax_errors: self.assertIsInstance(exc, BELSyntaxError) self.assertEqual(1, len(syntax_errors)) _, first_exc, _ = syntax_errors[0] self.assertEqual(98, first_exc.line_number) def test_slushy_json(self): graph_json = to_nodelink(self.slushy_graph) graph = from_nodelink(graph_json) self.bel_slushy_reconstituted(graph, check_warnings=False) def test_slushy_graphml(self): handle, path = tempfile.mkstemp() to_graphml(self.slushy_graph, path) os.close(handle) os.remove(path) def test_simple_compile(self): self.bel_simple_reconstituted(self.simple_graph) def test_isolated_compile(self): self.bel_isolated_reconstituted(self.isolated_graph) def test_isolated_upgrade(self): lines = to_bel_script_lines(self.isolated_graph) with mock_bel_resources: reconstituted = BELGraph() parse_lines(graph=reconstituted, lines=lines, manager=self.manager) self.bel_isolated_reconstituted(reconstituted) def test_misordered_compile(self): """Test that non-citation clearing mode works.""" self.assertEqual(4, self.misordered_graph.number_of_nodes()) self.assertEqual(3, self.misordered_graph.number_of_edges()) e1 = { RELATION: INCREASES, CITATION: citation_1, EVIDENCE: evidence_1, ANNOTATIONS: {"TESTAN1": {testan1: True}}, } self.assert_has_edge(self.misordered_graph, akt1, egfr, only=True, **e1) e2 = { RELATION: DECREASES, CITATION: citation_1, EVIDENCE: evidence_1, ANNOTATIONS: {"TESTAN1": {testan1: True}}, } self.assert_has_edge(self.misordered_graph, egfr, fadd, only=True, **e2) e3 = { RELATION: DIRECTLY_DECREASES, CITATION: citation_1, EVIDENCE: evidence_1, ANNOTATIONS: { "TESTAN1": {testan1: True}, }, } self.assert_has_edge(self.misordered_graph, egfr, casp8, only=True, **e3) namespace_to_term = { "TESTNS": { (None, "1"): "GRP", (None, "2"): "GRP", } } annotation_to_term = { "TestAnnotation1": {"A", "B", "C"}, "TestAnnotation2": {"X", "Y", "Z"}, "TestAnnotation3": {"D", "E", "F"}, } class TestFull(TestTokenParserBase): @classmethod def setUpClass(cls): cls.parser = BELParser( graph=BELGraph(), # gets overwritten in each test namespace_to_term_to_encoding=namespace_to_term, annotation_to_term=annotation_to_term, namespace_to_pattern={"dbSNP": re.compile("rs[0-9]*")}, ) def setUp(self): self.parser.clear() self.parser.graph = BELGraph() self.graph.annotation_list.update( { "TestAnnotation1": {"A", "B", "C"}, "TestAnnotation2": {"X", "Y", "Z"}, "TestAnnotation3": {"D", "E", "F"}, } ) self.parser.graph = self.graph self.assertIn( GRAPH_ANNOTATION_MIRIAM, self.graph.graph, msg=f"Graph metadata: {self.graph.graph}", ) def test_regex_match(self): line = "g(dbSNP:rs10234) -- g(dbSNP:rs10235)" self.add_default_provenance() self.parser.parseString(line) self.assertIn(Gene("dbSNP", "rs10234"), self.parser.graph) self.assertIn(Gene("dbSNP", "rs10235"), self.parser.graph) def test_regex_mismatch(self): statement = "g(dbSNP:10234) -- g(dbSNP:rr10235)" with self.assertRaises(MissingNamespaceRegexWarning): self.parser.parseString(statement) def test_semantic_failure(self): self.assertIsNotNone(self.parser.concept_parser.namespace_to_name_to_encoding) self.assertIn("TESTNS", self.parser.concept_parser.namespace_to_name_to_encoding) self.assertIn("1", self.parser.concept_parser.namespace_to_name_to_encoding["TESTNS"]) self.assertIn("2", self.parser.concept_parser.namespace_to_name_to_encoding["TESTNS"]) statement = "bp(TESTNS:1) -- p(TESTNS:2)" with self.assertRaises(InvalidFunctionSemantic): self.parser.parseString(statement) def test_missing_citation(self): statements = [ test_set_evidence, 'SET TestAnnotation1 = "A"', 'SET TestAnnotation2 = "X"', "g(TESTNS:1) -> g(TESTNS:2)", ] with self.assertRaises(MissingCitationException): self.parser.parse_lines(statements) def test_annotations(self): self.add_default_provenance() statements = [ 'SET TestAnnotation1 = "A"', 'SET TestAnnotation2 = "X"', "g(TESTNS:1) -> g(TESTNS:2)", ] self.parser.parse_lines(statements) self.assertEqual(2, len(self.parser.control_parser.annotations)) self.assertIn("TestAnnotation1", self.parser.control_parser.annotations) self.assertIn("TestAnnotation2", self.parser.control_parser.annotations) test_node_1 = Gene(namespace="TESTNS", name="1") test_node_2 = Gene(namespace="TESTNS", name="2") self.assertEqual(2, self.graph.number_of_nodes()) self.assertIn(test_node_1, self.graph) self.assertIn(test_node_2, self.graph) self.assertEqual(1, self.parser.graph.number_of_edges()) kwargs = { RELATION: INCREASES, ANNOTATIONS: { "TestAnnotation1": {"A": True}, "TestAnnotation2": {"X": True}, }, EVIDENCE: test_evidence_text, CITATION: test_citation_dict, } self.assert_has_edge(test_node_1, test_node_2, only=True, **kwargs) def test_annotations_with_list(self): self.assertIsNotNone(self.parser.graph) self.add_default_provenance() statements = [ 'SET TestAnnotation1 = {"A","B"}', 'SET TestAnnotation2 = "X"', "g(TESTNS:1) -> g(TESTNS:2)", ] self.parser.parse_lines(statements) self.assertEqual(2, len(self.parser.control_parser.annotations)) self.assertIn("TestAnnotation1", self.parser.control_parser.annotations) self.assertIn("TestAnnotation2", self.parser.control_parser.annotations) self.assertEqual(2, len(self.parser.control_parser.annotations["TestAnnotation1"])) self.assertEqual( [ Entity(namespace="TestAnnotation1", identifier="A"), Entity(namespace="TestAnnotation1", identifier="B"), ], self.parser.control_parser.annotations["TestAnnotation1"], ) self.assertEqual(1, len(self.parser.control_parser.annotations["TestAnnotation2"])) self.assertEqual( [ Entity(namespace="TestAnnotation2", identifier="X"), ], self.parser.control_parser.annotations["TestAnnotation2"], ) test_node_1_dict = Gene(namespace="TESTNS", name="1") test_node_2_dict = Gene(namespace="TESTNS", name="2") self.assertEqual(2, self.parser.graph.number_of_nodes()) self.assertIn(test_node_1_dict, self.graph) self.assertIn(test_node_2_dict, self.graph) self.assertEqual(1, self.parser.graph.number_of_edges()) kwargs = { RELATION: INCREASES, EVIDENCE: test_evidence_text, ANNOTATIONS: { "TestAnnotation1": {"A": True, "B": True}, "TestAnnotation2": {"X": True}, }, CITATION: test_citation_dict, } self.assert_has_edge(test_node_1_dict, test_node_2_dict, only=True, **kwargs) def test_annotations_with_multilist(self): self.add_default_provenance() statements = [ 'SET TestAnnotation1 = {"A","B"}', 'SET TestAnnotation2 = "X"', 'SET TestAnnotation3 = {"D","E"}', "g(TESTNS:1) -> g(TESTNS:2)", ] self.parser.parse_lines(statements) self.assertEqual(3, len(self.parser.control_parser.annotations)) self.assertIn("TestAnnotation1", self.parser.control_parser.annotations) self.assertIn("TestAnnotation2", self.parser.control_parser.annotations) self.assertIn("TestAnnotation3", self.parser.control_parser.annotations) test_node_1 = Gene(namespace="TESTNS", name="1") test_node_2 = Gene(namespace="TESTNS", name="2") self.assertEqual(2, self.parser.graph.number_of_nodes()) self.assertIn(test_node_1, self.graph) self.assertIn(test_node_2, self.graph) self.assertEqual(1, self.parser.graph.number_of_edges()) kwargs = { RELATION: INCREASES, EVIDENCE: test_evidence_text, ANNOTATIONS: { "TestAnnotation1": {"A": True, "B": True}, "TestAnnotation2": {"X": True}, "TestAnnotation3": {"D": True, "E": True}, }, CITATION: test_citation_dict, } self.assert_has_edge(test_node_1, test_node_2, only=True, **kwargs) class TestRandom(unittest.TestCase): def test_import_warning(self): """Tests an error is thrown when the version is set wrong""" graph = BELGraph() # Much with stuff that would normally be set graph.graph[GRAPH_PYBEL_VERSION] = "0.0.0" graph_bytes = to_bytes(graph) with self.assertRaises(ImportVersionWarning) as cm: from_bytes(graph_bytes) self.assertEqual( import_version_message_fmt.format("0.0.0", PYBEL_MINIMUM_IMPORT_VERSION), str(cm.exception), ) class TestNomenclature(TemporaryCacheMixin): """Test that `BEP-0008 nomenclature `_ gets validated properly.""" def test_bep_0008(self): """Test parsing works right""" graph = from_bel_script(test_bel_with_obo, manager=self.manager) self.assertIn("hgnc", graph.namespace_pattern) self.assertEqual(r"\d+", graph.namespace_pattern["hgnc"]) self.assertEqual(0, graph.number_of_warnings(), msg=",\n".join(map(str, graph.warnings))) self.assertEqual(2, graph.number_of_nodes()) self.assertIn(Protein(namespace="hgnc", identifier="391", name="AKT1"), graph) self.assertIn(Protein(namespace="hgnc", identifier="3236", name="EGFR"), graph) if __name__ == "__main__": unittest.main() pybel-0.15.5/tests/test_io/test_jgif.py000066400000000000000000000117771426625374700201120ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for interchange with JGIF.""" import json import logging import sys import unittest from pybel import from_cbn_jgif, to_jgif from pybel.constants import ( ACTIVITY, ANNOTATIONS, CITATION, CITATION_TYPE_OTHER, CITATION_TYPE_PUBMED, DECREASES, DIRECTLY_INCREASES, EFFECT, EVIDENCE, IDENTIFIER, MODIFIER, NAMESPACE, RELATION, TARGET_MODIFIER, ) from pybel.dsl import ( Abundance, BiologicalProcess, ComplexAbundance, NamedComplexAbundance, Pathology, Protein, ProteinModification, ) from pybel.language import activity_mapping from pybel.testing.constants import test_jgif_path from tests.constants import TestGraphMixin logging.getLogger("pybel.parser").setLevel(20) calcium = Abundance("SCHEM", "Calcium") calcineurin_complex = NamedComplexAbundance("SCOMP", "Calcineurin Complex") foxo3 = Protein("HGNC", "FOXO3") tcell_proliferation = BiologicalProcess("GO", "CD8-positive, alpha-beta T cell proliferation") il15 = Protein("HGNC", "IL15") il2rg = Protein("MGI", "Il2rg") jgif_expected_nodes = { calcium, calcineurin_complex, foxo3, tcell_proliferation, il15, il2rg, Protein("HGNC", "CXCR6"), Protein("HGNC", "IL15RA"), BiologicalProcess("GO", "lymphocyte chemotaxis"), Protein("HGNC", "IL2RG"), Protein("HGNC", "ZAP70"), NamedComplexAbundance("SCOMP", "T Cell Receptor Complex"), BiologicalProcess("GO", "T cell activation"), Protein("HGNC", "CCL3"), Protein("HGNC", "PLCG1"), Protein("HGNC", "FASLG"), Protein("HGNC", "IDO1"), Protein("HGNC", "IL2"), Protein("HGNC", "CD8A"), Protein("HGNC", "CD8B"), Protein("HGNC", "PLCG1"), Protein("HGNC", "BCL2"), Protein("HGNC", "CCR3"), Protein("HGNC", "IL2RB"), Protein("HGNC", "CD28"), Pathology("SDIS", "Cytotoxic T-cell activation"), Protein("HGNC", "FYN"), Protein("HGNC", "CXCL16"), Protein("HGNC", "CCR5"), Protein("HGNC", "LCK"), Protein("SFAM", "Chemokine Receptor Family"), Protein("HGNC", "CXCL9"), Pathology("SDIS", "T-cell migration"), Protein("HGNC", "CXCR3"), Abundance("CHEBI", "acrolein"), Protein("HGNC", "IDO2"), Pathology("MESHD", "Pulmonary Disease, Chronic Obstructive"), Protein("HGNC", "IFNG"), Protein("HGNC", "TNFRSF4"), Protein("HGNC", "CTLA4"), Protein("HGNC", "GZMA"), Protein("HGNC", "PRF1"), Protein("HGNC", "TNF"), Protein("SFAM", "Chemokine Receptor Family"), ComplexAbundance([Protein("HGNC", "CD8A"), Protein("HGNC", "CD8B")]), ComplexAbundance([Protein("HGNC", "CD8A"), Protein("HGNC", "CD8B")]), Protein("HGNC", "PLCG1", variants=ProteinModification("Ph", "Tyr")), Protein("EGID", "21577"), } jgif_expected_edges = [ ( calcium, calcineurin_complex, { RELATION: DIRECTLY_INCREASES, EVIDENCE: "NMDA-mediated influx of calcium led to activated of the calcium-dependent phosphatase calcineurin and the subsequent dephosphorylation and activation of the protein-tyrosine phosphatase STEP", CITATION: {NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: "12483215"}, TARGET_MODIFIER: {MODIFIER: ACTIVITY, EFFECT: activity_mapping["phos"]}, ANNOTATIONS: {"Species": {"10116": True}, "Cell": {"neuron": True}}, }, ), ( foxo3, tcell_proliferation, { RELATION: DECREASES, EVIDENCE: '"These data suggested that FOXO3 downregulates the accumulation of CD8 T cells in tissue specific fashion during an acute LCMV [lymphocytic choriomeningitis virus] infection." (p. 3)', CITATION: {NAMESPACE: CITATION_TYPE_OTHER, IDENTIFIER: "22359505"}, ANNOTATIONS: { "Species": {"10090": True}, "Disease": {"Viral infection": True}, }, }, ), ( il15, il2rg, { RELATION: DIRECTLY_INCREASES, EVIDENCE: "IL-15 utilizes ... the common cytokine receptor γ-chain (CD132) for signal transduction in lymphocytes", CITATION: {NAMESPACE: CITATION_TYPE_OTHER, IDENTIFIER: "20335267"}, TARGET_MODIFIER: {MODIFIER: ACTIVITY, EFFECT: activity_mapping["cat"]}, ANNOTATIONS: { "Tissue": {"lung": True}, "Species": {"9606": True}, }, }, ), ] class TestJgif(TestGraphMixin): """Tests data interchange of JGIF.""" @unittest.skipIf(sys.platform.startswith("win"), "does not work on Windows") def test_jgif_interchange(self): """Tests data from CBN""" with open(test_jgif_path) as f: graph_jgif_dict = json.load(f) graph = from_cbn_jgif(graph_jgif_dict) self.assertEqual(jgif_expected_nodes, set(graph)) for u, v, d in jgif_expected_edges: self.assert_has_edge(graph, u, v, permissive=False, **d) # TODO test more thoroughly? export_jgif = to_jgif(graph) self.assertIsInstance(export_jgif, dict) pybel-0.15.5/tests/test_io/test_jupyter.py000066400000000000000000000011051426625374700206550ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test for export functions in PyBEL-Jupyter.""" import unittest from pybel.examples import sialic_acid_graph from pybel.io.jupyter import to_html, to_jupyter_str class TestHTML(unittest.TestCase): """Text HTML functions.""" def test_to_html(self): """Test export to HTML.""" html = to_html(sialic_acid_graph) self.assertIsNotNone(html) def test_to_jupyter(self): """Test export to JavaScript for Jupyter.""" javascript = to_jupyter_str(sialic_acid_graph) self.assertIsNotNone(javascript) pybel-0.15.5/tests/test_io/test_pynpa.py000066400000000000000000000061641426625374700203140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for PyNPA.""" import unittest import pandas as pd from pybel import BELGraph from pybel.dsl import ComplexAbundance, Gene, Protein, Rna from pybel.io.pynpa import to_npa_dfs, to_npa_layers from pybel.struct.getters import get_tf_pairs from pybel.testing.utils import n g1 = Gene("hgnc", "1") r1 = Rna("hgnc", "1") p1 = Protein("hgnc", "1") g2 = Gene("hgnc", "2") r2 = Rna("hgnc", "2") p2 = Protein("hgnc", "2") g3 = Gene("hgnc", "3") p3 = Protein("hgnc", "3") class TestPyNPA(unittest.TestCase): """Tests for PyNPA.""" def setUp(self) -> None: """Set up a small test graph.""" self.graph = BELGraph() self.graph.add_increases(ComplexAbundance([p1, g2]), r2, citation=n(), evidence=n()) self.graph.add_increases(p2, p3, citation=n(), evidence=n()) def test_get_tf_pairs(self): """Test getting transcription factor-target pairs.""" tf_pairs = set(get_tf_pairs(self.graph)) self.assertLess(0, len(tf_pairs), msg="No TF pairs pairs found") self.assertEqual(1, len(tf_pairs)) example_tf, example_target, _ = list(tf_pairs)[0] self.assertEqual(p1, example_tf) self.assertEqual(r2, example_target) def test_export_layers(self): """Test that the layers are exported right.""" ppi_layer, tf_layer = to_npa_layers(self.graph) self.assertIsInstance(ppi_layer, dict) self.assertIsInstance(tf_layer, dict) self.assertLess(0, len(ppi_layer), msg="PPI layer was not populated") self.assertLess(0, len(tf_layer), msg="TF layer was not populated") self.assertIn((g2, g3), ppi_layer) self.assertEqual(+1, ppi_layer[g2, g3]) self.assertIn((g1, g2), tf_layer) self.assertEqual(+1, tf_layer[g1, g2]) def test_export_df_curie(self): """Test exporting dataframes with curie-based nomenclature.""" ppi_df, tf_df = to_npa_dfs(self.graph) self.assertIsInstance(ppi_df, pd.DataFrame) self.assertIsInstance(tf_df, pd.DataFrame) self.assertEqual(1, len(ppi_df.index)) self.assertEqual(1, len(tf_df.index)) self.assertEqual(("hgnc:2", "hgnc:3", 1), tuple(list(ppi_df.values)[0])) self.assertEqual(("hgnc:1", "hgnc:2", 1), tuple(list(tf_df.values)[0])) def test_export_df_name(self): """Test exporting dataframes with name-based nomenclature.""" ppi_df, tf_df = to_npa_dfs( self.graph, nomenclature_method_first_layer="name", nomenclature_method_second_layer="name", ) self.assertEqual(("2", "3", 1), tuple(list(ppi_df.values)[0])) self.assertEqual(("1", "2", 1), tuple(list(tf_df.values)[0])) def test_export_df_inode(self): """Test exporting dataframes with inode-based nomenclature in the second layer.""" ppi_df, tf_df = to_npa_dfs( self.graph, nomenclature_method_first_layer="name", nomenclature_method_second_layer="inodes", ) self.assertEqual(("2", "3", 1), tuple(list(ppi_df.values)[0])) self.assertEqual(("*1", "*2", 1), tuple(list(tf_df.values)[0])) pybel-0.15.5/tests/test_io/test_spia.py000066400000000000000000000246251426625374700201230ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module contains tests for the SPIA exporter.""" import unittest from pandas import DataFrame from pybel.dsl import activity, composite_abundance, pmod, protein, rna from pybel.examples.sialic_acid_example import ( cd33, citation, evidence_1, shp1, shp2, sialic_acid_cd33_complex, sialic_acid_graph, trem2, ) from pybel.io.spia import ( build_spia_matrices, get_matrix_index, to_spia_dfs, update_spia_matrices, ) class TestSpia(unittest.TestCase): """Test SPIA Exporter.""" def setUp(self): self.sialic_acid_graph = sialic_acid_graph.copy() def test_build_matrix(self): """Test build empty matrix.""" node_names = get_matrix_index(self.sialic_acid_graph) matrix_dict = build_spia_matrices(node_names) nodes = {"PTPN11", "TREM2", "PTPN6", "TYROBP", "CD33", "SYK"} self.assertEqual(set(matrix_dict["activation"].columns), nodes) self.assertEqual(set(matrix_dict["repression"].index), nodes) def test_update_matrix_inhibition_ubiquination(self): """Test updating the matrix with an inhibition ubiquitination.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = protein(namespace="HGNC", name="B", identifier="2", variants=[pmod("Ub")]) index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) # Initialize matrix correctly self.assertEqual(test_matrix.values.all(), 0) test_dict["inhibition_ubiquination"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "decreases"}) self.assertEqual(test_dict["inhibition_ubiquination"]["A"]["B"], 1) self.assertEqual(test_dict["inhibition_ubiquination"]["A"]["A"], 0) self.assertEqual(test_dict["inhibition_ubiquination"]["B"]["A"], 0) self.assertEqual(test_dict["inhibition_ubiquination"]["B"]["B"], 0) def test_update_matrix_activation_ubiquination(self): """Test updating the matrix with an activation ubiquitination.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = protein(namespace="HGNC", name="B", identifier="2", variants=[pmod("Ub")]) index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["activation_ubiquination"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "increases"}) self.assertEqual(test_dict["activation_ubiquination"]["A"]["B"], 1) self.assertEqual(test_dict["activation_ubiquination"]["A"]["A"], 0) self.assertEqual(test_dict["activation_ubiquination"]["B"]["A"], 0) self.assertEqual(test_dict["activation_ubiquination"]["B"]["B"], 0) def test_update_matrix_inhibition_phosphorylation(self): """Test updating the matrix with an inhibition phosphorylation.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = protein(namespace="HGNC", name="B", identifier="2", variants=[pmod("Ph")]) index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["inhibition_phosphorylation"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "decreases"}) self.assertEqual(test_dict["inhibition_phosphorylation"]["A"]["B"], 1) self.assertEqual(test_dict["inhibition_phosphorylation"]["A"]["A"], 0) self.assertEqual(test_dict["inhibition_phosphorylation"]["B"]["A"], 0) self.assertEqual(test_dict["inhibition_phosphorylation"]["B"]["B"], 0) def test_update_matrix_activation_phosphorylation(self): """Test updating the matrix with an activation phosphorylation.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = protein(namespace="HGNC", name="B", identifier="2", variants=[pmod("Ph")]) index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["activation_phosphorylation"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "increases"}) self.assertEqual(test_dict["activation_phosphorylation"]["A"]["B"], 1) self.assertEqual(test_dict["activation_phosphorylation"]["A"]["A"], 0) self.assertEqual(test_dict["activation_phosphorylation"]["B"]["A"], 0) self.assertEqual(test_dict["activation_phosphorylation"]["B"]["B"], 0) def test_update_matrix_expression(self): """Test updating the matrix with RNA expression.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = rna(namespace="HGNC", name="B", identifier="2") index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["expression"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "increases"}) self.assertEqual(test_dict["expression"]["A"]["B"], 1) self.assertEqual(test_dict["expression"]["A"]["A"], 0) self.assertEqual(test_dict["expression"]["B"]["A"], 0) self.assertEqual(test_dict["expression"]["B"]["B"], 0) def test_update_matrix_repression(self): """Test updating the matrix with RNA repression.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = rna(namespace="HGNC", name="B", identifier="2") index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["repression"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "decreases"}) self.assertEqual(test_dict["repression"]["A"]["B"], 1) self.assertEqual(test_dict["repression"]["A"]["A"], 0) self.assertEqual(test_dict["repression"]["B"]["A"], 0) self.assertEqual(test_dict["repression"]["B"]["B"], 0) def test_update_matrix_activation(self): """Test updating the matrix with activation.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = protein(namespace="HGNC", name="B", identifier="2") index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["activation"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "increases"}) self.assertEqual(test_dict["activation"]["A"]["B"], 1) self.assertEqual(test_dict["activation"]["A"]["A"], 0) self.assertEqual(test_dict["activation"]["B"]["A"], 0) self.assertEqual(test_dict["activation"]["B"]["B"], 0) def test_update_matrix_inhibition(self): """Test updating the matrix with activation.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = protein(namespace="HGNC", name="B", identifier="2") index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["inhibition"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "decreases"}) self.assertEqual(test_dict["inhibition"]["A"]["B"], 1) self.assertEqual(test_dict["inhibition"]["A"]["A"], 0) self.assertEqual(test_dict["inhibition"]["B"]["A"], 0) self.assertEqual(test_dict["inhibition"]["B"]["B"], 0) def test_update_matrix_association(self): """Test updating the matrix with association.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = protein(namespace="HGNC", name="B", identifier="2") index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["binding_association"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "association"}) self.assertEqual(test_dict["binding_association"]["A"]["B"], 1) self.assertEqual(test_dict["binding_association"]["A"]["A"], 0) self.assertEqual(test_dict["binding_association"]["B"]["A"], 0) self.assertEqual(test_dict["binding_association"]["B"]["B"], 0) def test_update_matrix_pmods(self): """Test updating the matrix with multiple protein modifications.""" sub = protein(namespace="HGNC", name="A", identifier="1") obj = protein( namespace="HGNC", name="B", identifier="2", variants=[pmod("Ub"), pmod("Ph")], ) index = {"A", "B"} test_dict = {} test_matrix = DataFrame(0, index=index, columns=index) test_dict["activation_ubiquination"] = test_matrix test_dict["activation_phosphorylation"] = test_matrix update_spia_matrices(test_dict, sub, obj, {"relation": "increases"}) self.assertEqual(test_dict["activation_ubiquination"]["A"]["B"], 1) self.assertEqual(test_dict["activation_ubiquination"]["A"]["A"], 0) self.assertEqual(test_dict["activation_ubiquination"]["B"]["A"], 0) self.assertEqual(test_dict["activation_ubiquination"]["B"]["B"], 0) self.assertEqual(test_dict["activation_phosphorylation"]["A"]["B"], 1) self.assertEqual(test_dict["activation_phosphorylation"]["A"]["A"], 0) self.assertEqual(test_dict["activation_phosphorylation"]["B"]["A"], 0) self.assertEqual(test_dict["activation_phosphorylation"]["B"]["B"], 0) def test_spia_matrix_complexes(self): """Test handling of complexes.""" self.sialic_acid_graph.add_increases( sialic_acid_cd33_complex, trem2, citation=citation, annotations={"Species": "9606", "Confidence": "High"}, evidence=evidence_1, target_modifier=activity(), ) spia_dfs = to_spia_dfs(self.sialic_acid_graph) self.assertEqual(spia_dfs["activation"][cd33.name][trem2.name], 1) def test_spia_matrix_composites(self): """Test handling of composites.""" shp = composite_abundance([shp1, shp2]) self.sialic_acid_graph.add_increases( shp, trem2, citation=citation, annotations={"Species": "9606", "Confidence": "High"}, evidence=evidence_1, target_modifier=activity(), ) spia_dfs = to_spia_dfs(self.sialic_acid_graph) self.assertEqual(spia_dfs["activation"][shp1.name][trem2.name], 1) self.assertEqual(spia_dfs["activation"][shp2.name][trem2.name], 1) pybel-0.15.5/tests/test_io/test_triples.py000066400000000000000000000225231426625374700206440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the conversion procedure.""" import unittest from typing import Tuple, Type from pybel import BELGraph from pybel.constants import ( ASSOCIATION, DECREASES, DIRECTLY_DECREASES, DIRECTLY_INCREASES, EQUIVALENT_TO, INCREASES, IS_A, NEGATIVE_CORRELATION, PART_OF, POSITIVE_CORRELATION, REGULATES, RELATION, TARGET_MODIFIER, ) from pybel.dsl import ( Abundance, BaseEntity, BiologicalProcess, ComplexAbundance, MicroRna, NamedComplexAbundance, Pathology, Population, Protein, Rna, activity, ) from pybel.io.triples import converters as tsvc from pybel.io.triples.api import to_triple from pybel.io.triples.converters import ( AbundanceDirectlyDecreasesProteinActivityConverter, AbundanceDirectlyIncreasesProteinActivityConverter, AbundancePartOfPopulationConverter, AssociationConverter, Converter, CorrelationConverter, DecreasesAmountConverter, DrugIndicationConverter, DrugSideEffectConverter, EquivalenceConverter, IncreasesAmountConverter, IsAConverter, MiRNADecreasesExpressionConverter, PartOfNamedComplexConverter, RegulatesActivityConverter, RegulatesAmountConverter, SubprocessPartOfBiologicalProcessConverter, TranscriptionFactorForConverter, ) from pybel.testing.utils import n from pybel.typing import EdgeData def _rel(x): return {RELATION: x} def _rela(x, y=None): return {RELATION: x, TARGET_MODIFIER: activity(y)} def _assoc(y): return {RELATION: ASSOCIATION, "association_type": y} a1 = Abundance("CHEBI", "1") p1 = Protein("HGNC", "1") pf1 = Protein("INTERPRO", "1") d1 = Pathology("MESH", "1") b1 = BiologicalProcess("GO", "1") b2 = BiologicalProcess("GO", "2") m1 = MicroRna("MIRBASE", "1") r1 = Rna("HGNC", "1") r2 = Rna("HGNC", "2") nca1 = NamedComplexAbundance("FPLX", "1") pop1 = Population("taxonomy", "1") p2 = Protein("HGNC", identifier="9236") p3 = Protein("HGNC", identifier="9212") r3 = p3.get_rna() g3 = r3.get_gene() c1 = ComplexAbundance([p2, g3]) c2 = ComplexAbundance([p1, p2]) c3 = ComplexAbundance([a1, p2]) p1_homodimer = ComplexAbundance([p1, p1]) p1_homotrimer = ComplexAbundance([p1, p1, p1]) converters_true_list = [ ( PartOfNamedComplexConverter, p1, nca1, _rel(PART_OF), ("HGNC:1", "partOf", "FPLX:1"), ), ( SubprocessPartOfBiologicalProcessConverter, b1, b2, _rel(PART_OF), ("GO:1", "partOf", "GO:2"), ), (tsvc.ProcessCausalConverter, b1, b2, _rel(INCREASES), ("GO:1", INCREASES, "GO:2")), (tsvc.ProcessCausalConverter, b1, b2, _rel(DECREASES), ("GO:1", DECREASES, "GO:2")), ( tsvc.ProcessCausalConverter, b1, b2, _rel(DIRECTLY_INCREASES), ("GO:1", DIRECTLY_INCREASES, "GO:2"), ), ( tsvc.ProcessCausalConverter, b1, b2, _rel(DIRECTLY_DECREASES), ("GO:1", DIRECTLY_DECREASES, "GO:2"), ), ( AssociationConverter, r1, r2, _rel(ASSOCIATION), ("HGNC:1", "association", "HGNC:2"), ), ( AssociationConverter, r1, r2, _assoc("similarity"), ("HGNC:1", "similarity", "HGNC:2"), ), ( CorrelationConverter, r1, r2, _rel(POSITIVE_CORRELATION), ("HGNC:1", "positiveCorrelation", "HGNC:2"), ), (IsAConverter, p1, pf1, _rel(IS_A), ("HGNC:1", "isA", "INTERPRO:1")), # Found in ADEPTUS ( CorrelationConverter, d1, r1, _rel(POSITIVE_CORRELATION), ("MESH:1", "positiveCorrelation", "HGNC:1"), ), ( CorrelationConverter, d1, r1, _rel(NEGATIVE_CORRELATION), ("MESH:1", "negativeCorrelation", "HGNC:1"), ), # Found in LINCS (not integrated yet) ( RegulatesAmountConverter, a1, r1, _rel(REGULATES), ("CHEBI:1", "regulatesAmountOf", "HGNC:1"), ), ( IncreasesAmountConverter, a1, r1, _rel(INCREASES), ("CHEBI:1", "increasesAmountOf", "HGNC:1"), ), ( DecreasesAmountConverter, a1, r1, _rel(DECREASES), ("CHEBI:1", "decreasesAmountOf", "HGNC:1"), ), # Found in SIDER ( DrugSideEffectConverter, a1, d1, _rel(INCREASES), ("CHEBI:1", "increases", "MESH:1"), ), ( DrugIndicationConverter, a1, d1, _rel(DECREASES), ("CHEBI:1", "decreases", "MESH:1"), ), # Found in miRTarBase ( MiRNADecreasesExpressionConverter, m1, r1, _rel(DECREASES), ("MIRBASE:1", "repressesExpressionOf", "HGNC:1"), ), # Found in chemogenomics databases (e.g., DrugBank) ( RegulatesActivityConverter, a1, p1, _rela(REGULATES), ("CHEBI:1", "activityDirectlyRegulatesActivityOf", "HGNC:1"), ), ( AbundanceDirectlyDecreasesProteinActivityConverter, a1, p1, _rela(DIRECTLY_DECREASES), ("CHEBI:1", "activityDirectlyNegativelyRegulatesActivityOf", "HGNC:1"), ), ( AbundanceDirectlyIncreasesProteinActivityConverter, a1, p1, _rela(DIRECTLY_INCREASES), ("CHEBI:1", "activityDirectlyPositivelyRegulatesActivityOf", "HGNC:1"), ), # Found in ComPath ( EquivalenceConverter, b1, b2, _rel(EQUIVALENT_TO), ("GO:1", "equivalentTo", "GO:2"), ), ( SubprocessPartOfBiologicalProcessConverter, b1, b2, _rel(PART_OF), ("GO:1", "partOf", "GO:2"), ), # Misc ( AbundancePartOfPopulationConverter, a1, pop1, _rel(PART_OF), ("CHEBI:1", "partOf", "taxonomy:1"), ), # complex(g(hgnc:9212), p(hgnc:9236)) directlyIncreases r(hgnc:9212) ( TranscriptionFactorForConverter, c1, r3, _rel(DIRECTLY_INCREASES), ("HGNC:9236", DIRECTLY_INCREASES, "HGNC:9212"), ), ( TranscriptionFactorForConverter, c1, r3, _rel(DIRECTLY_DECREASES), ("HGNC:9236", DIRECTLY_DECREASES, "HGNC:9212"), ), ( tsvc.BindsGeneConverter, p2, c1, _rel(DIRECTLY_INCREASES), (p2.curie, "bindsToGene", g3.curie), ), ( tsvc.BindsProteinConverter, p1, c2, _rel(DIRECTLY_INCREASES), (p1.curie, "bindsToProtein", p2.curie), ), ( tsvc.BindsProteinConverter, a1, c3, _rel(DIRECTLY_INCREASES), (a1.curie, "bindsToProtein", p2.curie), ), ( tsvc.HomomultimerConverter, p1, p1_homodimer, _rel(DIRECTLY_INCREASES), (p1.curie, "bindsToProtein", p1.curie), ), ( tsvc.HomomultimerConverter, p1, p1_homotrimer, _rel(DIRECTLY_INCREASES), (p1.curie, "bindsToProtein", p1.curie), ), ( tsvc.ProteinRegulatesComplex, p3, c2, _rel(DIRECTLY_INCREASES), (p3.curie, "increasesAmountOf", str(c2)), ), ( tsvc.ProteinRegulatesComplex, p3, c2, _rel(DIRECTLY_DECREASES), (p3.curie, "decreasesAmountOf", str(c2)), ), ( tsvc.ProteinRegulatesComplex, p3, c2, _rel(DECREASES), (p3.curie, "decreasesAmountOf", str(c2)), ), ( tsvc.ProteinRegulatesComplex, p3, c2, _rel(INCREASES), (p3.curie, "increasesAmountOf", str(c2)), ), ] converters_false_list = [ (PartOfNamedComplexConverter, nca1, p1, _rel(IS_A)), ] class TestConverters(unittest.TestCase): """Tests for the converter classes.""" def help_test_convert( self, converter: Type[Converter], u: BaseEntity, v: BaseEntity, edge_data: EdgeData, triple: Tuple[str, str, str], ) -> None: """Test a converter class.""" self.assertTrue( issubclass(converter, Converter), msg="Not a Converter: {}".format(converter.__name__), ) key = n() self.assertTrue( converter.predicate(u, v, key, edge_data), msg="Predicate failed: {}".format(converter.__name__), ) self.assertEqual( triple, converter.convert(u, v, key, edge_data), msg="Conversion failed: {}".format(converter.__name__), ) graph = BELGraph() graph.add_edge(u, v, key=key, **edge_data) self.assertEqual( triple, to_triple(graph, u, v, key), msg="get_triple failed: {}".format(converter.__name__), ) def test_converters_true(self): """Test passing converters.""" for converter, u, v, edge_data, triple in converters_true_list: with self.subTest(converter=converter.__qualname__): self.help_test_convert(converter, u, v, edge_data, triple) def test_converters_false(self): """Test falsification of converters.""" for converter, u, v, edge_data in converters_false_list: with self.subTest(converter=converter.__qualname__): self.assertFalse(converter.predicate(u, v, n(), edge_data)) pybel-0.15.5/tests/test_io/test_umbrella_nodelink.py000066400000000000000000000035141426625374700226470ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the umbrella node-link JSON exporter.""" import unittest from pybel.io.umbrella_nodelink import to_umbrella_nodelink from tests.test_io.test_cx.examples import example_graph class TestUmbrellaNodeLinkExporter(unittest.TestCase): """Tests for the umbrella node-link JSON exporter.""" def test_exporter_new_nodes(self): """Test new nodes created.""" # Check original number of nodes and edges in the example BEL Graph self.assertEqual(29, example_graph.number_of_nodes()) self.assertEqual(32, example_graph.number_of_edges()) custom_json_dict = to_umbrella_nodelink(example_graph) self.assertEqual(32, len(custom_json_dict["nodes"])) # 3 new nodes are created: self.assertIn( 'act(p(hgnc:MAPK1), ma(go:0016301 ! "kinase activity"))', custom_json_dict["nodes"], ) self.assertIn( 'act(p(hgnc:PTK2, pmod(go:0006468 ! "protein phosphorylation", Tyr, 925)), ma(go:0016301 ! "kinase activity"))', custom_json_dict["nodes"], ) self.assertIn( 'act(p(fus(hgnc:BCR, "?", hgnc:ABL1, "?")), ma(go:0016301 ! "kinase activity"))', custom_json_dict["nodes"], ) def test_exporter_edges(self): """Test no new edges created.""" # Check original number of nodes and edges in the example BEL Graph self.assertEqual(29, example_graph.number_of_nodes(), msg="Wrong number of nodes") self.assertEqual(32, example_graph.number_of_edges(), msg="Wrong number of edges") custom_json_dict = to_umbrella_nodelink(example_graph) # Number of edges is maintained self.assertEqual( 32, len(custom_json_dict["links"]), msg="Wrong number of links in Umbrella JSON", ) pybel-0.15.5/tests/test_manager/000077500000000000000000000000001426625374700165505ustar00rootroot00000000000000pybel-0.15.5/tests/test_manager/__init__.py000066400000000000000000000000771426625374700206650ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for :mod:`pybel.manager`.""" pybel-0.15.5/tests/test_manager/pmc_citation_data.json000066400000000000000000000024561426625374700231140ustar00rootroot00000000000000{ "PMC6611653": { "source": "PubMed", "accessed": { "date-parts": [ [ 2020, 10, 9 ] ] }, "id": "pmid:31233491", "title": "Open collaborative writing with Manubot", "author": [ { "family": "Himmelstein", "given": "Daniel S" }, { "family": "Rubinetti", "given": "Vincent" }, { "family": "Slochower", "given": "David R" }, { "family": "Hu", "given": "Dongbo" }, { "family": "Malladi", "given": "Venkat S" }, { "family": "Greene", "given": "Casey S" }, { "family": "Gitter", "given": "Anthony" } ], "container-title-short": "PLoS Comput Biol", "container-title": "PLoS computational biology", "publisher": "Public Library of Science", "ISSN": "1553-734X", "issued": { "date-parts": [ [ 2019, 6, 24 ] ] }, "page": "e1007128", "volume": "15", "issue": "6", "PMID": "31233491", "PMCID": "PMC6611653", "DOI": "10.1371/journal.pcbi.1007128", "type": "article-journal", "URL": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611653/" } }pybel-0.15.5/tests/test_manager/pubmed_citation_data.json000066400000000000000000000565431426625374700236170ustar00rootroot00000000000000{ "header": { "type": "esummary", "version": "0.3" }, "result": { "uids": [ "26438529", "29324713", "29359844", "27003210", "25818332", "9611787", "26649137" ], "26438529": { "uid": "26438529", "pubdate": "2015 Oct 5", "epubdate": "2015 Oct 5", "source": "Mol Neurodegener", "authors": [ { "name": "Malik M", "authtype": "Author", "clusterid": "" }, { "name": "Parikh I", "authtype": "Author", "clusterid": "" }, { "name": "Vasquez JB", "authtype": "Author", "clusterid": "" }, { "name": "Smith C", "authtype": "Author", "clusterid": "" }, { "name": "Tai L", "authtype": "Author", "clusterid": "" }, { "name": "Bu G", "authtype": "Author", "clusterid": "" }, { "name": "LaDu MJ", "authtype": "Author", "clusterid": "" }, { "name": "Fardo DW", "authtype": "Author", "clusterid": "" }, { "name": "Rebeck GW", "authtype": "Author", "clusterid": "" }, { "name": "Estus S", "authtype": "Author", "clusterid": "" } ], "lastauthor": "Estus S", "title": "Genetics ignite focus on microglial inflammation in Alzheimer's disease.", "sorttitle": "genetics ignite focus on microglial inflammation in alzheimer s disease", "volume": "10", "issue": "", "pages": "52", "lang": [ "eng" ], "nlmuniqueid": "101266600", "issn": "", "essn": "1750-1326", "pubtype": [ "Journal Article", "Review" ], "recordstatus": "PubMed - indexed for MEDLINE", "pubstatus": "3", "articleids": [ { "idtype": "pubmed", "idtypen": 1, "value": "26438529" }, { "idtype": "doi", "idtypen": 3, "value": "10.1186/s13024-015-0048-1" }, { "idtype": "pii", "idtypen": 4, "value": "10.1186/s13024-015-0048-1" }, { "idtype": "pmc", "idtypen": 8, "value": "PMC4595327" }, { "idtype": "rid", "idtypen": 8, "value": "26438529" }, { "idtype": "eid", "idtypen": 8, "value": "26438529" }, { "idtype": "pmcid", "idtypen": 5, "value": "pmc-id: PMC4595327;" } ], "history": [ { "pubstatus": "received", "date": "2015/08/03 00:00" }, { "pubstatus": "accepted", "date": "2015/09/23 00:00" }, { "pubstatus": "entrez", "date": "2015/10/07 06:00" }, { "pubstatus": "pubmed", "date": "2015/10/07 06:00" }, { "pubstatus": "medline", "date": "2016/08/25 06:00" } ], "references": [], "attributes": [ "Has Abstract" ], "pmcrefcount": 47, "fulljournalname": "Molecular neurodegeneration", "elocationid": "doi: 10.1186/s13024-015-0048-1", "doctype": "citation", "srccontriblist": [], "booktitle": "", "medium": "", "edition": "", "publisherlocation": "", "publishername": "", "srcdate": "", "reportnumber": "", "availablefromurl": "", "locationlabel": "", "doccontriblist": [], "docdate": "", "bookname": "", "chapter": "", "sortpubdate": "2015/10/05 00:00", "sortfirstauthor": "Malik M", "vernaculartitle": "" }, "29324713": { "uid": "29324713", "pubdate": "2018 Jan 11", "epubdate": "2018 Jan 11", "source": "Molecules", "authors": [ { "name": "Mart\u00ednez-Guill\u00e9n JR", "authtype": "Author", "clusterid": "" }, { "name": "Flores-Ferr\u00e1ndiz J", "authtype": "Author", "clusterid": "" }, { "name": "G\u00f3mez C", "authtype": "Author", "clusterid": "" }, { "name": "G\u00f3mez-Bengoa E", "authtype": "Author", "clusterid": "" }, { "name": "Chinchilla R", "authtype": "Author", "clusterid": "" } ], "lastauthor": "Chinchilla R", "title": "Asymmetric Conjugate Addition of \u03b1,\u03b1-Disubstituted Aldehydes to Nitroalkenes Organocatalyzed by Chiral Monosalicylamides from trans-Cyclohexane-1,2-Diamines.", "sorttitle": "asymmetric conjugate addition of disubstituted aldehydes to nitroalkenes organocatalyzed by chiral monosalicylamides from trans cyclohexane 1 2 diamines", "volume": "23", "issue": "1", "pages": "", "lang": [ "eng" ], "nlmuniqueid": "100964009", "issn": "", "essn": "1420-3049", "pubtype": [ "Journal Article" ], "recordstatus": "PubMed - indexed for MEDLINE", "pubstatus": "3", "articleids": [ { "idtype": "pubmed", "idtypen": 1, "value": "29324713" }, { "idtype": "pii", "idtypen": 4, "value": "molecules23010141" }, { "idtype": "doi", "idtypen": 3, "value": "10.3390/molecules23010141" }, { "idtype": "pmc", "idtypen": 8, "value": "PMC6017890" }, { "idtype": "rid", "idtypen": 8, "value": "29324713" }, { "idtype": "eid", "idtypen": 8, "value": "29324713" }, { "idtype": "pmcid", "idtypen": 5, "value": "pmc-id: PMC6017890;" } ], "history": [ { "pubstatus": "received", "date": "2017/12/25 00:00" }, { "pubstatus": "revised", "date": "2018/01/08 00:00" }, { "pubstatus": "accepted", "date": "2018/01/09 00:00" }, { "pubstatus": "entrez", "date": "2018/01/12 06:00" }, { "pubstatus": "pubmed", "date": "2018/01/13 06:00" }, { "pubstatus": "medline", "date": "2018/08/04 06:00" } ], "references": [], "attributes": [ "Has Abstract" ], "pmcrefcount": 3, "fulljournalname": "Molecules (Basel, Switzerland)", "elocationid": "pii: E141. doi: 10.3390/molecules23010141", "doctype": "citation", "srccontriblist": [], "booktitle": "", "medium": "", "edition": "", "publisherlocation": "", "publishername": "", "srcdate": "", "reportnumber": "", "availablefromurl": "", "locationlabel": "", "doccontriblist": [], "docdate": "", "bookname": "", "chapter": "", "sortpubdate": "2018/01/11 00:00", "sortfirstauthor": "Mart\u00ednez-Guill\u00e9n JR", "vernaculartitle": "" }, "29359844": { "uid": "29359844", "pubdate": "2018 Apr", "epubdate": "2018 Mar 13", "source": "Transpl Infect Dis", "authors": [ { "name": "Botero V", "authtype": "Author", "clusterid": "" }, { "name": "Garc\u00eda VH", "authtype": "Author", "clusterid": "" }, { "name": "Aristizabal AM", "authtype": "Author", "clusterid": "" }, { "name": "Gomez C", "authtype": "Author", "clusterid": "" }, { "name": "Perez P", "authtype": "Author", "clusterid": "" }, { "name": "Caicedo LA", "authtype": "Author", "clusterid": "" }, { "name": "Echeverri GJ", "authtype": "Author", "clusterid": "" } ], "lastauthor": "Echeverri GJ", "title": "Hepatitis A, cardiomyopathy, aplastic anemia, and acute liver failure: A devastating scenario.", "sorttitle": "hepatitis a cardiomyopathy aplastic anemia and acute liver failure a devastating scenario", "volume": "20", "issue": "2", "pages": "e12842", "lang": [ "eng" ], "nlmuniqueid": "100883688", "issn": "1398-2273", "essn": "1399-3062", "pubtype": [ "Journal Article" ], "recordstatus": "PubMed - indexed for MEDLINE", "pubstatus": "256", "articleids": [ { "idtype": "pubmed", "idtypen": 1, "value": "29359844" }, { "idtype": "doi", "idtypen": 3, "value": "10.1111/tid.12842" }, { "idtype": "rid", "idtypen": 8, "value": "29359844" }, { "idtype": "eid", "idtypen": 8, "value": "29359844" } ], "history": [ { "pubstatus": "received", "date": "2017/04/19 00:00" }, { "pubstatus": "revised", "date": "2017/09/14 00:00" }, { "pubstatus": "accepted", "date": "2017/09/24 00:00" }, { "pubstatus": "pubmed", "date": "2018/01/24 06:00" }, { "pubstatus": "medline", "date": "2018/09/18 06:00" }, { "pubstatus": "entrez", "date": "2018/01/24 06:00" } ], "references": [], "attributes": [ "Has Abstract" ], "pmcrefcount": 1, "fulljournalname": "Transplant infectious disease : an official journal of the Transplantation Society", "elocationid": "doi: 10.1111/tid.12842", "doctype": "citation", "srccontriblist": [], "booktitle": "", "medium": "", "edition": "", "publisherlocation": "", "publishername": "", "srcdate": "", "reportnumber": "", "availablefromurl": "", "locationlabel": "", "doccontriblist": [], "docdate": "", "bookname": "", "chapter": "", "sortpubdate": "2018/04/01 00:00", "sortfirstauthor": "Botero V", "vernaculartitle": "" }, "27003210": { "uid": "27003210", "pubdate": "2016 Mar 15", "epubdate": "", "source": "J Alzheimers Dis", "authors": [ { "name": "Wang HF", "authtype": "Author", "clusterid": "" }, { "name": "Wan Y", "authtype": "Author", "clusterid": "" }, { "name": "Hao XK", "authtype": "Author", "clusterid": "" }, { "name": "Cao L", "authtype": "Author", "clusterid": "" }, { "name": "Zhu XC", "authtype": "Author", "clusterid": "" }, { "name": "Jiang T", "authtype": "Author", "clusterid": "" }, { "name": "Tan MS", "authtype": "Author", "clusterid": "" }, { "name": "Tan L", "authtype": "Author", "clusterid": "" }, { "name": "Zhang DQ", "authtype": "Author", "clusterid": "" }, { "name": "Tan L", "authtype": "Author", "clusterid": "" }, { "name": "Yu JT", "authtype": "Author", "clusterid": "" }, { "name": "Disease Neuroimaging Initiative Alzheimer\u2019s.", "authtype": "CollectiveName", "clusterid": "" } ], "lastauthor": "Yu JT", "title": "Bridging Integrator 1 (BIN1) Genotypes Mediate Alzheimer's Disease Risk by Altering Neuronal Degeneration.", "sorttitle": "bridging integrator 1 bin1 genotypes mediate alzheimer s disease risk by altering neuronal degeneration", "volume": "52", "issue": "1", "pages": "179-90", "lang": [ "eng" ], "nlmuniqueid": "9814863", "issn": "1387-2877", "essn": "1875-8908", "pubtype": [ "Journal Article", "Meta-Analysis", "Multicenter Study" ], "recordstatus": "PubMed - indexed for MEDLINE", "pubstatus": "4", "articleids": [ { "idtype": "pubmed", "idtypen": 1, "value": "27003210" }, { "idtype": "pii", "idtypen": 4, "value": "JAD150972" }, { "idtype": "doi", "idtypen": 3, "value": "10.3233/JAD-150972" }, { "idtype": "rid", "idtypen": 8, "value": "27003210" }, { "idtype": "eid", "idtypen": 8, "value": "27003210" } ], "history": [ { "pubstatus": "entrez", "date": "2016/03/23 06:00" }, { "pubstatus": "pubmed", "date": "2016/03/24 06:00" }, { "pubstatus": "medline", "date": "2017/01/28 06:00" } ], "references": [], "attributes": [ "Has Abstract" ], "pmcrefcount": 9, "fulljournalname": "Journal of Alzheimer's disease : JAD", "elocationid": "doi: 10.3233/JAD-150972", "doctype": "citation", "srccontriblist": [], "booktitle": "", "medium": "", "edition": "", "publisherlocation": "", "publishername": "", "srcdate": "", "reportnumber": "", "availablefromurl": "", "locationlabel": "", "doccontriblist": [], "docdate": "", "bookname": "", "chapter": "", "sortpubdate": "2016/03/15 00:00", "sortfirstauthor": "Wang HF", "vernaculartitle": "" }, "25818332": { "uid": "25818332", "pubdate": "2015 May 6", "epubdate": "2015 Mar 25", "source": "Neurosci Lett", "authors": [ { "name": "Shi X", "authtype": "Author", "clusterid": "" }, { "name": "Zheng Z", "authtype": "Author", "clusterid": "" }, { "name": "Li J", "authtype": "Author", "clusterid": "" }, { "name": "Xiao Z", "authtype": "Author", "clusterid": "" }, { "name": "Qi W", "authtype": "Author", "clusterid": "" }, { "name": "Zhang A", "authtype": "Author", "clusterid": "" }, { "name": "Wu Q", "authtype": "Author", "clusterid": "" }, { "name": "Fang Y", "authtype": "Author", "clusterid": "" } ], "lastauthor": "Fang Y", "title": "Curcumin inhibits A\u03b2-induced microglial inflammatory responses in vitro: Involvement of ERK1/2 and p38 signaling pathways.", "sorttitle": "curcumin inhibits a induced microglial inflammatory responses in vitro involvement of erk1 2 and p38 signaling pathways", "volume": "594", "issue": "", "pages": "105-10", "lang": [ "eng" ], "nlmuniqueid": "7600130", "issn": "0304-3940", "essn": "1872-7972", "pubtype": [ "Journal Article" ], "recordstatus": "PubMed - indexed for MEDLINE", "pubstatus": "256", "articleids": [ { "idtype": "pubmed", "idtypen": 1, "value": "25818332" }, { "idtype": "pii", "idtypen": 4, "value": "S0304-3940(15)00238-4" }, { "idtype": "doi", "idtypen": 3, "value": "10.1016/j.neulet.2015.03.045" }, { "idtype": "rid", "idtypen": 8, "value": "25818332" }, { "idtype": "eid", "idtypen": 8, "value": "25818332" } ], "history": [ { "pubstatus": "received", "date": "2014/12/30 00:00" }, { "pubstatus": "revised", "date": "2015/02/18 00:00" }, { "pubstatus": "accepted", "date": "2015/03/21 00:00" }, { "pubstatus": "entrez", "date": "2015/03/31 06:00" }, { "pubstatus": "pubmed", "date": "2015/03/31 06:00" }, { "pubstatus": "medline", "date": "2015/09/15 06:00" } ], "references": [], "attributes": [ "Has Abstract" ], "pmcrefcount": 17, "fulljournalname": "Neuroscience letters", "elocationid": "doi: 10.1016/j.neulet.2015.03.045", "doctype": "citation", "srccontriblist": [], "booktitle": "", "medium": "", "edition": "", "publisherlocation": "", "publishername": "", "srcdate": "", "reportnumber": "", "availablefromurl": "", "locationlabel": "", "doccontriblist": [], "docdate": "", "bookname": "", "chapter": "", "sortpubdate": "2015/05/06 00:00", "sortfirstauthor": "Shi X", "vernaculartitle": "" }, "9611787": { "uid": "9611787", "pubdate": "1998 May-Jun", "epubdate": "", "source": "J Chem Inf Comput Sci", "authors": [ { "name": "Lewell XQ", "authtype": "Author", "clusterid": "" }, { "name": "Judd DB", "authtype": "Author", "clusterid": "" }, { "name": "Watson SP", "authtype": "Author", "clusterid": "" }, { "name": "Hann MM", "authtype": "Author", "clusterid": "" } ], "lastauthor": "Hann MM", "title": "RECAP--retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry.", "sorttitle": "recap retrosynthetic combinatorial analysis procedure a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry", "volume": "38", "issue": "3", "pages": "511-22", "lang": [ "eng" ], "nlmuniqueid": "7505012", "issn": "0095-2338", "essn": "", "pubtype": [ "Journal Article" ], "recordstatus": "PubMed - indexed for MEDLINE", "pubstatus": "4", "articleids": [ { "idtype": "pubmed", "idtypen": 1, "value": "9611787" }, { "idtype": "doi", "idtypen": 3, "value": "10.1021/ci970429i" }, { "idtype": "rid", "idtypen": 8, "value": "9611787" }, { "idtype": "eid", "idtypen": 8, "value": "9611787" } ], "history": [ { "pubstatus": "pubmed", "date": "1998/06/05 00:00" }, { "pubstatus": "medline", "date": "1998/06/05 00:01" }, { "pubstatus": "entrez", "date": "1998/06/05 00:00" } ], "references": [], "attributes": [ "Has Abstract" ], "pmcrefcount": 66, "fulljournalname": "Journal of chemical information and computer sciences", "elocationid": "", "doctype": "citation", "srccontriblist": [], "booktitle": "", "medium": "", "edition": "", "publisherlocation": "", "publishername": "", "srcdate": "", "reportnumber": "", "availablefromurl": "", "locationlabel": "", "doccontriblist": [], "docdate": "", "bookname": "", "chapter": "", "sortpubdate": "1998/05/01 00:00", "sortfirstauthor": "Lewell XQ", "vernaculartitle": "" }, "26649137": { "uid": "26649137", "pubdate": "2016", "epubdate": "2015 Nov 16", "source": "Oxid Med Cell Longev", "authors": [ { "name": "Yan T", "authtype": "Author", "clusterid": "" }, { "name": "Zhao Y", "authtype": "Author", "clusterid": "" }, { "name": "Zhang X", "authtype": "Author", "clusterid": "" } ], "lastauthor": "Zhang X", "title": "Acetaldehyde Induces Cytotoxicity of SH-SY5Y Cells via Inhibition of Akt Activation and Induction of Oxidative Stress.", "sorttitle": "acetaldehyde induces cytotoxicity of sh sy5y cells via inhibition of akt activation and induction of oxidative stress", "volume": "2016", "issue": "", "pages": "4512309", "lang": [ "eng" ], "nlmuniqueid": "101479826", "issn": "1942-0900", "essn": "1942-0994", "pubtype": [ "Journal Article" ], "recordstatus": "PubMed - indexed for MEDLINE", "pubstatus": "256", "articleids": [ { "idtype": "pubmed", "idtypen": 1, "value": "26649137" }, { "idtype": "doi", "idtypen": 3, "value": "10.1155/2016/4512309" }, { "idtype": "pmc", "idtypen": 8, "value": "PMC4663355" }, { "idtype": "rid", "idtypen": 8, "value": "26649137" }, { "idtype": "eid", "idtypen": 8, "value": "26649137" }, { "idtype": "pmcid", "idtypen": 5, "value": "pmc-id: PMC4663355;" } ], "history": [ { "pubstatus": "received", "date": "2015/06/13 00:00" }, { "pubstatus": "accepted", "date": "2015/07/14 00:00" }, { "pubstatus": "entrez", "date": "2015/12/10 06:00" }, { "pubstatus": "pubmed", "date": "2015/12/10 06:00" }, { "pubstatus": "medline", "date": "2016/09/13 06:00" } ], "references": [], "attributes": [ "Has Abstract" ], "pmcrefcount": 10, "fulljournalname": "Oxidative medicine and cellular longevity", "elocationid": "doi: 10.1155/2016/4512309", "doctype": "citation", "srccontriblist": [], "booktitle": "", "medium": "", "edition": "", "publisherlocation": "", "publishername": "", "srcdate": "", "reportnumber": "", "availablefromurl": "", "locationlabel": "", "doccontriblist": [], "docdate": "", "bookname": "", "chapter": "", "sortpubdate": "2016/01/01 00:00", "sortfirstauthor": "Yan T", "vernaculartitle": "" } } }pybel-0.15.5/tests/test_manager/test_citation_utils.py000066400000000000000000000212201426625374700232100ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test the manager's citation utilities. The test data can be created with the following script: .. code-block:: python import json from pybel.manager.citation_utils import get_pubmed_citation_response DATA = {'29324713', '29359844', '9611787', '25818332', '26438529', '26649137', '27003210'} rv = get_pubmed_citation_response(DATA) with open('/Users/cthoyt/dev/bel/pybel/tests/test_manager/citation_data.json', 'w') as file: json.dump(rv, file, indent=2) """ import json import os import time import unittest from typing import Any, Iterable, Mapping from unittest import mock from pybel import BELGraph from pybel.constants import ( CITATION, CITATION_AUTHORS, CITATION_DATE, CITATION_JOURNAL, CITATION_TYPE_PUBMED, ) from pybel.dsl import Protein from pybel.language import CitationDict from pybel.manager.citation_utils import ( _enrich_citations, enrich_pubmed_citations, get_citations_by_pmids, sanitize_date, ) from pybel.manager.models import Citation from pybel.testing.cases import TemporaryCacheMixin from pybel.testing.utils import n HERE = os.path.abspath(os.path.dirname(__file__)) PUBMED_DATA_PATH = os.path.join(HERE, "pubmed_citation_data.json") with open(PUBMED_DATA_PATH) as _file: PUBMED_DATA = json.load(_file) PMC_DATA_PATH = os.path.join(HERE, "pmc_citation_data.json") with open(PMC_DATA_PATH) as _file: PMC_DATA = json.load(_file) def _mock_fn(pubmed_identifiers: Iterable[str]) -> Mapping[str, Any]: result = { "uids": pubmed_identifiers, } for pmid in pubmed_identifiers: result[pmid] = PUBMED_DATA["result"][pmid] return {"result": result} mock_get_pubmed_citation_response = mock.patch( "pybel.manager.citation_utils.get_pubmed_citation_response", side_effect=_mock_fn, ) def _mock_get_pmc_csl_item(pmc_id: str) -> Mapping[str, Any]: return PMC_DATA[pmc_id] mock_get_pmc_csl_item = mock.patch( "pybel.manager.citation_utils.get_pmc_csl_item", side_effect=_mock_get_pmc_csl_item, ) class TestSanitizeDate(unittest.TestCase): """Test sanitization of dates in various formats.""" def test_sanitize_1(self): """Test YYYY Mon DD.""" self.assertEqual("2012-12-19", sanitize_date("2012 Dec 19")) def test_sanitize_2(self): """Test YYYY Mon.""" self.assertEqual("2012-12-01", sanitize_date("2012 Dec")) def test_sanitize_3(self): """Test YYYY.""" self.assertEqual("2012-01-01", sanitize_date("2012")) def test_sanitize_4(self): """Test YYYY Mon-Mon.""" self.assertEqual("2012-10-01", sanitize_date("2012 Oct-Dec")) def test_sanitize_5(self): """Test YYYY Season.""" self.assertEqual("2012-03-01", sanitize_date("2012 Spring")) def test_sanitize_6(self): """Test YYYY Mon DD-DD.""" self.assertEqual("2012-12-12", sanitize_date("2012 Dec 12-15")) def test_sanitize_7(self): """Test YYYY Mon DD-Mon DD.""" self.assertEqual("2005-01-29", sanitize_date("2005 Jan 29-Feb 4")) def test_sanitize_nope(self): """Test failure.""" self.assertEqual(None, sanitize_date("2012 Early Spring")) class TestPubmed(TemporaryCacheMixin): """Tests for citations.""" def setUp(self): super().setUp() self.u, self.v = (Protein(n(), n()) for _ in range(2)) self.pmid = "9611787" self.graph = BELGraph() self.graph.add_increases(self.u, self.v, citation=self.pmid, evidence=n()) @mock_get_pubmed_citation_response def test_enrich_pubmed(self, *_): self.assertEqual(0, self.manager.count_citations()) get_citations_by_pmids(manager=self.manager, pmids=[self.pmid]) self.assertEqual(1, self.manager.count_citations()) c = self.manager.get_citation_by_pmid(self.pmid) self.assertIsNotNone(c) self.assertIsInstance(c, Citation) self.assertEqual(CITATION_TYPE_PUBMED, c.db) self.assertEqual(self.pmid, c.db_id) @mock_get_pubmed_citation_response def test_enrich_pubmed_list(self, *_): pmids = [ "25818332", "27003210", "26438529", "26649137", ] get_citations_by_pmids(manager=self.manager, pmids=pmids) citation = self.manager.get_or_create_citation(namespace=CITATION_TYPE_PUBMED, identifier="25818332") self.assertIsNotNone(citation) @mock_get_pubmed_citation_response def test_enrich_pubmed_list_grouped(self, *_): pmids = [ "25818332", "27003210", "26438529", "26649137", ] get_citations_by_pmids(manager=self.manager, pmids=pmids, group_size=2) citation = self.manager.get_citation_by_pmid("25818332") self.assertIsNotNone(citation) @mock_get_pubmed_citation_response def test_enrich_pubmed_overwrite(self, *_): citation = self.manager.get_or_create_citation(namespace=CITATION_TYPE_PUBMED, identifier=self.pmid) self.manager.session.commit() self.assertIsNone(citation.date) self.assertIsNone(citation.title) enrich_pubmed_citations(manager=self.manager, graph=self.graph) _, _, d = list(self.graph.edges(data=True))[0] citation_dict = d[CITATION] self.assertIsInstance(citation_dict, CitationDict) self.assertIn(CITATION_JOURNAL, citation_dict) self.assertIn(CITATION_DATE, citation_dict) self.assertEqual("1998-05-01", citation_dict[CITATION_DATE]) self.assertIn(CITATION_AUTHORS, citation_dict) self.assertEqual( {"Lewell XQ", "Judd DB", "Watson SP", "Hann MM"}, set(citation_dict[CITATION_AUTHORS]), ) @mock_get_pubmed_citation_response def test_enrich_pubmed_graph(self, *_): enrich_pubmed_citations(manager=self.manager, graph=self.graph) _, _, d = list(self.graph.edges(data=True))[0] citation_dict = d[CITATION] self.assertIsInstance(citation_dict, CitationDict) self.assertIn(CITATION_JOURNAL, citation_dict) self.assertIn(CITATION_DATE, citation_dict) self.assertEqual("1998-05-01", citation_dict[CITATION_DATE]) self.assertIn(CITATION_AUTHORS, citation_dict) self.assertEqual( {"Lewell XQ", "Judd DB", "Watson SP", "Hann MM"}, set(citation_dict[CITATION_AUTHORS]), ) @mock_get_pubmed_citation_response @unittest.skipIf(os.environ.get("DB") == "mysql", reason="MySQL collation is wonky") def test_enrich_pubmed_accent_duplicate(self, *_): """Test when two authors, Gomez C and Goméz C are both checked that they are not counted as duplicates.""" g1 = "Gomez C" g2 = "Gómez C" pmid_1, pmid_2 = pmids = [ "29324713", "29359844", ] get_citations_by_pmids(manager=self.manager, pmids=pmids) time.sleep(1) x = self.manager.get_citation_by_pmid(pmid_1) self.assertIsNotNone(x) self.assertEqual("Martínez-Guillén JR", x.first.name, msg="wrong first author name") self.assertIn(g1, self.manager.object_cache_author) self.assertIn(g2, self.manager.object_cache_author) a1 = self.manager.get_author_by_name(g1) self.assertEqual(g1, a1.name) a2 = self.manager.get_author_by_name(g2) self.assertEqual(g2, a2.name) class TestPMC(TemporaryCacheMixin): """Tests for citations.""" def setUp(self): super().setUp() self.u, self.v = (Protein(n(), n()) for _ in range(2)) self.citation_identifier = "PMC6611653" self.graph = BELGraph() self.graph.add_increases(self.u, self.v, citation=("pmc", self.citation_identifier), evidence=n()) @mock_get_pmc_csl_item def test_enrich_pmc(self, *_): errors = _enrich_citations(manager=self.manager, graph=self.graph, prefix="pmc") self.assertEqual(0, len(errors), msg=f"Got errors: {errors}") _, _, d = list(self.graph.edges(data=True))[0] citation_dict = d[CITATION] self.assertIsInstance(citation_dict, CitationDict) self.assertEqual("pmc", citation_dict.namespace) self.assertEqual(self.citation_identifier, citation_dict.identifier) self.assertIn(CITATION_JOURNAL, citation_dict) self.assertEqual("PLoS computational biology", citation_dict[CITATION_JOURNAL]) self.assertIn(CITATION_DATE, citation_dict) self.assertEqual("2019-06-24", citation_dict[CITATION_DATE]) self.assertIn(CITATION_AUTHORS, citation_dict) self.assertLess(0, len(citation_dict[CITATION_AUTHORS])) # TODO the eUtils and CSL thing both normalize the way autors look pybel-0.15.5/tests/test_manager/test_connection.py000066400000000000000000000055271426625374700223310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for instantiating the manager""" import os import tempfile import unittest from pybel import Manager from pybel.manager.base_manager import build_engine_session try: from unittest import mock except ImportError: import mock class TestInstantiation(unittest.TestCase): """Allows for testing with a consistent connection without changing the configuration.""" def setUp(self): """Add two class-level variables: ``mock_global_connection`` and ``mock_module_connection`` that can be used as context managers to mock the bio2bel connection getter functions.""" self.fd, self.path = tempfile.mkstemp() self.connection = "sqlite:///" + self.path def mock_connection(): """Get the connection enclosed by this class. :rtype: str """ return self.connection self.mock_connection = mock.patch("pybel.manager.cache_manager.get_cache_connection", mock_connection) def tearDown(self): os.close(self.fd) os.remove(self.path) def test_fail_connection_none(self): """Test that a None causes a huge error.""" with self.assertRaises(ValueError): build_engine_session(None) def test_instantiate_init(self): """Test what happens when no connection is specified for the normal constructor.""" with self.mock_connection: manager = Manager() self.assertEqual(self.connection, str(manager.engine.url)) def test_instantiate_manager_positional(self): manager = Manager(self.connection) self.assertEqual(self.connection, str(manager.engine.url)) def test_instantiate_manager_positional_with_keyword(self): manager = Manager(self.connection, echo=False) self.assertEqual(self.connection, str(manager.engine.url)) def test_instantiate_manager_fail_positional(self): with self.assertRaises(ValueError): Manager(self.connection, True) def test_instantiate_manager_keyword(self): manager = Manager(connection=self.connection) self.assertEqual(self.connection, str(manager.engine.url)) def test_instantiate_manager_connection_fail_too_many_keyword(self): with self.assertRaises(ValueError): Manager(connection=self.connection, engine="something", session="something") def test_instantiate_manager_engine_fail_too_many_keywords(self): with self.assertRaises(ValueError): Manager(engine="something", session="something", echo=False) def test_instantiate_manager_engine_missing(self): with self.assertRaises(ValueError): Manager(engine=None, session="fake-session") def test_instantiate_manager_session_missing(self): with self.assertRaises(ValueError): Manager(engine="fake-engine", session=None) pybel-0.15.5/tests/test_manager/test_manager_definitions.py000066400000000000000000000105561426625374700241750ustar00rootroot00000000000000# -*- coding: utf-8 -*- import os from pathlib import Path from pybel import BELGraph, Manager from pybel.constants import ANNOTATIONS from pybel.resources import HGNC_URL from pybel.testing.cases import TemporaryCacheClsMixin from pybel.testing.constants import belns_dir_path from pybel.testing.mocks import mock_bel_resources from tests.constants import OPENBEL_ANNOTATION_RESOURCES ns1 = Path(os.path.join(belns_dir_path, "disease-ontology.belns")).as_uri() ns1_url = "http://resources.openbel.org/belframework/20150611/namespace/disease-ontology-ids.belns" ns2 = Path(os.path.join(belns_dir_path, "mesh-diseases.belns")).as_uri() ns2_url = "http://resources.openbel.org/belframework/20150611/namespace/mesh-diseases.belns" CELL_LINE_URL = OPENBEL_ANNOTATION_RESOURCES + "cell-line.belanno" CELL_LINE_KEYWORD = "CellLine" class TestDefinitionManagers(TemporaryCacheClsMixin): def _help_check_hgnc(self, manager: Manager) -> None: """Help check the HGNC namespace was loaded properly.""" entry = manager.get_namespace_entry(HGNC_URL, "MHS2") self.assertIsNotNone(entry) self.assertEqual("MHS2", entry.name) self.assertIn("G", entry.encoding) entry = manager.get_namespace_entry(HGNC_URL, "MIATNB") self.assertIsNotNone(entry) self.assertEqual("MIATNB", entry.name) self.assertIn("G", entry.encoding) self.assertIn("R", entry.encoding) entry = manager.get_namespace_entry(HGNC_URL, "MIA") self.assertIsNotNone(entry) self.assertEqual("MIA", entry.name) self.assertIn("G", entry.encoding) self.assertIn("P", entry.encoding) self.assertIn("R", entry.encoding) @mock_bel_resources def test_insert_namespace_persistent(self, mock_get): self.assertEqual(0, self.manager.count_namespaces()) self.assertEqual(0, self.manager.count_namespace_entries()) self.manager.get_or_create_namespace(HGNC_URL) self._help_check_hgnc(self.manager) self.manager.get_or_create_namespace(HGNC_URL) self._help_check_hgnc(self.manager) self.manager.drop_namespace_by_url(HGNC_URL) self.assertEqual(0, self.manager.count_namespaces()) self.assertEqual(0, self.manager.count_namespace_entries()) @mock_bel_resources def test_insert_annotation(self, mock_get): self.assertEqual(0, self.manager.count_annotations()) self.assertEqual(0, self.manager.count_annotation_entries()) annotation = self.manager.get_or_create_annotation(CELL_LINE_URL) self.assertIsNotNone(annotation) self.assertEqual(CELL_LINE_URL, annotation.url) entry = self.manager.get_namespace_entry(CELL_LINE_URL, "1321N1 cell") self.assertEqual("1321N1 cell", entry.name) self.assertEqual("CLO_0001072", entry.identifier) entries = self.manager.get_annotation_entries_by_names(CELL_LINE_URL, ["1321N1 cell"]) self.assertIsNotNone(entries) self.assertEqual(1, len(entries)) entry = entries[0] self.assertEqual("1321N1 cell", entry.name) self.assertEqual("CLO_0001072", entry.identifier) graph = BELGraph() graph.annotation_url[CELL_LINE_KEYWORD] = CELL_LINE_URL data = { ANNOTATIONS: { CELL_LINE_KEYWORD: { "1321N1 cell": True, }, }, } annotations_iter = dict(self.manager._iter_from_annotations_dict(graph, annotations_dict=data[ANNOTATIONS])) self.assertIn(CELL_LINE_URL, annotations_iter) self.assertIn("1321N1 cell", annotations_iter[CELL_LINE_URL]) entries = self.manager._get_annotation_entries_from_data(graph, data) self.assertIsNotNone(entries) self.assertEqual(1, len(entries)) entry = entries[0] self.assertEqual("1321N1 cell", entry.name) self.assertEqual("CLO_0001072", entry.identifier) self.manager.drop_namespace_by_url(CELL_LINE_URL) self.assertEqual(0, self.manager.count_annotations()) self.assertEqual(0, self.manager.count_annotation_entries()) def test_get_annotation_entries_no_data(self): """Test that if there's no ANNOTATIONS entry in the data, it just returns none.""" graph = BELGraph() data = {} entries = self.manager._get_annotation_entries_from_data(graph, data) self.assertIsNone(entries) pybel-0.15.5/tests/test_manager/test_manager_drop.py000066400000000000000000000135721426625374700226270ustar00rootroot00000000000000# -*- coding: utf-8 -*- import json from pybel import BELGraph from pybel.constants import INCREASES, RELATION from pybel.dsl import hgnc from pybel.manager.models import Edge, Namespace, NamespaceEntry, Network, Node from pybel.testing.cases import TemporaryCacheMixin from pybel.testing.mocks import mock_bel_resources from pybel.testing.utils import make_dummy_annotations, make_dummy_namespaces, n from tests.constants import test_citation_dict, test_evidence_text yfg1 = hgnc(identifier="1", name="YFG1") yfg2 = hgnc(identifier="2", name="YFG1") yfg3 = hgnc(identifier="3", name="YFG3") def make_increase_edge(u, v): bel = "{} {} {}".format(u.as_bel(), INCREASES, v.as_bel()) data = json.dumps({RELATION: INCREASES}) assert data return Edge(source=u, target=v, relation=INCREASES, bel=bel, data=data) class TestReconstituteNodeTuples(TemporaryCacheMixin): @mock_bel_resources def test_simple(self, mock): """This test checks that the network can be added and dropped""" graph = BELGraph(name="test", version="0.0.0") graph.annotation_pattern["Disease"] = ".*" graph.annotation_pattern["Cell"] = ".*" graph.add_increases( yfg1, yfg2, evidence=test_evidence_text, citation=test_citation_dict, annotations={ "Disease": {"Disease1": True}, "Cell": {"Cell1": True}, }, ) make_dummy_namespaces(self.manager, graph) make_dummy_annotations(self.manager, graph) network = self.manager.insert_graph(graph) self.manager.drop_network_by_id(network.id) class TestCascades(TemporaryCacheMixin): def setUp(self): super(TestCascades, self).setUp() self.n1 = Node._start_from_base_entity(yfg1) self.n2 = Node._start_from_base_entity(yfg2) self.n3 = Node._start_from_base_entity(yfg3) self.e1 = make_increase_edge(self.n1, self.n2) self.e2 = make_increase_edge(self.n2, self.n3) self.e3 = make_increase_edge(self.n1, self.n3) self.g1 = Network(name=n(), version=n(), edges=[self.e1, self.e2, self.e3]) self.g2 = Network(name=n(), version=n(), edges=[self.e1]) self.manager.session.add_all([self.n1, self.n2, self.n3, self.e1, self.e2, self.e3, self.g1, self.g2]) self.manager.session.commit() self.assertEqual(3, self.manager.count_nodes()) self.assertEqual(3, self.manager.count_edges()) self.assertEqual(2, self.manager.count_networks()) def test_drop_node(self): """Makes sure that when a node gets dropped, its in-edges AND out-edges also do""" self.manager.session.delete(self.n2) self.manager.session.commit() self.assertEqual(2, self.manager.count_nodes()) self.assertEqual(1, self.manager.count_edges()) self.assertEqual(2, self.manager.count_networks()) self.assertEqual(1, self.g1.edges.count()) self.assertEqual(0, self.g2.edges.count()) def test_drop_edge(self): """When an edge gets dropped, make sure the network doesn't have as many edges, but nodes get to stay""" self.manager.session.delete(self.e1) self.manager.session.commit() self.assertEqual(3, self.manager.count_nodes()) self.assertEqual(2, self.manager.count_edges()) self.assertEqual(2, self.manager.count_networks()) self.assertEqual(2, self.g1.edges.count()) self.assertEqual(0, self.g2.edges.count()) def test_get_orphan_edges(self): edges = [result.edge_id for result in self.manager.query_singleton_edges_from_network(self.g1)] self.assertEqual(2, len(edges)) self.assertIn(self.e2.id, edges) self.assertIn(self.e3.id, edges) def test_drop_network_1(self): """When a network gets dropped, drop all of the edges if they don't appear in other networks""" self.manager.drop_network(self.g1) self.assertEqual(3, self.manager.count_nodes()) self.assertEqual(1, self.manager.count_edges()) self.assertEqual(1, self.manager.count_networks()) self.assertEqual(1, self.g2.edges.count()) def test_drop_network_2(self): """When a network gets dropped, drop all of the edges if they don't appear in other networks""" self.manager.drop_network(self.g2) self.assertEqual(3, self.manager.count_nodes()) self.assertEqual(3, self.manager.count_edges()) self.assertEqual(1, self.manager.count_networks()) self.assertEqual(3, self.g1.edges.count()) def test_drop_all_networks(self): """When all networks are dropped, make sure all the edges and network_edge mappings are gone too""" self.manager.drop_networks() self.assertEqual(0, self.manager.count_edges()) self.assertEqual(0, self.manager.count_networks()) def test_drop_modification(self): """Don't let this happen""" def test_drop_property(self): """Don't let this happen""" def test_drop_namespace(self): keyword, url = n(), n() namespace = Namespace(keyword=keyword, url=url) self.manager.session.add(namespace) n_entries = 5 for _ in range(n_entries): entry = NamespaceEntry(name=n(), namespace=namespace) self.manager.session.add(entry) self.manager.session.commit() self.assertEqual(1, self.manager.count_namespaces(), msg="Should have one namespace") self.assertEqual( n_entries, self.manager.count_namespace_entries(), msg="Should have {} entries".format(n_entries), ) self.manager.drop_namespace_by_url(url) self.assertEqual(0, self.manager.count_namespaces(), msg="Should have no namespaces") self.assertEqual( 0, self.manager.count_namespace_entries(), msg="Entries should have been dropped", ) pybel-0.15.5/tests/test_manager/test_manager_graph.py000066400000000000000000001312721426625374700227620ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for manager functions handling BEL networks.""" import time import unittest from collections import Counter from random import randint from pybel import BELGraph, from_bel_script, from_database, to_database from pybel.constants import ( ABUNDANCE, BIOPROCESS, CITATION_TYPE_PUBMED, DECREASES, HAS_PRODUCT, HAS_REACTANT, IDENTIFIER, INCREASES, METADATA_NAME, METADATA_VERSION, MIRNA, NAMESPACE, PART_OF, PATHOLOGY, PROTEIN, RELATION, ) from pybel.dsl import ( BaseEntity, ComplexAbundance, CompositeAbundance, EnumeratedFusionRange, Fragment, Gene, GeneFusion, GeneModification, Hgvs, NamedComplexAbundance, Pathology, Protein, ProteinModification, Reaction, activity, degradation, location, secretion, translocation, ) from pybel.dsl.namespaces import chebi, hgnc, mirbase from pybel.examples import ras_tloc_graph, sialic_acid_graph from pybel.language import Entity from pybel.manager import models from pybel.manager.models import Author, Citation, Edge, Evidence, NamespaceEntry, Node from pybel.testing.cases import ( FleetingTemporaryCacheMixin, TemporaryCacheClsMixin, TemporaryCacheMixin, ) from pybel.testing.constants import test_bel_simple from pybel.testing.mocks import mock_bel_resources from pybel.testing.utils import make_dummy_annotations, make_dummy_namespaces, n from tests.constants import ( BelReconstitutionMixin, akt1, casp8, egfr, expected_test_simple_metadata, fadd, test_citation_dict, test_evidence_text, ) fos = hgnc(name="FOS") jun = hgnc(name="JUN") mirna_1 = mirbase(name=n()) mirna_2 = mirbase(name=n()) pathology_1 = Pathology("DO", n()) ap1_complex = ComplexAbundance([fos, jun]) egfr_dimer = ComplexAbundance([egfr, egfr]) yfg_data = hgnc(name="YFG") e2f4_data = hgnc(name="E2F4") bound_ap1_e2f4 = ComplexAbundance([ap1_complex, e2f4_data]) superoxide = chebi(name="superoxide") hydrogen_peroxide = chebi(name="hydrogen peroxide") oxygen = chebi(name="oxygen") superoxide_decomposition = Reaction(reactants=[superoxide], products=[hydrogen_peroxide, oxygen]) def assert_unqualified_edge(test_case, u: BaseEntity, v: BaseEntity, rel: str) -> None: """Assert there's only one edge and get the data for it""" test_case.assertIn(u, test_case.graph) test_case.assertIn(v, test_case.graph[u]) edges = list(test_case.graph[u][v].values()) test_case.assertEqual(1, len(edges)) data = edges[0] test_case.assertEqual(rel, data[RELATION]) class TestNetworkCache(BelReconstitutionMixin, FleetingTemporaryCacheMixin): def test_get_network_missing(self): network = self.manager.get_most_recent_network_by_name("This network is not here") self.assertIsNone(network) def test_get_graph_missing(self): network = self.manager.get_graph_by_most_recent("This network is not here") self.assertIsNone(network) @mock_bel_resources def test_reload(self, mock_get): """Test that a graph with the same name and version can't be added twice.""" graph = sialic_acid_graph.copy() self.assertEqual("1.0.0", graph.version) to_database(graph, manager=self.manager) time.sleep(1) self.assertEqual(1, self.manager.count_networks()) networks = self.manager.list_networks() self.assertEqual(1, len(networks)) network = networks[0] self.assertEqual(graph.name, network.name) self.assertEqual(graph.version, network.version) self.assertEqual(graph.description, network.description) reconstituted = self.manager.get_graph_by_name_version(graph.name, graph.version) self.assertIsInstance(reconstituted, BELGraph) self.assertEqual(graph.nodes(data=True), reconstituted.nodes(data=True)) # self.bel_thorough_reconstituted(reconstituted) self.assertEqual(1, self.manager.count_networks()) graph_copy = graph.copy() graph_copy.version = "1.0.1" network_copy = self.manager.insert_graph(graph_copy) time.sleep(1) # Sleep so the first graph always definitely goes in first self.assertNotEqual(network.id, network_copy.id) self.assertTrue(self.manager.has_name_version(graph_copy.name, graph_copy.version)) self.assertFalse(self.manager.has_name_version("wrong name", "0.1.2")) self.assertFalse(self.manager.has_name_version(graph_copy.name, "0.1.2")) self.assertFalse(self.manager.has_name_version("wrong name", graph_copy.version)) self.assertEqual(2, self.manager.count_networks()) self.assertEqual("1.0.1", self.manager.get_most_recent_network_by_name(graph.name).version) query_ids = {-1, network.id, network_copy.id} query_networks_result = self.manager.get_networks_by_ids(query_ids) self.assertEqual(2, len(query_networks_result)) self.assertEqual( {network.id, network_copy.id}, {network.id for network in query_networks_result}, ) expected_versions = {"1.0.1", "1.0.0"} self.assertEqual(expected_versions, set(self.manager.get_network_versions(graph.name))) exact_name_version = from_database(graph.name, graph.version, manager=self.manager) self.assertEqual(graph.name, exact_name_version.name) self.assertEqual(graph.version, exact_name_version.version) exact_name_version = from_database(graph.name, "1.0.1", manager=self.manager) self.assertEqual(graph.name, exact_name_version.name) self.assertEqual("1.0.1", exact_name_version.version) most_recent_version = from_database(graph.name, manager=self.manager) self.assertEqual(graph.name, most_recent_version.name) self.assertEqual("1.0.1", exact_name_version.version) recent_networks = list(self.manager.list_recent_networks()) # just try it to see if it fails self.assertIsNotNone(recent_networks) self.assertEqual([(network.name, "1.0.1")], [(n.name, n.version) for n in recent_networks]) self.assertEqual("1.0.1", recent_networks[0].version) @mock_bel_resources def test_upload_with_tloc(self, mock_get): """Test that the RAS translocation example graph can be uploaded.""" make_dummy_namespaces(self.manager, ras_tloc_graph) to_database(ras_tloc_graph, manager=self.manager) class TestTemporaryInsertNetwork(TemporaryCacheMixin): def test_insert_with_list_annotations(self): """This test checks that graphs that contain list annotations, which aren't cached, can be loaded properly into the database.""" graph = BELGraph(name="test", version="0.0.0") graph.annotation_list["TEST"] = {"a", "b", "c"} graph.add_increases( fos, jun, evidence=test_evidence_text, citation=test_citation_dict, annotations={"TEST": "a"}, ) make_dummy_namespaces(self.manager, graph) with mock_bel_resources: self.manager.insert_graph(graph) # TODO check that the database doesn't have anything for TEST in it class TestTypedQuery(TemporaryCacheMixin): def setUp(self): super().setUp() graph = BELGraph(name="test", version="0.0.0") graph.annotation_list["TEST"] = {"a", "b", "c"} graph.add_positive_correlation( mirna_1, pathology_1, evidence=n(), citation=n(), ) graph.add_negative_correlation( mirna_2, pathology_1, evidence=n(), citation=n(), ) make_dummy_namespaces(self.manager, graph) make_dummy_annotations(self.manager, graph) with mock_bel_resources: self.manager.insert_graph(graph) def test_query_edge_source_type(self): rv = self.manager.query_edges(source_function=MIRNA).all() self.assertEqual(2, len(rv)) rv = self.manager.query_edges(target_function=PATHOLOGY).all() self.assertEqual(2, len(rv)) class TestQuery(TemporaryCacheMixin): def setUp(self): super(TestQuery, self).setUp() graph = BELGraph(name="test", version="0.0.0") graph.annotation_list["TEST"] = {"a", "b", "c"} graph.add_increases( fos, jun, evidence=test_evidence_text, citation=test_citation_dict, annotations={"TEST": "a"}, ) make_dummy_namespaces(self.manager, graph) make_dummy_annotations(self.manager, graph) with mock_bel_resources: self.manager.insert_graph(graph) def test_query_node_bel_1(self): rv = self.manager.query_nodes(bel="p(HGNC:FOS)").all() self.assertEqual(1, len(rv)) self.assertEqual(fos, rv[0].to_json()) def test_query_node_bel_2(self): rv = self.manager.query_nodes(bel="p(HGNC:JUN)").all() self.assertEqual(1, len(rv)) self.assertEqual(jun, rv[0].to_json()) def test_query_node_namespace_wildcard(self): rv = self.manager.query_nodes(namespace="HG%").all() self.assertEqual(2, len(rv)) self.assertTrue(any(x.to_json() == fos for x in rv)) self.assertTrue(any(x.to_json() == jun for x in rv)) def test_query_node_name_wildcard(self): rv = self.manager.query_nodes(name="%J%").all() self.assertEqual(1, len(rv), 1) self.assertEqual(jun, rv[0].to_json()) def test_query_node_type(self): rv = self.manager.query_nodes(type=PROTEIN).all() self.assertEqual(2, len(rv)) def test_query_node_type_missing(self): rv = self.manager.query_nodes(type=ABUNDANCE).all() self.assertEqual(0, len(rv)) def test_query_edge_by_bel(self): rv = self.manager.query_edges(bel="p(HGNC:FOS) increases p(HGNC:JUN)").all() self.assertEqual(1, len(rv)) def test_query_edge_by_relation_wildcard(self): # relation like, data increased_list = self.manager.query_edges(relation="increase%").all() self.assertEqual(1, len(increased_list)) # self.assertIn(..., increased_list) def test_query_edge_by_evidence_wildcard(self): # evidence like, data evidence_list = self.manager.search_edges_with_evidence(evidence="%3%") self.assertEqual(len(evidence_list), 0) evidence_list = self.manager.search_edges_with_evidence(evidence="%Twit%") self.assertEqual(len(evidence_list), 1) def test_query_edge_by_mixed_no_result(self): # no result # FIXME what should this return empty_list = self.manager.query_edges(source="p(HGNC:FADD)", relation=DECREASES) self.assertEqual(len(empty_list), 0) def test_query_edge_by_mixed(self): # source, relation, data source_list = self.manager.query_edges(source="p(HGNC:FOS)", relation=INCREASES).all() self.assertEqual(len(source_list), 1) def test_query_edge_by_source_function(self): edges = self.manager.query_edges(source_function=PROTEIN).all() self.assertEqual(1, len(edges), msg="Wrong number of edges: {}".format(edges)) edges = self.manager.query_edges(source_function=BIOPROCESS).all() self.assertEqual(0, len(edges), msg="Wrong number of edges: {}".format(edges)) def test_query_edge_by_target_function(self): edges = self.manager.query_edges(target_function=PROTEIN).all() self.assertEqual(1, len(edges), msg="Wrong number of edges: {}".format(edges)) edges = self.manager.query_edges(target_function=PATHOLOGY).all() self.assertEqual(0, len(edges), msg="Wrong number of edges: {}".format(edges)) def test_query_citation_by_type(self): rv = self.manager.query_citations(db=CITATION_TYPE_PUBMED) self.assertEqual(1, len(rv)) self.assertTrue(rv[0].is_pubmed) self.assertFalse(rv[0].is_enriched) def test_query_citaiton_by_reference(self): rv = self.manager.query_citations(db=CITATION_TYPE_PUBMED, db_id=test_citation_dict[IDENTIFIER]) self.assertEqual(1, len(rv)) self.assertTrue(rv[0].is_pubmed) self.assertFalse(rv[0].is_enriched) self.assertEqual(test_citation_dict, rv[0].to_json()) @unittest.skip def test_query_by_author_wildcard(self): author_list = self.manager.query_citations(author="Example%") self.assertEqual(len(author_list), 1) @unittest.skip def test_query_by_name(self): # type, name, data name_dict_list = self.manager.query_citations( db=CITATION_TYPE_PUBMED, name="That other article from last week", ) self.assertEqual(len(name_dict_list), 1) # self.assertIn(..., name_dict_list) @unittest.skip def test_query_by_name_wildcard(self): # type, name like, data name_dict_list2 = self.manager.query_citations(db=CITATION_TYPE_PUBMED, name="%article from%") self.assertEqual(len(name_dict_list2), 2) # self.assertIn(..., name_dict_list2) # self.assertIn(..., name_dict_list2) class TestEnsure(TemporaryCacheMixin): def test_get_or_create_citation(self): reference = str(randint(1, 1000000)) citation_dict = { NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: reference, } citation = self.manager.get_or_create_citation(**citation_dict) self.manager.session.commit() self.assertIsInstance(citation, Citation) self.assertEqual(citation_dict, citation.to_json()) citation_reloaded_from_reference = self.manager.get_citation_by_pmid(reference) self.assertIsNotNone(citation_reloaded_from_reference) self.assertEqual(citation_dict, citation_reloaded_from_reference.to_json()) citation_reloaded_from_dict = self.manager.get_or_create_citation(**citation_dict) self.assertIsNotNone(citation_reloaded_from_dict) self.assertEqual(citation_dict, citation_reloaded_from_dict.to_json()) citation_reloaded_from_reference = self.manager.get_citation_by_reference( citation_dict[NAMESPACE], citation_dict[IDENTIFIER], ) self.assertIsNotNone(citation_reloaded_from_reference) self.assertEqual(citation_dict, citation_reloaded_from_reference.to_json()) def test_get_or_create_evidence(self): citation_db, citation_ref = CITATION_TYPE_PUBMED, str(randint(1, 1000000)) basic_citation = self.manager.get_or_create_citation(namespace=citation_db, identifier=citation_ref) utf8_test_evidence = "Yes, all the information is true! This contains a unicode alpha: α" evidence = self.manager.get_or_create_evidence(basic_citation, utf8_test_evidence) self.assertIsInstance(evidence, Evidence) self.assertIn(evidence, self.manager.object_cache_evidence.values()) # Objects cached? reloaded_evidence = self.manager.get_or_create_evidence(basic_citation, utf8_test_evidence) self.assertEqual(evidence, reloaded_evidence) def test_get_or_create_author(self): """This tests getting or creating author with unicode characters""" author_name = "Jαckson M" # Create author = self.manager.get_or_create_author(author_name) self.manager.session.commit() self.assertIsInstance(author, Author) self.assertEqual(author.name, author_name) author_from_name = self.manager.get_author_by_name(author_name) self.assertIsNotNone(author_from_name) self.assertEqual(author_name, author_from_name.name) # Get author_from_get = self.manager.get_or_create_author(author_name) self.assertEqual(author.name, author_from_get.name) self.assertEqual(author, author_from_get) class TestEdgeStore(TemporaryCacheClsMixin, BelReconstitutionMixin): """Tests that the cache can be queried.""" @classmethod def setUpClass(cls): """Set up the class with a BEL graph and its corresponding SQLAlchemy model.""" super().setUpClass() with mock_bel_resources: cls.graph = from_bel_script(test_bel_simple, manager=cls.manager, disallow_nested=False) cls.network = cls.manager.insert_graph(cls.graph) def test_citations(self): citations = self.manager.session.query(Citation).all() self.assertEqual(2, len(citations), msg="Citations: {}".format(citations)) citation_db_ids = {"123455", "123456"} self.assertEqual(citation_db_ids, {citation.db_id for citation in citations}) def test_evidences(self): evidences = self.manager.session.query(Evidence).all() self.assertEqual(3, len(evidences)) evidences_texts = {"Evidence 1 w extra notes", "Evidence 2", "Evidence 3"} self.assertEqual(evidences_texts, {evidence.text for evidence in evidences}) def test_nodes(self): nodes = self.manager.session.query(Node).all() self.assertEqual(4, len(nodes)) def test_edges(self): edges = self.manager.session.query(Edge).all() x = Counter((e.source.bel, e.target.bel) for e in edges) d = { (akt1.as_bel(), egfr.as_bel()): 1, (egfr.as_bel(), fadd.as_bel()): 1, (egfr.as_bel(), casp8.as_bel()): 1, (fadd.as_bel(), casp8.as_bel()): 1, (akt1.as_bel(), casp8.as_bel()): 1, # two way association (casp8.as_bel(), akt1.as_bel()): 1, # two way association } self.assertEqual(dict(x), d) network_edge_associations = ( self.manager.session.query(models.network_edge).filter_by(network_id=self.network.id).all() ) self.assertEqual( {network_edge_association.edge_id for network_edge_association in network_edge_associations}, {edge.id for edge in edges}, ) def test_reconstitute(self): g2 = self.manager.get_graph_by_name_version( expected_test_simple_metadata[METADATA_NAME], expected_test_simple_metadata[METADATA_VERSION], ) self.bel_simple_reconstituted(g2) class TestAddNodeFromData(unittest.TestCase): def setUp(self): self.graph = BELGraph() def test_simple(self): self.graph.add_node_from_data(yfg_data) self.assertIn(yfg_data, self.graph) self.assertEqual(1, self.graph.number_of_nodes()) def test_single_variant(self): node_data = Gene("HGNC", "AKT1", variants=Hgvs("p.Phe508del")) node_parent_data = node_data.get_parent() self.graph.add_node_from_data(node_data) self.assertIn(node_data, self.graph) self.assertIn(node_parent_data, self.graph) self.assertEqual(2, self.graph.number_of_nodes()) self.assertEqual(1, self.graph.number_of_edges()) def test_multiple_variants(self): node_data = Gene("HGNC", "AKT1", variants=[Hgvs("p.Phe508del"), Hgvs("p.Phe509del")]) node_parent_data = node_data.get_parent() node_parent_tuple = node_parent_data self.graph.add_node_from_data(node_data) self.assertIn(node_data, self.graph) self.assertIn(node_parent_tuple, self.graph) self.assertEqual(2, self.graph.number_of_nodes()) self.assertEqual(1, self.graph.number_of_edges()) def test_fusion(self): node_data = GeneFusion( partner_5p=Gene("HGNC", "TMPRSS2"), partner_3p=Gene("HGNC", "ERG"), range_5p=EnumeratedFusionRange("c", 1, 79), range_3p=EnumeratedFusionRange("c", 312, 5034), ) node_data = node_data self.graph.add_node_from_data(node_data) self.assertIn(node_data, self.graph) self.assertEqual(1, self.graph.number_of_nodes()) self.assertEqual(0, self.graph.number_of_edges()) def test_composite(self): il23 = NamedComplexAbundance(namespace="GO", name="interleukin-23 complex") il6 = Protein(namespace="HGNC", name="IL6") node_data = CompositeAbundance([il23, il6]) self.graph.add_node_from_data(node_data) self.assertIn(node_data, self.graph) self.assertEqual(3, self.graph.number_of_nodes()) self.assertIn(il6, self.graph, msg="Nodes:\n".format("\n".join(map(str, self.graph)))) self.assertIn(il23, self.graph) self.assertEqual(2, self.graph.number_of_edges()) self.assertIn(node_data, self.graph[il6]) edges = list(self.graph[il6][node_data].values()) self.assertEqual(1, len(edges)) data = edges[0] self.assertEqual(PART_OF, data[RELATION]) self.assertIn(node_data, self.graph[il23]) edges = list(self.graph[il23][node_data].values()) self.assertEqual(1, len(edges)) data = edges[0] self.assertEqual(PART_OF, data[RELATION]) def test_reaction(self): self.graph.add_node_from_data(superoxide_decomposition) self.assertIn(superoxide_decomposition, self.graph) self.assertEqual(4, self.graph.number_of_nodes()) self.assertEqual(3, self.graph.number_of_edges()) assert_unqualified_edge(self, superoxide_decomposition, superoxide, HAS_REACTANT) assert_unqualified_edge(self, superoxide_decomposition, hydrogen_peroxide, HAS_PRODUCT) assert_unqualified_edge(self, superoxide_decomposition, oxygen, HAS_PRODUCT) def test_complex(self): node = ComplexAbundance(members=[fos, jun]) self.graph.add_node_from_data(node) self.assertIn(node, self.graph) self.assertEqual(3, self.graph.number_of_nodes()) self.assertEqual(2, self.graph.number_of_edges()) assert_unqualified_edge(self, fos, node, PART_OF) assert_unqualified_edge(self, jun, node, PART_OF) def test_dimer_complex(self): """Tests what happens if a BEL statement complex(p(X), p(X)) is added""" self.graph.add_node_from_data(egfr_dimer) self.assertIn(egfr, self.graph) self.assertIn(egfr_dimer, self.graph) self.assertEqual(2, self.graph.number_of_nodes()) self.assertEqual(1, self.graph.number_of_edges()) assert_unqualified_edge(self, egfr, egfr_dimer, PART_OF) def test_nested_complex(self): """Checks what happens if a theoretical BEL statement `complex(p(X), complex(p(Y), p(Z)))` is added""" self.graph.add_node_from_data(bound_ap1_e2f4) self.assertIn(bound_ap1_e2f4, self.graph) self.assertEqual(5, self.graph.number_of_nodes()) self.assertIn(fos, self.graph) self.assertIn(jun, self.graph) self.assertIn(e2f4_data, self.graph) self.assertIn(ap1_complex, self.graph) self.assertEqual(4, self.graph.number_of_edges()) assert_unqualified_edge(self, fos, ap1_complex, PART_OF) assert_unqualified_edge(self, jun, ap1_complex, PART_OF) assert_unqualified_edge(self, ap1_complex, bound_ap1_e2f4, PART_OF) assert_unqualified_edge(self, e2f4_data, bound_ap1_e2f4, PART_OF) class TestReconstituteNodeTuples(TemporaryCacheMixin): """Tests the ability to go from PyBEL to relational database""" def _help_reconstitute(self, node: BaseEntity, number_nodes: int, number_edges: int): """Help test the round-trip conversion from PyBEL data dictionary to node model.""" self.assertIsInstance(node, BaseEntity) graph = BELGraph(name="test", version="0.0.0") graph.add_node_from_data(node) make_dummy_namespaces(self.manager, graph) self.manager.insert_graph(graph) self.assertEqual(number_nodes, self.manager.count_nodes()) self.assertEqual(number_edges, self.manager.count_edges()) node_model = self.manager.get_or_create_node(graph, node) self.assertEqual(node.md5, node_model.md5) self.manager.session.commit() self.assertEqual(node, node_model.to_json()) self.assertEqual(node, self.manager.get_dsl_by_hash(node.md5)) @mock_bel_resources def test_simple(self, mock): self._help_reconstitute(yfg_data, 1, 0) @mock_bel_resources def test_Hgvs(self, mock): node_data = Gene(namespace="HGNC", name="AKT1", variants=Hgvs("p.Phe508del")) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_fragment_unspecified(self, mock): dummy_namespace = n() dummy_name = n() node_data = Protein(namespace=dummy_namespace, name=dummy_name, variants=[Fragment()]) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_fragment_specified(self, mock): dummy_namespace = n() dummy_name = n() node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[Fragment(start=5, stop=8)], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_fragment_specified_start_only(self, mock): dummy_namespace = n() dummy_name = n() node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[Fragment(start=5, stop="*")], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_fragment_specified_end_only(self, mock): dummy_namespace = n() dummy_name = n() node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[Fragment(start="*", stop=1000)], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_gmod_custom(self, mock): """Tests a gene modification that uses a non-default namespace""" dummy_namespace = "HGNC" dummy_name = "AKT1" dummy_mod_namespace = "GO" dummy_mod_name = "DNA Methylation" node_data = Gene( namespace=dummy_namespace, name=dummy_name, variants=[GeneModification(name=dummy_mod_name, namespace=dummy_mod_namespace)], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_gmod_default(self, mock): """Test a gene modification that uses the BEL default namespace.""" dummy_namespace = n() dummy_name = n() node_data = Gene( namespace=dummy_namespace, name=dummy_name, variants=[GeneModification("Me")], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_pmod_default_simple(self, mock): dummy_namespace = n() dummy_name = n() node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[ProteinModification("Me")], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_pmod_custom_simple(self, mock): dummy_namespace = "HGNC" dummy_name = "AKT1" dummy_mod_namespace = "GO" dummy_mod_name = "Protein phosphorylation" node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[ProteinModification(name=dummy_mod_name, namespace=dummy_mod_namespace)], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_pmod_default_with_residue(self, mock): dummy_namespace = n() dummy_name = n() node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[ProteinModification("Me", code="Ser")], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_pmod_custom_with_residue(self, mock): dummy_namespace = "HGNC" dummy_name = "AKT1" dummy_mod_namespace = "GO" dummy_mod_name = "Protein phosphorylation" node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[ProteinModification(name=dummy_mod_name, namespace=dummy_mod_namespace, code="Ser")], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_pmod_default_full(self, mock): dummy_namespace = n() dummy_name = n() node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[ProteinModification("Me", code="Ser", position=5)], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_pmod_custom_full(self, mock): dummy_namespace = "HGNC" dummy_name = "AKT1" dummy_mod_namespace = "GO" dummy_mod_name = "Protein phosphorylation" node_data = Protein( namespace=dummy_namespace, name=dummy_name, variants=[ ProteinModification( name=dummy_mod_name, namespace=dummy_mod_namespace, code="Ser", position=5, ) ], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_multiple_variants(self, mock): node_data = Gene( namespace="HGNC", name="AKT1", variants=[Hgvs("p.Phe508del"), Hgvs("p.Phe509del")], ) self._help_reconstitute(node_data, 2, 1) @mock_bel_resources def test_fusion_specified(self, mock): node_data = GeneFusion( Gene("HGNC", "TMPRSS2"), Gene("HGNC", "ERG"), EnumeratedFusionRange("c", 1, 79), EnumeratedFusionRange("c", 312, 5034), ) self._help_reconstitute(node_data, 1, 0) @mock_bel_resources def test_fusion_unspecified(self, mock): node_data = GeneFusion( Gene("HGNC", "TMPRSS2"), Gene("HGNC", "ERG"), ) self._help_reconstitute(node_data, 1, 0) @mock_bel_resources def test_composite(self, mock): interleukin_23_complex = NamedComplexAbundance("GO", "interleukin-23 complex") il6 = hgnc(name="IL6") interleukin_23_and_il6 = CompositeAbundance([interleukin_23_complex, il6]) self._help_reconstitute(interleukin_23_and_il6, 3, 2) @mock_bel_resources def test_reaction(self, mock): self._help_reconstitute(superoxide_decomposition, 4, 3) @mock_bel_resources def test_complex(self, mock): self._help_reconstitute(ap1_complex, 3, 2) @mock_bel_resources def test_nested_complex(self, mock): self._help_reconstitute(bound_ap1_e2f4, 5, 4) class TestReconstituteEdges(TemporaryCacheMixin): """This class tests that edges with varying properties can be added and extracted losslessly""" def setUp(self): """Creates a unit test with a manager and graph""" super().setUp() self.graph = BELGraph(name=n(), version=n()) self.graph.annotation_pattern["Species"] = r"\d+" @mock_bel_resources def test_translocation_default(self, mock): """This test checks that a translocation gets in the database properly""" self.graph.add_increases( Protein(name="F2", namespace="HGNC"), Protein(name="EDN1", namespace="HGNC"), evidence="In endothelial cells, ET-1 secretion is detectable under basal conditions, whereas thrombin " "induces its secretion.", citation="10473669", source_modifier=secretion(), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count(), msg="Missing one or both of the nodes.") self.assertEqual(1, network.edges.count(), msg="Missing the edge") # edge = network.edges.first() # self.assertEqual(2, edge.properties.count()) @mock_bel_resources def test_subject_translocation_custom_to_loc(self, mock): self.graph.add_increases( Protein(name="F2", namespace="HGNC"), Protein(name="EDN1", namespace="HGNC"), evidence="In endothelial cells, ET-1 secretion is detectable under basal conditions, whereas thrombin induces its secretion.", citation="10473669", source_modifier=translocation( from_loc=Entity(namespace="TEST", name="A"), to_loc=Entity(namespace="GO", name="extracellular space"), ), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) edge = network.edges.first() # self.assertEqual(2, edge.properties.count()) @mock_bel_resources def test_subject_activity_default(self, mock): p1_name = n() p2_name = n() self.graph.add_increases( Protein(name=p1_name, namespace="HGNC"), Protein(name=p2_name, namespace="HGNC"), evidence=n(), citation=n(), source_modifier=activity("kin"), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count(), msg="number of nodes") self.assertEqual(1, network.edges.count(), msg="number of edges") kin_list = self.manager.session.query(NamespaceEntry).filter(NamespaceEntry.name == "kinase activity").all() self.assertEqual(1, len(kin_list), msg="number of kinase NamespaceEntrys") kin = list(kin_list)[0] self.assertEqual("kinase activity", kin.name) @mock_bel_resources def test_subject_activity_custom(self, mock): p1_name = n() p2_name = n() dummy_activity_namespace = n() dummy_activity_name = n() self.graph.add_increases( Protein(name=p1_name, namespace="HGNC"), Protein(name=p2_name, namespace="HGNC"), evidence=n(), citation=n(), source_modifier=activity(name=dummy_activity_name, namespace=dummy_activity_namespace), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) kin_list = self.manager.session.query(NamespaceEntry).filter(NamespaceEntry.name == dummy_activity_name).all() self.assertEqual(1, len(kin_list)) kin = list(kin_list)[0] self.assertEqual(dummy_activity_name, kin.name) @mock_bel_resources def test_object_activity_default(self, mock): p1_name = n() p2_name = n() self.graph.add_increases( Protein(name=p1_name, namespace="HGNC"), Protein(name=p2_name, namespace="HGNC"), evidence=n(), citation=n(), target_modifier=activity("kin"), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) kin_list = self.manager.session.query(NamespaceEntry).filter(NamespaceEntry.name == "kinase activity").all() self.assertEqual(1, len(kin_list)) kin = list(kin_list)[0] self.assertEqual("kinase activity", kin.name) @mock_bel_resources def test_object_activity_custom(self, mock): p1_name = n() p2_name = n() dummy_activity_namespace = n() dummy_activity_name = n() self.graph.add_increases( Protein(name=p1_name, namespace="HGNC"), Protein(name=p2_name, namespace="HGNC"), evidence=n(), citation=n(), target_modifier=activity(name=dummy_activity_name, namespace=dummy_activity_namespace), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) kin_list = self.manager.session.query(NamespaceEntry).filter(NamespaceEntry.name == dummy_activity_name).all() self.assertEqual(1, len(kin_list)) kin = list(kin_list)[0] self.assertEqual(dummy_activity_name, kin.name) def test_subject_degradation(self): self.graph.add_increases( Protein(name="YFG", namespace="HGNC"), Protein(name="YFG2", namespace="HGNC"), evidence=n(), citation=n(), source_modifier=degradation(), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) edge = network.edges.first() # self.assertEqual(1, edge.properties.count()) def test_object_degradation(self): self.graph.add_increases( Protein(name="YFG", namespace="HGNC"), Protein(name="YFG2", namespace="HGNC"), evidence=n(), citation=n(), target_modifier=degradation(), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) edge = network.edges.first() # self.assertEqual(1, edge.properties.count()) def test_subject_location(self): self.graph.add_increases( Protein(name="YFG", namespace="HGNC"), Protein(name="YFG2", namespace="HGNC"), evidence=n(), citation=n(), source_modifier=location(Entity(namespace="GO", name="nucleus", identifier="0005634")), ) make_dummy_namespaces(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) edge = network.edges.first() # self.assertEqual(1, edge.properties.count()) def test_mixed_1(self): """Test mixed having location and something else.""" self.graph.add_increases( Protein(namespace="HGNC", name="CDC42"), Protein(namespace="HGNC", name="PAK2"), evidence="""Summary: PAK proteins, a family of serine/threonine p21-activating kinases, include PAK1, PAK2, PAK3 and PAK4. PAK proteins are critical effectors that link Rho GTPases to cytoskeleton reorganization and nuclear signaling. They serve as targets for the small GTP binding proteins Cdc42 and Rac and have been implicated in a wide range of biological activities. PAK4 interacts specifically with the GTP-bound form of Cdc42Hs and weakly activates the JNK family of MAP kinases. PAK4 is a mediator of filopodia formation and may play a role in the reorganization of the actin cytoskeleton. Multiple alternatively spliced transcript variants encoding distinct isoforms have been found for this gene.""", citation={ NAMESPACE: "Online Resource", IDENTIFIER: "PAK4 Hs ENTREZ Gene Summary", }, annotations={"Species": "9606"}, source_modifier=activity("gtp"), target_modifier=activity("kin"), ) make_dummy_namespaces(self.manager, self.graph) make_dummy_annotations(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) edge = network.edges.first() # self.assertEqual(2, edge.properties.count()) # FIXME def test_mixed_2(self): """Tests both subject and object activity with location information as well.""" self.graph.add_directly_increases( Protein(namespace="HGNC", name="HDAC4"), Protein(namespace="HGNC", name="MEF2A"), citation="10487761", evidence=""""In the nucleus, HDAC4 associates with the myocyte enhancer factor MEF2A. Binding of HDAC4 to MEF2A results in the repression of MEF2A transcriptional activation, a function that requires the deacetylase domain of HDAC4.""", annotations={"Species": "9606"}, source_modifier=activity("cat", location=Entity(namespace="GO", name="nucleus")), target_modifier=activity("tscript", location=Entity(namespace="GO", name="nucleus")), ) make_dummy_namespaces(self.manager, self.graph) make_dummy_annotations(self.manager, self.graph) network = self.manager.insert_graph(self.graph) self.assertEqual(2, network.nodes.count()) self.assertEqual(1, network.edges.count()) edge = network.edges.first() # self.assertEqual(4, edge.properties.count()) # self.assertEqual(2, edge.properties.filter(Property.is_subject).count()) # self.assertEqual(2, edge.properties.filter(not_(Property.is_subject)).count()) class TestNoAddNode(TemporaryCacheMixin): """Tests scenarios where an instance of :class:`BELGraph` may contain edges that refer to uncached resources, and therefore should not be added to the edge store.""" @mock_bel_resources def test_regex_lookup(self, mock): # FIXME this test needs to be put somewhere else """Test that regular expression nodes get love too.""" graph = BELGraph( name="Regular Expression Test Graph", description="Help test regular expression namespaces", version="1.0.0", ) dbsnp = "dbSNP" DBSNP_PATTERN = "rs[0-9]+" graph.namespace_pattern[dbsnp] = DBSNP_PATTERN rs1234 = Gene(namespace=dbsnp, name="rs1234") rs1235 = Gene(namespace=dbsnp, name="rs1235") graph.add_node_from_data(rs1234) graph.add_node_from_data(rs1235) rs1234_hash = rs1234.md5 rs1235_hash = rs1235.md5 self.manager.insert_graph(graph) rs1234_lookup = self.manager.get_node_by_hash(rs1234_hash) self.assertIsNotNone(rs1234_lookup) self.assertEqual("Gene", rs1234_lookup.type) self.assertEqual("g(dbSNP:rs1234)", rs1234_lookup.bel) self.assertEqual(rs1234_hash, rs1234_lookup.md5) self.assertIsNotNone(rs1234_lookup.namespace_entry) self.assertEqual("rs1234", rs1234_lookup.namespace_entry.name) self.assertEqual("dbSNP", rs1234_lookup.namespace_entry.namespace.keyword) self.assertEqual(DBSNP_PATTERN, rs1234_lookup.namespace_entry.namespace.pattern) rs1235_lookup = self.manager.get_node_by_hash(rs1235_hash) self.assertIsNotNone(rs1235_lookup) self.assertEqual("Gene", rs1235_lookup.type) self.assertEqual("g(dbSNP:rs1235)", rs1235_lookup.bel) self.assertEqual(rs1235_hash, rs1235_lookup.md5) self.assertIsNotNone(rs1235_lookup.namespace_entry) self.assertEqual("rs1235", rs1235_lookup.namespace_entry.name) self.assertEqual("dbSNP", rs1235_lookup.namespace_entry.namespace.keyword) self.assertEqual(DBSNP_PATTERN, rs1235_lookup.namespace_entry.namespace.pattern) class TestEquivalentNodes(unittest.TestCase): def test_direct_has_namespace(self): graph = BELGraph() n1 = Protein(namespace="HGNC", name="CD33", identifier="1659") n2 = Protein(namespace="NOPE", name="NOPE", identifier="NOPE") graph.add_increases(n1, n2, citation=n(), evidence=n()) self.assertEqual({n1}, graph.get_equivalent_nodes(n1)) self.assertTrue(graph.node_has_namespace(n1, "HGNC")) self.assertFalse(graph.node_has_namespace(n2, "HGNC")) def test_indirect_has_namespace(self): graph = BELGraph() a = Protein(namespace="HGNC", name="CD33") b = Protein(namespace="HGNCID", identifier="1659") graph.add_equivalence(a, b) self.assertEqual({a, b}, graph.get_equivalent_nodes(a)) self.assertEqual({a, b}, graph.get_equivalent_nodes(b)) self.assertTrue(graph.node_has_namespace(a, "HGNC")) self.assertTrue(graph.node_has_namespace(b, "HGNC")) def test_triangle_has_namespace(self): graph = BELGraph() a = Protein(namespace="A", name="CD33") b = Protein(namespace="B", identifier="1659") c = Protein(namespace="C", identifier="1659") d = Protein(namespace="HGNC", identifier="1659") graph.add_equivalence(a, b) graph.add_equivalence(b, c) graph.add_equivalence(c, a) graph.add_equivalence(c, d) self.assertEqual({a, b, c, d}, graph.get_equivalent_nodes(a)) self.assertEqual({a, b, c, d}, graph.get_equivalent_nodes(b)) self.assertEqual({a, b, c, d}, graph.get_equivalent_nodes(c)) self.assertEqual({a, b, c, d}, graph.get_equivalent_nodes(d)) self.assertTrue(graph.node_has_namespace(a, "HGNC")) self.assertTrue(graph.node_has_namespace(b, "HGNC")) self.assertTrue(graph.node_has_namespace(c, "HGNC")) self.assertTrue(graph.node_has_namespace(d, "HGNC")) if __name__ == "__main__": unittest.main() pybel-0.15.5/tests/test_manager/test_manager_model.py000066400000000000000000000113511426625374700227540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """This module tests the to_json functions for all of the database models.""" import datetime import json import unittest from pybel.constants import ( CITATION_TYPE_PUBMED, IDENTIFIER, METADATA_AUTHORS, METADATA_CONTACT, METADATA_COPYRIGHT, METADATA_DESCRIPTION, METADATA_DISCLAIMER, METADATA_LICENSES, METADATA_NAME, METADATA_VERSION, NAME, NAMESPACE, NAMESPACE_DOMAIN_OTHER, ) from pybel.language import citation_dict from pybel.manager.models import Citation, Namespace, NamespaceEntry, Network from pybel.testing.utils import n class TestNetwork(unittest.TestCase): def setUp(self): self.name = n() self.version = n() self.created = datetime.datetime.utcnow() self.model = Network( name=self.name, version=self.version, created=self.created, ) self.expected = { METADATA_NAME: self.name, METADATA_VERSION: self.version, "created": str(self.created), } def test_to_json(self): model_json = self.model.to_json() self.assertIn(METADATA_NAME, model_json) self.assertEqual(self.name, model_json[METADATA_NAME]) self.assertIn(METADATA_VERSION, model_json) self.assertEqual(self.version, model_json[METADATA_VERSION]) self.assertIn("created", model_json) self.assertEqual(str(self.created), model_json["created"]) self.assertEqual(self.expected, model_json) def test_dump(self): json.dumps(self.model) def test_network(self): self.expected[METADATA_AUTHORS] = self.model.authors = n() self.assertEqual(self.expected, self.model.to_json()) self.expected[METADATA_CONTACT] = self.model.contact = n() self.assertEqual(self.expected, self.model.to_json()) self.expected[METADATA_DESCRIPTION] = self.model.description = n() self.assertEqual(self.expected, self.model.to_json()) self.expected[METADATA_COPYRIGHT] = self.model.copyright = n() self.assertEqual(self.expected, self.model.to_json()) self.expected[METADATA_DISCLAIMER] = self.model.disclaimer = n() self.assertEqual(self.expected, self.model.to_json()) self.expected[METADATA_LICENSES] = self.model.licenses = n() self.assertEqual(self.expected, self.model.to_json()) self.expected["id"] = None self.assertEqual(self.expected, self.model.to_json(include_id=True)) class TestModels(unittest.TestCase): def test_namespace_url(self): uploaded = datetime.datetime.now() model = Namespace( keyword="TEST", url="http://test.com", name="Test Namespace", domain=NAMESPACE_DOMAIN_OTHER, species="9606", version="1.0.0", author="Charles Tapley Hoyt", contact="cthoyt@gmail.com", uploaded=uploaded, ) expected = dict( keyword="TEST", url="http://test.com", name="Test Namespace", version="1.0.0", ) self.assertEqual(model.to_json(), expected) expected["id"] = model.id = 1 self.assertEqual(model.to_json(include_id=True), expected) def test_namespace_pattern(self): uploaded = datetime.datetime.now() model = Namespace( keyword="TEST", pattern=r"\w+", name="Test Namespace", domain=NAMESPACE_DOMAIN_OTHER, species="9606", version="1.0.0", author="Charles Tapley Hoyt", contact="cthoyt@gmail.com", uploaded=uploaded, ) expected = dict( keyword="TEST", pattern=r"\w+", name="Test Namespace", version="1.0.0", ) self.assertEqual(model.to_json(), expected) def test_namespace_entry(self): model = NamespaceEntry(name="entry", namespace=Namespace(keyword="test")) expected = { NAMESPACE: "test", NAME: "entry", } self.assertEqual(model.to_json(), expected) expected["id"] = model.id = 1 self.assertEqual(model.to_json(include_id=True), expected) expected[IDENTIFIER] = model.identifier = "test:00001" self.assertEqual(model.to_json(include_id=True), expected) def test_citation(self): db_id = n() model = Citation( db=CITATION_TYPE_PUBMED, db_id=db_id, ) expected = citation_dict(namespace=CITATION_TYPE_PUBMED, identifier=db_id) self.assertEqual(expected, model.to_json()) expected[NAME] = model.title = n() self.assertEqual(expected, model.to_json()) pybel-0.15.5/tests/test_manager/test_seeding.py000066400000000000000000000101101426625374700215700ustar00rootroot00000000000000# -*- coding: utf-8 -*- from pybel.examples import sialic_acid_graph from pybel.examples.sialic_acid_example import ( cd33, cd33_phosphorylated, shp2, syk, trem2, ) from pybel.manager.models import Edge, Namespace, Network from pybel.manager.query_manager import graph_from_edges from pybel.resources import CHEBI_URL, GO_URL, HGNC_URL from pybel.testing.cases import TemporaryCacheClsMixin from pybel.testing.mocks import mock_bel_resources class TestSeeding(TemporaryCacheClsMixin): """This module tests the seeding functions in the query manager.""" @classmethod def setUpClass(cls): """Add the Sialic Acid subgraph for all query tests.""" super().setUpClass() with mock_bel_resources: cls.manager.insert_graph(sialic_acid_graph) def test_namespace_existence(self): """Check the sialic acid graph has the right namespaces, and they're uploaded properly.""" ns = self.manager.session.query(Namespace).filter(Namespace.url == HGNC_URL).one() self.assertIsNotNone(ns) ns = self.manager.session.query(Namespace).filter(Namespace.url == CHEBI_URL).one() self.assertIsNotNone(ns) ns = self.manager.session.query(Namespace).filter(Namespace.url == GO_URL).one() self.assertIsNotNone(ns) def test_sialic_acid_in_node_store(self): r = "sialic acid" n = self.manager.get_namespace_entry(CHEBI_URL, r) self.assertIsNotNone(n) self.assertEqual(r, n.name) def test_network_existence(self): networks = self.manager.session.query(Network).all() self.assertEqual(1, len(networks)) def test_edge_existence(self): edges = self.manager.session.query(Edge).all() self.assertEqual(11, len(edges)) def test_seed_by_pmid(self): pmids = ["26438529"] edges = self.manager.query_edges_by_pubmed_identifiers(pmids) self.assertLessEqual(1, len(edges)) def test_seed_by_pmid_no_result(self): missing_pmids = ["11111"] edges = self.manager.query_edges_by_pubmed_identifiers(missing_pmids) self.assertEqual(0, len(edges)) def test_seed_by_induction_raise(self): """Test that seeding by induction fails when an empty list is given.""" with self.assertRaises(ValueError): self.manager.query_induction([]) def test_seed_by_induction_raise_length_one(self): """Test that seeding by induction fails when a list of length one is given.""" shp2_model = self.manager.get_node_by_dsl(shp2) with self.assertRaises(ValueError): self.manager.query_induction([shp2_model]) def test_seed_by_induction(self): """Test seeding by inducing over a list of nodes.""" shp2_model = self.manager.get_node_by_dsl(shp2) self.assertIsNotNone(shp2_model) syk_model = self.manager.get_node_by_dsl(syk) self.assertIsNotNone(syk_model) trem2_model = self.manager.get_node_by_dsl(trem2) self.assertIsNotNone(trem2_model) edges = self.manager.query_induction([shp2_model, syk_model, trem2_model]) self.assertEqual(2, len(edges)) graph = graph_from_edges(edges) self.assertEqual(3, graph.number_of_nodes()) self.assertIn(trem2, graph) self.assertIn(syk, graph) self.assertIn(shp2, graph) self.assertEqual(3, graph.number_of_nodes()) self.assertEqual(2, graph.number_of_edges()) def test_seed_by_neighbors(self): """Test seeding a graph by neighbors of a list of nodes.""" shp2_model = self.manager.get_node_by_dsl(shp2) self.assertIsNotNone(shp2_model) edges = self.manager.query_neighbors([shp2_model]) self.assertEqual(2, len(edges)) graph = graph_from_edges(edges) self.assertEqual(3, graph.number_of_edges()) self.assertEqual(4, graph.number_of_nodes()) self.assertIn(cd33_phosphorylated, graph) self.assertIn(cd33, graph) self.assertIn(syk, graph) self.assertIn(shp2, graph) self.assertEqual(3, graph.number_of_edges()) pybel-0.15.5/tests/test_parse/000077500000000000000000000000001426625374700162505ustar00rootroot00000000000000pybel-0.15.5/tests/test_parse/__init__.py000066400000000000000000000000761426625374700203640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for :mod:`pybel.parser`.""" pybel-0.15.5/tests/test_parse/test_parse_bel.py000066400000000000000000002217121426625374700216220ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the BEL parser.""" import logging import re import unittest from pybel import BELGraph from pybel.constants import ( ABUNDANCE, ACTIVITY, BIOPROCESS, CELL_SECRETION, CELL_SURFACE_EXPRESSION, COMPLEX, COMPOSITE, CONCEPT, DEGRADATION, DIRECTLY_INCREASES, DIRTY, EFFECT, FRAGMENT, FROM_LOC, FUNCTION, FUSION, FUSION_MISSING, FUSION_REFERENCE, FUSION_START, FUSION_STOP, GENE, HAS_VARIANT, HGVS, IDENTIFIER, KIND, LOCATION, MEMBERS, MIRNA, MODIFIER, NAME, NAMESPACE, PART_OF, PARTNER_3P, PARTNER_5P, PATHOLOGY, POPULATION, PRODUCTS, PROTEIN, RANGE_3P, RANGE_5P, REACTANTS, REACTION, RELATION, RNA, SOURCE, TARGET, TARGET_MODIFIER, TO_LOC, TRANSLOCATION, VARIANTS, ) from pybel.dsl import ( Fragment, Population, abundance, bioprocess, cell_surface_expression, complex_abundance, composite_abundance, fragment, fusion_range, gene, gene_fusion, gmod, hgvs, mirna, named_complex_abundance, pathology, pmod, protein, protein_fusion, reaction, rna, rna_fusion, secretion, translocation, ) from pybel.dsl.namespaces import hgnc from pybel.exceptions import MalformedTranslocationWarning from pybel.language import Entity, activity_mapping from pybel.parser import BELParser from pybel.parser.parse_bel import modifier_po_to_dict from tests.constants import ( TestTokenParserBase, assert_has_edge, assert_has_node, update_provenance, ) logger = logging.getLogger(__name__) TEST_GENE_VARIANT = "c.308G>A" TEST_PROTEIN_VARIANT = "p.Phe508del" class TestAbundance(TestTokenParserBase): """2.1.1""" def setUp(self): self.parser.clear() self.parser.general_abundance.setParseAction(self.parser.handle_term) self.expected_node = abundance(namespace="CHEBI", name="oxygen atom") self.expected_canonical_bel = 'a(CHEBI:"oxygen atom")' def _test_abundance_helper(self, statement): result = self.parser.general_abundance.parseString(statement) self.assertEqual(dict(self.expected_node), result.asDict()) self.assertIn(self.expected_node, self.graph) self.assertEqual(self.expected_canonical_bel, self.graph.node_to_bel(self.expected_node)) self.assertEqual( {}, modifier_po_to_dict(result), msg="The modifier dictionary should be empty", ) def test_abundance(self): """Test short/long abundance name.""" self._test_abundance_helper('a(CHEBI:"oxygen atom")') self._test_abundance_helper('abundance(CHEBI:"oxygen atom")') def _test_abundance_with_location_helper(self, statement): result = self.parser.general_abundance.parseString(statement) expected_result = { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "oxygen atom", }, LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}, } self.assertEqual(expected_result, result.asDict()) self.assertIn(self.expected_node, self.graph) self.assertEqual(self.expected_canonical_bel, self.graph.node_to_bel(self.expected_node)) modifier = modifier_po_to_dict(result) expected_modifier = {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}} self.assertEqual(expected_modifier, modifier) def test_abundance_with_location(self): """Test short/long abundance name and short/long location name.""" self._test_abundance_with_location_helper('a(CHEBI:"oxygen atom", loc(GO:intracellular))') self._test_abundance_with_location_helper('abundance(CHEBI:"oxygen atom", loc(GO:intracellular))') self._test_abundance_with_location_helper('a(CHEBI:"oxygen atom", location(GO:intracellular))') self._test_abundance_with_location_helper('abundance(CHEBI:"oxygen atom", location(GO:intracellular))') class TestAbundanceLabeled(TestTokenParserBase): """2.1.1""" def setUp(self): self.parser.clear() self.parser.general_abundance.setParseAction(self.parser.handle_term) self.expected_node = abundance(namespace="CHEBI", name="oxygen atom", identifier="CHEBI:25805") self.expected_canonical_bel = 'a(CHEBI:"CHEBI:25805" ! "oxygen atom")' def _test_abundance_helper(self, statement): result = self.parser.general_abundance.parseString(statement) self.assertEqual(dict(self.expected_node), result.asDict()) self.assertIn(self.expected_node, self.graph) node = list(self.graph)[0] self.assertEqual(self.expected_canonical_bel, node.as_bel()) self.assertEqual( {}, modifier_po_to_dict(result), msg="The modifier dictionary should be empty", ) def test_abundance(self): """Test short/long abundance name.""" for s in ( 'a(CHEBI:"CHEBI:25805"!"oxygen atom")', 'abundance(CHEBI:"CHEBI:25805"!"oxygen atom")', 'abundance(CHEBI:"CHEBI:25805" ! "oxygen atom")', ): with self.subTest(s=s): self._test_abundance_helper(s) def _test_abundance_with_location_helper(self, statement): result = self.parser.general_abundance.parseString(statement) expected_result = { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "oxygen atom", IDENTIFIER: "CHEBI:25805", }, LOCATION: { NAMESPACE: "GO", NAME: "intracellular", }, } self.assertEqual(expected_result, result.asDict()) self.assertIn(self.expected_node, self.graph) self.assertEqual(self.expected_canonical_bel, self.graph.node_to_bel(self.expected_node)) modifier = modifier_po_to_dict(result) expected_modifier = { LOCATION: { NAMESPACE: "GO", NAME: "intracellular", } } self.assertEqual(expected_modifier, modifier) def test_abundance_with_location(self): """Test short/long abundance name and short/long location name.""" self._test_abundance_with_location_helper('a(CHEBI:"CHEBI:25805"!"oxygen atom", loc(GO:intracellular))') self._test_abundance_with_location_helper('abundance(CHEBI:"CHEBI:25805"!"oxygen atom", loc(GO:intracellular))') self._test_abundance_with_location_helper('a(CHEBI:"CHEBI:25805"!"oxygen atom", location(GO:intracellular))') self._test_abundance_with_location_helper( 'abundance(CHEBI:"CHEBI:25805"!"oxygen atom", location(GO:intracellular))' ) class TestGene(TestTokenParserBase): """2.1.4 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XgeneA""" def setUp(self): self.parser.clear() self.parser.gene.setParseAction(self.parser.handle_term) def test_gene_simple(self): """Test parsing a simple gene.""" statement = "g(HGNC:AKT1)" result = self.parser.gene.parseString(statement) expected_list = [GENE, ["HGNC", "AKT1"]] self.assertEqual(expected_list, result.asList()) expected_dict = { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_dict, result.asDict()) expected_node = gene("HGNC", "AKT1") self.assert_has_node(expected_node) self.assertEqual("g(HGNC:AKT1)", self.graph.node_to_bel(expected_node)) self.assertEqual(1, len(self.graph)) def test_gene_with_location(self): """Test parsing a gene with a location.""" statement = "g(HGNC:AKT1, loc(GO:intracellular))" result = self.parser.gene.parseString(statement) expected_dict = { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, LOCATION: { NAMESPACE: "GO", NAME: "intracellular", }, } self.assertEqual(expected_dict, result.asDict()) expected_node = gene("HGNC", "AKT1") self.assert_has_node(expected_node) self.assertEqual("g(HGNC:AKT1)", self.graph.node_to_bel(expected_node)) def test_gene_with_hgvs(self): """Test parsing a gene with a variant.""" statement = "g(HGNC:AKT1, var(p.Phe508del))" result = self.parser.gene.parseString(statement) expected_result = { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, VARIANTS: [hgvs(TEST_PROTEIN_VARIANT)], } self.assertEqual(expected_result, result.asDict()) expected_node = gene("HGNC", "AKT1", variants=hgvs(TEST_PROTEIN_VARIANT)) self.assert_has_node(expected_node) self.assertEqual('g(HGNC:AKT1, var("p.Phe508del"))', self.graph.node_to_bel(expected_node)) parent = gene("HGNC", "AKT1") self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_gene_with_gmod(self): """Test parsing a gene with a gene modification.""" statement = "geneAbundance(HGNC:AKT1,gmod(M))" result = self.parser.gene.parseString(statement) expected_result = { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, VARIANTS: [gmod("Me")], } self.assertEqual(expected_result, result.asDict()) expected_node = gene("HGNC", "AKT1", variants=gmod("Me")) self.assert_has_node(expected_node) self.assertEqual( 'g(HGNC:AKT1, gmod(go:0006306 ! "DNA methylation"))', self.graph.node_to_bel(expected_node), ) parent = gene("HGNC", "AKT1") self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_gene_with_substitution(self): """Test BEL 1.0 gene substitution""" statement = "g(HGNC:AKT1,sub(G,308,A))" result = self.parser.gene.parseString(statement) expected_result = gene( name="AKT1", namespace="HGNC", variants=[hgvs(TEST_GENE_VARIANT)], ) self.assertEqual(dict(expected_result), result.asDict()) expected_node = gene("HGNC", "AKT1", variants=hgvs("c.308G>A")) self.assert_has_node(expected_node) self.assertEqual('g(HGNC:AKT1, var("c.308G>A"))', self.graph.node_to_bel(expected_node)) parent = gene("HGNC", "AKT1") self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_gene_with_substitution_and_location(self): """Test BEL 1.0 gene substitution with location tag""" statement = "g(HGNC:AKT1,sub(G,308,A),loc(GO:intracellular))" result = self.parser.gene.parseString(statement) expected_result = { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, VARIANTS: [ { KIND: HGVS, HGVS: TEST_GENE_VARIANT, }, ], LOCATION: { NAMESPACE: "GO", NAME: "intracellular", }, } self.assertEqual(expected_result, result.asDict()) expected_node = gene("HGNC", "AKT1", variants=hgvs("c.308G>A")) self.assert_has_node(expected_node) self.assertEqual('g(HGNC:AKT1, var("c.308G>A"))', self.graph.node_to_bel(expected_node)) parent = gene("HGNC", "AKT1") self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_multiple_variants(self): """Test multiple variants""" statement = "g(HGNC:AKT1, var(p.Phe508del), sub(G,308,A), var(c.1521_1523delCTT))" result = self.parser.gene.parseString(statement) expected_result = { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, VARIANTS: [ hgvs(TEST_PROTEIN_VARIANT), hgvs(TEST_GENE_VARIANT), hgvs("c.1521_1523delCTT"), ], } self.assertEqual(expected_result, result.asDict()) expected_node = gene( "HGNC", "AKT1", variants=[ hgvs("c.1521_1523delCTT"), hgvs(TEST_GENE_VARIANT), hgvs(TEST_PROTEIN_VARIANT), ], ) self.assert_has_node(expected_node) self.assertEqual( 'g(HGNC:AKT1, var("c.1521_1523delCTT"), var("c.308G>A"), var("p.Phe508del"))', self.graph.node_to_bel(expected_node), ) parent = gene("HGNC", "AKT1") self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def _help_test_gene_fusion_1(self, statement): result = self.parser.gene.parseString(statement) expected_dict = { FUNCTION: GENE, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "TMPRSS2"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "ERG"}}, RANGE_5P: { FUSION_REFERENCE: "c", FUSION_START: 1, FUSION_STOP: 79, }, RANGE_3P: { FUSION_REFERENCE: "c", FUSION_START: 312, FUSION_STOP: 5034, }, }, } self.assertEqual(expected_dict, result.asDict()) en = gene_fusion( partner_5p=gene("HGNC", "TMPRSS2"), range_5p=fusion_range("c", 1, 79), partner_3p=gene("HGNC", "ERG"), range_3p=fusion_range("c", 312, 5034), ) self.assert_has_node(en) self.assertEqual( 'g(fus(HGNC:TMPRSS2, "c.1_79", HGNC:ERG, "c.312_5034"))', self.graph.node_to_bel(en), ) def test_gene_fusion_1(self): # no quotes self._help_test_gene_fusion_1("g(fus(HGNC:TMPRSS2, c.1_79, HGNC:ERG, c.312_5034))") # quotes self._help_test_gene_fusion_1('g(fus(HGNC:TMPRSS2, "c.1_79", HGNC:ERG, "c.312_5034"))') def _help_test_gene_fusion_2(self, statement): result = self.parser.gene.parseString(statement) expected_dict = { FUNCTION: GENE, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "TMPRSS2"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "ERG"}}, RANGE_5P: {FUSION_REFERENCE: "c", FUSION_START: 1, FUSION_STOP: "?"}, RANGE_3P: {FUSION_REFERENCE: "c", FUSION_START: 312, FUSION_STOP: 5034}, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = gene_fusion( gene("HGNC", "TMPRSS2"), gene("HGNC", "ERG"), fusion_range("c", 1, "?"), fusion_range("c", 312, 5034), ) self.assert_has_node(expected_node) canonical_bel = self.graph.node_to_bel(expected_node) self.assertEqual('g(fus(HGNC:TMPRSS2, "c.1_?", HGNC:ERG, "c.312_5034"))', canonical_bel) def test_gene_fusion_2(self): # no quotes self._help_test_gene_fusion_2("g(fus(HGNC:TMPRSS2, c.1_?, HGNC:ERG, c.312_5034))") # correct self._help_test_gene_fusion_2('g(fus(HGNC:TMPRSS2, "c.1_?", HGNC:ERG, "c.312_5034"))') def _help_test_gene_fusion_3(self, statement): result = self.parser.gene.parseString(statement) expected_dict = { FUNCTION: GENE, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "TMPRSS2"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "ERG"}}, RANGE_5P: {FUSION_MISSING: "?"}, RANGE_3P: {FUSION_REFERENCE: "c", FUSION_START: 312, FUSION_STOP: 5034}, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = gene_fusion( gene("HGNC", "TMPRSS2"), gene("HGNC", "ERG"), range_3p=fusion_range("c", 312, 5034), ) self.assert_has_node(expected_node) self.assertEqual( 'g(fus(HGNC:TMPRSS2, "?", HGNC:ERG, "c.312_5034"))', self.graph.node_to_bel(expected_node), ) def test_gene_fusion_3(self): # no quotes self._help_test_gene_fusion_3("g(fus(HGNC:TMPRSS2, ?, HGNC:ERG, c.312_5034))") # correct self._help_test_gene_fusion_3('g(fus(HGNC:TMPRSS2, "?", HGNC:ERG, "c.312_5034"))') def _help_test_gene_fusion_legacy_1(self, statement): result = self.parser.gene.parseString(statement) expected_dict = { FUNCTION: GENE, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "BCR"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "JAK2"}}, RANGE_5P: {FUSION_REFERENCE: "c", FUSION_START: "?", FUSION_STOP: 1875}, RANGE_3P: {FUSION_REFERENCE: "c", FUSION_START: 2626, FUSION_STOP: "?"}, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = gene_fusion( gene("HGNC", "BCR"), gene("HGNC", "JAK2"), fusion_range("c", "?", 1875), fusion_range("c", 2626, "?"), ) self.assert_has_node(expected_node) self.assertEqual( 'g(fus(HGNC:BCR, "c.?_1875", HGNC:JAK2, "c.2626_?"))', self.graph.node_to_bel(expected_node), ) def test_gene_fusion_legacy_1(self): # legacy self._help_test_gene_fusion_legacy_1("g(HGNC:BCR, fus(HGNC:JAK2, 1875, 2626))") # no quotes self._help_test_gene_fusion_legacy_1("g(fus(HGNC:BCR, c.?_1875, HGNC:JAK2, c.2626_?))") # correct self._help_test_gene_fusion_legacy_1('g(fus(HGNC:BCR, "c.?_1875", HGNC:JAK2, "c.2626_?"))') def _help_test_gene_fusion_legacy_2(self, statement): result = self.parser.gene.parseString(statement) expected_dict = { FUNCTION: GENE, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "CHCHD4"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "AIFM1"}}, RANGE_5P: {FUSION_MISSING: "?"}, RANGE_3P: {FUSION_MISSING: "?"}, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = gene_fusion(gene("HGNC", "CHCHD4"), gene("HGNC", "AIFM1")) self.assert_has_node(expected_node) self.assertEqual( 'g(fus(HGNC:CHCHD4, "?", HGNC:AIFM1, "?"))', self.graph.node_to_bel(expected_node), ) def test_gene_fusion_legacy_2(self): # legacy self._help_test_gene_fusion_legacy_2("g(HGNC:CHCHD4, fusion(HGNC:AIFM1))") # no quotes self._help_test_gene_fusion_legacy_2("g(fus(HGNC:CHCHD4, ?, HGNC:AIFM1, ?))") # correct self._help_test_gene_fusion_legacy_2('g(fus(HGNC:CHCHD4, "?", HGNC:AIFM1, "?"))') def test_gene_variant_snp(self): """2.2.2 SNP""" statement = "g(SNP:rs113993960, var(c.1521_1523delCTT))" result = self.parser.gene.parseString(statement) expected_result = [GENE, ["SNP", "rs113993960"], [HGVS, "c.1521_1523delCTT"]] self.assertEqual(expected_result, result.asList()) expected_node = gene("SNP", "rs113993960", variants=hgvs("c.1521_1523delCTT")) self.assert_has_node(expected_node) self.assertEqual( 'g(SNP:rs113993960, var("c.1521_1523delCTT"))', self.graph.node_to_bel(expected_node), ) gene_node = expected_node.get_parent() self.assert_has_node(gene_node) self.assert_has_edge(gene_node, expected_node, relation=HAS_VARIANT) def test_gene_variant_chromosome(self): """2.2.2 chromosome""" statement = 'g(REF:"NC_000007.13", var(g.117199646_117199648delCTT))' result = self.parser.gene.parseString(statement) expected_result = [ GENE, ["REF", "NC_000007.13"], [HGVS, "g.117199646_117199648delCTT"], ] self.assertEqual(expected_result, result.asList()) gene_node = gene("REF", "NC_000007.13", variants=hgvs("g.117199646_117199648delCTT")) self.assert_has_node(gene_node) parent = gene_node.get_parent() self.assert_has_node(parent) self.assert_has_edge(parent, gene_node, relation=HAS_VARIANT) def test_gene_variant_deletion(self): """2.2.2 gene-coding DNA reference sequence""" statement = "g(HGNC:CFTR, var(c.1521_1523delCTT))" result = self.parser.gene.parseString(statement) expected_result = { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "CFTR", }, VARIANTS: [ { KIND: HGVS, HGVS: "c.1521_1523delCTT", }, ], } self.assertEqual(expected_result, result.asDict()) expected_node = gene("HGNC", "CFTR", variants=hgvs("c.1521_1523delCTT")) self.assert_has_node(expected_node) self.assertEqual( 'g(HGNC:CFTR, var("c.1521_1523delCTT"))', self.graph.node_to_bel(expected_node), ) gene_node = expected_node.get_parent() self.assert_has_node(gene_node) self.assert_has_edge(gene_node, expected_node, relation=HAS_VARIANT) class TestMicroRna(TestTokenParserBase): """2.1.5 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XmicroRNAA""" def setUp(self): self.parser.clear() self.parser.mirna.setParseAction(self.parser.handle_term) def _test_no_variant_helper(self, statement): result = self.parser.mirna.parseString(statement) expected_result = [MIRNA, ["HGNC", "MIR21"]] self.assertEqual(expected_result, result.asList()) expected_dict = { FUNCTION: MIRNA, CONCEPT: { NAMESPACE: "HGNC", NAME: "MIR21", }, } self.assertEqual(expected_dict, result.asDict()) node = mirna("HGNC", "MIR21") self.assert_has_node(node) def test_short(self): self._test_no_variant_helper("m(HGNC:MIR21)") self._test_no_variant_helper("microRNAAbundance(HGNC:MIR21)") def test_mirna_location(self): statement = "m(HGNC:MIR21,loc(GO:intracellular))" result = self.parser.mirna.parseString(statement) expected_dict = { FUNCTION: MIRNA, CONCEPT: { NAMESPACE: "HGNC", NAME: "MIR21", }, LOCATION: { NAMESPACE: "GO", NAME: "intracellular", }, } self.assertEqual(expected_dict, result.asDict()) expected_node = mirna("HGNC", "MIR21") self.assert_has_node(expected_node) def test_mirna_variant(self): statement = "m(HGNC:MIR21,var(p.Phe508del))" result = self.parser.mirna.parseString(statement) expected_dict = { FUNCTION: MIRNA, CONCEPT: { NAMESPACE: "HGNC", NAME: "MIR21", }, VARIANTS: [ hgvs(TEST_PROTEIN_VARIANT), ], } self.assertEqual(expected_dict, result.asDict()) node = mirna("HGNC", "MIR21", variants=hgvs(TEST_PROTEIN_VARIANT)) self.assert_has_node(node) self.assertEqual('m(HGNC:MIR21, var("p.Phe508del"))', self.graph.node_to_bel(node)) self.assertEqual(2, self.parser.graph.number_of_nodes()) expected_parent = node.get_parent() self.assert_has_node(expected_parent) self.assert_has_edge(expected_parent, node, relation=HAS_VARIANT) def test_mirna_variant_location(self): statement = "m(HGNC:MIR21,var(p.Phe508del),loc(GO:intracellular))" result = self.parser.mirna.parseString(statement) expected_dict = { FUNCTION: MIRNA, CONCEPT: { NAMESPACE: "HGNC", NAME: "MIR21", }, VARIANTS: [ { KIND: HGVS, HGVS: "p.Phe508del", }, ], LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}, } self.assertEqual(expected_dict, result.asDict()) node = mirna("HGNC", "MIR21", variants=hgvs(TEST_PROTEIN_VARIANT)) self.assert_has_node(node) self.assertEqual('m(HGNC:MIR21, var("p.Phe508del"))', self.graph.node_to_bel(node)) self.assertEqual(2, self.parser.graph.number_of_nodes()) expected_parent = node.get_parent() self.assert_has_node(expected_parent) self.assert_has_edge(expected_parent, node, relation=HAS_VARIANT) class TestProtein(TestTokenParserBase): """2.1.6 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XproteinA""" def setUp(self): self.parser.clear() self.parser.protein.setParseAction(self.parser.handle_term) def _test_reference_helper(self, statement): result = self.parser.protein.parseString(statement) expected_result = [PROTEIN, ["HGNC", "AKT1"]] self.assertEqual(expected_result, result.asList()) expected_dict = { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_dict, result.asDict()) node = protein("HGNC", "AKT1") self.assert_has_node(node) self.assertEqual("p(HGNC:AKT1)", self.graph.node_to_bel(node)) def test_reference(self): self._test_reference_helper("p(HGNC:AKT1)") self._test_reference_helper("proteinAbundance(HGNC:AKT1)") def test_protein_with_location(self): statement = "p(HGNC:AKT1, loc(GO:intracellular))" result = self.parser.protein.parseString(statement) expected_dict = { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}, } self.assertEqual(expected_dict, result.asDict()) node = protein("HGNC", "AKT1") self.assert_has_node(node) self.assertEqual("p(HGNC:AKT1)", self.graph.node_to_bel(node)) def test_multiple_variants(self): statement = "p(HGNC:AKT1,sub(A,127,Y),pmod(Ph, Ser),loc(GO:intracellular))" result = self.parser.protein.parseString(statement) expected_dict = { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}, VARIANTS: [hgvs("p.Ala127Tyr"), pmod(name="Ph", code="Ser")], } self.assertEqual(expected_dict, result.asDict()) parent = protein("HGNC", "AKT1") node = parent.with_variants([hgvs("p.Ala127Tyr"), pmod("Ph", code="Ser")]) self.assert_has_node(node) self.assertEqual( 'p(HGNC:AKT1, pmod(go:0006468 ! "protein phosphorylation", Ser), var("p.Ala127Tyr"))', self.graph.node_to_bel(node), ) self.assert_has_node(parent) self.assert_has_edge(parent, node, relation=HAS_VARIANT) def _help_test_protein_fusion_1(self, statement): result = self.parser.protein.parseString(statement) expected_dict = { FUNCTION: PROTEIN, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "TMPRSS2"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "ERG"}}, RANGE_5P: { FUSION_REFERENCE: "p", FUSION_START: 1, FUSION_STOP: 79, }, RANGE_3P: { FUSION_REFERENCE: "p", FUSION_START: 312, FUSION_STOP: 5034, }, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = protein_fusion( protein("HGNC", "TMPRSS2"), protein("HGNC", "ERG"), fusion_range("p", 1, 79), fusion_range("p", 312, 5034), ) self.assert_has_node(expected_node) self.assertEqual( 'p(fus(HGNC:TMPRSS2, "p.1_79", HGNC:ERG, "p.312_5034"))', self.graph.node_to_bel(expected_node), ) def test_protein_fusion_1(self): # no quotes self._help_test_protein_fusion_1("p(fus(HGNC:TMPRSS2, p.1_79, HGNC:ERG, p.312_5034))") # quotes self._help_test_protein_fusion_1('p(fus(HGNC:TMPRSS2, "p.1_79", HGNC:ERG, "p.312_5034"))') def _help_test_protein_fusion_legacy_1(self, statement): result = self.parser.protein.parseString(statement) expected_dict = { FUNCTION: PROTEIN, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "BCR"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "JAK2"}}, RANGE_5P: { FUSION_REFERENCE: "p", FUSION_START: "?", FUSION_STOP: 1875, }, RANGE_3P: { FUSION_REFERENCE: "p", FUSION_START: 2626, FUSION_STOP: "?", }, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = protein_fusion( protein("HGNC", "BCR"), protein("HGNC", "JAK2"), fusion_range("p", "?", 1875), fusion_range("p", 2626, "?"), ) self.assert_has_node(expected_node) canonical_bel = self.graph.node_to_bel(expected_node) self.assertEqual('p(fus(HGNC:BCR, "p.?_1875", HGNC:JAK2, "p.2626_?"))', canonical_bel) def test_protein_fusion_legacy_1(self): # legacy (BEL 1.0) self._help_test_protein_fusion_legacy_1("p(HGNC:BCR, fus(HGNC:JAK2, 1875, 2626))") # missing quotes self._help_test_protein_fusion_legacy_1("p(fus(HGNC:BCR, p.?_1875, HGNC:JAK2, p.2626_?))") # correct self._help_test_protein_fusion_legacy_1('p(fus(HGNC:BCR, "p.?_1875", HGNC:JAK2, "p.2626_?"))') def _help_test_protein_legacy_fusion_2(self, statement): result = self.parser.protein.parseString(statement) expected_dict = { FUNCTION: PROTEIN, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "CHCHD4"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "AIFM1"}}, RANGE_5P: {FUSION_MISSING: "?"}, RANGE_3P: {FUSION_MISSING: "?"}, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = protein_fusion( protein("HGNC", "CHCHD4"), protein("HGNC", "AIFM1"), ) self.assert_has_node(expected_node) canonical_bel = self.graph.node_to_bel(expected_node) self.assertEqual('p(fus(HGNC:CHCHD4, "?", HGNC:AIFM1, "?"))', canonical_bel) def test_protein_fusion_legacy_2(self): # legacy (BEL 1.0) self._help_test_protein_legacy_fusion_2("proteinAbundance(HGNC:CHCHD4, fusion(HGNC:AIFM1))") # legacy shorthand (BEL 1.0) self._help_test_protein_legacy_fusion_2("p(HGNC:CHCHD4, fus(HGNC:AIFM1))") # missing quotes self._help_test_protein_legacy_fusion_2("p(fus(HGNC:CHCHD4, ?, HGNC:AIFM1, ?))") # correct self._help_test_protein_legacy_fusion_2('p(fus(HGNC:CHCHD4, "?", HGNC:AIFM1, "?"))') def _help_test_protein_trunc_1(self, statement): result = self.parser.protein.parseString(statement) expected_node = protein("HGNC", "AKT1", variants=hgvs("p.40*")) self.assert_has_node(expected_node) canonical_bel = self.graph.node_to_bel(expected_node) self.assertEqual('p(HGNC:AKT1, var("p.40*"))', canonical_bel) protein_node = expected_node.get_parent() self.assert_has_node(protein_node) self.assert_has_edge(protein_node, expected_node, relation=HAS_VARIANT) def test_protein_trunc_1(self): # legacy self._help_test_protein_trunc_1("p(HGNC:AKT1, trunc(40))") # missing quotes self._help_test_protein_trunc_1("p(HGNC:AKT1, var(p.40*))") # correct self._help_test_protein_trunc_1('p(HGNC:AKT1, var("p.40*"))') def test_protein_trunc_2(self): statement = "p(HGNC:AKT1, var(p.Cys40*))" result = self.parser.protein.parseString(statement) expected_result = [PROTEIN, ["HGNC", "AKT1"], [HGVS, "p.Cys40*"]] self.assertEqual(expected_result, result.asList()) parent = protein("HGNC", "AKT1") expected_node = parent.with_variants(hgvs("p.Cys40*")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:AKT1, var("p.Cys40*"))', self.graph.node_to_bel(expected_node)) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_trunc_3(self): statement = "p(HGNC:AKT1, var(p.Arg1851*))" result = self.parser.protein.parseString(statement) expected_result = [PROTEIN, ["HGNC", "AKT1"], [HGVS, "p.Arg1851*"]] self.assertEqual(expected_result, result.asList()) parent = protein("HGNC", "AKT1") expected_node = parent.with_variants(hgvs("p.Arg1851*")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:AKT1, var("p.Arg1851*"))', self.graph.node_to_bel(expected_node)) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_pmod_1(self): """2.2.1 Test default BEL namespace and 1-letter amino acid code:""" statement = "p(HGNC:AKT1, pmod(Ph, S, 473))" result = self.parser.protein.parseString(statement) parent = protein("HGNC", "AKT1") expected_node = parent.with_variants(pmod("Ph", code="Ser", position=473)) self.assert_has_node(expected_node) self.assertEqual( 'p(HGNC:AKT1, pmod(go:0006468 ! "protein phosphorylation", Ser, 473))', self.graph.node_to_bel(expected_node), ) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_pmod_2(self): """2.2.1 Test default BEL namespace and 3-letter amino acid code:""" statement = "p(HGNC:AKT1, pmod(Ph, Ser, 473))" result = self.parser.protein.parseString(statement) parent = protein("HGNC", "AKT1") expected_node = parent.with_variants(pmod("Ph", code="Ser", position=473)) self.assert_has_node(expected_node) self.assertEqual( 'p(HGNC:AKT1, pmod(go:0006468 ! "protein phosphorylation", Ser, 473))', self.graph.node_to_bel(expected_node), ) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_pmod_3(self): """2.2.1 Test PSI-MOD namespace and 3-letter amino acid code:""" statement = "p(HGNC:AKT1, pmod(MOD:PhosRes,Ser,473))" result = self.parser.protein.parseString(statement) parent = protein("HGNC", "AKT1") expected_node = parent.with_variants(pmod(namespace="MOD", name="PhosRes", code="Ser", position=473)) self.assert_has_node(expected_node) self.assertEqual( "p(HGNC:AKT1, pmod(MOD:PhosRes, Ser, 473))", self.graph.node_to_bel(expected_node), ) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_pmod_4(self): """2.2.1 Test HRAS palmitoylated at an unspecified residue. Default BEL namespace""" statement = "p(HGNC:HRAS,pmod(Palm))" result = self.parser.protein.parseString(statement) parent = protein("HGNC", "HRAS") expected_node = parent.with_variants(pmod("Palm")) self.assert_has_node(expected_node) self.assertEqual( 'p(HGNC:HRAS, pmod(go:0018345 ! "protein palmitoylation"))', self.graph.node_to_bel(expected_node), ) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_variant_reference(self): """2.2.2 Test reference allele""" statement = "p(HGNC:CFTR, var(=))" result = self.parser.protein.parseString(statement) expected_result = [PROTEIN, ["HGNC", "CFTR"], [HGVS, "="]] self.assertEqual(expected_result, result.asList()) expected_node = protein("HGNC", "CFTR", variants=hgvs("=")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:CFTR, var("="))', self.graph.node_to_bel(expected_node)) protein_node = expected_node.get_parent() self.assert_has_node(protein_node) self.assert_has_edge(protein_node, expected_node, relation=HAS_VARIANT) def test_protein_variant_unspecified(self): """2.2.2 Test unspecified variant""" statement = "p(HGNC:CFTR, var(?))" result = self.parser.protein.parseString(statement) expected_result = [PROTEIN, ["HGNC", "CFTR"], [HGVS, "?"]] self.assertEqual(expected_result, result.asList()) expected_node = protein("HGNC", "CFTR", variants=hgvs("?")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:CFTR, var("?"))', self.graph.node_to_bel(expected_node)) parent = expected_node.get_parent() self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_variant_substitution(self): """2.2.2 Test substitution""" statement = "p(HGNC:CFTR, var(p.Gly576Ala))" result = self.parser.protein.parseString(statement) expected_result = [PROTEIN, ["HGNC", "CFTR"], [HGVS, "p.Gly576Ala"]] self.assertEqual(expected_result, result.asList()) expected_node = protein("HGNC", "CFTR", variants=hgvs("p.Gly576Ala")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:CFTR, var("p.Gly576Ala"))', self.graph.node_to_bel(expected_node)) parent = expected_node.get_parent() self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_variant_deletion(self): """2.2.2 deletion""" statement = "p(HGNC:CFTR, var(p.Phe508del))" result = self.parser.protein.parseString(statement) expected_result = [PROTEIN, ["HGNC", "CFTR"], [HGVS, TEST_PROTEIN_VARIANT]] self.assertEqual(expected_result, result.asList()) expected_node = protein("HGNC", "CFTR", variants=hgvs("p.Phe508del")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:CFTR, var("p.Phe508del"))', self.graph.node_to_bel(expected_node)) protein_node = expected_node.get_parent() self.assert_has_node(protein_node) self.assert_has_edge(protein_node, expected_node, relation=HAS_VARIANT) def test_protein_fragment_known(self): """2.2.3 fragment with known start/stop""" statement = "p(HGNC:YFG, frag(5_20))" self.parser.protein.parseString(statement) parent = protein("HGNC", "YFG") expected_node = parent.with_variants(fragment(5, 20)) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:YFG, frag("5_20"))', self.graph.node_to_bel(expected_node)) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_fragment_unbounded(self): """2.2.3 amino-terminal fragment of unknown length""" statement = "p(HGNC:YFG, frag(1_?))" result = self.parser.protein.parseString(statement) parent = protein("HGNC", "YFG") expected_node = parent.with_variants(fragment(1, "?")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:YFG, frag("1_?"))', self.graph.node_to_bel(expected_node)) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_fragment_unboundTerminal(self): """2.2.3 carboxyl-terminal fragment of unknown length""" statement = "p(HGNC:YFG, frag(?_*))" result = self.parser.protein.parseString(statement) parent = protein("HGNC", "YFG") expected_node = parent.with_variants(fragment("?", "*")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:YFG, frag("?_*"))', self.graph.node_to_bel(expected_node)) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_fragment_unknown(self): """2.2.3 fragment with unknown start/stop""" statement = "p(HGNC:YFG, frag(?))" result = self.parser.protein.parseString(statement) expected_result = [PROTEIN, ["HGNC", "YFG"], [FRAGMENT, "?"]] self.assertEqual(expected_result, result.asList()) parent = protein("HGNC", "YFG") expected_node = parent.with_variants(fragment()) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:YFG, frag("?"))', self.graph.node_to_bel(expected_node)) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_protein_fragment_descriptor(self): """2.2.3 fragment with unknown start/stop and a descriptor""" statement = 'p(HGNC:YFG, frag(?, "55kD"))' result = self.parser.protein.parseString(statement) parent = protein("HGNC", "YFG") expected_node = parent.with_variants(fragment("?", description="55kD")) self.assert_has_node(expected_node) self.assertEqual('p(HGNC:YFG, frag("?", "55kD"))', self.graph.node_to_bel(expected_node)) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def test_ensure_no_dup_edges(self): """Ensure node and edges aren't added twice, even if from different statements and has origin completion""" s1 = "p(HGNC:AKT1)" s2 = "deg(p(HGNC:AKT1))" node = protein("HGNC", "AKT1") self.parser.bel_term.parseString(s1) self.assert_has_node(node) self.assertEqual(1, self.parser.graph.number_of_nodes()) self.assertEqual(0, self.parser.graph.number_of_edges()) self.parser.bel_term.parseString(s2) self.assert_has_node(node) self.assertEqual(1, self.parser.graph.number_of_nodes()) self.assertEqual(0, self.parser.graph.number_of_edges()) class TestRna(TestTokenParserBase): """2.1.7 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XrnaA""" def setUp(self): self.parser.clear() self.parser.rna.setParseAction(self.parser.handle_term) def _help_test_reference(self, statement): result = self.parser.rna.parseString(statement) expected_result = [RNA, ["HGNC", "AKT1"]] self.assertEqual(expected_result, result.asList()) expected_dict = { FUNCTION: RNA, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_dict, result.asDict()) expected_node = rna("HGNC", "AKT1") self.assert_has_node(expected_node) self.assertEqual("r(HGNC:AKT1)", self.graph.node_to_bel(expected_node)) def test_reference(self): # short self._help_test_reference("r(HGNC:AKT1)") # long self._help_test_reference("rnaAbundance(HGNC:AKT1)") def test_multiple_variants(self): """Test multiple variants.""" statement = "r(HGNC:AKT1, var(p.Phe508del), var(c.1521_1523delCTT))" result = self.parser.rna.parseString(statement) expected_result = { FUNCTION: RNA, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, VARIANTS: [ hgvs(TEST_PROTEIN_VARIANT), hgvs("c.1521_1523delCTT"), ], } self.assertEqual(expected_result, result.asDict()) parent = rna("HGNC", "AKT1") expected_node = parent.with_variants([hgvs("c.1521_1523delCTT"), hgvs(TEST_PROTEIN_VARIANT)]) self.assert_has_node(expected_node) self.assertEqual( 'r(HGNC:AKT1, var("c.1521_1523delCTT"), var("p.Phe508del"))', self.graph.node_to_bel(expected_node), ) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) def _help_test_rna_fusion_1(self, statement): result = self.parser.rna.parseString(statement) expected_dict = { FUNCTION: RNA, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "TMPRSS2"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "ERG"}}, RANGE_5P: { FUSION_REFERENCE: "r", FUSION_START: 1, FUSION_STOP: 79, }, RANGE_3P: { FUSION_REFERENCE: "r", FUSION_START: 312, FUSION_STOP: 5034, }, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = rna_fusion( rna("HGNC", "TMPRSS2"), rna("HGNC", "ERG"), fusion_range("r", 1, 79), fusion_range("r", 312, 5034), ) self.assert_has_node(expected_node) self.assertEqual( 'r(fus(HGNC:TMPRSS2, "r.1_79", HGNC:ERG, "r.312_5034"))', self.graph.node_to_bel(expected_node), ) def test_rna_fusion_known_breakpoints(self): """Test RNA fusions (2.6.1) with known breakpoints (2.6.1).""" # missing quotes self._help_test_rna_fusion_1("r(fus(HGNC:TMPRSS2, r.1_79, HGNC:ERG, r.312_5034))") # correct (short form) self._help_test_rna_fusion_1('r(fus(HGNC:TMPRSS2, "r.1_79", HGNC:ERG, "r.312_5034"))') # correct (long form) self._help_test_rna_fusion_1('rnaAbundance(fusion(HGNC:TMPRSS2, "r.1_79", HGNC:ERG, "r.312_5034"))') def _help_test_rna_fusion_unspecified_breakpoints(self, statement): result = self.parser.rna.parseString(statement) expected_dict = { FUNCTION: RNA, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "TMPRSS2"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "ERG"}}, RANGE_5P: { FUSION_MISSING: "?", }, RANGE_3P: { FUSION_MISSING: "?", }, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = rna_fusion( rna("HGNC", "TMPRSS2"), rna("HGNC", "ERG"), ) self.assert_has_node(expected_node) self.assertEqual( 'r(fus(HGNC:TMPRSS2, "?", HGNC:ERG, "?"))', self.graph.node_to_bel(expected_node), ) def test_rna_fusion_unspecified_breakpoints(self): """Test RNA fusions (2.6.1) with unspecified breakpoints.""" # legacy self._help_test_rna_fusion_unspecified_breakpoints("r(HGNC:TMPRSS2, fusion(HGNC:ERG))") # missing quotes self._help_test_rna_fusion_unspecified_breakpoints("r(fus(HGNC:TMPRSS2, ?, HGNC:ERG, ?))") # correct (short form) self._help_test_rna_fusion_unspecified_breakpoints('r(fus(HGNC:TMPRSS2, "?", HGNC:ERG, "?"))') # correct (long form) self._help_test_rna_fusion_unspecified_breakpoints('rnaAbundance(fusion(HGNC:TMPRSS2, "?", HGNC:ERG, "?"))') def _help_test_rna_fusion_legacy_1(self, statement): result = self.parser.rna.parseString(statement) expected_dict = { FUNCTION: RNA, FUSION: { PARTNER_5P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "BCR"}}, PARTNER_3P: {CONCEPT: {NAMESPACE: "HGNC", NAME: "JAK2"}}, RANGE_5P: { FUSION_REFERENCE: "r", FUSION_START: "?", FUSION_STOP: 1875, }, RANGE_3P: { FUSION_REFERENCE: "r", FUSION_START: 2626, FUSION_STOP: "?", }, }, } self.assertEqual(expected_dict, result.asDict()) expected_node = rna_fusion( rna("HGNC", "BCR"), rna("HGNC", "JAK2"), fusion_range("r", "?", 1875), fusion_range("r", 2626, "?"), ) self.assert_has_node(expected_node) self.assertEqual( 'r(fus(HGNC:BCR, "r.?_1875", HGNC:JAK2, "r.2626_?"))', self.graph.node_to_bel(expected_node), ) def test_rna_fusion_legacy_1(self): # legacy self._help_test_rna_fusion_legacy_1("r(HGNC:BCR, fus(HGNC:JAK2, 1875, 2626))") # no quotes self._help_test_rna_fusion_legacy_1("r(fus(HGNC:BCR, r.?_1875, HGNC:JAK2, r.2626_?))") # correct self._help_test_rna_fusion_legacy_1('r(fus(HGNC:BCR, "r.?_1875", HGNC:JAK2, "r.2626_?"))') def test_rna_variant_codingReference(self): """2.2.2 RNA coding reference sequence""" statement = "r(HGNC:CFTR, var(r.1521_1523delcuu))" result = self.parser.rna.parseString(statement) expected_dict = { FUNCTION: RNA, CONCEPT: { NAMESPACE: "HGNC", NAME: "CFTR", }, VARIANTS: [hgvs("r.1521_1523delcuu")], } self.assertEqual(expected_dict, result.asDict()) parent = rna("HGNC", "CFTR") expected_node = parent.with_variants(hgvs("r.1521_1523delcuu")) self.assert_has_node(expected_node) self.assertEqual( 'r(HGNC:CFTR, var("r.1521_1523delcuu"))', self.graph.node_to_bel(expected_node), ) self.assert_has_node(parent) self.assert_has_edge(parent, expected_node, relation=HAS_VARIANT) class TestComplex(TestTokenParserBase): """2.1.2 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XcomplexA""" def setUp(self): self.parser.clear() self.parser.complex_abundances.setParseAction(self.parser.handle_term) def test_named_complex_singleton(self): statement = "complex(FPLX:AP1)" result = self.parser.complex_abundances.parseString(statement) expected_dict = { FUNCTION: COMPLEX, CONCEPT: { NAMESPACE: "FPLX", NAME: "AP1", }, } self.assertEqual(expected_dict, result.asDict()) expected_node = named_complex_abundance("FPLX", "AP1") self.assert_has_node(expected_node) def test_complex_list_short(self): statement = "complex(p(HGNC:FOS), p(HGNC:JUN))" result = self.parser.complex_abundances.parseString(statement) expected_result = [ COMPLEX, [PROTEIN, ["HGNC", "FOS"]], [PROTEIN, ["HGNC", "JUN"]], ] self.assertEqual(expected_result, result.asList()) expected_result = { FUNCTION: COMPLEX, MEMBERS: [ { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "FOS", }, }, { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "JUN", }, }, ], } self.assertEqual(expected_result, result.asDict()) child_1 = protein("HGNC", "FOS") self.assert_has_node(child_1) child_2 = protein("HGNC", "JUN") self.assert_has_node(child_2) expected_node = complex_abundance([child_1, child_2]) self.assert_has_node(expected_node) self.assert_has_edge(child_1, expected_node, relation=PART_OF) self.assert_has_edge(child_2, expected_node, relation=PART_OF) def test_complex_list_long(self): statement = "complexAbundance(proteinAbundance(HGNC:HBP1),geneAbundance(HGNC:NCF1))" self.parser.complex_abundances.parseString(statement) class TestComposite(TestTokenParserBase): """Tests the parsing of the composite function .. seealso:: `BEL 2.0 Specification 2.1.3 `_ """ def setUp(self): self.parser.clear() self.parser.composite_abundance.setParseAction(self.parser.handle_term) def test_213a(self): """Evidence: ``IL-6 and IL-23 synergistically induce Th17 differentiation""" statement = 'composite(p(HGNC:IL6), complex(GO:"interleukin-23 complex"))' result = self.parser.composite_abundance.parseString(statement) expected_result = [ COMPOSITE, [PROTEIN, ["HGNC", "IL6"]], [COMPLEX, ["GO", "interleukin-23 complex"]], ] self.assertEqual(expected_result, result.asList()) expected_dict = { FUNCTION: COMPOSITE, MEMBERS: [ { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "IL6", }, }, { FUNCTION: COMPLEX, CONCEPT: { NAMESPACE: "GO", NAME: "interleukin-23 complex", }, }, ], } self.assertEqual(expected_dict, result.asDict()) il23 = named_complex_abundance("GO", "interleukin-23 complex") il6 = protein("HGNC", "IL6") expected_node = composite_abundance([il23, il6]) self.assert_has_node(expected_node) self.assertEqual(2, len(expected_node[MEMBERS])) self.assertEqual(il23, expected_node[MEMBERS][0]) self.assertEqual(il6, expected_node[MEMBERS][1]) self.assertEqual( 'composite(complex(GO:"interleukin-23 complex"), p(HGNC:IL6))', self.graph.node_to_bel(expected_node), ) self.assertEqual(3, self.parser.graph.number_of_nodes()) self.assert_has_node(expected_node) self.assert_has_node(il23) self.assert_has_node(il6) self.assertEqual(2, self.parser.graph.number_of_edges()) class TestBiologicalProcess(TestTokenParserBase): def setUp(self): self.parser.clear() self.parser.biological_process.setParseAction(self.parser.handle_term) def test_231a(self): statement = 'bp(GO:"cell cycle arrest")' result = self.parser.biological_process.parseString(statement) expected_result = [BIOPROCESS, ["GO", "cell cycle arrest"]] self.assertEqual(expected_result, result.asList()) expected_dict = { FUNCTION: BIOPROCESS, CONCEPT: { NAMESPACE: "GO", NAME: "cell cycle arrest", }, } self.assertEqual(expected_dict, result.asDict()) expected_node = bioprocess("GO", "cell cycle arrest") self.assert_has_node(expected_node) class TestPathology(TestTokenParserBase): def setUp(self): self.parser.clear() self.parser.pathology.setParseAction(self.parser.handle_term) def test_232a(self): statement = "pathology(MESH:adenocarcinoma)" result = self.parser.pathology.parseString(statement) expected_dict = { FUNCTION: PATHOLOGY, CONCEPT: { NAMESPACE: "MESH", NAME: "adenocarcinoma", }, } self.assertEqual(expected_dict, result.asDict()) expected_node = pathology("MESH", "adenocarcinoma") self.assert_has_node(expected_node) self.assertEqual("path(MESH:adenocarcinoma)", self.graph.node_to_bel(expected_node)) class TestPopulation(TestTokenParserBase): def setUp(self): self.parser.clear() self.parser.population.setParseAction(self.parser.handle_term) def test_parse_population(self): statement = "pop(UBERON:blood)" result = self.parser.population.parseString(statement) expected_dict = { FUNCTION: POPULATION, CONCEPT: { NAMESPACE: "UBERON", NAME: "blood", }, } self.assertEqual(expected_dict, result.asDict()) expected_node = Population("UBERON", "blood") self.assert_has_node(expected_node) self.assertEqual( "pop(UBERON:blood)", self.graph.node_to_bel(expected_node), msg="Nodes: {}".format(list(self.graph)), ) class TestActivity(TestTokenParserBase): """Tests for molecular activity terms.""" def setUp(self): """Set up parser for testing the activity language.""" self.parser.clear() self.parser.activity.setParseAction(self.parser.handle_term) def test_activity_bare(self): statement = "act(p(HGNC:AKT1))" result = self.parser.activity.parseString(statement) expected_result = [ACTIVITY, PROTEIN, ["HGNC", "AKT1"]] self.assertEqual(expected_result, result.asList()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: ACTIVITY, } self.assertEqual(expected_mod, mod) def test_activity_withMolecularActivityDefault(self): """Tests activity modifier with molecular activity from default BEL namespace""" statement = "act(p(HGNC:AKT1), ma(kin))" result = self.parser.activity.parseString(statement) expected_dict = { MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"], FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"], } self.assertEqual(expected_mod, mod) def test_activity_withMolecularActivityDefaultLong(self): """Tests activity modifier with molecular activity from custom namespaced""" statement = "act(p(HGNC:AKT1), ma(catalyticActivity))" result = self.parser.activity.parseString(statement) expected_dict = { MODIFIER: ACTIVITY, EFFECT: activity_mapping["cat"], FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: ACTIVITY, EFFECT: activity_mapping["cat"], } self.assertEqual(expected_mod, mod) def test_activity_withMolecularActivityCustom(self): """Tests activity modifier with molecular activity from custom namespaced""" statement = 'act(p(HGNC:AKT1), ma(GO:"catalytic activity"))' result = self.parser.activity.parseString(statement) expected_dict = { MODIFIER: ACTIVITY, EFFECT: { NAMESPACE: "GO", NAME: "catalytic activity", }, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: ACTIVITY, EFFECT: {NAMESPACE: "GO", NAME: "catalytic activity"}, } self.assertEqual(expected_mod, mod) def test_activity_legacy(self): """Test BEL 1.0 style molecular activity annotation""" statement = "kin(p(HGNC:AKT1))" result = self.parser.activity.parseString(statement) expected_dict = { MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"], FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"], } self.assertEqual(expected_mod, mod) node = protein("HGNC", "AKT1") self.assert_has_node(node) def test_kinase_activity_on_named_complex(self): statement = "kin(complex(FPLX:C1))" self.parser.activity.parseString(statement) def test_activity_on_named_complex(self): statement = "act(complex(FPLX:C1), ma(kin))" self.parser.activity.parseString(statement) def test_kinase_activity_on_listed_complex(self): statement = "kin(complex(p(HGNC:A), p(HGNC:B)))" self.parser.activity.parseString(statement) def test_activity_on_listed_complex(self): statement = "act(complex(p(HGNC:A), p(HGNC:B)), ma(kin))" self.parser.activity.parseString(statement) class TestTranslocationPermissive(unittest.TestCase): @classmethod def setUpClass(cls): cls.graph = BELGraph() cls.parser = BELParser( cls.graph, disallow_unqualified_translocations=False, namespace_to_pattern={ "HGNC": re.compile(r".*"), "CHEBI": re.compile(r".*"), }, ) def setUp(self): self.parser.clear() self.parser.transformation.setParseAction(self.parser.handle_term) def assert_has_node(self, member, **kwargs): assert_has_node(self, member, self.parser.graph, **kwargs) def assert_has_edge(self, u, v, **kwargs): assert_has_edge(self, u, v, self.parser.graph, **kwargs) def test_unqualified_translocation_single(self): """translocation example""" statement = "tloc(p(HGNC:EGFR))" result = self.parser.transformation.parseString(statement) expected_dict = { MODIFIER: TRANSLOCATION, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "EGFR", }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: TRANSLOCATION, } self.assertEqual(expected_mod, mod) node = protein("HGNC", "EGFR") self.assert_has_node(node) def test_unqualified_translocation_relation(self): """Test translocation in object. 3.1.2 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XdIncreases """ update_provenance(self.parser.control_parser) statement = 'a(CHEBI:"Abeta_42") => tloc(a(CHEBI:"calcium(2+)"))' result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "Abeta_42", }, }, RELATION: DIRECTLY_INCREASES, TARGET: { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "calcium(2+)", }, MODIFIER: TRANSLOCATION, }, } self.assertEqual(expected_dict, result.asDict()) sub = abundance("CHEBI", "Abeta_42") self.assert_has_node(sub) obj = abundance("CHEBI", "calcium(2+)") self.assert_has_node(obj) expected_annotations = { RELATION: DIRECTLY_INCREASES, TARGET_MODIFIER: { MODIFIER: TRANSLOCATION, }, } self.assert_has_edge(sub, obj, **expected_annotations) class TestTransformation(TestTokenParserBase): def setUp(self): self.parser.clear() self.parser.transformation.setParseAction(self.parser.handle_term) def test_degradation_short(self): """Test the short form of degradation works""" statement = "deg(p(HGNC:AKT1))" result = self.parser.transformation.parseString(statement) expected_result = [DEGRADATION, PROTEIN, ["HGNC", "AKT1"]] self.assertEqual(expected_result, result.asList()) expected_dict = { MODIFIER: DEGRADATION, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: DEGRADATION, } self.assertEqual(expected_mod, mod) def test_degradation_long(self): """Test the long form of degradation works""" statement = "degradation(p(HGNC:EGFR))" result = self.parser.transformation.parseString(statement) expected_dict = { MODIFIER: DEGRADATION, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "EGFR", }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: DEGRADATION, } self.assertEqual(expected_mod, mod) node = protein("HGNC", "EGFR") self.assert_has_node(node) def test_translocation_standard(self): """translocation example""" statement = 'tloc(p(HGNC:EGFR), fromLoc(GO:"cell surface"), toLoc(GO:endosome))' result = self.parser.transformation.parseString(statement) expected_dict = { MODIFIER: TRANSLOCATION, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "EGFR", }, EFFECT: { FROM_LOC: { NAMESPACE: "GO", NAME: "cell surface", }, TO_LOC: { NAMESPACE: "GO", NAME: "endosome", }, }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = translocation( from_loc=Entity(namespace="GO", name="cell surface"), to_loc=Entity(namespace="GO", name="endosome"), ) self.assertEqual(expected_mod, mod) node = protein("HGNC", "EGFR") self.assert_has_node(node) def test_translocation_bare(self): """translocation example""" statement = 'tloc(p(HGNC:EGFR), GO:"cell surface", GO:endosome)' result = self.parser.transformation.parseString(statement) expected_dict = { MODIFIER: TRANSLOCATION, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "EGFR", }, EFFECT: { FROM_LOC: {NAMESPACE: "GO", NAME: "cell surface"}, TO_LOC: {NAMESPACE: "GO", NAME: "endosome"}, }, } self.assertEqual(expected_dict, result.asDict()) mod = modifier_po_to_dict(result) expected_mod = { MODIFIER: TRANSLOCATION, EFFECT: { FROM_LOC: {NAMESPACE: "GO", NAME: "cell surface"}, TO_LOC: {NAMESPACE: "GO", NAME: "endosome"}, }, } self.assertEqual(expected_mod, mod) node = protein("HGNC", "EGFR") self.assert_has_node(node) def test_unqualified_translocation_strict(self): """Fail on an improperly written single argument translocation""" statement = 'tloc(pop(EFO:"CD8-Positive T-Lymphocytes"))' with self.assertRaises(MalformedTranslocationWarning): self.parser.translocation.parseString(statement) def test_translocation_secretion(self): """cell secretion short form""" statement = "sec(p(HGNC:EGFR))" result = self.parser.transformation.parseString(statement) expected_result = [CELL_SECRETION, PROTEIN, ["HGNC", "EGFR"]] self.assertEqual(expected_result, result.asList()) mod = modifier_po_to_dict(result) expected_mod = secretion() self.assertEqual(expected_mod, mod) node = protein("HGNC", "EGFR") self.assert_has_node(node) def test_translocation_surface(self): """cell surface expression short form""" statement = "surf(p(HGNC:EGFR))" result = self.parser.transformation.parseString(statement) expected_result = [CELL_SURFACE_EXPRESSION, PROTEIN, ["HGNC", "EGFR"]] self.assertEqual(expected_result, result.asList()) expected_mod = cell_surface_expression() self.assertEqual(expected_mod, modifier_po_to_dict(result)) node = protein("HGNC", "EGFR") self.assert_has_node(node) def test_reaction_1(self): statement = 'rxn(reactants(a(CHEBI:superoxide)), products(a(CHEBI:"hydrogen peroxide"), a(CHEBI:oxygen)))' result = self.parser.transformation.parseString(statement) expected_dict = { FUNCTION: REACTION, REACTANTS: [ { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "superoxide", }, } ], PRODUCTS: [ { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "hydrogen peroxide", }, }, { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "oxygen", }, }, ], } self.assertEqual(expected_dict, result.asDict()) superoxide_node = abundance("CHEBI", "superoxide") hydrogen_peroxide = abundance("CHEBI", "hydrogen peroxide") oxygen_node = abundance("CHEBI", "oxygen") expected_node = reaction([superoxide_node], [hydrogen_peroxide, oxygen_node]) self.assert_has_node(expected_node) self.assertEqual(statement, self.graph.node_to_bel(expected_node)) self.assert_has_node(superoxide_node) self.assert_has_edge(expected_node, superoxide_node) self.assert_has_node(hydrogen_peroxide) self.assert_has_edge(expected_node, hydrogen_peroxide) self.assert_has_node(oxygen_node) self.assert_has_edge(expected_node, oxygen_node) def test_reaction_2(self): statement = "rxn(reactants(p(HGNC:APP)), products(p(HGNC:APP, frag(672_713))))" self.parser.transformation.parseString(statement) app = hgnc(name="APP") self.assertIn(app, self.graph) amyloid_beta_42 = app.with_variants(Fragment(start=672, stop=713)) self.assertIn(amyloid_beta_42, self.graph) expected_node = reaction(app, amyloid_beta_42) self.assertIn(expected_node, self.graph) def test_clearance(self): """Tests that after adding things, the graph and parser can be cleared properly""" s1 = "surf(p(HGNC:EGFR))" s2 = 'rxn(reactants(a(CHEBI:superoxide)),products(a(CHEBI:"hydrogen peroxide"), a(CHEBI:"oxygen")))' self.parser.transformation.parseString(s1) self.parser.transformation.parseString(s2) self.assertGreater(self.parser.graph.number_of_nodes(), 0) self.assertGreater(self.parser.graph.number_of_edges(), 0) self.parser.clear() self.assertEqual(0, self.parser.graph.number_of_nodes()) self.assertEqual(0, self.parser.graph.number_of_edges()) self.assertEqual(0, len(self.parser.control_parser.annotations)) self.assertFalse(self.parser.control_parser.citation_is_set) class TestSemantics(unittest.TestCase): def test_lenient_semantic_no_failure(self): graph = BELGraph() parser = BELParser(graph, allow_naked_names=True) update_provenance(parser.control_parser) parser.bel_term.addParseAction(parser.handle_term) parser.bel_term.parseString("bp(ABASD)") node = bioprocess(namespace=DIRTY, name="ABASD") self.assertIn(node, graph) pybel-0.15.5/tests/test_parse/test_parse_bel_relations.py000066400000000000000000001147221426625374700237040ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for parsing full BEL relations.""" import logging import unittest from pyparsing import ParseException from pybel import BELGraph from pybel.canonicalize import edge_to_bel from pybel.constants import ( ABUNDANCE, ACTIVITY, ANNOTATIONS, BIOPROCESS, CAUSES_NO_CHANGE, CITATION, COMPLEX, COMPOSITE, CONCEPT, CORRELATION, DECREASES, DIRECTLY_DECREASES, DIRECTLY_INCREASES, EFFECT, EQUIVALENT_TO, EVIDENCE, FROM_LOC, FUNCTION, GENE, GMOD, GRAPH_ANNOTATION_LIST, HAS_PRODUCT, HAS_REACTANT, HAS_VARIANT, HGVS, IDENTIFIER, INCREASES, IS_A, KIND, LOCATION, MEMBERS, MODIFIER, NAME, NAMESPACE, NEGATIVE_CORRELATION, NO_CORRELATION, ORTHOLOGOUS, PART_OF, PATHOLOGY, POSITIVE_CORRELATION, PRODUCTS, PROTEIN, RATE_LIMITING_STEP_OF, REACTANTS, REACTION, REGULATES, RELATION, RNA, SOURCE, SOURCE_MODIFIER, SUBPROCESS_OF, TARGET, TARGET_MODIFIER, TO_LOC, TRANSCRIBED_TO, TRANSLATED_TO, TRANSLOCATION, VARIANTS, ) from pybel.dsl import ( ComplexAbundance, Pathology, Protein, Rna, abundance, activity, bioprocess, complex_abundance, composite_abundance, gene, gmod, hgvs, named_complex_abundance, pmod, protein, reaction, rna, ) from pybel.dsl.namespaces import hgnc from pybel.exceptions import ( MissingNamespaceNameWarning, NestedRelationWarning, UndefinedNamespaceWarning, ) from pybel.language import Entity, activity_mapping from pybel.parser import BELParser from tests.constants import TestTokenParserBase, test_citation_dict, test_evidence_text logger = logging.getLogger(__name__) class TestRelations(TestTokenParserBase): @classmethod def setUpClass(cls): super().setUpClass() cls.parser.relation.streamline() def setUp(self): super().setUp() self.add_default_provenance() def test_ensure_no_dup_nodes(self): """Ensure node isn't added twice, even if from different statements""" self.parser.gene.addParseAction(self.parser.handle_term) result = self.parser.bel_term.parseString("g(HGNC:AKT1)") expected_result_dict = { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, } self.assertEqual(expected_result_dict, result.asDict()) self.parser.degradation.addParseAction(self.parser.handle_term) self.parser.degradation.parseString("deg(g(HGNC:AKT1))") akt1_gene = gene("HGNC", "AKT1") self.assertEqual(1, self.parser.graph.number_of_nodes()) self.assert_has_node(akt1_gene) def test_singleton(self): """Test singleton composite in subject.""" statement = 'composite(p(HGNC:CASP8),p(HGNC:FADD),a(CHEBI:"Abeta_42"))' result = self.parser.statement.parseString(statement) expected = [ COMPOSITE, [PROTEIN, ["HGNC", "CASP8"]], [PROTEIN, ["HGNC", "FADD"]], [ABUNDANCE, ["CHEBI", "Abeta_42"]], ] self.assertEqual(expected, result.asList()) sub_member_1 = protein("HGNC", "CASP8") self.assert_has_node(sub_member_1) sub_member_2 = protein("HGNC", "FADD") self.assert_has_node(sub_member_2) sub_member_3 = abundance("CHEBI", "Abeta_42") self.assert_has_node(sub_member_3) sub = composite_abundance([sub_member_1, sub_member_2, sub_member_3]) self.assert_has_node(sub) self.assert_has_edge(sub_member_1, sub, relation=PART_OF) self.assert_has_edge(sub_member_2, sub, relation=PART_OF) def test_predicate_failure(self): """Checks that if there's a problem with the relation/object, that an error gets thrown""" statement = 'composite(p(HGNC:CASP8),p(HGNC:FADD),a(CHEBI:"Abeta_42")) -> nope(GO:"neuron apoptotic process")' with self.assertRaises(ParseException): self.parser.relation.parseString(statement) def test_increases(self): """Test composite in subject. See BEL 2.0 specification `3.1.1 `_ """ statement = 'composite(p(HGNC:CASP8),p(HGNC:FADD),a(CHEBI:"Abeta_42")) -> bp(GO:"neuron apoptotic process")' result = self.parser.relation.parseString(statement) expected = [ [ COMPOSITE, [PROTEIN, ["HGNC", "CASP8"]], [PROTEIN, ["HGNC", "FADD"]], [ABUNDANCE, ["CHEBI", "Abeta_42"]], ], INCREASES, [BIOPROCESS, ["GO", "neuron apoptotic process"]], ] self.assertEqual(expected, result.asList()) sub_member_1 = protein("HGNC", "CASP8") self.assert_has_node(sub_member_1) sub_member_2 = protein("HGNC", "FADD") self.assert_has_node(sub_member_2) sub_member_3 = abundance("CHEBI", "Abeta_42") self.assert_has_node(sub_member_3) sub = composite_abundance([sub_member_1, sub_member_2, sub_member_3]) self.assert_has_node(sub) self.assert_has_edge(sub_member_1, sub, relation=PART_OF) self.assert_has_edge(sub_member_2, sub, relation=PART_OF) self.assert_has_edge(sub_member_3, sub, relation=PART_OF) obj = bioprocess("GO", "neuron apoptotic process") self.assert_has_node(obj) self.assert_has_edge(sub, obj, relation=INCREASES) def test_increases_methylation(self): """Test a causal statement with a gene modification.""" for gmod in ["Me", 'go:0006306 ! "DNA methylation"']: with self.subTest(gmod=gmod): self._help_test_increases_methylation(gmod) def _help_test_increases_methylation(self, x): statement = f'a(CHEBI:"lead atom") -> g(HGNC:APP, gmod({x}))' result = self.parser.relation.parseString(statement) expected_dict = { TARGET: { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "APP", }, VARIANTS: [ { KIND: GMOD, CONCEPT: { NAMESPACE: "go", IDENTIFIER: "0006306", NAME: "DNA methylation", }, }, ], }, RELATION: INCREASES, SOURCE: { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "lead atom", }, }, } self.assertEqual(expected_dict, result.asDict()) sub = abundance("CHEBI", "lead atom") obj = gene("HGNC", "APP", variants=gmod("Me")) self.assert_has_edge(sub, obj, relation=INCREASES) def test_directlyIncreases_withTlocObject(self): """Test translocation in object. See BEL 2.0 specification `3.1.2 `_ """ statement = ( 'a(CHEBI:"Abeta_42") => tloc(a(CHEBI:"calcium(2+)"),fromLoc(MESH:"Cell Membrane"),' 'toLoc(MESH:"Intracellular Space"))' ) result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "Abeta_42", }, }, RELATION: DIRECTLY_INCREASES, TARGET: { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "calcium(2+)", }, MODIFIER: TRANSLOCATION, EFFECT: { FROM_LOC: {NAMESPACE: "MESH", NAME: "Cell Membrane"}, TO_LOC: {NAMESPACE: "MESH", NAME: "Intracellular Space"}, }, }, } self.assertEqual(expected_dict, result.asDict()) sub = abundance("CHEBI", "Abeta_42") self.assert_has_node(sub) obj = abundance("CHEBI", "calcium(2+)") self.assert_has_node(obj) expected_annotations = { RELATION: DIRECTLY_INCREASES, TARGET_MODIFIER: { MODIFIER: TRANSLOCATION, EFFECT: { FROM_LOC: {NAMESPACE: "MESH", NAME: "Cell Membrane"}, TO_LOC: {NAMESPACE: "MESH", NAME: "Intracellular Space"}, }, }, } self.assert_has_edge(sub, obj, **expected_annotations) def test_decreases(self): """Test parsing a decreases relation with a reaction. 3.1.3 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#Xdecreases """ statement = "pep(p(FPLX:CAPN, location(GO:intracellular))) -| reaction(reactants(p(HGNC:CDK5R1)),products(p(HGNC:CDK5)))" result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { MODIFIER: ACTIVITY, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "FPLX", NAME: "CAPN", }, LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}, EFFECT: activity_mapping["pep"], }, RELATION: DECREASES, TARGET: { FUNCTION: REACTION, REACTANTS: [ { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "CDK5R1", }, } ], PRODUCTS: [ { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "CDK5", }, }, ], }, } self.assertEqual(expected_dict, result.asDict()) sub = protein("FPLX", "CAPN") self.assert_has_node(sub) obj_member_1 = protein("HGNC", "CDK5R1") self.assert_has_node(obj_member_1) obj_member_2 = protein("HGNC", "CDK5") self.assert_has_node(obj_member_2) obj = reaction(reactants=[obj_member_1], products=[obj_member_2]) self.assert_has_node(obj) self.assert_has_edge(obj, obj_member_1, relation=HAS_REACTANT) self.assert_has_edge(obj, obj_member_2, relation=HAS_PRODUCT) expected_edge_attributes = { RELATION: DECREASES, SOURCE_MODIFIER: { MODIFIER: ACTIVITY, EFFECT: activity_mapping["pep"], LOCATION: { NAMESPACE: "GO", NAME: "intracellular", }, }, } self.assertEqual( expected_edge_attributes[SOURCE_MODIFIER], activity(name="pep", location=Entity(name="intracellular", namespace="GO")), ) self.assert_has_edge(sub, obj, **expected_edge_attributes) def test_directlyDecreases(self): """ 3.1.4 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XdDecreases Tests simple triple""" statement = 'proteinAbundance(HGNC:CAT, location(GO:intracellular)) directlyDecreases abundance(CHEBI:"hydrogen peroxide")' result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "CAT", }, LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}, }, RELATION: DIRECTLY_DECREASES, TARGET: { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "hydrogen peroxide", }, }, } self.assertEqual(expected_dict, result.asDict()) sub = protein("HGNC", "CAT") self.assert_has_node(sub) obj = abundance("CHEBI", "hydrogen peroxide") self.assert_has_node(obj) expected_attrs = { SOURCE_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, RELATION: DIRECTLY_DECREASES, } self.assert_has_edge(sub, obj, **expected_attrs) def test_directlyDecreases_annotationExpansion(self): """ 3.1.4 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XdDecreases Tests simple triple""" statement = 'g(HGNC:CAT, location(GO:intracellular)) directlyDecreases abundance(CHEBI:"hydrogen peroxide")' self.graph.annotation_list.update( { "ListAnnotation": set("abef"), "ScalarAnnotation": set("cghi"), } ) annotations = self.parser.graph._clean_annotations( { "ListAnnotation": {"a", "b"}, "ScalarAnnotation": {"c"}, } ) self.parser.control_parser.annotations.update(annotations) result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "CAT", }, LOCATION: { NAMESPACE: "GO", NAME: "intracellular", }, }, RELATION: DIRECTLY_DECREASES, TARGET: { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: "CHEBI", NAME: "hydrogen peroxide", }, }, } self.assertEqual(expected_dict, result.asDict()) sub = gene("HGNC", "CAT") self.assert_has_node(sub) obj = abundance("CHEBI", "hydrogen peroxide") self.assert_has_node(obj) expected_attrs = { SOURCE_MODIFIER: {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}}, RELATION: DIRECTLY_DECREASES, CITATION: test_citation_dict, EVIDENCE: test_evidence_text, ANNOTATIONS: { "ListAnnotation": [ Entity(namespace="ListAnnotation", identifier="a"), Entity(namespace="ListAnnotation", identifier="b"), ], "ScalarAnnotation": [ Entity(namespace="ScalarAnnotation", identifier="c"), ], }, } self.assert_has_edge(sub, obj, only=True, **expected_attrs) def test_rateLimitingStepOf_subjectActivity(self): """3.1.5 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_ratelimitingstepof""" statement = 'act(p(HGNC:HMGCR), ma(cat)) rateLimitingStepOf bp(GO:"cholesterol biosynthetic process")' result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { MODIFIER: ACTIVITY, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "HMGCR", }, EFFECT: activity_mapping["cat"], }, RELATION: RATE_LIMITING_STEP_OF, TARGET: { FUNCTION: BIOPROCESS, CONCEPT: { NAMESPACE: "GO", NAME: "cholesterol biosynthetic process", }, }, } self.assertEqual(expected_dict, result.asDict()) sub = protein("HGNC", "HMGCR") self.assert_has_node(sub) obj = bioprocess("GO", "cholesterol biosynthetic process") self.assert_has_node(obj) self.assert_has_edge(sub, obj, relation=expected_dict[RELATION]) def test_cnc_with_subject_variant(self): """Test a causesNoChange relationship with a variant in the subject. See also: 3.1.6 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#Xcnc """ statement = 'g(HGNC:APP,sub(G,275341,C)) cnc path(MESH:"Alzheimer Disease")' result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "APP", }, VARIANTS: [ {KIND: HGVS, HGVS: "c.275341G>C"}, ], }, RELATION: CAUSES_NO_CHANGE, TARGET: { FUNCTION: PATHOLOGY, CONCEPT: { NAMESPACE: "MESH", NAME: "Alzheimer Disease", }, }, } self.assertEqual(expected_dict, result.asDict()) app_gene = gene(namespace="HGNC", name="APP") self.assert_has_node(app_gene) sub = app_gene.with_variants(hgvs("c.275341G>C")) self.assert_has_node(sub) obj = Pathology("MESH", "Alzheimer Disease") self.assert_has_node(obj) self.assert_has_edge(sub, obj, relation=expected_dict[RELATION]) def test_regulates_with_multiple_annotations(self): """ 3.1.7 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_regulates_reg Test nested definitions""" statement = "pep(complex(p(HGNC:F3),p(HGNC:F7))) regulates pep(p(HGNC:F9))" result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { MODIFIER: ACTIVITY, EFFECT: activity_mapping["pep"], FUNCTION: COMPLEX, MEMBERS: [ {FUNCTION: PROTEIN, CONCEPT: {NAMESPACE: "HGNC", NAME: "F3"}}, {FUNCTION: PROTEIN, CONCEPT: {NAMESPACE: "HGNC", NAME: "F7"}}, ], }, RELATION: REGULATES, TARGET: { MODIFIER: ACTIVITY, EFFECT: activity_mapping["pep"], FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "F9", }, }, } self.assertEqual(expected_dict, result.asDict()) sub_member_1 = protein("HGNC", "F3") self.assert_has_node(sub_member_1) sub_member_2 = protein("HGNC", "F7") self.assert_has_node(sub_member_2) sub = complex_abundance([sub_member_1, sub_member_2]) self.assert_has_node(sub) self.assert_has_edge(sub_member_1, sub, relation=PART_OF) self.assert_has_edge(sub_member_2, sub, relation=PART_OF) obj = protein("HGNC", "F9") self.assert_has_node(obj) self.assert_has_edge(sub, obj, relation=expected_dict[RELATION]) def test_nested_failure(self): """Test nested statement (3.1).""" statement = 'p(HGNC:CAT) -| (a(CHEBI:"hydrogen peroxide") -> bp(GO:"apoptotic process"))' self.parser.disallow_nested = True with self.assertRaises(NestedRelationWarning): self.parser.relation.parseString(statement) self.parser.disallow_nested = False def test_nested_lenient(self): """Test nested statement (3.1).""" self.parser.disallow_nested = False statement = 'p(HGNC:CAT) -| (a(CHEBI:"hydrogen peroxide") -> bp(GO:"apoptotic process"))' self.parser.relation.parseString(statement) cat = protein("HGNC", "CAT") h2o2 = abundance("CHEBI", "hydrogen peroxide") apoptosis = bioprocess("GO", "apoptotic process") self.assert_has_edge(cat, h2o2) self.assert_has_edge(h2o2, apoptosis) self.assertEqual(1, len(self.parser.graph.transitivities)) def test_negativeCorrelation_withObjectVariant(self): """ 3.2.1 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XnegCor Test phosphoralation tag""" for pmod in ["P", "Ph", 'go:0006468 ! "protein phosphorylation"']: with self.subTest(pmod=pmod): self._help_test_negative_correlation_with_object_variant(pmod) def _help_test_negative_correlation_with_object_variant(self, x): statement = f"kin(p(FPLX:GSK3)) neg p(HGNC:MAPT,pmod({x}))" result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { MODIFIER: ACTIVITY, EFFECT: activity_mapping["kin"], FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "FPLX", NAME: "GSK3", }, }, RELATION: NEGATIVE_CORRELATION, TARGET: { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "MAPT", }, VARIANTS: [pmod("Ph")], }, } self.assertEqual(expected_dict, result.asDict()) sub = protein("FPLX", "GSK3") self.assert_has_node(sub) obj = protein("HGNC", "MAPT", variants=pmod("Ph")) self.assert_has_node(obj) self.assert_has_edge(sub, obj, relation=expected_dict[RELATION]) self.assert_has_edge(obj, sub, relation=expected_dict[RELATION]) def test_positiveCorrelation_withSelfReferential(self): """ 3.2.2 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#XposCor Self-referential relationships""" statement = "p(HGNC:GSK3B, pmod(P, S, 9)) pos act(p(HGNC:GSK3B), ma(kin))" result = self.parser.relation.parseString(statement) expected_dict = { SOURCE: { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "GSK3B", }, VARIANTS: [pmod("Ph", position=9, code="Ser")], }, RELATION: POSITIVE_CORRELATION, TARGET: { MODIFIER: ACTIVITY, FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "GSK3B", }, EFFECT: activity_mapping["kin"], }, } self.assertEqual(expected_dict, result.asDict()) subject_node = protein("HGNC", "GSK3B", variants=pmod("Ph", code="Ser", position=9)) self.assert_has_node(subject_node) object_node = protein("HGNC", "GSK3B") self.assert_has_node(object_node) self.assert_has_edge(subject_node, object_node, relation=expected_dict[RELATION]) self.assert_has_edge(object_node, subject_node, relation=expected_dict[RELATION]) def test_orthologous(self): """ 3.3.1 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_orthologous """ statement = "g(HGNC:AKT1) orthologous g(MGI:AKT1)" result = self.parser.relation.parseString(statement) expected_result = [ [GENE, ["HGNC", "AKT1"]], ORTHOLOGOUS, [GENE, ["MGI", "AKT1"]], ] self.assertEqual(expected_result, result.asList()) sub = gene("HGNC", "AKT1") self.assert_has_node(sub) obj = gene("MGI", "AKT1") self.assert_has_node(obj) self.assert_has_edge(sub, obj, relation=ORTHOLOGOUS) self.assert_has_edge(obj, sub, relation=ORTHOLOGOUS) def test_transcription(self): """ 3.3.2 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_transcribedto """ statement = "g(HGNC:AKT1) :> r(HGNC:AKT1)" result = self.parser.relation.parseString(statement) expected_result = [ [GENE, ["HGNC", "AKT1"]], TRANSCRIBED_TO, [RNA, ["HGNC", "AKT1"]], ] self.assertEqual(expected_result, result.asList()) sub = gene("HGNC", "AKT1") self.assert_has_node(sub) obj = rna("HGNC", "AKT1") self.assert_has_node(obj) self.assert_has_edge(sub, obj, **{RELATION: TRANSCRIBED_TO}) def test_translation(self): """ 3.3.3 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_translatedto """ statement = "r(HGNC:AKT1,loc(GO:intracellular)) >> p(HGNC:AKT1)" result = self.parser.relation.parseString(statement) # [[RNA, ['HGNC', 'AKT1']], TRANSLATED_TO, [PROTEIN, ['HGNC', 'AKT1']]] expected_result = { SOURCE: { FUNCTION: RNA, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, LOCATION: { NAMESPACE: "GO", NAME: "intracellular", }, }, RELATION: TRANSLATED_TO, TARGET: { FUNCTION: PROTEIN, CONCEPT: { NAMESPACE: "HGNC", NAME: "AKT1", }, }, } self.assertEqual(expected_result, result.asDict()) self.assertEqual(2, self.graph.number_of_nodes()) source = rna(name="AKT1", namespace="HGNC") self.assertIn(source, self.graph) target = protein(name="AKT1", namespace="HGNC") self.assertIn(target, self.graph) self.assertEqual(1, self.graph.number_of_edges()) self.assertTrue(self.graph.has_edge(source, target)) key_data = self.parser.graph[source][target] self.assertEqual(1, len(key_data)) key = list(key_data)[0] data = key_data[key] self.assertIn(RELATION, data) self.assertEqual(TRANSLATED_TO, data[RELATION]) calculated_edge_bel = edge_to_bel(source, target, data=data) self.assertEqual( "r(HGNC:AKT1, loc(GO:intracellular)) translatedTo p(HGNC:AKT1)", calculated_edge_bel, ) def test_component_list(self): s = "complex(FPLX:C1) hasComponents list(p(HGNC:C1QB), p(HGNC:C1S))" result = self.parser.relation.parseString(s) expected_result_list = [ [COMPLEX, ["FPLX", "C1"]], "hasComponents", [ [PROTEIN, ["HGNC", "C1QB"]], [PROTEIN, ["HGNC", "C1S"]], ], ] self.assertEqual(expected_result_list, result.asList()) sub = named_complex_abundance("FPLX", "C1") self.assert_has_node(sub) child_1 = hgnc(name="C1QB") self.assert_has_node(child_1) self.assert_has_edge(child_1, sub, **{RELATION: PART_OF}) child_2 = hgnc(name="C1S") self.assert_has_node(child_2) self.assert_has_edge(child_2, sub, **{RELATION: PART_OF}) def test_member_list(self): """ 3.4.2 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_hasmembers """ statement = "p(FPLX:PKC) hasMembers list(p(HGNC:PRKCA), p(HGNC:PRKCB), p(HGNC:PRKCD), p(HGNC:PRKCE))" result = self.parser.relation.parseString(statement) expected_result = [ [PROTEIN, ["FPLX", "PKC"]], "hasMembers", [ [PROTEIN, ["HGNC", "PRKCA"]], [PROTEIN, ["HGNC", "PRKCB"]], [PROTEIN, ["HGNC", "PRKCD"]], [PROTEIN, ["HGNC", "PRKCE"]], ], ] self.assertEqual(expected_result, result.asList()) sub = protein("FPLX", "PKC") obj_1 = protein("HGNC", "PRKCA") obj_2 = protein("HGNC", "PRKCB") obj_3 = protein("HGNC", "PRKCD") obj_4 = protein("HGNC", "PRKCE") self.assert_has_node(sub) self.assert_has_node(obj_1) self.assert_has_edge(obj_1, sub, relation=IS_A) self.assert_has_node(obj_2) self.assert_has_edge(obj_2, sub, relation=IS_A) self.assert_has_node(obj_3) self.assert_has_edge(obj_3, sub, relation=IS_A) self.assert_has_node(obj_4) self.assert_has_edge(obj_4, sub, relation=IS_A) def test_is_a(self): """ 3.4.5 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_isa """ statement = 'pathology(MESH:Psoriasis) isA pathology(MESH:"Skin Diseases")' result = self.parser.relation.parseString(statement) expected_result = [ [PATHOLOGY, ["MESH", "Psoriasis"]], "isA", [PATHOLOGY, ["MESH", "Skin Diseases"]], ] self.assertEqual(expected_result, result.asList()) sub = Pathology("MESH", "Psoriasis") self.assert_has_node(sub) obj = Pathology("MESH", "Skin Diseases") self.assert_has_node(obj) self.assert_has_edge(sub, obj, relation=IS_A) def test_equivalentTo(self): statement = 'g(dbSNP:"rs123456") eq g(HGNC:YFG, var(c.123G>A))' result = self.parser.relation.parseString(statement) expected_result = { SOURCE: { FUNCTION: GENE, CONCEPT: { NAMESPACE: "dbSNP", NAME: "rs123456", }, }, RELATION: EQUIVALENT_TO, TARGET: { FUNCTION: GENE, CONCEPT: { NAMESPACE: "HGNC", NAME: "YFG", }, VARIANTS: [ { KIND: HGVS, HGVS: "c.123G>A", }, ], }, } self.assertEqual(expected_result, result.asDict()) sub = gene("dbSNP", "rs123456") self.assert_has_node(sub) obj = gene("HGNC", "YFG", variants=hgvs("c.123G>A")) self.assert_has_node(obj) self.assert_has_edge(sub, obj, **{RELATION: EQUIVALENT_TO}) self.assert_has_edge(obj, sub, **{RELATION: EQUIVALENT_TO}) def test_partOf(self): statement = 'a(UBERON:"corpus striatum") partOf a(UBERON:"basal ganglion")' self.parser.relation.parseString(statement) corpus_striatum = abundance(namespace="UBERON", name="corpus striatum") basal_ganglion = abundance(namespace="UBERON", name="basal ganglion") self.assert_has_node(corpus_striatum) self.assert_has_node(basal_ganglion) self.assert_has_edge(corpus_striatum, basal_ganglion, relation=PART_OF) v = list(self.parser.graph[corpus_striatum][basal_ganglion].values()) self.assertEqual(1, len(v)) v = v[0] self.assertIn(RELATION, v) self.assertEqual(PART_OF, v[RELATION]) def test_subProcessOf(self): """ 3.4.6 http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_subprocessof """ statement = 'rxn(reactants(a(CHEBI:"(S)-3-hydroxy-3-methylglutaryl-CoA"),a(CHEBI:NADPH), \ a(CHEBI:hydron)),products(a(CHEBI:mevalonate), a(CHEBI:"CoA-SH"), a(CHEBI:"NADP(+)"))) \ subProcessOf bp(GO:"cholesterol biosynthetic process")' result = self.parser.relation.parseString(statement) expected_result = [ [ REACTION, [ [ABUNDANCE, ["CHEBI", "(S)-3-hydroxy-3-methylglutaryl-CoA"]], [ABUNDANCE, ["CHEBI", "NADPH"]], [ABUNDANCE, ["CHEBI", "hydron"]], ], [ [ABUNDANCE, ["CHEBI", "mevalonate"]], [ABUNDANCE, ["CHEBI", "CoA-SH"]], [ABUNDANCE, ["CHEBI", "NADP(+)"]], ], ], SUBPROCESS_OF, [BIOPROCESS, ["GO", "cholesterol biosynthetic process"]], ] self.assertEqual(expected_result, result.asList()) sub_reactant_1 = abundance("CHEBI", "(S)-3-hydroxy-3-methylglutaryl-CoA") sub_reactant_2 = abundance("CHEBI", "NADPH") sub_reactant_3 = abundance("CHEBI", "hydron") sub_product_1 = abundance("CHEBI", "mevalonate") sub_product_2 = abundance("CHEBI", "CoA-SH") sub_product_3 = abundance("CHEBI", "NADP(+)") self.assert_has_node(sub_reactant_1) self.assert_has_node(sub_reactant_2) self.assert_has_node(sub_reactant_3) self.assert_has_node(sub_product_1) self.assert_has_node(sub_product_2) self.assert_has_node(sub_product_3) sub = reaction( [sub_reactant_1, sub_reactant_2, sub_reactant_3], [sub_product_1, sub_product_2, sub_product_3], ) self.assert_has_edge(sub, sub_reactant_1, relation=HAS_REACTANT) self.assert_has_edge(sub, sub_reactant_2, relation=HAS_REACTANT) self.assert_has_edge(sub, sub_reactant_3, relation=HAS_REACTANT) self.assert_has_edge(sub, sub_product_1, relation=HAS_PRODUCT) self.assert_has_edge(sub, sub_product_2, relation=HAS_PRODUCT) self.assert_has_edge(sub, sub_product_3, relation=HAS_PRODUCT) obj = bioprocess("GO", "cholesterol biosynthetic process") self.assert_has_node(obj) self.assert_has_edge(sub, obj, **{RELATION: SUBPROCESS_OF}) def test_extra_1(self): statement = 'abundance(CHEBI:"nitric oxide") increases cellSurfaceExpression(complexAbundance(proteinAbundance(HGNC:ITGAV),proteinAbundance(HGNC:ITGB3)))' self.parser.relation.parseString(statement) def test_has_variant(self): statement = "g(HGNC:AKT1) hasVariant g(HGNC:AKT1, gmod(M))" self.parser.relation.parseString(statement) expected_parent = gene("HGNC", "AKT1") expected_child = expected_parent.with_variants(gmod("Me")) self.assert_has_node(expected_parent) self.assert_has_node(expected_child) self.assertEqual("g(HGNC:AKT1)", self.graph.node_to_bel(expected_parent)) self.assertEqual( 'g(HGNC:AKT1, gmod(go:0006306 ! "DNA methylation"))', self.graph.node_to_bel(expected_child), ) self.assert_has_edge(expected_parent, expected_child, **{RELATION: HAS_VARIANT}) def test_has_reaction_component(self): statement = 'rxn(reactants(a(CHEBI:"(S)-3-hydroxy-3-methylglutaryl-CoA"),a(CHEBI:NADPH), \ a(CHEBI:hydron)),products(a(CHEBI:mevalonate), a(CHEBI:"CoA-SH"), a(CHEBI:"NADP(+)"))) \ hasReactant a(CHEBI:"(S)-3-hydroxy-3-methylglutaryl-CoA")' self.parser.relation.parseString(statement) sub_reactant_1 = abundance("CHEBI", "(S)-3-hydroxy-3-methylglutaryl-CoA") sub_reactant_2 = abundance("CHEBI", "NADPH") sub_reactant_3 = abundance("CHEBI", "hydron") sub_product_1 = abundance("CHEBI", "mevalonate") sub_product_2 = abundance("CHEBI", "CoA-SH") sub_product_3 = abundance("CHEBI", "NADP(+)") self.assert_has_node(sub_reactant_1) self.assert_has_node(sub_reactant_2) self.assert_has_node(sub_reactant_3) self.assert_has_node(sub_product_1) self.assert_has_node(sub_product_2) self.assert_has_node(sub_product_3) sub = reaction( reactants=[sub_reactant_1, sub_reactant_2, sub_reactant_3], products=[sub_product_1, sub_product_2, sub_product_3], ) self.assert_has_node(sub) self.assert_has_edge(sub, sub_reactant_1, relation=HAS_REACTANT) self.assert_has_edge(sub, sub_reactant_2, relation=HAS_REACTANT) self.assert_has_edge(sub, sub_reactant_3, relation=HAS_REACTANT) self.assert_has_edge(sub, sub_product_1, relation=HAS_PRODUCT) self.assert_has_edge(sub, sub_product_2, relation=HAS_PRODUCT) self.assert_has_edge(sub, sub_product_3, relation=HAS_PRODUCT) def assert_has_two_way_edge(self, source, target, relation): self.assert_has_node(source) self.assert_has_node(target) self.assert_has_edge(source, target, **{RELATION: relation}) self.assert_has_edge(target, source, **{RELATION: relation}) def test_no_correlation(self): """Test the ``noCorrelation`` relation.""" statement = "r(HGNC:X) noCorrelation r(HGNC:Y)" self.parser.relation.parseString(statement) source = Rna("HGNC", "X") target = Rna("HGNC", "Y") self.assert_has_two_way_edge(source, target, NO_CORRELATION) def test_correlation(self): """Test the ``correlation`` relation.""" statement = "r(HGNC:X) correlation r(HGNC:Y)" self.parser.relation.parseString(statement) source = Rna("HGNC", "X") target = Rna("HGNC", "Y") self.assert_has_two_way_edge(source, target, CORRELATION) def test_binds(self): """Test the ``binds`` relation.""" statement = "p(HGNC:X) binds p(HGNC:Y)" self.parser.relation.parseString(statement) source = Protein("HGNC", "X") target = Protein("HGNC", "Y") x_y_complex = ComplexAbundance([source, target]) self.assert_has_node(x_y_complex) self.assert_has_edge(source, x_y_complex, relation=PART_OF) self.assert_has_edge(target, x_y_complex, relation=PART_OF) class TestCustom(unittest.TestCase): def setUp(self): graph = BELGraph() namespace_to_term = { "HGNC": {(None, "AKT1"): "GRP", (None, "YFG"): "GRP"}, "MESH": {(None, "nucleus"): "A"}, } self.parser = BELParser(graph, namespace_to_term_to_encoding=namespace_to_term, autostreamline=False) def test_tloc_undefined_namespace(self): s = 'tloc(p(HGNC:AKT1), fromLoc(MESH:nucleus), toLoc(MISSING:"undefined"))' with self.assertRaises(UndefinedNamespaceWarning): self.parser.translocation.parseString(s) def test_tloc_undefined_name(self): s = 'tloc(p(HGNC:AKT1), fromLoc(MESH:nucleus), toLoc(MESH:"undefined"))' with self.assertRaises(MissingNamespaceNameWarning): self.parser.translocation.parseString(s) def test_location_undefined_namespace(self): s = 'p(HGNC:AKT1, loc(MISSING:"nucleus")' with self.assertRaises(UndefinedNamespaceWarning): self.parser.protein.parseString(s) def test_location_undefined_name(self): s = 'p(HGNC:AKT1, loc(MESH:"undefined")' with self.assertRaises(MissingNamespaceNameWarning): self.parser.protein.parseString(s) pybel-0.15.5/tests/test_parse/test_parse_bel_variants.py000066400000000000000000000410521426625374700235260ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test parsing variants.""" import logging import re import unittest from pybel.constants import ( CONCEPT, FRAGMENT, FRAGMENT_DESCRIPTION, FRAGMENT_MISSING, FRAGMENT_START, FRAGMENT_STOP, FUSION_MISSING, FUSION_REFERENCE, FUSION_START, FUSION_STOP, GMOD, IDENTIFIER, KIND, LOCATION, NAME, NAMESPACE, PARTNER_3P, PARTNER_5P, PMOD, PMOD_CODE, PMOD_POSITION, RANGE_3P, RANGE_5P, ) from pybel.dsl import GeneModification, Hgvs, ProteinModification from pybel.language import Entity from pybel.parser import ConceptParser from pybel.parser.modifiers import ( get_fragment_language, get_fusion_language, get_gene_modification_language, get_gene_substitution_language, get_hgvs_language, get_location_language, get_protein_modification_language, get_protein_substitution_language, get_truncation_language, ) log = logging.getLogger(__name__) class TestHGVSParser(unittest.TestCase): def setUp(self): self.parser = get_hgvs_language() def test_protein_del(self): statement = "variant(p.Phe508del)" expected = Hgvs("p.Phe508del") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_protein_del_quoted(self): statement = 'variant("p.Phe508del")' expected = Hgvs("p.Phe508del") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_protein_mut(self): statement = "var(p.Gly576Ala)" expected = Hgvs("p.Gly576Ala") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_unspecified(self): statement = "var(=)" expected = Hgvs("=") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_frameshift(self): statement = "variant(p.Thr1220Lysfs)" expected = Hgvs("p.Thr1220Lysfs") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_snp(self): statement = "var(c.1521_1523delCTT)" expected = Hgvs("c.1521_1523delCTT") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_chromosome_1(self): statement = "variant(g.117199646_117199648delCTT)" expected = Hgvs("g.117199646_117199648delCTT") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_chromosome_2(self): statement = "var(c.1521_1523delCTT)" expected = Hgvs("c.1521_1523delCTT") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_rna_del(self): statement = "var(r.1653_1655delcuu)" expected = Hgvs("r.1653_1655delcuu") result = self.parser.parseString(statement) self.assertEqual(expected, result.asDict()) def test_protein_trunc_triple(self): statement = "var(p.Cys65*)" result = self.parser.parseString(statement) expected = Hgvs("p.Cys65*") self.assertEqual(expected, result.asDict()) def test_protein_trunc_legacy(self): statement = "var(p.65*)" result = self.parser.parseString(statement) expected = Hgvs("p.65*") self.assertEqual(expected, result.asDict()) class TestPmod(unittest.TestCase): def setUp(self): identifier_parser = ConceptParser( namespace_to_pattern={ "MOD": re.compile(".*"), "HGNC": re.compile(".*"), } ) self.parser = get_protein_modification_language( concept_qualified=identifier_parser.identifier_qualified, concept_fqualified=identifier_parser.identifier_fqualified, ) def _help_test_pmod_simple(self, statement): result = self.parser.parseString(statement) expected = { KIND: PMOD, CONCEPT: { NAMESPACE: "go", NAME: "protein phosphorylation", IDENTIFIER: "0006468", }, } self.assertEqual(expected, ProteinModification("Ph")) self.assertEqual(expected, result.asDict()) def test_bel_name(self): # long function, legacy modification self._help_test_pmod_simple("proteinModification(P)") # long function, new modification self._help_test_pmod_simple("proteinModification(Ph)") # short function, legacy modification self._help_test_pmod_simple("pmod(P)") # short function, new modification self._help_test_pmod_simple("pmod(Ph)") def _help_test_pmod_with_residue(self, statement): result = self.parser.parseString(statement) expected = { KIND: PMOD, CONCEPT: { NAMESPACE: "go", NAME: "protein phosphorylation", IDENTIFIER: "0006468", }, PMOD_CODE: "Ser", } self.assertEqual(expected, ProteinModification("Ph", code="Ser")) self.assertEqual(expected, result.asDict()) def test_residue(self): # short amino acid self._help_test_pmod_with_residue("pmod(Ph, S)") # long amino acid self._help_test_pmod_with_residue("pmod(Ph, Ser)") def _help_test_pmod_full(self, statement): result = self.parser.parseString(statement) expected = { KIND: PMOD, CONCEPT: { NAMESPACE: "go", NAME: "protein phosphorylation", IDENTIFIER: "0006468", }, PMOD_CODE: "Ser", PMOD_POSITION: 473, } self.assertEqual(expected, ProteinModification("Ph", code="Ser", position=473)) self.assertEqual(expected, result.asDict()) def test_full(self): self._help_test_pmod_full("proteinModification(P, Ser, 473)") self._help_test_pmod_full("proteinModification(P, S, 473)") self._help_test_pmod_full("proteinModification(Ph, Ser, 473)") self._help_test_pmod_full("proteinModification(Ph, S, 473)") self._help_test_pmod_full("pmod(P, Ser, 473)") self._help_test_pmod_full("pmod(P, S, 473)") self._help_test_pmod_full("pmod(Ph, Ser, 473)") self._help_test_pmod_full("pmod(Ph, S, 473)") def _help_test_non_standard_namespace(self, statement): result = self.parser.parseString(statement) expected = { KIND: PMOD, CONCEPT: Entity(namespace="MOD", name="PhosRes"), PMOD_CODE: "Ser", PMOD_POSITION: 473, } self.assertEqual( expected, ProteinModification(name="PhosRes", namespace="MOD", code="Ser", position=473), ) self.assertEqual(expected, result.asDict()) def test_full_with_non_standard_namespace(self): self._help_test_non_standard_namespace("proteinModification(MOD:PhosRes, S, 473)") self._help_test_non_standard_namespace("proteinModification(MOD:PhosRes, Ser, 473)") self._help_test_non_standard_namespace("proteinModification(MOD:PhosRes, S, 473)") self._help_test_non_standard_namespace("proteinModification(MOD:PhosRes, Ser, 473)") class TestGeneModification(unittest.TestCase): def setUp(self): identifier_parser = ConceptParser() self.parser = get_gene_modification_language( concept_fqualified=identifier_parser.identifier_fqualified, concept_qualified=identifier_parser.identifier_qualified, ) self.expected = GeneModification("Me") def test_dsl(self): self.assertEqual( { KIND: GMOD, CONCEPT: { NAME: "DNA methylation", IDENTIFIER: "0006306", NAMESPACE: "go", }, }, self.expected, ) def test_gmod_short(self): statement = "geneModification(M)" result = self.parser.parseString(statement) self.assertEqual(self.expected, result.asDict()) def test_gmod_unabbreviated(self): statement = "geneModification(Me)" result = self.parser.parseString(statement) self.assertEqual(self.expected, result.asDict()) def test_gmod_long(self): statement = "geneModification(methylation)" result = self.parser.parseString(statement) self.assertEqual(self.expected, result.asDict()) class TestProteinSubstitution(unittest.TestCase): def setUp(self): self.parser = get_protein_substitution_language() def test_psub_1(self): statement = "sub(A, 127, Y)" result = self.parser.parseString(statement) expected_list = Hgvs("p.Ala127Tyr") self.assertEqual(expected_list, result.asDict()) def test_psub_2(self): statement = "sub(Ala, 127, Tyr)" result = self.parser.parseString(statement) expected_list = Hgvs("p.Ala127Tyr") self.assertEqual(expected_list, result.asDict()) class TestGeneSubstitutionParser(unittest.TestCase): def setUp(self): self.parser = get_gene_substitution_language() def test_gsub(self): statement = "sub(G,308,A)" result = self.parser.parseString(statement) expected_dict = Hgvs("c.308G>A") self.assertEqual(expected_dict, result.asDict()) class TestFragmentParser(unittest.TestCase): """See http://openbel.org/language/web/version_2.0/bel_specification_version_2.0.html#_examples_2""" def setUp(self): self.parser = get_fragment_language() def _help_test_known_length(self, s): result = self.parser.parseString(s) expected = {KIND: FRAGMENT, FRAGMENT_START: 5, FRAGMENT_STOP: 20} self.assertEqual(expected, result.asDict()) def test_known_length_unquoted(self): """test known length""" s = "frag(5_20)" self._help_test_known_length(s) def test_known_length_quotes(self): """test known length""" s = 'frag("5_20")' self._help_test_known_length(s) def _help_test_unknown_length(self, s): result = self.parser.parseString(s) expected = {KIND: FRAGMENT, FRAGMENT_START: 1, FRAGMENT_STOP: "?"} self.assertEqual(expected, result.asDict()) def test_unknown_length_unquoted(self): """amino-terminal fragment of unknown length""" s = "frag(1_?)" self._help_test_unknown_length(s) def test_unknown_length_quoted(self): """amino-terminal fragment of unknown length""" s = 'frag("1_?")' self._help_test_unknown_length(s) def _help_test_unknown_start_stop(self, s): result = self.parser.parseString(s) expected = {KIND: FRAGMENT, FRAGMENT_START: "?", FRAGMENT_STOP: "*"} self.assertEqual(expected, result.asDict()) def test_unknown_start_stop_unquoted(self): """fragment with unknown start/stop""" s = "frag(?_*)" self._help_test_unknown_start_stop(s) def test_unknown_start_stop_quoted(self): """fragment with unknown start/stop""" s = 'frag("?_*")' self._help_test_unknown_start_stop(s) def _help_test_descriptor(self, s): result = self.parser.parseString(s) expected = {KIND: FRAGMENT, FRAGMENT_MISSING: "?", FRAGMENT_DESCRIPTION: "55kD"} self.assertEqual(expected, result.asDict()) def test_descriptor_unquoted(self): """fragment with unknown start/stop and a descriptor""" s = 'frag(?, "55kD")' self._help_test_descriptor(s) def test_descriptor_quoted(self): """fragment with unknown start/stop and a descriptor""" s = 'frag("?", "55kD")' self._help_test_descriptor(s) class TestTruncationParser(unittest.TestCase): def setUp(self): self.parser = get_truncation_language() def test_trunc_1(self): statement = "trunc(40)" result = self.parser.parseString(statement) expected = Hgvs("p.40*") self.assertEqual(expected, result.asDict()) def test_trunc_2(self): """Test a truncation in which the amino acid is specified.""" statement = "trunc(Gly40)" result = self.parser.parseString(statement) expected = Hgvs("p.Gly40*") self.assertEqual(expected, result.asDict()) def test_trunc_missing_number(self): """Test that an error is raised for a truncation in which the position is omitted.""" statement = "trunc(Gly)" with self.assertRaises(Exception): self.parser.parseString(statement) class TestFusionParser(unittest.TestCase): def setUp(self): identifier_parser = ConceptParser(namespace_to_pattern={"HGNC": re.compile(".*")}) identifier_qualified = identifier_parser.identifier_qualified self.parser = get_fusion_language(identifier_qualified) def test_rna_fusion_known_breakpoints(self): """RNA abundance of fusion with known breakpoints""" statement = "fus(HGNC:TMPRSS2, r.1_79, HGNC:ERG, r.312_5034)" result = self.parser.parseString(statement) expected = { PARTNER_5P: { CONCEPT: { NAMESPACE: "HGNC", NAME: "TMPRSS2", }, }, RANGE_5P: {FUSION_REFERENCE: "r", FUSION_START: 1, FUSION_STOP: 79}, PARTNER_3P: { CONCEPT: { NAMESPACE: "HGNC", NAME: "ERG", }, }, RANGE_3P: { FUSION_REFERENCE: "r", FUSION_START: 312, FUSION_STOP: 5034, }, } self.assertEqual(expected, result.asDict()) def test_rna_fusion_unspecified_breakpoints(self): """RNA abundance of fusion with unspecified breakpoints""" statement = "fus(HGNC:TMPRSS2, ?, HGNC:ERG, ?)" result = self.parser.parseString(statement) expected = { PARTNER_5P: { CONCEPT: { NAMESPACE: "HGNC", NAME: "TMPRSS2", } }, RANGE_5P: {FUSION_MISSING: "?"}, PARTNER_3P: { CONCEPT: { NAMESPACE: "HGNC", NAME: "ERG", }, }, RANGE_3P: {FUSION_MISSING: "?"}, } self.assertEqual(expected, result.asDict()) def test_rna_fusion_specified_one_fuzzy_breakpoint(self): """RNA abundance of fusion with unspecified breakpoints""" statement = "fusion(HGNC:TMPRSS2, r.1_79, HGNC:ERG, r.?_1)" result = self.parser.parseString(statement) expected = { PARTNER_5P: { CONCEPT: { NAMESPACE: "HGNC", NAME: "TMPRSS2", }, }, RANGE_5P: {FUSION_REFERENCE: "r", FUSION_START: 1, FUSION_STOP: 79}, PARTNER_3P: { CONCEPT: { NAMESPACE: "HGNC", NAME: "ERG", }, }, RANGE_3P: {FUSION_REFERENCE: "r", FUSION_START: "?", FUSION_STOP: 1}, } self.assertEqual(expected, result.asDict()) def test_rna_fusion_specified_fuzzy_breakpoints(self): """RNA abundance of fusion with unspecified breakpoints""" statement = "fusion(HGNC:TMPRSS2, r.1_?, HGNC:ERG, r.?_1)" result = self.parser.parseString(statement) expected = { PARTNER_5P: { CONCEPT: { NAMESPACE: "HGNC", NAME: "TMPRSS2", }, }, RANGE_5P: {FUSION_REFERENCE: "r", FUSION_START: 1, FUSION_STOP: "?"}, PARTNER_3P: { CONCEPT: { NAMESPACE: "HGNC", NAME: "ERG", }, }, RANGE_3P: {FUSION_REFERENCE: "r", FUSION_START: "?", FUSION_STOP: 1}, } self.assertEqual(expected, result.asDict()) class TestLocation(unittest.TestCase): def setUp(self): identifier_parser = ConceptParser(namespace_to_pattern={"GO": re.compile(".*")}) identifier_qualified = identifier_parser.identifier_qualified self.parser = get_location_language(identifier_qualified) def test_a(self): statement = "loc(GO:intracellular)" result = self.parser.parseString(statement) expected = {LOCATION: {NAMESPACE: "GO", NAME: "intracellular"}} self.assertEqual(expected, result.asDict()) pybel-0.15.5/tests/test_parse/test_parse_control.py000066400000000000000000000312131426625374700225330ustar00rootroot00000000000000# -*- coding: utf-8 -*- import logging import re import unittest from random import randint from pybel.constants import ( ANNOTATIONS, CITATION, CITATION_TYPE_PUBMED, EVIDENCE, IDENTIFIER, NAMESPACE, ) from pybel.exceptions import ( CitationTooLongException, CitationTooShortException, IllegalAnnotationValueWarning, InvalidCitationType, InvalidPubMedIdentifierWarning, MissingAnnotationKeyWarning, MissingAnnotationRegexWarning, UndefinedAnnotationWarning, ) from pybel.language import Entity from pybel.parser import ControlParser from pybel.parser.parse_control import set_citation_stub from pybel.testing.utils import n from tests.constants import SET_CITATION_TEST, test_citation_dict logging.getLogger("requests").setLevel(logging.WARNING) class TestParseControl(unittest.TestCase): def setUp(self): self.annotation_to_term = { "Custom1": {"Custom1_A", "Custom1_B"}, "Custom2": {"Custom2_A", "Custom2_B"}, } self.annotation_to_pattern = {"CustomRegex": re.compile("[0-9]+")} self.parser = ControlParser( annotation_to_term=self.annotation_to_term, annotation_to_pattern=self.annotation_to_pattern, ) class TestParseControlUnsetStatementErrors(TestParseControl): def test_unset_missing_evidence(self): with self.assertRaises(MissingAnnotationKeyWarning): self.parser.parseString("UNSET Evidence") def test_unset_missing_citation(self): with self.assertRaises(MissingAnnotationKeyWarning): self.parser.parseString("UNSET Citation") def test_unset_missing_evidence_with_citation(self): """Tests that an evidence can't be unset without a citation""" s = [SET_CITATION_TEST, "UNSET Evidence"] with self.assertRaises(MissingAnnotationKeyWarning): self.parser.parse_lines(s) def test_unset_missing_statement_group(self): with self.assertRaises(MissingAnnotationKeyWarning): self.parser.parseString("UNSET STATEMENT_GROUP") def test_unset_missing_command(self): s = [SET_CITATION_TEST, "UNSET Custom1"] with self.assertRaises(MissingAnnotationKeyWarning): self.parser.parse_lines(s) def test_unset_invalid_command(self): s = [SET_CITATION_TEST, "UNSET MISSING"] with self.assertRaises(UndefinedAnnotationWarning): self.parser.parse_lines(s) def test_unset_list_compact(self): """Tests unsetting an annotation list, without spaces in it""" s = [ SET_CITATION_TEST, 'SET Custom1 = "Custom1_A"', 'SET Custom2 = "Custom2_A"', ] self.parser.parse_lines(s) self.assertIn("Custom1", self.parser.annotations) self.assertIn("Custom2", self.parser.annotations) self.parser.parseString("UNSET {Custom1,Custom2}") self.assertFalse(self.parser.annotations) def test_unset_list_spaced(self): """Tests unsetting an annotation list, with spaces in it""" s = [ SET_CITATION_TEST, 'SET Custom1 = "Custom1_A"', 'SET Custom2 = "Custom2_A"', ] self.parser.parse_lines(s) self.assertIn("Custom1", self.parser.annotations) self.assertIn("Custom2", self.parser.annotations) self.parser.parseString("UNSET {Custom1, Custom2}") self.assertFalse(self.parser.annotations) class TestSetCitation(unittest.TestCase): def test_parser_double(self): set_citation_stub.parseString('Citation = {"PubMed","12928037"}') def test_parser_double_spaced(self): set_citation_stub.parseString('Citation = {"PubMed", "12928037"}') def test_parser_triple(self): set_citation_stub.parseString('Citation = {"PubMed Central","Trends in molecular medicine","12928037"}') def test_parser_triple_spaced(self): set_citation_stub.parseString('Citation = {"PubMed Central", "Trends in molecular medicine", "12928037"}') class TestParseControlSetStatementErrors(TestParseControl): def test_invalid_citation_type(self): with self.assertRaises(InvalidCitationType): self.parser.parseString('SET Citation = {"PubMedCentral","Trends in molecular medicine","12928037"}') def test_invalid_pmid(self): with self.assertRaises(InvalidPubMedIdentifierWarning): self.parser.parseString('SET Citation = {"PubMed","Trends in molecular medicine","NOT VALID NUMBER"}') def test_invalid_pmid_short(self): with self.assertRaises(InvalidPubMedIdentifierWarning): self.parser.parseString('SET Citation = {"PubMed","NOT VALID NUMBER"}') def test_set_missing_statement(self): statements = [SET_CITATION_TEST, 'SET MissingKey = "lol"'] with self.assertRaises(UndefinedAnnotationWarning): self.parser.parse_lines(statements) def test_custom_annotation_list_withInvalid(self): statements = [ SET_CITATION_TEST, 'SET Custom1 = {"Custom1_A","Custom1_B","Evil invalid!!!"}', ] with self.assertRaises(IllegalAnnotationValueWarning): self.parser.parse_lines(statements) def test_custom_value_failure(self): """Tests what happens for a valid annotation key, but an invalid value""" s = [SET_CITATION_TEST, 'SET Custom1 = "Custom1_C"'] with self.assertRaises(IllegalAnnotationValueWarning): self.parser.parse_lines(s) def test_regex_failure(self): s = [SET_CITATION_TEST, 'SET CustomRegex = "abce13"'] with self.assertRaises(MissingAnnotationRegexWarning): self.parser.parse_lines(s) class TestParseControl2(TestParseControl): def test_set_statement_group(self): """Tests a statement group gets set properly""" s1 = 'SET STATEMENT_GROUP = "my group"' self.assertIsNone(self.parser.statement_group) self.parser.parseString(s1) self.assertEqual("my group", self.parser.statement_group, msg="problem with integration") s2 = "UNSET STATEMENT_GROUP" self.parser.parseString(s2) self.assertIsNone(self.parser.statement_group, msg="problem with unset") def test_citation_short(self): self.parser.parseString(SET_CITATION_TEST) self.assertEqual(test_citation_dict[IDENTIFIER], self.parser.citation_db_id) self.assertEqual(test_citation_dict[NAMESPACE], self.parser.citation_db) expected_annotations = { EVIDENCE: None, ANNOTATIONS: {}, CITATION: test_citation_dict, } self.assertEqual(expected_annotations, self.parser.get_annotations()) self.parser.parseString("UNSET Citation") self.assertFalse(self.parser.citation_is_set) def test_citation_invalid_date(self): s = 'SET Citation = {"PubMed","Trends in molecular medicine","12928037","01-12-1999","de Nigris"}' self.parser.parseString(s) self.assertEqual(CITATION_TYPE_PUBMED, self.parser.citation_db) self.assertEqual("12928037", self.parser.citation_db_id) expected_dict = { EVIDENCE: None, ANNOTATIONS: {}, CITATION: { NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: "12928037", }, } self.assertEqual(expected_dict, self.parser.get_annotations()) def test_citation_with_empty_comment(self): s = 'SET Citation = {"PubMed","Test Name","12928037","1999-01-01","de Nigris|Lerman A|Ignarro LJ",""}' self.parser.parseString(s) self.assertEqual(CITATION_TYPE_PUBMED, self.parser.citation_db) self.assertEqual("12928037", self.parser.citation_db_id) expected_dict = { EVIDENCE: None, ANNOTATIONS: {}, CITATION: { NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: "12928037", }, } self.assertEqual(expected_dict, self.parser.get_annotations()) def test_double(self): s = 'SET Citation = {"PubMed","12928037"}' self.parser.parseString(s) self.assertEqual(CITATION_TYPE_PUBMED, self.parser.citation_db) self.assertEqual("12928037", self.parser.citation_db_id) def test_double_with_space(self): """Same as test_double, but has a space between the comma and next entry""" s = 'SET Citation = {"PubMed", "12928037"}' self.parser.parseString(s) self.assertEqual(CITATION_TYPE_PUBMED, self.parser.citation_db) self.assertEqual("12928037", self.parser.citation_db_id) def test_citation_too_short(self): s = 'SET Citation = {"PubMed"}' with self.assertRaises(CitationTooShortException): self.parser.parseString(s) def test_citation_too_long(self): s = 'SET Citation = {"PubMed","Name","1234","1999-01-01","Nope|Noper","Nope", "nope nope"}' with self.assertRaises(CitationTooLongException): self.parser.parseString(s) def test_evidence(self): self.parser.parseString(SET_CITATION_TEST) s = 'SET Evidence = "For instance, during 7-ketocholesterol-induced apoptosis of U937 cells"' self.parser.parseString(s) self.assertIsNotNone(self.parser.evidence) expected_annotation = { CITATION: test_citation_dict, ANNOTATIONS: {}, EVIDENCE: "For instance, during 7-ketocholesterol-induced apoptosis of U937 cells", } self.assertEqual(expected_annotation, self.parser.get_annotations()) def test_custom_annotation(self): s = [SET_CITATION_TEST, 'SET Custom1 = "Custom1_A"'] self.parser.parse_lines(s) expected_annotation = { "Custom1": [Entity(namespace="Custom1", identifier="Custom1_A")], } self.assertEqual(expected_annotation, self.parser.annotations) def test_custom_annotation_list(self): s = [SET_CITATION_TEST, 'SET Custom1 = {"Custom1_A","Custom1_B"}'] self.parser.parse_lines(s) expected_annotation = { "Custom1": [ Entity(namespace="Custom1", identifier="Custom1_A"), Entity(namespace="Custom1", identifier="Custom1_B"), ], } self.assertEqual(expected_annotation, self.parser.annotations) expected_dict = { ANNOTATIONS: expected_annotation, CITATION: test_citation_dict, EVIDENCE: None, } self.assertEqual(expected_dict, self.parser.get_annotations()) def test_overwrite_evidence(self): s1 = 'SET Evidence = "a"' s2 = 'SET Evidence = "b"' self.parser.parseString(s1) self.parser.parseString(s2) self.assertEqual("b", self.parser.evidence) def test_unset_evidence(self): s1 = 'SET Evidence = "a"' s2 = "UNSET Evidence" self.parser.parseString(s1) self.parser.parseString(s2) self.assertEqual({}, self.parser.annotations) def test_unset_custom(self): statements = [SET_CITATION_TEST, 'SET Custom1 = "Custom1_A"', "UNSET Custom1"] self.parser.parse_lines(statements) self.assertEqual({}, self.parser.annotations) def test_reset_citation(self): s1_identifier = str(randint(0, 1e7)) s1 = 'SET Citation = {{"PubMed","Test Reference 1","{}"}}'.format(s1_identifier) s2 = 'SET Evidence = "d"' s3_identifier = str(randint(0, 1e7)) s3 = 'SET Citation = {{"PubMed","Test Reference 2","{}"}}'.format(s3_identifier) _test_evidence = n() s4 = 'SET Evidence = "{}"'.format(_test_evidence) s5 = 'SET Custom1 = "Custom1_A"' s6 = 'SET Custom2 = "Custom2_A"' statements = [s1, s2, s3, s4, s5, s6] self.parser.parse_lines(statements) self.assertEqual(_test_evidence, self.parser.evidence) self.assertEqual(CITATION_TYPE_PUBMED, self.parser.citation_db) self.assertEqual(s3_identifier, self.parser.citation_db_id) self.parser.parseString("UNSET {Custom1,Evidence}") self.assertNotIn("Custom1", self.parser.annotations) self.assertIsNone(self.parser.evidence) self.assertIn("Custom2", self.parser.annotations) self.assertTrue(self.parser.citation_is_set) self.parser.parseString("UNSET ALL") self.assertEqual(0, len(self.parser.annotations)) self.assertFalse(self.parser.citation_is_set) def test_set_regex(self): v = str(randint(0, 1e5)) s = [SET_CITATION_TEST, f'SET CustomRegex = "{v}"'] self.parser.parse_lines(s) self.assertEqual( [ Entity(namespace="CustomRegex", identifier=v), ], self.parser.annotations["CustomRegex"], ) pybel-0.15.5/tests/test_parse/test_parse_identifier.py000066400000000000000000000154361426625374700232060ustar00rootroot00000000000000# -*- coding: utf-8 -*- import re import unittest from pybel.constants import DIRTY from pybel.exceptions import MissingNamespaceRegexWarning, NakedNameWarning from pybel.parser import ConceptParser class _ParserMixin(unittest.TestCase): def setUp(self): self.namespace_to_term = { "A": { (None, "1"): "P", (None, "2"): "P", (None, "3"): "P", }, "B": { (None, "4"): "P", (None, "5"): "P", (None, "6"): "P", }, } class TestConceptEnumerated(_ParserMixin): def setUp(self): super().setUp() self.parser = ConceptParser(namespace_to_term_to_encoding=self.namespace_to_term) def test_valid_1(self): s = "A:3" result = self.parser.parseString(s) self.assertIn("namespace", result) self.assertIn("name", result) self.assertEqual("A", result["namespace"]) self.assertEqual("3", result["name"]) def test_valid_2(self): s = 'A:"3"' result = self.parser.parseString(s) self.assertIn("namespace", result) self.assertIn("name", result) self.assertEqual("A", result["namespace"]) self.assertEqual("3", result["name"]) def test_invalid_1(self): s = "C:4" with self.assertRaises(Exception): self.parser.parseString(s) def test_invalid_2(self): s = "A:4" with self.assertRaises(Exception): self.parser.parseString(s) def test_invalid_3(self): s = "bare" with self.assertRaises(NakedNameWarning): self.parser.parseString(s) def test_invalid_4(self): s = '"quoted"' with self.assertRaises(NakedNameWarning): self.parser.parseString(s) class TestConceptParserDefault(_ParserMixin): """Tests where the concept parser allows an enumerated list of unqualified entities.""" def setUp(self): super().setUp() default_namespace = {"X", "Y", "W Z"} self.parser = ConceptParser( namespace_to_term_to_encoding=self.namespace_to_term, default_namespace=default_namespace, ) def test_valid_1(self): s = "A:3" result = self.parser.parseString(s) self.assertIn("namespace", result) self.assertIn("name", result) self.assertNotIn("identifier", result) self.assertEqual("A", result["namespace"]) self.assertEqual("3", result["name"]) def test_valid_2(self): s = "X" result = self.parser.parseString(s) self.assertIn("name", result) self.assertEqual("X", result["name"]) def test_valid_3(self): s = '"W Z"' result = self.parser.parseString(s) self.assertIn("name", result) self.assertNotIn("identifier", result) self.assertEqual("W Z", result["name"]) def test_not_in_defaultNs(self): s = "D" with self.assertRaises(Exception): self.parser.parseString(s) class TestConceptParserLenient(_ParserMixin): """Tests where naked names are allowed.""" def setUp(self): super().setUp() self.parser = ConceptParser( namespace_to_term_to_encoding=self.namespace_to_term, allow_naked_names=True, ) def test_valid_1(self): s = "A:3" result = self.parser.parseString(s) self.assertIn("namespace", result) self.assertIn("name", result) self.assertNotIn("identifier", result) self.assertEqual("A", result["namespace"]) self.assertEqual("3", result["name"]) def test_valid_2(self): s = 'A:"3"' result = self.parser.parseString(s) self.assertIn("namespace", result) self.assertIn("name", result) self.assertNotIn("identifier", result) self.assertEqual("A", result["namespace"]) self.assertEqual("3", result["name"]) def test_invalid_1(self): s = "C:4" with self.assertRaises(Exception): self.parser.parseString(s) def test_invalid_2(self): s = "A:4" with self.assertRaises(Exception): self.parser.parseString(s) def test_not_invalid_3(self): s = "bare" result = self.parser.parseString(s) self.assertIn("namespace", result) self.assertIn("name", result) self.assertNotIn("identifier", result) self.assertEqual(DIRTY, result["namespace"]) self.assertEqual("bare", result["name"]) def test_not_invalid_4(self): s = '"quoted"' result = self.parser.parseString(s) self.assertIn("namespace", result) self.assertIn("name", result) self.assertNotIn("identifier", result) self.assertEqual(DIRTY, result["namespace"]) self.assertEqual("quoted", result["name"]) class TestConceptParserRegex(unittest.TestCase): """Tests for regular expression parsing""" def setUp(self) -> None: self.parser = ConceptParser( namespace_to_pattern={ "hgnc": re.compile(r"\d+"), "ec-code": re.compile(r".+"), } ) self.assertEqual({}, self.parser.namespace_to_identifier_to_encoding) self.assertEqual({}, self.parser.namespace_to_name_to_encoding) def test_valid(self): for curie, namespace, name in [ ("hgnc:391", "hgnc", "391"), ("ec-code:1.1.1.27", "ec-code", "1.1.1.27"), ]: with self.subTest(curie=curie): result = self.parser.parseString(curie) self.assertIn("namespace", result) self.assertIn("name", result) self.assertNotIn("identifier", result) self.assertEqual(namespace, result["namespace"]) self.assertEqual(name, result["name"]) def test_invalid(self): """Test invalid BEL term.""" s = "hgnc:AKT1" with self.assertRaises(MissingNamespaceRegexWarning): result = self.parser.parseString(s) print(result.asDict()) def test_valid_obo(self): """Test parsing an identifier that has a name.""" s = "hgnc:391 ! AKT1" result = self.parser.parseString(s) self.assertIn("namespace", result) self.assertIn("name", result) self.assertIn("identifier", result) self.assertEqual("hgnc", result["namespace"]) self.assertEqual("AKT1", result["name"]) self.assertEqual("391", result["identifier"]) def test_invalid_obo(self): """Test parsing an OBO-style identifier where the identifier and name are switched.""" s = "hgnc:AKT1 ! 391" with self.assertRaises(MissingNamespaceRegexWarning): result = self.parser.parseString(s) print(result.asDict()) pybel-0.15.5/tests/test_parse/test_parse_metadata.py000066400000000000000000000205621426625374700226400ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test parsing metadata from a BEL script.""" import logging import os import re import unittest from pathlib import Path from bel_resources.constants import ANNOTATION_URL_FMT, NAMESPACE_URL_FMT from pybel.exceptions import ( InvalidMetadataException, RedefinedAnnotationError, RedefinedNamespaceError, VersionFormatWarning, ) from pybel.parser import MetadataParser from pybel.resources import HGNC_URL from pybel.testing.cases import FleetingTemporaryCacheMixin from pybel.testing.constants import test_an_1, test_ns_1 from pybel.testing.mocks import mock_bel_resources from tests.constants import ( HGNC_KEYWORD, MESH_DISEASES_KEYWORD, MESH_DISEASES_URL, help_check_hgnc, ) logging.getLogger("requests").setLevel(logging.WARNING) LOCAL_TEST_PATH = os.path.expanduser("~/dev/pybel/src/pybel/testing/resources/belns/hgnc-names.belns") class TestParseMetadata(FleetingTemporaryCacheMixin): def setUp(self): super(TestParseMetadata, self).setUp() self.parser = MetadataParser(manager=self.manager) def _help_test_local_annotation(self, annotation: str) -> None: """Check that the annotation is defined locally.""" self.assertTrue(self.parser.has_annotation(annotation)) self.assertNotIn(annotation, self.parser.annotation_to_term) self.assertFalse(self.parser.has_enumerated_annotation(annotation)) self.assertNotIn(annotation, self.parser.annotation_to_pattern) self.assertFalse(self.parser.has_regex_annotation(annotation)) self.assertIn(annotation, self.parser.annotation_to_local) self.assertTrue(self.parser.has_local_annotation(annotation)) @mock_bel_resources def test_namespace_name_persistience(self, mock_get): """Test that a namespace defined by a URL can't be overwritten by a definition by another URL.""" s = NAMESPACE_URL_FMT.format(HGNC_KEYWORD, HGNC_URL) self.parser.parseString(s) self.parser.ensure_resources() help_check_hgnc(self, self.parser.namespace_to_term_to_encoding) s = NAMESPACE_URL_FMT.format(HGNC_KEYWORD, "XXXXX") with self.assertRaises(RedefinedNamespaceError): self.parser.parseString(s) help_check_hgnc(self, self.parser.namespace_to_term_to_encoding) @mock_bel_resources def test_annotation_name_persistience_1(self, mock_get): """Test that an annotation defined by a URL can't be overwritten by a definition by a list.""" s = ANNOTATION_URL_FMT.format(MESH_DISEASES_KEYWORD, MESH_DISEASES_URL) self.parser.parseString(s) self.parser.ensure_resources() self.assertIn(MESH_DISEASES_KEYWORD, self.parser.annotation_to_term) s = 'DEFINE ANNOTATION {} AS LIST {{"A","B","C"}}'.format(MESH_DISEASES_KEYWORD) with self.assertRaises(RedefinedAnnotationError): self.parser.parseString(s) self.assertIn(MESH_DISEASES_KEYWORD, self.parser.annotation_to_term) self.assertNotIn("A", self.parser.annotation_to_term[MESH_DISEASES_KEYWORD]) self.assertIn( "46, XX Disorders of Sex Development", self.parser.annotation_to_term[MESH_DISEASES_KEYWORD], ) def test_annotation_name_persistience_2(self): """Tests that an annotation defined by a list can't be overwritten by a definition by URL""" s = 'DEFINE ANNOTATION TextLocation AS LIST {"Abstract","Results","Legend","Review"}' self.parser.parseString(s) self._help_test_local_annotation("TextLocation") s = ANNOTATION_URL_FMT.format("TextLocation", MESH_DISEASES_URL) with self.assertRaises(RedefinedAnnotationError): self.parser.parseString(s) self._help_test_local_annotation("TextLocation") self.assertIn("Abstract", self.parser.annotation_to_local["TextLocation"]) def test_underscore(self): """Tests that an underscore is a valid character in an annotation name""" s = 'DEFINE ANNOTATION Text_Location AS LIST {"Abstract","Results","Legend","Review"}' self.parser.parseString(s) self._help_test_local_annotation("Text_Location") @mock_bel_resources def test_control_compound(self, mock_get): text_location = "TextLocation" lines = [ ANNOTATION_URL_FMT.format(MESH_DISEASES_KEYWORD, MESH_DISEASES_URL), NAMESPACE_URL_FMT.format(HGNC_KEYWORD, HGNC_URL), 'DEFINE ANNOTATION TextLocation AS LIST {"Abstract","Results","Legend","Review"}', ] self.parser.parse_lines(lines) self.parser.ensure_resources() self.assertIn(MESH_DISEASES_KEYWORD, self.parser.annotation_to_term) self.assertIn(HGNC_KEYWORD, self.parser.namespace_to_term_to_encoding) self._help_test_local_annotation(text_location) @unittest.skipUnless(os.path.exists(LOCAL_TEST_PATH), "Need local files to test local files") def test_squiggly_filepath(self): line = NAMESPACE_URL_FMT.format(HGNC_KEYWORD, LOCAL_TEST_PATH) self.parser.parseString(line) help_check_hgnc(self, self.parser.namespace_to_term_to_encoding) def test_document_metadata_exception(self): s = 'SET DOCUMENT InvalidKey = "nope"' with self.assertRaises(InvalidMetadataException): self.parser.parseString(s) def test_parse_document(self): s = '''SET DOCUMENT Name = "Alzheimer's Disease Model"''' self.parser.parseString(s) self.assertIn("name", self.parser.document_metadata) self.assertEqual("Alzheimer's Disease Model", self.parser.document_metadata["name"]) # Check nothing bad happens # with self.assertLogs('pybel', level='WARNING'): self.parser.parseString(s) @mock_bel_resources def test_parse_namespace_url_file(self, mock): """Tests parsing a namespace by file URL""" s = NAMESPACE_URL_FMT.format("TESTNS1", test_ns_1) self.parser.parseString(s) self.parser.ensure_resources() expected_values = { "TestValue1": {"O"}, "TestValue2": {"O"}, "TestValue3": {"O"}, "TestValue4": {"O"}, "TestValue5": {"O"}, } self.assertIn("TESTNS1", self.parser.namespace_to_term_to_encoding) for k, values in expected_values.items(): k = (None, k) self.assertIn(k, self.parser.namespace_to_term_to_encoding["TESTNS1"]) self.assertEqual( set(values), set(self.parser.namespace_to_term_to_encoding["TESTNS1"][k]), ) def test_parse_annotation_url_file(self): """Tests parsing an annotation by file URL""" keyword = "TESTAN1" url = Path(test_an_1).as_uri() line = ANNOTATION_URL_FMT.format(keyword, url) self.parser.parseString(line) self.parser.ensure_resources() expected_values = { "TestAnnot1": "O", "TestAnnot2": "O", "TestAnnot3": "O", "TestAnnot4": "O", "TestAnnot5": "O", } annotation = self.parser.manager.get_namespace_by_url(url) self.assertIsNotNone(annotation) self.assertEqual(set(expected_values), {e.name for e in annotation.entries}) def test_parse_annotation_pattern(self): s = r'DEFINE ANNOTATION Test AS PATTERN "\w+"' self.parser.parseString(s) self.assertNotIn("Test", self.parser.annotation_to_term) self.assertIn("Test", self.parser.annotation_to_pattern) self.assertEqual(re.compile(r"\w+"), self.parser.annotation_to_pattern["Test"]) def test_define_namespace_regex(self): for s, namespace, regex in [ ( 'DEFINE NAMESPACE dbSNP AS PATTERN "rs[0-9]*"', "dbSNP", re.compile(r"rs[0-9]*"), ), ('DEFINE NAMESPACE ec-code AS PATTERN ".*"', "ec-code", re.compile(r".*")), ]: with self.subTest(namespace=namespace): self.parser.parseString(s) self.assertNotIn(namespace, self.parser.namespace_to_term_to_encoding) self.assertIn(namespace, self.parser.namespace_to_pattern) self.assertEqual(regex, self.parser.namespace_to_pattern[namespace]) def test_not_semantic_version(self): s = 'SET DOCUMENT Version = "1.0"' with self.assertRaises(VersionFormatWarning): self.parser.parseString(s) pybel-0.15.5/tests/test_parse/test_parse_utils.py000066400000000000000000000046071426625374700222220ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for parsing utilities.""" import unittest import networkx as nx from pybel.utils import subdict_matches from tests.constants import any_subdict_matches class TestSubdictMatching(unittest.TestCase): """Tests for matching sub-dictionaries.""" def test_dict_matches_1(self): target = {"k1": "v1", "k2": "v2"} query = {"k1": "v1", "k2": "v2"} self.assertTrue(subdict_matches(target, query)) def test_dict_matches_2(self): target = {"k1": "v1", "k2": "v2", "k3": "v3"} query = {"k1": "v1", "k2": "v2"} self.assertTrue(subdict_matches(target, query)) def test_dict_matches_3(self): target = { "k1": "v1", } query = {"k1": "v1", "k2": "v2"} self.assertFalse(subdict_matches(target, query)) def test_dict_matches_4(self): target = {"k1": "v1", "k2": "v4", "k3": "v3"} query = {"k1": "v1", "k2": "v2"} self.assertFalse(subdict_matches(target, query)) def test_dict_matches_5(self): target = {"k1": "v1", "k2": "v2"} query = {"k1": "v1", "k2": ["v2", "v3"]} self.assertTrue(subdict_matches(target, query)) def test_dict_matches_6(self): target = {"k1": "v1", "k2": ["v2", "v3"]} query = {"k1": "v1", "k2": "v4"} self.assertFalse(subdict_matches(target, query)) def test_dict_matches_7_partial(self): """Tests a partial match""" target = {"k1": "v1", "k2": "v2"} query = {"k1": "v1", "k2": {"v2": "v3"}} self.assertFalse(subdict_matches(target, query)) def test_dict_matches_7_exact(self): """Test a partial match.""" target = {"k1": "v1", "k2": "v2"} query = {"k1": "v1", "k2": {"v2": "v3"}} self.assertFalse(subdict_matches(target, query, partial_match=False)) def test_dict_matches_8_partial(self): """Test a partial match.""" target = {"k1": "v1", "k2": {"v2": "v3", "v4": "v5"}} query = {"k1": "v1", "k2": {"v2": "v3"}} self.assertTrue(subdict_matches(target, query)) def test_dict_matches_graph(self): """Test matching a graph.""" g = nx.MultiDiGraph() g.add_node(1) g.add_node(2) g.add_edge(1, 2, relation="yup") g.add_edge(1, 2, relation="nope") d = {"relation": "yup"} self.assertTrue(any_subdict_matches(g[1][2], d)) pybel-0.15.5/tests/test_parse/test_user_api.py000066400000000000000000000016501426625374700214720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test the user API.""" import unittest import pybel class TestParse(unittest.TestCase): """Test user API.""" def test_bel_statement(self): for bel in [ "p(hgnc:1234) -> p(hgnc:1235)", "p(hgnc:1234) hasVariant p(hgnc:1234, pmod(Ph))", "p(hgnc:1) => rxn(reactants(a(chebi:1), a(chebi:2)), products(a(chebi:3), a(chebi:4)))", "r(hgnc:1) pos r(hgnc:2)", "r(hgnc:1) cor r(hgnc:2)", "r(hgnc:1) eq r(ncbigene:5)", "p(hgnc:1) -> (p(hgnc:2) -> p(hgnc:3))", "p(hgnc:1)", ]: with self.subTest(bel=bel): x = pybel.parse(bel) def test_citation(self): x = pybel.parse("SET Citation = pmid:1234") def test_control(self): x = pybel.parse('SET Test = "5"') def test_control_multiple(self): x = pybel.parse('SET Test = {"1","2","3"}') pybel-0.15.5/tests/test_schema.py000066400000000000000000000135501426625374700167540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the jsonschema node validation.""" import copy import json import os import unittest import pybel.dsl from pybel.schema import is_valid_edge, is_valid_node from pybel.testing.utils import n NAMESPACE, NAME, IDENTIFIER = n(), n(), n() BLANK_ABUNDANCE = {"function": "", "variants": []} # Abundances PROTEIN = pybel.dsl.Protein(namespace=NAMESPACE, name=NAME, identifier=IDENTIFIER) GENE = pybel.dsl.Gene(NAMESPACE, name=NAME, identifier=IDENTIFIER) # Variants GENE_MOD = pybel.dsl.GeneModification(NAME, namespace=NAMESPACE, identifier=IDENTIFIER) PROTEIN_SUB = pybel.dsl.ProteinSubstitution("Ala", 1, "Tyr") PROTEIN_MOD = pybel.dsl.ProteinModification(NAME, code="Ala", position=1, namespace=NAMESPACE, identifier=IDENTIFIER) FRAGMENT = pybel.dsl.Fragment(1, 2) class TestNodeSchema(unittest.TestCase): """Tests for the jsonschema node validation.""" def test_pure_abundances(self): """Test validating abundance nodes.""" self.assertTrue(is_valid_node(PROTEIN)) self.assertTrue(is_valid_node(GENE)) abundance = pybel.dsl.Abundance(namespace=NAMESPACE, name=NAME, identifier=IDENTIFIER) self.assertTrue(is_valid_node(abundance)) def test_variant_abundances(self): """Test validating abundance nodes with variants.""" gmod = GENE.with_variants(GENE_MOD) self.assertTrue(is_valid_node(gmod)) psub = PROTEIN.with_variants(PROTEIN_SUB) self.assertTrue(is_valid_node(psub)) pmod = PROTEIN.with_variants(PROTEIN_MOD) self.assertTrue(is_valid_node(pmod)) frag = PROTEIN.with_variants(FRAGMENT) self.assertTrue(is_valid_node(frag)) def test_matching_variants(self): """Test catching invalid abundance/variant pairs, e.g. ProteinModification on a Gene.""" invalid_gmod = PROTEIN.with_variants(GENE_MOD) self.assertFalse(is_valid_node(invalid_gmod)) invalid_psub = GENE.with_variants(PROTEIN_SUB) self.assertFalse(is_valid_node(invalid_psub)) invalid_pmod = GENE.with_variants(PROTEIN_MOD) self.assertFalse(is_valid_node(invalid_pmod)) invalid_frag = GENE.with_variants(FRAGMENT) self.assertFalse(is_valid_node(invalid_frag)) def test_fusions(self): """Test validating fusion nodes.""" protein = pybel.dsl.Protein(namespace=NAMESPACE, name=NAME, identifier=IDENTIFIER) enum_fusion_range = pybel.dsl.EnumeratedFusionRange("r", 1, 79) missing_fusion_range = pybel.dsl.MissingFusionRange() fusion_node = pybel.dsl.FusionBase(protein, protein, range_5p=enum_fusion_range, range_3p=missing_fusion_range) self.assertTrue(is_valid_node(fusion_node)) def test_list_abundances(self): """Test validating list abundance nodes.""" complex_abundance = pybel.dsl.ComplexAbundance( [GENE.with_variants(GENE_MOD), PROTEIN.with_variants(PROTEIN_SUB)], namespace=NAMESPACE, name=NAME, identifier=IDENTIFIER, ) composite_abundance = pybel.dsl.CompositeAbundance([PROTEIN, complex_abundance]) self.assertTrue(is_valid_node(complex_abundance)) self.assertTrue(is_valid_node(composite_abundance)) def test_reaction(self): """Test validating a reaction node.""" node = pybel.dsl.Rna(namespace=NAMESPACE, name=NAME, identifier=IDENTIFIER) node_list = [node, PROTEIN, GENE] rxn = pybel.dsl.Reaction(reactants=node_list, products=node_list) self.assertTrue(is_valid_node(rxn)) def test_invalid_abundances(self): """Test that invalid abundances are caught.""" missing_fn = BLANK_ABUNDANCE.copy() missing_fn.pop("function") self.assertFalse(is_valid_node(missing_fn)) def test_invalid_psub(self): """Test that invalid protein substitutions are caught.""" missing_hgvs = dict(PROTEIN_SUB) # Remove the required "hgvs" property missing_hgvs.pop("hgvs") protein = BLANK_ABUNDANCE.copy() protein["function"] = "Protein" protein["variants"] = [missing_hgvs] self.assertFalse(is_valid_node(protein)) def test_invalid_amino(self): """Test that protein variants with invalid amino acid codes are caught.""" invalid_psub = dict(PROTEIN_SUB) invalid_psub["hgvs"] = "p.Aaa0Bbb" self.assertFalse(is_valid_node(invalid_psub)) invalid_pmod = dict(PROTEIN_MOD) invalid_pmod["code"] = "Aaa" self.assertFalse(is_valid_node(invalid_pmod)) class TestEdgeSchema(unittest.TestCase): """Tests for the jsonschema edge validation.""" @classmethod def setUpClass(cls): """Load the edge contained in example_edge.json.""" here = os.path.abspath(os.path.dirname(__file__)) example_file = os.path.join(here, "example_edge.json") with open(example_file) as example_json: edge = json.load(example_json) cls.example_edge = edge def test_predefined_example(self): """Test a predefined edge example.""" edge = self.example_edge self.assertTrue(is_valid_edge(edge)) def test_missing_information(self): """Test removing information from the predefined edge.""" edge = self.example_edge missing_source = copy.deepcopy(edge) missing_source.pop("source") self.assertFalse(is_valid_edge(missing_source)) missing_relation = copy.deepcopy(edge) missing_relation.pop("relation") self.assertFalse(is_valid_edge(missing_relation)) missing_target = copy.deepcopy(edge) missing_target.pop("target") self.assertFalse(is_valid_edge(missing_target)) missing_location = copy.deepcopy(edge) missing_location["target"]["effect"].pop("fromLoc") self.assertFalse(is_valid_edge(missing_location)) if __name__ == "__main__": unittest.main() pybel-0.15.5/tests/test_struct/000077500000000000000000000000001426625374700164625ustar00rootroot00000000000000pybel-0.15.5/tests/test_struct/__init__.py000066400000000000000000000000761426625374700205760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for :mod:`pybel.struct`.""" pybel-0.15.5/tests/test_struct/test_filters/000077500000000000000000000000001426625374700211715ustar00rootroot00000000000000pybel-0.15.5/tests/test_struct/test_filters/__init__.py000066400000000000000000000001011426625374700232720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for PyBEL filter functions.""" pybel-0.15.5/tests/test_struct/test_filters/test_edge_predicate_builders.py000066400000000000000000000071751426625374700274310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for edge predicate builders.""" import unittest from pybel.constants import CITATION, CITATION_AUTHORS, CITATION_TYPE_PUBMED from pybel.language import citation_dict from pybel.struct.filters.edge_predicate_builders import ( build_author_inclusion_filter, build_pmid_inclusion_filter, ) pmid1 = "1" pmid2 = "2" pmid3 = "3" author1 = "1" author2 = "2" author3 = "3" class TestEdgePredicateBuilders(unittest.TestCase): """Tests for edge predicate builders.""" def test_build_pmid_inclusion_filter(self): """Test building a predicate for a single PubMed identifier.""" pmid_inclusion_filter = build_pmid_inclusion_filter(pmid1) self.assertTrue( pmid_inclusion_filter( { CITATION: citation_dict(namespace=CITATION_TYPE_PUBMED, identifier=pmid1), } ) ) self.assertFalse( pmid_inclusion_filter( { CITATION: citation_dict(namespace=CITATION_TYPE_PUBMED, identifier=pmid2), } ) ) def test_build_pmid_set_inclusion_filter(self): """Test building a predicate for multiple PubMed identifiers.""" pmids = {pmid1, pmid2} pmid_inclusion_filter = build_pmid_inclusion_filter(pmids) self.assertTrue( pmid_inclusion_filter( { CITATION: citation_dict(namespace=CITATION_TYPE_PUBMED, identifier=pmid1), } ) ) self.assertTrue( pmid_inclusion_filter( { CITATION: citation_dict(namespace=CITATION_TYPE_PUBMED, identifier=pmid2), } ) ) self.assertFalse( pmid_inclusion_filter( { CITATION: citation_dict(namespace=CITATION_TYPE_PUBMED, identifier=pmid3), } ) ) def test_build_author_inclusion_filter(self): """Test building a predicate for a single author.""" author_inclusion_filter = build_author_inclusion_filter(author1) self.assertTrue( author_inclusion_filter( { CITATION: citation_dict( namespace=CITATION_TYPE_PUBMED, identifier=pmid3, authors=[author1], ), } ) ) self.assertTrue( author_inclusion_filter( { CITATION: citation_dict( namespace=CITATION_TYPE_PUBMED, identifier=pmid3, authors=[author1, author2], ), } ) ) self.assertFalse( author_inclusion_filter( { CITATION: citation_dict( namespace=CITATION_TYPE_PUBMED, identifier=pmid3, authors=[author3], ), } ) ) def test_build_author_set_inclusion_filter(self): """Test building a predicate for multiple authors.""" author = {author1, author2} author_inclusion_filter = build_author_inclusion_filter(author) self.assertTrue(author_inclusion_filter({CITATION: {CITATION_AUTHORS: [author1]}})) self.assertTrue(author_inclusion_filter({CITATION: {CITATION_AUTHORS: [author1, author2]}})) self.assertFalse(author_inclusion_filter({CITATION: {CITATION_AUTHORS: [author3]}})) pybel-0.15.5/tests/test_struct/test_filters/test_edge_predicates.py000066400000000000000000000020651426625374700257140ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for edge predicates""" import unittest from pybel import BELGraph from pybel.dsl import pathology, protein from pybel.struct.filters.edge_predicates import has_pathology_causal from pybel.testing.utils import n class TestEdgePredicates(unittest.TestCase): """Tests for edge predicates.""" def test_has_pathology(self): """Test for checking edges that have a causal pathology.""" graph = BELGraph() a, b, c = protein(n(), n()), pathology(n(), n()), pathology(n(), n()) key = graph.add_increases(a, b, citation=n(), evidence=n()) self.assertFalse(has_pathology_causal(graph, a, b, key)) key = graph.add_increases(b, a, citation=n(), evidence=n()) self.assertTrue(has_pathology_causal(graph, b, a, key)) key = graph.add_association(b, a, citation=n(), evidence=n()) self.assertFalse(has_pathology_causal(graph, b, a, key)) key = graph.add_increases(a, c, citation=n(), evidence=n()) self.assertFalse(has_pathology_causal(graph, a, c, key)) pybel-0.15.5/tests/test_struct/test_filters/test_node_predicate_builders.py000066400000000000000000000101411426625374700274350ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for functions for building node predicates.""" import unittest from pybel import BELGraph from pybel.constants import GENE, PROTEIN from pybel.dsl import bioprocess, gene, protein from pybel.struct import filter_nodes from pybel.struct.filters import invert_node_predicate from pybel.struct.filters.node_predicate_builders import ( build_node_graph_data_search, build_node_name_search, data_missing_key_builder, function_inclusion_filter_builder, ) from pybel.testing.utils import n class TestFunctionInclusionFilterBuilder(unittest.TestCase): """Tests for the function_inclusion_filter_builder function.""" def test_type_error(self): """Test that a type error is thrown for an invalid argument type.""" with self.assertRaises(TypeError): function_inclusion_filter_builder(5) def test_empty_list_error(self): """Test that a value error is thrown for an empty list.""" with self.assertRaises(ValueError): function_inclusion_filter_builder([]) def test_single(self): """Test building a node predicate with a single function.""" f = function_inclusion_filter_builder(GENE) p1 = protein(n(), n()) g1 = gene(n(), n()) g = BELGraph() g.add_node_from_data(p1) g.add_node_from_data(g1) self.assertIn(p1, g) self.assertIn(g1, g) self.assertFalse(f(g, p1)) self.assertTrue(f(g, g1)) f = invert_node_predicate(f) self.assertTrue(f(g, p1)) self.assertFalse(f(g, g1)) def test_multiple(self): """Test building a node predicate with multiple functions.""" f = function_inclusion_filter_builder([GENE, PROTEIN]) p1 = protein(n(), n()) g1 = gene(n(), n()) b1 = bioprocess(n(), n()) g = BELGraph() g.add_node_from_data(p1) g.add_node_from_data(g1) g.add_node_from_data(b1) self.assertIn(p1, g) self.assertIn(g1, g) self.assertIn(b1, g) self.assertTrue(f(g, p1)) self.assertTrue(f(g, g1)) self.assertFalse(f(g, b1)) f = invert_node_predicate(f) self.assertFalse(f(g, p1)) self.assertFalse(f(g, g1)) self.assertTrue(f(g, b1)) class TestNodePredicateBuilders(unittest.TestCase): """Tests for node predicate builders.""" def test_data_missing_key_builder(self): """Test the data_missing_key_builder function.""" graph = BELGraph() p1 = protein("HGNC", n()) p2 = protein("HGNC", n()) graph.add_node_from_data(p1) graph.add_node_from_data(p2) key, other_key = "k1", "k2" data_missing_key = data_missing_key_builder(key) graph.nodes[p1][key] = n() graph.nodes[p2][other_key] = n() nodes = set(filter_nodes(graph, data_missing_key)) self.assertNotIn(p1, nodes) self.assertIn(p2, nodes) def test_build_node_data_search(self): """Test build_node_data_search.""" def test_key_predicate(datum): """Check the data is greater than zero. :rtype: bool """ return 0 < datum key = n() data_predicate = build_node_graph_data_search(key, test_key_predicate) graph = BELGraph() p1 = protein("HGNC", n()) graph.add_node_from_data(p1) graph.nodes[p1][key] = 0 self.assertFalse(data_predicate(graph, p1)) p2 = protein("HGNC", n()) graph.add_node_from_data(p2) graph.nodes[p2][key] = 5 self.assertTrue(data_predicate(graph, p2)) p3 = protein("HGNC", n()) graph.add_node_from_data(p3) self.assertFalse(data_predicate(graph, p3)) def test_build_node_name_search(self): graph = BELGraph() p1 = protein("HGNC", "APP") graph.add_node_from_data(p1) p2 = protein("HGNC", "MAPK") graph.add_node_from_data(p2) node_name_search = build_node_name_search(query="APP") self.assertTrue(node_name_search(graph, p1)) self.assertFalse(node_name_search(graph, p2)) pybel-0.15.5/tests/test_struct/test_filters/test_node_predicates.py000066400000000000000000000523231426625374700257370ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for node predicates.""" import unittest from pybel import BELGraph from pybel.constants import ( ACTIVITY, ANNOTATIONS, ASSOCIATION, CAUSES_NO_CHANGE, CITATION, CITATION_AUTHORS, CITATION_TYPE_PUBMED, CITATION_TYPE_URL, DECREASES, DEGRADATION, DIRECTLY_DECREASES, DIRECTLY_INCREASES, EVIDENCE, GMOD, IDENTIFIER, INCREASES, LOCATION, MODIFIER, NAMESPACE, POLAR_RELATIONS, POSITIVE_CORRELATION, RELATION, SOURCE_MODIFIER, TARGET_MODIFIER, TRANSLOCATION, ) from pybel.dsl import ( abundance, activity, degradation, fragment, gene, gmod, hgvs, pmod, protein, secretion, translocation, ) from pybel.language import Entity from pybel.struct.filters import false_node_predicate, true_node_predicate from pybel.struct.filters.edge_predicate_builders import build_relation_predicate from pybel.struct.filters.edge_predicates import ( edge_has_activity, edge_has_annotation, edge_has_degradation, edge_has_translocation, has_authors, has_polarity, has_provenance, has_pubmed, is_associative_relation, is_causal_relation, is_direct_causal_relation, ) from pybel.struct.filters.node_predicates import ( has_activity, has_causal_in_edges, has_causal_out_edges, has_fragment, has_gene_modification, has_hgvs, has_protein_modification, has_variant, is_abundance, is_causal_central, is_causal_sink, is_causal_source, is_degraded, is_gene, is_pathology, is_protein, is_translocated, none_of, not_pathology, one_of, ) from pybel.testing.utils import n p1 = protein(name="BRAF", namespace="HGNC") p2 = protein(name="BRAF", namespace="HGNC", variants=[hgvs("p.Val600Glu"), pmod("Ph")]) p3 = protein(name="APP", namespace="HGNC", variants=fragment(start=672, stop=713)) p4 = protein(name="2", namespace="HGNC") g1 = gene(name="BRAF", namespace="HGNC", variants=gmod("Me")) class TestNodePredicates(unittest.TestCase): """Tests for node predicates.""" def test_none_data(self): """Test permissive node predicate with a node data dictionary.""" self.assertTrue(true_node_predicate(p1)) self.assertFalse(false_node_predicate(p1)) def test_none(self): """Test permissive node predicate with graph and tuple.""" g = BELGraph() g.add_node_from_data(p1) self.assertTrue(true_node_predicate(g, p1)) self.assertFalse(false_node_predicate(g, p1)) def test_p1_data_variants(self): """Test node predicates on BRAF.""" self.assertFalse(is_abundance(p1)) self.assertFalse(is_gene(p1)) self.assertTrue(is_protein(p1)) self.assertFalse(is_pathology(p1)) self.assertTrue(not_pathology(p1)) self.assertFalse(has_variant(p1)) self.assertFalse(has_protein_modification(p1)) self.assertFalse(has_gene_modification(p1)) self.assertFalse(has_hgvs(p1)) self.assertFalse(has_fragment(p1)) def test_p1_tuple_variants(self): """Test node predicates on the node tuple from BRAF.s""" g = BELGraph() g.add_node_from_data(p1) self.assertFalse(is_abundance(g, p1)) self.assertFalse(is_gene(g, p1)) self.assertTrue(is_protein(g, p1)) self.assertFalse(is_pathology(g, p1)) self.assertTrue(not_pathology(g, p1)) self.assertFalse(has_variant(g, p1)) self.assertFalse(has_protein_modification(g, p1)) self.assertFalse(has_gene_modification(g, p1)) self.assertFalse(has_hgvs(g, p1)) def test_p2_data_variants(self): self.assertFalse(is_abundance(p2)) self.assertFalse(is_gene(p2)) self.assertTrue(is_protein(p2)) self.assertFalse(is_pathology(p2)) self.assertTrue(not_pathology(p2)) self.assertTrue(has_variant(p2)) self.assertFalse(has_gene_modification(p2)) self.assertTrue(has_protein_modification(p2)) self.assertTrue(has_hgvs(p2)) def test_p2_tuple_variants(self): g = BELGraph() g.add_node_from_data(p2) self.assertFalse(is_abundance(g, p2)) self.assertFalse(is_gene(g, p2)) self.assertTrue(is_protein(g, p2)) self.assertFalse(is_pathology(g, p2)) self.assertTrue(not_pathology(g, p2)) self.assertTrue(has_variant(g, p2)) self.assertFalse(has_gene_modification(g, p2)) self.assertTrue(has_protein_modification(g, p2)) self.assertTrue(has_hgvs(g, p2)) def test_p3(self): self.assertFalse(is_abundance(p3)) self.assertFalse(is_gene(p3)) self.assertTrue(is_protein(p3)) self.assertFalse(is_pathology(p3)) self.assertTrue(not_pathology(p3)) self.assertTrue(has_variant(p3)) self.assertFalse(has_gene_modification(p3)) self.assertFalse(has_protein_modification(p3)) self.assertFalse(has_hgvs(p3)) self.assertTrue(has_fragment(p3)) def test_g1_variants(self): self.assertFalse(is_abundance(g1)) self.assertTrue(is_gene(g1)) self.assertFalse(is_protein(g1)) self.assertFalse(is_pathology(g1)) self.assertTrue(has_variant(g1)) self.assertTrue(has_gene_modification(g1), msg="Should have {}: {}".format(GMOD, g1)) self.assertFalse(has_protein_modification(g1)) self.assertFalse(has_hgvs(g1)) def test_fragments(self): self.assertTrue( has_fragment( protein( name="APP", namespace="HGNC", variants=[fragment(start=672, stop=713, description="random text")], ) ) ) self.assertTrue(has_fragment(protein(name="APP", namespace="HGNC", variants=[fragment()]))) def test_p1_active(self): """cat(p(HGNC:HSD11B1)) increases deg(a(CHEBI:cortisol))""" g = BELGraph() g.annotation_pattern["Species"] = r"\d+" u = protein(name="HSD11B1", namespace="HGNC") v = abundance(name="cortisol", namespace="CHEBI", identifier="17650") g.add_increases( u, v, citation={ NAMESPACE: CITATION_TYPE_URL, IDENTIFIER: "https://www.ncbi.nlm.nih.gov/gene/3290", }, evidence="Entrez Gene Summary: Human: The protein encoded by this gene is a microsomal enzyme that " "catalyzes the conversion of the stress hormone cortisol to the inactive metabolite cortisone. " "In addition, the encoded protein can catalyze the reverse reaction, the conversion of cortisone " "to cortisol. Too much cortisol can lead to central obesity, and a particular variation in this " "gene has been associated with obesity and insulin resistance in children. Two transcript " "variants encoding the same protein have been found for this gene.", annotations={"Species": "9606"}, source_modifier=activity("cat"), target_modifier=degradation(), ) self.assertFalse(is_translocated(g, u)) self.assertFalse(is_degraded(g, u)) self.assertTrue(has_activity(g, u)) self.assertFalse(is_translocated(g, v)) self.assertTrue(is_degraded(g, v)) self.assertFalse(has_activity(g, v)) def test_object_has_translocation(self): """p(HGNC: EGF) increases tloc(p(HGNC: VCP), GO:0005634, GO:0005737)""" g = BELGraph() g.annotation_pattern["Species"] = r"\d+" u = protein(name="EFG", namespace="HGNC") v = protein(name="VCP", namespace="HGNC") g.add_increases( u, v, citation="10855792", evidence="Although found predominantly in the cytoplasm and, less abundantly, in the nucleus, VCP can be " "translocated from the nucleus after stimulation with epidermal growth factor.", annotations={"Species": "9606"}, target_modifier=translocation( from_loc=Entity(namespace="GO", identifier="0005634"), to_loc=Entity(namespace="GO", identifier="0005737"), ), ) self.assertFalse(is_translocated(g, u)) self.assertFalse(is_degraded(g, u)) self.assertFalse(has_activity(g, u)) self.assertFalse(has_causal_in_edges(g, u)) self.assertTrue(has_causal_out_edges(g, u)) self.assertTrue(is_translocated(g, v)) self.assertFalse(is_degraded(g, v)) self.assertFalse(has_activity(g, v)) self.assertTrue(has_causal_in_edges(g, v)) self.assertFalse(has_causal_out_edges(g, v)) def test_object_has_secretion(self): """p(MGI:Il4) increases sec(p(MGI:Cxcl1))""" g = BELGraph() g.annotation_pattern["Species"] = r"\d+" g.annotation_pattern["MeSH"] = ".*" u = protein(name="Il4", namespace="MGI") v = protein(name="Cxcl1", namespace="MGI") g.add_increases( u, v, citation="10072486", evidence="Compared with controls treated with culture medium alone, IL-4 and IL-5 induced significantly " "higher levels of MIP-2 and KC production; IL-4 also increased the production of MCP-1 " "(Fig. 2, A and B)....we only tested the effects of IL-3, IL-4, IL-5, and IL-13 on chemokine " "expression and cellular infiltration....Recombinant cytokines were used, ... to treat naive " "BALB/c mice.", annotations={"Species": "10090", "MeSH": "bronchoalveolar lavage fluid"}, target_modifier=secretion(), ) self.assertFalse(is_translocated(g, u)) self.assertFalse(is_degraded(g, u)) self.assertFalse(has_activity(g, u)) self.assertFalse(has_causal_in_edges(g, u)) self.assertTrue(has_causal_out_edges(g, u)) self.assertTrue(is_translocated(g, v)) self.assertFalse(is_degraded(g, v)) self.assertFalse(has_activity(g, v)) self.assertTrue(has_causal_in_edges(g, v)) self.assertFalse(has_causal_out_edges(g, v)) def test_subject_has_secretion(self): """sec(p(MGI:S100b)) increases a(CHEBI:"nitric oxide")""" g = BELGraph() g.annotation_pattern["Species"] = r"\d+" g.annotation_pattern["Cell"] = r".*" u = protein(name="S100b", namespace="MGI") v = abundance(name="nitric oxide", namespace="CHEBI") g.add_increases( u, v, citation="11180510", evidence="S100B protein is also secreted by astrocytes and acts on these cells to stimulate nitric oxide " "secretion in an autocrine manner.", annotations={"Species": "10090", "Cell": "astrocyte"}, source_modifier=secretion(), ) self.assertTrue(is_translocated(g, u)) self.assertFalse(is_degraded(g, u)) self.assertFalse(has_activity(g, u)) self.assertFalse(has_causal_in_edges(g, u)) self.assertTrue(has_causal_out_edges(g, u)) self.assertFalse(is_translocated(g, v)) self.assertFalse(is_degraded(g, v)) self.assertFalse(has_activity(g, v)) self.assertTrue(has_causal_in_edges(g, v)) self.assertFalse(has_causal_out_edges(g, v)) def test_node_exclusion_data(self): g = BELGraph() u = protein(name="S100b", namespace="MGI") v = abundance(name="nitric oxide", namespace="CHEBI") w = abundance(name="cortisol", namespace="CHEBI", identifier="17650") g.add_node_from_data(u) g.add_node_from_data(v) g.add_node_from_data(w) f = none_of([u]) self.assertFalse(f(u)) self.assertTrue(f(v)) self.assertTrue(f(w)) f = none_of([u, v]) self.assertFalse(f(u)) self.assertFalse(f(v)) self.assertTrue(f(w)) f = none_of([]) self.assertTrue(f(u)) self.assertTrue(f(v)) self.assertTrue(f(w)) def test_node_exclusion_tuples(self): g = BELGraph() u = protein(name="S100b", namespace="MGI") v = abundance(name="nitric oxide", namespace="CHEBI") w = abundance(name="cortisol", namespace="CHEBI", identifier="17650") g.add_node_from_data(u) g.add_node_from_data(v) g.add_node_from_data(w) f = none_of([u]) self.assertFalse(f(g, u)) self.assertTrue(f(g, v)) self.assertTrue(f(g, w)) f = none_of([u, v]) self.assertFalse(f(g, u)) self.assertFalse(f(g, v)) self.assertTrue(f(g, w)) f = none_of([]) self.assertTrue(f(g, u)) self.assertTrue(f(g, v)) self.assertTrue(f(g, w)) def test_node_inclusion_data(self): g = BELGraph() u = protein(name="S100b", namespace="MGI") v = abundance(name="nitric oxide", namespace="CHEBI") w = abundance(name="cortisol", namespace="CHEBI", identifier="17650") g.add_node_from_data(u) g.add_node_from_data(v) g.add_node_from_data(w) f = one_of([u]) self.assertTrue(f(u)) self.assertFalse(f(v)) self.assertFalse(f(w)) f = one_of([u, v]) self.assertTrue(f(u)) self.assertTrue(f(v)) self.assertFalse(f(w)) f = one_of([]) self.assertFalse(f(u)) self.assertFalse(f(v)) self.assertFalse(f(w)) def test_node_inclusion_tuples(self): g = BELGraph() u = protein(name="S100b", namespace="MGI") v = abundance(name="nitric oxide", namespace="CHEBI") w = abundance(name="cortisol", namespace="CHEBI", identifier="17650") g.add_node_from_data(u) g.add_node_from_data(v) g.add_node_from_data(w) f = one_of([u]) self.assertTrue(f(g, u)) self.assertFalse(f(g, v)) self.assertFalse(f(g, w)) f = one_of([u, v]) self.assertTrue(f(g, u)) self.assertTrue(f(g, v)) self.assertFalse(f(g, w)) f = one_of([]) self.assertFalse(f(g, u)) self.assertFalse(f(g, v)) self.assertFalse(f(g, w)) def test_causal_source(self): g = BELGraph() a, b, c = (protein(n(), n()) for _ in range(3)) g.add_increases(a, b, citation=n(), evidence=n()) g.add_increases(b, c, citation=n(), evidence=n()) self.assertTrue(is_causal_source(g, a)) self.assertFalse(is_causal_central(g, a)) self.assertFalse(is_causal_sink(g, a)) self.assertFalse(is_causal_source(g, b)) self.assertTrue(is_causal_central(g, b)) self.assertFalse(is_causal_sink(g, b)) self.assertFalse(is_causal_source(g, c)) self.assertFalse(is_causal_central(g, c)) self.assertTrue(is_causal_sink(g, c)) class TestEdgePredicate(unittest.TestCase): def test_has_polarity_dict(self): for relation in POLAR_RELATIONS: self.assertTrue(has_polarity({RELATION: relation})) self.assertFalse(has_polarity({RELATION: ASSOCIATION})) def test_has_polarity(self): g = BELGraph() a, b, c = (protein(n(), n()) for _ in range(3)) key1 = g.add_increases(a, b, citation=n(), evidence=n()) self.assertTrue(has_polarity(g, a, b, key1)) key2 = g.add_association(b, c, citation=n(), evidence=n()) self.assertFalse(has_polarity(g, b, c, key2)) def test_has_provenance(self): self.assertFalse(has_provenance({})) self.assertFalse(has_provenance({CITATION: {}})) self.assertFalse(has_provenance({EVIDENCE: ""})) self.assertTrue(has_provenance({CITATION: {}, EVIDENCE: ""})) def test_has_pubmed(self): self.assertTrue(has_pubmed({CITATION: {NAMESPACE: CITATION_TYPE_PUBMED}})) self.assertFalse(has_pubmed({CITATION: {NAMESPACE: CITATION_TYPE_URL}})) self.assertFalse(has_pubmed({})) def test_has_authors(self): self.assertFalse(has_authors({})) self.assertFalse(has_authors({CITATION: {}})) self.assertFalse(has_authors({CITATION: {CITATION_AUTHORS: []}})) self.assertTrue(has_authors({CITATION: {CITATION_AUTHORS: ["One guy"]}})) def test_is_causal(self): self.assertTrue(is_causal_relation({RELATION: INCREASES})) self.assertTrue(is_causal_relation({RELATION: DECREASES})) self.assertTrue(is_causal_relation({RELATION: DIRECTLY_INCREASES})) self.assertTrue(is_causal_relation({RELATION: DIRECTLY_DECREASES})) self.assertFalse(is_causal_relation({RELATION: ASSOCIATION})) self.assertFalse(is_causal_relation({RELATION: POSITIVE_CORRELATION})) def test_is_direct_causal(self): self.assertTrue(is_direct_causal_relation({RELATION: DIRECTLY_INCREASES})) self.assertTrue(is_direct_causal_relation({RELATION: DIRECTLY_DECREASES})) self.assertFalse(is_direct_causal_relation({RELATION: INCREASES})) self.assertFalse(is_direct_causal_relation({RELATION: DECREASES})) self.assertFalse(is_direct_causal_relation({RELATION: ASSOCIATION})) self.assertFalse(is_direct_causal_relation({RELATION: POSITIVE_CORRELATION})) def test_is_association(self): self.assertTrue(is_associative_relation({RELATION: ASSOCIATION})) self.assertFalse(is_associative_relation({RELATION: INCREASES})) self.assertFalse(is_associative_relation({RELATION: CAUSES_NO_CHANGE})) self.assertFalse(is_associative_relation({RELATION: DECREASES})) self.assertFalse(is_associative_relation({RELATION: DIRECTLY_INCREASES})) self.assertFalse(is_associative_relation({RELATION: DIRECTLY_DECREASES})) def test_build_is_association(self): """Test build_relation_predicate.""" alternate_is_associative_relation = build_relation_predicate(ASSOCIATION) g = BELGraph() g.add_edge(p1, p2, key=0, **{RELATION: ASSOCIATION}) g.add_edge(p2, p3, key=0, **{RELATION: INCREASES}) self.assertTrue(alternate_is_associative_relation(g, p1, p2, 0)) self.assertFalse(alternate_is_associative_relation(g, p2, p3, 0)) def test_build_is_increases_or_decreases(self): """Test build_relation_predicate with multiple relations.""" is_increase_or_decrease = build_relation_predicate([INCREASES, DECREASES]) g = BELGraph() g.add_edge(p1, p2, key=0, **{RELATION: ASSOCIATION}) g.add_edge(p2, p3, key=0, **{RELATION: INCREASES}) g.add_edge(p3, p4, key=0, **{RELATION: DECREASES}) self.assertFalse(is_increase_or_decrease(g, p1, p2, 0)) self.assertTrue(is_increase_or_decrease(g, p2, p3, 0)) self.assertTrue(is_increase_or_decrease(g, p3, p4, 0)) def test_has_degradation(self): self.assertTrue(edge_has_degradation({SOURCE_MODIFIER: {MODIFIER: DEGRADATION}})) self.assertTrue(edge_has_degradation({TARGET_MODIFIER: {MODIFIER: DEGRADATION}})) self.assertFalse(edge_has_degradation({SOURCE_MODIFIER: {MODIFIER: TRANSLOCATION}})) self.assertFalse(edge_has_degradation({SOURCE_MODIFIER: {MODIFIER: ACTIVITY}})) self.assertFalse(edge_has_degradation({SOURCE_MODIFIER: {LOCATION: None}})) self.assertFalse(edge_has_degradation({TARGET_MODIFIER: {MODIFIER: TRANSLOCATION}})) self.assertFalse(edge_has_degradation({TARGET_MODIFIER: {MODIFIER: ACTIVITY}})) self.assertFalse(edge_has_degradation({TARGET_MODIFIER: {LOCATION: None}})) def test_has_translocation(self): self.assertTrue(edge_has_translocation({SOURCE_MODIFIER: {MODIFIER: TRANSLOCATION}})) self.assertTrue(edge_has_translocation({TARGET_MODIFIER: {MODIFIER: TRANSLOCATION}})) self.assertFalse(edge_has_translocation({SOURCE_MODIFIER: {MODIFIER: ACTIVITY}})) self.assertFalse(edge_has_translocation({SOURCE_MODIFIER: {LOCATION: None}})) self.assertFalse(edge_has_translocation({SOURCE_MODIFIER: {MODIFIER: DEGRADATION}})) self.assertFalse(edge_has_translocation({TARGET_MODIFIER: {MODIFIER: ACTIVITY}})) self.assertFalse(edge_has_translocation({TARGET_MODIFIER: {LOCATION: None}})) self.assertFalse(edge_has_translocation({TARGET_MODIFIER: {MODIFIER: DEGRADATION}})) def test_has_activity(self): self.assertTrue(edge_has_activity({SOURCE_MODIFIER: {MODIFIER: ACTIVITY}})) self.assertTrue(edge_has_activity({TARGET_MODIFIER: {MODIFIER: ACTIVITY}})) self.assertFalse(edge_has_activity({SOURCE_MODIFIER: {MODIFIER: TRANSLOCATION}})) self.assertFalse(edge_has_activity({TARGET_MODIFIER: {MODIFIER: TRANSLOCATION}})) self.assertFalse(edge_has_activity({SOURCE_MODIFIER: {LOCATION: None}})) self.assertFalse(edge_has_activity({SOURCE_MODIFIER: {MODIFIER: DEGRADATION}})) self.assertFalse(edge_has_activity({TARGET_MODIFIER: {LOCATION: None}})) self.assertFalse(edge_has_activity({TARGET_MODIFIER: {MODIFIER: DEGRADATION}})) def test_has_annotation(self): self.assertFalse(edge_has_annotation({}, "Subgraph")) self.assertFalse(edge_has_annotation({ANNOTATIONS: {}}, "Subgraph")) self.assertFalse(edge_has_annotation({ANNOTATIONS: {"Subgraph": None}}, "Subgraph")) self.assertTrue(edge_has_annotation({ANNOTATIONS: {"Subgraph": "value"}}, "Subgraph")) self.assertFalse(edge_has_annotation({ANNOTATIONS: {"Nope": "value"}}, "Subgraph")) pybel-0.15.5/tests/test_struct/test_filters/test_node_selection.py000066400000000000000000000021241426625374700255730ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for node selection functions.""" import unittest from pybel import BELGraph from pybel.dsl import Protein from pybel.struct.filters import get_nodes_by_namespace class TestNodeSelection(unittest.TestCase): """Tests for node selection functions.""" def test_get_node_by_namespace(self): """Test getting nodes with a given namespace.""" g = BELGraph() a = Protein(namespace="N1", name="a") b = Protein(namespace="N1", name="b") c = Protein(namespace="N2", name="c") d = Protein(namespace="N3", name="d") g.add_node_from_data(a) g.add_node_from_data(b) g.add_node_from_data(c) g.add_node_from_data(d) nodes = set(get_nodes_by_namespace(g, "N1")) self.assertIn(a, nodes) self.assertIn(b, nodes) self.assertNotIn(c, nodes) self.assertNotIn(d, nodes) nodes = set(get_nodes_by_namespace(g, ("N1", "N2"))) self.assertIn(a, nodes) self.assertIn(b, nodes) self.assertIn(c, nodes) self.assertNotIn(d, nodes) pybel-0.15.5/tests/test_struct/test_filters/test_struct_filters.py000066400000000000000000000275541426625374700256730ustar00rootroot00000000000000# -*- coding: utf-8 -*- import unittest from typing import Set, Tuple from pybel import BELGraph from pybel.constants import ANNOTATIONS from pybel.dsl import BaseEntity, Protein from pybel.struct.filters import ( and_edge_predicates, concatenate_node_predicates, count_passed_edge_filter, count_passed_node_filter, filter_edges, get_nodes, invert_edge_predicate, ) from pybel.struct.filters.edge_predicate_builders import ( _annotation_dict_all_filter, _annotation_dict_any_filter, build_annotation_dict_all_filter, build_annotation_dict_any_filter, ) from pybel.struct.filters.edge_predicates import true_edge_predicate from pybel.struct.filters.node_predicates import true_node_predicate from pybel.struct.filters.typing import EdgeIterator from pybel.testing.utils import n def make_edge_iterator_set(it: EdgeIterator) -> Set[Tuple[BaseEntity, BaseEntity]]: return {(u, v) for u, v, _ in it} class TestNodeFilters(unittest.TestCase): def setUp(self): self.universe = BELGraph() self.universe.add_edge(1, 2) self.universe.add_edge(2, 3) self.universe.add_edge(3, 7) self.universe.add_edge(1, 4) self.universe.add_edge(1, 5) self.universe.add_edge(5, 6) self.universe.add_edge(8, 2) self.graph = BELGraph() self.graph.add_edge(1, 2) self.all_universe_nodes = {1, 2, 3, 4, 5, 6, 7, 8} self.all_graph_nodes = {1, 2} def test_no_node_filter_argument(self): nodes = get_nodes(self.universe, []) self.assertEqual(self.all_universe_nodes, nodes) def test_keep_node_permissive(self): nodes = get_nodes(self.universe, true_node_predicate) self.assertEqual(self.all_universe_nodes, nodes) def test_missing_node_filter(self): nodes = get_nodes(self.universe, concatenate_node_predicates([])) self.assertEqual(self.all_universe_nodes, nodes) def test_concatenate_single_node_filter(self): nodes = get_nodes(self.universe, [true_node_predicate]) self.assertEqual(self.all_universe_nodes, nodes) def test_concatenate_multiple_node_filters(self): def even(_, node) -> bool: return node % 2 == 0 def big(_, node) -> bool: return node > 3 nodes = get_nodes(self.universe, [even, big]) self.assertEqual({4, 6, 8}, nodes) self.assertEqual(3, count_passed_node_filter(self.universe, [even, big])) def test_no_edge_filter(self): edges = make_edge_iterator_set(filter_edges(self.graph, [])) self.assertEqual({(1, 2)}, edges) def test_keep_edge_permissive(self): edges = make_edge_iterator_set(filter_edges(self.graph, true_edge_predicate)) self.assertEqual({(1, 2)}, edges) def test_keep_edge_unpermissive(self): keep_edge_restrictive = invert_edge_predicate(true_edge_predicate) edges = make_edge_iterator_set(filter_edges(self.graph, keep_edge_restrictive)) self.assertEqual(set(), edges) def test_missing_edge_filter(self): edges = make_edge_iterator_set(filter_edges(self.graph, and_edge_predicates([]))) self.assertEqual(({(1, 2)}), edges) def test_concatenate_single_edge_filter(self): edges = make_edge_iterator_set(filter_edges(self.graph, [true_edge_predicate])) self.assertEqual({(1, 2)}, edges) def test_concatenate_multiple_edge_filter(self): def has_odd_source(graph, u, v, k): return u % 2 != 0 def has_even_target(graph, u, v, k): return v % 2 == 0 edges = make_edge_iterator_set(filter_edges(self.universe, [has_odd_source, has_even_target])) self.assertEqual({(1, 2), (1, 4), (5, 6)}, edges) self.assertEqual( 3, count_passed_edge_filter(self.universe, [has_odd_source, has_even_target]), ) has_even_source = invert_edge_predicate(has_odd_source) edges = make_edge_iterator_set(filter_edges(self.universe, has_even_source)) self.assertEqual({(2, 3), (8, 2)}, edges) class TestEdgeFilters(unittest.TestCase): def test_a(self): self.assertTrue(_annotation_dict_any_filter({ANNOTATIONS: {"A": {"1", "2"}}}, {"A": {"1"}})) self.assertTrue(_annotation_dict_any_filter({ANNOTATIONS: {"A": {"1", "2"}}}, {"A": {"1", "2"}})) self.assertTrue(_annotation_dict_any_filter({ANNOTATIONS: {"A": {"1", "2"}}}, {"A": {"1", "2", "3"}})) self.assertTrue( _annotation_dict_any_filter({ANNOTATIONS: {"A": {"1", "2"}, "B": {"X"}}}, {"A": {"3"}, "B": {"X"}}) ) self.assertFalse(_annotation_dict_any_filter({ANNOTATIONS: {"A": {"1", "2"}}}, {"A": {"3"}})) self.assertFalse( _annotation_dict_any_filter({ANNOTATIONS: {"A": {"1", "2"}, "B": {"X"}}}, {"A": {"3"}, "B": {"Y"}}) ) def test_any_filter_no_query(self): """Test that the all filter returns true when there's no argument""" graph = BELGraph() graph.add_increases(Protein(n(), n()), Protein(n(), n()), citation=n(), evidence=n()) self.assertEqual(1, count_passed_edge_filter(graph, build_annotation_dict_any_filter({}))) def test_any_filter_no_annotations(self): graph = BELGraph() graph.add_increases(Protein(n(), n()), Protein(n(), n()), citation=n(), evidence=n()) self.assertEqual( 0, count_passed_edge_filter(graph, build_annotation_dict_any_filter({"A": {"1"}})), ) def test_any_filter_empty_annotations(self): graph = BELGraph() graph.add_increases( Protein(n(), n()), Protein(n(), n()), citation=n(), evidence=n(), annotations={}, ) self.assertEqual( 0, count_passed_edge_filter(graph, build_annotation_dict_any_filter({"A": {"1"}})), ) def test_any_filter(self): graph = BELGraph() graph.annotation_list["A"] = set("12345") graph.add_increases( Protein(n(), n()), Protein(n(), n()), citation=n(), evidence=n(), annotations={"A": {"1", "2", "3"}}, ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_any_filter(graph._clean_annotations({"A": {"1"}})), ), ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_any_filter(graph._clean_annotations({"A": {"1", "2"}})), ), ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_any_filter(graph._clean_annotations({"A": {"1", "2", "3"}})), ), ) def test_b(self): self.assertTrue(_annotation_dict_all_filter({ANNOTATIONS: {"A": {"1"}}}, {"A": {"1"}})) self.assertTrue(_annotation_dict_all_filter({ANNOTATIONS: {"A": {"1", "2"}}}, {"A": {"1", "2"}})) self.assertTrue(_annotation_dict_all_filter({ANNOTATIONS: {"A": {"1", "2"}}}, {"A": {"1", "2"}})) self.assertTrue( _annotation_dict_all_filter( {ANNOTATIONS: {"A": {"1", "2"}, "B": {"X"}}}, {"A": {"1", "2"}, "B": {"X"}}, ) ) self.assertFalse( _annotation_dict_all_filter( {ANNOTATIONS: {"A": {"1", "2"}, "B": {"X"}}}, {"A": {"1", "2", "3"}, "B": {"X", "Y"}}, ) ) self.assertFalse(_annotation_dict_all_filter({ANNOTATIONS: {"A": {"1"}}}, {"A": {"1", "2"}})) self.assertFalse(_annotation_dict_all_filter({ANNOTATIONS: {"A": {"1"}}}, {"A": {"2"}})) self.assertFalse(_annotation_dict_all_filter({ANNOTATIONS: {"A": {"1"}}}, {"B": {"1"}})) def test_all_filter_no_query(self): """Test that the all filter returns true when there's no argument""" graph = BELGraph() graph.add_increases(Protein(n(), n()), Protein(n(), n()), citation=n(), evidence=n()) self.assertEqual(1, count_passed_edge_filter(graph, build_annotation_dict_all_filter({}))) def test_all_filter_no_annotations(self): graph = BELGraph() graph.add_increases(Protein(n(), n()), Protein(n(), n()), citation=n(), evidence=n()) self.assertEqual( 0, count_passed_edge_filter(graph, build_annotation_dict_all_filter({"A": {"1"}})), ) def test_all_filter_empty_annotations(self): graph = BELGraph() graph.add_increases( Protein(n(), n()), Protein(n(), n()), citation=n(), evidence=n(), annotations={}, ) self.assertEqual( 0, count_passed_edge_filter(graph, build_annotation_dict_all_filter({"A": {"1"}})), ) def test_all_filter(self): graph = BELGraph() graph.annotation_list["A"] = set("12345") graph.add_increases( Protein(n(), n()), Protein(n(), n()), citation=n(), evidence=n(), annotations={ "A": {"1", "2", "3"}, }, ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"1"}})), ), ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"1", "2"}})), ), ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"1", "2", "3"}})), ), ) self.assertEqual( 0, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"1", "2", "3", "4"}})), ), ) self.assertEqual( 0, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"4"}})), ), ) def test_all_filter_dict(self): graph = BELGraph() graph.annotation_list["A"] = set("12345") a, b = Protein(namespace="hgnc", identifier="1", name="A"), Protein(namespace="hgnc", identifier="2", name="B") graph.add_increases( a, b, citation=n(), evidence=n(), annotations={ "A": {"1", "2", "3"}, }, ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"1": True}})), ), ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"1": True, "2": True}})), ), ) self.assertEqual( 1, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"1": True, "2": True, "3": True}})), ), ) self.assertEqual( 0, count_passed_edge_filter( graph, build_annotation_dict_all_filter( graph._clean_annotations({"A": {"1": True, "2": True, "3": True, "4": True}}) ), ), ) self.assertEqual( 0, count_passed_edge_filter( graph, build_annotation_dict_all_filter(graph._clean_annotations({"A": {"4": True}})), ), ) pybel-0.15.5/tests/test_struct/test_getters.py000066400000000000000000000036531426625374700215570ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for getters.""" import unittest from pybel import BELGraph from pybel.dsl import ComplexAbundance, Protein, Rna from pybel.struct.getters import get_tf_pairs from pybel.testing.utils import n def _tf_up(graph, protein, rna): graph.add_directly_increases( ComplexAbundance([protein, rna.get_gene()]), rna, citation=n(), evidence=n(), ) def _tf_down(graph, protein, rna): graph.add_directly_decreases( ComplexAbundance([protein, rna.get_gene()]), rna, citation=n(), evidence=n(), ) def _bel_pair_key(k): return tuple(map(str, k)) class TestGetters(unittest.TestCase): """Tests for getters.""" def test_get_tf_pairs(self): """Test iterating over transcription factor pairs.""" graph = BELGraph() p1, p2, p3 = (Protein("test", str(i)) for i in range(1, 4)) r4, r5, r6 = (Rna("test", str(j)) for j in range(4, 7)) g4 = r4.get_gene() self.assertIsNotNone(g4) g5 = r5.get_gene() self.assertIsNotNone(g5) c14, c25 = ComplexAbundance([p1, g4]), ComplexAbundance([p2, g5]) _tf_up(graph, p1, r4) _tf_down(graph, p2, r5) graph.add_correlation(p3, r6, citation=n(), evidence=n()) self.assertEqual({p1, p2, p3, r4, r5, r6, g4, g5, c14, c25}, set(graph)) expected_edges = [ (c14, r4), (p1, c14), (g4, c14), (c25, r5), (p2, c25), (g5, c25), (p3, r6), (r6, p3), ] sorted_expected_edges = sorted(expected_edges, key=_bel_pair_key) sorted_actual_edges = sorted(graph.edges(), key=_bel_pair_key) self.assertEqual(sorted_expected_edges, sorted_actual_edges) pairs = set(get_tf_pairs(graph)) expected_pairs = {(p1, r4, +1), (p2, r5, -1)} self.assertEqual(expected_pairs, pairs) pybel-0.15.5/tests/test_struct/test_grouping.py000066400000000000000000000116331426625374700217310ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for functions for grouping BEL graphs into sub-graphs.""" import unittest from pybel import BELGraph from pybel.constants import CITATION_TYPE_PUBMED from pybel.dsl import protein from pybel.language import Entity from pybel.struct.grouping import get_subgraphs_by_annotation, get_subgraphs_by_citation from pybel.testing.utils import n test_namespace_url = n() test_annotation_url = n() citation, evidence = n(), n() a, b, c, d = [protein(namespace="test", name=str(i)) for i in range(4)] class TestAnnotation(unittest.TestCase): """Tests for getting sub-graphs by annotation.""" def setUp(self): """Set up the test case with a pre-populated BEL graph.""" self.graph = BELGraph() self.graph.namespace_url["test"] = test_namespace_url self.graph.annotation_url["subgraph"] = test_annotation_url self.graph.add_increases( a, b, citation=citation, evidence=evidence, annotations={"subgraph": {"1", "2"}}, ) self.graph.add_increases(a, c, citation=citation, evidence=evidence, annotations={"subgraph": {"1"}}) self.graph.add_increases( b, d, citation=citation, evidence=evidence, annotations={"subgraph": {"1", "2"}}, ) self.graph.add_increases(a, d, citation=citation, evidence=evidence, annotations={"subgraph": {"2"}}) self.graph.add_increases(c, d, citation=citation, evidence=evidence) def test_get_subgraphs_by_annotation(self): subgraphs = get_subgraphs_by_annotation(self.graph, annotation="subgraph") self.assertEqual(2, len(subgraphs)) self.assertIn(Entity(namespace="subgraph", identifier="1"), subgraphs) self.assertIn(Entity(namespace="subgraph", identifier="2"), subgraphs) subgraph_1 = subgraphs[Entity(namespace="subgraph", identifier="1")] self.assertIsInstance(subgraph_1, BELGraph) self.assertIn("test", subgraph_1.namespace_url) self.assertIn("subgraph", subgraph_1.annotation_url) self.assertIn(a, subgraph_1) self.assertIn(b, subgraph_1) self.assertIn(c, subgraph_1) self.assertIn(d, subgraph_1) self.assertIn(b, subgraph_1[a]) self.assertIn(c, subgraph_1[a]) self.assertIn(d, subgraph_1[b]) self.assertNotIn(d, subgraph_1[a]) self.assertNotIn(d, subgraph_1[c]) subgraph_2 = subgraphs[Entity(namespace="subgraph", identifier="2")] self.assertIsInstance(subgraph_2, BELGraph) self.assertIn("test", subgraph_2.namespace_url) self.assertIn("subgraph", subgraph_2.annotation_url) self.assertIn(a, subgraph_2) self.assertIn(b, subgraph_2) self.assertNotIn(c, subgraph_2) self.assertIn(d, subgraph_2) self.assertIn(b, subgraph_2[a]) self.assertNotIn(c, subgraph_2[a]) self.assertIn(d, subgraph_2[b]) self.assertIn(d, subgraph_2[a]) def test_get_subgraphs_by_annotation_with_sentinel(self): sentinel = n() subgraphs = get_subgraphs_by_annotation(self.graph, annotation="subgraph", sentinel=sentinel) self.assertEqual(3, len(subgraphs)) self.assertIn(Entity(namespace="subgraph", identifier="1"), subgraphs) self.assertIn(Entity(namespace="subgraph", identifier="2"), subgraphs) self.assertIn(sentinel, subgraphs) class TestProvenance(unittest.TestCase): """Tests for getting sub-graphs by provenance information (citation, etc.)""" def test_get_subgraphs_by_citation(self): """Test getting sub-graphs by citation.""" graph = BELGraph() c1, c2, c3 = n(), n(), n() graph.add_increases(a, b, citation=c1, evidence=n()) graph.add_increases(a, b, citation=c2, evidence=n()) graph.add_increases(b, c, citation=c1, evidence=n()) graph.add_increases(c, d, citation=c1, evidence=n()) graph.add_increases(a, d, citation=c3, evidence=n()) subgraphs = get_subgraphs_by_citation(graph) # TODO tests for metadata c1_pair = (CITATION_TYPE_PUBMED, c1) self.assertIn(c1_pair, subgraphs) c1_subgraph = subgraphs[c1_pair] self.assertIn(a, c1_subgraph) self.assertIn(b, c1_subgraph) self.assertIn(c, c1_subgraph) self.assertIn(d, c1_subgraph) c2_pair = (CITATION_TYPE_PUBMED, c2) self.assertIn(c2_pair, subgraphs) c2_subgraph = subgraphs[c2_pair] self.assertIn(a, c2_subgraph) self.assertIn(b, c2_subgraph) self.assertNotIn(c, c2_subgraph) self.assertNotIn(d, c2_subgraph) c3_pair = (CITATION_TYPE_PUBMED, c3) self.assertIn(c3_pair, subgraphs) c3_subgraph = subgraphs[c3_pair] self.assertIn(a, c3_subgraph) self.assertNotIn(b, c3_subgraph) self.assertNotIn(c, c3_subgraph) self.assertIn(d, c3_subgraph) pybel-0.15.5/tests/test_struct/test_node_utils.py000066400000000000000000000113621426625374700222430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for node utilities.""" import unittest from pybel import BELGraph from pybel.constants import INCREASES from pybel.dsl import ComplexAbundance as g from pybel.dsl import CompositeAbundance as c from pybel.dsl import Protein, Reaction from pybel.examples.various_example import ( adp, atp, glucose, glucose_6_phosphate, hk1, phosphate, single_reaction_graph, ) from pybel.struct.node_utils import flatten_list_abundance, reaction_cartesian_expansion class TestNodeUtils(unittest.TestCase): """Test node utilities.""" def test_flatten_complex(self): """Test flattening a nested complex.""" p1, p2, p3 = (Protein("N", str(i + 1)) for i in range(3)) pairs = [ # Mainly complexes (g([p1, p2, p3]), g([p1, p2, p3])), # no nesting (g([p1, p2, p3]), g([g([p1, p2]), p3])), # one nesting (g([p1, p2, p3]), g([g([p1]), p2, p3])), # one nesting (g([p1, p2, p3]), g([g([p1]), g([p2]), p3])), # one nesting # Mainly composites (c([p1, p2, p3]), c([p1, p2, p3])), # no nesting (c([p1, p2, p3]), c([c([p1, p2]), p3])), # one nesting (c([p1, p2, p3]), c([c([p1]), p2, p3])), # one nesting (c([p1, p2, p3]), c([c([p1]), c([p2]), p3])), # one nesting # TODO: mixtures of composites and complexes? ] for expected, source in pairs: self.assertEqual(expected, flatten_list_abundance(source)) def test_flatten_reaction(self): """Test flattening a reaction.""" single_reaction_graph_copy = single_reaction_graph.copy() self.assertEqual(single_reaction_graph_copy.number_of_nodes(), 7) self.assertEqual(single_reaction_graph_copy.number_of_edges(), 7) reaction_cartesian_expansion(single_reaction_graph_copy) self.assertEqual(single_reaction_graph_copy.number_of_nodes(), 6) self.assertEqual(single_reaction_graph_copy.number_of_edges(), 8) pairs = [ (glucose, INCREASES, glucose_6_phosphate), (glucose, INCREASES, adp), (hk1, INCREASES, glucose_6_phosphate), (hk1, INCREASES, adp), (atp, INCREASES, glucose_6_phosphate), (atp, INCREASES, adp), (phosphate, INCREASES, glucose_6_phosphate), (phosphate, INCREASES, adp), ] for source, target, data in single_reaction_graph_copy.edges(data=True): self.assertIn((source, INCREASES, target), pairs) def test_flatten_reaction_2(self): """Test flattening a qualified reaction.""" node_increases_reaction_graph = BELGraph() glycolisis_step_1 = Reaction(reactants=[glucose, hk1, atp], products=[glucose_6_phosphate, adp, hk1]) node_increases_reaction_graph.add_increases(glucose_6_phosphate, glycolisis_step_1, citation="X", evidence="X") self.assertEqual(node_increases_reaction_graph.number_of_nodes(), 6) self.assertEqual(node_increases_reaction_graph.number_of_edges(), 7) reaction_cartesian_expansion(node_increases_reaction_graph) self.assertEqual(node_increases_reaction_graph.number_of_nodes(), 5) # TODO Fix so unqualified duplicate edges are not created (it should be the 8 edges below) self.assertEqual(node_increases_reaction_graph.number_of_edges(), 12) # pairs = [ # (glucose, INCREASES, glucose_6_phosphate), # (glucose, INCREASES, adp), # (hk1, INCREASES, glucose_6_phosphate), # (hk1, INCREASES, adp), # (atp, INCREASES, glucose_6_phosphate), # (atp, INCREASES, adp), # (phosphate, INCREASES, glucose_6_phosphate), # (phosphate, INCREASES, adp), # ] # # for source, target, data in node_increases_reaction_graph.edges(data=True): # self.assertIn((source, INCREASES, target), pairs) def test_flatten_reaction_3(self): """Test flattening a graph containing 2 reactions connected to each other.""" two_reactions_graph = BELGraph() reaction_1 = Reaction(reactants=[glucose, atp], products=hk1) reaction_2 = Reaction(reactants=glucose_6_phosphate, products=adp) two_reactions_graph.add_increases(reaction_1, reaction_2, citation="X", evidence="X") self.assertEqual(two_reactions_graph.number_of_nodes(), 7) self.assertEqual(two_reactions_graph.number_of_edges(), 6) reaction_cartesian_expansion(two_reactions_graph) # TODO Fix so unqualified duplicate edges are not created (it should be the 6 edges below) self.assertEqual(two_reactions_graph.number_of_nodes(), 5) self.assertEqual(two_reactions_graph.number_of_edges(), 8) pybel-0.15.5/tests/test_struct/test_query/000077500000000000000000000000001426625374700206665ustar00rootroot00000000000000pybel-0.15.5/tests/test_struct/test_query/__init__.py000066400000000000000000000000001426625374700227650ustar00rootroot00000000000000pybel-0.15.5/tests/test_struct/test_query/test_mocks.py000066400000000000000000000016051426625374700234150ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the mocks for the query builder.""" import unittest from pybel.examples import egf_graph from pybel.testing.mock_manager import MockQueryManager class TestMockManager(unittest.TestCase): """Tests for the mock query manager.""" def test_make(self): """Test instantiating the mock query manager.""" manager = MockQueryManager() self.assertEqual(0, manager.count_networks()) def test_make_with_graph(self): """Test counting networks in the mock query manager.""" manager = MockQueryManager(graphs=[egf_graph]) self.assertEqual(1, manager.count_networks()) def test_add_graph(self): """Test adding a graph with insert_graph.""" manager = MockQueryManager() graph = egf_graph.copy() manager.insert_graph(graph) self.assertEqual(1, manager.count_networks()) pybel-0.15.5/tests/test_struct/test_query/test_query.py000066400000000000000000000247501426625374700234540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the query builder.""" import logging import unittest from pybel import BELGraph, Pipeline from pybel.dsl import Protein from pybel.examples.egf_example import egf_graph, vcp from pybel.examples.homology_example import ( homology_graph, mouse_csf1_protein, mouse_csf1_rna, mouse_mapk1_protein, mouse_mapk1_rna, ) from pybel.examples.sialic_acid_example import ( cd33_phosphorylated, dap12, shp1, shp2, sialic_acid_graph, syk, trem2, ) from pybel.struct import ( expand_node_neighborhood, expand_nodes_neighborhoods, get_subgraph_by_annotation_value, ) from pybel.struct.mutation import collapse_to_genes, enrich_protein_and_rna_origins from pybel.struct.query import Query, QueryMissingNetworksError, Seeding from pybel.testing.generate import generate_random_graph from pybel.testing.mock_manager import MockQueryManager from pybel.testing.utils import n log = logging.getLogger(__name__) def add(query, manager, graph): network = manager.insert_graph(graph) query.append_network(network.id) class TestSeedingConstructor(unittest.TestCase): def test_none(self): """Test construction of a seeding container.""" seeding = Seeding() self.assertEqual(0, len(seeding)) self.assertEqual("[]", seeding.dumps()) def test_append_sample(self): seeding = Seeding() seeding.append_sample() self.assertEqual(1, len(seeding)) s = seeding.dumps() self.assertIsInstance(s, str) class TestQueryConstructor(unittest.TestCase): """Test the construction of a Query.""" def test_network_ids_none(self): query = Query() self.assertIsInstance(query.network_ids, list) self.assertIsInstance(query.seeding, Seeding) self.assertIsInstance(query.pipeline, Pipeline) self.assertEqual(0, len(query.network_ids)) def test_network_ids_single(self): query = Query(network_ids=1) self.assertIsInstance(query.network_ids, list) self.assertEqual(1, len(query.network_ids)) def test_network_ids_multiple(self): query = Query(network_ids=[1, 2, 3]) self.assertIsInstance(query.network_ids, list) self.assertEqual(3, len(query.network_ids)) def test_network_ids_type_error(self): with self.assertRaises(TypeError): Query(network_ids="a") def test_seeding(self): query = Query(seeding=Seeding()) self.assertEqual(0, len(query.seeding)) def test_pipeline(self): query = Query(pipeline=Pipeline()) self.assertEqual(0, len(query.pipeline)) class QueryTestEgf(unittest.TestCase): """Test querying the EGF subgraph""" def setUp(self): """Set up each test with a mock query manager.""" self.manager = MockQueryManager() self.query = Query() def add_query(self, graph): add(self.query, self.manager, graph) return self.query def run_query(self): return self.query.run(self.manager) def test_fail_run_with_no_networks(self): with self.assertRaises(QueryMissingNetworksError): self.run_query() def test_no_seeding_no_pipeline(self): graph = egf_graph.copy() self.add_query(graph) result = self.run_query() self.assertEqual(graph.number_of_nodes(), result.number_of_nodes()) self.assertEqual(graph.number_of_edges(), result.number_of_edges()) def test_seed_by_neighbor(self): graph = BELGraph() a, b, c, d = (Protein(namespace=n(), name=str(i)) for i in range(4)) graph.add_increases(a, b, citation=n(), evidence=n()) graph.add_increases(b, c, citation=n(), evidence=n()) graph.add_increases(c, d, citation=n(), evidence=n()) self.add_query(graph).append_seeding_neighbors(b) result = self.run_query() self.assertIsInstance(result, BELGraph) # test nodes self.assertIn(a, result) self.assertIn(b, result) self.assertIn(c, result) self.assertNotIn(d, result) # test edges self.assertIn(b, result[a]) self.assertIn(c, result[b]) self.assertNotIn(d, result[c]) def test_seed_by_neighbors(self): graph = BELGraph() a, b, c, d, e = (Protein(namespace=n(), name=str(i)) for i in range(5)) graph.add_increases(a, b, citation=n(), evidence=n()) graph.add_increases(b, c, citation=n(), evidence=n()) graph.add_increases(c, d, citation=n(), evidence=n()) graph.add_increases(d, e, citation=n(), evidence=n()) self.add_query(graph).append_seeding_neighbors([b, c]) result = self.run_query() self.assertIsInstance(result, BELGraph) # test nodes self.assertIn(a, result) self.assertIn(b, result) self.assertIn(c, result) self.assertIn(d, result) self.assertNotIn(e, result) # test edges self.assertIn(b, result[a]) self.assertIn(c, result[b]) self.assertIn(d, result[c]) self.assertNotIn(e, result[d]) def test_random_sample(self): """Test generating multiple random samples and combining them.""" graph = generate_random_graph(50, 1000) query = self.add_query(graph) query.append_seeding_sample(number_edges=10) query.append_seeding_sample(number_edges=10) result = self.run_query() self.assertIn( result.number_of_edges(), {16, 17, 18, 19, 20}, msg="This will rail randomly sometimes, lol", ) class QueryTest(unittest.TestCase): """Test the query""" def setUp(self): """Setup each test with an empty mock query manager.""" self.manager = MockQueryManager() def test_pipeline(self): graph = egf_graph.copy() enrich_protein_and_rna_origins(graph) self.assertEqual( 32, # 10 protein nodes already there + complex + bp + 2*10 (genes and rnas) graph.number_of_nodes(), ) # 6 already there + 5 complex hasComponent edges + new 2*10 edges self.assertEqual(31, graph.number_of_edges()) network = self.manager.insert_graph(graph) pipeline = Pipeline() pipeline.append(collapse_to_genes) query = Query(network_ids=[network.id], pipeline=pipeline) result_graph = query.run(self.manager) self.assertEqual(12, result_graph.number_of_nodes()) # same number of nodes than there were self.assertEqual(11, result_graph.number_of_edges()) # same number of edges than there were def test_pipeline_2(self): graph = egf_graph.copy() network = self.manager.insert_graph(graph) network_id = network.id query = Query(network_ids=[network_id]) query.append_seeding_neighbors(vcp) query.append_pipeline(get_subgraph_by_annotation_value, "Species", "9606") result = query.run(self.manager) self.assertIsNotNone(result, msg="Query returned none") self.assertEqual(3, result.number_of_nodes()) def test_query_multiple_networks(self): sialic_acid_graph_id = self.manager.insert_graph(sialic_acid_graph.copy()).id egf_graph_id = self.manager.insert_graph(egf_graph.copy()).id query = Query() query.append_network(sialic_acid_graph_id) query.append_network(egf_graph_id) query.append_seeding_neighbors([syk]) query.append_pipeline(enrich_protein_and_rna_origins) result = query.run(self.manager) self.assertIsNotNone(result, msg="Query returned none") self.assertIn(shp1, result) self.assertIn(shp2, result) self.assertIn(trem2, result) self.assertIn(dap12, result) self.assertEqual(15, result.number_of_nodes()) self.assertEqual(14, result.number_of_edges()) def test_get_subgraph_by_annotation_value(self): graph = homology_graph.copy() result = get_subgraph_by_annotation_value(graph, "Species", "10090") self.assertIsNotNone(result, msg="Query returned none") self.assertIsInstance(result, BELGraph) self.assertLess(0, result.number_of_nodes()) self.assertIn(mouse_mapk1_protein, result, msg="nodes:\n{}".format(list(map(repr, graph)))) self.assertIn(mouse_csf1_protein, result) self.assertEqual(2, result.number_of_nodes()) self.assertEqual(1, result.number_of_edges()) def test_seeding_1(self): test_network_1 = self.manager.insert_graph(homology_graph.copy()) query = Query(network_ids=[test_network_1.id]) query.append_seeding_neighbors([mouse_csf1_rna, mouse_mapk1_rna]) result = query.run(self.manager) self.assertIsNotNone(result, msg="Query returned none") self.assertIsInstance(result, BELGraph) self.assertIn(mouse_mapk1_rna, result) self.assertIn(mouse_csf1_rna, result) self.assertIn(mouse_mapk1_protein, result) self.assertIn(mouse_csf1_protein, result) self.assertEqual(6, result.number_of_nodes()) self.assertEqual(4, result.number_of_edges()) def test_seeding_with_pipeline(self): test_network_1 = self.manager.insert_graph(sialic_acid_graph.copy()) query = Query(network_ids=[test_network_1.id]) query.append_seeding_neighbors([trem2, dap12, shp2]) query.append_pipeline(expand_nodes_neighborhoods, [trem2, dap12, shp2]) result = query.run(self.manager) self.assertIsNotNone(result, msg="Query returned none") self.assertIsInstance(result, BELGraph) self.assertIn(trem2, result) self.assertIn(dap12, result) self.assertIn(shp2, result) self.assertIn(syk, result) self.assertIn(cd33_phosphorylated, result) self.assertEqual(5, result.number_of_nodes()) self.assertEqual(4, result.number_of_edges()) def test_query_multiple_networks_with_api(self): test_network_1 = self.manager.insert_graph(homology_graph.copy()) pipeline = Pipeline() pipeline.append(expand_node_neighborhood, mouse_mapk1_protein) query = Query(network_ids=[test_network_1.id], pipeline=pipeline) query.append_seeding_annotation("Species", {"10090"}) result = query.run(self.manager) self.assertIsNotNone(result, msg="Query returned none") self.assertEqual(3, result.number_of_nodes()) self.assertIn(mouse_mapk1_protein, result) self.assertIn(mouse_csf1_protein, result) self.assertEqual(2, result.number_of_edges()) pybel-0.15.5/tests/test_struct/test_query/test_seeding.py000066400000000000000000000060511426625374700237170ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for the query builder.""" import logging import unittest from pybel import BELGraph from pybel.dsl import Protein from pybel.examples.egf_example import egf_graph from pybel.struct.query import Seeding from pybel.testing.generate import generate_random_graph from pybel.testing.utils import n log = logging.getLogger(__name__) class TestSeedingConstructor(unittest.TestCase): def test_none(self): seeding = Seeding() self.assertEqual(0, len(seeding)) self.assertEqual("[]", seeding.dumps()) def test_append_sample(self): seeding = Seeding() seeding.append_sample() self.assertEqual(1, len(seeding)) s = seeding.dumps() self.assertIsInstance(s, str) def test_no_seeding(self): graph = egf_graph.copy() seeding = Seeding() result = seeding.run(graph) self.assertEqual(graph.number_of_nodes(), result.number_of_nodes()) self.assertEqual(graph.number_of_edges(), result.number_of_edges()) def test_seed_by_neighbor(self): graph = BELGraph() a, b, c, d = (Protein(namespace=n(), name=str(i)) for i in range(4)) graph.add_increases(a, b, citation=n(), evidence=n()) graph.add_increases(b, c, citation=n(), evidence=n()) graph.add_increases(c, d, citation=n(), evidence=n()) seeding = Seeding() seeding.append_neighbors(b) result = seeding.run(graph) self.assertIsInstance(result, BELGraph) # test nodes self.assertIn(a, result) self.assertIn(b, result) self.assertIn(c, result) self.assertNotIn(d, result) # test edges self.assertIn(b, result[a]) self.assertIn(c, result[b]) self.assertNotIn(d, result[c]) def test_seed_by_neighbors(self): graph = BELGraph() a, b, c, d, e = (Protein(namespace=n(), name=str(i)) for i in range(5)) graph.add_increases(a, b, citation=n(), evidence=n()) graph.add_increases(b, c, citation=n(), evidence=n()) graph.add_increases(c, d, citation=n(), evidence=n()) graph.add_increases(d, e, citation=n(), evidence=n()) seeding = Seeding() seeding.append_neighbors([b, c]) result = seeding.run(graph) self.assertIsInstance(result, BELGraph) # test nodes self.assertIn(a, result) self.assertIn(b, result) self.assertIn(c, result) self.assertIn(d, result) self.assertNotIn(e, result) # test edges self.assertIn(b, result[a]) self.assertIn(c, result[b]) self.assertIn(d, result[c]) self.assertNotIn(e, result[d]) def test_random_sample(self): graph = generate_random_graph(50, 1000) seeding = Seeding() seeding.append_sample(number_edges=10) seeding.append_sample(number_edges=10) result = seeding.run(graph) # TODO this will fail randomly some times lol, so make allowed to be sort of wrong self.assertIn(result.number_of_edges(), {18, 19, 20}) pybel-0.15.5/tests/test_struct/test_query/test_struct_pipeline.py000066400000000000000000000077541426625374700255250ustar00rootroot00000000000000# -*- coding: utf-8 -*- import logging import unittest from io import StringIO from pybel import BELGraph from pybel.examples.egf_example import egf_graph from pybel.struct.mutation import enrich_protein_and_rna_origins from pybel.struct.pipeline import Pipeline from pybel.struct.pipeline.decorators import get_transformation, mapped from pybel.struct.pipeline.exc import MetaValueError, MissingPipelineFunctionError log = logging.getLogger(__name__) log.setLevel(10) class TestEgfExample(unittest.TestCase): """Random test for mutation functions""" def setUp(self): self.graph = egf_graph.copy() self.original_number_nodes = self.graph.number_of_nodes() self.original_number_edges = self.graph.number_of_edges() def check_original_unchanged(self): self.assertEqual( self.original_number_nodes, self.graph.number_of_nodes(), msg="original graph nodes should remain unchanged", ) self.assertEqual( self.original_number_edges, self.graph.number_of_edges(), msg="original graph edges should remain unchanged", ) class TestPipelineFailures(unittest.TestCase): def test_assert_failure(self): with self.assertRaises(MissingPipelineFunctionError): get_transformation("missing function") def test_assert_success(self): m = list(mapped) self.assertLess(0, len(m)) m = m[0] f = get_transformation(m) self.assertIsNotNone(f) def test_append_invalid(self): """Test when an invalid type is given to a :class:`pybel.struct.Pipeline`.""" p = Pipeline() with self.assertRaises(TypeError): p.append(4) def test_get_function_failure(self): p = Pipeline() with self.assertRaises(MissingPipelineFunctionError): p._get_function("nonsense name") def test_build_meta_failure(self): p1, p2 = Pipeline(), Pipeline() p = Pipeline._build_meta("wrong", [p1, p2]) with self.assertRaises(MetaValueError): p(BELGraph()) def test_fail_add(self): pipeline = Pipeline() with self.assertRaises(MissingPipelineFunctionError): pipeline.append("missing function") class TestPipeline(TestEgfExample): def test_append(self): pipeline = Pipeline() self.assertEqual(0, len(pipeline)) pipeline.append("enrich_protein_and_rna_origins") self.assertEqual(1, len(pipeline)) def test_extend(self): p1 = Pipeline.from_functions(["enrich_protein_and_rna_origins"]) self.assertEqual(1, len(p1)) p2 = Pipeline.from_functions(["remove_pathologies"]) p1.extend(p2) self.assertEqual(2, len(p1)) def test_serialize_string(self): p = Pipeline.from_functions(["enrich_protein_and_rna_origins"]) s = p.dumps() p_reconstituted = Pipeline.loads(s) self.assertEqual(p.protocol, p_reconstituted.protocol) def test_serialize_file(self): p = Pipeline.from_functions(["enrich_protein_and_rna_origins"]) sio = StringIO() p.dump(sio) sio.seek(0) p_reconstituted = Pipeline.load(sio) self.assertEqual(p.protocol, p_reconstituted.protocol) def test_pipeline_by_string(self): pipeline = Pipeline.from_functions( [ "enrich_protein_and_rna_origins", ] ) result = pipeline(self.graph) self.assertEqual(32, result.number_of_nodes()) for node in self.graph: self.assertIn(node, result) self.check_original_unchanged() def test_pipeline_by_function(self): pipeline = Pipeline.from_functions( [ enrich_protein_and_rna_origins, ] ) result = pipeline(self.graph) self.assertEqual(32, result.number_of_nodes()) for node in self.graph: self.assertIn(node, result) self.check_original_unchanged() pybel-0.15.5/tests/test_struct/test_struct_graph.py000066400000000000000000000211561426625374700226050ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for data structures in PyBEL.""" import os import random import tempfile import unittest from io import StringIO import pybel import pybel.examples from pybel import BELGraph from pybel.constants import CITATION_TYPE_PUBMED, IDENTIFIER, NAMESPACE from pybel.dsl import hgvs, protein from pybel.io.api import InvalidExtensionError from pybel.language import Entity from pybel.testing.utils import n class TestGraphProperties(unittest.TestCase): """Test setting and access to graph properties.""" def setUp(self): """Make fake metadata for the graphs.""" ( self.name, self.version, self.description, self.authors, self.contact, self.licenses, self.copyrights, self.disclaimer, ) = [n() for _ in range(8)] def _help_test_metadata(self, graph: BELGraph) -> None: """Help test the right metadata got in the graph.""" self.assertEqual(self.name, graph.name) self.assertEqual(self.version, graph.version) self.assertEqual(self.description, graph.description) self.assertEqual(self.authors, graph.authors) self.assertEqual(self.contact, graph.contact) self.assertEqual(self.licenses, graph.license) self.assertEqual(self.copyrights, graph.copyright) self.assertEqual(self.disclaimer, graph.disclaimer) self.assertEqual("{name} v{version}".format(name=self.name, version=self.version), str(graph)) def test_str_kwargs(self): """Test setting of metadata through keyword arguments.""" graph = BELGraph( name=self.name, version=self.version, description=self.description, authors=self.authors, contact=self.contact, license=self.licenses, copyright=self.copyrights, disclaimer=self.disclaimer, ) self._help_test_metadata(graph) def test_name(self): """Test setting of metadata through attributes.""" graph = BELGraph() graph.name = self.name graph.version = self.version graph.description = self.description graph.authors = self.authors graph.contact = self.contact graph.license = self.licenses graph.copyright = self.copyrights graph.disclaimer = self.disclaimer self._help_test_metadata(graph) class TestStruct(unittest.TestCase): """Test the BEL graph data structure.""" def test_add_simple(self): """Test that a simple node can be added, but not duplicated.""" graph = BELGraph() node = protein(namespace="TEST", name="YFG") graph.add_node_from_data(node) self.assertEqual(1, graph.number_of_nodes()) graph.add_node_from_data(node) self.assertEqual(1, graph.number_of_nodes(), msg="should not add same node again") def test_summarize(self): """Test summarizing a graph.""" self.maxDiff = None sio = StringIO() random.seed(5) pybel.examples.sialic_acid_graph.version = "1.0.0" pybel.examples.sialic_acid_graph.summarize(file=sio, examples=False) test_str = """--------------------- ------------------- Name Sialic Acid Graph Version 1.0.0 Authors Charles Tapley Hoyt Number of Nodes 9 Number of Namespaces 3 Number of Edges 11 Number of Annotations 2 Number of Citations 1 Number of Authors 0 Number of Components 1 Number of Warnings 0 Network Density 1.53E-01 --------------------- ------------------- Type (3) Count ---------- ------- Protein 7 Complex 1 Abundance 1 Namespace (3) Count --------------- ------- go 15 hgnc 8 chebi 2 Edge Type (7) Count --------------------------------- ------- Protein increases Protein 3 Protein directlyIncreases Protein 2 Protein directlyDecreases Protein 2 Complex increases Protein 1 Abundance partOf Complex 1 Protein partOf Complex 1 Protein hasVariant Protein 1""" self.assertEqual(test_str.strip(), sio.getvalue().strip()) def test_citation_type_error(self): """Test error handling on adding qualified edges.""" graph = BELGraph() with self.assertRaises(TypeError): graph.add_increases( protein(namespace="TEST", name="YFG1"), protein(namespace="TEST", name="YFG2"), evidence=n(), citation=5, ) class TestGetGraphProperties(unittest.TestCase): """The tests in this class check the getting and setting of node properties.""" def setUp(self): """Set up the test case with a fresh BEL graph.""" self.graph = BELGraph() self.graph.annotation_pattern["Species"] = r"\d+" self.graph.annotation_list["Confidence"] = { "Very Low", "Low", "Medium", "High", "Very High", } def test_get_qualified_edge(self): """Test adding an edge to a graph.""" test_source = protein(namespace="TEST", name="YFG") test_target = protein(namespace="TEST", name="YFG2") self.graph.add_node_from_data(test_source) self.graph.add_node_from_data(test_target) test_evidence = n() test_pmid = n() test_key = self.graph.add_increases( test_source, test_target, citation=test_pmid, evidence=test_evidence, annotations={"Species": "9606", "Confidence": "Very High"}, ) citation = self.graph.get_edge_citation(test_source, test_target, test_key) self.assertIsNotNone(citation) self.assertIsInstance(citation, dict) self.assertIn(NAMESPACE, citation) self.assertEqual(CITATION_TYPE_PUBMED, citation[NAMESPACE]) self.assertIn(IDENTIFIER, citation) self.assertEqual(test_pmid, citation[IDENTIFIER]) evidence = self.graph.get_edge_evidence(test_source, test_target, test_key) self.assertIsNotNone(evidence) self.assertIsInstance(evidence, str) self.assertEqual(test_evidence, evidence) annotations = self.graph.get_edge_annotations(test_source, test_target, test_key) self.assertIsNotNone(annotations) self.assertIsInstance(annotations, dict) self.assertIn("Species", annotations) self.assertIn(Entity(namespace="Species", identifier="9606"), annotations["Species"]) self.assertIn("Confidence", annotations) self.assertIn( Entity(namespace="Confidence", identifier="Very High"), annotations["Confidence"], ) def test_get_unqualified_edge(self): """Test adding an unqualified edge.""" test_source = protein(namespace="TEST", name="YFG") test_target = protein(namespace="TEST", name="YFG2") key = self.graph.add_part_of(test_source, test_target) citation = self.graph.get_edge_citation(test_source, test_target, key) self.assertIsNone(citation) evidence = self.graph.get_edge_evidence(test_source, test_target, key) self.assertIsNone(evidence) annotations = self.graph.get_edge_annotations(test_source, test_target, key) self.assertIsNone(annotations) def test_add_node_with_variant(self): """Test that the identifier is carried through to the child.""" graph = BELGraph() namespace, name, identifier, variant_name = n(), n(), n(), n() node = protein( namespace=namespace, name=name, identifier=identifier, variants=hgvs(variant_name), ) node.get_parent() graph.add_node_from_data(node) self.assertEqual(2, graph.number_of_nodes()) class TestExtensionIO(unittest.TestCase): def test_io(self): with tempfile.TemporaryDirectory() as directory: path = os.path.join(directory, "ampk.bel.nodelink.json") pybel.dump(pybel.examples.ampk_graph, path) self.assertTrue(os.path.exists(path)) new_graph = pybel.load(path) self.assertIsNotNone(new_graph) def test_invalid_io(self): with tempfile.TemporaryDirectory() as directory: path = os.path.join(directory, "ampk.bel.invalid.json") with self.assertRaises(InvalidExtensionError): pybel.dump(pybel.examples.ampk_graph, path) self.assertFalse(os.path.exists(path)) pybel-0.15.5/tests/test_struct/test_struct_operations.py000066400000000000000000000210601426625374700236610ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for graph operations.""" import unittest from pybel import BELGraph from pybel.dsl import protein from pybel.struct.operations import ( left_full_join, left_node_intersection_join, left_outer_join, node_intersection, union, ) from pybel.testing.utils import n p1, p2, p3, p4, p5, p6, p7, p8 = (protein(namespace="HGNC", name=n()) for _ in range(8)) class TestLeftFullJoin(unittest.TestCase): """Tests the variants of the left full join, including the exhaustive vs. hash algorithms and calling by function or magic functions""" def setUp(self): """Set up tests for the left full join with two example graphs.""" g = BELGraph() g.add_increases(p1, p2, citation="PMID1", evidence="Evidence 1") h = BELGraph() h.add_increases(p1, p2, citation="PMID1", evidence="Evidence 1") h.add_increases(p1, p2, citation="PMID2", evidence="Evidence 2") h.add_increases(p1, p3, citation="PMID1", evidence="Evidence 3") self.g = g self.h = h self._help_check_initial_g(self.g) self._help_check_initial_h(self.h) def _help_check_initial_g(self, graph: BELGraph): """Test the initial G graph.""" self.assertEqual(2, graph.number_of_nodes(), msg="initial graph G had wrong number of nodes") self.assertEqual(1, graph.number_of_edges(), msg="initial graph G had wrong number of edges") def _help_check_initial_h(self, graph: BELGraph): """Test the initial H graph.""" self.assertEqual(3, graph.number_of_nodes(), msg="initial graph H had wrong number of nodes") self.assertEqual(3, graph.number_of_edges(), msg="initial graph H had wrong number of edges") def _help_check_result(self, j: BELGraph): """Help check the result of left joining H into G. :param j: The resulting graph from G += H """ self.assertEqual(3, j.number_of_nodes()) self.assertEqual( 3, j.number_of_edges(), msg="G edges:\n{}".format("\n".join(map(str, j.edges(data=True)))), ) def test_function(self): """Test full joining two networks using the function.""" left_full_join(self.g, self.h) self._help_check_result(self.g) self._help_check_initial_h(self.h) def test_full_join_with_isolated_nodes(self): """Test what happens when there are isolated nodes.""" a = BELGraph() a.add_increases(p1, p2, citation=n(), evidence=n()) a.add_node_from_data(p4) b = BELGraph() b.add_increases(p2, p3, citation=n(), evidence=n()) b.add_node_from_data(p5) left_full_join(a, b) for node in p1, p2, p3, p4, p5: self.assertIn(node, a) def test_in_place_operator_failure(self): """Test that using the wrong type with the in-place addition operator raises an error.""" with self.assertRaises(TypeError): self.g += None def test_in_place_operator(self): """Test full joining two networks using the BELGraph in-place addition operator.""" self.g += self.h self._help_check_result(self.g) self._help_check_initial_h(self.h) def test_operator_failure(self): """Test that using the wrong type with the addition operator raises an error.""" with self.assertRaises(TypeError): self.g + None def test_operator(self): """Test full joining two networks using the BELGraph addition operator.""" j = self.g + self.h self._help_check_result(j) self._help_check_initial_g(self.g) self._help_check_initial_h(self.h) def test_union_failure(self): """Test that the union of no graphs raises a value error.""" with self.assertRaises(ValueError): union([]) def test_union_trivial(self): """Test that the union of a single graph returns that graph.""" res = union([self.g]) self.assertEqual(self.g, res) def test_union(self): """Test that the union of a pair of graphs is the same as the full join.""" j = union([self.g, self.h]) self._help_check_result(j) self._help_check_initial_g(self.g) self._help_check_initial_h(self.h) class TestLeftFullOuterJoin(unittest.TestCase): def setUp(self): g = BELGraph() g.add_edge(p1, p2) h = BELGraph() h.add_edge(p1, p3) h.add_edge(p1, p4) h.add_edge(p5, p6) h.add_node(p7) self.g = g self.h = h def _help_check_initial_g(self, g): self.assertEqual(2, g.number_of_nodes()) self.assertEqual({p1, p2}, set(g)) self.assertEqual(1, g.number_of_edges()) self.assertEqual({(p1, p2)}, set(g.edges())) def _help_check_initial_h(self, h): self.assertEqual(6, h.number_of_nodes()) self.assertEqual({p1, p3, p4, p5, p6, p7}, set(h)) self.assertEqual(3, h.number_of_edges()) self.assertEqual({(p1, p3), (p1, p4), (p5, p6)}, set(h.edges())) def _help_check_result(self, j): """After H has been full outer joined into G, this is what it should be""" self.assertEqual(4, j.number_of_nodes()) self.assertEqual({p1, p2, p3, p4}, set(j)) self.assertEqual(3, j.number_of_edges()) self.assertEqual({(p1, p2), (p1, p3), (p1, p4)}, set(j.edges())) def test_in_place_type_failure(self): with self.assertRaises(TypeError): self.g &= None def test_type_failure(self): with self.assertRaises(TypeError): self.g & None def test_magic(self): # left_outer_join(g, h) self.g &= self.h self._help_check_initial_h(self.h) self._help_check_result(self.g) def test_operator(self): # left_outer_join(g, h) j = self.g & self.h self._help_check_initial_h(self.h) self._help_check_initial_g(self.g) self._help_check_result(j) def test_left_outer_join(self): left_outer_join(self.g, self.h) self._help_check_initial_h(self.h) self._help_check_result(self.g) def test_left_outer_exhaustive_join(self): self.g &= self.h left_outer_join(self.g, self.h) self._help_check_initial_h(self.h) self._help_check_result(self.g) class TestInnerJoin(unittest.TestCase): """Tests various graph merging procedures""" def setUp(self): g = BELGraph() g.add_edge(p1, p2) g.add_edge(p1, p3) g.add_edge(p8, p3) h = BELGraph() h.add_edge(p1, p3) h.add_edge(p1, p4) h.add_edge(p5, p6) h.add_node(p7) self.g = g self.h = h def _help_check_initialize_g(self, graph): self.assertEqual(4, graph.number_of_nodes()) self.assertEqual(3, graph.number_of_edges()) def _help_check_initialize_h(self, graph): self.assertEqual(6, graph.number_of_nodes()) self.assertEqual({p1, p3, p4, p5, p6, p7}, set(graph)) self.assertEqual(3, graph.number_of_edges()) self.assertEqual({(p1, p3), (p1, p4), (p5, p6)}, set(graph.edges())) def test_initialize(self): self._help_check_initialize_g(self.g) self._help_check_initialize_h(self.h) def _help_check_join(self, j): self.assertEqual(2, j.number_of_nodes()) self.assertEqual({p1, p3}, set(j)) self.assertEqual(1, j.number_of_edges()) self.assertEqual( { (p1, p3), }, set(j.edges()), ) def test_in_place_type_failure(self): with self.assertRaises(TypeError): self.g ^ None def test_type_failure(self): with self.assertRaises(TypeError): self.g ^= None def test_magic(self): j = self.g ^ self.h self._help_check_join(j) self._help_check_initialize_h(self.h) self._help_check_initialize_g(self.g) def test_left_node_intersection_join(self): j = left_node_intersection_join(self.g, self.h) self._help_check_join(j) self._help_check_initialize_h(self.h) self._help_check_initialize_g(self.g) def test_node_intersection(self): j = node_intersection([self.h, self.g]) self._help_check_join(j) self._help_check_initialize_h(self.h) self._help_check_initialize_g(self.g) def test_intersection_failure(self): with self.assertRaises(ValueError): node_intersection([]) def test_intersection_trivial(self): res = node_intersection([self.g]) self.assertEqual(self.g, res) pybel-0.15.5/tests/test_struct/test_summary/000077500000000000000000000000001426625374700212165ustar00rootroot00000000000000pybel-0.15.5/tests/test_struct/test_summary/__init__.py000066400000000000000000000001061426625374700233240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for :mod:`pybel.struct.summary`.""" pybel-0.15.5/tests/test_struct/test_summary/test_errors.py000066400000000000000000000037451426625374700241540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for :mod:`pybel.struct.summary.errors`.""" import unittest from pybel import BELGraph from pybel.exceptions import NakedNameWarning, UndefinedAnnotationWarning from pybel.struct.summary import count_error_types, count_naked_names, get_naked_names from pybel.testing.utils import n class TestErrors(unittest.TestCase): """Test :mod:`pybel.struct.summary.errors`.""" def test_count_error_types(self): """Test counting error types.""" graph = BELGraph() line_number = 30 position = 4 line = n() annotation = n() exc = UndefinedAnnotationWarning( line_number=line_number, line=line, position=position, annotation=annotation, ) graph.add_warning(exc) error_types = count_error_types(graph) self.assertEqual(1, len(error_types)) self.assertIn(UndefinedAnnotationWarning.__name__, error_types) self.assertEqual(1, error_types[UndefinedAnnotationWarning.__name__]) def test_get_naked_names(self): """Retrieve the naked names from a graph.""" graph = BELGraph() n_names = 5 line_number = 30 position = 4 line = n() names = {n() for _ in range(n_names)} exceptions = [ NakedNameWarning( line_number=line_number, line=line, position=position, name=name, ) for name in names ] for exception in exceptions: graph.add_warning(exception=exception) graph.add_warning(exception=exceptions[0]) self.assertEqual(6, graph.number_of_warnings()) naked_names = get_naked_names(graph) self.assertEqual(names, naked_names) naked_name_counter = count_naked_names(graph) self.assertEqual(n_names, len(naked_name_counter)) self.assertEqual(2, naked_name_counter[exceptions[0].name]) pybel-0.15.5/tests/test_struct/test_summary/test_provenance.py000066400000000000000000000006121426625374700247660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for provenance summary functions.""" import unittest from pybel.examples import sialic_acid_graph class TestProvenance(unittest.TestCase): """Tests for provenance summary functions.""" def test_count_citations(self): """Test counting citations.""" count = sialic_acid_graph.number_of_citations() self.assertEqual(1, count) pybel-0.15.5/tests/test_struct/test_summary/test_struct_summary_edges.py000066400000000000000000000103611426625374700271000ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test summary functions for edges.""" import unittest from collections import Counter from pybel import BELGraph from pybel.dsl import protein from pybel.examples import sialic_acid_graph from pybel.language import Entity from pybel.struct.summary.edge_summary import ( count_annotations, get_annotation_values, get_annotation_values_by_annotation, get_annotations, get_unused_annotations, get_unused_list_annotation_values, iter_annotation_value_pairs, iter_annotation_values, ) from pybel.testing.utils import n class TestEdgeSummary(unittest.TestCase): """Test summary functions for edges.""" def test_1(self): """Test iterating over annotation/value pairs.""" graph = BELGraph() graph.annotation_list.update( { "A": set("1234"), "B": set("XYZ"), "C": set("abcde"), } ) u = protein("HGNC", name="U") v = protein("HGNC", name="V") w = protein("HGNC", name="W") graph.add_increases(u, v, evidence=n(), citation=n(), annotations={"A": {"1", "2"}, "B": {"X"}}) graph.add_increases( u, w, evidence=n(), citation=n(), annotations={ "A": {"1", "3"}, "C": {"a"}, }, ) graph.add_increases( w, v, evidence=n(), citation=n(), ) x = dict(Counter((key, entity.identifier) for key, entity in iter_annotation_value_pairs(graph))) self.assertEqual( { ("A", "1"): 2, ("A", "2"): 1, ("A", "3"): 1, ("B", "X"): 1, ("C", "a"): 1, }, x, ) y = Counter(iter_annotation_values(graph, "A")) self.assertEqual(x["A", "1"] + x["A", "2"] + x["A", "3"], sum(y.values())) y = Counter(iter_annotation_values(graph, "B")) self.assertEqual(x["B", "X"], sum(y.values())) y = Counter(iter_annotation_values(graph, "C")) self.assertEqual(x["C", "a"], sum(y.values())) def test_get_annotation_values(self): """Test getting annotation values.""" expected = { "Confidence": { Entity(namespace="Confidence", identifier="High"), Entity(namespace="Confidence", identifier="Low"), }, "Species": { Entity(namespace="Species", identifier="9606"), }, } self.assertEqual({"Confidence", "Species"}, get_annotations(sialic_acid_graph)) self.assertEqual({"Confidence": 8, "Species": 8}, dict(count_annotations(sialic_acid_graph))) annotation_values_by_annotation = get_annotation_values_by_annotation(sialic_acid_graph) self.assertEqual(expected, annotation_values_by_annotation) annotation_values = get_annotation_values(sialic_acid_graph, "Confidence") self.assertEqual(expected["Confidence"], annotation_values) def test_get_unused_annotation_url(self): graph = BELGraph() name = n() graph.annotation_url[name] = n() self.assertEqual({name}, get_unused_annotations(graph)) def test_get_unused_annotation_pattern(self): graph = BELGraph() name = n() graph.annotation_pattern[name] = n() self.assertEqual({name}, get_unused_annotations(graph)) def test_get_unused_annotation_list(self): graph = BELGraph() name = n() graph.annotation_pattern[name] = {n(), n(), n()} self.assertEqual({name}, get_unused_annotations(graph)) def test_get_unused_annotation_list_values(self): """Test getting unused annotation list values.""" graph = BELGraph() annotation_key = "test" graph.annotation_list[annotation_key] = set("abc") graph.add_increases( protein(n(), n()), protein(n(), n()), citation=n(), evidence=n(), annotations={annotation_key: {"a"}}, ) rv = get_unused_list_annotation_values(graph) self.assertIsInstance(rv, dict) self.assertEqual({annotation_key: set("bc")}, rv) pybel-0.15.5/tests/test_struct/test_summary/test_summary_nodes.py000066400000000000000000000136041426625374700255200ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for summary functions for nodes.""" import unittest from collections import Counter from pybel import BELGraph from pybel.constants import ABUNDANCE, BIOPROCESS, COMPLEX, PROTEIN from pybel.dsl import fusion_range, pathology, protein, protein_fusion from pybel.examples import egf_graph, sialic_acid_graph from pybel.struct.summary.node_summary import ( count_functions, count_names_by_namespace, count_namespaces, count_pathologies, count_variants, get_functions, get_names, get_names_by_namespace, get_namespaces, get_top_hubs, get_top_pathologies, ) from pybel.testing.utils import n class TestSummary(unittest.TestCase): """Test node summary functions.""" def test_functions_sialic(self): """Test counting nodes and grouping by function on the sialic acid graph.""" result = { PROTEIN: 7, COMPLEX: 1, ABUNDANCE: 1, } self.assertEqual(set(result), get_functions(sialic_acid_graph)) self.assertEqual(Counter(result), count_functions(sialic_acid_graph)) def test_functions_egf(self): """Test counting nodes and grouping by function on the EGF graph.""" result = { PROTEIN: 10, COMPLEX: 1, BIOPROCESS: 1, } self.assertEqual(set(result), get_functions(egf_graph)) self.assertEqual(result, count_functions(egf_graph)) def test_summarize_sialic(self): """Test getting and counting namespaces' contents on the sialic acid graph.""" namespace_result = { "hgnc": 8, "chebi": 2, "go": 15, } self.assertEqual(set(namespace_result), get_namespaces(sialic_acid_graph)) self.assertEqual(Counter(namespace_result), count_namespaces(sialic_acid_graph)) hgnc_result = { "CD33": 3, # once as reference, once in complex, and once as variant "TYROBP": 1, "SYK": 1, "PTPN6": 1, "PTPN11": 1, "TREM2": 1, } chebi_result = { "sialic acid": 2, } names = get_names(sialic_acid_graph) self.assertEqual(set(namespace_result), set(names)) self.assertEqual(set(hgnc_result), names["hgnc"]) self.assertEqual(set(chebi_result), names["chebi"]) self.assertEqual(set(hgnc_result), get_names_by_namespace(sialic_acid_graph, "hgnc")) self.assertEqual(set(chebi_result), get_names_by_namespace(sialic_acid_graph, "chebi")) self.assertEqual(hgnc_result, dict(count_names_by_namespace(sialic_acid_graph, "hgnc"))) self.assertEqual(chebi_result, dict(count_names_by_namespace(sialic_acid_graph, "chebi"))) def test_namespaces_egf(self): """Test getting and counting namespaces' contents on the EGF graph.""" result = { "hgnc": 15, "go": 7, } self.assertEqual(set(result), get_namespaces(egf_graph)) self.assertEqual(Counter(result), count_namespaces(egf_graph)) def test_names_fusions(self): """Test that names inside fusions are still found by the iterator.""" graph = BELGraph() graph.namespace_url["HGNC"] = "http://dummy" node = protein_fusion( partner_5p=protein(name="A", namespace="HGNC"), range_5p=fusion_range("p", 1, 15), partner_3p=protein(name="B", namespace="HGNC"), range_3p=fusion_range("p", 1, 100), ) graph.add_node_from_data(node) result = { "A": 1, "B": 1, } self.assertEqual(set(result), get_names_by_namespace(graph, "HGNC")) self.assertEqual(result, count_names_by_namespace(graph, "HGNC")) def test_get_names_raise(self): """Test that an index error is raised when trying to get names from a namespace that isn't present.""" with self.assertRaises(IndexError): get_names_by_namespace(sialic_acid_graph, "NOPE") def test_count_names_raise(self): """Test that an index error is raised when trying to count a namespace that isn't present.""" with self.assertRaises(IndexError): count_names_by_namespace(sialic_acid_graph, "NOPE") def test_count_variants(self): """Test counting the number of variants in a graph.""" variants = count_variants(sialic_acid_graph) self.assertEqual(1, variants["pmod"]) def test_count_pathologies(self): """Test counting pathologies in the graph.""" graph = BELGraph() a, b = (protein(namespace="HGNC", name=n()) for _ in range(2)) c, d = (pathology(namespace="DOID", name=n()) for _ in range(2)) graph.add_association(a, c, citation=n(), evidence=n()) graph.add_association(a, d, citation=n(), evidence=n()) graph.add_association(b, d, citation=n(), evidence=n()) pathology_counter = count_pathologies(graph) self.assertIn(c, pathology_counter) self.assertIn(d, pathology_counter) self.assertEqual(1, pathology_counter[c]) self.assertEqual(2, pathology_counter[d]) top_pathology_counter = get_top_pathologies(graph, n=1) self.assertEqual(1, len(top_pathology_counter)) node, count = top_pathology_counter[0] self.assertEqual(d, node) self.assertEqual(2, count) def test_get_top_hubs(self): """Test counting pathologies in the graph.""" graph = BELGraph() a, b, c = protein(n(), n()), protein(n(), n()), pathology(n(), n()) graph.add_association(a, b, citation=n(), evidence=n()) graph.add_association(a, c, citation=n(), evidence=n()) top_hubs = get_top_hubs(graph, n=1) self.assertEqual(1, len(top_hubs)) node, degree = top_hubs[0] self.assertEqual(a, node) self.assertEqual(4, degree) # higher than expected because association edges pybel-0.15.5/tests/test_struct/test_transformations/000077500000000000000000000000001426625374700227525ustar00rootroot00000000000000pybel-0.15.5/tests/test_struct/test_transformations/__init__.py000066400000000000000000000001071426625374700250610ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for :mod:`pybel.struct.mutation`.""" pybel-0.15.5/tests/test_struct/test_transformations/test_collapse.py000066400000000000000000000103311426625374700261630ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for collapse functions.""" import unittest from pybel import BELGraph from pybel.constants import DIRECTLY_INCREASES from pybel.dsl import gene, mirna, pathology, pmod, protein, rna from pybel.struct.mutation.collapse import ( collapse_all_variants, collapse_nodes, collapse_to_genes, surviors_are_inconsistent, ) from pybel.testing.utils import n HGNC = "HGNC" GO = "GO" CHEBI = "CHEBI" g1 = gene(HGNC, "1") r1 = rna(HGNC, "1") p1 = protein(HGNC, "1") p1_phosphorylated = protein(HGNC, "1", variants=[pmod("Ph")]) g2 = gene(HGNC, "2") r2 = rna(HGNC, "2") p2 = protein(HGNC, "2") g3 = gene(HGNC, "3") r3 = rna(HGNC, "3") p3 = protein(HGNC, "3") g4 = gene(HGNC, "4") m4 = mirna(HGNC, "4") p5 = pathology(GO, "5") class TestCollapse(unittest.TestCase): """Tests for collapse functions.""" def test_check_survivors_consistent(self): """Test the survivor mapping is consistent.""" inconsistencies = surviors_are_inconsistent( { 1: {2}, 3: {4}, } ) self.assertEqual(0, len(inconsistencies)) self.assertFalse(inconsistencies) inconsistencies = surviors_are_inconsistent( { 1: {2}, 2: {3}, 3: {4}, 5: {4}, } ) self.assertEqual(2, len(inconsistencies)) self.assertIn(2, inconsistencies) self.assertIn(3, inconsistencies) def test_collapse_by_dict(self): """Test collapsing nodes by a dictionary.""" graph = BELGraph() graph.add_node_from_data(p1) graph.add_node_from_data(p2) graph.add_node_from_data(p3) graph.add_increases(p1, p3, citation=n(), evidence=n()) graph.add_qualified_edge(p2, p3, relation=DIRECTLY_INCREASES, citation=n(), evidence=n()) self.assertEqual(3, graph.number_of_nodes()) self.assertEqual(2, graph.number_of_edges()) d = {p1: {p2}} collapse_nodes(graph, d) self.assertEqual({p1, p3}, set(graph)) self.assertEqual({(p1, p3), (p1, p3)}, set(graph.edges())) self.assertEqual(2, graph.number_of_edges(), msg=graph.edges(data=True, keys=True)) def test_collapse_dogma_1(self): """Test collapsing to genes, only with translations.""" graph = BELGraph() graph.add_translation(r1, p1) self.assertEqual(2, graph.number_of_nodes()) self.assertEqual(1, graph.number_of_edges()) collapse_to_genes(graph) self.assertIn(g1, graph) self.assertEqual(1, graph.number_of_nodes()) self.assertEqual(0, graph.number_of_edges()) def test_collapse_dogma_2(self): """Test collapsing to genes with translations and transcriptions.""" graph = BELGraph() graph.add_transcription(g1, r1) graph.add_translation(r1, p1) self.assertEqual(3, graph.number_of_nodes()) self.assertEqual(2, graph.number_of_edges()) collapse_to_genes(graph) self.assertIn(g1, graph) self.assertEqual(1, graph.number_of_nodes()) self.assertEqual(0, graph.number_of_edges()) def test_collapse_dogma_3(self): """Test collapsing to genes, only with transcriptions.""" graph = BELGraph() graph.add_transcription(g1, r1) self.assertEqual(2, graph.number_of_nodes()) self.assertEqual(1, graph.number_of_edges()) collapse_to_genes(graph) self.assertIn(g1, graph) self.assertEqual(1, graph.number_of_nodes()) self.assertEqual(0, graph.number_of_edges()) def test_collapse_all_variants(self): """Test collapsing all variants to their reference nodes.""" graph = BELGraph() graph.add_node_from_data(p1_phosphorylated) graph.add_increases(p1_phosphorylated, p2, citation=n(), evidence=n()) self.assertEqual(3, graph.number_of_nodes()) self.assertEqual(2, graph.number_of_edges()) collapse_all_variants(graph) self.assertEqual(2, graph.number_of_nodes()) self.assertEqual(1, graph.number_of_edges()) self.assertIn(p1, graph) self.assertNotIn(p1_phosphorylated, graph) self.assertIn(p2, graph) pybel-0.15.5/tests/test_struct/test_transformations/test_deletions.py000066400000000000000000000153101426625374700263510ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for node deletion functions.""" import unittest from pybel import BELGraph from pybel.constants import POSITIVE_CORRELATION, RELATION from pybel.dsl import ( CompositeAbundance, Protein, gene, hgvs, pathology, protein_fusion, rna, rna_fusion, ) from pybel.struct.mutation import ( enrich_protein_and_rna_origins, prune_protein_rna_origins, remove_associations, remove_isolated_list_abundances, remove_pathologies, ) from pybel.struct.mutation.utils import remove_isolated_nodes, remove_isolated_nodes_op from pybel.testing.utils import n trem2_gene = gene(namespace="HGNC", name="TREM2") trem2_rna = rna(namespace="HGNC", name="TREM2") trem2_protein = Protein(namespace="HGNC", name="TREM2") class TestDeletions(unittest.TestCase): """Test cases for deletion functions.""" def test_remove_pathologies(self): """Test removal of pathologies.""" g = BELGraph() p1, p2, p3 = (Protein(namespace="HGNC", name=n()) for _ in range(3)) d1, d2 = (pathology(namespace="MESH", name=n()) for _ in range(2)) g.add_increases(p1, p2, citation=n(), evidence=n()) g.add_increases(p2, p3, citation=n(), evidence=n()) g.add_positive_correlation(p1, d1, citation=n(), evidence=n()) g.add_positive_correlation(p2, d1, citation=n(), evidence=n()) g.add_association(p2, d1, citation=n(), evidence=n()) g.add_positive_correlation(d1, d2, citation=n(), evidence=n()) g.add_positive_correlation(d1, d2, citation=n(), evidence=n()) self.assertEqual(5, g.number_of_nodes()) self.assertEqual(12, g.number_of_edges()) self.assertEqual(2, len(g[p2][d1])) remove_associations(g) relations = list(g[p2][d1].values()) self.assertEqual(1, len(relations)) self.assertEqual(POSITIVE_CORRELATION, relations[0][RELATION]) self.assertEqual(5, g.number_of_nodes()) self.assertEqual(10, g.number_of_edges()) self.assertEqual(5, g.number_of_nodes()) remove_pathologies(g) self.assertTrue(p1, g) self.assertTrue(p2, g) self.assertTrue(p3, g) self.assertEqual(3, g.number_of_nodes()) self.assertEqual(2, g.number_of_edges()) def test_remove_isolated_in_place(self): """Test removing isolated nodes (in-place).""" g = BELGraph() g.add_edge(1, 2) g.add_edge(2, 3) g.add_node(4) remove_isolated_nodes(g) self.assertEqual(3, g.number_of_nodes()) self.assertEqual(2, g.number_of_edges()) def test_remove_isolated_out_of_place(self): """Test removing isolated nodes (out-of-place).""" g = BELGraph() g.add_edge(1, 2) g.add_edge(2, 3) g.add_node(4) g = remove_isolated_nodes_op(g) self.assertEqual(3, g.number_of_nodes()) self.assertEqual(2, g.number_of_edges()) def test_remove_isolated_list_abundances(self): """Test removing isolated list abundances.""" g = BELGraph() p1, p2 = [Protein("HGNC", n()) for _ in range(2)] node = CompositeAbundance([p1, p2]) g.add_node_from_data(node) self.assertEqual(3, g.number_of_nodes()) remove_isolated_list_abundances(g) self.assertEqual(2, g.number_of_nodes()) self.assertIn(p1, g) self.assertIn(p2, g) class TestProcessing(unittest.TestCase): """Test inference of the central dogma.""" def assert_in_graph(self, node, graph): """Assert the node is in the graph. :type node: pybel.dsl.BaseEntity :type graph: pybel.BELGraph :rtype: bool """ self.assertIn(node, graph) def assert_not_in_graph(self, node, graph): """Assert the node is not in the graph. :type node: pybel.dsl.BaseEntity :type graph: pybel.BELGraph :rtype: bool """ self.assertNotIn(node, graph) def test_infer_on_sialic_acid_example(self): """Test infer_central_dogma on the sialic acid example.""" graph = BELGraph() graph.add_node_from_data(trem2_protein) self.assert_in_graph(trem2_protein, graph) self.assert_not_in_graph(trem2_gene, graph) self.assert_not_in_graph(trem2_rna, graph) enrich_protein_and_rna_origins(graph) self.assert_in_graph(trem2_gene, graph) self.assert_in_graph(trem2_rna, graph) prune_protein_rna_origins(graph) self.assert_not_in_graph(trem2_gene, graph) self.assert_not_in_graph(trem2_rna, graph) self.assert_in_graph(trem2_protein, graph) def test_no_infer_on_protein_variants(self): """Test that expansion doesn't occur on protein variants.""" p = Protein("HGNC", n(), variants=[hgvs(n())]) graph = BELGraph() graph.add_node_from_data(p) self.assertEqual(2, graph.number_of_nodes()) self.assertEqual(1, graph.number_of_edges()) enrich_protein_and_rna_origins(graph) self.assertEqual(4, graph.number_of_nodes()) self.assertEqual(3, graph.number_of_edges()) def test_no_infer_on_rna_variants(self): """Test that expansion doesn't occur on RNA variants.""" r = rna("HGNC", n(), variants=[hgvs(n())]) graph = BELGraph() graph.add_node_from_data(r) self.assertEqual(2, graph.number_of_nodes()) self.assertEqual(1, graph.number_of_edges()) enrich_protein_and_rna_origins(graph) self.assertEqual(3, graph.number_of_nodes()) self.assertEqual(2, graph.number_of_edges()) def test_no_infer_protein_fusion(self): """Test that no gene is inferred from a RNA fusion node.""" partner5p = Protein(n(), n()) partner3p = Protein(n(), n()) p = protein_fusion(partner_3p=partner3p, partner_5p=partner5p) graph = BELGraph() graph.add_node_from_data(p) self.assertEqual(1, graph.number_of_nodes()) self.assertEqual(0, graph.number_of_edges()) enrich_protein_and_rna_origins(graph) self.assertEqual(1, graph.number_of_nodes()) self.assertEqual(0, graph.number_of_edges()) def test_no_infer_rna_fusion(self): """Test that no RNA nor gene is inferred from a protein fusion node.""" partner5p = rna(n(), n()) partner3p = rna(n(), n()) p = rna_fusion(partner_3p=partner3p, partner_5p=partner5p) graph = BELGraph() graph.add_node_from_data(p) self.assertEqual(1, graph.number_of_nodes()) self.assertEqual(0, graph.number_of_edges()) enrich_protein_and_rna_origins(graph) self.assertEqual(1, graph.number_of_nodes()) self.assertEqual(0, graph.number_of_edges()) pybel-0.15.5/tests/test_struct/test_transformations/test_expansion.py000066400000000000000000000064751426625374700264030ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for expansion functions.""" import unittest from pybel import BELGraph from pybel.examples.sialic_acid_example import ( cd33, cd33_phosphorylated, shp1, shp2, sialic_acid, sialic_acid_cd33_complex, sialic_acid_graph, syk, ) from pybel.struct.mutation.expansion.neighborhood import ( expand_node_neighborhood, expand_node_predecessors, expand_node_successors, expand_nodes_neighborhoods, ) class TestExpansion(unittest.TestCase): """Test expansion functions.""" def test_neighborhood(self): """Test expansion around the neighborhood of CD33 in the sialic acid graph given node.""" graph = BELGraph() graph.add_node_from_data(cd33) self.assertEqual(1, graph.number_of_nodes()) _sialic_acid_graph = sialic_acid_graph.copy() expand_node_neighborhood(graph=graph, universe=_sialic_acid_graph, node=cd33) self.assertEqual( {cd33, sialic_acid, sialic_acid_cd33_complex, cd33_phosphorylated}, set(graph), ) def test_neighborhood_with_predecessors(self): """Test expansion on the predecessors of a given node.""" graph = BELGraph() graph.add_node_from_data(cd33) graph.add_node_from_data(sialic_acid_cd33_complex) self.assertEqual(3, graph.number_of_nodes()) _sialic_acid_graph = sialic_acid_graph.copy() expand_node_predecessors(universe=_sialic_acid_graph, graph=graph, node=cd33) self.assertEqual(4, graph.number_of_nodes()) self.assertIn(sialic_acid, graph) self.assertIn(sialic_acid_cd33_complex, graph) self.assertIn(cd33_phosphorylated, graph) def test_neighborhood_with_successors(self): """Test expansion on the successors of a given node.""" graph = BELGraph() graph.add_node_from_data(cd33) graph.add_node_from_data(cd33_phosphorylated) self.assertEqual(2, graph.number_of_nodes()) _sialic_acid_graph = sialic_acid_graph.copy() expand_node_successors(universe=_sialic_acid_graph, graph=graph, node=cd33) self.assertEqual( {sialic_acid_cd33_complex, sialic_acid, cd33_phosphorylated, cd33}, set(graph), ) def test_neighborhoods(self): """Test expansion on the neighborhood of given nodes. The edge between PTPN6/CD33ph should not be added. """ graph = BELGraph() graph.add_node_from_data(cd33) graph.add_node_from_data(syk) self.assertEqual(2, graph.number_of_nodes()) _sialic_acid_graph = sialic_acid_graph.copy() expand_nodes_neighborhoods(universe=_sialic_acid_graph, graph=graph, nodes=[cd33, syk]) self.assertNotIn(shp1, graph[cd33_phosphorylated]) self.assertNotIn(shp2, graph[cd33_phosphorylated]) self.assertEqual( 9, graph.number_of_nodes(), msg="wrong number of nodes: {}".format(list(graph)), ) self.assertEqual(8, graph.number_of_edges(), msg="wrong number of edges") self.assertIn(sialic_acid, graph) self.assertIn(sialic_acid_cd33_complex, graph) self.assertIn(cd33_phosphorylated, graph) # TODO test that if new nodes with metadata that's missing (namespace_url definition, etc) then that gets added too pybel-0.15.5/tests/test_struct/test_transformations/test_induction.py000066400000000000000000000363361426625374700263720ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for PyBEL induction functions.""" import string import unittest from pybel import BELGraph from pybel.constants import ( CITATION_AUTHORS, CITATION_TYPE_PUBMED, IDENTIFIER, NAMESPACE, ) from pybel.dsl import BaseEntity, gene, protein, rna from pybel.struct.mutation.expansion import expand_upstream_causal from pybel.struct.mutation.induction import get_subgraph_by_annotation_value from pybel.struct.mutation.induction.citation import ( get_subgraph_by_authors, get_subgraph_by_pubmed, ) from pybel.struct.mutation.induction.paths import ( get_nodes_in_all_shortest_paths, get_subgraph_by_all_shortest_paths, ) from pybel.struct.mutation.induction.upstream import get_upstream_causal_subgraph from pybel.struct.mutation.induction.utils import get_subgraph_by_induction from pybel.testing.utils import n trem2_gene = gene(namespace="HGNC", name="TREM2") trem2_rna = rna(namespace="HGNC", name="TREM2") trem2_protein = protein(namespace="HGNC", name="TREM2") class TestGraphMixin(unittest.TestCase): """A mixin to enable testing nodes and edge membership in the graph.""" def assert_in_edge(self, source, target, graph): """Assert the edge is in the graph. :param source: :param target: :type graph: pybel.BELGraph :rtype: bool """ self.assertIn(target, graph[source]) def assert_all_nodes_are_base_entities(self, graph): """Assert that all nodes are base entities.""" for node in graph: self.assertIsInstance(node, BaseEntity) class TestInduction(TestGraphMixin): """Test induction functions.""" def test_get_subgraph_by_induction(self): """Test get_subgraph_by_induction.""" graph = BELGraph() keyword, url = n(), n() graph.namespace_url[keyword] = url a, b, c, d = [protein(namespace="test", name=str(i)) for i in range(4)] graph.add_directly_increases(a, b, citation=n(), evidence=n()) graph.add_directly_increases(b, c, citation=n(), evidence=n()) graph.add_directly_increases(c, d, citation=n(), evidence=n()) graph.add_increases(a, d, citation=n(), evidence=n()) nodes = [b, c] subgraph = get_subgraph_by_induction(graph, nodes) self.assertIsInstance(subgraph, BELGraph) self.assert_all_nodes_are_base_entities(subgraph) self.assertNotEqual( 0, len(subgraph.namespace_url), msg="improperly found metadata: {}".format(subgraph.graph), ) self.assertIn(keyword, subgraph.namespace_url) self.assertEqual(url, subgraph.namespace_url[keyword]) self.assertNotIn(a, subgraph) self.assertIn(b, subgraph) self.assertIn(c, subgraph) self.assertNotIn(d, subgraph) def test_get_subgraph_by_all_shortest_paths(self): """Test get_subgraph_by_all_shortest_paths.""" graph = BELGraph() keyword, url = n(), n() graph.namespace_url[keyword] = url a, b, c, d, e, f = [protein(namespace="test", name=n()) for _ in range(6)] graph.add_increases(a, b, citation=n(), evidence=n()) graph.add_increases(a, c, citation=n(), evidence=n()) graph.add_increases(b, d, citation=n(), evidence=n()) graph.add_increases(c, d, citation=n(), evidence=n()) graph.add_increases(a, e, citation=n(), evidence=n()) graph.add_increases(e, f, citation=n(), evidence=n()) graph.add_increases(f, d, citation=n(), evidence=n()) query_nodes = [a, d] shortest_paths_nodes = get_nodes_in_all_shortest_paths(graph, query_nodes) self.assertIn(a, shortest_paths_nodes) self.assertIn(b, shortest_paths_nodes) self.assertIn(c, shortest_paths_nodes) self.assertIn(d, shortest_paths_nodes) subgraph = get_subgraph_by_all_shortest_paths(graph, query_nodes) self.assert_all_nodes_are_base_entities(subgraph) self.assertIsInstance(subgraph, BELGraph) self.assertIn(keyword, subgraph.namespace_url) self.assertEqual(url, subgraph.namespace_url[keyword]) self.assertIn(a, subgraph) self.assertIn(b, subgraph) self.assertIn(c, subgraph) self.assertIn(d, subgraph) self.assertNotIn(e, subgraph) self.assertNotIn(f, subgraph) def test_get_upstream_causal_subgraph(self): """Test get_upstream_causal_subgraph.""" a, b, c, d, e, f = [protein(namespace="test", name=n()) for _ in range(6)] universe = BELGraph() universe.namespace_pattern["test"] = "test-url" universe.add_increases(a, b, citation=n(), evidence=n()) universe.add_increases(b, c, citation=n(), evidence=n()) universe.add_association(d, a, citation=n(), evidence=n()) universe.add_increases(e, a, citation=n(), evidence=n()) universe.add_decreases(f, b, citation=n(), evidence=n()) subgraph = get_upstream_causal_subgraph(universe, [a, b]) self.assertIsInstance(subgraph, BELGraph) self.assert_all_nodes_are_base_entities(subgraph) self.assertIn("test", subgraph.namespace_pattern) self.assertEqual("test-url", subgraph.namespace_pattern["test"]) self.assertIn(a, subgraph) self.assertIn(b, subgraph) self.assertNotIn(c, subgraph) self.assertNotIn(d, subgraph) self.assertIn(e, subgraph) self.assertIn(f, subgraph) self.assertEqual(4, subgraph.number_of_nodes()) self.assert_in_edge(e, a, subgraph) self.assert_in_edge(a, b, subgraph) self.assert_in_edge(f, b, subgraph) self.assertEqual(3, subgraph.number_of_edges()) def test_expand_upstream_causal_subgraph(self): """Test expanding on the upstream causal subgraph.""" a, b, c, d, e, f = [protein(namespace="test", name=i) for i in string.ascii_lowercase[:6]] universe = BELGraph() universe.add_increases(a, b, citation=n(), evidence=n()) universe.add_increases(b, c, citation=n(), evidence=n()) universe.add_association(d, a, citation=n(), evidence=n()) universe.add_increases(e, a, citation=n(), evidence=n()) universe.add_decreases(f, b, citation=n(), evidence=n()) subgraph = BELGraph() subgraph.add_increases(a, b, citation=n(), evidence=n()) expand_upstream_causal(universe, subgraph) self.assertIsInstance(subgraph, BELGraph) self.assert_all_nodes_are_base_entities(subgraph) self.assertIn(a, subgraph) self.assertIn(b, subgraph) self.assertNotIn(c, subgraph) self.assertNotIn(d, subgraph) self.assertIn(e, subgraph) self.assertIn(f, subgraph) self.assertEqual(4, subgraph.number_of_nodes()) self.assert_in_edge(e, a, subgraph) self.assert_in_edge(a, b, subgraph) self.assert_in_edge(f, b, subgraph) self.assertEqual(2, len(subgraph[a][b])) self.assertEqual(4, subgraph.number_of_edges(), msg="\n".join(map(str, subgraph.edges()))) class TestEdgePredicateBuilders(TestGraphMixin): """Tests for edge predicate builders.""" def test_build_pmid_inclusion_filter(self): """Test getting a sub-graph by a single PubMed identifier.""" a, b, c, d = [protein(namespace="test", name=n()) for _ in range(4)] p1, p2, p3, p4 = n(), n(), n(), n() graph = BELGraph() keyword, url = n(), n() graph.namespace_url[keyword] = url graph.add_increases(a, b, evidence=n(), citation=p1) graph.add_increases(a, b, evidence=n(), citation=p2) graph.add_increases(b, c, evidence=n(), citation=p1) graph.add_increases(b, c, evidence=n(), citation=p3) graph.add_increases(c, d, evidence=n(), citation=p3) subgraph = get_subgraph_by_pubmed(graph, p1) self.assertIsInstance(subgraph, BELGraph) self.assert_all_nodes_are_base_entities(subgraph) self.assertIn(keyword, subgraph.namespace_url) self.assertEqual(url, subgraph.namespace_url[keyword]) self.assertIn(a, subgraph) self.assertIn(b, subgraph) self.assertIn(c, subgraph) self.assertNotIn(d, subgraph) empty_subgraph = get_subgraph_by_pubmed(graph, p4) self.assertIn(keyword, subgraph.namespace_url) self.assertEqual(url, subgraph.namespace_url[keyword]) self.assertEqual(0, empty_subgraph.number_of_nodes()) def test_build_pmid_set_inclusion_filter(self): """Test getting a sub-graph by a set of PubMed identifiers.""" a, b, c, d, e, f = [protein(namespace="test", name=n()) for _ in range(6)] p1, p2, p3, p4, p5, p6 = n(), n(), n(), n(), n(), n() graph = BELGraph() keyword, url = n(), n() graph.namespace_url[keyword] = url graph.add_increases(a, b, evidence=n(), citation=p1) graph.add_increases(a, b, evidence=n(), citation=p2) graph.add_increases(b, c, evidence=n(), citation=p1) graph.add_increases(b, c, evidence=n(), citation=p3) graph.add_increases(c, d, evidence=n(), citation=p3) graph.add_increases(e, f, evidence=n(), citation=p4) subgraph = get_subgraph_by_pubmed(graph, [p1, p4]) self.assertIsInstance(subgraph, BELGraph) self.assert_all_nodes_are_base_entities(subgraph) self.assertIn(keyword, subgraph.namespace_url) self.assertEqual(url, subgraph.namespace_url[keyword]) self.assertIn(a, subgraph) self.assertIn(b, subgraph) self.assertIn(c, subgraph) self.assertNotIn(d, subgraph) self.assertIn(e, subgraph) self.assertIn(f, subgraph) empty_subgraph = get_subgraph_by_pubmed(graph, [p5, p6]) self.assertIn(keyword, subgraph.namespace_url) self.assertEqual(url, subgraph.namespace_url[keyword]) self.assertEqual(0, empty_subgraph.number_of_nodes()) def test_build_author_inclusion_filter(self): """Test getting a sub-graph by a single author.""" a, b, c, d = [protein(namespace="test", name=n()) for _ in range(4)] a1, a2, a3, a4, a5 = n(), n(), n(), n(), n() c1 = { NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: n(), CITATION_AUTHORS: [a1, a2, a3], } c2 = { NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: n(), CITATION_AUTHORS: [a1, a4], } graph = BELGraph() keyword, url = n(), n() graph.namespace_url[keyword] = url graph.add_increases(a, b, evidence=n(), citation=c1) graph.add_increases(a, b, evidence=n(), citation=c2) graph.add_increases(b, c, evidence=n(), citation=c1) graph.add_increases(c, d, evidence=n(), citation=c2) subgraph1 = get_subgraph_by_authors(graph, a1) self.assertIsInstance(subgraph1, BELGraph) self.assert_all_nodes_are_base_entities(subgraph1) self.assertIn(keyword, subgraph1.namespace_url) self.assertEqual(url, subgraph1.namespace_url[keyword]) self.assertIn(a, subgraph1) self.assertIn(b, subgraph1) self.assertIn(c, subgraph1) self.assertIn(d, subgraph1) subgraph2 = get_subgraph_by_authors(graph, a2) self.assertIsInstance(subgraph2, BELGraph) self.assert_all_nodes_are_base_entities(subgraph2) self.assertIn(keyword, subgraph2.namespace_url) self.assertEqual(url, subgraph2.namespace_url[keyword]) self.assertIn(a, subgraph2) self.assertIn(b, subgraph2) self.assertIn(c, subgraph2) self.assertNotIn(d, subgraph2) subgraph3 = get_subgraph_by_authors(graph, a5) self.assertIsInstance(subgraph3, BELGraph) self.assert_all_nodes_are_base_entities(subgraph3) self.assertIn(keyword, subgraph3.namespace_url) self.assertEqual(url, subgraph3.namespace_url[keyword]) self.assertEqual(0, subgraph3.number_of_nodes()) def test_build_author_set_inclusion_filter(self): """Test getting a sub-graph by a set of authors.""" a, b, c, d = [protein(namespace="test", name=n()) for _ in range(4)] a1, a2, a3, a4 = n(), n(), n(), n() c1 = { NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: n(), CITATION_AUTHORS: [a1, a2, a3], } c2 = { NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: n(), CITATION_AUTHORS: [a1, a4], } graph = BELGraph() keyword, url = n(), n() graph.namespace_url[keyword] = url graph.add_increases(a, b, evidence=n(), citation=c1) graph.add_increases(a, b, evidence=n(), citation=c2) graph.add_increases(b, c, evidence=n(), citation=c1) graph.add_increases(c, d, evidence=n(), citation=c2) subgraph1 = get_subgraph_by_authors(graph, [a1, a2]) self.assertIsInstance(subgraph1, BELGraph) self.assert_all_nodes_are_base_entities(subgraph1) self.assertIn(keyword, subgraph1.namespace_url) self.assertEqual(url, subgraph1.namespace_url[keyword]) self.assertIn(a, subgraph1) self.assertIn(b, subgraph1) self.assertIn(c, subgraph1) self.assertIn(d, subgraph1) class TestEdgeInduction(unittest.TestCase): """Test induction over edges.""" def test_get_subgraph_by_annotation_value(self): """Test getting a subgraph by a single annotation value.""" graph = BELGraph() graph.annotation_url["Subgraph"] = n() a, b, c, d = [protein(namespace="test", name=n()) for _ in range(4)] k1 = graph.add_increases(a, b, citation=n(), evidence=n(), annotations={"Subgraph": {"A"}}) k2 = graph.add_increases(a, b, citation=n(), evidence=n(), annotations={"Subgraph": {"B"}}) k3 = graph.add_increases(a, b, citation=n(), evidence=n(), annotations={"Subgraph": {"A", "C", "D"}}) subgraph = get_subgraph_by_annotation_value(graph, "Subgraph", "A") self.assertIsInstance(subgraph, BELGraph) self.assertIn(a, subgraph) self.assertIn(b, subgraph) self.assertIn(b, subgraph[a]) self.assertIn(k1, subgraph[a][b]) self.assertNotIn(k2, subgraph[a][b]) self.assertIn(k3, subgraph[a][b]) def test_get_subgraph_by_annotation_values(self): """Test getting a subgraph by multiple annotation value.""" graph = BELGraph() graph.annotation_list["Subgraph"] = set("ABCDE") a, b, c, d = [protein(namespace="test", name=n()) for _ in range(4)] k1 = graph.add_increases(a, b, citation=n(), evidence=n(), annotations={"Subgraph": {"A"}}) k2 = graph.add_increases(a, b, citation=n(), evidence=n(), annotations={"Subgraph": {"B"}}) k3 = graph.add_increases(a, b, citation=n(), evidence=n(), annotations={"Subgraph": {"A", "C", "D"}}) k4 = graph.add_increases(a, b, citation=n(), evidence=n(), annotations={"Subgraph": {"C", "D"}}) subgraph = get_subgraph_by_annotation_value(graph, "Subgraph", {"A", "C"}) self.assertIsInstance(subgraph, BELGraph) self.assertIn(a, subgraph) self.assertIn(b, subgraph) self.assertIn(b, subgraph[a]) self.assertIn(k1, subgraph[a][b]) self.assertNotIn(k2, subgraph[a][b]) self.assertIn(k3, subgraph[a][b]) self.assertIn(k4, subgraph[a][b]) pybel-0.15.5/tests/test_struct/test_transformations/test_metadata.py000066400000000000000000000070251426625374700261470ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for metadata transforations.""" import unittest from pybel import BELGraph from pybel.constants import ( ANNOTATIONS, CITATION, CITATION_AUTHORS, CITATION_DATE, CITATION_TYPE_PUBMED, IDENTIFIER, NAMESPACE, ) from pybel.dsl import protein from pybel.examples import sialic_acid_graph from pybel.struct.mutation import ( add_annotation_value, remove_annotation_value, remove_extra_citation_metadata, strip_annotations, ) from pybel.testing.utils import n class TestMetadata(unittest.TestCase): """Test metadata transformations.""" def test_strip_annotations(self): """Test the strip_annotation function.""" x = protein(namespace="HGNC", name="X") y = protein(namespace="HGNC", name="X") graph = BELGraph() graph.annotation_list["A"] = set("ABC") key = graph.add_increases( x, y, citation="123456", evidence="Fake", annotations={"A": {"B": True}}, ) self.assertIn(ANNOTATIONS, graph[x][y][key]) strip_annotations(graph) self.assertNotIn(ANNOTATIONS, graph[x][y][key]) def test_add_and_remove_annotation(self): """Test adding and removing annotations. See: :func:`pybel.struct.mutation.add_annotation_value` and :func:`pybel.struct.mutation.remove_annotation_value` functions. """ graph = sialic_acid_graph.copy() annotation = "test-annotation" value = "test-value" url = n() graph.annotation_url[annotation] = url add_annotation_value(graph, annotation, value) for u, v, d in graph.edges(data=True): annotations = d.get(ANNOTATIONS) if annotations is None: continue self.assertIn(annotation, annotations) self.assertIn(value, annotations[annotation]) remove_annotation_value(graph, annotation, value) for u, v, d in graph.edges(data=True): annotations = d.get(ANNOTATIONS) if annotations is None: continue annotation_values = annotations.get(annotation) if annotation_values is None: continue self.assertNotIn(value, annotation_values) def test_remove_citation_metadata(self): """Test removing citation metadata from a graph.""" x = protein(namespace="HGNC", name="X") y = protein(namespace="HGNC", name="X") graph = BELGraph() graph.annotation_list["A"] = set("ABC") k0 = graph.add_part_of(x, y) k1 = graph.add_increases( x, y, citation="123456", evidence="Fake", annotations={"A": {"B": True}}, ) k2 = graph.add_increases( x, y, citation={ NAMESPACE: CITATION_TYPE_PUBMED, IDENTIFIER: "12345678", CITATION_DATE: "2018-12-10", }, evidence="Fake", annotations={"A": {"B": True}}, ) remove_extra_citation_metadata(graph) self.assertNotIn(CITATION, graph[x][y][k0]) for k in k1, k2: self.assertIn(CITATION, graph[x][y][k]) self.assertIn(NAMESPACE, graph[x][y][k][CITATION]) self.assertIn(IDENTIFIER, graph[x][y][k][CITATION]) self.assertNotIn(CITATION_DATE, graph[x][y][k][CITATION]) self.assertNotIn(CITATION_AUTHORS, graph[x][y][k][CITATION]) pybel-0.15.5/tests/test_struct/test_transformations/test_random.py000066400000000000000000000112341426625374700256440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Test for functions for inducing random sub-graphs.""" import random import sys import unittest from collections import Counter import networkx as nx from pybel.examples import sialic_acid_graph, statin_graph from pybel.struct.mutation.induction.paths import get_random_path from pybel.struct.mutation.induction.random_subgraph import ( _helper, get_graph_with_random_edges, get_random_node, get_random_subgraph, ) from pybel.testing.generate import generate_random_graph @unittest.skipIf(sys.version_info < (3,), "Will not support random operations on python2") class TestRandom(unittest.TestCase): """Test random graph induction functions.""" def setUp(self): """Set the random seed before each test.""" random.seed(125) # love that number def test_random_edges(self): """Test getting a graph by random edges.""" n_nodes, n_edges, n_sample_edges = 15, 80, 40 graph = generate_random_graph(n_nodes=n_nodes, n_edges=n_edges) subgraph = get_graph_with_random_edges(graph, n_edges=n_sample_edges) self.assertEqual(n_sample_edges, subgraph.number_of_edges()) def test_random_nodes(self): """Test getting random nodes.""" graph = nx.MultiDiGraph() graph.add_edge(1, 2) graph.add_edge(1, 3) graph.add_edge(1, 4) graph.add_edge(1, 5) n = 30000 r = Counter(get_random_node(graph, set(), invert_degrees=False) for _ in range(n)) degree_sum = 4 + 1 + 1 + 1 + 1 self.assertAlmostEqual(4 / degree_sum, r[1] / n, places=2) self.assertAlmostEqual(1 / degree_sum, r[2] / n, places=2) self.assertAlmostEqual(1 / degree_sum, r[3] / n, places=2) self.assertAlmostEqual(1 / degree_sum, r[4] / n, places=2) self.assertAlmostEqual(1 / degree_sum, r[5] / n, places=2) def test_random_nodes_inverted(self): """Test getting random nodes.""" graph = nx.MultiDiGraph() graph.add_edge(1, 2) graph.add_edge(1, 3) graph.add_edge(1, 4) graph.add_edge(1, 5) n = 30000 r = Counter(get_random_node(graph, set(), invert_degrees=True) for _ in range(n)) degree_sum = (1 / 4) + (1 / 1) + (1 / 1) + (1 / 1) + (1 / 1) self.assertAlmostEqual((1 / 4) / degree_sum, r[1] / n, places=2) self.assertAlmostEqual((1 / 1) / degree_sum, r[2] / n, places=2) self.assertAlmostEqual((1 / 1) / degree_sum, r[3] / n, places=2) self.assertAlmostEqual((1 / 1) / degree_sum, r[4] / n, places=2) self.assertAlmostEqual((1 / 1) / degree_sum, r[5] / n, places=2) def test_random_sample(self): """Test randomly sampling a graph.""" n_nodes, n_edges = 50, 500 graph = generate_random_graph(n_nodes=n_nodes, n_edges=n_edges) self.assertEqual(n_edges, graph.number_of_edges()) sg_1 = get_random_subgraph(graph, number_edges=250, number_seed_edges=10, invert_degrees=False) self.assertEqual(250, sg_1.number_of_edges()) sg_2 = get_random_subgraph(graph, number_edges=250, number_seed_edges=10, invert_degrees=True) self.assertEqual(250, sg_2.number_of_edges()) def test_random_sample_small(self): """Test a graph that is too small to sample.""" n_nodes, n_edges = 11, 25 graph = generate_random_graph(n_nodes, n_edges) self.assertEqual(n_edges, graph.number_of_edges()) sg_1 = get_random_subgraph(graph, number_edges=250, number_seed_edges=5, invert_degrees=False) self.assertEqual( graph.number_of_edges(), sg_1.number_of_edges(), msg="since graph is too small, the subgraph should contain the whole thing", ) sg_2 = get_random_subgraph(graph, number_edges=250, number_seed_edges=5, invert_degrees=True) self.assertEqual( graph.number_of_edges(), sg_2.number_of_edges(), msg="since graph is too small, the subgraph should contain the whole thing", ) def test_helper_failure(self): graph = nx.MultiDiGraph() graph.add_edge(1, 2) graph.add_edge(2, 3) result = nx.MultiDiGraph() result.add_edge(1, 2) _helper( result, graph, number_edges_remaining=5, node_blacklist={1, 2, 3}, ) self.assertNotIn(3, result) class TestRandomPath(unittest.TestCase): """Test getting random paths.""" def test_get_random_path(self): """Test getting random paths doesn't crash.""" for graph in (sialic_acid_graph, statin_graph): for _ in range(100): get_random_path(graph.copy()) pybel-0.15.5/tests/test_struct/test_transformations/test_transfer.py000066400000000000000000000076611426625374700262210ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for transfer of knowledge and inference functions.""" import unittest from pybel.examples.statin_example import ( avorastatin, ec_11134, ec_11188, fluvastatin, hmgcr, hmgcr_inhibitor, mevinolinic_acid, statin, statin_graph, synthetic_statin, ) from pybel.struct.mutation import infer_child_relations from pybel.struct.mutation.inference.transfer import iter_children class TestTransfer(unittest.TestCase): """Tests for transfer of knowledge and inference functions.""" def test_get_children(self): """Test iterating over the children of a node.""" children = list(iter_children(statin_graph, hmgcr_inhibitor)) self.assertNotEqual(0, len(children), msg="no children found") self.assertIn(mevinolinic_acid, children, msg="direct child not found") def test_infer(self): """Test inferring child relations.""" graph = statin_graph.copy() self.assertEqual(9, graph.number_of_nodes()) self.assertEqual(8, graph.number_of_edges()) self.assertNotIn(ec_11134, graph[fluvastatin]) self.assertNotIn(ec_11188, graph[fluvastatin]) self.assertNotIn(ec_11134, graph[avorastatin]) self.assertNotIn(ec_11188, graph[avorastatin]) self.assertNotIn(ec_11134, graph[synthetic_statin]) self.assertNotIn(ec_11188, graph[synthetic_statin]) self.assertNotIn(ec_11134, graph[statin]) self.assertNotIn(ec_11188, graph[statin]) self.assertNotIn(ec_11134, graph[mevinolinic_acid]) self.assertNotIn(ec_11188, graph[mevinolinic_acid]) self.assertIn(ec_11134, graph[hmgcr_inhibitor]) self.assertIn(ec_11188, graph[hmgcr_inhibitor]) infer_child_relations(graph, hmgcr_inhibitor) self.assertIn(ec_11134, graph[fluvastatin]) self.assertIn(ec_11188, graph[fluvastatin]) self.assertIn(ec_11134, graph[avorastatin]) self.assertIn(ec_11188, graph[avorastatin]) self.assertIn(ec_11134, graph[synthetic_statin]) self.assertIn(ec_11188, graph[synthetic_statin]) self.assertIn(ec_11134, graph[statin]) self.assertIn(ec_11188, graph[statin]) self.assertIn(ec_11134, graph[mevinolinic_acid]) self.assertIn(ec_11188, graph[mevinolinic_acid]) self.assertIn(ec_11134, graph[hmgcr_inhibitor]) self.assertIn(ec_11188, graph[hmgcr_inhibitor]) self.assertEqual(9, graph.number_of_nodes()) self.assertEqual(18, graph.number_of_edges()) infer_child_relations(graph, ec_11134) self.assertIn(hmgcr, graph[fluvastatin]) self.assertIn(hmgcr, graph[avorastatin]) self.assertIn(hmgcr, graph[synthetic_statin]) self.assertIn(hmgcr, graph[statin]) self.assertIn(hmgcr, graph[mevinolinic_acid]) self.assertIn(hmgcr, graph[hmgcr_inhibitor]) self.assertEqual(9, graph.number_of_nodes()) self.assertEqual(24, graph.number_of_edges()) self.assertEqual( 9, statin_graph.number_of_nodes(), msg="original graph nodes should not be modified", ) self.assertEqual( 8, statin_graph.number_of_edges(), msg="original graph edges should not be modified", ) def test_does_not_redo(self): """Test that :func:`propagate_node_relations` does not add the same edges twice.""" graph = statin_graph.copy() self.assertEqual(9, graph.number_of_nodes()) self.assertEqual(8, graph.number_of_edges()) infer_child_relations(graph, hmgcr_inhibitor) self.assertEqual(9, graph.number_of_nodes()) self.assertEqual(18, graph.number_of_edges()) infer_child_relations(graph, hmgcr_inhibitor) self.assertEqual(9, graph.number_of_nodes()) self.assertEqual(18, graph.number_of_edges(), msg="edges should not be added again") if __name__ == "__main__": unittest.main() pybel-0.15.5/tests/test_tokens.py000066400000000000000000000035661426625374700170250ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for ``pybel.tokens``.""" import unittest from pybel.constants import ABUNDANCE, CONCEPT, FUNCTION, IDENTIFIER, NAME, NAMESPACE from pybel.dsl import Abundance from pybel.testing.utils import n from pybel.tokens import _simple_po_to_dict class TestRecover(unittest.TestCase): """Test converting dictionaries to DSL.""" def test_simple(self): """Test converting a simple dictionary.""" namespace, name, identifier = n(), n(), n() self.assertEqual( Abundance(namespace=namespace, name=name), _simple_po_to_dict( { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: namespace, NAME: name, }, } ), ) self.assertEqual( Abundance(namespace=namespace, name=name, identifier=identifier), _simple_po_to_dict( { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: namespace, NAME: name, IDENTIFIER: identifier, }, } ), ) self.assertEqual( Abundance(namespace=namespace, identifier=identifier), _simple_po_to_dict( { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: namespace, IDENTIFIER: identifier, }, } ), ) with self.assertRaises(ValueError): _simple_po_to_dict( { FUNCTION: ABUNDANCE, CONCEPT: { NAMESPACE: namespace, }, } ) pybel-0.15.5/tests/test_utils.py000066400000000000000000000042441426625374700166540ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Tests for PyBEL utilities.""" import unittest from pybel.exceptions import PlaceholderAminoAcidWarning from pybel.parser.modifiers.constants import amino_acid from pybel.parser.utils import nest from pybel.utils import expand_dict, flatten_dict, tokenize_version class TestTokenizeVersion(unittest.TestCase): """Test tokenization of version strings.""" def test_simple(self): """Test the simplest version string case.""" version_str = "0.1.2" version_tuple = 0, 1, 2 self.assertEqual(version_tuple, tokenize_version(version_str)) def test_long(self): """Test when the version pieces have more than 1 digit.""" version_str = "0.12.20" version_tuple = 0, 12, 20 self.assertEqual(version_tuple, tokenize_version(version_str)) def test_dev(self): """Test when there's a dash after.""" version_str = "0.1.2-dev" version_tuple = 0, 1, 2 self.assertEqual(version_tuple, tokenize_version(version_str)) class TestRandom(unittest.TestCase): def test_nest_failure(self): with self.assertRaises(ValueError): nest() def test_bad_aminoAcid(self): with self.assertRaises(PlaceholderAminoAcidWarning): amino_acid.parseString("X") class TestUtils(unittest.TestCase): def test_expand_dict(self): flat_dict = { "k1": "v1", "k2_k2a": "v2", "k2_k2b": "v3", "k2_k2c_k2ci": "v4", "k2_k2c_k2cii": "v5", } expected_dict = { "k1": "v1", "k2": {"k2a": "v2", "k2b": "v3", "k2c": {"k2ci": "v4", "k2cii": "v5"}}, } self.assertEqual(expected_dict, expand_dict(flat_dict)) def test_flatten_dict(self): d = {"A": 5, "B": "b", "C": {"D": "d", "E": "e"}} expected = {"A": 5, "B": "b", "C_D": "d", "C_E": "e"} self.assertEqual(expected, flatten_dict(d)) def test_flatten_dict_withLists(self): d = {"A": 5, "B": "b", "C": {"D": ["d", "delta"], "E": "e"}} expected = {"A": 5, "B": "b", "C_D": "d,delta", "C_E": "e"} self.assertEqual(expected, flatten_dict(d)) pybel-0.15.5/tox.ini000066400000000000000000000106421426625374700142530ustar00rootroot00000000000000# Tox (http://tox.testrun.org/) is a tool for running tests # in multiple virtualenvs. This configuration file will run the # test suite on all supported python versions. To use it, "pip install tox" # and then run "tox" from this directory. [tox] isolated_build = true envlist = # always keep coverage-clean first coverage-clean # code linters/stylers lint manifest pyroma flake8 # documentation linters/checkers doc8 readme docs # the actual tests py # always keep coverage-report last coverage-report [testenv] commands = {[testenv:doctests]commands} coverage run -p -m pytest --durations=20 {posargs:tests} passenv = PYBEL_TEST_CONNECTOR PYBEL_TEST_CONNECTION HOME deps = coverage pytest {env:PYBEL_TEST_CONNECTOR:} extras = jupyter grounding whitelist_externals = /bin/cat /bin/cp /bin/mkdir /usr/bin/git [testenv:doctests] commands = pytest --doctest-modules \ src/pybel/struct/summary \ src/pybel/struct/filters \ src/pybel/struct/mutation \ src/pybel/struct/graph.py deps = pytest [testenv:coverage-clean] deps = coverage skip_install = true commands = coverage erase [testenv:manifest] deps = check-manifest skip_install = true commands = check-manifest [testenv:lint] deps = black isort skip_install = true commands = black src/ tests/ isort src/ tests/ description = Run linters. [testenv:flake8] skip_install = true deps = flake8 flake8-bandit flake8-colors flake8-docstrings flake8-isort flake8-bugbear flake8-broken-line flake8-black pep8-naming pydocstyle commands = flake8 src/pybel/ \ tests/test_schema.py \ tests/test_grounding.py description = Run the flake8 tool with several plugins (bandit, docstrings, import order, pep8 naming). [testenv:xenon] deps = xenon skip_install = true commands = xenon --max-average A --max-modules A --max-absolute B . description = Run the xenon tool to monitor code complexity. [testenv:pyroma] deps = pygments pyroma skip_install = true commands = pyroma --min=10 . description = Run the pyroma tool to check the package friendliness of the project. [testenv:mypy] deps = mypy skip_install = true commands = mypy --ignore-missing-imports src/pybel/ description = Run the mypy tool to check static typing on the project. [testenv:doc8] skip_install = true deps = sphinx doc8 commands = doc8 docs/source/ AUTHORS.rst CHANGELOG.rst README.rst description = Run the doc8 tool to check the style of the RST files in the project docs. [testenv:readme] commands = rst-lint README.rst skip_install = true deps = restructuredtext_lint pygments description = Run the rst-lint tool to check the style of the README. [testenv:docs] changedir = docs extras = docs jupyter grounding commands = mkdir -p {envtmpdir} cp -r source {envtmpdir}/source sphinx-build -W -b html -d {envtmpdir}/build/doctrees {envtmpdir}/source {envtmpdir}/build/html sphinx-build -W -b coverage -d {envtmpdir}/build/doctrees {envtmpdir}/source {envtmpdir}/build/coverage cat {envtmpdir}/build/coverage/c.txt cat {envtmpdir}/build/coverage/python.txt [testenv:coverage-report] deps = coverage skip_install = true commands = coverage combine coverage report #################### # Deployment tools # #################### [testenv:bumpversion] commands = bumpversion {posargs} skip_install = true passenv = HOME deps = bumpversion [testenv:build] skip_install = true deps = wheel build commands = python -m build --sdist --wheel --no-isolation [testenv:release] description = Release the code to PyPI so users can pip install it skip_install = true deps = {[testenv:build]deps} twine >= 1.5.0 commands = {[testenv:build]commands} twine upload --skip-existing dist/* [testenv:release-test] description = Release the code to the test PyPI site skip_install = true deps = {[testenv:build]deps} twine >= 1.5.0 commands = {[testenv:build]commands} twine upload --skip-existing --repository-url https://test.pypi.org/simple/ dist/* [testenv:finish] skip_install = true passenv = HOME TWINE_USERNAME TWINE_PASSWORD deps = {[testenv:release]deps} bump2version commands = bump2version release --tag {[testenv:release]commands} git push --tags bump2version patch git push whitelist_externals = /usr/bin/git